diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md index 505fe77f896e..309524886e6e 100644 --- a/.github/ISSUE_TEMPLATE.md +++ b/.github/ISSUE_TEMPLATE.md @@ -34,11 +34,11 @@ This is a template helping you to create an issue which can be processed as quic - [ ] I report the issue, it's not a question - [ ] I checked the problem with documentation, FAQ, open issues, - forum.opencv.org, Stack Overflow, etc and have not found solution + forum.opencv.org, Stack Overflow, etc and have not found any solution - - [ ] I updated to latest OpenCV version and the issue is still there + - [ ] I updated to the latest OpenCV version and the issue is still there - [ ] There is reproducer code and related data files: videos, images, onnx, etc diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 0454d1aed8bc..5e2e911cc884 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -3,9 +3,9 @@ See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. -- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV -- [ ] The PR is proposed to proper branch -- [ ] There is reference to original bug report and related work +- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV +- [ ] The PR is proposed to the proper branch +- [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake diff --git a/.github/workflows/PR-4.x.yaml b/.github/workflows/PR-4.x.yaml new file mode 100644 index 000000000000..980bf753de53 --- /dev/null +++ b/.github/workflows/PR-4.x.yaml @@ -0,0 +1,19 @@ +name: PR:4.x + +on: + pull_request: + branches: + - 4.x + +jobs: + ARM64: + uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-4.x-ARM64.yaml@main + + U20: + uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-4.x-U20.yaml@main + + W10: + uses: opencv/ci-gha-workflow/.github/workflows/OCV-PR-4.x-W10.yaml@main + + TIM-VX: + uses: opencv/ci-gha-workflow/.github/workflows/OCV-timvx-backend-tests-4.x.yml@main \ No newline at end of file diff --git a/3rdparty/ffmpeg/ffmpeg.cmake b/3rdparty/ffmpeg/ffmpeg.cmake index f6c148659891..6dcb24db6b83 100644 --- a/3rdparty/ffmpeg/ffmpeg.cmake +++ b/3rdparty/ffmpeg/ffmpeg.cmake @@ -1,8 +1,8 @@ -# Binaries branch name: ffmpeg/4.x_20211220 -# Binaries were created for OpenCV: 0e274fc4bed8a64ba4f1c201a21304286e217afc -ocv_update(FFMPEG_BINARIES_COMMIT "4d348507d156ec797a88a887cfa7f9129a35afac") -ocv_update(FFMPEG_FILE_HASH_BIN32 "eece4ec8304188117ffc7d5dfd0fc0ae") -ocv_update(FFMPEG_FILE_HASH_BIN64 "20deefbfe023c8b8d11a52e5a6527c6a") +# Binaries branch name: ffmpeg/4.x_20220524 +# Binaries were created for OpenCV: d6e9616256b46bd59be0a93d397f6ab958d39cd2 +ocv_update(FFMPEG_BINARIES_COMMIT "65ec04d4573dcdfa4531f0b9e67f35d8ffff873e") +ocv_update(FFMPEG_FILE_HASH_BIN32 "5573e2262ad1298e603122b7759fc2f6") +ocv_update(FFMPEG_FILE_HASH_BIN64 "5f9e2b2e04c15f080f40e844de80c867") ocv_update(FFMPEG_FILE_HASH_CMAKE "8862c87496e2e8c375965e1277dee1c7") function(download_win_ffmpeg script_var) diff --git a/3rdparty/ittnotify/CMakeLists.txt b/3rdparty/ittnotify/CMakeLists.txt index a227aff88e35..0f39adcd4ba2 100644 --- a/3rdparty/ittnotify/CMakeLists.txt +++ b/3rdparty/ittnotify/CMakeLists.txt @@ -54,6 +54,7 @@ set_target_properties(${ITT_LIBRARY} PROPERTIES ) ocv_warnings_disable(CMAKE_C_FLAGS -Wundef -Wsign-compare) +ocv_warnings_disable(CMAKE_C_FLAGS -Wstrict-prototypes) # clang15 if(ENABLE_SOLUTION_FOLDERS) set_target_properties(${ITT_LIBRARY} PROPERTIES FOLDER "3rdparty") diff --git a/3rdparty/libpng/CMakeLists.txt b/3rdparty/libpng/CMakeLists.txt index efa59627ebdc..973f39cafafa 100644 --- a/3rdparty/libpng/CMakeLists.txt +++ b/3rdparty/libpng/CMakeLists.txt @@ -77,7 +77,11 @@ endif(MSVC) add_library(${PNG_LIBRARY} STATIC ${OPENCV_3RDPARTY_EXCLUDE_FROM_ALL} ${lib_srcs} ${lib_hdrs}) target_link_libraries(${PNG_LIBRARY} ${ZLIB_LIBRARIES}) -ocv_warnings_disable(CMAKE_C_FLAGS -Wundef -Wcast-align -Wimplicit-fallthrough -Wunused-parameter -Wsign-compare) +ocv_warnings_disable(CMAKE_C_FLAGS -Wundef -Wcast-align -Wimplicit-fallthrough -Wunused-parameter -Wsign-compare + -Wmaybe-uninitialized + -Wnull-pointer-subtraction # clang15 + -Wunused-but-set-variable # clang15 +) set_target_properties(${PNG_LIBRARY} PROPERTIES OUTPUT_NAME ${PNG_LIBRARY} diff --git a/3rdparty/libtiff/CMakeLists.txt b/3rdparty/libtiff/CMakeLists.txt index 2074888a52f6..826c5e2316c1 100644 --- a/3rdparty/libtiff/CMakeLists.txt +++ b/3rdparty/libtiff/CMakeLists.txt @@ -452,8 +452,10 @@ ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-protot -Wcast-align -Wshadow -Wno-maybe-uninitialized -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast -Wmisleading-indentation -Wimplicit-fallthrough + -Wunused-parameter # clang + -Warray-parameter + -Wstrict-prototypes # clang15 ) -ocv_warnings_disable(CMAKE_C_FLAGS -Wunused-parameter) # clang ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations -Wunused-parameter -Wmissing-prototypes -Wundef # tiffiop.h: #if __clang_major__ >= 4 ) diff --git a/3rdparty/libtim-vx/tim-vx.cmake b/3rdparty/libtim-vx/tim-vx.cmake new file mode 100644 index 000000000000..7fb9f3435214 --- /dev/null +++ b/3rdparty/libtim-vx/tim-vx.cmake @@ -0,0 +1,73 @@ +set(TIMVX_COMMIT_HASH "1d9c7ab941b3d8d9c4d28d80058402725731e3d6") +set(OCV_TIMVX_DIR "${OpenCV_BINARY_DIR}/3rdparty/libtim-vx") +set(OCV_TIMVX_SOURCE_PATH "${OCV_TIMVX_DIR}/TIM-VX-${TIMVX_COMMIT_HASH}") + +# Download TIM-VX source code +if(EXISTS "${OCV_TIMVX_SOURCE_PATH}") + message(STATUS "TIM-VX: Use cache of TIM-VX source code at ${OCV_TIMVX_SOURCE_PATH}") + set(TIMVX_FOUND ON) +else() + set(OCV_TIMVX_FILENAME "${TIMVX_COMMIT_HASH}.zip") + set(OCV_TIMVX_URL "https://github.com/VeriSilicon/TIM-VX/archive/") + set(timvx_zip_md5sum 92619cc4498014ac7a09834d5e33ebd5) + + ocv_download(FILENAME ${OCV_TIMVX_FILENAME} + HASH ${timvx_zip_md5sum} + URL "${OCV_TIMVX_URL}" + DESTINATION_DIR "${OCV_TIMVX_DIR}" + ID "TIM-VX" + STATUS res + UNPACK RELATIVE_URL) + if(res) + set(TIMVX_FOUND ON) + message(STATUS "TIM-VX: Source code downloaded at ${OCV_TIMVX_SOURCE_PATH}.") + else() + set(TIMVX_FOUND OFF) + message(STATUS "TIM-VX: Failed to download source code from github. Turning off TIMVX_FOUND") + return() + endif() +endif() + +# set VIVANTE SDK especially for x86_64 which comes along with TIM-VX source code +if(CMAKE_SYSTEM_PROCESSOR STREQUAL x86_64) + set(VIVANTE_SDK_DIR "${OCV_TIMVX_SOURCE_PATH}/prebuilt-sdk/x86_64_linux") + message(STATUS "TIM-VX: Build from source using prebuilt x86_64 VIVANTE SDK.") +endif() + +# Verify if requested VIVANTE SDK libraries are all found +find_vivante_sdk_libs(missing ${VIVANTE_SDK_DIR}) +if(missing) + message(STATUS "TIM-VX: Failed to find ${missing} in ${VIVANTE_SDK_DIR}/lib. Turning off TIMVX_VIV_FOUND") + set(TIMVX_VIV_FOUND OFF) +else() + message(STATUS "TIM-VX: dependent VIVANTE SDK libraries are found at ${VIVANTE_SDK_DIR}/lib.") + set(TIMVX_VIV_FOUND ON) +endif() + +if(TIMVX_VIV_FOUND) + # vars used by TIM-VX CMake scripts + set(EXTERNAL_VIV_SDK "${VIVANTE_SDK_DIR}" CACHE INTERNAL "" FORCE) + set(VIV_SDK_DRIVER_PREFIX "lib" CACHE INTERNAL "" FORCE) +endif() + +if(TIMVX_FOUND AND TIMVX_VIV_FOUND) + set(BUILD_TIMVX ON) +else() + return() +endif() + +if(BUILD_TIMVX) + set(HAVE_TIMVX 1) + + ocv_warnings_disable(CMAKE_C_FLAGS -Wunused-parameter -Wstrict-prototypes -Wundef -Wsign-compare -Wmissing-prototypes -Wmissing-declarations -Wstrict-aliasing -Wunused-but-set-variable -Wmaybe-uninitialized -Wshadow -Wsuggest-override -Wswitch) + ocv_warnings_disable(CMAKE_CXX_FLAGS -Wunused-parameter -Wstrict-prototypes -Wundef -Wsign-compare -Wunused-but-set-variable -Wshadow -Wsuggest-override -Wmissing-declarations -Wswitch) + + set(TIMVX_INC_DIR "${OCV_TIMVX_SOURCE_PATH}/include" CACHE INTERNAL "TIM-VX include directory") + if(EXISTS "${OCV_TIMVX_SOURCE_PATH}/CMakeLists.txt") + add_subdirectory("${OCV_TIMVX_SOURCE_PATH}" "${OCV_TIMVX_DIR}/build") + else() + message(WARNING "TIM-VX: Missing 'CMakeLists.txt' in the source code: ${OCV_TIMVX_SOURCE_PATH}") + endif() + ocv_install_target(tim-vx EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev) + set(TIMVX_LIB "tim-vx") +endif() diff --git a/3rdparty/libwebp/CMakeLists.txt b/3rdparty/libwebp/CMakeLists.txt index 9160e2024ca0..723575c8db3d 100644 --- a/3rdparty/libwebp/CMakeLists.txt +++ b/3rdparty/libwebp/CMakeLists.txt @@ -45,6 +45,7 @@ ocv_warnings_disable(CMAKE_C_FLAGS -Wunused-variable -Wunused-function -Wshadow -Wmissing-prototypes # clang -Wmissing-declarations # gcc -Wimplicit-fallthrough + -Wunused-but-set-variable # clang15 ) ocv_warnings_disable(CMAKE_C_FLAGS /wd4244 /wd4267) # vs2005 diff --git a/3rdparty/openjpeg/CMakeLists.txt b/3rdparty/openjpeg/CMakeLists.txt index fe766101d08d..42730bab2b1c 100644 --- a/3rdparty/openjpeg/CMakeLists.txt +++ b/3rdparty/openjpeg/CMakeLists.txt @@ -13,6 +13,7 @@ project(openjpeg C) ocv_warnings_disable(CMAKE_C_FLAGS -Wimplicit-const-int-float-conversion # clang + -Wunused-but-set-variable # clang15 ) #----------------------------------------------------------------------------- diff --git a/3rdparty/openvx/README.md b/3rdparty/openvx/README.md index 2f869a42bfa2..339b5072cc18 100644 --- a/3rdparty/openvx/README.md +++ b/3rdparty/openvx/README.md @@ -77,7 +77,7 @@ E.g. external ref-counting is implemented for 1.0 version and native OpenVX one Also there are some **C++ 11** features are used (e.g. rvalue ref-s) when their availability is detected at ***compile time***. -C++ exceptions are used for errors indication instead of return codes. There are two types of exceptions are defined: `RuntimeError` is thrown when OpenVX C call returned unsuccessful result and `WrapperError` is thrown when a problem is occured in the wrappers code. Both exception calsses are derived from `std::exception` (actually from its inheritants). +C++ exceptions are used for errors indication instead of return codes. There are two types of exceptions are defined: `RuntimeError` is thrown when OpenVX C call returned unsuccessful result and `WrapperError` is thrown when a problem is occurred in the wrappers code. Both exception calsses are derived from `std::exception` (actually from its inheritants). The so called **OpenVX objects** (e.g. `vx_image`) are represented as C++ classes in wrappers. All these classes use automatic ref-counting that allows development of exception-safe code. diff --git a/3rdparty/readme.txt b/3rdparty/readme.txt index 4e4a6ba0a653..e67304c5ef61 100644 --- a/3rdparty/readme.txt +++ b/3rdparty/readme.txt @@ -37,7 +37,7 @@ libtiff Tag Image File Format (TIFF) Software WITH_TIFF CMake option must be ON to add libtiff & zlib support to imgcodecs. ------------------------------------------------------------------------------------ zlib General purpose LZ77 compression library - Copyright (C) 1995-2012 Jean-loup Gailly and Mark Adler. + Copyright (C) 1995-2022 Jean-loup Gailly and Mark Adler. See zlib home page http://www.zlib.net for details and links to the source code ------------------------------------------------------------------------------------ diff --git a/3rdparty/zlib/CMakeLists.txt b/3rdparty/zlib/CMakeLists.txt index 9758861a6b33..709e293c2855 100644 --- a/3rdparty/zlib/CMakeLists.txt +++ b/3rdparty/zlib/CMakeLists.txt @@ -83,6 +83,7 @@ ocv_warnings_disable(CMAKE_C_FLAGS -Wshorten-64-to-32 -Wattributes -Wstrict-prot -Wundef # _LFS64_LARGEFILE is not defined /wd4267 # MSVS 2015 (x64) + zlib 1.2.11 -Wimplicit-fallthrough + /wd4244 # MSVS + zlib 1.2.12: warning C4244: '=': conversion from 'ush' to 'uchf', possible loss of data ) set_target_properties(${ZLIB_LIBRARY} PROPERTIES diff --git a/3rdparty/zlib/ChangeLog b/3rdparty/zlib/ChangeLog index 30199a65a03d..f0b0e6180921 100644 --- a/3rdparty/zlib/ChangeLog +++ b/3rdparty/zlib/ChangeLog @@ -1,6 +1,69 @@ ChangeLog file for zlib +Changes in 1.2.12 (27 Mar 2022) +- Cygwin does not have _wopen(), so do not create gzopen_w() there +- Permit a deflateParams() parameter change as soon as possible +- Limit hash table inserts after switch from stored deflate +- Fix bug when window full in deflate_stored() +- Fix CLEAR_HASH macro to be usable as a single statement +- Avoid a conversion error in gzseek when off_t type too small +- Have Makefile return non-zero error code on test failure +- Avoid some conversion warnings in gzread.c and gzwrite.c +- Update use of errno for newer Windows CE versions +- Small speedup to inflate [psumbera] +- Return an error if the gzputs string length can't fit in an int +- Add address checking in clang to -w option of configure +- Don't compute check value for raw inflate if asked to validate +- Handle case where inflateSync used when header never processed +- Avoid the use of ptrdiff_t +- Avoid an undefined behavior of memcpy() in gzappend() +- Avoid undefined behaviors of memcpy() in gz*printf() +- Avoid an undefined behavior of memcpy() in _tr_stored_block() +- Make the names in functions declarations identical to definitions +- Remove old assembler code in which bugs have manifested +- Fix deflateEnd() to not report an error at start of raw deflate +- Add legal disclaimer to README +- Emphasize the need to continue decompressing gzip members +- Correct the initialization requirements for deflateInit2() +- Fix a bug that can crash deflate on some input when using Z_FIXED +- Assure that the number of bits for deflatePrime() is valid +- Use a structure to make globals in enough.c evident +- Use a macro for the printf format of big_t in enough.c +- Clean up code style in enough.c, update version +- Use inline function instead of macro for index in enough.c +- Clarify that prefix codes are counted in enough.c +- Show all the codes for the maximum tables size in enough.c +- Add gznorm.c example, which normalizes gzip files +- Fix the zran.c example to work on a multiple-member gzip file +- Add tables for crc32_combine(), to speed it up by a factor of 200 +- Add crc32_combine_gen() and crc32_combine_op() for fast combines +- Speed up software CRC-32 computation by a factor of 1.5 to 3 +- Use atomic test and set, if available, for dynamic CRC tables +- Don't bother computing check value after successful inflateSync() +- Correct comment in crc32.c +- Add use of the ARMv8 crc32 instructions when requested +- Use ARM crc32 instructions if the ARM architecture has them +- Explicitly note that the 32-bit check values are 32 bits +- Avoid adding empty gzip member after gzflush with Z_FINISH +- Fix memory leak on error in gzlog.c +- Fix error in comment on the polynomial representation of a byte +- Clarify gz* function interfaces, referring to parameter names +- Change macro name in inflate.c to avoid collision in VxWorks +- Correct typo in blast.c +- Improve portability of contrib/minizip +- Fix indentation in minizip's zip.c +- Replace black/white with allow/block. (theresa-m) +- minizip warning fix if MAXU32 already defined. (gvollant) +- Fix unztell64() in minizip to work past 4GB. (Daniël Hörchner) +- Clean up minizip to reduce warnings for testing +- Add fallthrough comments for gcc +- Eliminate use of ULL constants +- Separate out address sanitizing from warnings in configure +- Remove destructive aspects of make distclean +- Check for cc masquerading as gcc or clang in configure +- Fix crc32.c to compile local functions only if used + Changes in 1.2.11 (15 Jan 2017) - Fix deflate stored bug when pulling last block from window - Permit immediate deflateParams changes before any deflate input @@ -511,7 +574,7 @@ Changes in 1.2.3.5 (8 Jan 2010) - Don't use _vsnprintf on later versions of MSVC [Lowman] - Add CMake build script and input file [Lowman] - Update contrib/minizip to 1.1 [Svensson, Vollant] -- Moved nintendods directory from contrib to . +- Moved nintendods directory from contrib to root - Replace gzio.c with a new set of routines with the same functionality - Add gzbuffer(), gzoffset(), gzclose_r(), gzclose_w() as part of above - Update contrib/minizip to 1.1b @@ -685,7 +748,7 @@ Changes in 1.2.2.4 (11 July 2005) - Be more strict on incomplete code sets in inflate_table() and increase ENOUGH and MAXD -- this repairs a possible security vulnerability for invalid inflate input. Thanks to Tavis Ormandy and Markus Oberhumer for - discovering the vulnerability and providing test cases. + discovering the vulnerability and providing test cases - Add ia64 support to configure for HP-UX [Smith] - Add error return to gzread() for format or i/o error [Levin] - Use malloc.h for OS/2 [Necasek] @@ -721,7 +784,7 @@ Changes in 1.2.2.2 (30 December 2004) - Add Z_FIXED strategy option to deflateInit2() to force fixed trees - Add updated make_vms.com [Coghlan], update README - Create a new "examples" directory, move gzappend.c there, add zpipe.c, - fitblk.c, gzlog.[ch], gzjoin.c, and zlib_how.html. + fitblk.c, gzlog.[ch], gzjoin.c, and zlib_how.html - Add FAQ entry and comments in deflate.c on uninitialized memory access - Add Solaris 9 make options in configure [Gilbert] - Allow strerror() usage in gzio.c for STDC @@ -792,7 +855,7 @@ Changes in 1.2.1.1 (9 January 2004) - Fix a big fat bug in inftrees.c that prevented decoding valid dynamic blocks with only literals and no distance codes -- Thanks to "Hot Emu" for the bug report and sample file -- Add a note to puff.c on no distance codes case. +- Add a note to puff.c on no distance codes case Changes in 1.2.1 (17 November 2003) - Remove a tab in contrib/gzappend/gzappend.c @@ -1036,14 +1099,14 @@ Changes in 1.2.0 (9 March 2003) - Add contrib/puff/ simple inflate for deflate format description Changes in 1.1.4 (11 March 2002) -- ZFREE was repeated on same allocation on some error conditions. +- ZFREE was repeated on same allocation on some error conditions This creates a security problem described in http://www.zlib.org/advisory-2002-03-11.txt - Returned incorrect error (Z_MEM_ERROR) on some invalid data - Avoid accesses before window for invalid distances with inflate window - less than 32K. + less than 32K - force windowBits > 8 to avoid a bug in the encoder for a window size - of 256 bytes. (A complete fix will be available in 1.1.5). + of 256 bytes. (A complete fix will be available in 1.1.5) Changes in 1.1.3 (9 July 1998) - fix "an inflate input buffer bug that shows up on rare but persistent @@ -1117,7 +1180,7 @@ Changes in 1.1.1 (27 Feb 98) - remove block truncation heuristic which had very marginal effect for zlib (smaller lit_bufsize than in gzip 1.2.4) and degraded a little the compression ratio on some files. This also allows inlining _tr_tally for - matches in deflate_slow. + matches in deflate_slow - added msdos/Makefile.w32 for WIN32 Microsoft Visual C++ (Bob Frazier) Changes in 1.1.0 (24 Feb 98) @@ -1162,7 +1225,7 @@ Changes in 1.0.8 (27 Jan 1998) - include sys/types.h to get off_t on some systems (Marc Lehmann & QingLong) - use constant arrays for the static trees in trees.c instead of computing them at run time (thanks to Ken Raeburn for this suggestion). To create - trees.h, compile with GEN_TREES_H and run "make test". + trees.h, compile with GEN_TREES_H and run "make test" - check return code of example in "make test" and display result - pass minigzip command line options to file_compress - simplifying code of inflateSync to avoid gcc 2.8 bug @@ -1201,12 +1264,12 @@ Changes in 1.0.6 (19 Jan 1998) - add functions gzprintf, gzputc, gzgetc, gztell, gzeof, gzseek, gzrewind and gzsetparams (thanks to Roland Giersig and Kevin Ruland for some of this code) - Fix a deflate bug occurring only with compression level 0 (thanks to - Andy Buckler for finding this one). -- In minigzip, pass transparently also the first byte for .Z files. + Andy Buckler for finding this one) +- In minigzip, pass transparently also the first byte for .Z files - return Z_BUF_ERROR instead of Z_OK if output buffer full in uncompress() - check Z_FINISH in inflate (thanks to Marc Schluper) - Implement deflateCopy (thanks to Adam Costello) -- make static libraries by default in configure, add --shared option. +- make static libraries by default in configure, add --shared option - move MSDOS or Windows specific files to directory msdos - suppress the notion of partial flush to simplify the interface (but the symbol Z_PARTIAL_FLUSH is kept for compatibility with 1.0.4) @@ -1218,7 +1281,7 @@ Changes in 1.0.6 (19 Jan 1998) - added Makefile.nt (thanks to Stephen Williams) - added the unsupported "contrib" directory: contrib/asm386/ by Gilles Vollant - 386 asm code replacing longest_match(). + 386 asm code replacing longest_match() contrib/iostream/ by Kevin Ruland A C++ I/O streams interface to the zlib gz* functions contrib/iostream2/ by Tyge Løvset @@ -1226,7 +1289,7 @@ Changes in 1.0.6 (19 Jan 1998) contrib/untgz/ by "Pedro A. Aranda Guti\irrez" A very simple tar.gz file extractor using zlib contrib/visual-basic.txt by Carlos Rios - How to use compress(), uncompress() and the gz* functions from VB. + How to use compress(), uncompress() and the gz* functions from VB - pass params -f (filtered data), -h (huffman only), -1 to -9 (compression level) in minigzip (thanks to Tom Lane) @@ -1235,8 +1298,8 @@ Changes in 1.0.6 (19 Jan 1998) - add undocumented function inflateSyncPoint() (hack for Paul Mackerras) - add undocumented function zError to convert error code to string (for Tim Smithers) -- Allow compilation of gzio with -DNO_DEFLATE to avoid the compression code. -- Use default memcpy for Symantec MSDOS compiler. +- Allow compilation of gzio with -DNO_DEFLATE to avoid the compression code +- Use default memcpy for Symantec MSDOS compiler - Add EXPORT keyword for check_func (needed for Windows DLL) - add current directory to LD_LIBRARY_PATH for "make test" - create also a link for libz.so.1 @@ -1249,7 +1312,7 @@ Changes in 1.0.6 (19 Jan 1998) - allow compilation with ANSI keywords only enabled for TurboC in large model - avoid "versionString"[0] (Borland bug) - add NEED_DUMMY_RETURN for Borland -- use variable z_verbose for tracing in debug mode (L. Peter Deutsch). +- use variable z_verbose for tracing in debug mode (L. Peter Deutsch) - allow compilation with CC - defined STDC for OS/2 (David Charlap) - limit external names to 8 chars for MVS (Thomas Lund) @@ -1259,7 +1322,7 @@ Changes in 1.0.6 (19 Jan 1998) - use _fdopen instead of fdopen for MSC >= 6.0 (Thomas Fanslau) - added makelcc.bat for lcc-win32 (Tom St Denis) - in Makefile.dj2, use copy and del instead of install and rm (Frank Donahoe) -- Avoid expanded $Id$. Use "rcs -kb" or "cvs admin -kb" to avoid Id expansion. +- Avoid expanded $Id$. Use "rcs -kb" or "cvs admin -kb" to avoid Id expansion - check for unistd.h in configure (for off_t) - remove useless check parameter in inflate_blocks_free - avoid useless assignment of s->check to itself in inflate_blocks_new @@ -1280,7 +1343,7 @@ Changes in 1.0.5 (3 Jan 98) Changes in 1.0.4 (24 Jul 96) - In very rare conditions, deflate(s, Z_FINISH) could fail to produce an EOF bit, so the decompressor could decompress all the correct data but went - on to attempt decompressing extra garbage data. This affected minigzip too. + on to attempt decompressing extra garbage data. This affected minigzip too - zlibVersion and gzerror return const char* (needed for DLL) - port to RISCOS (no fdopen, no multiple dots, no unlink, no fileno) - use z_error only for DEBUG (avoid problem with DLLs) @@ -1310,7 +1373,7 @@ Changes in 1.0.1 (20 May 96) [1.0 skipped to avoid confusion] - fix array overlay in deflate.c which sometimes caused bad compressed data - fix inflate bug with empty stored block - fix MSDOS medium model which was broken in 0.99 -- fix deflateParams() which could generate bad compressed data. +- fix deflateParams() which could generate bad compressed data - Bytef is define'd instead of typedef'ed (work around Borland bug) - added an INDEX file - new makefiles for DJGPP (Makefile.dj2), 32-bit Borland (Makefile.b32), @@ -1331,7 +1394,7 @@ Changes in 0.99 (27 Jan 96) - allow preset dictionary shared between compressor and decompressor - allow compression level 0 (no compression) - add deflateParams in zlib.h: allow dynamic change of compression level - and compression strategy. + and compression strategy - test large buffers and deflateParams in example.c - add optional "configure" to build zlib as a shared library - suppress Makefile.qnx, use configure instead @@ -1373,30 +1436,30 @@ Changes in 0.99 (27 Jan 96) - use STDC instead of __GO32__ to avoid redeclaring exit, calloc, etc... - use Z_BINARY instead of BINARY - document that gzclose after gzdopen will close the file -- allow "a" as mode in gzopen. +- allow "a" as mode in gzopen - fix error checking in gzread - allow skipping .gz extra-field on pipes - added reference to Perl interface in README - put the crc table in FAR data (I dislike more and more the medium model :) - added get_crc_table -- added a dimension to all arrays (Borland C can't count). +- added a dimension to all arrays (Borland C can't count) - workaround Borland C bug in declaration of inflate_codes_new & inflate_fast - guard against multiple inclusion of *.h (for precompiled header on Mac) -- Watcom C pretends to be Microsoft C small model even in 32 bit mode. +- Watcom C pretends to be Microsoft C small model even in 32 bit mode - don't use unsized arrays to avoid silly warnings by Visual C++: warning C4746: 'inflate_mask' : unsized array treated as '__far' - (what's wrong with far data in far model?). + (what's wrong with far data in far model?) - define enum out of inflate_blocks_state to allow compilation with C++ Changes in 0.95 (16 Aug 95) - fix MSDOS small and medium model (now easier to adapt to any compiler) - inlined send_bits - fix the final (:-) bug for deflate with flush (output was correct but - not completely flushed in rare occasions). + not completely flushed in rare occasions) - default window size is same for compression and decompression - (it's now sufficient to set MAX_WBITS in zconf.h). + (it's now sufficient to set MAX_WBITS in zconf.h) - voidp -> voidpf and voidnp -> voidp (for consistency with other - typedefs and because voidnp was not near in large model). + typedefs and because voidnp was not near in large model) Changes in 0.94 (13 Aug 95) - support MSDOS medium model @@ -1405,12 +1468,12 @@ Changes in 0.94 (13 Aug 95) - added support for VMS - allow a compression level in gzopen() - gzflush now calls fflush -- For deflate with flush, flush even if no more input is provided. +- For deflate with flush, flush even if no more input is provided - rename libgz.a as libz.a - avoid complex expression in infcodes.c triggering Turbo C bug - work around a problem with gcc on Alpha (in INSERT_STRING) - don't use inline functions (problem with some gcc versions) -- allow renaming of Byte, uInt, etc... with #define. +- allow renaming of Byte, uInt, etc... with #define - avoid warning about (unused) pointer before start of array in deflate.c - avoid various warnings in gzio.c, example.c, infblock.c, adler32.c, zutil.c - avoid reserved word 'new' in trees.c @@ -1429,7 +1492,7 @@ Changes in 0.92 (3 May 95) - no memcpy on Pyramid - suppressed inftest.c - optimized fill_window, put longest_match inline for gcc -- optimized inflate on stored blocks. +- optimized inflate on stored blocks - untabify all sources to simplify patches Changes in 0.91 (2 May 95) @@ -1447,7 +1510,7 @@ Changes in 0.9 (1 May 95) - let again gzread copy uncompressed data unchanged (was working in 0.71) - deflate(Z_FULL_FLUSH), inflateReset and inflateSync are now fully implemented - added a test of inflateSync in example.c -- moved MAX_WBITS to zconf.h because users might want to change that. +- moved MAX_WBITS to zconf.h because users might want to change that - document explicitly that zalloc(64K) on MSDOS must return a normalized pointer (zero offset) - added Makefiles for Microsoft C, Turbo C, Borland C++ @@ -1456,7 +1519,7 @@ Changes in 0.9 (1 May 95) Changes in 0.8 (29 April 95) - added fast inflate (inffast.c) - deflate(Z_FINISH) now returns Z_STREAM_END when done. Warning: this - is incompatible with previous versions of zlib which returned Z_OK. + is incompatible with previous versions of zlib which returned Z_OK - work around a TurboC compiler bug (bad code for b << 0, see infutil.h) (actually that was not a compiler bug, see 0.81 above) - gzread no longer reads one extra byte in certain cases @@ -1466,50 +1529,50 @@ Changes in 0.8 (29 April 95) Changes in 0.71 (14 April 95) - Fixed more MSDOS compilation problems :( There is still a bug with - TurboC large model. + TurboC large model Changes in 0.7 (14 April 95) -- Added full inflate support. +- Added full inflate support - Simplified the crc32() interface. The pre- and post-conditioning (one's complement) is now done inside crc32(). WARNING: this is - incompatible with previous versions; see zlib.h for the new usage. + incompatible with previous versions; see zlib.h for the new usage Changes in 0.61 (12 April 95) -- workaround for a bug in TurboC. example and minigzip now work on MSDOS. +- workaround for a bug in TurboC. example and minigzip now work on MSDOS Changes in 0.6 (11 April 95) - added minigzip.c - added gzdopen to reopen a file descriptor as gzFile -- added transparent reading of non-gziped files in gzread. +- added transparent reading of non-gziped files in gzread - fixed bug in gzread (don't read crc as data) -- fixed bug in destroy (gzio.c) (don't return Z_STREAM_END for gzclose). +- fixed bug in destroy (gzio.c) (don't return Z_STREAM_END for gzclose) - don't allocate big arrays in the stack (for MSDOS) - fix some MSDOS compilation problems Changes in 0.5: - do real compression in deflate.c. Z_PARTIAL_FLUSH is supported but - not yet Z_FULL_FLUSH. + not yet Z_FULL_FLUSH - support decompression but only in a single step (forced Z_FINISH) -- added opaque object for zalloc and zfree. +- added opaque object for zalloc and zfree - added deflateReset and inflateReset -- added a variable zlib_version for consistency checking. -- renamed the 'filter' parameter of deflateInit2 as 'strategy'. - Added Z_FILTERED and Z_HUFFMAN_ONLY constants. +- added a variable zlib_version for consistency checking +- renamed the 'filter' parameter of deflateInit2 as 'strategy' + Added Z_FILTERED and Z_HUFFMAN_ONLY constants Changes in 0.4: -- avoid "zip" everywhere, use zlib instead of ziplib. +- avoid "zip" everywhere, use zlib instead of ziplib - suppress Z_BLOCK_FLUSH, interpret Z_PARTIAL_FLUSH as block flush - if compression method == 8. + if compression method == 8 - added adler32 and crc32 - renamed deflateOptions as deflateInit2, call one or the other but not both -- added the method parameter for deflateInit2. +- added the method parameter for deflateInit2 - added inflateInit2 - simplied considerably deflateInit and inflateInit by not supporting user-provided history buffer. This is supported only in deflateInit2 - and inflateInit2. + and inflateInit2 Changes in 0.3: - prefix all macro names with Z_ -- use Z_FINISH instead of deflateEnd to finish compression. +- use Z_FINISH instead of deflateEnd to finish compression - added Z_HUFFMAN_ONLY - added gzerror() diff --git a/3rdparty/zlib/README b/3rdparty/zlib/README index 51106de47532..024b79d3d8c8 100644 --- a/3rdparty/zlib/README +++ b/3rdparty/zlib/README @@ -1,6 +1,6 @@ ZLIB DATA COMPRESSION LIBRARY -zlib 1.2.11 is a general purpose data compression library. All the code is +zlib 1.2.12 is a general purpose data compression library. All the code is thread safe. The data format used by the zlib library is described by RFCs (Request for Comments) 1950 to 1952 in the files http://tools.ietf.org/html/rfc1950 (zlib format), rfc1951 (deflate format) and @@ -31,7 +31,7 @@ Mark Nelson wrote an article about zlib for the Jan. 1997 issue of Dr. Dobb's Journal; a copy of the article is available at http://marknelson.us/1997/01/01/zlib-engine/ . -The changes made in version 1.2.11 are documented in the file ChangeLog. +The changes made in version 1.2.12 are documented in the file ChangeLog. Unsupported third party contributions are provided in directory contrib/ . @@ -84,7 +84,7 @@ Acknowledgments: Copyright notice: - (C) 1995-2017 Jean-loup Gailly and Mark Adler + (C) 1995-2022 Jean-loup Gailly and Mark Adler This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages @@ -108,7 +108,10 @@ Copyright notice: If you use the zlib library in a product, we would appreciate *not* receiving lengthy legal documents to sign. The sources are provided for free but without warranty of any kind. The library has been entirely written by Jean-loup -Gailly and Mark Adler; it does not include third-party code. +Gailly and Mark Adler; it does not include third-party code. We make all +contributions to and distributions of this project solely in our personal +capacity, and are not conveying any rights to any intellectual property of +any third parties. If you redistribute modified sources, we would appreciate that you include in the file ChangeLog history information documenting your changes. Please read diff --git a/3rdparty/zlib/crc32.c b/3rdparty/zlib/crc32.c index 9580440c0e6b..a1bdce5c23c6 100644 --- a/3rdparty/zlib/crc32.c +++ b/3rdparty/zlib/crc32.c @@ -1,12 +1,10 @@ /* crc32.c -- compute the CRC-32 of a data stream - * Copyright (C) 1995-2006, 2010, 2011, 2012, 2016 Mark Adler + * Copyright (C) 1995-2022 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h * - * Thanks to Rodney Brown for his contribution of faster - * CRC methods: exclusive-oring 32 bits of data at a time, and pre-computing - * tables for updating the shift register in one step with three exclusive-ors - * instead of four steps with four exclusive-ors. This results in about a - * factor of two increase in speed on a Power PC G4 (PPC7455) using gcc -O3. + * This interleaved implementation of a CRC makes use of pipelined multiple + * arithmetic-logic units, commonly found in modern CPU cores. It is due to + * Kadatch and Jenkins (2010). See doc/crc-doc.1.0.pdf in this distribution. */ /* @(#) $Id$ */ @@ -14,11 +12,12 @@ /* Note on the use of DYNAMIC_CRC_TABLE: there is no mutex or semaphore protection on the static variables used to control the first-use generation - of the crc tables. Therefore, if you #define DYNAMIC_CRC_TABLE, you should + of the crc tables. Therefore, if you #define DYNAMIC_CRC_TABLE, you should first call get_crc_table() to initialize the tables before allowing more than one thread to use crc32(). - DYNAMIC_CRC_TABLE and MAKECRCH can be #defined to write out crc32.h. + MAKECRCH can be #defined to write out crc32.h. A main() routine is also + produced, so that this one source file can be compiled to an executable. */ #ifdef MAKECRCH @@ -28,415 +27,1090 @@ # endif /* !DYNAMIC_CRC_TABLE */ #endif /* MAKECRCH */ -#include "zutil.h" /* for STDC and FAR definitions */ +#include "zutil.h" /* for Z_U4, Z_U8, z_crc_t, and FAR definitions */ -/* Definitions for doing the crc four data bytes at a time. */ -#if !defined(NOBYFOUR) && defined(Z_U4) -# define BYFOUR + /* + A CRC of a message is computed on N braids of words in the message, where + each word consists of W bytes (4 or 8). If N is 3, for example, then three + running sparse CRCs are calculated respectively on each braid, at these + indices in the array of words: 0, 3, 6, ..., 1, 4, 7, ..., and 2, 5, 8, ... + This is done starting at a word boundary, and continues until as many blocks + of N * W bytes as are available have been processed. The results are combined + into a single CRC at the end. For this code, N must be in the range 1..6 and + W must be 4 or 8. The upper limit on N can be increased if desired by adding + more #if blocks, extending the patterns apparent in the code. In addition, + crc32.h would need to be regenerated, if the maximum N value is increased. + + N and W are chosen empirically by benchmarking the execution time on a given + processor. The choices for N and W below were based on testing on Intel Kaby + Lake i7, AMD Ryzen 7, ARM Cortex-A57, Sparc64-VII, PowerPC POWER9, and MIPS64 + Octeon II processors. The Intel, AMD, and ARM processors were all fastest + with N=5, W=8. The Sparc, PowerPC, and MIPS64 were all fastest at N=5, W=4. + They were all tested with either gcc or clang, all using the -O3 optimization + level. Your mileage may vary. + */ + +/* Define N */ +#ifdef Z_TESTN +# define N Z_TESTN +#else +# define N 5 +#endif +#if N < 1 || N > 6 +# error N must be in 1..6 #endif -#ifdef BYFOUR - local unsigned long crc32_little OF((unsigned long, - const unsigned char FAR *, z_size_t)); - local unsigned long crc32_big OF((unsigned long, - const unsigned char FAR *, z_size_t)); -# define TBLS 8 + +/* + z_crc_t must be at least 32 bits. z_word_t must be at least as long as + z_crc_t. It is assumed here that z_word_t is either 32 bits or 64 bits, and + that bytes are eight bits. + */ + +/* + Define W and the associated z_word_t type. If W is not defined, then a + braided calculation is not used, and the associated tables and code are not + compiled. + */ +#ifdef Z_TESTW +# if Z_TESTW-1 != -1 +# define W Z_TESTW +# endif #else -# define TBLS 1 -#endif /* BYFOUR */ +# ifdef MAKECRCH +# define W 8 /* required for MAKECRCH */ +# else +# if defined(__x86_64__) || defined(__aarch64__) +# define W 8 +# else +# define W 4 +# endif +# endif +#endif +#ifdef W +# if W == 8 && defined(Z_U8) + typedef Z_U8 z_word_t; +# elif defined(Z_U4) +# undef W +# define W 4 + typedef Z_U4 z_word_t; +# else +# undef W +# endif +#endif -/* Local functions for crc concatenation */ -local unsigned long gf2_matrix_times OF((unsigned long *mat, - unsigned long vec)); -local void gf2_matrix_square OF((unsigned long *square, unsigned long *mat)); -local uLong crc32_combine_ OF((uLong crc1, uLong crc2, z_off64_t len2)); +/* Local functions. */ +local z_crc_t multmodp OF((z_crc_t a, z_crc_t b)); +local z_crc_t x2nmodp OF((z_off64_t n, unsigned k)); +/* If available, use the ARM processor CRC32 instruction. */ +#if defined(__aarch64__) && defined(__ARM_FEATURE_CRC32) && W == 8 +# define ARMCRC32 +#endif + +#if defined(W) && (!defined(ARMCRC32) || defined(DYNAMIC_CRC_TABLE)) +/* + Swap the bytes in a z_word_t to convert between little and big endian. Any + self-respecting compiler will optimize this to a single machine byte-swap + instruction, if one is available. This assumes that word_t is either 32 bits + or 64 bits. + */ +local z_word_t byte_swap(word) + z_word_t word; +{ +# if W == 8 + return + (word & 0xff00000000000000) >> 56 | + (word & 0xff000000000000) >> 40 | + (word & 0xff0000000000) >> 24 | + (word & 0xff00000000) >> 8 | + (word & 0xff000000) << 8 | + (word & 0xff0000) << 24 | + (word & 0xff00) << 40 | + (word & 0xff) << 56; +# else /* W == 4 */ + return + (word & 0xff000000) >> 24 | + (word & 0xff0000) >> 8 | + (word & 0xff00) << 8 | + (word & 0xff) << 24; +# endif +} +#endif + +/* CRC polynomial. */ +#define POLY 0xedb88320 /* p(x) reflected, with x^32 implied */ #ifdef DYNAMIC_CRC_TABLE -local volatile int crc_table_empty = 1; -local z_crc_t FAR crc_table[TBLS][256]; +local z_crc_t FAR crc_table[256]; +local z_crc_t FAR x2n_table[32]; local void make_crc_table OF((void)); +#ifdef W + local z_word_t FAR crc_big_table[256]; + local z_crc_t FAR crc_braid_table[W][256]; + local z_word_t FAR crc_braid_big_table[W][256]; + local void braid OF((z_crc_t [][256], z_word_t [][256], int, int)); +#endif #ifdef MAKECRCH - local void write_table OF((FILE *, const z_crc_t FAR *)); + local void write_table OF((FILE *, const z_crc_t FAR *, int)); + local void write_table32hi OF((FILE *, const z_word_t FAR *, int)); + local void write_table64 OF((FILE *, const z_word_t FAR *, int)); #endif /* MAKECRCH */ + +/* + Define a once() function depending on the availability of atomics. If this is + compiled with DYNAMIC_CRC_TABLE defined, and if CRCs will be computed in + multiple threads, and if atomics are not available, then get_crc_table() must + be called to initialize the tables and must return before any threads are + allowed to compute or combine CRCs. + */ + +/* Definition of once functionality. */ +typedef struct once_s once_t; +local void once OF((once_t *, void (*)(void))); + +/* Check for the availability of atomics. */ +#if defined(__STDC__) && __STDC_VERSION__ >= 201112L && \ + !defined(__STDC_NO_ATOMICS__) + +#include + +/* Structure for once(), which must be initialized with ONCE_INIT. */ +struct once_s { + atomic_flag begun; + atomic_int done; +}; +#define ONCE_INIT {ATOMIC_FLAG_INIT, 0} + +/* + Run the provided init() function exactly once, even if multiple threads + invoke once() at the same time. The state must be a once_t initialized with + ONCE_INIT. + */ +local void once(state, init) + once_t *state; + void (*init)(void); +{ + if (!atomic_load(&state->done)) { + if (atomic_flag_test_and_set(&state->begun)) + while (!atomic_load(&state->done)) + ; + else { + init(); + atomic_store(&state->done, 1); + } + } +} + +#else /* no atomics */ + +/* Structure for once(), which must be initialized with ONCE_INIT. */ +struct once_s { + volatile int begun; + volatile int done; +}; +#define ONCE_INIT {0, 0} + +/* Test and set. Alas, not atomic, but tries to minimize the period of + vulnerability. */ +local int test_and_set OF((int volatile *)); +local int test_and_set(flag) + int volatile *flag; +{ + int was; + + was = *flag; + *flag = 1; + return was; +} + +/* Run the provided init() function once. This is not thread-safe. */ +local void once(state, init) + once_t *state; + void (*init)(void); +{ + if (!state->done) { + if (test_and_set(&state->begun)) + while (!state->done) + ; + else { + init(); + state->done = 1; + } + } +} + +#endif + +/* State for once(). */ +local once_t made = ONCE_INIT; + /* Generate tables for a byte-wise 32-bit CRC calculation on the polynomial: x^32+x^26+x^23+x^22+x^16+x^12+x^11+x^10+x^8+x^7+x^5+x^4+x^2+x+1. Polynomials over GF(2) are represented in binary, one bit per coefficient, - with the lowest powers in the most significant bit. Then adding polynomials + with the lowest powers in the most significant bit. Then adding polynomials is just exclusive-or, and multiplying a polynomial by x is a right shift by - one. If we call the above polynomial p, and represent a byte as the + one. If we call the above polynomial p, and represent a byte as the polynomial q, also with the lowest power in the most significant bit (so the - byte 0xb1 is the polynomial x^7+x^3+x+1), then the CRC is (q*x^32) mod p, + byte 0xb1 is the polynomial x^7+x^3+x^2+1), then the CRC is (q*x^32) mod p, where a mod b means the remainder after dividing a by b. This calculation is done using the shift-register method of multiplying and - taking the remainder. The register is initialized to zero, and for each + taking the remainder. The register is initialized to zero, and for each incoming bit, x^32 is added mod p to the register if the bit is a one (where - x^32 mod p is p+x^32 = x^26+...+1), and the register is multiplied mod p by - x (which is shifting right by one and adding x^32 mod p if the bit shifted - out is a one). We start with the highest power (least significant bit) of - q and repeat for all eight bits of q. - - The first table is simply the CRC of all possible eight bit values. This is - all the information needed to generate CRCs on data a byte at a time for all - combinations of CRC register values and incoming bytes. The remaining tables - allow for word-at-a-time CRC calculation for both big-endian and little- - endian machines, where a word is four bytes. -*/ + x^32 mod p is p+x^32 = x^26+...+1), and the register is multiplied mod p by x + (which is shifting right by one and adding x^32 mod p if the bit shifted out + is a one). We start with the highest power (least significant bit) of q and + repeat for all eight bits of q. + + The table is simply the CRC of all possible eight bit values. This is all the + information needed to generate CRCs on data a byte at a time for all + combinations of CRC register values and incoming bytes. + */ + local void make_crc_table() { - z_crc_t c; - int n, k; - z_crc_t poly; /* polynomial exclusive-or pattern */ - /* terms of polynomial defining this crc (except x^32): */ - static volatile int first = 1; /* flag to limit concurrent making */ - static const unsigned char p[] = {0,1,2,4,5,7,8,10,11,12,16,22,23,26}; - - /* See if another task is already doing this (not thread-safe, but better - than nothing -- significantly reduces duration of vulnerability in - case the advice about DYNAMIC_CRC_TABLE is ignored) */ - if (first) { - first = 0; - - /* make exclusive-or pattern from polynomial (0xedb88320UL) */ - poly = 0; - for (n = 0; n < (int)(sizeof(p)/sizeof(unsigned char)); n++) - poly |= (z_crc_t)1 << (31 - p[n]); - - /* generate a crc for every 8-bit value */ - for (n = 0; n < 256; n++) { - c = (z_crc_t)n; - for (k = 0; k < 8; k++) - c = c & 1 ? poly ^ (c >> 1) : c >> 1; - crc_table[0][n] = c; - } + unsigned i, j, n; + z_crc_t p; -#ifdef BYFOUR - /* generate crc for each value followed by one, two, and three zeros, - and then the byte reversal of those as well as the first table */ - for (n = 0; n < 256; n++) { - c = crc_table[0][n]; - crc_table[4][n] = ZSWAP32(c); - for (k = 1; k < 4; k++) { - c = crc_table[0][c & 0xff] ^ (c >> 8); - crc_table[k][n] = c; - crc_table[k + 4][n] = ZSWAP32(c); - } - } -#endif /* BYFOUR */ - - crc_table_empty = 0; - } - else { /* not first */ - /* wait for the other guy to finish (not efficient, but rare) */ - while (crc_table_empty) - ; + /* initialize the CRC of bytes tables */ + for (i = 0; i < 256; i++) { + p = i; + for (j = 0; j < 8; j++) + p = p & 1 ? (p >> 1) ^ POLY : p >> 1; + crc_table[i] = p; +#ifdef W + crc_big_table[i] = byte_swap(p); +#endif } + /* initialize the x^2^n mod p(x) table */ + p = (z_crc_t)1 << 30; /* x^1 */ + x2n_table[0] = p; + for (n = 1; n < 32; n++) + x2n_table[n] = p = multmodp(p, p); + +#ifdef W + /* initialize the braiding tables -- needs x2n_table[] */ + braid(crc_braid_table, crc_braid_big_table, N, W); +#endif + #ifdef MAKECRCH - /* write out CRC tables to crc32.h */ { + /* + The crc32.h header file contains tables for both 32-bit and 64-bit + z_word_t's, and so requires a 64-bit type be available. In that case, + z_word_t must be defined to be 64-bits. This code then also generates + and writes out the tables for the case that z_word_t is 32 bits. + */ +#if !defined(W) || W != 8 +# error Need a 64-bit integer type in order to generate crc32.h. +#endif FILE *out; + int k, n; + z_crc_t ltl[8][256]; + z_word_t big[8][256]; out = fopen("crc32.h", "w"); if (out == NULL) return; - fprintf(out, "/* crc32.h -- tables for rapid CRC calculation\n"); - fprintf(out, " * Generated automatically by crc32.c\n */\n\n"); - fprintf(out, "local const z_crc_t FAR "); - fprintf(out, "crc_table[TBLS][256] =\n{\n {\n"); - write_table(out, crc_table[0]); -# ifdef BYFOUR - fprintf(out, "#ifdef BYFOUR\n"); - for (k = 1; k < 8; k++) { - fprintf(out, " },\n {\n"); - write_table(out, crc_table[k]); + + /* write out little-endian CRC table to crc32.h */ + fprintf(out, + "/* crc32.h -- tables for rapid CRC calculation\n" + " * Generated automatically by crc32.c\n */\n" + "\n" + "local const z_crc_t FAR crc_table[] = {\n" + " "); + write_table(out, crc_table, 256); + fprintf(out, + "};\n"); + + /* write out big-endian CRC table for 64-bit z_word_t to crc32.h */ + fprintf(out, + "\n" + "#ifdef W\n" + "\n" + "#if W == 8\n" + "\n" + "local const z_word_t FAR crc_big_table[] = {\n" + " "); + write_table64(out, crc_big_table, 256); + fprintf(out, + "};\n"); + + /* write out big-endian CRC table for 32-bit z_word_t to crc32.h */ + fprintf(out, + "\n" + "#else /* W == 4 */\n" + "\n" + "local const z_word_t FAR crc_big_table[] = {\n" + " "); + write_table32hi(out, crc_big_table, 256); + fprintf(out, + "};\n" + "\n" + "#endif\n"); + + /* write out braid tables for each value of N */ + for (n = 1; n <= 6; n++) { + fprintf(out, + "\n" + "#if N == %d\n", n); + + /* compute braid tables for this N and 64-bit word_t */ + braid(ltl, big, n, 8); + + /* write out braid tables for 64-bit z_word_t to crc32.h */ + fprintf(out, + "\n" + "#if W == 8\n" + "\n" + "local const z_crc_t FAR crc_braid_table[][256] = {\n"); + for (k = 0; k < 8; k++) { + fprintf(out, " {"); + write_table(out, ltl[k], 256); + fprintf(out, "}%s", k < 7 ? ",\n" : ""); + } + fprintf(out, + "};\n" + "\n" + "local const z_word_t FAR crc_braid_big_table[][256] = {\n"); + for (k = 0; k < 8; k++) { + fprintf(out, " {"); + write_table64(out, big[k], 256); + fprintf(out, "}%s", k < 7 ? ",\n" : ""); + } + fprintf(out, + "};\n"); + + /* compute braid tables for this N and 32-bit word_t */ + braid(ltl, big, n, 4); + + /* write out braid tables for 32-bit z_word_t to crc32.h */ + fprintf(out, + "\n" + "#else /* W == 4 */\n" + "\n" + "local const z_crc_t FAR crc_braid_table[][256] = {\n"); + for (k = 0; k < 4; k++) { + fprintf(out, " {"); + write_table(out, ltl[k], 256); + fprintf(out, "}%s", k < 3 ? ",\n" : ""); + } + fprintf(out, + "};\n" + "\n" + "local const z_word_t FAR crc_braid_big_table[][256] = {\n"); + for (k = 0; k < 4; k++) { + fprintf(out, " {"); + write_table32hi(out, big[k], 256); + fprintf(out, "}%s", k < 3 ? ",\n" : ""); + } + fprintf(out, + "};\n" + "\n" + "#endif\n" + "\n" + "#endif\n"); } - fprintf(out, "#endif\n"); -# endif /* BYFOUR */ - fprintf(out, " }\n};\n"); + fprintf(out, + "\n" + "#endif\n"); + + /* write out zeros operator table to crc32.h */ + fprintf(out, + "\n" + "local const z_crc_t FAR x2n_table[] = {\n" + " "); + write_table(out, x2n_table, 32); + fprintf(out, + "};\n"); fclose(out); } #endif /* MAKECRCH */ } #ifdef MAKECRCH -local void write_table(out, table) + +/* + Write the 32-bit values in table[0..k-1] to out, five per line in + hexadecimal separated by commas. + */ +local void write_table(out, table, k) FILE *out; const z_crc_t FAR *table; + int k; { int n; - for (n = 0; n < 256; n++) - fprintf(out, "%s0x%08lxUL%s", n % 5 ? "" : " ", + for (n = 0; n < k; n++) + fprintf(out, "%s0x%08lx%s", n == 0 || n % 5 ? "" : " ", (unsigned long)(table[n]), - n == 255 ? "\n" : (n % 5 == 4 ? ",\n" : ", ")); + n == k - 1 ? "" : (n % 5 == 4 ? ",\n" : ", ")); } + +/* + Write the high 32-bits of each value in table[0..k-1] to out, five per line + in hexadecimal separated by commas. + */ +local void write_table32hi(out, table, k) +FILE *out; +const z_word_t FAR *table; +int k; +{ + int n; + + for (n = 0; n < k; n++) + fprintf(out, "%s0x%08lx%s", n == 0 || n % 5 ? "" : " ", + (unsigned long)(table[n] >> 32), + n == k - 1 ? "" : (n % 5 == 4 ? ",\n" : ", ")); +} + +/* + Write the 64-bit values in table[0..k-1] to out, three per line in + hexadecimal separated by commas. This assumes that if there is a 64-bit + type, then there is also a long long integer type, and it is at least 64 + bits. If not, then the type cast and format string can be adjusted + accordingly. + */ +local void write_table64(out, table, k) + FILE *out; + const z_word_t FAR *table; + int k; +{ + int n; + + for (n = 0; n < k; n++) + fprintf(out, "%s0x%016llx%s", n == 0 || n % 3 ? "" : " ", + (unsigned long long)(table[n]), + n == k - 1 ? "" : (n % 3 == 2 ? ",\n" : ", ")); +} + +/* Actually do the deed. */ +int main() +{ + make_crc_table(); + return 0; +} + #endif /* MAKECRCH */ +#ifdef W +/* + Generate the little and big-endian braid tables for the given n and z_word_t + size w. Each array must have room for w blocks of 256 elements. + */ +local void braid(ltl, big, n, w) + z_crc_t ltl[][256]; + z_word_t big[][256]; + int n; + int w; +{ + int k; + z_crc_t i, p, q; + for (k = 0; k < w; k++) { + p = x2nmodp((n * w + 3 - k) << 3, 0); + ltl[k][0] = 0; + big[w - 1 - k][0] = 0; + for (i = 1; i < 256; i++) { + ltl[k][i] = q = multmodp(i << 24, p); + big[w - 1 - k][i] = byte_swap(q); + } + } +} +#endif + #else /* !DYNAMIC_CRC_TABLE */ /* ======================================================================== - * Tables of CRC-32s of all single-byte values, made by make_crc_table(). + * Tables for byte-wise and braided CRC-32 calculations, and a table of powers + * of x for combining CRC-32s, all made by make_crc_table(). */ #include "crc32.h" #endif /* DYNAMIC_CRC_TABLE */ +/* ======================================================================== + * Routines used for CRC calculation. Some are also required for the table + * generation above. + */ + +/* + Return a(x) multiplied by b(x) modulo p(x), where p(x) is the CRC polynomial, + reflected. For speed, this requires that a not be zero. + */ +local z_crc_t multmodp(a, b) + z_crc_t a; + z_crc_t b; +{ + z_crc_t m, p; + + m = (z_crc_t)1 << 31; + p = 0; + for (;;) { + if (a & m) { + p ^= b; + if ((a & (m - 1)) == 0) + break; + } + m >>= 1; + b = b & 1 ? (b >> 1) ^ POLY : b >> 1; + } + return p; +} + +/* + Return x^(n * 2^k) modulo p(x). Requires that x2n_table[] has been + initialized. + */ +local z_crc_t x2nmodp(n, k) + z_off64_t n; + unsigned k; +{ + z_crc_t p; + + p = (z_crc_t)1 << 31; /* x^0 == 1 */ + while (n) { + if (n & 1) + p = multmodp(x2n_table[k & 31], p); + n >>= 1; + k++; + } + return p; +} + /* ========================================================================= - * This function can be used by asm versions of crc32() + * This function can be used by asm versions of crc32(), and to force the + * generation of the CRC tables in a threaded application. */ const z_crc_t FAR * ZEXPORT get_crc_table() { #ifdef DYNAMIC_CRC_TABLE - if (crc_table_empty) - make_crc_table(); + once(&made, make_crc_table); #endif /* DYNAMIC_CRC_TABLE */ return (const z_crc_t FAR *)crc_table; } -/* ========================================================================= */ -#define DO1 crc = crc_table[0][((int)crc ^ (*buf++)) & 0xff] ^ (crc >> 8) -#define DO8 DO1; DO1; DO1; DO1; DO1; DO1; DO1; DO1 +/* ========================================================================= + * Use ARM machine instructions if available. This will compute the CRC about + * ten times faster than the braided calculation. This code does not check for + * the presence of the CRC instruction at run time. __ARM_FEATURE_CRC32 will + * only be defined if the compilation specifies an ARM processor architecture + * that has the instructions. For example, compiling with -march=armv8.1-a or + * -march=armv8-a+crc, or -march=native if the compile machine has the crc32 + * instructions. + */ +#ifdef ARMCRC32 + +/* + Constants empirically determined to maximize speed. These values are from + measurements on a Cortex-A57. Your mileage may vary. + */ +#define Z_BATCH 3990 /* number of words in a batch */ +#define Z_BATCH_ZEROS 0xa10d3d0c /* computed from Z_BATCH = 3990 */ +#define Z_BATCH_MIN 800 /* fewest words in a final batch */ -/* ========================================================================= */ unsigned long ZEXPORT crc32_z(crc, buf, len) unsigned long crc; const unsigned char FAR *buf; z_size_t len; { - if (buf == Z_NULL) return 0UL; + z_crc_t val; + z_word_t crc1, crc2; + const z_word_t *word; + z_word_t val0, val1, val2; + z_size_t last, last2, i; + z_size_t num; + + /* Return initial CRC, if requested. */ + if (buf == Z_NULL) return 0; #ifdef DYNAMIC_CRC_TABLE - if (crc_table_empty) - make_crc_table(); + once(&made, make_crc_table); #endif /* DYNAMIC_CRC_TABLE */ -#ifdef BYFOUR - if (sizeof(void *) == sizeof(ptrdiff_t)) { - z_crc_t endian; + /* Pre-condition the CRC */ + crc ^= 0xffffffff; - endian = 1; - if (*((unsigned char *)(&endian))) - return crc32_little(crc, buf, len); - else - return crc32_big(crc, buf, len); + /* Compute the CRC up to a word boundary. */ + while (len && ((z_size_t)buf & 7) != 0) { + len--; + val = *buf++; + __asm__ volatile("crc32b %w0, %w0, %w1" : "+r"(crc) : "r"(val)); } -#endif /* BYFOUR */ - crc = crc ^ 0xffffffffUL; - while (len >= 8) { - DO8; - len -= 8; + + /* Prepare to compute the CRC on full 64-bit words word[0..num-1]. */ + word = (z_word_t const *)buf; + num = len >> 3; + len &= 7; + + /* Do three interleaved CRCs to realize the throughput of one crc32x + instruction per cycle. Each CRC is calcuated on Z_BATCH words. The three + CRCs are combined into a single CRC after each set of batches. */ + while (num >= 3 * Z_BATCH) { + crc1 = 0; + crc2 = 0; + for (i = 0; i < Z_BATCH; i++) { + val0 = word[i]; + val1 = word[i + Z_BATCH]; + val2 = word[i + 2 * Z_BATCH]; + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc) : "r"(val0)); + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc1) : "r"(val1)); + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc2) : "r"(val2)); + } + word += 3 * Z_BATCH; + num -= 3 * Z_BATCH; + crc = multmodp(Z_BATCH_ZEROS, crc) ^ crc1; + crc = multmodp(Z_BATCH_ZEROS, crc) ^ crc2; } - if (len) do { - DO1; - } while (--len); - return crc ^ 0xffffffffUL; -} -/* ========================================================================= */ -unsigned long ZEXPORT crc32(crc, buf, len) - unsigned long crc; - const unsigned char FAR *buf; - uInt len; -{ - return crc32_z(crc, buf, len); + /* Do one last smaller batch with the remaining words, if there are enough + to pay for the combination of CRCs. */ + last = num / 3; + if (last >= Z_BATCH_MIN) { + last2 = last << 1; + crc1 = 0; + crc2 = 0; + for (i = 0; i < last; i++) { + val0 = word[i]; + val1 = word[i + last]; + val2 = word[i + last2]; + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc) : "r"(val0)); + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc1) : "r"(val1)); + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc2) : "r"(val2)); + } + word += 3 * last; + num -= 3 * last; + val = x2nmodp(last, 6); + crc = multmodp(val, crc) ^ crc1; + crc = multmodp(val, crc) ^ crc2; + } + + /* Compute the CRC on any remaining words. */ + for (i = 0; i < num; i++) { + val0 = word[i]; + __asm__ volatile("crc32x %w0, %w0, %x1" : "+r"(crc) : "r"(val0)); + } + word += num; + + /* Complete the CRC on any remaining bytes. */ + buf = (const unsigned char FAR *)word; + while (len) { + len--; + val = *buf++; + __asm__ volatile("crc32b %w0, %w0, %w1" : "+r"(crc) : "r"(val)); + } + + /* Return the CRC, post-conditioned. */ + return crc ^ 0xffffffff; } -#ifdef BYFOUR +#else + +#ifdef W /* - This BYFOUR code accesses the passed unsigned char * buffer with a 32-bit - integer pointer type. This violates the strict aliasing rule, where a - compiler can assume, for optimization purposes, that two pointers to - fundamentally different types won't ever point to the same memory. This can - manifest as a problem only if one of the pointers is written to. This code - only reads from those pointers. So long as this code remains isolated in - this compilation unit, there won't be a problem. For this reason, this code - should not be copied and pasted into a compilation unit in which other code - writes to the buffer that is passed to these routines. + Return the CRC of the W bytes in the word_t data, taking the + least-significant byte of the word as the first byte of data, without any pre + or post conditioning. This is used to combine the CRCs of each braid. */ +local z_crc_t crc_word(data) + z_word_t data; +{ + int k; + for (k = 0; k < W; k++) + data = (data >> 8) ^ crc_table[data & 0xff]; + return (z_crc_t)data; +} -/* ========================================================================= */ -#define DOLIT4 c ^= *buf4++; \ - c = crc_table[3][c & 0xff] ^ crc_table[2][(c >> 8) & 0xff] ^ \ - crc_table[1][(c >> 16) & 0xff] ^ crc_table[0][c >> 24] -#define DOLIT32 DOLIT4; DOLIT4; DOLIT4; DOLIT4; DOLIT4; DOLIT4; DOLIT4; DOLIT4 +local z_word_t crc_word_big(data) + z_word_t data; +{ + int k; + for (k = 0; k < W; k++) + data = (data << 8) ^ + crc_big_table[(data >> ((W - 1) << 3)) & 0xff]; + return data; +} + +#endif /* ========================================================================= */ -local unsigned long crc32_little(crc, buf, len) +unsigned long ZEXPORT crc32_z(crc, buf, len) unsigned long crc; const unsigned char FAR *buf; z_size_t len; { - register z_crc_t c; - register const z_crc_t FAR *buf4; + /* Return initial CRC, if requested. */ + if (buf == Z_NULL) return 0; - c = (z_crc_t)crc; - c = ~c; - while (len && ((ptrdiff_t)buf & 3)) { - c = crc_table[0][(c ^ *buf++) & 0xff] ^ (c >> 8); - len--; - } +#ifdef DYNAMIC_CRC_TABLE + once(&made, make_crc_table); +#endif /* DYNAMIC_CRC_TABLE */ - buf4 = (const z_crc_t FAR *)(const void FAR *)buf; - while (len >= 32) { - DOLIT32; - len -= 32; - } - while (len >= 4) { - DOLIT4; - len -= 4; - } - buf = (const unsigned char FAR *)buf4; + /* Pre-condition the CRC */ + crc ^= 0xffffffff; - if (len) do { - c = crc_table[0][(c ^ *buf++) & 0xff] ^ (c >> 8); - } while (--len); - c = ~c; - return (unsigned long)c; -} +#ifdef W -/* ========================================================================= */ -#define DOBIG4 c ^= *buf4++; \ - c = crc_table[4][c & 0xff] ^ crc_table[5][(c >> 8) & 0xff] ^ \ - crc_table[6][(c >> 16) & 0xff] ^ crc_table[7][c >> 24] -#define DOBIG32 DOBIG4; DOBIG4; DOBIG4; DOBIG4; DOBIG4; DOBIG4; DOBIG4; DOBIG4 + /* If provided enough bytes, do a braided CRC calculation. */ + if (len >= N * W + W - 1) { + z_size_t blks; + z_word_t const *words; + unsigned endian; + int k; -/* ========================================================================= */ -local unsigned long crc32_big(crc, buf, len) - unsigned long crc; - const unsigned char FAR *buf; - z_size_t len; -{ - register z_crc_t c; - register const z_crc_t FAR *buf4; + /* Compute the CRC up to a z_word_t boundary. */ + while (len && ((z_size_t)buf & (W - 1)) != 0) { + len--; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + } - c = ZSWAP32((z_crc_t)crc); - c = ~c; - while (len && ((ptrdiff_t)buf & 3)) { - c = crc_table[4][(c >> 24) ^ *buf++] ^ (c << 8); - len--; + /* Compute the CRC on as many N z_word_t blocks as are available. */ + blks = len / (N * W); + len -= blks * N * W; + words = (z_word_t const *)buf; + + /* Do endian check at execution time instead of compile time, since ARM + processors can change the endianess at execution time. If the + compiler knows what the endianess will be, it can optimize out the + check and the unused branch. */ + endian = 1; + if (*(unsigned char *)&endian) { + /* Little endian. */ + + z_crc_t crc0; + z_word_t word0; +#if N > 1 + z_crc_t crc1; + z_word_t word1; +#if N > 2 + z_crc_t crc2; + z_word_t word2; +#if N > 3 + z_crc_t crc3; + z_word_t word3; +#if N > 4 + z_crc_t crc4; + z_word_t word4; +#if N > 5 + z_crc_t crc5; + z_word_t word5; +#endif +#endif +#endif +#endif +#endif + + /* Initialize the CRC for each braid. */ + crc0 = crc; +#if N > 1 + crc1 = 0; +#if N > 2 + crc2 = 0; +#if N > 3 + crc3 = 0; +#if N > 4 + crc4 = 0; +#if N > 5 + crc5 = 0; +#endif +#endif +#endif +#endif +#endif + + /* + Process the first blks-1 blocks, computing the CRCs on each braid + independently. + */ + while (--blks) { + /* Load the word for each braid into registers. */ + word0 = crc0 ^ words[0]; +#if N > 1 + word1 = crc1 ^ words[1]; +#if N > 2 + word2 = crc2 ^ words[2]; +#if N > 3 + word3 = crc3 ^ words[3]; +#if N > 4 + word4 = crc4 ^ words[4]; +#if N > 5 + word5 = crc5 ^ words[5]; +#endif +#endif +#endif +#endif +#endif + words += N; + + /* Compute and update the CRC for each word. The loop should + get unrolled. */ + crc0 = crc_braid_table[0][word0 & 0xff]; +#if N > 1 + crc1 = crc_braid_table[0][word1 & 0xff]; +#if N > 2 + crc2 = crc_braid_table[0][word2 & 0xff]; +#if N > 3 + crc3 = crc_braid_table[0][word3 & 0xff]; +#if N > 4 + crc4 = crc_braid_table[0][word4 & 0xff]; +#if N > 5 + crc5 = crc_braid_table[0][word5 & 0xff]; +#endif +#endif +#endif +#endif +#endif + for (k = 1; k < W; k++) { + crc0 ^= crc_braid_table[k][(word0 >> (k << 3)) & 0xff]; +#if N > 1 + crc1 ^= crc_braid_table[k][(word1 >> (k << 3)) & 0xff]; +#if N > 2 + crc2 ^= crc_braid_table[k][(word2 >> (k << 3)) & 0xff]; +#if N > 3 + crc3 ^= crc_braid_table[k][(word3 >> (k << 3)) & 0xff]; +#if N > 4 + crc4 ^= crc_braid_table[k][(word4 >> (k << 3)) & 0xff]; +#if N > 5 + crc5 ^= crc_braid_table[k][(word5 >> (k << 3)) & 0xff]; +#endif +#endif +#endif +#endif +#endif + } + } + + /* + Process the last block, combining the CRCs of the N braids at the + same time. + */ + crc = crc_word(crc0 ^ words[0]); +#if N > 1 + crc = crc_word(crc1 ^ words[1] ^ crc); +#if N > 2 + crc = crc_word(crc2 ^ words[2] ^ crc); +#if N > 3 + crc = crc_word(crc3 ^ words[3] ^ crc); +#if N > 4 + crc = crc_word(crc4 ^ words[4] ^ crc); +#if N > 5 + crc = crc_word(crc5 ^ words[5] ^ crc); +#endif +#endif +#endif +#endif +#endif + words += N; + } + else { + /* Big endian. */ + + z_word_t crc0, word0, comb; +#if N > 1 + z_word_t crc1, word1; +#if N > 2 + z_word_t crc2, word2; +#if N > 3 + z_word_t crc3, word3; +#if N > 4 + z_word_t crc4, word4; +#if N > 5 + z_word_t crc5, word5; +#endif +#endif +#endif +#endif +#endif + + /* Initialize the CRC for each braid. */ + crc0 = byte_swap(crc); +#if N > 1 + crc1 = 0; +#if N > 2 + crc2 = 0; +#if N > 3 + crc3 = 0; +#if N > 4 + crc4 = 0; +#if N > 5 + crc5 = 0; +#endif +#endif +#endif +#endif +#endif + + /* + Process the first blks-1 blocks, computing the CRCs on each braid + independently. + */ + while (--blks) { + /* Load the word for each braid into registers. */ + word0 = crc0 ^ words[0]; +#if N > 1 + word1 = crc1 ^ words[1]; +#if N > 2 + word2 = crc2 ^ words[2]; +#if N > 3 + word3 = crc3 ^ words[3]; +#if N > 4 + word4 = crc4 ^ words[4]; +#if N > 5 + word5 = crc5 ^ words[5]; +#endif +#endif +#endif +#endif +#endif + words += N; + + /* Compute and update the CRC for each word. The loop should + get unrolled. */ + crc0 = crc_braid_big_table[0][word0 & 0xff]; +#if N > 1 + crc1 = crc_braid_big_table[0][word1 & 0xff]; +#if N > 2 + crc2 = crc_braid_big_table[0][word2 & 0xff]; +#if N > 3 + crc3 = crc_braid_big_table[0][word3 & 0xff]; +#if N > 4 + crc4 = crc_braid_big_table[0][word4 & 0xff]; +#if N > 5 + crc5 = crc_braid_big_table[0][word5 & 0xff]; +#endif +#endif +#endif +#endif +#endif + for (k = 1; k < W; k++) { + crc0 ^= crc_braid_big_table[k][(word0 >> (k << 3)) & 0xff]; +#if N > 1 + crc1 ^= crc_braid_big_table[k][(word1 >> (k << 3)) & 0xff]; +#if N > 2 + crc2 ^= crc_braid_big_table[k][(word2 >> (k << 3)) & 0xff]; +#if N > 3 + crc3 ^= crc_braid_big_table[k][(word3 >> (k << 3)) & 0xff]; +#if N > 4 + crc4 ^= crc_braid_big_table[k][(word4 >> (k << 3)) & 0xff]; +#if N > 5 + crc5 ^= crc_braid_big_table[k][(word5 >> (k << 3)) & 0xff]; +#endif +#endif +#endif +#endif +#endif + } + } + + /* + Process the last block, combining the CRCs of the N braids at the + same time. + */ + comb = crc_word_big(crc0 ^ words[0]); +#if N > 1 + comb = crc_word_big(crc1 ^ words[1] ^ comb); +#if N > 2 + comb = crc_word_big(crc2 ^ words[2] ^ comb); +#if N > 3 + comb = crc_word_big(crc3 ^ words[3] ^ comb); +#if N > 4 + comb = crc_word_big(crc4 ^ words[4] ^ comb); +#if N > 5 + comb = crc_word_big(crc5 ^ words[5] ^ comb); +#endif +#endif +#endif +#endif +#endif + words += N; + crc = byte_swap(comb); + } + + /* + Update the pointer to the remaining bytes to process. + */ + buf = (unsigned char const *)words; } - buf4 = (const z_crc_t FAR *)(const void FAR *)buf; - while (len >= 32) { - DOBIG32; - len -= 32; +#endif /* W */ + + /* Complete the computation of the CRC on any remaining bytes. */ + while (len >= 8) { + len -= 8; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; } - while (len >= 4) { - DOBIG4; - len -= 4; + while (len) { + len--; + crc = (crc >> 8) ^ crc_table[(crc ^ *buf++) & 0xff]; } - buf = (const unsigned char FAR *)buf4; - if (len) do { - c = crc_table[4][(c >> 24) ^ *buf++] ^ (c << 8); - } while (--len); - c = ~c; - return (unsigned long)(ZSWAP32(c)); + /* Return the CRC, post-conditioned. */ + return crc ^ 0xffffffff; } -#endif /* BYFOUR */ - -#define GF2_DIM 32 /* dimension of GF(2) vectors (length of CRC) */ +#endif /* ========================================================================= */ -local unsigned long gf2_matrix_times(mat, vec) - unsigned long *mat; - unsigned long vec; +unsigned long ZEXPORT crc32(crc, buf, len) + unsigned long crc; + const unsigned char FAR *buf; + uInt len; { - unsigned long sum; - - sum = 0; - while (vec) { - if (vec & 1) - sum ^= *mat; - vec >>= 1; - mat++; - } - return sum; + return crc32_z(crc, buf, len); } /* ========================================================================= */ -local void gf2_matrix_square(square, mat) - unsigned long *square; - unsigned long *mat; +uLong ZEXPORT crc32_combine64(crc1, crc2, len2) + uLong crc1; + uLong crc2; + z_off64_t len2; { - int n; - - for (n = 0; n < GF2_DIM; n++) - square[n] = gf2_matrix_times(mat, mat[n]); +#ifdef DYNAMIC_CRC_TABLE + once(&made, make_crc_table); +#endif /* DYNAMIC_CRC_TABLE */ + return multmodp(x2nmodp(len2, 3), crc1) ^ crc2; } /* ========================================================================= */ -local uLong crc32_combine_(crc1, crc2, len2) +uLong ZEXPORT crc32_combine(crc1, crc2, len2) uLong crc1; uLong crc2; - z_off64_t len2; + z_off_t len2; { - int n; - unsigned long row; - unsigned long even[GF2_DIM]; /* even-power-of-two zeros operator */ - unsigned long odd[GF2_DIM]; /* odd-power-of-two zeros operator */ - - /* degenerate case (also disallow negative lengths) */ - if (len2 <= 0) - return crc1; - - /* put operator for one zero bit in odd */ - odd[0] = 0xedb88320UL; /* CRC-32 polynomial */ - row = 1; - for (n = 1; n < GF2_DIM; n++) { - odd[n] = row; - row <<= 1; - } + return crc32_combine64(crc1, crc2, len2); +} - /* put operator for two zero bits in even */ - gf2_matrix_square(even, odd); - - /* put operator for four zero bits in odd */ - gf2_matrix_square(odd, even); - - /* apply len2 zeros to crc1 (first square will put the operator for one - zero byte, eight zero bits, in even) */ - do { - /* apply zeros operator for this bit of len2 */ - gf2_matrix_square(even, odd); - if (len2 & 1) - crc1 = gf2_matrix_times(even, crc1); - len2 >>= 1; - - /* if no more bits set, then done */ - if (len2 == 0) - break; - - /* another iteration of the loop with odd and even swapped */ - gf2_matrix_square(odd, even); - if (len2 & 1) - crc1 = gf2_matrix_times(odd, crc1); - len2 >>= 1; - - /* if no more bits set, then done */ - } while (len2 != 0); - - /* return combined crc */ - crc1 ^= crc2; - return crc1; +/* ========================================================================= */ +uLong ZEXPORT crc32_combine_gen64(len2) + z_off64_t len2; +{ +#ifdef DYNAMIC_CRC_TABLE + once(&made, make_crc_table); +#endif /* DYNAMIC_CRC_TABLE */ + return x2nmodp(len2, 3); } /* ========================================================================= */ -uLong ZEXPORT crc32_combine(crc1, crc2, len2) - uLong crc1; - uLong crc2; +uLong ZEXPORT crc32_combine_gen(len2) z_off_t len2; { - return crc32_combine_(crc1, crc2, len2); + return crc32_combine_gen64(len2); } -uLong ZEXPORT crc32_combine64(crc1, crc2, len2) +/* ========================================================================= */ +uLong crc32_combine_op(crc1, crc2, op) uLong crc1; uLong crc2; - z_off64_t len2; + uLong op; { - return crc32_combine_(crc1, crc2, len2); + return multmodp(op, crc1) ^ crc2; } diff --git a/3rdparty/zlib/crc32.h b/3rdparty/zlib/crc32.h index 9e0c77810251..137df68d616c 100644 --- a/3rdparty/zlib/crc32.h +++ b/3rdparty/zlib/crc32.h @@ -2,440 +2,9445 @@ * Generated automatically by crc32.c */ -local const z_crc_t FAR crc_table[TBLS][256] = -{ - { - 0x00000000UL, 0x77073096UL, 0xee0e612cUL, 0x990951baUL, 0x076dc419UL, - 0x706af48fUL, 0xe963a535UL, 0x9e6495a3UL, 0x0edb8832UL, 0x79dcb8a4UL, - 0xe0d5e91eUL, 0x97d2d988UL, 0x09b64c2bUL, 0x7eb17cbdUL, 0xe7b82d07UL, - 0x90bf1d91UL, 0x1db71064UL, 0x6ab020f2UL, 0xf3b97148UL, 0x84be41deUL, - 0x1adad47dUL, 0x6ddde4ebUL, 0xf4d4b551UL, 0x83d385c7UL, 0x136c9856UL, - 0x646ba8c0UL, 0xfd62f97aUL, 0x8a65c9ecUL, 0x14015c4fUL, 0x63066cd9UL, - 0xfa0f3d63UL, 0x8d080df5UL, 0x3b6e20c8UL, 0x4c69105eUL, 0xd56041e4UL, - 0xa2677172UL, 0x3c03e4d1UL, 0x4b04d447UL, 0xd20d85fdUL, 0xa50ab56bUL, - 0x35b5a8faUL, 0x42b2986cUL, 0xdbbbc9d6UL, 0xacbcf940UL, 0x32d86ce3UL, - 0x45df5c75UL, 0xdcd60dcfUL, 0xabd13d59UL, 0x26d930acUL, 0x51de003aUL, - 0xc8d75180UL, 0xbfd06116UL, 0x21b4f4b5UL, 0x56b3c423UL, 0xcfba9599UL, - 0xb8bda50fUL, 0x2802b89eUL, 0x5f058808UL, 0xc60cd9b2UL, 0xb10be924UL, - 0x2f6f7c87UL, 0x58684c11UL, 0xc1611dabUL, 0xb6662d3dUL, 0x76dc4190UL, - 0x01db7106UL, 0x98d220bcUL, 0xefd5102aUL, 0x71b18589UL, 0x06b6b51fUL, - 0x9fbfe4a5UL, 0xe8b8d433UL, 0x7807c9a2UL, 0x0f00f934UL, 0x9609a88eUL, - 0xe10e9818UL, 0x7f6a0dbbUL, 0x086d3d2dUL, 0x91646c97UL, 0xe6635c01UL, - 0x6b6b51f4UL, 0x1c6c6162UL, 0x856530d8UL, 0xf262004eUL, 0x6c0695edUL, - 0x1b01a57bUL, 0x8208f4c1UL, 0xf50fc457UL, 0x65b0d9c6UL, 0x12b7e950UL, - 0x8bbeb8eaUL, 0xfcb9887cUL, 0x62dd1ddfUL, 0x15da2d49UL, 0x8cd37cf3UL, - 0xfbd44c65UL, 0x4db26158UL, 0x3ab551ceUL, 0xa3bc0074UL, 0xd4bb30e2UL, - 0x4adfa541UL, 0x3dd895d7UL, 0xa4d1c46dUL, 0xd3d6f4fbUL, 0x4369e96aUL, - 0x346ed9fcUL, 0xad678846UL, 0xda60b8d0UL, 0x44042d73UL, 0x33031de5UL, - 0xaa0a4c5fUL, 0xdd0d7cc9UL, 0x5005713cUL, 0x270241aaUL, 0xbe0b1010UL, - 0xc90c2086UL, 0x5768b525UL, 0x206f85b3UL, 0xb966d409UL, 0xce61e49fUL, - 0x5edef90eUL, 0x29d9c998UL, 0xb0d09822UL, 0xc7d7a8b4UL, 0x59b33d17UL, - 0x2eb40d81UL, 0xb7bd5c3bUL, 0xc0ba6cadUL, 0xedb88320UL, 0x9abfb3b6UL, - 0x03b6e20cUL, 0x74b1d29aUL, 0xead54739UL, 0x9dd277afUL, 0x04db2615UL, - 0x73dc1683UL, 0xe3630b12UL, 0x94643b84UL, 0x0d6d6a3eUL, 0x7a6a5aa8UL, - 0xe40ecf0bUL, 0x9309ff9dUL, 0x0a00ae27UL, 0x7d079eb1UL, 0xf00f9344UL, - 0x8708a3d2UL, 0x1e01f268UL, 0x6906c2feUL, 0xf762575dUL, 0x806567cbUL, - 0x196c3671UL, 0x6e6b06e7UL, 0xfed41b76UL, 0x89d32be0UL, 0x10da7a5aUL, - 0x67dd4accUL, 0xf9b9df6fUL, 0x8ebeeff9UL, 0x17b7be43UL, 0x60b08ed5UL, - 0xd6d6a3e8UL, 0xa1d1937eUL, 0x38d8c2c4UL, 0x4fdff252UL, 0xd1bb67f1UL, - 0xa6bc5767UL, 0x3fb506ddUL, 0x48b2364bUL, 0xd80d2bdaUL, 0xaf0a1b4cUL, - 0x36034af6UL, 0x41047a60UL, 0xdf60efc3UL, 0xa867df55UL, 0x316e8eefUL, - 0x4669be79UL, 0xcb61b38cUL, 0xbc66831aUL, 0x256fd2a0UL, 0x5268e236UL, - 0xcc0c7795UL, 0xbb0b4703UL, 0x220216b9UL, 0x5505262fUL, 0xc5ba3bbeUL, - 0xb2bd0b28UL, 0x2bb45a92UL, 0x5cb36a04UL, 0xc2d7ffa7UL, 0xb5d0cf31UL, - 0x2cd99e8bUL, 0x5bdeae1dUL, 0x9b64c2b0UL, 0xec63f226UL, 0x756aa39cUL, - 0x026d930aUL, 0x9c0906a9UL, 0xeb0e363fUL, 0x72076785UL, 0x05005713UL, - 0x95bf4a82UL, 0xe2b87a14UL, 0x7bb12baeUL, 0x0cb61b38UL, 0x92d28e9bUL, - 0xe5d5be0dUL, 0x7cdcefb7UL, 0x0bdbdf21UL, 0x86d3d2d4UL, 0xf1d4e242UL, - 0x68ddb3f8UL, 0x1fda836eUL, 0x81be16cdUL, 0xf6b9265bUL, 0x6fb077e1UL, - 0x18b74777UL, 0x88085ae6UL, 0xff0f6a70UL, 0x66063bcaUL, 0x11010b5cUL, - 0x8f659effUL, 0xf862ae69UL, 0x616bffd3UL, 0x166ccf45UL, 0xa00ae278UL, - 0xd70dd2eeUL, 0x4e048354UL, 0x3903b3c2UL, 0xa7672661UL, 0xd06016f7UL, - 0x4969474dUL, 0x3e6e77dbUL, 0xaed16a4aUL, 0xd9d65adcUL, 0x40df0b66UL, - 0x37d83bf0UL, 0xa9bcae53UL, 0xdebb9ec5UL, 0x47b2cf7fUL, 0x30b5ffe9UL, - 0xbdbdf21cUL, 0xcabac28aUL, 0x53b39330UL, 0x24b4a3a6UL, 0xbad03605UL, - 0xcdd70693UL, 0x54de5729UL, 0x23d967bfUL, 0xb3667a2eUL, 0xc4614ab8UL, - 0x5d681b02UL, 0x2a6f2b94UL, 0xb40bbe37UL, 0xc30c8ea1UL, 0x5a05df1bUL, - 0x2d02ef8dUL -#ifdef BYFOUR - }, - { - 0x00000000UL, 0x191b3141UL, 0x32366282UL, 0x2b2d53c3UL, 0x646cc504UL, - 0x7d77f445UL, 0x565aa786UL, 0x4f4196c7UL, 0xc8d98a08UL, 0xd1c2bb49UL, - 0xfaefe88aUL, 0xe3f4d9cbUL, 0xacb54f0cUL, 0xb5ae7e4dUL, 0x9e832d8eUL, - 0x87981ccfUL, 0x4ac21251UL, 0x53d92310UL, 0x78f470d3UL, 0x61ef4192UL, - 0x2eaed755UL, 0x37b5e614UL, 0x1c98b5d7UL, 0x05838496UL, 0x821b9859UL, - 0x9b00a918UL, 0xb02dfadbUL, 0xa936cb9aUL, 0xe6775d5dUL, 0xff6c6c1cUL, - 0xd4413fdfUL, 0xcd5a0e9eUL, 0x958424a2UL, 0x8c9f15e3UL, 0xa7b24620UL, - 0xbea97761UL, 0xf1e8e1a6UL, 0xe8f3d0e7UL, 0xc3de8324UL, 0xdac5b265UL, - 0x5d5daeaaUL, 0x44469febUL, 0x6f6bcc28UL, 0x7670fd69UL, 0x39316baeUL, - 0x202a5aefUL, 0x0b07092cUL, 0x121c386dUL, 0xdf4636f3UL, 0xc65d07b2UL, - 0xed705471UL, 0xf46b6530UL, 0xbb2af3f7UL, 0xa231c2b6UL, 0x891c9175UL, - 0x9007a034UL, 0x179fbcfbUL, 0x0e848dbaUL, 0x25a9de79UL, 0x3cb2ef38UL, - 0x73f379ffUL, 0x6ae848beUL, 0x41c51b7dUL, 0x58de2a3cUL, 0xf0794f05UL, - 0xe9627e44UL, 0xc24f2d87UL, 0xdb541cc6UL, 0x94158a01UL, 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0xbcf571deUL, - 0x8b9fb3dfUL, 0xd221f5ddUL, 0xe54b37dcUL, 0xd80c6bd7UL, 0xef66a9d6UL, - 0xb6d8efd4UL, 0x81b22dd5UL, 0x04a462d0UL, 0x33cea0d1UL, 0x6a70e6d3UL, - 0x5d1a24d2UL, 0x10fe5ec5UL, 0x27949cc4UL, 0x7e2adac6UL, 0x494018c7UL, - 0xcc5657c2UL, 0xfb3c95c3UL, 0xa282d3c1UL, 0x95e811c0UL, 0xa8af4dcbUL, - 0x9fc58fcaUL, 0xc67bc9c8UL, 0xf1110bc9UL, 0x740744ccUL, 0x436d86cdUL, - 0x1ad3c0cfUL, 0x2db902ceUL, 0x4096af91UL, 0x77fc6d90UL, 0x2e422b92UL, - 0x1928e993UL, 0x9c3ea696UL, 0xab546497UL, 0xf2ea2295UL, 0xc580e094UL, - 0xf8c7bc9fUL, 0xcfad7e9eUL, 0x9613389cUL, 0xa179fa9dUL, 0x246fb598UL, - 0x13057799UL, 0x4abb319bUL, 0x7dd1f39aUL, 0x3035898dUL, 0x075f4b8cUL, - 0x5ee10d8eUL, 0x698bcf8fUL, 0xec9d808aUL, 0xdbf7428bUL, 0x82490489UL, - 0xb523c688UL, 0x88649a83UL, 0xbf0e5882UL, 0xe6b01e80UL, 0xd1dadc81UL, - 0x54cc9384UL, 0x63a65185UL, 0x3a181787UL, 0x0d72d586UL, 0xa0d0e2a9UL, - 0x97ba20a8UL, 0xce0466aaUL, 0xf96ea4abUL, 0x7c78ebaeUL, 0x4b1229afUL, - 0x12ac6fadUL, 0x25c6adacUL, 0x1881f1a7UL, 0x2feb33a6UL, 0x765575a4UL, - 0x413fb7a5UL, 0xc429f8a0UL, 0xf3433aa1UL, 0xaafd7ca3UL, 0x9d97bea2UL, - 0xd073c4b5UL, 0xe71906b4UL, 0xbea740b6UL, 0x89cd82b7UL, 0x0cdbcdb2UL, - 0x3bb10fb3UL, 0x620f49b1UL, 0x55658bb0UL, 0x6822d7bbUL, 0x5f4815baUL, - 0x06f653b8UL, 0x319c91b9UL, 0xb48adebcUL, 0x83e01cbdUL, 0xda5e5abfUL, - 0xed3498beUL - }, - { - 0x00000000UL, 0x6567bcb8UL, 0x8bc809aaUL, 0xeeafb512UL, 0x5797628fUL, - 0x32f0de37UL, 0xdc5f6b25UL, 0xb938d79dUL, 0xef28b4c5UL, 0x8a4f087dUL, - 0x64e0bd6fUL, 0x018701d7UL, 0xb8bfd64aUL, 0xddd86af2UL, 0x3377dfe0UL, - 0x56106358UL, 0x9f571950UL, 0xfa30a5e8UL, 0x149f10faUL, 0x71f8ac42UL, - 0xc8c07bdfUL, 0xada7c767UL, 0x43087275UL, 0x266fcecdUL, 0x707fad95UL, - 0x1518112dUL, 0xfbb7a43fUL, 0x9ed01887UL, 0x27e8cf1aUL, 0x428f73a2UL, - 0xac20c6b0UL, 0xc9477a08UL, 0x3eaf32a0UL, 0x5bc88e18UL, 0xb5673b0aUL, - 0xd00087b2UL, 0x6938502fUL, 0x0c5fec97UL, 0xe2f05985UL, 0x8797e53dUL, - 0xd1878665UL, 0xb4e03addUL, 0x5a4f8fcfUL, 0x3f283377UL, 0x8610e4eaUL, - 0xe3775852UL, 0x0dd8ed40UL, 0x68bf51f8UL, 0xa1f82bf0UL, 0xc49f9748UL, - 0x2a30225aUL, 0x4f579ee2UL, 0xf66f497fUL, 0x9308f5c7UL, 0x7da740d5UL, - 0x18c0fc6dUL, 0x4ed09f35UL, 0x2bb7238dUL, 0xc518969fUL, 0xa07f2a27UL, - 0x1947fdbaUL, 0x7c204102UL, 0x928ff410UL, 0xf7e848a8UL, 0x3d58149bUL, - 0x583fa823UL, 0xb6901d31UL, 0xd3f7a189UL, 0x6acf7614UL, 0x0fa8caacUL, - 0xe1077fbeUL, 0x8460c306UL, 0xd270a05eUL, 0xb7171ce6UL, 0x59b8a9f4UL, - 0x3cdf154cUL, 0x85e7c2d1UL, 0xe0807e69UL, 0x0e2fcb7bUL, 0x6b4877c3UL, - 0xa20f0dcbUL, 0xc768b173UL, 0x29c70461UL, 0x4ca0b8d9UL, 0xf5986f44UL, - 0x90ffd3fcUL, 0x7e5066eeUL, 0x1b37da56UL, 0x4d27b90eUL, 0x284005b6UL, - 0xc6efb0a4UL, 0xa3880c1cUL, 0x1ab0db81UL, 0x7fd76739UL, 0x9178d22bUL, - 0xf41f6e93UL, 0x03f7263bUL, 0x66909a83UL, 0x883f2f91UL, 0xed589329UL, - 0x546044b4UL, 0x3107f80cUL, 0xdfa84d1eUL, 0xbacff1a6UL, 0xecdf92feUL, - 0x89b82e46UL, 0x67179b54UL, 0x027027ecUL, 0xbb48f071UL, 0xde2f4cc9UL, - 0x3080f9dbUL, 0x55e74563UL, 0x9ca03f6bUL, 0xf9c783d3UL, 0x176836c1UL, - 0x720f8a79UL, 0xcb375de4UL, 0xae50e15cUL, 0x40ff544eUL, 0x2598e8f6UL, - 0x73888baeUL, 0x16ef3716UL, 0xf8408204UL, 0x9d273ebcUL, 0x241fe921UL, - 0x41785599UL, 0xafd7e08bUL, 0xcab05c33UL, 0x3bb659edUL, 0x5ed1e555UL, - 0xb07e5047UL, 0xd519ecffUL, 0x6c213b62UL, 0x094687daUL, 0xe7e932c8UL, - 0x828e8e70UL, 0xd49eed28UL, 0xb1f95190UL, 0x5f56e482UL, 0x3a31583aUL, - 0x83098fa7UL, 0xe66e331fUL, 0x08c1860dUL, 0x6da63ab5UL, 0xa4e140bdUL, - 0xc186fc05UL, 0x2f294917UL, 0x4a4ef5afUL, 0xf3762232UL, 0x96119e8aUL, - 0x78be2b98UL, 0x1dd99720UL, 0x4bc9f478UL, 0x2eae48c0UL, 0xc001fdd2UL, - 0xa566416aUL, 0x1c5e96f7UL, 0x79392a4fUL, 0x97969f5dUL, 0xf2f123e5UL, - 0x05196b4dUL, 0x607ed7f5UL, 0x8ed162e7UL, 0xebb6de5fUL, 0x528e09c2UL, - 0x37e9b57aUL, 0xd9460068UL, 0xbc21bcd0UL, 0xea31df88UL, 0x8f566330UL, - 0x61f9d622UL, 0x049e6a9aUL, 0xbda6bd07UL, 0xd8c101bfUL, 0x366eb4adUL, - 0x53090815UL, 0x9a4e721dUL, 0xff29cea5UL, 0x11867bb7UL, 0x74e1c70fUL, - 0xcdd91092UL, 0xa8beac2aUL, 0x46111938UL, 0x2376a580UL, 0x7566c6d8UL, - 0x10017a60UL, 0xfeaecf72UL, 0x9bc973caUL, 0x22f1a457UL, 0x479618efUL, - 0xa939adfdUL, 0xcc5e1145UL, 0x06ee4d76UL, 0x6389f1ceUL, 0x8d2644dcUL, - 0xe841f864UL, 0x51792ff9UL, 0x341e9341UL, 0xdab12653UL, 0xbfd69aebUL, - 0xe9c6f9b3UL, 0x8ca1450bUL, 0x620ef019UL, 0x07694ca1UL, 0xbe519b3cUL, - 0xdb362784UL, 0x35999296UL, 0x50fe2e2eUL, 0x99b95426UL, 0xfcdee89eUL, - 0x12715d8cUL, 0x7716e134UL, 0xce2e36a9UL, 0xab498a11UL, 0x45e63f03UL, - 0x208183bbUL, 0x7691e0e3UL, 0x13f65c5bUL, 0xfd59e949UL, 0x983e55f1UL, - 0x2106826cUL, 0x44613ed4UL, 0xaace8bc6UL, 0xcfa9377eUL, 0x38417fd6UL, - 0x5d26c36eUL, 0xb389767cUL, 0xd6eecac4UL, 0x6fd61d59UL, 0x0ab1a1e1UL, - 0xe41e14f3UL, 0x8179a84bUL, 0xd769cb13UL, 0xb20e77abUL, 0x5ca1c2b9UL, - 0x39c67e01UL, 0x80fea99cUL, 0xe5991524UL, 0x0b36a036UL, 0x6e511c8eUL, - 0xa7166686UL, 0xc271da3eUL, 0x2cde6f2cUL, 0x49b9d394UL, 0xf0810409UL, - 0x95e6b8b1UL, 0x7b490da3UL, 0x1e2eb11bUL, 0x483ed243UL, 0x2d596efbUL, - 0xc3f6dbe9UL, 0xa6916751UL, 0x1fa9b0ccUL, 0x7ace0c74UL, 0x9461b966UL, - 0xf10605deUL +local const z_crc_t FAR crc_table[] = { + 0x00000000, 0x77073096, 0xee0e612c, 0x990951ba, 0x076dc419, + 0x706af48f, 0xe963a535, 0x9e6495a3, 0x0edb8832, 0x79dcb8a4, + 0xe0d5e91e, 0x97d2d988, 0x09b64c2b, 0x7eb17cbd, 0xe7b82d07, + 0x90bf1d91, 0x1db71064, 0x6ab020f2, 0xf3b97148, 0x84be41de, + 0x1adad47d, 0x6ddde4eb, 0xf4d4b551, 0x83d385c7, 0x136c9856, + 0x646ba8c0, 0xfd62f97a, 0x8a65c9ec, 0x14015c4f, 0x63066cd9, + 0xfa0f3d63, 0x8d080df5, 0x3b6e20c8, 0x4c69105e, 0xd56041e4, + 0xa2677172, 0x3c03e4d1, 0x4b04d447, 0xd20d85fd, 0xa50ab56b, + 0x35b5a8fa, 0x42b2986c, 0xdbbbc9d6, 0xacbcf940, 0x32d86ce3, + 0x45df5c75, 0xdcd60dcf, 0xabd13d59, 0x26d930ac, 0x51de003a, + 0xc8d75180, 0xbfd06116, 0x21b4f4b5, 0x56b3c423, 0xcfba9599, + 0xb8bda50f, 0x2802b89e, 0x5f058808, 0xc60cd9b2, 0xb10be924, + 0x2f6f7c87, 0x58684c11, 0xc1611dab, 0xb6662d3d, 0x76dc4190, + 0x01db7106, 0x98d220bc, 0xefd5102a, 0x71b18589, 0x06b6b51f, + 0x9fbfe4a5, 0xe8b8d433, 0x7807c9a2, 0x0f00f934, 0x9609a88e, + 0xe10e9818, 0x7f6a0dbb, 0x086d3d2d, 0x91646c97, 0xe6635c01, + 0x6b6b51f4, 0x1c6c6162, 0x856530d8, 0xf262004e, 0x6c0695ed, + 0x1b01a57b, 0x8208f4c1, 0xf50fc457, 0x65b0d9c6, 0x12b7e950, + 0x8bbeb8ea, 0xfcb9887c, 0x62dd1ddf, 0x15da2d49, 0x8cd37cf3, + 0xfbd44c65, 0x4db26158, 0x3ab551ce, 0xa3bc0074, 0xd4bb30e2, + 0x4adfa541, 0x3dd895d7, 0xa4d1c46d, 0xd3d6f4fb, 0x4369e96a, + 0x346ed9fc, 0xad678846, 0xda60b8d0, 0x44042d73, 0x33031de5, + 0xaa0a4c5f, 0xdd0d7cc9, 0x5005713c, 0x270241aa, 0xbe0b1010, + 0xc90c2086, 0x5768b525, 0x206f85b3, 0xb966d409, 0xce61e49f, + 0x5edef90e, 0x29d9c998, 0xb0d09822, 0xc7d7a8b4, 0x59b33d17, + 0x2eb40d81, 0xb7bd5c3b, 0xc0ba6cad, 0xedb88320, 0x9abfb3b6, + 0x03b6e20c, 0x74b1d29a, 0xead54739, 0x9dd277af, 0x04db2615, + 0x73dc1683, 0xe3630b12, 0x94643b84, 0x0d6d6a3e, 0x7a6a5aa8, + 0xe40ecf0b, 0x9309ff9d, 0x0a00ae27, 0x7d079eb1, 0xf00f9344, + 0x8708a3d2, 0x1e01f268, 0x6906c2fe, 0xf762575d, 0x806567cb, + 0x196c3671, 0x6e6b06e7, 0xfed41b76, 0x89d32be0, 0x10da7a5a, + 0x67dd4acc, 0xf9b9df6f, 0x8ebeeff9, 0x17b7be43, 0x60b08ed5, + 0xd6d6a3e8, 0xa1d1937e, 0x38d8c2c4, 0x4fdff252, 0xd1bb67f1, + 0xa6bc5767, 0x3fb506dd, 0x48b2364b, 0xd80d2bda, 0xaf0a1b4c, + 0x36034af6, 0x41047a60, 0xdf60efc3, 0xa867df55, 0x316e8eef, + 0x4669be79, 0xcb61b38c, 0xbc66831a, 0x256fd2a0, 0x5268e236, + 0xcc0c7795, 0xbb0b4703, 0x220216b9, 0x5505262f, 0xc5ba3bbe, + 0xb2bd0b28, 0x2bb45a92, 0x5cb36a04, 0xc2d7ffa7, 0xb5d0cf31, + 0x2cd99e8b, 0x5bdeae1d, 0x9b64c2b0, 0xec63f226, 0x756aa39c, + 0x026d930a, 0x9c0906a9, 0xeb0e363f, 0x72076785, 0x05005713, + 0x95bf4a82, 0xe2b87a14, 0x7bb12bae, 0x0cb61b38, 0x92d28e9b, + 0xe5d5be0d, 0x7cdcefb7, 0x0bdbdf21, 0x86d3d2d4, 0xf1d4e242, + 0x68ddb3f8, 0x1fda836e, 0x81be16cd, 0xf6b9265b, 0x6fb077e1, + 0x18b74777, 0x88085ae6, 0xff0f6a70, 0x66063bca, 0x11010b5c, + 0x8f659eff, 0xf862ae69, 0x616bffd3, 0x166ccf45, 0xa00ae278, + 0xd70dd2ee, 0x4e048354, 0x3903b3c2, 0xa7672661, 0xd06016f7, + 0x4969474d, 0x3e6e77db, 0xaed16a4a, 0xd9d65adc, 0x40df0b66, + 0x37d83bf0, 0xa9bcae53, 0xdebb9ec5, 0x47b2cf7f, 0x30b5ffe9, + 0xbdbdf21c, 0xcabac28a, 0x53b39330, 0x24b4a3a6, 0xbad03605, + 0xcdd70693, 0x54de5729, 0x23d967bf, 0xb3667a2e, 0xc4614ab8, + 0x5d681b02, 0x2a6f2b94, 0xb40bbe37, 0xc30c8ea1, 0x5a05df1b, + 0x2d02ef8d}; + +#ifdef W + +#if W == 8 + +local const z_word_t FAR crc_big_table[] = { + 0x0000000000000000, 0x9630077700000000, 0x2c610eee00000000, + 0xba51099900000000, 0x19c46d0700000000, 0x8ff46a7000000000, + 0x35a563e900000000, 0xa395649e00000000, 0x3288db0e00000000, + 0xa4b8dc7900000000, 0x1ee9d5e000000000, 0x88d9d29700000000, + 0x2b4cb60900000000, 0xbd7cb17e00000000, 0x072db8e700000000, + 0x911dbf9000000000, 0x6410b71d00000000, 0xf220b06a00000000, + 0x4871b9f300000000, 0xde41be8400000000, 0x7dd4da1a00000000, + 0xebe4dd6d00000000, 0x51b5d4f400000000, 0xc785d38300000000, + 0x56986c1300000000, 0xc0a86b6400000000, 0x7af962fd00000000, + 0xecc9658a00000000, 0x4f5c011400000000, 0xd96c066300000000, + 0x633d0ffa00000000, 0xf50d088d00000000, 0xc8206e3b00000000, + 0x5e10694c00000000, 0xe44160d500000000, 0x727167a200000000, + 0xd1e4033c00000000, 0x47d4044b00000000, 0xfd850dd200000000, + 0x6bb50aa500000000, 0xfaa8b53500000000, 0x6c98b24200000000, + 0xd6c9bbdb00000000, 0x40f9bcac00000000, 0xe36cd83200000000, + 0x755cdf4500000000, 0xcf0dd6dc00000000, 0x593dd1ab00000000, + 0xac30d92600000000, 0x3a00de5100000000, 0x8051d7c800000000, + 0x1661d0bf00000000, 0xb5f4b42100000000, 0x23c4b35600000000, + 0x9995bacf00000000, 0x0fa5bdb800000000, 0x9eb8022800000000, + 0x0888055f00000000, 0xb2d90cc600000000, 0x24e90bb100000000, + 0x877c6f2f00000000, 0x114c685800000000, 0xab1d61c100000000, + 0x3d2d66b600000000, 0x9041dc7600000000, 0x0671db0100000000, + 0xbc20d29800000000, 0x2a10d5ef00000000, 0x8985b17100000000, + 0x1fb5b60600000000, 0xa5e4bf9f00000000, 0x33d4b8e800000000, + 0xa2c9077800000000, 0x34f9000f00000000, 0x8ea8099600000000, + 0x18980ee100000000, 0xbb0d6a7f00000000, 0x2d3d6d0800000000, + 0x976c649100000000, 0x015c63e600000000, 0xf4516b6b00000000, + 0x62616c1c00000000, 0xd830658500000000, 0x4e0062f200000000, + 0xed95066c00000000, 0x7ba5011b00000000, 0xc1f4088200000000, + 0x57c40ff500000000, 0xc6d9b06500000000, 0x50e9b71200000000, + 0xeab8be8b00000000, 0x7c88b9fc00000000, 0xdf1ddd6200000000, + 0x492dda1500000000, 0xf37cd38c00000000, 0x654cd4fb00000000, + 0x5861b24d00000000, 0xce51b53a00000000, 0x7400bca300000000, + 0xe230bbd400000000, 0x41a5df4a00000000, 0xd795d83d00000000, + 0x6dc4d1a400000000, 0xfbf4d6d300000000, 0x6ae9694300000000, + 0xfcd96e3400000000, 0x468867ad00000000, 0xd0b860da00000000, + 0x732d044400000000, 0xe51d033300000000, 0x5f4c0aaa00000000, + 0xc97c0ddd00000000, 0x3c71055000000000, 0xaa41022700000000, + 0x10100bbe00000000, 0x86200cc900000000, 0x25b5685700000000, + 0xb3856f2000000000, 0x09d466b900000000, 0x9fe461ce00000000, + 0x0ef9de5e00000000, 0x98c9d92900000000, 0x2298d0b000000000, + 0xb4a8d7c700000000, 0x173db35900000000, 0x810db42e00000000, + 0x3b5cbdb700000000, 0xad6cbac000000000, 0x2083b8ed00000000, + 0xb6b3bf9a00000000, 0x0ce2b60300000000, 0x9ad2b17400000000, + 0x3947d5ea00000000, 0xaf77d29d00000000, 0x1526db0400000000, + 0x8316dc7300000000, 0x120b63e300000000, 0x843b649400000000, + 0x3e6a6d0d00000000, 0xa85a6a7a00000000, 0x0bcf0ee400000000, + 0x9dff099300000000, 0x27ae000a00000000, 0xb19e077d00000000, + 0x44930ff000000000, 0xd2a3088700000000, 0x68f2011e00000000, + 0xfec2066900000000, 0x5d5762f700000000, 0xcb67658000000000, + 0x71366c1900000000, 0xe7066b6e00000000, 0x761bd4fe00000000, + 0xe02bd38900000000, 0x5a7ada1000000000, 0xcc4add6700000000, + 0x6fdfb9f900000000, 0xf9efbe8e00000000, 0x43beb71700000000, + 0xd58eb06000000000, 0xe8a3d6d600000000, 0x7e93d1a100000000, + 0xc4c2d83800000000, 0x52f2df4f00000000, 0xf167bbd100000000, + 0x6757bca600000000, 0xdd06b53f00000000, 0x4b36b24800000000, + 0xda2b0dd800000000, 0x4c1b0aaf00000000, 0xf64a033600000000, + 0x607a044100000000, 0xc3ef60df00000000, 0x55df67a800000000, + 0xef8e6e3100000000, 0x79be694600000000, 0x8cb361cb00000000, + 0x1a8366bc00000000, 0xa0d26f2500000000, 0x36e2685200000000, + 0x95770ccc00000000, 0x03470bbb00000000, 0xb916022200000000, + 0x2f26055500000000, 0xbe3bbac500000000, 0x280bbdb200000000, + 0x925ab42b00000000, 0x046ab35c00000000, 0xa7ffd7c200000000, + 0x31cfd0b500000000, 0x8b9ed92c00000000, 0x1daede5b00000000, + 0xb0c2649b00000000, 0x26f263ec00000000, 0x9ca36a7500000000, + 0x0a936d0200000000, 0xa906099c00000000, 0x3f360eeb00000000, + 0x8567077200000000, 0x1357000500000000, 0x824abf9500000000, + 0x147ab8e200000000, 0xae2bb17b00000000, 0x381bb60c00000000, + 0x9b8ed29200000000, 0x0dbed5e500000000, 0xb7efdc7c00000000, + 0x21dfdb0b00000000, 0xd4d2d38600000000, 0x42e2d4f100000000, + 0xf8b3dd6800000000, 0x6e83da1f00000000, 0xcd16be8100000000, + 0x5b26b9f600000000, 0xe177b06f00000000, 0x7747b71800000000, + 0xe65a088800000000, 0x706a0fff00000000, 0xca3b066600000000, + 0x5c0b011100000000, 0xff9e658f00000000, 0x69ae62f800000000, + 0xd3ff6b6100000000, 0x45cf6c1600000000, 0x78e20aa000000000, + 0xeed20dd700000000, 0x5483044e00000000, 0xc2b3033900000000, + 0x612667a700000000, 0xf71660d000000000, 0x4d47694900000000, + 0xdb776e3e00000000, 0x4a6ad1ae00000000, 0xdc5ad6d900000000, + 0x660bdf4000000000, 0xf03bd83700000000, 0x53aebca900000000, + 0xc59ebbde00000000, 0x7fcfb24700000000, 0xe9ffb53000000000, + 0x1cf2bdbd00000000, 0x8ac2baca00000000, 0x3093b35300000000, + 0xa6a3b42400000000, 0x0536d0ba00000000, 0x9306d7cd00000000, + 0x2957de5400000000, 0xbf67d92300000000, 0x2e7a66b300000000, + 0xb84a61c400000000, 0x021b685d00000000, 0x942b6f2a00000000, + 0x37be0bb400000000, 0xa18e0cc300000000, 0x1bdf055a00000000, + 0x8def022d00000000}; + +#else /* W == 4 */ + +local const z_word_t FAR crc_big_table[] = { + 0x00000000, 0x96300777, 0x2c610eee, 0xba510999, 0x19c46d07, + 0x8ff46a70, 0x35a563e9, 0xa395649e, 0x3288db0e, 0xa4b8dc79, + 0x1ee9d5e0, 0x88d9d297, 0x2b4cb609, 0xbd7cb17e, 0x072db8e7, + 0x911dbf90, 0x6410b71d, 0xf220b06a, 0x4871b9f3, 0xde41be84, + 0x7dd4da1a, 0xebe4dd6d, 0x51b5d4f4, 0xc785d383, 0x56986c13, + 0xc0a86b64, 0x7af962fd, 0xecc9658a, 0x4f5c0114, 0xd96c0663, + 0x633d0ffa, 0xf50d088d, 0xc8206e3b, 0x5e10694c, 0xe44160d5, + 0x727167a2, 0xd1e4033c, 0x47d4044b, 0xfd850dd2, 0x6bb50aa5, + 0xfaa8b535, 0x6c98b242, 0xd6c9bbdb, 0x40f9bcac, 0xe36cd832, + 0x755cdf45, 0xcf0dd6dc, 0x593dd1ab, 0xac30d926, 0x3a00de51, + 0x8051d7c8, 0x1661d0bf, 0xb5f4b421, 0x23c4b356, 0x9995bacf, + 0x0fa5bdb8, 0x9eb80228, 0x0888055f, 0xb2d90cc6, 0x24e90bb1, + 0x877c6f2f, 0x114c6858, 0xab1d61c1, 0x3d2d66b6, 0x9041dc76, + 0x0671db01, 0xbc20d298, 0x2a10d5ef, 0x8985b171, 0x1fb5b606, + 0xa5e4bf9f, 0x33d4b8e8, 0xa2c90778, 0x34f9000f, 0x8ea80996, + 0x18980ee1, 0xbb0d6a7f, 0x2d3d6d08, 0x976c6491, 0x015c63e6, + 0xf4516b6b, 0x62616c1c, 0xd8306585, 0x4e0062f2, 0xed95066c, + 0x7ba5011b, 0xc1f40882, 0x57c40ff5, 0xc6d9b065, 0x50e9b712, + 0xeab8be8b, 0x7c88b9fc, 0xdf1ddd62, 0x492dda15, 0xf37cd38c, + 0x654cd4fb, 0x5861b24d, 0xce51b53a, 0x7400bca3, 0xe230bbd4, + 0x41a5df4a, 0xd795d83d, 0x6dc4d1a4, 0xfbf4d6d3, 0x6ae96943, + 0xfcd96e34, 0x468867ad, 0xd0b860da, 0x732d0444, 0xe51d0333, + 0x5f4c0aaa, 0xc97c0ddd, 0x3c710550, 0xaa410227, 0x10100bbe, + 0x86200cc9, 0x25b56857, 0xb3856f20, 0x09d466b9, 0x9fe461ce, + 0x0ef9de5e, 0x98c9d929, 0x2298d0b0, 0xb4a8d7c7, 0x173db359, + 0x810db42e, 0x3b5cbdb7, 0xad6cbac0, 0x2083b8ed, 0xb6b3bf9a, + 0x0ce2b603, 0x9ad2b174, 0x3947d5ea, 0xaf77d29d, 0x1526db04, + 0x8316dc73, 0x120b63e3, 0x843b6494, 0x3e6a6d0d, 0xa85a6a7a, + 0x0bcf0ee4, 0x9dff0993, 0x27ae000a, 0xb19e077d, 0x44930ff0, + 0xd2a30887, 0x68f2011e, 0xfec20669, 0x5d5762f7, 0xcb676580, + 0x71366c19, 0xe7066b6e, 0x761bd4fe, 0xe02bd389, 0x5a7ada10, + 0xcc4add67, 0x6fdfb9f9, 0xf9efbe8e, 0x43beb717, 0xd58eb060, + 0xe8a3d6d6, 0x7e93d1a1, 0xc4c2d838, 0x52f2df4f, 0xf167bbd1, + 0x6757bca6, 0xdd06b53f, 0x4b36b248, 0xda2b0dd8, 0x4c1b0aaf, + 0xf64a0336, 0x607a0441, 0xc3ef60df, 0x55df67a8, 0xef8e6e31, + 0x79be6946, 0x8cb361cb, 0x1a8366bc, 0xa0d26f25, 0x36e26852, + 0x95770ccc, 0x03470bbb, 0xb9160222, 0x2f260555, 0xbe3bbac5, + 0x280bbdb2, 0x925ab42b, 0x046ab35c, 0xa7ffd7c2, 0x31cfd0b5, + 0x8b9ed92c, 0x1daede5b, 0xb0c2649b, 0x26f263ec, 0x9ca36a75, + 0x0a936d02, 0xa906099c, 0x3f360eeb, 0x85670772, 0x13570005, + 0x824abf95, 0x147ab8e2, 0xae2bb17b, 0x381bb60c, 0x9b8ed292, + 0x0dbed5e5, 0xb7efdc7c, 0x21dfdb0b, 0xd4d2d386, 0x42e2d4f1, + 0xf8b3dd68, 0x6e83da1f, 0xcd16be81, 0x5b26b9f6, 0xe177b06f, + 0x7747b718, 0xe65a0888, 0x706a0fff, 0xca3b0666, 0x5c0b0111, + 0xff9e658f, 0x69ae62f8, 0xd3ff6b61, 0x45cf6c16, 0x78e20aa0, + 0xeed20dd7, 0x5483044e, 0xc2b30339, 0x612667a7, 0xf71660d0, + 0x4d476949, 0xdb776e3e, 0x4a6ad1ae, 0xdc5ad6d9, 0x660bdf40, + 0xf03bd837, 0x53aebca9, 0xc59ebbde, 0x7fcfb247, 0xe9ffb530, + 0x1cf2bdbd, 0x8ac2baca, 0x3093b353, 0xa6a3b424, 0x0536d0ba, + 0x9306d7cd, 0x2957de54, 0xbf67d923, 0x2e7a66b3, 0xb84a61c4, + 0x021b685d, 0x942b6f2a, 0x37be0bb4, 0xa18e0cc3, 0x1bdf055a, + 0x8def022d}; + +#endif + +#if N == 1 + +#if W == 8 + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xccaa009e, 0x4225077d, 0x8e8f07e3, 0x844a0efa, + 0x48e00e64, 0xc66f0987, 0x0ac50919, 0xd3e51bb5, 0x1f4f1b2b, + 0x91c01cc8, 0x5d6a1c56, 0x57af154f, 0x9b0515d1, 0x158a1232, + 0xd92012ac, 0x7cbb312b, 0xb01131b5, 0x3e9e3656, 0xf23436c8, + 0xf8f13fd1, 0x345b3f4f, 0xbad438ac, 0x767e3832, 0xaf5e2a9e, + 0x63f42a00, 0xed7b2de3, 0x21d12d7d, 0x2b142464, 0xe7be24fa, + 0x69312319, 0xa59b2387, 0xf9766256, 0x35dc62c8, 0xbb53652b, + 0x77f965b5, 0x7d3c6cac, 0xb1966c32, 0x3f196bd1, 0xf3b36b4f, + 0x2a9379e3, 0xe639797d, 0x68b67e9e, 0xa41c7e00, 0xaed97719, + 0x62737787, 0xecfc7064, 0x205670fa, 0x85cd537d, 0x496753e3, + 0xc7e85400, 0x0b42549e, 0x01875d87, 0xcd2d5d19, 0x43a25afa, + 0x8f085a64, 0x562848c8, 0x9a824856, 0x140d4fb5, 0xd8a74f2b, + 0xd2624632, 0x1ec846ac, 0x9047414f, 0x5ced41d1, 0x299dc2ed, + 0xe537c273, 0x6bb8c590, 0xa712c50e, 0xadd7cc17, 0x617dcc89, + 0xeff2cb6a, 0x2358cbf4, 0xfa78d958, 0x36d2d9c6, 0xb85dde25, + 0x74f7debb, 0x7e32d7a2, 0xb298d73c, 0x3c17d0df, 0xf0bdd041, + 0x5526f3c6, 0x998cf358, 0x1703f4bb, 0xdba9f425, 0xd16cfd3c, + 0x1dc6fda2, 0x9349fa41, 0x5fe3fadf, 0x86c3e873, 0x4a69e8ed, + 0xc4e6ef0e, 0x084cef90, 0x0289e689, 0xce23e617, 0x40ace1f4, + 0x8c06e16a, 0xd0eba0bb, 0x1c41a025, 0x92cea7c6, 0x5e64a758, + 0x54a1ae41, 0x980baedf, 0x1684a93c, 0xda2ea9a2, 0x030ebb0e, + 0xcfa4bb90, 0x412bbc73, 0x8d81bced, 0x8744b5f4, 0x4beeb56a, + 0xc561b289, 0x09cbb217, 0xac509190, 0x60fa910e, 0xee7596ed, + 0x22df9673, 0x281a9f6a, 0xe4b09ff4, 0x6a3f9817, 0xa6959889, + 0x7fb58a25, 0xb31f8abb, 0x3d908d58, 0xf13a8dc6, 0xfbff84df, + 0x37558441, 0xb9da83a2, 0x7570833c, 0x533b85da, 0x9f918544, + 0x111e82a7, 0xddb48239, 0xd7718b20, 0x1bdb8bbe, 0x95548c5d, + 0x59fe8cc3, 0x80de9e6f, 0x4c749ef1, 0xc2fb9912, 0x0e51998c, + 0x04949095, 0xc83e900b, 0x46b197e8, 0x8a1b9776, 0x2f80b4f1, + 0xe32ab46f, 0x6da5b38c, 0xa10fb312, 0xabcaba0b, 0x6760ba95, + 0xe9efbd76, 0x2545bde8, 0xfc65af44, 0x30cfafda, 0xbe40a839, + 0x72eaa8a7, 0x782fa1be, 0xb485a120, 0x3a0aa6c3, 0xf6a0a65d, + 0xaa4de78c, 0x66e7e712, 0xe868e0f1, 0x24c2e06f, 0x2e07e976, + 0xe2ade9e8, 0x6c22ee0b, 0xa088ee95, 0x79a8fc39, 0xb502fca7, + 0x3b8dfb44, 0xf727fbda, 0xfde2f2c3, 0x3148f25d, 0xbfc7f5be, + 0x736df520, 0xd6f6d6a7, 0x1a5cd639, 0x94d3d1da, 0x5879d144, + 0x52bcd85d, 0x9e16d8c3, 0x1099df20, 0xdc33dfbe, 0x0513cd12, + 0xc9b9cd8c, 0x4736ca6f, 0x8b9ccaf1, 0x8159c3e8, 0x4df3c376, + 0xc37cc495, 0x0fd6c40b, 0x7aa64737, 0xb60c47a9, 0x3883404a, + 0xf42940d4, 0xfeec49cd, 0x32464953, 0xbcc94eb0, 0x70634e2e, + 0xa9435c82, 0x65e95c1c, 0xeb665bff, 0x27cc5b61, 0x2d095278, + 0xe1a352e6, 0x6f2c5505, 0xa386559b, 0x061d761c, 0xcab77682, + 0x44387161, 0x889271ff, 0x825778e6, 0x4efd7878, 0xc0727f9b, + 0x0cd87f05, 0xd5f86da9, 0x19526d37, 0x97dd6ad4, 0x5b776a4a, + 0x51b26353, 0x9d1863cd, 0x1397642e, 0xdf3d64b0, 0x83d02561, + 0x4f7a25ff, 0xc1f5221c, 0x0d5f2282, 0x079a2b9b, 0xcb302b05, + 0x45bf2ce6, 0x89152c78, 0x50353ed4, 0x9c9f3e4a, 0x121039a9, + 0xdeba3937, 0xd47f302e, 0x18d530b0, 0x965a3753, 0x5af037cd, + 0xff6b144a, 0x33c114d4, 0xbd4e1337, 0x71e413a9, 0x7b211ab0, + 0xb78b1a2e, 0x39041dcd, 0xf5ae1d53, 0x2c8e0fff, 0xe0240f61, + 0x6eab0882, 0xa201081c, 0xa8c40105, 0x646e019b, 0xeae10678, + 0x264b06e6}, + {0x00000000, 0xa6770bb4, 0x979f1129, 0x31e81a9d, 0xf44f2413, + 0x52382fa7, 0x63d0353a, 0xc5a73e8e, 0x33ef4e67, 0x959845d3, + 0xa4705f4e, 0x020754fa, 0xc7a06a74, 0x61d761c0, 0x503f7b5d, + 0xf64870e9, 0x67de9cce, 0xc1a9977a, 0xf0418de7, 0x56368653, + 0x9391b8dd, 0x35e6b369, 0x040ea9f4, 0xa279a240, 0x5431d2a9, + 0xf246d91d, 0xc3aec380, 0x65d9c834, 0xa07ef6ba, 0x0609fd0e, + 0x37e1e793, 0x9196ec27, 0xcfbd399c, 0x69ca3228, 0x582228b5, + 0xfe552301, 0x3bf21d8f, 0x9d85163b, 0xac6d0ca6, 0x0a1a0712, + 0xfc5277fb, 0x5a257c4f, 0x6bcd66d2, 0xcdba6d66, 0x081d53e8, + 0xae6a585c, 0x9f8242c1, 0x39f54975, 0xa863a552, 0x0e14aee6, + 0x3ffcb47b, 0x998bbfcf, 0x5c2c8141, 0xfa5b8af5, 0xcbb39068, + 0x6dc49bdc, 0x9b8ceb35, 0x3dfbe081, 0x0c13fa1c, 0xaa64f1a8, + 0x6fc3cf26, 0xc9b4c492, 0xf85cde0f, 0x5e2bd5bb, 0x440b7579, + 0xe27c7ecd, 0xd3946450, 0x75e36fe4, 0xb044516a, 0x16335ade, + 0x27db4043, 0x81ac4bf7, 0x77e43b1e, 0xd19330aa, 0xe07b2a37, + 0x460c2183, 0x83ab1f0d, 0x25dc14b9, 0x14340e24, 0xb2430590, + 0x23d5e9b7, 0x85a2e203, 0xb44af89e, 0x123df32a, 0xd79acda4, + 0x71edc610, 0x4005dc8d, 0xe672d739, 0x103aa7d0, 0xb64dac64, + 0x87a5b6f9, 0x21d2bd4d, 0xe47583c3, 0x42028877, 0x73ea92ea, + 0xd59d995e, 0x8bb64ce5, 0x2dc14751, 0x1c295dcc, 0xba5e5678, + 0x7ff968f6, 0xd98e6342, 0xe86679df, 0x4e11726b, 0xb8590282, + 0x1e2e0936, 0x2fc613ab, 0x89b1181f, 0x4c162691, 0xea612d25, + 0xdb8937b8, 0x7dfe3c0c, 0xec68d02b, 0x4a1fdb9f, 0x7bf7c102, + 0xdd80cab6, 0x1827f438, 0xbe50ff8c, 0x8fb8e511, 0x29cfeea5, + 0xdf879e4c, 0x79f095f8, 0x48188f65, 0xee6f84d1, 0x2bc8ba5f, + 0x8dbfb1eb, 0xbc57ab76, 0x1a20a0c2, 0x8816eaf2, 0x2e61e146, + 0x1f89fbdb, 0xb9fef06f, 0x7c59cee1, 0xda2ec555, 0xebc6dfc8, + 0x4db1d47c, 0xbbf9a495, 0x1d8eaf21, 0x2c66b5bc, 0x8a11be08, + 0x4fb68086, 0xe9c18b32, 0xd82991af, 0x7e5e9a1b, 0xefc8763c, + 0x49bf7d88, 0x78576715, 0xde206ca1, 0x1b87522f, 0xbdf0599b, + 0x8c184306, 0x2a6f48b2, 0xdc27385b, 0x7a5033ef, 0x4bb82972, + 0xedcf22c6, 0x28681c48, 0x8e1f17fc, 0xbff70d61, 0x198006d5, + 0x47abd36e, 0xe1dcd8da, 0xd034c247, 0x7643c9f3, 0xb3e4f77d, + 0x1593fcc9, 0x247be654, 0x820cede0, 0x74449d09, 0xd23396bd, + 0xe3db8c20, 0x45ac8794, 0x800bb91a, 0x267cb2ae, 0x1794a833, + 0xb1e3a387, 0x20754fa0, 0x86024414, 0xb7ea5e89, 0x119d553d, + 0xd43a6bb3, 0x724d6007, 0x43a57a9a, 0xe5d2712e, 0x139a01c7, + 0xb5ed0a73, 0x840510ee, 0x22721b5a, 0xe7d525d4, 0x41a22e60, + 0x704a34fd, 0xd63d3f49, 0xcc1d9f8b, 0x6a6a943f, 0x5b828ea2, + 0xfdf58516, 0x3852bb98, 0x9e25b02c, 0xafcdaab1, 0x09baa105, + 0xfff2d1ec, 0x5985da58, 0x686dc0c5, 0xce1acb71, 0x0bbdf5ff, + 0xadcafe4b, 0x9c22e4d6, 0x3a55ef62, 0xabc30345, 0x0db408f1, + 0x3c5c126c, 0x9a2b19d8, 0x5f8c2756, 0xf9fb2ce2, 0xc813367f, + 0x6e643dcb, 0x982c4d22, 0x3e5b4696, 0x0fb35c0b, 0xa9c457bf, + 0x6c636931, 0xca146285, 0xfbfc7818, 0x5d8b73ac, 0x03a0a617, + 0xa5d7ada3, 0x943fb73e, 0x3248bc8a, 0xf7ef8204, 0x519889b0, + 0x6070932d, 0xc6079899, 0x304fe870, 0x9638e3c4, 0xa7d0f959, + 0x01a7f2ed, 0xc400cc63, 0x6277c7d7, 0x539fdd4a, 0xf5e8d6fe, + 0x647e3ad9, 0xc209316d, 0xf3e12bf0, 0x55962044, 0x90311eca, + 0x3646157e, 0x07ae0fe3, 0xa1d90457, 0x579174be, 0xf1e67f0a, + 0xc00e6597, 0x66796e23, 0xa3de50ad, 0x05a95b19, 0x34414184, + 0x92364a30}, + {0x00000000, 0xcb5cd3a5, 0x4dc8a10b, 0x869472ae, 0x9b914216, + 0x50cd91b3, 0xd659e31d, 0x1d0530b8, 0xec53826d, 0x270f51c8, + 0xa19b2366, 0x6ac7f0c3, 0x77c2c07b, 0xbc9e13de, 0x3a0a6170, + 0xf156b2d5, 0x03d6029b, 0xc88ad13e, 0x4e1ea390, 0x85427035, + 0x9847408d, 0x531b9328, 0xd58fe186, 0x1ed33223, 0xef8580f6, + 0x24d95353, 0xa24d21fd, 0x6911f258, 0x7414c2e0, 0xbf481145, + 0x39dc63eb, 0xf280b04e, 0x07ac0536, 0xccf0d693, 0x4a64a43d, + 0x81387798, 0x9c3d4720, 0x57619485, 0xd1f5e62b, 0x1aa9358e, + 0xebff875b, 0x20a354fe, 0xa6372650, 0x6d6bf5f5, 0x706ec54d, + 0xbb3216e8, 0x3da66446, 0xf6fab7e3, 0x047a07ad, 0xcf26d408, + 0x49b2a6a6, 0x82ee7503, 0x9feb45bb, 0x54b7961e, 0xd223e4b0, + 0x197f3715, 0xe82985c0, 0x23755665, 0xa5e124cb, 0x6ebdf76e, + 0x73b8c7d6, 0xb8e41473, 0x3e7066dd, 0xf52cb578, 0x0f580a6c, + 0xc404d9c9, 0x4290ab67, 0x89cc78c2, 0x94c9487a, 0x5f959bdf, + 0xd901e971, 0x125d3ad4, 0xe30b8801, 0x28575ba4, 0xaec3290a, + 0x659ffaaf, 0x789aca17, 0xb3c619b2, 0x35526b1c, 0xfe0eb8b9, + 0x0c8e08f7, 0xc7d2db52, 0x4146a9fc, 0x8a1a7a59, 0x971f4ae1, + 0x5c439944, 0xdad7ebea, 0x118b384f, 0xe0dd8a9a, 0x2b81593f, + 0xad152b91, 0x6649f834, 0x7b4cc88c, 0xb0101b29, 0x36846987, + 0xfdd8ba22, 0x08f40f5a, 0xc3a8dcff, 0x453cae51, 0x8e607df4, + 0x93654d4c, 0x58399ee9, 0xdeadec47, 0x15f13fe2, 0xe4a78d37, + 0x2ffb5e92, 0xa96f2c3c, 0x6233ff99, 0x7f36cf21, 0xb46a1c84, + 0x32fe6e2a, 0xf9a2bd8f, 0x0b220dc1, 0xc07ede64, 0x46eaacca, + 0x8db67f6f, 0x90b34fd7, 0x5bef9c72, 0xdd7beedc, 0x16273d79, + 0xe7718fac, 0x2c2d5c09, 0xaab92ea7, 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0x516791a6, 0xccb0a91f, 0x740cce7a, 0x66b96194, + 0xde0506f1}, + {0x00000000, 0x01c26a37, 0x0384d46e, 0x0246be59, 0x0709a8dc, + 0x06cbc2eb, 0x048d7cb2, 0x054f1685, 0x0e1351b8, 0x0fd13b8f, + 0x0d9785d6, 0x0c55efe1, 0x091af964, 0x08d89353, 0x0a9e2d0a, + 0x0b5c473d, 0x1c26a370, 0x1de4c947, 0x1fa2771e, 0x1e601d29, + 0x1b2f0bac, 0x1aed619b, 0x18abdfc2, 0x1969b5f5, 0x1235f2c8, + 0x13f798ff, 0x11b126a6, 0x10734c91, 0x153c5a14, 0x14fe3023, + 0x16b88e7a, 0x177ae44d, 0x384d46e0, 0x398f2cd7, 0x3bc9928e, + 0x3a0bf8b9, 0x3f44ee3c, 0x3e86840b, 0x3cc03a52, 0x3d025065, + 0x365e1758, 0x379c7d6f, 0x35dac336, 0x3418a901, 0x3157bf84, + 0x3095d5b3, 0x32d36bea, 0x331101dd, 0x246be590, 0x25a98fa7, + 0x27ef31fe, 0x262d5bc9, 0x23624d4c, 0x22a0277b, 0x20e69922, + 0x2124f315, 0x2a78b428, 0x2bbade1f, 0x29fc6046, 0x283e0a71, + 0x2d711cf4, 0x2cb376c3, 0x2ef5c89a, 0x2f37a2ad, 0x709a8dc0, + 0x7158e7f7, 0x731e59ae, 0x72dc3399, 0x7793251c, 0x76514f2b, + 0x7417f172, 0x75d59b45, 0x7e89dc78, 0x7f4bb64f, 0x7d0d0816, + 0x7ccf6221, 0x798074a4, 0x78421e93, 0x7a04a0ca, 0x7bc6cafd, + 0x6cbc2eb0, 0x6d7e4487, 0x6f38fade, 0x6efa90e9, 0x6bb5866c, + 0x6a77ec5b, 0x68315202, 0x69f33835, 0x62af7f08, 0x636d153f, + 0x612bab66, 0x60e9c151, 0x65a6d7d4, 0x6464bde3, 0x662203ba, + 0x67e0698d, 0x48d7cb20, 0x4915a117, 0x4b531f4e, 0x4a917579, + 0x4fde63fc, 0x4e1c09cb, 0x4c5ab792, 0x4d98dda5, 0x46c49a98, + 0x4706f0af, 0x45404ef6, 0x448224c1, 0x41cd3244, 0x400f5873, + 0x4249e62a, 0x438b8c1d, 0x54f16850, 0x55330267, 0x5775bc3e, + 0x56b7d609, 0x53f8c08c, 0x523aaabb, 0x507c14e2, 0x51be7ed5, + 0x5ae239e8, 0x5b2053df, 0x5966ed86, 0x58a487b1, 0x5deb9134, + 0x5c29fb03, 0x5e6f455a, 0x5fad2f6d, 0xe1351b80, 0xe0f771b7, + 0xe2b1cfee, 0xe373a5d9, 0xe63cb35c, 0xe7fed96b, 0xe5b86732, + 0xe47a0d05, 0xef264a38, 0xeee4200f, 0xeca29e56, 0xed60f461, + 0xe82fe2e4, 0xe9ed88d3, 0xebab368a, 0xea695cbd, 0xfd13b8f0, + 0xfcd1d2c7, 0xfe976c9e, 0xff5506a9, 0xfa1a102c, 0xfbd87a1b, + 0xf99ec442, 0xf85cae75, 0xf300e948, 0xf2c2837f, 0xf0843d26, + 0xf1465711, 0xf4094194, 0xf5cb2ba3, 0xf78d95fa, 0xf64fffcd, + 0xd9785d60, 0xd8ba3757, 0xdafc890e, 0xdb3ee339, 0xde71f5bc, + 0xdfb39f8b, 0xddf521d2, 0xdc374be5, 0xd76b0cd8, 0xd6a966ef, + 0xd4efd8b6, 0xd52db281, 0xd062a404, 0xd1a0ce33, 0xd3e6706a, + 0xd2241a5d, 0xc55efe10, 0xc49c9427, 0xc6da2a7e, 0xc7184049, + 0xc25756cc, 0xc3953cfb, 0xc1d382a2, 0xc011e895, 0xcb4dafa8, + 0xca8fc59f, 0xc8c97bc6, 0xc90b11f1, 0xcc440774, 0xcd866d43, + 0xcfc0d31a, 0xce02b92d, 0x91af9640, 0x906dfc77, 0x922b422e, + 0x93e92819, 0x96a63e9c, 0x976454ab, 0x9522eaf2, 0x94e080c5, + 0x9fbcc7f8, 0x9e7eadcf, 0x9c381396, 0x9dfa79a1, 0x98b56f24, + 0x99770513, 0x9b31bb4a, 0x9af3d17d, 0x8d893530, 0x8c4b5f07, + 0x8e0de15e, 0x8fcf8b69, 0x8a809dec, 0x8b42f7db, 0x89044982, + 0x88c623b5, 0x839a6488, 0x82580ebf, 0x801eb0e6, 0x81dcdad1, + 0x8493cc54, 0x8551a663, 0x8717183a, 0x86d5720d, 0xa9e2d0a0, + 0xa820ba97, 0xaa6604ce, 0xaba46ef9, 0xaeeb787c, 0xaf29124b, + 0xad6fac12, 0xacadc625, 0xa7f18118, 0xa633eb2f, 0xa4755576, + 0xa5b73f41, 0xa0f829c4, 0xa13a43f3, 0xa37cfdaa, 0xa2be979d, + 0xb5c473d0, 0xb40619e7, 0xb640a7be, 0xb782cd89, 0xb2cddb0c, + 0xb30fb13b, 0xb1490f62, 0xb08b6555, 0xbbd72268, 0xba15485f, + 0xb853f606, 0xb9919c31, 0xbcde8ab4, 0xbd1ce083, 0xbf5a5eda, + 0xbe9834ed}, + {0x00000000, 0x191b3141, 0x32366282, 0x2b2d53c3, 0x646cc504, + 0x7d77f445, 0x565aa786, 0x4f4196c7, 0xc8d98a08, 0xd1c2bb49, + 0xfaefe88a, 0xe3f4d9cb, 0xacb54f0c, 0xb5ae7e4d, 0x9e832d8e, + 0x87981ccf, 0x4ac21251, 0x53d92310, 0x78f470d3, 0x61ef4192, + 0x2eaed755, 0x37b5e614, 0x1c98b5d7, 0x05838496, 0x821b9859, + 0x9b00a918, 0xb02dfadb, 0xa936cb9a, 0xe6775d5d, 0xff6c6c1c, + 0xd4413fdf, 0xcd5a0e9e, 0x958424a2, 0x8c9f15e3, 0xa7b24620, + 0xbea97761, 0xf1e8e1a6, 0xe8f3d0e7, 0xc3de8324, 0xdac5b265, + 0x5d5daeaa, 0x44469feb, 0x6f6bcc28, 0x7670fd69, 0x39316bae, + 0x202a5aef, 0x0b07092c, 0x121c386d, 0xdf4636f3, 0xc65d07b2, + 0xed705471, 0xf46b6530, 0xbb2af3f7, 0xa231c2b6, 0x891c9175, + 0x9007a034, 0x179fbcfb, 0x0e848dba, 0x25a9de79, 0x3cb2ef38, + 0x73f379ff, 0x6ae848be, 0x41c51b7d, 0x58de2a3c, 0xf0794f05, + 0xe9627e44, 0xc24f2d87, 0xdb541cc6, 0x94158a01, 0x8d0ebb40, + 0xa623e883, 0xbf38d9c2, 0x38a0c50d, 0x21bbf44c, 0x0a96a78f, + 0x138d96ce, 0x5ccc0009, 0x45d73148, 0x6efa628b, 0x77e153ca, + 0xbabb5d54, 0xa3a06c15, 0x888d3fd6, 0x91960e97, 0xded79850, + 0xc7cca911, 0xece1fad2, 0xf5facb93, 0x7262d75c, 0x6b79e61d, + 0x4054b5de, 0x594f849f, 0x160e1258, 0x0f152319, 0x243870da, + 0x3d23419b, 0x65fd6ba7, 0x7ce65ae6, 0x57cb0925, 0x4ed03864, + 0x0191aea3, 0x188a9fe2, 0x33a7cc21, 0x2abcfd60, 0xad24e1af, + 0xb43fd0ee, 0x9f12832d, 0x8609b26c, 0xc94824ab, 0xd05315ea, + 0xfb7e4629, 0xe2657768, 0x2f3f79f6, 0x362448b7, 0x1d091b74, + 0x04122a35, 0x4b53bcf2, 0x52488db3, 0x7965de70, 0x607eef31, + 0xe7e6f3fe, 0xfefdc2bf, 0xd5d0917c, 0xcccba03d, 0x838a36fa, + 0x9a9107bb, 0xb1bc5478, 0xa8a76539, 0x3b83984b, 0x2298a90a, + 0x09b5fac9, 0x10aecb88, 0x5fef5d4f, 0x46f46c0e, 0x6dd93fcd, + 0x74c20e8c, 0xf35a1243, 0xea412302, 0xc16c70c1, 0xd8774180, + 0x9736d747, 0x8e2de606, 0xa500b5c5, 0xbc1b8484, 0x71418a1a, + 0x685abb5b, 0x4377e898, 0x5a6cd9d9, 0x152d4f1e, 0x0c367e5f, + 0x271b2d9c, 0x3e001cdd, 0xb9980012, 0xa0833153, 0x8bae6290, + 0x92b553d1, 0xddf4c516, 0xc4eff457, 0xefc2a794, 0xf6d996d5, + 0xae07bce9, 0xb71c8da8, 0x9c31de6b, 0x852aef2a, 0xca6b79ed, + 0xd37048ac, 0xf85d1b6f, 0xe1462a2e, 0x66de36e1, 0x7fc507a0, + 0x54e85463, 0x4df36522, 0x02b2f3e5, 0x1ba9c2a4, 0x30849167, + 0x299fa026, 0xe4c5aeb8, 0xfdde9ff9, 0xd6f3cc3a, 0xcfe8fd7b, + 0x80a96bbc, 0x99b25afd, 0xb29f093e, 0xab84387f, 0x2c1c24b0, + 0x350715f1, 0x1e2a4632, 0x07317773, 0x4870e1b4, 0x516bd0f5, + 0x7a468336, 0x635db277, 0xcbfad74e, 0xd2e1e60f, 0xf9ccb5cc, + 0xe0d7848d, 0xaf96124a, 0xb68d230b, 0x9da070c8, 0x84bb4189, + 0x03235d46, 0x1a386c07, 0x31153fc4, 0x280e0e85, 0x674f9842, + 0x7e54a903, 0x5579fac0, 0x4c62cb81, 0x8138c51f, 0x9823f45e, + 0xb30ea79d, 0xaa1596dc, 0xe554001b, 0xfc4f315a, 0xd7626299, + 0xce7953d8, 0x49e14f17, 0x50fa7e56, 0x7bd72d95, 0x62cc1cd4, + 0x2d8d8a13, 0x3496bb52, 0x1fbbe891, 0x06a0d9d0, 0x5e7ef3ec, + 0x4765c2ad, 0x6c48916e, 0x7553a02f, 0x3a1236e8, 0x230907a9, + 0x0824546a, 0x113f652b, 0x96a779e4, 0x8fbc48a5, 0xa4911b66, + 0xbd8a2a27, 0xf2cbbce0, 0xebd08da1, 0xc0fdde62, 0xd9e6ef23, + 0x14bce1bd, 0x0da7d0fc, 0x268a833f, 0x3f91b27e, 0x70d024b9, + 0x69cb15f8, 0x42e6463b, 0x5bfd777a, 0xdc656bb5, 0xc57e5af4, + 0xee530937, 0xf7483876, 0xb809aeb1, 0xa1129ff0, 0x8a3fcc33, + 0x9324fd72}, + {0x00000000, 0x77073096, 0xee0e612c, 0x990951ba, 0x076dc419, + 0x706af48f, 0xe963a535, 0x9e6495a3, 0x0edb8832, 0x79dcb8a4, + 0xe0d5e91e, 0x97d2d988, 0x09b64c2b, 0x7eb17cbd, 0xe7b82d07, + 0x90bf1d91, 0x1db71064, 0x6ab020f2, 0xf3b97148, 0x84be41de, + 0x1adad47d, 0x6ddde4eb, 0xf4d4b551, 0x83d385c7, 0x136c9856, + 0x646ba8c0, 0xfd62f97a, 0x8a65c9ec, 0x14015c4f, 0x63066cd9, + 0xfa0f3d63, 0x8d080df5, 0x3b6e20c8, 0x4c69105e, 0xd56041e4, + 0xa2677172, 0x3c03e4d1, 0x4b04d447, 0xd20d85fd, 0xa50ab56b, + 0x35b5a8fa, 0x42b2986c, 0xdbbbc9d6, 0xacbcf940, 0x32d86ce3, + 0x45df5c75, 0xdcd60dcf, 0xabd13d59, 0x26d930ac, 0x51de003a, + 0xc8d75180, 0xbfd06116, 0x21b4f4b5, 0x56b3c423, 0xcfba9599, + 0xb8bda50f, 0x2802b89e, 0x5f058808, 0xc60cd9b2, 0xb10be924, + 0x2f6f7c87, 0x58684c11, 0xc1611dab, 0xb6662d3d, 0x76dc4190, + 0x01db7106, 0x98d220bc, 0xefd5102a, 0x71b18589, 0x06b6b51f, + 0x9fbfe4a5, 0xe8b8d433, 0x7807c9a2, 0x0f00f934, 0x9609a88e, + 0xe10e9818, 0x7f6a0dbb, 0x086d3d2d, 0x91646c97, 0xe6635c01, + 0x6b6b51f4, 0x1c6c6162, 0x856530d8, 0xf262004e, 0x6c0695ed, + 0x1b01a57b, 0x8208f4c1, 0xf50fc457, 0x65b0d9c6, 0x12b7e950, + 0x8bbeb8ea, 0xfcb9887c, 0x62dd1ddf, 0x15da2d49, 0x8cd37cf3, + 0xfbd44c65, 0x4db26158, 0x3ab551ce, 0xa3bc0074, 0xd4bb30e2, + 0x4adfa541, 0x3dd895d7, 0xa4d1c46d, 0xd3d6f4fb, 0x4369e96a, + 0x346ed9fc, 0xad678846, 0xda60b8d0, 0x44042d73, 0x33031de5, + 0xaa0a4c5f, 0xdd0d7cc9, 0x5005713c, 0x270241aa, 0xbe0b1010, + 0xc90c2086, 0x5768b525, 0x206f85b3, 0xb966d409, 0xce61e49f, + 0x5edef90e, 0x29d9c998, 0xb0d09822, 0xc7d7a8b4, 0x59b33d17, + 0x2eb40d81, 0xb7bd5c3b, 0xc0ba6cad, 0xedb88320, 0x9abfb3b6, + 0x03b6e20c, 0x74b1d29a, 0xead54739, 0x9dd277af, 0x04db2615, + 0x73dc1683, 0xe3630b12, 0x94643b84, 0x0d6d6a3e, 0x7a6a5aa8, + 0xe40ecf0b, 0x9309ff9d, 0x0a00ae27, 0x7d079eb1, 0xf00f9344, + 0x8708a3d2, 0x1e01f268, 0x6906c2fe, 0xf762575d, 0x806567cb, + 0x196c3671, 0x6e6b06e7, 0xfed41b76, 0x89d32be0, 0x10da7a5a, + 0x67dd4acc, 0xf9b9df6f, 0x8ebeeff9, 0x17b7be43, 0x60b08ed5, + 0xd6d6a3e8, 0xa1d1937e, 0x38d8c2c4, 0x4fdff252, 0xd1bb67f1, + 0xa6bc5767, 0x3fb506dd, 0x48b2364b, 0xd80d2bda, 0xaf0a1b4c, + 0x36034af6, 0x41047a60, 0xdf60efc3, 0xa867df55, 0x316e8eef, + 0x4669be79, 0xcb61b38c, 0xbc66831a, 0x256fd2a0, 0x5268e236, + 0xcc0c7795, 0xbb0b4703, 0x220216b9, 0x5505262f, 0xc5ba3bbe, + 0xb2bd0b28, 0x2bb45a92, 0x5cb36a04, 0xc2d7ffa7, 0xb5d0cf31, + 0x2cd99e8b, 0x5bdeae1d, 0x9b64c2b0, 0xec63f226, 0x756aa39c, + 0x026d930a, 0x9c0906a9, 0xeb0e363f, 0x72076785, 0x05005713, + 0x95bf4a82, 0xe2b87a14, 0x7bb12bae, 0x0cb61b38, 0x92d28e9b, + 0xe5d5be0d, 0x7cdcefb7, 0x0bdbdf21, 0x86d3d2d4, 0xf1d4e242, + 0x68ddb3f8, 0x1fda836e, 0x81be16cd, 0xf6b9265b, 0x6fb077e1, + 0x18b74777, 0x88085ae6, 0xff0f6a70, 0x66063bca, 0x11010b5c, + 0x8f659eff, 0xf862ae69, 0x616bffd3, 0x166ccf45, 0xa00ae278, + 0xd70dd2ee, 0x4e048354, 0x3903b3c2, 0xa7672661, 0xd06016f7, + 0x4969474d, 0x3e6e77db, 0xaed16a4a, 0xd9d65adc, 0x40df0b66, + 0x37d83bf0, 0xa9bcae53, 0xdebb9ec5, 0x47b2cf7f, 0x30b5ffe9, + 0xbdbdf21c, 0xcabac28a, 0x53b39330, 0x24b4a3a6, 0xbad03605, + 0xcdd70693, 0x54de5729, 0x23d967bf, 0xb3667a2e, 0xc4614ab8, + 0x5d681b02, 0x2a6f2b94, 0xb40bbe37, 0xc30c8ea1, 0x5a05df1b, + 0x2d02ef8d}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x0000000000000000, 0x9630077700000000, 0x2c610eee00000000, + 0xba51099900000000, 0x19c46d0700000000, 0x8ff46a7000000000, + 0x35a563e900000000, 0xa395649e00000000, 0x3288db0e00000000, + 0xa4b8dc7900000000, 0x1ee9d5e000000000, 0x88d9d29700000000, + 0x2b4cb60900000000, 0xbd7cb17e00000000, 0x072db8e700000000, + 0x911dbf9000000000, 0x6410b71d00000000, 0xf220b06a00000000, + 0x4871b9f300000000, 0xde41be8400000000, 0x7dd4da1a00000000, + 0xebe4dd6d00000000, 0x51b5d4f400000000, 0xc785d38300000000, + 0x56986c1300000000, 0xc0a86b6400000000, 0x7af962fd00000000, + 0xecc9658a00000000, 0x4f5c011400000000, 0xd96c066300000000, + 0x633d0ffa00000000, 0xf50d088d00000000, 0xc8206e3b00000000, + 0x5e10694c00000000, 0xe44160d500000000, 0x727167a200000000, + 0xd1e4033c00000000, 0x47d4044b00000000, 0xfd850dd200000000, + 0x6bb50aa500000000, 0xfaa8b53500000000, 0x6c98b24200000000, + 0xd6c9bbdb00000000, 0x40f9bcac00000000, 0xe36cd83200000000, + 0x755cdf4500000000, 0xcf0dd6dc00000000, 0x593dd1ab00000000, + 0xac30d92600000000, 0x3a00de5100000000, 0x8051d7c800000000, + 0x1661d0bf00000000, 0xb5f4b42100000000, 0x23c4b35600000000, + 0x9995bacf00000000, 0x0fa5bdb800000000, 0x9eb8022800000000, + 0x0888055f00000000, 0xb2d90cc600000000, 0x24e90bb100000000, + 0x877c6f2f00000000, 0x114c685800000000, 0xab1d61c100000000, + 0x3d2d66b600000000, 0x9041dc7600000000, 0x0671db0100000000, + 0xbc20d29800000000, 0x2a10d5ef00000000, 0x8985b17100000000, + 0x1fb5b60600000000, 0xa5e4bf9f00000000, 0x33d4b8e800000000, + 0xa2c9077800000000, 0x34f9000f00000000, 0x8ea8099600000000, + 0x18980ee100000000, 0xbb0d6a7f00000000, 0x2d3d6d0800000000, + 0x976c649100000000, 0x015c63e600000000, 0xf4516b6b00000000, + 0x62616c1c00000000, 0xd830658500000000, 0x4e0062f200000000, + 0xed95066c00000000, 0x7ba5011b00000000, 0xc1f4088200000000, + 0x57c40ff500000000, 0xc6d9b06500000000, 0x50e9b71200000000, + 0xeab8be8b00000000, 0x7c88b9fc00000000, 0xdf1ddd6200000000, + 0x492dda1500000000, 0xf37cd38c00000000, 0x654cd4fb00000000, + 0x5861b24d00000000, 0xce51b53a00000000, 0x7400bca300000000, + 0xe230bbd400000000, 0x41a5df4a00000000, 0xd795d83d00000000, + 0x6dc4d1a400000000, 0xfbf4d6d300000000, 0x6ae9694300000000, + 0xfcd96e3400000000, 0x468867ad00000000, 0xd0b860da00000000, + 0x732d044400000000, 0xe51d033300000000, 0x5f4c0aaa00000000, + 0xc97c0ddd00000000, 0x3c71055000000000, 0xaa41022700000000, + 0x10100bbe00000000, 0x86200cc900000000, 0x25b5685700000000, + 0xb3856f2000000000, 0x09d466b900000000, 0x9fe461ce00000000, + 0x0ef9de5e00000000, 0x98c9d92900000000, 0x2298d0b000000000, + 0xb4a8d7c700000000, 0x173db35900000000, 0x810db42e00000000, + 0x3b5cbdb700000000, 0xad6cbac000000000, 0x2083b8ed00000000, + 0xb6b3bf9a00000000, 0x0ce2b60300000000, 0x9ad2b17400000000, + 0x3947d5ea00000000, 0xaf77d29d00000000, 0x1526db0400000000, + 0x8316dc7300000000, 0x120b63e300000000, 0x843b649400000000, + 0x3e6a6d0d00000000, 0xa85a6a7a00000000, 0x0bcf0ee400000000, + 0x9dff099300000000, 0x27ae000a00000000, 0xb19e077d00000000, + 0x44930ff000000000, 0xd2a3088700000000, 0x68f2011e00000000, + 0xfec2066900000000, 0x5d5762f700000000, 0xcb67658000000000, + 0x71366c1900000000, 0xe7066b6e00000000, 0x761bd4fe00000000, + 0xe02bd38900000000, 0x5a7ada1000000000, 0xcc4add6700000000, + 0x6fdfb9f900000000, 0xf9efbe8e00000000, 0x43beb71700000000, + 0xd58eb06000000000, 0xe8a3d6d600000000, 0x7e93d1a100000000, + 0xc4c2d83800000000, 0x52f2df4f00000000, 0xf167bbd100000000, + 0x6757bca600000000, 0xdd06b53f00000000, 0x4b36b24800000000, + 0xda2b0dd800000000, 0x4c1b0aaf00000000, 0xf64a033600000000, + 0x607a044100000000, 0xc3ef60df00000000, 0x55df67a800000000, + 0xef8e6e3100000000, 0x79be694600000000, 0x8cb361cb00000000, + 0x1a8366bc00000000, 0xa0d26f2500000000, 0x36e2685200000000, + 0x95770ccc00000000, 0x03470bbb00000000, 0xb916022200000000, + 0x2f26055500000000, 0xbe3bbac500000000, 0x280bbdb200000000, + 0x925ab42b00000000, 0x046ab35c00000000, 0xa7ffd7c200000000, + 0x31cfd0b500000000, 0x8b9ed92c00000000, 0x1daede5b00000000, + 0xb0c2649b00000000, 0x26f263ec00000000, 0x9ca36a7500000000, + 0x0a936d0200000000, 0xa906099c00000000, 0x3f360eeb00000000, + 0x8567077200000000, 0x1357000500000000, 0x824abf9500000000, + 0x147ab8e200000000, 0xae2bb17b00000000, 0x381bb60c00000000, + 0x9b8ed29200000000, 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0x252d61ea00000000, 0xb83789db00000000, + 0x0c3cfe7d00000000, 0x2bd068ec00000000, 0x9fdb1f4a00000000, + 0x02c1f77b00000000, 0xb6ca80dd00000000, 0x38f4271800000000, + 0x8cff50be00000000, 0x11e5b88f00000000, 0xa5eecf2900000000, + 0x4c9e87df00000000, 0xf895f07900000000, 0x658f184800000000, + 0xd1846fee00000000, 0x5fbac82b00000000, 0xebb1bf8d00000000, + 0x76ab57bc00000000, 0xc2a0201a00000000, 0xf2ea168800000000, + 0x46e1612e00000000, 0xdbfb891f00000000, 0x6ff0feb900000000, + 0xe1ce597c00000000, 0x55c52eda00000000, 0xc8dfc6eb00000000, + 0x7cd4b14d00000000, 0x95a4f9bb00000000, 0x21af8e1d00000000, + 0xbcb5662c00000000, 0x08be118a00000000, 0x8680b64f00000000, + 0x328bc1e900000000, 0xaf9129d800000000, 0x1b9a5e7e00000000, + 0x3c76c8ef00000000, 0x887dbf4900000000, 0x1567577800000000, + 0xa16c20de00000000, 0x2f52871b00000000, 0x9b59f0bd00000000, + 0x0643188c00000000, 0xb2486f2a00000000, 0x5b3827dc00000000, + 0xef33507a00000000, 0x7229b84b00000000, 0xc622cfed00000000, + 0x481c682800000000, 0xfc171f8e00000000, 0x610df7bf00000000, + 0xd506801900000000, 0x6ed3ab4700000000, 0xdad8dce100000000, + 0x47c234d000000000, 0xf3c9437600000000, 0x7df7e4b300000000, + 0xc9fc931500000000, 0x54e67b2400000000, 0xe0ed0c8200000000, + 0x099d447400000000, 0xbd9633d200000000, 0x208cdbe300000000, + 0x9487ac4500000000, 0x1ab90b8000000000, 0xaeb27c2600000000, + 0x33a8941700000000, 0x87a3e3b100000000, 0xa04f752000000000, + 0x1444028600000000, 0x895eeab700000000, 0x3d559d1100000000, + 0xb36b3ad400000000, 0x07604d7200000000, 0x9a7aa54300000000, + 0x2e71d2e500000000, 0xc7019a1300000000, 0x730aedb500000000, + 0xee10058400000000, 0x5a1b722200000000, 0xd425d5e700000000, + 0x602ea24100000000, 0xfd344a7000000000, 0x493f3dd600000000, + 0x8b9f1dcc00000000, 0x3f946a6a00000000, 0xa28e825b00000000, + 0x1685f5fd00000000, 0x98bb523800000000, 0x2cb0259e00000000, + 0xb1aacdaf00000000, 0x05a1ba0900000000, 0xecd1f2ff00000000, + 0x58da855900000000, 0xc5c06d6800000000, 0x71cb1ace00000000, + 0xfff5bd0b00000000, 0x4bfecaad00000000, 0xd6e4229c00000000, + 0x62ef553a00000000, 0x4503c3ab00000000, 0xf108b40d00000000, + 0x6c125c3c00000000, 0xd8192b9a00000000, 0x56278c5f00000000, + 0xe22cfbf900000000, 0x7f3613c800000000, 0xcb3d646e00000000, + 0x224d2c9800000000, 0x96465b3e00000000, 0x0b5cb30f00000000, + 0xbf57c4a900000000, 0x3169636c00000000, 0x856214ca00000000, + 0x1878fcfb00000000, 0xac738b5d00000000, 0x17a6a00300000000, + 0xa3add7a500000000, 0x3eb73f9400000000, 0x8abc483200000000, + 0x0482eff700000000, 0xb089985100000000, 0x2d93706000000000, + 0x999807c600000000, 0x70e84f3000000000, 0xc4e3389600000000, + 0x59f9d0a700000000, 0xedf2a70100000000, 0x63cc00c400000000, + 0xd7c7776200000000, 0x4add9f5300000000, 0xfed6e8f500000000, + 0xd93a7e6400000000, 0x6d3109c200000000, 0xf02be1f300000000, + 0x4420965500000000, 0xca1e319000000000, 0x7e15463600000000, + 0xe30fae0700000000, 0x5704d9a100000000, 0xbe74915700000000, + 0x0a7fe6f100000000, 0x97650ec000000000, 0x236e796600000000, + 0xad50dea300000000, 0x195ba90500000000, 0x8441413400000000, + 0x304a369200000000}, + {0x0000000000000000, 0x9e00aacc00000000, 0x7d07254200000000, + 0xe3078f8e00000000, 0xfa0e4a8400000000, 0x640ee04800000000, + 0x87096fc600000000, 0x1909c50a00000000, 0xb51be5d300000000, + 0x2b1b4f1f00000000, 0xc81cc09100000000, 0x561c6a5d00000000, + 0x4f15af5700000000, 0xd115059b00000000, 0x32128a1500000000, + 0xac1220d900000000, 0x2b31bb7c00000000, 0xb53111b000000000, + 0x56369e3e00000000, 0xc83634f200000000, 0xd13ff1f800000000, + 0x4f3f5b3400000000, 0xac38d4ba00000000, 0x32387e7600000000, + 0x9e2a5eaf00000000, 0x002af46300000000, 0xe32d7bed00000000, + 0x7d2dd12100000000, 0x6424142b00000000, 0xfa24bee700000000, + 0x1923316900000000, 0x87239ba500000000, 0x566276f900000000, + 0xc862dc3500000000, 0x2b6553bb00000000, 0xb565f97700000000, + 0xac6c3c7d00000000, 0x326c96b100000000, 0xd16b193f00000000, + 0x4f6bb3f300000000, 0xe379932a00000000, 0x7d7939e600000000, + 0x9e7eb66800000000, 0x007e1ca400000000, 0x1977d9ae00000000, + 0x8777736200000000, 0x6470fcec00000000, 0xfa70562000000000, + 0x7d53cd8500000000, 0xe353674900000000, 0x0054e8c700000000, + 0x9e54420b00000000, 0x875d870100000000, 0x195d2dcd00000000, + 0xfa5aa24300000000, 0x645a088f00000000, 0xc848285600000000, + 0x5648829a00000000, 0xb54f0d1400000000, 0x2b4fa7d800000000, + 0x324662d200000000, 0xac46c81e00000000, 0x4f41479000000000, + 0xd141ed5c00000000, 0xedc29d2900000000, 0x73c237e500000000, + 0x90c5b86b00000000, 0x0ec512a700000000, 0x17ccd7ad00000000, + 0x89cc7d6100000000, 0x6acbf2ef00000000, 0xf4cb582300000000, + 0x58d978fa00000000, 0xc6d9d23600000000, 0x25de5db800000000, + 0xbbdef77400000000, 0xa2d7327e00000000, 0x3cd798b200000000, + 0xdfd0173c00000000, 0x41d0bdf000000000, 0xc6f3265500000000, + 0x58f38c9900000000, 0xbbf4031700000000, 0x25f4a9db00000000, + 0x3cfd6cd100000000, 0xa2fdc61d00000000, 0x41fa499300000000, + 0xdffae35f00000000, 0x73e8c38600000000, 0xede8694a00000000, + 0x0eefe6c400000000, 0x90ef4c0800000000, 0x89e6890200000000, + 0x17e623ce00000000, 0xf4e1ac4000000000, 0x6ae1068c00000000, + 0xbba0ebd000000000, 0x25a0411c00000000, 0xc6a7ce9200000000, + 0x58a7645e00000000, 0x41aea15400000000, 0xdfae0b9800000000, + 0x3ca9841600000000, 0xa2a92eda00000000, 0x0ebb0e0300000000, + 0x90bba4cf00000000, 0x73bc2b4100000000, 0xedbc818d00000000, + 0xf4b5448700000000, 0x6ab5ee4b00000000, 0x89b261c500000000, + 0x17b2cb0900000000, 0x909150ac00000000, 0x0e91fa6000000000, + 0xed9675ee00000000, 0x7396df2200000000, 0x6a9f1a2800000000, + 0xf49fb0e400000000, 0x17983f6a00000000, 0x899895a600000000, + 0x258ab57f00000000, 0xbb8a1fb300000000, 0x588d903d00000000, + 0xc68d3af100000000, 0xdf84fffb00000000, 0x4184553700000000, + 0xa283dab900000000, 0x3c83707500000000, 0xda853b5300000000, + 0x4485919f00000000, 0xa7821e1100000000, 0x3982b4dd00000000, + 0x208b71d700000000, 0xbe8bdb1b00000000, 0x5d8c549500000000, + 0xc38cfe5900000000, 0x6f9ede8000000000, 0xf19e744c00000000, + 0x1299fbc200000000, 0x8c99510e00000000, 0x9590940400000000, + 0x0b903ec800000000, 0xe897b14600000000, 0x76971b8a00000000, + 0xf1b4802f00000000, 0x6fb42ae300000000, 0x8cb3a56d00000000, + 0x12b30fa100000000, 0x0bbacaab00000000, 0x95ba606700000000, + 0x76bdefe900000000, 0xe8bd452500000000, 0x44af65fc00000000, + 0xdaafcf3000000000, 0x39a840be00000000, 0xa7a8ea7200000000, + 0xbea12f7800000000, 0x20a185b400000000, 0xc3a60a3a00000000, + 0x5da6a0f600000000, 0x8ce74daa00000000, 0x12e7e76600000000, + 0xf1e068e800000000, 0x6fe0c22400000000, 0x76e9072e00000000, + 0xe8e9ade200000000, 0x0bee226c00000000, 0x95ee88a000000000, + 0x39fca87900000000, 0xa7fc02b500000000, 0x44fb8d3b00000000, + 0xdafb27f700000000, 0xc3f2e2fd00000000, 0x5df2483100000000, + 0xbef5c7bf00000000, 0x20f56d7300000000, 0xa7d6f6d600000000, + 0x39d65c1a00000000, 0xdad1d39400000000, 0x44d1795800000000, + 0x5dd8bc5200000000, 0xc3d8169e00000000, 0x20df991000000000, + 0xbedf33dc00000000, 0x12cd130500000000, 0x8ccdb9c900000000, + 0x6fca364700000000, 0xf1ca9c8b00000000, 0xe8c3598100000000, + 0x76c3f34d00000000, 0x95c47cc300000000, 0x0bc4d60f00000000, + 0x3747a67a00000000, 0xa9470cb600000000, 0x4a40833800000000, + 0xd44029f400000000, 0xcd49ecfe00000000, 0x5349463200000000, + 0xb04ec9bc00000000, 0x2e4e637000000000, 0x825c43a900000000, + 0x1c5ce96500000000, 0xff5b66eb00000000, 0x615bcc2700000000, + 0x7852092d00000000, 0xe652a3e100000000, 0x05552c6f00000000, + 0x9b5586a300000000, 0x1c761d0600000000, 0x8276b7ca00000000, + 0x6171384400000000, 0xff71928800000000, 0xe678578200000000, + 0x7878fd4e00000000, 0x9b7f72c000000000, 0x057fd80c00000000, + 0xa96df8d500000000, 0x376d521900000000, 0xd46add9700000000, + 0x4a6a775b00000000, 0x5363b25100000000, 0xcd63189d00000000, + 0x2e64971300000000, 0xb0643ddf00000000, 0x6125d08300000000, + 0xff257a4f00000000, 0x1c22f5c100000000, 0x82225f0d00000000, + 0x9b2b9a0700000000, 0x052b30cb00000000, 0xe62cbf4500000000, + 0x782c158900000000, 0xd43e355000000000, 0x4a3e9f9c00000000, + 0xa939101200000000, 0x3739bade00000000, 0x2e307fd400000000, + 0xb030d51800000000, 0x53375a9600000000, 0xcd37f05a00000000, + 0x4a146bff00000000, 0xd414c13300000000, 0x37134ebd00000000, + 0xa913e47100000000, 0xb01a217b00000000, 0x2e1a8bb700000000, + 0xcd1d043900000000, 0x531daef500000000, 0xff0f8e2c00000000, + 0x610f24e000000000, 0x8208ab6e00000000, 0x1c0801a200000000, + 0x0501c4a800000000, 0x9b016e6400000000, 0x7806e1ea00000000, + 0xe6064b2600000000}}; + +#else /* W == 4 */ + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xb8bc6765, 0xaa09c88b, 0x12b5afee, 0x8f629757, + 0x37def032, 0x256b5fdc, 0x9dd738b9, 0xc5b428ef, 0x7d084f8a, + 0x6fbde064, 0xd7018701, 0x4ad6bfb8, 0xf26ad8dd, 0xe0df7733, + 0x58631056, 0x5019579f, 0xe8a530fa, 0xfa109f14, 0x42acf871, + 0xdf7bc0c8, 0x67c7a7ad, 0x75720843, 0xcdce6f26, 0x95ad7f70, + 0x2d111815, 0x3fa4b7fb, 0x8718d09e, 0x1acfe827, 0xa2738f42, + 0xb0c620ac, 0x087a47c9, 0xa032af3e, 0x188ec85b, 0x0a3b67b5, + 0xb28700d0, 0x2f503869, 0x97ec5f0c, 0x8559f0e2, 0x3de59787, + 0x658687d1, 0xdd3ae0b4, 0xcf8f4f5a, 0x7733283f, 0xeae41086, + 0x525877e3, 0x40edd80d, 0xf851bf68, 0xf02bf8a1, 0x48979fc4, + 0x5a22302a, 0xe29e574f, 0x7f496ff6, 0xc7f50893, 0xd540a77d, + 0x6dfcc018, 0x359fd04e, 0x8d23b72b, 0x9f9618c5, 0x272a7fa0, + 0xbafd4719, 0x0241207c, 0x10f48f92, 0xa848e8f7, 0x9b14583d, + 0x23a83f58, 0x311d90b6, 0x89a1f7d3, 0x1476cf6a, 0xaccaa80f, + 0xbe7f07e1, 0x06c36084, 0x5ea070d2, 0xe61c17b7, 0xf4a9b859, + 0x4c15df3c, 0xd1c2e785, 0x697e80e0, 0x7bcb2f0e, 0xc377486b, + 0xcb0d0fa2, 0x73b168c7, 0x6104c729, 0xd9b8a04c, 0x446f98f5, + 0xfcd3ff90, 0xee66507e, 0x56da371b, 0x0eb9274d, 0xb6054028, + 0xa4b0efc6, 0x1c0c88a3, 0x81dbb01a, 0x3967d77f, 0x2bd27891, + 0x936e1ff4, 0x3b26f703, 0x839a9066, 0x912f3f88, 0x299358ed, + 0xb4446054, 0x0cf80731, 0x1e4da8df, 0xa6f1cfba, 0xfe92dfec, + 0x462eb889, 0x549b1767, 0xec277002, 0x71f048bb, 0xc94c2fde, + 0xdbf98030, 0x6345e755, 0x6b3fa09c, 0xd383c7f9, 0xc1366817, + 0x798a0f72, 0xe45d37cb, 0x5ce150ae, 0x4e54ff40, 0xf6e89825, + 0xae8b8873, 0x1637ef16, 0x048240f8, 0xbc3e279d, 0x21e91f24, + 0x99557841, 0x8be0d7af, 0x335cb0ca, 0xed59b63b, 0x55e5d15e, + 0x47507eb0, 0xffec19d5, 0x623b216c, 0xda874609, 0xc832e9e7, + 0x708e8e82, 0x28ed9ed4, 0x9051f9b1, 0x82e4565f, 0x3a58313a, + 0xa78f0983, 0x1f336ee6, 0x0d86c108, 0xb53aa66d, 0xbd40e1a4, + 0x05fc86c1, 0x1749292f, 0xaff54e4a, 0x322276f3, 0x8a9e1196, + 0x982bbe78, 0x2097d91d, 0x78f4c94b, 0xc048ae2e, 0xd2fd01c0, + 0x6a4166a5, 0xf7965e1c, 0x4f2a3979, 0x5d9f9697, 0xe523f1f2, + 0x4d6b1905, 0xf5d77e60, 0xe762d18e, 0x5fdeb6eb, 0xc2098e52, + 0x7ab5e937, 0x680046d9, 0xd0bc21bc, 0x88df31ea, 0x3063568f, + 0x22d6f961, 0x9a6a9e04, 0x07bda6bd, 0xbf01c1d8, 0xadb46e36, + 0x15080953, 0x1d724e9a, 0xa5ce29ff, 0xb77b8611, 0x0fc7e174, + 0x9210d9cd, 0x2aacbea8, 0x38191146, 0x80a57623, 0xd8c66675, + 0x607a0110, 0x72cfaefe, 0xca73c99b, 0x57a4f122, 0xef189647, + 0xfdad39a9, 0x45115ecc, 0x764dee06, 0xcef18963, 0xdc44268d, + 0x64f841e8, 0xf92f7951, 0x41931e34, 0x5326b1da, 0xeb9ad6bf, + 0xb3f9c6e9, 0x0b45a18c, 0x19f00e62, 0xa14c6907, 0x3c9b51be, + 0x842736db, 0x96929935, 0x2e2efe50, 0x2654b999, 0x9ee8defc, + 0x8c5d7112, 0x34e11677, 0xa9362ece, 0x118a49ab, 0x033fe645, + 0xbb838120, 0xe3e09176, 0x5b5cf613, 0x49e959fd, 0xf1553e98, + 0x6c820621, 0xd43e6144, 0xc68bceaa, 0x7e37a9cf, 0xd67f4138, + 0x6ec3265d, 0x7c7689b3, 0xc4caeed6, 0x591dd66f, 0xe1a1b10a, + 0xf3141ee4, 0x4ba87981, 0x13cb69d7, 0xab770eb2, 0xb9c2a15c, + 0x017ec639, 0x9ca9fe80, 0x241599e5, 0x36a0360b, 0x8e1c516e, + 0x866616a7, 0x3eda71c2, 0x2c6fde2c, 0x94d3b949, 0x090481f0, + 0xb1b8e695, 0xa30d497b, 0x1bb12e1e, 0x43d23e48, 0xfb6e592d, + 0xe9dbf6c3, 0x516791a6, 0xccb0a91f, 0x740cce7a, 0x66b96194, + 0xde0506f1}, + {0x00000000, 0x01c26a37, 0x0384d46e, 0x0246be59, 0x0709a8dc, + 0x06cbc2eb, 0x048d7cb2, 0x054f1685, 0x0e1351b8, 0x0fd13b8f, + 0x0d9785d6, 0x0c55efe1, 0x091af964, 0x08d89353, 0x0a9e2d0a, + 0x0b5c473d, 0x1c26a370, 0x1de4c947, 0x1fa2771e, 0x1e601d29, + 0x1b2f0bac, 0x1aed619b, 0x18abdfc2, 0x1969b5f5, 0x1235f2c8, + 0x13f798ff, 0x11b126a6, 0x10734c91, 0x153c5a14, 0x14fe3023, + 0x16b88e7a, 0x177ae44d, 0x384d46e0, 0x398f2cd7, 0x3bc9928e, + 0x3a0bf8b9, 0x3f44ee3c, 0x3e86840b, 0x3cc03a52, 0x3d025065, + 0x365e1758, 0x379c7d6f, 0x35dac336, 0x3418a901, 0x3157bf84, + 0x3095d5b3, 0x32d36bea, 0x331101dd, 0x246be590, 0x25a98fa7, + 0x27ef31fe, 0x262d5bc9, 0x23624d4c, 0x22a0277b, 0x20e69922, + 0x2124f315, 0x2a78b428, 0x2bbade1f, 0x29fc6046, 0x283e0a71, + 0x2d711cf4, 0x2cb376c3, 0x2ef5c89a, 0x2f37a2ad, 0x709a8dc0, + 0x7158e7f7, 0x731e59ae, 0x72dc3399, 0x7793251c, 0x76514f2b, + 0x7417f172, 0x75d59b45, 0x7e89dc78, 0x7f4bb64f, 0x7d0d0816, + 0x7ccf6221, 0x798074a4, 0x78421e93, 0x7a04a0ca, 0x7bc6cafd, + 0x6cbc2eb0, 0x6d7e4487, 0x6f38fade, 0x6efa90e9, 0x6bb5866c, + 0x6a77ec5b, 0x68315202, 0x69f33835, 0x62af7f08, 0x636d153f, + 0x612bab66, 0x60e9c151, 0x65a6d7d4, 0x6464bde3, 0x662203ba, + 0x67e0698d, 0x48d7cb20, 0x4915a117, 0x4b531f4e, 0x4a917579, + 0x4fde63fc, 0x4e1c09cb, 0x4c5ab792, 0x4d98dda5, 0x46c49a98, + 0x4706f0af, 0x45404ef6, 0x448224c1, 0x41cd3244, 0x400f5873, + 0x4249e62a, 0x438b8c1d, 0x54f16850, 0x55330267, 0x5775bc3e, + 0x56b7d609, 0x53f8c08c, 0x523aaabb, 0x507c14e2, 0x51be7ed5, + 0x5ae239e8, 0x5b2053df, 0x5966ed86, 0x58a487b1, 0x5deb9134, + 0x5c29fb03, 0x5e6f455a, 0x5fad2f6d, 0xe1351b80, 0xe0f771b7, + 0xe2b1cfee, 0xe373a5d9, 0xe63cb35c, 0xe7fed96b, 0xe5b86732, + 0xe47a0d05, 0xef264a38, 0xeee4200f, 0xeca29e56, 0xed60f461, + 0xe82fe2e4, 0xe9ed88d3, 0xebab368a, 0xea695cbd, 0xfd13b8f0, + 0xfcd1d2c7, 0xfe976c9e, 0xff5506a9, 0xfa1a102c, 0xfbd87a1b, + 0xf99ec442, 0xf85cae75, 0xf300e948, 0xf2c2837f, 0xf0843d26, + 0xf1465711, 0xf4094194, 0xf5cb2ba3, 0xf78d95fa, 0xf64fffcd, + 0xd9785d60, 0xd8ba3757, 0xdafc890e, 0xdb3ee339, 0xde71f5bc, + 0xdfb39f8b, 0xddf521d2, 0xdc374be5, 0xd76b0cd8, 0xd6a966ef, + 0xd4efd8b6, 0xd52db281, 0xd062a404, 0xd1a0ce33, 0xd3e6706a, + 0xd2241a5d, 0xc55efe10, 0xc49c9427, 0xc6da2a7e, 0xc7184049, + 0xc25756cc, 0xc3953cfb, 0xc1d382a2, 0xc011e895, 0xcb4dafa8, + 0xca8fc59f, 0xc8c97bc6, 0xc90b11f1, 0xcc440774, 0xcd866d43, + 0xcfc0d31a, 0xce02b92d, 0x91af9640, 0x906dfc77, 0x922b422e, + 0x93e92819, 0x96a63e9c, 0x976454ab, 0x9522eaf2, 0x94e080c5, + 0x9fbcc7f8, 0x9e7eadcf, 0x9c381396, 0x9dfa79a1, 0x98b56f24, + 0x99770513, 0x9b31bb4a, 0x9af3d17d, 0x8d893530, 0x8c4b5f07, + 0x8e0de15e, 0x8fcf8b69, 0x8a809dec, 0x8b42f7db, 0x89044982, + 0x88c623b5, 0x839a6488, 0x82580ebf, 0x801eb0e6, 0x81dcdad1, + 0x8493cc54, 0x8551a663, 0x8717183a, 0x86d5720d, 0xa9e2d0a0, + 0xa820ba97, 0xaa6604ce, 0xaba46ef9, 0xaeeb787c, 0xaf29124b, + 0xad6fac12, 0xacadc625, 0xa7f18118, 0xa633eb2f, 0xa4755576, + 0xa5b73f41, 0xa0f829c4, 0xa13a43f3, 0xa37cfdaa, 0xa2be979d, + 0xb5c473d0, 0xb40619e7, 0xb640a7be, 0xb782cd89, 0xb2cddb0c, + 0xb30fb13b, 0xb1490f62, 0xb08b6555, 0xbbd72268, 0xba15485f, + 0xb853f606, 0xb9919c31, 0xbcde8ab4, 0xbd1ce083, 0xbf5a5eda, + 0xbe9834ed}, + {0x00000000, 0x191b3141, 0x32366282, 0x2b2d53c3, 0x646cc504, + 0x7d77f445, 0x565aa786, 0x4f4196c7, 0xc8d98a08, 0xd1c2bb49, + 0xfaefe88a, 0xe3f4d9cb, 0xacb54f0c, 0xb5ae7e4d, 0x9e832d8e, + 0x87981ccf, 0x4ac21251, 0x53d92310, 0x78f470d3, 0x61ef4192, + 0x2eaed755, 0x37b5e614, 0x1c98b5d7, 0x05838496, 0x821b9859, + 0x9b00a918, 0xb02dfadb, 0xa936cb9a, 0xe6775d5d, 0xff6c6c1c, + 0xd4413fdf, 0xcd5a0e9e, 0x958424a2, 0x8c9f15e3, 0xa7b24620, + 0xbea97761, 0xf1e8e1a6, 0xe8f3d0e7, 0xc3de8324, 0xdac5b265, + 0x5d5daeaa, 0x44469feb, 0x6f6bcc28, 0x7670fd69, 0x39316bae, + 0x202a5aef, 0x0b07092c, 0x121c386d, 0xdf4636f3, 0xc65d07b2, + 0xed705471, 0xf46b6530, 0xbb2af3f7, 0xa231c2b6, 0x891c9175, + 0x9007a034, 0x179fbcfb, 0x0e848dba, 0x25a9de79, 0x3cb2ef38, + 0x73f379ff, 0x6ae848be, 0x41c51b7d, 0x58de2a3c, 0xf0794f05, + 0xe9627e44, 0xc24f2d87, 0xdb541cc6, 0x94158a01, 0x8d0ebb40, + 0xa623e883, 0xbf38d9c2, 0x38a0c50d, 0x21bbf44c, 0x0a96a78f, + 0x138d96ce, 0x5ccc0009, 0x45d73148, 0x6efa628b, 0x77e153ca, + 0xbabb5d54, 0xa3a06c15, 0x888d3fd6, 0x91960e97, 0xded79850, + 0xc7cca911, 0xece1fad2, 0xf5facb93, 0x7262d75c, 0x6b79e61d, + 0x4054b5de, 0x594f849f, 0x160e1258, 0x0f152319, 0x243870da, + 0x3d23419b, 0x65fd6ba7, 0x7ce65ae6, 0x57cb0925, 0x4ed03864, + 0x0191aea3, 0x188a9fe2, 0x33a7cc21, 0x2abcfd60, 0xad24e1af, + 0xb43fd0ee, 0x9f12832d, 0x8609b26c, 0xc94824ab, 0xd05315ea, + 0xfb7e4629, 0xe2657768, 0x2f3f79f6, 0x362448b7, 0x1d091b74, + 0x04122a35, 0x4b53bcf2, 0x52488db3, 0x7965de70, 0x607eef31, + 0xe7e6f3fe, 0xfefdc2bf, 0xd5d0917c, 0xcccba03d, 0x838a36fa, + 0x9a9107bb, 0xb1bc5478, 0xa8a76539, 0x3b83984b, 0x2298a90a, + 0x09b5fac9, 0x10aecb88, 0x5fef5d4f, 0x46f46c0e, 0x6dd93fcd, + 0x74c20e8c, 0xf35a1243, 0xea412302, 0xc16c70c1, 0xd8774180, + 0x9736d747, 0x8e2de606, 0xa500b5c5, 0xbc1b8484, 0x71418a1a, + 0x685abb5b, 0x4377e898, 0x5a6cd9d9, 0x152d4f1e, 0x0c367e5f, + 0x271b2d9c, 0x3e001cdd, 0xb9980012, 0xa0833153, 0x8bae6290, + 0x92b553d1, 0xddf4c516, 0xc4eff457, 0xefc2a794, 0xf6d996d5, + 0xae07bce9, 0xb71c8da8, 0x9c31de6b, 0x852aef2a, 0xca6b79ed, + 0xd37048ac, 0xf85d1b6f, 0xe1462a2e, 0x66de36e1, 0x7fc507a0, + 0x54e85463, 0x4df36522, 0x02b2f3e5, 0x1ba9c2a4, 0x30849167, + 0x299fa026, 0xe4c5aeb8, 0xfdde9ff9, 0xd6f3cc3a, 0xcfe8fd7b, + 0x80a96bbc, 0x99b25afd, 0xb29f093e, 0xab84387f, 0x2c1c24b0, + 0x350715f1, 0x1e2a4632, 0x07317773, 0x4870e1b4, 0x516bd0f5, + 0x7a468336, 0x635db277, 0xcbfad74e, 0xd2e1e60f, 0xf9ccb5cc, + 0xe0d7848d, 0xaf96124a, 0xb68d230b, 0x9da070c8, 0x84bb4189, + 0x03235d46, 0x1a386c07, 0x31153fc4, 0x280e0e85, 0x674f9842, + 0x7e54a903, 0x5579fac0, 0x4c62cb81, 0x8138c51f, 0x9823f45e, + 0xb30ea79d, 0xaa1596dc, 0xe554001b, 0xfc4f315a, 0xd7626299, + 0xce7953d8, 0x49e14f17, 0x50fa7e56, 0x7bd72d95, 0x62cc1cd4, + 0x2d8d8a13, 0x3496bb52, 0x1fbbe891, 0x06a0d9d0, 0x5e7ef3ec, + 0x4765c2ad, 0x6c48916e, 0x7553a02f, 0x3a1236e8, 0x230907a9, + 0x0824546a, 0x113f652b, 0x96a779e4, 0x8fbc48a5, 0xa4911b66, + 0xbd8a2a27, 0xf2cbbce0, 0xebd08da1, 0xc0fdde62, 0xd9e6ef23, + 0x14bce1bd, 0x0da7d0fc, 0x268a833f, 0x3f91b27e, 0x70d024b9, + 0x69cb15f8, 0x42e6463b, 0x5bfd777a, 0xdc656bb5, 0xc57e5af4, + 0xee530937, 0xf7483876, 0xb809aeb1, 0xa1129ff0, 0x8a3fcc33, + 0x9324fd72}, + {0x00000000, 0x77073096, 0xee0e612c, 0x990951ba, 0x076dc419, + 0x706af48f, 0xe963a535, 0x9e6495a3, 0x0edb8832, 0x79dcb8a4, + 0xe0d5e91e, 0x97d2d988, 0x09b64c2b, 0x7eb17cbd, 0xe7b82d07, + 0x90bf1d91, 0x1db71064, 0x6ab020f2, 0xf3b97148, 0x84be41de, + 0x1adad47d, 0x6ddde4eb, 0xf4d4b551, 0x83d385c7, 0x136c9856, + 0x646ba8c0, 0xfd62f97a, 0x8a65c9ec, 0x14015c4f, 0x63066cd9, + 0xfa0f3d63, 0x8d080df5, 0x3b6e20c8, 0x4c69105e, 0xd56041e4, + 0xa2677172, 0x3c03e4d1, 0x4b04d447, 0xd20d85fd, 0xa50ab56b, + 0x35b5a8fa, 0x42b2986c, 0xdbbbc9d6, 0xacbcf940, 0x32d86ce3, + 0x45df5c75, 0xdcd60dcf, 0xabd13d59, 0x26d930ac, 0x51de003a, + 0xc8d75180, 0xbfd06116, 0x21b4f4b5, 0x56b3c423, 0xcfba9599, + 0xb8bda50f, 0x2802b89e, 0x5f058808, 0xc60cd9b2, 0xb10be924, + 0x2f6f7c87, 0x58684c11, 0xc1611dab, 0xb6662d3d, 0x76dc4190, + 0x01db7106, 0x98d220bc, 0xefd5102a, 0x71b18589, 0x06b6b51f, + 0x9fbfe4a5, 0xe8b8d433, 0x7807c9a2, 0x0f00f934, 0x9609a88e, + 0xe10e9818, 0x7f6a0dbb, 0x086d3d2d, 0x91646c97, 0xe6635c01, + 0x6b6b51f4, 0x1c6c6162, 0x856530d8, 0xf262004e, 0x6c0695ed, + 0x1b01a57b, 0x8208f4c1, 0xf50fc457, 0x65b0d9c6, 0x12b7e950, + 0x8bbeb8ea, 0xfcb9887c, 0x62dd1ddf, 0x15da2d49, 0x8cd37cf3, + 0xfbd44c65, 0x4db26158, 0x3ab551ce, 0xa3bc0074, 0xd4bb30e2, + 0x4adfa541, 0x3dd895d7, 0xa4d1c46d, 0xd3d6f4fb, 0x4369e96a, + 0x346ed9fc, 0xad678846, 0xda60b8d0, 0x44042d73, 0x33031de5, + 0xaa0a4c5f, 0xdd0d7cc9, 0x5005713c, 0x270241aa, 0xbe0b1010, + 0xc90c2086, 0x5768b525, 0x206f85b3, 0xb966d409, 0xce61e49f, + 0x5edef90e, 0x29d9c998, 0xb0d09822, 0xc7d7a8b4, 0x59b33d17, + 0x2eb40d81, 0xb7bd5c3b, 0xc0ba6cad, 0xedb88320, 0x9abfb3b6, + 0x03b6e20c, 0x74b1d29a, 0xead54739, 0x9dd277af, 0x04db2615, + 0x73dc1683, 0xe3630b12, 0x94643b84, 0x0d6d6a3e, 0x7a6a5aa8, + 0xe40ecf0b, 0x9309ff9d, 0x0a00ae27, 0x7d079eb1, 0xf00f9344, + 0x8708a3d2, 0x1e01f268, 0x6906c2fe, 0xf762575d, 0x806567cb, + 0x196c3671, 0x6e6b06e7, 0xfed41b76, 0x89d32be0, 0x10da7a5a, + 0x67dd4acc, 0xf9b9df6f, 0x8ebeeff9, 0x17b7be43, 0x60b08ed5, + 0xd6d6a3e8, 0xa1d1937e, 0x38d8c2c4, 0x4fdff252, 0xd1bb67f1, + 0xa6bc5767, 0x3fb506dd, 0x48b2364b, 0xd80d2bda, 0xaf0a1b4c, + 0x36034af6, 0x41047a60, 0xdf60efc3, 0xa867df55, 0x316e8eef, + 0x4669be79, 0xcb61b38c, 0xbc66831a, 0x256fd2a0, 0x5268e236, + 0xcc0c7795, 0xbb0b4703, 0x220216b9, 0x5505262f, 0xc5ba3bbe, + 0xb2bd0b28, 0x2bb45a92, 0x5cb36a04, 0xc2d7ffa7, 0xb5d0cf31, + 0x2cd99e8b, 0x5bdeae1d, 0x9b64c2b0, 0xec63f226, 0x756aa39c, + 0x026d930a, 0x9c0906a9, 0xeb0e363f, 0x72076785, 0x05005713, + 0x95bf4a82, 0xe2b87a14, 0x7bb12bae, 0x0cb61b38, 0x92d28e9b, + 0xe5d5be0d, 0x7cdcefb7, 0x0bdbdf21, 0x86d3d2d4, 0xf1d4e242, + 0x68ddb3f8, 0x1fda836e, 0x81be16cd, 0xf6b9265b, 0x6fb077e1, + 0x18b74777, 0x88085ae6, 0xff0f6a70, 0x66063bca, 0x11010b5c, + 0x8f659eff, 0xf862ae69, 0x616bffd3, 0x166ccf45, 0xa00ae278, + 0xd70dd2ee, 0x4e048354, 0x3903b3c2, 0xa7672661, 0xd06016f7, + 0x4969474d, 0x3e6e77db, 0xaed16a4a, 0xd9d65adc, 0x40df0b66, + 0x37d83bf0, 0xa9bcae53, 0xdebb9ec5, 0x47b2cf7f, 0x30b5ffe9, + 0xbdbdf21c, 0xcabac28a, 0x53b39330, 0x24b4a3a6, 0xbad03605, + 0xcdd70693, 0x54de5729, 0x23d967bf, 0xb3667a2e, 0xc4614ab8, + 0x5d681b02, 0x2a6f2b94, 0xb40bbe37, 0xc30c8ea1, 0x5a05df1b, + 0x2d02ef8d}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x00000000, 0x96300777, 0x2c610eee, 0xba510999, 0x19c46d07, + 0x8ff46a70, 0x35a563e9, 0xa395649e, 0x3288db0e, 0xa4b8dc79, + 0x1ee9d5e0, 0x88d9d297, 0x2b4cb609, 0xbd7cb17e, 0x072db8e7, + 0x911dbf90, 0x6410b71d, 0xf220b06a, 0x4871b9f3, 0xde41be84, + 0x7dd4da1a, 0xebe4dd6d, 0x51b5d4f4, 0xc785d383, 0x56986c13, + 0xc0a86b64, 0x7af962fd, 0xecc9658a, 0x4f5c0114, 0xd96c0663, + 0x633d0ffa, 0xf50d088d, 0xc8206e3b, 0x5e10694c, 0xe44160d5, + 0x727167a2, 0xd1e4033c, 0x47d4044b, 0xfd850dd2, 0x6bb50aa5, + 0xfaa8b535, 0x6c98b242, 0xd6c9bbdb, 0x40f9bcac, 0xe36cd832, + 0x755cdf45, 0xcf0dd6dc, 0x593dd1ab, 0xac30d926, 0x3a00de51, + 0x8051d7c8, 0x1661d0bf, 0xb5f4b421, 0x23c4b356, 0x9995bacf, + 0x0fa5bdb8, 0x9eb80228, 0x0888055f, 0xb2d90cc6, 0x24e90bb1, + 0x877c6f2f, 0x114c6858, 0xab1d61c1, 0x3d2d66b6, 0x9041dc76, + 0x0671db01, 0xbc20d298, 0x2a10d5ef, 0x8985b171, 0x1fb5b606, + 0xa5e4bf9f, 0x33d4b8e8, 0xa2c90778, 0x34f9000f, 0x8ea80996, + 0x18980ee1, 0xbb0d6a7f, 0x2d3d6d08, 0x976c6491, 0x015c63e6, + 0xf4516b6b, 0x62616c1c, 0xd8306585, 0x4e0062f2, 0xed95066c, + 0x7ba5011b, 0xc1f40882, 0x57c40ff5, 0xc6d9b065, 0x50e9b712, + 0xeab8be8b, 0x7c88b9fc, 0xdf1ddd62, 0x492dda15, 0xf37cd38c, + 0x654cd4fb, 0x5861b24d, 0xce51b53a, 0x7400bca3, 0xe230bbd4, + 0x41a5df4a, 0xd795d83d, 0x6dc4d1a4, 0xfbf4d6d3, 0x6ae96943, + 0xfcd96e34, 0x468867ad, 0xd0b860da, 0x732d0444, 0xe51d0333, + 0x5f4c0aaa, 0xc97c0ddd, 0x3c710550, 0xaa410227, 0x10100bbe, + 0x86200cc9, 0x25b56857, 0xb3856f20, 0x09d466b9, 0x9fe461ce, + 0x0ef9de5e, 0x98c9d929, 0x2298d0b0, 0xb4a8d7c7, 0x173db359, + 0x810db42e, 0x3b5cbdb7, 0xad6cbac0, 0x2083b8ed, 0xb6b3bf9a, + 0x0ce2b603, 0x9ad2b174, 0x3947d5ea, 0xaf77d29d, 0x1526db04, + 0x8316dc73, 0x120b63e3, 0x843b6494, 0x3e6a6d0d, 0xa85a6a7a, + 0x0bcf0ee4, 0x9dff0993, 0x27ae000a, 0xb19e077d, 0x44930ff0, + 0xd2a30887, 0x68f2011e, 0xfec20669, 0x5d5762f7, 0xcb676580, + 0x71366c19, 0xe7066b6e, 0x761bd4fe, 0xe02bd389, 0x5a7ada10, + 0xcc4add67, 0x6fdfb9f9, 0xf9efbe8e, 0x43beb717, 0xd58eb060, + 0xe8a3d6d6, 0x7e93d1a1, 0xc4c2d838, 0x52f2df4f, 0xf167bbd1, + 0x6757bca6, 0xdd06b53f, 0x4b36b248, 0xda2b0dd8, 0x4c1b0aaf, + 0xf64a0336, 0x607a0441, 0xc3ef60df, 0x55df67a8, 0xef8e6e31, + 0x79be6946, 0x8cb361cb, 0x1a8366bc, 0xa0d26f25, 0x36e26852, + 0x95770ccc, 0x03470bbb, 0xb9160222, 0x2f260555, 0xbe3bbac5, + 0x280bbdb2, 0x925ab42b, 0x046ab35c, 0xa7ffd7c2, 0x31cfd0b5, + 0x8b9ed92c, 0x1daede5b, 0xb0c2649b, 0x26f263ec, 0x9ca36a75, + 0x0a936d02, 0xa906099c, 0x3f360eeb, 0x85670772, 0x13570005, + 0x824abf95, 0x147ab8e2, 0xae2bb17b, 0x381bb60c, 0x9b8ed292, + 0x0dbed5e5, 0xb7efdc7c, 0x21dfdb0b, 0xd4d2d386, 0x42e2d4f1, + 0xf8b3dd68, 0x6e83da1f, 0xcd16be81, 0x5b26b9f6, 0xe177b06f, + 0x7747b718, 0xe65a0888, 0x706a0fff, 0xca3b0666, 0x5c0b0111, + 0xff9e658f, 0x69ae62f8, 0xd3ff6b61, 0x45cf6c16, 0x78e20aa0, + 0xeed20dd7, 0x5483044e, 0xc2b30339, 0x612667a7, 0xf71660d0, + 0x4d476949, 0xdb776e3e, 0x4a6ad1ae, 0xdc5ad6d9, 0x660bdf40, + 0xf03bd837, 0x53aebca9, 0xc59ebbde, 0x7fcfb247, 0xe9ffb530, + 0x1cf2bdbd, 0x8ac2baca, 0x3093b353, 0xa6a3b424, 0x0536d0ba, + 0x9306d7cd, 0x2957de54, 0xbf67d923, 0x2e7a66b3, 0xb84a61c4, + 0x021b685d, 0x942b6f2a, 0x37be0bb4, 0xa18e0cc3, 0x1bdf055a, + 0x8def022d}, + {0x00000000, 0x41311b19, 0x82623632, 0xc3532d2b, 0x04c56c64, + 0x45f4777d, 0x86a75a56, 0xc796414f, 0x088ad9c8, 0x49bbc2d1, + 0x8ae8effa, 0xcbd9f4e3, 0x0c4fb5ac, 0x4d7eaeb5, 0x8e2d839e, + 0xcf1c9887, 0x5112c24a, 0x1023d953, 0xd370f478, 0x9241ef61, + 0x55d7ae2e, 0x14e6b537, 0xd7b5981c, 0x96848305, 0x59981b82, + 0x18a9009b, 0xdbfa2db0, 0x9acb36a9, 0x5d5d77e6, 0x1c6c6cff, + 0xdf3f41d4, 0x9e0e5acd, 0xa2248495, 0xe3159f8c, 0x2046b2a7, + 0x6177a9be, 0xa6e1e8f1, 0xe7d0f3e8, 0x2483dec3, 0x65b2c5da, + 0xaaae5d5d, 0xeb9f4644, 0x28cc6b6f, 0x69fd7076, 0xae6b3139, + 0xef5a2a20, 0x2c09070b, 0x6d381c12, 0xf33646df, 0xb2075dc6, + 0x715470ed, 0x30656bf4, 0xf7f32abb, 0xb6c231a2, 0x75911c89, + 0x34a00790, 0xfbbc9f17, 0xba8d840e, 0x79dea925, 0x38efb23c, + 0xff79f373, 0xbe48e86a, 0x7d1bc541, 0x3c2ade58, 0x054f79f0, + 0x447e62e9, 0x872d4fc2, 0xc61c54db, 0x018a1594, 0x40bb0e8d, + 0x83e823a6, 0xc2d938bf, 0x0dc5a038, 0x4cf4bb21, 0x8fa7960a, + 0xce968d13, 0x0900cc5c, 0x4831d745, 0x8b62fa6e, 0xca53e177, + 0x545dbbba, 0x156ca0a3, 0xd63f8d88, 0x970e9691, 0x5098d7de, + 0x11a9ccc7, 0xd2fae1ec, 0x93cbfaf5, 0x5cd76272, 0x1de6796b, + 0xdeb55440, 0x9f844f59, 0x58120e16, 0x1923150f, 0xda703824, + 0x9b41233d, 0xa76bfd65, 0xe65ae67c, 0x2509cb57, 0x6438d04e, + 0xa3ae9101, 0xe29f8a18, 0x21cca733, 0x60fdbc2a, 0xafe124ad, + 0xeed03fb4, 0x2d83129f, 0x6cb20986, 0xab2448c9, 0xea1553d0, + 0x29467efb, 0x687765e2, 0xf6793f2f, 0xb7482436, 0x741b091d, + 0x352a1204, 0xf2bc534b, 0xb38d4852, 0x70de6579, 0x31ef7e60, + 0xfef3e6e7, 0xbfc2fdfe, 0x7c91d0d5, 0x3da0cbcc, 0xfa368a83, + 0xbb07919a, 0x7854bcb1, 0x3965a7a8, 0x4b98833b, 0x0aa99822, + 0xc9fab509, 0x88cbae10, 0x4f5def5f, 0x0e6cf446, 0xcd3fd96d, + 0x8c0ec274, 0x43125af3, 0x022341ea, 0xc1706cc1, 0x804177d8, + 0x47d73697, 0x06e62d8e, 0xc5b500a5, 0x84841bbc, 0x1a8a4171, + 0x5bbb5a68, 0x98e87743, 0xd9d96c5a, 0x1e4f2d15, 0x5f7e360c, + 0x9c2d1b27, 0xdd1c003e, 0x120098b9, 0x533183a0, 0x9062ae8b, + 0xd153b592, 0x16c5f4dd, 0x57f4efc4, 0x94a7c2ef, 0xd596d9f6, + 0xe9bc07ae, 0xa88d1cb7, 0x6bde319c, 0x2aef2a85, 0xed796bca, + 0xac4870d3, 0x6f1b5df8, 0x2e2a46e1, 0xe136de66, 0xa007c57f, + 0x6354e854, 0x2265f34d, 0xe5f3b202, 0xa4c2a91b, 0x67918430, + 0x26a09f29, 0xb8aec5e4, 0xf99fdefd, 0x3accf3d6, 0x7bfde8cf, + 0xbc6ba980, 0xfd5ab299, 0x3e099fb2, 0x7f3884ab, 0xb0241c2c, + 0xf1150735, 0x32462a1e, 0x73773107, 0xb4e17048, 0xf5d06b51, + 0x3683467a, 0x77b25d63, 0x4ed7facb, 0x0fe6e1d2, 0xccb5ccf9, + 0x8d84d7e0, 0x4a1296af, 0x0b238db6, 0xc870a09d, 0x8941bb84, + 0x465d2303, 0x076c381a, 0xc43f1531, 0x850e0e28, 0x42984f67, + 0x03a9547e, 0xc0fa7955, 0x81cb624c, 0x1fc53881, 0x5ef42398, + 0x9da70eb3, 0xdc9615aa, 0x1b0054e5, 0x5a314ffc, 0x996262d7, + 0xd85379ce, 0x174fe149, 0x567efa50, 0x952dd77b, 0xd41ccc62, + 0x138a8d2d, 0x52bb9634, 0x91e8bb1f, 0xd0d9a006, 0xecf37e5e, + 0xadc26547, 0x6e91486c, 0x2fa05375, 0xe836123a, 0xa9070923, + 0x6a542408, 0x2b653f11, 0xe479a796, 0xa548bc8f, 0x661b91a4, + 0x272a8abd, 0xe0bccbf2, 0xa18dd0eb, 0x62defdc0, 0x23efe6d9, + 0xbde1bc14, 0xfcd0a70d, 0x3f838a26, 0x7eb2913f, 0xb924d070, + 0xf815cb69, 0x3b46e642, 0x7a77fd5b, 0xb56b65dc, 0xf45a7ec5, + 0x370953ee, 0x763848f7, 0xb1ae09b8, 0xf09f12a1, 0x33cc3f8a, + 0x72fd2493}, + {0x00000000, 0x376ac201, 0x6ed48403, 0x59be4602, 0xdca80907, + 0xebc2cb06, 0xb27c8d04, 0x85164f05, 0xb851130e, 0x8f3bd10f, + 0xd685970d, 0xe1ef550c, 0x64f91a09, 0x5393d808, 0x0a2d9e0a, + 0x3d475c0b, 0x70a3261c, 0x47c9e41d, 0x1e77a21f, 0x291d601e, + 0xac0b2f1b, 0x9b61ed1a, 0xc2dfab18, 0xf5b56919, 0xc8f23512, + 0xff98f713, 0xa626b111, 0x914c7310, 0x145a3c15, 0x2330fe14, + 0x7a8eb816, 0x4de47a17, 0xe0464d38, 0xd72c8f39, 0x8e92c93b, + 0xb9f80b3a, 0x3cee443f, 0x0b84863e, 0x523ac03c, 0x6550023d, + 0x58175e36, 0x6f7d9c37, 0x36c3da35, 0x01a91834, 0x84bf5731, + 0xb3d59530, 0xea6bd332, 0xdd011133, 0x90e56b24, 0xa78fa925, + 0xfe31ef27, 0xc95b2d26, 0x4c4d6223, 0x7b27a022, 0x2299e620, + 0x15f32421, 0x28b4782a, 0x1fdeba2b, 0x4660fc29, 0x710a3e28, + 0xf41c712d, 0xc376b32c, 0x9ac8f52e, 0xada2372f, 0xc08d9a70, + 0xf7e75871, 0xae591e73, 0x9933dc72, 0x1c259377, 0x2b4f5176, + 0x72f11774, 0x459bd575, 0x78dc897e, 0x4fb64b7f, 0x16080d7d, + 0x2162cf7c, 0xa4748079, 0x931e4278, 0xcaa0047a, 0xfdcac67b, + 0xb02ebc6c, 0x87447e6d, 0xdefa386f, 0xe990fa6e, 0x6c86b56b, + 0x5bec776a, 0x02523168, 0x3538f369, 0x087faf62, 0x3f156d63, + 0x66ab2b61, 0x51c1e960, 0xd4d7a665, 0xe3bd6464, 0xba032266, + 0x8d69e067, 0x20cbd748, 0x17a11549, 0x4e1f534b, 0x7975914a, + 0xfc63de4f, 0xcb091c4e, 0x92b75a4c, 0xa5dd984d, 0x989ac446, + 0xaff00647, 0xf64e4045, 0xc1248244, 0x4432cd41, 0x73580f40, + 0x2ae64942, 0x1d8c8b43, 0x5068f154, 0x67023355, 0x3ebc7557, + 0x09d6b756, 0x8cc0f853, 0xbbaa3a52, 0xe2147c50, 0xd57ebe51, + 0xe839e25a, 0xdf53205b, 0x86ed6659, 0xb187a458, 0x3491eb5d, + 0x03fb295c, 0x5a456f5e, 0x6d2fad5f, 0x801b35e1, 0xb771f7e0, + 0xeecfb1e2, 0xd9a573e3, 0x5cb33ce6, 0x6bd9fee7, 0x3267b8e5, + 0x050d7ae4, 0x384a26ef, 0x0f20e4ee, 0x569ea2ec, 0x61f460ed, + 0xe4e22fe8, 0xd388ede9, 0x8a36abeb, 0xbd5c69ea, 0xf0b813fd, + 0xc7d2d1fc, 0x9e6c97fe, 0xa90655ff, 0x2c101afa, 0x1b7ad8fb, + 0x42c49ef9, 0x75ae5cf8, 0x48e900f3, 0x7f83c2f2, 0x263d84f0, + 0x115746f1, 0x944109f4, 0xa32bcbf5, 0xfa958df7, 0xcdff4ff6, + 0x605d78d9, 0x5737bad8, 0x0e89fcda, 0x39e33edb, 0xbcf571de, + 0x8b9fb3df, 0xd221f5dd, 0xe54b37dc, 0xd80c6bd7, 0xef66a9d6, + 0xb6d8efd4, 0x81b22dd5, 0x04a462d0, 0x33cea0d1, 0x6a70e6d3, + 0x5d1a24d2, 0x10fe5ec5, 0x27949cc4, 0x7e2adac6, 0x494018c7, + 0xcc5657c2, 0xfb3c95c3, 0xa282d3c1, 0x95e811c0, 0xa8af4dcb, + 0x9fc58fca, 0xc67bc9c8, 0xf1110bc9, 0x740744cc, 0x436d86cd, + 0x1ad3c0cf, 0x2db902ce, 0x4096af91, 0x77fc6d90, 0x2e422b92, + 0x1928e993, 0x9c3ea696, 0xab546497, 0xf2ea2295, 0xc580e094, + 0xf8c7bc9f, 0xcfad7e9e, 0x9613389c, 0xa179fa9d, 0x246fb598, + 0x13057799, 0x4abb319b, 0x7dd1f39a, 0x3035898d, 0x075f4b8c, + 0x5ee10d8e, 0x698bcf8f, 0xec9d808a, 0xdbf7428b, 0x82490489, + 0xb523c688, 0x88649a83, 0xbf0e5882, 0xe6b01e80, 0xd1dadc81, + 0x54cc9384, 0x63a65185, 0x3a181787, 0x0d72d586, 0xa0d0e2a9, + 0x97ba20a8, 0xce0466aa, 0xf96ea4ab, 0x7c78ebae, 0x4b1229af, + 0x12ac6fad, 0x25c6adac, 0x1881f1a7, 0x2feb33a6, 0x765575a4, + 0x413fb7a5, 0xc429f8a0, 0xf3433aa1, 0xaafd7ca3, 0x9d97bea2, + 0xd073c4b5, 0xe71906b4, 0xbea740b6, 0x89cd82b7, 0x0cdbcdb2, + 0x3bb10fb3, 0x620f49b1, 0x55658bb0, 0x6822d7bb, 0x5f4815ba, + 0x06f653b8, 0x319c91b9, 0xb48adebc, 0x83e01cbd, 0xda5e5abf, + 0xed3498be}, + {0x00000000, 0x6567bcb8, 0x8bc809aa, 0xeeafb512, 0x5797628f, + 0x32f0de37, 0xdc5f6b25, 0xb938d79d, 0xef28b4c5, 0x8a4f087d, + 0x64e0bd6f, 0x018701d7, 0xb8bfd64a, 0xddd86af2, 0x3377dfe0, + 0x56106358, 0x9f571950, 0xfa30a5e8, 0x149f10fa, 0x71f8ac42, + 0xc8c07bdf, 0xada7c767, 0x43087275, 0x266fcecd, 0x707fad95, + 0x1518112d, 0xfbb7a43f, 0x9ed01887, 0x27e8cf1a, 0x428f73a2, + 0xac20c6b0, 0xc9477a08, 0x3eaf32a0, 0x5bc88e18, 0xb5673b0a, + 0xd00087b2, 0x6938502f, 0x0c5fec97, 0xe2f05985, 0x8797e53d, + 0xd1878665, 0xb4e03add, 0x5a4f8fcf, 0x3f283377, 0x8610e4ea, + 0xe3775852, 0x0dd8ed40, 0x68bf51f8, 0xa1f82bf0, 0xc49f9748, + 0x2a30225a, 0x4f579ee2, 0xf66f497f, 0x9308f5c7, 0x7da740d5, + 0x18c0fc6d, 0x4ed09f35, 0x2bb7238d, 0xc518969f, 0xa07f2a27, + 0x1947fdba, 0x7c204102, 0x928ff410, 0xf7e848a8, 0x3d58149b, + 0x583fa823, 0xb6901d31, 0xd3f7a189, 0x6acf7614, 0x0fa8caac, + 0xe1077fbe, 0x8460c306, 0xd270a05e, 0xb7171ce6, 0x59b8a9f4, + 0x3cdf154c, 0x85e7c2d1, 0xe0807e69, 0x0e2fcb7b, 0x6b4877c3, + 0xa20f0dcb, 0xc768b173, 0x29c70461, 0x4ca0b8d9, 0xf5986f44, + 0x90ffd3fc, 0x7e5066ee, 0x1b37da56, 0x4d27b90e, 0x284005b6, + 0xc6efb0a4, 0xa3880c1c, 0x1ab0db81, 0x7fd76739, 0x9178d22b, + 0xf41f6e93, 0x03f7263b, 0x66909a83, 0x883f2f91, 0xed589329, + 0x546044b4, 0x3107f80c, 0xdfa84d1e, 0xbacff1a6, 0xecdf92fe, + 0x89b82e46, 0x67179b54, 0x027027ec, 0xbb48f071, 0xde2f4cc9, + 0x3080f9db, 0x55e74563, 0x9ca03f6b, 0xf9c783d3, 0x176836c1, + 0x720f8a79, 0xcb375de4, 0xae50e15c, 0x40ff544e, 0x2598e8f6, + 0x73888bae, 0x16ef3716, 0xf8408204, 0x9d273ebc, 0x241fe921, + 0x41785599, 0xafd7e08b, 0xcab05c33, 0x3bb659ed, 0x5ed1e555, + 0xb07e5047, 0xd519ecff, 0x6c213b62, 0x094687da, 0xe7e932c8, + 0x828e8e70, 0xd49eed28, 0xb1f95190, 0x5f56e482, 0x3a31583a, + 0x83098fa7, 0xe66e331f, 0x08c1860d, 0x6da63ab5, 0xa4e140bd, + 0xc186fc05, 0x2f294917, 0x4a4ef5af, 0xf3762232, 0x96119e8a, + 0x78be2b98, 0x1dd99720, 0x4bc9f478, 0x2eae48c0, 0xc001fdd2, + 0xa566416a, 0x1c5e96f7, 0x79392a4f, 0x97969f5d, 0xf2f123e5, + 0x05196b4d, 0x607ed7f5, 0x8ed162e7, 0xebb6de5f, 0x528e09c2, + 0x37e9b57a, 0xd9460068, 0xbc21bcd0, 0xea31df88, 0x8f566330, + 0x61f9d622, 0x049e6a9a, 0xbda6bd07, 0xd8c101bf, 0x366eb4ad, + 0x53090815, 0x9a4e721d, 0xff29cea5, 0x11867bb7, 0x74e1c70f, + 0xcdd91092, 0xa8beac2a, 0x46111938, 0x2376a580, 0x7566c6d8, + 0x10017a60, 0xfeaecf72, 0x9bc973ca, 0x22f1a457, 0x479618ef, + 0xa939adfd, 0xcc5e1145, 0x06ee4d76, 0x6389f1ce, 0x8d2644dc, + 0xe841f864, 0x51792ff9, 0x341e9341, 0xdab12653, 0xbfd69aeb, + 0xe9c6f9b3, 0x8ca1450b, 0x620ef019, 0x07694ca1, 0xbe519b3c, + 0xdb362784, 0x35999296, 0x50fe2e2e, 0x99b95426, 0xfcdee89e, + 0x12715d8c, 0x7716e134, 0xce2e36a9, 0xab498a11, 0x45e63f03, + 0x208183bb, 0x7691e0e3, 0x13f65c5b, 0xfd59e949, 0x983e55f1, + 0x2106826c, 0x44613ed4, 0xaace8bc6, 0xcfa9377e, 0x38417fd6, + 0x5d26c36e, 0xb389767c, 0xd6eecac4, 0x6fd61d59, 0x0ab1a1e1, + 0xe41e14f3, 0x8179a84b, 0xd769cb13, 0xb20e77ab, 0x5ca1c2b9, + 0x39c67e01, 0x80fea99c, 0xe5991524, 0x0b36a036, 0x6e511c8e, + 0xa7166686, 0xc271da3e, 0x2cde6f2c, 0x49b9d394, 0xf0810409, + 0x95e6b8b1, 0x7b490da3, 0x1e2eb11b, 0x483ed243, 0x2d596efb, + 0xc3f6dbe9, 0xa6916751, 0x1fa9b0cc, 0x7ace0c74, 0x9461b966, + 0xf10605de}}; + +#endif + +#endif + +#if N == 2 + +#if W == 8 + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xae689191, 0x87a02563, 0x29c8b4f2, 0xd4314c87, + 0x7a59dd16, 0x539169e4, 0xfdf9f875, 0x73139f4f, 0xdd7b0ede, + 0xf4b3ba2c, 0x5adb2bbd, 0xa722d3c8, 0x094a4259, 0x2082f6ab, + 0x8eea673a, 0xe6273e9e, 0x484faf0f, 0x61871bfd, 0xcfef8a6c, + 0x32167219, 0x9c7ee388, 0xb5b6577a, 0x1bdec6eb, 0x9534a1d1, + 0x3b5c3040, 0x129484b2, 0xbcfc1523, 0x4105ed56, 0xef6d7cc7, + 0xc6a5c835, 0x68cd59a4, 0x173f7b7d, 0xb957eaec, 0x909f5e1e, + 0x3ef7cf8f, 0xc30e37fa, 0x6d66a66b, 0x44ae1299, 0xeac68308, + 0x642ce432, 0xca4475a3, 0xe38cc151, 0x4de450c0, 0xb01da8b5, + 0x1e753924, 0x37bd8dd6, 0x99d51c47, 0xf11845e3, 0x5f70d472, + 0x76b86080, 0xd8d0f111, 0x25290964, 0x8b4198f5, 0xa2892c07, + 0x0ce1bd96, 0x820bdaac, 0x2c634b3d, 0x05abffcf, 0xabc36e5e, + 0x563a962b, 0xf85207ba, 0xd19ab348, 0x7ff222d9, 0x2e7ef6fa, + 0x8016676b, 0xa9ded399, 0x07b64208, 0xfa4fba7d, 0x54272bec, + 0x7def9f1e, 0xd3870e8f, 0x5d6d69b5, 0xf305f824, 0xdacd4cd6, + 0x74a5dd47, 0x895c2532, 0x2734b4a3, 0x0efc0051, 0xa09491c0, + 0xc859c864, 0x663159f5, 0x4ff9ed07, 0xe1917c96, 0x1c6884e3, + 0xb2001572, 0x9bc8a180, 0x35a03011, 0xbb4a572b, 0x1522c6ba, + 0x3cea7248, 0x9282e3d9, 0x6f7b1bac, 0xc1138a3d, 0xe8db3ecf, + 0x46b3af5e, 0x39418d87, 0x97291c16, 0xbee1a8e4, 0x10893975, + 0xed70c100, 0x43185091, 0x6ad0e463, 0xc4b875f2, 0x4a5212c8, + 0xe43a8359, 0xcdf237ab, 0x639aa63a, 0x9e635e4f, 0x300bcfde, + 0x19c37b2c, 0xb7abeabd, 0xdf66b319, 0x710e2288, 0x58c6967a, + 0xf6ae07eb, 0x0b57ff9e, 0xa53f6e0f, 0x8cf7dafd, 0x229f4b6c, + 0xac752c56, 0x021dbdc7, 0x2bd50935, 0x85bd98a4, 0x784460d1, + 0xd62cf140, 0xffe445b2, 0x518cd423, 0x5cfdedf4, 0xf2957c65, + 0xdb5dc897, 0x75355906, 0x88cca173, 0x26a430e2, 0x0f6c8410, + 0xa1041581, 0x2fee72bb, 0x8186e32a, 0xa84e57d8, 0x0626c649, + 0xfbdf3e3c, 0x55b7afad, 0x7c7f1b5f, 0xd2178ace, 0xbadad36a, + 0x14b242fb, 0x3d7af609, 0x93126798, 0x6eeb9fed, 0xc0830e7c, + 0xe94bba8e, 0x47232b1f, 0xc9c94c25, 0x67a1ddb4, 0x4e696946, + 0xe001f8d7, 0x1df800a2, 0xb3909133, 0x9a5825c1, 0x3430b450, + 0x4bc29689, 0xe5aa0718, 0xcc62b3ea, 0x620a227b, 0x9ff3da0e, + 0x319b4b9f, 0x1853ff6d, 0xb63b6efc, 0x38d109c6, 0x96b99857, + 0xbf712ca5, 0x1119bd34, 0xece04541, 0x4288d4d0, 0x6b406022, + 0xc528f1b3, 0xade5a817, 0x038d3986, 0x2a458d74, 0x842d1ce5, + 0x79d4e490, 0xd7bc7501, 0xfe74c1f3, 0x501c5062, 0xdef63758, + 0x709ea6c9, 0x5956123b, 0xf73e83aa, 0x0ac77bdf, 0xa4afea4e, + 0x8d675ebc, 0x230fcf2d, 0x72831b0e, 0xdceb8a9f, 0xf5233e6d, + 0x5b4baffc, 0xa6b25789, 0x08dac618, 0x211272ea, 0x8f7ae37b, + 0x01908441, 0xaff815d0, 0x8630a122, 0x285830b3, 0xd5a1c8c6, + 0x7bc95957, 0x5201eda5, 0xfc697c34, 0x94a42590, 0x3accb401, + 0x130400f3, 0xbd6c9162, 0x40956917, 0xeefdf886, 0xc7354c74, + 0x695ddde5, 0xe7b7badf, 0x49df2b4e, 0x60179fbc, 0xce7f0e2d, + 0x3386f658, 0x9dee67c9, 0xb426d33b, 0x1a4e42aa, 0x65bc6073, + 0xcbd4f1e2, 0xe21c4510, 0x4c74d481, 0xb18d2cf4, 0x1fe5bd65, + 0x362d0997, 0x98459806, 0x16afff3c, 0xb8c76ead, 0x910fda5f, + 0x3f674bce, 0xc29eb3bb, 0x6cf6222a, 0x453e96d8, 0xeb560749, + 0x839b5eed, 0x2df3cf7c, 0x043b7b8e, 0xaa53ea1f, 0x57aa126a, + 0xf9c283fb, 0xd00a3709, 0x7e62a698, 0xf088c1a2, 0x5ee05033, + 0x7728e4c1, 0xd9407550, 0x24b98d25, 0x8ad11cb4, 0xa319a846, + 0x0d7139d7}, + {0x00000000, 0xb9fbdbe8, 0xa886b191, 0x117d6a79, 0x8a7c6563, + 0x3387be8b, 0x22fad4f2, 0x9b010f1a, 0xcf89cc87, 0x7672176f, + 0x670f7d16, 0xdef4a6fe, 0x45f5a9e4, 0xfc0e720c, 0xed731875, + 0x5488c39d, 0x44629f4f, 0xfd9944a7, 0xece42ede, 0x551ff536, + 0xce1efa2c, 0x77e521c4, 0x66984bbd, 0xdf639055, 0x8beb53c8, + 0x32108820, 0x236de259, 0x9a9639b1, 0x019736ab, 0xb86ced43, + 0xa911873a, 0x10ea5cd2, 0x88c53e9e, 0x313ee576, 0x20438f0f, + 0x99b854e7, 0x02b95bfd, 0xbb428015, 0xaa3fea6c, 0x13c43184, + 0x474cf219, 0xfeb729f1, 0xefca4388, 0x56319860, 0xcd30977a, + 0x74cb4c92, 0x65b626eb, 0xdc4dfd03, 0xcca7a1d1, 0x755c7a39, + 0x64211040, 0xdddacba8, 0x46dbc4b2, 0xff201f5a, 0xee5d7523, + 0x57a6aecb, 0x032e6d56, 0xbad5b6be, 0xaba8dcc7, 0x1253072f, + 0x89520835, 0x30a9d3dd, 0x21d4b9a4, 0x982f624c, 0xcafb7b7d, + 0x7300a095, 0x627dcaec, 0xdb861104, 0x40871e1e, 0xf97cc5f6, + 0xe801af8f, 0x51fa7467, 0x0572b7fa, 0xbc896c12, 0xadf4066b, + 0x140fdd83, 0x8f0ed299, 0x36f50971, 0x27886308, 0x9e73b8e0, + 0x8e99e432, 0x37623fda, 0x261f55a3, 0x9fe48e4b, 0x04e58151, + 0xbd1e5ab9, 0xac6330c0, 0x1598eb28, 0x411028b5, 0xf8ebf35d, + 0xe9969924, 0x506d42cc, 0xcb6c4dd6, 0x7297963e, 0x63eafc47, + 0xda1127af, 0x423e45e3, 0xfbc59e0b, 0xeab8f472, 0x53432f9a, + 0xc8422080, 0x71b9fb68, 0x60c49111, 0xd93f4af9, 0x8db78964, + 0x344c528c, 0x253138f5, 0x9ccae31d, 0x07cbec07, 0xbe3037ef, + 0xaf4d5d96, 0x16b6867e, 0x065cdaac, 0xbfa70144, 0xaeda6b3d, + 0x1721b0d5, 0x8c20bfcf, 0x35db6427, 0x24a60e5e, 0x9d5dd5b6, + 0xc9d5162b, 0x702ecdc3, 0x6153a7ba, 0xd8a87c52, 0x43a97348, + 0xfa52a8a0, 0xeb2fc2d9, 0x52d41931, 0x4e87f0bb, 0xf77c2b53, + 0xe601412a, 0x5ffa9ac2, 0xc4fb95d8, 0x7d004e30, 0x6c7d2449, + 0xd586ffa1, 0x810e3c3c, 0x38f5e7d4, 0x29888dad, 0x90735645, + 0x0b72595f, 0xb28982b7, 0xa3f4e8ce, 0x1a0f3326, 0x0ae56ff4, + 0xb31eb41c, 0xa263de65, 0x1b98058d, 0x80990a97, 0x3962d17f, + 0x281fbb06, 0x91e460ee, 0xc56ca373, 0x7c97789b, 0x6dea12e2, + 0xd411c90a, 0x4f10c610, 0xf6eb1df8, 0xe7967781, 0x5e6dac69, + 0xc642ce25, 0x7fb915cd, 0x6ec47fb4, 0xd73fa45c, 0x4c3eab46, + 0xf5c570ae, 0xe4b81ad7, 0x5d43c13f, 0x09cb02a2, 0xb030d94a, + 0xa14db333, 0x18b668db, 0x83b767c1, 0x3a4cbc29, 0x2b31d650, + 0x92ca0db8, 0x8220516a, 0x3bdb8a82, 0x2aa6e0fb, 0x935d3b13, + 0x085c3409, 0xb1a7efe1, 0xa0da8598, 0x19215e70, 0x4da99ded, + 0xf4524605, 0xe52f2c7c, 0x5cd4f794, 0xc7d5f88e, 0x7e2e2366, + 0x6f53491f, 0xd6a892f7, 0x847c8bc6, 0x3d87502e, 0x2cfa3a57, + 0x9501e1bf, 0x0e00eea5, 0xb7fb354d, 0xa6865f34, 0x1f7d84dc, + 0x4bf54741, 0xf20e9ca9, 0xe373f6d0, 0x5a882d38, 0xc1892222, + 0x7872f9ca, 0x690f93b3, 0xd0f4485b, 0xc01e1489, 0x79e5cf61, + 0x6898a518, 0xd1637ef0, 0x4a6271ea, 0xf399aa02, 0xe2e4c07b, + 0x5b1f1b93, 0x0f97d80e, 0xb66c03e6, 0xa711699f, 0x1eeab277, + 0x85ebbd6d, 0x3c106685, 0x2d6d0cfc, 0x9496d714, 0x0cb9b558, + 0xb5426eb0, 0xa43f04c9, 0x1dc4df21, 0x86c5d03b, 0x3f3e0bd3, + 0x2e4361aa, 0x97b8ba42, 0xc33079df, 0x7acba237, 0x6bb6c84e, + 0xd24d13a6, 0x494c1cbc, 0xf0b7c754, 0xe1caad2d, 0x583176c5, + 0x48db2a17, 0xf120f1ff, 0xe05d9b86, 0x59a6406e, 0xc2a74f74, + 0x7b5c949c, 0x6a21fee5, 0xd3da250d, 0x8752e690, 0x3ea93d78, + 0x2fd45701, 0x962f8ce9, 0x0d2e83f3, 0xb4d5581b, 0xa5a83262, + 0x1c53e98a}, + {0x00000000, 0x9d0fe176, 0xe16ec4ad, 0x7c6125db, 0x19ac8f1b, + 0x84a36e6d, 0xf8c24bb6, 0x65cdaac0, 0x33591e36, 0xae56ff40, + 0xd237da9b, 0x4f383bed, 0x2af5912d, 0xb7fa705b, 0xcb9b5580, + 0x5694b4f6, 0x66b23c6c, 0xfbbddd1a, 0x87dcf8c1, 0x1ad319b7, + 0x7f1eb377, 0xe2115201, 0x9e7077da, 0x037f96ac, 0x55eb225a, + 0xc8e4c32c, 0xb485e6f7, 0x298a0781, 0x4c47ad41, 0xd1484c37, + 0xad2969ec, 0x3026889a, 0xcd6478d8, 0x506b99ae, 0x2c0abc75, + 0xb1055d03, 0xd4c8f7c3, 0x49c716b5, 0x35a6336e, 0xa8a9d218, + 0xfe3d66ee, 0x63328798, 0x1f53a243, 0x825c4335, 0xe791e9f5, + 0x7a9e0883, 0x06ff2d58, 0x9bf0cc2e, 0xabd644b4, 0x36d9a5c2, + 0x4ab88019, 0xd7b7616f, 0xb27acbaf, 0x2f752ad9, 0x53140f02, + 0xce1bee74, 0x988f5a82, 0x0580bbf4, 0x79e19e2f, 0xe4ee7f59, + 0x8123d599, 0x1c2c34ef, 0x604d1134, 0xfd42f042, 0x41b9f7f1, + 0xdcb61687, 0xa0d7335c, 0x3dd8d22a, 0x581578ea, 0xc51a999c, + 0xb97bbc47, 0x24745d31, 0x72e0e9c7, 0xefef08b1, 0x938e2d6a, + 0x0e81cc1c, 0x6b4c66dc, 0xf64387aa, 0x8a22a271, 0x172d4307, + 0x270bcb9d, 0xba042aeb, 0xc6650f30, 0x5b6aee46, 0x3ea74486, + 0xa3a8a5f0, 0xdfc9802b, 0x42c6615d, 0x1452d5ab, 0x895d34dd, + 0xf53c1106, 0x6833f070, 0x0dfe5ab0, 0x90f1bbc6, 0xec909e1d, + 0x719f7f6b, 0x8cdd8f29, 0x11d26e5f, 0x6db34b84, 0xf0bcaaf2, + 0x95710032, 0x087ee144, 0x741fc49f, 0xe91025e9, 0xbf84911f, + 0x228b7069, 0x5eea55b2, 0xc3e5b4c4, 0xa6281e04, 0x3b27ff72, + 0x4746daa9, 0xda493bdf, 0xea6fb345, 0x77605233, 0x0b0177e8, + 0x960e969e, 0xf3c33c5e, 0x6eccdd28, 0x12adf8f3, 0x8fa21985, + 0xd936ad73, 0x44394c05, 0x385869de, 0xa55788a8, 0xc09a2268, + 0x5d95c31e, 0x21f4e6c5, 0xbcfb07b3, 0x8373efe2, 0x1e7c0e94, + 0x621d2b4f, 0xff12ca39, 0x9adf60f9, 0x07d0818f, 0x7bb1a454, + 0xe6be4522, 0xb02af1d4, 0x2d2510a2, 0x51443579, 0xcc4bd40f, + 0xa9867ecf, 0x34899fb9, 0x48e8ba62, 0xd5e75b14, 0xe5c1d38e, + 0x78ce32f8, 0x04af1723, 0x99a0f655, 0xfc6d5c95, 0x6162bde3, + 0x1d039838, 0x800c794e, 0xd698cdb8, 0x4b972cce, 0x37f60915, + 0xaaf9e863, 0xcf3442a3, 0x523ba3d5, 0x2e5a860e, 0xb3556778, + 0x4e17973a, 0xd318764c, 0xaf795397, 0x3276b2e1, 0x57bb1821, + 0xcab4f957, 0xb6d5dc8c, 0x2bda3dfa, 0x7d4e890c, 0xe041687a, + 0x9c204da1, 0x012facd7, 0x64e20617, 0xf9ede761, 0x858cc2ba, + 0x188323cc, 0x28a5ab56, 0xb5aa4a20, 0xc9cb6ffb, 0x54c48e8d, + 0x3109244d, 0xac06c53b, 0xd067e0e0, 0x4d680196, 0x1bfcb560, + 0x86f35416, 0xfa9271cd, 0x679d90bb, 0x02503a7b, 0x9f5fdb0d, + 0xe33efed6, 0x7e311fa0, 0xc2ca1813, 0x5fc5f965, 0x23a4dcbe, + 0xbeab3dc8, 0xdb669708, 0x4669767e, 0x3a0853a5, 0xa707b2d3, + 0xf1930625, 0x6c9ce753, 0x10fdc288, 0x8df223fe, 0xe83f893e, + 0x75306848, 0x09514d93, 0x945eace5, 0xa478247f, 0x3977c509, + 0x4516e0d2, 0xd81901a4, 0xbdd4ab64, 0x20db4a12, 0x5cba6fc9, + 0xc1b58ebf, 0x97213a49, 0x0a2edb3f, 0x764ffee4, 0xeb401f92, + 0x8e8db552, 0x13825424, 0x6fe371ff, 0xf2ec9089, 0x0fae60cb, + 0x92a181bd, 0xeec0a466, 0x73cf4510, 0x1602efd0, 0x8b0d0ea6, + 0xf76c2b7d, 0x6a63ca0b, 0x3cf77efd, 0xa1f89f8b, 0xdd99ba50, + 0x40965b26, 0x255bf1e6, 0xb8541090, 0xc435354b, 0x593ad43d, + 0x691c5ca7, 0xf413bdd1, 0x8872980a, 0x157d797c, 0x70b0d3bc, + 0xedbf32ca, 0x91de1711, 0x0cd1f667, 0x5a454291, 0xc74aa3e7, + 0xbb2b863c, 0x2624674a, 0x43e9cd8a, 0xdee62cfc, 0xa2870927, + 0x3f88e851}, + {0x00000000, 0xdd96d985, 0x605cb54b, 0xbdca6cce, 0xc0b96a96, + 0x1d2fb313, 0xa0e5dfdd, 0x7d730658, 0x5a03d36d, 0x87950ae8, + 0x3a5f6626, 0xe7c9bfa3, 0x9abab9fb, 0x472c607e, 0xfae60cb0, + 0x2770d535, 0xb407a6da, 0x69917f5f, 0xd45b1391, 0x09cdca14, + 0x74becc4c, 0xa92815c9, 0x14e27907, 0xc974a082, 0xee0475b7, + 0x3392ac32, 0x8e58c0fc, 0x53ce1979, 0x2ebd1f21, 0xf32bc6a4, + 0x4ee1aa6a, 0x937773ef, 0xb37e4bf5, 0x6ee89270, 0xd322febe, + 0x0eb4273b, 0x73c72163, 0xae51f8e6, 0x139b9428, 0xce0d4dad, + 0xe97d9898, 0x34eb411d, 0x89212dd3, 0x54b7f456, 0x29c4f20e, + 0xf4522b8b, 0x49984745, 0x940e9ec0, 0x0779ed2f, 0xdaef34aa, + 0x67255864, 0xbab381e1, 0xc7c087b9, 0x1a565e3c, 0xa79c32f2, + 0x7a0aeb77, 0x5d7a3e42, 0x80ece7c7, 0x3d268b09, 0xe0b0528c, + 0x9dc354d4, 0x40558d51, 0xfd9fe19f, 0x2009381a, 0xbd8d91ab, + 0x601b482e, 0xddd124e0, 0x0047fd65, 0x7d34fb3d, 0xa0a222b8, + 0x1d684e76, 0xc0fe97f3, 0xe78e42c6, 0x3a189b43, 0x87d2f78d, + 0x5a442e08, 0x27372850, 0xfaa1f1d5, 0x476b9d1b, 0x9afd449e, + 0x098a3771, 0xd41ceef4, 0x69d6823a, 0xb4405bbf, 0xc9335de7, + 0x14a58462, 0xa96fe8ac, 0x74f93129, 0x5389e41c, 0x8e1f3d99, + 0x33d55157, 0xee4388d2, 0x93308e8a, 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0xc4cba5bc, 0x5d29c3bd, 0x9ca71c7d, 0x2735a3a7, + 0xe6bb7c67, 0x7f591a66, 0xbed7c5a6, 0x97ecd025, 0x56620fe5, + 0xcf8069e4, 0x0e0eb624, 0x9df642e2, 0x5c789d22, 0xc59afb23, + 0x041424e3, 0x2d2f3160, 0xeca1eea0, 0x754388a1, 0xb4cd5761, + 0x89c3676c, 0x484db8ac, 0xd1afdead, 0x1021016d, 0x391a14ee, + 0xf894cb2e, 0x6176ad2f, 0xa0f872ef, 0x33008629, 0xf28e59e9, + 0x6b6c3fe8, 0xaae2e028, 0x83d9f5ab, 0x42572a6b, 0xdbb54c6a, + 0x1a3b93aa}, + {0x00000000, 0xefc26b3e, 0x04f5d03d, 0xeb37bb03, 0x09eba07a, + 0xe629cb44, 0x0d1e7047, 0xe2dc1b79, 0x13d740f4, 0xfc152bca, + 0x172290c9, 0xf8e0fbf7, 0x1a3ce08e, 0xf5fe8bb0, 0x1ec930b3, + 0xf10b5b8d, 0x27ae81e8, 0xc86cead6, 0x235b51d5, 0xcc993aeb, + 0x2e452192, 0xc1874aac, 0x2ab0f1af, 0xc5729a91, 0x3479c11c, + 0xdbbbaa22, 0x308c1121, 0xdf4e7a1f, 0x3d926166, 0xd2500a58, + 0x3967b15b, 0xd6a5da65, 0x4f5d03d0, 0xa09f68ee, 0x4ba8d3ed, + 0xa46ab8d3, 0x46b6a3aa, 0xa974c894, 0x42437397, 0xad8118a9, + 0x5c8a4324, 0xb348281a, 0x587f9319, 0xb7bdf827, 0x5561e35e, + 0xbaa38860, 0x51943363, 0xbe56585d, 0x68f38238, 0x8731e906, + 0x6c065205, 0x83c4393b, 0x61182242, 0x8eda497c, 0x65edf27f, + 0x8a2f9941, 0x7b24c2cc, 0x94e6a9f2, 0x7fd112f1, 0x901379cf, + 0x72cf62b6, 0x9d0d0988, 0x763ab28b, 0x99f8d9b5, 0x9eba07a0, + 0x71786c9e, 0x9a4fd79d, 0x758dbca3, 0x9751a7da, 0x7893cce4, + 0x93a477e7, 0x7c661cd9, 0x8d6d4754, 0x62af2c6a, 0x89989769, + 0x665afc57, 0x8486e72e, 0x6b448c10, 0x80733713, 0x6fb15c2d, + 0xb9148648, 0x56d6ed76, 0xbde15675, 0x52233d4b, 0xb0ff2632, + 0x5f3d4d0c, 0xb40af60f, 0x5bc89d31, 0xaac3c6bc, 0x4501ad82, + 0xae361681, 0x41f47dbf, 0xa32866c6, 0x4cea0df8, 0xa7ddb6fb, + 0x481fddc5, 0xd1e70470, 0x3e256f4e, 0xd512d44d, 0x3ad0bf73, + 0xd80ca40a, 0x37cecf34, 0xdcf97437, 0x333b1f09, 0xc2304484, + 0x2df22fba, 0xc6c594b9, 0x2907ff87, 0xcbdbe4fe, 0x24198fc0, + 0xcf2e34c3, 0x20ec5ffd, 0xf6498598, 0x198beea6, 0xf2bc55a5, + 0x1d7e3e9b, 0xffa225e2, 0x10604edc, 0xfb57f5df, 0x14959ee1, + 0xe59ec56c, 0x0a5cae52, 0xe16b1551, 0x0ea97e6f, 0xec756516, + 0x03b70e28, 0xe880b52b, 0x0742de15, 0xe6050901, 0x09c7623f, + 0xe2f0d93c, 0x0d32b202, 0xefeea97b, 0x002cc245, 0xeb1b7946, + 0x04d91278, 0xf5d249f5, 0x1a1022cb, 0xf12799c8, 0x1ee5f2f6, + 0xfc39e98f, 0x13fb82b1, 0xf8cc39b2, 0x170e528c, 0xc1ab88e9, + 0x2e69e3d7, 0xc55e58d4, 0x2a9c33ea, 0xc8402893, 0x278243ad, + 0xccb5f8ae, 0x23779390, 0xd27cc81d, 0x3dbea323, 0xd6891820, + 0x394b731e, 0xdb976867, 0x34550359, 0xdf62b85a, 0x30a0d364, + 0xa9580ad1, 0x469a61ef, 0xadaddaec, 0x426fb1d2, 0xa0b3aaab, + 0x4f71c195, 0xa4467a96, 0x4b8411a8, 0xba8f4a25, 0x554d211b, + 0xbe7a9a18, 0x51b8f126, 0xb364ea5f, 0x5ca68161, 0xb7913a62, + 0x5853515c, 0x8ef68b39, 0x6134e007, 0x8a035b04, 0x65c1303a, + 0x871d2b43, 0x68df407d, 0x83e8fb7e, 0x6c2a9040, 0x9d21cbcd, + 0x72e3a0f3, 0x99d41bf0, 0x761670ce, 0x94ca6bb7, 0x7b080089, + 0x903fbb8a, 0x7ffdd0b4, 0x78bf0ea1, 0x977d659f, 0x7c4ade9c, + 0x9388b5a2, 0x7154aedb, 0x9e96c5e5, 0x75a17ee6, 0x9a6315d8, + 0x6b684e55, 0x84aa256b, 0x6f9d9e68, 0x805ff556, 0x6283ee2f, + 0x8d418511, 0x66763e12, 0x89b4552c, 0x5f118f49, 0xb0d3e477, + 0x5be45f74, 0xb426344a, 0x56fa2f33, 0xb938440d, 0x520fff0e, + 0xbdcd9430, 0x4cc6cfbd, 0xa304a483, 0x48331f80, 0xa7f174be, + 0x452d6fc7, 0xaaef04f9, 0x41d8bffa, 0xae1ad4c4, 0x37e20d71, + 0xd820664f, 0x3317dd4c, 0xdcd5b672, 0x3e09ad0b, 0xd1cbc635, + 0x3afc7d36, 0xd53e1608, 0x24354d85, 0xcbf726bb, 0x20c09db8, + 0xcf02f686, 0x2ddeedff, 0xc21c86c1, 0x292b3dc2, 0xc6e956fc, + 0x104c8c99, 0xff8ee7a7, 0x14b95ca4, 0xfb7b379a, 0x19a72ce3, + 0xf66547dd, 0x1d52fcde, 0xf29097e0, 0x039bcc6d, 0xec59a753, + 0x076e1c50, 0xe8ac776e, 0x0a706c17, 0xe5b20729, 0x0e85bc2a, + 0xe147d714}, + {0x00000000, 0x177b1443, 0x2ef62886, 0x398d3cc5, 0x5dec510c, + 0x4a97454f, 0x731a798a, 0x64616dc9, 0xbbd8a218, 0xaca3b65b, + 0x952e8a9e, 0x82559edd, 0xe634f314, 0xf14fe757, 0xc8c2db92, + 0xdfb9cfd1, 0xacc04271, 0xbbbb5632, 0x82366af7, 0x954d7eb4, + 0xf12c137d, 0xe657073e, 0xdfda3bfb, 0xc8a12fb8, 0x1718e069, + 0x0063f42a, 0x39eec8ef, 0x2e95dcac, 0x4af4b165, 0x5d8fa526, + 0x640299e3, 0x73798da0, 0x82f182a3, 0x958a96e0, 0xac07aa25, + 0xbb7cbe66, 0xdf1dd3af, 0xc866c7ec, 0xf1ebfb29, 0xe690ef6a, + 0x392920bb, 0x2e5234f8, 0x17df083d, 0x00a41c7e, 0x64c571b7, + 0x73be65f4, 0x4a335931, 0x5d484d72, 0x2e31c0d2, 0x394ad491, + 0x00c7e854, 0x17bcfc17, 0x73dd91de, 0x64a6859d, 0x5d2bb958, + 0x4a50ad1b, 0x95e962ca, 0x82927689, 0xbb1f4a4c, 0xac645e0f, + 0xc80533c6, 0xdf7e2785, 0xe6f31b40, 0xf1880f03, 0xde920307, + 0xc9e91744, 0xf0642b81, 0xe71f3fc2, 0x837e520b, 0x94054648, + 0xad887a8d, 0xbaf36ece, 0x654aa11f, 0x7231b55c, 0x4bbc8999, + 0x5cc79dda, 0x38a6f013, 0x2fdde450, 0x1650d895, 0x012bccd6, + 0x72524176, 0x65295535, 0x5ca469f0, 0x4bdf7db3, 0x2fbe107a, + 0x38c50439, 0x014838fc, 0x16332cbf, 0xc98ae36e, 0xdef1f72d, + 0xe77ccbe8, 0xf007dfab, 0x9466b262, 0x831da621, 0xba909ae4, + 0xadeb8ea7, 0x5c6381a4, 0x4b1895e7, 0x7295a922, 0x65eebd61, + 0x018fd0a8, 0x16f4c4eb, 0x2f79f82e, 0x3802ec6d, 0xe7bb23bc, + 0xf0c037ff, 0xc94d0b3a, 0xde361f79, 0xba5772b0, 0xad2c66f3, + 0x94a15a36, 0x83da4e75, 0xf0a3c3d5, 0xe7d8d796, 0xde55eb53, + 0xc92eff10, 0xad4f92d9, 0xba34869a, 0x83b9ba5f, 0x94c2ae1c, + 0x4b7b61cd, 0x5c00758e, 0x658d494b, 0x72f65d08, 0x169730c1, + 0x01ec2482, 0x38611847, 0x2f1a0c04, 0x6655004f, 0x712e140c, + 0x48a328c9, 0x5fd83c8a, 0x3bb95143, 0x2cc24500, 0x154f79c5, + 0x02346d86, 0xdd8da257, 0xcaf6b614, 0xf37b8ad1, 0xe4009e92, + 0x8061f35b, 0x971ae718, 0xae97dbdd, 0xb9eccf9e, 0xca95423e, + 0xddee567d, 0xe4636ab8, 0xf3187efb, 0x97791332, 0x80020771, + 0xb98f3bb4, 0xaef42ff7, 0x714de026, 0x6636f465, 0x5fbbc8a0, + 0x48c0dce3, 0x2ca1b12a, 0x3bdaa569, 0x025799ac, 0x152c8def, + 0xe4a482ec, 0xf3df96af, 0xca52aa6a, 0xdd29be29, 0xb948d3e0, + 0xae33c7a3, 0x97befb66, 0x80c5ef25, 0x5f7c20f4, 0x480734b7, + 0x718a0872, 0x66f11c31, 0x029071f8, 0x15eb65bb, 0x2c66597e, + 0x3b1d4d3d, 0x4864c09d, 0x5f1fd4de, 0x6692e81b, 0x71e9fc58, + 0x15889191, 0x02f385d2, 0x3b7eb917, 0x2c05ad54, 0xf3bc6285, + 0xe4c776c6, 0xdd4a4a03, 0xca315e40, 0xae503389, 0xb92b27ca, + 0x80a61b0f, 0x97dd0f4c, 0xb8c70348, 0xafbc170b, 0x96312bce, + 0x814a3f8d, 0xe52b5244, 0xf2504607, 0xcbdd7ac2, 0xdca66e81, + 0x031fa150, 0x1464b513, 0x2de989d6, 0x3a929d95, 0x5ef3f05c, + 0x4988e41f, 0x7005d8da, 0x677ecc99, 0x14074139, 0x037c557a, + 0x3af169bf, 0x2d8a7dfc, 0x49eb1035, 0x5e900476, 0x671d38b3, + 0x70662cf0, 0xafdfe321, 0xb8a4f762, 0x8129cba7, 0x9652dfe4, + 0xf233b22d, 0xe548a66e, 0xdcc59aab, 0xcbbe8ee8, 0x3a3681eb, + 0x2d4d95a8, 0x14c0a96d, 0x03bbbd2e, 0x67dad0e7, 0x70a1c4a4, + 0x492cf861, 0x5e57ec22, 0x81ee23f3, 0x969537b0, 0xaf180b75, + 0xb8631f36, 0xdc0272ff, 0xcb7966bc, 0xf2f45a79, 0xe58f4e3a, + 0x96f6c39a, 0x818dd7d9, 0xb800eb1c, 0xaf7bff5f, 0xcb1a9296, + 0xdc6186d5, 0xe5ecba10, 0xf297ae53, 0x2d2e6182, 0x3a5575c1, + 0x03d84904, 0x14a35d47, 0x70c2308e, 0x67b924cd, 0x5e341808, + 0x494f0c4b}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x0000000000000000, 0x43147b1700000000, 0x8628f62e00000000, + 0xc53c8d3900000000, 0x0c51ec5d00000000, 0x4f45974a00000000, + 0x8a791a7300000000, 0xc96d616400000000, 0x18a2d8bb00000000, + 0x5bb6a3ac00000000, 0x9e8a2e9500000000, 0xdd9e558200000000, + 0x14f334e600000000, 0x57e74ff100000000, 0x92dbc2c800000000, + 0xd1cfb9df00000000, 0x7142c0ac00000000, 0x3256bbbb00000000, + 0xf76a368200000000, 0xb47e4d9500000000, 0x7d132cf100000000, + 0x3e0757e600000000, 0xfb3bdadf00000000, 0xb82fa1c800000000, + 0x69e0181700000000, 0x2af4630000000000, 0xefc8ee3900000000, + 0xacdc952e00000000, 0x65b1f44a00000000, 0x26a58f5d00000000, + 0xe399026400000000, 0xa08d797300000000, 0xa382f18200000000, + 0xe0968a9500000000, 0x25aa07ac00000000, 0x66be7cbb00000000, + 0xafd31ddf00000000, 0xecc766c800000000, 0x29fbebf100000000, + 0x6aef90e600000000, 0xbb20293900000000, 0xf834522e00000000, + 0x3d08df1700000000, 0x7e1ca40000000000, 0xb771c56400000000, + 0xf465be7300000000, 0x3159334a00000000, 0x724d485d00000000, + 0xd2c0312e00000000, 0x91d44a3900000000, 0x54e8c70000000000, + 0x17fcbc1700000000, 0xde91dd7300000000, 0x9d85a66400000000, + 0x58b92b5d00000000, 0x1bad504a00000000, 0xca62e99500000000, + 0x8976928200000000, 0x4c4a1fbb00000000, 0x0f5e64ac00000000, + 0xc63305c800000000, 0x85277edf00000000, 0x401bf3e600000000, + 0x030f88f100000000, 0x070392de00000000, 0x4417e9c900000000, + 0x812b64f000000000, 0xc23f1fe700000000, 0x0b527e8300000000, + 0x4846059400000000, 0x8d7a88ad00000000, 0xce6ef3ba00000000, + 0x1fa14a6500000000, 0x5cb5317200000000, 0x9989bc4b00000000, + 0xda9dc75c00000000, 0x13f0a63800000000, 0x50e4dd2f00000000, + 0x95d8501600000000, 0xd6cc2b0100000000, 0x7641527200000000, + 0x3555296500000000, 0xf069a45c00000000, 0xb37ddf4b00000000, + 0x7a10be2f00000000, 0x3904c53800000000, 0xfc38480100000000, + 0xbf2c331600000000, 0x6ee38ac900000000, 0x2df7f1de00000000, + 0xe8cb7ce700000000, 0xabdf07f000000000, 0x62b2669400000000, + 0x21a61d8300000000, 0xe49a90ba00000000, 0xa78eebad00000000, + 0xa481635c00000000, 0xe795184b00000000, 0x22a9957200000000, + 0x61bdee6500000000, 0xa8d08f0100000000, 0xebc4f41600000000, + 0x2ef8792f00000000, 0x6dec023800000000, 0xbc23bbe700000000, + 0xff37c0f000000000, 0x3a0b4dc900000000, 0x791f36de00000000, + 0xb07257ba00000000, 0xf3662cad00000000, 0x365aa19400000000, + 0x754eda8300000000, 0xd5c3a3f000000000, 0x96d7d8e700000000, + 0x53eb55de00000000, 0x10ff2ec900000000, 0xd9924fad00000000, + 0x9a8634ba00000000, 0x5fbab98300000000, 0x1caec29400000000, + 0xcd617b4b00000000, 0x8e75005c00000000, 0x4b498d6500000000, + 0x085df67200000000, 0xc130971600000000, 0x8224ec0100000000, + 0x4718613800000000, 0x040c1a2f00000000, 0x4f00556600000000, + 0x0c142e7100000000, 0xc928a34800000000, 0x8a3cd85f00000000, + 0x4351b93b00000000, 0x0045c22c00000000, 0xc5794f1500000000, + 0x866d340200000000, 0x57a28ddd00000000, 0x14b6f6ca00000000, + 0xd18a7bf300000000, 0x929e00e400000000, 0x5bf3618000000000, + 0x18e71a9700000000, 0xdddb97ae00000000, 0x9ecfecb900000000, + 0x3e4295ca00000000, 0x7d56eedd00000000, 0xb86a63e400000000, + 0xfb7e18f300000000, 0x3213799700000000, 0x7107028000000000, + 0xb43b8fb900000000, 0xf72ff4ae00000000, 0x26e04d7100000000, + 0x65f4366600000000, 0xa0c8bb5f00000000, 0xe3dcc04800000000, + 0x2ab1a12c00000000, 0x69a5da3b00000000, 0xac99570200000000, + 0xef8d2c1500000000, 0xec82a4e400000000, 0xaf96dff300000000, + 0x6aaa52ca00000000, 0x29be29dd00000000, 0xe0d348b900000000, + 0xa3c733ae00000000, 0x66fbbe9700000000, 0x25efc58000000000, + 0xf4207c5f00000000, 0xb734074800000000, 0x72088a7100000000, + 0x311cf16600000000, 0xf871900200000000, 0xbb65eb1500000000, + 0x7e59662c00000000, 0x3d4d1d3b00000000, 0x9dc0644800000000, + 0xded41f5f00000000, 0x1be8926600000000, 0x58fce97100000000, + 0x9191881500000000, 0xd285f30200000000, 0x17b97e3b00000000, + 0x54ad052c00000000, 0x8562bcf300000000, 0xc676c7e400000000, + 0x034a4add00000000, 0x405e31ca00000000, 0x893350ae00000000, + 0xca272bb900000000, 0x0f1ba68000000000, 0x4c0fdd9700000000, + 0x4803c7b800000000, 0x0b17bcaf00000000, 0xce2b319600000000, + 0x8d3f4a8100000000, 0x44522be500000000, 0x074650f200000000, + 0xc27addcb00000000, 0x816ea6dc00000000, 0x50a11f0300000000, + 0x13b5641400000000, 0xd689e92d00000000, 0x959d923a00000000, + 0x5cf0f35e00000000, 0x1fe4884900000000, 0xdad8057000000000, + 0x99cc7e6700000000, 0x3941071400000000, 0x7a557c0300000000, + 0xbf69f13a00000000, 0xfc7d8a2d00000000, 0x3510eb4900000000, + 0x7604905e00000000, 0xb3381d6700000000, 0xf02c667000000000, + 0x21e3dfaf00000000, 0x62f7a4b800000000, 0xa7cb298100000000, + 0xe4df529600000000, 0x2db233f200000000, 0x6ea648e500000000, + 0xab9ac5dc00000000, 0xe88ebecb00000000, 0xeb81363a00000000, + 0xa8954d2d00000000, 0x6da9c01400000000, 0x2ebdbb0300000000, + 0xe7d0da6700000000, 0xa4c4a17000000000, 0x61f82c4900000000, + 0x22ec575e00000000, 0xf323ee8100000000, 0xb037959600000000, + 0x750b18af00000000, 0x361f63b800000000, 0xff7202dc00000000, + 0xbc6679cb00000000, 0x795af4f200000000, 0x3a4e8fe500000000, + 0x9ac3f69600000000, 0xd9d78d8100000000, 0x1ceb00b800000000, + 0x5fff7baf00000000, 0x96921acb00000000, 0xd58661dc00000000, + 0x10baece500000000, 0x53ae97f200000000, 0x82612e2d00000000, + 0xc175553a00000000, 0x0449d80300000000, 0x475da31400000000, + 0x8e30c27000000000, 0xcd24b96700000000, 0x0818345e00000000, + 0x4b0c4f4900000000}, + {0x0000000000000000, 0x3e6bc2ef00000000, 0x3dd0f50400000000, + 0x03bb37eb00000000, 0x7aa0eb0900000000, 0x44cb29e600000000, + 0x47701e0d00000000, 0x791bdce200000000, 0xf440d71300000000, + 0xca2b15fc00000000, 0xc990221700000000, 0xf7fbe0f800000000, + 0x8ee03c1a00000000, 0xb08bfef500000000, 0xb330c91e00000000, + 0x8d5b0bf100000000, 0xe881ae2700000000, 0xd6ea6cc800000000, + 0xd5515b2300000000, 0xeb3a99cc00000000, 0x9221452e00000000, + 0xac4a87c100000000, 0xaff1b02a00000000, 0x919a72c500000000, + 0x1cc1793400000000, 0x22aabbdb00000000, 0x21118c3000000000, + 0x1f7a4edf00000000, 0x6661923d00000000, 0x580a50d200000000, + 0x5bb1673900000000, 0x65daa5d600000000, 0xd0035d4f00000000, + 0xee689fa000000000, 0xedd3a84b00000000, 0xd3b86aa400000000, + 0xaaa3b64600000000, 0x94c874a900000000, 0x9773434200000000, + 0xa91881ad00000000, 0x24438a5c00000000, 0x1a2848b300000000, + 0x19937f5800000000, 0x27f8bdb700000000, 0x5ee3615500000000, + 0x6088a3ba00000000, 0x6333945100000000, 0x5d5856be00000000, + 0x3882f36800000000, 0x06e9318700000000, 0x0552066c00000000, + 0x3b39c48300000000, 0x4222186100000000, 0x7c49da8e00000000, + 0x7ff2ed6500000000, 0x41992f8a00000000, 0xccc2247b00000000, + 0xf2a9e69400000000, 0xf112d17f00000000, 0xcf79139000000000, + 0xb662cf7200000000, 0x88090d9d00000000, 0x8bb23a7600000000, + 0xb5d9f89900000000, 0xa007ba9e00000000, 0x9e6c787100000000, + 0x9dd74f9a00000000, 0xa3bc8d7500000000, 0xdaa7519700000000, + 0xe4cc937800000000, 0xe777a49300000000, 0xd91c667c00000000, + 0x54476d8d00000000, 0x6a2caf6200000000, 0x6997988900000000, + 0x57fc5a6600000000, 0x2ee7868400000000, 0x108c446b00000000, + 0x1337738000000000, 0x2d5cb16f00000000, 0x488614b900000000, + 0x76edd65600000000, 0x7556e1bd00000000, 0x4b3d235200000000, + 0x3226ffb000000000, 0x0c4d3d5f00000000, 0x0ff60ab400000000, + 0x319dc85b00000000, 0xbcc6c3aa00000000, 0x82ad014500000000, + 0x811636ae00000000, 0xbf7df44100000000, 0xc66628a300000000, + 0xf80dea4c00000000, 0xfbb6dda700000000, 0xc5dd1f4800000000, + 0x7004e7d100000000, 0x4e6f253e00000000, 0x4dd412d500000000, + 0x73bfd03a00000000, 0x0aa40cd800000000, 0x34cfce3700000000, + 0x3774f9dc00000000, 0x091f3b3300000000, 0x844430c200000000, + 0xba2ff22d00000000, 0xb994c5c600000000, 0x87ff072900000000, + 0xfee4dbcb00000000, 0xc08f192400000000, 0xc3342ecf00000000, + 0xfd5fec2000000000, 0x988549f600000000, 0xa6ee8b1900000000, + 0xa555bcf200000000, 0x9b3e7e1d00000000, 0xe225a2ff00000000, + 0xdc4e601000000000, 0xdff557fb00000000, 0xe19e951400000000, + 0x6cc59ee500000000, 0x52ae5c0a00000000, 0x51156be100000000, + 0x6f7ea90e00000000, 0x166575ec00000000, 0x280eb70300000000, + 0x2bb580e800000000, 0x15de420700000000, 0x010905e600000000, + 0x3f62c70900000000, 0x3cd9f0e200000000, 0x02b2320d00000000, + 0x7ba9eeef00000000, 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0x6ba6666d00000000, 0x9912ae4400000000, + 0x0883c6ea00000000, 0x32e42c6400000000, 0xa37544ca00000000, + 0x51c18ce300000000, 0xc050e44d00000000, 0xb5a81db000000000, + 0x2439751e00000000, 0xd68dbd3700000000, 0x471cd59900000000, + 0xe34518f100000000, 0x72d4705f00000000, 0x8060b87600000000, + 0x11f1d0d800000000, 0x6409292500000000, 0xf598418b00000000, + 0x072c89a200000000, 0x96bde10c00000000, 0xacda0b8200000000, + 0x3d4b632c00000000, 0xcfffab0500000000, 0x5e6ec3ab00000000, + 0x2b963a5600000000, 0xba0752f800000000, 0x48b39ad100000000, + 0xd922f27f00000000, 0xfaf67e2e00000000, 0x6b67168000000000, + 0x99d3dea900000000, 0x0842b60700000000, 0x7dba4ffa00000000, + 0xec2b275400000000, 0x1e9fef7d00000000, 0x8f0e87d300000000, + 0xb5696d5d00000000, 0x24f805f300000000, 0xd64ccdda00000000, + 0x47dda57400000000, 0x32255c8900000000, 0xa3b4342700000000, + 0x5100fc0e00000000, 0xc09194a000000000, 0x64c859c800000000, + 0xf559316600000000, 0x07edf94f00000000, 0x967c91e100000000, + 0xe384681c00000000, 0x721500b200000000, 0x80a1c89b00000000, + 0x1130a03500000000, 0x2b574abb00000000, 0xbac6221500000000, + 0x4872ea3c00000000, 0xd9e3829200000000, 0xac1b7b6f00000000, + 0x3d8a13c100000000, 0xcf3edbe800000000, 0x5eafb34600000000, + 0x878d413900000000, 0x161c299700000000, 0xe4a8e1be00000000, + 0x7539891000000000, 0x00c170ed00000000, 0x9150184300000000, + 0x63e4d06a00000000, 0xf275b8c400000000, 0xc812524a00000000, + 0x59833ae400000000, 0xab37f2cd00000000, 0x3aa69a6300000000, + 0x4f5e639e00000000, 0xdecf0b3000000000, 0x2c7bc31900000000, + 0xbdeaabb700000000, 0x19b366df00000000, 0x88220e7100000000, + 0x7a96c65800000000, 0xeb07aef600000000, 0x9eff570b00000000, + 0x0f6e3fa500000000, 0xfddaf78c00000000, 0x6c4b9f2200000000, + 0x562c75ac00000000, 0xc7bd1d0200000000, 0x3509d52b00000000, + 0xa498bd8500000000, 0xd160447800000000, 0x40f12cd600000000, + 0xb245e4ff00000000, 0x23d48c5100000000, 0xf4edfd5c00000000, + 0x657c95f200000000, 0x97c85ddb00000000, 0x0659357500000000, + 0x73a1cc8800000000, 0xe230a42600000000, 0x10846c0f00000000, + 0x811504a100000000, 0xbb72ee2f00000000, 0x2ae3868100000000, + 0xd8574ea800000000, 0x49c6260600000000, 0x3c3edffb00000000, + 0xadafb75500000000, 0x5f1b7f7c00000000, 0xce8a17d200000000, + 0x6ad3daba00000000, 0xfb42b21400000000, 0x09f67a3d00000000, + 0x9867129300000000, 0xed9feb6e00000000, 0x7c0e83c000000000, + 0x8eba4be900000000, 0x1f2b234700000000, 0x254cc9c900000000, + 0xb4dda16700000000, 0x4669694e00000000, 0xd7f801e000000000, + 0xa200f81d00000000, 0x339190b300000000, 0xc125589a00000000, + 0x50b4303400000000, 0x8996c24b00000000, 0x1807aae500000000, + 0xeab362cc00000000, 0x7b220a6200000000, 0x0edaf39f00000000, + 0x9f4b9b3100000000, 0x6dff531800000000, 0xfc6e3bb600000000, + 0xc609d13800000000, 0x5798b99600000000, 0xa52c71bf00000000, + 0x34bd191100000000, 0x4145e0ec00000000, 0xd0d4884200000000, + 0x2260406b00000000, 0xb3f128c500000000, 0x17a8e5ad00000000, + 0x86398d0300000000, 0x748d452a00000000, 0xe51c2d8400000000, + 0x90e4d47900000000, 0x0175bcd700000000, 0xf3c174fe00000000, + 0x62501c5000000000, 0x5837f6de00000000, 0xc9a69e7000000000, + 0x3b12565900000000, 0xaa833ef700000000, 0xdf7bc70a00000000, + 0x4eeaafa400000000, 0xbc5e678d00000000, 0x2dcf0f2300000000, + 0x0e1b837200000000, 0x9f8aebdc00000000, 0x6d3e23f500000000, + 0xfcaf4b5b00000000, 0x8957b2a600000000, 0x18c6da0800000000, + 0xea72122100000000, 0x7be37a8f00000000, 0x4184900100000000, + 0xd015f8af00000000, 0x22a1308600000000, 0xb330582800000000, + 0xc6c8a1d500000000, 0x5759c97b00000000, 0xa5ed015200000000, + 0x347c69fc00000000, 0x9025a49400000000, 0x01b4cc3a00000000, + 0xf300041300000000, 0x62916cbd00000000, 0x1769954000000000, + 0x86f8fdee00000000, 0x744c35c700000000, 0xe5dd5d6900000000, + 0xdfbab7e700000000, 0x4e2bdf4900000000, 0xbc9f176000000000, + 0x2d0e7fce00000000, 0x58f6863300000000, 0xc967ee9d00000000, + 0x3bd326b400000000, 0xaa424e1a00000000, 0x7360bc6500000000, + 0xe2f1d4cb00000000, 0x10451ce200000000, 0x81d4744c00000000, + 0xf42c8db100000000, 0x65bde51f00000000, 0x97092d3600000000, + 0x0698459800000000, 0x3cffaf1600000000, 0xad6ec7b800000000, + 0x5fda0f9100000000, 0xce4b673f00000000, 0xbbb39ec200000000, + 0x2a22f66c00000000, 0xd8963e4500000000, 0x490756eb00000000, + 0xed5e9b8300000000, 0x7ccff32d00000000, 0x8e7b3b0400000000, + 0x1fea53aa00000000, 0x6a12aa5700000000, 0xfb83c2f900000000, + 0x09370ad000000000, 0x98a6627e00000000, 0xa2c188f000000000, + 0x3350e05e00000000, 0xc1e4287700000000, 0x507540d900000000, + 0x258db92400000000, 0xb41cd18a00000000, 0x46a819a300000000, + 0xd739710d00000000}}; + +#else /* W == 4 */ + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xccaa009e, 0x4225077d, 0x8e8f07e3, 0x844a0efa, + 0x48e00e64, 0xc66f0987, 0x0ac50919, 0xd3e51bb5, 0x1f4f1b2b, + 0x91c01cc8, 0x5d6a1c56, 0x57af154f, 0x9b0515d1, 0x158a1232, + 0xd92012ac, 0x7cbb312b, 0xb01131b5, 0x3e9e3656, 0xf23436c8, + 0xf8f13fd1, 0x345b3f4f, 0xbad438ac, 0x767e3832, 0xaf5e2a9e, + 0x63f42a00, 0xed7b2de3, 0x21d12d7d, 0x2b142464, 0xe7be24fa, + 0x69312319, 0xa59b2387, 0xf9766256, 0x35dc62c8, 0xbb53652b, + 0x77f965b5, 0x7d3c6cac, 0xb1966c32, 0x3f196bd1, 0xf3b36b4f, + 0x2a9379e3, 0xe639797d, 0x68b67e9e, 0xa41c7e00, 0xaed97719, + 0x62737787, 0xecfc7064, 0x205670fa, 0x85cd537d, 0x496753e3, + 0xc7e85400, 0x0b42549e, 0x01875d87, 0xcd2d5d19, 0x43a25afa, + 0x8f085a64, 0x562848c8, 0x9a824856, 0x140d4fb5, 0xd8a74f2b, + 0xd2624632, 0x1ec846ac, 0x9047414f, 0x5ced41d1, 0x299dc2ed, + 0xe537c273, 0x6bb8c590, 0xa712c50e, 0xadd7cc17, 0x617dcc89, + 0xeff2cb6a, 0x2358cbf4, 0xfa78d958, 0x36d2d9c6, 0xb85dde25, + 0x74f7debb, 0x7e32d7a2, 0xb298d73c, 0x3c17d0df, 0xf0bdd041, + 0x5526f3c6, 0x998cf358, 0x1703f4bb, 0xdba9f425, 0xd16cfd3c, + 0x1dc6fda2, 0x9349fa41, 0x5fe3fadf, 0x86c3e873, 0x4a69e8ed, + 0xc4e6ef0e, 0x084cef90, 0x0289e689, 0xce23e617, 0x40ace1f4, + 0x8c06e16a, 0xd0eba0bb, 0x1c41a025, 0x92cea7c6, 0x5e64a758, + 0x54a1ae41, 0x980baedf, 0x1684a93c, 0xda2ea9a2, 0x030ebb0e, + 0xcfa4bb90, 0x412bbc73, 0x8d81bced, 0x8744b5f4, 0x4beeb56a, + 0xc561b289, 0x09cbb217, 0xac509190, 0x60fa910e, 0xee7596ed, + 0x22df9673, 0x281a9f6a, 0xe4b09ff4, 0x6a3f9817, 0xa6959889, + 0x7fb58a25, 0xb31f8abb, 0x3d908d58, 0xf13a8dc6, 0xfbff84df, + 0x37558441, 0xb9da83a2, 0x7570833c, 0x533b85da, 0x9f918544, + 0x111e82a7, 0xddb48239, 0xd7718b20, 0x1bdb8bbe, 0x95548c5d, + 0x59fe8cc3, 0x80de9e6f, 0x4c749ef1, 0xc2fb9912, 0x0e51998c, + 0x04949095, 0xc83e900b, 0x46b197e8, 0x8a1b9776, 0x2f80b4f1, + 0xe32ab46f, 0x6da5b38c, 0xa10fb312, 0xabcaba0b, 0x6760ba95, + 0xe9efbd76, 0x2545bde8, 0xfc65af44, 0x30cfafda, 0xbe40a839, + 0x72eaa8a7, 0x782fa1be, 0xb485a120, 0x3a0aa6c3, 0xf6a0a65d, + 0xaa4de78c, 0x66e7e712, 0xe868e0f1, 0x24c2e06f, 0x2e07e976, + 0xe2ade9e8, 0x6c22ee0b, 0xa088ee95, 0x79a8fc39, 0xb502fca7, + 0x3b8dfb44, 0xf727fbda, 0xfde2f2c3, 0x3148f25d, 0xbfc7f5be, + 0x736df520, 0xd6f6d6a7, 0x1a5cd639, 0x94d3d1da, 0x5879d144, + 0x52bcd85d, 0x9e16d8c3, 0x1099df20, 0xdc33dfbe, 0x0513cd12, + 0xc9b9cd8c, 0x4736ca6f, 0x8b9ccaf1, 0x8159c3e8, 0x4df3c376, + 0xc37cc495, 0x0fd6c40b, 0x7aa64737, 0xb60c47a9, 0x3883404a, + 0xf42940d4, 0xfeec49cd, 0x32464953, 0xbcc94eb0, 0x70634e2e, + 0xa9435c82, 0x65e95c1c, 0xeb665bff, 0x27cc5b61, 0x2d095278, + 0xe1a352e6, 0x6f2c5505, 0xa386559b, 0x061d761c, 0xcab77682, + 0x44387161, 0x889271ff, 0x825778e6, 0x4efd7878, 0xc0727f9b, + 0x0cd87f05, 0xd5f86da9, 0x19526d37, 0x97dd6ad4, 0x5b776a4a, + 0x51b26353, 0x9d1863cd, 0x1397642e, 0xdf3d64b0, 0x83d02561, + 0x4f7a25ff, 0xc1f5221c, 0x0d5f2282, 0x079a2b9b, 0xcb302b05, + 0x45bf2ce6, 0x89152c78, 0x50353ed4, 0x9c9f3e4a, 0x121039a9, + 0xdeba3937, 0xd47f302e, 0x18d530b0, 0x965a3753, 0x5af037cd, + 0xff6b144a, 0x33c114d4, 0xbd4e1337, 0x71e413a9, 0x7b211ab0, + 0xb78b1a2e, 0x39041dcd, 0xf5ae1d53, 0x2c8e0fff, 0xe0240f61, + 0x6eab0882, 0xa201081c, 0xa8c40105, 0x646e019b, 0xeae10678, + 0x264b06e6}, + {0x00000000, 0xa6770bb4, 0x979f1129, 0x31e81a9d, 0xf44f2413, + 0x52382fa7, 0x63d0353a, 0xc5a73e8e, 0x33ef4e67, 0x959845d3, + 0xa4705f4e, 0x020754fa, 0xc7a06a74, 0x61d761c0, 0x503f7b5d, + 0xf64870e9, 0x67de9cce, 0xc1a9977a, 0xf0418de7, 0x56368653, + 0x9391b8dd, 0x35e6b369, 0x040ea9f4, 0xa279a240, 0x5431d2a9, + 0xf246d91d, 0xc3aec380, 0x65d9c834, 0xa07ef6ba, 0x0609fd0e, + 0x37e1e793, 0x9196ec27, 0xcfbd399c, 0x69ca3228, 0x582228b5, + 0xfe552301, 0x3bf21d8f, 0x9d85163b, 0xac6d0ca6, 0x0a1a0712, + 0xfc5277fb, 0x5a257c4f, 0x6bcd66d2, 0xcdba6d66, 0x081d53e8, + 0xae6a585c, 0x9f8242c1, 0x39f54975, 0xa863a552, 0x0e14aee6, + 0x3ffcb47b, 0x998bbfcf, 0x5c2c8141, 0xfa5b8af5, 0xcbb39068, + 0x6dc49bdc, 0x9b8ceb35, 0x3dfbe081, 0x0c13fa1c, 0xaa64f1a8, + 0x6fc3cf26, 0xc9b4c492, 0xf85cde0f, 0x5e2bd5bb, 0x440b7579, + 0xe27c7ecd, 0xd3946450, 0x75e36fe4, 0xb044516a, 0x16335ade, + 0x27db4043, 0x81ac4bf7, 0x77e43b1e, 0xd19330aa, 0xe07b2a37, + 0x460c2183, 0x83ab1f0d, 0x25dc14b9, 0x14340e24, 0xb2430590, + 0x23d5e9b7, 0x85a2e203, 0xb44af89e, 0x123df32a, 0xd79acda4, + 0x71edc610, 0x4005dc8d, 0xe672d739, 0x103aa7d0, 0xb64dac64, + 0x87a5b6f9, 0x21d2bd4d, 0xe47583c3, 0x42028877, 0x73ea92ea, + 0xd59d995e, 0x8bb64ce5, 0x2dc14751, 0x1c295dcc, 0xba5e5678, + 0x7ff968f6, 0xd98e6342, 0xe86679df, 0x4e11726b, 0xb8590282, + 0x1e2e0936, 0x2fc613ab, 0x89b1181f, 0x4c162691, 0xea612d25, + 0xdb8937b8, 0x7dfe3c0c, 0xec68d02b, 0x4a1fdb9f, 0x7bf7c102, + 0xdd80cab6, 0x1827f438, 0xbe50ff8c, 0x8fb8e511, 0x29cfeea5, + 0xdf879e4c, 0x79f095f8, 0x48188f65, 0xee6f84d1, 0x2bc8ba5f, + 0x8dbfb1eb, 0xbc57ab76, 0x1a20a0c2, 0x8816eaf2, 0x2e61e146, + 0x1f89fbdb, 0xb9fef06f, 0x7c59cee1, 0xda2ec555, 0xebc6dfc8, + 0x4db1d47c, 0xbbf9a495, 0x1d8eaf21, 0x2c66b5bc, 0x8a11be08, + 0x4fb68086, 0xe9c18b32, 0xd82991af, 0x7e5e9a1b, 0xefc8763c, + 0x49bf7d88, 0x78576715, 0xde206ca1, 0x1b87522f, 0xbdf0599b, + 0x8c184306, 0x2a6f48b2, 0xdc27385b, 0x7a5033ef, 0x4bb82972, + 0xedcf22c6, 0x28681c48, 0x8e1f17fc, 0xbff70d61, 0x198006d5, + 0x47abd36e, 0xe1dcd8da, 0xd034c247, 0x7643c9f3, 0xb3e4f77d, + 0x1593fcc9, 0x247be654, 0x820cede0, 0x74449d09, 0xd23396bd, + 0xe3db8c20, 0x45ac8794, 0x800bb91a, 0x267cb2ae, 0x1794a833, + 0xb1e3a387, 0x20754fa0, 0x86024414, 0xb7ea5e89, 0x119d553d, + 0xd43a6bb3, 0x724d6007, 0x43a57a9a, 0xe5d2712e, 0x139a01c7, + 0xb5ed0a73, 0x840510ee, 0x22721b5a, 0xe7d525d4, 0x41a22e60, + 0x704a34fd, 0xd63d3f49, 0xcc1d9f8b, 0x6a6a943f, 0x5b828ea2, + 0xfdf58516, 0x3852bb98, 0x9e25b02c, 0xafcdaab1, 0x09baa105, + 0xfff2d1ec, 0x5985da58, 0x686dc0c5, 0xce1acb71, 0x0bbdf5ff, + 0xadcafe4b, 0x9c22e4d6, 0x3a55ef62, 0xabc30345, 0x0db408f1, + 0x3c5c126c, 0x9a2b19d8, 0x5f8c2756, 0xf9fb2ce2, 0xc813367f, + 0x6e643dcb, 0x982c4d22, 0x3e5b4696, 0x0fb35c0b, 0xa9c457bf, + 0x6c636931, 0xca146285, 0xfbfc7818, 0x5d8b73ac, 0x03a0a617, + 0xa5d7ada3, 0x943fb73e, 0x3248bc8a, 0xf7ef8204, 0x519889b0, + 0x6070932d, 0xc6079899, 0x304fe870, 0x9638e3c4, 0xa7d0f959, + 0x01a7f2ed, 0xc400cc63, 0x6277c7d7, 0x539fdd4a, 0xf5e8d6fe, + 0x647e3ad9, 0xc209316d, 0xf3e12bf0, 0x55962044, 0x90311eca, + 0x3646157e, 0x07ae0fe3, 0xa1d90457, 0x579174be, 0xf1e67f0a, + 0xc00e6597, 0x66796e23, 0xa3de50ad, 0x05a95b19, 0x34414184, + 0x92364a30}, + {0x00000000, 0xcb5cd3a5, 0x4dc8a10b, 0x869472ae, 0x9b914216, + 0x50cd91b3, 0xd659e31d, 0x1d0530b8, 0xec53826d, 0x270f51c8, + 0xa19b2366, 0x6ac7f0c3, 0x77c2c07b, 0xbc9e13de, 0x3a0a6170, + 0xf156b2d5, 0x03d6029b, 0xc88ad13e, 0x4e1ea390, 0x85427035, + 0x9847408d, 0x531b9328, 0xd58fe186, 0x1ed33223, 0xef8580f6, + 0x24d95353, 0xa24d21fd, 0x6911f258, 0x7414c2e0, 0xbf481145, + 0x39dc63eb, 0xf280b04e, 0x07ac0536, 0xccf0d693, 0x4a64a43d, + 0x81387798, 0x9c3d4720, 0x57619485, 0xd1f5e62b, 0x1aa9358e, + 0xebff875b, 0x20a354fe, 0xa6372650, 0x6d6bf5f5, 0x706ec54d, + 0xbb3216e8, 0x3da66446, 0xf6fab7e3, 0x047a07ad, 0xcf26d408, + 0x49b2a6a6, 0x82ee7503, 0x9feb45bb, 0x54b7961e, 0xd223e4b0, + 0x197f3715, 0xe82985c0, 0x23755665, 0xa5e124cb, 0x6ebdf76e, + 0x73b8c7d6, 0xb8e41473, 0x3e7066dd, 0xf52cb578, 0x0f580a6c, + 0xc404d9c9, 0x4290ab67, 0x89cc78c2, 0x94c9487a, 0x5f959bdf, + 0xd901e971, 0x125d3ad4, 0xe30b8801, 0x28575ba4, 0xaec3290a, + 0x659ffaaf, 0x789aca17, 0xb3c619b2, 0x35526b1c, 0xfe0eb8b9, + 0x0c8e08f7, 0xc7d2db52, 0x4146a9fc, 0x8a1a7a59, 0x971f4ae1, + 0x5c439944, 0xdad7ebea, 0x118b384f, 0xe0dd8a9a, 0x2b81593f, + 0xad152b91, 0x6649f834, 0x7b4cc88c, 0xb0101b29, 0x36846987, + 0xfdd8ba22, 0x08f40f5a, 0xc3a8dcff, 0x453cae51, 0x8e607df4, + 0x93654d4c, 0x58399ee9, 0xdeadec47, 0x15f13fe2, 0xe4a78d37, + 0x2ffb5e92, 0xa96f2c3c, 0x6233ff99, 0x7f36cf21, 0xb46a1c84, + 0x32fe6e2a, 0xf9a2bd8f, 0x0b220dc1, 0xc07ede64, 0x46eaacca, + 0x8db67f6f, 0x90b34fd7, 0x5bef9c72, 0xdd7beedc, 0x16273d79, + 0xe7718fac, 0x2c2d5c09, 0xaab92ea7, 0x61e5fd02, 0x7ce0cdba, + 0xb7bc1e1f, 0x31286cb1, 0xfa74bf14, 0x1eb014d8, 0xd5ecc77d, + 0x5378b5d3, 0x98246676, 0x852156ce, 0x4e7d856b, 0xc8e9f7c5, + 0x03b52460, 0xf2e396b5, 0x39bf4510, 0xbf2b37be, 0x7477e41b, + 0x6972d4a3, 0xa22e0706, 0x24ba75a8, 0xefe6a60d, 0x1d661643, + 0xd63ac5e6, 0x50aeb748, 0x9bf264ed, 0x86f75455, 0x4dab87f0, + 0xcb3ff55e, 0x006326fb, 0xf135942e, 0x3a69478b, 0xbcfd3525, + 0x77a1e680, 0x6aa4d638, 0xa1f8059d, 0x276c7733, 0xec30a496, + 0x191c11ee, 0xd240c24b, 0x54d4b0e5, 0x9f886340, 0x828d53f8, + 0x49d1805d, 0xcf45f2f3, 0x04192156, 0xf54f9383, 0x3e134026, + 0xb8873288, 0x73dbe12d, 0x6eded195, 0xa5820230, 0x2316709e, + 0xe84aa33b, 0x1aca1375, 0xd196c0d0, 0x5702b27e, 0x9c5e61db, + 0x815b5163, 0x4a0782c6, 0xcc93f068, 0x07cf23cd, 0xf6999118, + 0x3dc542bd, 0xbb513013, 0x700de3b6, 0x6d08d30e, 0xa65400ab, + 0x20c07205, 0xeb9ca1a0, 0x11e81eb4, 0xdab4cd11, 0x5c20bfbf, + 0x977c6c1a, 0x8a795ca2, 0x41258f07, 0xc7b1fda9, 0x0ced2e0c, + 0xfdbb9cd9, 0x36e74f7c, 0xb0733dd2, 0x7b2fee77, 0x662adecf, + 0xad760d6a, 0x2be27fc4, 0xe0beac61, 0x123e1c2f, 0xd962cf8a, + 0x5ff6bd24, 0x94aa6e81, 0x89af5e39, 0x42f38d9c, 0xc467ff32, + 0x0f3b2c97, 0xfe6d9e42, 0x35314de7, 0xb3a53f49, 0x78f9ecec, + 0x65fcdc54, 0xaea00ff1, 0x28347d5f, 0xe368aefa, 0x16441b82, + 0xdd18c827, 0x5b8cba89, 0x90d0692c, 0x8dd55994, 0x46898a31, + 0xc01df89f, 0x0b412b3a, 0xfa1799ef, 0x314b4a4a, 0xb7df38e4, + 0x7c83eb41, 0x6186dbf9, 0xaada085c, 0x2c4e7af2, 0xe712a957, + 0x15921919, 0xdececabc, 0x585ab812, 0x93066bb7, 0x8e035b0f, + 0x455f88aa, 0xc3cbfa04, 0x089729a1, 0xf9c19b74, 0x329d48d1, + 0xb4093a7f, 0x7f55e9da, 0x6250d962, 0xa90c0ac7, 0x2f987869, + 0xe4c4abcc}, + {0x00000000, 0x3d6029b0, 0x7ac05360, 0x47a07ad0, 0xf580a6c0, + 0xc8e08f70, 0x8f40f5a0, 0xb220dc10, 0x30704bc1, 0x0d106271, + 0x4ab018a1, 0x77d03111, 0xc5f0ed01, 0xf890c4b1, 0xbf30be61, + 0x825097d1, 0x60e09782, 0x5d80be32, 0x1a20c4e2, 0x2740ed52, + 0x95603142, 0xa80018f2, 0xefa06222, 0xd2c04b92, 0x5090dc43, + 0x6df0f5f3, 0x2a508f23, 0x1730a693, 0xa5107a83, 0x98705333, + 0xdfd029e3, 0xe2b00053, 0xc1c12f04, 0xfca106b4, 0xbb017c64, + 0x866155d4, 0x344189c4, 0x0921a074, 0x4e81daa4, 0x73e1f314, + 0xf1b164c5, 0xccd14d75, 0x8b7137a5, 0xb6111e15, 0x0431c205, + 0x3951ebb5, 0x7ef19165, 0x4391b8d5, 0xa121b886, 0x9c419136, + 0xdbe1ebe6, 0xe681c256, 0x54a11e46, 0x69c137f6, 0x2e614d26, + 0x13016496, 0x9151f347, 0xac31daf7, 0xeb91a027, 0xd6f18997, + 0x64d15587, 0x59b17c37, 0x1e1106e7, 0x23712f57, 0x58f35849, + 0x659371f9, 0x22330b29, 0x1f532299, 0xad73fe89, 0x9013d739, + 0xd7b3ade9, 0xead38459, 0x68831388, 0x55e33a38, 0x124340e8, + 0x2f236958, 0x9d03b548, 0xa0639cf8, 0xe7c3e628, 0xdaa3cf98, + 0x3813cfcb, 0x0573e67b, 0x42d39cab, 0x7fb3b51b, 0xcd93690b, + 0xf0f340bb, 0xb7533a6b, 0x8a3313db, 0x0863840a, 0x3503adba, + 0x72a3d76a, 0x4fc3feda, 0xfde322ca, 0xc0830b7a, 0x872371aa, + 0xba43581a, 0x9932774d, 0xa4525efd, 0xe3f2242d, 0xde920d9d, + 0x6cb2d18d, 0x51d2f83d, 0x167282ed, 0x2b12ab5d, 0xa9423c8c, + 0x9422153c, 0xd3826fec, 0xeee2465c, 0x5cc29a4c, 0x61a2b3fc, + 0x2602c92c, 0x1b62e09c, 0xf9d2e0cf, 0xc4b2c97f, 0x8312b3af, + 0xbe729a1f, 0x0c52460f, 0x31326fbf, 0x7692156f, 0x4bf23cdf, + 0xc9a2ab0e, 0xf4c282be, 0xb362f86e, 0x8e02d1de, 0x3c220dce, + 0x0142247e, 0x46e25eae, 0x7b82771e, 0xb1e6b092, 0x8c869922, + 0xcb26e3f2, 0xf646ca42, 0x44661652, 0x79063fe2, 0x3ea64532, + 0x03c66c82, 0x8196fb53, 0xbcf6d2e3, 0xfb56a833, 0xc6368183, + 0x74165d93, 0x49767423, 0x0ed60ef3, 0x33b62743, 0xd1062710, + 0xec660ea0, 0xabc67470, 0x96a65dc0, 0x248681d0, 0x19e6a860, + 0x5e46d2b0, 0x6326fb00, 0xe1766cd1, 0xdc164561, 0x9bb63fb1, + 0xa6d61601, 0x14f6ca11, 0x2996e3a1, 0x6e369971, 0x5356b0c1, + 0x70279f96, 0x4d47b626, 0x0ae7ccf6, 0x3787e546, 0x85a73956, + 0xb8c710e6, 0xff676a36, 0xc2074386, 0x4057d457, 0x7d37fde7, + 0x3a978737, 0x07f7ae87, 0xb5d77297, 0x88b75b27, 0xcf1721f7, + 0xf2770847, 0x10c70814, 0x2da721a4, 0x6a075b74, 0x576772c4, + 0xe547aed4, 0xd8278764, 0x9f87fdb4, 0xa2e7d404, 0x20b743d5, + 0x1dd76a65, 0x5a7710b5, 0x67173905, 0xd537e515, 0xe857cca5, + 0xaff7b675, 0x92979fc5, 0xe915e8db, 0xd475c16b, 0x93d5bbbb, + 0xaeb5920b, 0x1c954e1b, 0x21f567ab, 0x66551d7b, 0x5b3534cb, + 0xd965a31a, 0xe4058aaa, 0xa3a5f07a, 0x9ec5d9ca, 0x2ce505da, + 0x11852c6a, 0x562556ba, 0x6b457f0a, 0x89f57f59, 0xb49556e9, + 0xf3352c39, 0xce550589, 0x7c75d999, 0x4115f029, 0x06b58af9, + 0x3bd5a349, 0xb9853498, 0x84e51d28, 0xc34567f8, 0xfe254e48, + 0x4c059258, 0x7165bbe8, 0x36c5c138, 0x0ba5e888, 0x28d4c7df, + 0x15b4ee6f, 0x521494bf, 0x6f74bd0f, 0xdd54611f, 0xe03448af, + 0xa794327f, 0x9af41bcf, 0x18a48c1e, 0x25c4a5ae, 0x6264df7e, + 0x5f04f6ce, 0xed242ade, 0xd044036e, 0x97e479be, 0xaa84500e, + 0x4834505d, 0x755479ed, 0x32f4033d, 0x0f942a8d, 0xbdb4f69d, + 0x80d4df2d, 0xc774a5fd, 0xfa148c4d, 0x78441b9c, 0x4524322c, + 0x028448fc, 0x3fe4614c, 0x8dc4bd5c, 0xb0a494ec, 0xf704ee3c, + 0xca64c78c}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x00000000, 0xb029603d, 0x6053c07a, 0xd07aa047, 0xc0a680f5, + 0x708fe0c8, 0xa0f5408f, 0x10dc20b2, 0xc14b7030, 0x7162100d, + 0xa118b04a, 0x1131d077, 0x01edf0c5, 0xb1c490f8, 0x61be30bf, + 0xd1975082, 0x8297e060, 0x32be805d, 0xe2c4201a, 0x52ed4027, + 0x42316095, 0xf21800a8, 0x2262a0ef, 0x924bc0d2, 0x43dc9050, + 0xf3f5f06d, 0x238f502a, 0x93a63017, 0x837a10a5, 0x33537098, + 0xe329d0df, 0x5300b0e2, 0x042fc1c1, 0xb406a1fc, 0x647c01bb, + 0xd4556186, 0xc4894134, 0x74a02109, 0xa4da814e, 0x14f3e173, + 0xc564b1f1, 0x754dd1cc, 0xa537718b, 0x151e11b6, 0x05c23104, + 0xb5eb5139, 0x6591f17e, 0xd5b89143, 0x86b821a1, 0x3691419c, + 0xe6ebe1db, 0x56c281e6, 0x461ea154, 0xf637c169, 0x264d612e, + 0x96640113, 0x47f35191, 0xf7da31ac, 0x27a091eb, 0x9789f1d6, + 0x8755d164, 0x377cb159, 0xe706111e, 0x572f7123, 0x4958f358, + 0xf9719365, 0x290b3322, 0x9922531f, 0x89fe73ad, 0x39d71390, + 0xe9adb3d7, 0x5984d3ea, 0x88138368, 0x383ae355, 0xe8404312, + 0x5869232f, 0x48b5039d, 0xf89c63a0, 0x28e6c3e7, 0x98cfa3da, + 0xcbcf1338, 0x7be67305, 0xab9cd342, 0x1bb5b37f, 0x0b6993cd, + 0xbb40f3f0, 0x6b3a53b7, 0xdb13338a, 0x0a846308, 0xbaad0335, + 0x6ad7a372, 0xdafec34f, 0xca22e3fd, 0x7a0b83c0, 0xaa712387, + 0x1a5843ba, 0x4d773299, 0xfd5e52a4, 0x2d24f2e3, 0x9d0d92de, + 0x8dd1b26c, 0x3df8d251, 0xed827216, 0x5dab122b, 0x8c3c42a9, + 0x3c152294, 0xec6f82d3, 0x5c46e2ee, 0x4c9ac25c, 0xfcb3a261, + 0x2cc90226, 0x9ce0621b, 0xcfe0d2f9, 0x7fc9b2c4, 0xafb31283, + 0x1f9a72be, 0x0f46520c, 0xbf6f3231, 0x6f159276, 0xdf3cf24b, + 0x0eaba2c9, 0xbe82c2f4, 0x6ef862b3, 0xded1028e, 0xce0d223c, + 0x7e244201, 0xae5ee246, 0x1e77827b, 0x92b0e6b1, 0x2299868c, + 0xf2e326cb, 0x42ca46f6, 0x52166644, 0xe23f0679, 0x3245a63e, + 0x826cc603, 0x53fb9681, 0xe3d2f6bc, 0x33a856fb, 0x838136c6, + 0x935d1674, 0x23747649, 0xf30ed60e, 0x4327b633, 0x102706d1, + 0xa00e66ec, 0x7074c6ab, 0xc05da696, 0xd0818624, 0x60a8e619, + 0xb0d2465e, 0x00fb2663, 0xd16c76e1, 0x614516dc, 0xb13fb69b, + 0x0116d6a6, 0x11caf614, 0xa1e39629, 0x7199366e, 0xc1b05653, + 0x969f2770, 0x26b6474d, 0xf6cce70a, 0x46e58737, 0x5639a785, + 0xe610c7b8, 0x366a67ff, 0x864307c2, 0x57d45740, 0xe7fd377d, + 0x3787973a, 0x87aef707, 0x9772d7b5, 0x275bb788, 0xf72117cf, + 0x470877f2, 0x1408c710, 0xa421a72d, 0x745b076a, 0xc4726757, + 0xd4ae47e5, 0x648727d8, 0xb4fd879f, 0x04d4e7a2, 0xd543b720, + 0x656ad71d, 0xb510775a, 0x05391767, 0x15e537d5, 0xa5cc57e8, + 0x75b6f7af, 0xc59f9792, 0xdbe815e9, 0x6bc175d4, 0xbbbbd593, + 0x0b92b5ae, 0x1b4e951c, 0xab67f521, 0x7b1d5566, 0xcb34355b, + 0x1aa365d9, 0xaa8a05e4, 0x7af0a5a3, 0xcad9c59e, 0xda05e52c, + 0x6a2c8511, 0xba562556, 0x0a7f456b, 0x597ff589, 0xe95695b4, + 0x392c35f3, 0x890555ce, 0x99d9757c, 0x29f01541, 0xf98ab506, + 0x49a3d53b, 0x983485b9, 0x281de584, 0xf86745c3, 0x484e25fe, + 0x5892054c, 0xe8bb6571, 0x38c1c536, 0x88e8a50b, 0xdfc7d428, + 0x6feeb415, 0xbf941452, 0x0fbd746f, 0x1f6154dd, 0xaf4834e0, + 0x7f3294a7, 0xcf1bf49a, 0x1e8ca418, 0xaea5c425, 0x7edf6462, + 0xcef6045f, 0xde2a24ed, 0x6e0344d0, 0xbe79e497, 0x0e5084aa, + 0x5d503448, 0xed795475, 0x3d03f432, 0x8d2a940f, 0x9df6b4bd, + 0x2ddfd480, 0xfda574c7, 0x4d8c14fa, 0x9c1b4478, 0x2c322445, + 0xfc488402, 0x4c61e43f, 0x5cbdc48d, 0xec94a4b0, 0x3cee04f7, + 0x8cc764ca}, + {0x00000000, 0xa5d35ccb, 0x0ba1c84d, 0xae729486, 0x1642919b, + 0xb391cd50, 0x1de359d6, 0xb830051d, 0x6d8253ec, 0xc8510f27, + 0x66239ba1, 0xc3f0c76a, 0x7bc0c277, 0xde139ebc, 0x70610a3a, + 0xd5b256f1, 0x9b02d603, 0x3ed18ac8, 0x90a31e4e, 0x35704285, + 0x8d404798, 0x28931b53, 0x86e18fd5, 0x2332d31e, 0xf68085ef, + 0x5353d924, 0xfd214da2, 0x58f21169, 0xe0c21474, 0x451148bf, + 0xeb63dc39, 0x4eb080f2, 0x3605ac07, 0x93d6f0cc, 0x3da4644a, + 0x98773881, 0x20473d9c, 0x85946157, 0x2be6f5d1, 0x8e35a91a, + 0x5b87ffeb, 0xfe54a320, 0x502637a6, 0xf5f56b6d, 0x4dc56e70, + 0xe81632bb, 0x4664a63d, 0xe3b7faf6, 0xad077a04, 0x08d426cf, + 0xa6a6b249, 0x0375ee82, 0xbb45eb9f, 0x1e96b754, 0xb0e423d2, + 0x15377f19, 0xc08529e8, 0x65567523, 0xcb24e1a5, 0x6ef7bd6e, + 0xd6c7b873, 0x7314e4b8, 0xdd66703e, 0x78b52cf5, 0x6c0a580f, + 0xc9d904c4, 0x67ab9042, 0xc278cc89, 0x7a48c994, 0xdf9b955f, + 0x71e901d9, 0xd43a5d12, 0x01880be3, 0xa45b5728, 0x0a29c3ae, + 0xaffa9f65, 0x17ca9a78, 0xb219c6b3, 0x1c6b5235, 0xb9b80efe, + 0xf7088e0c, 0x52dbd2c7, 0xfca94641, 0x597a1a8a, 0xe14a1f97, + 0x4499435c, 0xeaebd7da, 0x4f388b11, 0x9a8adde0, 0x3f59812b, + 0x912b15ad, 0x34f84966, 0x8cc84c7b, 0x291b10b0, 0x87698436, + 0x22bad8fd, 0x5a0ff408, 0xffdca8c3, 0x51ae3c45, 0xf47d608e, + 0x4c4d6593, 0xe99e3958, 0x47ecadde, 0xe23ff115, 0x378da7e4, + 0x925efb2f, 0x3c2c6fa9, 0x99ff3362, 0x21cf367f, 0x841c6ab4, + 0x2a6efe32, 0x8fbda2f9, 0xc10d220b, 0x64de7ec0, 0xcaacea46, + 0x6f7fb68d, 0xd74fb390, 0x729cef5b, 0xdcee7bdd, 0x793d2716, + 0xac8f71e7, 0x095c2d2c, 0xa72eb9aa, 0x02fde561, 0xbacde07c, + 0x1f1ebcb7, 0xb16c2831, 0x14bf74fa, 0xd814b01e, 0x7dc7ecd5, + 0xd3b57853, 0x76662498, 0xce562185, 0x6b857d4e, 0xc5f7e9c8, + 0x6024b503, 0xb596e3f2, 0x1045bf39, 0xbe372bbf, 0x1be47774, + 0xa3d47269, 0x06072ea2, 0xa875ba24, 0x0da6e6ef, 0x4316661d, + 0xe6c53ad6, 0x48b7ae50, 0xed64f29b, 0x5554f786, 0xf087ab4d, + 0x5ef53fcb, 0xfb266300, 0x2e9435f1, 0x8b47693a, 0x2535fdbc, + 0x80e6a177, 0x38d6a46a, 0x9d05f8a1, 0x33776c27, 0x96a430ec, + 0xee111c19, 0x4bc240d2, 0xe5b0d454, 0x4063889f, 0xf8538d82, + 0x5d80d149, 0xf3f245cf, 0x56211904, 0x83934ff5, 0x2640133e, + 0x883287b8, 0x2de1db73, 0x95d1de6e, 0x300282a5, 0x9e701623, + 0x3ba34ae8, 0x7513ca1a, 0xd0c096d1, 0x7eb20257, 0xdb615e9c, + 0x63515b81, 0xc682074a, 0x68f093cc, 0xcd23cf07, 0x189199f6, + 0xbd42c53d, 0x133051bb, 0xb6e30d70, 0x0ed3086d, 0xab0054a6, + 0x0572c020, 0xa0a19ceb, 0xb41ee811, 0x11cdb4da, 0xbfbf205c, + 0x1a6c7c97, 0xa25c798a, 0x078f2541, 0xa9fdb1c7, 0x0c2eed0c, + 0xd99cbbfd, 0x7c4fe736, 0xd23d73b0, 0x77ee2f7b, 0xcfde2a66, + 0x6a0d76ad, 0xc47fe22b, 0x61acbee0, 0x2f1c3e12, 0x8acf62d9, + 0x24bdf65f, 0x816eaa94, 0x395eaf89, 0x9c8df342, 0x32ff67c4, + 0x972c3b0f, 0x429e6dfe, 0xe74d3135, 0x493fa5b3, 0xececf978, + 0x54dcfc65, 0xf10fa0ae, 0x5f7d3428, 0xfaae68e3, 0x821b4416, + 0x27c818dd, 0x89ba8c5b, 0x2c69d090, 0x9459d58d, 0x318a8946, + 0x9ff81dc0, 0x3a2b410b, 0xef9917fa, 0x4a4a4b31, 0xe438dfb7, + 0x41eb837c, 0xf9db8661, 0x5c08daaa, 0xf27a4e2c, 0x57a912e7, + 0x19199215, 0xbccacede, 0x12b85a58, 0xb76b0693, 0x0f5b038e, + 0xaa885f45, 0x04facbc3, 0xa1299708, 0x749bc1f9, 0xd1489d32, + 0x7f3a09b4, 0xdae9557f, 0x62d95062, 0xc70a0ca9, 0x6978982f, + 0xccabc4e4}, + {0x00000000, 0xb40b77a6, 0x29119f97, 0x9d1ae831, 0x13244ff4, + 0xa72f3852, 0x3a35d063, 0x8e3ea7c5, 0x674eef33, 0xd3459895, + 0x4e5f70a4, 0xfa540702, 0x746aa0c7, 0xc061d761, 0x5d7b3f50, + 0xe97048f6, 0xce9cde67, 0x7a97a9c1, 0xe78d41f0, 0x53863656, + 0xddb89193, 0x69b3e635, 0xf4a90e04, 0x40a279a2, 0xa9d23154, + 0x1dd946f2, 0x80c3aec3, 0x34c8d965, 0xbaf67ea0, 0x0efd0906, + 0x93e7e137, 0x27ec9691, 0x9c39bdcf, 0x2832ca69, 0xb5282258, + 0x012355fe, 0x8f1df23b, 0x3b16859d, 0xa60c6dac, 0x12071a0a, + 0xfb7752fc, 0x4f7c255a, 0xd266cd6b, 0x666dbacd, 0xe8531d08, + 0x5c586aae, 0xc142829f, 0x7549f539, 0x52a563a8, 0xe6ae140e, + 0x7bb4fc3f, 0xcfbf8b99, 0x41812c5c, 0xf58a5bfa, 0x6890b3cb, + 0xdc9bc46d, 0x35eb8c9b, 0x81e0fb3d, 0x1cfa130c, 0xa8f164aa, + 0x26cfc36f, 0x92c4b4c9, 0x0fde5cf8, 0xbbd52b5e, 0x79750b44, + 0xcd7e7ce2, 0x506494d3, 0xe46fe375, 0x6a5144b0, 0xde5a3316, + 0x4340db27, 0xf74bac81, 0x1e3be477, 0xaa3093d1, 0x372a7be0, + 0x83210c46, 0x0d1fab83, 0xb914dc25, 0x240e3414, 0x900543b2, + 0xb7e9d523, 0x03e2a285, 0x9ef84ab4, 0x2af33d12, 0xa4cd9ad7, + 0x10c6ed71, 0x8ddc0540, 0x39d772e6, 0xd0a73a10, 0x64ac4db6, + 0xf9b6a587, 0x4dbdd221, 0xc38375e4, 0x77880242, 0xea92ea73, + 0x5e999dd5, 0xe54cb68b, 0x5147c12d, 0xcc5d291c, 0x78565eba, + 0xf668f97f, 0x42638ed9, 0xdf7966e8, 0x6b72114e, 0x820259b8, + 0x36092e1e, 0xab13c62f, 0x1f18b189, 0x9126164c, 0x252d61ea, + 0xb83789db, 0x0c3cfe7d, 0x2bd068ec, 0x9fdb1f4a, 0x02c1f77b, + 0xb6ca80dd, 0x38f42718, 0x8cff50be, 0x11e5b88f, 0xa5eecf29, + 0x4c9e87df, 0xf895f079, 0x658f1848, 0xd1846fee, 0x5fbac82b, + 0xebb1bf8d, 0x76ab57bc, 0xc2a0201a, 0xf2ea1688, 0x46e1612e, + 0xdbfb891f, 0x6ff0feb9, 0xe1ce597c, 0x55c52eda, 0xc8dfc6eb, + 0x7cd4b14d, 0x95a4f9bb, 0x21af8e1d, 0xbcb5662c, 0x08be118a, + 0x8680b64f, 0x328bc1e9, 0xaf9129d8, 0x1b9a5e7e, 0x3c76c8ef, + 0x887dbf49, 0x15675778, 0xa16c20de, 0x2f52871b, 0x9b59f0bd, + 0x0643188c, 0xb2486f2a, 0x5b3827dc, 0xef33507a, 0x7229b84b, + 0xc622cfed, 0x481c6828, 0xfc171f8e, 0x610df7bf, 0xd5068019, + 0x6ed3ab47, 0xdad8dce1, 0x47c234d0, 0xf3c94376, 0x7df7e4b3, + 0xc9fc9315, 0x54e67b24, 0xe0ed0c82, 0x099d4474, 0xbd9633d2, + 0x208cdbe3, 0x9487ac45, 0x1ab90b80, 0xaeb27c26, 0x33a89417, + 0x87a3e3b1, 0xa04f7520, 0x14440286, 0x895eeab7, 0x3d559d11, + 0xb36b3ad4, 0x07604d72, 0x9a7aa543, 0x2e71d2e5, 0xc7019a13, + 0x730aedb5, 0xee100584, 0x5a1b7222, 0xd425d5e7, 0x602ea241, + 0xfd344a70, 0x493f3dd6, 0x8b9f1dcc, 0x3f946a6a, 0xa28e825b, + 0x1685f5fd, 0x98bb5238, 0x2cb0259e, 0xb1aacdaf, 0x05a1ba09, + 0xecd1f2ff, 0x58da8559, 0xc5c06d68, 0x71cb1ace, 0xfff5bd0b, + 0x4bfecaad, 0xd6e4229c, 0x62ef553a, 0x4503c3ab, 0xf108b40d, + 0x6c125c3c, 0xd8192b9a, 0x56278c5f, 0xe22cfbf9, 0x7f3613c8, + 0xcb3d646e, 0x224d2c98, 0x96465b3e, 0x0b5cb30f, 0xbf57c4a9, + 0x3169636c, 0x856214ca, 0x1878fcfb, 0xac738b5d, 0x17a6a003, + 0xa3add7a5, 0x3eb73f94, 0x8abc4832, 0x0482eff7, 0xb0899851, + 0x2d937060, 0x999807c6, 0x70e84f30, 0xc4e33896, 0x59f9d0a7, + 0xedf2a701, 0x63cc00c4, 0xd7c77762, 0x4add9f53, 0xfed6e8f5, + 0xd93a7e64, 0x6d3109c2, 0xf02be1f3, 0x44209655, 0xca1e3190, + 0x7e154636, 0xe30fae07, 0x5704d9a1, 0xbe749157, 0x0a7fe6f1, + 0x97650ec0, 0x236e7966, 0xad50dea3, 0x195ba905, 0x84414134, + 0x304a3692}, + {0x00000000, 0x9e00aacc, 0x7d072542, 0xe3078f8e, 0xfa0e4a84, + 0x640ee048, 0x87096fc6, 0x1909c50a, 0xb51be5d3, 0x2b1b4f1f, + 0xc81cc091, 0x561c6a5d, 0x4f15af57, 0xd115059b, 0x32128a15, + 0xac1220d9, 0x2b31bb7c, 0xb53111b0, 0x56369e3e, 0xc83634f2, + 0xd13ff1f8, 0x4f3f5b34, 0xac38d4ba, 0x32387e76, 0x9e2a5eaf, + 0x002af463, 0xe32d7bed, 0x7d2dd121, 0x6424142b, 0xfa24bee7, + 0x19233169, 0x87239ba5, 0x566276f9, 0xc862dc35, 0x2b6553bb, + 0xb565f977, 0xac6c3c7d, 0x326c96b1, 0xd16b193f, 0x4f6bb3f3, + 0xe379932a, 0x7d7939e6, 0x9e7eb668, 0x007e1ca4, 0x1977d9ae, + 0x87777362, 0x6470fcec, 0xfa705620, 0x7d53cd85, 0xe3536749, + 0x0054e8c7, 0x9e54420b, 0x875d8701, 0x195d2dcd, 0xfa5aa243, + 0x645a088f, 0xc8482856, 0x5648829a, 0xb54f0d14, 0x2b4fa7d8, + 0x324662d2, 0xac46c81e, 0x4f414790, 0xd141ed5c, 0xedc29d29, + 0x73c237e5, 0x90c5b86b, 0x0ec512a7, 0x17ccd7ad, 0x89cc7d61, + 0x6acbf2ef, 0xf4cb5823, 0x58d978fa, 0xc6d9d236, 0x25de5db8, + 0xbbdef774, 0xa2d7327e, 0x3cd798b2, 0xdfd0173c, 0x41d0bdf0, + 0xc6f32655, 0x58f38c99, 0xbbf40317, 0x25f4a9db, 0x3cfd6cd1, + 0xa2fdc61d, 0x41fa4993, 0xdffae35f, 0x73e8c386, 0xede8694a, + 0x0eefe6c4, 0x90ef4c08, 0x89e68902, 0x17e623ce, 0xf4e1ac40, + 0x6ae1068c, 0xbba0ebd0, 0x25a0411c, 0xc6a7ce92, 0x58a7645e, + 0x41aea154, 0xdfae0b98, 0x3ca98416, 0xa2a92eda, 0x0ebb0e03, + 0x90bba4cf, 0x73bc2b41, 0xedbc818d, 0xf4b54487, 0x6ab5ee4b, + 0x89b261c5, 0x17b2cb09, 0x909150ac, 0x0e91fa60, 0xed9675ee, + 0x7396df22, 0x6a9f1a28, 0xf49fb0e4, 0x17983f6a, 0x899895a6, + 0x258ab57f, 0xbb8a1fb3, 0x588d903d, 0xc68d3af1, 0xdf84fffb, + 0x41845537, 0xa283dab9, 0x3c837075, 0xda853b53, 0x4485919f, + 0xa7821e11, 0x3982b4dd, 0x208b71d7, 0xbe8bdb1b, 0x5d8c5495, + 0xc38cfe59, 0x6f9ede80, 0xf19e744c, 0x1299fbc2, 0x8c99510e, + 0x95909404, 0x0b903ec8, 0xe897b146, 0x76971b8a, 0xf1b4802f, + 0x6fb42ae3, 0x8cb3a56d, 0x12b30fa1, 0x0bbacaab, 0x95ba6067, + 0x76bdefe9, 0xe8bd4525, 0x44af65fc, 0xdaafcf30, 0x39a840be, + 0xa7a8ea72, 0xbea12f78, 0x20a185b4, 0xc3a60a3a, 0x5da6a0f6, + 0x8ce74daa, 0x12e7e766, 0xf1e068e8, 0x6fe0c224, 0x76e9072e, + 0xe8e9ade2, 0x0bee226c, 0x95ee88a0, 0x39fca879, 0xa7fc02b5, + 0x44fb8d3b, 0xdafb27f7, 0xc3f2e2fd, 0x5df24831, 0xbef5c7bf, + 0x20f56d73, 0xa7d6f6d6, 0x39d65c1a, 0xdad1d394, 0x44d17958, + 0x5dd8bc52, 0xc3d8169e, 0x20df9910, 0xbedf33dc, 0x12cd1305, + 0x8ccdb9c9, 0x6fca3647, 0xf1ca9c8b, 0xe8c35981, 0x76c3f34d, + 0x95c47cc3, 0x0bc4d60f, 0x3747a67a, 0xa9470cb6, 0x4a408338, + 0xd44029f4, 0xcd49ecfe, 0x53494632, 0xb04ec9bc, 0x2e4e6370, + 0x825c43a9, 0x1c5ce965, 0xff5b66eb, 0x615bcc27, 0x7852092d, + 0xe652a3e1, 0x05552c6f, 0x9b5586a3, 0x1c761d06, 0x8276b7ca, + 0x61713844, 0xff719288, 0xe6785782, 0x7878fd4e, 0x9b7f72c0, + 0x057fd80c, 0xa96df8d5, 0x376d5219, 0xd46add97, 0x4a6a775b, + 0x5363b251, 0xcd63189d, 0x2e649713, 0xb0643ddf, 0x6125d083, + 0xff257a4f, 0x1c22f5c1, 0x82225f0d, 0x9b2b9a07, 0x052b30cb, + 0xe62cbf45, 0x782c1589, 0xd43e3550, 0x4a3e9f9c, 0xa9391012, + 0x3739bade, 0x2e307fd4, 0xb030d518, 0x53375a96, 0xcd37f05a, + 0x4a146bff, 0xd414c133, 0x37134ebd, 0xa913e471, 0xb01a217b, + 0x2e1a8bb7, 0xcd1d0439, 0x531daef5, 0xff0f8e2c, 0x610f24e0, + 0x8208ab6e, 0x1c0801a2, 0x0501c4a8, 0x9b016e64, 0x7806e1ea, + 0xe6064b26}}; + #endif - } -}; + +#endif + +#if N == 3 + +#if W == 8 + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0x81256527, 0xd93bcc0f, 0x581ea928, 0x69069e5f, + 0xe823fb78, 0xb03d5250, 0x31183777, 0xd20d3cbe, 0x53285999, + 0x0b36f0b1, 0x8a139596, 0xbb0ba2e1, 0x3a2ec7c6, 0x62306eee, + 0xe3150bc9, 0x7f6b7f3d, 0xfe4e1a1a, 0xa650b332, 0x2775d615, + 0x166de162, 0x97488445, 0xcf562d6d, 0x4e73484a, 0xad664383, + 0x2c4326a4, 0x745d8f8c, 0xf578eaab, 0xc460dddc, 0x4545b8fb, + 0x1d5b11d3, 0x9c7e74f4, 0xfed6fe7a, 0x7ff39b5d, 0x27ed3275, + 0xa6c85752, 0x97d06025, 0x16f50502, 0x4eebac2a, 0xcfcec90d, + 0x2cdbc2c4, 0xadfea7e3, 0xf5e00ecb, 0x74c56bec, 0x45dd5c9b, + 0xc4f839bc, 0x9ce69094, 0x1dc3f5b3, 0x81bd8147, 0x0098e460, + 0x58864d48, 0xd9a3286f, 0xe8bb1f18, 0x699e7a3f, 0x3180d317, + 0xb0a5b630, 0x53b0bdf9, 0xd295d8de, 0x8a8b71f6, 0x0bae14d1, + 0x3ab623a6, 0xbb934681, 0xe38defa9, 0x62a88a8e, 0x26dcfab5, + 0xa7f99f92, 0xffe736ba, 0x7ec2539d, 0x4fda64ea, 0xceff01cd, + 0x96e1a8e5, 0x17c4cdc2, 0xf4d1c60b, 0x75f4a32c, 0x2dea0a04, + 0xaccf6f23, 0x9dd75854, 0x1cf23d73, 0x44ec945b, 0xc5c9f17c, + 0x59b78588, 0xd892e0af, 0x808c4987, 0x01a92ca0, 0x30b11bd7, + 0xb1947ef0, 0xe98ad7d8, 0x68afb2ff, 0x8bbab936, 0x0a9fdc11, + 0x52817539, 0xd3a4101e, 0xe2bc2769, 0x6399424e, 0x3b87eb66, + 0xbaa28e41, 0xd80a04cf, 0x592f61e8, 0x0131c8c0, 0x8014ade7, + 0xb10c9a90, 0x3029ffb7, 0x6837569f, 0xe91233b8, 0x0a073871, + 0x8b225d56, 0xd33cf47e, 0x52199159, 0x6301a62e, 0xe224c309, + 0xba3a6a21, 0x3b1f0f06, 0xa7617bf2, 0x26441ed5, 0x7e5ab7fd, + 0xff7fd2da, 0xce67e5ad, 0x4f42808a, 0x175c29a2, 0x96794c85, + 0x756c474c, 0xf449226b, 0xac578b43, 0x2d72ee64, 0x1c6ad913, + 0x9d4fbc34, 0xc551151c, 0x4474703b, 0x4db9f56a, 0xcc9c904d, + 0x94823965, 0x15a75c42, 0x24bf6b35, 0xa59a0e12, 0xfd84a73a, + 0x7ca1c21d, 0x9fb4c9d4, 0x1e91acf3, 0x468f05db, 0xc7aa60fc, + 0xf6b2578b, 0x779732ac, 0x2f899b84, 0xaeacfea3, 0x32d28a57, + 0xb3f7ef70, 0xebe94658, 0x6acc237f, 0x5bd41408, 0xdaf1712f, + 0x82efd807, 0x03cabd20, 0xe0dfb6e9, 0x61fad3ce, 0x39e47ae6, + 0xb8c11fc1, 0x89d928b6, 0x08fc4d91, 0x50e2e4b9, 0xd1c7819e, + 0xb36f0b10, 0x324a6e37, 0x6a54c71f, 0xeb71a238, 0xda69954f, + 0x5b4cf068, 0x03525940, 0x82773c67, 0x616237ae, 0xe0475289, + 0xb859fba1, 0x397c9e86, 0x0864a9f1, 0x8941ccd6, 0xd15f65fe, + 0x507a00d9, 0xcc04742d, 0x4d21110a, 0x153fb822, 0x941add05, + 0xa502ea72, 0x24278f55, 0x7c39267d, 0xfd1c435a, 0x1e094893, + 0x9f2c2db4, 0xc732849c, 0x4617e1bb, 0x770fd6cc, 0xf62ab3eb, + 0xae341ac3, 0x2f117fe4, 0x6b650fdf, 0xea406af8, 0xb25ec3d0, + 0x337ba6f7, 0x02639180, 0x8346f4a7, 0xdb585d8f, 0x5a7d38a8, + 0xb9683361, 0x384d5646, 0x6053ff6e, 0xe1769a49, 0xd06ead3e, + 0x514bc819, 0x09556131, 0x88700416, 0x140e70e2, 0x952b15c5, + 0xcd35bced, 0x4c10d9ca, 0x7d08eebd, 0xfc2d8b9a, 0xa43322b2, + 0x25164795, 0xc6034c5c, 0x4726297b, 0x1f388053, 0x9e1de574, + 0xaf05d203, 0x2e20b724, 0x763e1e0c, 0xf71b7b2b, 0x95b3f1a5, + 0x14969482, 0x4c883daa, 0xcdad588d, 0xfcb56ffa, 0x7d900add, + 0x258ea3f5, 0xa4abc6d2, 0x47becd1b, 0xc69ba83c, 0x9e850114, + 0x1fa06433, 0x2eb85344, 0xaf9d3663, 0xf7839f4b, 0x76a6fa6c, + 0xead88e98, 0x6bfdebbf, 0x33e34297, 0xb2c627b0, 0x83de10c7, + 0x02fb75e0, 0x5ae5dcc8, 0xdbc0b9ef, 0x38d5b226, 0xb9f0d701, + 0xe1ee7e29, 0x60cb1b0e, 0x51d32c79, 0xd0f6495e, 0x88e8e076, + 0x09cd8551}, + {0x00000000, 0x9b73ead4, 0xed96d3e9, 0x76e5393d, 0x005ca193, + 0x9b2f4b47, 0xedca727a, 0x76b998ae, 0x00b94326, 0x9bcaa9f2, + 0xed2f90cf, 0x765c7a1b, 0x00e5e2b5, 0x9b960861, 0xed73315c, + 0x7600db88, 0x0172864c, 0x9a016c98, 0xece455a5, 0x7797bf71, + 0x012e27df, 0x9a5dcd0b, 0xecb8f436, 0x77cb1ee2, 0x01cbc56a, + 0x9ab82fbe, 0xec5d1683, 0x772efc57, 0x019764f9, 0x9ae48e2d, + 0xec01b710, 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0x2bf1a6c1, 0x14e696e1, 0xfbb42000, 0x1132fd62, + 0xfe604b83, 0x1f4e41e7, 0xf01cf706, 0x1a9a2a64, 0xf5c89c85, + 0x03b738ed, 0xece58e0c, 0x0663536e, 0xe931e58f, 0x081fefeb, + 0xe74d590a, 0x0dcb8468, 0xe2993289, 0x3a45caf9, 0xd5177c18, + 0x3f91a17a, 0xd0c3179b, 0x31ed1dff, 0xdebfab1e, 0x3439767c, + 0xdb6bc09d, 0x2d1464f5, 0xc246d214, 0x28c00f76, 0xc792b997, + 0x26bcb3f3, 0xc9ee0512, 0x2368d870, 0xcc3a6e91, 0x49a02ed1, + 0xa6f29830, 0x4c744552, 0xa326f3b3, 0x4208f9d7, 0xad5a4f36, + 0x47dc9254, 0xa88e24b5, 0x5ef180dd, 0xb1a3363c, 0x5b25eb5e, + 0xb4775dbf, 0x555957db, 0xba0be13a, 0x508d3c58, 0xbfdf8ab9, + 0x670372c9, 0x8851c428, 0x62d7194a, 0x8d85afab, 0x6caba5cf, + 0x83f9132e, 0x697fce4c, 0x862d78ad, 0x7052dcc5, 0x9f006a24, + 0x7586b746, 0x9ad401a7, 0x7bfa0bc3, 0x94a8bd22, 0x7e2e6040, + 0x917cd6a1}, + {0x00000000, 0x87a6cb43, 0xd43c90c7, 0x539a5b84, 0x730827cf, + 0xf4aeec8c, 0xa734b708, 0x20927c4b, 0xe6104f9e, 0x61b684dd, + 0x322cdf59, 0xb58a141a, 0x95186851, 0x12bea312, 0x4124f896, + 0xc68233d5, 0x1751997d, 0x90f7523e, 0xc36d09ba, 0x44cbc2f9, + 0x6459beb2, 0xe3ff75f1, 0xb0652e75, 0x37c3e536, 0xf141d6e3, + 0x76e71da0, 0x257d4624, 0xa2db8d67, 0x8249f12c, 0x05ef3a6f, + 0x567561eb, 0xd1d3aaa8, 0x2ea332fa, 0xa905f9b9, 0xfa9fa23d, + 0x7d39697e, 0x5dab1535, 0xda0dde76, 0x899785f2, 0x0e314eb1, + 0xc8b37d64, 0x4f15b627, 0x1c8feda3, 0x9b2926e0, 0xbbbb5aab, + 0x3c1d91e8, 0x6f87ca6c, 0xe821012f, 0x39f2ab87, 0xbe5460c4, + 0xedce3b40, 0x6a68f003, 0x4afa8c48, 0xcd5c470b, 0x9ec61c8f, + 0x1960d7cc, 0xdfe2e419, 0x58442f5a, 0x0bde74de, 0x8c78bf9d, + 0xaceac3d6, 0x2b4c0895, 0x78d65311, 0xff709852, 0x5d4665f4, + 0xdae0aeb7, 0x897af533, 0x0edc3e70, 0x2e4e423b, 0xa9e88978, + 0xfa72d2fc, 0x7dd419bf, 0xbb562a6a, 0x3cf0e129, 0x6f6abaad, + 0xe8cc71ee, 0xc85e0da5, 0x4ff8c6e6, 0x1c629d62, 0x9bc45621, + 0x4a17fc89, 0xcdb137ca, 0x9e2b6c4e, 0x198da70d, 0x391fdb46, + 0xbeb91005, 0xed234b81, 0x6a8580c2, 0xac07b317, 0x2ba17854, + 0x783b23d0, 0xff9de893, 0xdf0f94d8, 0x58a95f9b, 0x0b33041f, + 0x8c95cf5c, 0x73e5570e, 0xf4439c4d, 0xa7d9c7c9, 0x207f0c8a, + 0x00ed70c1, 0x874bbb82, 0xd4d1e006, 0x53772b45, 0x95f51890, + 0x1253d3d3, 0x41c98857, 0xc66f4314, 0xe6fd3f5f, 0x615bf41c, + 0x32c1af98, 0xb56764db, 0x64b4ce73, 0xe3120530, 0xb0885eb4, + 0x372e95f7, 0x17bce9bc, 0x901a22ff, 0xc380797b, 0x4426b238, + 0x82a481ed, 0x05024aae, 0x5698112a, 0xd13eda69, 0xf1aca622, + 0x760a6d61, 0x259036e5, 0xa236fda6, 0xba8ccbe8, 0x3d2a00ab, + 0x6eb05b2f, 0xe916906c, 0xc984ec27, 0x4e222764, 0x1db87ce0, + 0x9a1eb7a3, 0x5c9c8476, 0xdb3a4f35, 0x88a014b1, 0x0f06dff2, + 0x2f94a3b9, 0xa83268fa, 0xfba8337e, 0x7c0ef83d, 0xaddd5295, + 0x2a7b99d6, 0x79e1c252, 0xfe470911, 0xded5755a, 0x5973be19, + 0x0ae9e59d, 0x8d4f2ede, 0x4bcd1d0b, 0xcc6bd648, 0x9ff18dcc, + 0x1857468f, 0x38c53ac4, 0xbf63f187, 0xecf9aa03, 0x6b5f6140, + 0x942ff912, 0x13893251, 0x401369d5, 0xc7b5a296, 0xe727dedd, + 0x6081159e, 0x331b4e1a, 0xb4bd8559, 0x723fb68c, 0xf5997dcf, + 0xa603264b, 0x21a5ed08, 0x01379143, 0x86915a00, 0xd50b0184, + 0x52adcac7, 0x837e606f, 0x04d8ab2c, 0x5742f0a8, 0xd0e43beb, + 0xf07647a0, 0x77d08ce3, 0x244ad767, 0xa3ec1c24, 0x656e2ff1, + 0xe2c8e4b2, 0xb152bf36, 0x36f47475, 0x1666083e, 0x91c0c37d, + 0xc25a98f9, 0x45fc53ba, 0xe7caae1c, 0x606c655f, 0x33f63edb, + 0xb450f598, 0x94c289d3, 0x13644290, 0x40fe1914, 0xc758d257, + 0x01dae182, 0x867c2ac1, 0xd5e67145, 0x5240ba06, 0x72d2c64d, + 0xf5740d0e, 0xa6ee568a, 0x21489dc9, 0xf09b3761, 0x773dfc22, + 0x24a7a7a6, 0xa3016ce5, 0x839310ae, 0x0435dbed, 0x57af8069, + 0xd0094b2a, 0x168b78ff, 0x912db3bc, 0xc2b7e838, 0x4511237b, + 0x65835f30, 0xe2259473, 0xb1bfcff7, 0x361904b4, 0xc9699ce6, + 0x4ecf57a5, 0x1d550c21, 0x9af3c762, 0xba61bb29, 0x3dc7706a, + 0x6e5d2bee, 0xe9fbe0ad, 0x2f79d378, 0xa8df183b, 0xfb4543bf, + 0x7ce388fc, 0x5c71f4b7, 0xdbd73ff4, 0x884d6470, 0x0febaf33, + 0xde38059b, 0x599eced8, 0x0a04955c, 0x8da25e1f, 0xad302254, + 0x2a96e917, 0x790cb293, 0xfeaa79d0, 0x38284a05, 0xbf8e8146, + 0xec14dac2, 0x6bb21181, 0x4b206dca, 0xcc86a689, 0x9f1cfd0d, + 0x18ba364e}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x0000000000000000, 0x43cba68700000000, 0xc7903cd400000000, + 0x845b9a5300000000, 0xcf27087300000000, 0x8cecaef400000000, + 0x08b734a700000000, 0x4b7c922000000000, 0x9e4f10e600000000, + 0xdd84b66100000000, 0x59df2c3200000000, 0x1a148ab500000000, + 0x5168189500000000, 0x12a3be1200000000, 0x96f8244100000000, + 0xd53382c600000000, 0x7d99511700000000, 0x3e52f79000000000, + 0xba096dc300000000, 0xf9c2cb4400000000, 0xb2be596400000000, + 0xf175ffe300000000, 0x752e65b000000000, 0x36e5c33700000000, + 0xe3d641f100000000, 0xa01de77600000000, 0x24467d2500000000, + 0x678ddba200000000, 0x2cf1498200000000, 0x6f3aef0500000000, + 0xeb61755600000000, 0xa8aad3d100000000, 0xfa32a32e00000000, + 0xb9f905a900000000, 0x3da29ffa00000000, 0x7e69397d00000000, + 0x3515ab5d00000000, 0x76de0dda00000000, 0xf285978900000000, + 0xb14e310e00000000, 0x647db3c800000000, 0x27b6154f00000000, + 0xa3ed8f1c00000000, 0xe026299b00000000, 0xab5abbbb00000000, + 0xe8911d3c00000000, 0x6cca876f00000000, 0x2f0121e800000000, + 0x87abf23900000000, 0xc46054be00000000, 0x403bceed00000000, + 0x03f0686a00000000, 0x488cfa4a00000000, 0x0b475ccd00000000, + 0x8f1cc69e00000000, 0xccd7601900000000, 0x19e4e2df00000000, + 0x5a2f445800000000, 0xde74de0b00000000, 0x9dbf788c00000000, + 0xd6c3eaac00000000, 0x95084c2b00000000, 0x1153d67800000000, + 0x529870ff00000000, 0xf465465d00000000, 0xb7aee0da00000000, + 0x33f57a8900000000, 0x703edc0e00000000, 0x3b424e2e00000000, + 0x7889e8a900000000, 0xfcd272fa00000000, 0xbf19d47d00000000, + 0x6a2a56bb00000000, 0x29e1f03c00000000, 0xadba6a6f00000000, + 0xee71cce800000000, 0xa50d5ec800000000, 0xe6c6f84f00000000, + 0x629d621c00000000, 0x2156c49b00000000, 0x89fc174a00000000, + 0xca37b1cd00000000, 0x4e6c2b9e00000000, 0x0da78d1900000000, + 0x46db1f3900000000, 0x0510b9be00000000, 0x814b23ed00000000, + 0xc280856a00000000, 0x17b307ac00000000, 0x5478a12b00000000, + 0xd0233b7800000000, 0x93e89dff00000000, 0xd8940fdf00000000, + 0x9b5fa95800000000, 0x1f04330b00000000, 0x5ccf958c00000000, + 0x0e57e57300000000, 0x4d9c43f400000000, 0xc9c7d9a700000000, + 0x8a0c7f2000000000, 0xc170ed0000000000, 0x82bb4b8700000000, + 0x06e0d1d400000000, 0x452b775300000000, 0x9018f59500000000, + 0xd3d3531200000000, 0x5788c94100000000, 0x14436fc600000000, + 0x5f3ffde600000000, 0x1cf45b6100000000, 0x98afc13200000000, + 0xdb6467b500000000, 0x73ceb46400000000, 0x300512e300000000, + 0xb45e88b000000000, 0xf7952e3700000000, 0xbce9bc1700000000, + 0xff221a9000000000, 0x7b7980c300000000, 0x38b2264400000000, + 0xed81a48200000000, 0xae4a020500000000, 0x2a11985600000000, + 0x69da3ed100000000, 0x22a6acf100000000, 0x616d0a7600000000, + 0xe536902500000000, 0xa6fd36a200000000, 0xe8cb8cba00000000, + 0xab002a3d00000000, 0x2f5bb06e00000000, 0x6c9016e900000000, + 0x27ec84c900000000, 0x6427224e00000000, 0xe07cb81d00000000, + 0xa3b71e9a00000000, 0x76849c5c00000000, 0x354f3adb00000000, + 0xb114a08800000000, 0xf2df060f00000000, 0xb9a3942f00000000, + 0xfa6832a800000000, 0x7e33a8fb00000000, 0x3df80e7c00000000, + 0x9552ddad00000000, 0xd6997b2a00000000, 0x52c2e17900000000, + 0x110947fe00000000, 0x5a75d5de00000000, 0x19be735900000000, + 0x9de5e90a00000000, 0xde2e4f8d00000000, 0x0b1dcd4b00000000, + 0x48d66bcc00000000, 0xcc8df19f00000000, 0x8f46571800000000, + 0xc43ac53800000000, 0x87f163bf00000000, 0x03aaf9ec00000000, + 0x40615f6b00000000, 0x12f92f9400000000, 0x5132891300000000, + 0xd569134000000000, 0x96a2b5c700000000, 0xddde27e700000000, + 0x9e15816000000000, 0x1a4e1b3300000000, 0x5985bdb400000000, + 0x8cb63f7200000000, 0xcf7d99f500000000, 0x4b2603a600000000, + 0x08eda52100000000, 0x4391370100000000, 0x005a918600000000, + 0x84010bd500000000, 0xc7caad5200000000, 0x6f607e8300000000, + 0x2cabd80400000000, 0xa8f0425700000000, 0xeb3be4d000000000, + 0xa04776f000000000, 0xe38cd07700000000, 0x67d74a2400000000, + 0x241ceca300000000, 0xf12f6e6500000000, 0xb2e4c8e200000000, + 0x36bf52b100000000, 0x7574f43600000000, 0x3e08661600000000, + 0x7dc3c09100000000, 0xf9985ac200000000, 0xba53fc4500000000, + 0x1caecae700000000, 0x5f656c6000000000, 0xdb3ef63300000000, + 0x98f550b400000000, 0xd389c29400000000, 0x9042641300000000, + 0x1419fe4000000000, 0x57d258c700000000, 0x82e1da0100000000, + 0xc12a7c8600000000, 0x4571e6d500000000, 0x06ba405200000000, + 0x4dc6d27200000000, 0x0e0d74f500000000, 0x8a56eea600000000, + 0xc99d482100000000, 0x61379bf000000000, 0x22fc3d7700000000, + 0xa6a7a72400000000, 0xe56c01a300000000, 0xae10938300000000, + 0xeddb350400000000, 0x6980af5700000000, 0x2a4b09d000000000, + 0xff788b1600000000, 0xbcb32d9100000000, 0x38e8b7c200000000, + 0x7b23114500000000, 0x305f836500000000, 0x739425e200000000, + 0xf7cfbfb100000000, 0xb404193600000000, 0xe69c69c900000000, + 0xa557cf4e00000000, 0x210c551d00000000, 0x62c7f39a00000000, + 0x29bb61ba00000000, 0x6a70c73d00000000, 0xee2b5d6e00000000, + 0xade0fbe900000000, 0x78d3792f00000000, 0x3b18dfa800000000, + 0xbf4345fb00000000, 0xfc88e37c00000000, 0xb7f4715c00000000, + 0xf43fd7db00000000, 0x70644d8800000000, 0x33afeb0f00000000, + 0x9b0538de00000000, 0xd8ce9e5900000000, 0x5c95040a00000000, + 0x1f5ea28d00000000, 0x542230ad00000000, 0x17e9962a00000000, + 0x93b20c7900000000, 0xd079aafe00000000, 0x054a283800000000, + 0x46818ebf00000000, 0xc2da14ec00000000, 0x8111b26b00000000, + 0xca6d204b00000000, 0x89a686cc00000000, 0x0dfd1c9f00000000, + 0x4e36ba1800000000}, + {0x0000000000000000, 0xe1b652ef00000000, 0x836bd40500000000, + 0x62dd86ea00000000, 0x06d7a80b00000000, 0xe761fae400000000, + 0x85bc7c0e00000000, 0x640a2ee100000000, 0x0cae511700000000, + 0xed1803f800000000, 0x8fc5851200000000, 0x6e73d7fd00000000, + 0x0a79f91c00000000, 0xebcfabf300000000, 0x89122d1900000000, + 0x68a47ff600000000, 0x185ca32e00000000, 0xf9eaf1c100000000, + 0x9b37772b00000000, 0x7a8125c400000000, 0x1e8b0b2500000000, + 0xff3d59ca00000000, 0x9de0df2000000000, 0x7c568dcf00000000, + 0x14f2f23900000000, 0xf544a0d600000000, 0x9799263c00000000, + 0x762f74d300000000, 0x12255a3200000000, 0xf39308dd00000000, + 0x914e8e3700000000, 0x70f8dcd800000000, 0x30b8465d00000000, + 0xd10e14b200000000, 0xb3d3925800000000, 0x5265c0b700000000, + 0x366fee5600000000, 0xd7d9bcb900000000, 0xb5043a5300000000, + 0x54b268bc00000000, 0x3c16174a00000000, 0xdda045a500000000, + 0xbf7dc34f00000000, 0x5ecb91a000000000, 0x3ac1bf4100000000, + 0xdb77edae00000000, 0xb9aa6b4400000000, 0x581c39ab00000000, + 0x28e4e57300000000, 0xc952b79c00000000, 0xab8f317600000000, + 0x4a39639900000000, 0x2e334d7800000000, 0xcf851f9700000000, + 0xad58997d00000000, 0x4ceecb9200000000, 0x244ab46400000000, + 0xc5fce68b00000000, 0xa721606100000000, 0x4697328e00000000, + 0x229d1c6f00000000, 0xc32b4e8000000000, 0xa1f6c86a00000000, + 0x40409a8500000000, 0x60708dba00000000, 0x81c6df5500000000, + 0xe31b59bf00000000, 0x02ad0b5000000000, 0x66a725b100000000, + 0x8711775e00000000, 0xe5ccf1b400000000, 0x047aa35b00000000, + 0x6cdedcad00000000, 0x8d688e4200000000, 0xefb508a800000000, + 0x0e035a4700000000, 0x6a0974a600000000, 0x8bbf264900000000, + 0xe962a0a300000000, 0x08d4f24c00000000, 0x782c2e9400000000, + 0x999a7c7b00000000, 0xfb47fa9100000000, 0x1af1a87e00000000, + 0x7efb869f00000000, 0x9f4dd47000000000, 0xfd90529a00000000, + 0x1c26007500000000, 0x74827f8300000000, 0x95342d6c00000000, + 0xf7e9ab8600000000, 0x165ff96900000000, 0x7255d78800000000, + 0x93e3856700000000, 0xf13e038d00000000, 0x1088516200000000, + 0x50c8cbe700000000, 0xb17e990800000000, 0xd3a31fe200000000, + 0x32154d0d00000000, 0x561f63ec00000000, 0xb7a9310300000000, + 0xd574b7e900000000, 0x34c2e50600000000, 0x5c669af000000000, + 0xbdd0c81f00000000, 0xdf0d4ef500000000, 0x3ebb1c1a00000000, + 0x5ab132fb00000000, 0xbb07601400000000, 0xd9dae6fe00000000, + 0x386cb41100000000, 0x489468c900000000, 0xa9223a2600000000, + 0xcbffbccc00000000, 0x2a49ee2300000000, 0x4e43c0c200000000, + 0xaff5922d00000000, 0xcd2814c700000000, 0x2c9e462800000000, + 0x443a39de00000000, 0xa58c6b3100000000, 0xc751eddb00000000, + 0x26e7bf3400000000, 0x42ed91d500000000, 0xa35bc33a00000000, + 0xc18645d000000000, 0x2030173f00000000, 0x81e66bae00000000, + 0x6050394100000000, 0x028dbfab00000000, 0xe33bed4400000000, + 0x8731c3a500000000, 0x6687914a00000000, 0x045a17a000000000, + 0xe5ec454f00000000, 0x8d483ab900000000, 0x6cfe685600000000, + 0x0e23eebc00000000, 0xef95bc5300000000, 0x8b9f92b200000000, + 0x6a29c05d00000000, 0x08f446b700000000, 0xe942145800000000, + 0x99bac88000000000, 0x780c9a6f00000000, 0x1ad11c8500000000, + 0xfb674e6a00000000, 0x9f6d608b00000000, 0x7edb326400000000, + 0x1c06b48e00000000, 0xfdb0e66100000000, 0x9514999700000000, + 0x74a2cb7800000000, 0x167f4d9200000000, 0xf7c91f7d00000000, + 0x93c3319c00000000, 0x7275637300000000, 0x10a8e59900000000, + 0xf11eb77600000000, 0xb15e2df300000000, 0x50e87f1c00000000, + 0x3235f9f600000000, 0xd383ab1900000000, 0xb78985f800000000, + 0x563fd71700000000, 0x34e251fd00000000, 0xd554031200000000, + 0xbdf07ce400000000, 0x5c462e0b00000000, 0x3e9ba8e100000000, + 0xdf2dfa0e00000000, 0xbb27d4ef00000000, 0x5a91860000000000, + 0x384c00ea00000000, 0xd9fa520500000000, 0xa9028edd00000000, + 0x48b4dc3200000000, 0x2a695ad800000000, 0xcbdf083700000000, + 0xafd526d600000000, 0x4e63743900000000, 0x2cbef2d300000000, + 0xcd08a03c00000000, 0xa5acdfca00000000, 0x441a8d2500000000, + 0x26c70bcf00000000, 0xc771592000000000, 0xa37b77c100000000, + 0x42cd252e00000000, 0x2010a3c400000000, 0xc1a6f12b00000000, + 0xe196e61400000000, 0x0020b4fb00000000, 0x62fd321100000000, + 0x834b60fe00000000, 0xe7414e1f00000000, 0x06f71cf000000000, + 0x642a9a1a00000000, 0x859cc8f500000000, 0xed38b70300000000, + 0x0c8ee5ec00000000, 0x6e53630600000000, 0x8fe531e900000000, + 0xebef1f0800000000, 0x0a594de700000000, 0x6884cb0d00000000, + 0x893299e200000000, 0xf9ca453a00000000, 0x187c17d500000000, + 0x7aa1913f00000000, 0x9b17c3d000000000, 0xff1ded3100000000, + 0x1eabbfde00000000, 0x7c76393400000000, 0x9dc06bdb00000000, + 0xf564142d00000000, 0x14d246c200000000, 0x760fc02800000000, + 0x97b992c700000000, 0xf3b3bc2600000000, 0x1205eec900000000, + 0x70d8682300000000, 0x916e3acc00000000, 0xd12ea04900000000, + 0x3098f2a600000000, 0x5245744c00000000, 0xb3f326a300000000, + 0xd7f9084200000000, 0x364f5aad00000000, 0x5492dc4700000000, + 0xb5248ea800000000, 0xdd80f15e00000000, 0x3c36a3b100000000, + 0x5eeb255b00000000, 0xbf5d77b400000000, 0xdb57595500000000, + 0x3ae10bba00000000, 0x583c8d5000000000, 0xb98adfbf00000000, + 0xc972036700000000, 0x28c4518800000000, 0x4a19d76200000000, + 0xabaf858d00000000, 0xcfa5ab6c00000000, 0x2e13f98300000000, + 0x4cce7f6900000000, 0xad782d8600000000, 0xc5dc527000000000, + 0x246a009f00000000, 0x46b7867500000000, 0xa701d49a00000000, + 0xc30bfa7b00000000, 0x22bda89400000000, 0x40602e7e00000000, + 0xa1d67c9100000000}, + {0x0000000000000000, 0x5880e2d700000000, 0xf106b47400000000, + 0xa98656a300000000, 0xe20d68e900000000, 0xba8d8a3e00000000, + 0x130bdc9d00000000, 0x4b8b3e4a00000000, 0x851da10900000000, + 0xdd9d43de00000000, 0x741b157d00000000, 0x2c9bf7aa00000000, + 0x6710c9e000000000, 0x3f902b3700000000, 0x96167d9400000000, + 0xce969f4300000000, 0x0a3b421300000000, 0x52bba0c400000000, + 0xfb3df66700000000, 0xa3bd14b000000000, 0xe8362afa00000000, + 0xb0b6c82d00000000, 0x19309e8e00000000, 0x41b07c5900000000, + 0x8f26e31a00000000, 0xd7a601cd00000000, 0x7e20576e00000000, + 0x26a0b5b900000000, 0x6d2b8bf300000000, 0x35ab692400000000, + 0x9c2d3f8700000000, 0xc4addd5000000000, 0x1476842600000000, + 0x4cf666f100000000, 0xe570305200000000, 0xbdf0d28500000000, + 0xf67beccf00000000, 0xaefb0e1800000000, 0x077d58bb00000000, + 0x5ffdba6c00000000, 0x916b252f00000000, 0xc9ebc7f800000000, + 0x606d915b00000000, 0x38ed738c00000000, 0x73664dc600000000, + 0x2be6af1100000000, 0x8260f9b200000000, 0xdae01b6500000000, + 0x1e4dc63500000000, 0x46cd24e200000000, 0xef4b724100000000, + 0xb7cb909600000000, 0xfc40aedc00000000, 0xa4c04c0b00000000, + 0x0d461aa800000000, 0x55c6f87f00000000, 0x9b50673c00000000, + 0xc3d085eb00000000, 0x6a56d34800000000, 0x32d6319f00000000, + 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0x73cfbdba00000000, 0xed4c672500000000, + 0x0ece795e00000000, 0x904da3c100000000, 0x8af59a5100000000, + 0x147640ce00000000, 0xf7f45eb500000000, 0x6977842a00000000, + 0x31f1634300000000, 0xaf72b9dc00000000, 0x4cf0a7a700000000, + 0xd2737d3800000000, 0xfcfc687400000000, 0x627fb2eb00000000, + 0x81fdac9000000000, 0x1f7e760f00000000, 0x47f8916600000000, + 0xd97b4bf900000000, 0x3af9558200000000, 0xa47a8f1d00000000, + 0x66e77e1a00000000, 0xf864a48500000000, 0x1be6bafe00000000, + 0x8565606100000000, 0xdde3870800000000, 0x43605d9700000000, + 0xa0e243ec00000000, 0x3e61997300000000, 0x10ee8c3f00000000, + 0x8e6d56a000000000, 0x6def48db00000000, 0xf36c924400000000, + 0xabea752d00000000, 0x3569afb200000000, 0xd6ebb1c900000000, + 0x48686b5600000000}, + {0x0000000000000000, 0xc064281700000000, 0x80c9502e00000000, + 0x40ad783900000000, 0x0093a15c00000000, 0xc0f7894b00000000, + 0x805af17200000000, 0x403ed96500000000, 0x002643b900000000, + 0xc0426bae00000000, 0x80ef139700000000, 0x408b3b8000000000, + 0x00b5e2e500000000, 0xc0d1caf200000000, 0x807cb2cb00000000, + 0x40189adc00000000, 0x414af7a900000000, 0x812edfbe00000000, + 0xc183a78700000000, 0x01e78f9000000000, 0x41d956f500000000, + 0x81bd7ee200000000, 0xc11006db00000000, 0x01742ecc00000000, + 0x416cb41000000000, 0x81089c0700000000, 0xc1a5e43e00000000, + 0x01c1cc2900000000, 0x41ff154c00000000, 0x819b3d5b00000000, + 0xc136456200000000, 0x01526d7500000000, 0xc3929f8800000000, + 0x03f6b79f00000000, 0x435bcfa600000000, 0x833fe7b100000000, + 0xc3013ed400000000, 0x036516c300000000, 0x43c86efa00000000, + 0x83ac46ed00000000, 0xc3b4dc3100000000, 0x03d0f42600000000, + 0x437d8c1f00000000, 0x8319a40800000000, 0xc3277d6d00000000, + 0x0343557a00000000, 0x43ee2d4300000000, 0x838a055400000000, + 0x82d8682100000000, 0x42bc403600000000, 0x0211380f00000000, + 0xc275101800000000, 0x824bc97d00000000, 0x422fe16a00000000, + 0x0282995300000000, 0xc2e6b14400000000, 0x82fe2b9800000000, + 0x429a038f00000000, 0x02377bb600000000, 0xc25353a100000000, + 0x826d8ac400000000, 0x4209a2d300000000, 0x02a4daea00000000, + 0xc2c0f2fd00000000, 0xc7234eca00000000, 0x074766dd00000000, + 0x47ea1ee400000000, 0x878e36f300000000, 0xc7b0ef9600000000, + 0x07d4c78100000000, 0x4779bfb800000000, 0x871d97af00000000, + 0xc7050d7300000000, 0x0761256400000000, 0x47cc5d5d00000000, + 0x87a8754a00000000, 0xc796ac2f00000000, 0x07f2843800000000, + 0x475ffc0100000000, 0x873bd41600000000, 0x8669b96300000000, + 0x460d917400000000, 0x06a0e94d00000000, 0xc6c4c15a00000000, + 0x86fa183f00000000, 0x469e302800000000, 0x0633481100000000, + 0xc657600600000000, 0x864ffada00000000, 0x462bd2cd00000000, + 0x0686aaf400000000, 0xc6e282e300000000, 0x86dc5b8600000000, + 0x46b8739100000000, 0x06150ba800000000, 0xc67123bf00000000, + 0x04b1d14200000000, 0xc4d5f95500000000, 0x8478816c00000000, + 0x441ca97b00000000, 0x0422701e00000000, 0xc446580900000000, + 0x84eb203000000000, 0x448f082700000000, 0x049792fb00000000, + 0xc4f3baec00000000, 0x845ec2d500000000, 0x443aeac200000000, + 0x040433a700000000, 0xc4601bb000000000, 0x84cd638900000000, + 0x44a94b9e00000000, 0x45fb26eb00000000, 0x859f0efc00000000, + 0xc53276c500000000, 0x05565ed200000000, 0x456887b700000000, + 0x850cafa000000000, 0xc5a1d79900000000, 0x05c5ff8e00000000, + 0x45dd655200000000, 0x85b94d4500000000, 0xc514357c00000000, + 0x05701d6b00000000, 0x454ec40e00000000, 0x852aec1900000000, + 0xc587942000000000, 0x05e3bc3700000000, 0xcf41ed4f00000000, + 0x0f25c55800000000, 0x4f88bd6100000000, 0x8fec957600000000, + 0xcfd24c1300000000, 0x0fb6640400000000, 0x4f1b1c3d00000000, + 0x8f7f342a00000000, 0xcf67aef600000000, 0x0f0386e100000000, + 0x4faefed800000000, 0x8fcad6cf00000000, 0xcff40faa00000000, + 0x0f9027bd00000000, 0x4f3d5f8400000000, 0x8f59779300000000, + 0x8e0b1ae600000000, 0x4e6f32f100000000, 0x0ec24ac800000000, + 0xcea662df00000000, 0x8e98bbba00000000, 0x4efc93ad00000000, + 0x0e51eb9400000000, 0xce35c38300000000, 0x8e2d595f00000000, + 0x4e49714800000000, 0x0ee4097100000000, 0xce80216600000000, + 0x8ebef80300000000, 0x4edad01400000000, 0x0e77a82d00000000, + 0xce13803a00000000, 0x0cd372c700000000, 0xccb75ad000000000, + 0x8c1a22e900000000, 0x4c7e0afe00000000, 0x0c40d39b00000000, + 0xcc24fb8c00000000, 0x8c8983b500000000, 0x4cedaba200000000, + 0x0cf5317e00000000, 0xcc91196900000000, 0x8c3c615000000000, + 0x4c58494700000000, 0x0c66902200000000, 0xcc02b83500000000, + 0x8cafc00c00000000, 0x4ccbe81b00000000, 0x4d99856e00000000, + 0x8dfdad7900000000, 0xcd50d54000000000, 0x0d34fd5700000000, + 0x4d0a243200000000, 0x8d6e0c2500000000, 0xcdc3741c00000000, + 0x0da75c0b00000000, 0x4dbfc6d700000000, 0x8ddbeec000000000, + 0xcd7696f900000000, 0x0d12beee00000000, 0x4d2c678b00000000, + 0x8d484f9c00000000, 0xcde537a500000000, 0x0d811fb200000000, + 0x0862a38500000000, 0xc8068b9200000000, 0x88abf3ab00000000, + 0x48cfdbbc00000000, 0x08f102d900000000, 0xc8952ace00000000, + 0x883852f700000000, 0x485c7ae000000000, 0x0844e03c00000000, + 0xc820c82b00000000, 0x888db01200000000, 0x48e9980500000000, + 0x08d7416000000000, 0xc8b3697700000000, 0x881e114e00000000, + 0x487a395900000000, 0x4928542c00000000, 0x894c7c3b00000000, + 0xc9e1040200000000, 0x09852c1500000000, 0x49bbf57000000000, + 0x89dfdd6700000000, 0xc972a55e00000000, 0x09168d4900000000, + 0x490e179500000000, 0x896a3f8200000000, 0xc9c747bb00000000, + 0x09a36fac00000000, 0x499db6c900000000, 0x89f99ede00000000, + 0xc954e6e700000000, 0x0930cef000000000, 0xcbf03c0d00000000, + 0x0b94141a00000000, 0x4b396c2300000000, 0x8b5d443400000000, + 0xcb639d5100000000, 0x0b07b54600000000, 0x4baacd7f00000000, + 0x8bcee56800000000, 0xcbd67fb400000000, 0x0bb257a300000000, + 0x4b1f2f9a00000000, 0x8b7b078d00000000, 0xcb45dee800000000, + 0x0b21f6ff00000000, 0x4b8c8ec600000000, 0x8be8a6d100000000, + 0x8abacba400000000, 0x4adee3b300000000, 0x0a739b8a00000000, + 0xca17b39d00000000, 0x8a296af800000000, 0x4a4d42ef00000000, + 0x0ae03ad600000000, 0xca8412c100000000, 0x8a9c881d00000000, + 0x4af8a00a00000000, 0x0a55d83300000000, 0xca31f02400000000, + 0x8a0f294100000000, 0x4a6b015600000000, 0x0ac6796f00000000, + 0xcaa2517800000000}, + {0x0000000000000000, 0xd4ea739b00000000, 0xe9d396ed00000000, + 0x3d39e57600000000, 0x93a15c0000000000, 0x474b2f9b00000000, + 0x7a72caed00000000, 0xae98b97600000000, 0x2643b90000000000, + 0xf2a9ca9b00000000, 0xcf902fed00000000, 0x1b7a5c7600000000, + 0xb5e2e50000000000, 0x6108969b00000000, 0x5c3173ed00000000, + 0x88db007600000000, 0x4c86720100000000, 0x986c019a00000000, + 0xa555e4ec00000000, 0x71bf977700000000, 0xdf272e0100000000, + 0x0bcd5d9a00000000, 0x36f4b8ec00000000, 0xe21ecb7700000000, + 0x6ac5cb0100000000, 0xbe2fb89a00000000, 0x83165dec00000000, + 0x57fc2e7700000000, 0xf964970100000000, 0x2d8ee49a00000000, + 0x10b701ec00000000, 0xc45d727700000000, 0x980ce50200000000, + 0x4ce6969900000000, 0x71df73ef00000000, 0xa535007400000000, + 0x0badb90200000000, 0xdf47ca9900000000, 0xe27e2fef00000000, + 0x36945c7400000000, 0xbe4f5c0200000000, 0x6aa52f9900000000, + 0x579ccaef00000000, 0x8376b97400000000, 0x2dee000200000000, + 0xf904739900000000, 0xc43d96ef00000000, 0x10d7e57400000000, + 0xd48a970300000000, 0x0060e49800000000, 0x3d5901ee00000000, + 0xe9b3727500000000, 0x472bcb0300000000, 0x93c1b89800000000, + 0xaef85dee00000000, 0x7a122e7500000000, 0xf2c92e0300000000, + 0x26235d9800000000, 0x1b1ab8ee00000000, 0xcff0cb7500000000, + 0x6168720300000000, 0xb582019800000000, 0x88bbe4ee00000000, + 0x5c51977500000000, 0x3019ca0500000000, 0xe4f3b99e00000000, + 0xd9ca5ce800000000, 0x0d202f7300000000, 0xa3b8960500000000, + 0x7752e59e00000000, 0x4a6b00e800000000, 0x9e81737300000000, + 0x165a730500000000, 0xc2b0009e00000000, 0xff89e5e800000000, + 0x2b63967300000000, 0x85fb2f0500000000, 0x51115c9e00000000, + 0x6c28b9e800000000, 0xb8c2ca7300000000, 0x7c9fb80400000000, + 0xa875cb9f00000000, 0x954c2ee900000000, 0x41a65d7200000000, + 0xef3ee40400000000, 0x3bd4979f00000000, 0x06ed72e900000000, + 0xd207017200000000, 0x5adc010400000000, 0x8e36729f00000000, + 0xb30f97e900000000, 0x67e5e47200000000, 0xc97d5d0400000000, + 0x1d972e9f00000000, 0x20aecbe900000000, 0xf444b87200000000, + 0xa8152f0700000000, 0x7cff5c9c00000000, 0x41c6b9ea00000000, + 0x952cca7100000000, 0x3bb4730700000000, 0xef5e009c00000000, + 0xd267e5ea00000000, 0x068d967100000000, 0x8e56960700000000, + 0x5abce59c00000000, 0x678500ea00000000, 0xb36f737100000000, + 0x1df7ca0700000000, 0xc91db99c00000000, 0xf4245cea00000000, + 0x20ce2f7100000000, 0xe4935d0600000000, 0x30792e9d00000000, + 0x0d40cbeb00000000, 0xd9aab87000000000, 0x7732010600000000, + 0xa3d8729d00000000, 0x9ee197eb00000000, 0x4a0be47000000000, + 0xc2d0e40600000000, 0x163a979d00000000, 0x2b0372eb00000000, + 0xffe9017000000000, 0x5171b80600000000, 0x859bcb9d00000000, + 0xb8a22eeb00000000, 0x6c485d7000000000, 0x6032940b00000000, + 0xb4d8e79000000000, 0x89e102e600000000, 0x5d0b717d00000000, + 0xf393c80b00000000, 0x2779bb9000000000, 0x1a405ee600000000, + 0xceaa2d7d00000000, 0x46712d0b00000000, 0x929b5e9000000000, + 0xafa2bbe600000000, 0x7b48c87d00000000, 0xd5d0710b00000000, + 0x013a029000000000, 0x3c03e7e600000000, 0xe8e9947d00000000, + 0x2cb4e60a00000000, 0xf85e959100000000, 0xc56770e700000000, + 0x118d037c00000000, 0xbf15ba0a00000000, 0x6bffc99100000000, + 0x56c62ce700000000, 0x822c5f7c00000000, 0x0af75f0a00000000, + 0xde1d2c9100000000, 0xe324c9e700000000, 0x37ceba7c00000000, + 0x9956030a00000000, 0x4dbc709100000000, 0x708595e700000000, + 0xa46fe67c00000000, 0xf83e710900000000, 0x2cd4029200000000, + 0x11ede7e400000000, 0xc507947f00000000, 0x6b9f2d0900000000, + 0xbf755e9200000000, 0x824cbbe400000000, 0x56a6c87f00000000, + 0xde7dc80900000000, 0x0a97bb9200000000, 0x37ae5ee400000000, + 0xe3442d7f00000000, 0x4ddc940900000000, 0x9936e79200000000, + 0xa40f02e400000000, 0x70e5717f00000000, 0xb4b8030800000000, + 0x6052709300000000, 0x5d6b95e500000000, 0x8981e67e00000000, + 0x27195f0800000000, 0xf3f32c9300000000, 0xcecac9e500000000, + 0x1a20ba7e00000000, 0x92fbba0800000000, 0x4611c99300000000, + 0x7b282ce500000000, 0xafc25f7e00000000, 0x015ae60800000000, + 0xd5b0959300000000, 0xe88970e500000000, 0x3c63037e00000000, + 0x502b5e0e00000000, 0x84c12d9500000000, 0xb9f8c8e300000000, + 0x6d12bb7800000000, 0xc38a020e00000000, 0x1760719500000000, + 0x2a5994e300000000, 0xfeb3e77800000000, 0x7668e70e00000000, + 0xa282949500000000, 0x9fbb71e300000000, 0x4b51027800000000, + 0xe5c9bb0e00000000, 0x3123c89500000000, 0x0c1a2de300000000, + 0xd8f05e7800000000, 0x1cad2c0f00000000, 0xc8475f9400000000, + 0xf57ebae200000000, 0x2194c97900000000, 0x8f0c700f00000000, + 0x5be6039400000000, 0x66dfe6e200000000, 0xb235957900000000, + 0x3aee950f00000000, 0xee04e69400000000, 0xd33d03e200000000, + 0x07d7707900000000, 0xa94fc90f00000000, 0x7da5ba9400000000, + 0x409c5fe200000000, 0x94762c7900000000, 0xc827bb0c00000000, + 0x1ccdc89700000000, 0x21f42de100000000, 0xf51e5e7a00000000, + 0x5b86e70c00000000, 0x8f6c949700000000, 0xb25571e100000000, + 0x66bf027a00000000, 0xee64020c00000000, 0x3a8e719700000000, + 0x07b794e100000000, 0xd35de77a00000000, 0x7dc55e0c00000000, + 0xa92f2d9700000000, 0x9416c8e100000000, 0x40fcbb7a00000000, + 0x84a1c90d00000000, 0x504bba9600000000, 0x6d725fe000000000, + 0xb9982c7b00000000, 0x1700950d00000000, 0xc3eae69600000000, + 0xfed303e000000000, 0x2a39707b00000000, 0xa2e2700d00000000, + 0x7608039600000000, 0x4b31e6e000000000, 0x9fdb957b00000000, + 0x31432c0d00000000, 0xe5a95f9600000000, 0xd890bae000000000, + 0x0c7ac97b00000000}, + {0x0000000000000000, 0x2765258100000000, 0x0fcc3bd900000000, + 0x28a91e5800000000, 0x5f9e066900000000, 0x78fb23e800000000, + 0x50523db000000000, 0x7737183100000000, 0xbe3c0dd200000000, + 0x9959285300000000, 0xb1f0360b00000000, 0x9695138a00000000, + 0xe1a20bbb00000000, 0xc6c72e3a00000000, 0xee6e306200000000, + 0xc90b15e300000000, 0x3d7f6b7f00000000, 0x1a1a4efe00000000, + 0x32b350a600000000, 0x15d6752700000000, 0x62e16d1600000000, + 0x4584489700000000, 0x6d2d56cf00000000, 0x4a48734e00000000, + 0x834366ad00000000, 0xa426432c00000000, 0x8c8f5d7400000000, + 0xabea78f500000000, 0xdcdd60c400000000, 0xfbb8454500000000, + 0xd3115b1d00000000, 0xf4747e9c00000000, 0x7afed6fe00000000, + 0x5d9bf37f00000000, 0x7532ed2700000000, 0x5257c8a600000000, + 0x2560d09700000000, 0x0205f51600000000, 0x2aaceb4e00000000, + 0x0dc9cecf00000000, 0xc4c2db2c00000000, 0xe3a7fead00000000, + 0xcb0ee0f500000000, 0xec6bc57400000000, 0x9b5cdd4500000000, + 0xbc39f8c400000000, 0x9490e69c00000000, 0xb3f5c31d00000000, + 0x4781bd8100000000, 0x60e4980000000000, 0x484d865800000000, + 0x6f28a3d900000000, 0x181fbbe800000000, 0x3f7a9e6900000000, + 0x17d3803100000000, 0x30b6a5b000000000, 0xf9bdb05300000000, + 0xded895d200000000, 0xf6718b8a00000000, 0xd114ae0b00000000, + 0xa623b63a00000000, 0x814693bb00000000, 0xa9ef8de300000000, + 0x8e8aa86200000000, 0xb5fadc2600000000, 0x929ff9a700000000, + 0xba36e7ff00000000, 0x9d53c27e00000000, 0xea64da4f00000000, + 0xcd01ffce00000000, 0xe5a8e19600000000, 0xc2cdc41700000000, + 0x0bc6d1f400000000, 0x2ca3f47500000000, 0x040aea2d00000000, + 0x236fcfac00000000, 0x5458d79d00000000, 0x733df21c00000000, + 0x5b94ec4400000000, 0x7cf1c9c500000000, 0x8885b75900000000, + 0xafe092d800000000, 0x87498c8000000000, 0xa02ca90100000000, + 0xd71bb13000000000, 0xf07e94b100000000, 0xd8d78ae900000000, + 0xffb2af6800000000, 0x36b9ba8b00000000, 0x11dc9f0a00000000, + 0x3975815200000000, 0x1e10a4d300000000, 0x6927bce200000000, + 0x4e42996300000000, 0x66eb873b00000000, 0x418ea2ba00000000, + 0xcf040ad800000000, 0xe8612f5900000000, 0xc0c8310100000000, + 0xe7ad148000000000, 0x909a0cb100000000, 0xb7ff293000000000, + 0x9f56376800000000, 0xb83312e900000000, 0x7138070a00000000, + 0x565d228b00000000, 0x7ef43cd300000000, 0x5991195200000000, + 0x2ea6016300000000, 0x09c324e200000000, 0x216a3aba00000000, + 0x060f1f3b00000000, 0xf27b61a700000000, 0xd51e442600000000, + 0xfdb75a7e00000000, 0xdad27fff00000000, 0xade567ce00000000, + 0x8a80424f00000000, 0xa2295c1700000000, 0x854c799600000000, + 0x4c476c7500000000, 0x6b2249f400000000, 0x438b57ac00000000, + 0x64ee722d00000000, 0x13d96a1c00000000, 0x34bc4f9d00000000, + 0x1c1551c500000000, 0x3b70744400000000, 0x6af5b94d00000000, + 0x4d909ccc00000000, 0x6539829400000000, 0x425ca71500000000, + 0x356bbf2400000000, 0x120e9aa500000000, 0x3aa784fd00000000, + 0x1dc2a17c00000000, 0xd4c9b49f00000000, 0xf3ac911e00000000, + 0xdb058f4600000000, 0xfc60aac700000000, 0x8b57b2f600000000, + 0xac32977700000000, 0x849b892f00000000, 0xa3feacae00000000, + 0x578ad23200000000, 0x70eff7b300000000, 0x5846e9eb00000000, + 0x7f23cc6a00000000, 0x0814d45b00000000, 0x2f71f1da00000000, + 0x07d8ef8200000000, 0x20bdca0300000000, 0xe9b6dfe000000000, + 0xced3fa6100000000, 0xe67ae43900000000, 0xc11fc1b800000000, + 0xb628d98900000000, 0x914dfc0800000000, 0xb9e4e25000000000, + 0x9e81c7d100000000, 0x100b6fb300000000, 0x376e4a3200000000, + 0x1fc7546a00000000, 0x38a271eb00000000, 0x4f9569da00000000, + 0x68f04c5b00000000, 0x4059520300000000, 0x673c778200000000, + 0xae37626100000000, 0x895247e000000000, 0xa1fb59b800000000, + 0x869e7c3900000000, 0xf1a9640800000000, 0xd6cc418900000000, + 0xfe655fd100000000, 0xd9007a5000000000, 0x2d7404cc00000000, + 0x0a11214d00000000, 0x22b83f1500000000, 0x05dd1a9400000000, + 0x72ea02a500000000, 0x558f272400000000, 0x7d26397c00000000, + 0x5a431cfd00000000, 0x9348091e00000000, 0xb42d2c9f00000000, + 0x9c8432c700000000, 0xbbe1174600000000, 0xccd60f7700000000, + 0xebb32af600000000, 0xc31a34ae00000000, 0xe47f112f00000000, + 0xdf0f656b00000000, 0xf86a40ea00000000, 0xd0c35eb200000000, + 0xf7a67b3300000000, 0x8091630200000000, 0xa7f4468300000000, + 0x8f5d58db00000000, 0xa8387d5a00000000, 0x613368b900000000, + 0x46564d3800000000, 0x6eff536000000000, 0x499a76e100000000, + 0x3ead6ed000000000, 0x19c84b5100000000, 0x3161550900000000, + 0x1604708800000000, 0xe2700e1400000000, 0xc5152b9500000000, + 0xedbc35cd00000000, 0xcad9104c00000000, 0xbdee087d00000000, + 0x9a8b2dfc00000000, 0xb22233a400000000, 0x9547162500000000, + 0x5c4c03c600000000, 0x7b29264700000000, 0x5380381f00000000, + 0x74e51d9e00000000, 0x03d205af00000000, 0x24b7202e00000000, + 0x0c1e3e7600000000, 0x2b7b1bf700000000, 0xa5f1b39500000000, + 0x8294961400000000, 0xaa3d884c00000000, 0x8d58adcd00000000, + 0xfa6fb5fc00000000, 0xdd0a907d00000000, 0xf5a38e2500000000, + 0xd2c6aba400000000, 0x1bcdbe4700000000, 0x3ca89bc600000000, + 0x1401859e00000000, 0x3364a01f00000000, 0x4453b82e00000000, + 0x63369daf00000000, 0x4b9f83f700000000, 0x6cfaa67600000000, + 0x988ed8ea00000000, 0xbfebfd6b00000000, 0x9742e33300000000, + 0xb027c6b200000000, 0xc710de8300000000, 0xe075fb0200000000, + 0xc8dce55a00000000, 0xefb9c0db00000000, 0x26b2d53800000000, + 0x01d7f0b900000000, 0x297eeee100000000, 0x0e1bcb6000000000, + 0x792cd35100000000, 0x5e49f6d000000000, 0x76e0e88800000000, + 0x5185cd0900000000}}; + +#else /* W == 4 */ + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0x9ba54c6f, 0xec3b9e9f, 0x779ed2f0, 0x03063b7f, + 0x98a37710, 0xef3da5e0, 0x7498e98f, 0x060c76fe, 0x9da93a91, + 0xea37e861, 0x7192a40e, 0x050a4d81, 0x9eaf01ee, 0xe931d31e, + 0x72949f71, 0x0c18edfc, 0x97bda193, 0xe0237363, 0x7b863f0c, + 0x0f1ed683, 0x94bb9aec, 0xe325481c, 0x78800473, 0x0a149b02, + 0x91b1d76d, 0xe62f059d, 0x7d8a49f2, 0x0912a07d, 0x92b7ec12, + 0xe5293ee2, 0x7e8c728d, 0x1831dbf8, 0x83949797, 0xf40a4567, + 0x6faf0908, 0x1b37e087, 0x8092ace8, 0xf70c7e18, 0x6ca93277, + 0x1e3dad06, 0x8598e169, 0xf2063399, 0x69a37ff6, 0x1d3b9679, + 0x869eda16, 0xf10008e6, 0x6aa54489, 0x14293604, 0x8f8c7a6b, + 0xf812a89b, 0x63b7e4f4, 0x172f0d7b, 0x8c8a4114, 0xfb1493e4, + 0x60b1df8b, 0x122540fa, 0x89800c95, 0xfe1ede65, 0x65bb920a, + 0x11237b85, 0x8a8637ea, 0xfd18e51a, 0x66bda975, 0x3063b7f0, + 0xabc6fb9f, 0xdc58296f, 0x47fd6500, 0x33658c8f, 0xa8c0c0e0, + 0xdf5e1210, 0x44fb5e7f, 0x366fc10e, 0xadca8d61, 0xda545f91, + 0x41f113fe, 0x3569fa71, 0xaeccb61e, 0xd95264ee, 0x42f72881, + 0x3c7b5a0c, 0xa7de1663, 0xd040c493, 0x4be588fc, 0x3f7d6173, + 0xa4d82d1c, 0xd346ffec, 0x48e3b383, 0x3a772cf2, 0xa1d2609d, + 0xd64cb26d, 0x4de9fe02, 0x3971178d, 0xa2d45be2, 0xd54a8912, + 0x4eefc57d, 0x28526c08, 0xb3f72067, 0xc469f297, 0x5fccbef8, + 0x2b545777, 0xb0f11b18, 0xc76fc9e8, 0x5cca8587, 0x2e5e1af6, + 0xb5fb5699, 0xc2658469, 0x59c0c806, 0x2d582189, 0xb6fd6de6, + 0xc163bf16, 0x5ac6f379, 0x244a81f4, 0xbfefcd9b, 0xc8711f6b, + 0x53d45304, 0x274cba8b, 0xbce9f6e4, 0xcb772414, 0x50d2687b, + 0x2246f70a, 0xb9e3bb65, 0xce7d6995, 0x55d825fa, 0x2140cc75, + 0xbae5801a, 0xcd7b52ea, 0x56de1e85, 0x60c76fe0, 0xfb62238f, + 0x8cfcf17f, 0x1759bd10, 0x63c1549f, 0xf86418f0, 0x8ffaca00, + 0x145f866f, 0x66cb191e, 0xfd6e5571, 0x8af08781, 0x1155cbee, + 0x65cd2261, 0xfe686e0e, 0x89f6bcfe, 0x1253f091, 0x6cdf821c, + 0xf77ace73, 0x80e41c83, 0x1b4150ec, 0x6fd9b963, 0xf47cf50c, + 0x83e227fc, 0x18476b93, 0x6ad3f4e2, 0xf176b88d, 0x86e86a7d, + 0x1d4d2612, 0x69d5cf9d, 0xf27083f2, 0x85ee5102, 0x1e4b1d6d, + 0x78f6b418, 0xe353f877, 0x94cd2a87, 0x0f6866e8, 0x7bf08f67, + 0xe055c308, 0x97cb11f8, 0x0c6e5d97, 0x7efac2e6, 0xe55f8e89, + 0x92c15c79, 0x09641016, 0x7dfcf999, 0xe659b5f6, 0x91c76706, + 0x0a622b69, 0x74ee59e4, 0xef4b158b, 0x98d5c77b, 0x03708b14, + 0x77e8629b, 0xec4d2ef4, 0x9bd3fc04, 0x0076b06b, 0x72e22f1a, + 0xe9476375, 0x9ed9b185, 0x057cfdea, 0x71e41465, 0xea41580a, + 0x9ddf8afa, 0x067ac695, 0x50a4d810, 0xcb01947f, 0xbc9f468f, + 0x273a0ae0, 0x53a2e36f, 0xc807af00, 0xbf997df0, 0x243c319f, + 0x56a8aeee, 0xcd0de281, 0xba933071, 0x21367c1e, 0x55ae9591, + 0xce0bd9fe, 0xb9950b0e, 0x22304761, 0x5cbc35ec, 0xc7197983, + 0xb087ab73, 0x2b22e71c, 0x5fba0e93, 0xc41f42fc, 0xb381900c, + 0x2824dc63, 0x5ab04312, 0xc1150f7d, 0xb68bdd8d, 0x2d2e91e2, + 0x59b6786d, 0xc2133402, 0xb58de6f2, 0x2e28aa9d, 0x489503e8, + 0xd3304f87, 0xa4ae9d77, 0x3f0bd118, 0x4b933897, 0xd03674f8, + 0xa7a8a608, 0x3c0dea67, 0x4e997516, 0xd53c3979, 0xa2a2eb89, + 0x3907a7e6, 0x4d9f4e69, 0xd63a0206, 0xa1a4d0f6, 0x3a019c99, + 0x448dee14, 0xdf28a27b, 0xa8b6708b, 0x33133ce4, 0x478bd56b, + 0xdc2e9904, 0xabb04bf4, 0x3015079b, 0x428198ea, 0xd924d485, + 0xaeba0675, 0x351f4a1a, 0x4187a395, 0xda22effa, 0xadbc3d0a, + 0x36197165}, + {0x00000000, 0xc18edfc0, 0x586cb9c1, 0x99e26601, 0xb0d97382, + 0x7157ac42, 0xe8b5ca43, 0x293b1583, 0xbac3e145, 0x7b4d3e85, + 0xe2af5884, 0x23218744, 0x0a1a92c7, 0xcb944d07, 0x52762b06, + 0x93f8f4c6, 0xaef6c4cb, 0x6f781b0b, 0xf69a7d0a, 0x3714a2ca, + 0x1e2fb749, 0xdfa16889, 0x46430e88, 0x87cdd148, 0x1435258e, + 0xd5bbfa4e, 0x4c599c4f, 0x8dd7438f, 0xa4ec560c, 0x656289cc, + 0xfc80efcd, 0x3d0e300d, 0x869c8fd7, 0x47125017, 0xdef03616, + 0x1f7ee9d6, 0x3645fc55, 0xf7cb2395, 0x6e294594, 0xafa79a54, + 0x3c5f6e92, 0xfdd1b152, 0x6433d753, 0xa5bd0893, 0x8c861d10, + 0x4d08c2d0, 0xd4eaa4d1, 0x15647b11, 0x286a4b1c, 0xe9e494dc, + 0x7006f2dd, 0xb1882d1d, 0x98b3389e, 0x593de75e, 0xc0df815f, + 0x01515e9f, 0x92a9aa59, 0x53277599, 0xcac51398, 0x0b4bcc58, + 0x2270d9db, 0xe3fe061b, 0x7a1c601a, 0xbb92bfda, 0xd64819ef, + 0x17c6c62f, 0x8e24a02e, 0x4faa7fee, 0x66916a6d, 0xa71fb5ad, + 0x3efdd3ac, 0xff730c6c, 0x6c8bf8aa, 0xad05276a, 0x34e7416b, + 0xf5699eab, 0xdc528b28, 0x1ddc54e8, 0x843e32e9, 0x45b0ed29, + 0x78bedd24, 0xb93002e4, 0x20d264e5, 0xe15cbb25, 0xc867aea6, + 0x09e97166, 0x900b1767, 0x5185c8a7, 0xc27d3c61, 0x03f3e3a1, + 0x9a1185a0, 0x5b9f5a60, 0x72a44fe3, 0xb32a9023, 0x2ac8f622, + 0xeb4629e2, 0x50d49638, 0x915a49f8, 0x08b82ff9, 0xc936f039, + 0xe00de5ba, 0x21833a7a, 0xb8615c7b, 0x79ef83bb, 0xea17777d, + 0x2b99a8bd, 0xb27bcebc, 0x73f5117c, 0x5ace04ff, 0x9b40db3f, + 0x02a2bd3e, 0xc32c62fe, 0xfe2252f3, 0x3fac8d33, 0xa64eeb32, + 0x67c034f2, 0x4efb2171, 0x8f75feb1, 0x169798b0, 0xd7194770, + 0x44e1b3b6, 0x856f6c76, 0x1c8d0a77, 0xdd03d5b7, 0xf438c034, + 0x35b61ff4, 0xac5479f5, 0x6ddaa635, 0x77e1359f, 0xb66fea5f, + 0x2f8d8c5e, 0xee03539e, 0xc738461d, 0x06b699dd, 0x9f54ffdc, + 0x5eda201c, 0xcd22d4da, 0x0cac0b1a, 0x954e6d1b, 0x54c0b2db, + 0x7dfba758, 0xbc757898, 0x25971e99, 0xe419c159, 0xd917f154, + 0x18992e94, 0x817b4895, 0x40f59755, 0x69ce82d6, 0xa8405d16, + 0x31a23b17, 0xf02ce4d7, 0x63d41011, 0xa25acfd1, 0x3bb8a9d0, + 0xfa367610, 0xd30d6393, 0x1283bc53, 0x8b61da52, 0x4aef0592, + 0xf17dba48, 0x30f36588, 0xa9110389, 0x689fdc49, 0x41a4c9ca, + 0x802a160a, 0x19c8700b, 0xd846afcb, 0x4bbe5b0d, 0x8a3084cd, + 0x13d2e2cc, 0xd25c3d0c, 0xfb67288f, 0x3ae9f74f, 0xa30b914e, + 0x62854e8e, 0x5f8b7e83, 0x9e05a143, 0x07e7c742, 0xc6691882, + 0xef520d01, 0x2edcd2c1, 0xb73eb4c0, 0x76b06b00, 0xe5489fc6, + 0x24c64006, 0xbd242607, 0x7caaf9c7, 0x5591ec44, 0x941f3384, + 0x0dfd5585, 0xcc738a45, 0xa1a92c70, 0x6027f3b0, 0xf9c595b1, + 0x384b4a71, 0x11705ff2, 0xd0fe8032, 0x491ce633, 0x889239f3, + 0x1b6acd35, 0xdae412f5, 0x430674f4, 0x8288ab34, 0xabb3beb7, + 0x6a3d6177, 0xf3df0776, 0x3251d8b6, 0x0f5fe8bb, 0xced1377b, + 0x5733517a, 0x96bd8eba, 0xbf869b39, 0x7e0844f9, 0xe7ea22f8, + 0x2664fd38, 0xb59c09fe, 0x7412d63e, 0xedf0b03f, 0x2c7e6fff, + 0x05457a7c, 0xc4cba5bc, 0x5d29c3bd, 0x9ca71c7d, 0x2735a3a7, + 0xe6bb7c67, 0x7f591a66, 0xbed7c5a6, 0x97ecd025, 0x56620fe5, + 0xcf8069e4, 0x0e0eb624, 0x9df642e2, 0x5c789d22, 0xc59afb23, + 0x041424e3, 0x2d2f3160, 0xeca1eea0, 0x754388a1, 0xb4cd5761, + 0x89c3676c, 0x484db8ac, 0xd1afdead, 0x1021016d, 0x391a14ee, + 0xf894cb2e, 0x6176ad2f, 0xa0f872ef, 0x33008629, 0xf28e59e9, + 0x6b6c3fe8, 0xaae2e028, 0x83d9f5ab, 0x42572a6b, 0xdbb54c6a, + 0x1a3b93aa}, + {0x00000000, 0xefc26b3e, 0x04f5d03d, 0xeb37bb03, 0x09eba07a, + 0xe629cb44, 0x0d1e7047, 0xe2dc1b79, 0x13d740f4, 0xfc152bca, + 0x172290c9, 0xf8e0fbf7, 0x1a3ce08e, 0xf5fe8bb0, 0x1ec930b3, + 0xf10b5b8d, 0x27ae81e8, 0xc86cead6, 0x235b51d5, 0xcc993aeb, + 0x2e452192, 0xc1874aac, 0x2ab0f1af, 0xc5729a91, 0x3479c11c, + 0xdbbbaa22, 0x308c1121, 0xdf4e7a1f, 0x3d926166, 0xd2500a58, + 0x3967b15b, 0xd6a5da65, 0x4f5d03d0, 0xa09f68ee, 0x4ba8d3ed, + 0xa46ab8d3, 0x46b6a3aa, 0xa974c894, 0x42437397, 0xad8118a9, + 0x5c8a4324, 0xb348281a, 0x587f9319, 0xb7bdf827, 0x5561e35e, + 0xbaa38860, 0x51943363, 0xbe56585d, 0x68f38238, 0x8731e906, + 0x6c065205, 0x83c4393b, 0x61182242, 0x8eda497c, 0x65edf27f, + 0x8a2f9941, 0x7b24c2cc, 0x94e6a9f2, 0x7fd112f1, 0x901379cf, + 0x72cf62b6, 0x9d0d0988, 0x763ab28b, 0x99f8d9b5, 0x9eba07a0, + 0x71786c9e, 0x9a4fd79d, 0x758dbca3, 0x9751a7da, 0x7893cce4, + 0x93a477e7, 0x7c661cd9, 0x8d6d4754, 0x62af2c6a, 0x89989769, + 0x665afc57, 0x8486e72e, 0x6b448c10, 0x80733713, 0x6fb15c2d, + 0xb9148648, 0x56d6ed76, 0xbde15675, 0x52233d4b, 0xb0ff2632, + 0x5f3d4d0c, 0xb40af60f, 0x5bc89d31, 0xaac3c6bc, 0x4501ad82, + 0xae361681, 0x41f47dbf, 0xa32866c6, 0x4cea0df8, 0xa7ddb6fb, + 0x481fddc5, 0xd1e70470, 0x3e256f4e, 0xd512d44d, 0x3ad0bf73, + 0xd80ca40a, 0x37cecf34, 0xdcf97437, 0x333b1f09, 0xc2304484, + 0x2df22fba, 0xc6c594b9, 0x2907ff87, 0xcbdbe4fe, 0x24198fc0, + 0xcf2e34c3, 0x20ec5ffd, 0xf6498598, 0x198beea6, 0xf2bc55a5, + 0x1d7e3e9b, 0xffa225e2, 0x10604edc, 0xfb57f5df, 0x14959ee1, + 0xe59ec56c, 0x0a5cae52, 0xe16b1551, 0x0ea97e6f, 0xec756516, + 0x03b70e28, 0xe880b52b, 0x0742de15, 0xe6050901, 0x09c7623f, + 0xe2f0d93c, 0x0d32b202, 0xefeea97b, 0x002cc245, 0xeb1b7946, + 0x04d91278, 0xf5d249f5, 0x1a1022cb, 0xf12799c8, 0x1ee5f2f6, + 0xfc39e98f, 0x13fb82b1, 0xf8cc39b2, 0x170e528c, 0xc1ab88e9, + 0x2e69e3d7, 0xc55e58d4, 0x2a9c33ea, 0xc8402893, 0x278243ad, + 0xccb5f8ae, 0x23779390, 0xd27cc81d, 0x3dbea323, 0xd6891820, + 0x394b731e, 0xdb976867, 0x34550359, 0xdf62b85a, 0x30a0d364, + 0xa9580ad1, 0x469a61ef, 0xadaddaec, 0x426fb1d2, 0xa0b3aaab, + 0x4f71c195, 0xa4467a96, 0x4b8411a8, 0xba8f4a25, 0x554d211b, + 0xbe7a9a18, 0x51b8f126, 0xb364ea5f, 0x5ca68161, 0xb7913a62, + 0x5853515c, 0x8ef68b39, 0x6134e007, 0x8a035b04, 0x65c1303a, + 0x871d2b43, 0x68df407d, 0x83e8fb7e, 0x6c2a9040, 0x9d21cbcd, + 0x72e3a0f3, 0x99d41bf0, 0x761670ce, 0x94ca6bb7, 0x7b080089, + 0x903fbb8a, 0x7ffdd0b4, 0x78bf0ea1, 0x977d659f, 0x7c4ade9c, + 0x9388b5a2, 0x7154aedb, 0x9e96c5e5, 0x75a17ee6, 0x9a6315d8, + 0x6b684e55, 0x84aa256b, 0x6f9d9e68, 0x805ff556, 0x6283ee2f, + 0x8d418511, 0x66763e12, 0x89b4552c, 0x5f118f49, 0xb0d3e477, + 0x5be45f74, 0xb426344a, 0x56fa2f33, 0xb938440d, 0x520fff0e, + 0xbdcd9430, 0x4cc6cfbd, 0xa304a483, 0x48331f80, 0xa7f174be, + 0x452d6fc7, 0xaaef04f9, 0x41d8bffa, 0xae1ad4c4, 0x37e20d71, + 0xd820664f, 0x3317dd4c, 0xdcd5b672, 0x3e09ad0b, 0xd1cbc635, + 0x3afc7d36, 0xd53e1608, 0x24354d85, 0xcbf726bb, 0x20c09db8, + 0xcf02f686, 0x2ddeedff, 0xc21c86c1, 0x292b3dc2, 0xc6e956fc, + 0x104c8c99, 0xff8ee7a7, 0x14b95ca4, 0xfb7b379a, 0x19a72ce3, + 0xf66547dd, 0x1d52fcde, 0xf29097e0, 0x039bcc6d, 0xec59a753, + 0x076e1c50, 0xe8ac776e, 0x0a706c17, 0xe5b20729, 0x0e85bc2a, + 0xe147d714}, + {0x00000000, 0x177b1443, 0x2ef62886, 0x398d3cc5, 0x5dec510c, + 0x4a97454f, 0x731a798a, 0x64616dc9, 0xbbd8a218, 0xaca3b65b, + 0x952e8a9e, 0x82559edd, 0xe634f314, 0xf14fe757, 0xc8c2db92, + 0xdfb9cfd1, 0xacc04271, 0xbbbb5632, 0x82366af7, 0x954d7eb4, + 0xf12c137d, 0xe657073e, 0xdfda3bfb, 0xc8a12fb8, 0x1718e069, + 0x0063f42a, 0x39eec8ef, 0x2e95dcac, 0x4af4b165, 0x5d8fa526, + 0x640299e3, 0x73798da0, 0x82f182a3, 0x958a96e0, 0xac07aa25, + 0xbb7cbe66, 0xdf1dd3af, 0xc866c7ec, 0xf1ebfb29, 0xe690ef6a, + 0x392920bb, 0x2e5234f8, 0x17df083d, 0x00a41c7e, 0x64c571b7, + 0x73be65f4, 0x4a335931, 0x5d484d72, 0x2e31c0d2, 0x394ad491, + 0x00c7e854, 0x17bcfc17, 0x73dd91de, 0x64a6859d, 0x5d2bb958, + 0x4a50ad1b, 0x95e962ca, 0x82927689, 0xbb1f4a4c, 0xac645e0f, + 0xc80533c6, 0xdf7e2785, 0xe6f31b40, 0xf1880f03, 0xde920307, + 0xc9e91744, 0xf0642b81, 0xe71f3fc2, 0x837e520b, 0x94054648, + 0xad887a8d, 0xbaf36ece, 0x654aa11f, 0x7231b55c, 0x4bbc8999, + 0x5cc79dda, 0x38a6f013, 0x2fdde450, 0x1650d895, 0x012bccd6, + 0x72524176, 0x65295535, 0x5ca469f0, 0x4bdf7db3, 0x2fbe107a, + 0x38c50439, 0x014838fc, 0x16332cbf, 0xc98ae36e, 0xdef1f72d, + 0xe77ccbe8, 0xf007dfab, 0x9466b262, 0x831da621, 0xba909ae4, + 0xadeb8ea7, 0x5c6381a4, 0x4b1895e7, 0x7295a922, 0x65eebd61, + 0x018fd0a8, 0x16f4c4eb, 0x2f79f82e, 0x3802ec6d, 0xe7bb23bc, + 0xf0c037ff, 0xc94d0b3a, 0xde361f79, 0xba5772b0, 0xad2c66f3, + 0x94a15a36, 0x83da4e75, 0xf0a3c3d5, 0xe7d8d796, 0xde55eb53, + 0xc92eff10, 0xad4f92d9, 0xba34869a, 0x83b9ba5f, 0x94c2ae1c, + 0x4b7b61cd, 0x5c00758e, 0x658d494b, 0x72f65d08, 0x169730c1, + 0x01ec2482, 0x38611847, 0x2f1a0c04, 0x6655004f, 0x712e140c, + 0x48a328c9, 0x5fd83c8a, 0x3bb95143, 0x2cc24500, 0x154f79c5, + 0x02346d86, 0xdd8da257, 0xcaf6b614, 0xf37b8ad1, 0xe4009e92, + 0x8061f35b, 0x971ae718, 0xae97dbdd, 0xb9eccf9e, 0xca95423e, + 0xddee567d, 0xe4636ab8, 0xf3187efb, 0x97791332, 0x80020771, + 0xb98f3bb4, 0xaef42ff7, 0x714de026, 0x6636f465, 0x5fbbc8a0, + 0x48c0dce3, 0x2ca1b12a, 0x3bdaa569, 0x025799ac, 0x152c8def, + 0xe4a482ec, 0xf3df96af, 0xca52aa6a, 0xdd29be29, 0xb948d3e0, + 0xae33c7a3, 0x97befb66, 0x80c5ef25, 0x5f7c20f4, 0x480734b7, + 0x718a0872, 0x66f11c31, 0x029071f8, 0x15eb65bb, 0x2c66597e, + 0x3b1d4d3d, 0x4864c09d, 0x5f1fd4de, 0x6692e81b, 0x71e9fc58, + 0x15889191, 0x02f385d2, 0x3b7eb917, 0x2c05ad54, 0xf3bc6285, + 0xe4c776c6, 0xdd4a4a03, 0xca315e40, 0xae503389, 0xb92b27ca, + 0x80a61b0f, 0x97dd0f4c, 0xb8c70348, 0xafbc170b, 0x96312bce, + 0x814a3f8d, 0xe52b5244, 0xf2504607, 0xcbdd7ac2, 0xdca66e81, + 0x031fa150, 0x1464b513, 0x2de989d6, 0x3a929d95, 0x5ef3f05c, + 0x4988e41f, 0x7005d8da, 0x677ecc99, 0x14074139, 0x037c557a, + 0x3af169bf, 0x2d8a7dfc, 0x49eb1035, 0x5e900476, 0x671d38b3, + 0x70662cf0, 0xafdfe321, 0xb8a4f762, 0x8129cba7, 0x9652dfe4, + 0xf233b22d, 0xe548a66e, 0xdcc59aab, 0xcbbe8ee8, 0x3a3681eb, + 0x2d4d95a8, 0x14c0a96d, 0x03bbbd2e, 0x67dad0e7, 0x70a1c4a4, + 0x492cf861, 0x5e57ec22, 0x81ee23f3, 0x969537b0, 0xaf180b75, + 0xb8631f36, 0xdc0272ff, 0xcb7966bc, 0xf2f45a79, 0xe58f4e3a, + 0x96f6c39a, 0x818dd7d9, 0xb800eb1c, 0xaf7bff5f, 0xcb1a9296, + 0xdc6186d5, 0xe5ecba10, 0xf297ae53, 0x2d2e6182, 0x3a5575c1, + 0x03d84904, 0x14a35d47, 0x70c2308e, 0x67b924cd, 0x5e341808, + 0x494f0c4b}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x00000000, 0x43147b17, 0x8628f62e, 0xc53c8d39, 0x0c51ec5d, + 0x4f45974a, 0x8a791a73, 0xc96d6164, 0x18a2d8bb, 0x5bb6a3ac, + 0x9e8a2e95, 0xdd9e5582, 0x14f334e6, 0x57e74ff1, 0x92dbc2c8, + 0xd1cfb9df, 0x7142c0ac, 0x3256bbbb, 0xf76a3682, 0xb47e4d95, + 0x7d132cf1, 0x3e0757e6, 0xfb3bdadf, 0xb82fa1c8, 0x69e01817, + 0x2af46300, 0xefc8ee39, 0xacdc952e, 0x65b1f44a, 0x26a58f5d, + 0xe3990264, 0xa08d7973, 0xa382f182, 0xe0968a95, 0x25aa07ac, + 0x66be7cbb, 0xafd31ddf, 0xecc766c8, 0x29fbebf1, 0x6aef90e6, + 0xbb202939, 0xf834522e, 0x3d08df17, 0x7e1ca400, 0xb771c564, + 0xf465be73, 0x3159334a, 0x724d485d, 0xd2c0312e, 0x91d44a39, + 0x54e8c700, 0x17fcbc17, 0xde91dd73, 0x9d85a664, 0x58b92b5d, + 0x1bad504a, 0xca62e995, 0x89769282, 0x4c4a1fbb, 0x0f5e64ac, + 0xc63305c8, 0x85277edf, 0x401bf3e6, 0x030f88f1, 0x070392de, + 0x4417e9c9, 0x812b64f0, 0xc23f1fe7, 0x0b527e83, 0x48460594, + 0x8d7a88ad, 0xce6ef3ba, 0x1fa14a65, 0x5cb53172, 0x9989bc4b, + 0xda9dc75c, 0x13f0a638, 0x50e4dd2f, 0x95d85016, 0xd6cc2b01, + 0x76415272, 0x35552965, 0xf069a45c, 0xb37ddf4b, 0x7a10be2f, + 0x3904c538, 0xfc384801, 0xbf2c3316, 0x6ee38ac9, 0x2df7f1de, + 0xe8cb7ce7, 0xabdf07f0, 0x62b26694, 0x21a61d83, 0xe49a90ba, + 0xa78eebad, 0xa481635c, 0xe795184b, 0x22a99572, 0x61bdee65, + 0xa8d08f01, 0xebc4f416, 0x2ef8792f, 0x6dec0238, 0xbc23bbe7, + 0xff37c0f0, 0x3a0b4dc9, 0x791f36de, 0xb07257ba, 0xf3662cad, + 0x365aa194, 0x754eda83, 0xd5c3a3f0, 0x96d7d8e7, 0x53eb55de, + 0x10ff2ec9, 0xd9924fad, 0x9a8634ba, 0x5fbab983, 0x1caec294, + 0xcd617b4b, 0x8e75005c, 0x4b498d65, 0x085df672, 0xc1309716, + 0x8224ec01, 0x47186138, 0x040c1a2f, 0x4f005566, 0x0c142e71, + 0xc928a348, 0x8a3cd85f, 0x4351b93b, 0x0045c22c, 0xc5794f15, + 0x866d3402, 0x57a28ddd, 0x14b6f6ca, 0xd18a7bf3, 0x929e00e4, + 0x5bf36180, 0x18e71a97, 0xdddb97ae, 0x9ecfecb9, 0x3e4295ca, + 0x7d56eedd, 0xb86a63e4, 0xfb7e18f3, 0x32137997, 0x71070280, + 0xb43b8fb9, 0xf72ff4ae, 0x26e04d71, 0x65f43666, 0xa0c8bb5f, + 0xe3dcc048, 0x2ab1a12c, 0x69a5da3b, 0xac995702, 0xef8d2c15, + 0xec82a4e4, 0xaf96dff3, 0x6aaa52ca, 0x29be29dd, 0xe0d348b9, + 0xa3c733ae, 0x66fbbe97, 0x25efc580, 0xf4207c5f, 0xb7340748, + 0x72088a71, 0x311cf166, 0xf8719002, 0xbb65eb15, 0x7e59662c, + 0x3d4d1d3b, 0x9dc06448, 0xded41f5f, 0x1be89266, 0x58fce971, + 0x91918815, 0xd285f302, 0x17b97e3b, 0x54ad052c, 0x8562bcf3, + 0xc676c7e4, 0x034a4add, 0x405e31ca, 0x893350ae, 0xca272bb9, + 0x0f1ba680, 0x4c0fdd97, 0x4803c7b8, 0x0b17bcaf, 0xce2b3196, + 0x8d3f4a81, 0x44522be5, 0x074650f2, 0xc27addcb, 0x816ea6dc, + 0x50a11f03, 0x13b56414, 0xd689e92d, 0x959d923a, 0x5cf0f35e, + 0x1fe48849, 0xdad80570, 0x99cc7e67, 0x39410714, 0x7a557c03, + 0xbf69f13a, 0xfc7d8a2d, 0x3510eb49, 0x7604905e, 0xb3381d67, + 0xf02c6670, 0x21e3dfaf, 0x62f7a4b8, 0xa7cb2981, 0xe4df5296, + 0x2db233f2, 0x6ea648e5, 0xab9ac5dc, 0xe88ebecb, 0xeb81363a, + 0xa8954d2d, 0x6da9c014, 0x2ebdbb03, 0xe7d0da67, 0xa4c4a170, + 0x61f82c49, 0x22ec575e, 0xf323ee81, 0xb0379596, 0x750b18af, + 0x361f63b8, 0xff7202dc, 0xbc6679cb, 0x795af4f2, 0x3a4e8fe5, + 0x9ac3f696, 0xd9d78d81, 0x1ceb00b8, 0x5fff7baf, 0x96921acb, + 0xd58661dc, 0x10baece5, 0x53ae97f2, 0x82612e2d, 0xc175553a, + 0x0449d803, 0x475da314, 0x8e30c270, 0xcd24b967, 0x0818345e, + 0x4b0c4f49}, + {0x00000000, 0x3e6bc2ef, 0x3dd0f504, 0x03bb37eb, 0x7aa0eb09, + 0x44cb29e6, 0x47701e0d, 0x791bdce2, 0xf440d713, 0xca2b15fc, + 0xc9902217, 0xf7fbe0f8, 0x8ee03c1a, 0xb08bfef5, 0xb330c91e, + 0x8d5b0bf1, 0xe881ae27, 0xd6ea6cc8, 0xd5515b23, 0xeb3a99cc, + 0x9221452e, 0xac4a87c1, 0xaff1b02a, 0x919a72c5, 0x1cc17934, + 0x22aabbdb, 0x21118c30, 0x1f7a4edf, 0x6661923d, 0x580a50d2, + 0x5bb16739, 0x65daa5d6, 0xd0035d4f, 0xee689fa0, 0xedd3a84b, + 0xd3b86aa4, 0xaaa3b646, 0x94c874a9, 0x97734342, 0xa91881ad, + 0x24438a5c, 0x1a2848b3, 0x19937f58, 0x27f8bdb7, 0x5ee36155, + 0x6088a3ba, 0x63339451, 0x5d5856be, 0x3882f368, 0x06e93187, + 0x0552066c, 0x3b39c483, 0x42221861, 0x7c49da8e, 0x7ff2ed65, + 0x41992f8a, 0xccc2247b, 0xf2a9e694, 0xf112d17f, 0xcf791390, + 0xb662cf72, 0x88090d9d, 0x8bb23a76, 0xb5d9f899, 0xa007ba9e, + 0x9e6c7871, 0x9dd74f9a, 0xa3bc8d75, 0xdaa75197, 0xe4cc9378, + 0xe777a493, 0xd91c667c, 0x54476d8d, 0x6a2caf62, 0x69979889, + 0x57fc5a66, 0x2ee78684, 0x108c446b, 0x13377380, 0x2d5cb16f, + 0x488614b9, 0x76edd656, 0x7556e1bd, 0x4b3d2352, 0x3226ffb0, + 0x0c4d3d5f, 0x0ff60ab4, 0x319dc85b, 0xbcc6c3aa, 0x82ad0145, + 0x811636ae, 0xbf7df441, 0xc66628a3, 0xf80dea4c, 0xfbb6dda7, + 0xc5dd1f48, 0x7004e7d1, 0x4e6f253e, 0x4dd412d5, 0x73bfd03a, + 0x0aa40cd8, 0x34cfce37, 0x3774f9dc, 0x091f3b33, 0x844430c2, + 0xba2ff22d, 0xb994c5c6, 0x87ff0729, 0xfee4dbcb, 0xc08f1924, + 0xc3342ecf, 0xfd5fec20, 0x988549f6, 0xa6ee8b19, 0xa555bcf2, + 0x9b3e7e1d, 0xe225a2ff, 0xdc4e6010, 0xdff557fb, 0xe19e9514, + 0x6cc59ee5, 0x52ae5c0a, 0x51156be1, 0x6f7ea90e, 0x166575ec, + 0x280eb703, 0x2bb580e8, 0x15de4207, 0x010905e6, 0x3f62c709, + 0x3cd9f0e2, 0x02b2320d, 0x7ba9eeef, 0x45c22c00, 0x46791beb, + 0x7812d904, 0xf549d2f5, 0xcb22101a, 0xc89927f1, 0xf6f2e51e, + 0x8fe939fc, 0xb182fb13, 0xb239ccf8, 0x8c520e17, 0xe988abc1, + 0xd7e3692e, 0xd4585ec5, 0xea339c2a, 0x932840c8, 0xad438227, + 0xaef8b5cc, 0x90937723, 0x1dc87cd2, 0x23a3be3d, 0x201889d6, + 0x1e734b39, 0x676897db, 0x59035534, 0x5ab862df, 0x64d3a030, + 0xd10a58a9, 0xef619a46, 0xecdaadad, 0xd2b16f42, 0xabaab3a0, + 0x95c1714f, 0x967a46a4, 0xa811844b, 0x254a8fba, 0x1b214d55, + 0x189a7abe, 0x26f1b851, 0x5fea64b3, 0x6181a65c, 0x623a91b7, + 0x5c515358, 0x398bf68e, 0x07e03461, 0x045b038a, 0x3a30c165, + 0x432b1d87, 0x7d40df68, 0x7efbe883, 0x40902a6c, 0xcdcb219d, + 0xf3a0e372, 0xf01bd499, 0xce701676, 0xb76bca94, 0x8900087b, + 0x8abb3f90, 0xb4d0fd7f, 0xa10ebf78, 0x9f657d97, 0x9cde4a7c, + 0xa2b58893, 0xdbae5471, 0xe5c5969e, 0xe67ea175, 0xd815639a, + 0x554e686b, 0x6b25aa84, 0x689e9d6f, 0x56f55f80, 0x2fee8362, + 0x1185418d, 0x123e7666, 0x2c55b489, 0x498f115f, 0x77e4d3b0, + 0x745fe45b, 0x4a3426b4, 0x332ffa56, 0x0d4438b9, 0x0eff0f52, + 0x3094cdbd, 0xbdcfc64c, 0x83a404a3, 0x801f3348, 0xbe74f1a7, + 0xc76f2d45, 0xf904efaa, 0xfabfd841, 0xc4d41aae, 0x710de237, + 0x4f6620d8, 0x4cdd1733, 0x72b6d5dc, 0x0bad093e, 0x35c6cbd1, + 0x367dfc3a, 0x08163ed5, 0x854d3524, 0xbb26f7cb, 0xb89dc020, + 0x86f602cf, 0xffedde2d, 0xc1861cc2, 0xc23d2b29, 0xfc56e9c6, + 0x998c4c10, 0xa7e78eff, 0xa45cb914, 0x9a377bfb, 0xe32ca719, + 0xdd4765f6, 0xdefc521d, 0xe09790f2, 0x6dcc9b03, 0x53a759ec, + 0x501c6e07, 0x6e77ace8, 0x176c700a, 0x2907b2e5, 0x2abc850e, + 0x14d747e1}, + {0x00000000, 0xc0df8ec1, 0xc1b96c58, 0x0166e299, 0x8273d9b0, + 0x42ac5771, 0x43cab5e8, 0x83153b29, 0x45e1c3ba, 0x853e4d7b, + 0x8458afe2, 0x44872123, 0xc7921a0a, 0x074d94cb, 0x062b7652, + 0xc6f4f893, 0xcbc4f6ae, 0x0b1b786f, 0x0a7d9af6, 0xcaa21437, + 0x49b72f1e, 0x8968a1df, 0x880e4346, 0x48d1cd87, 0x8e253514, + 0x4efabbd5, 0x4f9c594c, 0x8f43d78d, 0x0c56eca4, 0xcc896265, + 0xcdef80fc, 0x0d300e3d, 0xd78f9c86, 0x17501247, 0x1636f0de, + 0xd6e97e1f, 0x55fc4536, 0x9523cbf7, 0x9445296e, 0x549aa7af, + 0x926e5f3c, 0x52b1d1fd, 0x53d73364, 0x9308bda5, 0x101d868c, + 0xd0c2084d, 0xd1a4ead4, 0x117b6415, 0x1c4b6a28, 0xdc94e4e9, + 0xddf20670, 0x1d2d88b1, 0x9e38b398, 0x5ee73d59, 0x5f81dfc0, + 0x9f5e5101, 0x59aaa992, 0x99752753, 0x9813c5ca, 0x58cc4b0b, + 0xdbd97022, 0x1b06fee3, 0x1a601c7a, 0xdabf92bb, 0xef1948d6, + 0x2fc6c617, 0x2ea0248e, 0xee7faa4f, 0x6d6a9166, 0xadb51fa7, + 0xacd3fd3e, 0x6c0c73ff, 0xaaf88b6c, 0x6a2705ad, 0x6b41e734, + 0xab9e69f5, 0x288b52dc, 0xe854dc1d, 0xe9323e84, 0x29edb045, + 0x24ddbe78, 0xe40230b9, 0xe564d220, 0x25bb5ce1, 0xa6ae67c8, + 0x6671e909, 0x67170b90, 0xa7c88551, 0x613c7dc2, 0xa1e3f303, + 0xa085119a, 0x605a9f5b, 0xe34fa472, 0x23902ab3, 0x22f6c82a, + 0xe22946eb, 0x3896d450, 0xf8495a91, 0xf92fb808, 0x39f036c9, + 0xbae50de0, 0x7a3a8321, 0x7b5c61b8, 0xbb83ef79, 0x7d7717ea, + 0xbda8992b, 0xbcce7bb2, 0x7c11f573, 0xff04ce5a, 0x3fdb409b, + 0x3ebda202, 0xfe622cc3, 0xf35222fe, 0x338dac3f, 0x32eb4ea6, + 0xf234c067, 0x7121fb4e, 0xb1fe758f, 0xb0989716, 0x704719d7, + 0xb6b3e144, 0x766c6f85, 0x770a8d1c, 0xb7d503dd, 0x34c038f4, + 0xf41fb635, 0xf57954ac, 0x35a6da6d, 0x9f35e177, 0x5fea6fb6, + 0x5e8c8d2f, 0x9e5303ee, 0x1d4638c7, 0xdd99b606, 0xdcff549f, + 0x1c20da5e, 0xdad422cd, 0x1a0bac0c, 0x1b6d4e95, 0xdbb2c054, + 0x58a7fb7d, 0x987875bc, 0x991e9725, 0x59c119e4, 0x54f117d9, + 0x942e9918, 0x95487b81, 0x5597f540, 0xd682ce69, 0x165d40a8, + 0x173ba231, 0xd7e42cf0, 0x1110d463, 0xd1cf5aa2, 0xd0a9b83b, + 0x107636fa, 0x93630dd3, 0x53bc8312, 0x52da618b, 0x9205ef4a, + 0x48ba7df1, 0x8865f330, 0x890311a9, 0x49dc9f68, 0xcac9a441, + 0x0a162a80, 0x0b70c819, 0xcbaf46d8, 0x0d5bbe4b, 0xcd84308a, + 0xcce2d213, 0x0c3d5cd2, 0x8f2867fb, 0x4ff7e93a, 0x4e910ba3, + 0x8e4e8562, 0x837e8b5f, 0x43a1059e, 0x42c7e707, 0x821869c6, + 0x010d52ef, 0xc1d2dc2e, 0xc0b43eb7, 0x006bb076, 0xc69f48e5, + 0x0640c624, 0x072624bd, 0xc7f9aa7c, 0x44ec9155, 0x84331f94, + 0x8555fd0d, 0x458a73cc, 0x702ca9a1, 0xb0f32760, 0xb195c5f9, + 0x714a4b38, 0xf25f7011, 0x3280fed0, 0x33e61c49, 0xf3399288, + 0x35cd6a1b, 0xf512e4da, 0xf4740643, 0x34ab8882, 0xb7beb3ab, + 0x77613d6a, 0x7607dff3, 0xb6d85132, 0xbbe85f0f, 0x7b37d1ce, + 0x7a513357, 0xba8ebd96, 0x399b86bf, 0xf944087e, 0xf822eae7, + 0x38fd6426, 0xfe099cb5, 0x3ed61274, 0x3fb0f0ed, 0xff6f7e2c, + 0x7c7a4505, 0xbca5cbc4, 0xbdc3295d, 0x7d1ca79c, 0xa7a33527, + 0x677cbbe6, 0x661a597f, 0xa6c5d7be, 0x25d0ec97, 0xe50f6256, + 0xe46980cf, 0x24b60e0e, 0xe242f69d, 0x229d785c, 0x23fb9ac5, + 0xe3241404, 0x60312f2d, 0xa0eea1ec, 0xa1884375, 0x6157cdb4, + 0x6c67c389, 0xacb84d48, 0xaddeafd1, 0x6d012110, 0xee141a39, + 0x2ecb94f8, 0x2fad7661, 0xef72f8a0, 0x29860033, 0xe9598ef2, + 0xe83f6c6b, 0x28e0e2aa, 0xabf5d983, 0x6b2a5742, 0x6a4cb5db, + 0xaa933b1a}, + {0x00000000, 0x6f4ca59b, 0x9f9e3bec, 0xf0d29e77, 0x7f3b0603, + 0x1077a398, 0xe0a53def, 0x8fe99874, 0xfe760c06, 0x913aa99d, + 0x61e837ea, 0x0ea49271, 0x814d0a05, 0xee01af9e, 0x1ed331e9, + 0x719f9472, 0xfced180c, 0x93a1bd97, 0x637323e0, 0x0c3f867b, + 0x83d61e0f, 0xec9abb94, 0x1c4825e3, 0x73048078, 0x029b140a, + 0x6dd7b191, 0x9d052fe6, 0xf2498a7d, 0x7da01209, 0x12ecb792, + 0xe23e29e5, 0x8d728c7e, 0xf8db3118, 0x97979483, 0x67450af4, + 0x0809af6f, 0x87e0371b, 0xe8ac9280, 0x187e0cf7, 0x7732a96c, + 0x06ad3d1e, 0x69e19885, 0x993306f2, 0xf67fa369, 0x79963b1d, + 0x16da9e86, 0xe60800f1, 0x8944a56a, 0x04362914, 0x6b7a8c8f, + 0x9ba812f8, 0xf4e4b763, 0x7b0d2f17, 0x14418a8c, 0xe49314fb, + 0x8bdfb160, 0xfa402512, 0x950c8089, 0x65de1efe, 0x0a92bb65, + 0x857b2311, 0xea37868a, 0x1ae518fd, 0x75a9bd66, 0xf0b76330, + 0x9ffbc6ab, 0x6f2958dc, 0x0065fd47, 0x8f8c6533, 0xe0c0c0a8, + 0x10125edf, 0x7f5efb44, 0x0ec16f36, 0x618dcaad, 0x915f54da, + 0xfe13f141, 0x71fa6935, 0x1eb6ccae, 0xee6452d9, 0x8128f742, + 0x0c5a7b3c, 0x6316dea7, 0x93c440d0, 0xfc88e54b, 0x73617d3f, + 0x1c2dd8a4, 0xecff46d3, 0x83b3e348, 0xf22c773a, 0x9d60d2a1, + 0x6db24cd6, 0x02fee94d, 0x8d177139, 0xe25bd4a2, 0x12894ad5, + 0x7dc5ef4e, 0x086c5228, 0x6720f7b3, 0x97f269c4, 0xf8becc5f, + 0x7757542b, 0x181bf1b0, 0xe8c96fc7, 0x8785ca5c, 0xf61a5e2e, + 0x9956fbb5, 0x698465c2, 0x06c8c059, 0x8921582d, 0xe66dfdb6, + 0x16bf63c1, 0x79f3c65a, 0xf4814a24, 0x9bcdefbf, 0x6b1f71c8, + 0x0453d453, 0x8bba4c27, 0xe4f6e9bc, 0x142477cb, 0x7b68d250, + 0x0af74622, 0x65bbe3b9, 0x95697dce, 0xfa25d855, 0x75cc4021, + 0x1a80e5ba, 0xea527bcd, 0x851ede56, 0xe06fc760, 0x8f2362fb, + 0x7ff1fc8c, 0x10bd5917, 0x9f54c163, 0xf01864f8, 0x00cafa8f, + 0x6f865f14, 0x1e19cb66, 0x71556efd, 0x8187f08a, 0xeecb5511, + 0x6122cd65, 0x0e6e68fe, 0xfebcf689, 0x91f05312, 0x1c82df6c, + 0x73ce7af7, 0x831ce480, 0xec50411b, 0x63b9d96f, 0x0cf57cf4, + 0xfc27e283, 0x936b4718, 0xe2f4d36a, 0x8db876f1, 0x7d6ae886, + 0x12264d1d, 0x9dcfd569, 0xf28370f2, 0x0251ee85, 0x6d1d4b1e, + 0x18b4f678, 0x77f853e3, 0x872acd94, 0xe866680f, 0x678ff07b, + 0x08c355e0, 0xf811cb97, 0x975d6e0c, 0xe6c2fa7e, 0x898e5fe5, + 0x795cc192, 0x16106409, 0x99f9fc7d, 0xf6b559e6, 0x0667c791, + 0x692b620a, 0xe459ee74, 0x8b154bef, 0x7bc7d598, 0x148b7003, + 0x9b62e877, 0xf42e4dec, 0x04fcd39b, 0x6bb07600, 0x1a2fe272, + 0x756347e9, 0x85b1d99e, 0xeafd7c05, 0x6514e471, 0x0a5841ea, + 0xfa8adf9d, 0x95c67a06, 0x10d8a450, 0x7f9401cb, 0x8f469fbc, + 0xe00a3a27, 0x6fe3a253, 0x00af07c8, 0xf07d99bf, 0x9f313c24, + 0xeeaea856, 0x81e20dcd, 0x713093ba, 0x1e7c3621, 0x9195ae55, + 0xfed90bce, 0x0e0b95b9, 0x61473022, 0xec35bc5c, 0x837919c7, + 0x73ab87b0, 0x1ce7222b, 0x930eba5f, 0xfc421fc4, 0x0c9081b3, + 0x63dc2428, 0x1243b05a, 0x7d0f15c1, 0x8ddd8bb6, 0xe2912e2d, + 0x6d78b659, 0x023413c2, 0xf2e68db5, 0x9daa282e, 0xe8039548, + 0x874f30d3, 0x779daea4, 0x18d10b3f, 0x9738934b, 0xf87436d0, + 0x08a6a8a7, 0x67ea0d3c, 0x1675994e, 0x79393cd5, 0x89eba2a2, + 0xe6a70739, 0x694e9f4d, 0x06023ad6, 0xf6d0a4a1, 0x999c013a, + 0x14ee8d44, 0x7ba228df, 0x8b70b6a8, 0xe43c1333, 0x6bd58b47, + 0x04992edc, 0xf44bb0ab, 0x9b071530, 0xea988142, 0x85d424d9, + 0x7506baae, 0x1a4a1f35, 0x95a38741, 0xfaef22da, 0x0a3dbcad, + 0x65711936}}; + +#endif + +#endif + +#if N == 4 + +#if W == 8 + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xf1da05aa, 0x38c50d15, 0xc91f08bf, 0x718a1a2a, + 0x80501f80, 0x494f173f, 0xb8951295, 0xe3143454, 0x12ce31fe, + 0xdbd13941, 0x2a0b3ceb, 0x929e2e7e, 0x63442bd4, 0xaa5b236b, + 0x5b8126c1, 0x1d596ee9, 0xec836b43, 0x259c63fc, 0xd4466656, + 0x6cd374c3, 0x9d097169, 0x541679d6, 0xa5cc7c7c, 0xfe4d5abd, + 0x0f975f17, 0xc68857a8, 0x37525202, 0x8fc74097, 0x7e1d453d, + 0xb7024d82, 0x46d84828, 0x3ab2ddd2, 0xcb68d878, 0x0277d0c7, + 0xf3add56d, 0x4b38c7f8, 0xbae2c252, 0x73fdcaed, 0x8227cf47, + 0xd9a6e986, 0x287cec2c, 0xe163e493, 0x10b9e139, 0xa82cf3ac, + 0x59f6f606, 0x90e9feb9, 0x6133fb13, 0x27ebb33b, 0xd631b691, + 0x1f2ebe2e, 0xeef4bb84, 0x5661a911, 0xa7bbacbb, 0x6ea4a404, + 0x9f7ea1ae, 0xc4ff876f, 0x352582c5, 0xfc3a8a7a, 0x0de08fd0, + 0xb5759d45, 0x44af98ef, 0x8db09050, 0x7c6a95fa, 0x7565bba4, + 0x84bfbe0e, 0x4da0b6b1, 0xbc7ab31b, 0x04efa18e, 0xf535a424, + 0x3c2aac9b, 0xcdf0a931, 0x96718ff0, 0x67ab8a5a, 0xaeb482e5, + 0x5f6e874f, 0xe7fb95da, 0x16219070, 0xdf3e98cf, 0x2ee49d65, + 0x683cd54d, 0x99e6d0e7, 0x50f9d858, 0xa123ddf2, 0x19b6cf67, + 0xe86ccacd, 0x2173c272, 0xd0a9c7d8, 0x8b28e119, 0x7af2e4b3, + 0xb3edec0c, 0x4237e9a6, 0xfaa2fb33, 0x0b78fe99, 0xc267f626, + 0x33bdf38c, 0x4fd76676, 0xbe0d63dc, 0x77126b63, 0x86c86ec9, + 0x3e5d7c5c, 0xcf8779f6, 0x06987149, 0xf74274e3, 0xacc35222, + 0x5d195788, 0x94065f37, 0x65dc5a9d, 0xdd494808, 0x2c934da2, + 0xe58c451d, 0x145640b7, 0x528e089f, 0xa3540d35, 0x6a4b058a, + 0x9b910020, 0x230412b5, 0xd2de171f, 0x1bc11fa0, 0xea1b1a0a, + 0xb19a3ccb, 0x40403961, 0x895f31de, 0x78853474, 0xc01026e1, + 0x31ca234b, 0xf8d52bf4, 0x090f2e5e, 0xeacb7748, 0x1b1172e2, + 0xd20e7a5d, 0x23d47ff7, 0x9b416d62, 0x6a9b68c8, 0xa3846077, + 0x525e65dd, 0x09df431c, 0xf80546b6, 0x311a4e09, 0xc0c04ba3, + 0x78555936, 0x898f5c9c, 0x40905423, 0xb14a5189, 0xf79219a1, + 0x06481c0b, 0xcf5714b4, 0x3e8d111e, 0x8618038b, 0x77c20621, + 0xbedd0e9e, 0x4f070b34, 0x14862df5, 0xe55c285f, 0x2c4320e0, + 0xdd99254a, 0x650c37df, 0x94d63275, 0x5dc93aca, 0xac133f60, + 0xd079aa9a, 0x21a3af30, 0xe8bca78f, 0x1966a225, 0xa1f3b0b0, + 0x5029b51a, 0x9936bda5, 0x68ecb80f, 0x336d9ece, 0xc2b79b64, + 0x0ba893db, 0xfa729671, 0x42e784e4, 0xb33d814e, 0x7a2289f1, + 0x8bf88c5b, 0xcd20c473, 0x3cfac1d9, 0xf5e5c966, 0x043fcccc, + 0xbcaade59, 0x4d70dbf3, 0x846fd34c, 0x75b5d6e6, 0x2e34f027, + 0xdfeef58d, 0x16f1fd32, 0xe72bf898, 0x5fbeea0d, 0xae64efa7, + 0x677be718, 0x96a1e2b2, 0x9faeccec, 0x6e74c946, 0xa76bc1f9, + 0x56b1c453, 0xee24d6c6, 0x1ffed36c, 0xd6e1dbd3, 0x273bde79, + 0x7cbaf8b8, 0x8d60fd12, 0x447ff5ad, 0xb5a5f007, 0x0d30e292, + 0xfceae738, 0x35f5ef87, 0xc42fea2d, 0x82f7a205, 0x732da7af, + 0xba32af10, 0x4be8aaba, 0xf37db82f, 0x02a7bd85, 0xcbb8b53a, + 0x3a62b090, 0x61e39651, 0x903993fb, 0x59269b44, 0xa8fc9eee, + 0x10698c7b, 0xe1b389d1, 0x28ac816e, 0xd97684c4, 0xa51c113e, + 0x54c61494, 0x9dd91c2b, 0x6c031981, 0xd4960b14, 0x254c0ebe, + 0xec530601, 0x1d8903ab, 0x4608256a, 0xb7d220c0, 0x7ecd287f, + 0x8f172dd5, 0x37823f40, 0xc6583aea, 0x0f473255, 0xfe9d37ff, + 0xb8457fd7, 0x499f7a7d, 0x808072c2, 0x715a7768, 0xc9cf65fd, + 0x38156057, 0xf10a68e8, 0x00d06d42, 0x5b514b83, 0xaa8b4e29, + 0x63944696, 0x924e433c, 0x2adb51a9, 0xdb015403, 0x121e5cbc, + 0xe3c45916}, + {0x00000000, 0x0ee7e8d1, 0x1dcfd1a2, 0x13283973, 0x3b9fa344, + 0x35784b95, 0x265072e6, 0x28b79a37, 0x773f4688, 0x79d8ae59, + 0x6af0972a, 0x64177ffb, 0x4ca0e5cc, 0x42470d1d, 0x516f346e, + 0x5f88dcbf, 0xee7e8d10, 0xe09965c1, 0xf3b15cb2, 0xfd56b463, + 0xd5e12e54, 0xdb06c685, 0xc82efff6, 0xc6c91727, 0x9941cb98, + 0x97a62349, 0x848e1a3a, 0x8a69f2eb, 0xa2de68dc, 0xac39800d, + 0xbf11b97e, 0xb1f651af, 0x078c1c61, 0x096bf4b0, 0x1a43cdc3, + 0x14a42512, 0x3c13bf25, 0x32f457f4, 0x21dc6e87, 0x2f3b8656, + 0x70b35ae9, 0x7e54b238, 0x6d7c8b4b, 0x639b639a, 0x4b2cf9ad, + 0x45cb117c, 0x56e3280f, 0x5804c0de, 0xe9f29171, 0xe71579a0, + 0xf43d40d3, 0xfadaa802, 0xd26d3235, 0xdc8adae4, 0xcfa2e397, + 0xc1450b46, 0x9ecdd7f9, 0x902a3f28, 0x8302065b, 0x8de5ee8a, + 0xa55274bd, 0xabb59c6c, 0xb89da51f, 0xb67a4dce, 0x0f1838c2, + 0x01ffd013, 0x12d7e960, 0x1c3001b1, 0x34879b86, 0x3a607357, + 0x29484a24, 0x27afa2f5, 0x78277e4a, 0x76c0969b, 0x65e8afe8, + 0x6b0f4739, 0x43b8dd0e, 0x4d5f35df, 0x5e770cac, 0x5090e47d, + 0xe166b5d2, 0xef815d03, 0xfca96470, 0xf24e8ca1, 0xdaf91696, + 0xd41efe47, 0xc736c734, 0xc9d12fe5, 0x9659f35a, 0x98be1b8b, + 0x8b9622f8, 0x8571ca29, 0xadc6501e, 0xa321b8cf, 0xb00981bc, + 0xbeee696d, 0x089424a3, 0x0673cc72, 0x155bf501, 0x1bbc1dd0, + 0x330b87e7, 0x3dec6f36, 0x2ec45645, 0x2023be94, 0x7fab622b, + 0x714c8afa, 0x6264b389, 0x6c835b58, 0x4434c16f, 0x4ad329be, + 0x59fb10cd, 0x571cf81c, 0xe6eaa9b3, 0xe80d4162, 0xfb257811, + 0xf5c290c0, 0xdd750af7, 0xd392e226, 0xc0badb55, 0xce5d3384, + 0x91d5ef3b, 0x9f3207ea, 0x8c1a3e99, 0x82fdd648, 0xaa4a4c7f, + 0xa4ada4ae, 0xb7859ddd, 0xb962750c, 0x1e307184, 0x10d79955, + 0x03ffa026, 0x0d1848f7, 0x25afd2c0, 0x2b483a11, 0x38600362, + 0x3687ebb3, 0x690f370c, 0x67e8dfdd, 0x74c0e6ae, 0x7a270e7f, + 0x52909448, 0x5c777c99, 0x4f5f45ea, 0x41b8ad3b, 0xf04efc94, + 0xfea91445, 0xed812d36, 0xe366c5e7, 0xcbd15fd0, 0xc536b701, + 0xd61e8e72, 0xd8f966a3, 0x8771ba1c, 0x899652cd, 0x9abe6bbe, + 0x9459836f, 0xbcee1958, 0xb209f189, 0xa121c8fa, 0xafc6202b, + 0x19bc6de5, 0x175b8534, 0x0473bc47, 0x0a945496, 0x2223cea1, + 0x2cc42670, 0x3fec1f03, 0x310bf7d2, 0x6e832b6d, 0x6064c3bc, + 0x734cfacf, 0x7dab121e, 0x551c8829, 0x5bfb60f8, 0x48d3598b, + 0x4634b15a, 0xf7c2e0f5, 0xf9250824, 0xea0d3157, 0xe4ead986, + 0xcc5d43b1, 0xc2baab60, 0xd1929213, 0xdf757ac2, 0x80fda67d, + 0x8e1a4eac, 0x9d3277df, 0x93d59f0e, 0xbb620539, 0xb585ede8, + 0xa6add49b, 0xa84a3c4a, 0x11284946, 0x1fcfa197, 0x0ce798e4, + 0x02007035, 0x2ab7ea02, 0x245002d3, 0x37783ba0, 0x399fd371, + 0x66170fce, 0x68f0e71f, 0x7bd8de6c, 0x753f36bd, 0x5d88ac8a, + 0x536f445b, 0x40477d28, 0x4ea095f9, 0xff56c456, 0xf1b12c87, + 0xe29915f4, 0xec7efd25, 0xc4c96712, 0xca2e8fc3, 0xd906b6b0, + 0xd7e15e61, 0x886982de, 0x868e6a0f, 0x95a6537c, 0x9b41bbad, + 0xb3f6219a, 0xbd11c94b, 0xae39f038, 0xa0de18e9, 0x16a45527, + 0x1843bdf6, 0x0b6b8485, 0x058c6c54, 0x2d3bf663, 0x23dc1eb2, + 0x30f427c1, 0x3e13cf10, 0x619b13af, 0x6f7cfb7e, 0x7c54c20d, + 0x72b32adc, 0x5a04b0eb, 0x54e3583a, 0x47cb6149, 0x492c8998, + 0xf8dad837, 0xf63d30e6, 0xe5150995, 0xebf2e144, 0xc3457b73, + 0xcda293a2, 0xde8aaad1, 0xd06d4200, 0x8fe59ebf, 0x8102766e, + 0x922a4f1d, 0x9ccda7cc, 0xb47a3dfb, 0xba9dd52a, 0xa9b5ec59, + 0xa7520488}, + {0x00000000, 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0xdb4b739d, + 0xd99574ba, 0xd82089a7, 0xdc297af4, 0xdd9c87e9, 0xdf4280ce, + 0xdef77dd3, 0xd7516668, 0xd6e49b75, 0xd43a9c52, 0xd58f614f, + 0xd186921c, 0xd0336f01, 0xd2ed6826, 0xd358953b, 0xc1a15f50, + 0xc014a24d, 0xc2caa56a, 0xc37f5877, 0xc776ab24, 0xc6c35639, + 0xc41d511e, 0xc5a8ac03, 0xcc0eb7b8, 0xcdbb4aa5, 0xcf654d82, + 0xced0b09f, 0xcad943cc, 0xcb6cbed1, 0xc9b2b9f6, 0xc80744eb, + 0xec412d20, 0xedf4d03d, 0xef2ad71a, 0xee9f2a07, 0xea96d954, + 0xeb232449, 0xe9fd236e, 0xe848de73, 0xe1eec5c8, 0xe05b38d5, + 0xe2853ff2, 0xe330c2ef, 0xe73931bc, 0xe68ccca1, 0xe452cb86, + 0xe5e7369b, 0xf71efcf0, 0xf6ab01ed, 0xf47506ca, 0xf5c0fbd7, + 0xf1c90884, 0xf07cf599, 0xf2a2f2be, 0xf3170fa3, 0xfab11418, + 0xfb04e905, 0xf9daee22, 0xf86f133f, 0xfc66e06c, 0xfdd31d71, + 0xff0d1a56, 0xfeb8e74b, 0xb781c9c0, 0xb63434dd, 0xb4ea33fa, + 0xb55fcee7, 0xb1563db4, 0xb0e3c0a9, 0xb23dc78e, 0xb3883a93, + 0xba2e2128, 0xbb9bdc35, 0xb945db12, 0xb8f0260f, 0xbcf9d55c, + 0xbd4c2841, 0xbf922f66, 0xbe27d27b, 0xacde1810, 0xad6be50d, + 0xafb5e22a, 0xae001f37, 0xaa09ec64, 0xabbc1179, 0xa962165e, + 0xa8d7eb43, 0xa171f0f8, 0xa0c40de5, 0xa21a0ac2, 0xa3aff7df, + 0xa7a6048c, 0xa613f991, 0xa4cdfeb6, 0xa57803ab, 0x813e6a60, + 0x808b977d, 0x8255905a, 0x83e06d47, 0x87e99e14, 0x865c6309, + 0x8482642e, 0x85379933, 0x8c918288, 0x8d247f95, 0x8ffa78b2, + 0x8e4f85af, 0x8a4676fc, 0x8bf38be1, 0x892d8cc6, 0x889871db, + 0x9a61bbb0, 0x9bd446ad, 0x990a418a, 0x98bfbc97, 0x9cb64fc4, + 0x9d03b2d9, 0x9fddb5fe, 0x9e6848e3, 0x97ce5358, 0x967bae45, + 0x94a5a962, 0x9510547f, 0x9119a72c, 0x90ac5a31, 0x92725d16, + 0x93c7a00b}, + {0x00000000, 0x6e8c1b41, 0xdd183682, 0xb3942dc3, 0x61416b45, + 0x0fcd7004, 0xbc595dc7, 0xd2d54686, 0xc282d68a, 0xac0ecdcb, + 0x1f9ae008, 0x7116fb49, 0xa3c3bdcf, 0xcd4fa68e, 0x7edb8b4d, + 0x1057900c, 0x5e74ab55, 0x30f8b014, 0x836c9dd7, 0xede08696, + 0x3f35c010, 0x51b9db51, 0xe22df692, 0x8ca1edd3, 0x9cf67ddf, + 0xf27a669e, 0x41ee4b5d, 0x2f62501c, 0xfdb7169a, 0x933b0ddb, + 0x20af2018, 0x4e233b59, 0xbce956aa, 0xd2654deb, 0x61f16028, + 0x0f7d7b69, 0xdda83def, 0xb32426ae, 0x00b00b6d, 0x6e3c102c, + 0x7e6b8020, 0x10e79b61, 0xa373b6a2, 0xcdffade3, 0x1f2aeb65, + 0x71a6f024, 0xc232dde7, 0xacbec6a6, 0xe29dfdff, 0x8c11e6be, + 0x3f85cb7d, 0x5109d03c, 0x83dc96ba, 0xed508dfb, 0x5ec4a038, + 0x3048bb79, 0x201f2b75, 0x4e933034, 0xfd071df7, 0x938b06b6, + 0x415e4030, 0x2fd25b71, 0x9c4676b2, 0xf2ca6df3, 0xa2a3ab15, + 0xcc2fb054, 0x7fbb9d97, 0x113786d6, 0xc3e2c050, 0xad6edb11, + 0x1efaf6d2, 0x7076ed93, 0x60217d9f, 0x0ead66de, 0xbd394b1d, + 0xd3b5505c, 0x016016da, 0x6fec0d9b, 0xdc782058, 0xb2f43b19, + 0xfcd70040, 0x925b1b01, 0x21cf36c2, 0x4f432d83, 0x9d966b05, + 0xf31a7044, 0x408e5d87, 0x2e0246c6, 0x3e55d6ca, 0x50d9cd8b, + 0xe34de048, 0x8dc1fb09, 0x5f14bd8f, 0x3198a6ce, 0x820c8b0d, + 0xec80904c, 0x1e4afdbf, 0x70c6e6fe, 0xc352cb3d, 0xadded07c, + 0x7f0b96fa, 0x11878dbb, 0xa213a078, 0xcc9fbb39, 0xdcc82b35, + 0xb2443074, 0x01d01db7, 0x6f5c06f6, 0xbd894070, 0xd3055b31, + 0x609176f2, 0x0e1d6db3, 0x403e56ea, 0x2eb24dab, 0x9d266068, + 0xf3aa7b29, 0x217f3daf, 0x4ff326ee, 0xfc670b2d, 0x92eb106c, + 0x82bc8060, 0xec309b21, 0x5fa4b6e2, 0x3128ada3, 0xe3fdeb25, + 0x8d71f064, 0x3ee5dda7, 0x5069c6e6, 0x9e36506b, 0xf0ba4b2a, + 0x432e66e9, 0x2da27da8, 0xff773b2e, 0x91fb206f, 0x226f0dac, + 0x4ce316ed, 0x5cb486e1, 0x32389da0, 0x81acb063, 0xef20ab22, + 0x3df5eda4, 0x5379f6e5, 0xe0eddb26, 0x8e61c067, 0xc042fb3e, + 0xaecee07f, 0x1d5acdbc, 0x73d6d6fd, 0xa103907b, 0xcf8f8b3a, + 0x7c1ba6f9, 0x1297bdb8, 0x02c02db4, 0x6c4c36f5, 0xdfd81b36, + 0xb1540077, 0x638146f1, 0x0d0d5db0, 0xbe997073, 0xd0156b32, + 0x22df06c1, 0x4c531d80, 0xffc73043, 0x914b2b02, 0x439e6d84, + 0x2d1276c5, 0x9e865b06, 0xf00a4047, 0xe05dd04b, 0x8ed1cb0a, + 0x3d45e6c9, 0x53c9fd88, 0x811cbb0e, 0xef90a04f, 0x5c048d8c, + 0x328896cd, 0x7cabad94, 0x1227b6d5, 0xa1b39b16, 0xcf3f8057, + 0x1deac6d1, 0x7366dd90, 0xc0f2f053, 0xae7eeb12, 0xbe297b1e, + 0xd0a5605f, 0x63314d9c, 0x0dbd56dd, 0xdf68105b, 0xb1e40b1a, + 0x027026d9, 0x6cfc3d98, 0x3c95fb7e, 0x5219e03f, 0xe18dcdfc, + 0x8f01d6bd, 0x5dd4903b, 0x33588b7a, 0x80cca6b9, 0xee40bdf8, + 0xfe172df4, 0x909b36b5, 0x230f1b76, 0x4d830037, 0x9f5646b1, + 0xf1da5df0, 0x424e7033, 0x2cc26b72, 0x62e1502b, 0x0c6d4b6a, + 0xbff966a9, 0xd1757de8, 0x03a03b6e, 0x6d2c202f, 0xdeb80dec, + 0xb03416ad, 0xa06386a1, 0xceef9de0, 0x7d7bb023, 0x13f7ab62, + 0xc122ede4, 0xafaef6a5, 0x1c3adb66, 0x72b6c027, 0x807cadd4, + 0xeef0b695, 0x5d649b56, 0x33e88017, 0xe13dc691, 0x8fb1ddd0, + 0x3c25f013, 0x52a9eb52, 0x42fe7b5e, 0x2c72601f, 0x9fe64ddc, + 0xf16a569d, 0x23bf101b, 0x4d330b5a, 0xfea72699, 0x902b3dd8, + 0xde080681, 0xb0841dc0, 0x03103003, 0x6d9c2b42, 0xbf496dc4, + 0xd1c57685, 0x62515b46, 0x0cdd4007, 0x1c8ad00b, 0x7206cb4a, + 0xc192e689, 0xaf1efdc8, 0x7dcbbb4e, 0x1347a00f, 0xa0d38dcc, + 0xce5f968d}, + {0x00000000, 0xe71da697, 0x154a4b6f, 0xf257edf8, 0x2a9496de, + 0xcd893049, 0x3fdeddb1, 0xd8c37b26, 0x55292dbc, 0xb2348b2b, + 0x406366d3, 0xa77ec044, 0x7fbdbb62, 0x98a01df5, 0x6af7f00d, + 0x8dea569a, 0xaa525b78, 0x4d4ffdef, 0xbf181017, 0x5805b680, + 0x80c6cda6, 0x67db6b31, 0x958c86c9, 0x7291205e, 0xff7b76c4, + 0x1866d053, 0xea313dab, 0x0d2c9b3c, 0xd5efe01a, 0x32f2468d, + 0xc0a5ab75, 0x27b80de2, 0x8fd5b0b1, 0x68c81626, 0x9a9ffbde, + 0x7d825d49, 0xa541266f, 0x425c80f8, 0xb00b6d00, 0x5716cb97, + 0xdafc9d0d, 0x3de13b9a, 0xcfb6d662, 0x28ab70f5, 0xf0680bd3, + 0x1775ad44, 0xe52240bc, 0x023fe62b, 0x2587ebc9, 0xc29a4d5e, + 0x30cda0a6, 0xd7d00631, 0x0f137d17, 0xe80edb80, 0x1a593678, + 0xfd4490ef, 0x70aec675, 0x97b360e2, 0x65e48d1a, 0x82f92b8d, + 0x5a3a50ab, 0xbd27f63c, 0x4f701bc4, 0xa86dbd53, 0xc4da6723, + 0x23c7c1b4, 0xd1902c4c, 0x368d8adb, 0xee4ef1fd, 0x0953576a, + 0xfb04ba92, 0x1c191c05, 0x91f34a9f, 0x76eeec08, 0x84b901f0, + 0x63a4a767, 0xbb67dc41, 0x5c7a7ad6, 0xae2d972e, 0x493031b9, + 0x6e883c5b, 0x89959acc, 0x7bc27734, 0x9cdfd1a3, 0x441caa85, + 0xa3010c12, 0x5156e1ea, 0xb64b477d, 0x3ba111e7, 0xdcbcb770, + 0x2eeb5a88, 0xc9f6fc1f, 0x11358739, 0xf62821ae, 0x047fcc56, + 0xe3626ac1, 0x4b0fd792, 0xac127105, 0x5e459cfd, 0xb9583a6a, + 0x619b414c, 0x8686e7db, 0x74d10a23, 0x93ccacb4, 0x1e26fa2e, + 0xf93b5cb9, 0x0b6cb141, 0xec7117d6, 0x34b26cf0, 0xd3afca67, + 0x21f8279f, 0xc6e58108, 0xe15d8cea, 0x06402a7d, 0xf417c785, + 0x130a6112, 0xcbc91a34, 0x2cd4bca3, 0xde83515b, 0x399ef7cc, + 0xb474a156, 0x536907c1, 0xa13eea39, 0x46234cae, 0x9ee03788, + 0x79fd911f, 0x8baa7ce7, 0x6cb7da70, 0x52c5c807, 0xb5d86e90, + 0x478f8368, 0xa09225ff, 0x78515ed9, 0x9f4cf84e, 0x6d1b15b6, + 0x8a06b321, 0x07ece5bb, 0xe0f1432c, 0x12a6aed4, 0xf5bb0843, + 0x2d787365, 0xca65d5f2, 0x3832380a, 0xdf2f9e9d, 0xf897937f, + 0x1f8a35e8, 0xedddd810, 0x0ac07e87, 0xd20305a1, 0x351ea336, + 0xc7494ece, 0x2054e859, 0xadbebec3, 0x4aa31854, 0xb8f4f5ac, + 0x5fe9533b, 0x872a281d, 0x60378e8a, 0x92606372, 0x757dc5e5, + 0xdd1078b6, 0x3a0dde21, 0xc85a33d9, 0x2f47954e, 0xf784ee68, + 0x109948ff, 0xe2cea507, 0x05d30390, 0x8839550a, 0x6f24f39d, + 0x9d731e65, 0x7a6eb8f2, 0xa2adc3d4, 0x45b06543, 0xb7e788bb, + 0x50fa2e2c, 0x774223ce, 0x905f8559, 0x620868a1, 0x8515ce36, + 0x5dd6b510, 0xbacb1387, 0x489cfe7f, 0xaf8158e8, 0x226b0e72, + 0xc576a8e5, 0x3721451d, 0xd03ce38a, 0x08ff98ac, 0xefe23e3b, + 0x1db5d3c3, 0xfaa87554, 0x961faf24, 0x710209b3, 0x8355e44b, + 0x644842dc, 0xbc8b39fa, 0x5b969f6d, 0xa9c17295, 0x4edcd402, + 0xc3368298, 0x242b240f, 0xd67cc9f7, 0x31616f60, 0xe9a21446, + 0x0ebfb2d1, 0xfce85f29, 0x1bf5f9be, 0x3c4df45c, 0xdb5052cb, + 0x2907bf33, 0xce1a19a4, 0x16d96282, 0xf1c4c415, 0x039329ed, + 0xe48e8f7a, 0x6964d9e0, 0x8e797f77, 0x7c2e928f, 0x9b333418, + 0x43f04f3e, 0xa4ede9a9, 0x56ba0451, 0xb1a7a2c6, 0x19ca1f95, + 0xfed7b902, 0x0c8054fa, 0xeb9df26d, 0x335e894b, 0xd4432fdc, + 0x2614c224, 0xc10964b3, 0x4ce33229, 0xabfe94be, 0x59a97946, + 0xbeb4dfd1, 0x6677a4f7, 0x816a0260, 0x733def98, 0x9420490f, + 0xb39844ed, 0x5485e27a, 0xa6d20f82, 0x41cfa915, 0x990cd233, + 0x7e1174a4, 0x8c46995c, 0x6b5b3fcb, 0xe6b16951, 0x01accfc6, + 0xf3fb223e, 0x14e684a9, 0xcc25ff8f, 0x2b385918, 0xd96fb4e0, + 0x3e721277}, + {0x00000000, 0xa58b900e, 0x9066265d, 0x35edb653, 0xfbbd4afb, + 0x5e36daf5, 0x6bdb6ca6, 0xce50fca8, 0x2c0b93b7, 0x898003b9, + 0xbc6db5ea, 0x19e625e4, 0xd7b6d94c, 0x723d4942, 0x47d0ff11, + 0xe25b6f1f, 0x5817276e, 0xfd9cb760, 0xc8710133, 0x6dfa913d, + 0xa3aa6d95, 0x0621fd9b, 0x33cc4bc8, 0x9647dbc6, 0x741cb4d9, + 0xd19724d7, 0xe47a9284, 0x41f1028a, 0x8fa1fe22, 0x2a2a6e2c, + 0x1fc7d87f, 0xba4c4871, 0xb02e4edc, 0x15a5ded2, 0x20486881, + 0x85c3f88f, 0x4b930427, 0xee189429, 0xdbf5227a, 0x7e7eb274, + 0x9c25dd6b, 0x39ae4d65, 0x0c43fb36, 0xa9c86b38, 0x67989790, + 0xc213079e, 0xf7feb1cd, 0x527521c3, 0xe83969b2, 0x4db2f9bc, + 0x785f4fef, 0xddd4dfe1, 0x13842349, 0xb60fb347, 0x83e20514, + 0x2669951a, 0xc432fa05, 0x61b96a0b, 0x5454dc58, 0xf1df4c56, + 0x3f8fb0fe, 0x9a0420f0, 0xafe996a3, 0x0a6206ad, 0xbb2d9bf9, + 0x1ea60bf7, 0x2b4bbda4, 0x8ec02daa, 0x4090d102, 0xe51b410c, + 0xd0f6f75f, 0x757d6751, 0x9726084e, 0x32ad9840, 0x07402e13, + 0xa2cbbe1d, 0x6c9b42b5, 0xc910d2bb, 0xfcfd64e8, 0x5976f4e6, + 0xe33abc97, 0x46b12c99, 0x735c9aca, 0xd6d70ac4, 0x1887f66c, + 0xbd0c6662, 0x88e1d031, 0x2d6a403f, 0xcf312f20, 0x6ababf2e, + 0x5f57097d, 0xfadc9973, 0x348c65db, 0x9107f5d5, 0xa4ea4386, + 0x0161d388, 0x0b03d525, 0xae88452b, 0x9b65f378, 0x3eee6376, + 0xf0be9fde, 0x55350fd0, 0x60d8b983, 0xc553298d, 0x27084692, + 0x8283d69c, 0xb76e60cf, 0x12e5f0c1, 0xdcb50c69, 0x793e9c67, + 0x4cd32a34, 0xe958ba3a, 0x5314f24b, 0xf69f6245, 0xc372d416, + 0x66f94418, 0xa8a9b8b0, 0x0d2228be, 0x38cf9eed, 0x9d440ee3, + 0x7f1f61fc, 0xda94f1f2, 0xef7947a1, 0x4af2d7af, 0x84a22b07, + 0x2129bb09, 0x14c40d5a, 0xb14f9d54, 0xad2a31b3, 0x08a1a1bd, + 0x3d4c17ee, 0x98c787e0, 0x56977b48, 0xf31ceb46, 0xc6f15d15, + 0x637acd1b, 0x8121a204, 0x24aa320a, 0x11478459, 0xb4cc1457, + 0x7a9ce8ff, 0xdf1778f1, 0xeafacea2, 0x4f715eac, 0xf53d16dd, + 0x50b686d3, 0x655b3080, 0xc0d0a08e, 0x0e805c26, 0xab0bcc28, + 0x9ee67a7b, 0x3b6dea75, 0xd936856a, 0x7cbd1564, 0x4950a337, + 0xecdb3339, 0x228bcf91, 0x87005f9f, 0xb2ede9cc, 0x176679c2, + 0x1d047f6f, 0xb88fef61, 0x8d625932, 0x28e9c93c, 0xe6b93594, + 0x4332a59a, 0x76df13c9, 0xd35483c7, 0x310fecd8, 0x94847cd6, + 0xa169ca85, 0x04e25a8b, 0xcab2a623, 0x6f39362d, 0x5ad4807e, + 0xff5f1070, 0x45135801, 0xe098c80f, 0xd5757e5c, 0x70feee52, + 0xbeae12fa, 0x1b2582f4, 0x2ec834a7, 0x8b43a4a9, 0x6918cbb6, + 0xcc935bb8, 0xf97eedeb, 0x5cf57de5, 0x92a5814d, 0x372e1143, + 0x02c3a710, 0xa748371e, 0x1607aa4a, 0xb38c3a44, 0x86618c17, + 0x23ea1c19, 0xedbae0b1, 0x483170bf, 0x7ddcc6ec, 0xd85756e2, + 0x3a0c39fd, 0x9f87a9f3, 0xaa6a1fa0, 0x0fe18fae, 0xc1b17306, + 0x643ae308, 0x51d7555b, 0xf45cc555, 0x4e108d24, 0xeb9b1d2a, + 0xde76ab79, 0x7bfd3b77, 0xb5adc7df, 0x102657d1, 0x25cbe182, + 0x8040718c, 0x621b1e93, 0xc7908e9d, 0xf27d38ce, 0x57f6a8c0, + 0x99a65468, 0x3c2dc466, 0x09c07235, 0xac4be23b, 0xa629e496, + 0x03a27498, 0x364fc2cb, 0x93c452c5, 0x5d94ae6d, 0xf81f3e63, + 0xcdf28830, 0x6879183e, 0x8a227721, 0x2fa9e72f, 0x1a44517c, + 0xbfcfc172, 0x719f3dda, 0xd414add4, 0xe1f91b87, 0x44728b89, + 0xfe3ec3f8, 0x5bb553f6, 0x6e58e5a5, 0xcbd375ab, 0x05838903, + 0xa008190d, 0x95e5af5e, 0x306e3f50, 0xd235504f, 0x77bec041, + 0x42537612, 0xe7d8e61c, 0x29881ab4, 0x8c038aba, 0xb9ee3ce9, + 0x1c65ace7}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x0000000000000000, 0x0e908ba500000000, 0x5d26669000000000, + 0x53b6ed3500000000, 0xfb4abdfb00000000, 0xf5da365e00000000, + 0xa66cdb6b00000000, 0xa8fc50ce00000000, 0xb7930b2c00000000, + 0xb903808900000000, 0xeab56dbc00000000, 0xe425e61900000000, + 0x4cd9b6d700000000, 0x42493d7200000000, 0x11ffd04700000000, + 0x1f6f5be200000000, 0x6e27175800000000, 0x60b79cfd00000000, + 0x330171c800000000, 0x3d91fa6d00000000, 0x956daaa300000000, + 0x9bfd210600000000, 0xc84bcc3300000000, 0xc6db479600000000, + 0xd9b41c7400000000, 0xd72497d100000000, 0x84927ae400000000, + 0x8a02f14100000000, 0x22fea18f00000000, 0x2c6e2a2a00000000, + 0x7fd8c71f00000000, 0x71484cba00000000, 0xdc4e2eb000000000, + 0xd2dea51500000000, 0x8168482000000000, 0x8ff8c38500000000, + 0x2704934b00000000, 0x299418ee00000000, 0x7a22f5db00000000, + 0x74b27e7e00000000, 0x6bdd259c00000000, 0x654dae3900000000, + 0x36fb430c00000000, 0x386bc8a900000000, 0x9097986700000000, + 0x9e0713c200000000, 0xcdb1fef700000000, 0xc321755200000000, + 0xb26939e800000000, 0xbcf9b24d00000000, 0xef4f5f7800000000, + 0xe1dfd4dd00000000, 0x4923841300000000, 0x47b30fb600000000, + 0x1405e28300000000, 0x1a95692600000000, 0x05fa32c400000000, + 0x0b6ab96100000000, 0x58dc545400000000, 0x564cdff100000000, + 0xfeb08f3f00000000, 0xf020049a00000000, 0xa396e9af00000000, + 0xad06620a00000000, 0xf99b2dbb00000000, 0xf70ba61e00000000, + 0xa4bd4b2b00000000, 0xaa2dc08e00000000, 0x02d1904000000000, + 0x0c411be500000000, 0x5ff7f6d000000000, 0x51677d7500000000, + 0x4e08269700000000, 0x4098ad3200000000, 0x132e400700000000, + 0x1dbecba200000000, 0xb5429b6c00000000, 0xbbd210c900000000, + 0xe864fdfc00000000, 0xe6f4765900000000, 0x97bc3ae300000000, + 0x992cb14600000000, 0xca9a5c7300000000, 0xc40ad7d600000000, + 0x6cf6871800000000, 0x62660cbd00000000, 0x31d0e18800000000, + 0x3f406a2d00000000, 0x202f31cf00000000, 0x2ebfba6a00000000, + 0x7d09575f00000000, 0x7399dcfa00000000, 0xdb658c3400000000, + 0xd5f5079100000000, 0x8643eaa400000000, 0x88d3610100000000, + 0x25d5030b00000000, 0x2b4588ae00000000, 0x78f3659b00000000, + 0x7663ee3e00000000, 0xde9fbef000000000, 0xd00f355500000000, + 0x83b9d86000000000, 0x8d2953c500000000, 0x9246082700000000, + 0x9cd6838200000000, 0xcf606eb700000000, 0xc1f0e51200000000, + 0x690cb5dc00000000, 0x679c3e7900000000, 0x342ad34c00000000, + 0x3aba58e900000000, 0x4bf2145300000000, 0x45629ff600000000, + 0x16d472c300000000, 0x1844f96600000000, 0xb0b8a9a800000000, + 0xbe28220d00000000, 0xed9ecf3800000000, 0xe30e449d00000000, + 0xfc611f7f00000000, 0xf2f194da00000000, 0xa14779ef00000000, + 0xafd7f24a00000000, 0x072ba28400000000, 0x09bb292100000000, + 0x5a0dc41400000000, 0x549d4fb100000000, 0xb3312aad00000000, + 0xbda1a10800000000, 0xee174c3d00000000, 0xe087c79800000000, + 0x487b975600000000, 0x46eb1cf300000000, 0x155df1c600000000, + 0x1bcd7a6300000000, 0x04a2218100000000, 0x0a32aa2400000000, + 0x5984471100000000, 0x5714ccb400000000, 0xffe89c7a00000000, + 0xf17817df00000000, 0xa2cefaea00000000, 0xac5e714f00000000, + 0xdd163df500000000, 0xd386b65000000000, 0x80305b6500000000, + 0x8ea0d0c000000000, 0x265c800e00000000, 0x28cc0bab00000000, + 0x7b7ae69e00000000, 0x75ea6d3b00000000, 0x6a8536d900000000, + 0x6415bd7c00000000, 0x37a3504900000000, 0x3933dbec00000000, + 0x91cf8b2200000000, 0x9f5f008700000000, 0xcce9edb200000000, + 0xc279661700000000, 0x6f7f041d00000000, 0x61ef8fb800000000, + 0x3259628d00000000, 0x3cc9e92800000000, 0x9435b9e600000000, + 0x9aa5324300000000, 0xc913df7600000000, 0xc78354d300000000, + 0xd8ec0f3100000000, 0xd67c849400000000, 0x85ca69a100000000, + 0x8b5ae20400000000, 0x23a6b2ca00000000, 0x2d36396f00000000, + 0x7e80d45a00000000, 0x70105fff00000000, 0x0158134500000000, + 0x0fc898e000000000, 0x5c7e75d500000000, 0x52eefe7000000000, + 0xfa12aebe00000000, 0xf482251b00000000, 0xa734c82e00000000, + 0xa9a4438b00000000, 0xb6cb186900000000, 0xb85b93cc00000000, + 0xebed7ef900000000, 0xe57df55c00000000, 0x4d81a59200000000, + 0x43112e3700000000, 0x10a7c30200000000, 0x1e3748a700000000, + 0x4aaa071600000000, 0x443a8cb300000000, 0x178c618600000000, + 0x191cea2300000000, 0xb1e0baed00000000, 0xbf70314800000000, + 0xecc6dc7d00000000, 0xe25657d800000000, 0xfd390c3a00000000, + 0xf3a9879f00000000, 0xa01f6aaa00000000, 0xae8fe10f00000000, + 0x0673b1c100000000, 0x08e33a6400000000, 0x5b55d75100000000, + 0x55c55cf400000000, 0x248d104e00000000, 0x2a1d9beb00000000, + 0x79ab76de00000000, 0x773bfd7b00000000, 0xdfc7adb500000000, + 0xd157261000000000, 0x82e1cb2500000000, 0x8c71408000000000, + 0x931e1b6200000000, 0x9d8e90c700000000, 0xce387df200000000, + 0xc0a8f65700000000, 0x6854a69900000000, 0x66c42d3c00000000, + 0x3572c00900000000, 0x3be24bac00000000, 0x96e429a600000000, + 0x9874a20300000000, 0xcbc24f3600000000, 0xc552c49300000000, + 0x6dae945d00000000, 0x633e1ff800000000, 0x3088f2cd00000000, + 0x3e18796800000000, 0x2177228a00000000, 0x2fe7a92f00000000, + 0x7c51441a00000000, 0x72c1cfbf00000000, 0xda3d9f7100000000, + 0xd4ad14d400000000, 0x871bf9e100000000, 0x898b724400000000, + 0xf8c33efe00000000, 0xf653b55b00000000, 0xa5e5586e00000000, + 0xab75d3cb00000000, 0x0389830500000000, 0x0d1908a000000000, + 0x5eafe59500000000, 0x503f6e3000000000, 0x4f5035d200000000, + 0x41c0be7700000000, 0x1276534200000000, 0x1ce6d8e700000000, + 0xb41a882900000000, 0xba8a038c00000000, 0xe93ceeb900000000, + 0xe7ac651c00000000}, + {0x0000000000000000, 0x97a61de700000000, 0x6f4b4a1500000000, + 0xf8ed57f200000000, 0xde96942a00000000, 0x493089cd00000000, + 0xb1ddde3f00000000, 0x267bc3d800000000, 0xbc2d295500000000, + 0x2b8b34b200000000, 0xd366634000000000, 0x44c07ea700000000, + 0x62bbbd7f00000000, 0xf51da09800000000, 0x0df0f76a00000000, + 0x9a56ea8d00000000, 0x785b52aa00000000, 0xeffd4f4d00000000, + 0x171018bf00000000, 0x80b6055800000000, 0xa6cdc68000000000, + 0x316bdb6700000000, 0xc9868c9500000000, 0x5e20917200000000, + 0xc4767bff00000000, 0x53d0661800000000, 0xab3d31ea00000000, + 0x3c9b2c0d00000000, 0x1ae0efd500000000, 0x8d46f23200000000, + 0x75aba5c000000000, 0xe20db82700000000, 0xb1b0d58f00000000, + 0x2616c86800000000, 0xdefb9f9a00000000, 0x495d827d00000000, + 0x6f2641a500000000, 0xf8805c4200000000, 0x006d0bb000000000, + 0x97cb165700000000, 0x0d9dfcda00000000, 0x9a3be13d00000000, + 0x62d6b6cf00000000, 0xf570ab2800000000, 0xd30b68f000000000, + 0x44ad751700000000, 0xbc4022e500000000, 0x2be63f0200000000, + 0xc9eb872500000000, 0x5e4d9ac200000000, 0xa6a0cd3000000000, + 0x3106d0d700000000, 0x177d130f00000000, 0x80db0ee800000000, + 0x7836591a00000000, 0xef9044fd00000000, 0x75c6ae7000000000, + 0xe260b39700000000, 0x1a8de46500000000, 0x8d2bf98200000000, + 0xab503a5a00000000, 0x3cf627bd00000000, 0xc41b704f00000000, + 0x53bd6da800000000, 0x2367dac400000000, 0xb4c1c72300000000, + 0x4c2c90d100000000, 0xdb8a8d3600000000, 0xfdf14eee00000000, + 0x6a57530900000000, 0x92ba04fb00000000, 0x051c191c00000000, + 0x9f4af39100000000, 0x08ecee7600000000, 0xf001b98400000000, + 0x67a7a46300000000, 0x41dc67bb00000000, 0xd67a7a5c00000000, + 0x2e972dae00000000, 0xb931304900000000, 0x5b3c886e00000000, + 0xcc9a958900000000, 0x3477c27b00000000, 0xa3d1df9c00000000, + 0x85aa1c4400000000, 0x120c01a300000000, 0xeae1565100000000, + 0x7d474bb600000000, 0xe711a13b00000000, 0x70b7bcdc00000000, + 0x885aeb2e00000000, 0x1ffcf6c900000000, 0x3987351100000000, + 0xae2128f600000000, 0x56cc7f0400000000, 0xc16a62e300000000, + 0x92d70f4b00000000, 0x057112ac00000000, 0xfd9c455e00000000, + 0x6a3a58b900000000, 0x4c419b6100000000, 0xdbe7868600000000, + 0x230ad17400000000, 0xb4accc9300000000, 0x2efa261e00000000, + 0xb95c3bf900000000, 0x41b16c0b00000000, 0xd61771ec00000000, + 0xf06cb23400000000, 0x67caafd300000000, 0x9f27f82100000000, + 0x0881e5c600000000, 0xea8c5de100000000, 0x7d2a400600000000, + 0x85c717f400000000, 0x12610a1300000000, 0x341ac9cb00000000, + 0xa3bcd42c00000000, 0x5b5183de00000000, 0xccf79e3900000000, + 0x56a174b400000000, 0xc107695300000000, 0x39ea3ea100000000, + 0xae4c234600000000, 0x8837e09e00000000, 0x1f91fd7900000000, + 0xe77caa8b00000000, 0x70dab76c00000000, 0x07c8c55200000000, + 0x906ed8b500000000, 0x68838f4700000000, 0xff2592a000000000, + 0xd95e517800000000, 0x4ef84c9f00000000, 0xb6151b6d00000000, + 0x21b3068a00000000, 0xbbe5ec0700000000, 0x2c43f1e000000000, + 0xd4aea61200000000, 0x4308bbf500000000, 0x6573782d00000000, + 0xf2d565ca00000000, 0x0a38323800000000, 0x9d9e2fdf00000000, + 0x7f9397f800000000, 0xe8358a1f00000000, 0x10d8dded00000000, + 0x877ec00a00000000, 0xa10503d200000000, 0x36a31e3500000000, + 0xce4e49c700000000, 0x59e8542000000000, 0xc3bebead00000000, + 0x5418a34a00000000, 0xacf5f4b800000000, 0x3b53e95f00000000, + 0x1d282a8700000000, 0x8a8e376000000000, 0x7263609200000000, + 0xe5c57d7500000000, 0xb67810dd00000000, 0x21de0d3a00000000, + 0xd9335ac800000000, 0x4e95472f00000000, 0x68ee84f700000000, + 0xff48991000000000, 0x07a5cee200000000, 0x9003d30500000000, + 0x0a55398800000000, 0x9df3246f00000000, 0x651e739d00000000, + 0xf2b86e7a00000000, 0xd4c3ada200000000, 0x4365b04500000000, + 0xbb88e7b700000000, 0x2c2efa5000000000, 0xce23427700000000, + 0x59855f9000000000, 0xa168086200000000, 0x36ce158500000000, + 0x10b5d65d00000000, 0x8713cbba00000000, 0x7ffe9c4800000000, + 0xe85881af00000000, 0x720e6b2200000000, 0xe5a876c500000000, + 0x1d45213700000000, 0x8ae33cd000000000, 0xac98ff0800000000, + 0x3b3ee2ef00000000, 0xc3d3b51d00000000, 0x5475a8fa00000000, + 0x24af1f9600000000, 0xb309027100000000, 0x4be4558300000000, + 0xdc42486400000000, 0xfa398bbc00000000, 0x6d9f965b00000000, + 0x9572c1a900000000, 0x02d4dc4e00000000, 0x988236c300000000, + 0x0f242b2400000000, 0xf7c97cd600000000, 0x606f613100000000, + 0x4614a2e900000000, 0xd1b2bf0e00000000, 0x295fe8fc00000000, + 0xbef9f51b00000000, 0x5cf44d3c00000000, 0xcb5250db00000000, + 0x33bf072900000000, 0xa4191ace00000000, 0x8262d91600000000, + 0x15c4c4f100000000, 0xed29930300000000, 0x7a8f8ee400000000, + 0xe0d9646900000000, 0x777f798e00000000, 0x8f922e7c00000000, + 0x1834339b00000000, 0x3e4ff04300000000, 0xa9e9eda400000000, + 0x5104ba5600000000, 0xc6a2a7b100000000, 0x951fca1900000000, + 0x02b9d7fe00000000, 0xfa54800c00000000, 0x6df29deb00000000, + 0x4b895e3300000000, 0xdc2f43d400000000, 0x24c2142600000000, + 0xb36409c100000000, 0x2932e34c00000000, 0xbe94feab00000000, + 0x4679a95900000000, 0xd1dfb4be00000000, 0xf7a4776600000000, + 0x60026a8100000000, 0x98ef3d7300000000, 0x0f49209400000000, + 0xed4498b300000000, 0x7ae2855400000000, 0x820fd2a600000000, + 0x15a9cf4100000000, 0x33d20c9900000000, 0xa474117e00000000, + 0x5c99468c00000000, 0xcb3f5b6b00000000, 0x5169b1e600000000, + 0xc6cfac0100000000, 0x3e22fbf300000000, 0xa984e61400000000, + 0x8fff25cc00000000, 0x1859382b00000000, 0xe0b46fd900000000, + 0x7712723e00000000}, + {0x0000000000000000, 0x411b8c6e00000000, 0x823618dd00000000, + 0xc32d94b300000000, 0x456b416100000000, 0x0470cd0f00000000, + 0xc75d59bc00000000, 0x8646d5d200000000, 0x8ad682c200000000, + 0xcbcd0eac00000000, 0x08e09a1f00000000, 0x49fb167100000000, + 0xcfbdc3a300000000, 0x8ea64fcd00000000, 0x4d8bdb7e00000000, + 0x0c90571000000000, 0x55ab745e00000000, 0x14b0f83000000000, + 0xd79d6c8300000000, 0x9686e0ed00000000, 0x10c0353f00000000, + 0x51dbb95100000000, 0x92f62de200000000, 0xd3eda18c00000000, + 0xdf7df69c00000000, 0x9e667af200000000, 0x5d4bee4100000000, + 0x1c50622f00000000, 0x9a16b7fd00000000, 0xdb0d3b9300000000, + 0x1820af2000000000, 0x593b234e00000000, 0xaa56e9bc00000000, + 0xeb4d65d200000000, 0x2860f16100000000, 0x697b7d0f00000000, + 0xef3da8dd00000000, 0xae2624b300000000, 0x6d0bb00000000000, + 0x2c103c6e00000000, 0x20806b7e00000000, 0x619be71000000000, + 0xa2b673a300000000, 0xe3adffcd00000000, 0x65eb2a1f00000000, + 0x24f0a67100000000, 0xe7dd32c200000000, 0xa6c6beac00000000, + 0xfffd9de200000000, 0xbee6118c00000000, 0x7dcb853f00000000, + 0x3cd0095100000000, 0xba96dc8300000000, 0xfb8d50ed00000000, + 0x38a0c45e00000000, 0x79bb483000000000, 0x752b1f2000000000, + 0x3430934e00000000, 0xf71d07fd00000000, 0xb6068b9300000000, + 0x30405e4100000000, 0x715bd22f00000000, 0xb276469c00000000, + 0xf36dcaf200000000, 0x15aba3a200000000, 0x54b02fcc00000000, + 0x979dbb7f00000000, 0xd686371100000000, 0x50c0e2c300000000, + 0x11db6ead00000000, 0xd2f6fa1e00000000, 0x93ed767000000000, + 0x9f7d216000000000, 0xde66ad0e00000000, 0x1d4b39bd00000000, + 0x5c50b5d300000000, 0xda16600100000000, 0x9b0dec6f00000000, + 0x582078dc00000000, 0x193bf4b200000000, 0x4000d7fc00000000, + 0x011b5b9200000000, 0xc236cf2100000000, 0x832d434f00000000, + 0x056b969d00000000, 0x44701af300000000, 0x875d8e4000000000, + 0xc646022e00000000, 0xcad6553e00000000, 0x8bcdd95000000000, + 0x48e04de300000000, 0x09fbc18d00000000, 0x8fbd145f00000000, + 0xcea6983100000000, 0x0d8b0c8200000000, 0x4c9080ec00000000, + 0xbffd4a1e00000000, 0xfee6c67000000000, 0x3dcb52c300000000, + 0x7cd0dead00000000, 0xfa960b7f00000000, 0xbb8d871100000000, + 0x78a013a200000000, 0x39bb9fcc00000000, 0x352bc8dc00000000, + 0x743044b200000000, 0xb71dd00100000000, 0xf6065c6f00000000, + 0x704089bd00000000, 0x315b05d300000000, 0xf276916000000000, + 0xb36d1d0e00000000, 0xea563e4000000000, 0xab4db22e00000000, + 0x6860269d00000000, 0x297baaf300000000, 0xaf3d7f2100000000, + 0xee26f34f00000000, 0x2d0b67fc00000000, 0x6c10eb9200000000, + 0x6080bc8200000000, 0x219b30ec00000000, 0xe2b6a45f00000000, + 0xa3ad283100000000, 0x25ebfde300000000, 0x64f0718d00000000, + 0xa7dde53e00000000, 0xe6c6695000000000, 0x6b50369e00000000, + 0x2a4bbaf000000000, 0xe9662e4300000000, 0xa87da22d00000000, + 0x2e3b77ff00000000, 0x6f20fb9100000000, 0xac0d6f2200000000, + 0xed16e34c00000000, 0xe186b45c00000000, 0xa09d383200000000, + 0x63b0ac8100000000, 0x22ab20ef00000000, 0xa4edf53d00000000, + 0xe5f6795300000000, 0x26dbede000000000, 0x67c0618e00000000, + 0x3efb42c000000000, 0x7fe0ceae00000000, 0xbccd5a1d00000000, + 0xfdd6d67300000000, 0x7b9003a100000000, 0x3a8b8fcf00000000, + 0xf9a61b7c00000000, 0xb8bd971200000000, 0xb42dc00200000000, + 0xf5364c6c00000000, 0x361bd8df00000000, 0x770054b100000000, + 0xf146816300000000, 0xb05d0d0d00000000, 0x737099be00000000, + 0x326b15d000000000, 0xc106df2200000000, 0x801d534c00000000, + 0x4330c7ff00000000, 0x022b4b9100000000, 0x846d9e4300000000, + 0xc576122d00000000, 0x065b869e00000000, 0x47400af000000000, + 0x4bd05de000000000, 0x0acbd18e00000000, 0xc9e6453d00000000, + 0x88fdc95300000000, 0x0ebb1c8100000000, 0x4fa090ef00000000, + 0x8c8d045c00000000, 0xcd96883200000000, 0x94adab7c00000000, + 0xd5b6271200000000, 0x169bb3a100000000, 0x57803fcf00000000, + 0xd1c6ea1d00000000, 0x90dd667300000000, 0x53f0f2c000000000, + 0x12eb7eae00000000, 0x1e7b29be00000000, 0x5f60a5d000000000, + 0x9c4d316300000000, 0xdd56bd0d00000000, 0x5b1068df00000000, + 0x1a0be4b100000000, 0xd926700200000000, 0x983dfc6c00000000, + 0x7efb953c00000000, 0x3fe0195200000000, 0xfccd8de100000000, + 0xbdd6018f00000000, 0x3b90d45d00000000, 0x7a8b583300000000, + 0xb9a6cc8000000000, 0xf8bd40ee00000000, 0xf42d17fe00000000, + 0xb5369b9000000000, 0x761b0f2300000000, 0x3700834d00000000, + 0xb146569f00000000, 0xf05ddaf100000000, 0x33704e4200000000, + 0x726bc22c00000000, 0x2b50e16200000000, 0x6a4b6d0c00000000, + 0xa966f9bf00000000, 0xe87d75d100000000, 0x6e3ba00300000000, + 0x2f202c6d00000000, 0xec0db8de00000000, 0xad1634b000000000, + 0xa18663a000000000, 0xe09defce00000000, 0x23b07b7d00000000, + 0x62abf71300000000, 0xe4ed22c100000000, 0xa5f6aeaf00000000, + 0x66db3a1c00000000, 0x27c0b67200000000, 0xd4ad7c8000000000, + 0x95b6f0ee00000000, 0x569b645d00000000, 0x1780e83300000000, + 0x91c63de100000000, 0xd0ddb18f00000000, 0x13f0253c00000000, + 0x52eba95200000000, 0x5e7bfe4200000000, 0x1f60722c00000000, + 0xdc4de69f00000000, 0x9d566af100000000, 0x1b10bf2300000000, + 0x5a0b334d00000000, 0x9926a7fe00000000, 0xd83d2b9000000000, + 0x810608de00000000, 0xc01d84b000000000, 0x0330100300000000, + 0x422b9c6d00000000, 0xc46d49bf00000000, 0x8576c5d100000000, + 0x465b516200000000, 0x0740dd0c00000000, 0x0bd08a1c00000000, + 0x4acb067200000000, 0x89e692c100000000, 0xc8fd1eaf00000000, + 0x4ebbcb7d00000000, 0x0fa0471300000000, 0xcc8dd3a000000000, + 0x8d965fce00000000}, + {0x0000000000000000, 0x1dfdb50100000000, 0x3afa6b0300000000, + 0x2707de0200000000, 0x74f4d70600000000, 0x6909620700000000, + 0x4e0ebc0500000000, 0x53f3090400000000, 0xe8e8af0d00000000, + 0xf5151a0c00000000, 0xd212c40e00000000, 0xcfef710f00000000, + 0x9c1c780b00000000, 0x81e1cd0a00000000, 0xa6e6130800000000, + 0xbb1ba60900000000, 0xd0d15f1b00000000, 0xcd2cea1a00000000, + 0xea2b341800000000, 0xf7d6811900000000, 0xa425881d00000000, + 0xb9d83d1c00000000, 0x9edfe31e00000000, 0x8322561f00000000, + 0x3839f01600000000, 0x25c4451700000000, 0x02c39b1500000000, + 0x1f3e2e1400000000, 0x4ccd271000000000, 0x5130921100000000, + 0x76374c1300000000, 0x6bcaf91200000000, 0xa0a3bf3600000000, + 0xbd5e0a3700000000, 0x9a59d43500000000, 0x87a4613400000000, + 0xd457683000000000, 0xc9aadd3100000000, 0xeead033300000000, + 0xf350b63200000000, 0x484b103b00000000, 0x55b6a53a00000000, + 0x72b17b3800000000, 0x6f4cce3900000000, 0x3cbfc73d00000000, + 0x2142723c00000000, 0x0645ac3e00000000, 0x1bb8193f00000000, + 0x7072e02d00000000, 0x6d8f552c00000000, 0x4a888b2e00000000, + 0x57753e2f00000000, 0x0486372b00000000, 0x197b822a00000000, + 0x3e7c5c2800000000, 0x2381e92900000000, 0x989a4f2000000000, + 0x8567fa2100000000, 0xa260242300000000, 0xbf9d912200000000, + 0xec6e982600000000, 0xf1932d2700000000, 0xd694f32500000000, + 0xcb69462400000000, 0x40477f6d00000000, 0x5dbaca6c00000000, + 0x7abd146e00000000, 0x6740a16f00000000, 0x34b3a86b00000000, + 0x294e1d6a00000000, 0x0e49c36800000000, 0x13b4766900000000, + 0xa8afd06000000000, 0xb552656100000000, 0x9255bb6300000000, + 0x8fa80e6200000000, 0xdc5b076600000000, 0xc1a6b26700000000, + 0xe6a16c6500000000, 0xfb5cd96400000000, 0x9096207600000000, + 0x8d6b957700000000, 0xaa6c4b7500000000, 0xb791fe7400000000, + 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0xfdd2f54900000000, 0xf531957500000000, + 0xed14343100000000, 0xe5f7540d00000000, 0xdc40008000000000, + 0xd4a360bc00000000, 0xcc86c1f800000000, 0xc465a1c400000000, + 0xfccc837100000000, 0xf42fe34d00000000, 0xec0a420900000000, + 0xe4e9223500000000}, + {0x0000000000000000, 0xd1e8e70e00000000, 0xa2d1cf1d00000000, + 0x7339281300000000, 0x44a39f3b00000000, 0x954b783500000000, + 0xe672502600000000, 0x379ab72800000000, 0x88463f7700000000, + 0x59aed87900000000, 0x2a97f06a00000000, 0xfb7f176400000000, + 0xcce5a04c00000000, 0x1d0d474200000000, 0x6e346f5100000000, + 0xbfdc885f00000000, 0x108d7eee00000000, 0xc16599e000000000, + 0xb25cb1f300000000, 0x63b456fd00000000, 0x542ee1d500000000, + 0x85c606db00000000, 0xf6ff2ec800000000, 0x2717c9c600000000, + 0x98cb419900000000, 0x4923a69700000000, 0x3a1a8e8400000000, + 0xebf2698a00000000, 0xdc68dea200000000, 0x0d8039ac00000000, + 0x7eb911bf00000000, 0xaf51f6b100000000, 0x611c8c0700000000, + 0xb0f46b0900000000, 0xc3cd431a00000000, 0x1225a41400000000, + 0x25bf133c00000000, 0xf457f43200000000, 0x876edc2100000000, + 0x56863b2f00000000, 0xe95ab37000000000, 0x38b2547e00000000, + 0x4b8b7c6d00000000, 0x9a639b6300000000, 0xadf92c4b00000000, + 0x7c11cb4500000000, 0x0f28e35600000000, 0xdec0045800000000, + 0x7191f2e900000000, 0xa07915e700000000, 0xd3403df400000000, + 0x02a8dafa00000000, 0x35326dd200000000, 0xe4da8adc00000000, + 0x97e3a2cf00000000, 0x460b45c100000000, 0xf9d7cd9e00000000, + 0x283f2a9000000000, 0x5b06028300000000, 0x8aeee58d00000000, + 0xbd7452a500000000, 0x6c9cb5ab00000000, 0x1fa59db800000000, + 0xce4d7ab600000000, 0xc238180f00000000, 0x13d0ff0100000000, + 0x60e9d71200000000, 0xb101301c00000000, 0x869b873400000000, + 0x5773603a00000000, 0x244a482900000000, 0xf5a2af2700000000, + 0x4a7e277800000000, 0x9b96c07600000000, 0xe8afe86500000000, + 0x39470f6b00000000, 0x0eddb84300000000, 0xdf355f4d00000000, + 0xac0c775e00000000, 0x7de4905000000000, 0xd2b566e100000000, + 0x035d81ef00000000, 0x7064a9fc00000000, 0xa18c4ef200000000, + 0x9616f9da00000000, 0x47fe1ed400000000, 0x34c736c700000000, + 0xe52fd1c900000000, 0x5af3599600000000, 0x8b1bbe9800000000, + 0xf822968b00000000, 0x29ca718500000000, 0x1e50c6ad00000000, + 0xcfb821a300000000, 0xbc8109b000000000, 0x6d69eebe00000000, + 0xa324940800000000, 0x72cc730600000000, 0x01f55b1500000000, + 0xd01dbc1b00000000, 0xe7870b3300000000, 0x366fec3d00000000, + 0x4556c42e00000000, 0x94be232000000000, 0x2b62ab7f00000000, + 0xfa8a4c7100000000, 0x89b3646200000000, 0x585b836c00000000, + 0x6fc1344400000000, 0xbe29d34a00000000, 0xcd10fb5900000000, + 0x1cf81c5700000000, 0xb3a9eae600000000, 0x62410de800000000, + 0x117825fb00000000, 0xc090c2f500000000, 0xf70a75dd00000000, + 0x26e292d300000000, 0x55dbbac000000000, 0x84335dce00000000, + 0x3befd59100000000, 0xea07329f00000000, 0x993e1a8c00000000, + 0x48d6fd8200000000, 0x7f4c4aaa00000000, 0xaea4ada400000000, + 0xdd9d85b700000000, 0x0c7562b900000000, 0x8471301e00000000, + 0x5599d71000000000, 0x26a0ff0300000000, 0xf748180d00000000, + 0xc0d2af2500000000, 0x113a482b00000000, 0x6203603800000000, + 0xb3eb873600000000, 0x0c370f6900000000, 0xdddfe86700000000, + 0xaee6c07400000000, 0x7f0e277a00000000, 0x4894905200000000, + 0x997c775c00000000, 0xea455f4f00000000, 0x3badb84100000000, + 0x94fc4ef000000000, 0x4514a9fe00000000, 0x362d81ed00000000, + 0xe7c566e300000000, 0xd05fd1cb00000000, 0x01b736c500000000, + 0x728e1ed600000000, 0xa366f9d800000000, 0x1cba718700000000, + 0xcd52968900000000, 0xbe6bbe9a00000000, 0x6f83599400000000, + 0x5819eebc00000000, 0x89f109b200000000, 0xfac821a100000000, + 0x2b20c6af00000000, 0xe56dbc1900000000, 0x34855b1700000000, + 0x47bc730400000000, 0x9654940a00000000, 0xa1ce232200000000, + 0x7026c42c00000000, 0x031fec3f00000000, 0xd2f70b3100000000, + 0x6d2b836e00000000, 0xbcc3646000000000, 0xcffa4c7300000000, + 0x1e12ab7d00000000, 0x29881c5500000000, 0xf860fb5b00000000, + 0x8b59d34800000000, 0x5ab1344600000000, 0xf5e0c2f700000000, + 0x240825f900000000, 0x57310dea00000000, 0x86d9eae400000000, + 0xb1435dcc00000000, 0x60abbac200000000, 0x139292d100000000, + 0xc27a75df00000000, 0x7da6fd8000000000, 0xac4e1a8e00000000, + 0xdf77329d00000000, 0x0e9fd59300000000, 0x390562bb00000000, + 0xe8ed85b500000000, 0x9bd4ada600000000, 0x4a3c4aa800000000, + 0x4649281100000000, 0x97a1cf1f00000000, 0xe498e70c00000000, + 0x3570000200000000, 0x02eab72a00000000, 0xd302502400000000, + 0xa03b783700000000, 0x71d39f3900000000, 0xce0f176600000000, + 0x1fe7f06800000000, 0x6cded87b00000000, 0xbd363f7500000000, + 0x8aac885d00000000, 0x5b446f5300000000, 0x287d474000000000, + 0xf995a04e00000000, 0x56c456ff00000000, 0x872cb1f100000000, + 0xf41599e200000000, 0x25fd7eec00000000, 0x1267c9c400000000, + 0xc38f2eca00000000, 0xb0b606d900000000, 0x615ee1d700000000, + 0xde82698800000000, 0x0f6a8e8600000000, 0x7c53a69500000000, + 0xadbb419b00000000, 0x9a21f6b300000000, 0x4bc911bd00000000, + 0x38f039ae00000000, 0xe918dea000000000, 0x2755a41600000000, + 0xf6bd431800000000, 0x85846b0b00000000, 0x546c8c0500000000, + 0x63f63b2d00000000, 0xb21edc2300000000, 0xc127f43000000000, + 0x10cf133e00000000, 0xaf139b6100000000, 0x7efb7c6f00000000, + 0x0dc2547c00000000, 0xdc2ab37200000000, 0xebb0045a00000000, + 0x3a58e35400000000, 0x4961cb4700000000, 0x98892c4900000000, + 0x37d8daf800000000, 0xe6303df600000000, 0x950915e500000000, + 0x44e1f2eb00000000, 0x737b45c300000000, 0xa293a2cd00000000, + 0xd1aa8ade00000000, 0x00426dd000000000, 0xbf9ee58f00000000, + 0x6e76028100000000, 0x1d4f2a9200000000, 0xcca7cd9c00000000, + 0xfb3d7ab400000000, 0x2ad59dba00000000, 0x59ecb5a900000000, + 0x880452a700000000}, + {0x0000000000000000, 0xaa05daf100000000, 0x150dc53800000000, + 0xbf081fc900000000, 0x2a1a8a7100000000, 0x801f508000000000, + 0x3f174f4900000000, 0x951295b800000000, 0x543414e300000000, + 0xfe31ce1200000000, 0x4139d1db00000000, 0xeb3c0b2a00000000, + 0x7e2e9e9200000000, 0xd42b446300000000, 0x6b235baa00000000, + 0xc126815b00000000, 0xe96e591d00000000, 0x436b83ec00000000, + 0xfc639c2500000000, 0x566646d400000000, 0xc374d36c00000000, + 0x6971099d00000000, 0xd679165400000000, 0x7c7ccca500000000, + 0xbd5a4dfe00000000, 0x175f970f00000000, 0xa85788c600000000, + 0x0252523700000000, 0x9740c78f00000000, 0x3d451d7e00000000, + 0x824d02b700000000, 0x2848d84600000000, 0xd2ddb23a00000000, + 0x78d868cb00000000, 0xc7d0770200000000, 0x6dd5adf300000000, + 0xf8c7384b00000000, 0x52c2e2ba00000000, 0xedcafd7300000000, + 0x47cf278200000000, 0x86e9a6d900000000, 0x2cec7c2800000000, + 0x93e463e100000000, 0x39e1b91000000000, 0xacf32ca800000000, + 0x06f6f65900000000, 0xb9fee99000000000, 0x13fb336100000000, + 0x3bb3eb2700000000, 0x91b631d600000000, 0x2ebe2e1f00000000, + 0x84bbf4ee00000000, 0x11a9615600000000, 0xbbacbba700000000, + 0x04a4a46e00000000, 0xaea17e9f00000000, 0x6f87ffc400000000, + 0xc582253500000000, 0x7a8a3afc00000000, 0xd08fe00d00000000, + 0x459d75b500000000, 0xef98af4400000000, 0x5090b08d00000000, + 0xfa956a7c00000000, 0xa4bb657500000000, 0x0ebebf8400000000, + 0xb1b6a04d00000000, 0x1bb37abc00000000, 0x8ea1ef0400000000, + 0x24a435f500000000, 0x9bac2a3c00000000, 0x31a9f0cd00000000, + 0xf08f719600000000, 0x5a8aab6700000000, 0xe582b4ae00000000, + 0x4f876e5f00000000, 0xda95fbe700000000, 0x7090211600000000, + 0xcf983edf00000000, 0x659de42e00000000, 0x4dd53c6800000000, + 0xe7d0e69900000000, 0x58d8f95000000000, 0xf2dd23a100000000, + 0x67cfb61900000000, 0xcdca6ce800000000, 0x72c2732100000000, + 0xd8c7a9d000000000, 0x19e1288b00000000, 0xb3e4f27a00000000, + 0x0cecedb300000000, 0xa6e9374200000000, 0x33fba2fa00000000, + 0x99fe780b00000000, 0x26f667c200000000, 0x8cf3bd3300000000, + 0x7666d74f00000000, 0xdc630dbe00000000, 0x636b127700000000, + 0xc96ec88600000000, 0x5c7c5d3e00000000, 0xf67987cf00000000, + 0x4971980600000000, 0xe37442f700000000, 0x2252c3ac00000000, + 0x8857195d00000000, 0x375f069400000000, 0x9d5adc6500000000, + 0x084849dd00000000, 0xa24d932c00000000, 0x1d458ce500000000, + 0xb740561400000000, 0x9f088e5200000000, 0x350d54a300000000, + 0x8a054b6a00000000, 0x2000919b00000000, 0xb512042300000000, + 0x1f17ded200000000, 0xa01fc11b00000000, 0x0a1a1bea00000000, + 0xcb3c9ab100000000, 0x6139404000000000, 0xde315f8900000000, + 0x7434857800000000, 0xe12610c000000000, 0x4b23ca3100000000, + 0xf42bd5f800000000, 0x5e2e0f0900000000, 0x4877cbea00000000, + 0xe272111b00000000, 0x5d7a0ed200000000, 0xf77fd42300000000, + 0x626d419b00000000, 0xc8689b6a00000000, 0x776084a300000000, + 0xdd655e5200000000, 0x1c43df0900000000, 0xb64605f800000000, + 0x094e1a3100000000, 0xa34bc0c000000000, 0x3659557800000000, + 0x9c5c8f8900000000, 0x2354904000000000, 0x89514ab100000000, + 0xa11992f700000000, 0x0b1c480600000000, 0xb41457cf00000000, + 0x1e118d3e00000000, 0x8b03188600000000, 0x2106c27700000000, + 0x9e0eddbe00000000, 0x340b074f00000000, 0xf52d861400000000, + 0x5f285ce500000000, 0xe020432c00000000, 0x4a2599dd00000000, + 0xdf370c6500000000, 0x7532d69400000000, 0xca3ac95d00000000, + 0x603f13ac00000000, 0x9aaa79d000000000, 0x30afa32100000000, + 0x8fa7bce800000000, 0x25a2661900000000, 0xb0b0f3a100000000, + 0x1ab5295000000000, 0xa5bd369900000000, 0x0fb8ec6800000000, + 0xce9e6d3300000000, 0x649bb7c200000000, 0xdb93a80b00000000, + 0x719672fa00000000, 0xe484e74200000000, 0x4e813db300000000, + 0xf189227a00000000, 0x5b8cf88b00000000, 0x73c420cd00000000, + 0xd9c1fa3c00000000, 0x66c9e5f500000000, 0xcccc3f0400000000, + 0x59deaabc00000000, 0xf3db704d00000000, 0x4cd36f8400000000, + 0xe6d6b57500000000, 0x27f0342e00000000, 0x8df5eedf00000000, + 0x32fdf11600000000, 0x98f82be700000000, 0x0deabe5f00000000, + 0xa7ef64ae00000000, 0x18e77b6700000000, 0xb2e2a19600000000, + 0xecccae9f00000000, 0x46c9746e00000000, 0xf9c16ba700000000, + 0x53c4b15600000000, 0xc6d624ee00000000, 0x6cd3fe1f00000000, + 0xd3dbe1d600000000, 0x79de3b2700000000, 0xb8f8ba7c00000000, + 0x12fd608d00000000, 0xadf57f4400000000, 0x07f0a5b500000000, + 0x92e2300d00000000, 0x38e7eafc00000000, 0x87eff53500000000, + 0x2dea2fc400000000, 0x05a2f78200000000, 0xafa72d7300000000, + 0x10af32ba00000000, 0xbaaae84b00000000, 0x2fb87df300000000, + 0x85bda70200000000, 0x3ab5b8cb00000000, 0x90b0623a00000000, + 0x5196e36100000000, 0xfb93399000000000, 0x449b265900000000, + 0xee9efca800000000, 0x7b8c691000000000, 0xd189b3e100000000, + 0x6e81ac2800000000, 0xc48476d900000000, 0x3e111ca500000000, + 0x9414c65400000000, 0x2b1cd99d00000000, 0x8119036c00000000, + 0x140b96d400000000, 0xbe0e4c2500000000, 0x010653ec00000000, + 0xab03891d00000000, 0x6a25084600000000, 0xc020d2b700000000, + 0x7f28cd7e00000000, 0xd52d178f00000000, 0x403f823700000000, + 0xea3a58c600000000, 0x5532470f00000000, 0xff379dfe00000000, + 0xd77f45b800000000, 0x7d7a9f4900000000, 0xc272808000000000, + 0x68775a7100000000, 0xfd65cfc900000000, 0x5760153800000000, + 0xe8680af100000000, 0x426dd00000000000, 0x834b515b00000000, + 0x294e8baa00000000, 0x9646946300000000, 0x3c434e9200000000, + 0xa951db2a00000000, 0x035401db00000000, 0xbc5c1e1200000000, + 0x1659c4e300000000}}; + +#else /* W == 4 */ + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xae689191, 0x87a02563, 0x29c8b4f2, 0xd4314c87, + 0x7a59dd16, 0x539169e4, 0xfdf9f875, 0x73139f4f, 0xdd7b0ede, + 0xf4b3ba2c, 0x5adb2bbd, 0xa722d3c8, 0x094a4259, 0x2082f6ab, + 0x8eea673a, 0xe6273e9e, 0x484faf0f, 0x61871bfd, 0xcfef8a6c, + 0x32167219, 0x9c7ee388, 0xb5b6577a, 0x1bdec6eb, 0x9534a1d1, + 0x3b5c3040, 0x129484b2, 0xbcfc1523, 0x4105ed56, 0xef6d7cc7, + 0xc6a5c835, 0x68cd59a4, 0x173f7b7d, 0xb957eaec, 0x909f5e1e, + 0x3ef7cf8f, 0xc30e37fa, 0x6d66a66b, 0x44ae1299, 0xeac68308, + 0x642ce432, 0xca4475a3, 0xe38cc151, 0x4de450c0, 0xb01da8b5, + 0x1e753924, 0x37bd8dd6, 0x99d51c47, 0xf11845e3, 0x5f70d472, + 0x76b86080, 0xd8d0f111, 0x25290964, 0x8b4198f5, 0xa2892c07, + 0x0ce1bd96, 0x820bdaac, 0x2c634b3d, 0x05abffcf, 0xabc36e5e, + 0x563a962b, 0xf85207ba, 0xd19ab348, 0x7ff222d9, 0x2e7ef6fa, + 0x8016676b, 0xa9ded399, 0x07b64208, 0xfa4fba7d, 0x54272bec, + 0x7def9f1e, 0xd3870e8f, 0x5d6d69b5, 0xf305f824, 0xdacd4cd6, + 0x74a5dd47, 0x895c2532, 0x2734b4a3, 0x0efc0051, 0xa09491c0, + 0xc859c864, 0x663159f5, 0x4ff9ed07, 0xe1917c96, 0x1c6884e3, + 0xb2001572, 0x9bc8a180, 0x35a03011, 0xbb4a572b, 0x1522c6ba, + 0x3cea7248, 0x9282e3d9, 0x6f7b1bac, 0xc1138a3d, 0xe8db3ecf, + 0x46b3af5e, 0x39418d87, 0x97291c16, 0xbee1a8e4, 0x10893975, + 0xed70c100, 0x43185091, 0x6ad0e463, 0xc4b875f2, 0x4a5212c8, + 0xe43a8359, 0xcdf237ab, 0x639aa63a, 0x9e635e4f, 0x300bcfde, + 0x19c37b2c, 0xb7abeabd, 0xdf66b319, 0x710e2288, 0x58c6967a, + 0xf6ae07eb, 0x0b57ff9e, 0xa53f6e0f, 0x8cf7dafd, 0x229f4b6c, + 0xac752c56, 0x021dbdc7, 0x2bd50935, 0x85bd98a4, 0x784460d1, + 0xd62cf140, 0xffe445b2, 0x518cd423, 0x5cfdedf4, 0xf2957c65, + 0xdb5dc897, 0x75355906, 0x88cca173, 0x26a430e2, 0x0f6c8410, + 0xa1041581, 0x2fee72bb, 0x8186e32a, 0xa84e57d8, 0x0626c649, + 0xfbdf3e3c, 0x55b7afad, 0x7c7f1b5f, 0xd2178ace, 0xbadad36a, + 0x14b242fb, 0x3d7af609, 0x93126798, 0x6eeb9fed, 0xc0830e7c, + 0xe94bba8e, 0x47232b1f, 0xc9c94c25, 0x67a1ddb4, 0x4e696946, + 0xe001f8d7, 0x1df800a2, 0xb3909133, 0x9a5825c1, 0x3430b450, + 0x4bc29689, 0xe5aa0718, 0xcc62b3ea, 0x620a227b, 0x9ff3da0e, + 0x319b4b9f, 0x1853ff6d, 0xb63b6efc, 0x38d109c6, 0x96b99857, + 0xbf712ca5, 0x1119bd34, 0xece04541, 0x4288d4d0, 0x6b406022, + 0xc528f1b3, 0xade5a817, 0x038d3986, 0x2a458d74, 0x842d1ce5, + 0x79d4e490, 0xd7bc7501, 0xfe74c1f3, 0x501c5062, 0xdef63758, + 0x709ea6c9, 0x5956123b, 0xf73e83aa, 0x0ac77bdf, 0xa4afea4e, + 0x8d675ebc, 0x230fcf2d, 0x72831b0e, 0xdceb8a9f, 0xf5233e6d, + 0x5b4baffc, 0xa6b25789, 0x08dac618, 0x211272ea, 0x8f7ae37b, + 0x01908441, 0xaff815d0, 0x8630a122, 0x285830b3, 0xd5a1c8c6, + 0x7bc95957, 0x5201eda5, 0xfc697c34, 0x94a42590, 0x3accb401, + 0x130400f3, 0xbd6c9162, 0x40956917, 0xeefdf886, 0xc7354c74, + 0x695ddde5, 0xe7b7badf, 0x49df2b4e, 0x60179fbc, 0xce7f0e2d, + 0x3386f658, 0x9dee67c9, 0xb426d33b, 0x1a4e42aa, 0x65bc6073, + 0xcbd4f1e2, 0xe21c4510, 0x4c74d481, 0xb18d2cf4, 0x1fe5bd65, + 0x362d0997, 0x98459806, 0x16afff3c, 0xb8c76ead, 0x910fda5f, + 0x3f674bce, 0xc29eb3bb, 0x6cf6222a, 0x453e96d8, 0xeb560749, + 0x839b5eed, 0x2df3cf7c, 0x043b7b8e, 0xaa53ea1f, 0x57aa126a, + 0xf9c283fb, 0xd00a3709, 0x7e62a698, 0xf088c1a2, 0x5ee05033, + 0x7728e4c1, 0xd9407550, 0x24b98d25, 0x8ad11cb4, 0xa319a846, + 0x0d7139d7}, + {0x00000000, 0xb9fbdbe8, 0xa886b191, 0x117d6a79, 0x8a7c6563, + 0x3387be8b, 0x22fad4f2, 0x9b010f1a, 0xcf89cc87, 0x7672176f, + 0x670f7d16, 0xdef4a6fe, 0x45f5a9e4, 0xfc0e720c, 0xed731875, + 0x5488c39d, 0x44629f4f, 0xfd9944a7, 0xece42ede, 0x551ff536, + 0xce1efa2c, 0x77e521c4, 0x66984bbd, 0xdf639055, 0x8beb53c8, + 0x32108820, 0x236de259, 0x9a9639b1, 0x019736ab, 0xb86ced43, + 0xa911873a, 0x10ea5cd2, 0x88c53e9e, 0x313ee576, 0x20438f0f, + 0x99b854e7, 0x02b95bfd, 0xbb428015, 0xaa3fea6c, 0x13c43184, + 0x474cf219, 0xfeb729f1, 0xefca4388, 0x56319860, 0xcd30977a, + 0x74cb4c92, 0x65b626eb, 0xdc4dfd03, 0xcca7a1d1, 0x755c7a39, + 0x64211040, 0xdddacba8, 0x46dbc4b2, 0xff201f5a, 0xee5d7523, + 0x57a6aecb, 0x032e6d56, 0xbad5b6be, 0xaba8dcc7, 0x1253072f, + 0x89520835, 0x30a9d3dd, 0x21d4b9a4, 0x982f624c, 0xcafb7b7d, + 0x7300a095, 0x627dcaec, 0xdb861104, 0x40871e1e, 0xf97cc5f6, + 0xe801af8f, 0x51fa7467, 0x0572b7fa, 0xbc896c12, 0xadf4066b, + 0x140fdd83, 0x8f0ed299, 0x36f50971, 0x27886308, 0x9e73b8e0, + 0x8e99e432, 0x37623fda, 0x261f55a3, 0x9fe48e4b, 0x04e58151, + 0xbd1e5ab9, 0xac6330c0, 0x1598eb28, 0x411028b5, 0xf8ebf35d, + 0xe9969924, 0x506d42cc, 0xcb6c4dd6, 0x7297963e, 0x63eafc47, + 0xda1127af, 0x423e45e3, 0xfbc59e0b, 0xeab8f472, 0x53432f9a, + 0xc8422080, 0x71b9fb68, 0x60c49111, 0xd93f4af9, 0x8db78964, + 0x344c528c, 0x253138f5, 0x9ccae31d, 0x07cbec07, 0xbe3037ef, + 0xaf4d5d96, 0x16b6867e, 0x065cdaac, 0xbfa70144, 0xaeda6b3d, + 0x1721b0d5, 0x8c20bfcf, 0x35db6427, 0x24a60e5e, 0x9d5dd5b6, + 0xc9d5162b, 0x702ecdc3, 0x6153a7ba, 0xd8a87c52, 0x43a97348, + 0xfa52a8a0, 0xeb2fc2d9, 0x52d41931, 0x4e87f0bb, 0xf77c2b53, + 0xe601412a, 0x5ffa9ac2, 0xc4fb95d8, 0x7d004e30, 0x6c7d2449, + 0xd586ffa1, 0x810e3c3c, 0x38f5e7d4, 0x29888dad, 0x90735645, + 0x0b72595f, 0xb28982b7, 0xa3f4e8ce, 0x1a0f3326, 0x0ae56ff4, + 0xb31eb41c, 0xa263de65, 0x1b98058d, 0x80990a97, 0x3962d17f, + 0x281fbb06, 0x91e460ee, 0xc56ca373, 0x7c97789b, 0x6dea12e2, + 0xd411c90a, 0x4f10c610, 0xf6eb1df8, 0xe7967781, 0x5e6dac69, + 0xc642ce25, 0x7fb915cd, 0x6ec47fb4, 0xd73fa45c, 0x4c3eab46, + 0xf5c570ae, 0xe4b81ad7, 0x5d43c13f, 0x09cb02a2, 0xb030d94a, + 0xa14db333, 0x18b668db, 0x83b767c1, 0x3a4cbc29, 0x2b31d650, + 0x92ca0db8, 0x8220516a, 0x3bdb8a82, 0x2aa6e0fb, 0x935d3b13, + 0x085c3409, 0xb1a7efe1, 0xa0da8598, 0x19215e70, 0x4da99ded, + 0xf4524605, 0xe52f2c7c, 0x5cd4f794, 0xc7d5f88e, 0x7e2e2366, + 0x6f53491f, 0xd6a892f7, 0x847c8bc6, 0x3d87502e, 0x2cfa3a57, + 0x9501e1bf, 0x0e00eea5, 0xb7fb354d, 0xa6865f34, 0x1f7d84dc, + 0x4bf54741, 0xf20e9ca9, 0xe373f6d0, 0x5a882d38, 0xc1892222, + 0x7872f9ca, 0x690f93b3, 0xd0f4485b, 0xc01e1489, 0x79e5cf61, + 0x6898a518, 0xd1637ef0, 0x4a6271ea, 0xf399aa02, 0xe2e4c07b, + 0x5b1f1b93, 0x0f97d80e, 0xb66c03e6, 0xa711699f, 0x1eeab277, + 0x85ebbd6d, 0x3c106685, 0x2d6d0cfc, 0x9496d714, 0x0cb9b558, + 0xb5426eb0, 0xa43f04c9, 0x1dc4df21, 0x86c5d03b, 0x3f3e0bd3, + 0x2e4361aa, 0x97b8ba42, 0xc33079df, 0x7acba237, 0x6bb6c84e, + 0xd24d13a6, 0x494c1cbc, 0xf0b7c754, 0xe1caad2d, 0x583176c5, + 0x48db2a17, 0xf120f1ff, 0xe05d9b86, 0x59a6406e, 0xc2a74f74, + 0x7b5c949c, 0x6a21fee5, 0xd3da250d, 0x8752e690, 0x3ea93d78, + 0x2fd45701, 0x962f8ce9, 0x0d2e83f3, 0xb4d5581b, 0xa5a83262, + 0x1c53e98a}, + {0x00000000, 0x9d0fe176, 0xe16ec4ad, 0x7c6125db, 0x19ac8f1b, + 0x84a36e6d, 0xf8c24bb6, 0x65cdaac0, 0x33591e36, 0xae56ff40, + 0xd237da9b, 0x4f383bed, 0x2af5912d, 0xb7fa705b, 0xcb9b5580, + 0x5694b4f6, 0x66b23c6c, 0xfbbddd1a, 0x87dcf8c1, 0x1ad319b7, + 0x7f1eb377, 0xe2115201, 0x9e7077da, 0x037f96ac, 0x55eb225a, + 0xc8e4c32c, 0xb485e6f7, 0x298a0781, 0x4c47ad41, 0xd1484c37, + 0xad2969ec, 0x3026889a, 0xcd6478d8, 0x506b99ae, 0x2c0abc75, + 0xb1055d03, 0xd4c8f7c3, 0x49c716b5, 0x35a6336e, 0xa8a9d218, + 0xfe3d66ee, 0x63328798, 0x1f53a243, 0x825c4335, 0xe791e9f5, + 0x7a9e0883, 0x06ff2d58, 0x9bf0cc2e, 0xabd644b4, 0x36d9a5c2, + 0x4ab88019, 0xd7b7616f, 0xb27acbaf, 0x2f752ad9, 0x53140f02, + 0xce1bee74, 0x988f5a82, 0x0580bbf4, 0x79e19e2f, 0xe4ee7f59, + 0x8123d599, 0x1c2c34ef, 0x604d1134, 0xfd42f042, 0x41b9f7f1, + 0xdcb61687, 0xa0d7335c, 0x3dd8d22a, 0x581578ea, 0xc51a999c, + 0xb97bbc47, 0x24745d31, 0x72e0e9c7, 0xefef08b1, 0x938e2d6a, + 0x0e81cc1c, 0x6b4c66dc, 0xf64387aa, 0x8a22a271, 0x172d4307, + 0x270bcb9d, 0xba042aeb, 0xc6650f30, 0x5b6aee46, 0x3ea74486, + 0xa3a8a5f0, 0xdfc9802b, 0x42c6615d, 0x1452d5ab, 0x895d34dd, + 0xf53c1106, 0x6833f070, 0x0dfe5ab0, 0x90f1bbc6, 0xec909e1d, + 0x719f7f6b, 0x8cdd8f29, 0x11d26e5f, 0x6db34b84, 0xf0bcaaf2, + 0x95710032, 0x087ee144, 0x741fc49f, 0xe91025e9, 0xbf84911f, + 0x228b7069, 0x5eea55b2, 0xc3e5b4c4, 0xa6281e04, 0x3b27ff72, + 0x4746daa9, 0xda493bdf, 0xea6fb345, 0x77605233, 0x0b0177e8, + 0x960e969e, 0xf3c33c5e, 0x6eccdd28, 0x12adf8f3, 0x8fa21985, + 0xd936ad73, 0x44394c05, 0x385869de, 0xa55788a8, 0xc09a2268, + 0x5d95c31e, 0x21f4e6c5, 0xbcfb07b3, 0x8373efe2, 0x1e7c0e94, + 0x621d2b4f, 0xff12ca39, 0x9adf60f9, 0x07d0818f, 0x7bb1a454, + 0xe6be4522, 0xb02af1d4, 0x2d2510a2, 0x51443579, 0xcc4bd40f, + 0xa9867ecf, 0x34899fb9, 0x48e8ba62, 0xd5e75b14, 0xe5c1d38e, + 0x78ce32f8, 0x04af1723, 0x99a0f655, 0xfc6d5c95, 0x6162bde3, + 0x1d039838, 0x800c794e, 0xd698cdb8, 0x4b972cce, 0x37f60915, + 0xaaf9e863, 0xcf3442a3, 0x523ba3d5, 0x2e5a860e, 0xb3556778, + 0x4e17973a, 0xd318764c, 0xaf795397, 0x3276b2e1, 0x57bb1821, + 0xcab4f957, 0xb6d5dc8c, 0x2bda3dfa, 0x7d4e890c, 0xe041687a, + 0x9c204da1, 0x012facd7, 0x64e20617, 0xf9ede761, 0x858cc2ba, + 0x188323cc, 0x28a5ab56, 0xb5aa4a20, 0xc9cb6ffb, 0x54c48e8d, + 0x3109244d, 0xac06c53b, 0xd067e0e0, 0x4d680196, 0x1bfcb560, + 0x86f35416, 0xfa9271cd, 0x679d90bb, 0x02503a7b, 0x9f5fdb0d, + 0xe33efed6, 0x7e311fa0, 0xc2ca1813, 0x5fc5f965, 0x23a4dcbe, + 0xbeab3dc8, 0xdb669708, 0x4669767e, 0x3a0853a5, 0xa707b2d3, + 0xf1930625, 0x6c9ce753, 0x10fdc288, 0x8df223fe, 0xe83f893e, + 0x75306848, 0x09514d93, 0x945eace5, 0xa478247f, 0x3977c509, + 0x4516e0d2, 0xd81901a4, 0xbdd4ab64, 0x20db4a12, 0x5cba6fc9, + 0xc1b58ebf, 0x97213a49, 0x0a2edb3f, 0x764ffee4, 0xeb401f92, + 0x8e8db552, 0x13825424, 0x6fe371ff, 0xf2ec9089, 0x0fae60cb, + 0x92a181bd, 0xeec0a466, 0x73cf4510, 0x1602efd0, 0x8b0d0ea6, + 0xf76c2b7d, 0x6a63ca0b, 0x3cf77efd, 0xa1f89f8b, 0xdd99ba50, + 0x40965b26, 0x255bf1e6, 0xb8541090, 0xc435354b, 0x593ad43d, + 0x691c5ca7, 0xf413bdd1, 0x8872980a, 0x157d797c, 0x70b0d3bc, + 0xedbf32ca, 0x91de1711, 0x0cd1f667, 0x5a454291, 0xc74aa3e7, + 0xbb2b863c, 0x2624674a, 0x43e9cd8a, 0xdee62cfc, 0xa2870927, + 0x3f88e851}, + {0x00000000, 0xdd96d985, 0x605cb54b, 0xbdca6cce, 0xc0b96a96, + 0x1d2fb313, 0xa0e5dfdd, 0x7d730658, 0x5a03d36d, 0x87950ae8, + 0x3a5f6626, 0xe7c9bfa3, 0x9abab9fb, 0x472c607e, 0xfae60cb0, + 0x2770d535, 0xb407a6da, 0x69917f5f, 0xd45b1391, 0x09cdca14, + 0x74becc4c, 0xa92815c9, 0x14e27907, 0xc974a082, 0xee0475b7, + 0x3392ac32, 0x8e58c0fc, 0x53ce1979, 0x2ebd1f21, 0xf32bc6a4, + 0x4ee1aa6a, 0x937773ef, 0xb37e4bf5, 0x6ee89270, 0xd322febe, + 0x0eb4273b, 0x73c72163, 0xae51f8e6, 0x139b9428, 0xce0d4dad, + 0xe97d9898, 0x34eb411d, 0x89212dd3, 0x54b7f456, 0x29c4f20e, + 0xf4522b8b, 0x49984745, 0x940e9ec0, 0x0779ed2f, 0xdaef34aa, + 0x67255864, 0xbab381e1, 0xc7c087b9, 0x1a565e3c, 0xa79c32f2, + 0x7a0aeb77, 0x5d7a3e42, 0x80ece7c7, 0x3d268b09, 0xe0b0528c, + 0x9dc354d4, 0x40558d51, 0xfd9fe19f, 0x2009381a, 0xbd8d91ab, + 0x601b482e, 0xddd124e0, 0x0047fd65, 0x7d34fb3d, 0xa0a222b8, + 0x1d684e76, 0xc0fe97f3, 0xe78e42c6, 0x3a189b43, 0x87d2f78d, + 0x5a442e08, 0x27372850, 0xfaa1f1d5, 0x476b9d1b, 0x9afd449e, + 0x098a3771, 0xd41ceef4, 0x69d6823a, 0xb4405bbf, 0xc9335de7, + 0x14a58462, 0xa96fe8ac, 0x74f93129, 0x5389e41c, 0x8e1f3d99, + 0x33d55157, 0xee4388d2, 0x93308e8a, 0x4ea6570f, 0xf36c3bc1, + 0x2efae244, 0x0ef3da5e, 0xd36503db, 0x6eaf6f15, 0xb339b690, + 0xce4ab0c8, 0x13dc694d, 0xae160583, 0x7380dc06, 0x54f00933, + 0x8966d0b6, 0x34acbc78, 0xe93a65fd, 0x944963a5, 0x49dfba20, + 0xf415d6ee, 0x29830f6b, 0xbaf47c84, 0x6762a501, 0xdaa8c9cf, + 0x073e104a, 0x7a4d1612, 0xa7dbcf97, 0x1a11a359, 0xc7877adc, + 0xe0f7afe9, 0x3d61766c, 0x80ab1aa2, 0x5d3dc327, 0x204ec57f, + 0xfdd81cfa, 0x40127034, 0x9d84a9b1, 0xa06a2517, 0x7dfcfc92, + 0xc036905c, 0x1da049d9, 0x60d34f81, 0xbd459604, 0x008ffaca, + 0xdd19234f, 0xfa69f67a, 0x27ff2fff, 0x9a354331, 0x47a39ab4, + 0x3ad09cec, 0xe7464569, 0x5a8c29a7, 0x871af022, 0x146d83cd, + 0xc9fb5a48, 0x74313686, 0xa9a7ef03, 0xd4d4e95b, 0x094230de, + 0xb4885c10, 0x691e8595, 0x4e6e50a0, 0x93f88925, 0x2e32e5eb, + 0xf3a43c6e, 0x8ed73a36, 0x5341e3b3, 0xee8b8f7d, 0x331d56f8, + 0x13146ee2, 0xce82b767, 0x7348dba9, 0xaede022c, 0xd3ad0474, + 0x0e3bddf1, 0xb3f1b13f, 0x6e6768ba, 0x4917bd8f, 0x9481640a, + 0x294b08c4, 0xf4ddd141, 0x89aed719, 0x54380e9c, 0xe9f26252, + 0x3464bbd7, 0xa713c838, 0x7a8511bd, 0xc74f7d73, 0x1ad9a4f6, + 0x67aaa2ae, 0xba3c7b2b, 0x07f617e5, 0xda60ce60, 0xfd101b55, + 0x2086c2d0, 0x9d4cae1e, 0x40da779b, 0x3da971c3, 0xe03fa846, + 0x5df5c488, 0x80631d0d, 0x1de7b4bc, 0xc0716d39, 0x7dbb01f7, + 0xa02dd872, 0xdd5ede2a, 0x00c807af, 0xbd026b61, 0x6094b2e4, + 0x47e467d1, 0x9a72be54, 0x27b8d29a, 0xfa2e0b1f, 0x875d0d47, + 0x5acbd4c2, 0xe701b80c, 0x3a976189, 0xa9e01266, 0x7476cbe3, + 0xc9bca72d, 0x142a7ea8, 0x695978f0, 0xb4cfa175, 0x0905cdbb, + 0xd493143e, 0xf3e3c10b, 0x2e75188e, 0x93bf7440, 0x4e29adc5, + 0x335aab9d, 0xeecc7218, 0x53061ed6, 0x8e90c753, 0xae99ff49, + 0x730f26cc, 0xcec54a02, 0x13539387, 0x6e2095df, 0xb3b64c5a, + 0x0e7c2094, 0xd3eaf911, 0xf49a2c24, 0x290cf5a1, 0x94c6996f, + 0x495040ea, 0x342346b2, 0xe9b59f37, 0x547ff3f9, 0x89e92a7c, + 0x1a9e5993, 0xc7088016, 0x7ac2ecd8, 0xa754355d, 0xda273305, + 0x07b1ea80, 0xba7b864e, 0x67ed5fcb, 0x409d8afe, 0x9d0b537b, + 0x20c13fb5, 0xfd57e630, 0x8024e068, 0x5db239ed, 0xe0785523, + 0x3dee8ca6}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x00000000, 0x85d996dd, 0x4bb55c60, 0xce6ccabd, 0x966ab9c0, + 0x13b32f1d, 0xdddfe5a0, 0x5806737d, 0x6dd3035a, 0xe80a9587, + 0x26665f3a, 0xa3bfc9e7, 0xfbb9ba9a, 0x7e602c47, 0xb00ce6fa, + 0x35d57027, 0xdaa607b4, 0x5f7f9169, 0x91135bd4, 0x14cacd09, + 0x4cccbe74, 0xc91528a9, 0x0779e214, 0x82a074c9, 0xb77504ee, + 0x32ac9233, 0xfcc0588e, 0x7919ce53, 0x211fbd2e, 0xa4c62bf3, + 0x6aaae14e, 0xef737793, 0xf54b7eb3, 0x7092e86e, 0xbefe22d3, + 0x3b27b40e, 0x6321c773, 0xe6f851ae, 0x28949b13, 0xad4d0dce, + 0x98987de9, 0x1d41eb34, 0xd32d2189, 0x56f4b754, 0x0ef2c429, + 0x8b2b52f4, 0x45479849, 0xc09e0e94, 0x2fed7907, 0xaa34efda, + 0x64582567, 0xe181b3ba, 0xb987c0c7, 0x3c5e561a, 0xf2329ca7, + 0x77eb0a7a, 0x423e7a5d, 0xc7e7ec80, 0x098b263d, 0x8c52b0e0, + 0xd454c39d, 0x518d5540, 0x9fe19ffd, 0x1a380920, 0xab918dbd, + 0x2e481b60, 0xe024d1dd, 0x65fd4700, 0x3dfb347d, 0xb822a2a0, + 0x764e681d, 0xf397fec0, 0xc6428ee7, 0x439b183a, 0x8df7d287, + 0x082e445a, 0x50283727, 0xd5f1a1fa, 0x1b9d6b47, 0x9e44fd9a, + 0x71378a09, 0xf4ee1cd4, 0x3a82d669, 0xbf5b40b4, 0xe75d33c9, + 0x6284a514, 0xace86fa9, 0x2931f974, 0x1ce48953, 0x993d1f8e, + 0x5751d533, 0xd28843ee, 0x8a8e3093, 0x0f57a64e, 0xc13b6cf3, + 0x44e2fa2e, 0x5edaf30e, 0xdb0365d3, 0x156faf6e, 0x90b639b3, + 0xc8b04ace, 0x4d69dc13, 0x830516ae, 0x06dc8073, 0x3309f054, + 0xb6d06689, 0x78bcac34, 0xfd653ae9, 0xa5634994, 0x20badf49, + 0xeed615f4, 0x6b0f8329, 0x847cf4ba, 0x01a56267, 0xcfc9a8da, + 0x4a103e07, 0x12164d7a, 0x97cfdba7, 0x59a3111a, 0xdc7a87c7, + 0xe9aff7e0, 0x6c76613d, 0xa21aab80, 0x27c33d5d, 0x7fc54e20, + 0xfa1cd8fd, 0x34701240, 0xb1a9849d, 0x17256aa0, 0x92fcfc7d, + 0x5c9036c0, 0xd949a01d, 0x814fd360, 0x049645bd, 0xcafa8f00, + 0x4f2319dd, 0x7af669fa, 0xff2fff27, 0x3143359a, 0xb49aa347, + 0xec9cd03a, 0x694546e7, 0xa7298c5a, 0x22f01a87, 0xcd836d14, + 0x485afbc9, 0x86363174, 0x03efa7a9, 0x5be9d4d4, 0xde304209, + 0x105c88b4, 0x95851e69, 0xa0506e4e, 0x2589f893, 0xebe5322e, + 0x6e3ca4f3, 0x363ad78e, 0xb3e34153, 0x7d8f8bee, 0xf8561d33, + 0xe26e1413, 0x67b782ce, 0xa9db4873, 0x2c02deae, 0x7404add3, + 0xf1dd3b0e, 0x3fb1f1b3, 0xba68676e, 0x8fbd1749, 0x0a648194, + 0xc4084b29, 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0xc050e44d, 0xb5a81db0, + 0x2439751e, 0xd68dbd37, 0x471cd599, 0xe34518f1, 0x72d4705f, + 0x8060b876, 0x11f1d0d8, 0x64092925, 0xf598418b, 0x072c89a2, + 0x96bde10c, 0xacda0b82, 0x3d4b632c, 0xcfffab05, 0x5e6ec3ab, + 0x2b963a56, 0xba0752f8, 0x48b39ad1, 0xd922f27f, 0xfaf67e2e, + 0x6b671680, 0x99d3dea9, 0x0842b607, 0x7dba4ffa, 0xec2b2754, + 0x1e9fef7d, 0x8f0e87d3, 0xb5696d5d, 0x24f805f3, 0xd64ccdda, + 0x47dda574, 0x32255c89, 0xa3b43427, 0x5100fc0e, 0xc09194a0, + 0x64c859c8, 0xf5593166, 0x07edf94f, 0x967c91e1, 0xe384681c, + 0x721500b2, 0x80a1c89b, 0x1130a035, 0x2b574abb, 0xbac62215, + 0x4872ea3c, 0xd9e38292, 0xac1b7b6f, 0x3d8a13c1, 0xcf3edbe8, + 0x5eafb346, 0x878d4139, 0x161c2997, 0xe4a8e1be, 0x75398910, + 0x00c170ed, 0x91501843, 0x63e4d06a, 0xf275b8c4, 0xc812524a, + 0x59833ae4, 0xab37f2cd, 0x3aa69a63, 0x4f5e639e, 0xdecf0b30, + 0x2c7bc319, 0xbdeaabb7, 0x19b366df, 0x88220e71, 0x7a96c658, + 0xeb07aef6, 0x9eff570b, 0x0f6e3fa5, 0xfddaf78c, 0x6c4b9f22, + 0x562c75ac, 0xc7bd1d02, 0x3509d52b, 0xa498bd85, 0xd1604478, + 0x40f12cd6, 0xb245e4ff, 0x23d48c51, 0xf4edfd5c, 0x657c95f2, + 0x97c85ddb, 0x06593575, 0x73a1cc88, 0xe230a426, 0x10846c0f, + 0x811504a1, 0xbb72ee2f, 0x2ae38681, 0xd8574ea8, 0x49c62606, + 0x3c3edffb, 0xadafb755, 0x5f1b7f7c, 0xce8a17d2, 0x6ad3daba, + 0xfb42b214, 0x09f67a3d, 0x98671293, 0xed9feb6e, 0x7c0e83c0, + 0x8eba4be9, 0x1f2b2347, 0x254cc9c9, 0xb4dda167, 0x4669694e, + 0xd7f801e0, 0xa200f81d, 0x339190b3, 0xc125589a, 0x50b43034, + 0x8996c24b, 0x1807aae5, 0xeab362cc, 0x7b220a62, 0x0edaf39f, + 0x9f4b9b31, 0x6dff5318, 0xfc6e3bb6, 0xc609d138, 0x5798b996, + 0xa52c71bf, 0x34bd1911, 0x4145e0ec, 0xd0d48842, 0x2260406b, + 0xb3f128c5, 0x17a8e5ad, 0x86398d03, 0x748d452a, 0xe51c2d84, + 0x90e4d479, 0x0175bcd7, 0xf3c174fe, 0x62501c50, 0x5837f6de, + 0xc9a69e70, 0x3b125659, 0xaa833ef7, 0xdf7bc70a, 0x4eeaafa4, + 0xbc5e678d, 0x2dcf0f23, 0x0e1b8372, 0x9f8aebdc, 0x6d3e23f5, + 0xfcaf4b5b, 0x8957b2a6, 0x18c6da08, 0xea721221, 0x7be37a8f, + 0x41849001, 0xd015f8af, 0x22a13086, 0xb3305828, 0xc6c8a1d5, + 0x5759c97b, 0xa5ed0152, 0x347c69fc, 0x9025a494, 0x01b4cc3a, + 0xf3000413, 0x62916cbd, 0x17699540, 0x86f8fdee, 0x744c35c7, + 0xe5dd5d69, 0xdfbab7e7, 0x4e2bdf49, 0xbc9f1760, 0x2d0e7fce, + 0x58f68633, 0xc967ee9d, 0x3bd326b4, 0xaa424e1a, 0x7360bc65, + 0xe2f1d4cb, 0x10451ce2, 0x81d4744c, 0xf42c8db1, 0x65bde51f, + 0x97092d36, 0x06984598, 0x3cffaf16, 0xad6ec7b8, 0x5fda0f91, + 0xce4b673f, 0xbbb39ec2, 0x2a22f66c, 0xd8963e45, 0x490756eb, + 0xed5e9b83, 0x7ccff32d, 0x8e7b3b04, 0x1fea53aa, 0x6a12aa57, + 0xfb83c2f9, 0x09370ad0, 0x98a6627e, 0xa2c188f0, 0x3350e05e, + 0xc1e42877, 0x507540d9, 0x258db924, 0xb41cd18a, 0x46a819a3, + 0xd739710d}}; + +#endif + +#endif + +#if N == 5 + +#if W == 8 + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0xaf449247, 0x85f822cf, 0x2abcb088, 0xd08143df, + 0x7fc5d198, 0x55796110, 0xfa3df357, 0x7a7381ff, 0xd53713b8, + 0xff8ba330, 0x50cf3177, 0xaaf2c220, 0x05b65067, 0x2f0ae0ef, + 0x804e72a8, 0xf4e703fe, 0x5ba391b9, 0x711f2131, 0xde5bb376, + 0x24664021, 0x8b22d266, 0xa19e62ee, 0x0edaf0a9, 0x8e948201, + 0x21d01046, 0x0b6ca0ce, 0xa4283289, 0x5e15c1de, 0xf1515399, + 0xdbede311, 0x74a97156, 0x32bf01bd, 0x9dfb93fa, 0xb7472372, + 0x1803b135, 0xe23e4262, 0x4d7ad025, 0x67c660ad, 0xc882f2ea, + 0x48cc8042, 0xe7881205, 0xcd34a28d, 0x627030ca, 0x984dc39d, + 0x370951da, 0x1db5e152, 0xb2f17315, 0xc6580243, 0x691c9004, + 0x43a0208c, 0xece4b2cb, 0x16d9419c, 0xb99dd3db, 0x93216353, + 0x3c65f114, 0xbc2b83bc, 0x136f11fb, 0x39d3a173, 0x96973334, + 0x6caac063, 0xc3ee5224, 0xe952e2ac, 0x461670eb, 0x657e037a, + 0xca3a913d, 0xe08621b5, 0x4fc2b3f2, 0xb5ff40a5, 0x1abbd2e2, + 0x3007626a, 0x9f43f02d, 0x1f0d8285, 0xb04910c2, 0x9af5a04a, + 0x35b1320d, 0xcf8cc15a, 0x60c8531d, 0x4a74e395, 0xe53071d2, + 0x91990084, 0x3edd92c3, 0x1461224b, 0xbb25b00c, 0x4118435b, + 0xee5cd11c, 0xc4e06194, 0x6ba4f3d3, 0xebea817b, 0x44ae133c, + 0x6e12a3b4, 0xc15631f3, 0x3b6bc2a4, 0x942f50e3, 0xbe93e06b, + 0x11d7722c, 0x57c102c7, 0xf8859080, 0xd2392008, 0x7d7db24f, + 0x87404118, 0x2804d35f, 0x02b863d7, 0xadfcf190, 0x2db28338, + 0x82f6117f, 0xa84aa1f7, 0x070e33b0, 0xfd33c0e7, 0x527752a0, + 0x78cbe228, 0xd78f706f, 0xa3260139, 0x0c62937e, 0x26de23f6, + 0x899ab1b1, 0x73a742e6, 0xdce3d0a1, 0xf65f6029, 0x591bf26e, + 0xd95580c6, 0x76111281, 0x5cada209, 0xf3e9304e, 0x09d4c319, + 0xa690515e, 0x8c2ce1d6, 0x23687391, 0xcafc06f4, 0x65b894b3, + 0x4f04243b, 0xe040b67c, 0x1a7d452b, 0xb539d76c, 0x9f8567e4, + 0x30c1f5a3, 0xb08f870b, 0x1fcb154c, 0x3577a5c4, 0x9a333783, + 0x600ec4d4, 0xcf4a5693, 0xe5f6e61b, 0x4ab2745c, 0x3e1b050a, + 0x915f974d, 0xbbe327c5, 0x14a7b582, 0xee9a46d5, 0x41ded492, + 0x6b62641a, 0xc426f65d, 0x446884f5, 0xeb2c16b2, 0xc190a63a, + 0x6ed4347d, 0x94e9c72a, 0x3bad556d, 0x1111e5e5, 0xbe5577a2, + 0xf8430749, 0x5707950e, 0x7dbb2586, 0xd2ffb7c1, 0x28c24496, + 0x8786d6d1, 0xad3a6659, 0x027ef41e, 0x823086b6, 0x2d7414f1, + 0x07c8a479, 0xa88c363e, 0x52b1c569, 0xfdf5572e, 0xd749e7a6, + 0x780d75e1, 0x0ca404b7, 0xa3e096f0, 0x895c2678, 0x2618b43f, + 0xdc254768, 0x7361d52f, 0x59dd65a7, 0xf699f7e0, 0x76d78548, + 0xd993170f, 0xf32fa787, 0x5c6b35c0, 0xa656c697, 0x091254d0, + 0x23aee458, 0x8cea761f, 0xaf82058e, 0x00c697c9, 0x2a7a2741, + 0x853eb506, 0x7f034651, 0xd047d416, 0xfafb649e, 0x55bff6d9, + 0xd5f18471, 0x7ab51636, 0x5009a6be, 0xff4d34f9, 0x0570c7ae, + 0xaa3455e9, 0x8088e561, 0x2fcc7726, 0x5b650670, 0xf4219437, + 0xde9d24bf, 0x71d9b6f8, 0x8be445af, 0x24a0d7e8, 0x0e1c6760, + 0xa158f527, 0x2116878f, 0x8e5215c8, 0xa4eea540, 0x0baa3707, + 0xf197c450, 0x5ed35617, 0x746fe69f, 0xdb2b74d8, 0x9d3d0433, + 0x32799674, 0x18c526fc, 0xb781b4bb, 0x4dbc47ec, 0xe2f8d5ab, + 0xc8446523, 0x6700f764, 0xe74e85cc, 0x480a178b, 0x62b6a703, + 0xcdf23544, 0x37cfc613, 0x988b5454, 0xb237e4dc, 0x1d73769b, + 0x69da07cd, 0xc69e958a, 0xec222502, 0x4366b745, 0xb95b4412, + 0x161fd655, 0x3ca366dd, 0x93e7f49a, 0x13a98632, 0xbced1475, + 0x9651a4fd, 0x391536ba, 0xc328c5ed, 0x6c6c57aa, 0x46d0e722, + 0xe9947565}, + {0x00000000, 0x4e890ba9, 0x9d121752, 0xd39b1cfb, 0xe15528e5, + 0xafdc234c, 0x7c473fb7, 0x32ce341e, 0x19db578b, 0x57525c22, + 0x84c940d9, 0xca404b70, 0xf88e7f6e, 0xb60774c7, 0x659c683c, + 0x2b156395, 0x33b6af16, 0x7d3fa4bf, 0xaea4b844, 0xe02db3ed, + 0xd2e387f3, 0x9c6a8c5a, 0x4ff190a1, 0x01789b08, 0x2a6df89d, + 0x64e4f334, 0xb77fefcf, 0xf9f6e466, 0xcb38d078, 0x85b1dbd1, + 0x562ac72a, 0x18a3cc83, 0x676d5e2c, 0x29e45585, 0xfa7f497e, + 0xb4f642d7, 0x863876c9, 0xc8b17d60, 0x1b2a619b, 0x55a36a32, + 0x7eb609a7, 0x303f020e, 0xe3a41ef5, 0xad2d155c, 0x9fe32142, + 0xd16a2aeb, 0x02f13610, 0x4c783db9, 0x54dbf13a, 0x1a52fa93, + 0xc9c9e668, 0x8740edc1, 0xb58ed9df, 0xfb07d276, 0x289cce8d, + 0x6615c524, 0x4d00a6b1, 0x0389ad18, 0xd012b1e3, 0x9e9bba4a, + 0xac558e54, 0xe2dc85fd, 0x31479906, 0x7fce92af, 0xcedabc58, + 0x8053b7f1, 0x53c8ab0a, 0x1d41a0a3, 0x2f8f94bd, 0x61069f14, + 0xb29d83ef, 0xfc148846, 0xd701ebd3, 0x9988e07a, 0x4a13fc81, + 0x049af728, 0x3654c336, 0x78ddc89f, 0xab46d464, 0xe5cfdfcd, + 0xfd6c134e, 0xb3e518e7, 0x607e041c, 0x2ef70fb5, 0x1c393bab, + 0x52b03002, 0x812b2cf9, 0xcfa22750, 0xe4b744c5, 0xaa3e4f6c, + 0x79a55397, 0x372c583e, 0x05e26c20, 0x4b6b6789, 0x98f07b72, + 0xd67970db, 0xa9b7e274, 0xe73ee9dd, 0x34a5f526, 0x7a2cfe8f, + 0x48e2ca91, 0x066bc138, 0xd5f0ddc3, 0x9b79d66a, 0xb06cb5ff, + 0xfee5be56, 0x2d7ea2ad, 0x63f7a904, 0x51399d1a, 0x1fb096b3, + 0xcc2b8a48, 0x82a281e1, 0x9a014d62, 0xd48846cb, 0x07135a30, + 0x499a5199, 0x7b546587, 0x35dd6e2e, 0xe64672d5, 0xa8cf797c, + 0x83da1ae9, 0xcd531140, 0x1ec80dbb, 0x50410612, 0x628f320c, + 0x2c0639a5, 0xff9d255e, 0xb1142ef7, 0x46c47ef1, 0x084d7558, + 0xdbd669a3, 0x955f620a, 0xa7915614, 0xe9185dbd, 0x3a834146, + 0x740a4aef, 0x5f1f297a, 0x119622d3, 0xc20d3e28, 0x8c843581, + 0xbe4a019f, 0xf0c30a36, 0x235816cd, 0x6dd11d64, 0x7572d1e7, + 0x3bfbda4e, 0xe860c6b5, 0xa6e9cd1c, 0x9427f902, 0xdaaef2ab, + 0x0935ee50, 0x47bce5f9, 0x6ca9866c, 0x22208dc5, 0xf1bb913e, + 0xbf329a97, 0x8dfcae89, 0xc375a520, 0x10eeb9db, 0x5e67b272, + 0x21a920dd, 0x6f202b74, 0xbcbb378f, 0xf2323c26, 0xc0fc0838, + 0x8e750391, 0x5dee1f6a, 0x136714c3, 0x38727756, 0x76fb7cff, + 0xa5606004, 0xebe96bad, 0xd9275fb3, 0x97ae541a, 0x443548e1, + 0x0abc4348, 0x121f8fcb, 0x5c968462, 0x8f0d9899, 0xc1849330, + 0xf34aa72e, 0xbdc3ac87, 0x6e58b07c, 0x20d1bbd5, 0x0bc4d840, + 0x454dd3e9, 0x96d6cf12, 0xd85fc4bb, 0xea91f0a5, 0xa418fb0c, + 0x7783e7f7, 0x390aec5e, 0x881ec2a9, 0xc697c900, 0x150cd5fb, + 0x5b85de52, 0x694bea4c, 0x27c2e1e5, 0xf459fd1e, 0xbad0f6b7, + 0x91c59522, 0xdf4c9e8b, 0x0cd78270, 0x425e89d9, 0x7090bdc7, + 0x3e19b66e, 0xed82aa95, 0xa30ba13c, 0xbba86dbf, 0xf5216616, + 0x26ba7aed, 0x68337144, 0x5afd455a, 0x14744ef3, 0xc7ef5208, + 0x896659a1, 0xa2733a34, 0xecfa319d, 0x3f612d66, 0x71e826cf, + 0x432612d1, 0x0daf1978, 0xde340583, 0x90bd0e2a, 0xef739c85, + 0xa1fa972c, 0x72618bd7, 0x3ce8807e, 0x0e26b460, 0x40afbfc9, + 0x9334a332, 0xddbda89b, 0xf6a8cb0e, 0xb821c0a7, 0x6bbadc5c, + 0x2533d7f5, 0x17fde3eb, 0x5974e842, 0x8aeff4b9, 0xc466ff10, + 0xdcc53393, 0x924c383a, 0x41d724c1, 0x0f5e2f68, 0x3d901b76, + 0x731910df, 0xa0820c24, 0xee0b078d, 0xc51e6418, 0x8b976fb1, + 0x580c734a, 0x168578e3, 0x244b4cfd, 0x6ac24754, 0xb9595baf, + 0xf7d05006}, + {0x00000000, 0x8d88fde2, 0xc060fd85, 0x4de80067, 0x5bb0fd4b, + 0xd63800a9, 0x9bd000ce, 0x1658fd2c, 0xb761fa96, 0x3ae90774, + 0x77010713, 0xfa89faf1, 0xecd107dd, 0x6159fa3f, 0x2cb1fa58, + 0xa13907ba, 0xb5b2f36d, 0x383a0e8f, 0x75d20ee8, 0xf85af30a, + 0xee020e26, 0x638af3c4, 0x2e62f3a3, 0xa3ea0e41, 0x02d309fb, + 0x8f5bf419, 0xc2b3f47e, 0x4f3b099c, 0x5963f4b0, 0xd4eb0952, + 0x99030935, 0x148bf4d7, 0xb014e09b, 0x3d9c1d79, 0x70741d1e, + 0xfdfce0fc, 0xeba41dd0, 0x662ce032, 0x2bc4e055, 0xa64c1db7, + 0x07751a0d, 0x8afde7ef, 0xc715e788, 0x4a9d1a6a, 0x5cc5e746, + 0xd14d1aa4, 0x9ca51ac3, 0x112de721, 0x05a613f6, 0x882eee14, + 0xc5c6ee73, 0x484e1391, 0x5e16eebd, 0xd39e135f, 0x9e761338, + 0x13feeeda, 0xb2c7e960, 0x3f4f1482, 0x72a714e5, 0xff2fe907, + 0xe977142b, 0x64ffe9c9, 0x2917e9ae, 0xa49f144c, 0xbb58c777, + 0x36d03a95, 0x7b383af2, 0xf6b0c710, 0xe0e83a3c, 0x6d60c7de, + 0x2088c7b9, 0xad003a5b, 0x0c393de1, 0x81b1c003, 0xcc59c064, + 0x41d13d86, 0x5789c0aa, 0xda013d48, 0x97e93d2f, 0x1a61c0cd, + 0x0eea341a, 0x8362c9f8, 0xce8ac99f, 0x4302347d, 0x555ac951, + 0xd8d234b3, 0x953a34d4, 0x18b2c936, 0xb98bce8c, 0x3403336e, + 0x79eb3309, 0xf463ceeb, 0xe23b33c7, 0x6fb3ce25, 0x225bce42, + 0xafd333a0, 0x0b4c27ec, 0x86c4da0e, 0xcb2cda69, 0x46a4278b, + 0x50fcdaa7, 0xdd742745, 0x909c2722, 0x1d14dac0, 0xbc2ddd7a, + 0x31a52098, 0x7c4d20ff, 0xf1c5dd1d, 0xe79d2031, 0x6a15ddd3, + 0x27fdddb4, 0xaa752056, 0xbefed481, 0x33762963, 0x7e9e2904, + 0xf316d4e6, 0xe54e29ca, 0x68c6d428, 0x252ed44f, 0xa8a629ad, + 0x099f2e17, 0x8417d3f5, 0xc9ffd392, 0x44772e70, 0x522fd35c, + 0xdfa72ebe, 0x924f2ed9, 0x1fc7d33b, 0xadc088af, 0x2048754d, + 0x6da0752a, 0xe02888c8, 0xf67075e4, 0x7bf88806, 0x36108861, + 0xbb987583, 0x1aa17239, 0x97298fdb, 0xdac18fbc, 0x5749725e, + 0x41118f72, 0xcc997290, 0x817172f7, 0x0cf98f15, 0x18727bc2, + 0x95fa8620, 0xd8128647, 0x559a7ba5, 0x43c28689, 0xce4a7b6b, + 0x83a27b0c, 0x0e2a86ee, 0xaf138154, 0x229b7cb6, 0x6f737cd1, + 0xe2fb8133, 0xf4a37c1f, 0x792b81fd, 0x34c3819a, 0xb94b7c78, + 0x1dd46834, 0x905c95d6, 0xddb495b1, 0x503c6853, 0x4664957f, + 0xcbec689d, 0x860468fa, 0x0b8c9518, 0xaab592a2, 0x273d6f40, + 0x6ad56f27, 0xe75d92c5, 0xf1056fe9, 0x7c8d920b, 0x3165926c, + 0xbced6f8e, 0xa8669b59, 0x25ee66bb, 0x680666dc, 0xe58e9b3e, + 0xf3d66612, 0x7e5e9bf0, 0x33b69b97, 0xbe3e6675, 0x1f0761cf, + 0x928f9c2d, 0xdf679c4a, 0x52ef61a8, 0x44b79c84, 0xc93f6166, + 0x84d76101, 0x095f9ce3, 0x16984fd8, 0x9b10b23a, 0xd6f8b25d, + 0x5b704fbf, 0x4d28b293, 0xc0a04f71, 0x8d484f16, 0x00c0b2f4, + 0xa1f9b54e, 0x2c7148ac, 0x619948cb, 0xec11b529, 0xfa494805, + 0x77c1b5e7, 0x3a29b580, 0xb7a14862, 0xa32abcb5, 0x2ea24157, + 0x634a4130, 0xeec2bcd2, 0xf89a41fe, 0x7512bc1c, 0x38fabc7b, + 0xb5724199, 0x144b4623, 0x99c3bbc1, 0xd42bbba6, 0x59a34644, + 0x4ffbbb68, 0xc273468a, 0x8f9b46ed, 0x0213bb0f, 0xa68caf43, + 0x2b0452a1, 0x66ec52c6, 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0xf0bfa1e2, + 0xf3f6cc59, 0x3c68db91, 0xb7bbe588, 0x7825f240, 0xb1293efe, + 0x7eb72936, 0xf564172f, 0x3afa00e7, 0x39b36d5c, 0xf62d7a94, + 0x7dfe448d, 0xb2605345, 0x3496dbb0, 0xfb08cc78, 0x70dbf261, + 0xbf45e5a9, 0xbc0c8812, 0x73929fda, 0xf841a1c3, 0x37dfb60b, + 0xfed37ab5, 0x314d6d7d, 0xba9e5364, 0x750044ac, 0x76492917, + 0xb9d73edf, 0x320400c6, 0xfd9a170e, 0x1241289b, 0xdddf3f53, + 0x560c014a, 0x99921682, 0x9adb7b39, 0x55456cf1, 0xde9652e8, + 0x11084520, 0xd804899e, 0x179a9e56, 0x9c49a04f, 0x53d7b787, + 0x509eda3c, 0x9f00cdf4, 0x14d3f3ed, 0xdb4de425, 0x5dbb6cd0, + 0x92257b18, 0x19f64501, 0xd66852c9, 0xd5213f72, 0x1abf28ba, + 0x916c16a3, 0x5ef2016b, 0x97fecdd5, 0x5860da1d, 0xd3b3e404, + 0x1c2df3cc, 0x1f649e77, 0xd0fa89bf, 0x5b29b7a6, 0x94b7a06e, + 0x8db5a00d, 0x422bb7c5, 0xc9f889dc, 0x06669e14, 0x052ff3af, + 0xcab1e467, 0x4162da7e, 0x8efccdb6, 0x47f00108, 0x886e16c0, + 0x03bd28d9, 0xcc233f11, 0xcf6a52aa, 0x00f44562, 0x8b277b7b, + 0x44b96cb3, 0xc24fe446, 0x0dd1f38e, 0x8602cd97, 0x499cda5f, + 0x4ad5b7e4, 0x854ba02c, 0x0e989e35, 0xc10689fd, 0x080a4543, + 0xc794528b, 0x4c476c92, 0x83d97b5a, 0x809016e1, 0x4f0e0129, + 0xc4dd3f30, 0x0b4328f8, 0xf6d93ff6, 0x3947283e, 0xb2941627, + 0x7d0a01ef, 0x7e436c54, 0xb1dd7b9c, 0x3a0e4585, 0xf590524d, + 0x3c9c9ef3, 0xf302893b, 0x78d1b722, 0xb74fa0ea, 0xb406cd51, + 0x7b98da99, 0xf04be480, 0x3fd5f348, 0xb9237bbd, 0x76bd6c75, + 0xfd6e526c, 0x32f045a4, 0x31b9281f, 0xfe273fd7, 0x75f401ce, + 0xba6a1606, 0x7366dab8, 0xbcf8cd70, 0x372bf369, 0xf8b5e4a1, + 0xfbfc891a, 0x34629ed2, 0xbfb1a0cb, 0x702fb703, 0x692db760, + 0xa6b3a0a8, 0x2d609eb1, 0xe2fe8979, 0xe1b7e4c2, 0x2e29f30a, + 0xa5facd13, 0x6a64dadb, 0xa3681665, 0x6cf601ad, 0xe7253fb4, + 0x28bb287c, 0x2bf245c7, 0xe46c520f, 0x6fbf6c16, 0xa0217bde, + 0x26d7f32b, 0xe949e4e3, 0x629adafa, 0xad04cd32, 0xae4da089, + 0x61d3b741, 0xea008958, 0x259e9e90, 0xec92522e, 0x230c45e6, + 0xa8df7bff, 0x67416c37, 0x6408018c, 0xab961644, 0x2045285d, + 0xefdb3f95}, + {0x00000000, 0x24825136, 0x4904a26c, 0x6d86f35a, 0x920944d8, + 0xb68b15ee, 0xdb0de6b4, 0xff8fb782, 0xff638ff1, 0xdbe1dec7, + 0xb6672d9d, 0x92e57cab, 0x6d6acb29, 0x49e89a1f, 0x246e6945, + 0x00ec3873, 0x25b619a3, 0x01344895, 0x6cb2bbcf, 0x4830eaf9, + 0xb7bf5d7b, 0x933d0c4d, 0xfebbff17, 0xda39ae21, 0xdad59652, + 0xfe57c764, 0x93d1343e, 0xb7536508, 0x48dcd28a, 0x6c5e83bc, + 0x01d870e6, 0x255a21d0, 0x4b6c3346, 0x6fee6270, 0x0268912a, + 0x26eac01c, 0xd965779e, 0xfde726a8, 0x9061d5f2, 0xb4e384c4, + 0xb40fbcb7, 0x908ded81, 0xfd0b1edb, 0xd9894fed, 0x2606f86f, + 0x0284a959, 0x6f025a03, 0x4b800b35, 0x6eda2ae5, 0x4a587bd3, + 0x27de8889, 0x035cd9bf, 0xfcd36e3d, 0xd8513f0b, 0xb5d7cc51, + 0x91559d67, 0x91b9a514, 0xb53bf422, 0xd8bd0778, 0xfc3f564e, + 0x03b0e1cc, 0x2732b0fa, 0x4ab443a0, 0x6e361296, 0x96d8668c, + 0xb25a37ba, 0xdfdcc4e0, 0xfb5e95d6, 0x04d12254, 0x20537362, + 0x4dd58038, 0x6957d10e, 0x69bbe97d, 0x4d39b84b, 0x20bf4b11, + 0x043d1a27, 0xfbb2ada5, 0xdf30fc93, 0xb2b60fc9, 0x96345eff, + 0xb36e7f2f, 0x97ec2e19, 0xfa6add43, 0xdee88c75, 0x21673bf7, + 0x05e56ac1, 0x6863999b, 0x4ce1c8ad, 0x4c0df0de, 0x688fa1e8, + 0x050952b2, 0x218b0384, 0xde04b406, 0xfa86e530, 0x9700166a, + 0xb382475c, 0xddb455ca, 0xf93604fc, 0x94b0f7a6, 0xb032a690, + 0x4fbd1112, 0x6b3f4024, 0x06b9b37e, 0x223be248, 0x22d7da3b, + 0x06558b0d, 0x6bd37857, 0x4f512961, 0xb0de9ee3, 0x945ccfd5, + 0xf9da3c8f, 0xdd586db9, 0xf8024c69, 0xdc801d5f, 0xb106ee05, + 0x9584bf33, 0x6a0b08b1, 0x4e895987, 0x230faadd, 0x078dfbeb, + 0x0761c398, 0x23e392ae, 0x4e6561f4, 0x6ae730c2, 0x95688740, + 0xb1ead676, 0xdc6c252c, 0xf8ee741a, 0xf6c1cb59, 0xd2439a6f, + 0xbfc56935, 0x9b473803, 0x64c88f81, 0x404adeb7, 0x2dcc2ded, + 0x094e7cdb, 0x09a244a8, 0x2d20159e, 0x40a6e6c4, 0x6424b7f2, + 0x9bab0070, 0xbf295146, 0xd2afa21c, 0xf62df32a, 0xd377d2fa, + 0xf7f583cc, 0x9a737096, 0xbef121a0, 0x417e9622, 0x65fcc714, + 0x087a344e, 0x2cf86578, 0x2c145d0b, 0x08960c3d, 0x6510ff67, + 0x4192ae51, 0xbe1d19d3, 0x9a9f48e5, 0xf719bbbf, 0xd39bea89, + 0xbdadf81f, 0x992fa929, 0xf4a95a73, 0xd02b0b45, 0x2fa4bcc7, + 0x0b26edf1, 0x66a01eab, 0x42224f9d, 0x42ce77ee, 0x664c26d8, + 0x0bcad582, 0x2f4884b4, 0xd0c73336, 0xf4456200, 0x99c3915a, + 0xbd41c06c, 0x981be1bc, 0xbc99b08a, 0xd11f43d0, 0xf59d12e6, + 0x0a12a564, 0x2e90f452, 0x43160708, 0x6794563e, 0x67786e4d, + 0x43fa3f7b, 0x2e7ccc21, 0x0afe9d17, 0xf5712a95, 0xd1f37ba3, + 0xbc7588f9, 0x98f7d9cf, 0x6019add5, 0x449bfce3, 0x291d0fb9, + 0x0d9f5e8f, 0xf210e90d, 0xd692b83b, 0xbb144b61, 0x9f961a57, + 0x9f7a2224, 0xbbf87312, 0xd67e8048, 0xf2fcd17e, 0x0d7366fc, + 0x29f137ca, 0x4477c490, 0x60f595a6, 0x45afb476, 0x612de540, + 0x0cab161a, 0x2829472c, 0xd7a6f0ae, 0xf324a198, 0x9ea252c2, + 0xba2003f4, 0xbacc3b87, 0x9e4e6ab1, 0xf3c899eb, 0xd74ac8dd, + 0x28c57f5f, 0x0c472e69, 0x61c1dd33, 0x45438c05, 0x2b759e93, + 0x0ff7cfa5, 0x62713cff, 0x46f36dc9, 0xb97cda4b, 0x9dfe8b7d, + 0xf0787827, 0xd4fa2911, 0xd4161162, 0xf0944054, 0x9d12b30e, + 0xb990e238, 0x461f55ba, 0x629d048c, 0x0f1bf7d6, 0x2b99a6e0, + 0x0ec38730, 0x2a41d606, 0x47c7255c, 0x6345746a, 0x9ccac3e8, + 0xb84892de, 0xd5ce6184, 0xf14c30b2, 0xf1a008c1, 0xd52259f7, + 0xb8a4aaad, 0x9c26fb9b, 0x63a94c19, 0x472b1d2f, 0x2aadee75, + 0x0e2fbf43}, + {0x00000000, 0x36f290f3, 0x6de521e6, 0x5b17b115, 0xdbca43cc, + 0xed38d33f, 0xb62f622a, 0x80ddf2d9, 0x6ce581d9, 0x5a17112a, + 0x0100a03f, 0x37f230cc, 0xb72fc215, 0x81dd52e6, 0xdacae3f3, + 0xec387300, 0xd9cb03b2, 0xef399341, 0xb42e2254, 0x82dcb2a7, + 0x0201407e, 0x34f3d08d, 0x6fe46198, 0x5916f16b, 0xb52e826b, + 0x83dc1298, 0xd8cba38d, 0xee39337e, 0x6ee4c1a7, 0x58165154, + 0x0301e041, 0x35f370b2, 0x68e70125, 0x5e1591d6, 0x050220c3, + 0x33f0b030, 0xb32d42e9, 0x85dfd21a, 0xdec8630f, 0xe83af3fc, + 0x040280fc, 0x32f0100f, 0x69e7a11a, 0x5f1531e9, 0xdfc8c330, + 0xe93a53c3, 0xb22de2d6, 0x84df7225, 0xb12c0297, 0x87de9264, + 0xdcc92371, 0xea3bb382, 0x6ae6415b, 0x5c14d1a8, 0x070360bd, + 0x31f1f04e, 0xddc9834e, 0xeb3b13bd, 0xb02ca2a8, 0x86de325b, + 0x0603c082, 0x30f15071, 0x6be6e164, 0x5d147197, 0xd1ce024a, + 0xe73c92b9, 0xbc2b23ac, 0x8ad9b35f, 0x0a044186, 0x3cf6d175, + 0x67e16060, 0x5113f093, 0xbd2b8393, 0x8bd91360, 0xd0cea275, + 0xe63c3286, 0x66e1c05f, 0x501350ac, 0x0b04e1b9, 0x3df6714a, + 0x080501f8, 0x3ef7910b, 0x65e0201e, 0x5312b0ed, 0xd3cf4234, + 0xe53dd2c7, 0xbe2a63d2, 0x88d8f321, 0x64e08021, 0x521210d2, + 0x0905a1c7, 0x3ff73134, 0xbf2ac3ed, 0x89d8531e, 0xd2cfe20b, + 0xe43d72f8, 0xb929036f, 0x8fdb939c, 0xd4cc2289, 0xe23eb27a, + 0x62e340a3, 0x5411d050, 0x0f066145, 0x39f4f1b6, 0xd5cc82b6, + 0xe33e1245, 0xb829a350, 0x8edb33a3, 0x0e06c17a, 0x38f45189, + 0x63e3e09c, 0x5511706f, 0x60e200dd, 0x5610902e, 0x0d07213b, + 0x3bf5b1c8, 0xbb284311, 0x8ddad3e2, 0xd6cd62f7, 0xe03ff204, + 0x0c078104, 0x3af511f7, 0x61e2a0e2, 0x57103011, 0xd7cdc2c8, + 0xe13f523b, 0xba28e32e, 0x8cda73dd, 0x78ed02d5, 0x4e1f9226, + 0x15082333, 0x23fab3c0, 0xa3274119, 0x95d5d1ea, 0xcec260ff, + 0xf830f00c, 0x1408830c, 0x22fa13ff, 0x79eda2ea, 0x4f1f3219, + 0xcfc2c0c0, 0xf9305033, 0xa227e126, 0x94d571d5, 0xa1260167, + 0x97d49194, 0xccc32081, 0xfa31b072, 0x7aec42ab, 0x4c1ed258, + 0x1709634d, 0x21fbf3be, 0xcdc380be, 0xfb31104d, 0xa026a158, + 0x96d431ab, 0x1609c372, 0x20fb5381, 0x7bece294, 0x4d1e7267, + 0x100a03f0, 0x26f89303, 0x7def2216, 0x4b1db2e5, 0xcbc0403c, + 0xfd32d0cf, 0xa62561da, 0x90d7f129, 0x7cef8229, 0x4a1d12da, + 0x110aa3cf, 0x27f8333c, 0xa725c1e5, 0x91d75116, 0xcac0e003, + 0xfc3270f0, 0xc9c10042, 0xff3390b1, 0xa42421a4, 0x92d6b157, + 0x120b438e, 0x24f9d37d, 0x7fee6268, 0x491cf29b, 0xa524819b, + 0x93d61168, 0xc8c1a07d, 0xfe33308e, 0x7eeec257, 0x481c52a4, + 0x130be3b1, 0x25f97342, 0xa923009f, 0x9fd1906c, 0xc4c62179, + 0xf234b18a, 0x72e94353, 0x441bd3a0, 0x1f0c62b5, 0x29fef246, + 0xc5c68146, 0xf33411b5, 0xa823a0a0, 0x9ed13053, 0x1e0cc28a, + 0x28fe5279, 0x73e9e36c, 0x451b739f, 0x70e8032d, 0x461a93de, + 0x1d0d22cb, 0x2bffb238, 0xab2240e1, 0x9dd0d012, 0xc6c76107, + 0xf035f1f4, 0x1c0d82f4, 0x2aff1207, 0x71e8a312, 0x471a33e1, + 0xc7c7c138, 0xf13551cb, 0xaa22e0de, 0x9cd0702d, 0xc1c401ba, + 0xf7369149, 0xac21205c, 0x9ad3b0af, 0x1a0e4276, 0x2cfcd285, + 0x77eb6390, 0x4119f363, 0xad218063, 0x9bd31090, 0xc0c4a185, + 0xf6363176, 0x76ebc3af, 0x4019535c, 0x1b0ee249, 0x2dfc72ba, + 0x180f0208, 0x2efd92fb, 0x75ea23ee, 0x4318b31d, 0xc3c541c4, + 0xf537d137, 0xae206022, 0x98d2f0d1, 0x74ea83d1, 0x42181322, + 0x190fa237, 0x2ffd32c4, 0xaf20c01d, 0x99d250ee, 0xc2c5e1fb, + 0xf4377108}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x0000000000000000, 0xf390f23600000000, 0xe621e56d00000000, + 0x15b1175b00000000, 0xcc43cadb00000000, 0x3fd338ed00000000, + 0x2a622fb600000000, 0xd9f2dd8000000000, 0xd981e56c00000000, + 0x2a11175a00000000, 0x3fa0000100000000, 0xcc30f23700000000, + 0x15c22fb700000000, 0xe652dd8100000000, 0xf3e3cada00000000, + 0x007338ec00000000, 0xb203cbd900000000, 0x419339ef00000000, + 0x54222eb400000000, 0xa7b2dc8200000000, 0x7e40010200000000, + 0x8dd0f33400000000, 0x9861e46f00000000, 0x6bf1165900000000, + 0x6b822eb500000000, 0x9812dc8300000000, 0x8da3cbd800000000, + 0x7e3339ee00000000, 0xa7c1e46e00000000, 0x5451165800000000, + 0x41e0010300000000, 0xb270f33500000000, 0x2501e76800000000, + 0xd691155e00000000, 0xc320020500000000, 0x30b0f03300000000, + 0xe9422db300000000, 0x1ad2df8500000000, 0x0f63c8de00000000, + 0xfcf33ae800000000, 0xfc80020400000000, 0x0f10f03200000000, + 0x1aa1e76900000000, 0xe931155f00000000, 0x30c3c8df00000000, + 0xc3533ae900000000, 0xd6e22db200000000, 0x2572df8400000000, + 0x97022cb100000000, 0x6492de8700000000, 0x7123c9dc00000000, + 0x82b33bea00000000, 0x5b41e66a00000000, 0xa8d1145c00000000, + 0xbd60030700000000, 0x4ef0f13100000000, 0x4e83c9dd00000000, + 0xbd133beb00000000, 0xa8a22cb000000000, 0x5b32de8600000000, + 0x82c0030600000000, 0x7150f13000000000, 0x64e1e66b00000000, + 0x9771145d00000000, 0x4a02ced100000000, 0xb9923ce700000000, + 0xac232bbc00000000, 0x5fb3d98a00000000, 0x8641040a00000000, + 0x75d1f63c00000000, 0x6060e16700000000, 0x93f0135100000000, + 0x93832bbd00000000, 0x6013d98b00000000, 0x75a2ced000000000, + 0x86323ce600000000, 0x5fc0e16600000000, 0xac50135000000000, + 0xb9e1040b00000000, 0x4a71f63d00000000, 0xf801050800000000, + 0x0b91f73e00000000, 0x1e20e06500000000, 0xedb0125300000000, + 0x3442cfd300000000, 0xc7d23de500000000, 0xd2632abe00000000, + 0x21f3d88800000000, 0x2180e06400000000, 0xd210125200000000, + 0xc7a1050900000000, 0x3431f73f00000000, 0xedc32abf00000000, + 0x1e53d88900000000, 0x0be2cfd200000000, 0xf8723de400000000, + 0x6f0329b900000000, 0x9c93db8f00000000, 0x8922ccd400000000, + 0x7ab23ee200000000, 0xa340e36200000000, 0x50d0115400000000, + 0x4561060f00000000, 0xb6f1f43900000000, 0xb682ccd500000000, + 0x45123ee300000000, 0x50a329b800000000, 0xa333db8e00000000, + 0x7ac1060e00000000, 0x8951f43800000000, 0x9ce0e36300000000, + 0x6f70115500000000, 0xdd00e26000000000, 0x2e90105600000000, + 0x3b21070d00000000, 0xc8b1f53b00000000, 0x114328bb00000000, + 0xe2d3da8d00000000, 0xf762cdd600000000, 0x04f23fe000000000, + 0x0481070c00000000, 0xf711f53a00000000, 0xe2a0e26100000000, + 0x1130105700000000, 0xc8c2cdd700000000, 0x3b523fe100000000, + 0x2ee328ba00000000, 0xdd73da8c00000000, 0xd502ed7800000000, + 0x26921f4e00000000, 0x3323081500000000, 0xc0b3fa2300000000, + 0x194127a300000000, 0xead1d59500000000, 0xff60c2ce00000000, + 0x0cf030f800000000, 0x0c83081400000000, 0xff13fa2200000000, + 0xeaa2ed7900000000, 0x19321f4f00000000, 0xc0c0c2cf00000000, + 0x335030f900000000, 0x26e127a200000000, 0xd571d59400000000, + 0x670126a100000000, 0x9491d49700000000, 0x8120c3cc00000000, + 0x72b031fa00000000, 0xab42ec7a00000000, 0x58d21e4c00000000, + 0x4d63091700000000, 0xbef3fb2100000000, 0xbe80c3cd00000000, + 0x4d1031fb00000000, 0x58a126a000000000, 0xab31d49600000000, + 0x72c3091600000000, 0x8153fb2000000000, 0x94e2ec7b00000000, + 0x67721e4d00000000, 0xf0030a1000000000, 0x0393f82600000000, + 0x1622ef7d00000000, 0xe5b21d4b00000000, 0x3c40c0cb00000000, + 0xcfd032fd00000000, 0xda6125a600000000, 0x29f1d79000000000, + 0x2982ef7c00000000, 0xda121d4a00000000, 0xcfa30a1100000000, + 0x3c33f82700000000, 0xe5c125a700000000, 0x1651d79100000000, + 0x03e0c0ca00000000, 0xf07032fc00000000, 0x4200c1c900000000, + 0xb19033ff00000000, 0xa42124a400000000, 0x57b1d69200000000, + 0x8e430b1200000000, 0x7dd3f92400000000, 0x6862ee7f00000000, + 0x9bf21c4900000000, 0x9b8124a500000000, 0x6811d69300000000, + 0x7da0c1c800000000, 0x8e3033fe00000000, 0x57c2ee7e00000000, + 0xa4521c4800000000, 0xb1e30b1300000000, 0x4273f92500000000, + 0x9f0023a900000000, 0x6c90d19f00000000, 0x7921c6c400000000, + 0x8ab134f200000000, 0x5343e97200000000, 0xa0d31b4400000000, + 0xb5620c1f00000000, 0x46f2fe2900000000, 0x4681c6c500000000, + 0xb51134f300000000, 0xa0a023a800000000, 0x5330d19e00000000, + 0x8ac20c1e00000000, 0x7952fe2800000000, 0x6ce3e97300000000, + 0x9f731b4500000000, 0x2d03e87000000000, 0xde931a4600000000, + 0xcb220d1d00000000, 0x38b2ff2b00000000, 0xe14022ab00000000, + 0x12d0d09d00000000, 0x0761c7c600000000, 0xf4f135f000000000, + 0xf4820d1c00000000, 0x0712ff2a00000000, 0x12a3e87100000000, + 0xe1331a4700000000, 0x38c1c7c700000000, 0xcb5135f100000000, + 0xdee022aa00000000, 0x2d70d09c00000000, 0xba01c4c100000000, + 0x499136f700000000, 0x5c2021ac00000000, 0xafb0d39a00000000, + 0x76420e1a00000000, 0x85d2fc2c00000000, 0x9063eb7700000000, + 0x63f3194100000000, 0x638021ad00000000, 0x9010d39b00000000, + 0x85a1c4c000000000, 0x763136f600000000, 0xafc3eb7600000000, + 0x5c53194000000000, 0x49e20e1b00000000, 0xba72fc2d00000000, + 0x08020f1800000000, 0xfb92fd2e00000000, 0xee23ea7500000000, + 0x1db3184300000000, 0xc441c5c300000000, 0x37d137f500000000, + 0x226020ae00000000, 0xd1f0d29800000000, 0xd183ea7400000000, + 0x2213184200000000, 0x37a20f1900000000, 0xc432fd2f00000000, + 0x1dc020af00000000, 0xee50d29900000000, 0xfbe1c5c200000000, + 0x087137f400000000}, + {0x0000000000000000, 0x3651822400000000, 0x6ca2044900000000, + 0x5af3866d00000000, 0xd844099200000000, 0xee158bb600000000, + 0xb4e60ddb00000000, 0x82b78fff00000000, 0xf18f63ff00000000, + 0xc7dee1db00000000, 0x9d2d67b600000000, 0xab7ce59200000000, + 0x29cb6a6d00000000, 0x1f9ae84900000000, 0x45696e2400000000, + 0x7338ec0000000000, 0xa319b62500000000, 0x9548340100000000, + 0xcfbbb26c00000000, 0xf9ea304800000000, 0x7b5dbfb700000000, + 0x4d0c3d9300000000, 0x17ffbbfe00000000, 0x21ae39da00000000, + 0x5296d5da00000000, 0x64c757fe00000000, 0x3e34d19300000000, + 0x086553b700000000, 0x8ad2dc4800000000, 0xbc835e6c00000000, + 0xe670d80100000000, 0xd0215a2500000000, 0x46336c4b00000000, + 0x7062ee6f00000000, 0x2a91680200000000, 0x1cc0ea2600000000, + 0x9e7765d900000000, 0xa826e7fd00000000, 0xf2d5619000000000, + 0xc484e3b400000000, 0xb7bc0fb400000000, 0x81ed8d9000000000, + 0xdb1e0bfd00000000, 0xed4f89d900000000, 0x6ff8062600000000, + 0x59a9840200000000, 0x035a026f00000000, 0x350b804b00000000, + 0xe52ada6e00000000, 0xd37b584a00000000, 0x8988de2700000000, + 0xbfd95c0300000000, 0x3d6ed3fc00000000, 0x0b3f51d800000000, + 0x51ccd7b500000000, 0x679d559100000000, 0x14a5b99100000000, + 0x22f43bb500000000, 0x7807bdd800000000, 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0xcf26e87100000000, 0xd112264300000000, 0x7819af0d00000000, + 0x830534de00000000, 0x2a0ebd9000000000, 0x859c73ef00000000, + 0x2c97faa100000000, 0xd78b617200000000, 0x7e80e83c00000000, + 0x60b4260e00000000, 0xc9bfaf4000000000, 0x32a3349300000000, + 0x9ba8bddd00000000, 0x0ecba8f600000000, 0xa7c021b800000000, + 0x5cdcba6b00000000, 0xf5d7332500000000, 0xebe3fd1700000000, + 0x42e8745900000000, 0xb9f4ef8a00000000, 0x10ff66c400000000, + 0x9333c5dc00000000, 0x3a384c9200000000, 0xc124d74100000000, + 0x682f5e0f00000000, 0x761b903d00000000, 0xdf10197300000000, + 0x240c82a000000000, 0x8d070bee00000000, 0x18641ec500000000, + 0xb16f978b00000000, 0x4a730c5800000000, 0xe378851600000000, + 0xfd4c4b2400000000, 0x5447c26a00000000, 0xaf5b59b900000000, + 0x0650d0f700000000}, + {0x0000000000000000, 0x479244af00000000, 0xcf22f88500000000, + 0x88b0bc2a00000000, 0xdf4381d000000000, 0x98d1c57f00000000, + 0x1061795500000000, 0x57f33dfa00000000, 0xff81737a00000000, + 0xb81337d500000000, 0x30a38bff00000000, 0x7731cf5000000000, + 0x20c2f2aa00000000, 0x6750b60500000000, 0xefe00a2f00000000, + 0xa8724e8000000000, 0xfe03e7f400000000, 0xb991a35b00000000, + 0x31211f7100000000, 0x76b35bde00000000, 0x2140662400000000, + 0x66d2228b00000000, 0xee629ea100000000, 0xa9f0da0e00000000, + 0x0182948e00000000, 0x4610d02100000000, 0xcea06c0b00000000, + 0x893228a400000000, 0xdec1155e00000000, 0x995351f100000000, + 0x11e3eddb00000000, 0x5671a97400000000, 0xbd01bf3200000000, + 0xfa93fb9d00000000, 0x722347b700000000, 0x35b1031800000000, + 0x62423ee200000000, 0x25d07a4d00000000, 0xad60c66700000000, + 0xeaf282c800000000, 0x4280cc4800000000, 0x051288e700000000, + 0x8da234cd00000000, 0xca30706200000000, 0x9dc34d9800000000, + 0xda51093700000000, 0x52e1b51d00000000, 0x1573f1b200000000, + 0x430258c600000000, 0x04901c6900000000, 0x8c20a04300000000, + 0xcbb2e4ec00000000, 0x9c41d91600000000, 0xdbd39db900000000, + 0x5363219300000000, 0x14f1653c00000000, 0xbc832bbc00000000, + 0xfb116f1300000000, 0x73a1d33900000000, 0x3433979600000000, + 0x63c0aa6c00000000, 0x2452eec300000000, 0xace252e900000000, + 0xeb70164600000000, 0x7a037e6500000000, 0x3d913aca00000000, + 0xb52186e000000000, 0xf2b3c24f00000000, 0xa540ffb500000000, + 0xe2d2bb1a00000000, 0x6a62073000000000, 0x2df0439f00000000, + 0x85820d1f00000000, 0xc21049b000000000, 0x4aa0f59a00000000, + 0x0d32b13500000000, 0x5ac18ccf00000000, 0x1d53c86000000000, + 0x95e3744a00000000, 0xd27130e500000000, 0x8400999100000000, + 0xc392dd3e00000000, 0x4b22611400000000, 0x0cb025bb00000000, + 0x5b43184100000000, 0x1cd15cee00000000, 0x9461e0c400000000, + 0xd3f3a46b00000000, 0x7b81eaeb00000000, 0x3c13ae4400000000, + 0xb4a3126e00000000, 0xf33156c100000000, 0xa4c26b3b00000000, + 0xe3502f9400000000, 0x6be093be00000000, 0x2c72d71100000000, + 0xc702c15700000000, 0x809085f800000000, 0x082039d200000000, + 0x4fb27d7d00000000, 0x1841408700000000, 0x5fd3042800000000, + 0xd763b80200000000, 0x90f1fcad00000000, 0x3883b22d00000000, + 0x7f11f68200000000, 0xf7a14aa800000000, 0xb0330e0700000000, + 0xe7c033fd00000000, 0xa052775200000000, 0x28e2cb7800000000, + 0x6f708fd700000000, 0x390126a300000000, 0x7e93620c00000000, + 0xf623de2600000000, 0xb1b19a8900000000, 0xe642a77300000000, + 0xa1d0e3dc00000000, 0x29605ff600000000, 0x6ef21b5900000000, + 0xc68055d900000000, 0x8112117600000000, 0x09a2ad5c00000000, + 0x4e30e9f300000000, 0x19c3d40900000000, 0x5e5190a600000000, + 0xd6e12c8c00000000, 0x9173682300000000, 0xf406fcca00000000, + 0xb394b86500000000, 0x3b24044f00000000, 0x7cb640e000000000, + 0x2b457d1a00000000, 0x6cd739b500000000, 0xe467859f00000000, + 0xa3f5c13000000000, 0x0b878fb000000000, 0x4c15cb1f00000000, + 0xc4a5773500000000, 0x8337339a00000000, 0xd4c40e6000000000, + 0x93564acf00000000, 0x1be6f6e500000000, 0x5c74b24a00000000, + 0x0a051b3e00000000, 0x4d975f9100000000, 0xc527e3bb00000000, + 0x82b5a71400000000, 0xd5469aee00000000, 0x92d4de4100000000, + 0x1a64626b00000000, 0x5df626c400000000, 0xf584684400000000, + 0xb2162ceb00000000, 0x3aa690c100000000, 0x7d34d46e00000000, + 0x2ac7e99400000000, 0x6d55ad3b00000000, 0xe5e5111100000000, + 0xa27755be00000000, 0x490743f800000000, 0x0e95075700000000, + 0x8625bb7d00000000, 0xc1b7ffd200000000, 0x9644c22800000000, + 0xd1d6868700000000, 0x59663aad00000000, 0x1ef47e0200000000, + 0xb686308200000000, 0xf114742d00000000, 0x79a4c80700000000, + 0x3e368ca800000000, 0x69c5b15200000000, 0x2e57f5fd00000000, + 0xa6e749d700000000, 0xe1750d7800000000, 0xb704a40c00000000, + 0xf096e0a300000000, 0x78265c8900000000, 0x3fb4182600000000, + 0x684725dc00000000, 0x2fd5617300000000, 0xa765dd5900000000, + 0xe0f799f600000000, 0x4885d77600000000, 0x0f1793d900000000, + 0x87a72ff300000000, 0xc0356b5c00000000, 0x97c656a600000000, + 0xd054120900000000, 0x58e4ae2300000000, 0x1f76ea8c00000000, + 0x8e0582af00000000, 0xc997c60000000000, 0x41277a2a00000000, + 0x06b53e8500000000, 0x5146037f00000000, 0x16d447d000000000, + 0x9e64fbfa00000000, 0xd9f6bf5500000000, 0x7184f1d500000000, + 0x3616b57a00000000, 0xbea6095000000000, 0xf9344dff00000000, + 0xaec7700500000000, 0xe95534aa00000000, 0x61e5888000000000, + 0x2677cc2f00000000, 0x7006655b00000000, 0x379421f400000000, + 0xbf249dde00000000, 0xf8b6d97100000000, 0xaf45e48b00000000, + 0xe8d7a02400000000, 0x60671c0e00000000, 0x27f558a100000000, + 0x8f87162100000000, 0xc815528e00000000, 0x40a5eea400000000, + 0x0737aa0b00000000, 0x50c497f100000000, 0x1756d35e00000000, + 0x9fe66f7400000000, 0xd8742bdb00000000, 0x33043d9d00000000, + 0x7496793200000000, 0xfc26c51800000000, 0xbbb481b700000000, + 0xec47bc4d00000000, 0xabd5f8e200000000, 0x236544c800000000, + 0x64f7006700000000, 0xcc854ee700000000, 0x8b170a4800000000, + 0x03a7b66200000000, 0x4435f2cd00000000, 0x13c6cf3700000000, + 0x54548b9800000000, 0xdce437b200000000, 0x9b76731d00000000, + 0xcd07da6900000000, 0x8a959ec600000000, 0x022522ec00000000, + 0x45b7664300000000, 0x12445bb900000000, 0x55d61f1600000000, + 0xdd66a33c00000000, 0x9af4e79300000000, 0x3286a91300000000, + 0x7514edbc00000000, 0xfda4519600000000, 0xba36153900000000, + 0xedc528c300000000, 0xaa576c6c00000000, 0x22e7d04600000000, + 0x657594e900000000}}; + +#else /* W == 4 */ + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0x65673b46, 0xcace768c, 0xafa94dca, 0x4eedeb59, + 0x2b8ad01f, 0x84239dd5, 0xe144a693, 0x9ddbd6b2, 0xf8bcedf4, + 0x5715a03e, 0x32729b78, 0xd3363deb, 0xb65106ad, 0x19f84b67, + 0x7c9f7021, 0xe0c6ab25, 0x85a19063, 0x2a08dda9, 0x4f6fe6ef, + 0xae2b407c, 0xcb4c7b3a, 0x64e536f0, 0x01820db6, 0x7d1d7d97, + 0x187a46d1, 0xb7d30b1b, 0xd2b4305d, 0x33f096ce, 0x5697ad88, + 0xf93ee042, 0x9c59db04, 0x1afc500b, 0x7f9b6b4d, 0xd0322687, + 0xb5551dc1, 0x5411bb52, 0x31768014, 0x9edfcdde, 0xfbb8f698, + 0x872786b9, 0xe240bdff, 0x4de9f035, 0x288ecb73, 0xc9ca6de0, + 0xacad56a6, 0x03041b6c, 0x6663202a, 0xfa3afb2e, 0x9f5dc068, + 0x30f48da2, 0x5593b6e4, 0xb4d71077, 0xd1b02b31, 0x7e1966fb, + 0x1b7e5dbd, 0x67e12d9c, 0x028616da, 0xad2f5b10, 0xc8486056, + 0x290cc6c5, 0x4c6bfd83, 0xe3c2b049, 0x86a58b0f, 0x35f8a016, + 0x509f9b50, 0xff36d69a, 0x9a51eddc, 0x7b154b4f, 0x1e727009, + 0xb1db3dc3, 0xd4bc0685, 0xa82376a4, 0xcd444de2, 0x62ed0028, + 0x078a3b6e, 0xe6ce9dfd, 0x83a9a6bb, 0x2c00eb71, 0x4967d037, + 0xd53e0b33, 0xb0593075, 0x1ff07dbf, 0x7a9746f9, 0x9bd3e06a, + 0xfeb4db2c, 0x511d96e6, 0x347aada0, 0x48e5dd81, 0x2d82e6c7, + 0x822bab0d, 0xe74c904b, 0x060836d8, 0x636f0d9e, 0xccc64054, + 0xa9a17b12, 0x2f04f01d, 0x4a63cb5b, 0xe5ca8691, 0x80adbdd7, + 0x61e91b44, 0x048e2002, 0xab276dc8, 0xce40568e, 0xb2df26af, + 0xd7b81de9, 0x78115023, 0x1d766b65, 0xfc32cdf6, 0x9955f6b0, + 0x36fcbb7a, 0x539b803c, 0xcfc25b38, 0xaaa5607e, 0x050c2db4, + 0x606b16f2, 0x812fb061, 0xe4488b27, 0x4be1c6ed, 0x2e86fdab, + 0x52198d8a, 0x377eb6cc, 0x98d7fb06, 0xfdb0c040, 0x1cf466d3, + 0x79935d95, 0xd63a105f, 0xb35d2b19, 0x6bf1402c, 0x0e967b6a, + 0xa13f36a0, 0xc4580de6, 0x251cab75, 0x407b9033, 0xefd2ddf9, + 0x8ab5e6bf, 0xf62a969e, 0x934dadd8, 0x3ce4e012, 0x5983db54, + 0xb8c77dc7, 0xdda04681, 0x72090b4b, 0x176e300d, 0x8b37eb09, + 0xee50d04f, 0x41f99d85, 0x249ea6c3, 0xc5da0050, 0xa0bd3b16, + 0x0f1476dc, 0x6a734d9a, 0x16ec3dbb, 0x738b06fd, 0xdc224b37, + 0xb9457071, 0x5801d6e2, 0x3d66eda4, 0x92cfa06e, 0xf7a89b28, + 0x710d1027, 0x146a2b61, 0xbbc366ab, 0xdea45ded, 0x3fe0fb7e, + 0x5a87c038, 0xf52e8df2, 0x9049b6b4, 0xecd6c695, 0x89b1fdd3, + 0x2618b019, 0x437f8b5f, 0xa23b2dcc, 0xc75c168a, 0x68f55b40, + 0x0d926006, 0x91cbbb02, 0xf4ac8044, 0x5b05cd8e, 0x3e62f6c8, + 0xdf26505b, 0xba416b1d, 0x15e826d7, 0x708f1d91, 0x0c106db0, + 0x697756f6, 0xc6de1b3c, 0xa3b9207a, 0x42fd86e9, 0x279abdaf, + 0x8833f065, 0xed54cb23, 0x5e09e03a, 0x3b6edb7c, 0x94c796b6, + 0xf1a0adf0, 0x10e40b63, 0x75833025, 0xda2a7def, 0xbf4d46a9, + 0xc3d23688, 0xa6b50dce, 0x091c4004, 0x6c7b7b42, 0x8d3fddd1, + 0xe858e697, 0x47f1ab5d, 0x2296901b, 0xbecf4b1f, 0xdba87059, + 0x74013d93, 0x116606d5, 0xf022a046, 0x95459b00, 0x3aecd6ca, + 0x5f8bed8c, 0x23149dad, 0x4673a6eb, 0xe9daeb21, 0x8cbdd067, + 0x6df976f4, 0x089e4db2, 0xa7370078, 0xc2503b3e, 0x44f5b031, + 0x21928b77, 0x8e3bc6bd, 0xeb5cfdfb, 0x0a185b68, 0x6f7f602e, + 0xc0d62de4, 0xa5b116a2, 0xd92e6683, 0xbc495dc5, 0x13e0100f, + 0x76872b49, 0x97c38dda, 0xf2a4b69c, 0x5d0dfb56, 0x386ac010, + 0xa4331b14, 0xc1542052, 0x6efd6d98, 0x0b9a56de, 0xeadef04d, + 0x8fb9cb0b, 0x201086c1, 0x4577bd87, 0x39e8cda6, 0x5c8ff6e0, + 0xf326bb2a, 0x9641806c, 0x770526ff, 0x12621db9, 0xbdcb5073, + 0xd8ac6b35}, + {0x00000000, 0xd7e28058, 0x74b406f1, 0xa35686a9, 0xe9680de2, + 0x3e8a8dba, 0x9ddc0b13, 0x4a3e8b4b, 0x09a11d85, 0xde439ddd, + 0x7d151b74, 0xaaf79b2c, 0xe0c91067, 0x372b903f, 0x947d1696, + 0x439f96ce, 0x13423b0a, 0xc4a0bb52, 0x67f63dfb, 0xb014bda3, + 0xfa2a36e8, 0x2dc8b6b0, 0x8e9e3019, 0x597cb041, 0x1ae3268f, + 0xcd01a6d7, 0x6e57207e, 0xb9b5a026, 0xf38b2b6d, 0x2469ab35, + 0x873f2d9c, 0x50ddadc4, 0x26847614, 0xf166f64c, 0x523070e5, + 0x85d2f0bd, 0xcfec7bf6, 0x180efbae, 0xbb587d07, 0x6cbafd5f, + 0x2f256b91, 0xf8c7ebc9, 0x5b916d60, 0x8c73ed38, 0xc64d6673, + 0x11afe62b, 0xb2f96082, 0x651be0da, 0x35c64d1e, 0xe224cd46, + 0x41724bef, 0x9690cbb7, 0xdcae40fc, 0x0b4cc0a4, 0xa81a460d, + 0x7ff8c655, 0x3c67509b, 0xeb85d0c3, 0x48d3566a, 0x9f31d632, + 0xd50f5d79, 0x02eddd21, 0xa1bb5b88, 0x7659dbd0, 0x4d08ec28, + 0x9aea6c70, 0x39bcead9, 0xee5e6a81, 0xa460e1ca, 0x73826192, + 0xd0d4e73b, 0x07366763, 0x44a9f1ad, 0x934b71f5, 0x301df75c, + 0xe7ff7704, 0xadc1fc4f, 0x7a237c17, 0xd975fabe, 0x0e977ae6, + 0x5e4ad722, 0x89a8577a, 0x2afed1d3, 0xfd1c518b, 0xb722dac0, + 0x60c05a98, 0xc396dc31, 0x14745c69, 0x57ebcaa7, 0x80094aff, + 0x235fcc56, 0xf4bd4c0e, 0xbe83c745, 0x6961471d, 0xca37c1b4, + 0x1dd541ec, 0x6b8c9a3c, 0xbc6e1a64, 0x1f389ccd, 0xc8da1c95, + 0x82e497de, 0x55061786, 0xf650912f, 0x21b21177, 0x622d87b9, + 0xb5cf07e1, 0x16998148, 0xc17b0110, 0x8b458a5b, 0x5ca70a03, + 0xfff18caa, 0x28130cf2, 0x78cea136, 0xaf2c216e, 0x0c7aa7c7, + 0xdb98279f, 0x91a6acd4, 0x46442c8c, 0xe512aa25, 0x32f02a7d, + 0x716fbcb3, 0xa68d3ceb, 0x05dbba42, 0xd2393a1a, 0x9807b151, + 0x4fe53109, 0xecb3b7a0, 0x3b5137f8, 0x9a11d850, 0x4df35808, + 0xeea5dea1, 0x39475ef9, 0x7379d5b2, 0xa49b55ea, 0x07cdd343, + 0xd02f531b, 0x93b0c5d5, 0x4452458d, 0xe704c324, 0x30e6437c, + 0x7ad8c837, 0xad3a486f, 0x0e6ccec6, 0xd98e4e9e, 0x8953e35a, + 0x5eb16302, 0xfde7e5ab, 0x2a0565f3, 0x603beeb8, 0xb7d96ee0, + 0x148fe849, 0xc36d6811, 0x80f2fedf, 0x57107e87, 0xf446f82e, + 0x23a47876, 0x699af33d, 0xbe787365, 0x1d2ef5cc, 0xcacc7594, + 0xbc95ae44, 0x6b772e1c, 0xc821a8b5, 0x1fc328ed, 0x55fda3a6, + 0x821f23fe, 0x2149a557, 0xf6ab250f, 0xb534b3c1, 0x62d63399, + 0xc180b530, 0x16623568, 0x5c5cbe23, 0x8bbe3e7b, 0x28e8b8d2, + 0xff0a388a, 0xafd7954e, 0x78351516, 0xdb6393bf, 0x0c8113e7, + 0x46bf98ac, 0x915d18f4, 0x320b9e5d, 0xe5e91e05, 0xa67688cb, + 0x71940893, 0xd2c28e3a, 0x05200e62, 0x4f1e8529, 0x98fc0571, + 0x3baa83d8, 0xec480380, 0xd7193478, 0x00fbb420, 0xa3ad3289, + 0x744fb2d1, 0x3e71399a, 0xe993b9c2, 0x4ac53f6b, 0x9d27bf33, + 0xdeb829fd, 0x095aa9a5, 0xaa0c2f0c, 0x7deeaf54, 0x37d0241f, + 0xe032a447, 0x436422ee, 0x9486a2b6, 0xc45b0f72, 0x13b98f2a, + 0xb0ef0983, 0x670d89db, 0x2d330290, 0xfad182c8, 0x59870461, + 0x8e658439, 0xcdfa12f7, 0x1a1892af, 0xb94e1406, 0x6eac945e, + 0x24921f15, 0xf3709f4d, 0x502619e4, 0x87c499bc, 0xf19d426c, + 0x267fc234, 0x8529449d, 0x52cbc4c5, 0x18f54f8e, 0xcf17cfd6, + 0x6c41497f, 0xbba3c927, 0xf83c5fe9, 0x2fdedfb1, 0x8c885918, + 0x5b6ad940, 0x1154520b, 0xc6b6d253, 0x65e054fa, 0xb202d4a2, + 0xe2df7966, 0x353df93e, 0x966b7f97, 0x4189ffcf, 0x0bb77484, + 0xdc55f4dc, 0x7f037275, 0xa8e1f22d, 0xeb7e64e3, 0x3c9ce4bb, + 0x9fca6212, 0x4828e24a, 0x02166901, 0xd5f4e959, 0x76a26ff0, + 0xa140efa8}, + {0x00000000, 0xef52b6e1, 0x05d46b83, 0xea86dd62, 0x0ba8d706, + 0xe4fa61e7, 0x0e7cbc85, 0xe12e0a64, 0x1751ae0c, 0xf80318ed, + 0x1285c58f, 0xfdd7736e, 0x1cf9790a, 0xf3abcfeb, 0x192d1289, + 0xf67fa468, 0x2ea35c18, 0xc1f1eaf9, 0x2b77379b, 0xc425817a, + 0x250b8b1e, 0xca593dff, 0x20dfe09d, 0xcf8d567c, 0x39f2f214, + 0xd6a044f5, 0x3c269997, 0xd3742f76, 0x325a2512, 0xdd0893f3, + 0x378e4e91, 0xd8dcf870, 0x5d46b830, 0xb2140ed1, 0x5892d3b3, + 0xb7c06552, 0x56ee6f36, 0xb9bcd9d7, 0x533a04b5, 0xbc68b254, + 0x4a17163c, 0xa545a0dd, 0x4fc37dbf, 0xa091cb5e, 0x41bfc13a, + 0xaeed77db, 0x446baab9, 0xab391c58, 0x73e5e428, 0x9cb752c9, + 0x76318fab, 0x9963394a, 0x784d332e, 0x971f85cf, 0x7d9958ad, + 0x92cbee4c, 0x64b44a24, 0x8be6fcc5, 0x616021a7, 0x8e329746, + 0x6f1c9d22, 0x804e2bc3, 0x6ac8f6a1, 0x859a4040, 0xba8d7060, + 0x55dfc681, 0xbf591be3, 0x500bad02, 0xb125a766, 0x5e771187, + 0xb4f1cce5, 0x5ba37a04, 0xaddcde6c, 0x428e688d, 0xa808b5ef, + 0x475a030e, 0xa674096a, 0x4926bf8b, 0xa3a062e9, 0x4cf2d408, + 0x942e2c78, 0x7b7c9a99, 0x91fa47fb, 0x7ea8f11a, 0x9f86fb7e, + 0x70d44d9f, 0x9a5290fd, 0x7500261c, 0x837f8274, 0x6c2d3495, + 0x86abe9f7, 0x69f95f16, 0x88d75572, 0x6785e393, 0x8d033ef1, + 0x62518810, 0xe7cbc850, 0x08997eb1, 0xe21fa3d3, 0x0d4d1532, + 0xec631f56, 0x0331a9b7, 0xe9b774d5, 0x06e5c234, 0xf09a665c, + 0x1fc8d0bd, 0xf54e0ddf, 0x1a1cbb3e, 0xfb32b15a, 0x146007bb, + 0xfee6dad9, 0x11b46c38, 0xc9689448, 0x263a22a9, 0xccbcffcb, + 0x23ee492a, 0xc2c0434e, 0x2d92f5af, 0xc71428cd, 0x28469e2c, + 0xde393a44, 0x316b8ca5, 0xdbed51c7, 0x34bfe726, 0xd591ed42, + 0x3ac35ba3, 0xd04586c1, 0x3f173020, 0xae6be681, 0x41395060, + 0xabbf8d02, 0x44ed3be3, 0xa5c33187, 0x4a918766, 0xa0175a04, + 0x4f45ece5, 0xb93a488d, 0x5668fe6c, 0xbcee230e, 0x53bc95ef, + 0xb2929f8b, 0x5dc0296a, 0xb746f408, 0x581442e9, 0x80c8ba99, + 0x6f9a0c78, 0x851cd11a, 0x6a4e67fb, 0x8b606d9f, 0x6432db7e, + 0x8eb4061c, 0x61e6b0fd, 0x97991495, 0x78cba274, 0x924d7f16, + 0x7d1fc9f7, 0x9c31c393, 0x73637572, 0x99e5a810, 0x76b71ef1, + 0xf32d5eb1, 0x1c7fe850, 0xf6f93532, 0x19ab83d3, 0xf88589b7, + 0x17d73f56, 0xfd51e234, 0x120354d5, 0xe47cf0bd, 0x0b2e465c, + 0xe1a89b3e, 0x0efa2ddf, 0xefd427bb, 0x0086915a, 0xea004c38, + 0x0552fad9, 0xdd8e02a9, 0x32dcb448, 0xd85a692a, 0x3708dfcb, + 0xd626d5af, 0x3974634e, 0xd3f2be2c, 0x3ca008cd, 0xcadfaca5, + 0x258d1a44, 0xcf0bc726, 0x205971c7, 0xc1777ba3, 0x2e25cd42, + 0xc4a31020, 0x2bf1a6c1, 0x14e696e1, 0xfbb42000, 0x1132fd62, + 0xfe604b83, 0x1f4e41e7, 0xf01cf706, 0x1a9a2a64, 0xf5c89c85, + 0x03b738ed, 0xece58e0c, 0x0663536e, 0xe931e58f, 0x081fefeb, + 0xe74d590a, 0x0dcb8468, 0xe2993289, 0x3a45caf9, 0xd5177c18, + 0x3f91a17a, 0xd0c3179b, 0x31ed1dff, 0xdebfab1e, 0x3439767c, + 0xdb6bc09d, 0x2d1464f5, 0xc246d214, 0x28c00f76, 0xc792b997, + 0x26bcb3f3, 0xc9ee0512, 0x2368d870, 0xcc3a6e91, 0x49a02ed1, + 0xa6f29830, 0x4c744552, 0xa326f3b3, 0x4208f9d7, 0xad5a4f36, + 0x47dc9254, 0xa88e24b5, 0x5ef180dd, 0xb1a3363c, 0x5b25eb5e, + 0xb4775dbf, 0x555957db, 0xba0be13a, 0x508d3c58, 0xbfdf8ab9, + 0x670372c9, 0x8851c428, 0x62d7194a, 0x8d85afab, 0x6caba5cf, + 0x83f9132e, 0x697fce4c, 0x862d78ad, 0x7052dcc5, 0x9f006a24, + 0x7586b746, 0x9ad401a7, 0x7bfa0bc3, 0x94a8bd22, 0x7e2e6040, + 0x917cd6a1}, + {0x00000000, 0x87a6cb43, 0xd43c90c7, 0x539a5b84, 0x730827cf, + 0xf4aeec8c, 0xa734b708, 0x20927c4b, 0xe6104f9e, 0x61b684dd, + 0x322cdf59, 0xb58a141a, 0x95186851, 0x12bea312, 0x4124f896, + 0xc68233d5, 0x1751997d, 0x90f7523e, 0xc36d09ba, 0x44cbc2f9, + 0x6459beb2, 0xe3ff75f1, 0xb0652e75, 0x37c3e536, 0xf141d6e3, + 0x76e71da0, 0x257d4624, 0xa2db8d67, 0x8249f12c, 0x05ef3a6f, + 0x567561eb, 0xd1d3aaa8, 0x2ea332fa, 0xa905f9b9, 0xfa9fa23d, + 0x7d39697e, 0x5dab1535, 0xda0dde76, 0x899785f2, 0x0e314eb1, + 0xc8b37d64, 0x4f15b627, 0x1c8feda3, 0x9b2926e0, 0xbbbb5aab, + 0x3c1d91e8, 0x6f87ca6c, 0xe821012f, 0x39f2ab87, 0xbe5460c4, + 0xedce3b40, 0x6a68f003, 0x4afa8c48, 0xcd5c470b, 0x9ec61c8f, + 0x1960d7cc, 0xdfe2e419, 0x58442f5a, 0x0bde74de, 0x8c78bf9d, + 0xaceac3d6, 0x2b4c0895, 0x78d65311, 0xff709852, 0x5d4665f4, + 0xdae0aeb7, 0x897af533, 0x0edc3e70, 0x2e4e423b, 0xa9e88978, + 0xfa72d2fc, 0x7dd419bf, 0xbb562a6a, 0x3cf0e129, 0x6f6abaad, + 0xe8cc71ee, 0xc85e0da5, 0x4ff8c6e6, 0x1c629d62, 0x9bc45621, + 0x4a17fc89, 0xcdb137ca, 0x9e2b6c4e, 0x198da70d, 0x391fdb46, + 0xbeb91005, 0xed234b81, 0x6a8580c2, 0xac07b317, 0x2ba17854, + 0x783b23d0, 0xff9de893, 0xdf0f94d8, 0x58a95f9b, 0x0b33041f, + 0x8c95cf5c, 0x73e5570e, 0xf4439c4d, 0xa7d9c7c9, 0x207f0c8a, + 0x00ed70c1, 0x874bbb82, 0xd4d1e006, 0x53772b45, 0x95f51890, + 0x1253d3d3, 0x41c98857, 0xc66f4314, 0xe6fd3f5f, 0x615bf41c, + 0x32c1af98, 0xb56764db, 0x64b4ce73, 0xe3120530, 0xb0885eb4, + 0x372e95f7, 0x17bce9bc, 0x901a22ff, 0xc380797b, 0x4426b238, + 0x82a481ed, 0x05024aae, 0x5698112a, 0xd13eda69, 0xf1aca622, + 0x760a6d61, 0x259036e5, 0xa236fda6, 0xba8ccbe8, 0x3d2a00ab, + 0x6eb05b2f, 0xe916906c, 0xc984ec27, 0x4e222764, 0x1db87ce0, + 0x9a1eb7a3, 0x5c9c8476, 0xdb3a4f35, 0x88a014b1, 0x0f06dff2, + 0x2f94a3b9, 0xa83268fa, 0xfba8337e, 0x7c0ef83d, 0xaddd5295, + 0x2a7b99d6, 0x79e1c252, 0xfe470911, 0xded5755a, 0x5973be19, + 0x0ae9e59d, 0x8d4f2ede, 0x4bcd1d0b, 0xcc6bd648, 0x9ff18dcc, + 0x1857468f, 0x38c53ac4, 0xbf63f187, 0xecf9aa03, 0x6b5f6140, + 0x942ff912, 0x13893251, 0x401369d5, 0xc7b5a296, 0xe727dedd, + 0x6081159e, 0x331b4e1a, 0xb4bd8559, 0x723fb68c, 0xf5997dcf, + 0xa603264b, 0x21a5ed08, 0x01379143, 0x86915a00, 0xd50b0184, + 0x52adcac7, 0x837e606f, 0x04d8ab2c, 0x5742f0a8, 0xd0e43beb, + 0xf07647a0, 0x77d08ce3, 0x244ad767, 0xa3ec1c24, 0x656e2ff1, + 0xe2c8e4b2, 0xb152bf36, 0x36f47475, 0x1666083e, 0x91c0c37d, + 0xc25a98f9, 0x45fc53ba, 0xe7caae1c, 0x606c655f, 0x33f63edb, + 0xb450f598, 0x94c289d3, 0x13644290, 0x40fe1914, 0xc758d257, + 0x01dae182, 0x867c2ac1, 0xd5e67145, 0x5240ba06, 0x72d2c64d, + 0xf5740d0e, 0xa6ee568a, 0x21489dc9, 0xf09b3761, 0x773dfc22, + 0x24a7a7a6, 0xa3016ce5, 0x839310ae, 0x0435dbed, 0x57af8069, + 0xd0094b2a, 0x168b78ff, 0x912db3bc, 0xc2b7e838, 0x4511237b, + 0x65835f30, 0xe2259473, 0xb1bfcff7, 0x361904b4, 0xc9699ce6, + 0x4ecf57a5, 0x1d550c21, 0x9af3c762, 0xba61bb29, 0x3dc7706a, + 0x6e5d2bee, 0xe9fbe0ad, 0x2f79d378, 0xa8df183b, 0xfb4543bf, + 0x7ce388fc, 0x5c71f4b7, 0xdbd73ff4, 0x884d6470, 0x0febaf33, + 0xde38059b, 0x599eced8, 0x0a04955c, 0x8da25e1f, 0xad302254, + 0x2a96e917, 0x790cb293, 0xfeaa79d0, 0x38284a05, 0xbf8e8146, + 0xec14dac2, 0x6bb21181, 0x4b206dca, 0xcc86a689, 0x9f1cfd0d, + 0x18ba364e}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x00000000, 0x43cba687, 0xc7903cd4, 0x845b9a53, 0xcf270873, + 0x8cecaef4, 0x08b734a7, 0x4b7c9220, 0x9e4f10e6, 0xdd84b661, + 0x59df2c32, 0x1a148ab5, 0x51681895, 0x12a3be12, 0x96f82441, + 0xd53382c6, 0x7d995117, 0x3e52f790, 0xba096dc3, 0xf9c2cb44, + 0xb2be5964, 0xf175ffe3, 0x752e65b0, 0x36e5c337, 0xe3d641f1, + 0xa01de776, 0x24467d25, 0x678ddba2, 0x2cf14982, 0x6f3aef05, + 0xeb617556, 0xa8aad3d1, 0xfa32a32e, 0xb9f905a9, 0x3da29ffa, + 0x7e69397d, 0x3515ab5d, 0x76de0dda, 0xf2859789, 0xb14e310e, + 0x647db3c8, 0x27b6154f, 0xa3ed8f1c, 0xe026299b, 0xab5abbbb, + 0xe8911d3c, 0x6cca876f, 0x2f0121e8, 0x87abf239, 0xc46054be, + 0x403bceed, 0x03f0686a, 0x488cfa4a, 0x0b475ccd, 0x8f1cc69e, + 0xccd76019, 0x19e4e2df, 0x5a2f4458, 0xde74de0b, 0x9dbf788c, + 0xd6c3eaac, 0x95084c2b, 0x1153d678, 0x529870ff, 0xf465465d, + 0xb7aee0da, 0x33f57a89, 0x703edc0e, 0x3b424e2e, 0x7889e8a9, + 0xfcd272fa, 0xbf19d47d, 0x6a2a56bb, 0x29e1f03c, 0xadba6a6f, + 0xee71cce8, 0xa50d5ec8, 0xe6c6f84f, 0x629d621c, 0x2156c49b, + 0x89fc174a, 0xca37b1cd, 0x4e6c2b9e, 0x0da78d19, 0x46db1f39, + 0x0510b9be, 0x814b23ed, 0xc280856a, 0x17b307ac, 0x5478a12b, + 0xd0233b78, 0x93e89dff, 0xd8940fdf, 0x9b5fa958, 0x1f04330b, + 0x5ccf958c, 0x0e57e573, 0x4d9c43f4, 0xc9c7d9a7, 0x8a0c7f20, + 0xc170ed00, 0x82bb4b87, 0x06e0d1d4, 0x452b7753, 0x9018f595, + 0xd3d35312, 0x5788c941, 0x14436fc6, 0x5f3ffde6, 0x1cf45b61, + 0x98afc132, 0xdb6467b5, 0x73ceb464, 0x300512e3, 0xb45e88b0, + 0xf7952e37, 0xbce9bc17, 0xff221a90, 0x7b7980c3, 0x38b22644, + 0xed81a482, 0xae4a0205, 0x2a119856, 0x69da3ed1, 0x22a6acf1, + 0x616d0a76, 0xe5369025, 0xa6fd36a2, 0xe8cb8cba, 0xab002a3d, + 0x2f5bb06e, 0x6c9016e9, 0x27ec84c9, 0x6427224e, 0xe07cb81d, + 0xa3b71e9a, 0x76849c5c, 0x354f3adb, 0xb114a088, 0xf2df060f, + 0xb9a3942f, 0xfa6832a8, 0x7e33a8fb, 0x3df80e7c, 0x9552ddad, + 0xd6997b2a, 0x52c2e179, 0x110947fe, 0x5a75d5de, 0x19be7359, + 0x9de5e90a, 0xde2e4f8d, 0x0b1dcd4b, 0x48d66bcc, 0xcc8df19f, + 0x8f465718, 0xc43ac538, 0x87f163bf, 0x03aaf9ec, 0x40615f6b, + 0x12f92f94, 0x51328913, 0xd5691340, 0x96a2b5c7, 0xddde27e7, + 0x9e158160, 0x1a4e1b33, 0x5985bdb4, 0x8cb63f72, 0xcf7d99f5, + 0x4b2603a6, 0x08eda521, 0x43913701, 0x005a9186, 0x84010bd5, + 0xc7caad52, 0x6f607e83, 0x2cabd804, 0xa8f04257, 0xeb3be4d0, + 0xa04776f0, 0xe38cd077, 0x67d74a24, 0x241ceca3, 0xf12f6e65, + 0xb2e4c8e2, 0x36bf52b1, 0x7574f436, 0x3e086616, 0x7dc3c091, + 0xf9985ac2, 0xba53fc45, 0x1caecae7, 0x5f656c60, 0xdb3ef633, + 0x98f550b4, 0xd389c294, 0x90426413, 0x1419fe40, 0x57d258c7, + 0x82e1da01, 0xc12a7c86, 0x4571e6d5, 0x06ba4052, 0x4dc6d272, + 0x0e0d74f5, 0x8a56eea6, 0xc99d4821, 0x61379bf0, 0x22fc3d77, + 0xa6a7a724, 0xe56c01a3, 0xae109383, 0xeddb3504, 0x6980af57, + 0x2a4b09d0, 0xff788b16, 0xbcb32d91, 0x38e8b7c2, 0x7b231145, + 0x305f8365, 0x739425e2, 0xf7cfbfb1, 0xb4041936, 0xe69c69c9, + 0xa557cf4e, 0x210c551d, 0x62c7f39a, 0x29bb61ba, 0x6a70c73d, + 0xee2b5d6e, 0xade0fbe9, 0x78d3792f, 0x3b18dfa8, 0xbf4345fb, + 0xfc88e37c, 0xb7f4715c, 0xf43fd7db, 0x70644d88, 0x33afeb0f, + 0x9b0538de, 0xd8ce9e59, 0x5c95040a, 0x1f5ea28d, 0x542230ad, + 0x17e9962a, 0x93b20c79, 0xd079aafe, 0x054a2838, 0x46818ebf, + 0xc2da14ec, 0x8111b26b, 0xca6d204b, 0x89a686cc, 0x0dfd1c9f, + 0x4e36ba18}, + {0x00000000, 0xe1b652ef, 0x836bd405, 0x62dd86ea, 0x06d7a80b, + 0xe761fae4, 0x85bc7c0e, 0x640a2ee1, 0x0cae5117, 0xed1803f8, + 0x8fc58512, 0x6e73d7fd, 0x0a79f91c, 0xebcfabf3, 0x89122d19, + 0x68a47ff6, 0x185ca32e, 0xf9eaf1c1, 0x9b37772b, 0x7a8125c4, + 0x1e8b0b25, 0xff3d59ca, 0x9de0df20, 0x7c568dcf, 0x14f2f239, + 0xf544a0d6, 0x9799263c, 0x762f74d3, 0x12255a32, 0xf39308dd, + 0x914e8e37, 0x70f8dcd8, 0x30b8465d, 0xd10e14b2, 0xb3d39258, + 0x5265c0b7, 0x366fee56, 0xd7d9bcb9, 0xb5043a53, 0x54b268bc, + 0x3c16174a, 0xdda045a5, 0xbf7dc34f, 0x5ecb91a0, 0x3ac1bf41, + 0xdb77edae, 0xb9aa6b44, 0x581c39ab, 0x28e4e573, 0xc952b79c, + 0xab8f3176, 0x4a396399, 0x2e334d78, 0xcf851f97, 0xad58997d, + 0x4ceecb92, 0x244ab464, 0xc5fce68b, 0xa7216061, 0x4697328e, + 0x229d1c6f, 0xc32b4e80, 0xa1f6c86a, 0x40409a85, 0x60708dba, + 0x81c6df55, 0xe31b59bf, 0x02ad0b50, 0x66a725b1, 0x8711775e, + 0xe5ccf1b4, 0x047aa35b, 0x6cdedcad, 0x8d688e42, 0xefb508a8, + 0x0e035a47, 0x6a0974a6, 0x8bbf2649, 0xe962a0a3, 0x08d4f24c, + 0x782c2e94, 0x999a7c7b, 0xfb47fa91, 0x1af1a87e, 0x7efb869f, + 0x9f4dd470, 0xfd90529a, 0x1c260075, 0x74827f83, 0x95342d6c, + 0xf7e9ab86, 0x165ff969, 0x7255d788, 0x93e38567, 0xf13e038d, + 0x10885162, 0x50c8cbe7, 0xb17e9908, 0xd3a31fe2, 0x32154d0d, + 0x561f63ec, 0xb7a93103, 0xd574b7e9, 0x34c2e506, 0x5c669af0, + 0xbdd0c81f, 0xdf0d4ef5, 0x3ebb1c1a, 0x5ab132fb, 0xbb076014, + 0xd9dae6fe, 0x386cb411, 0x489468c9, 0xa9223a26, 0xcbffbccc, + 0x2a49ee23, 0x4e43c0c2, 0xaff5922d, 0xcd2814c7, 0x2c9e4628, + 0x443a39de, 0xa58c6b31, 0xc751eddb, 0x26e7bf34, 0x42ed91d5, + 0xa35bc33a, 0xc18645d0, 0x2030173f, 0x81e66bae, 0x60503941, + 0x028dbfab, 0xe33bed44, 0x8731c3a5, 0x6687914a, 0x045a17a0, + 0xe5ec454f, 0x8d483ab9, 0x6cfe6856, 0x0e23eebc, 0xef95bc53, + 0x8b9f92b2, 0x6a29c05d, 0x08f446b7, 0xe9421458, 0x99bac880, + 0x780c9a6f, 0x1ad11c85, 0xfb674e6a, 0x9f6d608b, 0x7edb3264, + 0x1c06b48e, 0xfdb0e661, 0x95149997, 0x74a2cb78, 0x167f4d92, + 0xf7c91f7d, 0x93c3319c, 0x72756373, 0x10a8e599, 0xf11eb776, + 0xb15e2df3, 0x50e87f1c, 0x3235f9f6, 0xd383ab19, 0xb78985f8, + 0x563fd717, 0x34e251fd, 0xd5540312, 0xbdf07ce4, 0x5c462e0b, + 0x3e9ba8e1, 0xdf2dfa0e, 0xbb27d4ef, 0x5a918600, 0x384c00ea, + 0xd9fa5205, 0xa9028edd, 0x48b4dc32, 0x2a695ad8, 0xcbdf0837, + 0xafd526d6, 0x4e637439, 0x2cbef2d3, 0xcd08a03c, 0xa5acdfca, + 0x441a8d25, 0x26c70bcf, 0xc7715920, 0xa37b77c1, 0x42cd252e, + 0x2010a3c4, 0xc1a6f12b, 0xe196e614, 0x0020b4fb, 0x62fd3211, + 0x834b60fe, 0xe7414e1f, 0x06f71cf0, 0x642a9a1a, 0x859cc8f5, + 0xed38b703, 0x0c8ee5ec, 0x6e536306, 0x8fe531e9, 0xebef1f08, + 0x0a594de7, 0x6884cb0d, 0x893299e2, 0xf9ca453a, 0x187c17d5, + 0x7aa1913f, 0x9b17c3d0, 0xff1ded31, 0x1eabbfde, 0x7c763934, + 0x9dc06bdb, 0xf564142d, 0x14d246c2, 0x760fc028, 0x97b992c7, + 0xf3b3bc26, 0x1205eec9, 0x70d86823, 0x916e3acc, 0xd12ea049, + 0x3098f2a6, 0x5245744c, 0xb3f326a3, 0xd7f90842, 0x364f5aad, + 0x5492dc47, 0xb5248ea8, 0xdd80f15e, 0x3c36a3b1, 0x5eeb255b, + 0xbf5d77b4, 0xdb575955, 0x3ae10bba, 0x583c8d50, 0xb98adfbf, + 0xc9720367, 0x28c45188, 0x4a19d762, 0xabaf858d, 0xcfa5ab6c, + 0x2e13f983, 0x4cce7f69, 0xad782d86, 0xc5dc5270, 0x246a009f, + 0x46b78675, 0xa701d49a, 0xc30bfa7b, 0x22bda894, 0x40602e7e, + 0xa1d67c91}, + {0x00000000, 0x5880e2d7, 0xf106b474, 0xa98656a3, 0xe20d68e9, + 0xba8d8a3e, 0x130bdc9d, 0x4b8b3e4a, 0x851da109, 0xdd9d43de, + 0x741b157d, 0x2c9bf7aa, 0x6710c9e0, 0x3f902b37, 0x96167d94, + 0xce969f43, 0x0a3b4213, 0x52bba0c4, 0xfb3df667, 0xa3bd14b0, + 0xe8362afa, 0xb0b6c82d, 0x19309e8e, 0x41b07c59, 0x8f26e31a, + 0xd7a601cd, 0x7e20576e, 0x26a0b5b9, 0x6d2b8bf3, 0x35ab6924, + 0x9c2d3f87, 0xc4addd50, 0x14768426, 0x4cf666f1, 0xe5703052, + 0xbdf0d285, 0xf67beccf, 0xaefb0e18, 0x077d58bb, 0x5ffdba6c, + 0x916b252f, 0xc9ebc7f8, 0x606d915b, 0x38ed738c, 0x73664dc6, + 0x2be6af11, 0x8260f9b2, 0xdae01b65, 0x1e4dc635, 0x46cd24e2, + 0xef4b7241, 0xb7cb9096, 0xfc40aedc, 0xa4c04c0b, 0x0d461aa8, + 0x55c6f87f, 0x9b50673c, 0xc3d085eb, 0x6a56d348, 0x32d6319f, + 0x795d0fd5, 0x21dded02, 0x885bbba1, 0xd0db5976, 0x28ec084d, + 0x706cea9a, 0xd9eabc39, 0x816a5eee, 0xcae160a4, 0x92618273, + 0x3be7d4d0, 0x63673607, 0xadf1a944, 0xf5714b93, 0x5cf71d30, + 0x0477ffe7, 0x4ffcc1ad, 0x177c237a, 0xbefa75d9, 0xe67a970e, + 0x22d74a5e, 0x7a57a889, 0xd3d1fe2a, 0x8b511cfd, 0xc0da22b7, + 0x985ac060, 0x31dc96c3, 0x695c7414, 0xa7caeb57, 0xff4a0980, + 0x56cc5f23, 0x0e4cbdf4, 0x45c783be, 0x1d476169, 0xb4c137ca, + 0xec41d51d, 0x3c9a8c6b, 0x641a6ebc, 0xcd9c381f, 0x951cdac8, + 0xde97e482, 0x86170655, 0x2f9150f6, 0x7711b221, 0xb9872d62, + 0xe107cfb5, 0x48819916, 0x10017bc1, 0x5b8a458b, 0x030aa75c, + 0xaa8cf1ff, 0xf20c1328, 0x36a1ce78, 0x6e212caf, 0xc7a77a0c, + 0x9f2798db, 0xd4aca691, 0x8c2c4446, 0x25aa12e5, 0x7d2af032, + 0xb3bc6f71, 0xeb3c8da6, 0x42badb05, 0x1a3a39d2, 0x51b10798, + 0x0931e54f, 0xa0b7b3ec, 0xf837513b, 0x50d8119a, 0x0858f34d, + 0xa1dea5ee, 0xf95e4739, 0xb2d57973, 0xea559ba4, 0x43d3cd07, + 0x1b532fd0, 0xd5c5b093, 0x8d455244, 0x24c304e7, 0x7c43e630, + 0x37c8d87a, 0x6f483aad, 0xc6ce6c0e, 0x9e4e8ed9, 0x5ae35389, + 0x0263b15e, 0xabe5e7fd, 0xf365052a, 0xb8ee3b60, 0xe06ed9b7, + 0x49e88f14, 0x11686dc3, 0xdffef280, 0x877e1057, 0x2ef846f4, + 0x7678a423, 0x3df39a69, 0x657378be, 0xccf52e1d, 0x9475ccca, + 0x44ae95bc, 0x1c2e776b, 0xb5a821c8, 0xed28c31f, 0xa6a3fd55, + 0xfe231f82, 0x57a54921, 0x0f25abf6, 0xc1b334b5, 0x9933d662, + 0x30b580c1, 0x68356216, 0x23be5c5c, 0x7b3ebe8b, 0xd2b8e828, + 0x8a380aff, 0x4e95d7af, 0x16153578, 0xbf9363db, 0xe713810c, + 0xac98bf46, 0xf4185d91, 0x5d9e0b32, 0x051ee9e5, 0xcb8876a6, + 0x93089471, 0x3a8ec2d2, 0x620e2005, 0x29851e4f, 0x7105fc98, + 0xd883aa3b, 0x800348ec, 0x783419d7, 0x20b4fb00, 0x8932ada3, + 0xd1b24f74, 0x9a39713e, 0xc2b993e9, 0x6b3fc54a, 0x33bf279d, + 0xfd29b8de, 0xa5a95a09, 0x0c2f0caa, 0x54afee7d, 0x1f24d037, + 0x47a432e0, 0xee226443, 0xb6a28694, 0x720f5bc4, 0x2a8fb913, + 0x8309efb0, 0xdb890d67, 0x9002332d, 0xc882d1fa, 0x61048759, + 0x3984658e, 0xf712facd, 0xaf92181a, 0x06144eb9, 0x5e94ac6e, + 0x151f9224, 0x4d9f70f3, 0xe4192650, 0xbc99c487, 0x6c429df1, + 0x34c27f26, 0x9d442985, 0xc5c4cb52, 0x8e4ff518, 0xd6cf17cf, + 0x7f49416c, 0x27c9a3bb, 0xe95f3cf8, 0xb1dfde2f, 0x1859888c, + 0x40d96a5b, 0x0b525411, 0x53d2b6c6, 0xfa54e065, 0xa2d402b2, + 0x6679dfe2, 0x3ef93d35, 0x977f6b96, 0xcfff8941, 0x8474b70b, + 0xdcf455dc, 0x7572037f, 0x2df2e1a8, 0xe3647eeb, 0xbbe49c3c, + 0x1262ca9f, 0x4ae22848, 0x01691602, 0x59e9f4d5, 0xf06fa276, + 0xa8ef40a1}, + {0x00000000, 0x463b6765, 0x8c76ceca, 0xca4da9af, 0x59ebed4e, + 0x1fd08a2b, 0xd59d2384, 0x93a644e1, 0xb2d6db9d, 0xf4edbcf8, + 0x3ea01557, 0x789b7232, 0xeb3d36d3, 0xad0651b6, 0x674bf819, + 0x21709f7c, 0x25abc6e0, 0x6390a185, 0xa9dd082a, 0xefe66f4f, + 0x7c402bae, 0x3a7b4ccb, 0xf036e564, 0xb60d8201, 0x977d1d7d, + 0xd1467a18, 0x1b0bd3b7, 0x5d30b4d2, 0xce96f033, 0x88ad9756, + 0x42e03ef9, 0x04db599c, 0x0b50fc1a, 0x4d6b9b7f, 0x872632d0, + 0xc11d55b5, 0x52bb1154, 0x14807631, 0xdecddf9e, 0x98f6b8fb, + 0xb9862787, 0xffbd40e2, 0x35f0e94d, 0x73cb8e28, 0xe06dcac9, + 0xa656adac, 0x6c1b0403, 0x2a206366, 0x2efb3afa, 0x68c05d9f, + 0xa28df430, 0xe4b69355, 0x7710d7b4, 0x312bb0d1, 0xfb66197e, + 0xbd5d7e1b, 0x9c2de167, 0xda168602, 0x105b2fad, 0x566048c8, + 0xc5c60c29, 0x83fd6b4c, 0x49b0c2e3, 0x0f8ba586, 0x16a0f835, + 0x509b9f50, 0x9ad636ff, 0xdced519a, 0x4f4b157b, 0x0970721e, + 0xc33ddbb1, 0x8506bcd4, 0xa47623a8, 0xe24d44cd, 0x2800ed62, + 0x6e3b8a07, 0xfd9dcee6, 0xbba6a983, 0x71eb002c, 0x37d06749, + 0x330b3ed5, 0x753059b0, 0xbf7df01f, 0xf946977a, 0x6ae0d39b, + 0x2cdbb4fe, 0xe6961d51, 0xa0ad7a34, 0x81dde548, 0xc7e6822d, + 0x0dab2b82, 0x4b904ce7, 0xd8360806, 0x9e0d6f63, 0x5440c6cc, + 0x127ba1a9, 0x1df0042f, 0x5bcb634a, 0x9186cae5, 0xd7bdad80, + 0x441be961, 0x02208e04, 0xc86d27ab, 0x8e5640ce, 0xaf26dfb2, + 0xe91db8d7, 0x23501178, 0x656b761d, 0xf6cd32fc, 0xb0f65599, + 0x7abbfc36, 0x3c809b53, 0x385bc2cf, 0x7e60a5aa, 0xb42d0c05, + 0xf2166b60, 0x61b02f81, 0x278b48e4, 0xedc6e14b, 0xabfd862e, + 0x8a8d1952, 0xccb67e37, 0x06fbd798, 0x40c0b0fd, 0xd366f41c, + 0x955d9379, 0x5f103ad6, 0x192b5db3, 0x2c40f16b, 0x6a7b960e, + 0xa0363fa1, 0xe60d58c4, 0x75ab1c25, 0x33907b40, 0xf9ddd2ef, + 0xbfe6b58a, 0x9e962af6, 0xd8ad4d93, 0x12e0e43c, 0x54db8359, + 0xc77dc7b8, 0x8146a0dd, 0x4b0b0972, 0x0d306e17, 0x09eb378b, + 0x4fd050ee, 0x859df941, 0xc3a69e24, 0x5000dac5, 0x163bbda0, + 0xdc76140f, 0x9a4d736a, 0xbb3dec16, 0xfd068b73, 0x374b22dc, + 0x717045b9, 0xe2d60158, 0xa4ed663d, 0x6ea0cf92, 0x289ba8f7, + 0x27100d71, 0x612b6a14, 0xab66c3bb, 0xed5da4de, 0x7efbe03f, + 0x38c0875a, 0xf28d2ef5, 0xb4b64990, 0x95c6d6ec, 0xd3fdb189, + 0x19b01826, 0x5f8b7f43, 0xcc2d3ba2, 0x8a165cc7, 0x405bf568, + 0x0660920d, 0x02bbcb91, 0x4480acf4, 0x8ecd055b, 0xc8f6623e, + 0x5b5026df, 0x1d6b41ba, 0xd726e815, 0x911d8f70, 0xb06d100c, + 0xf6567769, 0x3c1bdec6, 0x7a20b9a3, 0xe986fd42, 0xafbd9a27, + 0x65f03388, 0x23cb54ed, 0x3ae0095e, 0x7cdb6e3b, 0xb696c794, + 0xf0ada0f1, 0x630be410, 0x25308375, 0xef7d2ada, 0xa9464dbf, + 0x8836d2c3, 0xce0db5a6, 0x04401c09, 0x427b7b6c, 0xd1dd3f8d, + 0x97e658e8, 0x5dabf147, 0x1b909622, 0x1f4bcfbe, 0x5970a8db, + 0x933d0174, 0xd5066611, 0x46a022f0, 0x009b4595, 0xcad6ec3a, + 0x8ced8b5f, 0xad9d1423, 0xeba67346, 0x21ebdae9, 0x67d0bd8c, + 0xf476f96d, 0xb24d9e08, 0x780037a7, 0x3e3b50c2, 0x31b0f544, + 0x778b9221, 0xbdc63b8e, 0xfbfd5ceb, 0x685b180a, 0x2e607f6f, + 0xe42dd6c0, 0xa216b1a5, 0x83662ed9, 0xc55d49bc, 0x0f10e013, + 0x492b8776, 0xda8dc397, 0x9cb6a4f2, 0x56fb0d5d, 0x10c06a38, + 0x141b33a4, 0x522054c1, 0x986dfd6e, 0xde569a0b, 0x4df0deea, + 0x0bcbb98f, 0xc1861020, 0x87bd7745, 0xa6cde839, 0xe0f68f5c, + 0x2abb26f3, 0x6c804196, 0xff260577, 0xb91d6212, 0x7350cbbd, + 0x356bacd8}}; + +#endif + +#endif + +#if N == 6 + +#if W == 8 + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0x3db1ecdc, 0x7b63d9b8, 0x46d23564, 0xf6c7b370, + 0xcb765fac, 0x8da46ac8, 0xb0158614, 0x36fe60a1, 0x0b4f8c7d, + 0x4d9db919, 0x702c55c5, 0xc039d3d1, 0xfd883f0d, 0xbb5a0a69, + 0x86ebe6b5, 0x6dfcc142, 0x504d2d9e, 0x169f18fa, 0x2b2ef426, + 0x9b3b7232, 0xa68a9eee, 0xe058ab8a, 0xdde94756, 0x5b02a1e3, + 0x66b34d3f, 0x2061785b, 0x1dd09487, 0xadc51293, 0x9074fe4f, + 0xd6a6cb2b, 0xeb1727f7, 0xdbf98284, 0xe6486e58, 0xa09a5b3c, + 0x9d2bb7e0, 0x2d3e31f4, 0x108fdd28, 0x565de84c, 0x6bec0490, + 0xed07e225, 0xd0b60ef9, 0x96643b9d, 0xabd5d741, 0x1bc05155, + 0x2671bd89, 0x60a388ed, 0x5d126431, 0xb60543c6, 0x8bb4af1a, + 0xcd669a7e, 0xf0d776a2, 0x40c2f0b6, 0x7d731c6a, 0x3ba1290e, + 0x0610c5d2, 0x80fb2367, 0xbd4acfbb, 0xfb98fadf, 0xc6291603, + 0x763c9017, 0x4b8d7ccb, 0x0d5f49af, 0x30eea573, 0x6c820349, + 0x5133ef95, 0x17e1daf1, 0x2a50362d, 0x9a45b039, 0xa7f45ce5, + 0xe1266981, 0xdc97855d, 0x5a7c63e8, 0x67cd8f34, 0x211fba50, + 0x1cae568c, 0xacbbd098, 0x910a3c44, 0xd7d80920, 0xea69e5fc, + 0x017ec20b, 0x3ccf2ed7, 0x7a1d1bb3, 0x47acf76f, 0xf7b9717b, + 0xca089da7, 0x8cdaa8c3, 0xb16b441f, 0x3780a2aa, 0x0a314e76, + 0x4ce37b12, 0x715297ce, 0xc14711da, 0xfcf6fd06, 0xba24c862, + 0x879524be, 0xb77b81cd, 0x8aca6d11, 0xcc185875, 0xf1a9b4a9, + 0x41bc32bd, 0x7c0dde61, 0x3adfeb05, 0x076e07d9, 0x8185e16c, + 0xbc340db0, 0xfae638d4, 0xc757d408, 0x7742521c, 0x4af3bec0, + 0x0c218ba4, 0x31906778, 0xda87408f, 0xe736ac53, 0xa1e49937, + 0x9c5575eb, 0x2c40f3ff, 0x11f11f23, 0x57232a47, 0x6a92c69b, + 0xec79202e, 0xd1c8ccf2, 0x971af996, 0xaaab154a, 0x1abe935e, + 0x270f7f82, 0x61dd4ae6, 0x5c6ca63a, 0xd9040692, 0xe4b5ea4e, + 0xa267df2a, 0x9fd633f6, 0x2fc3b5e2, 0x1272593e, 0x54a06c5a, + 0x69118086, 0xeffa6633, 0xd24b8aef, 0x9499bf8b, 0xa9285357, + 0x193dd543, 0x248c399f, 0x625e0cfb, 0x5fefe027, 0xb4f8c7d0, + 0x89492b0c, 0xcf9b1e68, 0xf22af2b4, 0x423f74a0, 0x7f8e987c, + 0x395cad18, 0x04ed41c4, 0x8206a771, 0xbfb74bad, 0xf9657ec9, + 0xc4d49215, 0x74c11401, 0x4970f8dd, 0x0fa2cdb9, 0x32132165, + 0x02fd8416, 0x3f4c68ca, 0x799e5dae, 0x442fb172, 0xf43a3766, + 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0x0495382d, + 0x3caeb35e, 0x9d9737b0, 0x4542910f, 0xe47b15e1, 0xdc409e92, + 0x7d791a7c, 0xac378874, 0x0d0e0c9a, 0x353587e9, 0x940c0307, + 0x5fefccd6, 0xfed64838, 0xc6edc34b, 0x67d447a5, 0xb69ad5ad, + 0x17a35143, 0x2f98da30, 0x8ea15ede, 0x5674f861, 0xf74d7c8f, + 0xcf76f7fc, 0x6e4f7312, 0xbf01e11a, 0x1e3865f4, 0x2603ee87, + 0x873a6a69, 0x6ab57764, 0xcb8cf38a, 0xf3b778f9, 0x528efc17, + 0x83c06e1f, 0x22f9eaf1, 0x1ac26182, 0xbbfbe56c, 0x632e43d3, + 0xc217c73d, 0xfa2c4c4e, 0x5b15c8a0, 0x8a5b5aa8, 0x2b62de46, + 0x13595535, 0xb260d1db, 0x79831e0a, 0xd8ba9ae4, 0xe0811197, + 0x41b89579, 0x90f60771, 0x31cf839f, 0x09f408ec, 0xa8cd8c02, + 0x70182abd, 0xd121ae53, 0xe91a2520, 0x4823a1ce, 0x996d33c6, + 0x3854b728, 0x006f3c5b, 0xa156b8b5, 0x99b34b70, 0x388acf9e, + 0x00b144ed, 0xa188c003, 0x70c6520b, 0xd1ffd6e5, 0xe9c45d96, + 0x48fdd978, 0x90287fc7, 0x3111fb29, 0x092a705a, 0xa813f4b4, + 0x795d66bc, 0xd864e252, 0xe05f6921, 0x4166edcf, 0x8a85221e, + 0x2bbca6f0, 0x13872d83, 0xb2bea96d, 0x63f03b65, 0xc2c9bf8b, + 0xfaf234f8, 0x5bcbb016, 0x831e16a9, 0x22279247, 0x1a1c1934, + 0xbb259dda, 0x6a6b0fd2, 0xcb528b3c, 0xf369004f, 0x525084a1, + 0xbfdf99ac, 0x1ee61d42, 0x26dd9631, 0x87e412df, 0x56aa80d7, + 0xf7930439, 0xcfa88f4a, 0x6e910ba4, 0xb644ad1b, 0x177d29f5, + 0x2f46a286, 0x8e7f2668, 0x5f31b460, 0xfe08308e, 0xc633bbfd, + 0x670a3f13, 0xace9f0c2, 0x0dd0742c, 0x35ebff5f, 0x94d27bb1, + 0x459ce9b9, 0xe4a56d57, 0xdc9ee624, 0x7da762ca, 0xa572c475, + 0x044b409b, 0x3c70cbe8, 0x9d494f06, 0x4c07dd0e, 0xed3e59e0, + 0xd505d293, 0x743c567d, 0xd56aeec8, 0x74536a26, 0x4c68e155, + 0xed5165bb, 0x3c1ff7b3, 0x9d26735d, 0xa51df82e, 0x04247cc0, + 0xdcf1da7f, 0x7dc85e91, 0x45f3d5e2, 0xe4ca510c, 0x3584c304, + 0x94bd47ea, 0xac86cc99, 0x0dbf4877, 0xc65c87a6, 0x67650348, + 0x5f5e883b, 0xfe670cd5, 0x2f299edd, 0x8e101a33, 0xb62b9140, + 0x171215ae, 0xcfc7b311, 0x6efe37ff, 0x56c5bc8c, 0xf7fc3862, + 0x26b2aa6a, 0x878b2e84, 0xbfb0a5f7, 0x1e892119, 0xf3063c14, + 0x523fb8fa, 0x6a043389, 0xcb3db767, 0x1a73256f, 0xbb4aa181, + 0x83712af2, 0x2248ae1c, 0xfa9d08a3, 0x5ba48c4d, 0x639f073e, + 0xc2a683d0, 0x13e811d8, 0xb2d19536, 0x8aea1e45, 0x2bd39aab, + 0xe030557a, 0x4109d194, 0x79325ae7, 0xd80bde09, 0x09454c01, + 0xa87cc8ef, 0x9047439c, 0x317ec772, 0xe9ab61cd, 0x4892e523, + 0x70a96e50, 0xd190eabe, 0x00de78b6, 0xa1e7fc58, 0x99dc772b, + 0x38e5f3c5}, + {0x00000000, 0xe81790a1, 0x0b5e2703, 0xe349b7a2, 0x16bc4e06, + 0xfeabdea7, 0x1de26905, 0xf5f5f9a4, 0x2d789c0c, 0xc56f0cad, + 0x2626bb0f, 0xce312bae, 0x3bc4d20a, 0xd3d342ab, 0x309af509, + 0xd88d65a8, 0x5af13818, 0xb2e6a8b9, 0x51af1f1b, 0xb9b88fba, + 0x4c4d761e, 0xa45ae6bf, 0x4713511d, 0xaf04c1bc, 0x7789a414, + 0x9f9e34b5, 0x7cd78317, 0x94c013b6, 0x6135ea12, 0x89227ab3, + 0x6a6bcd11, 0x827c5db0, 0xb5e27030, 0x5df5e091, 0xbebc5733, + 0x56abc792, 0xa35e3e36, 0x4b49ae97, 0xa8001935, 0x40178994, + 0x989aec3c, 0x708d7c9d, 0x93c4cb3f, 0x7bd35b9e, 0x8e26a23a, + 0x6631329b, 0x85788539, 0x6d6f1598, 0xef134828, 0x0704d889, + 0xe44d6f2b, 0x0c5aff8a, 0xf9af062e, 0x11b8968f, 0xf2f1212d, + 0x1ae6b18c, 0xc26bd424, 0x2a7c4485, 0xc935f327, 0x21226386, + 0xd4d79a22, 0x3cc00a83, 0xdf89bd21, 0x379e2d80, 0xb0b5e621, + 0x58a27680, 0xbbebc122, 0x53fc5183, 0xa609a827, 0x4e1e3886, + 0xad578f24, 0x45401f85, 0x9dcd7a2d, 0x75daea8c, 0x96935d2e, + 0x7e84cd8f, 0x8b71342b, 0x6366a48a, 0x802f1328, 0x68388389, + 0xea44de39, 0x02534e98, 0xe11af93a, 0x090d699b, 0xfcf8903f, + 0x14ef009e, 0xf7a6b73c, 0x1fb1279d, 0xc73c4235, 0x2f2bd294, + 0xcc626536, 0x2475f597, 0xd1800c33, 0x39979c92, 0xdade2b30, + 0x32c9bb91, 0x05579611, 0xed4006b0, 0x0e09b112, 0xe61e21b3, + 0x13ebd817, 0xfbfc48b6, 0x18b5ff14, 0xf0a26fb5, 0x282f0a1d, + 0xc0389abc, 0x23712d1e, 0xcb66bdbf, 0x3e93441b, 0xd684d4ba, + 0x35cd6318, 0xdddaf3b9, 0x5fa6ae09, 0xb7b13ea8, 0x54f8890a, + 0xbcef19ab, 0x491ae00f, 0xa10d70ae, 0x4244c70c, 0xaa5357ad, + 0x72de3205, 0x9ac9a2a4, 0x79801506, 0x919785a7, 0x64627c03, + 0x8c75eca2, 0x6f3c5b00, 0x872bcba1, 0xba1aca03, 0x520d5aa2, + 0xb144ed00, 0x59537da1, 0xaca68405, 0x44b114a4, 0xa7f8a306, + 0x4fef33a7, 0x9762560f, 0x7f75c6ae, 0x9c3c710c, 0x742be1ad, + 0x81de1809, 0x69c988a8, 0x8a803f0a, 0x6297afab, 0xe0ebf21b, + 0x08fc62ba, 0xebb5d518, 0x03a245b9, 0xf657bc1d, 0x1e402cbc, + 0xfd099b1e, 0x151e0bbf, 0xcd936e17, 0x2584feb6, 0xc6cd4914, + 0x2edad9b5, 0xdb2f2011, 0x3338b0b0, 0xd0710712, 0x386697b3, + 0x0ff8ba33, 0xe7ef2a92, 0x04a69d30, 0xecb10d91, 0x1944f435, + 0xf1536494, 0x121ad336, 0xfa0d4397, 0x2280263f, 0xca97b69e, + 0x29de013c, 0xc1c9919d, 0x343c6839, 0xdc2bf898, 0x3f624f3a, + 0xd775df9b, 0x5509822b, 0xbd1e128a, 0x5e57a528, 0xb6403589, + 0x43b5cc2d, 0xaba25c8c, 0x48ebeb2e, 0xa0fc7b8f, 0x78711e27, + 0x90668e86, 0x732f3924, 0x9b38a985, 0x6ecd5021, 0x86dac080, + 0x65937722, 0x8d84e783, 0x0aaf2c22, 0xe2b8bc83, 0x01f10b21, + 0xe9e69b80, 0x1c136224, 0xf404f285, 0x174d4527, 0xff5ad586, + 0x27d7b02e, 0xcfc0208f, 0x2c89972d, 0xc49e078c, 0x316bfe28, + 0xd97c6e89, 0x3a35d92b, 0xd222498a, 0x505e143a, 0xb849849b, + 0x5b003339, 0xb317a398, 0x46e25a3c, 0xaef5ca9d, 0x4dbc7d3f, + 0xa5abed9e, 0x7d268836, 0x95311897, 0x7678af35, 0x9e6f3f94, + 0x6b9ac630, 0x838d5691, 0x60c4e133, 0x88d37192, 0xbf4d5c12, + 0x575accb3, 0xb4137b11, 0x5c04ebb0, 0xa9f11214, 0x41e682b5, + 0xa2af3517, 0x4ab8a5b6, 0x9235c01e, 0x7a2250bf, 0x996be71d, + 0x717c77bc, 0x84898e18, 0x6c9e1eb9, 0x8fd7a91b, 0x67c039ba, + 0xe5bc640a, 0x0dabf4ab, 0xeee24309, 0x06f5d3a8, 0xf3002a0c, + 0x1b17baad, 0xf85e0d0f, 0x10499dae, 0xc8c4f806, 0x20d368a7, + 0xc39adf05, 0x2b8d4fa4, 0xde78b600, 0x366f26a1, 0xd5269103, + 0x3d3101a2}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x0000000000000000, 0xa19017e800000000, 0x03275e0b00000000, + 0xa2b749e300000000, 0x064ebc1600000000, 0xa7deabfe00000000, + 0x0569e21d00000000, 0xa4f9f5f500000000, 0x0c9c782d00000000, + 0xad0c6fc500000000, 0x0fbb262600000000, 0xae2b31ce00000000, + 0x0ad2c43b00000000, 0xab42d3d300000000, 0x09f59a3000000000, + 0xa8658dd800000000, 0x1838f15a00000000, 0xb9a8e6b200000000, + 0x1b1faf5100000000, 0xba8fb8b900000000, 0x1e764d4c00000000, + 0xbfe65aa400000000, 0x1d51134700000000, 0xbcc104af00000000, + 0x14a4897700000000, 0xb5349e9f00000000, 0x1783d77c00000000, + 0xb613c09400000000, 0x12ea356100000000, 0xb37a228900000000, + 0x11cd6b6a00000000, 0xb05d7c8200000000, 0x3070e2b500000000, + 0x91e0f55d00000000, 0x3357bcbe00000000, 0x92c7ab5600000000, + 0x363e5ea300000000, 0x97ae494b00000000, 0x351900a800000000, + 0x9489174000000000, 0x3cec9a9800000000, 0x9d7c8d7000000000, + 0x3fcbc49300000000, 0x9e5bd37b00000000, 0x3aa2268e00000000, + 0x9b32316600000000, 0x3985788500000000, 0x98156f6d00000000, + 0x284813ef00000000, 0x89d8040700000000, 0x2b6f4de400000000, + 0x8aff5a0c00000000, 0x2e06aff900000000, 0x8f96b81100000000, + 0x2d21f1f200000000, 0x8cb1e61a00000000, 0x24d46bc200000000, + 0x85447c2a00000000, 0x27f335c900000000, 0x8663222100000000, + 0x229ad7d400000000, 0x830ac03c00000000, 0x21bd89df00000000, + 0x802d9e3700000000, 0x21e6b5b000000000, 0x8076a25800000000, + 0x22c1ebbb00000000, 0x8351fc5300000000, 0x27a809a600000000, + 0x86381e4e00000000, 0x248f57ad00000000, 0x851f404500000000, + 0x2d7acd9d00000000, 0x8ceada7500000000, 0x2e5d939600000000, + 0x8fcd847e00000000, 0x2b34718b00000000, 0x8aa4666300000000, + 0x28132f8000000000, 0x8983386800000000, 0x39de44ea00000000, + 0x984e530200000000, 0x3af91ae100000000, 0x9b690d0900000000, + 0x3f90f8fc00000000, 0x9e00ef1400000000, 0x3cb7a6f700000000, + 0x9d27b11f00000000, 0x35423cc700000000, 0x94d22b2f00000000, + 0x366562cc00000000, 0x97f5752400000000, 0x330c80d100000000, + 0x929c973900000000, 0x302bdeda00000000, 0x91bbc93200000000, + 0x1196570500000000, 0xb00640ed00000000, 0x12b1090e00000000, + 0xb3211ee600000000, 0x17d8eb1300000000, 0xb648fcfb00000000, + 0x14ffb51800000000, 0xb56fa2f000000000, 0x1d0a2f2800000000, + 0xbc9a38c000000000, 0x1e2d712300000000, 0xbfbd66cb00000000, + 0x1b44933e00000000, 0xbad484d600000000, 0x1863cd3500000000, + 0xb9f3dadd00000000, 0x09aea65f00000000, 0xa83eb1b700000000, + 0x0a89f85400000000, 0xab19efbc00000000, 0x0fe01a4900000000, + 0xae700da100000000, 0x0cc7444200000000, 0xad5753aa00000000, + 0x0532de7200000000, 0xa4a2c99a00000000, 0x0615807900000000, + 0xa785979100000000, 0x037c626400000000, 0xa2ec758c00000000, + 0x005b3c6f00000000, 0xa1cb2b8700000000, 0x03ca1aba00000000, + 0xa25a0d5200000000, 0x00ed44b100000000, 0xa17d535900000000, + 0x0584a6ac00000000, 0xa414b14400000000, 0x06a3f8a700000000, + 0xa733ef4f00000000, 0x0f56629700000000, 0xaec6757f00000000, + 0x0c713c9c00000000, 0xade12b7400000000, 0x0918de8100000000, + 0xa888c96900000000, 0x0a3f808a00000000, 0xabaf976200000000, + 0x1bf2ebe000000000, 0xba62fc0800000000, 0x18d5b5eb00000000, + 0xb945a20300000000, 0x1dbc57f600000000, 0xbc2c401e00000000, + 0x1e9b09fd00000000, 0xbf0b1e1500000000, 0x176e93cd00000000, + 0xb6fe842500000000, 0x1449cdc600000000, 0xb5d9da2e00000000, + 0x11202fdb00000000, 0xb0b0383300000000, 0x120771d000000000, + 0xb397663800000000, 0x33baf80f00000000, 0x922aefe700000000, + 0x309da60400000000, 0x910db1ec00000000, 0x35f4441900000000, + 0x946453f100000000, 0x36d31a1200000000, 0x97430dfa00000000, + 0x3f26802200000000, 0x9eb697ca00000000, 0x3c01de2900000000, + 0x9d91c9c100000000, 0x39683c3400000000, 0x98f82bdc00000000, + 0x3a4f623f00000000, 0x9bdf75d700000000, 0x2b82095500000000, + 0x8a121ebd00000000, 0x28a5575e00000000, 0x893540b600000000, + 0x2dccb54300000000, 0x8c5ca2ab00000000, 0x2eebeb4800000000, + 0x8f7bfca000000000, 0x271e717800000000, 0x868e669000000000, + 0x24392f7300000000, 0x85a9389b00000000, 0x2150cd6e00000000, + 0x80c0da8600000000, 0x2277936500000000, 0x83e7848d00000000, + 0x222caf0a00000000, 0x83bcb8e200000000, 0x210bf10100000000, + 0x809be6e900000000, 0x2462131c00000000, 0x85f204f400000000, + 0x27454d1700000000, 0x86d55aff00000000, 0x2eb0d72700000000, + 0x8f20c0cf00000000, 0x2d97892c00000000, 0x8c079ec400000000, + 0x28fe6b3100000000, 0x896e7cd900000000, 0x2bd9353a00000000, + 0x8a4922d200000000, 0x3a145e5000000000, 0x9b8449b800000000, + 0x3933005b00000000, 0x98a317b300000000, 0x3c5ae24600000000, + 0x9dcaf5ae00000000, 0x3f7dbc4d00000000, 0x9eedaba500000000, + 0x3688267d00000000, 0x9718319500000000, 0x35af787600000000, + 0x943f6f9e00000000, 0x30c69a6b00000000, 0x91568d8300000000, + 0x33e1c46000000000, 0x9271d38800000000, 0x125c4dbf00000000, + 0xb3cc5a5700000000, 0x117b13b400000000, 0xb0eb045c00000000, + 0x1412f1a900000000, 0xb582e64100000000, 0x1735afa200000000, + 0xb6a5b84a00000000, 0x1ec0359200000000, 0xbf50227a00000000, + 0x1de76b9900000000, 0xbc777c7100000000, 0x188e898400000000, + 0xb91e9e6c00000000, 0x1ba9d78f00000000, 0xba39c06700000000, + 0x0a64bce500000000, 0xabf4ab0d00000000, 0x0943e2ee00000000, + 0xa8d3f50600000000, 0x0c2a00f300000000, 0xadba171b00000000, + 0x0f0d5ef800000000, 0xae9d491000000000, 0x06f8c4c800000000, + 0xa768d32000000000, 0x05df9ac300000000, 0xa44f8d2b00000000, + 0x00b678de00000000, 0xa1266f3600000000, 0x039126d500000000, + 0xa201313d00000000}, + {0x0000000000000000, 0xee8439a100000000, 0x9d0f029900000000, + 0x738b3b3800000000, 0x7b1975e900000000, 0x959d4c4800000000, + 0xe616777000000000, 0x08924ed100000000, 0xb7349b0900000000, + 0x59b0a2a800000000, 0x2a3b999000000000, 0xc4bfa03100000000, + 0xcc2deee000000000, 0x22a9d74100000000, 0x5122ec7900000000, + 0xbfa6d5d800000000, 0x6e69361300000000, 0x80ed0fb200000000, + 0xf366348a00000000, 0x1de20d2b00000000, 0x157043fa00000000, + 0xfbf47a5b00000000, 0x887f416300000000, 0x66fb78c200000000, + 0xd95dad1a00000000, 0x37d994bb00000000, 0x4452af8300000000, + 0xaad6962200000000, 0xa244d8f300000000, 0x4cc0e15200000000, + 0x3f4bda6a00000000, 0xd1cfe3cb00000000, 0xdcd26c2600000000, + 0x3256558700000000, 0x41dd6ebf00000000, 0xaf59571e00000000, + 0xa7cb19cf00000000, 0x494f206e00000000, 0x3ac41b5600000000, + 0xd44022f700000000, 0x6be6f72f00000000, 0x8562ce8e00000000, + 0xf6e9f5b600000000, 0x186dcc1700000000, 0x10ff82c600000000, + 0xfe7bbb6700000000, 0x8df0805f00000000, 0x6374b9fe00000000, + 0xb2bb5a3500000000, 0x5c3f639400000000, 0x2fb458ac00000000, + 0xc130610d00000000, 0xc9a22fdc00000000, 0x2726167d00000000, + 0x54ad2d4500000000, 0xba2914e400000000, 0x058fc13c00000000, + 0xeb0bf89d00000000, 0x9880c3a500000000, 0x7604fa0400000000, + 0x7e96b4d500000000, 0x90128d7400000000, 0xe399b64c00000000, + 0x0d1d8fed00000000, 0xb8a5d94c00000000, 0x5621e0ed00000000, + 0x25aadbd500000000, 0xcb2ee27400000000, 0xc3bcaca500000000, + 0x2d38950400000000, 0x5eb3ae3c00000000, 0xb037979d00000000, + 0x0f91424500000000, 0xe1157be400000000, 0x929e40dc00000000, + 0x7c1a797d00000000, 0x748837ac00000000, 0x9a0c0e0d00000000, + 0xe987353500000000, 0x07030c9400000000, 0xd6ccef5f00000000, + 0x3848d6fe00000000, 0x4bc3edc600000000, 0xa547d46700000000, + 0xadd59ab600000000, 0x4351a31700000000, 0x30da982f00000000, + 0xde5ea18e00000000, 0x61f8745600000000, 0x8f7c4df700000000, + 0xfcf776cf00000000, 0x12734f6e00000000, 0x1ae101bf00000000, + 0xf465381e00000000, 0x87ee032600000000, 0x696a3a8700000000, + 0x6477b56a00000000, 0x8af38ccb00000000, 0xf978b7f300000000, + 0x17fc8e5200000000, 0x1f6ec08300000000, 0xf1eaf92200000000, + 0x8261c21a00000000, 0x6ce5fbbb00000000, 0xd3432e6300000000, + 0x3dc717c200000000, 0x4e4c2cfa00000000, 0xa0c8155b00000000, + 0xa85a5b8a00000000, 0x46de622b00000000, 0x3555591300000000, + 0xdbd160b200000000, 0x0a1e837900000000, 0xe49abad800000000, + 0x971181e000000000, 0x7995b84100000000, 0x7107f69000000000, + 0x9f83cf3100000000, 0xec08f40900000000, 0x028ccda800000000, + 0xbd2a187000000000, 0x53ae21d100000000, 0x20251ae900000000, + 0xcea1234800000000, 0xc6336d9900000000, 0x28b7543800000000, + 0x5b3c6f0000000000, 0xb5b856a100000000, 0x704bb39900000000, + 0x9ecf8a3800000000, 0xed44b10000000000, 0x03c088a100000000, + 0x0b52c67000000000, 0xe5d6ffd100000000, 0x965dc4e900000000, + 0x78d9fd4800000000, 0xc77f289000000000, 0x29fb113100000000, + 0x5a702a0900000000, 0xb4f413a800000000, 0xbc665d7900000000, + 0x52e264d800000000, 0x21695fe000000000, 0xcfed664100000000, + 0x1e22858a00000000, 0xf0a6bc2b00000000, 0x832d871300000000, + 0x6da9beb200000000, 0x653bf06300000000, 0x8bbfc9c200000000, + 0xf834f2fa00000000, 0x16b0cb5b00000000, 0xa9161e8300000000, + 0x4792272200000000, 0x34191c1a00000000, 0xda9d25bb00000000, + 0xd20f6b6a00000000, 0x3c8b52cb00000000, 0x4f0069f300000000, + 0xa184505200000000, 0xac99dfbf00000000, 0x421de61e00000000, + 0x3196dd2600000000, 0xdf12e48700000000, 0xd780aa5600000000, + 0x390493f700000000, 0x4a8fa8cf00000000, 0xa40b916e00000000, + 0x1bad44b600000000, 0xf5297d1700000000, 0x86a2462f00000000, + 0x68267f8e00000000, 0x60b4315f00000000, 0x8e3008fe00000000, + 0xfdbb33c600000000, 0x133f0a6700000000, 0xc2f0e9ac00000000, + 0x2c74d00d00000000, 0x5fffeb3500000000, 0xb17bd29400000000, + 0xb9e99c4500000000, 0x576da5e400000000, 0x24e69edc00000000, + 0xca62a77d00000000, 0x75c472a500000000, 0x9b404b0400000000, + 0xe8cb703c00000000, 0x064f499d00000000, 0x0edd074c00000000, + 0xe0593eed00000000, 0x93d205d500000000, 0x7d563c7400000000, + 0xc8ee6ad500000000, 0x266a537400000000, 0x55e1684c00000000, + 0xbb6551ed00000000, 0xb3f71f3c00000000, 0x5d73269d00000000, + 0x2ef81da500000000, 0xc07c240400000000, 0x7fdaf1dc00000000, + 0x915ec87d00000000, 0xe2d5f34500000000, 0x0c51cae400000000, + 0x04c3843500000000, 0xea47bd9400000000, 0x99cc86ac00000000, + 0x7748bf0d00000000, 0xa6875cc600000000, 0x4803656700000000, + 0x3b885e5f00000000, 0xd50c67fe00000000, 0xdd9e292f00000000, + 0x331a108e00000000, 0x40912bb600000000, 0xae15121700000000, + 0x11b3c7cf00000000, 0xff37fe6e00000000, 0x8cbcc55600000000, + 0x6238fcf700000000, 0x6aaab22600000000, 0x842e8b8700000000, + 0xf7a5b0bf00000000, 0x1921891e00000000, 0x143c06f300000000, + 0xfab83f5200000000, 0x8933046a00000000, 0x67b73dcb00000000, + 0x6f25731a00000000, 0x81a14abb00000000, 0xf22a718300000000, + 0x1cae482200000000, 0xa3089dfa00000000, 0x4d8ca45b00000000, + 0x3e079f6300000000, 0xd083a6c200000000, 0xd811e81300000000, + 0x3695d1b200000000, 0x451eea8a00000000, 0xab9ad32b00000000, + 0x7a5530e000000000, 0x94d1094100000000, 0xe75a327900000000, + 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0xb4f22af200000000, 0xa0743f4200000000, 0x7c988e7f00000000, + 0x18ad5c3900000000, 0xc441ed0400000000, 0x71a7068200000000, + 0xad4bb7bf00000000, 0xc97e65f900000000, 0x1592d4c400000000, + 0x0114c17400000000, 0xddf8704900000000, 0xb9cda20f00000000, + 0x6521133200000000, 0x1684fd0200000000, 0xca684c3f00000000, + 0xae5d9e7900000000, 0x72b12f4400000000, 0x66373af400000000, + 0xbadb8bc900000000, 0xdeee598f00000000, 0x0202e8b200000000, + 0xb7e4033400000000, 0x6b08b20900000000, 0x0f3d604f00000000, + 0xd3d1d17200000000, 0xc757c4c200000000, 0x1bbb75ff00000000, + 0x7f8ea7b900000000, 0xa362168400000000, 0x5445016f00000000, + 0x88a9b05200000000, 0xec9c621400000000, 0x3070d32900000000, + 0x24f6c69900000000, 0xf81a77a400000000, 0x9c2fa5e200000000, + 0x40c314df00000000, 0xf525ff5900000000, 0x29c94e6400000000, + 0x4dfc9c2200000000, 0x91102d1f00000000, 0x859638af00000000, + 0x597a899200000000, 0x3d4f5bd400000000, 0xe1a3eae900000000, + 0xdb0586b500000000, 0x07e9378800000000, 0x63dce5ce00000000, + 0xbf3054f300000000, 0xabb6414300000000, 0x775af07e00000000, + 0x136f223800000000, 0xcf83930500000000, 0x7a65788300000000, + 0xa689c9be00000000, 0xc2bc1bf800000000, 0x1e50aac500000000, + 0x0ad6bf7500000000, 0xd63a0e4800000000, 0xb20fdc0e00000000, + 0x6ee36d3300000000, 0x99c47ad800000000, 0x4528cbe500000000, + 0x211d19a300000000, 0xfdf1a89e00000000, 0xe977bd2e00000000, + 0x359b0c1300000000, 0x51aede5500000000, 0x8d426f6800000000, + 0x38a484ee00000000, 0xe44835d300000000, 0x807de79500000000, + 0x5c9156a800000000, 0x4817431800000000, 0x94fbf22500000000, + 0xf0ce206300000000, 0x2c22915e00000000, 0x5f877f6e00000000, + 0x836bce5300000000, 0xe75e1c1500000000, 0x3bb2ad2800000000, + 0x2f34b89800000000, 0xf3d809a500000000, 0x97eddbe300000000, + 0x4b016ade00000000, 0xfee7815800000000, 0x220b306500000000, + 0x463ee22300000000, 0x9ad2531e00000000, 0x8e5446ae00000000, + 0x52b8f79300000000, 0x368d25d500000000, 0xea6194e800000000, + 0x1d46830300000000, 0xc1aa323e00000000, 0xa59fe07800000000, + 0x7973514500000000, 0x6df544f500000000, 0xb119f5c800000000, + 0xd52c278e00000000, 0x09c096b300000000, 0xbc267d3500000000, + 0x60cacc0800000000, 0x04ff1e4e00000000, 0xd813af7300000000, + 0xcc95bac300000000, 0x10790bfe00000000, 0x744cd9b800000000, + 0xa8a0688500000000}}; + +#else /* W == 4 */ + +local const z_crc_t FAR crc_braid_table[][256] = { + {0x00000000, 0x81256527, 0xd93bcc0f, 0x581ea928, 0x69069e5f, + 0xe823fb78, 0xb03d5250, 0x31183777, 0xd20d3cbe, 0x53285999, + 0x0b36f0b1, 0x8a139596, 0xbb0ba2e1, 0x3a2ec7c6, 0x62306eee, + 0xe3150bc9, 0x7f6b7f3d, 0xfe4e1a1a, 0xa650b332, 0x2775d615, + 0x166de162, 0x97488445, 0xcf562d6d, 0x4e73484a, 0xad664383, + 0x2c4326a4, 0x745d8f8c, 0xf578eaab, 0xc460dddc, 0x4545b8fb, + 0x1d5b11d3, 0x9c7e74f4, 0xfed6fe7a, 0x7ff39b5d, 0x27ed3275, + 0xa6c85752, 0x97d06025, 0x16f50502, 0x4eebac2a, 0xcfcec90d, + 0x2cdbc2c4, 0xadfea7e3, 0xf5e00ecb, 0x74c56bec, 0x45dd5c9b, + 0xc4f839bc, 0x9ce69094, 0x1dc3f5b3, 0x81bd8147, 0x0098e460, + 0x58864d48, 0xd9a3286f, 0xe8bb1f18, 0x699e7a3f, 0x3180d317, + 0xb0a5b630, 0x53b0bdf9, 0xd295d8de, 0x8a8b71f6, 0x0bae14d1, + 0x3ab623a6, 0xbb934681, 0xe38defa9, 0x62a88a8e, 0x26dcfab5, + 0xa7f99f92, 0xffe736ba, 0x7ec2539d, 0x4fda64ea, 0xceff01cd, + 0x96e1a8e5, 0x17c4cdc2, 0xf4d1c60b, 0x75f4a32c, 0x2dea0a04, + 0xaccf6f23, 0x9dd75854, 0x1cf23d73, 0x44ec945b, 0xc5c9f17c, + 0x59b78588, 0xd892e0af, 0x808c4987, 0x01a92ca0, 0x30b11bd7, + 0xb1947ef0, 0xe98ad7d8, 0x68afb2ff, 0x8bbab936, 0x0a9fdc11, + 0x52817539, 0xd3a4101e, 0xe2bc2769, 0x6399424e, 0x3b87eb66, + 0xbaa28e41, 0xd80a04cf, 0x592f61e8, 0x0131c8c0, 0x8014ade7, + 0xb10c9a90, 0x3029ffb7, 0x6837569f, 0xe91233b8, 0x0a073871, + 0x8b225d56, 0xd33cf47e, 0x52199159, 0x6301a62e, 0xe224c309, + 0xba3a6a21, 0x3b1f0f06, 0xa7617bf2, 0x26441ed5, 0x7e5ab7fd, + 0xff7fd2da, 0xce67e5ad, 0x4f42808a, 0x175c29a2, 0x96794c85, + 0x756c474c, 0xf449226b, 0xac578b43, 0x2d72ee64, 0x1c6ad913, + 0x9d4fbc34, 0xc551151c, 0x4474703b, 0x4db9f56a, 0xcc9c904d, + 0x94823965, 0x15a75c42, 0x24bf6b35, 0xa59a0e12, 0xfd84a73a, + 0x7ca1c21d, 0x9fb4c9d4, 0x1e91acf3, 0x468f05db, 0xc7aa60fc, + 0xf6b2578b, 0x779732ac, 0x2f899b84, 0xaeacfea3, 0x32d28a57, + 0xb3f7ef70, 0xebe94658, 0x6acc237f, 0x5bd41408, 0xdaf1712f, + 0x82efd807, 0x03cabd20, 0xe0dfb6e9, 0x61fad3ce, 0x39e47ae6, + 0xb8c11fc1, 0x89d928b6, 0x08fc4d91, 0x50e2e4b9, 0xd1c7819e, + 0xb36f0b10, 0x324a6e37, 0x6a54c71f, 0xeb71a238, 0xda69954f, + 0x5b4cf068, 0x03525940, 0x82773c67, 0x616237ae, 0xe0475289, + 0xb859fba1, 0x397c9e86, 0x0864a9f1, 0x8941ccd6, 0xd15f65fe, + 0x507a00d9, 0xcc04742d, 0x4d21110a, 0x153fb822, 0x941add05, + 0xa502ea72, 0x24278f55, 0x7c39267d, 0xfd1c435a, 0x1e094893, + 0x9f2c2db4, 0xc732849c, 0x4617e1bb, 0x770fd6cc, 0xf62ab3eb, + 0xae341ac3, 0x2f117fe4, 0x6b650fdf, 0xea406af8, 0xb25ec3d0, + 0x337ba6f7, 0x02639180, 0x8346f4a7, 0xdb585d8f, 0x5a7d38a8, + 0xb9683361, 0x384d5646, 0x6053ff6e, 0xe1769a49, 0xd06ead3e, + 0x514bc819, 0x09556131, 0x88700416, 0x140e70e2, 0x952b15c5, + 0xcd35bced, 0x4c10d9ca, 0x7d08eebd, 0xfc2d8b9a, 0xa43322b2, + 0x25164795, 0xc6034c5c, 0x4726297b, 0x1f388053, 0x9e1de574, + 0xaf05d203, 0x2e20b724, 0x763e1e0c, 0xf71b7b2b, 0x95b3f1a5, + 0x14969482, 0x4c883daa, 0xcdad588d, 0xfcb56ffa, 0x7d900add, + 0x258ea3f5, 0xa4abc6d2, 0x47becd1b, 0xc69ba83c, 0x9e850114, + 0x1fa06433, 0x2eb85344, 0xaf9d3663, 0xf7839f4b, 0x76a6fa6c, + 0xead88e98, 0x6bfdebbf, 0x33e34297, 0xb2c627b0, 0x83de10c7, + 0x02fb75e0, 0x5ae5dcc8, 0xdbc0b9ef, 0x38d5b226, 0xb9f0d701, + 0xe1ee7e29, 0x60cb1b0e, 0x51d32c79, 0xd0f6495e, 0x88e8e076, + 0x09cd8551}, + {0x00000000, 0x9b73ead4, 0xed96d3e9, 0x76e5393d, 0x005ca193, + 0x9b2f4b47, 0xedca727a, 0x76b998ae, 0x00b94326, 0x9bcaa9f2, + 0xed2f90cf, 0x765c7a1b, 0x00e5e2b5, 0x9b960861, 0xed73315c, + 0x7600db88, 0x0172864c, 0x9a016c98, 0xece455a5, 0x7797bf71, + 0x012e27df, 0x9a5dcd0b, 0xecb8f436, 0x77cb1ee2, 0x01cbc56a, + 0x9ab82fbe, 0xec5d1683, 0x772efc57, 0x019764f9, 0x9ae48e2d, + 0xec01b710, 0x77725dc4, 0x02e50c98, 0x9996e64c, 0xef73df71, + 0x740035a5, 0x02b9ad0b, 0x99ca47df, 0xef2f7ee2, 0x745c9436, + 0x025c4fbe, 0x992fa56a, 0xefca9c57, 0x74b97683, 0x0200ee2d, + 0x997304f9, 0xef963dc4, 0x74e5d710, 0x03978ad4, 0x98e46000, + 0xee01593d, 0x7572b3e9, 0x03cb2b47, 0x98b8c193, 0xee5df8ae, + 0x752e127a, 0x032ec9f2, 0x985d2326, 0xeeb81a1b, 0x75cbf0cf, + 0x03726861, 0x980182b5, 0xeee4bb88, 0x7597515c, 0x05ca1930, + 0x9eb9f3e4, 0xe85ccad9, 0x732f200d, 0x0596b8a3, 0x9ee55277, + 0xe8006b4a, 0x7373819e, 0x05735a16, 0x9e00b0c2, 0xe8e589ff, + 0x7396632b, 0x052ffb85, 0x9e5c1151, 0xe8b9286c, 0x73cac2b8, + 0x04b89f7c, 0x9fcb75a8, 0xe92e4c95, 0x725da641, 0x04e43eef, + 0x9f97d43b, 0xe972ed06, 0x720107d2, 0x0401dc5a, 0x9f72368e, + 0xe9970fb3, 0x72e4e567, 0x045d7dc9, 0x9f2e971d, 0xe9cbae20, + 0x72b844f4, 0x072f15a8, 0x9c5cff7c, 0xeab9c641, 0x71ca2c95, + 0x0773b43b, 0x9c005eef, 0xeae567d2, 0x71968d06, 0x0796568e, + 0x9ce5bc5a, 0xea008567, 0x71736fb3, 0x07caf71d, 0x9cb91dc9, + 0xea5c24f4, 0x712fce20, 0x065d93e4, 0x9d2e7930, 0xebcb400d, + 0x70b8aad9, 0x06013277, 0x9d72d8a3, 0xeb97e19e, 0x70e40b4a, + 0x06e4d0c2, 0x9d973a16, 0xeb72032b, 0x7001e9ff, 0x06b87151, + 0x9dcb9b85, 0xeb2ea2b8, 0x705d486c, 0x0b943260, 0x90e7d8b4, + 0xe602e189, 0x7d710b5d, 0x0bc893f3, 0x90bb7927, 0xe65e401a, + 0x7d2daace, 0x0b2d7146, 0x905e9b92, 0xe6bba2af, 0x7dc8487b, + 0x0b71d0d5, 0x90023a01, 0xe6e7033c, 0x7d94e9e8, 0x0ae6b42c, + 0x91955ef8, 0xe77067c5, 0x7c038d11, 0x0aba15bf, 0x91c9ff6b, + 0xe72cc656, 0x7c5f2c82, 0x0a5ff70a, 0x912c1dde, 0xe7c924e3, + 0x7cbace37, 0x0a035699, 0x9170bc4d, 0xe7958570, 0x7ce66fa4, + 0x09713ef8, 0x9202d42c, 0xe4e7ed11, 0x7f9407c5, 0x092d9f6b, + 0x925e75bf, 0xe4bb4c82, 0x7fc8a656, 0x09c87dde, 0x92bb970a, + 0xe45eae37, 0x7f2d44e3, 0x0994dc4d, 0x92e73699, 0xe4020fa4, + 0x7f71e570, 0x0803b8b4, 0x93705260, 0xe5956b5d, 0x7ee68189, + 0x085f1927, 0x932cf3f3, 0xe5c9cace, 0x7eba201a, 0x08bafb92, + 0x93c91146, 0xe52c287b, 0x7e5fc2af, 0x08e65a01, 0x9395b0d5, + 0xe57089e8, 0x7e03633c, 0x0e5e2b50, 0x952dc184, 0xe3c8f8b9, + 0x78bb126d, 0x0e028ac3, 0x95716017, 0xe394592a, 0x78e7b3fe, + 0x0ee76876, 0x959482a2, 0xe371bb9f, 0x7802514b, 0x0ebbc9e5, + 0x95c82331, 0xe32d1a0c, 0x785ef0d8, 0x0f2cad1c, 0x945f47c8, + 0xe2ba7ef5, 0x79c99421, 0x0f700c8f, 0x9403e65b, 0xe2e6df66, + 0x799535b2, 0x0f95ee3a, 0x94e604ee, 0xe2033dd3, 0x7970d707, + 0x0fc94fa9, 0x94baa57d, 0xe25f9c40, 0x792c7694, 0x0cbb27c8, + 0x97c8cd1c, 0xe12df421, 0x7a5e1ef5, 0x0ce7865b, 0x97946c8f, + 0xe17155b2, 0x7a02bf66, 0x0c0264ee, 0x97718e3a, 0xe194b707, + 0x7ae75dd3, 0x0c5ec57d, 0x972d2fa9, 0xe1c81694, 0x7abbfc40, + 0x0dc9a184, 0x96ba4b50, 0xe05f726d, 0x7b2c98b9, 0x0d950017, + 0x96e6eac3, 0xe003d3fe, 0x7b70392a, 0x0d70e2a2, 0x96030876, + 0xe0e6314b, 0x7b95db9f, 0x0d2c4331, 0x965fa9e5, 0xe0ba90d8, + 0x7bc97a0c}, + {0x00000000, 0x172864c0, 0x2e50c980, 0x3978ad40, 0x5ca19300, + 0x4b89f7c0, 0x72f15a80, 0x65d93e40, 0xb9432600, 0xae6b42c0, + 0x9713ef80, 0x803b8b40, 0xe5e2b500, 0xf2cad1c0, 0xcbb27c80, + 0xdc9a1840, 0xa9f74a41, 0xbedf2e81, 0x87a783c1, 0x908fe701, + 0xf556d941, 0xe27ebd81, 0xdb0610c1, 0xcc2e7401, 0x10b46c41, + 0x079c0881, 0x3ee4a5c1, 0x29ccc101, 0x4c15ff41, 0x5b3d9b81, + 0x624536c1, 0x756d5201, 0x889f92c3, 0x9fb7f603, 0xa6cf5b43, + 0xb1e73f83, 0xd43e01c3, 0xc3166503, 0xfa6ec843, 0xed46ac83, + 0x31dcb4c3, 0x26f4d003, 0x1f8c7d43, 0x08a41983, 0x6d7d27c3, + 0x7a554303, 0x432dee43, 0x54058a83, 0x2168d882, 0x3640bc42, + 0x0f381102, 0x181075c2, 0x7dc94b82, 0x6ae12f42, 0x53998202, + 0x44b1e6c2, 0x982bfe82, 0x8f039a42, 0xb67b3702, 0xa15353c2, + 0xc48a6d82, 0xd3a20942, 0xeadaa402, 0xfdf2c0c2, 0xca4e23c7, + 0xdd664707, 0xe41eea47, 0xf3368e87, 0x96efb0c7, 0x81c7d407, + 0xb8bf7947, 0xaf971d87, 0x730d05c7, 0x64256107, 0x5d5dcc47, + 0x4a75a887, 0x2fac96c7, 0x3884f207, 0x01fc5f47, 0x16d43b87, + 0x63b96986, 0x74910d46, 0x4de9a006, 0x5ac1c4c6, 0x3f18fa86, + 0x28309e46, 0x11483306, 0x066057c6, 0xdafa4f86, 0xcdd22b46, + 0xf4aa8606, 0xe382e2c6, 0x865bdc86, 0x9173b846, 0xa80b1506, + 0xbf2371c6, 0x42d1b104, 0x55f9d5c4, 0x6c817884, 0x7ba91c44, + 0x1e702204, 0x095846c4, 0x3020eb84, 0x27088f44, 0xfb929704, + 0xecbaf3c4, 0xd5c25e84, 0xc2ea3a44, 0xa7330404, 0xb01b60c4, + 0x8963cd84, 0x9e4ba944, 0xeb26fb45, 0xfc0e9f85, 0xc57632c5, + 0xd25e5605, 0xb7876845, 0xa0af0c85, 0x99d7a1c5, 0x8effc505, + 0x5265dd45, 0x454db985, 0x7c3514c5, 0x6b1d7005, 0x0ec44e45, + 0x19ec2a85, 0x209487c5, 0x37bce305, 0x4fed41cf, 0x58c5250f, + 0x61bd884f, 0x7695ec8f, 0x134cd2cf, 0x0464b60f, 0x3d1c1b4f, + 0x2a347f8f, 0xf6ae67cf, 0xe186030f, 0xd8feae4f, 0xcfd6ca8f, + 0xaa0ff4cf, 0xbd27900f, 0x845f3d4f, 0x9377598f, 0xe61a0b8e, + 0xf1326f4e, 0xc84ac20e, 0xdf62a6ce, 0xbabb988e, 0xad93fc4e, + 0x94eb510e, 0x83c335ce, 0x5f592d8e, 0x4871494e, 0x7109e40e, + 0x662180ce, 0x03f8be8e, 0x14d0da4e, 0x2da8770e, 0x3a8013ce, + 0xc772d30c, 0xd05ab7cc, 0xe9221a8c, 0xfe0a7e4c, 0x9bd3400c, + 0x8cfb24cc, 0xb583898c, 0xa2abed4c, 0x7e31f50c, 0x691991cc, + 0x50613c8c, 0x4749584c, 0x2290660c, 0x35b802cc, 0x0cc0af8c, + 0x1be8cb4c, 0x6e85994d, 0x79adfd8d, 0x40d550cd, 0x57fd340d, + 0x32240a4d, 0x250c6e8d, 0x1c74c3cd, 0x0b5ca70d, 0xd7c6bf4d, + 0xc0eedb8d, 0xf99676cd, 0xeebe120d, 0x8b672c4d, 0x9c4f488d, + 0xa537e5cd, 0xb21f810d, 0x85a36208, 0x928b06c8, 0xabf3ab88, + 0xbcdbcf48, 0xd902f108, 0xce2a95c8, 0xf7523888, 0xe07a5c48, + 0x3ce04408, 0x2bc820c8, 0x12b08d88, 0x0598e948, 0x6041d708, + 0x7769b3c8, 0x4e111e88, 0x59397a48, 0x2c542849, 0x3b7c4c89, + 0x0204e1c9, 0x152c8509, 0x70f5bb49, 0x67dddf89, 0x5ea572c9, + 0x498d1609, 0x95170e49, 0x823f6a89, 0xbb47c7c9, 0xac6fa309, + 0xc9b69d49, 0xde9ef989, 0xe7e654c9, 0xf0ce3009, 0x0d3cf0cb, + 0x1a14940b, 0x236c394b, 0x34445d8b, 0x519d63cb, 0x46b5070b, + 0x7fcdaa4b, 0x68e5ce8b, 0xb47fd6cb, 0xa357b20b, 0x9a2f1f4b, + 0x8d077b8b, 0xe8de45cb, 0xfff6210b, 0xc68e8c4b, 0xd1a6e88b, + 0xa4cbba8a, 0xb3e3de4a, 0x8a9b730a, 0x9db317ca, 0xf86a298a, + 0xef424d4a, 0xd63ae00a, 0xc11284ca, 0x1d889c8a, 0x0aa0f84a, + 0x33d8550a, 0x24f031ca, 0x41290f8a, 0x56016b4a, 0x6f79c60a, + 0x7851a2ca}, + {0x00000000, 0x9fda839e, 0xe4c4017d, 0x7b1e82e3, 0x12f904bb, + 0x8d238725, 0xf63d05c6, 0x69e78658, 0x25f20976, 0xba288ae8, + 0xc136080b, 0x5eec8b95, 0x370b0dcd, 0xa8d18e53, 0xd3cf0cb0, + 0x4c158f2e, 0x4be412ec, 0xd43e9172, 0xaf201391, 0x30fa900f, + 0x591d1657, 0xc6c795c9, 0xbdd9172a, 0x220394b4, 0x6e161b9a, + 0xf1cc9804, 0x8ad21ae7, 0x15089979, 0x7cef1f21, 0xe3359cbf, + 0x982b1e5c, 0x07f19dc2, 0x97c825d8, 0x0812a646, 0x730c24a5, + 0xecd6a73b, 0x85312163, 0x1aeba2fd, 0x61f5201e, 0xfe2fa380, + 0xb23a2cae, 0x2de0af30, 0x56fe2dd3, 0xc924ae4d, 0xa0c32815, + 0x3f19ab8b, 0x44072968, 0xdbddaaf6, 0xdc2c3734, 0x43f6b4aa, + 0x38e83649, 0xa732b5d7, 0xced5338f, 0x510fb011, 0x2a1132f2, + 0xb5cbb16c, 0xf9de3e42, 0x6604bddc, 0x1d1a3f3f, 0x82c0bca1, + 0xeb273af9, 0x74fdb967, 0x0fe33b84, 0x9039b81a, 0xf4e14df1, + 0x6b3bce6f, 0x10254c8c, 0x8fffcf12, 0xe618494a, 0x79c2cad4, + 0x02dc4837, 0x9d06cba9, 0xd1134487, 0x4ec9c719, 0x35d745fa, + 0xaa0dc664, 0xc3ea403c, 0x5c30c3a2, 0x272e4141, 0xb8f4c2df, + 0xbf055f1d, 0x20dfdc83, 0x5bc15e60, 0xc41bddfe, 0xadfc5ba6, + 0x3226d838, 0x49385adb, 0xd6e2d945, 0x9af7566b, 0x052dd5f5, + 0x7e335716, 0xe1e9d488, 0x880e52d0, 0x17d4d14e, 0x6cca53ad, + 0xf310d033, 0x63296829, 0xfcf3ebb7, 0x87ed6954, 0x1837eaca, + 0x71d06c92, 0xee0aef0c, 0x95146def, 0x0aceee71, 0x46db615f, + 0xd901e2c1, 0xa21f6022, 0x3dc5e3bc, 0x542265e4, 0xcbf8e67a, + 0xb0e66499, 0x2f3ce707, 0x28cd7ac5, 0xb717f95b, 0xcc097bb8, + 0x53d3f826, 0x3a347e7e, 0xa5eefde0, 0xdef07f03, 0x412afc9d, + 0x0d3f73b3, 0x92e5f02d, 0xe9fb72ce, 0x7621f150, 0x1fc67708, + 0x801cf496, 0xfb027675, 0x64d8f5eb, 0x32b39da3, 0xad691e3d, + 0xd6779cde, 0x49ad1f40, 0x204a9918, 0xbf901a86, 0xc48e9865, + 0x5b541bfb, 0x174194d5, 0x889b174b, 0xf38595a8, 0x6c5f1636, + 0x05b8906e, 0x9a6213f0, 0xe17c9113, 0x7ea6128d, 0x79578f4f, + 0xe68d0cd1, 0x9d938e32, 0x02490dac, 0x6bae8bf4, 0xf474086a, + 0x8f6a8a89, 0x10b00917, 0x5ca58639, 0xc37f05a7, 0xb8618744, + 0x27bb04da, 0x4e5c8282, 0xd186011c, 0xaa9883ff, 0x35420061, + 0xa57bb87b, 0x3aa13be5, 0x41bfb906, 0xde653a98, 0xb782bcc0, + 0x28583f5e, 0x5346bdbd, 0xcc9c3e23, 0x8089b10d, 0x1f533293, + 0x644db070, 0xfb9733ee, 0x9270b5b6, 0x0daa3628, 0x76b4b4cb, + 0xe96e3755, 0xee9faa97, 0x71452909, 0x0a5babea, 0x95812874, + 0xfc66ae2c, 0x63bc2db2, 0x18a2af51, 0x87782ccf, 0xcb6da3e1, + 0x54b7207f, 0x2fa9a29c, 0xb0732102, 0xd994a75a, 0x464e24c4, + 0x3d50a627, 0xa28a25b9, 0xc652d052, 0x598853cc, 0x2296d12f, + 0xbd4c52b1, 0xd4abd4e9, 0x4b715777, 0x306fd594, 0xafb5560a, + 0xe3a0d924, 0x7c7a5aba, 0x0764d859, 0x98be5bc7, 0xf159dd9f, + 0x6e835e01, 0x159ddce2, 0x8a475f7c, 0x8db6c2be, 0x126c4120, + 0x6972c3c3, 0xf6a8405d, 0x9f4fc605, 0x0095459b, 0x7b8bc778, + 0xe45144e6, 0xa844cbc8, 0x379e4856, 0x4c80cab5, 0xd35a492b, + 0xbabdcf73, 0x25674ced, 0x5e79ce0e, 0xc1a34d90, 0x519af58a, + 0xce407614, 0xb55ef4f7, 0x2a847769, 0x4363f131, 0xdcb972af, + 0xa7a7f04c, 0x387d73d2, 0x7468fcfc, 0xebb27f62, 0x90acfd81, + 0x0f767e1f, 0x6691f847, 0xf94b7bd9, 0x8255f93a, 0x1d8f7aa4, + 0x1a7ee766, 0x85a464f8, 0xfebae61b, 0x61606585, 0x0887e3dd, + 0x975d6043, 0xec43e2a0, 0x7399613e, 0x3f8cee10, 0xa0566d8e, + 0xdb48ef6d, 0x44926cf3, 0x2d75eaab, 0xb2af6935, 0xc9b1ebd6, + 0x566b6848}}; + +local const z_word_t FAR crc_braid_big_table[][256] = { + {0x00000000, 0x9e83da9f, 0x7d01c4e4, 0xe3821e7b, 0xbb04f912, + 0x2587238d, 0xc6053df6, 0x5886e769, 0x7609f225, 0xe88a28ba, + 0x0b0836c1, 0x958bec5e, 0xcd0d0b37, 0x538ed1a8, 0xb00ccfd3, + 0x2e8f154c, 0xec12e44b, 0x72913ed4, 0x911320af, 0x0f90fa30, + 0x57161d59, 0xc995c7c6, 0x2a17d9bd, 0xb4940322, 0x9a1b166e, + 0x0498ccf1, 0xe71ad28a, 0x79990815, 0x211fef7c, 0xbf9c35e3, + 0x5c1e2b98, 0xc29df107, 0xd825c897, 0x46a61208, 0xa5240c73, + 0x3ba7d6ec, 0x63213185, 0xfda2eb1a, 0x1e20f561, 0x80a32ffe, + 0xae2c3ab2, 0x30afe02d, 0xd32dfe56, 0x4dae24c9, 0x1528c3a0, + 0x8bab193f, 0x68290744, 0xf6aadddb, 0x34372cdc, 0xaab4f643, + 0x4936e838, 0xd7b532a7, 0x8f33d5ce, 0x11b00f51, 0xf232112a, + 0x6cb1cbb5, 0x423edef9, 0xdcbd0466, 0x3f3f1a1d, 0xa1bcc082, + 0xf93a27eb, 0x67b9fd74, 0x843be30f, 0x1ab83990, 0xf14de1f4, + 0x6fce3b6b, 0x8c4c2510, 0x12cfff8f, 0x4a4918e6, 0xd4cac279, + 0x3748dc02, 0xa9cb069d, 0x874413d1, 0x19c7c94e, 0xfa45d735, + 0x64c60daa, 0x3c40eac3, 0xa2c3305c, 0x41412e27, 0xdfc2f4b8, + 0x1d5f05bf, 0x83dcdf20, 0x605ec15b, 0xfedd1bc4, 0xa65bfcad, + 0x38d82632, 0xdb5a3849, 0x45d9e2d6, 0x6b56f79a, 0xf5d52d05, + 0x1657337e, 0x88d4e9e1, 0xd0520e88, 0x4ed1d417, 0xad53ca6c, + 0x33d010f3, 0x29682963, 0xb7ebf3fc, 0x5469ed87, 0xcaea3718, + 0x926cd071, 0x0cef0aee, 0xef6d1495, 0x71eece0a, 0x5f61db46, + 0xc1e201d9, 0x22601fa2, 0xbce3c53d, 0xe4652254, 0x7ae6f8cb, + 0x9964e6b0, 0x07e73c2f, 0xc57acd28, 0x5bf917b7, 0xb87b09cc, + 0x26f8d353, 0x7e7e343a, 0xe0fdeea5, 0x037ff0de, 0x9dfc2a41, + 0xb3733f0d, 0x2df0e592, 0xce72fbe9, 0x50f12176, 0x0877c61f, + 0x96f41c80, 0x757602fb, 0xebf5d864, 0xa39db332, 0x3d1e69ad, + 0xde9c77d6, 0x401fad49, 0x18994a20, 0x861a90bf, 0x65988ec4, + 0xfb1b545b, 0xd5944117, 0x4b179b88, 0xa89585f3, 0x36165f6c, + 0x6e90b805, 0xf013629a, 0x13917ce1, 0x8d12a67e, 0x4f8f5779, + 0xd10c8de6, 0x328e939d, 0xac0d4902, 0xf48bae6b, 0x6a0874f4, + 0x898a6a8f, 0x1709b010, 0x3986a55c, 0xa7057fc3, 0x448761b8, + 0xda04bb27, 0x82825c4e, 0x1c0186d1, 0xff8398aa, 0x61004235, + 0x7bb87ba5, 0xe53ba13a, 0x06b9bf41, 0x983a65de, 0xc0bc82b7, + 0x5e3f5828, 0xbdbd4653, 0x233e9ccc, 0x0db18980, 0x9332531f, + 0x70b04d64, 0xee3397fb, 0xb6b57092, 0x2836aa0d, 0xcbb4b476, + 0x55376ee9, 0x97aa9fee, 0x09294571, 0xeaab5b0a, 0x74288195, + 0x2cae66fc, 0xb22dbc63, 0x51afa218, 0xcf2c7887, 0xe1a36dcb, + 0x7f20b754, 0x9ca2a92f, 0x022173b0, 0x5aa794d9, 0xc4244e46, + 0x27a6503d, 0xb9258aa2, 0x52d052c6, 0xcc538859, 0x2fd19622, + 0xb1524cbd, 0xe9d4abd4, 0x7757714b, 0x94d56f30, 0x0a56b5af, + 0x24d9a0e3, 0xba5a7a7c, 0x59d86407, 0xc75bbe98, 0x9fdd59f1, + 0x015e836e, 0xe2dc9d15, 0x7c5f478a, 0xbec2b68d, 0x20416c12, + 0xc3c37269, 0x5d40a8f6, 0x05c64f9f, 0x9b459500, 0x78c78b7b, + 0xe64451e4, 0xc8cb44a8, 0x56489e37, 0xb5ca804c, 0x2b495ad3, + 0x73cfbdba, 0xed4c6725, 0x0ece795e, 0x904da3c1, 0x8af59a51, + 0x147640ce, 0xf7f45eb5, 0x6977842a, 0x31f16343, 0xaf72b9dc, + 0x4cf0a7a7, 0xd2737d38, 0xfcfc6874, 0x627fb2eb, 0x81fdac90, + 0x1f7e760f, 0x47f89166, 0xd97b4bf9, 0x3af95582, 0xa47a8f1d, + 0x66e77e1a, 0xf864a485, 0x1be6bafe, 0x85656061, 0xdde38708, + 0x43605d97, 0xa0e243ec, 0x3e619973, 0x10ee8c3f, 0x8e6d56a0, + 0x6def48db, 0xf36c9244, 0xabea752d, 0x3569afb2, 0xd6ebb1c9, + 0x48686b56}, + {0x00000000, 0xc0642817, 0x80c9502e, 0x40ad7839, 0x0093a15c, + 0xc0f7894b, 0x805af172, 0x403ed965, 0x002643b9, 0xc0426bae, + 0x80ef1397, 0x408b3b80, 0x00b5e2e5, 0xc0d1caf2, 0x807cb2cb, + 0x40189adc, 0x414af7a9, 0x812edfbe, 0xc183a787, 0x01e78f90, + 0x41d956f5, 0x81bd7ee2, 0xc11006db, 0x01742ecc, 0x416cb410, + 0x81089c07, 0xc1a5e43e, 0x01c1cc29, 0x41ff154c, 0x819b3d5b, + 0xc1364562, 0x01526d75, 0xc3929f88, 0x03f6b79f, 0x435bcfa6, + 0x833fe7b1, 0xc3013ed4, 0x036516c3, 0x43c86efa, 0x83ac46ed, + 0xc3b4dc31, 0x03d0f426, 0x437d8c1f, 0x8319a408, 0xc3277d6d, + 0x0343557a, 0x43ee2d43, 0x838a0554, 0x82d86821, 0x42bc4036, + 0x0211380f, 0xc2751018, 0x824bc97d, 0x422fe16a, 0x02829953, + 0xc2e6b144, 0x82fe2b98, 0x429a038f, 0x02377bb6, 0xc25353a1, + 0x826d8ac4, 0x4209a2d3, 0x02a4daea, 0xc2c0f2fd, 0xc7234eca, + 0x074766dd, 0x47ea1ee4, 0x878e36f3, 0xc7b0ef96, 0x07d4c781, + 0x4779bfb8, 0x871d97af, 0xc7050d73, 0x07612564, 0x47cc5d5d, + 0x87a8754a, 0xc796ac2f, 0x07f28438, 0x475ffc01, 0x873bd416, + 0x8669b963, 0x460d9174, 0x06a0e94d, 0xc6c4c15a, 0x86fa183f, + 0x469e3028, 0x06334811, 0xc6576006, 0x864ffada, 0x462bd2cd, + 0x0686aaf4, 0xc6e282e3, 0x86dc5b86, 0x46b87391, 0x06150ba8, + 0xc67123bf, 0x04b1d142, 0xc4d5f955, 0x8478816c, 0x441ca97b, + 0x0422701e, 0xc4465809, 0x84eb2030, 0x448f0827, 0x049792fb, + 0xc4f3baec, 0x845ec2d5, 0x443aeac2, 0x040433a7, 0xc4601bb0, + 0x84cd6389, 0x44a94b9e, 0x45fb26eb, 0x859f0efc, 0xc53276c5, + 0x05565ed2, 0x456887b7, 0x850cafa0, 0xc5a1d799, 0x05c5ff8e, + 0x45dd6552, 0x85b94d45, 0xc514357c, 0x05701d6b, 0x454ec40e, + 0x852aec19, 0xc5879420, 0x05e3bc37, 0xcf41ed4f, 0x0f25c558, + 0x4f88bd61, 0x8fec9576, 0xcfd24c13, 0x0fb66404, 0x4f1b1c3d, + 0x8f7f342a, 0xcf67aef6, 0x0f0386e1, 0x4faefed8, 0x8fcad6cf, + 0xcff40faa, 0x0f9027bd, 0x4f3d5f84, 0x8f597793, 0x8e0b1ae6, + 0x4e6f32f1, 0x0ec24ac8, 0xcea662df, 0x8e98bbba, 0x4efc93ad, + 0x0e51eb94, 0xce35c383, 0x8e2d595f, 0x4e497148, 0x0ee40971, + 0xce802166, 0x8ebef803, 0x4edad014, 0x0e77a82d, 0xce13803a, + 0x0cd372c7, 0xccb75ad0, 0x8c1a22e9, 0x4c7e0afe, 0x0c40d39b, + 0xcc24fb8c, 0x8c8983b5, 0x4cedaba2, 0x0cf5317e, 0xcc911969, + 0x8c3c6150, 0x4c584947, 0x0c669022, 0xcc02b835, 0x8cafc00c, + 0x4ccbe81b, 0x4d99856e, 0x8dfdad79, 0xcd50d540, 0x0d34fd57, + 0x4d0a2432, 0x8d6e0c25, 0xcdc3741c, 0x0da75c0b, 0x4dbfc6d7, + 0x8ddbeec0, 0xcd7696f9, 0x0d12beee, 0x4d2c678b, 0x8d484f9c, + 0xcde537a5, 0x0d811fb2, 0x0862a385, 0xc8068b92, 0x88abf3ab, + 0x48cfdbbc, 0x08f102d9, 0xc8952ace, 0x883852f7, 0x485c7ae0, + 0x0844e03c, 0xc820c82b, 0x888db012, 0x48e99805, 0x08d74160, + 0xc8b36977, 0x881e114e, 0x487a3959, 0x4928542c, 0x894c7c3b, + 0xc9e10402, 0x09852c15, 0x49bbf570, 0x89dfdd67, 0xc972a55e, + 0x09168d49, 0x490e1795, 0x896a3f82, 0xc9c747bb, 0x09a36fac, + 0x499db6c9, 0x89f99ede, 0xc954e6e7, 0x0930cef0, 0xcbf03c0d, + 0x0b94141a, 0x4b396c23, 0x8b5d4434, 0xcb639d51, 0x0b07b546, + 0x4baacd7f, 0x8bcee568, 0xcbd67fb4, 0x0bb257a3, 0x4b1f2f9a, + 0x8b7b078d, 0xcb45dee8, 0x0b21f6ff, 0x4b8c8ec6, 0x8be8a6d1, + 0x8abacba4, 0x4adee3b3, 0x0a739b8a, 0xca17b39d, 0x8a296af8, + 0x4a4d42ef, 0x0ae03ad6, 0xca8412c1, 0x8a9c881d, 0x4af8a00a, + 0x0a55d833, 0xca31f024, 0x8a0f2941, 0x4a6b0156, 0x0ac6796f, + 0xcaa25178}, + {0x00000000, 0xd4ea739b, 0xe9d396ed, 0x3d39e576, 0x93a15c00, + 0x474b2f9b, 0x7a72caed, 0xae98b976, 0x2643b900, 0xf2a9ca9b, + 0xcf902fed, 0x1b7a5c76, 0xb5e2e500, 0x6108969b, 0x5c3173ed, + 0x88db0076, 0x4c867201, 0x986c019a, 0xa555e4ec, 0x71bf9777, + 0xdf272e01, 0x0bcd5d9a, 0x36f4b8ec, 0xe21ecb77, 0x6ac5cb01, + 0xbe2fb89a, 0x83165dec, 0x57fc2e77, 0xf9649701, 0x2d8ee49a, + 0x10b701ec, 0xc45d7277, 0x980ce502, 0x4ce69699, 0x71df73ef, + 0xa5350074, 0x0badb902, 0xdf47ca99, 0xe27e2fef, 0x36945c74, + 0xbe4f5c02, 0x6aa52f99, 0x579ccaef, 0x8376b974, 0x2dee0002, + 0xf9047399, 0xc43d96ef, 0x10d7e574, 0xd48a9703, 0x0060e498, + 0x3d5901ee, 0xe9b37275, 0x472bcb03, 0x93c1b898, 0xaef85dee, + 0x7a122e75, 0xf2c92e03, 0x26235d98, 0x1b1ab8ee, 0xcff0cb75, + 0x61687203, 0xb5820198, 0x88bbe4ee, 0x5c519775, 0x3019ca05, + 0xe4f3b99e, 0xd9ca5ce8, 0x0d202f73, 0xa3b89605, 0x7752e59e, + 0x4a6b00e8, 0x9e817373, 0x165a7305, 0xc2b0009e, 0xff89e5e8, + 0x2b639673, 0x85fb2f05, 0x51115c9e, 0x6c28b9e8, 0xb8c2ca73, + 0x7c9fb804, 0xa875cb9f, 0x954c2ee9, 0x41a65d72, 0xef3ee404, + 0x3bd4979f, 0x06ed72e9, 0xd2070172, 0x5adc0104, 0x8e36729f, + 0xb30f97e9, 0x67e5e472, 0xc97d5d04, 0x1d972e9f, 0x20aecbe9, + 0xf444b872, 0xa8152f07, 0x7cff5c9c, 0x41c6b9ea, 0x952cca71, + 0x3bb47307, 0xef5e009c, 0xd267e5ea, 0x068d9671, 0x8e569607, + 0x5abce59c, 0x678500ea, 0xb36f7371, 0x1df7ca07, 0xc91db99c, + 0xf4245cea, 0x20ce2f71, 0xe4935d06, 0x30792e9d, 0x0d40cbeb, + 0xd9aab870, 0x77320106, 0xa3d8729d, 0x9ee197eb, 0x4a0be470, + 0xc2d0e406, 0x163a979d, 0x2b0372eb, 0xffe90170, 0x5171b806, + 0x859bcb9d, 0xb8a22eeb, 0x6c485d70, 0x6032940b, 0xb4d8e790, + 0x89e102e6, 0x5d0b717d, 0xf393c80b, 0x2779bb90, 0x1a405ee6, + 0xceaa2d7d, 0x46712d0b, 0x929b5e90, 0xafa2bbe6, 0x7b48c87d, + 0xd5d0710b, 0x013a0290, 0x3c03e7e6, 0xe8e9947d, 0x2cb4e60a, + 0xf85e9591, 0xc56770e7, 0x118d037c, 0xbf15ba0a, 0x6bffc991, + 0x56c62ce7, 0x822c5f7c, 0x0af75f0a, 0xde1d2c91, 0xe324c9e7, + 0x37ceba7c, 0x9956030a, 0x4dbc7091, 0x708595e7, 0xa46fe67c, + 0xf83e7109, 0x2cd40292, 0x11ede7e4, 0xc507947f, 0x6b9f2d09, + 0xbf755e92, 0x824cbbe4, 0x56a6c87f, 0xde7dc809, 0x0a97bb92, + 0x37ae5ee4, 0xe3442d7f, 0x4ddc9409, 0x9936e792, 0xa40f02e4, + 0x70e5717f, 0xb4b80308, 0x60527093, 0x5d6b95e5, 0x8981e67e, + 0x27195f08, 0xf3f32c93, 0xcecac9e5, 0x1a20ba7e, 0x92fbba08, + 0x4611c993, 0x7b282ce5, 0xafc25f7e, 0x015ae608, 0xd5b09593, + 0xe88970e5, 0x3c63037e, 0x502b5e0e, 0x84c12d95, 0xb9f8c8e3, + 0x6d12bb78, 0xc38a020e, 0x17607195, 0x2a5994e3, 0xfeb3e778, + 0x7668e70e, 0xa2829495, 0x9fbb71e3, 0x4b510278, 0xe5c9bb0e, + 0x3123c895, 0x0c1a2de3, 0xd8f05e78, 0x1cad2c0f, 0xc8475f94, + 0xf57ebae2, 0x2194c979, 0x8f0c700f, 0x5be60394, 0x66dfe6e2, + 0xb2359579, 0x3aee950f, 0xee04e694, 0xd33d03e2, 0x07d77079, + 0xa94fc90f, 0x7da5ba94, 0x409c5fe2, 0x94762c79, 0xc827bb0c, + 0x1ccdc897, 0x21f42de1, 0xf51e5e7a, 0x5b86e70c, 0x8f6c9497, + 0xb25571e1, 0x66bf027a, 0xee64020c, 0x3a8e7197, 0x07b794e1, + 0xd35de77a, 0x7dc55e0c, 0xa92f2d97, 0x9416c8e1, 0x40fcbb7a, + 0x84a1c90d, 0x504bba96, 0x6d725fe0, 0xb9982c7b, 0x1700950d, + 0xc3eae696, 0xfed303e0, 0x2a39707b, 0xa2e2700d, 0x76080396, + 0x4b31e6e0, 0x9fdb957b, 0x31432c0d, 0xe5a95f96, 0xd890bae0, + 0x0c7ac97b}, + {0x00000000, 0x27652581, 0x0fcc3bd9, 0x28a91e58, 0x5f9e0669, + 0x78fb23e8, 0x50523db0, 0x77371831, 0xbe3c0dd2, 0x99592853, + 0xb1f0360b, 0x9695138a, 0xe1a20bbb, 0xc6c72e3a, 0xee6e3062, + 0xc90b15e3, 0x3d7f6b7f, 0x1a1a4efe, 0x32b350a6, 0x15d67527, + 0x62e16d16, 0x45844897, 0x6d2d56cf, 0x4a48734e, 0x834366ad, + 0xa426432c, 0x8c8f5d74, 0xabea78f5, 0xdcdd60c4, 0xfbb84545, + 0xd3115b1d, 0xf4747e9c, 0x7afed6fe, 0x5d9bf37f, 0x7532ed27, + 0x5257c8a6, 0x2560d097, 0x0205f516, 0x2aaceb4e, 0x0dc9cecf, + 0xc4c2db2c, 0xe3a7fead, 0xcb0ee0f5, 0xec6bc574, 0x9b5cdd45, + 0xbc39f8c4, 0x9490e69c, 0xb3f5c31d, 0x4781bd81, 0x60e49800, + 0x484d8658, 0x6f28a3d9, 0x181fbbe8, 0x3f7a9e69, 0x17d38031, + 0x30b6a5b0, 0xf9bdb053, 0xded895d2, 0xf6718b8a, 0xd114ae0b, + 0xa623b63a, 0x814693bb, 0xa9ef8de3, 0x8e8aa862, 0xb5fadc26, + 0x929ff9a7, 0xba36e7ff, 0x9d53c27e, 0xea64da4f, 0xcd01ffce, + 0xe5a8e196, 0xc2cdc417, 0x0bc6d1f4, 0x2ca3f475, 0x040aea2d, + 0x236fcfac, 0x5458d79d, 0x733df21c, 0x5b94ec44, 0x7cf1c9c5, + 0x8885b759, 0xafe092d8, 0x87498c80, 0xa02ca901, 0xd71bb130, + 0xf07e94b1, 0xd8d78ae9, 0xffb2af68, 0x36b9ba8b, 0x11dc9f0a, + 0x39758152, 0x1e10a4d3, 0x6927bce2, 0x4e429963, 0x66eb873b, + 0x418ea2ba, 0xcf040ad8, 0xe8612f59, 0xc0c83101, 0xe7ad1480, + 0x909a0cb1, 0xb7ff2930, 0x9f563768, 0xb83312e9, 0x7138070a, + 0x565d228b, 0x7ef43cd3, 0x59911952, 0x2ea60163, 0x09c324e2, + 0x216a3aba, 0x060f1f3b, 0xf27b61a7, 0xd51e4426, 0xfdb75a7e, + 0xdad27fff, 0xade567ce, 0x8a80424f, 0xa2295c17, 0x854c7996, + 0x4c476c75, 0x6b2249f4, 0x438b57ac, 0x64ee722d, 0x13d96a1c, + 0x34bc4f9d, 0x1c1551c5, 0x3b707444, 0x6af5b94d, 0x4d909ccc, + 0x65398294, 0x425ca715, 0x356bbf24, 0x120e9aa5, 0x3aa784fd, + 0x1dc2a17c, 0xd4c9b49f, 0xf3ac911e, 0xdb058f46, 0xfc60aac7, + 0x8b57b2f6, 0xac329777, 0x849b892f, 0xa3feacae, 0x578ad232, + 0x70eff7b3, 0x5846e9eb, 0x7f23cc6a, 0x0814d45b, 0x2f71f1da, + 0x07d8ef82, 0x20bdca03, 0xe9b6dfe0, 0xced3fa61, 0xe67ae439, + 0xc11fc1b8, 0xb628d989, 0x914dfc08, 0xb9e4e250, 0x9e81c7d1, + 0x100b6fb3, 0x376e4a32, 0x1fc7546a, 0x38a271eb, 0x4f9569da, + 0x68f04c5b, 0x40595203, 0x673c7782, 0xae376261, 0x895247e0, + 0xa1fb59b8, 0x869e7c39, 0xf1a96408, 0xd6cc4189, 0xfe655fd1, + 0xd9007a50, 0x2d7404cc, 0x0a11214d, 0x22b83f15, 0x05dd1a94, + 0x72ea02a5, 0x558f2724, 0x7d26397c, 0x5a431cfd, 0x9348091e, + 0xb42d2c9f, 0x9c8432c7, 0xbbe11746, 0xccd60f77, 0xebb32af6, + 0xc31a34ae, 0xe47f112f, 0xdf0f656b, 0xf86a40ea, 0xd0c35eb2, + 0xf7a67b33, 0x80916302, 0xa7f44683, 0x8f5d58db, 0xa8387d5a, + 0x613368b9, 0x46564d38, 0x6eff5360, 0x499a76e1, 0x3ead6ed0, + 0x19c84b51, 0x31615509, 0x16047088, 0xe2700e14, 0xc5152b95, + 0xedbc35cd, 0xcad9104c, 0xbdee087d, 0x9a8b2dfc, 0xb22233a4, + 0x95471625, 0x5c4c03c6, 0x7b292647, 0x5380381f, 0x74e51d9e, + 0x03d205af, 0x24b7202e, 0x0c1e3e76, 0x2b7b1bf7, 0xa5f1b395, + 0x82949614, 0xaa3d884c, 0x8d58adcd, 0xfa6fb5fc, 0xdd0a907d, + 0xf5a38e25, 0xd2c6aba4, 0x1bcdbe47, 0x3ca89bc6, 0x1401859e, + 0x3364a01f, 0x4453b82e, 0x63369daf, 0x4b9f83f7, 0x6cfaa676, + 0x988ed8ea, 0xbfebfd6b, 0x9742e333, 0xb027c6b2, 0xc710de83, + 0xe075fb02, 0xc8dce55a, 0xefb9c0db, 0x26b2d538, 0x01d7f0b9, + 0x297eeee1, 0x0e1bcb60, 0x792cd351, 0x5e49f6d0, 0x76e0e888, + 0x5185cd09}}; + +#endif + +#endif + +#endif + +local const z_crc_t FAR x2n_table[] = { + 0x40000000, 0x20000000, 0x08000000, 0x00800000, 0x00008000, + 0xedb88320, 0xb1e6b092, 0xa06a2517, 0xed627dae, 0x88d14467, + 0xd7bbfe6a, 0xec447f11, 0x8e7ea170, 0x6427800e, 0x4d47bae0, + 0x09fe548f, 0x83852d0f, 0x30362f1a, 0x7b5a9cc3, 0x31fec169, + 0x9fec022a, 0x6c8dedc4, 0x15d6874d, 0x5fde7a4e, 0xbad90e37, + 0x2e4e5eef, 0x4eaba214, 0xa8a472c0, 0x429a969e, 0x148d302a, + 0xc40ba6d0, 0xc4e22c3c}; diff --git a/3rdparty/zlib/deflate.c b/3rdparty/zlib/deflate.c index 1ec761448de9..799fb93cc04b 100644 --- a/3rdparty/zlib/deflate.c +++ b/3rdparty/zlib/deflate.c @@ -1,5 +1,5 @@ /* deflate.c -- compress data using the deflation algorithm - * Copyright (C) 1995-2017 Jean-loup Gailly and Mark Adler + * Copyright (C) 1995-2022 Jean-loup Gailly and Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -52,7 +52,7 @@ #include "deflate.h" const char deflate_copyright[] = - " deflate 1.2.11 Copyright 1995-2017 Jean-loup Gailly and Mark Adler "; + " deflate 1.2.12 Copyright 1995-2022 Jean-loup Gailly and Mark Adler "; /* If you use the zlib library in a product, an acknowledgment is welcome in the documentation of your product. If for some reason you cannot @@ -190,8 +190,11 @@ local const config configuration_table[10] = { * prev[] will be initialized on the fly. */ #define CLEAR_HASH(s) \ - s->head[s->hash_size-1] = NIL; \ - zmemzero((Bytef *)s->head, (unsigned)(s->hash_size-1)*sizeof(*s->head)); + do { \ + s->head[s->hash_size-1] = NIL; \ + zmemzero((Bytef *)s->head, \ + (unsigned)(s->hash_size-1)*sizeof(*s->head)); \ + } while (0) /* =========================================================================== * Slide the hash table when sliding the window down (could be avoided with 32 @@ -252,11 +255,6 @@ int ZEXPORT deflateInit2_(strm, level, method, windowBits, memLevel, strategy, int wrap = 1; static const char my_version[] = ZLIB_VERSION; - ushf *overlay; - /* We overlay pending_buf and d_buf+l_buf. This works since the average - * output size for (length,distance) codes is <= 24 bits. - */ - if (version == Z_NULL || version[0] != my_version[0] || stream_size != sizeof(z_stream)) { return Z_VERSION_ERROR; @@ -326,9 +324,47 @@ int ZEXPORT deflateInit2_(strm, level, method, windowBits, memLevel, strategy, s->lit_bufsize = 1 << (memLevel + 6); /* 16K elements by default */ - overlay = (ushf *) ZALLOC(strm, s->lit_bufsize, sizeof(ush)+2); - s->pending_buf = (uchf *) overlay; - s->pending_buf_size = (ulg)s->lit_bufsize * (sizeof(ush)+2L); + /* We overlay pending_buf and sym_buf. This works since the average size + * for length/distance pairs over any compressed block is assured to be 31 + * bits or less. + * + * Analysis: The longest fixed codes are a length code of 8 bits plus 5 + * extra bits, for lengths 131 to 257. The longest fixed distance codes are + * 5 bits plus 13 extra bits, for distances 16385 to 32768. The longest + * possible fixed-codes length/distance pair is then 31 bits total. + * + * sym_buf starts one-fourth of the way into pending_buf. So there are + * three bytes in sym_buf for every four bytes in pending_buf. Each symbol + * in sym_buf is three bytes -- two for the distance and one for the + * literal/length. As each symbol is consumed, the pointer to the next + * sym_buf value to read moves forward three bytes. From that symbol, up to + * 31 bits are written to pending_buf. The closest the written pending_buf + * bits gets to the next sym_buf symbol to read is just before the last + * code is written. At that time, 31*(n-2) bits have been written, just + * after 24*(n-2) bits have been consumed from sym_buf. sym_buf starts at + * 8*n bits into pending_buf. (Note that the symbol buffer fills when n-1 + * symbols are written.) The closest the writing gets to what is unread is + * then n+14 bits. Here n is lit_bufsize, which is 16384 by default, and + * can range from 128 to 32768. + * + * Therefore, at a minimum, there are 142 bits of space between what is + * written and what is read in the overlain buffers, so the symbols cannot + * be overwritten by the compressed data. That space is actually 139 bits, + * due to the three-bit fixed-code block header. + * + * That covers the case where either Z_FIXED is specified, forcing fixed + * codes, or when the use of fixed codes is chosen, because that choice + * results in a smaller compressed block than dynamic codes. That latter + * condition then assures that the above analysis also covers all dynamic + * blocks. A dynamic-code block will only be chosen to be emitted if it has + * fewer bits than a fixed-code block would for the same set of symbols. + * Therefore its average symbol length is assured to be less than 31. So + * the compressed data for a dynamic block also cannot overwrite the + * symbols from which it is being constructed. + */ + + s->pending_buf = (uchf *) ZALLOC(strm, s->lit_bufsize, 4); + s->pending_buf_size = (ulg)s->lit_bufsize * 4; if (s->window == Z_NULL || s->prev == Z_NULL || s->head == Z_NULL || s->pending_buf == Z_NULL) { @@ -337,8 +373,12 @@ int ZEXPORT deflateInit2_(strm, level, method, windowBits, memLevel, strategy, deflateEnd (strm); return Z_MEM_ERROR; } - s->d_buf = overlay + s->lit_bufsize/sizeof(ush); - s->l_buf = s->pending_buf + (1+sizeof(ush))*s->lit_bufsize; + s->sym_buf = s->pending_buf + s->lit_bufsize; + s->sym_end = (s->lit_bufsize - 1) * 3; + /* We avoid equality with lit_bufsize*3 because of wraparound at 64K + * on 16 bit machines and because stored blocks are restricted to + * 64K-1 bytes. + */ s->level = level; s->strategy = strategy; @@ -488,13 +528,13 @@ int ZEXPORT deflateResetKeep (strm) #ifdef GZIP s->wrap == 2 ? GZIP_STATE : #endif - s->wrap ? INIT_STATE : BUSY_STATE; + INIT_STATE; strm->adler = #ifdef GZIP s->wrap == 2 ? crc32(0L, Z_NULL, 0) : #endif adler32(0L, Z_NULL, 0); - s->last_flush = Z_NO_FLUSH; + s->last_flush = -2; _tr_init(s); @@ -549,7 +589,8 @@ int ZEXPORT deflatePrime (strm, bits, value) if (deflateStateCheck(strm)) return Z_STREAM_ERROR; s = strm->state; - if ((Bytef *)(s->d_buf) < s->pending_out + ((Buf_size + 7) >> 3)) + if (bits < 0 || bits > 16 || + s->sym_buf < s->pending_out + ((Buf_size + 7) >> 3)) return Z_BUF_ERROR; do { put = Buf_size - s->bi_valid; @@ -587,12 +628,12 @@ int ZEXPORT deflateParams(strm, level, strategy) func = configuration_table[s->level].func; if ((strategy != s->strategy || func != configuration_table[level].func) && - s->high_water) { + s->last_flush != -2) { /* Flush the last buffer: */ int err = deflate(strm, Z_BLOCK); if (err == Z_STREAM_ERROR) return err; - if (strm->avail_out == 0) + if (strm->avail_in || (s->strstart - s->block_start) + s->lookahead) return Z_BUF_ERROR; } if (s->level != level) { @@ -811,6 +852,8 @@ int ZEXPORT deflate (strm, flush) } /* Write the header */ + if (s->status == INIT_STATE && s->wrap == 0) + s->status = BUSY_STATE; if (s->status == INIT_STATE) { /* zlib header */ uInt header = (Z_DEFLATED + ((s->w_bits-8)<<4)) << 8; @@ -1108,7 +1151,6 @@ int ZEXPORT deflateCopy (dest, source) #else deflate_state *ds; deflate_state *ss; - ushf *overlay; if (deflateStateCheck(source) || dest == Z_NULL) { @@ -1128,8 +1170,7 @@ int ZEXPORT deflateCopy (dest, source) ds->window = (Bytef *) ZALLOC(dest, ds->w_size, 2*sizeof(Byte)); ds->prev = (Posf *) ZALLOC(dest, ds->w_size, sizeof(Pos)); ds->head = (Posf *) ZALLOC(dest, ds->hash_size, sizeof(Pos)); - overlay = (ushf *) ZALLOC(dest, ds->lit_bufsize, sizeof(ush)+2); - ds->pending_buf = (uchf *) overlay; + ds->pending_buf = (uchf *) ZALLOC(dest, ds->lit_bufsize, 4); if (ds->window == Z_NULL || ds->prev == Z_NULL || ds->head == Z_NULL || ds->pending_buf == Z_NULL) { @@ -1143,8 +1184,7 @@ int ZEXPORT deflateCopy (dest, source) zmemcpy(ds->pending_buf, ss->pending_buf, (uInt)ds->pending_buf_size); ds->pending_out = ds->pending_buf + (ss->pending_out - ss->pending_buf); - ds->d_buf = overlay + ds->lit_bufsize/sizeof(ush); - ds->l_buf = ds->pending_buf + (1+sizeof(ush))*ds->lit_bufsize; + ds->sym_buf = ds->pending_buf + ds->lit_bufsize; ds->l_desc.dyn_tree = ds->dyn_ltree; ds->d_desc.dyn_tree = ds->dyn_dtree; @@ -1513,6 +1553,8 @@ local void fill_window(s) s->match_start -= wsize; s->strstart -= wsize; /* we now have strstart >= MAX_DIST */ s->block_start -= (long) wsize; + if (s->insert > s->strstart) + s->insert = s->strstart; slide_hash(s); more += wsize; } @@ -1742,6 +1784,7 @@ local block_state deflate_stored(s, flush) s->matches = 2; /* clear hash */ zmemcpy(s->window, s->strm->next_in - s->w_size, s->w_size); s->strstart = s->w_size; + s->insert = s->strstart; } else { if (s->window_size - s->strstart <= used) { @@ -1750,12 +1793,14 @@ local block_state deflate_stored(s, flush) zmemcpy(s->window, s->window + s->w_size, s->strstart); if (s->matches < 2) s->matches++; /* add a pending slide_hash() */ + if (s->insert > s->strstart) + s->insert = s->strstart; } zmemcpy(s->window + s->strstart, s->strm->next_in - used, used); s->strstart += used; + s->insert += MIN(used, s->w_size - s->insert); } s->block_start = s->strstart; - s->insert += MIN(used, s->w_size - s->insert); } if (s->high_water < s->strstart) s->high_water = s->strstart; @@ -1770,7 +1815,7 @@ local block_state deflate_stored(s, flush) return block_done; /* Fill the window with any remaining input. */ - have = s->window_size - s->strstart - 1; + have = s->window_size - s->strstart; if (s->strm->avail_in > have && s->block_start >= (long)s->w_size) { /* Slide the window down. */ s->block_start -= s->w_size; @@ -1779,12 +1824,15 @@ local block_state deflate_stored(s, flush) if (s->matches < 2) s->matches++; /* add a pending slide_hash() */ have += s->w_size; /* more space now */ + if (s->insert > s->strstart) + s->insert = s->strstart; } if (have > s->strm->avail_in) have = s->strm->avail_in; if (have) { read_buf(s->strm, s->window + s->strstart, have); s->strstart += have; + s->insert += MIN(have, s->w_size - s->insert); } if (s->high_water < s->strstart) s->high_water = s->strstart; @@ -1912,7 +1960,7 @@ local block_state deflate_fast(s, flush) FLUSH_BLOCK(s, 1); return finish_done; } - if (s->last_lit) + if (s->sym_next) FLUSH_BLOCK(s, 0); return block_done; } @@ -2043,7 +2091,7 @@ local block_state deflate_slow(s, flush) FLUSH_BLOCK(s, 1); return finish_done; } - if (s->last_lit) + if (s->sym_next) FLUSH_BLOCK(s, 0); return block_done; } @@ -2118,7 +2166,7 @@ local block_state deflate_rle(s, flush) FLUSH_BLOCK(s, 1); return finish_done; } - if (s->last_lit) + if (s->sym_next) FLUSH_BLOCK(s, 0); return block_done; } @@ -2157,7 +2205,7 @@ local block_state deflate_huff(s, flush) FLUSH_BLOCK(s, 1); return finish_done; } - if (s->last_lit) + if (s->sym_next) FLUSH_BLOCK(s, 0); return block_done; } diff --git a/3rdparty/zlib/deflate.h b/3rdparty/zlib/deflate.h index 23ecdd312bc0..17c226113b08 100644 --- a/3rdparty/zlib/deflate.h +++ b/3rdparty/zlib/deflate.h @@ -1,5 +1,5 @@ /* deflate.h -- internal compression state - * Copyright (C) 1995-2016 Jean-loup Gailly + * Copyright (C) 1995-2018 Jean-loup Gailly * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -217,7 +217,7 @@ typedef struct internal_state { /* Depth of each subtree used as tie breaker for trees of equal frequency */ - uchf *l_buf; /* buffer for literals or lengths */ + uchf *sym_buf; /* buffer for distances and literals/lengths */ uInt lit_bufsize; /* Size of match buffer for literals/lengths. There are 4 reasons for @@ -239,13 +239,8 @@ typedef struct internal_state { * - I can't count above 4 */ - uInt last_lit; /* running index in l_buf */ - - ushf *d_buf; - /* Buffer for distances. To simplify the code, d_buf and l_buf have - * the same number of elements. To use different lengths, an extra flag - * array would be necessary. - */ + uInt sym_next; /* running index in sym_buf */ + uInt sym_end; /* symbol table full when sym_next reaches this */ ulg opt_len; /* bit length of current block with optimal trees */ ulg static_len; /* bit length of current block with static trees */ @@ -325,20 +320,22 @@ void ZLIB_INTERNAL _tr_stored_block OF((deflate_state *s, charf *buf, # define _tr_tally_lit(s, c, flush) \ { uch cc = (c); \ - s->d_buf[s->last_lit] = 0; \ - s->l_buf[s->last_lit++] = cc; \ + s->sym_buf[s->sym_next++] = 0; \ + s->sym_buf[s->sym_next++] = 0; \ + s->sym_buf[s->sym_next++] = cc; \ s->dyn_ltree[cc].Freq++; \ - flush = (s->last_lit == s->lit_bufsize-1); \ + flush = (s->sym_next == s->sym_end); \ } # define _tr_tally_dist(s, distance, length, flush) \ { uch len = (uch)(length); \ ush dist = (ush)(distance); \ - s->d_buf[s->last_lit] = dist; \ - s->l_buf[s->last_lit++] = len; \ + s->sym_buf[s->sym_next++] = dist; \ + s->sym_buf[s->sym_next++] = dist >> 8; \ + s->sym_buf[s->sym_next++] = len; \ dist--; \ s->dyn_ltree[_length_code[len]+LITERALS+1].Freq++; \ s->dyn_dtree[d_code(dist)].Freq++; \ - flush = (s->last_lit == s->lit_bufsize-1); \ + flush = (s->sym_next == s->sym_end); \ } #else # define _tr_tally_lit(s, c, flush) flush = _tr_tally(s, 0, c) diff --git a/3rdparty/zlib/gzguts.h b/3rdparty/zlib/gzguts.h index 990a4d251493..57faf37165a3 100644 --- a/3rdparty/zlib/gzguts.h +++ b/3rdparty/zlib/gzguts.h @@ -1,5 +1,5 @@ /* gzguts.h -- zlib internal header definitions for gz* operations - * Copyright (C) 2004, 2005, 2010, 2011, 2012, 2013, 2016 Mark Adler + * Copyright (C) 2004-2019 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -39,7 +39,7 @@ # include #endif -#if defined(_WIN32) || defined(__CYGWIN__) +#if defined(_WIN32) # define WIDECHAR #endif @@ -190,6 +190,7 @@ typedef struct { /* just for writing */ int level; /* compression level */ int strategy; /* compression strategy */ + int reset; /* true if a reset is pending after a Z_FINISH */ /* seek request */ z_off64_t skip; /* amount to skip (already rewound if backwards) */ int seek; /* true if seek request pending */ diff --git a/3rdparty/zlib/gzlib.c b/3rdparty/zlib/gzlib.c index 4105e6aff925..dddaf2687303 100644 --- a/3rdparty/zlib/gzlib.c +++ b/3rdparty/zlib/gzlib.c @@ -1,11 +1,11 @@ /* gzlib.c -- zlib functions common to reading and writing gzip files - * Copyright (C) 2004-2017 Mark Adler + * Copyright (C) 2004-2019 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ #include "gzguts.h" -#if defined(_WIN32) && !defined(__BORLANDC__) && !defined(__MINGW32__) +#if defined(_WIN32) && !defined(__BORLANDC__) # define LSEEK _lseeki64 #else #if defined(_LARGEFILE64_SOURCE) && _LFS64_LARGEFILE-0 @@ -81,6 +81,8 @@ local void gz_reset(state) state->past = 0; /* have not read past end yet */ state->how = LOOK; /* look for gzip header */ } + else /* for writing ... */ + state->reset = 0; /* no deflateReset pending */ state->seek = 0; /* no seek request pending */ gz_error(state, Z_OK, NULL); /* clear error */ state->x.pos = 0; /* no uncompressed data yet */ @@ -397,7 +399,7 @@ z_off64_t ZEXPORT gzseek64(file, offset, whence) /* if within raw area while reading, just go there */ if (state->mode == GZ_READ && state->how == COPY && state->x.pos + offset >= 0) { - ret = LSEEK(state->fd, offset - state->x.have, SEEK_CUR); + ret = LSEEK(state->fd, offset - (z_off64_t)state->x.have, SEEK_CUR); if (ret == -1) return -1; state->x.have = 0; diff --git a/3rdparty/zlib/gzread.c b/3rdparty/zlib/gzread.c index e75cae64dcda..884c9bfe4cfb 100644 --- a/3rdparty/zlib/gzread.c +++ b/3rdparty/zlib/gzread.c @@ -1,5 +1,5 @@ /* gzread.c -- zlib functions for reading gzip files - * Copyright (C) 2004, 2005, 2010, 2011, 2012, 2013, 2016 Mark Adler + * Copyright (C) 2004-2017 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -314,9 +314,9 @@ local z_size_t gz_read(state, buf, len) got = 0; do { /* set n to the maximum amount of len that fits in an unsigned int */ - n = -1; + n = (unsigned)-1; if (n > len) - n = (int)len; + n = (unsigned)len; /* first just try copying data from the output buffer */ if (state->x.have) { @@ -397,7 +397,7 @@ int ZEXPORT gzread(file, buf, len) } /* read len or fewer bytes to buf */ - len = (int)gz_read(state, buf, len); + len = (unsigned)gz_read(state, buf, len); /* check for an error */ if (len == 0 && state->err != Z_OK && state->err != Z_BUF_ERROR) @@ -447,7 +447,6 @@ z_size_t ZEXPORT gzfread(buf, size, nitems, file) int ZEXPORT gzgetc(file) gzFile file; { - int ret; unsigned char buf[1]; gz_statep state; @@ -469,8 +468,7 @@ int ZEXPORT gzgetc(file) } /* nothing there -- try gz_read() */ - ret = (int)gz_read(state, buf, 1); - return ret < 1 ? -1 : buf[0]; + return gz_read(state, buf, 1) < 1 ? -1 : buf[0]; } int ZEXPORT gzgetc_(file) diff --git a/3rdparty/zlib/gzwrite.c b/3rdparty/zlib/gzwrite.c index 57a1cc70c858..a8ffc8f53da7 100644 --- a/3rdparty/zlib/gzwrite.c +++ b/3rdparty/zlib/gzwrite.c @@ -1,5 +1,5 @@ /* gzwrite.c -- zlib functions for writing gzip files - * Copyright (C) 2004-2017 Mark Adler + * Copyright (C) 2004-2019 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -97,6 +97,15 @@ local int gz_comp(state, flush) return 0; } + /* check for a pending reset */ + if (state->reset) { + /* don't start a new gzip member unless there is data to write */ + if (strm->avail_in == 0) + return 0; + deflateReset(strm); + state->reset = 0; + } + /* run deflate() on provided input until it produces no more output */ ret = Z_OK; do { @@ -134,7 +143,7 @@ local int gz_comp(state, flush) /* if that completed a deflate stream, allow another to start */ if (flush == Z_FINISH) - deflateReset(strm); + state->reset = 1; /* all done, no errors */ return 0; @@ -209,7 +218,7 @@ local z_size_t gz_write(state, buf, len) state->in); copy = state->size - have; if (copy > len) - copy = (int)len; + copy = (unsigned)len; memcpy(state->in + have, buf, copy); state->strm.avail_in += copy; state->x.pos += copy; @@ -229,7 +238,7 @@ local z_size_t gz_write(state, buf, len) do { unsigned n = (unsigned)-1; if (n > len) - n = (int)len; + n = (unsigned)len; state->strm.avail_in = n; state->x.pos += n; if (gz_comp(state, Z_NO_FLUSH) == -1) @@ -349,12 +358,11 @@ int ZEXPORT gzputc(file, c) } /* -- see zlib.h -- */ -int ZEXPORT gzputs(file, str) +int ZEXPORT gzputs(file, s) gzFile file; - const char *str; + const char *s; { - int ret; - z_size_t len; + z_size_t len, put; gz_statep state; /* get internal structure */ @@ -367,9 +375,13 @@ int ZEXPORT gzputs(file, str) return -1; /* write string */ - len = strlen(str); - ret = (int)gz_write(state, str, len); - return ret == 0 && len != 0 ? -1 : ret; + len = strlen(s); + if ((int)len < 0 || (unsigned)len != len) { + gz_error(state, Z_STREAM_ERROR, "string length does not fit in int"); + return -1; + } + put = gz_write(state, s, len); + return put < len ? -1 : (int)len; } #if defined(STDC) || defined(Z_HAVE_STDARG_H) @@ -441,7 +453,7 @@ int ZEXPORTVA gzvprintf(gzFile file, const char *format, va_list va) strm->avail_in = state->size; if (gz_comp(state, Z_NO_FLUSH) == -1) return state->err; - memcpy(state->in, state->in + state->size, left); + memmove(state->in, state->in + state->size, left); strm->next_in = state->in; strm->avail_in = left; } @@ -540,7 +552,7 @@ int ZEXPORTVA gzprintf (file, format, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, strm->avail_in = state->size; if (gz_comp(state, Z_NO_FLUSH) == -1) return state->err; - memcpy(state->in, state->in + state->size, left); + memmove(state->in, state->in + state->size, left); strm->next_in = state->in; strm->avail_in = left; } diff --git a/3rdparty/zlib/infback.c b/3rdparty/zlib/infback.c index 59679ecbfc5d..a390c58e816f 100644 --- a/3rdparty/zlib/infback.c +++ b/3rdparty/zlib/infback.c @@ -1,5 +1,5 @@ /* infback.c -- inflate using a call-back interface - * Copyright (C) 1995-2016 Mark Adler + * Copyright (C) 1995-2022 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -477,6 +477,7 @@ void FAR *out_desc; } Tracev((stderr, "inflate: codes ok\n")); state->mode = LEN; + /* fallthrough */ case LEN: /* use inflate_fast() if we have enough input and output */ diff --git a/3rdparty/zlib/inffast.c b/3rdparty/zlib/inffast.c index 0dbd1dbc09f2..1fec7f363fa6 100644 --- a/3rdparty/zlib/inffast.c +++ b/3rdparty/zlib/inffast.c @@ -70,7 +70,7 @@ unsigned start; /* inflate()'s starting value for strm->avail_out */ code const FAR *dcode; /* local strm->distcode */ unsigned lmask; /* mask for first level of length codes */ unsigned dmask; /* mask for first level of distance codes */ - code here; /* retrieved table entry */ + code const *here; /* retrieved table entry */ unsigned op; /* code bits, operation, extra bits, or */ /* window position, window bytes to copy */ unsigned len; /* match length, unused bytes */ @@ -107,20 +107,20 @@ unsigned start; /* inflate()'s starting value for strm->avail_out */ hold += (unsigned long)(*in++) << bits; bits += 8; } - here = lcode[hold & lmask]; + here = lcode + (hold & lmask); dolen: - op = (unsigned)(here.bits); + op = (unsigned)(here->bits); hold >>= op; bits -= op; - op = (unsigned)(here.op); + op = (unsigned)(here->op); if (op == 0) { /* literal */ - Tracevv((stderr, here.val >= 0x20 && here.val < 0x7f ? + Tracevv((stderr, here->val >= 0x20 && here->val < 0x7f ? "inflate: literal '%c'\n" : - "inflate: literal 0x%02x\n", here.val)); - *out++ = (unsigned char)(here.val); + "inflate: literal 0x%02x\n", here->val)); + *out++ = (unsigned char)(here->val); } else if (op & 16) { /* length base */ - len = (unsigned)(here.val); + len = (unsigned)(here->val); op &= 15; /* number of extra bits */ if (op) { if (bits < op) { @@ -138,14 +138,14 @@ unsigned start; /* inflate()'s starting value for strm->avail_out */ hold += (unsigned long)(*in++) << bits; bits += 8; } - here = dcode[hold & dmask]; + here = dcode + (hold & dmask); dodist: - op = (unsigned)(here.bits); + op = (unsigned)(here->bits); hold >>= op; bits -= op; - op = (unsigned)(here.op); + op = (unsigned)(here->op); if (op & 16) { /* distance base */ - dist = (unsigned)(here.val); + dist = (unsigned)(here->val); op &= 15; /* number of extra bits */ if (bits < op) { hold += (unsigned long)(*in++) << bits; @@ -264,7 +264,7 @@ unsigned start; /* inflate()'s starting value for strm->avail_out */ } } else if ((op & 64) == 0) { /* 2nd level distance code */ - here = dcode[here.val + (hold & ((1U << op) - 1))]; + here = dcode + here->val + (hold & ((1U << op) - 1)); goto dodist; } else { @@ -274,7 +274,7 @@ unsigned start; /* inflate()'s starting value for strm->avail_out */ } } else if ((op & 64) == 0) { /* 2nd level length code */ - here = lcode[here.val + (hold & ((1U << op) - 1))]; + here = lcode + here->val + (hold & ((1U << op) - 1)); goto dolen; } else if (op & 32) { /* end-of-block */ diff --git a/3rdparty/zlib/inflate.c b/3rdparty/zlib/inflate.c index 19a2cf2ed8a2..0e7c4f26b1f1 100644 --- a/3rdparty/zlib/inflate.c +++ b/3rdparty/zlib/inflate.c @@ -1,5 +1,5 @@ /* inflate.c -- zlib decompression - * Copyright (C) 1995-2016 Mark Adler + * Copyright (C) 1995-2022 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -130,6 +130,7 @@ z_streamp strm; state->mode = HEAD; state->last = 0; state->havedict = 0; + state->flags = -1; state->dmax = 32768U; state->head = Z_NULL; state->hold = 0; @@ -448,10 +449,10 @@ unsigned copy; /* check function to use adler32() for zlib or crc32() for gzip */ #ifdef GUNZIP -# define UPDATE(check, buf, len) \ +# define UPDATE_CHECK(check, buf, len) \ (state->flags ? crc32(check, buf, len) : adler32(check, buf, len)) #else -# define UPDATE(check, buf, len) adler32(check, buf, len) +# define UPDATE_CHECK(check, buf, len) adler32(check, buf, len) #endif /* check macros for header crc */ @@ -671,7 +672,6 @@ int flush; state->mode = FLAGS; break; } - state->flags = 0; /* expect zlib header */ if (state->head != Z_NULL) state->head->done = -1; if (!(state->wrap & 1) || /* check if zlib header allowed */ @@ -698,6 +698,7 @@ int flush; break; } state->dmax = 1U << len; + state->flags = 0; /* indicate zlib header */ Tracev((stderr, "inflate: zlib header ok\n")); strm->adler = state->check = adler32(0L, Z_NULL, 0); state->mode = hold & 0x200 ? DICTID : TYPE; @@ -723,6 +724,7 @@ int flush; CRC2(state->check, hold); INITBITS(); state->mode = TIME; + /* fallthrough */ case TIME: NEEDBITS(32); if (state->head != Z_NULL) @@ -731,6 +733,7 @@ int flush; CRC4(state->check, hold); INITBITS(); state->mode = OS; + /* fallthrough */ case OS: NEEDBITS(16); if (state->head != Z_NULL) { @@ -741,6 +744,7 @@ int flush; CRC2(state->check, hold); INITBITS(); state->mode = EXLEN; + /* fallthrough */ case EXLEN: if (state->flags & 0x0400) { NEEDBITS(16); @@ -754,6 +758,7 @@ int flush; else if (state->head != Z_NULL) state->head->extra = Z_NULL; state->mode = EXTRA; + /* fallthrough */ case EXTRA: if (state->flags & 0x0400) { copy = state->length; @@ -776,6 +781,7 @@ int flush; } state->length = 0; state->mode = NAME; + /* fallthrough */ case NAME: if (state->flags & 0x0800) { if (have == 0) goto inf_leave; @@ -797,6 +803,7 @@ int flush; state->head->name = Z_NULL; state->length = 0; state->mode = COMMENT; + /* fallthrough */ case COMMENT: if (state->flags & 0x1000) { if (have == 0) goto inf_leave; @@ -817,6 +824,7 @@ int flush; else if (state->head != Z_NULL) state->head->comment = Z_NULL; state->mode = HCRC; + /* fallthrough */ case HCRC: if (state->flags & 0x0200) { NEEDBITS(16); @@ -840,6 +848,7 @@ int flush; strm->adler = state->check = ZSWAP32(hold); INITBITS(); state->mode = DICT; + /* fallthrough */ case DICT: if (state->havedict == 0) { RESTORE(); @@ -847,8 +856,10 @@ int flush; } strm->adler = state->check = adler32(0L, Z_NULL, 0); state->mode = TYPE; + /* fallthrough */ case TYPE: if (flush == Z_BLOCK || flush == Z_TREES) goto inf_leave; + /* fallthrough */ case TYPEDO: if (state->last) { BYTEBITS(); @@ -899,8 +910,10 @@ int flush; INITBITS(); state->mode = COPY_; if (flush == Z_TREES) goto inf_leave; + /* fallthrough */ case COPY_: state->mode = COPY; + /* fallthrough */ case COPY: copy = state->length; if (copy) { @@ -936,6 +949,7 @@ int flush; Tracev((stderr, "inflate: table sizes ok\n")); state->have = 0; state->mode = LENLENS; + /* fallthrough */ case LENLENS: while (state->have < state->ncode) { NEEDBITS(3); @@ -957,6 +971,7 @@ int flush; Tracev((stderr, "inflate: code lengths ok\n")); state->have = 0; state->mode = CODELENS; + /* fallthrough */ case CODELENS: while (state->have < state->nlen + state->ndist) { for (;;) { @@ -1040,8 +1055,10 @@ int flush; Tracev((stderr, "inflate: codes ok\n")); state->mode = LEN_; if (flush == Z_TREES) goto inf_leave; + /* fallthrough */ case LEN_: state->mode = LEN; + /* fallthrough */ case LEN: if (have >= 6 && left >= 258) { RESTORE(); @@ -1091,6 +1108,7 @@ int flush; } state->extra = (unsigned)(here.op) & 15; state->mode = LENEXT; + /* fallthrough */ case LENEXT: if (state->extra) { NEEDBITS(state->extra); @@ -1101,6 +1119,7 @@ int flush; Tracevv((stderr, "inflate: length %u\n", state->length)); state->was = state->length; state->mode = DIST; + /* fallthrough */ case DIST: for (;;) { here = state->distcode[BITS(state->distbits)]; @@ -1128,6 +1147,7 @@ int flush; state->offset = (unsigned)here.val; state->extra = (unsigned)(here.op) & 15; state->mode = DISTEXT; + /* fallthrough */ case DISTEXT: if (state->extra) { NEEDBITS(state->extra); @@ -1144,6 +1164,7 @@ int flush; #endif Tracevv((stderr, "inflate: distance %u\n", state->offset)); state->mode = MATCH; + /* fallthrough */ case MATCH: if (left == 0) goto inf_leave; copy = out - left; @@ -1203,7 +1224,7 @@ int flush; state->total += out; if ((state->wrap & 4) && out) strm->adler = state->check = - UPDATE(state->check, put - out, out); + UPDATE_CHECK(state->check, put - out, out); out = left; if ((state->wrap & 4) && ( #ifdef GUNZIP @@ -1219,10 +1240,11 @@ int flush; } #ifdef GUNZIP state->mode = LENGTH; + /* fallthrough */ case LENGTH: if (state->wrap && state->flags) { NEEDBITS(32); - if (hold != (state->total & 0xffffffffUL)) { + if ((state->wrap & 4) && hold != (state->total & 0xffffffff)) { strm->msg = (char *)"incorrect length check"; state->mode = BAD; break; @@ -1232,6 +1254,7 @@ int flush; } #endif state->mode = DONE; + /* fallthrough */ case DONE: ret = Z_STREAM_END; goto inf_leave; @@ -1241,6 +1264,7 @@ int flush; case MEM: return Z_MEM_ERROR; case SYNC: + /* fallthrough */ default: return Z_STREAM_ERROR; } @@ -1266,7 +1290,7 @@ int flush; state->total += out; if ((state->wrap & 4) && out) strm->adler = state->check = - UPDATE(state->check, strm->next_out - out, out); + UPDATE_CHECK(state->check, strm->next_out - out, out); strm->data_type = (int)state->bits + (state->last ? 64 : 0) + (state->mode == TYPE ? 128 : 0) + (state->mode == LEN_ || state->mode == COPY_ ? 256 : 0); @@ -1402,6 +1426,7 @@ int ZEXPORT inflateSync(strm) z_streamp strm; { unsigned len; /* number of bytes to look at or looked at */ + int flags; /* temporary to save header status */ unsigned long in, out; /* temporary to save total_in and total_out */ unsigned char buf[4]; /* to restore bit buffer to byte string */ struct inflate_state FAR *state; @@ -1434,9 +1459,15 @@ z_streamp strm; /* return no joy or set up to restart inflate() on a new block */ if (state->have != 4) return Z_DATA_ERROR; + if (state->flags == -1) + state->wrap = 0; /* if no header yet, treat as raw */ + else + state->wrap &= ~4; /* no point in computing a check value now */ + flags = state->flags; in = strm->total_in; out = strm->total_out; inflateReset(strm); strm->total_in = in; strm->total_out = out; + state->flags = flags; state->mode = TYPE; return Z_OK; } @@ -1532,7 +1563,7 @@ int check; if (inflateStateCheck(strm)) return Z_STREAM_ERROR; state = (struct inflate_state FAR *)strm->state; - if (check) + if (check && state->wrap) state->wrap |= 4; else state->wrap &= ~4; diff --git a/3rdparty/zlib/inflate.h b/3rdparty/zlib/inflate.h index a46cce6b6d05..f127b6b1fa5f 100644 --- a/3rdparty/zlib/inflate.h +++ b/3rdparty/zlib/inflate.h @@ -1,5 +1,5 @@ /* inflate.h -- internal inflate state definition - * Copyright (C) 1995-2016 Mark Adler + * Copyright (C) 1995-2019 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -86,7 +86,8 @@ struct inflate_state { int wrap; /* bit 0 true for zlib, bit 1 true for gzip, bit 2 true to validate check value */ int havedict; /* true if dictionary provided */ - int flags; /* gzip header method and flags (0 if zlib) */ + int flags; /* gzip header method and flags, 0 if zlib, or + -1 if raw or no header yet */ unsigned dmax; /* zlib header max distance (INFLATE_STRICT) */ unsigned long check; /* protected copy of check value */ unsigned long total; /* protected copy of output count */ diff --git a/3rdparty/zlib/inftrees.c b/3rdparty/zlib/inftrees.c index 2ea08fc13ea8..09462a740b12 100644 --- a/3rdparty/zlib/inftrees.c +++ b/3rdparty/zlib/inftrees.c @@ -1,5 +1,5 @@ /* inftrees.c -- generate Huffman trees for efficient decoding - * Copyright (C) 1995-2017 Mark Adler + * Copyright (C) 1995-2022 Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -9,7 +9,7 @@ #define MAXBITS 15 const char inflate_copyright[] = - " inflate 1.2.11 Copyright 1995-2017 Mark Adler "; + " inflate 1.2.12 Copyright 1995-2022 Mark Adler "; /* If you use the zlib library in a product, an acknowledgment is welcome in the documentation of your product. If for some reason you cannot @@ -62,7 +62,7 @@ unsigned short FAR *work; 35, 43, 51, 59, 67, 83, 99, 115, 131, 163, 195, 227, 258, 0, 0}; static const unsigned short lext[31] = { /* Length codes 257..285 extra */ 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, - 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 16, 77, 202}; + 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 16, 199, 202}; static const unsigned short dbase[32] = { /* Distance codes 0..29 base */ 1, 2, 3, 4, 5, 7, 9, 13, 17, 25, 33, 49, 65, 97, 129, 193, 257, 385, 513, 769, 1025, 1537, 2049, 3073, 4097, 6145, diff --git a/3rdparty/zlib/trees.c b/3rdparty/zlib/trees.c index 50cf4b4571cf..f73fd99c37bd 100644 --- a/3rdparty/zlib/trees.c +++ b/3rdparty/zlib/trees.c @@ -1,5 +1,5 @@ /* trees.c -- output deflated data using Huffman coding - * Copyright (C) 1995-2017 Jean-loup Gailly + * Copyright (C) 1995-2021 Jean-loup Gailly * detect_data_type() function provided freely by Cosmin Truta, 2006 * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -149,7 +149,7 @@ local void send_all_trees OF((deflate_state *s, int lcodes, int dcodes, local void compress_block OF((deflate_state *s, const ct_data *ltree, const ct_data *dtree)); local int detect_data_type OF((deflate_state *s)); -local unsigned bi_reverse OF((unsigned value, int length)); +local unsigned bi_reverse OF((unsigned code, int len)); local void bi_windup OF((deflate_state *s)); local void bi_flush OF((deflate_state *s)); @@ -416,7 +416,7 @@ local void init_block(s) s->dyn_ltree[END_BLOCK].Freq = 1; s->opt_len = s->static_len = 0L; - s->last_lit = s->matches = 0; + s->sym_next = s->matches = 0; } #define SMALLEST 1 @@ -870,7 +870,8 @@ void ZLIB_INTERNAL _tr_stored_block(s, buf, stored_len, last) bi_windup(s); /* align on byte boundary */ put_short(s, (ush)stored_len); put_short(s, (ush)~stored_len); - zmemcpy(s->pending_buf + s->pending, (Bytef *)buf, stored_len); + if (stored_len) + zmemcpy(s->pending_buf + s->pending, (Bytef *)buf, stored_len); s->pending += stored_len; #ifdef ZLIB_DEBUG s->compressed_len = (s->compressed_len + 3 + 7) & (ulg)~7L; @@ -947,7 +948,7 @@ void ZLIB_INTERNAL _tr_flush_block(s, buf, stored_len, last) Tracev((stderr, "\nopt %lu(%lu) stat %lu(%lu) stored %lu lit %u ", opt_lenb, s->opt_len, static_lenb, s->static_len, stored_len, - s->last_lit)); + s->sym_next / 3)); if (static_lenb <= opt_lenb) opt_lenb = static_lenb; @@ -1016,8 +1017,9 @@ int ZLIB_INTERNAL _tr_tally (s, dist, lc) unsigned dist; /* distance of matched string */ unsigned lc; /* match length-MIN_MATCH or unmatched char (if dist==0) */ { - s->d_buf[s->last_lit] = (ush)dist; - s->l_buf[s->last_lit++] = (uch)lc; + s->sym_buf[s->sym_next++] = dist; + s->sym_buf[s->sym_next++] = dist >> 8; + s->sym_buf[s->sym_next++] = lc; if (dist == 0) { /* lc is the unmatched char */ s->dyn_ltree[lc].Freq++; @@ -1032,30 +1034,7 @@ int ZLIB_INTERNAL _tr_tally (s, dist, lc) s->dyn_ltree[_length_code[lc]+LITERALS+1].Freq++; s->dyn_dtree[d_code(dist)].Freq++; } - -#ifdef TRUNCATE_BLOCK - /* Try to guess if it is profitable to stop the current block here */ - if ((s->last_lit & 0x1fff) == 0 && s->level > 2) { - /* Compute an upper bound for the compressed length */ - ulg out_length = (ulg)s->last_lit*8L; - ulg in_length = (ulg)((long)s->strstart - s->block_start); - int dcode; - for (dcode = 0; dcode < D_CODES; dcode++) { - out_length += (ulg)s->dyn_dtree[dcode].Freq * - (5L+extra_dbits[dcode]); - } - out_length >>= 3; - Tracev((stderr,"\nlast_lit %u, in %ld, out ~%ld(%ld%%) ", - s->last_lit, in_length, out_length, - 100L - out_length*100L/in_length)); - if (s->matches < s->last_lit/2 && out_length < in_length/2) return 1; - } -#endif - return (s->last_lit == s->lit_bufsize-1); - /* We avoid equality with lit_bufsize because of wraparound at 64K - * on 16 bit machines and because stored blocks are restricted to - * 64K-1 bytes. - */ + return (s->sym_next == s->sym_end); } /* =========================================================================== @@ -1068,13 +1047,14 @@ local void compress_block(s, ltree, dtree) { unsigned dist; /* distance of matched string */ int lc; /* match length or unmatched char (if dist == 0) */ - unsigned lx = 0; /* running index in l_buf */ + unsigned sx = 0; /* running index in sym_buf */ unsigned code; /* the code to send */ int extra; /* number of extra bits to send */ - if (s->last_lit != 0) do { - dist = s->d_buf[lx]; - lc = s->l_buf[lx++]; + if (s->sym_next != 0) do { + dist = s->sym_buf[sx++] & 0xff; + dist += (unsigned)(s->sym_buf[sx++] & 0xff) << 8; + lc = s->sym_buf[sx++]; if (dist == 0) { send_code(s, lc, ltree); /* send a literal byte */ Tracecv(isgraph(lc), (stderr," '%c' ", lc)); @@ -1099,11 +1079,10 @@ local void compress_block(s, ltree, dtree) } } /* literal or match pair ? */ - /* Check that the overlay between pending_buf and d_buf+l_buf is ok: */ - Assert((uInt)(s->pending) < s->lit_bufsize + 2*lx, - "pendingBuf overflow"); + /* Check that the overlay between pending_buf and sym_buf is ok: */ + Assert(s->pending < s->lit_bufsize + sx, "pendingBuf overflow"); - } while (lx < s->last_lit); + } while (sx < s->sym_next); send_code(s, END_BLOCK, ltree); } @@ -1112,9 +1091,9 @@ local void compress_block(s, ltree, dtree) * Check if the data type is TEXT or BINARY, using the following algorithm: * - TEXT if the two conditions below are satisfied: * a) There are no non-portable control characters belonging to the - * "black list" (0..6, 14..25, 28..31). + * "block list" (0..6, 14..25, 28..31). * b) There is at least one printable character belonging to the - * "white list" (9 {TAB}, 10 {LF}, 13 {CR}, 32..255). + * "allow list" (9 {TAB}, 10 {LF}, 13 {CR}, 32..255). * - BINARY otherwise. * - The following partially-portable control characters form a * "gray list" that is ignored in this detection algorithm: @@ -1124,19 +1103,19 @@ local void compress_block(s, ltree, dtree) local int detect_data_type(s) deflate_state *s; { - /* black_mask is the bit mask of black-listed bytes + /* block_mask is the bit mask of block-listed bytes * set bits 0..6, 14..25, and 28..31 * 0xf3ffc07f = binary 11110011111111111100000001111111 */ - unsigned long black_mask = 0xf3ffc07fUL; + unsigned long block_mask = 0xf3ffc07fUL; int n; - /* Check for non-textual ("black-listed") bytes. */ - for (n = 0; n <= 31; n++, black_mask >>= 1) - if ((black_mask & 1) && (s->dyn_ltree[n].Freq != 0)) + /* Check for non-textual ("block-listed") bytes. */ + for (n = 0; n <= 31; n++, block_mask >>= 1) + if ((block_mask & 1) && (s->dyn_ltree[n].Freq != 0)) return Z_BINARY; - /* Check for textual ("white-listed") bytes. */ + /* Check for textual ("allow-listed") bytes. */ if (s->dyn_ltree[9].Freq != 0 || s->dyn_ltree[10].Freq != 0 || s->dyn_ltree[13].Freq != 0) return Z_TEXT; @@ -1144,7 +1123,7 @@ local int detect_data_type(s) if (s->dyn_ltree[n].Freq != 0) return Z_TEXT; - /* There are no "black-listed" or "white-listed" bytes: + /* There are no "block-listed" or "allow-listed" bytes: * this stream either is empty or has tolerated ("gray-listed") bytes only. */ return Z_BINARY; diff --git a/3rdparty/zlib/zlib.h b/3rdparty/zlib/zlib.h index f09cdaf1e054..4a98e38bf34c 100644 --- a/3rdparty/zlib/zlib.h +++ b/3rdparty/zlib/zlib.h @@ -1,7 +1,7 @@ /* zlib.h -- interface of the 'zlib' general purpose compression library - version 1.2.11, January 15th, 2017 + version 1.2.12, March 11th, 2022 - Copyright (C) 1995-2017 Jean-loup Gailly and Mark Adler + Copyright (C) 1995-2022 Jean-loup Gailly and Mark Adler This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages @@ -37,11 +37,11 @@ extern "C" { #endif -#define ZLIB_VERSION "1.2.11" -#define ZLIB_VERNUM 0x12b0 +#define ZLIB_VERSION "1.2.12" +#define ZLIB_VERNUM 0x12c0 #define ZLIB_VER_MAJOR 1 #define ZLIB_VER_MINOR 2 -#define ZLIB_VER_REVISION 11 +#define ZLIB_VER_REVISION 12 #define ZLIB_VER_SUBREVISION 0 /* @@ -543,8 +543,7 @@ ZEXTERN int ZEXPORT deflateInit2 OF((z_streamp strm, int strategy)); This is another version of deflateInit with more compression options. The - fields next_in, zalloc, zfree and opaque must be initialized before by the - caller. + fields zalloc, zfree and opaque must be initialized before by the caller. The method parameter is the compression method. It must be Z_DEFLATED in this version of the library. @@ -712,11 +711,12 @@ ZEXTERN int ZEXPORT deflateParams OF((z_streamp strm, used to switch between compression and straight copy of the input data, or to switch to a different kind of input data requiring a different strategy. If the compression approach (which is a function of the level) or the - strategy is changed, and if any input has been consumed in a previous - deflate() call, then the input available so far is compressed with the old - level and strategy using deflate(strm, Z_BLOCK). There are three approaches - for the compression levels 0, 1..3, and 4..9 respectively. The new level - and strategy will take effect at the next call of deflate(). + strategy is changed, and if there have been any deflate() calls since the + state was initialized or reset, then the input available so far is + compressed with the old level and strategy using deflate(strm, Z_BLOCK). + There are three approaches for the compression levels 0, 1..3, and 4..9 + respectively. The new level and strategy will take effect at the next call + of deflate(). If a deflate(strm, Z_BLOCK) is performed by deflateParams(), and it does not have enough output space to complete, then the parameter change will not @@ -865,9 +865,11 @@ ZEXTERN int ZEXPORT inflateInit2 OF((z_streamp strm, detection, or add 16 to decode only the gzip format (the zlib format will return a Z_DATA_ERROR). If a gzip stream is being decoded, strm->adler is a CRC-32 instead of an Adler-32. Unlike the gunzip utility and gzread() (see - below), inflate() will not automatically decode concatenated gzip streams. - inflate() will return Z_STREAM_END at the end of the gzip stream. The state - would need to be reset to continue decoding a subsequent gzip stream. + below), inflate() will *not* automatically decode concatenated gzip members. + inflate() will return Z_STREAM_END at the end of the gzip member. The state + would need to be reset to continue decoding a subsequent gzip member. This + *must* be done if there is more data after a gzip member, in order for the + decompression to be compliant with the gzip standard (RFC 1952). inflateInit2 returns Z_OK if success, Z_MEM_ERROR if there was not enough memory, Z_VERSION_ERROR if the zlib library version is incompatible with the @@ -1302,14 +1304,14 @@ typedef struct gzFile_s *gzFile; /* semi-opaque gzip file descriptor */ /* ZEXTERN gzFile ZEXPORT gzopen OF((const char *path, const char *mode)); - Opens a gzip (.gz) file for reading or writing. The mode parameter is as - in fopen ("rb" or "wb") but can also include a compression level ("wb9") or - a strategy: 'f' for filtered data as in "wb6f", 'h' for Huffman-only - compression as in "wb1h", 'R' for run-length encoding as in "wb1R", or 'F' - for fixed code compression as in "wb9F". (See the description of - deflateInit2 for more information about the strategy parameter.) 'T' will - request transparent writing or appending with no compression and not using - the gzip format. + Open the gzip (.gz) file at path for reading and decompressing, or + compressing and writing. The mode parameter is as in fopen ("rb" or "wb") + but can also include a compression level ("wb9") or a strategy: 'f' for + filtered data as in "wb6f", 'h' for Huffman-only compression as in "wb1h", + 'R' for run-length encoding as in "wb1R", or 'F' for fixed code compression + as in "wb9F". (See the description of deflateInit2 for more information + about the strategy parameter.) 'T' will request transparent writing or + appending with no compression and not using the gzip format. "a" can be used instead of "w" to request that the gzip stream that will be written be appended to the file. "+" will result in an error, since @@ -1339,9 +1341,9 @@ ZEXTERN gzFile ZEXPORT gzopen OF((const char *path, const char *mode)); ZEXTERN gzFile ZEXPORT gzdopen OF((int fd, const char *mode)); /* - gzdopen associates a gzFile with the file descriptor fd. File descriptors - are obtained from calls like open, dup, creat, pipe or fileno (if the file - has been previously opened with fopen). The mode parameter is as in gzopen. + Associate a gzFile with the file descriptor fd. File descriptors are + obtained from calls like open, dup, creat, pipe or fileno (if the file has + been previously opened with fopen). The mode parameter is as in gzopen. The next call of gzclose on the returned gzFile will also close the file descriptor fd, just like fclose(fdopen(fd, mode)) closes the file descriptor @@ -1362,13 +1364,13 @@ ZEXTERN gzFile ZEXPORT gzdopen OF((int fd, const char *mode)); ZEXTERN int ZEXPORT gzbuffer OF((gzFile file, unsigned size)); /* - Set the internal buffer size used by this library's functions. The - default buffer size is 8192 bytes. This function must be called after - gzopen() or gzdopen(), and before any other calls that read or write the - file. The buffer memory allocation is always deferred to the first read or - write. Three times that size in buffer space is allocated. A larger buffer - size of, for example, 64K or 128K bytes will noticeably increase the speed - of decompression (reading). + Set the internal buffer size used by this library's functions for file to + size. The default buffer size is 8192 bytes. This function must be called + after gzopen() or gzdopen(), and before any other calls that read or write + the file. The buffer memory allocation is always deferred to the first read + or write. Three times that size in buffer space is allocated. A larger + buffer size of, for example, 64K or 128K bytes will noticeably increase the + speed of decompression (reading). The new buffer size also affects the maximum length for gzprintf(). @@ -1378,9 +1380,9 @@ ZEXTERN int ZEXPORT gzbuffer OF((gzFile file, unsigned size)); ZEXTERN int ZEXPORT gzsetparams OF((gzFile file, int level, int strategy)); /* - Dynamically update the compression level or strategy. See the description - of deflateInit2 for the meaning of these parameters. Previously provided - data is flushed before the parameter change. + Dynamically update the compression level and strategy for file. See the + description of deflateInit2 for the meaning of these parameters. Previously + provided data is flushed before applying the parameter changes. gzsetparams returns Z_OK if success, Z_STREAM_ERROR if the file was not opened for writing, Z_ERRNO if there is an error writing the flushed data, @@ -1389,7 +1391,7 @@ ZEXTERN int ZEXPORT gzsetparams OF((gzFile file, int level, int strategy)); ZEXTERN int ZEXPORT gzread OF((gzFile file, voidp buf, unsigned len)); /* - Reads the given number of uncompressed bytes from the compressed file. If + Read and decompress up to len uncompressed bytes from file into buf. If the input file is not in gzip format, gzread copies the given number of bytes into the buffer directly from the file. @@ -1420,11 +1422,11 @@ ZEXTERN int ZEXPORT gzread OF((gzFile file, voidp buf, unsigned len)); ZEXTERN z_size_t ZEXPORT gzfread OF((voidp buf, z_size_t size, z_size_t nitems, gzFile file)); /* - Read up to nitems items of size size from file to buf, otherwise operating - as gzread() does. This duplicates the interface of stdio's fread(), with - size_t request and return types. If the library defines size_t, then - z_size_t is identical to size_t. If not, then z_size_t is an unsigned - integer type that can contain a pointer. + Read and decompress up to nitems items of size size from file into buf, + otherwise operating as gzread() does. This duplicates the interface of + stdio's fread(), with size_t request and return types. If the library + defines size_t, then z_size_t is identical to size_t. If not, then z_size_t + is an unsigned integer type that can contain a pointer. gzfread() returns the number of full items read of size size, or zero if the end of the file was reached and a full item could not be read, or if @@ -1443,18 +1445,16 @@ ZEXTERN z_size_t ZEXPORT gzfread OF((voidp buf, z_size_t size, z_size_t nitems, file, reseting and retrying on end-of-file, when size is not 1. */ -ZEXTERN int ZEXPORT gzwrite OF((gzFile file, - voidpc buf, unsigned len)); +ZEXTERN int ZEXPORT gzwrite OF((gzFile file, voidpc buf, unsigned len)); /* - Writes the given number of uncompressed bytes into the compressed file. - gzwrite returns the number of uncompressed bytes written or 0 in case of - error. + Compress and write the len uncompressed bytes at buf to file. gzwrite + returns the number of uncompressed bytes written or 0 in case of error. */ ZEXTERN z_size_t ZEXPORT gzfwrite OF((voidpc buf, z_size_t size, z_size_t nitems, gzFile file)); /* - gzfwrite() writes nitems items of size size from buf to file, duplicating + Compress and write nitems items of size size from buf to file, duplicating the interface of stdio's fwrite(), with size_t request and return types. If the library defines size_t, then z_size_t is identical to size_t. If not, then z_size_t is an unsigned integer type that can contain a pointer. @@ -1467,22 +1467,22 @@ ZEXTERN z_size_t ZEXPORT gzfwrite OF((voidpc buf, z_size_t size, ZEXTERN int ZEXPORTVA gzprintf Z_ARG((gzFile file, const char *format, ...)); /* - Converts, formats, and writes the arguments to the compressed file under - control of the format string, as in fprintf. gzprintf returns the number of + Convert, format, compress, and write the arguments (...) to file under + control of the string format, as in fprintf. gzprintf returns the number of uncompressed bytes actually written, or a negative zlib error code in case of error. The number of uncompressed bytes written is limited to 8191, or one less than the buffer size given to gzbuffer(). The caller should assure that this limit is not exceeded. If it is exceeded, then gzprintf() will return an error (0) with nothing written. In this case, there may also be a buffer overflow with unpredictable consequences, which is possible only if - zlib was compiled with the insecure functions sprintf() or vsprintf() + zlib was compiled with the insecure functions sprintf() or vsprintf(), because the secure snprintf() or vsnprintf() functions were not available. This can be determined using zlibCompileFlags(). */ ZEXTERN int ZEXPORT gzputs OF((gzFile file, const char *s)); /* - Writes the given null-terminated string to the compressed file, excluding + Compress and write the given null-terminated string s to file, excluding the terminating null character. gzputs returns the number of characters written, or -1 in case of error. @@ -1490,11 +1490,12 @@ ZEXTERN int ZEXPORT gzputs OF((gzFile file, const char *s)); ZEXTERN char * ZEXPORT gzgets OF((gzFile file, char *buf, int len)); /* - Reads bytes from the compressed file until len-1 characters are read, or a - newline character is read and transferred to buf, or an end-of-file - condition is encountered. If any characters are read or if len == 1, the - string is terminated with a null character. If no characters are read due - to an end-of-file or len < 1, then the buffer is left untouched. + Read and decompress bytes from file into buf, until len-1 characters are + read, or until a newline character is read and transferred to buf, or an + end-of-file condition is encountered. If any characters are read or if len + is one, the string is terminated with a null character. If no characters + are read due to an end-of-file or len is less than one, then the buffer is + left untouched. gzgets returns buf which is a null-terminated string, or it returns NULL for end-of-file or in case of error. If there was an error, the contents at @@ -1503,13 +1504,13 @@ ZEXTERN char * ZEXPORT gzgets OF((gzFile file, char *buf, int len)); ZEXTERN int ZEXPORT gzputc OF((gzFile file, int c)); /* - Writes c, converted to an unsigned char, into the compressed file. gzputc + Compress and write c, converted to an unsigned char, into file. gzputc returns the value that was written, or -1 in case of error. */ ZEXTERN int ZEXPORT gzgetc OF((gzFile file)); /* - Reads one byte from the compressed file. gzgetc returns this byte or -1 + Read and decompress one byte from file. gzgetc returns this byte or -1 in case of end of file or error. This is implemented as a macro for speed. As such, it does not do all of the checking the other functions do. I.e. it does not check to see if file is NULL, nor whether the structure file @@ -1518,8 +1519,8 @@ ZEXTERN int ZEXPORT gzgetc OF((gzFile file)); ZEXTERN int ZEXPORT gzungetc OF((int c, gzFile file)); /* - Push one character back onto the stream to be read as the first character - on the next read. At least one character of push-back is allowed. + Push c back onto the stream for file to be read as the first character on + the next read. At least one character of push-back is always allowed. gzungetc() returns the character pushed, or -1 on failure. gzungetc() will fail if c is -1, and may fail if a character has been pushed but not read yet. If gzungetc is used immediately after gzopen or gzdopen, at least the @@ -1530,9 +1531,9 @@ ZEXTERN int ZEXPORT gzungetc OF((int c, gzFile file)); ZEXTERN int ZEXPORT gzflush OF((gzFile file, int flush)); /* - Flushes all pending output into the compressed file. The parameter flush - is as in the deflate() function. The return value is the zlib error number - (see function gzerror below). gzflush is only permitted when writing. + Flush all pending output to file. The parameter flush is as in the + deflate() function. The return value is the zlib error number (see function + gzerror below). gzflush is only permitted when writing. If the flush parameter is Z_FINISH, the remaining data is written and the gzip stream is completed in the output. If gzwrite() is called again, a new @@ -1547,8 +1548,8 @@ ZEXTERN int ZEXPORT gzflush OF((gzFile file, int flush)); ZEXTERN z_off_t ZEXPORT gzseek OF((gzFile file, z_off_t offset, int whence)); - Sets the starting position for the next gzread or gzwrite on the given - compressed file. The offset represents a number of bytes in the + Set the starting position to offset relative to whence for the next gzread + or gzwrite on file. The offset represents a number of bytes in the uncompressed data stream. The whence parameter is defined as in lseek(2); the value SEEK_END is not supported. @@ -1565,18 +1566,18 @@ ZEXTERN z_off_t ZEXPORT gzseek OF((gzFile file, ZEXTERN int ZEXPORT gzrewind OF((gzFile file)); /* - Rewinds the given file. This function is supported only for reading. + Rewind file. This function is supported only for reading. - gzrewind(file) is equivalent to (int)gzseek(file, 0L, SEEK_SET) + gzrewind(file) is equivalent to (int)gzseek(file, 0L, SEEK_SET). */ /* ZEXTERN z_off_t ZEXPORT gztell OF((gzFile file)); - Returns the starting position for the next gzread or gzwrite on the given - compressed file. This position represents a number of bytes in the - uncompressed data stream, and is zero when starting, even if appending or - reading a gzip stream from the middle of a file using gzdopen(). + Return the starting position for the next gzread or gzwrite on file. + This position represents a number of bytes in the uncompressed data stream, + and is zero when starting, even if appending or reading a gzip stream from + the middle of a file using gzdopen(). gztell(file) is equivalent to gzseek(file, 0L, SEEK_CUR) */ @@ -1584,22 +1585,22 @@ ZEXTERN z_off_t ZEXPORT gztell OF((gzFile file)); /* ZEXTERN z_off_t ZEXPORT gzoffset OF((gzFile file)); - Returns the current offset in the file being read or written. This offset - includes the count of bytes that precede the gzip stream, for example when - appending or when using gzdopen() for reading. When reading, the offset - does not include as yet unused buffered input. This information can be used - for a progress indicator. On error, gzoffset() returns -1. + Return the current compressed (actual) read or write offset of file. This + offset includes the count of bytes that precede the gzip stream, for example + when appending or when using gzdopen() for reading. When reading, the + offset does not include as yet unused buffered input. This information can + be used for a progress indicator. On error, gzoffset() returns -1. */ ZEXTERN int ZEXPORT gzeof OF((gzFile file)); /* - Returns true (1) if the end-of-file indicator has been set while reading, - false (0) otherwise. Note that the end-of-file indicator is set only if the - read tried to go past the end of the input, but came up short. Therefore, - just like feof(), gzeof() may return false even if there is no more data to - read, in the event that the last read request was for the exact number of - bytes remaining in the input file. This will happen if the input file size - is an exact multiple of the buffer size. + Return true (1) if the end-of-file indicator for file has been set while + reading, false (0) otherwise. Note that the end-of-file indicator is set + only if the read tried to go past the end of the input, but came up short. + Therefore, just like feof(), gzeof() may return false even if there is no + more data to read, in the event that the last read request was for the exact + number of bytes remaining in the input file. This will happen if the input + file size is an exact multiple of the buffer size. If gzeof() returns true, then the read functions will return no more data, unless the end-of-file indicator is reset by gzclearerr() and the input file @@ -1608,7 +1609,7 @@ ZEXTERN int ZEXPORT gzeof OF((gzFile file)); ZEXTERN int ZEXPORT gzdirect OF((gzFile file)); /* - Returns true (1) if file is being copied directly while reading, or false + Return true (1) if file is being copied directly while reading, or false (0) if file is a gzip stream being decompressed. If the input file is empty, gzdirect() will return true, since the input @@ -1629,8 +1630,8 @@ ZEXTERN int ZEXPORT gzdirect OF((gzFile file)); ZEXTERN int ZEXPORT gzclose OF((gzFile file)); /* - Flushes all pending output if necessary, closes the compressed file and - deallocates the (de)compression state. Note that once file is closed, you + Flush all pending output for file, if necessary, close file and + deallocate the (de)compression state. Note that once file is closed, you cannot call gzerror with file, since its structures have been deallocated. gzclose must not be called more than once on the same file, just as free must not be called more than once on the same allocation. @@ -1654,10 +1655,10 @@ ZEXTERN int ZEXPORT gzclose_w OF((gzFile file)); ZEXTERN const char * ZEXPORT gzerror OF((gzFile file, int *errnum)); /* - Returns the error message for the last error which occurred on the given - compressed file. errnum is set to zlib error number. If an error occurred - in the file system and not in the compression library, errnum is set to - Z_ERRNO and the application may consult errno to get the exact error code. + Return the error message for the last error which occurred on file. + errnum is set to zlib error number. If an error occurred in the file system + and not in the compression library, errnum is set to Z_ERRNO and the + application may consult errno to get the exact error code. The application must not modify the returned string. Future calls to this function may invalidate the previously returned string. If file is @@ -1670,7 +1671,7 @@ ZEXTERN const char * ZEXPORT gzerror OF((gzFile file, int *errnum)); ZEXTERN void ZEXPORT gzclearerr OF((gzFile file)); /* - Clears the error and end-of-file flags for file. This is analogous to the + Clear the error and end-of-file flags for file. This is analogous to the clearerr() function in stdio. This is useful for continuing to read a gzip file that is being written concurrently. */ @@ -1688,8 +1689,9 @@ ZEXTERN void ZEXPORT gzclearerr OF((gzFile file)); ZEXTERN uLong ZEXPORT adler32 OF((uLong adler, const Bytef *buf, uInt len)); /* Update a running Adler-32 checksum with the bytes buf[0..len-1] and - return the updated checksum. If buf is Z_NULL, this function returns the - required initial value for the checksum. + return the updated checksum. An Adler-32 value is in the range of a 32-bit + unsigned integer. If buf is Z_NULL, this function returns the required + initial value for the checksum. An Adler-32 checksum is almost as reliable as a CRC-32 but can be computed much faster. @@ -1722,12 +1724,13 @@ ZEXTERN uLong ZEXPORT adler32_combine OF((uLong adler1, uLong adler2, negative, the result has no meaning or utility. */ -ZEXTERN uLong ZEXPORT crc32 OF((uLong crc, const Bytef *buf, uInt len)); +ZEXTERN uLong ZEXPORT crc32 OF((uLong crc, const Bytef *buf, uInt len)); /* Update a running CRC-32 with the bytes buf[0..len-1] and return the - updated CRC-32. If buf is Z_NULL, this function returns the required - initial value for the crc. Pre- and post-conditioning (one's complement) is - performed within this function so it shouldn't be done by the application. + updated CRC-32. A CRC-32 value is in the range of a 32-bit unsigned integer. + If buf is Z_NULL, this function returns the required initial value for the + crc. Pre- and post-conditioning (one's complement) is performed within this + function so it shouldn't be done by the application. Usage example: @@ -1739,7 +1742,7 @@ ZEXTERN uLong ZEXPORT crc32 OF((uLong crc, const Bytef *buf, uInt len)); if (crc != original_crc) error(); */ -ZEXTERN uLong ZEXPORT crc32_z OF((uLong adler, const Bytef *buf, +ZEXTERN uLong ZEXPORT crc32_z OF((uLong crc, const Bytef *buf, z_size_t len)); /* Same as crc32(), but with a size_t length. @@ -1755,6 +1758,20 @@ ZEXTERN uLong ZEXPORT crc32_combine OF((uLong crc1, uLong crc2, z_off_t len2)); len2. */ +/* +ZEXTERN uLong ZEXPORT crc32_combine_gen OF((z_off_t len2)); + + Return the operator corresponding to length len2, to be used with + crc32_combine_op(). +*/ + +ZEXTERN uLong ZEXPORT crc32_combine_op OF((uLong crc1, uLong crc2, uLong op)); +/* + Give the same result as crc32_combine(), using op in place of len2. op is + is generated from len2 by crc32_combine_gen(). This will be faster than + crc32_combine() if the generated op is used more than once. +*/ + /* various hacks, don't look :) */ @@ -1842,6 +1859,7 @@ ZEXTERN int ZEXPORT gzgetc_ OF((gzFile file)); /* backward compatibility */ ZEXTERN z_off64_t ZEXPORT gzoffset64 OF((gzFile)); ZEXTERN uLong ZEXPORT adler32_combine64 OF((uLong, uLong, z_off64_t)); ZEXTERN uLong ZEXPORT crc32_combine64 OF((uLong, uLong, z_off64_t)); + ZEXTERN uLong ZEXPORT crc32_combine_gen64 OF((z_off64_t)); #endif #if !defined(ZLIB_INTERNAL) && defined(Z_WANT64) @@ -1852,6 +1870,7 @@ ZEXTERN int ZEXPORT gzgetc_ OF((gzFile file)); /* backward compatibility */ # define z_gzoffset z_gzoffset64 # define z_adler32_combine z_adler32_combine64 # define z_crc32_combine z_crc32_combine64 +# define z_crc32_combine_gen z_crc32_combine_gen64 # else # define gzopen gzopen64 # define gzseek gzseek64 @@ -1859,6 +1878,7 @@ ZEXTERN int ZEXPORT gzgetc_ OF((gzFile file)); /* backward compatibility */ # define gzoffset gzoffset64 # define adler32_combine adler32_combine64 # define crc32_combine crc32_combine64 +# define crc32_combine_gen crc32_combine_gen64 # endif # ifndef Z_LARGE64 ZEXTERN gzFile ZEXPORT gzopen64 OF((const char *, const char *)); @@ -1867,6 +1887,7 @@ ZEXTERN int ZEXPORT gzgetc_ OF((gzFile file)); /* backward compatibility */ ZEXTERN z_off_t ZEXPORT gzoffset64 OF((gzFile)); ZEXTERN uLong ZEXPORT adler32_combine64 OF((uLong, uLong, z_off_t)); ZEXTERN uLong ZEXPORT crc32_combine64 OF((uLong, uLong, z_off_t)); + ZEXTERN uLong ZEXPORT crc32_combine_gen64 OF((z_off_t)); # endif #else ZEXTERN gzFile ZEXPORT gzopen OF((const char *, const char *)); @@ -1875,12 +1896,14 @@ ZEXTERN int ZEXPORT gzgetc_ OF((gzFile file)); /* backward compatibility */ ZEXTERN z_off_t ZEXPORT gzoffset OF((gzFile)); ZEXTERN uLong ZEXPORT adler32_combine OF((uLong, uLong, z_off_t)); ZEXTERN uLong ZEXPORT crc32_combine OF((uLong, uLong, z_off_t)); + ZEXTERN uLong ZEXPORT crc32_combine_gen OF((z_off_t)); #endif #else /* Z_SOLO */ ZEXTERN uLong ZEXPORT adler32_combine OF((uLong, uLong, z_off_t)); ZEXTERN uLong ZEXPORT crc32_combine OF((uLong, uLong, z_off_t)); + ZEXTERN uLong ZEXPORT crc32_combine_gen OF((z_off_t)); #endif /* !Z_SOLO */ @@ -1893,7 +1916,7 @@ ZEXTERN int ZEXPORT inflateValidate OF((z_streamp, int)); ZEXTERN unsigned long ZEXPORT inflateCodesUsed OF ((z_streamp)); ZEXTERN int ZEXPORT inflateResetKeep OF((z_streamp)); ZEXTERN int ZEXPORT deflateResetKeep OF((z_streamp)); -#if (defined(_WIN32) || defined(__CYGWIN__)) && !defined(Z_SOLO) +#if defined(_WIN32) && !defined(Z_SOLO) ZEXTERN gzFile ZEXPORT gzopen_w OF((const wchar_t *path, const char *mode)); #endif diff --git a/3rdparty/zlib/zutil.c b/3rdparty/zlib/zutil.c index 7271667fd7be..dcab28a0d517 100644 --- a/3rdparty/zlib/zutil.c +++ b/3rdparty/zlib/zutil.c @@ -137,7 +137,7 @@ const char * ZEXPORT zError(err) } #if defined(_WIN32_WCE) && _WIN32_WCE < 0x800 - /* The Microsoft C Run-Time Library for Windows CE doesn't have + /* The older Microsoft C Run-Time Library for Windows CE doesn't have * errno. We define it as a global variable to simplify porting. * Its value is always 0 and should not be used. */ diff --git a/3rdparty/zlib/zutil.h b/3rdparty/zlib/zutil.h index 4774fc3235f6..d9a20ae1bf4c 100644 --- a/3rdparty/zlib/zutil.h +++ b/3rdparty/zlib/zutil.h @@ -1,5 +1,5 @@ /* zutil.h -- internal interface and configuration of the compression library - * Copyright (C) 1995-2016 Jean-loup Gailly, Mark Adler + * Copyright (C) 1995-2022 Jean-loup Gailly, Mark Adler * For conditions of distribution and use, see copyright notice in zlib.h */ @@ -29,10 +29,6 @@ # include #endif -#ifdef Z_SOLO - typedef long ptrdiff_t; /* guess -- will be caught if guess is wrong */ -#endif - #ifndef local # define local static #endif @@ -46,6 +42,17 @@ typedef unsigned short ush; typedef ush FAR ushf; typedef unsigned long ulg; +#if !defined(Z_U8) && !defined(Z_SOLO) && defined(STDC) +# include +# if (ULONG_MAX == 0xffffffffffffffff) +# define Z_U8 unsigned long +# elif (ULLONG_MAX == 0xffffffffffffffff) +# define Z_U8 unsigned long long +# elif (UINT_MAX == 0xffffffffffffffff) +# define Z_U8 unsigned +# endif +#endif + extern z_const char * const z_errmsg[10]; /* indexed by 2-zlib_error */ /* (size given to avoid silly warnings with Visual C++) */ @@ -169,13 +176,7 @@ extern z_const char * const z_errmsg[10]; /* indexed by 2-zlib_error */ #if (defined(_MSC_VER) && (_MSC_VER > 600)) && !defined __INTERIX # if defined(_WIN32_WCE) -# if _WIN32_WCE < 0x800 -# define fdopen(fd,mode) NULL /* No fdopen() */ -# ifndef _PTRDIFF_T_DEFINED - typedef int ptrdiff_t; -# define _PTRDIFF_T_DEFINED -# endif -# endif +# define fdopen(fd,mode) NULL /* No fdopen() */ # else # define fdopen(fd,type) _fdopen(fd,type) # endif diff --git a/CMakeLists.txt b/CMakeLists.txt index f05adb32daef..fa409f516c93 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -243,7 +243,7 @@ OCV_OPTION(WITH_AVFOUNDATION "Use AVFoundation for Video I/O (iOS/Mac)" ON OCV_OPTION(WITH_CAP_IOS "Enable iOS video capture" ON VISIBLE_IF IOS VERIFY HAVE_CAP_IOS) -OCV_OPTION(WITH_CAROTENE "Use NVidia carotene acceleration library for ARM platform" ON +OCV_OPTION(WITH_CAROTENE "Use NVidia carotene acceleration library for ARM platform" (NOT CV_DISABLE_OPTIMIZATION) VISIBLE_IF (ARM OR AARCH64) AND NOT IOS) OCV_OPTION(WITH_CPUFEATURES "Use cpufeatures Android library" ON VISIBLE_IF ANDROID @@ -290,12 +290,10 @@ OCV_OPTION(WITH_HALIDE "Include Halide support" OFF OCV_OPTION(WITH_VULKAN "Include Vulkan support" OFF VISIBLE_IF TRUE VERIFY HAVE_VULKAN) -OCV_OPTION(WITH_INF_ENGINE "Include Intel Inference Engine support" OFF +# replacement for deprecated options: WITH_INF_ENGINE, WITH_NGRAPH +OCV_OPTION(WITH_OPENVINO "Include Intel OpenVINO toolkit support" (WITH_INF_ENGINE) VISIBLE_IF TRUE - VERIFY INF_ENGINE_TARGET) -OCV_OPTION(WITH_NGRAPH "Include nGraph support" WITH_INF_ENGINE - VISIBLE_IF TRUE - VERIFY TARGET ngraph::ngraph) + VERIFY TARGET ocv.3rdparty.openvino) OCV_OPTION(WITH_WEBNN "Include WebNN support" OFF VISIBLE_IF TRUE VERIFY HAVE_WEBNN) @@ -455,6 +453,9 @@ OCV_OPTION(WITH_TENGINE "Include Arm Inference Tengine support" OFF OCV_OPTION(WITH_ONNX "Include Microsoft ONNX Runtime support" OFF VISIBLE_IF TRUE VERIFY HAVE_ONNX) +OCV_OPTION(WITH_TIMVX "Include Tim-VX support" OFF + VISIBLE_IF TRUE + VERIFY HAVE_TIMVX) # OpenCV build components # =================================================== @@ -735,6 +736,9 @@ include(cmake/OpenCVFindProtobuf.cmake) if(WITH_TENGINE) include(cmake/OpenCVFindTengine.cmake) endif() +if(WITH_TIMVX) + include(cmake/OpenCVFindTIMVX.cmake) +endif() # ---------------------------------------------------------------------------- # Detect other 3rd-party libraries/tools @@ -802,7 +806,7 @@ if(WITH_WEBNN) endif() # --- Inference Engine --- -if(WITH_INF_ENGINE) +if(WITH_INF_ENGINE OR WITH_OPENVINO) include(cmake/OpenCVDetectInferenceEngine.cmake) endif() @@ -1537,55 +1541,61 @@ if(WITH_HALIDE OR HAVE_HALIDE) status(" Halide:" HAVE_HALIDE THEN "YES (${HALIDE_LIBRARIES} ${HALIDE_INCLUDE_DIRS})" ELSE NO) endif() -if(WITH_INF_ENGINE OR INF_ENGINE_TARGET) - if(INF_ENGINE_TARGET) - list(GET INF_ENGINE_TARGET 0 ie_target) - set(__msg "YES (${INF_ENGINE_RELEASE} / ${INF_ENGINE_VERSION})") - ocv_get_imported_target(ie_target "${ie_target}") - get_target_property(_lib ${ie_target} IMPORTED_LOCATION) - get_target_property(_lib_imp_rel ${ie_target} IMPORTED_IMPLIB_RELEASE) - get_target_property(_lib_imp_dbg ${ie_target} IMPORTED_IMPLIB_DEBUG) - get_target_property(_lib_rel ${ie_target} IMPORTED_LOCATION_RELEASE) - get_target_property(_lib_dbg ${ie_target} IMPORTED_LOCATION_DEBUG) - ocv_build_features_string(_lib - IF _lib THEN "${_lib}" - IF _lib_imp_rel AND _lib_imp_dbg THEN "${_lib_imp_rel} / ${_lib_imp_dbg}" - IF _lib_rel AND _lib_dbg THEN "${_lib_rel} / ${_lib_dbg}" - IF _lib_rel THEN "${_lib_rel}" - IF _lib_dbg THEN "${_lib_dbg}" - ELSE "unknown" - ) - get_target_property(_inc ${ie_target} INTERFACE_INCLUDE_DIRECTORIES) - status(" Inference Engine:" "${__msg}") - status(" * libs:" "${_lib}") - status(" * includes:" "${_inc}") - else() - status(" Inference Engine:" "NO") +if(HAVE_OPENVINO + OR (WITH_OPENVINO AND NOT WITH_INF_ENGINE AND NOT INF_ENGINE_TARGET) +) + status(" OpenVINO:" TARGET openvino::runtime THEN "YES (${OpenVINO_VERSION})" ELSE "NO") +else() + if(WITH_INF_ENGINE OR INF_ENGINE_TARGET) + if(INF_ENGINE_TARGET) + list(GET INF_ENGINE_TARGET 0 ie_target) + set(__msg "YES (${INF_ENGINE_RELEASE} / ${INF_ENGINE_VERSION})") + ocv_get_imported_target(ie_target "${ie_target}") + get_target_property(_lib ${ie_target} IMPORTED_LOCATION) + get_target_property(_lib_imp_rel ${ie_target} IMPORTED_IMPLIB_RELEASE) + get_target_property(_lib_imp_dbg ${ie_target} IMPORTED_IMPLIB_DEBUG) + get_target_property(_lib_rel ${ie_target} IMPORTED_LOCATION_RELEASE) + get_target_property(_lib_dbg ${ie_target} IMPORTED_LOCATION_DEBUG) + ocv_build_features_string(_lib + IF _lib THEN "${_lib}" + IF _lib_imp_rel AND _lib_imp_dbg THEN "${_lib_imp_rel} / ${_lib_imp_dbg}" + IF _lib_rel AND _lib_dbg THEN "${_lib_rel} / ${_lib_dbg}" + IF _lib_rel THEN "${_lib_rel}" + IF _lib_dbg THEN "${_lib_dbg}" + ELSE "unknown" + ) + get_target_property(_inc ${ie_target} INTERFACE_INCLUDE_DIRECTORIES) + status(" Inference Engine:" "${__msg}") + status(" * libs:" "${_lib}") + status(" * includes:" "${_inc}") + else() + status(" Inference Engine:" "NO") + endif() endif() -endif() -if(WITH_NGRAPH OR HAVE_NGRAPH) - if(HAVE_NGRAPH) - ocv_get_imported_target(__target ngraph::ngraph) - set(__msg "YES (${ngraph_VERSION})") - get_target_property(_lib ${__target} IMPORTED_LOCATION) - get_target_property(_lib_imp_rel ${__target} IMPORTED_IMPLIB_RELEASE) - get_target_property(_lib_imp_dbg ${__target} IMPORTED_IMPLIB_DEBUG) - get_target_property(_lib_rel ${__target} IMPORTED_LOCATION_RELEASE) - get_target_property(_lib_dbg ${__target} IMPORTED_LOCATION_DEBUG) - ocv_build_features_string(_lib - IF _lib THEN "${_lib}" - IF _lib_imp_rel AND _lib_imp_dbg THEN "${_lib_imp_rel} / ${_lib_imp_dbg}" - IF _lib_rel AND _lib_dbg THEN "${_lib_rel} / ${_lib_dbg}" - IF _lib_rel THEN "${_lib_rel}" - IF _lib_dbg THEN "${_lib_dbg}" - ELSE "unknown" - ) - get_target_property(_inc ${__target} INTERFACE_INCLUDE_DIRECTORIES) - status(" nGraph:" "${__msg}") - status(" * libs:" "${_lib}") - status(" * includes:" "${_inc}") - else() - status(" nGraph:" "NO") + if(WITH_NGRAPH OR HAVE_NGRAPH) + if(HAVE_NGRAPH) + ocv_get_imported_target(__target ngraph::ngraph) + set(__msg "YES (${ngraph_VERSION})") + get_target_property(_lib ${__target} IMPORTED_LOCATION) + get_target_property(_lib_imp_rel ${__target} IMPORTED_IMPLIB_RELEASE) + get_target_property(_lib_imp_dbg ${__target} IMPORTED_IMPLIB_DEBUG) + get_target_property(_lib_rel ${__target} IMPORTED_LOCATION_RELEASE) + get_target_property(_lib_dbg ${__target} IMPORTED_LOCATION_DEBUG) + ocv_build_features_string(_lib + IF _lib THEN "${_lib}" + IF _lib_imp_rel AND _lib_imp_dbg THEN "${_lib_imp_rel} / ${_lib_imp_dbg}" + IF _lib_rel AND _lib_dbg THEN "${_lib_rel} / ${_lib_dbg}" + IF _lib_rel THEN "${_lib_rel}" + IF _lib_dbg THEN "${_lib_dbg}" + ELSE "unknown" + ) + get_target_property(_inc ${__target} INTERFACE_INCLUDE_DIRECTORIES) + status(" nGraph:" "${__msg}") + status(" * libs:" "${_lib}") + status(" * includes:" "${_inc}") + else() + status(" nGraph:" "NO") + endif() endif() endif() @@ -1641,6 +1651,16 @@ if(WITH_WEBNN OR HAVE_WEBNN) endif() endif() +if(WITH_TIMVX) + status("") + status(" Tim-VX:" HAVE_TIMVX THEN "YES" ELSE "NO") + if(HAVE_TIMVX) + status(" Include path" TIMVX_INCLUDE_DIR THEN "${TIMVX_INCLUDE_DIR}" ELSE "NO") + status(" Link libraries:" TIMVX_LIBRARY THEN "${TIMVX_LIBRARY}" ELSE "NO") + status(" VIVANTE SDK path" VIVANTE_SDK_DIR THEN "${VIVANTE_SDK_DIR}" ELSE "NO") + endif() +endif() + if(WITH_OPENCL OR HAVE_OPENCL) ocv_build_features_string(opencl_features IF HAVE_OPENCL_SVM THEN "SVM" diff --git a/COPYRIGHT b/COPYRIGHT index b0137f251046..d5875e986430 100644 --- a/COPYRIGHT +++ b/COPYRIGHT @@ -1,11 +1,11 @@ -Copyright (C) 2000-2021, Intel Corporation, all rights reserved. +Copyright (C) 2000-2022, Intel Corporation, all rights reserved. Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. Copyright (C) 2009-2016, NVIDIA Corporation, all rights reserved. Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -Copyright (C) 2015-2021, OpenCV Foundation, all rights reserved. +Copyright (C) 2015-2022, OpenCV Foundation, all rights reserved. Copyright (C) 2008-2016, Itseez Inc., all rights reserved. -Copyright (C) 2019-2021, Xperience AI, all rights reserved. -Copyright (C) 2019-2021, Shenzhen Institute of Artificial Intelligence and +Copyright (C) 2019-2022, Xperience AI, all rights reserved. +Copyright (C) 2019-2022, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved. Third party copyrights are property of their respective owners. diff --git a/apps/opencv_stitching_tool/README.md b/apps/opencv_stitching_tool/README.md index 1cf3f019d030..2f4ce2362522 100644 --- a/apps/opencv_stitching_tool/README.md +++ b/apps/opencv_stitching_tool/README.md @@ -1,3 +1,3 @@ -## In-Depth Stitching Tool for experiments and research +## MOVED: opencv_stitching_tool -Visit [opencv_stitching_tutorial](https://github.com/lukasalexanderweber/opencv_stitching_tutorial) for a detailed Tutorial +As the stitching package is now available on [PyPI](https://pypi.org/project/stitching/) the tool and belonging package are now maintained [here](https://github.com/lukasalexanderweber/stitching). The Tutorial is maintained [here](https://github.com/lukasalexanderweber/stitching_tutorial). diff --git a/apps/opencv_stitching_tool/opencv_stitching/.gitignore b/apps/opencv_stitching_tool/opencv_stitching/.gitignore deleted file mode 100644 index 1f4d07f716a5..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -# python binary files -*.pyc -__pycache__ -.pylint* diff --git a/apps/opencv_stitching_tool/opencv_stitching/__init__.py b/apps/opencv_stitching_tool/opencv_stitching/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/apps/opencv_stitching_tool/opencv_stitching/blender.py b/apps/opencv_stitching_tool/opencv_stitching/blender.py deleted file mode 100644 index 886aef67efd0..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/blender.py +++ /dev/null @@ -1,48 +0,0 @@ -import cv2 as cv -import numpy as np - - -class Blender: - - BLENDER_CHOICES = ('multiband', 'feather', 'no',) - DEFAULT_BLENDER = 'multiband' - DEFAULT_BLEND_STRENGTH = 5 - - def __init__(self, blender_type=DEFAULT_BLENDER, - blend_strength=DEFAULT_BLEND_STRENGTH): - self.blender_type = blender_type - self.blend_strength = blend_strength - self.blender = None - - def prepare(self, corners, sizes): - dst_sz = cv.detail.resultRoi(corners=corners, sizes=sizes) - blend_width = (np.sqrt(dst_sz[2] * dst_sz[3]) * - self.blend_strength / 100) - - if self.blender_type == 'no' or blend_width < 1: - self.blender = cv.detail.Blender_createDefault( - cv.detail.Blender_NO - ) - - elif self.blender_type == "multiband": - self.blender = cv.detail_MultiBandBlender() - self.blender.setNumBands((np.log(blend_width) / - np.log(2.) - 1.).astype(np.int)) - - elif self.blender_type == "feather": - self.blender = cv.detail_FeatherBlender() - self.blender.setSharpness(1. / blend_width) - - self.blender.prepare(dst_sz) - - def feed(self, img, mask, corner): - """https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html#a64837308bcf4e414a6219beff6cbe37a""" # noqa - self.blender.feed(cv.UMat(img.astype(np.int16)), mask, corner) - - def blend(self): - """https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html#aa0a91ce0d6046d3a63e0123cbb1b5c00""" # noqa - result = None - result_mask = None - result, result_mask = self.blender.blend(result, result_mask) - result = cv.convertScaleAbs(result) - return result diff --git a/apps/opencv_stitching_tool/opencv_stitching/camera_adjuster.py b/apps/opencv_stitching_tool/opencv_stitching/camera_adjuster.py deleted file mode 100644 index 684fd3d4fa4a..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/camera_adjuster.py +++ /dev/null @@ -1,49 +0,0 @@ -from collections import OrderedDict -import cv2 as cv -import numpy as np - -from .stitching_error import StitchingError - - -class CameraAdjuster: - """https://docs.opencv.org/4.x/d5/d56/classcv_1_1detail_1_1BundleAdjusterBase.html""" # noqa - - CAMERA_ADJUSTER_CHOICES = OrderedDict() - CAMERA_ADJUSTER_CHOICES['ray'] = cv.detail_BundleAdjusterRay - CAMERA_ADJUSTER_CHOICES['reproj'] = cv.detail_BundleAdjusterReproj - CAMERA_ADJUSTER_CHOICES['affine'] = cv.detail_BundleAdjusterAffinePartial - CAMERA_ADJUSTER_CHOICES['no'] = cv.detail_NoBundleAdjuster - - DEFAULT_CAMERA_ADJUSTER = list(CAMERA_ADJUSTER_CHOICES.keys())[0] - DEFAULT_REFINEMENT_MASK = "xxxxx" - - def __init__(self, - adjuster=DEFAULT_CAMERA_ADJUSTER, - refinement_mask=DEFAULT_REFINEMENT_MASK): - - self.adjuster = CameraAdjuster.CAMERA_ADJUSTER_CHOICES[adjuster]() - self.set_refinement_mask(refinement_mask) - self.adjuster.setConfThresh(1) - - def set_refinement_mask(self, refinement_mask): - mask_matrix = np.zeros((3, 3), np.uint8) - if refinement_mask[0] == 'x': - mask_matrix[0, 0] = 1 - if refinement_mask[1] == 'x': - mask_matrix[0, 1] = 1 - if refinement_mask[2] == 'x': - mask_matrix[0, 2] = 1 - if refinement_mask[3] == 'x': - mask_matrix[1, 1] = 1 - if refinement_mask[4] == 'x': - mask_matrix[1, 2] = 1 - self.adjuster.setRefinementMask(mask_matrix) - - def adjust(self, features, pairwise_matches, estimated_cameras): - b, cameras = self.adjuster.apply(features, - pairwise_matches, - estimated_cameras) - if not b: - raise StitchingError("Camera parameters adjusting failed.") - - return cameras diff --git a/apps/opencv_stitching_tool/opencv_stitching/camera_estimator.py b/apps/opencv_stitching_tool/opencv_stitching/camera_estimator.py deleted file mode 100644 index 8520eb0ddf2c..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/camera_estimator.py +++ /dev/null @@ -1,27 +0,0 @@ -from collections import OrderedDict -import cv2 as cv -import numpy as np - -from .stitching_error import StitchingError - - -class CameraEstimator: - - CAMERA_ESTIMATOR_CHOICES = OrderedDict() - CAMERA_ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator - CAMERA_ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator - - DEFAULT_CAMERA_ESTIMATOR = list(CAMERA_ESTIMATOR_CHOICES.keys())[0] - - def __init__(self, estimator=DEFAULT_CAMERA_ESTIMATOR, **kwargs): - self.estimator = CameraEstimator.CAMERA_ESTIMATOR_CHOICES[estimator]( - **kwargs - ) - - def estimate(self, features, pairwise_matches): - b, cameras = self.estimator.apply(features, pairwise_matches, None) - if not b: - raise StitchingError("Homography estimation failed.") - for cam in cameras: - cam.R = cam.R.astype(np.float32) - return cameras diff --git a/apps/opencv_stitching_tool/opencv_stitching/camera_wave_corrector.py b/apps/opencv_stitching_tool/opencv_stitching/camera_wave_corrector.py deleted file mode 100644 index 97b821b955bd..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/camera_wave_corrector.py +++ /dev/null @@ -1,28 +0,0 @@ -from collections import OrderedDict -import cv2 as cv -import numpy as np - - -class WaveCorrector: - """https://docs.opencv.org/4.x/d7/d74/group__stitching__rotation.html#ga83b24d4c3e93584986a56d9e43b9cf7f""" # noqa - WAVE_CORRECT_CHOICES = OrderedDict() - WAVE_CORRECT_CHOICES['horiz'] = cv.detail.WAVE_CORRECT_HORIZ - WAVE_CORRECT_CHOICES['vert'] = cv.detail.WAVE_CORRECT_VERT - WAVE_CORRECT_CHOICES['auto'] = cv.detail.WAVE_CORRECT_AUTO - WAVE_CORRECT_CHOICES['no'] = None - - DEFAULT_WAVE_CORRECTION = list(WAVE_CORRECT_CHOICES.keys())[0] - - def __init__(self, wave_correct_kind=DEFAULT_WAVE_CORRECTION): - self.wave_correct_kind = WaveCorrector.WAVE_CORRECT_CHOICES[ - wave_correct_kind - ] - - def correct(self, cameras): - if self.wave_correct_kind is not None: - rmats = [np.copy(cam.R) for cam in cameras] - rmats = cv.detail.waveCorrect(rmats, self.wave_correct_kind) - for idx, cam in enumerate(cameras): - cam.R = rmats[idx] - return cameras - return cameras diff --git a/apps/opencv_stitching_tool/opencv_stitching/exposure_error_compensator.py b/apps/opencv_stitching_tool/opencv_stitching/exposure_error_compensator.py deleted file mode 100644 index f28fd83f1492..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/exposure_error_compensator.py +++ /dev/null @@ -1,40 +0,0 @@ -from collections import OrderedDict -import cv2 as cv - - -class ExposureErrorCompensator: - - COMPENSATOR_CHOICES = OrderedDict() - COMPENSATOR_CHOICES['gain_blocks'] = cv.detail.ExposureCompensator_GAIN_BLOCKS # noqa - COMPENSATOR_CHOICES['gain'] = cv.detail.ExposureCompensator_GAIN - COMPENSATOR_CHOICES['channel'] = cv.detail.ExposureCompensator_CHANNELS - COMPENSATOR_CHOICES['channel_blocks'] = cv.detail.ExposureCompensator_CHANNELS_BLOCKS # noqa - COMPENSATOR_CHOICES['no'] = cv.detail.ExposureCompensator_NO - - DEFAULT_COMPENSATOR = list(COMPENSATOR_CHOICES.keys())[0] - DEFAULT_NR_FEEDS = 1 - DEFAULT_BLOCK_SIZE = 32 - - def __init__(self, - compensator=DEFAULT_COMPENSATOR, - nr_feeds=DEFAULT_NR_FEEDS, - block_size=DEFAULT_BLOCK_SIZE): - - if compensator == 'channel': - self.compensator = cv.detail_ChannelsCompensator(nr_feeds) - elif compensator == 'channel_blocks': - self.compensator = cv.detail_BlocksChannelsCompensator( - block_size, block_size, nr_feeds - ) - else: - self.compensator = cv.detail.ExposureCompensator_createDefault( - ExposureErrorCompensator.COMPENSATOR_CHOICES[compensator] - ) - - def feed(self, *args): - """https://docs.opencv.org/4.x/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#ae6b0cc69a7bc53818ddea53eddb6bdba""" # noqa - self.compensator.feed(*args) - - def apply(self, *args): - """https://docs.opencv.org/4.x/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#a473eaf1e585804c08d77c91e004f93aa""" # noqa - return self.compensator.apply(*args) diff --git a/apps/opencv_stitching_tool/opencv_stitching/feature_detector.py b/apps/opencv_stitching_tool/opencv_stitching/feature_detector.py deleted file mode 100644 index 995517b01bf5..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/feature_detector.py +++ /dev/null @@ -1,44 +0,0 @@ -from collections import OrderedDict -import cv2 as cv - - -class FeatureDetector: - DETECTOR_CHOICES = OrderedDict() - try: - cv.xfeatures2d_SURF.create() # check if the function can be called - DETECTOR_CHOICES['surf'] = cv.xfeatures2d_SURF.create - except (AttributeError, cv.error): - print("SURF not available") - - # if SURF not available, ORB is default - DETECTOR_CHOICES['orb'] = cv.ORB.create - - try: - DETECTOR_CHOICES['sift'] = cv.SIFT_create - except AttributeError: - print("SIFT not available") - - try: - DETECTOR_CHOICES['brisk'] = cv.BRISK_create - except AttributeError: - print("BRISK not available") - - try: - DETECTOR_CHOICES['akaze'] = cv.AKAZE_create - except AttributeError: - print("AKAZE not available") - - DEFAULT_DETECTOR = list(DETECTOR_CHOICES.keys())[0] - - def __init__(self, detector=DEFAULT_DETECTOR, **kwargs): - self.detector = FeatureDetector.DETECTOR_CHOICES[detector](**kwargs) - - def detect_features(self, img, *args, **kwargs): - return cv.detail.computeImageFeatures2(self.detector, img, - *args, **kwargs) - - @staticmethod - def draw_keypoints(img, features, **kwargs): - kwargs.setdefault('color', (0, 255, 0)) - keypoints = features.getKeypoints() - return cv.drawKeypoints(img, keypoints, None, **kwargs) diff --git a/apps/opencv_stitching_tool/opencv_stitching/feature_matcher.py b/apps/opencv_stitching_tool/opencv_stitching/feature_matcher.py deleted file mode 100644 index f2c7183e25db..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/feature_matcher.py +++ /dev/null @@ -1,98 +0,0 @@ -import math -import cv2 as cv -import numpy as np - - -class FeatureMatcher: - - MATCHER_CHOICES = ('homography', 'affine') - DEFAULT_MATCHER = 'homography' - DEFAULT_RANGE_WIDTH = -1 - - def __init__(self, - matcher_type=DEFAULT_MATCHER, - range_width=DEFAULT_RANGE_WIDTH, - **kwargs): - - if matcher_type == "affine": - """https://docs.opencv.org/4.x/d3/dda/classcv_1_1detail_1_1AffineBestOf2NearestMatcher.html""" # noqa - self.matcher = cv.detail_AffineBestOf2NearestMatcher(**kwargs) - elif range_width == -1: - """https://docs.opencv.org/4.x/d4/d26/classcv_1_1detail_1_1BestOf2NearestMatcher.html""" # noqa - self.matcher = cv.detail.BestOf2NearestMatcher_create(**kwargs) - else: - """https://docs.opencv.org/4.x/d8/d72/classcv_1_1detail_1_1BestOf2NearestRangeMatcher.html""" # noqa - self.matcher = cv.detail.BestOf2NearestRangeMatcher_create( - range_width, **kwargs - ) - - def match_features(self, features, *args, **kwargs): - pairwise_matches = self.matcher.apply2(features, *args, **kwargs) - self.matcher.collectGarbage() - return pairwise_matches - - @staticmethod - def draw_matches_matrix(imgs, features, matches, conf_thresh=1, - inliers=False, **kwargs): - matches_matrix = FeatureMatcher.get_matches_matrix(matches) - for idx1, idx2 in FeatureMatcher.get_all_img_combinations(len(imgs)): - match = matches_matrix[idx1, idx2] - if match.confidence < conf_thresh: - continue - if inliers: - kwargs['matchesMask'] = match.getInliers() - yield idx1, idx2, FeatureMatcher.draw_matches( - imgs[idx1], features[idx1], - imgs[idx2], features[idx2], - match, - **kwargs - ) - - @staticmethod - def draw_matches(img1, features1, img2, features2, match1to2, **kwargs): - kwargs.setdefault('flags', cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) - - keypoints1 = features1.getKeypoints() - keypoints2 = features2.getKeypoints() - matches = match1to2.getMatches() - - return cv.drawMatches( - img1, keypoints1, img2, keypoints2, matches, None, **kwargs - ) - - @staticmethod - def get_matches_matrix(pairwise_matches): - return FeatureMatcher.array_in_sqare_matrix(pairwise_matches) - - @staticmethod - def get_confidence_matrix(pairwise_matches): - matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches) - match_confs = [[m.confidence for m in row] for row in matches_matrix] - match_conf_matrix = np.array(match_confs) - return match_conf_matrix - - @staticmethod - def array_in_sqare_matrix(array): - matrix_dimension = int(math.sqrt(len(array))) - rows = [] - for i in range(0, len(array), matrix_dimension): - rows.append(array[i:i+matrix_dimension]) - return np.array(rows) - - def get_all_img_combinations(number_imgs): - ii, jj = np.triu_indices(number_imgs, k=1) - for i, j in zip(ii, jj): - yield i, j - - @staticmethod - def get_match_conf(match_conf, feature_detector_type): - if match_conf is None: - match_conf = \ - FeatureMatcher.get_default_match_conf(feature_detector_type) - return match_conf - - @staticmethod - def get_default_match_conf(feature_detector_type): - if feature_detector_type == 'orb': - return 0.3 - return 0.65 diff --git a/apps/opencv_stitching_tool/opencv_stitching/image_handler.py b/apps/opencv_stitching_tool/opencv_stitching/image_handler.py deleted file mode 100644 index a3b76b288ab5..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/image_handler.py +++ /dev/null @@ -1,94 +0,0 @@ -import cv2 as cv - -from .megapix_downscaler import MegapixDownscaler -from .stitching_error import StitchingError - -class ImageHandler: - - DEFAULT_MEDIUM_MEGAPIX = 0.6 - DEFAULT_LOW_MEGAPIX = 0.1 - DEFAULT_FINAL_MEGAPIX = -1 - - def __init__(self, - medium_megapix=DEFAULT_MEDIUM_MEGAPIX, - low_megapix=DEFAULT_LOW_MEGAPIX, - final_megapix=DEFAULT_FINAL_MEGAPIX): - - if medium_megapix < low_megapix: - raise StitchingError("Medium resolution megapix need to be " - "greater or equal than low resolution " - "megapix") - - self.medium_scaler = MegapixDownscaler(medium_megapix) - self.low_scaler = MegapixDownscaler(low_megapix) - self.final_scaler = MegapixDownscaler(final_megapix) - - self.scales_set = False - self.img_names = [] - self.img_sizes = [] - - def set_img_names(self, img_names): - self.img_names = img_names - - def resize_to_medium_resolution(self): - return self.read_and_resize_imgs(self.medium_scaler) - - def resize_to_low_resolution(self, medium_imgs=None): - if medium_imgs and self.scales_set: - return self.resize_medium_to_low(medium_imgs) - return self.read_and_resize_imgs(self.low_scaler) - - def resize_to_final_resolution(self): - return self.read_and_resize_imgs(self.final_scaler) - - def read_and_resize_imgs(self, scaler): - for img, size in self.input_images(): - yield self.resize_img_by_scaler(scaler, size, img) - - def resize_medium_to_low(self, medium_imgs): - for img, size in zip(medium_imgs, self.img_sizes): - yield self.resize_img_by_scaler(self.low_scaler, size, img) - - @staticmethod - def resize_img_by_scaler(scaler, size, img): - desired_size = scaler.get_scaled_img_size(size) - return cv.resize(img, desired_size, - interpolation=cv.INTER_LINEAR_EXACT) - - def input_images(self): - self.img_sizes = [] - for name in self.img_names: - img = self.read_image(name) - size = self.get_image_size(img) - self.img_sizes.append(size) - self.set_scaler_scales() - yield img, size - - @staticmethod - def get_image_size(img): - """(width, height)""" - return (img.shape[1], img.shape[0]) - - @staticmethod - def read_image(img_name): - img = cv.imread(img_name) - if img is None: - raise StitchingError("Cannot read image " + img_name) - return img - - def set_scaler_scales(self): - if not self.scales_set: - first_img_size = self.img_sizes[0] - self.medium_scaler.set_scale_by_img_size(first_img_size) - self.low_scaler.set_scale_by_img_size(first_img_size) - self.final_scaler.set_scale_by_img_size(first_img_size) - self.scales_set = True - - def get_medium_to_final_ratio(self): - return self.final_scaler.scale / self.medium_scaler.scale - - def get_medium_to_low_ratio(self): - return self.low_scaler.scale / self.medium_scaler.scale - - def get_final_to_low_ratio(self): - return self.low_scaler.scale / self.final_scaler.scale diff --git a/apps/opencv_stitching_tool/opencv_stitching/megapix_downscaler.py b/apps/opencv_stitching_tool/opencv_stitching/megapix_downscaler.py deleted file mode 100644 index f7553acc2eea..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/megapix_downscaler.py +++ /dev/null @@ -1,12 +0,0 @@ -from .megapix_scaler import MegapixScaler - - -class MegapixDownscaler(MegapixScaler): - - @staticmethod - def force_downscale(scale): - return min(1.0, scale) - - def set_scale(self, scale): - scale = self.force_downscale(scale) - super().set_scale(scale) diff --git a/apps/opencv_stitching_tool/opencv_stitching/megapix_scaler.py b/apps/opencv_stitching_tool/opencv_stitching/megapix_scaler.py deleted file mode 100644 index 96d47536f951..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/megapix_scaler.py +++ /dev/null @@ -1,27 +0,0 @@ -import numpy as np - - -class MegapixScaler: - def __init__(self, megapix): - self.megapix = megapix - self.is_scale_set = False - self.scale = None - - def set_scale_by_img_size(self, img_size): - self.set_scale( - self.get_scale_by_resolution(img_size[0] * img_size[1]) - ) - - def set_scale(self, scale): - self.scale = scale - self.is_scale_set = True - - def get_scale_by_resolution(self, resolution): - if self.megapix > 0: - return np.sqrt(self.megapix * 1e6 / resolution) - return 1.0 - - def get_scaled_img_size(self, img_size): - width = int(round(img_size[0] * self.scale)) - height = int(round(img_size[1] * self.scale)) - return (width, height) diff --git a/apps/opencv_stitching_tool/opencv_stitching/panorama_estimation.py b/apps/opencv_stitching_tool/opencv_stitching/panorama_estimation.py deleted file mode 100644 index e3a45773ea2c..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/panorama_estimation.py +++ /dev/null @@ -1,27 +0,0 @@ -import statistics - - -def estimate_final_panorama_dimensions(cameras, warper, img_handler): - medium_to_final_ratio = img_handler.get_medium_to_final_ratio() - - panorama_scale_determined_on_medium_img = \ - estimate_panorama_scale(cameras) - - panorama_scale = (panorama_scale_determined_on_medium_img * - medium_to_final_ratio) - panorama_corners = [] - panorama_sizes = [] - - for size, camera in zip(img_handler.img_sizes, cameras): - width, height = img_handler.final_scaler.get_scaled_img_size(size) - roi = warper.warp_roi(width, height, camera, panorama_scale, medium_to_final_ratio) - panorama_corners.append(roi[0:2]) - panorama_sizes.append(roi[2:4]) - - return panorama_scale, panorama_corners, panorama_sizes - - -def estimate_panorama_scale(cameras): - focals = [cam.focal for cam in cameras] - panorama_scale = statistics.median(focals) - return panorama_scale diff --git a/apps/opencv_stitching_tool/opencv_stitching/seam_finder.py b/apps/opencv_stitching_tool/opencv_stitching/seam_finder.py deleted file mode 100644 index edf1ed6624be..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/seam_finder.py +++ /dev/null @@ -1,127 +0,0 @@ -from collections import OrderedDict -import cv2 as cv -import numpy as np - -from .blender import Blender - - -class SeamFinder: - """https://docs.opencv.org/4.x/d7/d09/classcv_1_1detail_1_1SeamFinder.html""" # noqa - SEAM_FINDER_CHOICES = OrderedDict() - SEAM_FINDER_CHOICES['dp_color'] = cv.detail_DpSeamFinder('COLOR') - SEAM_FINDER_CHOICES['dp_colorgrad'] = cv.detail_DpSeamFinder('COLOR_GRAD') - SEAM_FINDER_CHOICES['voronoi'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM) # noqa - SEAM_FINDER_CHOICES['no'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) # noqa - - DEFAULT_SEAM_FINDER = list(SEAM_FINDER_CHOICES.keys())[0] - - def __init__(self, finder=DEFAULT_SEAM_FINDER): - self.finder = SeamFinder.SEAM_FINDER_CHOICES[finder] - - def find(self, imgs, corners, masks): - """https://docs.opencv.org/4.x/d0/dd5/classcv_1_1detail_1_1DpSeamFinder.html#a7914624907986f7a94dd424209a8a609""" # noqa - imgs_float = [img.astype(np.float32) for img in imgs] - return self.finder.find(imgs_float, corners, masks) - - @staticmethod - def resize(seam_mask, mask): - dilated_mask = cv.dilate(seam_mask, None) - resized_seam_mask = cv.resize(dilated_mask, (mask.shape[1], - mask.shape[0]), - 0, 0, cv.INTER_LINEAR_EXACT) - return cv.bitwise_and(resized_seam_mask, mask) - - @staticmethod - def draw_seam_mask(img, seam_mask, color=(0, 0, 0)): - seam_mask = cv.UMat.get(seam_mask) - overlayed_img = np.copy(img) - overlayed_img[seam_mask == 0] = color - return overlayed_img - - @staticmethod - def draw_seam_polygons(panorama, blended_seam_masks, alpha=0.5): - return add_weighted_image(panorama, blended_seam_masks, alpha) - - @staticmethod - def draw_seam_lines(panorama, blended_seam_masks, - linesize=1, color=(0, 0, 255)): - seam_lines = \ - SeamFinder.exctract_seam_lines(blended_seam_masks, linesize) - panorama_with_seam_lines = panorama.copy() - panorama_with_seam_lines[seam_lines == 255] = color - return panorama_with_seam_lines - - @staticmethod - def exctract_seam_lines(blended_seam_masks, linesize=1): - seam_lines = cv.Canny(np.uint8(blended_seam_masks), 100, 200) - seam_indices = (seam_lines == 255).nonzero() - seam_lines = remove_invalid_line_pixels( - seam_indices, seam_lines, blended_seam_masks - ) - kernelsize = linesize + linesize - 1 - kernel = np.ones((kernelsize, kernelsize), np.uint8) - return cv.dilate(seam_lines, kernel) - - @staticmethod - def blend_seam_masks(seam_masks, corners, sizes, colors=[ - (255, 000, 000), # Blue - (000, 000, 255), # Red - (000, 255, 000), # Green - (000, 255, 255), # Yellow - (255, 000, 255), # Magenta - (128, 128, 255), # Pink - (128, 128, 128), # Gray - (000, 000, 128), # Brown - (000, 128, 255)] # Orange - ): - - blender = Blender("no") - blender.prepare(corners, sizes) - - for idx, (seam_mask, size, corner) in enumerate( - zip(seam_masks, sizes, corners)): - if idx+1 > len(colors): - raise ValueError("Not enough default colors! Pass additional " - "colors to \"colors\" parameter") - one_color_img = create_img_by_size(size, colors[idx]) - blender.feed(one_color_img, seam_mask, corner) - - return blender.blend() - - -def create_img_by_size(size, color=(0, 0, 0)): - width, height = size - img = np.zeros((height, width, 3), np.uint8) - img[:] = color - return img - - -def add_weighted_image(img1, img2, alpha): - return cv.addWeighted( - img1, alpha, img2, (1.0 - alpha), 0.0 - ) - - -def remove_invalid_line_pixels(indices, lines, mask): - for x, y in zip(*indices): - if check_if_pixel_or_neighbor_is_black(mask, x, y): - lines[x, y] = 0 - return lines - - -def check_if_pixel_or_neighbor_is_black(img, x, y): - check = [is_pixel_black(img, x, y), - is_pixel_black(img, x+1, y), is_pixel_black(img, x-1, y), - is_pixel_black(img, x, y+1), is_pixel_black(img, x, y-1)] - return any(check) - - -def is_pixel_black(img, x, y): - return np.all(get_pixel_value(img, x, y) == 0) - - -def get_pixel_value(img, x, y): - try: - return img[x, y] - except IndexError: - pass diff --git a/apps/opencv_stitching_tool/opencv_stitching/stitcher.py b/apps/opencv_stitching_tool/opencv_stitching/stitcher.py deleted file mode 100644 index c08112664f02..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/stitcher.py +++ /dev/null @@ -1,207 +0,0 @@ -from types import SimpleNamespace - -from .image_handler import ImageHandler -from .feature_detector import FeatureDetector -from .feature_matcher import FeatureMatcher -from .subsetter import Subsetter -from .camera_estimator import CameraEstimator -from .camera_adjuster import CameraAdjuster -from .camera_wave_corrector import WaveCorrector -from .warper import Warper -from .panorama_estimation import estimate_final_panorama_dimensions -from .exposure_error_compensator import ExposureErrorCompensator -from .seam_finder import SeamFinder -from .blender import Blender -from .timelapser import Timelapser -from .stitching_error import StitchingError - - -class Stitcher: - DEFAULT_SETTINGS = { - "medium_megapix": ImageHandler.DEFAULT_MEDIUM_MEGAPIX, - "detector": FeatureDetector.DEFAULT_DETECTOR, - "nfeatures": 500, - "matcher_type": FeatureMatcher.DEFAULT_MATCHER, - "range_width": FeatureMatcher.DEFAULT_RANGE_WIDTH, - "try_use_gpu": False, - "match_conf": None, - "confidence_threshold": Subsetter.DEFAULT_CONFIDENCE_THRESHOLD, - "matches_graph_dot_file": Subsetter.DEFAULT_MATCHES_GRAPH_DOT_FILE, - "estimator": CameraEstimator.DEFAULT_CAMERA_ESTIMATOR, - "adjuster": CameraAdjuster.DEFAULT_CAMERA_ADJUSTER, - "refinement_mask": CameraAdjuster.DEFAULT_REFINEMENT_MASK, - "wave_correct_kind": WaveCorrector.DEFAULT_WAVE_CORRECTION, - "warper_type": Warper.DEFAULT_WARP_TYPE, - "low_megapix": ImageHandler.DEFAULT_LOW_MEGAPIX, - "compensator": ExposureErrorCompensator.DEFAULT_COMPENSATOR, - "nr_feeds": ExposureErrorCompensator.DEFAULT_NR_FEEDS, - "block_size": ExposureErrorCompensator.DEFAULT_BLOCK_SIZE, - "finder": SeamFinder.DEFAULT_SEAM_FINDER, - "final_megapix": ImageHandler.DEFAULT_FINAL_MEGAPIX, - "blender_type": Blender.DEFAULT_BLENDER, - "blend_strength": Blender.DEFAULT_BLEND_STRENGTH, - "timelapse": Timelapser.DEFAULT_TIMELAPSE} - - def __init__(self, **kwargs): - self.initialize_stitcher(**kwargs) - - def initialize_stitcher(self, **kwargs): - self.settings = Stitcher.DEFAULT_SETTINGS.copy() - self.validate_kwargs(kwargs) - self.settings.update(kwargs) - - args = SimpleNamespace(**self.settings) - self.img_handler = ImageHandler(args.medium_megapix, - args.low_megapix, - args.final_megapix) - self.detector = \ - FeatureDetector(args.detector, nfeatures=args.nfeatures) - match_conf = \ - FeatureMatcher.get_match_conf(args.match_conf, args.detector) - self.matcher = FeatureMatcher(args.matcher_type, args.range_width, - try_use_gpu=args.try_use_gpu, - match_conf=match_conf) - self.subsetter = \ - Subsetter(args.confidence_threshold, args.matches_graph_dot_file) - self.camera_estimator = CameraEstimator(args.estimator) - self.camera_adjuster = \ - CameraAdjuster(args.adjuster, args.refinement_mask) - self.wave_corrector = WaveCorrector(args.wave_correct_kind) - self.warper = Warper(args.warper_type) - self.compensator = \ - ExposureErrorCompensator(args.compensator, args.nr_feeds, - args.block_size) - self.seam_finder = SeamFinder(args.finder) - self.blender = Blender(args.blender_type, args.blend_strength) - self.timelapser = Timelapser(args.timelapse) - - def stitch(self, img_names): - self.initialize_registration(img_names) - - imgs = self.resize_medium_resolution() - features = self.find_features(imgs) - matches = self.match_features(features) - imgs, features, matches = self.subset(imgs, features, matches) - cameras = self.estimate_camera_parameters(features, matches) - cameras = self.refine_camera_parameters(features, matches, cameras) - cameras = self.perform_wave_correction(cameras) - panorama_scale, panorama_corners, panorama_sizes = \ - self.estimate_final_panorama_dimensions(cameras) - - self.initialize_composition(panorama_corners, panorama_sizes) - - imgs = self.resize_low_resolution(imgs) - imgs = self.warp_low_resolution_images(imgs, cameras, panorama_scale) - self.estimate_exposure_errors(imgs) - seam_masks = self.find_seam_masks(imgs) - - imgs = self.resize_final_resolution() - imgs = self.warp_final_resolution_images(imgs, cameras, panorama_scale) - imgs = self.compensate_exposure_errors(imgs) - seam_masks = self.resize_seam_masks(seam_masks) - self.blend_images(imgs, seam_masks) - - return self.create_final_panorama() - - def initialize_registration(self, img_names): - self.img_handler.set_img_names(img_names) - - def resize_medium_resolution(self): - return list(self.img_handler.resize_to_medium_resolution()) - - def find_features(self, imgs): - return [self.detector.detect_features(img) for img in imgs] - - def match_features(self, features): - return self.matcher.match_features(features) - - def subset(self, imgs, features, matches): - names, sizes, imgs, features, matches = \ - self.subsetter.subset(self.img_handler.img_names, - self.img_handler.img_sizes, - imgs, features, matches) - self.img_handler.img_names, self.img_handler.img_sizes = names, sizes - return imgs, features, matches - - def estimate_camera_parameters(self, features, matches): - return self.camera_estimator.estimate(features, matches) - - def refine_camera_parameters(self, features, matches, cameras): - return self.camera_adjuster.adjust(features, matches, cameras) - - def perform_wave_correction(self, cameras): - return self.wave_corrector.correct(cameras) - - def estimate_final_panorama_dimensions(self, cameras): - return estimate_final_panorama_dimensions(cameras, self.warper, - self.img_handler) - - def initialize_composition(self, corners, sizes): - if self.timelapser.do_timelapse: - self.timelapser.initialize(corners, sizes) - else: - self.blender.prepare(corners, sizes) - - def resize_low_resolution(self, imgs=None): - return list(self.img_handler.resize_to_low_resolution(imgs)) - - def warp_low_resolution_images(self, imgs, cameras, final_scale): - camera_aspect = self.img_handler.get_medium_to_low_ratio() - scale = final_scale * self.img_handler.get_final_to_low_ratio() - return list(self.warp_images(imgs, cameras, scale, camera_aspect)) - - def warp_final_resolution_images(self, imgs, cameras, scale): - camera_aspect = self.img_handler.get_medium_to_final_ratio() - return self.warp_images(imgs, cameras, scale, camera_aspect) - - def warp_images(self, imgs, cameras, scale, aspect=1): - self._masks = [] - self._corners = [] - for img_warped, mask_warped, corner in \ - self.warper.warp_images_and_image_masks( - imgs, cameras, scale, aspect - ): - self._masks.append(mask_warped) - self._corners.append(corner) - yield img_warped - - def estimate_exposure_errors(self, imgs): - self.compensator.feed(self._corners, imgs, self._masks) - - def find_seam_masks(self, imgs): - return self.seam_finder.find(imgs, self._corners, self._masks) - - def resize_final_resolution(self): - return self.img_handler.resize_to_final_resolution() - - def compensate_exposure_errors(self, imgs): - for idx, img in enumerate(imgs): - yield self.compensator.apply(idx, self._corners[idx], - img, self._masks[idx]) - - def resize_seam_masks(self, seam_masks): - for idx, seam_mask in enumerate(seam_masks): - yield SeamFinder.resize(seam_mask, self._masks[idx]) - - def blend_images(self, imgs, masks): - for idx, (img, mask) in enumerate(zip(imgs, masks)): - if self.timelapser.do_timelapse: - self.timelapser.process_and_save_frame( - self.img_handler.img_names[idx], img, self._corners[idx] - ) - else: - self.blender.feed(img, mask, self._corners[idx]) - - def create_final_panorama(self): - if not self.timelapser.do_timelapse: - return self.blender.blend() - - @staticmethod - def validate_kwargs(kwargs): - for arg in kwargs: - if arg not in Stitcher.DEFAULT_SETTINGS: - raise StitchingError("Invalid Argument: " + arg) - - def collect_garbage(self): - del self.img_handler.img_names, self.img_handler.img_sizes, - del self._corners, self._masks diff --git a/apps/opencv_stitching_tool/opencv_stitching/stitching_error.py b/apps/opencv_stitching_tool/opencv_stitching/stitching_error.py deleted file mode 100644 index 6d42e95437b3..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/stitching_error.py +++ /dev/null @@ -1,2 +0,0 @@ -class StitchingError(Exception): - pass diff --git a/apps/opencv_stitching_tool/opencv_stitching/subsetter.py b/apps/opencv_stitching_tool/opencv_stitching/subsetter.py deleted file mode 100644 index 4ea6acc60de2..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/subsetter.py +++ /dev/null @@ -1,95 +0,0 @@ -from itertools import chain -import math -import cv2 as cv -import numpy as np - -from .feature_matcher import FeatureMatcher -from .stitching_error import StitchingError - - -class Subsetter: - - DEFAULT_CONFIDENCE_THRESHOLD = 1 - DEFAULT_MATCHES_GRAPH_DOT_FILE = None - - def __init__(self, - confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD, - matches_graph_dot_file=DEFAULT_MATCHES_GRAPH_DOT_FILE): - self.confidence_threshold = confidence_threshold - self.save_file = matches_graph_dot_file - - def subset(self, img_names, img_sizes, imgs, features, matches): - self.save_matches_graph_dot_file(img_names, matches) - indices = self.get_indices_to_keep(features, matches) - - img_names = Subsetter.subset_list(img_names, indices) - img_sizes = Subsetter.subset_list(img_sizes, indices) - imgs = Subsetter.subset_list(imgs, indices) - features = Subsetter.subset_list(features, indices) - matches = Subsetter.subset_matches(matches, indices) - return img_names, img_sizes, imgs, features, matches - - def save_matches_graph_dot_file(self, img_names, pairwise_matches): - if self.save_file: - with open(self.save_file, 'w') as filehandler: - filehandler.write(self.get_matches_graph(img_names, - pairwise_matches) - ) - - def get_matches_graph(self, img_names, pairwise_matches): - return cv.detail.matchesGraphAsString(img_names, pairwise_matches, - self.confidence_threshold) - - def get_indices_to_keep(self, features, pairwise_matches): - indices = cv.detail.leaveBiggestComponent(features, - pairwise_matches, - self.confidence_threshold) - indices_as_list = [int(idx) for idx in list(indices[:, 0])] - - if len(indices_as_list) < 2: - raise StitchingError("No match exceeds the " - "given confidence theshold.") - - return indices_as_list - - @staticmethod - def subset_list(list_to_subset, indices): - return [list_to_subset[i] for i in indices] - - @staticmethod - def subset_matches(pairwise_matches, indices): - indices_to_delete = Subsetter.get_indices_to_delete( - math.sqrt(len(pairwise_matches)), - indices - ) - - matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches) - matches_matrix_subset = Subsetter.subset_matrix(matches_matrix, - indices_to_delete) - matches_subset = Subsetter.matrix_rows_to_list(matches_matrix_subset) - - return matches_subset - - @staticmethod - def get_indices_to_delete(nr_elements, indices_to_keep): - return list(set(range(int(nr_elements))) - set(indices_to_keep)) - - @staticmethod - def subset_matrix(matrix_to_subset, indices_to_delete): - for idx, idx_to_delete in enumerate(indices_to_delete): - matrix_to_subset = Subsetter.delete_index_from_matrix( - matrix_to_subset, - idx_to_delete-idx # matrix shape reduced by one at each step - ) - - return matrix_to_subset - - @staticmethod - def delete_index_from_matrix(matrix, idx): - mask = np.ones(matrix.shape[0], bool) - mask[idx] = 0 - return matrix[mask, :][:, mask] - - @staticmethod - def matrix_rows_to_list(matrix): - return list(chain.from_iterable(matrix.tolist())) diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/.gitignore b/apps/opencv_stitching_tool/opencv_stitching/test/.gitignore deleted file mode 100644 index 93426ff2b0f9..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/.gitignore +++ /dev/null @@ -1,13 +0,0 @@ -# Ignore everything -* - -# But not these files... -!.gitignore -!test_matcher.py -!test_stitcher.py -!test_megapix_scaler.py -!test_registration.py -!test_composition.py -!test_performance.py -!stitching_detailed.py -!SAMPLE_IMAGES_TO_DOWNLOAD.txt \ No newline at end of file diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/SAMPLE_IMAGES_TO_DOWNLOAD.txt b/apps/opencv_stitching_tool/opencv_stitching/test/SAMPLE_IMAGES_TO_DOWNLOAD.txt deleted file mode 100644 index 236d3607decc..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/SAMPLE_IMAGES_TO_DOWNLOAD.txt +++ /dev/null @@ -1,5 +0,0 @@ -https://github.com/opencv/opencv_extra/tree/4.x/testdata/stitching - -s1.jpg s2.jpg -boat1.jpg boat2.jpg boat3.jpg boat4.jpg boat5.jpg boat6.jpg -budapest1.jpg budapest2.jpg budapest3.jpg budapest4.jpg budapest5.jpg budapest6.jpg \ No newline at end of file diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/stitching_detailed.py b/apps/opencv_stitching_tool/opencv_stitching/test/stitching_detailed.py deleted file mode 100644 index ef9d78fe736b..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/stitching_detailed.py +++ /dev/null @@ -1,406 +0,0 @@ -""" -Stitching sample (advanced) -=========================== -Show how to use Stitcher API from python. -""" - -# Python 2/3 compatibility -from __future__ import print_function - -from types import SimpleNamespace -from collections import OrderedDict - -import cv2 as cv -import numpy as np - -EXPOS_COMP_CHOICES = OrderedDict() -EXPOS_COMP_CHOICES['gain_blocks'] = cv.detail.ExposureCompensator_GAIN_BLOCKS -EXPOS_COMP_CHOICES['gain'] = cv.detail.ExposureCompensator_GAIN -EXPOS_COMP_CHOICES['channel'] = cv.detail.ExposureCompensator_CHANNELS -EXPOS_COMP_CHOICES['channel_blocks'] = cv.detail.ExposureCompensator_CHANNELS_BLOCKS -EXPOS_COMP_CHOICES['no'] = cv.detail.ExposureCompensator_NO - -BA_COST_CHOICES = OrderedDict() -BA_COST_CHOICES['ray'] = cv.detail_BundleAdjusterRay -BA_COST_CHOICES['reproj'] = cv.detail_BundleAdjusterReproj -BA_COST_CHOICES['affine'] = cv.detail_BundleAdjusterAffinePartial -BA_COST_CHOICES['no'] = cv.detail_NoBundleAdjuster - -FEATURES_FIND_CHOICES = OrderedDict() -try: - cv.xfeatures2d_SURF.create() # check if the function can be called - FEATURES_FIND_CHOICES['surf'] = cv.xfeatures2d_SURF.create -except (AttributeError, cv.error) as e: - print("SURF not available") -# if SURF not available, ORB is default -FEATURES_FIND_CHOICES['orb'] = cv.ORB.create -try: - FEATURES_FIND_CHOICES['sift'] = cv.xfeatures2d_SIFT.create -except AttributeError: - print("SIFT not available") -try: - FEATURES_FIND_CHOICES['brisk'] = cv.BRISK_create -except AttributeError: - print("BRISK not available") -try: - FEATURES_FIND_CHOICES['akaze'] = cv.AKAZE_create -except AttributeError: - print("AKAZE not available") - -SEAM_FIND_CHOICES = OrderedDict() -SEAM_FIND_CHOICES['dp_color'] = cv.detail_DpSeamFinder('COLOR') -SEAM_FIND_CHOICES['dp_colorgrad'] = cv.detail_DpSeamFinder('COLOR_GRAD') -SEAM_FIND_CHOICES['voronoi'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM) -SEAM_FIND_CHOICES['no'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) - -ESTIMATOR_CHOICES = OrderedDict() -ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator -ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator - -WARP_CHOICES = ( - 'spherical', - 'plane', - 'affine', - 'cylindrical', - 'fisheye', - 'stereographic', - 'compressedPlaneA2B1', - 'compressedPlaneA1.5B1', - 'compressedPlanePortraitA2B1', - 'compressedPlanePortraitA1.5B1', - 'paniniA2B1', - 'paniniA1.5B1', - 'paniniPortraitA2B1', - 'paniniPortraitA1.5B1', - 'mercator', - 'transverseMercator', -) - -WAVE_CORRECT_CHOICES = OrderedDict() -WAVE_CORRECT_CHOICES['horiz'] = cv.detail.WAVE_CORRECT_HORIZ -WAVE_CORRECT_CHOICES['no'] = None -WAVE_CORRECT_CHOICES['vert'] = cv.detail.WAVE_CORRECT_VERT - -BLEND_CHOICES = ('multiband', 'feather', 'no',) - -def get_matcher(args): - try_cuda = args.try_cuda - matcher_type = args.matcher - if args.match_conf is None: - if args.features == 'orb': - match_conf = 0.3 - else: - match_conf = 0.65 - else: - match_conf = args.match_conf - range_width = args.rangewidth - if matcher_type == "affine": - matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf) - elif range_width == -1: - matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf) - else: - matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf) - return matcher - - -def get_compensator(args): - expos_comp_type = EXPOS_COMP_CHOICES[args.expos_comp] - expos_comp_nr_feeds = args.expos_comp_nr_feeds - expos_comp_block_size = args.expos_comp_block_size - # expos_comp_nr_filtering = args.expos_comp_nr_filtering - if expos_comp_type == cv.detail.ExposureCompensator_CHANNELS: - compensator = cv.detail_ChannelsCompensator(expos_comp_nr_feeds) - # compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering) - elif expos_comp_type == cv.detail.ExposureCompensator_CHANNELS_BLOCKS: - compensator = cv.detail_BlocksChannelsCompensator( - expos_comp_block_size, expos_comp_block_size, - expos_comp_nr_feeds - ) - # compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering) - else: - compensator = cv.detail.ExposureCompensator_createDefault(expos_comp_type) - return compensator - - -def main(): - - args = { - "img_names":["boat5.jpg", "boat2.jpg", - "boat3.jpg", "boat4.jpg", - "boat1.jpg", "boat6.jpg"], - "try_cuda": False, - "work_megapix": 0.6, - "features": "orb", - "matcher": "homography", - "estimator": "homography", - "match_conf": None, - "conf_thresh": 1.0, - "ba": "ray", - "ba_refine_mask": "xxxxx", - "wave_correct": "horiz", - "save_graph": None, - "warp": "spherical", - "seam_megapix": 0.1, - "seam": "dp_color", - "compose_megapix": 3, - "expos_comp": "gain_blocks", - "expos_comp_nr_feeds": 1, - "expos_comp_nr_filtering": 2, - "expos_comp_block_size": 32, - "blend": "multiband", - "blend_strength": 5, - "output": "time_test.jpg", - "timelapse": None, - "rangewidth": -1 - } - - args = SimpleNamespace(**args) - img_names = args.img_names - work_megapix = args.work_megapix - seam_megapix = args.seam_megapix - compose_megapix = args.compose_megapix - conf_thresh = args.conf_thresh - ba_refine_mask = args.ba_refine_mask - wave_correct = WAVE_CORRECT_CHOICES[args.wave_correct] - if args.save_graph is None: - save_graph = False - else: - save_graph = True - warp_type = args.warp - blend_type = args.blend - blend_strength = args.blend_strength - result_name = args.output - if args.timelapse is not None: - timelapse = True - if args.timelapse == "as_is": - timelapse_type = cv.detail.Timelapser_AS_IS - elif args.timelapse == "crop": - timelapse_type = cv.detail.Timelapser_CROP - else: - print("Bad timelapse method") - exit() - else: - timelapse = False - finder = FEATURES_FIND_CHOICES[args.features]() - seam_work_aspect = 1 - full_img_sizes = [] - features = [] - images = [] - is_work_scale_set = False - is_seam_scale_set = False - is_compose_scale_set = False - for name in img_names: - full_img = cv.imread(cv.samples.findFile(name)) - if full_img is None: - print("Cannot read image ", name) - exit() - full_img_sizes.append((full_img.shape[1], full_img.shape[0])) - if work_megapix < 0: - img = full_img - work_scale = 1 - is_work_scale_set = True - else: - if is_work_scale_set is False: - work_scale = min(1.0, np.sqrt(work_megapix * 1e6 / (full_img.shape[0] * full_img.shape[1]))) - is_work_scale_set = True - img = cv.resize(src=full_img, dsize=None, fx=work_scale, fy=work_scale, interpolation=cv.INTER_LINEAR_EXACT) - if is_seam_scale_set is False: - seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0] * full_img.shape[1]))) - seam_work_aspect = seam_scale / work_scale - is_seam_scale_set = True - img_feat = cv.detail.computeImageFeatures2(finder, img) - features.append(img_feat) - img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT) - images.append(img) - - matcher = get_matcher(args) - p = matcher.apply2(features) - matcher.collectGarbage() - - if save_graph: - with open(args.save_graph, 'w') as fh: - fh.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh)) - - indices = cv.detail.leaveBiggestComponent(features, p, conf_thresh) - img_subset = [] - img_names_subset = [] - full_img_sizes_subset = [] - for i in range(len(indices)): - img_names_subset.append(img_names[indices[i, 0]]) - img_subset.append(images[indices[i, 0]]) - full_img_sizes_subset.append(full_img_sizes[indices[i, 0]]) - images = img_subset - img_names = img_names_subset - full_img_sizes = full_img_sizes_subset - num_images = len(img_names) - if num_images < 2: - print("Need more images") - exit() - - estimator = ESTIMATOR_CHOICES[args.estimator]() - b, cameras = estimator.apply(features, p, None) - if not b: - print("Homography estimation failed.") - exit() - for cam in cameras: - cam.R = cam.R.astype(np.float32) - - adjuster = BA_COST_CHOICES[args.ba]() - adjuster.setConfThresh(1) - refine_mask = np.zeros((3, 3), np.uint8) - if ba_refine_mask[0] == 'x': - refine_mask[0, 0] = 1 - if ba_refine_mask[1] == 'x': - refine_mask[0, 1] = 1 - if ba_refine_mask[2] == 'x': - refine_mask[0, 2] = 1 - if ba_refine_mask[3] == 'x': - refine_mask[1, 1] = 1 - if ba_refine_mask[4] == 'x': - refine_mask[1, 2] = 1 - adjuster.setRefinementMask(refine_mask) - b, cameras = adjuster.apply(features, p, cameras) - if not b: - print("Camera parameters adjusting failed.") - exit() - focals = [] - for cam in cameras: - focals.append(cam.focal) - focals.sort() - if len(focals) % 2 == 1: - warped_image_scale = focals[len(focals) // 2] - else: - warped_image_scale = (focals[len(focals) // 2] + focals[len(focals) // 2 - 1]) / 2 - if wave_correct is not None: - rmats = [] - for cam in cameras: - rmats.append(np.copy(cam.R)) - rmats = cv.detail.waveCorrect(rmats, wave_correct) - for idx, cam in enumerate(cameras): - cam.R = rmats[idx] - corners = [] - masks_warped = [] - images_warped = [] - sizes = [] - masks = [] - for i in range(0, num_images): - um = cv.UMat(255 * np.ones((images[i].shape[0], images[i].shape[1]), np.uint8)) - masks.append(um) - - warper = cv.PyRotationWarper(warp_type, warped_image_scale * seam_work_aspect) # warper could be nullptr? - for idx in range(0, num_images): - K = cameras[idx].K().astype(np.float32) - swa = seam_work_aspect - K[0, 0] *= swa - K[0, 2] *= swa - K[1, 1] *= swa - K[1, 2] *= swa - corner, image_wp = warper.warp(images[idx], K, cameras[idx].R, cv.INTER_LINEAR, cv.BORDER_REFLECT) - corners.append(corner) - sizes.append((image_wp.shape[1], image_wp.shape[0])) - images_warped.append(image_wp) - p, mask_wp = warper.warp(masks[idx], K, cameras[idx].R, cv.INTER_NEAREST, cv.BORDER_CONSTANT) - masks_warped.append(mask_wp.get()) - - images_warped_f = [] - for img in images_warped: - imgf = img.astype(np.float32) - images_warped_f.append(imgf) - - compensator = get_compensator(args) - compensator.feed(corners=corners, images=images_warped, masks=masks_warped) - - seam_finder = SEAM_FIND_CHOICES[args.seam] - masks_warped = seam_finder.find(images_warped_f, corners, masks_warped) - compose_scale = 1 - corners = [] - sizes = [] - blender = None - timelapser = None - # https://github.com/opencv/opencv/blob/4.x/samples/cpp/stitching_detailed.cpp#L725 ? - for idx, name in enumerate(img_names): - full_img = cv.imread(name) - if not is_compose_scale_set: - if compose_megapix > 0: - compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0] * full_img.shape[1]))) - is_compose_scale_set = True - compose_work_aspect = compose_scale / work_scale - warped_image_scale *= compose_work_aspect - warper = cv.PyRotationWarper(warp_type, warped_image_scale) - for i in range(0, len(img_names)): - cameras[i].focal *= compose_work_aspect - cameras[i].ppx *= compose_work_aspect - cameras[i].ppy *= compose_work_aspect - sz = (int(round(full_img_sizes[i][0] * compose_scale)), - int(round(full_img_sizes[i][1] * compose_scale))) - K = cameras[i].K().astype(np.float32) - roi = warper.warpRoi(sz, K, cameras[i].R) - corners.append(roi[0:2]) - sizes.append(roi[2:4]) - if abs(compose_scale - 1) > 1e-1: - img = cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, - interpolation=cv.INTER_LINEAR_EXACT) - else: - img = full_img - _img_size = (img.shape[1], img.shape[0]) - K = cameras[idx].K().astype(np.float32) - corner, image_warped = warper.warp(img, K, cameras[idx].R, cv.INTER_LINEAR, cv.BORDER_REFLECT) - mask = 255 * np.ones((img.shape[0], img.shape[1]), np.uint8) - p, mask_warped = warper.warp(mask, K, cameras[idx].R, cv.INTER_NEAREST, cv.BORDER_CONSTANT) - compensator.apply(idx, corners[idx], image_warped, mask_warped) - image_warped_s = image_warped.astype(np.int16) - dilated_mask = cv.dilate(masks_warped[idx], None) - seam_mask = cv.resize(dilated_mask, (mask_warped.shape[1], mask_warped.shape[0]), 0, 0, cv.INTER_LINEAR_EXACT) - mask_warped = cv.bitwise_and(seam_mask, mask_warped) - if blender is None and not timelapse: - blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO) - dst_sz = cv.detail.resultRoi(corners=corners, sizes=sizes) - blend_width = np.sqrt(dst_sz[2] * dst_sz[3]) * blend_strength / 100 - if blend_width < 1: - blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO) - elif blend_type == "multiband": - blender = cv.detail_MultiBandBlender() - blender.setNumBands((np.log(blend_width) / np.log(2.) - 1.).astype(np.int64)) - elif blend_type == "feather": - blender = cv.detail_FeatherBlender() - blender.setSharpness(1. / blend_width) - blender.prepare(dst_sz) - elif timelapser is None and timelapse: - timelapser = cv.detail.Timelapser_createDefault(timelapse_type) - timelapser.initialize(corners, sizes) - if timelapse: - ma_tones = np.ones((image_warped_s.shape[0], image_warped_s.shape[1]), np.uint8) - timelapser.process(image_warped_s, ma_tones, corners[idx]) - pos_s = img_names[idx].rfind("/") - if pos_s == -1: - fixed_file_name = "fixed_" + img_names[idx] - else: - fixed_file_name = img_names[idx][:pos_s + 1] + "fixed_" + img_names[idx][pos_s + 1:] - cv.imwrite(fixed_file_name, timelapser.getDst()) - else: - blender.feed(cv.UMat(image_warped_s), mask_warped, corners[idx]) - if not timelapse: - result = None - result_mask = None - result, result_mask = blender.blend(result, result_mask) - # cv.imwrite(result_name, result) - return result - # zoom_x = 600.0 / result.shape[1] - # dst = cv.normalize(src=result, dst=None, alpha=255., norm_type=cv.NORM_MINMAX, dtype=cv.CV_8U) - # dst = cv.resize(dst, dsize=None, fx=zoom_x, fy=zoom_x) - # cv.imshow(result_name, dst) - # cv.waitKey() - - - -if __name__ == '__main__': - import tracemalloc - import time - tracemalloc.start() - start = time.time() - result = main() - current, peak = tracemalloc.get_traced_memory() - print(f"Current memory usage is {current / 10**6}MB; Peak was {peak / 10**6}MB") - tracemalloc.stop() - end = time.time() - print(end - start) diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/test_composition.py b/apps/opencv_stitching_tool/opencv_stitching/test/test_composition.py deleted file mode 100644 index b0b4d76c87ac..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/test_composition.py +++ /dev/null @@ -1,67 +0,0 @@ -import unittest -import os -import sys - -import numpy as np -import cv2 as cv - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), - '..', '..'))) - -from opencv_stitching.stitcher import Stitcher - - -class TestImageComposition(unittest.TestCase): - - # visual test: look especially in the sky - def test_exposure_compensation(self): - img = cv.imread("s1.jpg") - img = increase_brightness(img, value=25) - cv.imwrite("s1_bright.jpg", img) - - stitcher = Stitcher(compensator="no", blender_type="no") - result = stitcher.stitch(["s1_bright.jpg", "s2.jpg"]) - - cv.imwrite("without_exposure_comp.jpg", result) - - stitcher = Stitcher(blender_type="no") - result = stitcher.stitch(["s1_bright.jpg", "s2.jpg"]) - - cv.imwrite("with_exposure_comp.jpg", result) - - def test_timelapse(self): - stitcher = Stitcher(timelapse='as_is') - _ = stitcher.stitch(["s1.jpg", "s2.jpg"]) - frame1 = cv.imread("fixed_s1.jpg") - - max_image_shape_derivation = 3 - np.testing.assert_allclose(frame1.shape[:2], - (700, 1811), - atol=max_image_shape_derivation) - - left = cv.cvtColor(frame1[:, :1300, ], cv.COLOR_BGR2GRAY) - right = cv.cvtColor(frame1[:, 1300:, ], cv.COLOR_BGR2GRAY) - - self.assertGreater(cv.countNonZero(left), 800000) - self.assertEqual(cv.countNonZero(right), 0) - - -def increase_brightness(img, value=30): - hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV) - h, s, v = cv.split(hsv) - - lim = 255 - value - v[v > lim] = 255 - v[v <= lim] += value - - final_hsv = cv.merge((h, s, v)) - img = cv.cvtColor(final_hsv, cv.COLOR_HSV2BGR) - return img - - -def starttest(): - unittest.main() - - -if __name__ == "__main__": - starttest() diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/test_matcher.py b/apps/opencv_stitching_tool/opencv_stitching/test/test_matcher.py deleted file mode 100644 index a2424ec9ce81..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/test_matcher.py +++ /dev/null @@ -1,47 +0,0 @@ -import unittest -import os -import sys - -import numpy as np - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), - '..', '..'))) - -from opencv_stitching.feature_matcher import FeatureMatcher -# %% - - -class TestMatcher(unittest.TestCase): - - def test_array_in_sqare_matrix(self): - array = np.zeros(9) - - matrix = FeatureMatcher.array_in_sqare_matrix(array) - - np.testing.assert_array_equal(matrix, np.array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]])) - - def test_get_all_img_combinations(self): - nimgs = 3 - - combinations = list(FeatureMatcher.get_all_img_combinations(nimgs)) - - self.assertEqual(combinations, [(0, 1), (0, 2), (1, 2)]) - - def test_get_match_conf(self): - explicit_match_conf = FeatureMatcher.get_match_conf(1, 'orb') - implicit_match_conf_orb = FeatureMatcher.get_match_conf(None, 'orb') - implicit_match_conf_other = FeatureMatcher.get_match_conf(None, 'surf') - - self.assertEqual(explicit_match_conf, 1) - self.assertEqual(implicit_match_conf_orb, 0.3) - self.assertEqual(implicit_match_conf_other, 0.65) - - -def starttest(): - unittest.main() - - -if __name__ == "__main__": - starttest() diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/test_megapix_scaler.py b/apps/opencv_stitching_tool/opencv_stitching/test/test_megapix_scaler.py deleted file mode 100644 index 0afdad2628b7..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/test_megapix_scaler.py +++ /dev/null @@ -1,59 +0,0 @@ -import unittest -import os -import sys - -import cv2 as cv - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), - '..', '..'))) - -from opencv_stitching.megapix_scaler import MegapixScaler -from opencv_stitching.megapix_downscaler import MegapixDownscaler -#%% - - -class TestScaler(unittest.TestCase): - - def setUp(self): - self.img = cv.imread("s1.jpg") - self.size = (self.img.shape[1], self.img.shape[0]) - - def test_get_scale_by_resolution(self): - scaler = MegapixScaler(0.6) - - scale = scaler.get_scale_by_resolution(1_200_000) - - self.assertEqual(scale, 0.7071067811865476) - - def test_get_scale_by_image(self): - scaler = MegapixScaler(0.6) - - scaler.set_scale_by_img_size(self.size) - - self.assertEqual(scaler.scale, 0.8294067854101966) - - def test_get_scaled_img_size(self): - scaler = MegapixScaler(0.6) - scaler.set_scale_by_img_size(self.size) - - size = scaler.get_scaled_img_size(self.size) - self.assertEqual(size, (1033, 581)) - # 581*1033 = 600173 px = ~0.6 MP - - def test_force_of_downscaling(self): - normal_scaler = MegapixScaler(2) - downscaler = MegapixDownscaler(2) - - normal_scaler.set_scale_by_img_size(self.size) - downscaler.set_scale_by_img_size(self.size) - - self.assertEqual(normal_scaler.scale, 1.5142826857233715) - self.assertEqual(downscaler.scale, 1.0) - - -def starttest(): - unittest.main() - - -if __name__ == "__main__": - starttest() diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/test_performance.py b/apps/opencv_stitching_tool/opencv_stitching/test/test_performance.py deleted file mode 100644 index 60b03a8bfe2b..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/test_performance.py +++ /dev/null @@ -1,65 +0,0 @@ -import unittest -import os -import sys -import time -import tracemalloc - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), - '..', '..'))) - -from opencv_stitching.stitcher import Stitcher -from stitching_detailed import main -# %% - - -class TestStitcher(unittest.TestCase): - - def test_performance(self): - - print("Run new Stitcher class:") - - start = time.time() - tracemalloc.start() - - stitcher = Stitcher(final_megapix=3) - stitcher.stitch(["boat5.jpg", "boat2.jpg", - "boat3.jpg", "boat4.jpg", - "boat1.jpg", "boat6.jpg"]) - stitcher.collect_garbage() - - _, peak_memory = tracemalloc.get_traced_memory() - tracemalloc.stop() - end = time.time() - time_needed = end - start - - print(f"Peak was {peak_memory / 10**6} MB") - print(f"Time was {time_needed} s") - - print("Run original stitching_detailed.py:") - - start = time.time() - tracemalloc.start() - - main() - - _, peak_memory_detailed = tracemalloc.get_traced_memory() - tracemalloc.stop() - end = time.time() - time_needed_detailed = end - start - - print(f"Peak was {peak_memory_detailed / 10**6} MB") - print(f"Time was {time_needed_detailed} s") - - self.assertLessEqual(peak_memory / 10**6, - peak_memory_detailed / 10**6) - uncertainty_based_on_run = 0.25 - self.assertLessEqual(time_needed - uncertainty_based_on_run, - time_needed_detailed) - - -def starttest(): - unittest.main() - - -if __name__ == "__main__": - starttest() diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/test_registration.py b/apps/opencv_stitching_tool/opencv_stitching/test/test_registration.py deleted file mode 100644 index 98e792fd01fe..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/test_registration.py +++ /dev/null @@ -1,100 +0,0 @@ -import unittest -import os -import sys - -import numpy as np -import cv2 as cv - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), - '..', '..'))) - -from opencv_stitching.feature_detector import FeatureDetector -from opencv_stitching.feature_matcher import FeatureMatcher -from opencv_stitching.subsetter import Subsetter - - -class TestImageRegistration(unittest.TestCase): - - def test_feature_detector(self): - img1 = cv.imread("s1.jpg") - - default_number_of_keypoints = 500 - detector = FeatureDetector("orb") - features = detector.detect_features(img1) - self.assertEqual(len(features.getKeypoints()), - default_number_of_keypoints) - - other_keypoints = 1000 - detector = FeatureDetector("orb", nfeatures=other_keypoints) - features = detector.detect_features(img1) - self.assertEqual(len(features.getKeypoints()), other_keypoints) - - def test_feature_matcher(self): - img1, img2 = cv.imread("s1.jpg"), cv.imread("s2.jpg") - - detector = FeatureDetector("orb") - features = [detector.detect_features(img1), - detector.detect_features(img2)] - - matcher = FeatureMatcher() - pairwise_matches = matcher.match_features(features) - self.assertEqual(len(pairwise_matches), len(features)**2) - self.assertGreater(pairwise_matches[1].confidence, 2) - - matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches) - self.assertEqual(matches_matrix.shape, (2, 2)) - conf_matrix = FeatureMatcher.get_confidence_matrix(pairwise_matches) - self.assertTrue(np.array_equal( - conf_matrix > 2, - np.array([[False, True], [True, False]]) - )) - - def test_subsetting(self): - img1, img2 = cv.imread("s1.jpg"), cv.imread("s2.jpg") - img3, img4 = cv.imread("boat1.jpg"), cv.imread("boat2.jpg") - img5 = cv.imread("boat3.jpg") - img_names = ["s1.jpg", "s2.jpg", "boat1.jpg", "boat2.jpg", "boat3.jpg"] - - detector = FeatureDetector("orb") - features = [detector.detect_features(img1), - detector.detect_features(img2), - detector.detect_features(img3), - detector.detect_features(img4), - detector.detect_features(img5)] - matcher = FeatureMatcher() - pairwise_matches = matcher.match_features(features) - subsetter = Subsetter(confidence_threshold=1, - matches_graph_dot_file="dot_graph.txt") # view in https://dreampuf.github.io # noqa - - indices = subsetter.get_indices_to_keep(features, pairwise_matches) - indices_to_delete = subsetter.get_indices_to_delete(len(img_names), - indices) - - self.assertEqual(indices, [2, 3, 4]) - self.assertEqual(indices_to_delete, [0, 1]) - - subsetted_image_names = subsetter.subset_list(img_names, indices) - self.assertEqual(subsetted_image_names, - ['boat1.jpg', 'boat2.jpg', 'boat3.jpg']) - - matches_subset = subsetter.subset_matches(pairwise_matches, indices) - # FeatureMatcher.get_confidence_matrix(pairwise_matches) - # FeatureMatcher.get_confidence_matrix(subsetted_matches) - self.assertEqual(pairwise_matches[13].confidence, - matches_subset[1].confidence) - - graph = subsetter.get_matches_graph(img_names, pairwise_matches) - self.assertTrue(graph.startswith("graph matches_graph{")) - - subsetter.save_matches_graph_dot_file(img_names, pairwise_matches) - with open('dot_graph.txt', 'r') as file: - graph = file.read() - self.assertTrue(graph.startswith("graph matches_graph{")) - - -def starttest(): - unittest.main() - - -if __name__ == "__main__": - starttest() diff --git a/apps/opencv_stitching_tool/opencv_stitching/test/test_stitcher.py b/apps/opencv_stitching_tool/opencv_stitching/test/test_stitcher.py deleted file mode 100644 index 5a24f752c0b3..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/test/test_stitcher.py +++ /dev/null @@ -1,108 +0,0 @@ -import unittest -import os -import sys - -import numpy as np -import cv2 as cv - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), - '..', '..'))) - -from opencv_stitching.stitcher import Stitcher -# %% - - -class TestStitcher(unittest.TestCase): - - def test_stitcher_aquaduct(self): - stitcher = Stitcher(n_features=250) - result = stitcher.stitch(["s1.jpg", "s2.jpg"]) - cv.imwrite("result.jpg", result) - - max_image_shape_derivation = 3 - np.testing.assert_allclose(result.shape[:2], - (700, 1811), - atol=max_image_shape_derivation) - - @unittest.skip("skip boat test (high resuolution ran >30s)") - def test_stitcher_boat1(self): - settings = {"warper_type": "fisheye", - "wave_correct_kind": "no", - "finder": "dp_colorgrad", - "compensator": "no", - "conf_thresh": 0.3} - - stitcher = Stitcher(**settings) - result = stitcher.stitch(["boat5.jpg", "boat2.jpg", - "boat3.jpg", "boat4.jpg", - "boat1.jpg", "boat6.jpg"]) - - cv.imwrite("boat_fisheye.jpg", result) - - max_image_shape_derivation = 600 - np.testing.assert_allclose(result.shape[:2], - (14488, 7556), - atol=max_image_shape_derivation) - - @unittest.skip("skip boat test (high resuolution ran >30s)") - def test_stitcher_boat2(self): - settings = {"warper_type": "compressedPlaneA2B1", - "finder": "dp_colorgrad", - "compensator": "channel_blocks", - "conf_thresh": 0.3} - - stitcher = Stitcher(**settings) - result = stitcher.stitch(["boat5.jpg", "boat2.jpg", - "boat3.jpg", "boat4.jpg", - "boat1.jpg", "boat6.jpg"]) - - cv.imwrite("boat_plane.jpg", result) - - max_image_shape_derivation = 600 - np.testing.assert_allclose(result.shape[:2], - (7400, 12340), - atol=max_image_shape_derivation) - - def test_stitcher_boat_aquaduct_subset(self): - settings = {"final_megapix": 1} - - stitcher = Stitcher(**settings) - result = stitcher.stitch(["boat5.jpg", - "s1.jpg", "s2.jpg", - "boat2.jpg", - "boat3.jpg", "boat4.jpg", - "boat1.jpg", "boat6.jpg"]) - cv.imwrite("subset_low_res.jpg", result) - - max_image_shape_derivation = 100 - np.testing.assert_allclose(result.shape[:2], - (839, 3384), - atol=max_image_shape_derivation) - - def test_stitcher_budapest(self): - settings = {"matcher_type": "affine", - "estimator": "affine", - "adjuster": "affine", - "warper_type": "affine", - "wave_correct_kind": "no", - "confidence_threshold": 0.3} - - stitcher = Stitcher(**settings) - result = stitcher.stitch(["budapest1.jpg", "budapest2.jpg", - "budapest3.jpg", "budapest4.jpg", - "budapest5.jpg", "budapest6.jpg"]) - - cv.imwrite("budapest.jpg", result) - - max_image_shape_derivation = 50 - np.testing.assert_allclose(result.shape[:2], - (1155, 2310), - atol=max_image_shape_derivation) - - -def starttest(): - unittest.main() - - -if __name__ == "__main__": - starttest() diff --git a/apps/opencv_stitching_tool/opencv_stitching/timelapser.py b/apps/opencv_stitching_tool/opencv_stitching/timelapser.py deleted file mode 100644 index 894294bd420c..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/timelapser.py +++ /dev/null @@ -1,50 +0,0 @@ -import os -import cv2 as cv -import numpy as np - - -class Timelapser: - - TIMELAPSE_CHOICES = ('no', 'as_is', 'crop',) - DEFAULT_TIMELAPSE = 'no' - - def __init__(self, timelapse=DEFAULT_TIMELAPSE): - self.do_timelapse = True - self.timelapse_type = None - self.timelapser = None - - if timelapse == "as_is": - self.timelapse_type = cv.detail.Timelapser_AS_IS - elif timelapse == "crop": - self.timelapse_type = cv.detail.Timelapser_CROP - else: - self.do_timelapse = False - - if self.do_timelapse: - self.timelapser = cv.detail.Timelapser_createDefault( - self.timelapse_type - ) - - def initialize(self, *args): - """https://docs.opencv.org/4.x/dd/dac/classcv_1_1detail_1_1Timelapser.html#aaf0f7c4128009f02473332a0c41f6345""" # noqa - self.timelapser.initialize(*args) - - def process_and_save_frame(self, img_name, img, corner): - self.process_frame(img, corner) - cv.imwrite(self.get_fixed_filename(img_name), self.get_frame()) - - def process_frame(self, img, corner): - mask = np.ones((img.shape[0], img.shape[1]), np.uint8) - img = img.astype(np.int16) - self.timelapser.process(img, mask, corner) - - def get_frame(self): - frame = self.timelapser.getDst() - frame = np.float32(cv.UMat.get(frame)) - frame = cv.convertScaleAbs(frame) - return frame - - @staticmethod - def get_fixed_filename(img_name): - dirname, filename = os.path.split(img_name) - return os.path.join(dirname, "fixed_" + filename) diff --git a/apps/opencv_stitching_tool/opencv_stitching/warper.py b/apps/opencv_stitching_tool/opencv_stitching/warper.py deleted file mode 100644 index e21521d10933..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching/warper.py +++ /dev/null @@ -1,71 +0,0 @@ -import cv2 as cv -import numpy as np - - -class Warper: - - WARP_TYPE_CHOICES = ('spherical', 'plane', 'affine', 'cylindrical', - 'fisheye', 'stereographic', 'compressedPlaneA2B1', - 'compressedPlaneA1.5B1', - 'compressedPlanePortraitA2B1', - 'compressedPlanePortraitA1.5B1', - 'paniniA2B1', 'paniniA1.5B1', 'paniniPortraitA2B1', - 'paniniPortraitA1.5B1', 'mercator', - 'transverseMercator') - - DEFAULT_WARP_TYPE = 'spherical' - - def __init__(self, warper_type=DEFAULT_WARP_TYPE, scale=1): - self.warper_type = warper_type - self.warper = cv.PyRotationWarper(warper_type, scale) - self.scale = scale - - def warp_images_and_image_masks(self, imgs, cameras, scale=None, aspect=1): - self.update_scale(scale) - for img, camera in zip(imgs, cameras): - yield self.warp_image_and_image_mask(img, camera, scale, aspect) - - def warp_image_and_image_mask(self, img, camera, scale=None, aspect=1): - self.update_scale(scale) - corner, img_warped = self.warp_image(img, camera, aspect) - mask = 255 * np.ones((img.shape[0], img.shape[1]), np.uint8) - _, mask_warped = self.warp_image(mask, camera, aspect, mask=True) - return img_warped, mask_warped, corner - - def warp_image(self, image, camera, aspect=1, mask=False): - if mask: - interp_mode = cv.INTER_NEAREST - border_mode = cv.BORDER_CONSTANT - else: - interp_mode = cv.INTER_LINEAR - border_mode = cv.BORDER_REFLECT - - corner, warped_image = self.warper.warp(image, - Warper.get_K(camera, aspect), - camera.R, - interp_mode, - border_mode) - return corner, warped_image - - def warp_roi(self, width, height, camera, scale=None, aspect=1): - self.update_scale(scale) - roi = (width, height) - K = Warper.get_K(camera, aspect) - return self.warper.warpRoi(roi, K, camera.R) - - def update_scale(self, scale): - if scale is not None and scale != self.scale: - self.warper = cv.PyRotationWarper(self.warper_type, scale) # setScale not working: https://docs.opencv.org/4.x/d5/d76/classcv_1_1PyRotationWarper.html#a90b000bb75f95294f9b0b6ec9859eb55 - self.scale = scale - - @staticmethod - def get_K(camera, aspect=1): - K = camera.K().astype(np.float32) - """ Modification of intrinsic parameters needed if cameras were - obtained on different scale than the scale of the Images which should - be warped """ - K[0, 0] *= aspect - K[0, 2] *= aspect - K[1, 1] *= aspect - K[1, 2] *= aspect - return K diff --git a/apps/opencv_stitching_tool/opencv_stitching_tool.py b/apps/opencv_stitching_tool/opencv_stitching_tool.py deleted file mode 100644 index 1ee96aa8cb4a..000000000000 --- a/apps/opencv_stitching_tool/opencv_stitching_tool.py +++ /dev/null @@ -1,232 +0,0 @@ -""" -Stitching sample (advanced) -=========================== - -Show how to use Stitcher API from python. -""" - -# Python 2/3 compatibility -from __future__ import print_function - -import argparse - -import cv2 as cv -import numpy as np - -from opencv_stitching.stitcher import Stitcher - -from opencv_stitching.image_handler import ImageHandler -from opencv_stitching.feature_detector import FeatureDetector -from opencv_stitching.feature_matcher import FeatureMatcher -from opencv_stitching.subsetter import Subsetter -from opencv_stitching.camera_estimator import CameraEstimator -from opencv_stitching.camera_adjuster import CameraAdjuster -from opencv_stitching.camera_wave_corrector import WaveCorrector -from opencv_stitching.warper import Warper -from opencv_stitching.exposure_error_compensator import ExposureErrorCompensator # noqa -from opencv_stitching.seam_finder import SeamFinder -from opencv_stitching.blender import Blender -from opencv_stitching.timelapser import Timelapser - -parser = argparse.ArgumentParser( - prog="opencv_stitching_tool.py", - description="Rotation model images stitcher" -) -parser.add_argument( - 'img_names', nargs='+', - help="Files to stitch", type=str -) -parser.add_argument( - '--medium_megapix', action='store', - default=ImageHandler.DEFAULT_MEDIUM_MEGAPIX, - help="Resolution for image registration step. " - "The default is %s Mpx" % ImageHandler.DEFAULT_MEDIUM_MEGAPIX, - type=float, dest='medium_megapix' -) -parser.add_argument( - '--detector', action='store', - default=FeatureDetector.DEFAULT_DETECTOR, - help="Type of features used for images matching. " - "The default is '%s'." % FeatureDetector.DEFAULT_DETECTOR, - choices=FeatureDetector.DETECTOR_CHOICES.keys(), - type=str, dest='detector' -) -parser.add_argument( - '--nfeatures', action='store', - default=500, - help="Type of features used for images matching. " - "The default is 500.", - type=int, dest='nfeatures' -) -parser.add_argument( - '--matcher_type', action='store', default=FeatureMatcher.DEFAULT_MATCHER, - help="Matcher used for pairwise image matching. " - "The default is '%s'." % FeatureMatcher.DEFAULT_MATCHER, - choices=FeatureMatcher.MATCHER_CHOICES, - type=str, dest='matcher_type' -) -parser.add_argument( - '--range_width', action='store', - default=FeatureMatcher.DEFAULT_RANGE_WIDTH, - help="uses range_width to limit number of images to match with.", - type=int, dest='range_width' -) -parser.add_argument( - '--try_use_gpu', - action='store', - default=False, - help="Try to use CUDA. The default value is no. " - "All default values are for CPU mode.", - type=bool, dest='try_use_gpu' -) -parser.add_argument( - '--match_conf', action='store', - help="Confidence for feature matching step. " - "The default is 0.3 for ORB and 0.65 for other feature types.", - type=float, dest='match_conf' -) -parser.add_argument( - '--confidence_threshold', action='store', - default=Subsetter.DEFAULT_CONFIDENCE_THRESHOLD, - help="Threshold for two images are from the same panorama confidence. " - "The default is '%s'." % Subsetter.DEFAULT_CONFIDENCE_THRESHOLD, - type=float, dest='confidence_threshold' -) -parser.add_argument( - '--matches_graph_dot_file', action='store', - default=Subsetter.DEFAULT_MATCHES_GRAPH_DOT_FILE, - help="Save matches graph represented in DOT language to file.", - type=str, dest='matches_graph_dot_file' -) -parser.add_argument( - '--estimator', action='store', - default=CameraEstimator.DEFAULT_CAMERA_ESTIMATOR, - help="Type of estimator used for transformation estimation. " - "The default is '%s'." % CameraEstimator.DEFAULT_CAMERA_ESTIMATOR, - choices=CameraEstimator.CAMERA_ESTIMATOR_CHOICES.keys(), - type=str, dest='estimator' -) -parser.add_argument( - '--adjuster', action='store', - default=CameraAdjuster.DEFAULT_CAMERA_ADJUSTER, - help="Bundle adjustment cost function. " - "The default is '%s'." % CameraAdjuster.DEFAULT_CAMERA_ADJUSTER, - choices=CameraAdjuster.CAMERA_ADJUSTER_CHOICES.keys(), - type=str, dest='adjuster' -) -parser.add_argument( - '--refinement_mask', action='store', - default=CameraAdjuster.DEFAULT_REFINEMENT_MASK, - help="Set refinement mask for bundle adjustment. It looks like 'x_xxx', " - "where 'x' means refine respective parameter and '_' means don't " - "refine, and has the following format:. " - "The default mask is '%s'. " - "If bundle adjustment doesn't support estimation of selected " - "parameter then the respective flag is ignored." - "" % CameraAdjuster.DEFAULT_REFINEMENT_MASK, - type=str, dest='refinement_mask' -) -parser.add_argument( - '--wave_correct_kind', action='store', - default=WaveCorrector.DEFAULT_WAVE_CORRECTION, - help="Perform wave effect correction. " - "The default is '%s'" % WaveCorrector.DEFAULT_WAVE_CORRECTION, - choices=WaveCorrector.WAVE_CORRECT_CHOICES.keys(), - type=str, dest='wave_correct_kind' -) -parser.add_argument( - '--warper_type', action='store', default=Warper.DEFAULT_WARP_TYPE, - help="Warp surface type. The default is '%s'." % Warper.DEFAULT_WARP_TYPE, - choices=Warper.WARP_TYPE_CHOICES, - type=str, dest='warper_type' -) -parser.add_argument( - '--low_megapix', action='store', default=ImageHandler.DEFAULT_LOW_MEGAPIX, - help="Resolution for seam estimation and exposure estimation step. " - "The default is %s Mpx." % ImageHandler.DEFAULT_LOW_MEGAPIX, - type=float, dest='low_megapix' -) -parser.add_argument( - '--compensator', action='store', - default=ExposureErrorCompensator.DEFAULT_COMPENSATOR, - help="Exposure compensation method. " - "The default is '%s'." % ExposureErrorCompensator.DEFAULT_COMPENSATOR, - choices=ExposureErrorCompensator.COMPENSATOR_CHOICES.keys(), - type=str, dest='compensator' -) -parser.add_argument( - '--nr_feeds', action='store', - default=ExposureErrorCompensator.DEFAULT_NR_FEEDS, - help="Number of exposure compensation feed.", - type=np.int32, dest='nr_feeds' -) -parser.add_argument( - '--block_size', action='store', - default=ExposureErrorCompensator.DEFAULT_BLOCK_SIZE, - help="BLock size in pixels used by the exposure compensator. " - "The default is '%s'." % ExposureErrorCompensator.DEFAULT_BLOCK_SIZE, - type=np.int32, dest='block_size' -) -parser.add_argument( - '--finder', action='store', default=SeamFinder.DEFAULT_SEAM_FINDER, - help="Seam estimation method. " - "The default is '%s'." % SeamFinder.DEFAULT_SEAM_FINDER, - choices=SeamFinder.SEAM_FINDER_CHOICES.keys(), - type=str, dest='finder' -) -parser.add_argument( - '--final_megapix', action='store', - default=ImageHandler.DEFAULT_FINAL_MEGAPIX, - help="Resolution for compositing step. Use -1 for original resolution. " - "The default is %s" % ImageHandler.DEFAULT_FINAL_MEGAPIX, - type=float, dest='final_megapix' -) -parser.add_argument( - '--blender_type', action='store', default=Blender.DEFAULT_BLENDER, - help="Blending method. The default is '%s'." % Blender.DEFAULT_BLENDER, - choices=Blender.BLENDER_CHOICES, - type=str, dest='blender_type' -) -parser.add_argument( - '--blend_strength', action='store', default=Blender.DEFAULT_BLEND_STRENGTH, - help="Blending strength from [0,100] range. " - "The default is '%s'." % Blender.DEFAULT_BLEND_STRENGTH, - type=np.int32, dest='blend_strength' -) -parser.add_argument( - '--timelapse', action='store', default=Timelapser.DEFAULT_TIMELAPSE, - help="Output warped images separately as frames of a time lapse movie, " - "with 'fixed_' prepended to input file names. " - "The default is '%s'." % Timelapser.DEFAULT_TIMELAPSE, - choices=Timelapser.TIMELAPSE_CHOICES, - type=str, dest='timelapse' -) -parser.add_argument( - '--output', action='store', default='result.jpg', - help="The default is 'result.jpg'", - type=str, dest='output' -) - -__doc__ += '\n' + parser.format_help() - -if __name__ == '__main__': - print(__doc__) - args = parser.parse_args() - args_dict = vars(args) - - # Extract In- and Output - img_names = args_dict.pop("img_names") - img_names = [cv.samples.findFile(img_name) for img_name in img_names] - output = args_dict.pop("output") - - stitcher = Stitcher(**args_dict) - panorama = stitcher.stitch(img_names) - - cv.imwrite(output, panorama) - - zoom_x = 600.0 / panorama.shape[1] - preview = cv.resize(panorama, dsize=None, fx=zoom_x, fy=zoom_x) - - cv.imshow(output, preview) - cv.waitKey() - cv.destroyAllWindows() diff --git a/apps/traincascade/cascadeclassifier.cpp b/apps/traincascade/cascadeclassifier.cpp index 3d7b3832586d..5a83746bc48a 100644 --- a/apps/traincascade/cascadeclassifier.cpp +++ b/apps/traincascade/cascadeclassifier.cpp @@ -252,7 +252,7 @@ bool CvCascadeClassifier::train( const string _cascadeDirName, fs << "}"; } // save current stage - char buf[10]; + char buf[32]; sprintf(buf, "%s%d", "stage", i ); string stageFilename = dirName + buf + ".xml"; FileStorage fs( stageFilename, FileStorage::WRITE ); diff --git a/cmake/OpenCVCompilerOptions.cmake b/cmake/OpenCVCompilerOptions.cmake index 037c7fb5ba79..4f5c35398077 100644 --- a/cmake/OpenCVCompilerOptions.cmake +++ b/cmake/OpenCVCompilerOptions.cmake @@ -119,12 +119,12 @@ if(CV_GCC OR CV_CLANG) # we want. add_extra_compiler_option(-Wall) endif() - add_extra_compiler_option(-Werror=return-type) - add_extra_compiler_option(-Werror=non-virtual-dtor) - add_extra_compiler_option(-Werror=address) - add_extra_compiler_option(-Werror=sequence-point) + add_extra_compiler_option(-Wreturn-type) + add_extra_compiler_option(-Wnon-virtual-dtor) + add_extra_compiler_option(-Waddress) + add_extra_compiler_option(-Wsequence-point) add_extra_compiler_option(-Wformat) - add_extra_compiler_option(-Werror=format-security -Wformat) + add_extra_compiler_option(-Wformat-security -Wformat) add_extra_compiler_option(-Wmissing-declarations) add_extra_compiler_option(-Wmissing-prototypes) add_extra_compiler_option(-Wstrict-prototypes) @@ -314,6 +314,10 @@ if(MSVC) set(OPENCV_EXTRA_C_FLAGS "${OPENCV_EXTRA_C_FLAGS} /FS") set(OPENCV_EXTRA_CXX_FLAGS "${OPENCV_EXTRA_CXX_FLAGS} /FS") endif() + + if(AARCH64 AND NOT MSVC_VERSION LESS 1930) + set(OPENCV_EXTRA_FLAGS "${OPENCV_EXTRA_FLAGS} /D _ARM64_DISTINCT_NEON_TYPES") + endif() endif() if(PROJECT_NAME STREQUAL "OpenCV") @@ -367,6 +371,22 @@ if(NOT OPENCV_SKIP_LINK_AS_NEEDED) endif() endif() +# Apply "-Wl,--no-undefined" linker flags: https://github.com/opencv/opencv/pull/21347 +if(NOT OPENCV_SKIP_LINK_NO_UNDEFINED) + if(UNIX AND (NOT APPLE OR NOT CMAKE_VERSION VERSION_LESS "3.2")) + set(_option "-Wl,--no-undefined") + set(_saved_CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS}") + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${_option}") # requires CMake 3.2+ and CMP0056 + ocv_check_compiler_flag(CXX "" HAVE_LINK_NO_UNDEFINED) + set(CMAKE_EXE_LINKER_FLAGS "${_saved_CMAKE_EXE_LINKER_FLAGS}") + if(HAVE_LINK_NO_UNDEFINED) + set(OPENCV_EXTRA_EXE_LINKER_FLAGS "${OPENCV_EXTRA_EXE_LINKER_FLAGS} ${_option}") + set(OPENCV_EXTRA_SHARED_LINKER_FLAGS "${OPENCV_EXTRA_SHARED_LINKER_FLAGS} ${_option}") + set(OPENCV_EXTRA_MODULE_LINKER_FLAGS "${OPENCV_EXTRA_MODULE_LINKER_FLAGS} ${_option}") + endif() + endif() +endif() + # combine all "extra" options if(NOT OPENCV_SKIP_EXTRA_COMPILER_FLAGS) set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENCV_EXTRA_FLAGS} ${OPENCV_EXTRA_C_FLAGS}") diff --git a/cmake/OpenCVDetectInferenceEngine.cmake b/cmake/OpenCVDetectInferenceEngine.cmake index 6521fbab9315..319fd5bf0ad2 100644 --- a/cmake/OpenCVDetectInferenceEngine.cmake +++ b/cmake/OpenCVDetectInferenceEngine.cmake @@ -1,101 +1,38 @@ -# The script detects Intel(R) Inference Engine installation -# -# Cache variables: -# INF_ENGINE_RELEASE - a number reflecting IE source interface (linked with OpenVINO release) -# -# Detect parameters: -# 1. Native cmake IE package: -# - environment variable InferenceEngine_DIR is set to location of cmake module -# 2. Custom location: -# - INF_ENGINE_INCLUDE_DIRS - headers search location -# - INF_ENGINE_LIB_DIRS - library search location -# 3. OpenVINO location: -# - environment variable INTEL_OPENVINO_DIR is set to location of OpenVINO installation dir -# - INF_ENGINE_PLATFORM - part of name of library directory representing its platform +# The script detects Intel(R) OpenVINO(TM) runtime installation # # Result: -# INF_ENGINE_TARGET - set to name of imported library target representing InferenceEngine -# - +# - target ocv.3rdparty.openvino -macro(ocv_ie_find_extra_libraries find_prefix find_suffix) - file(GLOB libraries "${INF_ENGINE_LIB_DIRS}/${find_prefix}inference_engine*${find_suffix}") - foreach(full_path IN LISTS libraries) - get_filename_component(library "${full_path}" NAME_WE) - string(REPLACE "${find_prefix}" "" library "${library}") - if(library STREQUAL "inference_engine" OR library STREQUAL "inference_engined") - # skip - else() - add_library(${library} UNKNOWN IMPORTED) - set_target_properties(${library} PROPERTIES - IMPORTED_LOCATION "${full_path}") - list(APPEND custom_libraries ${library}) - endif() - endforeach() -endmacro() - -function(add_custom_ie_build _inc _lib _lib_rel _lib_dbg _msg) - if(NOT _inc OR NOT (_lib OR _lib_rel OR _lib_dbg)) +if(WITH_OPENVINO) + find_package(OpenVINO QUIET) + if(OpenVINO_FOUND) + message(STATUS "OpenVINO FOUND: ${OpenVINO_VERSION}") + math(EXPR ver "${OpenVINO_VERSION_MAJOR} * 1000000 + ${OpenVINO_VERSION_MINOR} * 10000 + ${OpenVINO_VERSION_PATCH} * 100") + ocv_add_external_target(openvino "" "openvino::runtime" "INF_ENGINE_RELEASE=${ver};HAVE_NGRAPH;HAVE_DNN_NGRAPH;HAVE_INF_ENGINE") + set(HAVE_OPENVINO 1) return() endif() - if(NOT _lib) - if(_lib_rel) - set(_lib "${_lib_rel}") - else() - set(_lib "${_lib_dbg}") - endif() - endif() - add_library(inference_engine UNKNOWN IMPORTED) - set_target_properties(inference_engine PROPERTIES - IMPORTED_LOCATION "${_lib}" - IMPORTED_IMPLIB_RELEASE "${_lib_rel}" - IMPORTED_IMPLIB_DEBUG "${_lib_dbg}" - INTERFACE_INCLUDE_DIRECTORIES "${_inc}" - ) +endif() - set(custom_libraries "") - set(__prefixes "${CMAKE_FIND_LIBRARY_PREFIXES}") - if(NOT __prefixes) - set(__prefixes "_empty_") - endif() - foreach(find_prefix ${__prefixes}) - if(find_prefix STREQUAL "_empty_") # foreach doesn't iterate over empty elements - set(find_prefix "") - endif() - if(NOT DEFINED INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES) # allow custom override - set(INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES}) - if(APPLE) - ocv_list_filterout(INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES "^.so$") # skip plugins (can't be linked) - endif() - endif() - foreach(find_suffix ${INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES}) - ocv_ie_find_extra_libraries("${find_prefix}" "${find_suffix}") - endforeach() - if(NOT CMAKE_FIND_LIBRARY_SUFFIXES) - ocv_ie_find_extra_libraries("${find_prefix}" "") - endif() - endforeach() +# ====================== - if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000") - find_library(INF_ENGINE_OMP_LIBRARY iomp5 PATHS "${INF_ENGINE_OMP_DIR}" NO_DEFAULT_PATH) - if(NOT INF_ENGINE_OMP_LIBRARY) - message(WARNING "OpenMP for IE have not been found. Set INF_ENGINE_OMP_DIR variable if you experience build errors.") - endif() - endif() - if(EXISTS "${INF_ENGINE_OMP_LIBRARY}") - set_target_properties(inference_engine PROPERTIES IMPORTED_LINK_INTERFACE_LIBRARIES "${INF_ENGINE_OMP_LIBRARY}") +if(WITH_OPENVINO) + find_package(OpenVINO QUIET) + if(OpenVINO_FOUND) + message(STATUS "OpenVINO FOUND: ${OpenVINO_VERSION}") + math(EXPR ver "${OpenVINO_VERSION_MAJOR} * 1000000 + ${OpenVINO_VERSION_MINOR} * 10000 + ${OpenVINO_VERSION_PATCH} * 100") + ocv_add_external_target(openvino "" "openvino::runtime" "INF_ENGINE_RELEASE=${ver};HAVE_NGRAPH;HAVE_DNN_NGRAPH;HAVE_INF_ENGINE") + set(HAVE_OPENVINO 1) + return() endif() - set(INF_ENGINE_VERSION "Unknown" CACHE STRING "") - set(INF_ENGINE_TARGET "inference_engine;${custom_libraries}" PARENT_SCOPE) - message(STATUS "Detected InferenceEngine: ${_msg}") -endfunction() +endif() # ====================== find_package(InferenceEngine QUIET) if(InferenceEngine_FOUND) set(INF_ENGINE_TARGET ${InferenceEngine_LIBRARIES}) - set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}" CACHE STRING "") + set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}") message(STATUS "Detected InferenceEngine: cmake package (${InferenceEngine_VERSION})") endif() @@ -113,47 +50,19 @@ elseif(DEFINED INF_ENGINE_RELEASE) endif() set(INF_ENGINE_RELEASE "${INF_ENGINE_RELEASE_INIT}" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)") -if(NOT INF_ENGINE_TARGET AND INF_ENGINE_LIB_DIRS AND INF_ENGINE_INCLUDE_DIRS) - find_path(ie_custom_inc "inference_engine.hpp" PATHS "${INF_ENGINE_INCLUDE_DIRS}" NO_DEFAULT_PATH) - if(CMAKE_BUILD_TYPE STREQUAL "Debug") - find_library(ie_custom_lib_dbg "inference_engined" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH) # Win32 and MacOSX - endif() - find_library(ie_custom_lib "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH) - find_library(ie_custom_lib_rel "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Release" NO_DEFAULT_PATH) - find_library(ie_custom_lib_dbg "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Debug" NO_DEFAULT_PATH) - add_custom_ie_build("${ie_custom_inc}" "${ie_custom_lib}" "${ie_custom_lib_rel}" "${ie_custom_lib_dbg}" "INF_ENGINE_{INCLUDE,LIB}_DIRS") -endif() - -set(_loc "$ENV{INTEL_OPENVINO_DIR}") -if(NOT _loc AND DEFINED ENV{INTEL_CVSDK_DIR}) - set(_loc "$ENV{INTEL_CVSDK_DIR}") # OpenVINO 2018.x -endif() -if(NOT INF_ENGINE_TARGET AND _loc) - if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000") - set(INF_ENGINE_PLATFORM_DEFAULT "ubuntu_16.04") - else() - set(INF_ENGINE_PLATFORM_DEFAULT "") - endif() - set(INF_ENGINE_PLATFORM "${INF_ENGINE_PLATFORM_DEFAULT}" CACHE STRING "InferenceEngine platform (library dir)") - find_path(ie_custom_env_inc "inference_engine.hpp" PATHS "${_loc}/deployment_tools/inference_engine/include" NO_DEFAULT_PATH) - if(CMAKE_BUILD_TYPE STREQUAL "Debug") - find_library(ie_custom_env_lib_dbg "inference_engined" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH) - endif() - find_library(ie_custom_env_lib "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH) - find_library(ie_custom_env_lib_rel "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Release" NO_DEFAULT_PATH) - find_library(ie_custom_env_lib_dbg "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Debug" NO_DEFAULT_PATH) - add_custom_ie_build("${ie_custom_env_inc}" "${ie_custom_env_lib}" "${ie_custom_env_lib_rel}" "${ie_custom_env_lib_dbg}" "OpenVINO (${_loc})") -endif() +set(tgts) +set(defs) # Add more features to the target - if(INF_ENGINE_TARGET) set_target_properties(${INF_ENGINE_TARGET} PROPERTIES INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}" ) + list(APPEND tgts ${INF_ENGINE_TARGET}) + list(APPEND defs "INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}" "HAVE_INF_ENGINE") endif() -if(WITH_NGRAPH) +if(WITH_NGRAPH OR NOT DEFINED WITH_NGRAPH) find_package(ngraph QUIET) if(ngraph_FOUND) ocv_assert(TARGET ngraph::ngraph) @@ -162,5 +71,9 @@ if(WITH_NGRAPH) endif() message(STATUS "Detected ngraph: cmake package (${ngraph_VERSION})") set(HAVE_NGRAPH ON) + list(APPEND tgts ngraph::ngraph) + list(APPEND defs "HAVE_NGRAPH" "HAVE_DNN_NGRAPH") endif() endif() + +ocv_add_external_target(openvino "" "${tgts}" "${defs}") diff --git a/cmake/OpenCVDownload.cmake b/cmake/OpenCVDownload.cmake index a427a4122734..3e4651553771 100644 --- a/cmake/OpenCVDownload.cmake +++ b/cmake/OpenCVDownload.cmake @@ -37,6 +37,50 @@ file(WRITE "${OPENCV_DOWNLOAD_LOG}" "#use_cache \"${OPENCV_DOWNLOAD_PATH}\"\n") file(REMOVE "${OPENCV_DOWNLOAD_WITH_CURL}") file(REMOVE "${OPENCV_DOWNLOAD_WITH_WGET}") +ocv_check_environment_variables(OPENCV_DOWNLOAD_MIRROR_ID) + +function(ocv_init_download_mirror) + if(NOT DEFINED OPENCV_DOWNLOAD_MIRROR_ID) + # Run `git remote get-url origin` to get remote source + execute_process( + COMMAND + git remote get-url origin + WORKING_DIRECTORY + ${CMAKE_SOURCE_DIR} + RESULT_VARIABLE + RESULT_STATUS + OUTPUT_VARIABLE + OCV_GIT_ORIGIN_URL_OUTPUT + ERROR_QUIET + ) + # if non-git, OCV_GIT_ORIGIN_URL_OUTPUT is empty + if(NOT OCV_GIT_ORIGIN_URL_OUTPUT) + message(STATUS "ocv_init_download: OpenCV source tree is not fetched as git repository. 3rdparty resources will be downloaded from github.com by default.") + return() + else() + # Check if git origin is github.com + string(FIND "${OCV_GIT_ORIGIN_URL_OUTPUT}" "github.com" _found_github) + if(NOT ${_found_github} EQUAL -1) + set(OPENCV_DOWNLOAD_MIRROR_ID "github" CACHE STRING "") + endif() + # Check if git origin is gitcode.net + string(FIND "${OCV_GIT_ORIGIN_URL_OUTPUT}" "gitcode.net" _found_gitcode) + if(NOT ${_found_gitcode} EQUAL -1) + set(OPENCV_DOWNLOAD_MIRROR_ID "gitcode" CACHE STRING "") + endif() + endif() + endif() + + if(OPENCV_DOWNLOAD_MIRROR_ID STREQUAL "gitcode" OR OPENCV_DOWNLOAD_MIRROR_ID STREQUAL "custom") + message(STATUS "ocv_init_download: Using ${OPENCV_DOWNLOAD_MIRROR_ID}-hosted mirror to download 3rdparty components.") + ocv_cmake_hook_append(OPENCV_DOWNLOAD_PRE "${CMAKE_CURRENT_SOURCE_DIR}/cmake/mirrors/${OPENCV_DOWNLOAD_MIRROR_ID}.cmake") + elseif(OPENCV_DOWNLOAD_MIRROR_ID STREQUAL "github") + return() + else() + message(STATUS "ocv_init_download: Unable to recognize git server of OpenCV source code. Using github.com to download 3rdparty components.") + endif() +endfunction() + function(ocv_download) cmake_parse_arguments(DL "UNPACK;RELATIVE_URL" "FILENAME;HASH;DESTINATION_DIR;ID;STATUS" "URL" ${ARGN}) @@ -67,6 +111,8 @@ function(ocv_download) set(${DL_STATUS} TRUE PARENT_SCOPE) endif() + ocv_cmake_hook(OPENCV_DOWNLOAD_PRE) + # Check CMake cache for already processed tasks string(FIND "${DL_DESTINATION_DIR}" "${CMAKE_BINARY_DIR}" DL_BINARY_PATH_POS) if(DL_BINARY_PATH_POS EQUAL 0) @@ -115,7 +161,7 @@ function(ocv_download) if(DL_ID) set(__msg_prefix "${DL_ID}: ") endif() - message(STATUS "${__msg_prefix}Download: ${DL_FILENAME}") + message(STATUS "${__msg_prefix}Downloading ${DL_FILENAME} from ${DL_URL}") # Copy mode: check if copy destination exists and is correct if(NOT DL_UNPACK) @@ -252,3 +298,8 @@ ${OPENCV_DOWNLOAD_LOG} set(${OCV_DOWNLOAD_HASH_NAME} "${DL_HASH}" CACHE INTERNAL "") endif() endfunction() + +# ---------------------------------------------------------------------------- +# Initialize download in case mirror is used +# ---------------------------------------------------------------------------- +ocv_init_download_mirror() diff --git a/cmake/OpenCVFindLibsGrfmt.cmake b/cmake/OpenCVFindLibsGrfmt.cmake index 95d1d92f6856..00886cc131df 100644 --- a/cmake/OpenCVFindLibsGrfmt.cmake +++ b/cmake/OpenCVFindLibsGrfmt.cmake @@ -17,8 +17,10 @@ else() unset(_zlib_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES) endif() if(ZLIB_FOUND AND ANDROID) - if(ZLIB_LIBRARIES MATCHES "/usr/lib.*/libz.so$") + if(ZLIB_LIBRARY MATCHES "/usr/lib.*/libz.so$") + set(ZLIB_LIBRARY z) set(ZLIB_LIBRARIES z) + set(ZLIB_LIBRARY_RELEASE z) endif() endif() endif() diff --git a/cmake/OpenCVFindTIMVX.cmake b/cmake/OpenCVFindTIMVX.cmake new file mode 100644 index 000000000000..339f726bd984 --- /dev/null +++ b/cmake/OpenCVFindTIMVX.cmake @@ -0,0 +1,69 @@ +set(TIMVX_INSTALL_DIR "" CACHE PATH "Path to libtim-vx installation") +set(VIVANTE_SDK_DIR "" CACHE PATH "Path to VIVANTE SDK needed by TIM-VX.") +set(VIVANTE_SDK_LIB_CANDIDATES "OpenVX;VSC;GAL;ArchModelSw;NNArchPerf" CACHE STRING "VIVANTE SDK library candidates") + +# Ensure VIVANTE SDK library candidates are present in given search path +function(find_vivante_sdk_libs _viv_notfound _viv_search_path) + foreach(one ${VIVANTE_SDK_LIB_CANDIDATES}) + #NO_DEFAULT_PATH is used to ensure VIVANTE SDK libs are from one only source + find_library(VIV_${one}_LIB ${one} PATHS "${_viv_search_path}/lib" NO_DEFAULT_PATH) + if(NOT VIV_${one}_LIB) + list(APPEND _viv_notfound_list ${one}) + endif() + endforeach() + set(${_viv_notfound} ${_viv_notfound_list} PARENT_SCOPE) +endfunction() +# Default value for VIVANTE_SDK_DIR: /usr +if(NOT VIVANTE_SDK_DIR) + set(VIVANTE_SDK_DIR "/usr") +endif() +# Environment variable VIVANTE_SDK_DIR overrides the one in this script +if(DEFINED ENV{VIVANTE_SDK_DIR}) + set(VIVANTE_SDK_DIR $ENV{VIVANTE_SDK_DIR}) + message(STATUS "TIM-VX: Load VIVANTE_SDK_DIR from system environment: ${VIVANTE_SDK_DIR}") +endif() + + +# Compile with pre-installed TIM-VX; Or compile together with TIM-VX from source +if(TIMVX_INSTALL_DIR AND NOT BUILD_TIMVX) + message(STATUS "TIM-VX: Use binaries at ${TIMVX_INSTALL_DIR}") + set(BUILD_TIMVX OFF) + + set(TIMVX_INC_DIR "${TIMVX_INSTALL_DIR}/include" CACHE INTERNAL "TIM-VX include directory") + find_library(TIMVX_LIB "tim-vx" PATHS "${TIMVX_INSTALL_DIR}/lib") + if(TIMVX_LIB) + set(TIMVX_FOUND ON) + else() + set(TIMVX_FOUND OFF) + endif() + + # Verify if requested VIVANTE SDK libraries are all found + find_vivante_sdk_libs(missing ${VIVANTE_SDK_DIR}) + if(missing) + message(STATUS "TIM-VX: Failed to find ${missing} in ${VIVANTE_SDK_DIR}/lib. Turning off TIMVX_VIV_FOUND") + set(TIMVX_VIV_FOUND OFF) + else() + message(STATUS "TIM-VX: dependent VIVANTE SDK libraries are found at ${VIVANTE_SDK_DIR}/lib.") + set(TIMVX_VIV_FOUND ON) + endif() +else() + message(STATUS "TIM-VX: Build from source") + include("${OpenCV_SOURCE_DIR}/3rdparty/libtim-vx/tim-vx.cmake") +endif() + +if(TIMVX_FOUND AND TIMVX_VIV_FOUND) + set(HAVE_TIMVX 1) + + message(STATUS "TIM-VX: Found TIM-VX includes: ${TIMVX_INC_DIR}") + message(STATUS "TIM-VX: Found TIM-VX library: ${TIMVX_LIB}") + set(TIMVX_LIBRARY ${TIMVX_LIB}) + set(TIMVX_INCLUDE_DIR ${TIMVX_INC_DIR}) + + message(STATUS "TIM-VX: Found VIVANTE SDK libraries: ${VIVANTE_SDK_DIR}/lib") + link_directories(${VIVANTE_SDK_DIR}/lib) +endif() + +MARK_AS_ADVANCED( + TIMVX_INC_DIR + TIMVX_LIB +) diff --git a/cmake/OpenCVPluginStandalone.cmake b/cmake/OpenCVPluginStandalone.cmake index 15b7a8085e3d..129ede1ae14b 100644 --- a/cmake/OpenCVPluginStandalone.cmake +++ b/cmake/OpenCVPluginStandalone.cmake @@ -78,10 +78,17 @@ function(ocv_create_plugin module default_name dependency_target dependency_targ set_target_properties(${OPENCV_PLUGIN_NAME} PROPERTIES PREFIX "${OPENCV_PLUGIN_MODULE_PREFIX}") endif() - if(APPLE) - set_target_properties(${OPENCV_PLUGIN_NAME} PROPERTIES LINK_FLAGS "-undefined dynamic_lookup") - elseif(WIN32) - # Hack for Windows only, Linux/MacOS uses global symbol table (without exact .so binding) + if(WIN32 OR NOT APPLE) + set(OPENCV_PLUGIN_NO_LINK FALSE CACHE BOOL "") + else() + set(OPENCV_PLUGIN_NO_LINK TRUE CACHE BOOL "") + endif() + + if(OPENCV_PLUGIN_NO_LINK) + if(APPLE) + set_target_properties(${OPENCV_PLUGIN_NAME} PROPERTIES LINK_FLAGS "-undefined dynamic_lookup") + endif() + else() find_package(OpenCV REQUIRED ${module} ${OPENCV_PLUGIN_DEPS}) target_link_libraries(${OPENCV_PLUGIN_NAME} PRIVATE ${OpenCV_LIBRARIES}) endif() diff --git a/cmake/OpenCVUtils.cmake b/cmake/OpenCVUtils.cmake index d7097fdc30b9..579c5ce54c5d 100644 --- a/cmake/OpenCVUtils.cmake +++ b/cmake/OpenCVUtils.cmake @@ -1619,6 +1619,7 @@ function(ocv_add_external_target name inc link def) endif() endfunction() + # Returns the first non-interface target function(ocv_get_imported_target imported interface) set(__result "${interface}") diff --git a/cmake/checks/cpu_neon.cpp b/cmake/checks/cpu_neon.cpp index c309e8504996..bb103ec3661d 100644 --- a/cmake/checks/cpu_neon.cpp +++ b/cmake/checks/cpu_neon.cpp @@ -1,6 +1,7 @@ #include #if defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) +# define _ARM64_DISTINCT_NEON_TYPES # include # include # define CV_NEON 1 diff --git a/cmake/mirrors/custom.cmake b/cmake/mirrors/custom.cmake new file mode 100644 index 000000000000..3cdf700e1926 --- /dev/null +++ b/cmake/mirrors/custom.cmake @@ -0,0 +1,91 @@ +# Gitlab-style mirror +# CMake scripts look for opencv/opencv_3rdparty, +# OAID/Tengine, 01org/tbb(oneAPI/oneTBB), opencv/ade +# from OPENCV_DOWNLOAD_MIRROR +ocv_update(OPENCV_DOWNLOAD_MIRROR_URL "") + +###### +# Download via commit id +###### +# Tengine +ocv_update(TENGINE_PKG_MD5_CUSTOM "") +ocv_update(TENGINE_PKG_MD5_ORIGINAL 23f61ebb1dd419f1207d8876496289c5) # same as tengine_md5sum for TENGINE commit of e89cf8870de2ff0a80cfe626c0b52b2a16fb302e +# NVIDIA_OPTICAL_FLOW +ocv_update(NVIDIA_OPTICAL_FLOW_PKG_MD5_GITCODE "") +ocv_update(NVIDIA_OPTICAL_FLOW_PKG_MD5_ORIGINAL a73cd48b18dcc0cc8933b30796074191) +# TIM-VX +ocv_update(TIM-VX_PKG_MD5_GITCODE "") +ocv_update(TIM-VX_PKG_MD5_ORIGINAL 92619cc4498014ac7a09834d5e33ebd5) + +###### +# Download from release page +##### +# TBB +ocv_update(TBB_RELEASE_CUSTOM "") +ocv_update(TBB_PKG_NAME_CUSTOM "") +ocv_update(TBB_PKG_MD5_CUSTOM "") +ocv_update(TBB_PKG_MD5_ORIGINAL 5af6f6c2a24c2043e62e47205e273b1f) # same as OPENCV_TBB_RELEASE_MD5 for TBB release of v2020.2 +# ADE +ocv_update(ADE_RELEASE_CUSTOM "") +ocv_update(ADE_PKG_NAME_CUSTOM "") +ocv_update(ADE_PKG_MD5_CUSTOM "") +ocv_update(ADE_PKG_MD5_ORIGINAL b624b995ec9c439cbc2e9e6ee940d3a2) # same as ade_md5 for ADE release of v0.1.1f + +macro(ocv_download_url_custom_usercontent OWNER) + string(REPLACE "/" ";" DL_URL_split ${DL_URL}) + list(GET DL_URL_split 5 __COMMIT_ID) + list(GET DL_URL_split 6 __PKG_NAME) + set(DL_URL "https://${OPENCV_DOWNLOAD_MIRROR_URL}/${OWNER}/opencv_3rdparty/-/raw/${__COMMIT_ID}/${__PKG_NAME}/") +endmacro() +macro(ocv_download_url_custom_archive_commit_id) + if("m${${DL_ID}_PKG_MD5_CUSTOM}" STREQUAL "m") + message(WARNING "ocv_download: specify ${DL_ID}_PKG_MD5_CUSTOM to download ${DL_ID} from custom source.") + elseif(${DL_ID}_PKG_MD5_ORIGINAL STREQUAL "${DL_HASH}") + string(REPLACE "/" ";" DL_URL_split ${DL_URL}) + list(GET DL_URL_split 3 __OWNER) + list(GET DL_URL_split 4 __REPO_NAME) + set(DL_URL "https://${OPENCV_DOWNLOAD_MIRROR_URL}/${__OWNER}/${__REPO_NAME}/-/archive/") + set(DL_HASH "${${DL_ID}_PKG_MD5_CUSTOM}") + else() + message(WARNING "No information about mirrors for downloading ${DL_FILENAME} from URL='${DL_URL}' and MD5=${DL_HASH}.") + endif() +endmacro() +macro(ocv_download_url_custom_archive_release) + if("m${${DL_ID}_RELEASE_CUSTOM}" STREQUAL "m") + message(WARNING "ocv_download: specify ${DL_ID}_RELEASE_CUSTOM to download ${DL_ID} from custom source.") + return() + endif() + if("m${${DL_ID}_PKG_NAME_CUSTOM}" STREQUAL "m") + message(WARNING "ocv_download: specify ${DL_ID}_PKG_NAME_CUSTOM to download ${DL_ID} from custom source.") + return() + endif() + if("m${${DL_ID}_PKG_MD5_CUSTOM}" STREQUAL "m") + message(WARNING "ocv_download: specify ${DL_ID}_PKG_MD5_CUSTOM to download ${DL_ID} from custom source.") + return() + endif() + string(REPLACE "/" ";" DL_URL_split ${DL_URL}) + list(GET DL_URL_split 3 __OWNER) + list(GET DL_URL_split 4 __REPO_NAME) + set(DL_URL "https://${OPENCV_DOWNLOAD_MIRROR_URL}/${__OWNER}/${__REPO_NAME}/-/archive/${${DL_ID}_RELEASE_CUSTOM}/${__REPO_NAME}-") + set(DL_HASH "${${DL_ID}_PKG_MD5_CUSTOM}") +endmacro() + +if("m${OPENCV_DOWNLOAD_MIRROR_URL}" STREQUAL "m") + message(WARNING "ocv_download: specify OPENCV_DOWNLOAD_MIRROR_URL to use custom mirror.") +else() + if((DL_ID STREQUAL "FFMPEG") OR (DL_ID STREQUAL "IPPICV") OR (DL_ID STREQUAL "data") OR (DL_ID STREQUAL "xfeatures2d/boostdesc") OR (DL_ID STREQUAL "xfeatures2d/vgg")) + ocv_download_url_custom_usercontent(opencv) + elseif(DL_ID STREQUAL "wechat_qrcode") + ocv_download_url_gitcode_usercontent(WeChatCV) + elseif((DL_ID STREQUAL "TENGINE") OR (DL_ID STREQUAL "NVIDIA_OPTICAL_FLOW") OR (DL_ID STREQUAL "TIM-VX")) + ocv_download_url_custom_archive_commit_id() + elseif(DL_ID STREQUAL "TBB") + ocv_download_url_custom_archive_release() + set(OPENCV_TBB_SUBDIR "${TBB_PKG_NAME_CUSTOM}" PARENT_SCOPE) + elseif(DL_ID STREQUAL "ADE") + ocv_download_url_custom_archive_release() + set(ade_subdir "${ADE_PKG_NAME_CUSTOM}" PARENT_SCOPE) + else() + message(STATUS "ocv_download: Unknown download ID ${DL_ID} for using mirror ${OPENCV_DOWNLOAD_MIRROR_URL}. Use original source instead.") + endif() +endif() diff --git a/cmake/mirrors/gitcode.cmake b/cmake/mirrors/gitcode.cmake new file mode 100644 index 000000000000..abd7a29be488 --- /dev/null +++ b/cmake/mirrors/gitcode.cmake @@ -0,0 +1,86 @@ +###### +# Download via commit id +###### +# Tengine +ocv_update(TENGINE_PKG_MD5_GITCODE 1b5908632b557275cd6e85b0c03f9690) +ocv_update(TENGINE_PKG_MD5_ORIGINAL 23f61ebb1dd419f1207d8876496289c5) # same as tengine_md5sum for TENGINE commit of e89cf8870de2ff0a80cfe626c0b52b2a16fb302e +# NVIDIA_OPTICAL_FLOW +ocv_update(NVIDIA_OPTICAL_FLOW_PKG_MD5_GITCODE 8d5b7eeb24d6ca9c6bcfdff4196d5b47) +ocv_update(NVIDIA_OPTICAL_FLOW_PKG_MD5_ORIGINAL a73cd48b18dcc0cc8933b30796074191) +# TIM-VX +ocv_update(TIM-VX_PKG_MD5_GITCODE 3f2a548b40b170668aaa60d4f60ba40b) +ocv_update(TIM-VX_PKG_MD5_ORIGINAL 92619cc4498014ac7a09834d5e33ebd5) + +###### +# Download from release page +##### +# TBB +ocv_update(TBB_RELEASE_GITCODE "v2020.2") +ocv_update(TBB_PKG_NAME_GITCODE "tbb-${TBB_RELEASE_GITCODE}") +ocv_update(TBB_PKG_MD5_GITCODE 4eeafdf16a90cb66e39a31c8d6c6804e) +ocv_update(TBB_PKG_MD5_ORIGINAL 5af6f6c2a24c2043e62e47205e273b1f) # same as OPENCV_TBB_RELEASE_MD5 for TBB release of v2020.2 +# ADE +ocv_update(ADE_RELEASE_GITCODE "v0.1.1f") +ocv_update(ADE_PKG_NAME_GITCODE "ade-${ADE_RELEASE_GITCODE}") +ocv_update(ADE_PKG_MD5_GITCODE c12909e0ccfa93138c820ba91ff37b3c) +ocv_update(ADE_PKG_MD5_ORIGINAL b624b995ec9c439cbc2e9e6ee940d3a2) # same as ade_md5 for ADE release of v0.1.1f + +# +# Replace download links for packages in opencv/opencv_3rdparty: +# 1. Extract repo owner and repo name from DL_URL. +# 2. Put repo owner and repo name into the placeholders of new DL_URL. +# +macro(ocv_download_url_gitcode_usercontent OWNER) + string(REPLACE "/" ";" DL_URL_split ${DL_URL}) + list(GET DL_URL_split 5 __COMMIT_ID) + list(GET DL_URL_split 6 __PKG_NAME) + set(DL_URL "https://gitcode.net/${OWNER}/opencv_3rdparty/-/raw/${__COMMIT_ID}/") + if(__PKG_NAME) + set(DL_URL "${DL_URL}${__PKG_NAME}/") + endif() +endmacro() +# +# Replace download links and checksums for archives/releases in other repositories: +# 1. Check if versions matched. If not matched, download from github instead. +# 2. Extract repo owner and repo name from DL_URL. +# 3. Put repo owner and repo name into the placeholders of new DL_URL. +# 4. Replace DL_HASH with the one downloaded from gitcode.net. +# +macro(ocv_download_url_gitcode_archive_commit_id) + if(DL_HASH STREQUAL "${${DL_ID}_PKG_MD5_ORIGINAL}") + string(REPLACE "/" ";" DL_URL_split ${DL_URL}) + list(GET DL_URL_split 3 __OWNER) + list(GET DL_URL_split 4 __REPO_NAME) + set(DL_URL "https://gitcode.net/mirrors/${__OWNER}/${__REPO_NAME}/-/archive/") + set(DL_HASH "${${DL_ID}_PKG_MD5_GITCODE}") + else() + message(WARNING "Package ${DL_ID} from mirror gitcode.net is outdated and will be downloaded from github.com instead.") + endif() +endmacro() +macro(ocv_download_url_gitcode_archive_release) + if(DL_HASH STREQUAL "${${DL_ID}_PKG_MD5_ORIGINAL}") + string(REPLACE "/" ";" DL_URL_split ${DL_URL}) + list(GET DL_URL_split 3 __OWNER) + list(GET DL_URL_split 4 __REPO_NAME) + set(DL_URL "https://gitcode.net/${__OWNER}/${__REPO_NAME}/-/archive/${${DL_ID}_RELEASE_GITCODE}/${__REPO_NAME}-") + set(DL_HASH "${${DL_ID}_PKG_MD5_GITCODE}") + else() + message(WARNING "Package ${DL_ID} from mirror gitcode.net is outdated and will be downloaded from github.com instead.") + endif() +endmacro() + +if((DL_ID STREQUAL "FFMPEG") OR (DL_ID STREQUAL "IPPICV") OR (DL_ID STREQUAL "data") OR (DL_ID STREQUAL "xfeatures2d/boostdesc") OR (DL_ID STREQUAL "xfeatures2d/vgg")) + ocv_download_url_gitcode_usercontent(opencv) +elseif(DL_ID STREQUAL "wechat_qrcode") + ocv_download_url_gitcode_usercontent(mirrors/WeChatCV) +elseif((DL_ID STREQUAL "TENGINE") OR (DL_ID STREQUAL "NVIDIA_OPTICAL_FLOW") OR (DL_ID STREQUAL "TIM-VX")) + ocv_download_url_gitcode_archive_commit_id() +elseif(DL_ID STREQUAL "TBB") + ocv_download_url_gitcode_archive_release() + set(OPENCV_TBB_SUBDIR "${TBB_PKG_NAME_GITCODE}" PARENT_SCOPE) +elseif(DL_ID STREQUAL "ADE") + ocv_download_url_gitcode_archive_release() + set(ade_subdir "${ADE_PKG_NAME_GITCODE}" PARENT_SCOPE) +else() + message(STATUS "ocv_download: Unknown download ID ${DL_ID} for using mirror gitcode.net. Use original source instead.") +endif() diff --git a/doc/js_tutorials/js_assets/webnn-electron/package.json b/doc/js_tutorials/js_assets/webnn-electron/package.json index e6a258ee4053..9c3c817db796 100644 --- a/doc/js_tutorials/js_assets/webnn-electron/package.json +++ b/doc/js_tutorials/js_assets/webnn-electron/package.json @@ -1,7 +1,7 @@ { "name": "image_classification", "version": "0.0.1", - "description": "An Electon.js example of image_classification using webnn-native", + "description": "An Electron.js example of image_classification using webnn-native", "main": "main.js", "author": "WebNN-native Authors", "license": "Apache-2.0", diff --git a/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown b/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown index b5e10e0e674c..9ffa218bb2a5 100644 --- a/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown +++ b/doc/js_tutorials/js_imgproc/js_morphological_ops/js_morphological_ops.markdown @@ -52,7 +52,7 @@ Try it ### 2. Dilation -It is just opposite of erosion. Here, a pixel element is '1' if atleast one pixel under the kernel +It is just opposite of erosion. Here, a pixel element is '1' if at least one pixel under the kernel is '1'. So it increases the white region in the image or size of foreground object increases. Normally, in cases like noise removal, erosion is followed by dilation. Because, erosion removes white noises, but it also shrinks our object. So we dilate it. Since noise is gone, they won't come diff --git a/doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown b/doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown index 361543502b20..f424d0ca812a 100644 --- a/doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown +++ b/doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown @@ -223,7 +223,7 @@ Before the example, is worth consider first how files are handled in emscripten These C++ sources use standard APIs to access the filesystem and the implementation often ends up in system calls that read a file in the hard drive. Since JavaScript applications in the browser don't have access to the local filesystem, [emscripten emulates a standard filesystem](https://emscripten.org/docs/api_reference/Filesystem-API.html) so compiled C++ code works out of the box. -In the browser, this filesystem is emulated in memory while in Node.js there's also the possibility of using the local filesystem directly. This is often preferable since there's no need of copy file's content in memory. This section is explains how to do do just that, this is, configuring emscripten so files are accessed directly from our local filesystem and relative paths match files relative to the current local directory as expected. +In the browser, this filesystem is emulated in memory while in Node.js there's also the possibility of using the local filesystem directly. This is often preferable since there's no need of copy file's content in memory. This section explains how to do just that, this is, configuring emscripten so files are accessed directly from our local filesystem and relative paths match files relative to the current local directory as expected. ### The example ### diff --git a/doc/js_tutorials/js_setup/js_setup/js_setup.markdown b/doc/js_tutorials/js_setup/js_setup/js_setup.markdown index 992747744327..2a7a111d8a8f 100644 --- a/doc/js_tutorials/js_setup/js_setup/js_setup.markdown +++ b/doc/js_tutorials/js_setup/js_setup/js_setup.markdown @@ -97,10 +97,10 @@ Building OpenCV.js from Source @endcode @note - The loader is implemented as a js file in the path `/bin/loader.js`. The loader utilizes the [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) to detect the features of the broswer and load corresponding OpenCV.js automatically. To use it, you need to use the UMD version of [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) and introduce the `loader.js` in your Web application. + The loader is implemented as a js file in the path `/bin/loader.js`. The loader utilizes the [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) to detect the features of the browser and load corresponding OpenCV.js automatically. To use it, you need to use the UMD version of [WebAssembly Feature Detection](https://github.com/GoogleChromeLabs/wasm-feature-detect) and introduce the `loader.js` in your Web application. Example Code: - @code{.javascipt} + @code{.javascript} // Set paths configuration let pathsConfig = { wasm: "../../build_wasm/opencv.js", @@ -173,7 +173,7 @@ This snippet and the following require [Node.js](https://nodejs.org) to be insta ### Headless with Puppeteer -Alternatively tests can run with [GoogleChrome/puppeteer](https://github.com/GoogleChrome/puppeteer#readme) which is a version of Google Chrome that runs in the terminal (useful for Continuos integration like travis CI, etc) +Alternatively tests can run with [GoogleChrome/puppeteer](https://github.com/GoogleChrome/puppeteer#readme) which is a version of Google Chrome that runs in the terminal (useful for Continuous integration like travis CI, etc) @code{.sh} cd build_js/bin @@ -229,7 +229,7 @@ node tests.js The simd optimization is experimental as wasm simd is still in development. @note - Now only emscripten LLVM upstream backend supports wasm simd, refering to https://emscripten.org/docs/porting/simd.html. So you need to setup upstream backend environment with the following command first: + Now only emscripten LLVM upstream backend supports wasm simd, referring to https://emscripten.org/docs/porting/simd.html. So you need to setup upstream backend environment with the following command first: @code{.bash} ./emsdk update ./emsdk install latest-upstream diff --git a/doc/js_tutorials/js_setup/js_usage/js_usage.markdown b/doc/js_tutorials/js_setup/js_usage/js_usage.markdown index dbf4f1ec1540..4034977f0869 100644 --- a/doc/js_tutorials/js_setup/js_usage/js_usage.markdown +++ b/doc/js_tutorials/js_setup/js_usage/js_usage.markdown @@ -122,11 +122,14 @@ imgElement.onload = function() { mat.delete(); }; -function onOpenCvReady() { - document.getElementById('status').innerHTML = 'OpenCV.js is ready.'; -} +var Module = { + // https://emscripten.org/docs/api_reference/module.html#Module.onRuntimeInitialized + onRuntimeInitialized() { + document.getElementById('status').innerHTML = 'OpenCV.js is ready.'; + } +}; - + @endcode diff --git a/doc/opencv.bib b/doc/opencv.bib index 62c1c799124c..a5b17335b2e5 100644 --- a/doc/opencv.bib +++ b/doc/opencv.bib @@ -1348,3 +1348,13 @@ @article{umeyama1991least year={1991}, publisher={IEEE Computer Society} } +@article{Kannala2006, + author = {Kannala, Juho and Brandt, Sami}, + year = {2006}, + month = {09}, + pages = {1335-40}, + title = {A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses}, + volume = {28}, + journal = {IEEE transactions on pattern analysis and machine intelligence}, + doi = {10.1109/TPAMI.2006.153} +} diff --git a/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown b/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown index f8836b095b59..6abac2c57bbd 100644 --- a/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown +++ b/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown @@ -18,7 +18,7 @@ is sufficient to find the object exactly on the trainImage. For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set of points from both the images, it will find the perspective transformation of that object. Then we -can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to +can use **cv.perspectiveTransform()** to find the object. It needs at least four correct points to find the transformation. We have seen that there can be some possible errors while matching which may affect the result. To @@ -64,7 +64,7 @@ for m,n in matches: if m.distance < 0.7*n.distance: good.append(m) @endcode -Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are to be there to +Now we set a condition that at least 10 matches (defined by MIN_MATCH_COUNT) are to be there to find the object. Otherwise simply show a message saying not enough matches are present. If enough matches are found, we extract the locations of matched keypoints in both the images. They diff --git a/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown b/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown index 35e716d8e95d..84a62d14cdc8 100644 --- a/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown +++ b/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.markdown @@ -48,7 +48,7 @@ Result: ### 2. Dilation -It is just opposite of erosion. Here, a pixel element is '1' if atleast one pixel under the kernel +It is just opposite of erosion. Here, a pixel element is '1' if at least one pixel under the kernel is '1'. So it increases the white region in the image or size of foreground object increases. Normally, in cases like noise removal, erosion is followed by dilation. Because, erosion removes white noises, but it also shrinks our object. So we dilate it. Since noise is gone, they won't come diff --git a/doc/tutorials/calib3d/usac.markdown b/doc/tutorials/calib3d/usac.markdown index 27d590be3a0d..df9e25f907b9 100644 --- a/doc/tutorials/calib3d/usac.markdown +++ b/doc/tutorials/calib3d/usac.markdown @@ -244,9 +244,9 @@ Samples: There are three new sample files in opencv/samples directory. 1. `epipolar_lines.cpp` – input arguments of `main` function are two - pathes to images. Then correspondences are found using + paths to images. Then correspondences are found using SIFT detector. Fundamental matrix is found using RANSAC from - tentaive correspondences and epipolar lines are plot. + tentative correspondences and epipolar lines are plot. 2. `essential_mat_reconstr.cpp` – input arguments are path to data file containing image names and single intrinsic matrix and directory diff --git a/doc/tutorials/core/how_to_use_OpenCV_parallel_for_new/how_to_use_OpenCV_parallel_for_new.markdown b/doc/tutorials/core/how_to_use_OpenCV_parallel_for_new/how_to_use_OpenCV_parallel_for_new.markdown index 5ef63ed6f4f7..57cec4cba16f 100644 --- a/doc/tutorials/core/how_to_use_OpenCV_parallel_for_new/how_to_use_OpenCV_parallel_for_new.markdown +++ b/doc/tutorials/core/how_to_use_OpenCV_parallel_for_new/how_to_use_OpenCV_parallel_for_new.markdown @@ -92,7 +92,7 @@ We then fill value to the corresponding pixel in the dst image. ### Parallel implementation -When looking at the sequential implementation, we can notice that each pixel depends on multiple neighbouring pixels but only one pixel is edited at a time. Thus, to optimize the computation, we can split the image into stripes and parallely perform convolution on each, by exploiting the multi-core architecture of modern processor. The OpenCV @ref cv::parallel_for_ framework automatically decides how to split the computation efficiently and does most of the work for us. +When looking at the sequential implementation, we can notice that each pixel depends on multiple neighbouring pixels but only one pixel is edited at a time. Thus, to optimize the computation, we can split the image into stripes and parallelly perform convolution on each, by exploiting the multi-core architecture of modern processor. The OpenCV @ref cv::parallel_for_ framework automatically decides how to split the computation efficiently and does most of the work for us. @note Although values of a pixel in a particular stripe may depend on pixel values outside the stripe, these are only read only operations and hence will not cause undefined behaviour. diff --git a/doc/tutorials/dnn/dnn_face/dnn_face.markdown b/doc/tutorials/dnn/dnn_face/dnn_face.markdown index d85eb3c2ee18..e55f0d1bc29a 100644 --- a/doc/tutorials/dnn/dnn_face/dnn_face.markdown +++ b/doc/tutorials/dnn/dnn_face/dnn_face.markdown @@ -8,19 +8,19 @@ | | | | -: | :- | | Original Author | Chengrui Wang, Yuantao Feng | -| Compatibility | OpenCV >= 4.5.1 | +| Compatibility | OpenCV >= 4.5.4 | ## Introduction -In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizerSF` are presented in [Usage](#Usage). +In this section, we introduce cv::FaceDetectorYN class for face detection and cv::FaceRecognizerSF class for face recognition. ## Models There are two models (ONNX format) pre-trained and required for this module: -- [Face Detection](https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx): - - Size: 337KB +- [Face Detection](https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet): + - Size: 338KB - Results on WIDER Face Val set: 0.830(easy), 0.824(medium), 0.708(hard) -- [Face Recognition](https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view?usp=sharing) +- [Face Recognition](https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface) - Size: 36.9MB - Results: @@ -32,9 +32,7 @@ There are two models (ONNX format) pre-trained and required for this module: | AgeDB-30 | 94.90% | 1.202 | 0.277 | | CFP-FP | 94.80% | 1.253 | 0.212 | -## Usage - -### DNNFaceDetector +## Code @add_toggle_cpp - **Downloadable code**: Click diff --git a/doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown b/doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown index 38324610be98..6d2751a467fe 100644 --- a/doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown +++ b/doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown @@ -70,7 +70,7 @@ Sometimes networks built using blocked structure that means some layer are identical or quite similar. If you want to apply the same scheduling for different layers accurate to tiling or vectorization factors, define scheduling patterns in section `patterns` at the beginning of scheduling file. -Also, your patters may use some parametric variables. +Also, your patterns may use some parametric variables. @code # At the beginning of the file patterns: diff --git a/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown b/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown index c2b3ec8d7115..b6f4e120fb7a 100644 --- a/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown +++ b/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown @@ -29,8 +29,8 @@ Before recognition, you should `setVocabulary` and `setDecodeType`. - "CTC-prefix-beam-search", the output of the text recognition model should be a probability matrix same with "CTC-greedy". - The algorithm is proposed at Hannun's [paper](https://arxiv.org/abs/1408.2873). - `setDecodeOptsCTCPrefixBeamSearch` could be used to control the beam size in search step. - - To futher optimize for big vocabulary, a new option `vocPruneSize` is introduced to avoid iterate the whole vocbulary - but only the number of `vocPruneSize` tokens with top probabilty. + - To further optimize for big vocabulary, a new option `vocPruneSize` is introduced to avoid iterate the whole vocbulary + but only the number of `vocPruneSize` tokens with top probability. @ref cv::dnn::TextRecognitionModel::recognize() is the main function for text recognition. - The input image should be a cropped text image or an image with `roiRects` diff --git a/doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown b/doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown index fa25c2b0b37b..64b68e644c92 100644 --- a/doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown +++ b/doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown @@ -142,7 +142,7 @@ being a Graph API, doesn't force its users to do that. However, a graph is still built implicitly when a cv::GComputation object is defined. It may be useful to inspect how the resulting graph looks like to check if it is generated correctly and if it really -represents our alrogithm. It is also useful to learn the structure of +represents our algorithm. It is also useful to learn the structure of the graph to see if it has any redundancies. G-API allows to dump generated graphs to `.dot` files which then @@ -295,7 +295,7 @@ and specify it to G-API: In G-API, kernels (or operation implementations) are objects. Kernels are organized into collections, or _kernel packages_, represented by class -cv::gapi::GKernelPackage. The main purpose of a kernel package is to +cv::GKernelPackage. The main purpose of a kernel package is to capture which kernels we would like to use in our graph, and pass it as a _graph compilation option_: diff --git a/doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown b/doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown index 6f8b03bb6120..27916b4176ec 100644 --- a/doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown +++ b/doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown @@ -241,7 +241,7 @@ pipeline is compiled for streaming: cv::GComputation::compileStreaming() triggers a special video-oriented form of graph compilation where G-API is trying to optimize throughput. Result of this compilation is an object of special type -cv::GStreamingCompiled -- in constract to a traditional callable +cv::GStreamingCompiled -- in contrast to a traditional callable cv::GCompiled, these objects are closer to media players in their semantics. diff --git a/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown b/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown index 952b54249d6c..937e29134222 100644 --- a/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown +++ b/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown @@ -81,11 +81,11 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o @end_toggle @add_toggle_java -@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py load +@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java load @end_toggle @add_toggle_python -@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java load +@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py load @end_toggle #### Convert it to grayscale: @@ -95,11 +95,11 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o @end_toggle @add_toggle_java -@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py convert_to_gray +@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java convert_to_gray @end_toggle @add_toggle_python -@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java convert_to_gray +@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py convert_to_gray @end_toggle #### Apply a Median blur to reduce noise and avoid false circle detection: @@ -109,11 +109,11 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o @end_toggle @add_toggle_java -@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py reduce_noise +@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java reduce_noise @end_toggle @add_toggle_python -@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java reduce_noise +@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py reduce_noise @end_toggle #### Proceed to apply Hough Circle Transform: @@ -123,11 +123,11 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o @end_toggle @add_toggle_java -@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py houghcircles +@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java houghcircles @end_toggle @add_toggle_python -@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java houghcircles +@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py houghcircles @end_toggle - with the arguments: @@ -151,11 +151,11 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o @end_toggle @add_toggle_java -@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py draw +@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java draw @end_toggle @add_toggle_python -@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java draw +@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py draw @end_toggle You can see that we will draw the circle(s) on red and the center(s) with a small green dot @@ -167,11 +167,11 @@ You can see that we will draw the circle(s) on red and the center(s) with a smal @end_toggle @add_toggle_java -@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py display +@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java display @end_toggle @add_toggle_python -@snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java display +@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py display @end_toggle Result diff --git a/doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md b/doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md index ce5223c5218d..2d0f5de98d1c 100644 --- a/doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md +++ b/doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md @@ -130,7 +130,7 @@ Get image from [here](https://raw.githubusercontent.com/opencv/opencv/4.x/doc/tu #### Output images -Now we are ready to apply morphological operations in order to extract the horizontal and vertical lines and as a consequence to separate the the music notes from the music sheet, but first let's initialize the output images that we will use for that reason: +Now we are ready to apply morphological operations in order to extract the horizontal and vertical lines and as a consequence to separate the music notes from the music sheet, but first let's initialize the output images that we will use for that reason: @add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp init diff --git a/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown b/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown index 520d8761eb8b..14b3105b6843 100644 --- a/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown +++ b/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown @@ -79,7 +79,7 @@ The main function is rather simple, as follows from the comments we do the follo In general callback functions are used to react to some kind of signal, in our case it's trackbar's state change. Explicit one-time call of `thresh_callback` is necessary to display - the "Contours" window simultaniously with the "Source" window. + the "Contours" window simultaneously with the "Source" window. @add_toggle_cpp @snippet samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp trackbar diff --git a/doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown b/doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown index 5acdbc41ed96..d37721a188c2 100644 --- a/doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown +++ b/doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.markdown @@ -240,7 +240,7 @@ taken: Hello OpenCV Sample ------------------- -Here are basic steps to guide you trough the process of creating a simple OpenCV-centric +Here are basic steps to guide you through the process of creating a simple OpenCV-centric application. It will be capable of accessing camera output, processing it and displaying the result. -# Open Eclipse IDE, create a new clean workspace, create a new Android project diff --git a/doc/tutorials/introduction/config_reference/config_reference.markdown b/doc/tutorials/introduction/config_reference/config_reference.markdown index dda14108c442..388b0cc7c1e1 100644 --- a/doc/tutorials/introduction/config_reference/config_reference.markdown +++ b/doc/tutorials/introduction/config_reference/config_reference.markdown @@ -476,9 +476,10 @@ OpenCV have own DNN inference module which have own build-in engine, but can als | `BUILD_PROTOBUF` | _ON_ | Build own copy of _protobuf_. Must be disabled if you want to use external library. | | `PROTOBUF_UPDATE_FILES` | _OFF_ | Re-generate all .proto files. _protoc_ compiler compatible with used version of _protobuf_ must be installed. | | `OPENCV_DNN_OPENCL` | _ON_ | Enable built-in OpenCL inference backend. | -| `WITH_INF_ENGINE` | _OFF_ | Enables [Intel Inference Engine (IE)](https://github.com/openvinotoolkit/openvino) backend. Allows to execute networks in IE format (.xml + .bin). Inference Engine must be installed either as part of [OpenVINO toolkit](https://en.wikipedia.org/wiki/OpenVINO), either as a standalone library built from sources. | -| `INF_ENGINE_RELEASE` | _2020040000_ | Defines version of Inference Engine library which is tied to OpenVINO toolkit version. Must be a 10-digit string, e.g. _2020040000_ for OpenVINO 2020.4. | -| `WITH_NGRAPH` | _OFF_ | Enables Intel NGraph library support. This library is part of Inference Engine backend which allows executing arbitrary networks read from files in multiple formats supported by OpenCV: Caffe, TensorFlow, PyTorch, Darknet, etc.. NGraph library must be installed, it is included into Inference Engine. | +| `WITH_INF_ENGINE` | _OFF_ | **Deprecated since OpenVINO 2022.1** Enables [Intel Inference Engine (IE)](https://github.com/openvinotoolkit/openvino) backend. Allows to execute networks in IE format (.xml + .bin). Inference Engine must be installed either as part of [OpenVINO toolkit](https://en.wikipedia.org/wiki/OpenVINO), either as a standalone library built from sources. | +| `INF_ENGINE_RELEASE` | _2020040000_ | **Deprecated since OpenVINO 2022.1** Defines version of Inference Engine library which is tied to OpenVINO toolkit version. Must be a 10-digit string, e.g. _2020040000_ for OpenVINO 2020.4. | +| `WITH_NGRAPH` | _OFF_ | **Deprecated since OpenVINO 2022.1** Enables Intel NGraph library support. This library is part of Inference Engine backend which allows executing arbitrary networks read from files in multiple formats supported by OpenCV: Caffe, TensorFlow, PyTorch, Darknet, etc.. NGraph library must be installed, it is included into Inference Engine. | +| `WITH_OPENVINO` | _OFF_ | Enable Intel OpenVINO Toolkit support. Should be used for OpenVINO>=2022.1 instead of `WITH_INF_ENGINE` and `WITH_NGRAPH`. | | `OPENCV_DNN_CUDA` | _OFF_ | Enable CUDA backend. [CUDA](https://en.wikipedia.org/wiki/CUDA), CUBLAS and [CUDNN](https://developer.nvidia.com/cudnn) must be installed. | | `WITH_HALIDE` | _OFF_ | Use experimental [Halide](https://en.wikipedia.org/wiki/Halide_(programming_language)) backend which can generate optimized code for dnn-layers at runtime. Halide must be installed. | | `WITH_VULKAN` | _OFF_ | Enable experimental [Vulkan](https://en.wikipedia.org/wiki/Vulkan_(API)) backend. Does not require additional dependencies, but can use external Vulkan headers (`VULKAN_INCLUDE_DIRS`). | diff --git a/doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown b/doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown index dfd244fbedeb..ccef42a482c6 100644 --- a/doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown +++ b/doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown @@ -46,14 +46,14 @@ Open your Doxyfile using your favorite text editor and search for the key `TAGFILES`. Change it as follows: @code -TAGFILES = ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/4.5.5 +TAGFILES = ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/4.6.0 @endcode If you had other definitions already, you can append the line using a `\`: @code TAGFILES = ./docs/doxygen-tags/libstdc++.tag=https://gcc.gnu.org/onlinedocs/libstdc++/latest-doxygen \ - ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/4.5.5 + ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/4.6.0 @endcode Doxygen can now use the information from the tag file to link to the OpenCV diff --git a/doc/tutorials/introduction/linux_gdb_pretty_printer/linux_gdb_pretty_printer.markdown b/doc/tutorials/introduction/linux_gdb_pretty_printer/linux_gdb_pretty_printer.markdown index 9d6446992000..b0b8d404a0f7 100644 --- a/doc/tutorials/introduction/linux_gdb_pretty_printer/linux_gdb_pretty_printer.markdown +++ b/doc/tutorials/introduction/linux_gdb_pretty_printer/linux_gdb_pretty_printer.markdown @@ -20,7 +20,7 @@ This pretty-printer can show element type, `is_continuous`, `is_submatrix` flags # Installation {#tutorial_linux_gdb_pretty_printer_installation} -Move into `opencv/samples/gdb/`. Place `mat_pretty_printer.py` in a convinient place, rename `gdbinit` to `.gdbinit` and move it into your home folder. Change 'source' line of `.gdbinit` to point to your `mat_pretty_printer.py` path. +Move into `opencv/samples/gdb/`. Place `mat_pretty_printer.py` in a convenient place, rename `gdbinit` to `.gdbinit` and move it into your home folder. Change 'source' line of `.gdbinit` to point to your `mat_pretty_printer.py` path. In order to check version of python bundled with your gdb, use the following commands from the gdb shell: @@ -34,5 +34,5 @@ If the version of python 3 installed in your system doesn't match the version in # Usage {#tutorial_linux_gdb_pretty_printer_usage} -The fields in a debugger prefixed with `view_` are pseudo-fields added for convinience, the rest are left as is. -If you feel that the number of elements in truncated view is too low, you can edit `mat_pretty_printer.py` - `np.set_printoptions` controlls everything matrix display-related. +The fields in a debugger prefixed with `view_` are pseudo-fields added for convenience, the rest are left as is. +If you feel that the number of elements in truncated view is too low, you can edit `mat_pretty_printer.py` - `np.set_printoptions` controls everything matrix display-related. diff --git a/doc/tutorials/ios/image_manipulation/image_manipulation.markdown b/doc/tutorials/ios/image_manipulation/image_manipulation.markdown index f01aa6e4f857..57f34e8e4cc2 100644 --- a/doc/tutorials/ios/image_manipulation/image_manipulation.markdown +++ b/doc/tutorials/ios/image_manipulation/image_manipulation.markdown @@ -22,7 +22,7 @@ Introduction In *OpenCV* all the image processing operations are usually carried out on the *Mat* structure. In iOS however, to render an image on screen it have to be an instance of the *UIImage* class. To convert an *OpenCV Mat* to an *UIImage* we use the *Core Graphics* framework available in iOS. Below -is the code needed to covert back and forth between Mat's and UIImage's. +is the code needed to convert back and forth between Mat's and UIImage's. @code{.m} - (cv::Mat)cvMatFromUIImage:(UIImage *)image { diff --git a/modules/calib3d/include/opencv2/calib3d.hpp b/modules/calib3d/include/opencv2/calib3d.hpp index b3709c8cc25b..6558d2caf9b1 100644 --- a/modules/calib3d/include/opencv2/calib3d.hpp +++ b/modules/calib3d/include/opencv2/calib3d.hpp @@ -45,6 +45,7 @@ #define OPENCV_CALIB3D_HPP #include "opencv2/core.hpp" +#include "opencv2/core/types.hpp" #include "opencv2/features2d.hpp" #include "opencv2/core/affine.hpp" @@ -429,6 +430,9 @@ R & t \\ \f[u = f_x (x' + \alpha y') + c_x \\ v = f_y y' + c_y\f] + Summary: + Generic camera model @cite Kannala2006 with perspective projection and without distortion correction + @defgroup calib3d_c C API @} @@ -3721,6 +3725,21 @@ void undistortPoints(InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, InputArray R, InputArray P, TermCriteria criteria); +/** + * @brief Compute undistorted image points position + * + * @param src Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or +CV_64FC2) (or vector\ ). + * @param dst Output undistorted points position (1xN/Nx1 2-channel or vector\ ). + * @param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . + * @param distCoeffs Distortion coefficients + */ +CV_EXPORTS_W +void undistortImagePoints(InputArray src, OutputArray dst, InputArray cameraMatrix, + InputArray distCoeffs, + TermCriteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, + 0.01)); + //! @} calib3d /** @brief The methods in this namespace use a so-called fisheye camera model. @@ -3783,8 +3802,7 @@ namespace fisheye @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. - This means if you want to transform back points undistorted with #fisheye::undistortPoints you have to - multiply them with \f$P^{-1}\f$. + This means if you want to distort image points you have to multiply them with \f$K^{-1}\f$. */ CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); @@ -3797,10 +3815,12 @@ namespace fisheye @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4) + @param criteria Termination criteria @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . */ CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, - InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); + InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray(), + TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 10, 1e-8)); /** @brief Computes undistortion and rectification maps for image transform by #remap. If D is empty zero distortion is used, if R or P is empty identity matrixes are used. @@ -3866,7 +3886,7 @@ namespace fisheye CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); - /** @brief Performs camera calibaration + /** @brief Performs camera calibration @param objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space. diff --git a/modules/calib3d/perf/perf_undistort.cpp b/modules/calib3d/perf/perf_undistort.cpp index e15d2aefe3a1..fa0d7ea2ecd0 100644 --- a/modules/calib3d/perf/perf_undistort.cpp +++ b/modules/calib3d/perf/perf_undistort.cpp @@ -27,4 +27,36 @@ PERF_TEST(Undistort, DISABLED_InitInverseRectificationMap) SANITY_CHECK_NOTHING(); } +PERF_TEST(Undistort, fisheye_undistortPoints_100k_10iter) +{ + const int pointsNumber = 100000; + const Size imageSize(1280, 800); + + /* Set camera matrix */ + const Matx33d K(558.478087865323, 0, 620.458515360843, + 0, 560.506767351568, 381.939424848348, + 0, 0, 1); + + /* Set distortion coefficients */ + const Matx14d D(2.81e-06, 1.31e-06, -4.42e-06, -1.25e-06); + + /* Create two-channel points matrix */ + Mat xy[2] = {}; + xy[0].create(pointsNumber, 1, CV_64F); + theRNG().fill(xy[0], RNG::UNIFORM, 0, imageSize.width); // x + xy[1].create(pointsNumber, 1, CV_64F); + theRNG().fill(xy[1], RNG::UNIFORM, 0, imageSize.height); // y + + Mat points; + merge(xy, 2, points); + + /* Set fixed iteration number to check only c++ code, not algo convergence */ + TermCriteria termCriteria(TermCriteria::MAX_ITER, 10, 0); + + Mat undistortedPoints; + TEST_CYCLE() fisheye::undistortPoints(points, undistortedPoints, K, D, noArray(), noArray(), termCriteria); + + SANITY_CHECK_NOTHING(); +} + } // namespace diff --git a/modules/calib3d/src/calibinit.cpp b/modules/calib3d/src/calibinit.cpp index e25dd26d21bb..9e96802388ee 100644 --- a/modules/calib3d/src/calibinit.cpp +++ b/modules/calib3d/src/calibinit.cpp @@ -1469,7 +1469,7 @@ int ChessBoardDetector::checkQuadGroup(std::vector& quad_group, first = below; // remember the first corner in the next row // find and store the first row (or column) - for (int j = 1; ; ++j) + while( 1 ) { right->row = 0; out_corners.push_back(right); diff --git a/modules/calib3d/src/dls.cpp b/modules/calib3d/src/dls.cpp index a0ff0c3e1e8a..f0b9d850e987 100644 --- a/modules/calib3d/src/dls.cpp +++ b/modules/calib3d/src/dls.cpp @@ -21,15 +21,15 @@ # include "opencv2/core/eigen.hpp" #endif -using namespace std; +namespace cv { -dls::dls(const cv::Mat& opoints, const cv::Mat& ipoints) +dls::dls(const Mat& opoints, const Mat& ipoints) { - N = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); - p = cv::Mat(3, N, CV_64F); - z = cv::Mat(3, N, CV_64F); - mn = cv::Mat::zeros(3, 1, CV_64F); + N = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); + p = Mat(3, N, CV_64F); + z = Mat(3, N, CV_64F); + mn = Mat::zeros(3, 1, CV_64F); cost__ = 9999; @@ -40,14 +40,14 @@ dls::dls(const cv::Mat& opoints, const cv::Mat& ipoints) if (opoints.depth() == ipoints.depth()) { if (opoints.depth() == CV_32F) - init_points(opoints, ipoints); + init_points(opoints, ipoints); else - init_points(opoints, ipoints); + init_points(opoints, ipoints); } else if (opoints.depth() == CV_32F) - init_points(opoints, ipoints); + init_points(opoints, ipoints); else - init_points(opoints, ipoints); + init_points(opoints, ipoints); } dls::~dls() @@ -55,10 +55,10 @@ dls::~dls() // TODO Auto-generated destructor stub } -bool dls::compute_pose(cv::Mat& R, cv::Mat& t) +bool dls::compute_pose(Mat& R, Mat& t) { - std::vector R_; + std::vector R_; R_.push_back(rotx(CV_PI/2)); R_.push_back(roty(CV_PI/2)); R_.push_back(rotz(CV_PI/2)); @@ -67,7 +67,7 @@ bool dls::compute_pose(cv::Mat& R, cv::Mat& t) for (int i = 0; i < 3; ++i) { // Make a random rotation - cv::Mat pp = R_[i] * ( p - cv::repeat(mn, 1, p.cols) ); + Mat pp = R_[i] * ( p - repeat(mn, 1, p.cols) ); // clear for new data C_est_.clear(); @@ -99,13 +99,13 @@ bool dls::compute_pose(cv::Mat& R, cv::Mat& t) return false; } -void dls::run_kernel(const cv::Mat& pp) +void dls::run_kernel(const Mat& pp) { - cv::Mat Mtilde(27, 27, CV_64F); - cv::Mat D = cv::Mat::zeros(9, 9, CV_64F); + Mat Mtilde(27, 27, CV_64F); + Mat D = Mat::zeros(9, 9, CV_64F); build_coeff_matrix(pp, Mtilde, D); - cv::Mat eigenval_r, eigenval_i, eigenvec_r, eigenvec_i; + Mat eigenval_r, eigenval_i, eigenvec_r, eigenvec_i; compute_eigenvec(Mtilde, eigenval_r, eigenval_i, eigenvec_r, eigenvec_i); /* @@ -115,16 +115,16 @@ void dls::run_kernel(const cv::Mat& pp) // extract the optimal solutions from the eigen decomposition of the // Multiplication matrix - cv::Mat sols = cv::Mat::zeros(3, 27, CV_64F); + Mat sols = Mat::zeros(3, 27, CV_64F); std::vector cost; int count = 0; for (int k = 0; k < 27; ++k) { // V(:,k) = V(:,k)/V(1,k); - cv::Mat V_kA = eigenvec_r.col(k); // 27x1 - cv::Mat V_kB = cv::Mat(1, 1, z.depth(), V_kA.at(0)); // 1x1 - cv::Mat V_k; cv::solve(V_kB.t(), V_kA.t(), V_k); // A/B = B'\A' - cv::Mat( V_k.t()).copyTo( eigenvec_r.col(k) ); + Mat V_kA = eigenvec_r.col(k); // 27x1 + Mat V_kB = Mat(1, 1, z.depth(), V_kA.at(0)); // 1x1 + Mat V_k; solve(V_kB.t(), V_kA.t(), V_k); // A/B = B'\A' + Mat( V_k.t()).copyTo( eigenvec_r.col(k) ); //if (imag(V(2,k)) == 0) #ifdef HAVE_EIGEN @@ -138,24 +138,24 @@ void dls::run_kernel(const cv::Mat& pp) stmp[1] = eigenvec_r.at(3, k); stmp[2] = eigenvec_r.at(1, k); - cv::Mat H = Hessian(stmp); + Mat H = Hessian(stmp); - cv::Mat eigenvalues, eigenvectors; - cv::eigen(H, eigenvalues, eigenvectors); + Mat eigenvalues, eigenvectors; + eigen(H, eigenvalues, eigenvectors); if(positive_eigenvalues(&eigenvalues)) { // sols(:,i) = stmp; - cv::Mat stmp_mat(3, 1, CV_64F, &stmp); + Mat stmp_mat(3, 1, CV_64F, &stmp); stmp_mat.copyTo( sols.col(count) ); - cv::Mat Cbar = cayley2rotbar(stmp_mat); - cv::Mat Cbarvec = Cbar.reshape(1,1).t(); + Mat Cbar = cayley2rotbar(stmp_mat); + Mat Cbarvec = Cbar.reshape(1,1).t(); // cost(i) = CbarVec' * D * CbarVec; - cv::Mat cost_mat = Cbarvec.t() * D * Cbarvec; + Mat cost_mat = Cbarvec.t() * D * Cbarvec; cost.push_back( cost_mat.at(0) ); count++; @@ -166,30 +166,30 @@ void dls::run_kernel(const cv::Mat& pp) // extract solutions sols = sols.clone().colRange(0, count); - std::vector C_est, t_est; + std::vector C_est, t_est; for (int j = 0; j < sols.cols; ++j) { // recover the optimal orientation // C_est(:,:,j) = 1/(1 + sols(:,j)' * sols(:,j)) * cayley2rotbar(sols(:,j)); - cv::Mat sols_j = sols.col(j); - double sols_mult = 1./(1.+cv::Mat( sols_j.t() * sols_j ).at(0)); - cv::Mat C_est_j = cayley2rotbar(sols_j).mul(sols_mult); + Mat sols_j = sols.col(j); + double sols_mult = 1./(1.+Mat( sols_j.t() * sols_j ).at(0)); + Mat C_est_j = cayley2rotbar(sols_j).mul(sols_mult); C_est.push_back( C_est_j ); - cv::Mat A2 = cv::Mat::zeros(3, 3, CV_64F); - cv::Mat b2 = cv::Mat::zeros(3, 1, CV_64F); + Mat A2 = Mat::zeros(3, 3, CV_64F); + Mat b2 = Mat::zeros(3, 1, CV_64F); for (int i = 0; i < N; ++i) { - cv::Mat eye = cv::Mat::eye(3, 3, CV_64F); - cv::Mat z_mul = z.col(i)*z.col(i).t(); + Mat eye = Mat::eye(3, 3, CV_64F); + Mat z_mul = z.col(i)*z.col(i).t(); A2 += eye - z_mul; b2 += (z_mul - eye) * C_est_j * pp.col(i); } // recover the optimal translation - cv::Mat X2; cv::solve(A2, b2, X2); // A\B + Mat X2; solve(A2, b2, X2); // A\B t_est.push_back(X2); } @@ -197,12 +197,12 @@ void dls::run_kernel(const cv::Mat& pp) // check that the points are infront of the center of perspectivity for (int k = 0; k < sols.cols; ++k) { - cv::Mat cam_points = C_est[k] * pp + cv::repeat(t_est[k], 1, pp.cols); - cv::Mat cam_points_k = cam_points.row(2); + Mat cam_points = C_est[k] * pp + repeat(t_est[k], 1, pp.cols); + Mat cam_points_k = cam_points.row(2); if(is_empty(&cam_points_k)) { - cv::Mat C_valid = C_est[k], t_valid = t_est[k]; + Mat C_valid = C_est[k], t_valid = t_est[k]; double cost_valid = cost[k]; C_est_.push_back(C_valid); @@ -213,20 +213,20 @@ void dls::run_kernel(const cv::Mat& pp) } -void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D) +void dls::build_coeff_matrix(const Mat& pp, Mat& Mtilde, Mat& D) { CV_Assert(!pp.empty() && N > 0); - cv::Mat eye = cv::Mat::eye(3, 3, CV_64F); + Mat eye = Mat::eye(3, 3, CV_64F); // build coeff matrix // An intermediate matrix, the inverse of what is called "H" in the paper // (see eq. 25) - cv::Mat H = cv::Mat::zeros(3, 3, CV_64F); - cv::Mat A = cv::Mat::zeros(3, 9, CV_64F); - cv::Mat pp_i(3, 1, CV_64F); + Mat H = Mat::zeros(3, 3, CV_64F); + Mat A = Mat::zeros(3, 9, CV_64F); + Mat pp_i(3, 1, CV_64F); - cv::Mat z_i(3, 1, CV_64F); + Mat z_i(3, 1, CV_64F); for (int i = 0; i < N; ++i) { z.col(i).copyTo(z_i); @@ -236,10 +236,10 @@ void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D) H = eye.mul(N) - z * z.t(); // A\B - cv::solve(H, A, A, cv::DECOMP_NORMAL); + solve(H, A, A, DECOMP_NORMAL); H.release(); - cv::Mat ppi_A(3, 1, CV_64F); + Mat ppi_A(3, 1, CV_64F); for (int i = 0; i < N; ++i) { z.col(i).copyTo(z_i); @@ -253,18 +253,18 @@ void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D) // generate random samples std::vector u(5); - cv::randn(u, 0, 200); + randn(u, 0, 200); - cv::Mat M2 = cayley_LS_M(f1coeff, f2coeff, f3coeff, u); + Mat M2 = cayley_LS_M(f1coeff, f2coeff, f3coeff, u); - cv::Mat M2_1 = M2(cv::Range(0,27), cv::Range(0,27)); - cv::Mat M2_2 = M2(cv::Range(0,27), cv::Range(27,120)); - cv::Mat M2_3 = M2(cv::Range(27,120), cv::Range(27,120)); - cv::Mat M2_4 = M2(cv::Range(27,120), cv::Range(0,27)); + Mat M2_1 = M2(Range(0,27), Range(0,27)); + Mat M2_2 = M2(Range(0,27), Range(27,120)); + Mat M2_3 = M2(Range(27,120), Range(27,120)); + Mat M2_4 = M2(Range(27,120), Range(0,27)); M2.release(); // A/B = B'\A' - cv::Mat M2_5; cv::solve(M2_3.t(), M2_2.t(), M2_5); + Mat M2_5; solve(M2_3.t(), M2_2.t(), M2_5); M2_2.release(); M2_3.release(); // construct the multiplication matrix via schur compliment of the Macaulay @@ -273,13 +273,13 @@ void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D) } -void dls::compute_eigenvec(const cv::Mat& Mtilde, cv::Mat& eigenval_real, cv::Mat& eigenval_imag, - cv::Mat& eigenvec_real, cv::Mat& eigenvec_imag) +void dls::compute_eigenvec(const Mat& Mtilde, Mat& eigenval_real, Mat& eigenval_imag, + Mat& eigenvec_real, Mat& eigenvec_imag) { #ifdef HAVE_EIGEN Eigen::MatrixXd Mtilde_eig, zeros_eig; - cv::cv2eigen(Mtilde, Mtilde_eig); - cv::cv2eigen(cv::Mat::zeros(27, 27, CV_64F), zeros_eig); + cv2eigen(Mtilde, Mtilde_eig); + cv2eigen(Mat::zeros(27, 27, CV_64F), zeros_eig); Eigen::MatrixXcd Mtilde_eig_cmplx(27, 27); Mtilde_eig_cmplx.real() = Mtilde_eig; @@ -293,20 +293,20 @@ void dls::compute_eigenvec(const cv::Mat& Mtilde, cv::Mat& eigenval_real, cv::Ma Eigen::MatrixXd eigvec_real = ces.eigenvectors().real(); Eigen::MatrixXd eigvec_imag = ces.eigenvectors().imag(); - cv::eigen2cv(eigval_real, eigenval_real); - cv::eigen2cv(eigval_imag, eigenval_imag); - cv::eigen2cv(eigvec_real, eigenvec_real); - cv::eigen2cv(eigvec_imag, eigenvec_imag); + eigen2cv(eigval_real, eigenval_real); + eigen2cv(eigval_imag, eigenval_imag); + eigen2cv(eigvec_real, eigenvec_real); + eigen2cv(eigvec_imag, eigenvec_imag); #else EigenvalueDecomposition es(Mtilde); eigenval_real = es.eigenvalues(); eigenvec_real = es.eigenvectors(); - eigenval_imag = eigenvec_imag = cv::Mat(); + eigenval_imag = eigenvec_imag = Mat(); #endif } -void dls::fill_coeff(const cv::Mat * D_mat) +void dls::fill_coeff(const Mat * D_mat) { // TODO: shift D and coefficients one position to left @@ -394,9 +394,9 @@ void dls::fill_coeff(const cv::Mat * D_mat) } -cv::Mat dls::LeftMultVec(const cv::Mat& v) +Mat dls::LeftMultVec(const Mat& v) { - cv::Mat mat_ = cv::Mat::zeros(3, 9, CV_64F); + Mat mat_ = Mat::zeros(3, 9, CV_64F); for (int i = 0; i < 3; ++i) { @@ -407,12 +407,12 @@ cv::Mat dls::LeftMultVec(const cv::Mat& v) return mat_; } -cv::Mat dls::cayley_LS_M(const std::vector& a, const std::vector& b, const std::vector& c, const std::vector& u) +Mat dls::cayley_LS_M(const std::vector& a, const std::vector& b, const std::vector& c, const std::vector& u) { // TODO: input matrix pointer // TODO: shift coefficients one position to left - cv::Mat M = cv::Mat::zeros(120, 120, CV_64F); + Mat M = Mat::zeros(120, 120, CV_64F); M.at(0,0)=u[1]; M.at(0,35)=a[1]; M.at(0,83)=b[1]; M.at(0,118)=c[1]; M.at(1,0)=u[4]; M.at(1,1)=u[1]; M.at(1,34)=a[1]; M.at(1,35)=a[10]; M.at(1,54)=b[1]; M.at(1,83)=b[10]; M.at(1,99)=c[1]; M.at(1,118)=c[10]; @@ -538,7 +538,7 @@ cv::Mat dls::cayley_LS_M(const std::vector& a, const std::vector return M.t(); } -cv::Mat dls::Hessian(const double s[]) +Mat dls::Hessian(const double s[]) { // the vector of monomials is // m = [ const ; s1^2 * s2 ; s1 * s2 ; s1 * s3 ; s2 * s3 ; s2^2 * s3 ; s2^3 ; ... @@ -577,73 +577,73 @@ cv::Mat dls::Hessian(const double s[]) Hs3[14]=0; Hs3[15]=3*s[2]*s[2]; Hs3[16]=s[0]*s[1]; Hs3[17]=0; Hs3[18]=s[0]*s[0]; Hs3[19]=0; // fill Hessian matrix - cv::Mat H(3, 3, CV_64F); - H.at(0,0) = cv::Mat(cv::Mat(f1coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs1)).at(0,0); - H.at(0,1) = cv::Mat(cv::Mat(f1coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs2)).at(0,0); - H.at(0,2) = cv::Mat(cv::Mat(f1coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs3)).at(0,0); + Mat H(3, 3, CV_64F); + H.at(0,0) = Mat(Mat(f1coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs1)).at(0,0); + H.at(0,1) = Mat(Mat(f1coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs2)).at(0,0); + H.at(0,2) = Mat(Mat(f1coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs3)).at(0,0); - H.at(1,0) = cv::Mat(cv::Mat(f2coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs1)).at(0,0); - H.at(1,1) = cv::Mat(cv::Mat(f2coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs2)).at(0,0); - H.at(1,2) = cv::Mat(cv::Mat(f2coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs3)).at(0,0); + H.at(1,0) = Mat(Mat(f2coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs1)).at(0,0); + H.at(1,1) = Mat(Mat(f2coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs2)).at(0,0); + H.at(1,2) = Mat(Mat(f2coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs3)).at(0,0); - H.at(2,0) = cv::Mat(cv::Mat(f3coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs1)).at(0,0); - H.at(2,1) = cv::Mat(cv::Mat(f3coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs2)).at(0,0); - H.at(2,2) = cv::Mat(cv::Mat(f3coeff).rowRange(1,21).t()*cv::Mat(20, 1, CV_64F, &Hs3)).at(0,0); + H.at(2,0) = Mat(Mat(f3coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs1)).at(0,0); + H.at(2,1) = Mat(Mat(f3coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs2)).at(0,0); + H.at(2,2) = Mat(Mat(f3coeff).rowRange(1,21).t()*Mat(20, 1, CV_64F, &Hs3)).at(0,0); return H; } -cv::Mat dls::cayley2rotbar(const cv::Mat& s) +Mat dls::cayley2rotbar(const Mat& s) { - double s_mul1 = cv::Mat(s.t()*s).at(0,0); - cv::Mat s_mul2 = s*s.t(); - cv::Mat eye = cv::Mat::eye(3, 3, CV_64F); + double s_mul1 = Mat(s.t()*s).at(0,0); + Mat s_mul2 = s*s.t(); + Mat eye = Mat::eye(3, 3, CV_64F); - return cv::Mat( eye.mul(1.-s_mul1) + skewsymm(&s).mul(2.) + s_mul2.mul(2.) ).t(); + return Mat( eye.mul(1.-s_mul1) + skewsymm(&s).mul(2.) + s_mul2.mul(2.) ).t(); } -cv::Mat dls::skewsymm(const cv::Mat * X1) +Mat dls::skewsymm(const Mat * X1) { - cv::MatConstIterator_ it = X1->begin(); - return (cv::Mat_(3,3) << 0, -*(it+2), *(it+1), - *(it+2), 0, -*(it+0), - -*(it+1), *(it+0), 0); + MatConstIterator_ it = X1->begin(); + return (Mat_(3,3) << 0, -*(it+2), *(it+1), + *(it+2), 0, -*(it+0), + -*(it+1), *(it+0), 0); } -cv::Mat dls::rotx(const double t) +Mat dls::rotx(const double t) { // rotx: rotation about y-axis double ct = cos(t); double st = sin(t); - return (cv::Mat_(3,3) << 1, 0, 0, 0, ct, -st, 0, st, ct); + return (Mat_(3,3) << 1, 0, 0, 0, ct, -st, 0, st, ct); } -cv::Mat dls::roty(const double t) +Mat dls::roty(const double t) { // roty: rotation about y-axis double ct = cos(t); double st = sin(t); - return (cv::Mat_(3,3) << ct, 0, st, 0, 1, 0, -st, 0, ct); + return (Mat_(3,3) << ct, 0, st, 0, 1, 0, -st, 0, ct); } -cv::Mat dls::rotz(const double t) +Mat dls::rotz(const double t) { // rotz: rotation about y-axis double ct = cos(t); double st = sin(t); - return (cv::Mat_(3,3) << ct, -st, 0, st, ct, 0, 0, 0, 1); + return (Mat_(3,3) << ct, -st, 0, st, ct, 0, 0, 0, 1); } -cv::Mat dls::mean(const cv::Mat& M) +Mat dls::mean(const Mat& M) { - cv::Mat m = cv::Mat::zeros(3, 1, CV_64F); + Mat m = Mat::zeros(3, 1, CV_64F); for (int i = 0; i < M.cols; ++i) m += M.col(i); return m.mul(1./(double)M.cols); } -bool dls::is_empty(const cv::Mat * M) +bool dls::is_empty(const Mat * M) { - cv::MatConstIterator_ it = M->begin(), it_end = M->end(); + MatConstIterator_ it = M->begin(), it_end = M->end(); for(; it != it_end; ++it) { if(*it < 0) return false; @@ -651,9 +651,11 @@ bool dls::is_empty(const cv::Mat * M) return true; } -bool dls::positive_eigenvalues(const cv::Mat * eigenvalues) +bool dls::positive_eigenvalues(const Mat * eigenvalues) { CV_Assert(eigenvalues && !eigenvalues->empty()); - cv::MatConstIterator_ it = eigenvalues->begin(); + MatConstIterator_ it = eigenvalues->begin(); return *(it) > 0 && *(it+1) > 0 && *(it+2) > 0; } + +} // namespace cv diff --git a/modules/calib3d/src/dls.h b/modules/calib3d/src/dls.h index f03bee49d740..a3382498c994 100644 --- a/modules/calib3d/src/dls.h +++ b/modules/calib3d/src/dls.h @@ -5,22 +5,21 @@ #include -using namespace std; -using namespace cv; +namespace cv { class dls { public: - dls(const cv::Mat& opoints, const cv::Mat& ipoints); + dls(const Mat& opoints, const Mat& ipoints); ~dls(); - bool compute_pose(cv::Mat& R, cv::Mat& t); + bool compute_pose(Mat& R, Mat& t); private: // initialisation template - void init_points(const cv::Mat& opoints, const cv::Mat& ipoints) + void init_points(const Mat& opoints, const Mat& ipoints) { for(int i = 0; i < N; i++) { @@ -49,33 +48,33 @@ class dls } // main algorithm - cv::Mat LeftMultVec(const cv::Mat& v); - void run_kernel(const cv::Mat& pp); - void build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D); - void compute_eigenvec(const cv::Mat& Mtilde, cv::Mat& eigenval_real, cv::Mat& eigenval_imag, - cv::Mat& eigenvec_real, cv::Mat& eigenvec_imag); - void fill_coeff(const cv::Mat * D); + Mat LeftMultVec(const Mat& v); + void run_kernel(const Mat& pp); + void build_coeff_matrix(const Mat& pp, Mat& Mtilde, Mat& D); + void compute_eigenvec(const Mat& Mtilde, Mat& eigenval_real, Mat& eigenval_imag, + Mat& eigenvec_real, Mat& eigenvec_imag); + void fill_coeff(const Mat * D); // useful functions - cv::Mat cayley_LS_M(const std::vector& a, const std::vector& b, - const std::vector& c, const std::vector& u); - cv::Mat Hessian(const double s[]); - cv::Mat cayley2rotbar(const cv::Mat& s); - cv::Mat skewsymm(const cv::Mat * X1); + Mat cayley_LS_M(const std::vector& a, const std::vector& b, + const std::vector& c, const std::vector& u); + Mat Hessian(const double s[]); + Mat cayley2rotbar(const Mat& s); + Mat skewsymm(const Mat * X1); // extra functions - cv::Mat rotx(const double t); - cv::Mat roty(const double t); - cv::Mat rotz(const double t); - cv::Mat mean(const cv::Mat& M); - bool is_empty(const cv::Mat * v); - bool positive_eigenvalues(const cv::Mat * eigenvalues); - - cv::Mat p, z, mn; // object-image points + Mat rotx(const double t); + Mat roty(const double t); + Mat rotz(const double t); + Mat mean(const Mat& M); + bool is_empty(const Mat * v); + bool positive_eigenvalues(const Mat * eigenvalues); + + Mat p, z, mn; // object-image points int N; // number of input points std::vector f1coeff, f2coeff, f3coeff, cost_; // coefficient for coefficients matrix - std::vector C_est_, t_est_; // optimal candidates - cv::Mat C_est__, t_est__; // optimal found solution + std::vector C_est_, t_est_; // optimal candidates + Mat C_est__, t_est__; // optimal found solution double cost__; // optimal found solution }; @@ -738,7 +737,7 @@ class EigenvalueDecomposition { { /*if(isSymmetric(src)) { // Fall back to OpenCV for a symmetric matrix! - cv::eigen(src, _eigenvalues, _eigenvectors); + eigen(src, _eigenvalues, _eigenvectors); } else {*/ Mat tmp; // Convert the given input matrix to double. Is there any way to @@ -770,4 +769,5 @@ class EigenvalueDecomposition { Mat eigenvectors() { return _eigenvectors; } }; +} // namespace cv #endif // DLS_H diff --git a/modules/calib3d/src/fisheye.cpp b/modules/calib3d/src/fisheye.cpp index 43b679f9e40b..346638d4c13a 100644 --- a/modules/calib3d/src/fisheye.cpp +++ b/modules/calib3d/src/fisheye.cpp @@ -318,7 +318,8 @@ void cv::fisheye::distortPoints(InputArray undistorted, OutputArray distorted, I ////////////////////////////////////////////////////////////////////////////////////////////////////////////// /// cv::fisheye::undistortPoints -void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray R, InputArray P) +void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, + InputArray R, InputArray P, TermCriteria criteria) { CV_INSTRUMENT_REGION(); @@ -330,6 +331,8 @@ void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted CV_Assert(R.empty() || R.size() == Size(3, 3) || R.total() * R.channels() == 3); CV_Assert(D.total() == 4 && K.size() == Size(3, 3) && (K.depth() == CV_32F || K.depth() == CV_64F)); + CV_Assert(criteria.isValid()); + cv::Vec2d f, c; if (K.depth() == CV_32F) { @@ -372,6 +375,15 @@ void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted size_t n = distorted.total(); int sdepth = distorted.depth(); + const bool isEps = (criteria.type & TermCriteria::EPS) != 0; + + /* Define max count for solver iterations */ + int maxCount = std::numeric_limits::max(); + if (criteria.type & TermCriteria::MAX_ITER) { + maxCount = criteria.maxCount; + } + + for(size_t i = 0; i < n; i++ ) { Vec2d pi = sdepth == CV_32F ? (Vec2d)srcf[i] : srcd[i]; // image point @@ -389,13 +401,11 @@ void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted double scale = 0.0; - if (fabs(theta_d) > 1e-8) + if (!isEps || fabs(theta_d) > criteria.epsilon) { // compensate distortion iteratively - const double EPS = 1e-8; // or std::numeric_limits::epsilon(); - - for (int j = 0; j < 10; j++) + for (int j = 0; j < maxCount; j++) { double theta2 = theta*theta, theta4 = theta2*theta2, theta6 = theta4*theta2, theta8 = theta6*theta2; double k0_theta2 = k[0] * theta2, k1_theta4 = k[1] * theta4, k2_theta6 = k[2] * theta6, k3_theta8 = k[3] * theta8; @@ -403,7 +413,8 @@ void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted double theta_fix = (theta * (1 + k0_theta2 + k1_theta4 + k2_theta6 + k3_theta8) - theta_d) / (1 + 3*k0_theta2 + 5*k1_theta4 + 7*k2_theta6 + 9*k3_theta8); theta = theta - theta_fix; - if (fabs(theta_fix) < EPS) + + if (isEps && (fabs(theta_fix) < criteria.epsilon)) { converged = true; break; @@ -422,7 +433,7 @@ void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted // so we can check whether theta has changed the sign during the optimization bool theta_flipped = ((theta_d < 0 && theta > 0) || (theta_d > 0 && theta < 0)); - if (converged && !theta_flipped) + if ((converged || !isEps) && !theta_flipped) { Vec2d pu = pw * scale; //undistorted point diff --git a/modules/calib3d/src/solvepnp.cpp b/modules/calib3d/src/solvepnp.cpp index 1e5591f710b9..07780c4b1777 100644 --- a/modules/calib3d/src/solvepnp.cpp +++ b/modules/calib3d/src/solvepnp.cpp @@ -103,12 +103,12 @@ void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray d CV_Assert(length > 0); // project axes points - vector axesPoints; + std::vector axesPoints; axesPoints.push_back(Point3f(0, 0, 0)); axesPoints.push_back(Point3f(length, 0, 0)); axesPoints.push_back(Point3f(0, length, 0)); axesPoints.push_back(Point3f(0, 0, length)); - vector imagePoints; + std::vector imagePoints; projectPoints(axesPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints); // draw axes lines @@ -123,7 +123,7 @@ bool solvePnP( InputArray opoints, InputArray ipoints, { CV_INSTRUMENT_REGION(); - vector rvecs, tvecs; + std::vector rvecs, tvecs; int solutions = solvePnPGeneric(opoints, ipoints, cameraMatrix, distCoeffs, rvecs, tvecs, useExtrinsicGuess, (SolvePnPMethod)flags, rvec, tvec); if (solutions > 0) @@ -321,8 +321,8 @@ bool solvePnPRansac(InputArray _opoints, InputArray _ipoints, return false; } - vector opoints_inliers; - vector ipoints_inliers; + std::vector opoints_inliers; + std::vector ipoints_inliers; opoints = opoints.reshape(3); ipoints = ipoints.reshape(2); opoints.convertTo(opoints_inliers, CV_64F); @@ -472,7 +472,7 @@ int solveP3P( InputArray _opoints, InputArray _ipoints, else imgPts = imgPts.reshape(1, 2*imgPts.rows); - vector reproj_errors(solutions); + std::vector reproj_errors(solutions); for (size_t i = 0; i < reproj_errors.size(); i++) { Mat rvec; @@ -762,7 +762,7 @@ static void solvePnPRefine(InputArray _objectPoints, InputArray _imagePoints, rvec0.convertTo(rvec, CV_64F); tvec0.convertTo(tvec, CV_64F); - vector ipoints_normalized; + std::vector ipoints_normalized; undistortPoints(ipoints, ipoints_normalized, cameraMatrix, distCoeffs); Mat sd = Mat(ipoints_normalized).reshape(1, npoints*2); Mat objectPoints0 = opoints.reshape(1, npoints); @@ -856,7 +856,7 @@ int solvePnPGeneric( InputArray _opoints, InputArray _ipoints, Mat cameraMatrix = Mat_(cameraMatrix0); Mat distCoeffs = Mat_(distCoeffs0); - vector vec_rvecs, vec_tvecs; + std::vector vec_rvecs, vec_tvecs; if (flags == SOLVEPNP_EPNP || flags == SOLVEPNP_DLS || flags == SOLVEPNP_UPNP) { if (flags == SOLVEPNP_DLS) @@ -881,7 +881,7 @@ int solvePnPGeneric( InputArray _opoints, InputArray _ipoints, } else if (flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) { - vector rvecs, tvecs; + std::vector rvecs, tvecs; solveP3P(opoints, ipoints, _cameraMatrix, _distCoeffs, rvecs, tvecs, flags); vec_rvecs.insert(vec_rvecs.end(), rvecs.begin(), rvecs.end()); vec_tvecs.insert(vec_tvecs.end(), tvecs.begin(), tvecs.end()); @@ -1134,7 +1134,7 @@ int solvePnPGeneric( InputArray _opoints, InputArray _ipoints, for (size_t i = 0; i < vec_rvecs.size(); i++) { - vector projectedPoints; + std::vector projectedPoints; projectPoints(objectPoints, vec_rvecs[i], vec_tvecs[i], cameraMatrix, distCoeffs, projectedPoints); double rmse = norm(Mat(projectedPoints, false), imagePoints, NORM_L2) / sqrt(2*projectedPoints.size()); diff --git a/modules/calib3d/src/sqpnp.cpp b/modules/calib3d/src/sqpnp.cpp index 7117e61c96c2..3e0d7ace495f 100644 --- a/modules/calib3d/src/sqpnp.cpp +++ b/modules/calib3d/src/sqpnp.cpp @@ -118,7 +118,7 @@ void PoseSolver::solve(InputArray objectPoints, InputArray imagePoints, OutputAr num_solutions_ = 0; computeOmega(_objectPoints, _imagePoints); - solveInternal(); + solveInternal(_objectPoints); int depthRot = rvecs.fixedType() ? rvecs.depth() : CV_64F; int depthTrans = tvecs.fixedType() ? tvecs.depth() : CV_64F; @@ -194,37 +194,41 @@ void PoseSolver::computeOmega(InputArray objectPoints, InputArray imagePoints) omega_(7, 7) += sq_norm * Y2; omega_(7, 8) += sq_norm * YZ; omega_(8, 8) += sq_norm * Z2; - //Compute qa_sum + //Compute qa_sum. Certain pairs of elements are equal, so filling them outside the loop saves some operations qa_sum(0, 0) += X; qa_sum(0, 1) += Y; qa_sum(0, 2) += Z; - qa_sum(1, 3) += X; qa_sum(1, 4) += Y; qa_sum(1, 5) += Z; qa_sum(0, 6) += -x * X; qa_sum(0, 7) += -x * Y; qa_sum(0, 8) += -x * Z; qa_sum(1, 6) += -y * X; qa_sum(1, 7) += -y * Y; qa_sum(1, 8) += -y * Z; - qa_sum(2, 0) += -x * X; qa_sum(2, 1) += -x * Y; qa_sum(2, 2) += -x * Z; - qa_sum(2, 3) += -y * X; qa_sum(2, 4) += -y * Y; qa_sum(2, 5) += -y * Z; - qa_sum(2, 6) += sq_norm * X; qa_sum(2, 7) += sq_norm * Y; qa_sum(2, 8) += sq_norm * Z; } + //Complete qa_sum + qa_sum(1, 3) = qa_sum(0, 0); qa_sum(1, 4) = qa_sum(0, 1); qa_sum(1, 5) = qa_sum(0, 2); + qa_sum(2, 0) = qa_sum(0, 6); qa_sum(2, 1) = qa_sum(0, 7); qa_sum(2, 2) = qa_sum(0, 8); + qa_sum(2, 3) = qa_sum(1, 6); qa_sum(2, 4) = qa_sum(1, 7); qa_sum(2, 5) = qa_sum(1, 8); + + //lower triangles of omega_'s off-diagonal blocks (0:2, 6:8), (3:5, 6:8) and (6:8, 6:8) omega_(1, 6) = omega_(0, 7); omega_(2, 6) = omega_(0, 8); omega_(2, 7) = omega_(1, 8); omega_(4, 6) = omega_(3, 7); omega_(5, 6) = omega_(3, 8); omega_(5, 7) = omega_(4, 8); omega_(7, 6) = omega_(6, 7); omega_(8, 6) = omega_(6, 8); omega_(8, 7) = omega_(7, 8); - + //upper triangle of omega_'s block (3:5, 3:5) omega_(3, 3) = omega_(0, 0); omega_(3, 4) = omega_(0, 1); omega_(3, 5) = omega_(0, 2); - omega_(4, 4) = omega_(1, 1); omega_(4, 5) = omega_(1, 2); - omega_(5, 5) = omega_(2, 2); - - //Mirror upper triangle to lower triangle - for (int r = 0; r < 9; r++) - { - for (int c = 0; c < r; c++) - { - omega_(r, c) = omega_(c, r); - } - } + omega_(4, 4) = omega_(1, 1); omega_(4, 5) = omega_(1, 2); + omega_(5, 5) = omega_(2, 2); + + //Mirror omega_'s upper triangle to lower triangle + //Note that elements (7, 6), (8, 6) & (8, 7) have already been assigned above + omega_(1, 0) = omega_(0, 1); + omega_(2, 0) = omega_(0, 2); omega_(2, 1) = omega_(1, 2); + omega_(3, 0) = omega_(0, 3); omega_(3, 1) = omega_(1, 3); omega_(3, 2) = omega_(2, 3); + omega_(4, 0) = omega_(0, 4); omega_(4, 1) = omega_(1, 4); omega_(4, 2) = omega_(2, 4); omega_(4, 3) = omega_(3, 4); + omega_(5, 0) = omega_(0, 5); omega_(5, 1) = omega_(1, 5); omega_(5, 2) = omega_(2, 5); omega_(5, 3) = omega_(3, 5); omega_(5, 4) = omega_(4, 5); + omega_(6, 0) = omega_(0, 6); omega_(6, 1) = omega_(1, 6); omega_(6, 2) = omega_(2, 6); omega_(6, 3) = omega_(3, 6); omega_(6, 4) = omega_(4, 6); omega_(6, 5) = omega_(5, 6); + omega_(7, 0) = omega_(0, 7); omega_(7, 1) = omega_(1, 7); omega_(7, 2) = omega_(2, 7); omega_(7, 3) = omega_(3, 7); omega_(7, 4) = omega_(4, 7); omega_(7, 5) = omega_(5, 7); + omega_(8, 0) = omega_(0, 8); omega_(8, 1) = omega_(1, 8); omega_(8, 2) = omega_(2, 8); omega_(8, 3) = omega_(3, 8); omega_(8, 4) = omega_(4, 8); omega_(8, 5) = omega_(5, 8); cv::Matx q; q(0, 0) = n; q(0, 1) = 0; q(0, 2) = -sum_img.x; @@ -247,6 +251,11 @@ void PoseSolver::computeOmega(InputArray objectPoints, InputArray imagePoints) cv::SVD omega_svd(omega_, cv::SVD::FULL_UV); s_ = omega_svd.w; u_ = cv::Mat(omega_svd.vt.t()); +#if 0 + // EVD equivalent of the SVD; less accurate + cv::eigen(omega_, s_, u_); + u_ = u_.t(); // eigenvectors were returned as rows +#endif CV_Assert(s_(0) >= 1e-7); @@ -257,7 +266,7 @@ void PoseSolver::computeOmega(InputArray objectPoints, InputArray imagePoints) point_mean_ = cv::Vec3d(sum_obj.x / n, sum_obj.y / n, sum_obj.z / n); } -void PoseSolver::solveInternal() +void PoseSolver::solveInternal(InputArray objectPoints) { double min_sq_err = std::numeric_limits::max(); int num_eigen_points = num_null_vectors_ > 0 ? num_null_vectors_ : 1; @@ -274,42 +283,39 @@ void PoseSolver::solveInternal() { solutions[0].r_hat = det3x3(e) * e; solutions[0].t = p_ * solutions[0].r_hat; - checkSolution(solutions[0], min_sq_err); + checkSolution(solutions[0], objectPoints, min_sq_err); } else { Matx r; - nearestRotationMatrix(e, r); + nearestRotationMatrixFOAM(e, r); solutions[0] = runSQP(r); solutions[0].t = p_ * solutions[0].r_hat; - checkSolution(solutions[0], min_sq_err); + checkSolution(solutions[0], objectPoints, min_sq_err); - nearestRotationMatrix(-e, r); + nearestRotationMatrixFOAM(-e, r); solutions[1] = runSQP(r); solutions[1].t = p_ * solutions[1].r_hat; - checkSolution(solutions[1], min_sq_err); + checkSolution(solutions[1], objectPoints, min_sq_err); } } - int c = 1; - - while (min_sq_err > 3 * s_[9 - num_eigen_points - c] && 9 - num_eigen_points - c > 0) + int index, c = 1; + while ((index = 9 - num_eigen_points - c) > 0 && min_sq_err > 3 * s_[index]) { - int index = 9 - num_eigen_points - c; - const cv::Matx e = u_.col(index); SQPSolution solutions[2]; Matx r; - nearestRotationMatrix(e, r); + nearestRotationMatrixFOAM(e, r); solutions[0] = runSQP(r); solutions[0].t = p_ * solutions[0].r_hat; - checkSolution(solutions[0], min_sq_err); + checkSolution(solutions[0], objectPoints, min_sq_err); - nearestRotationMatrix(-e, r); + nearestRotationMatrixFOAM(-e, r); solutions[1] = runSQP(r); solutions[1].t = p_ * solutions[1].r_hat; - checkSolution(solutions[1], min_sq_err); + checkSolution(solutions[1], objectPoints, min_sq_err); c++; } @@ -341,7 +347,7 @@ PoseSolver::SQPSolution PoseSolver::runSQP(const cv::Matx& r0) if (det_r > SQP_DET_THRESHOLD) { - nearestRotationMatrix(r, solution.r_hat); + nearestRotationMatrixFOAM(r, solution.r_hat); } else { @@ -615,12 +621,26 @@ void PoseSolver::computeRowAndNullspace(const cv::Matx& r, } -// faster nearest rotation computation based on FOAM (see: http://users.ics.forth.gr/~lourakis/publ/2018_iros.pdf ) +// if e = u*w*vt then r=u*diag([1, 1, det(u)*det(v)])*vt +void PoseSolver::nearestRotationMatrixSVD(const cv::Matx& e, + cv::Matx& r) +{ + cv::Matx e33 = e.reshape<3, 3>(); + cv::SVD e33_svd(e33, cv::SVD::FULL_UV); + double detuv = cv::determinant(e33_svd.u)*cv::determinant(e33_svd.vt); + cv::Matx diag = cv::Matx33d::eye(); + diag(2, 2) = detuv; + cv::Matx r33 = cv::Mat(e33_svd.u*diag*e33_svd.vt); + r = r33.reshape<9, 1>(); +} + +// Faster nearest rotation computation based on FOAM. See M. Lourakis: "An Efficient Solution to Absolute Orientation", ICPR 2016 +// and M. Lourakis, G. Terzakis: "Efficient Absolute Orientation Revisited", IROS 2018. /* Solve the nearest orthogonal approximation problem * i.e., given e, find R minimizing ||R-e||_F * * The computation borrows from Markley's FOAM algorithm - * "Attitude Determination Using Vector Observations: A Fast Optimal Matrix Algorithm", J. Astronaut. Sci. + * "Attitude Determination Using Vector Observations: A Fast Optimal Matrix Algorithm", J. Astronaut. Sci. 1993. * * See also M. Lourakis: "An Efficient Solution to Absolute Orientation", ICPR 2016 * @@ -628,24 +648,32 @@ void PoseSolver::computeRowAndNullspace(const cv::Matx& r, * Institute of Computer Science, Foundation for Research & Technology - Hellas * Heraklion, Crete, Greece. */ -void PoseSolver::nearestRotationMatrix(const cv::Matx& e, +void PoseSolver::nearestRotationMatrixFOAM(const cv::Matx& e, cv::Matx& r) { int i; double l, lprev, det_e, e_sq, adj_e_sq, adj_e[9]; + // det(e) + det_e = e(0) * e(4) * e(8) - e(0) * e(5) * e(7) - e(1) * e(3) * e(8) + e(2) * e(3) * e(7) + e(1) * e(6) * e(5) - e(2) * e(6) * e(4); + if (fabs(det_e) < 1E-04) { // singular, handle it with SVD + PoseSolver::nearestRotationMatrixSVD(e, r); + return; + } + // e's adjoint adj_e[0] = e(4) * e(8) - e(5) * e(7); adj_e[1] = e(2) * e(7) - e(1) * e(8); adj_e[2] = e(1) * e(5) - e(2) * e(4); adj_e[3] = e(5) * e(6) - e(3) * e(8); adj_e[4] = e(0) * e(8) - e(2) * e(6); adj_e[5] = e(2) * e(3) - e(0) * e(5); adj_e[6] = e(3) * e(7) - e(4) * e(6); adj_e[7] = e(1) * e(6) - e(0) * e(7); adj_e[8] = e(0) * e(4) - e(1) * e(3); - // det(e), ||e||^2, ||adj(e)||^2 - det_e = e(0) * e(4) * e(8) - e(0) * e(5) * e(7) - e(1) * e(3) * e(8) + e(2) * e(3) * e(7) + e(1) * e(6) * e(5) - e(2) * e(6) * e(4); + // ||e||^2, ||adj(e)||^2 e_sq = e(0) * e(0) + e(1) * e(1) + e(2) * e(2) + e(3) * e(3) + e(4) * e(4) + e(5) * e(5) + e(6) * e(6) + e(7) * e(7) + e(8) * e(8); adj_e_sq = adj_e[0] * adj_e[0] + adj_e[1] * adj_e[1] + adj_e[2] * adj_e[2] + adj_e[3] * adj_e[3] + adj_e[4] * adj_e[4] + adj_e[5] * adj_e[5] + adj_e[6] * adj_e[6] + adj_e[7] * adj_e[7] + adj_e[8] * adj_e[8]; // compute l_max with Newton-Raphson from FOAM's characteristic polynomial, i.e. eq.(23) - (26) - for (i = 200, l = 2.0, lprev = 0.0; fabs(l - lprev) > 1E-12 * fabs(lprev) && i > 0; --i) { + l = 0.5*(e_sq + 3.0); // 1/2*(trace(mat(e)*mat(e)') + trace(eye(3))) + if (det_e < 0.0) l = -l; + for (i = 15, lprev = 0.0; fabs(l - lprev) > 1E-12 * fabs(lprev) && i > 0; --i) { double tmp, p, pp; tmp = (l * l - e_sq); @@ -719,9 +747,31 @@ inline bool PoseSolver::positiveDepth(const SQPSolution& solution) const return (r(6) * mean(0) + r(7) * mean(1) + r(8) * mean(2) + t(2) > 0); } -void PoseSolver::checkSolution(SQPSolution& solution, double& min_error) +inline bool PoseSolver::positiveMajorityDepths(const SQPSolution& solution, InputArray objectPoints) const +{ + const cv::Matx& r = solution.r_hat; + const cv::Matx& t = solution.t; + int npos = 0, nneg = 0; + + Mat _objectPoints = objectPoints.getMat(); + + int n = _objectPoints.cols * _objectPoints.rows; + + for (int i = 0; i < n; i++) + { + const cv::Point3d& obj_pt = _objectPoints.at(i); + if (r(6) * obj_pt.x + r(7) * obj_pt.y + r(8) * obj_pt.z + t(2) > 0) ++npos; + else ++nneg; + } + + return npos >= nneg; +} + + +void PoseSolver::checkSolution(SQPSolution& solution, InputArray objectPoints, double& min_error) { - if (positiveDepth(solution)) + bool cheirok = positiveDepth(solution) || positiveMajorityDepths(solution, objectPoints); // check the majority if the check with centroid fails + if (cheirok) { solution.sq_error = (omega_ * solution.r_hat).ddot(solution.r_hat); if (fabs(min_error - solution.sq_error) > EQUAL_SQUARED_ERRORS_DIFF) diff --git a/modules/calib3d/src/sqpnp.hpp b/modules/calib3d/src/sqpnp.hpp index 97c10e34e733..078c07e906cf 100644 --- a/modules/calib3d/src/sqpnp.hpp +++ b/modules/calib3d/src/sqpnp.hpp @@ -85,13 +85,14 @@ class PoseSolver { /* * @brief Computes the 9x9 PSD Omega matrix and supporting matrices. + * @param objectPoints The 3D points in object coordinates. */ - void solveInternal(); + void solveInternal(InputArray objectPoints); /* * @brief Produces the distance from being orthogonal for a given 3x3 matrix - * in row-major form. - * @param e The vector to test representing a 3x3 matrix in row major form. + * in row-major order. + * @param e The vector to test representing a 3x3 matrix in row-major order. * @return The distance the matrix is from being orthogonal. */ static double orthogonalityError(const cv::Matx& e); @@ -99,31 +100,49 @@ class PoseSolver { /* * @brief Processes a solution and sorts it by error. * @param solution The solution to evaluate. - * @param min_error The current minimum error. + * @param objectPoints The 3D points in object coordinates. + * @param min_error The current minimum error. */ - void checkSolution(SQPSolution& solution, double& min_error); + void checkSolution(SQPSolution& solution, InputArray objectPoints, double& min_error); /* - * @brief Computes the determinant of a matrix stored in row-major format. - * @param e Vector representing a 3x3 matrix stored in row-major format. + * @brief Computes the determinant of a matrix stored in row-major order. + * @param e Vector representing a 3x3 matrix stored in row-major order. * @return The determinant of the matrix. */ static double det3x3(const cv::Matx& e); /* - * @brief Tests the cheirality for a given solution. + * @brief Tests the cheirality on the mean object point for a given solution. * @param solution The solution to evaluate. */ inline bool positiveDepth(const SQPSolution& solution) const; /* - * @brief Determines the nearest rotation matrix to a given rotaiton matrix. - * Input and output are 9x1 vector representing a vector stored in row-major - * form. - * @param e The input 3x3 matrix stored in a vector in row-major form. - * @param r The nearest rotation matrix to the input e (again in row-major form). + * @brief Tests the cheirality on all object points for a given solution. + * @param solution The solution to evaluate. + * @param objectPoints The 3D points in object coordinates. + */ + inline bool positiveMajorityDepths(const SQPSolution& solution, InputArray objectPoints) const; + + /* + * @brief Determines the nearest rotation matrix to a given rotation matrix using SVD. + * Input and output are 9x1 vector representing a matrix stored in row-major + * order. + * @param e The input 3x3 matrix stored in a vector in row-major order. + * @param r The nearest rotation matrix to the input e (again in row-major order). + */ + static void nearestRotationMatrixSVD(const cv::Matx& e, + cv::Matx& r); + + /* + * @brief Determines the nearest rotation matrix to a given rotation matrix using the FOAM algorithm. + * Input and output are 9x1 vector representing a matrix stored in row-major + * order. + * @param e The input 3x3 matrix stored in a vector in row-major order. + * @param r The nearest rotation matrix to the input e (again in row-major order). */ - static void nearestRotationMatrix(const cv::Matx& e, + static void nearestRotationMatrixFOAM(const cv::Matx& e, cv::Matx& r); /* diff --git a/modules/calib3d/src/undistort.dispatch.cpp b/modules/calib3d/src/undistort.dispatch.cpp index 146befd955f0..6c3d32941c37 100644 --- a/modules/calib3d/src/undistort.dispatch.cpp +++ b/modules/calib3d/src/undistort.dispatch.cpp @@ -40,6 +40,7 @@ // //M*/ +#include "opencv2/core/types.hpp" #include "precomp.hpp" #include "distortion_model.hpp" @@ -607,6 +608,11 @@ void undistortPoints(InputArray _src, OutputArray _dst, cvUndistortPointsInternal(&_csrc, &_cdst, &_ccameraMatrix, pD, pR, pP, criteria); } +void undistortImagePoints(InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, TermCriteria termCriteria) +{ + undistortPoints(src, dst, cameraMatrix, distCoeffs, noArray(), cameraMatrix, termCriteria); +} + static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, enum UndistortTypes projType) { double x = p.x, y = p.y; diff --git a/modules/calib3d/test/test_cameracalibration.cpp b/modules/calib3d/test/test_cameracalibration.cpp index c1704ad928e1..27e0e83b8f01 100644 --- a/modules/calib3d/test/test_cameracalibration.cpp +++ b/modules/calib3d/test/test_cameracalibration.cpp @@ -484,7 +484,8 @@ void CV_CameraCalibrationTest::run( int start_from ) for( i = 0; i < 3; i++ ) for( j = 0; j < 3; j++ ) { - values_read = fscanf(file, "%lf", &goodRotMatrs[currImage].val[i*3+j]); + // Yes, load with transpose + values_read = fscanf(file, "%lf", &goodRotMatrs[currImage].val[j*3+i]); CV_Assert(values_read == 1); } } @@ -568,12 +569,7 @@ void CV_CameraCalibrationTest::run( int start_from ) /* ----- Compute reprojection error ----- */ double dx,dy; double rx,ry; - double meanDx,meanDy; - double maxDx = 0.0; - double maxDy = 0.0; - meanDx = 0; - meanDy = 0; for( currImage = 0; currImage < numImages; currImage++ ) { double imageMeanDx = 0; @@ -585,20 +581,8 @@ void CV_CameraCalibrationTest::run( int start_from ) dx = rx - imagePoints[currImage][currPoint].x; dy = ry - imagePoints[currImage][currPoint].y; - meanDx += dx; - meanDy += dy; - imageMeanDx += dx*dx; imageMeanDy += dy*dy; - - dx = fabs(dx); - dy = fabs(dy); - - if( dx > maxDx ) - maxDx = dx; - - if( dy > maxDy ) - maxDy = dy; } goodPerViewErrors[currImage] = sqrt( (imageMeanDx + imageMeanDy) / (etalonSize.width * etalonSize.height)); @@ -609,9 +593,6 @@ void CV_CameraCalibrationTest::run( int start_from ) perViewErrors[currImage] = goodPerViewErrors[currImage]; } - meanDx /= numImages * etalonSize.width * etalonSize.height; - meanDy /= numImages * etalonSize.width * etalonSize.height; - /* ========= Compare parameters ========= */ CV_Assert(cameraMatrix.type() == CV_64F && cameraMatrix.size() == Size(3, 3)); CV_Assert(distortion.type() == CV_64F); @@ -681,7 +662,7 @@ void CV_CameraCalibrationTest::run( int start_from ) /* ----- Compare per view re-projection errors ----- */ CV_Assert(perViewErrors.size() == (size_t)numImages); - code = compare(&perViewErrors[0], &goodPerViewErrors[0], numImages, 1.1, "per view errors vector"); + code = compare(&perViewErrors[0], &goodPerViewErrors[0], numImages, 0.1, "per view errors vector"); if( code < 0 ) break; @@ -812,7 +793,6 @@ void CV_CameraCalibrationTest_CPP::calibrate(Size imageSize, { Mat r9; cvtest::Rodrigues( rvecs[i], r9 ); - cv::transpose(r9, r9); r9.convertTo(rotationMatrices[i], CV_64F); tvecs[i].convertTo(translationVectors[i], CV_64F); } diff --git a/modules/calib3d/test/test_fisheye.cpp b/modules/calib3d/test/test_fisheye.cpp index 310804d23365..23cfa98889f0 100644 --- a/modules/calib3d/test/test_fisheye.cpp +++ b/modules/calib3d/test/test_fisheye.cpp @@ -101,6 +101,55 @@ TEST_F(fisheyeTest, projectPoints) EXPECT_MAT_NEAR(distorted0, distorted2, 1e-10); } +TEST_F(fisheyeTest, distortUndistortPoints) +{ + int width = imageSize.width; + int height = imageSize.height; + + /* Create test points */ + std::vector points0Vector; + cv::Mat principalPoints = (cv::Mat_(5, 2) << K(0, 2), K(1, 2), // (cx, cy) + /* Image corners */ + 0, 0, + 0, height, + width, 0, + width, height + ); + + /* Random points inside image */ + cv::Mat xy[2] = {}; + xy[0].create(100, 1, CV_64F); + theRNG().fill(xy[0], cv::RNG::UNIFORM, 0, width); // x + xy[1].create(100, 1, CV_64F); + theRNG().fill(xy[1], cv::RNG::UNIFORM, 0, height); // y + + cv::Mat randomPoints; + merge(xy, 2, randomPoints); + + cv::Mat points0; + cv::vconcat(principalPoints.reshape(2), randomPoints, points0); + + /* Test with random D set */ + for (size_t i = 0; i < 10; ++i) { + cv::Mat distortion(1, 4, CV_64F); + theRNG().fill(distortion, cv::RNG::UNIFORM, -0.00001, 0.00001); + + /* Distort -> Undistort */ + cv::Mat distortedPoints; + cv::fisheye::distortPoints(points0, distortedPoints, K, distortion); + cv::Mat undistortedPoints; + cv::fisheye::undistortPoints(distortedPoints, undistortedPoints, K, distortion); + + EXPECT_MAT_NEAR(points0, undistortedPoints, 1e-8); + + /* Undistort -> Distort */ + cv::fisheye::undistortPoints(points0, undistortedPoints, K, distortion); + cv::fisheye::distortPoints(undistortedPoints, distortedPoints, K, distortion); + + EXPECT_MAT_NEAR(points0, distortedPoints, 1e-8); + } +} + TEST_F(fisheyeTest, undistortImage) { cv::Matx33d theK = this->K; @@ -178,7 +227,7 @@ TEST_F(fisheyeTest, undistortAndDistortImage) cv::Mat undPointsGt(imageHeight, imageWidth, CV_32FC2); cv::Mat imageGt(imageHeight, imageWidth, CV_8UC3); - for(int y = 0, k = 0; y < imageHeight; ++y) + for(int y = 0; y < imageHeight; ++y) { for(int x = 0; x < imageWidth; ++x) { @@ -212,7 +261,6 @@ TEST_F(fisheyeTest, undistortAndDistortImage) pixel_gt[2] = pixel[2]; } - k++; } } diff --git a/modules/calib3d/test/test_undistort_points.cpp b/modules/calib3d/test/test_undistort_points.cpp index 8765e2c5eb22..f92bec068b89 100644 --- a/modules/calib3d/test/test_undistort_points.cpp +++ b/modules/calib3d/test/test_undistort_points.cpp @@ -1,34 +1,26 @@ // This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. + +#include // EXPECT_MAT_NEAR +#include "opencv2/core/types.hpp" #include "test_precomp.hpp" namespace opencv_test { namespace { -class CV_UndistortTest : public cvtest::BaseTest +class UndistortPointsTest : public ::testing::Test { -public: - CV_UndistortTest(); - ~CV_UndistortTest(); protected: - void run(int); -private: void generate3DPointCloud(vector& points, Point3f pmin = Point3f(-1, -1, 5), Point3f pmax = Point3f(1, 1, 10)); void generateCameraMatrix(Mat& cameraMatrix); void generateDistCoeffs(Mat& distCoeffs, int count); + cv::Mat generateRotationVector(); - double thresh; - RNG rng; + double thresh = 1.0e-2; }; -CV_UndistortTest::CV_UndistortTest() -{ - thresh = 1.0e-2; -} -CV_UndistortTest::~CV_UndistortTest() {} - -void CV_UndistortTest::generate3DPointCloud(vector& points, Point3f pmin, Point3f pmax) +void UndistortPointsTest::generate3DPointCloud(vector& points, Point3f pmin, Point3f pmax) { RNG rng_Point = cv::theRNG(); // fix the seed to use "fixed" input 3D points for (size_t i = 0; i < points.size(); i++) @@ -39,87 +31,146 @@ void CV_UndistortTest::generate3DPointCloud(vector& points, Point3f pmi points[i] = Point3f(_x, _y, _z); } } -void CV_UndistortTest::generateCameraMatrix(Mat& cameraMatrix) + +void UndistortPointsTest::generateCameraMatrix(Mat& cameraMatrix) { const double fcMinVal = 1e-3; const double fcMaxVal = 100; cameraMatrix.create(3, 3, CV_64FC1); cameraMatrix.setTo(Scalar(0)); - cameraMatrix.at(0,0) = rng.uniform(fcMinVal, fcMaxVal); - cameraMatrix.at(1,1) = rng.uniform(fcMinVal, fcMaxVal); - cameraMatrix.at(0,2) = rng.uniform(fcMinVal, fcMaxVal); - cameraMatrix.at(1,2) = rng.uniform(fcMinVal, fcMaxVal); + cameraMatrix.at(0,0) = theRNG().uniform(fcMinVal, fcMaxVal); + cameraMatrix.at(1,1) = theRNG().uniform(fcMinVal, fcMaxVal); + cameraMatrix.at(0,2) = theRNG().uniform(fcMinVal, fcMaxVal); + cameraMatrix.at(1,2) = theRNG().uniform(fcMinVal, fcMaxVal); cameraMatrix.at(2,2) = 1; } -void CV_UndistortTest::generateDistCoeffs(Mat& distCoeffs, int count) + +void UndistortPointsTest::generateDistCoeffs(Mat& distCoeffs, int count) { distCoeffs = Mat::zeros(count, 1, CV_64FC1); for (int i = 0; i < count; i++) - distCoeffs.at(i,0) = rng.uniform(0.0, 1.0e-3); + distCoeffs.at(i,0) = theRNG().uniform(-0.1, 0.1); } -void CV_UndistortTest::run(int /* start_from */) +cv::Mat UndistortPointsTest::generateRotationVector() +{ + Mat rvec(1, 3, CV_64F); + theRNG().fill(rvec, RNG::UNIFORM, -0.2, 0.2); + + return rvec; +} + +TEST_F(UndistortPointsTest, accuracy) { Mat intrinsics, distCoeffs; generateCameraMatrix(intrinsics); + vector points(500); generate3DPointCloud(points); - vector projectedPoints; - projectedPoints.resize(points.size()); + + Mat rvec = generateRotationVector(); + Mat R; + cv::Rodrigues(rvec, R); + int modelMembersCount[] = {4,5,8}; for (int idx = 0; idx < 3; idx++) { generateDistCoeffs(distCoeffs, modelMembersCount[idx]); - projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1), Mat::zeros(3,1,CV_64FC1), intrinsics, distCoeffs, projectedPoints); + /* Project points with distortion */ + vector projectedPoints; + projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1), + Mat::zeros(3,1,CV_64FC1), intrinsics, + distCoeffs, projectedPoints); + + /* Project points without distortion */ vector realUndistortedPoints; - projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1), Mat::zeros(3,1,CV_64FC1), intrinsics, Mat::zeros(4,1,CV_64FC1), realUndistortedPoints); + projectPoints(Mat(points), rvec, + Mat::zeros(3,1,CV_64FC1), intrinsics, + Mat::zeros(4,1,CV_64FC1), realUndistortedPoints); + /* Undistort points */ Mat undistortedPoints; - undistortPoints(Mat(projectedPoints), undistortedPoints, intrinsics, distCoeffs); - - Mat p; - perspectiveTransform(undistortedPoints, p, intrinsics); - undistortedPoints = p; - double diff = cvtest::norm(Mat(realUndistortedPoints), undistortedPoints, NORM_L2); - if (diff > thresh) - { - ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); - return; - } - ts->set_failed_test_info(cvtest::TS::OK); + undistortPoints(Mat(projectedPoints), undistortedPoints, intrinsics, distCoeffs, R, intrinsics); + + EXPECT_MAT_NEAR(realUndistortedPoints, undistortedPoints.t(), thresh); } } -TEST(Calib3d_Undistort, accuracy) { CV_UndistortTest test; test.safe_run(); } +TEST_F(UndistortPointsTest, undistortImagePointsAccuracy) +{ + Mat intrinsics, distCoeffs; + generateCameraMatrix(intrinsics); -TEST(Calib3d_Undistort, stop_criteria) + vector points(500); + generate3DPointCloud(points); + + + int modelMembersCount[] = {4,5,8}; + for (int idx = 0; idx < 3; idx++) + { + generateDistCoeffs(distCoeffs, modelMembersCount[idx]); + + /* Project points with distortion */ + vector projectedPoints; + projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1), + Mat::zeros(3,1,CV_64FC1), intrinsics, + distCoeffs, projectedPoints); + + /* Project points without distortion */ + vector realUndistortedPoints; + projectPoints(Mat(points), Mat::zeros(3, 1, CV_64FC1), + Mat::zeros(3,1,CV_64FC1), intrinsics, + Mat::zeros(4,1,CV_64FC1), realUndistortedPoints); + + /* Undistort points */ + Mat undistortedPoints; + TermCriteria termCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, thresh / 2); + undistortImagePoints(Mat(projectedPoints), undistortedPoints, intrinsics, distCoeffs, + termCriteria); + + EXPECT_MAT_NEAR(realUndistortedPoints, undistortedPoints.t(), thresh); + } +} + + +TEST_F(UndistortPointsTest, stop_criteria) { Mat cameraMatrix = (Mat_(3,3,CV_64F) << 857.48296979, 0, 968.06224829, 0, 876.71824265, 556.37145899, 0, 0, 1); Mat distCoeffs = (Mat_(5,1,CV_64F) << -2.57614020e-01, 8.77086999e-02, -2.56970803e-04, -5.93390389e-04, -1.52194091e-02); - RNG rng(2); - Point2d pt_distorted(rng.uniform(0.0, 1920.0), rng.uniform(0.0, 1080.0)); + + Point2d pt_distorted(theRNG().uniform(0.0, 1920.0), theRNG().uniform(0.0, 1080.0)); + std::vector pt_distorted_vec; pt_distorted_vec.push_back(pt_distorted); + const double maxError = 1e-6; TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 100, maxError); + std::vector pt_undist_vec; - undistortPoints(pt_distorted_vec, pt_undist_vec, cameraMatrix, distCoeffs, noArray(), noArray(), criteria); + Mat rVec = Mat(Matx31d(0.1, -0.2, 0.2)); + Mat R; + cv::Rodrigues(rVec, R); + + undistortPoints(pt_distorted_vec, pt_undist_vec, cameraMatrix, distCoeffs, R, noArray(), criteria); - std::vector pt_redistorted_vec; std::vector pt_undist_vec_homogeneous; - pt_undist_vec_homogeneous.push_back( Point3d(pt_undist_vec[0].x, pt_undist_vec[0].y, 1.0) ); - projectPoints(pt_undist_vec_homogeneous, Mat::zeros(3,1,CV_64F), Mat::zeros(3,1,CV_64F), cameraMatrix, distCoeffs, pt_redistorted_vec); + pt_undist_vec_homogeneous.emplace_back(pt_undist_vec[0].x, pt_undist_vec[0].y, 1.0 ); + + std::vector pt_redistorted_vec; + projectPoints(pt_undist_vec_homogeneous, -rVec, + Mat::zeros(3,1,CV_64F), cameraMatrix, distCoeffs, pt_redistorted_vec); + const double obtainedError = sqrt( pow(pt_distorted.x - pt_redistorted_vec[0].x, 2) + pow(pt_distorted.y - pt_redistorted_vec[0].y, 2) ); ASSERT_LE(obtainedError, maxError); } -TEST(undistortPoints, regression_14583) +TEST_F(UndistortPointsTest, regression_14583) { const int col = 720; // const int row = 540; diff --git a/modules/core/include/opencv2/core.hpp b/modules/core/include/opencv2/core.hpp index 70ea4f8c1fa2..f7807e37ec07 100644 --- a/modules/core/include/opencv2/core.hpp +++ b/modules/core/include/opencv2/core.hpp @@ -1739,6 +1739,16 @@ should be done separately if needed. */ CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); +/** @brief Transpose for n-dimensional matrices. + * + * @note Input should be continuous single-channel matrix. + * @param src input array. + * @param order a permutation of [0,1,..,N-1] where N is the number of axes of src. + * The i’th axis of dst will correspond to the axis numbered order[i] of the input. + * @param dst output array of the same type as src. + */ +CV_EXPORTS_W void transposeND(InputArray src, const std::vector& order, OutputArray dst); + /** @brief Performs the matrix transformation of every array element. The function cv::transform performs the matrix transformation of every diff --git a/modules/core/include/opencv2/core/bindings_utils.hpp b/modules/core/include/opencv2/core/bindings_utils.hpp index 22a86ff9beb8..4f7eb532b955 100644 --- a/modules/core/include/opencv2/core/bindings_utils.hpp +++ b/modules/core/include/opencv2/core/bindings_utils.hpp @@ -219,6 +219,59 @@ AsyncArray testAsyncException() return p.getArrayResult(); } +namespace nested { +CV_WRAP static inline bool testEchoBooleanFunction(bool flag) { + return flag; +} + +class CV_EXPORTS_W CV_WRAP_AS(ExportClassName) OriginalClassName +{ +public: + struct CV_EXPORTS_W_SIMPLE Params + { + CV_PROP_RW int int_value; + CV_PROP_RW float float_value; + + CV_WRAP explicit Params(int int_param = 123, float float_param = 3.5f) + { + int_value = int_param; + float_value = float_param; + } + }; + + explicit OriginalClassName(const OriginalClassName::Params& params = OriginalClassName::Params()) + { + params_ = params; + } + + CV_WRAP int getIntParam() const + { + return params_.int_value; + } + + CV_WRAP float getFloatParam() const + { + return params_.float_value; + } + + CV_WRAP static std::string originalName() + { + return "OriginalClassName"; + } + + CV_WRAP static Ptr + create(const OriginalClassName::Params& params = OriginalClassName::Params()) + { + return makePtr(params); + } + +private: + OriginalClassName::Params params_; +}; + +typedef OriginalClassName::Params OriginalClassName_Params; +} // namespace nested + namespace fs { CV_EXPORTS_W cv::String getCacheDirectoryForDownloads(); } // namespace fs diff --git a/modules/core/include/opencv2/core/check.hpp b/modules/core/include/opencv2/core/check.hpp index d975223cc514..a32b8111cea2 100644 --- a/modules/core/include/opencv2/core/check.hpp +++ b/modules/core/include/opencv2/core/check.hpp @@ -13,7 +13,7 @@ namespace cv { CV_EXPORTS const char* depthToString(int depth); /** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "" */ -CV_EXPORTS const String typeToString(int type); +CV_EXPORTS String typeToString(int type); //! @cond IGNORED @@ -23,7 +23,7 @@ namespace detail { CV_EXPORTS const char* depthToString_(int depth); /** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */ -CV_EXPORTS const cv::String typeToString_(int type); +CV_EXPORTS cv::String typeToString_(int type); enum TestOp { TEST_CUSTOM = 0, diff --git a/modules/core/include/opencv2/core/core_c.h b/modules/core/include/opencv2/core/core_c.h index 09ac1e789a89..7b686b86f315 100644 --- a/modules/core/include/opencv2/core/core_c.h +++ b/modules/core/include/opencv2/core/core_c.h @@ -48,16 +48,19 @@ #include "opencv2/core/types_c.h" #ifdef __cplusplus -# ifdef _MSC_VER -/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' - which is incompatible with C +/* disable MSVC warning C4190 / clang-cl -Wreturn-type-c-linkage: + 'function' has C-linkage specified, but returns UDT 'typename' + which is incompatible with C It is OK to disable it because we only extend few plain structures with C++ constructors for simpler interoperability with C++ API of the library */ -# pragma warning(disable:4190) -# elif defined __clang__ && __clang_major__ >= 3 +# if defined(__clang__) + // handle clang on Linux and clang-cl (i. e. clang on Windows) first # pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" +# elif defined(_MSC_VER) + // then handle MSVC +# pragma warning(disable:4190) # endif #endif diff --git a/modules/core/include/opencv2/core/cuda.hpp b/modules/core/include/opencv2/core/cuda.hpp index 716b8bf2a8e3..1ebea07c0d8d 100644 --- a/modules/core/include/opencv2/core/cuda.hpp +++ b/modules/core/include/opencv2/core/cuda.hpp @@ -155,7 +155,7 @@ class CV_EXPORTS_W GpuMat CV_WRAP void create(Size size, int type); //! decreases reference counter, deallocate the data when reference counter reaches 0 - void release(); + CV_WRAP void release(); //! swaps with other smart pointer CV_WRAP void swap(GpuMat& mat); @@ -689,7 +689,7 @@ class CV_EXPORTS_W BufferPool public: //! Gets the BufferPool for the given stream. - explicit BufferPool(Stream& stream); + CV_WRAP explicit BufferPool(Stream& stream); //! Allocates a new GpuMat of given size and type. CV_WRAP GpuMat getBuffer(int rows, int cols, int type); @@ -924,7 +924,7 @@ class CV_EXPORTS_W Event INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ }; - CV_WRAP explicit Event(Event::CreateFlags flags = Event::CreateFlags::DEFAULT); + CV_WRAP explicit Event(const Event::CreateFlags flags = Event::CreateFlags::DEFAULT); //! records an event CV_WRAP void record(Stream& stream = Stream::Null()); @@ -946,6 +946,7 @@ class CV_EXPORTS_W Event friend struct EventAccessor; }; +CV_ENUM_FLAGS(Event::CreateFlags) //! @} cudacore_struct diff --git a/modules/core/include/opencv2/core/hal/intrin_msa.hpp b/modules/core/include/opencv2/core/hal/intrin_msa.hpp index a1fbb093a84d..c035fdad602a 100644 --- a/modules/core/include/opencv2/core/hal/intrin_msa.hpp +++ b/modules/core/include/opencv2/core/hal/intrin_msa.hpp @@ -1037,12 +1037,12 @@ inline scalartype v_reduce_sum(const _Tpvec& a) \ return (scalartype)msa_sum_##suffix(a.val); \ } -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint8x16, unsigned char, u8) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int8x16, char, s8) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint16x8, unsigned short, u16) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int16x8, short, s16) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int32x4, int, s32) +OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint8x16, unsigned short, u8) +OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int8x16, short, s8) +OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint16x8, unsigned, u16) +OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int16x8, int, s16) +OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint32x4, uint64_t, u32) +OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int32x4, int64_t, s32) OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_float32x4, float, f32) inline uint64 v_reduce_sum(const v_uint64x2& a) diff --git a/modules/core/include/opencv2/core/hal/intrin_neon.hpp b/modules/core/include/opencv2/core/hal/intrin_neon.hpp index e17972a3fc4a..28cf81337922 100644 --- a/modules/core/include/opencv2/core/hal/intrin_neon.hpp +++ b/modules/core/include/opencv2/core/hal/intrin_neon.hpp @@ -591,28 +591,26 @@ inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) { - return v_int16x8(vcombine_s16( - vshrn_n_s32(vmull_s16( vget_low_s16(a.val), vget_low_s16(b.val)), 16), - vshrn_n_s32( #if CV_NEON_AARCH64 - vmull_high_s16(a.val, b.val) + int32x4_t c = vmull_high_s16(a.val, b.val); #else // #if CV_NEON_AARCH64 - vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)) + int32x4_t c = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); #endif // #if CV_NEON_AARCH64 - , 16) + return v_int16x8(vcombine_s16( + vshrn_n_s32(vmull_s16( vget_low_s16(a.val), vget_low_s16(b.val)), 16), + vshrn_n_s32(c, 16) )); } inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) { - return v_uint16x8(vcombine_u16( - vshrn_n_u32(vmull_u16( vget_low_u16(a.val), vget_low_u16(b.val)), 16), - vshrn_n_u32( #if CV_NEON_AARCH64 - vmull_high_u16(a.val, b.val) + uint32x4_t c = vmull_high_u16(a.val, b.val); #else // #if CV_NEON_AARCH64 - vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)) + uint32x4_t c = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); #endif // #if CV_NEON_AARCH64 - , 16) + return v_uint16x8(vcombine_u16( + vshrn_n_u32(vmull_u16( vget_low_u16(a.val), vget_low_u16(b.val)), 16), + vshrn_n_u32(c, 16) )); } @@ -1937,10 +1935,14 @@ inline v_int32x4 v_round(const v_float32x4& a) { float32x4_t a_ = a.val; int32x4_t result; +#if defined _MSC_VER + result = vcvtnq_s32_f32(a_); +#else __asm__ ("fcvtns %0.4s, %1.4s" : "=w"(result) : "w"(a_) : /* No clobbers */); +#endif return v_int32x4(result); } #else diff --git a/modules/core/include/opencv2/core/hal/intrin_rvv.hpp b/modules/core/include/opencv2/core/hal/intrin_rvv.hpp index fe6c077639fa..a592976827c7 100644 --- a/modules/core/include/opencv2/core/hal/intrin_rvv.hpp +++ b/modules/core/include/opencv2/core/hal/intrin_rvv.hpp @@ -230,6 +230,7 @@ inline vint16mf2_t vwcvt_x_x_v_i16mf2 (vint8mf4_t src, size_t vl) //////////// Types //////////// +#ifndef __clang__ struct v_uint8x16 { typedef uchar lane_type; @@ -531,7 +532,358 @@ struct v_float64x2 double val[2]; }; #endif +#else +struct v_uint8x16 +{ + typedef uchar lane_type; + enum { nlanes = 16 }; + + v_uint8x16() {} + explicit v_uint8x16(vuint8m1_t v) + { + *pval = v; + } + v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, + uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) + { + uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + *pval = vle8_v_u8m1(v, nlanes); + } + operator vuint8m1_t() const + { + return *pval; + } + uchar get0() const + { + return vmv_x(*pval); + } + inline v_uint8x16& operator=(const v_uint8x16& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_uint8x16(const v_uint8x16& vec) { + *pval = *(vec.pval); + } + uchar val[16]; + vuint8m1_t* pval = (vuint8m1_t*)val; +}; + +struct v_int8x16 +{ + typedef schar lane_type; + enum { nlanes = 16 }; + + v_int8x16() {} + explicit v_int8x16(vint8m1_t v) + { + *pval = v; + } + v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, + schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) + { + schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; + *pval = vle8_v_i8m1(v, nlanes); + } + operator vint8m1_t() const + { + return *pval; + } + schar get0() const + { + return vmv_x(*pval); + } + inline v_int8x16& operator=(const v_int8x16& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_int8x16(const v_int8x16& vec) { + *pval = *(vec.pval); + } + schar val[16]; + vint8m1_t* pval = (vint8m1_t*)val; +}; + +struct v_uint16x8 +{ + typedef ushort lane_type; + enum { nlanes = 8 }; + + v_uint16x8() {} + explicit v_uint16x8(vuint16m1_t v) + { + *pval = v; + } + v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) + { + ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + *pval = vle16_v_u16m1(v, nlanes); + } + operator vuint16m1_t() const + { + return *pval; + } + ushort get0() const + { + return vmv_x(*pval); + } + + inline v_uint16x8& operator=(const v_uint16x8& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_uint16x8(const v_uint16x8& vec) { + *pval = *(vec.pval); + } + ushort val[8]; + vuint16m1_t* pval = (vuint16m1_t*)val; +}; + +struct v_int16x8 +{ + typedef short lane_type; + enum { nlanes = 8 }; + + v_int16x8() {} + explicit v_int16x8(vint16m1_t v) + { + *pval = v; + } + v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) + { + short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; + *pval = vle16_v_i16m1(v, nlanes); + } + operator vint16m1_t() const + { + return *pval; + } + short get0() const + { + return vmv_x(*pval); + } + + inline v_int16x8& operator=(const v_int16x8& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_int16x8(const v_int16x8& vec) { + *pval = *(vec.pval); + } + short val[8]; + vint16m1_t* pval = (vint16m1_t*)val; +}; + +struct v_uint32x4 +{ + typedef unsigned lane_type; + enum { nlanes = 4 }; + + v_uint32x4() {} + explicit v_uint32x4(vuint32m1_t v) + { + *pval = v; + } + v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) + { + unsigned v[] = {v0, v1, v2, v3}; + *pval = vle32_v_u32m1(v, nlanes); + } + operator vuint32m1_t() const + { + return *pval; + } + unsigned get0() const + { + return vmv_x(*pval); + } + inline v_uint32x4& operator=(const v_uint32x4& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_uint32x4(const v_uint32x4& vec) { + *pval = *(vec.pval); + } + unsigned val[4]; + vuint32m1_t* pval = (vuint32m1_t*)val; +}; + +struct v_int32x4 +{ + typedef int lane_type; + enum { nlanes = 4 }; + + v_int32x4() {} + explicit v_int32x4(vint32m1_t v) + { + *pval = v; + } + v_int32x4(int v0, int v1, int v2, int v3) + { + int v[] = {v0, v1, v2, v3}; + *pval = vle32_v_i32m1(v, nlanes); + } + operator vint32m1_t() const + { + return *pval; + } + int get0() const + { + return vmv_x(*pval); + } + + inline v_int32x4& operator=(const v_int32x4& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_int32x4(const v_int32x4& vec) { + *pval = *(vec.pval); + } + int val[4]; + vint32m1_t* pval = (vint32m1_t*)val; +}; + +struct v_float32x4 +{ + typedef float lane_type; + enum { nlanes = 4 }; + + v_float32x4() {} + explicit v_float32x4(vfloat32m1_t v) + { + *pval = v; + } + v_float32x4(float v0, float v1, float v2, float v3) + { + float v[] = {v0, v1, v2, v3}; + *pval = vle32_v_f32m1(v, nlanes); + } + operator vfloat32m1_t() const + { + return *pval; + } + float get0() const + { + return vfmv_f(*pval); + } + inline v_float32x4& operator=(const v_float32x4& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_float32x4(const v_float32x4& vec) { + *pval = *(vec.pval); + } + float val[4]; + vfloat32m1_t* pval = (vfloat32m1_t*)val; +}; + +struct v_uint64x2 +{ + typedef uint64 lane_type; + enum { nlanes = 2 }; + + v_uint64x2() {} + explicit v_uint64x2(vuint64m1_t v) + { + *pval = v; + } + v_uint64x2(uint64 v0, uint64 v1) + { + uint64 v[] = {v0, v1}; + *pval = vle64_v_u64m1(v, nlanes); + } + operator vuint64m1_t() const + { + return *pval; + } + uint64 get0() const + { + return vmv_x(*pval); + } + + inline v_uint64x2& operator=(const v_uint64x2& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_uint64x2(const v_uint64x2& vec) { + *pval = *(vec.pval); + } + uint64 val[2]; + vuint64m1_t* pval = (vuint64m1_t*)val; +}; + +struct v_int64x2 +{ + typedef int64 lane_type; + enum { nlanes = 2 }; + + v_int64x2() {} + explicit v_int64x2(vint64m1_t v) + { + *pval = v; + } + v_int64x2(int64 v0, int64 v1) + { + int64 v[] = {v0, v1}; + *pval = vle64_v_i64m1(v, nlanes); + } + operator vint64m1_t() const + { + return *pval; + } + int64 get0() const + { + return vmv_x(*pval); + } + + inline v_int64x2& operator=(const v_int64x2& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_int64x2(const v_int64x2& vec) { + *pval = *(vec.pval); + } + int64 val[2]; + vint64m1_t* pval = (vint64m1_t*)val; +}; + +#if CV_SIMD128_64F +struct v_float64x2 +{ + typedef double lane_type; + enum { nlanes = 2 }; + + v_float64x2() {} + explicit v_float64x2(vfloat64m1_t v) + { + *pval = v; + } + v_float64x2(double v0, double v1) + { + double v[] = {v0, v1}; + *pval = vle64_v_f64m1(v, nlanes); + } + operator vfloat64m1_t() const + { + return *pval; + } + double get0() const + { + return vfmv_f(*pval); + } + + inline v_float64x2& operator=(const v_float64x2& vec) { + *pval = *(vec.pval); + return *this; + } + inline v_float64x2(const v_float64x2& vec) { + *pval = *(vec.pval); + } + double val[2]; + vfloat64m1_t* pval = (vfloat64m1_t*)val; +}; +#endif // CV_SIMD128_64F +#endif // __clang__ //////////// Initial //////////// @@ -1819,6 +2171,7 @@ inline v_float32x4 v_cvt_f32(const v_int32x4& a) } #if CV_SIMD128_64F +#ifndef __clang__ inline v_float32x4 v_cvt_f32(const v_float64x2& a) { double arr[4] = {a.val[0], a.val[1], 0, 0}; @@ -1832,6 +2185,18 @@ inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) vfloat64m2_t tmp = vle64_v_f64m2(arr, 4); return v_float32x4(vfncvt_f_f_w_f32m1(tmp, 4)); } +#else +inline v_float32x4 v_cvt_f32(const v_float64x2& a) +{ + vfloat64m2_t zero = vfmv_v_f_f64m2(0, 4); + return v_float32x4(vfncvt_f_f_w_f32m1(vset_v_f64m1_f64m2(zero, 0, a), 4)); +} +inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) +{ + vfloat64m2_t dst = vlmul_ext_v_f64m1_f64m2(a); + return v_float32x4(vfncvt_f_f_w_f32m1(vset_v_f64m1_f64m2(dst, 1, b), 4)); +} +#endif inline v_float64x2 v_cvt_f64(const v_int32x4& a) { @@ -2351,6 +2716,7 @@ OPENCV_HAL_IMPL_RVV_POPCOUNT_OP(v_uint64x2, v_int64x2, uint64, int64, u64) //////////// SignMask //////////// +#ifndef __clang__ #define OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(_Tpvec, _Tp, suffix, vl, shift) \ inline int v_signmask(const _Tpvec& a) \ { \ @@ -2381,6 +2747,36 @@ inline int v_signmask(const v_float64x2& a) { return v_signmask(v_reinterpret_as_u64(a)); } #endif +#else +#define OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(_Tpvec, width, vl) \ +inline int v_signmask(const _Tpvec& a) \ +{ \ + uint8_t ans[16] = {0};\ + vsm(ans, vmslt(a, 0, vl), vl);\ + return reinterpret_cast(ans)[0];\ +} + +OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_int8x16, 8, 16) +OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_int16x8, 16, 8) +OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_int32x4, 32, 4) +OPENCV_HAL_IMPL_RVV_SIGNMASK_OP(v_int64x2, 64, 2) + +inline int v_signmask(const v_uint8x16& a) +{ return v_signmask(v_reinterpret_as_s8(a)); } +inline int v_signmask(const v_uint16x8& a) +{ return v_signmask(v_reinterpret_as_s16(a)); } +inline int v_signmask(const v_uint32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } +inline int v_signmask(const v_float32x4& a) +{ return v_signmask(v_reinterpret_as_s32(a)); } +inline int v_signmask(const v_uint64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } +#if CV_SIMD128_64F +inline int v_signmask(const v_float64x2& a) +{ return v_signmask(v_reinterpret_as_s64(a)); } +#endif + +#endif //////////// Scan forward //////////// @@ -2520,6 +2916,7 @@ inline v_int32x4 v_trunc(const v_float32x4& a) return v_int32x4(vfcvt_rtz_x_f_v_i32m1(a, 4)); } #if CV_SIMD128_64F +#ifndef __clang__ inline v_int32x4 v_round(const v_float64x2& a) { double arr[4] = {a.val[0], a.val[1], 0, 0}; @@ -2554,6 +2951,42 @@ inline v_int32x4 v_trunc(const v_float64x2& a) vfloat64m2_t tmp = vle64_v_f64m2(arr, 4); return v_int32x4(vfncvt_rtz_x_f_w_i32m1(tmp, 4)); } + +#else +inline v_int32x4 v_round(const v_float64x2& a) +{ + vfloat64m2_t zero = vfmv_v_f_f64m2(0, 4); + return v_int32x4(vfncvt_x_f_w_i32m1(vset_v_f64m1_f64m2(zero, 0, a), 4)); +} + +inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) +{ + vfloat64m2_t dst = vlmul_ext_v_f64m1_f64m2(a); + return v_int32x4(vfncvt_x_f_w_i32m1(vset_v_f64m1_f64m2(dst, 1, b), 4)); +} + +inline v_int32x4 v_floor(const v_float64x2& a) +{ + vfloat64m2_t dst = vfmv_v_f_f64m2(0, 4); + dst = vset_v_f64m1_f64m2(dst, 0, a); + dst = vfsub_vf_f64m2(dst, 0.5, 2); + return v_int32x4(vfncvt_x_f_w_i32m1(dst, 4)); +} + +inline v_int32x4 v_ceil(const v_float64x2& a) +{ + vfloat64m2_t dst = vfmv_v_f_f64m2(0, 4); + dst = vset_v_f64m1_f64m2(dst, 0, a); + dst = vfadd_vf_f64m2(dst, 0.5, 2); + return v_int32x4(vfncvt_x_f_w_i32m1(dst, 4)); +} + +inline v_int32x4 v_trunc(const v_float64x2& a) +{ + vfloat64m2_t zero = vfmv_v_f_f64m2(0, 4); + return v_int32x4(vfncvt_rtz_x_f_w_i32m1(vset_v_f64m1_f64m2(zero, 0, a), 4)); +} +#endif #endif diff --git a/modules/core/include/opencv2/core/hal/msa_macros.h b/modules/core/include/opencv2/core/hal/msa_macros.h index bd6ddb127aac..fad8c5adda25 100644 --- a/modules/core/include/opencv2/core/hal/msa_macros.h +++ b/modules/core/include/opencv2/core/hal/msa_macros.h @@ -719,7 +719,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8))); v2i64 _c; \ _b = __builtin_msa_hadd_s_w(__a, __a); \ _c = __builtin_msa_hadd_s_d(_b, _b); \ - (int16_t)(_c[0] + _c[1]); \ + (int32_t)(_c[0] + _c[1]); \ }) @@ -736,7 +736,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8))); ({ \ v2i64 _b; \ _b = __builtin_msa_hadd_s_d(__a, __a); \ - (int32_t)(_b[0] + _b[1]); \ + (int64_t)(_b[0] + _b[1]); \ }) /* uint8_t msa_sum_u8(v16u8 __a)*/ @@ -756,7 +756,7 @@ typedef double v1f64 __attribute__ ((vector_size(8), aligned(8))); v4i32 _c32; \ _b16 = __builtin_msa_hadd_s_h(__a, __a); \ _c32 = __builtin_msa_hadd_s_w(_b16, _b16); \ - (int8_t)msa_sum_s32(_c32); \ + (int16_t)msa_sum_s32(_c32); \ }) /* float msa_sum_f32(v4f32 __a)*/ diff --git a/modules/core/include/opencv2/core/mat.hpp b/modules/core/include/opencv2/core/mat.hpp index 1ef0eb5a0245..2aba15c4b917 100644 --- a/modules/core/include/opencv2/core/mat.hpp +++ b/modules/core/include/opencv2/core/mat.hpp @@ -449,7 +449,16 @@ CV_EXPORTS InputOutputArray noArray(); /////////////////////////////////// MatAllocator ////////////////////////////////////// -//! Usage flags for allocator +/** @brief Usage flags for allocator + + @warning All flags except `USAGE_DEFAULT` are experimental. + + @warning For the OpenCL allocator, `USAGE_ALLOCATE_SHARED_MEMORY` depends on + OpenCV's optional, experimental integration with OpenCL SVM. To enable this + integration, build OpenCV using the `WITH_OPENCL_SVM=ON` CMake option and, at + runtime, call `cv::ocl::Context::getDefault().setUseSVM(true);` or similar + code. Note that SVM is incompatible with OpenCL 1.x. +*/ enum UMatUsageFlags { USAGE_DEFAULT = 0, @@ -1009,7 +1018,7 @@ class CV_EXPORTS Mat @param copyData Flag to specify whether the underlying data of the STL vector should be copied to (true) or shared with (false) the newly constructed matrix. When the data is copied, the allocated buffer is managed using Mat reference counting mechanism. While the data is shared, - the reference counter is NULL, and you should not deallocate the data until the matrix is not + the reference counter is NULL, and you should not deallocate the data until the matrix is destructed. */ template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); @@ -2077,7 +2086,7 @@ class CV_EXPORTS Mat Mat_ image = Mat::zeros(3, sizes, CV_8UC3); - image.forEach([&](Pixel& pixel, const int position[]) -> void { + image.forEach([](Pixel& pixel, const int position[]) -> void { pixel.x = position[0]; pixel.y = position[1]; pixel.z = position[2]; @@ -2267,7 +2276,7 @@ template class Mat_ : public Mat std::reverse_iterator rbegin() const; std::reverse_iterator rend() const; - //! template methods for for operation over all matrix elements. + //! template methods for operation over all matrix elements. // the operations take care of skipping gaps in the end of rows (if any) template void forEach(const Functor& operation); template void forEach(const Functor& operation) const; diff --git a/modules/core/include/opencv2/core/matx.hpp b/modules/core/include/opencv2/core/matx.hpp index 3c92e3a21db5..162ce6e7f82c 100644 --- a/modules/core/include/opencv2/core/matx.hpp +++ b/modules/core/include/opencv2/core/matx.hpp @@ -372,6 +372,14 @@ template class Vec : public Matx<_Tp, cn, 1> Vec(const Vec<_Tp, cn>& v); static Vec all(_Tp alpha); + static Vec ones(); + static Vec randn(_Tp a, _Tp b); + static Vec randu(_Tp a, _Tp b); + static Vec zeros(); +#ifdef CV_CXX11 + static Vec diag(_Tp alpha) = delete; + static Vec eye() = delete; +#endif //! per-element multiplication Vec mul(const Vec<_Tp, cn>& v) const; @@ -1053,6 +1061,18 @@ Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) return v; } +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::ones() +{ + return Vec::all(1); +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::zeros() +{ + return Vec::all(0); +} + template inline Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const { diff --git a/modules/core/include/opencv2/core/operations.hpp b/modules/core/include/opencv2/core/operations.hpp index bde28c49b272..43a9eb8603b1 100644 --- a/modules/core/include/opencv2/core/operations.hpp +++ b/modules/core/include/opencv2/core/operations.hpp @@ -230,6 +230,22 @@ Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) return M; } +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::randu(_Tp a, _Tp b) +{ + Vec<_Tp,cn> V; + cv::randu(V, Scalar(a), Scalar(b)); + return V; +} + +template inline +Vec<_Tp, cn> Vec<_Tp, cn>::randn(_Tp a, _Tp b) +{ + Vec<_Tp,cn> V; + cv::randn(V, Scalar(a), Scalar(b)); + return V; +} + template inline Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const { diff --git a/modules/core/include/opencv2/core/persistence.hpp b/modules/core/include/opencv2/core/persistence.hpp index 276f640323e7..8e135d1a1109 100644 --- a/modules/core/include/opencv2/core/persistence.hpp +++ b/modules/core/include/opencv2/core/persistence.hpp @@ -309,8 +309,8 @@ class CV_EXPORTS_W FileStorage READ = 0, //!< value, open the file for reading WRITE = 1, //!< value, open the file for writing APPEND = 2, //!< value, open the file for appending - MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is - //!< returned by FileStorage::release) + MEMORY = 4, /**< flag, read data from source or write data to the internal buffer (which is + returned by FileStorage::release) */ FORMAT_MASK = (7<<3), //!< mask for format flags FORMAT_AUTO = 0, //!< flag, auto format FORMAT_XML = (1<<3), //!< flag, XML format diff --git a/modules/core/include/opencv2/core/utils/fp_control.private.hpp b/modules/core/include/opencv2/core/utils/fp_control.private.hpp new file mode 100644 index 000000000000..12ee363dd89a --- /dev/null +++ b/modules/core/include/opencv2/core/utils/fp_control.private.hpp @@ -0,0 +1,29 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_FP_CONTROL_UTILS_PRIVATE_HPP +#define OPENCV_CORE_FP_CONTROL_UTILS_PRIVATE_HPP + +#include "fp_control_utils.hpp" + +#if OPENCV_SUPPORTS_FP_DENORMALS_HINT == 0 + // disabled +#elif defined(OPENCV_IMPL_FP_HINTS) + // custom +#elif defined(OPENCV_IMPL_FP_HINTS_X86) + // custom +#elif defined(__SSE__) || defined(__SSE2__) || defined(_M_X64) || (defined(_M_IX86_FP) && _M_IX86_FP >= 1) + #include + #define OPENCV_IMPL_FP_HINTS_X86 1 + #define OPENCV_IMPL_FP_HINTS 1 +#endif + +#ifndef OPENCV_IMPL_FP_HINTS +#define OPENCV_IMPL_FP_HINTS 0 +#endif +#ifndef OPENCV_IMPL_FP_HINTS_X86 +#define OPENCV_IMPL_FP_HINTS_X86 0 +#endif + +#endif // OPENCV_CORE_FP_CONTROL_UTILS_PRIVATE_HPP diff --git a/modules/core/include/opencv2/core/utils/fp_control_utils.hpp b/modules/core/include/opencv2/core/utils/fp_control_utils.hpp new file mode 100644 index 000000000000..930bc5d36770 --- /dev/null +++ b/modules/core/include/opencv2/core/utils/fp_control_utils.hpp @@ -0,0 +1,69 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_CORE_FP_CONTROL_UTILS_HPP +#define OPENCV_CORE_FP_CONTROL_UTILS_HPP + +namespace cv { + +namespace details { + +struct FPDenormalsModeState +{ + uint32_t reserved[16]; // 64-bytes +}; // FPDenormalsModeState + +CV_EXPORTS void setFPDenormalsIgnoreHint(bool ignore, CV_OUT FPDenormalsModeState& state); +CV_EXPORTS int saveFPDenormalsState(CV_OUT FPDenormalsModeState& state); +CV_EXPORTS bool restoreFPDenormalsState(const FPDenormalsModeState& state); + +class FPDenormalsIgnoreHintScope +{ +public: + inline explicit FPDenormalsIgnoreHintScope(bool ignore = true) + { + details::setFPDenormalsIgnoreHint(ignore, saved_state); + } + + inline explicit FPDenormalsIgnoreHintScope(const FPDenormalsModeState& state) + { + details::saveFPDenormalsState(saved_state); + details::restoreFPDenormalsState(state); + } + + inline ~FPDenormalsIgnoreHintScope() + { + details::restoreFPDenormalsState(saved_state); + } + +protected: + FPDenormalsModeState saved_state; +}; // FPDenormalsIgnoreHintScope + +class FPDenormalsIgnoreHintScopeNOOP +{ +public: + inline FPDenormalsIgnoreHintScopeNOOP(bool ignore = true) { CV_UNUSED(ignore); } + inline FPDenormalsIgnoreHintScopeNOOP(const FPDenormalsModeState& state) { CV_UNUSED(state); } + inline ~FPDenormalsIgnoreHintScopeNOOP() { } +}; // FPDenormalsIgnoreHintScopeNOOP + +} // namespace details + + +// Should depend on target compilation architecture only +// Note: previously added archs should NOT be removed to preserve ABI compatibility +#if defined(OPENCV_SUPPORTS_FP_DENORMALS_HINT) + // preserve configuration overloading through ports +#elif defined(__i386__) || defined(__x86_64__) || defined(_M_X64) || defined(_X86_) +typedef details::FPDenormalsIgnoreHintScope FPDenormalsIgnoreHintScope; +#define OPENCV_SUPPORTS_FP_DENORMALS_HINT 1 +#else +#define OPENCV_SUPPORTS_FP_DENORMALS_HINT 0 +typedef details::FPDenormalsIgnoreHintScopeNOOP FPDenormalsIgnoreHintScope; +#endif + +} // namespace cv + +#endif // OPENCV_CORE_FP_CONTROL_UTILS_HPP diff --git a/modules/core/include/opencv2/core/utils/plugin_loader.private.hpp b/modules/core/include/opencv2/core/utils/plugin_loader.private.hpp index d6390fc74a48..53b8c48c38d1 100644 --- a/modules/core/include/opencv2/core/utils/plugin_loader.private.hpp +++ b/modules/core/include/opencv2/core/utils/plugin_loader.private.hpp @@ -147,7 +147,7 @@ class CV_EXPORTS DynamicLib return handle != NULL; } void* getSymbol(const char* symbolName) const; - const std::string getName() const; + std::string getName() const; private: void libraryLoad(const FileSystemPath_t& filename); void libraryRelease(); diff --git a/modules/core/include/opencv2/core/version.hpp b/modules/core/include/opencv2/core/version.hpp index 2fbd4995c6b3..7b129b6dd306 100644 --- a/modules/core/include/opencv2/core/version.hpp +++ b/modules/core/include/opencv2/core/version.hpp @@ -6,8 +6,8 @@ #define OPENCV_VERSION_HPP #define CV_VERSION_MAJOR 4 -#define CV_VERSION_MINOR 5 -#define CV_VERSION_REVISION 5 +#define CV_VERSION_MINOR 6 +#define CV_VERSION_REVISION 0 #define CV_VERSION_STATUS "" #define CVAUX_STR_EXP(__A) #__A diff --git a/modules/core/include/opencv2/core/vsx_utils.hpp b/modules/core/include/opencv2/core/vsx_utils.hpp index 68863ffb36f2..79a1074d59ff 100644 --- a/modules/core/include/opencv2/core/vsx_utils.hpp +++ b/modules/core/include/opencv2/core/vsx_utils.hpp @@ -324,6 +324,7 @@ VSX_IMPL_1RG(vec_udword2, vec_float4, xvcvspuxds, vec_ctulo) #define VSX_IMPL_CONVERT(rt, rg, fnm) \ VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); } +#ifndef vec_permi #if __clang_major__ < 5 // implement vec_permi in a dirty way # define VSX_IMPL_CLANG_4_PERMI(Tvec) \ @@ -351,12 +352,14 @@ VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); } // vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4 # define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) #endif // __clang_major__ < 5 +#endif // shift left double by word immediate #ifndef vec_sldw # define vec_sldw vec_xxsldwi #endif +#if __clang_major__ < 13 // Implement vec_rsqrt since clang only supports vec_rsqrte #ifndef vec_rsqrt VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a) @@ -380,6 +383,7 @@ VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b) ret[b & 1] = a; return ret; } +#endif // vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt #define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast) \ @@ -684,7 +688,8 @@ VSX_IMPL_LOAD_L8(vec_double2, double) #endif // absolute difference -#ifndef vec_absd +#ifndef _ARCH_PWR9 +# undef vec_absd # define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b)) #endif diff --git a/modules/core/misc/objc/gen_dict.json b/modules/core/misc/objc/gen_dict.json index 9ade8ccb9fd3..58300255dcb3 100644 --- a/modules/core/misc/objc/gen_dict.json +++ b/modules/core/misc/objc/gen_dict.json @@ -78,6 +78,26 @@ "(void)divide:(double)scale src2:(Mat*)src2 dst:(Mat*)dst dtype:(int)dtype" : { "src2" : {"name" : "src"} } } }, + "header_fix" : { + "Core": { + "pow" : { + "prolog" : "#pragma push_macro(\"pow\")\n#undef pow", + "epilog" : "#pragma pop_macro(\"pow\")" + }, + "sqrt" : { + "prolog" : "#pragma push_macro(\"sqrt\")\n#undef sqrt", + "epilog" : "#pragma pop_macro(\"sqrt\")" + }, + "exp" : { + "prolog" : "#pragma push_macro(\"exp\")\n#undef exp", + "epilog" : "#pragma pop_macro(\"exp\")" + }, + "log" : { + "prolog" : "#pragma push_macro(\"log\")\n#undef log", + "epilog" : "#pragma pop_macro(\"log\")" + } + } + }, "type_dict" : { "Algorithm": { "objc_type": "Algorithm*" diff --git a/modules/core/perf/opencl/perf_matop.cpp b/modules/core/perf/opencl/perf_matop.cpp index 5be1f431f647..b763a98e2acd 100644 --- a/modules/core/perf/opencl/perf_matop.cpp +++ b/modules/core/perf/opencl/perf_matop.cpp @@ -233,6 +233,7 @@ PERF_TEST_P_(OpenCLBuffer, cpu_read) for (size_t x_bytes = 0; x_bytes < width_bytes; x_bytes++) counter += (unsigned)(ptr[x_bytes]); } + (void)counter; // To avoid -Wunused-but-set-variable } SANITY_CHECK_NOTHING(); diff --git a/modules/core/perf/perf_arithm.cpp b/modules/core/perf/perf_arithm.cpp index 70e2f49210e5..3ac9a2463979 100644 --- a/modules/core/perf/perf_arithm.cpp +++ b/modules/core/perf/perf_arithm.cpp @@ -1,4 +1,5 @@ #include "perf_precomp.hpp" +#include namespace opencv_test { @@ -393,6 +394,29 @@ PERF_TEST_P_(BinaryOpTest, reciprocal) SANITY_CHECK_NOTHING(); } + +PERF_TEST_P_(BinaryOpTest, transposeND) +{ + Size sz = get<0>(GetParam()); + int type = get<1>(GetParam()); + cv::Mat a = Mat(sz, type).reshape(1); + + std::vector order(a.dims); + std::iota(order.begin(), order.end(), 0); + std::reverse(order.begin(), order.end()); + + std::vector new_sz(a.dims); + std::copy(a.size.p, a.size.p + a.dims, new_sz.begin()); + std::reverse(new_sz.begin(), new_sz.end()); + cv::Mat b = Mat(new_sz, type); + + declare.in(a,WARMUP_RNG).out(b); + + TEST_CYCLE() cv::transposeND(a, order, b); + + SANITY_CHECK_NOTHING(); +} + INSTANTIATE_TEST_CASE_P(/*nothing*/ , BinaryOpTest, testing::Combine( testing::Values(szVGA, sz720p, sz1080p), diff --git a/modules/core/perf/perf_reduce.cpp b/modules/core/perf/perf_reduce.cpp index 8f9c2e8349f9..dcc0205fdc04 100644 --- a/modules/core/perf/perf_reduce.cpp +++ b/modules/core/perf/perf_reduce.cpp @@ -23,7 +23,7 @@ PERF_TEST_P(Size_MatType_ROp, reduceR, int reduceOp = get<2>(GetParam()); int ddepth = -1; - if( CV_MAT_DEPTH(matType) < CV_32S && (reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG) ) + if( CV_MAT_DEPTH(matType) < CV_32S && (reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG) ) ddepth = CV_32S; Mat src(sz, matType); @@ -51,7 +51,7 @@ PERF_TEST_P(Size_MatType_ROp, reduceC, int reduceOp = get<2>(GetParam()); int ddepth = -1; - if( CV_MAT_DEPTH(matType)< CV_32S && (reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG) ) + if( CV_MAT_DEPTH(matType)< CV_32S && (reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG) ) ddepth = CV_32S; Mat src(sz, matType); diff --git a/modules/core/src/check.cpp b/modules/core/src/check.cpp index 90df841e63a0..7f1310ad0aff 100644 --- a/modules/core/src/check.cpp +++ b/modules/core/src/check.cpp @@ -14,7 +14,7 @@ const char* depthToString(int depth) return s ? s : ""; } -const cv::String typeToString(int type) +cv::String typeToString(int type) { cv::String s = detail::typeToString_(type); if (s.empty()) @@ -47,7 +47,7 @@ const char* depthToString_(int depth) return (depth <= CV_16F && depth >= 0) ? depthNames[depth] : NULL; } -const cv::String typeToString_(int type) +cv::String typeToString_(int type) { int depth = CV_MAT_DEPTH(type); int cn = CV_MAT_CN(type); diff --git a/modules/core/src/cuda/gpu_mat.cu b/modules/core/src/cuda/gpu_mat.cu index f31f78a87aa7..c286f28eb038 100644 --- a/modules/core/src/cuda/gpu_mat.cu +++ b/modules/core/src/cuda/gpu_mat.cu @@ -184,11 +184,8 @@ void cv::cuda::GpuMat::create(int _rows, int _cols, int _type) if (esz * cols == step) flags |= Mat::CONTINUOUS_FLAG; - int64 _nettosize = static_cast(step) * rows; - size_t nettosize = static_cast(_nettosize); - datastart = data; - dataend = data + nettosize; + dataend = data + step * (rows - 1) + cols * esz; if (refcount) *refcount = 1; diff --git a/modules/core/src/cuda_stream.cpp b/modules/core/src/cuda_stream.cpp index 3680e0720ad6..3f647c8d55f2 100644 --- a/modules/core/src/cuda_stream.cpp +++ b/modules/core/src/cuda_stream.cpp @@ -811,7 +811,7 @@ Event cv::cuda::EventAccessor::wrapEvent(cudaEvent_t event) #endif -cv::cuda::Event::Event(CreateFlags flags) +cv::cuda::Event::Event(const Event::CreateFlags flags) { #ifndef HAVE_CUDA CV_UNUSED(flags); diff --git a/modules/core/src/datastructs.cpp b/modules/core/src/datastructs.cpp index eec7ade02bed..80b02283dc90 100644 --- a/modules/core/src/datastructs.cpp +++ b/modules/core/src/datastructs.cpp @@ -133,8 +133,6 @@ cvCreateChildMemStorage( CvMemStorage * parent ) static void icvDestroyMemStorage( CvMemStorage* storage ) { - int k = 0; - CvMemBlock *block; CvMemBlock *dst_top = 0; @@ -144,7 +142,7 @@ icvDestroyMemStorage( CvMemStorage* storage ) if( storage->parent ) dst_top = storage->parent->top; - for( block = storage->bottom; block != 0; k++ ) + for( block = storage->bottom; block != 0; ) { CvMemBlock *temp = block; diff --git a/modules/core/src/directx.cpp b/modules/core/src/directx.cpp index 2dbc3e27635e..7b423cc29f95 100644 --- a/modules/core/src/directx.cpp +++ b/modules/core/src/directx.cpp @@ -1385,6 +1385,7 @@ void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst OpenCL_D3D11_NV* impl_nv = ctx.getUserContext().get(); if (impl_nv) { __convertFromD3D11Texture2DNV(pD3D11Texture2D,dst); + return; } #endif OpenCL_D3D11* impl = ctx.getUserContext().get(); diff --git a/modules/core/src/hal_internal.cpp b/modules/core/src/hal_internal.cpp index cbe02780d23b..5239acc585dc 100644 --- a/modules/core/src/hal_internal.cpp +++ b/modules/core/src/hal_internal.cpp @@ -64,6 +64,16 @@ #define HAL_LU_SMALL_MATRIX_THRESH 100 #define HAL_CHOLESKY_SMALL_MATRIX_THRESH 100 +#if defined(__clang__) && defined(__has_feature) +#if __has_feature(memory_sanitizer) +#define CV_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \ +__msan_unpoison(adresse, size) +#endif +#endif +#ifndef CV_ANNOTATE_MEMORY_IS_INITIALIZED +#define CV_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) do { } while(0) +#endif + //lapack stores matrices in column-major order so transposing is needed everywhere template static inline void transpose_square_inplace(fptype *src, size_t src_ld, size_t m) @@ -239,6 +249,17 @@ lapack_SVD(fptype* a, size_t a_step, fptype *w, fptype* u, size_t u_step, fptype else if(typeid(fptype) == typeid(double)) OCV_LAPACK_FUNC(dgesdd)(mode, &m, &n, (double*)a, &lda, (double*)w, (double*)u, &ldu, (double*)vt, &ldv, (double*)buffer, &lwork, iworkBuf, info); + // Make sure MSAN sees the memory as having been written. + // MSAN does not think it has been written because a different language was called. + CV_ANNOTATE_MEMORY_IS_INITIALIZED(a, a_step * n); + CV_ANNOTATE_MEMORY_IS_INITIALIZED(buffer, sizeof(fptype) * (lwork + 1)); + if (u) + CV_ANNOTATE_MEMORY_IS_INITIALIZED(u, u_step * m); + if (vt) + CV_ANNOTATE_MEMORY_IS_INITIALIZED(vt, v_step * n); + if (w) + CV_ANNOTATE_MEMORY_IS_INITIALIZED(w, sizeof(fptype) * std::min(m, n)); + if(!(flags & CV_HAL_SVD_NO_UV)) transpose_square_inplace(vt, ldv, n); @@ -342,6 +363,7 @@ lapack_QR(fptype* a, size_t a_step, int m, int n, int k, fptype* b, size_t b_ste dgeqrf_(&m, &n, (double*)tmpA, &ldtmpA, (double*)dst, (double*)buffer, &lwork, info); } + CV_ANNOTATE_MEMORY_IS_INITIALIZED(info, sizeof(int)); if (m == n) transpose_square_inplace(a, lda, m); else diff --git a/modules/core/src/kmeans.cpp b/modules/core/src/kmeans.cpp index 544882ef1d81..f5a5bc5c6c59 100644 --- a/modules/core/src/kmeans.cpp +++ b/modules/core/src/kmeans.cpp @@ -240,7 +240,7 @@ double cv::kmeans( InputArray _data, int K, attempts = std::max(attempts, 1); CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 ); - CV_CheckGE(N, K, "Number of clusters should be more than number of elements"); + CV_CheckGE(N, K, "There can't be more clusters than elements"); Mat data(N, dims, CV_32F, data0.ptr(), isrow ? dims * sizeof(float) : static_cast(data0.step)); diff --git a/modules/core/src/lpsolver.cpp b/modules/core/src/lpsolver.cpp index 951da3fd7f26..43fa73a02553 100644 --- a/modules/core/src/lpsolver.cpp +++ b/modules/core/src/lpsolver.cpp @@ -253,11 +253,8 @@ static int initialize_simplex(Mat_& c, Mat_& b,double& v,vector< } static int inner_simplex(Mat_& c, Mat_& b,double& v,vector& N,vector& B,vector& indexToRow){ - int count=0; - for(;;){ - dprintf(("iteration #%d\n",count)); - count++; + for(;;){ static MatIterator_ pos_ptr; int e=-1,pos_ctr=0,min_var=INT_MAX; bool all_nonzero=true; diff --git a/modules/core/src/mathfuncs.cpp b/modules/core/src/mathfuncs.cpp index 8e9a07da19dc..056be63a716a 100644 --- a/modules/core/src/mathfuncs.cpp +++ b/modules/core/src/mathfuncs.cpp @@ -270,6 +270,9 @@ void cartToPolar( InputArray src1, InputArray src2, { CV_INSTRUMENT_REGION(); + CV_Assert(src1.getObj() != dst1.getObj() && src1.getObj() != dst2.getObj() && + src2.getObj() != dst1.getObj() && src2.getObj() != dst2.getObj()); + CV_OCL_RUN(dst1.isUMat() && dst2.isUMat(), ocl_cartToPolar(src1, src2, dst1, dst2, angleInDegrees)) @@ -564,6 +567,9 @@ void polarToCart( InputArray src1, InputArray src2, { CV_INSTRUMENT_REGION(); + CV_Assert(src1.getObj() != dst1.getObj() && src1.getObj() != dst2.getObj() && + src2.getObj() != dst1.getObj() && src2.getObj() != dst2.getObj()); + int type = src2.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); CV_Assert((depth == CV_32F || depth == CV_64F) && (src1.empty() || src1.type() == type)); diff --git a/modules/core/src/matmul.dispatch.cpp b/modules/core/src/matmul.dispatch.cpp index 92e44f45c929..52200f097ccb 100644 --- a/modules/core/src/matmul.dispatch.cpp +++ b/modules/core/src/matmul.dispatch.cpp @@ -804,7 +804,7 @@ void calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mea else { ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F); - reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype ); + reduce( _src, _mean, takeRows ? 0 : 1, REDUCE_AVG, ctype ); mean = _mean.getMat(); } diff --git a/modules/core/src/matrix.cpp b/modules/core/src/matrix.cpp index 1729862cb78d..8111dc223036 100644 --- a/modules/core/src/matrix.cpp +++ b/modules/core/src/matrix.cpp @@ -176,27 +176,23 @@ class StdMatAllocator CV_FINAL : public MatAllocator } }; -namespace +static +MatAllocator*& getDefaultAllocatorMatRef() { - MatAllocator* volatile g_matAllocator = NULL; + static MatAllocator* g_matAllocator = Mat::getStdAllocator(); + return g_matAllocator; } MatAllocator* Mat::getDefaultAllocator() { - if (g_matAllocator == NULL) - { - cv::AutoLock lock(cv::getInitializationMutex()); - if (g_matAllocator == NULL) - { - g_matAllocator = getStdAllocator(); - } - } - return g_matAllocator; + return getDefaultAllocatorMatRef(); } + void Mat::setDefaultAllocator(MatAllocator* allocator) { - g_matAllocator = allocator; + getDefaultAllocatorMatRef() = allocator; } + MatAllocator* Mat::getStdAllocator() { CV_SINGLETON_LAZY_INIT(MatAllocator, new StdMatAllocator()) @@ -269,7 +265,7 @@ void setSize( Mat& m, int _dims, const int* _sz, const size_t* _steps, bool auto else if( autoSteps ) { m.step.p[i] = total; - int64 total1 = (int64)total*s; + uint64 total1 = (uint64)total*s; if( (uint64)total1 != (size_t)total1 ) CV_Error( CV_StsOutOfRange, "The total matrix size does not fit to \"size_t\" type" ); total = (size_t)total1; @@ -668,6 +664,8 @@ void Mat::create(int d, const int* _sizes, int _type) if( data && (d == dims || (d == 1 && dims <= 2)) && _type == type() ) { + if ( dims == 1 && (d == 1 && _sizes[0] == size[0]) ) + return; if( d == 2 && rows == _sizes[0] && cols == _sizes[1] ) return; for( i = 0; i < d; i++ ) diff --git a/modules/core/src/matrix_operations.cpp b/modules/core/src/matrix_operations.cpp index 227c7aaef774..f9a50cd0ee65 100644 --- a/modules/core/src/matrix_operations.cpp +++ b/modules/core/src/matrix_operations.cpp @@ -616,7 +616,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst, if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F)) return false; - if (op == CV_REDUCE_AVG) + if (op == REDUCE_AVG) { if (sdepth < CV_32S && ddepth < CV_32S) ddepth = CV_32S; @@ -654,7 +654,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst, _dst.create(dsize, dtype); UMat dst = _dst.getUMat(); - if (op0 == CV_REDUCE_AVG) + if (op0 == REDUCE_AVG) k.args(ocl::KernelArg::ReadOnly(src), ocl::KernelArg::WriteOnlyNoSize(dst), 1.0f / src.cols); else @@ -690,7 +690,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst, ocl::KernelArg srcarg = ocl::KernelArg::ReadOnly(src), temparg = ocl::KernelArg::WriteOnlyNoSize(dst); - if (op0 == CV_REDUCE_AVG) + if (op0 == REDUCE_AVG) k.args(srcarg, temparg, 1.0f / (dim == 0 ? src.rows : src.cols)); else k.args(srcarg, temparg); @@ -717,8 +717,8 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) int ddepth = CV_MAT_DEPTH(dtype); CV_Assert( cn == CV_MAT_CN(dtype) ); - CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX || - op == CV_REDUCE_MIN || op == CV_REDUCE_AVG ); + CV_Assert( op == REDUCE_SUM || op == REDUCE_MAX || + op == REDUCE_MIN || op == REDUCE_AVG ); CV_OCL_RUN(_dst.isUMat(), ocl_reduce(_src, _dst, dim, op, op0, stype, dtype)) @@ -732,9 +732,9 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) _dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, dtype); Mat dst = _dst.getMat(), temp = dst; - if( op == CV_REDUCE_AVG ) + if( op == REDUCE_AVG ) { - op = CV_REDUCE_SUM; + op = REDUCE_SUM; if( sdepth < CV_32S && ddepth < CV_32S ) { temp.create(dst.rows, dst.cols, CV_32SC(cn)); @@ -745,7 +745,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) ReduceFunc func = 0; if( dim == 0 ) { - if( op == CV_REDUCE_SUM ) + if( op == REDUCE_SUM ) { if(sdepth == CV_8U && ddepth == CV_32S) func = GET_OPTIMIZED(reduceSumR8u32s); @@ -768,7 +768,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) else if(sdepth == CV_64F && ddepth == CV_64F) func = reduceSumR64f64f; } - else if(op == CV_REDUCE_MAX) + else if(op == REDUCE_MAX) { if(sdepth == CV_8U && ddepth == CV_8U) func = GET_OPTIMIZED(reduceMaxR8u); @@ -781,7 +781,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) else if(sdepth == CV_64F && ddepth == CV_64F) func = reduceMaxR64f; } - else if(op == CV_REDUCE_MIN) + else if(op == REDUCE_MIN) { if(sdepth == CV_8U && ddepth == CV_8U) func = GET_OPTIMIZED(reduceMinR8u); @@ -797,7 +797,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) } else { - if(op == CV_REDUCE_SUM) + if(op == REDUCE_SUM) { if(sdepth == CV_8U && ddepth == CV_32S) func = GET_OPTIMIZED(reduceSumC8u32s); @@ -820,7 +820,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) else if(sdepth == CV_64F && ddepth == CV_64F) func = reduceSumC64f64f; } - else if(op == CV_REDUCE_MAX) + else if(op == REDUCE_MAX) { if(sdepth == CV_8U && ddepth == CV_8U) func = GET_OPTIMIZED(reduceMaxC8u); @@ -833,7 +833,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) else if(sdepth == CV_64F && ddepth == CV_64F) func = reduceMaxC64f; } - else if(op == CV_REDUCE_MIN) + else if(op == REDUCE_MIN) { if(sdepth == CV_8U && ddepth == CV_8U) func = GET_OPTIMIZED(reduceMinC8u); @@ -854,7 +854,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype) func( src, temp ); - if( op0 == CV_REDUCE_AVG ) + if( op0 == REDUCE_AVG ) temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols)); } @@ -868,9 +868,9 @@ template static void sort_( const Mat& src, Mat& dst, int flags ) { AutoBuffer buf; int n, len; - bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW; + bool sortRows = (flags & 1) == SORT_EVERY_ROW; bool inplace = src.data == dst.data; - bool sortDescending = (flags & CV_SORT_DESCENDING) != 0; + bool sortDescending = (flags & SORT_DESCENDING) != 0; if( sortRows ) n = src.rows, len = src.cols; @@ -940,8 +940,8 @@ static bool ipp_sort(const Mat& src, Mat& dst, int flags) { CV_INSTRUMENT_REGION_IPP(); - bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW; - bool sortDescending = (flags & CV_SORT_DESCENDING) != 0; + bool sortRows = (flags & 1) == SORT_EVERY_ROW; + bool sortDescending = (flags & SORT_DESCENDING) != 0; bool inplace = (src.data == dst.data); int depth = src.depth(); IppDataType type = ippiGetDataType(depth); @@ -1013,8 +1013,8 @@ template static void sortIdx_( const Mat& src, Mat& dst, int flags ) { AutoBuffer buf; AutoBuffer ibuf; - bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW; - bool sortDescending = (flags & CV_SORT_DESCENDING) != 0; + bool sortRows = (flags & 1) == SORT_EVERY_ROW; + bool sortDescending = (flags & SORT_DESCENDING) != 0; CV_Assert( src.data != dst.data ); diff --git a/modules/core/src/matrix_transform.cpp b/modules/core/src/matrix_transform.cpp index 727eaf7feef2..05ecf450e127 100644 --- a/modules/core/src/matrix_transform.cpp +++ b/modules/core/src/matrix_transform.cpp @@ -4,6 +4,7 @@ #include "precomp.hpp" #include "opencl_kernels_core.hpp" +#include "opencv2/core/detail/dispatch_helper.impl.hpp" namespace cv { @@ -282,6 +283,72 @@ void transpose( InputArray _src, OutputArray _dst ) } +void transposeND(InputArray src_, const std::vector& order, OutputArray dst_) +{ + Mat inp = src_.getMat(); + CV_Assert(inp.isContinuous()); + CV_CheckEQ(inp.channels(), 1, "Input array should be single-channel"); + CV_CheckEQ(order.size(), static_cast(inp.dims), "Number of dimensions shouldn't change"); + + auto order_ = order; + std::sort(order_.begin(), order_.end()); + for (size_t i = 0; i < order_.size(); ++i) + { + CV_CheckEQ(static_cast(order_[i]), i, "New order should be a valid permutation of the old one"); + } + + std::vector newShape(order.size()); + for (size_t i = 0; i < order.size(); ++i) + { + newShape[i] = inp.size[order[i]]; + } + + dst_.create(static_cast(newShape.size()), newShape.data(), inp.type()); + Mat out = dst_.getMat(); + CV_Assert(out.isContinuous()); + CV_Assert(inp.data != out.data); + + int continuous_idx = 0; + for (int i = static_cast(order.size()) - 1; i >= 0; --i) + { + if (order[i] != i) + { + continuous_idx = i + 1; + break; + } + } + + size_t continuous_size = continuous_idx == 0 ? out.total() : out.step1(continuous_idx - 1); + size_t outer_size = out.total() / continuous_size; + + std::vector steps(order.size()); + for (int i = 0; i < static_cast(steps.size()); ++i) + { + steps[i] = inp.step1(order[i]); + } + + auto* src = inp.ptr(); + auto* dst = out.ptr(); + + size_t src_offset = 0; + size_t es = out.elemSize(); + for (size_t i = 0; i < outer_size; ++i) + { + std::memcpy(dst, src + es * src_offset, es * continuous_size); + dst += es * continuous_size; + for (int j = continuous_idx - 1; j >= 0; --j) + { + src_offset += steps[j]; + if ((src_offset / steps[j]) % out.size[j] != 0) + { + break; + } + src_offset -= steps[j] * out.size[j]; + } + } +} + + #if CV_SIMD128 template CV_ALWAYS_INLINE void flipHoriz_single( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size size, size_t esz ) { diff --git a/modules/core/src/ocl.cpp b/modules/core/src/ocl.cpp index 8e545f629366..51acc68879d6 100644 --- a/modules/core/src/ocl.cpp +++ b/modules/core/src/ocl.cpp @@ -153,6 +153,17 @@ static bool isRaiseError() } #endif +static void onOpenCLKernelBuildError() +{ + // NB: no need to cache this value + bool value = cv::utils::getConfigurationParameterBool("OPENCV_OPENCL_ABORT_ON_BUILD_ERROR", false); + if (value) + { + fprintf(stderr, "Abort on OpenCL kernel build failure!\n"); + abort(); + } +} + #if CV_OPENCL_TRACE_CHECK static inline void traceOpenCLCheck(cl_int status, const char* message) @@ -220,7 +231,7 @@ static const bool CV_OPENCL_DISABLE_BUFFER_RECT_OPERATIONS = utils::getConfigura #endif ); -static const String getBuildExtraOptions() +static String getBuildExtraOptions() { static String param_buildExtraOptions; static bool initialized = false; @@ -4593,6 +4604,12 @@ struct Program::Impl CV_OCL_DBG_CHECK(clReleaseProgram(handle)); handle = NULL; } + if (retval != CL_SUCCESS && + sourceName_ != "dummy" // used for testing of compilation flags + ) + { + onOpenCLKernelBuildError(); + } } #if CV_OPENCL_VALIDATE_BINARY_PROGRAMS if (handle && CV_OPENCL_VALIDATE_BINARY_PROGRAMS_VALUE) diff --git a/modules/core/src/parallel.cpp b/modules/core/src/parallel.cpp index fe64d5d7c993..26e5e8c19b8b 100644 --- a/modules/core/src/parallel.cpp +++ b/modules/core/src/parallel.cpp @@ -153,6 +153,9 @@ #include "opencv2/core/detail/exception_ptr.hpp" // CV__EXCEPTION_PTR = 1 if std::exception_ptr is available +#include +#include + using namespace cv; namespace cv { @@ -203,6 +206,9 @@ namespace { // propagate main thread state rng = cv::theRNG(); +#if OPENCV_SUPPORTS_FP_DENORMALS_HINT && OPENCV_IMPL_FP_HINTS + details::saveFPDenormalsState(fp_denormals_base_state); +#endif #ifdef OPENCV_TRACE traceRootRegion = CV_TRACE_NS::details::getCurrentRegion(); @@ -283,6 +289,11 @@ namespace { } } } + +#if OPENCV_SUPPORTS_FP_DENORMALS_HINT && OPENCV_IMPL_FP_HINTS + details::FPDenormalsModeState fp_denormals_base_state; +#endif + private: ParallelLoopBodyWrapperContext(const ParallelLoopBodyWrapperContext&); // disabled ParallelLoopBodyWrapperContext& operator=(const ParallelLoopBodyWrapperContext&); // disabled @@ -319,6 +330,9 @@ namespace { // propagate main thread state cv::theRNG() = ctx.rng; +#if OPENCV_SUPPORTS_FP_DENORMALS_HINT && OPENCV_IMPL_FP_HINTS + FPDenormalsIgnoreHintScope fp_denormals_scope(ctx.fp_denormals_base_state); +#endif cv::Range r; cv::Range wholeRange = ctx.wholeRange; @@ -972,7 +986,7 @@ int getNumberOfCPUs_() #endif -#if !defined(_WIN32) && !defined(__APPLE__) +#if !defined(_WIN32) && !defined(__APPLE__) && defined(_SC_NPROCESSORS_ONLN) static unsigned cpu_count_sysconf = (unsigned)sysconf( _SC_NPROCESSORS_ONLN ); ncpus = minNonZero(ncpus, cpu_count_sysconf); diff --git a/modules/core/src/persistence.cpp b/modules/core/src/persistence.cpp index ae6a5a04fe7f..09435b585069 100644 --- a/modules/core/src/persistence.cpp +++ b/modules/core/src/persistence.cpp @@ -9,6 +9,8 @@ #include #include +#include + namespace cv { @@ -499,21 +501,29 @@ bool FileStorage::Impl::open(const char *filename_or_buf, int _flags, const char if (!isGZ) { file = fopen(filename.c_str(), !write_mode ? "rt" : !append ? "wt" : "a+t"); if (!file) + { + CV_LOG_ERROR(NULL, "Can't open file: '" << filename << "' in " << (!write_mode ? "read" : !append ? "write" : "append") << " mode"); return false; + } } else { #if USE_ZLIB char mode[] = {write_mode ? 'w' : 'r', 'b', compression ? compression : '3', '\0'}; gzfile = gzopen(filename.c_str(), mode); if (!gzfile) + { + CV_LOG_ERROR(NULL, "Can't open archive: '" << filename << "' mode=" << mode); return false; + } #else CV_Error(cv::Error::StsNotImplemented, "There is no compressed file storage support in this configuration"); #endif } } + // FIXIT release() must do that, use CV_Assert() here instead roots.clear(); fs_data.clear(); + wrap_margin = 71; fmt = FileStorage::FORMAT_AUTO; @@ -616,14 +626,14 @@ bool FileStorage::Impl::open(const char *filename_or_buf, int _flags, const char puts("\n"); } - emitter = createXMLEmitter(this); + emitter_do_not_use_direct_dereference = createXMLEmitter(this); } else if (fmt == FileStorage::FORMAT_YAML) { if (!append) puts("%YAML:1.0\n---\n"); else puts("...\n---\n"); - emitter = createYAMLEmitter(this); + emitter_do_not_use_direct_dereference = createYAMLEmitter(this); } else { CV_Assert(fmt == FileStorage::FORMAT_JSON); if (!append) @@ -653,7 +663,7 @@ bool FileStorage::Impl::open(const char *filename_or_buf, int _flags, const char } } write_stack.back().indent = 4; - emitter = createJSONEmitter(this); + emitter_do_not_use_direct_dereference = createJSONEmitter(this); } is_opened = true; } else { @@ -701,20 +711,20 @@ bool FileStorage::Impl::open(const char *filename_or_buf, int _flags, const char switch (fmt) { case FileStorage::FORMAT_XML: - parser = createXMLParser(this); + parser_do_not_use_direct_dereference = createXMLParser(this); break; case FileStorage::FORMAT_YAML: - parser = createYAMLParser(this); + parser_do_not_use_direct_dereference = createYAMLParser(this); break; case FileStorage::FORMAT_JSON: - parser = createJSONParser(this); + parser_do_not_use_direct_dereference = createJSONParser(this); break; default: - parser = Ptr(); + parser_do_not_use_direct_dereference = Ptr(); } - if (!parser.empty()) { - ok = parser->parse(ptr); + if (!parser_do_not_use_direct_dereference.empty()) { + ok = getParser().parse(ptr); if (ok) { finalizeCollection(root_nodes); @@ -728,7 +738,9 @@ bool FileStorage::Impl::open(const char *filename_or_buf, int _flags, const char } } } - catch (...) { + catch (...) + { + // FIXIT log error message is_opened = true; release(); throw; @@ -805,7 +817,7 @@ char *FileStorage::Impl::gets(size_t maxCount) { int delta = (int) strlen(ptr); ofs += delta; maxCount -= delta; - if (ptr[delta - 1] == '\n' || maxCount == 0) + if (delta == 0 || ptr[delta - 1] == '\n' || maxCount == 0) break; if (delta == count) buffer.resize((size_t) (buffer.size() * 1.5)); @@ -926,7 +938,7 @@ void FileStorage::Impl::endWriteStruct() { if (fmt == FileStorage::FORMAT_JSON && !FileNode::isFlow(current_struct.flags) && write_stack.size() > 1) current_struct.indent = write_stack[write_stack.size() - 2].indent; - emitter->endWriteStruct(current_struct); + getEmitter().endWriteStruct(current_struct); write_stack.pop_back(); if (!write_stack.empty()) @@ -945,7 +957,7 @@ void FileStorage::Impl::startWriteStruct_helper(const char *key, int struct_flag if (type_name && type_name[0] == '\0') type_name = 0; - FStructData s = emitter->startWriteStruct(write_stack.back(), key, struct_flags, type_name); + FStructData s = getEmitter().startWriteStruct(write_stack.back(), key, struct_flags, type_name); write_stack.push_back(s); size_t write_stack_size = write_stack.size(); @@ -956,7 +968,7 @@ void FileStorage::Impl::startWriteStruct_helper(const char *key, int struct_flag flush(); if (fmt == FileStorage::FORMAT_JSON && type_name && type_name[0] && FileNode::isMap(struct_flags)) { - emitter->write("type_id", type_name, false); + getEmitter().write("type_id", type_name, false); } } @@ -997,7 +1009,7 @@ void FileStorage::Impl::startWriteStruct(const char *key, int struct_flags, void FileStorage::Impl::writeComment(const char *comment, bool eol_comment) { CV_Assert(write_mode); - emitter->writeComment(comment, eol_comment); + getEmitter().writeComment(comment, eol_comment); } void FileStorage::Impl::startNextStream() { @@ -1006,7 +1018,7 @@ void FileStorage::Impl::startNextStream() { while (!write_stack.empty()) endWriteStruct(); flush(); - emitter->startNextStream(); + getEmitter().startNextStream(); empty_stream = true; write_stack.push_back(FStructData("", FileNode::EMPTY, 0)); bufofs = 0; @@ -1015,17 +1027,17 @@ void FileStorage::Impl::startNextStream() { void FileStorage::Impl::write(const String &key, int value) { CV_Assert(write_mode); - emitter->write(key.c_str(), value); + getEmitter().write(key.c_str(), value); } void FileStorage::Impl::write(const String &key, double value) { CV_Assert(write_mode); - emitter->write(key.c_str(), value); + getEmitter().write(key.c_str(), value); } void FileStorage::Impl::write(const String &key, const String &value) { CV_Assert(write_mode); - emitter->write(key.c_str(), value.c_str(), false); + getEmitter().write(key.c_str(), value.c_str(), false); } void FileStorage::Impl::writeRawData(const std::string &dt, const void *_data, size_t len) { @@ -1111,7 +1123,7 @@ void FileStorage::Impl::writeRawData(const std::string &dt, const void *_data, s return; } - emitter->writeScalar(0, ptr); + getEmitter().writeScalar(0, ptr); } offset = (int) (data - data0); @@ -1597,8 +1609,8 @@ FileStorage::Impl::Base64Decoder::Base64Decoder() { eos = true; } -void FileStorage::Impl::Base64Decoder::init(Ptr &_parser, char *_ptr, int _indent) { - parser = _parser; +void FileStorage::Impl::Base64Decoder::init(const Ptr &_parser, char *_ptr, int _indent) { + parser_do_not_use_direct_dereference = _parser; ptr = _ptr; indent = _indent; encoded.clear(); @@ -1641,9 +1653,9 @@ bool FileStorage::Impl::Base64Decoder::readMore(int needed) { decoded.resize(sz); ofs = 0; - CV_Assert(!parser.empty() && ptr); + CV_Assert(ptr); char *beg = 0, *end = 0; - bool ok = parser->getBase64Row(ptr, indent, beg, end); + bool ok = getParser().getBase64Row(ptr, indent, beg, end); ptr = end; std::copy(beg, end, std::back_inserter(encoded)); totalchars += end - beg; @@ -1730,7 +1742,7 @@ char *FileStorage::Impl::Base64Decoder::getPtr() const { return ptr; } char *FileStorage::Impl::parseBase64(char *ptr, int indent, FileNode &collection) { const int BASE64_HDR_SIZE = 24; char dt[BASE64_HDR_SIZE + 1] = {0}; - base64decoder.init(parser, ptr, indent); + base64decoder.init(parser_do_not_use_direct_dereference, ptr, indent); int i, k; diff --git a/modules/core/src/persistence_impl.hpp b/modules/core/src/persistence_impl.hpp index 4ea2dc350282..1c261ce77290 100644 --- a/modules/core/src/persistence_impl.hpp +++ b/modules/core/src/persistence_impl.hpp @@ -139,7 +139,7 @@ class cv::FileStorage::Impl : public FileStorage_API { public: Base64Decoder(); - void init(Ptr& _parser, char* _ptr, int _indent); + void init(const Ptr& _parser, char* _ptr, int _indent); bool readMore(int needed); @@ -155,7 +155,13 @@ class cv::FileStorage::Impl : public FileStorage_API char* getPtr() const; protected: - Ptr parser; + Ptr parser_do_not_use_direct_dereference; + FileStorageParser& getParser() const + { + if (!parser_do_not_use_direct_dereference) + CV_Error(Error::StsNullPtr, "Parser is not available"); + return *parser_do_not_use_direct_dereference; + } char* ptr; int indent; std::vector encoded; @@ -205,8 +211,20 @@ class cv::FileStorage::Impl : public FileStorage_API std::deque outbuf; - Ptr emitter; - Ptr parser; + Ptr emitter_do_not_use_direct_dereference; + FileStorageEmitter& getEmitter() + { + if (!emitter_do_not_use_direct_dereference) + CV_Error(Error::StsNullPtr, "Emitter is not available"); + return *emitter_do_not_use_direct_dereference; + } + Ptr parser_do_not_use_direct_dereference; + FileStorageParser& getParser() const + { + if (!parser_do_not_use_direct_dereference) + CV_Error(Error::StsNullPtr, "Parser is not available"); + return *parser_do_not_use_direct_dereference; + } Base64Decoder base64decoder; base64::Base64Writer* base64_writer; @@ -228,4 +246,4 @@ class cv::FileStorage::Impl : public FileStorage_API } -#endif \ No newline at end of file +#endif diff --git a/modules/core/src/precomp.hpp b/modules/core/src/precomp.hpp index 9e60c235a173..33c3b3291ce5 100644 --- a/modules/core/src/precomp.hpp +++ b/modules/core/src/precomp.hpp @@ -365,6 +365,7 @@ extern CV_EXPORTS bool __termination; // skip some cleanups, because process is terminating // (for example, if ExitProcess() was already called) +CV_EXPORTS cv::Mutex& getInitializationMutex(); /// @brief Returns timestamp in nanoseconds since program launch diff --git a/modules/core/src/system.cpp b/modules/core/src/system.cpp index 3923f13b63da..d2231fe952ee 100644 --- a/modules/core/src/system.cpp +++ b/modules/core/src/system.cpp @@ -55,6 +55,9 @@ #include +#include +#include + #ifndef OPENCV_WITH_THREAD_SANITIZER #if defined(__clang__) && defined(__has_feature) #if __has_feature(thread_sanitizer) @@ -612,6 +615,9 @@ struct HWFeatures #if defined _ARM_ && (defined(_WIN32_WCE) && _WIN32_WCE >= 0x800) have[CV_CPU_NEON] = true; #endif + #if defined _M_ARM64 + have[CV_CPU_NEON] = true; + #endif #ifdef __riscv_vector have[CV_CPU_RISCVV] = true; #endif @@ -630,7 +636,7 @@ struct HWFeatures } } #elif (defined __ppc64__ || defined __PPC64__) && defined __FreeBSD__ - unsigned int hwcap = 0; + unsigned long hwcap = 0; elf_aux_info(AT_HWCAP, &hwcap, sizeof(hwcap)); if (hwcap & PPC_FEATURE_HAS_VSX) { elf_aux_info(AT_HWCAP2, &hwcap, sizeof(hwcap)); @@ -2772,6 +2778,82 @@ void setUseIPP_NotExact(bool flag) } // namespace ipp + +namespace details { + +#if OPENCV_IMPL_FP_HINTS_X86 +#ifndef _MM_DENORMALS_ZERO_ON // requires pmmintrin.h (SSE3) +#define _MM_DENORMALS_ZERO_ON 0x0040 +#endif +#ifndef _MM_DENORMALS_ZERO_MASK // requires pmmintrin.h (SSE3) +#define _MM_DENORMALS_ZERO_MASK 0x0040 +#endif +#endif + +void setFPDenormalsIgnoreHint(bool ignore, CV_OUT FPDenormalsModeState& state) +{ +#if OPENCV_IMPL_FP_HINTS_X86 + unsigned mask = _MM_FLUSH_ZERO_MASK; + unsigned value = ignore ? _MM_FLUSH_ZERO_ON : 0; + if (featuresEnabled.have[CPU_SSE3]) + { + mask |= _MM_DENORMALS_ZERO_MASK; + value |= ignore ? _MM_DENORMALS_ZERO_ON : 0; + } + const unsigned old_flags = _mm_getcsr(); + const unsigned old_value = old_flags & mask; + unsigned flags = (old_flags & ~mask) | value; + CV_LOG_DEBUG(NULL, "core: update FP mxcsr flags = " << cv::format("0x%08x", flags)); + // save state + state.reserved[0] = (uint32_t)mask; + state.reserved[1] = (uint32_t)old_value; + _mm_setcsr(flags); +#else + CV_UNUSED(ignore); CV_UNUSED(state); +#endif +} + +int saveFPDenormalsState(CV_OUT FPDenormalsModeState& state) +{ +#if OPENCV_IMPL_FP_HINTS_X86 + unsigned mask = _MM_FLUSH_ZERO_MASK; + if (featuresEnabled.have[CPU_SSE3]) + { + mask |= _MM_DENORMALS_ZERO_MASK; + } + const unsigned old_flags = _mm_getcsr(); + const unsigned old_value = old_flags & mask; + // save state + state.reserved[0] = (uint32_t)mask; + state.reserved[1] = (uint32_t)old_value; + return 2; +#else + CV_UNUSED(state); + return 0; +#endif +} + +bool restoreFPDenormalsState(const FPDenormalsModeState& state) +{ +#if OPENCV_IMPL_FP_HINTS_X86 + const unsigned mask = (unsigned)state.reserved[0]; + CV_DbgAssert(mask != 0); // invalid state (ensure that state is properly saved earlier) + const unsigned value = (unsigned)state.reserved[1]; + CV_DbgCheck((int)value, value == (value & mask), "invalid SSE FP state"); + const unsigned old_flags = _mm_getcsr(); + unsigned flags = (old_flags & ~mask) | value; + CV_LOG_DEBUG(NULL, "core: restore FP mxcsr flags = " << cv::format("0x%08x", flags)); + _mm_setcsr(flags); + return true; +#else + CV_UNUSED(state); + return false; +#endif +} + +} // namespace details + + } // namespace cv /* End of file. */ diff --git a/modules/core/src/trace.cpp b/modules/core/src/trace.cpp index 1011db5e3be2..05acbde7c978 100644 --- a/modules/core/src/trace.cpp +++ b/modules/core/src/trace.cpp @@ -988,7 +988,6 @@ void parallelForFinalize(const Region& rootRegion) std::vector threads_ctx; getTraceManager().tls.gather(threads_ctx); RegionStatistics parallel_for_stat; - int threads = 0; for (size_t i = 0; i < threads_ctx.size(); i++) { TraceManagerThreadLocal* child_ctx = threads_ctx[i]; @@ -996,7 +995,6 @@ void parallelForFinalize(const Region& rootRegion) if (child_ctx && child_ctx->stackTopRegion() == &rootRegion) { CV_LOG_PARALLEL(NULL, "Thread=" << child_ctx->threadID << " " << child_ctx->stat); - threads++; RegionStatistics child_stat; child_ctx->stat.grab(child_stat); parallel_for_stat.append(child_stat); @@ -1012,6 +1010,7 @@ void parallelForFinalize(const Region& rootRegion) } } } + float parallel_coeff = std::min(1.0f, duration / (float)(parallel_for_stat.duration)); CV_LOG_PARALLEL(NULL, "parallel_coeff=" << 1.0f / parallel_coeff); CV_LOG_PARALLEL(NULL, parallel_for_stat); diff --git a/modules/core/src/utils/plugin_loader.impl.hpp b/modules/core/src/utils/plugin_loader.impl.hpp index 4173c9d8024d..be04b4c131b4 100644 --- a/modules/core/src/utils/plugin_loader.impl.hpp +++ b/modules/core/src/utils/plugin_loader.impl.hpp @@ -56,7 +56,7 @@ void* DynamicLib::getSymbol(const char* symbolName) const return res; } -const std::string DynamicLib::getName() const +std::string DynamicLib::getName() const { return toPrintablePath(fname); } diff --git a/modules/core/src/va_intel.cpp b/modules/core/src/va_intel.cpp index a7623c37f47b..c2859a7d1107 100644 --- a/modules/core/src/va_intel.cpp +++ b/modules/core/src/va_intel.cpp @@ -606,10 +606,36 @@ void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, if (status != VA_STATUS_SUCCESS) CV_Error(cv::Error::StsError, "VA-API: vaSyncSurface failed"); + bool indirect_buffer = false; VAImage image; status = vaDeriveImage(display, surface, &image); - if (status != VA_STATUS_SUCCESS) - CV_Error(cv::Error::StsError, "VA-API: vaDeriveImage failed"); + if (status != VA_STATUS_SUCCESS){ + //try vaCreateImage + vaPutImage + //pick a format + indirect_buffer = true; + int num_formats = vaMaxNumImageFormats(display); + if (num_formats <= 0) + CV_Error(cv::Error::StsError, "VA-API: vaMaxNumImageFormats failed"); + std::vector fmt_list(num_formats); + + status = vaQueryImageFormats(display, fmt_list.data(), &num_formats); + if (status != VA_STATUS_SUCCESS) + CV_Error(cv::Error::StsError, "VA-API: vaQueryImageFormats failed"); + VAImageFormat *selected_format = nullptr; + for (auto &fmt : fmt_list){ + if (fmt.fourcc == VA_FOURCC_NV12 || fmt.fourcc == VA_FOURCC_YV12){ + selected_format = &fmt; + break; + } + } + if (selected_format == nullptr) + CV_Error(cv::Error::StsError, "VA-API: vaQueryImageFormats did not return a supported format"); + + status = vaCreateImage(display, selected_format, size.width, size.height, &image); + if (status != VA_STATUS_SUCCESS) + CV_Error(cv::Error::StsError, "VA-API: vaCreateImage failed"); + + } unsigned char* buffer = 0; status = vaMapBuffer(display, image.buf, (void **)&buffer); @@ -627,6 +653,14 @@ void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, if (status != VA_STATUS_SUCCESS) CV_Error(cv::Error::StsError, "VA-API: vaUnmapBuffer failed"); + if (indirect_buffer){ + status = vaPutImage(display, surface, image.image_id, 0, 0, size.width, size.height, 0, 0, size.width, size.height); + if (status != VA_STATUS_SUCCESS){ + vaDestroyImage(display, image.image_id); + CV_Error(cv::Error::StsError, "VA-API: vaPutImage failed"); + } + } + status = vaDestroyImage(display, image.image_id); if (status != VA_STATUS_SUCCESS) CV_Error(cv::Error::StsError, "VA-API: vaDestroyImage failed"); @@ -711,8 +745,37 @@ void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, Out VAImage image; status = vaDeriveImage(display, surface, &image); - if (status != VA_STATUS_SUCCESS) - CV_Error(cv::Error::StsError, "VA-API: vaDeriveImage failed"); + if (status != VA_STATUS_SUCCESS){ + //try vaCreateImage + vaGetImage + //pick a format + int num_formats = vaMaxNumImageFormats(display); + if (num_formats <= 0) + CV_Error(cv::Error::StsError, "VA-API: vaMaxNumImageFormats failed"); + std::vector fmt_list(num_formats); + + status = vaQueryImageFormats(display, fmt_list.data(), &num_formats); + if (status != VA_STATUS_SUCCESS) + CV_Error(cv::Error::StsError, "VA-API: vaQueryImageFormats failed"); + VAImageFormat *selected_format = nullptr; + for (auto &fmt : fmt_list){ + if (fmt.fourcc == VA_FOURCC_NV12 || fmt.fourcc == VA_FOURCC_YV12){ + selected_format = &fmt; + break; + } + } + if (selected_format == nullptr) + CV_Error(cv::Error::StsError, "VA-API: vaQueryImageFormats did not return a supported format"); + + status = vaCreateImage(display, selected_format, size.width, size.height, &image); + if (status != VA_STATUS_SUCCESS) + CV_Error(cv::Error::StsError, "VA-API: vaCreateImage failed"); + + status = vaGetImage(display, surface, 0, 0, size.width, size.height, image.image_id); + if (status != VA_STATUS_SUCCESS){ + vaDestroyImage(display, image.image_id); + CV_Error(cv::Error::StsError, "VA-API: vaPutImage failed"); + } + } unsigned char* buffer = 0; status = vaMapBuffer(display, image.buf, (void **)&buffer); diff --git a/modules/core/src/va_wrapper.impl.hpp b/modules/core/src/va_wrapper.impl.hpp index 260d3ba49b55..77faa984d066 100644 --- a/modules/core/src/va_wrapper.impl.hpp +++ b/modules/core/src/va_wrapper.impl.hpp @@ -15,18 +15,33 @@ typedef VAStatus (*FN_vaDestroyImage)(VADisplay dpy, VAImageID image); typedef VAStatus (*FN_vaMapBuffer)(VADisplay dpy, VABufferID buf_id, void **pbuf); typedef VAStatus (*FN_vaSyncSurface)(VADisplay dpy, VASurfaceID render_target); typedef VAStatus (*FN_vaUnmapBuffer)(VADisplay dpy, VABufferID buf_id); +typedef int (*FN_vaMaxNumImageFormats)(VADisplay dpy); +typedef VAStatus (*FN_vaQueryImageFormats)(VADisplay dpy, VAImageFormat *format_list, int *num_formats); +typedef VAStatus (*FN_vaCreateImage)(VADisplay dpy, VAImageFormat *format, int width, int height, VAImage *image); +typedef VAStatus (*FN_vaPutImage)(VADisplay dpy, VASurfaceID surface, VAImageID image, int src_x, int src_y, unsigned int src_width, unsigned int src_height, int dest_x, int dest_y, unsigned int dest_width, unsigned int dest_height); +typedef VAStatus (*FN_vaGetImage)(VADisplay dpy, VASurfaceID surface, int x, int y, unsigned int width, unsigned int height, VAImageID image); static FN_vaDeriveImage fn_vaDeriveImage = NULL; static FN_vaDestroyImage fn_vaDestroyImage = NULL; static FN_vaMapBuffer fn_vaMapBuffer = NULL; static FN_vaSyncSurface fn_vaSyncSurface = NULL; static FN_vaUnmapBuffer fn_vaUnmapBuffer = NULL; +static FN_vaMaxNumImageFormats fn_vaMaxNumImageFormats = NULL; +static FN_vaQueryImageFormats fn_vaQueryImageFormats = NULL; +static FN_vaCreateImage fn_vaCreateImage = NULL; +static FN_vaPutImage fn_vaPutImage = NULL; +static FN_vaGetImage fn_vaGetImage = NULL; #define vaDeriveImage fn_vaDeriveImage #define vaDestroyImage fn_vaDestroyImage #define vaMapBuffer fn_vaMapBuffer #define vaSyncSurface fn_vaSyncSurface #define vaUnmapBuffer fn_vaUnmapBuffer +#define vaMaxNumImageFormats fn_vaMaxNumImageFormats +#define vaQueryImageFormats fn_vaQueryImageFormats +#define vaCreateImage fn_vaCreateImage +#define vaPutImage fn_vaPutImage +#define vaGetImage fn_vaGetImage static std::shared_ptr loadLibVA() @@ -76,6 +91,11 @@ static void init_libva() VA_LOAD_SYMBOL(vaMapBuffer); VA_LOAD_SYMBOL(vaSyncSurface); VA_LOAD_SYMBOL(vaUnmapBuffer); + VA_LOAD_SYMBOL(vaMaxNumImageFormats); + VA_LOAD_SYMBOL(vaQueryImageFormats); + VA_LOAD_SYMBOL(vaCreateImage); + VA_LOAD_SYMBOL(vaPutImage); + VA_LOAD_SYMBOL(vaGetImage); initialized = true; } if (!library) diff --git a/modules/core/test/ocl/test_arithm.cpp b/modules/core/test/ocl/test_arithm.cpp index e6cb82919a12..20e3a177553b 100644 --- a/modules/core/test/ocl/test_arithm.cpp +++ b/modules/core/test/ocl/test_arithm.cpp @@ -1819,8 +1819,8 @@ OCL_TEST_P(ReduceSum, Mat) { generateTestData(); - OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_SUM, dtype)); - OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_SUM, dtype)); + OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_SUM, dtype)); + OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_SUM, dtype)); double eps = ddepth <= CV_32S ? 1 : 7e-4; OCL_EXPECT_MATS_NEAR(dst, eps); @@ -1835,8 +1835,8 @@ OCL_TEST_P(ReduceMax, Mat) { generateTestData(); - OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_MAX, dtype)); - OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_MAX, dtype)); + OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_MAX, dtype)); + OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_MAX, dtype)); OCL_EXPECT_MATS_NEAR(dst, 0); } @@ -1850,8 +1850,8 @@ OCL_TEST_P(ReduceMin, Mat) { generateTestData(); - OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_MIN, dtype)); - OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_MIN, dtype)); + OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_MIN, dtype)); + OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_MIN, dtype)); OCL_EXPECT_MATS_NEAR(dst, 0); } @@ -1865,8 +1865,8 @@ OCL_TEST_P(ReduceAvg, Mat) { generateTestData(); - OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_AVG, dtype)); - OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_AVG, dtype)); + OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_AVG, dtype)); + OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_AVG, dtype)); double eps = ddepth <= CV_32S ? 1 : 6e-6; OCL_EXPECT_MATS_NEAR(dst, eps); diff --git a/modules/core/test/test_arithm.cpp b/modules/core/test/test_arithm.cpp index 014a0cff0a1c..6d50b5a8f796 100644 --- a/modules/core/test/test_arithm.cpp +++ b/modules/core/test/test_arithm.cpp @@ -3,6 +3,7 @@ // of this distribution and at http://opencv.org/license.html. #include "test_precomp.hpp" #include "ref_reduce_arg.impl.hpp" +#include namespace opencv_test { namespace { @@ -2128,6 +2129,79 @@ TEST(Core_minMaxIdx, regression_9207_1) } +class TransposeND : public testing::TestWithParam< tuple, perf::MatType> > +{ +public: + std::vector m_shape; + int m_type; + + void SetUp() + { + std::tie(m_shape, m_type) = GetParam(); + } +}; + + +TEST_P(TransposeND, basic) +{ + Mat inp(m_shape, m_type); + randu(inp, 0, 255); + + std::vector order(m_shape.size()); + std::iota(order.begin(), order.end(), 0); + auto transposer = [&order] (const std::vector& id) + { + std::vector ret(id.size()); + for (size_t i = 0; i < id.size(); ++i) + { + ret[i] = id[order[i]]; + } + return ret; + }; + auto advancer = [&inp] (std::vector& id) + { + for (int j = static_cast(id.size() - 1); j >= 0; --j) + { + ++id[j]; + if (id[j] != inp.size[j]) + { + break; + } + id[j] = 0; + } + }; + + do + { + Mat out; + cv::transposeND(inp, order, out); + std::vector id(order.size()); + for (size_t i = 0; i < inp.total(); ++i) + { + auto new_id = transposer(id); + switch (inp.type()) + { + case CV_8UC1: + ASSERT_EQ(inp.at(id.data()), out.at(new_id.data())); + break; + case CV_32FC1: + ASSERT_EQ(inp.at(id.data()), out.at(new_id.data())); + break; + default: + FAIL() << "Unsupported type: " << inp.type(); + } + advancer(id); + } + } while (std::next_permutation(order.begin(), order.end())); +} + + +INSTANTIATE_TEST_CASE_P(Arithm, TransposeND, testing::Combine( + testing::Values(std::vector{2, 3, 4}, std::vector{5, 10}), + testing::Values(perf::MatType(CV_8UC1), CV_32FC1) +)); + + TEST(Core_minMaxIdx, regression_9207_2) { const int rows = 13; @@ -2546,5 +2620,36 @@ TEST(Core_Magnitude, regression_19506) } } +TEST(Core_CartPolar, inplace) +{ + RNG& rng = TS::ptr()->get_rng(); + cv::Mat1d A[2] = {cv::Mat1d(10, 10), cv::Mat1d(10, 10)}; + cv::Mat1d B[2], C[2]; + cv::UMat uA[2]; + + for(int i = 0; i < 2; ++i) + { + cvtest::randUni(rng, A[i], Scalar::all(-1000), Scalar::all(1000)); + A[i].copyTo(uA[i]); + } + + // Reverse + cv::cartToPolar(A[0], A[1], B[0], B[1], false); + cv::polarToCart(B[0], B[1], C[0], C[1], false); + EXPECT_MAT_NEAR(A[0], C[0], 2); + EXPECT_MAT_NEAR(A[1], C[1], 2); + + // Inplace + EXPECT_THROW(cv::polarToCart(B[0], B[1], B[0], B[1], false), cv::Exception); + EXPECT_THROW(cv::polarToCart(B[0], B[1], B[1], B[0], false), cv::Exception); + EXPECT_THROW(cv::cartToPolar(A[0], A[1], A[0], A[1], false), cv::Exception); + EXPECT_THROW(cv::cartToPolar(A[0], A[1], A[1], A[0], false), cv::Exception); + // Inplace OCL + EXPECT_THROW(cv::polarToCart(uA[0], uA[1], uA[0], uA[1]), cv::Exception); + EXPECT_THROW(cv::polarToCart(uA[0], uA[1], uA[1], uA[0]), cv::Exception); + EXPECT_THROW(cv::cartToPolar(uA[0], uA[1], uA[0], uA[1]), cv::Exception); + EXPECT_THROW(cv::cartToPolar(uA[0], uA[1], uA[0], uA[1]), cv::Exception); + +} }} // namespace diff --git a/modules/core/test/test_io.cpp b/modules/core/test/test_io.cpp index 3712be9f2e39..04426e58f7ed 100644 --- a/modules/core/test/test_io.cpp +++ b/modules/core/test/test_io.cpp @@ -3,6 +3,8 @@ // of this distribution and at http://opencv.org/license.html. #include "test_precomp.hpp" +#include + namespace opencv_test { namespace { static SparseMat cvTsGetRandomSparseMat(int dims, const int* sz, int type, @@ -799,6 +801,25 @@ TEST(Core_InputOutput, filestorage_base64_basic_memory_JSON) test_filestorage_basic(cv::FileStorage::WRITE_BASE64, ".json", true, true); } +// issue #21851 +TEST(Core_InputOutput, filestorage_heap_overflow) +{ + const ::testing::TestInfo* const test_info = ::testing::UnitTest::GetInstance()->current_test_info(); + CV_Assert(test_info); + + std::string name = std::string(test_info->test_case_name()) + "--" + test_info->name(); + const char data[] = {0x00, 0x2f, 0x4a, 0x4a, 0x50, 0x4a, 0x4a }; + + std::ofstream file; + file.open(name, std::ios_base::binary); + assert(file.is_open()); + + file.write(data, sizeof(data)); + file.close(); + + // This just shouldn't segfault, otherwise it's fine + EXPECT_ANY_THROW(FileStorage(name, FileStorage::READ)); +} TEST(Core_InputOutput, filestorage_base64_valid_call) { @@ -1918,5 +1939,29 @@ TEST(Core_InputOutput, FileStorage_16F_json) test_20279(fs); } +TEST(Core_InputOutput, FileStorage_invalid_path_regression_21448_YAML) +{ + FileStorage fs("invalid_path/test.yaml", cv::FileStorage::WRITE); + EXPECT_FALSE(fs.isOpened()); + EXPECT_ANY_THROW(fs.write("K", 1)); + fs.release(); +} + +TEST(Core_InputOutput, FileStorage_invalid_path_regression_21448_XML) +{ + FileStorage fs("invalid_path/test.xml", cv::FileStorage::WRITE); + EXPECT_FALSE(fs.isOpened()); + EXPECT_ANY_THROW(fs.write("K", 1)); + fs.release(); +} + +TEST(Core_InputOutput, FileStorage_invalid_path_regression_21448_JSON) +{ + FileStorage fs("invalid_path/test.json", cv::FileStorage::WRITE); + EXPECT_FALSE(fs.isOpened()); + EXPECT_ANY_THROW(fs.write("K", 1)); + fs.release(); +} + }} // namespace diff --git a/modules/core/test/test_mat.cpp b/modules/core/test/test_mat.cpp index bfc51b8a3e70..61ba306ba69b 100644 --- a/modules/core/test/test_mat.cpp +++ b/modules/core/test/test_mat.cpp @@ -93,7 +93,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat { int srcType = src.type(); bool support = false; - if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG ) + if( opType == REDUCE_SUM || opType == REDUCE_AVG ) { if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) ) support = true; @@ -106,7 +106,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat if( srcType == CV_64F && dstType == CV_64F) support = true; } - else if( opType == CV_REDUCE_MAX ) + else if( opType == REDUCE_MAX ) { if( srcType == CV_8U && dstType == CV_8U ) support = true; @@ -115,7 +115,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat if( srcType == CV_64F && dstType == CV_64F ) support = true; } - else if( opType == CV_REDUCE_MIN ) + else if( opType == REDUCE_MIN ) { if( srcType == CV_8U && dstType == CV_8U) support = true; @@ -128,7 +128,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat return cvtest::TS::OK; double eps = 0.0; - if ( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG ) + if ( opType == REDUCE_SUM || opType == REDUCE_AVG ) { if ( dstType == CV_32F ) eps = 1.e-5; @@ -152,10 +152,10 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat if( check ) { char msg[100]; - const char* opTypeStr = opType == CV_REDUCE_SUM ? "CV_REDUCE_SUM" : - opType == CV_REDUCE_AVG ? "CV_REDUCE_AVG" : - opType == CV_REDUCE_MAX ? "CV_REDUCE_MAX" : - opType == CV_REDUCE_MIN ? "CV_REDUCE_MIN" : "unknown operation type"; + const char* opTypeStr = opType == REDUCE_SUM ? "REDUCE_SUM" : + opType == REDUCE_AVG ? "REDUCE_AVG" : + opType == REDUCE_MAX ? "REDUCE_MAX" : + opType == REDUCE_MIN ? "REDUCE_MIN" : "unknown operation type"; string srcTypeStr, dstTypeStr; getMatTypeStr( src.type(), srcTypeStr ); getMatTypeStr( dstType, dstTypeStr ); @@ -195,19 +195,19 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz ) CV_Assert( 0 ); // 1. sum - tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim ); + tempCode = checkOp( src, dstType, REDUCE_SUM, sum, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; // 2. avg - tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim ); + tempCode = checkOp( src, dstType, REDUCE_AVG, avg, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; // 3. max - tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim ); + tempCode = checkOp( src, dstType, REDUCE_MAX, max, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; // 4. min - tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim ); + tempCode = checkOp( src, dstType, REDUCE_MIN, min, dim ); code = tempCode != cvtest::TS::OK ? tempCode : code; return code; @@ -315,7 +315,7 @@ TEST(Core_PCA, accuracy) Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints ); Mat avg(1, sz.width, CV_32FC1 ); - cv::reduce( rPoints, avg, 0, CV_REDUCE_AVG ); + cv::reduce( rPoints, avg, 0, REDUCE_AVG ); Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec; Q = Qt * Q; Q = Q /(float)rPoints.rows; @@ -1559,10 +1559,10 @@ TEST(Reduce, regression_should_fail_bug_4594) cv::Mat src = cv::Mat::eye(4, 4, CV_8U); std::vector dst; - EXPECT_THROW(cv::reduce(src, dst, 0, CV_REDUCE_MIN, CV_32S), cv::Exception); - EXPECT_THROW(cv::reduce(src, dst, 0, CV_REDUCE_MAX, CV_32S), cv::Exception); - EXPECT_NO_THROW(cv::reduce(src, dst, 0, CV_REDUCE_SUM, CV_32S)); - EXPECT_NO_THROW(cv::reduce(src, dst, 0, CV_REDUCE_AVG, CV_32S)); + EXPECT_THROW(cv::reduce(src, dst, 0, REDUCE_MIN, CV_32S), cv::Exception); + EXPECT_THROW(cv::reduce(src, dst, 0, REDUCE_MAX, CV_32S), cv::Exception); + EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_SUM, CV_32S)); + EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_AVG, CV_32S)); } TEST(Mat, push_back_vector) @@ -2371,6 +2371,18 @@ TEST(Mat, ptrVecni_20044) EXPECT_EQ(int(6), *(ci)); } + +TEST(Mat, VecMatx_4650) +{ + // Makes sure the following compiles. + cv::Vec3b a; + a = cv::Vec3b::ones(); + a = cv::Vec3b::zeros(); + a = cv::Vec3b::randn(0, 10); + a = cv::Vec3b::randu(0, 10); +} + + TEST(Mat, reverse_iterator_19967) { // empty iterator (#16855) @@ -2449,4 +2461,16 @@ TEST(Mat, reverse_iterator_19967) } +TEST(Mat, Recreate1DMatWithSameMeta) +{ + std::vector dims = {100}; + auto depth = CV_8U; + cv::Mat m(dims, depth); + + // By default m has dims: [1, 100] + m.dims = 1; + + EXPECT_NO_THROW(m.create(dims, depth)); +} + }} // namespace diff --git a/modules/core/test/test_math.cpp b/modules/core/test/test_math.cpp index 1e5239e38f61..580b4dcb10f4 100644 --- a/modules/core/test/test_math.cpp +++ b/modules/core/test/test_math.cpp @@ -3018,7 +3018,7 @@ TEST(CovariationMatrixVectorOfMatWithMean, accuracy) cv::randu(src,cv::Scalar(-128), cv::Scalar(128)); cv::Mat goldMean; - cv::reduce(src,goldMean,0 ,CV_REDUCE_AVG, CV_32F); + cv::reduce(src,goldMean,0 ,REDUCE_AVG, CV_32F); cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F); diff --git a/modules/core/test/test_misc.cpp b/modules/core/test/test_misc.cpp index d9df475fa69b..8ed0afe77160 100644 --- a/modules/core/test/test_misc.cpp +++ b/modules/core/test/test_misc.cpp @@ -4,6 +4,15 @@ #include "test_precomp.hpp" #include +#include "opencv2/core/utils/logger.hpp" + +#include + +#ifdef CV_CXX11 +#include +#include +#endif + namespace opencv_test { namespace { TEST(Core_OutputArrayCreate, _1997) @@ -243,6 +252,62 @@ TEST(Core_Parallel, propagate_exceptions) }, cv::Exception); } +class FPDenormalsHintCheckerParallelLoopBody : public cv::ParallelLoopBody +{ +public: + FPDenormalsHintCheckerParallelLoopBody() + : isOK(true) + { + state_values_to_check = cv::details::saveFPDenormalsState(base_state); + } + ~FPDenormalsHintCheckerParallelLoopBody() {} + void operator()(const cv::Range& r) const + { + CV_UNUSED(r); + cv::details::FPDenormalsModeState state; + if (cv::details::saveFPDenormalsState(state)) + { + for (int i = 0; i < state_values_to_check; ++i) + { + if (base_state.reserved[i] != state.reserved[i]) + { + CV_LOG_ERROR(NULL, cv::format("FP state[%d] mismatch: base=0x%08x thread=0x%08x", i, base_state.reserved[i], state.reserved[i])); + isOK = false; + cv::details::restoreFPDenormalsState(base_state); + } + } + } + else + { + // FP state is not supported + // no checks + } +#ifdef CV_CXX11 + std::this_thread::sleep_for(std::chrono::milliseconds(100)); +#endif + } + + cv::details::FPDenormalsModeState base_state; + int state_values_to_check; + + mutable bool isOK; +}; + +TEST(Core_Parallel, propagate_fp_denormals_ignore_hint) +{ + int nThreads = std::max(1, cv::getNumThreads()) * 3; + for (int i = 0; i < 4; ++i) + { + SCOPED_TRACE(cv::format("Case=%d: FP denormals ignore hint: %s\n", i, ((i & 1) != 0) ? "enable" : "disable")); + FPDenormalsIgnoreHintScope fp_denormals_scope((i & 1) != 0); + FPDenormalsHintCheckerParallelLoopBody job; + ASSERT_NO_THROW({ + parallel_for_(cv::Range(0, nThreads), job); + }); + EXPECT_TRUE(job.isOK); + } +} + TEST(Core_Version, consistency) { // this test verifies that OpenCV version loaded in runtime diff --git a/modules/core/test/test_precomp.hpp b/modules/core/test/test_precomp.hpp index a82f5cc12c9d..81ddf45de9eb 100644 --- a/modules/core/test/test_precomp.hpp +++ b/modules/core/test/test_precomp.hpp @@ -6,9 +6,6 @@ #include "opencv2/ts.hpp" #include "opencv2/ts/ocl_test.hpp" -#include "opencv2/core/core_c.h" - -#include "opencv2/core/cvdef.h" #include "opencv2/core/private.hpp" #include "opencv2/core/hal/hal.hpp" diff --git a/modules/core/test/test_umat.cpp b/modules/core/test/test_umat.cpp index c323d17c06d3..a89972762a4d 100644 --- a/modules/core/test/test_umat.cpp +++ b/modules/core/test/test_umat.cpp @@ -1398,8 +1398,8 @@ TEST(UMat, testTempObjects_Mat_issue_8693) randu(srcUMat, -1.f, 1.f); srcUMat.copyTo(srcMat); - reduce(srcUMat, srcUMat, 0, CV_REDUCE_SUM); - reduce(srcMat, srcMat, 0, CV_REDUCE_SUM); + reduce(srcUMat, srcUMat, 0, REDUCE_SUM); + reduce(srcMat, srcMat, 0, REDUCE_SUM); srcUMat.convertTo(srcUMat, CV_64FC1); srcMat.convertTo(srcMat, CV_64FC1); diff --git a/modules/dnn/CMakeLists.txt b/modules/dnn/CMakeLists.txt index dfc08e8b9bdf..e0773d52146f 100644 --- a/modules/dnn/CMakeLists.txt +++ b/modules/dnn/CMakeLists.txt @@ -23,6 +23,10 @@ if(WITH_WEBNN AND HAVE_WEBNN) add_definitions(-DHAVE_WEBNN=1) endif() +if(HAVE_TIMVX) + add_definitions(-DHAVE_TIMVX=1) +endif() + ocv_option(OPENCV_DNN_CUDA "Build with CUDA support" HAVE_CUDA AND HAVE_CUBLAS @@ -146,6 +150,11 @@ if(HAVE_TENGINE) list(APPEND libs -Wl,--whole-archive ${TENGINE_LIBRARIES} -Wl,--no-whole-archive) endif() +if(HAVE_TIMVX) + list(APPEND include_dirs ${TIMVX_INCLUDE_DIR}) + list(APPEND libs -Wl,--whole-archive ${TIMVX_LIBRARY} -Wl,--no-whole-archive) +endif() + set(webnn_srcs "") if(NOT EMSCRIPTEN) if(HAVE_WEBNN) @@ -165,24 +174,13 @@ elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang") endif() set(dnn_runtime_libs "") -if(INF_ENGINE_TARGET) - set(use_nn_builder OFF) - if(TARGET inference_engine_nn_builder OR # custom imported target - TARGET IE::inference_engine_nn_builder OR # default imported target via InferenceEngineConfig.cmake - INF_ENGINE_RELEASE VERSION_LESS "2020000000") # compatibility with older versions on IE - set(use_nn_builder ON) - endif() - ocv_option(OPENCV_DNN_IE_NN_BUILDER_2019 "Build with Inference Engine NN Builder API support" ${use_nn_builder}) # future: NOT HAVE_NGRAPH - if(OPENCV_DNN_IE_NN_BUILDER_2019) - message(STATUS "DNN: Enabling Inference Engine NN Builder API support") - add_definitions(-DHAVE_DNN_IE_NN_BUILDER_2019=1) + +ocv_option(OPENCV_DNN_OPENVINO "Build with OpenVINO support (2021.4+)" (TARGET ocv.3rdparty.openvino)) +if(TARGET ocv.3rdparty.openvino AND OPENCV_DNN_OPENVINO) + if(NOT HAVE_OPENVINO AND NOT HAVE_NGRAPH) + message(FATAL_ERROR "DNN: Inference Engine is not supported without enabled 'nGraph'. Check build configuration.") endif() - list(APPEND dnn_runtime_libs ${INF_ENGINE_TARGET}) -endif() -if(HAVE_NGRAPH) - message(STATUS "DNN: Enabling Inference Engine nGraph API support") - add_definitions(-DHAVE_DNN_NGRAPH) - list(APPEND dnn_runtime_libs ngraph::ngraph) + list(APPEND dnn_runtime_libs ocv.3rdparty.openvino) endif() ocv_glob_module_sources(${sources_options} SOURCES ${fw_srcs} ${webnn_srcs}) @@ -193,7 +191,7 @@ ocv_add_accuracy_tests(${dnn_runtime_libs}) set(perf_path "${CMAKE_CURRENT_LIST_DIR}/perf") file(GLOB_RECURSE perf_srcs "${perf_path}/*.cpp") file(GLOB_RECURSE perf_hdrs "${perf_path}/*.hpp" "${perf_path}/*.h") -ocv_add_perf_tests(${INF_ENGINE_TARGET} +ocv_add_perf_tests(${dnn_runtime_libs} FILES test_common "${CMAKE_CURRENT_LIST_DIR}/test/test_common.hpp" "${CMAKE_CURRENT_LIST_DIR}/test/test_common.impl.hpp" FILES Src ${perf_srcs} FILES Include ${perf_hdrs} diff --git a/modules/dnn/include/opencv2/dnn/all_layers.hpp b/modules/dnn/include/opencv2/dnn/all_layers.hpp index 44b16f780066..dae870197033 100644 --- a/modules/dnn/include/opencv2/dnn/all_layers.hpp +++ b/modules/dnn/include/opencv2/dnn/all_layers.hpp @@ -262,7 +262,7 @@ CV__DNN_INLINE_NS_BEGIN { public: int input_zp, output_zp; - float output_sc; + float input_sc, output_sc; static Ptr create(const LayerParams& params); }; @@ -322,9 +322,24 @@ CV__DNN_INLINE_NS_BEGIN { public: int input_zp, output_zp; + float input_sc, output_sc; static Ptr create(const LayerParams& params); }; + class CV_EXPORTS ReduceLayer : public Layer + { + public: + int reduceType; + std::vector reduceDims; + static Ptr create(const LayerParams& params); + }; + + class CV_EXPORTS ReduceLayerInt8 : public ReduceLayer + { + public: + static Ptr create(const LayerParams& params); + }; + class CV_EXPORTS SoftmaxLayer : public Layer { public: @@ -351,7 +366,8 @@ CV__DNN_INLINE_NS_BEGIN class CV_EXPORTS InnerProductLayerInt8 : public InnerProductLayer { public: - int output_zp; + int input_zp, output_zp; + float input_sc, output_sc; static Ptr create(const LayerParams& params); }; @@ -778,6 +794,26 @@ CV__DNN_INLINE_NS_BEGIN static Ptr create(const LayerParams ¶ms); }; + class CV_EXPORTS SignLayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS ShrinkLayer : public ActivationLayer + { + public: + float bias; + float lambd; + static Ptr create(const LayerParams ¶ms); + }; + + class CV_EXPORTS ReciprocalLayer : public ActivationLayer + { + public: + static Ptr create(const LayerParams ¶ms); + }; + /* Layers used in semantic segmentation */ class CV_EXPORTS CropLayer : public Layer diff --git a/modules/dnn/include/opencv2/dnn/dict.hpp b/modules/dnn/include/opencv2/dnn/dict.hpp index 463d314bee57..059ce9b28ef5 100644 --- a/modules/dnn/include/opencv2/dnn/dict.hpp +++ b/modules/dnn/include/opencv2/dnn/dict.hpp @@ -60,13 +60,13 @@ CV__DNN_INLINE_NS_BEGIN struct CV_EXPORTS_W DictValue { DictValue(const DictValue &r); - DictValue(bool i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i ? 1 : 0; } //!< Constructs integer scalar - DictValue(int64 i = 0) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar - CV_WRAP DictValue(int i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar - DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = p; } //!< Constructs integer scalar - CV_WRAP DictValue(double p) : type(Param::REAL), pd(new AutoBuffer) { (*pd)[0] = p; } //!< Constructs floating point scalar - CV_WRAP DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< Constructs string scalar - DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< @overload + explicit DictValue(bool i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i ? 1 : 0; } //!< Constructs integer scalar + explicit DictValue(int64 i = 0) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar + CV_WRAP explicit DictValue(int i) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = i; } //!< Constructs integer scalar + explicit DictValue(unsigned p) : type(Param::INT), pi(new AutoBuffer) { (*pi)[0] = p; } //!< Constructs integer scalar + CV_WRAP explicit DictValue(double p) : type(Param::REAL), pd(new AutoBuffer) { (*pd)[0] = p; } //!< Constructs floating point scalar + CV_WRAP explicit DictValue(const String &s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< Constructs string scalar + explicit DictValue(const char *s) : type(Param::STRING), ps(new AutoBuffer) { (*ps)[0] = s; } //!< @overload template static DictValue arrayInt(TypeIter begin, int size); //!< Constructs integer array diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp index d6b29cfcf335..8bca6c538ba7 100644 --- a/modules/dnn/include/opencv2/dnn/dnn.hpp +++ b/modules/dnn/include/opencv2/dnn/dnn.hpp @@ -75,6 +75,7 @@ CV__DNN_INLINE_NS_BEGIN DNN_BACKEND_VKCOM, DNN_BACKEND_CUDA, DNN_BACKEND_WEBNN, + DNN_BACKEND_TIMVX, #ifdef __OPENCV_BUILD DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = 1000000, // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType() DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType() @@ -95,7 +96,8 @@ CV__DNN_INLINE_NS_BEGIN DNN_TARGET_FPGA, //!< FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin. DNN_TARGET_CUDA, DNN_TARGET_CUDA_FP16, - DNN_TARGET_HDDL + DNN_TARGET_HDDL, + DNN_TARGET_NPU, }; CV_EXPORTS std::vector< std::pair > getAvailableBackends(); @@ -134,7 +136,7 @@ CV__DNN_INLINE_NS_BEGIN class BackendNode { public: - BackendNode(int backendId); + explicit BackendNode(int backendId); virtual ~BackendNode(); //!< Virtual destructor to make polymorphism. @@ -277,18 +279,18 @@ CV__DNN_INLINE_NS_BEGIN * Each layer input and output can be labeled to easily identify them using "%[.output_name]" notation. * This method maps label of input blob to its index into input vector. */ - virtual int inputNameToIndex(String inputName); + virtual int inputNameToIndex(String inputName); // FIXIT const /** @brief Returns index of output blob in output array. * @see inputNameToIndex() */ - CV_WRAP virtual int outputNameToIndex(const String& outputName); + CV_WRAP virtual int outputNameToIndex(const String& outputName); // FIXIT const /** * @brief Ask layer if it support specific backend for doing computations. * @param[in] backendId computation backend identifier. * @see Backend */ - virtual bool supportBackend(int backendId); + virtual bool supportBackend(int backendId); // FIXIT const /** * @brief Returns Halide backend node. @@ -302,8 +304,6 @@ CV__DNN_INLINE_NS_BEGIN */ virtual Ptr initHalide(const std::vector > &inputs); - virtual Ptr initInfEngine(const std::vector > &inputs); - virtual Ptr initNgraph(const std::vector > &inputs, const std::vector >& nodes); virtual Ptr initVkCom(const std::vector > &inputs); @@ -323,6 +323,19 @@ CV__DNN_INLINE_NS_BEGIN const std::vector>& outputs ); + /** + * @brief Returns a TimVX backend node + * + * @param timVxInfo void pointer to CSLContext object + * @param inputsWrapper layer inputs + * @param outputsWrapper layer outputs + * @param isLast if the node is the last one of the TimVX Graph. + */ + virtual Ptr initTimVX(void* timVxInfo, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast); + /** * @brief Automatic Halide scheduling based on layer hyper-parameters. * @param[in] node Backend node with Halide functions. @@ -391,7 +404,7 @@ CV__DNN_INLINE_NS_BEGIN /** - * @brief "Deattaches" all the layers, attached to particular layer. + * @brief "Detaches" all the layers, attached to particular layer. */ virtual void unsetAttached(); @@ -495,18 +508,29 @@ CV__DNN_INLINE_NS_BEGIN /** @brief Converts string name of the layer to the integer identifier. * @returns id of the layer, or -1 if the layer wasn't found. */ - CV_WRAP int getLayerId(const String &layer); + CV_WRAP int getLayerId(const String &layer) const; CV_WRAP std::vector getLayerNames() const; - /** @brief Container for strings and integers. */ + /** @brief Container for strings and integers. + * + * @deprecated Use getLayerId() with int result. + */ typedef DictValue LayerId; /** @brief Returns pointer to layer with specified id or name which the network use. */ - CV_WRAP Ptr getLayer(LayerId layerId); + CV_WRAP Ptr getLayer(int layerId) const; + /** @overload + * @deprecated Use int getLayerId(const String &layer) + */ + CV_WRAP inline Ptr getLayer(const String& layerName) const { return getLayer(getLayerId(layerName)); } + /** @overload + * @deprecated to be removed + */ + CV_WRAP Ptr getLayer(const LayerId& layerId) const; /** @brief Returns pointers to input layers of specific layer. */ - std::vector > getLayerInputs(LayerId layerId); // FIXIT: CV_WRAP + std::vector > getLayerInputs(int layerId) const; // FIXIT: CV_WRAP /** @brief Connects output of the first layer to input of the second layer. * @param outPin descriptor of the first layer output. @@ -531,6 +555,18 @@ CV__DNN_INLINE_NS_BEGIN */ void connect(int outLayerId, int outNum, int inpLayerId, int inpNum); + /** @brief Registers network output with name + * + * Function may create additional 'Identity' layer. + * + * @param outputName identifier of the output + * @param layerId identifier of the second layer + * @param outputPort number of the second layer input + * + * @returns index of bound layer (the same as layerId or newly created) + */ + int registerOutput(const std::string& outputName, int layerId, int outputPort); + /** @brief Sets outputs names of the network input pseudo layer. * * Each net always has special own the network input pseudo layer with id=0. @@ -662,20 +698,26 @@ CV__DNN_INLINE_NS_BEGIN * @note If shape of the new blob differs from the previous shape, * then the following forward pass may fail. */ - CV_WRAP void setParam(LayerId layer, int numParam, const Mat &blob); + CV_WRAP void setParam(int layer, int numParam, const Mat &blob); + CV_WRAP inline void setParam(const String& layerName, int numParam, const Mat &blob) { return setParam(getLayerId(layerName), numParam, blob); } /** @brief Returns parameter blob of the layer. * @param layer name or id of the layer. * @param numParam index of the layer parameter in the Layer::blobs array. * @see Layer::blobs */ - CV_WRAP Mat getParam(LayerId layer, int numParam = 0); + CV_WRAP Mat getParam(int layer, int numParam = 0) const; + CV_WRAP inline Mat getParam(const String& layerName, int numParam = 0) const { return getParam(getLayerId(layerName), numParam); } /** @brief Returns indexes of layers with unconnected outputs. + * + * FIXIT: Rework API to registerOutput() approach, deprecate this call */ CV_WRAP std::vector getUnconnectedOutLayers() const; /** @brief Returns names of layers with unconnected outputs. + * + * FIXIT: Rework API to registerOutput() approach, deprecate this call */ CV_WRAP std::vector getUnconnectedOutLayersNames() const; @@ -1283,6 +1325,9 @@ CV__DNN_INLINE_NS_BEGIN class CV_EXPORTS_W_SIMPLE ClassificationModel : public Model { public: + CV_DEPRECATED_EXTERNAL // avoid using in C++ code, will be moved to "protected" (need to fix bindings first) + ClassificationModel(); + /** * @brief Create classification model from network represented in one of the supported formats. * An order of @p model and @p config arguments does not matter. @@ -1297,6 +1342,24 @@ CV__DNN_INLINE_NS_BEGIN */ CV_WRAP ClassificationModel(const Net& network); + /** + * @brief Set enable/disable softmax post processing option. + * + * If this option is true, softmax is applied after forward inference within the classify() function + * to convert the confidences range to [0.0-1.0]. + * This function allows you to toggle this behavior. + * Please turn true when not contain softmax layer in model. + * @param[in] enable Set enable softmax post processing within the classify() function. + */ + CV_WRAP ClassificationModel& setEnableSoftmaxPostProcessing(bool enable); + + /** + * @brief Get enable/disable softmax post processing option. + * + * This option defaults to false, softmax post processing is not applied within the classify() function. + */ + CV_WRAP bool getEnableSoftmaxPostProcessing() const; + /** @brief Given the @p input frame, create input blob, run net and return top-1 prediction. * @param[in] frame The input image. */ @@ -1531,7 +1594,7 @@ class CV_EXPORTS_W_SIMPLE TextDetectionModel : public Model * - top-right * - bottom-right * - * Use cv::getPerspectiveTransform function to retrive image region without perspective transformations. + * Use cv::getPerspectiveTransform function to retrieve image region without perspective transformations. * * @note If DL model doesn't support that kind of output then result may be derived from detectTextRectangles() output. * diff --git a/modules/dnn/include/opencv2/dnn/layer.hpp b/modules/dnn/include/opencv2/dnn/layer.hpp index 85005993716d..a4d167564d90 100644 --- a/modules/dnn/include/opencv2/dnn/layer.hpp +++ b/modules/dnn/include/opencv2/dnn/layer.hpp @@ -66,6 +66,9 @@ class CV_EXPORTS LayerFactory //! Unregisters registered layer with specified type name. Thread-safe. static void unregisterLayer(const String &type); + //! Check if layer is registered. + static bool isLayerRegistered(const std::string& type); + /** @brief Creates instance of registered layer. * @param type type name of creating layer. * @param params parameters which will be used for layer initialization. diff --git a/modules/dnn/include/opencv2/dnn/shape_utils.hpp b/modules/dnn/include/opencv2/dnn/shape_utils.hpp index 4c610f6cefb6..9bbbc806a87f 100644 --- a/modules/dnn/include/opencv2/dnn/shape_utils.hpp +++ b/modules/dnn/include/opencv2/dnn/shape_utils.hpp @@ -184,7 +184,8 @@ static inline MatShape concat(const MatShape& a, const MatShape& b) return c; } -static inline std::string toString(const MatShape& shape, const String& name = "") +template +static inline std::string toString(const std::vector<_Tp>& shape, const String& name = "") { std::ostringstream ss; if (!name.empty()) @@ -195,11 +196,14 @@ static inline std::string toString(const MatShape& shape, const String& name = " ss << " ]"; return ss.str(); } -static inline void print(const MatShape& shape, const String& name = "") + +template +static inline void print(const std::vector<_Tp>& shape, const String& name = "") { std::cout << toString(shape, name) << std::endl; } -static inline std::ostream& operator<<(std::ostream &out, const MatShape& shape) +template +static inline std::ostream& operator<<(std::ostream &out, const std::vector<_Tp>& shape) { out << toString(shape); return out; diff --git a/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp b/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp index 333b1bfdd23b..b81806ed5a1a 100644 --- a/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp +++ b/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp @@ -15,14 +15,18 @@ CV__DNN_INLINE_NS_BEGIN /* Values for 'OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE' parameter */ +/// @deprecated #define CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API "NN_BUILDER" +/// @deprecated #define CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH "NGRAPH" /** @brief Returns Inference Engine internal backend API. * * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros. * - * Default value is controlled through `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable). + * `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable) is ignored since 4.6.0. + * + * @deprecated */ CV_EXPORTS_W cv::String getInferenceEngineBackendType(); @@ -31,6 +35,8 @@ CV_EXPORTS_W cv::String getInferenceEngineBackendType(); * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros. * * @returns previous value of internal backend API + * + * @deprecated */ CV_EXPORTS_W cv::String setInferenceEngineBackendType(const cv::String& newBackendType); diff --git a/modules/dnn/include/opencv2/dnn/version.hpp b/modules/dnn/include/opencv2/dnn/version.hpp index a0a1754901a5..630cccf75831 100644 --- a/modules/dnn/include/opencv2/dnn/version.hpp +++ b/modules/dnn/include/opencv2/dnn/version.hpp @@ -6,7 +6,7 @@ #define OPENCV_DNN_VERSION_HPP /// Use with major OpenCV version only. -#define OPENCV_DNN_API_VERSION 20211220 +#define OPENCV_DNN_API_VERSION 20220524 #if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_INLINE_NS #define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION) diff --git a/modules/dnn/misc/objc/gen_dict.json b/modules/dnn/misc/objc/gen_dict.json index e6d561fba025..6072bdfc01d8 100644 --- a/modules/dnn/misc/objc/gen_dict.json +++ b/modules/dnn/misc/objc/gen_dict.json @@ -18,8 +18,12 @@ "(long)getFLOPS:(NSArray*)netInputShapes" : { "getFLOPS" : {"name" : "getFLOPSWithNetInputShapes"} }, "(long)getFLOPS:(int)layerId netInputShape:(IntVector*)netInputShape" : { "getFLOPS" : {"name" : "getFLOPSWithLayerId"} }, "(long)getFLOPS:(int)layerId netInputShapes:(NSArray*)netInputShapes" : { "getFLOPS" : {"name" : "getFLOPSWithLayerId"} }, + "(Layer*)getLayer:(NSString*)layerName" : { "getLayer" : {"name" : "getLayerByName"} }, + "(Layer*)getLayer:(DictValue*)layerId" : { "getLayer" : {"name" : "getLayerByDictValue"} }, "(void)getLayersShapes:(IntVector*)netInputShape layersIds:(IntVector*)layersIds inLayersShapes:(NSMutableArray*>*)inLayersShapes outLayersShapes:(NSMutableArray*>*)outLayersShapes" : { "getLayersShapes" : {"name" : "getLayersShapesWithNetInputShape"} }, - "(void)getLayersShapes:(NSArray*)netInputShapes layersIds:(IntVector*)layersIds inLayersShapes:(NSMutableArray*>*)inLayersShapes outLayersShapes:(NSMutableArray*>*)outLayersShapes" : { "getLayersShapes" : {"name" : "getLayersShapesWithNetInputShapes"} } + "(void)getLayersShapes:(NSArray*)netInputShapes layersIds:(IntVector*)layersIds inLayersShapes:(NSMutableArray*>*)inLayersShapes outLayersShapes:(NSMutableArray*>*)outLayersShapes" : { "getLayersShapes" : {"name" : "getLayersShapesWithNetInputShapes"} }, + "(Mat*)getParam:(NSString*)layerName numParam:(int)numParam" : { "getParam" : {"name" : "getParamByName"} }, + "(void)setParam:(NSString*)layerName numParam:(int)numParam blob:(Mat*)blob" : { "setParam" : {"name" : "setParamByName"} } } }, "type_dict": { diff --git a/modules/dnn/src/caffe/caffe_importer.cpp b/modules/dnn/src/caffe/caffe_importer.cpp index 7fb64c7c0d15..a8d2f28ca61a 100644 --- a/modules/dnn/src/caffe/caffe_importer.cpp +++ b/modules/dnn/src/caffe/caffe_importer.cpp @@ -53,6 +53,8 @@ #include "caffe_io.hpp" #endif +#include + namespace cv { namespace dnn { CV__DNN_INLINE_NS_BEGIN @@ -88,6 +90,8 @@ MatShape parseBlobShape(const caffe::BlobShape& _input_shape) class CaffeImporter { + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + caffe::NetParameter net; caffe::NetParameter netBinary; diff --git a/modules/dnn/src/cuda/activations.cu b/modules/dnn/src/cuda/activations.cu index f5dafcea7fe5..e12457a164ce 100644 --- a/modules/dnn/src/cuda/activations.cu +++ b/modules/dnn/src/cuda/activations.cu @@ -248,6 +248,21 @@ void selu(const Stream& stream, Span output, View input, T alpha, T gamma) generic_op>(stream, output, input, {alpha, gamma}); } +template +void sign(const Stream& stream, Span output, View input) { + generic_op>(stream, output, input); +} + +template +void shrink(const Stream& stream, Span output, View input, T bias, T lambd) { + generic_op>(stream, output, input, {bias, lambd}); +} + +template +void reciprocal(const Stream& stream, Span output, View input) { + generic_op>(stream, output, input); +} + template void thresholdedrelu(const Stream& stream, Span output, View input, T alpha) { generic_op>(stream, output, input, {alpha}); @@ -312,6 +327,9 @@ template void selu<__half>(const Stream&, Span<__half>, View<__half>, __half, __ template void thresholdedrelu<__half>(const Stream&, Span<__half>, View<__half>, __half); template void power<__half>(const Stream&, Span<__half>, View<__half>, __half, __half, __half); template void exp<__half>(const Stream&, Span<__half>, View<__half>, __half, __half); +template void sign<__half>(const Stream&, Span<__half>, View<__half>); +template void shrink<__half>(const Stream&, Span<__half>, View<__half>, __half, __half); +template void reciprocal<__half>(const Stream&, Span<__half>, View<__half>); #endif @@ -351,6 +369,9 @@ template void selu(const Stream&, Span, View, float, float) template void thresholdedrelu(const Stream&, Span, View, float); template void power(const Stream&, Span, View, float, float, float); template void exp(const Stream&, Span, View, float, float); +template void sign(const Stream&, Span, View); +template void shrink(const Stream&, Span, View, float, float); +template void reciprocal(const Stream&, Span, View); template static void launch_vectorized_axiswise_relu(const Stream& stream, Span output, View input, std::size_t inner_size, View slope) { diff --git a/modules/dnn/src/cuda/concat.cu b/modules/dnn/src/cuda/concat.cu index ac1be7568224..5250b5951820 100644 --- a/modules/dnn/src/cuda/concat.cu +++ b/modules/dnn/src/cuda/concat.cu @@ -100,7 +100,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels { CV_Assert(output.rank() == input.rank()); CV_Assert(output_axis_offset < output.get_axis_size(axis)); - /* if axes preceeding the concat axis are all singleton, the concat blocks are contiguous + /* if axes preceding the concat axis are all singleton, the concat blocks are contiguous * in the output and we can copy each block directly */ if (output.size_range(0, axis) == 1) diff --git a/modules/dnn/src/cuda/functors.hpp b/modules/dnn/src/cuda/functors.hpp index 640c7c8ad6dc..83a949f8e7eb 100644 --- a/modules/dnn/src/cuda/functors.hpp +++ b/modules/dnn/src/cuda/functors.hpp @@ -726,6 +726,52 @@ struct DivFunctor { CUDA4DNN_DEVICE T operator()(T x, T y) { return x / y; } }; +template +struct SignFunctor { + struct Params { + CUDA4DNN_HOST_DEVICE Params() {} + }; + + CUDA4DNN_DEVICE SignFunctor() { } + CUDA4DNN_DEVICE SignFunctor(const Params& params) { } + + CUDA4DNN_DEVICE T operator()(T value) { + return value > T(0) ? T(1) : (value < T(0) ? T(-1) : T(0)); + } +}; + +template +struct ShrinkFunctor { + struct Params { + CUDA4DNN_HOST_DEVICE Params() : bias(0), lambd(0.5) { } + CUDA4DNN_HOST_DEVICE Params(T bias_, T lambd_) : bias(bias_), lambd(lambd_) { } + T bias, lambd; + }; + + CUDA4DNN_DEVICE ShrinkFunctor() : ShrinkFunctor(Params{}) { } + CUDA4DNN_DEVICE ShrinkFunctor(const Params& params) : bias{params.bias}, lambd{params.lambd} { } + + CUDA4DNN_DEVICE T operator()(T value) { + return value > lambd ? value - bias : (value < -lambd ? value + bias : T(0)); + } + + T bias, lambd; +}; + +template +struct ReciprocalFunctor { + struct Params { + CUDA4DNN_HOST_DEVICE Params() {} + }; + + CUDA4DNN_DEVICE ReciprocalFunctor() { } + CUDA4DNN_DEVICE ReciprocalFunctor(const Params& params) { } + + CUDA4DNN_DEVICE T operator()(T value) { + return T(1.f)/value; + } +}; + }}}} /* namespace cv::dnn::cuda4dnn::kernels */ #endif /* OPENCV_DNN_SRC_CUDA_FUNCTORS_HPP */ diff --git a/modules/dnn/src/cuda/kernel_dispatcher.hpp b/modules/dnn/src/cuda/kernel_dispatcher.hpp index b0fc65885073..0f3e7c4fc43a 100644 --- a/modules/dnn/src/cuda/kernel_dispatcher.hpp +++ b/modules/dnn/src/cuda/kernel_dispatcher.hpp @@ -33,7 +33,7 @@ * template * void launch_some_kernel(...); * - * // creates the dispatcher named "some_dispatcher" which invokves the correct instantiation of "launch_some_kernel" + * // creates the dispatcher named "some_dispatcher" which invokes the correct instantiation of "launch_some_kernel" * GENERATE_KERNEL_DISPATCHER(some_dispatcher, launch_some_kernel); * * // internal API function diff --git a/modules/dnn/src/cuda/permute.cu b/modules/dnn/src/cuda/permute.cu index 082c1bf75e1c..35c95a67378a 100644 --- a/modules/dnn/src/cuda/permute.cu +++ b/modules/dnn/src/cuda/permute.cu @@ -72,7 +72,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels { __syncthreads(); /* We interchange `threadIdx.x` and `threadIdx.y` so that consecutive output indices map to - * consecutive threads. This would allow writes across threds in a warp to be coalesced. + * consecutive threads. This would allow writes across threads in a warp to be coalesced. */ const index_type out_x = blockIdx.y * TILE_SIZE + threadIdx.x; const index_type out_y_begin = blockIdx.x * TILE_SIZE + threadIdx.y; @@ -156,7 +156,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels { * tensor indices be [o1, o2, ...]. The permutation operation essentially copies items * from the input tensor to new locations in the output tensor as dictated by the indices. * - * If the size of the nth axis (say i2) of the input is one the input and output indicies for + * If the size of the nth axis (say i2) of the input is one the input and output indices for * all the elements will be of the form be [i1, 0, ...] and [..., 0, ...] respectively. * The index does not contribute to the element's address calculation and hence would give * identical result if it weren't there. diff --git a/modules/dnn/src/cuda/slice.cu b/modules/dnn/src/cuda/slice.cu index 37b718cd63b0..461e87e549f9 100644 --- a/modules/dnn/src/cuda/slice.cu +++ b/modules/dnn/src/cuda/slice.cu @@ -159,7 +159,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels { /* We can do a copy if the reduced rank is two and only the first axis is sliced. * The general requirement is that only one axis is sliced and all the axes that - * preceed the sliced axis are singleton. However, the reductions above will remove + * precede the sliced axis are singleton. However, the reductions above will remove * all the leading singleton axes and merge the trailing unsliced axes into one, or * zero if there are no trailing unsliced axes. The latter is handled separately. */ diff --git a/modules/dnn/src/cuda4dnn/csl/cudnn/cudnn.hpp b/modules/dnn/src/cuda4dnn/csl/cudnn/cudnn.hpp index 2370492ad530..9bd8fcfe3bef 100644 --- a/modules/dnn/src/cuda4dnn/csl/cudnn/cudnn.hpp +++ b/modules/dnn/src/cuda4dnn/csl/cudnn/cudnn.hpp @@ -287,6 +287,51 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cu cudnnTensorDescriptor_t descriptor; }; + /** An array of number fully packed tensor descriptors + * + * @tparam T type of elements in the tensor + */ + template + class TensorDescriptorsArray + { + public: + TensorDescriptorsArray() noexcept = default; + TensorDescriptorsArray(const TensorDescriptorsArray&) = delete; + TensorDescriptorsArray(TensorDescriptorsArray&& other) noexcept + : descriptors{std::move(other.descriptors)} {} + + TensorDescriptorsArray(int seqLength, std::array dims) + { + for (int i = 0; i < seqLength; ++i) + { + descriptors.emplace_back(dims); + } + } + + ~TensorDescriptorsArray() noexcept = default; + + TensorDescriptorsArray& operator=(const TensorDescriptorsArray&) = delete; + TensorDescriptorsArray& operator=(TensorDescriptorsArray&& other) noexcept + { + descriptors = std::move(other.descriptors); + return *this; + }; + + std::vector get() const noexcept + { + std::vector descPtrs; + descPtrs.reserve(descriptors.size()); + for (auto& desc : descriptors) + { + descPtrs.push_back(desc.get()); + } + return descPtrs; + } + + private: + std::vector> descriptors; + }; + }}}}} /* namespace cv::dnn::cuda4dnn::csl::cudnn */ #endif /* OPENCV_DNN_CUDA4DNN_CSL_CUDNN_HPP */ diff --git a/modules/dnn/src/cuda4dnn/csl/cudnn/recurrent.hpp b/modules/dnn/src/cuda4dnn/csl/cudnn/recurrent.hpp new file mode 100644 index 000000000000..7ba6acdf173c --- /dev/null +++ b/modules/dnn/src/cuda4dnn/csl/cudnn/recurrent.hpp @@ -0,0 +1,195 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_DNN_CUDA4DNN_CSL_CUDNN_RECURRENT_HPP +#define OPENCV_DNN_CUDA4DNN_CSL_CUDNN_RECURRENT_HPP + +#include "cudnn.hpp" +#include + + +namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace cudnn { + +/** + */ +class DropoutDescriptor +{ +public: + DropoutDescriptor() noexcept = default; + DropoutDescriptor(const DropoutDescriptor &) = delete; + DropoutDescriptor(DropoutDescriptor &&other) noexcept : descriptor{other.descriptor} + { + states = std::move(other.states); + other.descriptor = nullptr; + } + + /** + */ + DropoutDescriptor(const Handle &handle, float dropout) + { + CUDA4DNN_CHECK_CUDNN(cudnnCreateDropoutDescriptor(&descriptor)); + + // we need additional memory for dropout descriptor + size_t stateSize; + CUDA4DNN_CHECK_CUDNN(cudnnDropoutGetStatesSize(handle.get(), &stateSize)); + states.reset(stateSize); + + try + { + auto seed = 1234ull; // Pick a seed. + CUDA4DNN_CHECK_CUDNN(cudnnSetDropoutDescriptor(descriptor, handle.get(), dropout, + states.get().get(), stateSize, seed)); + } + catch (...) + { + CUDA4DNN_CHECK_CUDNN(cudnnDestroyDropoutDescriptor(descriptor)); + throw; + } + } + + ~DropoutDescriptor() noexcept + { + if (descriptor) + { + CUDA4DNN_CHECK_CUDNN(cudnnDestroyDropoutDescriptor(descriptor)); + } + } + + DropoutDescriptor &operator=(const DropoutDescriptor &) = delete; + DropoutDescriptor &operator=(DropoutDescriptor &&other) noexcept + { + descriptor = other.descriptor; + states = std::move(other.states); + other.descriptor = nullptr; + return *this; + }; + + cudnnDropoutDescriptor_t get() const noexcept { return descriptor; } + +private: + cudnnDropoutDescriptor_t descriptor{nullptr}; + + using value_type = typename ManagedPtr::element_type; + ManagedPtr states; +}; + +/** + */ +template +class RNNDescriptor +{ +public: + enum class RNNMode + { + RNN_RELU, + RNN_TANH, + LSTM, + GRU + }; + + RNNDescriptor() noexcept = default; + RNNDescriptor(const RNNDescriptor &) = delete; + RNNDescriptor(RNNDescriptor &&other) noexcept : descriptor{other.descriptor} + { + other.descriptor = nullptr; + } + + /** + */ + RNNDescriptor(const Handle &handle, RNNMode mode, int hidden_size, int num_layers, + bool bidirectional, const DropoutDescriptor &dropoutDesc) + { + CUDA4DNN_CHECK_CUDNN(cudnnCreateRNNDescriptor(&descriptor)); + const auto rnn_mode = [mode] { + switch (mode) + { + case RNNMode::RNN_RELU: + return CUDNN_RNN_RELU; + case RNNMode::RNN_TANH: + return CUDNN_RNN_TANH; + case RNNMode::LSTM: + return CUDNN_LSTM; + case RNNMode::GRU: + return CUDNN_GRU; + default: + return CUDNN_LSTM; + } + }(); + + try + { + CUDA4DNN_CHECK_CUDNN(cudnnSetRNNDescriptor_v6( + handle.get(), descriptor, hidden_size, num_layers, dropoutDesc.get(), + CUDNN_LINEAR_INPUT, bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, + rnn_mode, + algo, //CUDNN_RNN_ALGO_STANDARD, + detail::get_data_type())); + } + catch (...) + { + CUDA4DNN_CHECK_CUDNN(cudnnDestroyRNNDescriptor(descriptor)); + throw; + } + } + + ~RNNDescriptor() noexcept + { + if (descriptor) + { + CUDA4DNN_CHECK_CUDNN(cudnnDestroyRNNDescriptor(descriptor)); + } + } + + RNNDescriptor &operator=(const RNNDescriptor &) = delete; + RNNDescriptor &operator=(RNNDescriptor &&other) noexcept + { + descriptor = other.descriptor; + other.descriptor = nullptr; + return *this; + }; + + cudnnRNNDescriptor_t get() const noexcept { return descriptor; } + +private: + cudnnRNNDescriptor_t descriptor{nullptr}; + cudnnRNNMode_t mode{CUDNN_LSTM}; + // support only one algo for a while + cudnnRNNAlgo_t algo{CUDNN_RNN_ALGO_STANDARD}; +}; + +template +size_t getRNNWorkspaceSize(const Handle &handle, const RNNDescriptor &rnnDesc, + const int seqLength, const TensorDescriptorsArray &inputDesc) +{ + size_t workSize; + CUDA4DNN_CHECK_CUDNN(cudnnGetRNNWorkspaceSize(handle.get(), rnnDesc.get(), seqLength, + inputDesc.get().data(), &workSize)); + return workSize; +} + +template +void LSTMForward(const Handle &handle, const RNNDescriptor &rnnDesc, + const FilterDescriptor &filterDesc, DevicePtr filterPtr, + const TensorDescriptorsArray &inputDesc, DevicePtr inputPtr, + const TensorDescriptor &initialHDesc, DevicePtr initialH, + const TensorDescriptor &initialCDesc, DevicePtr initialC, + const int seqLength, const TensorDescriptorsArray &outputDesc, + DevicePtr yOutputPtr, DevicePtr ycOutputPtr, WorkspaceInstance workspace) +{ + CV_Assert(handle); + + CUDA4DNN_CHECK_CUDNN(cudnnRNNForwardInference(handle.get(), rnnDesc.get(), seqLength, + inputDesc.get().data(), inputPtr.get(), // input sequence + initialHDesc.get(), initialH.get(), + initialCDesc.get(), initialC.get(), // hidden + filterDesc.get(), filterPtr.get(), // weights + outputDesc.get().data(), yOutputPtr.get(), // output + nullptr, nullptr, + initialCDesc.get(), ycOutputPtr.get(), + static_cast(workspace.get()), workspace.size_in_bytes())); +} + +}}}}} /* namespace cv::dnn::cuda4dnn::csl::cudnn */ + +#endif //OPENCV_DNN_CUDA4DNN_CSL_CUDNN_RECURRENT_HPP \ No newline at end of file diff --git a/modules/dnn/src/cuda4dnn/csl/memory.hpp b/modules/dnn/src/cuda4dnn/csl/memory.hpp index 40918cd4b3ca..683ed6205910 100644 --- a/modules/dnn/src/cuda4dnn/csl/memory.hpp +++ b/modules/dnn/src/cuda4dnn/csl/memory.hpp @@ -68,7 +68,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { } } }); - /* std::shared_ptr::reset invokves the deleter if an exception occurs; hence, we don't + /* std::shared_ptr::reset invokes the deleter if an exception occurs; hence, we don't * need to have a try-catch block to free the allocated device memory */ diff --git a/modules/dnn/src/cuda4dnn/csl/pointer.hpp b/modules/dnn/src/cuda4dnn/csl/pointer.hpp index 45bf94bf0a3b..4d7a23209393 100644 --- a/modules/dnn/src/cuda4dnn/csl/pointer.hpp +++ b/modules/dnn/src/cuda4dnn/csl/pointer.hpp @@ -147,7 +147,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { /* host const void pointer to const void device pointer */ CUDA4DNN_HOST_DEVICE explicit DevicePtr(pointer ptr_) noexcept : ptr{ ptr_ } { } - /* allow any device pointer to be implicitly convereted to void device pointer */ + /* allow any device pointer to be implicitly converted to void device pointer */ template CUDA4DNN_HOST_DEVICE DevicePtr(DevicePtr ptr_) noexcept : ptr{ ptr_.get() } { } @@ -199,7 +199,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { /* host pointer to device pointer */ CUDA4DNN_HOST_DEVICE explicit DevicePtr(pointer ptr_) noexcept : ptr{ ptr_ } { } - /* allow any device pointer to mutable memory to be implicitly convereted to void device pointer */ + /* allow any device pointer to mutable memory to be implicitly converted to void device pointer */ template ::value, bool>::type = false> CUDA4DNN_HOST_DEVICE DevicePtr(DevicePtr ptr_) noexcept : ptr { ptr_.get() } { } diff --git a/modules/dnn/src/cuda4dnn/csl/tensor_ops.hpp b/modules/dnn/src/cuda4dnn/csl/tensor_ops.hpp index 4ee0e8ab77cf..27f8306bf337 100644 --- a/modules/dnn/src/cuda4dnn/csl/tensor_ops.hpp +++ b/modules/dnn/src/cuda4dnn/csl/tensor_ops.hpp @@ -18,6 +18,7 @@ #include "cudnn/softmax.hpp" #include "cudnn/transform.hpp" #include "cudnn/transpose_convolution.hpp" +#include "cudnn/recurrent.hpp" #include @@ -472,6 +473,90 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { TensorTransformDescriptor transDesc; }; + template + class LSTM + { + using TensorDescriptor = cudnn::TensorDescriptor; + using DropoutDescriptor = cudnn::DropoutDescriptor; + using RNNDescriptor = cudnn::RNNDescriptor; + using FilterDescriptor = cudnn::FilterDescriptor; + using TensorDescriptorsArray = cudnn::TensorDescriptorsArray; + + public: + using RNNMode = typename RNNDescriptor::RNNMode; + + struct params_type + { + std::vector weights_shape; + + int seqLength; + int numLayers; + int hiddenSize; + int inputSize; + int miniBatch; + bool bidirectional; + + float dropout; + RNNMode type; + }; + + LSTM() = default; + LSTM(const LSTM&) = delete; + LSTM(LSTM&&) = default; + LSTM(cudnn::Handle handle, const params_type& params) + : cudnnHandle(std::move(handle)), seqLength{params.seqLength}, + inputDesc(seqLength, {params.miniBatch, params.inputSize, 1}), + outputDesc(seqLength, + {params.miniBatch, + params.bidirectional ? params.hiddenSize * 2 : params.hiddenSize, + 1}) + { + dropoutDesc = DropoutDescriptor(cudnnHandle, params.dropout); + filterDesc = FilterDescriptor(params.weights_shape); + rnnDesc = RNNDescriptor(cudnnHandle, params.type, params.hiddenSize, + params.numLayers, params.bidirectional, dropoutDesc); + + int num_direction = params.bidirectional ? 2 : 1; + h0TensorDesc = TensorDescriptor( + {num_direction, params.miniBatch, params.hiddenSize}); + c0TensorDesc = TensorDescriptor( + {num_direction, params.miniBatch, params.hiddenSize}); + + // Get amount of work space required to execute the RNN described by rnnDesc + // with input dimensions defined by inputDesc + csl::WorkspaceBuilder builder; + builder.require(cudnn::getRNNWorkspaceSize(cudnnHandle, rnnDesc, seqLength, inputDesc)); + scratch_mem_in_bytes = builder.required_workspace_size(); + } + + LSTM& operator=(const LSTM&) = delete; + LSTM& operator=(LSTM&&) = default; + + void inference(TensorView input, TensorSpan y_output, TensorSpan yc_output, TensorView filters, + TensorView h0, TensorView c0, WorkspaceInstance workspace) + { + cudnn::LSTMForward(cudnnHandle, rnnDesc, filterDesc, filters.get(), inputDesc, + input.get(), h0TensorDesc, h0.get(), c0TensorDesc, c0.get(), + seqLength, outputDesc, y_output.get(), yc_output.get(), workspace); + } + + std::size_t get_workspace_memory_in_bytes() const noexcept { return scratch_mem_in_bytes; } + + private: + cudnn::Handle cudnnHandle; + std::size_t scratch_mem_in_bytes{0}; + int seqLength; + + RNNDescriptor rnnDesc; + DropoutDescriptor dropoutDesc; + + FilterDescriptor filterDesc; + TensorDescriptor h0TensorDesc, c0TensorDesc; + + TensorDescriptorsArray inputDesc; + TensorDescriptorsArray outputDesc; + }; + }}}} /* namespace cv::dnn::cuda4dnn::csl */ #endif /* OPENCV_DNN_SRC_CUDA4DNN_CSL_TENSOR_OPS_HPP */ diff --git a/modules/dnn/src/cuda4dnn/kernels/activations.hpp b/modules/dnn/src/cuda4dnn/kernels/activations.hpp index ef1f6da3e6d1..6958b93d5efc 100644 --- a/modules/dnn/src/cuda4dnn/kernels/activations.hpp +++ b/modules/dnn/src/cuda4dnn/kernels/activations.hpp @@ -123,6 +123,14 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels { template void exp(const csl::Stream& stream, csl::Span output, csl::View input, T normScale, T normShift); + template + void sign(const csl::Stream& stream, csl::Span output, csl::View input); + + template + void shrink(const csl::Stream& stream, csl::Span output, csl::View input, T bias, T lambd); + + template + void reciprocal(const csl::Stream& stream, csl::Span output, csl::View input); }}}} /* namespace cv::dnn::cuda4dnn::kernels */ #endif /* OPENCV_DNN_SRC_CUDA4DNN_KERNELS_ACTIVATIONS_HPP */ diff --git a/modules/dnn/src/cuda4dnn/primitives/activation.hpp b/modules/dnn/src/cuda4dnn/primitives/activation.hpp index 39ebf513a7a6..564202e8c0bc 100644 --- a/modules/dnn/src/cuda4dnn/primitives/activation.hpp +++ b/modules/dnn/src/cuda4dnn/primitives/activation.hpp @@ -584,6 +584,52 @@ namespace cv { namespace dnn { namespace cuda4dnn { const T normScale, normShift; }; + template + class ShrinkOp final : public BaseOp { + public: + ShrinkOp(csl::Stream stream_, T bias_, T lambd_) + : stream(std::move(stream_)), bias{ bias_ }, lambd{ lambd_ } { } + + void calculate(csl::TensorSpan output, csl::TensorView input) const + { + kernels::shrink(stream, output, input, bias, lambd); + } + + private: + csl::Stream stream; + const T bias, lambd; + }; + + template + class SignOp final : public BaseOp { + public: + SignOp(csl::Stream stream_) + : stream(std::move(stream_)) { } + + void calculate(csl::TensorSpan output, csl::TensorView input) const + { + kernels::sign(stream, output, input); + } + + private: + csl::Stream stream; + }; + + template + class ReciprocalOp final : public BaseOp { + public: + ReciprocalOp(csl::Stream stream_) + : stream(std::move(stream_)) { } + + void calculate(csl::TensorSpan output, csl::TensorView input) const + { + kernels::reciprocal(stream, output, input); + } + + private: + csl::Stream stream; + }; + }}} /* namespace cv::dnn::cuda4dnn */ #endif /* OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_ACTIVATION_HPP */ diff --git a/modules/dnn/src/cuda4dnn/primitives/recurrent_cells.hpp b/modules/dnn/src/cuda4dnn/primitives/recurrent_cells.hpp new file mode 100644 index 000000000000..5cba78800812 --- /dev/null +++ b/modules/dnn/src/cuda4dnn/primitives/recurrent_cells.hpp @@ -0,0 +1,97 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_CELLS_HPP +#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_CELLS_HPP + +#include "../../op_cuda.hpp" + +#include "../csl/cudnn.hpp" +#include "../csl/tensor_ops.hpp" +#include "../csl/cudnn/recurrent.hpp" + +namespace cv { namespace dnn { namespace cuda4dnn { + +struct RNNConfiguration +{ + int seqLength; + int numLayers; + int hiddenSize; + int inputSize; + int miniBatch; + bool bidirectional; +}; + +template +class LSTMOp final : public CUDABackendNode +{ +public: + using wrapper_type = GetCUDABackendWrapperType; + + LSTMOp(csl::Stream stream_, csl::cudnn::Handle handle, const Mat& filters, const Mat& h0, + const Mat& c0, const RNNConfiguration& config) + : stream(std::move(stream_)) + { + typename csl::LSTM::params_type params{ + {filters.total(), 1, 1}, // reshape + config.seqLength, + config.numLayers, + config.hiddenSize, + config.inputSize, + config.miniBatch, + config.bidirectional, + 0.0, /* dropout */ + csl::cudnn::RNNDescriptor::RNNMode::LSTM + }; + + lstm = csl::LSTM(handle, params); + auto correct_shape_filters = filters.reshape(1, {static_cast(filters.total()), 1, 1}); + filtersTensor = csl::makeTensorHeader(correct_shape_filters); + csl::copyMatToTensor(correct_shape_filters, filtersTensor, stream); + + h0Tensor = csl::makeTensorHeader(h0); + csl::copyMatToTensor(h0, h0Tensor, stream); + + c0Tensor = csl::makeTensorHeader(c0); + csl::copyMatToTensor(c0, c0Tensor, stream); + + csl::WorkspaceBuilder builder; + builder.require(lstm.get_workspace_memory_in_bytes()); + } + + void forward(const std::vector>& inputs, + const std::vector>& outputs, + csl::Workspace& workspace) override + { + CV_Assert(inputs.size() == 1 && !outputs.empty()); + + auto input_wrapper = inputs[0].dynamicCast(); + auto input = input_wrapper->getView(); + + auto y_output_wrapper = outputs[0].dynamicCast(); + auto y_output = y_output_wrapper->getSpan(); + + Ptr yc_output_wrapper = outputs.size() == 2 ? outputs[1].dynamicCast() : Ptr(); + csl::TensorSpan yc_output = yc_output_wrapper.empty() ? csl::TensorSpan() : yc_output_wrapper->getSpan(); + + csl::WorkspaceAllocator allocator(workspace); + lstm.inference(input, y_output, yc_output, filtersTensor, h0Tensor, c0Tensor, allocator.get_instance()); + } + + std::size_t get_workspace_memory_in_bytes() const noexcept override + { + return lstm.get_workspace_memory_in_bytes(); + } + +private: + csl::LSTM lstm; + csl::Stream stream; + csl::Tensor filtersTensor; + csl::Tensor h0Tensor; + csl::Tensor c0Tensor; +}; + +}}} /* namespace cv::dnn::cuda4dnn */ + +#endif //OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_RECURRENT_CELLS_HPP \ No newline at end of file diff --git a/modules/dnn/src/darknet/darknet_importer.cpp b/modules/dnn/src/darknet/darknet_importer.cpp index f1269bd979d6..024c0b6c5005 100644 --- a/modules/dnn/src/darknet/darknet_importer.cpp +++ b/modules/dnn/src/darknet/darknet_importer.cpp @@ -51,6 +51,7 @@ #include "darknet_io.hpp" +#include namespace cv { namespace dnn { @@ -61,6 +62,8 @@ namespace class DarknetImporter { + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + darknet::NetParameter net; public: @@ -204,7 +207,7 @@ Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= Str std::ifstream cfgStream(cfgFile.c_str()); if (!cfgStream.is_open()) { - CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(cfgFile)); + CV_Error(cv::Error::StsParseError, "Failed to open NetParameter file: " + std::string(cfgFile)); } if (darknetModel != String()) { diff --git a/modules/dnn/src/darknet/darknet_io.cpp b/modules/dnn/src/darknet/darknet_io.cpp index 99715df829f1..520f3c94bed1 100644 --- a/modules/dnn/src/darknet/darknet_io.cpp +++ b/modules/dnn/src/darknet/darknet_io.cpp @@ -376,7 +376,7 @@ namespace cv { int begin[] = {0, split_size * group_id, 0, 0}; cv::dnn::DictValue paramBegin = cv::dnn::DictValue::arrayInt(begin, 4); - int end[] = {-1, begin[1] + split_size, -1, -1}; + int end[] = {INT_MAX, begin[1] + split_size, INT_MAX, INT_MAX}; cv::dnn::DictValue paramEnd = cv::dnn::DictValue::arrayInt(end, 4); darknet::LayerParameter lp; @@ -791,7 +791,7 @@ namespace cv { if (layers_vec.size() > 1) { // layer ids in layers_vec - inputs of Slice layers - // after adding offset to layers_vec: layer ids - ouputs of Slice layers + // after adding offset to layers_vec: layer ids - outputs of Slice layers for (size_t k = 0; k < layers_vec.size(); ++k) layers_vec[k] += layers_vec.size(); diff --git a/modules/dnn/src/debug_utils.cpp b/modules/dnn/src/debug_utils.cpp index d951205bd876..0e1ba1023633 100644 --- a/modules/dnn/src/debug_utils.cpp +++ b/modules/dnn/src/debug_utils.cpp @@ -37,11 +37,8 @@ void skipModelImport(bool skip) void detail::LayerHandler::addMissing(const std::string& name, const std::string& type) { - cv::AutoLock lock(getLayerFactoryMutex()); - auto& registeredLayers = getLayerFactoryImpl(); - // If we didn't add it, but can create it, it's custom and not missing. - if (layers.find(type) == layers.end() && registeredLayers.find(type) != registeredLayers.end()) + if (!contains(type) && LayerFactory::isLayerRegistered(type)) { return; } @@ -51,17 +48,17 @@ void detail::LayerHandler::addMissing(const std::string& name, const std::string bool detail::LayerHandler::contains(const std::string& type) const { - return layers.find(type) != layers.end(); + return layers.count(type) != 0; } -void detail::LayerHandler::printMissing() +void detail::LayerHandler::printMissing() const { if (layers.empty()) { return; } - std::stringstream ss; + std::ostringstream ss; ss << "DNN: Not supported types:\n"; for (const auto& type_names : layers) { diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp index 67312dba7831..8c397b14f2e8 100644 --- a/modules/dnn/src/dnn.cpp +++ b/modules/dnn/src/dnn.cpp @@ -1,6222 +1,10 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. #include "precomp.hpp" -#include "op_halide.hpp" -#include "op_inf_engine.hpp" -#include "ie_ngraph.hpp" -#include "op_vkcom.hpp" -#include "op_cuda.hpp" -#include "op_webnn.hpp" -#ifdef HAVE_CUDA -#include "cuda4dnn/init.hpp" -#include "cuda4dnn/primitives/eltwise.hpp" // required by fuseLayers -#endif - -#include "halide_scheduler.hpp" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include - -namespace cv { -namespace dnn { -CV__DNN_INLINE_NS_BEGIN - -static size_t DNN_NETWORK_DUMP = utils::getConfigurationParameterSizeT("OPENCV_DNN_NETWORK_DUMP", 0); - -// this option is useful to run valgrind memory errors detection -static bool DNN_DISABLE_MEMORY_OPTIMIZATIONS = utils::getConfigurationParameterBool("OPENCV_DNN_DISABLE_MEMORY_OPTIMIZATIONS", false); - -#ifdef HAVE_OPENCL -static bool DNN_OPENCL_ALLOW_ALL_DEVICES = utils::getConfigurationParameterBool("OPENCV_DNN_OPENCL_ALLOW_ALL_DEVICES", false); -#endif - -static int PARAM_DNN_BACKEND_DEFAULT = (int)utils::getConfigurationParameterSizeT("OPENCV_DNN_BACKEND_DEFAULT", -#ifdef HAVE_INF_ENGINE - (size_t)DNN_BACKEND_INFERENCE_ENGINE -#else - (size_t)DNN_BACKEND_OPENCV -#endif -); - -// Additional checks (slowdowns execution!) -static bool DNN_CHECK_NAN_INF = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF", false); -static bool DNN_CHECK_NAN_INF_DUMP = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_DUMP", false); -static bool DNN_CHECK_NAN_INF_RAISE_ERROR = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_RAISE_ERROR", false); - -using std::vector; -using std::map; -using std::make_pair; -using std::set; -using std::string; - -//================================================================================================== - -class BackendRegistry -{ -public: - typedef std::vector< std::pair > BackendsList; - const BackendsList & getBackends() const { return backends; } - static BackendRegistry & getRegistry() - { - static BackendRegistry impl; - return impl; - } - -#ifdef HAVE_INF_ENGINE - static inline bool checkIETarget(Target target) - { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R3) - // Lightweight detection - const std::vector devices = getCore("").GetAvailableDevices(); - for (std::vector::const_iterator i = devices.begin(); i != devices.end(); ++i) - { - if (std::string::npos != i->find("MYRIAD") && target == DNN_TARGET_MYRIAD) - return true; - if (std::string::npos != i->find("HDDL") && target == DNN_TARGET_HDDL) - return true; - else if (std::string::npos != i->find("FPGA") && target == DNN_TARGET_FPGA) - return true; - else if (std::string::npos != i->find("CPU") && target == DNN_TARGET_CPU) - return true; - else if (std::string::npos != i->find("GPU") && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) - return true; - } - return false; -#else - cv::dnn::Net net; - cv::dnn::LayerParams lp; - lp.set("kernel_size", 1); - lp.set("num_output", 1); - lp.set("bias_term", false); - lp.type = "Convolution"; - lp.name = "testLayer"; - lp.blobs.push_back(Mat({1, 2, 1, 1}, CV_32F, Scalar(1))); - net.addLayerToPrev(lp.name, lp.type, lp); - net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE); - net.setPreferableTarget(target); - static int inpDims[] = {1, 2, 3, 4}; - net.setInput(cv::Mat(4, &inpDims[0], CV_32FC1, cv::Scalar(0))); - try - { - net.forward(); - } - catch(const std::exception& e) - { - CV_LOG_INFO(NULL, "checkIETarget(" << (int)target << ") has failed with message: " << e.what()); - return false; - } - return true; -#endif - } -#endif - -private: - BackendRegistry() - { -#ifdef HAVE_HALIDE - backends.push_back(std::make_pair(DNN_BACKEND_HALIDE, DNN_TARGET_CPU)); -# ifdef HAVE_OPENCL - if (cv::ocl::useOpenCL()) - backends.push_back(std::make_pair(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL)); -# endif -#endif // HAVE_HALIDE - -#ifdef HAVE_INF_ENGINE - if (checkIETarget(DNN_TARGET_CPU)) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_CPU)); -#endif -#ifdef HAVE_DNN_NGRAPH - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU)); -#endif - } - if (checkIETarget(DNN_TARGET_MYRIAD)) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_MYRIAD)); -#endif -#ifdef HAVE_DNN_NGRAPH - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD)); -#endif - } - if (checkIETarget(DNN_TARGET_HDDL)) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_HDDL)); -#endif -#ifdef HAVE_DNN_NGRAPH - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_HDDL)); -#endif - } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - if (checkIETarget(DNN_TARGET_FPGA)) - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_FPGA)); -#endif -#ifdef HAVE_OPENCL - if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel()) - { - if (checkIETarget(DNN_TARGET_OPENCL)) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL)); -#endif -#ifdef HAVE_DNN_NGRAPH - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL)); -#endif - } - if (checkIETarget(DNN_TARGET_OPENCL_FP16)) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL_FP16)); -#endif -#ifdef HAVE_DNN_NGRAPH - backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16)); -#endif - } - } -#endif -#endif // HAVE_INF_ENGINE - -#ifdef HAVE_WEBNN - if (haveWebnn()) - { - backends.push_back(std::make_pair(DNN_BACKEND_WEBNN, DNN_TARGET_CPU)); - } -#endif // HAVE_WEBNN - -#ifdef HAVE_OPENCL - if (cv::ocl::useOpenCL()) - { - backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL)); - backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)); - } -#endif - - backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)); - -#ifdef HAVE_VULKAN - if (haveVulkan()) - backends.push_back(std::make_pair(DNN_BACKEND_VKCOM, DNN_TARGET_VULKAN)); -#endif - -#ifdef HAVE_CUDA - if (haveCUDA()) - { - backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA)); - backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA_FP16)); - } -#endif - } - - BackendsList backends; -}; - - -std::vector< std::pair > getAvailableBackends() -{ - return BackendRegistry::getRegistry().getBackends(); -} - -std::vector getAvailableTargets(Backend be) -{ - if (be == DNN_BACKEND_DEFAULT) - be = (Backend)PARAM_DNN_BACKEND_DEFAULT; -#ifdef HAVE_INF_ENGINE - if (be == DNN_BACKEND_INFERENCE_ENGINE) - be = getInferenceEngineBackendTypeParam(); -#endif - - std::vector result; - const BackendRegistry::BackendsList all_backends = getAvailableBackends(); - for(BackendRegistry::BackendsList::const_iterator i = all_backends.begin(); i != all_backends.end(); ++i ) - { - if (i->first == be) - result.push_back(i->second); - } - return result; -} - -//================================================================================================== - -namespace -{ - struct LayerShapes - { - ShapesVec in, out, internal; - // No guarantees that layer which support in-place computations - // will be computed in-place (input.data_ptr == output.data_ptr). - // If layer said that it could work in-place and layers after it - // no longer use input blob, we'll set output = input. - bool supportInPlace; - LayerShapes() {supportInPlace = false;} - }; -} - -Mat blobFromImage(InputArray image, double scalefactor, const Size& size, - const Scalar& mean, bool swapRB, bool crop, int ddepth) -{ - CV_TRACE_FUNCTION(); - Mat blob; - blobFromImage(image, blob, scalefactor, size, mean, swapRB, crop, ddepth); - return blob; -} - -void blobFromImage(InputArray image, OutputArray blob, double scalefactor, - const Size& size, const Scalar& mean, bool swapRB, bool crop, int ddepth) -{ - CV_TRACE_FUNCTION(); - std::vector images(1, image.getMat()); - blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth); -} - -Mat blobFromImages(InputArrayOfArrays images, double scalefactor, Size size, - const Scalar& mean, bool swapRB, bool crop, int ddepth) -{ - CV_TRACE_FUNCTION(); - Mat blob; - blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth); - return blob; -} - -void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalefactor, - Size size, const Scalar& mean_, bool swapRB, bool crop, int ddepth) -{ - CV_TRACE_FUNCTION(); - CV_CheckType(ddepth, ddepth == CV_32F || ddepth == CV_8U, "Blob depth should be CV_32F or CV_8U"); - if (ddepth == CV_8U) - { - CV_CheckEQ(scalefactor, 1.0, "Scaling is not supported for CV_8U blob depth"); - CV_Assert(mean_ == Scalar() && "Mean subtraction is not supported for CV_8U blob depth"); - } - - std::vector images; - images_.getMatVector(images); - CV_Assert(!images.empty()); - for (size_t i = 0; i < images.size(); i++) - { - Size imgSize = images[i].size(); - if (size == Size()) - size = imgSize; - if (size != imgSize) - { - if(crop) - { - float resizeFactor = std::max(size.width / (float)imgSize.width, - size.height / (float)imgSize.height); - resize(images[i], images[i], Size(), resizeFactor, resizeFactor, INTER_LINEAR); - Rect crop(Point(0.5 * (images[i].cols - size.width), - 0.5 * (images[i].rows - size.height)), - size); - images[i] = images[i](crop); - } - else - resize(images[i], images[i], size, 0, 0, INTER_LINEAR); - } - if(images[i].depth() == CV_8U && ddepth == CV_32F) - images[i].convertTo(images[i], CV_32F); - Scalar mean = mean_; - if (swapRB) - std::swap(mean[0], mean[2]); - - images[i] -= mean; - images[i] *= scalefactor; - } - - size_t nimages = images.size(); - Mat image0 = images[0]; - int nch = image0.channels(); - CV_Assert(image0.dims == 2); - if (nch == 3 || nch == 4) - { - int sz[] = { (int)nimages, nch, image0.rows, image0.cols }; - blob_.create(4, sz, ddepth); - Mat blob = blob_.getMat(); - Mat ch[4]; - - for(size_t i = 0; i < nimages; i++ ) - { - const Mat& image = images[i]; - CV_Assert(image.depth() == blob_.depth()); - nch = image.channels(); - CV_Assert(image.dims == 2 && (nch == 3 || nch == 4)); - CV_Assert(image.size() == image0.size()); - - for( int j = 0; j < nch; j++ ) - ch[j] = Mat(image.rows, image.cols, ddepth, blob.ptr((int)i, j)); - if(swapRB) - std::swap(ch[0], ch[2]); - split(image, ch); - } - } - else - { - CV_Assert(nch == 1); - int sz[] = { (int)nimages, 1, image0.rows, image0.cols }; - blob_.create(4, sz, ddepth); - Mat blob = blob_.getMat(); - - for(size_t i = 0; i < nimages; i++ ) - { - const Mat& image = images[i]; - CV_Assert(image.depth() == blob_.depth()); - nch = image.channels(); - CV_Assert(image.dims == 2 && (nch == 1)); - CV_Assert(image.size() == image0.size()); - - image.copyTo(Mat(image.rows, image.cols, ddepth, blob.ptr((int)i, 0))); - } - } -} - -void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_) -{ - CV_TRACE_FUNCTION(); - - //A blob is a 4 dimensional matrix in floating point precision - //blob_[0] = batchSize = nbOfImages - //blob_[1] = nbOfChannels - //blob_[2] = height - //blob_[3] = width - CV_Assert(blob_.depth() == CV_32F); - CV_Assert(blob_.dims == 4); - - images_.create(cv::Size(1, blob_.size[0]), blob_.depth()); - - std::vector vectorOfChannels(blob_.size[1]); - for (int n = 0; n < blob_.size[0]; ++n) - { - for (int c = 0; c < blob_.size[1]; ++c) - { - vectorOfChannels[c] = getPlane(blob_, n, c); - } - cv::merge(vectorOfChannels, images_.getMatRef(n)); - } -} - -#ifdef HAVE_OPENCL -class OpenCLBackendWrapper : public BackendWrapper -{ -public: - OpenCLBackendWrapper(Mat& m) : BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL) - { - m.copyTo(umat); - host = &m; - hostDirty = false; - } - - OpenCLBackendWrapper(const Ptr& baseBuffer, Mat& m) - : BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL) - { - Ptr base = baseBuffer.dynamicCast(); - CV_Assert(!base.empty()); - - host = &m; - - int shape[] = {1, (int)base->umat.total()}; - umat = base->umat.reshape(1, 2, &shape[0]) - .colRange(0, host->total()) - .reshape(1, host->dims, &host->size[0]); - hostDirty = false; - } - - static Ptr create(Mat& m) - { - return Ptr(new OpenCLBackendWrapper(m)); - } - - static Ptr create(const Ptr& baseBuffer, Mat& m) - { - return Ptr(new OpenCLBackendWrapper(baseBuffer, m)); - } - - static std::vector getUMatVector(const std::vector >& wrappers) - { - const int numWrappers = wrappers.size(); - std::vector mats(wrappers.size()); - for (int i = 0; i < numWrappers; ++i) - { - Ptr umatWrapper = wrappers[i].dynamicCast(); - CV_Assert(!umatWrapper.empty()); - umatWrapper->copyToDevice(); - mats[i] = umatWrapper->umat; - } - return mats; - } - - // Replaces all umats in wrappers to specific ones. - static void update(const std::vector >& wrappers, - const std::vector& umats) - { - CV_Assert(wrappers.size() == umats.size()); - for (int i = 0, n = umats.size(); i < n; ++i) - { - Ptr umatWrapper = wrappers[i].dynamicCast(); - CV_Assert(!umatWrapper.empty()); - umatWrapper->umat = umats[i]; - } - } - - ~OpenCLBackendWrapper() {} - - // Copies data from device to a host memory. - virtual void copyToHost() CV_OVERRIDE - { - umat.copyTo(*host); - } - - virtual void setHostDirty() CV_OVERRIDE - { - hostDirty = true; - }; - - void copyToDevice() - { - if (hostDirty) - { - host->copyTo(umat); - hostDirty = false; - } - } - -private: - UMat umat; - Mat* host; - bool hostDirty; -}; -#endif - -struct LayerPin -{ - int lid; - int oid; - - LayerPin(int layerId = -1, int outputId = -1) - : lid(layerId), oid(outputId) {} - - bool valid() const - { - return (lid >= 0 && oid >= 0); - } - - bool equal(const LayerPin &r) const - { - return (lid == r.lid && oid == r.oid); - } - - bool operator<(const LayerPin &r) const - { - return lid < r.lid || (lid == r.lid && oid < r.oid); - } - - bool operator ==(const LayerPin &r) const - { - return lid == r.lid && oid == r.oid; - } -}; - -struct LayerData -{ - LayerData() : id(-1), dtype(CV_32F), skip(false), flag(0) {} - LayerData(int _id, const String &_name, const String &_type, const int &_dtype, LayerParams &_params) - : id(_id), name(_name), type(_type), dtype(_dtype), params(_params), skip(false), flag(0) - { - CV_TRACE_FUNCTION(); - - //add logging info - params.name = name; - params.type = type; - } - - int id; - String name; - String type; - int dtype; // Datatype of output blobs. - LayerParams params; - - std::vector inputBlobsId; - std::set inputLayersId; - std::set requiredOutputs; - std::vector consumers; - std::vector > outputBlobsWrappers; - std::vector > inputBlobsWrappers; - std::vector > internalBlobsWrappers; - -#ifdef HAVE_CUDA - /* output ids which must be transferred to the host in the background - * after the completion of the forward pass of the layer - */ - std::vector cudaD2HBackgroundTransfers; -#endif - - Ptr layerInstance; - std::vector outputBlobs; - std::vector inputBlobs; - std::vector internals; - // Computation nodes of implemented backends (except DEFAULT). - std::map > backendNodes; - // Flag for skip layer computation for specific backend. - bool skip; - - int flag; - - Ptr getLayerInstance() - { - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(type, "type", type.c_str()); - - if (layerInstance) - return layerInstance; - - layerInstance = LayerFactory::createLayerInstance(type, params); - if (!layerInstance) - { - CV_Error(Error::StsError, "Can't create layer \"" + name + "\" of type \"" + type + "\""); - } - - return layerInstance; - } -}; - -//fake layer containing network input blobs -struct DataLayer : public Layer -{ - DataLayer() : Layer() - { - skip = false; - } - - virtual bool supportBackend(int backendId) CV_OVERRIDE - { - return backendId == DNN_BACKEND_OPENCV || - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && inputsData.size() == 1); - } - - void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE - { - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(name, "name", name.c_str()); - - // FIXIT: add wrapper without exception suppression - CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget), - forward_ocl(inputs_arr, outputs_arr, internals_arr)) - - bool isFP16 = outputs_arr.depth() == CV_16S; - - std::vector outputs, internals; - outputs_arr.getMatVector(outputs); - internals_arr.getMatVector(internals); - - for (int i = 0; i < inputsData.size(); ++i) - { - double scale = scaleFactors[i]; - Scalar& mean = means[i]; - - CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4); - if (isFP16) - CV_CheckTypeEQ(outputs[i].type(), CV_16SC1, ""); - else - CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, ""); - - bool singleMean = true; - for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j) - { - singleMean = mean[j] == mean[j - 1]; - } - - if (singleMean) - { - if (isFP16) - { - Mat input_f32; - inputsData[i].convertTo(input_f32, CV_32F, scale, -mean[0] * scale); - convertFp16(input_f32, outputs[i]); - } - else - { - inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale); - } - } - else - { - for (int n = 0; n < inputsData[i].size[0]; ++n) - { - for (int c = 0; c < inputsData[i].size[1]; ++c) - { - Mat inp = getPlane(inputsData[i], n, c); - Mat out = getPlane(outputs[i], n, c); - if (isFP16) - { - Mat input_f32; - inp.convertTo(input_f32, CV_32F, scale, -mean[c] * scale); - convertFp16(input_f32, out); - } - else - { - inp.convertTo(out, CV_32F, scale, -mean[c] * scale); - } - } - } - } - } - } - -#ifdef HAVE_OPENCL - bool forward_ocl(InputArrayOfArrays, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_) - { - bool isFP16 = outputs_.depth() == CV_16S; - - std::vector outputs; - outputs_.getUMatVector(outputs); - - for (int i = 0; i < inputsData.size(); ++i) - { - Mat inputData = inputsData[i]; - - double scale = scaleFactors[i]; - Scalar& mean = means[i]; - - CV_Assert(mean == Scalar() || inputData.size[1] <= 4); - if (isFP16) - CV_CheckTypeEQ(outputs[i].type(), CV_16SC1, ""); - else - CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, ""); - - bool singleMean = true; - for (int j = 1; j < std::min(4, inputData.size[1]) && singleMean; ++j) - { - singleMean = mean[j] == mean[j - 1]; - } - - if (singleMean) - { - if (isFP16) - { - UMat input_i; - inputData.convertTo(input_i, CV_32F, scale, -mean[0] * scale); - convertFp16(input_i, outputs[i]); - } - else - { - inputData.convertTo(outputs[i], CV_32F, scale, -mean[0] * scale); - } - } - else - { - for (int n = 0; n < inputData.size[0]; ++n) - { - for (int c = 0; c < inputData.size[1]; ++c) - { - Mat inp = getPlane(inputData, n, c); - - std::vector plane(4, Range::all()); - plane[0] = Range(n, n + 1); - plane[1] = Range(c, c + 1); - UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size); - - if (isFP16) - { - UMat input_i; - inp.convertTo(input_i, CV_32F, scale, -mean[c] * scale); - convertFp16(input_i, out); - } - else - { - inp.convertTo(out, CV_32F, scale, -mean[c] * scale); - } - } - } - } - } - return true; - } -#endif - - int outputNameToIndex(const String& tgtName) CV_OVERRIDE - { - int idx = (int)(std::find(outNames.begin(), outNames.end(), tgtName) - outNames.begin()); - return (idx < (int)outNames.size()) ? idx : -1; - } - - void setNames(const std::vector &names) - { - outNames.assign(names.begin(), names.end()); - shapes.clear(); shapes.resize(outNames.size()); - } - - void setInputShape(const String& tgtName, const MatShape& shape) - { - std::vector::const_iterator it = std::find(outNames.begin(), outNames.end(), tgtName); - CV_Check(tgtName, it != outNames.end(), "Unknown input"); - int idx = (int)(it - outNames.begin()); - - CV_Assert(idx < (int)shapes.size()); - CV_Check(tgtName, shapes[idx].empty(), "Input shape redefinition is not allowed"); - shapes[idx] = shape; - } - - bool getMemoryShapes(const std::vector &inputs, - const int requiredOutputs, - std::vector &outputs, - std::vector &internals) const CV_OVERRIDE - { - CV_Assert(inputs.size() == requiredOutputs); - outputs.assign(inputs.begin(), inputs.end()); - return false; - } - - virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays outputs_arr) CV_OVERRIDE - { - std::vector outputs; - outputs_arr.getMatVector(outputs); - - CV_Assert_N(outputs.size() == scaleFactors.size(), outputs.size() == means.size(), - inputsData.size() == outputs.size()); - skip = true; - for (int i = 0; skip && i < inputsData.size(); ++i) - { - if (inputsData[i].data != outputs[i].data || scaleFactors[i] != 1.0 || means[i] != Scalar()) - skip = false; - } - } - -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - CV_CheckEQ(inputsData.size(), (size_t)1, ""); - CV_CheckEQ(inputsData[0].dims, 4, ""); - const size_t numChannels = inputsData[0].size[1]; - CV_Assert(numChannels <= 4); - - // Scale - InferenceEngine::TensorDesc td(InferenceEngine::Precision::FP32, {numChannels}, - InferenceEngine::Layout::C); - auto weights = InferenceEngine::make_shared_blob(td); - weights->allocate(); - - float* weight_buf = weights->buffer().as(); - std::fill(weight_buf, weight_buf + numChannels, scaleFactors[0]); - - // Mean subtraction - auto biases = InferenceEngine::make_shared_blob(td); - biases->allocate(); - float* bias_buf = biases->buffer().as(); - - for (int i = 0; i < numChannels; ++i) - { - bias_buf[i] = -means[0][i] * scaleFactors[0]; - } - - InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name); - addConstantData("weights", weights, ieLayer); - addConstantData("biases", biases, ieLayer); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - std::vector outNames; - std::vector shapes; - // Preprocessing parameters for each network's input. - std::vector scaleFactors; - std::vector means; - std::vector inputsData; - bool skip; -}; - -struct BlobManager -{ -public: - // Increase references counter to layer output. - void addReference(const LayerPin& lp) - { - std::map::iterator it = refCounter.find(lp); - if (it == refCounter.end()) - refCounter[lp] = 1; - else - it->second += 1; - } - - void addReferences(const std::vector& pins) - { - for (int i = 0; i < pins.size(); i++) - { - addReference(pins[i]); - } - } - - // Returns number of references to allocated memory that used in specific - // layer blob. - int numReferences(const LayerPin& lp) - { - std::map::iterator mapIt = reuseMap.find(lp); - CV_Assert(mapIt != reuseMap.end()); - LayerPin memHost = mapIt->second; - - std::map::iterator refIt = refCounter.find(memHost); - CV_Assert(refIt != refCounter.end()); - return refIt->second; - } - - // Reuse data allocated in inside the blob. - void reuse(const LayerPin& host, const LayerPin& user) - { - CV_Assert(reuseMap.find(user) == reuseMap.end()); - CV_Assert(reuseMap.find(host) != reuseMap.end()); - LayerPin memHost = reuseMap[host]; - reuseMap[user] = memHost; - if (refCounter.find(memHost) != refCounter.end()) - { - std::map::iterator userRefIt = refCounter.find(user); - if (userRefIt != refCounter.end()) - { - refCounter[memHost] += userRefIt->second; - refCounter.erase(userRefIt); - } - else - refCounter[memHost] += 1; - } - } - - // Decrease references counter to allocated memory inside specific blob. - void releaseReference(const LayerPin& lp) - { - std::map::iterator mapIt = reuseMap.find(lp); - CV_Assert(mapIt != reuseMap.end()); - - std::map::iterator refIt = refCounter.find(mapIt->second); - CV_Assert(refIt != refCounter.end()); - CV_Assert(refIt->second > 0); - refIt->second -= 1; - } - - void releaseReferences(const std::vector& pins) - { - for (int i = 0; i < pins.size(); i++) - { - releaseReference(pins[i]); - } - } - - void reuseOrCreate(const MatShape& shape, const LayerPin& lp, Mat& dst, const int& dtype) - { - if (!DNN_DISABLE_MEMORY_OPTIMIZATIONS) - { - Mat bestBlob; - LayerPin bestBlobPin; - - std::map::iterator hostIt; - std::map::iterator refIt; - - const int targetTotal = total(shape); - int bestBlobTotal = INT_MAX; - - for (hostIt = memHosts.begin(); hostIt != memHosts.end(); ++hostIt) - { - refIt = refCounter.find(hostIt->first); - // Use only blobs that had references before because if not, - // it might be used as output. - if (refIt != refCounter.end() && refIt->second == 0) - { - Mat& unusedBlob = hostIt->second; - if (unusedBlob.total() >= targetTotal && - unusedBlob.total() < bestBlobTotal && - unusedBlob.type() == dtype) - { - bestBlobPin = hostIt->first; - bestBlob = unusedBlob; - bestBlobTotal = unusedBlob.total(); - } - } - } - if (!bestBlob.empty()) - { - reuse(bestBlobPin, lp); - dst = bestBlob.reshape(1, 1).colRange(0, targetTotal).reshape(1, shape); - return; - } - } - - { - // if dst already has been allocated with total(shape) elements, - // it won't be recreated and pointer of dst.data remains the same. - dst.create(shape, dtype); - addHost(lp, dst); - } - } - - void allocateBlobsForLayer(LayerData &ld, const LayerShapes& layerShapes, - std::vector& pinsForInternalBlobs) - { - CV_TRACE_FUNCTION(); - - pinsForInternalBlobs.clear(); - - std::vector& outputBlobs = ld.outputBlobs, - &internalBlobs = ld.internals; - - const ShapesVec& outShapes = layerShapes.out, - internalShapes = layerShapes.internal; - - outputBlobs.resize(std::max((size_t)1, outShapes.size())); //layer produce at least one output blob - internalBlobs.resize(internalShapes.size()); - - CV_Assert(ld.requiredOutputs.size() <= outShapes.size()); - - // Check that layer could work in-place. - bool inPlace = false; - if (layerShapes.supportInPlace) - { - if (ld.inputBlobs.size() == 1) - { - // Get number of references to the input memory. - int numRef = numReferences(ld.inputBlobsId[0]); - // If current layer is one and only customer of this blob. - inPlace = numRef == 1; - } - } - - ShapesVec shapes(outShapes); - shapes.insert(shapes.end(), internalShapes.begin(), internalShapes.end()); - std::vector blobs; - for(int i = 0; i < outputBlobs.size(); i++) - { - blobs.push_back(&outputBlobs[i]); - } - - for(int i = 0; i < internalBlobs.size(); i++) - { - blobs.push_back(&internalBlobs[i]); - if (total(internalShapes[i])) - { - pinsForInternalBlobs.push_back(LayerPin(ld.id, ld.outputBlobs.size() + i)); - } - } - - addReferences(pinsForInternalBlobs); - - std::map > idxSizes; - for(int i = 0; i < shapes.size(); i++) - { - idxSizes[total(shapes[i])].push_back(i); - } - - std::map >::reverse_iterator it; - for(it = idxSizes.rbegin(); it != idxSizes.rend(); it++) - { - for(int j = 0; j < it->second.size(); j++) - { - int index = it->second[j]; - if (total(shapes[index])) - { - LayerPin blobPin(ld.id, index); - if (index < outShapes.size() && inPlace) - { - CV_Assert(ld.inputBlobs[0]->total() == total(shapes[index])); - ld.outputBlobs[index] = ld.inputBlobs[0]->reshape(1, shapes[index]); - reuse(ld.inputBlobsId[0], blobPin); - } - else - reuseOrCreate(shapes[index], blobPin, *blobs[index], ld.dtype); - } - } - } - } - - // Clear internal state. Calls before an every reallocation. - void reset() - { - CV_TRACE_FUNCTION(); - - refCounter.clear(); - reuseMap.clear(); - memHosts.clear(); - } - -private: - // Register allocated memory. - void addHost(const LayerPin& lp, const Mat& mat) - { - CV_Assert(memHosts.find(lp) == memHosts.end()); - reuseMap[lp] = lp; - memHosts[lp] = mat; - } - - std::map refCounter; - // Maps pin to origin blob (for whom memory was allocated firstly). - // For origin blobs key == value. - std::map reuseMap; - std::map memHosts; -}; - -static Ptr wrapMat(int backendId, int targetId, cv::Mat& m) -{ - if (backendId == DNN_BACKEND_OPENCV) - { - if (targetId == DNN_TARGET_CPU) - return Ptr(); -#ifdef HAVE_OPENCL - else if (IS_DNN_OPENCL_TARGET(targetId)) - return OpenCLBackendWrapper::create(m); -#endif - else - CV_Error(Error::StsNotImplemented, "Unknown/unsupported target identifier"); - } - else if (backendId == DNN_BACKEND_HALIDE) - { - CV_Assert(haveHalide()); -#ifdef HAVE_HALIDE - return Ptr(new HalideBackendWrapper(targetId, m)); -#endif // HAVE_HALIDE - } - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - return Ptr(new InfEngineBackendWrapper(targetId, m)); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support"); -#endif - } - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { -#ifdef HAVE_DNN_NGRAPH - return Ptr(new NgraphBackendWrapper(targetId, m)); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph"); -#endif - } - else if (backendId == DNN_BACKEND_WEBNN) - { -#ifdef HAVE_WEBNN - return Ptr(new WebnnBackendWrapper(targetId, m)); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of WebNN"); -#endif - } - else if (backendId == DNN_BACKEND_VKCOM) - { - CV_Assert(haveVulkan()); -#ifdef HAVE_VULKAN - return Ptr(new VkComBackendWrapper(m)); -#endif // HAVE_VULKAN - } - else if (backendId == DNN_BACKEND_CUDA) - { - CV_Assert(haveCUDA()); - -#ifdef HAVE_CUDA - switch (targetId) - { - case DNN_TARGET_CUDA: - return CUDABackendWrapperFP32::create(m); - case DNN_TARGET_CUDA_FP16: - return CUDABackendWrapperFP16::create(m); - default: - CV_Assert(IS_DNN_CUDA_TARGET(targetId)); - } -#endif - } - else - CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); - return Ptr(); // TODO Error? -} - -static int g_networkId = 0; - -detail::NetImplBase::NetImplBase() - : networkId(CV_XADD(&g_networkId, 1)) - , networkDumpCounter(0) - , dumpLevel(DNN_NETWORK_DUMP) -{ - // nothing -} - -std::string detail::NetImplBase::getDumpFileNameBase() -{ - std::string dumpFileNameBase = cv::format("ocv_dnn_net_%05d_%02d", networkId, networkDumpCounter++); - return dumpFileNameBase; -} - -struct Net::Impl : public detail::NetImplBase -{ - typedef std::map LayersShapesMap; - typedef std::map MapIdToLayerData; - - Impl() - { - //allocate fake net input layer - netInputLayer = Ptr(new DataLayer()); - LayerData &inpl = layers.insert( make_pair(0, LayerData()) ).first->second; - inpl.id = 0; - netInputLayer->name = inpl.name = "_input"; - inpl.type = "__NetInputLayer__"; - inpl.layerInstance = netInputLayer; - layerNameToId.insert(std::make_pair(inpl.name, inpl.id)); - - lastLayerId = 0; - netWasAllocated = false; - netWasQuantized = false; - fusion = true; - isAsync = false; - preferableBackend = DNN_BACKEND_DEFAULT; - preferableTarget = DNN_TARGET_CPU; - skipInfEngineInit = false; - hasDynamicShapes = false; - } - - Ptr netInputLayer; - std::vector blobsToKeep; - MapIdToLayerData layers; - std::map layerNameToId; - BlobManager blobManager; - int preferableBackend; - int preferableTarget; - String halideConfigFile; - bool skipInfEngineInit; - bool hasDynamicShapes; - // Map host data to backend specific wrapper. - std::map > backendWrappers; - - int lastLayerId; - - bool netWasAllocated; - bool netWasQuantized; - bool fusion; - bool isAsync; - std::vector layersTimings; - Mat output_blob; - -#ifdef HAVE_CUDA - struct CudaInfo_t - { - CudaInfo_t(cuda4dnn::csl::CSLContext ctxt, cuda4dnn::csl::Stream d2h_stream_) - : context(std::move(ctxt)), d2h_stream(std::move(d2h_stream_)) { } - cuda4dnn::csl::CSLContext context; - cuda4dnn::csl::Stream d2h_stream; - cuda4dnn::csl::Workspace workspace; - }; - - std::unique_ptr cudaInfo; -#endif - - Ptr wrap(Mat& host) - { - if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU) - return Ptr(); - - MatShape shape(host.dims); - for (int i = 0; i < host.dims; ++i) - shape[i] = host.size[i]; - - void* data = host.data; - if (backendWrappers.find(data) != backendWrappers.end()) - { - Ptr baseBuffer = backendWrappers[data]; - if (preferableBackend == DNN_BACKEND_OPENCV) - { -#ifdef HAVE_OPENCL - CV_Assert(IS_DNN_OPENCL_TARGET(preferableTarget)); - return OpenCLBackendWrapper::create(baseBuffer, host); -#else - CV_Error(Error::StsInternal, ""); -#endif - } - else if (preferableBackend == DNN_BACKEND_HALIDE) - { - CV_Assert(haveHalide()); -#ifdef HAVE_HALIDE - return Ptr(new HalideBackendWrapper(baseBuffer, shape)); -#endif - } - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { - return wrapMat(preferableBackend, preferableTarget, host); - } - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - return wrapMat(preferableBackend, preferableTarget, host); - } - else if (preferableBackend == DNN_BACKEND_WEBNN) - { -#ifdef HAVE_WEBNN - return wrapMat(preferableBackend, preferableTarget, host); -#endif - } - else if (preferableBackend == DNN_BACKEND_VKCOM) - { - #ifdef HAVE_VULKAN - return Ptr(new VkComBackendWrapper(baseBuffer, host)); - #endif - } - else if (preferableBackend == DNN_BACKEND_CUDA) - { - CV_Assert(haveCUDA()); -#ifdef HAVE_CUDA - switch (preferableTarget) - { - case DNN_TARGET_CUDA: - return CUDABackendWrapperFP32::create(baseBuffer, shape); - case DNN_TARGET_CUDA_FP16: - return CUDABackendWrapperFP16::create(baseBuffer, shape); - default: - CV_Assert(IS_DNN_CUDA_TARGET(preferableTarget)); - } -#endif - } - else - CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); - } - - Ptr wrapper = wrapMat(preferableBackend, preferableTarget, host); - backendWrappers[data] = wrapper; - return wrapper; - } - -#ifdef HAVE_HALIDE - void compileHalide() - { - CV_TRACE_FUNCTION(); - - CV_Assert(preferableBackend == DNN_BACKEND_HALIDE); - - HalideScheduler scheduler(halideConfigFile); - std::vector< std::reference_wrapper > compileList; compileList.reserve(64); - for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - Ptr layer = ld.layerInstance; - if (layer->supportBackend(DNN_BACKEND_HALIDE) && !ld.skip) - { - CV_Assert(!ld.backendNodes[DNN_BACKEND_HALIDE].empty()); - bool scheduled = scheduler.process(ld.backendNodes[DNN_BACKEND_HALIDE]); - if (!scheduled) - { - // Use automatic scheduling provided by layer. - layer->applyHalideScheduler(ld.backendNodes[DNN_BACKEND_HALIDE], - ld.inputBlobs, ld.outputBlobs, - preferableTarget); - } - compileList.emplace_back(ld); - } - } - std::atomic progress(0); - auto fn = ([&] () -> void - { - for (;;) - { - int id = progress.fetch_add(1); - if ((size_t)id >= compileList.size()) - return; - const LayerData& ld = compileList[id].get(); - Ptr node = ld.backendNodes.find(DNN_BACKEND_HALIDE)->second; - dnn::compileHalide(ld.outputBlobs, node, preferableTarget); - } - }); - size_t num_threads = std::min(compileList.size(), (size_t)std::thread::hardware_concurrency()); - num_threads = std::max((size_t)1u, std::min((size_t)8u, num_threads)); - std::vector threads(num_threads - 1); - for (auto& t: threads) t = std::thread(fn); - fn(); // process own tasks - for (auto& t: threads) t.join(); - } -#endif - - void clear() - { - CV_TRACE_FUNCTION(); - - MapIdToLayerData::iterator it; - for (it = layers.begin(); it != layers.end(); it++) - { - if (it->second.id != 0) { - it->second.inputBlobs.clear(); - it->second.outputBlobs.clear(); - it->second.internals.clear(); - } - it->second.skip = false; - //it->second.consumers.clear(); - Ptr currLayer = it->second.layerInstance; - - if( currLayer.empty() ) - continue; - - currLayer->unsetAttached(); - } - netWasAllocated = false; - layersTimings.clear(); - } - - void setUpNet(const std::vector& blobsToKeep_ = std::vector()) - { - CV_TRACE_FUNCTION(); - - if (dumpLevel && networkDumpCounter == 0) - { - dumpNetworkToFile(); - } - - if (preferableBackend == DNN_BACKEND_DEFAULT) - preferableBackend = (Backend)PARAM_DNN_BACKEND_DEFAULT; -#ifdef HAVE_INF_ENGINE - if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE) - preferableBackend = getInferenceEngineBackendTypeParam(); -#endif - - CV_Assert(preferableBackend != DNN_BACKEND_OPENCV || - preferableTarget == DNN_TARGET_CPU || - preferableTarget == DNN_TARGET_OPENCL || - preferableTarget == DNN_TARGET_OPENCL_FP16); - CV_Assert(preferableBackend != DNN_BACKEND_HALIDE || - preferableTarget == DNN_TARGET_CPU || - preferableTarget == DNN_TARGET_OPENCL); -#ifdef HAVE_INF_ENGINE - if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || - preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - CV_Assert( - (preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) || - preferableTarget == DNN_TARGET_OPENCL || - preferableTarget == DNN_TARGET_OPENCL_FP16 || - preferableTarget == DNN_TARGET_MYRIAD || - preferableTarget == DNN_TARGET_HDDL || - preferableTarget == DNN_TARGET_FPGA - ); - } -#endif -#ifdef HAVE_WEBNN - if (preferableBackend == DNN_BACKEND_WEBNN) - { - CV_Assert(preferableTarget == DNN_TARGET_CPU || - preferableTarget == DNN_TARGET_OPENCL); - } -#endif - CV_Assert(preferableBackend != DNN_BACKEND_VKCOM || - preferableTarget == DNN_TARGET_VULKAN); - CV_Assert(preferableBackend != DNN_BACKEND_CUDA || - IS_DNN_CUDA_TARGET(preferableTarget)); - if (!netWasAllocated || this->blobsToKeep != blobsToKeep_) - { - if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) -#ifndef HAVE_OPENCL - { - CV_LOG_WARNING(NULL, "DNN: OpenCL target is not available in this OpenCV build, switching to CPU."); - preferableTarget = DNN_TARGET_CPU; - } -#else - { - if (!DNN_OPENCL_ALLOW_ALL_DEVICES) - { - // Current implementation is only valid for GPU (#11494) - if (ocl::Device::getDefault().type() != ocl::Device::TYPE_GPU) - { - CV_LOG_WARNING(NULL, "DNN: OpenCL target is not supported with current OpenCL device (tested with GPUs only), switching to CPU."); - preferableTarget = DNN_TARGET_CPU; - } - else if (preferableTarget == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel()) - { - CV_LOG_WARNING(NULL, - "DNN: OpenCL target with fp16 precision is not supported " - "with current OpenCL device (tested with Intel GPUs only), " - "switching to OpenCL with fp32 precision."); - preferableTarget = DNN_TARGET_OPENCL; - } - } - } -#endif - if (preferableBackend == DNN_BACKEND_VKCOM && !haveVulkan()) - { - preferableBackend = DNN_BACKEND_OPENCV; - preferableTarget = DNN_TARGET_CPU; - } - - if (preferableBackend == DNN_BACKEND_CUDA && !haveCUDA()) - { -#ifdef HAVE_CUDA - CV_LOG_WARNING(NULL, "unable to use CUDA backend; switching to CPU"); -#else - CV_LOG_WARNING(NULL, "DNN module was not built with CUDA backend; switching to CPU"); -#endif - preferableBackend = DNN_BACKEND_OPENCV; - preferableTarget = DNN_TARGET_CPU; - } - - clear(); - - if (hasDynamicShapes) - { - updateLayersShapes(); - } - - this->blobsToKeep = blobsToKeep_; - - allocateLayers(blobsToKeep_); - - MapIdToLayerData::iterator it = layers.find(0); - CV_Assert(it != layers.end()); - it->second.skip = netInputLayer->skip; - - initBackend(blobsToKeep_); - - if (!netWasAllocated) - { -#ifdef HAVE_HALIDE - if (preferableBackend == DNN_BACKEND_HALIDE) - compileHalide(); -#else - CV_Assert(preferableBackend != DNN_BACKEND_HALIDE); -#endif - } - - netWasAllocated = true; - - if (dumpLevel) - { - dumpNetworkToFile(); - } - } - } - - int getLayerId(const String &layerName) - { - std::map::iterator it = layerNameToId.find(layerName); - return (it != layerNameToId.end()) ? it->second : -1; - } - - int getLayerId(int id) - { - MapIdToLayerData::iterator it = layers.find(id); - return (it != layers.end()) ? id : -1; - } - - int getLayerId(DictValue &layerDesc) - { - if (layerDesc.isInt()) - return getLayerId(layerDesc.get()); - else if (layerDesc.isString()) - return getLayerId(layerDesc.get()); - - CV_Assert(layerDesc.isInt() || layerDesc.isString()); - return -1; - } - - String getLayerName(int id) - { - MapIdToLayerData::iterator it = layers.find(id); - return (it != layers.end()) ? it->second.name : "(unknown layer)"; - } - - LayerData& getLayerData(int id) - { - MapIdToLayerData::iterator it = layers.find(id); - - if (it == layers.end()) - CV_Error(Error::StsObjectNotFound, format("Layer with requested id=%d not found", id)); - - return it->second; - } - - LayerData& getLayerData(const String &layerName) - { - int id = getLayerId(layerName); - - if (id < 0) - CV_Error(Error::StsError, "Requested layer \"" + layerName + "\" not found"); - - return getLayerData(id); - } - - LayerData& getLayerData(const DictValue &layerDesc) - { - CV_Assert(layerDesc.isInt() || layerDesc.isString()); - if (layerDesc.isInt()) - return getLayerData(layerDesc.get()); - else /*if (layerDesc.isString())*/ - return getLayerData(layerDesc.get()); - } - - static void addLayerInput(LayerData &ld, int inNum, LayerPin from) - { - if ((int)ld.inputBlobsId.size() <= inNum) - { - ld.inputBlobsId.resize(inNum + 1); - } - else - { - LayerPin storedFrom = ld.inputBlobsId[inNum]; - if (storedFrom.valid() && !storedFrom.equal(from)) - CV_Error(Error::StsError, format("Input #%d of layer \"%s\" already was connected", - inNum, ld.name.c_str())); - } - - ld.inputBlobsId[inNum] = from; - } - - int resolvePinOutputName(LayerData &ld, const String &outName) - { - if (outName.empty()) - return 0; - return ld.getLayerInstance()->outputNameToIndex(outName); - } - - LayerPin getPinByAlias(const String &layerName) - { - LayerPin pin; - pin.lid = (layerName.empty()) ? 0 : getLayerId(layerName); - - if (pin.lid >= 0) - pin.oid = resolvePinOutputName(getLayerData(pin.lid), layerName); - - return pin; - } - - std::vector getLayerOutPins(const String &layerName) - { - int lid = (layerName.empty()) ? 0 : getLayerId(layerName); - - std::vector pins; - - for (int i = 0; i < layers[lid].outputBlobs.size(); i++) - { - pins.push_back(LayerPin(lid, i)); - } - - return pins; - } - - void connect(int outLayerId, int outNum, int inLayerId, int inNum) - { - CV_Assert(outLayerId < inLayerId); - LayerData &ldOut = getLayerData(outLayerId); - LayerData &ldInp = getLayerData(inLayerId); - - addLayerInput(ldInp, inNum, LayerPin(outLayerId, outNum)); - ldOut.requiredOutputs.insert(outNum); - ldOut.consumers.push_back(LayerPin(inLayerId, outNum)); - } - - void initBackend(const std::vector& blobsToKeep_) - { - CV_TRACE_FUNCTION(); - if (preferableBackend == DNN_BACKEND_OPENCV) - { - CV_Assert(preferableTarget == DNN_TARGET_CPU || IS_DNN_OPENCL_TARGET(preferableTarget)); - } - else if (preferableBackend == DNN_BACKEND_HALIDE) - initHalideBackend(); - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - initInfEngineBackend(blobsToKeep_); -#else - CV_Assert(false && "This OpenCV version is built without Inference Engine NN Builder API support"); -#endif - } - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { -#ifdef HAVE_DNN_NGRAPH - initNgraphBackend(blobsToKeep_); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph"); -#endif - } - else if (preferableBackend == DNN_BACKEND_WEBNN) - { -#ifdef HAVE_WEBNN - initWebnnBackend(blobsToKeep_); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of WebNN"); -#endif - } - else if (preferableBackend == DNN_BACKEND_VKCOM) - initVkComBackend(); - else if (preferableBackend == DNN_BACKEND_CUDA) - initCUDABackend(blobsToKeep_); - else - CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); - } - - void initHalideBackend() - { - CV_TRACE_FUNCTION(); - CV_Assert_N(preferableBackend == DNN_BACKEND_HALIDE, haveHalide()); - - // Iterator to current layer. - MapIdToLayerData::iterator it = layers.begin(); - // Iterator to base layer for fusion. In example, in case of conv+bn+relu - // it'll be a conv layer. - MapIdToLayerData::iterator baseIt = layers.begin(); - for (; it != layers.end(); it++) - { - LayerData &ldTop = it->second; - Ptr layerTop = ldTop.layerInstance; - if (!layerTop->supportBackend(preferableBackend)) - { - // Move base iterator to layer that don't support preferable - // backend to prevent fusion over layer of different backend. - baseIt = it; - continue; - } - // Try to do layers fusion. - LayerData &ldBot = baseIt->second; - Ptr layerBot = ldBot.layerInstance; - // 1. Check that bottom and top from the same backends. - if (it != layers.begin() && layerBot->supportBackend(preferableBackend)) - { - // 2. Check that current layer works in-place. - bool inPlace = ldTop.inputBlobs.size() == 1 && - ldBot.outputBlobs.size() == 1 && - ldTop.inputBlobs[0]->data == - ldBot.outputBlobs[0].data; - if (inPlace) - { - // 3. Try to attach node. - CV_Assert(!ldBot.backendNodes[preferableBackend].empty()); - Ptr fusedNode = - layerTop->tryAttach(ldBot.backendNodes[preferableBackend]); - if (!fusedNode.empty()) - { - ldTop.skip = true; - ldBot.backendNodes[preferableBackend] = fusedNode; - ldBot.outputBlobsWrappers = ldTop.outputBlobsWrappers; - continue; - } - } - } - // No layers fusion. - ldTop.skip = false; - ldTop.backendNodes[DNN_BACKEND_HALIDE] = - layerTop->initHalide(ldTop.inputBlobsWrappers); - baseIt = it; - } - } - -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - // Before launching Inference Engine graph we need to specify output blobs. - // This function requests output blobs based on inputs references of - // layers from default backend or layers from different graphs. - void addInfEngineNetOutputs(LayerData &ld) - { - CV_TRACE_FUNCTION(); - Ptr layerNet; - if (ld.backendNodes.find(preferableBackend) != ld.backendNodes.end()) - { - Ptr node = ld.backendNodes[preferableBackend]; - if (!node.empty()) - { - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty()); - layerNet = ieNode->net; - } - } - // For an every input reference we check that it belongs to one of - // the Inference Engine backend graphs. Request an output blob if it is. - // Do nothing if layer's input is from the same graph. - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr ieInpNode = inpNode.dynamicCast(); - CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty()); - if (layerNet != ieInpNode->net) - { - // layerNet is empty or nodes are from different graphs. - ieInpNode->net->addOutput(ieInpNode->layer.getName()); - } - } - } - } - - void initInfEngineBackend(const std::vector& blobsToKeep_) - { - CV_TRACE_FUNCTION(); - CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, haveInfEngine()); - MapIdToLayerData::iterator it; - Ptr net; - - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - if (ld.id == 0) - { - CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) || - (netInputLayer->outNames.size() == ld.outputBlobsWrappers.size())); - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000) - dataPtr->name = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]; -#else - dataPtr->setName(netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]); -#endif - } - } - else - { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000) - dataPtr->name = ld.name; -#else - dataPtr->setName(ld.name); -#endif - } - } - } - - if (skipInfEngineInit) - { - Ptr node = layers[lastLayerId].backendNodes[preferableBackend]; - CV_Assert(!node.empty()); - - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); - ieNode->net->reset(); - - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - if (ld.id == 0) - { - for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.inputBlobsWrappers[i]); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000) - dataPtr->name = netInputLayer->outNames[i]; -#else - dataPtr->setName(netInputLayer->outNames[i]); -#endif - } - } - else - { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000) - dataPtr->name = ld.name; -#else - dataPtr->setName(ld.name); -#endif - } - } - ieNode->net->addBlobs(ld.inputBlobsWrappers); - ieNode->net->addBlobs(ld.outputBlobsWrappers); - ld.skip = true; - } - layers[lastLayerId].skip = false; - ieNode->net->init((Target)preferableTarget); - return; - } - - // Build Inference Engine networks from sets of layers that support this - // backend. Split a whole model on several Inference Engine networks if - // some of layers are not implemented. - - bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU || - BackendRegistry::checkIETarget(DNN_TARGET_CPU); - - // Set of all input and output blobs wrappers for current network. - std::map > netBlobsWrappers; - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - if (ld.id == 0 && ld.skip) - continue; - bool fused = ld.skip; - - Ptr layer = ld.layerInstance; - if (!fused && !layer->supportBackend(preferableBackend)) - { - bool customizable = ld.id != 0 && - INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R2) && - supportsCPUFallback; - // TODO: there is a bug in Myriad plugin with custom layers shape infer. - if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) - { - for (int i = 0; customizable && i < ld.inputBlobs.size(); ++i) - { - customizable = ld.inputBlobs[i]->size[0] == 1; - } - } - - // TODO: fix these workarounds - if (preferableTarget == DNN_TARGET_MYRIAD || - preferableTarget == DNN_TARGET_HDDL || - preferableTarget == DNN_TARGET_OPENCL || - preferableTarget == DNN_TARGET_OPENCL_FP16) - customizable &= ld.type != "Concat"; - - if (preferableTarget == DNN_TARGET_OPENCL || - preferableTarget == DNN_TARGET_OPENCL_FP16) - customizable &= ld.type != "Power"; - - if (preferableTarget == DNN_TARGET_OPENCL) - customizable &= ld.type != "Eltwise"; - - if (!customizable) - { - addInfEngineNetOutputs(ld); - net = Ptr(); - netBlobsWrappers.clear(); // Is not used for R5 release but we don't wrap it to #ifdef. - layer->preferableTarget = DNN_TARGET_CPU; - continue; - } - } - ld.skip = true; // Initially skip all Inference Engine supported layers. - - // Create a new network if one of inputs from different Inference Engine graph. - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr ieInpNode = inpNode.dynamicCast(); - CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty()); - if (ieInpNode->net != net) - { - net = Ptr(); - netBlobsWrappers.clear(); // Is not used for R5 release but we don't wrap it to #ifdef. - break; - } - } - } - - Ptr node; - if (!net.empty()) - { - if (fused) - { - bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 && - ld.inputBlobs[0]->data == ld.outputBlobs[0].data; - CV_Assert(inPlace); - node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend]; - ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers; - } - } - else - net = Ptr(new InfEngineBackendNet()); - - if (!fused) - { - if (layer->supportBackend(preferableBackend)) - node = layer->initInfEngine(ld.inputBlobsWrappers); - else - { - node = Ptr(new InfEngineBackendNode( - ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals)); - } - } - else if (node.empty()) - continue; - - CV_Assert(!node.empty()); - ld.backendNodes[preferableBackend] = node; - - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); - ieNode->net = net; - - for (const auto& pin : blobsToKeep_) - { - if (pin.lid == ld.id) - { - ieNode->net->addOutput(ieNode->layer.getName()); - break; - } - } - - // Convert weights in FP16 for specific targets. - if ((preferableTarget == DNN_TARGET_OPENCL_FP16 || - preferableTarget == DNN_TARGET_MYRIAD || - preferableTarget == DNN_TARGET_HDDL || - preferableTarget == DNN_TARGET_FPGA) && !fused) - { -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) - for (const std::string& name : {"weights", "biases"}) - { - auto it = ieNode->layer.getParameters().find(name); - if (it != ieNode->layer.getParameters().end()) - { - InferenceEngine::Blob::Ptr bp = it->second.as(); - it->second = convertFp16(std::const_pointer_cast(bp)); - } - } -#else - auto& blobs = ieNode->layer.getConstantData(); - if (blobs.empty()) - { - // In case of non weightable layer we have to specify - // it's precision adding dummy blob. - auto blob = InferenceEngine::make_shared_blob( - InferenceEngine::Precision::FP16, - InferenceEngine::Layout::C, {1}); - blob->allocate(); - blobs[""] = blob; - } - else - { - for (auto& it : blobs) - it.second = convertFp16(std::const_pointer_cast(it.second)); - } -#endif - } - - if (!fused) - net->addLayer(ieNode->layer); - - net->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers, ieNode->layer.getName()); - net->addBlobs(ld.inputBlobsWrappers); - net->addBlobs(ld.outputBlobsWrappers); - addInfEngineNetOutputs(ld); - } - - // Initialize all networks. - for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) - { - LayerData &ld = it->second; - if (ld.backendNodes.find(preferableBackend) == ld.backendNodes.end()) - continue; - - Ptr node = ld.backendNodes[preferableBackend]; - if (node.empty()) - continue; - - Ptr ieNode = node.dynamicCast(); - if (ieNode.empty()) - continue; - - CV_Assert(!ieNode->net.empty()); - - if (!ieNode->net->isInitialized()) - { - ieNode->net->init((Target)preferableTarget); - ld.skip = false; - } - } - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - -#ifdef HAVE_DNN_NGRAPH - void addNgraphOutputs(LayerData &ld) - { - CV_TRACE_FUNCTION(); - - Ptr layerNet; - auto it = ld.backendNodes.find(preferableBackend); - if (it != ld.backendNodes.end()) - { - Ptr node = it->second; - if (!node.empty()) - { - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty()); - layerNet = ieNode->net; - } - } - - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr ieInpNode = inpNode.dynamicCast(); - CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty()); - if (layerNet != ieInpNode->net) - { - ieInpNode->net->addOutput(ieInpNode->node->get_friendly_name()); - ieInpNode->net->setUnconnectedNodes(ieInpNode); - } - } - } - } - - void initNgraphBackend(const std::vector& blobsToKeep_) - { - CV_TRACE_FUNCTION(); - CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, haveInfEngine()); - - MapIdToLayerData::iterator it; - Ptr net; - - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - if (ld.id == 0) - { - CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) || - (netInputLayer->outNames.size() == ld.outputBlobsWrappers.size())); - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); - std::string outputName = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]; - outputName = ld.outputBlobsWrappers.size() > 1 ? (outputName + "." + std::to_string(i)) : outputName; - dataPtr->setName(outputName); - } - } - else - { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); - std::string outputName = ld.outputBlobsWrappers.size() > 1 ? (ld.name + "." + std::to_string(i)) : ld.name; - dataPtr->setName(outputName); - } - } - } - - if (skipInfEngineInit) - { - Ptr node = layers[lastLayerId].backendNodes[preferableBackend]; - CV_Assert(!node.empty()); - - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); - - CV_Assert(ieNode->net); - InfEngineNgraphNet& ienet = *ieNode->net; - ienet.reset(); - - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - if (ld.id == 0) - { - for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.inputBlobsWrappers[i]); - dataPtr->setName(netInputLayer->outNames[i]); - } - } - else - { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - auto it = ienet.outputsDesc.find(ld.name); - if (it != ienet.outputsDesc.end()) - { - const InferenceEngine::TensorDesc& descriptor = it->second; - InferenceEngine::DataPtr dataPtr = ngraphDataOutputNode(ld.outputBlobsWrappers[i], descriptor, ld.name); - dataPtr->setName(ld.name); - } - else - { - InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); - dataPtr->setName(ld.name); - } - } - } - ienet.addBlobs(ld.inputBlobsWrappers); - ienet.addBlobs(ld.outputBlobsWrappers); - ld.skip = true; - } - layers[lastLayerId].skip = false; - ienet.init((Target)preferableTarget); - return; - } - - bool supportsCPUFallback = !isArmComputePlugin() && (preferableTarget == DNN_TARGET_CPU || - BackendRegistry::checkIETarget(DNN_TARGET_CPU)); - - // Build Inference Engine networks from sets of layers that support this - // backend. Split a whole model on several Inference Engine networks if - // some of layers are not implemented. - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - - if (ld.id == 0 && ld.skip) - continue; - - bool fused = ld.skip; - Ptr layer = ld.layerInstance; - if (!fused && !layer->supportBackend(preferableBackend)) - { - bool customizable = ld.id != 0 && supportsCPUFallback; - - // TODO: there is a bug in Myriad plugin with custom layers shape infer. - if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) - { - for (int i = 0; customizable && i < ld.inputBlobs.size(); ++i) - { - customizable = ld.inputBlobs[i]->size[0] == 1; - } - } - - // TODO: fix these workarounds - if (preferableTarget == DNN_TARGET_MYRIAD || - preferableTarget == DNN_TARGET_HDDL || - preferableTarget == DNN_TARGET_OPENCL || - preferableTarget == DNN_TARGET_OPENCL_FP16) - customizable &= ld.type != "Concat"; - - if (preferableTarget == DNN_TARGET_OPENCL || - preferableTarget == DNN_TARGET_OPENCL_FP16) - customizable &= ld.type != "Power"; - - if (preferableTarget == DNN_TARGET_OPENCL) - customizable &= ld.type != "Eltwise"; - - if (!customizable) - { - addNgraphOutputs(ld); - net = Ptr(); - layer->preferableTarget = DNN_TARGET_CPU; - - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) { - Ptr ieNode = inpNode.dynamicCast(); - CV_Assert(!ieNode.empty()); - ieNode->net->setUnconnectedNodes(ieNode); - } - } - continue; - } - } - ld.skip = true; // Initially skip all Inference Engine supported layers. - - // Create a new network if one of inputs from different Inference Engine graph. - std::vector> inputNodes; - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - // Layer_Test_ROIPooling.Accuracy has 2 inputs inpLD = 0, 0 -> has 4 inputNodes (input, rois, input, rois) - if (inputNodes.size() == ld.inputBlobsId.size()) { - break; - } - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr ieInpNode = inpNode.dynamicCast(); - CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty()); - if (ieInpNode->net == net && !fused) { - inputNodes.push_back(inpNode); - continue; - } - } - - if (net.empty()) { - net = Ptr(new InfEngineNgraphNet(*this)); - } - - if (!fused) { - std::vector inputNames; - std::vector inputs; - - auto curr_pos = inpLd.consumers.begin(); - auto compare = [&ld] (const LayerPin& lp) { return lp.lid == ld.id; }; - auto cons = curr_pos; - while ((cons = std::find_if(curr_pos, inpLd.consumers.end(), compare)) != - inpLd.consumers.end()) { - int cons_inp = cons->oid; - Ptr inpWrapper = inpLd.outputBlobsWrappers[cons_inp]. - dynamicCast(); - CV_Assert(!inpWrapper.empty()); - auto iter = std::find(inputNames.begin(), inputNames.end(), - inpWrapper->dataPtr->getName()); - if (iter == inputNames.end()) { - inputNames.push_back(inpWrapper->dataPtr->getName()); - inputs.push_back(inpLd.outputBlobs[cons_inp]); - } - curr_pos = cons + 1; - } - - auto inps = net->setInputs(inputs, inputNames); - for (auto& inp : inps) { - inputNodes.emplace_back(Ptr(new InfEngineNgraphNode(inp))); - } - } - } - - Ptr node; - if (!net.empty()) - { - if (fused) - { - bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 && - ld.inputBlobs[0]->data == ld.outputBlobs[0].data; - CV_Assert(inPlace); - node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend]; - ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers; - } - } - else { - net = Ptr(new InfEngineNgraphNet(*this)); - } - - if (!fused) - { - CV_Assert(ld.inputBlobsId.size() == inputNodes.size()); - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - int lid = ld.inputBlobsId[i].lid; - int oid = ld.inputBlobsId[i].oid; - if (oid == 0 || lid == 0) - continue; - - auto ieInpNode = inputNodes[i].dynamicCast(); - CV_Assert(oid < ieInpNode->node->get_output_size()); -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) - inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node)); -#elif INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3) - inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid))); -#else - inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false))); -#endif - } - - if (layer->supportBackend(preferableBackend)) - { - node = layer->initNgraph(ld.inputBlobsWrappers, inputNodes); - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); - node.dynamicCast()->setName(dataPtr->getName()); - } - } - else - { - node = Ptr(new InfEngineNgraphNode(inputNodes, - ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals)); - } - } - else if (node.empty()) - continue; - - ld.backendNodes[preferableBackend] = node; - - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); - ieNode->net = net; - - if (ld.consumers.empty()) { - // TF EAST_text_detection - ieNode->net->setUnconnectedNodes(ieNode); - } - for (const auto& pin : blobsToKeep_) - { - if (pin.lid == ld.id) - { - ieNode->net->addOutput(ieNode->node->get_friendly_name()); - break; - } - } - ieNode->net->setNodePtr(&ieNode->node); - - net->addBlobs(ld.inputBlobsWrappers); - net->addBlobs(ld.outputBlobsWrappers); - addNgraphOutputs(ld); - } - - // Initialize all networks. - for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) - { - LayerData &ld = it->second; - auto iter = ld.backendNodes.find(preferableBackend); - if (iter == ld.backendNodes.end()) - continue; - - Ptr& node = iter->second; - if (node.empty()) - continue; - - Ptr ieNode = node.dynamicCast(); - if (ieNode.empty()) - continue; - - CV_Assert(!ieNode->net.empty()); - - if (!ieNode->net->isInitialized()) - { - ieNode->net->setUnconnectedNodes(ieNode); - ieNode->net->createNet((Target)preferableTarget); - ld.skip = false; - } - } - } -#endif // HAVE_DNN_NGRAPH - -#ifdef HAVE_WEBNN - void addWebnnOutputs(LayerData &ld) - { - CV_TRACE_FUNCTION(); - - Ptr layerNet; - auto it = ld.backendNodes.find(preferableBackend); - if (it != ld.backendNodes.end()) - { - Ptr node = it->second; - if (!node.empty()) - { - Ptr webnnNode = node.dynamicCast(); - CV_Assert(!webnnNode.empty()); CV_Assert(!webnnNode->net.empty()); - layerNet = webnnNode->net; - } - } - - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr webnnInpNode = inpNode.dynamicCast(); - CV_Assert(!webnnInpNode.empty()); CV_Assert(!webnnInpNode->net.empty()); - if (layerNet != webnnInpNode->net) - { - webnnInpNode->net->addOutput(webnnInpNode->name); - webnnInpNode->net->setUnconnectedNodes(webnnInpNode); - } - } - } - } - - void initWebnnBackend(const std::vector& blobsToKeep_) - { - CV_TRACE_FUNCTION(); - CV_Assert_N(preferableBackend == DNN_BACKEND_WEBNN, haveWebnn()); - - MapIdToLayerData::iterator it; - Ptr net; - - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - if (ld.id == 0) - { - CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) || - (netInputLayer->outNames.size() == ld.outputBlobsWrappers.size())); - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - Ptr wrapper = ld.outputBlobsWrappers[i].dynamicCast(); - std::string outputName = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]; - outputName = ld.outputBlobsWrappers.size() > 1 ? (outputName + "." + std::to_string(i)) : outputName; - wrapper->name = outputName; - } - } - else - { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - Ptr wrapper = ld.outputBlobsWrappers[i].dynamicCast(); - std::string outputName = ld.outputBlobsWrappers.size() > 1 ? (ld.name + "." + std::to_string(i)) : ld.name; - wrapper->name = outputName; - } - } - } - - // Build WebNN networks from sets of layers that support this - // backend. Split a whole model on several WebNN networks if - // some of layers are not implemented. - for (it = layers.begin(); it != layers.end(); ++it) - { - LayerData &ld = it->second; - - if (ld.id == 0 && ld.skip) - continue; - - bool fused = ld.skip; - Ptr layer = ld.layerInstance; - if (!fused && !layer->supportBackend(preferableBackend)) - { - // For test use. when not using WebNN, the test case will fail - // with the following code. - CV_LOG_WARNING(NULL, "Layer " + ld.type + " name " + ld.name + " is unsupported by WebNN backend."); - - addWebnnOutputs(ld); - net = Ptr(); - layer->preferableTarget = DNN_TARGET_CPU; - - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) { - Ptr webnnNode = inpNode.dynamicCast(); - CV_Assert(!webnnNode.empty()); - webnnNode->net->setUnconnectedNodes(webnnNode); - } - } - continue; - } - ld.skip = true; // Initially skip all WebNN supported layers. - - // Create a new network if one of inputs from different WebNN graph. - std::vector> inputNodes; - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - // Layer_Test_ROIPooling.Accuracy has 2 inputs inpLD = 0, 0 -> has 4 inputNodes (input, rois, input, rois) - if (inputNodes.size() == ld.inputBlobsId.size()) { - break; - } - LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; - Ptr inpNode = inpLd.backendNodes[preferableBackend]; - if (!inpNode.empty()) - { - Ptr webnnInpNode = inpNode.dynamicCast(); - CV_Assert(!webnnInpNode.empty()); CV_Assert(!webnnInpNode->net.empty()); - if (webnnInpNode->net == net && !fused) { - inputNodes.push_back(inpNode); - continue; - } - } - - if (net.empty()) { - net = Ptr(new WebnnNet()); - } - - if (!fused) { - std::vector inputNames; - std::vector inputs; - - auto curr_pos = inpLd.consumers.begin(); - auto compare = [&ld] (const LayerPin& lp) { return lp.lid == ld.id; }; - auto cons = curr_pos; - while ((cons = std::find_if(curr_pos, inpLd.consumers.end(), compare)) != - inpLd.consumers.end()) { - int cons_inp = cons->oid; - Ptr inpWrapper = inpLd.outputBlobsWrappers[cons_inp]. - dynamicCast(); - CV_Assert(!inpWrapper.empty()); - auto iter = std::find(inputNames.begin(), inputNames.end(), - inpWrapper->name); - if (iter == inputNames.end()) { - inputNames.push_back(inpWrapper->name); - inputs.push_back(inpLd.outputBlobs[cons_inp]); - } - curr_pos = cons + 1; - } - - auto inps = net->setInputs(inputs, inputNames); - for (auto& inp : inps) { - WebnnBackendNode* node = new WebnnBackendNode(inp); - node->net = net; - inputNodes.emplace_back(Ptr(node)); - } - } - } - - Ptr node; - if (!net.empty()) - { - if (fused) - { - bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 && - ld.inputBlobs[0]->data == ld.outputBlobs[0].data; - CV_Assert(inPlace); - node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend]; - ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers; - } - } - else { - net = Ptr(new WebnnNet()); - } - - if (!fused) - { - CV_Assert(ld.inputBlobsId.size() == inputNodes.size()); - for (int i = 0; i < ld.inputBlobsId.size(); ++i) - { - int lid = ld.inputBlobsId[i].lid; - int oid = ld.inputBlobsId[i].oid; - if (oid == 0 || lid == 0) - continue; - - auto webnnInpNode = inputNodes[i].dynamicCast(); - inputNodes[i] = Ptr(new WebnnBackendNode(webnnInpNode->operand)); - } - - if (layer->supportBackend(preferableBackend)) - { - if (ld.type == "Const") { - ml::Operand fake_operand; - Ptr fake_input_node = Ptr(new WebnnBackendNode(fake_operand)); - fake_input_node->net = net; - inputNodes.push_back(fake_input_node); - } - node = layer->initWebnn(ld.inputBlobsWrappers, inputNodes); - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - Ptr wrapper = ld.outputBlobsWrappers[i].dynamicCast(); - node.dynamicCast()->name = wrapper->name; - } - } - else - { - continue; - } - } - else if (node.empty()) - continue; - - ld.backendNodes[preferableBackend] = node; - - Ptr webnnNode = node.dynamicCast(); - CV_Assert(!webnnNode.empty()); - webnnNode->net = net; - - if (ld.consumers.empty()) { - // TF EAST_text_detection - webnnNode->net->setUnconnectedNodes(webnnNode); - } - for (const auto& pin : blobsToKeep_) - { - if (pin.lid == ld.id) - { - webnnNode->net->addOutput(webnnNode->name); - break; - } - } - net->addBlobs(ld.inputBlobsWrappers); - net->addBlobs(ld.outputBlobsWrappers); - addWebnnOutputs(ld); - } - - // Initialize all networks. - for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) - { - LayerData &ld = it->second; - auto iter = ld.backendNodes.find(preferableBackend); - if (iter == ld.backendNodes.end()) - continue; - - Ptr& node = iter->second; - if (node.empty()) - continue; - - Ptr webnnNode = node.dynamicCast(); - if (webnnNode.empty()) - continue; - - CV_Assert(!webnnNode->net.empty()); - - if (!webnnNode->net->isInitialized()) - { - webnnNode->net->setUnconnectedNodes(webnnNode); - webnnNode->net->createNet((Target)preferableTarget); - ld.skip = false; - } - } - } -#endif - - void initVkComBackend() - { - CV_TRACE_FUNCTION(); - CV_Assert(preferableBackend == DNN_BACKEND_VKCOM); -#ifdef HAVE_VULKAN - if (!haveVulkan()) - return; - - MapIdToLayerData::iterator it = layers.begin(); - for (; it != layers.end(); it++) - { - LayerData &ld = it->second; - Ptr layer = ld.layerInstance; - if (!layer->supportBackend(preferableBackend)) - { - continue; - } - - ld.skip = false; - - try - { - ld.backendNodes[DNN_BACKEND_VKCOM] = - layer->initVkCom(ld.inputBlobsWrappers); - } - catch (const cv::Exception& e) - { - CV_LOG_ERROR(NULL, "initVkCom failed, fallback to CPU implementation. " << e.what()); - ld.backendNodes[DNN_BACKEND_VKCOM] = Ptr(); - } - } -#endif - } - - void initCUDABackend(const std::vector& blobsToKeep_) - { - CV_Assert(haveCUDA()); - CV_Assert(preferableBackend == DNN_BACKEND_CUDA); - -#ifdef HAVE_CUDA - if (!cudaInfo) /* we need to check only once */ - cuda4dnn::checkVersions(); - - if (cuda4dnn::getDeviceCount() <= 0) - CV_Error(Error::StsError, "No CUDA capable device found."); - - if (cuda4dnn::getDevice() < 0) - CV_Error(Error::StsError, "No CUDA capable device selected."); - - if (!cuda4dnn::isDeviceCompatible()) - CV_Error(Error::GpuNotSupported, "OpenCV was not built to work with the selected device. Please check CUDA_ARCH_PTX or CUDA_ARCH_BIN in your build configuration."); - - if (preferableTarget == DNN_TARGET_CUDA_FP16 && !cuda4dnn::doesDeviceSupportFP16()) - { - CV_LOG_WARNING(NULL, "The selected CUDA device does not support FP16 target; switching to FP32 target."); - preferableTarget = DNN_TARGET_CUDA; - } - - if (!cudaInfo) - { - cuda4dnn::csl::CSLContext context; - context.stream = cuda4dnn::csl::Stream(true); - context.cublas_handle = cuda4dnn::csl::cublas::Handle(context.stream); - context.cudnn_handle = cuda4dnn::csl::cudnn::Handle(context.stream); - - auto d2h_stream = cuda4dnn::csl::Stream(true); // stream for background D2H data transfers - cudaInfo = std::unique_ptr(new CudaInfo_t(std::move(context), std::move(d2h_stream))); - } - - cudaInfo->workspace = cuda4dnn::csl::Workspace(); // release workspace memory if any - - for (auto& layer : layers) - { - auto& ld = layer.second; - if (ld.id == 0) - { - for (auto& wrapper : ld.inputBlobsWrappers) - { - auto cudaWrapper = wrapper.dynamicCast(); - cudaWrapper->setStream(cudaInfo->context.stream, cudaInfo->d2h_stream); - } - } - - for (auto& wrapper : ld.outputBlobsWrappers) - { - auto cudaWrapper = wrapper.dynamicCast(); - cudaWrapper->setStream(cudaInfo->context.stream, cudaInfo->d2h_stream); - } - } - - for (auto& layer : layers) - { - auto& ld = layer.second; - auto& layerInstance = ld.layerInstance; - - if (!layerInstance->supportBackend(DNN_BACKEND_CUDA)) - { - std::ostringstream os; - os << "CUDA backend will fallback to the CPU implementation for the layer \"" << ld.name - << "\" of type " << ld.type << '\n'; - CV_LOG_INFO(NULL, os.str().c_str()); - continue; - } - - /* we make a copy so that `initCUDA` doesn't modify `cudaInfo->context` */ - auto context = cudaInfo->context; - auto node = layerInstance->initCUDA(&context, ld.inputBlobsWrappers, ld.outputBlobsWrappers); - ld.backendNodes[DNN_BACKEND_CUDA] = node; - - auto cudaNode = node.dynamicCast(); - cudaInfo->workspace.require(cudaNode->get_workspace_memory_in_bytes()); - } - - if (blobsToKeep_.size() > 1) - { - for (const auto& pin : blobsToKeep_) - { - LayerData& ld = layers[pin.lid]; - ld.cudaD2HBackgroundTransfers.push_back(pin.oid); - } - } -#endif - } - - void allocateLayer(int lid, const LayersShapesMap& layersShapes) - { - CV_TRACE_FUNCTION(); - - LayerData &ld = layers[lid]; - - //already allocated - if (ld.flag) - return; - - size_t ninputs = ld.inputBlobsId.size(); -#if 0 - printf("layer %s:", ld.name.c_str()); - for (size_t i = 0; i < ninputs; i++) - { - int inp_lid = ld.inputBlobsId[i].lid; - LayerData &inp_ld = layers[inp_lid]; - int inp_outputs = (int)inp_ld.outputBlobs.size(); - std::cout << " " << inp_ld.name << "(" << inp_outputs; - - for( int j = 0; j < inp_outputs; j++ ) - { - std::cout << (j == 0 ? ": " : ", ") << inp_ld.outputBlobs[j].size; - } - std::cout << ")"; - } - printf("\n"); -#endif - - //determine parent layers - for (size_t i = 0; i < ninputs; i++) - ld.inputLayersId.insert(ld.inputBlobsId[i].lid); - - //allocate parents - for (set::iterator i = ld.inputLayersId.begin(); i != ld.inputLayersId.end(); i++) - allocateLayer(*i, layersShapes); - - //bind inputs - if (ld.id == 0) // DataLayer - { - ninputs = netInputLayer->inputsData.size(); - ld.inputBlobsWrappers.resize(ninputs); - for (size_t i = 0; i < ninputs; i++) - ld.inputBlobsWrappers[i] = wrap(netInputLayer->inputsData[i]); - } - else - { - ld.inputBlobs.resize(ninputs); - ld.inputBlobsWrappers.resize(ninputs); - for (size_t i = 0; i < ninputs; i++) - { - LayerPin from = ld.inputBlobsId[i]; - CV_Assert(from.valid()); - CV_DbgAssert(layers.count(from.lid) && (int)layers[from.lid].outputBlobs.size() > from.oid); - ld.inputBlobs[i] = &layers[from.lid].outputBlobs[from.oid]; - ld.inputBlobsWrappers[i] = layers[from.lid].outputBlobsWrappers[from.oid]; - } - } - - LayersShapesMap::const_iterator layerShapesIt = layersShapes.find(lid); - - CV_Assert(layerShapesIt != layersShapes.end()); - - if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_OPENCL_FP16 && ld.dtype == CV_32F) - ld.dtype = CV_16S; - - std::vector pinsForInternalBlobs; - blobManager.allocateBlobsForLayer(ld, layerShapesIt->second, pinsForInternalBlobs); - ld.outputBlobsWrappers.resize(ld.outputBlobs.size()); - for (int i = 0; i < ld.outputBlobs.size(); ++i) - ld.outputBlobsWrappers[i] = wrap(ld.outputBlobs[i]); - - /* CUDA backend has its own system for internal blobs; we don't need these */ - ld.internalBlobsWrappers.resize((preferableBackend == DNN_BACKEND_CUDA) ? 0 : ld.internals.size()); - for (int i = 0; i < ld.internalBlobsWrappers.size(); ++i) - ld.internalBlobsWrappers[i] = wrap(ld.internals[i]); - - Ptr layerPtr = ld.getLayerInstance(); - { - std::vector inps(ld.inputBlobs.size()); - for (int i = 0; i < ld.inputBlobs.size(); ++i) - { - inps[i] = *ld.inputBlobs[i]; - } - layerPtr->finalize(inps, ld.outputBlobs); - layerPtr->preferableTarget = preferableTarget; -#if 0 - std::cout << "\toutputs:"; - size_t noutputs = ld.outputBlobs.size(); - for (size_t j = 0; j < noutputs; j++) - { - std::cout << (j == 0 ? " " : ", ") << ld.outputBlobs[j].size; - } - std::cout << "\n"; -#endif - } - - // After allocation of layer, we decrease counters to it's input blobs. - blobManager.releaseReferences(ld.inputBlobsId); - blobManager.releaseReferences(pinsForInternalBlobs); - - ld.flag = 1; - } - -#if 0 -#define printf_(args) printf args -#else -#define printf_(args) -#endif - - void fuseLayers(const std::vector& blobsToKeep_) - { - CV_TRACE_FUNCTION(); - - if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV && - preferableBackend != DNN_BACKEND_CUDA && - preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && - preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) - return; - - // scan through all the layers. If there is convolution layer followed by the activation layer, - // we try to embed this activation into the convolution and disable separate execution of the activation - std::set pinsToKeep(blobsToKeep_.begin(), - blobsToKeep_.end()); - MapIdToLayerData::iterator it; - for (it = layers.begin(); it != layers.end(); it++) - { - int lid = it->first; - LayerData& ld = layers[lid]; - if( ld.skip ) - { - printf_(("skipped %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str())); - continue; - } - printf_(("analyzing %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str())); - - // the optimization #1. try to fuse batch norm, scaling and/or activation layers - // with the current layer if they follow it. Normally, the are fused with the convolution layer, - // but some of them (like activation) may be fused with fully-connected, elemwise (+) and - // some other layers. - Ptr& currLayer = ld.layerInstance; - if( ld.consumers.size() == 1 && pinsToKeep.count(LayerPin(lid, 0)) == 0 ) - { - LayerData* nextData = &layers[ld.consumers[0].lid]; - LayerPin lpNext(ld.consumers[0].lid, 0); - while (nextData) - { - /* we use `tryFuse` member of convolution layer to fuse eltwise later - * it's not intended to be fused here; hence, we stop when we encounter eltwise - */ - if (preferableBackend == DNN_BACKEND_CUDA && ld.type == "Convolution" && nextData->type == "Eltwise") - break; - Ptr nextLayer = nextData->layerInstance; - if (currLayer->tryFuse(nextLayer)) - { - printf_(("\tfused with %s\n", nextLayer->name.c_str())); - nextData->skip = true; - ld.outputBlobs = layers[lpNext.lid].outputBlobs; - ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers; - if (nextData->consumers.size() == 1) - { - int nextLayerId = nextData->consumers[0].lid; - nextData = &layers[nextLayerId]; - lpNext = LayerPin(nextLayerId, 0); - } - else - { - nextData = 0; - break; - } - } - else - break; - } - - if (preferableBackend != DNN_BACKEND_OPENCV && preferableBackend != DNN_BACKEND_CUDA) - continue; // Go to the next layer. - - // TODO: OpenCL target support more fusion styles. - if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) && - (!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" && - ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" && - ld.layerInstance->type != "Concat")) ) - continue; - - if (preferableBackend == DNN_BACKEND_CUDA && IS_DNN_CUDA_TARGET(preferableTarget) - && ld.layerInstance->type != "Convolution" - && ld.layerInstance->type != "Concat") - continue; - - while (nextData) - { - // For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh - if (IS_DNN_OPENCL_TARGET(preferableTarget) && - nextData->type != "ReLU" && - nextData->type != "ChannelsPReLU" && - nextData->type != "ReLU6" && - nextData->type != "TanH" && - nextData->type != "Power") - break; - - Ptr nextActivLayer = nextData->layerInstance.dynamicCast(); - if (nextActivLayer.empty()) - break; - - if (currLayer->setActivation(nextActivLayer)) - { - printf_(("\tfused with %s\n", nextActivLayer->name.c_str())); - nextData->skip = true; - ld.outputBlobs = layers[lpNext.lid].outputBlobs; - ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers; - if (nextData->consumers.size() == 1) - { - int nextLayerId = nextData->consumers[0].lid; - nextData = &layers[nextLayerId]; - lpNext = LayerPin(nextLayerId, 0); - } - else - { - nextData = 0; - break; - } - } - else - break; - } - - // OpenCL: fuse convolution layer followed by eltwise + relu - // CUDA: fuse convolution layer followed by eltwise (and optional activation) - while (nextData && - (IS_DNN_OPENCL_TARGET(preferableTarget) || IS_DNN_CUDA_TARGET(preferableTarget)) && - ld.layerInstance->type == "Convolution" - ) // semantic of 'if' - { - Ptr nextEltwiseLayer = nextData->layerInstance.dynamicCast(); - if (nextEltwiseLayer.empty()) - break; - -#ifdef HAVE_CUDA - // CUDA backend supports fusion with eltwise sum (without variable channels) - if (IS_DNN_CUDA_TARGET(preferableTarget) && !nextEltwiseLayer.empty()) - { - // we create a temporary backend node for eltwise layer to obtain the eltwise configuration - cuda4dnn::csl::CSLContext context; // assume that initCUDA and EltwiseOp do not use the context during init - const auto node = nextData->layerInstance->initCUDA(&context, nextData->inputBlobsWrappers, nextData->outputBlobsWrappers); - auto eltwiseNode = node.dynamicCast(); - - // broadcasting not supported in fused ops - auto required_shape = shape(nextData->outputBlobs[0]); - for (int i = 0; i < nextData->inputBlobs.size(); i++) - { - if (shape(*nextData->inputBlobs[i]) != required_shape) - { - eltwiseNode.reset(); - break; - } - } - - // CUDA backend uses EltwiseOp when all operands have the same number of channels; otherwise, ShortcutOp is used. - // Hence, a successful cast to EltwiseOp implies that the number of channels is same in all operand tensors. - if (eltwiseNode.empty() || eltwiseNode->op != cuda4dnn::EltwiseOpType::SUM || !eltwiseNode->coeffs.empty()) - break; - } -#endif - - if (IS_DNN_OPENCL_TARGET(preferableTarget) && pinsToKeep.count(lpNext) != 0) - break; - if (nextData->inputBlobsId.size() != 2) - break; - - if (IS_DNN_OPENCL_TARGET(preferableTarget)) - { - if (!nextData->params.has("operation") || toLowerCase(nextData->params.get("operation")) == "sum") - { - if (nextData->params.has("coeff")) - { - DictValue paramCoeff = nextData->params.get("coeff"); - int n = paramCoeff.size(); - bool isCoeffOneOne = (n == 2); - for (int i = 0; isCoeffOneOne && i < n; i++) - { - float c = paramCoeff.get(i); - isCoeffOneOne &= (c == 1.0f); - } - if (!isCoeffOneOne) - { - CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion of 'Sum' without coeffs (or {1.0, 1.0}) is supported only"); - break; - } - } - } - else - { - CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion with eltwise operation is not supported: " << nextData->params.get("operation")); - break; - } - } - - { - LayerData *eltwiseData = nextData; - - // Eltwise layer has two inputs. We need to determine which - // is a base convolution layer and which could be used as it's bias. - LayerData* biasLayerData = 0; - for (int i = 0; i < 2; ++i) - { - LayerData *downLayerData = &layers[eltwiseData->inputBlobsId[i].lid]; - CV_Assert(downLayerData); - while (downLayerData->skip) - { - if (downLayerData->inputBlobsId.size() == 1) - downLayerData = &layers[downLayerData->inputBlobsId[0].lid]; - else - { - downLayerData = 0; - break; - } - } - if (downLayerData && ld.id == downLayerData->id) - { - biasLayerData = &layers[eltwiseData->inputBlobsId[1 - i].lid]; - break; - } - } - CV_Assert(biasLayerData); - { - // fuse eltwise + activation layer - // bias must already be computed to fuse => bias layer must appear before convolution - if (biasLayerData->id < ld.id) - { - /* we can fuse activation if: - * => activation layer that follows is the only consumer of eltwise output - * => activation layer does not process multiple inputs - * => we do not require to keep the output of eltwise - */ - Ptr nextFusabeleActivLayer; - if (eltwiseData->consumers.size() == 1 && pinsToKeep.count(lpNext) == 0) - { - nextData = &layers[eltwiseData->consumers[0].lid]; - lpNext = LayerPin(eltwiseData->consumers[0].lid, 0); - CV_Assert(nextData); - if (nextData->outputBlobs.size() == 1) - nextFusabeleActivLayer = nextData->layerInstance.dynamicCast(); - } - else - { - // OCL backend cannot fuse in this case but the CUDA backend can continue with just eltwise - nextData = 0; - } - - // the requirements of OCV OpenCL backend and CUDA backend are different - // we need to check them separately; hence, the fuse variables - bool fuse_eltwise = false, fuse_activation = false; - - Ptr activ_power; - if (IS_DNN_OPENCL_TARGET(preferableTarget) && !nextFusabeleActivLayer.empty() && - nextData && - (!nextData->type.compare("ReLU") || - !nextData->type.compare("ChannelsPReLU") || - (!nextData->type.compare("Power") && (activ_power = nextFusabeleActivLayer.dynamicCast()) && activ_power->scale == 1.0f) - ) && - currLayer->setActivation(nextFusabeleActivLayer)) - { - fuse_eltwise = true; - fuse_activation = true; - } - - if (IS_DNN_CUDA_TARGET(preferableTarget)) - { - /* supported fusion options: - * => convolution + eltwise - * => activation(convolution) + eltwise - * > convolution + activation would have been fused already; we have to fuse eltwise - * => activation(convolution + eltwise) - * > fuse eltwise and then activation - */ - auto layer = nextEltwiseLayer.staticCast(); - if (currLayer->tryFuse(layer)) - { - fuse_eltwise = true; /* eltwise was successfully fused */ - if (!nextFusabeleActivLayer.empty() && nextData) - { - if ((!nextData->type.compare("ReLU") || - !nextData->type.compare("ReLU6") || - !nextData->type.compare("Power") || - !nextData->type.compare("TanH") || - !nextData->type.compare("Sigmoid") || - !nextData->type.compare("Swish") || - !nextData->type.compare("Mish")) && - currLayer->setActivation(nextFusabeleActivLayer)) - { - // activation was fused - fuse_activation = true; - } - } - } - } - - CV_Assert(!fuse_activation || fuse_eltwise); /* cannot fuse activation without eltwise */ - if(fuse_eltwise && fuse_activation) - { - CV_Assert(nextData); - CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1); - ld.inputBlobsWrappers.push_back(biasLayerData->outputBlobsWrappers[0]); - printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str())); - printf_(("\tfused with %s\n", nextFusabeleActivLayer->name.c_str())); - eltwiseData->skip = true; - nextData->skip = true; - // This optimization for cases like - // some_layer conv - // | | - // +-- eltwise --+ - // | - // activ - // This way all the element-wise computations - // (i.e. some_layer+conv or some_layer*conv) - // would be done at [conv] layer. So we need to - // replace [conv]'s output blob to [eltwise]'s one - // considering that [activ] is an in-place layer. - // Also we need to move all the consumers' references. - // To prevent memory collisions (i.e. when input of - // [conv] and output of [eltwise] is the same blob) - // we allocate a new blob. - CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1); - ld.outputBlobs[0] = ld.outputBlobs[0].clone(); - ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]); - - eltwiseData->outputBlobs = ld.outputBlobs; - nextData->outputBlobs = ld.outputBlobs; - eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers; - nextData->outputBlobsWrappers = ld.outputBlobsWrappers; - - // Move references of [activ] layer consumers to the newly allocated blob. - for (int i = 0; i < nextData->consumers.size(); ++i) - { - LayerData& consumer = layers[nextData->consumers[i].lid]; - for (int j = 0; j < consumer.inputBlobsId.size(); ++j) - { - if (consumer.inputBlobsId[j].lid == lpNext.lid) - { - consumer.inputBlobs[j] = &ld.outputBlobs[0]; - consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0]; - break; - } - } - } - } - else if (fuse_eltwise) // conv + eltwise (note: conv could have fused activations before eltwise) - { - CV_Assert(IS_DNN_CUDA_TARGET(preferableTarget)); - CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1); - ld.inputBlobsWrappers.push_back(biasLayerData->outputBlobsWrappers[0]); - printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str())); - eltwiseData->skip = true; - // This optimization is for cases like - // some_layer conv (maybe fused with activ) - // | | - // +-- eltwise --+ - // - // This way all the element-wise computations - // (i.e. some_layer+conv or some_layer*conv) - // would be done at [conv] layer. So we need to - // replace [conv]'s output blob to [eltwise]'s one. - // Also we need to move all the consumers' references. - // To prevent memory collisions (i.e. when input of - // [conv] and output of [eltwise] is the same blob) - // we allocate a new blob. - CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1); - ld.outputBlobs[0] = ld.outputBlobs[0].clone(); - ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]); - - eltwiseData->outputBlobs = ld.outputBlobs; - eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers; - - // Move references of [eltwise] layer consumers to the newly allocated blob. - for (int i = 0; i < eltwiseData->consumers.size(); ++i) - { - LayerData& consumer = layers[eltwiseData->consumers[i].lid]; - for (int j = 0; j < consumer.inputBlobsId.size(); ++j) - { - if (consumer.inputBlobsId[j].lid == eltwiseData->id) - { - consumer.inputBlobs[j] = &ld.outputBlobs[0]; - consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0]; - break; - } - } - } - } - } - } - } - - break; - } - } - - if (preferableBackend != DNN_BACKEND_OPENCV && preferableBackend != DNN_BACKEND_CUDA) - continue; // Go to the next layer. - - // the optimization #2. if there is concat layer that concatenates channels - // from the inputs together (i.e. axis == 1) then we make the inputs of - // the concat layer to write to the concatenation output buffer - // (and so we eliminate the concatenation layer, because the channels - // are concatenated implicitly). - Ptr concatLayer = ld.layerInstance.dynamicCast(); - if( !concatLayer.empty() && !concatLayer->padding && ld.outputBlobs.size() == 1 ) - { - Mat& output = ld.outputBlobs[0]; - UMat umat_output; -#ifdef HAVE_OPENCL - if (!ld.outputBlobsWrappers.empty() && - (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))) - { - size_t i, ninputs = ld.inputBlobsId.size(); - bool conv_layer = true; - for( i = 0; i < ninputs; i++ ) - { - LayerPin pin = ld.inputBlobsId[i]; - LayerData* inp_i_data = &layers[pin.lid]; - while(inp_i_data->skip && - inp_i_data->inputBlobsId.size() == 1 && - inp_i_data->consumers.size() == 1) - { - pin = inp_i_data->inputBlobsId[0]; - inp_i_data = &layers[pin.lid]; - } - conv_layer = conv_layer && (inp_i_data->getLayerInstance()->type == "Convolution"); - } - if (!conv_layer) - continue; - std::vector umat_outputBlobs; - umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); - umat_output = umat_outputBlobs[0]; - } -#endif - - // TODO: in general, this optimization can always be done, but - // many layers currently check that the input/output blobs are - // continuous arrays. Unfortunately, this is not true when - // the concatenation optimization is applied with batch_size > 1. - // so, for now, we only apply this optimization in the most popular - // case batch_size == 1. - int axis = normalize_axis(concatLayer->axis, output.dims); - if( output.total(0, axis) == 1 ) - { - size_t i, ninputs = ld.inputBlobsId.size(); - std::vector realinputs(ninputs); - for( i = 0; i < ninputs; i++ ) - { - LayerPin pin = ld.inputBlobsId[i]; - LayerData* inp_i_data = &layers[pin.lid]; - while(inp_i_data->skip && - inp_i_data->inputBlobsId.size() == 1 && - inp_i_data->consumers.size() == 1) - { - pin = inp_i_data->inputBlobsId[0]; - inp_i_data = &layers[pin.lid]; - } - printf_(("\treal input for %s is %s\n", - layers[ld.inputBlobsId[i].lid].getLayerInstance()->name.c_str(), - inp_i_data->getLayerInstance()->name.c_str())); - - if(inp_i_data->skip || inp_i_data->consumers.size() != 1) - break; -#ifdef HAVE_CUDA - if (preferableBackend == DNN_BACKEND_CUDA && - (inp_i_data->layerInstance->supportBackend(DNN_BACKEND_CUDA) == false || - (inp_i_data->layerInstance->type != "Convolution" && - inp_i_data->layerInstance->type != "Pooling" && - inp_i_data->layerInstance->type != "Resize" && - inp_i_data->layerInstance->type != "Flatten" && - inp_i_data->layerInstance->type != "Permute" && - inp_i_data->layerInstance->type != "Reorg" && - inp_i_data->layerInstance->type != "Eltwise" && - inp_i_data->layerInstance.dynamicCast().empty()))) - { - break; - } -#endif - realinputs[i] = pin; - } - - if( i >= ninputs ) - { - // Allocate new memory to prevent collisions during memory - // reusing (see https://github.com/opencv/opencv/pull/10456). - output = output.clone(); -#ifdef HAVE_OPENCL - if (preferableBackend == DNN_BACKEND_OPENCV && - IS_DNN_OPENCL_TARGET(preferableTarget)) - { - std::vector umats(1); - umat_output = umat_output.clone(); - umats[0] = umat_output; - OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umats); - } -#endif - -#ifdef HAVE_CUDA - if (preferableBackend == DNN_BACKEND_CUDA) - ld.outputBlobsWrappers[0] = wrap(output); -#endif - std::vector chrange(output.dims, Range::all()); - int ofs = 0; - for( i = 0; i < ninputs; i++ ) - { - LayerPin pin = realinputs[i]; - LayerData* inp_i_data = &layers[pin.lid]; - int channels_i = ld.inputBlobs[i]->size[axis]; - chrange[axis] = Range(ofs, ofs + channels_i); - printf_(("\toutput %s(%d) to channels (%d, %d)\n", inp_i_data->layerInstance->name.c_str(), - pin.oid, ofs, ofs + channels_i)); - ofs += channels_i; - Mat output_slice = output(chrange); - Mat& curr_output = inp_i_data->outputBlobs[pin.oid]; - CV_Assert(output_slice.isContinuous() && output_slice.size == curr_output.size); - Mat* oldPtr = &curr_output; - curr_output = output_slice; -#ifdef HAVE_OPENCL - if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) - { - std::vector umats(inp_i_data->outputBlobsWrappers.size()); - umats[pin.oid] = umat_output(chrange); - OpenCLBackendWrapper::update(inp_i_data->outputBlobsWrappers, umats); - } -#endif -#ifdef HAVE_CUDA - if (preferableBackend == DNN_BACKEND_CUDA) - { - auto cuda_wrapper = wrap(output).dynamicCast(); - auto offset = chrange[axis].start * output_slice.total(axis + 1, output.dims); - auto new_shape = shape(output_slice); - cuda_wrapper->update(new_shape, offset); - inp_i_data->outputBlobsWrappers[pin.oid] = cuda_wrapper.staticCast(); - } -#endif - // Layers that refer old input Mat will refer to the - // new data but the same Mat object. - CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output); - } - -#ifdef HAVE_CUDA - if (preferableBackend == DNN_BACKEND_CUDA) - { - for (int i = 0; i < ld.consumers.size(); i++) - { - LayerData& consumer = layers[ld.consumers[i].lid]; - for (int j = 0; j < consumer.inputBlobsId.size(); j++) - { - if (consumer.inputBlobsId[j].lid == ld.id) - { - CV_Assert(consumer.inputBlobs[j]->data == ld.outputBlobs[0].data); - consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0]; - break; - } - } - } - } -#endif - ld.skip = true; - printf_(("\toptimized out Concat layer %s\n", concatLayer->name.c_str())); - } - } - } - } - } - - void allocateLayers(const std::vector& blobsToKeep_) - { - CV_TRACE_FUNCTION(); - - MapIdToLayerData::iterator it; - for (it = layers.begin(); it != layers.end(); it++) - it->second.flag = 0; - - CV_Assert(!layers[0].outputBlobs.empty()); - ShapesVec inputShapes; - for(int i = 0; i < layers[0].outputBlobs.size(); i++) - { - Mat& inp = layers[0].outputBlobs[i]; - CV_Assert(inp.total()); - if (preferableBackend == DNN_BACKEND_OPENCV && - preferableTarget == DNN_TARGET_OPENCL_FP16 && - layers[0].dtype == CV_32F) - { - layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S); - } - inputShapes.push_back(shape(inp)); - } - LayersShapesMap layersShapes; - getLayersShapes(inputShapes, layersShapes); - - blobManager.reset(); - backendWrappers.clear(); - - for(auto& layer : layers) - { - auto& ld = layer.second; - ld.inputBlobsWrappers.clear(); - ld.outputBlobsWrappers.clear(); - ld.internalBlobsWrappers.clear(); - } - - // Fake references to input blobs. - for (int i = 0; i < layers[0].outputBlobs.size(); ++i) - blobManager.addReference(LayerPin(0, i)); - for (it = layers.begin(); it != layers.end(); ++it) - { - const LayerData& ld = it->second; - blobManager.addReferences(ld.inputBlobsId); - } - - for (int i = 0; i < blobsToKeep_.size(); i++) - { - blobManager.addReference(blobsToKeep_[i]); - } - - for (it = layers.begin(); it != layers.end(); it++) - { - int lid = it->first; - allocateLayer(lid, layersShapes); - } - - layersTimings.resize(lastLayerId + 1, 0); - fuseLayers(blobsToKeep_); - } - - void forwardLayer(LayerData &ld) - { - CV_TRACE_FUNCTION(); - - Ptr layer = ld.layerInstance; - - if( !ld.skip ) - { - TickMeter tm; - tm.start(); - - std::map >::iterator it = ld.backendNodes.find(preferableBackend); - if (preferableBackend == DNN_BACKEND_OPENCV || it == ld.backendNodes.end() || it->second.empty()) - { - if (isAsync) - CV_Error(Error::StsNotImplemented, "Default implementation fallbacks in asynchronous mode"); - - if (!layer->supportBackend(DNN_BACKEND_OPENCV)) - CV_Error(Error::StsNotImplemented, format("Layer \"%s\" of type \"%s\" unsupported on OpenCV backend", - ld.name.c_str(), ld.type.c_str())); - -#ifdef HAVE_OPENCL - if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) - { - std::vector umat_inputBlobs = OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers); - std::vector umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); - std::vector umat_internalBlobs = OpenCLBackendWrapper::getUMatVector(ld.internalBlobsWrappers); - layer->forward(umat_inputBlobs, - umat_outputBlobs, - umat_internalBlobs); - if (DNN_CHECK_NAN_INF) - { - bool fail = false; - for (size_t i = 0; i < umat_outputBlobs.size(); ++i) - { - UMat& u = umat_outputBlobs[i]; - Mat m; - if (u.depth() == CV_16S) // FP16 - convertFp16(u, m); - else - m = u.getMat(ACCESS_READ); - if (!checkRange(m)) - { - std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; - std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; - fail = true; - } - else if (!checkRange(m, true, NULL, -1e6, 1e6)) - { - std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; - std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; - fail = true; - } - } - if (fail) - { - for (size_t i = 0; i < umat_inputBlobs.size(); ++i) - { - UMat& u = umat_inputBlobs[i]; - Mat m; - if (u.depth() == CV_16S) // FP16 - convertFp16(u, m); - else - m = u.getMat(ACCESS_READ); - std::cout << "INPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl; - if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl; - } - for (size_t i = 0; i < umat_outputBlobs.size(); ++i) - { - UMat& u = umat_outputBlobs[i]; - Mat m; - if (u.depth() == CV_16S) // FP16 - convertFp16(u, m); - else - m = u.getMat(ACCESS_READ); - std::cout << "OUTPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl; - if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl; - } - for (size_t i = 0; i < umat_internalBlobs.size(); ++i) - { - UMat& u = umat_internalBlobs[i]; - Mat m; - if (u.depth() == CV_16S) // FP16 - convertFp16(u, m); - else - m = u.getMat(ACCESS_READ); - std::cout << "INTERNAL " << i << " " << shape(m) << std::endl; - if (DNN_CHECK_NAN_INF_DUMP) std::cout << cv::typeToString(u.type()) << " " << m.reshape(1, 1) << std::endl; - } - if (DNN_CHECK_NAN_INF_RAISE_ERROR) - CV_Assert(!fail); - } - } - OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umat_outputBlobs); - } - else -#endif - { - for (int i = 0, n = ld.inputBlobsWrappers.size(); i < n; ++i) - { - if (!ld.inputBlobsWrappers[i].empty()) - ld.inputBlobsWrappers[i]->copyToHost(); - } - - std::vector inps(ld.inputBlobs.size()); - for (int i = 0; i < ld.inputBlobs.size(); ++i) - { - inps[i] = *ld.inputBlobs[i]; - } - layer->forward(inps, ld.outputBlobs, ld.internals); - - if (DNN_CHECK_NAN_INF) - { - bool fail = false; - for (size_t i = 0; i < ld.outputBlobs.size(); ++i) - { - const Mat& m = ld.outputBlobs[i]; - if (!checkRange(m)) - { - std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; - std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; - fail = true; - } - else if (!checkRange(m, true, NULL, -1e6, 1e6)) - { - std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; - std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; - fail = true; - } - } - if (fail) - { - for (size_t i = 0; i < ld.inputBlobs.size(); ++i) - { - const Mat* pM = ld.inputBlobs[i]; - if (!pM) - { - std::cout << "INPUT " << i << " is NULL" << std::endl; - continue; - } - const Mat& m = *pM; - std::cout << "INPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl; - if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl; - } - for (size_t i = 0; i < ld.outputBlobs.size(); ++i) - { - const Mat& m = ld.outputBlobs[i]; - std::cout << "OUTPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl; - if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl; - } - for (size_t i = 0; i < ld.internals.size(); ++i) - { - const Mat& m = ld.internals[i]; - std::cout << "INTERNAL " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl; - if (DNN_CHECK_NAN_INF_DUMP) std::cout << m.reshape(1, 1) << std::endl; - } - if (DNN_CHECK_NAN_INF_RAISE_ERROR) - CV_Assert(!fail); - } - } - - for (int i = 0, n = ld.outputBlobsWrappers.size(); i < n; ++i) - { - if (!ld.outputBlobsWrappers[i].empty()) - ld.outputBlobsWrappers[i]->setHostDirty(); - } - } - } - else - { - Ptr node = it->second; - CV_Assert(!node.empty()); - if (preferableBackend == DNN_BACKEND_CUDA) - { - CV_Assert(haveCUDA()); - -#ifdef HAVE_CUDA - Ptr cudaNode = node.dynamicCast(); - CV_Assert(!cudaNode.empty()); - - cudaNode->forward(ld.inputBlobsWrappers, ld.outputBlobsWrappers, cudaInfo->workspace); - - for (auto id : ld.cudaD2HBackgroundTransfers) - { - auto wrapper = ld.outputBlobsWrappers[id].dynamicCast(); - wrapper->copyToHostInBackground(); - } -#endif - } - else if (preferableBackend == DNN_BACKEND_HALIDE) - { - forwardHalide(ld.outputBlobsWrappers, node); - } - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { - forwardInfEngine(ld.outputBlobsWrappers, node, isAsync); - } - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - forwardNgraph(ld.outputBlobsWrappers, node, isAsync); - } - else if (preferableBackend == DNN_BACKEND_WEBNN) - { - forwardWebnn(ld.outputBlobsWrappers, node, isAsync); - } - else if (preferableBackend == DNN_BACKEND_VKCOM) - { - try - { - forwardVkCom(ld.outputBlobsWrappers, node); - } - catch (const cv::Exception& e) - { - CV_LOG_ERROR(NULL, "forwardVkCom failed, fallback to CPU implementation. " << e.what()); - it->second = Ptr(); - forwardLayer(ld); - } - } - else - { - CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); - } - } - - tm.stop(); - int64 t = tm.getTimeTicks(); - layersTimings[ld.id] = (t > 0) ? t : t + 1; // zero for skipped layers only - } - else - { - layersTimings[ld.id] = 0; - } - - ld.flag = 1; - } - - void forwardToLayer(LayerData &ld, bool clearFlags = true) - { - CV_TRACE_FUNCTION(); - - if (clearFlags) - { - MapIdToLayerData::iterator it; - for (it = layers.begin(); it != layers.end(); it++) - it->second.flag = 0; - } - - //already was forwarded - if (ld.flag) - return; - - //forward parents - MapIdToLayerData::iterator it; - for (it = layers.begin(); it != layers.end() && (it->second.id < ld.id); ++it) - { - LayerData &ld = it->second; - if (ld.flag) - continue; - forwardLayer(ld); - } - - //forward itself - forwardLayer(ld); - -#ifdef HAVE_CUDA - if (preferableBackend == DNN_BACKEND_CUDA) - cudaInfo->context.stream.synchronize(); -#endif - } - - void getQuantizationParams(const Mat& src, std::vector& scales, std::vector& zeropoints) - { - const int qmin = -128; // INT8_MIN - const int qmax = 127; // INT8_MAX - - double rmin, rmax, sc, zp; - cv::minMaxIdx(src, &rmin, &rmax); - - // 0 must be present in the range [rmin, rmax] - rmin = std::min(rmin, 0.0); - rmax = std::max(rmax, 0.0); - - sc = (rmax == rmin) ? 1.0 : (rmax - rmin)/(qmax - qmin); - zp = qmin - (rmin/sc); - - scales.push_back((float)sc); - zeropoints.push_back((int)std::round(zp)); - } - - void getLayerShapesRecursively(int id, LayersShapesMap& inOutShapes) - { - CV_CheckGE(id, 0, ""); - CV_CheckLT(id, (int)layers.size(), ""); - LayerData& layerData = layers[id]; - std::vector& inputLayerIds = layerData.inputBlobsId; - LayerShapes& layerShapes = inOutShapes[id]; - - if (id == 0 && layerShapes.in[0].empty()) - { - if (!layerData.outputBlobs.empty()) - { - ShapesVec shapes; - for (int i = 0; i < layerData.outputBlobs.size(); i++) - { - Mat& inp = layerData.outputBlobs[i]; - CV_Assert(!inp.empty()); - shapes.push_back(shape(inp)); - } - layerShapes.in = shapes; - } - else - { - const std::vector& inputShapes = netInputLayer->shapes; - bool none = true; - for (size_t i = 0; i < inputShapes.size(); i++) - { - if (!inputShapes[i].empty()) - { - none = false; - break; - } - } - if (none) - { - layerShapes.out.clear(); - return; - } - else - { - layerShapes.in = inputShapes; - } - } - } - - if (layerShapes.in.empty()) - { - for(int i = 0; i < inputLayerIds.size(); i++) - { - int layerId = inputLayerIds[i].lid; - LayersShapesMap::iterator it = - inOutShapes.find(layerId); - if(it == inOutShapes.end() || - it->second.out.empty()) - { - getLayerShapesRecursively(layerId, inOutShapes); - } - const MatShape& shape = inOutShapes[layerId].out[inputLayerIds[i].oid]; - layerShapes.in.push_back(shape); - } - } - const ShapesVec& is = layerShapes.in; - ShapesVec& os = layerShapes.out; - ShapesVec& ints = layerShapes.internal; - int requiredOutputs = layerData.requiredOutputs.size(); - Ptr l = layerData.getLayerInstance(); - CV_Assert(l); - bool layerSupportInPlace = false; - try - { - layerSupportInPlace = l->getMemoryShapes(is, requiredOutputs, os, ints); - } - catch (const cv::Exception& e) - { - CV_LOG_ERROR(NULL, "OPENCV/DNN: [" << l->type << "]:(" << l->name << "): getMemoryShapes() throws exception." << - " inputs=" << is.size() << - " outputs=" << os.size() << "/" << requiredOutputs << - " blobs=" << l->blobs.size()); - for (size_t i = 0; i < is.size(); ++i) - { - CV_LOG_ERROR(NULL, " input[" << i << "] = " << toString(is[i])); - } - for (size_t i = 0; i < os.size(); ++i) - { - CV_LOG_ERROR(NULL, " output[" << i << "] = " << toString(os[i])); - } - for (size_t i = 0; i < l->blobs.size(); ++i) - { - CV_LOG_ERROR(NULL, " blobs[" << i << "] = " << typeToString(l->blobs[i].type()) << " " << toString(shape(l->blobs[i]))); - } - CV_LOG_ERROR(NULL, "Exception message: " << e.what()); - throw; - } - layerShapes.supportInPlace = layerSupportInPlace; - - try - { - for (int i = 0; i < ints.size(); i++) - CV_CheckGT(total(ints[i]), 0, ""); - - for (int i = 0; i < os.size(); i++) - CV_CheckGT(total(os[i]), 0, ""); - } - catch (const cv::Exception& e) - { - CV_LOG_ERROR(NULL, "OPENCV/DNN: [" << l->type << "]:(" << l->name << "): getMemoryShapes() post validation failed." << - " inputs=" << is.size() << - " outputs=" << os.size() << "/" << requiredOutputs << - " blobs=" << l->blobs.size() << - " inplace=" << layerSupportInPlace); - for (size_t i = 0; i < is.size(); ++i) - { - CV_LOG_ERROR(NULL, " input[" << i << "] = " << toString(is[i])); - } - for (size_t i = 0; i < os.size(); ++i) - { - CV_LOG_ERROR(NULL, " output[" << i << "] = " << toString(os[i])); - } - for (size_t i = 0; i < l->blobs.size(); ++i) - { - CV_LOG_ERROR(NULL, " blobs[" << i << "] = " << typeToString(l->blobs[i].type()) << " " << toString(shape(l->blobs[i]))); - } - CV_LOG_ERROR(NULL, "Exception message: " << e.what()); - throw; - } - } - - void getLayersShapes(const ShapesVec& netInputShapes, - LayersShapesMap& inOutShapes) - { - inOutShapes.clear(); - - inOutShapes[0].in = netInputShapes; //insert shape for first input layer - for (MapIdToLayerData::iterator it = layers.begin(); - it != layers.end(); it++) - { - getLayerShapesRecursively(it->first, inOutShapes); - } - } - - void getLayerShapes(const ShapesVec& netInputShapes, - const int layerId, - LayerShapes& shapes) - { - LayersShapesMap inOutShapes; - inOutShapes[0].in = netInputShapes; //insert shape for first input layer - getLayerShapesRecursively(layerId, inOutShapes); - shapes = inOutShapes[layerId]; - } - - void updateLayersShapes() - { - CV_LOG_DEBUG(NULL, "updateLayersShapes() with layers.size=" << layers.size()); - CV_Assert(netInputLayer); - DataLayer& inputLayer = *netInputLayer; - LayerData& inputLayerData = layers[0]; - CV_Assert(inputLayerData.layerInstance.get() == &inputLayer); - CV_Assert(!inputLayerData.outputBlobs.empty()); - ShapesVec inputShapes; - for(int i = 0; i < inputLayerData.outputBlobs.size(); i++) - { - Mat& inp = inputLayerData.outputBlobs[i]; - CV_Assert(!inp.empty()); - if (preferableBackend == DNN_BACKEND_OPENCV && // FIXIT: wrong place for output allocation - preferableTarget == DNN_TARGET_OPENCL_FP16 && - inputLayerData.dtype == CV_32F) - { - inp.create(inp.dims, inp.size, CV_16S); - } - inputShapes.push_back(shape(inp)); - } - CV_LOG_DEBUG(NULL, toString(inputShapes, "Network input shapes")); - LayersShapesMap layersShapes; - layersShapes[0].in = inputShapes; - for (MapIdToLayerData::iterator it = layers.begin(); - it != layers.end(); it++) - { - int layerId = it->first; - LayerData& layerData = it->second; - std::vector& inputLayerIds = layerData.inputBlobsId; - LayerShapes& layerShapes = layersShapes[layerId]; - CV_LOG_DEBUG(NULL, "layer " << layerId << ": [" << layerData.type << "]:(" << layerData.name << ") with inputs.size=" << inputLayerIds.size()); - if (layerShapes.in.empty()) - { - for(int i = 0; i < inputLayerIds.size(); i++) - { - const LayerPin& inputPin = inputLayerIds[i]; - int inputLayerId = inputPin.lid; - CV_LOG_DEBUG(NULL, " input[" << i << "] " << inputLayerId << ":" << inputPin.oid << " as [" << layers[inputLayerId].type << "]:(" << layers[inputLayerId].name << ")"); - LayersShapesMap::iterator inputIt = layersShapes.find(inputLayerId); - if (inputIt == layersShapes.end() || inputIt->second.out.empty()) - { - getLayerShapesRecursively(inputLayerId, layersShapes); - } - const MatShape& shape = layersShapes[inputLayerId].out[inputPin.oid]; - layerShapes.in.push_back(shape); - } - layerData.getLayerInstance()->updateMemoryShapes(layerShapes.in); - } - CV_LOG_DEBUG(NULL, "Layer " << layerId << ": " << toString(layerShapes.in, "input shapes")); - CV_LOG_IF_DEBUG(NULL, !layerShapes.out.empty(), "Layer " << layerId << ": " << toString(layerShapes.out, "output shapes")); - CV_LOG_IF_DEBUG(NULL, !layerShapes.internal.empty(), "Layer " << layerId << ": " << toString(layerShapes.internal, "internal shapes")); - } - CV_LOG_DEBUG(NULL, "updateLayersShapes() - DONE"); - } - - LayerPin getLatestLayerPin(const std::vector& pins) - { - return *std::max_element(pins.begin(), pins.end()); - } - - Mat getBlob(const LayerPin& pin) - { - CV_TRACE_FUNCTION(); - - if (!pin.valid()) - CV_Error(Error::StsObjectNotFound, "Requested blob not found"); - - LayerData &ld = layers[pin.lid]; - if ((size_t)pin.oid >= ld.outputBlobs.size()) - { - CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %zu outputs, " - "the #%d was requested", ld.name.c_str(), - ld.outputBlobs.size(), pin.oid)); - } - if (preferableTarget != DNN_TARGET_CPU) - { - CV_Assert(!ld.outputBlobsWrappers.empty() && !ld.outputBlobsWrappers[pin.oid].empty()); - // Transfer data to CPU if it's require. - ld.outputBlobsWrappers[pin.oid]->copyToHost(); - } - - if (ld.outputBlobs[pin.oid].depth() == CV_16S) - { - convertFp16(ld.outputBlobs[pin.oid], output_blob); - return output_blob; - } - else - return ld.outputBlobs[pin.oid]; - } - - Mat getBlob(String outputName) - { - return getBlob(getPinByAlias(outputName)); - } - -#ifdef CV_CXX11 - AsyncArray getBlobAsync(const LayerPin& pin) - { - CV_TRACE_FUNCTION(); -#ifdef HAVE_INF_ENGINE - if (!pin.valid()) - CV_Error(Error::StsObjectNotFound, "Requested blob not found"); - - LayerData &ld = layers[pin.lid]; - if ((size_t)pin.oid >= ld.outputBlobs.size()) - { - CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %d outputs, " - "the #%d was requested", ld.name.c_str(), - (int)ld.outputBlobs.size(), (int)pin.oid)); - } - if (preferableTarget != DNN_TARGET_CPU) - { - CV_Assert(!ld.outputBlobsWrappers.empty() && !ld.outputBlobsWrappers[pin.oid].empty()); - // Transfer data to CPU if it's require. - ld.outputBlobsWrappers[pin.oid]->copyToHost(); - } - CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - - if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - Ptr wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast(); - return std::move(wrapper->futureMat); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support"); -#endif - } - else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { -#ifdef HAVE_DNN_NGRAPH - Ptr wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast(); - return std::move(wrapper->futureMat); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph"); -#endif - } -#endif // HAVE_INF_ENGINE - CV_Error(Error::StsNotImplemented, "DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 backend is required"); - } - - AsyncArray getBlobAsync(String outputName) - { - return getBlobAsync(getPinByAlias(outputName)); - } -#endif // CV_CXX11 - -#ifdef HAVE_INF_ENGINE - static - Net createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet); -#endif - - string dump(); - - void dumpNetworkToFile() - { -#ifndef OPENCV_DNN_DISABLE_NETWORK_AUTO_DUMP - string dumpFileNameBase = getDumpFileNameBase(); - string dumpFileName = dumpFileNameBase + ".dot"; - try - { - string dumpStr = dump(); - std::ofstream out(dumpFileName.c_str(), std::ios::out | std::ios::binary); - out << dumpStr; - } - catch (const std::exception& e) - { - std::ofstream out((dumpFileName + ".error").c_str(), std::ios::out); - out << "Exception: " << e.what() << std::endl; - } - catch (...) - { - std::ofstream out((dumpFileName + ".error").c_str(), std::ios::out); - out << "Can't dump: unknown exception" << std::endl; - } -#endif - } -}; - -Net::Net() : impl(new Net::Impl) -{ -} - -#ifdef HAVE_INF_ENGINE -/*static*/ -Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet) -{ - CV_TRACE_FUNCTION(); - - CV_TRACE_REGION("register_inputs"); - - std::vector inputsNames; - std::vector inp_shapes; - for (auto& it : ieNet.getInputsInfo()) - { - inputsNames.push_back(it.first); - std::vector dims = it.second->getTensorDesc().getDims(); - inp_shapes.push_back(std::vector(dims.begin(), dims.end())); - } - - Net cvNet; - cvNet.setInputsNames(inputsNames); - - // set empty input to determine input shapes - for (int inp_id = 0; inp_id < inputsNames.size(); ++inp_id) - { - cvNet.setInputShape(inputsNames[inp_id], inp_shapes[inp_id]); - } - - CV_TRACE_REGION_NEXT("backendNode"); - - Ptr backendNode; -#ifdef HAVE_DNN_NGRAPH - if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam()) - { - auto fake_node = std::make_shared(ngraph::element::f32, ngraph::Shape{}); - Ptr backendNodeNGraph(new InfEngineNgraphNode(fake_node)); - backendNodeNGraph->net = Ptr(new InfEngineNgraphNet(*(cvNet.impl), ieNet)); - backendNode = backendNodeNGraph; - } - else -#endif - { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - Ptr backendNodeNN(new InfEngineBackendNode(InferenceEngine::Builder::Layer(""))); - backendNodeNN->net = Ptr(new InfEngineBackendNet(ieNet)); - backendNode = backendNodeNN; -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support"); -#endif - } - - CV_TRACE_REGION_NEXT("register_outputs"); - -#ifdef HAVE_DNN_NGRAPH - auto ngraphFunction = ieNet.getFunction(); -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2) - std::list< std::shared_ptr > ngraphOperations; -#else - std::vector< std::shared_ptr > ngraphOperations; -#endif - if (ngraphFunction) - { - ngraphOperations = ngraphFunction->get_ops(); - } -#endif - - for (auto& it : ieNet.getOutputsInfo()) - { - CV_TRACE_REGION("output"); - const auto& outputName = it.first; - - LayerParams lp; - int lid = cvNet.addLayer(it.first, "", lp); - - LayerData& ld = cvNet.impl->layers[lid]; - -#ifdef HAVE_DNN_NGRAPH - if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam()) - { - Ptr cvLayer(new NgraphBackendLayer(ieNet)); - cvLayer->name = outputName; - cvLayer->type = "_unknown_"; - - auto process_layer = [&](const std::string& name) -> bool - { - if (ngraphFunction) - { - CV_TRACE_REGION("ngraph_function"); - for (const auto& op : ngraphOperations) - { - CV_Assert(op); - if (op->get_friendly_name() == name) - { - const std::string typeName = op->get_type_info().name; - cvLayer->type = typeName; - return true; - } - } - return false; - } - else - { -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) - CV_Error(Error::StsNotImplemented, "This OpenCV version is built with Inference Engine which has dropped IR v7 support"); -#else - CV_TRACE_REGION("legacy_cnn_layer"); - try - { - InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(name.c_str()); - CV_Assert(ieLayer); - - cvLayer->type = ieLayer->type; - return true; - } - catch (const std::exception& e) - { - CV_UNUSED(e); - CV_LOG_DEBUG(NULL, "IE layer extraction failure: '" << name << "' - " << e.what()); - return false; - } -#endif - - } - }; - - bool found = process_layer(outputName); - if (!found) - { - auto pos = outputName.rfind('.'); // cut port number: ".0" - if (pos != std::string::npos) - { - std::string layerName = outputName.substr(0, pos); - found = process_layer(layerName); - } - } - if (!found) - CV_LOG_WARNING(NULL, "DNN/IE: Can't determine output layer type: '" << outputName << "'"); - - ld.layerInstance = cvLayer; - ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode; - } - else -#endif - { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - Ptr cvLayer(new InfEngineBackendLayer(ieNet)); - - InferenceEngine::CNNLayerPtr ieLayer; - try - { - ieLayer = ieNet.getLayerByName(outputName.c_str()); - } - catch (...) - { - auto pos = outputName.rfind('.'); // cut port number: ".0" - if (pos != std::string::npos) - { - std::string layerName = outputName.substr(0, pos); - ieLayer = ieNet.getLayerByName(layerName.c_str()); - } - } - CV_Assert(ieLayer); - - cvLayer->name = outputName; - cvLayer->type = ieLayer->type; - ld.layerInstance = cvLayer; - - ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019] = backendNode; -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support"); -#endif - } - - for (int i = 0; i < inputsNames.size(); ++i) - cvNet.connect(0, i, lid, i); - } - - CV_TRACE_REGION_NEXT("finalize"); - - cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam()); - - cvNet.impl->skipInfEngineInit = true; - return cvNet; -} -#endif // HAVE_INF_ENGINE - -Net Net::readFromModelOptimizer(const String& xml, const String& bin) -{ - CV_TRACE_FUNCTION(); -#ifndef HAVE_INF_ENGINE - CV_UNUSED(xml); CV_UNUSED(bin); - CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer."); -#else -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3) - InferenceEngine::CNNNetReader reader; - reader.ReadNetwork(xml); - reader.ReadWeights(bin); - - InferenceEngine::CNNNetwork ieNet = reader.getNetwork(); -#else - InferenceEngine::Core& ie = getCore(""); - InferenceEngine::CNNNetwork ieNet = ie.ReadNetwork(xml, bin); -#endif - - return Impl::createNetworkFromModelOptimizer(ieNet); -#endif // HAVE_INF_ENGINE -} - -Net Net::readFromModelOptimizer(const std::vector& bufferModelConfig, const std::vector& bufferWeights) -{ - CV_TRACE_FUNCTION(); - CV_Assert(!bufferModelConfig.empty()); - CV_Assert(!bufferWeights.empty()); - return readFromModelOptimizer(bufferModelConfig.data(), bufferModelConfig.size(), - bufferWeights.data(), bufferWeights.size()); -} - -Net Net::readFromModelOptimizer( - const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize, - const uchar* bufferWeightsPtr, size_t bufferWeightsSize -) -{ - CV_TRACE_FUNCTION(); -#ifndef HAVE_INF_ENGINE - CV_UNUSED(bufferModelConfigPtr); CV_UNUSED(bufferWeightsPtr); - CV_UNUSED(bufferModelConfigSize); CV_UNUSED(bufferModelConfigSize); - CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer."); -#else - -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3) - InferenceEngine::CNNNetReader reader; - - try - { - reader.ReadNetwork(bufferModelConfigPtr, bufferModelConfigSize); - - InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C); - InferenceEngine::TBlob::Ptr weightsBlobPtr(new InferenceEngine::TBlob(tensorDesc)); - weightsBlobPtr->allocate(); - std::memcpy(weightsBlobPtr->buffer(), (uchar*)bufferWeightsPtr, bufferWeightsSize); - reader.SetWeights(weightsBlobPtr); - } - catch (const std::exception& e) - { - CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what()); - } - - InferenceEngine::CNNNetwork ieNet = reader.getNetwork(); -#else - InferenceEngine::Core& ie = getCore(""); - - std::string model; model.assign((char*)bufferModelConfigPtr, bufferModelConfigSize); - - InferenceEngine::CNNNetwork ieNet; - try - { - InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C); - InferenceEngine::Blob::CPtr weights_blob = InferenceEngine::make_shared_blob(tensorDesc, (uint8_t*)bufferWeightsPtr, bufferWeightsSize); - - ieNet = ie.ReadNetwork(model, weights_blob); - } - catch (const std::exception& e) - { - CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what()); - } -#endif - - return Impl::createNetworkFromModelOptimizer(ieNet); -#endif // HAVE_INF_ENGINE -} - - -Net::~Net() -{ -} - -int Net::addLayer(const String &name, const String &type, const int &dtype, LayerParams ¶ms) -{ - CV_TRACE_FUNCTION(); - - int id = impl->getLayerId(name); - if (id >= 0) - { - if (!DNN_DIAGNOSTICS_RUN || type != "NotImplemented") - { - CV_Error(Error::StsBadArg, "Layer \"" + name + "\" already into net"); - return -1; - } - else - { - LayerData& ld = impl->layers.find(id)->second; - ld.type = type; - ld.params = params; - return -1; - } - } - - id = ++impl->lastLayerId; - impl->layerNameToId.insert(std::make_pair(name, id)); - impl->layers.insert(std::make_pair(id, LayerData(id, name, type, dtype, params))); - if (params.get("has_dynamic_shapes", false)) - impl->hasDynamicShapes = true; - - if (dtype == CV_8S) - impl->netWasQuantized = true; - - return id; -} - -int Net::addLayer(const String &name, const String &type, LayerParams ¶ms) -{ - CV_TRACE_FUNCTION(); - return addLayer(name, type, CV_32F, params); -} - -int Net::addLayerToPrev(const String &name, const String &type, const int &dtype, LayerParams ¶ms) -{ - CV_TRACE_FUNCTION(); - - int prvLid = impl->lastLayerId; - int newLid = this->addLayer(name, type, dtype, params); - this->connect(prvLid, 0, newLid, 0); - return newLid; -} - -int Net::addLayerToPrev(const String &name, const String &type, LayerParams ¶ms) -{ - CV_TRACE_FUNCTION(); - return addLayerToPrev(name, type, CV_32F, params); -} - -void Net::connect(int outLayerId, int outNum, int inpLayerId, int inpNum) -{ - CV_TRACE_FUNCTION(); - - impl->connect(outLayerId, outNum, inpLayerId, inpNum); -} - -void Net::connect(String _outPin, String _inPin) -{ - CV_TRACE_FUNCTION(); - - LayerPin outPin = impl->getPinByAlias(_outPin); - LayerPin inpPin = impl->getPinByAlias(_inPin); - - CV_Assert(outPin.valid() && inpPin.valid()); - - impl->connect(outPin.lid, outPin.oid, inpPin.lid, inpPin.oid); -} - -Mat Net::forward(const String& outputName) -{ - CV_TRACE_FUNCTION(); - CV_Assert(!empty()); - - String layerName = outputName; - - if (layerName.empty()) - { - std::vector layerNames = getLayerNames(); - CV_Assert(!layerNames.empty()); - layerName = layerNames.back(); - } - - std::vector pins(1, impl->getPinByAlias(layerName)); - impl->setUpNet(pins); - impl->forwardToLayer(impl->getLayerData(layerName)); - - return impl->getBlob(layerName); -} - -AsyncArray Net::forwardAsync(const String& outputName) -{ - CV_TRACE_FUNCTION(); - CV_Assert(!empty()); - -#ifdef CV_CXX11 - String layerName = outputName; - - if (layerName.empty()) - { - std::vector layerNames = getLayerNames(); - CV_Assert(!layerNames.empty()); - layerName = layerNames.back(); - } - - std::vector pins(1, impl->getPinByAlias(layerName)); - impl->setUpNet(pins); - - if (!(impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) - CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backends only"); - - impl->isAsync = true; - impl->forwardToLayer(impl->getLayerData(layerName)); - impl->isAsync = false; - - return impl->getBlobAsync(layerName); -#else - CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward requires build with enabled C++11"); -#endif // CV_CXX11 -} - -void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName) -{ - CV_TRACE_FUNCTION(); - CV_Assert(!empty()); - - String layerName = outputName; - - if (layerName.empty()) - { - std::vector layerNames = getLayerNames(); - CV_Assert(!layerNames.empty()); - layerName = layerNames.back(); - } - - std::vector pins(1, impl->getPinByAlias(layerName)); - impl->setUpNet(pins); - impl->forwardToLayer(impl->getLayerData(layerName)); - - LayerPin pin = impl->getPinByAlias(layerName); - LayerData &ld = impl->layers[pin.lid]; - - if (outputBlobs.isUMat()) - { - impl->getBlob(layerName).copyTo(outputBlobs); - } - else if (outputBlobs.isMat()) - { - outputBlobs.assign(impl->getBlob(layerName)); - } - else if (outputBlobs.isMatVector()) - { - if (impl->preferableTarget != DNN_TARGET_CPU) - { - for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) - { - CV_Assert(!ld.outputBlobsWrappers[i].empty()); - ld.outputBlobsWrappers[i]->copyToHost(); - } - } - if (ld.outputBlobs[0].depth() == CV_16S) - { - std::vector & outputvec = *(std::vector *)outputBlobs.getObj(); - outputvec.resize(ld.outputBlobs.size()); - for (int i = 0; i < outputvec.size(); i++) - convertFp16(ld.outputBlobs[i], outputvec[i]); - } - else - { - // Output depth can be CV_32F or CV_8S - std::vector & outputvec = *(std::vector *)outputBlobs.getObj(); - outputvec = ld.outputBlobs; - } - } - else if (outputBlobs.isUMatVector()) - { - std::vector & outputvec = *(std::vector *)outputBlobs.getObj(); - -#ifdef HAVE_OPENCL - if (impl->preferableBackend == DNN_BACKEND_OPENCV && - IS_DNN_OPENCL_TARGET(impl->preferableTarget)) - { - if (impl->preferableTarget == DNN_TARGET_OPENCL) - outputvec = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); - else if (impl->preferableTarget == DNN_TARGET_OPENCL_FP16) - { - std::vector out_vec = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); - outputvec.resize(out_vec.size()); - for (int i = 0; i < out_vec.size(); i++) - convertFp16(out_vec[i], outputvec[i]); - } - } - else -#endif - { - outputvec.resize(ld.outputBlobs.size()); - for (int i = 0; i < outputvec.size(); ++i) - ld.outputBlobs[i].copyTo(outputvec[i]); - } - } -} - -void Net::forward(OutputArrayOfArrays outputBlobs, - const std::vector& outBlobNames) -{ - CV_TRACE_FUNCTION(); - - std::vector pins; - for (int i = 0; i < outBlobNames.size(); i++) - { - pins.push_back(impl->getPinByAlias(outBlobNames[i])); - } - - impl->setUpNet(pins); - - LayerPin out = impl->getLatestLayerPin(pins); - - impl->forwardToLayer(impl->getLayerData(out.lid)); - - std::vector matvec; - for (int i = 0; i < pins.size(); i++) - { - matvec.push_back(impl->getBlob(pins[i])); - } - - outputBlobs.create((int)matvec.size(), 1, CV_32F/*FIXIT*/, -1); // allocate vector - outputBlobs.assign(matvec); -} - -void Net::forward(std::vector >& outputBlobs, - const std::vector& outBlobNames) -{ - CV_TRACE_FUNCTION(); - - std::vector pins; - for (int i = 0; i < outBlobNames.size(); i++) - { - pins.push_back(impl->getPinByAlias(outBlobNames[i])); - } - - impl->setUpNet(pins); - - LayerPin out = impl->getLatestLayerPin(pins); - - impl->forwardToLayer(impl->getLayerData(out.lid)); - - outputBlobs.resize(outBlobNames.size()); - for (int i = 0; i < outBlobNames.size(); i++) - { - std::vector lp = impl->getLayerOutPins(outBlobNames[i]); - outputBlobs[i].resize(lp.size()); - for (int j = 0; j < lp.size(); j++) - { - outputBlobs[i][j] = impl->getBlob(lp[j]); - } - } -} - -Net Net::quantize(InputArrayOfArrays calibData, int inputsDtype, int outputsDtype) -{ - CV_TRACE_FUNCTION(); - - // Net can be quantized only once. - if (impl->netWasQuantized) - CV_Error(Error::StsBadArg, "Cannot quantize a quantized net"); - - CV_CheckType(inputsDtype, inputsDtype == CV_32F || inputsDtype == CV_8S, "Input depth should be CV_32F or CV_8S"); - CV_CheckType(outputsDtype, outputsDtype == CV_32F || outputsDtype == CV_8S, "Output depth should be CV_32F or CV_8S"); - - bool originalFusion = impl->fusion; - int prefBackend = impl->preferableBackend; - int prefTarget = impl->preferableTarget; - - // Disable fusions and use CPU backend to quantize net - setPreferableBackend(DNN_BACKEND_OPENCV); - setPreferableTarget(DNN_TARGET_CPU); - enableFusion(false); - - if (calibData.isMat()) - { - setInput(calibData.getMat()); - } - else if (calibData.isMatVector()) - { - std::vector calibDataVec; - calibData.getMatVector(calibDataVec); - - std::vector inpNames = impl->netInputLayer->outNames; - CV_CheckEQ(calibDataVec.size(), inpNames.size(), "Calibration data size should be equal to number of inputs"); - for (int i = 0; i < calibDataVec.size(); i++) - setInput(calibDataVec[i], inpNames[i]); - } - - std::vector outNames = getUnconnectedOutLayersNames(); - std::vector pins; - for (int i = 0; i < outNames.size(); i++) - pins.push_back(impl->getPinByAlias(outNames[i])); - impl->setUpNet(pins); - - // Compute scales and zeropoints for all the layers - std::vector > scales; - std::vector > zeropoints; - for (Impl::MapIdToLayerData::iterator it = impl->layers.begin(); it != impl->layers.end(); it++) - { - LayerData& ld = it->second; - if (!ld.skip) - { - Ptr layer = ld.layerInstance; - std::vector inps(ld.inputBlobs.size()); - for (int i = 0; i < ld.inputBlobs.size(); ++i) - inps[i] = *ld.inputBlobs[i]; - layer->forward(inps, ld.outputBlobs, ld.internals); - } - - std::vector sc; - std::vector zp; - if (ld.type == "TanH") - { - sc.push_back(1.f/128); - zp.push_back(0); - } - else if (ld.type == "Sigmoid" || ld.type == "Softmax" || ld.type == "SoftMax") - { - if (ld.params.get("log_softmax", false)) - { - sc.push_back(16.f/256); - zp.push_back(127); - } - else - { - sc.push_back(1.f/256); - zp.push_back(-128); - } - } - else if (ld.type == "Split" || ld.type == "Slice" || ld.type == "Crop") - { - std::vector inp_sc; std::vector inp_zp; - impl->getQuantizationParams(*ld.inputBlobs[0], inp_sc, inp_zp); - sc.assign(ld.outputBlobs.size(), inp_sc[0]); - zp.assign(ld.outputBlobs.size(), inp_zp[0]); - } - else - { - for (int i = 0; i < ld.outputBlobs.size(); i++) - impl->getQuantizationParams(ld.outputBlobs[i], sc, zp); - } - scales.push_back(sc); - zeropoints.push_back(zp); - } - - // For some layers, the input and output scales/zeropoints must be equal so that rescaling of inputs - // is not needed during quantized inference. We start from the last layer and modify the layer's input scales/zeropoints - // TODO : Need a different approach. Current solution fails when 2 such layers have the same input layer - for (Impl::MapIdToLayerData::reverse_iterator it = impl->layers.rbegin(); it != impl->layers.rend(); ++it) - { - LayerData& ld = it->second; - // Layers with multiple outputs. Number of outputs is equal to number of inputs - if (ld.type == "Blank" || ld.type == "Dropout" || ld.type == "Identity" || ld.type == "Silence" || - ld.type == "Flatten" || ld.type == "Padding" || ld.type == "Permute" || ld.type == "Reshape" || - ld.type == "ReLU6" || ld.type == "Reorg" || ld.type == "ShuffleChannel" || ld.type == "Resize" || - (ld.type == "ReLU" && !ld.params.get("negative_slope", 0.f)) /* ReLU with negative slope 0 */) - { - for (int i = 0; i < ld.outputBlobs.size(); i++) - { - LayerPin &pin = ld.inputBlobsId[i]; - scales[pin.lid][pin.oid] = scales[ld.id][i]; - zeropoints[pin.lid][pin.oid] = zeropoints[ld.id][i]; - } - } - // Layers with multiple inputs and single output. - else if ((ld.type == "Pooling" && toLowerCase(ld.params.get("pool", "max")) == "max") /* Max Pooling */ || - (ld.type == "Eltwise" && toLowerCase(ld.params.get("operation", "sum")) == "max") /* Elementwise max */ || - ld.type == "Concat") - { - for (int i = 0; i < ld.inputBlobsId.size(); i++) - { - LayerPin &pin = ld.inputBlobsId[i]; - scales[pin.lid][pin.oid] = scales[ld.id][0]; - zeropoints[pin.lid][pin.oid] = zeropoints[ld.id][0]; - } - } - } - - // Create a new Net and add quantized layers to it. - Net dstNet; - dstNet.impl->netWasQuantized = true; - dstNet.setInputsNames(impl->netInputLayer->outNames); - dstNet.setPreferableBackend(prefBackend); - dstNet.setPreferableTarget(prefTarget); - dstNet.enableFusion(originalFusion); - - for (Impl::MapIdToLayerData::iterator it = impl->layers.begin(); it != impl->layers.end(); it++) - { - LayerData ld = it->second; - if (ld.id == 0) - { - LayerData &quantInpLd = dstNet.impl->layers[0]; - quantInpLd.dtype = inputsDtype; - quantInpLd.params.set("scales", DictValue::arrayReal(scales[0].data(), scales[0].size())); - quantInpLd.params.set("zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size())); - continue; - } - - std::vector inpPins = ld.inputBlobsId; - // Fill input and output scales/zeropoints for the layer - std::vector > inp_out_sc(2); - std::vector > inp_out_zp(2); - for (int i = 0; i < inpPins.size(); i++) - { - LayerPin &pin = inpPins[i]; - inp_out_sc[0].push_back(scales[pin.lid][pin.oid]); - inp_out_zp[0].push_back(zeropoints[pin.lid][pin.oid]); - } - inp_out_sc[1] = scales[ld.id]; - inp_out_zp[1] = zeropoints[ld.id]; - - // Quantize layer - Ptr layer = ld.layerInstance; - if (layer->tryQuantize(inp_out_sc, inp_out_zp, ld.params)) - { - ld.type += "Int8"; - ld.dtype = CV_8S; - } - ld.params.set("scales", DictValue::arrayReal(inp_out_sc[1].data(), inp_out_sc[1].size())); - ld.params.set("zeropoints", DictValue::arrayInt(inp_out_zp[1].data(), inp_out_zp[1].size())); - - // Check and add quantize/dequantize node before layer - for (int i = 0; i < inpPins.size(); i++) - { - LayerPin &pin = inpPins[i]; - LayerData &inpLd = dstNet.impl->getLayerData(impl->getLayerName(pin.lid)); - pin.lid = inpLd.id; - if (inpLd.dtype != ld.dtype) - { - String layerName = (inpLd.dtype == CV_32F && ld.dtype == CV_8S) ? cv::format("quantize/%s/%d", inpLd.name.c_str(), pin.oid) - : cv::format("dequantize/%s/%d", inpLd.name.c_str(), pin.oid); - // Check if quantize/dequantize node for the input layer already exists - if (dstNet.impl->getLayerId(layerName) >= 0) - { - pin.lid = dstNet.impl->getLayerId(layerName); - pin.oid = 0; - } - else - { - LayerParams lp; - lp.set("scales", inp_out_sc[0][i]); - lp.set("zeropoints", inp_out_zp[0][i]); - lp.name = layerName; - lp.type = (inpLd.dtype == CV_32F && ld.dtype == CV_8S) ? "Quantize" : "Dequantize"; - int newLid = dstNet.addLayer(lp.name, lp.type, ld.dtype, lp); - dstNet.connect(pin.lid, pin.oid, newLid, 0); - pin.lid = newLid; pin.oid = 0; - } - } - } - - // Add quantized layer to Net and connect to its inputs. - int newLid = dstNet.addLayer(ld.name, ld.type, ld.dtype, ld.params); - for( int i = 0; i < inpPins.size(); i++ ) - dstNet.connect(inpPins[i].lid, inpPins[i].oid, newLid, i); - - // If the layer is a output layer, add quantize/dequantize node after it based on output's data type. - if (ld.requiredOutputs.size() == 0 && ld.dtype != outputsDtype) - { - LayerParams lp; - lp.set("scales", inp_out_sc[1][0]); - lp.set("zeropoints", inp_out_zp[1][0]); - lp.name = ((ld.dtype == CV_32F && outputsDtype == CV_8S) ? "quantize/" : "dequantize/") + ld.name; - lp.type = (ld.dtype == CV_32F && outputsDtype == CV_8S) ? "Quantize" : "Dequantize"; - dstNet.addLayerToPrev(lp.name, lp.type, outputsDtype, lp); - } - } - // Restore FP32 Net's backend, target and fusion - setPreferableBackend(prefBackend); - setPreferableTarget(prefTarget); - enableFusion(originalFusion); - return dstNet; -} - -void Net::getInputDetails(std::vector& scales, std::vector& zeropoints) const -{ - if (!impl->netWasQuantized) - CV_Error(Error::StsBadFunc, "Net isn't quantized"); - - LayerParams &lp = impl->layers[0].params; - DictValue sc = lp.get("scales"); - DictValue zp = lp.get("zeropoints"); - - for (int i = 0; i < sc.size(); i++) - { - scales.push_back(sc.get(i)); - zeropoints.push_back(zp.get(i)); - } -} - -void Net::getOutputDetails(std::vector& scales, std::vector& zeropoints) const -{ - if (!impl->netWasQuantized) - CV_Error(Error::StsBadFunc, "Net isn't quantized"); - - std::vector outLayerIds = getUnconnectedOutLayers(); - for (auto &lid : outLayerIds) - { - LayerParams &lp = impl->layers[lid].params; - DictValue sc = lp.get("scales"); - DictValue zp = lp.get("zeropoints"); - - for (int i = 0; i < sc.size(); i++) - { - scales.push_back(sc.get(i)); - zeropoints.push_back(zp.get(i)); - } - } -} - -void Net::setPreferableBackend(int backendId) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG(backendId); - - if (backendId == DNN_BACKEND_DEFAULT) - backendId = (Backend)PARAM_DNN_BACKEND_DEFAULT; - - if (impl->netWasQuantized && backendId != DNN_BACKEND_OPENCV) - { - CV_LOG_WARNING(NULL, "DNN: Only default backend supports quantized networks"); - backendId = DNN_BACKEND_OPENCV; - } - -#ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE) - backendId = getInferenceEngineBackendTypeParam(); -#endif - - if( impl->preferableBackend != backendId ) - { - impl->preferableBackend = backendId; - impl->clear(); - } -} - -void Net::setPreferableTarget(int targetId) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG(targetId); - - if (impl->netWasQuantized && targetId != DNN_TARGET_CPU && - targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16) - { - CV_LOG_WARNING(NULL, "DNN: Only CPU and OpenCL/OpenCL FP16 target is supported by quantized networks"); - targetId = DNN_TARGET_CPU; - } - - if( impl->preferableTarget != targetId ) - { - impl->preferableTarget = targetId; - if (IS_DNN_OPENCL_TARGET(targetId)) - { -#ifndef HAVE_OPENCL -#ifdef HAVE_INF_ENGINE - if (impl->preferableBackend == DNN_BACKEND_OPENCV) -#else - if (impl->preferableBackend == DNN_BACKEND_DEFAULT || - impl->preferableBackend == DNN_BACKEND_OPENCV) -#endif // HAVE_INF_ENGINE - impl->preferableTarget = DNN_TARGET_CPU; -#else - bool fp16 = ocl::Device::getDefault().isExtensionSupported("cl_khr_fp16"); - if (!fp16 && targetId == DNN_TARGET_OPENCL_FP16) - impl->preferableTarget = DNN_TARGET_OPENCL; -#endif - } - impl->clear(); - } -} - -void Net::setInputsNames(const std::vector &inputBlobNames) -{ - CV_TRACE_FUNCTION(); - - impl->netInputLayer->setNames(inputBlobNames); -} - -void Net::setInputShape(const String &inputName, const MatShape& shape) -{ - CV_TRACE_FUNCTION(); - - impl->netInputLayer->setInputShape(inputName, shape); -} - -void Net::setInput(InputArray blob, const String& name, double scalefactor, const Scalar& mean) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(name, "name", name.c_str()); - - LayerPin pin; - pin.lid = 0; - pin.oid = impl->resolvePinOutputName(impl->getLayerData(pin.lid), name); - - if (!pin.valid()) - CV_Error(Error::StsObjectNotFound, "Requested blob \"" + name + "\" not found"); - - Mat blob_ = blob.getMat(); // can't use InputArray directly due MatExpr stuff - MatShape blobShape = shape(blob_); - - if (pin.lid == 0) - { - CV_Assert(!impl->netInputLayer.empty()); - const DataLayer& netInputLayer = *impl->netInputLayer.get(); - if (!netInputLayer.shapes.empty()) - { - CV_CheckLT(pin.oid, (int)netInputLayer.shapes.size(), ""); - const MatShape& inputShapeLimitation = netInputLayer.shapes[pin.oid]; - if (!inputShapeLimitation.empty()) - { - CV_CheckEQ(inputShapeLimitation.size(), blobShape.size(), ""); -#if 0 // TODO: DNNTestNetwork.MobileNet_SSD_Caffe_Different_Width_Height/0 - const size_t dims = inputShapeLimitation.size(); - for (size_t dim = 0; dim < dims; dim++) - { - if (dims >= 3 && dim == 0 && inputShapeLimitation[0] == 1) - continue; // don't limit batch - CV_CheckEQ(inputShapeLimitation[dim], blobShape[dim], ""); - } -#endif - } - } - } - - LayerData &ld = impl->layers[pin.lid]; - const int numInputs = std::max(pin.oid+1, (int)ld.requiredOutputs.size()); - ld.outputBlobs.resize(numInputs); - ld.outputBlobsWrappers.resize(numInputs); - impl->netInputLayer->inputsData.resize(numInputs); - impl->netInputLayer->scaleFactors.resize(numInputs); - impl->netInputLayer->means.resize(numInputs); - - MatShape prevShape = shape(impl->netInputLayer->inputsData[pin.oid]); - bool oldShape = prevShape == blobShape; - - blob_.copyTo(impl->netInputLayer->inputsData[pin.oid]); - if (!oldShape) - ld.outputBlobs[pin.oid] = impl->netInputLayer->inputsData[pin.oid]; - - if (!ld.outputBlobsWrappers[pin.oid].empty()) - { - ld.outputBlobsWrappers[pin.oid]->setHostDirty(); - } - impl->netInputLayer->scaleFactors[pin.oid] = scalefactor; - impl->netInputLayer->means[pin.oid] = mean; - impl->netWasAllocated = impl->netWasAllocated && oldShape; -} - -Mat Net::getParam(LayerId layer, int numParam) -{ - LayerData &ld = impl->getLayerData(layer); - std::vector &layerBlobs = ld.getLayerInstance()->blobs; - CV_Assert(numParam < (int)layerBlobs.size()); - return layerBlobs[numParam]; -} - -void Net::setParam(LayerId layer, int numParam, const Mat &blob) -{ - LayerData &ld = impl->getLayerData(layer); - - std::vector &layerBlobs = ld.getLayerInstance()->blobs; - CV_Assert(numParam < (int)layerBlobs.size()); - //we don't make strong checks, use this function carefully - layerBlobs[numParam] = blob; -} - -int Net::getLayerId(const String &layer) -{ - return impl->getLayerId(layer); -} - -static -string dumpLayerParameterSize(const string& name, const LayerParams& lp) -{ - std::ostringstream out(name, std::ios::ate); - DictValue param = lp.get(name); - switch (param.size()) - { - case 1: out << " : "; break; - case 2: out << " (HxW): "; break; - case 3: out << " (DxHxW): "; break; - default: - CV_LOG_INFO(NULL, format("DNN/dumpLayerParameterSize(): Unsupported '%s' size = %d", name.c_str(), param.size())); - out << ": "; - } - for (size_t i = 0; i < param.size(); i++) - { - if (i > 0) - out << " x "; - out << param.get(i); - } - return out.str(); -} - -String Net::dump() -{ - CV_Assert(!empty()); - - bool hasInput = !impl->netInputLayer->inputsData.empty(); - - if (hasInput) - { - if (!impl->netWasAllocated) - impl->setUpNet(); - } - - return impl->dump(); -} - -string Net::Impl::dump() -{ - bool hasInput = !netInputLayer->inputsData.empty(); - - std::ostringstream out; - const std::map& map = layers; - - Backend prefBackend = (Backend)preferableBackend; - std::vector > skippedLayers; - std::vector skipId; - std::vector allLayers(map.size(), -1); - int idPrev = -1; - Ptr prevNode; - for (std::map::const_reverse_iterator rit = map.rbegin(); rit != map.rend(); ++rit) - { - std::map >::const_iterator itBackend = rit->second.backendNodes.find(prefBackend); - if (prefBackend == DNN_BACKEND_OPENCV || itBackend == rit->second.backendNodes.end() || - itBackend->second.empty()) - { - if (rit->second.skip) - skipId.push_back(rit->first); - else if (!skipId.empty()) - { - if (prefBackend == DNN_BACKEND_OPENCV || prevNode.empty()) - skipId.push_back(rit->first); - else if (idPrev != -1) - skipId.push_back(idPrev); - - std::sort(skipId.begin(), skipId.end()); - for (int i = 0; i < skipId.size(); i++) { - allLayers[skipId[i]] = skippedLayers.size(); - } - skippedLayers.push_back(skipId); - skipId.clear(); - } - } - else - { - if (itBackend->second == prevNode) - skipId.push_back(idPrev); - else if (!skipId.empty()) - { - skipId.push_back(idPrev); - std::sort(skipId.begin(), skipId.end()); - for (int i = 0; i < skipId.size(); i++) { - allLayers[skipId[i]] = skippedLayers.size(); - } - skippedLayers.push_back(skipId); - skipId.clear(); - } - idPrev = rit->first; - prevNode = itBackend->second; - } - } - std::vector colors = {"#ffffb3", "#fccde5", "#8dd3c7", "#bebada", "#80b1d3", "#fdb462", "#ff4848", "#b35151", "#b266ff"}; - string backend; - switch (prefBackend) - { - case DNN_BACKEND_DEFAULT: backend = "DEFAULT/"; break; - case DNN_BACKEND_HALIDE: backend = "HALIDE/"; break; - case DNN_BACKEND_INFERENCE_ENGINE: // fallthru - case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: backend = "DLIE/"; break; - case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "NGRAPH/"; break; - case DNN_BACKEND_OPENCV: backend = "OCV/"; break; - case DNN_BACKEND_VKCOM: backend = "VULKAN/"; break; - case DNN_BACKEND_CUDA: backend = "CUDA/"; break; - case DNN_BACKEND_WEBNN: backend = "WEBNN/"; break; - // don't use default: - } - out << "digraph G {\n"; - // Add nodes - for (std::map::const_iterator it = map.begin(); it != map.end(); ++it) - { - const LayerData& ld = it->second; - string name = ld.params.name; - std::vector clusterIds(1, it->first); - if (allLayers[it->first] == -1 && !name.empty()) - { - out << "\t\"" << name << "\" [label=\""; - } - else if (name.empty() || it->first != skippedLayers[allLayers[it->first]][0]) - { - continue; - } - else // first node in cluster : it->first == skippedLayers[allLayers[it->first]][0] - { - int cluster = allLayers[it->first]; - out << "\t\"" << "cluster_" << cluster << "\" [label=\"{"; - clusterIds = skippedLayers[allLayers[it->first]]; // vertices in current cluster - } - for (int i = 0; i < clusterIds.size(); i++) - { - CV_DbgAssert(map.find(clusterIds[i]) != map.end()); - const LayerParams& lp = map.find(clusterIds[i])->second.params; - if (!lp.name.empty()) { - if (i > 0) { - out << " | "; - } - out << lp.name << "\\n" << lp.type << "\\n"; // align center - if (lp.has("kernel_size")) - { - string kernel = dumpLayerParameterSize("kernel_size", lp); - out << kernel; - out << "\\l"; // align left - } else if (lp.has("kernel_h") && lp.has("kernel_w")) { - DictValue h = lp.get("kernel_h"); - DictValue w = lp.get("kernel_w"); - out << "kernel (HxW): " << h << " x " << w; - out << "\\l"; // align left - } - if (lp.has("stride")) { - string stride = dumpLayerParameterSize("stride", lp); - out << stride; - out << "\\l"; // align left - } else if (lp.has("stride_h") && lp.has("stride_w")) { - DictValue h = lp.get("stride_h"); - DictValue w = lp.get("stride_w"); - out << "stride (HxW): " << h << " x " << w; - out << "\\l"; // align left - } - if (lp.has("dilation")) { - string dilation = dumpLayerParameterSize("dilation", lp); - out << dilation; - out << "\\l"; // align left - } else if (lp.has("dilation_h") && lp.has("dilation_w")) { - DictValue h = lp.get("dilation_h"); - DictValue w = lp.get("dilation_w"); - out << "dilation (HxW): " << h << " x " << w; - out << "\\l"; // align left - } - if (lp.has("pad")) { - DictValue pad = lp.get("pad"); - out << "pad "; - switch (pad.size()) - { - case 1: out << ": " << pad; break; - case 2: - out << "(HxW): (" << pad.get(0) << " x " << pad.get(1) << ")"; - break; - case 4: - out << "(HxW): (" << pad.get(0) << ", " << pad.get(2) - << ") x (" << pad.get(1) << ", " << pad.get(3) << ")"; - break; - case 6: - out << "(DxHxW): (" << pad.get(0) << ", " << pad.get(3) - << ") x (" << pad.get(1) << ", " << pad.get(4) - << ") x (" << pad.get(2) << ", " << pad.get(5) << ")"; - break; - default: CV_Error(Error::StsNotImplemented, format("Unsupported pad size = %d", pad.size())); - } - out << "\\l"; // align left - } else if (lp.has("pad_l") && lp.has("pad_t") && lp.has("pad_r") && lp.has("pad_b")) { - DictValue l = lp.get("pad_l"); - DictValue t = lp.get("pad_t"); - DictValue r = lp.get("pad_r"); - DictValue b = lp.get("pad_b"); - out << "pad (HxW): (" << t << ", " << b << ") x (" << l << ", " << r << ")"; - out << "\\l"; // align left - } - else if (lp.has("pooled_w") || lp.has("pooled_h")) { - DictValue h = lp.get("pooled_h"); - DictValue w = lp.get("pooled_w"); - out << "pad pooled (HxW): " << h << " x " << w; - out << "\\l"; // align left - } - if (lp.has("pool")) { - out << "pool: " << lp.get("pool"); - out << "\\l"; // align left - } - if (lp.has("global_pooling")) { - out << "global_pooling: " << lp.get("global_pooling"); - out << "\\l"; // align left - } - if (lp.has("group")) { - out << "group: " << lp.get("group"); - out << "\\l"; // align left - } - } - } - if (!ld.outputBlobs.empty()) - { - out << "output: " << ld.outputBlobs[0].size; - out << "\\l"; // align left - } - - Ptr layerBackend; - std::map >::const_iterator ibn = ld.backendNodes.find(prefBackend); - if (ibn != ld.backendNodes.end()) - layerBackend = ibn->second; - out << (!layerBackend.empty() ? backend : "OCV/"); - int colorId = 0; - const Target target = ld.layerInstance.empty() - ? DNN_TARGET_CPU - : (Target)(ld.layerInstance->preferableTarget); // TODO fix preferableTarget type - switch (target) - { - case DNN_TARGET_CPU: out << "CPU"; colorId = layerBackend.empty() ? 0 : 5; break; - case DNN_TARGET_OPENCL: out << "OCL"; colorId = 1; break; - case DNN_TARGET_OPENCL_FP16: out << "OCL_FP16"; colorId = 2; break; - case DNN_TARGET_MYRIAD: out << "MYRIAD"; colorId = 3; break; - case DNN_TARGET_HDDL: out << "HDDL"; colorId = 8; break; - case DNN_TARGET_VULKAN: out << "VULKAN"; colorId = 7; break; - case DNN_TARGET_FPGA: out << "FPGA"; colorId = 4; break; - case DNN_TARGET_CUDA: out << "CUDA"; colorId = 5; break; - case DNN_TARGET_CUDA_FP16: out << "CUDA_FP16"; colorId = 6; break; - // don't use default: - } - CV_Assert(colorId < colors.size()); - out << "\\n"; // align center - out << ((clusterIds.size() == 1)? "\" " : " }\" "); - out << "fillcolor=\"" << colors[colorId] << "\" "; - out << "style=filled "; - out << "shape=" << ((clusterIds.size() == 1)? "box" : "record") << "]\n"; - } - out << '\n'; - // Add edges - int inputsSize = hasInput ? netInputLayer->outNames.size() : 0; - for (std::map::const_iterator it = map.begin(); it != map.end(); ++it) - { - const LayerData& ld = it->second; - if (allLayers[it->first] == -1) // node - { - for (int i = 0; i < ld.consumers.size(); i++) - { - int outId = ld.consumers[i].lid; - if (it == map.begin() && inputsSize > 1) - out << "\t\"" << ld.name << "_" << i << "\"" << " -> "; - else - out << "\t\"" << ld.name << "\"" << " -> "; - if (allLayers[outId] == -1) // node - { - CV_DbgAssert(map.find(outId) != map.end()); - out << "\"" << map.find(outId)->second.name << "\"\n"; - } - else // cluster - { - out << "\"" << "cluster_" << allLayers[outId] << "\"\n"; - } - } - } - else if (it->first == skippedLayers[allLayers[it->first]].back()) // edges from last layer in cluster - { - for (int i = 0; i < ld.consumers.size(); i++) - { - int outId = ld.consumers[i].lid; - if (allLayers[outId] == -1) // node - { - CV_DbgAssert(map.find(outId) != map.end()); - out << "\t\"" << "cluster_" << allLayers[it->first] << "\"" << " -> "; - out << "\"" << map.find(outId)->second.name << "\"\n"; - } - else if (allLayers[outId] != allLayers[it->first]) { // another cluster - out << "\t\"" << "cluster_" << allLayers[it->first] << "\"" << " -> "; - out << "\"" << "cluster_" << allLayers[outId] << "\"\n"; - } - } - } - } - out << "}\n"; - return out.str(); -} - -void Net::dumpToFile(const String& path) { - std::ofstream file(path.c_str()); - file << dump(); - file.close(); -} - -Ptr Net::getLayer(LayerId layerId) -{ - LayerData &ld = impl->getLayerData(layerId); - return ld.getLayerInstance(); -} - -std::vector > Net::getLayerInputs(LayerId layerId) -{ - LayerData &ld = impl->getLayerData(layerId); - - std::vector > inputLayers; - inputLayers.reserve(ld.inputBlobsId.size()); - for (int i = 0; i < ld.inputBlobsId.size(); ++i) { - inputLayers.push_back(getLayer(ld.inputBlobsId[i].lid)); - } - return inputLayers; -} - -std::vector Net::getLayerNames() const -{ - CV_TRACE_FUNCTION(); - - std::vector res; - res.reserve(impl->layers.size()); - - Impl::MapIdToLayerData::iterator it; - for (it = impl->layers.begin(); it != impl->layers.end(); it++) - { - if (it->second.id) //skip Data layer - res.push_back(it->second.name); - } - - return res; -} - -bool Net::empty() const -{ - return impl->layers.size() <= 1; //first layer is default Data layer -} - -std::vector Net::getUnconnectedOutLayers() const -{ - std::vector layersIds; - - Impl::MapIdToLayerData::iterator it; - for (it = impl->layers.begin(); it != impl->layers.end(); it++) - { - int lid = it->first; - LayerData &ld = it->second; - - if (ld.requiredOutputs.size() == 0) - layersIds.push_back(lid); - } - - return layersIds; -} - -std::vector Net::getUnconnectedOutLayersNames() const -{ - std::vector ids = getUnconnectedOutLayers(); - const size_t n = ids.size(); - std::vector names(n); - for (size_t i = 0; i < n; ++i) - { - names[i] = impl->layers[ids[i]].name; - } - return names; -} - -void Net::getLayersShapes(const ShapesVec& netInputShapes, - std::vector& layersIds, - std::vector& inLayersShapes, - std::vector& outLayersShapes) const -{ - layersIds.clear(); - inLayersShapes.clear(); - outLayersShapes.clear(); - - Impl::LayersShapesMap inOutShapes; - impl->getLayersShapes(netInputShapes, inOutShapes); - - for(Impl::LayersShapesMap::const_iterator it = inOutShapes.begin(); - it != inOutShapes.end(); it++) - { - layersIds.push_back(it->first); - inLayersShapes.push_back(it->second.in); - outLayersShapes.push_back(it->second.out); - } -} - -void Net::getLayersShapes(const MatShape& netInputShape, - std::vector& layerIds, - std::vector& inLayersShapes, - std::vector& outLayersShapes) const -{ - getLayersShapes(ShapesVec(1, netInputShape), - layerIds, inLayersShapes, outLayersShapes); -} - -void Net::getLayerShapes(const MatShape& netInputShape, - const int layerId, - ShapesVec& inLayerShapes, - ShapesVec& outLayerShapes) const -{ - getLayerShapes(ShapesVec(1, netInputShape), - layerId, inLayerShapes, outLayerShapes); - -} - -void Net::getLayerShapes(const ShapesVec& netInputShapes, - const int layerId, - ShapesVec& inLayerShapes, - ShapesVec& outLayerShapes) const -{ - LayerShapes shapes; - impl->getLayerShapes(netInputShapes, layerId, shapes); - inLayerShapes = shapes.in; - outLayerShapes = shapes.out; -} - -int64 Net::getFLOPS(const std::vector& netInputShapes) const -{ - CV_TRACE_FUNCTION(); - - int64 flops = 0; - std::vector ids; - std::vector > inShapes, outShapes; - getLayersShapes(netInputShapes, ids, inShapes, outShapes); - CV_Assert(inShapes.size() == outShapes.size()); - CV_Assert(inShapes.size() == ids.size()); - - for(int i = 0; i < ids.size(); i++) - { - flops += impl->layers[ids[i]].getLayerInstance()->getFLOPS(inShapes[i], - outShapes[i]); - } - - return flops; -} - -int64 Net::getFLOPS(const MatShape& netInputShape) const -{ - return getFLOPS(std::vector(1, netInputShape)); -} - -int64 Net::getFLOPS(const int layerId, - const std::vector& netInputShapes) const -{ - Impl::MapIdToLayerData::iterator layer = impl->layers.find(layerId); - CV_Assert(layer != impl->layers.end()); - - LayerShapes shapes; - impl->getLayerShapes(netInputShapes, layerId, shapes); - - return layer->second.getLayerInstance()->getFLOPS(shapes.in, shapes.out); -} - -int64 Net::getFLOPS(const int layerId, - const MatShape& netInputShape) const -{ - return getFLOPS(layerId, std::vector(1, netInputShape)); -} - -void Net::getLayerTypes(std::vector& layersTypes) const -{ - layersTypes.clear(); - - std::map layers; - for (Impl::MapIdToLayerData::iterator it = impl->layers.begin(); - it != impl->layers.end(); it++) - { - if (layers.find(it->second.type) == layers.end()) - layers[it->second.type] = 0; - layers[it->second.type]++; - } - - for (std::map::iterator it = layers.begin(); - it != layers.end(); it++) - { - layersTypes.push_back(it->first); - } -} - -int Net::getLayersCount(const String& layerType) const -{ - int count = 0; - for (Impl::MapIdToLayerData::iterator it = impl->layers.begin(); - it != impl->layers.end(); it++) - { - if (it->second.type == layerType) - count++; - } - return count; -} - -void Net::getMemoryConsumption(const int layerId, - const std::vector& netInputShapes, - size_t& weights, size_t& blobs) const -{ - CV_TRACE_FUNCTION(); - - Impl::MapIdToLayerData::iterator layer = impl->layers.find(layerId); - CV_Assert(layer != impl->layers.end()); - - weights = blobs = 0; - - for(int i = 0; i < layer->second.params.blobs.size(); i++) - { - const Mat& weightsBlob = layer->second.params.blobs[i]; - weights += weightsBlob.total()*weightsBlob.elemSize(); - } - - ShapesVec inLayerShapes, outLayerShapes; - getLayerShapes(netInputShapes, layerId, inLayerShapes, outLayerShapes); - size_t elemSize = (impl->netWasQuantized) ? sizeof(char) : sizeof(float); - for(int i = 0; i < outLayerShapes.size(); i++) - { - blobs += total(outLayerShapes[i]) * elemSize; - } -} - -void Net::getMemoryConsumption(const std::vector& netInputShapes, - size_t& weights, size_t& blobs) const -{ - CV_TRACE_FUNCTION(); - - std::vector layerIds; - std::vector w, b; - getMemoryConsumption(netInputShapes, layerIds, w, b); - - weights = blobs = 0; - for(int i = 0; i < layerIds.size(); i++) - { - weights += w[i]; - blobs += b[i]; - } -} - -void Net::getMemoryConsumption(const int layerId, - const MatShape& netInputShape, - size_t& weights, size_t& blobs) const -{ - getMemoryConsumption(layerId, std::vector(1, netInputShape), - weights, blobs); -} - -void Net::getMemoryConsumption(const MatShape& netInputShape, - size_t& weights, size_t& blobs) const -{ - getMemoryConsumption(std::vector(1, netInputShape), - weights, blobs); -} - -void Net::getMemoryConsumption(const std::vector& netInputShapes, - std::vector& layerIds, std::vector& weights, - std::vector& blobs) const -{ - CV_TRACE_FUNCTION(); - - layerIds.clear(); - weights.clear(); - blobs.clear(); - - std::vector > inLayerShapes, outLayerShapes; - - getLayersShapes(netInputShapes, layerIds, inLayerShapes, outLayerShapes); - size_t elemSize = (impl->netWasQuantized) ? sizeof(char) : sizeof(float); - for(int i = 0; i < layerIds.size(); i++) - { - int w = 0, b = 0; - Impl::MapIdToLayerData::iterator layer = impl->layers.find(layerIds[i]); - CV_Assert(layer != impl->layers.end()); - - for(int j = 0; j < layer->second.params.blobs.size(); j++) - { - const Mat& weightsBlob = layer->second.params.blobs[j]; - w += weightsBlob.total()*weightsBlob.elemSize(); - } - - for(int j = 0; j < outLayerShapes[i].size(); j++) - { - b += total(outLayerShapes[i][j]) * elemSize; - } - - weights.push_back(w); - blobs.push_back(b); - } -} - -void Net::getMemoryConsumption(const MatShape& netInputShape, std::vector& layerIds, - std::vector& weights, std::vector& blobs) const -{ - getMemoryConsumption(std::vector(1, netInputShape), layerIds, - weights, blobs); -} - -void Net::enableFusion(bool fusion) -{ - if( impl->fusion != fusion ) - { - impl->fusion = fusion; - impl->clear(); - } -} - -void Net::setHalideScheduler(const String& scheduler) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(scheduler, "scheduler", scheduler.c_str()); - - impl->halideConfigFile = scheduler; -} - -int64 Net::getPerfProfile(std::vector& timings) -{ - timings = std::vector(impl->layersTimings.begin() + 1, impl->layersTimings.end()); - int64 total = (int64)std::accumulate(timings.begin(), timings.end(), 0.0); - return total; -} - -////////////////////////////////////////////////////////////////////////// - -Layer::Layer() { preferableTarget = DNN_TARGET_CPU; } - -Layer::Layer(const LayerParams ¶ms) - : blobs(params.blobs), name(params.name), type(params.type) -{ - preferableTarget = DNN_TARGET_CPU; -} - -void Layer::setParamsFrom(const LayerParams ¶ms) -{ - blobs = params.blobs; - name = params.name; - type = params.type; -} - -int Layer::inputNameToIndex(String) -{ - return -1; -} - -int Layer::outputNameToIndex(const String&) -{ - return 0; -} - -bool Layer::supportBackend(int backendId) -{ - return backendId == DNN_BACKEND_OPENCV; -} - -Ptr Layer::initCUDA( - void*, - const std::vector>&, - const std::vector>&) -{ - CV_Error(Error::StsNotImplemented, "CUDA pipeline of " + type + - " layers is not defined."); - return Ptr(); -} - -Ptr Layer::initVkCom(const std::vector > &) -{ - CV_Error(Error::StsNotImplemented, "VkCom pipeline of " + type + - " layers is not defined."); - return Ptr(); -} - -Ptr Layer::initHalide(const std::vector > &) -{ - CV_Error(Error::StsNotImplemented, "Halide pipeline of " + type + - " layers is not defined."); - return Ptr(); -} - -Ptr Layer::initInfEngine(const std::vector > &) -{ - CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type + - " layers is not defined."); - return Ptr(); -} - -Ptr Layer::initNgraph(const std::vector > & inputs, const std::vector >& nodes) -{ - CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type + - " layers is not defined."); - return Ptr(); -} - -Ptr Layer::initWebnn(const std::vector > & inputs, const std::vector >& nodes) -{ - CV_Error(Error::StsNotImplemented, "WebNN pipeline of " + type + - " layers is not defined."); - return Ptr(); -} - -void Layer::applyHalideScheduler(Ptr& node, const std::vector &inputs, - const std::vector &outputs, int targetId) const -{ -#ifdef HAVE_HALIDE - CV_TRACE_FUNCTION(); - - Halide::Var x("x"), y("y"), c("c"), n("n"), co("co"), ci("ci"), - xo("xo"), xi("xi"), yo("yo"), yi("yi"), tile("tile"); - Halide::Func& top = node.dynamicCast()->funcs.back(); - - int outW, outH, outC, outN; - getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN); - - if (targetId == DNN_TARGET_CPU) - { - if (outW == 1 && outH == 1) - { - if (outC + outN == 1) - return; - - if (outC > 8) - top.split(c, co, ci, 8) - .fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile) - .parallel(tile) - .vectorize(ci, 8); - else - top.fuse(x, y, tile).fuse(c, tile, tile).fuse(n, tile, tile) - .parallel(tile); - } - else - { - if (outH > 2) - { - top.reorder(x, c, y) - .split(y, yo, yi, 2) - .fuse(yo, n, tile) - .parallel(tile) - .unroll(yi) - .vectorize(x, outW >= 16 ? 16 : outW); - } - } - } - else if (targetId == DNN_TARGET_OPENCL) - { - if (outW == 1 && outH == 1) - { - int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC; - top.split(c, co, ci, c_split) - .fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile) - .gpu_blocks(tile) - .gpu_threads(ci); - } - else - { - int x_split = outW > 8 ? (outW >= 32 ? 16 : 8) : outW; - int y_split = outH > 8 ? (outH >= 32 ? 16 : 8) : outH; - // Supported vectorization widths: 2, 3, 4, 8, 16 - int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : std::min(4, outC); - top.split(x, xo, xi, x_split).split(y, yo, yi, y_split) - .split(c, co, ci, c_split) - .gpu_blocks(xo, yo, co) - .gpu_threads(xi, yi) - .reorder(xi, yi, ci, xo, yo, co) - .vectorize(ci); - } - } - else - CV_Error(Error::StsNotImplemented, "Unknown target identifier"); -#endif // HAVE_HALIDE -} - -Ptr Layer::tryAttach(const Ptr& node) -{ - return Ptr(); -} - -bool Layer::setActivation(const Ptr&) { return false; } -bool Layer::tryFuse(Ptr&) { return false; } -void Layer::getScaleShift(Mat& scale, Mat& shift) const -{ - scale = Mat(); - shift = Mat(); -} - -void Layer::getScaleZeropoint(float& scale, int& zeropoint) const -{ - scale = 1.f; - zeropoint = 0; -} - -void Layer::unsetAttached() -{ - setActivation(Ptr()); -} - -template -static void vecToPVec(const std::vector &v, std::vector &pv) -{ - pv.resize(v.size()); - for (size_t i = 0; i < v.size(); i++) - pv[i] = const_cast(&v[i]); -} - -void Layer::finalize(const std::vector &inputs, std::vector &outputs) -{ - CV_TRACE_FUNCTION(); - this->finalize((InputArrayOfArrays)inputs, (OutputArrayOfArrays)outputs); -} - -void Layer::finalize(const std::vector &input, std::vector &output) -{ - CV_UNUSED(input);CV_UNUSED(output); -} - -void Layer::finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) -{ - CV_TRACE_FUNCTION(); - std::vector inputs, outputs; - inputs_arr.getMatVector(inputs); - outputs_arr.getMatVector(outputs); - - std::vector inputsp; - vecToPVec(inputs, inputsp); - this->finalize(inputsp, outputs); -} - -std::vector Layer::finalize(const std::vector &inputs) -{ - CV_TRACE_FUNCTION(); - - std::vector outputs; - this->finalize(inputs, outputs); - return outputs; -} - -void Layer::forward(std::vector &input, std::vector &output, std::vector &internals) -{ - // We kept this method for compatibility. DNN calls it now only to support users' implementations. -} - -void Layer::forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(name, "name", name.c_str()); - - Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); -} - -void Layer::forward_fallback(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(name, "name", name.c_str()); - - if (preferableTarget == DNN_TARGET_OPENCL_FP16 && inputs_arr.depth() == CV_16S) - { - std::vector inputs; - std::vector outputs; - std::vector internals; - - std::vector orig_inputs; - std::vector orig_outputs; - std::vector orig_internals; - - inputs_arr.getUMatVector(orig_inputs); - outputs_arr.getUMatVector(orig_outputs); - internals_arr.getUMatVector(orig_internals); - - inputs.resize(orig_inputs.size()); - for (size_t i = 0; i < orig_inputs.size(); i++) - convertFp16(orig_inputs[i], inputs[i]); - - outputs.resize(orig_outputs.size()); - for (size_t i = 0; i < orig_outputs.size(); i++) - outputs[i].create(shape(orig_outputs[i]), CV_32F); - - internals.resize(orig_internals.size()); - for (size_t i = 0; i < orig_internals.size(); i++) - internals[i].create(shape(orig_internals[i]), CV_32F); - - forward(inputs, outputs, internals); - - for (size_t i = 0; i < outputs.size(); i++) - convertFp16(outputs[i], orig_outputs[i]); - - // sync results back - outputs_arr.assign(orig_outputs); - internals_arr.assign(orig_internals); - return; - } - std::vector inpvec; - std::vector outputs; - std::vector internals; - - inputs_arr.getMatVector(inpvec); - outputs_arr.getMatVector(outputs); - internals_arr.getMatVector(internals); - - std::vector inputs(inpvec.size()); - for (int i = 0; i < inpvec.size(); i++) - inputs[i] = &inpvec[i]; - - this->forward(inputs, outputs, internals); - - // sync results back - outputs_arr.assign(outputs); - internals_arr.assign(internals); -} - -void Layer::run(const std::vector &inputs, std::vector &outputs, std::vector &internals) -{ - CV_TRACE_FUNCTION(); - - this->finalize(inputs, outputs); - this->forward(inputs, outputs, internals); -} - -bool Layer::tryQuantize(const std::vector > &scales, - const std::vector > &zeropoints, LayerParams& params) -{ - return false; -} - -Layer::~Layer() {} - -bool Layer::getMemoryShapes(const std::vector &inputs, - const int requiredOutputs, - std::vector &outputs, - std::vector &internals) const -{ - CV_Assert(inputs.size()); - outputs.assign(std::max(requiredOutputs, (int)inputs.size()), inputs[0]); - return false; -} - -bool Layer::updateMemoryShapes(const std::vector &inputs) -{ - return true; -} -////////////////////////////////////////////////////////////////////////// - -Mutex& getLayerFactoryMutex() -{ - static Mutex* volatile instance = NULL; - if (instance == NULL) - { - cv::AutoLock lock(getInitializationMutex()); - if (instance == NULL) - instance = new Mutex(); - } - return *instance; -} - -static LayerFactory_Impl& getLayerFactoryImpl_() -{ - static LayerFactory_Impl impl; - return impl; -} - -LayerFactory_Impl& getLayerFactoryImpl() -{ - static LayerFactory_Impl* volatile instance = NULL; - if (instance == NULL) - { - cv::AutoLock lock(getLayerFactoryMutex()); - if (instance == NULL) - { - instance = &getLayerFactoryImpl_(); - initializeLayerFactory(); - } - } - return *instance; -} - -void LayerFactory::registerLayer(const String &type, Constructor constructor) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(type, "type", type.c_str()); - - cv::AutoLock lock(getLayerFactoryMutex()); - LayerFactory_Impl::iterator it = getLayerFactoryImpl().find(type); - - if (it != getLayerFactoryImpl().end()) - { - if (it->second.back() == constructor) - CV_Error(cv::Error::StsBadArg, "Layer \"" + type + "\" already was registered"); - it->second.push_back(constructor); - } - getLayerFactoryImpl().insert(std::make_pair(type, std::vector(1, constructor))); -} - -void LayerFactory::unregisterLayer(const String &type) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(type, "type", type.c_str()); - - cv::AutoLock lock(getLayerFactoryMutex()); - - LayerFactory_Impl::iterator it = getLayerFactoryImpl().find(type); - if (it != getLayerFactoryImpl().end()) - { - if (it->second.size() > 1) - it->second.pop_back(); - else - getLayerFactoryImpl().erase(it); - } -} - -Ptr LayerFactory::createLayerInstance(const String &type, LayerParams& params) -{ - CV_TRACE_FUNCTION(); - CV_TRACE_ARG_VALUE(type, "type", type.c_str()); - - cv::AutoLock lock(getLayerFactoryMutex()); - LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type); - - if (it != getLayerFactoryImpl().end()) - { - CV_Assert(!it->second.empty()); - return it->second.back()(params); - } - else - { - return Ptr(); //NULL - } -} - -BackendNode::BackendNode(int backendId) : backendId(backendId) {} - -BackendNode::~BackendNode() {}; - -BackendWrapper::BackendWrapper(int backendId, int targetId) - : backendId(backendId), targetId(targetId) {} - -BackendWrapper::BackendWrapper(int targetId, const cv::Mat& m) -{ - CV_Error(Error::StsNotImplemented, - "Constructor of backend wrapper must be implemented"); -} - -BackendWrapper::BackendWrapper(const Ptr& base, const MatShape& shape) -{ - CV_Error(Error::StsNotImplemented, - "Constructor of backend wrapper must be implemented"); -} - -BackendWrapper::~BackendWrapper() {} - -Net readNet(const String& _model, const String& _config, const String& _framework) -{ - String framework = toLowerCase(_framework); - String model = _model; - String config = _config; - const std::string modelExt = model.substr(model.rfind('.') + 1); - const std::string configExt = config.substr(config.rfind('.') + 1); - if (framework == "caffe" || modelExt == "caffemodel" || configExt == "caffemodel" || - modelExt == "prototxt" || configExt == "prototxt") - { - if (modelExt == "prototxt" || configExt == "caffemodel") - std::swap(model, config); - return readNetFromCaffe(config, model); - } - if (framework == "tensorflow" || modelExt == "pb" || configExt == "pb" || - modelExt == "pbtxt" || configExt == "pbtxt") - { - if (modelExt == "pbtxt" || configExt == "pb") - std::swap(model, config); - return readNetFromTensorflow(model, config); - } - if (framework == "torch" || modelExt == "t7" || modelExt == "net" || - configExt == "t7" || configExt == "net") - { - return readNetFromTorch(model.empty() ? config : model); - } - if (framework == "darknet" || modelExt == "weights" || configExt == "weights" || - modelExt == "cfg" || configExt == "cfg") - { - if (modelExt == "cfg" || configExt == "weights") - std::swap(model, config); - return readNetFromDarknet(config, model); - } - if (framework == "dldt" || modelExt == "bin" || configExt == "bin" || - modelExt == "xml" || configExt == "xml") - { - if (modelExt == "xml" || configExt == "bin") - std::swap(model, config); - return readNetFromModelOptimizer(config, model); - } - if (framework == "onnx" || modelExt == "onnx") - { - return readNetFromONNX(model); - } - CV_Error(Error::StsError, "Cannot determine an origin framework of files: " + - model + (config.empty() ? "" : ", " + config)); -} - -Net readNet(const String& _framework, const std::vector& bufferModel, - const std::vector& bufferConfig) -{ - String framework = toLowerCase(_framework); - if (framework == "caffe") - return readNetFromCaffe(bufferConfig, bufferModel); - else if (framework == "tensorflow") - return readNetFromTensorflow(bufferModel, bufferConfig); - else if (framework == "darknet") - return readNetFromDarknet(bufferConfig, bufferModel); - else if (framework == "torch") - CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers"); - else if (framework == "dldt") - return readNetFromModelOptimizer(bufferConfig, bufferModel); - CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework); -} - -Net readNetFromModelOptimizer(const String &xml, const String &bin) -{ - return Net::readFromModelOptimizer(xml, bin); -} - -Net readNetFromModelOptimizer(const std::vector& bufferCfg, const std::vector& bufferModel) -{ - return Net::readFromModelOptimizer(bufferCfg, bufferModel); -} - -Net readNetFromModelOptimizer( - const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize, - const uchar* bufferWeightsPtr, size_t bufferWeightsSize -) -{ - return Net::readFromModelOptimizer( - bufferModelConfigPtr, bufferModelConfigSize, - bufferWeightsPtr, bufferWeightsSize - ); -} - -CV__DNN_INLINE_NS_END -}} // namespace +// This is a stub file to provide git history information. +// Content has been moved, see PR: https://github.com/opencv/opencv/pull/21662 +// Base commit: 19926e2979ef049a89dd029e2231555db40c2776 +// Original dnn.cpp content: https://github.com/opencv/opencv/blame/19926e2979ef049a89dd029e2231555db40c2776/modules/dnn/src/dnn.cpp diff --git a/modules/dnn/src/dnn_common.hpp b/modules/dnn/src/dnn_common.hpp index ffeb3bfda1ad..ae4d9c295e5d 100644 --- a/modules/dnn/src/dnn_common.hpp +++ b/modules/dnn/src/dnn_common.hpp @@ -5,8 +5,8 @@ #ifndef __OPENCV_DNN_COMMON_HPP__ #define __OPENCV_DNN_COMMON_HPP__ -#include #include +#include #include @@ -19,7 +19,44 @@ void initializeLayerFactory(); extern bool DNN_DIAGNOSTICS_RUN; extern bool DNN_SKIP_REAL_IMPORT; -namespace detail { +// +// dnn_params.cpp +// + +/// Network dump level +size_t getParam_DNN_NETWORK_DUMP(); + +/// This parameter is useful to run with valgrind memory errors detection +bool getParam_DNN_DISABLE_MEMORY_OPTIMIZATIONS(); + +#ifdef HAVE_OPENCL +bool getParam_DNN_OPENCL_ALLOW_ALL_DEVICES(); +#endif + +int getParam_DNN_BACKEND_DEFAULT(); + +// Additional checks (slowdowns execution!) +bool getParam_DNN_CHECK_NAN_INF(); +bool getParam_DNN_CHECK_NAN_INF_DUMP(); +bool getParam_DNN_CHECK_NAN_INF_RAISE_ERROR(); + + +inline namespace detail { + +typedef std::vector ShapesVec; + +struct LayerShapes +{ + ShapesVec in, out, internal; + // No guarantees that layer which support in-place computations + // will be computed in-place (input.data_ptr == output.data_ptr). + // If layer said that it could work in-place and layers after it + // no longer use input blob, we'll set output = input. + bool supportInPlace; + LayerShapes() {supportInPlace = false;} +}; + + #define CALL_MEMBER_FN(object, ptrToMemFn) ((object).*(ptrToMemFn)) class NotImplemented : public Layer @@ -59,7 +96,7 @@ class LayerHandler public: void addMissing(const std::string& name, const std::string& type); bool contains(const std::string& type) const; - void printMissing(); + void printMissing() const; protected: LayerParams getNotImplementedParams(const std::string& name, const std::string& op); @@ -71,19 +108,17 @@ class LayerHandler struct NetImplBase { const int networkId; // network global identifier - int networkDumpCounter; // dump counter + mutable int networkDumpCounter; // dump counter int dumpLevel; // level of information dumps (initialized through OPENCV_DNN_NETWORK_DUMP parameter) NetImplBase(); - std::string getDumpFileNameBase(); + std::string getDumpFileNameBase() const; }; } // namespace detail -typedef std::vector ShapesVec; - static inline std::string toString(const ShapesVec& shapes, const std::string& name = std::string()) { std::ostringstream ss; diff --git a/modules/dnn/src/dnn_params.cpp b/modules/dnn/src/dnn_params.cpp new file mode 100644 index 000000000000..48e89c6facda --- /dev/null +++ b/modules/dnn/src/dnn_params.cpp @@ -0,0 +1,67 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "dnn_common.hpp" +#include + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +size_t getParam_DNN_NETWORK_DUMP() +{ + static size_t DNN_NETWORK_DUMP = utils::getConfigurationParameterSizeT("OPENCV_DNN_NETWORK_DUMP", 0); + return DNN_NETWORK_DUMP; +} + +// this option is useful to run with valgrind memory errors detection +bool getParam_DNN_DISABLE_MEMORY_OPTIMIZATIONS() +{ + static bool DNN_DISABLE_MEMORY_OPTIMIZATIONS = utils::getConfigurationParameterBool("OPENCV_DNN_DISABLE_MEMORY_OPTIMIZATIONS", false); + return DNN_DISABLE_MEMORY_OPTIMIZATIONS; +} + +#ifdef HAVE_OPENCL +bool getParam_DNN_OPENCL_ALLOW_ALL_DEVICES() +{ + static bool DNN_OPENCL_ALLOW_ALL_DEVICES = utils::getConfigurationParameterBool("OPENCV_DNN_OPENCL_ALLOW_ALL_DEVICES", false); + return DNN_OPENCL_ALLOW_ALL_DEVICES; +} +#endif + +int getParam_DNN_BACKEND_DEFAULT() +{ + static int PARAM_DNN_BACKEND_DEFAULT = (int)utils::getConfigurationParameterSizeT("OPENCV_DNN_BACKEND_DEFAULT", +#ifdef HAVE_INF_ENGINE + (size_t)DNN_BACKEND_INFERENCE_ENGINE +#else + (size_t)DNN_BACKEND_OPENCV +#endif + ); + return PARAM_DNN_BACKEND_DEFAULT; +} + +// Additional checks (slowdowns execution!) +bool getParam_DNN_CHECK_NAN_INF() +{ + static bool DNN_CHECK_NAN_INF = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF", false); + return DNN_CHECK_NAN_INF; +} +bool getParam_DNN_CHECK_NAN_INF_DUMP() +{ + static bool DNN_CHECK_NAN_INF_DUMP = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_DUMP", false); + return DNN_CHECK_NAN_INF_DUMP; +} +bool getParam_DNN_CHECK_NAN_INF_RAISE_ERROR() +{ + static bool DNN_CHECK_NAN_INF_RAISE_ERROR = utils::getConfigurationParameterBool("OPENCV_DNN_CHECK_NAN_INF_RAISE_ERROR", false); + return DNN_CHECK_NAN_INF_RAISE_ERROR; +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/dnn_read.cpp b/modules/dnn/src/dnn_read.cpp new file mode 100644 index 000000000000..931170722b2d --- /dev/null +++ b/modules/dnn/src/dnn_read.cpp @@ -0,0 +1,93 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +Net readNet(const String& _model, const String& _config, const String& _framework) +{ + String framework = toLowerCase(_framework); + String model = _model; + String config = _config; + const std::string modelExt = model.substr(model.rfind('.') + 1); + const std::string configExt = config.substr(config.rfind('.') + 1); + if (framework == "caffe" || modelExt == "caffemodel" || configExt == "caffemodel" || modelExt == "prototxt" || configExt == "prototxt") + { + if (modelExt == "prototxt" || configExt == "caffemodel") + std::swap(model, config); + return readNetFromCaffe(config, model); + } + if (framework == "tensorflow" || modelExt == "pb" || configExt == "pb" || modelExt == "pbtxt" || configExt == "pbtxt") + { + if (modelExt == "pbtxt" || configExt == "pb") + std::swap(model, config); + return readNetFromTensorflow(model, config); + } + if (framework == "torch" || modelExt == "t7" || modelExt == "net" || configExt == "t7" || configExt == "net") + { + return readNetFromTorch(model.empty() ? config : model); + } + if (framework == "darknet" || modelExt == "weights" || configExt == "weights" || modelExt == "cfg" || configExt == "cfg") + { + if (modelExt == "cfg" || configExt == "weights") + std::swap(model, config); + return readNetFromDarknet(config, model); + } + if (framework == "dldt" || modelExt == "bin" || configExt == "bin" || modelExt == "xml" || configExt == "xml") + { + if (modelExt == "xml" || configExt == "bin") + std::swap(model, config); + return readNetFromModelOptimizer(config, model); + } + if (framework == "onnx" || modelExt == "onnx") + { + return readNetFromONNX(model); + } + CV_Error(Error::StsError, "Cannot determine an origin framework of files: " + model + (config.empty() ? "" : ", " + config)); +} + +Net readNet(const String& _framework, const std::vector& bufferModel, + const std::vector& bufferConfig) +{ + String framework = toLowerCase(_framework); + if (framework == "caffe") + return readNetFromCaffe(bufferConfig, bufferModel); + else if (framework == "tensorflow") + return readNetFromTensorflow(bufferModel, bufferConfig); + else if (framework == "darknet") + return readNetFromDarknet(bufferConfig, bufferModel); + else if (framework == "torch") + CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers"); + else if (framework == "dldt") + return readNetFromModelOptimizer(bufferConfig, bufferModel); + CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework); +} + +Net readNetFromModelOptimizer(const String& xml, const String& bin) +{ + return Net::readFromModelOptimizer(xml, bin); +} + +Net readNetFromModelOptimizer(const std::vector& bufferCfg, const std::vector& bufferModel) +{ + return Net::readFromModelOptimizer(bufferCfg, bufferModel); +} + +Net readNetFromModelOptimizer( + const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize, + const uchar* bufferWeightsPtr, size_t bufferWeightsSize) +{ + return Net::readFromModelOptimizer( + bufferModelConfigPtr, bufferModelConfigSize, + bufferWeightsPtr, bufferWeightsSize); +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/dnn_utils.cpp b/modules/dnn/src/dnn_utils.cpp new file mode 100644 index 000000000000..aa4a6eadf1a8 --- /dev/null +++ b/modules/dnn/src/dnn_utils.cpp @@ -0,0 +1,158 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include + + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +Mat blobFromImage(InputArray image, double scalefactor, const Size& size, + const Scalar& mean, bool swapRB, bool crop, int ddepth) +{ + CV_TRACE_FUNCTION(); + Mat blob; + blobFromImage(image, blob, scalefactor, size, mean, swapRB, crop, ddepth); + return blob; +} + +void blobFromImage(InputArray image, OutputArray blob, double scalefactor, + const Size& size, const Scalar& mean, bool swapRB, bool crop, int ddepth) +{ + CV_TRACE_FUNCTION(); + std::vector images(1, image.getMat()); + blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth); +} + +Mat blobFromImages(InputArrayOfArrays images, double scalefactor, Size size, + const Scalar& mean, bool swapRB, bool crop, int ddepth) +{ + CV_TRACE_FUNCTION(); + Mat blob; + blobFromImages(images, blob, scalefactor, size, mean, swapRB, crop, ddepth); + return blob; +} + +void blobFromImages(InputArrayOfArrays images_, OutputArray blob_, double scalefactor, + Size size, const Scalar& mean_, bool swapRB, bool crop, int ddepth) +{ + CV_TRACE_FUNCTION(); + CV_CheckType(ddepth, ddepth == CV_32F || ddepth == CV_8U, "Blob depth should be CV_32F or CV_8U"); + if (ddepth == CV_8U) + { + CV_CheckEQ(scalefactor, 1.0, "Scaling is not supported for CV_8U blob depth"); + CV_Assert(mean_ == Scalar() && "Mean subtraction is not supported for CV_8U blob depth"); + } + + std::vector images; + images_.getMatVector(images); + CV_Assert(!images.empty()); + for (size_t i = 0; i < images.size(); i++) + { + Size imgSize = images[i].size(); + if (size == Size()) + size = imgSize; + if (size != imgSize) + { + if (crop) + { + float resizeFactor = std::max(size.width / (float)imgSize.width, + size.height / (float)imgSize.height); + resize(images[i], images[i], Size(), resizeFactor, resizeFactor, INTER_LINEAR); + Rect crop(Point(0.5 * (images[i].cols - size.width), + 0.5 * (images[i].rows - size.height)), + size); + images[i] = images[i](crop); + } + else + resize(images[i], images[i], size, 0, 0, INTER_LINEAR); + } + if (images[i].depth() == CV_8U && ddepth == CV_32F) + images[i].convertTo(images[i], CV_32F); + Scalar mean = mean_; + if (swapRB) + std::swap(mean[0], mean[2]); + + images[i] -= mean; + images[i] *= scalefactor; + } + + size_t nimages = images.size(); + Mat image0 = images[0]; + int nch = image0.channels(); + CV_Assert(image0.dims == 2); + if (nch == 3 || nch == 4) + { + int sz[] = { (int)nimages, nch, image0.rows, image0.cols }; + blob_.create(4, sz, ddepth); + Mat blob = blob_.getMat(); + Mat ch[4]; + + for (size_t i = 0; i < nimages; i++) + { + const Mat& image = images[i]; + CV_Assert(image.depth() == blob_.depth()); + nch = image.channels(); + CV_Assert(image.dims == 2 && (nch == 3 || nch == 4)); + CV_Assert(image.size() == image0.size()); + + for (int j = 0; j < nch; j++) + ch[j] = Mat(image.rows, image.cols, ddepth, blob.ptr((int)i, j)); + if (swapRB) + std::swap(ch[0], ch[2]); + split(image, ch); + } + } + else + { + CV_Assert(nch == 1); + int sz[] = { (int)nimages, 1, image0.rows, image0.cols }; + blob_.create(4, sz, ddepth); + Mat blob = blob_.getMat(); + + for (size_t i = 0; i < nimages; i++) + { + const Mat& image = images[i]; + CV_Assert(image.depth() == blob_.depth()); + nch = image.channels(); + CV_Assert(image.dims == 2 && (nch == 1)); + CV_Assert(image.size() == image0.size()); + + image.copyTo(Mat(image.rows, image.cols, ddepth, blob.ptr((int)i, 0))); + } + } +} + +void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_) +{ + CV_TRACE_FUNCTION(); + + // A blob is a 4 dimensional matrix in floating point precision + // blob_[0] = batchSize = nbOfImages + // blob_[1] = nbOfChannels + // blob_[2] = height + // blob_[3] = width + CV_Assert(blob_.depth() == CV_32F); + CV_Assert(blob_.dims == 4); + + images_.create(cv::Size(1, blob_.size[0]), blob_.depth()); + + std::vector vectorOfChannels(blob_.size[1]); + for (int n = 0; n < blob_.size[0]; ++n) + { + for (int c = 0; c < blob_.size[1]; ++c) + { + vectorOfChannels[c] = getPlane(blob_, n, c); + } + cv::merge(vectorOfChannels, images_.getMatRef(n)); + } +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/graph_simplifier.cpp b/modules/dnn/src/graph_simplifier.cpp index a23fce30f58d..e58e0e38e853 100644 --- a/modules/dnn/src/graph_simplifier.cpp +++ b/modules/dnn/src/graph_simplifier.cpp @@ -108,7 +108,7 @@ bool Subgraph::match(const Ptr& net, int nodeId, for (int j = 0; j < inputNodes.size(); ++j) { - if (nodes[inputNodes[j]].empty()) // Unknown input node type. + if (nodes[inputNodes[j]].empty() || node->getInputName(j).empty()) // Unknown input node type. continue; nodeId = getInputNodeId(net, node, j); const Ptr inpNode = net->getNode(nodeId); diff --git a/modules/dnn/src/ie_ngraph.cpp b/modules/dnn/src/ie_ngraph.cpp index a61766337e30..d2bb2f189c42 100644 --- a/modules/dnn/src/ie_ngraph.cpp +++ b/modules/dnn/src/ie_ngraph.cpp @@ -80,7 +80,7 @@ class NgraphCustomOp: public ngraph::op::Op { public: const ngraph::NodeTypeInfo& get_type_info() const override { - static constexpr ngraph::NodeTypeInfo type_info{kOpenCVLayersType, 0}; + static constexpr ngraph::NodeTypeInfo type_info{kOpenCVLayersType, static_cast(0)}; return type_info; } @@ -330,7 +330,7 @@ class InfEngineNgraphExtension : public InferenceEngine::IExtension InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr&& _node) : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) {} -InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr& _node) +InfEngineNgraphNode::InfEngineNgraphNode(const std::shared_ptr& _node) : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(_node) {} InfEngineNgraphNode::InfEngineNgraphNode(const std::vector >& nodes, @@ -379,16 +379,21 @@ InfEngineNgraphNet::InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEn device_name = "CPU"; } -void InfEngineNgraphNet::addOutput(const std::string& name) +void InfEngineNgraphNet::addOutput(const Ptr& node) { - requestedOutputs.push_back(name); + CV_Assert(node); + CV_Assert(node->node); + const std::string& name = node->node->get_friendly_name(); + requestedOutputs.insert({name, node}); } void InfEngineNgraphNet::setNodePtr(std::shared_ptr* ptr) { all_nodes.emplace((*ptr)->get_friendly_name(), ptr); } - void InfEngineNgraphNet::release() { + void InfEngineNgraphNet::release() + { + // FIXIT release should not be conditional, release ALL for (auto& node : components.back()) { #if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) if (!(ngraph::op::is_parameter(node) || ngraph::op::is_output(node) || ngraph::op::is_constant(node)) ) { @@ -397,7 +402,6 @@ void InfEngineNgraphNet::setNodePtr(std::shared_ptr* ptr) { #endif auto it = all_nodes.find(node->get_friendly_name()); if (it != all_nodes.end()) { - unconnectedNodes.erase(*(it->second)); it->second->reset(); all_nodes.erase(it); } @@ -422,7 +426,8 @@ void InfEngineNgraphNet::dfs(std::shared_ptr& node, } } -int InfEngineNgraphNet::getNumComponents() { +int InfEngineNgraphNet::getNumComponents() +{ if (!components.empty()) { return components.size(); } @@ -445,17 +450,21 @@ int InfEngineNgraphNet::getNumComponents() { void InfEngineNgraphNet::createNet(Target targetId) { if (!hasNetOwner) { - CV_Assert(!unconnectedNodes.empty()); + CV_Assert(!requestedOutputs.empty()); ngraph::ResultVector outs; - for (auto& node : unconnectedNodes) + + for (auto output_node_it = requestedOutputs.begin(); output_node_it != requestedOutputs.end(); ++output_node_it) { - auto out = std::make_shared(node); + CV_LOG_DEBUG(NULL, "DNN/NGRAPH: Add 'Result' output: " << output_node_it->first); + CV_Assert(output_node_it->second); + auto out = std::make_shared(output_node_it->second->node); outs.push_back(out); } CV_Assert_N(!inputs_vec.empty(), !outs.empty()); ngraph_function = std::make_shared(outs, inputs_vec); int num_comp = getNumComponents(); + CV_LOG_DEBUG(NULL, "DNN/IE: number of subgraphs: " << num_comp); if (num_comp > 1) { for (int i = num_comp - 1; i >= 0; --i) { ngraph::ResultVector outputs; @@ -466,6 +475,7 @@ void InfEngineNgraphNet::createNet(Target targetId) { #else if (node->is_parameter()) { #endif + CV_LOG_DEBUG(NULL, "DNN/IE: subgraph[" << i << "]: +input[" << inps.size() << "] = '" << node->get_friendly_name() << "'"); auto parameter = std::dynamic_pointer_cast(node); inps.push_back(parameter); } @@ -474,10 +484,12 @@ void InfEngineNgraphNet::createNet(Target targetId) { #else else if (node->is_output()) { #endif + CV_LOG_DEBUG(NULL, "DNN/IE: subgraph[" << i << "]: +output[" << outputs.size() << "] = '" << node->get_friendly_name() << "'"); auto result = std::dynamic_pointer_cast(node); outputs.push_back(result); } } + CV_LOG_DEBUG(NULL, "DNN/IE: subgraph[" << i << ": nodes=" << components.back().size() << " inputs=" << inps.size() << " outputs=" << outputs.size()); isInit = false; CV_Assert_N(!inps.empty(), !outputs.empty()); ngraph_function = std::make_shared(outputs, inps); @@ -574,7 +586,7 @@ void InfEngineNgraphNet::init(Target targetId) auto node = ngraph_function->output(i).get_node(); for (size_t j = 0; j < node->get_input_size(); ++j) { std::string name = node->input_value(j).get_node()->get_friendly_name(); - auto iter = std::find(requestedOutputs.begin(), requestedOutputs.end(), name); + auto iter = requestedOutputs.find(name); if (iter != requestedOutputs.end()) { requestedOutputs.erase(iter); cnn.addOutput(name); @@ -582,10 +594,6 @@ void InfEngineNgraphNet::init(Target targetId) } } } - for (const auto& name : requestedOutputs) - { - cnn.addOutput(name); - } for (const auto& it : cnn.getInputsInfo()) { @@ -630,9 +638,6 @@ ngraph::ParameterVector InfEngineNgraphNet::setInputs(const std::vector return current_inp; } -void InfEngineNgraphNet::setUnconnectedNodes(Ptr& node) { - unconnectedNodes.insert(node->node); -} void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) { @@ -732,10 +737,10 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net) } } } - if (isHetero) - netExec = ie.LoadNetwork(net, "HETERO:" + device_name + ",CPU", config); - else - netExec = ie.LoadNetwork(net, device_name, config); + + std::string ieDevice = isHetero ? ("HETERO:" + device_name + ",CPU") : device_name; + CV_LOG_INFO(NULL, "DNN/IE: Calling LoadNetwork(device=" << ieDevice << ")..."); + netExec = ie.LoadNetwork(net, ieDevice, config); } catch (const std::exception& ex) { diff --git a/modules/dnn/src/ie_ngraph.hpp b/modules/dnn/src/ie_ngraph.hpp index 617f1d454232..0d287a22a5fb 100644 --- a/modules/dnn/src/ie_ngraph.hpp +++ b/modules/dnn/src/ie_ngraph.hpp @@ -37,7 +37,7 @@ class InfEngineNgraphNet InfEngineNgraphNet(detail::NetImplBase& netImpl); InfEngineNgraphNet(detail::NetImplBase& netImpl, InferenceEngine::CNNNetwork& net); - void addOutput(const std::string& name); + void addOutput(const Ptr& node); bool isInitialized(); void init(Target targetId); @@ -47,7 +47,6 @@ class InfEngineNgraphNet void initPlugin(InferenceEngine::CNNNetwork& net); ngraph::ParameterVector setInputs(const std::vector& inputs, const std::vector& names); - void setUnconnectedNodes(Ptr& node); void addBlobs(const std::vector >& ptrs); void createNet(Target targetId); @@ -88,8 +87,7 @@ class InfEngineNgraphNet InferenceEngine::CNNNetwork cnn; bool hasNetOwner; - std::vector requestedOutputs; - std::unordered_set> unconnectedNodes; + std::unordered_map > requestedOutputs; std::map outputsDesc; }; @@ -102,7 +100,7 @@ class InfEngineNgraphNode : public BackendNode std::vector& internals); InfEngineNgraphNode(std::shared_ptr&& _node); - InfEngineNgraphNode(std::shared_ptr& _node); + InfEngineNgraphNode(const std::shared_ptr& _node); void setName(const std::string& name); diff --git a/modules/dnn/src/init.cpp b/modules/dnn/src/init.cpp index 55ed1e5d17c6..6979d1864d7d 100644 --- a/modules/dnn/src/init.cpp +++ b/modules/dnn/src/init.cpp @@ -92,6 +92,7 @@ void initializeLayerFactory() CV_DNN_REGISTER_LAYER_CLASS(Pooling, PoolingLayer); CV_DNN_REGISTER_LAYER_CLASS(ROIPooling, PoolingLayer); CV_DNN_REGISTER_LAYER_CLASS(PSROIPooling, PoolingLayer); + CV_DNN_REGISTER_LAYER_CLASS(Reduce, ReduceLayer); CV_DNN_REGISTER_LAYER_CLASS(LRN, LRNLayer); CV_DNN_REGISTER_LAYER_CLASS(InnerProduct, InnerProductLayer); CV_DNN_REGISTER_LAYER_CLASS(Softmax, SoftmaxLayer); @@ -129,6 +130,8 @@ void initializeLayerFactory() CV_DNN_REGISTER_LAYER_CLASS(HardSwish, HardSwishLayer); CV_DNN_REGISTER_LAYER_CLASS(Sin, SinLayer); CV_DNN_REGISTER_LAYER_CLASS(Sinh, SinhLayer); + CV_DNN_REGISTER_LAYER_CLASS(Sign, SignLayer); + CV_DNN_REGISTER_LAYER_CLASS(Shrink, ShrinkLayer); CV_DNN_REGISTER_LAYER_CLASS(Softplus, SoftplusLayer); CV_DNN_REGISTER_LAYER_CLASS(Softsign, SoftsignLayer); CV_DNN_REGISTER_LAYER_CLASS(Tan, TanLayer); @@ -143,6 +146,7 @@ void initializeLayerFactory() CV_DNN_REGISTER_LAYER_CLASS(Silence, BlankLayer); CV_DNN_REGISTER_LAYER_CLASS(Const, ConstLayer); CV_DNN_REGISTER_LAYER_CLASS(Arg, ArgLayer); + CV_DNN_REGISTER_LAYER_CLASS(Reciprocal, ReciprocalLayer); CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer); CV_DNN_REGISTER_LAYER_CLASS(Eltwise, EltwiseLayer); @@ -175,6 +179,7 @@ void initializeLayerFactory() CV_DNN_REGISTER_LAYER_CLASS(ConvolutionInt8, ConvolutionLayerInt8); CV_DNN_REGISTER_LAYER_CLASS(InnerProductInt8, InnerProductLayerInt8); CV_DNN_REGISTER_LAYER_CLASS(PoolingInt8, PoolingLayerInt8); + CV_DNN_REGISTER_LAYER_CLASS(ReduceInt8, ReduceLayerInt8); CV_DNN_REGISTER_LAYER_CLASS(EltwiseInt8, EltwiseLayerInt8); CV_DNN_REGISTER_LAYER_CLASS(BatchNormInt8, BatchNormLayerInt8); CV_DNN_REGISTER_LAYER_CLASS(ScaleInt8, ScaleLayerInt8); diff --git a/modules/dnn/src/int8layers/batch_norm_layer.cpp b/modules/dnn/src/int8layers/batch_norm_layer.cpp index c5b8c3d9e9b0..a3a9ebb261f1 100644 --- a/modules/dnn/src/int8layers/batch_norm_layer.cpp +++ b/modules/dnn/src/int8layers/batch_norm_layer.cpp @@ -4,6 +4,8 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" + #include namespace cv @@ -103,6 +105,11 @@ class BatchNormLayerInt8Impl CV_FINAL : public BatchNormLayerInt8 virtual bool supportBackend(int backendId) CV_OVERRIDE { + if (backendId == DNN_BACKEND_TIMVX && haveTimVX()) + { + return true; + } + return backendId == DNN_BACKEND_OPENCV; } @@ -116,6 +123,121 @@ class BatchNormLayerInt8Impl CV_FINAL : public BatchNormLayerInt8 return false; } + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + const int numChannels = (int)origin_bias.total(); + Mat tvGamma = origin_weights.reshape(1, numChannels); + Mat tvBeta = origin_bias.reshape(1, numChannels); + + std::vector inputsIndex; + std::vector outputsIndex; + + Mat tvMean = Mat::zeros(1, numChannels, CV_32F); + tvMean = tvMean.reshape(1, numChannels); + Mat tvVar = Mat::ones(1, numChannels, CV_32F); + tvVar = tvVar.reshape(1, numChannels); + + CV_Assert(inputsWrapper.size() == 1); + if (outputsWrapper.size() > 1) + return Ptr(); + + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp)); + + // input Tensor + auto inputWrapper = inputsWrapper[0].dynamicCast(); + Mat tmpInput = inputWrapper->getMat(); + + if (tmpInput.dims != 4) // Only support 4 dim input. + return Ptr(); + + int input_index = -1, mean_index = -1, var_index = -1, gamma_index = -1, beta_index = -1, output_index = -1; + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + // Mean tensor + Ptr meanWrapper = Ptr(new TimVXBackendWrapper(tvMean)); + Ptr meanQuant; + meanWrapper->createTensor(graph, tim::vx::TensorAttribute::CONSTANT); + mean_index = tvGraph->addWrapper(meanWrapper); + inputsIndex.push_back(mean_index); + + // Var tensor + Ptr varWrapper = Ptr(new TimVXBackendWrapper(tvVar)); + varWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT); + var_index = tvGraph->addWrapper(varWrapper); + inputsIndex.push_back(var_index); + + // Gamma tensor + Ptr gammaWrapper = Ptr(new TimVXBackendWrapper(tvGamma)); + gammaWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT); + gamma_index = tvGraph->addWrapper(gammaWrapper); + inputsIndex.push_back(gamma_index); + + // Beta tensor + Ptr betaWrapper = Ptr(new TimVXBackendWrapper(tvBeta)); + betaWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT); + beta_index = tvGraph->addWrapper(betaWrapper); + inputsIndex.push_back(beta_index); + + // Output tensor + CV_Assert(outputsWrapper.size() == 1); + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvBatchNorm = graph->CreateOperation(0.f); + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvBatchNorm, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE { CV_TRACE_FUNCTION(); diff --git a/modules/dnn/src/int8layers/convolution_layer.cpp b/modules/dnn/src/int8layers/convolution_layer.cpp index ea29610222e7..45aaa3bc1977 100644 --- a/modules/dnn/src/int8layers/convolution_layer.cpp +++ b/modules/dnn/src/int8layers/convolution_layer.cpp @@ -9,6 +9,7 @@ #include "opencv2/core/hal/hal.hpp" #include "opencv2/core/hal/intrin.hpp" +#include "../op_timvx.hpp" #include #include @@ -46,6 +47,7 @@ class BaseConvolutionLayerInt8Impl : public ConvolutionLayerInt8 int ngroups = params.get("group", 1); CV_Assert(numOutput % ngroups == 0); + input_sc = params.get("input_scale"); input_zp = params.get("input_zeropoint"); output_zp = params.get("zeropoints"); output_sc = params.get("scales"); @@ -181,6 +183,16 @@ class ConvolutionLayerInt8Impl CV_FINAL : public BaseConvolutionLayerInt8Impl virtual bool supportBackend(int backendId) CV_OVERRIDE { size_t ksize = kernel_size.size(); + +#ifdef HAVE_TIMVX + if (backendId == DNN_BACKEND_TIMVX) + { + /* only Conv1d and Conv2d supported. */ + if (ksize == 2 || ksize == 1) + return true; + return false; + } +#endif // Only default backend and Conv1D/Conv2D/Conv3D are supported return backendId == DNN_BACKEND_OPENCV && ksize >= 1 && ksize <= 3; } @@ -261,6 +273,11 @@ class ConvolutionLayerInt8Impl CV_FINAL : public BaseConvolutionLayerInt8Impl bool setActivation(const Ptr& layer) CV_OVERRIDE { + // TODO! add activation in convolution. +#ifdef HAVE_TIMVX + if (preferableTarget == DNN_TARGET_NPU) + return false; +#endif Ptr activ_int8 = layer.dynamicCast(); if (!activ_int8.empty()) { @@ -300,6 +317,249 @@ class ConvolutionLayerInt8Impl CV_FINAL : public BaseConvolutionLayerInt8Impl outputMultiplier[outCn] = outputMultiplier[outCn+1] = outputMultiplier[outCn-1]; } + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + /* TODO :support GroupConv; + Ref: + https://github.com/VeriSilicon/TIM-VX/blob/main/docs/Operators.md#conv2d + Link Reference: https://github.com/VeriSilicon/TIM-VX/blob/main/src/tim/vx/ops/conv1d_test.cc + */ + + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + Mat tvWeightMat = blobs[0]; + + std::vector tvBiasVec; + tvBiasVec.assign(biasvec.begin(), biasvec.end() - 2); + Mat tvBiasMat(tvBiasVec); + + for (int i = 0; i < numOutput; i++) + { + tvBiasVec[i] += input_zp * (cv::sum(blobs[0].row(i))[0]); + } + + // Padding Type + tim::vx::PadType tvPadType; + + if (padMode.empty()) + { + tvPadType = tim::vx::PadType::AUTO; // TODO! check the padding type. + } + else if(padMode == "VALID") + { + tvPadType = tim::vx::PadType::VALID; + } + else if (padMode == "SAME") + { + tvPadType = tim::vx::PadType::SAME; + } + else + { + CV_Error(Error::StsError, "Unsupported padding mode in TimVXBackend!"); + } + + size_t ksize = kernel_size.size(); + + std::vector inputsIndex; + std::vector outputsIndex; + + CV_Assert(inputsWrapper.size() == 1); + CV_Assert(ksize == 2 || ksize == 1); + + std::vector weight_scs, bias_scs; + std::vector weight_zps, bias_zps; + + weight_scs.resize(numOutput); + bias_scs.resize(numOutput); + + for (int i = 0; i < numOutput; i++) + { + bias_scs[i] = outputMultiplier[i] * output_sc; + weight_scs[i] = bias_scs[i] / input_sc; + } + + weight_zps.assign(numOutput, 0); + bias_zps.assign(numOutput, 0); + + bool tvSymmetric; + tvSymmetric = getQuantType(weight_scs, numOutput); + + // input Tensor + auto inputWrapper = inputsWrapper[0].dynamicCast(); + int input_index = -1, weight_index = -1, bias_index = -1, output_index = -1; + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp)); + inputWrapper->createTensor(graph, tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + // weight Tensor + auto tvConvWeightShape = shape(tvWeightMat); + Mat tvInputMat = inputWrapper->getMat(); + // calculate group value. + int group = tvInputMat.size[1] / tvWeightMat.size[1]; + + // TODO! It will be supported in future. + if (tvSymmetric && tvWeightMat.total() == tvConvWeightShape[0]) + return Ptr(); + // Reverse weight shape From OpenCV NCHW to TimVX WHCN. + std::reverse(tvConvWeightShape.begin(), tvConvWeightShape.end()); + + Ptr weightWrapper = Ptr(new TimVXBackendWrapper(tvWeightMat)); + Ptr weightQuant; + + if (tvSymmetric) + { + int wtChanneldim = tvWeightMat.dims - 1; + weightQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, wtChanneldim, + weight_scs, weight_zps)); + } + else + { + weightQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, weight_scs[0], 0)); + } + weightWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT, weightQuant); + + weight_index = tvGraph->addWrapper(weightWrapper); + inputsIndex.push_back(weight_index); + + // Bias Tensor + Ptr biasWrapper = Ptr(new TimVXBackendWrapper(tvBiasMat)); + Ptr biasQuant; + + if (tvSymmetric) + { + biasQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0, + bias_scs, bias_zps)); + } + else + { + biasQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, weight_scs[0] * input_sc, 0)); + } + + biasWrapper->createTensor(graph, tim::vx::TensorAttribute::CONSTANT, biasQuant); + bias_index = tvGraph->addWrapper(biasWrapper); + inputsIndex.push_back(bias_index); + // Output tensor + CV_Assert(outputsWrapper.size() == 1); + auto outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + if (isLast) + { + // From OpenCV NCHW, to TimVX WHCN + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvConv; + + if (ksize == 2) // for conv2d + { + int multiplier = 0; + if(group == tvConvWeightShape[3] && group != 1) + multiplier = 1; + if (group == 1 || (group == tvConvWeightShape[3] && group != 1)) // Conv2D || DeConv2D + { + if (tvPadType == tim::vx::PadType::AUTO) { + tvConv = graph->CreateOperation( + tvConvWeightShape[3], tvPadType, + std::array({(uint32_t) kernel_size[1], (uint32_t) kernel_size[0]}), + std::array({(uint32_t) strides[1], (uint32_t) strides[0]}), + std::array({(uint32_t) dilations[1], (uint32_t) dilations[0]}), + std::array({(uint32_t) pads_begin[1], (uint32_t) pads_end[1], + (uint32_t) pads_begin[0], (uint32_t) pads_end[0]}), + multiplier); + } + else + { + tvConv = graph->CreateOperation( + tvPadType, + std::array({(uint32_t) strides[1], (uint32_t) strides[0]}), + std::array({(uint32_t) dilations[1], (uint32_t) dilations[0]}), + multiplier); + } + } + else + { + // GroupedConv2d + if (tvPadType == tim::vx::PadType::AUTO) + { + tvConv = graph->CreateOperation( + std::array({(uint32_t) pads_begin[1], (uint32_t) pads_end[1], + (uint32_t) pads_begin[0], (uint32_t) pads_end[0]}), + std::array({(uint32_t)strides[1], (uint32_t)strides[0]}), + std::array({(uint32_t)dilations[1], (uint32_t)dilations[0]}), + group); + } + else + { + tvConv = graph->CreateOperation( + tvPadType, + std::array({(uint32_t)strides[1], (uint32_t)strides[0]}), + std::array({(uint32_t)dilations[1], (uint32_t)dilations[0]}), + group); + } + } + } + else + { + // for Conv1d + if (group != 1) + CV_Error( CV_StsNotImplemented, " Grouped Conv1d or Depth-Wise Conv1d are not supported by " + "TimVX Backend. Please try OpenCV Backend."); + tvConv = graph->CreateOperation( + tvConvWeightShape[2], tvPadType, (uint32_t)kernel_size[0], + (uint32_t)strides[0],(uint32_t)dilations[0], + std::array({(uint32_t)pads_begin[0], (uint32_t)pads_end[0]})); + } + // Create TimVXBackendNode + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvConv, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + class ParallelConv : public cv::ParallelLoopBody { public: diff --git a/modules/dnn/src/int8layers/elementwise_layers.cpp b/modules/dnn/src/int8layers/elementwise_layers.cpp index 75118b6bc123..f1b78f48fb6d 100644 --- a/modules/dnn/src/int8layers/elementwise_layers.cpp +++ b/modules/dnn/src/int8layers/elementwise_layers.cpp @@ -4,6 +4,7 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" #include #include @@ -16,14 +17,45 @@ namespace dnn class ActivationLayerInt8Impl CV_FINAL : public ActivationLayerInt8 { public: + int input_zp, output_zp; + float input_sc, output_sc; + float slope = 0.0f; + +#ifdef HAVE_TIMVX + tvActivationType tvActType; +#endif ActivationLayerInt8Impl(const LayerParams ¶ms) { setParamsFrom(params); activationLUT = !blobs.empty() ? blobs[0] : Mat(); + + input_zp = params.get("input_zeropoint"); + input_sc = params.get("input_scale"); + output_zp = params.get("zeropoints"); + output_sc = params.get("scales"); + + if (params.has("slope")) + { + slope = params.get("slope"); + } + +#ifdef HAVE_TIMVX + tvActType = getTimVXActType(type); +#endif + } virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_TIMVX + if (backendId == DNN_BACKEND_TIMVX) + { + // TODO!: Leaky ReLU will be supported in future. + if (tvActType == tvActReLU && slope != 0.f) + return false; + return tvActType != tvActNotSupported; + } +#endif return backendId == DNN_BACKEND_OPENCV; } @@ -106,6 +138,112 @@ class ActivationLayerInt8Impl CV_FINAL : public ActivationLayerInt8 } }; + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + std::vector inputsIndex, outputsIndex; + int input_index, output_index; + CV_Assert(inputsWrapper.size() == 1); + + // input Tensor + Ptr inputWrapper = inputsWrapper[0].dynamicCast(); + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if(input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp)); + inputWrapper->createTensor(graph, tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + + inputsIndex.push_back(input_index); + + // output tensor + CV_Assert(outputsWrapper.size() == 1); + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + Ptr outputTensor; + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvAct; + + switch(tvActType) { + case tvActReLU: + { + if (slope != 0.f) + tvAct = graph->CreateOperation(slope); + else + tvAct = graph->CreateOperation(); + break; + } + case tvActReLU6: + tvAct = graph->CreateOperation(); + break; + case tvActTanH: + tvAct = graph->CreateOperation(); + break; + case tvActSwish: + tvAct = graph->CreateOperation(); + break; + case tvActMish: + tvAct = graph->CreateOperation(); + break; + case tvActSigmoid: + tvAct = graph->CreateOperation(); + break; + case tvActELU: + tvAct = graph->CreateOperation(); + break; + default: + // TODO! check the default function. + tvAct = graph->CreateOperation(); + break; + } + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvAct, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE { CV_TRACE_FUNCTION(); diff --git a/modules/dnn/src/int8layers/eltwise_layer.cpp b/modules/dnn/src/int8layers/eltwise_layer.cpp index a522bc90314a..e0a8d4787cd5 100644 --- a/modules/dnn/src/int8layers/eltwise_layer.cpp +++ b/modules/dnn/src/int8layers/eltwise_layer.cpp @@ -4,6 +4,7 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" #include namespace cv @@ -22,6 +23,10 @@ class EltwiseLayerInt8Impl CV_FINAL : public EltwiseLayerInt8 } op; std::vector coeffs; std::vector zeropoints; + std::vector scales; + + int output_zp; + float output_sc; enum OutputChannelsMode { @@ -84,6 +89,20 @@ class EltwiseLayerInt8Impl CV_FINAL : public EltwiseLayerInt8 } } + if (params.has("input_scales")) + { + DictValue sc = params.get("input_scales"); + int i, n = sc.size(); + scales.resize(n); + for (i = 0; i < n; i++) + { + scales[i] = sc.get(i); + } + } + + output_zp = params.get("zeropoints"); + output_sc = params.get("scales"); + channelsModeInput = ELTWISE_CHANNNELS_SAME; if (params.has("output_channels_mode")) { @@ -116,6 +135,9 @@ class EltwiseLayerInt8Impl CV_FINAL : public EltwiseLayerInt8 virtual bool supportBackend(int backendId) CV_OVERRIDE { + // For TimVX Backend, only ELTWISE_CHANNNELS_SAME was supported. + if (backendId == DNN_BACKEND_TIMVX && haveTimVX()) + return channelsModeInput == ELTWISE_CHANNNELS_SAME; return backendId == DNN_BACKEND_OPENCV; } @@ -219,6 +241,134 @@ class EltwiseLayerInt8Impl CV_FINAL : public EltwiseLayerInt8 } } + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + if (inputsWrapper.size() != 2) + return Ptr(); + + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + bool isSub = false; + // TODO: support variable coeffs. + if (op == SUM) + { + CV_Assert(coeffs.size() == scales.size()); + std::vector originalCoeffs; + + for (int i = 0; i < coeffs.size(); i++) + { + originalCoeffs.push_back(coeffs[i] * output_sc / scales[i]); + } + + float eps = std::numeric_limits::epsilon(); + if (std::fabs(originalCoeffs[0] - 1.0f) <= eps * std::fabs(originalCoeffs[0] + 1.0f) && + std::fabs(originalCoeffs[1] + 1.0f) <= eps * std::fabs(originalCoeffs[1] - 1.0f)) + { + // Sub, if coeffs = {1., -1.}, isSub = true. + isSub = true; + } + else if (std::fabs(originalCoeffs[0] - 1.0f) <= eps * std::fabs(originalCoeffs[0] + 1.0f) && + std::abs(originalCoeffs[1] - 1.0f) <= eps * std::abs(originalCoeffs[1] + 1.0f)) + { + // Sum, if coeff = {1., 1.}, isSub = false. + isSub = false; + } + else + { + return Ptr(); + } + } + + std::vector inputsIndex, outputsIndex; + int input_index = -1, output_index = -1; + CV_Assert(channelsModeInput == ELTWISE_CHANNNELS_SAME); + + // Input + Ptr inputWrapper; + + CV_Assert(!scales.empty() && !zeropoints.empty()); + + for (int i = 0; i(); + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scales[i], zeropoints[i])); + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + + inputsIndex.push_back(input_index); + } + + // Output + CV_Assert(outputsWrapper.size() == 1); + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvEltwise; + + switch (op) { + case SUM: + if (isSub) + tvEltwise = graph->CreateOperation(); + else + tvEltwise = graph->CreateOperation(); + break; + case PROD: + tvEltwise = graph->CreateOperation(); + break; + case MAX: + tvEltwise = graph->CreateOperation(); + break; + default: + CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation"); + } + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvEltwise, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + class EltwiseInvoker : public ParallelLoopBody { EltwiseLayerInt8Impl& self; diff --git a/modules/dnn/src/int8layers/fully_connected_layer.cpp b/modules/dnn/src/int8layers/fully_connected_layer.cpp index 83da677a47f6..0887388b0b13 100644 --- a/modules/dnn/src/int8layers/fully_connected_layer.cpp +++ b/modules/dnn/src/int8layers/fully_connected_layer.cpp @@ -4,6 +4,7 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" #include @@ -19,7 +20,11 @@ class FullyConnectedLayerInt8Impl CV_FINAL : public InnerProductLayerInt8 FullyConnectedLayerInt8Impl(const LayerParams& params) { setParamsFrom(params); + + input_sc = params.get("input_scale"); + input_zp = params.get("input_zeropoint"); output_zp = params.get("zeropoints"); + output_sc = params.get("scales"); axis = params.get("axis", 1); if (blobs.size() == 3) { @@ -71,11 +76,25 @@ class FullyConnectedLayerInt8Impl CV_FINAL : public InnerProductLayerInt8 virtual bool supportBackend(int backendId) CV_OVERRIDE { + if (backendId == DNN_BACKEND_TIMVX && haveTimVX()) + { + if (biasMat.empty()) + return true; + else + return false; + } + return backendId == DNN_BACKEND_OPENCV; } virtual bool setActivation(const Ptr& layer) CV_OVERRIDE { + // TODO! add activation in Fully connection. +#ifdef HAVE_TIMVX + if(preferableTarget == DNN_TARGET_NPU) + return false; +#endif + Ptr activ_int8 = layer.dynamicCast(); if (!activ_int8.empty()) { @@ -87,6 +106,120 @@ class FullyConnectedLayerInt8Impl CV_FINAL : public InnerProductLayerInt8 return false; } + + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + int numOutput = blobs[0].size[0]; + Mat weightMat = blobs[0]; + + std::vector inputsIndex; + std::vector outputsIndex; + + std::vector weight_scs, bias_scs; + std::vector weight_zps; + + bias_scs.resize(numOutput); + weight_scs.resize(numOutput); + + for (int i = 0; i < numOutput; i++) + { + bias_scs[i] = outputMultiplier.at(i) * output_sc; + weight_scs[i] = bias_scs[i] / input_sc; + } + + weight_zps.assign(numOutput, 0); + + // input Tensor + auto inputWrapper = inputsWrapper[0].dynamicCast(); + int input_index = -1, weight_index = -1, output_index = -1; + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor() || input_index == -1) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp)); + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + // weight tensor + Ptr weightWrapper = Ptr(new TimVXBackendWrapper(weightMat)); + Ptr weightQuant; + + bool tvSymmetric; + tvSymmetric = getQuantType(weight_scs, numOutput); + + if (tvSymmetric) + { + // TODO! fix the following issue. + // TimVX does not support the SYMMETRIC PER CHANNEL MatMul. + return Ptr(); + } + else + { + weightQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, weight_scs[0], 0)); + } + weightWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT, weightQuant); + + weight_index = tvGraph->addWrapper(weightWrapper); + inputsIndex.push_back(weight_index); + + // Output tensor + CV_Assert(outputsWrapper.size() == 1); + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvMatmul; + + tvMatmul = graph->CreateOperation(false, true); + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvMatmul, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + class FullyConnected : public ParallelLoopBody { public: diff --git a/modules/dnn/src/int8layers/pooling_layer.cpp b/modules/dnn/src/int8layers/pooling_layer.cpp index 20a0486a4625..98cf17c06cf9 100644 --- a/modules/dnn/src/int8layers/pooling_layer.cpp +++ b/modules/dnn/src/int8layers/pooling_layer.cpp @@ -4,6 +4,7 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" #include "opencv2/core/hal/intrin.hpp" #include @@ -26,9 +27,12 @@ class PoolingLayerInt8Impl CV_FINAL : public PoolingLayerInt8 globalPooling = false; isGlobalPooling = std::vector(3, false); output_zp = params.get("zeropoints"); - input_zp = params.get("input_zeropoint", 0); + input_zp = params.get("input_zeropoint", output_zp); multiplier = params.get("multiplier", 1.f); + output_sc = params.get("scales"); + input_sc = multiplier * output_sc; + hasDynamicShapes = params.get("has_dynamic_shapes", false); shapesInitialized = !hasDynamicShapes; @@ -103,6 +107,24 @@ class PoolingLayerInt8Impl CV_FINAL : public PoolingLayerInt8 else return false; } + else if (backendId == DNN_BACKEND_TIMVX && haveTimVX()) + { + // Only pool 2d and pool 1d were supported. + if (kernel_size.size() == 3) + { + // fallback to CPU implementation. + preferableTarget = DNN_TARGET_CPU; + return false; + } + if (!avePoolPaddedArea) // TimVX does not support exclude padding. + return false; + if (globalPooling) // TODO support globalPooling in TimVX backend. + return false; + if (kernel_size.size() == 2) + return type == MAX || type == AVE; + return false; + } + return false; } @@ -116,6 +138,139 @@ class PoolingLayerInt8Impl CV_FINAL : public PoolingLayerInt8 return false; } + + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + tim::vx::PoolType tvPoolType; + tim::vx::RoundType tvRoundType; + size_t ksize = kernel_size.size(); + if (ksize != 2) + return Ptr(); + + // type Change from OpenCV to TimVX only MAX and AVG are supported. + switch (type) { + case MAX: { + tvPoolType = tim::vx::PoolType::MAX; + break; + } + case AVE:{ + tvPoolType = tim::vx::PoolType::AVG; + break; + } + default: + CV_Error(Error::StsNotImplemented, "Not implemented Pooling type in TimVX Backend."); + } + + // Padding Type + tim::vx::PadType tvPadType; + if (padMode.empty()) + { + tvPadType = tim::vx::PadType::AUTO; // TODO! check the padding type. + } + else if(padMode == "VALID") + { + tvPadType = tim::vx::PadType::VALID; + } + else if (padMode == "SAME") + { + tvPadType = tim::vx::PadType::SAME; + } + else + { + CV_Error(Error::StsError, "Unsupported padding mode in TimVXBackend!"); + } + + if (ceilMode) + tvRoundType = tim::vx::RoundType::CEILING; + else + tvRoundType = tim::vx::RoundType::FLOOR; + + auto input = inputsWrapper[0]; + std::vector inputsIndex; + std::vector outputsIndex; + + // input Tensor + auto inputWrapper = inputsWrapper[0].dynamicCast(); + int input_index, output_index; + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp)); + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + // Output tensor + CV_Assert(outputsWrapper.size() == 1); + auto outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + std::shared_ptr tvPool; + + if (tvPadType == tim::vx::PadType::AUTO) + { + tvPool = graph->CreateOperation( tvPoolType, + std::array({(uint32_t) pads_begin[1], (uint32_t) pads_end[1], + (uint32_t) pads_begin[0], (uint32_t) pads_end[0]}), + std::array({(uint32_t)kernel_size[1], (uint32_t)kernel_size[0]}), + std::array({(uint32_t)strides[1], (uint32_t)strides[0]}), + tvRoundType); + } + else + { + tvPool = graph->CreateOperation( + tvPoolType, tvPadType, + std::array({(uint32_t)kernel_size[1], (uint32_t)kernel_size[0]}), + std::array({(uint32_t)strides[1], (uint32_t)strides[0]}), + tvRoundType); + } + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvPool, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE { CV_TRACE_FUNCTION(); diff --git a/modules/dnn/src/int8layers/quantization_utils.cpp b/modules/dnn/src/int8layers/quantization_utils.cpp index d72487639e9e..6e2f0bb61cbf 100644 --- a/modules/dnn/src/int8layers/quantization_utils.cpp +++ b/modules/dnn/src/int8layers/quantization_utils.cpp @@ -4,6 +4,7 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" namespace cv { @@ -149,15 +150,21 @@ class DequantizeLayerImpl CV_FINAL : public DequantizeLayer class RequantizeLayerImpl CV_FINAL : public RequantizeLayer { public: + bool isEltwise; RequantizeLayerImpl(const LayerParams& params) { scale = params.get("scale", 1.f); shift = params.get("shift", 0.f); + isEltwise = params.get("isEltwise", false); setParamsFrom(params); } virtual bool supportBackend(int backendId) CV_OVERRIDE { + if (backendId == DNN_BACKEND_TIMVX && haveTimVX() && !isEltwise) + { + return true; + } return backendId == DNN_BACKEND_OPENCV; } @@ -178,6 +185,82 @@ class RequantizeLayerImpl CV_FINAL : public RequantizeLayer outputs_arr.getMatVector(outputs); } + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // preprocessing + // Check if data is 8-bit. + CV_Assert(inputsWrapper.size() == 1 && outputsWrapper.size() == 1); + Ptr inputWrapper = inputsWrapper[0].dynamicCast(); + + if (!inputWrapper->isTensor()) + { + return Ptr(); + } + + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + std::vector inputsIndex, outputsIndex; + int input_index = -1, output_index = -1; + + // Input + std::shared_ptr inputTensor = inputWrapper->getTensor(); + input_index = tvGraph->getTensorIndex(inputTensor); + if (input_index == -1) + return Ptr(); + + inputsIndex.push_back(input_index); + + Ptr inputQuant = inputWrapper->getTensorQuantization(); + + tim::vx::QuantType quanType = inputQuant->Type(); + CV_Assert(quanType == tim::vx::QuantType::ASYMMETRIC); + + std::vector scales = inputQuant->Scales(); + std::vector zeropoints = inputQuant->ZeroPoints(); + CV_Assert(!scales.empty() && !zeropoints.empty()); + int input_zp = int(zeropoints[0]); + float input_scale = scales[0]; + + float tmpOut_sc = input_scale/scale; + int tmpOut_zp = int(shift + scale * input_zp); + + // Output + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, tmpOut_sc, tmpOut_zp)); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvRequantize = graph->CreateOperation(); + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvRequantize, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE { CV_TRACE_FUNCTION(); diff --git a/modules/dnn/src/int8layers/reduce_layer.cpp b/modules/dnn/src/int8layers/reduce_layer.cpp new file mode 100644 index 000000000000..935bdc065978 --- /dev/null +++ b/modules/dnn/src/int8layers/reduce_layer.cpp @@ -0,0 +1,213 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "../precomp.hpp" +#include "layers_common.hpp" + +#include +#include +#include + +namespace cv +{ +namespace dnn +{ + +class ReduceLayerInt8Impl CV_FINAL : public ReduceLayerInt8 +{ +public: + ReduceLayerInt8Impl(const LayerParams& params) + { + // Set reduce type + CV_Assert(params.has("reduce")); + String typeString = toLowerCase(params.get("reduce")); + if (typeString == "max") + reduceType = MAX; + else if (typeString == "min") + reduceType = MIN; + else + CV_Error(Error::StsBadArg, "Unknown reduce type \"" + typeString + "\""); + + // Set deleted dims + CV_Assert(params.has("deleted_dims")); + DictValue tempDims = params.get("deleted_dims"); + int i, n = tempDims.size(); + reduceDims.resize(n); + for (i = 0; i < n; i++) + { + reduceDims[i] = tempDims.get(i); + } + } + + virtual bool supportBackend(int backendId) CV_OVERRIDE + { + if (backendId == DNN_BACKEND_OPENCV) + { + return true; + } + return false; + } + + // reduceType == MIN + struct ReduceOpMIN + { + int8_t apply(const int8_t* first, const int8_t* last) + { + return std::accumulate(first, last, *first, + [](int8_t a, int8_t b) + { + return std::min(a, b); + }); + } + }; + + // reduceType == MAX + struct ReduceOpMAX + { + int8_t apply(const int8_t* first, const int8_t* last) + { + return std::accumulate(first, last, *first, + [](int8_t a, int8_t b) + { + return std::max(a, b); + }); + } + }; + + template + class ReduceInvoker : public ParallelLoopBody + { + public: + const Mat* src; + Mat *dst; + std::vector reduceDims; + int nstripes; + int reduceType; + Ptr func; + + ReduceInvoker() : src(0), dst(0), nstripes(0), reduceType(MAX), func(makePtr()) {} + + static void run(const Mat& src, Mat& dst, std::vector reduceDims, int reduceType, int nstripes) + { + CV_Assert_N(src.isContinuous(), dst.isContinuous(), src.type() == CV_8S, src.type() == dst.type()); + + ReduceInvoker p; + + p.src = &src; + p.dst = &dst; + + p.reduceDims = reduceDims; + p.nstripes = nstripes; + p.reduceType = reduceType; + + parallel_for_(Range(0, nstripes), p, nstripes); + } + + void operator()(const Range& r) const CV_OVERRIDE + { + size_t total = dst->total(); + size_t stripeSize = (total + nstripes - 1)/nstripes; + size_t stripeStart = r.start*stripeSize; + size_t stripeEnd = std::min(r.end*stripeSize, total); + size_t totalDeleted = std::accumulate(reduceDims.begin(), reduceDims.end(), 1, std::multiplies()); + + int8_t *dstData = (int8_t *)dst->data; + int8_t *srcData = (int8_t *)src->data; + + for (size_t ofs = stripeStart; ofs < stripeEnd;) + { + const int8_t* first = srcData + ofs * totalDeleted; + const int8_t* last = srcData + (ofs + 1) * totalDeleted; + + dstData[ofs] = func->apply(first, last); + ofs += 1; + } + } + }; + + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE + { + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(name, "name", name.c_str()); + + std::vector inputs, outputs; + inputs_arr.getMatVector(inputs); + outputs_arr.getMatVector(outputs); + CV_Assert(inputs.size() == 1); + const int nstripes = getNumThreads(); + + switch (reduceType) + { + case MIN: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case MAX: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + default: + CV_Error(Error::StsNotImplemented, "Not implemented"); + break; + } + } + + bool getMemoryShapes(const std::vector &inputs, + const int requiredOutputs, + std::vector &outputs, + std::vector &internals) const CV_OVERRIDE + { + CV_Assert(inputs.size() > 0); + CV_Assert(reduceDims.size() != 0 && inputs[0].size() >= reduceDims.size()); + + std::vector outShape; + if (inputs[0].size() == reduceDims.size()) + outShape.push_back(1); + else + { + for (int i = 0; i < inputs[0].size() - reduceDims.size(); i++) + { + outShape.push_back(inputs[0][i]); + } + } + outputs.assign(1, outShape); + + return false; + } + + virtual bool tryQuantize(const std::vector > &scales, + const std::vector > &zeropoints, LayerParams& params) CV_OVERRIDE + { + return false; + } + + virtual int64 getFLOPS(const std::vector &inputs, + const std::vector &outputs) const CV_OVERRIDE + { + CV_UNUSED(inputs); // suppress unused variable warning + long flops = 0; + size_t totalDeleted = std::accumulate(reduceDims.begin(), reduceDims.end(), 1, std::multiplies()); + for (int i = 0; i < outputs.size(); i++) + { + flops += total(outputs[i])*(totalDeleted); + } + return flops; + } +private: + enum Type + { + MAX, + MIN + }; +}; + +Ptr ReduceLayerInt8::create(const LayerParams& params) +{ + return Ptr(new ReduceLayerInt8Impl(params)); +} + +} +} diff --git a/modules/dnn/src/int8layers/softmax_layer.cpp b/modules/dnn/src/int8layers/softmax_layer.cpp index 7e3c82bc21ab..b2caf56fb092 100644 --- a/modules/dnn/src/int8layers/softmax_layer.cpp +++ b/modules/dnn/src/int8layers/softmax_layer.cpp @@ -4,6 +4,7 @@ #include "../precomp.hpp" #include "layers_common.hpp" +#include "../op_timvx.hpp" #include #include @@ -16,11 +17,17 @@ namespace dnn class SoftMaxLayerInt8Impl CV_FINAL : public SoftmaxLayerInt8 { public: + float input_sc; + int input_zp; SoftMaxLayerInt8Impl(const LayerParams& params) { axisRaw = params.get("axis", 1); logSoftMax = params.get("log_softmax", false); + + input_sc = params.get("input_scale"); + input_zp = params.get("input_zeropoint"); + output_sc = params.get("scales"); output_zp = params.get("zeropoints"); setParamsFrom(params); @@ -41,7 +48,8 @@ class SoftMaxLayerInt8Impl CV_FINAL : public SoftmaxLayerInt8 virtual bool supportBackend(int backendId) CV_OVERRIDE { - return backendId == DNN_BACKEND_OPENCV; + return backendId == DNN_BACKEND_OPENCV || + (backendId == DNN_BACKEND_TIMVX && haveTimVX()); } virtual bool tryFuse(Ptr& top) CV_OVERRIDE @@ -50,6 +58,102 @@ class SoftMaxLayerInt8Impl CV_FINAL : public SoftmaxLayerInt8 return !dequantize_layer.empty() && preferableTarget != DNN_TARGET_OPENCL_FP16; } + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + std::vector inputsIndex, outputsIndex; + int input_index, output_index; + + // input Tensor + CV_Assert(inputsWrapper.size() == 1); + Ptr inputWrapper = inputsWrapper[0].dynamicCast(); + const Mat &src = inputWrapper->getMat(); + + // convert axis from OpenCV NCHW toTimVX WHCN. + int axis = normalize_axis(axisRaw, src.dims); + int tvAxis = src.dims - 1 - axis; + if(tvAxis < 0) + tvAxis = 0; // default value is 0. + + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp)); + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + // output tensor + CV_Assert(outputsWrapper.size() == 1); + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + Mat dstMat = outputWrapper->getMat(); + Ptr outputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp)); + + Ptr outputTensor; + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + if (dstMat.type() == CV_32F) + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT); + else + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + if (dstMat.type() == CV_32F) + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT); + else + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvSoftmax; + + if (logSoftMax) + { + tvSoftmax = graph->CreateOperation(tvAxis); + + } + else + { + tvSoftmax = graph->CreateOperation(1.0f, tvAxis); + } + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvSoftmax, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE { CV_TRACE_FUNCTION(); diff --git a/modules/dnn/src/layer.cpp b/modules/dnn/src/layer.cpp new file mode 100644 index 000000000000..0ed3488da6d2 --- /dev/null +++ b/modules/dnn/src/layer.cpp @@ -0,0 +1,257 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +Layer::Layer() { preferableTarget = DNN_TARGET_CPU; } + +Layer::Layer(const LayerParams& params) + : blobs(params.blobs) + , name(params.name) + , type(params.type) +{ + preferableTarget = DNN_TARGET_CPU; +} + +void Layer::setParamsFrom(const LayerParams& params) +{ + blobs = params.blobs; + name = params.name; + type = params.type; +} + +int Layer::inputNameToIndex(String) +{ + return -1; +} + +int Layer::outputNameToIndex(const String&) +{ + return 0; +} + +bool Layer::supportBackend(int backendId) +{ + return backendId == DNN_BACKEND_OPENCV; +} + +Ptr Layer::initCUDA( + void*, + const std::vector>&, + const std::vector>&) +{ + CV_Error(Error::StsNotImplemented, "CUDA pipeline of " + type + " layers is not defined."); + return Ptr(); +} + +Ptr Layer::initVkCom(const std::vector>&) +{ + CV_Error(Error::StsNotImplemented, "VkCom pipeline of " + type + " layers is not defined."); + return Ptr(); +} + +Ptr Layer::initHalide(const std::vector>&) +{ + CV_Error(Error::StsNotImplemented, "Halide pipeline of " + type + " layers is not defined."); + return Ptr(); +} + +Ptr Layer::initNgraph(const std::vector>& inputs, const std::vector>& nodes) +{ + CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type + " layers is not defined."); + return Ptr(); +} + +Ptr Layer::initWebnn(const std::vector>& inputs, const std::vector>& nodes) +{ + CV_Error(Error::StsNotImplemented, "WebNN pipeline of " + type + " layers is not defined."); + return Ptr(); +} + +Ptr Layer::initTimVX(void* timVxInfo, + const std::vector > & inputsWrapper, + const std::vector > & outputsWrapper, + bool isLast) +{ + CV_Error(Error::StsNotImplemented, "TimVX pipeline of " + type + + " layers is not defined."); + return Ptr(); +} + +Ptr Layer::tryAttach(const Ptr& node) +{ + return Ptr(); +} + +bool Layer::setActivation(const Ptr&) { return false; } +bool Layer::tryFuse(Ptr&) { return false; } +void Layer::getScaleShift(Mat& scale, Mat& shift) const +{ + scale = Mat(); + shift = Mat(); +} + +void Layer::getScaleZeropoint(float& scale, int& zeropoint) const +{ + scale = 1.f; + zeropoint = 0; +} + +void Layer::unsetAttached() +{ + setActivation(Ptr()); +} + +template +static void vecToPVec(const std::vector& v, std::vector& pv) +{ + pv.resize(v.size()); + for (size_t i = 0; i < v.size(); i++) + pv[i] = const_cast(&v[i]); +} + +void Layer::finalize(const std::vector& inputs, std::vector& outputs) +{ + CV_TRACE_FUNCTION(); + this->finalize((InputArrayOfArrays)inputs, (OutputArrayOfArrays)outputs); +} + +void Layer::finalize(const std::vector& input, std::vector& output) +{ + CV_UNUSED(input); + CV_UNUSED(output); +} + +void Layer::finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) +{ + CV_TRACE_FUNCTION(); + std::vector inputs, outputs; + inputs_arr.getMatVector(inputs); + outputs_arr.getMatVector(outputs); + + std::vector inputsp; + vecToPVec(inputs, inputsp); + this->finalize(inputsp, outputs); +} + +std::vector Layer::finalize(const std::vector& inputs) +{ + CV_TRACE_FUNCTION(); + + std::vector outputs; + this->finalize(inputs, outputs); + return outputs; +} + +void Layer::forward(std::vector& input, std::vector& output, std::vector& internals) +{ + // We kept this method for compatibility. DNN calls it now only to support users' implementations. +} + +void Layer::forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(name, "name", name.c_str()); + + Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr); +} + +void Layer::forward_fallback(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(name, "name", name.c_str()); + + if (preferableTarget == DNN_TARGET_OPENCL_FP16 && inputs_arr.depth() == CV_16S) + { + std::vector inputs; + std::vector outputs; + std::vector internals; + + std::vector orig_inputs; + std::vector orig_outputs; + std::vector orig_internals; + + inputs_arr.getUMatVector(orig_inputs); + outputs_arr.getUMatVector(orig_outputs); + internals_arr.getUMatVector(orig_internals); + + inputs.resize(orig_inputs.size()); + for (size_t i = 0; i < orig_inputs.size(); i++) + convertFp16(orig_inputs[i], inputs[i]); + + outputs.resize(orig_outputs.size()); + for (size_t i = 0; i < orig_outputs.size(); i++) + outputs[i].create(shape(orig_outputs[i]), CV_32F); + + internals.resize(orig_internals.size()); + for (size_t i = 0; i < orig_internals.size(); i++) + internals[i].create(shape(orig_internals[i]), CV_32F); + + forward(inputs, outputs, internals); + + for (size_t i = 0; i < outputs.size(); i++) + convertFp16(outputs[i], orig_outputs[i]); + + // sync results back + outputs_arr.assign(orig_outputs); + internals_arr.assign(orig_internals); + return; + } + std::vector inpvec; + std::vector outputs; + std::vector internals; + + inputs_arr.getMatVector(inpvec); + outputs_arr.getMatVector(outputs); + internals_arr.getMatVector(internals); + + std::vector inputs(inpvec.size()); + for (int i = 0; i < inpvec.size(); i++) + inputs[i] = &inpvec[i]; + + this->forward(inputs, outputs, internals); + + // sync results back + outputs_arr.assign(outputs); + internals_arr.assign(internals); +} + +void Layer::run(const std::vector& inputs, std::vector& outputs, std::vector& internals) +{ + CV_TRACE_FUNCTION(); + + this->finalize(inputs, outputs); + this->forward(inputs, outputs, internals); +} + +bool Layer::tryQuantize(const std::vector>& scales, + const std::vector>& zeropoints, LayerParams& params) +{ + return false; +} + +Layer::~Layer() {} + +bool Layer::getMemoryShapes(const std::vector& inputs, + const int requiredOutputs, + std::vector& outputs, + std::vector& internals) const +{ + CV_Assert(inputs.size()); + outputs.assign(std::max(requiredOutputs, (int)inputs.size()), inputs[0]); + return false; +} + +bool Layer::updateMemoryShapes(const std::vector& inputs) +{ + return true; +} + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/layer_factory.cpp b/modules/dnn/src/layer_factory.cpp new file mode 100644 index 000000000000..5c80cd09add7 --- /dev/null +++ b/modules/dnn/src/layer_factory.cpp @@ -0,0 +1,111 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include + +#include // getLayerFactoryImpl + + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + +Mutex& getLayerFactoryMutex() +{ + static Mutex* volatile instance = NULL; + if (instance == NULL) + { + cv::AutoLock lock(getInitializationMutex()); + if (instance == NULL) + instance = new Mutex(); + } + return *instance; +} + +static LayerFactory_Impl& getLayerFactoryImpl_() +{ + static LayerFactory_Impl impl; + return impl; +} + +LayerFactory_Impl& getLayerFactoryImpl() +{ + static LayerFactory_Impl* volatile instance = NULL; + if (instance == NULL) + { + cv::AutoLock lock(getLayerFactoryMutex()); + if (instance == NULL) + { + instance = &getLayerFactoryImpl_(); + initializeLayerFactory(); + } + } + return *instance; +} + +void LayerFactory::registerLayer(const String& type, Constructor constructor) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(type, "type", type.c_str()); + + cv::AutoLock lock(getLayerFactoryMutex()); + LayerFactory_Impl::iterator it = getLayerFactoryImpl().find(type); + + if (it != getLayerFactoryImpl().end()) + { + if (it->second.back() == constructor) + CV_Error(cv::Error::StsBadArg, "Layer \"" + type + "\" already was registered"); + it->second.push_back(constructor); + } + getLayerFactoryImpl().insert(std::make_pair(type, std::vector(1, constructor))); +} + +void LayerFactory::unregisterLayer(const String& type) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(type, "type", type.c_str()); + + cv::AutoLock lock(getLayerFactoryMutex()); + + LayerFactory_Impl::iterator it = getLayerFactoryImpl().find(type); + if (it != getLayerFactoryImpl().end()) + { + if (it->second.size() > 1) + it->second.pop_back(); + else + getLayerFactoryImpl().erase(it); + } +} + +bool LayerFactory::isLayerRegistered(const std::string& type) +{ + cv::AutoLock lock(getLayerFactoryMutex()); + auto& registeredLayers = getLayerFactoryImpl(); + return registeredLayers.find(type) != registeredLayers.end(); +} + +Ptr LayerFactory::createLayerInstance(const String& type, LayerParams& params) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(type, "type", type.c_str()); + + cv::AutoLock lock(getLayerFactoryMutex()); + LayerFactory_Impl::const_iterator it = getLayerFactoryImpl().find(type); + + if (it != getLayerFactoryImpl().end()) + { + CV_Assert(!it->second.empty()); + return it->second.back()(params); + } + else + { + return Ptr(); // NULL + } +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/layer_internals.hpp b/modules/dnn/src/layer_internals.hpp new file mode 100644 index 000000000000..9ded3543e176 --- /dev/null +++ b/modules/dnn/src/layer_internals.hpp @@ -0,0 +1,335 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef __OPENCV_DNN_SRC_LAYER_INTERNALS_HPP__ +#define __OPENCV_DNN_SRC_LAYER_INTERNALS_HPP__ + +namespace cv { namespace dnn { +CV__DNN_INLINE_NS_BEGIN +inline namespace detail { + +struct LayerPin +{ + int lid; + int oid; + + LayerPin(int layerId = -1, int outputId = -1) + : lid(layerId) + , oid(outputId) + {} + + bool valid() const + { + return (lid >= 0 && oid >= 0); + } + + bool equal(const LayerPin& r) const + { + return (lid == r.lid && oid == r.oid); + } + + bool operator<(const LayerPin& r) const + { + return lid < r.lid || (lid == r.lid && oid < r.oid); + } + + bool operator==(const LayerPin& r) const + { + return lid == r.lid && oid == r.oid; + } +}; + +struct LayerData +{ + LayerData() + : id(-1) + , dtype(CV_32F) + , skip(false) + , flag(0) + {} + LayerData(int _id, const String& _name, const String& _type, const int& _dtype, LayerParams& _params) + : id(_id) + , name(_name) + , type(_type) + , dtype(_dtype) + , params(_params) + , skip(false) + , flag(0) + { + CV_TRACE_FUNCTION(); + + // add logging info + params.name = name; + params.type = type; + } + + int id; + String name; + String type; + int dtype; // Datatype of output blobs. + LayerParams params; + + std::vector inputBlobsId; + std::set inputLayersId; + std::set requiredOutputs; + std::vector consumers; + std::vector> outputBlobsWrappers; + std::vector> inputBlobsWrappers; + std::vector> internalBlobsWrappers; + +#ifdef HAVE_CUDA + /* output ids which must be transferred to the host in the background + * after the completion of the forward pass of the layer + */ + std::vector cudaD2HBackgroundTransfers; +#endif + + Ptr layerInstance; + std::vector outputBlobs; + std::vector inputBlobs; + std::vector internals; + // Computation nodes of implemented backends (except DEFAULT). + std::map> backendNodes; + // Flag for skip layer computation for specific backend. + bool skip; + + int flag; + + Ptr getLayerInstance() + { + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(type, "type", type.c_str()); + + if (layerInstance) + return layerInstance; + + layerInstance = LayerFactory::createLayerInstance(type, params); + if (!layerInstance) + { + CV_Error(Error::StsError, "Can't create layer \"" + name + "\" of type \"" + type + "\""); + } + + return layerInstance; + } +}; + + +// fake layer containing network input blobs +struct DataLayer : public Layer +{ + DataLayer() + : Layer() + { + skip = false; + } + + virtual bool supportBackend(int backendId) CV_OVERRIDE + { + return backendId == DNN_BACKEND_OPENCV; + } + + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE + { + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(name, "name", name.c_str()); + + // FIXIT: add wrapper without exception suppression + CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget), + forward_ocl(inputs_arr, outputs_arr, internals_arr)) + + bool isFP16 = outputs_arr.depth() == CV_16S; + + std::vector outputs, internals; + outputs_arr.getMatVector(outputs); + internals_arr.getMatVector(internals); + + for (int i = 0; i < inputsData.size(); ++i) + { + double scale = scaleFactors[i]; + Scalar& mean = means[i]; + + CV_Assert(mean == Scalar() || inputsData[i].size[1] <= 4); + if (isFP16) + CV_CheckTypeEQ(outputs[i].type(), CV_16SC1, ""); + else + CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, ""); + + bool singleMean = true; + for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j) + { + singleMean = mean[j] == mean[j - 1]; + } + + if (singleMean) + { + if (isFP16) + { + Mat input_f32; + inputsData[i].convertTo(input_f32, CV_32F, scale, -mean[0] * scale); + convertFp16(input_f32, outputs[i]); + } + else + { + inputsData[i].convertTo(outputs[i], CV_32F, scale, -mean[0] * scale); + } + } + else + { + for (int n = 0; n < inputsData[i].size[0]; ++n) + { + for (int c = 0; c < inputsData[i].size[1]; ++c) + { + Mat inp = getPlane(inputsData[i], n, c); + Mat out = getPlane(outputs[i], n, c); + if (isFP16) + { + Mat input_f32; + inp.convertTo(input_f32, CV_32F, scale, -mean[c] * scale); + convertFp16(input_f32, out); + } + else + { + inp.convertTo(out, CV_32F, scale, -mean[c] * scale); + } + } + } + } + } + } + +#ifdef HAVE_OPENCL + bool forward_ocl(InputArrayOfArrays, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_) + { + bool isFP16 = outputs_.depth() == CV_16S; + + std::vector outputs; + outputs_.getUMatVector(outputs); + + for (int i = 0; i < inputsData.size(); ++i) + { + Mat inputData = inputsData[i]; + + double scale = scaleFactors[i]; + Scalar& mean = means[i]; + + CV_Assert(mean == Scalar() || inputData.size[1] <= 4); + if (isFP16) + CV_CheckTypeEQ(outputs[i].type(), CV_16SC1, ""); + else + CV_CheckTypeEQ(outputs[i].type(), CV_32FC1, ""); + + bool singleMean = true; + for (int j = 1; j < std::min(4, inputData.size[1]) && singleMean; ++j) + { + singleMean = mean[j] == mean[j - 1]; + } + + if (singleMean) + { + if (isFP16) + { + UMat input_i; + inputData.convertTo(input_i, CV_32F, scale, -mean[0] * scale); + convertFp16(input_i, outputs[i]); + } + else + { + inputData.convertTo(outputs[i], CV_32F, scale, -mean[0] * scale); + } + } + else + { + for (int n = 0; n < inputData.size[0]; ++n) + { + for (int c = 0; c < inputData.size[1]; ++c) + { + Mat inp = getPlane(inputData, n, c); + + std::vector plane(4, Range::all()); + plane[0] = Range(n, n + 1); + plane[1] = Range(c, c + 1); + UMat out = outputs[i](plane).reshape(1, inp.dims, inp.size); + + if (isFP16) + { + UMat input_i; + inp.convertTo(input_i, CV_32F, scale, -mean[c] * scale); + convertFp16(input_i, out); + } + else + { + inp.convertTo(out, CV_32F, scale, -mean[c] * scale); + } + } + } + } + } + return true; + } +#endif + + int outputNameToIndex(const String& tgtName) CV_OVERRIDE + { + int idx = (int)(std::find(outNames.begin(), outNames.end(), tgtName) - outNames.begin()); + return (idx < (int)outNames.size()) ? idx : -1; + } + + void setNames(const std::vector& names) + { + outNames.assign(names.begin(), names.end()); + shapes.clear(); + shapes.resize(outNames.size()); + } + + void setInputShape(const String& tgtName, const MatShape& shape) + { + std::vector::const_iterator it = std::find(outNames.begin(), outNames.end(), tgtName); + CV_Check(tgtName, it != outNames.end(), "Unknown input"); + int idx = (int)(it - outNames.begin()); + + CV_Assert(idx < (int)shapes.size()); + CV_Check(tgtName, shapes[idx].empty(), "Input shape redefinition is not allowed"); + shapes[idx] = shape; + } + + bool getMemoryShapes(const std::vector& inputs, + const int requiredOutputs, + std::vector& outputs, + std::vector& internals) const CV_OVERRIDE + { + CV_Assert(inputs.size() == requiredOutputs); + outputs.assign(inputs.begin(), inputs.end()); + return false; + } + + virtual void finalize(InputArrayOfArrays, OutputArrayOfArrays outputs_arr) CV_OVERRIDE + { + std::vector outputs; + outputs_arr.getMatVector(outputs); + + CV_Assert_N(outputs.size() == scaleFactors.size(), outputs.size() == means.size(), + inputsData.size() == outputs.size()); + skip = true; + for (int i = 0; skip && i < inputsData.size(); ++i) + { + if (inputsData[i].data != outputs[i].data || scaleFactors[i] != 1.0 || means[i] != Scalar()) + skip = false; + } + } + + + std::vector outNames; + std::vector shapes; + // Preprocessing parameters for each network's input. + std::vector scaleFactors; + std::vector means; + std::vector inputsData; + bool skip; +}; // DataLayer + + +} // namespace detail +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn +#endif // __OPENCV_DNN_SRC_LAYER_INTERNALS_HPP__ diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp index d22a07080594..377e05f5cc6f 100644 --- a/modules/dnn/src/layers/batch_norm_layer.cpp +++ b/modules/dnn/src/layers/batch_norm_layer.cpp @@ -170,11 +170,14 @@ class BatchNormLayerImpl CV_FINAL : public BatchNormLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return preferableTarget == DNN_TARGET_CPU || dims == 4; +#endif return (backendId == DNN_BACKEND_OPENCV) || backendId == DNN_BACKEND_CUDA || (backendId == DNN_BACKEND_HALIDE && haveHalide()) || - backendId == DNN_BACKEND_WEBNN || - ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4)); + backendId == DNN_BACKEND_WEBNN; } #ifdef HAVE_OPENCL @@ -382,16 +385,6 @@ class BatchNormLayerImpl CV_FINAL : public BatchNormLayer } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name); - const size_t numChannels = weights_.total(); - addConstantData("weights", wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C), ieLayer); - addConstantData("biases", wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C), ieLayer); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE @@ -416,6 +409,7 @@ class BatchNormLayerImpl CV_FINAL : public BatchNormLayer { params.set("input_scale", scales[0][0]); params.set("input_zeropoint", zeropoints[0][0]); + params.set("eps", epsilon); params.blobs.clear(); params.blobs.push_back(origin_weights); diff --git a/modules/dnn/src/layers/blank_layer.cpp b/modules/dnn/src/layers/blank_layer.cpp index 59548a9c0c51..0d6ab19e4d28 100644 --- a/modules/dnn/src/layers/blank_layer.cpp +++ b/modules/dnn/src/layers/blank_layer.cpp @@ -63,9 +63,12 @@ class BlankLayerImpl CV_FINAL : public BlankLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || - backendId == DNN_BACKEND_CUDA || - ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()); + backendId == DNN_BACKEND_CUDA; } bool getMemoryShapes(const std::vector &inputs, @@ -116,32 +119,6 @@ class BlankLayerImpl CV_FINAL : public BlankLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); - std::vector dims = input->getDims(); - CV_Assert(!dims.empty()); - - InferenceEngine::Builder::Layer ieLayer(name); - ieLayer.setName(name); - if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) - { - ieLayer.setType("Copy"); - } - else - { - ieLayer.setType("Split"); - ieLayer.getParameters()["axis"] = dims.size() - 1; - ieLayer.getParameters()["out_sizes"] = dims[0]; - } - ieLayer.setInputPorts({InferenceEngine::Port(dims)}); - ieLayer.setOutputPorts(std::vector(1)); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/concat_layer.cpp b/modules/dnn/src/layers/concat_layer.cpp index f620d66a39da..5ba0cd199bf7 100644 --- a/modules/dnn/src/layers/concat_layer.cpp +++ b/modules/dnn/src/layers/concat_layer.cpp @@ -48,6 +48,7 @@ #include "../ie_ngraph.hpp" #include "../op_vkcom.hpp" #include "../op_webnn.hpp" +#include "../op_timvx.hpp" #ifdef HAVE_OPENCL #include "opencl_kernels_dnn.hpp" @@ -72,6 +73,9 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer axis = params.get("axis", 1); padding = params.get("padding", false); paddingValue = params.get("padding_value", 0); + + zeropoint = params.get("zeropoints", 0); + scale = params.get("scales", 1.0f); } virtual bool getMemoryShapes(const std::vector &inputs, @@ -113,11 +117,28 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_TIMVX + if (backendId == DNN_BACKEND_TIMVX && haveTimVX() && !padding) + { + if (axis == -1) + return false; + int len = this->type.length(); + if (len <= 4) + return false; + if (this->type.substr(len - 4) == "Int8") + return true; + else + return false; + } +#endif + +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) || // By channels - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !padding) || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || (backendId == DNN_BACKEND_WEBNN && !padding) || (backendId == DNN_BACKEND_VKCOM && haveVulkan() && !padding); } @@ -343,18 +364,6 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); - - InferenceEngine::Builder::ConcatLayer ieLayer(name); - ieLayer.setAxis(normalize_axis(axis, input->getDims().size())); - ieLayer.setInputPorts(std::vector(inputs.size())); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, @@ -403,6 +412,86 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer } #endif // HAVE_DNN_NGRAPH +#ifdef HAVE_TIMVX + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + Ptr inputWrapper = inputsWrapper[0].dynamicCast(); + // convert axis from OpenCV NCHW toTimVX WHCN. + Mat blob0 = inputWrapper->getMat(); + + // TODO! support TimVX 5 dim in future. + if(blob0.dims >4) + return Ptr(); + + int cAxis = normalize_axis(axis, blob0.dims); + int tvAxis = blob0.dims - 1 - cAxis; + CV_Assert(tvAxis>= 0); + std::vector inputsIndex, outputsIndex; + int input_index = -1, output_index = -1; + + // Input + Ptr tvQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint)); + + for (int i = 0; i(); + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + } + + //Output + CV_Assert(outputsWrapper.size() == 1); + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, tvQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, tvQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::shared_ptr tvConcate = graph->CreateOperation(tvAxis, inputsWrapper.size()); + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvConcate, inputsIndex, outputsIndex); + + return tvBackendNode; + } +#endif // HAVE_TIMVX + virtual bool tryQuantize(const std::vector > &scales, const std::vector > &zeropoints, LayerParams& params) CV_OVERRIDE { @@ -426,6 +515,8 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer } #endif + int zeropoint; + float scale; }; Ptr ConcatLayer::create(const LayerParams& params) diff --git a/modules/dnn/src/layers/const_layer.cpp b/modules/dnn/src/layers/const_layer.cpp index 1f307b8fa6aa..4392763be784 100644 --- a/modules/dnn/src/layers/const_layer.cpp +++ b/modules/dnn/src/layers/const_layer.cpp @@ -34,9 +34,11 @@ class ConstLayerImpl CV_FINAL : public ConstLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || backendId == DNN_BACKEND_WEBNN || backendId == DNN_BACKEND_CUDA; } @@ -78,16 +80,6 @@ class ConstLayerImpl CV_FINAL : public ConstLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::ConstLayer ieLayer(name); - ieLayer.setData(wrapToInfEngineBlob(blobs[0])); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp index bcc783d8a0b6..0bf39f93b33d 100644 --- a/modules/dnn/src/layers/convolution_layer.cpp +++ b/modules/dnn/src/layers/convolution_layer.cpp @@ -330,7 +330,7 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl } #endif #ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin(); if (isArmTarget && blobs.empty()) @@ -340,7 +340,7 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl if (ksize == 3) return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget; bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL; - if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || !isMyriad) && blobs.empty()) + if (!isMyriad && blobs.empty()) return false; return (!isMyriad || dilation.width == dilation.height); } @@ -421,7 +421,9 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl if (!blobs.empty()) { Mat wm = blobs[0].reshape(1, numOutput); - if( wm.step1() % VEC_ALIGN != 0 ) + if ((wm.step1() % VEC_ALIGN != 0) || + !isAligned(wm.data) + ) { int newcols = (int)alignSize(wm.step1(), VEC_ALIGN); Mat wm_buffer = Mat(numOutput, newcols, wm.type()); @@ -759,69 +761,6 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector > &inputs) CV_OVERRIDE - { - CV_Assert(!blobs.empty()); - InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); - std::vector dims = input->getDims(); - CV_Assert(dims.size() == 4 || dims.size() == 5); - const int inpCn = dims[1]; - const int outCn = blobs[0].size[0]; - const int inpGroupCn = blobs[0].size[1]; - const int group = inpCn / inpGroupCn; - InferenceEngine::Layout layout = (dims.size() == 4) ? InferenceEngine::Layout::OIHW : - InferenceEngine::Layout::NCDHW; - - auto ieWeights = wrapToInfEngineBlob(blobs[0], layout); - if (fusedWeights) - { - if (weightsMat.isContinuous()) - { - Mat cvWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size); - ieWeights = wrapToInfEngineBlob(cvWeights, layout); - } - else - { - ieWeights = InferenceEngine::make_shared_blob({ - InferenceEngine::Precision::FP32, - ieWeights->getTensorDesc().getDims(), layout - }); - ieWeights->allocate(); - - Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn); - Mat cvWeights = weightsMat.colRange(0, newWeights.cols); - cvWeights.copyTo(newWeights); - } - } - InferenceEngine::Blob::Ptr ieBiases; - if (hasBias() || fusedBias) - { - Mat biasesMat({outCn}, CV_32F, &biasvec[0]); - ieBiases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C); - } - - InferenceEngine::Builder::ConvolutionLayer ieLayer(name); - - ieLayer.setKernel(kernel_size); - ieLayer.setStrides(strides); - ieLayer.setDilation(dilations); - ieLayer.setPaddingsBegin(pads_begin); - ieLayer.setPaddingsEnd(pads_end); - ieLayer.setGroup((size_t)group); - ieLayer.setOutDepth((size_t)outCn); - - InferenceEngine::Builder::Layer l = ieLayer; - addConstantData("weights", ieWeights, l); - if (ieBiases) - addConstantData("biases", ieBiases, l); - - if (!padMode.empty()) - l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper"); - - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector > &inputs, @@ -1660,7 +1599,6 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl } } } - // now compute dot product of the weights // and im2row-transformed part of the tensor #if CV_TRY_AVX512_SKX @@ -1995,13 +1933,6 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl CV_TRACE_FUNCTION(); CV_TRACE_ARG_VALUE(name, "name", name.c_str()); -#if CV_SSE3 - uint32_t ftzMode = _MM_GET_FLUSH_ZERO_MODE(); - uint32_t dazMode = _MM_GET_DENORMALS_ZERO_MODE(); - _MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON); - _MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON); -#endif - CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget), forward_ocl(inputs_arr, outputs_arr, internals_arr)) @@ -2138,10 +2069,6 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl ParallelConv::run(inputs[0], outputs[0], weightsMat, biasvec, reluslope, kernel_size, strides, pads_begin, pads_end, dilations, activ.get(), ngroups, nstripes); } -#if CV_SSE3 - _MM_SET_FLUSH_ZERO_MODE(ftzMode); - _MM_SET_DENORMALS_ZERO_MODE(dazMode); -#endif } #ifdef HAVE_CUDA @@ -2153,6 +2080,7 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl { auto context = reinterpret_cast(context_); + // TODO: extract bias from inputs and pass it CV_Assert(inputs.size() == 1 || inputs.size() == 2); auto input_wrapper = inputs[0].dynamicCast(); auto input_shape = input_wrapper->getShape(); @@ -2241,6 +2169,7 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl float inputScale = scales[0][0], outputScale = scales[1][0]; int inputZp = zeropoints[0][0]; params.set("input_zeropoint", inputZp); + params.set("input_scale", inputScale); Mat weightsQuantized(weightsMat.rows, weightsMat.cols, CV_8S); Mat biasQuantized(1, numOutput, CV_32S); @@ -2329,52 +2258,6 @@ class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { return group == 1; } - -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { - if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) { - return false; - } - - if (std::accumulate(adjust_pads.begin(), adjust_pads.end(), 0, std::plus()) > 0) - { - if (padMode.empty()) - { - if (preferableTarget != DNN_TARGET_CPU && group != 1) - { - for (int i = 0; i < adjust_pads.size(); i++) { - if (adjust_pads[i] && pads_begin[i]) - return false; - } - } - for (int i = 0; i < adjust_pads.size(); i++) { - if (pads_end[i] < adjust_pads[i]) - return false; - } - return true; - } - else if (padMode == "SAME") - { - for (int i = 0; i < adjust_pads.size(); i++) { - if (kernel_size[i] < pads_begin[i] + 1 + adjust_pads[i]) - return false; - } - return true; - } - else if (padMode == "VALID") - return false; - } - - if (group != 1) - { - return preferableTarget == DNN_TARGET_CPU; - } - if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16) - return std::accumulate(dilations.begin(), dilations.end(), 1, std::multiplies()) == 1; - return true; - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #endif // HAVE_INF_ENGINE { return backendId == DNN_BACKEND_CUDA || @@ -3032,64 +2915,6 @@ class DeConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector > &) CV_OVERRIDE - { - CV_Assert(!blobs.empty()); - InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW : - InferenceEngine::Layout::OIHW; - - auto ieWeights = wrapToInfEngineBlob(blobs[0], layout); - if (fusedWeights) - { - ieWeights = InferenceEngine::make_shared_blob({ - InferenceEngine::Precision::FP32, - ieWeights->getTensorDesc().getDims(), layout - }); - ieWeights->allocate(); - - int inpCn = blobs[0].size[0]; - Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, inpCn); - transpose(weightsMat, newWeights); - } - - const int outGroupCn = blobs[0].size[1]; // Weights are in IOHW or OIDHW layout - const int group = numOutput / outGroupCn; - - InferenceEngine::Builder::DeconvolutionLayer ieLayer(name); - - ieLayer.setKernel(kernel_size); - ieLayer.setStrides(strides); - ieLayer.setDilation(dilations); - ieLayer.setPaddingsBegin(pads_begin); - - if (padMode.empty()) - { - std::vector paddings_end; - for (int i = 0; i < pads_end.size(); i++) { - paddings_end.push_back(pads_end[i] - adjust_pads[i]); - } - ieLayer.setPaddingsEnd(paddings_end); - } - else if (padMode == "SAME") - { - std::vector paddings_end; - for (int i = 0; i < pads_begin.size(); i++) { - paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]); - } - ieLayer.setPaddingsEnd(paddings_end); - } - ieLayer.setGroup((size_t)group); - ieLayer.setOutDepth((size_t)numOutput); - - InferenceEngine::Builder::Layer l = ieLayer; - addConstantData("weights", ieWeights, l); - if (hasBias()) - addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l); - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector > &inputs, diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp index 77d86d5652e5..61d4f444328c 100644 --- a/modules/dnn/src/layers/detection_output_layer.cpp +++ b/modules/dnn/src/layers/detection_output_layer.cpp @@ -221,7 +221,7 @@ class DetectionOutputLayerImpl CV_FINAL : public DetectionOutputLayer { return backendId == DNN_BACKEND_OPENCV || (backendId == DNN_BACKEND_CUDA && !_groupByClasses) || - ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !_locPredTransposed && _bboxesNormalized); + (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && !_locPredTransposed && _bboxesNormalized); } bool getMemoryShapes(const std::vector &inputs, @@ -1001,30 +1001,6 @@ class DetectionOutputLayerImpl CV_FINAL : public DetectionOutputLayer } #endif -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::DetectionOutputLayer ieLayer(name); - - ieLayer.setNumClasses(_numClasses); - ieLayer.setShareLocation(_shareLocation); - ieLayer.setBackgroudLabelId(_backgroundLabelId); - ieLayer.setNMSThreshold(_nmsThreshold); - ieLayer.setTopK(_topK > 0 ? _topK : _keepTopK); - ieLayer.setKeepTopK(_keepTopK); - ieLayer.setConfidenceThreshold(_confidenceThreshold); - ieLayer.setVariantEncodedInTarget(_varianceEncodedInTarget); - ieLayer.setCodeType("caffe.PriorBoxParameter." + _codeType); - ieLayer.setInputPorts(std::vector(3)); - - InferenceEngine::Builder::Layer l = ieLayer; - l.getParameters()["eta"] = std::string("1.0"); - l.getParameters()["clip"] = _clip; - - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp index bfabef9d68b3..353ce8c0b42e 100644 --- a/modules/dnn/src/layers/elementwise_layers.cpp +++ b/modules/dnn/src/layers/elementwise_layers.cpp @@ -186,14 +186,6 @@ class ElementWiseLayer : public Func::Layer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI(); - ieLayer.setName(this->name); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE @@ -341,10 +333,6 @@ struct ReLUFunctor : public BaseFunctor bool supportBackend(int backendId, int) { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1); -#endif #ifdef HAVE_DNN_NGRAPH if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) return true; @@ -462,13 +450,6 @@ struct ReLUFunctor : public BaseFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) { @@ -515,6 +496,9 @@ struct ReLUFunctor : public BaseFunctor params.blobs.clear(); params.blobs.push_back(lookUpTable); } + params.set("input_scale", scales[0][0]); + params.set("input_zeropoint", zeropoints[0][0]); + params.set("slope", slope); return true; } @@ -534,11 +518,14 @@ struct ReLU6Functor : public BaseFunctor bool supportBackend(int backendId, int) { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || backendId == DNN_BACKEND_HALIDE || - backendId == DNN_BACKEND_WEBNN || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; + backendId == DNN_BACKEND_WEBNN; } void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const @@ -620,12 +607,6 @@ struct ReLU6Functor : public BaseFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) @@ -657,6 +638,8 @@ struct ReLU6Functor : public BaseFunctor bool tryQuantize(const std::vector > &scales, const std::vector > &zeropoints, LayerParams& params) { + params.set("input_scale", scales[0][0]); + params.set("input_zeropoint", zeropoints[0][0]); return true; } @@ -726,6 +709,8 @@ struct BaseDefaultFunctor : public BaseFunctor } params.blobs.clear(); params.blobs.push_back(lookUpTable); + params.set("input_scale", scales[0][0]); + params.set("input_zeropoint", zeropoints[0][0]); return true; } @@ -743,12 +728,6 @@ struct BaseDefaultFunctor : public BaseFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - CV_Error(Error::StsNotImplemented, ""); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) @@ -782,10 +761,13 @@ struct TanHFunctor : public BaseDefaultFunctor bool supportBackend(int backendId, int) { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || - backendId == DNN_BACKEND_HALIDE || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; + backendId == DNN_BACKEND_HALIDE; } inline float calculate(float x) const @@ -808,13 +790,6 @@ struct TanHFunctor : public BaseDefaultFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::TanHLayer(""); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) { @@ -937,10 +912,13 @@ struct SigmoidFunctor : public BaseDefaultFunctor bool supportBackend(int backendId, int) { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || - backendId == DNN_BACKEND_HALIDE || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; + backendId == DNN_BACKEND_HALIDE; } inline float calculate(float x) const @@ -963,12 +941,6 @@ struct SigmoidFunctor : public BaseDefaultFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::SigmoidLayer(""); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) @@ -992,10 +964,13 @@ struct ELUFunctor : public BaseDefaultFunctor bool supportBackend(int backendId, int) { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || - backendId == DNN_BACKEND_HALIDE || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; + backendId == DNN_BACKEND_HALIDE; } inline float calculate(float x) const @@ -1023,13 +998,6 @@ struct ELUFunctor : public BaseDefaultFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::ELULayer(""); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) { @@ -1050,8 +1018,8 @@ struct AbsValFunctor : public BaseDefaultFunctor bool supportBackend(int backendId, int) { #ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - return !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1); + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; #endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || @@ -1078,12 +1046,6 @@ struct AbsValFunctor : public BaseDefaultFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) @@ -1930,14 +1892,15 @@ struct PowerFunctor : public BaseFunctor bool supportBackend(int backendId, int targetId) { - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - return (targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16) || power == 1.0 || power == 0.5; +#ifdef HAVE_INF_ENGINE if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) return true; - else +#endif + { return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || backendId == DNN_BACKEND_HALIDE; + } } void finalize() @@ -2029,14 +1992,6 @@ struct PowerFunctor : public BaseFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - return InferenceEngine::Builder::PowerLayer("").setPower(power) - .setScale(scale) - .setShift(shift); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) @@ -2189,10 +2144,13 @@ struct ChannelsPReLUFunctor : public BaseFunctor bool supportBackend(int backendId, int) { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || - backendId == DNN_BACKEND_HALIDE || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; + backendId == DNN_BACKEND_HALIDE; } void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const @@ -2282,15 +2240,6 @@ struct ChannelsPReLUFunctor : public BaseFunctor } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - InferenceEngine::Builder::Layer initInfEngineBuilderAPI() - { - InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer(""); - const size_t numChannels = scale.total(); - addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l); - return l; - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH std::shared_ptr initNgraphAPI(const std::shared_ptr& node) @@ -2321,6 +2270,96 @@ struct ChannelsPReLUFunctor : public BaseFunctor int64 getFLOPSPerElement() const { return 1; } }; +struct SignFunctor : public BaseDefaultFunctor +{ + typedef SignLayer Layer; + + bool supportBackend(int backendId, int) + { + return backendId == DNN_BACKEND_OPENCV || + backendId == DNN_BACKEND_CUDA; + } + + inline float calculate(float x) const + { + return x > 0.f ? 1.f : (x < 0.f ? -1.f : 0.f); + } + +#ifdef HAVE_CUDA + Ptr initCUDA(int target, csl::Stream stream) + { + return make_cuda_node(target, stream); + } +#endif + + int64 getFLOPSPerElement() const { return 1; } +}; + +template<> +const char* const SignFunctor::BaseDefaultFunctor::ocl_kernel_name = "SignForward"; + + +struct ShrinkFunctor : public BaseDefaultFunctor +{ + typedef ShrinkLayer Layer; + float bias; + float lambd; + + explicit ShrinkFunctor(float bias_ = 0.0f, float lambd_ = 0.5f) : bias(bias_), lambd(lambd_) {} + + bool supportBackend(int backendId, int) + { + return backendId == DNN_BACKEND_OPENCV || + backendId == DNN_BACKEND_CUDA; + } + + inline float calculate(float x) const + { + return x > lambd ? x - bias : (x < -lambd ? x + bias : 0.f); + } + +#ifdef HAVE_CUDA + Ptr initCUDA(int target, csl::Stream stream) + { + return make_cuda_node(target, stream, bias, lambd); + } +#endif + + int64 getFLOPSPerElement() const { return 1; } +}; + +template<> +const char* const ShrinkFunctor::BaseDefaultFunctor::ocl_kernel_name = "ShrinkForward"; + +struct ReciprocalFunctor : public BaseDefaultFunctor +{ + typedef ReciprocalLayer Layer; + + bool supportBackend(int backendId, int) + { + return backendId == DNN_BACKEND_OPENCV || + backendId == DNN_BACKEND_CUDA; + } + + inline float calculate(float x) const + { + return 1.f/x; + } + +#ifdef HAVE_CUDA + Ptr initCUDA(int target, csl::Stream stream) + { + return make_cuda_node(target, stream); + } +#endif + + int64 getFLOPSPerElement() const { return 1; } +}; + +template<> +const char* const ReciprocalFunctor::BaseDefaultFunctor::ocl_kernel_name = "ReciprocalForward"; + + #define ACTIVATION_CREATOR_FOR(_Layer, _Functor, ...) \ Ptr<_Layer> _Layer::create() { \ return return Ptr<_Layer>( new ElementWiseLayer<_Functor>(_Functor()) ); } @@ -2662,5 +2701,32 @@ Ptr ChannelsPReLULayer::create(const LayerParams& params) return l; } +Ptr SignLayer::create(const LayerParams& params) +{ + Ptr l(new ElementWiseLayer()); + l->setParamsFrom(params); + + return l; +} + +Ptr ReciprocalLayer::create(const LayerParams& params) +{ + Ptr l(new ElementWiseLayer()); + l->setParamsFrom(params); + + return l; +} + +Ptr ShrinkLayer::create(const LayerParams& params) +{ + float bias = params.get("bias", 0.f); + float lambd = params.get("lambd", 0.5f); + Ptr l(new ElementWiseLayer(ShrinkFunctor(bias, lambd))); + l->setParamsFrom(params); + l->bias = bias; + l->lambd = lambd; + + return l; +} } } diff --git a/modules/dnn/src/layers/eltwise_layer.cpp b/modules/dnn/src/layers/eltwise_layer.cpp index 2c473ff4129e..a67b0c4bb595 100644 --- a/modules/dnn/src/layers/eltwise_layer.cpp +++ b/modules/dnn/src/layers/eltwise_layer.cpp @@ -164,6 +164,11 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer if (hasVecInput && ELTWISE_CHANNNELS_SAME) return backendId == DNN_BACKEND_OPENCV; +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return channelsMode == ELTWISE_CHANNNELS_SAME; +#endif + if (backendId == DNN_BACKEND_CUDA) { if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE) @@ -172,9 +177,8 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer } return backendId == DNN_BACKEND_OPENCV || - (backendId == DNN_BACKEND_HALIDE && op != DIV) || // TODO: not implemented, see PR #15811 - ((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty())) - || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && channelsMode == ELTWISE_CHANNNELS_SAME)); + (backendId == DNN_BACKEND_HALIDE && op != DIV) // TODO: not implemented, see PR #15811 + ; } bool getMemoryShapes(const std::vector &inputs, @@ -837,34 +841,6 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::Builder::EltwiseLayer ieLayer(name); - - ieLayer.setInputPorts(std::vector(inputs.size())); - - if (op == SUM) - ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM); - else if (op == PROD) - ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL); - else if (op == DIV) - ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::DIV); - else if (op == MAX) - ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX); - else if (op == MIN) - ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MIN); - else - CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation"); - - InferenceEngine::Builder::Layer l = ieLayer; - if (!coeffs.empty()) - l.getParameters()["coeff"] = coeffs; - - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, @@ -899,6 +875,8 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer virtual bool tryQuantize(const std::vector > &scales, const std::vector > &zeropoints, LayerParams& params) CV_OVERRIDE { + params.set("input_scales", DictValue::arrayReal(scales[0].data(), scales[0].size())); + params.set("input_zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size())); if (op == SUM) { std::vector newCoeffs; @@ -921,7 +899,6 @@ class EltwiseLayerImpl CV_FINAL : public EltwiseLayer newCoeffs[0] /= scales[1][0]; params.set("coeff", DictValue::arrayReal(newCoeffs.data(), newCoeffs.size())); params.set("offset", zeropoints[1][0]); - params.set("input_zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size())); return true; } return op == MAX; diff --git a/modules/dnn/src/layers/flatten_layer.cpp b/modules/dnn/src/layers/flatten_layer.cpp index 69bc422ee348..b3f57dc7cdb9 100644 --- a/modules/dnn/src/layers/flatten_layer.cpp +++ b/modules/dnn/src/layers/flatten_layer.cpp @@ -72,9 +72,12 @@ class FlattenLayerImpl CV_FINAL : public FlattenLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || - backendId == DNN_BACKEND_CUDA || - ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()); + backendId == DNN_BACKEND_CUDA; } bool getMemoryShapes(const std::vector &inputs, @@ -171,25 +174,10 @@ class FlattenLayerImpl CV_FINAL : public FlattenLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::Builder::Layer ieLayer(name); - ieLayer.setName(name); - ieLayer.setType("Flatten"); - ieLayer.getParameters()["axis"] = (size_t)_startAxis; - ieLayer.getParameters()["end_axis"] = _endAxis; // Do not cast to size_t because it might be negative. - ieLayer.setInputPorts(std::vector(1)); - ieLayer.setOutputPorts(std::vector(1)); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH -virtual Ptr initNgraph(const std::vector >& inputs, - const std::vector >& nodes) CV_OVERRIDE -{ + virtual Ptr initNgraph(const std::vector >& inputs, + const std::vector >& nodes) CV_OVERRIDE + { auto& ieInpNode = nodes[0].dynamicCast()->node; std::vector dims = ieInpNode->get_shape(); diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp index 99acba908b15..e9632e20be60 100644 --- a/modules/dnn/src/layers/fully_connected_layer.cpp +++ b/modules/dnn/src/layers/fully_connected_layer.cpp @@ -148,12 +148,15 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return axis == 1; +#endif + return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1) || - (backendId == DNN_BACKEND_WEBNN && axis == 1) || - (((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && !blobs.empty()) || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && axis == 1); + (backendId == DNN_BACKEND_WEBNN && axis == 1); } virtual bool setActivation(const Ptr& layer) CV_OVERRIDE @@ -570,23 +573,6 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::FullyConnectedLayer ieLayer(name); - - const int outNum = blobs[0].size[0]; - ieLayer.setOutputNum(outNum); - - InferenceEngine::Builder::Layer l = ieLayer; - addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW), l); - if (bias) - addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)outNum}, InferenceEngine::Layout::C), l); - - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, @@ -656,6 +642,8 @@ class FullyConnectedLayerImpl CV_FINAL : public InnerProductLayer params.blobs.push_back(weightsQuantized.reshape(1, shape(blobs[0]))); params.blobs.push_back(biasQuantized); params.blobs.push_back(outputMultiplier); + params.set("input_scale", inputScale); + params.set("input_zeropoint", inputZp); return true; } diff --git a/modules/dnn/src/layers/layers_common.simd.hpp b/modules/dnn/src/layers/layers_common.simd.hpp index 67a4b3c0652b..fd88a3c3d259 100644 --- a/modules/dnn/src/layers/layers_common.simd.hpp +++ b/modules/dnn/src/layers/layers_common.simd.hpp @@ -81,6 +81,8 @@ void fastConv( const float* weights, size_t wstep, const float* bias, int blockSize, int vecsize, int vecsize_aligned, const float* relu, bool initOutput ) { + CV_Assert(isAligned<32>(weights)); + int outCn = outShape[1]; size_t outPlaneSize = outShape[2]*outShape[3]; float r0 = 1.f, r1 = 1.f, r2 = 1.f; diff --git a/modules/dnn/src/layers/lrn_layer.cpp b/modules/dnn/src/layers/lrn_layer.cpp index 224441b0e780..6c3a65415978 100644 --- a/modules/dnn/src/layers/lrn_layer.cpp +++ b/modules/dnn/src/layers/lrn_layer.cpp @@ -99,12 +99,10 @@ class LRNLayerImpl CV_FINAL : public LRNLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) return bias == (int)bias; - } - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { - return bias == (int)bias; - } +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || backendId == DNN_BACKEND_HALIDE || @@ -444,24 +442,6 @@ class LRNLayerImpl CV_FINAL : public LRNLayer #endif // HAVE_HALIDE } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - float alphaSize = alpha; - if (!normBySize) - alphaSize *= (type == SPATIAL_NRM ? size*size : size); - - InferenceEngine::Builder::NormLayer ieLayer(name); - ieLayer.setSize(size); - ieLayer.setAlpha(alphaSize); - ieLayer.setBeta(beta); - ieLayer.setAcrossMaps(type == CHANNEL_NRM); - - InferenceEngine::Builder::Layer l = ieLayer; - l.getParameters()["k"] = bias; - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/mvn_layer.cpp b/modules/dnn/src/layers/mvn_layer.cpp index 455f4997df85..dc23656b7a78 100644 --- a/modules/dnn/src/layers/mvn_layer.cpp +++ b/modules/dnn/src/layers/mvn_layer.cpp @@ -124,14 +124,7 @@ class MVNLayerImpl CV_FINAL : public MVNLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { - bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL; - return !zeroDev && (!isMyriad || eps <= 1e-7f); - } -#endif -#ifdef HAVE_DNN_NGRAPH +#ifdef HAVE_INF_ENGINE if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) return true; #endif @@ -387,16 +380,6 @@ class MVNLayerImpl CV_FINAL : public MVNLayer } } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::MVNLayer ieLayer(name); - ieLayer.setAcrossChannels(acrossChannels); - ieLayer.setNormalize(normVariance); - ieLayer.setEpsilon(eps); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp index 236f2e43f11e..2017d768012d 100644 --- a/modules/dnn/src/layers/normalize_bbox_layer.cpp +++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp @@ -70,17 +70,15 @@ class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { if (pnorm != 2) return false; - bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL; - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && isMyriad) - return !acrossSpatial; - return startAxis == 1; } +#endif return backendId == DNN_BACKEND_OPENCV || (backendId == DNN_BACKEND_CUDA && (pnorm == 1 || pnorm == 2)); } @@ -270,58 +268,6 @@ class NormalizeBBoxLayerImpl CV_FINAL : public NormalizeBBoxLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); - std::vector dims = input->getDims(); - if (dims.size() == 4) - { - InferenceEngine::Builder::NormalizeLayer ieLayer(name); - - ieLayer.setChannelShared(false); - ieLayer.setAcrossMaps(acrossSpatial); - ieLayer.setEpsilon(epsilon); - - InferenceEngine::Builder::Layer l = ieLayer; - const int numChannels = dims[1]; - InferenceEngine::Blob::Ptr weights; - if (blobs.empty()) - { - weights = InferenceEngine::make_shared_blob({ - InferenceEngine::Precision::FP32, - {(size_t)numChannels}, InferenceEngine::Layout::C - }); - weights->allocate(); - - Mat weightsMat = infEngineBlobToMat(weights).reshape(1, numChannels); - Mat(numChannels, 1, CV_32F, Scalar(1)).copyTo(weightsMat); - l.getParameters()["channel_shared"] = false; - } - else - { - CV_Assert(numChannels == blobs[0].total()); - weights = wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C); - l.getParameters()["channel_shared"] = blobs[0].total() == 1; - } - addConstantData("weights", weights, l); - l.getParameters()["across_spatial"] = acrossSpatial; - return Ptr(new InfEngineBackendNode(l)); - } - else - { - InferenceEngine::Builder::GRNLayer ieLayer(name); - ieLayer.setBeta(epsilon); - - InferenceEngine::Builder::Layer l = ieLayer; - l.getParameters()["bias"] = epsilon; - - return Ptr(new InfEngineBackendNode(l)); - } - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/not_implemented_layer.cpp b/modules/dnn/src/layers/not_implemented_layer.cpp index c4b134390222..f66115521943 100644 --- a/modules/dnn/src/layers/not_implemented_layer.cpp +++ b/modules/dnn/src/layers/not_implemented_layer.cpp @@ -8,7 +8,7 @@ namespace cv { namespace dnn { CV__DNN_INLINE_NS_BEGIN -namespace detail { +inline namespace detail { class NotImplementedImpl CV_FINAL : public NotImplemented { @@ -87,11 +87,6 @@ class NotImplementedImpl CV_FINAL : public NotImplemented CV_Error(Error::StsNotImplemented, msg); } - virtual Ptr initInfEngine(const std::vector > &inputs) CV_OVERRIDE - { - CV_Error(Error::StsNotImplemented, msg); - } - virtual Ptr initNgraph(const std::vector > &inputs, const std::vector >& nodes) CV_OVERRIDE { diff --git a/modules/dnn/src/layers/padding_layer.cpp b/modules/dnn/src/layers/padding_layer.cpp index 7534145f5303..aea8ab3168ff 100644 --- a/modules/dnn/src/layers/padding_layer.cpp +++ b/modules/dnn/src/layers/padding_layer.cpp @@ -102,10 +102,10 @@ class PaddingLayerImpl CV_FINAL : public PaddingLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { #ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL; - if (INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && isMyriad) + if (isMyriad) return dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0; return (dstRanges.size() <= 4 || !isArmComputePlugin()); @@ -219,30 +219,6 @@ class PaddingLayerImpl CV_FINAL : public PaddingLayer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::Layer ieLayer(name); - ieLayer.setName(name); - ieLayer.setType("Pad"); - - std::vector begins(paddings.size(), 0), ends(paddings.size(), 0); - for (int i = 0; i < paddings.size(); ++i) - { - begins[i] = paddings[i].first; - ends[i] = paddings[i].second; - } - ieLayer.getParameters()["pads_begin"] = begins; - ieLayer.getParameters()["pads_end"] = ends; - ieLayer.getParameters()["pad_mode"] = paddingType; - if (paddingType == "constant") - ieLayer.getParameters()["pad_value"] = paddingValue; - - ieLayer.setInputPorts(std::vector(1)); - ieLayer.setOutputPorts(std::vector(1)); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, diff --git a/modules/dnn/src/layers/permute_layer.cpp b/modules/dnn/src/layers/permute_layer.cpp index 9e66eb6a648f..cce36b951ffd 100644 --- a/modules/dnn/src/layers/permute_layer.cpp +++ b/modules/dnn/src/layers/permute_layer.cpp @@ -47,6 +47,7 @@ #include "../ie_ngraph.hpp" #include "../op_vkcom.hpp" #include "../op_webnn.hpp" +#include "../op_timvx.hpp" #include #include @@ -108,6 +109,9 @@ class PermuteLayerImpl CV_FINAL : public PermuteLayer _order.push_back(currentOrder); } + zeropoint = params.get("zeropoints", 0); + scale = params.get("scales", 1.0f); + setParamsFrom(params); checkNeedForPermutation(); } @@ -115,13 +119,30 @@ class PermuteLayerImpl CV_FINAL : public PermuteLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { #ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && preferableTarget == DNN_TARGET_CPU) - return _order.size() <= 4 || !isArmComputePlugin(); + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + if (preferableTarget == DNN_TARGET_CPU) + return _order.size() <= 4 || !isArmComputePlugin(); + return true; + } +#endif + +#ifdef HAVE_TIMVX + if (backendId == DNN_BACKEND_TIMVX && haveTimVX()) + { + int len = this->type.length(); + if (len <= 4) + return false; + + if (this->type.substr(len - 4) == "Int8") + return true; + else + return false; + } #endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || backendId == DNN_BACKEND_WEBNN || - ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()) || (backendId == DNN_BACKEND_VKCOM && haveVulkan()); } @@ -418,16 +439,6 @@ class PermuteLayerImpl CV_FINAL : public PermuteLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::PermuteLayer ieLayer(name); - ieLayer.setOrder(_order); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE @@ -478,12 +489,120 @@ class PermuteLayerImpl CV_FINAL : public PermuteLayer } #endif // HAVE_VULKAN +#ifdef HAVE_TIMVX + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + std::vector inputsIndex, outputsIndex; + int input_index = -1, output_index = -1; + + if (outputsWrapper.size() != 1) // only work for single outputBlob + return Ptr(); + + // Input + Ptr inputWrapper = inputsWrapper[0].dynamicCast(); + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor()) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint)); + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + //Output + Ptr outputWrapper = outputsWrapper[0].dynamicCast(); + // output has the same quantized attrib. + Ptr outputQuant = inputWrapper->getTensorQuantization(); + + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + std::vector tvOrder; + if (getOrderWHCN(tvOrder)) + { + std::shared_ptr tvPermute = graph->CreateOperation(tvOrder); + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvPermute, inputsIndex, outputsIndex); + + return tvBackendNode; + } + else + { + return Ptr(); + } + } +#endif // HAVE_TIMVX + virtual bool tryQuantize(const std::vector > &scales, const std::vector > &zeropoints, LayerParams& params) CV_OVERRIDE { return true; } + // convert OpenCV NCHW order to WHCN order. + bool getOrderWHCN(std::vector& orderWHCN) + { + std::map lookup; + int orderLen = _order.size(); + if (orderLen <2) + return false; + orderWHCN.assign(_order.begin(), _order.end()); + + if (orderLen == 2) + { + return true; + } + else if (orderLen >= 3) + { + for (int i = 0; i < orderLen; i++) + { + lookup[i] = orderLen - i - 1; + } + + for (int i = 0; i < orderLen; i++) + { + orderWHCN[i] = lookup[_order[i]]; + } + std::reverse(orderWHCN.begin(), orderWHCN.end()); + return true; + } + else + return false; + } + size_t _count; std::vector _order; @@ -499,6 +618,8 @@ class PermuteLayerImpl CV_FINAL : public PermuteLayer #endif size_t _numAxes; + int zeropoint; + float scale; }; Ptr PermuteLayer::create(const LayerParams ¶ms) diff --git a/modules/dnn/src/layers/pooling_layer.cpp b/modules/dnn/src/layers/pooling_layer.cpp index 7cb86a95151a..6c584bf2ddd7 100644 --- a/modules/dnn/src/layers/pooling_layer.cpp +++ b/modules/dnn/src/layers/pooling_layer.cpp @@ -199,34 +199,13 @@ class PoolingLayerImpl CV_FINAL : public PoolingLayer { return type == MAX || type == AVE || type == ROI; } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - { - if (computeMaxIdx) - return false; - if (kernel_size.size() == 3) - return preferableTarget == DNN_TARGET_CPU; - if (kernel_size.size() == 1) - return false; - if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) { -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - if (type == MAX && (pads_begin[1] == 1 && pads_begin[0] == 1) && (strides[0] == 2 && strides[1] == 2)) { - return !isMyriadX(); - } -#endif - return type == MAX || type == AVE; - } - else - return type != STOCHASTIC && type != SUM; - } -#endif +#ifdef HAVE_INF_ENGINE if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { -#ifdef HAVE_DNN_NGRAPH return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin()); -#endif } - else if (backendId == DNN_BACKEND_OPENCV) +#endif + if (backendId == DNN_BACKEND_OPENCV) { if (kernel_size.size() == 3) return preferableTarget == DNN_TARGET_CPU; @@ -293,6 +272,17 @@ class PoolingLayerImpl CV_FINAL : public PoolingLayer return true; } } + else if (backendId == DNN_BACKEND_TIMVX) + { +#ifdef HAVE_TIMVX + if (kernel_size.size() == 3) + { + // fallback to CPU implementation. + preferableTarget = DNN_TARGET_CPU; + } +#endif + return false; + } return false; } @@ -550,54 +540,6 @@ class PoolingLayerImpl CV_FINAL : public PoolingLayer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - if (type == MAX || type == AVE) - { - InferenceEngine::Builder::PoolingLayer ieLayer(name); - - ieLayer.setKernel(kernel_size); - ieLayer.setStrides(strides); - ieLayer.setPaddingsBegin(pads_begin); - ieLayer.setPaddingsEnd(pads_end); - - ieLayer.setPoolingType(type == MAX ? - InferenceEngine::Builder::PoolingLayer::PoolingType::MAX : - InferenceEngine::Builder::PoolingLayer::PoolingType::AVG); - ieLayer.setRoundingType(ceilMode ? - InferenceEngine::Builder::PoolingLayer::RoundingType::CEIL : - InferenceEngine::Builder::PoolingLayer::RoundingType::FLOOR); - ieLayer.setExcludePad(!avePoolPaddedArea); - - InferenceEngine::Builder::Layer l = ieLayer; - if (!padMode.empty()) - l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper"); - return Ptr(new InfEngineBackendNode(l)); - } - else if (type == ROI) - { - InferenceEngine::Builder::ROIPoolingLayer ieLayer(name); - ieLayer.setSpatialScale(spatialScale); - ieLayer.setPooled({pooledSize.height, pooledSize.width}); - ieLayer.setInputPorts(std::vector(2)); - return Ptr(new InfEngineBackendNode(ieLayer)); - } - else if (type == PSROI) - { - InferenceEngine::Builder::PSROIPoolingLayer ieLayer(name); - ieLayer.setSpatialScale(spatialScale); - ieLayer.setOutputDim(psRoiOutChannels); - ieLayer.setGroupSize(pooledSize.width); - ieLayer.setInputPorts(std::vector(2)); - return Ptr(new InfEngineBackendNode(ieLayer)); - } - else - CV_Error(Error::StsNotImplemented, "Unsupported pooling type"); - return Ptr(); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp index f7340b1e675c..160b36c18df2 100644 --- a/modules/dnn/src/layers/prior_box_layer.cpp +++ b/modules/dnn/src/layers/prior_box_layer.cpp @@ -298,9 +298,7 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer #endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && - ( _explicitSizes || (_minSize.size() == 1 && _maxSize.size() <= 1))) - || (backendId == DNN_BACKEND_VKCOM && haveVulkan()); + (backendId == DNN_BACKEND_VKCOM && haveVulkan()); } bool getMemoryShapes(const std::vector &inputs, @@ -510,69 +508,6 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - if (_explicitSizes) - { - InferenceEngine::Builder::PriorBoxClusteredLayer ieLayer(name); - ieLayer.setSteps({_stepY, _stepX}); - - CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], ""); - ieLayer.setOffset(_offsetsX[0]); - - ieLayer.setClip(_clip); - ieLayer.setFlip(false); // We already flipped aspect ratios. - - InferenceEngine::Builder::Layer l = ieLayer; - - CV_Assert_N(!_boxWidths.empty(), !_boxHeights.empty(), !_variance.empty()); - CV_Assert(_boxWidths.size() == _boxHeights.size()); - l.getParameters()["width"] = _boxWidths; - l.getParameters()["height"] = _boxHeights; - l.getParameters()["variance"] = _variance; - return Ptr(new InfEngineBackendNode(l)); - } - else - { - InferenceEngine::Builder::PriorBoxLayer ieLayer(name); - - CV_Assert(!_explicitSizes); - ieLayer.setMinSize(_minSize[0]); - if (!_maxSize.empty()) - ieLayer.setMaxSize(_maxSize[0]); - - CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], ""); - ieLayer.setOffset(_offsetsX[0]); - - ieLayer.setClip(_clip); - ieLayer.setFlip(false); // We already flipped aspect ratios. - - InferenceEngine::Builder::Layer l = ieLayer; - if (_stepX == _stepY) - { - l.getParameters()["step"] = _stepX; - l.getParameters()["step_h"] = 0.0f; - l.getParameters()["step_w"] = 0.0f; - } - else - { - l.getParameters()["step"] = 0.0f; - l.getParameters()["step_h"] = _stepY; - l.getParameters()["step_w"] = _stepX; - } - if (!_aspectRatios.empty()) - { - l.getParameters()["aspect_ratio"] = _aspectRatios; - } - CV_Assert(!_variance.empty()); - l.getParameters()["variance"] = _variance; - return Ptr(new InfEngineBackendNode(l)); - } - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE { diff --git a/modules/dnn/src/layers/proposal_layer.cpp b/modules/dnn/src/layers/proposal_layer.cpp index aeb5d44a4742..e9edcf1547cc 100644 --- a/modules/dnn/src/layers/proposal_layer.cpp +++ b/modules/dnn/src/layers/proposal_layer.cpp @@ -96,7 +96,7 @@ class ProposalLayerImpl CV_FINAL : public ProposalLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { #ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL; return !isMyriad; @@ -338,32 +338,6 @@ class ProposalLayerImpl CV_FINAL : public ProposalLayer layerOutputs[0].col(2).copyTo(dst); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::ProposalLayer ieLayer(name); - - ieLayer.setBaseSize(baseSize); - ieLayer.setFeatStride(featStride); - ieLayer.setMinSize(16); - ieLayer.setNMSThresh(nmsThreshold); - ieLayer.setPostNMSTopN(keepTopAfterNMS); - ieLayer.setPreNMSTopN(keepTopBeforeNMS); - - std::vector scalesVec(scales.size()); - for (int i = 0; i < scales.size(); ++i) - scalesVec[i] = scales.get(i); - ieLayer.setScale(scalesVec); - - std::vector ratiosVec(ratios.size()); - for (int i = 0; i < ratios.size(); ++i) - ratiosVec[i] = ratios.get(i); - ieLayer.setRatio(ratiosVec); - - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, diff --git a/modules/dnn/src/layers/recurrent_layers.cpp b/modules/dnn/src/layers/recurrent_layers.cpp index 14210becb4a0..3961051c8ebc 100644 --- a/modules/dnn/src/layers/recurrent_layers.cpp +++ b/modules/dnn/src/layers/recurrent_layers.cpp @@ -42,10 +42,14 @@ #include "../precomp.hpp" #include -#include #include #include +#ifdef HAVE_CUDA +#include "../cuda4dnn/primitives/recurrent_cells.hpp" +using namespace cv::dnn::cuda4dnn; +#endif + #include "layers_common.hpp" namespace cv @@ -103,7 +107,7 @@ static ActivationFunction get_activation_function(const String& activation) { class LSTMLayerImpl CV_FINAL : public LSTMLayer { - int numTimeStamps, numSamples; + int numTimeStamps, numSamples, numHidden; bool allocated; MatShape outTailShape; //shape of single output sample @@ -119,6 +123,7 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer ActivationFunction f_activation; ActivationFunction g_activation; ActivationFunction h_activation; + bool isDefaultActivations{true}; #if CV_TRY_AVX bool useAVX; @@ -127,6 +132,10 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer bool useAVX2; #endif + // CUDA needs input blobs to be rearranged in a specific way, but some transformations + // in ONNXImporter are destructive, so we keep a copy. + std::vector originalBlobs; + public: LSTMLayerImpl(const LayerParams& params) @@ -140,6 +149,13 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer { setParamsFrom(params); + if (params.get("is_onnx", false)) + { + // collect copies of onnx blobs + originalBlobs.insert(originalBlobs.begin(), blobs.begin(), blobs.begin() + 3); + blobs.erase(blobs.begin(), blobs.begin() + 3); + } + bidirectional = params.get("bidirectional", false); if (!blobs.empty()) { @@ -181,20 +197,25 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer useCellClip = params.get("use_cell_clip", false); usePeephole = params.get("use_peephole", false); reverse = params.get("reverse", false); + numHidden = params.get("hidden_size", 1); CV_Assert(!reverse || !bidirectional); // read activations - DictValue activations = params.get("activations", ""); + DictValue activations = params.get("activations", DictValue(String())); if (activations.size() == 1) // if activations wasn't specified use default { f_activation = sigmoid; g_activation = tanh; h_activation = tanh; + isDefaultActivations = true; } else { CV_Assert(activations.size() == 3); f_activation = get_activation_function(activations.getStringValue(0)); g_activation = get_activation_function(activations.getStringValue(1)); h_activation = get_activation_function(activations.getStringValue(2)); + isDefaultActivations = activations.getStringValue(0) == "Sigmoid" + && activations.getStringValue(1) == "Tanh" + && activations.getStringValue(2) == "Tanh"; } allocated = false; @@ -233,6 +254,12 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer blobs[2] = Mat(bias.clone()).reshape(1, 1); } + bool supportBackend(int backendId) CV_OVERRIDE + { + return backendId == DNN_BACKEND_OPENCV + || (backendId == DNN_BACKEND_CUDA && isDefaultActivations && !reverse && !usePeephole); + } + bool getMemoryShapes(const std::vector &inputs, const int requiredOutputs, std::vector &outputs, @@ -269,8 +296,21 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer outResShape.insert(outResShape.end(), outTailShape_.begin(), outTailShape_.end()); outResShape.back() *= (1 + static_cast(bidirectional)); - size_t noutputs = produceCellOutput ? 2 : 1; - outputs.assign(noutputs, outResShape); + outputs.assign(1, outResShape); + if (produceCellOutput) + { + // the producer is ONNX, so CellState is different + if (!originalBlobs.empty()) + { + int shp[] = {(1 + static_cast(bidirectional)), _numSamples, numHidden}; + MatShape newShape(shp, shp + sizeof(shp)/sizeof(shp[0])); + outputs.push_back(newShape); + } + else + { + outputs.push_back(outResShape); + } + } internals.assign(1, shape(_numSamples, _numOut)); // hInternal internals.push_back(shape(_numSamples, _numOut)); // cInternal @@ -335,14 +375,39 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer outputs_arr.getMatVector(output); internals_arr.getMatVector(internals); + Mat cOut = produceCellOutput ? output[0].clone() : Mat(); + const bool needYcTransform = !originalBlobs.empty(); // if the producer is onnx const int numDirs = 1 + static_cast(bidirectional); for (int i = 0; i < numDirs; ++i) { - const Mat &Wh = blobs[0].rowRange(i * blobs[0].rows / numDirs, (i + 1) * blobs[0].rows / numDirs); - const Mat &Wx = blobs[1].rowRange(i * blobs[1].rows / numDirs, (i + 1) * blobs[1].rows / numDirs); - const Mat &bias = blobs[2].colRange(i * blobs[2].cols / numDirs, (i + 1) * blobs[2].cols / numDirs); - const Mat &h_0 = blobs[3].rowRange(i * blobs[3].rows / numDirs, (i + 1) * blobs[3].rows / numDirs); - const Mat &c_0 = blobs[4].rowRange(i * blobs[4].rows / numDirs, (i + 1) * blobs[4].rows / numDirs); + Mat Wh = blobs[0]; + Mat Wx = blobs[1]; + Mat bias = blobs[2]; + Mat h_0 = blobs[3]; + Mat c_0 = blobs[4]; + Mat pI, pF, pO; + + Wh = Wh.rowRange(i * Wh.rows / numDirs, (i + 1) * Wh.rows / numDirs); + Wx = Wx.rowRange(i * Wx.rows / numDirs, (i + 1) * Wx.rows / numDirs); + bias = bias.colRange(i * bias.cols / numDirs, (i + 1) * bias.cols / numDirs); + h_0 = h_0.rowRange(i * h_0.rows / numDirs, (i + 1) * h_0.rows / numDirs); + c_0 = c_0.rowRange(i * c_0.rows / numDirs, (i + 1) * c_0.rows / numDirs); + + if (usePeephole) + { + pI = blobs[5]; + pF = blobs[6]; + pO = blobs[7]; + + pI = pI.rowRange(i * pI.rows / numDirs, (i + 1) * pI.rows / numDirs); + pI = pI.colRange(i * pI.cols / numDirs, (i + 1) * pI.cols / numDirs); + + pF = pF.rowRange(i * pF.rows / numDirs, (i + 1) * pF.rows / numDirs); + pF = pF.colRange(i * pF.cols / numDirs, (i + 1) * pF.cols / numDirs); + + pO = pO.rowRange(i * pO.rows / numDirs, (i + 1) * pO.rows / numDirs); + pO = pO.colRange(i * pO.cols / numDirs, (i + 1) * pO.cols / numDirs); + } int numOut = Wh.size[1]; Mat hInternal = internals[0], cInternal = internals[1], @@ -356,7 +421,12 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer Mat hOutTs = output[0].reshape(1, numSamplesTotal); hOutTs = hOutTs.colRange(i * hOutTs.cols / numDirs, (i + 1) * hOutTs.cols / numDirs); - Mat cOutTs = produceCellOutput ? output[1].reshape(1, numSamplesTotal) : Mat(); + Mat cOutTs; + if (produceCellOutput) + { + cOutTs = cOut.reshape(1, numSamplesTotal); + cOutTs = cOutTs.colRange(i * cOutTs.cols / numDirs, (i + 1) * cOutTs.cols / numDirs); + } #if CV_TRY_AVX2 || CV_TRY_AVX bool canUseAvx = gates.isContinuous() && bias.isContinuous() @@ -471,8 +541,8 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer if (usePeephole) { Mat gatesIF = gates.colRange(0, 2*numOut); - gemm(cInternal, blobs[5], 1, gateI, 1, gateI); - gemm(cInternal, blobs[6], 1, gateF, 1, gateF); + gemm(cInternal, pI, 1, gateI, 1, gateI); + gemm(cInternal, pF, 1, gateF, 1, gateF); f_activation(gatesIF, gatesIF); } else @@ -495,7 +565,7 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer } if (usePeephole) { - gemm(cInternal, blobs[7], 1, gateO, 1, gateO); + gemm(cInternal, pO, 1, gateO, 1, gateO); f_activation(gateO, gateO); } @@ -509,7 +579,129 @@ class LSTMLayerImpl CV_FINAL : public LSTMLayer cInternal.copyTo(cOutTs.rowRange(curRowRange)); } } + + if (needYcTransform && produceCellOutput) + { + fixCellState(cOut, numDirs); + } + if (produceCellOutput) + { + cOut.copyTo(output[1]); + } } + + void fixCellState(Mat& cOut, int numDirs) + { + // seq, batch, dirs, hidden + int shp[] = {0, numSamples, numDirs, numHidden}; + cOut = cOut.reshape(1, sizeof(shp)/sizeof(shp[0]), shp); + + // permute to {0, 2, 1, 3}; + cv::Mat newCellState; + cv::transposeND(cOut, {0, 2, 1, 3}, newCellState); + cOut = newCellState; + + if (numDirs == 1) + { + // Slice: Yh = Y[-1, :, :, :] + Range ranges[] = {cv::Range(cOut.size[0] - 1, cOut.size[0]), cv::Range::all(), cv::Range::all(), cv::Range::all()}; + cOut = cOut(ranges); + // Reshape: 1x1xBxH -> 1xBxH + int shp[] = {1, numSamples, numHidden}; + cOut = cOut.reshape(1, sizeof(shp)/sizeof(shp[0]), shp); + } + else + { + // Slice: SxDxBxH -> last sequence, first direction + Range ranges1[] = {cv::Range(cOut.size[0] - 1, cOut.size[0]), cv::Range(0, 1), cv::Range::all(), cv::Range::all()}; + Mat part1 = cOut(ranges1); + + // Slice: SxDxBxH -> first sequence, last direction + Range ranges2[] = {cv::Range(0, 1), cv::Range(cOut.size[1] - 1, cOut.size[1]), cv::Range::all(), cv::Range::all()}; + Mat part2 = cOut(ranges2); + + int shp[] = {1, part1.size[2] * part1.size[3]}; + part1 = part1.reshape(1, sizeof(shp)/sizeof(shp[0]), shp); + part2 = part2.reshape(1, sizeof(shp)/sizeof(shp[0]), shp); + + vconcat(part1, part2, cOut); + + // Reshape: 1x2xBxH -> 2xBxH + int finalShape[] = {2, numSamples, numHidden}; + cOut = cOut.reshape(1, sizeof(finalShape)/sizeof(finalShape[0]), finalShape); + } + } + +#ifdef HAVE_CUDA + Ptr initCUDA(void *context_, const std::vector> &inputs, + const std::vector> &outputs) override + { + const int numDirs = 1 + static_cast(bidirectional); + auto toIFCO = [numDirs] (Mat& in) { + int first = in.size[0]; + int rest = in.total() / first / 4; + // every weight blob contains weights for Input, Output, Forget and Cell gates + Mat m = in.reshape(1, {first, 4, rest}); + Mat outputGate = m.col(1); + Mat forgetGate = m.col(2); + Mat cellGate = m.col(3); + // IOFC -> IFOC + std::swap_ranges(outputGate.begin(), outputGate.end(), forgetGate.begin()); + std::swap(outputGate, forgetGate); + // IFOC -> IFCO + std::swap_ranges(outputGate.begin(), outputGate.end(), cellGate.begin()); + in = in.reshape(1, numDirs); + }; + + Mat& b = originalBlobs[2]; + // B is a concatenation of biases for Wh and Wx + b = b.reshape(1, originalBlobs[2].size[0]*2); + + for (auto& m : originalBlobs) + { + toIFCO(m); + } + + b = b.reshape(1, static_cast(b.total())); + + Mat ordered_weights; + // Wx_f, Wh_f, [Wx_b, Wh_b,] b + for (int i = 0; i < numDirs; ++i) + { + for (size_t j = 0; j < 2; ++j) // Wx, Wh + { + Mat oneDirection = originalBlobs[j].row(i); + ordered_weights.push_back(oneDirection.reshape(1, static_cast(oneDirection.total()))); + } + } + ordered_weights.push_back(b); + + // Pass hidden states as is + Mat h0 = blobs[3]; + Mat c0 = blobs[4]; + + CV_Assert(!inputs.empty()); + auto input_wrapper = inputs[0].dynamicCast(); + auto input_shape = input_wrapper->getShape(); + + RNNConfiguration config + { + input_shape[0], // seqLength; + 1, // numLayers; + numHidden, // hiddenSize; + input_shape[2], // inputSize; + input_shape[1], // miniBatch; + bidirectional + }; + + + auto *context = reinterpret_cast(context_); + return make_cuda_node(preferableTarget, std::move(context->stream), + std::move(context->cudnn_handle), + ordered_weights, h0, c0, + config); + } +#endif }; Ptr LSTMLayer::create(const LayerParams& params) diff --git a/modules/dnn/src/layers/reduce_layer.cpp b/modules/dnn/src/layers/reduce_layer.cpp new file mode 100644 index 000000000000..47aec237c7bf --- /dev/null +++ b/modules/dnn/src/layers/reduce_layer.cpp @@ -0,0 +1,389 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "../precomp.hpp" +#include "opencv2/core/hal/intrin.hpp" +#include "../op_cuda.hpp" +#include "../op_webnn.hpp" + +#include +#include +#include +using std::max; +using std::min; + +#include + +namespace cv +{ +namespace dnn +{ + +class ReduceLayerImpl CV_FINAL : public ReduceLayer +{ +public: + ReduceLayerImpl(const LayerParams& params) + { + setParamsFrom(params); + // set reduce type + CV_Assert(params.has("reduce")); + String typeString = toLowerCase(params.get("reduce")); + if (typeString == "max") + reduceType= MAX; + else if (typeString == "min") + reduceType= MIN; + else if (typeString == "ave") + reduceType= AVE; + else if (typeString == "sum") + reduceType= SUM; + else if (typeString == "sum_square") + reduceType= SUM_SQUARE; + else if (typeString == "l1") + reduceType= L1; + else if (typeString == "l2") + reduceType= L2; + else if (typeString == "log_sum") + reduceType= LOG_SUM; + else if (typeString == "log_sum_exp") + reduceType= LOG_SUM_EXP; + else if (typeString == "prod") + reduceType= PROD; + else + CV_Error(Error::StsBadArg, "Unknown reduce type\"" + typeString + "\""); + + // set deleted dims + CV_Assert(params.has("deleted_dims")); + DictValue tempDims = params.get("deleted_dims"); + int i, n = tempDims.size(); + reduceDims.resize(n); + for (i = 0; i < n; i++) + { + reduceDims[i] = tempDims.get(i); + } + } + + virtual bool supportBackend(int backendId) CV_OVERRIDE + { + if (backendId == DNN_BACKEND_OPENCV) + { + return true; + } + return false; + } + + // reduceType == MIN + struct ReduceOpMIN + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + return std::accumulate(first, last, FLT_MAX, + [](float a, float b) + { + return std::min(a, b); + }); + } + }; + + // reduceType == MAX + struct ReduceOpMAX + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + return std::accumulate(first, last, -FLT_MAX, + [](float a, float b) + { + return std::max(a, b); + }); + } + }; + + // reduceType == SUM + struct ReduceOpSUM + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + return std::accumulate(first, last, 0.f); + } + }; + + // reduceType == AVE + struct ReduceOpAVE + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + float output = std::accumulate(first, last, 0.f); + return output * ikarea; + } + }; + + // reduceType == SUM_SQUARE + struct ReduceOpSUM_SQUARE + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + return std::accumulate(first, last, 0.f, + [](float a, float b) + { + return a + b * b; + }); + } + }; + + // reduceType == L1 + struct ReduceOpL1 + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + return std::accumulate(first, last, 0.f, + [](float a, float b) + { + return a + std::abs(b); + }); + } + }; + + // reduceType == L2 + struct ReduceOpL2 + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + float output = std::accumulate(first, last, 0.f, + [](float a, float b) + { + return a + b * b; + }); + return std::sqrt(output); + } + }; + + // reduceType == PROD + struct ReduceOpPROD + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + return std::accumulate(first, last, 1.0f, std::multiplies()); + } + }; + + // reduceType == LOG_SUM + struct ReduceOpLOG_SUM + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + float output = std::accumulate(first, last, 0.0f); + return std::log(output); + } + }; + + // reduceType == LOG_SUM_EXP + struct ReduceOpLOG_SUM_EXP + { + float apply(const float* first, const float* last, const float ikarea = 1.0f) + { + float output = std::accumulate(first, last, 0.0f, + [](float a, float b) + { + return a + std::exp(b); + }); + return std::log(output); + } + }; + + template + class ReduceInvoker : public ParallelLoopBody + { + public: + const Mat* src; + Mat *dst; + std::vector reduceDims; + int nstripes; + int reduceType; + Ptr func; + + ReduceInvoker() : src(0), dst(0), nstripes(0), reduceType(MAX), func(makePtr()) {} + + static void run(const Mat& src, Mat& dst, std::vector reduceDims, int reduceType, int nstripes) + { + CV_Assert_N( src.isContinuous(), dst.isContinuous(), src.type() == CV_32F, src.type() == dst.type()); + + ReduceInvoker p; + + p.src = &src; + p.dst = &dst; + + p.reduceDims = reduceDims; + p.nstripes = nstripes; + p.reduceType = reduceType; + + parallel_for_(Range(0, nstripes), p, nstripes); + } + + void operator()(const Range& r) const CV_OVERRIDE + { + size_t total = dst->total(); + size_t stripeSize = (total + nstripes - 1)/nstripes; + size_t stripeStart = r.start*stripeSize; + size_t stripeEnd = std::min(r.end*stripeSize, total); + size_t stride_w = std::accumulate(reduceDims.begin(), reduceDims.end(), 1, std::multiplies()); + + float *dstData = (float *)dst->data; + float *srcData = (float *)src->data; + + for (size_t ofs = stripeStart; ofs < stripeEnd;) + { + const float* first = srcData + ofs * stride_w; + const float* last = srcData + (ofs + 1) * stride_w; + + if (ofs < stripeEnd) + { + dstData[ofs] = func->apply(first, last, 1.0 / stride_w); + ofs += 1; + } + } + } + }; + + void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE + { + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(name, "name", name.c_str()); + + if (inputs_arr.depth() == CV_16S) + { + forward_fallback(inputs_arr, outputs_arr, internals_arr); + return; + } + + std::vector inputs, outputs; + inputs_arr.getMatVector(inputs); + outputs_arr.getMatVector(outputs); + CV_Assert(inputs.size() == 1 || (inputs.size() == 2 && reduceType== SUM)); + const int nstripes = getNumThreads(); + + switch (reduceType) + { + case MIN: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case MAX: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case AVE: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case SUM: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case L1: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case L2: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case SUM_SQUARE: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case PROD: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case LOG_SUM: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + case LOG_SUM_EXP: + { + ReduceInvoker::run(inputs[0], outputs[0], reduceDims, reduceType, nstripes); + break; + } + default: + CV_Error(Error::StsNotImplemented, "Not implemented"); + break; + } + } + + bool getMemoryShapes(const std::vector &inputs, + const int requiredOutputs, + std::vector &outputs, + std::vector &internals) const CV_OVERRIDE + { + CV_Assert(inputs.size() > 0); + CV_Assert(reduceDims.size() != 0 && inputs[0].size() >= reduceDims.size()); + + std::vector outShape; + if (inputs[0].size() == reduceDims.size()) + outShape.push_back(1); + else + { + for (int i = 0; i < inputs[0].size() - reduceDims.size(); i++) + { + outShape.push_back(inputs[0][i]); + } + } + outputs.assign(1, outShape); + + return false; + } + + virtual bool tryQuantize(const std::vector > &scales, + const std::vector > &zeropoints, LayerParams& params) CV_OVERRIDE + { + if (reduceType== MAX || reduceType== MIN) + { + return true; + } + return false; + } + + virtual int64 getFLOPS(const std::vector &inputs, + const std::vector &outputs) const CV_OVERRIDE + { + CV_UNUSED(inputs); // suppress unused variable warning + long flops = 0; + size_t stride_w = std::accumulate(reduceDims.begin(), reduceDims.end(), 1, std::multiplies()); + for (int i = 0; i < outputs.size(); i++) + { + flops += total(outputs[i])*(stride_w); + } + return flops; + } +private: + enum ReduceType + { + MAX, + MIN, + AVE, + SUM, + L1, + L2, + PROD, + SUM_SQUARE, + LOG_SUM, + LOG_SUM_EXP + }; +}; + +Ptr ReduceLayer::create(const LayerParams& params) +{ + return Ptr(new ReduceLayerImpl(params)); +} + +} +} diff --git a/modules/dnn/src/layers/reorg_layer.cpp b/modules/dnn/src/layers/reorg_layer.cpp index 797df4819d9e..ac7d1abfb1f2 100644 --- a/modules/dnn/src/layers/reorg_layer.cpp +++ b/modules/dnn/src/layers/reorg_layer.cpp @@ -151,10 +151,12 @@ class ReorgLayerImpl CV_FINAL : public ReorgLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || - backendId == DNN_BACKEND_CUDA || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; + backendId == DNN_BACKEND_CUDA; } #ifdef HAVE_OPENCL @@ -198,16 +200,6 @@ class ReorgLayerImpl CV_FINAL : public ReorgLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::ReorgYoloLayer ieLayer(name); - ieLayer.setStride(reorgStride); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector > &inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/reshape_layer.cpp b/modules/dnn/src/layers/reshape_layer.cpp index 0ba3abf04758..290effd38018 100644 --- a/modules/dnn/src/layers/reshape_layer.cpp +++ b/modules/dnn/src/layers/reshape_layer.cpp @@ -46,6 +46,7 @@ #include "../op_inf_engine.hpp" #include "../ie_ngraph.hpp" #include "../op_webnn.hpp" +#include "../op_timvx.hpp" #include @@ -167,6 +168,9 @@ class ReshapeLayerImpl CV_FINAL : public ReshapeLayer hasDynamicShapes = params.get("has_dynamic_shapes", false); shapesInitialized = !hasDynamicShapes; + zeropoint = params.get("zeropoints", 0); + scale = params.get("scales", 1.0f); + CV_Assert(numAxes >= -1); newShapeRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes); @@ -202,10 +206,25 @@ class ReshapeLayerImpl CV_FINAL : public ReshapeLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { + if (backendId == DNN_BACKEND_TIMVX && haveTimVX()) + { + int len = this->type.length(); + if (len <= 4) + return false; + + if (this->type.substr(len - 4) == "Int8") + return true; + else + return false; + } + +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || - backendId == DNN_BACKEND_WEBNN || - ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()); + backendId == DNN_BACKEND_WEBNN; } bool getMemoryShapes(const std::vector &inputs, @@ -306,17 +325,6 @@ class ReshapeLayerImpl CV_FINAL : public ReshapeLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::Builder::ReshapeLayer ieLayer(name); - CV_Assert(outShapes.size() == 1); - ieLayer.setDims(outShapes[0]); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE @@ -356,6 +364,99 @@ class ReshapeLayerImpl CV_FINAL : public ReshapeLayer } #endif + virtual Ptr initTimVX(void* timVXInfo_, + const std::vector > &inputsWrapper, + const std::vector > &outputsWrapper, + bool isLast) CV_OVERRIDE + { +#ifdef HAVE_TIMVX + // tvGraph Initialization. + auto timVxInfo = reinterpret_cast(timVXInfo_); + CV_Assert(timVxInfo); + Ptr tvGraph = timVxInfo->getGraph(); + CV_Assert(tvGraph); + Ptr graph = tvGraph->graph; + + std::vector inputsIndex, outputsIndex; + int input_index = -1, output_index = -1; + + int reshapeNum = 0; + Ptr tmpWrapper, inputWrapper, outputWrapper; + for (size_t i = 0; i < outputsWrapper.size(); i++) + { + tmpWrapper = inputsWrapper[i].dynamicCast(); + Mat srcBlob = tmpWrapper->getMat(); + + tmpWrapper = outputsWrapper[i].dynamicCast(); + Mat dstBlob = tmpWrapper->getMat(); + if (dstBlob.data != srcBlob.data) + { + reshapeNum++; + inputWrapper = inputsWrapper[i].dynamicCast(); + outputWrapper = outputsWrapper[i].dynamicCast(); + } + } + + // Only work for single reshape Mat + if (reshapeNum != 1) + { + return Ptr(); + } + + // Input + if (inputWrapper->isTensor()) + { + input_index = tvGraph->getTensorIndex(inputWrapper->getTensor()); + if (input_index == -1) + { + // Copy To New inputWrapper + Mat tmp = inputWrapper->getMat(); + inputWrapper = Ptr(new TimVXBackendWrapper(tmp)); + } + } + + if (!inputWrapper->isTensor() || input_index == -1) + { + Ptr tvInputQuant = Ptr( + new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint)); + inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT,tvInputQuant); + input_index = tvGraph->addWrapper(inputWrapper); + } + inputsIndex.push_back(input_index); + + //Output + // Output Tensor has the same quantized attrib as Input Tesor. + Ptr outputQuant = inputWrapper->getTensorQuantization(); + if (isLast) + { + auto shapeType = getShapeTypeFromMat(outputWrapper->getMat()); + + // For Graph Output tensor, we need to set tensor shape before createTensor(). + outputWrapper->setTensorShape(shapeType); + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant); + } + else + { + outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant); + } + output_index = tvGraph->addWrapper(outputWrapper); + outputsIndex.push_back(output_index); + + // generate output shape. + MatShape outputShape = shape(outputWrapper->getMat()); + // reverse shape, from NCHW to WHCN + std::reverse(outputShape.begin(), outputShape.end()); + std::vector tvShape(outputShape.begin(), outputShape.end()); + + std::shared_ptr tvReshape = graph->CreateOperation(tvShape); + + Ptr tvBackendNode = new TimVXBackendNode(tvGraph, tvReshape, inputsIndex, outputsIndex); + + return tvBackendNode; +#endif // HAVE_TIMVX + return Ptr(); + } + virtual bool tryQuantize(const std::vector > &scales, const std::vector > &zeropoints, LayerParams& params) CV_OVERRIDE { @@ -368,6 +469,8 @@ class ReshapeLayerImpl CV_FINAL : public ReshapeLayer std::vector inputIndices; // Which axes from input are needed to compute correct output shape bool hasDynamicShapes; bool shapesInitialized; + float scale; + int zeropoint; }; Ptr ReshapeLayer::create(const LayerParams& params) diff --git a/modules/dnn/src/layers/resize_layer.cpp b/modules/dnn/src/layers/resize_layer.cpp index 42eb2e2331fc..ab640dbf3fc0 100644 --- a/modules/dnn/src/layers/resize_layer.cpp +++ b/modules/dnn/src/layers/resize_layer.cpp @@ -78,7 +78,7 @@ class ResizeLayerImpl : public ResizeLayer return interpolation == "nearest" || interpolation == "bilinear" || interpolation == "opencv_linear"; #ifdef HAVE_INF_ENGINE - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { return (interpolation == "nearest" && scaleWidth == scaleHeight) || (interpolation == "bilinear"); @@ -308,38 +308,6 @@ class ResizeLayerImpl : public ResizeLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::Layer ieLayer(name); - ieLayer.setName(name); - if (interpolation == "nearest") - { - ieLayer.setType("Resample"); - ieLayer.getParameters()["type"] = std::string("caffe.ResampleParameter.NEAREST"); - ieLayer.getParameters()["antialias"] = false; - if (scaleWidth != scaleHeight) - CV_Error(Error::StsNotImplemented, "resample with sw != sh"); - ieLayer.getParameters()["factor"] = 1.0f / scaleWidth; - } - else if (interpolation == "bilinear") - { - ieLayer.setType("Interp"); - ieLayer.getParameters()["pad_beg"] = 0; - ieLayer.getParameters()["pad_end"] = 0; - ieLayer.getParameters()["align_corners"] = alignCorners; - } - else - CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation); - ieLayer.getParameters()["width"] = outWidth; - ieLayer.getParameters()["height"] = outHeight; - ieLayer.setInputPorts(std::vector(1)); - ieLayer.setOutputPorts(std::vector(1)); - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/scale_layer.cpp b/modules/dnn/src/layers/scale_layer.cpp index fcee4515567e..d727734cf3bb 100644 --- a/modules/dnn/src/layers/scale_layer.cpp +++ b/modules/dnn/src/layers/scale_layer.cpp @@ -78,11 +78,13 @@ class ScaleLayerImpl CV_FINAL : public ScaleLayer { return backendId == DNN_BACKEND_OPENCV; } +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return axis > 0; +#endif return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || backendId == DNN_BACKEND_HALIDE || - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && axis == 1 && !blobs.empty()) || - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && axis > 0) || (backendId == DNN_BACKEND_WEBNN && axis >0); } @@ -314,34 +316,6 @@ class ScaleLayerImpl CV_FINAL : public ScaleLayer } #endif // HAVE_HALIDE -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >&) CV_OVERRIDE - { - InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ScaleShiftLayer(name); - - CV_Assert(!blobs.empty()); - const size_t numChannels = blobs[0].total(); - if (hasWeights) - { - addConstantData("weights", wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C), l); - } - else - { - auto weights = InferenceEngine::make_shared_blob({ - InferenceEngine::Precision::FP32, {(size_t)numChannels}, - InferenceEngine::Layout::C - }); - weights->allocate(); - float* buf = weights->buffer().as(); - std::fill(buf, buf + numChannels, 1); - addConstantData("weights", weights, l); - } - if (hasBias) - addConstantData("biases", wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C), l); - return Ptr(new InfEngineBackendNode(l)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/slice_layer.cpp b/modules/dnn/src/layers/slice_layer.cpp index 20b493636a98..aa44e4a5b99b 100644 --- a/modules/dnn/src/layers/slice_layer.cpp +++ b/modules/dnn/src/layers/slice_layer.cpp @@ -64,12 +64,32 @@ namespace cv namespace dnn { -void sliceRangesFromShape(const MatShape& inpShape, int& axis, std::vector >& sliceRanges) +Range normalizeRange(const Range& input_range, int n) { + Range range = input_range; + + range.start = std::min(std::max(range.start, -n), n - 1); + if (range.start < 0) + { + range.start += n; + } + + range.end = std::min(std::max(range.end, -n), n); + if (range.end < 0) + { + range.end += n; + } + + return range; +} + +std::vector > finalizeSliceRange(const MatShape& inpShape, int& axis, + const std::vector >& inputSliceRanges) +{ + std::vector > sliceRanges = inputSliceRanges; CV_Assert(inpShape.size() > 0); bool axisNeg = (axis < 0); axis = (axis + static_cast(inpShape.size())) % inpShape.size(); - int n = inpShape[axis]; for (size_t i = 0; i < sliceRanges.size(); ++i){ std::vector& ranges = sliceRanges[i]; @@ -77,16 +97,20 @@ void sliceRangesFromShape(const MatShape& inpShape, int& axis, std::vector= 0) + for (size_t j = 0; j < ranges.size(); ++j) { - continue; - } + int n = inpShape[j]; + if (n <= 0) + { + continue; + } - CV_Assert(n != 0); - range.start = (n + range.start) % n; + ranges[j] = normalizeRange(ranges[j], n); + } } + + return sliceRanges; } class SliceLayerImpl : public SliceLayer @@ -136,7 +160,7 @@ class SliceLayerImpl : public SliceLayer { int size = sizeOrEnd; CV_Assert(size == -1 || size > 0); // -1 value means range [start, axis_size). - sliceRanges[0][i].end = size > 0 ? (start + size) : -1; // We'll finalize a negative value later. + sliceRanges[0][i].end = size > 0 ? (start + size) : INT_MAX; // We'll finalize a negative value later. } else { @@ -166,12 +190,7 @@ class SliceLayerImpl : public SliceLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && - sliceRanges.size() == 1 && sliceRanges[0].size() == 4 && !hasSteps; -#endif -#ifdef HAVE_DNN_NGRAPH +#ifdef HAVE_INF_ENGINE if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) return sliceRanges.size() == 1 && !hasSteps; #endif @@ -191,8 +210,7 @@ class SliceLayerImpl : public SliceLayer MatShape inpShape = inputs[0]; int axis_rw = axis; - std::vector > sliceRanges_rw = sliceRanges; - sliceRangesFromShape(inpShape, axis_rw, sliceRanges_rw); + std::vector > sliceRanges_rw = finalizeSliceRange(inpShape, axis_rw, sliceRanges); if (!sliceRanges_rw.empty()) { @@ -203,7 +221,7 @@ class SliceLayerImpl : public SliceLayer for (int j = 0; j < sliceRanges_rw[i].size(); ++j) { if (shapesInitialized || inpShape[j] > 0) - outputs[i][j] = normalize_axis_range(sliceRanges_rw[i][j], inpShape[j]).size(); + outputs[i][j] = normalizeRange(sliceRanges_rw[i][j], inpShape[j]).size(); if (!sliceSteps.empty() && (i < sliceSteps.size()) && (j < sliceSteps[i].size()) && (sliceSteps[i][j] > 1)) outputs[i][j] = (outputs[i][j] + sliceSteps[i][j] - 1) / sliceSteps[i][j]; @@ -240,8 +258,7 @@ class SliceLayerImpl : public SliceLayer CV_Assert(inputs.size() == 1); const MatSize& inpShape = inputs[0].size; - sliceRangesFromShape(shape(inputs[0]), axis, sliceRanges); - finalSliceRanges = sliceRanges; + finalSliceRanges = finalizeSliceRange(shape(inputs[0]), axis, sliceRanges); if (sliceRanges.empty()) { @@ -271,7 +288,7 @@ class SliceLayerImpl : public SliceLayer // Clamp. for (int j = 0; j < finalSliceRanges[i].size(); ++j) { - finalSliceRanges[i][j] = normalize_axis_range(finalSliceRanges[i][j], inpShape[j]); + finalSliceRanges[i][j] = normalizeRange(finalSliceRanges[i][j], inpShape[j]); } } @@ -573,64 +590,6 @@ class SliceLayerImpl : public SliceLayer } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - CV_Assert_N(finalSliceRanges.size() == 1, inputs.size() <= 2); - - std::vector axes, offsets, dims; - int from, to, step; - int numDims = finalSliceRanges[0].size(); - if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) - { - from = axis; - to = numDims; - step = 1; - } - else - { - from = numDims - 1; - to = axis - 1; - step = -1; - } - for (int i = from; i != to; i += step) - { - axes.push_back(i); - offsets.push_back(finalSliceRanges[0][i].start); - dims.push_back(finalSliceRanges[0][i].size()); - } - - InferenceEngine::Builder::Layer ieLayer(name); - ieLayer.setName(name); - ieLayer.setType("Crop"); - ieLayer.getParameters()["axis"] = axes; - ieLayer.getParameters()["dim"] = dims; - ieLayer.getParameters()["offset"] = offsets; - ieLayer.setInputPorts(std::vector(2)); - ieLayer.setOutputPorts(std::vector(1)); - - if (inputs.size() != 2) - { - std::vector outShape(numDims); - for (int i = 0; i < numDims; ++i) - outShape[i] = finalSliceRanges[0][i].size(); - - ieLayer.getInputPorts()[1].setParameter("type", "weights"); - - auto shapeSource = InferenceEngine::make_shared_blob({ - InferenceEngine::Precision::FP32, outShape, - InferenceEngine::Layout::ANY - }); - shapeSource->allocate(); - addConstantData("weights", shapeSource, ieLayer); - } - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif -#endif - - #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, const std::vector >& nodes) CV_OVERRIDE diff --git a/modules/dnn/src/layers/softmax_layer.cpp b/modules/dnn/src/layers/softmax_layer.cpp index db2951808ffd..b10aef34539e 100644 --- a/modules/dnn/src/layers/softmax_layer.cpp +++ b/modules/dnn/src/layers/softmax_layer.cpp @@ -99,6 +99,10 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer virtual bool supportBackend(int backendId) CV_OVERRIDE { +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return true; +#endif #ifdef HAVE_WEBNN if (backendId == DNN_BACKEND_WEBNN) { // TODO: support logSoftMax @@ -112,8 +116,6 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA || (backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1) || - backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !logSoftMax) || (backendId == DNN_BACKEND_VKCOM && haveVulkan()); } @@ -360,17 +362,6 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer return Ptr(); } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - virtual Ptr initInfEngine(const std::vector >& inputs) CV_OVERRIDE - { - InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]); - - InferenceEngine::Builder::SoftMaxLayer ieLayer(name); - ieLayer.setAxis(normalize_axis(axisRaw, input->getDims().size())); - - return Ptr(new InfEngineBackendNode(ieLayer)); - } -#endif // HAVE_DNN_IE_NN_BUILDER_2019 #ifdef HAVE_DNN_NGRAPH virtual Ptr initNgraph(const std::vector >& inputs, @@ -399,6 +390,8 @@ class SoftMaxLayerImpl CV_FINAL : public SoftmaxLayer } params.blobs.clear(); params.blobs.push_back(lookUpTable); + params.set("input_scale", inpScale); + params.set("input_zeropoint", zeropoints[0][0]); return true; } diff --git a/modules/dnn/src/legacy_backend.cpp b/modules/dnn/src/legacy_backend.cpp new file mode 100644 index 000000000000..fa9407aacdf2 --- /dev/null +++ b/modules/dnn/src/legacy_backend.cpp @@ -0,0 +1,130 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "legacy_backend.hpp" + +#include "op_halide.hpp" +#include "op_inf_engine.hpp" +#include "ie_ngraph.hpp" +#include "op_vkcom.hpp" +#include "op_cuda.hpp" +#include "op_webnn.hpp" +#include "op_timvx.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +BackendNode::BackendNode(int backendId) + : backendId(backendId) +{} + +BackendNode::~BackendNode() {}; + +BackendWrapper::BackendWrapper(int backendId, int targetId) + : backendId(backendId) + , targetId(targetId) +{} + +BackendWrapper::BackendWrapper(int targetId, const cv::Mat& m) +{ + CV_Error(Error::StsNotImplemented, + "Constructor of backend wrapper must be implemented"); +} + +BackendWrapper::BackendWrapper(const Ptr& base, const MatShape& shape) +{ + CV_Error(Error::StsNotImplemented, + "Constructor of backend wrapper must be implemented"); +} + +BackendWrapper::~BackendWrapper() {} + + + +inline namespace detail { + + +Ptr wrapMat(int backendId, int targetId, cv::Mat& m) +{ + if (backendId == DNN_BACKEND_OPENCV) + { + if (targetId == DNN_TARGET_CPU) + return Ptr(); +#ifdef HAVE_OPENCL + else if (IS_DNN_OPENCL_TARGET(targetId)) + return OpenCLBackendWrapper::create(m); +#endif + else + CV_Error(Error::StsNotImplemented, "Unknown/unsupported target identifier"); + } + else if (backendId == DNN_BACKEND_HALIDE) + { + CV_Assert(haveHalide()); +#ifdef HAVE_HALIDE + return Ptr(new HalideBackendWrapper(targetId, m)); +#endif // HAVE_HALIDE + } + else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) + { + CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019; + } + else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { +#ifdef HAVE_DNN_NGRAPH + return Ptr(new NgraphBackendWrapper(targetId, m)); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of OpenVINO / Inference Engine + nGraph"); +#endif + } + else if (backendId == DNN_BACKEND_WEBNN) + { +#ifdef HAVE_WEBNN + return Ptr(new WebnnBackendWrapper(targetId, m)); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of WebNN"); +#endif + } + else if (backendId == DNN_BACKEND_VKCOM) + { + CV_Assert(haveVulkan()); +#ifdef HAVE_VULKAN + return Ptr(new VkComBackendWrapper(m)); +#endif // HAVE_VULKAN + } + else if (backendId == DNN_BACKEND_CUDA) + { + CV_Assert(haveCUDA()); + +#ifdef HAVE_CUDA + switch (targetId) + { + case DNN_TARGET_CUDA: + return CUDABackendWrapperFP32::create(m); + case DNN_TARGET_CUDA_FP16: + return CUDABackendWrapperFP16::create(m); + default: + CV_Assert(IS_DNN_CUDA_TARGET(targetId)); + } +#endif + } + else if (backendId == DNN_BACKEND_TIMVX) + { + CV_Assert(haveTimVX()); +#ifdef HAVE_TIMVX + return Ptr(new TimVXBackendWrapper(m)); +#endif // HAVE_TIMVX + } + else + CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); + return Ptr(); // TODO Error? +} // wrapMat() + + +} // namespace detail +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/legacy_backend.hpp b/modules/dnn/src/legacy_backend.hpp new file mode 100644 index 000000000000..e9ca3fecb3ba --- /dev/null +++ b/modules/dnn/src/legacy_backend.hpp @@ -0,0 +1,339 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef __OPENCV_DNN_SRC_LEGACY_BACKEND_HPP__ +#define __OPENCV_DNN_SRC_LEGACY_BACKEND_HPP__ + +#include "layer_internals.hpp" // LayerPin LayerData DataLayer + +namespace cv { namespace dnn { +CV__DNN_INLINE_NS_BEGIN +inline namespace detail { + + +#ifdef HAVE_OPENCL +class OpenCLBackendWrapper : public BackendWrapper +{ +public: + OpenCLBackendWrapper(Mat& m) + : BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL) + { + m.copyTo(umat); + host = &m; + hostDirty = false; + } + + OpenCLBackendWrapper(const Ptr& baseBuffer, Mat& m) + : BackendWrapper(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL) + { + Ptr base = baseBuffer.dynamicCast(); + CV_Assert(!base.empty()); + + host = &m; + + int shape[] = { 1, (int)base->umat.total() }; + umat = base->umat.reshape(1, 2, &shape[0]) + .colRange(0, host->total()) + .reshape(1, host->dims, &host->size[0]); + hostDirty = false; + } + + static Ptr create(Mat& m) + { + return Ptr(new OpenCLBackendWrapper(m)); + } + + static Ptr create(const Ptr& baseBuffer, Mat& m) + { + return Ptr(new OpenCLBackendWrapper(baseBuffer, m)); + } + + static std::vector getUMatVector(const std::vector>& wrappers) + { + const int numWrappers = wrappers.size(); + std::vector mats(wrappers.size()); + for (int i = 0; i < numWrappers; ++i) + { + Ptr umatWrapper = wrappers[i].dynamicCast(); + CV_Assert(!umatWrapper.empty()); + umatWrapper->copyToDevice(); + mats[i] = umatWrapper->umat; + } + return mats; + } + + // Replaces all umats in wrappers to specific ones. + static void update(const std::vector>& wrappers, + const std::vector& umats) + { + CV_Assert(wrappers.size() == umats.size()); + for (int i = 0, n = umats.size(); i < n; ++i) + { + Ptr umatWrapper = wrappers[i].dynamicCast(); + CV_Assert(!umatWrapper.empty()); + umatWrapper->umat = umats[i]; + } + } + + ~OpenCLBackendWrapper() {} + + // Copies data from device to a host memory. + virtual void copyToHost() CV_OVERRIDE + { + umat.copyTo(*host); + } + + virtual void setHostDirty() CV_OVERRIDE + { + hostDirty = true; + }; + + void copyToDevice() + { + if (hostDirty) + { + host->copyTo(umat); + hostDirty = false; + } + } + +private: + UMat umat; + Mat* host; + bool hostDirty; +}; // OpenCLBackendWrapper +#endif // HAVE_OPENCL + + +struct BlobManager +{ +public: + // Increase references counter to layer output. + void addReference(const LayerPin& lp) + { + std::map::iterator it = refCounter.find(lp); + if (it == refCounter.end()) + refCounter[lp] = 1; + else + it->second += 1; + } + + void addReferences(const std::vector& pins) + { + for (int i = 0; i < pins.size(); i++) + { + addReference(pins[i]); + } + } + + // Returns number of references to allocated memory that used in specific + // layer blob. + int numReferences(const LayerPin& lp) + { + std::map::const_iterator mapIt = reuseMap.find(lp); + CV_Assert(mapIt != reuseMap.end()); + LayerPin memHost = mapIt->second; + + std::map::const_iterator refIt = refCounter.find(memHost); + CV_Assert(refIt != refCounter.end()); + return refIt->second; + } + + // Reuse data allocated in inside the blob. + void reuse(const LayerPin& host, const LayerPin& user) + { + CV_Assert(reuseMap.find(user) == reuseMap.end()); + CV_Assert(reuseMap.find(host) != reuseMap.end()); + LayerPin memHost = reuseMap[host]; + reuseMap[user] = memHost; + if (refCounter.find(memHost) != refCounter.end()) + { + std::map::iterator userRefIt = refCounter.find(user); + if (userRefIt != refCounter.end()) + { + refCounter[memHost] += userRefIt->second; + refCounter.erase(userRefIt); + } + else + refCounter[memHost] += 1; + } + } + + // Decrease references counter to allocated memory inside specific blob. + void releaseReference(const LayerPin& lp) + { + std::map::const_iterator mapIt = reuseMap.find(lp); + CV_Assert(mapIt != reuseMap.end()); + + std::map::iterator refIt = refCounter.find(mapIt->second); + CV_Assert(refIt != refCounter.end()); + CV_Assert(refIt->second > 0); + refIt->second -= 1; + } + + void releaseReferences(const std::vector& pins) + { + for (int i = 0; i < pins.size(); i++) + { + releaseReference(pins[i]); + } + } + + void reuseOrCreate(const MatShape& shape, const LayerPin& lp, Mat& dst, const int& dtype) + { + if (!getParam_DNN_DISABLE_MEMORY_OPTIMIZATIONS()) + { + Mat bestBlob; + LayerPin bestBlobPin; + + std::map::const_iterator hostIt; + std::map::const_iterator refIt; + + const int targetTotal = total(shape); + int bestBlobTotal = INT_MAX; + + for (hostIt = memHosts.begin(); hostIt != memHosts.end(); ++hostIt) + { + refIt = refCounter.find(hostIt->first); + // Use only blobs that had references before because if not, + // it might be used as output. + if (refIt != refCounter.end() && refIt->second == 0) + { + const Mat& unusedBlob = hostIt->second; + if (unusedBlob.total() >= targetTotal && unusedBlob.total() < bestBlobTotal && unusedBlob.type() == dtype) + { + bestBlobPin = hostIt->first; + bestBlob = unusedBlob; + bestBlobTotal = unusedBlob.total(); + } + } + } + if (!bestBlob.empty()) + { + reuse(bestBlobPin, lp); + dst = bestBlob.reshape(1, 1).colRange(0, targetTotal).reshape(1, shape); + return; + } + } + + { + // if dst already has been allocated with total(shape) elements, + // it won't be recreated and pointer of dst.data remains the same. + dst.create(shape, dtype); + addHost(lp, dst); + } + } + + void allocateBlobsForLayer(LayerData& ld, const LayerShapes& layerShapes, + std::vector& pinsForInternalBlobs) + { + CV_TRACE_FUNCTION(); + + pinsForInternalBlobs.clear(); + + std::vector&outputBlobs = ld.outputBlobs, + &internalBlobs = ld.internals; + + const ShapesVec &outShapes = layerShapes.out, + internalShapes = layerShapes.internal; + + outputBlobs.resize(std::max((size_t)1, outShapes.size())); // layer produce at least one output blob + internalBlobs.resize(internalShapes.size()); + + CV_Assert(ld.requiredOutputs.size() <= outShapes.size()); + + // Check that layer could work in-place. + bool inPlace = false; + if (layerShapes.supportInPlace) + { + if (ld.inputBlobs.size() == 1) + { + // Get number of references to the input memory. + int numRef = numReferences(ld.inputBlobsId[0]); + // If current layer is one and only customer of this blob. + inPlace = numRef == 1; + } + } + + ShapesVec shapes(outShapes); + shapes.insert(shapes.end(), internalShapes.begin(), internalShapes.end()); + std::vector blobs; + for (int i = 0; i < outputBlobs.size(); i++) + { + blobs.push_back(&outputBlobs[i]); + } + + for (int i = 0; i < internalBlobs.size(); i++) + { + blobs.push_back(&internalBlobs[i]); + if (total(internalShapes[i])) + { + pinsForInternalBlobs.push_back(LayerPin(ld.id, ld.outputBlobs.size() + i)); + } + } + + addReferences(pinsForInternalBlobs); + + std::map> idxSizes; + for (int i = 0; i < shapes.size(); i++) + { + idxSizes[total(shapes[i])].push_back(i); + } + + std::map>::reverse_iterator it; + for (it = idxSizes.rbegin(); it != idxSizes.rend(); it++) + { + for (int j = 0; j < it->second.size(); j++) + { + int index = it->second[j]; + if (total(shapes[index])) + { + LayerPin blobPin(ld.id, index); + if (index < outShapes.size() && inPlace) + { + CV_Assert(ld.inputBlobs[0]->total() == total(shapes[index])); + ld.outputBlobs[index] = ld.inputBlobs[0]->reshape(1, shapes[index]); + reuse(ld.inputBlobsId[0], blobPin); + } + else + reuseOrCreate(shapes[index], blobPin, *blobs[index], ld.dtype); + } + } + } + } + + // Clear internal state. Calls before an every reallocation. + void reset() + { + CV_TRACE_FUNCTION(); + + refCounter.clear(); + reuseMap.clear(); + memHosts.clear(); + } + +private: + // Register allocated memory. + void addHost(const LayerPin& lp, const Mat& mat) + { + CV_Assert(memHosts.find(lp) == memHosts.end()); + reuseMap[lp] = lp; + memHosts[lp] = mat; + } + + std::map refCounter; + // Maps pin to origin blob (for whom memory was allocated firstly). + // For origin blobs key == value. + std::map reuseMap; + std::map memHosts; +}; // BlobManager + + +Ptr wrapMat(int backendId, int targetId, cv::Mat& m); + + +} // namespace detail +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn +#endif // __OPENCV_DNN_SRC_LEGACY_BACKEND_HPP__ diff --git a/modules/dnn/src/model.cpp b/modules/dnn/src/model.cpp index bc8709d22edc..7444011a64fd 100644 --- a/modules/dnn/src/model.cpp +++ b/modules/dnn/src/model.cpp @@ -197,28 +197,95 @@ void Model::predict(InputArray frame, OutputArrayOfArrays outs) const } +class ClassificationModel_Impl : public Model::Impl +{ +public: + virtual ~ClassificationModel_Impl() {} + ClassificationModel_Impl() : Impl() {} + ClassificationModel_Impl(const ClassificationModel_Impl&) = delete; + ClassificationModel_Impl(ClassificationModel_Impl&&) = delete; + + void setEnableSoftmaxPostProcessing(bool enable) + { + applySoftmax = enable; + } + + bool getEnableSoftmaxPostProcessing() const + { + return applySoftmax; + } + + std::pair classify(InputArray frame) + { + std::vector outs; + processFrame(frame, outs); + CV_Assert(outs.size() == 1); + + Mat out = outs[0].reshape(1, 1); + + if(getEnableSoftmaxPostProcessing()) + { + softmax(out, out); + } + + double conf; + Point maxLoc; + cv::minMaxLoc(out, nullptr, &conf, nullptr, &maxLoc); + return {maxLoc.x, static_cast(conf)}; + } + +protected: + void softmax(InputArray inblob, OutputArray outblob) + { + const Mat input = inblob.getMat(); + outblob.create(inblob.size(), inblob.type()); + + Mat exp; + const float max = *std::max_element(input.begin(), input.end()); + cv::exp((input - max), exp); + outblob.getMat() = exp / cv::sum(exp)[0]; + } + +protected: + bool applySoftmax = false; +}; + +ClassificationModel::ClassificationModel() + : Model() +{ + // nothing +} + ClassificationModel::ClassificationModel(const String& model, const String& config) - : Model(model, config) + : ClassificationModel(readNet(model, config)) { // nothing } ClassificationModel::ClassificationModel(const Net& network) - : Model(network) + : Model() { - // nothing + impl = makePtr(); + impl->initNet(network); } -std::pair ClassificationModel::classify(InputArray frame) +ClassificationModel& ClassificationModel::setEnableSoftmaxPostProcessing(bool enable) { - std::vector outs; - impl->processFrame(frame, outs); - CV_Assert(outs.size() == 1); + CV_Assert(impl != nullptr && impl.dynamicCast() != nullptr); + impl.dynamicCast()->setEnableSoftmaxPostProcessing(enable); + return *this; +} - double conf; - cv::Point maxLoc; - minMaxLoc(outs[0].reshape(1, 1), nullptr, &conf, nullptr, &maxLoc); - return {maxLoc.x, static_cast(conf)}; +bool ClassificationModel::getEnableSoftmaxPostProcessing() const +{ + CV_Assert(impl != nullptr && impl.dynamicCast() != nullptr); + return impl.dynamicCast()->getEnableSoftmaxPostProcessing(); +} + +std::pair ClassificationModel::classify(InputArray frame) +{ + CV_Assert(impl != nullptr && impl.dynamicCast() != nullptr); + return impl.dynamicCast()->classify(frame); } void ClassificationModel::classify(InputArray frame, int& classId, float& conf) @@ -732,7 +799,7 @@ struct TextRecognitionModel_Impl : public Model::Impl virtual std::string ctcPrefixBeamSearchDecode(const Mat& prediction) { - // CTC prefix beam seach decode. + // CTC prefix beam search decode. // For more detail, refer to: // https://distill.pub/2017/ctc/#inference // https://gist.github.com/awni/56369a90d03953e370f3964c826ed4b0i @@ -1500,4 +1567,4 @@ int TextDetectionModel_DB::getMaxCandidates() const } -}} // namespace +}} // namespace diff --git a/modules/dnn/src/net.cpp b/modules/dnn/src/net.cpp new file mode 100644 index 000000000000..901101b1e02f --- /dev/null +++ b/modules/dnn/src/net.cpp @@ -0,0 +1,414 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "net_impl.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + +Net::Net() + : impl(makePtr()) +{ +} + +Net::~Net() +{ +} + +int Net::addLayer(const String& name, const String& type, const int& dtype, LayerParams& params) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->addLayer(name, type, dtype, params); +} + +int Net::addLayer(const String& name, const String& type, LayerParams& params) +{ + CV_TRACE_FUNCTION(); + return addLayer(name, type, CV_32F, params); +} + +int Net::addLayerToPrev(const String& name, const String& type, const int& dtype, LayerParams& params) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->addLayerToPrev(name, type, dtype, params); +} + +int Net::addLayerToPrev(const String& name, const String& type, LayerParams& params) +{ + CV_TRACE_FUNCTION(); + return addLayerToPrev(name, type, CV_32F, params); +} + +void Net::connect(int outLayerId, int outNum, int inpLayerId, int inpNum) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + impl->connect(outLayerId, outNum, inpLayerId, inpNum); +} + +void Net::connect(String _outPin, String _inPin) +{ + CV_TRACE_FUNCTION(); + + CV_Assert(impl); + LayerPin outPin = impl->getPinByAlias(_outPin); + LayerPin inpPin = impl->getPinByAlias(_inPin); + + CV_Assert(outPin.valid() && inpPin.valid()); + + impl->connect(outPin.lid, outPin.oid, inpPin.lid, inpPin.oid); +} + +int Net::registerOutput(const std::string& outputName, int layerId, int outputPort) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->registerOutput(outputName, layerId, outputPort); +} + +Mat Net::forward(const String& outputName) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->forward(outputName); +} + +AsyncArray Net::forwardAsync(const String& outputName) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->forwardAsync(outputName); +} + +void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->forward(outputBlobs, outputName); +} + +void Net::forward(OutputArrayOfArrays outputBlobs, + const std::vector& outBlobNames) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->forward(outputBlobs, outBlobNames); +} + +void Net::forward(std::vector>& outputBlobs, + const std::vector& outBlobNames) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->forward(outputBlobs, outBlobNames); +} + +// FIXIT drop from inference API +Net Net::quantize(InputArrayOfArrays calibData, int inputsDtype, int outputsDtype) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->quantize(calibData, inputsDtype, outputsDtype); +} + +// FIXIT drop from inference API +void Net::getInputDetails(std::vector& scales, std::vector& zeropoints) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->getInputDetails(scales, zeropoints); +} + +// FIXIT drop from inference API +void Net::getOutputDetails(std::vector& scales, std::vector& zeropoints) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->getOutputDetails(scales, zeropoints); +} + +void Net::setPreferableBackend(int backendId) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG(backendId); + CV_Assert(impl); + return impl->setPreferableBackend(backendId); +} + +void Net::setPreferableTarget(int targetId) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG(targetId); + CV_Assert(impl); + return impl->setPreferableTarget(targetId); +} + +void Net::setInputsNames(const std::vector& inputBlobNames) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->setInputsNames(inputBlobNames); +} + +void Net::setInputShape(const String& inputName, const MatShape& shape) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->setInputShape(inputName, shape); +} + +void Net::setInput(InputArray blob, const String& name, double scalefactor, const Scalar& mean) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(name, "name", name.c_str()); + CV_Assert(impl); + return impl->setInput(blob, name, scalefactor, mean); +} + +Mat Net::getParam(int layer, int numParam) const +{ + CV_Assert(impl); + return impl->getParam(layer, numParam); +} + +void Net::setParam(int layer, int numParam, const Mat& blob) +{ + CV_Assert(impl); + return impl->setParam(layer, numParam, blob); +} + +int Net::getLayerId(const String& layer) const +{ + CV_Assert(impl); + return impl->getLayerId(layer); +} + +String Net::dump() +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + return impl->dump(true); +} + +void Net::dumpToFile(const String& path) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + CV_Assert(!empty()); + std::ofstream file(path.c_str()); + file << dump(); + file.close(); +} + +Ptr Net::getLayer(int layerId) const +{ + CV_Assert(impl); + return impl->getLayer(layerId); +} +Ptr Net::getLayer(const LayerId& layerId) const +{ + CV_Assert(impl); + return impl->getLayer(layerId); +} + +std::vector> Net::getLayerInputs(int layerId) const +{ + CV_Assert(impl); + return impl->getLayerInputs(layerId); +} + +std::vector Net::getLayerNames() const +{ + CV_Assert(impl); + return impl->getLayerNames(); +} + +bool Net::empty() const +{ + CV_Assert(impl); + return impl->empty(); +} + +// FIXIT drop "unconnected" API +std::vector Net::getUnconnectedOutLayers() const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getUnconnectedOutLayers(); +} + +// FIXIT drop "unconnected" API +std::vector Net::getUnconnectedOutLayersNames() const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getUnconnectedOutLayersNames(); +} + +void Net::getLayersShapes(const ShapesVec& netInputShapes, + std::vector& layersIds, + std::vector& inLayersShapes, + std::vector& outLayersShapes) const +{ + CV_Assert(impl); + return impl->getLayersShapes(netInputShapes, layersIds, inLayersShapes, outLayersShapes); +} + +void Net::getLayersShapes(const MatShape& netInputShape, + std::vector& layerIds, + std::vector& inLayersShapes, + std::vector& outLayersShapes) const +{ + getLayersShapes(ShapesVec(1, netInputShape), + layerIds, inLayersShapes, outLayersShapes); +} + +void Net::getLayerShapes(const MatShape& netInputShape, + const int layerId, + ShapesVec& inLayerShapes, + ShapesVec& outLayerShapes) const +{ + getLayerShapes(ShapesVec(1, netInputShape), + layerId, inLayerShapes, outLayerShapes); +} + +void Net::getLayerShapes(const ShapesVec& netInputShapes, + const int layerId, + ShapesVec& inLayerShapes, + ShapesVec& outLayerShapes) const +{ + CV_Assert(impl); + LayerShapes shapes; + impl->getLayerShapes(netInputShapes, layerId, shapes); + inLayerShapes = shapes.in; + outLayerShapes = shapes.out; +} + +int64 Net::getFLOPS(const std::vector& netInputShapes) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getFLOPS(netInputShapes); +} + +int64 Net::getFLOPS(const MatShape& netInputShape) const +{ + return getFLOPS(std::vector(1, netInputShape)); +} + +int64 Net::getFLOPS(const int layerId, + const std::vector& netInputShapes) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getFLOPS(layerId, netInputShapes); +} + +int64 Net::getFLOPS(const int layerId, + const MatShape& netInputShape) const +{ + return getFLOPS(layerId, std::vector(1, netInputShape)); +} + +void Net::getLayerTypes(std::vector& layersTypes) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getLayerTypes(layersTypes); +} + +int Net::getLayersCount(const String& layerType) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getLayersCount(layerType); +} + +void Net::getMemoryConsumption(const int layerId, + const std::vector& netInputShapes, + size_t& weights, size_t& blobs) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getMemoryConsumption(layerId, netInputShapes, weights, blobs); +} + +void Net::getMemoryConsumption(const std::vector& netInputShapes, + size_t& weights, size_t& blobs) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getMemoryConsumption(netInputShapes, weights, blobs); +} + +void Net::getMemoryConsumption(const int layerId, + const MatShape& netInputShape, + size_t& weights, size_t& blobs) const +{ + getMemoryConsumption(layerId, std::vector(1, netInputShape), + weights, blobs); +} + +void Net::getMemoryConsumption(const MatShape& netInputShape, + size_t& weights, size_t& blobs) const +{ + getMemoryConsumption(std::vector(1, netInputShape), + weights, blobs); +} + +void Net::getMemoryConsumption(const std::vector& netInputShapes, + std::vector& layerIds, std::vector& weights, + std::vector& blobs) const +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getMemoryConsumption(netInputShapes, layerIds, weights, blobs); +} + +void Net::getMemoryConsumption(const MatShape& netInputShape, std::vector& layerIds, + std::vector& weights, std::vector& blobs) const +{ + getMemoryConsumption(std::vector(1, netInputShape), layerIds, + weights, blobs); +} + +// FIXIT return old value or add get method +void Net::enableFusion(bool fusion) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->enableFusion(fusion); +} + +void Net::setHalideScheduler(const String& scheduler) +{ + CV_TRACE_FUNCTION(); + CV_TRACE_ARG_VALUE(scheduler, "scheduler", scheduler.c_str()); + CV_Assert(impl); + return impl->setHalideScheduler(scheduler); +} + +int64 Net::getPerfProfile(std::vector& timings) +{ + CV_TRACE_FUNCTION(); + CV_Assert(impl); + return impl->getPerfProfile(timings); +} + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/net_impl.cpp b/modules/dnn/src/net_impl.cpp new file mode 100644 index 000000000000..24fb31f03e51 --- /dev/null +++ b/modules/dnn/src/net_impl.cpp @@ -0,0 +1,2107 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "net_impl.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +static int g_networkId = 0; + + +detail::NetImplBase::NetImplBase() + : networkId(CV_XADD(&g_networkId, 1)) + , networkDumpCounter(0) + , dumpLevel(getParam_DNN_NETWORK_DUMP()) +{ + // nothing +} + + +std::string detail::NetImplBase::getDumpFileNameBase() const +{ + std::string dumpFileNameBase = cv::format("ocv_dnn_net_%05d_%02d", networkId, networkDumpCounter++); + return dumpFileNameBase; +} + + +Net::Impl::Impl() +{ + // allocate fake net input layer + netInputLayer = Ptr(new DataLayer()); + LayerData& inpl = layers.insert(make_pair(0, LayerData())).first->second; + inpl.id = 0; + netInputLayer->name = inpl.name = "_input"; + inpl.type = "__NetInputLayer__"; + inpl.layerInstance = netInputLayer; + layerNameToId.insert(std::make_pair(inpl.name, inpl.id)); + + lastLayerId = 0; + netWasAllocated = false; + netWasQuantized = false; + fusion = true; + isAsync = false; + preferableBackend = DNN_BACKEND_DEFAULT; + preferableTarget = DNN_TARGET_CPU; + skipInfEngineInit = false; + hasDynamicShapes = false; +} + + +bool Net::Impl::empty() const +{ + return layers.size() <= 1; // first layer is default Data layer +} + + +void Net::Impl::clear() +{ + CV_TRACE_FUNCTION(); + + MapIdToLayerData::iterator it; + for (it = layers.begin(); it != layers.end(); it++) + { + if (it->second.id != 0) + { + it->second.inputBlobs.clear(); + it->second.outputBlobs.clear(); + it->second.internals.clear(); + } + it->second.skip = false; + // it->second.consumers.clear(); + Ptr currLayer = it->second.layerInstance; + + if (currLayer.empty()) + continue; + + currLayer->unsetAttached(); + } + netWasAllocated = false; + layersTimings.clear(); +} + + +void Net::Impl::setUpNet(const std::vector& blobsToKeep_) +{ + CV_TRACE_FUNCTION(); + + if (dumpLevel && networkDumpCounter == 0) + { + dumpNetworkToFile(); + } + + if (preferableBackend == DNN_BACKEND_DEFAULT) + preferableBackend = (Backend)getParam_DNN_BACKEND_DEFAULT(); +#ifdef HAVE_INF_ENGINE + if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE) + preferableBackend = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; // = getInferenceEngineBackendTypeParam(); +#endif + + CV_Assert(preferableBackend != DNN_BACKEND_OPENCV || + preferableTarget == DNN_TARGET_CPU || + preferableTarget == DNN_TARGET_OPENCL || + preferableTarget == DNN_TARGET_OPENCL_FP16); + CV_Assert(preferableBackend != DNN_BACKEND_HALIDE || + preferableTarget == DNN_TARGET_CPU || + preferableTarget == DNN_TARGET_OPENCL); +#ifdef HAVE_INF_ENGINE + if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + CV_Assert( + (preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) || + preferableTarget == DNN_TARGET_OPENCL || + preferableTarget == DNN_TARGET_OPENCL_FP16 || + preferableTarget == DNN_TARGET_MYRIAD || + preferableTarget == DNN_TARGET_HDDL || + preferableTarget == DNN_TARGET_FPGA + ); + } +#endif +#ifdef HAVE_WEBNN + if (preferableBackend == DNN_BACKEND_WEBNN) + { + CV_Assert(preferableTarget == DNN_TARGET_CPU || + preferableTarget == DNN_TARGET_OPENCL); + } +#endif + CV_Assert(preferableBackend != DNN_BACKEND_VKCOM || + preferableTarget == DNN_TARGET_VULKAN); + CV_Assert(preferableBackend != DNN_BACKEND_CUDA || + IS_DNN_CUDA_TARGET(preferableTarget)); + CV_Assert(preferableBackend != DNN_BACKEND_TIMVX || + preferableTarget == DNN_TARGET_NPU); + + if (!netWasAllocated || this->blobsToKeep != blobsToKeep_) + { + if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) +#ifndef HAVE_OPENCL + { + CV_LOG_WARNING(NULL, "DNN: OpenCL target is not available in this OpenCV build, switching to CPU."); + preferableTarget = DNN_TARGET_CPU; + } +#else + { + if (!getParam_DNN_OPENCL_ALLOW_ALL_DEVICES()) + { + // Current implementation is only valid for GPU (#11494) + if (ocl::Device::getDefault().type() != ocl::Device::TYPE_GPU) + { + CV_LOG_WARNING(NULL, "DNN: OpenCL target is not supported with current OpenCL device (tested with GPUs only), switching to CPU."); + preferableTarget = DNN_TARGET_CPU; + } + else if (preferableTarget == DNN_TARGET_OPENCL_FP16 && !ocl::Device::getDefault().isIntel()) + { + CV_LOG_WARNING(NULL, + "DNN: OpenCL target with fp16 precision is not supported " + "with current OpenCL device (tested with Intel GPUs only), " + "switching to OpenCL with fp32 precision."); + preferableTarget = DNN_TARGET_OPENCL; + } + } + } +#endif + if (preferableBackend == DNN_BACKEND_VKCOM && !haveVulkan()) + { + preferableBackend = DNN_BACKEND_OPENCV; + preferableTarget = DNN_TARGET_CPU; + } + + if (preferableBackend == DNN_BACKEND_CUDA && !haveCUDA()) + { +#ifdef HAVE_CUDA + CV_LOG_WARNING(NULL, "unable to use CUDA backend; switching to CPU"); +#else + CV_LOG_WARNING(NULL, "DNN module was not built with CUDA backend; switching to CPU"); +#endif + preferableBackend = DNN_BACKEND_OPENCV; + preferableTarget = DNN_TARGET_CPU; + } + + if (preferableBackend == DNN_BACKEND_TIMVX && !haveTimVX()) + { + preferableBackend = DNN_BACKEND_OPENCV; + preferableTarget = DNN_TARGET_CPU; + } + + clear(); + + if (hasDynamicShapes) + { + updateLayersShapes(); + } + + this->blobsToKeep = blobsToKeep_; + + allocateLayers(blobsToKeep_); + + MapIdToLayerData::iterator it = layers.find(0); + CV_Assert(it != layers.end()); + it->second.skip = netInputLayer->skip; + + initBackend(blobsToKeep_); + + if (!netWasAllocated) + { +#ifdef HAVE_HALIDE + if (preferableBackend == DNN_BACKEND_HALIDE) + compileHalide(); +#else + CV_Assert(preferableBackend != DNN_BACKEND_HALIDE); +#endif + } + + netWasAllocated = true; + + if (dumpLevel) + { + dumpNetworkToFile(); + } + } +} + + +Ptr Net::Impl::getLayer(int layerId) const +{ + LayerData& ld = getLayerData(layerId); + return ld.getLayerInstance(); +} + + +Ptr Net::Impl::getLayer(const LayerId& layerId) const +{ + LayerData& ld = getLayerData(layerId); + return ld.getLayerInstance(); +} + + +int Net::Impl::getLayerId(const String& layerName) const +{ + std::map::const_iterator it = layerNameToId.find(layerName); + return (it != layerNameToId.end()) ? it->second : -1; +} + + +int Net::Impl::getLayerId(int id) const +{ + MapIdToLayerData::const_iterator it = layers.find(id); + return (it != layers.end()) ? id : -1; +} + + +int Net::Impl::getLayerId(DictValue& layerDesc) const +{ + if (layerDesc.isInt()) + return getLayerId(layerDesc.get()); + else if (layerDesc.isString()) + return getLayerId(layerDesc.get()); + + CV_Assert(layerDesc.isInt() || layerDesc.isString()); + return -1; +} + + +String Net::Impl::getLayerName(int id) const +{ + MapIdToLayerData::const_iterator it = layers.find(id); + return (it != layers.end()) ? it->second.name : "(unknown layer)"; +} + + +LayerData& Net::Impl::getLayerData(int id) const +{ + MapIdToLayerData::const_iterator it = layers.find(id); + + if (it == layers.end()) + CV_Error(Error::StsObjectNotFound, format("Layer with requested id=%d not found", id)); + + return const_cast(it->second); +} + + +LayerData& Net::Impl::getLayerData(const String& layerName) const +{ + int id = getLayerId(layerName); + + if (id < 0) + CV_Error(Error::StsError, "Requested layer \"" + layerName + "\" not found"); + + return getLayerData(id); +} + + +LayerData& Net::Impl::getLayerData(const DictValue& layerDesc) const +{ + CV_Assert(layerDesc.isInt() || layerDesc.isString()); + if (layerDesc.isInt()) + return getLayerData(layerDesc.get()); + else /*if (layerDesc.isString())*/ + return getLayerData(layerDesc.get()); +} + + +/*static*/ +void Net::Impl::addLayerInput(LayerData& ld, int inNum, LayerPin from) +{ + if ((int)ld.inputBlobsId.size() <= inNum) + { + ld.inputBlobsId.resize(inNum + 1); + } + else + { + LayerPin storedFrom = ld.inputBlobsId[inNum]; + if (storedFrom.valid() && !storedFrom.equal(from)) + CV_Error(Error::StsError, format("Input #%d of layer \"%s\" already was connected", + inNum, ld.name.c_str())); + } + + ld.inputBlobsId[inNum] = from; +} + + +int Net::Impl::resolvePinOutputName(LayerData& ld, const String& outName) const +{ + if (outName.empty()) + return 0; + return ld.getLayerInstance()->outputNameToIndex(outName); +} + + +LayerPin Net::Impl::getPinByAlias(const String& layerName) const +{ + LayerPin pin; + pin.lid = (layerName.empty()) ? 0 : getLayerId(layerName); + + if (pin.lid >= 0) + pin.oid = resolvePinOutputName(getLayerData(pin.lid), layerName); + + return pin; +} + + +std::vector Net::Impl::getLayerOutPins(const String& layerName) const +{ + int lid = (layerName.empty()) ? 0 : getLayerId(layerName); + + MapIdToLayerData::const_iterator it = layers.find(lid); + if (it == layers.end()) + CV_Error_(Error::StsOutOfRange, ("Layer #%d is not valid", lid)); + const size_t nOutputs = it->second.outputBlobs.size(); + + std::vector pins; + for (int i = 0; i < nOutputs; i++) + { + pins.push_back(LayerPin(lid, i)); + } + + return pins; +} + + +// FIXIT remove dtype +int Net::Impl::addLayer(const String& name, const String& type, const int& dtype, LayerParams& params) +{ + int id = getLayerId(name); + if (id >= 0) + { + if (!DNN_DIAGNOSTICS_RUN || type != "NotImplemented") + { + CV_Error(Error::StsBadArg, "Layer \"" + name + "\" already into net"); + return -1; + } + else + { + LayerData& ld = layers.find(id)->second; + ld.type = type; + ld.params = params; + return -1; + } + } + + id = ++lastLayerId; + layerNameToId.insert(std::make_pair(name, id)); + layers.insert(std::make_pair(id, LayerData(id, name, type, dtype, params))); + if (params.get("has_dynamic_shapes", false)) + hasDynamicShapes = true; + + if (dtype == CV_8S) + netWasQuantized = true; + + return id; +} + + +int Net::Impl::addLayerToPrev(const String& name, const String& type, const int& dtype, LayerParams& params) +{ + int prvLid = lastLayerId; + int newLid = addLayer(name, type, dtype, params); + connect(prvLid, 0, newLid, 0); + return newLid; +} + + +void Net::Impl::connect(int outLayerId, int outNum, int inLayerId, int inNum) +{ + CV_Assert(outLayerId < inLayerId); + LayerData& ldOut = getLayerData(outLayerId); + LayerData& ldInp = getLayerData(inLayerId); + + addLayerInput(ldInp, inNum, LayerPin(outLayerId, outNum)); + ldOut.requiredOutputs.insert(outNum); + ldOut.consumers.push_back(LayerPin(inLayerId, outNum)); + + CV_LOG_VERBOSE(NULL, 0, "DNN: connect(" << outLayerId << ":" << outNum << " ==> " << inLayerId << ":" << inNum << ")"); +} + + +int Net::Impl::registerOutput(const std::string& outputName, int layerId, int outputPort) +{ + int checkLayerId = getLayerId(outputName); + if (checkLayerId >= 0) + { + if (checkLayerId == layerId) + { + if (outputPort == 0) + { + // layer name correlates with its output name + CV_LOG_DEBUG(NULL, "DNN: register output='" << outputName << "': reuse layer with the same name and id=" << layerId << " to be linked"); + outputNameToId.insert(std::make_pair(outputName, layerId)); + return checkLayerId; + } + } + CV_Error_(Error::StsBadArg, ("Layer with name='%s' already exists id=%d (to be linked with %d:%d)", outputName.c_str(), checkLayerId, layerId, outputPort)); + } +#if 0 // TODO + if (outputPort == 0) + // make alias only, need to adopt getUnconnectedOutLayers() call +#endif + LayerParams outputLayerParams; + outputLayerParams.name = outputName; + outputLayerParams.type = "Identity"; + int dtype = CV_32F; // FIXIT remove + int outputLayerId = addLayer(outputLayerParams.name, outputLayerParams.type, dtype, outputLayerParams); + connect(layerId, outputPort, outputLayerId, 0); + CV_LOG_DEBUG(NULL, "DNN: register output='" << outputName << "' id=" << outputLayerId << " defined as " << layerId << ":" << outputPort); + outputNameToId.insert(std::make_pair(outputName, outputLayerId)); + return outputLayerId; +} + + +void Net::Impl::allocateLayer(int lid, const LayersShapesMap& layersShapes) +{ + CV_TRACE_FUNCTION(); + + LayerData& ld = layers[lid]; + + // already allocated + if (ld.flag) + return; + + size_t ninputs = ld.inputBlobsId.size(); +#if 0 + printf("layer %s:", ld.name.c_str()); + for (size_t i = 0; i < ninputs; i++) + { + int inp_lid = ld.inputBlobsId[i].lid; + LayerData &inp_ld = layers[inp_lid]; + int inp_outputs = (int)inp_ld.outputBlobs.size(); + std::cout << " " << inp_ld.name << "(" << inp_outputs; + + for( int j = 0; j < inp_outputs; j++ ) + { + std::cout << (j == 0 ? ": " : ", ") << inp_ld.outputBlobs[j].size; + } + std::cout << ")"; + } + printf("\n"); +#endif + + // determine parent layers + for (size_t i = 0; i < ninputs; i++) + ld.inputLayersId.insert(ld.inputBlobsId[i].lid); + + // allocate parents + for (std::set::const_iterator i = ld.inputLayersId.begin(); i != ld.inputLayersId.end(); i++) + allocateLayer(*i, layersShapes); + + // bind inputs + if (ld.id == 0) // DataLayer + { + ninputs = netInputLayer->inputsData.size(); + ld.inputBlobsWrappers.resize(ninputs); + for (size_t i = 0; i < ninputs; i++) + ld.inputBlobsWrappers[i] = wrap(netInputLayer->inputsData[i]); + } + else + { + ld.inputBlobs.resize(ninputs); + ld.inputBlobsWrappers.resize(ninputs); + for (size_t i = 0; i < ninputs; i++) + { + LayerPin from = ld.inputBlobsId[i]; + CV_Assert(from.valid()); + CV_DbgAssert(layers.count(from.lid) && (int)layers[from.lid].outputBlobs.size() > from.oid); + ld.inputBlobs[i] = &layers[from.lid].outputBlobs[from.oid]; + ld.inputBlobsWrappers[i] = layers[from.lid].outputBlobsWrappers[from.oid]; + } + } + + LayersShapesMap::const_iterator layerShapesIt = layersShapes.find(lid); + + CV_Assert(layerShapesIt != layersShapes.end()); + + if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_OPENCL_FP16 && ld.dtype == CV_32F) + ld.dtype = CV_16S; + + std::vector pinsForInternalBlobs; + blobManager.allocateBlobsForLayer(ld, layerShapesIt->second, pinsForInternalBlobs); + ld.outputBlobsWrappers.resize(ld.outputBlobs.size()); + for (int i = 0; i < ld.outputBlobs.size(); ++i) + ld.outputBlobsWrappers[i] = wrap(ld.outputBlobs[i]); + + /* CUDA backend has its own system for internal blobs; we don't need these */ + ld.internalBlobsWrappers.resize((preferableBackend == DNN_BACKEND_CUDA || preferableBackend == DNN_BACKEND_TIMVX) ? 0 : ld.internals.size()); + for (int i = 0; i < ld.internalBlobsWrappers.size(); ++i) + ld.internalBlobsWrappers[i] = wrap(ld.internals[i]); + + Ptr layerPtr = ld.getLayerInstance(); + { + std::vector inps(ld.inputBlobs.size()); + for (int i = 0; i < ld.inputBlobs.size(); ++i) + { + inps[i] = *ld.inputBlobs[i]; + } + layerPtr->finalize(inps, ld.outputBlobs); + layerPtr->preferableTarget = preferableTarget; +#if 0 + std::cout << "\toutputs:"; + size_t noutputs = ld.outputBlobs.size(); + for (size_t j = 0; j < noutputs; j++) + { + std::cout << (j == 0 ? " " : ", ") << ld.outputBlobs[j].size; + } + std::cout << "\n"; +#endif + } + + // After allocation of layer, we decrease counters to it's input blobs. + blobManager.releaseReferences(ld.inputBlobsId); + blobManager.releaseReferences(pinsForInternalBlobs); + + ld.flag = 1; +} + + +void Net::Impl::allocateLayers(const std::vector& blobsToKeep_) +{ + CV_TRACE_FUNCTION(); + + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); it++) + it->second.flag = 0; + + CV_Assert(!layers[0].outputBlobs.empty()); + ShapesVec inputShapes; + for (int i = 0; i < layers[0].outputBlobs.size(); i++) + { + Mat& inp = layers[0].outputBlobs[i]; + CV_Assert(inp.total()); + if (preferableBackend == DNN_BACKEND_OPENCV && + preferableTarget == DNN_TARGET_OPENCL_FP16 && + layers[0].dtype == CV_32F) + { + layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S); + } + inputShapes.push_back(shape(inp)); + } + LayersShapesMap layersShapes; + getLayersShapes(inputShapes, layersShapes); + + blobManager.reset(); + backendWrappers.clear(); + + for (auto& layer : layers) + { + auto& ld = layer.second; + ld.inputBlobsWrappers.clear(); + ld.outputBlobsWrappers.clear(); + ld.internalBlobsWrappers.clear(); + } + + // Fake references to input blobs. + for (int i = 0; i < layers[0].outputBlobs.size(); ++i) + blobManager.addReference(LayerPin(0, i)); + for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); ++it) + { + const LayerData& ld = it->second; + blobManager.addReferences(ld.inputBlobsId); + } + + for (int i = 0; i < blobsToKeep_.size(); i++) + { + blobManager.addReference(blobsToKeep_[i]); + } + + for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); it++) + { + int lid = it->first; + allocateLayer(lid, layersShapes); + } + + layersTimings.resize(lastLayerId + 1, 0); + fuseLayers(blobsToKeep_); +} + + +void Net::Impl::forwardLayer(LayerData& ld) +{ + CV_TRACE_FUNCTION(); + + Ptr layer = ld.layerInstance; + + if (!ld.skip) + { + TickMeter tm; + tm.start(); + +#ifndef HAVE_VULKAN + std::map>::const_iterator it = ld.backendNodes.find(preferableBackend); +#else + std::map>::iterator it = ld.backendNodes.find(preferableBackend); +#endif + if (preferableBackend == DNN_BACKEND_OPENCV || it == ld.backendNodes.end() || it->second.empty()) + { + if (isAsync) + CV_Error(Error::StsNotImplemented, "Default implementation fallbacks in asynchronous mode"); + + if (!layer->supportBackend(DNN_BACKEND_OPENCV)) + CV_Error(Error::StsNotImplemented, format("Layer \"%s\" of type \"%s\" unsupported on OpenCV backend", + ld.name.c_str(), ld.type.c_str())); + +#ifdef HAVE_OPENCL + if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) + { + std::vector umat_inputBlobs = OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers); + std::vector umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); + std::vector umat_internalBlobs = OpenCLBackendWrapper::getUMatVector(ld.internalBlobsWrappers); + layer->forward(umat_inputBlobs, + umat_outputBlobs, + umat_internalBlobs); + if (getParam_DNN_CHECK_NAN_INF()) + { + bool fail = false; + for (size_t i = 0; i < umat_outputBlobs.size(); ++i) + { + UMat& u = umat_outputBlobs[i]; + Mat m; + if (u.depth() == CV_16S) // FP16 + convertFp16(u, m); + else + m = u.getMat(ACCESS_READ); + if (!checkRange(m)) + { + std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; + std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; + fail = true; + } + else if (!checkRange(m, true, NULL, -1e6, 1e6)) + { + std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; + std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; + fail = true; + } + } + if (fail) + { + for (size_t i = 0; i < umat_inputBlobs.size(); ++i) + { + UMat& u = umat_inputBlobs[i]; + Mat m; + if (u.depth() == CV_16S) // FP16 + convertFp16(u, m); + else + m = u.getMat(ACCESS_READ); + std::cout << "INPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl; + if (getParam_DNN_CHECK_NAN_INF_DUMP()) std::cout << m.reshape(1, 1) << std::endl; + } + for (size_t i = 0; i < umat_outputBlobs.size(); ++i) + { + UMat& u = umat_outputBlobs[i]; + Mat m; + if (u.depth() == CV_16S) // FP16 + convertFp16(u, m); + else + m = u.getMat(ACCESS_READ); + std::cout << "OUTPUT " << i << " " << cv::typeToString(u.type()) << " " << shape(m) << std::endl; + if (getParam_DNN_CHECK_NAN_INF_DUMP()) std::cout << m.reshape(1, 1) << std::endl; + } + for (size_t i = 0; i < umat_internalBlobs.size(); ++i) + { + UMat& u = umat_internalBlobs[i]; + Mat m; + if (u.depth() == CV_16S) // FP16 + convertFp16(u, m); + else + m = u.getMat(ACCESS_READ); + std::cout << "INTERNAL " << i << " " << shape(m) << std::endl; + if (getParam_DNN_CHECK_NAN_INF_DUMP()) std::cout << cv::typeToString(u.type()) << " " << m.reshape(1, 1) << std::endl; + } + if (getParam_DNN_CHECK_NAN_INF_RAISE_ERROR()) + CV_Assert(!fail); + } + } + OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umat_outputBlobs); + } + else +#endif + { + for (int i = 0, n = ld.inputBlobsWrappers.size(); i < n; ++i) + { + if (!ld.inputBlobsWrappers[i].empty()) + ld.inputBlobsWrappers[i]->copyToHost(); + } + + std::vector inps(ld.inputBlobs.size()); + for (int i = 0; i < ld.inputBlobs.size(); ++i) + { + inps[i] = *ld.inputBlobs[i]; + } + layer->forward(inps, ld.outputBlobs, ld.internals); + + if (getParam_DNN_CHECK_NAN_INF()) + { + bool fail = false; + for (size_t i = 0; i < ld.outputBlobs.size(); ++i) + { + const Mat& m = ld.outputBlobs[i]; + if (!checkRange(m)) + { + std::cerr << "WARNING: NaN detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; + std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; + fail = true; + } + else if (!checkRange(m, true, NULL, -1e6, 1e6)) + { + std::cerr << "WARNING: Inf detected in layer output: id=" << ld.id << " name=" << layer->name << std::endl; + std::cerr << "output id=" << i << " output shape=" << shape(m) << std::endl; + fail = true; + } + } + if (fail) + { + for (size_t i = 0; i < ld.inputBlobs.size(); ++i) + { + const Mat* pM = ld.inputBlobs[i]; + if (!pM) + { + std::cout << "INPUT " << i << " is NULL" << std::endl; + continue; + } + const Mat& m = *pM; + std::cout << "INPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl; + if (getParam_DNN_CHECK_NAN_INF_DUMP()) std::cout << m.reshape(1, 1) << std::endl; + } + for (size_t i = 0; i < ld.outputBlobs.size(); ++i) + { + const Mat& m = ld.outputBlobs[i]; + std::cout << "OUTPUT " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl; + if (getParam_DNN_CHECK_NAN_INF_DUMP()) std::cout << m.reshape(1, 1) << std::endl; + } + for (size_t i = 0; i < ld.internals.size(); ++i) + { + const Mat& m = ld.internals[i]; + std::cout << "INTERNAL " << i << " " << cv::typeToString(m.type()) << " " << shape(m) << std::endl; + if (getParam_DNN_CHECK_NAN_INF_DUMP()) std::cout << m.reshape(1, 1) << std::endl; + } + if (getParam_DNN_CHECK_NAN_INF_RAISE_ERROR()) + CV_Assert(!fail); + } + } + + for (int i = 0, n = ld.outputBlobsWrappers.size(); i < n; ++i) + { + if (!ld.outputBlobsWrappers[i].empty()) + ld.outputBlobsWrappers[i]->setHostDirty(); + } + } + } + else + { + Ptr node = it->second; + CV_Assert(!node.empty()); + if (preferableBackend == DNN_BACKEND_CUDA) + { + CV_Assert(haveCUDA()); + +#ifdef HAVE_CUDA + Ptr cudaNode = node.dynamicCast(); + CV_Assert(!cudaNode.empty()); + + cudaNode->forward(ld.inputBlobsWrappers, ld.outputBlobsWrappers, cudaInfo->workspace); + + for (auto id : ld.cudaD2HBackgroundTransfers) + { + auto wrapper = ld.outputBlobsWrappers[id].dynamicCast(); + wrapper->copyToHostInBackground(); + } +#endif + } + else if (preferableBackend == DNN_BACKEND_HALIDE) + { + forwardHalide(ld.outputBlobsWrappers, node); + } +#ifdef HAVE_INF_ENGINE + else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + forwardNgraph(ld.outputBlobsWrappers, node, isAsync); + } +#endif + else if (preferableBackend == DNN_BACKEND_WEBNN) + { + forwardWebnn(ld.outputBlobsWrappers, node, isAsync); + } + else if (preferableBackend == DNN_BACKEND_TIMVX) + { + forwardTimVX(ld.outputBlobsWrappers, node); + } +#ifdef HAVE_VULKAN + else if (preferableBackend == DNN_BACKEND_VKCOM) + { + try + { + forwardVkCom(ld.outputBlobsWrappers, node); + } + catch (const cv::Exception& e) + { + CV_LOG_ERROR(NULL, "forwardVkCom failed, fallback to CPU implementation. " << e.what()); + it->second = Ptr(); + forwardLayer(ld); + } + } +#endif + else + { + CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); + } + } + + tm.stop(); + int64 t = tm.getTimeTicks(); + layersTimings[ld.id] = (t > 0) ? t : t + 1; // zero for skipped layers only + } + else + { + layersTimings[ld.id] = 0; + } + + ld.flag = 1; +} + + +void Net::Impl::forwardToLayer(LayerData& ld, bool clearFlags) +{ + CV_TRACE_FUNCTION(); + + if (clearFlags) + { + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); it++) + it->second.flag = 0; + } + + // already was forwarded + if (ld.flag) + return; + + // forward parents + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end() && (it->second.id < ld.id); ++it) + { + LayerData& ld = it->second; + if (ld.flag) + continue; + forwardLayer(ld); + } + + // forward itself + forwardLayer(ld); + +#ifdef HAVE_CUDA + if (preferableBackend == DNN_BACKEND_CUDA) + cudaInfo->context.stream.synchronize(); +#endif +} + + +Mat Net::Impl::forward(const String& outputName) +{ + CV_Assert(!empty()); + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + String layerName = outputName; + + if (layerName.empty()) + { + std::vector layerNames = getLayerNames(); + CV_Assert(!layerNames.empty()); + layerName = layerNames.back(); + } + + std::vector pins(1, getPinByAlias(layerName)); + setUpNet(pins); + forwardToLayer(getLayerData(layerName)); + + return getBlob(layerName); +} + + +AsyncArray Net::Impl::forwardAsync(const String& outputName) +{ + CV_Assert(!empty()); + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + +#ifdef CV_CXX11 + String layerName = outputName; + + if (layerName.empty()) + { + std::vector layerNames = getLayerNames(); + CV_Assert(!layerNames.empty()); + layerName = layerNames.back(); + } + + std::vector pins(1, getPinByAlias(layerName)); + setUpNet(pins); + + if (preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backend only"); + + isAsync = true; + forwardToLayer(getLayerData(layerName)); + isAsync = false; + + return getBlobAsync(layerName); +#else + CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward requires build with enabled C++11"); +#endif // CV_CXX11 +} + + +void Net::Impl::forward(OutputArrayOfArrays outputBlobs, const String& outputName) +{ + CV_Assert(!empty()); + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + String layerName = outputName; + + if (layerName.empty()) + { + std::vector layerNames = getLayerNames(); + CV_Assert(!layerNames.empty()); + layerName = layerNames.back(); + } + + std::vector pins(1, getPinByAlias(layerName)); + setUpNet(pins); + forwardToLayer(getLayerData(layerName)); + + LayerPin pin = getPinByAlias(layerName); + LayerData& ld = layers[pin.lid]; + + if (outputBlobs.isUMat()) + { + getBlob(layerName).copyTo(outputBlobs); + } + else if (outputBlobs.isMat()) + { + outputBlobs.assign(getBlob(layerName)); + } + else if (outputBlobs.isMatVector()) + { + if (preferableTarget != DNN_TARGET_CPU) + { + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + CV_Assert(!ld.outputBlobsWrappers[i].empty()); + ld.outputBlobsWrappers[i]->copyToHost(); + } + } + if (ld.outputBlobs[0].depth() == CV_16S) + { + std::vector& outputvec = *(std::vector*)outputBlobs.getObj(); + outputvec.resize(ld.outputBlobs.size()); + for (int i = 0; i < outputvec.size(); i++) + convertFp16(ld.outputBlobs[i], outputvec[i]); + } + else + { + // Output depth can be CV_32F or CV_8S + std::vector& outputvec = *(std::vector*)outputBlobs.getObj(); + outputvec = ld.outputBlobs; + } + } + else if (outputBlobs.isUMatVector()) + { + std::vector& outputvec = *(std::vector*)outputBlobs.getObj(); + +#ifdef HAVE_OPENCL + if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) + { + if (preferableTarget == DNN_TARGET_OPENCL) + outputvec = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); + else if (preferableTarget == DNN_TARGET_OPENCL_FP16) + { + std::vector out_vec = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); + outputvec.resize(out_vec.size()); + for (int i = 0; i < out_vec.size(); i++) + convertFp16(out_vec[i], outputvec[i]); + } + } + else +#endif + { + outputvec.resize(ld.outputBlobs.size()); + for (int i = 0; i < outputvec.size(); ++i) + ld.outputBlobs[i].copyTo(outputvec[i]); + } + } +} + + +void Net::Impl::forward(OutputArrayOfArrays outputBlobs, + const std::vector& outBlobNames) +{ + CV_Assert(!empty()); + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + std::vector pins; + for (int i = 0; i < outBlobNames.size(); i++) + { + pins.push_back(getPinByAlias(outBlobNames[i])); + } + + setUpNet(pins); + + LayerPin out = getLatestLayerPin(pins); + + forwardToLayer(getLayerData(out.lid)); + + std::vector matvec; + for (int i = 0; i < pins.size(); i++) + { + matvec.push_back(getBlob(pins[i])); + } + + outputBlobs.create((int)matvec.size(), 1, CV_32F/*FIXIT*/, -1); // allocate vector + outputBlobs.assign(matvec); +} + + +void Net::Impl::forward(std::vector>& outputBlobs, + const std::vector& outBlobNames) +{ + CV_Assert(!empty()); + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + std::vector pins; + for (int i = 0; i < outBlobNames.size(); i++) + { + pins.push_back(getPinByAlias(outBlobNames[i])); + } + + setUpNet(pins); + + LayerPin out = getLatestLayerPin(pins); + + forwardToLayer(getLayerData(out.lid)); + + outputBlobs.resize(outBlobNames.size()); + for (int i = 0; i < outBlobNames.size(); i++) + { + std::vector lp = getLayerOutPins(outBlobNames[i]); + outputBlobs[i].resize(lp.size()); + for (int j = 0; j < lp.size(); j++) + { + outputBlobs[i][j] = getBlob(lp[j]); + } + } +} + + +void Net::Impl::getLayerShapesRecursively(int id, LayersShapesMap& inOutShapes) +{ + CV_CheckGE(id, 0, ""); + CV_CheckLT(id, (int)layers.size(), ""); + LayerData& layerData = layers[id]; + std::vector& inputLayerIds = layerData.inputBlobsId; + LayerShapes& layerShapes = inOutShapes[id]; + + if (id == 0 && layerShapes.in[0].empty()) + { + if (!layerData.outputBlobs.empty()) + { + ShapesVec shapes; + for (int i = 0; i < layerData.outputBlobs.size(); i++) + { + Mat& inp = layerData.outputBlobs[i]; + CV_Assert(!inp.empty()); + shapes.push_back(shape(inp)); + } + layerShapes.in = shapes; + } + else + { + const std::vector& inputShapes = netInputLayer->shapes; + bool none = true; + for (size_t i = 0; i < inputShapes.size(); i++) + { + if (!inputShapes[i].empty()) + { + none = false; + break; + } + } + if (none) + { + layerShapes.out.clear(); + return; + } + else + { + layerShapes.in = inputShapes; + } + } + } + + if (layerShapes.in.empty()) + { + for (int i = 0; i < inputLayerIds.size(); i++) + { + int layerId = inputLayerIds[i].lid; + LayersShapesMap::const_iterator it = inOutShapes.find(layerId); + if (it == inOutShapes.end() || it->second.out.empty()) + { + getLayerShapesRecursively(layerId, inOutShapes); + it = inOutShapes.find(layerId); + CV_Assert(it != inOutShapes.end()); + } + const int out_port = inputLayerIds[i].oid; + CV_CheckLT(out_port, (int)it->second.out.size(), ""); + const MatShape& shape = it->second.out[out_port]; + layerShapes.in.push_back(shape); + } + } + const ShapesVec& is = layerShapes.in; + ShapesVec& os = layerShapes.out; + ShapesVec& ints = layerShapes.internal; + int requiredOutputs = layerData.requiredOutputs.size(); + Ptr l = layerData.getLayerInstance(); + CV_Assert(l); + bool layerSupportInPlace = false; + try + { + layerSupportInPlace = l->getMemoryShapes(is, requiredOutputs, os, ints); + } + catch (const cv::Exception& e) + { + CV_LOG_ERROR(NULL, "OPENCV/DNN: [" << l->type << "]:(" << l->name << "): getMemoryShapes() throws exception." << + " inputs=" << is.size() << + " outputs=" << os.size() << "/" << requiredOutputs << + " blobs=" << l->blobs.size()); + for (size_t i = 0; i < is.size(); ++i) + { + CV_LOG_ERROR(NULL, " input[" << i << "] = " << toString(is[i])); + } + for (size_t i = 0; i < os.size(); ++i) + { + CV_LOG_ERROR(NULL, " output[" << i << "] = " << toString(os[i])); + } + for (size_t i = 0; i < l->blobs.size(); ++i) + { + CV_LOG_ERROR(NULL, " blobs[" << i << "] = " << typeToString(l->blobs[i].type()) << " " << toString(shape(l->blobs[i]))); + } + CV_LOG_ERROR(NULL, "Exception message: " << e.what()); + throw; + } + layerShapes.supportInPlace = layerSupportInPlace; + + try + { + for (int i = 0; i < ints.size(); i++) + CV_CheckGT(total(ints[i]), 0, ""); + + for (int i = 0; i < os.size(); i++) + CV_CheckGT(total(os[i]), 0, ""); + } + catch (const cv::Exception& e) + { + CV_LOG_ERROR(NULL, "OPENCV/DNN: [" << l->type << "]:(" << l->name << "): getMemoryShapes() post validation failed." << + " inputs=" << is.size() << + " outputs=" << os.size() << "/" << requiredOutputs << + " blobs=" << l->blobs.size() << + " inplace=" << layerSupportInPlace); + for (size_t i = 0; i < is.size(); ++i) + { + CV_LOG_ERROR(NULL, " input[" << i << "] = " << toString(is[i])); + } + for (size_t i = 0; i < os.size(); ++i) + { + CV_LOG_ERROR(NULL, " output[" << i << "] = " << toString(os[i])); + } + for (size_t i = 0; i < l->blobs.size(); ++i) + { + CV_LOG_ERROR(NULL, " blobs[" << i << "] = " << typeToString(l->blobs[i].type()) << " " << toString(shape(l->blobs[i]))); + } + CV_LOG_ERROR(NULL, "Exception message: " << e.what()); + throw; + } +} + +void Net::Impl::getLayersShapes( + const ShapesVec& netInputShapes, + std::vector& layersIds, + std::vector& inLayersShapes, + std::vector& outLayersShapes) /*const*/ +{ + layersIds.clear(); + inLayersShapes.clear(); + outLayersShapes.clear(); + + Impl::LayersShapesMap inOutShapes; + getLayersShapes(netInputShapes, inOutShapes); + + for (Impl::LayersShapesMap::const_iterator it = inOutShapes.begin(); + it != inOutShapes.end(); it++) + { + layersIds.push_back(it->first); + inLayersShapes.push_back(it->second.in); + outLayersShapes.push_back(it->second.out); + } +} + + +void Net::Impl::getLayersShapes(const ShapesVec& netInputShapes, + LayersShapesMap& inOutShapes) +{ + inOutShapes.clear(); + + inOutShapes[0].in = netInputShapes; // insert shape for first input layer + for (MapIdToLayerData::const_iterator it = layers.begin(); + it != layers.end(); it++) + { + getLayerShapesRecursively(it->first, inOutShapes); + } +} + +void Net::Impl::getLayerShapes(const ShapesVec& netInputShapes, + const int layerId, + LayerShapes& shapes) +{ + LayersShapesMap inOutShapes; + inOutShapes[0].in = netInputShapes; // insert shape for first input layer + getLayerShapesRecursively(layerId, inOutShapes); + shapes = inOutShapes[layerId]; +} + +void Net::Impl::updateLayersShapes() +{ + CV_LOG_DEBUG(NULL, "updateLayersShapes() with layers.size=" << layers.size()); + CV_Assert(netInputLayer); + DataLayer& inputLayer = *netInputLayer; + LayerData& inputLayerData = layers[0]; + CV_Assert(inputLayerData.layerInstance.get() == &inputLayer); + CV_Assert(!inputLayerData.outputBlobs.empty()); + ShapesVec inputShapes; + for (int i = 0; i < inputLayerData.outputBlobs.size(); i++) + { + Mat& inp = inputLayerData.outputBlobs[i]; + CV_Assert(!inp.empty()); + if (preferableBackend == DNN_BACKEND_OPENCV && // FIXIT: wrong place for output allocation + preferableTarget == DNN_TARGET_OPENCL_FP16 && + inputLayerData.dtype == CV_32F) + { + inp.create(inp.dims, inp.size, CV_16S); + } + inputShapes.push_back(shape(inp)); + } + CV_LOG_DEBUG(NULL, toString(inputShapes, "Network input shapes")); + LayersShapesMap layersShapes; + layersShapes[0].in = inputShapes; + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); it++) + { + int layerId = it->first; + LayerData& layerData = it->second; + const std::vector& inputLayerIds = layerData.inputBlobsId; + LayerShapes& layerShapes = layersShapes[layerId]; + CV_LOG_DEBUG(NULL, "layer " << layerId << ": [" << layerData.type << "]:(" << layerData.name << ") with inputs.size=" << inputLayerIds.size()); + if (layerShapes.in.empty()) + { + for (int i = 0; i < inputLayerIds.size(); i++) + { + const LayerPin& inputPin = inputLayerIds[i]; + int inputLayerId = inputPin.lid; + CV_LOG_DEBUG(NULL, " input[" << i << "] " << inputLayerId << ":" << inputPin.oid << " as [" << layers[inputLayerId].type << "]:(" << layers[inputLayerId].name << ")"); + LayersShapesMap::const_iterator inputIt = layersShapes.find(inputLayerId); + if (inputIt == layersShapes.end() || inputIt->second.out.empty()) + { + getLayerShapesRecursively(inputLayerId, layersShapes); + } + const MatShape& shape = layersShapes[inputLayerId].out[inputPin.oid]; + layerShapes.in.push_back(shape); + } + layerData.getLayerInstance()->updateMemoryShapes(layerShapes.in); + } + CV_LOG_DEBUG(NULL, "Layer " << layerId << ": " << toString(layerShapes.in, "input shapes")); + CV_LOG_IF_DEBUG(NULL, !layerShapes.out.empty(), "Layer " << layerId << ": " << toString(layerShapes.out, "output shapes")); + CV_LOG_IF_DEBUG(NULL, !layerShapes.internal.empty(), "Layer " << layerId << ": " << toString(layerShapes.internal, "internal shapes")); + } + CV_LOG_DEBUG(NULL, "updateLayersShapes() - DONE"); +} + + +LayerPin Net::Impl::getLatestLayerPin(const std::vector& pins) const +{ + return *std::max_element(pins.begin(), pins.end()); +} + +Mat Net::Impl::getBlob(const LayerPin& pin) const +{ + CV_TRACE_FUNCTION(); + + if (!pin.valid()) + CV_Error(Error::StsObjectNotFound, "Requested blob not found"); + + MapIdToLayerData::const_iterator it = layers.find(pin.lid); + if (it == layers.end()) + CV_Error_(Error::StsOutOfRange, ("Layer #%d is not valid (output #%d requested)", pin.lid, pin.oid)); + + const LayerData& ld = it->second; + if ((size_t)pin.oid >= ld.outputBlobs.size()) + { + CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %zu outputs, " + "the #%d was requested", + ld.name.c_str(), ld.outputBlobs.size(), pin.oid)); + } + if (preferableTarget != DNN_TARGET_CPU) + { + CV_Assert(!ld.outputBlobsWrappers.empty() && !ld.outputBlobsWrappers[pin.oid].empty()); + // Transfer data to CPU if it's require. + ld.outputBlobsWrappers[pin.oid]->copyToHost(); + } + + if (ld.outputBlobs[pin.oid].depth() == CV_16S) + { + Mat output_blob; + convertFp16(ld.outputBlobs[pin.oid], output_blob); + return output_blob; + } + else + return ld.outputBlobs[pin.oid]; +} + +Mat Net::Impl::getBlob(String outputName) const +{ + return getBlob(getPinByAlias(outputName)); +} + + +AsyncArray Net::Impl::getBlobAsync(const LayerPin& pin) +{ + CV_TRACE_FUNCTION(); +#ifdef HAVE_INF_ENGINE + if (!pin.valid()) + CV_Error(Error::StsObjectNotFound, "Requested blob not found"); + + LayerData& ld = layers[pin.lid]; + if ((size_t)pin.oid >= ld.outputBlobs.size()) + { + CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %d outputs, " + "the #%d was requested", + ld.name.c_str(), (int)ld.outputBlobs.size(), (int)pin.oid)); + } + if (preferableTarget != DNN_TARGET_CPU) + { + CV_Assert(!ld.outputBlobsWrappers.empty() && !ld.outputBlobsWrappers[pin.oid].empty()); + // Transfer data to CPU if it's require. + ld.outputBlobsWrappers[pin.oid]->copyToHost(); + } + CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); + + Ptr wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast(); + return std::move(wrapper->futureMat); +#else + CV_Error(Error::StsNotImplemented, "DNN: OpenVINO/nGraph backend is required"); +#endif // HAVE_INF_ENGINE +} + + +AsyncArray Net::Impl::getBlobAsync(String outputName) +{ + return getBlobAsync(getPinByAlias(outputName)); +} + + +void Net::Impl::setInputsNames(const std::vector& inputBlobNames) +{ + CV_Assert(netInputLayer); + netInputLayer->setNames(inputBlobNames); +} + + +void Net::Impl::setInputShape(const String& inputName, const MatShape& shape) +{ + CV_Assert(netInputLayer); + netInputLayer->setInputShape(inputName, shape); +} + + +void Net::Impl::setInput(InputArray blob, const String& name, double scalefactor, const Scalar& mean) +{ + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + LayerPin pin; + pin.lid = 0; + pin.oid = resolvePinOutputName(getLayerData(pin.lid), name); + + if (!pin.valid()) + CV_Error(Error::StsObjectNotFound, "Requested blob \"" + name + "\" not found"); + + Mat blob_ = blob.getMat(); // can't use InputArray directly due MatExpr stuff + MatShape blobShape = shape(blob_); + + if (pin.lid == 0) + { + CV_Assert(!netInputLayer.empty()); + const DataLayer& netInputLayer = *(this->netInputLayer); + if (!netInputLayer.shapes.empty()) + { + CV_CheckLT(pin.oid, (int)netInputLayer.shapes.size(), ""); + const MatShape& inputShapeLimitation = netInputLayer.shapes[pin.oid]; + if (!inputShapeLimitation.empty()) + { + CV_CheckEQ(inputShapeLimitation.size(), blobShape.size(), ""); +#if 0 // TODO: DNNTestNetwork.MobileNet_SSD_Caffe_Different_Width_Height/0 + const size_t dims = inputShapeLimitation.size(); + for (size_t dim = 0; dim < dims; dim++) + { + if (dims >= 3 && dim == 0 && inputShapeLimitation[0] == 1) + continue; // don't limit batch + CV_CheckEQ(inputShapeLimitation[dim], blobShape[dim], ""); + } +#endif + } + } + } + + LayerData& ld = layers[pin.lid]; + const int numInputs = std::max(pin.oid + 1, (int)ld.requiredOutputs.size()); + ld.outputBlobs.resize(numInputs); + ld.outputBlobsWrappers.resize(numInputs); + netInputLayer->inputsData.resize(numInputs); + netInputLayer->scaleFactors.resize(numInputs); + netInputLayer->means.resize(numInputs); + + MatShape prevShape = shape(netInputLayer->inputsData[pin.oid]); + bool oldShape = prevShape == blobShape; + + blob_.copyTo(netInputLayer->inputsData[pin.oid]); + if (!oldShape) + ld.outputBlobs[pin.oid] = netInputLayer->inputsData[pin.oid]; + + if (!ld.outputBlobsWrappers[pin.oid].empty()) + { + ld.outputBlobsWrappers[pin.oid]->setHostDirty(); + } + netInputLayer->scaleFactors[pin.oid] = scalefactor; + netInputLayer->means[pin.oid] = mean; + netWasAllocated = netWasAllocated && oldShape; +} + + +Mat Net::Impl::getParam(int layer, int numParam) const +{ + LayerData& ld = getLayerData(layer); + std::vector& layerBlobs = ld.getLayerInstance()->blobs; + CV_Assert(numParam < (int)layerBlobs.size()); + return layerBlobs[numParam]; +} + +void Net::Impl::setParam(int layer, int numParam, const Mat& blob) +{ + LayerData& ld = getLayerData(layer); + + std::vector& layerBlobs = ld.getLayerInstance()->blobs; + CV_Assert(numParam < (int)layerBlobs.size()); + // we don't make strong checks, use this function carefully + layerBlobs[numParam] = blob; +} + + +static +string dumpLayerParameterSize(const string& name, const LayerParams& lp) +{ + std::ostringstream out(name, std::ios::ate); + DictValue param = lp.get(name); + switch (param.size()) + { + case 1: out << " : "; break; + case 2: out << " (HxW): "; break; + case 3: out << " (DxHxW): "; break; + default: + CV_LOG_INFO(NULL, format("DNN/dumpLayerParameterSize(): Unsupported '%s' size = %d", name.c_str(), param.size())); + out << ": "; + } + for (size_t i = 0; i < param.size(); i++) + { + if (i > 0) + out << " x "; + out << param.get(i); + } + return out.str(); +} + +string Net::Impl::dump(bool forceAllocation) const +{ + bool hasInput = !netInputLayer->inputsData.empty(); + if (forceAllocation) + { + if (!netWasAllocated) + const_cast(this)->setUpNet(); + } + + std::ostringstream out; + const std::map& map = layers; + + Backend prefBackend = (Backend)preferableBackend; + std::vector> skippedLayers; + std::vector skipId; + std::vector allLayers(map.size(), -1); + int idPrev = -1; + Ptr prevNode; + for (std::map::const_reverse_iterator rit = map.rbegin(); rit != map.rend(); ++rit) + { + std::map>::const_iterator itBackend = rit->second.backendNodes.find(prefBackend); + if (prefBackend == DNN_BACKEND_OPENCV || itBackend == rit->second.backendNodes.end() || itBackend->second.empty()) + { + if (rit->second.skip) + skipId.push_back(rit->first); + else if (!skipId.empty()) + { + if (prefBackend == DNN_BACKEND_OPENCV || prevNode.empty()) + skipId.push_back(rit->first); + else if (idPrev != -1) + skipId.push_back(idPrev); + + std::sort(skipId.begin(), skipId.end()); + for (int i = 0; i < skipId.size(); i++) + { + allLayers[skipId[i]] = skippedLayers.size(); + } + skippedLayers.push_back(skipId); + skipId.clear(); + } + } + else + { + if (itBackend->second == prevNode) + skipId.push_back(idPrev); + else if (!skipId.empty()) + { + skipId.push_back(idPrev); + std::sort(skipId.begin(), skipId.end()); + for (int i = 0; i < skipId.size(); i++) + { + allLayers[skipId[i]] = skippedLayers.size(); + } + skippedLayers.push_back(skipId); + skipId.clear(); + } + idPrev = rit->first; + prevNode = itBackend->second; + } + } + std::vector colors = { "#ffffb3", "#fccde5", "#8dd3c7", "#bebada", "#80b1d3", "#fdb462", "#ff4848", "#b35151", "#b266ff", "#b266ff", "#3cb371"}; + string backend; + switch (prefBackend) + { + case DNN_BACKEND_DEFAULT: backend = "DEFAULT/"; break; + case DNN_BACKEND_HALIDE: backend = "HALIDE/"; break; + case DNN_BACKEND_INFERENCE_ENGINE: // fallthru + case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: // fallthru + case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "OpenVINO/"; break; + case DNN_BACKEND_OPENCV: backend = "OCV/"; break; + case DNN_BACKEND_VKCOM: backend = "VULKAN/"; break; + case DNN_BACKEND_CUDA: backend = "CUDA/"; break; + case DNN_BACKEND_WEBNN: backend = "WEBNN/"; break; + case DNN_BACKEND_TIMVX: backend = "TIMVX/"; break; + // don't use default: + } + out << "digraph G {\n"; + // Add nodes + for (std::map::const_iterator it = map.begin(); it != map.end(); ++it) + { + const LayerData& ld = it->second; + string name = ld.params.name; + std::vector clusterIds(1, it->first); + if (allLayers[it->first] == -1 && !name.empty()) + { + out << "\t\"" << name << "\" [label=\""; + } + else if (name.empty() || it->first != skippedLayers[allLayers[it->first]][0]) + { + continue; + } + else // first node in cluster : it->first == skippedLayers[allLayers[it->first]][0] + { + int cluster = allLayers[it->first]; + out << "\t\"" + << "cluster_" << cluster << "\" [label=\"{"; + clusterIds = skippedLayers[allLayers[it->first]]; // vertices in current cluster + } + for (int i = 0; i < clusterIds.size(); i++) + { + CV_DbgAssert(map.find(clusterIds[i]) != map.end()); + const LayerParams& lp = map.find(clusterIds[i])->second.params; + if (!lp.name.empty()) + { + if (i > 0) + { + out << " | "; + } + out << lp.name << "\\n" + << lp.type << "\\n"; // align center + if (lp.has("kernel_size")) + { + string kernel = dumpLayerParameterSize("kernel_size", lp); + out << kernel; + out << "\\l"; // align left + } + else if (lp.has("kernel_h") && lp.has("kernel_w")) + { + DictValue h = lp.get("kernel_h"); + DictValue w = lp.get("kernel_w"); + out << "kernel (HxW): " << h << " x " << w; + out << "\\l"; // align left + } + if (lp.has("stride")) + { + string stride = dumpLayerParameterSize("stride", lp); + out << stride; + out << "\\l"; // align left + } + else if (lp.has("stride_h") && lp.has("stride_w")) + { + DictValue h = lp.get("stride_h"); + DictValue w = lp.get("stride_w"); + out << "stride (HxW): " << h << " x " << w; + out << "\\l"; // align left + } + if (lp.has("dilation")) + { + string dilation = dumpLayerParameterSize("dilation", lp); + out << dilation; + out << "\\l"; // align left + } + else if (lp.has("dilation_h") && lp.has("dilation_w")) + { + DictValue h = lp.get("dilation_h"); + DictValue w = lp.get("dilation_w"); + out << "dilation (HxW): " << h << " x " << w; + out << "\\l"; // align left + } + if (lp.has("pad")) + { + DictValue pad = lp.get("pad"); + out << "pad "; + switch (pad.size()) + { + case 1: out << ": " << pad; break; + case 2: + out << "(HxW): (" << pad.get(0) << " x " << pad.get(1) << ")"; + break; + case 4: + out << "(HxW): (" << pad.get(0) << ", " << pad.get(2) + << ") x (" << pad.get(1) << ", " << pad.get(3) << ")"; + break; + case 6: + out << "(DxHxW): (" << pad.get(0) << ", " << pad.get(3) + << ") x (" << pad.get(1) << ", " << pad.get(4) + << ") x (" << pad.get(2) << ", " << pad.get(5) << ")"; + break; + default: CV_Error(Error::StsNotImplemented, format("Unsupported pad size = %d", pad.size())); + } + out << "\\l"; // align left + } + else if (lp.has("pad_l") && lp.has("pad_t") && lp.has("pad_r") && lp.has("pad_b")) + { + DictValue l = lp.get("pad_l"); + DictValue t = lp.get("pad_t"); + DictValue r = lp.get("pad_r"); + DictValue b = lp.get("pad_b"); + out << "pad (HxW): (" << t << ", " << b << ") x (" << l << ", " << r << ")"; + out << "\\l"; // align left + } + else if (lp.has("pooled_w") || lp.has("pooled_h")) + { + DictValue h = lp.get("pooled_h"); + DictValue w = lp.get("pooled_w"); + out << "pad pooled (HxW): " << h << " x " << w; + out << "\\l"; // align left + } + if (lp.has("pool")) + { + out << "pool: " << lp.get("pool"); + out << "\\l"; // align left + } + if (lp.has("global_pooling")) + { + out << "global_pooling: " << lp.get("global_pooling"); + out << "\\l"; // align left + } + if (lp.has("group")) + { + out << "group: " << lp.get("group"); + out << "\\l"; // align left + } + } + } + if (!ld.outputBlobs.empty()) + { + out << "output: " << ld.outputBlobs[0].size; + out << "\\l"; // align left + } + + Ptr layerBackend; + std::map>::const_iterator ibn = ld.backendNodes.find(prefBackend); + if (ibn != ld.backendNodes.end()) + layerBackend = ibn->second; + out << (!layerBackend.empty() ? backend : "OCV/"); + int colorId = 0; + const Target target = ld.layerInstance.empty() + ? DNN_TARGET_CPU + : (Target)(ld.layerInstance->preferableTarget); // TODO fix preferableTarget type + switch (target) + { + case DNN_TARGET_CPU: + out << "CPU"; + colorId = layerBackend.empty() ? 0 : 5; + break; + case DNN_TARGET_OPENCL: + out << "OCL"; + colorId = 1; + break; + case DNN_TARGET_OPENCL_FP16: + out << "OCL_FP16"; + colorId = 2; + break; + case DNN_TARGET_MYRIAD: + out << "MYRIAD"; + colorId = 3; + break; + case DNN_TARGET_HDDL: + out << "HDDL"; + colorId = 8; + break; + case DNN_TARGET_VULKAN: + out << "VULKAN"; + colorId = 7; + break; + case DNN_TARGET_FPGA: + out << "FPGA"; + colorId = 4; + break; + case DNN_TARGET_CUDA: + out << "CUDA"; + colorId = 5; + break; + case DNN_TARGET_CUDA_FP16: + out << "CUDA_FP16"; + colorId = 6; + break; + case DNN_TARGET_NPU: + out << "NPU"; + colorId = 9; + break; + // don't use default: + } + CV_Assert(colorId < colors.size()); + out << "\\n"; // align center + out << ((clusterIds.size() == 1) ? "\" " : " }\" "); + out << "fillcolor=\"" << colors[colorId] << "\" "; + out << "style=filled "; + out << "shape=" << ((clusterIds.size() == 1) ? "box" : "record") << "]\n"; + } + out << '\n'; + // Add edges + int inputsSize = hasInput ? netInputLayer->outNames.size() : 0; + for (std::map::const_iterator it = map.begin(); it != map.end(); ++it) + { + const LayerData& ld = it->second; + if (allLayers[it->first] == -1) // node + { + for (int i = 0; i < ld.consumers.size(); i++) + { + int outId = ld.consumers[i].lid; + if (it == map.begin() && inputsSize > 1) + out << "\t\"" << ld.name << "_" << i << "\"" + << " -> "; + else + out << "\t\"" << ld.name << "\"" + << " -> "; + if (allLayers[outId] == -1) // node + { + CV_DbgAssert(map.find(outId) != map.end()); + out << "\"" << map.find(outId)->second.name << "\"\n"; + } + else // cluster + { + out << "\"" + << "cluster_" << allLayers[outId] << "\"\n"; + } + } + } + else if (it->first == skippedLayers[allLayers[it->first]].back()) // edges from last layer in cluster + { + for (int i = 0; i < ld.consumers.size(); i++) + { + int outId = ld.consumers[i].lid; + if (allLayers[outId] == -1) // node + { + CV_DbgAssert(map.find(outId) != map.end()); + out << "\t\"" + << "cluster_" << allLayers[it->first] << "\"" + << " -> "; + out << "\"" << map.find(outId)->second.name << "\"\n"; + } + else if (allLayers[outId] != allLayers[it->first]) + { // another cluster + out << "\t\"" + << "cluster_" << allLayers[it->first] << "\"" + << " -> "; + out << "\"" + << "cluster_" << allLayers[outId] << "\"\n"; + } + } + } + } + out << "}\n"; + return out.str(); +} + + +void Net::Impl::dumpNetworkToFile() const +{ +#ifndef OPENCV_DNN_DISABLE_NETWORK_AUTO_DUMP + string dumpFileNameBase = getDumpFileNameBase(); + string dumpFileName = dumpFileNameBase + ".dot"; + try + { + string dumpStr = dump(); + std::ofstream out(dumpFileName.c_str(), std::ios::out | std::ios::binary); + out << dumpStr; + } + catch (const std::exception& e) + { + std::ofstream out((dumpFileName + ".error").c_str(), std::ios::out); + out << "Exception: " << e.what() << std::endl; + } + catch (...) + { + std::ofstream out((dumpFileName + ".error").c_str(), std::ios::out); + out << "Can't dump: unknown exception" << std::endl; + } +#endif +} + + +std::vector> Net::Impl::getLayerInputs(int layerId) const +{ + LayerData& ld = getLayerData(layerId); + + std::vector> inputLayers; + inputLayers.reserve(ld.inputBlobsId.size()); + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + inputLayers.push_back(getLayer(ld.inputBlobsId[i].lid)); + } + return inputLayers; +} + +std::vector Net::Impl::getLayerNames() const +{ + std::vector res; + res.reserve(layers.size()); + + Impl::MapIdToLayerData::const_iterator it; + for (it = layers.begin(); it != layers.end(); it++) + { + if (it->second.id) // skip Data layer + res.push_back(it->second.name); + } + + return res; +} + + +// FIXIT drop "unconnected" API +std::vector Net::Impl::getUnconnectedOutLayers() const +{ + std::vector layersIds; + + // registerOutput() flow + if (!outputNameToId.empty()) + { + for (std::map::const_iterator it = outputNameToId.begin(); it != outputNameToId.end(); ++it) + { + layersIds.push_back(it->second); + } + return layersIds; + } + + Impl::MapIdToLayerData::const_iterator it; + for (it = layers.begin(); it != layers.end(); it++) + { + int lid = it->first; + const LayerData& ld = it->second; + + if (ld.requiredOutputs.size() == 0) + layersIds.push_back(lid); + } + + return layersIds; +} + + +// FIXIT drop "unconnected" API +std::vector Net::Impl::getUnconnectedOutLayersNames() /*const*/ +{ + std::vector ids = getUnconnectedOutLayers(); + const size_t n = ids.size(); + std::vector names(n); + for (size_t i = 0; i < n; ++i) + { + names[i] = layers[ids[i]].name; + } + return names; +} + + +int64 Net::Impl::getFLOPS(const std::vector& netInputShapes) /*const*/ +{ + int64 flops = 0; + std::vector ids; + std::vector> inShapes, outShapes; + getLayersShapes(netInputShapes, ids, inShapes, outShapes); + CV_Assert(inShapes.size() == outShapes.size()); + CV_Assert(inShapes.size() == ids.size()); + + for (int i = 0; i < ids.size(); i++) + { + flops += layers[ids[i]].getLayerInstance()->getFLOPS(inShapes[i], outShapes[i]); + } + + return flops; +} + + +int64 Net::Impl::getFLOPS( + const int layerId, + const std::vector& netInputShapes) /*const*/ +{ + Impl::MapIdToLayerData::const_iterator layer = layers.find(layerId); + CV_Assert(layer != layers.end()); + + LayerShapes shapes; + getLayerShapes(netInputShapes, layerId, shapes); + + return const_cast(layer->second).getLayerInstance()->getFLOPS(shapes.in, shapes.out); +} + + +void Net::Impl::getMemoryConsumption( + const int layerId, + const std::vector& netInputShapes, + size_t& weights, size_t& blobs) /*const*/ +{ + Impl::MapIdToLayerData::const_iterator layer = layers.find(layerId); + CV_Assert(layer != layers.end()); + + weights = blobs = 0; + + for (int i = 0; i < layer->second.params.blobs.size(); i++) + { + const Mat& weightsBlob = layer->second.params.blobs[i]; + weights += weightsBlob.total() * weightsBlob.elemSize(); + } + + LayerShapes shapes; + getLayerShapes(netInputShapes, layerId, shapes); + const ShapesVec& outLayerShapes = shapes.out; + + // FIXIT netWasQuantized check is not enough - per layer check should be done + size_t elemSize = netWasQuantized ? sizeof(char) : sizeof(float); + for (int i = 0; i < outLayerShapes.size(); i++) + { + blobs += total(outLayerShapes[i]) * elemSize; + } +} + + +void Net::Impl::getMemoryConsumption( + const std::vector& netInputShapes, + size_t& weights, size_t& blobs) /*const*/ +{ + std::vector layerIds; + std::vector w, b; + getMemoryConsumption(netInputShapes, layerIds, w, b); + + weights = blobs = 0; + for (int i = 0; i < layerIds.size(); i++) + { + weights += w[i]; + blobs += b[i]; + } +} + + +int64 Net::Impl::getPerfProfile(std::vector& timings) const +{ + timings = std::vector(layersTimings.begin() + 1, layersTimings.end()); + int64 total = (int64)std::accumulate(timings.begin(), timings.end(), 0.0); + return total; +} + +void Net::Impl::getMemoryConsumption( + const std::vector& netInputShapes, + std::vector& layerIds, std::vector& weights, + std::vector& blobs) /*const*/ +{ + layerIds.clear(); + weights.clear(); + blobs.clear(); + + std::vector> inLayerShapes, outLayerShapes; + + getLayersShapes(netInputShapes, layerIds, inLayerShapes, outLayerShapes); + // FIXIT netWasQuantized check is not enough - per layer check should be done + size_t elemSize = netWasQuantized ? sizeof(char) : sizeof(float); + for (int i = 0; i < layerIds.size(); i++) + { + int w = 0, b = 0; + Impl::MapIdToLayerData::const_iterator layer = layers.find(layerIds[i]); + CV_Assert(layer != layers.end()); + + for (int j = 0; j < layer->second.params.blobs.size(); j++) + { + const Mat& weightsBlob = layer->second.params.blobs[j]; + w += weightsBlob.total() * weightsBlob.elemSize(); + } + + for (int j = 0; j < outLayerShapes[i].size(); j++) + { + b += total(outLayerShapes[i][j]) * elemSize; + } + + weights.push_back(w); + blobs.push_back(b); + } +} + + +// TODO drop? +void Net::Impl::getLayerTypes(std::vector& layersTypes) const +{ + layersTypes.clear(); + + std::map layers_type_map; + for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); it++) + { + if (layers_type_map.find(it->second.type) == layers_type_map.end()) + layers_type_map[it->second.type] = 0; + layers_type_map[it->second.type]++; + } + + for (std::map::const_iterator it = layers_type_map.begin(); it != layers_type_map.end(); it++) + { + layersTypes.push_back(it->first); + } +} + + +// TODO drop? +int Net::Impl::getLayersCount(const String& layerType) const +{ + int count = 0; + for (Impl::MapIdToLayerData::const_iterator it = layers.begin(); + it != layers.end(); it++) + { + if (it->second.type == layerType) + count++; + } + return count; +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/net_impl.hpp b/modules/dnn/src/net_impl.hpp new file mode 100644 index 000000000000..9dc96fe82dd5 --- /dev/null +++ b/modules/dnn/src/net_impl.hpp @@ -0,0 +1,270 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef __OPENCV_DNN_SRC_NET_IMPL_HPP__ +#define __OPENCV_DNN_SRC_NET_IMPL_HPP__ + +#include "op_halide.hpp" +#include "op_inf_engine.hpp" +#include "ie_ngraph.hpp" +#include "op_vkcom.hpp" +#include "op_cuda.hpp" +#include "op_webnn.hpp" +#include "op_timvx.hpp" + +#include +#include +#include + +#include + +#include + +#include "layer_internals.hpp" // LayerPin LayerData DataLayer + +#include "legacy_backend.hpp" // wrapMat BlobManager OpenCLBackendWrapper + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + +using std::make_pair; +using std::string; + +// NB: Implementation is divided between of multiple .cpp files +struct Net::Impl : public detail::NetImplBase +{ + typedef std::map LayersShapesMap; + typedef std::map MapIdToLayerData; + + Impl(); + + Ptr netInputLayer; + std::vector blobsToKeep; + MapIdToLayerData layers; + std::map layerNameToId; + std::map outputNameToId; // use registerOutput() to populate outputs + BlobManager blobManager; + int preferableBackend; + int preferableTarget; + String halideConfigFile; + bool skipInfEngineInit; + bool hasDynamicShapes; + // Map host data to backend specific wrapper. + std::map> backendWrappers; + + int lastLayerId; + + bool netWasAllocated; + bool netWasQuantized; + bool fusion; + bool isAsync; + std::vector layersTimings; + + + bool empty() const; + void setPreferableBackend(int backendId); + void setPreferableTarget(int targetId); + + // FIXIT use inheritance + Ptr wrap(Mat& host); + + + void clear(); + + void setUpNet(const std::vector& blobsToKeep_ = std::vector()); + + + Ptr getLayer(int layerId) const; + Ptr getLayer(const LayerId& layerId) const; + + int getLayerId(const String& layerName) const; + + int getLayerId(int id) const; + + int getLayerId(DictValue& layerDesc) const; + + String getLayerName(int id) const; + + LayerData& getLayerData(int id) const; + + LayerData& getLayerData(const String& layerName) const; + + LayerData& getLayerData(const DictValue& layerDesc) const; + + static void addLayerInput(LayerData& ld, int inNum, LayerPin from); + + int resolvePinOutputName(LayerData& ld, const String& outName) const; + + LayerPin getPinByAlias(const String& layerName) const; + + std::vector getLayerOutPins(const String& layerName) const; + + // FIXIT remove dtype + int addLayer(const String& name, const String& type, const int& dtype, LayerParams& params); + + int addLayerToPrev(const String& name, const String& type, const int& dtype, LayerParams& params); + + + void connect(int outLayerId, int outNum, int inLayerId, int inNum); + + int registerOutput(const std::string& outputName, int layerId, int outputPort); + + // FIXIT drop "unconnected" API + std::vector getUnconnectedOutLayers() const; + std::vector getUnconnectedOutLayersNames() /*const*/; + + + void setInputsNames(const std::vector& inputBlobNames); + void setInputShape(const String& inputName, const MatShape& shape); + void setInput(InputArray blob, const String& name, double scalefactor, const Scalar& mean); + Mat getParam(int layer, int numParam) const; + void setParam(int layer, int numParam, const Mat& blob); + std::vector> getLayerInputs(int layerId) const; + std::vector getLayerNames() const; + + + // TODO drop? + void getLayerTypes(std::vector& layersTypes) const; + int getLayersCount(const String& layerType) const; + + + // FIXIT use inheritance + void initBackend(const std::vector& blobsToKeep_); + + void setHalideScheduler(const String& scheduler); +#ifdef HAVE_HALIDE + void compileHalide(); + void initHalideBackend(); +#endif + +#ifdef HAVE_DNN_NGRAPH + void addNgraphOutputs(LayerData& ld); + void initNgraphBackend(const std::vector& blobsToKeep_); +#endif + +#ifdef HAVE_WEBNN + void addWebnnOutputs(LayerData& ld); + void initWebnnBackend(const std::vector& blobsToKeep_); +#endif + +#ifdef HAVE_VULKAN + void initVkComBackend(); +#endif + +#ifdef HAVE_TIMVX + // Create timVxInfo for reserve tvGraphList. + TimVXInfo timVxInfo = TimVXInfo(); + void tvUpdateConfictMap(int graphIndex, LayerData& ld, std::vector >& graphConflictMap); + void tvConvertToOutputNode(const LayerData& ld, Ptr& targetWrap); + void initTimVXBackend(); +#endif + +#ifdef HAVE_CUDA + struct CudaInfo_t + { + CudaInfo_t(cuda4dnn::csl::CSLContext ctxt, cuda4dnn::csl::Stream d2h_stream_) + : context(std::move(ctxt)) + , d2h_stream(std::move(d2h_stream_)) + {} + cuda4dnn::csl::CSLContext context; + cuda4dnn::csl::Stream d2h_stream; + cuda4dnn::csl::Workspace workspace; + }; + + std::unique_ptr cudaInfo; + + void initCUDABackend(const std::vector& blobsToKeep_); +#endif + + void allocateLayer(int lid, const LayersShapesMap& layersShapes); + + // TODO add getter + void enableFusion(bool fusion_); + + void fuseLayers(const std::vector& blobsToKeep_); + + void allocateLayers(const std::vector& blobsToKeep_); + + void forwardLayer(LayerData& ld); + + void forwardToLayer(LayerData& ld, bool clearFlags = true); + + Mat forward(const String& outputName); + AsyncArray forwardAsync(const String& outputName); + void forward(OutputArrayOfArrays outputBlobs, const String& outputName); + void forward(OutputArrayOfArrays outputBlobs, + const std::vector& outBlobNames); + void forward(std::vector>& outputBlobs, + const std::vector& outBlobNames); + + + void getLayerShapesRecursively(int id, LayersShapesMap& inOutShapes); + + void getLayersShapes( + const ShapesVec& netInputShapes, + std::vector& layersIds, + std::vector& inLayersShapes, + std::vector& outLayersShapes) /*const*/; + + void getLayersShapes(const ShapesVec& netInputShapes, + LayersShapesMap& inOutShapes); + + void getLayerShapes(const ShapesVec& netInputShapes, + const int layerId, + LayerShapes& shapes); + + void updateLayersShapes(); + + int64 getFLOPS(const std::vector& netInputShapes) /*const*/; + int64 getFLOPS( + const int layerId, + const std::vector& netInputShapes) /*const*/; + + void getMemoryConsumption( + const int layerId, + const std::vector& netInputShapes, + size_t& weights, size_t& blobs) /*const*/; + void getMemoryConsumption( + const std::vector& netInputShapes, + size_t& weights, size_t& blobs) /*const*/; + void getMemoryConsumption( + const std::vector& netInputShapes, + std::vector& layerIds, std::vector& weights, + std::vector& blobs) /*const*/; + int64 getPerfProfile(std::vector& timings) const; + + // TODO drop + LayerPin getLatestLayerPin(const std::vector& pins) const; + + Mat getBlob(const LayerPin& pin) const; + + Mat getBlob(String outputName) const; + +#ifdef CV_CXX11 + AsyncArray getBlobAsync(const LayerPin& pin); + + AsyncArray getBlobAsync(String outputName); +#endif // CV_CXX11 + +#ifdef HAVE_INF_ENGINE + static + Net createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet); +#endif + + string dump(bool forceAllocation = false) const; + + void dumpNetworkToFile() const; + + // FIXIT drop from inference API + Net quantize(InputArrayOfArrays calibData, int inputsDtype, int outputsDtype) /*const*/; + void getInputDetails(std::vector& scales, std::vector& zeropoints) /*const*/; + void getOutputDetails(std::vector& scales, std::vector& zeropoints) /*const*/; + +}; // Net::Impl + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn +#endif // __OPENCV_DNN_SRC_NET_IMPL_HPP__ diff --git a/modules/dnn/src/net_impl_backend.cpp b/modules/dnn/src/net_impl_backend.cpp new file mode 100644 index 000000000000..e26126d86c52 --- /dev/null +++ b/modules/dnn/src/net_impl_backend.cpp @@ -0,0 +1,214 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "net_impl.hpp" +#include "legacy_backend.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +Ptr Net::Impl::wrap(Mat& host) +{ + if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU) + return Ptr(); + + MatShape shape(host.dims); + for (int i = 0; i < host.dims; ++i) + shape[i] = host.size[i]; + + void* data = host.data; + if (backendWrappers.find(data) != backendWrappers.end()) + { + Ptr baseBuffer = backendWrappers[data]; + if (preferableBackend == DNN_BACKEND_OPENCV) + { +#ifdef HAVE_OPENCL + CV_Assert(IS_DNN_OPENCL_TARGET(preferableTarget)); + return OpenCLBackendWrapper::create(baseBuffer, host); +#else + CV_Error(Error::StsInternal, ""); +#endif + } + else if (preferableBackend == DNN_BACKEND_HALIDE) + { + CV_Assert(haveHalide()); +#ifdef HAVE_HALIDE + return Ptr(new HalideBackendWrapper(baseBuffer, shape)); +#endif + } + else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) + { + CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019; + } + else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + return wrapMat(preferableBackend, preferableTarget, host); + } + else if (preferableBackend == DNN_BACKEND_WEBNN) + { +#ifdef HAVE_WEBNN + return wrapMat(preferableBackend, preferableTarget, host); +#endif + } + else if (preferableBackend == DNN_BACKEND_VKCOM) + { +#ifdef HAVE_VULKAN + return Ptr(new VkComBackendWrapper(baseBuffer, host)); +#endif + } + else if (preferableBackend == DNN_BACKEND_CUDA) + { + CV_Assert(haveCUDA()); +#ifdef HAVE_CUDA + switch (preferableTarget) + { + case DNN_TARGET_CUDA: + return CUDABackendWrapperFP32::create(baseBuffer, shape); + case DNN_TARGET_CUDA_FP16: + return CUDABackendWrapperFP16::create(baseBuffer, shape); + default: + CV_Assert(IS_DNN_CUDA_TARGET(preferableTarget)); + } +#endif + } + else if (preferableBackend == DNN_BACKEND_TIMVX) + { +#ifdef HAVE_TIMVX + return Ptr(new TimVXBackendWrapper(baseBuffer, host)); +#endif + } + else + CV_Error(Error::StsNotImplemented, "Unknown backend identifier"); + } + + Ptr wrapper = wrapMat(preferableBackend, preferableTarget, host); + backendWrappers[data] = wrapper; + return wrapper; +} + + +void Net::Impl::initBackend(const std::vector& blobsToKeep_) +{ + CV_TRACE_FUNCTION(); + if (preferableBackend == DNN_BACKEND_OPENCV) + { + CV_Assert(preferableTarget == DNN_TARGET_CPU || IS_DNN_OPENCL_TARGET(preferableTarget)); + } + else if (preferableBackend == DNN_BACKEND_HALIDE) + { +#ifdef HAVE_HALIDE + initHalideBackend(); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Halide"); +#endif + } + else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { +#ifdef HAVE_DNN_NGRAPH + initNgraphBackend(blobsToKeep_); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of OpenVINO"); +#endif + } + else if (preferableBackend == DNN_BACKEND_WEBNN) + { +#ifdef HAVE_WEBNN + initWebnnBackend(blobsToKeep_); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of WebNN"); +#endif + } + else if (preferableBackend == DNN_BACKEND_VKCOM) + { +#ifdef HAVE_VULKAN + initVkComBackend(); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Vulkan"); +#endif + } + else if (preferableBackend == DNN_BACKEND_CUDA) + { +#ifdef HAVE_CUDA + initCUDABackend(blobsToKeep_); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of CUDA/CUDNN"); +#endif + } + else if (preferableBackend == DNN_BACKEND_TIMVX) + { +#ifdef HAVE_TIMVX + initTimVXBackend(); +#else + CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of TimVX"); +#endif + } + else + { + CV_Error(Error::StsNotImplemented, cv::format("Unknown backend identifier: %d", preferableBackend)); + } +} + + +void Net::Impl::setPreferableBackend(int backendId) +{ + if (backendId == DNN_BACKEND_DEFAULT) + backendId = (Backend)getParam_DNN_BACKEND_DEFAULT(); + + if (netWasQuantized && backendId != DNN_BACKEND_OPENCV && backendId != DNN_BACKEND_TIMVX) + { + CV_LOG_WARNING(NULL, "DNN: Only default and TIMVX backends support quantized networks"); + backendId = DNN_BACKEND_OPENCV; + } + +#ifdef HAVE_INF_ENGINE + if (backendId == DNN_BACKEND_INFERENCE_ENGINE) + backendId = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; +#endif + + if (preferableBackend != backendId) + { + preferableBackend = backendId; + clear(); + } +} + +void Net::Impl::setPreferableTarget(int targetId) +{ + if (netWasQuantized && targetId != DNN_TARGET_CPU && + targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16 && targetId != DNN_TARGET_NPU) + { + CV_LOG_WARNING(NULL, "DNN: Only CPU, OpenCL/OpenCL FP16 and NPU targets are supported by quantized networks"); + targetId = DNN_TARGET_CPU; + } + + if (preferableTarget != targetId) + { + preferableTarget = targetId; + if (IS_DNN_OPENCL_TARGET(targetId)) + { +#ifndef HAVE_OPENCL +#ifdef HAVE_INF_ENGINE + if (preferableBackend == DNN_BACKEND_OPENCV) +#else + if (preferableBackend == DNN_BACKEND_DEFAULT || + preferableBackend == DNN_BACKEND_OPENCV) +#endif // HAVE_INF_ENGINE + preferableTarget = DNN_TARGET_CPU; +#else + bool fp16 = ocl::Device::getDefault().isExtensionSupported("cl_khr_fp16"); + if (!fp16 && targetId == DNN_TARGET_OPENCL_FP16) + preferableTarget = DNN_TARGET_OPENCL; +#endif + } + clear(); + } +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/net_impl_fuse.cpp b/modules/dnn/src/net_impl_fuse.cpp new file mode 100644 index 000000000000..753c00de90c5 --- /dev/null +++ b/modules/dnn/src/net_impl_fuse.cpp @@ -0,0 +1,608 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "net_impl.hpp" + +#ifdef HAVE_CUDA +#include "cuda4dnn/primitives/eltwise.hpp" // required by fuseLayers +#endif + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +void Net::Impl::enableFusion(bool fusion_) +{ + if (fusion != fusion_) + { + fusion = fusion_; + clear(); + } +} + + +#if 0 +#define printf_(args) printf args +#else +#define printf_(args) +#endif + + +void Net::Impl::fuseLayers(const std::vector& blobsToKeep_) +{ + CV_TRACE_FUNCTION(); + + if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV && + preferableBackend != DNN_BACKEND_CUDA && + preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && + preferableBackend != DNN_BACKEND_TIMVX)) + return; + +#if 0 // FIXIT mode without fusion is broken due to unsupported layers and handling of "custom" nodes + if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + return; +#endif + + // scan through all the layers. If there is convolution layer followed by the activation layer, + // we try to embed this activation into the convolution and disable separate execution of the activation + + // FIXIT replace by layersToKeep to avoid hacks like "LayerPin(lid, 0)" + std::set pinsToKeep(blobsToKeep_.begin(), + blobsToKeep_.end()); + for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); it++) + { + int lid = it->first; + LayerData& ld = layers[lid]; + if (ld.skip) + { + printf_(("skipped %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str())); + continue; + } + printf_(("analyzing %s: %s\n", ld.layerInstance->name.c_str(), ld.layerInstance->type.c_str())); + + // the optimization #1. try to fuse batch norm, scaling and/or activation layers + // with the current layer if they follow it. Normally, the are fused with the convolution layer, + // but some of them (like activation) may be fused with fully-connected, elemwise (+) and + // some other layers. + Ptr& currLayer = ld.layerInstance; + if (ld.consumers.size() == 1 && pinsToKeep.count(LayerPin(lid, 0)) == 0) + { + LayerData* nextData = &layers[ld.consumers[0].lid]; + LayerPin lpNext(ld.consumers[0].lid, 0); + while (nextData) + { +#ifdef HAVE_INF_ENGINE + if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && pinsToKeep.count(lpNext) != 0) + { + CV_LOG_DEBUG(NULL, "DNN/IE: skip fusing with 'output' node: " << nextData->name << "@" << nextData->type); + break; + } +#endif + /* we use `tryFuse` member of convolution layer to fuse eltwise later + * it's not intended to be fused here; hence, we stop when we encounter eltwise + */ + if (preferableBackend == DNN_BACKEND_CUDA && ld.type == "Convolution" && nextData->type == "Eltwise") + break; + Ptr nextLayer = nextData->layerInstance; + if (currLayer->tryFuse(nextLayer)) + { + printf_(("\tfused with %s\n", nextLayer->name.c_str())); + nextData->skip = true; + ld.outputBlobs = layers[lpNext.lid].outputBlobs; + ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers; + if (nextData->consumers.size() == 1) + { + int nextLayerId = nextData->consumers[0].lid; + nextData = &layers[nextLayerId]; + lpNext = LayerPin(nextLayerId, 0); + } + else + { + nextData = 0; + break; + } + } + else + break; + } + + if (preferableBackend != DNN_BACKEND_OPENCV && preferableBackend != DNN_BACKEND_CUDA) + continue; // Go to the next layer. + + // TODO: OpenCL target support more fusion styles. + if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) && + (!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" && + ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" && + ld.layerInstance->type != "Concat")) ) + continue; + + if (preferableBackend == DNN_BACKEND_CUDA && IS_DNN_CUDA_TARGET(preferableTarget) + && ld.layerInstance->type != "Convolution" + && ld.layerInstance->type != "Concat") + continue; + + while (nextData) + { + // For now, OpenCL target support fusion with activation of ReLU/ChannelsPReLU/Power/Tanh + if (IS_DNN_OPENCL_TARGET(preferableTarget) && + nextData->type != "ReLU" && + nextData->type != "ChannelsPReLU" && + nextData->type != "ReLU6" && + nextData->type != "TanH" && + nextData->type != "Power") + break; + + Ptr nextActivLayer = nextData->layerInstance.dynamicCast(); + if (nextActivLayer.empty()) + break; + + if (currLayer->setActivation(nextActivLayer)) + { + printf_(("\tfused with %s\n", nextActivLayer->name.c_str())); + nextData->skip = true; + ld.outputBlobs = layers[lpNext.lid].outputBlobs; + ld.outputBlobsWrappers = layers[lpNext.lid].outputBlobsWrappers; + if (nextData->consumers.size() == 1) + { + int nextLayerId = nextData->consumers[0].lid; + nextData = &layers[nextLayerId]; + lpNext = LayerPin(nextLayerId, 0); + } + else + { + nextData = 0; + break; + } + } + else + break; + } + + // OpenCL: fuse convolution layer followed by eltwise + relu + // CUDA: fuse convolution layer followed by eltwise (and optional activation) + while (nextData && + (IS_DNN_OPENCL_TARGET(preferableTarget) || IS_DNN_CUDA_TARGET(preferableTarget)) && + ld.layerInstance->type == "Convolution" + ) // semantic of 'if' + { + Ptr nextEltwiseLayer = nextData->layerInstance.dynamicCast(); + if (nextEltwiseLayer.empty()) + break; + +#ifdef HAVE_CUDA + // CUDA backend supports fusion with eltwise sum (without variable channels) + if (IS_DNN_CUDA_TARGET(preferableTarget) && !nextEltwiseLayer.empty()) + { + // we create a temporary backend node for eltwise layer to obtain the eltwise configuration + cuda4dnn::csl::CSLContext context; // assume that initCUDA and EltwiseOp do not use the context during init + const auto node = nextData->layerInstance->initCUDA(&context, nextData->inputBlobsWrappers, nextData->outputBlobsWrappers); + auto eltwiseNode = node.dynamicCast(); + + // broadcasting not supported in fused ops + auto required_shape = shape(nextData->outputBlobs[0]); + for (int i = 0; i < nextData->inputBlobs.size(); i++) + { + if (shape(*nextData->inputBlobs[i]) != required_shape) + { + eltwiseNode.reset(); + break; + } + } + + // CUDA backend uses EltwiseOp when all operands have the same number of channels; otherwise, ShortcutOp is used. + // Hence, a successful cast to EltwiseOp implies that the number of channels is same in all operand tensors. + if (eltwiseNode.empty() || eltwiseNode->op != cuda4dnn::EltwiseOpType::SUM || !eltwiseNode->coeffs.empty()) + break; + } +#endif + + if (IS_DNN_OPENCL_TARGET(preferableTarget) && pinsToKeep.count(lpNext) != 0) + break; + if (nextData->inputBlobsId.size() != 2) + break; + + if (IS_DNN_OPENCL_TARGET(preferableTarget)) + { + if (!nextData->params.has("operation") || toLowerCase(nextData->params.get("operation")) == "sum") + { + if (nextData->params.has("coeff")) + { + DictValue paramCoeff = nextData->params.get("coeff"); + int n = paramCoeff.size(); + bool isCoeffOneOne = (n == 2); + for (int i = 0; isCoeffOneOne && i < n; i++) + { + float c = paramCoeff.get(i); + isCoeffOneOne &= (c == 1.0f); + } + if (!isCoeffOneOne) + { + CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion of 'Sum' without coeffs (or {1.0, 1.0}) is supported only"); + break; + } + } + } + else + { + CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion with eltwise operation is not supported: " << nextData->params.get("operation")); + break; + } + } + + { + LayerData *eltwiseData = nextData; + + // Eltwise layer has two inputs. We need to determine which + // is a base convolution layer and which could be used as it's bias. + LayerData* biasLayerData = 0; + for (int i = 0; i < 2; ++i) + { + LayerData *downLayerData = &layers[eltwiseData->inputBlobsId[i].lid]; + CV_Assert(downLayerData); + while (downLayerData->skip) + { + if (downLayerData->inputBlobsId.size() == 1) + downLayerData = &layers[downLayerData->inputBlobsId[0].lid]; + else + { + downLayerData = 0; + break; + } + } + if (downLayerData && ld.id == downLayerData->id) + { + biasLayerData = &layers[eltwiseData->inputBlobsId[1 - i].lid]; + break; + } + } + CV_Assert(biasLayerData); + { + // fuse eltwise + activation layer + // bias must already be computed to fuse => bias layer must appear before convolution + if (biasLayerData->id < ld.id) + { + /* we can fuse activation if: + * => activation layer that follows is the only consumer of eltwise output + * => activation layer does not process multiple inputs + * => we do not require to keep the output of eltwise + */ + Ptr nextFusabeleActivLayer; + if (eltwiseData->consumers.size() == 1 && pinsToKeep.count(lpNext) == 0) + { + nextData = &layers[eltwiseData->consumers[0].lid]; + lpNext = LayerPin(eltwiseData->consumers[0].lid, 0); + CV_Assert(nextData); + if (nextData->outputBlobs.size() == 1) + nextFusabeleActivLayer = nextData->layerInstance.dynamicCast(); + } + else + { + // OCL backend cannot fuse in this case but the CUDA backend can continue with just eltwise + nextData = 0; + } + + // the requirements of OCV OpenCL backend and CUDA backend are different + // we need to check them separately; hence, the fuse variables + bool fuse_eltwise = false, fuse_activation = false; + + Ptr activ_power; + if (IS_DNN_OPENCL_TARGET(preferableTarget) && !nextFusabeleActivLayer.empty() && + nextData && + (!nextData->type.compare("ReLU") || + !nextData->type.compare("ChannelsPReLU") || + (!nextData->type.compare("Power") && (activ_power = nextFusabeleActivLayer.dynamicCast()) && activ_power->scale == 1.0f) + ) && + currLayer->setActivation(nextFusabeleActivLayer)) + { + fuse_eltwise = true; + fuse_activation = true; + } + + if (IS_DNN_CUDA_TARGET(preferableTarget)) + { + /* supported fusion options: + * => convolution + eltwise + * => activation(convolution) + eltwise + * > convolution + activation would have been fused already; we have to fuse eltwise + * => activation(convolution + eltwise) + * > fuse eltwise and then activation + */ + auto layer = nextEltwiseLayer.staticCast(); + if (currLayer->tryFuse(layer)) + { + fuse_eltwise = true; /* eltwise was successfully fused */ + if (!nextFusabeleActivLayer.empty() && nextData) + { + if ((!nextData->type.compare("ReLU") || + !nextData->type.compare("ReLU6") || + !nextData->type.compare("Power") || + !nextData->type.compare("TanH") || + !nextData->type.compare("Sigmoid") || + !nextData->type.compare("Swish") || + !nextData->type.compare("Mish")) && + currLayer->setActivation(nextFusabeleActivLayer)) + { + // activation was fused + fuse_activation = true; + } + } + } + } + + CV_Assert(!fuse_activation || fuse_eltwise); /* cannot fuse activation without eltwise */ + if(fuse_eltwise && fuse_activation) + { + CV_Assert(nextData); + CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1); + ld.inputBlobsWrappers.push_back(biasLayerData->outputBlobsWrappers[0]); + printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str())); + printf_(("\tfused with %s\n", nextFusabeleActivLayer->name.c_str())); + eltwiseData->skip = true; + nextData->skip = true; + // This optimization for cases like + // some_layer conv + // | | + // +-- eltwise --+ + // | + // activ + // This way all the element-wise computations + // (i.e. some_layer+conv or some_layer*conv) + // would be done at [conv] layer. So we need to + // replace [conv]'s output blob to [eltwise]'s one + // considering that [activ] is an in-place layer. + // Also we need to move all the consumers' references. + // To prevent memory collisions (i.e. when input of + // [conv] and output of [eltwise] is the same blob) + // we allocate a new blob. + CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1); + ld.outputBlobs[0] = ld.outputBlobs[0].clone(); + ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]); + + eltwiseData->outputBlobs = ld.outputBlobs; + nextData->outputBlobs = ld.outputBlobs; + eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers; + nextData->outputBlobsWrappers = ld.outputBlobsWrappers; + + // Move references of [activ] layer consumers to the newly allocated blob. + for (int i = 0; i < nextData->consumers.size(); ++i) + { + LayerData& consumer = layers[nextData->consumers[i].lid]; + for (int j = 0; j < consumer.inputBlobsId.size(); ++j) + { + if (consumer.inputBlobsId[j].lid == lpNext.lid) + { + consumer.inputBlobs[j] = &ld.outputBlobs[0]; + consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0]; + break; + } + } + } + } + else if (fuse_eltwise) // conv + eltwise (note: conv could have fused activations before eltwise) + { + CV_Assert(IS_DNN_CUDA_TARGET(preferableTarget)); + CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1); + ld.inputBlobsWrappers.push_back(biasLayerData->outputBlobsWrappers[0]); + printf_(("\tfused with %s\n", nextEltwiseLayer->name.c_str())); + eltwiseData->skip = true; + // This optimization is for cases like + // some_layer conv (maybe fused with activ) + // | | + // +-- eltwise --+ + // + // This way all the element-wise computations + // (i.e. some_layer+conv or some_layer*conv) + // would be done at [conv] layer. So we need to + // replace [conv]'s output blob to [eltwise]'s one. + // Also we need to move all the consumers' references. + // To prevent memory collisions (i.e. when input of + // [conv] and output of [eltwise] is the same blob) + // we allocate a new blob. + CV_Assert_N(ld.outputBlobs.size() == 1, ld.outputBlobsWrappers.size() == 1); + ld.outputBlobs[0] = ld.outputBlobs[0].clone(); + ld.outputBlobsWrappers[0] = wrap(ld.outputBlobs[0]); + + eltwiseData->outputBlobs = ld.outputBlobs; + eltwiseData->outputBlobsWrappers = ld.outputBlobsWrappers; + + // Move references of [eltwise] layer consumers to the newly allocated blob. + for (int i = 0; i < eltwiseData->consumers.size(); ++i) + { + LayerData& consumer = layers[eltwiseData->consumers[i].lid]; + for (int j = 0; j < consumer.inputBlobsId.size(); ++j) + { + if (consumer.inputBlobsId[j].lid == eltwiseData->id) + { + consumer.inputBlobs[j] = &ld.outputBlobs[0]; + consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0]; + break; + } + } + } + } + } + } + } + + break; + } + } + + if (preferableBackend != DNN_BACKEND_OPENCV && preferableBackend != DNN_BACKEND_CUDA) + continue; // Go to the next layer. + + // the optimization #2. if there is concat layer that concatenates channels + // from the inputs together (i.e. axis == 1) then we make the inputs of + // the concat layer to write to the concatenation output buffer + // (and so we eliminate the concatenation layer, because the channels + // are concatenated implicitly). + Ptr concatLayer = ld.layerInstance.dynamicCast(); + if( !concatLayer.empty() && !concatLayer->padding && ld.outputBlobs.size() == 1 ) + { + Mat& output = ld.outputBlobs[0]; + UMat umat_output; +#ifdef HAVE_OPENCL + if (!ld.outputBlobsWrappers.empty() && + (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))) + { + size_t i, ninputs = ld.inputBlobsId.size(); + bool conv_layer = true; + for( i = 0; i < ninputs; i++ ) + { + LayerPin pin = ld.inputBlobsId[i]; + LayerData* inp_i_data = &layers[pin.lid]; + while(inp_i_data->skip && + inp_i_data->inputBlobsId.size() == 1 && + inp_i_data->consumers.size() == 1) + { + pin = inp_i_data->inputBlobsId[0]; + inp_i_data = &layers[pin.lid]; + } + conv_layer = conv_layer && (inp_i_data->getLayerInstance()->type == "Convolution"); + } + if (!conv_layer) + continue; + std::vector umat_outputBlobs; + umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers); + umat_output = umat_outputBlobs[0]; + } +#endif + + // TODO: in general, this optimization can always be done, but + // many layers currently check that the input/output blobs are + // continuous arrays. Unfortunately, this is not true when + // the concatenation optimization is applied with batch_size > 1. + // so, for now, we only apply this optimization in the most popular + // case batch_size == 1. + int axis = normalize_axis(concatLayer->axis, output.dims); + if( output.total(0, axis) == 1 ) + { + size_t i, ninputs = ld.inputBlobsId.size(); + std::vector realinputs(ninputs); + for( i = 0; i < ninputs; i++ ) + { + LayerPin pin = ld.inputBlobsId[i]; + LayerData* inp_i_data = &layers[pin.lid]; + while(inp_i_data->skip && + inp_i_data->inputBlobsId.size() == 1 && + inp_i_data->consumers.size() == 1) + { + pin = inp_i_data->inputBlobsId[0]; + inp_i_data = &layers[pin.lid]; + } + printf_(("\treal input for %s is %s\n", + layers[ld.inputBlobsId[i].lid].getLayerInstance()->name.c_str(), + inp_i_data->getLayerInstance()->name.c_str())); + + if(inp_i_data->skip || inp_i_data->consumers.size() != 1) + break; +#ifdef HAVE_CUDA + if (preferableBackend == DNN_BACKEND_CUDA && + (inp_i_data->layerInstance->supportBackend(DNN_BACKEND_CUDA) == false || + (inp_i_data->layerInstance->type != "Convolution" && + inp_i_data->layerInstance->type != "Pooling" && + inp_i_data->layerInstance->type != "Resize" && + inp_i_data->layerInstance->type != "Flatten" && + inp_i_data->layerInstance->type != "Permute" && + inp_i_data->layerInstance->type != "Reorg" && + inp_i_data->layerInstance->type != "Eltwise" && + inp_i_data->layerInstance.dynamicCast().empty()))) + { + break; + } +#endif + realinputs[i] = pin; + } + + if( i >= ninputs ) + { + // Allocate new memory to prevent collisions during memory + // reusing (see https://github.com/opencv/opencv/pull/10456). + output = output.clone(); +#ifdef HAVE_OPENCL + if (preferableBackend == DNN_BACKEND_OPENCV && + IS_DNN_OPENCL_TARGET(preferableTarget)) + { + std::vector umats(1); + umat_output = umat_output.clone(); + umats[0] = umat_output; + OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umats); + } +#endif + +#ifdef HAVE_CUDA + if (preferableBackend == DNN_BACKEND_CUDA) + ld.outputBlobsWrappers[0] = wrap(output); +#endif + std::vector chrange(output.dims, Range::all()); + int ofs = 0; + for( i = 0; i < ninputs; i++ ) + { + LayerPin pin = realinputs[i]; + LayerData* inp_i_data = &layers[pin.lid]; + int channels_i = ld.inputBlobs[i]->size[axis]; + chrange[axis] = Range(ofs, ofs + channels_i); + printf_(("\toutput %s(%d) to channels (%d, %d)\n", inp_i_data->layerInstance->name.c_str(), + pin.oid, ofs, ofs + channels_i)); + ofs += channels_i; + Mat output_slice = output(chrange); + Mat& curr_output = inp_i_data->outputBlobs[pin.oid]; + CV_Assert(output_slice.isContinuous() && output_slice.size == curr_output.size); + Mat* oldPtr = &curr_output; + curr_output = output_slice; +#ifdef HAVE_OPENCL + if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)) + { + std::vector umats(inp_i_data->outputBlobsWrappers.size()); + umats[pin.oid] = umat_output(chrange); + OpenCLBackendWrapper::update(inp_i_data->outputBlobsWrappers, umats); + } +#endif +#ifdef HAVE_CUDA + if (preferableBackend == DNN_BACKEND_CUDA) + { + auto cuda_wrapper = wrap(output).dynamicCast(); + auto offset = chrange[axis].start * output_slice.total(axis + 1, output.dims); + auto new_shape = shape(output_slice); + cuda_wrapper->update(new_shape, offset); + inp_i_data->outputBlobsWrappers[pin.oid] = cuda_wrapper.staticCast(); + } +#endif + // Layers that refer old input Mat will refer to the + // new data but the same Mat object. + CV_Assert_N(curr_output.data == output_slice.data, oldPtr == &curr_output); + } + +#ifdef HAVE_CUDA + if (preferableBackend == DNN_BACKEND_CUDA) + { + for (int i = 0; i < ld.consumers.size(); i++) + { + LayerData& consumer = layers[ld.consumers[i].lid]; + for (int j = 0; j < consumer.inputBlobsId.size(); j++) + { + if (consumer.inputBlobsId[j].lid == ld.id) + { + CV_Assert(consumer.inputBlobs[j]->data == ld.outputBlobs[0].data); + consumer.inputBlobsWrappers[j] = ld.outputBlobsWrappers[0]; + break; + } + } + } + } +#endif + ld.skip = true; + printf_(("\toptimized out Concat layer %s\n", concatLayer->name.c_str())); + } + } + } + } +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/net_openvino.cpp b/modules/dnn/src/net_openvino.cpp new file mode 100644 index 000000000000..a546b0237df4 --- /dev/null +++ b/modules/dnn/src/net_openvino.cpp @@ -0,0 +1,568 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include + +#include +#include + +#include "net_impl.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + +#ifdef HAVE_INF_ENGINE + + +/** mark input pins as outputs from other subnetworks + * FIXIT must be done by DNN engine not ngraph. + */ +void Net::Impl::addNgraphOutputs(LayerData& ld) +{ + CV_TRACE_FUNCTION(); + + CV_LOG_DEBUG(NULL, "DNN/IE: layer of new subnet: " << ld.name << "@" << ld.type); + + Ptr layerNet; + auto it = ld.backendNodes.find(preferableBackend); + if (it != ld.backendNodes.end()) + { + Ptr node = it->second; + if (!node.empty()) + { + Ptr ieNode = node.dynamicCast(); + CV_Assert(!ieNode.empty()); + CV_Assert(!ieNode->net.empty()); + layerNet = ieNode->net; + } + } + + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + LayerData& inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) + { + Ptr ieInpNode = inpNode.dynamicCast(); + CV_Assert(!ieInpNode.empty()); + CV_Assert(!ieInpNode->net.empty()); + if (layerNet != ieInpNode->net) + { + CV_LOG_DEBUG(NULL, "DNN/IE: pin output between subnets: " << ieInpNode->node->get_friendly_name()); + ieInpNode->net->addOutput(ieInpNode); + } + } + } +} + +void Net::Impl::initNgraphBackend(const std::vector& blobsToKeep_) +{ + CV_TRACE_FUNCTION(); + CV_CheckEQ(preferableBackend, DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, ""); + + Ptr net; + + for (MapIdToLayerData::const_iterator it = layers.begin(); it != layers.end(); ++it) + { + const LayerData& ld = it->second; + if (ld.id == 0) + { + CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) || + (netInputLayer->outNames.size() == ld.outputBlobsWrappers.size())); + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); + std::string outputName = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]; + outputName = ld.outputBlobsWrappers.size() > 1 ? (outputName + "." + std::to_string(i)) : outputName; + dataPtr->setName(outputName); + } + } + else + { + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); + std::string outputName = ld.outputBlobsWrappers.size() > 1 ? (ld.name + "." + std::to_string(i)) : ld.name; + dataPtr->setName(outputName); + } + } + } + + if (skipInfEngineInit) + { + Ptr node = layers[lastLayerId].backendNodes[preferableBackend]; + CV_Assert(!node.empty()); + + Ptr ieNode = node.dynamicCast(); + CV_Assert(!ieNode.empty()); + + CV_Assert(ieNode->net); + InfEngineNgraphNet& ienet = *ieNode->net; + ienet.reset(); + + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it) + { + LayerData& ld = it->second; + if (ld.id == 0) + { + for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i) + { + InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.inputBlobsWrappers[i]); + dataPtr->setName(netInputLayer->outNames[i]); + } + } + else + { + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + auto it = ienet.outputsDesc.find(ld.name); + if (it != ienet.outputsDesc.end()) + { + const InferenceEngine::TensorDesc& descriptor = it->second; + InferenceEngine::DataPtr dataPtr = ngraphDataOutputNode(ld.outputBlobsWrappers[i], descriptor, ld.name); + dataPtr->setName(ld.name); + } + else + { + InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); + dataPtr->setName(ld.name); + } + } + } + ienet.addBlobs(ld.inputBlobsWrappers); + ienet.addBlobs(ld.outputBlobsWrappers); + ld.skip = true; + } + layers[lastLayerId].skip = false; + ienet.init((Target)preferableTarget); + return; + } + + bool supportsCPUFallback = !isArmComputePlugin() && (preferableTarget == DNN_TARGET_CPU || + openvino::checkTarget(DNN_TARGET_CPU)); + + // Build Inference Engine networks from sets of layers that support this + // backend. Split a whole model on several Inference Engine networks if + // some of layers are not implemented. + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it) + { + LayerData& ld = it->second; + + CV_LOG_DEBUG(NULL, "DNN/IE: processing layer " << ld.name << "@" << ld.type << " (" << ld.id << ") ..."); + + if (ld.id == 0 && ld.skip) + { + CV_LOG_DEBUG(NULL, "DNN/IE: SKIP!"); + continue; + } + + bool fused = ld.skip; + Ptr layer = ld.layerInstance; + if (!fused && !layer->supportBackend(preferableBackend)) + { + CV_LOG_DEBUG(NULL, "DNN/IE: NOT supported!"); + bool customizable = ld.id != 0 && supportsCPUFallback; + + // TODO: there is a bug in Myriad plugin with custom layers shape infer. + if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) + { + for (int i = 0; customizable && i < ld.inputBlobs.size(); ++i) + { + customizable = ld.inputBlobs[i]->size[0] == 1; + } + } + + // TODO: fix these workarounds + if (preferableTarget == DNN_TARGET_MYRIAD || + preferableTarget == DNN_TARGET_HDDL || + preferableTarget == DNN_TARGET_OPENCL || + preferableTarget == DNN_TARGET_OPENCL_FP16) + customizable &= ld.type != "Concat"; + + if (preferableTarget == DNN_TARGET_OPENCL || + preferableTarget == DNN_TARGET_OPENCL_FP16) + customizable &= ld.type != "Power"; + + if (preferableTarget == DNN_TARGET_OPENCL) + customizable &= ld.type != "Eltwise"; + + if (!customizable) + { + CV_LOG_DEBUG(NULL, "DNN/IE: NOT customizable!"); + addNgraphOutputs(ld); + net = Ptr(); + layer->preferableTarget = DNN_TARGET_CPU; + + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + LayerData& inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) + { + Ptr ieNode = inpNode.dynamicCast(); + CV_Assert(!ieNode.empty()); + ieNode->net->addOutput(ieNode); + } + } + continue; + } + } + ld.skip = true; // Initially skip all Inference Engine supported layers. + + // Create a new network if one of inputs from different Inference Engine graph. + std::vector> inputNodes; + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + // Layer_Test_ROIPooling.Accuracy has 2 inputs inpLD = 0, 0 -> has 4 inputNodes (input, rois, input, rois) + if (inputNodes.size() == ld.inputBlobsId.size()) + { + break; + } + LayerData& inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) + { + Ptr ieInpNode = inpNode.dynamicCast(); + CV_Assert(!ieInpNode.empty()); + CV_Assert(!ieInpNode->net.empty()); + if (ieInpNode->net == net && !fused) + { + inputNodes.push_back(inpNode); + continue; + } + } + + if (net.empty()) + { + net = Ptr(new InfEngineNgraphNet(*this)); + } + + if (!fused) + { + std::vector inputNames; + std::vector inputs; + + auto curr_pos = inpLd.consumers.begin(); + auto compare = [&ld](const LayerPin& lp) { return lp.lid == ld.id; }; + auto cons = curr_pos; + while ((cons = std::find_if(curr_pos, inpLd.consumers.end(), compare)) != + inpLd.consumers.end()) { + int cons_inp = cons->oid; + Ptr inpWrapper = inpLd.outputBlobsWrappers[cons_inp]. + dynamicCast(); + CV_Assert(!inpWrapper.empty()); + auto iter = std::find(inputNames.begin(), inputNames.end(), + inpWrapper->dataPtr->getName()); + if (iter == inputNames.end()) + { + inputNames.push_back(inpWrapper->dataPtr->getName()); + inputs.push_back(inpLd.outputBlobs[cons_inp]); + } + curr_pos = cons + 1; + } + + auto inps = net->setInputs(inputs, inputNames); + for (auto& inp : inps) + { + inputNodes.emplace_back(Ptr(new InfEngineNgraphNode(inp))); + } + } + } + + Ptr node; + if (!net.empty()) + { + if (fused) + { + bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 && + ld.inputBlobs[0]->data == ld.outputBlobs[0].data; + CV_Assert(inPlace); + node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend]; + ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers; + } + } + else + { + net = Ptr(new InfEngineNgraphNet(*this)); + } + + if (!fused) + { + CV_Assert(ld.inputBlobsId.size() == inputNodes.size()); + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + int lid = ld.inputBlobsId[i].lid; + int oid = ld.inputBlobsId[i].oid; + if (oid == 0 || lid == 0) + continue; + + auto ieInpNode = inputNodes[i].dynamicCast(); + const auto& ngraph_input_node = ieInpNode->node; + CV_LOG_DEBUG(NULL, "DNN/IE: bind output port " << lid << ":" << oid << " (" << ngraph_input_node->get_friendly_name() << ":" << ngraph_input_node->get_type_info().name << ")"); + + // Handle parameters from other subnets. Output port is not used in this case + if ((ngraph::op::is_parameter(ngraph_input_node) || ngraph::op::is_constant(ngraph_input_node)) && + ngraph_input_node->get_output_size() == 1) + { + inputNodes[i] = Ptr(new InfEngineNgraphNode(ngraph_input_node)); + continue; + } + CV_CheckLT((size_t)oid, ngraph_input_node->get_output_size(), ""); +#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4) + // FIXIT refactor ".initNgraph()" API to use Output + // WA: use Concat to emulate Identity operation with requested output port + auto oid_node = std::make_shared(ngraph::OutputVector { ngraph_input_node->output(oid) }, 0); + inputNodes[i] = Ptr(new InfEngineNgraphNode(oid_node)); +#elif INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3) + inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid))); +#else + inputNodes[i] = Ptr(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false))); +#endif + } + + if (layer->supportBackend(preferableBackend)) + { + CV_LOG_DEBUG(NULL, "DNN/IE: wrap layer " << ld.name << "@" << ld.type << " - outputs: " << ld.outputBlobsWrappers.size()); + node = layer->initNgraph(ld.inputBlobsWrappers, inputNodes); +#if 0 // FIXIT doesn't work with multiple outputs (set name is applied to the same node) + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + InferenceEngine::DataPtr dataPtr = ngraphDataNode(ld.outputBlobsWrappers[i]); + node.dynamicCast()->setName(dataPtr->getName()); + } +#else + node.dynamicCast()->setName(layer->name); +#endif + } + else + { + CV_LOG_DEBUG(NULL, "DNN/IE: layer is not supported: " << ld.name << "@" << ld.type); + node = Ptr(new InfEngineNgraphNode(inputNodes, + ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals)); + } + } + else if (node.empty()) + { + CV_LOG_DEBUG(NULL, "DNN/IE: node.empty() bypass..."); + continue; + } + + ld.backendNodes[preferableBackend] = node; + + Ptr ieNode = node.dynamicCast(); + CV_Assert(!ieNode.empty()); + ieNode->net = net; + + for (const auto& pin : blobsToKeep_) + { + if (pin.lid == ld.id) + { + ieNode->net->addOutput(ieNode); + break; + } + } + ieNode->net->setNodePtr(&ieNode->node); + + net->addBlobs(ld.inputBlobsWrappers); + net->addBlobs(ld.outputBlobsWrappers); + addNgraphOutputs(ld); + } + + // Initialize all networks. + for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) + { + LayerData& ld = it->second; + auto iter = ld.backendNodes.find(preferableBackend); + if (iter == ld.backendNodes.end()) + continue; + + Ptr& node = iter->second; + if (node.empty()) + continue; + + Ptr ieNode = node.dynamicCast(); + if (ieNode.empty()) + continue; + + CV_Assert(!ieNode->net.empty()); + + if (!ieNode->net->isInitialized()) + { + ieNode->net->addOutput(ieNode); + ieNode->net->createNet((Target)preferableTarget); + ld.skip = false; + } + } +} + +//} // Net::Impl + +/*static*/ +Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet) +{ + CV_TRACE_FUNCTION(); + + CV_TRACE_REGION("register_inputs"); + + std::vector inputsNames; + std::vector inp_shapes; + for (auto& it : ieNet.getInputsInfo()) + { + inputsNames.push_back(it.first); + std::vector dims = it.second->getTensorDesc().getDims(); + inp_shapes.push_back(std::vector(dims.begin(), dims.end())); + } + + Net cvNet; + cvNet.setInputsNames(inputsNames); + + // set empty input to determine input shapes + for (int inp_id = 0; inp_id < inputsNames.size(); ++inp_id) + { + cvNet.setInputShape(inputsNames[inp_id], inp_shapes[inp_id]); + } + + CV_TRACE_REGION_NEXT("backendNode"); + + Ptr backendNode; + { + auto fake_node = std::make_shared(ngraph::element::f32, ngraph::Shape {}); + Ptr backendNodeNGraph(new InfEngineNgraphNode(fake_node)); + backendNodeNGraph->net = Ptr(new InfEngineNgraphNet(*(cvNet.impl), ieNet)); + backendNode = backendNodeNGraph; + } + + CV_TRACE_REGION_NEXT("register_outputs"); + + auto ngraphFunction = ieNet.getFunction(); + CV_Assert(ngraphFunction); + std::vector> ngraphOperations = ngraphFunction->get_ops(); + + for (auto& it : ieNet.getOutputsInfo()) + { + CV_TRACE_REGION("output"); + const auto& outputName = it.first; + + LayerParams lp; + int lid = cvNet.addLayer(it.first, "", lp); + + LayerData& ld = cvNet.impl->layers[lid]; + + { + Ptr cvLayer(new NgraphBackendLayer(ieNet)); + cvLayer->name = outputName; + cvLayer->type = "_unknown_"; + + auto process_layer = [&](const std::string& name) -> bool + { + CV_TRACE_REGION("ngraph_function"); + for (const auto& op : ngraphOperations) + { + CV_Assert(op); + if (op->get_friendly_name() == name) + { + const std::string typeName = op->get_type_info().name; + cvLayer->type = typeName; + return true; + } + } + return false; + }; + + bool found = process_layer(outputName); + if (!found) + { + auto pos = outputName.rfind('.'); // cut port number: ".0" + if (pos != std::string::npos) + { + std::string layerName = outputName.substr(0, pos); + found = process_layer(layerName); + } + } + if (!found) + CV_LOG_WARNING(NULL, "DNN/IE: Can't determine output layer type: '" << outputName << "'"); + + ld.layerInstance = cvLayer; + ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode; + } + + for (int i = 0; i < inputsNames.size(); ++i) + cvNet.connect(0, i, lid, i); + } + + CV_TRACE_REGION_NEXT("finalize"); + + cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); + + cvNet.impl->skipInfEngineInit = true; + return cvNet; +} +#endif // HAVE_INF_ENGINE + +Net Net::readFromModelOptimizer(const String& xml, const String& bin) +{ + CV_TRACE_FUNCTION(); +#ifndef HAVE_INF_ENGINE + CV_UNUSED(xml); CV_UNUSED(bin); + CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer."); +#else + + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + InferenceEngine::Core& ie = getCore(""); + InferenceEngine::CNNNetwork ieNet = ie.ReadNetwork(xml, bin); + + return Impl::createNetworkFromModelOptimizer(ieNet); +#endif // HAVE_INF_ENGINE +} + +Net Net::readFromModelOptimizer(const std::vector& bufferModelConfig, const std::vector& bufferWeights) +{ + CV_TRACE_FUNCTION(); + CV_Assert(!bufferModelConfig.empty()); + CV_Assert(!bufferWeights.empty()); + return readFromModelOptimizer(bufferModelConfig.data(), bufferModelConfig.size(), + bufferWeights.data(), bufferWeights.size()); +} + +Net Net::readFromModelOptimizer( + const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize, + const uchar* bufferWeightsPtr, size_t bufferWeightsSize +) +{ + CV_TRACE_FUNCTION(); +#ifndef HAVE_INF_ENGINE + CV_UNUSED(bufferModelConfigPtr); CV_UNUSED(bufferWeightsPtr); + CV_UNUSED(bufferModelConfigSize); CV_UNUSED(bufferModelConfigSize); + CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer."); +#else + + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + + InferenceEngine::Core& ie = getCore(""); + + std::string model; model.assign((char*)bufferModelConfigPtr, bufferModelConfigSize); + + InferenceEngine::CNNNetwork ieNet; + try + { + InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C); + InferenceEngine::Blob::CPtr weights_blob = InferenceEngine::make_shared_blob(tensorDesc, (uint8_t*)bufferWeightsPtr, bufferWeightsSize); + + ieNet = ie.ReadNetwork(model, weights_blob); + } + catch (const std::exception& e) + { + CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what()); + } + + return Impl::createNetworkFromModelOptimizer(ieNet); +#endif // HAVE_INF_ENGINE +} + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/net_quantization.cpp b/modules/dnn/src/net_quantization.cpp new file mode 100644 index 000000000000..ef1857a8e265 --- /dev/null +++ b/modules/dnn/src/net_quantization.cpp @@ -0,0 +1,298 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "net_impl.hpp" + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +// FIXIT drop from inference API +static +void getQuantizationParams(const Mat& src, std::vector& scales, std::vector& zeropoints) +{ + const int qmin = -128; // INT8_MIN + const int qmax = 127; // INT8_MAX + + double rmin, rmax, sc, zp; + cv::minMaxIdx(src, &rmin, &rmax); + + // 0 must be present in the range [rmin, rmax] + rmin = std::min(rmin, 0.0); + rmax = std::max(rmax, 0.0); + + sc = (rmax == rmin) ? 1.0 : (rmax - rmin)/(qmax - qmin); + zp = qmin - (rmin/sc); + + scales.push_back((float)sc); + zeropoints.push_back((int)std::round(zp)); +} + +// FIXIT drop from inference API +Net Net::Impl::quantize(InputArrayOfArrays calibData, int inputsDtype, int outputsDtype) +{ + // Net can be quantized only once. + if (netWasQuantized) + CV_Error(Error::StsBadArg, "Cannot quantize a quantized net"); + + CV_CheckType(inputsDtype, inputsDtype == CV_32F || inputsDtype == CV_8S, "Input depth should be CV_32F or CV_8S"); + CV_CheckType(outputsDtype, outputsDtype == CV_32F || outputsDtype == CV_8S, "Output depth should be CV_32F or CV_8S"); + + bool originalFusion = fusion; + int prefBackend = preferableBackend; + int prefTarget = preferableTarget; + + // Disable fusions and use CPU backend to quantize net + setPreferableBackend(DNN_BACKEND_OPENCV); + setPreferableTarget(DNN_TARGET_CPU); + enableFusion(false); + + if (calibData.isMat()) + { + setInput(calibData.getMat(), /*name=*/"", /*scalefactor=*/1.0, /*mean=*/Scalar()); + } + else if (calibData.isMatVector()) + { + std::vector calibDataVec; + calibData.getMatVector(calibDataVec); + + std::vector inpNames = netInputLayer->outNames; + CV_CheckEQ(calibDataVec.size(), inpNames.size(), "Calibration data size should be equal to number of inputs"); + for (int i = 0; i < calibDataVec.size(); i++) + setInput(calibDataVec[i], inpNames[i], /*scalefactor=*/1.0, /*mean=*/Scalar()); + } + + std::vector outNames = getUnconnectedOutLayersNames(); + std::vector pins; + for (int i = 0; i < outNames.size(); i++) + pins.push_back(getPinByAlias(outNames[i])); + setUpNet(pins); + + // Compute scales and zeropoints for all the layers + std::vector > scales; + std::vector > zeropoints; + for (Impl::MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); it++) + { + LayerData& ld = it->second; + if (!ld.skip) + { + Ptr layer = ld.layerInstance; + std::vector inps(ld.inputBlobs.size()); + for (int i = 0; i < ld.inputBlobs.size(); ++i) + inps[i] = *ld.inputBlobs[i]; + layer->forward(inps, ld.outputBlobs, ld.internals); + } + + std::vector sc; + std::vector zp; + if (ld.type == "TanH") + { + sc.push_back(1.f/128); + zp.push_back(0); + } + else if (ld.type == "Sigmoid" || ld.type == "Softmax" || ld.type == "SoftMax") + { + if (ld.params.get("log_softmax", false)) + { + sc.push_back(16.f/256); + zp.push_back(127); + } + else + { + sc.push_back(1.f/256); + zp.push_back(-128); + } + } + else if (ld.type == "Split" || ld.type == "Slice" || ld.type == "Crop") + { + std::vector inp_sc; std::vector inp_zp; + getQuantizationParams(*ld.inputBlobs[0], inp_sc, inp_zp); + sc.assign(ld.outputBlobs.size(), inp_sc[0]); + zp.assign(ld.outputBlobs.size(), inp_zp[0]); + } + else + { + for (int i = 0; i < ld.outputBlobs.size(); i++) + getQuantizationParams(ld.outputBlobs[i], sc, zp); + } + scales.push_back(sc); + zeropoints.push_back(zp); + } + + // For some layers, the input and output scales/zeropoints must be equal so that rescaling of inputs + // is not needed during quantized inference. We start from the last layer and modify the layer's input scales/zeropoints + // TODO : Need a different approach. Current solution fails when 2 such layers have the same input layer + for (Impl::MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) + { + LayerData& ld = it->second; + // Layers with multiple outputs. Number of outputs is equal to number of inputs + if (ld.type == "Blank" || ld.type == "Dropout" || ld.type == "Identity" || ld.type == "Silence" || + ld.type == "Flatten" || ld.type == "Padding" || ld.type == "Permute" || ld.type == "Reshape" || + ld.type == "ReLU6" || ld.type == "Reorg" || ld.type == "ShuffleChannel" || ld.type == "Resize" || + (ld.type == "ReLU" && !ld.params.get("negative_slope", 0.f)) || /* ReLU with negative slope 0 */ + (ld.type == "Reduce" && (toLowerCase(ld.params.get("reduce")) == "max" || + toLowerCase(ld.params.get("reduce")) == "min"))) + { + for (int i = 0; i < ld.outputBlobs.size(); i++) + { + LayerPin &pin = ld.inputBlobsId[i]; + scales[pin.lid][pin.oid] = scales[ld.id][i]; + zeropoints[pin.lid][pin.oid] = zeropoints[ld.id][i]; + } + } + // Layers with multiple inputs and single output. + else if ((ld.type == "Pooling" && toLowerCase(ld.params.get("pool", "max")) == "max") /* Max Pooling */ || + (ld.type == "Eltwise" && toLowerCase(ld.params.get("operation", "sum")) == "max") /* Elementwise max */ || + ld.type == "Concat") + { + for (int i = 0; i < ld.inputBlobsId.size(); i++) + { + LayerPin &pin = ld.inputBlobsId[i]; + scales[pin.lid][pin.oid] = scales[ld.id][0]; + zeropoints[pin.lid][pin.oid] = zeropoints[ld.id][0]; + } + } + } + + // Create a new Net and add quantized layers to it. + Net dstNet_; + Net::Impl& dstNet = *(dstNet_.impl); + dstNet.netWasQuantized = true; + dstNet.setInputsNames(netInputLayer->outNames); + dstNet.setPreferableBackend(prefBackend); + dstNet.setPreferableTarget(prefTarget); + dstNet.enableFusion(originalFusion); + + for (Impl::MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); it++) + { + LayerData ld = it->second; + if (ld.id == 0) + { + LayerData &quantInpLd = dstNet.layers[0]; + quantInpLd.dtype = inputsDtype; + quantInpLd.params.set("scales", DictValue::arrayReal(scales[0].data(), scales[0].size())); + quantInpLd.params.set("zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size())); + continue; + } + + std::vector inpPins = ld.inputBlobsId; + // Fill input and output scales/zeropoints for the layer + std::vector > inp_out_sc(2); + std::vector > inp_out_zp(2); + for (int i = 0; i < inpPins.size(); i++) + { + LayerPin &pin = inpPins[i]; + inp_out_sc[0].push_back(scales[pin.lid][pin.oid]); + inp_out_zp[0].push_back(zeropoints[pin.lid][pin.oid]); + } + inp_out_sc[1] = scales[ld.id]; + inp_out_zp[1] = zeropoints[ld.id]; + + // Quantize layer + Ptr layer = ld.layerInstance; + if (layer->tryQuantize(inp_out_sc, inp_out_zp, ld.params)) + { + ld.type += "Int8"; + ld.dtype = CV_8S; + } + ld.params.set("scales", DictValue::arrayReal(inp_out_sc[1].data(), inp_out_sc[1].size())); + ld.params.set("zeropoints", DictValue::arrayInt(inp_out_zp[1].data(), inp_out_zp[1].size())); + + // Check and add quantize/dequantize node before layer + for (int i = 0; i < inpPins.size(); i++) + { + LayerPin &pin = inpPins[i]; + LayerData &inpLd = dstNet.getLayerData(getLayerName(pin.lid)); + pin.lid = inpLd.id; + if (inpLd.dtype != ld.dtype) + { + String layerName = (inpLd.dtype == CV_32F && ld.dtype == CV_8S) ? cv::format("quantize/%s/%d", inpLd.name.c_str(), pin.oid) + : cv::format("dequantize/%s/%d", inpLd.name.c_str(), pin.oid); + // Check if quantize/dequantize node for the input layer already exists + if (dstNet.getLayerId(layerName) >= 0) + { + pin.lid = dstNet.getLayerId(layerName); + pin.oid = 0; + } + else + { + LayerParams lp; + lp.set("scales", inp_out_sc[0][i]); + lp.set("zeropoints", inp_out_zp[0][i]); + lp.name = layerName; + lp.type = (inpLd.dtype == CV_32F && ld.dtype == CV_8S) ? "Quantize" : "Dequantize"; + int newLid = dstNet.addLayer(lp.name, lp.type, ld.dtype, lp); + dstNet.connect(pin.lid, pin.oid, newLid, 0); + pin.lid = newLid; pin.oid = 0; + } + } + } + + // Add quantized layer to Net and connect to its inputs. + int newLid = dstNet.addLayer(ld.name, ld.type, ld.dtype, ld.params); + for( int i = 0; i < inpPins.size(); i++ ) + dstNet.connect(inpPins[i].lid, inpPins[i].oid, newLid, i); + + // If the layer is a output layer, add quantize/dequantize node after it based on output's data type. + if (ld.requiredOutputs.size() == 0 && ld.dtype != outputsDtype) + { + LayerParams lp; + lp.set("scales", inp_out_sc[1][0]); + lp.set("zeropoints", inp_out_zp[1][0]); + lp.name = ((ld.dtype == CV_32F && outputsDtype == CV_8S) ? "quantize/" : "dequantize/") + ld.name; + lp.type = (ld.dtype == CV_32F && outputsDtype == CV_8S) ? "Quantize" : "Dequantize"; + dstNet.addLayerToPrev(lp.name, lp.type, outputsDtype, lp); + } + } + // Restore FP32 Net's backend, target and fusion + setPreferableBackend(prefBackend); + setPreferableTarget(prefTarget); + enableFusion(originalFusion); + return dstNet_; +} + +// FIXIT drop from inference API +void Net::Impl::getInputDetails(std::vector& scales, std::vector& zeropoints) /*const*/ +{ + if (!netWasQuantized) + CV_Error(Error::StsBadFunc, "Net isn't quantized"); + + LayerParams &lp = layers[0].params; + DictValue sc = lp.get("scales"); + DictValue zp = lp.get("zeropoints"); + + for (int i = 0; i < sc.size(); i++) + { + scales.push_back(sc.get(i)); + zeropoints.push_back(zp.get(i)); + } +} + +// FIXIT drop from inference API +void Net::Impl::getOutputDetails(std::vector& scales, std::vector& zeropoints) /*const*/ +{ + if (!netWasQuantized) + CV_Error(Error::StsBadFunc, "Net isn't quantized"); + + std::vector outLayerIds = getUnconnectedOutLayers(); + for (auto &lid : outLayerIds) + { + LayerParams &lp = layers[lid].params; + DictValue sc = lp.get("scales"); + DictValue zp = lp.get("zeropoints"); + + for (int i = 0; i < sc.size(); i++) + { + scales.push_back(sc.get(i)); + zeropoints.push_back(zp.get(i)); + } + } +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp index a2b28462e8ef..f06ff32dbe06 100644 --- a/modules/dnn/src/onnx/onnx_importer.cpp +++ b/modules/dnn/src/onnx/onnx_importer.cpp @@ -10,6 +10,8 @@ #include +#include + #include #undef CV_LOG_STRIP_LEVEL #define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE + 1 @@ -48,8 +50,12 @@ CV__DNN_INLINE_NS_BEGIN extern bool DNN_DIAGNOSTICS_RUN; +class ONNXLayerHandler; + class ONNXImporter { + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + opencv_onnx::ModelProto model_proto; struct LayerInfo { int layerId; @@ -73,6 +79,14 @@ class ONNXImporter void expandMid(const std::string& prefix, opencv_onnx::NodeProto& node_proto, const std::string& input, size_t n); void addNegation(const LayerParams& layerParams, opencv_onnx::NodeProto& node_proto, int input_id); + void lstm_extractConsts(LayerParams& layerParams, const opencv_onnx::NodeProto& lstm_proto, size_t idx, int* blobShape_, int size); + void lstm_add_reshape(const std::string& input_name, const std::string& output_name, int* layerShape, size_t n); + std::string lstm_add_slice(int index, const std::string& input_name, int* begin, int* end, size_t n); + std::string lstm_fix_dims(LayerParams& layerParams, const opencv_onnx::NodeProto& lstm_proto, + int batch_size, int num_directions, int hidden_size, bool need_y, const std::string& y_name, + const int index); + void lstm_add_transform(int num_directions, int batch_size, int hidden_size, + int index, const std::string& input_name, const std::string& output_name); public: ONNXImporter(Net& net, const char *onnxFile); ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer); @@ -80,7 +94,7 @@ class ONNXImporter void populateNet(); protected: - std::unique_ptr missingLayerHandler; + std::unique_ptr layerHandler; Net& dstNet; opencv_onnx::GraphProto graph_proto; @@ -94,15 +108,19 @@ class ONNXImporter std::map layer_id; typedef std::map::iterator IterLayerId_t; + typedef std::map::const_iterator ConstIterLayerId_t; void handleNode(const opencv_onnx::NodeProto& node_proto); private: + friend class ONNXLayerHandler; typedef void (ONNXImporter::*ONNXImporterNodeParser)(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); typedef std::map DispatchMap; typedef std::map DomainDispatchMap; DomainDispatchMap domain_dispatch_map; + std::string getLayerTypeDomain(const opencv_onnx::NodeProto& node_proto); + const DispatchMap& getDispatchMap(const opencv_onnx::NodeProto& node_proto); void buildDispatchMap_ONNX_AI(int opset_version); void buildDispatchMap_COM_MICROSOFT(int opset_version); @@ -112,6 +130,7 @@ class ONNXImporter void parseMaxUnpool (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseMaxPool (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseAveragePool (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); + void parseGlobalPool (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseReduce (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseSlice (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseSplit (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); @@ -156,6 +175,8 @@ class ONNXImporter void parseSoftMax (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseDetectionOutput (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); void parseCumSum (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); + void parseDepthToSpace (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); + void parseSimpleLayers (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto); // Domain: com.microsoft // URL: https://github.com/microsoft/onnxruntime/blob/master/docs/ContribOperators.md @@ -176,13 +197,53 @@ class ONNXImporter void parseOperatorSet(); const std::string str_domain_ai_onnx = "ai.onnx"; + + + bool useLegacyNames; + bool getParamUseLegacyNames() + { + bool param = utils::getConfigurationParameterBool("OPENCV_DNN_ONNX_USE_LEGACY_NAMES", false); + return param; + } + std::string extractNodeName(const opencv_onnx::NodeProto& node_proto); }; +class ONNXLayerHandler : public detail::LayerHandler +{ +public: + explicit ONNXLayerHandler(ONNXImporter* importer_); + + void fillRegistry(const opencv_onnx::GraphProto& net); + +protected: + ONNXImporter* importer; +}; + +ONNXLayerHandler::ONNXLayerHandler(ONNXImporter* importer_) : importer(importer_){} + +void ONNXLayerHandler::fillRegistry(const opencv_onnx::GraphProto &net) +{ + int layersSize = net.node_size(); + for (int li = 0; li < layersSize; li++) { + const opencv_onnx::NodeProto &node_proto = net.node(li); + const std::string& name = node_proto.output(0); + const std::string& type = node_proto.op_type(); + const std::string& layer_type_domain = importer->getLayerTypeDomain(node_proto); + const auto& dispatch = importer->getDispatchMap(node_proto); + if (dispatch.find(type) == dispatch.end()) + { + addMissing(name, cv::format("%s.%s", layer_type_domain.c_str(), type.c_str())); + } + } + printMissing(); +} + ONNXImporter::ONNXImporter(Net& net, const char *onnxFile) - : missingLayerHandler(DNN_DIAGNOSTICS_RUN ? new detail::LayerHandler() : nullptr) + : layerHandler(DNN_DIAGNOSTICS_RUN ? new ONNXLayerHandler(this) : nullptr) , dstNet(net) , onnx_opset(0) + , useLegacyNames(getParamUseLegacyNames()) { hasDynamicShapes = false; CV_Assert(onnxFile); @@ -203,9 +264,10 @@ ONNXImporter::ONNXImporter(Net& net, const char *onnxFile) } ONNXImporter::ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer) - : missingLayerHandler(DNN_DIAGNOSTICS_RUN ? new detail::LayerHandler() : nullptr) + : layerHandler(DNN_DIAGNOSTICS_RUN ? new ONNXLayerHandler(this) : nullptr) , dstNet(net) , onnx_opset(0) + , useLegacyNames(getParamUseLegacyNames()) { hasDynamicShapes = false; CV_LOG_DEBUG(NULL, "DNN/ONNX: processing in-memory ONNX model (" << sizeBuffer << " bytes)"); @@ -228,6 +290,7 @@ ONNXImporter::ONNXImporter(Net& net, const char* buffer, size_t sizeBuffer) populateNet(); } + inline void replaceLayerParam(LayerParams& layerParams, const String& oldKey, const String& newKey) { if (layerParams.has(oldKey)) { @@ -517,7 +580,11 @@ void ONNXImporter::addLayer(LayerParams& layerParams, int id = dstNet.addLayer(layerParams.name, layerParams.type, depth, layerParams); for (int i = 0; i < node_proto.output_size(); ++i) { - layer_id.insert(std::make_pair(node_proto.output(i), LayerInfo(id, i))); + const std::string& output_name = node_proto.output(i); + if (!output_name.empty()) + { + layer_id.insert(std::make_pair(output_name, LayerInfo(id, i))); + } } std::vector layerInpShapes, layerOutShapes, layerInternalShapes; @@ -540,7 +607,11 @@ void ONNXImporter::addLayer(LayerParams& layerParams, layer->getMemoryShapes(layerInpShapes, 0, layerOutShapes, layerInternalShapes); for (int i = 0; i < node_proto.output_size() && i < (int)layerOutShapes.size(); ++i) { - outShapes[node_proto.output(i)] = layerOutShapes[i]; + const std::string& output_name = node_proto.output(i); + if (!output_name.empty()) + { + outShapes[node_proto.output(i)] = layerOutShapes[i]; + } } } @@ -795,6 +866,7 @@ void ONNXImporter::populateNet() if (DNN_DIAGNOSTICS_RUN) { CV_LOG_INFO(NULL, "DNN/ONNX: start diagnostic run!"); + layerHandler->fillRegistry(graph_proto); } for(int li = 0; li < layersSize; li++) @@ -803,57 +875,97 @@ void ONNXImporter::populateNet() handleNode(node_proto); } + // register outputs + for (int i = 0; i < graph_proto.output_size(); ++i) + { + const std::string& output_name = graph_proto.output(i).name(); + if (output_name.empty()) + { + CV_LOG_ERROR(NULL, "DNN/ONNX: can't register output without name: " << i); + continue; + } + ConstIterLayerId_t layerIt = layer_id.find(output_name); + if (layerIt == layer_id.end()) + { + CV_LOG_ERROR(NULL, "DNN/ONNX: can't find layer for output name: '" << output_name << "'. Does model imported properly?"); + continue; + } + + const LayerInfo& li = layerIt->second; + int outputId = dstNet.registerOutput(output_name, li.layerId, li.outputId); CV_UNUSED(outputId); + // no need to duplicate message from engine: CV_LOG_DEBUG(NULL, "DNN/ONNX: registered output='" << output_name << "' with id=" << outputId); + } + CV_LOG_DEBUG(NULL, (DNN_DIAGNOSTICS_RUN ? "DNN/ONNX: diagnostic run completed!" : "DNN/ONNX: import completed!")); } -void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto) +std::string ONNXImporter::getLayerTypeDomain(const opencv_onnx::NodeProto& node_proto) { - CV_Assert(node_proto.output_size() >= 1); - const std::string& name = node_proto.output(0); - const std::string& layer_type = node_proto.op_type(); - const std::string& layer_type_domain = [&]() + if (!node_proto.has_domain()) + return str_domain_ai_onnx; + const std::string& domain = node_proto.domain(); + if (domain.empty()) + return str_domain_ai_onnx; + return domain; +} + +const ONNXImporter::DispatchMap& ONNXImporter::getDispatchMap(const opencv_onnx::NodeProto& node_proto) +{ + static DispatchMap empty_map; + const std::string& layer_type_domain = getLayerTypeDomain(node_proto); + auto it = domain_dispatch_map.find(layer_type_domain); + if (it == domain_dispatch_map.end()) + { + return empty_map; + } + + return it->second; +} + +std::string ONNXImporter::extractNodeName(const opencv_onnx::NodeProto& node_proto) +{ + // We need to rework DNN outputs API, this is a workaround for #21698 + if (node_proto.has_name() && !node_proto.name().empty()) { - if (!node_proto.has_domain()) - return str_domain_ai_onnx; - const std::string& domain = node_proto.domain(); - if (domain.empty()) - return str_domain_ai_onnx; - return domain; - }(); - const auto& dispatch = [&]() + if (useLegacyNames) + return node_proto.name(); + return cv::format("onnx_node!%s", node_proto.name().c_str()); + } + for (int i = 0; i < node_proto.output_size(); ++i) { - if (layer_type_domain != str_domain_ai_onnx) + const std::string& name = node_proto.output(i); + // There are two ways to leave an optional input or output unspecified: + // the first, available only for trailing inputs and outputs, is to simply not provide that input; + // the second method is to use an empty string in place of an input or output name. + if (!name.empty()) { - if (onnx_opset_map.find(layer_type_domain) == onnx_opset_map.end()) - { - CV_LOG_WARNING(NULL, "DNN/ONNX: processing node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: " - << cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str()) - << " from undeclared domain='" << layer_type_domain << "'" - ); - } - else - { - CV_LOG_DEBUG(NULL, "DNN/ONNX: processing node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: " - << cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str()) - << " from domain='" << layer_type_domain << "'" - ); - } - auto it = domain_dispatch_map.find(layer_type_domain); - if (it == domain_dispatch_map.end()) - { - CV_LOG_WARNING(NULL, "DNN/ONNX: missing dispatch map for domain='" << layer_type_domain << "'"); - return DispatchMap(); - } - return it->second; + if (useLegacyNames) + return name.c_str(); + return cv::format("onnx_node_output_%d!%s", i, name.c_str()); } - else - { - CV_LOG_DEBUG(NULL, "DNN/ONNX: processing node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: " - << cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str()) - ); - return domain_dispatch_map[str_domain_ai_onnx]; - } - }(); + } + CV_Error(Error::StsAssert, "Couldn't deduce Node name."); +} + +void ONNXImporter::handleNode(const opencv_onnx::NodeProto& node_proto) +{ + CV_Assert(node_proto.output_size() >= 1); + const std::string& name = extractNodeName(node_proto); + const std::string& layer_type = node_proto.op_type(); + const std::string& layer_type_domain = getLayerTypeDomain(node_proto); + const auto& dispatch = getDispatchMap(node_proto); + + CV_LOG_DEBUG(NULL, "DNN/ONNX: processing node with " << node_proto.input_size() << " inputs and " + << node_proto.output_size() << " outputs: " + << cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str()) + << cv::format(" from %sdomain='", onnx_opset_map.count(layer_type_domain) == 1 ? "" : "undeclared ") + << layer_type_domain << "'" + ); + + if (dispatch.empty()) + { + CV_LOG_WARNING(NULL, "DNN/ONNX: missing dispatch map for domain='" << layer_type_domain << "'"); + } LayerParams layerParams; try @@ -1003,165 +1115,185 @@ void ONNXImporter::parseAveragePool(LayerParams& layerParams, const opencv_onnx: addLayer(layerParams, node_proto); } -void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) +void ONNXImporter::parseGlobalPool(LayerParams &layerParams, const opencv_onnx::NodeProto &node_proto_) { opencv_onnx::NodeProto node_proto = node_proto_; const std::string& layer_type = node_proto.op_type(); + const std::string output_name = node_proto.output(0); CV_Assert(node_proto.input_size() == 1); layerParams.type = "Pooling"; String pool; - if (layer_type == "GlobalMaxPool" || layer_type == "ReduceMax") + if (layer_type == "GlobalMaxPool") pool = "MAX"; - else if (layer_type == "ReduceSum") - pool = "SUM"; - else + else if (layer_type == "GlobalAveragePool") pool = "AVE"; + else + CV_Error(Error::StsNotImplemented, "Unsupported Pooling type of " + layer_type + " operation."); + + CV_Assert(!layerParams.has("axes")); + layerParams.set("global_pooling", true); layerParams.set("pool", pool); - layerParams.set("global_pooling", !layerParams.has("axes")); + addLayer(layerParams, node_proto); +} + +void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) +{ + opencv_onnx::NodeProto node_proto = node_proto_; + const std::string& layer_type = node_proto.op_type(); + const std::string output_name = node_proto.output(0); + int depth = layerParams.get("depth", CV_32F); + + CV_Assert(node_proto.input_size() <= 2); + String reduceType; + + if (layer_type == "ReduceMax") + reduceType = "MAX"; + else if (layer_type == "ReduceMin") + reduceType = "MIN"; + else if (layer_type == "ReduceSum") + reduceType = "SUM"; + else if (layer_type == "ReduceSumSquare") + reduceType = "SUM_SQUARE"; + else if (layer_type == "ReduceProd") + reduceType = "PROD"; + else if (layer_type == "ReduceL1") + reduceType = "L1"; + else if (layer_type == "ReduceL2") + reduceType = "L2"; + else if (layer_type == "ReduceLogSum") + reduceType = "LOG_SUM"; + else if (layer_type == "ReduceLogSumExp") + reduceType = "LOG_SUM_EXP"; + else if (layer_type == "ReduceMean") + reduceType = "AVE"; + else + CV_Error(Error::StsNotImplemented, "Unsupported Pooling type of " + layer_type + " operation."); + + // The ReduceInt8 can only support "MAX" and "MIN". + if (depth == CV_8S) + { + CV_Assert(reduceType == "MAX" || reduceType == "MIN"); + } + + layerParams.type = (depth == CV_8S) ? "ReduceInt8" : "Reduce"; + layerParams.set("reduce", reduceType); bool keepdims = layerParams.get("keepdims", 1) == 1; - if (layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax")) + + if (layer_type == "ReduceSum" && node_proto.input_size() == 2) + { + // TODO support the opset 13 of ReduceSum. + // in opset 13, the ReduceSum has two input, it takes axes as input instead of attribute + // details:https://github.com/onnx/onnx/issues/3420#issuecomment-844295687 + CV_Error(Error::StsNotImplemented, "Unsupported " + layer_type + " operation of opset 13, please try to " + "re-export the onnx model with opset 11."); + } + + MatShape inpShape = outShapes[node_proto.input(0)]; + std::vector shouldDelete(inpShape.size(), false); + + if (layerParams.has("axes")) { - MatShape inpShape = outShapes[node_proto.input(0)]; DictValue axes = layerParams.get("axes"); - MatShape targetShape; - std::vector shouldDelete(inpShape.size(), false); - for (int i = 0; i < axes.size(); i++) { + for (int i = 0; i < axes.size(); i++) + { int axis = normalize_axis(axes.get(i), inpShape.size()); shouldDelete[axis] = true; } - for (int axis = 0; axis < inpShape.size(); ++axis){ - if (!shouldDelete[axis]) - targetShape.push_back(inpShape[axis]); - else if (keepdims) - targetShape.push_back(1); + } + else + { + for (int i = 0; i < inpShape.size(); i++) + { + shouldDelete[i] = true; } + } - if (inpShape.size() == 3 && axes.size() <= 2) + MatShape targetShape; + for (int i = 0; i < inpShape.size(); ++i) + { + if (!shouldDelete[i]) { - int axis = normalize_axis(axes.get(0), inpShape.size()); - CV_CheckNE(axis, 0, ""); - - LayerParams reshapeLp; - reshapeLp.name = layerParams.name + "/reshape"; - reshapeLp.type = "Reshape"; - CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end()); - reshapeLp.set("axis", 0); - reshapeLp.set("num_axes", 1); - int newShape[] = {1, -1}; - reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], 2)); + targetShape.push_back(inpShape[i]); + } + else if (keepdims) + { + targetShape.push_back(1); + } + } - opencv_onnx::NodeProto proto; - proto.add_input(node_proto.input(0)); - proto.add_output(reshapeLp.name); - addLayer(reshapeLp, proto); + if (targetShape.empty()) + targetShape.push_back(1); - LayerParams avgLp; - avgLp.name = layerParams.name + "/avg"; - avgLp.type = "Pooling"; - CV_Assert(layer_id.find(avgLp.name) == layer_id.end()); - avgLp.set("pool", pool); - if (axes.size() == 2) - { - CV_CheckEQ(normalize_axis(axes.get(0), inpShape.size()), 1, "Unsupported mode"); - CV_CheckEQ(normalize_axis(axes.get(1), inpShape.size()), 2, "Unsupported mode"); - avgLp.set("global_pooling", true); - } - else - { - avgLp.set(axis == 2 ? "global_pooling_w" : "global_pooling_h", true); - avgLp.set(axis == 2 ? "kernel_h" : "kernel_w", 1); - } + // Using PermuteLayer to move the deleted axis to the last. + std::vector perm(inpShape.size(), 0); + for (int i = 0; i < inpShape.size(); i++) + perm[i] = i; - node_proto.set_input(0, reshapeLp.name); - node_proto.set_output(0, avgLp.name); - addLayer(avgLp, node_proto); - } - else + bool needPermuet = false; + for (int i = 0; i < inpShape.size(); i++) + { + if (shouldDelete[i]) { - if (inpShape.size() != 4 && inpShape.size() != 5) - CV_Error(Error::StsNotImplemented, "Unsupported input shape of " + layer_type + " operation."); + // find the first not deleted element. + std::vector::iterator iter = std::find(shouldDelete.begin() + i, shouldDelete.end(), false); - CV_Assert(axes.size() <= inpShape.size() - 2); - std::vector kernel_size(inpShape.size() - 2, 1); - if (axes.size() == 1 && (normalize_axis(axes.get(0), inpShape.size()) <= 1)) + if (iter != shouldDelete.end()) { - int axis = normalize_axis(axes.get(0), inpShape.size()); - MatShape newShape = inpShape; - newShape[axis + 1] = total(newShape, axis + 1); - newShape.resize(axis + 2); - newShape.insert(newShape.begin(), 2 - axis, 1); - - LayerParams reshapeLp; - reshapeLp.type = "Reshape"; - reshapeLp.name = layerParams.name + "/reshape"; - CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end()); - reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], newShape.size())); - - node_proto.set_output(0, reshapeLp.name); - addLayer(reshapeLp, node_proto); - - kernel_size.resize(2); - kernel_size[0] = inpShape[axis]; - node_proto.set_input(0, node_proto.output(0)); - } - else - { - for (int i = 0; i < axes.size(); i++) { - int axis = normalize_axis(axes.get(i), inpShape.size()); - CV_Assert_N(axis >= 2 + i, axis < inpShape.size()); - kernel_size[axis - 2] = inpShape[axis]; - } - } + int index = iter - shouldDelete.begin(); - LayerParams poolLp = layerParams; - poolLp.name = layerParams.name + "/avg"; - CV_Assert(layer_id.find(poolLp.name) == layer_id.end()); - poolLp.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size())); + bool temp = shouldDelete[index]; + shouldDelete[index] = shouldDelete[i]; + shouldDelete[i] = temp; - node_proto.set_output(0, poolLp.name); - addLayer(poolLp, node_proto); + std::swap(perm[index], perm[i]); + std::swap(inpShape[index], inpShape[i]); + needPermuet = true; + } + else + break; } + } - layerParams.type = "Reshape"; - layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size())); + auto inputString= node_proto.input(0); + if (needPermuet) + { + LayerParams permuteLp; + permuteLp.name = layerParams.name + "/permute"; + permuteLp.type = (depth == CV_8S) ? "PermuteInt8" : "Permute"; + permuteLp.set("order", DictValue::arrayInt(perm.data(), perm.size())); - node_proto.set_input(0, node_proto.output(0)); - node_proto.set_output(0, layerParams.name); + opencv_onnx::NodeProto protoPermute; + protoPermute.add_input(inputString); + protoPermute.add_output(permuteLp.name); + addLayer(permuteLp, protoPermute); + inputString = permuteLp.name; } - else if (!layerParams.has("axes") && (layer_type == "ReduceMean" || layer_type == "ReduceSum" || layer_type == "ReduceMax")) + + std::vector deletedDims; + for (int axis_i = 0; axis_i < inpShape.size(); ++axis_i) { - IterShape_t shapeIt = outShapes.find(node_proto.input(0)); - CV_Assert(shapeIt != outShapes.end()); - const size_t dims = keepdims ? shapeIt->second.size() : 1; + if (shouldDelete[axis_i]) + { + deletedDims.push_back(inpShape[axis_i]); + } + } - LayerParams reshapeLp; - reshapeLp.name = layerParams.name + "/reshape"; - reshapeLp.type = "Reshape"; - CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end()); - int newShape[] = {1, 1, 1, -1}; - reshapeLp.set("dim", DictValue::arrayInt(&newShape[0], 4)); + LayerParams reduceLp = layerParams; + reduceLp.name = layerParams.name + "/reduce"; + CV_Assert(layer_id.find(reduceLp.name) == layer_id.end()); + reduceLp.set("deleted_dims", DictValue::arrayInt(&deletedDims[0], deletedDims.size())); - opencv_onnx::NodeProto proto; - proto.add_input(node_proto.input(0)); - proto.add_output(reshapeLp.name); - addLayer(reshapeLp, proto); - - LayerParams poolLp = layerParams; - poolLp.name = layerParams.name + "/pool"; - CV_Assert(layer_id.find(poolLp.name) == layer_id.end()); + node_proto.set_input(0, inputString); + node_proto.set_output(0, reduceLp.name); + addLayer(reduceLp, node_proto); - node_proto.set_input(0, reshapeLp.name); - node_proto.set_output(0, poolLp.name); - addLayer(poolLp, node_proto); + layerParams.type = (depth == CV_8S) ? "ReshapeInt8" : "Reshape"; + layerParams.set("dim", DictValue::arrayInt(&targetShape[0], targetShape.size())); - layerParams.type = "Reshape"; - std::vector targetShape(dims, 1); - layerParams.set("dim", DictValue::arrayInt(targetShape.data(), targetShape.size())); + node_proto.set_input(0, node_proto.output(0)); + node_proto.set_output(0, output_name); - node_proto.set_input(0, node_proto.output(0)); - node_proto.set_output(0, layerParams.name); - } addLayer(layerParams, node_proto); } @@ -1188,14 +1320,14 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP CV_Assert(starts.size() == ends.size()); if (axis > 0) { + CV_CheckLE(axis, 1024, "Slice layer can't have more than 1024 axes"); // arbitrary limit begin.resize(axis, 0); - end.resize(axis, -1); + end.resize(axis, INT_MAX); } for (int i = 0; i < starts.size(); ++i) { begin.push_back(starts.get(i)); - int finish = ends.get(i); - end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim + end.push_back(ends.get(i)); } } else { // inp_size > 1 CV_Assert(inp_size >= 3); @@ -1219,14 +1351,10 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP const int* ends = end_blob.ptr(); if (axis > 0) { begin.resize(axis, 0); - end.resize(axis, -1); + end.resize(axis, INT_MAX); } std::copy(starts, starts + start_blob.total(), std::back_inserter(begin)); - for (int i = 0; i < end_blob.total(); ++i) - { - int finish = ends[i]; - end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim - } + std::copy(ends, ends + end_blob.total(), std::back_inserter(end)); if (inp_size == 5) { CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end()); @@ -1250,7 +1378,7 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP { Mat flipped; flip(inp, flipped, 0); - addConstant(layerParams.name, flipped); + addConstant(node_proto.output(0), flipped); return; } } @@ -1270,7 +1398,7 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP inputs.push_back(inp); runLayer(layerParams, inputs, sliced); CV_Assert(sliced.size() == 1); - addConstant(layerParams.name, sliced[0]); + addConstant(node_proto.output(0), sliced[0]); return; } addLayer(layerParams, node_proto); @@ -1335,7 +1463,7 @@ void ONNXImporter::parseBias(LayerParams& layerParams, const opencv_onnx::NodePr Mat blob_1 = getBlob(node_proto, 1); CV_Assert(blob_0.size == blob_1.size); Mat output = isSub ? (blob_0 - blob_1) : (blob_0 + blob_1); - addConstant(layerParams.name, output); + addConstant(node_proto.output(0), output); return; } else if (is_const_0 || is_const_1) @@ -1451,100 +1579,293 @@ void ONNXImporter::parseConstant(LayerParams& layerParams, const opencv_onnx::No { CV_Assert(node_proto.input_size() == 0); CV_Assert(layerParams.blobs.size() == 1); - addConstant(layerParams.name, layerParams.blobs[0]); + addConstant(node_proto.output(0), layerParams.blobs[0]); } -void ONNXImporter::parseLSTM(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) +void transformBlobs(std::vector& blobs) { - opencv_onnx::NodeProto node_proto = node_proto_; - LayerParams lstmParams = layerParams; - lstmParams.name += "/lstm"; + Mat Wx = blobs[0]; + Mat Wh = blobs[1]; + Mat b = blobs[2]; + std::vector cudaWorkaround; + cudaWorkaround.push_back(Wx.clone()); + cudaWorkaround.push_back(Wh.clone()); + cudaWorkaround.push_back(b.clone()); - // https://pytorch.org/docs/stable/nn.html#lstm - CV_Assert(node_proto.input_size() >= 7); - Mat Wx = getBlob(node_proto, 1); - Mat Wh = getBlob(node_proto, 2); - Mat b = getBlob(node_proto, 3); + const int numHidden = Wh.size[2]; - const int numHidden = lstmParams.get("hidden_size"); - const int numDirs = Wx.size[0]; // Is 1 for forward only and 2 for bidirectional LSTM. - const int numFeatures = Wx.size[2]; - - Mat h0, c0; - if (!node_proto.input(5).empty()) { - h0 = getBlob(node_proto, 5); - h0 = h0.reshape(1, h0.size[0] * h0.size[1]); - } else { - // initial_h attribute can be empty in case of keras2onnx producer. fill it with zeros - h0 = Mat::zeros(numDirs * numFeatures, numHidden, CV_32FC1); - } - if (!node_proto.input(6).empty()) { - c0 = getBlob(node_proto, 6); - c0 = c0.reshape(1, c0.size[0] * c0.size[1]); - } else { - // initial_c attribute can be empty in case of keras2onnx producer. fill it with zeros - c0 = Mat::zeros(numDirs * numFeatures, numHidden, CV_32FC1); - } + Mat h0 = blobs[3]; + h0 = h0.reshape(1, h0.size[0] * h0.size[1]); + Mat c0 = blobs[4]; + c0 = c0.reshape(1, c0.size[0] * c0.size[1]); b = b.reshape(1, b.size[0]); Mat bx = b.colRange(0, b.cols / 2); Mat bh = b.colRange(b.cols / 2, b.cols); b = bx + bh; - // IFGO->IGFO - for (int k = 0; k < numDirs; ++k) + auto toIFOC = [] (Mat& in) { + int first = in.size[0]; + int rest = in.total() / first / 4; + // every weight blob contains weights for Input, Output, Forget and Cell gates + Mat m = in.reshape(1, {first, 4, rest}); + Mat outputGate = m.col(1); + Mat forgetGate = m.col(2); + std::swap_ranges(outputGate.begin(), outputGate.end(), forgetGate.begin()); + }; + + toIFOC(Wx); + toIFOC(Wh); + toIFOC(b); + + Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]); + Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]); + + blobs[0] = Wh; + blobs[1] = Wx; + blobs[2] = b.reshape(1, 1); + blobs[3] = h0; + blobs[4] = c0; + + if (blobs.size() == 5) { + // so that future patch removing copies can leave all indexing as is + blobs.insert(blobs.begin(), cudaWorkaround.begin(), cudaWorkaround.end()); + return; + } + + Mat P = blobs[5]; + blobs[5] = P.colRange(0, numHidden); + blobs[5] = blobs[5].clone().reshape(1, blobs[5].total()); // Single column. + blobs[5] = Mat::diag(blobs[5]); + + blobs.push_back(P.colRange(numHidden, 2 * numHidden)); + blobs[6] = blobs[6].clone().reshape(1, blobs[6].total()); // Single column. + blobs[6] = Mat::diag(blobs[6]); + + blobs.push_back(P.colRange(2 * numHidden, 3 * numHidden)); + blobs[7] = blobs[7].clone().reshape(1, blobs[7].total()); // Single column. + blobs[7] = Mat::diag(blobs[7]); + + // so that future patch removing copies can leave all indexing as is + blobs.insert(blobs.begin(), cudaWorkaround.begin(), cudaWorkaround.end()); +} + +void ONNXImporter::lstm_extractConsts(LayerParams& layerParams, const opencv_onnx::NodeProto& lstm_proto, size_t idx, int* blobShape_, int size) +{ + MatShape blobShape(blobShape_, blobShape_ + size); + Mat blob; + if (idx < lstm_proto.input_size() && !lstm_proto.input(idx).empty()) + { + blob = getBlob(lstm_proto, idx); + CV_Assert(shape(blob) == blobShape); + } + else + { + blob = Mat(blobShape, CV_32FC1, 0.); + } + layerParams.blobs.push_back(blob); +}; + +void ONNXImporter::lstm_add_reshape(const std::string& input_name, const std::string& output_name, int* layerShape, size_t n) +{ + LayerParams reshapeLp; + reshapeLp.name = cv::format("%s/reshape", input_name.c_str()); + reshapeLp.type = "Reshape"; + CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end()); + + reshapeLp.set("dim", DictValue::arrayInt(layerShape, n)); + + opencv_onnx::NodeProto reshape_proto; + reshape_proto.add_input(input_name); + reshape_proto.add_output(output_name); + addLayer(reshapeLp, reshape_proto); +}; + +std::string ONNXImporter::lstm_add_slice(int index, const std::string& input_name, int* begin, int* end, size_t n) +{ + LayerParams sliceLP; + sliceLP.name = cv::format("%s/slice_%d", input_name.c_str(), index); + sliceLP.type = "Slice"; + CV_Assert(layer_id.find(sliceLP.name) == layer_id.end()); + + sliceLP.set("begin", DictValue::arrayInt(begin, n)); + sliceLP.set("end", DictValue::arrayInt(end, n)); + sliceLP.set("axis", 0); + + opencv_onnx::NodeProto slice_proto; + slice_proto.add_input(input_name); + slice_proto.add_output(sliceLP.name); + addLayer(sliceLP, slice_proto); + + return slice_proto.output(0); +}; + +std::string ONNXImporter::lstm_fix_dims(LayerParams& layerParams, const opencv_onnx::NodeProto& lstm_proto, + int batch_size, int num_directions, int hidden_size, bool need_y, const std::string& y_name, + const int index) +{ + std::string reshape_output = cv::format("%s/reshape_%d", layerParams.name.c_str(), index); + + // reshape from Seq, Batch, Dirs*Hidden to Seq, Batch, Dirs, Hidden + // to not confuse reshape with dynamic first dimension, zero means 'leave unchanged' + int layerShape[] = {0, batch_size, num_directions, hidden_size}; + lstm_add_reshape(lstm_proto.output(index), reshape_output, layerShape, sizeof(layerShape) / sizeof(layerShape[0])); + + // permute from Seq, Batch, Dirs, Hidden to Seq, Dirs, Batch, Hidden + LayerParams permuteLP; + permuteLP.name = reshape_output + "/permute"; + permuteLP.type = "Permute"; + CV_Assert(layer_id.find(permuteLP.name) == layer_id.end()); + + int order[] = {0, 2, 1, 3}; + permuteLP.set("order", DictValue::arrayInt(order, 4)); + + opencv_onnx::NodeProto permute_proto; + permute_proto.add_input(reshape_output); + permute_proto.add_output((need_y && index == 0) ? y_name : static_cast(permuteLP.name)); + addLayer(permuteLP, permute_proto); + + return permute_proto.output(0); +}; + +void ONNXImporter::lstm_add_transform(int num_directions, int batch_size, int hidden_size, + int index, const std::string& input_name, const std::string& output_name) +{ + if (num_directions == 1) + { + // Slice: Yh = Y[-1, :, :, :] + int begin[] = {-1}, end[] = {INT_MAX}; + std::string slice_output = lstm_add_slice(index, input_name, begin, end, sizeof(begin) / sizeof(begin[0])); + + // Reshape: 1x1xBxH -> 1xBxH + int layerShape[] = {1, batch_size, hidden_size}; + lstm_add_reshape(slice_output, output_name, layerShape, sizeof(layerShape) / sizeof(layerShape[0])); + } + else { - float* WxData = Wx.ptr(k); - float* WhData = Wh.ptr(k); - float* biasData = b.ptr(k); - for (int j = 0; j < numHidden; ++j) + // Slice: SxDxBxH -> last sequence, first direction + int begin0[] = {-1, 0}, end0[] = {INT_MAX, 1}; + std::string slice_0 = lstm_add_slice(0, input_name, begin0, end0, sizeof(begin0) / sizeof(begin0[0])); + + // Slice: SxDxBxH -> first sequence, last direction + int begin1[] = {0, -1}, end1[] = {1, INT_MAX}; + std::string slice_1 = lstm_add_slice(1, input_name, begin1, end1, sizeof(begin1) / sizeof(begin1[0])); + + LayerParams concatLP; + concatLP.name = cv::format("%s/concat", input_name.c_str()); + concatLP.type = "Concat"; + CV_Assert(layer_id.find(concatLP.name) == layer_id.end()); + + concatLP.set("axis", 1); // 1x1xBxH -> 1x2xBxH + + opencv_onnx::NodeProto concat_proto; + concat_proto.add_input(slice_0); + concat_proto.add_input(slice_1); + concat_proto.add_output(concatLP.name); + addLayer(concatLP, concat_proto); + + // Reshape: 1x2xBxH -> 2xBxH + int layerShape[] = {2, batch_size, hidden_size}; + lstm_add_reshape(concat_proto.output(0), output_name, layerShape, sizeof(layerShape) / sizeof(layerShape[0])); + } +}; + +void ONNXImporter::parseLSTM(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) +{ + opencv_onnx::NodeProto lstm_proto = node_proto_; + layerParams.name += "/lstm"; + + // https://github.com/onnx/onnx/blob/main/docs/Operators.md#LSTM + CV_Assert(lstm_proto.input_size() >= 3); + for (size_t i = 1; i < 3; ++i) + { + const std::string& name = lstm_proto.input(i); + CV_Assert(!name.empty() && constBlobs.count(name) == 1); + } + + IterShape_t shapeIt = outShapes.find(lstm_proto.input(0)); + CV_Assert(shapeIt != outShapes.end()); + const MatShape x_shape = shapeIt->second; + + const int seq_length = x_shape[0]; + const int batch_size = x_shape[1]; + const int input_size = x_shape[2]; + const int hidden_size = layerParams.get("hidden_size"); + const int num_directions = constBlobs[lstm_proto.input(1)].size[0]; + + int w_size[] = {num_directions, 4*hidden_size, input_size}; + lstm_extractConsts(layerParams, lstm_proto, 1, w_size, sizeof(w_size) / sizeof(w_size[0])); // W + + int r_size[] = {num_directions, 4*hidden_size, hidden_size}; + lstm_extractConsts(layerParams, lstm_proto, 2, r_size, sizeof(r_size) / sizeof(r_size[0])); // R + + int b_size[] = {num_directions, 8*hidden_size}; + lstm_extractConsts(layerParams, lstm_proto, 3, b_size, sizeof(b_size) / sizeof(b_size[0])); // B + + if (4 < lstm_proto.input_size() && !lstm_proto.input(4).empty()) + { + Mat blob = getBlob(lstm_proto, 4); + CV_Assert(blob.total() == batch_size); + for (MatIterator_ it = blob.begin(); it != blob.end(); ++it) { - for (int i = 0; i < numFeatures; ++i) - { - std::swap(WxData[(numHidden + j) * numFeatures + i], - WxData[(numHidden * 2 + j) * numFeatures + i]); - } - for (int i = 0; i < numHidden; ++i) - { - std::swap(WhData[(numHidden + j) * numHidden + i], - WhData[(numHidden * 2 + j) * numHidden + i]); - } - std::swap(biasData[numHidden + j], biasData[numHidden * 2 + j]); + CV_Assert(*it == seq_length); } } - Wx = Wx.reshape(1, Wx.size[0] * Wx.size[1]); - Wh = Wh.reshape(1, Wh.size[0] * Wh.size[1]); + int h_size[] = {num_directions, batch_size, hidden_size}; + lstm_extractConsts(layerParams, lstm_proto, 5, h_size, sizeof(h_size) / sizeof(h_size[0])); // initial_h - lstmParams.blobs.resize(5); - lstmParams.blobs[0] = Wh; - lstmParams.blobs[1] = Wx; - lstmParams.blobs[2] = b; - lstmParams.blobs[3] = h0; - lstmParams.blobs[4] = c0; + int c_size[] = {num_directions, batch_size, hidden_size}; + lstm_extractConsts(layerParams, lstm_proto, 6, c_size, sizeof(c_size) / sizeof(c_size[0])); // initial_c - // read direction attribute - lstmParams.set("reverse", lstmParams.get("direction", "") == "reverse"); - lstmParams.set("bidirectional", lstmParams.get("direction", "") == "bidirectional"); + if (lstm_proto.input_size() > 7 && !lstm_proto.input(7).empty()) + { + layerParams.set("use_peephole", true); + int p_size[] = {num_directions, 3 * hidden_size}; + lstm_extractConsts(layerParams, lstm_proto, 7, p_size, sizeof(p_size) / sizeof(p_size[0])); // P + } - node_proto.set_output(0, lstmParams.name); // set different name so output shapes will be registered on that name - addLayer(lstmParams, node_proto); + transformBlobs(layerParams.blobs); - MatShape lstmShape = outShapes[node_proto.output(0)]; + layerParams.set("is_onnx", true); + layerParams.set("reverse", layerParams.get("direction", "") == "reverse"); + layerParams.set("bidirectional", layerParams.get("direction", "") == "bidirectional"); - // Add fake 1 as it is done in ONNX - lstmShape.insert(lstmShape.begin() + 1, 1); + bool need_yc = lstm_proto.output_size() > 2 && !lstm_proto.output(2).empty(); + bool need_yh = lstm_proto.output_size() > 1 && !lstm_proto.output(1).empty(); + bool need_y = lstm_proto.output_size() > 0 && !lstm_proto.output(0).empty(); - layerParams.type = "Reshape"; - layerParams.set("dim", DictValue::arrayInt(&lstmShape[0], lstmShape.size())); - node_proto.set_input(0, lstmParams.name); // redirect input to LSTM - node_proto.set_output(0, layerParams.name); // keep origin LSTM's name - addLayer(layerParams, node_proto); + const std::string y_name = need_y ? lstm_proto.output(0) : ""; + const std::string yh_name = need_yh ? lstm_proto.output(1) : ""; + const std::string yc_name = need_yc ? lstm_proto.output(2) : ""; + + layerParams.set("produce_cell_output", need_yc); + + lstm_proto.clear_output(); + if (need_y || need_yh) + { + // give random names to LSTMLayer's outputs because every output needs postprocessing + lstm_proto.add_output(cv::format("%s_y", layerParams.name.c_str())); + } + if (need_yc) + { + lstm_proto.add_output(yc_name); + } + + addLayer(layerParams, lstm_proto); + + std::string y_output = lstm_fix_dims(layerParams, lstm_proto, batch_size, num_directions, hidden_size, need_y, + y_name, 0); + if (need_yh) + { + lstm_add_transform(num_directions, batch_size, hidden_size, 0, y_output, yh_name); + } } void ONNXImporter::parseGRU(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) { opencv_onnx::NodeProto node_proto = node_proto_; + const std::string output_name = node_proto.output(0); LayerParams gruParams = layerParams; gruParams.name += "/gru"; @@ -1578,7 +1899,7 @@ void ONNXImporter::parseGRU(LayerParams& layerParams, const opencv_onnx::NodePro layerParams.type = "Reshape"; layerParams.set("dim", DictValue::arrayInt(&gruShape[0], gruShape.size())); node_proto.set_input(0, gruParams.name); // redirect input to GRU - node_proto.set_output(0, layerParams.name); // keep origin GRU's name + node_proto.set_output(0, output_name); // keep origin GRU's name addLayer(layerParams, node_proto); } @@ -1852,6 +2173,7 @@ void ONNXImporter::parseMul(LayerParams& layerParams, const opencv_onnx::NodePro { opencv_onnx::NodeProto node_proto = node_proto_; const std::string& layer_type = node_proto.op_type(); + const std::string output_name = node_proto.output(0); CV_Assert(node_proto.input_size() == 2); bool isDiv = layer_type == "Div"; @@ -1936,7 +2258,7 @@ void ONNXImporter::parseMul(LayerParams& layerParams, const opencv_onnx::NodePro if (inp0.dims == 1 && inp1.dims == 1) out.dims = 1; // to workaround dims == 1 - addConstant(layerParams.name, out); + addConstant(output_name, out); return; } else if (outShapes[node_proto.input(0)] == outShapes[node_proto.input(1)]) @@ -1952,7 +2274,7 @@ void ONNXImporter::parseMul(LayerParams& layerParams, const opencv_onnx::NodePro opencv_onnx::NodeProto proto; proto.add_input(node_proto.input(1)); proto.add_input(node_proto.input(0)); - proto.add_output(layerParams.name); + proto.add_output(output_name); node_proto = proto; } @@ -2131,7 +2453,7 @@ void ONNXImporter::parseTranspose(LayerParams& layerParams, const opencv_onnx::N std::vector inputs(1, getBlob(node_proto, 0)), transposed; runLayer(layerParams, inputs, transposed); CV_Assert(transposed.size() == 1); - addConstant(layerParams.name, transposed[0]); + addConstant(node_proto.output(0), transposed[0]); return; } addLayer(layerParams, node_proto); @@ -2183,7 +2505,7 @@ void ONNXImporter::parseSqueeze(LayerParams& layerParams, const opencv_onnx::Nod Mat inp = getBlob(node_proto, 0); Mat out = inp.reshape(1, outShape); out.dims = outShape.size(); // to workaround dims == 1 - addConstant(layerParams.name, out); + addConstant(node_proto.output(0), out); return; } int depth = layerParams.get("depth", CV_32F); @@ -2212,7 +2534,7 @@ void ONNXImporter::parseFlatten(LayerParams& layerParams, const opencv_onnx::Nod } Mat output = input.reshape(1, 2, out_size); - addConstant(layerParams.name, output); + addConstant(node_proto.output(0), output); return; } IterShape_t shapeIt = outShapes.find(node_proto.input(0)); @@ -2284,7 +2606,7 @@ void ONNXImporter::parseUnsqueeze(LayerParams& layerParams, const opencv_onnx::N } Mat out = input.reshape(0, dims); - addConstant(layerParams.name, out); + addConstant(node_proto.output(0), out); return; } @@ -2323,6 +2645,7 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node CV_CheckEQ(node_proto.input_size(), 2, ""); const std::string& input0 = node_proto.input(0); const std::string& input1 = node_proto.input(1); + const std::string output_name = node_proto.output(0); Mat newShapeMat = getBlob(input1); MatShape targetShape(newShapeMat.ptr(), newShapeMat.ptr() + newShapeMat.total()); @@ -2385,14 +2708,20 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node if (!haveVariables) { - if (broadcast_axes.size() != 1) + if (broadcast_axes.size() > 1) CV_Error(Error::StsNotImplemented, "Expand op doesn't support multiple axes for constant input"); + if (broadcast_axes.empty()) + { + addConstant(output_name, getBlob(node_proto, 0)); + return; + } + Mat input = getBlob(node_proto, 0); input = input.reshape(0, total(inpShape, 0, broadcast_axes[0])); Mat output = cv::repeat(input, 1, targetShape[broadcast_axes[0]]); output = output.reshape(0, targetShape); - addConstant(layerParams.name, output); + addConstant(output_name, output); return; } @@ -2422,7 +2751,7 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node layerParams.set("axis", broadcast_axes[0]); layerParams.type = "Concat"; - node_proto.set_output(0, layerParams.name); + node_proto.set_output(0, output_name); } else if (broadcast_axes.empty()) { @@ -2448,7 +2777,7 @@ void ONNXImporter::parseReshape(LayerParams& layerParams, const opencv_onnx::Nod if (layer_id.find(node_proto.input(0)) == layer_id.end()) { std::vector inputs(1, getBlob(node_proto, 0)), outputs; runLayer(layerParams, inputs, outputs); - addConstant(layerParams.name, outputs[0]); + addConstant(node_proto.output(0), outputs[0]); return; } } @@ -2462,7 +2791,7 @@ void ONNXImporter::parseReshape(LayerParams& layerParams, const opencv_onnx::Nod if (layer_id.find(node_proto.input(0)) == layer_id.end()) { Mat input = getBlob(node_proto, 0); Mat out = input.reshape(0, dim); - addConstant(layerParams.name, out); + addConstant(node_proto.output(0), out); return; } replaceLayerParam(layerParams, "shape", "dim"); @@ -2514,11 +2843,9 @@ void ONNXImporter::parseShape(LayerParams& layerParams, const opencv_onnx::NodeP if (isDynamicShape) { CV_LOG_ERROR(NULL, "DNN/ONNX(Shape): dynamic 'zero' shapes are not supported, input " << toString(inpShape, node_proto.input(0))); - // FIXIT repair assertion - // Disabled to pass face detector tests from #20422 - // CV_Assert(!isDynamicShape); // not supported + CV_Assert(!isDynamicShape); // not supported } - addConstant(layerParams.name, shapeMat); + addConstant(node_proto.output(0), shapeMat); } void ONNXImporter::parseCast(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) @@ -2542,7 +2869,7 @@ void ONNXImporter::parseCast(LayerParams& layerParams, const opencv_onnx::NodePr Mat dst; blob.convertTo(dst, type); dst.dims = blob.dims; - addConstant(layerParams.name, dst); + addConstant(node_proto.output(0), dst); return; } else @@ -2569,7 +2896,7 @@ void ONNXImporter::parseConstantFill(LayerParams& layerParams, const opencv_onnx for (int i = 0; i < inpShape.size(); i++) CV_CheckGT(inpShape[i], 0, ""); Mat tensor(inpShape.size(), &inpShape[0], depth, Scalar(fill_value)); - addConstant(layerParams.name, tensor); + addConstant(node_proto.output(0), tensor); } void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) @@ -2597,7 +2924,7 @@ void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::Node } else { out.dims = 1; } - addConstant(layerParams.name, out); + addConstant(node_proto.output(0), out); return; } else @@ -2610,7 +2937,7 @@ void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::Node sliceLp.type = "Slice"; sliceLp.name = inpShape.size() > 1 ? layerParams.name + "/slice" : layerParams.name; std::vector begin(inpShape.size(), 0); - std::vector end(inpShape.size(), -1); + std::vector end(inpShape.size(), INT_MAX); begin[axis] = index; end[axis] = index + 1; @@ -2691,7 +3018,7 @@ void ONNXImporter::parseConcat(LayerParams& layerParams, const opencv_onnx::Node runLayer(layerParams, inputs, concatenated); CV_Assert(concatenated.size() == 1); - addConstant(layerParams.name, concatenated[0]); + addConstant(node_proto.output(0), concatenated[0]); return; } else @@ -2740,16 +3067,20 @@ void ONNXImporter::parseResize(LayerParams& layerParams, const opencv_onnx::Node // opset-10: input = [X, scales] // opset-11: input = [X, roi, scales] or [x, roi, scales, sizes] + // opset-13: may have empty input, [X, "", "", sizes] or [x, "", scales] int scalesInputId = node_proto.input_size() == 2 ? 1 : 2; + const std::string& scale_name = node_proto.input(scalesInputId); + Mat scales; + if(!scale_name.empty()) + scales = getBlob(node_proto, scalesInputId); - Mat scales = getBlob(node_proto, scalesInputId); if (!scales.empty()) { CV_CheckEQ(scales.total(), (size_t)4, "HCHW layout is expected"); layerParams.set("zoom_factor_y", scales.at(2)); layerParams.set("zoom_factor_x", scales.at(3)); } - else if (node_proto.input_size() >= 4) // opset-11 + else if (node_proto.input_size() >= 4) // opset-11 [x, roi, scales, sizes] or opset-13: input = [X, "", "", sizes] { const std::string& inputSizes = node_proto.input(3); if (constBlobs.find(inputSizes) != constBlobs.end()) @@ -2871,6 +3202,94 @@ void ONNXImporter::parseCumSum(LayerParams& layerParams, const opencv_onnx::Node addLayer(layerParams, node_proto); } +void ONNXImporter::parseDepthToSpace(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) +{ + // We parse "DepthToSpace" and "SpaceToDepth" in this function. + opencv_onnx::NodeProto node_proto = node_proto_; + const std::string& layer_type = node_proto.op_type(); + CV_Assert(layer_type == "DepthToSpace" || layer_type == "SpaceToDepth"); + + // Get blocksize + CV_Assert(layerParams.has("blocksize")); + int blocksize = layerParams.get("blocksize"); + CV_Assert(blocksize > 0); + + // Get mode, only for "DepthToSpace" + std::string modeType = layerParams.get("mode", "DCR"); + + MatShape inpShape = outShapes[node_proto.input(0)]; + CV_Assert(inpShape.size() == 4); + int N = inpShape[0], C = inpShape[1], H = inpShape[2], W = inpShape[3]; + + // Implement DepthToSpace and SpaceToDepth by the Reshape and Permute layer. + std::array shape0, perm; + std::array shape1; + + if (layer_type == "DepthToSpace") + { + if (modeType == "DCR") + { + shape0 = {N, blocksize, blocksize, C/(blocksize * blocksize), H, W}; + perm = {0, 3, 4, 1, 5, 2}; + shape1 = {N, C/(blocksize * blocksize), H * blocksize, W * blocksize}; + } + else if (modeType == "CRD") + { + shape0 = {N, C/(blocksize * blocksize), blocksize, blocksize, H, W}; + perm = {0, 1, 4, 2, 5, 3}; + shape1 = {N, C/(blocksize * blocksize), H * blocksize, W * blocksize}; + } + else + CV_Error(Error::StsNotImplemented, "The mode of " + modeType + " in " + layer_type + " Layer is not supported"); + } + else // SpaceToDepth + { + shape0 = {N, C, H/blocksize, blocksize, W/blocksize, blocksize}; + perm = {0, 3, 5, 1, 2, 4}; + shape1 = {N, C * blocksize * blocksize, H/blocksize, W/blocksize}; + } + + // Step1: Reshape + LayerParams reshapeLp; + reshapeLp.name = layerParams.name + "/reshape"; + reshapeLp.type = "Reshape"; + CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end()); + reshapeLp.set("dim", DictValue::arrayInt(shape0.data(), shape0.size())); + + opencv_onnx::NodeProto protoReshape; + protoReshape.add_input(node_proto.input(0)); + protoReshape.add_output(reshapeLp.name); + addLayer(reshapeLp, protoReshape); + + // Step2: Transpose + LayerParams permuteLp; + permuteLp.name = layerParams.name + "/permute"; + permuteLp.type = "Permute"; + CV_Assert(layer_id.find(permuteLp.name) == layer_id.end()); + permuteLp.set("order", DictValue::arrayInt(perm.data(), perm.size())); + + opencv_onnx::NodeProto protoPermute; + protoPermute.add_input(reshapeLp.name); + protoPermute.add_output(permuteLp.name); + addLayer(permuteLp, protoPermute); + + // Step3: Reshape + layerParams.type = "Reshape"; + layerParams.set("dim", DictValue::arrayInt(shape1.data(), shape1.size())); + + node_proto.set_input(0, permuteLp.name); + addLayer(layerParams, node_proto); +} + +void ONNXImporter::parseSimpleLayers(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) +{ + for (int j = 0; j < node_proto.input_size(); j++) { + if (layer_id.find(node_proto.input(j)) == layer_id.end()) + layerParams.blobs.push_back(getBlob(node_proto, j)); + } + addLayer(layerParams, node_proto); +} + void ONNXImporter::parseCustomLayer(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) { const std::string& name = layerParams.name; @@ -2886,20 +3305,11 @@ void ONNXImporter::parseCustomLayer(LayerParams& layerParams, const opencv_onnx: } } - CV_LOG_INFO(NULL, "DNN/ONNX: unknown node type, try using custom handler for node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: " + CV_LOG_IF_INFO(NULL, !LayerFactory::isLayerRegistered(layer_type), "DNN/ONNX: unknown node type, try using custom handler for node with " << node_proto.input_size() << " inputs and " << node_proto.output_size() << " outputs: " << cv::format("[%s]:(%s)", layer_type.c_str(), name.c_str()) ); - if (missingLayerHandler) - { - missingLayerHandler->addMissing(layerParams.name, layerParams.type); - } - - for (int j = 0; j < node_proto.input_size(); j++) { - if (layer_id.find(node_proto.input(j)) == layer_id.end()) - layerParams.blobs.push_back(getBlob(node_proto, j)); - } - addLayer(layerParams, node_proto); + parseSimpleLayers(layerParams, node_proto); } void ONNXImporter::parseQuantDequant(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) @@ -2918,14 +3328,54 @@ void ONNXImporter::parseQuantDequant(LayerParams& layerParams, const opencv_onnx addLayer(layerParams, node_proto); } -void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) +void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_) { + opencv_onnx::NodeProto node_proto = node_proto_; int ninputs = node_proto.input_size(); CV_Assert(ninputs == 8 || ninputs == 9); Mat inp_sc = getBlob(node_proto, 1); Mat inp_zp = getBlob(node_proto, 2); + if (layerParams.has("pad")) + { + bool asymmetricPadding = false; + DictValue pads = layerParams.get("pad"); + const int dims = pads.size() / 2; + + for (int i = 0; i < dims; ++i) + { + if (pads.get(i) != pads.get(i + dims)) + { + asymmetricPadding = true; + break; + } + } + if (asymmetricPadding && pads.size() == 4) + { + layerParams.erase("pad"); + std::vector paddings(4, 0); + for (int i = 0; i < dims; ++i) + { + paddings.push_back(pads.get(i)); + paddings.push_back(pads.get(dims + i)); + } + LayerParams padLp; + padLp.name = layerParams.name + "/pad"; + padLp.type = "PaddingInt8"; + padLp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size())); + padLp.set("depth", CV_8S); + padLp.set("value", inp_zp.at(0)); + + opencv_onnx::NodeProto proto; + proto.add_input(node_proto.input(0)); + proto.add_output(padLp.name); + + addLayer(padLp, proto); + node_proto.set_input(0, padLp.name); + } + } + Mat weights = getBlob(node_proto, 3); int outCn = weights.size[0]; Mat w_scale = getBlob(node_proto, 4); @@ -2947,6 +3397,7 @@ void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeP layerParams.type = "ConvolutionInt8"; layerParams.set("num_output", outCn); layerParams.set("input_zeropoint", inp_zp.at(0)); + layerParams.set("input_scale",inp_sc.at(0)); layerParams.blobs.push_back(weights); layerParams.blobs.push_back(biasFused); layerParams.blobs.push_back(outputMultiplier); @@ -2986,6 +3437,9 @@ void ONNXImporter::parseQMatMul(LayerParams& layerParams, const opencv_onnx::Nod layerParams.type = "InnerProductInt8"; layerParams.set("num_output", outCn); layerParams.set("axis", firstInpDims - secondInpDims + 1); + layerParams.set("input_scale", inp_sc.at(0)); + layerParams.set("input_zeropoint", inp_zp.at(0)); + layerParams.blobs.push_back(weights); layerParams.blobs.push_back(bias); layerParams.blobs.push_back(outputMultiplier); @@ -3056,6 +3510,7 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No rescaleParams.set("depth", CV_8S); rescaleParams.set("scale", scale); rescaleParams.set("shift", shift); + rescaleParams.set("isEltwise", true); addLayer(rescaleParams, node_proto); return; } @@ -3104,7 +3559,6 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No Mat blob_dequantized; blob.convertTo(blob_dequantized, CV_32F, inp_scales[1], -(inp_scales[1] * inp_zps[1])); layerParams.blobs.push_back(blob_dequantized); - layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size())); } } } @@ -3119,9 +3573,9 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No { layerParams.type = "ScaleInt8"; layerParams.set("bias_term", op == "sum"); - layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size())); } + layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size())); layerParams.set("input_zeropoints", DictValue::arrayInt(inp_zps.data(), inp_zps.size())); addLayer(layerParams, node_proto); } @@ -3147,6 +3601,9 @@ void ONNXImporter::parseQLeakyRelu(LayerParams& layerParams, const opencv_onnx:: } layerParams.type = "ReLUInt8"; + layerParams.set("input_scale", inp_sc); + layerParams.set("input_zeropoint", inp_zp); + layerParams.set("slope", slope); layerParams.blobs.push_back(lookUpTable); addLayer(layerParams, node_proto); } @@ -3171,6 +3628,8 @@ void ONNXImporter::parseQSigmoid(LayerParams& layerParams, const opencv_onnx::No } layerParams.type = "SigmoidInt8"; + layerParams.set("input_scale", inp_sc); + layerParams.set("input_zeropoint", inp_zp); layerParams.blobs.push_back(lookUpTable); addLayer(layerParams, node_proto); } @@ -3224,6 +3683,7 @@ void ONNXImporter::parseQConcat(LayerParams& layerParams, const opencv_onnx::Nod rescaleParams.set("depth", CV_8S); rescaleParams.set("scale", scale); rescaleParams.set("shift", shift); + rescaleParams.set("isEltwise", false); opencv_onnx::NodeProto proto; proto.add_input(node_proto.input(i)); @@ -3303,8 +3763,10 @@ void ONNXImporter::buildDispatchMap_ONNX_AI(int opset_version) dispatch["MaxUnpool"] = &ONNXImporter::parseMaxUnpool; dispatch["MaxPool"] = &ONNXImporter::parseMaxPool; dispatch["AveragePool"] = &ONNXImporter::parseAveragePool; - dispatch["GlobalAveragePool"] = dispatch["GlobalMaxPool"] = dispatch["ReduceMean"] = dispatch["ReduceSum"] = - dispatch["ReduceMax"] = &ONNXImporter::parseReduce; + dispatch["GlobalAveragePool"] = dispatch["GlobalMaxPool"] = &ONNXImporter::parseGlobalPool; + dispatch["ReduceMax"] = dispatch["ReduceMin"] = dispatch["ReduceMean"] = dispatch["ReduceSum"] = dispatch["ReduceMax"] = + dispatch["ReduceMin"] = dispatch["ReduceSumSquare"] = dispatch["ReduceProd"] = dispatch["ReduceL1"] = + dispatch["ReduceL2"] = dispatch["ReduceLogSum"] = dispatch["ReduceLogSumExp"] = &ONNXImporter::parseReduce; dispatch["Slice"] = &ONNXImporter::parseSlice; dispatch["Split"] = &ONNXImporter::parseSplit; dispatch["Add"] = dispatch["Sum"] = dispatch["Sub"] = &ONNXImporter::parseBias; @@ -3348,6 +3810,16 @@ void ONNXImporter::buildDispatchMap_ONNX_AI(int opset_version) dispatch["SoftMax"] = dispatch["LogSoftmax"] = &ONNXImporter::parseSoftMax; dispatch["DetectionOutput"] = &ONNXImporter::parseDetectionOutput; dispatch["CumSum"] = &ONNXImporter::parseCumSum; + dispatch["SpaceToDepth"] = dispatch["DepthToSpace"] = &ONNXImporter::parseDepthToSpace; + + std::vector simpleLayers{"Acos", "Acosh", "Asin", "Asinh", "Atan", "Atanh", "Ceil", "Celu", "Cos", + "Cosh", "Dropout", "Erf", "Exp", "Floor", "HardSigmoid", "HardSwish", + "Identity", "Log", "Round", "Reciprocal", "Selu", "Sign", "Sigmoid", "Sin", "Sinh", "Softmax", + "Softplus", "Softsign", "Shrink", "Sqrt", "Tan", "ThresholdedRelu"}; + for (const auto& name : simpleLayers) + { + dispatch[name] = &ONNXImporter::parseSimpleLayers; + } // ai.onnx: opset 10+ dispatch["QuantizeLinear"] = dispatch["DequantizeLinear"] = &ONNXImporter::parseQuantDequant; diff --git a/modules/dnn/src/onnx/opencv-onnx.proto b/modules/dnn/src/onnx/opencv-onnx.proto index b24220adb9a3..8dd69cb2d96b 100644 --- a/modules/dnn/src/onnx/opencv-onnx.proto +++ b/modules/dnn/src/onnx/opencv-onnx.proto @@ -61,7 +61,7 @@ enum Version { // The version field is always serialized and we will use it to store the // version that the graph is generated from. This helps us set up version // control. - // For the IR, we are using simple numbers starting with with 0x00000001, + // For the IR, we are using simple numbers starting with 0x00000001, // which was the version we published on Oct 10, 2017. IR_VERSION_2017_10_10 = 0x0000000000000001; diff --git a/modules/dnn/src/op_cuda.cpp b/modules/dnn/src/op_cuda.cpp new file mode 100644 index 000000000000..a1b588ecfb82 --- /dev/null +++ b/modules/dnn/src/op_cuda.cpp @@ -0,0 +1,106 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#ifdef HAVE_CUDA +#include "op_cuda.hpp" +#include "cuda4dnn/init.hpp" +#include "net_impl.hpp" + +namespace cv { namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +void Net::Impl::initCUDABackend(const std::vector& blobsToKeep_) +{ + CV_Assert(preferableBackend == DNN_BACKEND_CUDA); + + if (!cudaInfo) /* we need to check only once */ + cuda4dnn::checkVersions(); + + if (cuda4dnn::getDeviceCount() <= 0) + CV_Error(Error::StsError, "No CUDA capable device found."); + + if (cuda4dnn::getDevice() < 0) + CV_Error(Error::StsError, "No CUDA capable device selected."); + + if (!cuda4dnn::isDeviceCompatible()) + CV_Error(Error::GpuNotSupported, "OpenCV was not built to work with the selected device. Please check CUDA_ARCH_PTX or CUDA_ARCH_BIN in your build configuration."); + + if (preferableTarget == DNN_TARGET_CUDA_FP16 && !cuda4dnn::doesDeviceSupportFP16()) + { + CV_LOG_WARNING(NULL, "The selected CUDA device does not support FP16 target; switching to FP32 target."); + preferableTarget = DNN_TARGET_CUDA; + } + + if (!cudaInfo) + { + cuda4dnn::csl::CSLContext context; + context.stream = cuda4dnn::csl::Stream(true); + context.cublas_handle = cuda4dnn::csl::cublas::Handle(context.stream); + context.cudnn_handle = cuda4dnn::csl::cudnn::Handle(context.stream); + + auto d2h_stream = cuda4dnn::csl::Stream(true); // stream for background D2H data transfers + cudaInfo = std::unique_ptr(new CudaInfo_t(std::move(context), std::move(d2h_stream))); + } + + cudaInfo->workspace = cuda4dnn::csl::Workspace(); // release workspace memory if any + + for (auto& layer : layers) + { + auto& ld = layer.second; + if (ld.id == 0) + { + for (auto& wrapper : ld.inputBlobsWrappers) + { + auto cudaWrapper = wrapper.dynamicCast(); + cudaWrapper->setStream(cudaInfo->context.stream, cudaInfo->d2h_stream); + } + } + + for (auto& wrapper : ld.outputBlobsWrappers) + { + auto cudaWrapper = wrapper.dynamicCast(); + cudaWrapper->setStream(cudaInfo->context.stream, cudaInfo->d2h_stream); + } + } + + for (auto& layer : layers) + { + auto& ld = layer.second; + auto& layerInstance = ld.layerInstance; + + if (!layerInstance->supportBackend(DNN_BACKEND_CUDA)) + { + std::ostringstream os; + os << "CUDA backend will fallback to the CPU implementation for the layer \"" << ld.name + << "\" of type " << ld.type << '\n'; + CV_LOG_INFO(NULL, os.str().c_str()); + continue; + } + + /* we make a copy so that `initCUDA` doesn't modify `cudaInfo->context` */ + auto context = cudaInfo->context; + auto node = layerInstance->initCUDA(&context, ld.inputBlobsWrappers, ld.outputBlobsWrappers); + ld.backendNodes[DNN_BACKEND_CUDA] = node; + + auto cudaNode = node.dynamicCast(); + cudaInfo->workspace.require(cudaNode->get_workspace_memory_in_bytes()); + } + + if (blobsToKeep_.size() > 1) + { + for (const auto& pin : blobsToKeep_) + { + LayerData& ld = layers[pin.lid]; + ld.cudaD2HBackgroundTransfers.push_back(pin.oid); + } + } +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn +#endif // HAVE_CUDA diff --git a/modules/dnn/src/op_halide.cpp b/modules/dnn/src/op_halide.cpp index c96971bc6aac..653de36146cc 100644 --- a/modules/dnn/src/op_halide.cpp +++ b/modules/dnn/src/op_halide.cpp @@ -8,15 +8,135 @@ #include "precomp.hpp" #include #include "op_halide.hpp" +#include "net_impl.hpp" #ifdef HAVE_HALIDE +#include "halide_scheduler.hpp" + #include #endif // HAVE_HALIDE -namespace cv +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +void Net::Impl::setHalideScheduler(const String& scheduler) { -namespace dnn + halideConfigFile = scheduler; +} + + +#ifdef HAVE_HALIDE + + +void Net::Impl::compileHalide() { + CV_TRACE_FUNCTION(); + + CV_Assert(preferableBackend == DNN_BACKEND_HALIDE); + + HalideScheduler scheduler(halideConfigFile); + std::vector< std::reference_wrapper > compileList; compileList.reserve(64); + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it) + { + LayerData& ld = it->second; + Ptr layer = ld.layerInstance; + if (layer->supportBackend(DNN_BACKEND_HALIDE) && !ld.skip) + { + CV_Assert(!ld.backendNodes[DNN_BACKEND_HALIDE].empty()); + bool scheduled = scheduler.process(ld.backendNodes[DNN_BACKEND_HALIDE]); + if (!scheduled) + { + // Use automatic scheduling provided by layer. + layer->applyHalideScheduler(ld.backendNodes[DNN_BACKEND_HALIDE], + ld.inputBlobs, ld.outputBlobs, + preferableTarget); + } + compileList.emplace_back(ld); + } + } + std::atomic progress(0); + auto fn = ([&] () -> void + { + for (;;) + { + int id = progress.fetch_add(1); + if ((size_t)id >= compileList.size()) + return; + const LayerData& ld = compileList[id].get(); + Ptr node = ld.backendNodes.find(DNN_BACKEND_HALIDE)->second; + dnn::compileHalide(ld.outputBlobs, node, preferableTarget); + } + }); + size_t num_threads = std::min(compileList.size(), (size_t)std::thread::hardware_concurrency()); + num_threads = std::max((size_t)1u, std::min((size_t)8u, num_threads)); + std::vector threads(num_threads - 1); + for (auto& t: threads) t = std::thread(fn); + fn(); // process own tasks + for (auto& t: threads) t.join(); +} + + +void Net::Impl::initHalideBackend() +{ + CV_TRACE_FUNCTION(); + CV_Assert_N(preferableBackend == DNN_BACKEND_HALIDE, haveHalide()); + + // Iterator to current layer. + MapIdToLayerData::iterator it = layers.begin(); + // Iterator to base layer for fusion. In example, in case of conv+bn+relu + // it'll be a conv layer. + MapIdToLayerData::iterator baseIt = layers.begin(); + for (; it != layers.end(); it++) + { + LayerData &ldTop = it->second; + Ptr layerTop = ldTop.layerInstance; + if (!layerTop->supportBackend(preferableBackend)) + { + // Move base iterator to layer that don't support preferable + // backend to prevent fusion over layer of different backend. + baseIt = it; + continue; + } + // Try to do layers fusion. + LayerData &ldBot = baseIt->second; + Ptr layerBot = ldBot.layerInstance; + // 1. Check that bottom and top from the same backends. + if (it != layers.begin() && layerBot->supportBackend(preferableBackend)) + { + // 2. Check that current layer works in-place. + bool inPlace = ldTop.inputBlobs.size() == 1 && + ldBot.outputBlobs.size() == 1 && + ldTop.inputBlobs[0]->data == + ldBot.outputBlobs[0].data; + if (inPlace) + { + // 3. Try to attach node. + CV_Assert(!ldBot.backendNodes[preferableBackend].empty()); + Ptr fusedNode = + layerTop->tryAttach(ldBot.backendNodes[preferableBackend]); + if (!fusedNode.empty()) + { + ldTop.skip = true; + ldBot.backendNodes[preferableBackend] = fusedNode; + ldBot.outputBlobsWrappers = ldTop.outputBlobsWrappers; + continue; + } + } + } + // No layers fusion. + ldTop.skip = false; + ldTop.backendNodes[DNN_BACKEND_HALIDE] = + layerTop->initHalide(ldTop.inputBlobsWrappers); + baseIt = it; + } +} + + +#endif // HAVE_HALIDE +CV__DNN_INLINE_NS_END + #ifdef HAVE_HALIDE static MatShape getBufferShape(const MatShape& shape) @@ -226,5 +346,83 @@ bool haveHalide() #endif // HAVE_HALIDE } -} // namespace dnn -} // namespace cv + +CV__DNN_INLINE_NS_BEGIN + + +void Layer::applyHalideScheduler(Ptr& node, const std::vector &inputs, + const std::vector &outputs, int targetId) const +{ +#ifndef HAVE_HALIDE + CV_Error(Error::StsNotImplemented, ""); +#else + CV_TRACE_FUNCTION(); + + Halide::Var x("x"), y("y"), c("c"), n("n"), co("co"), ci("ci"), + xo("xo"), xi("xi"), yo("yo"), yi("yi"), tile("tile"); + Halide::Func& top = node.dynamicCast()->funcs.back(); + + int outW, outH, outC, outN; + getCanonicalSize(outputs[0].size, &outW, &outH, &outC, &outN); + + if (targetId == DNN_TARGET_CPU) + { + if (outW == 1 && outH == 1) + { + if (outC + outN == 1) + return; + + if (outC > 8) + top.split(c, co, ci, 8) + .fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile) + .parallel(tile) + .vectorize(ci, 8); + else + top.fuse(x, y, tile).fuse(c, tile, tile).fuse(n, tile, tile) + .parallel(tile); + } + else + { + if (outH > 2) + { + top.reorder(x, c, y) + .split(y, yo, yi, 2) + .fuse(yo, n, tile) + .parallel(tile) + .unroll(yi) + .vectorize(x, outW >= 16 ? 16 : outW); + } + } + } + else if (targetId == DNN_TARGET_OPENCL) + { + if (outW == 1 && outH == 1) + { + int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : outC; + top.split(c, co, ci, c_split) + .fuse(x, y, tile).fuse(co, tile, tile).fuse(n, tile, tile) + .gpu_blocks(tile) + .gpu_threads(ci); + } + else + { + int x_split = outW > 8 ? (outW >= 32 ? 16 : 8) : outW; + int y_split = outH > 8 ? (outH >= 32 ? 16 : 8) : outH; + // Supported vectorization widths: 2, 3, 4, 8, 16 + int c_split = outC > 8 ? (outC > 16 ? 8 : 4) : std::min(4, outC); + top.split(x, xo, xi, x_split).split(y, yo, yi, y_split) + .split(c, co, ci, c_split) + .gpu_blocks(xo, yo, co) + .gpu_threads(xi, yi) + .reorder(xi, yi, ci, xo, yo, co) + .vectorize(ci); + } + } + else + CV_Error(Error::StsNotImplemented, "Unknown target identifier"); +#endif // HAVE_HALIDE +} + + +CV__DNN_INLINE_NS_END +}} // namespace diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp index d9b98404c378..8a27dc2221a8 100644 --- a/modules/dnn/src/op_inf_engine.cpp +++ b/modules/dnn/src/op_inf_engine.cpp @@ -20,52 +20,17 @@ namespace cv { namespace dnn { #ifdef HAVE_INF_ENGINE -static Backend parseInferenceEngineBackendType(const cv::String& backend) -{ - CV_Assert(!backend.empty()); - if (backend == CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - return DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; - if (backend == CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API) - return DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019; - CV_Error(Error::StsBadArg, cv::format("Unknown IE backend: %s", backend.c_str())); -} -static const char* dumpInferenceEngineBackendType(Backend backend) -{ - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - return CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - return CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API; - CV_Error(Error::StsBadArg, cv::format("Invalid backend ID for IE: %d", backend)); -} -Backend& getInferenceEngineBackendTypeParam() -{ - static Backend param = parseInferenceEngineBackendType( - utils::getConfigurationParameterString("OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE", -#ifdef HAVE_DNN_NGRAPH - CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH -#elif defined(HAVE_DNN_IE_NN_BUILDER_2019) - CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API -#else -#error "Build configuration error: nGraph or NN Builder API backend should be enabled" -#endif - ) - ); - return param; -} - CV__DNN_INLINE_NS_BEGIN cv::String getInferenceEngineBackendType() { - return dumpInferenceEngineBackendType(getInferenceEngineBackendTypeParam()); + return "NGRAPH"; } cv::String setInferenceEngineBackendType(const cv::String& newBackendType) { - Backend newBackend = parseInferenceEngineBackendType(newBackendType); - Backend& param = getInferenceEngineBackendTypeParam(); - Backend old = param; - param = newBackend; - return dumpInferenceEngineBackendType(old); + if (newBackendType != "NGRAPH") + CV_Error(Error::StsNotImplemented, cv::format("DNN/IE: only NGRAPH backend is supported: %s", newBackendType.c_str())); + return newBackendType; } CV__DNN_INLINE_NS_END @@ -98,508 +63,6 @@ void infEngineBlobsToMats(const std::vector& blobs, } -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - -// For networks with input layer which has an empty name, IE generates a name id[some_number]. -// OpenCV lets users use an empty input name and to prevent unexpected naming, -// we can use some predefined name. -static std::string kDefaultInpLayerName = "empty_inp_layer_name"; -static std::string kOpenCVLayersType = "OpenCVLayer"; - -static std::string shapesToStr(const std::vector& mats) -{ - std::ostringstream shapes; - shapes << mats.size() << " "; - for (const Mat& m : mats) - { - shapes << m.dims << " "; - for (int i = 0; i < m.dims; ++i) - shapes << m.size[i] << " "; - } - return shapes.str(); -} - -static void strToShapes(const std::string& str, std::vector >& shapes) -{ - std::istringstream ss(str); - int num, dims; - ss >> num; - shapes.resize(num); - for (int i = 0; i < num; ++i) - { - ss >> dims; - shapes[i].resize(dims); - for (int j = 0; j < dims; ++j) - ss >> shapes[i][j]; - } -} - -class InfEngineCustomLayer : public InferenceEngine::ILayerExecImpl -{ -public: - explicit InfEngineCustomLayer(const InferenceEngine::CNNLayer& layer) : cnnLayer(layer) - { - std::istringstream iss(layer.GetParamAsString("impl")); - size_t ptr; - iss >> ptr; - cvLayer = (Layer*)ptr; - - std::vector > shapes; - strToShapes(layer.GetParamAsString("internals"), shapes); - internals.resize(shapes.size()); - for (int i = 0; i < shapes.size(); ++i) - internals[i].create(std::vector(shapes[i].begin(), shapes[i].end()), CV_32F); - } - - virtual InferenceEngine::StatusCode execute(std::vector& inputs, - std::vector& outputs, - InferenceEngine::ResponseDesc *resp) noexcept - { - std::vector inpMats, outMats; - infEngineBlobsToMats(inputs, inpMats); - infEngineBlobsToMats(outputs, outMats); - - try - { - cvLayer->forward(inpMats, outMats, internals); - return InferenceEngine::StatusCode::OK; - } - catch (...) - { - return InferenceEngine::StatusCode::GENERAL_ERROR; - } - } - - virtual InferenceEngine::StatusCode - getSupportedConfigurations(std::vector& conf, - InferenceEngine::ResponseDesc* resp) noexcept - { - std::vector inDataConfig; - std::vector outDataConfig; - for (auto& it : cnnLayer.insData) - { - InferenceEngine::DataConfig conf; - conf.desc = it.lock()->getTensorDesc(); - inDataConfig.push_back(conf); - } - - for (auto& it : cnnLayer.outData) - { - InferenceEngine::DataConfig conf; - conf.desc = it->getTensorDesc(); - outDataConfig.push_back(conf); - } - - InferenceEngine::LayerConfig layerConfig; - layerConfig.inConfs = inDataConfig; - layerConfig.outConfs = outDataConfig; - - conf.push_back(layerConfig); - return InferenceEngine::StatusCode::OK; - } - - InferenceEngine::StatusCode init(InferenceEngine::LayerConfig& config, - InferenceEngine::ResponseDesc *resp) noexcept - { - return InferenceEngine::StatusCode::OK; - } - -private: - InferenceEngine::CNNLayer cnnLayer; - dnn::Layer* cvLayer; - std::vector internals; -}; - -class InfEngineCustomLayerShapeInfer : public InferenceEngine::IShapeInferImpl -{ -public: - InferenceEngine::StatusCode - inferShapes(const std::vector& inBlobs, - const std::map& params, - const std::map& blobs, - std::vector& outShapes, - InferenceEngine::ResponseDesc* desc) noexcept override - { - strToShapes(params.at("outputs"), outShapes); - return InferenceEngine::StatusCode::OK; - } -}; - -class InfEngineCustomLayerFactory : public InferenceEngine::ILayerImplFactory { -public: - explicit InfEngineCustomLayerFactory(const InferenceEngine::CNNLayer* layer) : cnnLayer(*layer) {} - - InferenceEngine::StatusCode - getImplementations(std::vector& impls, - InferenceEngine::ResponseDesc* resp) noexcept override { - impls.push_back(std::make_shared(cnnLayer)); - return InferenceEngine::StatusCode::OK; - } - -private: - InferenceEngine::CNNLayer cnnLayer; -}; - -InferenceEngine::StatusCode InfEngineExtension::getFactoryFor( - InferenceEngine::ILayerImplFactory*& factory, - const InferenceEngine::CNNLayer* cnnLayer, - InferenceEngine::ResponseDesc* resp -) noexcept -{ - if (cnnLayer->type != kOpenCVLayersType) - return InferenceEngine::StatusCode::NOT_IMPLEMENTED; - factory = new InfEngineCustomLayerFactory(cnnLayer); - return InferenceEngine::StatusCode::OK; -} - -InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::Builder::Layer& _layer) - : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019), layer(_layer) {} - - InfEngineBackendNode::InfEngineBackendNode(Ptr& cvLayer_, std::vector& inputs, - std::vector& outputs, - std::vector& internals) - : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019), layer(cvLayer_->name), - cvLayer(cvLayer_) -{ - CV_Assert(!cvLayer->name.empty()); - layer.setName(cvLayer->name); - layer.setType(kOpenCVLayersType); - layer.getParameters()["impl"] = (size_t)cvLayer.get(); - layer.getParameters()["outputs"] = shapesToStr(outputs); - layer.getParameters()["internals"] = shapesToStr(internals); - layer.setInputPorts(std::vector(inputs.size())); - layer.setOutputPorts(std::vector(outputs.size())); -} - -static std::vector > -infEngineWrappers(const std::vector >& ptrs) -{ - std::vector > wrappers(ptrs.size()); - for (int i = 0; i < ptrs.size(); ++i) - { - CV_Assert(!ptrs[i].empty()); - wrappers[i] = ptrs[i].dynamicCast(); - CV_Assert(!wrappers[i].empty()); - } - return wrappers; -} - -InfEngineBackendNet::InfEngineBackendNet() : netBuilder("") -{ - hasNetOwner = false; - device_name = "CPU"; -} - -InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net) : netBuilder(""), cnn(net) -{ - hasNetOwner = true; - device_name = "CPU"; -} - -void InfEngineBackendNet::connect(const std::vector >& inputs, - const std::vector >& outputs, - const std::string& layerName) -{ - std::vector > inpWrappers = infEngineWrappers(inputs); - std::map::iterator it = layers.find(layerName); - CV_Assert(it != layers.end()); - - const int layerId = it->second; - for (size_t i = 0; i < inpWrappers.size(); ++i) - { - const auto& inp = inpWrappers[i]; - const std::string& inpName = inp->dataPtr->getName(); - - std::string inpLayerName = inpName; - size_t inpPortId = inpName.rfind('.'); - if (inpPortId != std::string::npos) - { - std::string portIdStr = inpName.substr(inpPortId + 1); - if (std::all_of(portIdStr.begin(), portIdStr.end(), ::isdigit)) - { - inpLayerName = inpName.substr(0, inpPortId); - inpPortId = atoi(portIdStr.c_str()); - } - else - inpPortId = 0; - } - else - inpPortId = 0; - - int inpId; - it = layers.find(inpLayerName); - if (it == layers.end()) - { - InferenceEngine::Builder::InputLayer inpLayer(!inpLayerName.empty() ? inpLayerName : kDefaultInpLayerName); - std::vector shape(inp->blob->getTensorDesc().getDims()); - inpLayer.setPort(InferenceEngine::Port(shape)); - inpId = netBuilder.addLayer(inpLayer); - - layers.insert({inpName, inpId}); - } - else - inpId = it->second; - - netBuilder.connect({(size_t)inpId, inpPortId}, {(size_t)layerId, i}); - unconnectedPorts.erase({inpId, inpPortId}); - } - CV_Assert(!outputs.empty()); - for (int i = 0; i < outputs.size(); ++i) - { - InferenceEngine::DataPtr dataPtr = infEngineDataNode(outputs[i]); - std::string outputName = outputs.size() > 1 ? (layerName + "." + std::to_string(i)) : layerName; -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - dataPtr->name = outputName; -#else - dataPtr->setName(outputName); -#endif - } -} - -void InfEngineBackendNet::init(Target targetId) -{ - if (!hasNetOwner) - { - CV_Assert(!unconnectedPorts.empty()); - for (const auto& port : unconnectedPorts) - { - InferenceEngine::Builder::OutputLayer outLayer("myconv1"); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) - // Inference Engine determines network precision by ports. - InferenceEngine::Precision p = (targetId == DNN_TARGET_MYRIAD || - targetId == DNN_TARGET_HDDL || - targetId == DNN_TARGET_OPENCL_FP16) ? - InferenceEngine::Precision::FP16 : - InferenceEngine::Precision::FP32; - outLayer.setPort(InferenceEngine::Port({}, p)); -#endif - netBuilder.addLayer({InferenceEngine::PortInfo(port.first, port.second)}, outLayer); - } - netBuilder.getContext().addShapeInferImpl(kOpenCVLayersType, - std::make_shared()); - cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build())); - } - - switch (targetId) - { - case DNN_TARGET_CPU: - device_name = "CPU"; - break; - case DNN_TARGET_OPENCL: - case DNN_TARGET_OPENCL_FP16: - device_name = "GPU"; - break; - case DNN_TARGET_MYRIAD: - device_name = "MYRIAD"; - break; - case DNN_TARGET_HDDL: - device_name = "HDDL"; - break; - case DNN_TARGET_FPGA: - device_name = "FPGA"; - break; - default: - CV_Error(Error::StsNotImplemented, "Unknown target"); - }; - - for (const auto& name : requestedOutputs) - { - cnn.addOutput(name); - } - - for (const auto& it : cnn.getInputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision()); - } - for (const auto& it : cnn.getOutputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision()); // Should be always FP32 - } - - initPlugin(cnn); -} - -void InfEngineBackendNet::addLayer(InferenceEngine::Builder::Layer& layer) -{ -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) - // Add weights to network and connect them after input blobs. - std::map& params = layer.getParameters(); - std::vector blobsIds; - std::vector portIds; - for (const std::string& name : {"weights", "biases"}) - { - bool asInput = false; - int portId = 0; - for (int i = 0; i < layer.getInputPorts().size(); ++i) - { - const auto& port = layer.getInputPorts()[i]; - auto it = port.getParameters().find("type"); - if (it != port.getParameters().end() && it->second == name) - { - portId = i; - asInput = true; - break; - } - } - - if (!asInput) - continue; - - auto it = params.find(name); - if (it != params.end()) - { - InferenceEngine::Blob::Ptr blob = it->second.as(); - params.erase(it); - int blobId = netBuilder.addLayer(InferenceEngine::Builder::ConstLayer(name).setData(blob)); - blobsIds.push_back(blobId); - portIds.push_back(portId); - } - } -#endif - - int id = netBuilder.addLayer(layer); - const std::string& layerName = layer.getName(); - - CV_Assert(layers.insert({layerName, id}).second); - for (int i = 0; i < layer.getOutputPorts().size(); ++i) - unconnectedPorts.insert({id, i}); - -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) - // By default, all the weights are connected to last ports ids. - for (int i = 0; i < blobsIds.size(); ++i) - { - netBuilder.connect((size_t)blobsIds[i], {(size_t)id, (size_t)portIds[i]}); - } -#endif -} - -void InfEngineBackendNet::addOutput(const std::string& name) -{ - requestedOutputs.push_back(name); -} - -static InferenceEngine::Layout estimateLayout(const Mat& m) -{ - if (m.dims == 4) - return InferenceEngine::Layout::NCHW; - else if (m.dims == 2) - return InferenceEngine::Layout::NC; - else - return InferenceEngine::Layout::ANY; -} - -static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "") -{ - std::vector shape = getShape(m); - if (m.type() == CV_32F) - return InferenceEngine::DataPtr(new InferenceEngine::Data(name, - {InferenceEngine::Precision::FP32, shape, estimateLayout(m)})); - else if (m.type() == CV_8U) - return InferenceEngine::DataPtr(new InferenceEngine::Data(name, - {InferenceEngine::Precision::U8, shape, estimateLayout(m)})); - else - CV_Error(Error::StsNotImplemented, format("Unsupported data type %d", m.type())); -} - -InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector& shape, - InferenceEngine::Layout layout) -{ - if (m.type() == CV_32F) - return InferenceEngine::make_shared_blob( - {InferenceEngine::Precision::FP32, shape, layout}, (float*)m.data); - else if (m.type() == CV_8U) - return InferenceEngine::make_shared_blob( - {InferenceEngine::Precision::U8, shape, layout}, (uint8_t*)m.data); - else - CV_Error(Error::StsNotImplemented, format("Unsupported data type %d", m.type())); -} - -InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout) -{ - std::vector shape = getShape(m); - return wrapToInfEngineBlob(m, shape, layout); -} - -InferenceEngine::Blob::Ptr cloneBlob(const InferenceEngine::Blob::Ptr& blob) -{ - InferenceEngine::Blob::Ptr copy; - auto description = blob->getTensorDesc(); - InferenceEngine::Precision precision = description.getPrecision(); - if (precision == InferenceEngine::Precision::FP32) - { - copy = InferenceEngine::make_shared_blob(description); - } - else if (precision == InferenceEngine::Precision::U8) - { - copy = InferenceEngine::make_shared_blob(description); - } - else - CV_Error(Error::StsNotImplemented, "Unsupported blob precision"); - copy->allocate(); - return copy; -} - -InferenceEngine::DataPtr infEngineDataNode(const Ptr& ptr) -{ - CV_Assert(!ptr.empty()); - Ptr p = ptr.dynamicCast(); - CV_Assert(!p.empty()); - return p->dataPtr; -} - -InfEngineBackendWrapper::InfEngineBackendWrapper(int targetId, const cv::Mat& m) - : BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, targetId) -{ - dataPtr = wrapToInfEngineDataNode(m); - blob = wrapToInfEngineBlob(m, estimateLayout(m)); -} - -InfEngineBackendWrapper::InfEngineBackendWrapper(Ptr wrapper) - : BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, wrapper->targetId) -{ - Ptr ieWrapper = wrapper.dynamicCast(); - CV_Assert(!ieWrapper.empty()); - InferenceEngine::DataPtr srcData = ieWrapper->dataPtr; - - dataPtr = InferenceEngine::DataPtr(new InferenceEngine::Data(srcData->getName(), srcData->getTensorDesc())); - blob = ieWrapper->blob; -} - -Ptr InfEngineBackendWrapper::create(Ptr wrapper) -{ - return Ptr(new InfEngineBackendWrapper(wrapper)); -} - -InfEngineBackendWrapper::~InfEngineBackendWrapper() -{ - -} - -void InfEngineBackendWrapper::copyToHost() -{ - -} - -void InfEngineBackendWrapper::setHostDirty() -{ - -} - -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) -static std::map& getSharedPlugins() -{ - static std::map sharedPlugins; - return sharedPlugins; -} -#else static bool init_IE_plugins() { // load and hold IE plugins @@ -653,7 +116,7 @@ InferenceEngine::Core& getCore(const std::string& id) : create_IE_Core_instance(id); return core; } -#endif + static bool detectArmPlugin_() { @@ -672,10 +135,10 @@ static bool detectArmPlugin_() } #if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT) -static bool detectMyriadX_(std::string device) +static bool detectMyriadX_(const std::string& device) { AutoLock lock(getInitializationMutex()); -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R3) + // Lightweight detection InferenceEngine::Core& ie = getCore(device); const std::vector devices = ie.GetAvailableDevices(); @@ -689,481 +152,22 @@ static bool detectMyriadX_(std::string device) } } return false; -#else - InferenceEngine::Builder::Network builder(""); - InferenceEngine::idx_t inpId = builder.addLayer( - InferenceEngine::Builder::InputLayer().setPort(InferenceEngine::Port({1}))); - -#if INF_ENGINE_RELEASE <= 2018050000 - InferenceEngine::idx_t clampId; - { - InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ClampLayer(); - auto& blobs = l.getConstantData(); - auto blob = InferenceEngine::make_shared_blob( - InferenceEngine::Precision::FP16, - InferenceEngine::Layout::C, {1}); - blob->allocate(); - blobs[""] = blob; - clampId = builder.addLayer({inpId}, l); - } - builder.addLayer({InferenceEngine::PortInfo(clampId)}, InferenceEngine::Builder::OutputLayer()); -#else - - InferenceEngine::idx_t clampId = builder.addLayer({inpId}, InferenceEngine::Builder::ClampLayer()); - builder.addLayer({InferenceEngine::PortInfo(clampId)}, - InferenceEngine::Builder::OutputLayer().setPort(InferenceEngine::Port({}, - InferenceEngine::Precision::FP16))); -#endif - - InferenceEngine::CNNNetwork cnn = InferenceEngine::CNNNetwork( - InferenceEngine::Builder::convertToICNNNetwork(builder.build())); - -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - InferenceEngine::InferenceEnginePluginPtr enginePtr; - { - auto& sharedPlugins = getSharedPlugins(); - auto pluginIt = sharedPlugins.find(device); - if (pluginIt != sharedPlugins.end()) { - enginePtr = pluginIt->second; - } else { - auto dispatcher = InferenceEngine::PluginDispatcher({""}); - enginePtr = dispatcher.getPluginByDevice(device); - sharedPlugins[device] = enginePtr; - } - } - auto plugin = InferenceEngine::InferencePlugin(enginePtr); - try - { - auto netExec = plugin.LoadNetwork(cnn, {{"VPU_PLATFORM", "VPU_2480"}}); -#else - try - { -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3) - auto netExec = getCore(device).LoadNetwork(cnn, device, {{"VPU_PLATFORM", "VPU_2480"}}); -#else - auto netExec = getCore(device).LoadNetwork(cnn, device, {{"VPU_MYRIAD_PLATFORM", "VPU_MYRIAD_2480"}}); -#endif -#endif - auto infRequest = netExec.CreateInferRequest(); - } catch(...) { - return false; - } - return true; -#endif } #endif // !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT) -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - -void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net) -{ - CV_Assert(!isInitialized()); - - try - { - AutoLock lock(getInitializationMutex()); -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - auto& sharedPlugins = getSharedPlugins(); - auto pluginIt = sharedPlugins.find(device_name); - if (pluginIt != sharedPlugins.end()) - { - enginePtr = pluginIt->second; - } - else -#else - InferenceEngine::Core& ie = getCore(device_name); -#endif - { -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - auto dispatcher = InferenceEngine::PluginDispatcher({""}); - if (device_name == "FPGA") - enginePtr = dispatcher.getPluginByDevice("HETERO:FPGA,CPU"); - else - enginePtr = dispatcher.getPluginByDevice(device_name); - sharedPlugins[device_name] = enginePtr; -#else - isInit = true; -#endif - std::vector candidates; - std::string param_pluginPath = utils::getConfigurationParameterString("OPENCV_DNN_IE_EXTRA_PLUGIN_PATH", ""); - if (!param_pluginPath.empty()) - { - candidates.push_back(param_pluginPath); - } -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3) - if (device_name == "CPU" || device_name == "FPGA") - { - std::string suffixes[] = {"_avx2", "_sse4", ""}; - bool haveFeature[] = { - checkHardwareSupport(CPU_AVX2), - checkHardwareSupport(CPU_SSE4_2), - true - }; - for (int i = 0; i < 3; ++i) - { - if (!haveFeature[i]) - continue; -#ifdef _WIN32 - candidates.push_back("cpu_extension" + suffixes[i] + ".dll"); -#elif defined(__APPLE__) - candidates.push_back("libcpu_extension" + suffixes[i] + ".so"); // built as loadable module - candidates.push_back("libcpu_extension" + suffixes[i] + ".dylib"); // built as shared library -#else - candidates.push_back("libcpu_extension" + suffixes[i] + ".so"); -#endif // _WIN32 - } - } -#endif - bool found = false; - for (size_t i = 0; i != candidates.size(); ++i) - { - const std::string& libName = candidates[i]; - try - { - InferenceEngine::IExtensionPtr extension = - InferenceEngine::make_so_pointer(libName); - -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - enginePtr->AddExtension(extension, 0); -#else - ie.AddExtension(extension, "CPU"); -#endif - CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName); - found = true; - break; - } - catch(...) {} - } - if (!found && !candidates.empty()) - { - CV_LOG_WARNING(NULL, "DNN-IE: Can't load extension plugin (extra layers for some networks). Specify path via OPENCV_DNN_IE_EXTRA_PLUGIN_PATH parameter"); - } - // Some of networks can work without a library of extra layers. -#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R1) - // OpenCV fallbacks as extensions. - try - { - ie.AddExtension(std::make_shared(), "CPU"); - } - catch(const std::exception& e) - { - CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers extension: " << e.what()); - } -#endif - // Limit the number of CPU threads. -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) -#ifndef _WIN32 - enginePtr->SetConfig({{ - InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()), - }}, 0); -#endif // _WIN32 -#else - if (device_name == "CPU") - ie.SetConfig({{ - InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()), - }}, device_name); -#endif - } -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - plugin = InferenceEngine::InferencePlugin(enginePtr); - netExec = plugin.LoadNetwork(net, {}); -#else - bool isHetero = false; - if (device_name != "CPU") - { - isHetero = device_name == "FPGA"; - for (auto& layer : net) - { - if (layer->type == kOpenCVLayersType) - { - isHetero = true; -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2019R3) - // Not sure about lower versions but in 2019R3 we do not need this - layer->affinity = "CPU"; - } - else - { - layer->affinity = device_name; -#endif - } - } - } - if (isHetero) - netExec = ie.LoadNetwork(net, "HETERO:" + device_name + ",CPU"); - else - netExec = ie.LoadNetwork(net, device_name); -#endif - } - catch (const std::exception& ex) - { - CV_Error(Error::StsError, format("Failed to initialize Inference Engine backend (device = %s): %s", device_name.c_str(), ex.what())); - } -} - -bool InfEngineBackendNet::isInitialized() -{ -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - return (bool)enginePtr; -#else - return isInit; -#endif -} - -void InfEngineBackendNet::reset() -{ - allBlobs.clear(); - infRequests.clear(); - isInit = false; -} - -void InfEngineBackendNet::addBlobs(const std::vector >& ptrs) -{ - auto wrappers = infEngineWrappers(ptrs); - for (const auto& wrapper : wrappers) - { - std::string name = wrapper->dataPtr->getName(); - name = name.empty() ? kDefaultInpLayerName : name; - allBlobs.insert({name, wrapper->blob}); - } -} - -void InfEngineBackendNet::InfEngineReqWrapper::makePromises(const std::vector >& outsWrappers) -{ - auto outs = infEngineWrappers(outsWrappers); - outProms.clear(); - outProms.resize(outs.size()); - outsNames.resize(outs.size()); - for (int i = 0; i < outs.size(); ++i) - { - outs[i]->futureMat = outProms[i].getArrayResult(); - outsNames[i] = outs[i]->dataPtr->getName(); - } -} - -void InfEngineBackendNet::forward(const std::vector >& outBlobsWrappers, - bool isAsync) -{ - CV_LOG_DEBUG(NULL, "InfEngineBackendNet::forward(" << (isAsync ? "async" : "sync") << ")"); - // Look for finished requests. - Ptr reqWrapper; - for (auto& wrapper : infRequests) - { - if (wrapper->isReady) - { - reqWrapper = wrapper; - break; - } - } - if (reqWrapper.empty()) - { - reqWrapper = Ptr(new InfEngineReqWrapper()); - try - { - reqWrapper->req = netExec.CreateInferRequest(); - } - catch (const std::exception& ex) - { - CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what())); - } - infRequests.push_back(reqWrapper); - - InferenceEngine::BlobMap inpBlobs, outBlobs; - for (const auto& it : cnn.getInputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - inpBlobs[name] = isAsync ? cloneBlob(blobIt->second) : blobIt->second; - } - for (const auto& it : cnn.getOutputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - CV_Assert(blobIt != allBlobs.end()); - outBlobs[name] = isAsync ? cloneBlob(blobIt->second) : blobIt->second; - } - reqWrapper->req.SetInput(inpBlobs); - reqWrapper->req.SetOutput(outBlobs); - - InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req; - infRequestPtr->SetUserData(reqWrapper.get(), 0); - - infRequestPtr->SetCompletionCallback( - [](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status) - { - CV_LOG_DEBUG(NULL, "DNN(IE): completionCallback(" << (int)status << ")"); - - InfEngineReqWrapper* wrapper; - request->GetUserData((void**)&wrapper, 0); - CV_Assert(wrapper && "Internal error"); - - size_t processedOutputs = 0; - try - { - for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs) - { - const std::string& name = wrapper->outsNames[processedOutputs]; - Mat m = infEngineBlobToMat(wrapper->req.GetBlob(name)); - - try - { - CV_Assert(status == InferenceEngine::StatusCode::OK); - wrapper->outProms[processedOutputs].setValue(m.clone()); - } - catch (...) - { - try { - wrapper->outProms[processedOutputs].setException(std::current_exception()); - } catch(...) { - CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation"); - } - } - } - } - catch (...) - { - std::exception_ptr e = std::current_exception(); - for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs) - { - try { - wrapper->outProms[processedOutputs].setException(e); - } catch(...) { - CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation"); - } - } - } - wrapper->isReady = true; - } - ); - } - if (isAsync) - { - // Copy actual data to infer request's input blobs. - for (const auto& it : cnn.getInputsInfo()) - { - const std::string& name = it.first; - auto blobIt = allBlobs.find(name); - Mat srcMat = infEngineBlobToMat(blobIt->second); - Mat dstMat = infEngineBlobToMat(reqWrapper->req.GetBlob(name)); - srcMat.copyTo(dstMat); - } - - // Set promises to output blobs wrappers. - reqWrapper->makePromises(outBlobsWrappers); - - reqWrapper->isReady = false; - reqWrapper->req.StartAsync(); - } - else - { - reqWrapper->req.Infer(); - } -} - -bool InfEngineBackendLayer::getMemoryShapes(const std::vector &inputs, - const int requiredOutputs, - std::vector &outputs, - std::vector &internals) const -{ - InferenceEngine::ICNNNetwork::InputShapes inShapes = t_net.getInputShapes(); - InferenceEngine::ICNNNetwork::InputShapes::iterator itr; - bool equal_flag = true; - size_t i = 0; - for (itr = inShapes.begin(); itr != inShapes.end(); ++itr) - { - InferenceEngine::SizeVector currentInShape(inputs[i].begin(), inputs[i].end()); - if (itr->second != currentInShape) - { - itr->second = currentInShape; - equal_flag = false; - } - i++; - } - - if (!equal_flag) - { - InferenceEngine::CNNNetwork curr_t_net(t_net); - curr_t_net.reshape(inShapes); - } - std::vector dims = t_net.getOutputsInfo()[name]->getDims(); - outputs.push_back(MatShape(dims.begin(), dims.end())); - return false; -} - -bool InfEngineBackendLayer::supportBackend(int backendId) -{ - CV_LOG_DEBUG(NULL, "InfEngineBackendLayer::supportBackend(" << backendId << ")"); - return backendId == DNN_BACKEND_DEFAULT || - (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019); -} - -void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, - OutputArrayOfArrays internals) -{ - CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend."); -} - -InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob) -{ - auto halfs = InferenceEngine::make_shared_blob({ - InferenceEngine::Precision::FP16, blob->getTensorDesc().getDims(), - blob->getTensorDesc().getLayout() - }); - halfs->allocate(); - Mat floatsData(1, blob->size(), CV_32F, blob->buffer()); - Mat halfsData(1, blob->size(), CV_16SC1, halfs->buffer()); - convertFp16(floatsData, halfsData); - return halfs; -} - -void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data, - InferenceEngine::Builder::Layer& l) -{ -#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) - l.getParameters()[name] = data; -#else - l.addConstantData(name, data); -#endif -} - -#endif // HAVE_DNN_IE_NN_BUILDER_2019 - #endif // HAVE_INF_ENGINE -bool haveInfEngine() -{ -#ifdef HAVE_INF_ENGINE - return true; -#else - return false; -#endif // HAVE_INF_ENGINE -} - -void forwardInfEngine(const std::vector >& outBlobsWrappers, - Ptr& node, bool isAsync) -{ - CV_Assert(haveInfEngine()); -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - CV_Assert(!node.empty()); - Ptr ieNode = node.dynamicCast(); - CV_Assert(!ieNode.empty()); - ieNode->net->forward(outBlobsWrappers, isAsync); -#else - CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support"); -#endif // HAVE_INF_ENGINE -} CV__DNN_INLINE_NS_BEGIN void resetMyriadDevice() { #ifdef HAVE_INF_ENGINE + CV_LOG_INFO(NULL, "DNN: Unregistering both 'MYRIAD' and 'HETERO:MYRIAD,CPU' plugins"); + AutoLock lock(getInitializationMutex()); -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - getSharedPlugins().erase("MYRIAD"); -#else - // Unregister both "MYRIAD" and "HETERO:MYRIAD,CPU" plugins + InferenceEngine::Core& ie = getCore("MYRIAD"); try { @@ -1171,18 +175,16 @@ void resetMyriadDevice() ie.UnregisterPlugin("HETERO"); } catch (...) {} -#endif #endif // HAVE_INF_ENGINE } void releaseHDDLPlugin() { #ifdef HAVE_INF_ENGINE + CV_LOG_INFO(NULL, "DNN: Unregistering both 'HDDL' and 'HETERO:HDDL,CPU' plugins"); + AutoLock lock(getInitializationMutex()); -#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1) - getSharedPlugins().erase("HDDL"); -#else - // Unregister both "HDDL" and "HETERO:HDDL,CPU" plugins + InferenceEngine::Core& ie = getCore("HDDL"); try { @@ -1190,7 +192,6 @@ void releaseHDDLPlugin() ie.UnregisterPlugin("HETERO"); } catch (...) {} -#endif #endif // HAVE_INF_ENGINE } @@ -1253,6 +254,31 @@ cv::String getInferenceEngineCPUType() return cpu_type; } + +namespace openvino { + +bool checkTarget(Target target) +{ + // Lightweight detection + const std::vector devices = getCore("").GetAvailableDevices(); + for (std::vector::const_iterator i = devices.begin(); i != devices.end(); ++i) + { + if (std::string::npos != i->find("MYRIAD") && target == DNN_TARGET_MYRIAD) + return true; + if (std::string::npos != i->find("HDDL") && target == DNN_TARGET_HDDL) + return true; + else if (std::string::npos != i->find("FPGA") && target == DNN_TARGET_FPGA) + return true; + else if (std::string::npos != i->find("CPU") && target == DNN_TARGET_CPU) + return true; + else if (std::string::npos != i->find("GPU") && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + return true; + } + return false; +} + +} // namespace openvino + #else // HAVE_INF_ENGINE cv::String getInferenceEngineBackendType() diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp index ab2f161eaf1b..856441e71deb 100644 --- a/modules/dnn/src/op_inf_engine.hpp +++ b/modules/dnn/src/op_inf_engine.hpp @@ -48,37 +48,16 @@ #pragma GCC diagnostic ignored "-Wsuggest-override" #endif -#if defined(HAVE_DNN_IE_NN_BUILDER_2019) || INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2020_4) -//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE -//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Wdeprecated-declarations" -#endif -#ifdef _MSC_VER -#pragma warning(disable: 4996) // was declared deprecated -#endif -#endif - -#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1) -#pragma GCC visibility push(default) -#endif - #include -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 -#include -#endif - -#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1) -#pragma GCC visibility pop -#endif - #if defined(__GNUC__) && __GNUC__ >= 5 //#pragma GCC diagnostic pop #endif #endif // HAVE_INF_ENGINE +#define CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 do { CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support (legacy API is not supported anymore)"); } while (0) + namespace cv { namespace dnn { #ifdef HAVE_INF_ENGINE @@ -90,170 +69,16 @@ Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob); void infEngineBlobsToMats(const std::vector& blobs, std::vector& mats); -#ifdef HAVE_DNN_IE_NN_BUILDER_2019 - -class InfEngineBackendNet -{ -public: - InfEngineBackendNet(); - - InfEngineBackendNet(InferenceEngine::CNNNetwork& net); - - void addLayer(InferenceEngine::Builder::Layer& layer); - - void addOutput(const std::string& name); - - void connect(const std::vector >& inputs, - const std::vector >& outputs, - const std::string& layerName); - - bool isInitialized(); - - void init(Target targetId); - - void forward(const std::vector >& outBlobsWrappers, - bool isAsync); - - void initPlugin(InferenceEngine::CNNNetwork& net); - - void addBlobs(const std::vector >& ptrs); - - void reset(); - -private: - InferenceEngine::Builder::Network netBuilder; - - InferenceEngine::ExecutableNetwork netExec; - InferenceEngine::BlobMap allBlobs; - std::string device_name; -#if INF_ENGINE_VER_MAJOR_LE(2019010000) - InferenceEngine::InferenceEnginePluginPtr enginePtr; - InferenceEngine::InferencePlugin plugin; -#else - bool isInit = false; -#endif - - struct InfEngineReqWrapper - { - InfEngineReqWrapper() : isReady(true) {} - - void makePromises(const std::vector >& outs); - - InferenceEngine::InferRequest req; - std::vector outProms; - std::vector outsNames; - bool isReady; - }; - - std::vector > infRequests; - - InferenceEngine::CNNNetwork cnn; - bool hasNetOwner; - - std::map layers; - std::vector requestedOutputs; - - std::set > unconnectedPorts; -}; -class InfEngineBackendNode : public BackendNode -{ -public: - InfEngineBackendNode(const InferenceEngine::Builder::Layer& layer); - - InfEngineBackendNode(Ptr& layer, std::vector& inputs, - std::vector& outputs, std::vector& internals); - - void connect(std::vector >& inputs, - std::vector >& outputs); - - // Inference Engine network object that allows to obtain the outputs of this layer. - InferenceEngine::Builder::Layer layer; - Ptr net; - // CPU fallback in case of unsupported Inference Engine layer. - Ptr cvLayer; -}; - -class InfEngineBackendWrapper : public BackendWrapper -{ -public: - InfEngineBackendWrapper(int targetId, const Mat& m); - - InfEngineBackendWrapper(Ptr wrapper); - - ~InfEngineBackendWrapper(); - - static Ptr create(Ptr wrapper); - - virtual void copyToHost() CV_OVERRIDE; - - virtual void setHostDirty() CV_OVERRIDE; - - InferenceEngine::DataPtr dataPtr; - InferenceEngine::Blob::Ptr blob; - AsyncArray futureMat; -}; - -InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout = InferenceEngine::Layout::ANY); - -InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector& shape, InferenceEngine::Layout layout); - -InferenceEngine::DataPtr infEngineDataNode(const Ptr& ptr); - -// Convert Inference Engine blob with FP32 precision to FP16 precision. -// Allocates memory for a new blob. -InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob); - -void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data, InferenceEngine::Builder::Layer& l); -// This is a fake class to run networks from Model Optimizer. Objects of that -// class simulate responses of layers are imported by OpenCV and supported by -// Inference Engine. The main difference is that they do not perform forward pass. -class InfEngineBackendLayer : public Layer -{ -public: - InfEngineBackendLayer(const InferenceEngine::CNNNetwork &t_net_) : t_net(t_net_) {}; - - virtual bool getMemoryShapes(const std::vector &inputs, - const int requiredOutputs, - std::vector &outputs, - std::vector &internals) const CV_OVERRIDE; - - virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, - OutputArrayOfArrays internals) CV_OVERRIDE; - - virtual bool supportBackend(int backendId) CV_OVERRIDE; - -private: - InferenceEngine::CNNNetwork t_net; -}; - - -class InfEngineExtension : public InferenceEngine::IExtension -{ -public: -#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2) - virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {} -#endif - virtual void Unload() noexcept {} - virtual void Release() noexcept {} - virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {} - - virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&, - InferenceEngine::ResponseDesc*) noexcept - { - return InferenceEngine::StatusCode::OK; - } - - InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory, - const InferenceEngine::CNNLayer* cnnLayer, - InferenceEngine::ResponseDesc* resp) noexcept; -}; +CV__DNN_INLINE_NS_BEGIN -#endif // HAVE_DNN_IE_NN_BUILDER_2019 +namespace openvino { +// TODO: use std::string as parameter +bool checkTarget(Target target); -CV__DNN_INLINE_NS_BEGIN +} // namespace openvino bool isMyriadX(); @@ -273,14 +98,8 @@ static inline std::vector getShape(const Mat& mat) return result; } - #endif // HAVE_INF_ENGINE -bool haveInfEngine(); - -void forwardInfEngine(const std::vector >& outBlobsWrappers, - Ptr& node, bool isAsync); - }} // namespace dnn, namespace cv #endif // __OPENCV_DNN_OP_INF_ENGINE_HPP__ diff --git a/modules/dnn/src/op_timvx.cpp b/modules/dnn/src/op_timvx.cpp new file mode 100644 index 000000000000..107d660d3974 --- /dev/null +++ b/modules/dnn/src/op_timvx.cpp @@ -0,0 +1,931 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2019-2021, Shenzhen Institute of Artificial Intelligence and +// Robotics for Society, all rights reserved. +// Third party copyrights are property of their respective owners. + +#include "precomp.hpp" +#include +#include "op_timvx.hpp" +#include "net_impl.hpp" + +namespace cv +{ +namespace dnn +{ +#ifdef HAVE_TIMVX + +CV__DNN_INLINE_NS_BEGIN + +// update all comsumer +void Net::Impl::tvUpdateConfictMap(int graphIndex, LayerData& ld, std::vector >& graphConflictMap) +{ + if (ld.consumers.empty()) + return; + for (int i = 0; i < ld.consumers.size(); i++) + { + LayerData &consumerld = layers[ld.consumers[i].lid]; + std::vector::iterator it = std::find(graphConflictMap[ld.consumers[i].lid].begin(), + graphConflictMap[ld.consumers[i].lid].end(), graphIndex); + + if (it == graphConflictMap[ld.consumers[i].lid].end()) + { + graphConflictMap[ld.consumers[i].lid].push_back(graphIndex); + tvUpdateConfictMap(graphIndex, consumerld, graphConflictMap); + } + else + continue; + } +} + +// Convert TRANSIENT to OUTPUT +void Net::Impl::tvConvertToOutputNode(const LayerData& ld, Ptr& targetWrap) +{ + // find right layer. + for (auto& inputLayerId : ld.inputLayersId) + { + LayerData &inputld = layers[inputLayerId]; + auto itWrap = std::find(inputld.outputBlobsWrappers.begin(), + inputld.outputBlobsWrappers.end(), targetWrap); + if (itWrap != inputld.outputBlobsWrappers.end()) + { + auto outputWrap = (*itWrap).dynamicCast(); + if (!outputWrap->isTensor()) + continue; + + auto inputNode = inputld.backendNodes[DNN_BACKEND_TIMVX].dynamicCast(); + if (!inputNode->isLast && inputNode->opIndex != -1) + { + CV_Assert(outputWrap->getTensorAttr() == tim::vx::TRANSIENT); + // set last + inputNode->isLast = true; + + auto shapeType = getShapeTypeFromMat(outputWrap->getMat()); + auto outQuant = outputWrap->getTensorQuantization(); + + outputWrap->setTensorShape(shapeType); + outputWrap->createTensor(inputNode->tvGraph->graph, + tim::vx::TensorAttribute::OUTPUT, outQuant); + int outIndex = inputNode->tvGraph->addWrapper(outputWrap); + inputNode->outputIndexList.clear(); + inputNode->outputIndexList.push_back(outIndex); + } + } + } +} + +void Net::Impl::initTimVXBackend() +{ + CV_TRACE_FUNCTION(); + CV_Assert(preferableBackend == DNN_BACKEND_TIMVX); + + // Build TimVX Graph from sets of layers that support this TimVX backend. + // Split a whole model on several TimVX Graph if some of layers are not implemented by TimVX backend. + if (!haveTimVX()) + return; + + // Allocate graphConflictMap + if (timVxInfo.graphConflictMap.empty()) + timVxInfo.graphConflictMap.resize(layers.size()); + + auto it = layers.begin(); + bool isLast = false; // If the node is the last node in current tvGraph. + + for (; it != layers.end(); it++) + { + isLast = false; + LayerData &ld = it->second; + if(ld.skip) + continue; + Ptr layer = ld.layerInstance; + if (!layer->supportBackend(preferableBackend)) + { + continue; + } + + // If layer consumers are more than one, set isLast true. + // For now, TimVX backend divides multiple branchs into multiple tvGraph. + if (ld.consumers.size() == 0) + { + isLast = true; + } + else if(ld.consumers.size() == 1) + { + LayerData* consumerld = &layers[ld.consumers[0].lid]; + + while (consumerld) + { + if (consumerld->skip) + { + if (consumerld->consumers.size() == 1) + { + int nextLayerId = consumerld->consumers[0].lid; + consumerld = &layers[nextLayerId]; + } + else + { + isLast = true; + break; + } + } + else + { + break; + } + } + Ptr& consumerLayer = consumerld->layerInstance; + + if (!isLast && !consumerLayer->supportBackend(preferableBackend)) + { + isLast = true; + } + } + else + { + // If there are is multiple input, and only one of them is supported. + int tvSupportNum = 0; + for (int i = 0; iskip) + { + if (consumerld->consumers.size() == 1) + { + int nextLayerId = consumerld->consumers[0].lid; + consumerld = &layers[nextLayerId]; + } + else + { + isLast = true; + break; + } + } + else + { + break; + } + } + Ptr& consumerLayer = consumerld->layerInstance; + + if (consumerLayer->supportBackend(preferableBackend)) + { + tvSupportNum++; + } + } + + if (tvSupportNum != 1) + isLast = true; + } + + int graphIndex = -1; + bool needRecorrect = !timVxInfo.findGraphIndex(ld.inputBlobsWrappers, graphIndex); + + if (graphIndex != -1 && !needRecorrect) + { + needRecorrect = timVxInfo.isConflict(ld.id, graphIndex); + } + + // Recorrect the input layer. + if (needRecorrect) + { + // set all inputLayers' as last layer, and convert TRANSIENT to output. + for (int i = 0; i < ld.inputBlobsWrappers.size(); i++) + { + auto inputWrap = ld.inputBlobsWrappers[i]; + auto tvInputWrap = inputWrap.dynamicCast(); + if (!tvInputWrap->isTensor()) + continue; + + auto attr = tvInputWrap->getTensorAttr(); + if (attr == tim::vx::TensorAttribute::OUTPUT) + { + continue; + } + else if (attr == tim::vx::TensorAttribute::INPUT) + { + Mat matTmp = tvInputWrap->getMat(); + tvInputWrap = Ptr(new TimVXBackendWrapper(matTmp)); + + } + else if (attr == tim::vx::TensorAttribute::TRANSIENT) + { + tvConvertToOutputNode(ld, tvInputWrap); + // updateConflictMap + tvUpdateConfictMap(graphIndex, ld, timVxInfo.graphConflictMap); + } + } + graphIndex = -1; + } + + if (graphIndex == -1) + { + graphIndex = timVxInfo.createGraph(); + } + timVxInfo.setTmpGraphIndex(graphIndex); + + ld.backendNodes[DNN_BACKEND_TIMVX] = + layer->initTimVX(&timVxInfo, ld.inputBlobsWrappers, ld.outputBlobsWrappers, isLast); + + // post process, create last node correctly. + if (isLast && ld.backendNodes[DNN_BACKEND_TIMVX]) + { + auto tmpNode = ld.backendNodes[DNN_BACKEND_TIMVX].dynamicCast(); + tmpNode->isLast = true; + // update graphConflictMap + tvUpdateConfictMap(graphIndex, ld, timVxInfo.graphConflictMap); + } + + // post process for failing to create timvx Node. + if (!ld.backendNodes[DNN_BACKEND_TIMVX]) + { + for (int i = 0; i < ld.inputBlobsWrappers.size(); i++) + { + auto inputWrap = ld.inputBlobsWrappers[i]; + auto tvInputWrap = inputWrap.dynamicCast(); + if (!tvInputWrap->isTensor()) + continue; + + auto attr = tvInputWrap->getTensorAttr(); + if (attr == tim::vx::TensorAttribute::TRANSIENT) + { + tvConvertToOutputNode(ld, tvInputWrap); + } + } + } + } + + // Op Binding + it = layers.begin(); + Ptr node; + std::vector > tmpGrapList; + for (; it != layers.end(); it++) + { + LayerData &ld = it->second; + + if (ld.backendNodes[DNN_BACKEND_TIMVX]) + node = ld.backendNodes[DNN_BACKEND_TIMVX].dynamicCast(); + else + continue; + + // Binding tvTensor and tvOp + if (node->opIndex >= 0) + node->opBinding(); + } +} + +CV__DNN_INLINE_NS_END + +// from CPU to NPU +bool copyToTensor(std::shared_ptr &dst, const Mat &src) +{ + CV_Assert(src.isContinuous() && (src.type() == CV_8S || src.type() == CV_32F)); + if (dst->CopyDataToTensor(src.data, src.total())) + { + return true; + } + else + return false; +} + +// from NPU to CPU +bool copyToMat(const Mat &dst, std::shared_ptr &src) +{ + CV_Assert(dst.isContinuous() && (dst.type() == CV_8S || dst.type() == CV_32F)); + if (src->CopyDataFromTensor(dst.data)) + { + return true; + } + else + return false; +} + +tvActivationType getTimVXActType(String & actString) +{ + if (actString == "ReLUInt8") return tvActReLU; + if (actString == "ReLU6Int8") return tvActReLU6; + if (actString == "TanHInt8") return tvActTanH; + if (actString == "SwishInt8") return tvActSwish; + if (actString == "MishInt8") return tvActMish; + if (actString == "SigmoidInt8") return tvActSigmoid; + if (actString == "ELUInt8") return tvActELU; + + return tvActNotSupported; +} + +tim::vx::ShapeType getShapeTypeFromMat(const Mat& mat, bool ifConst) +{ + /* Convert Mat shape to TimVX Tensor shape. + DataLayout in TimVX is WHCN, while NCHW in OpenCV. + So we do vector reverse. + */ + CV_Assert(!mat.empty()); + tim::vx::ShapeType tvInputShape; + auto matShape = shape(mat); + tvInputShape.assign(matShape.begin(), matShape.end()); + + if ( matShape.size() > 1 ) // TODO: check when we need reverse the shape vector. + { + if (ifConst && tvInputShape.size() == 2 && tvInputShape[1] == 1) + { // if bias vector, shape [n, 1] to [n]. + tvInputShape.resize(1); + } + else + std::reverse(tvInputShape.begin(), tvInputShape.end()); + } + return tvInputShape; +} + +bool getQuantType(const std::vector& scales, int numOutput) +{ + CV_Assert(!scales.empty()); + if (numOutput == -1) + { + numOutput = scales.size(); + } + bool tvSymmetric = false; + + for (int i =1; i < numOutput; i++) + { + if (std::abs(scales[0] - scales[i]) > std::numeric_limits::epsilon()) + { + tvSymmetric = true; + break; + } + } + + return tvSymmetric; +} + +// convert mat Depth to tensorDataType +tim::vx::DataType dataTypeConvert(int matDepth) +{ + tim::vx::DataType tensorDataType; + switch(matDepth) + { + case CV_8U: + { + tensorDataType = tim::vx::DataType::UINT8; + break; + } + case CV_8S: + { + tensorDataType = tim::vx::DataType::INT8; + break; + } + case CV_16U: + { + tensorDataType = tim::vx::DataType::UINT16; + break; + } + case CV_16S: + { + tensorDataType = tim::vx::DataType::INT16; + break; + } + case CV_32S: + { + tensorDataType = tim::vx::DataType::INT32; + break; + } + case CV_32F: + { + tensorDataType = tim::vx::DataType::FLOAT32; + break; + } + case CV_16F: + { + tensorDataType = tim::vx::DataType::FLOAT16; + break; + } + default: + { + tensorDataType = tim::vx::DataType::UNKNOWN; + break; + } + } + return tensorDataType; +} + +std::vector > getWrappers(const std::vector wrappersIndex, + Ptr tvGraph) +{ + std::vector > wrappers; + for (int i = 0; igetWrapper(wrappersIndex[i]); + if (wrapper) + wrappers.push_back(wrapper); + } + + return wrappers; +} + +// *********************** TimVXGraph ******************** +TimVXGraph::TimVXGraph() +{ + // new TimVX Graph + context = tim::vx::Context::Create(); + graph = context->CreateGraph(); + isCompiled = false; +} + +TimVXGraph::~TimVXGraph() +{ + + // release opList + for (auto& tensor: tensorList) + tensor.reset(); + + // release tensorList + for (auto& op: opList) + op.reset(); + + // release graph + graph.reset(); + + // release context + context.reset(); +} + +std::shared_ptr TimVXGraph::getOp(const int opIndex) +{ + CV_Assert(0 <= opIndex && !opList.empty() && opIndex < opList.size()); + return opList[opIndex]; +} + +int TimVXGraph::addWrapper(Ptr& tensorWrapper) +{ + CV_Assert(tensorWrapper->isTensor()); + tim::vx::TensorAttribute tensorAttr = tensorWrapper->getTensorAttr(); + + wrapperList.push_back(tensorWrapper); + tensorList.push_back(tensorWrapper->getTensor()); + int wrapperIndex = wrapperList.size() -1; + + if (tensorAttr == tim::vx::TensorAttribute::INPUT) + { + inputWrappersIndex.push_back(wrapperIndex); + } + + if (tensorAttr == tim::vx::TensorAttribute::OUTPUT) + { + outputWrappersIndex.push_back(wrapperIndex); + } + + return wrapperIndex; +} + +Ptr TimVXGraph::getWrapper(int wrapperIndex) +{ + CV_Assert(wrapperIndex>=0 && wrapperIndex < wrapperList.size()); + return wrapperList[wrapperIndex]; +} + +int TimVXGraph::addOp(const std::shared_ptr& op) +{ + CV_Assert(op); + opList.emplace_back(op); + return opList.size()-1; +} + +int TimVXGraph::getTensorIndex(const std::shared_ptr& tensor) +{ + auto it = find(tensorList.begin(), tensorList.end(), tensor); + if (it != tensorList.end()) + return it - tensorList.begin(); + else + return -1; +} + +void TimVXGraph::forward() +{ + CV_Assert(!inputWrappersIndex.empty() && !outputWrappersIndex.empty()); + + // Every TimVXGraph Instance only compiles once. + if (!this->isCompiled) + { + if (!graph->Compile()) + CV_Error(cv::Error::StsBadArg, " Fail to compile TimVX graph!"); + this->isCompiled = true; + } + + if (!graph->Run()) + CV_Error(cv::Error::StsBadArg, " Fail to run TimVX graph!"); +} + +// *********************** TimVXBackendNode ******************** +TimVXBackendNode::TimVXBackendNode(const Ptr& tvGraph_): BackendNode(DNN_BACKEND_TIMVX) +{ + opIndex = -1; + tvGraph = tvGraph_; + isLast = false; +} + +TimVXBackendNode::TimVXBackendNode(const Ptr& tvGraph_, + const std::shared_ptr& op_): BackendNode(DNN_BACKEND_TIMVX) +{ + tvGraph = tvGraph_; + opIndex = tvGraph->addOp(op_); + isLast = false; +} + +TimVXBackendNode::TimVXBackendNode(const Ptr& tvGraph_, std::shared_ptr& op_, + std::vector& inputsIndex, std::vector& outpusIndex) + :BackendNode(DNN_BACKEND_TIMVX) +{ + tvGraph = tvGraph_; + opIndex = tvGraph->addOp(op_); + isLast = false; + + if (!inputsIndex.empty()) + inputIndexList.assign(inputsIndex.begin(), inputsIndex.end()); + + if (!outpusIndex.empty()) + outputIndexList.assign(outpusIndex.begin(), outpusIndex.end()); +} + +bool TimVXBackendNode::opBinding() +{ + if (!tvGraph || tvGraph->isCompiled || opIndex == -1) + return false; + + std::shared_ptr op = tvGraph->getOp(opIndex); + + if (!inputIndexList.empty()) + { + std::vector > inputsWrapper = getWrappers(inputIndexList, tvGraph); + // Binding input Tensor. + for (auto& warpper: inputsWrapper) + { + op->BindInput(warpper->getTensor()); + } + } + + if (!outputIndexList.empty()) + { + std::vector > outputsWrapper = getWrappers(outputIndexList, tvGraph); + for (auto& warpper: outputsWrapper) + { + op->BindOutput(warpper->getTensor()); + } + } + return true; +} + +void TimVXBackendNode::setInputTensor() +{ + if (!tvGraph || opIndex == -1) + return; + + if (!inputIndexList.empty()) + { + std::vector > inputsWrapper = getWrappers(inputIndexList, tvGraph); + + // Binding input Tensor. + for (auto& warpper: inputsWrapper) + { + if (warpper->getTensorAttr() == tim::vx::TensorAttribute::INPUT) + { + warpper->setHostDirty(); + warpper->copyToDevice(); + } + } + } +} + +// *********************** TimVXBackendWrapper ******************** +// Default Constructor +TimVXBackendWrapper::TimVXBackendWrapper() : BackendWrapper(DNN_BACKEND_TIMVX, DNN_TARGET_NPU) +{ + isTensor_ = false; + deviceDirty = false; + hostDirty = false; + tensorType = tim::vx::DataType::UNKNOWN; + tensorShape = {}; + tensorIndex = -1; + tensorAttr = tim::vx::TensorAttribute::CONSTANT; +} + +TimVXBackendWrapper::TimVXBackendWrapper(Mat& m) : BackendWrapper(DNN_BACKEND_TIMVX, + DNN_TARGET_NPU) +{ + host = m; + isTensor_ = false; + deviceDirty = false; + hostDirty = true; + tensorType = dataTypeConvert(m.type()); + tensorShape = {}; + tensorIndex = -1; + tensorAttr = tim::vx::TensorAttribute::CONSTANT; + + // TODO: unsupported data by TimVX should run convert function first. + CV_Assert(tensorType != tim::vx::DataType::UNKNOWN); +} + +TimVXBackendWrapper::TimVXBackendWrapper(const Ptr& baseBuffer, Mat& m) + :BackendWrapper(DNN_BACKEND_TIMVX, DNN_TARGET_NPU) +{ + Ptr base = baseBuffer.dynamicCast(); + CV_Assert(!base.empty()); + tensor = base->tensor; + isTensor_ = base->isTensor_; + tensorIndex = base->tensorIndex; + tensorType = base->tensorType; + tensorAttr = base->tensorAttr; + tensorShape = base->tensorShape; + deviceDirty = base->deviceDirty; + hostDirty = base->hostDirty; + host = m; +} + +TimVXBackendWrapper::TimVXBackendWrapper(std::shared_ptr& tensor_) + :BackendWrapper(DNN_BACKEND_TIMVX, DNN_TARGET_NPU) +{ + tensor = tensor_; + isTensor_ = true; + deviceDirty = true; + hostDirty = false; + tensorType = tensor_->GetDataType(); // getTensor DataType. + tensorAttr = tensor_->GetSpec().attr_; // getTensor Attribution. + tensorShape = tensor_->GetShape(); + tensorIndex = -1; +} + +void TimVXBackendWrapper::setTensorShape(const tim::vx::ShapeType & matShape) +{ + CV_Assert(!matShape.empty()); + tensorShape.assign(matShape.begin(), matShape.end()); +} + +int TimVXBackendWrapper::getTensorIndex() +{ + CV_Assert(isTensor_); + return tensorIndex; +} + +tim::vx::TensorAttribute TimVXBackendWrapper::getTensorAttr() +{ + CV_Assert(isTensor_); + return tensorAttr; +} + +// Create tensor +void TimVXBackendWrapper::createTensor(std::shared_ptr& graph, + tim::vx::TensorAttribute tensorAttribute) +{ + Ptr epmtyQuant = nullptr; + return this->createTensor(graph, tensorAttribute, epmtyQuant); +} + +// Create tensor +void TimVXBackendWrapper::createTensor(std::shared_ptr& graph, + tim::vx::TensorAttribute tensorAttribute, Ptr& tvQuant) +{ + CV_Assert(graph); + tim::vx::TensorSpec tensorSpec; + + if (tensorAttribute == tim::vx::INPUT) + { + CV_Assert(!host.empty()); + tensorShape = getShapeTypeFromMat(host); + } + else if (tensorAttribute == tim::vx::OUTPUT) + { + CV_Assert(!tensorShape.empty() && !host.empty()); + tensorShape = getShapeTypeFromMat(host); + } + else if (tensorAttribute == tim::vx::CONSTANT) + { + if (!host.empty()) + tensorShape = getShapeTypeFromMat(host, true); + } + else + { + if (!host.empty()) + tensorShape = getShapeTypeFromMat(host); + } + + // Tensor shape + if (tvQuant) + { + tensorSpec = tim::vx::TensorSpec(tensorType, tensorShape, tensorAttribute, *tvQuant); + } + else + { + tensorSpec = tim::vx::TensorSpec(tensorType, tensorShape, tensorAttribute); + } + + if (!host.empty() && tensorAttribute != tim::vx::INPUT && tensorAttribute != tim::vx::OUTPUT && tensorAttribute != tim::vx::TRANSIENT) + { + tensor = graph->CreateTensor(tensorSpec, (void *)(host.data)); + } + else + { + tensor = graph->CreateTensor(tensorSpec); + } + isTensor_ = true; + + // set Attribution + tensorAttr = tensorAttribute; +} + +Ptr TimVXBackendWrapper::getTensorQuantization() +{ + CV_Assert(isTensor_ && tensor); + auto quantize = tensor->GetQuantization(); + return makePtr(quantize); +} + +std::shared_ptr TimVXBackendWrapper::getTensor() +{ + CV_Assert(isTensor_); + return tensor; +} + +Mat TimVXBackendWrapper::getMat() +{ + if (host.empty()) + return {}; + return host; +} + + +bool TimVXBackendWrapper::isTensor() +{ + return isTensor_; +} + +void TimVXBackendWrapper::copyToHost() +{ + if (deviceDirty && !host.empty()) + { + copyToMat(host, tensor); + deviceDirty = false; + } +} + +void TimVXBackendWrapper::setHostDirty() +{ + hostDirty = true; +} + +void TimVXBackendWrapper::setDeviceDirty() +{ + deviceDirty = true; +} + +void TimVXBackendWrapper::copyToDevice() +{ + if (isTensor_ && hostDirty && !host.empty()) + { + copyToTensor(tensor, host); + hostDirty = false; + } +} + +// *********************** TimVXInfo ******************** +TimVXInfo::TimVXInfo() +{ + graphIndex = -1; +} + +TimVXInfo::~TimVXInfo() +{} + +int TimVXInfo::createGraph() +{ + Ptr tmpGraph = Ptr(new TimVXGraph()); + this->tvGraphList.push_back(tmpGraph); + return this->tvGraphList.size() - 1; +} + +bool TimVXInfo::findGraphIndex(const std::vector > &inputsWrapper, int& graphIndex) +{ + graphIndex = -1; + int wrapperSize = inputsWrapper.size(); + int graphSize = tvGraphList.size(); + + if (wrapperSize != 0 && graphSize == 0) + { + return true; + } + + int tensorIndex = -1; + Ptr wrapper; + Ptr tvGraph; + + for (int i = 0; i < graphSize; i++) + { + tvGraph = tvGraphList[i]; + for (int j = 0; j < wrapperSize; j++ ) + { + wrapper = inputsWrapper[j].dynamicCast(); + + if (!wrapper->isTensor()) // Skip wrapper without Tensor. + continue; + + tensorIndex = tvGraph->getTensorIndex(wrapper->getTensor()); + if (tensorIndex != -1 && wrapper->getTensorAttr() == tim::vx::TensorAttribute::TRANSIENT) + { + if (graphIndex == -1) + graphIndex = i; + else if (graphIndex != i) // if inputs of the same inputWrapper are from differen tvGraph. + { + graphIndex = -1; + return false; + } + } + } + } + return true; +} + +void TimVXInfo::setTmpGraphIndex(int graphIndex) +{ + this->graphIndex = graphIndex; +} + +int TimVXInfo::getTmpGraphIndex() +{ + int res = -1; + if (graphIndex != -1) + { + res = graphIndex; + graphIndex = -1; + } + return res; +} + +bool TimVXInfo::isConflict(int layerId, int graphIndex) +{ + if (graphConflictMap[layerId].empty()) + return false; + + std::vector::iterator it = std::find(graphConflictMap[layerId].begin(), + graphConflictMap[layerId].end(), graphIndex); + if (it != graphConflictMap[layerId].end()) + return true; + else + return false; +} + +Ptr TimVXInfo::getGraph() +{ + int index = getTmpGraphIndex(); + if (0 <= index && index < tvGraphList.size()) + return tvGraphList[index]; + else + return {}; +} + +#endif + +void forwardTimVX(std::vector >& outputs, const Ptr& node_) +{ +#ifdef HAVE_TIMVX + CV_Assert(!node_.empty()); + Ptr node = node_.dynamicCast(); + + if (node) + { + // set input + node->setInputTensor(); + + // graph Forward + if (node->isLast) + { + node->tvGraph->forward(); + } + } + else + return; + + // set ouput + Ptr outWarpper; + for (int i = 0; i < outputs.size(); i++) + { + outWarpper = outputs[i].dynamicCast(); + if (outWarpper->isTensor() && outWarpper->getTensorAttr() == tim::vx::TensorAttribute::OUTPUT) + { + outWarpper->setDeviceDirty(); + outWarpper->copyToHost(); + } + } +#endif +} + +bool haveTimVX() +{ +#ifdef HAVE_TIMVX + return true; +#else + return false; +#endif +} +} // namespace dnn +} // namespace cv \ No newline at end of file diff --git a/modules/dnn/src/op_timvx.hpp b/modules/dnn/src/op_timvx.hpp new file mode 100644 index 000000000000..d08cf64bb556 --- /dev/null +++ b/modules/dnn/src/op_timvx.hpp @@ -0,0 +1,187 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2019-2021, Shenzhen Institute of Artificial Intelligence and +// Robotics for Society, all rights reserved. +// Third party copyrights are property of their respective owners. + +#ifndef OPENCV_DNN_OP_TIMVX_HPP +#define OPENCV_DNN_OP_TIMVX_HPP + +#include + +// TimVX head file. +#ifdef HAVE_TIMVX +#include "tim/vx/context.h" +#include "tim/vx/graph.h" +#include "tim/vx/operation.h" +#include "tim/vx/ops.h" +#include "tim/vx/tensor.h" +#endif // HAVE_TIMVX + +namespace cv +{ +namespace dnn +{ +#ifdef HAVE_TIMVX + +enum tvActivationType{ + tvActNotSupported = -1, + tvActReLU, + tvActReLU6, + tvActTanH, + tvActSwish, + tvActMish, + tvActSigmoid, + tvActELU +}; + +// Data copied from/to Mat to/from Tensor. Change the shape of dst if +// needed to make it the same shape as src. +bool copyToTensor(Ptr &dst, const Mat &src); +bool copyToMat(const Mat &dst, Ptr &src); +tvActivationType getTimVXActType(String & actString); + +// Convert Mat shape to TimVX TensorShape +tim::vx::ShapeType getShapeTypeFromMat(const Mat& mat, bool ifConst = false); + +// if all value in weight +bool getQuantType(const std::vector& scales, int numOutput = -1); + +class TimVXInfo; +class TimVXGraph; +class TimVXBackendNode; +class TimVXBackendWrapper; + +// Maintain the tvGraph and tvTensor List. For now, every tvGraph only have one output node, and each node +// in tvGraph has only one output too. It could be optimized in future. +// TODO: tvGraph supports multiple output node. +class TimVXGraph +{ +public: + TimVXGraph(); + ~TimVXGraph(); + std::shared_ptr getOp(const int opIndex); + + // It will add tensorWrapper to wrapperList, and return index. + // And add tensor Ptr to tensorList. + int addWrapper(Ptr& tensorWrapper); + + void forward(); + + // Add new op to opList, and return the index. + int addOp(const std::shared_ptr& op); + + // If tensor existed in tensorList, return the tensorIndex, otherwise return -1. + int getTensorIndex(const std::shared_ptr& tensor); + + Ptr getWrapper(int wrapperIndex); + + std::shared_ptr graph; + bool isCompiled; // Every tvGraph can only be compiled once. + +private: + std::shared_ptr context; + std::vector inputWrappersIndex; + std::vector outputWrappersIndex; + std::vector > wrapperList; + std::vector > tensorList; + std::vector > opList; +}; + +class TimVXBackendNode : public BackendNode +{ +public: + TimVXBackendNode(const Ptr& tvGraph); + TimVXBackendNode(const Ptr& tvGraph, const std::shared_ptr& op); + TimVXBackendNode(const Ptr& tvGraph, std::shared_ptr& op, + std::vector& inputsIndex, std::vector& outpusIndex); + + void setInputTensor(); + bool opBinding(); + + // flag for marking OutputNode of tvGraph this node is the last node in this TimVX Graph. + bool isLast; + int opIndex; + + // index of tensor and wrapper. + std::vector inputIndexList; + std::vector outputIndexList; + Ptr tvGraph; +}; + +class TimVXBackendWrapper : public BackendWrapper +{ +public: + TimVXBackendWrapper(); + TimVXBackendWrapper(Mat& m); + TimVXBackendWrapper(const Ptr& baseBuffer, Mat& m); + TimVXBackendWrapper(std::shared_ptr& tensor); + + // Create Output Tensor + void createTensor(std::shared_ptr& graph, tim::vx::TensorAttribute tensorAttribute); + void createTensor(std::shared_ptr& graph, tim::vx::TensorAttribute tensorAttribute, + Ptr& tvQuant); + std::shared_ptr getTensor(); + Mat getMat(); + + // The Output tensor in TimVX doesn't have HostMat, The shape can only be given. + void setTensorShape(const tim::vx::ShapeType & matShape); + int getTensorIndex(); + Ptr getTensorQuantization(); + tim::vx::TensorAttribute getTensorAttr(); + bool isTensor(); + + // Data Copy, CPU <==> NPU + virtual void copyToHost() CV_OVERRIDE; + virtual void setHostDirty() CV_OVERRIDE; + void setDeviceDirty(); + void copyToDevice(); + +private: + tim::vx::DataType tensorType; + bool deviceDirty; + bool hostDirty; + int tensorIndex; // index of tensorList in specific TimVXGraph. + bool isTensor_; + Mat host; + + tim::vx::ShapeType tensorShape; + std::shared_ptr tensor; + tim::vx::TensorAttribute tensorAttr; +}; + +// Contain all created tvGraphList, used in every +class TimVXInfo{ +public: + TimVXInfo(); + ~TimVXInfo(); + + // Find the right graph Index set as graphIndex, if cannot find, return empty ptr. + Ptr getGraph(); + bool findGraphIndex(const std::vector > &inputsWrapper, int& graphIndex); + void setTmpGraphIndex(int graphIndex); + bool isConflict(int layerId, int graphIndex); + + // create a TimVXGraph, add it to tvGraphList, and return the index in tvGraphList. + int createGraph(); + + // graphConflictIndex[layerIndex] saves conflict graph index, which should be excluded + std::vector > graphConflictMap; + +private: + int getTmpGraphIndex(); + std::vector > tvGraphList; + int graphIndex; + +}; + +#endif + +void forwardTimVX(std::vector > &outputs, const Ptr& node); +bool haveTimVX(); +} // namespace dnn +} // namespace cv + +#endif // OPENCV_DNN_OP_TIMVX_HPP \ No newline at end of file diff --git a/modules/dnn/src/op_vkcom.cpp b/modules/dnn/src/op_vkcom.cpp index a252104240c6..7249c27ce253 100644 --- a/modules/dnn/src/op_vkcom.cpp +++ b/modules/dnn/src/op_vkcom.cpp @@ -8,12 +8,51 @@ #include "precomp.hpp" #include #include "op_vkcom.hpp" +#include "net_impl.hpp" namespace cv { namespace dnn { #ifdef HAVE_VULKAN + +CV__DNN_INLINE_NS_BEGIN + + +void Net::Impl::initVkComBackend() +{ + CV_TRACE_FUNCTION(); + CV_Assert(preferableBackend == DNN_BACKEND_VKCOM); + + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); it++) + { + LayerData &ld = it->second; + Ptr layer = ld.layerInstance; + if (!layer->supportBackend(preferableBackend)) + { + continue; + } + + ld.skip = false; + + try + { + ld.backendNodes[DNN_BACKEND_VKCOM] = + layer->initVkCom(ld.inputBlobsWrappers); + } + catch (const cv::Exception& e) + { + CV_LOG_ERROR(NULL, "initVkCom failed, fallback to CPU implementation. " << e.what()); + ld.backendNodes[DNN_BACKEND_VKCOM] = Ptr(); + } + } +} + +CV__DNN_INLINE_NS_END + + +/////////////////////////////////////////////////////////////////////////////// + void copyToTensor(vkcom::Tensor &dst, const Mat &src) { CV_Assert(src.isContinuous() && src.type() == CV_32F); diff --git a/modules/dnn/src/op_webnn.cpp b/modules/dnn/src/op_webnn.cpp index 4dba55bcbe80..d5c17404235f 100644 --- a/modules/dnn/src/op_webnn.cpp +++ b/modules/dnn/src/op_webnn.cpp @@ -2,6 +2,7 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. +#include "precomp.hpp" #include #include "op_webnn.hpp" @@ -13,10 +14,281 @@ #include +#include "net_impl.hpp" + namespace cv { namespace dnn { #ifdef HAVE_WEBNN +CV__DNN_INLINE_NS_BEGIN + + +void Net::Impl::addWebnnOutputs(LayerData &ld) +{ + CV_TRACE_FUNCTION(); + + Ptr layerNet; + auto it = ld.backendNodes.find(preferableBackend); + if (it != ld.backendNodes.end()) + { + Ptr node = it->second; + if (!node.empty()) + { + Ptr webnnNode = node.dynamicCast(); + CV_Assert(!webnnNode.empty()); CV_Assert(!webnnNode->net.empty()); + layerNet = webnnNode->net; + } + } + + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) + { + Ptr webnnInpNode = inpNode.dynamicCast(); + CV_Assert(!webnnInpNode.empty()); CV_Assert(!webnnInpNode->net.empty()); + if (layerNet != webnnInpNode->net) + { + webnnInpNode->net->addOutput(webnnInpNode->name); + webnnInpNode->net->setUnconnectedNodes(webnnInpNode); + } + } + } +} + + +void Net::Impl::initWebnnBackend(const std::vector& blobsToKeep_) +{ + CV_TRACE_FUNCTION(); + CV_Assert_N(preferableBackend == DNN_BACKEND_WEBNN, haveWebnn()); + + Ptr net; + + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it) + { + LayerData &ld = it->second; + if (ld.id == 0) + { + CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) || + (netInputLayer->outNames.size() == ld.outputBlobsWrappers.size())); + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + Ptr wrapper = ld.outputBlobsWrappers[i].dynamicCast(); + std::string outputName = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]; + outputName = ld.outputBlobsWrappers.size() > 1 ? (outputName + "." + std::to_string(i)) : outputName; + wrapper->name = outputName; + } + } + else + { + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + Ptr wrapper = ld.outputBlobsWrappers[i].dynamicCast(); + std::string outputName = ld.outputBlobsWrappers.size() > 1 ? (ld.name + "." + std::to_string(i)) : ld.name; + wrapper->name = outputName; + } + } + } + + // Build WebNN networks from sets of layers that support this + // backend. Split a whole model on several WebNN networks if + // some of layers are not implemented. + for (MapIdToLayerData::iterator it = layers.begin(); it != layers.end(); ++it) + { + LayerData &ld = it->second; + + if (ld.id == 0 && ld.skip) + continue; + + bool fused = ld.skip; + Ptr layer = ld.layerInstance; + if (!fused && !layer->supportBackend(preferableBackend)) + { + // For test use. when not using WebNN, the test case will fail + // with the following code. + CV_LOG_WARNING(NULL, "Layer " + ld.type + " name " + ld.name + " is unsupported by WebNN backend."); + + addWebnnOutputs(ld); + net = Ptr(); + layer->preferableTarget = DNN_TARGET_CPU; + + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) { + Ptr webnnNode = inpNode.dynamicCast(); + CV_Assert(!webnnNode.empty()); + webnnNode->net->setUnconnectedNodes(webnnNode); + } + } + continue; + } + ld.skip = true; // Initially skip all WebNN supported layers. + + // Create a new network if one of inputs from different WebNN graph. + std::vector> inputNodes; + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + // Layer_Test_ROIPooling.Accuracy has 2 inputs inpLD = 0, 0 -> has 4 inputNodes (input, rois, input, rois) + if (inputNodes.size() == ld.inputBlobsId.size()) { + break; + } + LayerData &inpLd = layers[ld.inputBlobsId[i].lid]; + Ptr inpNode = inpLd.backendNodes[preferableBackend]; + if (!inpNode.empty()) + { + Ptr webnnInpNode = inpNode.dynamicCast(); + CV_Assert(!webnnInpNode.empty()); CV_Assert(!webnnInpNode->net.empty()); + if (webnnInpNode->net == net && !fused) { + inputNodes.push_back(inpNode); + continue; + } + } + + if (net.empty()) { + net = Ptr(new WebnnNet()); + } + + if (!fused) { + std::vector inputNames; + std::vector inputs; + + auto curr_pos = inpLd.consumers.begin(); + auto compare = [&ld] (const LayerPin& lp) { return lp.lid == ld.id; }; + auto cons = curr_pos; + while ((cons = std::find_if(curr_pos, inpLd.consumers.end(), compare)) != + inpLd.consumers.end()) { + int cons_inp = cons->oid; + Ptr inpWrapper = inpLd.outputBlobsWrappers[cons_inp]. + dynamicCast(); + CV_Assert(!inpWrapper.empty()); + auto iter = std::find(inputNames.begin(), inputNames.end(), + inpWrapper->name); + if (iter == inputNames.end()) { + inputNames.push_back(inpWrapper->name); + inputs.push_back(inpLd.outputBlobs[cons_inp]); + } + curr_pos = cons + 1; + } + + auto inps = net->setInputs(inputs, inputNames); + for (auto& inp : inps) { + WebnnBackendNode* node = new WebnnBackendNode(inp); + node->net = net; + inputNodes.emplace_back(Ptr(node)); + } + } + } + + Ptr node; + if (!net.empty()) + { + if (fused) + { + bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 && + ld.inputBlobs[0]->data == ld.outputBlobs[0].data; + CV_Assert(inPlace); + node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend]; + ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers; + } + } + else { + net = Ptr(new WebnnNet()); + } + + if (!fused) + { + CV_Assert(ld.inputBlobsId.size() == inputNodes.size()); + for (int i = 0; i < ld.inputBlobsId.size(); ++i) + { + int lid = ld.inputBlobsId[i].lid; + int oid = ld.inputBlobsId[i].oid; + if (oid == 0 || lid == 0) + continue; + + auto webnnInpNode = inputNodes[i].dynamicCast(); + inputNodes[i] = Ptr(new WebnnBackendNode(webnnInpNode->operand)); + } + + if (layer->supportBackend(preferableBackend)) + { + if (ld.type == "Const") { + ml::Operand fake_operand; + Ptr fake_input_node = Ptr(new WebnnBackendNode(fake_operand)); + fake_input_node->net = net; + inputNodes.push_back(fake_input_node); + } + node = layer->initWebnn(ld.inputBlobsWrappers, inputNodes); + for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i) + { + Ptr wrapper = ld.outputBlobsWrappers[i].dynamicCast(); + node.dynamicCast()->name = wrapper->name; + } + } + else + { + continue; + } + } + else if (node.empty()) + continue; + + ld.backendNodes[preferableBackend] = node; + + Ptr webnnNode = node.dynamicCast(); + CV_Assert(!webnnNode.empty()); + webnnNode->net = net; + + if (ld.consumers.empty()) { + // TF EAST_text_detection + webnnNode->net->setUnconnectedNodes(webnnNode); + } + for (const auto& pin : blobsToKeep_) + { + if (pin.lid == ld.id) + { + webnnNode->net->addOutput(webnnNode->name); + break; + } + } + net->addBlobs(ld.inputBlobsWrappers); + net->addBlobs(ld.outputBlobsWrappers); + addWebnnOutputs(ld); + } + + // Initialize all networks. + for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it) + { + LayerData &ld = it->second; + auto iter = ld.backendNodes.find(preferableBackend); + if (iter == ld.backendNodes.end()) + continue; + + Ptr& node = iter->second; + if (node.empty()) + continue; + + Ptr webnnNode = node.dynamicCast(); + if (webnnNode.empty()) + continue; + + CV_Assert(!webnnNode->net.empty()); + + if (!webnnNode->net->isInitialized()) + { + webnnNode->net->setUnconnectedNodes(webnnNode); + webnnNode->net->createNet((Target)preferableTarget); + ld.skip = false; + } + } +} + + +CV__DNN_INLINE_NS_END + + namespace webnn { ml::Operand BuildConstant(const ml::GraphBuilder& builder, const std::vector& dimensions, diff --git a/modules/dnn/src/opencl/activations.cl b/modules/dnn/src/opencl/activations.cl index 040ee20d8ae0..0624f48e194c 100644 --- a/modules/dnn/src/opencl/activations.cl +++ b/modules/dnn/src/opencl/activations.cl @@ -278,7 +278,7 @@ __kernel void CeluForward(const int n, __global T* in, __global T* out, { int index = get_global_id(0); if(index < n) - out[index] = max(0.f, in[index]) + min(0.f, alpha * expm1(in[index] / alpha)); + out[index] = max((T)0.f, in[index]) + (T)min(0.f, alpha * expm1(in[index] / alpha)); } __kernel void HardSigmoidForward(const int n, __global T* in, __global T* out, @@ -287,7 +287,7 @@ __kernel void HardSigmoidForward(const int n, __global T* in, __global T* out, { int index = get_global_id(0); if(index < n) - out[index] = max(0.f, min(1.f, alpha * in[index] + beta)); + out[index] = max((T)0.f, (T)min(1.f, alpha * in[index] + beta)); } __kernel void SeluForward(const int n, __global T* in, __global T* out, @@ -306,3 +306,26 @@ __kernel void ThresholdedReluForward(const int n, __global T* in, __global T* ou if(index < n) out[index] = (in[index] > alpha ? in[index] : 0.f); } + +__kernel void ShrinkForward(const int n, __global T* in, __global T* out, + const KERNEL_ARG_DTYPE bias, + const KERNEL_ARG_DTYPE lambd) +{ + int index = get_global_id(0); + if(index < n) + out[index] = in[index] < -lambd ? in[index] + bias : (in[index] > lambd ? in[index] - bias : 0.f); +} + +__kernel void SignForward(const int n, __global T* in, __global T* out) +{ + int index = get_global_id(0); + if(index < n) + out[index] = in[index] > 0.f ? 1.0f : ((in[index] < 0.f) ? -1.0f : 0.0f); +} + +__kernel void ReciprocalForward(const int n, __global T* in, __global T* out) +{ + int index = get_global_id(0); + if(index < n) + out[index] = 1.0f/in[index]; +} \ No newline at end of file diff --git a/modules/dnn/src/precomp.hpp b/modules/dnn/src/precomp.hpp index 6ee693dd6b36..abcd3745f964 100644 --- a/modules/dnn/src/precomp.hpp +++ b/modules/dnn/src/precomp.hpp @@ -66,6 +66,15 @@ #undef HAVE_CUDA #endif +#include +#include +#include +#include +#include +#include +#include +#include + #include #include diff --git a/modules/dnn/src/registry.cpp b/modules/dnn/src/registry.cpp new file mode 100644 index 000000000000..697fca601595 --- /dev/null +++ b/modules/dnn/src/registry.cpp @@ -0,0 +1,152 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" + +#include "op_halide.hpp" +#include "op_inf_engine.hpp" +#include "ie_ngraph.hpp" +#include "op_vkcom.hpp" +#include "op_cuda.hpp" +#include "op_webnn.hpp" +#include "op_timvx.hpp" + +#include "halide_scheduler.hpp" + + +namespace cv { +namespace dnn { +CV__DNN_INLINE_NS_BEGIN + + +class BackendRegistry +{ +public: + typedef std::vector< std::pair > BackendsList; + const BackendsList & getBackends() const { return backends; } + static BackendRegistry & getRegistry() + { + static BackendRegistry impl; + return impl; + } + + +private: + BackendRegistry() + { +#ifdef HAVE_HALIDE + backends.push_back(std::make_pair(DNN_BACKEND_HALIDE, DNN_TARGET_CPU)); +#ifdef HAVE_OPENCL + if (cv::ocl::useOpenCL()) + backends.push_back(std::make_pair(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL)); +#endif +#endif // HAVE_HALIDE + +#ifdef HAVE_INF_ENGINE + if (openvino::checkTarget(DNN_TARGET_CPU)) + { +#ifdef HAVE_DNN_NGRAPH + backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU)); +#endif + } + if (openvino::checkTarget(DNN_TARGET_MYRIAD)) + { +#ifdef HAVE_DNN_NGRAPH + backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD)); +#endif + } + if (openvino::checkTarget(DNN_TARGET_HDDL)) + { +#ifdef HAVE_DNN_NGRAPH + backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_HDDL)); +#endif + } +#ifdef HAVE_OPENCL + if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel()) + { + if (openvino::checkTarget(DNN_TARGET_OPENCL)) + { +#ifdef HAVE_DNN_NGRAPH + backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL)); +#endif + } + if (openvino::checkTarget(DNN_TARGET_OPENCL_FP16)) + { +#ifdef HAVE_DNN_NGRAPH + backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16)); +#endif + } + } +#endif +#endif // HAVE_INF_ENGINE + +#ifdef HAVE_WEBNN + if (haveWebnn()) + { + backends.push_back(std::make_pair(DNN_BACKEND_WEBNN, DNN_TARGET_CPU)); + } +#endif // HAVE_WEBNN + +#ifdef HAVE_OPENCL + if (cv::ocl::useOpenCL()) + { + backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL)); + backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)); + } +#endif + + backends.push_back(std::make_pair(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)); + +#ifdef HAVE_VULKAN + if (haveVulkan()) + backends.push_back(std::make_pair(DNN_BACKEND_VKCOM, DNN_TARGET_VULKAN)); +#endif + +#ifdef HAVE_CUDA + if (haveCUDA()) + { + backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA)); + backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA_FP16)); + } +#endif + +#ifdef HAVE_TIMVX + if (haveTimVX()) + { + backends.push_back(std::make_pair(DNN_BACKEND_TIMVX, DNN_TARGET_NPU)); + } +#endif + } + + BackendsList backends; +}; + + +std::vector> getAvailableBackends() +{ + return BackendRegistry::getRegistry().getBackends(); +} + +std::vector getAvailableTargets(Backend be) +{ + if (be == DNN_BACKEND_DEFAULT) + be = (Backend)getParam_DNN_BACKEND_DEFAULT(); +#ifdef HAVE_INF_ENGINE + if (be == DNN_BACKEND_INFERENCE_ENGINE) + be = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH; +#endif + + std::vector result; + const BackendRegistry::BackendsList all_backends = getAvailableBackends(); + for (BackendRegistry::BackendsList::const_iterator i = all_backends.begin(); i != all_backends.end(); ++i) + { + if (i->first == be) + result.push_back(i->second); + } + return result; +} + + +CV__DNN_INLINE_NS_END +}} // namespace cv::dnn diff --git a/modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp b/modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp index ecb5c62f5667..daadc32ad265 100644 --- a/modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp +++ b/modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp @@ -331,7 +331,7 @@ teng_graph_t tengine_init(const char* layer_name, float* input_, int inch, int g teg_weight = kernel_; } - /* initial the resoruce of tengine */ + /* initial the resource of tengine */ if(false == tengine_init_flag) { init_tengine(); diff --git a/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp b/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp index 4c856592abd1..72f546ead975 100644 --- a/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp +++ b/modules/dnn/src/tensorflow/tf_graph_simplifier.cpp @@ -990,6 +990,7 @@ void sortByExecutionOrder(tensorflow::GraphDef& net) nodesMap.insert(std::make_pair(node.name(), i)); } + CV_CheckEQ(nodesMap.size(), (size_t)net.node_size(), "Node names must be unique"); // Indices of nodes which use specific node as input. std::vector > edges(nodesMap.size()); std::vector numRefsToAdd(nodesMap.size(), 0); @@ -1007,7 +1008,7 @@ void sortByExecutionOrder(tensorflow::GraphDef& net) nodesMapIt = nodesMap.find(inpName); if (nodesMapIt != nodesMap.end()) { - edges[nodesMapIt->second].push_back(i); + edges.at(nodesMapIt->second).push_back(i); numInputsInGraph += 1; } } @@ -1019,11 +1020,11 @@ void sortByExecutionOrder(tensorflow::GraphDef& net) { int numControlEdges = 0; for (int j = 0; j < numInputsInGraph; ++j) - numControlEdges += node.input(j)[0] == '^'; - numRefsToAdd[i] = numControlEdges + 1; + numControlEdges += node.input(j).at(0) == '^'; + numRefsToAdd.at(i) = numControlEdges + 1; } else - numRefsToAdd[i] = numInputsInGraph; + numRefsToAdd.at(i) = numInputsInGraph; } } @@ -1035,17 +1036,16 @@ void sortByExecutionOrder(tensorflow::GraphDef& net) nodesToAdd.pop_back(); permIds.push_back(nodeToAdd); - - for (int i = 0; i < edges[nodeToAdd].size(); ++i) + for (int i = 0; i < edges.at(nodeToAdd).size(); ++i) { - int consumerId = edges[nodeToAdd][i]; - if (numRefsToAdd[consumerId] > 0) + int consumerId = edges.at(nodeToAdd).at(i); + if (numRefsToAdd.at(consumerId) > 0) { - if (numRefsToAdd[consumerId] == 1) + if (numRefsToAdd.at(consumerId) == 1) nodesToAdd.push_back(consumerId); else - CV_Assert(numRefsToAdd[consumerId] >= 0); - numRefsToAdd[consumerId] -= 1; + CV_Assert(numRefsToAdd.at(consumerId) >= 0); + numRefsToAdd.at(consumerId) -= 1; } } } diff --git a/modules/dnn/src/tensorflow/tf_importer.cpp b/modules/dnn/src/tensorflow/tf_importer.cpp index efaedfaab1ff..cd413c1ad762 100644 --- a/modules/dnn/src/tensorflow/tf_importer.cpp +++ b/modules/dnn/src/tensorflow/tf_importer.cpp @@ -11,6 +11,8 @@ Implementation of Tensorflow models parser #include "../precomp.hpp" +#include + #include #include #undef CV_LOG_STRIP_LEVEL @@ -513,6 +515,7 @@ class TFLayerHandler; class TFImporter { + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; public: TFImporter(Net& net, const char *model, const char *config = NULL); TFImporter(Net& net, const char *dataModel, size_t lenModel, @@ -565,7 +568,7 @@ class TFImporter typedef std::map DispatchMap; const DispatchMap dispatch; - static const DispatchMap buildDispatchMap(); + static DispatchMap buildDispatchMap(); void parseConvolution (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams); void parseBias (tensorflow::GraphDef& net, const tensorflow::NodeDef& layer, LayerParams& layerParams); @@ -642,7 +645,7 @@ class TFLayerHandler : public detail::LayerHandler TFImporter* importer; }; -const TFImporter::DispatchMap TFImporter::buildDispatchMap() +TFImporter::DispatchMap TFImporter::buildDispatchMap() { static DispatchMap dispatch; dispatch["Conv2D"] = dispatch["SpaceToBatchND"] = dispatch["DepthwiseConv2dNative"] = @@ -1678,10 +1681,8 @@ void TFImporter::parseStridedSlice(tensorflow::GraphDef& net, const tensorflow:: int end_mask = getLayerAttr(layer, "end_mask").i(); for (int i = 0; i < num; ++i) { - if (ends.at(i) < 0) - ends.at(i) -= 1; if (end_mask & (1 << i)) - ends.at(i) = -1; + ends.at(i) = INT_MAX; if (strides.at(i) != 1) CV_Error(Error::StsNotImplemented, format("StridedSlice with stride %d", strides.at(i))); @@ -1979,15 +1980,16 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso int64_t pads[8]; bool explicit_pads = getExplicitPadding(layerParams, layer, pads); int64_t begs[4] = {}; - int64_t ends[4] = {-1, -1, -1, -1}; + int64_t ends[4] = {}; if (explicit_pads) { name += "/deconv"; layerParams.set("pad_mode", "VALID"); + ends[0] = ends[1] = INT_MAX; for (int i = 2; i < 4; ++i) // begins=[0, 0, a, b], ends=[-1, -1, c, d] { begs[i] = pads[2*i]; - ends[i] = -1 - pads[2*i + 1]; + ends[i] = -pads[2*i + 1]; } } @@ -2007,8 +2009,8 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso const int strideX = layerParams.get("stride_w"); Mat outShape = getTensorContent(getConstBlob(layer, value_id, 0)); int shift = (getDataLayout(layer) == DATA_LAYOUT_NCHW); - const int outH = outShape.at(1 + shift) + begs[2] - 1 - ends[2]; - const int outW = outShape.at(2 + shift) + begs[3] - 1 - ends[3]; + const int outH = outShape.at(1 + shift) + begs[2] - ends[2]; + const int outW = outShape.at(2 + shift) + begs[3] - ends[3]; if (layerParams.get("pad_mode") == "SAME") { layerParams.set("adj_w", (outW - 1) % strideX); @@ -3090,10 +3092,8 @@ void TFImporter::populateNet() { const tensorflow::NodeDef& layer = net.node(li); - const std::string name = layer.name(); - const std::string type = layer.op(); - const int ninputs = layer.input_size(); - CV_LOG_DEBUG(NULL, "DNN/TF: (" << li << "/" << layersSize << ") Parse layer " << name << " @ " << type << " with " << ninputs << " inputs"); + CV_LOG_DEBUG(NULL, "DNN/TF: processing node (" << li << "/" << layersSize << ") with " << layer.input_size() << " inputs: " + << cv::format("[%s]:(%s)", layer.op().c_str(), layer.name().c_str())); parseNode(layer); } diff --git a/modules/dnn/src/torch/torch_importer.cpp b/modules/dnn/src/torch/torch_importer.cpp index 1e7f07a47880..3a46c8f7c0dc 100644 --- a/modules/dnn/src/torch/torch_importer.cpp +++ b/modules/dnn/src/torch/torch_importer.cpp @@ -40,6 +40,9 @@ //M*/ #include "../precomp.hpp" + +#include + #include #include #include @@ -106,6 +109,8 @@ static inline bool endsWith(const String &str, const char *substr) struct TorchImporter { + FPDenormalsIgnoreHintScope fp_denormals_ignore_scope; + typedef std::map > TensorsMap; Net net; @@ -949,7 +954,7 @@ struct TorchImporter int size = scalarParams.get("size"); int begins[] = {0, 0, size, size}; - int ends[] = {-1, -1, -size - 1, -size - 1}; + int ends[] = {INT_MAX, INT_MAX, -size, -size}; newModule->apiType = "Slice"; layerParams.set("begin", DictValue::arrayInt(&begins[0], 4)); diff --git a/modules/dnn/test/test_backends.cpp b/modules/dnn/test/test_backends.cpp index 2b0c75e25371..f2c6f1e5a0f9 100644 --- a/modules/dnn/test/test_backends.cpp +++ b/modules/dnn/test/test_backends.cpp @@ -214,17 +214,31 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height) { if (backend == DNN_BACKEND_HALIDE) applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE); -#if defined(INF_ENGINE_RELEASE) - if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && - target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // May hang on some configurations + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE exception: Ngraph operation Transpose with name conv15_2_mbox_conf_perm has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); + if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && + target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) + if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && + target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif Mat sample = imread(findDataFile("dnn/street.png")); @@ -467,8 +481,8 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow) float scoreDiff = 0.0, iouDiff = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { - scoreDiff = 0.015; - iouDiff = 0.0731; + scoreDiff = 0.02; + iouDiff = 0.1; } else if (target == DNN_TARGET_CUDA_FP16) { @@ -502,7 +516,7 @@ TEST_P(DNNTestNetwork, DenseNet_121) else if (target == DNN_TARGET_CUDA_FP16) { l1 = 0.008; - lInf = 0.05; + lInf = 0.06; } processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf); if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) @@ -542,6 +556,20 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16) l1 = 0.3; lInf = 7.6; } + +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) + { + l1 = 5e-3; + lInf = 5e-3; + } + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + { + lInf = 25; + } +#endif + + processNet("dnn/fast_neural_style_eccv16_starry_night.t7", "", inp, "", "", l1, lInf); #if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000) expectNoFallbacksFromIE(net); diff --git a/modules/dnn/test/test_caffe_importer.cpp b/modules/dnn/test/test_caffe_importer.cpp index bc50c804f203..b747b476602b 100644 --- a/modules/dnn/test/test_caffe_importer.cpp +++ b/modules/dnn/test/test_caffe_importer.cpp @@ -557,7 +557,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121) if (target == DNN_TARGET_OPENCL_FP16) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000) - l1 = 0.045; lInf = 0.21; + l1 = 0.05; lInf = 0.3; #else l1 = 0.017; lInf = 0.0795; #endif @@ -633,6 +633,9 @@ TEST_P(opencv_face_detector, Accuracy) std::string model = findDataFile(get<0>(GetParam()), false); dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam()); + if (targetId == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); + Net net = readNetFromCaffe(proto, model); Mat img = imread(findDataFile("gpu/lbpcascade/er.png")); Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false); @@ -660,6 +663,9 @@ TEST_P(opencv_face_detector, issue_15106) std::string model = findDataFile(get<0>(GetParam()), false); dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam()); + if (targetId == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); + Net net = readNetFromCaffe(proto, model); Mat img = imread(findDataFile("cv/shared/lena.png")); img = img.rowRange(img.rows / 4, 3 * img.rows / 4).colRange(img.cols / 4, 3 * img.cols / 4); @@ -673,13 +679,13 @@ TEST_P(opencv_face_detector, issue_15106) // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom] Mat out = net.forward(); Mat ref = (Mat_(1, 7) << 0, 1, 0.9149431, 0.30424616, 0.26964942, 0.88733053, 0.99815309); - normAssertDetections(ref, out, "", 0.2, 6e-5, 1e-4); + normAssertDetections(ref, out, "", 0.89, 6e-5, 1e-4); } INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector, Combine( Values("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector_fp16.caffemodel"), - Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL) + testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV)) ) ); @@ -719,6 +725,18 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + + // Check 'backward_compatible_check || in_out_elements_equal' failed at core/src/op/reshape.cpp:427: + // While validating node 'v1::Reshape bbox_pred_reshape (bbox_pred[0]:f32{1,84}, Constant_265242[0]:i64{4}) -> (f32{?,?,?,?})' with friendly_name 'bbox_pred_reshape': + // Requested output shape {1,6300,4,1} is incompatible with input shape {1, 84} + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + static Mat ref = (Mat_(3, 7) << 0, 2, 0.949398, 99.2454, 210.141, 601.205, 462.849, 0, 7, 0.997022, 481.841, 92.3218, 722.685, 175.953, 0, 12, 0.993028, 133.221, 189.377, 350.994, 563.166); @@ -742,6 +760,13 @@ TEST_P(Test_Caffe_nets, FasterRCNN_zf) CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #endif + +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); @@ -763,17 +788,7 @@ TEST_P(Test_Caffe_nets, RFCN) CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG ); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif - if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || - backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); - if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || - backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); + float scoreDiff = default_l1, iouDiff = default_lInf; if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) { @@ -785,6 +800,41 @@ TEST_P(Test_Caffe_nets, RFCN) scoreDiff = 0.0034; iouDiff = 0.12; } + +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Sporadic: "Cannot get memory!" + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + { + scoreDiff = 0.1f; + iouDiff = 0.2f; + } + + // Check 'backward_compatible_check || in_out_elements_equal' failed at core/src/op/reshape.cpp:427: + // While validating node 'v1::Reshape bbox_pred_reshape (ave_bbox_pred_rois[0]:f32{1,8,1,1}, Constant_388[0]:i64{4}) -> (f32{?,?,?,?})' with friendly_name 'bbox_pred_reshape': + // Requested output shape {1,300,8,1} is incompatible with input shape {1, 8, 1, 1} + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) + if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || + backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); + if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || + backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); +#endif + static Mat ref = (Mat_(2, 7) << 0, 7, 0.991359, 491.822, 81.1668, 702.573, 178.234, 0, 12, 0.94786, 132.093, 223.903, 338.077, 566.16); testFaster("rfcn_pascal_voc_resnet50.prototxt", "resnet50_rfcn_final.caffemodel", ref, scoreDiff, iouDiff); diff --git a/modules/dnn/test/test_common.hpp b/modules/dnn/test/test_common.hpp index fb6469757462..3d616e17dac2 100644 --- a/modules/dnn/test/test_common.hpp +++ b/modules/dnn/test/test_common.hpp @@ -48,6 +48,7 @@ #define CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE "dnn_skip_onnx_conformance" #define CV_TEST_TAG_DNN_SKIP_PARSER "dnn_skip_parser" +#define CV_TEST_TAG_DNN_SKIP_TIMVX "dnn_skip_timvx" #ifdef HAVE_INF_ENGINE #if INF_ENGINE_VER_MAJOR_EQ(2018050000) diff --git a/modules/dnn/test/test_common.impl.hpp b/modules/dnn/test/test_common.impl.hpp index 9de1ac6821a4..35f658cc9025 100644 --- a/modules/dnn/test/test_common.impl.hpp +++ b/modules/dnn/test/test_common.impl.hpp @@ -30,6 +30,7 @@ void PrintTo(const cv::dnn::Backend& v, std::ostream* os) case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: *os << "DLIE"; return; case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: *os << "NGRAPH"; return; case DNN_BACKEND_WEBNN: *os << "WEBNN"; return; + case DNN_BACKEND_TIMVX: *os << "TIMVX"; return; } // don't use "default:" to emit compiler warnings *os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")"; } @@ -46,6 +47,7 @@ void PrintTo(const cv::dnn::Target& v, std::ostream* os) case DNN_TARGET_FPGA: *os << "FPGA"; return; case DNN_TARGET_CUDA: *os << "CUDA"; return; case DNN_TARGET_CUDA_FP16: *os << "CUDA_FP16"; return; + case DNN_TARGET_NPU: *os << "NPU"; return; } // don't use "default:" to emit compiler warnings *os << "DNN_TARGET_UNKNOWN(" << (int)v << ")"; } @@ -337,16 +339,6 @@ testing::internal::ParamGenerator< tuple > dnnBackendsAndTarget std::vector< tuple > targets; std::vector< Target > available; - { - available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019); - for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i) - { - if ((*i == DNN_TARGET_MYRIAD || *i == DNN_TARGET_HDDL) && !withVPU) - continue; - targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, *i)); - } - } - { available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i) @@ -488,6 +480,11 @@ void initDNNTests() registerGlobalSkipTag( CV_TEST_TAG_DNN_SKIP_CUDA, CV_TEST_TAG_DNN_SKIP_CUDA_FP32, CV_TEST_TAG_DNN_SKIP_CUDA_FP16 ); +#endif +#ifdef HAVE_TIMVX + registerGlobalSkipTag( + CV_TEST_TAG_DNN_SKIP_TIMVX + ); #endif registerGlobalSkipTag( CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE, diff --git a/modules/dnn/test/test_darknet_importer.cpp b/modules/dnn/test/test_darknet_importer.cpp index 3853e1ca72b6..75942b4f1013 100644 --- a/modules/dnn/test/test_darknet_importer.cpp +++ b/modules/dnn/test/test_darknet_importer.cpp @@ -139,7 +139,14 @@ class Test_Darknet_layers : public DNNTestLayer inp.copyTo(inp2(ranges1)); net2.setInput(inp2); Mat out2 = net2.forward(); - EXPECT_EQ(0, cv::norm(out2(ranges0), out2(ranges1), NORM_INF)) << "Batch result is not equal: " << name; + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + EXPECT_LT(cv::norm(out2(ranges0), out2(ranges1), NORM_INF), 1e-4) << "Batch result is not similar: " << name; + } + else + { + EXPECT_EQ(0, cv::norm(out2(ranges0), out2(ranges1), NORM_INF)) << "Batch result is not equal: " << name; + } Mat ref2 = ref; if (ref.dims == 2 && out2.dims == 3) @@ -328,12 +335,10 @@ TEST_P(Test_Darknet_nets, YoloVoc) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); -#endif -#if defined(INF_ENGINE_RELEASE) +#elif defined(INF_ENGINE_RELEASE) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); // need to update check function @@ -359,12 +364,28 @@ TEST_P(Test_Darknet_nets, YoloVoc) scoreDiff = 0.03; iouDiff = 0.018; } -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) { iouDiff = std::numeric_limits::quiet_NaN(); } + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + { + iouDiff = std::numeric_limits::quiet_NaN(); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + { + iouDiff = std::numeric_limits::quiet_NaN(); + } + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + { + iouDiff = std::numeric_limits::quiet_NaN(); + } #endif std::string config_file = "yolo-voc.cfg"; @@ -375,12 +396,25 @@ TEST_P(Test_Darknet_nets, YoloVoc) testDarknetModel(config_file, weights_file, ref.rowRange(0, 3), scoreDiff, iouDiff); } +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Exception: input != output + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // [ GENERAL_ERROR ] AssertionFailed: input != output + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif { SCOPED_TRACE("batch size 2"); testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, nmsThreshold); } -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); @@ -457,14 +491,15 @@ TEST_P(Test_Darknet_nets_async, Accuracy) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) throw SkipTestException("No support for async forward"); -#if defined(INF_ENGINE_RELEASE) -#if INF_ENGINE_VER_MAJOR_GE(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov3") // NC_OUT_OF_MEMORY - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#else + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov3") // NC_OUT_OF_MEMORY + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov4") // NC_OUT_OF_MEMORY applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif #endif const int numInputs = 2; @@ -519,6 +554,21 @@ TEST_P(Test_Darknet_nets_async, Accuracy) l1 = 0.001; lInf = 0.005; } + if (INF_ENGINE_VER_MAJOR_EQ(2021040000) && targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4-tiny") // FIXIT: 4.x only, 3.4 branch works well + { + l1 = 0.001; + lInf = 0.005; + } + if (INF_ENGINE_VER_MAJOR_EQ(2022010000) && targetId == DNN_TARGET_OPENCL_FP16 && prefix == "yolov4-tiny") // FIXIT: 4.x only, 3.4 branch works well + { + l1 = 0.001; + lInf = 0.005; + } + if (targetId == DNN_TARGET_MYRIAD && prefix == "yolov4") + { + l1 = 0.005; + lInf = 1.6f; // |ref| = 0.95431125164031982 + } } #endif @@ -631,7 +681,7 @@ TEST_P(Test_Darknet_nets, YOLOv4) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif -#if defined(INF_ENGINE_RELEASE) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2022010000) if (target == DNN_TARGET_MYRIAD) // NC_OUT_OF_MEMORY applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -666,14 +716,24 @@ TEST_P(Test_Darknet_nets, YOLOv4) std::string weights_file = "yolov4.weights"; -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy (batch 1): no detections + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + { + iouDiff = std::numeric_limits::quiet_NaN(); + } + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + { + iouDiff = std::numeric_limits::quiet_NaN(); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy (batch 1) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) { iouDiff = std::numeric_limits::quiet_NaN(); } -#endif -#if defined(INF_ENGINE_RELEASE) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2022010000) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) @@ -691,14 +751,24 @@ TEST_P(Test_Darknet_nets, YOLOv4) { SCOPED_TRACE("batch size 2"); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - // accuracy (batch 1) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy (batch 2) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + { + iouDiff = 0.05f; + } + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + { + iouDiff = std::numeric_limits::quiet_NaN(); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy (batch 2) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) { iouDiff = 0.45f; } -#endif -#if defined(INF_ENGINE_RELEASE) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2022010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_OPENCL) @@ -714,7 +784,14 @@ TEST_P(Test_Darknet_nets, YOLOv4) testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff); } -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); @@ -876,8 +953,27 @@ INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets()); TEST_P(Test_Darknet_layers, shortcut) { testDarknetLayer("shortcut"); +} +TEST_P(Test_Darknet_layers, shortcut_leaky) +{ testDarknetLayer("shortcut_leaky"); +} +TEST_P(Test_Darknet_layers, shortcut_unequal) +{ +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif testDarknetLayer("shortcut_unequal"); +} +TEST_P(Test_Darknet_layers, shortcut_unequal_2) +{ +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif testDarknetLayer("shortcut_unequal_2"); } @@ -912,7 +1008,19 @@ TEST_P(Test_Darknet_layers, region) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy on CPU, OpenCL + // Expected: (normL1) <= (l1), actual: 0.000358148 vs 1e-05 + // |ref| = 1.207319974899292 + // Expected: (normInf) <= (lInf), actual: 0.763223 vs 0.0001 + // |ref| = 1.207319974899292 + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy on CPU, OpenCL // Expected: (normInf) <= (lInf), actual: 0.763223 vs 0.0001 // |ref| = 1.207319974899292 @@ -949,10 +1057,12 @@ TEST_P(Test_Darknet_layers, maxpool) TEST_P(Test_Darknet_layers, convolutional) { +#if defined(INF_ENGINE_RELEASE) if (target == DNN_TARGET_MYRIAD) { default_l1 = 0.01f; } +#endif testDarknetLayer("convolutional", true); } diff --git a/modules/dnn/test/test_halide_layers.cpp b/modules/dnn/test/test_halide_layers.cpp index 17c6b48101ea..104d7e73470b 100644 --- a/modules/dnn/test/test_halide_layers.cpp +++ b/modules/dnn/test/test_halide_layers.cpp @@ -170,6 +170,23 @@ TEST_P(Deconvolution, Accuracy) Backend backendId = get<0>(get<7>(GetParam())); Target targetId = get<1>(get<7>(GetParam())); +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) + && inChannels == 6 && outChannels == 4 && group == 1 + && kernel == Size(3, 1) && pad == Size(0, 1) + && stride == Size(1, 1) && dilation == Size(1, 1)) + applyTestTag(targetId == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) + && inChannels == 6 && outChannels == 4 && group == 1 + && kernel == Size(1, 3) && pad == Size(1, 0) + && stride == Size(1, 1) && dilation == Size(1, 1)) + applyTestTag(targetId == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#endif + #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X @@ -624,9 +641,16 @@ TEST_P(NoParamActivation, Accuracy) { Backend backendId = get<0>(get<1>(GetParam())); Target targetId = get<1>(get<1>(GetParam())); + std::string layer_type = get<0>(GetParam()); + +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_CPU && layer_type == "BNLL") + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif LayerParams lp; - lp.type = get<0>(GetParam()); + lp.type = layer_type; lp.name = "testLayer"; testInPlaceActivation(lp, backendId, targetId); } diff --git a/modules/dnn/test/test_ie_models.cpp b/modules/dnn/test/test_ie_models.cpp index 2846f9ae7695..3622f69bdbd5 100644 --- a/modules/dnn/test/test_ie_models.cpp +++ b/modules/dnn/test/test_ie_models.cpp @@ -290,7 +290,7 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath if (cvtest::debugLevel > 0) { const std::vector& dims = desc.getDims(); - std::cout << "Input: '" << it.first << "' precison=" << desc.getPrecision() << " dims=" << dims.size() << " ["; + std::cout << "Input: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " ["; for (auto d : dims) std::cout << " " << d; std::cout << "] ocv_mat=" << inputsMap[it.first].size << " of " << typeToString(inputsMap[it.first].type()) << std::endl; @@ -308,7 +308,7 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath if (cvtest::debugLevel > 0) { const std::vector& dims = desc.getDims(); - std::cout << "Output: '" << it.first << "' precison=" << desc.getPrecision() << " dims=" << dims.size() << " ["; + std::cout << "Output: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " ["; for (auto d : dims) std::cout << " " << d; std::cout << "] ocv_mat=" << outputsMap[it.first].size << " of " << typeToString(outputsMap[it.first].type()) << std::endl; @@ -371,17 +371,17 @@ TEST_P(DNNTestOpenVINO, models) || modelName == "person-vehicle-bike-detection-2004" // 2021.4+: ncDeviceOpen:1013 Failed to find booted device after boot ) ) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (targetId == DNN_TARGET_OPENCL && (false || modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported ) ) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (targetId == DNN_TARGET_OPENCL_FP16 && (false || modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported ) ) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif #if INF_ENGINE_VER_MAJOR_GE(2020020000) @@ -397,12 +397,7 @@ TEST_P(DNNTestOpenVINO, models) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD); diff --git a/modules/dnn/test/test_int8_layers.cpp b/modules/dnn/test/test_int8_layers.cpp index c181dfa5ebbe..844601269ba0 100644 --- a/modules/dnn/test/test_int8_layers.cpp +++ b/modules/dnn/test/test_int8_layers.cpp @@ -12,6 +12,9 @@ testing::internal::ParamGenerator< tuple > dnnBackendsAndTarget { std::vector< tuple > targets; targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)); +#ifdef HAVE_TIMVX + targets.push_back(make_tuple(DNN_BACKEND_TIMVX, DNN_TARGET_NPU)); +#endif return testing::ValuesIn(targets); } @@ -104,14 +107,29 @@ TEST_P(Test_Int8_layers, Convolution1D) TEST_P(Test_Int8_layers, Convolution2D) { - testLayer("layer_convolution", "Caffe", 0.0174, 0.0758, 1, 1, true); - testLayer("single_conv", "TensorFlow", 0.00413, 0.02201); - testLayer("depthwise_conv2d", "TensorFlow", 0.0388, 0.169); + if(backend == DNN_BACKEND_TIMVX) + testLayer("single_conv", "TensorFlow", 0.00424, 0.02201); + else + testLayer("single_conv", "TensorFlow", 0.00413, 0.02201); + testLayer("atrous_conv2d_valid", "TensorFlow", 0.0193, 0.0633); testLayer("atrous_conv2d_same", "TensorFlow", 0.0185, 0.1322); testLayer("keras_atrous_conv2d_same", "TensorFlow", 0.0056, 0.0244); - testLayer("convolution", "ONNX", 0.0052, 0.01516); - testLayer("two_convolution", "ONNX", 0.00295, 0.00840); + + if(backend == DNN_BACKEND_TIMVX) + testLayer("convolution", "ONNX", 0.00534, 0.01516); + else + testLayer("convolution", "ONNX", 0.0052, 0.01516); + + if(backend == DNN_BACKEND_TIMVX) + testLayer("two_convolution", "ONNX", 0.0033, 0.01); + else + testLayer("two_convolution", "ONNX", 0.00295, 0.00840); + + if(backend == DNN_BACKEND_TIMVX) + applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX); + testLayer("layer_convolution", "Caffe", 0.0174, 0.0758, 1, 1, true); + testLayer("depthwise_conv2d", "TensorFlow", 0.0388, 0.169); } TEST_P(Test_Int8_layers, Convolution3D) @@ -130,9 +148,21 @@ TEST_P(Test_Int8_layers, Flatten) TEST_P(Test_Int8_layers, Padding) { - testLayer("padding_valid", "TensorFlow", 0.0026, 0.0064); - testLayer("padding_same", "TensorFlow", 0.0081, 0.032); - testLayer("spatial_padding", "TensorFlow", 0.0078, 0.028); + if (backend == DNN_BACKEND_TIMVX) + testLayer("padding_valid", "TensorFlow", 0.0292, 0.0105); + else + testLayer("padding_valid", "TensorFlow", 0.0026, 0.0064); + + if (backend == DNN_BACKEND_TIMVX) + testLayer("padding_same", "TensorFlow", 0.0085, 0.032); + else + testLayer("padding_same", "TensorFlow", 0.0081, 0.032); + + if (backend == DNN_BACKEND_TIMVX) + testLayer("spatial_padding", "TensorFlow", 0.0079, 0.028); + else + testLayer("spatial_padding", "TensorFlow", 0.0078, 0.028); + testLayer("mirror_pad", "TensorFlow", 0.0064, 0.013); testLayer("pad_and_concat", "TensorFlow", 0.0021, 0.0098); testLayer("padding", "ONNX", 0.0005, 0.0069); @@ -218,8 +248,16 @@ TEST_P(Test_Int8_layers, ReLU6) TEST_P(Test_Int8_layers, Sigmoid) { testLayer("maxpooling_sigmoid", "ONNX", 0.0011, 0.0032); - testLayer("maxpooling_sigmoid_dynamic_axes", "ONNX", 0.0011, 0.0032); - testLayer("maxpooling_sigmoid_1d", "ONNX", 0.0011, 0.0037); +} + +TEST_P(Test_Int8_layers, Sigmoid_dynamic_axes) +{ + testLayer("maxpooling_sigmoid_dynamic_axes", "ONNX", 0.002, 0.0032); +} + +TEST_P(Test_Int8_layers, Sigmoid_1d) +{ + testLayer("maxpooling_sigmoid_1d", "ONNX", 0.002, 0.0037); } TEST_P(Test_Int8_layers, Mish) @@ -227,14 +265,32 @@ TEST_P(Test_Int8_layers, Mish) testLayer("mish", "ONNX", 0.0015, 0.0025); } -TEST_P(Test_Int8_layers, Softmax) +TEST_P(Test_Int8_layers, Softmax_Caffe) { testLayer("layer_softmax", "Caffe", 0.0011, 0.0036); +} +TEST_P(Test_Int8_layers, Softmax_keras_TF) +{ testLayer("keras_softmax", "TensorFlow", 0.00093, 0.0027); +} +TEST_P(Test_Int8_layers, Softmax_slim_TF) +{ testLayer("slim_softmax", "TensorFlow", 0.0016, 0.0034); +} +TEST_P(Test_Int8_layers, Softmax_slim_v2_TF) +{ testLayer("slim_softmax_v2", "TensorFlow", 0.0029, 0.017); +} +TEST_P(Test_Int8_layers, Softmax_ONNX) +{ testLayer("softmax", "ONNX", 0.0016, 0.0028); +} +TEST_P(Test_Int8_layers, Softmax_log_ONNX) +{ testLayer("log_softmax", "ONNX", 0.014, 0.025); +} +TEST_P(Test_Int8_layers, DISABLED_Softmax_unfused_ONNX) // FIXIT Support 'Identity' layer for outputs (#22022) +{ testLayer("softmax_unfused", "ONNX", 0.0009, 0.0021); } @@ -275,20 +331,35 @@ TEST_P(Test_Int8_layers, InnerProduct) { testLayer("layer_inner_product", "Caffe", 0.005, 0.02, 1, 1, true); testLayer("matmul", "TensorFlow", 0.0061, 0.019); - testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0009, 0.0091); + + if (backend == DNN_BACKEND_TIMVX) + testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0018, 0.0175); + else + testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0009, 0.0091); + testLayer("nhwc_reshape_matmul", "TensorFlow", 0.03, 0.071); testLayer("matmul_layout", "TensorFlow", 0.035, 0.06); testLayer("tf2_dense", "TensorFlow", 0, 0); testLayer("matmul_add", "ONNX", 0.041, 0.082); testLayer("linear", "ONNX", 0.0018, 0.0029); - testLayer("constant", "ONNX", 0.00021, 0.0006); + + if (backend == DNN_BACKEND_TIMVX) + testLayer("constant", "ONNX", 0.00048, 0.0013); + else + testLayer("constant", "ONNX", 0.00021, 0.0006); + testLayer("lin_with_constant", "ONNX", 0.0011, 0.0016); } TEST_P(Test_Int8_layers, Reshape) { testLayer("reshape_layer", "TensorFlow", 0.0032, 0.0082); - testLayer("reshape_nchw", "TensorFlow", 0.0089, 0.029); + + if (backend == DNN_BACKEND_TIMVX) + testLayer("reshape_nchw", "TensorFlow", 0.0092, 0.0495); + else + testLayer("reshape_nchw", "TensorFlow", 0.0089, 0.029); + testLayer("reshape_conv", "TensorFlow", 0.035, 0.054); testLayer("reshape_reduce", "TensorFlow", 0.0042, 0.0078); testLayer("reshape_as_shape", "TensorFlow", 0.0014, 0.0028); @@ -299,7 +370,12 @@ TEST_P(Test_Int8_layers, Reshape) testLayer("flatten_by_prod", "ONNX", 0.0048, 0.0081); testLayer("squeeze", "ONNX", 0.0048, 0.0081); testLayer("unsqueeze", "ONNX", 0.0033, 0.0053); - testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.0054, 0.0154); + + if (backend == DNN_BACKEND_TIMVX) + testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.006, 0.0212); + else + testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.0054, 0.0154); + testLayer("unsqueeze_and_conv_dynamic_axes", "ONNX", 0.0037, 0.0151); } @@ -316,16 +392,48 @@ TEST_P(Test_Int8_layers, Identity) testLayer("expand_neg_batch", "ONNX", 0.00071, 0.0019); } -TEST_P(Test_Int8_layers, Slice) +TEST_P(Test_Int8_layers, Slice_split_tf) { testLayer("split", "TensorFlow", 0.0033, 0.0056); +} + +TEST_P(Test_Int8_layers, Slice_4d_tf) +{ testLayer("slice_4d", "TensorFlow", 0.003, 0.0073); +} + +TEST_P(Test_Int8_layers, Slice_strided_tf) +{ testLayer("strided_slice", "TensorFlow", 0.008, 0.0142); +} + +TEST_P(Test_Int8_layers, DISABLED_Slice_onnx) // FIXIT Support 'Identity' layer for outputs (#22022) +{ testLayer("slice", "ONNX", 0.0046, 0.0077); - testLayer("slice_dynamic_axes", "ONNX", 0.0039, 0.0084); - testLayer("slice_opset_11_steps_2d", "ONNX", 0.0052, 0.0124); +} + +TEST_P(Test_Int8_layers, Slice_dynamic_axes_onnx) +{ + testLayer("slice_dynamic_axes", "ONNX", 0.0039, 0.02); +} + +TEST_P(Test_Int8_layers, Slice_steps_2d_onnx11) +{ + testLayer("slice_opset_11_steps_2d", "ONNX", 0.01, 0.0124); +} + +TEST_P(Test_Int8_layers, Slice_steps_3d_onnx11) +{ testLayer("slice_opset_11_steps_3d", "ONNX", 0.0068, 0.014); +} + +TEST_P(Test_Int8_layers, Slice_steps_4d_onnx11) +{ testLayer("slice_opset_11_steps_4d", "ONNX", 0.0041, 0.008); +} + +TEST_P(Test_Int8_layers, Slice_steps_5d_onnx11) +{ testLayer("slice_opset_11_steps_5d", "ONNX", 0.0085, 0.021); } @@ -338,6 +446,10 @@ TEST_P(Test_Int8_layers, Dropout) TEST_P(Test_Int8_layers, Eltwise) { testLayer("layer_eltwise", "Caffe", 0.062, 0.15); + + if (backend == DNN_BACKEND_TIMVX) + applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX); + testLayer("conv_2_inps", "Caffe", 0.0086, 0.0232, 2, 1, true, false); testLayer("eltwise_sub", "TensorFlow", 0.015, 0.047); testLayer("eltwise_add_vec", "TensorFlow", 0.037, 0.21); // tflite 0.0095, 0.0365 @@ -812,7 +924,7 @@ TEST_P(Test_Int8_nets, opencv_face_detector) 0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494, 0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801); - float confThreshold = 0.5, scoreDiff = 0.002, iouDiff = 0.21; + float confThreshold = 0.5, scoreDiff = 0.002, iouDiff = 0.4; testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff); } @@ -822,6 +934,8 @@ TEST_P(Test_Int8_nets, EfficientDet) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel()) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL); + if (backend == DNN_BACKEND_TIMVX) + applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX); if (target != DNN_TARGET_CPU) { diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp index 8bbccdbc967b..14a19266ccc5 100644 --- a/modules/dnn/test/test_layers.cpp +++ b/modules/dnn/test/test_layers.cpp @@ -1265,12 +1265,7 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) throw SkipTestException("No support for async forward"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt")); Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin")); @@ -1310,12 +1305,7 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) throw SkipTestException("No support for async forward"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); int blobSize[] = {2, 6, 75, 113}; Mat inputs[] = {Mat(4, &blobSize[0], CV_8U), Mat()}; @@ -1348,12 +1338,7 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading) if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) throw SkipTestException("No support for async forward"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); std::string xmlPath = _tf("layer_convolution.xml"); std::string binPath = _tf("layer_convolution.bin"); @@ -1500,6 +1485,12 @@ static void test_dldt_fused_output(Backend backend, Target target) TEST_P(Test_DLDT_layers, fused_output) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + CV_DNN_REGISTER_LAYER_CLASS(Unsupported, UnsupportedLayer); try { @@ -1659,7 +1650,16 @@ TEST_P(Test_Caffe_layers, Interp) TEST_P(Test_Caffe_layers, DISABLED_Interp) // requires patched protobuf (available in OpenCV source tree only) #endif { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception #endif @@ -2097,7 +2097,7 @@ TEST_P(Layer_Test_Slice, variable_input_shape) int targetId = get<1>(GetParam()); int begin[] = {0, 0, 0, 0}; - int end[] = {-1, -1, -1, -1}; + int end[] = {INT_MAX, INT_MAX, INT_MAX, INT_MAX}; Net net; LayerParams lp; diff --git a/modules/dnn/test/test_misc.cpp b/modules/dnn/test/test_misc.cpp index 39bb73a9185c..0fab7551a5bf 100644 --- a/modules/dnn/test/test_misc.cpp +++ b/modules/dnn/test/test_misc.cpp @@ -117,12 +117,7 @@ void test_readNet_IE_do_not_call_setInput(Backend backendId) const std::string& model = findDataFile("dnn/layers/layer_convolution.bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net net = readNet(model, proto); net.setPreferableBackend(backendId); @@ -462,12 +457,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single) const std::string& model = findDataFile("dnn/layers/layer_convolution.bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net netSync = readNet(model, proto); netSync.setPreferableBackend(backendId); @@ -523,12 +513,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_all) const std::string& model = findDataFile("dnn/layers/layer_convolution.bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net netSync = readNet(model, proto); netSync.setPreferableBackend(backendId); @@ -586,12 +571,7 @@ TEST_P(Async, create_layer_pipeline_set_and_forward_all) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net netSync; Net netAsync; @@ -697,12 +677,7 @@ TEST_P(Test_Model_Optimizer, forward_two_nets) const std::string& model = findDataFile("dnn/layers/layer_convolution.bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net net0 = readNet(model, proto); net0.setPreferableTarget(targetId); @@ -741,12 +716,7 @@ TEST_P(Test_Model_Optimizer, readFromBuffer) const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution.bin"); const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.xml"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net net1 = readNetFromModelOptimizer(modelFile, weightsFile); net1.setPreferableBackend(backendId); @@ -793,12 +763,7 @@ TEST_P(Test_Model_Optimizer, flexible_inputs) const std::string& model = findDataFile("dnn/layers/layer_convolution.bin"); const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml"); - if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); - else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH); - else - FAIL() << "Unknown backendId"; + ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId); Net net0 = readNet(model, proto); net0.setPreferableTarget(targetId); @@ -879,8 +844,9 @@ TEST_P(Test_two_inputs, basic) Mat ref; addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F); - double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6; - double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5; + double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD || targetId == DNN_TARGET_CUDA_FP16) ? 0.06 : 1e-6; + double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD || targetId == DNN_TARGET_CUDA_FP16) ? 0.3 : 1e-5; + normAssert(out, ref, "", l1, lInf); if (cvtest::debugLevel > 0 || HasFailure()) diff --git a/modules/dnn/test/test_model.cpp b/modules/dnn/test/test_model.cpp index 4f5922182a07..25d1a18d5262 100644 --- a/modules/dnn/test/test_model.cpp +++ b/modules/dnn/test/test_model.cpp @@ -110,6 +110,9 @@ class Test_Model : public DNNTestLayer model.setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); + model.setPreferableBackend(backend); + model.setPreferableTarget(target); + model.segment(frame, mask); normAssert(mask, exp, "", norm, norm); } @@ -287,20 +290,20 @@ TEST_P(Test_Model, DetectRegion) CV_TEST_TAG_MEMORY_2GB ); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif - -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif - -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) // FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); @@ -347,20 +350,20 @@ TEST_P(Test_Model, DetectRegionWithNmsAcrossClasses) CV_TEST_TAG_MEMORY_2GB ); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif - -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif - -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); #endif @@ -403,13 +406,28 @@ TEST_P(Test_Model, DetectRegionWithNmsAcrossClasses) TEST_P(Test_Model, DetectionOutput) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Check 'backward_compatible_check || in_out_elements_equal' failed at core/src/op/reshape.cpp:427: + // While validating node 'v1::Reshape bbox_pred_reshape (ave_bbox_pred_rois[0]:f32{1,8,1,1}, Constant_388[0]:i64{4}) -> (f32{?,?,?,?})' with friendly_name 'bbox_pred_reshape': + // Requested output shape {1,300,8,1} is incompatible with input shape {1, 8, 1, 1} + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif -#if defined(INF_ENGINE_RELEASE) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) // FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); @@ -494,7 +512,7 @@ TEST_P(Test_Model, DetectionMobilenetSSD) } else if (target == DNN_TARGET_CUDA_FP16) { - scoreDiff = 0.002; + scoreDiff = 0.0021; iouDiff = 1e-2; } float confThreshold = FLT_MIN; @@ -613,13 +631,48 @@ TEST_P(Test_Model, Segmentation) CV_TEST_TAG_MEMORY_2GB ); + float norm = 0; + +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Failed to allocate graph: NC_ERROR + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + { + norm = 25.0f; // depends on OS/OpenCL version + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // Failed to allocate graph: NC_ERROR + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // cnn_network_ngraph_impl.cpp:104 Function contains several inputs and outputs with one friendly name: 'upscore2'! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // cnn_network_ngraph_impl.cpp:104 Function contains several inputs and outputs with one friendly name: 'upscore2'! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) + // Failed to allocate graph: NC_ERROR + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + + if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) + || (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)) + { + norm = 2.0f; // l1 = 0.01 lInf = 2 + } + std::string inp = _tf("dog416.png"); std::string weights_file = _tf("fcn8s-heavy-pascal.prototxt"); std::string config_file = _tf("fcn8s-heavy-pascal.caffemodel", false); std::string exp = _tf("segmentation_exp.png"); Size size{128, 128}; - float norm = 0; double scale = 1.0; Scalar mean = Scalar(); bool swapRB = false; @@ -629,7 +682,16 @@ TEST_P(Test_Model, Segmentation) TEST_P(Test_Model, TextRecognition) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // FIXIT: dnn/src/ie_ngraph.cpp:494: error: (-215:Assertion failed) !inps.empty() in function 'createNet' + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Node Transpose_79 was not assigned on any pointed device + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE Exception: Ngraph operation Reshape with name 71 has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, @@ -653,7 +715,13 @@ TEST_P(Test_Model, TextRecognition) TEST_P(Test_Model, TextRecognitionWithCTCPrefixBeamSearch) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Node Transpose_79 was not assigned on any pointed device + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE Exception: Ngraph operation Reshape with name 71 has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, diff --git a/modules/dnn/test/test_onnx_conformance.cpp b/modules/dnn/test/test_onnx_conformance.cpp index 1c3877b7b2dd..e9bc0e418797 100644 --- a/modules/dnn/test/test_onnx_conformance.cpp +++ b/modules/dnn/test/test_onnx_conformance.cpp @@ -954,7 +954,7 @@ class Test_ONNX_conformance : public TestWithParam if (target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) { - default_l1 = 4e-3; + default_l1 = 7e-3; default_lInf = 2e-2; } else @@ -1181,10 +1181,10 @@ TEST_P(Test_ONNX_conformance, Layer_Test) } std::vector layerNames = net.getUnconnectedOutLayersNames(); - std::vector< std::vector > outputs_; + std::vector outputs; try { - net.forward(outputs_, layerNames); + net.forward(outputs, layerNames); } catch (...) { @@ -1192,8 +1192,7 @@ TEST_P(Test_ONNX_conformance, Layer_Test) applyTestTag(CV_TEST_TAG_DNN_ERROR_FORWARD); throw; } - ASSERT_GE(outputs_.size(), 1); - const std::vector& outputs = outputs_[0]; + ASSERT_GE(outputs.size(), 1); if (checkLayersFallbacks && checkFallbacks(net)) { diff --git a/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp index 0f5f3871320b..c18ced0c5945 100644 --- a/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp +++ b/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp @@ -66,6 +66,15 @@ "test_maxunpool_export_with_output_shape", "test_mul_bcast", "test_mul_uint8", +"test_reduce_prod_default_axes_keepdims_example", // FP16 only +"test_reduce_prod_default_axes_keepdims_random", // FP16 only +"test_reduce_prod_do_not_keepdims_random", // FP16 only +"test_reduce_prod_keepdims_random", // FP16 only +"test_reduce_prod_negative_axes_keepdims_random", // FP16 only +"test_reduce_sum_square_default_axes_keepdims_random", // FP16 only +"test_reduce_sum_square_do_not_keepdims_random", // FP16 only +"test_reduce_sum_square_keepdims_random", // FP16 only +"test_reduce_sum_square_negative_axes_keepdims_random", // FP16 only "test_softmax_default_axis", "test_softmax_large_number", // FP16 only "test_softmax_large_number_expanded", // FP16 only diff --git a/modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp index ec262ed3011f..cad914d05ac2 100644 --- a/modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp +++ b/modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp @@ -59,6 +59,12 @@ if (name == #t) \ bool filterApplied = false; +#if INF_ENGINE_VER_MAJOR_EQ(2021040000) || INF_ENGINE_VER_MAJOR_EQ(2022010000) +#define SKIP_SET_1 1 +#else +#define SKIP_SET_1 0 +#endif + // Update note: execute /testdata/dnn/onnx/generate_conformance_list.py BEGIN_SWITCH() CASE(test_abs) @@ -82,11 +88,11 @@ CASE(test_adam_multiple) CASE(test_add) // no filter CASE(test_add_bcast) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_add_uint8) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_and2d) @@ -106,131 +112,131 @@ CASE(test_and_bcast4v3d) CASE(test_and_bcast4v4d) // no filter CASE(test_argmax_default_axis_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_default_axis_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_default_axis_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_default_axis_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_keepdims_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_keepdims_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_keepdims_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_keepdims_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_negative_axis_keepdims_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_negative_axis_keepdims_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_negative_axis_keepdims_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_negative_axis_keepdims_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_no_keepdims_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_no_keepdims_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_no_keepdims_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmax_no_keepdims_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_default_axis_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_default_axis_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_default_axis_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_default_axis_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_keepdims_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_keepdims_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_keepdims_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_keepdims_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_negative_axis_keepdims_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_negative_axis_keepdims_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_negative_axis_keepdims_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_negative_axis_keepdims_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_no_keepdims_example) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_no_keepdims_example_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_no_keepdims_random) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_argmin_no_keepdims_random_select_last_index) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_asin) @@ -256,11 +262,11 @@ CASE(test_averagepool_2d_ceil) CASE(test_averagepool_2d_default) // no filter CASE(test_averagepool_2d_pads) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_averagepool_2d_pads_count_include_pad) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_CPU; // MYRIAD is ok SKIP_OPENCL; @@ -269,7 +275,7 @@ CASE(test_averagepool_2d_pads_count_include_pad) CASE(test_averagepool_2d_precomputed_pads) // no filter CASE(test_averagepool_2d_precomputed_pads_count_include_pad) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_averagepool_2d_precomputed_same_upper) @@ -277,7 +283,7 @@ CASE(test_averagepool_2d_precomputed_same_upper) CASE(test_averagepool_2d_precomputed_strides) // no filter CASE(test_averagepool_2d_same_lower) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_averagepool_2d_same_upper) @@ -287,11 +293,11 @@ CASE(test_averagepool_2d_strides) CASE(test_averagepool_3d_default) // no filter CASE(test_basic_conv_with_padding) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_basic_conv_without_padding) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_basic_convinteger) @@ -349,11 +355,11 @@ CASE(test_cast_FLOAT_to_DOUBLE) CASE(test_cast_FLOAT_to_FLOAT16) // no filter CASE(test_cast_FLOAT_to_STRING) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_cast_STRING_to_FLOAT) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_castlike_BFLOAT16_to_FLOAT) @@ -391,13 +397,13 @@ CASE(test_castlike_FLOAT_to_FLOAT16_expanded) CASE(test_castlike_FLOAT_to_STRING) // no filter CASE(test_castlike_FLOAT_to_STRING_expanded) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_castlike_STRING_to_FLOAT) // no filter CASE(test_castlike_STRING_to_FLOAT_expanded) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_ceil) @@ -441,7 +447,7 @@ CASE(test_compress_negative_axis) CASE(test_concat_1d_axis_0) // no filter CASE(test_concat_1d_axis_negative_1) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_concat_2d_axis_0) @@ -475,19 +481,19 @@ CASE(test_constantofshape_int_shape_zero) CASE(test_constantofshape_int_zeros) // no filter CASE(test_conv_with_autopad_same) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_conv_with_strides_and_asymmetric_padding) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_conv_with_strides_no_padding) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_conv_with_strides_padding) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_convinteger_with_padding) @@ -555,13 +561,13 @@ CASE(test_det_nd) CASE(test_div) // no filter CASE(test_div_bcast) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_div_example) // no filter CASE(test_div_uint8) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_dropout_default) @@ -573,7 +579,7 @@ CASE(test_dropout_default_mask_ratio) CASE(test_dropout_default_old) // no filter CASE(test_dropout_default_ratio) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_dropout_random_old) @@ -697,11 +703,11 @@ CASE(test_globalaveragepool) CASE(test_globalaveragepool_precomputed) // no filter CASE(test_globalmaxpool) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_globalmaxpool_precomputed) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_greater) @@ -811,12 +817,12 @@ CASE(test_log) CASE(test_log_example) // no filter CASE(test_logsoftmax_axis_0) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL; SKIP_OPENCL_FP16; #endif CASE(test_logsoftmax_axis_0_expanded) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL; SKIP_OPENCL_FP16; #endif @@ -829,7 +835,7 @@ CASE(test_logsoftmax_axis_2) CASE(test_logsoftmax_axis_2_expanded) // no filter CASE(test_logsoftmax_default_axis) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_logsoftmax_default_axis_expanded) @@ -839,12 +845,12 @@ CASE(test_logsoftmax_example_1) CASE(test_logsoftmax_example_1_expanded) // no filter CASE(test_logsoftmax_large_number) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL_FP16; SKIP_MYRIAD; #endif CASE(test_logsoftmax_large_number_expanded) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL_FP16; SKIP_MYRIAD; #endif @@ -907,71 +913,71 @@ CASE(test_max_uint64) CASE(test_max_uint8) // no filter CASE(test_maxpool_1d_default) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_ceil) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_default) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_dilations) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_maxpool_2d_pads) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_precomputed_pads) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_precomputed_same_upper) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_precomputed_strides) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_same_lower) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_maxpool_2d_same_upper) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_strides) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_MYRIAD; #endif CASE(test_maxpool_2d_uint8) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_maxpool_3d_default) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_NON_CPU; #endif CASE(test_maxpool_with_argmax_2d_precomputed_pads) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_maxpool_with_argmax_2d_precomputed_strides) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_maxunpool_export_with_output_shape) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_maxunpool_export_without_output_shape) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_mean_example) @@ -1041,13 +1047,13 @@ CASE(test_momentum_multiple) CASE(test_mul) // no filter CASE(test_mul_bcast) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_mul_example) // no filter CASE(test_mul_uint8) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_mvn) @@ -1261,7 +1267,10 @@ CASE(test_reduce_l1_negative_axes_keep_dims_example) CASE(test_reduce_l1_negative_axes_keep_dims_random) // no filter CASE(test_reduce_l2_default_axes_keepdims_example) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00490189 vs 0.004) +#endif CASE(test_reduce_l2_default_axes_keepdims_random) // no filter CASE(test_reduce_l2_do_not_keepdims_example) @@ -1285,7 +1294,10 @@ CASE(test_reduce_log_sum_default) CASE(test_reduce_log_sum_desc_axes) // no filter CASE(test_reduce_log_sum_exp_default_axes_keepdims_example) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00671387 vs 0.004 +#endif CASE(test_reduce_log_sum_exp_default_axes_keepdims_random) // no filter CASE(test_reduce_log_sum_exp_do_not_keepdims_example) @@ -1351,21 +1363,61 @@ CASE(test_reduce_min_negative_axes_keepdims_example) CASE(test_reduce_min_negative_axes_keepdims_random) // no filter CASE(test_reduce_prod_default_axes_keepdims_example) - // no filter +#if SKIP_SET_1 + SKIP_MYRIAD; // accuracy (Expected: (normL1) <= (l1), actual: inf vs 0.004) +#endif CASE(test_reduce_prod_default_axes_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 5; // Expected: (normL1) <= (l1), actual: 2.66211 vs 0.004 |ref| = 24621.337890625 + default_lInf = 5; // Expected: (normInf) <= (lInf), actual: 2.66211 vs 0.02 |ref| = 24621.337890625 + } +#endif CASE(test_reduce_prod_do_not_keepdims_example) // no filter CASE(test_reduce_prod_do_not_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 + } +#endif CASE(test_reduce_prod_keepdims_example) // no filter CASE(test_reduce_prod_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 + } +#endif +#if INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (target == DNN_TARGET_OPENCL_FP16) + { + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 + } +#endif CASE(test_reduce_prod_negative_axes_keepdims_example) // no filter CASE(test_reduce_prod_negative_axes_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 + } +#endif +#if INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (target == DNN_TARGET_OPENCL_FP16) + { + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 + } +#endif CASE(test_reduce_sum_default_axes_keepdims_example) // no filter CASE(test_reduce_sum_default_axes_keepdims_random) @@ -1389,19 +1441,61 @@ CASE(test_reduce_sum_negative_axes_keepdims_random) CASE(test_reduce_sum_square_default_axes_keepdims_example) // no filter CASE(test_reduce_sum_square_default_axes_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + default_l1 = 0.05f; // Expected: (normL1) <= (l1), actual: 0.0183411 vs 0.004 +#endif CASE(test_reduce_sum_square_do_not_keepdims_example) // no filter CASE(test_reduce_sum_square_do_not_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 0.05f; // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 + } +#endif +#if INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (target == DNN_TARGET_OPENCL_FP16) + { + default_l1 = 0.01f; // Expected: (normL1) <= (l1), actual: 0.00723048 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0201416 vs 0.02 + } +#endif CASE(test_reduce_sum_square_keepdims_example) // no filter CASE(test_reduce_sum_square_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 0.05f; // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 + } +#endif +#if INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (target == DNN_TARGET_OPENCL_FP16) + { + default_l1 = 0.05f; // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 + } +#endif CASE(test_reduce_sum_square_negative_axes_keepdims_example) // no filter CASE(test_reduce_sum_square_negative_axes_keepdims_random) - // no filter +#if SKIP_SET_1 + if (target == DNN_TARGET_MYRIAD) + { + default_l1 = 0.05f; // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 + } +#endif +#if INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (target == DNN_TARGET_OPENCL_FP16) + { + default_l1 = 0.05f; // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004 + default_lInf = 0.05f; // Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 + } +#endif CASE(test_reflect_pad) // no filter CASE(test_relu) @@ -1717,12 +1811,12 @@ CASE(test_slice_negative_axes) CASE(test_slice_start_out_of_bounds) // no filter CASE(test_softmax_axis_0) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL; SKIP_OPENCL_FP16; #endif CASE(test_softmax_axis_0_expanded) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL; SKIP_OPENCL_FP16; #endif @@ -1735,7 +1829,7 @@ CASE(test_softmax_axis_2) CASE(test_softmax_axis_2_expanded) // no filter CASE(test_softmax_default_axis) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_softmax_default_axis_expanded) @@ -1745,12 +1839,12 @@ CASE(test_softmax_example) CASE(test_softmax_example_expanded) // no filter CASE(test_softmax_large_number) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL_FP16; SKIP_MYRIAD; #endif CASE(test_softmax_large_number_expanded) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP_OPENCL_FP16; SKIP_MYRIAD; #endif @@ -1771,26 +1865,11 @@ CASE(test_spacetodepth) CASE(test_spacetodepth_example) // no filter CASE(test_split_equal_parts_1d) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) - SKIP_CPU; - // MYRIAD is ok - SKIP_OPENCL; - SKIP_OPENCL_FP16; -#endif + // no filter CASE(test_split_equal_parts_2d) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) - SKIP_CPU; - // MYRIAD is ok - SKIP_OPENCL; - SKIP_OPENCL_FP16; -#endif + // no filter CASE(test_split_equal_parts_default_axis) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) - SKIP_CPU; - // MYRIAD is ok - SKIP_OPENCL; - SKIP_OPENCL_FP16; -#endif + // no filter CASE(test_split_variable_parts_1d) // no filter CASE(test_split_variable_parts_2d) @@ -1822,13 +1901,13 @@ CASE(test_strnormalizer_nostopwords_nochangecase) CASE(test_sub) // no filter CASE(test_sub_bcast) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_sub_example) // no filter CASE(test_sub_uint8) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_sum_example) @@ -1964,7 +2043,7 @@ CASE(test_unsqueeze_two_axes) CASE(test_unsqueeze_unsorted_axes) // no filter CASE(test_upsample_nearest) -#if INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if SKIP_SET_1 SKIP; #endif CASE(test_where_example) diff --git a/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp index ccd156884587..c2425d469fcb 100644 --- a/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp +++ b/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp @@ -20,3 +20,14 @@ "test_split_equal_parts_2d", "test_split_equal_parts_default_axis", "test_tan", +"test_reduce_l2_default_axes_keepdims_example", // Expected: (normL1) <= (l1), actual: 0.00490189 vs 0.004 +"test_reduce_log_sum_exp_default_axes_keepdims_example", // Expected: (normL1) <= (l1), actual: 0.00671387 vs 0.004 +"test_reduce_prod_default_axes_keepdims_example", // Expected: (normL1) <= (l1), actual: inf vs 0.004 +"test_reduce_prod_default_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 18.6621 vs 0.004, Expected: (normInf) <= (lInf), actual: 18.6621 vs 0.02 +"test_reduce_prod_do_not_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 +"test_reduce_prod_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 +"test_reduce_prod_negative_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.00436729 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0201836 vs 0.02 +"test_reduce_sum_square_default_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.0183411 vs 0.004 +"test_reduce_sum_square_do_not_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 +"test_reduce_sum_square_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 +"test_reduce_sum_square_negative_axes_keepdims_random", // Expected: (normL1) <= (l1), actual: 0.010789 vs 0.004, Expected: (normInf) <= (lInf), actual: 0.0290298 vs 0.02 \ No newline at end of file diff --git a/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp index e5d0ead9da03..1437e5475b56 100644 --- a/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp +++ b/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp @@ -96,10 +96,6 @@ "test_cumsum_2d_axis_0", "test_cumsum_2d_axis_1", "test_cumsum_2d_negative_axis", -"test_depthtospace_crd_mode", -"test_depthtospace_crd_mode_example", -"test_depthtospace_dcr_mode", -"test_depthtospace_example", "test_dequantizelinear", "test_dequantizelinear_axis", "test_det_2d", @@ -337,53 +333,6 @@ "test_range_float_type_positive_delta_expanded", "test_range_int32_type_negative_delta", "test_range_int32_type_negative_delta_expanded", -"test_reciprocal", -"test_reciprocal_example", -"test_reduce_l1_default_axes_keepdims_example", -"test_reduce_l1_default_axes_keepdims_random", -"test_reduce_l1_do_not_keepdims_example", -"test_reduce_l1_do_not_keepdims_random", -"test_reduce_l1_keep_dims_example", -"test_reduce_l1_keep_dims_random", -"test_reduce_l1_negative_axes_keep_dims_example", -"test_reduce_l1_negative_axes_keep_dims_random", -"test_reduce_l2_default_axes_keepdims_example", -"test_reduce_l2_default_axes_keepdims_random", -"test_reduce_l2_do_not_keepdims_example", -"test_reduce_l2_do_not_keepdims_random", -"test_reduce_l2_keep_dims_example", -"test_reduce_l2_keep_dims_random", -"test_reduce_l2_negative_axes_keep_dims_example", -"test_reduce_l2_negative_axes_keep_dims_random", -"test_reduce_log_sum", -"test_reduce_log_sum_asc_axes", -"test_reduce_log_sum_default", -"test_reduce_log_sum_desc_axes", -"test_reduce_log_sum_exp_default_axes_keepdims_example", -"test_reduce_log_sum_exp_default_axes_keepdims_random", -"test_reduce_log_sum_exp_do_not_keepdims_example", -"test_reduce_log_sum_exp_do_not_keepdims_random", -"test_reduce_log_sum_exp_keepdims_example", -"test_reduce_log_sum_exp_keepdims_random", -"test_reduce_log_sum_exp_negative_axes_keepdims_example", -"test_reduce_log_sum_exp_negative_axes_keepdims_random", -"test_reduce_log_sum_negative_axes", -"test_reduce_min_default_axes_keepdims_example", -"test_reduce_min_default_axes_keepdims_random", -"test_reduce_min_do_not_keepdims_example", -"test_reduce_min_do_not_keepdims_random", -"test_reduce_min_keepdims_example", -"test_reduce_min_keepdims_random", -"test_reduce_min_negative_axes_keepdims_example", -"test_reduce_min_negative_axes_keepdims_random", -"test_reduce_prod_default_axes_keepdims_example", -"test_reduce_prod_default_axes_keepdims_random", -"test_reduce_prod_do_not_keepdims_example", -"test_reduce_prod_do_not_keepdims_random", -"test_reduce_prod_keepdims_example", -"test_reduce_prod_keepdims_random", -"test_reduce_prod_negative_axes_keepdims_example", -"test_reduce_prod_negative_axes_keepdims_random", "test_reduce_sum_default_axes_keepdims_example", "test_reduce_sum_default_axes_keepdims_random", "test_reduce_sum_do_not_keepdims_example", @@ -394,14 +343,6 @@ "test_reduce_sum_keepdims_random", "test_reduce_sum_negative_axes_keepdims_example", "test_reduce_sum_negative_axes_keepdims_random", -"test_reduce_sum_square_default_axes_keepdims_example", -"test_reduce_sum_square_default_axes_keepdims_random", -"test_reduce_sum_square_do_not_keepdims_example", -"test_reduce_sum_square_do_not_keepdims_random", -"test_reduce_sum_square_keepdims_example", -"test_reduce_sum_square_keepdims_random", -"test_reduce_sum_square_negative_axes_keepdims_example", -"test_reduce_sum_square_negative_axes_keepdims_random", "test_reflect_pad", "test_reshape_allowzero_reordered", "test_reshape_extended_dims", @@ -532,9 +473,6 @@ "test_shape_start_1_end_2", "test_shape_start_1_end_negative_1", "test_shape_start_negative_1", -"test_shrink_hard", -"test_shrink_soft", -"test_sign", "test_simple_rnn_batchwise", "test_simple_rnn_defaults", "test_simple_rnn_with_initial_bias", @@ -548,8 +486,6 @@ "test_slice_neg_steps", "test_slice_negative_axes", "test_slice_start_out_of_bounds", -"test_spacetodepth", -"test_spacetodepth_example", "test_split_variable_parts_1d", "test_split_variable_parts_2d", "test_split_variable_parts_default_axis", diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp index 9f13727e95b7..50540cd04326 100644 --- a/modules/dnn/test/test_onnx_importer.cpp +++ b/modules/dnn/test/test_onnx_importer.cpp @@ -106,6 +106,12 @@ TEST_P(Test_ONNX_layers, MaxPooling) } TEST_P(Test_ONNX_layers, MaxPooling_2) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + testONNXModels("two_maxpooling", npy, 0, 0, false, false); } @@ -148,18 +154,31 @@ TEST_P(Test_ONNX_layers, Convolution_variable_weight) TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // openvino/src/plugins/intel_myriad/common/src/ngraph/transformations/extract_dynamic_batch/slice_convolution.cpp:14 Expecting operation v1::GroupConvolution GroupConvolution_6904725 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904719[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904719[0]:f32{4,1,1,2,2} + // openvino\src\plugins\intel_myriad\common\src\ngraph\transformations\extract_dynamic_batch\slice_convolution.cpp:15 Expecting operation v1::GroupConvolution GroupConvolution_6904692 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904686[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904686[0]:f32{4,1,1,2,2} + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // accuracy (depends on OpenCL version / HW) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH || backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); - if (backend == DNN_BACKEND_CUDA) - applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported - if (backend == DNN_BACKEND_VKCOM) - applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU && getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); +#endif + + if (backend == DNN_BACKEND_CUDA) + applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // supports only <= 2 inputs + + if (backend == DNN_BACKEND_VKCOM) + applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported String basename = "conv_variable_wb"; Net net = readNetFromONNX(_tf("models/" + basename + ".onnx")); @@ -229,7 +248,15 @@ TEST_P(Test_ONNX_layers, Deconvolution) TEST_P(Test_ONNX_layers, Deconvolution3D) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "2": + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively) + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": @@ -250,7 +277,15 @@ TEST_P(Test_ONNX_layers, Deconvolution3D) TEST_P(Test_ONNX_layers, Deconvolution3D_bias) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3": + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (270 and 810 respectively) + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": @@ -271,7 +306,15 @@ TEST_P(Test_ONNX_layers, Deconvolution3D_bias) TEST_P(Test_ONNX_layers, Deconvolution3D_pad) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3": + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (108 and 432 respectively) + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": @@ -292,7 +335,15 @@ TEST_P(Test_ONNX_layers, Deconvolution3D_pad) TEST_P(Test_ONNX_layers, Deconvolution3D_adjpad) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3": + // [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (90 and 180 respectively) + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2": @@ -358,7 +409,18 @@ TEST_P(Test_ONNX_layers, ReduceSum) TEST_P(Test_ONNX_layers, ReduceMax) { testONNXModels("reduce_max"); +} +TEST_P(Test_ONNX_layers, ReduceMax_axis_0) +{ testONNXModels("reduce_max_axis_0"); +} +TEST_P(Test_ONNX_layers, ReduceMax_axis_1) +{ +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif testONNXModels("reduce_max_axis_1"); } @@ -378,10 +440,40 @@ TEST_P(Test_ONNX_layers, ArgLayer) TEST_P(Test_ONNX_layers, Scale) { - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy (inf/nan) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: mkldnn_node.cpp:238 Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif testONNXModels("scale"); +} + +TEST_P(Test_ONNX_layers, Scale_broadcast) +{ + if (backend == DNN_BACKEND_CUDA) + applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // doesn't support broadcasting testONNXModels("scale_broadcast", npy, 0, 0, false, true, 3); +} + +TEST_P(Test_ONNX_layers, Scale_broadcast_mid) +{ + if (backend == DNN_BACKEND_CUDA) + applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // doesn't support broadcasting testONNXModels("scale_broadcast_mid", npy, 0, 0, false, true, 2); } @@ -481,7 +573,19 @@ TEST_P(Test_ONNX_layers, Elementwise_Sqrt) TEST_P(Test_ONNX_layers, Elementwise_not) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) @@ -492,145 +596,151 @@ TEST_P(Test_ONNX_layers, Elementwise_not) TEST_P(Test_ONNX_layers, Compare_EQ) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION - ); - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); - } -#endif + testONNXModels("equal"); } TEST_P(Test_ONNX_layers, Compare_GT) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION - ); - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); - } -#endif + testONNXModels("greater"); } TEST_P(Test_ONNX_layers, Compare_LT) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION - ); - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); - } -#endif + testONNXModels("less"); } TEST_P(Test_ONNX_layers, CompareSameDims_EQ) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION - ); - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); - } -#endif + testONNXModels("equal_same_dims", npy, 0, 0, false, true, 2); } TEST_P(Test_ONNX_layers, CompareSameDims_GT) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION - ); - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); - } -#endif + testONNXModels("greater_same_dims", npy, 0, 0, false, true, 2); } TEST_P(Test_ONNX_layers, CompareSameDims_LT) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: Function contains several inputs and outputs with one friendly name! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - { - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION - ); - // IE exception: Function contains several inputs and outputs with one friendly name! - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); - } -#endif + testONNXModels("less_same_dims", npy, 0, 0, false, true, 2); } @@ -664,7 +774,19 @@ TEST_P(Test_ONNX_layers, AveragePooling) TEST_P(Test_ONNX_layers, MaxPooling3D) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + { + // accuracy + if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired() + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // accuracy @@ -797,8 +919,14 @@ TEST_P(Test_ONNX_layers, MatMul) TEST_P(Test_ONNX_layers, MatMulAdd) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); +#endif if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); testONNXModels("matmul_add"); @@ -885,9 +1013,14 @@ TEST_P(Test_ONNX_layers, DynamicResize) testONNXModels("dynamic_resize_9", npy, 0, 0, false, true, 2); testONNXModels("dynamic_resize_10", npy, 0, 0, false, true, 2); testONNXModels("dynamic_resize_11", npy, 0, 0, false, true, 2); + testONNXModels("dynamic_resize_13", npy, 0, 0, false, true, 2); testONNXModels("dynamic_resize_scale_9", npy, 0, 0, false, true, 2); testONNXModels("dynamic_resize_scale_10", npy, 0, 0, false, true, 2); testONNXModels("dynamic_resize_scale_11", npy, 0, 0, false, true, 2); + testONNXModels("dynamic_resize_scale_13", npy, 0, 0, false, true, 2); + + testONNXModels("resize_size_opset11"); + testONNXModels("resize_size_opset13"); } TEST_P(Test_ONNX_layers, Resize_HumanSeg) @@ -985,6 +1118,17 @@ TEST_P(Test_ONNX_layers, Slice_Steps_2DInput) TEST_P(Test_ONNX_layers, Slice_Steps_3DInput) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#endif + testONNXModels("slice_opset_11_steps_3d"); } @@ -995,6 +1139,20 @@ TEST_P(Test_ONNX_layers, Slice_Steps_4DInput) TEST_P(Test_ONNX_layers, Slice_Steps_5DInput) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + testONNXModels("slice_opset_11_steps_5d"); } @@ -1016,7 +1174,16 @@ TEST_P(Test_ONNX_layers, Split_EltwiseMax) TEST_P(Test_ONNX_layers, LSTM_Activations) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: Node Block1326/lstm/reshape_0/permute was not assigned on any pointed device + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE Exception: Ngraph operation Reshape with name Block1237_Output_0_before_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, @@ -1027,12 +1194,14 @@ TEST_P(Test_ONNX_layers, LSTM_Activations) testONNXModels("lstm_cntk_tanh", pb, 0, 0, false, false); } -TEST_P(Test_ONNX_layers, LSTM) +// disabled due to poor handling of 1-d mats +TEST_P(Test_ONNX_layers, DISABLED_LSTM) { testONNXModels("lstm", npy, 0, 0, false, false); } -TEST_P(Test_ONNX_layers, LSTM_bidirectional) +// disabled due to poor handling of 1-d mats +TEST_P(Test_ONNX_layers, DISABLED_LSTM_bidirectional) { testONNXModels("lstm_bidirectional", npy, 0, 0, false, false); } @@ -1044,11 +1213,32 @@ TEST_P(Test_ONNX_layers, LSTM_hidden) TEST_P(Test_ONNX_layers, LSTM_hidden_bidirectional) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: Node Transpose_45 was not assigned on any pointed device. + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#endif + testONNXModels("hidden_lstm_bi", npy, 0, 0, false, false); } TEST_P(Test_ONNX_layers, GRU) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // IE exception: Node GRU_22 was not assigned on any pointed device + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#endif testONNXModels("gru", npy, 0, 0, false, false); } @@ -1057,6 +1247,41 @@ TEST_P(Test_ONNX_layers, GRU_bidirectional) testONNXModels("gru_bi", npy, 0, 0, false, false); } +TEST_P(Test_ONNX_layers, LSTM_cell_forward) +{ +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + testONNXModels("lstm_cell_forward", npy, 0, 0, false, false); +} +TEST_P(Test_ONNX_layers, LSTM_cell_bidirectional) +{ +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif + testONNXModels("lstm_cell_bidirectional", npy, 0, 0, false, false); +} +TEST_P(Test_ONNX_layers, LSTM_cell_with_peepholes) +{ + testONNXModels("lstm_cell_with_peepholes", npy, 0, 0, false, false); +} + TEST_P(Test_ONNX_layers, Pad2d_Unfused) { testONNXModels("ReflectionPad2d"); @@ -1243,14 +1468,19 @@ TEST_P(Test_ONNX_layers, DynamicAxes_gather) TEST_P(Test_ONNX_layers, DynamicAxes_gather_scalar) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); -#endif -#if defined(INF_ENGINE_RELEASE) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); @@ -1392,7 +1622,10 @@ TEST_P(Test_ONNX_layers, MaxPool1d) TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); @@ -1407,7 +1640,16 @@ TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d) TEST_P(Test_ONNX_layers, MaxPool1d_Twise) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); @@ -1422,7 +1664,11 @@ TEST_P(Test_ONNX_layers, MaxPool1d_Twise) TEST_P(Test_ONNX_layers, AvePool1d) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); @@ -1437,7 +1683,19 @@ TEST_P(Test_ONNX_layers, AvePool1d) TEST_P(Test_ONNX_layers, PoolConv1d) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) { if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); @@ -1494,6 +1752,8 @@ TEST_P(Test_ONNX_layers, Quantized_Convolution) testONNXModels("quantized_conv_uint8_weights", npy, 0.004, 0.02); testONNXModels("quantized_conv_int8_weights", npy, 0.03, 0.5); testONNXModels("quantized_conv_per_channel_weights", npy, 0.06, 0.4); + + testONNXModels("quantized_conv_asymmetric_pads_int8_weights"); } TEST_P(Test_ONNX_layers, Quantized_MatMul) @@ -1595,6 +1855,11 @@ TEST_P(Test_ONNX_layers, Quantized_Constant) testONNXModels("quantized_constant", npy, 0.002, 0.008); } +TEST_P(Test_ONNX_layers, OutputRegistration) +{ + testONNXModels("output_registration", npy, 0, 0, false, true, 2); +} + INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets()); class Test_ONNX_nets : public Test_ONNX_layers @@ -1638,18 +1903,21 @@ TEST_P(Test_ONNX_nets, Squeezenet) TEST_P(Test_ONNX_nets, Googlenet) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // accuracy + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // accuracy + if (target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) - // accuracy - if (target == DNN_TARGET_MYRIAD) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif const String model = _tf("models/googlenet.onnx", false); @@ -1875,7 +2143,7 @@ TEST_P(Test_ONNX_nets, Emotion_ferplus) double lInf = default_lInf; // Output values are in range [-2.011, 2.111] - if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) + if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) || (target == DNN_TARGET_CUDA_FP16)) l1 = 0.007; else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16) { @@ -1934,19 +2202,27 @@ TEST_P(Test_ONNX_nets, Shufflenet) TEST_P(Test_ONNX_nets, Resnet34_kinetics) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported - else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported -#endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: Failed to allocate graph: MYRIAD device is not opened + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // IE exception: Function contains several inputs and outputs with one friendly name! if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported #endif if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU) throw SkipTestException("Only CPU is supported"); // FIXIT use tags diff --git a/modules/dnn/test/test_tf_importer.cpp b/modules/dnn/test/test_tf_importer.cpp index 7a4bfc96f2d4..582d8b0c38da 100644 --- a/modules/dnn/test/test_tf_importer.cpp +++ b/modules/dnn/test/test_tf_importer.cpp @@ -215,7 +215,14 @@ TEST_P(Test_TensorFlow_layers, conv_keras_atrous_conv2d_same) } TEST_P(Test_TensorFlow_layers, conv_pool_nchw) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -275,13 +282,25 @@ TEST_P(Test_TensorFlow_layers, padding_asymmetric_3) TEST_P(Test_TensorFlow_layers, padding_asymmetric_4) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Unsupported pad value + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // Exception: Unsupported pad value + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) // Exception: Unsupported pad value applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#endif #endif runTensorFlowNet("max_pool2d_asymmetric_pads_nhwc"); } @@ -470,7 +489,14 @@ TEST_P(Test_TensorFlow_layers, slim_batch_norm) TEST_P(Test_TensorFlow_layers, pooling_max_pool_even) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -478,7 +504,14 @@ TEST_P(Test_TensorFlow_layers, pooling_max_pool_even) } TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_valid) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -486,7 +519,14 @@ TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_valid) } TEST_P(Test_TensorFlow_layers, pooling_max_pool_odd_same) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -605,7 +645,16 @@ TEST_P(Test_TensorFlow_layers, ave_pool_same) TEST_P(Test_TensorFlow_layers, MaxPooling3D) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // accuracy @@ -617,8 +666,7 @@ TEST_P(Test_TensorFlow_layers, MaxPooling3D) if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); } -#endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) @@ -794,14 +842,19 @@ TEST_P(Test_TensorFlow_layers, ExpandDims) // TODO: fix it and add to l2_normalize TEST_P(Test_TensorFlow_layers, l2_normalize_3d) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 - && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) - ) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); -#endif -#if defined(INF_ENGINE_RELEASE) + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); + // accuracy + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) @@ -1058,15 +1111,28 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN_resnet50_coco_2018_01_28) CV_TEST_TAG_DEBUG_VERYLONG ); -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep < subgraphs.size() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep < subgraphs.size() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) + // [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep++ < subgraphs.size() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // IE exception: Ngraph operation Transpose with name FirstStageBoxPredictor/ClassPredictor/reshape_1/nhwc has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); -#endif - -#ifdef INF_ENGINE_RELEASE +#elif defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU)) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); @@ -1074,12 +1140,12 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN_resnet50_coco_2018_01_28) if (INF_ENGINE_VER_MAJOR_GT(2019030000) && backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); -#endif #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) // segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111: // Assertion `prior_height > 0' failed. if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); +#endif #endif if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) @@ -1287,7 +1353,14 @@ TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_single_conv) } TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_odd_same) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -1311,7 +1384,14 @@ TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_padding_valid) } TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_even) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -1331,7 +1411,14 @@ TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_deconvolution) } TEST_P(Test_TensorFlow_layers, fp16_weights_fp16_max_pool_odd_valid) { -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // [ GENERAL_ERROR ] AssertionFailed: !expired() + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif @@ -1364,13 +1451,11 @@ TEST_P(Test_TensorFlow_layers, lstm) { if(backend == DNN_BACKEND_CUDA) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* not supported */ -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); -#endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Xlink, Failed to allocate graph: NC_ERROR + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // Exception: Ngraph operation Reshape with name Reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape @@ -1382,6 +1467,11 @@ TEST_P(Test_TensorFlow_layers, lstm) if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); @@ -1411,7 +1501,20 @@ TEST_P(Test_TensorFlow_layers, split_equals) TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#endif runTensorFlowNet("resize_nearest_neighbor"); +} +TEST_P(Test_TensorFlow_layers, resize_nearest_neighbor_keras_upsampling2d) +{ runTensorFlowNet("keras_upsampling2d"); } @@ -1435,25 +1538,30 @@ TEST_P(Test_TensorFlow_layers, fused_resize_conv) runTensorFlowNet("fused_resize_conv"); } -TEST_P(Test_TensorFlow_layers, slice) +TEST_P(Test_TensorFlow_layers, slice_crop2d) { - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && - (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) - applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, - CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); double l1 = target == DNN_TARGET_MYRIAD ? 4.9e-3 : default_l1; runTensorFlowNet("crop2d", false, l1); +} +TEST_P(Test_TensorFlow_layers, slice_4d) +{ runTensorFlowNet("slice_4d"); +} +TEST_P(Test_TensorFlow_layers, slice_strided) +{ runTensorFlowNet("strided_slice"); } -TEST_P(Test_TensorFlow_layers, softmax) +TEST_P(Test_TensorFlow_layers, softmax_keras) { runTensorFlowNet("keras_softmax"); +} +TEST_P(Test_TensorFlow_layers, softmax_slim) +{ runTensorFlowNet("slim_softmax"); } -TEST_P(Test_TensorFlow_layers, slim_softmax_v2) +TEST_P(Test_TensorFlow_layers, softmax_slim_v2) { #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD && @@ -1472,10 +1580,19 @@ TEST_P(Test_TensorFlow_layers, relu6) TEST_P(Test_TensorFlow_layers, subpixel) { +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); +#elif defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); +#endif runTensorFlowNet("subpixel"); } @@ -1566,13 +1683,16 @@ TEST_P(Test_TensorFlow_layers, tf2_prelu) { if (backend == DNN_BACKEND_CUDA) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported; only across channels is supported -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); - if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) - applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); -#endif -#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Eltwise executor got invalid input/output dims configuration + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { // IE exception: Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map @@ -1584,6 +1704,11 @@ TEST_P(Test_TensorFlow_layers, tf2_prelu) if (target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); } +#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000) + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); #endif runTensorFlowNet("tf2_prelu"); @@ -1767,4 +1892,25 @@ TEST_P(Test_TensorFlow_nets, EfficientDet) expectNoFallbacksFromIE(net); } +TEST(Test_TensorFlow_Importer, tf_graph_simplifier_buffer_overflow_21852) +{ + uint8_t payload[] = {0x08, 0x08, 0x0a, 0x00, 0x0a, 0x00}; + EXPECT_ANY_THROW(readNetFromTensorflow(reinterpret_cast(payload), sizeof(payload) / sizeof(payload[0]))); +} + +// can be triggered with -fsanitize=address +TEST(Test_TensorFlow_Importer, tf_graph_simplifier_buffer_overflow_21947) +{ + uint8_t payload[] = {0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, + 0xba, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, + 0x0a, 0xbd, 0x00, 0x1a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, + 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, + 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, 0x0a, 0x00, + 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0xba, + 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, + 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x2a, 0x00, 0xba, 0x0a, 0x00, + 0x0a, 0x00, 0x5d, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x00, 0x0a, 0x40}; + EXPECT_ANY_THROW(readNetFromTensorflow(reinterpret_cast(payload), sizeof(payload) / sizeof(payload[0]))); +} + } diff --git a/modules/dnn/test/test_torch_importer.cpp b/modules/dnn/test/test_torch_importer.cpp index 1365df78ea8e..520887480dcc 100644 --- a/modules/dnn/test/test_torch_importer.cpp +++ b/modules/dnn/test/test_torch_importer.cpp @@ -287,6 +287,21 @@ TEST_P(Test_Torch_layers, net_normalize) { if(backend == DNN_BACKEND_CUDA) applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* only L1 and L2 norms are supported */ + +#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) + applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); + // Cannot get memory! + if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) + applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, + CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION + ); +#endif + runTorchNet("net_normalize", "", false, true); } @@ -551,6 +566,8 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy) double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total(); if (target == DNN_TARGET_MYRIAD) EXPECT_LE(normL1, 4.0f); + else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) + EXPECT_LE(normL1, 1.0f); else EXPECT_LE(normL1, 0.6f); } diff --git a/modules/features2d/include/opencv2/features2d.hpp b/modules/features2d/include/opencv2/features2d.hpp index 26c2b950c9f1..952d24ca0cfa 100644 --- a/modules/features2d/include/opencv2/features2d.hpp +++ b/modules/features2d/include/opencv2/features2d.hpp @@ -1143,8 +1143,8 @@ class CV_EXPORTS_W DescriptorMatcher : public Algorithm virtual void clear(); const Mat& getDescriptors() const; - const Mat getDescriptor( int imgIdx, int localDescIdx ) const; - const Mat getDescriptor( int globalDescIdx ) const; + Mat getDescriptor( int imgIdx, int localDescIdx ) const; + Mat getDescriptor( int globalDescIdx ) const; void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; int size() const; diff --git a/modules/features2d/src/evaluation.cpp b/modules/features2d/src/evaluation.cpp index ca7ab145008d..7135891e0d86 100644 --- a/modules/features2d/src/evaluation.cpp +++ b/modules/features2d/src/evaluation.cpp @@ -315,6 +315,8 @@ struct SIdx const SIdx& used; bool operator()(const SIdx& v) const { return (v.i1 == used.i1 || v.i2 == used.i2); } UsedFinder& operator=(const UsedFinder&) = delete; + // To avoid -Wdeprecated-copy warning, copy constructor is needed. + UsedFinder(const UsedFinder&) = default; }; }; diff --git a/modules/features2d/src/kaze/KAZEFeatures.cpp b/modules/features2d/src/kaze/KAZEFeatures.cpp index ab591d417c69..7bd0ba6dcd82 100644 --- a/modules/features2d/src/kaze/KAZEFeatures.cpp +++ b/modules/features2d/src/kaze/KAZEFeatures.cpp @@ -312,7 +312,7 @@ void KAZEFeatures::Determinant_Hessian(std::vector& kpts) { int level = 0; float smax = 3.0; - int npoints = 0, id_repeated = 0; + int id_repeated = 0; int left_x = 0, right_x = 0, up_y = 0, down_y = 0; bool is_extremum = false, is_repeated = false, is_out = false; @@ -383,7 +383,6 @@ void KAZEFeatures::Determinant_Hessian(std::vector& kpts) if (is_out == false) { if (is_repeated == false) { kpts.push_back(kpts_par_ij); - npoints++; } else { kpts[id_repeated] = kpts_par_ij; diff --git a/modules/features2d/src/keypoint.cpp b/modules/features2d/src/keypoint.cpp index e14c9da94c2a..21d9eb30f792 100644 --- a/modules/features2d/src/keypoint.cpp +++ b/modules/features2d/src/keypoint.cpp @@ -148,10 +148,12 @@ class MaskPredicate { return mask.at( (int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f) ) == 0; } + MaskPredicate& operator=(const MaskPredicate&) = delete; + // To avoid -Wdeprecated-copy warning, copy constructor is needed. + MaskPredicate(const MaskPredicate&) = default; private: const Mat mask; - MaskPredicate& operator=(const MaskPredicate&) = delete; }; void KeyPointsFilter::runByPixelsMask( std::vector& keypoints, const Mat& mask ) diff --git a/modules/features2d/src/matchers.cpp b/modules/features2d/src/matchers.cpp index 97875d409563..97d0a0241766 100644 --- a/modules/features2d/src/matchers.cpp +++ b/modules/features2d/src/matchers.cpp @@ -475,7 +475,7 @@ void DescriptorMatcher::DescriptorCollection::clear() mergedDescriptors.release(); } -const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int imgIdx, int localDescIdx ) const +Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int imgIdx, int localDescIdx ) const { CV_Assert( imgIdx < (int)startIdxs.size() ); int globalIdx = startIdxs[imgIdx] + localDescIdx; @@ -489,7 +489,7 @@ const Mat& DescriptorMatcher::DescriptorCollection::getDescriptors() const return mergedDescriptors; } -const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int globalDescIdx ) const +Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int globalDescIdx ) const { CV_Assert( globalDescIdx < size() ); return mergedDescriptors.row( globalDescIdx ); diff --git a/modules/flann/include/opencv2/flann.hpp b/modules/flann/include/opencv2/flann.hpp index 04689037ebc3..90ee59e0b869 100644 --- a/modules/flann/include/opencv2/flann.hpp +++ b/modules/flann/include/opencv2/flann.hpp @@ -116,7 +116,7 @@ cv::flann::L2 - Squared Euclidean distance functor, optimized version. cv::flann::L1 - Manhattan distance functor, optimized version. -cv::flann::MinkowskiDistance - The Minkowsky distance functor. +cv::flann::MinkowskiDistance - The Minkowski distance functor. This is highly optimised with loop unrolling. The computation of squared root at the end is omitted for efficiency. diff --git a/modules/flann/include/opencv2/flann/dist.h b/modules/flann/include/opencv2/flann/dist.h index 608f8a507a32..2d7cbf13de99 100644 --- a/modules/flann/include/opencv2/flann/dist.h +++ b/modules/flann/include/opencv2/flann/dist.h @@ -375,7 +375,7 @@ struct MinkowskiDistance MinkowskiDistance(int order_) : order(order_) {} /** - * Compute the Minkowsky (L_p) distance between two vectors. + * Compute the Minkowski (L_p) distance between two vectors. * * This is highly optimised, with loop unrolling, as it is one * of the most expensive inner loops. diff --git a/modules/flann/include/opencv2/flann/index_testing.h b/modules/flann/include/opencv2/flann/index_testing.h index 207adef44986..4c0014332628 100644 --- a/modules/flann/include/opencv2/flann/index_testing.h +++ b/modules/flann/include/opencv2/flann/index_testing.h @@ -246,7 +246,6 @@ void test_index_precisions(NNIndex& index, const Matrix& index, const Matrix 0)&&(time > maxTime)&&(p2=1.2 libva-drm>=1.2) + if(PKG_LIBVA_FOUND) + set(CMAKE_THREAD_PREFER_PTHREAD TRUE) + set(THREADS_PREFER_PTHREAD_FLAG TRUE) + find_package(Threads REQUIRED) + else() + message(FATAL_ERROR "libva not found: building HAVE_GAPI_ONEVPL without libVA support is impossible on UNIX systems") + endif() + else() + message(FATAL_ERROR "PkgConfig not found: building HAVE_GAPI_ONEVPL without libVA support is impossible on UNIX systems") + endif() + ocv_target_link_libraries(${the_module} PRIVATE ${PKG_LIBVA_LIBRARIES} ${PKG_THREAD_LIBRARIES}) + endif() endif() ocv_option(OPENCV_GAPI_GSTREAMER "Build G-API with GStreamer support" HAVE_GSTREAMER) @@ -329,20 +366,27 @@ ocv_add_samples() # Required for sample with inference on host -if (TARGET example_gapi_onevpl_infer_single_roi) - if(OPENCV_GAPI_INF_ENGINE) - ocv_target_link_libraries(example_gapi_onevpl_infer_single_roi PRIVATE ${INF_ENGINE_TARGET}) - ocv_target_compile_definitions(example_gapi_onevpl_infer_single_roi PRIVATE -DHAVE_INF_ENGINE) +if(TARGET example_gapi_onevpl_infer_single_roi) + if(TARGET ocv.3rdparty.openvino AND OPENCV_GAPI_WITH_OPENVINO) + ocv_target_link_libraries(example_gapi_onevpl_infer_single_roi PRIVATE ocv.3rdparty.openvino) endif() if(HAVE_D3D11 AND HAVE_OPENCL) ocv_target_include_directories(example_gapi_onevpl_infer_single_roi SYSTEM PRIVATE ${OPENCL_INCLUDE_DIRS}) endif() endif() +if(TARGET example_gapi_pipeline_modeling_tool) + if(WIN32) + ocv_target_link_libraries(example_gapi_pipeline_modeling_tool winmm.lib) + endif() +endif() + # perf test dependencies postprocessing if(HAVE_GAPI_ONEVPL) # NB: TARGET opencv_perf_gapi doesn't exist before `ocv_add_perf_tests` + # src/ is specified to include dirs for INTERNAL tests only. if(TARGET opencv_perf_gapi) + target_include_directories(opencv_perf_gapi PRIVATE "${CMAKE_CURRENT_LIST_DIR}/src") ocv_target_compile_definitions(opencv_perf_gapi PRIVATE -DHAVE_ONEVPL) ocv_target_link_libraries(opencv_perf_gapi PRIVATE ${VPL_IMPORTED_TARGETS}) if(HAVE_D3D11 AND HAVE_OPENCL) diff --git a/modules/gapi/cmake/init.cmake b/modules/gapi/cmake/init.cmake index 1c464328ca1d..dd4b0bccfa33 100644 --- a/modules/gapi/cmake/init.cmake +++ b/modules/gapi/cmake/init.cmake @@ -1,7 +1,8 @@ OCV_OPTION(WITH_ADE "Enable ADE framework (required for Graph API module)" ON) -OCV_OPTION(WITH_FREETYPE "Enable FreeType framework" OFF) -OCV_OPTION(WITH_PLAIDML "Include PlaidML2 support" OFF) +OCV_OPTION(WITH_FREETYPE "Enable FreeType framework" OFF) +OCV_OPTION(WITH_PLAIDML "Include PlaidML2 support" OFF) +OCV_OPTION(WITH_OAK "Include OpenCV AI Kit support" OFF) if(NOT WITH_ADE) return() @@ -39,3 +40,10 @@ if(WITH_GAPI_ONEVPL) set(HAVE_GAPI_ONEVPL TRUE) endif() endif() + +if(WITH_OAK) + find_package(depthai QUIET) + if(depthai_FOUND) + set(HAVE_OAK TRUE) + endif() +endif() diff --git a/modules/gapi/doc/slides/gapi_overview.org b/modules/gapi/doc/slides/gapi_overview.org index 676c914b6645..7ed85baeca70 100644 --- a/modules/gapi/doc/slides/gapi_overview.org +++ b/modules/gapi/doc/slides/gapi_overview.org @@ -592,7 +592,7 @@ subgraph cluster_3 {style=filled;color=azure2; C}; *** But how does it run? -- Since ~infer~ is an *Operation*, backends may provide *Kernels* implenting it; +- Since ~infer~ is an *Operation*, backends may provide *Kernels* implementing it; - The only publicly available inference backend now is *OpenVINO™*: - Brings its ~infer~ kernel atop of the Inference Engine; - NN model data is passed through G-API compile arguments (like kernels); diff --git a/modules/gapi/include/opencv2/gapi/core.hpp b/modules/gapi/include/opencv2/gapi/core.hpp index 791aa4ce564e..f46f159adf73 100644 --- a/modules/gapi/include/opencv2/gapi/core.hpp +++ b/modules/gapi/include/opencv2/gapi/core.hpp @@ -9,10 +9,10 @@ #define OPENCV_GAPI_CORE_HPP #include - #include // std::tuple #include +#include #include #include @@ -34,6 +34,9 @@ namespace cv { namespace gapi { * Core module functionality. */ namespace core { + using GResize = cv::gapi::imgproc::GResize; + using GResizeP = cv::gapi::imgproc::GResizeP; + using GMat2 = std::tuple; using GMat3 = std::tuple; // FIXME: how to avoid this? using GMat4 = std::tuple; @@ -100,7 +103,7 @@ namespace core { } }; - G_TYPED_KERNEL(GMulC, , "org.opencv.core.math.mulC"){ + G_TYPED_KERNEL(GMulC, , "org.opencv.core.math.mulC") { static GMatDesc outMeta(GMatDesc a, GScalarDesc, int ddepth) { return a.withDepth(ddepth); } @@ -201,37 +204,37 @@ namespace core { } }; - G_TYPED_KERNEL(GCmpGTScalar, , "org.opencv.core.pixelwise.compare.cmpGTScalar"){ + G_TYPED_KERNEL(GCmpGTScalar, , "org.opencv.core.pixelwise.compare.cmpGTScalar") { static GMatDesc outMeta(GMatDesc a, GScalarDesc) { return a.withDepth(CV_8U); } }; - G_TYPED_KERNEL(GCmpGEScalar, , "org.opencv.core.pixelwise.compare.cmpGEScalar"){ + G_TYPED_KERNEL(GCmpGEScalar, , "org.opencv.core.pixelwise.compare.cmpGEScalar") { static GMatDesc outMeta(GMatDesc a, GScalarDesc) { return a.withDepth(CV_8U); } }; - G_TYPED_KERNEL(GCmpLEScalar, , "org.opencv.core.pixelwise.compare.cmpLEScalar"){ + G_TYPED_KERNEL(GCmpLEScalar, , "org.opencv.core.pixelwise.compare.cmpLEScalar") { static GMatDesc outMeta(GMatDesc a, GScalarDesc) { return a.withDepth(CV_8U); } }; - G_TYPED_KERNEL(GCmpLTScalar, , "org.opencv.core.pixelwise.compare.cmpLTScalar"){ + G_TYPED_KERNEL(GCmpLTScalar, , "org.opencv.core.pixelwise.compare.cmpLTScalar") { static GMatDesc outMeta(GMatDesc a, GScalarDesc) { return a.withDepth(CV_8U); } }; - G_TYPED_KERNEL(GCmpEQScalar, , "org.opencv.core.pixelwise.compare.cmpEQScalar"){ + G_TYPED_KERNEL(GCmpEQScalar, , "org.opencv.core.pixelwise.compare.cmpEQScalar") { static GMatDesc outMeta(GMatDesc a, GScalarDesc) { return a.withDepth(CV_8U); } }; - G_TYPED_KERNEL(GCmpNEScalar, , "org.opencv.core.pixelwise.compare.cmpNEScalar"){ + G_TYPED_KERNEL(GCmpNEScalar, , "org.opencv.core.pixelwise.compare.cmpNEScalar") { static GMatDesc outMeta(GMatDesc a, GScalarDesc) { return a.withDepth(CV_8U); } @@ -398,32 +401,6 @@ namespace core { } }; - G_TYPED_KERNEL(GResize, , "org.opencv.core.transform.resize") { - static GMatDesc outMeta(GMatDesc in, Size sz, double fx, double fy, int /*interp*/) { - if (sz.width != 0 && sz.height != 0) - { - return in.withSize(sz); - } - else - { - int outSz_w = static_cast(round(in.size.width * fx)); - int outSz_h = static_cast(round(in.size.height * fy)); - GAPI_Assert(outSz_w > 0 && outSz_h > 0); - return in.withSize(Size(outSz_w, outSz_h)); - } - } - }; - - G_TYPED_KERNEL(GResizeP, , "org.opencv.core.transform.resizeP") { - static GMatDesc outMeta(GMatDesc in, Size sz, int interp) { - GAPI_Assert(in.depth == CV_8U); - GAPI_Assert(in.chan == 3); - GAPI_Assert(in.planar); - GAPI_Assert(interp == cv::INTER_LINEAR); - return in.withSize(sz); - } - }; - G_TYPED_KERNEL(GMerge3, , "org.opencv.core.transform.merge3") { static GMatDesc outMeta(GMatDesc in, GMatDesc, GMatDesc) { // Preserve depth and add channel component @@ -1467,63 +1444,6 @@ GAPI_EXPORTS GMat inRange(const GMat& src, const GScalar& threshLow, const GScal //! @addtogroup gapi_transform //! @{ -/** @brief Resizes an image. - -The function resizes the image src down to or up to the specified size. - -Output image size will have the size dsize (when dsize is non-zero) or the size computed from -src.size(), fx, and fy; the depth of output is the same as of src. - -If you want to resize src so that it fits the pre-created dst, -you may call the function as follows: -@code - // explicitly specify dsize=dst.size(); fx and fy will be computed from that. - resize(src, dst, dst.size(), 0, 0, interpolation); -@endcode -If you want to decimate the image by factor of 2 in each direction, you can call the function this -way: -@code - // specify fx and fy and let the function compute the destination image size. - resize(src, dst, Size(), 0.5, 0.5, interpolation); -@endcode -To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to -enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR -(faster but still looks OK). - -@note Function textual ID is "org.opencv.core.transform.resize" - -@param src input image. -@param dsize output image size; if it equals zero, it is computed as: - \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] - Either dsize or both fx and fy must be non-zero. -@param fx scale factor along the horizontal axis; when it equals 0, it is computed as -\f[\texttt{(double)dsize.width/src.cols}\f] -@param fy scale factor along the vertical axis; when it equals 0, it is computed as -\f[\texttt{(double)dsize.height/src.rows}\f] -@param interpolation interpolation method, see cv::InterpolationFlags - -@sa warpAffine, warpPerspective, remap, resizeP - */ -GAPI_EXPORTS_W GMat resize(const GMat& src, const Size& dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR); - -/** @brief Resizes a planar image. - -The function resizes the image src down to or up to the specified size. -Planar image memory layout is three planes laying in the memory contiguously, -so the image height should be plane_height*plane_number, image type is @ref CV_8UC1. - -Output image size will have the size dsize, the depth of output is the same as of src. - -@note Function textual ID is "org.opencv.core.transform.resizeP" - -@param src input image, must be of @ref CV_8UC1 type; -@param dsize output image size; -@param interpolation interpolation method, only cv::INTER_LINEAR is supported at the moment - -@sa warpAffine, warpPerspective, remap, resize - */ -GAPI_EXPORTS GMatP resizeP(const GMatP& src, const Size& dsize, int interpolation = cv::INTER_LINEAR); - /** @brief Creates one 4-channel matrix out of 4 single-channel ones. The function merges several matrices to make a single multi-channel matrix. That is, each diff --git a/modules/gapi/include/opencv2/gapi/cpu/core.hpp b/modules/gapi/include/opencv2/gapi/cpu/core.hpp index ac08f91c78fd..ee86fb72c234 100644 --- a/modules/gapi/include/opencv2/gapi/cpu/core.hpp +++ b/modules/gapi/include/opencv2/gapi/cpu/core.hpp @@ -16,7 +16,7 @@ namespace gapi { namespace core { namespace cpu { -GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels(); +GAPI_EXPORTS_W cv::GKernelPackage kernels(); } // namespace cpu } // namespace core diff --git a/modules/gapi/include/opencv2/gapi/cpu/gcpukernel.hpp b/modules/gapi/include/opencv2/gapi/cpu/gcpukernel.hpp index 48909a84fc0b..ff3ee45ed3a8 100644 --- a/modules/gapi/include/opencv2/gapi/cpu/gcpukernel.hpp +++ b/modules/gapi/include/opencv2/gapi/cpu/gcpukernel.hpp @@ -2,12 +2,17 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2018-2020 Intel Corporation +// Copyright (C) 2018-2022 Intel Corporation #ifndef OPENCV_GAPI_GCPUKERNEL_HPP #define OPENCV_GAPI_GCPUKERNEL_HPP +#ifdef _MSC_VER +#pragma warning(disable: 4702) // "Unreachable code" +// on postprocess(...) call inside OCVCallHelper +#endif + #include #include #include diff --git a/modules/gapi/include/opencv2/gapi/fluid/core.hpp b/modules/gapi/include/opencv2/gapi/fluid/core.hpp index 9eceb82cb26c..a4329d6f50f8 100644 --- a/modules/gapi/include/opencv2/gapi/fluid/core.hpp +++ b/modules/gapi/include/opencv2/gapi/fluid/core.hpp @@ -13,7 +13,7 @@ namespace cv { namespace gapi { namespace core { namespace fluid { -GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels(); +GAPI_EXPORTS_W cv::GKernelPackage kernels(); }}}} diff --git a/modules/gapi/include/opencv2/gapi/fluid/gfluidkernel.hpp b/modules/gapi/include/opencv2/gapi/fluid/gfluidkernel.hpp index 53b68c4e2188..92f1ccc87f6f 100644 --- a/modules/gapi/include/opencv2/gapi/fluid/gfluidkernel.hpp +++ b/modules/gapi/include/opencv2/gapi/fluid/gfluidkernel.hpp @@ -181,7 +181,7 @@ template<> struct fluid_get_in template<> struct fluid_get_in { // FIXME: change to return by reference when moved to own::Scalar - static const cv::Scalar get(const cv::GArgs &in_args, int idx) + static cv::Scalar get(const cv::GArgs &in_args, int idx) { return in_args[idx].unsafe_get(); } diff --git a/modules/gapi/include/opencv2/gapi/garray.hpp b/modules/gapi/include/opencv2/gapi/garray.hpp index 17b03332e007..55f4d11b1223 100644 --- a/modules/gapi/include/opencv2/gapi/garray.hpp +++ b/modules/gapi/include/opencv2/gapi/garray.hpp @@ -381,7 +381,7 @@ template class GArray * * @note The value of `cv::GArray` may be overwritten by assigning some * other `cv::GArray` to the object using `operator=` -- on the - * assigment, the old association or value is discarded. + * assignment, the old association or value is discarded. * * @param v a std::vector to associate with this * `cv::GArray` object. Vector data is copied into the diff --git a/modules/gapi/include/opencv2/gapi/gframe.hpp b/modules/gapi/include/opencv2/gapi/gframe.hpp index 96913dc4cc57..54fb30789e33 100644 --- a/modules/gapi/include/opencv2/gapi/gframe.hpp +++ b/modules/gapi/include/opencv2/gapi/gframe.hpp @@ -48,7 +48,7 @@ struct GOrigin; * operations like BGR(), Y(), UV() -- these operations provide * access to frame's data in the familiar cv::GMat form, which can be * used with the majority of the existing G-API operations. These - * accessor functions may perform color space converion on the fly if + * accessor functions may perform color space conversion on the fly if * the image format of the GFrame they are applied to differs from the * operation's semantic (e.g. the BGR() accessor is called on an NV12 * image frame). @@ -86,6 +86,7 @@ enum class MediaFormat: int { BGR = 0, NV12, + GRAY, }; /** diff --git a/modules/gapi/include/opencv2/gapi/gkernel.hpp b/modules/gapi/include/opencv2/gapi/gkernel.hpp index 4d3fbd82c577..59beb556b5ba 100644 --- a/modules/gapi/include/opencv2/gapi/gkernel.hpp +++ b/modules/gapi/include/opencv2/gapi/gkernel.hpp @@ -410,9 +410,13 @@ namespace std }; } // namespace std - namespace cv { + class GAPI_EXPORTS_W_SIMPLE GKernelPackage; + namespace gapi { + GAPI_EXPORTS cv::GKernelPackage combine(const cv::GKernelPackage &lhs, + const cv::GKernelPackage &rhs); + /// @private class GFunctor { @@ -427,6 +431,7 @@ namespace gapi { private: const char* m_id; }; +} // namespace gapi /** \addtogroup gapi_compile_args * @{ @@ -463,7 +468,7 @@ namespace gapi { { /// @private - using M = std::unordered_map>; + using M = std::unordered_map>; /// @private M m_id_kernels; @@ -500,10 +505,8 @@ namespace gapi { } public: - void include(const GFunctor& functor) - { - m_id_kernels[functor.id()] = std::make_pair(functor.backend(), functor.impl()); - } + void include(const cv::gapi::GFunctor& functor); + /** * @brief Returns total number of kernels * in the package (across all backends included) @@ -555,7 +558,7 @@ namespace gapi { * * @param backend backend which kernels to remove */ - void remove(const GBackend& backend); + void remove(const cv::gapi::GBackend& backend); /** * @brief Remove all kernels implementing the given API from @@ -595,7 +598,7 @@ namespace gapi { * */ template - GBackend lookup() const + cv::gapi::GBackend lookup() const { return lookup(KAPI::id()).first; } @@ -621,18 +624,14 @@ namespace gapi { * @param backend backend associated with the kernel * @param kernel_id a name/id of the kernel */ - void include(const cv::gapi::GBackend& backend, const std::string& kernel_id) - { - removeAPI(kernel_id); - m_id_kernels[kernel_id] = std::make_pair(backend, GKernelImpl{{}, {}}); - } + void include(const cv::gapi::GBackend& backend, const std::string& kernel_id); /** * @brief Lists all backends which are included into package * * @return vector of backends */ - std::vector backends() const; + std::vector backends() const; // TODO: Doxygen bug -- it wants me to place this comment // here, not below. @@ -643,9 +642,17 @@ namespace gapi { * @param rhs "Right-hand-side" package in the process * @return a new kernel package. */ - friend GAPI_EXPORTS GKernelPackage combine(const GKernelPackage &lhs, - const GKernelPackage &rhs); + friend GAPI_EXPORTS GKernelPackage cv::gapi::combine(const GKernelPackage &lhs, + const GKernelPackage &rhs); }; + /** @} */ + +namespace gapi { + using GKernelPackage = cv::GKernelPackage; // Keep backward compatibility + + /** \addtogroup gapi_compile_args + * @{ + */ /** * @brief Create a kernel package object containing kernels @@ -695,10 +702,6 @@ namespace gapi { /** @} */ - // FYI - this function is already commented above - GAPI_EXPORTS GKernelPackage combine(const GKernelPackage &lhs, - const GKernelPackage &rhs); - /** * @brief Combines multiple G-API kernel packages into one * @@ -710,7 +713,7 @@ namespace gapi { * @return The resulting kernel package */ template - GKernelPackage combine(const GKernelPackage &a, const GKernelPackage &b, Ps&&... rest) + cv::GKernelPackage combine(const cv::GKernelPackage &a, const cv::GKernelPackage &b, Ps&&... rest) { return combine(a, combine(b, rest...)); } @@ -733,7 +736,7 @@ namespace gapi { namespace detail { - template<> struct CompileArgTag + template<> struct CompileArgTag { static const char* tag() { return "gapi.kernel_package"; } }; diff --git a/modules/gapi/include/opencv2/gapi/gscalar.hpp b/modules/gapi/include/opencv2/gapi/gscalar.hpp index 7ebededcf0cd..d8a47c8ea8b5 100644 --- a/modules/gapi/include/opencv2/gapi/gscalar.hpp +++ b/modules/gapi/include/opencv2/gapi/gscalar.hpp @@ -63,7 +63,7 @@ class GAPI_EXPORTS_W_SIMPLE GScalar * * @note The value of GScalar may be overwritten by assigning some * other GScalar to the object using `operator=` -- on the - * assigment, the old GScalar value is discarded. + * assignment, the old GScalar value is discarded. * * @param s a cv::Scalar value to associate with this GScalar object. */ diff --git a/modules/gapi/include/opencv2/gapi/gstreaming.hpp b/modules/gapi/include/opencv2/gapi/gstreaming.hpp index 890eb584fb0c..5677768a96fe 100644 --- a/modules/gapi/include/opencv2/gapi/gstreaming.hpp +++ b/modules/gapi/include/opencv2/gapi/gstreaming.hpp @@ -225,7 +225,7 @@ class GAPI_EXPORTS_W_SIMPLE GStreamingCompiled * setSource() to run the graph on a new video stream. * * @overload - * @param args arguments used to contruct and initialize a stream + * @param args arguments used to construct and initialize a stream * source. */ template diff --git a/modules/gapi/include/opencv2/gapi/gtype_traits.hpp b/modules/gapi/include/opencv2/gapi/gtype_traits.hpp index 2b43421907a5..b56175788f2a 100644 --- a/modules/gapi/include/opencv2/gapi/gtype_traits.hpp +++ b/modules/gapi/include/opencv2/gapi/gtype_traits.hpp @@ -204,7 +204,7 @@ namespace detail { static_assert(!(cv::detail::has_gshape>::value || cv::detail::contains::type, GAPI_OWN_TYPES_LIST>::value), - "gin/gout must not be used with G* classses or cv::gapi::own::*"); + "gin/gout must not be used with G* classes or cv::gapi::own::*"); return GTypeTraits::wrap_out(u); } }; diff --git a/modules/gapi/include/opencv2/gapi/imgproc.hpp b/modules/gapi/include/opencv2/gapi/imgproc.hpp index 5c4c6f7031e6..44f05281536c 100644 --- a/modules/gapi/include/opencv2/gapi/imgproc.hpp +++ b/modules/gapi/include/opencv2/gapi/imgproc.hpp @@ -23,6 +23,7 @@ @defgroup gapi_colorconvert Graph API: Converting image from one color space to another @defgroup gapi_feature Graph API: Image Feature Detection @defgroup gapi_shape Graph API: Image Structural Analysis and Shape Descriptors + @defgroup gapi_transform Graph API: Image and channel composition functions @} */ @@ -56,7 +57,7 @@ namespace imgproc { using GMat3 = std::tuple; // FIXME: how to avoid this? using GFindContoursOutput = std::tuple>,GArray>; - G_TYPED_KERNEL(GFilter2D, ,"org.opencv.imgproc.filters.filter2D") { + G_TYPED_KERNEL(GFilter2D, , "org.opencv.imgproc.filters.filter2D") { static GMatDesc outMeta(GMatDesc in, int ddepth, Mat, Point, Scalar, int, Scalar) { return in.withDepth(ddepth); } @@ -74,7 +75,7 @@ namespace imgproc { } }; - G_TYPED_KERNEL(GBlur, , "org.opencv.imgproc.filters.blur"){ + G_TYPED_KERNEL(GBlur, , "org.opencv.imgproc.filters.blur") { static GMatDesc outMeta(GMatDesc in, Size, Point, int, Scalar) { return in; } @@ -138,13 +139,13 @@ namespace imgproc { } }; - G_TYPED_KERNEL(GEqHist, , "org.opencv.imgproc.equalizeHist"){ + G_TYPED_KERNEL(GEqHist, , "org.opencv.imgproc.equalizeHist") { static GMatDesc outMeta(GMatDesc in) { return in.withType(CV_8U, 1); } }; - G_TYPED_KERNEL(GCanny, , "org.opencv.imgproc.feature.canny"){ + G_TYPED_KERNEL(GCanny, , "org.opencv.imgproc.feature.canny") { static GMatDesc outMeta(GMatDesc in, double, double, int, bool) { return in.withType(CV_8U, 1); } @@ -495,6 +496,32 @@ namespace imgproc { } }; + G_TYPED_KERNEL(GResize, , "org.opencv.imgproc.transform.resize") { + static GMatDesc outMeta(GMatDesc in, Size sz, double fx, double fy, int /*interp*/) { + if (sz.width != 0 && sz.height != 0) + { + return in.withSize(sz); + } + else + { + int outSz_w = saturate_cast(in.size.width * fx); + int outSz_h = saturate_cast(in.size.height * fy); + GAPI_Assert(outSz_w > 0 && outSz_h > 0); + return in.withSize(Size(outSz_w, outSz_h)); + } + } + }; + + G_TYPED_KERNEL(GResizeP, , "org.opencv.imgproc.transform.resizeP") { + static GMatDesc outMeta(GMatDesc in, Size sz, int interp) { + GAPI_Assert(in.depth == CV_8U); + GAPI_Assert(in.chan == 3); + GAPI_Assert(in.planar); + GAPI_Assert(interp == cv::INTER_LINEAR); + return in.withSize(sz); + } + }; + } //namespace imgproc //! @addtogroup gapi_filters @@ -1214,7 +1241,7 @@ or column if there are N channels, or have N columns if there is a single channe @param src Input set of 2D points stored in one of possible containers: Mat, std::vector, std::vector, std::vector. @param distType Distance used by the M-estimator, see #DistanceTypes. @ref DIST_USER -and @ref DIST_C are not suppored. +and @ref DIST_C are not supported. @param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value is chosen. @param reps Sufficient accuracy for the radius (distance between the coordinate origin and the @@ -1286,7 +1313,7 @@ or column if there are N channels, or have N columns if there is a single channe @param src Input set of 3D points stored in one of possible containers: Mat, std::vector, std::vector, std::vector. @param distType Distance used by the M-estimator, see #DistanceTypes. @ref DIST_USER -and @ref DIST_C are not suppored. +and @ref DIST_C are not supported. @param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value is chosen. @param reps Sufficient accuracy for the radius (distance between the coordinate origin and the @@ -1676,6 +1703,66 @@ image type is @ref CV_8UC1. GAPI_EXPORTS GMatP NV12toBGRp(const GMat &src_y, const GMat &src_uv); //! @} gapi_colorconvert +//! @addtogroup gapi_transform +//! @{ +/** @brief Resizes an image. + +The function resizes the image src down to or up to the specified size. + +Output image size will have the size dsize (when dsize is non-zero) or the size computed from +src.size(), fx, and fy; the depth of output is the same as of src. + +If you want to resize src so that it fits the pre-created dst, +you may call the function as follows: +@code + // explicitly specify dsize=dst.size(); fx and fy will be computed from that. + resize(src, dst, dst.size(), 0, 0, interpolation); +@endcode +If you want to decimate the image by factor of 2 in each direction, you can call the function this +way: +@code + // specify fx and fy and let the function compute the destination image size. + resize(src, dst, Size(), 0.5, 0.5, interpolation); +@endcode +To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to +enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR +(faster but still looks OK). + +@note Function textual ID is "org.opencv.imgproc.transform.resize" + +@param src input image. +@param dsize output image size; if it equals zero, it is computed as: + \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] + Either dsize or both fx and fy must be non-zero. +@param fx scale factor along the horizontal axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.width/src.cols}\f] +@param fy scale factor along the vertical axis; when it equals 0, it is computed as +\f[\texttt{(double)dsize.height/src.rows}\f] +@param interpolation interpolation method, see cv::InterpolationFlags + +@sa warpAffine, warpPerspective, remap, resizeP + */ +GAPI_EXPORTS_W GMat resize(const GMat& src, const Size& dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR); + +/** @brief Resizes a planar image. + +The function resizes the image src down to or up to the specified size. +Planar image memory layout is three planes laying in the memory contiguously, +so the image height should be plane_height*plane_number, image type is @ref CV_8UC1. + +Output image size will have the size dsize, the depth of output is the same as of src. + +@note Function textual ID is "org.opencv.imgproc.transform.resizeP" + +@param src input image, must be of @ref CV_8UC1 type; +@param dsize output image size; +@param interpolation interpolation method, only cv::INTER_LINEAR is supported at the moment + +@sa warpAffine, warpPerspective, remap, resize + */ +GAPI_EXPORTS GMatP resizeP(const GMatP& src, const Size& dsize, int interpolation = cv::INTER_LINEAR); + +//! @} gapi_transform } //namespace gapi } //namespace cv diff --git a/modules/gapi/include/opencv2/gapi/infer/ie.hpp b/modules/gapi/include/opencv2/gapi/infer/ie.hpp index e6b7be58adf8..204bd8f266bc 100644 --- a/modules/gapi/include/opencv2/gapi/infer/ie.hpp +++ b/modules/gapi/include/opencv2/gapi/infer/ie.hpp @@ -20,6 +20,7 @@ #include // GAPI_EXPORTS #include // GKernelPackage #include // Generic +#include // Preproc Dev & Ctx namespace cv { namespace gapi { @@ -84,6 +85,9 @@ struct ParamDesc { // have 2D (Layout::NC) input and if the first dimension not equal to 1 // net.setBatchSize(1) will overwrite it. cv::optional batch_size; + + cv::optional vpl_preproc_device; + cv::optional vpl_preproc_ctx; }; } // namespace detail @@ -126,6 +130,8 @@ template class Params { , {} , 1u , {} + , {} + , {} , {}} { }; @@ -148,6 +154,8 @@ template class Params { , {} , 1u , {} + , {} + , {} , {}} { }; @@ -336,6 +344,13 @@ template class Params { return *this; } + Params& cfgPreprocessingParams(const cv::gapi::wip::onevpl::Device &device, + const cv::gapi::wip::onevpl::Context &ctx) { + desc.vpl_preproc_device = cv::util::make_optional(device); + desc.vpl_preproc_ctx = cv::util::make_optional(ctx); + return *this; + } + // BEGIN(G-API's network parametrization API) GBackend backend() const { return cv::gapi::ie::backend(); } std::string tag() const { return Net::tag(); } @@ -370,7 +385,7 @@ class Params { const std::string &device) : desc{ model, weights, device, {}, {}, {}, 0u, 0u, detail::ParamDesc::Kind::Load, true, {}, {}, {}, 1u, - {}, {}}, + {}, {}, {}, {}}, m_tag(tag) { }; @@ -388,7 +403,7 @@ class Params { const std::string &device) : desc{ model, {}, device, {}, {}, {}, 0u, 0u, detail::ParamDesc::Kind::Import, true, {}, {}, {}, 1u, - {}, {}}, + {}, {}, {}, {}}, m_tag(tag) { }; diff --git a/modules/gapi/include/opencv2/gapi/infer/onnx.hpp b/modules/gapi/include/opencv2/gapi/infer/onnx.hpp index bb5ef6c59e12..16fc42eb63f3 100644 --- a/modules/gapi/include/opencv2/gapi/infer/onnx.hpp +++ b/modules/gapi/include/opencv2/gapi/infer/onnx.hpp @@ -126,7 +126,7 @@ template class Params { The function is used to associate data of graph outputs with output layers of network topology. If a network has only one output layer, there is no need to call it - as the layer is associated with ouput automatically but this doesn't prevent + as the layer is associated with output automatically but this doesn't prevent you from doing it yourself. Count of names has to match to number of network outputs or you can set your own output but for this case you have to additionally use @ref cfgPostProc function. diff --git a/modules/gapi/include/opencv2/gapi/infer/parsers.hpp b/modules/gapi/include/opencv2/gapi/infer/parsers.hpp index c7308dd39f47..e39d6fd4c6a2 100644 --- a/modules/gapi/include/opencv2/gapi/infer/parsers.hpp +++ b/modules/gapi/include/opencv2/gapi/infer/parsers.hpp @@ -95,7 +95,7 @@ GAPI_EXPORTS_W GArray parseSSD(const GMat& in, /** @brief Parses output of Yolo network. Extracts detection information (box, confidence, label) from Yolo output, -filters it by given confidence and performs non-maximum supression for overlapping boxes. +filters it by given confidence and performs non-maximum suppression for overlapping boxes. @note Function textual ID is "org.opencv.nn.parsers.parseYolo" @@ -105,7 +105,7 @@ where num_classes - a number of classes Yolo network was trained with. @param inSz Size to project detected boxes to (size of the input image). @param confidenceThreshold If confidence of the detection is smaller than confidence threshold, detection is rejected. -@param nmsThreshold Non-maximum supression threshold which controls minimum +@param nmsThreshold Non-maximum suppression threshold which controls minimum relative box intersection area required for rejecting the box with a smaller confidence. If 1.f, nms is not performed and no boxes are rejected. @param anchors Anchors Yolo network was trained with. diff --git a/modules/gapi/include/opencv2/gapi/oak/infer.hpp b/modules/gapi/include/opencv2/gapi/oak/infer.hpp new file mode 100644 index 000000000000..4a1b9f6db6a2 --- /dev/null +++ b/modules/gapi/include/opencv2/gapi/oak/infer.hpp @@ -0,0 +1,66 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef OPENCV_GAPI_OAK_INFER_HPP +#define OPENCV_GAPI_OAK_INFER_HPP + +#include +#include +#include +#include + +#include +#include + +#include // GAPI_EXPORTS +#include // GKernelPackage + +namespace cv { +namespace gapi { +namespace oak { + +namespace detail { +/** +* @brief This structure contains description of inference parameters +* which is specific to OAK models. +*/ +struct ParamDesc { + std::string blob_file; +}; +} // namespace detail + +/** + * Contains description of inference parameters and kit of functions that + * fill this parameters. + */ +template class Params { +public: + /** @brief Class constructor. + + Constructs Params based on model information and sets default values for other + inference description parameters. + + @param model Path to model (.blob file) + */ + explicit Params(const std::string &model) { + desc.blob_file = model; + }; + + // BEGIN(G-API's network parametrization API) + GBackend backend() const { return cv::gapi::oak::backend(); } + std::string tag() const { return Net::tag(); } + cv::util::any params() const { return { desc }; } + // END(G-API's network parametrization API) + +protected: + detail::ParamDesc desc; +}; + +} // namespace oak +} // namespace gapi +} // namespace cv + +#endif // OPENCV_GAPI_OAK_INFER_HPP diff --git a/modules/gapi/include/opencv2/gapi/oak/oak.hpp b/modules/gapi/include/opencv2/gapi/oak/oak.hpp new file mode 100644 index 000000000000..8b56b8a3658b --- /dev/null +++ b/modules/gapi/include/opencv2/gapi/oak/oak.hpp @@ -0,0 +1,158 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#ifndef OPENCV_GAPI_OAK_HPP +#define OPENCV_GAPI_OAK_HPP + +#include // IStreamSource +#include // GKernelPackage +#include // GOptRunArgsP + +namespace cv { +namespace gapi { +namespace oak { + +// FIXME: copypasted from dai library +struct EncoderConfig { + /** + * Rate control mode specifies if constant or variable bitrate should be used (H264 / H265) + */ + enum class RateControlMode: int { CBR, VBR }; + + /** + * Encoding profile, H264, H265 or MJPEG + */ + enum class Profile: int { H264_BASELINE, H264_HIGH, H264_MAIN, H265_MAIN, MJPEG }; + /** + * Specifies preferred bitrate (kb) of compressed output bitstream + */ + std::int32_t bitrate = 8000; + /** + * Every x number of frames a keyframe will be inserted + */ + std::int32_t keyframeFrequency = 30; + /** + * Specifies maximum bitrate (kb) of compressed output bitstream + */ + std::int32_t maxBitrate = 8000; + /** + * Specifies number of B frames to be inserted + */ + std::int32_t numBFrames = 0; + /** + * This options specifies how many frames are available in this nodes pool (can help if + * receiver node is slow at consuming + */ + std::uint32_t numFramesPool = 4; + /** + * Encoding profile, H264, H265 or MJPEG + */ + Profile profile = Profile::H265_MAIN; + /** + * Value between 0-100% (approximates quality) + */ + std::int32_t quality = 80; + /** + * Lossless mode ([M]JPEG only) + */ + bool lossless = false; + /** + * Rate control mode specifies if constant or variable bitrate should be used (H264 / H265) + */ + RateControlMode rateCtrlMode = RateControlMode::CBR; + /** + * Input and compressed output frame width + */ + std::int32_t width = 1920; + /** + * Input and compressed output frame height + */ + std::int32_t height = 1080; + /** + * Frame rate + */ + float frameRate = 30.0f; +}; + +G_API_OP(GEncFrame, (GFrame, EncoderConfig)>, "org.opencv.oak.enc_frame") { + static GArrayDesc outMeta(const GFrameDesc&, const EncoderConfig&) { + return cv::empty_array_desc(); + } +}; + +G_API_OP(GSobelXY, , "org.opencv.oak.sobelxy") { + static GFrameDesc outMeta(const GFrameDesc& in, const cv::Mat&, const cv::Mat&) { + return in; + } +}; + +G_API_OP(GCopy, , "org.opencv.oak.copy") { + static GFrameDesc outMeta(const GFrameDesc& in) { + return in; + } +}; + +// FIXME: add documentation on operations below + +GAPI_EXPORTS GArray encode(const GFrame& in, const EncoderConfig&); + +GAPI_EXPORTS GFrame sobelXY(const GFrame& in, + const cv::Mat& hk, + const cv::Mat& vk); + +GAPI_EXPORTS GFrame copy(const GFrame& in); + +// OAK backend & kernels //////////////////////////////////////////////////////// +GAPI_EXPORTS cv::gapi::GBackend backend(); +GAPI_EXPORTS cv::gapi::GKernelPackage kernels(); + +// Camera object /////////////////////////////////////////////////////////////// + +struct GAPI_EXPORTS ColorCameraParams { + /** + * Format of the frame one gets from the camera + */ + bool interleaved = false; + + // FIXME: extend + enum class BoardSocket: int { RGB, BGR }; + + BoardSocket board_socket = BoardSocket::RGB; + + // FIXME: extend + enum class Resolution: int { THE_1080_P }; + + Resolution resolution = Resolution::THE_1080_P; +}; + +class GAPI_EXPORTS ColorCamera: public cv::gapi::wip::IStreamSource { + cv::MediaFrame m_dummy; + ColorCameraParams m_params; + + virtual bool pull(cv::gapi::wip::Data &data) override; + virtual GMetaArg descr_of() const override; + +public: + ColorCamera(); + explicit ColorCamera(const ColorCameraParams& params); +}; + +} // namespace oak +} // namespace gapi + +namespace detail { +template<> struct CompileArgTag { + static const char* tag() { return "gapi.oak.colorCameraParams"; } +}; + +template<> struct CompileArgTag { + static const char* tag() { return "gapi.oak.encoderConfig"; } +}; +} // namespace detail + +} // namespace cv + +#endif // OPENCV_GAPI_OAK_HPP diff --git a/modules/gapi/include/opencv2/gapi/ocl/core.hpp b/modules/gapi/include/opencv2/gapi/ocl/core.hpp index 6c7587096c6f..b79aace0ca7b 100644 --- a/modules/gapi/include/opencv2/gapi/ocl/core.hpp +++ b/modules/gapi/include/opencv2/gapi/ocl/core.hpp @@ -16,7 +16,7 @@ namespace gapi { namespace core { namespace ocl { - GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels(); +GAPI_EXPORTS_W cv::GKernelPackage kernels(); } // namespace ocl } // namespace core diff --git a/modules/gapi/include/opencv2/gapi/own/assert.hpp b/modules/gapi/include/opencv2/gapi/own/assert.hpp index d50543fdac91..4bd3eaaf507d 100644 --- a/modules/gapi/include/opencv2/gapi/own/assert.hpp +++ b/modules/gapi/include/opencv2/gapi/own/assert.hpp @@ -43,7 +43,6 @@ namespace detail #define GAPI_Assert(expr) \ { if (!(expr)) ::detail::assert_abort(#expr, __LINE__, __FILE__, __func__); } - #ifdef NDEBUG # define GAPI_DbgAssert(expr) GAPI_DbgAssertNoOp(expr) #else diff --git a/modules/gapi/include/opencv2/gapi/own/types.hpp b/modules/gapi/include/opencv2/gapi/own/types.hpp index 38143660bca3..3ec97878392c 100644 --- a/modules/gapi/include/opencv2/gapi/own/types.hpp +++ b/modules/gapi/include/opencv2/gapi/own/types.hpp @@ -89,7 +89,7 @@ inline Rect& operator&=(Rect& lhs, const Rect& rhs) return lhs; } -inline const Rect operator&(const Rect& lhs, const Rect& rhs) +inline Rect operator&(const Rect& lhs, const Rect& rhs) { Rect result = lhs; return result &= rhs; diff --git a/modules/gapi/include/opencv2/gapi/plaidml/core.hpp b/modules/gapi/include/opencv2/gapi/plaidml/core.hpp index 3c63fed93d87..20e8812b3abc 100644 --- a/modules/gapi/include/opencv2/gapi/plaidml/core.hpp +++ b/modules/gapi/include/opencv2/gapi/plaidml/core.hpp @@ -13,7 +13,7 @@ namespace cv { namespace gapi { namespace core { namespace plaidml { -GAPI_EXPORTS cv::gapi::GKernelPackage kernels(); +GAPI_EXPORTS cv::GKernelPackage kernels(); }}}} diff --git a/modules/gapi/include/opencv2/gapi/render/render.hpp b/modules/gapi/include/opencv2/gapi/render/render.hpp index 537541222414..8d93a6efc028 100644 --- a/modules/gapi/include/opencv2/gapi/render/render.hpp +++ b/modules/gapi/include/opencv2/gapi/render/render.hpp @@ -177,7 +177,7 @@ namespace render { namespace ocv { - GAPI_EXPORTS_W cv::gapi::GKernelPackage kernels(); + GAPI_EXPORTS_W cv::GKernelPackage kernels(); } // namespace ocv } // namespace render diff --git a/modules/gapi/include/opencv2/gapi/s11n.hpp b/modules/gapi/include/opencv2/gapi/s11n.hpp index 6863a5ecabb9..adbcfdbdeb94 100644 --- a/modules/gapi/include/opencv2/gapi/s11n.hpp +++ b/modules/gapi/include/opencv2/gapi/s11n.hpp @@ -241,7 +241,7 @@ GAPI_EXPORTS IIStream& operator>> (IIStream& is, cv::Scalar &s); GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::Mat &m); GAPI_EXPORTS IIStream& operator>> (IIStream& is, cv::Mat &m); -// FIXME: for GRunArgs serailization +// FIXME: for GRunArgs serialization #if !defined(GAPI_STANDALONE) GAPI_EXPORTS IOStream& operator<< (IOStream& os, const cv::UMat & um); GAPI_EXPORTS IIStream& operator>> (IIStream& is, cv::UMat & um); diff --git a/modules/gapi/include/opencv2/gapi/streaming/cap.hpp b/modules/gapi/include/opencv2/gapi/streaming/cap.hpp index aad6af618c9a..73d5bfcbeb9f 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/cap.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/cap.hpp @@ -119,6 +119,12 @@ GAPI_EXPORTS_W cv::Ptr inline make_capture_src(const std::string& return make_src(path); } +// NB: Overload for using from python +GAPI_EXPORTS_W cv::Ptr inline make_capture_src(const int id) +{ + return make_src(id); +} + } // namespace wip } // namespace gapi } // namespace cv diff --git a/modules/gapi/include/opencv2/gapi/streaming/desync.hpp b/modules/gapi/include/opencv2/gapi/streaming/desync.hpp index 1ed6e24b49a4..9e927872a312 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/desync.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/desync.hpp @@ -67,7 +67,7 @@ G desync(const G &g) { * always produce their full output vectors. * * This operation only makes sense when a GComputation is compiled in - * straming mode with cv::GComputation::compileStreaming(). If this + * streaming mode with cv::GComputation::compileStreaming(). If this * operation is used and there are desynchronized outputs, the user * should use a special version of cv::GStreamingCompiled::pull() * which produces an array of cv::util::optional<> objects. diff --git a/modules/gapi/include/opencv2/gapi/streaming/format.hpp b/modules/gapi/include/opencv2/gapi/streaming/format.hpp index f7c3bd457dfb..739a3852a64d 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/format.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/format.hpp @@ -13,7 +13,7 @@ namespace cv { namespace gapi { namespace streaming { -GAPI_EXPORTS cv::gapi::GKernelPackage kernels(); +GAPI_EXPORTS cv::GKernelPackage kernels(); G_API_OP(GBGR, , "org.opencv.streaming.BGR") { diff --git a/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp b/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp index 83afc9939384..c566656cb61a 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamerpipeline.hpp @@ -19,12 +19,12 @@ namespace gapi { namespace wip { namespace gst { -class GAPI_EXPORTS GStreamerPipeline +class GAPI_EXPORTS_W GStreamerPipeline { public: class Priv; - explicit GStreamerPipeline(const std::string& pipeline); + GAPI_WRAP explicit GStreamerPipeline(const std::string& pipeline); IStreamSource::Ptr getStreamingSource(const std::string& appsinkName, const GStreamerSource::OutputType outputType = GStreamerSource::OutputType::MAT); @@ -40,6 +40,18 @@ class GAPI_EXPORTS GStreamerPipeline using GStreamerPipeline = gst::GStreamerPipeline; +// NB: Function for using from python +// FIXME: a separate function is created due to absence of wrappers for `shared_ptr<> ` +// Ideally would be to wrap the `GStreamerPipeline::getStreamingSource()` method as is +GAPI_EXPORTS_W cv::Ptr +inline get_streaming_source(cv::Ptr& pipeline, + const std::string& appsinkName, + const GStreamerSource::OutputType outputType + = GStreamerSource::OutputType::MAT) +{ + return pipeline->getStreamingSource(appsinkName, outputType); +} + } // namespace wip } // namespace gapi } // namespace cv diff --git a/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp b/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp index b81bad31b8b2..8b8a5ae3121f 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/gstreamer/gstreamersource.hpp @@ -32,13 +32,13 @@ namespace gst { * Pipeline can actually contain many sink elements, but it must have one and only one * appsink among them. * - * - data passed to appsink should be video-frame in NV12 format. + * - data passed to appsink should be video-frame in NV12 or GRAY8 format. * * 'outputType' is used to select type of output data to produce: 'cv::MediaFrame' or 'cv::Mat'. * To produce 'cv::MediaFrame'-s you need to pass 'GStreamerSource::OutputType::FRAME' and, * correspondingly, 'GStreamerSource::OutputType::MAT' to produce 'cv::Mat'-s. * Please note, that in the last case, output 'cv::Mat' will be of BGR format, internal conversion - * from NV12 GStreamer data will happen. + * from NV12 / GRAY8 GStreamer data will happen. * Default value for 'outputType' is 'GStreamerSource::OutputType::MAT'. * * @note Stream sources are passed to G-API via shared pointers, so please use gapi::make_src<> @@ -82,6 +82,14 @@ class GAPI_EXPORTS GStreamerSource : public IStreamSource using GStreamerSource = gst::GStreamerSource; +// NB: Overload for using from python +GAPI_EXPORTS_W cv::Ptr +inline make_gst_src(const std::string& pipeline, + const GStreamerSource::OutputType outputType = + GStreamerSource::OutputType::MAT) +{ + return make_src(pipeline, outputType); +} } // namespace wip } // namespace gapi } // namespace cv diff --git a/modules/gapi/include/opencv2/gapi/streaming/onevpl/accel_types.hpp b/modules/gapi/include/opencv2/gapi/streaming/onevpl/accel_types.hpp new file mode 100644 index 000000000000..c53b1b31db4a --- /dev/null +++ b/modules/gapi/include/opencv2/gapi/streaming/onevpl/accel_types.hpp @@ -0,0 +1,73 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONEVPL_ACCEL_TYPES_HPP +#define GAPI_STREAMING_ONEVPL_ACCEL_TYPES_HPP + +#include +#include + +#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { + +enum class AccelType: uint8_t { + HOST, + DX11, + VAAPI, + + LAST_VALUE = std::numeric_limits::max() +}; + +GAPI_EXPORTS const char* to_cstring(AccelType type); + +struct IDeviceSelector; +struct GAPI_EXPORTS Device { + friend struct IDeviceSelector; + using Ptr = void*; + + ~Device(); + const std::string& get_name() const; + Ptr get_ptr() const; + AccelType get_type() const; +private: + Device(Ptr device_ptr, const std::string& device_name, + AccelType device_type); + + std::string name; + Ptr ptr; + AccelType type; +}; + +struct GAPI_EXPORTS Context { + friend struct IDeviceSelector; + using Ptr = void*; + + ~Context(); + Ptr get_ptr() const; + AccelType get_type() const; +private: + Context(Ptr ctx_ptr, AccelType ctx_type); + Ptr ptr; + AccelType type; +}; + +GAPI_EXPORTS Device create_host_device(); +GAPI_EXPORTS Context create_host_context(); + +GAPI_EXPORTS Device create_dx11_device(Device::Ptr device_ptr, + const std::string& device_name); +GAPI_EXPORTS Context create_dx11_context(Context::Ptr ctx_ptr); + +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv + +#endif // GAPI_STREAMING_ONEVPL_ACCEL_TYPES_HPP diff --git a/modules/gapi/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp b/modules/gapi/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp index bfd922496aa3..25573afd4cfa 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/onevpl/cfg_params.hpp @@ -22,14 +22,14 @@ namespace onevpl { /** * @brief Public class is using for creation of onevpl::GSource instances. * - * Class members availaible through methods @ref CfgParam::get_name() and @ref CfgParam::get_value() are used by + * Class members available through methods @ref CfgParam::get_name() and @ref CfgParam::get_value() are used by * onevpl::GSource inner logic to create or find oneVPL particular implementation * (software/hardware, specific API version and etc.). * * @note Because oneVPL may provide several implementations which are satisfying with multiple (or single one) @ref CfgParam * criteria therefore it is possible to configure `preferred` parameters. This kind of CfgParams are created * using `is_major = false` argument in @ref CfgParam::create method and are not used by creating oneVPL particular implementations. - * Instead they fill out a "score table" to select preferrable implementation from available list. Implementation are satisfying + * Instead they fill out a "score table" to select preferable implementation from available list. Implementation are satisfying * with most of these optional params would be chosen. * If no one optional CfgParam params were present then first of available oneVPL implementation would be applied. * Please get on https://spec.oneapi.io/versions/latest/elements/oneVPL/source/API_ref/VPL_disp_api_func.html?highlight=mfxcreateconfig#mfxsetconfigfilterproperty @@ -110,6 +110,62 @@ struct GAPI_EXPORTS CfgParam { static CfgParam create_implementation(uint32_t value); static CfgParam create_implementation(const char* value); + + static constexpr const char *vpp_frames_pool_size_name() { return "vpp_frames_pool_size"; } + static CfgParam create_vpp_frames_pool_size(size_t value); + + static constexpr const char *vpp_in_width_name() { return "vpp.In.Width"; } + static CfgParam create_vpp_in_width(uint16_t value); + + static constexpr const char *vpp_in_height_name() { return "vpp.In.Height"; } + static CfgParam create_vpp_in_height(uint16_t value); + + static constexpr const char *vpp_in_crop_x_name() { return "vpp.In.CropX"; } + static CfgParam create_vpp_in_crop_x(uint16_t value); + + static constexpr const char *vpp_in_crop_y_name() { return "vpp.In.CropY"; } + static CfgParam create_vpp_in_crop_y(uint16_t value); + + static constexpr const char *vpp_in_crop_w_name() { return "vpp.In.CropW"; } + static CfgParam create_vpp_in_crop_w(uint16_t value); + + static constexpr const char *vpp_in_crop_h_name() { return "vpp.In.CropH"; } + static CfgParam create_vpp_in_crop_h(uint16_t value); + + + static constexpr const char *vpp_out_fourcc_name() { return "vpp.Out.FourCC"; } + static CfgParam create_vpp_out_fourcc(uint32_t value); + + static constexpr const char *vpp_out_chroma_format_name() { return "vpp.Out.ChromaFormat"; } + static CfgParam create_vpp_out_chroma_format(uint16_t value); + + static constexpr const char *vpp_out_width_name() { return "vpp.Out.Width"; } + static CfgParam create_vpp_out_width(uint16_t value); + + static constexpr const char *vpp_out_height_name() { return "vpp.Out.Height"; } + static CfgParam create_vpp_out_height(uint16_t value); + + static constexpr const char *vpp_out_crop_x_name() { return "vpp.Out.CropX"; } + static CfgParam create_vpp_out_crop_x(uint16_t value); + + static constexpr const char *vpp_out_crop_y_name() { return "vpp.Out.CropY"; } + static CfgParam create_vpp_out_crop_y(uint16_t value); + + static constexpr const char *vpp_out_crop_w_name() { return "vpp.Out.CropW"; } + static CfgParam create_vpp_out_crop_w(uint16_t value); + + static constexpr const char *vpp_out_crop_h_name() { return "vpp.Out.CropH"; } + static CfgParam create_vpp_out_crop_h(uint16_t value); + + static constexpr const char *vpp_out_pic_struct_name() { return "vpp.Out.PicStruct"; } + static CfgParam create_vpp_out_pic_struct(uint16_t value); + + static constexpr const char *vpp_out_framerate_n_name() { return "vpp.Out.FrameRateExtN"; } + static CfgParam create_vpp_out_framerate_n(uint32_t value); + + static constexpr const char *vpp_out_framerate_d_name() { return "vpp.Out.FrameRateExtD"; } + static CfgParam create_vpp_out_framerate_d(uint32_t value); + /** * Create generic onevp::GSource configuration parameter. * diff --git a/modules/gapi/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp b/modules/gapi/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp index c70e3db0ac67..ec683a7527ff 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/onevpl/data_provider_interface.hpp @@ -27,7 +27,7 @@ struct GAPI_EXPORTS DataProviderException : public std::exception { }; struct GAPI_EXPORTS DataProviderSystemErrorException final : public DataProviderException { - DataProviderSystemErrorException(int error_code, const std::string& desription = std::string()); + DataProviderSystemErrorException(int error_code, const std::string& description = std::string()); ~DataProviderSystemErrorException() = default; }; diff --git a/modules/gapi/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp b/modules/gapi/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp index 04f8cae02a42..2e2d879fba6b 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/onevpl/device_selector_interface.hpp @@ -12,53 +12,12 @@ #include #include -#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS +#include namespace cv { namespace gapi { namespace wip { namespace onevpl { - -enum class AccelType: uint8_t { - HOST, - DX11, - - LAST_VALUE = std::numeric_limits::max() -}; - -GAPI_EXPORTS const char* to_cstring(AccelType type); - -struct IDeviceSelector; -struct GAPI_EXPORTS Device { - friend struct IDeviceSelector; - using Ptr = void*; - - ~Device(); - const std::string& get_name() const; - Ptr get_ptr() const; - AccelType get_type() const; -private: - Device(Ptr device_ptr, const std::string& device_name, - AccelType device_type); - - std::string name; - Ptr ptr; - AccelType type; -}; - -struct GAPI_EXPORTS Context { - friend struct IDeviceSelector; - using Ptr = void*; - - ~Context(); - Ptr get_ptr() const; - AccelType get_type() const; -private: - Context(Ptr ctx_ptr, AccelType ctx_type); - Ptr ptr; - AccelType type; -}; - struct GAPI_EXPORTS IDeviceSelector { using Ptr = std::shared_ptr; diff --git a/modules/gapi/include/opencv2/gapi/streaming/onevpl/source.hpp b/modules/gapi/include/opencv2/gapi/streaming/onevpl/source.hpp index 6334480c1bb7..04dc2e246d3e 100644 --- a/modules/gapi/include/opencv2/gapi/streaming/onevpl/source.hpp +++ b/modules/gapi/include/opencv2/gapi/streaming/onevpl/source.hpp @@ -46,6 +46,10 @@ class GAPI_EXPORTS GSource : public IStreamSource void* accel_device_ptr, void* accel_ctx_ptr); + GSource(const std::string& filePath, + const CfgParams& cfg_params, + const Device &device, const Context &ctx); + GSource(const std::string& filePath, const CfgParams& cfg_params, std::shared_ptr selector); diff --git a/modules/gapi/misc/python/package/gapi/__init__.py b/modules/gapi/misc/python/package/gapi/__init__.py index b1326712fcb6..87ad9e2086fa 100644 --- a/modules/gapi/misc/python/package/gapi/__init__.py +++ b/modules/gapi/misc/python/package/gapi/__init__.py @@ -218,7 +218,7 @@ def op_with_params(cls): for i, t in enumerate(out_types): if t not in [cv.GMat, cv.GScalar, *garray_types, *gopaque_types]: - raise Exception('{} unsupported output type: {} in possition: {}' + raise Exception('{} unsupported output type: {} in position: {}' .format(cls.__name__, t.__name__, i)) def on(*args): @@ -287,13 +287,4 @@ def kernel_with_params(cls): return kernel_with_params -# FIXME: On the c++ side every class is placed in cv2 module. -cv.gapi.wip.draw.Rect = cv.gapi_wip_draw_Rect -cv.gapi.wip.draw.Text = cv.gapi_wip_draw_Text -cv.gapi.wip.draw.Circle = cv.gapi_wip_draw_Circle -cv.gapi.wip.draw.Line = cv.gapi_wip_draw_Line -cv.gapi.wip.draw.Mosaic = cv.gapi_wip_draw_Mosaic -cv.gapi.wip.draw.Image = cv.gapi_wip_draw_Image -cv.gapi.wip.draw.Poly = cv.gapi_wip_draw_Poly - -cv.gapi.streaming.queue_capacity = cv.gapi_streaming_queue_capacity +cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline diff --git a/modules/gapi/misc/python/pyopencv_gapi.hpp b/modules/gapi/misc/python/pyopencv_gapi.hpp index 64f8277740d9..7b760920e720 100644 --- a/modules/gapi/misc/python/pyopencv_gapi.hpp +++ b/modules/gapi/misc/python/pyopencv_gapi.hpp @@ -11,7 +11,7 @@ #include // NB: Python wrapper replaces :: with _ for classes -using gapi_GKernelPackage = cv::gapi::GKernelPackage; +using gapi_GKernelPackage = cv::GKernelPackage; using gapi_GNetPackage = cv::gapi::GNetPackage; using gapi_ie_PyParams = cv::gapi::ie::PyParams; using gapi_wip_IStreamSource_Ptr = cv::Ptr; @@ -19,6 +19,7 @@ using detail_ExtractArgsCallback = cv::detail::ExtractArgsCallback; using detail_ExtractMetaCallback = cv::detail::ExtractMetaCallback; using vector_GNetParam = std::vector; using gapi_streaming_queue_capacity = cv::gapi::streaming::queue_capacity; +using GStreamerSource_OutputType = cv::gapi::wip::GStreamerSource::OutputType; // NB: Python wrapper generate T_U for T // This behavior is only observed for inputs @@ -230,7 +231,7 @@ PyObject* pyopencv_from(const cv::GArg& value) { HANDLE_CASE(BOOL, bool); HANDLE_CASE(INT, int); - HANDLE_CASE(INT64, int64_t); + HANDLE_CASE(INT64, int64_t); HANDLE_CASE(DOUBLE, double); HANDLE_CASE(FLOAT, float); HANDLE_CASE(STRING, std::string); @@ -700,7 +701,7 @@ static cv::GRunArgs run_py_kernel(cv::detail::PyObjectHolder kernel, PyErr_Clear(); throw std::logic_error("Python kernel failed with error!"); } - // NB: In fact it's impossible situation, becase errors were handled above. + // NB: In fact it's impossible situation, because errors were handled above. GAPI_Assert(result.get() && "Python kernel returned NULL!"); if (out_info.size() == 1) @@ -810,7 +811,7 @@ static GMetaArgs run_py_meta(cv::detail::PyObjectHolder out_meta, PyErr_Clear(); throw std::logic_error("Python outMeta failed with error!"); } - // NB: In fact it's impossible situation, becase errors were handled above. + // NB: In fact it's impossible situation, because errors were handled above. GAPI_Assert(result.get() && "Python outMeta returned NULL!"); out_metas = PyTuple_Check(result.get()) ? get_meta_args(result.get()) @@ -829,7 +830,7 @@ static GMetaArgs run_py_meta(cv::detail::PyObjectHolder out_meta, static PyObject* pyopencv_cv_gapi_kernels(PyObject* , PyObject* py_args, PyObject*) { using namespace cv; - gapi::GKernelPackage pkg; + GKernelPackage pkg; Py_ssize_t size = PyTuple_Size(py_args); for (int i = 0; i < size; ++i) diff --git a/modules/gapi/misc/python/samples/gaze_estimation.py b/modules/gapi/misc/python/samples/gaze_estimation.py index 5536787e608c..bdcc7851ee0a 100644 --- a/modules/gapi/misc/python/samples/gaze_estimation.py +++ b/modules/gapi/misc/python/samples/gaze_estimation.py @@ -27,7 +27,7 @@ def build_argparser(): parser = argparse.ArgumentParser(description='This is an OpenCV-based version of Gaze Estimation example') parser.add_argument('--input', - help='Path to the input video file') + help='Path to the input video file or camera device number') parser.add_argument('--out', help='Path to the output video file') parser.add_argument('--facem', @@ -323,7 +323,11 @@ def run(eyesl, eyesr): # ------------------------Execution part------------------------ ccomp = comp.compileStreaming(args=cv.gapi.compile_args(kernels, nets)) - source = cv.gapi.wip.make_capture_src(ARGUMENTS.input) + if ARGUMENTS.input.isdigit(): + source = cv.gapi.wip.make_capture_src(int(ARGUMENTS.input)) + else: + source = cv.gapi.wip.make_capture_src(ARGUMENTS.input) + ccomp.setSource(cv.gin(source)) ccomp.start() diff --git a/modules/gapi/misc/python/shadow_gapi.hpp b/modules/gapi/misc/python/shadow_gapi.hpp index 33a0e0fde1bf..802f4397a049 100644 --- a/modules/gapi/misc/python/shadow_gapi.hpp +++ b/modules/gapi/misc/python/shadow_gapi.hpp @@ -5,7 +5,7 @@ namespace cv { struct GAPI_EXPORTS_W_SIMPLE GCompileArg { - GAPI_WRAP GCompileArg(gapi::GKernelPackage arg); + GAPI_WRAP GCompileArg(GKernelPackage arg); GAPI_WRAP GCompileArg(gapi::GNetPackage arg); GAPI_WRAP GCompileArg(gapi::streaming::queue_capacity arg); }; diff --git a/modules/gapi/misc/python/test/test_gapi_sample_pipelines.py b/modules/gapi/misc/python/test/test_gapi_sample_pipelines.py index 34a07b535bf2..7763579ebf5d 100644 --- a/modules/gapi/misc/python/test/test_gapi_sample_pipelines.py +++ b/modules/gapi/misc/python/test/test_gapi_sample_pipelines.py @@ -187,7 +187,7 @@ def run(img, max_corners, quality_lvl, blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k) # NB: The operation output is cv::GArray, so it should be mapped - # to python paramaters like this: [(1.2, 3.4), (5.2, 3.2)], because the cv::Point2f + # to python parameters like this: [(1.2, 3.4), (5.2, 3.2)], because the cv::Point2f # according to opencv rules mapped to the tuple and cv::GArray<> mapped to the list. # OpenCV returns np.array with shape (n_features, 1, 2), so let's to convert it to list # tuples with size == n_features. @@ -203,7 +203,7 @@ class Op: @staticmethod def outMeta(desc): - raise NotImplementedError("outMeta isn't imlemented") + raise NotImplementedError("outMeta isn't implemented") return Op @@ -605,7 +605,7 @@ def run(img0, img1): img1 = np.array([1, 2, 3]) # FIXME: Cause Bad variant access. - # Need to provide more descriptive error messsage. + # Need to provide more descriptive error message. with self.assertRaises(Exception): comp.apply(cv.gin(img0, img1), args=cv.gapi.compile_args( cv.gapi.kernels(GAddImpl))) diff --git a/modules/gapi/misc/python/test/test_gapi_streaming.py b/modules/gapi/misc/python/test/test_gapi_streaming.py index d7914c5157b5..e4c5926204c0 100644 --- a/modules/gapi/misc/python/test/test_gapi_streaming.py +++ b/modules/gapi/misc/python/test/test_gapi_streaming.py @@ -34,6 +34,16 @@ def run(img): return img + def convertNV12p2BGR(in_nv12): + shape = in_nv12.shape + y_height = shape[0] // 3 * 2 + uv_shape = (shape[0] // 3, shape[1]) + new_uv_shape = (uv_shape[0], uv_shape[1] // 2, 2) + return cv.cvtColorTwoPlane(in_nv12[:y_height, :], + in_nv12[ y_height:, :].reshape(new_uv_shape), + cv.COLOR_YUV2BGR_NV12) + + class test_gapi_streaming(NewOpenCVTests): def test_image_input(self): @@ -229,7 +239,6 @@ def test_video_good_features_to_track(self): def test_gapi_streaming_meta(self): - ksize = 3 path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']]) # G-API @@ -350,6 +359,189 @@ def test_compile_streaming_meta_and_args(self): cv.gapi.compile_args(cv.gapi.streaming.queue_capacity(1))) + def get_gst_source(self, gstpipeline): + # NB: Skip test in case gstreamer isn't available. + try: + return cv.gapi.wip.make_gst_src(gstpipeline) + except cv.error as e: + if str(e).find('Built without GStreamer support!') == -1: + raise e + else: + raise unittest.SkipTest(str(e)) + + + def test_gst_source(self): + if not cv.videoio_registry.hasBackend(cv.CAP_GSTREAMER): + raise unittest.SkipTest("Backend is not available/disabled: GSTREAMER") + + gstpipeline = """videotestsrc is-live=true pattern=colors num-buffers=10 ! + videorate ! videoscale ! video/x-raw,width=1920,height=1080, + framerate=30/1 ! appsink""" + + g_in = cv.GMat() + g_out = cv.gapi.copy(g_in) + c = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out)) + + ccomp = c.compileStreaming() + + source = self.get_gst_source(gstpipeline) + + ccomp.setSource(cv.gin(source)) + ccomp.start() + + has_frame, output = ccomp.pull() + while has_frame: + self.assertTrue(output.size != 0) + has_frame, output = ccomp.pull() + + + def open_VideoCapture_gstreamer(self, gstpipeline): + try: + cap = cv.VideoCapture(gstpipeline, cv.CAP_GSTREAMER) + except Exception as e: + raise unittest.SkipTest("Backend GSTREAMER can't open the video; " + + "cause: " + str(e)) + if not cap.isOpened(): + raise unittest.SkipTest("Backend GSTREAMER can't open the video") + return cap + + + def test_gst_source_accuracy(self): + if not cv.videoio_registry.hasBackend(cv.CAP_GSTREAMER): + raise unittest.SkipTest("Backend is not available/disabled: GSTREAMER") + + path = self.find_file('highgui/video/big_buck_bunny.avi', + [os.environ['OPENCV_TEST_DATA_PATH']]) + gstpipeline = """filesrc location=""" + path + """ ! decodebin ! videoconvert ! + videoscale ! video/x-raw,format=NV12 ! appsink""" + + # G-API pipeline + g_in = cv.GMat() + g_out = cv.gapi.copy(g_in) + c = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out)) + + ccomp = c.compileStreaming() + + # G-API Gst-source + source = self.get_gst_source(gstpipeline) + ccomp.setSource(cv.gin(source)) + ccomp.start() + + # OpenCV Gst-source + cap = self.open_VideoCapture_gstreamer(gstpipeline) + + # Assert + max_num_frames = 10 + for _ in range(max_num_frames): + has_expected, expected = cap.read() + has_actual, actual = ccomp.pull() + + self.assertEqual(has_expected, has_actual) + + if not has_expected: + break + + self.assertEqual(0.0, cv.norm(convertNV12p2BGR(expected), actual, cv.NORM_INF)) + + + def get_gst_pipeline(self, gstpipeline): + # NB: Skip test in case gstreamer isn't available. + try: + return cv.gapi.wip.GStreamerPipeline(gstpipeline) + except cv.error as e: + if str(e).find('Built without GStreamer support!') == -1: + raise e + else: + raise unittest.SkipTest(str(e)) + except SystemError as e: + raise unittest.SkipTest(str(e) + ", caused by " + str(e.__cause__)) + + + def test_gst_multiple_sources(self): + if not cv.videoio_registry.hasBackend(cv.CAP_GSTREAMER): + raise unittest.SkipTest("Backend is not available/disabled: GSTREAMER") + + gstpipeline = """videotestsrc is-live=true pattern=colors num-buffers=10 ! + videorate ! videoscale ! + video/x-raw,width=1920,height=1080,framerate=30/1 ! + appsink name=sink1 + videotestsrc is-live=true pattern=colors num-buffers=10 ! + videorate ! videoscale ! + video/x-raw,width=1920,height=1080,framerate=30/1 ! + appsink name=sink2""" + + g_in1 = cv.GMat() + g_in2 = cv.GMat() + g_out = cv.gapi.add(g_in1, g_in2) + c = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out)) + + ccomp = c.compileStreaming() + + pp = self.get_gst_pipeline(gstpipeline) + src1 = cv.gapi.wip.get_streaming_source(pp, "sink1") + src2 = cv.gapi.wip.get_streaming_source(pp, "sink2") + + ccomp.setSource(cv.gin(src1, src2)) + ccomp.start() + + has_frame, out = ccomp.pull() + while has_frame: + self.assertTrue(out.size != 0) + has_frame, out = ccomp.pull() + + + def test_gst_multiple_sources_accuracy(self): + if not cv.videoio_registry.hasBackend(cv.CAP_GSTREAMER): + raise unittest.SkipTest("Backend is not available/disabled: GSTREAMER") + + path = self.find_file('highgui/video/big_buck_bunny.avi', + [os.environ['OPENCV_TEST_DATA_PATH']]) + gstpipeline1 = """filesrc location=""" + path + """ ! decodebin ! videoconvert ! + videoscale ! video/x-raw,format=NV12 ! appsink""" + gstpipeline2 = """filesrc location=""" + path + """ ! decodebin ! + videoflip method=clockwise ! videoconvert ! videoscale ! + video/x-raw,format=NV12 ! appsink""" + gstpipeline_gapi = gstpipeline1 + ' name=sink1 ' + gstpipeline2 + ' name=sink2' + + # G-API pipeline + g_in1 = cv.GMat() + g_in2 = cv.GMat() + g_out1 = cv.gapi.copy(g_in1) + g_out2 = cv.gapi.copy(g_in2) + c = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out1, g_out2)) + + ccomp = c.compileStreaming() + + # G-API Gst-source + pp = self.get_gst_pipeline(gstpipeline_gapi) + + src1 = cv.gapi.wip.get_streaming_source(pp, "sink1") + src2 = cv.gapi.wip.get_streaming_source(pp, "sink2") + ccomp.setSource(cv.gin(src1, src2)) + ccomp.start() + + # OpenCV Gst-source + cap1 = self.open_VideoCapture_gstreamer(gstpipeline1) + cap2 = self.open_VideoCapture_gstreamer(gstpipeline2) + + # Assert + max_num_frames = 10 + for _ in range(max_num_frames): + has_expected1, expected1 = cap1.read() + has_expected2, expected2 = cap2.read() + has_actual, (actual1, actual2) = ccomp.pull() + + self.assertEqual(has_expected1, has_expected2) + has_expected = has_expected1 and has_expected2 + self.assertEqual(has_expected, has_actual) + + if not has_expected: + break + + self.assertEqual(0.0, cv.norm(convertNV12p2BGR(expected1), actual1, cv.NORM_INF)) + self.assertEqual(0.0, cv.norm(convertNV12p2BGR(expected2), actual2, cv.NORM_INF)) + + except unittest.SkipTest as e: diff --git a/modules/gapi/perf/common/gapi_core_perf_tests.hpp b/modules/gapi/perf/common/gapi_core_perf_tests.hpp index 7a1568ad22ff..60294d21930f 100644 --- a/modules/gapi/perf/common/gapi_core_perf_tests.hpp +++ b/modules/gapi/perf/common/gapi_core_perf_tests.hpp @@ -78,10 +78,6 @@ namespace opencv_test class KMeans2DPerfTest : public TestPerfParams> {}; class KMeans3DPerfTest : public TestPerfParams> {}; class TransposePerfTest : public TestPerfParams> {}; - class ResizePerfTest : public TestPerfParams> {}; - class BottleneckKernelsConstInputPerfTest : public TestPerfParams> {}; - class ResizeFxFyPerfTest : public TestPerfParams> {}; - class ResizeInSimpleGraphPerfTest : public TestPerfParams> {}; class ParseSSDBLPerfTest : public TestPerfParams>, public ParserSSDTest {}; class ParseSSDPerfTest : public TestPerfParams>, public ParserSSDTest {}; class ParseYoloPerfTest : public TestPerfParams>, public ParserYoloTest {}; diff --git a/modules/gapi/perf/common/gapi_core_perf_tests_inl.hpp b/modules/gapi/perf/common/gapi_core_perf_tests_inl.hpp index d5a8d95f4657..83ef13008c26 100644 --- a/modules/gapi/perf/common/gapi_core_perf_tests_inl.hpp +++ b/modules/gapi/perf/common/gapi_core_perf_tests_inl.hpp @@ -436,8 +436,8 @@ PERF_TEST_P_(DivPerfTest, TestPerformance) // FIXIT Unstable input data for divide initMatsRandU(type, sz, dtype, false); - //This condition need to workaround bug in OpenCV. - //It reinitializes divider matrix without zero values. + //This condition need to workaround the #21044 issue in the OpenCV. + //It reinitializes divider matrix without zero values for CV_16S DST type. if (dtype == CV_16S && dtype != type) cv::randu(in_mat2, cv::Scalar::all(1), cv::Scalar::all(255)); @@ -482,6 +482,11 @@ PERF_TEST_P_(DivCPerfTest, TestPerformance) // FIXIT Unstable input data for divide initMatsRandU(type, sz, dtype, false); + //This condition need to workaround the #21044 issue in the OpenCV. + //It reinitializes divider scalar without zero values for CV_16S DST type. + if (dtype == CV_16S || (type == CV_16S && dtype == -1)) + cv::randu(sc, cv::Scalar::all(1), cv::Scalar::all(SHRT_MAX)); + // OpenCV code /////////////////////////////////////////////////////////// cv::divide(in_mat1, sc, out_mat_ocv, scale, dtype); @@ -523,6 +528,10 @@ PERF_TEST_P_(DivRCPerfTest, TestPerformance) // FIXIT Unstable input data for divide initMatsRandU(type, sz, dtype, false); + //This condition need to workaround the #21044 issue in the OpenCV. + //It reinitializes divider matrix without zero values for CV_16S DST type. + if (dtype == CV_16S || (type == CV_16S && dtype == -1)) + cv::randu(in_mat1, cv::Scalar::all(1), cv::Scalar::all(255)); // OpenCV code /////////////////////////////////////////////////////////// cv::divide(sc, in_mat1, out_mat_ocv, scale, dtype); @@ -2282,187 +2291,6 @@ PERF_TEST_P_(TransposePerfTest, TestPerformance) //------------------------------------------------------------------------------ -PERF_TEST_P_(ResizePerfTest, TestPerformance) -{ - compare_f cmpF; - MatType type = -1; - int interp = 1; - cv::Size sz; - cv::Size sz_out; - cv::GCompileArgs compile_args; - std::tie(cmpF, type, interp, sz, sz_out, compile_args) = GetParam(); - - in_mat1 = cv::Mat(sz, type); - cv::Scalar mean = cv::Scalar::all(127); - cv::Scalar stddev = cv::Scalar::all(40.f); - cv::randn(in_mat1, mean, stddev); - out_mat_gapi = cv::Mat(sz_out, type); - out_mat_ocv = cv::Mat(sz_out, type); - - // OpenCV code /////////////////////////////////////////////////////////// - cv::resize(in_mat1, out_mat_ocv, sz_out, 0.0, 0.0, interp); - - // G-API code ////////////////////////////////////////////////////////////// - cv::GMat in; - auto out = cv::gapi::resize(in, sz_out, 0.0, 0.0, interp); - cv::GComputation c(in, out); - - // Warm-up graph engine: - auto cc = c.compile(descr_of(gin(in_mat1)), - std::move(compile_args)); - cc(gin(in_mat1), gout(out_mat_gapi)); - - TEST_CYCLE() - { - cc(gin(in_mat1), gout(out_mat_gapi)); - } - - // Comparison //////////////////////////////////////////////////////////// - { - EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); - } - - SANITY_CHECK_NOTHING(); -} - -//------------------------------------------------------------------------------ - -PERF_TEST_P_(ResizeFxFyPerfTest, TestPerformance) -{ - compare_f cmpF; - MatType type = -1; - int interp = 1; - cv::Size sz; - double fx = 0.0; - double fy = 0.0; - cv::GCompileArgs compile_args; - std::tie(cmpF, type, interp, sz, fx, fy, compile_args) = GetParam(); - - in_mat1 = cv::Mat(sz, type); - cv::Scalar mean = cv::Scalar::all(127); - cv::Scalar stddev = cv::Scalar::all(40.f); - cv::randn(in_mat1, mean, stddev); - cv::Size sz_out = cv::Size(saturate_cast(sz.width *fx), saturate_cast(sz.height*fy)); - out_mat_gapi = cv::Mat(sz_out, type); - out_mat_ocv = cv::Mat(sz_out, type); - - // OpenCV code /////////////////////////////////////////////////////////// - cv::resize(in_mat1, out_mat_ocv, sz_out, fx, fy, interp); - - // G-API code ////////////////////////////////////////////////////////////// - cv::GMat in; - auto out = cv::gapi::resize(in, sz_out, fx, fy, interp); - cv::GComputation c(in, out); - - // Warm-up graph engine: - auto cc = c.compile(descr_of(gin(in_mat1)), - std::move(compile_args)); - cc(gin(in_mat1), gout(out_mat_gapi)); - - TEST_CYCLE() - { - cc(gin(in_mat1), gout(out_mat_gapi)); - } - // Comparison //////////////////////////////////////////////////////////// - { - EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); - } - - SANITY_CHECK_NOTHING(); -} - -//------------------------------------------------------------------------------ - -// This test cases were created to control performance result of test scenario mentioned here: -// https://stackoverflow.com/questions/60629331/opencv-gapi-performance-not-good-as-expected - -PERF_TEST_P_(BottleneckKernelsConstInputPerfTest, TestPerformance) -{ - compare_f cmpF; - std::string fileName = ""; - cv::GCompileArgs compile_args; - double fx = 0.5; - double fy = 0.5; - std::tie(cmpF, fileName, compile_args) = GetParam(); - - in_mat1 = cv::imread(findDataFile(fileName)); - - cv::Mat cvvga; - cv::Mat cvgray; - cv::Mat cvblurred; - - cv::resize(in_mat1, cvvga, cv::Size(), fx, fy); - cv::cvtColor(cvvga, cvgray, cv::COLOR_BGR2GRAY); - cv::blur(cvgray, cvblurred, cv::Size(3, 3)); - cv::Canny(cvblurred, out_mat_ocv, 32, 128, 3); - - cv::GMat in; - cv::GMat vga = cv::gapi::resize(in, cv::Size(), fx, fy, INTER_LINEAR); - cv::GMat gray = cv::gapi::BGR2Gray(vga); - cv::GMat blurred = cv::gapi::blur(gray, cv::Size(3, 3)); - cv::GMat out = cv::gapi::Canny(blurred, 32, 128, 3); - cv::GComputation ac(in, out); - - auto cc = ac.compile(descr_of(gin(in_mat1)), - std::move(compile_args)); - cc(gin(in_mat1), gout(out_mat_gapi)); - - TEST_CYCLE() - { - cc(gin(in_mat1), gout(out_mat_gapi)); - } - - // Comparison //////////////////////////////////////////////////////////// - { - EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); - } - - SANITY_CHECK_NOTHING(); -} - -//------------------------------------------------------------------------------ - -PERF_TEST_P_(ResizeInSimpleGraphPerfTest, TestPerformance) -{ - compare_f cmpF; - MatType type = -1; - cv::Size sz; - double fx = 0.5; - double fy = 0.5; - cv::GCompileArgs compile_args; - std::tie(cmpF, type, sz, fx, fy, compile_args) = GetParam(); - - initMatsRandU(type, sz, type, false); - - cv::Mat add_res_ocv; - - cv::add(in_mat1, in_mat2, add_res_ocv); - cv::resize(add_res_ocv, out_mat_ocv, cv::Size(), fx, fy); - - cv::GMat in1, in2; - cv::GMat add_res_gapi = cv::gapi::add(in1, in2); - cv::GMat out = cv::gapi::resize(add_res_gapi, cv::Size(), fx, fy, INTER_LINEAR); - cv::GComputation ac(GIn(in1, in2), GOut(out)); - - auto cc = ac.compile(descr_of(gin(in_mat1, in_mat2)), - std::move(compile_args)); - cc(gin(in_mat1, in_mat2), gout(out_mat_gapi)); - - TEST_CYCLE() - { - cc(gin(in_mat1, in_mat2), gout(out_mat_gapi)); - } - - // Comparison //////////////////////////////////////////////////////////// - { - EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); - } - - SANITY_CHECK_NOTHING(); -} - -//------------------------------------------------------------------------------ - PERF_TEST_P_(ParseSSDBLPerfTest, TestPerformance) { cv::Size sz; diff --git a/modules/gapi/perf/common/gapi_imgproc_perf_tests.hpp b/modules/gapi/perf/common/gapi_imgproc_perf_tests.hpp index 16e5f13729b6..c1afbfea6d0f 100644 --- a/modules/gapi/perf/common/gapi_imgproc_perf_tests.hpp +++ b/modules/gapi/perf/common/gapi_imgproc_perf_tests.hpp @@ -99,6 +99,10 @@ class YUV2BGRPerfTest : public TestPerfParams> {}; class BayerGR2RGBPerfTest : public TestPerfParams> {}; class RGB2YUV422PerfTest : public TestPerfParams> {}; +class ResizePerfTest : public TestPerfParams> {}; +class ResizeFxFyPerfTest : public TestPerfParams> {}; +class ResizeInSimpleGraphPerfTest : public TestPerfParams> {}; +class BottleneckKernelsConstInputPerfTest : public TestPerfParams> {}; } // opencv_test #endif //OPENCV_GAPI_IMGPROC_PERF_TESTS_HPP diff --git a/modules/gapi/perf/common/gapi_imgproc_perf_tests_inl.hpp b/modules/gapi/perf/common/gapi_imgproc_perf_tests_inl.hpp index 57c813033817..475daa84c1cb 100644 --- a/modules/gapi/perf/common/gapi_imgproc_perf_tests_inl.hpp +++ b/modules/gapi/perf/common/gapi_imgproc_perf_tests_inl.hpp @@ -1761,5 +1761,187 @@ PERF_TEST_P_(RGB2YUV422PerfTest, TestPerformance) //------------------------------------------------------------------------------ +PERF_TEST_P_(ResizePerfTest, TestPerformance) +{ + compare_f cmpF; + MatType type = -1; + int interp = 1; + cv::Size sz; + cv::Size sz_out; + cv::GCompileArgs compile_args; + std::tie(cmpF, type, interp, sz, sz_out, compile_args) = GetParam(); + + in_mat1 = cv::Mat(sz, type); + cv::Scalar mean = cv::Scalar::all(127); + cv::Scalar stddev = cv::Scalar::all(40.f); + cv::randn(in_mat1, mean, stddev); + out_mat_gapi = cv::Mat(sz_out, type); + out_mat_ocv = cv::Mat(sz_out, type); + + // OpenCV code /////////////////////////////////////////////////////////// + cv::resize(in_mat1, out_mat_ocv, sz_out, 0.0, 0.0, interp); + + // G-API code ////////////////////////////////////////////////////////////// + cv::GMat in; + auto out = cv::gapi::resize(in, sz_out, 0.0, 0.0, interp); + cv::GComputation c(in, out); + + // Warm-up graph engine: + auto cc = c.compile(descr_of(gin(in_mat1)), + std::move(compile_args)); + cc(gin(in_mat1), gout(out_mat_gapi)); + + TEST_CYCLE() + { + cc(gin(in_mat1), gout(out_mat_gapi)); + } + + // Comparison //////////////////////////////////////////////////////////// + { + EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); + } + + SANITY_CHECK_NOTHING(); +} + +//------------------------------------------------------------------------------ + +PERF_TEST_P_(ResizeFxFyPerfTest, TestPerformance) +{ + compare_f cmpF; + MatType type = -1; + int interp = 1; + cv::Size sz; + double fx = 1.0; + double fy = 1.0; + cv::GCompileArgs compile_args; + std::tie(cmpF, type, interp, sz, fx, fy, compile_args) = GetParam(); + + in_mat1 = cv::Mat(sz, type); + cv::Scalar mean = cv::Scalar::all(127); + cv::Scalar stddev = cv::Scalar::all(40.f); + cv::randn(in_mat1, mean, stddev); + cv::Size sz_out = cv:: Size(saturate_cast(sz.width*fx), saturate_cast(sz.height*fy)); + out_mat_gapi = cv::Mat(sz_out, type); + out_mat_ocv = cv::Mat(sz_out, type); + + // OpenCV code /////////////////////////////////////////////////////////// + cv::resize(in_mat1, out_mat_ocv, sz_out, fx, fy, interp); + + // G-API code ////////////////////////////////////////////////////////////// + cv::GMat in; + auto out = cv::gapi::resize(in, sz_out, fx, fy, interp); + cv::GComputation c(in, out); + + // Warm-up graph engine: + auto cc = c.compile(descr_of(gin(in_mat1)), + std::move(compile_args)); + cc(gin(in_mat1), gout(out_mat_gapi)); + + TEST_CYCLE() + { + cc(gin(in_mat1), gout(out_mat_gapi)); + } + + // Comparison //////////////////////////////////////////////////////////// + { + EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); + } + + SANITY_CHECK_NOTHING(); +} + +//------------------------------------------------------------------------------ + +PERF_TEST_P_(ResizeInSimpleGraphPerfTest, TestPerformance) +{ + compare_f cmpF; + MatType type = -1; + cv::Size sz; + double fx = 0.5; + double fy = 0.5; + cv::GCompileArgs compile_args; + std::tie(cmpF, type, sz, fx, fy, compile_args) = GetParam(); + + initMatsRandU(type, sz, type, false); + + cv::Mat add_res_ocv; + + cv::add(in_mat1, in_mat2, add_res_ocv); + cv::resize(add_res_ocv, out_mat_ocv, cv::Size(), fx, fy); + + cv::GMat in1, in2; + cv::GMat add_res_gapi = cv::gapi::add(in1, in2); + cv::GMat out = cv::gapi::resize(add_res_gapi, cv::Size(), fx, fy, INTER_LINEAR); + cv::GComputation ac(GIn(in1, in2), GOut(out)); + + auto cc = ac.compile(descr_of(gin(in_mat1, in_mat2)), + std::move(compile_args)); + cc(gin(in_mat1, in_mat2), gout(out_mat_gapi)); + + TEST_CYCLE() + { + cc(gin(in_mat1, in_mat2), gout(out_mat_gapi)); + } + + // Comparison //////////////////////////////////////////////////////////// + { + EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); + } + + SANITY_CHECK_NOTHING(); +} + +//------------------------------------------------------------------------------ + +// This test cases were created to control performance result of test scenario mentioned here: +// https://stackoverflow.com/questions/60629331/opencv-gapi-performance-not-good-as-expected + +PERF_TEST_P_(BottleneckKernelsConstInputPerfTest, TestPerformance) +{ + compare_f cmpF; + std::string fileName = ""; + cv::GCompileArgs compile_args; + double fx = 0.5; + double fy = 0.5; + std::tie(cmpF, fileName, compile_args) = GetParam(); + + in_mat1 = cv::imread(findDataFile(fileName)); + + cv::Mat cvvga; + cv::Mat cvgray; + cv::Mat cvblurred; + + cv::resize(in_mat1, cvvga, cv::Size(), fx, fy); + cv::cvtColor(cvvga, cvgray, cv::COLOR_BGR2GRAY); + cv::blur(cvgray, cvblurred, cv::Size(3, 3)); + cv::Canny(cvblurred, out_mat_ocv, 32, 128, 3); + + cv::GMat in; + cv::GMat vga = cv::gapi::resize(in, cv::Size(), fx, fy, INTER_LINEAR); + cv::GMat gray = cv::gapi::BGR2Gray(vga); + cv::GMat blurred = cv::gapi::blur(gray, cv::Size(3, 3)); + cv::GMat out = cv::gapi::Canny(blurred, 32, 128, 3); + cv::GComputation ac(in, out); + + auto cc = ac.compile(descr_of(gin(in_mat1)), + std::move(compile_args)); + cc(gin(in_mat1), gout(out_mat_gapi)); + + TEST_CYCLE() + { + cc(gin(in_mat1), gout(out_mat_gapi)); + } + + // Comparison //////////////////////////////////////////////////////////// + { + EXPECT_TRUE(cmpF(out_mat_gapi, out_mat_ocv)); + } + + SANITY_CHECK_NOTHING(); +} + +//------------------------------------------------------------------------------ + } #endif //OPENCV_GAPI_IMGPROC_PERF_TESTS_INL_HPP diff --git a/modules/gapi/perf/common/gapi_video_perf_tests_inl.hpp b/modules/gapi/perf/common/gapi_video_perf_tests_inl.hpp index 05439f6b4825..abbdc6969509 100644 --- a/modules/gapi/perf/common/gapi_video_perf_tests_inl.hpp +++ b/modules/gapi/perf/common/gapi_video_perf_tests_inl.hpp @@ -130,7 +130,7 @@ PERF_TEST_P_(BuildPyr_CalcOptFlow_PipelinePerfTest, TestPerformance) auto customKernel = gapi::kernels(); auto kernels = gapi::combine(customKernel, - params.compileArgs[0].get()); + params.compileArgs[0].get()); params.compileArgs = compile_args(kernels); OptFlowLKTestOutput outOCV { outPtsOCV, outStatusOCV, outErrOCV }; diff --git a/modules/gapi/perf/cpu/gapi_core_perf_tests_cpu.cpp b/modules/gapi/perf/cpu/gapi_core_perf_tests_cpu.cpp index 5323ea8f0862..2f91e07e52a4 100644 --- a/modules/gapi/perf/cpu/gapi_core_perf_tests_cpu.cpp +++ b/modules/gapi/perf/cpu/gapi_core_perf_tests_cpu.cpp @@ -369,38 +369,6 @@ INSTANTIATE_TEST_CASE_P(TransposePerfTestCPU, TransposePerfTest, CV_8UC3, CV_16UC3, CV_16SC3, CV_32FC3), Values(cv::compile_args(CORE_CPU)))); -INSTANTIATE_TEST_CASE_P(ResizePerfTestCPU, ResizePerfTest, - Combine(Values(AbsExact().to_compare_f()), - Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values(szSmall128, szVGA, sz720p, sz1080p), - Values( cv::Size(64, 64), - cv::Size(32, 32)), - Values(cv::compile_args(CORE_CPU)))); - -INSTANTIATE_TEST_CASE_P(BottleneckKernelsPerfTestCPU, BottleneckKernelsConstInputPerfTest, - Combine(Values(AbsExact().to_compare_f()), - Values( "cv/optflow/frames/1080p_00.png", "cv/optflow/frames/720p_00.png", - "cv/optflow/frames/VGA_00.png", "cv/dnn_face/recognition/Aaron_Tippin_0001.jpg"), - Values(cv::compile_args(CORE_CPU)))); - -INSTANTIATE_TEST_CASE_P(ResizeInSimpleGraphPerfTestCPU, ResizeInSimpleGraphPerfTest, - Combine(Values(AbsExact().to_compare_f()), - Values(CV_8UC3), - Values(szSmall128, szVGA, sz720p, sz1080p), - Values(0.5), - Values(0.5), - Values(cv::compile_args(CORE_CPU)))); - -INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestCPU, ResizeFxFyPerfTest, - Combine(Values(AbsExact().to_compare_f()), - Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values(szSmall128, szVGA, sz720p, sz1080p), - Values(0.5, 0.1), - Values(0.5, 0.1), - Values(cv::compile_args(CORE_CPU)))); - INSTANTIATE_TEST_CASE_P(ParseSSDBLPerfTestCPU, ParseSSDBLPerfTest, Combine(Values(sz720p, sz1080p), Values(0.3f, 0.7f), diff --git a/modules/gapi/perf/cpu/gapi_core_perf_tests_fluid.cpp b/modules/gapi/perf/cpu/gapi_core_perf_tests_fluid.cpp index e25029b83535..83de793a8185 100644 --- a/modules/gapi/perf/cpu/gapi_core_perf_tests_fluid.cpp +++ b/modules/gapi/perf/cpu/gapi_core_perf_tests_fluid.cpp @@ -28,8 +28,8 @@ INSTANTIATE_TEST_CASE_P(SqrtPerfTestFluid, SqrtPerfTest, INSTANTIATE_TEST_CASE_P(AddPerfTestFluid, AddPerfTest, Combine(Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(CV_8UC1, CV_8UC3, CV_16SC1, CV_32FC1), - Values(-1, CV_8U, CV_32F), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + Values(-1, CV_8U, CV_16U, CV_16S, CV_32F), Values(cv::compile_args(CORE_FLUID)))); INSTANTIATE_TEST_CASE_P(AddCPerfTestFluid, AddCPerfTest, @@ -40,17 +40,17 @@ INSTANTIATE_TEST_CASE_P(AddCPerfTestFluid, AddCPerfTest, Values(cv::compile_args(CORE_FLUID)))); INSTANTIATE_TEST_CASE_P(SubPerfTestFluid, SubPerfTest, - Combine(Values(AbsExact().to_compare_f()), + Combine(Values(Tolerance_FloatRel_IntAbs(1e-6, 0).to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(CV_8UC1, CV_8UC3, CV_16SC1, CV_32FC1), - Values(-1, CV_8U, CV_32F), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + Values(-1, CV_8U, CV_16U, CV_16S, CV_32F), Values(cv::compile_args(CORE_FLUID)))); INSTANTIATE_TEST_CASE_P(SubCPerfTestFluid, SubCPerfTest, Combine(Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(CV_8UC1, CV_8UC3, CV_16SC1, CV_32FC1), - Values(-1, CV_8U, CV_32F), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + Values(-1, CV_8U, CV_16U, CV_16S, CV_32F), Values(cv::compile_args(CORE_FLUID)))); INSTANTIATE_TEST_CASE_P(SubRCPerfTestFluid, SubRCPerfTest, @@ -78,8 +78,8 @@ INSTANTIATE_TEST_CASE_P(MulDoublePerfTestFluid, MulDoublePerfTest, INSTANTIATE_TEST_CASE_P(MulCPerfTestFluid, MulCPerfTest, Combine(Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(CV_8UC1, CV_8UC3, CV_16SC1, CV_32FC1), - Values(-1, CV_8U, CV_32F), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + Values(-1, CV_8U, CV_16U, CV_16S, CV_32F), Values(cv::compile_args(CORE_FLUID)))); INSTANTIATE_TEST_CASE_P(DivPerfTestFluid, DivPerfTest, @@ -93,16 +93,16 @@ INSTANTIATE_TEST_CASE_P(DivPerfTestFluid, DivPerfTest, INSTANTIATE_TEST_CASE_P(DivCPerfTestFluid, DivCPerfTest, Combine(Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(CV_8UC1, CV_8UC3, CV_16SC1, CV_32FC1), - Values(-1, CV_8U, CV_32F), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + Values(-1, CV_8U, CV_16U, CV_16S, CV_32F), Values(1.0), Values(cv::compile_args(CORE_FLUID)))); INSTANTIATE_TEST_CASE_P(DivRCPerfTestFluid, DivRCPerfTest, Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(CV_8UC1, CV_8UC3, CV_32FC1), - Values(-1, CV_8U, CV_32F), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + Values(-1, CV_8U, CV_16U, CV_16S, CV_32F), Values(1.0), Values(cv::compile_args(CORE_FLUID)))); @@ -324,40 +324,8 @@ INSTANTIATE_TEST_CASE_P(ConvertToPerfTestFluid, ConvertToPerfTest, Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1), Values(CV_8U, CV_16U, CV_16S, CV_32F), Values(szSmall128, szVGA, sz720p, sz1080p), - Values(2.5, 1.0), + Values(1.0, 2.5), Values(0.0), Values(cv::compile_args(CORE_FLUID)))); -INSTANTIATE_TEST_CASE_P(ResizePerfTestFluid, ResizePerfTest, - Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), - Values(CV_8UC3), - Values(cv::INTER_LINEAR), - Values(szSmall128, szVGA, sz720p, sz1080p), - Values(cv::Size(64, 64), - cv::Size(30, 30)), - Values(cv::compile_args(CORE_FLUID)))); - -#define IMGPROC_FLUID cv::gapi::imgproc::fluid::kernels() -INSTANTIATE_TEST_CASE_P(BottleneckKernelsPerfTestFluid, BottleneckKernelsConstInputPerfTest, - Combine(Values(AbsSimilarPoints(0, 1).to_compare_f()), - Values("cv/optflow/frames/1080p_00.png", "cv/optflow/frames/720p_00.png", - "cv/optflow/frames/VGA_00.png", "cv/dnn_face/recognition/Aaron_Tippin_0001.jpg"), - Values(cv::compile_args(CORE_FLUID, IMGPROC_FLUID)))); - -INSTANTIATE_TEST_CASE_P(ResizeInSimpleGraphPerfTestFluid, ResizeInSimpleGraphPerfTest, - Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), - Values(CV_8UC3), - Values(szSmall128, szVGA, sz720p, sz1080p), - Values(0.5), - Values(0.5), - Values(cv::compile_args(CORE_FLUID, IMGPROC_FLUID)))); - -INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestFluid, ResizeFxFyPerfTest, - Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), - Values(CV_8UC3), - Values(cv::INTER_LINEAR), - Values(szSmall128, szVGA, sz720p, sz1080p), - Values(0.5, 0.1), - Values(0.5, 0.1), - Values(cv::compile_args(CORE_FLUID)))); } // opencv_test diff --git a/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_cpu.cpp b/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_cpu.cpp index d4c37c10af99..dc4c65bf7472 100644 --- a/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_cpu.cpp +++ b/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_cpu.cpp @@ -403,4 +403,36 @@ INSTANTIATE_TEST_CASE_P(RGB2YUV422PerfTestCPU, RGB2YUV422PerfTest, Combine(Values(ToleranceColor(1e-3).to_compare_f()), Values(szVGA, sz720p, sz1080p), Values(cv::compile_args(IMGPROC_CPU)))); + +INSTANTIATE_TEST_CASE_P(ResizePerfTestCPU, ResizePerfTest, + Combine(Values(AbsExact().to_compare_f()), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values( cv::Size(64, 64), + cv::Size(32, 32)), + Values(cv::compile_args(IMGPROC_CPU)))); + +INSTANTIATE_TEST_CASE_P(BottleneckKernelsPerfTestCPU, BottleneckKernelsConstInputPerfTest, + Combine(Values(AbsExact().to_compare_f()), + Values( "cv/optflow/frames/1080p_00.png", "cv/optflow/frames/720p_00.png", + "cv/optflow/frames/VGA_00.png", "cv/dnn_face/recognition/Aaron_Tippin_0001.jpg"), + Values(cv::compile_args(IMGPROC_CPU)))); + +INSTANTIATE_TEST_CASE_P(ResizeInSimpleGraphPerfTestCPU, ResizeInSimpleGraphPerfTest, + Combine(Values(AbsExact().to_compare_f()), + Values(CV_8UC3), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values(0.5), + Values(0.5), + Values(cv::compile_args(IMGPROC_CPU)))); + +INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestCPU, ResizeFxFyPerfTest, + Combine(Values(AbsExact().to_compare_f()), + Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values(0.5, 0.1), + Values(0.5, 0.1), + Values(cv::compile_args(IMGPROC_CPU)))); } // opencv_test diff --git a/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_fluid.cpp b/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_fluid.cpp index 1ccd763099d0..a768875f32f2 100644 --- a/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_fluid.cpp +++ b/modules/gapi/perf/cpu/gapi_imgproc_perf_tests_fluid.cpp @@ -9,6 +9,7 @@ #include "../common/gapi_imgproc_perf_tests.hpp" #define IMGPROC_FLUID cv::gapi::imgproc::fluid::kernels() +#define CORE_FLUID cv::gapi::core::fluid::kernels() namespace opencv_test { @@ -198,4 +199,36 @@ INSTANTIATE_TEST_CASE_P(RGB2LabPerfTestFluid, RGB2LabPerfTest, Values(szVGA, sz720p, sz1080p), Values(cv::compile_args(IMGPROC_FLUID)))); +INSTANTIATE_TEST_CASE_P(ResizePerfTestFluid, ResizePerfTest, + Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), + Values(CV_8UC3, CV_32FC1), + Values(cv::INTER_LINEAR), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values(cv::Size(64, 64), + cv::Size(30, 30)), + Values(cv::compile_args(IMGPROC_FLUID)))); + +#define IMGPROC_FLUID cv::gapi::imgproc::fluid::kernels() +INSTANTIATE_TEST_CASE_P(BottleneckKernelsPerfTestFluid, BottleneckKernelsConstInputPerfTest, + Combine(Values(AbsSimilarPoints(0, 1).to_compare_f()), + Values("cv/optflow/frames/1080p_00.png", "cv/optflow/frames/720p_00.png", + "cv/optflow/frames/VGA_00.png", "cv/dnn_face/recognition/Aaron_Tippin_0001.jpg"), + Values(cv::compile_args(IMGPROC_FLUID)))); + +INSTANTIATE_TEST_CASE_P(ResizeInSimpleGraphPerfTestFluid, ResizeInSimpleGraphPerfTest, + Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), + Values(CV_8UC3, CV_32FC1), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values(0.5), + Values(0.5), + Values(cv::compile_args(cv::gapi::combine(IMGPROC_FLUID, CORE_FLUID))))); + +INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestFluid, ResizeFxFyPerfTest, + Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), + Values(CV_8UC3, CV_32FC1), + Values(cv::INTER_LINEAR), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values(0.5, 0.25, 2), + Values(0.5, 0.25, 2), + Values(cv::compile_args(IMGPROC_FLUID)))); } diff --git a/modules/gapi/perf/gpu/gapi_core_perf_tests_gpu.cpp b/modules/gapi/perf/gpu/gapi_core_perf_tests_gpu.cpp index 6aaec4d79a6c..8aaa304e5897 100644 --- a/modules/gapi/perf/gpu/gapi_core_perf_tests_gpu.cpp +++ b/modules/gapi/perf/gpu/gapi_core_perf_tests_gpu.cpp @@ -79,7 +79,7 @@ INSTANTIATE_TEST_CASE_P(DivPerfTestGPU, DivPerfTest, Values(cv::compile_args(CORE_GPU)))); INSTANTIATE_TEST_CASE_P(DivCPerfTestGPU, DivCPerfTest, - Combine(Values(AbsExact().to_compare_f()), + Combine(Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_f()), Values( szSmall128, szVGA, sz720p, sz1080p ), Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), Values( -1, CV_8U, CV_16U, CV_32F ), @@ -320,25 +320,7 @@ INSTANTIATE_TEST_CASE_P(TransposePerfTestGPU, TransposePerfTest, Combine(Values(AbsExact().to_compare_f()), Values(szSmall128, szVGA, sz720p, sz1080p), Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1, - CV_8UC2, CV_16UC2, CV_16SC2, CV_32FC2, - CV_8UC3, CV_16UC3, CV_16SC3, CV_32FC3), - Values(cv::compile_args(CORE_GPU)))); - -INSTANTIATE_TEST_CASE_P(ResizePerfTestGPU, ResizePerfTest, - Combine(Values(AbsSimilarPoints(2, 0.05).to_compare_f()), - Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values( szSmall128, szVGA, sz720p, sz1080p ), - Values(cv::Size(64,64), - cv::Size(30,30)), - Values(cv::compile_args(CORE_GPU)))); - -INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestGPU, ResizeFxFyPerfTest, - Combine(Values(AbsSimilarPoints(2, 0.05).to_compare_f()), - Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values( szSmall128, szVGA, sz720p, sz1080p ), - Values(0.5, 0.1), - Values(0.5, 0.1), + CV_8UC2, CV_16UC2, CV_16SC2, CV_32FC2, + CV_8UC3, CV_16UC3, CV_16SC3, CV_32FC3), Values(cv::compile_args(CORE_GPU)))); } // opencv_test diff --git a/modules/gapi/perf/gpu/gapi_imgproc_perf_tests_gpu.cpp b/modules/gapi/perf/gpu/gapi_imgproc_perf_tests_gpu.cpp index 1f4f3883d1a2..faacf4f25456 100644 --- a/modules/gapi/perf/gpu/gapi_imgproc_perf_tests_gpu.cpp +++ b/modules/gapi/perf/gpu/gapi_imgproc_perf_tests_gpu.cpp @@ -204,4 +204,21 @@ INSTANTIATE_TEST_CASE_P(YUV2BGRPerfTestGPU, YUV2BGRPerfTest, Values(szVGA, sz720p, sz1080p), Values(cv::compile_args(IMGPROC_GPU)))); +INSTANTIATE_TEST_CASE_P(ResizePerfTestGPU, ResizePerfTest, + Combine(Values(AbsSimilarPoints(2, 0.05).to_compare_f()), + Values(CV_8UC1, CV_16UC1, CV_16SC1), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values( szSmall128, szVGA, sz720p, sz1080p ), + Values(cv::Size(64,64), + cv::Size(30,30)), + Values(cv::compile_args(IMGPROC_GPU)))); + +INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestGPU, ResizeFxFyPerfTest, + Combine(Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_f()), + Values(CV_8UC1, CV_16UC1, CV_16SC1), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(szSmall128, szVGA, sz720p, sz1080p), + Values(0.5, 0.1), + Values(0.5, 0.1), + Values(cv::compile_args(IMGPROC_GPU)))); } diff --git a/modules/gapi/perf/streaming/gapi_streaming_source_perf_tests.cpp b/modules/gapi/perf/streaming/gapi_streaming_source_perf_tests.cpp index 7d06ad068b3b..8021eed3cf27 100644 --- a/modules/gapi/perf/streaming/gapi_streaming_source_perf_tests.cpp +++ b/modules/gapi/perf/streaming/gapi_streaming_source_perf_tests.cpp @@ -11,6 +11,14 @@ #include #include +#include "streaming/onevpl/engine/preproc/preproc_engine.hpp" +#include "streaming/onevpl/engine/preproc/preproc_session.hpp" +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" +#include "streaming/onevpl/cfg_param_device_selector.hpp" +#include "streaming/onevpl/accelerators/accel_policy_dx11.hpp" +#include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" +#include "streaming/onevpl/accelerators/accel_policy_va_api.hpp" + namespace opencv_test { using namespace perf; @@ -32,10 +40,10 @@ using codec_t = std::string; using accel_mode_t = std::string; using source_description_t = std::tuple; -class OneVPLSourcePerfTest : public TestPerfParams {}; -class VideoCapSourcePerfTest : public TestPerfParams {}; +class OneVPLSourcePerf_Test : public TestPerfParams {}; +class VideoCapSourcePerf_Test : public TestPerfParams {}; -PERF_TEST_P_(OneVPLSourcePerfTest, TestPerformance) +PERF_TEST_P_(OneVPLSourcePerf_Test, TestPerformance) { using namespace cv::gapi::wip::onevpl; @@ -67,7 +75,7 @@ PERF_TEST_P_(OneVPLSourcePerfTest, TestPerformance) SANITY_CHECK_NOTHING(); } -PERF_TEST_P_(VideoCapSourcePerfTest, TestPerformance) +PERF_TEST_P_(VideoCapSourcePerf_Test, TestPerformance) { using namespace cv::gapi::wip; @@ -82,18 +90,248 @@ PERF_TEST_P_(VideoCapSourcePerfTest, TestPerformance) SANITY_CHECK_NOTHING(); } -INSTANTIATE_TEST_CASE_P(Streaming, OneVPLSourcePerfTest, +#ifdef __WIN32__ +INSTANTIATE_TEST_CASE_P(Streaming, OneVPLSourcePerf_Test, Values(source_description_t(files[0], codec[0], ""), source_description_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_D3D11"), source_description_t(files[1], codec[1], ""), source_description_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_D3D11"), source_description_t(files[2], codec[2], ""), source_description_t(files[2], codec[2], "MFX_ACCEL_MODE_VIA_D3D11"))); +#elif __linux__ +INSTANTIATE_TEST_CASE_P(Streaming, OneVPLSourcePerf_Test, + Values(source_description_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_VAAPI"), + source_description_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_VAAPI"))); +#endif -INSTANTIATE_TEST_CASE_P(Streaming, VideoCapSourcePerfTest, +INSTANTIATE_TEST_CASE_P(Streaming, VideoCapSourcePerf_Test, Values(files[0], files[1], files[2])); + +using pp_out_param_t = cv::GFrameDesc; +using source_description_preproc_t = decltype(std::tuple_cat(std::declval(), + std::declval>())); +class OneVPLSourcePerf_PP_Test : public TestPerfParams {}; + +PERF_TEST_P_(OneVPLSourcePerf_PP_Test, TestPerformance) +{ + using namespace cv::gapi::wip::onevpl; + + const auto params = GetParam(); + source_t src = findDataFile(get<0>(params)); + codec_t type = get<1>(params); + accel_mode_t mode = get<2>(params); + pp_out_param_t res = get<3>(params); + + std::vector cfg_params { + CfgParam::create_implementation("MFX_IMPL_TYPE_HARDWARE"), + }; + + if (!type.empty()) { + cfg_params.push_back(CfgParam::create_decoder_id(type.c_str())); + } + + if (!mode.empty()) { + cfg_params.push_back(CfgParam::create_acceleration_mode(mode.c_str())); + } + + cfg_params.push_back(CfgParam::create_vpp_out_width(static_cast(res.size.width))); + cfg_params.push_back(CfgParam::create_vpp_out_height(static_cast(res.size.height))); + cfg_params.push_back(CfgParam::create_vpp_out_crop_x(0)); + cfg_params.push_back(CfgParam::create_vpp_out_crop_y(0)); + cfg_params.push_back(CfgParam::create_vpp_out_crop_w(static_cast(res.size.width))); + cfg_params.push_back(CfgParam::create_vpp_out_crop_h(static_cast(res.size.height))); + + auto source_ptr = cv::gapi::wip::make_onevpl_src(src, cfg_params); + + cv::gapi::wip::Data out; + TEST_CYCLE() + { + source_ptr->pull(out); + } + + SANITY_CHECK_NOTHING(); +} +static pp_out_param_t full_hd = pp_out_param_t {cv::MediaFormat::NV12, + {1920, 1080}}; + +static pp_out_param_t cif = pp_out_param_t {cv::MediaFormat::NV12, + {352, 288}}; + + +#ifdef __WIN32__ +INSTANTIATE_TEST_CASE_P(Streaming_Source_PP, OneVPLSourcePerf_PP_Test, + Values(source_description_preproc_t(files[0], codec[0], "", full_hd), + source_description_preproc_t(files[0], codec[0], "", cif), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_D3D11", full_hd), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_D3D11", cif), + source_description_preproc_t(files[1], codec[1], "", full_hd), + source_description_preproc_t(files[1], codec[1], "", cif), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_D3D11",full_hd), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_D3D11",cif), + source_description_preproc_t(files[2], codec[2], "", full_hd), + source_description_preproc_t(files[2], codec[2], "", cif), + source_description_preproc_t(files[2], codec[2], "MFX_ACCEL_MODE_VIA_D3D11", full_hd), + source_description_preproc_t(files[2], codec[2], "MFX_ACCEL_MODE_VIA_D3D11", cif))); +#elif __linux__ +INSTANTIATE_TEST_CASE_P(Streaming_Source_PP, OneVPLSourcePerf_PP_Test, + Values(source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_VAAPI", full_hd), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_VAAPI", cif), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_VAAPI",full_hd), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_VAAPI",cif))); +#endif + +class OneVPLSourcePerf_PP_Engine_Test : public TestPerfParams {}; + +PERF_TEST_P_(OneVPLSourcePerf_PP_Engine_Test, TestPerformance) +{ + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + + const auto params = GetParam(); + source_t src = findDataFile(get<0>(params)); + codec_t type = get<1>(params); + accel_mode_t mode = get<2>(params); + const pp_out_param_t &required_frame_param = get<3>(params); + + std::vector cfg_params { + CfgParam::create_implementation("MFX_IMPL_TYPE_HARDWARE"), + }; + + if (!type.empty()) { + cfg_params.push_back(CfgParam::create_decoder_id(type.c_str())); + } + + if (!mode.empty()) { + cfg_params.push_back(CfgParam::create_acceleration_mode(mode.c_str())); + } + + auto device_selector = std::make_shared(cfg_params); + auto source_ptr = cv::gapi::wip::make_onevpl_src(src, cfg_params, device_selector); + + // create VPP preproc engine + std::unique_ptr policy; + if (mode == "MFX_ACCEL_MODE_VIA_D3D11") { + policy.reset(new VPLDX11AccelerationPolicy(device_selector)); + } else if (mode == "MFX_ACCEL_MODE_VIA_VAAPI") { + policy.reset(new VPLVAAPIAccelerationPolicy(device_selector)); + } else if (mode.empty()){ + policy.reset(new VPLCPUAccelerationPolicy(device_selector)); + } else { + ASSERT_TRUE(false && "Unsupported acceleration policy type"); + } + VPPPreprocEngine preproc_engine(std::move(policy)); + cv::gapi::wip::Data out; + cv::util::optional empty_roi; + TEST_CYCLE() + { + source_ptr->pull(out); + cv::MediaFrame frame = cv::util::get(out); + cv::util::optional param = preproc_engine.is_applicable(frame); + pp_session sess = preproc_engine.initialize_preproc(param.value(), + required_frame_param); + (void)preproc_engine.run_sync(sess, frame, empty_roi); + } + + SANITY_CHECK_NOTHING(); +} + +#ifdef __WIN32__ +INSTANTIATE_TEST_CASE_P(Streaming_Engine_PP, OneVPLSourcePerf_PP_Engine_Test, + Values(source_description_preproc_t(files[0], codec[0], "", full_hd), + source_description_preproc_t(files[0], codec[0], "", cif), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_D3D11", full_hd), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_D3D11", cif), + source_description_preproc_t(files[1], codec[1], "", full_hd), + source_description_preproc_t(files[1], codec[1], "", cif), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_D3D11",full_hd), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_D3D11",cif), + source_description_preproc_t(files[2], codec[2], "", full_hd), + source_description_preproc_t(files[2], codec[2], "", cif), + source_description_preproc_t(files[2], codec[2], "MFX_ACCEL_MODE_VIA_D3D11", full_hd), + source_description_preproc_t(files[2], codec[2], "MFX_ACCEL_MODE_VIA_D3D11", cif))); +#elif __linux__ +INSTANTIATE_TEST_CASE_P(Streaming_Engine_PP, OneVPLSourcePerf_PP_Engine_Test, + Values(source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_VAAPI", full_hd), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_VAAPI", cif), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_VAAPI",full_hd), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_VAAPI",cif))); +#endif + +class OneVPLSourcePerf_PP_Engine_Bypass_Test : public TestPerfParams {}; + +PERF_TEST_P_(OneVPLSourcePerf_PP_Engine_Bypass_Test, TestPerformance) +{ + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + + const auto params = GetParam(); + source_t src = findDataFile(get<0>(params)); + codec_t type = get<1>(params); + accel_mode_t mode = get<2>(params); + const pp_out_param_t &required_frame_param = get<3>(params); + + std::vector cfg_params { + CfgParam::create_implementation("MFX_IMPL_TYPE_HARDWARE"), + }; + + if (!type.empty()) { + cfg_params.push_back(CfgParam::create_decoder_id(type.c_str())); + } + + if (!mode.empty()) { + cfg_params.push_back(CfgParam::create_acceleration_mode(mode.c_str())); + } + + auto device_selector = std::make_shared(cfg_params); + auto source_ptr = cv::gapi::wip::make_onevpl_src(src, cfg_params, device_selector); + + // create VPP preproc engine + std::unique_ptr policy; + if (mode == "MFX_ACCEL_MODE_VIA_D3D11") { + policy.reset(new VPLDX11AccelerationPolicy(device_selector)); + } else if (mode == "MFX_ACCEL_MODE_VIA_VAAPI") { + policy.reset(new VPLVAAPIAccelerationPolicy(device_selector)); + } else if (mode.empty()){ + policy.reset(new VPLCPUAccelerationPolicy(device_selector)); + } else { + ASSERT_TRUE(false && "Unsupported acceleration policy type"); + } + VPPPreprocEngine preproc_engine(std::move(policy)); + cv::gapi::wip::Data out; + cv::util::optional empty_roi; + TEST_CYCLE() + { + source_ptr->pull(out); + cv::MediaFrame frame = cv::util::get(out); + cv::util::optional param = preproc_engine.is_applicable(frame); + pp_session sess = preproc_engine.initialize_preproc(param.value(), + required_frame_param); + (void)preproc_engine.run_sync(sess, frame, empty_roi); + } + + SANITY_CHECK_NOTHING(); +} + +static pp_out_param_t res_672x384 = pp_out_param_t {cv::MediaFormat::NV12, + {672, 384}}; +static pp_out_param_t res_336x256 = pp_out_param_t {cv::MediaFormat::NV12, + {336, 256}}; + +#ifdef __WIN32__ +INSTANTIATE_TEST_CASE_P(Streaming_Engine_PP_Bypass, OneVPLSourcePerf_PP_Engine_Bypass_Test, + Values(source_description_preproc_t(files[0], codec[0], "", res_672x384), + source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_D3D11", res_672x384), + source_description_preproc_t(files[1], codec[1], "", res_672x384), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_D3D11", res_672x384), + source_description_preproc_t(files[2], codec[2], "", res_336x256), + source_description_preproc_t(files[2], codec[2], "MFX_ACCEL_MODE_VIA_D3D11", res_336x256))); +#elif __linux__ +INSTANTIATE_TEST_CASE_P(Streaming_Engine_PP_Bypass, OneVPLSourcePerf_PP_Engine_Bypass_Test, + Values(source_description_preproc_t(files[0], codec[0], "MFX_ACCEL_MODE_VIA_VAAPI", res_672x384), + source_description_preproc_t(files[1], codec[1], "MFX_ACCEL_MODE_VIA_VAAPI", res_672x384))); +#endif } // namespace opencv_test #endif // HAVE_ONEVPL diff --git a/modules/gapi/samples/data/config_template.yml b/modules/gapi/samples/data/config_template.yml new file mode 100644 index 000000000000..75012d959d25 --- /dev/null +++ b/modules/gapi/samples/data/config_template.yml @@ -0,0 +1,192 @@ +%YAML:1.0 + +# Application running time in milliseconds: integer. +work_time: 2000 + +Pipelines: + PL1: + source: + name: 'Src' + latency: 33.0 + output: + dims: [1, 3, 1280, 720] + precision: 'U8' + + nodes: + - name: 'PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + edges: + - from: 'Src' + to: 'PP' + - from: 'PP' + to: 'Infer' + + # Path to the dump file (*.dot)' + dump: 'pl1.dot' + + PL2: + source: + name: 'Src' + latency: 50.0 + output: + dims: [1, 3, 1280, 720] + precision: 'U8' + + nodes: + - name: 'M1_PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'M1_Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + - name: 'M2_PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'M2_Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + - name: 'M3_PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'M3_Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + - name: 'M4_PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'M4_Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + - name: 'M5_PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'M5_Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + edges: + - from: 'Src' + to: 'M1_PP' + - from: 'M1_PP' + to: 'M1_Infer' + - from: 'M1_Infer' + to: 'M2_PP' + - from: 'M2_PP' + to: 'M2_Infer' + - from: 'M2_Infer' + to: 'M3_PP' + - from: 'M3_PP' + to: 'M3_Infer' + - from: 'M3_Infer' + to: 'M4_PP' + - from: 'M4_PP' + to: 'M4_Infer' + - from: 'M4_Infer' + to: 'M5_PP' + - from: 'M5_PP' + to: 'M5_Infer' + + dump: 'pl2.dot' + + PL3: + source: + name: 'Src' + latency: 33.0 + output: + dims: [1, 3, 1280, 720] + precision: 'U8' + + nodes: + - name: 'PP' + type: 'Dummy' + time: 0.2 + output: + dims: [1, 3, 300, 300] + precision: 'U8' + + - name: 'Infer' + type: 'Infer' + xml: 'face-detection-retail-0004.xml' + bin: 'face-detection-retail-0004.bin' + device: 'CPU' + input_layers: + - 'data' + output_layers: + - 'detection_out' + + edges: + - from: 'Src' + to: 'PP' + - from: 'PP' + to: 'Infer' + + dump: 'pl3.dot' diff --git a/modules/gapi/samples/face_detection_mtcnn.cpp b/modules/gapi/samples/face_detection_mtcnn.cpp index 50cb666a90f5..583219b51db3 100644 --- a/modules/gapi/samples/face_detection_mtcnn.cpp +++ b/modules/gapi/samples/face_detection_mtcnn.cpp @@ -33,7 +33,7 @@ const std::string keys = "{ thrr | 0.7 | MTCNN R confidence threshold}" "{ thro | 0.7 | MTCNN O confidence threshold}" "{ half_scale | false | MTCNN P use half scale pyramid}" -"{ queue_capacity | 1 | Streaming executor queue capacity. Calculated automaticaly if 0}" +"{ queue_capacity | 1 | Streaming executor queue capacity. Calculated automatically if 0}" ; namespace { @@ -488,8 +488,8 @@ static inline std::string get_pnet_level_name(const cv::Size &in_size) { } int calculate_scales(const cv::Size &input_size, std::vector &out_scales, std::vector &out_sizes ) { - //calculate multi - scale and limit the maxinum side to 1000 - //pr_scale: limit the maxinum side to 1000, < 1.0 + //calculate multi - scale and limit the maximum side to 1000 + //pr_scale: limit the maximum side to 1000, < 1.0 double pr_scale = 1.0; double h = static_cast(input_size.height); double w = static_cast(input_size.width); @@ -602,7 +602,7 @@ int main(int argc, char* argv[]) { cv::GArray final_p_faces_for_bb2squares = custom::ApplyRegression::on(faces0, true); cv::GArray final_faces_pnet0 = custom::BBoxesToSquares::on(final_p_faces_for_bb2squares); total_faces[0] = custom::RunNMS::on(final_faces_pnet0, 0.5f, false); - //The rest PNet pyramid layers to accumlate all layers result in total_faces[PYRAMID_LEVELS - 1]] + //The rest PNet pyramid layers to accumulate all layers result in total_faces[PYRAMID_LEVELS - 1]] for (int i = 1; i < pyramid_levels; ++i) { std::tie(regressions[i], scores[i]) = run_mtcnn_p(in_transposedRGB, get_pnet_level_name(level_size[i])); diff --git a/modules/gapi/samples/infer_single_roi.cpp b/modules/gapi/samples/infer_single_roi.cpp index e9c26a9b6322..15af442a9db1 100644 --- a/modules/gapi/samples/infer_single_roi.cpp +++ b/modules/gapi/samples/infer_single_roi.cpp @@ -150,7 +150,7 @@ int main(int argc, char *argv[]) auto networks = cv::gapi::networks(face_net); // Now build the graph. The graph structure may vary - // pased on the input parameters + // passed on the input parameters cv::GStreamingCompiled pipeline; auto inputs = cv::gin(cv::gapi::wip::make_src(input)); diff --git a/modules/gapi/samples/oak_basic_infer.cpp b/modules/gapi/samples/oak_basic_infer.cpp new file mode 100644 index 000000000000..ee234e38061b --- /dev/null +++ b/modules/gapi/samples/oak_basic_infer.cpp @@ -0,0 +1,122 @@ +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +const std::string keys = + "{ h help | | Print this help message }" + "{ detector | | Path to compiled .blob face detector model }" + "{ duration | 100 | Number of frames to pull from camera and run inference on }"; + +namespace custom { + +G_API_NET(FaceDetector, , "sample.custom.face-detector"); + +using GDetections = cv::GArray; +using GSize = cv::GOpaque; +using GPrims = cv::GArray; + +G_API_OP(BBoxes, , "sample.custom.b-boxes") { + static cv::GArrayDesc outMeta(const cv::GArrayDesc &) { + return cv::empty_array_desc(); + } +}; + +GAPI_OCV_KERNEL(OCVBBoxes, BBoxes) { + // This kernel converts the rectangles into G-API's + // rendering primitives + static void run(const std::vector &in_face_rcs, + std::vector &out_prims) { + out_prims.clear(); + const auto cvt = [](const cv::Rect &rc, const cv::Scalar &clr) { + return cv::gapi::wip::draw::Rect(rc, clr, 2); + }; + for (auto &&rc : in_face_rcs) { + out_prims.emplace_back(cvt(rc, CV_RGB(0,255,0))); // green + } + } +}; + +} // namespace custom + +int main(int argc, char *argv[]) { + cv::CommandLineParser cmd(argc, argv, keys); + if (cmd.has("help")) { + cmd.printMessage(); + return 0; + } + + const auto det_name = cmd.get("detector"); + const auto duration = cmd.get("duration"); + + if (det_name.empty()) { + std::cerr << "FATAL: path to detection model is not provided for the sample." + << "Please specify it with --detector options." + << std::endl; + return 1; + } + + // Prepare G-API kernels and networks packages: + auto detector = cv::gapi::oak::Params(det_name); + auto networks = cv::gapi::networks(detector); + + auto kernels = cv::gapi::combine( + cv::gapi::kernels(), + cv::gapi::oak::kernels()); + + auto args = cv::compile_args(kernels, networks); + + // Initialize graph structure + cv::GFrame in; + cv::GFrame copy = cv::gapi::oak::copy(in); // NV12 transfered to host + passthrough copy for infer + cv::GOpaque sz = cv::gapi::streaming::size(copy); + + // infer is not affected by the actual copy here + cv::GMat blob = cv::gapi::infer(copy); + // FIXME: OAK infer detects faces slightly out of frame bounds + cv::GArray rcs = cv::gapi::parseSSD(blob, sz, 0.5f, true, false); + auto rendered = cv::gapi::wip::draw::renderFrame(copy, custom::BBoxes::on(rcs)); + // on-the-fly conversion NV12->BGR + cv::GMat out = cv::gapi::streaming::BGR(rendered); + + auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out, rcs)) + .compileStreaming(std::move(args)); + + // Graph execution + pipeline.setSource(cv::gapi::wip::make_src()); + pipeline.start(); + + cv::Mat out_mat; + std::vector out_dets; + int frames = 0; + while (pipeline.pull(cv::gout(out_mat, out_dets))) { + std::string name = "oak_infer_frame_" + std::to_string(frames) + ".png"; + + cv::imwrite(name, out_mat); + + if (!out_dets.empty()) { + std::cout << "Got " << out_dets.size() << " detections on frame #" << frames << std::endl; + } + + ++frames; + if (frames == duration) { + pipeline.stop(); + break; + } + } + std::cout << "Pipeline finished. Processed " << frames << " frames" << std::endl; + return 0; +} diff --git a/modules/gapi/samples/oak_copy.cpp b/modules/gapi/samples/oak_copy.cpp new file mode 100644 index 000000000000..3c4d455f5203 --- /dev/null +++ b/modules/gapi/samples/oak_copy.cpp @@ -0,0 +1,48 @@ +#include +#include +#include +#include +#include + +#include +#include // BGR accessor + +#include // CommandLineParser + +const std::string keys = + "{ h help | | Print this help message }" + "{ output | output.png | Path to the output file }"; + +int main(int argc, char *argv[]) { + cv::CommandLineParser cmd(argc, argv, keys); + if (cmd.has("help")) { + cmd.printMessage(); + return 0; + } + + const std::string output_name = cmd.get("output"); + + cv::GFrame in; + // Actually transfers data to host + cv::GFrame copy = cv::gapi::oak::copy(in); + // Default camera works only with nv12 format + cv::GMat out = cv::gapi::streaming::Y(copy); + + auto args = cv::compile_args(cv::gapi::oak::ColorCameraParams{}, + cv::gapi::oak::kernels()); + + auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out)).compileStreaming(std::move(args)); + + // Graph execution ///////////////////////////////////////////////////////// + cv::Mat out_mat(1920, 1080, CV_8UC1); + + pipeline.setSource(cv::gapi::wip::make_src()); + pipeline.start(); + + // pull 1 frame + pipeline.pull(cv::gout(out_mat)); + + cv::imwrite(output_name, out_mat); + + std::cout << "Pipeline finished: " << output_name << " file has been written." << std::endl; +} diff --git a/modules/gapi/samples/oak_rgb_camera_encoding.cpp b/modules/gapi/samples/oak_rgb_camera_encoding.cpp new file mode 100644 index 000000000000..ee07ef09e4b2 --- /dev/null +++ b/modules/gapi/samples/oak_rgb_camera_encoding.cpp @@ -0,0 +1,60 @@ +#include + +#include +#include +#include + +#include +#include // BGR accessor + +#include // CommandLineParser + +const std::string keys = + "{ h help | | Print this help message }" + "{ output | output.h265 | Path to the output .h265 video file }"; + +int main(int argc, char *argv[]) { + cv::CommandLineParser cmd(argc, argv, keys); + if (cmd.has("help")) { + cmd.printMessage(); + return 0; + } + + const std::string output_name = cmd.get("output"); + + cv::gapi::oak::EncoderConfig cfg; + cfg.profile = cv::gapi::oak::EncoderConfig::Profile::H265_MAIN; + + cv::GFrame in; + cv::GArray encoded = cv::gapi::oak::encode(in, cfg); + + auto args = cv::compile_args(cv::gapi::oak::ColorCameraParams{}, cv::gapi::oak::kernels()); + + auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(encoded)).compileStreaming(std::move(args)); + + // Graph execution ///////////////////////////////////////////////////////// + pipeline.setSource(cv::gapi::wip::make_src()); + pipeline.start(); + + std::vector out_h265_data; + + std::ofstream out_h265_file; + out_h265_file.open(output_name, std::ofstream::out | std::ofstream::binary | std::ofstream::trunc); + + // Pull 300 frames from the camera + uint32_t frames = 300; + uint32_t pulled = 0; + + while (pipeline.pull(cv::gout(out_h265_data))) { + if (out_h265_file.is_open()) { + out_h265_file.write(reinterpret_cast(out_h265_data.data()), + out_h265_data.size()); + } + if (pulled++ == frames) { + pipeline.stop(); + break; + } + } + + std::cout << "Pipeline finished: " << output_name << " file has been written." << std::endl; +} diff --git a/modules/gapi/samples/oak_small_hetero_pipeline.cpp b/modules/gapi/samples/oak_small_hetero_pipeline.cpp new file mode 100644 index 000000000000..5df3ba5da133 --- /dev/null +++ b/modules/gapi/samples/oak_small_hetero_pipeline.cpp @@ -0,0 +1,58 @@ +#include +#include +#include +#include +#include + +#include +#include // BGR accessor + +#include // CommandLineParser + +const std::string keys = + "{ h help | | Print this help message }" + "{ output | output.png | Path to the output file }"; + +int main(int argc, char *argv[]) { + cv::CommandLineParser cmd(argc, argv, keys); + if (cmd.has("help")) { + cmd.printMessage(); + return 0; + } + + const std::string output_name = cmd.get("output"); + + std::vector h = {1, 0, -1, + 2, 0, -2, + 1, 0, -1}; + std::vector v = { 1, 2, 1, + 0, 0, 0, + -1, -2, -1}; + cv::Mat hk(3, 3, CV_32SC1, h.data()); + cv::Mat vk(3, 3, CV_32SC1, v.data()); + + // Heterogeneous pipeline: + // OAK camera -> Sobel -> streaming accessor (CPU) + cv::GFrame in; + cv::GFrame sobel = cv::gapi::oak::sobelXY(in, hk, vk); + // Default camera and then sobel work only with nv12 format + cv::GMat out = cv::gapi::streaming::Y(sobel); + + auto args = cv::compile_args(cv::gapi::oak::ColorCameraParams{}, + cv::gapi::oak::kernels()); + + auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out)).compileStreaming(std::move(args)); + + // Graph execution ///////////////////////////////////////////////////////// + cv::Mat out_mat(1920, 1080, CV_8UC1); + + pipeline.setSource(cv::gapi::wip::make_src()); + pipeline.start(); + + // pull 1 frame + pipeline.pull(cv::gout(out_mat)); + + cv::imwrite(output_name, out_mat); + + std::cout << "Pipeline finished: " << output_name << " file has been written." << std::endl; +} diff --git a/modules/gapi/samples/onevpl_infer_single_roi.cpp b/modules/gapi/samples/onevpl_infer_single_roi.cpp index 06950bcabe17..a89cd5071b7e 100644 --- a/modules/gapi/samples/onevpl_infer_single_roi.cpp +++ b/modules/gapi/samples/onevpl_infer_single_roi.cpp @@ -11,7 +11,6 @@ #include #include #include -#include #include // CommandLineParser #include @@ -44,24 +43,47 @@ const std::string keys = "{ facem | face-detection-adas-0001.xml | Path to OpenVINO IE face detection model (.xml) }" "{ faced | AUTO | Target device for face detection model (e.g. AUTO, GPU, VPU, ...) }" "{ cfg_params | :;: | Semicolon separated list of oneVPL mfxVariants which is used for configuring source (see `MFXSetConfigFilterProperty` by https://spec.oneapi.io/versions/latest/elements/oneVPL/source/index.html) }" - "{ streaming_queue_capacity | 1 | Streaming executor queue capacity. Calculated automaticaly if 0 }" - "{ frames_pool_size | 0 | OneVPL source applies this parameter as preallocated frames pool size}"; - + "{ streaming_queue_capacity | 1 | Streaming executor queue capacity. Calculated automatically if 0 }" + "{ frames_pool_size | 0 | OneVPL source applies this parameter as preallocated frames pool size}" + "{ vpp_frames_pool_size | 0 | OneVPL source applies this parameter as preallocated frames pool size for VPP preprocessing results}" + "{ roi | -1,-1,-1,-1 | Region of interest (ROI) to use for inference. Identified automatically when not set }"; namespace { std::string get_weights_path(const std::string &model_path) { const auto EXT_LEN = 4u; const auto sz = model_path.size(); - CV_Assert(sz > EXT_LEN); + GAPI_Assert(sz > EXT_LEN); auto ext = model_path.substr(sz - EXT_LEN); std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){ return static_cast(std::tolower(c)); }); - CV_Assert(ext == ".xml"); + GAPI_Assert(ext == ".xml"); return model_path.substr(0u, sz - EXT_LEN) + ".bin"; } +// TODO: It duplicates infer_single_roi sample +cv::util::optional parse_roi(const std::string &rc) { + cv::Rect rv; + char delim[3]; + + std::stringstream is(rc); + is >> rv.x >> delim[0] >> rv.y >> delim[1] >> rv.width >> delim[2] >> rv.height; + if (is.bad()) { + return cv::util::optional(); // empty value + } + const auto is_delim = [](char c) { + return c == ','; + }; + if (!std::all_of(std::begin(delim), std::end(delim), is_delim)) { + return cv::util::optional(); // empty value + } + if (rv.x < 0 || rv.y < 0 || rv.width <= 0 || rv.height <= 0) { + return cv::util::optional(); // empty value + } + return cv::util::make_optional(std::move(rv)); +} + #ifdef HAVE_INF_ENGINE #ifdef HAVE_DIRECTX #ifdef HAVE_D3D11 @@ -123,6 +145,13 @@ using GRect = cv::GOpaque; using GSize = cv::GOpaque; using GPrims = cv::GArray; +G_API_OP(ParseSSD, , "sample.custom.parse-ssd") { + static cv::GArrayDesc outMeta(const cv::GMatDesc &, const cv::GOpaqueDesc &, const cv::GOpaqueDesc &) { + return cv::empty_array_desc(); + } +}; + +// TODO: It duplicates infer_single_roi sample G_API_OP(LocateROI, , "sample.custom.locate-roi") { static cv::GOpaqueDesc outMeta(const cv::GOpaqueDesc &) { return cv::empty_gopaque_desc(); @@ -145,7 +174,8 @@ GAPI_OCV_KERNEL(OCVLocateROI, LocateROI) { // but only crops the input image to square (this is // the most convenient aspect ratio for detectors to use) - static void run(const cv::Size& in_size, cv::Rect &out_rect) { + static void run(const cv::Size& in_size, + cv::Rect &out_rect) { // Identify the central point & square size (- some padding) const auto center = cv::Point{in_size.width/2, in_size.height/2}; @@ -153,10 +183,10 @@ GAPI_OCV_KERNEL(OCVLocateROI, LocateROI) { // Now build the central square ROI out_rect = cv::Rect{ center.x - sqside/2 - , center.y - sqside/2 - , sqside - , sqside - }; + , center.y - sqside/2 + , sqside + , sqside + }; } }; @@ -177,6 +207,55 @@ GAPI_OCV_KERNEL(OCVBBoxes, BBoxes) { } }; +GAPI_OCV_KERNEL(OCVParseSSD, ParseSSD) { + static void run(const cv::Mat &in_ssd_result, + const cv::Rect &in_roi, + const cv::Size &in_parent_size, + std::vector &out_objects) { + const auto &in_ssd_dims = in_ssd_result.size; + GAPI_Assert(in_ssd_dims.dims() == 4u); + + const int MAX_PROPOSALS = in_ssd_dims[2]; + const int OBJECT_SIZE = in_ssd_dims[3]; + GAPI_Assert(OBJECT_SIZE == 7); // fixed SSD object size + + const cv::Size up_roi = in_roi.size(); + const cv::Rect surface({0,0}, in_parent_size); + + out_objects.clear(); + + const float *data = in_ssd_result.ptr(); + for (int i = 0; i < MAX_PROPOSALS; i++) { + const float image_id = data[i * OBJECT_SIZE + 0]; + const float label = data[i * OBJECT_SIZE + 1]; + const float confidence = data[i * OBJECT_SIZE + 2]; + const float rc_left = data[i * OBJECT_SIZE + 3]; + const float rc_top = data[i * OBJECT_SIZE + 4]; + const float rc_right = data[i * OBJECT_SIZE + 5]; + const float rc_bottom = data[i * OBJECT_SIZE + 6]; + (void) label; // unused + + if (image_id < 0.f) { + break; // marks end-of-detections + } + if (confidence < 0.5f) { + continue; // skip objects with low confidence + } + + // map relative coordinates to the original image scale + // taking the ROI into account + cv::Rect rc; + rc.x = static_cast(rc_left * up_roi.width); + rc.y = static_cast(rc_top * up_roi.height); + rc.width = static_cast(rc_right * up_roi.width) - rc.x; + rc.height = static_cast(rc_bottom * up_roi.height) - rc.y; + rc.x += in_roi.x; + rc.y += in_roi.y; + out_objects.emplace_back(rc & surface); + } + } +}; + } // namespace custom namespace cfg { @@ -193,13 +272,16 @@ int main(int argc, char *argv[]) { } // get file name - std::string file_path = cmd.get("input"); - const std::string output = cmd.get("output"); + const auto file_path = cmd.get("input"); + const auto output = cmd.get("output"); + const auto opt_roi = parse_roi(cmd.get("roi")); const auto face_model_path = cmd.get("facem"); const auto streaming_queue_capacity = cmd.get("streaming_queue_capacity"); - const auto source_queue_capacity = cmd.get("frames_pool_size"); + const auto source_decode_queue_capacity = cmd.get("frames_pool_size"); + const auto source_vpp_queue_capacity = cmd.get("vpp_frames_pool_size"); + const auto device_id = cmd.get("faced"); - // check ouput file extension + // check output file extension if (!output.empty()) { auto ext = output.find_last_of("."); if (ext == std::string::npos || (output.substr(ext + 1) != "avi")) { @@ -221,11 +303,13 @@ int main(int argc, char *argv[]) { return -1; } - if (source_queue_capacity != 0) { - source_cfgs.push_back(cv::gapi::wip::onevpl::CfgParam::create_frames_pool_size(source_queue_capacity)); + if (source_decode_queue_capacity != 0) { + source_cfgs.push_back(cv::gapi::wip::onevpl::CfgParam::create_frames_pool_size(source_decode_queue_capacity)); + } + if (source_vpp_queue_capacity != 0) { + source_cfgs.push_back(cv::gapi::wip::onevpl::CfgParam::create_vpp_frames_pool_size(source_vpp_queue_capacity)); } - const std::string& device_id = cmd.get("faced"); auto face_net = cv::gapi::ie::Params { face_model_path, // path to topology IR get_weights_path(face_model_path), // path to weights @@ -238,8 +322,8 @@ int main(int argc, char *argv[]) { // GAPI InferenceEngine backend to provide interoperability with onevpl::GSource // So GAPI InferenceEngine backend and onevpl::GSource MUST share the same // device and context - void* accel_device_ptr = nullptr; - void* accel_ctx_ptr = nullptr; + cv::util::optional accel_device; + cv::util::optional accel_ctx; #ifdef HAVE_INF_ENGINE #ifdef HAVE_DIRECTX @@ -281,8 +365,13 @@ int main(int argc, char *argv[]) { } std::tie(dx11_dev, dx11_ctx) = create_device_with_ctx(intel_adapter.get()); - accel_device_ptr = reinterpret_cast(dx11_dev.get()); - accel_ctx_ptr = reinterpret_cast(dx11_ctx.get()); + accel_device = cv::util::make_optional( + cv::gapi::wip::onevpl::create_dx11_device( + reinterpret_cast(dx11_dev.get()), + device_id)); + accel_ctx = cv::util::make_optional( + cv::gapi::wip::onevpl::create_dx11_context( + reinterpret_cast(dx11_ctx.get()))); // put accel type description for VPL source source_cfgs.push_back(cfg::create_from_string( @@ -294,16 +383,27 @@ int main(int argc, char *argv[]) { #endif // HAVE_D3D11 #endif // HAVE_DIRECTX // set ctx_config for GPU device only - no need in case of CPU device type - if (device_id.find("GPU") != std::string::npos) { + if (accel_device.has_value() && + accel_device.value().get_name().find("GPU") != std::string::npos) { InferenceEngine::ParamMap ctx_config({{"CONTEXT_TYPE", "VA_SHARED"}, - {"VA_DEVICE", accel_device_ptr} }); - + {"VA_DEVICE", accel_device.value().get_ptr()} }); face_net.cfgContextParams(ctx_config); + + // NB: consider NV12 surface because it's one of native GPU image format + face_net.pluginConfig({{"GPU_NV12_TWO_INPUTS", "YES" }}); } #endif // HAVE_INF_ENGINE + // turn on preproc + if (accel_device.has_value() && accel_ctx.has_value()) { + face_net.cfgPreprocessingParams(accel_device.value(), + accel_ctx.value()); + std::cout << "enforce VPP preprocessing on " << device_id << std::endl; + } + auto kernels = cv::gapi::kernels < custom::OCVLocateROI + , custom::OCVParseSSD , custom::OCVBBoxes>(); auto networks = cv::gapi::networks(face_net); auto face_detection_args = cv::compile_args(networks, kernels); @@ -312,17 +412,16 @@ int main(int argc, char *argv[]) { } // Create source - cv::Ptr cap; + cv::gapi::wip::IStreamSource::Ptr cap; try { - if (device_id.find("GPU") != std::string::npos) { + if (accel_device.has_value() && accel_ctx.has_value()) { cap = cv::gapi::wip::make_onevpl_src(file_path, source_cfgs, - device_id, - accel_device_ptr, - accel_ctx_ptr); + accel_device.value(), + accel_ctx.value()); } else { cap = cv::gapi::wip::make_onevpl_src(file_path, source_cfgs); } - std::cout << "oneVPL source desription: " << cap->descr_of() << std::endl; + std::cout << "oneVPL source description: " << cap->descr_of() << std::endl; } catch (const std::exception& ex) { std::cerr << "Cannot create source: " << ex.what() << std::endl; return -1; @@ -330,29 +429,35 @@ int main(int argc, char *argv[]) { cv::GMetaArg descr = cap->descr_of(); auto frame_descr = cv::util::get(descr); + cv::GOpaque in_roi; + auto inputs = cv::gin(cap); // Now build the graph cv::GFrame in; auto size = cv::gapi::streaming::size(in); - auto roi = custom::LocateROI::on(size); - auto blob = cv::gapi::infer(roi, in); - cv::GArray rcs = cv::gapi::parseSSD(blob, size, 0.5f, true, true); - auto out_frame = cv::gapi::wip::draw::renderFrame(in, custom::BBoxes::on(rcs, roi)); - auto out = cv::gapi::streaming::BGR(out_frame); - - cv::GStreamingCompiled pipeline; - try { - pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out)) - .compileStreaming(std::move(face_detection_args)); - } catch (const std::exception& ex) { - std::cerr << "Exception occured during pipeline construction: " << ex.what() << std::endl; - return -1; + auto graph_inputs = cv::GIn(in); + if (!opt_roi.has_value()) { + // Automatically detect ROI to infer. Make it output parameter + std::cout << "ROI is not set or invalid. Locating it automatically" + << std::endl; + in_roi = custom::LocateROI::on(size); + } else { + // Use the value provided by user + std::cout << "Will run inference for static region " + << opt_roi.value() + << " only" + << std::endl; + graph_inputs += cv::GIn(in_roi); + inputs += cv::gin(opt_roi.value()); } + auto blob = cv::gapi::infer(in_roi, in); + cv::GArray rcs = custom::ParseSSD::on(blob, in_roi, size); + auto out_frame = cv::gapi::wip::draw::renderFrame(in, custom::BBoxes::on(rcs, in_roi)); + auto out = cv::gapi::streaming::BGR(out_frame); + cv::GStreamingCompiled pipeline = cv::GComputation(std::move(graph_inputs), cv::GOut(out)) // and move here + .compileStreaming(std::move(face_detection_args)); // The execution part - - // TODO USE may set pool size from outside and set queue_capacity size, - // compile arg: cv::gapi::streaming::queue_capacity - pipeline.setSource(std::move(cap)); + pipeline.setSource(std::move(inputs)); pipeline.start(); size_t frames = 0u; @@ -361,7 +466,7 @@ int main(int argc, char *argv[]) { if (!output.empty() && !writer.isOpened()) { const auto sz = cv::Size{frame_descr.size.width, frame_descr.size.height}; writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz); - CV_Assert(writer.isOpened()); + GAPI_Assert(writer.isOpened()); } cv::Mat outMat; @@ -376,6 +481,7 @@ int main(int argc, char *argv[]) { } tm.stop(); std::cout << "Processed " << frames << " frames" << " (" << frames / tm.getTimeSec() << " FPS)" << std::endl; + return 0; } @@ -397,6 +503,8 @@ typename cv::gapi::wip::onevpl::CfgParam create_from_string(const std::string &l std::string name = line.substr(0, name_endline_pos); std::string value = line.substr(name_endline_pos + 1); - return cv::gapi::wip::onevpl::CfgParam::create(name, value); + return cv::gapi::wip::onevpl::CfgParam::create(name, value, + /* vpp params strongly optional */ + name.find("vpp.") == std::string::npos); } } diff --git a/modules/gapi/samples/pipeline_modeling_tool.cpp b/modules/gapi/samples/pipeline_modeling_tool.cpp new file mode 100644 index 000000000000..7a0f94655c56 --- /dev/null +++ b/modules/gapi/samples/pipeline_modeling_tool.cpp @@ -0,0 +1,465 @@ +#include +#include +#include +#include +#include +#include + +#include +#include // cv::CommandLineParser +#include + +#if defined(_WIN32) +#define NOMINMAX +#include +#undef NOMINMAX +#endif + +#include "pipeline_modeling_tool/dummy_source.hpp" +#include "pipeline_modeling_tool/utils.hpp" +#include "pipeline_modeling_tool/pipeline_builder.hpp" + +enum class AppMode { + REALTIME, + BENCHMARK +}; + +static AppMode strToAppMode(const std::string& mode_str) { + if (mode_str == "realtime") { + return AppMode::REALTIME; + } else if (mode_str == "benchmark") { + return AppMode::BENCHMARK; + } else { + throw std::logic_error("Unsupported AppMode: " + mode_str + + "\nPlease chose between: realtime and benchmark"); + } +} + +template +T read(const cv::FileNode& node) { + return static_cast(node); +} + +static cv::FileNode check_and_get_fn(const cv::FileNode& fn, + const std::string& field, + const std::string& uplvl) { + const bool is_map = fn.isMap(); + if (!is_map || fn[field].empty()) { + throw std::logic_error(uplvl + " must contain field: " + field); + } + return fn[field]; +} + +static cv::FileNode check_and_get_fn(const cv::FileStorage& fs, + const std::string& field, + const std::string& uplvl) { + auto fn = fs[field]; + if (fn.empty()) { + throw std::logic_error(uplvl + " must contain field: " + field); + } + return fn; +} + +template +T check_and_read(const FileT& f, + const std::string& field, + const std::string& uplvl) { + auto fn = check_and_get_fn(f, field, uplvl); + return read(fn); +} + +template +cv::optional readOpt(const cv::FileNode& fn) { + return fn.empty() ? cv::optional() : cv::optional(read(fn)); +} + +template +std::vector readList(const cv::FileNode& fn, + const std::string& field, + const std::string& uplvl) { + auto fn_field = check_and_get_fn(fn, field, uplvl); + if (!fn_field.isSeq()) { + throw std::logic_error(field + " in " + uplvl + " must be a sequence"); + } + + std::vector vec; + for (auto iter : fn_field) { + vec.push_back(read(iter)); + } + return vec; +} + +template +std::vector readVec(const cv::FileNode& fn, + const std::string& field, + const std::string& uplvl) { + auto fn_field = check_and_get_fn(fn, field, uplvl); + + std::vector vec; + fn_field >> vec; + return vec; +} + +static int strToPrecision(const std::string& precision) { + static std::unordered_map str_to_precision = { + {"U8", CV_8U}, {"FP32", CV_32F}, {"FP16", CV_16F} + }; + auto it = str_to_precision.find(precision); + if (it == str_to_precision.end()) { + throw std::logic_error("Unsupported precision: " + precision); + } + return it->second; +} + +template <> +OutputDescr read(const cv::FileNode& fn) { + auto dims = readVec(fn, "dims", "output"); + auto str_prec = check_and_read(fn, "precision", "output"); + return OutputDescr{dims, strToPrecision(str_prec)}; +} + +template <> +Edge read(const cv::FileNode& fn) { + auto from = check_and_read(fn, "from", "edge"); + auto to = check_and_read(fn, "to", "edge"); + + auto splitNameAndPort = [](const std::string& str) { + auto pos = str.find(':'); + auto name = + pos == std::string::npos ? str : std::string(str.c_str(), pos); + size_t port = + pos == std::string::npos ? 0 : std::atoi(str.c_str() + pos + 1); + return std::make_pair(name, port); + }; + + auto p1 = splitNameAndPort(from); + auto p2 = splitNameAndPort(to); + return Edge{Edge::P{p1.first, p1.second}, Edge::P{p2.first, p2.second}}; +} + +static std::string getModelsPath() { + static char* models_path_c = std::getenv("PIPELINE_MODELS_PATH"); + static std::string models_path = models_path_c ? models_path_c : "."; + return models_path; +} + +template <> +ModelPath read(const cv::FileNode& fn) { + using cv::utils::fs::join; + if (!fn["xml"].empty() && !fn["bin"].empty()) { + return ModelPath{LoadPath{join(getModelsPath(), fn["xml"].string()), + join(getModelsPath(), fn["bin"].string())}}; + } else if (!fn["blob"].empty()){ + return ModelPath{ImportPath{join(getModelsPath(), fn["blob"].string())}}; + } else { + const std::string emsg = R""""( + Path to OpenVINO model must be specified in either of two formats: +1. + xml: path to *.xml + bin: path to *.bin +2. + blob: path to *.blob + )""""; + throw std::logic_error(emsg); + } +} + +static PLMode strToPLMode(const std::string& mode_str) { + if (mode_str == "streaming") { + return PLMode::STREAMING; + } else if (mode_str == "regular") { + return PLMode::REGULAR; + } else { + throw std::logic_error("Unsupported PLMode: " + mode_str + + "\nPlease chose between: streaming and regular"); + } +} + +template <> +CallParams read(const cv::FileNode& fn) { + auto name = + check_and_read(fn, "name", "node"); + // FIXME: Impossible to read size_t due OpenCV limitations. + auto call_every_nth_opt = readOpt(fn["call_every_nth"]); + auto call_every_nth = call_every_nth_opt.value_or(1); + if (call_every_nth <= 0) { + throw std::logic_error( + name + " call_every_nth must be greater than zero\n" + "Current call_every_nth: " + std::to_string(call_every_nth)); + } + return CallParams{std::move(name), static_cast(call_every_nth)}; +} + +template <> +InferParams read(const cv::FileNode& fn) { + auto name = + check_and_read(fn, "name", "node"); + + InferParams params; + params.path = read(fn); + params.device = check_and_read(fn, "device", name); + params.input_layers = readList(fn, "input_layers", name); + params.output_layers = readList(fn, "output_layers", name); + + return params; +} + +template <> +DummyParams read(const cv::FileNode& fn) { + auto name = + check_and_read(fn, "name", "node"); + + DummyParams params; + params.time = check_and_read(fn, "time", name); + if (params.time < 0) { + throw std::logic_error(name + " time must be positive"); + } + params.output = check_and_read(fn, "output", name); + + return params; +} + +static std::vector parseExecList(const std::string& exec_list) { + std::vector pl_types; + std::stringstream ss(exec_list); + std::string pl_type; + while (getline(ss, pl_type, ',')) { + pl_types.push_back(pl_type); + } + return pl_types; +} + +static void loadConfig(const std::string& filename, + std::map& config) { + cv::FileStorage fs(filename, cv::FileStorage::READ); + if (!fs.isOpened()) { + throw std::runtime_error("Failed to load config: " + filename); + } + + cv::FileNode root = fs.root(); + for (auto it = root.begin(); it != root.end(); ++it) { + auto device = *it; + if (!device.isMap()) { + throw std::runtime_error("Failed to parse config: " + filename); + } + for (auto item : device) { + config.emplace(item.name(), item.string()); + } + } +} + +int main(int argc, char* argv[]) { +#if defined(_WIN32) + timeBeginPeriod(1); +#endif + try { + const std::string keys = + "{ h help | | Print this help message. }" + "{ cfg | | Path to the config which is either" + " YAML file or string. }" + "{ load_config | | Optional. Path to XML/YAML/JSON file" + " to load custom IE parameters. }" + "{ cache_dir | | Optional. Enables caching of loaded models" + " to specified directory. }" + "{ log_file | | Optional. If file is specified, app will" + " dump expanded execution information. }" + "{ pl_mode | streaming | Optional. Pipeline mode: streaming/regular" + " if it's specified will be applied for" + " every pipeline. }" + "{ qc | 1 | Optional. Calculated automatically by G-API" + " if set to 0. If it's specified will be" + " applied for every pipeline. }" + "{ app_mode | realtime | Application mode (realtime/benchmark). }" + "{ drop_frames | false | Drop frames if they come earlier than pipeline is completed. }" + "{ exec_list | | A comma-separated list of pipelines that" + " will be executed. Spaces around commas" + " are prohibited. }"; + + cv::CommandLineParser cmd(argc, argv, keys); + if (cmd.has("help")) { + cmd.printMessage(); + return 0; + } + + const auto cfg = cmd.get("cfg"); + const auto load_config = cmd.get("load_config"); + const auto cached_dir = cmd.get("cache_dir"); + const auto log_file = cmd.get("log_file"); + const auto cmd_pl_mode = strToPLMode(cmd.get("pl_mode")); + const auto qc = cmd.get("qc"); + const auto app_mode = strToAppMode(cmd.get("app_mode")); + const auto exec_str = cmd.get("exec_list"); + const auto drop_frames = cmd.get("drop_frames"); + + cv::FileStorage fs; + if (cfg.empty()) { + throw std::logic_error("Config must be specified via --cfg option"); + } + // NB: *.yml + if (cfg.size() < 5) { + throw std::logic_error("--cfg string must contain at least 5 symbols" + " to determine if it's a file (*.yml) a or string"); + } + if (cfg.substr(cfg.size() - 4, cfg.size()) == ".yml") { + if (!fs.open(cfg, cv::FileStorage::READ)) { + throw std::logic_error("Failed to open config file: " + cfg); + } + } else { + fs = cv::FileStorage(cfg, cv::FileStorage::FORMAT_YAML | + cv::FileStorage::MEMORY); + } + + std::map config; + if (!load_config.empty()) { + loadConfig(load_config, config); + } + // NB: Takes priority over config from file + if (!cached_dir.empty()) { + config = + std::map{{"CACHE_DIR", cached_dir}}; + } + + const double work_time_ms = + check_and_read(fs, "work_time", "Config"); + if (work_time_ms < 0) { + throw std::logic_error("work_time must be positive"); + } + + auto pipelines_fn = check_and_get_fn(fs, "Pipelines", "Config"); + if (!pipelines_fn.isMap()) { + throw std::logic_error("Pipelines field must be a map"); + } + + auto exec_list = !exec_str.empty() ? parseExecList(exec_str) + : pipelines_fn.keys(); + + + std::vector pipelines; + pipelines.reserve(exec_list.size()); + // NB: Build pipelines based on config information + PipelineBuilder builder; + for (const auto& name : exec_list) { + const auto& pl_fn = check_and_get_fn(pipelines_fn, name, "Pipelines"); + builder.setName(name); + // NB: Set source + { + const auto& src_fn = check_and_get_fn(pl_fn, "source", name); + auto src_name = + check_and_read(src_fn, "name", "source"); + auto latency = + check_and_read(src_fn, "latency", "source"); + auto output = + check_and_read(src_fn, "output", "source"); + // NB: In case BENCHMARK mode sources work with zero latency. + if (app_mode == AppMode::BENCHMARK) { + latency = 0.0; + } + auto src = std::make_shared(latency, output, drop_frames); + builder.setSource(src_name, src); + } + + const auto& nodes_fn = check_and_get_fn(pl_fn, "nodes", name); + if (!nodes_fn.isSeq()) { + throw std::logic_error("nodes in " + name + " must be a sequence"); + } + + for (auto node_fn : nodes_fn) { + auto call_params = read(node_fn); + auto node_type = + check_and_read(node_fn, "type", "node"); + if (node_type == "Dummy") { + builder.addDummy(call_params, read(node_fn)); + } else if (node_type == "Infer") { + auto infer_params = read(node_fn); + infer_params.config = config; + builder.addInfer(call_params, infer_params); + } else { + throw std::logic_error("Unsupported node type: " + node_type); + } + } + + const auto edges_fn = check_and_get_fn(pl_fn, "edges", name); + if (!edges_fn.isSeq()) { + throw std::logic_error("edges in " + name + " must be a sequence"); + } + for (auto edge_fn : edges_fn) { + auto edge = read(edge_fn); + builder.addEdge(edge); + } + + auto cfg_pl_mode = readOpt(pl_fn["mode"]); + // NB: Pipeline mode from config takes priority over cmd. + auto pl_mode = cfg_pl_mode.has_value() + ? strToPLMode(cfg_pl_mode.value()) : cmd_pl_mode; + // NB: Using drop_frames with streaming pipelines will follow to + // incorrect performance results. + if (drop_frames && pl_mode == PLMode::STREAMING) { + throw std::logic_error( + "--drop_frames option is supported only for pipelines in \"regular\" mode"); + } + + builder.setMode(pl_mode); + + // NB: Queue capacity from config takes priority over cmd. + auto config_qc = readOpt(pl_fn["queue_capacity"]); + auto queue_capacity = config_qc.has_value() ? config_qc.value() : qc; + // NB: 0 is special constant that means + // queue capacity should be calculated automatically. + if (queue_capacity != 0) { + builder.setQueueCapacity(queue_capacity); + } + + auto dump = readOpt(pl_fn["dump"]); + if (dump) { + builder.setDumpFilePath(dump.value()); + } + + pipelines.emplace_back(builder.build()); + } + + // NB: Compille pipelines + for (size_t i = 0; i < pipelines.size(); ++i) { + pipelines[i]->compile(); + } + + // NB: Execute pipelines + std::vector eptrs(pipelines.size(), nullptr); + std::vector threads(pipelines.size()); + for (size_t i = 0; i < pipelines.size(); ++i) { + threads[i] = std::thread([&, i]() { + try { + pipelines[i]->run(work_time_ms); + } catch (...) { + eptrs[i] = std::current_exception(); + } + }); + } + + std::ofstream file; + if (!log_file.empty()) { + file.open(log_file); + } + + for (size_t i = 0; i < threads.size(); ++i) { + threads[i].join(); + } + + for (size_t i = 0; i < threads.size(); ++i) { + if (eptrs[i] != nullptr) { + try { + std::rethrow_exception(eptrs[i]); + } catch (std::exception& e) { + throw std::logic_error(pipelines[i]->name() + " failed: " + e.what()); + } + } + if (file.is_open()) { + file << pipelines[i]->report().toStr(true) << std::endl; + } + std::cout << pipelines[i]->report().toStr() << std::endl; + } + } catch (const std::exception& e) { + std::cout << e.what() << std::endl; + throw; + } + return 0; +} diff --git a/modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp b/modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp new file mode 100644 index 000000000000..d77e12008189 --- /dev/null +++ b/modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp @@ -0,0 +1,102 @@ +#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP +#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP + +#include +#include +#include + +#include +#include // cv::gapi::wip::IStreamSource + +#include "utils.hpp" + +class DummySource final: public cv::gapi::wip::IStreamSource { +public: + using Ptr = std::shared_ptr; + DummySource(const double latency, + const OutputDescr& output, + const bool drop_frames); + bool pull(cv::gapi::wip::Data& data) override; + cv::GMetaArg descr_of() const override; + +private: + double m_latency; + cv::Mat m_mat; + bool m_drop_frames; + double m_next_tick_ts = -1; + int64_t m_curr_seq_id = 0; +}; + +DummySource::DummySource(const double latency, + const OutputDescr& output, + const bool drop_frames) + : m_latency(latency), m_drop_frames(drop_frames) { + utils::createNDMat(m_mat, output.dims, output.precision); + utils::generateRandom(m_mat); +} + +bool DummySource::pull(cv::gapi::wip::Data& data) { + using namespace std::chrono; + using namespace cv::gapi::streaming; + + // NB: Wait m_latency before return the first frame. + if (m_next_tick_ts == -1) { + m_next_tick_ts = utils::timestamp() + m_latency; + } + + int64_t curr_ts = utils::timestamp(); + if (curr_ts < m_next_tick_ts) { + /* + * curr_ts + * | + * ------|----*-----|-------> + * ^ + * m_next_tick_ts + * + * + * NB: New frame will be produced at the m_next_tick_ts point. + */ + utils::sleep(m_next_tick_ts - curr_ts); + } else { + /* + * curr_ts + * +1 +2 | + * |----------|----------|----------|----*-----|-------> + * ^ ^ + * m_next_tick_ts -------------> + * + * + * NB: Shift m_next_tick_ts to the nearest tick before curr_ts and + * update current seq_id correspondingly. + * + * if drop_frames is enabled, wait for the next tick, otherwise + * return last written frame (+2 at the picture above) immediately. + */ + int64_t num_frames = + static_cast((curr_ts - m_next_tick_ts) / m_latency); + m_curr_seq_id += num_frames; + m_next_tick_ts += num_frames * m_latency; + if (m_drop_frames) { + m_next_tick_ts += m_latency; + ++m_curr_seq_id; + utils::sleep(m_next_tick_ts - curr_ts); + } + } + + // NB: Just increase reference counter not to release mat memory + // after assigning it to the data. + cv::Mat mat = m_mat; + + data.meta[meta_tag::timestamp] = utils::timestamp(); + data.meta[meta_tag::seq_id] = m_curr_seq_id++; + data = mat; + m_next_tick_ts += m_latency; + + return true; +} + +cv::GMetaArg DummySource::descr_of() const { + return cv::GMetaArg{cv::descr_of(m_mat)}; +} + +#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP diff --git a/modules/gapi/samples/pipeline_modeling_tool/pipeline.hpp b/modules/gapi/samples/pipeline_modeling_tool/pipeline.hpp new file mode 100644 index 000000000000..2951d456105a --- /dev/null +++ b/modules/gapi/samples/pipeline_modeling_tool/pipeline.hpp @@ -0,0 +1,205 @@ +#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_HPP +#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_HPP + +struct PerfReport { + std::string name; + double avg_latency = 0.0; + double throughput = 0.0; + int64_t first_run_latency = 0; + int64_t elapsed = 0; + int64_t compilation_time = 0; + std::vector latencies; + + std::string toStr(bool expanded = false) const; +}; + +std::string PerfReport::toStr(bool expand) const { + std::stringstream ss; + ss << name << ": Compilation time: " << compilation_time << " ms; " + << "Average latency: " << avg_latency << " ms; Throughput: " + << throughput << " FPS; First latency: " + << first_run_latency << " ms"; + + if (expand) { + ss << "\nTotal processed frames: " << latencies.size() + << "\nTotal elapsed time: " << elapsed << " ms" << std::endl; + for (size_t i = 0; i < latencies.size(); ++i) { + ss << std::endl; + ss << "Frame:" << i << "\nLatency: " + << latencies[i] << " ms"; + } + } + + return ss.str(); +} + +class Pipeline { +public: + using Ptr = std::shared_ptr; + + Pipeline(std::string&& name, + cv::GComputation&& comp, + cv::gapi::wip::IStreamSource::Ptr&& src, + cv::GCompileArgs&& args, + const size_t num_outputs); + + void compile(); + void run(double work_time_ms); + const PerfReport& report() const; + const std::string& name() const { return m_name;} + + virtual ~Pipeline() = default; + +protected: + struct RunPerf { + int64_t elapsed = 0; + std::vector latencies; + }; + + virtual void _compile() = 0; + virtual RunPerf _run(double work_time_ms) = 0; + + std::string m_name; + cv::GComputation m_comp; + cv::gapi::wip::IStreamSource::Ptr m_src; + cv::GCompileArgs m_args; + size_t m_num_outputs; + PerfReport m_perf; +}; + +Pipeline::Pipeline(std::string&& name, + cv::GComputation&& comp, + cv::gapi::wip::IStreamSource::Ptr&& src, + cv::GCompileArgs&& args, + const size_t num_outputs) + : m_name(std::move(name)), + m_comp(std::move(comp)), + m_src(std::move(src)), + m_args(std::move(args)), + m_num_outputs(num_outputs) { + m_perf.name = m_name; +} + +void Pipeline::compile() { + m_perf.compilation_time = + utils::measure([this]() { + _compile(); + }); +} + +void Pipeline::run(double work_time_ms) { + auto run_perf = _run(work_time_ms); + + m_perf.elapsed = run_perf.elapsed; + m_perf.latencies = std::move(run_perf.latencies); + + m_perf.avg_latency = + std::accumulate(m_perf.latencies.begin(), + m_perf.latencies.end(), + 0.0) / static_cast(m_perf.latencies.size()); + m_perf.throughput = + (m_perf.latencies.size() / static_cast(m_perf.elapsed)) * 1000; + + m_perf.first_run_latency = m_perf.latencies[0]; +} + +const PerfReport& Pipeline::report() const { + return m_perf; +} + +class StreamingPipeline : public Pipeline { +public: + using Pipeline::Pipeline; + +private: + void _compile() override { + m_compiled = + m_comp.compileStreaming({m_src->descr_of()}, + cv::GCompileArgs(m_args)); + } + + Pipeline::RunPerf _run(double work_time_ms) override { + // NB: Setup. + using namespace std::chrono; + // NB: N-1 buffers + timestamp. + std::vector out_mats(m_num_outputs - 1); + int64_t start_ts = -1; + cv::GRunArgsP pipeline_outputs; + for (auto& m : out_mats) { + pipeline_outputs += cv::gout(m); + } + pipeline_outputs += cv::gout(start_ts); + m_compiled.setSource(m_src); + + // NB: Start execution & measure performance statistics. + Pipeline::RunPerf perf; + auto start = high_resolution_clock::now(); + m_compiled.start(); + while (m_compiled.pull(cv::GRunArgsP{pipeline_outputs})) { + int64_t latency = utils::timestamp() - start_ts; + + perf.latencies.push_back(latency); + perf.elapsed = duration_cast( + high_resolution_clock::now() - start).count(); + + if (perf.elapsed >= work_time_ms) { + m_compiled.stop(); + break; + } + }; + return perf; + } + + cv::GStreamingCompiled m_compiled; +}; + +class RegularPipeline : public Pipeline { +public: + using Pipeline::Pipeline; + +private: + void _compile() override { + m_compiled = + m_comp.compile({m_src->descr_of()}, + cv::GCompileArgs(m_args)); + } + + Pipeline::RunPerf _run(double work_time_ms) override { + // NB: Setup + using namespace std::chrono; + cv::gapi::wip::Data d; + std::vector out_mats(m_num_outputs); + cv::GRunArgsP pipeline_outputs; + for (auto& m : out_mats) { + pipeline_outputs += cv::gout(m); + } + + // NB: Start execution & measure performance statistics. + Pipeline::RunPerf perf; + auto start = high_resolution_clock::now(); + while (m_src->pull(d)) { + auto in_mat = cv::util::get(d); + int64_t latency = utils::measure([&]{ + m_compiled(cv::gin(in_mat), cv::GRunArgsP{pipeline_outputs}); + }); + + perf.latencies.push_back(latency); + perf.elapsed = duration_cast( + high_resolution_clock::now() - start).count(); + + if (perf.elapsed >= work_time_ms) { + break; + } + }; + return perf; + } + + cv::GCompiled m_compiled; +}; + +enum class PLMode { + REGULAR, + STREAMING +}; + +#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_HPP diff --git a/modules/gapi/samples/pipeline_modeling_tool/pipeline_builder.hpp b/modules/gapi/samples/pipeline_modeling_tool/pipeline_builder.hpp new file mode 100644 index 000000000000..a3f187249d83 --- /dev/null +++ b/modules/gapi/samples/pipeline_modeling_tool/pipeline_builder.hpp @@ -0,0 +1,621 @@ +#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_BUILDER_HPP +#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_BUILDER_HPP + +#include + +#include // cv::gapi::GNetPackage +#include // cv::gapi::wip::IStreamSource +#include // cv::gapi::ie::Params +#include // cv::gapi::GCompileArgs +#include // GAPI_OCV_KERNEL +#include // G_API_OP + +#include "pipeline.hpp" +#include "utils.hpp" + +struct Edge { + struct P { + std::string name; + size_t port; + }; + + P src; + P dst; +}; + +struct CallParams { + std::string name; + size_t call_every_nth; +}; + +struct CallNode { + using F = std::function; + + CallParams params; + F run; +}; + +struct DataNode { + cv::optional arg; +}; + +struct Node { + using Ptr = std::shared_ptr; + using WPtr = std::weak_ptr; + using Kind = cv::util::variant; + + std::vector in_nodes; + std::vector out_nodes; + Kind kind; +}; + +struct SubGraphCall { + G_API_OP(GSubGraph, + , + "custom.subgraph") { + static cv::GMatDesc outMeta(const cv::GMatDesc& in, + cv::GComputation comp, + cv::GCompileArgs compile_args, + const size_t call_every_nth) { + GAPI_Assert(call_every_nth > 0); + auto out_metas = + comp.compile(in, std::move(compile_args)).outMetas(); + GAPI_Assert(out_metas.size() == 1u); + GAPI_Assert(cv::util::holds_alternative(out_metas[0])); + return cv::util::get(out_metas[0]); + } + + }; + + struct SubGraphState { + cv::Mat last_result; + cv::GCompiled cc; + int call_counter = 0; + }; + + GAPI_OCV_KERNEL_ST(SubGraphImpl, GSubGraph, SubGraphState) { + static void setup(const cv::GMatDesc& in, + cv::GComputation comp, + cv::GCompileArgs compile_args, + const size_t /*call_every_nth*/, + std::shared_ptr& state, + const cv::GCompileArgs& /*args*/) { + state.reset(new SubGraphState{}); + state->cc = comp.compile(in, std::move(compile_args)); + auto out_desc = + cv::util::get(state->cc.outMetas()[0]); + utils::createNDMat(state->last_result, + out_desc.dims, + out_desc.depth); + } + + static void run(const cv::Mat& in, + cv::GComputation /*comp*/, + cv::GCompileArgs /*compile_args*/, + const size_t call_every_nth, + cv::Mat& out, + SubGraphState& state) { + // NB: Make a call on the first iteration and skip the furthers. + if (state.call_counter == 0) { + state.cc(in, state.last_result); + } + state.last_result.copyTo(out); + state.call_counter = (state.call_counter + 1) % call_every_nth; + } + }; + + void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs); + + size_t numInputs() const { return 1; } + size_t numOutputs() const { return 1; } + + cv::GComputation comp; + cv::GCompileArgs compile_args; + size_t call_every_nth; +}; + +void SubGraphCall::operator()(const cv::GProtoArgs& inputs, + cv::GProtoArgs& outputs) { + GAPI_Assert(inputs.size() == 1u); + GAPI_Assert(cv::util::holds_alternative(inputs[0])); + GAPI_Assert(outputs.empty()); + auto in = cv::util::get(inputs[0]); + outputs.emplace_back(GSubGraph::on(in, comp, compile_args, call_every_nth)); +} + +struct DummyCall { + G_API_OP(GDummy, + , + "custom.dummy") { + static cv::GMatDesc outMeta(const cv::GMatDesc& /* in */, + double /* time */, + const OutputDescr& output) { + if (output.dims.size() == 2) { + return cv::GMatDesc(output.precision, + 1, + // NB: Dims[H, W] -> Size(W, H) + cv::Size(output.dims[1], output.dims[0])); + } + return cv::GMatDesc(output.precision, output.dims); + } + }; + + struct DummyState { + cv::Mat mat; + }; + + // NB: Generate random mat once and then + // copy to dst buffer on every iteration. + GAPI_OCV_KERNEL_ST(GCPUDummy, GDummy, DummyState) { + static void setup(const cv::GMatDesc& /*in*/, + double /*time*/, + const OutputDescr& output, + std::shared_ptr& state, + const cv::GCompileArgs& /*args*/) { + state.reset(new DummyState{}); + utils::createNDMat(state->mat, output.dims, output.precision); + utils::generateRandom(state->mat); + } + + static void run(const cv::Mat& /*in_mat*/, + double time, + const OutputDescr& /*output*/, + cv::Mat& out_mat, + DummyState& state) { + using namespace std::chrono; + double total = 0; + auto start = high_resolution_clock::now(); + state.mat.copyTo(out_mat); + while (total < time) { + total = duration_cast>( + high_resolution_clock::now() - start).count(); + } + } + }; + + void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs); + + size_t numInputs() const { return 1; } + size_t numOutputs() const { return 1; } + + double time; + OutputDescr output; +}; + +void DummyCall::operator()(const cv::GProtoArgs& inputs, + cv::GProtoArgs& outputs) { + GAPI_Assert(inputs.size() == 1u); + GAPI_Assert(cv::util::holds_alternative(inputs[0])); + GAPI_Assert(outputs.empty()); + auto in = cv::util::get(inputs[0]); + outputs.emplace_back(GDummy::on(in, time, output)); +} + +struct InferCall { + void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs); + size_t numInputs() const { return input_layers.size(); } + size_t numOutputs() const { return output_layers.size(); } + + std::string tag; + std::vector input_layers; + std::vector output_layers; +}; + +void InferCall::operator()(const cv::GProtoArgs& inputs, + cv::GProtoArgs& outputs) { + GAPI_Assert(inputs.size() == input_layers.size()); + GAPI_Assert(outputs.empty()); + + cv::GInferInputs g_inputs; + // TODO: Add an opportunity not specify input/output layers in case + // there is only single layer. + for (size_t i = 0; i < inputs.size(); ++i) { + // TODO: Support GFrame as well. + GAPI_Assert(cv::util::holds_alternative(inputs[i])); + auto in = cv::util::get(inputs[i]); + g_inputs[input_layers[i]] = in; + } + auto g_outputs = cv::gapi::infer(tag, g_inputs); + for (size_t i = 0; i < output_layers.size(); ++i) { + outputs.emplace_back(g_outputs.at(output_layers[i])); + } +} + +struct SourceCall { + void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs); + size_t numInputs() const { return 0; } + size_t numOutputs() const { return 1; } +}; + +void SourceCall::operator()(const cv::GProtoArgs& inputs, + cv::GProtoArgs& outputs) { + GAPI_Assert(inputs.empty()); + GAPI_Assert(outputs.empty()); + // NB: Since NV12 isn't exposed source always produce GMat. + outputs.emplace_back(cv::GMat()); +} + +struct LoadPath { + std::string xml; + std::string bin; +}; + +struct ImportPath { + std::string blob; +}; + +using ModelPath = cv::util::variant; + +struct DummyParams { + double time; + OutputDescr output; +}; + +struct InferParams { + std::string name; + ModelPath path; + std::string device; + std::vector input_layers; + std::vector output_layers; + std::map config; +}; + +class PipelineBuilder { +public: + PipelineBuilder(); + void addDummy(const CallParams& call_params, + const DummyParams& dummy_params); + + void addInfer(const CallParams& call_params, + const InferParams& infer_params); + + void setSource(const std::string& name, + std::shared_ptr src); + + void addEdge(const Edge& edge); + void setMode(PLMode mode); + void setDumpFilePath(const std::string& dump); + void setQueueCapacity(const size_t qc); + void setName(const std::string& name); + + Pipeline::Ptr build(); + +private: + template + void addCall(const CallParams& call_params, + CallT&& call); + + Pipeline::Ptr construct(); + + template + using M = std::unordered_map; + struct State { + struct NodeEdges { + std::vector input_edges; + std::vector output_edges; + }; + + M calls_map; + std::vector all_calls; + + cv::gapi::GNetPackage networks; + cv::gapi::GKernelPackage kernels; + cv::GCompileArgs compile_args; + cv::gapi::wip::IStreamSource::Ptr src; + PLMode mode = PLMode::STREAMING; + std::string name; + }; + + std::unique_ptr m_state; +}; + +PipelineBuilder::PipelineBuilder() : m_state(new State{}) { }; + +void PipelineBuilder::addDummy(const CallParams& call_params, + const DummyParams& dummy_params) { + m_state->kernels.include(); + addCall(call_params, + DummyCall{dummy_params.time, dummy_params.output}); +} + +template +void PipelineBuilder::addCall(const CallParams& call_params, + CallT&& call) { + + size_t num_inputs = call.numInputs(); + size_t num_outputs = call.numOutputs(); + Node::Ptr call_node(new Node{{},{},Node::Kind{CallNode{call_params, + std::move(call)}}}); + // NB: Create placeholders for inputs. + call_node->in_nodes.resize(num_inputs); + // NB: Create outputs with empty data. + for (size_t i = 0; i < num_outputs; ++i) { + call_node->out_nodes.emplace_back(new Node{{call_node}, + {}, + Node::Kind{DataNode{}}}); + } + + auto it = m_state->calls_map.find(call_params.name); + if (it != m_state->calls_map.end()) { + throw std::logic_error("Node: " + call_params.name + " already exists!"); + } + m_state->calls_map.emplace(call_params.name, call_node); + m_state->all_calls.emplace_back(call_node); +} + +void PipelineBuilder::addInfer(const CallParams& call_params, + const InferParams& infer_params) { + // NB: No default ctor for Params. + std::unique_ptr> pp; + if (cv::util::holds_alternative(infer_params.path)) { + auto load_path = cv::util::get(infer_params.path); + pp.reset(new cv::gapi::ie::Params(call_params.name, + load_path.xml, + load_path.bin, + infer_params.device)); + } else { + GAPI_Assert(cv::util::holds_alternative(infer_params.path)); + auto import_path = cv::util::get(infer_params.path); + pp.reset(new cv::gapi::ie::Params(call_params.name, + import_path.blob, + infer_params.device)); + } + + pp->pluginConfig(infer_params.config); + m_state->networks += cv::gapi::networks(*pp); + + addCall(call_params, + InferCall{call_params.name, + infer_params.input_layers, + infer_params.output_layers}); +} + +void PipelineBuilder::addEdge(const Edge& edge) { + const auto& src_it = m_state->calls_map.find(edge.src.name); + if (src_it == m_state->calls_map.end()) { + throw std::logic_error("Failed to find node: " + edge.src.name); + } + auto src_node = src_it->second; + if (src_node->out_nodes.size() <= edge.src.port) { + throw std::logic_error("Failed to access node: " + edge.src.name + + " by out port: " + std::to_string(edge.src.port)); + } + + auto dst_it = m_state->calls_map.find(edge.dst.name); + if (dst_it == m_state->calls_map.end()) { + throw std::logic_error("Failed to find node: " + edge.dst.name); + } + auto dst_node = dst_it->second; + if (dst_node->in_nodes.size() <= edge.dst.port) { + throw std::logic_error("Failed to access node: " + edge.dst.name + + " by in port: " + std::to_string(edge.dst.port)); + } + + auto out_data = src_node->out_nodes[edge.src.port]; + auto& in_data = dst_node->in_nodes[edge.dst.port]; + // NB: in_data != nullptr. + if (!in_data.expired()) { + throw std::logic_error("Node: " + edge.dst.name + + " already connected by in port: " + + std::to_string(edge.dst.port)); + } + dst_node->in_nodes[edge.dst.port] = out_data; + out_data->out_nodes.push_back(dst_node); +} + +void PipelineBuilder::setSource(const std::string& name, + std::shared_ptr src) { + GAPI_Assert(!m_state->src && "Only single source pipelines are supported!"); + m_state->src = src; + addCall(CallParams{name, 1u/*call_every_nth*/}, SourceCall{}); +} + +void PipelineBuilder::setMode(PLMode mode) { + m_state->mode = mode; +} + +void PipelineBuilder::setDumpFilePath(const std::string& dump) { + m_state->compile_args.emplace_back(cv::graph_dump_path{dump}); +} + +void PipelineBuilder::setQueueCapacity(const size_t qc) { + m_state->compile_args.emplace_back(cv::gapi::streaming::queue_capacity{qc}); +} + +void PipelineBuilder::setName(const std::string& name) { + m_state->name = name; +} + +static bool visit(Node::Ptr node, + std::vector& sorted, + std::unordered_map& visited) { + if (!node) { + throw std::logic_error("Found null node"); + } + + visited[node] = 1; + for (auto in : node->in_nodes) { + auto in_node = in.lock(); + if (visited[in_node] == 0) { + if (visit(in_node, sorted, visited)) { + return true; + } + } else if (visited[in_node] == 1) { + return true; + } + } + visited[node] = 2; + sorted.push_back(node); + return false; +} + +static cv::optional> +toposort(const std::vector nodes) { + std::vector sorted; + std::unordered_map visited; + for (auto n : nodes) { + if (visit(n, sorted, visited)) { + return cv::optional>{}; + } + } + return cv::util::make_optional(sorted); +} + +Pipeline::Ptr PipelineBuilder::construct() { + // NB: Unlike G-API, pipeline_builder_tool graph always starts with CALL node + // (not data) that produce datas, so the call node which doesn't have + // inputs is considered as "producer" node. + // + // Graph always starts with CALL node and ends with DATA node. + // Graph example: [source] -> (source:0) -> [PP] -> (PP:0) + // + // The algorithm is quite simple: + // 0. Verify that every call input node exists (connected). + // 1. Sort all nodes by visiting only call nodes, + // since there is no data nodes that's not connected with any call node, + // it's guarantee that every node will be visited. + // 2. Fillter call nodes. + // 3. Go through every call node. + // FIXME: Add toposort in case user passed nodes + // in arbitrary order which is unlikely happened. + // 4. Extract proto input from every input node + // 5. Run call and get outputs + // 6. If call node doesn't have inputs it means that it's "producer" node, + // so collect all outputs to graph_inputs vector. + // 7. Assign proto outputs to output data nodes, + // so the next calls can use them as inputs. + cv::GProtoArgs graph_inputs; + cv::GProtoArgs graph_outputs; + // 0. Verify that every call input node exists (connected). + for (auto call_node : m_state->all_calls) { + for (size_t i = 0; i < call_node->in_nodes.size(); ++i) { + const auto& in_data_node = call_node->in_nodes[i]; + // NB: in_data_node == nullptr. + if (in_data_node.expired()) { + const auto& call = cv::util::get(call_node->kind); + throw std::logic_error( + "Node: " + call.params.name + " in Pipeline: " + m_state->name + + " has dangling input by in port: " + std::to_string(i)); + } + } + } + // (0) Sort all nodes; + auto has_sorted = toposort(m_state->all_calls); + if (!has_sorted) { + throw std::logic_error( + "Pipeline: " + m_state->name + " has cyclic dependencies") ; + } + auto& sorted = has_sorted.value(); + // (1). Fillter call nodes. + std::vector sorted_calls; + for (auto n : sorted) { + if (cv::util::holds_alternative(n->kind)) { + sorted_calls.push_back(n); + } + } + + m_state->kernels.include(); + m_state->compile_args.emplace_back(m_state->networks); + m_state->compile_args.emplace_back(m_state->kernels); + + // (2). Go through every call node. + for (auto call_node : sorted_calls) { + auto& call = cv::util::get(call_node->kind); + cv::GProtoArgs outputs; + cv::GProtoArgs inputs; + for (size_t i = 0; i < call_node->in_nodes.size(); ++i) { + auto in_node = call_node->in_nodes.at(i); + auto in_data = cv::util::get(in_node.lock()->kind); + if (!in_data.arg.has_value()) { + throw std::logic_error("data hasn't been provided"); + } + // (3). Extract proto input from every input node. + inputs.push_back(in_data.arg.value()); + } + // NB: If node shouldn't be called on each iterations, + // it should be wrapped into subgraph which is able to skip calling. + if (call.params.call_every_nth != 1u) { + // FIXME: Limitation of the subgraph operation (). + // G-API doesn't support dynamic number of inputs/outputs. + if (inputs.size() > 1u) { + throw std::logic_error( + "skip_frame_nth is supported only for single input subgraphs\n" + "Current subgraph has " + std::to_string(inputs.size()) + " inputs"); + } + + if (outputs.size() > 1u) { + throw std::logic_error( + "skip_frame_nth is supported only for single output subgraphs\n" + "Current subgraph has " + std::to_string(inputs.size()) + " outputs"); + } + // FIXME: Should be generalized. + // Now every subgraph contains only single node + // which has single input/output. + GAPI_Assert(cv::util::holds_alternative(inputs[0])); + cv::GProtoArgs subgr_inputs{cv::GProtoArg{cv::GMat()}}; + cv::GProtoArgs subgr_outputs; + call.run(subgr_inputs, subgr_outputs); + auto comp = cv::GComputation(cv::GProtoInputArgs{subgr_inputs}, + cv::GProtoOutputArgs{subgr_outputs}); + call = CallNode{CallParams{call.params.name, 1u/*call_every_nth*/}, + SubGraphCall{std::move(comp), + m_state->compile_args, + call.params.call_every_nth}}; + } + // (4). Run call and get outputs. + call.run(inputs, outputs); + // (5) If call node doesn't have inputs + // it means that it's input producer node (Source). + if (call_node->in_nodes.empty()) { + for (auto out : outputs) { + graph_inputs.push_back(out); + } + } + // (6). Assign proto outputs to output data nodes, + // so the next calls can use them as inputs. + GAPI_Assert(outputs.size() == call_node->out_nodes.size()); + for (size_t i = 0; i < outputs.size(); ++i) { + auto out_node = call_node->out_nodes[i]; + auto& out_data = cv::util::get(out_node->kind); + out_data.arg = cv::util::make_optional(outputs[i]); + if (out_node->out_nodes.empty()) { + graph_outputs.push_back(out_data.arg.value()); + } + } + } + + if (m_state->mode == PLMode::STREAMING) { + GAPI_Assert(graph_inputs.size() == 1); + GAPI_Assert(cv::util::holds_alternative(graph_inputs[0])); + // FIXME: Handle GFrame when NV12 comes. + const auto& graph_input = cv::util::get(graph_inputs[0]); + // NB: In case streaming mode need to expose timestamp in order to + // calculate performance metrics. + graph_outputs.emplace_back( + cv::gapi::streaming::timestamp(graph_input).strip()); + + return std::make_shared(std::move(m_state->name), + cv::GComputation( + cv::GProtoInputArgs{graph_inputs}, + cv::GProtoOutputArgs{graph_outputs}), + std::move(m_state->src), + std::move(m_state->compile_args), + graph_outputs.size()); + } + GAPI_Assert(m_state->mode == PLMode::REGULAR); + return std::make_shared(std::move(m_state->name), + cv::GComputation( + cv::GProtoInputArgs{graph_inputs}, + cv::GProtoOutputArgs{graph_outputs}), + std::move(m_state->src), + std::move(m_state->compile_args), + graph_outputs.size()); +} + +Pipeline::Ptr PipelineBuilder::build() { + auto pipeline = construct(); + m_state.reset(new State{}); + return pipeline; +} + +#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_BUILDER_HPP diff --git a/modules/gapi/samples/pipeline_modeling_tool/test_pipeline_modeling_tool.py b/modules/gapi/samples/pipeline_modeling_tool/test_pipeline_modeling_tool.py new file mode 100644 index 000000000000..d56a0399e901 --- /dev/null +++ b/modules/gapi/samples/pipeline_modeling_tool/test_pipeline_modeling_tool.py @@ -0,0 +1,984 @@ +import os +import subprocess + +pipeline_modeling_tool = os.getenv('PIPELINE_MODELING_TOOL') + +def get_output(exec_str): + try: + out = subprocess.check_output(exec_str, + stderr=subprocess.STDOUT, + shell=True).strip().decode() + except subprocess.CalledProcessError as exc: + out = exc.output.strip().decode() + return out + + +def test_error_no_config_specified(): + out = get_output(pipeline_modeling_tool) + assert out.startswith('Config must be specified via --cfg option') + + +def test_error_no_config_exists(): + cfg_file = 'not_existing_cfg.yml' + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert 'Failed to open config file: not_existing_cfg.yml' in out + + +def test_error_no_work_time(): + cfg_file = """\"%YAML:1.0\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Config must contain field: work_time') + + +def test_error_work_time_not_positive(): + cfg_file = """\"%YAML:1.0 +work_time: -1\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('work_time must be positive') + + +def test_error_no_pipelines(): + cfg_file = """\"%YAML:1.0 +work_time: 1000\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Config must contain field: Pipelines') + + +def test_error_pipelines_node_not_map(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Pipelines field must be a map') + + +def test_error_config_not_contain_pl(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1:\" """ + + exec_str = '{} --cfg={} --exec_list=PL2'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Pipelines must contain field: PL2') + + +def test_error_no_source(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('PL1 must contain field: source') + + +def test_error_source_no_name(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('source must contain field: name') + + +def test_error_source_no_latency(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('source must contain field: latency') + + +def test_error_source_no_output(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('source must contain field: output') + + +def test_error_source_output_no_dims(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('output must contain field: dims') + + +def test_error_source_output_no_precision(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4]\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('output must contain field: precision') + + +def test_error_no_nodes(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('PL1 must contain field: nodes') + + +def test_error_nodes_not_sequence(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('nodes in PL1 must be a sequence') + + +def test_error_node_no_name(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + -\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('node must contain field: name') + + +def test_error_node_no_type(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('node must contain field: type') + + +def test_error_node_unknown_type(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Unknown'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Unsupported node type: Unknown') + + +def test_error_node_dummy_no_time(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Node0 must contain field: time') + + +def test_error_node_dummy_not_positive_time(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: -0.2\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Node0 time must be positive') + + +def test_error_node_dummy_no_output(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Node0 must contain field: output') + + +def test_error_node_infer_no_model_path(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Infer'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + error_msg = """Path to OpenVINO model must be specified in either of two formats: +1. + xml: path to *.xml + bin: path to *.bin +2. + blob: path to *.blob""" + assert out.startswith(error_msg) + + +def test_error_node_infer_no_input_layers(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Infer' + blob: model.blob + device: 'CPU'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Node0 must contain field: input_layers') + + +def test_error_node_infer_input_layers_are_empty(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Infer' + blob: model.blob + device: 'CPU' + input_layers: + \" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('input_layers in Node0 must be a sequence') + + +def test_error_node_infer_no_output_layers(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Infer' + blob: model.blob + device: 'CPU' + input_layers: + - 'layer_name'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Node0 must contain field: output_layers') + + +def test_error_node_infer_output_layers_are_empty(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Infer' + blob: model.blob + device: 'CPU' + input_layers: + - 'layer_name' + output_layers:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('output_layers in Node0 must be a sequence') + + +def test_error_no_edges(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('PL1 must contain field: edges') + + +def test_error_edges_not_sequence(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges:\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('edges in PL1 must be a sequence') + + +def test_error_edges_no_from(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + -\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('edge must contain field: from') + + +def test_error_edges_no_to(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Node0'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('edge must contain field: to') + + +def test_error_edges_from_not_exists(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Node1' + to: 'Node2'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Failed to find node: Node1') + + +def test_error_edges_from_port_not_exists(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Node0:10' + to: 'Node2'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Failed to access node: Node0 by out port: 10') + + +def test_error_edges_to_not_exists(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node2'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Failed to find node: Node2') + + +def test_error_edges_to_port_not_exists(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0:3'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Failed to access node: Node0 by in port: 3') + + +def test_error_connect_to_source(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Node0' + to: 'Src'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Failed to access node: Src by in port: 0') + + +def test_error_double_edge(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0' + - from: 'Src' + to: 'Node0'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Node: Node0 already connected by in port: 0') + + +def test_error_double_edge(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0' + - from: 'Src' + to: 'Node0'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Node: Node0 already connected by in port: 0') + + +def test_node_has_dangling_input(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + + - name: 'Node1' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Node0' + to: 'Node1'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + + assert out.startswith('Node: Node0 in Pipeline: PL1 has dangling input by in port: 0') + + +def test_error_has_cycle_0(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node' + type: 'Infer' + blob: 'model.blob' + device: 'CPU' + input_layers: + - 'in_layer_name_0' + - 'in_layer_name_1' + output_layers: + - 'out_layer_name' + edges: + - from: 'Src' + to: 'Node:0' + - from: 'Node:0' + to: 'Node:1'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Pipeline: PL1 has cyclic dependencies') + + +def test_error_has_cycle_0(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Infer' + blob: 'model.blob' + device: 'CPU' + input_layers: + - 'in_layer_name_0' + - 'in_layer_name_1' + output_layers: + - 'out_layer_name' + + - name: 'Node1' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0:0' + - from: 'Node0:0' + to: 'Node1:0' + - from: 'Node1' + to: 'Node0:1'\" """ + + exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Pipeline: PL1 has cyclic dependencies') + + +def test_error_no_load_config_exists(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0'\" """ + + exec_str = '{} --cfg={} --load_config=not_existing.yml'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert 'Failed to load config: not_existing.yml' in out + + +def test_error_invalid_app_mode(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0'\" """ + + exec_str = '{} --cfg={} --pl_mode=unknown'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Unsupported PLMode: unknown\n' + 'Please chose between: streaming and regular') + + +def test_error_invalid_pl_mode(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0'\" """ + + exec_str = '{} --cfg={} --app_mode=unknown'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('Unsupported AppMode: unknown\n' + 'Please chose between: realtime and benchmark') + + +def test_error_drop_frames_with_streaming(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + time: 0.2 + output: + dims: [1,2,3,4] + precision: 'U8' + edges: + - from: 'Src' + to: 'Node0'\" """ + + exec_str = '{} --cfg={} --pl_mode=streaming --drop_frames'.format(pipeline_modeling_tool, cfg_file) + out = get_output(exec_str) + assert out.startswith('--drop_frames option is supported only for pipelines in "regular" mode') + + +def test_incorrect_call_every_nth(): + cfg_file = """\"%YAML:1.0 +work_time: 1000 +Pipelines: + PL1: + source: + name: 'Src' + latency: 20 + output: + dims: [1,2,3,4] + precision: 'U8' + nodes: + - name: 'Node0' + type: 'Dummy' + call_every_nth: {}\" """ + + error = 'Node0 call_every_nth must be greater than zero\nCurrent call_every_nth: {}' + + def check(cfg_file, call_every_nth): + out = get_output('{} --cfg={}'.format(pipeline_modeling_tool, cfg_file.format(call_every_nth))) + assert out.startswith(error.format(call_every_nth)) + + check(cfg_file, -3) + check(cfg_file, 0) diff --git a/modules/gapi/samples/pipeline_modeling_tool/utils.hpp b/modules/gapi/samples/pipeline_modeling_tool/utils.hpp new file mode 100644 index 000000000000..c110bf3b47fe --- /dev/null +++ b/modules/gapi/samples/pipeline_modeling_tool/utils.hpp @@ -0,0 +1,96 @@ +#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_UTILS_HPP +#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_UTILS_HPP + +#include + +#if defined(_WIN32) +#include +#endif + +// FIXME: It's better to place it somewhere in common.hpp +struct OutputDescr { + std::vector dims; + int precision; +}; + +namespace utils { + +inline void createNDMat(cv::Mat& mat, const std::vector& dims, int depth) { + GAPI_Assert(!dims.empty()); + mat.create(dims, depth); + if (dims.size() == 1) { + //FIXME: Well-known 1D mat WA + mat.dims = 1; + } +} + +inline void generateRandom(cv::Mat& out) { + switch (out.depth()) { + case CV_8U: + cv::randu(out, 0, 255); + break; + case CV_32F: + cv::randu(out, 0.f, 1.f); + break; + case CV_16F: { + std::vector dims; + for (int i = 0; i < out.size.dims(); ++i) { + dims.push_back(out.size[i]); + } + cv::Mat fp32_mat; + createNDMat(fp32_mat, dims, CV_32F); + cv::randu(fp32_mat, 0.f, 1.f); + fp32_mat.convertTo(out, out.type()); + break; + } + default: + throw std::logic_error("Unsupported preprocessing depth"); + } +} + +inline void sleep(double ms) { +#if defined(_WIN32) + // NB: It takes portions of 100 nanoseconds. + int64_t ns_units = static_cast(ms * 1e4); + // FIXME: Wrap it to RAII and instance only once. + HANDLE timer = CreateWaitableTimer(NULL, true, NULL); + if (!timer) { + throw std::logic_error("Failed to create timer"); + } + + LARGE_INTEGER li; + li.QuadPart = -ns_units; + if(!SetWaitableTimer(timer, &li, 0, NULL, NULL, false)){ + CloseHandle(timer); + throw std::logic_error("Failed to set timer"); + } + if (WaitForSingleObject(timer, INFINITE) != WAIT_OBJECT_0) { + CloseHandle(timer); + throw std::logic_error("Failed to wait timer"); + } + CloseHandle(timer); +#else + using namespace std::chrono; + std::this_thread::sleep_for(duration(ms)); +#endif +} + +template +typename duration_t::rep measure(std::function f) { + using namespace std::chrono; + auto start = high_resolution_clock::now(); + f(); + return duration_cast( + high_resolution_clock::now() - start).count(); +} + +template +typename duration_t::rep timestamp() { + using namespace std::chrono; + auto now = high_resolution_clock::now(); + return duration_cast(now.time_since_epoch()).count(); +} + +} // namespace utils + +#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_UTILS_HPP diff --git a/modules/gapi/src/api/gbackend.cpp b/modules/gapi/src/api/gbackend.cpp index 1e7b8a2a8dfd..e3b1e7123df1 100644 --- a/modules/gapi/src/api/gbackend.cpp +++ b/modules/gapi/src/api/gbackend.cpp @@ -62,7 +62,7 @@ void cv::gapi::GBackend::Priv::addMetaSensitiveBackendPasses(ade::ExecutionEngin // which are sensitive to metadata } -cv::gapi::GKernelPackage cv::gapi::GBackend::Priv::auxiliaryKernels() const +cv::GKernelPackage cv::gapi::GBackend::Priv::auxiliaryKernels() const { return {}; } @@ -411,6 +411,12 @@ void createMat(const cv::GMatDesc &desc, cv::Mat& mat) { GAPI_Assert(!desc.planar); mat.create(desc.dims, desc.depth); +#if !defined(GAPI_STANDALONE) + // NB: WA for 1D mats. + if (desc.dims.size() == 1u) { + mat.dims = 1; + } +#endif } } diff --git a/modules/gapi/src/api/gbackend_priv.hpp b/modules/gapi/src/api/gbackend_priv.hpp index 45237514a53e..5609b304aa59 100644 --- a/modules/gapi/src/api/gbackend_priv.hpp +++ b/modules/gapi/src/api/gbackend_priv.hpp @@ -66,7 +66,7 @@ class GAPI_EXPORTS cv::gapi::GBackend::Priv // they are called when meta information becomes available. virtual void addMetaSensitiveBackendPasses(ade::ExecutionEngineSetupContext &); - virtual cv::gapi::GKernelPackage auxiliaryKernels() const; + virtual cv::GKernelPackage auxiliaryKernels() const; // Ask backend if it has a custom control over island fusion process // This method is quite redundant but there's nothing better fits diff --git a/modules/gapi/src/api/gframe.cpp b/modules/gapi/src/api/gframe.cpp index 1acaa9b76639..b0830b7a63a3 100644 --- a/modules/gapi/src/api/gframe.cpp +++ b/modules/gapi/src/api/gframe.cpp @@ -44,6 +44,7 @@ std::ostream& operator<<(std::ostream& os, const cv::GFrameDesc &d) { switch (d.fmt) { case MediaFormat::BGR: os << "BGR"; break; case MediaFormat::NV12: os << "NV12"; break; + case MediaFormat::GRAY: os << "GRAY"; break; default: GAPI_Assert(false && "Invalid media format"); } os << ' ' << d.size << ']'; diff --git a/modules/gapi/src/api/gkernel.cpp b/modules/gapi/src/api/gkernel.cpp index 2a68272a4c1f..5ba399ffefa9 100644 --- a/modules/gapi/src/api/gkernel.cpp +++ b/modules/gapi/src/api/gkernel.cpp @@ -18,7 +18,7 @@ #include "api/gbackend_priv.hpp" // GKernelPackage public implementation //////////////////////////////////////// -void cv::gapi::GKernelPackage::remove(const cv::gapi::GBackend& backend) +void cv::GKernelPackage::remove(const cv::gapi::GBackend& backend) { std::vector id_deleted_kernels; for (const auto& p : m_id_kernels) @@ -35,27 +35,38 @@ void cv::gapi::GKernelPackage::remove(const cv::gapi::GBackend& backend) } } -bool cv::gapi::GKernelPackage::includesAPI(const std::string &id) const +void cv::GKernelPackage::include(const cv::gapi::GFunctor& functor) +{ + m_id_kernels[functor.id()] = std::make_pair(functor.backend(), functor.impl()); +} + +void cv::GKernelPackage::include(const cv::gapi::GBackend& backend, const std::string& kernel_id) +{ + removeAPI(kernel_id); + m_id_kernels[kernel_id] = std::make_pair(backend, GKernelImpl{{}, {}}); +} + +bool cv::GKernelPackage::includesAPI(const std::string &id) const { return ade::util::contains(m_id_kernels, id); } -void cv::gapi::GKernelPackage::removeAPI(const std::string &id) +void cv::GKernelPackage::removeAPI(const std::string &id) { m_id_kernels.erase(id); } -std::size_t cv::gapi::GKernelPackage::size() const +std::size_t cv::GKernelPackage::size() const { return m_id_kernels.size(); } -const std::vector &cv::gapi::GKernelPackage::get_transformations() const +const std::vector &cv::GKernelPackage::get_transformations() const { return m_transformations; } -std::vector cv::gapi::GKernelPackage::get_kernel_ids() const +std::vector cv::GKernelPackage::get_kernel_ids() const { std::vector ids; for (auto &&id : m_id_kernels) @@ -65,13 +76,13 @@ std::vector cv::gapi::GKernelPackage::get_kernel_ids() const return ids; } -cv::gapi::GKernelPackage cv::gapi::combine(const GKernelPackage &lhs, - const GKernelPackage &rhs) +cv::GKernelPackage cv::gapi::combine(const cv::GKernelPackage &lhs, + const cv::GKernelPackage &rhs) { // If there is a collision, prefer RHS to LHS // since RHS package has a precedense, start with its copy - GKernelPackage result(rhs); + cv::GKernelPackage result(rhs); // now iterate over LHS package and put kernel if and only // if there's no such one for (const auto& kernel : lhs.m_id_kernels) @@ -88,7 +99,7 @@ cv::gapi::GKernelPackage cv::gapi::combine(const GKernelPackage &lhs, } std::pair -cv::gapi::GKernelPackage::lookup(const std::string &id) const +cv::GKernelPackage::lookup(const std::string &id) const { auto kernel_it = m_id_kernels.find(id); if (kernel_it != m_id_kernels.end()) @@ -99,7 +110,7 @@ cv::gapi::GKernelPackage::lookup(const std::string &id) const util::throw_error(std::logic_error("Kernel " + id + " was not found")); } -std::vector cv::gapi::GKernelPackage::backends() const +std::vector cv::GKernelPackage::backends() const { using kernel_type = std::pair>; std::unordered_set unique_set; diff --git a/modules/gapi/src/api/kernels_core.cpp b/modules/gapi/src/api/kernels_core.cpp index 4485e36f273f..b24f6a098cdb 100644 --- a/modules/gapi/src/api/kernels_core.cpp +++ b/modules/gapi/src/api/kernels_core.cpp @@ -301,16 +301,6 @@ GMat merge4(const GMat& src1, const GMat& src2, const GMat& src3, const GMat& sr return core::GMerge4::on(src1, src2, src3, src4); } -GMat resize(const GMat& src, const Size& dsize, double fx, double fy, int interpolation) -{ - return core::GResize::on(src, dsize, fx, fy, interpolation); -} - -GMatP resizeP(const GMatP& src, const Size& dsize, int interpolation) -{ - return core::GResizeP::on(src, dsize, interpolation); -} - GMat remap(const GMat& src, const Mat& map1, const Mat& map2, int interpolation, int borderMode, const Scalar& borderValue) diff --git a/modules/gapi/src/api/kernels_imgproc.cpp b/modules/gapi/src/api/kernels_imgproc.cpp index 41085a7ebf33..f94d986ed4b4 100644 --- a/modules/gapi/src/api/kernels_imgproc.cpp +++ b/modules/gapi/src/api/kernels_imgproc.cpp @@ -14,6 +14,16 @@ namespace cv { namespace gapi { +GMat resize(const GMat& src, const Size& dsize, double fx, double fy, int interpolation) +{ + return imgproc::GResize::on(src, dsize, fx, fy, interpolation); +} + +GMatP resizeP(const GMatP& src, const Size& dsize, int interpolation) +{ + return imgproc::GResizeP::on(src, dsize, interpolation); +} + GMat sepFilter(const GMat& src, int ddepth, const Mat& kernelX, const Mat& kernelY, const Point& anchor, const Scalar& delta, int borderType, const Scalar& borderVal) { diff --git a/modules/gapi/src/api/kernels_streaming.cpp b/modules/gapi/src/api/kernels_streaming.cpp index 2c50551f4ed4..55834ffb9af5 100644 --- a/modules/gapi/src/api/kernels_streaming.cpp +++ b/modules/gapi/src/api/kernels_streaming.cpp @@ -39,7 +39,7 @@ cv::GMat cv::gapi::streaming::desync(const cv::GMat &g) { // // At the same time, generally, every island in the streaming // graph gets its individual input as a queue (so normally, a - // writer pushes the same output MULTIPLE TIMES if it has mutliple + // writer pushes the same output MULTIPLE TIMES if it has multiple // readers): // // LWV diff --git a/modules/gapi/src/api/media.cpp b/modules/gapi/src/api/media.cpp index b1c455d40aef..a3643e378c38 100644 --- a/modules/gapi/src/api/media.cpp +++ b/modules/gapi/src/api/media.cpp @@ -36,7 +36,7 @@ cv::MediaFrame::IAdapter* cv::MediaFrame::getAdapter() const { } void cv::MediaFrame::serialize(cv::gapi::s11n::IOStream& os) const { - return m->adapter->serialize(os); + m->adapter->serialize(os); } cv::MediaFrame::View::View(Ptrs&& ptrs, Strides&& strs, Callback &&cb) diff --git a/modules/gapi/src/backends/common/gbackend.hpp b/modules/gapi/src/backends/common/gbackend.hpp index 99b8f5dd3795..b05d8e2c36a9 100644 --- a/modules/gapi/src/backends/common/gbackend.hpp +++ b/modules/gapi/src/backends/common/gbackend.hpp @@ -173,7 +173,7 @@ namespace magazine // without utilizing magazine at all void GAPI_EXPORTS bindInArg (Mag& mag, const RcDesc &rc, const GRunArg &arg, HandleRMat handleRMat = HandleRMat::BIND); - // Extracts a memory object reference fro GRunArgP, stores it in appropriate slot in a magazine + // Extracts a memory object reference from GRunArgP, stores it in appropriate slot in a magazine // Note on RMat handling from bindInArg above is also applied here void GAPI_EXPORTS bindOutArg(Mag& mag, const RcDesc &rc, const GRunArgP &arg, HandleRMat handleRMat = HandleRMat::BIND); diff --git a/modules/gapi/src/backends/common/gmetabackend.cpp b/modules/gapi/src/backends/common/gmetabackend.cpp index 40e87c3ea0aa..30b6add94739 100644 --- a/modules/gapi/src/backends/common/gmetabackend.cpp +++ b/modules/gapi/src/backends/common/gmetabackend.cpp @@ -113,6 +113,6 @@ struct InGraphMetaKernel final: public cv::detail::KernelTag { } // anonymous namespace -cv::gapi::GKernelPackage cv::gimpl::meta::kernels() { +cv::GKernelPackage cv::gimpl::meta::kernels() { return cv::gapi::kernels(); } diff --git a/modules/gapi/src/backends/common/gmetabackend.hpp b/modules/gapi/src/backends/common/gmetabackend.hpp index 56f61d0e3dfb..109a59e79794 100644 --- a/modules/gapi/src/backends/common/gmetabackend.hpp +++ b/modules/gapi/src/backends/common/gmetabackend.hpp @@ -7,7 +7,7 @@ namespace cv { namespace gimpl { namespace meta { -cv::gapi::GKernelPackage kernels(); +cv::GKernelPackage kernels(); } // namespace meta } // namespace gimpl diff --git a/modules/gapi/src/backends/common/serialization.hpp b/modules/gapi/src/backends/common/serialization.hpp index 529fdc635d5e..3ba2e83581ab 100644 --- a/modules/gapi/src/backends/common/serialization.hpp +++ b/modules/gapi/src/backends/common/serialization.hpp @@ -164,7 +164,7 @@ GAPI_EXPORTS void serialize( IOStream& os GAPI_EXPORTS GSerialized deserialize(IIStream& is); GAPI_EXPORTS void reconstruct(const GSerialized &s, ade::Graph &g); -// FIXME: Basic Stream implementaions ////////////////////////////////////////// +// FIXME: Basic Stream implementations ///////////////////////////////////////// // Basic in-memory stream implementations. class GAPI_EXPORTS ByteMemoryOutStream final: public IOStream { diff --git a/modules/gapi/src/backends/cpu/gcpubackend.cpp b/modules/gapi/src/backends/cpu/gcpubackend.cpp index dfcaf3d47847..f50f8ecd2816 100644 --- a/modules/gapi/src/backends/cpu/gcpubackend.cpp +++ b/modules/gapi/src/backends/cpu/gcpubackend.cpp @@ -2,7 +2,7 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2018-2021 Intel Corporation +// Copyright (C) 2018-2022 Intel Corporation #include "precomp.hpp" @@ -27,6 +27,7 @@ #include "api/gbackend_priv.hpp" // FIXME: Make it part of Backend SDK! #include "utils/itt.hpp" +#include "logger.hpp" // FIXME: Is there a way to take a typed graph (our GModel), // and create a new typed graph _ATOP_ of that (by extending with a couple of @@ -88,7 +89,7 @@ cv::gimpl::GCPUExecutable::GCPUExecutable(const ade::Graph &g, { case NodeType::OP: { - m_script.push_back({nh, GModel::collectOutputMeta(m_gm, nh)}); + m_opNodes.push_back(nh); // If kernel is stateful then prepare storage for its state. GCPUKernel k = gcm.metadata(nh).get().k; @@ -107,21 +108,12 @@ cv::gimpl::GCPUExecutable::GCPUExecutable(const ade::Graph &g, auto rc = RcDesc{desc.rc, desc.shape, desc.ctor}; magazine::bindInArg(m_res, rc, m_gm.metadata(nh).get().arg); } - //preallocate internal Mats in advance - if (desc.storage == Data::Storage::INTERNAL && desc.shape == GShape::GMAT) - { - const auto mat_desc = util::get(desc.meta); - auto& mat = m_res.slot()[desc.rc]; - createMat(mat_desc, mat); - } break; } default: util::throw_error(std::logic_error("Unsupported NodeType type")); } } - - // For each stateful kernel call 'setup' user callback to initialize state. - setupKernelStates(); + makeReshape(); } // FIXME: Document what it does @@ -176,9 +168,44 @@ void cv::gimpl::GCPUExecutable::setupKernelStates() } } +void cv::gimpl::GCPUExecutable::makeReshape() { + // Prepare the execution script + m_script.clear(); + for (auto &nh : m_opNodes) { + m_script.push_back({nh, GModel::collectOutputMeta(m_gm, nh)}); + } + + // Preallocate internal mats + for (auto& nh : m_dataNodes) { + const auto& desc = m_gm.metadata(nh).get(); + if (desc.storage == Data::Storage::INTERNAL && desc.shape == GShape::GMAT) { + const auto mat_desc = util::get(desc.meta); + auto& mat = m_res.slot()[desc.rc]; + createMat(mat_desc, mat); + } + } +} + +void cv::gimpl::GCPUExecutable::reshape(ade::Graph&, const GCompileArgs& args) { + m_compileArgs = args; + makeReshape(); + // TODO: Add an input meta sensitivity flag to stateful kernels. + // When reshape() happens, reset state for meta-sensitive kernels only + if (!m_nodesToStates.empty()) { + std::call_once(m_warnFlag, + [](){ + GAPI_LOG_WARNING(NULL, + "\nGCPUExecutable::reshape was called. Resetting states of stateful kernels."); + }); + setupKernelStates(); + } +} + void cv::gimpl::GCPUExecutable::handleNewStream() { - m_newStreamStarted = true; + // In case if new video-stream happens - for each stateful kernel + // call 'setup' user callback to re-initialize state. + setupKernelStates(); } void cv::gimpl::GCPUExecutable::run(std::vector &&input_objs, @@ -208,14 +235,6 @@ void cv::gimpl::GCPUExecutable::run(std::vector &&input_objs, } } - // In case if new video-stream happens - for each stateful kernel - // call 'setup' user callback to re-initialize state. - if (m_newStreamStarted) - { - setupKernelStates(); - m_newStreamStarted = false; - } - // OpenCV backend execution is not a rocket science at all. // Simply invoke our kernels in the proper order. GConstGCPUModel gcm(m_g); diff --git a/modules/gapi/src/backends/cpu/gcpubackend.hpp b/modules/gapi/src/backends/cpu/gcpubackend.hpp index 6328da03b060..c8bad6c84fce 100644 --- a/modules/gapi/src/backends/cpu/gcpubackend.hpp +++ b/modules/gapi/src/backends/cpu/gcpubackend.hpp @@ -2,7 +2,7 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2018-2020 Intel Corporation +// Copyright (C) 2018-2022 Intel Corporation #ifndef OPENCV_GAPI_GCPUBACKEND_HPP @@ -33,7 +33,7 @@ class GCPUExecutable final: public GIslandExecutable { const ade::Graph &m_g; GModel::ConstGraph m_gm; - const cv::GCompileArgs m_compileArgs; + cv::GCompileArgs m_compileArgs; struct OperationInfo { @@ -51,29 +51,26 @@ class GCPUExecutable final: public GIslandExecutable // List of all resources in graph (both internal and external) std::vector m_dataNodes; + std::vector m_opNodes; // Actual data of all resources in graph (both internal and external) Mag m_res; - // Flag which identifies if new stream was started - bool m_newStreamStarted = false; + // A flag for call_once() (used for log warnings) + std::once_flag m_warnFlag; GArg packArg(const GArg &arg); void setupKernelStates(); + void makeReshape(); + public: GCPUExecutable(const ade::Graph &graph, const cv::GCompileArgs &compileArgs, const std::vector &nodes); - virtual inline bool canReshape() const override { return false; } - virtual inline void reshape(ade::Graph&, const GCompileArgs&) override - { - // FIXME: CPU plugin is in fact reshapeable (as it was initially, - // even before outMeta() has been introduced), so this limitation - // should be dropped. - util::throw_error(std::logic_error("GCPUExecutable::reshape() should never be called")); - } + virtual inline bool canReshape() const override { return true; } + virtual void reshape(ade::Graph&, const GCompileArgs&) override; virtual void handleNewStream() override; diff --git a/modules/gapi/src/backends/cpu/gcpucore.cpp b/modules/gapi/src/backends/cpu/gcpucore.cpp index 1f7dfb234ff8..038bd8d81771 100644 --- a/modules/gapi/src/backends/cpu/gcpucore.cpp +++ b/modules/gapi/src/backends/cpu/gcpucore.cpp @@ -462,30 +462,6 @@ GAPI_OCV_KERNEL(GCPUMerge4, cv::gapi::core::GMerge4) } }; -GAPI_OCV_KERNEL(GCPUResize, cv::gapi::core::GResize) -{ - static void run(const cv::Mat& in, cv::Size sz, double fx, double fy, int interp, cv::Mat &out) - { - cv::resize(in, out, sz, fx, fy, interp); - } -}; - -GAPI_OCV_KERNEL(GCPUResizeP, cv::gapi::core::GResizeP) -{ - static void run(const cv::Mat& in, cv::Size out_sz, int interp, cv::Mat& out) - { - int inH = in.rows / 3; - int inW = in.cols; - int outH = out.rows / 3; - int outW = out.cols; - for (int i = 0; i < 3; i++) { - auto in_plane = in(cv::Rect(0, i*inH, inW, inH)); - auto out_plane = out(cv::Rect(0, i*outH, outW, outH)); - cv::resize(in_plane, out_plane, out_sz, 0, 0, interp); - } - } -}; - GAPI_OCV_KERNEL(GCPURemap, cv::gapi::core::GRemap) { static void run(const cv::Mat& in, const cv::Mat& x, const cv::Mat& y, int a, int b, cv::Scalar s, cv::Mat& out) @@ -720,7 +696,7 @@ GAPI_OCV_KERNEL(GCPUSizeMF, cv::gapi::streaming::GSizeMF) } }; -cv::gapi::GKernelPackage cv::gapi::core::cpu::kernels() +cv::GKernelPackage cv::gapi::core::cpu::kernels() { static auto pkg = cv::gapi::kernels < GCPUAdd @@ -775,8 +751,6 @@ cv::gapi::GKernelPackage cv::gapi::core::cpu::kernels() , GCPUInRange , GCPUSplit3 , GCPUSplit4 - , GCPUResize - , GCPUResizeP , GCPUMerge3 , GCPUMerge4 , GCPURemap diff --git a/modules/gapi/src/backends/cpu/gcpuimgproc.cpp b/modules/gapi/src/backends/cpu/gcpuimgproc.cpp index 6cbf0d32f067..eae7c0d803f3 100644 --- a/modules/gapi/src/backends/cpu/gcpuimgproc.cpp +++ b/modules/gapi/src/backends/cpu/gcpuimgproc.cpp @@ -28,6 +28,30 @@ namespace { } } +GAPI_OCV_KERNEL(GCPUResize, cv::gapi::imgproc::GResize) +{ + static void run(const cv::Mat& in, cv::Size sz, double fx, double fy, int interp, cv::Mat &out) + { + cv::resize(in, out, sz, fx, fy, interp); + } +}; + +GAPI_OCV_KERNEL(GCPUResizeP, cv::gapi::imgproc::GResizeP) +{ + static void run(const cv::Mat& in, cv::Size out_sz, int interp, cv::Mat& out) + { + int inH = in.rows / 3; + int inW = in.cols; + int outH = out.rows / 3; + int outW = out.cols; + for (int i = 0; i < 3; i++) { + auto in_plane = in(cv::Rect(0, i*inH, inW, inH)); + auto out_plane = out(cv::Rect(0, i*outH, outW, outH)); + cv::resize(in_plane, out_plane, out_sz, 0, 0, interp); + } + } +}; + GAPI_OCV_KERNEL(GCPUSepFilter, cv::gapi::imgproc::GSepFilter) { static void run(const cv::Mat& in, int ddepth, const cv::Mat& kernX, const cv::Mat& kernY, const cv::Point& anchor, const cv::Scalar& delta, @@ -613,10 +637,12 @@ GAPI_OCV_KERNEL(GCPUNV12toBGRp, cv::gapi::imgproc::GNV12toBGRp) } }; -cv::gapi::GKernelPackage cv::gapi::imgproc::cpu::kernels() +cv::GKernelPackage cv::gapi::imgproc::cpu::kernels() { static auto pkg = cv::gapi::kernels < GCPUFilter2D + , GCPUResize + , GCPUResizeP , GCPUSepFilter , GCPUBoxFilter , GCPUBlur diff --git a/modules/gapi/src/backends/cpu/gcpustereo.cpp b/modules/gapi/src/backends/cpu/gcpustereo.cpp index 9b51d23992d5..6aec90d30a7b 100644 --- a/modules/gapi/src/backends/cpu/gcpustereo.cpp +++ b/modules/gapi/src/backends/cpu/gcpustereo.cpp @@ -70,14 +70,14 @@ GAPI_OCV_KERNEL_ST(GCPUStereo, cv::gapi::calib3d::GStereo, StereoSetup) } }; -cv::gapi::GKernelPackage cv::gapi::calib3d::cpu::kernels() { +cv::GKernelPackage cv::gapi::calib3d::cpu::kernels() { static auto pkg = cv::gapi::kernels(); return pkg; } #else -cv::gapi::GKernelPackage cv::gapi::calib3d::cpu::kernels() +cv::GKernelPackage cv::gapi::calib3d::cpu::kernels() { return GKernelPackage(); } diff --git a/modules/gapi/src/backends/cpu/gcpuvideo.cpp b/modules/gapi/src/backends/cpu/gcpuvideo.cpp index cf64c240d07c..b8fa82f0eb6d 100644 --- a/modules/gapi/src/backends/cpu/gcpuvideo.cpp +++ b/modules/gapi/src/backends/cpu/gcpuvideo.cpp @@ -174,7 +174,7 @@ GAPI_OCV_KERNEL_ST(GCPUKalmanFilterNoControl, cv::gapi::video::GKalmanFilterNoCo } }; -cv::gapi::GKernelPackage cv::gapi::video::cpu::kernels() +cv::GKernelPackage cv::gapi::video::cpu::kernels() { static auto pkg = cv::gapi::kernels < GCPUBuildOptFlowPyramid @@ -189,7 +189,7 @@ cv::gapi::GKernelPackage cv::gapi::video::cpu::kernels() #else -cv::gapi::GKernelPackage cv::gapi::video::cpu::kernels() +cv::GKernelPackage cv::gapi::video::cpu::kernels() { return GKernelPackage(); } diff --git a/modules/gapi/src/backends/fluid/gfluidbackend.cpp b/modules/gapi/src/backends/fluid/gfluidbackend.cpp index 0e33ca9c0fbb..ed4dda7d498b 100644 --- a/modules/gapi/src/backends/fluid/gfluidbackend.cpp +++ b/modules/gapi/src/backends/fluid/gfluidbackend.cpp @@ -2,7 +2,7 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2018-2020 Intel Corporation +// Copyright (C) 2018-2022 Intel Corporation #include "precomp.hpp" @@ -954,7 +954,7 @@ namespace GFluidModel fg(graph); for (const auto& node : g.nodes()) { - if (g.metadata(node).get().t == NodeType::DATA) + if (fg.metadata(node).contains()) { auto& fd = fg.metadata(node).get(); fd.latency = 0; diff --git a/modules/gapi/src/backends/fluid/gfluidcore.cpp b/modules/gapi/src/backends/fluid/gfluidcore.cpp index 105ae7ce72c5..7a8f1f5ed8b5 100644 --- a/modules/gapi/src/backends/fluid/gfluidcore.cpp +++ b/modules/gapi/src/backends/fluid/gfluidcore.cpp @@ -23,10 +23,6 @@ #include #include -#if CV_SSE4_1 -#include "gfluidcore_simd_sse41.hpp" -#endif - #include "gfluidbuffer_priv.hpp" #include "gfluidbackend.hpp" #include "gfluidutils.hpp" @@ -382,271 +378,11 @@ CV_ALWAYS_INLINE int absdiff_simd(const T in1[], const T in2[], T out[], int len return 0; } - -template -CV_ALWAYS_INLINE int add_simd_sametype(const T in1[], const T in2[], T out[], int length) -{ - constexpr int nlanes = static_cast(VT::nlanes); - - if (length < nlanes) - return 0; - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - VT a = vx_load(&in1[x]); - VT b = vx_load(&in2[x]); - vx_store(&out[x], a + b); - } - - if (x < length && (in1 != out) && (in2 != out)) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - - return x; -} - -template -CV_ALWAYS_INLINE int add_simd(const SRC in1[], const SRC in2[], DST out[], int length) -{ - if (std::is_same::value && !std::is_same::value) - return 0; - - if (std::is_same::value) - { - if (std::is_same::value) - { - return add_simd_sametype(reinterpret_cast(in1), - reinterpret_cast(in2), - reinterpret_cast(out), length); - } - else if (std::is_same::value) - { - return add_simd_sametype(reinterpret_cast(in1), - reinterpret_cast(in2), - reinterpret_cast(out), length); - } - else if (std::is_same::value) - { - return add_simd_sametype(reinterpret_cast(in1), - reinterpret_cast(in2), - reinterpret_cast(out), length); - } - } - else if (std::is_same::value && std::is_same::value) - { - constexpr int nlanes = static_cast(v_uint8::nlanes); - - if (length < nlanes) - return 0; - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_int16 a1 = vx_load(reinterpret_cast(&in1[x])); - v_int16 a2 = vx_load(reinterpret_cast(&in1[x + nlanes / 2])); - v_int16 b1 = vx_load(reinterpret_cast(&in2[x])); - v_int16 b2 = vx_load(reinterpret_cast(&in2[x + nlanes / 2])); - - vx_store(reinterpret_cast(&out[x]), v_pack_u(a1 + b1, a2 + b2)); - } - - if (x < length) - { - CV_DbgAssert((reinterpret_cast(in1) != reinterpret_cast(out)) && - (reinterpret_cast(in2) != reinterpret_cast(out))); - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - - return x; - } - else if (std::is_same::value && std::is_same::value) - { - constexpr int nlanes = static_cast(v_uint8::nlanes); - - if (length < nlanes) - return 0; - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vx_load(reinterpret_cast(&in1[x])); - v_float32 a2 = vx_load(reinterpret_cast(&in1[x + nlanes / 4])); - v_float32 a3 = vx_load(reinterpret_cast(&in1[x + 2 * nlanes / 4])); - v_float32 a4 = vx_load(reinterpret_cast(&in1[x + 3 * nlanes / 4])); - - v_float32 b1 = vx_load(reinterpret_cast(&in2[x])); - v_float32 b2 = vx_load(reinterpret_cast(&in2[x + nlanes / 4])); - v_float32 b3 = vx_load(reinterpret_cast(&in2[x + 2 * nlanes / 4])); - v_float32 b4 = vx_load(reinterpret_cast(&in2[x + 3 * nlanes / 4])); - - vx_store(reinterpret_cast(&out[x]), v_pack_u(v_pack(v_round(a1 + b1), v_round(a2 + b2)), - v_pack(v_round(a3 + b3), v_round(a4 + b4)))); - } - - if (x < length) - { - CV_DbgAssert((reinterpret_cast(in1) != reinterpret_cast(out)) && - (reinterpret_cast(in2) != reinterpret_cast(out))); - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - - return x; - } - - return 0; -} - -template -CV_ALWAYS_INLINE int sub_simd_sametype(const T in1[], const T in2[], T out[], int length) -{ - constexpr int nlanes = static_cast(VT::nlanes); - - if (length < nlanes) - return 0; - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - VT a = vx_load(&in1[x]); - VT b = vx_load(&in2[x]); - vx_store(&out[x], a - b); - } - - if (x < length && (in1 != out) && (in2 != out)) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - - return x; -} - -template -CV_ALWAYS_INLINE int sub_simd(const SRC in1[], const SRC in2[], DST out[], int length) -{ - if (std::is_same::value && !std::is_same::value) - return 0; - - if (std::is_same::value) - { - if (std::is_same::value) - { - return sub_simd_sametype(reinterpret_cast(in1), - reinterpret_cast(in2), - reinterpret_cast(out), length); - } - else if (std::is_same::value) - { - return sub_simd_sametype(reinterpret_cast(in1), - reinterpret_cast(in2), - reinterpret_cast(out), length); - } - else if (std::is_same::value) - { - return sub_simd_sametype(reinterpret_cast(in1), - reinterpret_cast(in2), - reinterpret_cast(out), length); - } - } - else if (std::is_same::value && std::is_same::value) - { - constexpr int nlanes = static_cast(v_uint8::nlanes); - - if (length < nlanes) - return 0; - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_int16 a1 = vx_load(reinterpret_cast(&in1[x])); - v_int16 a2 = vx_load(reinterpret_cast(&in1[x + nlanes / 2])); - v_int16 b1 = vx_load(reinterpret_cast(&in2[x])); - v_int16 b2 = vx_load(reinterpret_cast(&in2[x + nlanes / 2])); - - vx_store(reinterpret_cast(&out[x]), v_pack_u(a1 - b1, a2 - b2)); - } - - if (x < length) - { - CV_DbgAssert((reinterpret_cast(in1) != reinterpret_cast(out)) && - (reinterpret_cast(in2) != reinterpret_cast(out))); - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - - return x; - } - else if (std::is_same::value && std::is_same::value) - { - constexpr int nlanes = static_cast(v_uint8::nlanes); - - if (length < nlanes) - return 0; - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vx_load(reinterpret_cast(&in1[x])); - v_float32 a2 = vx_load(reinterpret_cast(&in1[x + nlanes / 4])); - v_float32 a3 = vx_load(reinterpret_cast(&in1[x + 2 * nlanes / 4])); - v_float32 a4 = vx_load(reinterpret_cast(&in1[x + 3 * nlanes / 4])); - - v_float32 b1 = vx_load(reinterpret_cast(&in2[x])); - v_float32 b2 = vx_load(reinterpret_cast(&in2[x + nlanes / 4])); - v_float32 b3 = vx_load(reinterpret_cast(&in2[x + 2 * nlanes / 4])); - v_float32 b4 = vx_load(reinterpret_cast(&in2[x + 3 * nlanes / 4])); - - vx_store(reinterpret_cast(&out[x]), v_pack_u(v_pack(v_round(a1 - b1), v_round(a2 - b2)), - v_pack(v_round(a3 - b3), v_round(a4 - b4)))); - } - - if (x < length) - { - CV_DbgAssert((reinterpret_cast(in1) != reinterpret_cast(out)) && - (reinterpret_cast(in2) != reinterpret_cast(out))); - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - - return x; - } - - return 0; -} #endif // CV_SIMD template -static CV_ALWAYS_INLINE void run_arithm(Buffer &dst, const View &src1, const View &src2, - Arithm arithm, double scale=1) +CV_ALWAYS_INLINE void run_arithm(Buffer &dst, const View &src1, const View &src2, + Arithm arithm, double scale=1) { static_assert(std::is_same::value, "wrong types"); @@ -656,7 +392,7 @@ static CV_ALWAYS_INLINE void run_arithm(Buffer &dst, const View &src1, const Vie int width = dst.length(); int chan = dst.meta().chan; - int length = width * chan; + const int length = width * chan; // NB: assume in/out types are not 64-bits float _scale = static_cast( scale ); @@ -712,13 +448,22 @@ GAPI_FLUID_KERNEL(GFluidAdd, cv::gapi::core::GAdd, false) static void run(const View &src1, const View &src2, int /*dtype*/, Buffer &dst) { // DST SRC1 SRC2 OP __VA_ARGS__ - BINARY_(uchar , uchar , uchar , run_arithm, dst, src1, src2, ARITHM_ADD); - BINARY_(uchar , short, short, run_arithm, dst, src1, src2, ARITHM_ADD); - BINARY_(uchar , float, float, run_arithm, dst, src1, src2, ARITHM_ADD); - BINARY_( short, short, short, run_arithm, dst, src1, src2, ARITHM_ADD); - BINARY_( float, uchar , uchar , run_arithm, dst, src1, src2, ARITHM_ADD); - BINARY_( float, short, short, run_arithm, dst, src1, src2, ARITHM_ADD); - BINARY_( float, float, float, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(uchar, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(uchar, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(uchar, short, short, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(uchar, float, float, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(short, short, short, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(short, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(short, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(short, float, float, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(ushort, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(ushort, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(ushort, short, short, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(ushort, float, float, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(float, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(float, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(float, short, short, run_arithm, dst, src1, src2, ARITHM_ADD); + BINARY_(float, float, float, run_arithm, dst, src1, src2, ARITHM_ADD); CV_Error(cv::Error::StsBadArg, "unsupported combination of types"); } @@ -732,10 +477,19 @@ GAPI_FLUID_KERNEL(GFluidSub, cv::gapi::core::GSub, false) { // DST SRC1 SRC2 OP __VA_ARGS__ BINARY_(uchar , uchar , uchar , run_arithm, dst, src1, src2, ARITHM_SUBTRACT); - BINARY_(uchar , short, short, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); - BINARY_(uchar , float, float, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); - BINARY_( short, short, short, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); - BINARY_( float, uchar , uchar , run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(uchar, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(uchar, short, short, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(uchar, float, float, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(short, short, short, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(short, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(short, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(short, float, float, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(ushort, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(ushort, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(ushort, short, short, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(ushort, float, float, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(float, uchar, uchar, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); + BINARY_(float, ushort, ushort, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); BINARY_( float, short, short, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); BINARY_( float, float, float, run_arithm, dst, src1, src2, ARITHM_SUBTRACT); @@ -890,25 +644,6 @@ static void run_arithm_s(DST out[], const SRC in[], int width, int chan, CV_Error(cv::Error::StsBadArg, "unsupported number of channels"); } -template -static void run_absdiffc(Buffer &dst, const View &src, const float scalar[]) -{ - const auto *in = src.InLine(0); - auto *out = dst.OutLine(); - - int width = dst.length(); - int chan = dst.meta().chan; - const int length = width * chan; - - int w = 0; -#if CV_SIMD - w = absdiffc_simd(in, scalar, out, length, chan); -#endif - - for (; w < length; ++w) - out[w] = absdiff(in[w], scalar[w%chan]); -} - template CV_ALWAYS_INLINE void run_arithm_s(Buffer &dst, const View &src, const float scalar[], Arithm arithm, float scale=1) @@ -954,18 +689,13 @@ CV_ALWAYS_INLINE void run_arithm_s(Buffer &dst, const View &src, const float sca out[chan * w + c] = mul(in[chan * w + c], scalar[c], scale); break; } - case ARITHM_DIVIDE: - for (int w=0; w < width; w++) - for (int c=0; c < chan; c++) - out[chan*w + c] = div(in[chan*w + c], scalar[c], scale); - break; default: CV_Error(cv::Error::StsBadArg, "unsupported arithmetic operation"); } } template -static void run_arithm_rs(Buffer &dst, const View &src, const float scalar[4], Arithm arithm, - float scale=1) +CV_ALWAYS_INLINE void run_arithm_rs(Buffer &dst, const View &src, const float scalar[], + Arithm arithm, float scale=1) { const auto *in = src.InLine(0); auto *out = dst.OutLine(); @@ -983,19 +713,35 @@ static void run_arithm_rs(Buffer &dst, const View &src, const float scalar[4], A w = subrc_simd(scalar, in, out, length, chan); #endif for (; w < length; ++w) + { out[w] = subr(in[w], scalar[w % chan]); + } break; } - // TODO: optimize division case ARITHM_DIVIDE: - for (int w=0; w < width; w++) - for (int c=0; c < chan; c++) - out[chan*w + c] = div(scalar[c], in[chan*w + c], scale); + { + int w = 0; +#if CV_SIMD + w = divrc_simd(scalar, in, out, length, chan, scale); +#endif + for (; w < length; ++w) + { + out[w] = div(scalar[w % chan], in[w], scale); + } break; + } default: CV_Error(cv::Error::StsBadArg, "unsupported arithmetic operation"); } } +CV_ALWAYS_INLINE void setScratchSize(Buffer& scratch, const int buflen) +{ + cv::Size bufsize(buflen, 1); + GMatDesc bufdesc = { CV_32F, 1, bufsize }; + Buffer buffer(bufdesc); + scratch = std::move(buffer); +} + CV_ALWAYS_INLINE void initScratchBuffer(Buffer& scratch) { #if CV_SIMD @@ -1016,25 +762,47 @@ CV_ALWAYS_INLINE void initScratchBuffer(Buffer& scratch) #else constexpr int buflen = 4; #endif - cv::Size bufsize(buflen, 1); - GMatDesc bufdesc = { CV_32F, 1, bufsize }; - Buffer buffer(bufdesc); - scratch = std::move(buffer); + setScratchSize(scratch, buflen); +} + +CV_ALWAYS_INLINE void scalar_to_scratch(const cv::Scalar& scalar, + float scratch[], const int length, const int chan) +{ + for (int i = 0; i < length; ++i) + scratch[i] = static_cast(scalar[i % chan]); +} + +template +CV_ALWAYS_INLINE void run_absdiffc(Buffer& dst, const View& src, const float scalar[]) +{ + const auto* in = src.InLine(0); + auto* out = dst.OutLine(); + + int width = dst.length(); + int chan = dst.meta().chan; + const int length = width * chan; + + int w = 0; +#if CV_SIMD + w = absdiffc_simd(in, scalar, out, length, chan); +#endif + + for (; w < length; ++w) + out[w] = absdiff(in[w], scalar[w % chan]); } GAPI_FLUID_KERNEL(GFluidAbsDiffC, cv::gapi::core::GAbsDiffC, true) { static const int Window = 1; - static void run(const View &src, const cv::Scalar& _scalar, Buffer &dst, Buffer& scratch) + static void run(const View& src, const cv::Scalar& _scalar, Buffer& dst, Buffer& scratch) { if (dst.y() == 0) { const int chan = src.meta().chan; - float* sc = scratch.OutLine(); + float* _scratch = scratch.OutLine(); - for (int i = 0; i < scratch.length(); ++i) - sc[i] = static_cast(_scalar[i % chan]); + scalar_to_scratch(_scalar, _scratch, scratch.length(), chan); } const float* scalar = scratch.OutLine(); @@ -1062,17 +830,16 @@ GAPI_FLUID_KERNEL(GFluidAddC, cv::gapi::core::GAddC, true) { static const int Window = 1; - static void run(const View &src, const cv::Scalar &_scalar, int /*dtype*/, Buffer &dst, Buffer &scratch) + static void run(const View& src, const cv::Scalar& _scalar, int /*dtype*/, Buffer& dst, Buffer& scratch) { GAPI_Assert(src.meta().chan <= 4); if (dst.y() == 0) { const int chan = src.meta().chan; - float* sc = scratch.OutLine(); + float* _scratch = scratch.OutLine(); - for (int i = 0; i < scratch.length(); ++i) - sc[i] = static_cast(_scalar[i % chan]); + scalar_to_scratch(_scalar, _scratch, scratch.length(), chan); } const float* scalar = scratch.OutLine(); @@ -1119,10 +886,9 @@ GAPI_FLUID_KERNEL(GFluidSubC, cv::gapi::core::GSubC, true) if (dst.y() == 0) { const int chan = src.meta().chan; - float* sc = scratch.OutLine(); + float* _scratch = scratch.OutLine(); - for (int i = 0; i < scratch.length(); ++i) - sc[i] = static_cast(_scalar[i % chan]); + scalar_to_scratch(_scalar, _scratch, scratch.length(), chan); } const float* scalar = scratch.OutLine(); @@ -1169,10 +935,9 @@ GAPI_FLUID_KERNEL(GFluidSubRC, cv::gapi::core::GSubRC, true) if (dst.y() == 0) { const int chan = src.meta().chan; - float* sc = scratch.OutLine(); + float* _scratch = scratch.OutLine(); - for (int i = 0; i < scratch.length(); ++i) - sc[i] = static_cast(_scalar[i % chan]); + scalar_to_scratch(_scalar, _scratch, scratch.length(), chan); } const float* scalar = scratch.OutLine(); @@ -1220,10 +985,9 @@ GAPI_FLUID_KERNEL(GFluidMulC, cv::gapi::core::GMulC, true) if (dst.y() == 0) { const int chan = src.meta().chan; - float* sc = scratch.OutLine(); + float* _scratch = scratch.OutLine(); - for (int i = 0; i < scratch.length(); ++i) - sc[i] = static_cast(_scalar[i % chan]); + scalar_to_scratch(_scalar, _scratch, scratch.length(), chan); } const float* scalar = scratch.OutLine(); const float scale = 1.0; @@ -1263,7 +1027,7 @@ GAPI_FLUID_KERNEL(GFluidMulCOld, cv::gapi::core::GMulCOld, true) { static const int Window = 1; - static void run(const View &src, double _scalar, int /*dtype*/, Buffer &dst, Buffer& scratch) + static void run(const View& src, double _scalar, int /*dtype*/, Buffer& dst, Buffer& scratch) { GAPI_Assert(src.meta().chan <= 4); @@ -1299,60 +1063,162 @@ GAPI_FLUID_KERNEL(GFluidMulCOld, cv::gapi::core::GMulCOld, true) } }; -GAPI_FLUID_KERNEL(GFluidDivC, cv::gapi::core::GDivC, false) +template +CV_ALWAYS_INLINE void run_divc(Buffer& dst, const View& src, Buffer& scratch, + float scale) +{ + const auto* in = src.InLine(0); + auto* out = dst.OutLine(); + const float* scalar = scratch.OutLine(); + + int width = dst.length(); + int chan = dst.meta().chan; + const int length = width * chan; + + int w = 0; +#if CV_SIMD + int scratch_length = scratch.length(); + int indicator_offset = scratch_length - 1; + const int set_mask_indicator = static_cast(*(scratch.OutLine() + (indicator_offset))); + + w = divc_simd(in, scalar, out, length, chan, scale, set_mask_indicator); +#endif + + for (; w < length; ++w) + { + out[w] = div(in[w], scalar[w % chan], scale); + } +} + +GAPI_FLUID_KERNEL(GFluidDivC, cv::gapi::core::GDivC, true) { static const int Window = 1; - static void run(const View &src, const cv::Scalar &_scalar, double _scale, int /*dtype*/, - Buffer &dst) + static void run(const View& src, const cv::Scalar& _scalar, double _scale, int /*dtype*/, + Buffer& dst, Buffer& scratch) { - const float scalar[4] = { - static_cast(_scalar[0]), - static_cast(_scalar[1]), - static_cast(_scalar[2]), - static_cast(_scalar[3]) - }; - const float scale = static_cast(_scale); + GAPI_Assert(src.meta().chan <= 4); + + if (dst.y() == 0) + { + const int chan = src.meta().chan; + float* _scratch = scratch.OutLine(); + int scratch_length = scratch.length(); + + scalar_to_scratch(_scalar, _scratch, scratch_length - 1, chan); + + _scratch[scratch_length - 1] = 0.0; + for (int j = 0; j < chan; ++j) + { + if (std::fabs(static_cast(_scalar[j])) <= FLT_EPSILON) + { + _scratch[scratch_length - 1] = 1.0; + break; + } + } + } + + float scale = static_cast(_scale); // DST SRC OP __VA_ARGS__ - UNARY_(uchar , uchar , run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_(uchar , short, run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_(uchar , float, run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( short, short, run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( float, uchar , run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( float, short, run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( float, float, run_arithm_s, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(uchar, uchar, run_divc, dst, src, scratch, scale); + UNARY_(uchar, ushort, run_divc, dst, src, scratch, scale); + UNARY_(uchar, short, run_divc, dst, src, scratch, scale); + UNARY_(uchar, float, run_divc, dst, src, scratch, scale); + UNARY_(ushort, ushort, run_divc, dst, src, scratch, scale); + UNARY_(ushort, uchar, run_divc, dst, src, scratch, scale); + UNARY_(ushort, short, run_divc, dst, src, scratch, scale); + UNARY_(ushort, float, run_divc, dst, src, scratch, scale); + UNARY_(short, short, run_divc, dst, src, scratch, scale); + UNARY_(short, ushort, run_divc, dst, src, scratch, scale); + UNARY_(short, uchar, run_divc, dst, src, scratch, scale); + UNARY_(short, float, run_divc, dst, src, scratch, scale); + UNARY_(float, uchar, run_divc, dst, src, scratch, scale); + UNARY_(float, short, run_divc, dst, src, scratch, scale); + UNARY_(float, ushort, run_divc, dst, src, scratch, scale); + UNARY_(float, float, run_divc, dst, src, scratch, scale); CV_Error(cv::Error::StsBadArg, "unsupported combination of types"); } + + static void initScratch(const GMatDesc&, const GScalarDesc&, double, int, Buffer& scratch) + { +#if CV_SIMD + // 512 bits / 32 bits = 16 elements of float32 a AVX512 SIMD vector can contain. + constexpr int maxNlanes = 16; + + // +2 is offset for 3-channel case. + // Offset is need to right load coefficients from scalar array to SIMD vectors for 3-channel case. + // Scalar array looks like: scalar[] = {C1, C2, C3, C1, C2, C3, ...} + // The first scalar SIMD vector should looks like: + // C1 C2 C3 C1 + // The second: + // C2 C3 C1 C2 + // The third: + // C3 C1 C2 C3 + constexpr int offset = 2; + constexpr int zero_scalar_elem_indicator = 1; + constexpr int buflen = maxNlanes + offset + zero_scalar_elem_indicator; +#else + constexpr int buflen = 4; +#endif + setScratchSize(scratch, buflen); + } + + static void resetScratch(Buffer& /*scratch*/) + { + } }; -GAPI_FLUID_KERNEL(GFluidDivRC, cv::gapi::core::GDivRC, false) +GAPI_FLUID_KERNEL(GFluidDivRC, cv::gapi::core::GDivRC, true) { static const int Window = 1; - static void run(const cv::Scalar &_scalar, const View &src, double _scale, int /*dtype*/, - Buffer &dst) + static void run(const cv::Scalar& _scalar, const View& src, double _scale, int /*dtype*/, + Buffer& dst, Buffer& scratch) { - const float scalar[4] = { - static_cast(_scalar[0]), - static_cast(_scalar[1]), - static_cast(_scalar[2]), - static_cast(_scalar[3]) - }; + GAPI_Assert(src.meta().chan <= 4); + + if (dst.y() == 0) + { + const int chan = src.meta().chan; + float* _scratch = scratch.OutLine(); + + scalar_to_scratch(_scalar, _scratch, scratch.length(), chan); + } + + const float* scalar = scratch.OutLine(); const float scale = static_cast(_scale); // DST SRC OP __VA_ARGS__ - UNARY_(uchar , uchar , run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_(uchar , short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_(uchar , float, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( short, short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( float, uchar , run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( float, short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); - UNARY_( float, float, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(uchar, uchar, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(uchar, ushort, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(uchar, short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(uchar, float, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(ushort, ushort, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(ushort, uchar, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(ushort, short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(ushort, float, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(short, short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(short, uchar, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(short, ushort, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(short, float, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(float, uchar, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(float, ushort, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(float, short, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); + UNARY_(float, float, run_arithm_rs, dst, src, scalar, ARITHM_DIVIDE, scale); CV_Error(cv::Error::StsBadArg, "unsupported combination of types"); } + + static void initScratch(const GScalarDesc&, const GMatDesc&, double, int, Buffer& scratch) + { + initScratchBuffer(scratch); + } + + static void resetScratch(Buffer& /*scratch*/) + { + } }; //------------------- @@ -1689,102 +1555,43 @@ GAPI_FLUID_KERNEL(GFluidLUT, cv::gapi::core::GLUT, false) // //------------------------- -#if CV_SIMD128 -template -CV_ALWAYS_INLINE int run_convertto_simd(DST*, const SRC*, int) -{ - return 0; -} -CV_ALWAYS_INLINE int run_convertto_simd(uchar *out, const float *in, const int length) -{ - int l = 0; - for (; l <= length - 16; l += 16) - { - v_int32x4 i0, i1, i2, i3; - i0 = v_round( v_load( (float*)& in[l ] ) ); - i1 = v_round( v_load( (float*)& in[l + 4] ) ); - i2 = v_round( v_load( (float*)& in[l + 8] ) ); - i3 = v_round( v_load( (float*)& in[l + 12] ) ); - - v_uint16x8 us0, us1; - us0 = v_pack_u(i0, i1); - us1 = v_pack_u(i2, i3); - - v_uint8x16 uc; - uc = v_pack(us0, us1); - v_store((uchar*)& out[l], uc); - } - return l; -} -CV_ALWAYS_INLINE int run_convertto_simd(ushort *out, const float *in, const int length) +template +CV_ALWAYS_INLINE void convertto_impl(const T in[], T out[], const int length) { - int l = 0; - for (; l <= length - 8; l += 8) - { - v_int32x4 i0, i1; - i0 = v_round( v_load( (float*)& in[l ] ) ); - i1 = v_round( v_load( (float*)& in[l + 4] ) ); - - v_uint16x8 us; - us = v_pack_u(i0, i1); - v_store((ushort*)& out[l], us); - } - return l; + memcpy(out, in, length * sizeof(T)); } -#endif -template::value && - std::is_floating_point::value, bool> = true > -CV_ALWAYS_INLINE void run_convertto(DST *out, const SRC *in, const int length) +template +CV_ALWAYS_INLINE void convertto_impl(const SRC in[], DST out[], const int length) { - // manual SIMD if need rounding - static_assert(std::is_same::value, "64-bit floating-point source is not supported"); - int l = 0; // cycle index -#if CV_SIMD128 - l = run_convertto_simd(out, in, length); + int x = 0; +#if CV_SIMD + x = convertto_simd(in, out, length); #endif // tail of SIMD cycle - for (; l < length; l++) - { - out[l] = saturate(in[l], rintf); - } -} -template::value && - std::is_integral::value , bool> = true > -CV_ALWAYS_INLINE void run_convertto(DST *out, const SRC *in, const int length) -{ - for (int l = 0; l < length; l++) - { - out[l] = saturate(in[l]); - } -} -template::value, bool> = true > -CV_ALWAYS_INLINE void run_convertto(DST *out, const SRC *in, const int length) -{ - static_assert(!std::is_same::value, "64-bit floating-point source is not supported"); - for (int l = 0; l < length; l++) + for (; x < length; ++x) { - out[l] = static_cast(in[l]); + out[x] = saturate(in[x], rintf); } } -template -CV_ALWAYS_INLINE void run_convertto(DST *out, const SRC *in, const float alpha, const float beta, - const int length) +template +CV_ALWAYS_INLINE void convertto_impl(const SRC *in, DST* out, const float alpha, const float beta, + const int length) { - static_assert(!std::is_same::value, "64-bit floating-point source is not supported"); - // TODO: optimize if alpha and beta and data are integral - for (int l = 0; l < length; l++) + int x = 0; +#if CV_SIMD + x = convertto_scaled_simd(in, out, alpha, beta, length); +#endif + + for (; x < length; ++x) { - out[l] = saturate(in[l] * alpha + beta, rintf); + out[x] = saturate(in[x] * alpha + beta, rintf); } } template -static void run_convertto(Buffer &dst, const View &src, double _alpha, double _beta) +CV_ALWAYS_INLINE void run_convertto(Buffer &dst, const View &src, double _alpha, double _beta) { const auto *in = src.InLine(0); auto *out = dst.OutLine(); @@ -1798,13 +1605,13 @@ static void run_convertto(Buffer &dst, const View &src, double _alpha, double _b const auto beta = static_cast( _beta ); // compute faster if no alpha no beta - if (1.f == alpha && 0.f == beta) + if ((std::fabs(alpha - 1.f) < FLT_EPSILON) && (std::fabs(beta) < FLT_EPSILON)) { - run_convertto(out, in, length); + convertto_impl(in, out, length); } else // if alpha or beta is non-trivial { - run_convertto(out, in, alpha, beta, length); + convertto_impl(in, out, alpha, beta, length); } } @@ -1815,22 +1622,22 @@ GAPI_FLUID_KERNEL(GFluidConvertTo, cv::gapi::core::GConvertTo, false) static void run(const View &src, int /*rtype*/, double alpha, double beta, Buffer &dst) { // DST SRC OP __VA_ARGS__ - UNARY_(uchar , uchar , run_convertto, dst, src, alpha, beta); - UNARY_(uchar , ushort, run_convertto, dst, src, alpha, beta); - UNARY_(uchar , short, run_convertto, dst, src, alpha, beta); - UNARY_(uchar , float, run_convertto, dst, src, alpha, beta); + UNARY_(uchar, uchar , run_convertto, dst, src, alpha, beta); + UNARY_(uchar, ushort, run_convertto, dst, src, alpha, beta); + UNARY_(uchar, short, run_convertto, dst, src, alpha, beta); + UNARY_(uchar, float, run_convertto, dst, src, alpha, beta); UNARY_(ushort, uchar , run_convertto, dst, src, alpha, beta); UNARY_(ushort, ushort, run_convertto, dst, src, alpha, beta); UNARY_(ushort, short, run_convertto, dst, src, alpha, beta); UNARY_(ushort, float, run_convertto, dst, src, alpha, beta); - UNARY_( short, uchar , run_convertto, dst, src, alpha, beta); - UNARY_( short, ushort, run_convertto, dst, src, alpha, beta); - UNARY_( short, short, run_convertto, dst, src, alpha, beta); - UNARY_( short, float, run_convertto, dst, src, alpha, beta); - UNARY_( float, uchar , run_convertto, dst, src, alpha, beta); - UNARY_( float, ushort, run_convertto, dst, src, alpha, beta); - UNARY_( float, short, run_convertto, dst, src, alpha, beta); - UNARY_( float, float, run_convertto, dst, src, alpha, beta); + UNARY_(short, uchar , run_convertto, dst, src, alpha, beta); + UNARY_(short, ushort, run_convertto, dst, src, alpha, beta); + UNARY_(short, short, run_convertto, dst, src, alpha, beta); + UNARY_(short, float, run_convertto, dst, src, alpha, beta); + UNARY_(float, uchar , run_convertto, dst, src, alpha, beta); + UNARY_(float, ushort, run_convertto, dst, src, alpha, beta); + UNARY_(float, short, run_convertto, dst, src, alpha, beta); + UNARY_(float, float, run_convertto, dst, src, alpha, beta); CV_Error(cv::Error::StsBadArg, "unsupported combination of types"); } @@ -2513,26 +2320,18 @@ GAPI_FLUID_KERNEL(GFluidSplit3, cv::gapi::core::GSplit3, false) static void run(const View &src, Buffer &dst1, Buffer &dst2, Buffer &dst3) { - const auto *in = src.InLine(0); + const auto *in = src.InLine(0); auto *out1 = dst1.OutLine(); auto *out2 = dst2.OutLine(); auto *out3 = dst3.OutLine(); GAPI_Assert(3 == src.meta().chan); int width = src.length(); + int w = 0; - int w = 0; // cycle counter - - #if CV_SIMD128 - for (; w <= width-16; w+=16) - { - v_uint8x16 a, b, c; - v_load_deinterleave(&in[3*w], a, b, c); - v_store(&out1[w], a); - v_store(&out2[w], b); - v_store(&out3[w], c); - } - #endif +#if CV_SIMD + w = split3_simd(in, out1, out2, out3, width); +#endif for (; w < width; w++) { @@ -2549,7 +2348,7 @@ GAPI_FLUID_KERNEL(GFluidSplit4, cv::gapi::core::GSplit4, false) static void run(const View &src, Buffer &dst1, Buffer &dst2, Buffer &dst3, Buffer &dst4) { - const auto *in = src.InLine(0); + const auto *in = src.InLine(0); auto *out1 = dst1.OutLine(); auto *out2 = dst2.OutLine(); auto *out3 = dst3.OutLine(); @@ -2557,19 +2356,10 @@ GAPI_FLUID_KERNEL(GFluidSplit4, cv::gapi::core::GSplit4, false) GAPI_Assert(4 == src.meta().chan); int width = src.length(); + int w = 0; - int w = 0; // cycle counter - - #if CV_SIMD128 - for (; w <= width-16; w+=16) - { - v_uint8x16 a, b, c, d; - v_load_deinterleave(&in[4*w], a, b, c, d); - v_store(&out1[w], a); - v_store(&out2[w], b); - v_store(&out3[w], c); - v_store(&out4[w], d); - } + #if CV_SIMD + w = split4_simd(in, out1, out2, out3, out4, width); #endif for (; w < width; w++) @@ -2595,18 +2385,10 @@ GAPI_FLUID_KERNEL(GFluidMerge3, cv::gapi::core::GMerge3, false) GAPI_Assert(3 == dst.meta().chan); int width = dst.length(); + int w = 0; - int w = 0; // cycle counter - - #if CV_SIMD128 - for (; w <= width-16; w+=16) - { - v_uint8x16 a, b, c; - a = v_load(&in1[w]); - b = v_load(&in2[w]); - c = v_load(&in3[w]); - v_store_interleave(&out[3*w], a, b, c); - } + #if CV_SIMD + w = merge3_simd(in1, in2, in3, out, width); #endif for (; w < width; w++) @@ -2636,16 +2418,8 @@ GAPI_FLUID_KERNEL(GFluidMerge4, cv::gapi::core::GMerge4, false) int w = 0; // cycle counter - #if CV_SIMD128 - for (; w <= width-16; w+=16) - { - v_uint8x16 a, b, c, d; - a = v_load(&in1[w]); - b = v_load(&in2[w]); - c = v_load(&in3[w]); - d = v_load(&in4[w]); - v_store_interleave(&out[4*w], a, b, c, d); - } + #if CV_SIMD + w = merge4_simd(in1, in2, in3, in4, out, width); #endif for (; w < width; w++) @@ -2760,301 +2534,6 @@ GAPI_FLUID_KERNEL(GFluidPhase, cv::gapi::core::GPhase, false) } }; -template -struct LinearScratchDesc { - using alpha_t = typename Mapper::alpha_type; - using index_t = typename Mapper::index_type; - - alpha_t* alpha; - alpha_t* clone; - index_t* mapsx; - alpha_t* beta; - index_t* mapsy; - T* tmp; - - LinearScratchDesc(int /*inW*/, int /*inH*/, int outW, int outH, void* data) { - alpha = reinterpret_cast(data); - clone = reinterpret_cast(alpha + outW); - mapsx = reinterpret_cast(clone + outW*4); - beta = reinterpret_cast(mapsx + outW); - mapsy = reinterpret_cast(beta + outH); - tmp = reinterpret_cast (mapsy + outH*2); - } - - static int bufSize(int inW, int /*inH*/, int outW, int outH, int lpi) { - auto size = outW * sizeof(alpha_t) + - outW * sizeof(alpha_t) * 4 + // alpha clones - outW * sizeof(index_t) + - outH * sizeof(alpha_t) + - outH * sizeof(index_t) * 2 + - inW * sizeof(T) * lpi * chanNum; - - return static_cast(size); - } -}; -static inline double invRatio(int inSz, int outSz) { - return static_cast(outSz) / inSz; -} - -static inline double ratio(int inSz, int outSz) { - return 1 / invRatio(inSz, outSz); -} - -template -static inline void initScratchLinear(const cv::GMatDesc& in, - const Size& outSz, - cv::gapi::fluid::Buffer& scratch, - int lpi) { - using alpha_type = typename Mapper::alpha_type; - static const auto unity = Mapper::unity; - - auto inSz = in.size; - auto sbufsize = LinearScratchDesc::bufSize(inSz.width, inSz.height, outSz.width, outSz.height, lpi); - - Size scratch_size{sbufsize, 1}; - - cv::GMatDesc desc; - desc.chan = 1; - desc.depth = CV_8UC1; - desc.size = scratch_size; - - cv::gapi::fluid::Buffer buffer(desc); - scratch = std::move(buffer); - - double hRatio = ratio(in.size.width, outSz.width); - double vRatio = ratio(in.size.height, outSz.height); - - LinearScratchDesc scr(inSz.width, inSz.height, outSz.width, outSz.height, scratch.OutLineB()); - - auto *alpha = scr.alpha; - auto *clone = scr.clone; - auto *index = scr.mapsx; - - for (int x = 0; x < outSz.width; x++) { - auto map = Mapper::map(hRatio, 0, in.size.width, x); - auto alpha0 = map.alpha0; - auto index0 = map.index0; - - // TRICK: - // Algorithm takes pair of input pixels, sx0'th and sx1'th, - // and compute result as alpha0*src[sx0] + alpha1*src[sx1]. - // By definition: sx1 == sx0 + 1 either sx1 == sx0, and - // alpha0 + alpha1 == unity (scaled appropriately). - // Here we modify formulas for alpha0 and sx1: by assuming - // that sx1 == sx0 + 1 always, and patching alpha0 so that - // result remains intact. - // Note that we need in.size.width >= 2, for both sx0 and - // sx0+1 were indexing pixels inside the input's width. - if (map.index1 != map.index0 + 1) { - GAPI_DbgAssert(map.index1 == map.index0); - GAPI_DbgAssert(in.size.width >= 2); - if (map.index0 < in.size.width-1) { - // sx1=sx0+1 fits inside row, - // make sure alpha0=unity and alpha1=0, - // so that result equals src[sx0]*unity - alpha0 = saturate_cast(unity); - } else { - // shift sx0 to left by 1 pixel, - // and make sure that alpha0=0 and alpha1==1, - // so that result equals to src[sx0+1]*unity - alpha0 = 0; - index0--; - } - } - - alpha[x] = alpha0; - index[x] = index0; - - for (int l = 0; l < 4; l++) { - clone[4*x + l] = alpha0; - } - } - - auto *beta = scr.beta; - auto *index_y = scr.mapsy; - - for (int y = 0; y < outSz.height; y++) { - auto mapY = Mapper::map(vRatio, 0, in.size.height, y); - beta[y] = mapY.alpha0; - index_y[y] = mapY.index0; - index_y[outSz.height + y] = mapY.index1; - } -} - -template -struct MapperUnit { - F alpha0, alpha1; - I index0, index1; -}; - -inline static uint8_t calc(short alpha0, uint8_t src0, short alpha1, uint8_t src1) { - constexpr static const int half = 1 << 14; - return (src0 * alpha0 + src1 * alpha1 + half) >> 15; -} -struct Mapper { - constexpr static const int ONE = 1 << 15; - typedef short alpha_type; - typedef short index_type; - constexpr static const int unity = ONE; - - typedef MapperUnit Unit; - - static inline Unit map(double ratio, int start, int max, int outCoord) { - float f = static_cast((outCoord + 0.5) * ratio - 0.5); - int s = cvFloor(f); - f -= s; - - Unit u; - - u.index0 = static_cast(std::max(s - start, 0)); - u.index1 = static_cast(((f == 0.0) || s + 1 >= max) ? s - start : s - start + 1); - - u.alpha0 = saturate_cast(ONE * (1.0f - f)); - u.alpha1 = saturate_cast(ONE * f); - - return u; - } -}; - -template -static void calcRowLinearC(const cv::gapi::fluid::View & in, - cv::gapi::fluid::Buffer& out, - cv::gapi::fluid::Buffer& scratch) { - using alpha_type = typename Mapper::alpha_type; - - auto inSz = in.meta().size; - auto outSz = out.meta().size; - - auto inY = in.y(); - int outY = out.y(); - int lpi = out.lpi(); - - GAPI_DbgAssert(outY + lpi <= outSz.height); - GAPI_DbgAssert(lpi <= 4); - - LinearScratchDesc scr(inSz.width, inSz.height, outSz.width, outSz.height, scratch.OutLineB()); - - const auto *alpha = scr.alpha; - const auto *mapsx = scr.mapsx; - const auto *beta_0 = scr.beta; - const auto *mapsy = scr.mapsy; - - const auto *beta = beta_0 + outY; - const T *src0[4]; - const T *src1[4]; - T* dst[4]; - - for (int l = 0; l < lpi; l++) { - auto index0 = mapsy[outY + l] - inY; - auto index1 = mapsy[outSz.height + outY + l] - inY; - src0[l] = in.InLine(index0); - src1[l] = in.InLine(index1); - dst[l] = out.OutLine(l); - } - -#if 0 // Disabling SSE4.1 path due to Valgrind issues: https://github.com/opencv/opencv/issues/21097 -#if CV_SSE4_1 - const auto* clone = scr.clone; - auto* tmp = scr.tmp; - - if (inSz.width >= 16 && outSz.width >= 16) - { - sse42::calcRowLinear_8UC_Impl_(reinterpret_cast(dst), - reinterpret_cast(src0), - reinterpret_cast(src1), - reinterpret_cast(alpha), - reinterpret_cast(clone), - reinterpret_cast(mapsx), - reinterpret_cast(beta), - reinterpret_cast(tmp), - inSz, outSz, lpi); - - return; - } -#endif // CV_SSE4_1 -#endif - int length = out.length(); - for (int l = 0; l < lpi; l++) { - constexpr static const auto unity = Mapper::unity; - - auto beta0 = beta[l]; - auto beta1 = saturate_cast(unity - beta[l]); - - for (int x = 0; x < length; x++) { - auto alpha0 = alpha[x]; - auto alpha1 = saturate_cast(unity - alpha[x]); - auto sx0 = mapsx[x]; - auto sx1 = sx0 + 1; - - for (int c = 0; c < numChan; c++) { - auto idx0 = numChan*sx0 + c; - auto idx1 = numChan*sx1 + c; - T tmp0 = calc(beta0, src0[l][idx0], beta1, src1[l][idx0]); - T tmp1 = calc(beta0, src0[l][idx1], beta1, src1[l][idx1]); - dst[l][numChan * x + c] = calc(alpha0, tmp0, alpha1, tmp1); - } - } - } -} - -GAPI_FLUID_KERNEL(GFluidResize, cv::gapi::core::GResize, true) -{ - static const int Window = 1; - static const int LPI = 4; - static const auto Kind = GFluidKernel::Kind::Resize; - - constexpr static const int INTER_RESIZE_COEF_BITS = 11; - constexpr static const int INTER_RESIZE_COEF_SCALE = 1 << INTER_RESIZE_COEF_BITS; - constexpr static const short ONE = INTER_RESIZE_COEF_SCALE; - - static void initScratch(const cv::GMatDesc& in, - cv::Size outSz, double fx, double fy, int /*interp*/, - cv::gapi::fluid::Buffer &scratch) - { - int outSz_w; - int outSz_h; - if (outSz.width == 0 || outSz.height == 0) - { - outSz_w = static_cast(round(in.size.width * fx)); - outSz_h = static_cast(round(in.size.height * fy)); - } - else - { - outSz_w = outSz.width; - outSz_h = outSz.height; - } - cv::Size outSize(outSz_w, outSz_h); - - if (in.chan == 3) - { - initScratchLinear(in, outSize, scratch, LPI); - } - else if (in.chan == 4) - { - initScratchLinear(in, outSize, scratch, LPI); - } - } - - static void resetScratch(cv::gapi::fluid::Buffer& /*scratch*/) - {} - - static void run(const cv::gapi::fluid::View& in, cv::Size /*sz*/, double /*fx*/, double /*fy*/, int interp, - cv::gapi::fluid::Buffer& out, - cv::gapi::fluid::Buffer& scratch) { - const int channels = in.meta().chan; - GAPI_Assert((channels == 3 || channels == 4) && (interp == cv::INTER_LINEAR)); - - if (channels == 3) - { - calcRowLinearC(in, out, scratch); - } - else if (channels == 4) - { - calcRowLinearC(in, out, scratch); - } - } -}; - GAPI_FLUID_KERNEL(GFluidSqrt, cv::gapi::core::GSqrt, false) { static const int Window = 1; @@ -3081,7 +2560,7 @@ GAPI_FLUID_KERNEL(GFluidSqrt, cv::gapi::core::GSqrt, false) } // namespace gapi } // namespace cv -cv::gapi::GKernelPackage cv::gapi::core::fluid::kernels() +cv::GKernelPackage cv::gapi::core::fluid::kernels() { using namespace cv::gapi::fluid; @@ -3134,7 +2613,6 @@ cv::gapi::GKernelPackage cv::gapi::core::fluid::kernels() ,GFluidCmpNEScalar ,GFluidThreshold ,GFluidInRange - ,GFluidResize ,GFluidSqrt #if 0 ,GFluidMean -- not fluid diff --git a/modules/gapi/src/backends/fluid/gfluidcore_func.dispatch.cpp b/modules/gapi/src/backends/fluid/gfluidcore_func.dispatch.cpp index 348c00ed12b1..c9d329b2ff35 100644 --- a/modules/gapi/src/backends/fluid/gfluidcore_func.dispatch.cpp +++ b/modules/gapi/src/backends/fluid/gfluidcore_func.dispatch.cpp @@ -192,6 +192,34 @@ MULC_SIMD(float, float) #undef MULC_SIMD +#define DIVC_SIMD(SRC, DST) \ +int divc_simd(const SRC in[], const float scalar[], DST out[], \ + const int length, const int chan, const float scale, \ + const int set_mask_flag) \ +{ \ + CV_CPU_DISPATCH(divc_simd, (in, scalar, out, length, chan, scale, set_mask_flag), \ + CV_CPU_DISPATCH_MODES_ALL); \ +} + +DIVC_SIMD(uchar, uchar) +DIVC_SIMD(ushort, uchar) +DIVC_SIMD(short, uchar) +DIVC_SIMD(float, uchar) +DIVC_SIMD(short, short) +DIVC_SIMD(ushort, short) +DIVC_SIMD(uchar, short) +DIVC_SIMD(float, short) +DIVC_SIMD(ushort, ushort) +DIVC_SIMD(uchar, ushort) +DIVC_SIMD(short, ushort) +DIVC_SIMD(float, ushort) +DIVC_SIMD(uchar, float) +DIVC_SIMD(ushort, float) +DIVC_SIMD(short, float) +DIVC_SIMD(float, float) + +#undef DIVC_SIMD + #define ABSDIFFC_SIMD(SRC) \ int absdiffc_simd(const SRC in[], const float scalar[], SRC out[], \ const int length, const int chan) \ @@ -207,6 +235,162 @@ ABSDIFFC_SIMD(float) #undef ABSDIFFC_SIMD +#define DIVRC_SIMD(SRC, DST) \ +int divrc_simd(const float scalar[], const SRC in[], DST out[], \ + const int length, const int chan, const float scale) \ +{ \ + CV_CPU_DISPATCH(divrc_simd, (scalar, in, out, length, chan, scale), \ + CV_CPU_DISPATCH_MODES_ALL); \ +} + +DIVRC_SIMD(uchar, uchar) +DIVRC_SIMD(ushort, uchar) +DIVRC_SIMD(short, uchar) +DIVRC_SIMD(float, uchar) +DIVRC_SIMD(short, short) +DIVRC_SIMD(ushort, short) +DIVRC_SIMD(uchar, short) +DIVRC_SIMD(float, short) +DIVRC_SIMD(ushort, ushort) +DIVRC_SIMD(uchar, ushort) +DIVRC_SIMD(short, ushort) +DIVRC_SIMD(float, ushort) +DIVRC_SIMD(uchar, float) +DIVRC_SIMD(ushort, float) +DIVRC_SIMD(short, float) +DIVRC_SIMD(float, float) + +#undef DIVRC_SIMD + +int split3_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], const int width) +{ + CV_CPU_DISPATCH(split3_simd, (in, out1, out2, out3, width), + CV_CPU_DISPATCH_MODES_ALL); +} + +int split4_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], uchar out4[], const int width) +{ + CV_CPU_DISPATCH(split4_simd, (in, out1, out2, out3, out4, width), + CV_CPU_DISPATCH_MODES_ALL); +} + +int merge3_simd(const uchar in1[], const uchar in2[], const uchar in3[], + uchar out[], const int width) +{ + CV_CPU_DISPATCH(merge3_simd, (in1, in2, in3, out, width), + CV_CPU_DISPATCH_MODES_ALL); +} + +int merge4_simd(const uchar in1[], const uchar in2[], const uchar in3[], + const uchar in4[], uchar out[], const int width) +{ + CV_CPU_DISPATCH(merge4_simd, (in1, in2, in3, in4, out, width), + CV_CPU_DISPATCH_MODES_ALL); +} + +#define ADD_SIMD(SRC, DST) \ +int add_simd(const SRC in1[], const SRC in2[], DST out[], const int length) \ +{ \ + CV_CPU_DISPATCH(add_simd, (in1, in2, out, length), \ + CV_CPU_DISPATCH_MODES_ALL); \ +} + +ADD_SIMD(uchar, uchar) +ADD_SIMD(ushort, uchar) +ADD_SIMD(short, uchar) +ADD_SIMD(float, uchar) +ADD_SIMD(short, short) +ADD_SIMD(ushort, short) +ADD_SIMD(uchar, short) +ADD_SIMD(float, short) +ADD_SIMD(ushort, ushort) +ADD_SIMD(uchar, ushort) +ADD_SIMD(short, ushort) +ADD_SIMD(float, ushort) +ADD_SIMD(uchar, float) +ADD_SIMD(ushort, float) +ADD_SIMD(short, float) +ADD_SIMD(float, float) + +#undef ADD_SIMD + +#define SUB_SIMD(SRC, DST) \ +int sub_simd(const SRC in1[], const SRC in2[], DST out[], const int length) \ +{ \ + CV_CPU_DISPATCH(sub_simd, (in1, in2, out, length), \ + CV_CPU_DISPATCH_MODES_ALL); \ +} + +SUB_SIMD(uchar, uchar) +SUB_SIMD(ushort, uchar) +SUB_SIMD(short, uchar) +SUB_SIMD(float, uchar) +SUB_SIMD(short, short) +SUB_SIMD(ushort, short) +SUB_SIMD(uchar, short) +SUB_SIMD(float, short) +SUB_SIMD(ushort, ushort) +SUB_SIMD(uchar, ushort) +SUB_SIMD(short, ushort) +SUB_SIMD(float, ushort) +SUB_SIMD(uchar, float) +SUB_SIMD(ushort, float) +SUB_SIMD(short, float) +SUB_SIMD(float, float) + +#undef SUB_SIMD + +#define CONVERTTO_NOCOEF_SIMD(SRC, DST) \ +int convertto_simd(const SRC in[], DST out[], const int length) \ +{ \ + CV_CPU_DISPATCH(convertto_simd, (in, out, length), \ + CV_CPU_DISPATCH_MODES_ALL); \ +} + +CONVERTTO_NOCOEF_SIMD(ushort, uchar) +CONVERTTO_NOCOEF_SIMD(short, uchar) +CONVERTTO_NOCOEF_SIMD(float, uchar) +CONVERTTO_NOCOEF_SIMD(ushort, short) +CONVERTTO_NOCOEF_SIMD(uchar, short) +CONVERTTO_NOCOEF_SIMD(float, short) +CONVERTTO_NOCOEF_SIMD(uchar, ushort) +CONVERTTO_NOCOEF_SIMD(short, ushort) +CONVERTTO_NOCOEF_SIMD(float, ushort) +CONVERTTO_NOCOEF_SIMD(uchar, float) +CONVERTTO_NOCOEF_SIMD(ushort, float) +CONVERTTO_NOCOEF_SIMD(short, float) + +#undef CONVERTTO_NOCOEF_SIMD + +#define CONVERTTO_SCALED_SIMD(SRC, DST) \ +int convertto_scaled_simd(const SRC in[], DST out[], const float alpha, \ + const float beta, const int length) \ +{ \ + CV_CPU_DISPATCH(convertto_scaled_simd, (in, out, alpha, beta, length), \ + CV_CPU_DISPATCH_MODES_ALL); \ +} + +CONVERTTO_SCALED_SIMD(uchar, uchar) +CONVERTTO_SCALED_SIMD(ushort, uchar) +CONVERTTO_SCALED_SIMD(short, uchar) +CONVERTTO_SCALED_SIMD(float, uchar) +CONVERTTO_SCALED_SIMD(short, short) +CONVERTTO_SCALED_SIMD(ushort, short) +CONVERTTO_SCALED_SIMD(uchar, short) +CONVERTTO_SCALED_SIMD(float, short) +CONVERTTO_SCALED_SIMD(ushort, ushort) +CONVERTTO_SCALED_SIMD(uchar, ushort) +CONVERTTO_SCALED_SIMD(short, ushort) +CONVERTTO_SCALED_SIMD(float, ushort) +CONVERTTO_SCALED_SIMD(uchar, float) +CONVERTTO_SCALED_SIMD(ushort, float) +CONVERTTO_SCALED_SIMD(short, float) +CONVERTTO_SCALED_SIMD(float, float) + +#undef CONVERTTO_SCALED_SIMD + } // namespace fluid } // namespace gapi } // namespace cv diff --git a/modules/gapi/src/backends/fluid/gfluidcore_func.hpp b/modules/gapi/src/backends/fluid/gfluidcore_func.hpp index 6023a879d955..81aa098b6418 100644 --- a/modules/gapi/src/backends/fluid/gfluidcore_func.hpp +++ b/modules/gapi/src/backends/fluid/gfluidcore_func.hpp @@ -152,6 +152,30 @@ MULC_SIMD(float, float) #undef MULC_SIMD +#define DIVC_SIMD(SRC, DST) \ +int divc_simd(const SRC in[], const float scalar[], DST out[], \ + const int length, const int chan, const float scale, \ + const int set_mask_flag); + +DIVC_SIMD(uchar, uchar) +DIVC_SIMD(ushort, uchar) +DIVC_SIMD(short, uchar) +DIVC_SIMD(float, uchar) +DIVC_SIMD(short, short) +DIVC_SIMD(ushort, short) +DIVC_SIMD(uchar, short) +DIVC_SIMD(float, short) +DIVC_SIMD(ushort, ushort) +DIVC_SIMD(uchar, ushort) +DIVC_SIMD(short, ushort) +DIVC_SIMD(float, ushort) +DIVC_SIMD(uchar, float) +DIVC_SIMD(ushort, float) +DIVC_SIMD(short, float) +DIVC_SIMD(float, float) + +#undef DIVC_SIMD + #define ABSDIFFC_SIMD(T) \ int absdiffc_simd(const T in[], const float scalar[], T out[], \ const int length, const int chan); @@ -163,6 +187,126 @@ ABSDIFFC_SIMD(float) #undef ABSDIFFC_SIMD +#define DIVRC_SIMD(SRC, DST) \ +int divrc_simd(const float scalar[], const SRC in[], DST out[], \ + const int length, const int chan, const float scale); + +DIVRC_SIMD(uchar, uchar) +DIVRC_SIMD(ushort, uchar) +DIVRC_SIMD(short, uchar) +DIVRC_SIMD(float, uchar) +DIVRC_SIMD(short, short) +DIVRC_SIMD(ushort, short) +DIVRC_SIMD(uchar, short) +DIVRC_SIMD(float, short) +DIVRC_SIMD(ushort, ushort) +DIVRC_SIMD(uchar, ushort) +DIVRC_SIMD(short, ushort) +DIVRC_SIMD(float, ushort) +DIVRC_SIMD(uchar, float) +DIVRC_SIMD(ushort, float) +DIVRC_SIMD(short, float) +DIVRC_SIMD(float, float) + +#undef DIVRC_SIMD + +int split3_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], const int width); + +int split4_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], uchar out4[], const int width); + +int merge3_simd(const uchar in1[], const uchar in2[], const uchar in3[], + uchar out[], const int width); + +int merge4_simd(const uchar in1[], const uchar in2[], const uchar in3[], + const uchar in4[], uchar out[], const int width); + +#define ADD_SIMD(SRC, DST) \ +int add_simd(const SRC in1[], const SRC in2[], DST out[], const int length); + +ADD_SIMD(uchar, uchar) +ADD_SIMD(ushort, uchar) +ADD_SIMD(short, uchar) +ADD_SIMD(float, uchar) +ADD_SIMD(short, short) +ADD_SIMD(ushort, short) +ADD_SIMD(uchar, short) +ADD_SIMD(float, short) +ADD_SIMD(ushort, ushort) +ADD_SIMD(uchar, ushort) +ADD_SIMD(short, ushort) +ADD_SIMD(float, ushort) +ADD_SIMD(uchar, float) +ADD_SIMD(ushort, float) +ADD_SIMD(short, float) +ADD_SIMD(float, float) + +#undef ADD_SIMD + +#define SUB_SIMD(SRC, DST) \ +int sub_simd(const SRC in1[], const SRC in2[], DST out[], const int length); + +SUB_SIMD(uchar, uchar) +SUB_SIMD(ushort, uchar) +SUB_SIMD(short, uchar) +SUB_SIMD(float, uchar) +SUB_SIMD(short, short) +SUB_SIMD(ushort, short) +SUB_SIMD(uchar, short) +SUB_SIMD(float, short) +SUB_SIMD(ushort, ushort) +SUB_SIMD(uchar, ushort) +SUB_SIMD(short, ushort) +SUB_SIMD(float, ushort) +SUB_SIMD(uchar, float) +SUB_SIMD(ushort, float) +SUB_SIMD(short, float) +SUB_SIMD(float, float) + +#undef SUB_SIMD + +#define CONVERTTO_NOCOEF_SIMD(SRC, DST) \ +int convertto_simd(const SRC in[], DST out[], const int length); + +CONVERTTO_NOCOEF_SIMD(ushort, uchar) +CONVERTTO_NOCOEF_SIMD(short, uchar) +CONVERTTO_NOCOEF_SIMD(float, uchar) +CONVERTTO_NOCOEF_SIMD(ushort, short) +CONVERTTO_NOCOEF_SIMD(uchar, short) +CONVERTTO_NOCOEF_SIMD(float, short) +CONVERTTO_NOCOEF_SIMD(uchar, ushort) +CONVERTTO_NOCOEF_SIMD(short, ushort) +CONVERTTO_NOCOEF_SIMD(float, ushort) +CONVERTTO_NOCOEF_SIMD(uchar, float) +CONVERTTO_NOCOEF_SIMD(ushort, float) +CONVERTTO_NOCOEF_SIMD(short, float) + +#undef CONVERTTO_NOCOEF_SIMD + +#define CONVERTTO_SCALED_SIMD(SRC, DST) \ +int convertto_scaled_simd(const SRC in[], DST out[], const float alpha, \ + const float beta, const int length); + +CONVERTTO_SCALED_SIMD(uchar, uchar) +CONVERTTO_SCALED_SIMD(ushort, uchar) +CONVERTTO_SCALED_SIMD(short, uchar) +CONVERTTO_SCALED_SIMD(float, uchar) +CONVERTTO_SCALED_SIMD(short, short) +CONVERTTO_SCALED_SIMD(ushort, short) +CONVERTTO_SCALED_SIMD(uchar, short) +CONVERTTO_SCALED_SIMD(float, short) +CONVERTTO_SCALED_SIMD(ushort, ushort) +CONVERTTO_SCALED_SIMD(uchar, ushort) +CONVERTTO_SCALED_SIMD(short, ushort) +CONVERTTO_SCALED_SIMD(float, ushort) +CONVERTTO_SCALED_SIMD(uchar, float) +CONVERTTO_SCALED_SIMD(ushort, float) +CONVERTTO_SCALED_SIMD(short, float) +CONVERTTO_SCALED_SIMD(float, float) + +#undef CONVERTTO_SCALED_SIMD + } // namespace fluid } // namespace gapi } // namespace cv diff --git a/modules/gapi/src/backends/fluid/gfluidcore_func.simd.hpp b/modules/gapi/src/backends/fluid/gfluidcore_func.simd.hpp index 38c47072f4e4..d1fe33fa2e03 100644 --- a/modules/gapi/src/backends/fluid/gfluidcore_func.simd.hpp +++ b/modules/gapi/src/backends/fluid/gfluidcore_func.simd.hpp @@ -150,8 +150,8 @@ SUBRC_SIMD(float, float) #undef SUBRC_SIMD -#define MULC_SIMD(SRC, DST) \ -int mulc_simd(const SRC in[], const float scalar[], DST out[], \ +#define MULC_SIMD(SRC, DST) \ +int mulc_simd(const SRC in[], const float scalar[], DST out[], \ const int length, const int chan, const float scale); MULC_SIMD(uchar, uchar) @@ -173,6 +173,30 @@ MULC_SIMD(float, float) #undef MULC_SIMD +#define DIVC_SIMD(SRC, DST) \ +int divc_simd(const SRC in[], const float scalar[], DST out[], \ + const int length, const int chan, const float scale, \ + const int set_mask_flag); + +DIVC_SIMD(uchar, uchar) +DIVC_SIMD(ushort, uchar) +DIVC_SIMD(short, uchar) +DIVC_SIMD(float, uchar) +DIVC_SIMD(short, short) +DIVC_SIMD(ushort, short) +DIVC_SIMD(uchar, short) +DIVC_SIMD(float, short) +DIVC_SIMD(ushort, ushort) +DIVC_SIMD(uchar, ushort) +DIVC_SIMD(short, ushort) +DIVC_SIMD(float, ushort) +DIVC_SIMD(uchar, float) +DIVC_SIMD(ushort, float) +DIVC_SIMD(short, float) +DIVC_SIMD(float, float) + +#undef DIVC_SIMD + #define ABSDIFFC_SIMD(T) \ int absdiffc_simd(const T in[], const float scalar[], T out[], \ const int length, const int chan); @@ -184,8 +208,133 @@ ABSDIFFC_SIMD(float) #undef ABSDIFFC_SIMD +#define DIVRC_SIMD(SRC, DST) \ +int divrc_simd(const float scalar[], const SRC in[], DST out[], \ + const int length, const int chan, const float scale); + +DIVRC_SIMD(uchar, uchar) +DIVRC_SIMD(ushort, uchar) +DIVRC_SIMD(short, uchar) +DIVRC_SIMD(float, uchar) +DIVRC_SIMD(short, short) +DIVRC_SIMD(ushort, short) +DIVRC_SIMD(uchar, short) +DIVRC_SIMD(float, short) +DIVRC_SIMD(ushort, ushort) +DIVRC_SIMD(uchar, ushort) +DIVRC_SIMD(short, ushort) +DIVRC_SIMD(float, ushort) +DIVRC_SIMD(uchar, float) +DIVRC_SIMD(ushort, float) +DIVRC_SIMD(short, float) +DIVRC_SIMD(float, float) + +#undef DIVRC_SIMD + +#define ADD_SIMD(SRC, DST) \ +int add_simd(const SRC in1[], const SRC in2[], DST out[], const int length); + +ADD_SIMD(uchar, uchar) +ADD_SIMD(ushort, uchar) +ADD_SIMD(short, uchar) +ADD_SIMD(float, uchar) +ADD_SIMD(short, short) +ADD_SIMD(ushort, short) +ADD_SIMD(uchar, short) +ADD_SIMD(float, short) +ADD_SIMD(ushort, ushort) +ADD_SIMD(uchar, ushort) +ADD_SIMD(short, ushort) +ADD_SIMD(float, ushort) +ADD_SIMD(uchar, float) +ADD_SIMD(ushort, float) +ADD_SIMD(short, float) +ADD_SIMD(float, float) + +#undef ADD_SIMD + +#define SUB_SIMD(SRC, DST) \ +int sub_simd(const SRC in1[], const SRC in2[], DST out[], const int length); + +SUB_SIMD(uchar, uchar) +SUB_SIMD(ushort, uchar) +SUB_SIMD(short, uchar) +SUB_SIMD(float, uchar) +SUB_SIMD(short, short) +SUB_SIMD(ushort, short) +SUB_SIMD(uchar, short) +SUB_SIMD(float, short) +SUB_SIMD(ushort, ushort) +SUB_SIMD(uchar, ushort) +SUB_SIMD(short, ushort) +SUB_SIMD(float, ushort) +SUB_SIMD(uchar, float) +SUB_SIMD(ushort, float) +SUB_SIMD(short, float) +SUB_SIMD(float, float) + +#undef SUB_SIMD + +#define CONVERTTO_NOCOEF_SIMD(SRC, DST) \ +int convertto_simd(const SRC in[], DST out[], const int length); + +CONVERTTO_NOCOEF_SIMD(ushort, uchar) +CONVERTTO_NOCOEF_SIMD(short, uchar) +CONVERTTO_NOCOEF_SIMD(float, uchar) +CONVERTTO_NOCOEF_SIMD(ushort, short) +CONVERTTO_NOCOEF_SIMD(uchar, short) +CONVERTTO_NOCOEF_SIMD(float, short) +CONVERTTO_NOCOEF_SIMD(uchar, ushort) +CONVERTTO_NOCOEF_SIMD(short, ushort) +CONVERTTO_NOCOEF_SIMD(float, ushort) +CONVERTTO_NOCOEF_SIMD(uchar, float) +CONVERTTO_NOCOEF_SIMD(ushort, float) +CONVERTTO_NOCOEF_SIMD(short, float) + +#undef CONVERTTO_NOCOEF_SIMD + +#define CONVERTTO_SCALED_SIMD(SRC, DST) \ +int convertto_scaled_simd(const SRC in[], DST out[], const float alpha, \ + const float beta, const int length); + +CONVERTTO_SCALED_SIMD(uchar, uchar) +CONVERTTO_SCALED_SIMD(ushort, uchar) +CONVERTTO_SCALED_SIMD(short, uchar) +CONVERTTO_SCALED_SIMD(float, uchar) +CONVERTTO_SCALED_SIMD(short, short) +CONVERTTO_SCALED_SIMD(ushort, short) +CONVERTTO_SCALED_SIMD(uchar, short) +CONVERTTO_SCALED_SIMD(float, short) +CONVERTTO_SCALED_SIMD(ushort, ushort) +CONVERTTO_SCALED_SIMD(uchar, ushort) +CONVERTTO_SCALED_SIMD(short, ushort) +CONVERTTO_SCALED_SIMD(float, ushort) +CONVERTTO_SCALED_SIMD(uchar, float) +CONVERTTO_SCALED_SIMD(ushort, float) +CONVERTTO_SCALED_SIMD(short, float) +CONVERTTO_SCALED_SIMD(float, float) + +#undef CONVERTTO_SCALED_SIMD + +int split3_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], const int width); + +int split4_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], uchar out4[], const int width); + +int merge3_simd(const uchar in1[], const uchar in2[], const uchar in3[], + uchar out[], const int width); + +int merge4_simd(const uchar in1[], const uchar in2[], const uchar in3[], + const uchar in4[], uchar out[], const int width); + #ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY +#define SRC_SHORT_OR_USHORT std::is_same::value || std::is_same::value +#define DST_SHORT_OR_USHORT std::is_same::value || std::is_same::value +#define SRC_DST_SHORT_AND_USHORT (std::is_same::value && std::is_same::value) || (std::is_same::value && std::is_same::value) +#define SRC_DST_SHORT_OR_USHORT (std::is_same::value && std::is_same::value) || (std::is_same::value && std::is_same::value) + struct scale_tag {}; struct not_scale_tag {}; @@ -200,6 +349,28 @@ template<> struct vector_type_of { using type = v_uint16; }; template<> struct vector_type_of { using type = v_int16; }; template<> struct vector_type_of { using type = v_float32; }; +template +struct zero_vec_type_of; + +template +using zero_vec_type_of_t = typename zero_vec_type_of::type; + +template<> struct zero_vec_type_of { using type = v_int16; }; +template<> struct zero_vec_type_of { using type = v_int16; }; +template<> struct zero_vec_type_of { using type = v_int16; }; +template<> struct zero_vec_type_of { using type = v_float32; }; + +template +struct univ_zero_vec_type_of; + +template +using univ_zero_vec_type_of_t = typename univ_zero_vec_type_of::type; + +template<> struct univ_zero_vec_type_of { using type = v_uint8; }; +template<> struct univ_zero_vec_type_of { using type = v_int16; }; +template<> struct univ_zero_vec_type_of { using type = v_int16; }; +template<> struct univ_zero_vec_type_of { using type = v_float32; }; + CV_ALWAYS_INLINE v_float32 vg_load_f32(const float* in) { return vx_load(in); @@ -259,165 +430,111 @@ CV_ALWAYS_INLINE void v_store_select(short* dst, const v_int16& div, const v_int CV_ALWAYS_INLINE void v_store_select(ushort* dst, const v_int16& div, const v_int16& v_zero, const v_int32& res1, const v_int32& res2) { - v_uint16 sel = v_reinterpret_as_u16(v_select(div == v_zero, v_zero, v_pack(res1, res2))); - vx_store(dst, sel); + vx_store(dst, v_select(v_reinterpret_as_u16(div == v_zero), + v_reinterpret_as_u16(v_zero), v_pack_u(res1, res2))); } -//================================================================================================= +//============================================================================= -template +template CV_ALWAYS_INLINE -typename std::enable_if<(std::is_same::value && std::is_same::value) || - (std::is_same::value && std::is_same::value) || - (std::is_same::value && std::is_same::value), int>::type -div_hal(scale_tag_t t, const SRC in1[], const SRC in2[], DST out[], const int length, double _scale) +void div_simd_impl(scale_tag_t s_tag, const v_float32& a1, const v_float32& a2, + const v_float32& a3, const v_float32& a4, const uchar* in2x, + uchar* outx, const v_float32& v_scale, const v_int16& v_zero) { - constexpr int nlanes = vector_type_of_t::nlanes; - - if (length < nlanes) - return 0; - - v_int16 v_zero = vx_setall_s16(0); - v_float32 scale = vx_setall_f32(static_cast(_scale)); - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vg_load_f32(&in1[x]); - v_float32 a2 = vg_load_f32(&in1[x + nlanes / 2]); + constexpr int nlanes = v_uint8::nlanes; - v_int16 div = v_reinterpret_as_s16(vx_load(&in2[x])); + v_int16 div1 = v_reinterpret_as_s16(vx_load_expand(in2x)); + v_int16 div2 = v_reinterpret_as_s16(vx_load_expand(&in2x[nlanes/2])); - v_float32 fdiv1 = v_cvt_f32(v_expand_low(div)); - v_float32 fdiv2 = v_cvt_f32(v_expand_high(div)); + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div1)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div1)); + v_float32 fdiv3 = v_cvt_f32(v_expand_low(div2)); + v_float32 fdiv4 = v_cvt_f32(v_expand_high(div2)); - v_int32 r1 = v_round(div_op(t, a1, fdiv1, scale)); - v_int32 r2 = v_round(div_op(t, a2, fdiv2, scale)); + v_int32 sum1 = v_round(div_op(s_tag, a1, fdiv1, v_scale)), + sum2 = v_round(div_op(s_tag, a2, fdiv2, v_scale)), + sum3 = v_round(div_op(s_tag, a3, fdiv3, v_scale)), + sum4 = v_round(div_op(s_tag, a4, fdiv4, v_scale)); - v_store_select(&out[x], div, v_zero, r1, r2); - } + v_int16 res1 = v_select((div1 == v_zero), v_zero, v_pack(sum1, sum2)); + v_int16 res2 = v_select((div2 == v_zero), v_zero, v_pack(sum3, sum4)); - if (x < length) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - return x; + vx_store(outx, v_pack_u(res1, res2)); } -//------------------------------------------------------------------------------------------------- - template CV_ALWAYS_INLINE typename std::enable_if::value || - std::is_same::value, int>::type -div_hal(scale_tag_t t, const SRC in1[], const SRC in2[], uchar out[], const int length, double _scale) + std::is_same::value, void>::type +div_simd_impl(scale_tag_t s_tag, const v_float32& a1, const v_float32& a2, + const v_float32& a3, const v_float32& a4, const SRC* in2x, + uchar* outx, const v_float32& v_scale, const v_int16& v_zero) { constexpr int nlanes = v_uint8::nlanes; - if (length < nlanes) - return 0; - - v_float32 scale = vx_setall_f32(static_cast(_scale)); - v_int16 v_zero = vx_setall_s16(0); - - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vg_load_f32(&in1[x]); - v_float32 a2 = vg_load_f32(&in1[x + nlanes / 4]); - v_float32 a3 = vg_load_f32(&in1[x + nlanes / 2]); - v_float32 a4 = vg_load_f32(&in1[x + 3 * nlanes / 4]); - - v_int16 div1 = v_reinterpret_as_s16(vx_load(&in2[x])); - v_int16 div2 = v_reinterpret_as_s16(vx_load(&in2[x + nlanes/2])); + v_int16 div1 = v_reinterpret_as_s16(vx_load(in2x)); + v_int16 div2 = v_reinterpret_as_s16(vx_load(&in2x[nlanes/2])); - v_float32 fdiv1 = v_cvt_f32(v_expand_low(div1)); - v_float32 fdiv2 = v_cvt_f32(v_expand_high(div1)); - v_float32 fdiv3 = v_cvt_f32(v_expand_low(div2)); - v_float32 fdiv4 = v_cvt_f32(v_expand_high(div2)); + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div1)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div1)); + v_float32 fdiv3 = v_cvt_f32(v_expand_low(div2)); + v_float32 fdiv4 = v_cvt_f32(v_expand_high(div2)); - v_int32 sum1 = v_round(div_op(t, a1, fdiv1, scale)), - sum2 = v_round(div_op(t, a2, fdiv2, scale)), - sum3 = v_round(div_op(t, a3, fdiv3, scale)), - sum4 = v_round(div_op(t, a4, fdiv4, scale)); + v_int32 sum1 = v_round(div_op(s_tag, a1, fdiv1, v_scale)), + sum2 = v_round(div_op(s_tag, a2, fdiv2, v_scale)), + sum3 = v_round(div_op(s_tag, a3, fdiv3, v_scale)), + sum4 = v_round(div_op(s_tag, a4, fdiv4, v_scale)); - v_int16 res1 = v_select((div1 == v_zero), v_zero, v_pack(sum1, sum2)); - v_int16 res2 = v_select((div2 == v_zero), v_zero, v_pack(sum3, sum4)); + v_int16 res1 = v_select((div1 == v_zero), v_zero, v_pack(sum1, sum2)); + v_int16 res2 = v_select((div2 == v_zero), v_zero, v_pack(sum3, sum4)); - vx_store(&out[x], v_pack_u(res1, res2)); - } - - if (x < length) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - return x; + vx_store(outx, v_pack_u(res1, res2)); } -//------------------------------------------------------------------------------------------------- - template -CV_ALWAYS_INLINE int div_hal(scale_tag_t t, const float in1[], const float in2[], uchar out[], - const int length, double _scale) +CV_ALWAYS_INLINE void div_simd_impl(scale_tag_t s_tag, const v_float32& a1, + const v_float32& a2, const v_float32& a3, + const v_float32& a4, const float* in2x, uchar* outx, + const v_float32& v_scale, const v_float32& v_zero) { constexpr int nlanes = v_uint8::nlanes; - if (length < nlanes) - return 0; + v_float32 div1 = vg_load_f32(in2x); + v_float32 div2 = vg_load_f32(&in2x[nlanes / 4]); + v_float32 div3 = vg_load_f32(&in2x[nlanes / 2]); + v_float32 div4 = vg_load_f32(&in2x[3 * nlanes / 4]); - v_float32 scale = vx_setall_f32(static_cast(_scale)); - v_float32 v_zero = vx_setall_f32(0); - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vg_load_f32(&in1[x]); - v_float32 a2 = vg_load_f32(&in1[x + nlanes / 4]); - v_float32 a3 = vg_load_f32(&in1[x + nlanes / 2]); - v_float32 a4 = vg_load_f32(&in1[x + 3 * nlanes / 4]); + v_float32 r1 = div_op(s_tag, a1, div1, v_scale); + v_float32 r2 = div_op(s_tag, a2, div2, v_scale); + v_float32 r3 = div_op(s_tag, a3, div3, v_scale); + v_float32 r4 = div_op(s_tag, a4, div4, v_scale); - v_float32 div1 = vg_load_f32(&in2[x]); - v_float32 div2 = vg_load_f32(&in2[x + nlanes / 4]); - v_float32 div3 = vg_load_f32(&in2[x + nlanes / 2]); - v_float32 div4 = vg_load_f32(&in2[x + 3 * nlanes / 4]); + v_float32 sel1 = v_select((div1 == v_zero), v_zero, r1); + v_float32 sel2 = v_select((div2 == v_zero), v_zero, r2); + v_float32 sel3 = v_select((div3 == v_zero), v_zero, r3); + v_float32 sel4 = v_select((div4 == v_zero), v_zero, r4); - v_float32 r1 = div_op(t, a1, div1, scale); - v_float32 r2 = div_op(t, a2, div2, scale); - v_float32 r3 = div_op(t, a3, div3, scale); - v_float32 r4 = div_op(t, a4, div4, scale); + v_int32 res1 = v_round(sel1); + v_int32 res2 = v_round(sel2); + v_int32 res3 = v_round(sel3); + v_int32 res4 = v_round(sel4); - v_float32 sel1 = v_select((div1 == v_zero), v_zero, r1); - v_float32 sel2 = v_select((div2 == v_zero), v_zero, r2); - v_float32 sel3 = v_select((div3 == v_zero), v_zero, r3); - v_float32 sel4 = v_select((div4 == v_zero), v_zero, r4); + vx_store(outx, v_pack_u(v_pack(res1, res2), v_pack(res3, res4))); +} - v_int32 res1 = v_round(sel1); - v_int32 res2 = v_round(sel2); - v_int32 res3 = v_round(sel3); - v_int32 res4 = v_round(sel4); +template +CV_ALWAYS_INLINE void div_hal(scale_tag_t s_tag, const SRC* in1x, const SRC* in2x, uchar* outx, + const v_float32& v_scale, const Vtype& v_zero) +{ + constexpr int nlanes = v_uint8::nlanes; - vx_store(&out[x], v_pack_u(v_pack(res1, res2), v_pack(res3, res4))); - } + v_float32 a1 = vg_load_f32(in1x); + v_float32 a2 = vg_load_f32(&in1x[nlanes / 4]); + v_float32 a3 = vg_load_f32(&in1x[nlanes / 2]); + v_float32 a4 = vg_load_f32(&in1x[3 * nlanes / 4]); - if (x < length) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - return x; + div_simd_impl(s_tag, a1, a2, a3, a4, in2x, outx, v_scale, v_zero); } //------------------------------------------------------------------------------------------------- @@ -425,113 +542,117 @@ CV_ALWAYS_INLINE int div_hal(scale_tag_t t, const float in1[], const float in2[] template CV_ALWAYS_INLINE typename std::enable_if::value || - std::is_same::value, int>::type -div_hal(scale_tag_t t, const uchar in1[], const uchar in2[], DST out[], const int length, double _scale) + std::is_same::value, void>::type +div_simd_impl(scale_tag_t s_tag, const v_float32& a1, const v_float32& a2, + const uchar* in2x, DST* outx, const v_float32& v_scale, + const v_int16& v_zero) { - constexpr int nlanes = vector_type_of_t::nlanes; - - if (length < nlanes) - return 0; + v_int16 div = v_reinterpret_as_s16(vx_load_expand(in2x)); - v_float32 scale = vx_setall_f32(static_cast(_scale)); - v_int16 v_zero = vx_setall_s16(0); + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div)); - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vg_load_f32(&in1[x]); - v_float32 a2 = vg_load_f32(&in1[x + nlanes / 2]); + v_int32 r1 = v_round(div_op(s_tag, a1, fdiv1, v_scale)); + v_int32 r2 = v_round(div_op(s_tag, a2, fdiv2, v_scale)); - v_int16 div = v_reinterpret_as_s16(vx_load_expand(&in2[x])); + v_store_select(outx, div, v_zero, r1, r2); +} - v_float32 fdiv1 = v_cvt_f32(v_expand_low(div)); - v_float32 fdiv2 = v_cvt_f32(v_expand_high(div)); +template +CV_ALWAYS_INLINE +typename std::enable_if<(std::is_same::value && std::is_same::value) || + (std::is_same::value && std::is_same::value) || + (std::is_same::value && std::is_same::value) || + (std::is_same::value && std::is_same::value), void>::type +div_simd_impl(scale_tag_t s_tag, const v_float32& a1, const v_float32& a2, + const SRC* in2x, DST* outx, const v_float32& v_scale, const v_int16& v_zero) +{ + v_int16 div = v_reinterpret_as_s16(vx_load(in2x)); - v_int32 r1 = v_round(div_op(t, a1, fdiv1, scale)); - v_int32 r2 = v_round(div_op(t, a2, fdiv2, scale)); + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div)); - v_store_select(&out[x], div, v_zero, r1, r2); - } + v_int32 r1 = v_round(div_op(s_tag, a1, fdiv1, v_scale)); + v_int32 r2 = v_round(div_op(s_tag, a2, fdiv2, v_scale)); - if (x < length) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - return x; + v_store_select(outx, div, v_zero, r1, r2); } -//------------------------------------------------------------------------------------------------- - template CV_ALWAYS_INLINE typename std::enable_if::value || - std::is_same::value, int>::type -div_hal(scale_tag_t t, const float in1[], const float in2[], DST out[], const int length, double _scale) + std::is_same::value, void>::type +div_simd_impl(scale_tag_t s_tag, const v_float32& a1, const v_float32& a2, + const float* in2x, DST* outx, const v_float32& v_scale, + const v_float32& v_zero) { constexpr int nlanes = vector_type_of_t::nlanes; - if (length < nlanes) - return 0; + v_float32 fdiv1 = vg_load_f32(in2x); + v_float32 fdiv2 = vg_load_f32(&in2x[nlanes / 2]); - v_float32 scale = vx_setall_f32(static_cast(_scale)); - v_float32 v_zero = vx_setall_f32(0); - int x = 0; - for (;;) - { - for (; x <= length - nlanes; x += nlanes) - { - v_float32 a1 = vg_load_f32(&in1[x]); - v_float32 a2 = vg_load_f32(&in1[x + nlanes / 2]); + v_float32 r1 = div_op(s_tag, a1, fdiv1, v_scale); + v_float32 r2 = div_op(s_tag, a2, fdiv2, v_scale); - v_float32 fdiv1 = vg_load_f32(&in2[x]); - v_float32 fdiv2 = vg_load_f32(&in2[x + nlanes / 2]); + v_int32 res1 = v_round(v_select((fdiv1 == v_zero), v_zero, r1)); + v_int32 res2 = v_round(v_select((fdiv2 == v_zero), v_zero, r2)); - v_float32 r1 = div_op(t, a1, fdiv1, scale); - v_float32 r2 = div_op(t, a2, fdiv2, scale); + v_store_i16(outx, res1, res2); +} - v_int32 res1 = v_round(v_select((fdiv1 == v_zero), v_zero, r1)); - v_int32 res2 = v_round(v_select((fdiv2 == v_zero), v_zero, r2)); +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +div_hal(scale_tag_t s_tag, const SRC* in1x, const SRC* in2x, DST* outx, + const v_float32& v_scale, const Vtype& v_zero) +{ + constexpr int nlanes = vector_type_of_t::nlanes; - v_store_i16(&out[x], res1, res2); - } + v_float32 a1 = vg_load_f32(in1x); + v_float32 a2 = vg_load_f32(&in1x[nlanes / 2]); - if (x < length) - { - x = length - nlanes; - continue; // process one more time (unaligned tail) - } - break; - } - return x; + div_simd_impl(s_tag, a1, a2, in2x, outx, v_scale, v_zero); } //------------------------------------------------------------------------------------------------- template -CV_ALWAYS_INLINE int div_hal(scale_tag_t t, const SRC in1[], const SRC in2[], float out[], - const int length, double _scale) +CV_ALWAYS_INLINE void div_simd_impl(scale_tag_t s_tag, const v_float32& a1, const SRC* in2x, + float* outx, const v_float32& v_scale) { - constexpr int nlanes = v_float32::nlanes; + v_float32 b1 = vg_load_f32(in2x); + vx_store(outx, div_op(s_tag, a1, b1, v_scale)); +} + +template +CV_ALWAYS_INLINE void div_hal(scale_tag_t s_tag, const SRC* in1x, const SRC* in2x, float* outx, + const v_float32& v_scale, const Tvec&) +{ + v_float32 a1 = vg_load_f32(in1x); + div_simd_impl(s_tag, a1, in2x, outx, v_scale); +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int div_simd_common(scale_tag_t s_tag, const SRC in1[], const SRC in2[], + DST out[], const int length, float scale) +{ + constexpr int nlanes = vector_type_of_t::nlanes; if (length < nlanes) return 0; - v_float32 scale = vx_setall_f32(static_cast(_scale)); + const zero_vec_type_of_t v_zero = vx_setall::lane_type>(0); + v_float32 v_scale = vx_setall_f32(scale); int x = 0; for (;;) { for (; x <= length - nlanes; x += nlanes) { - v_float32 a1 = vg_load_f32(&in1[x]); - v_float32 b1 = vg_load_f32(&in2[x]); - - vx_store(&out[x], div_op(t, a1, b1, scale)); + div_hal(s_tag, &in1[x], &in2[x], &out[x], v_scale, v_zero); } if (x < length) @@ -544,28 +665,6 @@ CV_ALWAYS_INLINE int div_hal(scale_tag_t t, const SRC in1[], const SRC in2[], fl return x; } -//------------------------------------------------------------------------------------------------- - -template -CV_ALWAYS_INLINE int div_hal(scale_tag_t, const uchar in1[], const uchar in2[], uchar out[], - const int length, double scale) -{ - hal::div8u(in1, static_cast(length), in2, static_cast(length), - out, static_cast(length), length, 1, &scale); - return length; -} - -template -CV_ALWAYS_INLINE int div_hal(scale_tag_t, const short in1[], const short in2[], short out[], - const int length, double scale) -{ - hal::div16s(in1, static_cast(length), in2, static_cast(length), - out, static_cast(length), length, 1, &scale); - return length; -} - -//------------------------------------------------------------------------------------------------- - #define DIV_SIMD(SRC, DST) \ int div_simd(const SRC in1[], const SRC in2[], DST out[], \ const int length, double _scale) \ @@ -574,13 +673,11 @@ int div_simd(const SRC in1[], const SRC in2[], DST out[], float fscale = static_cast(_scale); \ if (std::fabs(fscale - 1.0f) <= FLT_EPSILON) \ { \ - not_scale_tag t; \ - x = div_hal(t, in1, in2, out, length, _scale); \ + x = div_simd_common(not_scale_tag{}, in1, in2, out, length, fscale); \ } \ else \ { \ - scale_tag t; \ - x = div_hal(t, in1, in2, out, length, _scale); \ + x = div_simd_common(scale_tag{}, in1, in2, out, length, fscale); \ } \ return x; \ } @@ -935,6 +1032,7 @@ struct add_tag {}; struct sub_tag {}; struct subr_tag {}; struct mul_tag {}; +struct div_tag {}; struct absdiff_tag {}; CV_ALWAYS_INLINE void arithmOpScalar_pack_store_c3(short* outx, const v_int32& c1, @@ -979,6 +1077,21 @@ CV_ALWAYS_INLINE v_float32 oper(mul_tag, const v_float32& a, const v_float32& sc return a * sc; } +CV_ALWAYS_INLINE v_float32 oper_scaled(mul_tag, const v_float32& a, const v_float32& v_scalar, const v_float32& v_scale) +{ + return v_scale * a * v_scalar; +} + +CV_ALWAYS_INLINE v_float32 oper(div_tag, const v_float32& a, const v_float32& sc) +{ + return a / sc; +} + +CV_ALWAYS_INLINE v_float32 oper_scaled(div_tag, const v_float32& a, const v_float32& v_scalar, const v_float32& v_scale) +{ + return a*v_scale / v_scalar; +} + CV_ALWAYS_INLINE v_float32 oper(absdiff_tag, const v_float32& a, const v_float32& sc) { return v_absdiff(a, sc); @@ -1288,16 +1401,17 @@ SUBRC_SIMD(float, float) //------------------------- // -// Fluid kernels: MulC +// Fluid kernels: MulC, DivC // //------------------------- -template +template CV_ALWAYS_INLINE typename std::enable_if::value || std::is_same::value, void>::type -mulc_scale_simd_c3_impl(const SRC* inx, DST* outx, const v_float32& s1, const v_float32& s2, - const v_float32& s3, const v_float32& scale, const int nlanes) +arithmOpScalarScaled_simd_c3_impl(oper_tag op, SRC* inx, DST* outx, const v_float32& s1, + const v_float32& s2, const v_float32& s3, + const v_float32& v_scale, const int nlanes) { v_float32 a1 = vg_load_f32(inx); v_float32 a2 = vg_load_f32(&inx[nlanes / 2]); @@ -1306,62 +1420,64 @@ mulc_scale_simd_c3_impl(const SRC* inx, DST* outx, const v_float32& s1, const v_ v_float32 a5 = vg_load_f32(&inx[2 * nlanes]); v_float32 a6 = vg_load_f32(&inx[5 * nlanes / 2]); - arithmOpScalar_pack_store_c3(outx, v_round(scale*a1*s1), - v_round(scale*a2*s2), - v_round(scale*a3*s3), - v_round(scale*a4*s1), - v_round(scale*a5*s2), - v_round(scale*a6*s3)); + arithmOpScalar_pack_store_c3(outx, v_round(oper_scaled(op, a1, s1, v_scale)), + v_round(oper_scaled(op, a2, s2, v_scale)), + v_round(oper_scaled(op, a3, s3, v_scale)), + v_round(oper_scaled(op, a4, s1, v_scale)), + v_round(oper_scaled(op, a5, s2, v_scale)), + v_round(oper_scaled(op, a6, s3, v_scale))); } //------------------------------------------------------------------------------------------------- -template -CV_ALWAYS_INLINE void mulc_scale_simd_c3_impl(const SRC* inx, uchar* outx, - const v_float32& s1, const v_float32& s2, - const v_float32& s3, const v_float32& scale, const int nlanes) +template +CV_ALWAYS_INLINE void arithmOpScalarScaled_simd_c3_impl(oper_tag op, const SRC* inx, uchar* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const int nlanes) { vx_store(outx, - v_pack_u(v_pack(v_round(scale * vg_load_f32(inx)* s1), - v_round(scale * vg_load_f32(&inx[nlanes/4])* s2)), - v_pack(v_round(scale * vg_load_f32(&inx[nlanes/2])* s3), - v_round(scale * vg_load_f32(&inx[3*nlanes/4])* s1)))); + v_pack_u(v_pack(v_round(oper_scaled(op, vg_load_f32(inx), s1, v_scale)), + v_round(oper_scaled(op, vg_load_f32(&inx[nlanes/4]), s2, v_scale))), + v_pack(v_round(oper_scaled(op, vg_load_f32(&inx[nlanes/2]), s3, v_scale)), + v_round(oper_scaled(op, vg_load_f32(&inx[3*nlanes/4]), s1, v_scale))))); vx_store(&outx[nlanes], - v_pack_u(v_pack(v_round(scale * vg_load_f32(&inx[nlanes])* s2), - v_round(scale * vg_load_f32(&inx[5*nlanes/4])* s3)), - v_pack(v_round(scale * vg_load_f32(&inx[3*nlanes/2])* s1), - v_round(scale * vg_load_f32(&inx[7*nlanes/4])* s2)))); + v_pack_u(v_pack(v_round(oper_scaled(op, vg_load_f32(&inx[nlanes]), s2, v_scale)), + v_round(oper_scaled(op, vg_load_f32(&inx[5*nlanes/4]), s3, v_scale))), + v_pack(v_round(oper_scaled(op, vg_load_f32(&inx[3*nlanes/2]), s1, v_scale)), + v_round(oper_scaled(op, vg_load_f32(&inx[7*nlanes/4]), s2, v_scale))))); vx_store(&outx[2 * nlanes], - v_pack_u(v_pack(v_round(scale * vg_load_f32(&inx[2*nlanes])* s3), - v_round(scale * vg_load_f32(&inx[9*nlanes/4])* s1)), - v_pack(v_round(scale * vg_load_f32(&inx[5*nlanes/2])* s2), - v_round(scale * vg_load_f32(&inx[11*nlanes/4])* s3)))); + v_pack_u(v_pack(v_round(oper_scaled(op, vg_load_f32(&inx[2*nlanes]), s3, v_scale)), + v_round(oper_scaled(op, vg_load_f32(&inx[9*nlanes/4]), s1, v_scale))), + v_pack(v_round(oper_scaled(op, vg_load_f32(&inx[5*nlanes/2]), s2, v_scale)), + v_round(oper_scaled(op, vg_load_f32(&inx[11*nlanes/4]), s3, v_scale))))); } //------------------------------------------------------------------------------------------------- -template -CV_ALWAYS_INLINE void mulc_scale_simd_c3_impl(const SRC* in, float* out, - const v_float32& s1, const v_float32& s2, - const v_float32& s3, const v_float32& scale, const int nlanes) +template +CV_ALWAYS_INLINE void arithmOpScalarScaled_simd_c3_impl(oper_tag op, const SRC* in, float* out, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const int nlanes) { v_float32 a1 = vg_load_f32(in); v_float32 a2 = vg_load_f32(&in[nlanes]); v_float32 a3 = vg_load_f32(&in[2*nlanes]); - vx_store(out, scale * a1* s1); - vx_store(&out[nlanes], scale * a2* s2); - vx_store(&out[2*nlanes], scale * a3* s3); + vx_store(out, oper_scaled(op, a1, s1, v_scale)); + vx_store(&out[nlanes], oper_scaled(op, a2, s2, v_scale)); + vx_store(&out[2*nlanes], oper_scaled(op, a3, s3, v_scale)); } //------------------------------------------------------------------------------------------------- -template -CV_ALWAYS_INLINE int mulc_scale_simd_c3(const SRC in[], - const float scalar[], DST out[], - const int length, const float _scale) +template +CV_ALWAYS_INLINE int arithmOpScalarScaled_simd_c3(oper_tag op, const SRC in[], + const float scalar[], DST out[], + const int length, const float scale) { constexpr int chan = 3; constexpr int nlanes = vector_type_of_t::nlanes; @@ -1370,7 +1486,7 @@ CV_ALWAYS_INLINE int mulc_scale_simd_c3(const SRC in[], if (length < lanes) return 0; - v_float32 scale = vx_setall_f32(_scale); + v_float32 v_scale = vx_setall_f32(scale); v_float32 s1 = vx_load(scalar); #if CV_SIMD_WIDTH == 32 @@ -1386,7 +1502,7 @@ CV_ALWAYS_INLINE int mulc_scale_simd_c3(const SRC in[], { for (; x <= length - lanes; x += lanes) { - mulc_scale_simd_c3_impl(&in[x], &out[x], s1, s2, s3, scale, nlanes); + arithmOpScalarScaled_simd_c3_impl(op, &in[x], &out[x], s1, s2, s3, v_scale, nlanes); } if (x < length) @@ -1401,70 +1517,70 @@ CV_ALWAYS_INLINE int mulc_scale_simd_c3(const SRC in[], //------------------------------------------------------------------------------------------------- -template +template CV_ALWAYS_INLINE typename std::enable_if<(std::is_same::value || std::is_same::value), void>::type -mulc_scale_simd_common_impl(const SRC* inx, DST* outx, - const v_float32& sc, const v_float32& scale, - const int nlanes) +arithmOpScalarScaled_simd_common_impl(oper_tag op, const SRC* inx, DST* outx, + const v_float32& v_scalar, const v_float32& v_scale, + const int nlanes) { v_float32 a1 = vg_load_f32(inx); v_float32 a2 = vg_load_f32(&inx[nlanes/2]); - v_store_i16(outx, v_round(scale * a1* sc), v_round(scale * a2* sc)); + v_store_i16(outx, v_round(oper_scaled(op, a1, v_scalar, v_scale)), v_round(oper_scaled(op, a2, v_scalar, v_scale))); } //------------------------------------------------------------------------------------------------- -template -CV_ALWAYS_INLINE void mulc_scale_simd_common_impl(const SRC* inx, - uchar* outx, const v_float32& sc, - const v_float32& scale, const int nlanes) +template +CV_ALWAYS_INLINE void arithmOpScalarScaled_simd_common_impl(oper_tag op, const SRC* inx, + uchar* outx, const v_float32& v_scalar, + const v_float32& v_scale, const int nlanes) { v_float32 a1 = vg_load_f32(inx); v_float32 a2 = vg_load_f32(&inx[nlanes/4]); v_float32 a3 = vg_load_f32(&inx[nlanes/2]); v_float32 a4 = vg_load_f32(&inx[3 * nlanes/4]); - vx_store(outx, v_pack_u(v_pack(v_round(scale * a1* sc), - v_round(scale * a2* sc)), - v_pack(v_round(scale * a3* sc), - v_round(scale * a4* sc)))); + vx_store(outx, v_pack_u(v_pack(v_round(oper_scaled(op, a1, v_scalar, v_scale)), + v_round(oper_scaled(op, a2, v_scalar, v_scale))), + v_pack(v_round(oper_scaled(op, a3, v_scalar, v_scale)), + v_round(oper_scaled(op, a4, v_scalar, v_scale))))); } //------------------------------------------------------------------------------------------------- -template -CV_ALWAYS_INLINE void mulc_scale_simd_common_impl(const SRC* inx, - float* outx, const v_float32& sc, - const v_float32& scale, const int) +template +CV_ALWAYS_INLINE void arithmOpScalarScaled_simd_common_impl(oper_tag op, const SRC* inx, + float* outx, const v_float32& v_scalar, + const v_float32& v_scale, const int) { - v_float32 a1 = vg_load_f32(inx); - vx_store(outx, scale * a1* sc); + v_float32 a = vg_load_f32(inx); + vx_store(outx, oper_scaled(op, a, v_scalar, v_scale)); } //------------------------------------------------------------------------------------------------- -template -CV_ALWAYS_INLINE int mulc_scale_simd_common(const SRC in[], - const float scalar[], DST out[], - const int length, const float _scale) +template +CV_ALWAYS_INLINE int arithmOpScalarScaled_simd_common(oper_tag op, const SRC in[], + const float scalar[], DST out[], + const int length, const float scale) { constexpr int nlanes = vector_type_of_t::nlanes; if (length < nlanes) return 0; - v_float32 _scalar = vx_load(scalar); - v_float32 scale = vx_setall_f32(_scale); + v_float32 v_scalar = vx_load(scalar); + v_float32 v_scale = vx_setall_f32(scale); int x = 0; for (;;) { for (; x <= length - nlanes; x += nlanes) { - mulc_scale_simd_common_impl(&in[x], &out[x], _scalar, scale, nlanes); + arithmOpScalarScaled_simd_common_impl(op, &in[x], &out[x], v_scalar, v_scale, nlanes); } if (x < length) @@ -1477,6 +1593,8 @@ CV_ALWAYS_INLINE int mulc_scale_simd_common(const SRC in[], return x; } +//------------------------------------------------------------------------------------------------- + #define MULC_SIMD(SRC, DST) \ int mulc_simd(const SRC in[], const float scalar[], DST out[], \ const int length, const int chan, const float scale) \ @@ -1495,7 +1613,8 @@ int mulc_simd(const SRC in[], const float scalar[], DST out[], \ } \ else \ { \ - return mulc_scale_simd_common(in, scalar, out, length, scale); \ + return arithmOpScalarScaled_simd_common(op_t, in, scalar, out, \ + length, scale); \ } \ } \ case 3: \ @@ -1507,7 +1626,8 @@ int mulc_simd(const SRC in[], const float scalar[], DST out[], \ } \ else \ { \ - return mulc_scale_simd_c3(in, scalar, out, length, scale); \ + return arithmOpScalarScaled_simd_c3(op_t, in, scalar, out, \ + length, scale); \ } \ } \ default: \ @@ -1536,37 +1656,1481 @@ MULC_SIMD(float, float) #undef MULC_SIMD -//------------------------- -// -// Fluid kernels: AbsDiffC -// -//------------------------- +//------------------------------------------------------------------------------------------------- -#define ABSDIFFC_SIMD(SRC) \ -int absdiffc_simd(const SRC in[], const float scalar[], SRC out[], \ - const int length, const int chan) \ -{ \ - switch (chan) \ - { \ - case 1: \ - case 2: \ - case 4: \ - return arithmOpScalar_simd_common(absdiff_tag{}, in, scalar, out, length); \ - case 3: \ - return arithmOpScalar_simd_c3(absdiff_tag{}, in, scalar, out, length); \ - default: \ - GAPI_Assert(chan <= 4); \ - break; \ - } \ - return 0; \ -} +template +CV_ALWAYS_INLINE +typename std::enable_if<(std::is_same::value || + std::is_same::value), int>::type +divc_simd_common_impl(scale_tag_t s_tag, const SRC in[], DST out[], + const v_float32& v_scalar, const v_float32& v_scale, + const int length) +{ + constexpr int nlanes = vector_type_of_t::nlanes; -ABSDIFFC_SIMD(uchar) -ABSDIFFC_SIMD(short) -ABSDIFFC_SIMD(ushort) -ABSDIFFC_SIMD(float) + v_float32 v_zero = vx_setzero_f32(); + v_float32 v_mask = (v_scalar == v_zero); -#undef ABSDIFFC_SIMD + int x = 0; + for (;;) + { + for (; x <= length - nlanes; x += nlanes) + { + v_float32 a1 = vg_load_f32(&in[x]); + v_float32 a2 = vg_load_f32(&in[x + nlanes/2]); + + v_store_i16(&out[x], v_round(v_select(v_mask, v_zero, div_op(s_tag, a1, v_scalar, v_scale))), + v_round(v_select(v_mask, v_zero, div_op(s_tag, a2, v_scalar, v_scale)))); + } + + if (x < length) + { + x = length - nlanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divc_simd_common_impl(scale_tag_t s_tag, const SRC in[], + uchar out[], const v_float32& v_scalar, + const v_float32& v_scale, const int length) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_float32 v_zero = vx_setzero_f32(); + v_float32 v_mask = (v_scalar == v_zero); + + int x = 0; + for (;;) + { + for (; x <= length - nlanes; x += nlanes) + { + v_float32 a1 = vg_load_f32(&in[x]); + v_float32 a2 = vg_load_f32(&in[x + nlanes/4]); + v_float32 a3 = vg_load_f32(&in[x + nlanes/2]); + v_float32 a4 = vg_load_f32(&in[x + 3 * nlanes/4]); + + vx_store(&out[x], v_pack_u(v_pack(v_round(v_select(v_mask, v_zero, div_op(s_tag, a1, v_scalar, v_scale))), + v_round(v_select(v_mask, v_zero, div_op(s_tag, a2, v_scalar, v_scale)))), + v_pack(v_round(v_select(v_mask, v_zero, div_op(s_tag, a3, v_scalar, v_scale))), + v_round(v_select(v_mask, v_zero, div_op(s_tag, a4, v_scalar, v_scale)))))); + } + + if (x < length) + { + x = length - nlanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divc_simd_common_impl(scale_tag_t s_tag, const SRC in[], + float out[], const v_float32& v_scalar, + const v_float32& v_scale, const int length) +{ + constexpr int nlanes = v_float32::nlanes; + int x = 0; + for (;;) + { + for (; x <= length - nlanes; x += nlanes) + { + v_float32 a1 = vg_load_f32(&in[x]); + vx_store(&out[x], div_op(s_tag, a1, v_scalar, v_scale)); + } + + if (x < length) + { + x = length - nlanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divc_mask_simd_common(scale_tag_t tag, const SRC in[], + const float scalar[], DST out[], + const int length, const float scale) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + if (length < nlanes) + return 0; + + v_float32 v_scalar = vx_load(scalar); + v_float32 v_scale = vx_setall_f32(scale); + return divc_simd_common_impl(tag, in, out, v_scalar, v_scale, length); +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, int>::type +divc_simd_c3_impl(scale_tag_t s_tag, SRC in[], DST out[], const v_float32& s1, + const v_float32& s2, const v_float32& s3, + const v_float32& v_scale, const int length, + const int nlanes, const int lanes) +{ + v_float32 v_zero = vx_setzero_f32(); + v_float32 v_mask1 = (s1 == v_zero); + v_float32 v_mask2 = (s2 == v_zero); + v_float32 v_mask3 = (s3 == v_zero); + + int x = 0; + for (;;) + { + for (; x <= length - lanes; x += lanes) + { + v_float32 a1 = vg_load_f32(&in[x]); + v_float32 a2 = vg_load_f32(&in[x + nlanes / 2]); + v_float32 a3 = vg_load_f32(&in[x + nlanes]); + v_float32 a4 = vg_load_f32(&in[x + 3 * nlanes / 2]); + v_float32 a5 = vg_load_f32(&in[x + 2 * nlanes]); + v_float32 a6 = vg_load_f32(&in[x + 5 * nlanes / 2]); + + arithmOpScalar_pack_store_c3(&out[x], v_round(v_select(v_mask1, v_zero, div_op(s_tag, a1, s1, v_scale))), + v_round(v_select(v_mask2, v_zero, div_op(s_tag, a2, s2, v_scale))), + v_round(v_select(v_mask3, v_zero, div_op(s_tag, a3, s3, v_scale))), + v_round(v_select(v_mask1, v_zero, div_op(s_tag, a4, s1, v_scale))), + v_round(v_select(v_mask2, v_zero, div_op(s_tag, a5, s2, v_scale))), + v_round(v_select(v_mask3, v_zero, div_op(s_tag, a6, s3, v_scale)))); + } + + if (x < length) + { + x = length - lanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divc_simd_c3_impl(scale_tag_t s_tag, const SRC* in, uchar* out, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const int length, const int nlanes, const int lanes) +{ + v_float32 v_zero = vx_setzero_f32(); + v_float32 v_mask1 = (s1 == v_zero); + v_float32 v_mask2 = (s2 == v_zero); + v_float32 v_mask3 = (s3 == v_zero); + + int x = 0; + for (;;) + { + for (; x <= length - lanes; x += lanes) + { + vx_store(&out[x], + v_pack_u(v_pack(v_round(v_select(v_mask1, v_zero, div_op(s_tag, vg_load_f32(&in[x]), s1, v_scale))), + v_round(v_select(v_mask2, v_zero, div_op(s_tag, vg_load_f32(&in[x + nlanes/4]), s2, v_scale)))), + v_pack(v_round(v_select(v_mask3, v_zero, div_op(s_tag, vg_load_f32(&in[x + nlanes/2]), s3, v_scale))), + v_round(v_select(v_mask1, v_zero, div_op(s_tag, vg_load_f32(&in[x + 3*nlanes/4]), s1, v_scale)))))); + + vx_store(&out[x + nlanes], + v_pack_u(v_pack(v_round(v_select(v_mask2, v_zero, div_op(s_tag, vg_load_f32(&in[x + nlanes]), s2, v_scale))), + v_round(v_select(v_mask3, v_zero, div_op(s_tag, vg_load_f32(&in[x + 5*nlanes/4]), s3, v_scale)))), + v_pack(v_round(v_select(v_mask1, v_zero, div_op(s_tag, vg_load_f32(&in[x + 3*nlanes/2]), s1, v_scale))), + v_round(v_select(v_mask2, v_zero, div_op(s_tag, vg_load_f32(&in[x + 7*nlanes/4]), s2, v_scale)))))); + + vx_store(&out[x + 2 * nlanes], + v_pack_u(v_pack(v_round(v_select(v_mask3, v_zero, div_op(s_tag, vg_load_f32(&in[x + 2*nlanes]), s3, v_scale))), + v_round(v_select(v_mask1, v_zero, div_op(s_tag, vg_load_f32(&in[x + 9*nlanes/4]), s1, v_scale)))), + v_pack(v_round(v_select(v_mask2, v_zero, div_op(s_tag, vg_load_f32(&in[x + 5*nlanes/2]), s2, v_scale))), + v_round(v_select(v_mask3, v_zero, div_op(s_tag, vg_load_f32(&in[x + 11*nlanes/4]), s3, v_scale)))))); + } + + if (x < length) + { + x = length - lanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divc_simd_c3_impl(scale_tag_t s_tag, const SRC* in, float* out, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, const int length, + const int nlanes, const int lanes) +{ + int x = 0; + for (;;) + { + for (; x <= length - lanes; x += lanes) + { + v_float32 a1 = vg_load_f32(&in[x]); + v_float32 a2 = vg_load_f32(&in[x + nlanes]); + v_float32 a3 = vg_load_f32(&in[x + 2*nlanes]); + + vx_store(&out[x], div_op(s_tag, a1, s1, v_scale)); + vx_store(&out[x + nlanes], div_op(s_tag, a2, s2, v_scale)); + vx_store(&out[x + 2*nlanes], div_op(s_tag, a3, s3, v_scale)); + } + + if (x < length) + { + x = length - lanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divc_mask_simd_c3(scale_tag_t s_tag, const SRC in[], + const float scalar[], DST out[], + const int length, const float scale) +{ + constexpr int chan = 3; + constexpr int nlanes = vector_type_of_t::nlanes; + constexpr int lanes = chan * nlanes; + + if (length < lanes) + return 0; + + v_float32 v_scale = vx_setall_f32(scale); + + v_float32 s1 = vx_load(scalar); +#if CV_SIMD_WIDTH == 32 + v_float32 s2 = vx_load(&scalar[2]); + v_float32 s3 = vx_load(&scalar[1]); +#else + v_float32 s2 = vx_load(&scalar[1]); + v_float32 s3 = vx_load(&scalar[2]); +#endif + return divc_simd_c3_impl(s_tag, in, out, s1, s2, s3, v_scale, length, nlanes, lanes); +} + +//------------------------------------------------------------------------------------------------- + +#define DIVC_SIMD(SRC, DST) \ +int divc_simd(const SRC in[], const float scalar[], DST out[], \ + const int length, const int chan, const float scale, \ + const int set_mask_flag) \ +{ \ + switch (chan) \ + { \ + case 1: \ + case 2: \ + case 4: \ + { \ + if (std::fabs(scale - 1.0f) <= FLT_EPSILON) \ + { \ + if (set_mask_flag == 1) \ + return divc_mask_simd_common(not_scale_tag{}, in, scalar, \ + out, length, scale); \ + else \ + return arithmOpScalar_simd_common(div_tag{}, in, scalar, \ + out, length); \ + } \ + else \ + { if (set_mask_flag == 1) \ + return divc_mask_simd_common(scale_tag{}, in, scalar, \ + out, length, scale); \ + else \ + return arithmOpScalarScaled_simd_common(div_tag{}, in, scalar, \ + out, length, scale); \ + } \ + } \ + case 3: \ + { \ + if (std::fabs(scale - 1.0f) <= FLT_EPSILON) \ + { \ + if (set_mask_flag == 1) \ + return divc_mask_simd_c3(not_scale_tag{}, in, scalar, \ + out, length, scale); \ + else \ + return arithmOpScalar_simd_c3(div_tag{}, in, scalar, \ + out, length); \ + } \ + else \ + { \ + if (set_mask_flag == 1) \ + return divc_mask_simd_c3(scale_tag{}, in, scalar, \ + out, length, scale); \ + else \ + return arithmOpScalarScaled_simd_c3(div_tag{}, in, scalar, out,\ + length, scale); \ + } \ + } \ + default: \ + GAPI_Assert(chan <= 4); \ + break; \ + } \ + return 0; \ +} + +DIVC_SIMD(uchar, uchar) +DIVC_SIMD(ushort, uchar) +DIVC_SIMD(short, uchar) +DIVC_SIMD(float, uchar) +DIVC_SIMD(short, short) +DIVC_SIMD(ushort, short) +DIVC_SIMD(uchar, short) +DIVC_SIMD(float, short) +DIVC_SIMD(ushort, ushort) +DIVC_SIMD(uchar, ushort) +DIVC_SIMD(short, ushort) +DIVC_SIMD(float, ushort) +DIVC_SIMD(uchar, float) +DIVC_SIMD(ushort, float) +DIVC_SIMD(short, float) +DIVC_SIMD(float, float) + +#undef DIVC_SIMD + +//------------------------- +// +// Fluid kernels: AbsDiffC +// +//------------------------- + +#define ABSDIFFC_SIMD(SRC) \ +int absdiffc_simd(const SRC in[], const float scalar[], SRC out[], \ + const int length, const int chan) \ +{ \ + switch (chan) \ + { \ + case 1: \ + case 2: \ + case 4: \ + return arithmOpScalar_simd_common(absdiff_tag{}, in, scalar, out, length); \ + case 3: \ + return arithmOpScalar_simd_c3(absdiff_tag{}, in, scalar, out, length); \ + default: \ + GAPI_Assert(chan <= 4); \ + break; \ + } \ + return 0; \ +} + +ABSDIFFC_SIMD(uchar) +ABSDIFFC_SIMD(short) +ABSDIFFC_SIMD(ushort) +ABSDIFFC_SIMD(float) + +#undef ABSDIFFC_SIMD + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +divrc_simd_common_impl(scale_tag_t s_tag, const SRC* inx, + const v_float32& v_scalar, DST* outx, + const v_float32& v_scale, const Tvec& v_zero) +{ + div_simd_impl(s_tag, v_scalar, v_scalar, inx, outx, v_scale, v_zero); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value, void>::type +divrc_simd_common_impl(scale_tag_t s_tag, const SRC* inx, + const v_float32& v_scalar, DST* outx, + const v_float32& v_scale, const Tvec& v_zero) +{ + div_simd_impl(s_tag, v_scalar, v_scalar, v_scalar, v_scalar, inx, outx, v_scale, v_zero); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value, void>::type +divrc_simd_common_impl(scale_tag_t s_tag, const SRC* inx, + const v_float32& v_scalar, DST* outx, + const v_float32& v_scale, const Tvec&) +{ + div_simd_impl(s_tag, v_scalar, inx, outx, v_scale); +} + +template +CV_ALWAYS_INLINE int divrc_simd_common(scale_tag_t s_tag, const SRC in[], + const float scalar[], DST out[], + const int length, const float scale) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + if (length < nlanes) + return 0; + + v_float32 v_scalar = vx_load(scalar); + v_float32 v_scale = vx_setall_f32(scale); + zero_vec_type_of_t v_zero = + vx_setall::lane_type>(0); + + int x = 0; + for (;;) + { + for (; x <= length - nlanes; x += nlanes) + { + divrc_simd_common_impl(s_tag, &in[x], v_scalar, &out[x], v_scale, v_zero); + } + + if (x < length) + { + x = length - nlanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE void divrc_simd_c3_calc(scale_tag_t s_tag, const uchar* inx, uchar* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const v_uint8& v_zero) +{ + v_uint8 div = vx_load(inx); + v_uint8 v_mask = (div == v_zero); + + v_uint16 div1 = v_expand_low(div); + v_uint16 div2 = v_expand_high(div); + + v_float32 fdiv1 = v_cvt_f32(v_reinterpret_as_s32(v_expand_low(div1))); + v_float32 fdiv2 = v_cvt_f32(v_reinterpret_as_s32(v_expand_high(div1))); + v_float32 fdiv3 = v_cvt_f32(v_reinterpret_as_s32(v_expand_low(div2))); + v_float32 fdiv4 = v_cvt_f32(v_reinterpret_as_s32(v_expand_high(div2))); + + vx_store(outx, + v_select(v_mask, v_zero, v_pack_u(v_pack(v_round(div_op(s_tag, s1, fdiv1, v_scale)), + v_round(div_op(s_tag, s2, fdiv2, v_scale))), + v_pack(v_round(div_op(s_tag, s3, fdiv3, v_scale)), + v_round(div_op(s_tag, s1, fdiv4, v_scale)))))); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +divrc_simd_c3_calc(scale_tag_t s_tag, const SRC* inx, uchar* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const v_int16& v_zero) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_int16 div1 = v_reinterpret_as_s16(vx_load(inx)); + v_int16 div2 = v_reinterpret_as_s16(vx_load(&inx[nlanes / 2])); + + v_int16 v_mask1 = (div1 == v_zero); + v_int16 v_mask2 = (div2 == v_zero); + + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div1)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div1)); + v_float32 fdiv3 = v_cvt_f32(v_expand_low(div2)); + v_float32 fdiv4 = v_cvt_f32(v_expand_high(div2)); + + vx_store(outx, + v_pack_u(v_select(v_mask1, v_zero, + v_pack(v_round(div_op(s_tag, s1, fdiv1, v_scale)), + v_round(div_op(s_tag, s2, fdiv2, v_scale)))), + v_select(v_mask2, v_zero, + v_pack(v_round(div_op(s_tag, s3, fdiv3, v_scale)), + v_round(div_op(s_tag, s1, fdiv4, v_scale)))))); +} + +template +CV_ALWAYS_INLINE void divrc_simd_c3_calc(scale_tag_t s_tag, const float* inx, uchar* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const v_float32& v_zero) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_float32 fdiv1 = vg_load_f32(inx); + v_float32 fdiv2 = vg_load_f32(&inx[nlanes / 4]); + v_float32 fdiv3 = vg_load_f32(&inx[nlanes / 2]); + v_float32 fdiv4 = vg_load_f32(&inx[3 * nlanes / 4]); + + v_float32 v_mask1 = (fdiv1 == v_zero); + v_float32 v_mask2 = (fdiv2 == v_zero); + v_float32 v_mask3 = (fdiv3 == v_zero); + v_float32 v_mask4 = (fdiv4 == v_zero); + + vx_store(outx, + v_pack_u(v_pack(v_round(v_select(v_mask1, v_zero, div_op(s_tag, s1, fdiv1, v_scale))), + v_round(v_select(v_mask2, v_zero, div_op(s_tag, s2, fdiv2, v_scale)))), + v_pack(v_round(v_select(v_mask3, v_zero, div_op(s_tag, s3, fdiv3, v_scale))), + v_round(v_select(v_mask4, v_zero, div_op(s_tag, s1, fdiv4, v_scale)))))); + +} + +template +CV_ALWAYS_INLINE int divrc_simd_c3_impl(scale_tag_t s_tag, const SRC in[], uchar out[], + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const int length, const int nlanes, const int lanes) +{ + univ_zero_vec_type_of_t v_zero = + vx_setall::lane_type>(0); + + int x = 0; + for (;;) + { + for (; x <= length - lanes; x += lanes) + { + divrc_simd_c3_calc(s_tag, &in[x], &out[x], s1, s2, s3, v_scale, v_zero); + divrc_simd_c3_calc(s_tag, &in[x + nlanes], &out[x + nlanes], s2, s3, s1, v_scale, v_zero); + divrc_simd_c3_calc(s_tag, &in[x + 2 * nlanes], &out[x + 2 * nlanes], s3, s1, s2, v_scale, v_zero); + } + + if (x < length) + { + x = length - lanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//--------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +divrc_simd_c3_calc(scale_tag_t s_tag, const uchar* inx, DST* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const v_int16& v_zero) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + v_uint8 div = vx_load(inx); + + v_int16 div1 = v_reinterpret_as_s16(v_expand_low(div)); + v_int16 div2 = v_reinterpret_as_s16(v_expand_high(div)); + v_int16 div3 = v_reinterpret_as_s16(vx_load_expand(&inx[2 * nlanes])); + + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div1)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div1)); + v_float32 fdiv3 = v_cvt_f32(v_expand_low(div2)); + v_float32 fdiv4 = v_cvt_f32(v_expand_high(div2)); + v_float32 fdiv5 = v_cvt_f32(v_expand_low(div3)); + v_float32 fdiv6 = v_cvt_f32(v_expand_high(div3)); + + v_store_select(outx, div1, v_zero, v_round(div_op(s_tag, s1, fdiv1, v_scale)), + v_round(div_op(s_tag, s2, fdiv2, v_scale))); + v_store_select(&outx[nlanes], div2, v_zero, v_round(div_op(s_tag, s3, fdiv3, v_scale)), + v_round(div_op(s_tag, s1, fdiv4, v_scale))); + v_store_select(&outx[2*nlanes], div3, v_zero, v_round(div_op(s_tag, s2, fdiv5, v_scale)), + v_round(div_op(s_tag, s3, fdiv6, v_scale))); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if<(std::is_same::value && std::is_same::value) || + (std::is_same::value && std::is_same::value) || + (std::is_same::value && std::is_same::value) || + (std::is_same::value && std::is_same::value), void>::type +divrc_simd_c3_calc(scale_tag_t s_tag, const SRC* inx, DST* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const v_int16& v_zero) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + v_int16 div1 = v_reinterpret_as_s16(vx_load(inx)); + v_int16 div2 = v_reinterpret_as_s16(vx_load(&inx[nlanes])); + v_int16 div3 = v_reinterpret_as_s16(vx_load(&inx[2*nlanes])); + + v_float32 fdiv1 = v_cvt_f32(v_expand_low(div1)); + v_float32 fdiv2 = v_cvt_f32(v_expand_high(div1)); + v_float32 fdiv3 = v_cvt_f32(v_expand_low(div2)); + v_float32 fdiv4 = v_cvt_f32(v_expand_high(div2)); + v_float32 fdiv5 = v_cvt_f32(v_expand_low(div3)); + v_float32 fdiv6 = v_cvt_f32(v_expand_high(div3)); + + v_store_select(outx, div1, v_zero, v_round(div_op(s_tag, s1, fdiv1, v_scale)), + v_round(div_op(s_tag, s2, fdiv2, v_scale))); + v_store_select(&outx[nlanes], div2, v_zero, v_round(div_op(s_tag, s3, fdiv3, v_scale)), + v_round(div_op(s_tag, s1, fdiv4, v_scale))); + v_store_select(&outx[2*nlanes], div3, v_zero, v_round(div_op(s_tag, s2, fdiv5, v_scale)), + v_round(div_op(s_tag, s3, fdiv6, v_scale))); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +divrc_simd_c3_calc(scale_tag_t s_tag, const float* inx, DST* outx, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const v_float32& v_zero) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + v_float32 fdiv1 = vg_load_f32(inx); + v_float32 fdiv2 = vg_load_f32(&inx[nlanes/2]); + v_float32 fdiv3 = vg_load_f32(&inx[nlanes]); + v_float32 fdiv4 = vg_load_f32(&inx[3*nlanes/2]); + v_float32 fdiv5 = vg_load_f32(&inx[2*nlanes]); + v_float32 fdiv6 = vg_load_f32(&inx[5*nlanes/2]); + + v_store_i16(outx, v_round(v_select(fdiv1 == v_zero, v_zero, div_op(s_tag, s1, fdiv1, v_scale))), + v_round(v_select(fdiv2 == v_zero, v_zero, div_op(s_tag, s2, fdiv2, v_scale)))); + v_store_i16(&outx[nlanes], v_round(v_select(fdiv3 == v_zero, v_zero, div_op(s_tag, s3, fdiv3, v_scale))), + v_round(v_select(fdiv4 == v_zero, v_zero, div_op(s_tag, s1, fdiv4, v_scale)))); + v_store_i16(&outx[2*nlanes], v_round(v_select(fdiv5 == v_zero, v_zero, div_op(s_tag, s2, fdiv5, v_scale))), + v_round(v_select(fdiv6 == v_zero, v_zero, div_op(s_tag, s3, fdiv6, v_scale)))); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, int>::type +divrc_simd_c3_impl(scale_tag_t s_tag, const SRC in[], DST out[], const v_float32& s1, + const v_float32& s2, const v_float32& s3, + const v_float32& v_scale, const int length, + const int, const int lanes) +{ + zero_vec_type_of_t v_zero = + vx_setall::lane_type>(0); + + int x = 0; + for (;;) + { + for (; x <= length - lanes; x += lanes) + { + divrc_simd_c3_calc(s_tag, &in[x], &out[x], s1, s2, s3, v_scale, v_zero); + } + + if (x < length) + { + x = length - lanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//--------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divrc_simd_c3_impl(scale_tag_t s_tag, const SRC* in, float* out, + const v_float32& s1, const v_float32& s2, + const v_float32& s3, const v_float32& v_scale, + const int length, const int nlanes, const int lanes) +{ + int x = 0; + for (;;) + { + for (; x <= length - lanes; x += lanes) + { + v_float32 div1 = vg_load_f32(&in[x]); + v_float32 div2 = vg_load_f32(&in[x + nlanes]); + v_float32 div3 = vg_load_f32(&in[x + 2*nlanes]); + + vx_store(&out[x], div_op(s_tag, s1, div1, v_scale)); + vx_store(&out[x + nlanes], div_op(s_tag, s2, div2, v_scale)); + vx_store(&out[x + 2*nlanes], div_op(s_tag, s3, div3, v_scale)); + } + + if (x < length) + { + x = length - lanes; + continue; // process unaligned tail + } + break; + } + return x; +} + +//------------------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE int divrc_simd_c3(scale_tag_t s_tag, const SRC in[], + const float scalar[], DST out[], + const int length, const float scale) +{ + constexpr int chan = 3; + constexpr int nlanes = vector_type_of_t::nlanes; + constexpr int lanes = chan * nlanes; + + if (length < lanes) + return 0; + + v_float32 v_scale = vx_setall_f32(scale); + + v_float32 s1 = vx_load(scalar); +#if CV_SIMD_WIDTH == 32 + v_float32 s2 = vx_load(&scalar[2]); + v_float32 s3 = vx_load(&scalar[1]); +#else + v_float32 s2 = vx_load(&scalar[1]); + v_float32 s3 = vx_load(&scalar[2]); +#endif + return divrc_simd_c3_impl(s_tag, in, out, s1, s2, s3, v_scale, length, nlanes, lanes); +} + +#define DIVRC_SIMD(SRC, DST) \ +int divrc_simd(const float scalar[], const SRC in[], DST out[], \ + const int length, const int chan, const float scale) \ +{ \ + switch (chan) \ + { \ + case 1: \ + case 2: \ + case 4: \ + { \ + if (std::fabs(scale - 1.0f) <= FLT_EPSILON) \ + { \ + return divrc_simd_common(not_scale_tag{}, in, scalar, \ + out, length, scale); \ + } \ + else \ + { \ + return divrc_simd_common(scale_tag{}, in, scalar, out, \ + length, scale); \ + } \ + } \ + case 3: \ + { \ + if (std::fabs(scale - 1.0f) <= FLT_EPSILON) \ + { \ + return divrc_simd_c3(not_scale_tag{}, in, scalar, \ + out, length, scale); \ + } \ + else \ + { \ + return divrc_simd_c3(scale_tag{}, in, scalar, out, \ + length, scale); \ + } \ + } \ + default: \ + GAPI_Assert(chan <= 4); \ + break; \ + } \ + return 0; \ +} + +DIVRC_SIMD(uchar, uchar) +DIVRC_SIMD(ushort, uchar) +DIVRC_SIMD(short, uchar) +DIVRC_SIMD(float, uchar) +DIVRC_SIMD(short, short) +DIVRC_SIMD(ushort, short) +DIVRC_SIMD(uchar, short) +DIVRC_SIMD(float, short) +DIVRC_SIMD(ushort, ushort) +DIVRC_SIMD(uchar, ushort) +DIVRC_SIMD(short, ushort) +DIVRC_SIMD(float, ushort) +DIVRC_SIMD(uchar, float) +DIVRC_SIMD(ushort, float) +DIVRC_SIMD(short, float) +DIVRC_SIMD(float, float) + +#undef DIVRC_SIMD + +//------------------------- +// +// Fluid kernels: Split3 +// +//------------------------- + +int split3_simd(const uchar in[], uchar out1[], uchar out2[], uchar out3[], + const int width) +{ + constexpr int nlanes = v_uint8::nlanes; + if (width < nlanes) + return 0; + + int x = 0; + for (;;) + { + for (; x <= width - nlanes; x += nlanes) + { + v_uint8 a, b, c; + v_load_deinterleave(&in[3 * x], a, b, c); + vx_store(&out1[x], a); + vx_store(&out2[x], b); + vx_store(&out3[x], c); + } + if (x < width) + { + x = width - nlanes; + continue; + } + break; + } + return x; +} + +//------------------------- +// +// Fluid kernels: Split4 +// +//------------------------- + +int split4_simd(const uchar in[], uchar out1[], uchar out2[], + uchar out3[], uchar out4[], const int width) +{ + constexpr int nlanes = v_uint8::nlanes; + if (width < nlanes) + return 0; + + int x = 0; + for (;;) + { + for (; x <= width - nlanes; x += nlanes) + { + v_uint8 a, b, c, d; + v_load_deinterleave(&in[4 * x], a, b, c, d); + vx_store(&out1[x], a); + vx_store(&out2[x], b); + vx_store(&out3[x], c); + vx_store(&out4[x], d); + } + if (x < width) + { + x = width - nlanes; + continue; + } + break; + } + return x; +} + +//------------------------- +// +// Fluid kernels: Merge3 +// +//------------------------- + +int merge3_simd(const uchar in1[], const uchar in2[], const uchar in3[], + uchar out[], const int width) +{ + constexpr int nlanes = v_uint8::nlanes; + if (width < nlanes) + return 0; + + int x = 0; + for (;;) + { + for (; x <= width - nlanes; x += nlanes) + { + v_uint8 a, b, c; + a = vx_load(&in1[x]); + b = vx_load(&in2[x]); + c = vx_load(&in3[x]); + v_store_interleave(&out[3 * x], a, b, c); + } + if (x < width) + { + x = width - nlanes; + continue; + } + break; + } + return x; +} + +//------------------------- +// +// Fluid kernels: Merge4 +// +//------------------------- + +int merge4_simd(const uchar in1[], const uchar in2[], const uchar in3[], + const uchar in4[], uchar out[], const int width) +{ + constexpr int nlanes = v_uint8::nlanes; + if (width < nlanes) + return 0; + + int x = 0; + for (;;) + { + for (; x <= width - nlanes; x += nlanes) + { + v_uint8 a, b, c, d; + a = vx_load(&in1[x]); + b = vx_load(&in2[x]); + c = vx_load(&in3[x]); + d = vx_load(&in4[x]); + v_store_interleave(&out[4 * x], a, b, c, d); + } + if (x < width) + { + x = width - nlanes; + continue; + } + break; + } + return x; +} + +//------------------------- +// +// Fluid kernels: Add +// +//------------------------- +template +CV_ALWAYS_INLINE VT oper(add_tag, const VT& a, const VT& b) +{ + return a + b; +} + +template +CV_ALWAYS_INLINE VT oper(sub_tag, const VT& a, const VT& b) +{ + return a - b; +} + +CV_ALWAYS_INLINE void pack_store_uchar(uchar* outx, const v_uint16& c1, const v_uint16& c2) +{ + vx_store(outx, v_pack(c1, c2)); +} + +CV_ALWAYS_INLINE void pack_store_uchar(uchar* outx, const v_int16& c1, const v_int16& c2) +{ + vx_store(outx, v_pack_u(c1, c2)); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value, void>::type +arithmOp_simd_impl(oper_tag op, const SRC* in1x, const SRC* in2x, DST* outx) +{ + vector_type_of_t a = vx_load(in1x); + vector_type_of_t b = vx_load(in2x); + vx_store(outx, oper(op, a, b)); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +arithmOp_simd_impl(oper_tag op, const SRC* in1x, const SRC* in2x, uchar* outx) +{ + constexpr int nlanes = v_uint8::nlanes; + + vector_type_of_t a1 = vx_load(in1x); + vector_type_of_t a2 = vx_load(&in1x[nlanes / 2]); + vector_type_of_t b1 = vx_load(in2x); + vector_type_of_t b2 = vx_load(&in2x[nlanes / 2]); + + pack_store_uchar(outx, oper(op, a1, b1), oper(op, a2, b2)); +} + +template +CV_ALWAYS_INLINE void arithmOp_simd_impl(oper_tag op, const float* in1x, + const float* in2x, uchar* outx) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_float32 a1 = vx_load(in1x); + v_float32 a2 = vx_load(&in1x[nlanes / 4]); + v_float32 a3 = vx_load(&in1x[2 * nlanes / 4]); + v_float32 a4 = vx_load(&in1x[3 * nlanes / 4]); + + v_float32 b1 = vx_load(in2x); + v_float32 b2 = vx_load(&in2x[nlanes / 4]); + v_float32 b3 = vx_load(&in2x[2 * nlanes / 4]); + v_float32 b4 = vx_load(&in2x[3 * nlanes / 4]); + + vx_store(outx, v_pack_u(v_pack(v_round(oper(op, a1, b1)), v_round(oper(op, a2, b2))), + v_pack(v_round(oper(op, a3, b3)), v_round(oper(op, a4, b4))))); +} + +template +CV_ALWAYS_INLINE void arithmOp_simd_impl(oper_tag op, const uchar* in1x, + const uchar* in2x, short* outx) +{ + v_int16 a = v_reinterpret_as_s16(vx_load_expand(in1x)); + v_int16 b = v_reinterpret_as_s16(vx_load_expand(in2x)); + + vx_store(outx, oper(op, a, b)); +} + +template +CV_ALWAYS_INLINE void arithmOp_simd_impl(oper_tag op, const uchar* in1x, + const uchar* in2x, ushort* outx) +{ + v_uint16 a = vx_load_expand(in1x); + v_uint16 b = vx_load_expand(in2x); + + vx_store(outx, oper(op, a, b)); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::value || + std::is_same::value, void>::type +arithmOp_simd_impl(oper_tag op, const float* in1x, const float* in2x, DST* outx) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + v_float32 a1 = vx_load(in1x); + v_float32 a2 = vx_load(&in1x[nlanes/2]); + v_float32 b1 = vx_load(in2x); + v_float32 b2 = vx_load(&in2x[nlanes/2]); + + v_store_i16(outx, v_round(oper(op, a1, b1)), v_round(oper(op, a2, b2))); +} + +template +CV_ALWAYS_INLINE void arithmOp_simd_impl(oper_tag op, const short* in1x, + const short* in2x, ushort* outx) +{ + v_int16 a = vx_load(in1x); + v_int32 a1 = v_expand_low(a); + v_int32 a2 = v_expand_high(a); + + v_int16 b = vx_load(in2x); + v_int32 b1 = v_expand_low(b); + v_int32 b2 = v_expand_high(b); + + vx_store(outx, v_pack_u(oper(op, a1, b1), oper(op, a2, b2))); +} + +template +CV_ALWAYS_INLINE void arithmOp_simd_impl(oper_tag op, const ushort* in1x, + const ushort* in2x, short* outx) +{ + v_int16 a = v_reinterpret_as_s16(vx_load(in1x)); + v_int32 a1 = v_expand_low(a); + v_int32 a2 = v_expand_high(a); + + v_int16 b = v_reinterpret_as_s16(vx_load(in2x)); + v_int32 b1 = v_expand_low(b); + v_int32 b2 = v_expand_high(b); + + vx_store(outx, v_pack(oper(op, a1, b1), oper(op, a2, b2))); +} + +template +CV_ALWAYS_INLINE void arithmOp_simd_impl(oper_tag op, const SRC* in1x, const SRC* in2x, float* outx) +{ + v_float32 a = vg_load_f32(in1x); + v_float32 b = vg_load_f32(in2x); + + vx_store(outx, oper(op, a, b)); +} + +template +CV_ALWAYS_INLINE int arithmOp_simd(oper_tag op, const SRC in1[], const SRC in2[], + DST out[], const int length) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + if (length < nlanes) + return 0; + + int x = 0; + for (;;) + { + for (; x <= length - nlanes; x += nlanes) + { + arithmOp_simd_impl(op, &in1[x], &in2[x], &out[x]); + } + + if (x < length) + { + x = length - nlanes; + continue; + } + break; + } + + return x; +} + +#define ADD_SIMD(SRC, DST) \ +int add_simd(const SRC in1[], const SRC in2[], DST out[], const int length) \ +{ \ + return arithmOp_simd(add_tag{}, in1, in2, out, length); \ +} \ + +ADD_SIMD(uchar, uchar) +ADD_SIMD(ushort, uchar) +ADD_SIMD(short, uchar) +ADD_SIMD(float, uchar) +ADD_SIMD(short, short) +ADD_SIMD(ushort, short) +ADD_SIMD(uchar, short) +ADD_SIMD(float, short) +ADD_SIMD(ushort, ushort) +ADD_SIMD(uchar, ushort) +ADD_SIMD(short, ushort) +ADD_SIMD(float, ushort) +ADD_SIMD(uchar, float) +ADD_SIMD(ushort, float) +ADD_SIMD(short, float) +ADD_SIMD(float, float) + +#undef ADD_SIMD + +//------------------------- +// +// Fluid kernels: Sub +// +//------------------------- + +#define SUB_SIMD(SRC, DST) \ +int sub_simd(const SRC in1[], const SRC in2[], DST out[], const int length) \ +{ \ + return arithmOp_simd(sub_tag{}, in1, in2, out, length); \ +} \ + +SUB_SIMD(uchar, uchar) +SUB_SIMD(ushort, uchar) +SUB_SIMD(short, uchar) +SUB_SIMD(float, uchar) +SUB_SIMD(short, short) +SUB_SIMD(ushort, short) +SUB_SIMD(uchar, short) +SUB_SIMD(float, short) +SUB_SIMD(ushort, ushort) +SUB_SIMD(uchar, ushort) +SUB_SIMD(short, ushort) +SUB_SIMD(float, ushort) +SUB_SIMD(uchar, float) +SUB_SIMD(ushort, float) +SUB_SIMD(short, float) +SUB_SIMD(float, float) + +#undef SUB_SIMD + +//------------------------- +// +// Fluid kernels: ConvertTo +// +//------------------------- + +CV_ALWAYS_INLINE void store_i16(ushort* outx, const v_uint16& res) +{ + vx_store(outx, res); +} + +CV_ALWAYS_INLINE void store_i16(short* outx, const v_uint16& res) +{ + vx_store(outx, v_reinterpret_as_s16(res)); +} + +CV_ALWAYS_INLINE void store_i16(ushort* outx, const v_int16& res) +{ + vx_store(outx, v_reinterpret_as_u16(res)); +} + +CV_ALWAYS_INLINE void store_i16(short* outx, const v_int16& res) +{ + vx_store(outx, res); +} + +CV_ALWAYS_INLINE void convertto_simd_nocoeff_impl(const float* inx, uchar* outx) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_int32 a1 = v_round(vx_load(inx)); + v_int32 a2 = v_round(vx_load(&inx[nlanes/4])); + v_int32 a3 = v_round(vx_load(&inx[nlanes/2])); + v_int32 a4 = v_round(vx_load(&inx[3*nlanes/4])); + + v_int16 r1 = v_pack(a1, a2); + v_int16 r2 = v_pack(a3, a4); + + vx_store(outx, v_pack_u(r1, r2)); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_simd_nocoeff_impl(const SRC* inx, uchar* outx) +{ + constexpr int nlanes = v_uint8::nlanes; + + vector_type_of_t a1 = vx_load(inx); + vector_type_of_t a2 = vx_load(&inx[nlanes/2]); + + pack_store_uchar(outx, a1, a2); +} + +//--------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_simd_nocoeff_impl(const float* inx, DST* outx) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + v_int32 a1 = v_round(vx_load(inx)); + v_int32 a2 = v_round(vx_load(&inx[nlanes/2])); + + v_store_i16(outx, a1, a2); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_simd_nocoeff_impl(const uchar* inx, DST* outx) +{ + v_uint8 a = vx_load(inx); + v_uint16 res = v_expand_low(a); + + store_i16(outx, res); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_simd_nocoeff_impl(const SRC* inx, DST* outx) +{ + vector_type_of_t a = vx_load(inx); + store_i16(outx, a); +} + +//--------------------------------------------------------------------------------------- + +template +CV_ALWAYS_INLINE void convertto_simd_nocoeff_impl(const SRC* inx, float* outx) +{ + v_float32 a = vg_load_f32(inx); + vx_store(outx, a); +} + +#define CONVERTTO_NOCOEF_SIMD(SRC, DST) \ +int convertto_simd(const SRC in[], DST out[], const int length) \ +{ \ + constexpr int nlanes = vector_type_of_t::nlanes; \ + \ + int x = 0; \ + for (;;) \ + { \ + for (; x <= length - nlanes; x += nlanes) \ + { \ + convertto_simd_nocoeff_impl(&in[x], &out[x]); \ + } \ + if (x < length) \ + { \ + x = length - nlanes; \ + continue; \ + } \ + break; \ + } \ + return x; \ +} + +CONVERTTO_NOCOEF_SIMD(ushort, uchar) +CONVERTTO_NOCOEF_SIMD(short, uchar) +CONVERTTO_NOCOEF_SIMD(float, uchar) +CONVERTTO_NOCOEF_SIMD(ushort, short) +CONVERTTO_NOCOEF_SIMD(uchar, short) +CONVERTTO_NOCOEF_SIMD(float, short) +CONVERTTO_NOCOEF_SIMD(uchar, ushort) +CONVERTTO_NOCOEF_SIMD(short, ushort) +CONVERTTO_NOCOEF_SIMD(float, ushort) +CONVERTTO_NOCOEF_SIMD(uchar, float) +CONVERTTO_NOCOEF_SIMD(ushort, float) +CONVERTTO_NOCOEF_SIMD(short, float) + +#undef CONVERTTO_NOCOEF_SIMD + +CV_ALWAYS_INLINE void convertto_scaled_simd_impl(const float* inx, uchar* outx, + const v_float32& v_alpha, + const v_float32& v_beta) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_float32 a1 = vx_load(inx); + v_float32 a2 = vx_load(&inx[nlanes / 4]); + v_float32 a3 = vx_load(&inx[nlanes / 2]); + v_float32 a4 = vx_load(&inx[3 * nlanes / 4]); + + v_int32 r1 = v_round(v_fma(a1, v_alpha, v_beta)); + v_int32 r2 = v_round(v_fma(a2, v_alpha, v_beta)); + v_int32 r3 = v_round(v_fma(a3, v_alpha, v_beta)); + v_int32 r4 = v_round(v_fma(a4, v_alpha, v_beta)); + + vx_store(outx, v_pack_u(v_pack(r1, r2), v_pack(r3, r4))); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_scaled_simd_impl(const SRC* inx, uchar* outx, const v_float32& v_alpha, + const v_float32& v_beta) +{ + constexpr int nlanes = v_uint8::nlanes; + + v_int16 a = v_reinterpret_as_s16(vx_load(inx)); + v_int16 b = v_reinterpret_as_s16(vx_load(&inx[nlanes / 2])); + + v_float32 a1 = v_cvt_f32(v_expand_low(a)); + v_float32 a2 = v_cvt_f32(v_expand_high(a)); + v_float32 b1 = v_cvt_f32(v_expand_low(b)); + v_float32 b2 = v_cvt_f32(v_expand_high(b)); + + v_int32 r1 = v_round(v_fma(a1, v_alpha, v_beta)); + v_int32 r2 = v_round(v_fma(a2, v_alpha, v_beta)); + v_int32 r3 = v_round(v_fma(b1, v_alpha, v_beta)); + v_int32 r4 = v_round(v_fma(b2, v_alpha, v_beta)); + + vx_store(outx, v_pack_u(v_pack(r1, r2), v_pack(r3, r4))); +} + +CV_ALWAYS_INLINE void convertto_scaled_simd_impl(const uchar* inx, uchar* outx, + const v_float32& v_alpha, + const v_float32& v_beta) +{ + v_uint8 a = vx_load(inx); + v_int16 a1 = v_reinterpret_as_s16(v_expand_low(a)); + v_int16 a2 = v_reinterpret_as_s16(v_expand_high(a)); + + v_float32 f1 = v_cvt_f32(v_expand_low(a1)); + v_float32 f2 = v_cvt_f32(v_expand_high(a1)); + + v_float32 f3 = v_cvt_f32(v_expand_low(a2)); + v_float32 f4 = v_cvt_f32(v_expand_high(a2)); + + v_int32 r1 = v_round(v_fma(f1, v_alpha, v_beta)); + v_int32 r2 = v_round(v_fma(f2, v_alpha, v_beta)); + v_int32 r3 = v_round(v_fma(f3, v_alpha, v_beta)); + v_int32 r4 = v_round(v_fma(f4, v_alpha, v_beta)); + + vx_store(outx, v_pack_u(v_pack(r1, r2), v_pack(r3, r4))); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_scaled_simd_impl(const float* inx, DST* outx, + const v_float32& v_alpha, + const v_float32& v_beta) +{ + constexpr int nlanes = vector_type_of_t::nlanes; + + v_float32 a1 = vx_load(inx); + v_float32 a2 = vx_load(&inx[nlanes / 2]); + + v_int32 r1 = v_round(v_fma(a1, v_alpha, v_beta)); + v_int32 r2 = v_round(v_fma(a2, v_alpha, v_beta)); + + v_store_i16(outx, r1, r2); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_scaled_simd_impl(const uchar* inx, DST* outx, + const v_float32& v_alpha, + const v_float32& v_beta) +{ + v_int16 a = v_reinterpret_as_s16(vx_load_expand(inx)); + + v_float32 a1 = v_cvt_f32(v_expand_low(a)); + v_float32 a2 = v_cvt_f32(v_expand_high(a)); + + v_int32 r1 = v_round(v_fma(a1, v_alpha, v_beta)); + v_int32 r2 = v_round(v_fma(a2, v_alpha, v_beta)); + + v_store_i16(outx, r1, r2); +} + +template +CV_ALWAYS_INLINE +typename std::enable_if::type +convertto_scaled_simd_impl(const SRC* inx, DST* outx, + const v_float32& v_alpha, + const v_float32& v_beta) +{ + v_int16 a = v_reinterpret_as_s16(vx_load(inx)); + + v_float32 a1 = v_cvt_f32(v_expand_low(a)); + v_float32 a2 = v_cvt_f32(v_expand_high(a)); + + v_int32 r1 = v_round(v_fma(a1, v_alpha, v_beta)); + v_int32 r2 = v_round(v_fma(a2, v_alpha, v_beta)); + + v_store_i16(outx, r1, r2); +} + +template +CV_ALWAYS_INLINE void convertto_scaled_simd_impl(const SRC* inx, float* outx, + const v_float32& v_alpha, + const v_float32& v_beta) +{ + v_float32 a = vg_load_f32(inx); + vx_store(outx, v_fma(a, v_alpha, v_beta)); +} + +#define CONVERTTO_SCALED_SIMD(SRC, DST) \ +int convertto_scaled_simd(const SRC in[], DST out[], const float alpha, \ + const float beta, const int length) \ +{ \ + constexpr int nlanes = vector_type_of_t::nlanes; \ + v_float32 v_alpha = vx_setall_f32(alpha); \ + v_float32 v_beta = vx_setall_f32(beta); \ + \ + int x = 0; \ + for (;;) \ + { \ + for (; x <= length - nlanes; x += nlanes) \ + { \ + convertto_scaled_simd_impl(&in[x], &out[x], v_alpha, v_beta); \ + } \ + if (x < length) \ + { \ + x = length - nlanes; \ + continue; \ + } \ + break; \ + } \ + return x; \ +} + +CONVERTTO_SCALED_SIMD(uchar, uchar) +CONVERTTO_SCALED_SIMD(ushort, uchar) +CONVERTTO_SCALED_SIMD(short, uchar) +CONVERTTO_SCALED_SIMD(float, uchar) +CONVERTTO_SCALED_SIMD(short, short) +CONVERTTO_SCALED_SIMD(ushort, short) +CONVERTTO_SCALED_SIMD(uchar, short) +CONVERTTO_SCALED_SIMD(float, short) +CONVERTTO_SCALED_SIMD(ushort, ushort) +CONVERTTO_SCALED_SIMD(uchar, ushort) +CONVERTTO_SCALED_SIMD(short, ushort) +CONVERTTO_SCALED_SIMD(float, ushort) +CONVERTTO_SCALED_SIMD(uchar, float) +CONVERTTO_SCALED_SIMD(ushort, float) +CONVERTTO_SCALED_SIMD(short, float) +CONVERTTO_SCALED_SIMD(float, float) + +#undef CONVERTTO_SCALED_SIMD #endif // CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY diff --git a/modules/gapi/src/backends/fluid/gfluidcore_simd_sse41.hpp b/modules/gapi/src/backends/fluid/gfluidcore_simd_sse41.hpp index 02fff3097787..3f2012807e03 100644 --- a/modules/gapi/src/backends/fluid/gfluidcore_simd_sse41.hpp +++ b/modules/gapi/src/backends/fluid/gfluidcore_simd_sse41.hpp @@ -28,7 +28,7 @@ namespace cv { namespace gapi { namespace fluid { -namespace sse42 { +namespace sse41 { CV_ALWAYS_INLINE void v_gather_pixel_map(v_uint8x16& vec, const uchar src[], const short* index, const int pos) { @@ -216,8 +216,8 @@ CV_ALWAYS_INLINE void calcRowLinear_8UC_Impl_<3>(uint8_t* dst[], const int lpi) { bool xRatioEq = inSz.width == outSz.width; bool yRatioEq = inSz.height == outSz.height; - constexpr int nlanes = 16; - constexpr int half_nlanes = 16 / 2; + constexpr int nlanes = 16; // number of 8-bit integers that fit into a 128-bit SIMD vector. + constexpr int half_nlanes = nlanes / 2; constexpr int chanNum = 3; if (!xRatioEq && !yRatioEq) { @@ -235,7 +235,7 @@ CV_ALWAYS_INLINE void calcRowLinear_8UC_Impl_<3>(uint8_t* dst[], for (int w = 0; w < inSz.width * chanNum; ) { for (; w <= inSz.width * chanNum - half_nlanes && w >= 0; w += half_nlanes) { -#ifdef __i386__ +#if defined(__i386__) || defined(_M_IX86) __m128i val0lo = _mm_castpd_si128(_mm_loadh_pd( _mm_load_sd(reinterpret_cast(&src0[0][w])), reinterpret_cast(&src0[1][w]))); @@ -298,84 +298,36 @@ CV_ALWAYS_INLINE void calcRowLinear_8UC_Impl_<3>(uint8_t* dst[], // horizontal pass __m128i horizontal_shuf_mask = _mm_setr_epi8(0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15); - - for (int x = 0; outSz.width >= nlanes; ) + __m128i horizontal_shuf_mask1 = _mm_setr_epi8(0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 3, 7, 11, 15); + constexpr int nproc_pixels = 5; + for (int x = 0; ; ) { - for (; x <= outSz.width - nlanes; x += nlanes) + for (; x <= outSz.width - (nproc_pixels + 1); x += nproc_pixels) { -#ifdef _WIN64 +#ifdef _MSC_VER __m128i a00 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * x]), *reinterpret_cast(&clone[4 * x])); - __m128i a01 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * x]), *reinterpret_cast(&clone[4 * (x + 1)])); - __m128i a11 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 1)]), *reinterpret_cast(&clone[4 * (x + 1)])); - __m128i a22 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 2)]), *reinterpret_cast(&clone[4 * (x + 2)])); - __m128i a23 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 2)]), *reinterpret_cast(&clone[4 * (x + 3)])); - __m128i a33 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 3)]), *reinterpret_cast(&clone[4 * (x + 3)])); - __m128i a44 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 4)]), *reinterpret_cast(&clone[4 * (x + 4)])); - __m128i a45 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 4)]), *reinterpret_cast(&clone[4 * (x + 5)])); - __m128i a55 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 5)]), *reinterpret_cast(&clone[4 * (x + 5)])); - __m128i a66 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 6)]), *reinterpret_cast(&clone[4 * (x + 6)])); - __m128i a67 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 6)]), *reinterpret_cast(&clone[4 * (x + 7)])); - __m128i a77 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 7)]), *reinterpret_cast(&clone[4 * (x + 7)])); - __m128i a88 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 8)]), *reinterpret_cast(&clone[4 * (x + 8)])); - __m128i a89 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 8)]), *reinterpret_cast(&clone[4 * (x + 9)])); - __m128i a99 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 9)]), *reinterpret_cast(&clone[4 * (x + 9)])); - __m128i a1010 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 10)]), *reinterpret_cast(&clone[4 * (x + 10)])); - __m128i a1011 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 10)]), *reinterpret_cast(&clone[4 * (x + 11)])); - __m128i a1111 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 11)]), *reinterpret_cast(&clone[4 * (x + 11)])); - __m128i a1212 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 12)]), *reinterpret_cast(&clone[4 * (x + 12)])); - __m128i a1213 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 12)]), *reinterpret_cast(&clone[4 * (x + 13)])); - __m128i a1313 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 13)]), *reinterpret_cast(&clone[4 * (x + 13)])); - __m128i a1414 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 14)]), *reinterpret_cast(&clone[4 * (x + 14)])); - __m128i a1415 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 14)]), *reinterpret_cast(&clone[4 * (x + 15)])); - __m128i a1515 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 15)]), *reinterpret_cast(&clone[4 * (x + 15)])); #else __m128i a00 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * x]), *reinterpret_cast(&clone[4 * x])); - __m128i a01 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * x]), *reinterpret_cast(&clone[4 * (x + 1)])); - __m128i a11 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 1)]), *reinterpret_cast(&clone[4 * (x + 1)])); - __m128i a22 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 2)]), *reinterpret_cast(&clone[4 * (x + 2)])); - __m128i a23 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 2)]), *reinterpret_cast(&clone[4 * (x + 3)])); - __m128i a33 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 3)]), *reinterpret_cast(&clone[4 * (x + 3)])); - __m128i a44 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 4)]), *reinterpret_cast(&clone[4 * (x + 4)])); - __m128i a45 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 4)]), *reinterpret_cast(&clone[4 * (x + 5)])); - __m128i a55 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 5)]), *reinterpret_cast(&clone[4 * (x + 5)])); - __m128i a66 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 6)]), *reinterpret_cast(&clone[4 * (x + 6)])); - __m128i a67 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 6)]), *reinterpret_cast(&clone[4 * (x + 7)])); - __m128i a77 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 7)]), *reinterpret_cast(&clone[4 * (x + 7)])); - __m128i a88 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 8)]), *reinterpret_cast(&clone[4 * (x + 8)])); - __m128i a89 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 8)]), *reinterpret_cast(&clone[4 * (x + 9)])); - __m128i a99 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 9)]), *reinterpret_cast(&clone[4 * (x + 9)])); - __m128i a1010 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 10)]), *reinterpret_cast(&clone[4 * (x + 10)])); - __m128i a1011 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 10)]), *reinterpret_cast(&clone[4 * (x + 11)])); - __m128i a1111 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 11)]), *reinterpret_cast(&clone[4 * (x + 11)])); - __m128i a1212 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 12)]), *reinterpret_cast(&clone[4 * (x + 12)])); - __m128i a1213 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 12)]), *reinterpret_cast(&clone[4 * (x + 13)])); - __m128i a1313 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 13)]), *reinterpret_cast(&clone[4 * (x + 13)])); - __m128i a1414 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 14)]), *reinterpret_cast(&clone[4 * (x + 14)])); - __m128i a1415 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 14)]), *reinterpret_cast(&clone[4 * (x + 15)])); - __m128i a1515 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 15)]), *reinterpret_cast(&clone[4 * (x + 15)])); #endif - - // load 3 channels of first pixel from first pair of 4-couple scope __m128i pix1 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x])])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix1 = _mm_insert_epi32(pix1, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 1])]), 3); + __m128i pix2 = _mm_setzero_si128(); +#if defined(__i386__) || defined(_M_IX86) + pix2 = _mm_castpd_si128(_mm_load_sd(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x] + 1))]))); +#else + pix2 = _mm_insert_epi64(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x] + 1))]), 0); +#endif - // load 3 channels of neighbor pixel from first pair of 4-couple scope - __m128i pix2 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x] + 1))])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix2 = _mm_insert_epi32(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 1] + 1))]), 3); + pix2 = _mm_insert_epi32(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x] + 1)) + 8]), 2); // expand 8-bit data to 16-bit __m128i val_0 = _mm_unpacklo_epi8(pix1, zero); __m128i val_1 = _mm_unpacklo_epi8(pix2, zero); - - // expand 8-bit data to 16-bit __m128i val_2 = _mm_unpackhi_epi8(pix1, zero); __m128i val_3 = _mm_unpackhi_epi8(pix2, zero); // the main calculations __m128i t0_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a00); - __m128i t1_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a01); + __m128i t1_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a00); __m128i r0_0 = _mm_add_epi16(val_1, t0_0); __m128i r1_0 = _mm_add_epi16(val_3, t1_0); @@ -384,111 +336,129 @@ CV_ALWAYS_INLINE void calcRowLinear_8UC_Impl_<3>(uint8_t* dst[], // gather data from the same lines together __m128i res1 = _mm_shuffle_epi8(q0_0, horizontal_shuf_mask); - val_0 = _mm_unpacklo_epi8(_mm_insert_epi64(val_0, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 1] + 1)]), 0), zero); - val_1 = _mm_unpacklo_epi8(_mm_insert_epi64(val_1, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 1] + 1) + 1)]), 0), zero); - - val_2 = _mm_insert_epi64(val_2, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 2])]), 0); - val_3 = _mm_insert_epi64(val_3, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 2] + 1))]), 0); - - val_2 = _mm_unpacklo_epi8(val_2, zero); - val_3 = _mm_unpacklo_epi8(val_3, zero); - - __m128i t0_1 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a11); - __m128i t1_1 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a22); - __m128i r0_1 = _mm_add_epi16(val_1, t0_1); - __m128i r1_1 = _mm_add_epi16(val_3, t1_1); - - __m128i q0_1 = _mm_packus_epi16(r0_1, r1_1); - __m128i res2 = _mm_shuffle_epi8(q0_1, horizontal_shuf_mask); - - __m128i pix7 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 3] - 1) + 2)])); - pix7 = _mm_insert_epi32(pix7, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 2] + 2)]), 0); - - __m128i pix8 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 3] + 2)])); - pix8 = _mm_insert_epi32(pix8, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 2] + 1) + 2)]), 0); +#ifdef _MSC_VER + __m128i a11 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 1)]), *reinterpret_cast(&clone[4 * (x + 1)])); +#else + __m128i a11 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 1)]), *reinterpret_cast(&clone[4 * (x + 1)])); +#endif - val_0 = _mm_unpacklo_epi8(pix7, zero); - val_1 = _mm_unpacklo_epi8(pix8, zero); + pix1 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 1])])); +#if defined(__i386__) || defined(_M_IX86) + pix2 = _mm_castpd_si128(_mm_load_sd(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 1] + 1))]))); +#else + pix2 = _mm_insert_epi64(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 1] + 1))]), 0); +#endif + pix2 = _mm_insert_epi32(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 1] + 1)) + 8]), 2); - val_2 = _mm_unpackhi_epi8(pix7, zero); - val_3 = _mm_unpackhi_epi8(pix8, zero); + // expand 8-bit data to 16-bit + val_0 = _mm_unpacklo_epi8(pix1, zero); + val_1 = _mm_unpacklo_epi8(pix2, zero); + val_2 = _mm_unpackhi_epi8(pix1, zero); + val_3 = _mm_unpackhi_epi8(pix2, zero); // the main calculations - __m128i t0_2 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a23); - __m128i t1_2 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a33); - __m128i r0_2 = _mm_add_epi16(val_1, t0_2); - __m128i r1_2 = _mm_add_epi16(val_3, t1_2); + t0_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a11); + t1_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a11); + r0_0 = _mm_add_epi16(val_1, t0_0); + r1_0 = _mm_add_epi16(val_3, t1_0); // pack 16-bit data to 8-bit - __m128i q0_2 = _mm_packus_epi16(r0_2, r1_2); - __m128i res3 = _mm_shuffle_epi8(q0_2, horizontal_shuf_mask); - - __m128i pix9 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 4])])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix9 = _mm_insert_epi32(pix9, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 5])]), 3); + q0_0 = _mm_packus_epi16(r0_0, r1_0); + // gather data from the same lines together + __m128i res2 = _mm_shuffle_epi8(q0_0, horizontal_shuf_mask); - // load 3 channels of neighbor pixel from first pair of 4-couple scope - __m128i pix10 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 4] + 1))])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix10 = _mm_insert_epi32(pix10, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 5] + 1))]), 3); +#ifdef _MSC_VER + __m128i a22 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 2)]), *reinterpret_cast(&clone[4 * (x + 2)])); +#else + __m128i a22 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 2)]), *reinterpret_cast(&clone[4 * (x + 2)])); +#endif - // expand 8-bit data to 16-bit - val_0 = _mm_unpacklo_epi8(pix9, zero); - val_1 = _mm_unpacklo_epi8(pix10, zero); + pix1 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 2])])); +#if defined(__i386__) || defined(_M_IX86) + pix2 = _mm_castpd_si128(_mm_load_sd(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 2] + 1))]))); +#else + pix2 = _mm_insert_epi64(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 2] + 1))]), 0); +#endif + pix2 = _mm_insert_epi32(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 2] + 1)) + 8]), 2); // expand 8-bit data to 16-bit - val_2 = _mm_unpackhi_epi8(pix9, zero); - val_3 = _mm_unpackhi_epi8(pix10, zero); + val_0 = _mm_unpacklo_epi8(pix1, zero); + val_1 = _mm_unpacklo_epi8(pix2, zero); + val_2 = _mm_unpackhi_epi8(pix1, zero); + val_3 = _mm_unpackhi_epi8(pix2, zero); // the main calculations - __m128i t0_3 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a44); - __m128i t1_3 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a45); - __m128i r0_3 = _mm_add_epi16(val_1, t0_3); - __m128i r1_3 = _mm_add_epi16(val_3, t1_3); + t0_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a22); + t1_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a22); + r0_0 = _mm_add_epi16(val_1, t0_0); + r1_0 = _mm_add_epi16(val_3, t1_0); // pack 16-bit data to 8-bit - __m128i q0_3 = _mm_packus_epi16(r0_3, r1_3); + q0_0 = _mm_packus_epi16(r0_0, r1_0); // gather data from the same lines together - __m128i res4 = _mm_shuffle_epi8(q0_3, horizontal_shuf_mask); + __m128i res3 = _mm_shuffle_epi8(q0_0, horizontal_shuf_mask); - val_0 = _mm_unpacklo_epi8(_mm_insert_epi64(val_0, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 5] + 1)]), 0), zero); - val_1 = _mm_unpacklo_epi8(_mm_insert_epi64(val_1, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 5] + 1) + 1)]), 0), zero); - - val_2 = _mm_insert_epi64(val_2, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 6])]), 0); - val_3 = _mm_insert_epi64(val_3, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 6] + 1))]), 0); +#ifdef _MSC_VER + __m128i a33 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 3)]), *reinterpret_cast(&clone[4 * (x + 3)])); +#else + __m128i a33 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 3)]), *reinterpret_cast(&clone[4 * (x + 3)])); +#endif - val_2 = _mm_unpacklo_epi8(val_2, zero); - val_3 = _mm_unpacklo_epi8(val_3, zero); + pix1 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 3])])); +#if defined(__i386__) || defined(_M_IX86) + pix2 = _mm_castpd_si128(_mm_load_sd(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 3] + 1))]))); +#else + pix2 = _mm_insert_epi64(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 3] + 1))]), 0); +#endif + pix2 = _mm_insert_epi32(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 3] + 1)) + 8]), 2); - __m128i t0_4 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a55); - __m128i t1_4 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a66); - __m128i r0_4 = _mm_add_epi16(val_1, t0_4); - __m128i r1_4 = _mm_add_epi16(val_3, t1_4); + // expand 8-bit data to 16-bit + val_0 = _mm_unpacklo_epi8(pix1, zero); + val_1 = _mm_unpacklo_epi8(pix2, zero); + val_2 = _mm_unpackhi_epi8(pix1, zero); + val_3 = _mm_unpackhi_epi8(pix2, zero); - __m128i q0_4 = _mm_packus_epi16(r0_4, r1_4); - __m128i res5 = _mm_shuffle_epi8(q0_4, horizontal_shuf_mask); + // the main calculations + t0_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a33); + t1_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a33); + r0_0 = _mm_add_epi16(val_1, t0_0); + r1_0 = _mm_add_epi16(val_3, t1_0); - __m128i pix15 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 7] - 1) + 2)])); - pix15 = _mm_insert_epi32(pix15, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 6] + 2)]), 0); + // pack 16-bit data to 8-bit + q0_0 = _mm_packus_epi16(r0_0, r1_0); + // gather data from the same lines together + __m128i res4 = _mm_shuffle_epi8(q0_0, horizontal_shuf_mask); - __m128i pix16 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 7] + 2)])); - pix16 = _mm_insert_epi32(pix16, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 6] + 1) + 2)]), 0); +#ifdef _MSC_VER + __m128i a44 = _mm_setr_epi64x(*reinterpret_cast(&clone[4 * (x + 4)]), *reinterpret_cast(&clone[4 * (x + 4)])); +#else + __m128i a44 = _mm_setr_epi64(*reinterpret_cast(&clone[4 * (x + 4)]), *reinterpret_cast(&clone[4 * (x + 4)])); +#endif - val_0 = _mm_unpacklo_epi8(pix15, zero); - val_1 = _mm_unpacklo_epi8(pix16, zero); + pix1 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 4])])); +#if defined(__i386__) || defined(_M_IX86) + pix2 = _mm_castpd_si128(_mm_load_sd(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 4] + 1))]))); +#else + pix2 = _mm_insert_epi64(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 4] + 1))]), 0); +#endif + pix2 = _mm_insert_epi32(pix2, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 4] + 1)) + 8]), 2); - val_2 = _mm_unpackhi_epi8(pix15, zero); - val_3 = _mm_unpackhi_epi8(pix16, zero); + // expand 8-bit data to 16-bit + val_0 = _mm_unpacklo_epi8(pix1, zero); + val_1 = _mm_unpacklo_epi8(pix2, zero); + val_2 = _mm_unpackhi_epi8(pix1, zero); + val_3 = _mm_unpackhi_epi8(pix2, zero); // the main calculations - __m128i t0_5 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a67); - __m128i t1_5 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a77); - __m128i r0_5 = _mm_add_epi16(val_1, t0_5); - __m128i r1_5 = _mm_add_epi16(val_3, t1_5); + t0_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a44); + t1_0 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a44); + r0_0 = _mm_add_epi16(val_1, t0_0); + r1_0 = _mm_add_epi16(val_3, t1_0); // pack 16-bit data to 8-bit - __m128i q0_5 = _mm_packus_epi16(r0_5, r1_5); - __m128i res6 = _mm_shuffle_epi8(q0_5, horizontal_shuf_mask); + q0_0 = _mm_packus_epi16(r0_0, r1_0); + // gather data from the same lines together + __m128i res5 = _mm_shuffle_epi8(q0_0, horizontal_shuf_mask); __m128i bl1 = _mm_blend_epi16(res1, _mm_slli_si128(res2, 4), 0xCC /*0b11001100*/); __m128i bl2 = _mm_blend_epi16(_mm_srli_si128(res1, 4), res2, 0xCC /*0b11001100*/); @@ -496,189 +466,47 @@ CV_ALWAYS_INLINE void calcRowLinear_8UC_Impl_<3>(uint8_t* dst[], __m128i bl3 = _mm_blend_epi16(res3, _mm_slli_si128(res4, 4), 0xCC /*0b11001100*/); __m128i bl4 = _mm_blend_epi16(_mm_srli_si128(res3, 4), res4, 0xCC /*0b11001100*/); - __m128i bl5 = _mm_blend_epi16(res5, _mm_slli_si128(res6, 4), 0xCC /*0b11001100*/); - __m128i bl6 = _mm_blend_epi16(_mm_srli_si128(res5, 4), res6, 0xCC /*0b11001100*/); - __m128i bl13 = _mm_blend_epi16(bl1, _mm_slli_si128(bl3, 8), 0xF0 /*0b11110000*/); __m128i bl31 = _mm_blend_epi16(_mm_srli_si128(bl1, 8), bl3, 0xF0 /*0b11110000*/); __m128i bl24 = _mm_blend_epi16(bl2, _mm_slli_si128(bl4, 8), 0xF0 /*0b11110000*/); __m128i bl42 = _mm_blend_epi16(_mm_srli_si128(bl2, 8), bl4, 0xF0 /*0b11110000*/); - // load 3 channels of first pixel from first pair of 4-couple scope - __m128i pix17 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 8])])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix17 = _mm_insert_epi32(pix17, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 9])]), 3); - - // load 3 channels of neighbor pixel from first pair of 4-couple scope - __m128i pix18 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 8] + 1))])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix18 = _mm_insert_epi32(pix18, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 9] + 1))]), 3); - - // expand 8-bit data to 16-bit - val_0 = _mm_unpacklo_epi8(pix17, zero); - val_1 = _mm_unpacklo_epi8(pix18, zero); - - // expand 8-bit data to 16-bit - val_2 = _mm_unpackhi_epi8(pix17, zero); - val_3 = _mm_unpackhi_epi8(pix18, zero); - - // the main calculations - __m128i t0_6 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a88); - __m128i t1_6 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a89); - __m128i r0_6 = _mm_add_epi16(val_1, t0_6); - __m128i r1_6 = _mm_add_epi16(val_3, t1_6); - - // pack 16-bit data to 8-bit - __m128i q0_6 = _mm_packus_epi16(r0_6, r1_6); - // gather data from the same lines together - __m128i res7 = _mm_shuffle_epi8(q0_6, horizontal_shuf_mask); - - val_0 = _mm_unpacklo_epi8(_mm_insert_epi64(val_0, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 9] + 1)]), 0), zero); - val_1 = _mm_unpacklo_epi8(_mm_insert_epi64(val_1, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 9] + 1) + 1)]), 0), zero); - - val_2 = _mm_insert_epi64(val_2, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 10])]), 0); - val_3 = _mm_insert_epi64(val_3, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 10] + 1))]), 0); - - val_2 = _mm_unpacklo_epi8(val_2, zero); - val_3 = _mm_unpacklo_epi8(val_3, zero); - - __m128i t0_7 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a99); - __m128i t1_7 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a1010); - __m128i r0_7 = _mm_add_epi16(val_1, t0_7); - __m128i r1_7 = _mm_add_epi16(val_3, t1_7); - - __m128i q0_7 = _mm_packus_epi16(r0_7, r1_7); - __m128i res8 = _mm_shuffle_epi8(q0_7, horizontal_shuf_mask); - - __m128i pix21 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 11] - 1) + 2)])); - pix21 = _mm_insert_epi32(pix21, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 10] + 2)]), 0); - - __m128i pix22 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 11] + 2)])); - pix22 = _mm_insert_epi32(pix22, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 10] + 1) + 2)]), 0); - - val_0 = _mm_unpacklo_epi8(pix21, zero); - val_1 = _mm_unpacklo_epi8(pix22, zero); - - val_2 = _mm_unpackhi_epi8(pix21, zero); - val_3 = _mm_unpackhi_epi8(pix22, zero); - - // the main calculations - __m128i t0_8 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a1011); - __m128i t1_8 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a1111); - __m128i r0_8 = _mm_add_epi16(val_1, t0_8); - __m128i r1_8 = _mm_add_epi16(val_3, t1_8); - - // pack 16-bit data to 8-bit - __m128i q0_8 = _mm_packus_epi16(r0_8, r1_8); - __m128i res9 = _mm_shuffle_epi8(q0_8, horizontal_shuf_mask); - - __m128i pix23 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 12])])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix23 = _mm_insert_epi32(pix23, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 13])]), 3); - - // load 3 channels of neighbor pixel from first pair of 4-couple scope - __m128i pix24 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 12] + 1))])); - // insert first channel from next couple of pixels to completely fill the simd vector - pix24 = _mm_insert_epi32(pix24, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 13] + 1))]), 3); - - // expand 8-bit data to 16-bit - val_0 = _mm_unpacklo_epi8(pix23, zero); - val_1 = _mm_unpacklo_epi8(pix24, zero); - - // expand 8-bit data to 16-bit - val_2 = _mm_unpackhi_epi8(pix23, zero); - val_3 = _mm_unpackhi_epi8(pix24, zero); - - // the main calculations - __m128i t0_9 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a1212); - __m128i t1_9 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a1213); - __m128i r0_9 = _mm_add_epi16(val_1, t0_9); - __m128i r1_9 = _mm_add_epi16(val_3, t1_9); - - // pack 16-bit data to 8-bit - __m128i q0_9 = _mm_packus_epi16(r0_9, r1_9); - // gather data from the same lines together - __m128i res10 = _mm_shuffle_epi8(q0_9, horizontal_shuf_mask); - - val_0 = _mm_unpacklo_epi8(_mm_insert_epi64(val_0, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 13] + 1)]), 0), zero); - val_1 = _mm_unpacklo_epi8(_mm_insert_epi64(val_1, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 13] + 1) + 1)]), 0), zero); - - val_2 = _mm_insert_epi64(val_2, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 14])]), 0); - val_3 = _mm_insert_epi64(val_3, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 14] + 1))]), 0); - - val_2 = _mm_unpacklo_epi8(val_2, zero); - val_3 = _mm_unpacklo_epi8(val_3, zero); - - __m128i t0_10 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a1313); - __m128i t1_10 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a1414); - __m128i r0_10 = _mm_add_epi16(val_1, t0_10); - __m128i r1_10 = _mm_add_epi16(val_3, t1_10); - - __m128i q0_10 = _mm_packus_epi16(r0_10, r1_10); - __m128i res11 = _mm_shuffle_epi8(q0_10, horizontal_shuf_mask); - - __m128i pix27 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 15] - 1) + 2)])); - pix27 = _mm_insert_epi32(pix27, *reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 14] + 2)]), 0); - - __m128i pix28 = _mm_lddqu_si128(reinterpret_cast(&tmp[4 * (chanNum * mapsx[x + 15] + 2)])); - pix28 = _mm_insert_epi32(pix28, *reinterpret_cast(&tmp[4 * (chanNum * (mapsx[x + 14] + 1) + 2)]), 0); - - val_0 = _mm_unpacklo_epi8(pix27, zero); - val_1 = _mm_unpacklo_epi8(pix28, zero); - - val_2 = _mm_unpackhi_epi8(pix27, zero); - val_3 = _mm_unpackhi_epi8(pix28, zero); - - // the main calculations - __m128i t0_11 = _mm_mulhrs_epi16(_mm_sub_epi16(val_0, val_1), a1415); - __m128i t1_11 = _mm_mulhrs_epi16(_mm_sub_epi16(val_2, val_3), a1515); - __m128i r0_11 = _mm_add_epi16(val_1, t0_11); - __m128i r1_11 = _mm_add_epi16(val_3, t1_11); - - // pack 16-bit data to 8-bit - __m128i q0_11 = _mm_packus_epi16(r0_11, r1_11); - __m128i res12 = _mm_shuffle_epi8(q0_11, horizontal_shuf_mask); - - __m128i bl7 = _mm_blend_epi16(res7, _mm_slli_si128(res8, 4), 0xCC /*0b11001100*/); - __m128i bl8 = _mm_blend_epi16(_mm_srli_si128(res7, 4), res8, 0xCC /*0b11001100*/); - - __m128i bl9 = _mm_blend_epi16(res9, _mm_slli_si128(res10, 4), 0xCC /*0b11001100*/); - __m128i bl10 = _mm_blend_epi16(_mm_srli_si128(res9, 4), res10, 0xCC /*0b11001100*/); - - __m128i bl11 = _mm_blend_epi16(res11, _mm_slli_si128(res12, 4), 0xCC /*0b11001100*/); - __m128i bl12 = _mm_blend_epi16(_mm_srli_si128(res11, 4), res12, 0xCC /*0b11001100*/); - - __m128i bl57 = _mm_blend_epi16(bl5, _mm_slli_si128(bl7, 8), 0xF0 /*0b11110000*/); - __m128i bl75 = _mm_blend_epi16(_mm_srli_si128(bl5, 8), bl7, 0xF0 /*0b11110000*/); - - __m128i bl68 = _mm_blend_epi16(bl6, _mm_slli_si128(bl8, 8), 0xF0 /*0b11110000*/); - __m128i bl86 = _mm_blend_epi16(_mm_srli_si128(bl6, 8), bl8, 0xF0 /*0b11110000*/); - - __m128i bl911 = _mm_blend_epi16(bl9, _mm_slli_si128(bl11, 8), 0xF0 /*0b11110000*/); - __m128i bl119 = _mm_blend_epi16(_mm_srli_si128(bl9, 8), bl11, 0xF0 /*0b11110000*/); - - __m128i bl1012 = _mm_blend_epi16(bl10, _mm_slli_si128(bl12, 8), 0xF0 /*0b11110000*/); - __m128i bl1210 = _mm_blend_epi16(_mm_srli_si128(bl10, 8), bl12, 0xF0 /*0b11110000*/); - - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[0][3 * x]), bl13); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[1][3 * x]), bl24); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[2][3 * x]), bl31); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[3][3 * x]), bl42); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[0][3 * x + 16]), bl57); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[1][3 * x + 16]), bl68); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[2][3 * x + 16]), bl75); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[3][3 * x + 16]), bl86); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[0][3 * x + 32]), bl911); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[1][3 * x + 32]), bl1012); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[2][3 * x + 32]), bl119); - _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[3][3 * x + 32]), bl1210); + bl1 = _mm_blend_epi16(_mm_shuffle_epi8(bl13, horizontal_shuf_mask1), + _mm_slli_si128(res5, 12), 192 /*0b11000000*/); + bl2 = _mm_blend_epi16(_mm_shuffle_epi8(bl24, horizontal_shuf_mask1), + _mm_slli_si128(res5, 8), 192 /*0b11000000*/); + bl3 = _mm_blend_epi16(_mm_shuffle_epi8(bl31, horizontal_shuf_mask1), + _mm_slli_si128(res5, 4), 192 /*0b11000000*/); + bl4 = _mm_blend_epi16(_mm_shuffle_epi8(bl42, horizontal_shuf_mask1), + res5, 192 /*0b11000000*/); + + _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[0][chanNum * x]), bl1); + _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[1][chanNum * x]), bl2); + _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[2][chanNum * x]), bl3); + _mm_storeu_si128(reinterpret_cast<__m128i*>(&dst[3][chanNum * x]), bl4); } - if (x < outSz.width) { - x = outSz.width - nlanes; - continue; + for (; x < outSz.width; ++x) + { + constexpr static const int ONE = 1 << 15; + constexpr static const int half = 1 << 14; + auto alpha0 = alpha[x]; + auto alpha1 = saturate_cast(ONE - alpha[x]); + + for (int c = 0; c < chanNum; ++c) + { + dst[0][chanNum * x + c] = (tmp[4 * (chanNum * mapsx[x] + c) ] * alpha0 + + tmp[4 * (chanNum * (mapsx[x] + 1) + c) ] * alpha1 + half) >> 15; + dst[1][chanNum * x + c] = (tmp[4 * (chanNum * mapsx[x] + c) + 1] * alpha0 + + tmp[4 * (chanNum * (mapsx[x] + 1) + c) + 1] * alpha1 + half) >> 15; + dst[2][chanNum * x + c] = (tmp[4 * (chanNum * mapsx[x] + c) + 2] * alpha0 + + tmp[4 * (chanNum * (mapsx[x] + 1) + c) + 2] * alpha1 + half) >> 15; + dst[3][chanNum * x + c] = (tmp[4 * (chanNum * mapsx[x] + c) + 3] * alpha0 + + tmp[4 * (chanNum * (mapsx[x] + 1) + c) + 3] * alpha1 + half) >> 15; + } } + break; } } diff --git a/modules/gapi/src/backends/fluid/gfluidimgproc.cpp b/modules/gapi/src/backends/fluid/gfluidimgproc.cpp index 7ddf91e1226c..bdd11b12149d 100644 --- a/modules/gapi/src/backends/fluid/gfluidimgproc.cpp +++ b/modules/gapi/src/backends/fluid/gfluidimgproc.cpp @@ -25,6 +25,13 @@ #include "gfluidimgproc_func.hpp" +#if CV_AVX2 +#include "gfluidimgproc_simd_avx2.hpp" +#endif +#if CV_SSE4_1 +#include "gfluidcore_simd_sse41.hpp" +#endif + #include #include @@ -1022,8 +1029,8 @@ GAPI_FLUID_KERNEL(GFluidSobel, cv::gapi::imgproc::GSobel, true) auto *kx = scratch.OutLine(); auto *ky = kx + ksz; - Mat kxmat(1, ksize, CV_32FC1, kx); - Mat kymat(ksize, 1, CV_32FC1, ky); + Mat kxmat(1, ksz, CV_32FC1, kx); + Mat kymat(ksz, 1, CV_32FC1, ky); getDerivKernels(kxmat, kymat, dx, dy, ksize); } @@ -1181,12 +1188,12 @@ GAPI_FLUID_KERNEL(GFluidSobelXY, cv::gapi::imgproc::GSobelXY, true) auto *kx_dy = buf_helper.kx_dy; auto *ky_dy = buf_helper.ky_dy; - Mat kxmatX(1, ksize, CV_32FC1, kx_dx); - Mat kymatX(ksize, 1, CV_32FC1, ky_dx); + Mat kxmatX(1, ksz, CV_32FC1, kx_dx); + Mat kymatX(ksz, 1, CV_32FC1, ky_dx); getDerivKernels(kxmatX, kymatX, order, 0, ksize); - Mat kxmatY(1, ksize, CV_32FC1, kx_dy); - Mat kymatY(ksize, 1, CV_32FC1, ky_dy); + Mat kxmatY(1, ksz, CV_32FC1, kx_dy); + Mat kymatY(ksz, 1, CV_32FC1, ky_dy); getDerivKernels(kxmatY, kymatY, 0, order, ksize); } @@ -1821,16 +1828,439 @@ GAPI_FLUID_KERNEL(GFluidBayerGR2RGB, cv::gapi::imgproc::GBayerGR2RGB, false) } }; +template +struct LinearScratchDesc { + using alpha_t = typename Mapper::alpha_type; + using index_t = typename Mapper::index_type; + + alpha_t* alpha; + alpha_t* clone; + index_t* mapsx; + alpha_t* beta; + index_t* mapsy; + T* tmp; + + LinearScratchDesc(int /*inW*/, int /*inH*/, int outW, int outH, void* data) { + alpha = reinterpret_cast(data); + clone = reinterpret_cast(alpha + outW); + mapsx = reinterpret_cast(clone + outW*4); + beta = reinterpret_cast(mapsx + outW); + mapsy = reinterpret_cast(beta + outH); + tmp = reinterpret_cast (mapsy + outH*2); + } + + static int bufSize(int inW, int /*inH*/, int outW, int outH, int lpi) { + auto size = outW * sizeof(alpha_t) + + outW * sizeof(alpha_t) * 4 + // alpha clones + outW * sizeof(index_t) + + outH * sizeof(alpha_t) + + outH * sizeof(index_t) * 2 + + inW * sizeof(T) * lpi * chanNum; + + return static_cast(size); + } +}; +static inline double invRatio(int inSz, int outSz) { + return static_cast(outSz) / inSz; +} + +static inline double ratio(int inSz, int outSz) { + return 1 / invRatio(inSz, outSz); +} + +template +CV_ALWAYS_INLINE void initScratchLinear(const cv::GMatDesc& in, + const Size& outSz, + cv::gapi::fluid::Buffer& scratch, + int lpi) +{ + using alpha_type = typename Mapper::alpha_type; + static const auto unity = Mapper::unity; + + auto inSz = in.size; + auto sbufsize = LinearScratchDesc::bufSize(inSz.width, inSz.height, outSz.width, outSz.height, lpi); + + Size scratch_size{sbufsize, 1}; + + cv::GMatDesc desc; + desc.chan = 1; + desc.depth = CV_8UC1; + desc.size = scratch_size; + + cv::gapi::fluid::Buffer buffer(desc); + scratch = std::move(buffer); + + double hRatio = ratio(in.size.width, outSz.width); + double vRatio = ratio(in.size.height, outSz.height); + + LinearScratchDesc scr(inSz.width, inSz.height, outSz.width, outSz.height, scratch.OutLineB()); + + auto *alpha = scr.alpha; + auto *clone = scr.clone; + auto *index = scr.mapsx; + + for (int x = 0; x < outSz.width; ++x) + { + auto map = Mapper::map(hRatio, 0, in.size.width, x); + auto alpha0 = map.alpha0; + auto index0 = map.index0; + + // TRICK: + // Algorithm takes pair of input pixels, sx0'th and sx1'th, + // and compute result as alpha0*src[sx0] + alpha1*src[sx1]. + // By definition: sx1 == sx0 + 1 either sx1 == sx0, and + // alpha0 + alpha1 == unity (scaled appropriately). + // Here we modify formulas for alpha0 and sx1: by assuming + // that sx1 == sx0 + 1 always, and patching alpha0 so that + // result remains intact. + // Note that we need in.size.width >= 2, for both sx0 and + // sx0+1 were indexing pixels inside the input's width. + if (map.index1 != map.index0 + 1) { + GAPI_DbgAssert(map.index1 == map.index0); + GAPI_DbgAssert(in.size.width >= 2); + if (map.index0 < in.size.width-1) { + // sx1=sx0+1 fits inside row, + // make sure alpha0=unity and alpha1=0, + // so that result equals src[sx0]*unity + alpha0 = saturate_cast(unity); + } else { + // shift sx0 to left by 1 pixel, + // and make sure that alpha0=0 and alpha1==1, + // so that result equals to src[sx0+1]*unity + alpha0 = 0; + index0--; + } + } + + alpha[x] = alpha0; + index[x] = index0; + + for (int l = 0; l < 4; ++l) { + clone[4*x + l] = alpha0; + } + } + + auto *beta = scr.beta; + auto *index_y = scr.mapsy; + + for (int y = 0; y < outSz.height; y++) { + auto mapY = Mapper::map(vRatio, 0, in.size.height, y); + beta[y] = mapY.alpha0; + index_y[y] = mapY.index0; + index_y[outSz.height + y] = mapY.index1; + } +} + +template +struct MapperUnit { + F alpha0, alpha1; + I index0, index1; +}; + +CV_ALWAYS_INLINE uint8_t resize_calc_revert_fixedpoint(short alpha0, uint8_t src0, short alpha1, uint8_t src1) +{ + constexpr static const int half = 1 << 14; + return (src0 * alpha0 + src1 * alpha1 + half) >> 15; +} + +CV_ALWAYS_INLINE float resize_main_calculation(float alpha0, float src0, float alpha1, float src1) +{ + return src0 * alpha0 + src1 * alpha1; +} + +namespace linear { +struct Mapper { + constexpr static const int ONE = 1 << 15; + typedef short alpha_type; + typedef short index_type; + constexpr static const int unity = ONE; + + typedef MapperUnit Unit; + + static inline Unit map(double ratio, int start, int max, int outCoord) { + float f = static_cast((outCoord + 0.5) * ratio - 0.5); + int s = cvFloor(f); + f -= s; + + Unit u; + + u.index0 = static_cast(std::max(s - start, 0)); + u.index1 = static_cast(((f == 0.0) || s + 1 >= max) ? s - start : s - start + 1); + + u.alpha0 = saturate_cast(ONE * (1.0f - f)); + u.alpha1 = saturate_cast(ONE * f); + + return u; + } +}; +} // namespace linear + +namespace linear32f { +struct Mapper { + typedef float alpha_type; + typedef int index_type; + constexpr static const float unity = 1; + + typedef MapperUnit Unit; + + static inline Unit map(double ratio, int start, int max, int outCoord) { + float f = static_cast((outCoord + 0.5) * ratio - 0.5); + int s = cvFloor(f); + f -= s; + + Unit u; + + u.index0 = std::max(s - start, 0); + u.index1 = ((std::fabs(f) <= FLT_EPSILON) || s + 1 >= max) ? s - start : s - start + 1; + + u.alpha0 = 1.f - f; + u.alpha1 = f; + + return u; + } +}; +} // namespace linear32f + +template +CV_ALWAYS_INLINE void calcRowLinearC(const cv::gapi::fluid::View & in, + cv::gapi::fluid::Buffer& out, + cv::gapi::fluid::Buffer& scratch) { + using alpha_type = typename Mapper::alpha_type; + + auto inSz = in.meta().size; + auto outSz = out.meta().size; + + auto inY = in.y(); + int outY = out.y(); + int lpi = out.lpi(); + + GAPI_DbgAssert(outY + lpi <= outSz.height); + GAPI_DbgAssert(lpi <= 4); + + LinearScratchDesc scr(inSz.width, inSz.height, outSz.width, outSz.height, scratch.OutLineB()); + + const auto *alpha = scr.alpha; + const auto *mapsx = scr.mapsx; + const auto *beta_0 = scr.beta; + const auto *mapsy = scr.mapsy; + + const auto *beta = beta_0 + outY; + const T *src0[4]; + const T *src1[4]; + T* dst[4]; + + for (int l = 0; l < lpi; l++) { + auto index0 = mapsy[outY + l] - inY; + auto index1 = mapsy[outSz.height + outY + l] - inY; + src0[l] = in.InLine(index0); + src1[l] = in.InLine(index1); + dst[l] = out.OutLine(l); + } + +#if CV_SSE4_1 + const auto* clone = scr.clone; + auto* tmp = scr.tmp; + + if (inSz.width >= 16 && outSz.width >= 16) + { + sse41::calcRowLinear_8UC_Impl_(reinterpret_cast(dst), + reinterpret_cast(src0), + reinterpret_cast(src1), + reinterpret_cast(alpha), + reinterpret_cast(clone), + reinterpret_cast(mapsx), + reinterpret_cast(beta), + reinterpret_cast(tmp), + inSz, outSz, lpi); + + return; + } +#endif // CV_SSE4_1 + int length = out.length(); + for (int l = 0; l < lpi; l++) { + constexpr static const auto unity = Mapper::unity; + + auto beta0 = beta[l]; + auto beta1 = saturate_cast(unity - beta[l]); + + for (int x = 0; x < length; x++) { + auto alpha0 = alpha[x]; + auto alpha1 = saturate_cast(unity - alpha[x]); + auto sx0 = mapsx[x]; + auto sx1 = sx0 + 1; + + for (int c = 0; c < numChan; c++) { + auto idx0 = numChan*sx0 + c; + auto idx1 = numChan*sx1 + c; + T tmp0 = resize_calc_revert_fixedpoint(beta0, src0[l][idx0], beta1, src1[l][idx0]); + T tmp1 = resize_calc_revert_fixedpoint(beta0, src0[l][idx1], beta1, src1[l][idx1]); + dst[l][numChan * x + c] = resize_calc_revert_fixedpoint(alpha0, tmp0, alpha1, tmp1); + } + } + } +} + +template +CV_ALWAYS_INLINE void calcRowLinear(const cv::gapi::fluid::View& in, + cv::gapi::fluid::Buffer& out, + cv::gapi::fluid::Buffer& scratch) +{ + GAPI_DbgAssert((out.meta().depth == CV_32F) && (out.meta().chan == 1)); + + auto inSz = in.meta().size; + auto outSz = out.meta().size; + + auto inY = in.y(); + int length = out.length(); + int outY = out.y(); + int lpi = out.lpi(); + GAPI_DbgAssert(outY + lpi <= outSz.height); + + GAPI_DbgAssert(lpi <= 4); + + LinearScratchDesc scr(inSz.width, inSz.height, outSz.width, + outSz.height, scratch.OutLineB()); + + const auto* alpha = scr.alpha; + const auto* mapsx = scr.mapsx; + const auto* beta0 = scr.beta; + const auto* mapsy = scr.mapsy; + + const auto* beta = beta0 + outY; + const float* src0[4]; + const float* src1[4]; + float* dst[4]; + + for (int l = 0; l < lpi; ++l) + { + auto index0 = mapsy[outY + l] - inY; + auto index1 = mapsy[outSz.height + outY + l] - inY; + + src0[l] = in.InLine(index0); + src1[l] = in.InLine(index1); + dst[l] = out.OutLine(l); + } + +#if CV_AVX2 + // number floats in AVX2 SIMD vector. + constexpr int nlanes = 8; + + if (inSz.width >= nlanes && outSz.width >= nlanes) + { + avx2::calcRowLinear32FC1Impl(dst, src0, src1, alpha, mapsx, beta, + inSz, outSz, lpi); + + return; + } +#endif // CV_AVX2 + + using alpha_type = typename Mapper::alpha_type; + for (int l = 0; l < lpi; ++l) + { + constexpr static const auto unity = Mapper::unity; + + auto b0 = beta[l]; + auto b1 = saturate_cast(unity - beta[l]); + + for (int x = 0; x < length; ++x) { + auto alpha0 = alpha[x]; + auto alpha1 = saturate_cast(unity - alpha[x]); + auto sx0 = mapsx[x]; + auto sx1 = sx0 + 1; + + float tmp0 = resize_main_calculation(b0, src0[l][sx0], b1, src1[l][sx0]); + float tmp1 = resize_main_calculation(b0, src0[l][sx1], b1, src1[l][sx1]); + dst[l][x] = resize_main_calculation(alpha0, tmp0, alpha1, tmp1); + } + } +} + +GAPI_FLUID_KERNEL(GFluidResize, cv::gapi::imgproc::GResize, true) +{ + static const int Window = 1; + static const int LPI = 4; + static const auto Kind = GFluidKernel::Kind::Resize; + + constexpr static const int INTER_RESIZE_COEF_BITS = 11; + constexpr static const int INTER_RESIZE_COEF_SCALE = 1 << INTER_RESIZE_COEF_BITS; + constexpr static const short ONE = INTER_RESIZE_COEF_SCALE; + + static void initScratch(const cv::GMatDesc& in, + cv::Size outSz, double fx, double fy, int interp, + cv::gapi::fluid::Buffer &scratch) + { + GAPI_Assert((in.depth == CV_8U && in.chan == 3) || + (in.depth == CV_32F && in.chan == 1)); + GAPI_Assert(interp == cv::INTER_LINEAR); + + int outSz_w; + int outSz_h; + if (outSz.width == 0 || outSz.height == 0) + { + outSz_w = saturate_cast(in.size.width * fx); + outSz_h = saturate_cast(in.size.height * fy); + } + else + { + outSz_w = outSz.width; + outSz_h = outSz.height; + } + cv::Size outSize(outSz_w, outSz_h); + + if (in.depth == CV_8U && in.chan == 3) + { + initScratchLinear(in, outSize, scratch, LPI); + } + else if (in.depth == CV_32F && in.chan == 1) + { + initScratchLinear(in, outSize, scratch, LPI); + } + else + { + CV_Error(cv::Error::StsBadArg, "unsupported combination of type and number of channel"); + } + } + + static void resetScratch(cv::gapi::fluid::Buffer& /*scratch*/) + {} + + static void run(const cv::gapi::fluid::View& in, cv::Size /*sz*/, double /*fx*/, + double /*fy*/, int interp, cv::gapi::fluid::Buffer& out, + cv::gapi::fluid::Buffer& scratch) + { + GAPI_Assert((in.meta().depth == CV_8U && in.meta().chan == 3) || + (in.meta().depth == CV_32F && in.meta().chan == 1)); + GAPI_Assert(interp == cv::INTER_LINEAR); + + const int channels = in.meta().chan; + const int depth = in.meta().depth; + + if (depth == CV_8U && channels == 3) + { + calcRowLinearC(in, out, scratch); + } + else if (depth == CV_32F && channels == 1) + { + calcRowLinear(in, out, scratch); + } + else + { + CV_Error(cv::Error::StsBadArg, "unsupported combination of type and number of channel"); + } + } +}; + } // namespace fluid } // namespace gapi } // namespace cv -cv::gapi::GKernelPackage cv::gapi::imgproc::fluid::kernels() +cv::GKernelPackage cv::gapi::imgproc::fluid::kernels() { using namespace cv::gapi::fluid; return cv::gapi::kernels < GFluidBGR2Gray + , GFluidResize , GFluidRGB2Gray , GFluidRGB2GrayCustom , GFluidRGB2YUV diff --git a/modules/gapi/src/backends/fluid/gfluidimgproc_simd_avx2.hpp b/modules/gapi/src/backends/fluid/gfluidimgproc_simd_avx2.hpp new file mode 100644 index 000000000000..e246f0613bab --- /dev/null +++ b/modules/gapi/src/backends/fluid/gfluidimgproc_simd_avx2.hpp @@ -0,0 +1,181 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#if !defined(GAPI_STANDALONE) + +#include "opencv2/gapi/own/saturate.hpp" + +#include + +#include "opencv2/core.hpp" + +#include + +#include +#include + +#include +#include +#include + +namespace cv { +namespace gapi { +namespace fluid { +namespace avx2 { + +CV_ALWAYS_INLINE void v_gather_pairs(const float src[], const int* mapsx, + v_float32x8& low, v_float32x8& high) +{ + low.val = _mm256_castsi256_ps(_mm256_setr_epi64x(*reinterpret_cast(&src[mapsx[0]]), + *reinterpret_cast(&src[mapsx[1]]), + *reinterpret_cast(&src[mapsx[2]]), + *reinterpret_cast(&src[mapsx[3]]))); + high.val = _mm256_castsi256_ps(_mm256_setr_epi64x(*reinterpret_cast(&src[mapsx[4]]), + *reinterpret_cast(&src[mapsx[5]]), + *reinterpret_cast(&src[mapsx[6]]), + *reinterpret_cast(&src[mapsx[7]]))); +} + +CV_ALWAYS_INLINE void v_deinterleave(const v_float32x8& low, const v_float32x8& high, + v_float32x8& even, v_float32x8& odd) +{ + __m256 tmp0 = _mm256_unpacklo_ps(low.val, high.val); + __m256 tmp1 = _mm256_unpackhi_ps(low.val, high.val); + __m256 tmp2 = _mm256_unpacklo_ps(tmp0, tmp1); + __m256 tmp3 = _mm256_unpackhi_ps(tmp0, tmp1); + even.val = _mm256_castsi256_ps(_mm256_permute4x64_epi64(_mm256_castps_si256(tmp2), 216 /*11011000*/)); + odd.val = _mm256_castsi256_ps(_mm256_permute4x64_epi64(_mm256_castps_si256(tmp3), 216 /*11011000*/)); +} + +// Resize (bi-linear, 32FC1) +CV_ALWAYS_INLINE void calcRowLinear32FC1Impl(float *dst[], + const float *src0[], + const float *src1[], + const float alpha[], + const int mapsx[], + const float beta[], + const Size& inSz, + const Size& outSz, + const int lpi) +{ + bool xRatioEq1 = inSz.width == outSz.width; + bool yRatioEq1 = inSz.height == outSz.height; + + constexpr int nlanes = v_float32x8::nlanes; + + if (!xRatioEq1 && !yRatioEq1) + { + for (int line = 0; line < lpi; ++line) { + float beta0 = beta[line]; + float beta1 = 1 - beta0; + v_float32x8 v_beta0 = v256_setall_f32(beta0); + int x = 0; + + v_float32x8 low1, high1, s00, s01; + v_float32x8 low2, high2, s10, s11; + for (; x <= outSz.width - nlanes; x += nlanes) + { + v_float32x8 alpha0 = v256_load(&alpha[x]); + // v_float32 alpha1 = 1.f - alpha0; + + v_gather_pairs(src0[line], &mapsx[x], low1, high1); + v_deinterleave(low1, high1, s00, s01); + + // v_float32 res0 = s00*alpha0 + s01*alpha1; + v_float32x8 res0 = v_fma(s00 - s01, alpha0, s01); + + v_gather_pairs(src1[line], &mapsx[x], low2, high2); + v_deinterleave(low2, high2, s10, s11); + + // v_float32 res1 = s10*alpha0 + s11*alpha1; + v_float32x8 res1 = v_fma(s10 - s11, alpha0, s11); + // v_float32 d = res0*beta0 + res1*beta1; + v_float32x8 d = v_fma(res0 - res1, v_beta0, res1); + + v_store(&dst[line][x], d); + } + + for (; x < outSz.width; ++x) + { + float alpha0 = alpha[x]; + float alpha1 = 1 - alpha0; + int sx0 = mapsx[x]; + int sx1 = sx0 + 1; + float res0 = src0[line][sx0] * alpha0 + src0[line][sx1] * alpha1; + float res1 = src1[line][sx0] * alpha0 + src1[line][sx1] * alpha1; + dst[line][x] = beta0 * res0 + beta1 * res1; + } + } + } + else if (!xRatioEq1) + { + + for (int line = 0; line < lpi; ++line) { + int x = 0; + + v_float32x8 low, high, s00, s01; + for (; x <= outSz.width - nlanes; x += nlanes) + { + v_float32x8 alpha0 = v256_load(&alpha[x]); + // v_float32 alpha1 = 1.f - alpha0; + + v_gather_pairs(src0[line], &mapsx[x], low, high); + v_deinterleave(low, high, s00, s01); + + // v_float32 d = s00*alpha0 + s01*alpha1; + v_float32x8 d = v_fma(s00 - s01, alpha0, s01); + + v_store(&dst[line][x], d); + } + + for (; x < outSz.width; ++x) { + float alpha0 = alpha[x]; + float alpha1 = 1 - alpha0; + int sx0 = mapsx[x]; + int sx1 = sx0 + 1; + dst[line][x] = src0[line][sx0] * alpha0 + src0[line][sx1] * alpha1; + } + } + + } + else if (!yRatioEq1) + { + int length = inSz.width; // == outSz.width + + for (int line = 0; line < lpi; ++line) { + float beta0 = beta[line]; + float beta1 = 1 - beta0; + v_float32x8 v_beta0 = v256_setall_f32(beta0); + int x = 0; + + for (; x <= length - nlanes; x += nlanes) + { + v_float32x8 s0 = v256_load(&src0[line][x]); + v_float32x8 s1 = v256_load(&src1[line][x]); + + // v_float32 d = s0*beta0 + s1*beta1; + v_float32x8 d = v_fma(s0 - s1, v_beta0, s1); + + v_store(&dst[line][x], d); + } + + for (; x < length; ++x) { + dst[line][x] = beta0 * src0[line][x] + beta1 * src1[line][x]; + } + } + + } + else + { + int length = inSz.width; // == outSz.width + memcpy(dst[0], src0[0], length * sizeof(float)*lpi); + } +} +} // namespace avx2 +} // namespace fliud +} // namespace gapi +} // namespace cv +#endif // !defined(GAPI_STANDALONE) diff --git a/modules/gapi/src/backends/ie/giebackend.cpp b/modules/gapi/src/backends/ie/giebackend.cpp index 5e0c63dc3a8c..6647e484b153 100644 --- a/modules/gapi/src/backends/ie/giebackend.cpp +++ b/modules/gapi/src/backends/ie/giebackend.cpp @@ -2,7 +2,7 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2018-2021 Intel Corporation +// Copyright (C) 2018-2022 Intel Corporation #include "precomp.hpp" @@ -19,6 +19,8 @@ #include #include #include +#include + #include @@ -62,6 +64,9 @@ template using QueueClass = cv::gapi::own::concurrent_bounded_queue< #include "utils/itt.hpp" +#include "streaming/onevpl/engine/preproc_engine_interface.hpp" +#include "streaming/onevpl/engine/preproc/preproc_dispatcher.hpp" + namespace IE = InferenceEngine; namespace { @@ -180,6 +185,10 @@ inline IE::Blob::Ptr wrapIE(const cv::MediaFrame::View& view, auto uv_plane = cv::Mat(desc.size / 2, CV_8UC2, view.ptr[1], view.stride[1]); return cv::gapi::ie::util::to_ie(y_plane, uv_plane); } + case cv::MediaFormat::GRAY: { + auto gray = cv::Mat(desc.size, CV_8UC1, view.ptr[0], view.stride[0]); + return wrapIE(gray, cv::gapi::ie::TraitAs::IMAGE); + } default: GAPI_Assert(false && "Unsupported media format for IE backend"); } @@ -210,6 +219,39 @@ inline void copyFromIE(const IE::Blob::Ptr &blob, MatType &mat) { } } +template +void checkLayerNames(const MapT& network_map, + const std::vector& layer_names, + const std::string& layer_type) { + for (const auto& layer_name : layer_names) { + const auto it = network_map.find(layer_name); + if (it == network_map.end()) { + std::stringstream ss; + ss << "Failed to find " << layer_type << " layer with name: " + << "\"" << layer_name << "\"" << std::endl; + ss << "Network " << layer_type << " layers: " << std::endl; + for (const auto& p : network_map) { + const auto& desc = p.second->getTensorDesc(); + ss << p.first << " : " << desc.getPrecision() + << " / " << desc.getLayout() << std::endl; + } + throw std::logic_error(ss.str()); + } + } +} + +template +void checkInputLayerNames(const MapT& network_map, + const std::vector& layer_names) { + checkLayerNames(network_map, layer_names, "input"); +} + +template +void checkOutputLayerNames(const MapT& network_map, + const std::vector& layer_names) { + checkLayerNames(network_map, layer_names, "output"); +} + // IE-specific metadata, represents a network with its parameters struct IEUnit { static const char *name() { return "IEModelConfig"; } @@ -222,12 +264,36 @@ struct IEUnit { InferenceEngine::RemoteContext::Ptr rctx = nullptr; + std::shared_ptr preproc_engine_impl; + // FIXME: Unlike loadNetwork case, importNetwork requires that preprocessing // should be passed as ExecutableNetwork::SetBlob method, so need to collect // and store this information at the graph compilation stage (outMeta) and use in runtime. using PreProcMap = std::unordered_map; PreProcMap preproc_map; + // NEW FIXME: Need to aggregate getInputInfo & GetInputInfo from network + // into generic wrapper and invoke it at once in single place instead of + // analyzing ParamDesc::Kind::Load/Import every time when we need to get access + // for network info. + // In term of introducing custom VPP/VPL preprocessing functionality + // It was decided to use GFrameDesc as such aggregated network info with limitation + // that VPP/VPL produces cv::MediaFrame only. But it should be not considered as + // final solution + class InputFramesDesc { + using input_name_type = std::string; + using description_type = cv::GFrameDesc; + std::map map; + public: + static bool is_applicable(const cv::GMetaArg &mm); + const description_type &get_param(const input_name_type &input) const; + + void set_param(const input_name_type &input, + const IE::TensorDesc& desc); + }; + + InputFramesDesc net_input_params; + explicit IEUnit(const cv::gapi::ie::detail::ParamDesc &pp) : params(pp) { InferenceEngine::ParamMap* ctx_params = @@ -287,6 +353,27 @@ struct IEUnit { params.num_in && "Number of layers to reshape must be less than or equal to number of inputs"); } + + if (params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Load) { + checkInputLayerNames(net.getInputsInfo(), params.input_names); + checkOutputLayerNames(net.getOutputsInfo(), params.output_names); + } else if (params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import) { + checkInputLayerNames(this_network.GetInputsInfo(), params.input_names); + checkOutputLayerNames(this_network.GetOutputsInfo(), params.output_names); + } else { + cv::util::throw_error(std::logic_error("Unsupported ParamDesc::Kind")); + } + + using namespace cv::gapi::wip::onevpl; + if (params.vpl_preproc_device.has_value() && params.vpl_preproc_ctx.has_value()) { + using namespace cv::gapi::wip; + GAPI_LOG_INFO(nullptr, "VPP preproc creation requested"); + preproc_engine_impl = + IPreprocEngine::create_preproc_engine( + params.vpl_preproc_device.value(), + params.vpl_preproc_ctx.value()); + GAPI_LOG_INFO(nullptr, "VPP preproc created successfuly"); + } } // This method is [supposed to be] called at Island compilation stage @@ -305,6 +392,39 @@ struct IEUnit { } }; +bool IEUnit::InputFramesDesc::is_applicable(const cv::GMetaArg &mm) { + return cv::util::holds_alternative(mm); +} + +const IEUnit::InputFramesDesc::description_type & +IEUnit::InputFramesDesc::get_param(const input_name_type &input) const { + auto it = map.find(input); + GAPI_Assert(it != map.end() && "No appropriate input is found in InputFramesDesc"); + return it->second; +} + +void IEUnit::InputFramesDesc::set_param(const input_name_type &input, + const IE::TensorDesc& desc) { + description_type ret; + ret.fmt = cv::MediaFormat::NV12; + const InferenceEngine::SizeVector& inDims = desc.getDims(); + auto layout = desc.getLayout(); + GAPI_LOG_DEBUG(nullptr, "network input: " << input << + ", tensor dims: " << inDims[0] << ", " << inDims[1] << + ", " << inDims[2] << ", " << inDims[3]); + if (layout != InferenceEngine::NHWC && layout != InferenceEngine::NCHW) { + GAPI_LOG_WARNING(nullptr, "Unsupported layout for VPP preproc: " << layout << + ", input name: " << input); + GAPI_Assert(false && "Unsupported layout for VPP preproc"); + } + GAPI_Assert(inDims.size() == 4u); + ret.size.width = static_cast(inDims[3]); + ret.size.height = static_cast(inDims[2]); + + auto res = map.emplace(input, ret); + GAPI_Assert(res.second && "Duplicated input info in InputFramesDesc are not allowable"); +} + class IECallContext { public: @@ -340,10 +460,16 @@ class IECallContext const IEUnit &uu; cv::gimpl::GIslandExecutable::IOutput &out; - // NB: Need to gurantee that MediaFrame::View don't die until request is over. + // NB: Need to guarantee that MediaFrame::View doesn't die until request is over. using Views = std::vector>; Views views; + // To store exception appeared in callback. + std::exception_ptr eptr; + + using req_key_t = void*; + cv::MediaFrame* prepareKeepAliveFrameSlot(req_key_t key); + size_t releaseKeepAliveFrame(req_key_t key); private: cv::detail::VectorRef& outVecRef(std::size_t idx); @@ -365,6 +491,10 @@ class IECallContext // Input parameters passed to an inference operation. cv::GArgs m_args; cv::GShapes m_in_shapes; + + // keep alive preprocessed frames + std::mutex keep_alive_frames_mutex; + std::unordered_map keep_alive_pp_frames; }; IECallContext::IECallContext(const IEUnit & unit, @@ -464,6 +594,36 @@ cv::GArg IECallContext::packArg(const cv::GArg &arg) { } } +cv::MediaFrame* IECallContext::prepareKeepAliveFrameSlot(req_key_t key) { + std::lock_guard lock(keep_alive_frames_mutex); + return &keep_alive_pp_frames[key]; +} + +size_t IECallContext::releaseKeepAliveFrame(req_key_t key) { + size_t elapsed_count = 0; + void *prev_slot = nullptr; + // NB: release MediaFrame previously captured by prepareKeepAliveFrameSlot + // We must capture it to keep a reference counter on inner media adapter + // to ensure that frame resource would be locked until inference done. + // Otherwise decoder could seized this frame resource as free/unlocked resource + // from resource pool + // Current function just take a unique frame `key` and overwrite stored + // actual frame by empty frame + { + std::lock_guard lock(keep_alive_frames_mutex); + auto ka_frame_it = keep_alive_pp_frames.find(key); + if (ka_frame_it != keep_alive_pp_frames.end()) { + prev_slot = &ka_frame_it->second; + ka_frame_it->second = cv::MediaFrame(); + } + elapsed_count = keep_alive_pp_frames.size(); + } + cv::util::suppress_unused_warning(prev_slot); + GAPI_LOG_DEBUG(nullptr, "Release keep alive frame, slot: " << prev_slot << + ", reserved frames count: " << elapsed_count); + return elapsed_count; +} + struct IECallable { static const char *name() { return "IERequestCallable"; } using Run = std::function, cv::gimpl::ie::RequestPool&)>; @@ -500,37 +660,108 @@ using GConstGIEModel = ade::ConstTypedGraph , IECallable >; -inline IE::Blob::Ptr extractRemoteBlob(IECallContext& ctx, std::size_t i) { +cv::MediaFrame preprocess_frame_impl(cv::MediaFrame &&in_frame, const std::string &layer_name, + IECallContext& ctx, + const cv::util::optional &opt_roi, + cv::MediaFrame* out_keep_alive_frame, + bool* out_is_preprocessed) { + cv::util::optional param = + ctx.uu.preproc_engine_impl->is_applicable(in_frame); + if (param.has_value()) { + GAPI_LOG_DEBUG(nullptr, "VPP preprocessing for decoded remote frame will be used"); + cv::GFrameDesc expected_net_input_descr = + ctx.uu.net_input_params.get_param(layer_name); + + // TODO: Find a better place to configure media format for GPU + // adjust color conversion to NV12 according to OV GPU limitation + if(ctx.uu.params.device_id.find("GPU") != std::string::npos && + ctx.uu.rctx) { + auto it = ctx.uu.params.config.find(std::string("GPU_NV12_TWO_INPUTS")); + if (it != ctx.uu.params.config.end()) { + if (it->second == "YES") { + GAPI_LOG_DEBUG(nullptr, "Adjust preprocessing GPU media format to NV12"); + expected_net_input_descr.fmt = cv::MediaFormat::NV12; + } + } + } + + cv::gapi::wip::pp_session pp_sess = + ctx.uu.preproc_engine_impl->initialize_preproc(param.value(), + expected_net_input_descr); + + in_frame = ctx.uu.preproc_engine_impl->run_sync(pp_sess, in_frame, opt_roi); + + if (out_keep_alive_frame != nullptr) { + GAPI_LOG_DEBUG(nullptr, "remember preprocessed remote frame to keep it busy from reuse, slot: " << + out_keep_alive_frame); + *out_keep_alive_frame = in_frame; + } + if (out_is_preprocessed) { + *out_is_preprocessed = true; + } + } // otherwise it is not suitable frame, then check on other preproc backend or rely on IE plugin + return std::move(in_frame); +} + +inline IE::Blob::Ptr extractRemoteBlob(IECallContext& ctx, std::size_t i, + const std::string &layer_name, + const cv::util::optional &opt_roi, + cv::MediaFrame* out_keep_alive_frame, + bool* out_is_preprocessed) { GAPI_Assert(ctx.inShape(i) == cv::GShape::GFRAME && "Remote blob is supported for MediaFrame only"); + cv::MediaFrame frame = ctx.inFrame(i); + if (ctx.uu.preproc_engine_impl) { + GAPI_LOG_DEBUG(nullptr, "Try to use preprocessing for decoded remote frame in remote ctx"); + frame = preprocess_frame_impl(std::move(frame), layer_name, ctx, opt_roi, + out_keep_alive_frame, out_is_preprocessed); + } - cv::util::any any_blob_params = ctx.inFrame(i).blobParams(); - auto ie_core = cv::gimpl::ie::wrap::getCore(); + // Request params for result frame whatever it got preprocessed or not + cv::util::any any_blob_params = frame.blobParams(); - using ParamType = std::pair; + using ParamType = std::pair; + using NV12ParamType = std::pair; - ParamType* blob_params = cv::util::any_cast(&any_blob_params); + NV12ParamType* blob_params = cv::util::any_cast(&any_blob_params); if (blob_params == nullptr) { - GAPI_Assert(false && "Incorrect type of blobParams: " - "expected std::pair"); + GAPI_Assert(false && "Incorrect type of blobParams:" + "expected std::pair," + "with ParamType std::pair>"); } - return ctx.uu.rctx->CreateBlob(blob_params->first, - blob_params->second); + //The parameters are TensorDesc and ParamMap for both y and uv blobs + auto y_blob = ctx.uu.rctx->CreateBlob(blob_params->first.first, blob_params->first.second); + auto uv_blob = ctx.uu.rctx->CreateBlob(blob_params->second.first, blob_params->second.second); + +#if INF_ENGINE_RELEASE >= 2021010000 + return IE::make_shared_blob(y_blob, uv_blob); +#else + return IE::make_shared_blob(y_blob, uv_blob); +#endif } inline IE::Blob::Ptr extractBlob(IECallContext& ctx, std::size_t i, - cv::gapi::ie::TraitAs hint) { + cv::gapi::ie::TraitAs hint, + const std::string& layer_name, + const cv::util::optional &opt_roi, + cv::MediaFrame* out_keep_alive_frame = nullptr, + bool* out_is_preprocessed = nullptr) { if (ctx.uu.rctx != nullptr) { - return extractRemoteBlob(ctx, i); + return extractRemoteBlob(ctx, i, layer_name, opt_roi, + out_keep_alive_frame, out_is_preprocessed); } switch (ctx.inShape(i)) { case cv::GShape::GFRAME: { - const auto& frame = ctx.inFrame(i); + auto frame = ctx.inFrame(i); + if (ctx.uu.preproc_engine_impl) { + GAPI_LOG_DEBUG(nullptr, "Try to use preprocessing for decoded frame in local ctx"); + frame = preprocess_frame_impl(std::move(frame), layer_name, ctx, opt_roi, + out_keep_alive_frame, out_is_preprocessed); + } ctx.views.emplace_back(new cv::MediaFrame::View(frame.access(cv::MediaFrame::Access::R))); return wrapIE(*(ctx.views.back()), frame.desc()); } @@ -560,6 +791,31 @@ static void setBlob(InferenceEngine::InferRequest& req, } } +static void setROIBlob(InferenceEngine::InferRequest& req, + const std::string& layer_name, + const IE::Blob::Ptr& blob, + const cv::Rect &roi, + const IECallContext& ctx) { + if (ctx.uu.params.device_id.find("GPU") != std::string::npos && + ctx.uu.rctx) { + try { + // NB: make_shared_blob() cannot work with GPU NV12 & ROI at the moment. + // OpenVINO produces exception with unsupported status. + // To do not encounter with silent crash situation we should catch OV exception + // and suggest to avoid this problem by using inner preprocessing feature. + // VPP/VPL proprocessing are supported at the moment + setBlob(req, layer_name, IE::make_shared_blob(blob, toIE(roi)), ctx); + } catch (const std::exception &ex) { + GAPI_LOG_WARNING(nullptr, "cannot set ROI blob for layer: " << layer_name << + ", reason:\n" << ex.what() << + "\nTry using self GAPI preprocessing feature: " + " Check method `cfgPreprocessingParams` in `cv::gapi::ie::Params`"); + throw; + } + } else { + setBlob(req, layer_name, IE::make_shared_blob(blob, toIE(roi)), ctx); + } +} } // anonymous namespace std::vector cv::gimpl::ie::IECompiled::createInferRequests() { @@ -585,7 +841,7 @@ std::vector cv::gimpl::ie::IECompiled::createInfe class cv::gimpl::ie::RequestPool { public: using RunF = std::function; - using CallbackF = std::function; + using CallbackF = std::function; // NB: The task is represented by: // RunF - function which is set blobs and run async inference. @@ -601,7 +857,10 @@ class cv::gimpl::ie::RequestPool { void waitAll(); private: - void callback(Task task, InferenceEngine::InferRequest& request, size_t id); + void callback(Task task, + size_t id, + IE::InferRequest request, + IE::StatusCode code) noexcept; void setup(); QueueClass m_idle_ids; @@ -626,19 +885,32 @@ void cv::gimpl::ie::RequestPool::execute(cv::gimpl::ie::RequestPool::Task&& t) { auto& request = m_requests[id]; + using namespace std::placeholders; + using callback_t = std::function; request.SetCompletionCallback( - std::bind(&cv::gimpl::ie::RequestPool::callback, this, t, std::ref(request), id)); - t.run(request); + static_cast( + std::bind(&cv::gimpl::ie::RequestPool::callback, this, + t, id, _1, _2))); + // NB: InferRequest is already marked as busy + // in case of exception need to return it back to the idle. + try { + t.run(request); + } catch (...) { + request.SetCompletionCallback([](){}); + m_idle_ids.push(id); + throw; + } } void cv::gimpl::ie::RequestPool::callback(cv::gimpl::ie::RequestPool::Task task, - InferenceEngine::InferRequest& request, - size_t id) { - task.callback(request); - // NB: IE::InferRequest keeps the callback until the new one is set. - // Since user's callback might keep resources that should be released, - // need to destroy its after execution. - // Let's set the empty one to cause the destruction of a callback. + size_t id, + IE::InferRequest request, + IE::StatusCode code) noexcept { + // NB: Inference is over. + // 1. Run callback + // 2. Destroy callback to free resources. + // 3. Mark InferRequest as idle. + task.callback(request, code); request.SetCompletionCallback([](){}); m_idle_ids.push(id); } @@ -691,22 +963,23 @@ cv::gimpl::ie::GIEExecutable::GIEExecutable(const ade::Graph &g, void cv::gimpl::ie::GIEExecutable::run(cv::gimpl::GIslandExecutable::IInput &in, cv::gimpl::GIslandExecutable::IOutput &out) { - // General alghoritm: + // General algorithm: // 1. Collect island inputs/outputs. // 2. Create kernel context. (Every kernel has his own context). // 3. If the EndOfStream message is recieved, wait until all passed task are done. - // 4. + // 4. If the Exception message is revieved, propagate it further. + // 5. // 5.1 Run the kernel. // 5.2 Kernel wait for all nececcary infer requests and start asynchronous execution. // 5.3 After the kernel is finished continue processing next frame. // - // 5. If graph is compiled in non-streaming mode, wait until all tasks are done. + // 6. If graph is compiled in non-streaming mode, wait until all tasks are done. std::vector input_objs; std::vector output_objs; - const auto &in_desc = in.desc(); - const auto in_msg = in.get(); + const auto &in_desc = in.desc(); + auto in_msg = in.get(); if (cv::util::holds_alternative(in_msg)) { @@ -744,10 +1017,20 @@ void cv::gimpl::ie::GIEExecutable::run(cv::gimpl::GIslandExecutable::IInput &in const auto &kk = giem.metadata(this_nh).get(); - // (4) Run the kernel. - kk.run(ctx, *m_reqPool); + // (5) Run the kernel. + try { + kk.run(ctx, *m_reqPool); + } catch (...) { + auto eptr = std::current_exception(); + for (auto i : ade::util::iota(ctx->uu.params.num_out)) + { + auto output = ctx->output(i); + ctx->out.post(std::move(output), eptr); + } + return; + } - // (5) In non-streaming mode need to wait until the all tasks are done + // (6) In non-streaming mode need to wait until the all tasks are done // FIXME: Is there more graceful way to handle this case ? if (!m_gm.metadata().contains()) { m_reqPool->waitAll(); @@ -815,6 +1098,9 @@ static void configureInputInfo(const IE::InputInfo::Ptr& ii, const cv::GMetaArg case cv::MediaFormat::BGR: // NB: Do nothing break; + case cv::MediaFormat::GRAY: + // NB: Do nothing + break; default: GAPI_Assert(false && "Unsupported media format for IE backend"); } @@ -826,6 +1112,13 @@ static void configureInputInfo(const IE::InputInfo::Ptr& ii, const cv::GMetaArg } } +static bool isApplicableForResize(const IE::TensorDesc& desc) { + const auto layout = desc.getLayout(); + const auto prec = desc.getPrecision(); + return (layout == IE::Layout::NCHW || layout == IE::Layout::NHWC) && + (prec == IE::Precision::FP32 || prec == IE::Precision::U8); +} + static IE::PreProcessInfo configurePreProcInfo(const IE::InputInfo::CPtr& ii, const cv::GMetaArg& mm) { IE::PreProcessInfo info; @@ -835,9 +1128,7 @@ static IE::PreProcessInfo configurePreProcInfo(const IE::InputInfo::CPtr& ii, info.setColorFormat(IE::ColorFormat::NV12); } } - const auto layout = ii->getTensorDesc().getLayout(); - if (layout == IE::Layout::NCHW || - layout == IE::Layout::NHWC) { + if (isApplicableForResize(ii->getTensorDesc())) { info.setResizeAlgorithm(IE::RESIZE_BILINEAR); } return info; @@ -845,20 +1136,29 @@ static IE::PreProcessInfo configurePreProcInfo(const IE::InputInfo::CPtr& ii, // NB: This is a callback used by async infer // to post outputs blobs (cv::GMat's). -static void PostOutputs(InferenceEngine::InferRequest &request, - std::shared_ptr ctx) { +static void PostOutputs(InferenceEngine::InferRequest &request, + InferenceEngine::StatusCode code, + std::shared_ptr ctx) { GAPI_ITT_STATIC_LOCAL_HANDLE(ie_cb_post_outputs_hndl, "IE_async_callback_PostOutputs"); GAPI_ITT_AUTO_TRACE_GUARD(ie_cb_post_outputs_hndl); - for (auto i : ade::util::iota(ctx->uu.params.num_out)) - { + if (code != IE::StatusCode::OK) { + std::stringstream ss; + ss << "InferRequest for model: " << ctx->uu.params.model_path + << " finished with InferenceEngine::StatusCode: " << static_cast(code); + ctx->eptr = std::make_exception_ptr(std::logic_error(ss.str())); + } + + for (auto i : ade::util::iota(ctx->uu.params.num_out)) { auto& out_mat = ctx->outMatR(i); IE::Blob::Ptr this_blob = request.GetBlob(ctx->uu.params.output_names[i]); copyFromIE(this_blob, out_mat); auto output = ctx->output(i); ctx->out.meta(output, ctx->input(0).meta); - ctx->out.post(std::move(output)); + ctx->out.post(std::move(output), ctx->eptr); } + + ctx->releaseKeepAliveFrame(&request); } class PostOutputsList { @@ -867,7 +1167,9 @@ class PostOutputsList { std::shared_ptr ctx, std::vector>&& cached_dims); - void operator()(InferenceEngine::InferRequest &request, size_t pos) const; + void operator()(InferenceEngine::InferRequest &request, + InferenceEngine::StatusCode code, + size_t pos) const; private: struct Priv { @@ -888,20 +1190,30 @@ PostOutputsList::PostOutputsList(size_t size, m_priv->cached_dims = std::move(cached_dims); } -void PostOutputsList::operator()(InferenceEngine::InferRequest &req, size_t pos) const { +void PostOutputsList::operator()(InferenceEngine::InferRequest &req, + InferenceEngine::StatusCode code, + size_t pos) const { auto&& ctx = m_priv->ctx; auto&& cached_dims = m_priv->cached_dims; auto&& finished = m_priv->finished; auto&& size = m_priv->size; - for (auto i : ade::util::iota(ctx->uu.params.num_out)) { - std::vector &out_vec = ctx->outVecR(i); - IE::Blob::Ptr out_blob = req.GetBlob(ctx->uu.params.output_names[i]); - GAPI_Assert(out_blob); + if (code != IE::StatusCode::OK) { + ctx->eptr = std::make_exception_ptr( + std::logic_error("IE::InferRequest finished with not OK status")); + } - // FIXME: Avoid data copy. Not sure if it is possible though - out_vec[pos].create(cached_dims[i], toCV(out_blob->getTensorDesc().getPrecision())); - copyFromIE(out_blob, out_vec[pos]); + if (!ctx->eptr) { + for (auto i : ade::util::iota(ctx->uu.params.num_out)) { + std::vector &out_vec = ctx->outVecR(i); + + IE::Blob::Ptr out_blob = req.GetBlob(ctx->uu.params.output_names[i]); + GAPI_Assert(out_blob); + + // FIXME: Avoid data copy. Not sure if it is possible though + out_vec[pos].create(cached_dims[i], toCV(out_blob->getTensorDesc().getPrecision())); + copyFromIE(out_blob, out_vec[pos]); + } } ++finished; @@ -909,7 +1221,7 @@ void PostOutputsList::operator()(InferenceEngine::InferRequest &req, size_t pos) for (auto i : ade::util::iota(ctx->uu.params.num_out)) { auto output = ctx->output(i); ctx->out.meta(output, ctx->input(0).meta); - ctx->out.post(std::move(output)); + ctx->out.post(std::move(output), ctx->eptr); } } } @@ -957,13 +1269,15 @@ struct Infer: public cv::detail::KernelTag { configureInputReshapeByImage(ii, mm, input_reshape_table); } - // NB: Configure resize only for NCHW/NHWC layout, - // since it isn't supposed to work with others. - auto layout = ii->getTensorDesc().getLayout(); - if (layout == IE::Layout::NCHW || - layout == IE::Layout::NHWC) { + if (isApplicableForResize(ii->getTensorDesc())) { ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); } + + // NB: configure input param for further preproc + if (uu.net_input_params.is_applicable(mm)) { + const_cast(uu.net_input_params) + .set_param(input_name, ii->getTensorDesc()); + } } // FIXME: This isn't the best place to call reshape function. @@ -983,6 +1297,12 @@ struct Infer: public cv::detail::KernelTag { auto ii = inputs.at(input_name); const auto & mm = std::get<1>(it); non_const_prepm->emplace(input_name, configurePreProcInfo(ii, mm)); + + // NB: configure input param for further preproc + if (uu.net_input_params.is_applicable(mm)) { + const_cast(uu.net_input_params) + .set_param(input_name, ii->getTensorDesc()); + } } } @@ -1021,14 +1341,16 @@ struct Infer: public cv::detail::KernelTag { (layout == IE::Layout::NCHW || layout == IE::Layout::NHWC) ? cv::gapi::ie::TraitAs::IMAGE : cv::gapi::ie::TraitAs::TENSOR; - IE::Blob::Ptr this_blob = extractBlob(*ctx, i, hint); + IE::Blob::Ptr this_blob = extractBlob(*ctx, i, hint, + layer_name, + cv::util::optional{}); setBlob(req, layer_name, this_blob, *ctx); } // FIXME: Should it be done by kernel ? // What about to do that in RequestPool ? req.StartAsync(); }, - std::bind(PostOutputs, _1, ctx) + std::bind(PostOutputs, _1, _2, ctx) } ); } @@ -1066,7 +1388,9 @@ struct InferROI: public cv::detail::KernelTag { uu.params.layer_names_to_reshape.end()) { configureInputReshapeByImage(ii, mm, input_reshape_table); } - ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); + if (isApplicableForResize(ii->getTensorDesc())) { + ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); + } // FIXME: This isn't the best place to call reshape function. // Сorrect solution would be to do this in compile() method of network, @@ -1074,6 +1398,12 @@ struct InferROI: public cv::detail::KernelTag { if (!input_reshape_table.empty()) { const_cast(&uu.net)->reshape(input_reshape_table); } + + // NB: configure input param for further preproc + if (uu.net_input_params.is_applicable(mm)) { + const_cast(uu.net_input_params) + .set_param(input_name, ii->getTensorDesc()); + } } else { GAPI_Assert(uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import); auto inputs = uu.this_network.GetInputsInfo(); @@ -1081,6 +1411,12 @@ struct InferROI: public cv::detail::KernelTag { auto* non_const_prepm = const_cast(&uu.preproc_map); auto ii = inputs.at(input_name); non_const_prepm->emplace(input_name, configurePreProcInfo(ii, mm)); + + // NB: configure intput param for further preproc + if (uu.net_input_params.is_applicable(mm)) { + const_cast(uu.net_input_params) + .set_param(input_name, ii->getTensorDesc()); + } } // FIXME: It would be nice here to have an exact number of network's @@ -1110,19 +1446,31 @@ struct InferROI: public cv::detail::KernelTag { GAPI_Assert(ctx->uu.params.num_in == 1); auto&& this_roi = ctx->inArg(0).rref(); + // reserve unique slot for keep alive preprocessed frame + cv::MediaFrame* slot_ptr = ctx->prepareKeepAliveFrameSlot(&req); + // NB: This blob will be used to make roi from its, so // it should be treated as image + bool preprocessed = false; IE::Blob::Ptr this_blob = - extractBlob(*ctx, 1, cv::gapi::ie::TraitAs::IMAGE); - setBlob(req, - *(ctx->uu.params.input_names.begin()), - IE::make_shared_blob(this_blob, toIE(this_roi)), - *ctx); + extractBlob(*ctx, 1, cv::gapi::ie::TraitAs::IMAGE, + *(ctx->uu.params.input_names.begin()), + cv::util::make_optional(this_roi), + slot_ptr, &preprocessed); + if (!preprocessed) { + setROIBlob(req, + *(ctx->uu.params.input_names.begin()), + this_blob, this_roi, *ctx); + } else { + setBlob(req, + *(ctx->uu.params.input_names.begin()), + this_blob, *ctx); + } // FIXME: Should it be done by kernel ? // What about to do that in RequestPool ? req.StartAsync(); }, - std::bind(PostOutputs, _1, ctx) + std::bind(PostOutputs, _1, _2, ctx) } ); } @@ -1165,7 +1513,9 @@ struct InferList: public cv::detail::KernelTag { uu.params.layer_names_to_reshape.end()) { configureInputReshapeByImage(ii, mm, input_reshape_table); } - ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); + if (isApplicableForResize(ii->getTensorDesc())) { + ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); + } } // FIXME: This isn't the best place to call reshape function. @@ -1196,7 +1546,6 @@ struct InferList: public cv::detail::KernelTag { static void run(std::shared_ptr ctx, cv::gimpl::ie::RequestPool &reqPool) { - const auto& in_roi_vec = ctx->inArg(0u).rref(); // NB: In case there is no input data need to post output anyway if (in_roi_vec.empty()) { @@ -1210,7 +1559,9 @@ struct InferList: public cv::detail::KernelTag { // NB: This blob will be used to make roi from its, so // it should be treated as image - IE::Blob::Ptr this_blob = extractBlob(*ctx, 1, cv::gapi::ie::TraitAs::IMAGE); + IE::Blob::Ptr this_blob = extractBlob(*ctx, 1, cv::gapi::ie::TraitAs::IMAGE, + ctx->uu.params.input_names[0u], + cv::util::optional{}); std::vector> cached_dims(ctx->uu.params.num_out); for (auto i : ade::util::iota(ctx->uu.params.num_out)) { @@ -1234,11 +1585,10 @@ struct InferList: public cv::detail::KernelTag { reqPool.execute( cv::gimpl::ie::RequestPool::Task { [ctx, rc, this_blob](InferenceEngine::InferRequest &req) { - IE::Blob::Ptr roi_blob = IE::make_shared_blob(this_blob, toIE(rc)); - setBlob(req, ctx->uu.params.input_names[0u], roi_blob, *ctx); + setROIBlob(req, ctx->uu.params.input_names[0u], this_blob, rc, *ctx); req.StartAsync(); }, - std::bind(callback, std::placeholders::_1, pos) + std::bind(callback, std::placeholders::_1, std::placeholders::_2, pos) } ); } @@ -1318,7 +1668,9 @@ struct InferList2: public cv::detail::KernelTag { uu.params.layer_names_to_reshape.end()) { configureInputReshapeByImage(ii, mm_0, input_reshape_table); } - ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); + if (isApplicableForResize(ii->getTensorDesc())) { + ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR); + } // FIXME: This isn't the best place to call reshape function. // Сorrect solution would be to do this in compile() method of network, @@ -1356,7 +1708,9 @@ struct InferList2: public cv::detail::KernelTag { && "This operation must have at least two arguments"); // NB: This blob will be used to make roi from its, so // it should be treated as image - IE::Blob::Ptr blob_0 = extractBlob(*ctx, 0, cv::gapi::ie::TraitAs::IMAGE); + IE::Blob::Ptr blob_0 = extractBlob(*ctx, 0, cv::gapi::ie::TraitAs::IMAGE, + ctx->uu.params.input_names[0u], + cv::util::optional{}); const auto list_size = ctx->inArg(1u).size(); if (list_size == 0u) { for (auto i : ade::util::iota(ctx->uu.params.num_out)) { @@ -1390,23 +1744,24 @@ struct InferList2: public cv::detail::KernelTag { for (auto in_idx : ade::util::iota(ctx->uu.params.num_in)) { const auto &this_vec = ctx->inArg(in_idx+1u); GAPI_Assert(this_vec.size() == list_size); - IE::Blob::Ptr this_blob; if (this_vec.getKind() == cv::detail::OpaqueKind::CV_RECT) { const auto &vec = this_vec.rref(); - this_blob = IE::make_shared_blob(blob_0, toIE(vec[list_idx])); + setROIBlob(req, ctx->uu.params.input_names[in_idx], + blob_0, vec[list_idx], *ctx); } else if (this_vec.getKind() == cv::detail::OpaqueKind::CV_MAT) { const auto &vec = this_vec.rref(); const auto &mat = vec[list_idx]; - this_blob = wrapIE(mat, cv::gapi::ie::TraitAs::TENSOR); + setBlob(req, ctx->uu.params.input_names[in_idx], + wrapIE(mat, cv::gapi::ie::TraitAs::TENSOR), + *ctx); } else { GAPI_Assert(false && "Only Rect and Mat types are supported for infer list 2!"); } - setBlob(req, ctx->uu.params.input_names[in_idx], this_blob, *ctx); } req.StartAsync(); }, - std::bind(callback, std::placeholders::_1, list_idx) + std::bind(callback, std::placeholders::_1, std::placeholders::_2, list_idx) } // task ); } // for @@ -1456,7 +1811,7 @@ namespace { return EPtr{new cv::gimpl::ie::GIEExecutable(graph, nodes)}; } - virtual cv::gapi::GKernelPackage auxiliaryKernels() const override { + virtual cv::GKernelPackage auxiliaryKernels() const override { return cv::gapi::kernels< cv::gimpl::ie::Infer , cv::gimpl::ie::InferROI , cv::gimpl::ie::InferList diff --git a/modules/gapi/src/backends/oak/goak.cpp b/modules/gapi/src/backends/oak/goak.cpp new file mode 100644 index 000000000000..022f8f596f19 --- /dev/null +++ b/modules/gapi/src/backends/oak/goak.cpp @@ -0,0 +1,58 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#include +#include + +#include "oak_memory_adapters.hpp" + +#include +#include + +namespace cv { +namespace gapi { +namespace oak { + +GArray encode(const GFrame& in, const EncoderConfig& cfg) { + return GEncFrame::on(in, cfg); +} + +GFrame sobelXY(const GFrame& in, const cv::Mat& hk, const cv::Mat& vk) { + return GSobelXY::on(in, hk, vk); +} + +GFrame copy(const GFrame& in) { + return GCopy::on(in); +} + +// This is a dummy oak::ColorCamera class that just makes our pipelining +// machinery work. The real data comes from the physical camera which +// is handled by DepthAI library. +ColorCamera::ColorCamera() + : m_dummy(cv::MediaFrame::Create()) { +} + +ColorCamera::ColorCamera(const ColorCameraParams& params) + : m_dummy(cv::MediaFrame::Create()), + m_params(params) { +} + +bool ColorCamera::pull(cv::gapi::wip::Data &data) { + // FIXME: Avoid passing this formal frame to the pipeline + std::this_thread::sleep_for(std::chrono::milliseconds(10)); + data = m_dummy; + return true; +} + +cv::GMetaArg ColorCamera::descr_of() const { + // FIXME: support other resolutions + GAPI_Assert(m_params.resolution == ColorCameraParams::Resolution::THE_1080_P); + return cv::GMetaArg{cv::GFrameDesc{cv::MediaFormat::NV12, cv::Size{1920, 1080}}}; +} + +} // namespace oak +} // namespace gapi +} // namespace cv diff --git a/modules/gapi/src/backends/oak/goak_memory_adapters.cpp b/modules/gapi/src/backends/oak/goak_memory_adapters.cpp new file mode 100644 index 000000000000..5805f7933f9b --- /dev/null +++ b/modules/gapi/src/backends/oak/goak_memory_adapters.cpp @@ -0,0 +1,54 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#include "oak_memory_adapters.hpp" + +namespace cv { +namespace gapi { +namespace oak { + +OAKMediaAdapter::OAKMediaAdapter(cv::Size sz, cv::MediaFormat fmt, std::vector&& buffer) +: m_sz(sz), m_fmt(fmt), m_buffer(buffer) { + GAPI_Assert(fmt == cv::MediaFormat::NV12 && "OAKMediaAdapter only supports NV12 format for now"); +} + +MediaFrame::View OAKMediaAdapter::OAKMediaAdapter::access(MediaFrame::Access) { + uint8_t* y_ptr = m_buffer.data(); + uint8_t* uv_ptr = m_buffer.data() + static_cast(m_buffer.size() / 3 * 2); + return MediaFrame::View{cv::MediaFrame::View::Ptrs{y_ptr, uv_ptr}, + cv::MediaFrame::View::Strides{static_cast(m_sz.width), + static_cast(m_sz.width)}}; +} + +cv::GFrameDesc OAKMediaAdapter::OAKMediaAdapter::meta() const { return {m_fmt, m_sz}; } + +OAKRMatAdapter::OAKRMatAdapter(const cv::Size& size, + int precision, + std::vector&& buffer) + : m_size(size), m_precision(precision), m_buffer(buffer) { + GAPI_Assert(m_precision == CV_16F); + + std::vector wrapped_dims{1, 1, m_size.width, m_size.height}; + + // FIXME: check layout and add strides + m_desc = cv::GMatDesc(m_precision, wrapped_dims); + m_mat = cv::Mat(static_cast(wrapped_dims.size()), + wrapped_dims.data(), + CV_16FC1, // FIXME: cover other precisions + m_buffer.data()); +} + +cv::GMatDesc OAKRMatAdapter::desc() const { + return m_desc; +} + +cv::RMat::View OAKRMatAdapter::access(cv::RMat::Access) { + return cv::RMat::View{m_desc, m_mat.data}; +} + +} // namespace oak +} // namespace gapi +} // namespace cv diff --git a/modules/gapi/src/backends/oak/goakbackend.cpp b/modules/gapi/src/backends/oak/goakbackend.cpp new file mode 100644 index 000000000000..e159160ba971 --- /dev/null +++ b/modules/gapi/src/backends/oak/goakbackend.cpp @@ -0,0 +1,1135 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021-2022 Intel Corporation + +#include // GKernelPackage +#include // kernels() + +#ifdef HAVE_OAK + +#include +#include +#include // any_of +#include // reference_wrapper + +#include + +#include +#include + +#include // GInferBase +#include // streaming::meta_tag + +#include "depthai/depthai.hpp" + +#include "oak_memory_adapters.hpp" + +#include // infer params + +namespace cv { namespace gimpl { + +// Forward declaration +class GOAKContext; +class OAKKernelParams; + +class GOAKExecutable final: public GIslandExecutable { + friend class GOAKContext; + friend class OAKKernelParams; + virtual void run(std::vector&&, + std::vector&&) override { + GAPI_Assert(false && "Not implemented"); + } + + virtual void run(GIslandExecutable::IInput &in, + GIslandExecutable::IOutput &out) override; + + void linkToParent(ade::NodeHandle handle); + void linkCopy(ade::NodeHandle handle); + + class ExtractTypeHelper : protected dai::Node { + public: + using Input = dai::Node::Input; + using Output = dai::Node::Output; + using InputPtr = dai::Node::Input*; + using OutputPtr = dai::Node::Output*; + }; + + struct OAKNodeInfo { + std::shared_ptr node = nullptr; + std::vector inputs = {}; + std::vector outputs = {}; + }; + + struct OAKOutQueueInfo { + std::shared_ptr xlink_output; + std::shared_ptr out_queue; + std::string out_queue_name; + size_t gapi_out_data_index; + }; + + cv::GArg packInArg(const GArg &arg, std::vector& oak_ins); + void packOutArg(const RcDesc &rc, std::vector& oak_outs); + + const ade::Graph& m_g; + GModel::ConstGraph m_gm; + cv::GCompileArgs m_args; + + std::unordered_map> m_oak_nodes; + + // Will be reworked later when XLinkIn will be introduced as input + std::shared_ptr m_camera_input; + cv::Size m_camera_size; + + // Backend outputs + std::unordered_map> m_out_queues; + + // Backend inputs + std::vector> m_in_queues; + + std::unordered_set> m_passthrough_copy_nodes; + + // Note: dai::Pipeline should be the only one for the whole pipeline, + // so there is no way to insert any non-OAK node in graph between other OAK nodes. + // The only heterogeneous case possible is if we insert other backends after or before + // OAK island. + std::unique_ptr m_device; + std::unique_ptr m_pipeline; + + // Camera config + cv::gapi::oak::ColorCameraParams m_ccp; + + // Infer info + std::unordered_map> m_oak_infer_info; + +public: + GOAKExecutable(const ade::Graph& g, + const cv::GCompileArgs& args, + const std::vector& nodes, + const std::vector& ins_data, + const std::vector& outs_data); + ~GOAKExecutable() = default; + + // FIXME: could it reshape? + virtual bool canReshape() const override { return false; } + virtual void reshape(ade::Graph&, const GCompileArgs&) override { + GAPI_Assert(false && "GOAKExecutable::reshape() is not supported"); + } + + virtual void handleNewStream() override; + virtual void handleStopStream() override; +}; + +class GOAKContext { +public: + // FIXME: make private? + using Input = GOAKExecutable::ExtractTypeHelper::Input; + using Output = GOAKExecutable::ExtractTypeHelper::Output; + using InputPtr = GOAKExecutable::ExtractTypeHelper::Input*; + using OutputPtr = GOAKExecutable::ExtractTypeHelper::Output*; + + GOAKContext(const std::unique_ptr& pipeline, + const cv::Size& camera_size, + std::vector& args, + std::vector& results); + + GOAKContext(const std::unique_ptr& pipeline, + const cv::Size& camera_size, + const cv::gapi::oak::detail::ParamDesc& infer_info, + std::vector& args, + std::vector& results); + + // Generic accessor API + template + T& inArg(int input) { return m_args.at(input).get(); } + + // FIXME: consider not using raw pointers + InputPtr& in(int input); + OutputPtr& out(int output); + + const std::unique_ptr& pipeline() const; + const cv::Size& camera_size() const; + const cv::gapi::oak::detail::ParamDesc& ii() const; + +private: + const std::unique_ptr& m_pipeline; + const cv::Size m_camera_size; + const cv::gapi::oak::detail::ParamDesc m_infer_info; + std::vector& m_args; + std::vector& m_outputs; +}; + +GOAKContext::GOAKContext(const std::unique_ptr& pipeline, + const cv::Size& camera_size, + std::vector& args, + std::vector& results) + : m_pipeline(pipeline), m_camera_size(camera_size), + m_args(args), m_outputs(results) {} + +GOAKContext::GOAKContext(const std::unique_ptr& pipeline, + const cv::Size& camera_size, + const cv::gapi::oak::detail::ParamDesc& infer_info, + std::vector& args, + std::vector& results) + : m_pipeline(pipeline), m_camera_size(camera_size), + m_infer_info(infer_info), m_args(args), m_outputs(results) {} + +const std::unique_ptr& GOAKContext::pipeline() const { + return m_pipeline; +} + +const cv::Size& GOAKContext::camera_size() const { + return m_camera_size; +} + +const cv::gapi::oak::detail::ParamDesc& GOAKContext::ii() const { + return m_infer_info; +} + +GOAKContext::InputPtr& GOAKContext::in(int input) { + return inArg>(input).get(); +} + +GOAKContext::OutputPtr& GOAKContext::out(int output) { + return m_outputs.at(output); +} + +class OAKKernelParams { +public: + const std::unique_ptr& pipeline; + const cv::Size& camera_size; + const cv::gapi::oak::detail::ParamDesc& infer_info; + std::vector>& in_queues; +}; + +namespace detail { +template struct get_in; +template<> struct get_in { + static GOAKContext::InputPtr& get(GOAKContext &ctx, int idx) { return ctx.in(idx); } +}; +template struct get_in { + static T get(GOAKContext &ctx, int idx) { return ctx.inArg(idx); } +}; +// FIXME: add support of other types + +template struct get_out; +template<> struct get_out { + static GOAKContext::OutputPtr& get(GOAKContext &ctx, int idx) { return ctx.out(idx); } +}; +template struct get_out> { + static GOAKContext::OutputPtr& get(GOAKContext &ctx, int idx) { return ctx.out(idx); } +}; +template<> struct get_out { + static GOAKContext::OutputPtr& get(GOAKContext &ctx, int idx) { return ctx.out(idx); } +}; +// FIXME: add support of other types + +template +struct OAKCallHelper; + +template +struct OAKCallHelper, std::tuple > { + template + static std::shared_ptr construct_impl( GOAKContext &ctx + , std::vector>& in_queues_params + , cv::detail::Seq + , cv::detail::Seq) { + return Impl::put(OAKKernelParams{ctx.pipeline(), + ctx.camera_size(), + ctx.ii(), + in_queues_params}, + get_in::get(ctx, IIs)..., + get_out::get(ctx, OIs)...); + } + + static std::shared_ptr construct(GOAKContext &ctx, + std::vector>& in_queues_params) { + return construct_impl(ctx, + in_queues_params, + typename cv::detail::MkSeq::type(), + typename cv::detail::MkSeq::type()); + } +}; + +} // namespace detail + +struct GOAKKernel { + using F = std::function(GOAKContext&, + std::vector>&)>; + explicit GOAKKernel(const F& f) : m_put_f(f) {} + const F m_put_f; +}; + +struct OAKComponent +{ + static const char *name() { return "OAK Component"; } + GOAKKernel k; +}; +} // namespace gimpl +} // namespace cv + +using OAKGraph = ade::TypedGraph + < cv::gimpl::Protocol + , cv::gimpl::Op + , cv::gimpl::NetworkParams + , cv::gimpl::CustomMetaFunction + // OAK specific + , cv::gimpl::OAKComponent + >; + +using ConstOAKGraph = ade::ConstTypedGraph + < cv::gimpl::Protocol + , cv::gimpl::Op + , cv::gimpl::NetworkParams + , cv::gimpl::CustomMetaFunction + // OAK specific + , cv::gimpl::OAKComponent + >; + +namespace +{ +std::pair +parseDaiInferMeta(const cv::gapi::oak::detail::ParamDesc& pd) { + dai::OpenVINO::Blob blob(pd.blob_file); + + GAPI_Assert(blob.networkInputs.size() == 1); + GAPI_Assert(blob.networkOutputs.size() == 1); + + return {blob.networkInputs.begin()->second, + blob.networkOutputs.begin()->second}; +} + +std::string +getDaiInferOutLayerName(const cv::gapi::oak::detail::ParamDesc& pd) { + dai::OpenVINO::Blob blob(pd.blob_file); + + GAPI_Assert(blob.networkInputs.size() == 1); + GAPI_Assert(blob.networkOutputs.size() == 1); + + return blob.networkOutputs.begin()->first; +} +} // anonymous namespace + +// Custom meta function for OAK backend for infer +static cv::GMetaArgs customOutMeta(const ade::Graph &gr, + const ade::NodeHandle &nh, + const cv::GMetaArgs &/*in_metas*/, + const cv::GArgs &/*in_args*/) { + cv::GMetaArgs result; + const auto &np = ConstOAKGraph(gr).metadata(nh).get(); + const auto &pd = cv::util::any_cast(np.opaque); + + // FIXME: Infer kernel and backend does rather the same + auto in_out_tensor_info = parseDaiInferMeta(pd); + + GAPI_Assert(in_out_tensor_info.second.dataType == + dai::TensorInfo::DataType::FP16); + + // FIXME: add proper layout converter here + GAPI_Assert(in_out_tensor_info.second.order == + dai::TensorInfo::StorageOrder::NCHW); + + // FIXME: DAI returns vector, remove workaround + std::vector wrapped_dims; + for (const auto& d : in_out_tensor_info.second.dims) { + wrapped_dims.push_back(d); + } + result = {cv::GMetaArg{cv::GMatDesc(CV_16F, 1, cv::Size(wrapped_dims[1], wrapped_dims[0]), false)}}; + + return result; +} + +// This function links DAI operation nodes - parent's output to child's input. +// It utilizes G-API graph to search for operation's node it's previous operation in graph +// when links them in DAI graph. +void cv::gimpl::GOAKExecutable::linkToParent(ade::NodeHandle handle) +{ + ade::NodeHandle parent; + for (const auto& data_nh : handle.get()->inNodes()) { + // Data node has only 1 input + GAPI_Assert(data_nh.get()->inNodes().size() == 1); + parent = data_nh.get()->inNodes().front(); + + // Don't link if parent is copy - the case is handled differently + // in linkCopy + const auto& op = m_gm.metadata(parent).get(); + if (op.k.name == "org.opencv.oak.copy") { + continue; + } + + // Assuming that OAK nodes are aligned for linking. + // FIXME: potential rework might be needed then + // counterexample is found. + GAPI_Assert(m_oak_nodes.at(handle).inputs.size() == + m_oak_nodes.at(parent).outputs.size() && + "Internal OAK nodes are not aligned for linking"); + for (auto && it : ade::util::zip(ade::util::toRange(m_oak_nodes.at(parent).outputs), + ade::util::toRange(m_oak_nodes.at(handle).inputs))) + { + auto &out = std::get<0>(it); + auto &in = std::get<1>(it); + out->link(*in); + } + } +} + +// This function links DAI operations for Copy OP in G-API graph +void cv::gimpl::GOAKExecutable::linkCopy(ade::NodeHandle handle) { + // 1. Check that there are no back-to-back Copy OPs in graph + auto copy_out = handle.get()->outNodes(); + GAPI_Assert(copy_out.size() == 1); + for (const auto& copy_next_op : copy_out.front().get()->outNodes()) { + const auto& op = m_gm.metadata(copy_next_op).get(); + if (op.k.name == "org.opencv.oak.copy") { + GAPI_Assert(false && "Back-to-back Copy operations are not supported in graph"); + } + } + + // 2. Link passthrough case + if (m_passthrough_copy_nodes.find(handle) != m_passthrough_copy_nodes.end()) { + ExtractTypeHelper::OutputPtr parent; + bool parent_is_camera = false; + // Copy has only 1 input data + GAPI_Assert(handle.get()->inNodes().size() == 1); + auto in_ops = handle.get()->inNodes().front().get()->inNodes(); + if (in_ops.size() == 0) { + // No parent nodes - parent = camera + parent = &m_camera_input->video; + parent_is_camera = true; + } else { + // Data has only 1 input + GAPI_Assert(in_ops.size() == 1); + auto node = m_oak_nodes.at(in_ops.front()); + // Should only have 1 output + GAPI_Assert(node.outputs.size() == 1); + parent = node.outputs[0]; + } + + // Now link DAI parent output to Copy's child's inputs ignoring the Copy operation + // FIXME: simplify this loop + auto copy_out_data = handle.get()->outNodes(); + // Copy has only 1 output + GAPI_Assert(copy_out_data.size() == 1); + for (const auto& copy_next_op : copy_out_data.front().get()->outNodes()) { + if (m_oak_nodes.find(copy_next_op) != m_oak_nodes.end()) { + // FIXME: consider a better approach + if (parent_is_camera) { + if (m_oak_infer_info.find(copy_next_op) != m_oak_infer_info.end()) { + parent = &m_camera_input->preview; + } else { + parent = &m_camera_input->video; + } + } + // Found next Copy OP which needs to be linked to Copy's parent + GAPI_Assert(m_oak_nodes.at(copy_next_op).inputs.size() == 1 && + "Internal OAK nodes are not aligned for linking (Copy operation)"); + parent->link(*(m_oak_nodes.at(copy_next_op).inputs.front())); + } + } + } + + // 3. Link output Copy case + if (m_out_queues.find(handle) != m_out_queues.end()) { + // DAI XLinkOutput node + auto xout = m_out_queues[handle].xlink_output->input; + + // Find parent node + // FIXME: copypasted from case 2 above + ExtractTypeHelper::OutputPtr parent; + // Copy has only 1 input data + GAPI_Assert(handle.get()->inNodes().size() == 1); + auto in_ops = handle.get()->inNodes().front().get()->inNodes(); + if (in_ops.size() == 0) { + // No parent nodes - parent = camera + parent = &m_camera_input->video; + } else { + // Data has only 1 input + GAPI_Assert(in_ops.size() == 1); + auto node = m_oak_nodes.at(in_ops.front()); + // Should only have 1 output + GAPI_Assert(node.outputs.size() == 1); + parent = node.outputs[0]; + } + + // Link parent to xout + parent->link(xout); + } +} + +cv::GArg +cv::gimpl::GOAKExecutable::packInArg(const GArg &arg, + std::vector& oak_ins) { + if (arg.kind != cv::detail::ArgKind::GOBJREF) { + GAPI_Assert( arg.kind != cv::detail::ArgKind::GMAT + && arg.kind != cv::detail::ArgKind::GSCALAR + && arg.kind != cv::detail::ArgKind::GARRAY + && arg.kind != cv::detail::ArgKind::GOPAQUE + && arg.kind != cv::detail::ArgKind::GFRAME); + // All other cases - pass as-is, with no transformations to + // GArg contents. + return const_cast(arg); + } + const cv::gimpl::RcDesc &ref = arg.get(); + switch (ref.shape) { + case GShape::GFRAME: + oak_ins.push_back(nullptr); + return GArg(std::reference_wrapper(oak_ins.back())); + break; + default: + util::throw_error(std::logic_error("Unsupported GShape type in OAK backend")); + break; + } +} + +void cv::gimpl::GOAKExecutable::packOutArg(const RcDesc &rc, + std::vector& oak_outs) { + switch (rc.shape) { + case GShape::GFRAME: + case GShape::GARRAY: + case GShape::GMAT: + oak_outs.push_back(nullptr); + break; + default: + util::throw_error(std::logic_error("Unsupported GShape type in OAK backend")); + break; + } +} + +namespace { +static dai::CameraBoardSocket extractCameraBoardSocket(cv::gapi::oak::ColorCameraParams ccp) { + switch (ccp.board_socket) { + case cv::gapi::oak::ColorCameraParams::BoardSocket::RGB: + return dai::CameraBoardSocket::RGB; + // FIXME: extend + default: + // basically unreachable + GAPI_Assert("Unsupported camera board socket"); + return {}; + } +} + +static dai::ColorCameraProperties::SensorResolution +extractCameraResolution(cv::gapi::oak::ColorCameraParams ccp) { + switch (ccp.resolution) { + case cv::gapi::oak::ColorCameraParams::Resolution::THE_1080_P: + return dai::ColorCameraProperties::SensorResolution::THE_1080_P; + // FIXME: extend + default: + // basically unreachable + GAPI_Assert("Unsupported camera board socket"); + return {}; + } +} +} // anonymous namespace + +cv::gimpl::GOAKExecutable::GOAKExecutable(const ade::Graph& g, + const cv::GCompileArgs &args, + const std::vector& nodes, + const std::vector& ins_data, + const std::vector& outs_data) + : m_g(g), m_gm(m_g), m_args(args), + m_device(nullptr), m_pipeline(new dai::Pipeline) + { + // FIXME: currently OAK backend only works with camera as input, + // so it must be a single object + GAPI_Assert(ins_data.size() == 1); + + // Check that there is only one OAK island in graph since there + // can only be one instance of dai::Pipeline in the application + auto isl_graph = m_gm.metadata().get().model; + GIslandModel::Graph gim(*isl_graph); + size_t oak_islands = 0; + + for (const auto& nh : gim.nodes()) + { + if (gim.metadata(nh).get().k == NodeKind::ISLAND) + { + const auto isl = gim.metadata(nh).get().object; + if (isl->backend() == cv::gapi::oak::backend()) + { + ++oak_islands; + } + if (oak_islands > 1) { + util::throw_error + (std::logic_error + ("There can only be one OAK island in graph")); + } + } + } + + m_ccp = cv::gimpl::getCompileArg(args) + .value_or(cv::gapi::oak::ColorCameraParams{}); + + // FIXME: change the hard-coded behavior (XLinkIn path) + auto camRgb = m_pipeline->create(); + // FIXME: extract camera compile arguments here and properly convert them for dai + camRgb->setBoardSocket(extractCameraBoardSocket(m_ccp)); + camRgb->setResolution(extractCameraResolution(m_ccp)); + camRgb->setInterleaved(m_ccp.interleaved); + + // Extract infer params + for (const auto& nh : nodes) { + if (m_gm.metadata(nh).get().t == NodeType::OP) { + if (ConstOAKGraph(m_g).metadata(nh).contains()) { + const auto &np = ConstOAKGraph(m_g).metadata(nh).get(); + const auto &pp = cv::util::any_cast(np.opaque); + m_oak_infer_info[nh] = pp; + break; + } + } + } + + // FIXME: handle multiple infers + if (!m_oak_infer_info.empty()) { + GAPI_Assert(m_oak_infer_info.size() == 1); + // FIXME: move to infer node? + auto in_out_tensor_info = parseDaiInferMeta(m_oak_infer_info.begin()->second); + + if (in_out_tensor_info.first.dataType == + dai::TensorInfo::DataType::FP16 || + in_out_tensor_info.first.dataType == + dai::TensorInfo::DataType::FP32) { + camRgb->setFp16(true); + } else { + camRgb->setFp16(false); + } + + // FIXME: add proper layout converter here + GAPI_Assert(in_out_tensor_info.first.order == + dai::TensorInfo::StorageOrder::NCHW); + camRgb->setPreviewSize(in_out_tensor_info.first.dims[0], in_out_tensor_info.first.dims[1]); + } + + m_camera_input = camRgb; + // FIXME: change when other camera censors are introduced + std::tuple video_size = m_camera_input->getVideoSize(); + m_camera_size = cv::Size{std::get<0>(video_size), std::get<1>(video_size)}; + + // Prepare XLinkOut nodes for each output object in graph + for (size_t i = 0; i < outs_data.size(); ++i) { + auto xout = m_pipeline->create(); + std::string xout_name = "xout" + std::to_string(i); + xout->setStreamName(xout_name); + + // Find parent OP's nh + ade::NodeHandle parent_op_nh; + for (const auto& nh : nodes) { + for (const auto& outdata : nh.get()->outNodes()) { + if (m_gm.metadata(outdata).get().t == NodeType::DATA) { + auto rc = m_gm.metadata(outdata).get().rc; + auto shape = m_gm.metadata(outdata).get().shape; + // Match outs_data with the actual operation + if (rc == outs_data[i].rc && shape == outs_data[i].shape) { + parent_op_nh = nh; + } + } + } + } + + m_out_queues[parent_op_nh] = {xout, nullptr, xout_name, i}; + } + + // Create OAK node for each node in this backend + for (const auto& nh : nodes) { + if (m_gm.metadata(nh).get().t == NodeType::OP) { + const auto& op = m_gm.metadata(nh).get(); + const auto &u = ConstOAKGraph(m_g).metadata(nh).get(); + // pass kernel input args and compile args to prepare OAK node and + // store it to link later + m_oak_nodes[nh] = {}; + m_oak_nodes.at(nh).inputs.reserve(op.args.size()); + m_oak_nodes.at(nh).outputs.reserve(op.outs.size()); + + // Copy operation in graph can fall into 3 cases: + // 1) Copy is an output of the island - + // in that case we link it to XLinkOut node from m_out_queues + // 2) Copy is between other two operations in the same OAK island - + // in that case we link its parent operation (could be camera) to + // the child one (those copy operations are placed in m_passthrough_copy_nodes) + // 3) Copy can fall into cases 1) and 2) at the same time + + // Prepare passthrough Copy operations + if (op.k.name == "org.opencv.oak.copy") { + // Copy has only 1 output + auto copy_out = nh.get()->outNodes(); + GAPI_Assert(copy_out.size() == 1); + for (const auto& copy_next_op : copy_out.front().get()->outNodes()) { + // Check that copy is a passthrough OP + if (std::find(nodes.begin(), nodes.end(), copy_next_op) != nodes.end()) { + m_passthrough_copy_nodes.insert(nh); + break; + } + } + } + + std::vector in_ctx_args; + in_ctx_args.reserve(op.args.size()); + for (auto &op_arg : op.args) in_ctx_args.push_back(packInArg(op_arg, + m_oak_nodes.at(nh).inputs)); + for (auto &&op_out : op.outs) packOutArg(op_out, m_oak_nodes.at(nh).outputs); + GAPI_Assert(!m_oak_nodes.at(nh).inputs.empty()); + GAPI_Assert(!m_oak_nodes.at(nh).outputs.empty()); + + if (ConstOAKGraph(m_g).metadata(nh).contains()) { + GOAKContext ctx(m_pipeline, m_camera_size, m_oak_infer_info[nh], + in_ctx_args, m_oak_nodes.at(nh).outputs); + m_oak_nodes.at(nh).node = u.k.m_put_f(ctx, m_in_queues); + } else { + GOAKContext ctx(m_pipeline, m_camera_size, + in_ctx_args, m_oak_nodes.at(nh).outputs); + m_oak_nodes.at(nh).node = u.k.m_put_f(ctx, m_in_queues); + } + + // Check that all inputs and outputs are properly filled after constructing kernels + // to then link it together + // FIXME: add more logging + const auto& node_info = m_oak_nodes.at(nh); + // Copy operations don't set their inputs/outputs properly + if (op.k.name != "org.opencv.oak.copy") { + GAPI_Assert(node_info.node != nullptr); + if (std::any_of(node_info.inputs.cbegin(), node_info.inputs.cend(), + [](ExtractTypeHelper::InputPtr ptr) { + return ptr == nullptr; + })) { + GAPI_Assert(false && "DAI input are not set"); + } + + if (std::any_of(node_info.outputs.cbegin(), node_info.outputs.cend(), + [](ExtractTypeHelper::OutputPtr ptr) { + return ptr == nullptr; + })) { + GAPI_Assert(false && "DAI outputs are not set"); + } + } + } + } + + // Prepare nodes for linking + std::unordered_set> in_nodes; + std::unordered_set> out_nodes; + std::unordered_set> inter_nodes; + std::unordered_set> copy_nodes; + + // TODO: optimize this loop + for (const auto& node : m_oak_nodes) { + auto nh = node.first; + // Check if it's a Copy OP - will be handled differently when linking + GAPI_Assert(m_gm.metadata(nh).get().t == NodeType::OP); + const auto& op = m_gm.metadata(nh).get(); + if (op.k.name == "org.opencv.oak.copy") { + copy_nodes.insert(nh); + continue; + } + + // Fill input op nodes + for (const auto& d : ins_data) { + for (const auto& indata : nh.get()->inNodes()) { + auto rc = m_gm.metadata(indata).get().rc; + auto shape = m_gm.metadata(indata).get().shape; + if (rc == d.rc && shape == d.shape) { + in_nodes.insert(nh); + } + } + } + // Fill output op nodes + for (const auto& d : outs_data) { + for (const auto& outdata : nh.get()->outNodes()) { + auto rc = m_gm.metadata(outdata).get().rc; + auto shape = m_gm.metadata(outdata).get().shape; + if (rc == d.rc && shape == d.shape) { + out_nodes.insert(nh); + } + } + } + // Fill internal op nodes + if (in_nodes.find(nh) == in_nodes.end() && + out_nodes.find(nh) == in_nodes.end()) { + inter_nodes.insert(nh); + } + } + + // Properly link all nodes + // 1. Link input nodes to camera + for (const auto& nh : in_nodes) { + GAPI_Assert(m_oak_nodes.at(nh).inputs.size() == 1); + // FIXME: convert other camera outputs + // Link preview to infer, video to all other nodes + if (m_oak_infer_info.find(nh) == m_oak_infer_info.end()) { + m_camera_input->video.link(*(m_oak_nodes.at(nh).inputs[0])); + } else { + m_camera_input->preview.link(*(m_oak_nodes.at(nh).inputs[0])); + } + } + + // 2. Link output nodes to XLinkOut nodes + for (const auto& nh : out_nodes) { + for (const auto& out : m_oak_nodes.at(nh).outputs) { + out->link(m_out_queues[nh].xlink_output->input); + } + // Input nodes in OAK doesn't have parent operation - just camera (for now) + if (in_nodes.find(nh) == in_nodes.end()) { + linkToParent(nh); + } + } + + // 3. Link internal nodes to their parents + for (const auto& nh : inter_nodes) { + linkToParent(nh); + } + + // 4. Link copy nodes + for (const auto& nh : copy_nodes) { + linkCopy(nh); + } + + m_device = std::unique_ptr(new dai::Device(*m_pipeline)); + + // Prepare OAK output queues + GAPI_Assert(m_out_queues.size() == outs_data.size()); + for (const auto out_it : ade::util::indexed(m_out_queues)) + { + auto& q = ade::util::value(out_it).second; + GAPI_Assert(q.out_queue == nullptr); // shouldn't be not filled till this point + // FIXME: add queue parameters + // Currently: 4 - max DAI queue capacity, true - blocking queue + q.out_queue = m_device->getOutputQueue(q.out_queue_name, 4, true); + } + } + +void cv::gimpl::GOAKExecutable::handleNewStream() { + // do nothing +} + +void cv::gimpl::GOAKExecutable::handleStopStream() { + // do nothing +} + +void cv::gimpl::GOAKExecutable::run(GIslandExecutable::IInput &in, + GIslandExecutable::IOutput &out) { + const auto in_msg = in.get(); + + if (cv::util::holds_alternative(in_msg)) { + out.post(cv::gimpl::EndOfStream{}); + return; + } + + for (const auto& in_q : m_in_queues) { + auto q = m_device->getInputQueue(in_q.first); + q->send(in_q.second); + } + + for (size_t i = 0; i < m_in_queues.size(); ++i) { + auto q = m_device->getInputQueue(m_in_queues[i].first); + q->send(m_in_queues[i].second); + } + + for (const auto el : m_out_queues) { + const auto out_q = el.second; + auto& q = out_q.out_queue; + + auto out_arg = out.get(out_q.gapi_out_data_index); + + // FIXME: misc info to be utilized in switch below + cv::GRunArg::Meta meta; + std::shared_ptr oak_frame; + + switch(out_arg.index()) { + case cv::GRunArgP::index_of(): + { + oak_frame = q->get(); + // FIXME: hard-coded NV12 + *cv::util::get(out_arg) = + cv::MediaFrame::Create( + cv::Size(static_cast(oak_frame->getWidth()), + static_cast(oak_frame->getHeight())), + cv::MediaFormat::NV12, + std::move(oak_frame->getData())); + + using namespace cv::gapi::streaming::meta_tag; + meta[timestamp] = oak_frame->getTimestamp(); + meta[seq_id] = oak_frame->getSequenceNum(); + + break; + } + case cv::GRunArgP::index_of(): + { + oak_frame = q->get(); + cv::util::get(out_arg).wref() = std::move(oak_frame->getData()); + + using namespace cv::gapi::streaming::meta_tag; + meta[timestamp] = oak_frame->getTimestamp(); + meta[seq_id] = oak_frame->getSequenceNum(); + + break; + } + case cv::GRunArgP::index_of(): // only supported for infer + { + auto nn_data = q->get(); + + auto out_layer_name = getDaiInferOutLayerName(m_oak_infer_info.begin()->second); + auto in_out_tensor_info = parseDaiInferMeta(m_oak_infer_info.begin()->second); + + auto layer = std::move(nn_data->getLayerFp16(out_layer_name)); + + // FIXME: add proper layout converter here + GAPI_Assert(in_out_tensor_info.second.order == + dai::TensorInfo::StorageOrder::NCHW); + // FIMXE: only 1-channel data is supported for now + GAPI_Assert(in_out_tensor_info.second.dims[2] == 1); + + *cv::util::get(out_arg) = + cv::make_rmat( + cv::Size(in_out_tensor_info.second.dims[1], + in_out_tensor_info.second.dims[0]), + CV_16F, // FIXME: cover other precisions + std::move(layer) + ); + + using namespace cv::gapi::streaming::meta_tag; + meta[timestamp] = nn_data->getTimestamp(); + meta[seq_id] = nn_data->getSequenceNum(); + + break; + } + // FIXME: Add support for remaining types + default: + GAPI_Assert(false && "Unsupported type in OAK backend"); + } + + out.meta(out_arg, meta); + out.post(std::move(out_arg)); + } +} + +namespace cv { +namespace gimpl { +namespace oak { + +namespace { +static dai::VideoEncoderProperties::Profile convertEncProfile(cv::gapi::oak::EncoderConfig::Profile pf) { + switch (pf) { + case cv::gapi::oak::EncoderConfig::Profile::H264_BASELINE: + return dai::VideoEncoderProperties::Profile::H264_BASELINE; + case cv::gapi::oak::EncoderConfig::Profile::H264_HIGH: + return dai::VideoEncoderProperties::Profile::H264_HIGH; + case cv::gapi::oak::EncoderConfig::Profile::H264_MAIN: + return dai::VideoEncoderProperties::Profile::H264_MAIN; + case cv::gapi::oak::EncoderConfig::Profile::H265_MAIN: + return dai::VideoEncoderProperties::Profile::H265_MAIN; + case cv::gapi::oak::EncoderConfig::Profile::MJPEG: + return dai::VideoEncoderProperties::Profile::MJPEG; + default: + // basically unreachable + GAPI_Assert("Unsupported encoder profile"); + return {}; + } +} +} // anonymous namespace + +// Kernels /////////////////////////////////////////////////////////////// + +// FIXME: consider a better solution - hard-coded API +// Is there a way to extract API from somewhereelse/utilize structs +// like in streaming/infer backends (mainly infer and copy operations) +template +class GOAKKernelImpl: public detail::OAKCallHelper + , public cv::detail::KernelTag { + using P = detail::OAKCallHelper; +public: + using API = K; + static cv::gapi::GBackend backend() { return cv::gapi::oak::backend(); } + static GOAKKernel kernel() { return GOAKKernel(&P::construct); } +}; + +#define GAPI_OAK_KERNEL(Name, API) \ + struct Name: public cv::gimpl::oak::GOAKKernelImpl + +#define GAPI_OAK_FIXED_API_KERNEL(Name, API, InArgs, OutArgs) \ + struct Name: public cv::gimpl::oak::GOAKKernelImpl + +namespace { +GAPI_OAK_FIXED_API_KERNEL(GOAKInfer, cv::GInferBase, std::tuple, std::tuple) { + static std::shared_ptr put(const cv::gimpl::OAKKernelParams& params, + GOAKContext::InputPtr& in, + GOAKContext::OutputPtr& out) { + auto nn = params.pipeline->create(); + + nn->input.setBlocking(true); + nn->input.setQueueSize(1); + + // FIXME: add G-API built-in preproc here (currently it's only setPreviewSize() on the camera node) + // Note: for some reason currently it leads to: + // "Fatal error. Please report to developers. Log: 'ImageManipHelper' '61'" + + nn->setBlobPath(params.infer_info.blob_file); + + in = &(nn->input); + out = &(nn->out); + + return nn; + } +}; + +GAPI_OAK_KERNEL(GOAKCopy, cv::gapi::oak::GCopy) { + static std::shared_ptr put(const cv::gimpl::OAKKernelParams&, + GOAKContext::InputPtr&, + GOAKContext::OutputPtr&) { + // Do nothing in Copy OP since it's either already represented + // by XLinkOut node (bonded to output queues) or it's a passthrough OP + return nullptr; + } +}; + +GAPI_OAK_KERNEL(GOAKEncFrame, cv::gapi::oak::GEncFrame) { + static std::shared_ptr put(const cv::gimpl::OAKKernelParams& params, + GOAKContext::InputPtr& in, + const cv::gapi::oak::EncoderConfig& cfg, + GOAKContext::OutputPtr& out) { + auto videoEnc = params.pipeline->create(); + + // FIXME: convert all the parameters to dai + videoEnc->setDefaultProfilePreset(cfg.frameRate, + convertEncProfile(cfg.profile)); + + in = &(videoEnc->input); + out = &(videoEnc->bitstream); + + return videoEnc; + } +}; + +GAPI_OAK_KERNEL(GOAKSobelXY, cv::gapi::oak::GSobelXY) { + static std::shared_ptr put(const cv::gimpl::OAKKernelParams& params, + GOAKContext::InputPtr& in, + const cv::Mat& hk, + const cv::Mat& vk, + GOAKContext::OutputPtr& out) { + auto edgeDetector = params.pipeline->create(); + + edgeDetector->setMaxOutputFrameSize(params.camera_size.width * params.camera_size.height); + + auto xinEdgeCfg = params.pipeline->create(); + xinEdgeCfg->setStreamName("sobel_cfg"); + + auto mat2vec = [&](cv::Mat m) { + std::vector> v(m.rows); + for (int i = 0; i < m.rows; ++i) + { + m.row(i).reshape(1,1).copyTo(v[i]); + } + return v; + }; + + dai::EdgeDetectorConfig cfg; + cfg.setSobelFilterKernels(mat2vec(hk), mat2vec(vk)); + + xinEdgeCfg->out.link(edgeDetector->inputConfig); + + params.in_queues.push_back({"sobel_cfg", cfg}); + + in = &(edgeDetector->inputImage); + out = &(edgeDetector->outputImage); + + return edgeDetector; + } +}; + +} // anonymous namespace +} // namespace oak +} // namespace gimpl +} // namespace cv + +class GOAKBackendImpl final : public cv::gapi::GBackend::Priv { + virtual void unpackKernel(ade::Graph &graph, + const ade::NodeHandle &op_node, + const cv::GKernelImpl &impl) override { + using namespace cv::gimpl; + + OAKGraph gm(graph); + + const auto &kimpl = cv::util::any_cast(impl.opaque); + gm.metadata(op_node).set(OAKComponent{kimpl}); + + // Set custom meta for infer + if (gm.metadata(op_node).contains()) { + gm.metadata(op_node).set(CustomMetaFunction{customOutMeta}); + } + } + + virtual EPtr compile(const ade::Graph &graph, + const cv::GCompileArgs &args, + const std::vector &nodes, + const std::vector& ins_data, + const std::vector& outs_data) const override { + cv::gimpl::GModel::ConstGraph gm(graph); + // FIXME: pass streaming/non-streaming option to support non-camera case + // NB: how could we have non-OAK source in streaming mode, then OAK backend in + // streaming mode but without camera input? + if (!gm.metadata().contains()) { + GAPI_Assert(false && "OAK backend only supports Streaming mode for now"); + } + return EPtr{new cv::gimpl::GOAKExecutable(graph, args, nodes, ins_data, outs_data)}; + } + + virtual cv::GKernelPackage auxiliaryKernels() const override { + return cv::gapi::kernels< cv::gimpl::oak::GOAKInfer + >(); + } +}; + +cv::gapi::GBackend cv::gapi::oak::backend() { + static cv::gapi::GBackend this_backend(std::make_shared()); + return this_backend; +} + +namespace cv { +namespace gapi { +namespace oak { + +cv::gapi::GKernelPackage kernels() { + return cv::gapi::kernels< cv::gimpl::oak::GOAKEncFrame + , cv::gimpl::oak::GOAKSobelXY + , cv::gimpl::oak::GOAKCopy + >(); +} + +} // namespace oak +} // namespace gapi +} // namespace cv + +#else + +namespace cv { +namespace gapi { +namespace oak { + +cv::gapi::GKernelPackage kernels() { + GAPI_Assert(false && "Built without OAK support"); + return {}; +} + +cv::gapi::GBackend backend() { + GAPI_Assert(false && "Built without OAK support"); + static cv::gapi::GBackend this_backend(nullptr); + return this_backend; +} + +} // namespace oak +} // namespace gapi +} // namespace cv + +#endif // HAVE_OAK diff --git a/modules/gapi/src/backends/oak/oak_memory_adapters.hpp b/modules/gapi/src/backends/oak/oak_memory_adapters.hpp new file mode 100644 index 000000000000..69677978d41c --- /dev/null +++ b/modules/gapi/src/backends/oak/oak_memory_adapters.hpp @@ -0,0 +1,57 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#ifndef OPENCV_GAPI_OAK_MEDIA_ADAPTER_HPP +#define OPENCV_GAPI_OAK_MEDIA_ADAPTER_HPP + +#include + +#include +#include + +namespace cv { +namespace gapi { +namespace oak { + +// Used for OAK backends outputs only. +// Filled from DepthAI's ImgFrame type and owns the memory. +// Used mainly for CV operations. +class GAPI_EXPORTS OAKMediaAdapter final : public cv::MediaFrame::IAdapter { +public: + OAKMediaAdapter() = default; + OAKMediaAdapter(cv::Size sz, cv::MediaFormat fmt, std::vector&& buffer); + cv::GFrameDesc meta() const override; + cv::MediaFrame::View access(cv::MediaFrame::Access) override; + ~OAKMediaAdapter() = default; +private: + cv::Size m_sz; + cv::MediaFormat m_fmt; + std::vector m_buffer; +}; + +// Used for OAK backends outputs only. +// Filled from DepthAI's NNData type and owns the memory. +// Used only for infer operations. +class GAPI_EXPORTS OAKRMatAdapter final : public cv::RMat::Adapter { +public: + OAKRMatAdapter() = default; + OAKRMatAdapter(const cv::Size& size, int precision, std::vector&& buffer); + cv::GMatDesc desc() const override; + cv::RMat::View access(cv::RMat::Access) override; + ~OAKRMatAdapter() = default; +private: + cv::Size m_size; + int m_precision; + std::vector m_buffer; + cv::GMatDesc m_desc; + cv::Mat m_mat; +}; + +} // namespace oak +} // namespace gapi +} // namespace cv + +#endif // OPENCV_GAPI_OAK_MEDIA_ADAPTER_HPP diff --git a/modules/gapi/src/backends/ocl/goclcore.cpp b/modules/gapi/src/backends/ocl/goclcore.cpp index d74d521953fc..f3c5aa32bc38 100644 --- a/modules/gapi/src/backends/ocl/goclcore.cpp +++ b/modules/gapi/src/backends/ocl/goclcore.cpp @@ -458,14 +458,6 @@ GAPI_OCL_KERNEL(GOCLMerge4, cv::gapi::core::GMerge4) } }; -GAPI_OCL_KERNEL(GOCLResize, cv::gapi::core::GResize) -{ - static void run(const cv::UMat& in, cv::Size sz, double fx, double fy, int interp, cv::UMat &out) - { - cv::resize(in, out, sz, fx, fy, interp); - } -}; - GAPI_OCL_KERNEL(GOCLRemap, cv::gapi::core::GRemap) { static void run(const cv::UMat& in, const cv::Mat& x, const cv::Mat& y, int a, int b, cv::Scalar s, cv::UMat& out) @@ -531,7 +523,7 @@ GAPI_OCL_KERNEL(GOCLTranspose, cv::gapi::core::GTranspose) } }; -cv::gapi::GKernelPackage cv::gapi::core::ocl::kernels() +cv::GKernelPackage cv::gapi::core::ocl::kernels() { static auto pkg = cv::gapi::kernels < GOCLAdd @@ -585,7 +577,6 @@ cv::gapi::GKernelPackage cv::gapi::core::ocl::kernels() , GOCLInRange , GOCLSplit3 , GOCLSplit4 - , GOCLResize , GOCLMerge3 , GOCLMerge4 , GOCLRemap diff --git a/modules/gapi/src/backends/ocl/goclimgproc.cpp b/modules/gapi/src/backends/ocl/goclimgproc.cpp index 07069ae83e90..72650aa3ef03 100644 --- a/modules/gapi/src/backends/ocl/goclimgproc.cpp +++ b/modules/gapi/src/backends/ocl/goclimgproc.cpp @@ -11,6 +11,13 @@ #include #include "backends/ocl/goclimgproc.hpp" +GAPI_OCL_KERNEL(GOCLResize, cv::gapi::imgproc::GResize) +{ + static void run(const cv::UMat& in, cv::Size sz, double fx, double fy, int interp, cv::UMat &out) + { + cv::resize(in, out, sz, fx, fy, interp); + } +}; GAPI_OCL_KERNEL(GOCLSepFilter, cv::gapi::imgproc::GSepFilter) { @@ -266,10 +273,11 @@ GAPI_OCL_KERNEL(GOCLRGB2GrayCustom, cv::gapi::imgproc::GRGB2GrayCustom) }; -cv::gapi::GKernelPackage cv::gapi::imgproc::ocl::kernels() +cv::GKernelPackage cv::gapi::imgproc::ocl::kernels() { static auto pkg = cv::gapi::kernels < GOCLFilter2D + , GOCLResize , GOCLSepFilter , GOCLBoxFilter , GOCLBlur diff --git a/modules/gapi/src/backends/onnx/gonnxbackend.cpp b/modules/gapi/src/backends/onnx/gonnxbackend.cpp index 07a42a855a8c..af1f7f894829 100644 --- a/modules/gapi/src/backends/onnx/gonnxbackend.cpp +++ b/modules/gapi/src/backends/onnx/gonnxbackend.cpp @@ -1102,7 +1102,7 @@ struct InferList2: public cv::detail::KernelTag { } else { GAPI_Assert(false && "Only Rect and Mat types are supported for infer list 2!"); } - // }}} (Preapre input) + // }}} (Prepare input) } // }}} (For every input of the net) std::vector out_mats(uu.oc->numOutputs()); @@ -1147,7 +1147,7 @@ namespace { return EPtr{new cv::gimpl::onnx::GONNXExecutable(graph, nodes)}; } - virtual cv::gapi::GKernelPackage auxiliaryKernels() const override { + virtual cv::GKernelPackage auxiliaryKernels() const override { return cv::gapi::kernels< cv::gimpl::onnx::Infer , cv::gimpl::onnx::InferROI , cv::gimpl::onnx::InferList diff --git a/modules/gapi/src/backends/plaidml/gplaidmlcore.cpp b/modules/gapi/src/backends/plaidml/gplaidmlcore.cpp index c15f84cd1f1a..60f732ff73c1 100644 --- a/modules/gapi/src/backends/plaidml/gplaidmlcore.cpp +++ b/modules/gapi/src/backends/plaidml/gplaidmlcore.cpp @@ -47,7 +47,7 @@ GAPI_PLAIDML_LOGICAL_OP(GPlaidMLOr , cv::gapi::core::GOr , |) GAPI_PLAIDML_ARITHMETIC_OP(GPlaidMLAdd, cv::gapi::core::GAdd, +); GAPI_PLAIDML_ARITHMETIC_OP(GPlaidMLSub, cv::gapi::core::GSub, -); -cv::gapi::GKernelPackage cv::gapi::core::plaidml::kernels() +cv::GKernelPackage cv::gapi::core::plaidml::kernels() { static auto pkg = cv::gapi::kernels(); return pkg; @@ -55,7 +55,7 @@ cv::gapi::GKernelPackage cv::gapi::core::plaidml::kernels() #else // HAVE_PLAIDML -cv::gapi::GKernelPackage cv::gapi::core::plaidml::kernels() +cv::GKernelPackage cv::gapi::core::plaidml::kernels() { // Still provide this symbol to avoid linking issues util::throw_error(std::runtime_error("G-API has been compiled without PlaidML2 support")); diff --git a/modules/gapi/src/backends/render/ft_render.cpp b/modules/gapi/src/backends/render/ft_render.cpp index fcf84713ff34..3c4ae2077fd2 100644 --- a/modules/gapi/src/backends/render/ft_render.cpp +++ b/modules/gapi/src/backends/render/ft_render.cpp @@ -80,7 +80,7 @@ cv::Size cv::gapi::wip::draw::FTTextRender::Priv::getTextSize(const std::wstring // See (1) on picture. // // 4) As we can see the last pen position is isn't horizontal size yet. - // We need to check if the glyph goes beyound the last position of the pen + // We need to check if the glyph goes beyond the last position of the pen // To do this we can: // a) Return to the previous position -advance // b) Shift on left value +left diff --git a/modules/gapi/src/backends/render/grenderocv.cpp b/modules/gapi/src/backends/render/grenderocv.cpp index da0e5831a1e9..244bc4eb177f 100644 --- a/modules/gapi/src/backends/render/grenderocv.cpp +++ b/modules/gapi/src/backends/render/grenderocv.cpp @@ -193,7 +193,7 @@ GAPI_OCV_KERNEL_ST(RenderFrameOCVImpl, cv::gapi::wip::draw::GRenderFrame, Render }; -cv::gapi::GKernelPackage cv::gapi::render::ocv::kernels() +cv::GKernelPackage cv::gapi::render::ocv::kernels() { const static auto pkg = cv::gapi::kernels(); return pkg; diff --git a/modules/gapi/src/backends/streaming/gstreamingbackend.cpp b/modules/gapi/src/backends/streaming/gstreamingbackend.cpp index 8dc3dfe73fab..69b5f6c72b37 100644 --- a/modules/gapi/src/backends/streaming/gstreamingbackend.cpp +++ b/modules/gapi/src/backends/streaming/gstreamingbackend.cpp @@ -172,6 +172,7 @@ void Copy::Actor::run(cv::gimpl::GIslandExecutable::IInput &in, return; } + GAPI_DbgAssert(cv::util::holds_alternative(in_msg)); const cv::GRunArgs &in_args = cv::util::get(in_msg); GAPI_Assert(in_args.size() == 1u); @@ -193,7 +194,7 @@ void Copy::Actor::run(cv::gimpl::GIslandExecutable::IInput &in, out.post(std::move(out_arg)); } -cv::gapi::GKernelPackage cv::gimpl::streaming::kernels() +cv::GKernelPackage cv::gimpl::streaming::kernels() { return cv::gapi::kernels(); } @@ -212,6 +213,7 @@ class GAccessorActorBase : public cv::gapi::streaming::IActor { return; } + GAPI_Assert(cv::util::holds_alternative(in_msg)); const cv::GRunArgs &in_args = cv::util::get(in_msg); GAPI_Assert(in_args.size() == 1u); auto frame = cv::util::get(in_args[0]); @@ -282,6 +284,23 @@ void GOCVBGR::Actor::extractRMat(const cv::MediaFrame& frame, cv::RMat& rmat) rmat = cv::make_rmat(bgr); break; } + case cv::MediaFormat::GRAY: + { + std::call_once(m_warnFlag, + []() { + GAPI_LOG_WARNING(NULL, "\nOn-the-fly conversion from GRAY to BGR will happen.\n" + "Conversion may cost a lot for images with high resolution.\n" + "To retrieve cv::Mat from GRAY cv::MediaFrame for free, you may use " + "cv::gapi::streaming::Y.\n"); + }); + cv::Mat bgr; + auto view = frame.access(cv::MediaFrame::Access::R); + cv::Mat gray(desc.size, CV_8UC1, view.ptr[0], view.stride[0]); + cv::cvtColor(gray, bgr, cv::COLOR_GRAY2BGR); + rmat = cv::make_rmat(bgr); + break; + } + default: cv::util::throw_error( std::logic_error("Unsupported MediaFormat for cv::gapi::streaming::BGR")); @@ -339,6 +358,15 @@ void GOCVY::Actor::extractRMat(const cv::MediaFrame& frame, cv::RMat& rmat) }); break; } + case cv::MediaFormat::GRAY: + { + rmat = cv::make_rmat(frame, + [](const cv::GFrameDesc& d) { return cv::GMatDesc(CV_8U, 1, d.size); }, + [](const cv::GFrameDesc& d, const cv::MediaFrame::View& v) { + return cv::Mat(d.size, CV_8UC1, v.ptr[0], v.stride[0]); + }); + break; + } default: cv::util::throw_error( std::logic_error("Unsupported MediaFormat for cv::gapi::streaming::Y")); @@ -408,20 +436,26 @@ void GOCVUV::Actor::extractRMat(const cv::MediaFrame& frame, cv::RMat& rmat) }); break; } + case cv::MediaFormat::GRAY: + { + cv::Mat uv(desc.size / 2, CV_8UC2, cv::Scalar::all(127)); + rmat = cv::make_rmat(uv); + break; + } default: cv::util::throw_error( std::logic_error("Unsupported MediaFormat for cv::gapi::streaming::UV")); } } -cv::gapi::GKernelPackage cv::gapi::streaming::kernels() +cv::GKernelPackage cv::gapi::streaming::kernels() { return cv::gapi::kernels(); } #else -cv::gapi::GKernelPackage cv::gapi::streaming::kernels() +cv::GKernelPackage cv::gapi::streaming::kernels() { // Still provide this symbol to avoid linking issues util::throw_error(std::runtime_error("cv::gapi::streaming::kernels() isn't supported in standalone")); diff --git a/modules/gapi/src/backends/streaming/gstreamingbackend.hpp b/modules/gapi/src/backends/streaming/gstreamingbackend.hpp index 27b5443bb21e..2448f5199518 100644 --- a/modules/gapi/src/backends/streaming/gstreamingbackend.hpp +++ b/modules/gapi/src/backends/streaming/gstreamingbackend.hpp @@ -15,7 +15,7 @@ namespace cv { namespace gimpl { namespace streaming { -cv::gapi::GKernelPackage kernels(); +cv::GKernelPackage kernels(); struct GCopy final : public cv::detail::NoTag { diff --git a/modules/gapi/src/compiler/gcompiler.cpp b/modules/gapi/src/compiler/gcompiler.cpp index 7dd3c80cdfbe..bcf91f7dcd6e 100644 --- a/modules/gapi/src/compiler/gcompiler.cpp +++ b/modules/gapi/src/compiler/gcompiler.cpp @@ -53,18 +53,18 @@ namespace { - cv::gapi::GKernelPackage getKernelPackage(cv::GCompileArgs &args) + cv::GKernelPackage getKernelPackage(cv::GCompileArgs &args) { - auto withAuxKernels = [](const cv::gapi::GKernelPackage& pkg) { - cv::gapi::GKernelPackage aux_pkg; + auto withAuxKernels = [](const cv::GKernelPackage& pkg) { + cv::GKernelPackage aux_pkg; for (const auto &b : pkg.backends()) { - aux_pkg = combine(aux_pkg, b.priv().auxiliaryKernels()); + aux_pkg = cv::gapi::combine(aux_pkg, b.priv().auxiliaryKernels()); } // Always include built-in meta<> and copy implementation - return combine(pkg, - aux_pkg, - cv::gimpl::meta::kernels(), - cv::gimpl::streaming::kernels()); + return cv::gapi::combine(pkg, + aux_pkg, + cv::gimpl::meta::kernels(), + cv::gimpl::streaming::kernels()); }; auto has_use_only = cv::gapi::getCompileArg(args); @@ -73,18 +73,18 @@ namespace static auto ocv_pkg = #if !defined(GAPI_STANDALONE) - combine(cv::gapi::core::cpu::kernels(), - cv::gapi::imgproc::cpu::kernels(), - cv::gapi::video::cpu::kernels(), - cv::gapi::render::ocv::kernels(), - cv::gapi::streaming::kernels()); + cv::gapi::combine(cv::gapi::core::cpu::kernels(), + cv::gapi::imgproc::cpu::kernels(), + cv::gapi::video::cpu::kernels(), + cv::gapi::render::ocv::kernels(), + cv::gapi::streaming::kernels()); #else - cv::gapi::GKernelPackage(); + cv::GKernelPackage(); #endif // !defined(GAPI_STANDALONE) - auto user_pkg = cv::gapi::getCompileArg(args); - auto user_pkg_with_aux = withAuxKernels(user_pkg.value_or(cv::gapi::GKernelPackage{})); - return combine(ocv_pkg, user_pkg_with_aux); + auto user_pkg = cv::gapi::getCompileArg(args); + auto user_pkg_with_aux = withAuxKernels(user_pkg.value_or(cv::GKernelPackage{})); + return cv::gapi::combine(ocv_pkg, user_pkg_with_aux); } cv::gapi::GNetPackage getNetworkPackage(cv::GCompileArgs &args) @@ -110,8 +110,8 @@ namespace } template - cv::gapi::GKernelPackage auxKernelsFrom(const C& c) { - cv::gapi::GKernelPackage result; + cv::GKernelPackage auxKernelsFrom(const C& c) { + cv::GKernelPackage result; for (const auto &b : c) { result = cv::gapi::combine(result, b.priv().auxiliaryKernels()); } @@ -121,7 +121,7 @@ namespace using adeGraphs = std::vector>; // Creates ADE graphs (patterns and substitutes) from pkg's transformations - void makeTransformationGraphs(const cv::gapi::GKernelPackage& pkg, + void makeTransformationGraphs(const cv::GKernelPackage& pkg, adeGraphs& patterns, adeGraphs& substitutes) { const auto& transforms = pkg.get_transformations(); @@ -142,7 +142,7 @@ namespace } } - void checkTransformations(const cv::gapi::GKernelPackage& pkg, + void checkTransformations(const cv::GKernelPackage& pkg, const adeGraphs& patterns, const adeGraphs& substitutes) { const auto& transforms = pkg.get_transformations(); diff --git a/modules/gapi/src/compiler/gcompiler.hpp b/modules/gapi/src/compiler/gcompiler.hpp index 2712c7939411..b8cff23216ca 100644 --- a/modules/gapi/src/compiler/gcompiler.hpp +++ b/modules/gapi/src/compiler/gcompiler.hpp @@ -26,7 +26,7 @@ class GAPI_EXPORTS GCompiler GCompileArgs m_args; ade::ExecutionEngine m_e; - cv::gapi::GKernelPackage m_all_kernels; + cv::GKernelPackage m_all_kernels; cv::gapi::GNetPackage m_all_networks; // Patterns built from transformations diff --git a/modules/gapi/src/compiler/gislandmodel.cpp b/modules/gapi/src/compiler/gislandmodel.cpp index 1a8e0939e2ed..0567a90e3a23 100644 --- a/modules/gapi/src/compiler/gislandmodel.cpp +++ b/modules/gapi/src/compiler/gislandmodel.cpp @@ -346,9 +346,9 @@ std::string GIslandModel::traceIslandName(const ade::NodeHandle& island_nh, cons auto& backend_impl = island_ptr->backend().priv(); std::string backend_impl_type_name = typeid(backend_impl).name(); - // NOTE: Major part of already existing backends implementaion classes are called using + // NOTE: Major part of already existing backends implementation classes are called using // "*G[Name]BackendImpl*" pattern. - // We are trying to match against this pattern and retrive just [Name] part. + // We are trying to match against this pattern and retrieve just [Name] part. // If matching isn't successful, full mangled class name will be used. // // To match we use following algorithm: @@ -412,7 +412,17 @@ void GIslandExecutable::run(GIslandExecutable::IInput &in, GIslandExecutable::IO out_objs.emplace_back(ade::util::value(it), out.get(ade::util::checked_cast(ade::util::index(it)))); } - run(std::move(in_objs), std::move(out_objs)); + + try { + run(std::move(in_objs), std::move(out_objs)); + } catch (...) { + auto eptr = std::current_exception(); + for (auto &&it: out_objs) + { + out.post(std::move(it.second), eptr); + } + return; + } // Propagate in-graph meta down to the graph // Note: this is not a complete implementation! Mainly this is a stub diff --git a/modules/gapi/src/compiler/gislandmodel.hpp b/modules/gapi/src/compiler/gislandmodel.hpp index 063504a92227..565b3c4f2141 100644 --- a/modules/gapi/src/compiler/gislandmodel.hpp +++ b/modules/gapi/src/compiler/gislandmodel.hpp @@ -161,7 +161,12 @@ class GIslandExecutable::IODesc { const std::vector &desc() const { return d; } }; struct EndOfStream {}; -using StreamMsg = cv::util::variant; + +struct Exception { + std::exception_ptr eptr; +}; + +using StreamMsg = cv::util::variant; struct GIslandExecutable::IInput: public GIslandExecutable::IODesc { virtual ~IInput() = default; virtual StreamMsg get() = 0; // Get a new input vector (blocking) @@ -169,9 +174,11 @@ struct GIslandExecutable::IInput: public GIslandExecutable::IODesc { }; struct GIslandExecutable::IOutput: public GIslandExecutable::IODesc { virtual ~IOutput() = default; - virtual GRunArgP get(int idx) = 0; // Allocate (wrap) a new data object for output idx - virtual void post(GRunArgP&&) = 0; // Release the object back to the framework (mark available) - virtual void post(EndOfStream&&) = 0; // Post end-of-stream marker back to the framework + virtual GRunArgP get(int idx) = 0; // Allocate (wrap) a new data object for output idx + virtual void post(GRunArgP&&, const std::exception_ptr& = {}) = 0; // Release the object back to the framework (mark available) + virtual void post(EndOfStream&&) = 0; // Post end-of-stream marker back to the framework + virtual void post(Exception&&) = 0; + // Assign accumulated metadata to the given output object. // This method can only be called after get() and before post(). diff --git a/modules/gapi/src/compiler/gmodel.hpp b/modules/gapi/src/compiler/gmodel.hpp index d016766fb507..592678dae7fd 100644 --- a/modules/gapi/src/compiler/gmodel.hpp +++ b/modules/gapi/src/compiler/gmodel.hpp @@ -70,7 +70,7 @@ struct Data // FIXME: This is a _pure_ duplication of RcDesc now! (except storage) GShape shape; // FIXME: Probably to be replaced by GMetaArg? - int rc; + int rc; // rc is unique but local to shape GMetaArg meta; HostCtor ctor; // T-specific helper to deal with unknown types in our code cv::detail::OpaqueKind kind; // FIXME: is needed to store GArray/GOpaque type diff --git a/modules/gapi/src/compiler/passes/exec.cpp b/modules/gapi/src/compiler/passes/exec.cpp index f6a73489eb39..93d833d6024d 100644 --- a/modules/gapi/src/compiler/passes/exec.cpp +++ b/modules/gapi/src/compiler/passes/exec.cpp @@ -606,7 +606,9 @@ namespace MergeContext mc; bool there_was_a_merge = false; +#ifdef DEBUG_MERGE std::size_t iteration = 0u; +#endif do { there_was_a_merge = false; @@ -615,8 +617,8 @@ namespace #ifdef DEBUG_MERGE GAPI_LOG_INFO(NULL, "Before next merge attempt " << iteration << "..."); merge_debug(g, iteration); -#endif iteration++; +#endif auto sorted = pass_helpers::topoSort(im); for (auto nh : sorted) { diff --git a/modules/gapi/src/compiler/passes/intrin.cpp b/modules/gapi/src/compiler/passes/intrin.cpp index 56f2db69e036..8920be6d4e20 100644 --- a/modules/gapi/src/compiler/passes/intrin.cpp +++ b/modules/gapi/src/compiler/passes/intrin.cpp @@ -254,7 +254,7 @@ void apply(cv::gimpl::GModel::Graph &g) { } // Probably the simplest case: desync makes no sense in the regular -// compilation process, so just drop all its occurences in the graph, +// compilation process, so just drop all its occurrences in the graph, // reconnecting nodes properly. void drop(cv::gimpl::GModel::Graph &g) { // FIXME: LOG here that we're dropping the desync operations as diff --git a/modules/gapi/src/compiler/passes/kernels.cpp b/modules/gapi/src/compiler/passes/kernels.cpp index 4298b2e527c2..22b791650f65 100644 --- a/modules/gapi/src/compiler/passes/kernels.cpp +++ b/modules/gapi/src/compiler/passes/kernels.cpp @@ -157,7 +157,7 @@ void cv::gimpl::passes::bindNetParams(ade::passes::PassContext &ctx, // kernels, but if not, they are handled by the framework itself in // its optimization/execution passes. void cv::gimpl::passes::resolveKernels(ade::passes::PassContext &ctx, - const gapi::GKernelPackage &kernels) + const GKernelPackage &kernels) { std::unordered_set active_backends; @@ -212,7 +212,7 @@ void cv::gimpl::passes::resolveKernels(ade::passes::PassContext &ctx, GAPI_Assert(op.k.outMeta == nullptr); const_cast(op.k.outMeta) = selected_impl.outMeta; } else { - // Sanity check: the metadata funciton must be present + // Sanity check: the metadata function must be present GAPI_Assert(op.k.outMeta != nullptr); } } @@ -220,7 +220,7 @@ void cv::gimpl::passes::resolveKernels(ade::passes::PassContext &ctx, gr.metadata().set(ActiveBackends{active_backends}); } -void cv::gimpl::passes::expandKernels(ade::passes::PassContext &ctx, const gapi::GKernelPackage &kernels) +void cv::gimpl::passes::expandKernels(ade::passes::PassContext &ctx, const GKernelPackage &kernels) { GModel::Graph gr(ctx.graph); diff --git a/modules/gapi/src/compiler/passes/passes.hpp b/modules/gapi/src/compiler/passes/passes.hpp index 8f187f6bb75d..291b782b7116 100644 --- a/modules/gapi/src/compiler/passes/passes.hpp +++ b/modules/gapi/src/compiler/passes/passes.hpp @@ -23,11 +23,11 @@ namespace ade { } } +// Forward declarations - internal namespace cv { + class GKernelPackage; -// Forward declarations - internal namespace gapi { - class GKernelPackage; struct GNetPackage; } // namespace gapi @@ -52,20 +52,20 @@ void inferMeta(ade::passes::PassContext &ctx, bool meta_is_initialized); void storeResultingMeta(ade::passes::PassContext &ctx); void expandKernels(ade::passes::PassContext &ctx, - const gapi::GKernelPackage& kernels); + const GKernelPackage& kernels); void bindNetParams(ade::passes::PassContext &ctx, const gapi::GNetPackage &networks); void resolveKernels(ade::passes::PassContext &ctx, - const gapi::GKernelPackage &kernels); + const GKernelPackage &kernels); void fuseIslands(ade::passes::PassContext &ctx); void syncIslandTags(ade::passes::PassContext &ctx); void topoSortIslands(ade::passes::PassContext &ctx); void applyTransformations(ade::passes::PassContext &ctx, - const gapi::GKernelPackage &pkg, + const GKernelPackage &pkg, const std::vector> &preGeneratedPatterns); void addStreaming(ade::passes::PassContext &ctx); diff --git a/modules/gapi/src/compiler/passes/transformations.cpp b/modules/gapi/src/compiler/passes/transformations.cpp index 62407fe5a8ee..f61a8b2158b4 100644 --- a/modules/gapi/src/compiler/passes/transformations.cpp +++ b/modules/gapi/src/compiler/passes/transformations.cpp @@ -99,7 +99,7 @@ bool tryToSubstitute(ade::Graph& main, } // anonymous namespace void applyTransformations(ade::passes::PassContext& ctx, - const gapi::GKernelPackage& pkg, + const GKernelPackage& pkg, const std::vector>& patterns) { const auto& transforms = pkg.get_transformations(); diff --git a/modules/gapi/src/executor/gexecutor.cpp b/modules/gapi/src/executor/gexecutor.cpp index 6c15d1dfc971..472abaaa149d 100644 --- a/modules/gapi/src/executor/gexecutor.cpp +++ b/modules/gapi/src/executor/gexecutor.cpp @@ -30,10 +30,11 @@ cv::gimpl::GExecutor::GExecutor(std::unique_ptr &&g_model) // 1. Allocate all internal resources first (NB - CPU plugin doesn't do it) // 2. Put input/output GComputation arguments to the storage // 3. For every Island, prepare vectors of input/output parameter descs - // 4. Iterate over a list of operations (sorted in the topological order) - // 5. For every operation, form a list of input/output data objects - // 6. Run GIslandExecutable - // 7. writeBack + // 4. Ask every GIslandExecutable to prepare its internal states for a new stream + // 5. Iterate over a list of operations (sorted in the topological order) + // 6. For every operation, form a list of input/output data objects + // 7. Run GIslandExecutable + // 8. writeBack auto sorted = m_gim.metadata().get(); for (auto nh : sorted.nodes()) @@ -82,6 +83,9 @@ cv::gimpl::GExecutor::GExecutor(std::unique_ptr &&g_model) break; } // switch(kind) } // for(gim nodes) + + // (4) + prepareForNewStream(); } namespace cv { @@ -149,7 +153,7 @@ void writeBackExec(const Mag& mag, const RcDesc &rc, GRunArgP &g_arg) { case GRunArgP::index_of() : { // If there is a copy intrinsic at the end of the graph - // we need to actualy copy the data to the user buffer + // we need to actually copy the data to the user buffer // since output runarg was optimized to simply point // to the input of the copy kernel // FIXME: @@ -270,6 +274,7 @@ class cv::gimpl::GExecutor::Output final: public cv::gimpl::GIslandExecutable::I { cv::gimpl::Mag &mag; std::unordered_map out_idx; + std::exception_ptr eptr; GRunArgP get(int idx) override { @@ -278,8 +283,18 @@ class cv::gimpl::GExecutor::Output final: public cv::gimpl::GIslandExecutable::I out_idx[cv::gimpl::proto::ptr(r)] = idx; return r; } - void post(GRunArgP&&) override { } // Do nothing here + void post(GRunArgP&&, const std::exception_ptr& e) override + { + if (e) + { + eptr = e; + } + } void post(EndOfStream&&) override {} // Do nothing here too + void post(Exception&& ex) override + { + eptr = std::move(ex.eptr); + } void meta(const GRunArgP &out, const GRunArg::Meta &m) override { const auto idx = out_idx.at(cv::gimpl::proto::ptr(out)); @@ -291,6 +306,14 @@ class cv::gimpl::GExecutor::Output final: public cv::gimpl::GIslandExecutable::I { set(rcs); } + + void verify() + { + if (eptr) + { + std::rethrow_exception(eptr); + } + } }; void cv::gimpl::GExecutor::run(cv::gimpl::GRuntimeArgs &&args) @@ -382,16 +405,18 @@ void cv::gimpl::GExecutor::run(cv::gimpl::GRuntimeArgs &&args) magazine::resetInternalData(m_res, data); } - // Run the script + // Run the script (5) for (auto &op : m_ops) { - // (5), (6) + // (6), (7) Input i{m_res, op.in_objects}; Output o{m_res, op.out_objects}; op.isl_exec->run(i, o); + // NB: Check if execution finished without exception. + o.verify(); } - // (7) + // (8) for (auto it : ade::util::zip(ade::util::toRange(proto.outputs), ade::util::toRange(args.outObjs))) { diff --git a/modules/gapi/src/executor/gstreamingexecutor.cpp b/modules/gapi/src/executor/gstreamingexecutor.cpp index d15e17ea2839..557e5ceee49d 100644 --- a/modules/gapi/src/executor/gstreamingexecutor.cpp +++ b/modules/gapi/src/executor/gstreamingexecutor.cpp @@ -31,6 +31,8 @@ #include #include +#include + namespace { using namespace cv::gimpl::stream; @@ -310,29 +312,52 @@ class QueueReader const std::size_t this_id); public: - bool getInputVector (std::vector &in_queues, - cv::GRunArgs &in_constants, - cv::GRunArgs &isl_inputs); - - bool getResultsVector(std::vector &in_queues, - const std::vector &in_mapping, - const std::size_t out_size, - cv::GRunArgs &out_results); + cv::gimpl::StreamMsg getInputVector (std::vector &in_queues, + cv::GRunArgs &in_constants); + + using V = cv::util::variant; + V getResultsVector(std::vector &in_queues, + const std::vector &in_mapping, + const std::size_t out_size); }; void rewindToStop(std::vector &in_queues, const std::size_t this_id) { - for (auto &&qit : ade::util::indexed(in_queues)) - { - auto id2 = ade::util::index(qit); - auto &q2 = ade::util::value(qit); - if (this_id == id2) continue; + size_t expected_stop_count = std::count_if(in_queues.begin(), in_queues.end(), [] (const Q* ptr) { + return ptr != nullptr; + }); - Cmd cmd; - while (q2 && !cv::util::holds_alternative(cmd)) - q2->pop(cmd); + if (expected_stop_count > 0) { + // NB: it requires to substract own queues id from total waiting queue count + // because it had got stop message before rewind was called + expected_stop_count--; } + GAPI_LOG_DEBUG(nullptr, "id: " << this_id << ", queues count: " << in_queues.size() << + ", expected stop msg count: " << expected_stop_count); + size_t got_stop_count = 0; + while(got_stop_count < expected_stop_count) { + for (auto &&qit : ade::util::indexed(in_queues)) { + auto id2 = ade::util::index(qit); + auto &q2 = ade::util::value(qit); + if (this_id == id2) continue; + + GAPI_LOG_DEBUG(nullptr, "drain next id: " << id2 << + ", stop count (" << got_stop_count << "/" << + expected_stop_count << ")"); + bool got_cmd = true; + while (q2 && got_cmd) { + Cmd cmd; + got_cmd = q2->try_pop(cmd); + if (got_cmd && cv::util::holds_alternative(cmd)) { + got_stop_count ++; + GAPI_LOG_DEBUG(nullptr, "got stop from id: " << id2); + break; + } + } + } + } + GAPI_LOG_DEBUG(nullptr, "completed"); } // This method handles a stop sign got from some input @@ -345,9 +370,8 @@ void QueueReader::rewindToStop(std::vector &in_queues, ::rewindToStop(in_queues, this_id); } -bool QueueReader::getInputVector(std::vector &in_queues, - cv::GRunArgs &in_constants, - cv::GRunArgs &isl_inputs) +cv::gimpl::StreamMsg QueueReader::getInputVector(std::vector &in_queues, + cv::GRunArgs &in_constants) { // NB: Need to release resources from the previous step, to fetch new ones. // On some systems it might be impossible to allocate new memory @@ -357,72 +381,98 @@ bool QueueReader::getInputVector(std::vector &in_queues, // lifetime, keep the whole cmd vector (of size == # of inputs) // in memory. m_cmd.resize(in_queues.size()); - isl_inputs.resize(in_queues.size()); + cv::GRunArgs isl_inputs(in_queues.size()); + cv::optional exception; for (auto &&it : ade::util::indexed(in_queues)) { - auto id = ade::util::index(it); - auto &q = ade::util::value(it); - - if (q == nullptr) - { - GAPI_Assert(!in_constants.empty()); - // NULL queue means a graph-constant value (like a - // value-initialized scalar) - // It can also hold a constant value received with - // Stop::Kind::CNST message (see above). - isl_inputs[id] = in_constants[id]; - continue; - } - - q->pop(m_cmd[id]); - if (!cv::util::holds_alternative(m_cmd[id])) - { - isl_inputs[id] = cv::util::get(m_cmd[id]); - } - else // A Stop sign - { - const auto &stop = cv::util::get(m_cmd[id]); - if (stop.kind == Stop::Kind::CNST) - { - // We've got a Stop signal from a const source, - // propagated as a result of real stream reaching its - // end. Sometimes these signals come earlier than - // real EOS Stops so are deprioritized -- just - // remember the Const value here and continue - // processing other queues. Set queue pointer to - // nullptr and update the const_val vector - // appropriately - m_finishing = true; - in_queues[id] = nullptr; - in_constants.resize(in_queues.size()); - in_constants[id] = std::move(stop.cdata); - - // NEXT time (on a next call to getInputVector()), the - // "q==nullptr" check above will be triggered, but now - // we need to make it manually: - isl_inputs[id] = in_constants[id]; - } - else - { - GAPI_Assert(stop.kind == Stop::Kind::HARD); - rewindToStop(in_queues, id); - // After queues are read to the proper indicator, - // indicate end-of-stream - return false; - } // if(Cnst) - } // if(Stop) + auto id = ade::util::index(it); + auto &q = ade::util::value(it); + + if (q == nullptr) + { + GAPI_Assert(!in_constants.empty()); + // NULL queue means a graph-constant value (like a + // value-initialized scalar) + // It can also hold a constant value received with + // Stop::Kind::CNST message (see above). + isl_inputs[id] = in_constants[id]; + continue; + } + + q->pop(m_cmd[id]); + switch (m_cmd[id].index()) + { + case Cmd::index_of(): + isl_inputs[id] = cv::util::get(m_cmd[id]); + break; + case Cmd::index_of(): + { + const auto &stop = cv::util::get(m_cmd[id]); + if (stop.kind == Stop::Kind::CNST) + { + // We've got a Stop signal from a const source, + // propagated as a result of real stream reaching its + // end. Sometimes these signals come earlier than + // real EOS Stops so are deprioritized -- just + // remember the Const value here and continue + // processing other queues. Set queue pointer to + // nullptr and update the const_val vector + // appropriately + m_finishing = true; + in_queues[id] = nullptr; + in_constants.resize(in_queues.size()); + in_constants[id] = std::move(stop.cdata); + + // NEXT time (on a next call to getInputVector()), the + // "q==nullptr" check above will be triggered, but now + // we need to make it manually: + isl_inputs[id] = in_constants[id]; + } + else + { + GAPI_Assert(stop.kind == Stop::Kind::HARD); + rewindToStop(in_queues, id); + // After queues are read to the proper indicator, + // indicate end-of-stream + return cv::gimpl::StreamMsg{cv::gimpl::EndOfStream{}}; + } // if(Cnst) + break; + } + case Cmd::index_of(): + { + exception = + cv::util::make_optional(cv::util::get(m_cmd[id])); + break; + } + default: + GAPI_Assert(false && "Unsupported cmd type in getInputVector()"); + } } // for(in_queues) + if (exception.has_value()) { + return cv::gimpl::StreamMsg{exception.value()}; + } + if (m_finishing) { // If the process is about to end (a soft Stop was received // already) and an island has no other inputs than constant // inputs, its queues may all become nullptrs. Indicate it as // "no data". - return !ade::util::all_of(in_queues, [](Q *ptr){return ptr == nullptr;}); + if (ade::util::all_of(in_queues, [](Q *ptr){return ptr == nullptr;})) { + return cv::gimpl::StreamMsg{cv::gimpl::EndOfStream{}}; + } + } + // A regular case - there is data to process + for (auto& arg : isl_inputs) { + if (arg.index() == cv::GRunArg::index_of()) { + arg = cv::GRunArg{ cv::make_rmat(cv::util::get(arg)) + , arg.meta + }; + } } - return true; // A regular case - there is data to process. + return cv::gimpl::StreamMsg{std::move(isl_inputs)}; } // This is a special method to obtain a result vector @@ -450,33 +500,47 @@ bool QueueReader::getInputVector(std::vector &in_queues, // (_may be_ partially filled) to the same final output queue. // The receiver part at the GStreamingExecutor level won't change // because of that. -bool QueueReader::getResultsVector(std::vector &in_queues, - const std::vector &in_mapping, - const std::size_t out_size, - cv::GRunArgs &out_results) + +QueueReader::V QueueReader::getResultsVector(std::vector &in_queues, + const std::vector &in_mapping, + const std::size_t out_size) { + cv::GRunArgs out_results(out_size); m_cmd.resize(out_size); + cv::optional exception; for (auto &&it : ade::util::indexed(in_queues)) { auto ii = ade::util::index(it); auto oi = in_mapping[ii]; auto &q = ade::util::value(it); q->pop(m_cmd[oi]); - if (!cv::util::holds_alternative(m_cmd[oi])) - { - out_results[oi] = std::move(cv::util::get(m_cmd[oi])); - } - else // A Stop sign - { - // In theory, the CNST should never reach here. - // Collector thread never handles the inputs directly - // (collector's input queues are always produced by - // islands in the graph). - rewindToStop(in_queues, ii); - return false; - } // if(Stop) + + switch (m_cmd[oi].index()) { + case Cmd::index_of(): + out_results[oi] = std::move(cv::util::get(m_cmd[oi])); + break; + case Cmd::index_of(): + // In theory, the CNST should never reach here. + // Collector thread never handles the inputs directly + // (collector's input queues are always produced by + // islands in the graph). + rewindToStop(in_queues, ii); + return QueueReader::V(Stop{}); + case Cmd::index_of(): + exception = + cv::util::make_optional(cv::util::get(m_cmd[oi])); + break; + default: + cv::util::throw_error( + std::logic_error("Unexpected cmd kind in getResultsVector")); + } // switch } // for(in_queues) - return true; + + if (exception.has_value()) { + return QueueReader::V(exception.value()); + } + + return QueueReader::V(out_results); } @@ -497,7 +561,9 @@ void emitterActorThread(std::shared_ptr emitter, || cv::util::holds_alternative(cmd)); if (cv::util::holds_alternative(cmd)) { - for (auto &&oq : out_queues) oq->push(cmd); + for (auto &&oq : out_queues) { + oq->push(cmd); + } return; } @@ -523,10 +589,21 @@ void emitterActorThread(std::shared_ptr emitter, // Try to obtain next data chunk from the source cv::GRunArg data; - const bool result = [&](){ - GAPI_ITT_AUTO_TRACE_GUARD(emitter_pull_hndl); - return emitter->pull(data); - }(); + bool result = false; + try { + result = [&](){ + GAPI_ITT_AUTO_TRACE_GUARD(emitter_pull_hndl); + return emitter->pull(data); + }(); + } catch (...) { + auto eptr = std::current_exception(); + for (auto &&oq : out_queues) + { + oq->push(Cmd{cv::gimpl::Exception{eptr}}); + } + // NB: Go to the next iteration. + continue; + } if (result) { @@ -649,28 +726,8 @@ class StreamingInput final: public cv::gimpl::GIslandExecutable::IInput std::vector &in_queues; // FIXME: This can be part of QueueReader cv::GRunArgs &in_constants; // FIXME: This can be part of QueueReader - virtual cv::gimpl::StreamMsg get() override - { - GAPI_ITT_STATIC_LOCAL_HANDLE(inputs_get_hndl, "StreamingInput::get"); - GAPI_ITT_AUTO_TRACE_GUARD(inputs_get_hndl); - - cv::GRunArgs isl_input_args; + cv::optional last_read_msg; - if (!qr.getInputVector(in_queues, in_constants, isl_input_args)) - { - // Stop case - return cv::gimpl::StreamMsg{cv::gimpl::EndOfStream{}}; - } - // Wrap all input cv::Mats with RMats - for (auto& arg : isl_input_args) { - if (arg.index() == cv::GRunArg::index_of()) { - arg = cv::GRunArg{ cv::make_rmat(cv::util::get(arg)) - , arg.meta - }; - } - } - return cv::gimpl::StreamMsg{std::move(isl_input_args)}; - } virtual cv::gimpl::StreamMsg try_get() override { // FIXME: This is not very usable at the moment! @@ -685,17 +742,43 @@ class StreamingInput final: public cv::gimpl::GIslandExecutable::IInput { set(in_descs); } + + const cv::gimpl::StreamMsg& read() + { + GAPI_ITT_STATIC_LOCAL_HANDLE(inputs_get_hndl, "StreamingInput::read"); + GAPI_ITT_AUTO_TRACE_GUARD(inputs_get_hndl); + + last_read_msg = + cv::optional( + qr.getInputVector(in_queues, in_constants)); + return last_read_msg.value(); + } + + virtual cv::gimpl::StreamMsg get() override + { + GAPI_ITT_STATIC_LOCAL_HANDLE(inputs_get_hndl, "StreamingInput::get"); + GAPI_ITT_AUTO_TRACE_GUARD(inputs_get_hndl); + + if (!last_read_msg.has_value()) { + (void)read(); + } + auto msg = std::move(last_read_msg.value()); + last_read_msg = cv::optional(); + return msg; + } }; class StreamingOutput final: public cv::gimpl::GIslandExecutable::IOutput { // These objects form an internal state of the StreamingOutput struct Posting - { - using V = cv::util::variant; - V data; - bool ready = false; - }; + { + using V = cv::util::variant; + V data; + bool ready = false; + }; using PostingList = std::list; std::vector m_postings; std::unordered_map< const void* @@ -796,7 +879,7 @@ class StreamingOutput final: public cv::gimpl::GIslandExecutable::IOutput return ret_val; } - virtual void post(cv::GRunArgP&& argp) override + virtual void post(cv::GRunArgP&& argp, const std::exception_ptr& exptr) override { GAPI_ITT_STATIC_LOCAL_HANDLE(outputs_post_hndl, "StreamingOutput::post"); GAPI_ITT_AUTO_TRACE_GUARD(outputs_post_hndl); @@ -810,6 +893,9 @@ class StreamingOutput final: public cv::gimpl::GIslandExecutable::IOutput const int out_idx = it->second.first; const auto out_iter = it->second.second; out_iter->ready = true; + if (exptr) { + out_iter->data = cv::gimpl::Exception{exptr}; + } m_postIdx.erase(it); // Drop the link from the cache anyway if (out_iter != m_postings[out_idx].begin()) { @@ -821,16 +907,22 @@ class StreamingOutput final: public cv::gimpl::GIslandExecutable::IOutput while (post_iter != m_postings[out_idx].end() && post_iter->ready == true) { Cmd cmd; - if (cv::util::holds_alternative(post_iter->data)) - { - cmd = Cmd{cv::util::get(post_iter->data)}; - } - else + switch (post_iter->data.index()) { - GAPI_Assert(cv::util::holds_alternative(post_iter->data)); - cmd = Cmd{Stop{}}; - m_stops_sent++; + case Posting::V::index_of(): + cmd = Cmd{cv::util::get(post_iter->data)}; + break; + case Posting::V::index_of(): + cmd = Cmd{cv::util::get(post_iter->data)}; + break; + case Posting::V::index_of(): + cmd = Cmd{Stop{}}; + m_stops_sent++; + break; + default: + GAPI_Assert(false && "Unreachable code"); } + for (auto &&q : m_out_queues[out_idx]) { q->push(cmd); @@ -865,6 +957,7 @@ class StreamingOutput final: public cv::gimpl::GIslandExecutable::IOutput } } } + void meta(const cv::GRunArgP &out, const cv::GRunArg::Meta &m) override { std::lock_guard lock{m_mutex}; @@ -895,6 +988,32 @@ class StreamingOutput final: public cv::gimpl::GIslandExecutable::IOutput // when it posted/resent all STOP messages to all its outputs. return m_stops_sent == desc().size(); } + + virtual void post(cv::gimpl::Exception&& error) override + { + std::lock_guard lock{m_mutex}; + // If the posting list is empty, just broadcast the stop message. + // If it is not, enqueue the Stop message in the postings list. + for (auto &&it : ade::util::indexed(m_postings)) + { + const auto idx = ade::util::index(it); + auto &lst = ade::util::value(it); + if (lst.empty()) + { + for (auto &&q : m_out_queues[idx]) + { + q->push(Cmd(std::move(error))); + } + } + else + { + Posting p; + p.data = Posting::V{std::move(error)}; + p.ready = true; + lst.push_back(std::move(p)); // FIXME: For some reason {}-ctor didn't work here + } + } + } }; // This thread is a plain dumb processing actor. What it do is just: @@ -923,7 +1042,17 @@ void islandActorThread(std::vector in_rcs, while (!output.done()) { GAPI_ITT_AUTO_TRACE_GUARD(island_hndl); - island_exec->run(input, output); + // NB: In case the input message is an cv::gimpl::Exception + // handle it in a general way. + if (cv::util::holds_alternative(input.read())) + { + auto in_msg = input.get(); + output.post(std::move(cv::util::get(in_msg))); + } + else + { + island_exec->run(input, output); + } } } @@ -960,26 +1089,33 @@ void collectorThread(std::vector in_queues, while (true) { GAPI_ITT_AUTO_TRACE_GUARD(collector_hndl); - cv::GRunArgs this_result(out_size); - const bool ok = [&](){ + const auto result = [&](){ GAPI_ITT_AUTO_TRACE_GUARD(collector_get_results_hndl); - return qr.getResultsVector(in_queues, in_mapping, out_size, this_result); + return qr.getResultsVector(in_queues, in_mapping, out_size); }(); - if (!ok) + switch (result.index()) { - if (handle_stop) + case QueueReader::V::index_of(): { - out_queue.push(Cmd{Stop{}}); + GAPI_ITT_AUTO_TRACE_GUARD(collector_push_hndl); + auto this_result = cv::util::get(result); + out_queue.push(Cmd{Result{std::move(this_result), flags}}); + break; } - // Terminate the thread anyway - return; - } - - { - GAPI_ITT_AUTO_TRACE_GUARD(collector_push_hndl); - out_queue.push(Cmd{Result{std::move(this_result), flags}}); + case QueueReader::V::index_of(): + if (handle_stop) + { + out_queue.push(Cmd{Stop{}}); + } + // Terminate the thread anyway + return; + case QueueReader::V::index_of(): + out_queue.push(Cmd{cv::util::get(result)}); + break; + default: + GAPI_Assert(false && "Unreachable code"); } } } @@ -1384,7 +1520,7 @@ cv::gimpl::GStreamingExecutor::GStreamingExecutor(std::unique_ptr && cv::gimpl::GStreamingExecutor::~GStreamingExecutor() { - // FIXME: this is a temporary try-catch exception hadling. + // FIXME: this is a temporary try-catch exception handling. // Need to eliminate throwings from stop() try { if (state == State::READY || state == State::RUNNING) @@ -1428,7 +1564,7 @@ void cv::gimpl::GStreamingExecutor::setSource(GRunArgs &&ins) } } }; - bool islandsRecompiled = false; + const auto new_meta = cv::descr_of(ins); // 0 if (gm.metadata().contains()) // (1) { @@ -1450,8 +1586,6 @@ void cv::gimpl::GStreamingExecutor::setSource(GRunArgs &&ins) } update_int_metas(); // (7) m_reshapable = util::make_optional(is_reshapable); - - islandsRecompiled = true; } else // (8) { @@ -1485,7 +1619,7 @@ void cv::gimpl::GStreamingExecutor::setSource(GRunArgs &&ins) case T::index_of(): #if !defined(GAPI_STANDALONE) emitter.reset(new VideoEmitter{emit_arg}); - // Currently all video inputs are syncronized if sync policy is to drop, + // Currently all video inputs are synchronized if sync policy is to drop, // there is no different fps branches etc, so all video emitters are registered video_emitters.emplace_back(emit_nh); #else @@ -1573,14 +1707,8 @@ void cv::gimpl::GStreamingExecutor::setSource(GRunArgs &&ins) island_meta_info = GIslandModel::traceIslandName(op.nh, m_gim); #endif // OPENCV_WITH_ITT - // If Island Executable is recompiled, all its stuff including internal kernel states - // are recreated and re-initialized automatically. - // But if not, we should notify Island Executable about new started stream to let it update - // its internal variables. - if (!islandsRecompiled) - { - op.isl_exec->handleNewStream(); - } + // Notify island executable about a new stream to let it update its internal variables. + op.isl_exec->handleNewStream(); m_threads.emplace_back(islandActorThread, op.in_objects, @@ -1683,16 +1811,24 @@ bool cv::gimpl::GStreamingExecutor::pull(cv::GRunArgsP &&outs) Cmd cmd; m_out_queue.pop(cmd); - if (cv::util::holds_alternative(cmd)) - { - wait_shutdown(); - return false; + switch (cmd.index()) { + case Cmd::index_of(): + wait_shutdown(); + return false; + case Cmd::index_of(): { + GAPI_Assert(cv::util::holds_alternative(cmd)); + cv::GRunArgs &this_result = cv::util::get(cmd).args; + sync_data(this_result, outs); + return true; + } + case Cmd::index_of(): { + std::rethrow_exception(cv::util::get(cmd).eptr); + return true; + } + default: + GAPI_Assert(false && "Unsupported cmd type in pull"); } - - GAPI_Assert(cv::util::holds_alternative(cmd)); - cv::GRunArgs &this_result = cv::util::get(cmd).args; - sync_data(this_result, outs); - return true; + GAPI_Assert(false && "Unreachable code"); } bool cv::gimpl::GStreamingExecutor::pull(cv::GOptRunArgsP &&outs) @@ -1710,15 +1846,20 @@ bool cv::gimpl::GStreamingExecutor::pull(cv::GOptRunArgsP &&outs) Cmd cmd; m_out_queue.pop(cmd); - if (cv::util::holds_alternative(cmd)) - { - wait_shutdown(); - return false; + switch (cmd.index()) { + case Cmd::index_of(): + wait_shutdown(); + return false; + case Cmd::index_of(): { + sync_data(cv::util::get(cmd), outs); + return true; + } + case Cmd::index_of(): { + std::rethrow_exception(cv::util::get(cmd).eptr); + return true; + } } - - GAPI_Assert(cv::util::holds_alternative(cmd)); - sync_data(cv::util::get(cmd), outs); - return true; + GAPI_Assert(false && "Unreachable code"); } std::tuple> cv::gimpl::GStreamingExecutor::pull() diff --git a/modules/gapi/src/executor/gstreamingexecutor.hpp b/modules/gapi/src/executor/gstreamingexecutor.hpp index b4aadcbbaf4d..da27f6a646b3 100644 --- a/modules/gapi/src/executor/gstreamingexecutor.hpp +++ b/modules/gapi/src/executor/gstreamingexecutor.hpp @@ -50,11 +50,12 @@ struct Result { using Cmd = cv::util::variant < cv::util::monostate - , Start // Tells emitters to start working. Not broadcasted to workers. - , Stop // Tells emitters to stop working. Broadcasted to workers. - , cv::GRunArg // Workers data payload to process. - , Result // Pipeline's data for gout() - >; + , Start // Tells emitters to start working. Not broadcasted to workers. + , Stop // Tells emitters to stop working. Broadcasted to workers. + , cv::GRunArg // Workers data payload to process. + , Result // Pipeline's data for gout() + , cv::gimpl::Exception // Exception which is thrown while execution. + >; // Interface over a queue. The underlying queue implementation may be // different. This class is mainly introduced to bring some diff --git a/modules/gapi/src/executor/gtbbexecutor.cpp b/modules/gapi/src/executor/gtbbexecutor.cpp index cc6ccf9ef4b4..64cd9b5313aa 100644 --- a/modules/gapi/src/executor/gtbbexecutor.cpp +++ b/modules/gapi/src/executor/gtbbexecutor.cpp @@ -353,7 +353,7 @@ namespace graph { } ctx.executed++; - // reset dependecy_count to initial state to simplify re-execution of the same graph + // reset dependency_count to initial state to simplify re-execution of the same graph node->dependency_count = node->dependencies; return result; diff --git a/modules/gapi/src/logger.hpp b/modules/gapi/src/logger.hpp index cb169bf4be7f..7ac3c983fcff 100644 --- a/modules/gapi/src/logger.hpp +++ b/modules/gapi/src/logger.hpp @@ -14,10 +14,12 @@ # define GAPI_LOG_INFO(tag, ...) CV_LOG_INFO(tag, __VA_ARGS__) # define GAPI_LOG_WARNING(tag, ...) CV_LOG_WARNING(tag, __VA_ARGS__) # define GAPI_LOG_DEBUG(tag, ...) CV_LOG_DEBUG(tag, __VA_ARGS__) +# define GAPI_LOG_FATAL(tag, ...) CV_LOG_FATAL(tag, __VA_ARGS__) #else # define GAPI_LOG_INFO(tag, ...) # define GAPI_LOG_WARNING(tag, ...) # define GAPI_LOG_DEBUG(tag, ...) +# define GAPI_LOG_FATAL(tag, ...) #endif // !defined(GAPI_STANDALONE) diff --git a/modules/gapi/src/streaming/gstreamer/gstreamer_media_adapter.cpp b/modules/gapi/src/streaming/gstreamer/gstreamer_media_adapter.cpp index 9019289ae472..188f162ffd5c 100644 --- a/modules/gapi/src/streaming/gstreamer/gstreamer_media_adapter.cpp +++ b/modules/gapi/src/streaming/gstreamer/gstreamer_media_adapter.cpp @@ -28,13 +28,41 @@ GStreamerMediaAdapter::GStreamerMediaAdapter(const cv::GFrameDesc& frameDesc, GstVideoMeta* videoMeta = gst_buffer_get_video_meta(m_buffer); if (videoMeta != nullptr) { - m_strides = { videoMeta->stride[0], videoMeta->stride[1] }; - m_offsets = { videoMeta->offset[0], videoMeta->offset[1] }; + switch (m_frameDesc.fmt) { + case cv::MediaFormat::NV12: { + m_strides = { videoMeta->stride[0], videoMeta->stride[1] }; + m_offsets = { videoMeta->offset[0], videoMeta->offset[1] }; + break; + } + case cv::MediaFormat::GRAY: { + m_strides = { videoMeta->stride[0]}; + m_offsets = { videoMeta->offset[0]}; + break; + } + default: { + GAPI_Assert(false && "Non NV12 or GRAY Media format is not expected here"); + break; + } + } } else { - m_strides = { GST_VIDEO_INFO_PLANE_STRIDE(m_videoInfo.get(), 0), - GST_VIDEO_INFO_PLANE_STRIDE(m_videoInfo.get(), 1) }; - m_offsets = { GST_VIDEO_INFO_PLANE_OFFSET(m_videoInfo.get(), 0), - GST_VIDEO_INFO_PLANE_OFFSET(m_videoInfo.get(), 1) }; + switch (m_frameDesc.fmt) { + case cv::MediaFormat::NV12: { + m_strides = { GST_VIDEO_INFO_PLANE_STRIDE(m_videoInfo.get(), 0), + GST_VIDEO_INFO_PLANE_STRIDE(m_videoInfo.get(), 1) }; + m_offsets = { GST_VIDEO_INFO_PLANE_OFFSET(m_videoInfo.get(), 0), + GST_VIDEO_INFO_PLANE_OFFSET(m_videoInfo.get(), 1) }; + break; + } + case cv::MediaFormat::GRAY: { + m_strides = { GST_VIDEO_INFO_PLANE_STRIDE(m_videoInfo.get(), 0)}; + m_offsets = { GST_VIDEO_INFO_PLANE_OFFSET(m_videoInfo.get(), 0)}; + break; + } + default: { + GAPI_Assert(false && "Non NV12 or GRAY Media format is not expected here"); + break; + } + } } } @@ -71,8 +99,10 @@ cv::MediaFrame::View GStreamerMediaAdapter::access(cv::MediaFrame::Access access if(!m_isMapped.load(std::memory_order_relaxed)) { - GAPI_Assert(GST_VIDEO_INFO_N_PLANES(m_videoInfo.get()) == 2); - GAPI_Assert(GST_VIDEO_INFO_FORMAT(m_videoInfo.get()) == GST_VIDEO_FORMAT_NV12); + GAPI_Assert(GST_VIDEO_INFO_N_PLANES(m_videoInfo.get()) == 2 || + GST_VIDEO_INFO_N_PLANES(m_videoInfo.get()) == 1); + GAPI_Assert(GST_VIDEO_INFO_FORMAT(m_videoInfo.get()) == GST_VIDEO_FORMAT_NV12 || + GST_VIDEO_INFO_FORMAT(m_videoInfo.get()) == GST_VIDEO_FORMAT_GRAY8); // TODO: Use RAII for map/unmap if (access == cv::MediaFrame::Access::W) { @@ -85,27 +115,56 @@ cv::MediaFrame::View GStreamerMediaAdapter::access(cv::MediaFrame::Access access } GAPI_Assert(GST_VIDEO_FRAME_PLANE_STRIDE(&m_videoFrame, 0) == m_strides[0]); - GAPI_Assert(GST_VIDEO_FRAME_PLANE_STRIDE(&m_videoFrame, 1) == m_strides[1]); GAPI_Assert(GST_VIDEO_FRAME_PLANE_OFFSET(&m_videoFrame, 0) == m_offsets[0]); - GAPI_Assert(GST_VIDEO_FRAME_PLANE_OFFSET(&m_videoFrame, 1) == m_offsets[1]); + if (m_frameDesc.fmt == cv::MediaFormat::NV12) { + GAPI_Assert(GST_VIDEO_FRAME_PLANE_STRIDE(&m_videoFrame, 1) == m_strides[1]); + GAPI_Assert(GST_VIDEO_FRAME_PLANE_OFFSET(&m_videoFrame, 1) == m_offsets[1]); + } m_isMapped.store(true, std::memory_order_release); } } - cv::MediaFrame::View::Ptrs ps { - static_cast(GST_VIDEO_FRAME_PLANE_DATA(&m_videoFrame, 0)) + m_offsets[0], // Y-plane - static_cast(GST_VIDEO_FRAME_PLANE_DATA(&m_videoFrame, 0)) + m_offsets[1], // UV-plane - nullptr, - nullptr - }; - - cv::MediaFrame::View::Strides ss = { - static_cast(m_strides[0]), // Y-plane stride - static_cast(m_strides[1]), // UV-plane stride - 0u, - 0u - }; + cv::MediaFrame::View::Ptrs ps; + cv::MediaFrame::View::Strides ss; + + switch (m_frameDesc.fmt) { + case cv::MediaFormat::NV12: { + ps = { + static_cast(GST_VIDEO_FRAME_PLANE_DATA(&m_videoFrame, 0)) + m_offsets[0], // Y-plane + static_cast(GST_VIDEO_FRAME_PLANE_DATA(&m_videoFrame, 0)) + m_offsets[1], // UV-plane + nullptr, + nullptr + }; + ss = { + static_cast(m_strides[0]), // Y-plane stride + static_cast(m_strides[1]), // UV-plane stride + 0u, + 0u + }; + break; + } + case cv::MediaFormat::GRAY: { + ps = { + static_cast(GST_VIDEO_FRAME_PLANE_DATA(&m_videoFrame, 0)) + m_offsets[0], // Y-plane + nullptr, + nullptr, + nullptr + }; + ss = { + static_cast(m_strides[0]), // Y-plane stride + 0u, + 0u, + 0u + }; + break; + } + default: { + GAPI_Assert(false && "Non NV12 or GRAY Media format is not expected here"); + break; + } + } + --thread_counters; return cv::MediaFrame::View(std::move(ps), std::move(ss)); diff --git a/modules/gapi/src/streaming/gstreamer/gstreamer_pipeline_facade.cpp b/modules/gapi/src/streaming/gstreamer/gstreamer_pipeline_facade.cpp index cd782537ca51..d0f5bd5db206 100644 --- a/modules/gapi/src/streaming/gstreamer/gstreamer_pipeline_facade.cpp +++ b/modules/gapi/src/streaming/gstreamer/gstreamer_pipeline_facade.cpp @@ -111,11 +111,11 @@ GstElement* GStreamerPipelineFacade::getElementByName(const std::string& element void GStreamerPipelineFacade::completePreroll() { // FIXME: If there are multiple sources in pipeline and one of them is live, then pipeline // will return GST_STATE_CHANGE_NO_PREROLL while pipeline pausing. - // But appsink may not be connected to this live source and only to anothers, + // But appsink may not be connected to this live source and only to another, // not-live ones. So, it is not required to start the playback for appsink to complete // the preroll. // Starting of playback for the not-live sources before the first frame pull will lead - // to loosing of some amount of frames and pulling of the first frame can return frame + // to losing of some amount of frames and pulling of the first frame can return frame // which is far from the first. // // Need to handle this case or forbid to mix multiples sources of different diff --git a/modules/gapi/src/streaming/gstreamer/gstreamersource.cpp b/modules/gapi/src/streaming/gstreamer/gstreamersource.cpp index 661125657c19..f1bd438ce2c0 100644 --- a/modules/gapi/src/streaming/gstreamer/gstreamersource.cpp +++ b/modules/gapi/src/streaming/gstreamer/gstreamersource.cpp @@ -30,8 +30,9 @@ namespace gst { #ifdef HAVE_GSTREAMER -constexpr char NV12_CAPS_STRING[] = - "video/x-raw,format=NV12;video/x-raw(memory:DMABuf),format=NV12"; +constexpr char ALLOWED_CAPS_STRING[] = + "video/x-raw,format=(string){NV12, GRAY8};video/x-raw(memory:DMABuf),format=(string){NV12, GRAY8}"; + namespace { GstPadProbeReturn appsinkQueryCallback(GstPad*, GstPadProbeInfo* info, gpointer) @@ -137,17 +138,17 @@ void GStreamerSource::Priv::configureAppsink() { // Do not emit signals: all calls will be synchronous and blocking. gst_app_sink_set_emit_signals(GST_APP_SINK(m_appsink.get()), FALSE); - GStreamerPtr nv12Caps(gst_caps_from_string(NV12_CAPS_STRING)); + GStreamerPtr gstCaps(gst_caps_from_string(ALLOWED_CAPS_STRING)); GStreamerPtr appsinkPad(gst_element_get_static_pad(m_appsink, "sink")); GStreamerPtr peerCaps(gst_pad_peer_query_caps(appsinkPad, NULL)); - if (!gst_caps_can_intersect(peerCaps, nv12Caps)) { + if (!gst_caps_can_intersect(peerCaps, gstCaps)) { cv::util::throw_error( - std::logic_error("appsink element can only consume video-frame in NV12 format in " + std::logic_error("appsink element can only consume video-frame in NV12 or GRAY8 format in " "GStreamerSource")); } - gst_app_sink_set_caps(GST_APP_SINK(m_appsink.get()), nv12Caps); + gst_app_sink_set_caps(GST_APP_SINK(m_appsink.get()), gstCaps); gst_pad_add_probe(appsinkPad, GST_PAD_PROBE_TYPE_QUERY_DOWNSTREAM, appsinkQueryCallback, NULL, NULL); @@ -184,10 +185,29 @@ void GStreamerSource::Priv::prepareVideoMeta() cv::util::throw_error(std::logic_error("Cannot query video width/height.")); } + // Fill GstVideoInfo structure to work further with GstVideoFrame class. + if (!gst_video_info_from_caps(&m_videoInfo, prerollCaps)) { + cv::util::throw_error(std::logic_error("preroll sample has invalid caps.")); + } + m_type = GST_VIDEO_INFO_FORMAT(&m_videoInfo); switch(m_outputType) { case GStreamerSource::OutputType::FRAME: { // Construct metadata for media frame. - m_mediaFrameMeta = GFrameDesc { cv::MediaFormat::NV12, cv::Size(width, height) }; + switch (m_type) { + case GST_VIDEO_FORMAT_NV12: { + m_mediaFrameMeta = GFrameDesc{ cv::MediaFormat::NV12, cv::Size(width, height) }; + GAPI_Assert(GST_VIDEO_INFO_N_PLANES(&m_videoInfo) == 2); + break; + } + case GST_VIDEO_FORMAT_GRAY8: { + m_mediaFrameMeta = GFrameDesc{ cv::MediaFormat::GRAY, cv::Size(width, height) }; + GAPI_Assert(GST_VIDEO_INFO_N_PLANES(&m_videoInfo) == 1); + break; + } + default: { + GAPI_Assert(false && "Unsupported GStreamerSource FRAME type."); + } + } break; } case GStreamerSource::OutputType::MAT: { @@ -197,13 +217,6 @@ void GStreamerSource::Priv::prepareVideoMeta() } } - // Fill GstVideoInfo structure to work further with GstVideoFrame class. - if (!gst_video_info_from_caps(&m_videoInfo, prerollCaps)) { - cv::util::throw_error(std::logic_error("preroll sample has invalid caps.")); - } - GAPI_Assert(GST_VIDEO_INFO_N_PLANES(&m_videoInfo) == 2); - GAPI_Assert(GST_VIDEO_INFO_FORMAT(&m_videoInfo) == GST_VIDEO_FORMAT_NV12); - m_isMetaPrepared = true; } } @@ -272,28 +285,46 @@ bool GStreamerSource::Priv::retrieveFrame(cv::Mat& data) try { - // m_matMeta holds width and height for 8U BGR frame, but actual - // frame m_buffer we request from GStreamer pipeline has 8U NV12 format. - // Constructing y and uv cv::Mat-s from such a m_buffer: - GAPI_Assert((uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 1) == + switch (m_type) { + case GST_VIDEO_FORMAT_NV12: { + // m_matMeta holds width and height for 8U BGR frame, but actual + // frame m_buffer we request from GStreamer pipeline has 8U NV12 format. + // Constructing y and uv cv::Mat-s from such a m_buffer: + GAPI_Assert((uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 1) == (uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 0) + GST_VIDEO_FRAME_PLANE_OFFSET(&videoFrame, 1)); + GAPI_Assert(GST_VIDEO_INFO_N_PLANES(&m_videoInfo) == 2); - cv::Mat y(m_matMeta.size, CV_8UC1, - (uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 0) + - GST_VIDEO_FRAME_PLANE_OFFSET(&videoFrame, 0), - GST_VIDEO_FRAME_PLANE_STRIDE(&videoFrame, 0)); - cv::Mat uv(m_matMeta.size / 2, CV_8UC2, - (uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 0) + - GST_VIDEO_FRAME_PLANE_OFFSET(&videoFrame, 1), - GST_VIDEO_FRAME_PLANE_STRIDE(&videoFrame, 1)); + cv::Mat y(m_matMeta.size, CV_8UC1, + (uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 0) + + GST_VIDEO_FRAME_PLANE_OFFSET(&videoFrame, 0), + GST_VIDEO_FRAME_PLANE_STRIDE(&videoFrame, 0)); + cv::Mat uv(m_matMeta.size / 2, CV_8UC2, + (uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 0) + + GST_VIDEO_FRAME_PLANE_OFFSET(&videoFrame, 1), + GST_VIDEO_FRAME_PLANE_STRIDE(&videoFrame, 1)); - cv::cvtColorTwoPlane(y, uv, data, cv::COLOR_YUV2BGR_NV12); + cv::cvtColorTwoPlane(y, uv, data, cv::COLOR_YUV2BGR_NV12); + break; + } + case GST_VIDEO_FORMAT_GRAY8: { + GAPI_Assert(GST_VIDEO_INFO_N_PLANES(&m_videoInfo) == 1); + cv::Mat y(m_matMeta.size, CV_8UC1, + (uint8_t*)GST_VIDEO_FRAME_PLANE_DATA(&videoFrame, 0) + + GST_VIDEO_FRAME_PLANE_OFFSET(&videoFrame, 0), + GST_VIDEO_FRAME_PLANE_STRIDE(&videoFrame, 0)); + cv::cvtColor(y, data, cv::COLOR_GRAY2BGR); + break; + } + default: { + GAPI_Assert(false && "retrieveFrame - unsupported GStreamerSource FRAME type."); + } + } } catch (...) { gst_video_frame_unmap(&videoFrame); - cv::util::throw_error(std::runtime_error("NV12 buffer conversion to BGR is failed!")); + cv::util::throw_error(std::runtime_error("NV12 or GRAY8 buffer conversion to BGR is failed!")); } gst_video_frame_unmap(&videoFrame); diff --git a/modules/gapi/src/streaming/gstreamer/gstreamersource_priv.hpp b/modules/gapi/src/streaming/gstreamer/gstreamersource_priv.hpp index b0940c48a31b..0671213197a6 100644 --- a/modules/gapi/src/streaming/gstreamer/gstreamersource_priv.hpp +++ b/modules/gapi/src/streaming/gstreamer/gstreamersource_priv.hpp @@ -59,6 +59,7 @@ class GStreamerSource::Priv bool m_isPipelinePlaying = false; int64_t m_frameId = 0L; + size_t m_type = 0; //Gstreamer video format type protected: void configureAppsink(); diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.cpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.cpp index 2cdf1c2b4455..67ffdf937789 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.cpp @@ -11,6 +11,7 @@ #include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" #include "streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp" #include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/utils.hpp" #include "logger.hpp" #ifdef _WIN32 @@ -22,7 +23,7 @@ namespace gapi { namespace wip { namespace onevpl { namespace utils { -mfxU32 GetSurfaceSize_(mfxU32 FourCC, mfxU32 width, mfxU32 height) { +static mfxU32 GetSurfaceSize_(mfxU32 FourCC, mfxU32 width, mfxU32 height) { mfxU32 nbytes = 0; mfxU32 half_width = width / 2; @@ -47,10 +48,10 @@ mfxU32 GetSurfaceSize_(mfxU32 FourCC, mfxU32 width, mfxU32 height) { return nbytes; } -surface_ptr_t create_surface_RGB4_(mfxFrameInfo frameInfo, - std::shared_ptr out_buf_ptr, - size_t out_buf_ptr_offset, - size_t out_buf_size) +static surface_ptr_t create_surface_RGB4_(mfxFrameInfo frameInfo, + std::shared_ptr out_buf_ptr, + size_t out_buf_ptr_offset, + size_t out_buf_size) { mfxU8* buf = reinterpret_cast(out_buf_ptr.get()); mfxU16 surfW = frameInfo.Width * 4; @@ -80,10 +81,10 @@ surface_ptr_t create_surface_RGB4_(mfxFrameInfo frameInfo, return Surface::create_surface(std::move(handle), out_buf_ptr); } -surface_ptr_t create_surface_other_(mfxFrameInfo frameInfo, - std::shared_ptr out_buf_ptr, - size_t out_buf_ptr_offset, - size_t out_buf_size) +static surface_ptr_t create_surface_other_(mfxFrameInfo frameInfo, + std::shared_ptr out_buf_ptr, + size_t out_buf_ptr_offset, + size_t out_buf_size) { mfxU8* buf = reinterpret_cast(out_buf_ptr.get()); mfxU16 surfH = frameInfo.Height; @@ -155,8 +156,12 @@ VPLCPUAccelerationPolicy::create_surface_pool(size_t pool_size, size_t surface_s GAPI_LOG_DEBUG(nullptr, "page size: " << page_size_bytes << ", preallocated_raw_bytes: " << preallocated_raw_bytes); preallocated_pool_memory_ptr = _aligned_malloc(preallocated_raw_bytes, page_size_bytes); #else - GAPI_Assert(false && "Compatibility is not tested for systems differ than \"_WIN32\". " - "Please feel free to set it up under OPENCV contribution policy"); + int err = posix_memalign(&preallocated_pool_memory_ptr, page_size_bytes, preallocated_raw_bytes); + if (err) { + GAPI_LOG_WARNING(nullptr, "Cannot allocate aligned memory, size: " << preallocated_raw_bytes << + ", alignment: " << page_size_bytes << ", error: " << + strerror(err)); + } #endif if (!preallocated_pool_memory_ptr) { @@ -173,8 +178,9 @@ VPLCPUAccelerationPolicy::create_surface_pool(size_t pool_size, size_t surface_s GAPI_LOG_INFO(nullptr, "Released workspace memory: " << ptr); ptr = nullptr; #else - GAPI_Assert(false && "Not implemented for systems differ than \"_WIN32\". " - "Please feel free to set it up under OPENCV contribution policy"); + free(ptr); + GAPI_LOG_INFO(nullptr, "Released workspace memory: " << ptr); + ptr = nullptr; #endif }); @@ -210,30 +216,27 @@ VPLCPUAccelerationPolicy::create_surface_pool(size_t pool_size, size_t surface_s } VPLCPUAccelerationPolicy::pool_key_t -VPLCPUAccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxVideoParam& param) { +VPLCPUAccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxFrameInfo& info) { // External (application) allocation of decode surfaces GAPI_LOG_DEBUG(nullptr, "Query mfxFrameAllocRequest.NumFrameSuggested: " << alloc_request.NumFrameSuggested << ", mfxFrameAllocRequest.Type: " << alloc_request.Type); - mfxU32 singleSurfaceSize = utils::GetSurfaceSize_(param.mfx.FrameInfo.FourCC, - param.mfx.FrameInfo.Width, - param.mfx.FrameInfo.Height); + mfxU32 singleSurfaceSize = utils::GetSurfaceSize_(info.FourCC, + info.Width, + info.Height); if (!singleSurfaceSize) { - throw std::runtime_error("Cannot determine surface size for: fourCC: " + - std::to_string(param.mfx.FrameInfo.FourCC) + - ", width: " + std::to_string(param.mfx.FrameInfo.Width) + - ", height: " + std::to_string(param.mfx.FrameInfo.Height)); + throw std::runtime_error("Cannot determine surface size from frame: " + + mfx_frame_info_to_string(info)); } - const auto &frameInfo = param.mfx.FrameInfo; auto surface_creator = - [&frameInfo] (std::shared_ptr out_buf_ptr, size_t out_buf_ptr_offset, + [&info] (std::shared_ptr out_buf_ptr, size_t out_buf_ptr_offset, size_t out_buf_size) -> surface_ptr_t { - return (frameInfo.FourCC == MFX_FOURCC_RGB4) ? - utils::create_surface_RGB4_(frameInfo, out_buf_ptr, out_buf_ptr_offset, + return (info.FourCC == MFX_FOURCC_RGB4) ? + utils::create_surface_RGB4_(info, out_buf_ptr, out_buf_ptr_offset, out_buf_size) : - utils::create_surface_other_(frameInfo, out_buf_ptr, out_buf_ptr_offset, + utils::create_surface_other_(info, out_buf_ptr, out_buf_ptr_offset, out_buf_size);}; return create_surface_pool(alloc_request.NumFrameSuggested, @@ -274,7 +277,7 @@ size_t VPLCPUAccelerationPolicy::get_surface_count(pool_key_t key) const { } cv::MediaFrame::AdapterPtr VPLCPUAccelerationPolicy::create_frame_adapter(pool_key_t key, - mfxFrameSurface1* surface) { + const FrameConstructorArgs ¶ms) { auto pool_it = pool_table.find(key); if (pool_it == pool_table.end()) { std::stringstream ss; @@ -285,7 +288,8 @@ cv::MediaFrame::AdapterPtr VPLCPUAccelerationPolicy::create_frame_adapter(pool_k } pool_t& requested_pool = pool_it->second; - return cv::MediaFrame::AdapterPtr{new VPLMediaFrameCPUAdapter(requested_pool.find_by_handle(surface))}; + return cv::MediaFrame::AdapterPtr{new VPLMediaFrameCPUAdapter(requested_pool.find_by_handle(params.assoc_surface), + params.assoc_handle)}; } } // namespace onevpl } // namespace wip diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.hpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.hpp index fdc0afd4bfaa..57c14ad792cd 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_cpu.hpp @@ -32,13 +32,13 @@ struct GAPI_EXPORTS VPLCPUAccelerationPolicy final : public VPLAccelerationPolic void init(session_t session) override; void deinit(session_t session) override; pool_key_t create_surface_pool(size_t pool_size, size_t surface_size_bytes, surface_ptr_ctr_t creator); - pool_key_t create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxVideoParam& param) override; + pool_key_t create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxFrameInfo& info) override; surface_weak_ptr_t get_free_surface(pool_key_t key) override; size_t get_free_surface_count(pool_key_t key) const override; size_t get_surface_count(pool_key_t key) const override; cv::MediaFrame::AdapterPtr create_frame_adapter(pool_key_t key, - mfxFrameSurface1* surface) override; + const FrameConstructorArgs& args) override; private: std::map pool_table; diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.cpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.cpp index f528190ad568..dba05f0169f2 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.cpp @@ -13,8 +13,7 @@ #include "streaming/onevpl/utils.hpp" #include "logger.hpp" -#ifdef HAVE_DIRECTX -#ifdef HAVE_D3D11 +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) #pragma comment(lib,"d3d11.lib") #define D3D11_NO_HELPERS @@ -98,9 +97,7 @@ void VPLDX11AccelerationPolicy::deinit(session_t session) { VPLDX11AccelerationPolicy::pool_key_t VPLDX11AccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest& alloc_req, - mfxVideoParam& param) { - param.IOPattern = MFX_IOPATTERN_OUT_VIDEO_MEMORY; - + mfxFrameInfo& info) { // allocate textures by explicit request mfxFrameAllocResponse mfxResponse; mfxStatus sts = on_alloc(&alloc_req, &mfxResponse); @@ -120,7 +117,7 @@ VPLDX11AccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest& alloc pool_t pool(numSurfaces); for (int i = 0; i < numSurfaces; i++) { std::unique_ptr handle(new mfxFrameSurface1 {}); - handle->Info = param.mfx.FrameInfo; + handle->Info = info; handle->Data.MemId = mfxResponse.mids[i]; pool.push_back(Surface::create_surface(std::move(handle), table_it->second)); @@ -159,12 +156,21 @@ size_t VPLDX11AccelerationPolicy::get_free_surface_count(pool_key_t) const { GAPI_Assert(false && "get_free_surface_count() is not implemented"); } -size_t VPLDX11AccelerationPolicy::get_surface_count(pool_key_t) const { - GAPI_Assert(false && "VPLDX11AccelerationPolicy::get_surface_count() is not implemented"); +size_t VPLDX11AccelerationPolicy::get_surface_count(pool_key_t key) const { + auto pool_it = pool_table.find(key); + if (pool_it == pool_table.end()) { + std::stringstream ss; + ss << "key is not found: " << key << ", table size: " << pool_table.size(); + const std::string& str = ss.str(); + GAPI_LOG_WARNING(nullptr, str); + throw std::runtime_error(std::string(__FUNCTION__) + " - " + str); + } + return pool_it->second.total_size(); } -cv::MediaFrame::AdapterPtr VPLDX11AccelerationPolicy::create_frame_adapter(pool_key_t key, - mfxFrameSurface1* surface) { +cv::MediaFrame::AdapterPtr +VPLDX11AccelerationPolicy::create_frame_adapter(pool_key_t key, + const FrameConstructorArgs ¶ms) { auto pool_it = pool_table.find(key); if (pool_it == pool_table.end()) { std::stringstream ss; @@ -175,7 +181,8 @@ cv::MediaFrame::AdapterPtr VPLDX11AccelerationPolicy::create_frame_adapter(pool_ } pool_t& requested_pool = pool_it->second; - return cv::MediaFrame::AdapterPtr{new VPLMediaFrameDX11Adapter(requested_pool.find_by_handle(surface))}; + return cv::MediaFrame::AdapterPtr{new VPLMediaFrameDX11Adapter(requested_pool.find_by_handle(params.assoc_surface), + params.assoc_handle)}; } mfxStatus VPLDX11AccelerationPolicy::alloc_cb(mfxHDL pthis, mfxFrameAllocRequest *request, @@ -261,24 +268,70 @@ mfxStatus VPLDX11AccelerationPolicy::on_alloc(const mfxFrameAllocRequest *reques desc.Format = colorFormat; desc.SampleDesc.Count = 1; desc.Usage = D3D11_USAGE_DEFAULT; - desc.MiscFlags = D3D11_RESOURCE_MISC_SHARED; + desc.MiscFlags = 0; desc.BindFlags = D3D11_BIND_DECODER; + if ((MFX_MEMTYPE_FROM_VPPIN & request->Type) && (DXGI_FORMAT_YUY2 == desc.Format) || + (DXGI_FORMAT_B8G8R8A8_UNORM == desc.Format) || + (DXGI_FORMAT_R10G10B10A2_UNORM == desc.Format) || + (DXGI_FORMAT_R16G16B16A16_UNORM == desc.Format)) { + desc.BindFlags = D3D11_BIND_RENDER_TARGET; + } + + if ((MFX_MEMTYPE_FROM_VPPOUT & request->Type) || + (MFX_MEMTYPE_VIDEO_MEMORY_PROCESSOR_TARGET & request->Type)) { + desc.BindFlags = D3D11_BIND_RENDER_TARGET; + } + if (request->Type & MFX_MEMTYPE_SHARED_RESOURCE) { desc.BindFlags |= D3D11_BIND_SHADER_RESOURCE; desc.MiscFlags = D3D11_RESOURCE_MISC_SHARED; } - ComPtrGuard main_texture = createCOMPtrGuard(); + if (DXGI_FORMAT_P8 == desc.Format) { + desc.BindFlags = 0; + } + + /* NB: + * On the one hand current OpenVINO API doesn't support texture array and + * D3D11 API doesn't allow to address specific texture element in array. + * On the other hand using textures array should be more performant case + * in applications (according to community experience) + * So, to be compliant with OV let's turn off textures array feature, but keep + * this code in commented section to consider such "optimization" in future + */ +#if 0 + size_t main_textures_count = 1; + if (D3D11_BIND_RENDER_TARGET & desc.BindFlags) { + GAPI_LOG_DEBUG(nullptr, "Use array of testures instead of texture array"); + desc.ArraySize = 1; + main_textures_count = request->NumFrameSuggested; + } +#else + // enforcement to use array of textures + size_t main_textures_count = request->NumFrameSuggested; + + // enforcement to do not use texture array as subresources as part of a single texture + desc.ArraySize = 1; +#endif + + // create GPU textures HRESULT err = S_OK; - { - ID3D11Texture2D *pTexture2D = nullptr; - err = hw_handle->CreateTexture2D(&desc, nullptr, &pTexture2D); - if (FAILED(err)) { - GAPI_LOG_WARNING(nullptr, "Cannot create texture, error: " + std::to_string(HRESULT_CODE(err))); - return MFX_ERR_MEMORY_ALLOC; + std::vector> main_textures; + main_textures.reserve(main_textures_count); + for (size_t i = 0; i < main_textures_count; i++) { + ComPtrGuard main_texture = createCOMPtrGuard(); + { + ID3D11Texture2D *pTexture2D = nullptr; + err = hw_handle->CreateTexture2D(&desc, nullptr, &pTexture2D); + if (FAILED(err)) { + GAPI_LOG_WARNING(nullptr, "Cannot create texture by index: " << i << + ", error: " << std::to_string(HRESULT_CODE(err))); + return MFX_ERR_MEMORY_ALLOC; + } + main_texture.reset(pTexture2D); } - main_texture.reset(pTexture2D); + main_textures.push_back(std::move(main_texture)); } // create staging texture to read it from @@ -308,7 +361,7 @@ mfxStatus VPLDX11AccelerationPolicy::on_alloc(const mfxFrameAllocRequest *reques DX11AllocationRecord::create(request->NumFrameSuggested, device_context, allocator, - std::move(main_texture), + std::move(main_textures), std::move(staging_textures))); if (!inserted_it.second) { GAPI_LOG_WARNING(nullptr, "Cannot assign allocation by id: " + std::to_string(request->AllocId) + @@ -363,7 +416,7 @@ mfxStatus VPLDX11AccelerationPolicy::on_get_hdl(mfxMemId mid, mfxHDL *handle) { pPair->second = static_cast(reinterpret_cast( static_cast(data->get_subresource()))); - GAPI_LOG_DEBUG(nullptr, "texture : " << pPair->first << ", sub id: " << pPair->second); + GAPI_LOG_DEBUG(nullptr, "ID3D11Texture2D : " << pPair->first << ", sub id: " << pPair->second); return MFX_ERR_NONE; } @@ -379,12 +432,60 @@ mfxStatus VPLDX11AccelerationPolicy::on_free(mfxFrameAllocResponse *response) { } allocation_table.erase(table_it); + GAPI_LOG_DEBUG(nullptr, "Allocation by requested id: " << response->AllocId << + " has been erased"); return MFX_ERR_NONE; } } // namespace onevpl } // namespace wip } // namespace gapi } // namespace cv -#endif // HAVE_D3D11 -#endif // HAVE_DIRECTX + +#else // #if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +VPLDX11AccelerationPolicy::VPLDX11AccelerationPolicy(device_selector_ptr_t selector) : + VPLAccelerationPolicy(selector) { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +VPLDX11AccelerationPolicy::~VPLDX11AccelerationPolicy() = default; + +void VPLDX11AccelerationPolicy::init(session_t ) { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +void VPLDX11AccelerationPolicy::deinit(session_t) { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +VPLDX11AccelerationPolicy::pool_key_t VPLDX11AccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest&, + mfxFrameInfo&) { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +VPLDX11AccelerationPolicy::surface_weak_ptr_t VPLDX11AccelerationPolicy::get_free_surface(pool_key_t) { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +size_t VPLDX11AccelerationPolicy::get_free_surface_count(pool_key_t) const { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +size_t VPLDX11AccelerationPolicy::get_surface_count(pool_key_t) const { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} + +cv::MediaFrame::AdapterPtr VPLDX11AccelerationPolicy::create_frame_adapter(pool_key_t, + const FrameConstructorArgs &) { + GAPI_Assert(false && "VPLDX11AccelerationPolicy unavailable in current configuration"); +} +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // #if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) #endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.hpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.hpp index e053089587a1..f82ae645376e 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_dx11.hpp @@ -15,8 +15,7 @@ #include "streaming/onevpl/accelerators/surface/surface_pool.hpp" #include "streaming/onevpl/accelerators/dx11_alloc_resource.hpp" -#ifdef HAVE_DIRECTX -#ifdef HAVE_D3D11 +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) #define D3D11_NO_HELPERS #define NOMINMAX #include @@ -24,7 +23,9 @@ #include "opencv2/core/directx.hpp" #ifdef HAVE_OPENCL #include -#endif +#endif // HAVE_OPENCL +#undef NOMINMAX +#endif // HAVE_DIRECTX && HAVE_D3D11 namespace cv { namespace gapi { @@ -43,14 +44,16 @@ struct GAPI_EXPORTS VPLDX11AccelerationPolicy final: public VPLAccelerationPolic void init(session_t session) override; void deinit(session_t session) override; pool_key_t create_surface_pool(const mfxFrameAllocRequest& alloc_request, - mfxVideoParam& param) override; + mfxFrameInfo& info) override; surface_weak_ptr_t get_free_surface(pool_key_t key) override; size_t get_free_surface_count(pool_key_t key) const override; size_t get_surface_count(pool_key_t key) const override; cv::MediaFrame::AdapterPtr create_frame_adapter(pool_key_t key, - mfxFrameSurface1* surface) override; + const FrameConstructorArgs ¶ms) override; private: +#ifdef HAVE_DIRECTX +#ifdef HAVE_D3D11 ID3D11Device *hw_handle; ID3D11DeviceContext* device_context; @@ -75,14 +78,13 @@ struct GAPI_EXPORTS VPLDX11AccelerationPolicy final: public VPLAccelerationPolic std::map allocation_table; std::map pool_table; +#endif // HAVE_D3D11 +#endif // HAVE_DIRECTX }; } // namespace onevpl } // namespace wip } // namespace gapi } // namespace cv -#undef NOMINMAX -#endif // HAVE_D3D11 -#endif // HAVE_DIRECTX #endif // HAVE_ONEVPL #endif // GAPI_STREAMING_ONEVPL_ACCELERATORS_ACCEL_POLICY_DX11_HPP diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_interface.hpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_interface.hpp index a9059c29ef2f..c955b7529a81 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_interface.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_interface.hpp @@ -16,13 +16,12 @@ #ifdef HAVE_ONEVPL #include "streaming/onevpl/onevpl_export.hpp" +#include "streaming/onevpl/accelerators/surface/base_frame_adapter.hpp" namespace cv { namespace gapi { namespace wip { namespace onevpl { - -class Surface; struct VPLAccelerationPolicy { using device_selector_ptr_t = std::shared_ptr; @@ -40,6 +39,11 @@ struct VPLAccelerationPolicy size_t out_buf_ptr_offset, size_t out_buf_ptr_size)>; + struct FrameConstructorArgs { + surface_t::handle_t *assoc_surface; + session_t assoc_handle; + }; + device_selector_ptr_t get_device_selector() { return device_selector; } @@ -50,19 +54,18 @@ struct VPLAccelerationPolicy virtual void init(session_t session) = 0; virtual void deinit(session_t session) = 0; - // Limitation: cannot give guarantee in succesful memory realloccation + // Limitation: cannot give guarantee in successful memory realloccation // for existing workspace in existing pool (see realloc) // thus it is not implemented, // PLEASE provide initial memory area large enough - virtual pool_key_t create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxVideoParam& param) = 0; + virtual pool_key_t create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxFrameInfo& info) = 0; virtual surface_weak_ptr_t get_free_surface(pool_key_t key) = 0; virtual size_t get_free_surface_count(pool_key_t key) const = 0; virtual size_t get_surface_count(pool_key_t key) const = 0; virtual cv::MediaFrame::AdapterPtr create_frame_adapter(pool_key_t key, - mfxFrameSurface1* surface) = 0; -private: + const FrameConstructorArgs ¶ms) = 0; device_selector_ptr_t device_selector; }; } // namespace onevpl diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_va_api.cpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_va_api.cpp new file mode 100644 index 000000000000..8fa0be991470 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_va_api.cpp @@ -0,0 +1,150 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifdef HAVE_ONEVPL +#include +#include +#include + +#ifdef __linux__ +#include +#include +#include +#include +#endif // __linux__ + +#include "streaming/onevpl/accelerators/accel_policy_va_api.hpp" +#include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" +#include "streaming/onevpl/utils.hpp" +#include "logger.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +#ifdef __linux__ +VPLVAAPIAccelerationPolicy::VPLVAAPIAccelerationPolicy(device_selector_ptr_t selector) : + VPLAccelerationPolicy(selector), + cpu_dispatcher(new VPLCPUAccelerationPolicy(selector)), + va_handle(), + device_fd(-1) { +#if defined(HAVE_VA) || defined(HAVE_VA_INTEL) + // TODO Move it out in device selector + device_fd = open("/dev/dri/renderD128", O_RDWR); + if (device_fd < 0) { + GAPI_LOG_WARNING(nullptr, "VAAPI device descriptor \"/dev/dri/renderD128\" has not found"); + throw std::runtime_error("cannot open VAAPI device"); + } + va_handle = vaGetDisplayDRM(device_fd); + if (!va_handle) { + GAPI_LOG_WARNING(nullptr, "VAAPI device vaGetDisplayDRM failed, error: " << strerror(errno)); + close(device_fd); + throw std::runtime_error("vaGetDisplayDRM failed"); + } + int major_version = 0, minor_version = 0; + VAStatus status {}; + status = vaInitialize(va_handle, &major_version, &minor_version); + if (VA_STATUS_SUCCESS != status) { + GAPI_LOG_WARNING(nullptr, "Cannot initialize VAAPI device, error: " << vaErrorStr(status)); + close(device_fd); + throw std::runtime_error("vaInitialize failed"); + } + GAPI_LOG_INFO(nullptr, "created"); +#else // defined(HAVE_VA) || defined(HAVE_VA_INTEL) + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +#endif // defined(HAVE_VA) || defined(HAVE_VA_INTEL) +} + +VPLVAAPIAccelerationPolicy::~VPLVAAPIAccelerationPolicy() { + vaTerminate(va_handle); + close(device_fd); + GAPI_LOG_INFO(nullptr, "destroyed"); +} + +void VPLVAAPIAccelerationPolicy::init(session_t session) { + GAPI_LOG_INFO(nullptr, "session: " << session); + + cpu_dispatcher->init(session); + mfxStatus sts = MFXVideoCORE_SetHandle(session, + static_cast(MFX_HANDLE_VA_DISPLAY), + va_handle); + if (sts != MFX_ERR_NONE) + { + throw std::logic_error("Cannot create VPLVAAPIAccelerationPolicy, MFXVideoCORE_SetHandle error: " + + mfxstatus_to_string(sts)); + } + GAPI_LOG_INFO(nullptr, "finished successfully, session: " << session); +} + +void VPLVAAPIAccelerationPolicy::deinit(session_t session) { + GAPI_LOG_INFO(nullptr, "session: " << session); +} + +VPLVAAPIAccelerationPolicy::pool_key_t +VPLVAAPIAccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxFrameInfo& info) { + + return cpu_dispatcher->create_surface_pool(alloc_request, info); +} + +VPLVAAPIAccelerationPolicy::surface_weak_ptr_t VPLVAAPIAccelerationPolicy::get_free_surface(pool_key_t key) { + return cpu_dispatcher->get_free_surface(key); +} + +size_t VPLVAAPIAccelerationPolicy::get_free_surface_count(pool_key_t key) const { + return cpu_dispatcher->get_free_surface_count(key); +} + +size_t VPLVAAPIAccelerationPolicy::get_surface_count(pool_key_t key) const { + return cpu_dispatcher->get_surface_count(key); +} + +cv::MediaFrame::AdapterPtr VPLVAAPIAccelerationPolicy::create_frame_adapter(pool_key_t key, + const FrameConstructorArgs ¶ms) { + return cpu_dispatcher->create_frame_adapter(key, params); +} + +#else // __linux__ + +VPLVAAPIAccelerationPolicy::VPLVAAPIAccelerationPolicy(device_selector_ptr_t selector) : + VPLAccelerationPolicy(selector) { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +VPLVAAPIAccelerationPolicy::~VPLVAAPIAccelerationPolicy() = default; + +void VPLVAAPIAccelerationPolicy::init(session_t ) { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +void VPLVAAPIAccelerationPolicy::deinit(session_t) { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +VPLVAAPIAccelerationPolicy::pool_key_t VPLVAAPIAccelerationPolicy::create_surface_pool(const mfxFrameAllocRequest&, + mfxFrameInfo&) { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +VPLVAAPIAccelerationPolicy::surface_weak_ptr_t VPLVAAPIAccelerationPolicy::get_free_surface(pool_key_t) { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +size_t VPLVAAPIAccelerationPolicy::get_free_surface_count(pool_key_t) const { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +size_t VPLVAAPIAccelerationPolicy::get_surface_count(pool_key_t) const { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} + +cv::MediaFrame::AdapterPtr VPLVAAPIAccelerationPolicy::create_frame_adapter(pool_key_t, + const FrameConstructorArgs &) { + GAPI_Assert(false && "VPLVAAPIAccelerationPolicy unavailable in current configuration"); +} +#endif // __linux__ +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_va_api.hpp b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_va_api.hpp new file mode 100644 index 000000000000..ee7453f982dc --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/accelerators/accel_policy_va_api.hpp @@ -0,0 +1,62 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#ifndef GAPI_STREAMING_ONEVPL_ACCELERATORS_ACCEL_POLICY_VA_API_HPP +#define GAPI_STREAMING_ONEVPL_ACCELERATORS_ACCEL_POLICY_VA_API_HPP + +#include +#include + +#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" +#include "streaming/onevpl/accelerators/surface/surface_pool.hpp" + +#ifdef __linux__ +#if defined(HAVE_VA) || defined(HAVE_VA_INTEL) +#include "va/va.h" +#include "va/va_drm.h" +#else + typedef void* VADisplay; +#endif // defined(HAVE_VA) || defined(HAVE_VA_INTEL) +#endif // __linux__ + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { + +// GAPI_EXPORTS for tests +struct GAPI_EXPORTS VPLVAAPIAccelerationPolicy final : public VPLAccelerationPolicy +{ + VPLVAAPIAccelerationPolicy(device_selector_ptr_t selector); + ~VPLVAAPIAccelerationPolicy(); + + using pool_t = CachedPool; + + void init(session_t session) override; + void deinit(session_t session) override; + pool_key_t create_surface_pool(const mfxFrameAllocRequest& alloc_request, mfxFrameInfo& info) override; + surface_weak_ptr_t get_free_surface(pool_key_t key) override; + size_t get_free_surface_count(pool_key_t key) const override; + size_t get_surface_count(pool_key_t key) const override; + + cv::MediaFrame::AdapterPtr create_frame_adapter(pool_key_t key, + const FrameConstructorArgs& args) override; + +private: + std::unique_ptr cpu_dispatcher; +#ifdef __linux__ + VADisplay va_handle; + int device_fd; // TODO Move it out in device selector +#endif // __linux__ +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv + +#endif // HAVE_ONEVPL +#endif // GAPI_STREAMING_ONEVPL_ACCELERATORS_ACCEL_POLICY_VA_API_HPP diff --git a/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.cpp b/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.cpp index 3bbfb25b0af7..574860e03da3 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.cpp @@ -96,6 +96,7 @@ void LockAdapter::unlock_write(mfxMemId mid, mfxFrameData &data) { SharedLock* LockAdapter::set_adaptee(SharedLock* new_impl) { SharedLock* old_impl = impl; + GAPI_DbgAssert(old_impl == nullptr || new_impl == nullptr && "Must not be previous impl"); impl = new_impl; return old_impl; } @@ -355,13 +356,14 @@ DX11AllocationRecord::~DX11AllocationRecord() { GAPI_LOG_DEBUG(nullptr, "release final referenced texture: " << texture_ptr.get()); } -void DX11AllocationRecord::init(unsigned int items, - ID3D11DeviceContext* origin_ctx, +void DX11AllocationRecord::init(unsigned int items, ID3D11DeviceContext* origin_ctx, mfxFrameAllocator origin_allocator, - ComPtrGuard&& texture, + std::vector> &&textures, std::vector> &&staging_textures) { + GAPI_DbgAssert(items != 0 && "Cannot create DX11AllocationRecord with empty items"); GAPI_DbgAssert(items == staging_textures.size() && "Allocation items count and staging size are not equal"); + GAPI_DbgAssert(textures.size() != 1 ? items == textures.size() : true && "Allocation items count and staging size are not equal"); GAPI_DbgAssert(origin_ctx && "Cannot create DX11AllocationItem for empty origin_ctx"); auto shared_allocator_copy = origin_allocator; @@ -374,13 +376,22 @@ void DX11AllocationRecord::init(unsigned int items, shared_allocator_copy.pthis = nullptr; - GAPI_LOG_DEBUG(nullptr, "subresources count: " << items << ", text: " << texture.get()); + GAPI_LOG_DEBUG(nullptr, "subresources count: " << items); resources.reserve(items); - // no AddRef here, because DX11AllocationRecord receive ownership it here - texture_ptr = createCOMSharedPtrGuard(std::move(texture)); + + if (textures.size() == 1) { + texture_ptr = createCOMSharedPtrGuard(std::move(textures[0])); + } for(unsigned int i = 0; i < items; i++) { - resources.emplace_back(new DX11AllocationItem(get_ptr(), origin_ctx, shared_allocator_copy, - texture_ptr, i, std::move(staging_textures[i]))); + if (textures.size() == 1) { + GAPI_LOG_DEBUG(nullptr, "subresources: [" << i <<", " << items << "], ID3D11Texture2D: " << texture_ptr.get()); + resources.emplace_back(new DX11AllocationItem(get_ptr(), origin_ctx, shared_allocator_copy, + texture_ptr, i, std::move(staging_textures[i]))); + } else { + GAPI_LOG_DEBUG(nullptr, "subresources: [" << i <<", " << items << "], ID3D11Texture2D: " << textures[i].get()); + resources.emplace_back(new DX11AllocationItem(get_ptr(), origin_ctx, shared_allocator_copy, + std::move(textures[i]), 0, std::move(staging_textures[i]))); + } } } diff --git a/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.hpp b/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.hpp index 46ddff86a4c2..c68a08a3f8a2 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/dx11_alloc_resource.hpp @@ -133,10 +133,10 @@ struct DX11AllocationRecord : public std::enable_shared_from_this&& texture, std::vector> &&staging_textures); - + std::vector>&& textures, std::vector> &&staging_textures); std::vector resources; ComSharedPtrGuard texture_ptr; }; diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/base_frame_adapter.cpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/base_frame_adapter.cpp new file mode 100644 index 000000000000..6f2dbd137a51 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/base_frame_adapter.cpp @@ -0,0 +1,77 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#include "streaming/onevpl/accelerators/surface/base_frame_adapter.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "logger.hpp" + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +BaseFrameAdapter::BaseFrameAdapter(std::shared_ptr surface, + SessionHandle assoc_handle, + AccelType accel): + parent_surface_ptr(surface), parent_handle(assoc_handle), + acceleration_type(accel) { + GAPI_Assert(parent_surface_ptr && "Surface is nullptr"); + GAPI_Assert(parent_handle && "mfxSession is nullptr"); + + const Surface::info_t& info = parent_surface_ptr->get_info(); + GAPI_LOG_DEBUG(nullptr, "surface: " << parent_surface_ptr->get_handle() << + ", w: " << info.Width << ", h: " << info.Height << + ", p: " << parent_surface_ptr->get_data().Pitch << + ", frame id: " << reinterpret_cast(this)); + switch(info.FourCC) { + case MFX_FOURCC_I420: + throw std::runtime_error("MediaFrame doesn't support I420 type"); + break; + case MFX_FOURCC_NV12: + frame_desc.fmt = MediaFormat::NV12; + break; + default: + throw std::runtime_error("MediaFrame unknown 'fmt' type: " + std::to_string(info.FourCC)); + } + + frame_desc.size = cv::Size{info.Width, info.Height}; + parent_surface_ptr->obtain_lock(); +} + +BaseFrameAdapter::~BaseFrameAdapter() { + // Each BaseFrameAdapter releases mfx surface counter + // The last BaseFrameAdapter releases shared Surface pointer + // The last surface pointer releases workspace memory + GAPI_LOG_DEBUG(nullptr, "destroy frame id: " << reinterpret_cast(this)); + parent_surface_ptr->release_lock(); +} + +const std::shared_ptr& BaseFrameAdapter::get_surface() const { + return parent_surface_ptr; +} + +std::shared_ptr BaseFrameAdapter::surface() { + return parent_surface_ptr; +} + +BaseFrameAdapter::SessionHandle BaseFrameAdapter::get_session_handle() const { + return parent_handle; +} + +cv::GFrameDesc BaseFrameAdapter::meta() const { + return frame_desc; +} +AccelType BaseFrameAdapter::accel_type() const { + return acceleration_type; +} + +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/base_frame_adapter.hpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/base_frame_adapter.hpp new file mode 100644 index 000000000000..4231131b84a0 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/base_frame_adapter.hpp @@ -0,0 +1,47 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONEVPL_ACCELERATORS_SURFACE_BASE_FRAME_ADAPTER_HPP +#define GAPI_STREAMING_ONEVPL_ACCELERATORS_SURFACE_BASE_FRAME_ADAPTER_HPP +#include + +#include +#include +#include "streaming/onevpl/accelerators/surface/surface.hpp" + +#ifdef HAVE_ONEVPL + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +class BaseFrameAdapter : public cv::MediaFrame::IAdapter { +public: + using SessionHandle = mfxSession; + + const std::shared_ptr& get_surface() const; + SessionHandle get_session_handle() const; + + cv::GFrameDesc meta() const override; + AccelType accel_type() const; +protected: + BaseFrameAdapter(std::shared_ptr assoc_surface, SessionHandle assoc_handle, + AccelType accel); + ~BaseFrameAdapter(); + std::shared_ptr surface(); + + std::shared_ptr parent_surface_ptr; + SessionHandle parent_handle; + GFrameDesc frame_desc; + AccelType acceleration_type; +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv + +#endif // HAVE_ONEVPL +#endif // GAPI_STREAMING_ONEVPL_ACCELERATORS_SURFACE_BASE_FRAME_ADAPTER_HPP diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.cpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.cpp index 39094c9bc372..751ed7abbd0e 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.cpp @@ -16,46 +16,16 @@ namespace gapi { namespace wip { namespace onevpl { -VPLMediaFrameCPUAdapter::VPLMediaFrameCPUAdapter(std::shared_ptr surface): - parent_surface_ptr(surface) { - - GAPI_Assert(parent_surface_ptr && "Surface is nullptr"); - GAPI_LOG_DEBUG(nullptr, "surface: " << parent_surface_ptr->get_handle() << - ", w: " << parent_surface_ptr->get_info().Width << - ", h: " << parent_surface_ptr->get_info().Height << - ", p: " << parent_surface_ptr->get_data().Pitch); - const Surface::info_t& info = parent_surface_ptr->get_info(); - switch(info.FourCC) - { - case MFX_FOURCC_I420: - throw std::runtime_error("MediaFrame doesn't support I420 type"); - break; - case MFX_FOURCC_NV12: - frame_desc.fmt = MediaFormat::NV12; - break; - default: - throw std::runtime_error("MediaFrame unknown 'fmt' type: " + std::to_string(info.FourCC)); - } - - frame_desc.size = cv::Size{info.Width, info.Height}; - parent_surface_ptr->obtain_lock(); -} - -VPLMediaFrameCPUAdapter::~VPLMediaFrameCPUAdapter() { - - // Each VPLMediaFrameCPUAdapter releases mfx surface counter - // The last VPLMediaFrameCPUAdapter releases shared Surface pointer - // The last surface pointer releases workspace memory - parent_surface_ptr->release_lock(); +VPLMediaFrameCPUAdapter::VPLMediaFrameCPUAdapter(std::shared_ptr surface, + SessionHandle assoc_handle): + BaseFrameAdapter(surface, assoc_handle, AccelType::HOST) { } -cv::GFrameDesc VPLMediaFrameCPUAdapter::meta() const { - return frame_desc; -} +VPLMediaFrameCPUAdapter::~VPLMediaFrameCPUAdapter() = default; MediaFrame::View VPLMediaFrameCPUAdapter::access(MediaFrame::Access) { - const Surface::data_t& data = parent_surface_ptr->get_data(); - const Surface::info_t& info = parent_surface_ptr->get_info(); + const Surface::data_t& data = get_surface()->get_data(); + const Surface::info_t& info = get_surface()->get_info(); using stride_t = typename cv::MediaFrame::View::Strides::value_type; stride_t pitch = static_cast(data.Pitch); diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp index 1c51ad74738e..849c3a277517 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp @@ -6,10 +6,8 @@ #ifndef GAPI_STREAMING_ONEVPL_ACCELERATORS_SURFACE_CPU_FRAME_ADAPTER_HPP #define GAPI_STREAMING_ONEVPL_ACCELERATORS_SURFACE_CPU_FRAME_ADAPTER_HPP -#include -#include -#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS +#include "streaming/onevpl/accelerators/surface/base_frame_adapter.hpp" #ifdef HAVE_ONEVPL @@ -18,22 +16,20 @@ namespace gapi { namespace wip { namespace onevpl { -class Surface; -class VPLMediaFrameCPUAdapter : public cv::MediaFrame::IAdapter { +class VPLMediaFrameCPUAdapter : public BaseFrameAdapter { public: // GAPI_EXPORTS for tests - GAPI_EXPORTS explicit VPLMediaFrameCPUAdapter(std::shared_ptr assoc_surface); + GAPI_EXPORTS explicit VPLMediaFrameCPUAdapter(std::shared_ptr assoc_surface, + SessionHandle assoc_handle); GAPI_EXPORTS ~VPLMediaFrameCPUAdapter(); - cv::GFrameDesc meta() const override; + MediaFrame::View access(MediaFrame::Access) override; // The default implementation does nothing cv::util::any blobParams() const override; void serialize(cv::gapi::s11n::IOStream&) override; void deserialize(cv::gapi::s11n::IIStream&) override; -private: - std::shared_ptr parent_surface_ptr; - GFrameDesc frame_desc; + }; } // namespace onevpl } // namespace wip diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.cpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.cpp index 04cf10c8d7b9..fad26e50a81c 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.cpp @@ -12,6 +12,8 @@ #ifdef HAVE_ONEVPL #include "streaming/onevpl/onevpl_export.hpp" +#ifdef HAVE_DIRECTX +#ifdef HAVE_D3D11 #ifdef HAVE_INF_ENGINE // For IE classes (ParamMap, etc) #include @@ -40,113 +42,71 @@ void unlock_mid(mfxMemId mid, mfxFrameData &data, MediaFrame::Access mode) { } } -VPLMediaFrameDX11Adapter::VPLMediaFrameDX11Adapter(std::shared_ptr surface): - parent_surface_ptr(surface) { - GAPI_Assert(parent_surface_ptr && "Surface is nullptr"); - - const Surface::info_t& info = parent_surface_ptr->get_info(); - Surface::data_t& data = parent_surface_ptr->get_data(); - GAPI_LOG_DEBUG(nullptr, "surface: " << parent_surface_ptr->get_handle() << - ", w: " << info.Width << ", h: " << info.Height << - ", p: " << data.Pitch); - switch(info.FourCC) - { - case MFX_FOURCC_I420: - throw std::runtime_error("MediaFrame doesn't support I420 type"); - break; - case MFX_FOURCC_NV12: - frame_desc.fmt = MediaFormat::NV12; - break; - default: - throw std::runtime_error("MediaFrame unknown 'fmt' type: " + std::to_string(info.FourCC)); - } - frame_desc.size = cv::Size{info.Width, info.Height}; +VPLMediaFrameDX11Adapter::VPLMediaFrameDX11Adapter(std::shared_ptr assoc_surface, + SessionHandle assoc_handle): + BaseFrameAdapter(assoc_surface, assoc_handle, AccelType::DX11) { + Surface::data_t& data = assoc_surface->get_data(); LockAdapter* alloc_data = reinterpret_cast(data.MemId); alloc_data->set_adaptee(this); - - parent_surface_ptr->obtain_lock(); } VPLMediaFrameDX11Adapter::~VPLMediaFrameDX11Adapter() { - // Each VPLMediaFrameDX11Adapter releases mfx surface counter - // The last VPLMediaFrameDX11Adapter releases shared Surface pointer - // The last surface pointer releases workspace memory - Surface::data_t& data = parent_surface_ptr->get_data(); + Surface::data_t& data = surface()->get_data(); LockAdapter* alloc_data = reinterpret_cast(data.MemId); alloc_data->set_adaptee(nullptr); - - parent_surface_ptr->release_lock(); -} - -cv::GFrameDesc VPLMediaFrameDX11Adapter::meta() const { - return frame_desc; } MediaFrame::View VPLMediaFrameDX11Adapter::access(MediaFrame::Access mode) { - Surface::data_t& data = parent_surface_ptr->get_data(); - const Surface::info_t& info = parent_surface_ptr->get_info(); + // NB: make copy for some copyable object, because access release may be happened + // after source/pool destruction, so we need a copy + auto surface_ptr_copy = surface(); + Surface::data_t& data = surface_ptr_copy->get_data(); + const Surface::info_t& info = surface_ptr_copy->get_info(); void* frame_id = reinterpret_cast(this); - GAPI_LOG_DEBUG(nullptr, "START lock frame in surface: " << parent_surface_ptr->get_handle() << + GAPI_LOG_DEBUG(nullptr, "START lock frame in surface: " << surface_ptr_copy->get_handle() << ", frame id: " << frame_id); // lock MT lock_mid(data.MemId, data, mode); - GAPI_LOG_DEBUG(nullptr, "FINISH lock frame in surface: " << parent_surface_ptr->get_handle() << + GAPI_LOG_DEBUG(nullptr, "FINISH lock frame in surface: " << surface_ptr_copy->get_handle() << ", frame id: " << frame_id); using stride_t = typename cv::MediaFrame::View::Strides::value_type; stride_t pitch = static_cast(data.Pitch); - // NB: make copy for some copyable object, because access release may be happened - // after source/pool destruction, so we need a copy - auto parent_surface_ptr_copy = parent_surface_ptr; + auto release_guard = [surface_ptr_copy, frame_id, mode] () { + surface_ptr_copy->obtain_lock(); + + auto& data = surface_ptr_copy->get_data(); + GAPI_LOG_DEBUG(nullptr, "START unlock frame in surface: " << surface_ptr_copy->get_handle() << + ", frame id: " << frame_id); + unlock_mid(data.MemId, data, mode); + + GAPI_LOG_DEBUG(nullptr, "FINISH unlock frame in surface: " << surface_ptr_copy->get_handle() << + ", frame id: " << frame_id); + surface_ptr_copy->release_lock(); + }; + switch(info.FourCC) { case MFX_FOURCC_I420: { GAPI_Assert(data.Y && data.U && data.V && "MFX_FOURCC_I420 frame data is nullptr"); cv::MediaFrame::View::Ptrs pp = { data.Y, data.U, data.V, nullptr }; cv::MediaFrame::View::Strides ss = { pitch, pitch / 2, pitch / 2, 0u }; - return cv::MediaFrame::View(std::move(pp), std::move(ss), - [parent_surface_ptr_copy, - frame_id, mode] () { - parent_surface_ptr_copy->obtain_lock(); - - auto& data = parent_surface_ptr_copy->get_data(); - GAPI_LOG_DEBUG(nullptr, "START unlock frame in surface: " << parent_surface_ptr_copy->get_handle() << - ", frame id: " << frame_id); - unlock_mid(data.MemId, data, mode); - - GAPI_LOG_DEBUG(nullptr, "FINISH unlock frame in surface: " << parent_surface_ptr_copy->get_handle() << - ", frame id: " << frame_id); - - parent_surface_ptr_copy->release_lock(); - }); + return cv::MediaFrame::View(std::move(pp), std::move(ss), release_guard); } case MFX_FOURCC_NV12: { if (!data.Y || !data.UV) { - GAPI_LOG_WARNING(nullptr, "Empty data detected!!! for surface: " << parent_surface_ptr->get_handle() << + GAPI_LOG_WARNING(nullptr, "Empty data detected!!! for surface: " << surface_ptr_copy->get_handle() << ", frame id: " << frame_id); } GAPI_Assert(data.Y && data.UV && "MFX_FOURCC_NV12 frame data is nullptr"); cv::MediaFrame::View::Ptrs pp = { data.Y, data.UV, nullptr, nullptr }; cv::MediaFrame::View::Strides ss = { pitch, pitch, 0u, 0u }; - return cv::MediaFrame::View(std::move(pp), std::move(ss), - [parent_surface_ptr_copy, - frame_id, mode] () { - parent_surface_ptr_copy->obtain_lock(); - - auto& data = parent_surface_ptr_copy->get_data(); - GAPI_LOG_DEBUG(nullptr, "START unlock frame in surface: " << parent_surface_ptr_copy->get_handle() << - ", frame id: " << frame_id); - unlock_mid(data.MemId, data, mode); - - GAPI_LOG_DEBUG(nullptr, "FINISH unlock frame in surface: " << parent_surface_ptr_copy->get_handle() << - ", frame id: " << frame_id); - parent_surface_ptr_copy->release_lock(); - }); + return cv::MediaFrame::View(std::move(pp), std::move(ss), release_guard); } break; default: @@ -155,30 +115,45 @@ MediaFrame::View VPLMediaFrameDX11Adapter::access(MediaFrame::Access mode) { } cv::util::any VPLMediaFrameDX11Adapter::blobParams() const { + /*GAPI_Assert(false && "VPLMediaFrameDX11Adapter::blobParams() is not fully integrated" + "in OpenVINO InferenceEngine and would be temporary disable.");*/ #ifdef HAVE_INF_ENGINE - GAPI_Assert(false && "VPLMediaFrameDX11Adapter::blobParams() is not fully operable " - "in G-API streaming. Please waiting for future PRs"); - - Surface::data_t& data = parent_surface_ptr->get_data(); + auto surface_ptr_copy = get_surface(); + Surface::data_t& data = surface_ptr_copy->get_data(); + const Surface::info_t& info = surface_ptr_copy->get_info(); NativeHandleAdapter* native_handle_getter = reinterpret_cast(data.MemId); mfxHDLPair handle{}; native_handle_getter->get_handle(data.MemId, reinterpret_cast(handle)); - InferenceEngine::ParamMap params{{"SHARED_MEM_TYPE", "VA_SURFACE"}, - {"DEV_OBJECT_HANDLE", handle.first}, - {"COLOR_FORMAT", InferenceEngine::ColorFormat::NV12}, - {"VA_PLANE", + GAPI_Assert(frame_desc.fmt == MediaFormat::NV12 && + "blobParams() for VPLMediaFrameDX11Adapter supports NV12 only"); + + InferenceEngine::ParamMap y_params{{"SHARED_MEM_TYPE", "VA_SURFACE"}, + {"DEV_OBJECT_HANDLE", handle.first}, + {"COLOR_FORMAT", InferenceEngine::ColorFormat::NV12}, + {"VA_PLANE", static_cast( reinterpret_cast( reinterpret_cast( handle.second)))}};//, - const Surface::info_t& info = parent_surface_ptr->get_info(); - InferenceEngine::TensorDesc tdesc({InferenceEngine::Precision::U8, - {1, 3, static_cast(info.Height), - static_cast(info.Width)}, - InferenceEngine::Layout::NCHW}); - return std::make_pair(tdesc, params); + InferenceEngine::TensorDesc y_tdesc({InferenceEngine::Precision::U8, + {1, 1, static_cast(info.Height), + static_cast(info.Width)}, + InferenceEngine::Layout::NHWC}); + + InferenceEngine::ParamMap uv_params = y_params; + uv_params["MEM_HANDLE"] = handle.first; + uv_params["VA_PLANE"] = static_cast( + reinterpret_cast( + reinterpret_cast( + handle.second))) + 1; + InferenceEngine::TensorDesc uv_tdesc({InferenceEngine::Precision::U8, + {1, 2, static_cast(info.Height) / 2, + static_cast(info.Width) / 2}, + InferenceEngine::Layout::NHWC}); + return std::make_pair(std::make_pair(y_tdesc, y_params), + std::make_pair(uv_tdesc, uv_params)); #else GAPI_Assert(false && "VPLMediaFrameDX11Adapter::blobParams() is not implemented"); #endif // HAVE_INF_ENGINE @@ -229,4 +204,6 @@ DXGI_FORMAT VPLMediaFrameDX11Adapter::get_dx11_color_format(uint32_t mfx_fourcc) } // namespace wip } // namespace gapi } // namespace cv +#endif // HAVE_D3D11 +#endif // HAVE_DIRECTX #endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.hpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.hpp index ca6602353b75..39528ca6a556 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/dx11_frame_adapter.hpp @@ -8,9 +8,7 @@ #define GAPI_STREAMING_ONEVPL_ACCELERATORS_SURFACE_DX11_FRAME_ADAPTER_HPP #include -#include -#include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS - +#include "streaming/onevpl/accelerators/surface/base_frame_adapter.hpp" #include "streaming/onevpl/accelerators/utils/shared_lock.hpp" #ifdef HAVE_ONEVPL #include "streaming/onevpl/onevpl_export.hpp" @@ -30,15 +28,13 @@ namespace cv { namespace gapi { namespace wip { namespace onevpl { - -class Surface; -class VPLMediaFrameDX11Adapter final: public cv::MediaFrame::IAdapter, +class VPLMediaFrameDX11Adapter final: public BaseFrameAdapter, public SharedLock { public: // GAPI_EXPORTS for tests - GAPI_EXPORTS VPLMediaFrameDX11Adapter(std::shared_ptr assoc_surface); + GAPI_EXPORTS VPLMediaFrameDX11Adapter(std::shared_ptr assoc_surface, + SessionHandle assoc_handle); GAPI_EXPORTS ~VPLMediaFrameDX11Adapter(); - cv::GFrameDesc meta() const override; MediaFrame::View access(MediaFrame::Access) override; // The default implementation does nothing @@ -48,9 +44,7 @@ class VPLMediaFrameDX11Adapter final: public cv::MediaFrame::IAdapter, static DXGI_FORMAT get_dx11_color_format(uint32_t mfx_fourcc); private: - std::shared_ptr parent_surface_ptr; mfxFrameAllocator allocator; - GFrameDesc frame_desc; }; } // namespace onevpl } // namespace wip diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.cpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.cpp index c09dc8033821..88df8b2f94c3 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.cpp @@ -4,6 +4,7 @@ // // Copyright (C) 2021 Intel Corporation +#include #include "streaming/onevpl/accelerators/surface/surface.hpp" #include "logger.hpp" @@ -20,12 +21,12 @@ Surface::Surface(std::unique_ptr&& surf, std::shared_ptr associa mirrored_locked_count() { GAPI_Assert(mfx_surface && "Surface is nullptr"); - GAPI_LOG_DEBUG(nullptr, "create surface: " << mfx_surface << + GAPI_LOG_DEBUG(nullptr, "create surface: " << get_handle() << ", locked count: " << mfx_surface->Data.Locked); } Surface::~Surface() { - GAPI_LOG_DEBUG(nullptr, "destroy surface: " << mfx_surface << + GAPI_LOG_DEBUG(nullptr, "destroy surface: " << get_handle() << ", worspace memory counter: " << workspace_memory_ptr.use_count()); } @@ -60,7 +61,7 @@ size_t Surface::get_locks_count() const { size_t Surface::obtain_lock() { size_t locked_count = mirrored_locked_count.fetch_add(1); - GAPI_LOG_DEBUG(nullptr, "surface: " << mfx_surface.get() << + GAPI_LOG_DEBUG(nullptr, "surface: " << get_handle() << ", locked times: " << locked_count + 1); return locked_count; // return preceding value } @@ -68,7 +69,7 @@ size_t Surface::obtain_lock() { size_t Surface::release_lock() { size_t locked_count = mirrored_locked_count.fetch_sub(1); GAPI_Assert(locked_count && "Surface lock counter is invalid"); - GAPI_LOG_DEBUG(nullptr, "surface: " << mfx_surface.get() << + GAPI_LOG_DEBUG(nullptr, "surface: " << get_handle() << ", locked times: " << locked_count - 1); return locked_count; // return preceding value } diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.hpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.hpp index 4f93312e24f0..68d7a697e7a4 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/surface.hpp @@ -59,7 +59,7 @@ class GAPI_EXPORTS Surface final { // GAPI_EXPORTS for tests /** * Extract value thread-safe lock counter (see @ref Surface description). * It's usual situation that counter may be instantly decreased in other thread after this method called. - * We need instantaneous value. This method syncronized in inter-threading way with @ref Surface::release_lock() + * We need instantaneous value. This method synchronized in inter-threading way with @ref Surface::release_lock() * * @return fetched locks count. */ diff --git a/modules/gapi/src/streaming/onevpl/accelerators/surface/surface_pool.cpp b/modules/gapi/src/streaming/onevpl/accelerators/surface/surface_pool.cpp index bd6a0c69d157..454741369328 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/surface/surface_pool.cpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/surface/surface_pool.cpp @@ -1,3 +1,4 @@ +#include #include "streaming/onevpl/accelerators/surface/surface_pool.hpp" #include "streaming/onevpl/accelerators/surface/surface.hpp" #include "logger.hpp" diff --git a/modules/gapi/src/streaming/onevpl/accelerators/utils/elastic_barrier.hpp b/modules/gapi/src/streaming/onevpl/accelerators/utils/elastic_barrier.hpp index 827392f8be9c..b91554f43575 100644 --- a/modules/gapi/src/streaming/onevpl/accelerators/utils/elastic_barrier.hpp +++ b/modules/gapi/src/streaming/onevpl/accelerators/utils/elastic_barrier.hpp @@ -163,7 +163,7 @@ class elastic_barrier { * deinitialization called off in `on_unlock` * because new `incoming` request had appeared at here before * `on_unlock` started deinit procedure in another thread. - * So no reinit required because no deinit had happended + * So no reinit required because no deinit had happened * * main `busy-wait` request must break busy-wait state * and become `outgoing` request. diff --git a/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.cpp b/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.cpp index a4d85f2598e2..90bf3e8849bb 100644 --- a/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.cpp +++ b/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.cpp @@ -9,6 +9,7 @@ #include #include +#include #include "streaming/onevpl/cfg_param_device_selector.hpp" #include "streaming/onevpl/cfg_params_parser.hpp" #include "streaming/onevpl/utils.hpp" @@ -26,18 +27,34 @@ #pragma comment(lib, "dxgi") #undef D3D11_NO_HELPERS #undef NOMINMAX +#endif // HAVE_D3D11 +#endif // HAVE_DIRECTX #include #include "opencv2/core/directx.hpp" -#ifdef HAVE_OPENCL -#include -#endif namespace cv { namespace gapi { namespace wip { namespace onevpl { +static std::vector insertCfgparam(std::vector &¶m_array, AccelType type) { + switch (type) { + case AccelType::HOST: + break; + case AccelType::DX11: + param_array.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); + break; + case AccelType::VAAPI: + param_array.push_back(CfgParam::create_acceleration_mode(MFX_IMPL_VIA_VAAPI)); + break; + default: + GAPI_DbgAssert(false && "Unexpected AccelType"); + break; + } + return std::move(param_array); +} + CfgParamDeviceSelector::CfgParamDeviceSelector(const CfgParams& cfg_params) : suggested_device(IDeviceSelector::create(nullptr, "CPU", AccelType::HOST)), suggested_context(IDeviceSelector::create(nullptr, AccelType::HOST)) { @@ -57,8 +74,7 @@ CfgParamDeviceSelector::CfgParamDeviceSelector(const CfgParams& cfg_params) : switch(accel_mode.Data.U32) { case MFX_ACCEL_MODE_VIA_D3D11: { -#ifdef HAVE_DIRECTX -#ifdef HAVE_D3D11 +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) ID3D11Device *hw_handle = nullptr; ID3D11DeviceContext* device_context = nullptr; @@ -127,14 +143,17 @@ CfgParamDeviceSelector::CfgParamDeviceSelector(const CfgParams& cfg_params) : suggested_device = IDeviceSelector::create(hw_handle, "GPU", AccelType::DX11); suggested_context = IDeviceSelector::create(device_context, AccelType::DX11); -#else +#else // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) GAPI_LOG_WARNING(nullptr, "Unavailable \"" << CfgParam::acceleration_mode_name() << ": MFX_ACCEL_MODE_VIA_D3D11\"" "was chosen for current project configuration"); throw std::logic_error(std::string("Unsupported \"") + CfgParam::acceleration_mode_name() + ": MFX_ACCEL_MODE_VIA_D3D11\""); -#endif // HAVE_DIRECTX -#endif // HAVE_D3D11 +#endif // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) + break; + } + case MFX_IMPL_VIA_VAAPI : { + GAPI_LOG_WARNING(nullptr, "TODO MFX_IMPL_VIA_VAAPI falls back to CPU case") break; } case MFX_ACCEL_MODE_NA: { @@ -184,10 +203,10 @@ CfgParamDeviceSelector::CfgParamDeviceSelector(Device::Ptr device_ptr, } mfxVariant accel_mode = cfg_param_to_mfx_variant(*accel_mode_it); + cv::util::suppress_unused_warning(device_id); switch(accel_mode.Data.U32) { case MFX_ACCEL_MODE_VIA_D3D11: { -#ifdef HAVE_DIRECTX -#ifdef HAVE_D3D11 +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) suggested_device = IDeviceSelector::create(device_ptr, device_id, AccelType::DX11); ID3D11Device* dx_device_ptr = reinterpret_cast(suggested_device.get_ptr()); @@ -206,14 +225,13 @@ CfgParamDeviceSelector::CfgParamDeviceSelector(Device::Ptr device_ptr, } dx_ctx_ptr->AddRef(); -#else +#else // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) GAPI_LOG_WARNING(nullptr, "Unavailable \"" << CfgParam::acceleration_mode_name() << ": MFX_ACCEL_MODE_VIA_D3D11\"" "was chosen for current project configuration"); throw std::logic_error(std::string("Unsupported \"") + CfgParam::acceleration_mode_name() + ": MFX_ACCEL_MODE_VIA_D3D11\""); -#endif // HAVE_DIRECTX -#endif // HAVE_D3D11 +#endif // #if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) break; } case MFX_ACCEL_MODE_NA: { @@ -231,6 +249,53 @@ CfgParamDeviceSelector::CfgParamDeviceSelector(Device::Ptr device_ptr, } } +CfgParamDeviceSelector::CfgParamDeviceSelector(const Device &device, + const Context &ctx, + CfgParams) : + suggested_device(device), + suggested_context(ctx) { + + switch(device.get_type()) { + case AccelType::DX11: { +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) + ID3D11Device* dx_device_ptr = + reinterpret_cast(suggested_device.get_ptr()); + dx_device_ptr->AddRef(); + + ID3D11DeviceContext* dx_ctx_ptr = + reinterpret_cast(suggested_context.get_ptr()); + + // oneVPL recommendation + { + ID3D11Multithread *pD11Multithread = nullptr; + dx_ctx_ptr->QueryInterface(IID_PPV_ARGS(&pD11Multithread)); + pD11Multithread->SetMultithreadProtected(true); + pD11Multithread->Release(); + } + + dx_ctx_ptr->AddRef(); + break; +#else // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) + GAPI_LOG_WARNING(nullptr, "Unavailable \"" << CfgParam::acceleration_mode_name() << + ": MFX_ACCEL_MODE_VIA_D3D11\"" + "was chosen for current project configuration"); + throw std::logic_error(std::string("Unsupported \"") + + CfgParam::acceleration_mode_name() + ": MFX_ACCEL_MODE_VIA_D3D11\""); +#endif // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) + } + case AccelType::VAAPI: + GAPI_LOG_WARNING(nullptr, "TODO MFX_IMPL_VIA_VAAPI falls back to CPU case") + break; + case AccelType::HOST: + break; + default: + throw std::logic_error(std::string("Unsupported \"") + CfgParam::acceleration_mode_name() + + "\" requested: " + + to_cstring(device.get_type())); + break; + } +} + CfgParamDeviceSelector::~CfgParamDeviceSelector() { GAPI_LOG_INFO(nullptr, "release context: " << suggested_context.get_ptr()); AccelType ctype = suggested_context.get_type(); @@ -239,14 +304,12 @@ CfgParamDeviceSelector::~CfgParamDeviceSelector() { //nothing to do break; case AccelType::DX11: { -#ifdef HAVE_DIRECTX -#ifdef HAVE_D3D11 +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) ID3D11DeviceContext* device_ctx_ptr = reinterpret_cast(suggested_context.get_ptr()); device_ctx_ptr->Release(); device_ctx_ptr = nullptr; -#endif // HAVE_DIRECTX -#endif // HAVE_D3D11 +#endif // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) break; } default: @@ -262,13 +325,11 @@ CfgParamDeviceSelector::~CfgParamDeviceSelector() { //nothing to do break; case AccelType::DX11: { -#ifdef HAVE_DIRECTX -#ifdef HAVE_D3D11 +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) ID3D11Device* device_ptr = reinterpret_cast(suggested_device.get_ptr()); device_ptr->Release(); device_ptr = nullptr; -#endif // HAVE_DIRECTX -#endif // HAVE_D3D11 +#endif // defined(HAVE_DIRECTX) && defined(HAVE_D3D11) break; } default: @@ -277,7 +338,7 @@ CfgParamDeviceSelector::~CfgParamDeviceSelector() { } CfgParamDeviceSelector::DeviceScoreTable CfgParamDeviceSelector::select_devices() const { - return {std::make_pair(Score::MaxActivePriority, suggested_device)}; + return {std::make_pair(Score::Type(Score::MaxActivePriority), suggested_device)}; } CfgParamDeviceSelector::DeviceContexts CfgParamDeviceSelector::select_context() { @@ -288,6 +349,4 @@ CfgParamDeviceSelector::DeviceContexts CfgParamDeviceSelector::select_context() } // namespace wip } // namespace gapi } // namespace cv -#endif // HAVE_D3D11 -#endif // HAVE_DIRECTX #endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.hpp b/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.hpp index 2a55fb09cf53..5dae1c508d9f 100644 --- a/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.hpp +++ b/modules/gapi/src/streaming/onevpl/cfg_param_device_selector.hpp @@ -7,14 +7,14 @@ #ifndef GAPI_STREAMING_ONEVPL_CFG_PARAM_DEVICE_SELECTOR_HPP #define GAPI_STREAMING_ONEVPL_CFG_PARAM_DEVICE_SELECTOR_HPP -#ifdef HAVE_ONEVPL - #include #include #include #include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS +#ifdef HAVE_ONEVPL + namespace cv { namespace gapi { namespace wip { @@ -26,6 +26,9 @@ struct GAPI_EXPORTS CfgParamDeviceSelector final: public IDeviceSelector { const std::string& device_id, Context::Ptr ctx_ptr, const CfgParams& params); + CfgParamDeviceSelector(const Device &device_ptr, + const Context &ctx_ptr, + CfgParams params); ~CfgParamDeviceSelector(); DeviceScoreTable select_devices() const override; diff --git a/modules/gapi/src/streaming/onevpl/cfg_params.cpp b/modules/gapi/src/streaming/onevpl/cfg_params.cpp index 599f751358dc..b13f9cadb126 100644 --- a/modules/gapi/src/streaming/onevpl/cfg_params.cpp +++ b/modules/gapi/src/streaming/onevpl/cfg_params.cpp @@ -118,6 +118,82 @@ CfgParam CfgParam::create_implementation(const char* value) { return CfgParam::create(CfgParam::implementation_name(), std::string(value)); } +CfgParam CfgParam::create_vpp_frames_pool_size(size_t value) { + // NB: cast to uint64_t because CfgParam inner variant works over + // uint64_t instead of size_t and mirrored VPL types variety + // but size_t looks more friendly for C++ high-level development + return CfgParam::create(CfgParam::vpp_frames_pool_size_name(), + static_cast(value), false); +} + +CfgParam CfgParam::create_vpp_in_width(uint16_t value) { + return CfgParam::create(CfgParam::vpp_in_width_name(), value, false); +} + +CfgParam CfgParam::create_vpp_in_height(uint16_t value) { + return CfgParam::create(CfgParam::vpp_in_height_name(), value, false); +} + +CfgParam CfgParam::create_vpp_in_crop_x(uint16_t value) { + return CfgParam::create(CfgParam::vpp_in_crop_x_name(), value, false); +} + +CfgParam CfgParam::create_vpp_in_crop_y(uint16_t value) { + return CfgParam::create(CfgParam::vpp_in_crop_y_name(), value, false); +} + +CfgParam CfgParam::create_vpp_in_crop_w(uint16_t value) { + return CfgParam::create(CfgParam::vpp_in_crop_w_name(), value, false); +} + +CfgParam CfgParam::create_vpp_in_crop_h(uint16_t value) { + return CfgParam::create(CfgParam::vpp_in_crop_h_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_fourcc(uint32_t value) { + return CfgParam::create(CfgParam::vpp_out_fourcc_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_chroma_format(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_chroma_format_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_width(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_width_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_height(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_height_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_crop_x(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_crop_x_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_crop_y(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_crop_y_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_crop_w(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_crop_w_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_crop_h(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_crop_h_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_pic_struct(uint16_t value) { + return CfgParam::create(CfgParam::vpp_out_pic_struct_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_framerate_n(uint32_t value) { + return CfgParam::create(CfgParam::vpp_out_framerate_n_name(), value, false); +} + +CfgParam CfgParam::create_vpp_out_framerate_d(uint32_t value) { + return CfgParam::create(CfgParam::vpp_out_framerate_d_name(), value, false); +} + CfgParam& CfgParam::operator=(const CfgParam& src) { if (this != &src) { m_priv = src.m_priv; diff --git a/modules/gapi/src/streaming/onevpl/cfg_params_parser.cpp b/modules/gapi/src/streaming/onevpl/cfg_params_parser.cpp index 07c639faa227..9f5a68a43117 100644 --- a/modules/gapi/src/streaming/onevpl/cfg_params_parser.cpp +++ b/modules/gapi/src/streaming/onevpl/cfg_params_parser.cpp @@ -35,6 +35,12 @@ struct ParamCreator { return create_impl(name, value); } private: + mfxVariant create_impl(const std::string&, mfxU16 value) { + mfxVariant ret; + ret.Type = MFX_VARIANT_TYPE_U16; + ret.Data.U16 = value; + return ret; + } mfxVariant create_impl(const std::string&, mfxU32 value) { mfxVariant ret; ret.Type = MFX_VARIANT_TYPE_U32; @@ -53,6 +59,10 @@ struct ParamCreator { ret.Data.U64 = value; return ret; } + mfxVariant create_impl(const std::string&, const std::string&) { + GAPI_Assert(false && "Something wrong: you should not create mfxVariant " + "from string directly - native type is lost in this case"); + } }; template @@ -77,15 +87,32 @@ std::vector get_params_from_string(const std::string& str) { ParamCreator creator; if (name == CfgParam::implementation_name()) { - ret.push_back(creator.create(name, cstr_to_mfx_impl(value.c_str()))); + ret.push_back(creator.template create(name, cstr_to_mfx_impl(value.c_str()))); } else if (name == CfgParam::decoder_id_name()) { - ret.push_back(creator.create(name, cstr_to_mfx_codec_id(value.c_str()))); + ret.push_back(creator.template create(name, cstr_to_mfx_codec_id(value.c_str()))); } else if (name == CfgParam::acceleration_mode_name()) { - ret.push_back(creator.create(name, cstr_to_mfx_accel_mode(value.c_str()))); + ret.push_back(creator.template create(name, cstr_to_mfx_accel_mode(value.c_str()))); } else if (name == "mfxImplDescription.ApiVersion.Version") { - ret.push_back(creator.create(name, cstr_to_mfx_version(value.c_str()))); - } else if (name == CfgParam::frames_pool_size_name()) { - ret.push_back(creator.create(name, strtoull_or_throw(value.c_str()), false)); + ret.push_back(creator.template create(name, cstr_to_mfx_version(value.c_str()))); + } else if ((name == CfgParam::frames_pool_size_name()) || (name == CfgParam::vpp_frames_pool_size_name())) { + ret.push_back(creator.create(name, static_cast(strtoull_or_throw(value.c_str()), false))); + } else if ((name == CfgParam::vpp_in_width_name()) || (name == CfgParam::vpp_in_height_name()) || + (name == CfgParam::vpp_in_crop_w_name()) || (name == CfgParam::vpp_in_crop_h_name()) || + (name == CfgParam::vpp_in_crop_x_name()) || (name == CfgParam::vpp_in_crop_y_name()) || + (name == CfgParam::vpp_out_chroma_format_name()) || + (name == CfgParam::vpp_out_width_name()) || (name == CfgParam::vpp_out_height_name()) || + (name == CfgParam::vpp_out_crop_w_name()) || (name == CfgParam::vpp_out_crop_h_name()) || + (name == CfgParam::vpp_out_crop_x_name()) || (name == CfgParam::vpp_out_crop_y_name()) || + (name == CfgParam::vpp_out_pic_struct_name())) { + ret.push_back(creator.create(name, + static_cast(strtoul_or_throw(value.c_str())), + false)); + } else if ((name == CfgParam::vpp_out_fourcc_name()) || + (name == CfgParam::vpp_out_framerate_n_name()) || + (name == CfgParam::vpp_out_framerate_d_name())) { + ret.push_back(creator.create(name, + static_cast(strtoul_or_throw(value.c_str())), + false)); } else { GAPI_LOG_DEBUG(nullptr, "Cannot parse configuration param, name: " << name << ", value: " << value); @@ -128,12 +155,51 @@ mfxVariant cfg_param_to_mfx_variant(const CfgParam& cfg_val) { return ret; } +void extract_optional_param_by_name(const std::string &name, + const std::vector &in_params, + cv::util::optional &out_param) { + auto it = std::find_if(in_params.begin(), in_params.end(), [&name] (const CfgParam& value) { + return value.get_name() == name; + }); + if (it != in_params.end()) { + cv::util::visit(cv::util::overload_lambdas( + [&out_param](uint8_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](int8_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](uint16_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](int16_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](uint32_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](int32_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](uint64_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](int64_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](float_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](double_t value) { out_param = cv::util::make_optional(static_cast(value)); }, + [&out_param](void*) { GAPI_Assert(false && "`void*` is unsupported type"); }, + [&out_param](const std::string& value) { + out_param = cv::util::make_optional(strtoull_or_throw(value.c_str())); + }), + it->get_value()); + } +} + +unsigned long strtoul_or_throw(const char* str) { + char *end_ptr = nullptr; + errno = 0; + unsigned long ret = strtoul(str, &end_ptr, 10); + if ((end_ptr == str) || + ((ret == ULONG_MAX) && errno == ERANGE)) { + // nothing parsed from the string, handle errors or exit + GAPI_LOG_WARNING(nullptr, "strtoul failed for: " << str); + GAPI_Assert(false && "strtoul_or_throw"); + } + return ret; +} + size_t strtoull_or_throw(const char* str) { char *end_ptr = nullptr; errno = 0; size_t ret = strtoull(str, &end_ptr, 10); if ((end_ptr == str) || - ((ret == LONG_MAX || ret == LONG_MIN) && errno == ERANGE)) { + ((ret == ULLONG_MAX) && errno == ERANGE)) { // nothing parsed from the string, handle errors or exit GAPI_LOG_WARNING(nullptr, "strtoull failed for: " << str); GAPI_Assert(false && "strtoull_or_throw"); diff --git a/modules/gapi/src/streaming/onevpl/cfg_params_parser.hpp b/modules/gapi/src/streaming/onevpl/cfg_params_parser.hpp index c5e768575650..e01d5c412a0f 100644 --- a/modules/gapi/src/streaming/onevpl/cfg_params_parser.hpp +++ b/modules/gapi/src/streaming/onevpl/cfg_params_parser.hpp @@ -31,6 +31,11 @@ struct ParamCreator { mfxVariant cfg_param_to_mfx_variant(const CfgParam& value); +void extract_optional_param_by_name(const std::string &name, + const std::vector& in_params, + cv::util::optional &out_param); + +unsigned long strtoul_or_throw(const char* str); size_t strtoull_or_throw(const char* str); int64_t strtoll_or_throw(const char* str); diff --git a/modules/gapi/src/streaming/onevpl/demux/async_mfp_demux_data_provider.cpp b/modules/gapi/src/streaming/onevpl/demux/async_mfp_demux_data_provider.cpp index 85d7d1aaf01d..5d139af824bb 100644 --- a/modules/gapi/src/streaming/onevpl/demux/async_mfp_demux_data_provider.cpp +++ b/modules/gapi/src/streaming/onevpl/demux/async_mfp_demux_data_provider.cpp @@ -14,6 +14,7 @@ #pragma comment(lib, "mfreadwrite.lib") #endif // _WIN32 +#include #include "streaming/onevpl/demux/async_mfp_demux_data_provider.hpp" #include "logger.hpp" @@ -807,7 +808,7 @@ bool MFPAsyncDemuxDataProvider::fetch_bitstream_data(std::shared_ptr #ifdef _WIN32 #define NOMINMAX @@ -28,7 +29,6 @@ #include #undef NOMINMAX -#include #include "streaming/onevpl/data_provider_defines.hpp" #include "streaming/onevpl/utils.hpp" diff --git a/modules/gapi/src/streaming/onevpl/device_selector_interface.cpp b/modules/gapi/src/streaming/onevpl/device_selector_interface.cpp index 1ac88bd80772..d58d1d3d3c57 100644 --- a/modules/gapi/src/streaming/onevpl/device_selector_interface.cpp +++ b/modules/gapi/src/streaming/onevpl/device_selector_interface.cpp @@ -20,6 +20,8 @@ const char* to_cstring(AccelType type) { return "HOST"; case AccelType::DX11: return "DX11"; + case AccelType::VAAPI: + return "VAAPI"; default: GAPI_DbgAssert(false && "Unexpected AccelType"); break; @@ -81,6 +83,42 @@ IDeviceSelector::Score::Type IDeviceSelector::Score::get() const { IDeviceSelector::~IDeviceSelector() { } +namespace detail +{ +struct DeviceContextCreator : public IDeviceSelector { + DeviceScoreTable select_devices() const override { return {};} + DeviceContexts select_context() override { return {};} + + template + static Entity create_entity(Args &&...args) { + return IDeviceSelector::create(std::forward(args)...); + } +}; +} + +Device create_host_device() { + return detail::DeviceContextCreator::create_entity(nullptr, + "CPU", + AccelType::HOST); +} + +Context create_host_context() { + return detail::DeviceContextCreator::create_entity(nullptr, + AccelType::HOST); +} + +Device create_dx11_device(Device::Ptr device_ptr, + const std::string& device_name) { + return detail::DeviceContextCreator::create_entity(device_ptr, + device_name, + AccelType::DX11); +} + +Context create_dx11_context(Context::Ptr ctx_ptr) { + return detail::DeviceContextCreator::create_entity(ctx_ptr, + AccelType::DX11); +} + } // namespace onevpl } // namespace wip } // namespace gapi diff --git a/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.cpp b/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.cpp index 6707a401b19b..34db1bebfa2c 100644 --- a/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.cpp +++ b/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.cpp @@ -26,6 +26,31 @@ namespace gapi { namespace wip { namespace onevpl { +void VPLLegacyDecodeEngine::try_modify_pool_size_request_param(const char* param_name, + size_t new_frames_count, + mfxFrameAllocRequest& request) { + if (new_frames_count < request.NumFrameMin) { + GAPI_LOG_WARNING(nullptr, "Cannot proceed with CfgParam \"" << param_name << "\": " << + new_frames_count << ". It must be equal or greater than " + "mfxFrameAllocRequest.NumFrameMin: " << request.NumFrameMin); + throw std::runtime_error(std::string("Invalid value of param: ") + + param_name + ", underflow"); + } else { + if (static_cast(std::numeric_limits::max()) < new_frames_count) { + GAPI_LOG_WARNING(nullptr, "Cannot proceed with CfgParam \"" << param_name << "\": " << + new_frames_count << ". It must not be greater than " << + std::numeric_limits::max()); + throw std::runtime_error(std::string("Invalid value of param: ") + + param_name + ", overflow"); + } + request.NumFrameSuggested = static_cast(new_frames_count); + GAPI_LOG_DEBUG(nullptr, "mfxFrameAllocRequest overridden by user input: " << + ", mfxFrameAllocRequest.NumFrameMin: " << request.NumFrameMin << + ", mfxFrameAllocRequest.NumFrameSuggested: " << request.NumFrameSuggested << + ", mfxFrameAllocRequest.Type: " << request.Type); + } +} + VPLLegacyDecodeEngine::VPLLegacyDecodeEngine(std::unique_ptr&& accel) : ProcessingEngineBase(std::move(accel)) { @@ -58,11 +83,8 @@ VPLLegacyDecodeEngine::VPLLegacyDecodeEngine(std::unique_ptrDataLength)) - ? my_sess.stream.get() - - : nullptr, /* No more data to read, start decode draining mode*/ - my_sess.procesing_surface_ptr.lock()->get_handle(), + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), &sync_pair.second, &sync_pair.first); @@ -73,12 +95,12 @@ VPLLegacyDecodeEngine::VPLLegacyDecodeEngine(std::unique_ptrget_handle(), + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), &sync_pair.second, &sync_pair.first); @@ -130,7 +152,7 @@ VPLLegacyDecodeEngine::VPLLegacyDecodeEngine(std::unique_ptr ExecutionStatus { return this->process_error(sess.last_status, static_cast(sess)); @@ -138,11 +160,12 @@ VPLLegacyDecodeEngine::VPLLegacyDecodeEngine(std::unique_ptr& cfg_params, - std::shared_ptr provider) { - GAPI_DbgAssert(provider && "Cannot create decoder, data provider is nullptr"); +VPLLegacyDecodeEngine::SessionParam VPLLegacyDecodeEngine::prepare_session_param( + mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider) { + + GAPI_DbgAssert(provider && "Cannot create decoder, data provider is nullptr"); // init session acceleration_policy->init(mfx_session); @@ -152,9 +175,10 @@ VPLLegacyDecodeEngine::initialize_session(mfxSession mfx_session, // Prepare video param mfxVideoParam mfxDecParams {}; + memset(&mfxDecParams, 0, sizeof(mfxDecParams)); mfxDecParams.mfx.CodecId = decoder_id_name; - // set memory stream direction accroding to accelearion policy device type + // set memory stream direction according to acceleration policy device type IDeviceSelector::DeviceScoreTable devices = acceleration_policy->get_device_selector()->select_devices(); GAPI_Assert(devices.size() == 1 && "Multiple(or zero) acceleration devices case is unsupported"); AccelType accel_type = devices.begin()->second.get_type(); @@ -206,95 +230,70 @@ VPLLegacyDecodeEngine::initialize_session(mfxSession mfx_session, ", mfxFrameAllocRequest.Type: " << decRequest.Type); // NB: override NumFrameSuggested preallocation size (how many frames we can hold) - size_t preallocated_frames_count = decRequest.NumFrameSuggested; - // NB: if you see bunch of WARNING about "cannot get free surface from pool" - // and have abundant RAM size then increase `preallocated_frames_count` + // if you see bunch of WARNING about "cannot get free surface from pool" + // and have abundant RAM size then increase `CfgParam::frames_pool_size_name()` // to keep more free surfaces in a round. Otherwise VPL decode pipeline will be waiting // till application is freeing unusable surface on its side. // - auto queue_capacity_it = std::find_if(cfg_params.begin(), cfg_params.end(), [] (const CfgParam& value) { - return value.get_name() == CfgParam::frames_pool_size_name(); - }); - if (queue_capacity_it != cfg_params.end()) { - cv::util::visit(cv::util::overload_lambdas( - [&preallocated_frames_count](uint8_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](int8_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](uint16_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](int16_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](uint32_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](int32_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](uint64_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](int64_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](float_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](double_t value) { preallocated_frames_count = static_cast(value); }, - [&preallocated_frames_count](void*) { GAPI_Assert(false && "`void*` is unsupported type"); }, - [&preallocated_frames_count](const std::string& value) { - preallocated_frames_count = strtoull_or_throw(value.c_str()); - }), - queue_capacity_it->get_value()); - + cv::optional preallocated_frames_count_cfg; + extract_optional_param_by_name(CfgParam::frames_pool_size_name(), + cfg_params, + preallocated_frames_count_cfg); + if (preallocated_frames_count_cfg.has_value()) { GAPI_LOG_INFO(nullptr, "Try to use CfgParam \"" << CfgParam::frames_pool_size_name() << "\": " << - preallocated_frames_count << ", for session: " << mfx_session); + preallocated_frames_count_cfg.value() << ", for session: " << mfx_session); + try_modify_pool_size_request_param(CfgParam::frames_pool_size_name(), + preallocated_frames_count_cfg.value(), + decRequest); } - if (preallocated_frames_count < decRequest.NumFrameMin) { - GAPI_LOG_WARNING(nullptr, "Cannot proceed with CfgParam \"" << CfgParam::frames_pool_size_name() << "\": " << - preallocated_frames_count << ". It must be equal or greater than " - "mfxFrameAllocRequest.NumFrameMin: " << decRequest.NumFrameMin); - throw std::runtime_error(std::string("Invalid value of param: ") + - CfgParam::frames_pool_size_name() + ", underflow"); - } else { - if (static_cast(std::numeric_limits::max()) < preallocated_frames_count) { - GAPI_LOG_WARNING(nullptr, "Cannot proceed with CfgParam \"" << CfgParam::frames_pool_size_name() << "\": " << - preallocated_frames_count << ". It must not be equal than " << - std::numeric_limits::max()); - throw std::runtime_error(std::string("Invalid value of param: ") + - CfgParam::frames_pool_size_name() + ", overflow"); - } - decRequest.NumFrameSuggested = static_cast(preallocated_frames_count); - GAPI_LOG_DEBUG(nullptr, "mfxFrameAllocRequest overriden by user input for session: " << mfx_session << - ", mfxFrameAllocRequest.NumFrameMin: " << decRequest.NumFrameMin << - ", mfxFrameAllocRequest.NumFrameSuggested: " << decRequest.NumFrameSuggested << - ", mfxFrameAllocRequest.Type: " << decRequest.Type); - } + decRequest.Type |= MFX_MEMTYPE_EXTERNAL_FRAME | MFX_MEMTYPE_FROM_DECODE | MFX_MEMTYPE_FROM_VPPIN; VPLAccelerationPolicy::pool_key_t decode_pool_key = - acceleration_policy->create_surface_pool(decRequest, mfxDecParams); + acceleration_policy->create_surface_pool(decRequest, mfxDecParams.mfx.FrameInfo); // Input parameters finished, now initialize decode - // create decoder for session accoring to header recovered from source file + // create decoder for session according to header recovered from source file + GAPI_LOG_INFO(nullptr, "Initialize decoder for session: " << mfx_session << + ", frame info: " << mfx_frame_info_to_string(mfxDecParams.mfx.FrameInfo)); sts = MFXVideoDECODE_Init(mfx_session, &mfxDecParams); if (MFX_ERR_NONE != sts) { throw std::runtime_error("Error initializing Decode, error: " + mfxstatus_to_string(sts)); } - DecoderParams decoder_param {bitstream, mfxDecParams}; + return {decode_pool_key, {bitstream, mfxDecParams, preallocated_frames_count_cfg}}; +} + + +ProcessingEngineBase::session_ptr +VPLLegacyDecodeEngine::initialize_session(mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider) { + + SessionParam param = prepare_session_param(mfx_session, cfg_params, provider); // create session std::shared_ptr sess_ptr = register_session(mfx_session, - std::move(decoder_param), + std::move(param.decoder_params), provider); - sess_ptr->init_surface_pool(decode_pool_key); + sess_ptr->init_surface_pool(param.decode_pool_key); // prepare working decode surface - sess_ptr->swap_surface(*this); + sess_ptr->swap_decode_surface(*this); return sess_ptr; } -ProcessingEngineBase::ExecutionStatus VPLLegacyDecodeEngine::execute_op(operation_t& op, EngineSession& sess) { - return op(sess); -} - void VPLLegacyDecodeEngine::on_frame_ready(LegacyDecodeSession& sess, mfxFrameSurface1* ready_surface) { GAPI_LOG_DEBUG(nullptr, "[" << sess.session << "], frame ready"); // manage memory ownership rely on acceleration policy + VPLAccelerationPolicy::FrameConstructorArgs args{ready_surface, sess.session}; auto frame_adapter = acceleration_policy->create_frame_adapter(sess.decoder_pool_id, - ready_surface); + args); ready_frames.emplace(cv::MediaFrame(std::move(frame_adapter)), sess.generate_frame_meta()); // pop away synced out object @@ -310,7 +309,7 @@ ProcessingEngineBase::ExecutionStatus VPLLegacyDecodeEngine::process_error(mfxSt { // prepare sync object for new surface try { - sess.swap_surface(*this); + sess.swap_decode_surface(*this); return ExecutionStatus::Continue; } catch (const std::runtime_error& ex) { GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] error: " << ex.what()); @@ -331,7 +330,7 @@ ProcessingEngineBase::ExecutionStatus VPLLegacyDecodeEngine::process_error(mfxSt // This applies to external memory allocations and should not be expected for // a simple internal allocation case like this try { - sess.swap_surface(*this); + sess.swap_decode_surface(*this); return ExecutionStatus::Continue; } catch (const std::runtime_error& ex) { GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] error: " << ex.what()); @@ -355,9 +354,7 @@ ProcessingEngineBase::ExecutionStatus VPLLegacyDecodeEngine::process_error(mfxSt // The decoder detected a new sequence header in the bitstream. // Video parameters may have changed. // In external memory allocation case, might need to reallocate the output surface - /*GAPI_DbgAssert(false && "VPLLegacyDecodeEngine::process_error - " - "MFX_WRN_VIDEO_PARAM_CHANGED is not processed"); - */ + GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] got MFX_WRN_VIDEO_PARAM_CHANGED"); return ExecutionStatus::Continue; break; case MFX_ERR_INCOMPATIBLE_VIDEO_PARAM: @@ -377,7 +374,7 @@ ProcessingEngineBase::ExecutionStatus VPLLegacyDecodeEngine::process_error(mfxSt break; case MFX_WRN_IN_EXECUTION: try { - sess.swap_surface(*this); + sess.swap_decode_surface(*this); return ExecutionStatus::Continue; } catch (const std::runtime_error& ex) { GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] error: " << ex.what()); diff --git a/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.hpp b/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.hpp index f6a02db3db65..5b4142b69324 100644 --- a/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.hpp +++ b/modules/gapi/src/streaming/onevpl/engine/decode/decode_engine_legacy.hpp @@ -24,20 +24,30 @@ struct DecoderParams; struct IDataProvider; struct VPLAccelerationPolicy; -class VPLLegacyDecodeEngine : public ProcessingEngineBase { +class GAPI_EXPORTS VPLLegacyDecodeEngine : public ProcessingEngineBase { public: VPLLegacyDecodeEngine(std::unique_ptr&& accel); - session_ptr initialize_session(mfxSession mfx_session, - const std::vector& cfg_params, - std::shared_ptr provider) override; + virtual session_ptr initialize_session(mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider) override; +protected: + struct SessionParam { + void* decode_pool_key; + DecoderParams decoder_params; + }; + + SessionParam prepare_session_param(mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider); -private: - ExecutionStatus execute_op(operation_t& op, EngineSession& sess) override; ExecutionStatus process_error(mfxStatus status, LegacyDecodeSession& sess); void on_frame_ready(LegacyDecodeSession& sess, mfxFrameSurface1* ready_surface); + static void try_modify_pool_size_request_param(const char* param_name, + size_t new_frames_count, + mfxFrameAllocRequest& request); }; } // namespace onevpl } // namespace wip diff --git a/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.cpp b/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.cpp index bbb1378767e3..4d522e6db694 100644 --- a/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.cpp +++ b/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.cpp @@ -11,7 +11,6 @@ #include "streaming/onevpl/engine/decode/decode_session.hpp" #include "streaming/onevpl/engine/decode/decode_engine_legacy.hpp" -#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" #include "streaming/onevpl/accelerators/surface/surface.hpp" #include "streaming/onevpl/utils.hpp" @@ -23,10 +22,11 @@ namespace onevpl { LegacyDecodeSession::LegacyDecodeSession(mfxSession sess, DecoderParams&& decoder_param, std::shared_ptr provider) : - EngineSession(sess, std::move(decoder_param.stream)), + EngineSession(sess), mfx_decoder_param(std::move(decoder_param.param)), data_provider(std::move(provider)), - procesing_surface_ptr(), + stream(std::move(decoder_param.stream)), + processing_surface_ptr(), sync_queue(), decoded_frames_count() { @@ -38,25 +38,10 @@ LegacyDecodeSession::~LegacyDecodeSession() MFXVideoDECODE_Close(session); } -void LegacyDecodeSession::swap_surface(VPLLegacyDecodeEngine& engine) { +void LegacyDecodeSession::swap_decode_surface(VPLLegacyDecodeEngine& engine) { VPLAccelerationPolicy* acceleration_policy = engine.get_accel(); GAPI_Assert(acceleration_policy && "Empty acceleration_policy"); - try { - auto cand = acceleration_policy->get_free_surface(decoder_pool_id).lock(); - - GAPI_LOG_DEBUG(nullptr, "[" << session << "] swap surface" - ", old: " << (!procesing_surface_ptr.expired() - ? procesing_surface_ptr.lock()->get_handle() - : nullptr) << - ", new: "<< cand->get_handle()); - - procesing_surface_ptr = cand; - } catch (const std::runtime_error& ex) { - GAPI_LOG_WARNING(nullptr, "[" << session << "] error: " << ex.what()); - - // Delegate exception processing on caller - throw; - } + request_free_surface(session, decoder_pool_id, *acceleration_policy, processing_surface_ptr); } void LegacyDecodeSession::init_surface_pool(VPLAccelerationPolicy::pool_key_t key) { @@ -75,8 +60,13 @@ Data::Meta LegacyDecodeSession::generate_frame_meta() { return meta; } -const mfxVideoParam& LegacyDecodeSession::get_video_param() const { - return mfx_decoder_param; +const mfxFrameInfo& LegacyDecodeSession::get_video_param() const { + return mfx_decoder_param.mfx.FrameInfo; +} + +IDataProvider::mfx_bitstream *LegacyDecodeSession::get_mfx_bitstream_ptr() { + return (data_provider || (stream && stream->DataLength)) ? + stream.get() : nullptr; } } // namespace onevpl } // namespace wip diff --git a/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.hpp b/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.hpp index 476a575172d8..676564f066b8 100644 --- a/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.hpp +++ b/modules/gapi/src/streaming/onevpl/engine/decode/decode_session.hpp @@ -13,7 +13,6 @@ #include #include "streaming/onevpl/engine/engine_session.hpp" -#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" #ifdef HAVE_ONEVPL #include "streaming/onevpl/onevpl_export.hpp" @@ -21,32 +20,35 @@ namespace cv { namespace gapi { namespace wip { namespace onevpl { - -struct IDataProvider; class Surface; struct VPLAccelerationPolicy; +class VPLLegacyDecodeEngine; -class LegacyDecodeSession : public EngineSession { +class GAPI_EXPORTS LegacyDecodeSession : public EngineSession { public: friend class VPLLegacyDecodeEngine; + friend class VPLLegacyTranscodeEngine; //TODO: remove friend add method LegacyDecodeSession(mfxSession sess, DecoderParams&& decoder_param, std::shared_ptr provider); ~LegacyDecodeSession(); using EngineSession::EngineSession; - void swap_surface(VPLLegacyDecodeEngine& engine); + void swap_decode_surface(VPLLegacyDecodeEngine& engine); void init_surface_pool(VPLAccelerationPolicy::pool_key_t key); Data::Meta generate_frame_meta(); - const mfxVideoParam& get_video_param() const override; + virtual const mfxFrameInfo& get_video_param() const override; + + IDataProvider::mfx_bitstream *get_mfx_bitstream_ptr(); private: mfxVideoParam mfx_decoder_param; - std::shared_ptr data_provider; VPLAccelerationPolicy::pool_key_t decoder_pool_id; - mfxFrameAllocRequest request; - std::weak_ptr procesing_surface_ptr; + std::shared_ptr data_provider; + std::shared_ptr stream; +protected: + std::weak_ptr processing_surface_ptr; using op_handle_t = std::pair; std::queue sync_queue; diff --git a/modules/gapi/src/streaming/onevpl/engine/engine_session.cpp b/modules/gapi/src/streaming/onevpl/engine/engine_session.cpp index 4915b51e3476..9a2b812db3e5 100644 --- a/modules/gapi/src/streaming/onevpl/engine/engine_session.cpp +++ b/modules/gapi/src/streaming/onevpl/engine/engine_session.cpp @@ -14,8 +14,10 @@ namespace gapi { namespace wip { namespace onevpl { -EngineSession::EngineSession(mfxSession sess, std::shared_ptr&& str) : - session(sess), stream(std::move(str)) {} +EngineSession::EngineSession(mfxSession sess) : + session(sess) { +} + EngineSession::~EngineSession() { GAPI_LOG_INFO(nullptr, "Close session: " << session); @@ -26,6 +28,31 @@ std::string EngineSession::error_code_to_str() const { return mfxstatus_to_string(last_status); } + +void EngineSession::request_free_surface(mfxSession session, + VPLAccelerationPolicy::pool_key_t key, + VPLAccelerationPolicy &acceleration_policy, + std::weak_ptr &surface_to_exchange, + bool reset_if_not_found) { + try { + auto cand = acceleration_policy.get_free_surface(key).lock(); + + GAPI_LOG_DEBUG(nullptr, "[" << session << "] swap surface" + ", old: " << (!surface_to_exchange.expired() + ? surface_to_exchange.lock()->get_handle() + : nullptr) << + ", new: "<< cand->get_handle()); + + surface_to_exchange = cand; + } catch (const std::runtime_error& ex) { + GAPI_LOG_WARNING(nullptr, "[" << session << "] error: " << ex.what()); + if (reset_if_not_found) { + surface_to_exchange.reset(); + } + // Delegate exception processing on caller side + throw; + } +} } // namespace onevpl } // namespace wip } // namespace gapi diff --git a/modules/gapi/src/streaming/onevpl/engine/engine_session.hpp b/modules/gapi/src/streaming/onevpl/engine/engine_session.hpp index 67018d0fd70b..c684941069a0 100644 --- a/modules/gapi/src/streaming/onevpl/engine/engine_session.hpp +++ b/modules/gapi/src/streaming/onevpl/engine/engine_session.hpp @@ -14,8 +14,11 @@ #include #include +#include "opencv2/gapi/util/optional.hpp" #include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS #include +#include "streaming/onevpl/data_provider_defines.hpp" +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" #ifdef HAVE_ONEVPL #include "streaming/onevpl/onevpl_export.hpp" @@ -29,19 +32,28 @@ namespace onevpl { struct GAPI_EXPORTS DecoderParams { std::shared_ptr stream; mfxVideoParam param; + cv::util::optional preallocated_frames_count; +}; + +struct GAPI_EXPORTS TranscoderParams { + mfxVideoParam param; }; struct GAPI_EXPORTS EngineSession { mfxSession session; - std::shared_ptr stream; - mfxSyncPoint sync; mfxStatus last_status; - EngineSession(mfxSession sess, std::shared_ptr&& str); + EngineSession(mfxSession sess); std::string error_code_to_str() const; virtual ~EngineSession(); - virtual const mfxVideoParam& get_video_param() const = 0; + virtual const mfxFrameInfo& get_video_param() const = 0; + + static void request_free_surface(mfxSession session, + VPLAccelerationPolicy::pool_key_t key, + VPLAccelerationPolicy &acceleration_policy, + std::weak_ptr &surface_to_exchange, + bool reset_if_not_found = false); }; } // namespace onevpl } // namespace wip diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_dispatcher.cpp b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_dispatcher.cpp new file mode 100644 index 000000000000..5a08f2bd093d --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_dispatcher.cpp @@ -0,0 +1,105 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#include +#include + +#include +#include "streaming/onevpl/engine/preproc/preproc_dispatcher.hpp" + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" +#include "streaming/onevpl/engine/preproc/preproc_engine.hpp" +#include "streaming/onevpl/engine/preproc/preproc_session.hpp" + +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/cfg_params_parser.hpp" +#endif // HAVE_ONEVPL + +#include "logger.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +#ifdef HAVE_ONEVPL +cv::util::optional VPPPreprocDispatcher::is_applicable(const cv::MediaFrame& in_frame) { + cv::util::optional param; + GAPI_LOG_DEBUG(nullptr, "workers: " << workers.size()); + bool worker_found = false; + for (const auto &w : workers) { + param = w->is_applicable(in_frame); + if (param.has_value()) { + auto &vpp_param = param.value().get(); + BaseFrameAdapter* adapter = reinterpret_cast(vpp_param.reserved); + const IDeviceSelector::DeviceScoreTable &devs = + (std::static_pointer_cast(w))->get_accel()->get_device_selector()->select_devices(); + GAPI_DbgAssert(devs.size() >= 1 && "Invalid device selector"); + auto worker_accel_type = std::get<1>(*devs.begin()).get_type(); + GAPI_LOG_DEBUG(nullptr, "acceleration types for frame: " << to_cstring(adapter->accel_type()) << + ", for worker: " << to_cstring(worker_accel_type)); + if (worker_accel_type == adapter->accel_type()){ + vpp_param.reserved = reinterpret_cast(w.get()); + GAPI_LOG_DEBUG(nullptr, "selected worker: " << vpp_param.reserved); + worker_found = true; + break; + } + } + } + return worker_found ? param : cv::util::optional{}; +} + +pp_session VPPPreprocDispatcher::initialize_preproc(const pp_params& initial_frame_param, + const GFrameDesc& required_frame_descr) { + const auto &vpp_param = initial_frame_param.get(); + GAPI_LOG_DEBUG(nullptr, "workers: " << workers.size()); + for (auto &w : workers) { + if (reinterpret_cast(w.get()) == vpp_param.reserved) { + pp_session sess = w->initialize_preproc(initial_frame_param, required_frame_descr); + vpp_pp_session &vpp_sess = sess.get(); + vpp_sess.reserved = reinterpret_cast(w.get()); + GAPI_LOG_DEBUG(nullptr, "initialized session preproc for worker: " << vpp_sess.reserved); + return sess; + } + } + GAPI_Assert(false && "Cannot initialize VPP preproc in dispatcher, no suitable worker"); +} + +cv::MediaFrame VPPPreprocDispatcher::run_sync(const pp_session &session_handle, + const cv::MediaFrame& in_frame, + const cv::util::optional &opt_roi) { + const auto &vpp_sess = session_handle.get(); + GAPI_LOG_DEBUG(nullptr, "workers: " << workers.size()); + for (auto &w : workers) { + if (reinterpret_cast(w.get()) == vpp_sess.reserved) { + GAPI_LOG_DEBUG(nullptr, "trigger execution on worker: " << vpp_sess.reserved); + return w->run_sync(session_handle, in_frame, opt_roi); + } + } + GAPI_Assert(false && "Cannot invoke VPP preproc in dispatcher, no suitable worker"); +} + +#else // HAVE_ONEVPL +cv::util::optional VPPPreprocDispatcher::is_applicable(const cv::MediaFrame&) { + return cv::util::optional{}; +} + +pp_session VPPPreprocDispatcher::initialize_preproc(const pp_params&, + const GFrameDesc&) { + GAPI_Assert(false && "Unsupported: G-API compiled without `WITH_GAPI_ONEVPL=ON`"); +} + +cv::MediaFrame VPPPreprocDispatcher::run_sync(const pp_session &, + const cv::MediaFrame&, + const cv::util::optional &) { + GAPI_Assert(false && "Unsupported: G-API compiled without `WITH_GAPI_ONEVPL=ON`"); +} +#endif // HAVE_ONEVPL +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_dispatcher.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_dispatcher.hpp new file mode 100644 index 000000000000..ea808bd54200 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_dispatcher.hpp @@ -0,0 +1,48 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONEVPL_PREPROC_DISPATCHER_HPP +#define GAPI_STREAMING_ONEVPL_PREPROC_DISPATCHER_HPP + +#include +#include + +#include "streaming/onevpl/engine/preproc_engine_interface.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { + +// GAPI_EXPORTS for tests +class GAPI_EXPORTS VPPPreprocDispatcher final : public cv::gapi::wip::IPreprocEngine { +public: + + cv::util::optional is_applicable(const cv::MediaFrame& in_frame) override; + + pp_session initialize_preproc(const pp_params& initial_frame_param, + const GFrameDesc& required_frame_descr) override; + + cv::MediaFrame run_sync(const pp_session &session_handle, + const cv::MediaFrame& in_frame, + const cv::util::optional &opt_roi) override; + + template + void insert_worker(Args&& ...args) { + workers.emplace_back(std::make_shared(std::forward(args)...)); + } + + size_t size() const { + return workers.size(); + } +private: + std::vector> workers; +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // GAPI_STREAMING_ONEVPL_PREPROC_DISPATCHER_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_engine.cpp b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_engine.cpp new file mode 100644 index 000000000000..10ce92e20aad --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_engine.cpp @@ -0,0 +1,473 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifdef HAVE_ONEVPL + +#include +#include + +#include + +#include "streaming/onevpl/engine/preproc/preproc_engine.hpp" +#include "streaming/onevpl/engine/preproc/preproc_session.hpp" + +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/cfg_params_parser.hpp" +#include "logger.hpp" + +#define ALIGN16(value) (((value + 15) >> 4) << 4) + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { + +bool FrameInfoComparator::operator()(const mfxFrameInfo& lhs, const mfxFrameInfo& rhs) const { + return lhs < rhs; +} + +bool FrameInfoComparator::equal_to(const mfxFrameInfo& lhs, const mfxFrameInfo& rhs) { + return lhs == rhs; +} + +static void apply_roi(mfxFrameSurface1* surface_handle, + const cv::util::optional &opt_roi) { + if (opt_roi.has_value()) { + const cv::Rect &roi = opt_roi.value(); + surface_handle->Info.CropX = static_cast(roi.x); + surface_handle->Info.CropY = static_cast(roi.y); + surface_handle->Info.CropW = static_cast(roi.width); + surface_handle->Info.CropH = static_cast(roi.height); + GAPI_LOG_DEBUG(nullptr, "applied ROI {" << surface_handle->Info.CropX << + ", " << surface_handle->Info.CropY << "}, " + "{ " << surface_handle->Info.CropX + surface_handle->Info.CropW << + ", " << surface_handle->Info.CropY + surface_handle->Info.CropH << "}"); + } +} + +VPPPreprocEngine::VPPPreprocEngine(std::unique_ptr&& accel) : + ProcessingEngineBase(std::move(accel)) { + GAPI_LOG_DEBUG(nullptr, "Create VPP preprocessing engine"); + preprocessed_frames_count = 0; + create_pipeline( + // 0) preproc decoded surface with VPP params + [this] (EngineSession& sess) -> ExecutionStatus + { + session_type &my_sess = static_cast(sess); + while (!my_sess.sync_in_queue.empty()) { + do { + if (!my_sess.processing_surface_ptr.expired()) { + session_type::incoming_task pending_op = my_sess.sync_in_queue.front(); + GAPI_LOG_DEBUG(nullptr, "pending IN operations count: " << + my_sess.sync_in_queue.size() << + ", sync id: " << + pending_op.sync_handle << + ", surface: " << + pending_op.decoded_surface_ptr); + + my_sess.sync_in_queue.pop(); + auto *vpp_suface = my_sess.processing_surface_ptr.lock()->get_handle(); + + apply_roi(pending_op.decoded_surface_ptr, pending_op.roi); + + mfxSyncPoint vpp_sync_handle{}; + my_sess.last_status = MFXVideoVPP_RunFrameVPPAsync(my_sess.session, + pending_op.decoded_surface_ptr, + vpp_suface, + nullptr, &vpp_sync_handle); + session_type::outgoing_task vpp_pending_op {vpp_sync_handle, + vpp_suface, + std::move(pending_op) }; + GAPI_LOG_DEBUG(nullptr, "Got VPP async operation" << + ", sync id: " << + vpp_pending_op.sync_handle << + ", dec surface: " << + vpp_pending_op.original_surface_ptr << + ", trans surface: " << + vpp_pending_op.vpp_surface_ptr << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + // NB: process status + if (my_sess.last_status == MFX_ERR_MORE_SURFACE || + my_sess.last_status == MFX_ERR_NONE) { + vpp_pending_op.vpp_surface_ptr->Data.Locked++; // TODO -S- workaround + my_sess.vpp_out_queue.emplace(vpp_pending_op); + } + } + + try { + my_sess.swap_surface(*this); + } catch (const std::runtime_error& ex) { + // NB: not an error, yield CPU ticks to check + // surface availability at a next phase. + // But print WARNING to notify user about pipeline stuck + GAPI_LOG_WARNING(nullptr, "[" << my_sess.session << + "] has no VPP surface, reason: " << + ex.what()); + my_sess.processing_surface_ptr.reset(); + break; + } + } while(my_sess.last_status == MFX_ERR_MORE_SURFACE); + + if (my_sess.processing_surface_ptr.expired()) { + // TODO break main loop + break; + } + } + return ExecutionStatus::Continue; + }, + // 1) Wait for ASYNC decode result + [this] (EngineSession& sess) -> ExecutionStatus + { + session_type& my_sess = static_cast(sess); + do { + if (!my_sess.vpp_out_queue.empty()) { // FIFO: check the oldest async operation complete + session_type::outgoing_task& pending_op = my_sess.vpp_out_queue.front(); + sess.last_status = MFXVideoCORE_SyncOperation(sess.session, pending_op.sync_handle, 0); + + GAPI_LOG_DEBUG(nullptr, "pending VPP operations count: " << + my_sess.vpp_out_queue.size() << + ", sync id: " << + pending_op.sync_handle << + ", surface: " << + pending_op.vpp_surface_ptr << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + + // put frames in ready queue on success + if (MFX_ERR_NONE == sess.last_status) { + pending_op.release_frame(); + on_frame_ready(my_sess, pending_op.vpp_surface_ptr); + } + } + } while (MFX_ERR_NONE == sess.last_status && !my_sess.vpp_out_queue.empty()); + return ExecutionStatus::Continue; + }, + // 2) Falls back on generic status processing + [this] (EngineSession& sess) -> ExecutionStatus + { + return this->process_error(sess.last_status, static_cast(sess)); + } + ); +} + +cv::util::optional VPPPreprocEngine::is_applicable(const cv::MediaFrame& in_frame) { + // TODO consider something smarter than RTI + cv::util::optional ret; + BaseFrameAdapter *vpl_adapter = in_frame.get(); + GAPI_LOG_DEBUG(nullptr, "validate VPP preprocessing is applicable for frame"); + if (vpl_adapter) { + ret = cv::util::make_optional( + pp_params::create(vpl_adapter->get_session_handle(), + vpl_adapter->get_surface()->get_info(), + vpl_adapter)); + GAPI_LOG_DEBUG(nullptr, "VPP preprocessing applicable, session [" << + vpl_adapter->get_session_handle() << "]"); + } + return ret; +} + +pp_session VPPPreprocEngine::initialize_preproc(const pp_params& initial_frame_param, + const GFrameDesc& required_frame_descr) { + const vpp_pp_params ¶ms = initial_frame_param.get(); + + // adjust preprocessing settings + mfxVideoParam mfxVPPParams{}; + memset(&mfxVPPParams, 0, sizeof(mfxVideoParam)); + // NB: IN params for VPP session must be equal to decoded surface params + mfxVPPParams.vpp.In = params.info; + + // NB: OUT params must refer to IN params of a network + GAPI_LOG_DEBUG(nullptr, "network input size: " << required_frame_descr.size.width << + "x" << required_frame_descr.size.height); + mfxVPPParams.vpp.Out = mfxVPPParams.vpp.In; + switch (required_frame_descr.fmt) { + case MediaFormat::NV12: + mfxVPPParams.vpp.Out.FourCC = MFX_FOURCC_NV12; + break; + default: + GAPI_LOG_WARNING(nullptr, "Unsupported MediaFormat in preprocessing: " << + static_cast(required_frame_descr.fmt) << + ". Frame will be rejected"); + throw std::runtime_error("unsupported MediaFormat value in VPP preprocessing"); + } + + mfxVPPParams.vpp.Out.ChromaFormat = MFX_CHROMAFORMAT_YUV420; + mfxVPPParams.vpp.Out.Width = static_cast(required_frame_descr.size.width); + mfxVPPParams.vpp.Out.Height = static_cast(required_frame_descr.size.height); + mfxVPPParams.vpp.Out.CropW = mfxVPPParams.vpp.Out.Width; + mfxVPPParams.vpp.Out.CropH = mfxVPPParams.vpp.Out.Height; + + // check In & Out equally to bypass preproc + if (mfxVPPParams.vpp.Out == mfxVPPParams.vpp.In) { + GAPI_LOG_DEBUG(nullptr, "no preproc required"); + return pp_session::create(nullptr); + } + + // recalculate size param according to VPP alignment + mfxVPPParams.vpp.Out.Width = ALIGN16(mfxVPPParams.vpp.Out.Width); + mfxVPPParams.vpp.Out.Height = ALIGN16(mfxVPPParams.vpp.Out.Height); + mfxVPPParams.vpp.Out.CropW = mfxVPPParams.vpp.Out.Width; + mfxVPPParams.vpp.Out.CropH = mfxVPPParams.vpp.Out.Height; + + GAPI_LOG_DEBUG(nullptr, "\nFrom:\n{\n" << mfx_frame_info_to_string(mfxVPPParams.vpp.In) << + "}\nTo:\n{\n" << mfx_frame_info_to_string(mfxVPPParams.vpp.Out) << "}"); + + // find existing session + GAPI_LOG_DEBUG(nullptr, "Find existing VPPPreprocSession for requested frame params" + ", total sessions: " << preproc_session_map.size()); + auto it = preproc_session_map.find(mfxVPPParams.vpp.In); + if (it != preproc_session_map.end()) { + GAPI_LOG_DEBUG(nullptr, "[" << it->second->session << "] found"); + return pp_session::create(std::static_pointer_cast(it->second)); + } + + // NB: make some sanity checks + IDeviceSelector::DeviceScoreTable devices = acceleration_policy->get_device_selector()->select_devices(); + GAPI_Assert(devices.size() == 1 && "Multiple(or zero) acceleration devices case is unsupported"); + AccelType accel_type = devices.begin()->second.get_type(); + // assign acceleration + if (accel_type == AccelType::DX11) { + mfxVPPParams.IOPattern = MFX_IOPATTERN_IN_VIDEO_MEMORY | MFX_IOPATTERN_OUT_VIDEO_MEMORY; + } else { + mfxVPPParams.IOPattern = MFX_IOPATTERN_IN_SYSTEM_MEMORY | MFX_IOPATTERN_OUT_SYSTEM_MEMORY; + } + + // clone existing VPL session to inherit VPL loader configuration + // and avoid refer to any global state + // TODO no clone due to clone issue + + mfxSession mfx_vpp_session = params.handle; + mfxStatus sts = MFX_ERR_NONE; + + // TODO: simply use clone after VPL bug fixing + //sts = MFXCloneSession(params.handle, &mfx_vpp_session); + sts = MFXCreateSession(mfx_handle, impl_number, &mfx_vpp_session); + if (sts != MFX_ERR_NONE) { + GAPI_LOG_WARNING(nullptr, "Cannot clone VPP session, error: " << mfxstatus_to_string(sts)); + GAPI_Assert(false && "Cannot continue VPP preprocessing"); + } + + sts = MFXJoinSession(params.handle, mfx_vpp_session); + if (sts != MFX_ERR_NONE) { + GAPI_LOG_WARNING(nullptr, "Cannot join VPP sessions, error: " << mfxstatus_to_string(sts)); + GAPI_Assert(false && "Cannot continue VPP preprocessing"); + } + + GAPI_LOG_INFO(nullptr, "[" << mfx_vpp_session << "] starting pool allocation"); + VPLAccelerationPolicy::pool_key_t vpp_out_pool_key {}; + try { + // assign HW acceleration processor + acceleration_policy->init(mfx_vpp_session); + try { + // ask to allocate external memory pool + mfxFrameAllocRequest vppRequests[2]; + memset(&vppRequests, 0, sizeof(mfxFrameAllocRequest) * 2); + sts = MFXVideoVPP_QueryIOSurf(mfx_vpp_session, &mfxVPPParams, vppRequests); + if (MFX_ERR_NONE != sts) { + GAPI_LOG_WARNING(nullptr, "cannot execute MFXVideoVPP_QueryIOSurf, error: " << + mfxstatus_to_string(sts)); + throw std::runtime_error("Cannot execute MFXVideoVPP_QueryIOSurf"); + } + + // NB: Assign ID as upper limit descendant to distinguish specific VPP allocation + // from decode allocations witch started from 0: by local module convention + + static uint16_t request_id = 0; + vppRequests[1].AllocId = std::numeric_limits::max() - request_id++; + GAPI_Assert(request_id != std::numeric_limits::max() && "Something wrong"); + + vppRequests[1].Type |= MFX_MEMTYPE_FROM_VPPIN; + vpp_out_pool_key = acceleration_policy->create_surface_pool(vppRequests[1], + mfxVPPParams.vpp.Out); + + sts = MFXVideoVPP_Init(mfx_vpp_session, &mfxVPPParams); + if (MFX_ERR_NONE != sts) { + GAPI_LOG_WARNING(nullptr, "cannot Init VPP, error: " << + mfxstatus_to_string(sts)); + // TODO consider deallocate pool + // but not necessary now cause every fail processed as GAPI_Assert + throw std::runtime_error("Cannot init VPP, error: " + + mfxstatus_to_string(sts)); + } + } catch (const std::exception&) { + GAPI_LOG_WARNING(nullptr, "[" << mfx_vpp_session << "] allocation failed, rollback"); + acceleration_policy->deinit(mfx_vpp_session); + throw; + } + } catch (const std::exception&) { + MFXClose(mfx_vpp_session); + GAPI_Assert(false && "Cannot init preproc resources"); + } + + // create engine session after all + session_ptr_type sess_ptr = register_session(mfx_vpp_session, + mfxVPPParams); + sess_ptr->init_surface_pool(vpp_out_pool_key); + sess_ptr->swap_surface(*this); + + bool inserted = preproc_session_map.emplace(mfxVPPParams.vpp.In, sess_ptr).second; + GAPI_Assert(inserted && "preproc session is exist"); + GAPI_LOG_INFO(nullptr, "VPPPreprocSession created, total sessions: " << preproc_session_map.size()); + return pp_session::create(std::static_pointer_cast(sess_ptr)); +} + +void VPPPreprocEngine::on_frame_ready(session_type& sess, + mfxFrameSurface1* ready_surface) +{ + GAPI_LOG_DEBUG(nullptr, "[" << sess.session << "], frame ready"); + + // manage memory ownership rely on acceleration policy + ready_surface->Data.Locked--; // TODO -S- workaround + VPLAccelerationPolicy::FrameConstructorArgs args{ready_surface, sess.session}; + auto frame_adapter = acceleration_policy->create_frame_adapter(sess.vpp_pool_id, + args); + ready_frames.emplace(cv::MediaFrame(std::move(frame_adapter)), sess.generate_frame_meta()); + + // pop away synced out object + sess.vpp_out_queue.pop(); +} + +VPPPreprocEngine::session_ptr +VPPPreprocEngine::initialize_session(mfxSession, + const std::vector&, + std::shared_ptr) { + return {}; +} + +cv::MediaFrame VPPPreprocEngine::run_sync(const pp_session& sess, const cv::MediaFrame& in_frame, + const cv::util::optional &roi) { + vpp_pp_session pp_sess_impl = sess.get(); + if (!pp_sess_impl.handle) { + // bypass case + return in_frame; + } + session_ptr_type s = std::static_pointer_cast(pp_sess_impl.handle); + GAPI_DbgAssert(s && "Session is nullptr"); + GAPI_DbgAssert(is_applicable(in_frame) && + "VPP preproc is not applicable for the given frame"); + BaseFrameAdapter *vpl_adapter = in_frame.get(); + if (!vpl_adapter) { + GAPI_LOG_WARNING(nullptr, "VPP preproc is inapplicable for a given frame. " + "Make sure the frame is collected using onevpl::GSource"); + throw std::runtime_error("VPP preproc is inapplicable for given frame"); + } + + // schedule decoded surface into preproc queue + session_type::incoming_task in_preproc_request {nullptr, + vpl_adapter->get_surface()->get_handle(), + vpl_adapter->get_surface()->get_info(), + in_frame, + roi}; + s->sync_in_queue.emplace(in_preproc_request); + + // invoke pipeline to transform decoded surface into preprocessed surface + try + { + ExecutionStatus status = ExecutionStatus::Continue; + while (0 == get_ready_frames_count() && + status == ExecutionStatus::Continue) { + status = process(s->session); + } + + if (get_ready_frames_count() == 0) { + GAPI_LOG_WARNING(nullptr, "failed: cannot obtain preprocessed frames, last status: " << + ProcessingEngineBase::status_to_string(status)); + throw std::runtime_error("cannot finalize VPP preprocessing operation"); + } + } catch(const std::exception&) { + throw; + } + // obtain new frame is available + cv::gapi::wip::Data data; + get_frame(data); + preprocessed_frames_count++; + GAPI_LOG_DEBUG(nullptr, "processed frames count: " << preprocessed_frames_count); + return cv::util::get(data); +} + +ProcessingEngineBase::ExecutionStatus VPPPreprocEngine::process_error(mfxStatus status, session_type& sess) { + GAPI_LOG_DEBUG(nullptr, "status: " << mfxstatus_to_string(status)); + + switch (status) { + case MFX_ERR_NONE: + { + // prepare sync object for new surface + try { + sess.swap_surface(*this); + return ExecutionStatus::Continue; + } catch (const std::runtime_error& ex) { + GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] error: " << ex.what()); + return ExecutionStatus::Continue; // read more data + } + } + case MFX_ERR_MORE_DATA: // The function requires more bitstream at input before decoding can proceed + return ExecutionStatus::Processed; + case MFX_ERR_MORE_SURFACE: + { + // The function requires more frame surface at output before decoding can proceed. + // This applies to external memory allocations and should not be expected for + // a simple internal allocation case like this + try { + sess.swap_surface(*this); + return ExecutionStatus::Continue; + } catch (const std::runtime_error& ex) { + GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] error: " << ex.what()); + return ExecutionStatus::Continue; // read more data + } + break; + } + case MFX_ERR_DEVICE_LOST: + // For non-CPU implementations, + // Cleanup if device is lost + GAPI_DbgAssert(false && "VPPPreprocEngine::process_error - " + "MFX_ERR_DEVICE_LOST is not processed"); + break; + case MFX_WRN_DEVICE_BUSY: + // For non-CPU implementations, + // Wait a few milliseconds then try again + GAPI_DbgAssert(false && "VPPPreprocEngine::process_error - " + "MFX_WRN_DEVICE_BUSY is not processed"); + break; + case MFX_WRN_VIDEO_PARAM_CHANGED: + // The decoder detected a new sequence header in the bitstream. + // Video parameters may have changed. + // In external memory allocation case, might need to reallocate the output surface + GAPI_LOG_WARNING(nullptr, "[" << sess.session << "] got MFX_WRN_VIDEO_PARAM_CHANGED"); + return ExecutionStatus::Continue; + break; + case MFX_ERR_INCOMPATIBLE_VIDEO_PARAM: + // The function detected that video parameters provided by the application + // are incompatible with initialization parameters. + // The application should close the component and then reinitialize it + GAPI_DbgAssert(false && "VPPPreprocEngine::process_error - " + "MFX_ERR_INCOMPATIBLE_VIDEO_PARAM is not processed"); + break; + case MFX_ERR_REALLOC_SURFACE: + // Bigger surface_work required. May be returned only if + // mfxInfoMFX::EnableReallocRequest was set to ON during initialization. + // This applies to external memory allocations and should not be expected for + // a simple internal allocation case like this + GAPI_DbgAssert(false && "VPPPreprocEngine::process_error - " + "MFX_ERR_REALLOC_SURFACE is not processed"); + break; + case MFX_WRN_IN_EXECUTION: + GAPI_LOG_DEBUG(nullptr, "[" << sess.session << "] got MFX_WRN_IN_EXECUTION"); + return ExecutionStatus::Continue; + default: + GAPI_LOG_WARNING(nullptr, "Unknown status code: " << mfxstatus_to_string(status) << + ", decoded frames: " << sess.preprocessed_frames_count); + break; + } + + return ExecutionStatus::Failed; +} +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_engine.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_engine.hpp new file mode 100644 index 000000000000..b1d0cee2643a --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_engine.hpp @@ -0,0 +1,68 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONVPL_PREPROC_ENGINE_HPP +#define GAPI_STREAMING_ONVPL_PREPROC_ENGINE_HPP +#include +#include +#include + +#include "streaming/onevpl/engine/processing_engine_base.hpp" +#include "streaming/onevpl/accelerators/utils/shared_lock.hpp" + +#include "streaming/onevpl/engine/preproc_engine_interface.hpp" + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +// GAPI_EXPORTS for tests +struct GAPI_EXPORTS FrameInfoComparator { + bool operator()(const mfxFrameInfo& lhs, const mfxFrameInfo& rhs) const; + static bool equal_to(const mfxFrameInfo& lhs, const mfxFrameInfo& rhs); +}; + +class VPPPreprocSession; +struct IDataProvider; +struct VPLAccelerationPolicy; + +// GAPI_EXPORTS for tests +class GAPI_EXPORTS VPPPreprocEngine final : public ProcessingEngineBase, + public cv::gapi::wip::IPreprocEngine { +public: + using session_type = VPPPreprocSession; + using session_ptr_type = std::shared_ptr; + + VPPPreprocEngine(std::unique_ptr&& accel); + + cv::util::optional is_applicable(const cv::MediaFrame& in_frame) override; + + pp_session initialize_preproc(const pp_params& initial_frame_param, + const GFrameDesc& required_frame_descr) override; + + cv::MediaFrame run_sync(const pp_session &session_handle, + const cv::MediaFrame& in_frame, + const cv::util::optional &opt_roi) override; + +private: + std::map preproc_session_map; + void on_frame_ready(session_type& sess, + mfxFrameSurface1* ready_surface); + ExecutionStatus process_error(mfxStatus status, session_type& sess); + session_ptr initialize_session(mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider) override; + size_t preprocessed_frames_count; +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL +#endif // GAPI_STREAMING_ONVPL_PREPROC_ENGINE_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_session.cpp b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_session.cpp new file mode 100644 index 000000000000..2695a2604992 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_session.cpp @@ -0,0 +1,85 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifdef HAVE_ONEVPL + +#include +#include + +#include "streaming/onevpl/engine/preproc/preproc_session.hpp" +#include "streaming/onevpl/engine/preproc/preproc_engine.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/utils.hpp" +#include "logger.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +VPPPreprocSession::VPPPreprocSession(mfxSession sess, const mfxVideoParam& vpp_out_param) : + EngineSession(sess), + mfx_vpp_out_param(vpp_out_param), + processing_surface_ptr(), + sync_in_queue(), + vpp_out_queue(), + preprocessed_frames_count() +{ +} + +VPPPreprocSession::~VPPPreprocSession() { + GAPI_LOG_INFO(nullptr, "Close VPP for session: " << session); + MFXVideoVPP_Close(session); +} + +Data::Meta VPPPreprocSession::generate_frame_meta() { + const auto now = std::chrono::system_clock::now(); + const auto dur = std::chrono::duration_cast + (now.time_since_epoch()); + Data::Meta meta { + {cv::gapi::streaming::meta_tag::timestamp, int64_t{dur.count()} }, + {cv::gapi::streaming::meta_tag::seq_id, int64_t{preprocessed_frames_count++}} + }; + return meta; +} + +void VPPPreprocSession::swap_surface(VPPPreprocEngine& engine) { + VPLAccelerationPolicy* acceleration_policy = engine.get_accel(); + GAPI_Assert(acceleration_policy && "Empty acceleration_policy"); + request_free_surface(session, vpp_pool_id, *acceleration_policy, + processing_surface_ptr, true); +} + +void VPPPreprocSession::init_surface_pool(VPLAccelerationPolicy::pool_key_t key) { + GAPI_Assert(key && "Init preproc pull with empty key"); + vpp_pool_id = key; +} + +const mfxFrameInfo& VPPPreprocSession::get_video_param() const { + return mfx_vpp_out_param.vpp.Out; +} + +VPPPreprocSession::outgoing_task::outgoing_task(mfxSyncPoint acquired_sync_handle, + mfxFrameSurface1* acquired_surface_ptr, + VPPPreprocSession::incoming_task &&in) : + sync_handle(acquired_sync_handle), + vpp_surface_ptr(acquired_surface_ptr), + original_surface_ptr(in.decoded_surface_ptr), + original_frame_info(std::move(in.decoded_frame_info)), + original_frame(in.decoded_frame_copy) { +} + +void VPPPreprocSession::outgoing_task::release_frame() { + // restore initial surface params + memcpy(&(original_surface_ptr->Info), + &original_frame_info, sizeof(Surface::info_t)); + // release references on frame adapter + original_frame = cv::MediaFrame(); +} +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_session.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_session.hpp new file mode 100644 index 000000000000..b6800c3f7639 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/preproc_session.hpp @@ -0,0 +1,73 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONVPL_PREPROC_SESSION_HPP +#define GAPI_STREAMING_ONVPL_PREPROC_SESSION_HPP +#include +#include + +#include +#include "streaming/onevpl/engine/engine_session.hpp" +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" +#include "streaming/onevpl/engine/preproc/vpp_preproc_defines.hpp" + +#ifdef HAVE_ONEVPL + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +class VPPPreprocEngine; + +class VPPPreprocSession : public EngineSession { +public: + friend class VPPPreprocEngine; + VPPPreprocSession(mfxSession sess, const mfxVideoParam &vpp_out_param); + ~VPPPreprocSession(); + + Data::Meta generate_frame_meta(); + void swap_surface(VPPPreprocEngine& engine); + void init_surface_pool(VPLAccelerationPolicy::pool_key_t key); + + virtual const mfxFrameInfo& get_video_param() const override; +private: + mfxVideoParam mfx_vpp_out_param; + VPLAccelerationPolicy::pool_key_t vpp_pool_id; + std::weak_ptr processing_surface_ptr; + + struct incoming_task { + mfxSyncPoint sync_handle; + mfxFrameSurface1* decoded_surface_ptr; + Surface::info_t decoded_frame_info; + cv::MediaFrame decoded_frame_copy; + cv::util::optional roi; + }; + + struct outgoing_task { + outgoing_task() = default; + outgoing_task(mfxSyncPoint acquired_sync_handle, + mfxFrameSurface1* acquired_surface_ptr, + incoming_task &&in); + mfxSyncPoint sync_handle; + mfxFrameSurface1* vpp_surface_ptr; + + mfxFrameSurface1* original_surface_ptr; + void release_frame(); + private: + Surface::info_t original_frame_info; + cv::MediaFrame original_frame; + }; + + std::queue sync_in_queue; + std::queue vpp_out_queue; + int64_t preprocessed_frames_count; +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL +#endif // GAPI_STREAMING_ONVPL_PREPROC_SESSION_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/utils.cpp b/modules/gapi/src/streaming/onevpl/engine/preproc/utils.cpp new file mode 100644 index 000000000000..6cf7212f3e8d --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/utils.cpp @@ -0,0 +1,86 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#include + +#include "streaming/onevpl/engine/preproc/utils.hpp" + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" +#include "logger.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +namespace utils { + +cv::MediaFormat fourcc_to_MediaFormat(int value) { + switch (value) + { + case MFX_FOURCC_BGRP: + return cv::MediaFormat::BGR; + case MFX_FOURCC_NV12: + return cv::MediaFormat::NV12; + default: + GAPI_LOG_WARNING(nullptr, "Unsupported FourCC format requested: " << value << + ". Cannot cast to cv::MediaFrame"); + GAPI_Assert(false && "Unsupported FOURCC"); + + } +} + +int MediaFormat_to_fourcc(cv::MediaFormat value) { + switch (value) + { + case cv::MediaFormat::BGR: + return MFX_FOURCC_BGRP; + case cv::MediaFormat::NV12: + return MFX_FOURCC_NV12; + default: + GAPI_LOG_WARNING(nullptr, "Unsupported cv::MediaFormat format requested: " << + static_cast::type>(value) << + ". Cannot cast to FourCC"); + GAPI_Assert(false && "Unsupported cv::MediaFormat"); + } +} +int MediaFormat_to_chroma(cv::MediaFormat value) { + switch (value) + { + case cv::MediaFormat::BGR: + return MFX_CHROMAFORMAT_MONOCHROME; + case cv::MediaFormat::NV12: + return MFX_CHROMAFORMAT_YUV420; + default: + GAPI_LOG_WARNING(nullptr, "Unsupported cv::MediaFormat format requested: " << + static_cast::type>(value) << + ". Cannot cast to ChromaFormateIdc"); + GAPI_Assert(false && "Unsupported cv::MediaFormat"); + } +} + +mfxFrameInfo to_mfxFrameInfo(const cv::GFrameDesc& frame_info) { + mfxFrameInfo ret {0}; + ret.FourCC = MediaFormat_to_fourcc(frame_info.fmt); + ret.ChromaFormat = MediaFormat_to_chroma(frame_info.fmt); + ret.Width = frame_info.size.width; + ret.Height = frame_info.size.height; + ret.CropX = 0; + ret.CropY = 0; + ret.CropW = 0; + ret.CropH = 0; + ret.PicStruct = MFX_PICSTRUCT_UNKNOWN; + ret.FrameRateExtN = 0; + ret.FrameRateExtD = 0; + return ret; +} +} // namespace utils +} // namespace cv +} // namespace gapi +} // namespace wip +} // namespace onevpl + +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/utils.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc/utils.hpp new file mode 100644 index 000000000000..b52a8ad1e81f --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/utils.hpp @@ -0,0 +1,32 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONEVPL_PREPROC_UTILS_HPP +#define GAPI_STREAMING_ONEVPL_PREPROC_UTILS_HPP + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" + +#include + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +namespace utils { + +cv::MediaFormat fourcc_to_MediaFormat(int value); +int MediaFormat_to_fourcc(cv::MediaFormat value); +int MediaFormat_to_chroma(cv::MediaFormat value); + +mfxFrameInfo to_mfxFrameInfo(const cv::GFrameDesc& frame_info); +} // namespace utils +} // namespace cv +} // namespace gapi +} // namespace wip +} // namespace onevpl +#endif // #ifdef HAVE_ONEVPL +#endif // GAPI_STREAMING_ONEVPL_PREPROC_UTILS_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc/vpp_preproc_defines.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc/vpp_preproc_defines.hpp new file mode 100644 index 000000000000..e500c6466e2e --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc/vpp_preproc_defines.hpp @@ -0,0 +1,39 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifdef HAVE_ONEVPL + +#ifndef VPP_PREPROC_ENGINE +#define VPP_PREPROC_ENGINE +#include "streaming/onevpl/onevpl_export.hpp" +#include "streaming/onevpl/engine/engine_session.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +struct vpp_pp_params { + vpp_pp_params() : handle(), info(), reserved() {} + vpp_pp_params(mfxSession s, mfxFrameInfo i, void *r = nullptr) : + handle(s), info(i), reserved(r) {} + mfxSession handle; + mfxFrameInfo info; + void *reserved = nullptr; +}; + +struct vpp_pp_session { + vpp_pp_session() : handle(), reserved() {} + vpp_pp_session(std::shared_ptr h, void *r = nullptr) : + handle(h), reserved(r) {} + std::shared_ptr handle; + void *reserved = nullptr; +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // VPP_PREPROC_ENGINE +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc_defines.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc_defines.hpp new file mode 100644 index 000000000000..be215fec74e4 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc_defines.hpp @@ -0,0 +1,88 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONEVPL_ENGINE_PREPROC_DEFINES_HPP +#define GAPI_STREAMING_ONEVPL_ENGINE_PREPROC_DEFINES_HPP + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/utils.hpp" +#include "streaming/onevpl/engine/preproc/vpp_preproc_defines.hpp" +#endif // HAVE_ONEVPL + + +namespace cv { +namespace gapi { +namespace wip { + +#ifdef VPP_PREPROC_ENGINE +#define GAPI_BACKEND_PP_PARAMS cv::gapi::wip::onevpl::vpp_pp_params +#define GAPI_BACKEND_PP_SESSIONS cv::gapi::wip::onevpl::vpp_pp_session +#else // VPP_PREPROC_ENGINE +struct empty_pp_params {}; +struct empty_pp_session {}; +#define GAPI_BACKEND_PP_PARAMS cv::gapi::wip::empty_pp_params +#define GAPI_BACKEND_PP_SESSIONS cv::gapi::wip::empty_pp_session +#endif // VPP_PREPROC_ENGINE + +struct pp_params { + using value_type = cv::util::variant; + + template + static pp_params create(Args&& ...args) { + static_assert(cv::detail::contains::value, + "Invalid BackendSpecificParamType requested"); + pp_params ret; + ret.value = BackendSpecificParamType{std::forward(args)...}; + return ret; + } + + template + BackendSpecificParamType& get() { + static_assert(cv::detail::contains::value, + "Invalid BackendSpecificParamType requested"); + return cv::util::get(value); + } + + template + const BackendSpecificParamType& get() const { + return static_cast(const_cast(this)->get()); + } +private: + value_type value; +}; + +struct pp_session { + using value_type = cv::util::variant; + + template + static pp_session create(Args&& ...args) { + static_assert(cv::detail::contains::value, + "Invalid BackendSpecificSesionType requested"); + pp_session ret; + ret.value = BackendSpecificSesionType{std::forward(args)...};; + return ret; + } + + template + BackendSpecificSesionType &get() { + static_assert(cv::detail::contains::value, + "Invalid BackendSpecificSesionType requested"); + return cv::util::get(value); + } + + template + const BackendSpecificSesionType &get() const { + return const_cast(this)->get(); + } +private: + value_type value; +}; +} // namespace wip +} // namespace gapi +} // namespace cv + +#endif // GAPI_STREAMING_ONEVPL_ENGINE_PREPROC_DEFINES_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc_engine_interface.cpp b/modules/gapi/src/streaming/onevpl/engine/preproc_engine_interface.cpp new file mode 100644 index 000000000000..ff9f103b5af5 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc_engine_interface.cpp @@ -0,0 +1,83 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#include +#include "streaming/onevpl/engine/preproc_engine_interface.hpp" +#include "streaming/onevpl/engine/preproc/preproc_dispatcher.hpp" + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" +#include "streaming/onevpl/engine/preproc/preproc_engine.hpp" + +#include "streaming/onevpl/accelerators/accel_policy_dx11.hpp" +#include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/cfg_param_device_selector.hpp" +#include "streaming/onevpl/cfg_params_parser.hpp" + +#endif //HAVE_ONEVPL + +#include "logger.hpp" + +namespace cv { +namespace gapi { +namespace wip { + +template +std::unique_ptr +IPreprocEngine::create_preproc_engine_impl(const PreprocEngineArgs& ...) { + GAPI_Assert(false && "Unsupported "); +} + +template <> +std::unique_ptr +IPreprocEngine::create_preproc_engine_impl(const onevpl::Device &device, + const onevpl::Context &context) { + using namespace onevpl; + cv::util::suppress_unused_warning(device); + cv::util::suppress_unused_warning(context); + std::unique_ptr dispatcher(new VPPPreprocDispatcher); +#ifdef HAVE_ONEVPL + if (device.get_type() == onevpl::AccelType::DX11) { + bool gpu_pp_is_created = false; +#ifdef HAVE_DIRECTX +#ifdef HAVE_D3D11 + GAPI_LOG_INFO(nullptr, "Creating DX11 VPP preprocessing engine"); + // create GPU VPP preproc engine + dispatcher->insert_worker( + std::unique_ptr{ + new VPLDX11AccelerationPolicy( + std::make_shared( + device, context, CfgParams{})) + }); + GAPI_LOG_INFO(nullptr, "DX11 VPP preprocessing engine created"); + gpu_pp_is_created = true; +#endif +#endif + GAPI_Assert(gpu_pp_is_created && "VPP preproc for GPU is requested, but it is avaiable only for DX11 at now"); + } else { + GAPI_LOG_INFO(nullptr, "Creating CPU VPP preprocessing engine"); + dispatcher->insert_worker( + std::unique_ptr{ + new VPLCPUAccelerationPolicy( + std::make_shared(CfgParams{}))}); + GAPI_LOG_INFO(nullptr, "CPU VPP preprocessing engine created"); + } +#endif // HAVE_ONEVPL + return dispatcher; +} + + +// Force instantiation +template +std::unique_ptr +IPreprocEngine::create_preproc_engine_impl + (const onevpl::Device &device, + const onevpl::Context &ctx); +} // namespace wip +} // namespace gapi +} // namespace cv diff --git a/modules/gapi/src/streaming/onevpl/engine/preproc_engine_interface.hpp b/modules/gapi/src/streaming/onevpl/engine/preproc_engine_interface.hpp new file mode 100644 index 000000000000..72c1dbd0a72e --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/preproc_engine_interface.hpp @@ -0,0 +1,46 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + +#ifndef GAPI_STREAMING_ONEVPL_ENGINE_PROCESSING_ENGINE_INTERFACE_HPP +#define GAPI_STREAMING_ONEVPL_ENGINE_PROCESSING_ENGINE_INTERFACE_HPP + +#include "precomp.hpp" +#include +#include + +#include "streaming/onevpl/engine/preproc_defines.hpp" + +namespace cv { +namespace gapi { +namespace wip { + +struct IPreprocEngine { + virtual ~IPreprocEngine() = default; + + virtual cv::util::optional + is_applicable(const cv::MediaFrame& in_frame) = 0; + + virtual pp_session + initialize_preproc(const pp_params& initial_frame_param, + const GFrameDesc& required_frame_descr) = 0; + virtual cv::MediaFrame + run_sync(const pp_session &sess, const cv::MediaFrame& in_frame, + const cv::util::optional &opt_roi = {}) = 0; + + template + static std::unique_ptr create_preproc_engine(const PreprocEngineArgs& ...args) { + static_assert(std::is_base_of::value, + "SpecificPreprocEngine must have reachable ancessor IPreprocEngine"); + return create_preproc_engine_impl(args...); + } +private: + template + static std::unique_ptr create_preproc_engine_impl(const PreprocEngineArgs &...args); +}; +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // GAPI_STREAMING_ONEVPL_ENGINE_PROCESSING_ENGINE_INTERFACE_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.cpp b/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.cpp index 72f2f62fc47f..35cd66421937 100644 --- a/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.cpp +++ b/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.cpp @@ -36,7 +36,7 @@ ProcessingEngineBase::ExecutionStatus ProcessingEngineBase::process(mfxSession s session_ptr processing_session = sess_it->second; ExecutionData& exec_data = execution_table[session]; - GAPI_LOG_DEBUG(nullptr, "[" << session <<"] start op id: " << exec_data.op_id); + GAPI_LOG_DEBUG(nullptr, "[" << session << "] start op id: " << exec_data.op_id); ExecutionStatus status = execute_op(pipeline.at(exec_data.op_id), *processing_session); size_t old_op_id = exec_data.op_id++; if (exec_data.op_id == pipeline.size()) @@ -44,10 +44,10 @@ ProcessingEngineBase::ExecutionStatus ProcessingEngineBase::process(mfxSession s exec_data.op_id = 0; } cv::util::suppress_unused_warning(old_op_id); - GAPI_LOG_DEBUG(nullptr, "[" << session <<"] finish op id: " << old_op_id << - ", " << processing_session->error_code_to_str() << - ", " << ProcessingEngineBase::status_to_string(status) << - ", next op id: " << exec_data.op_id); + GAPI_LOG_DEBUG(nullptr, "[" << session << "] finish op id: " << old_op_id << + ", " << processing_session->error_code_to_str() << + ", " << ProcessingEngineBase::status_to_string(status) << + ", next op id: " << exec_data.op_id); if (status == ExecutionStatus::Failed) { @@ -81,7 +81,7 @@ const char* ProcessingEngineBase::status_to_string(ExecutionStatus status) ProcessingEngineBase::ExecutionStatus ProcessingEngineBase::execute_op(operation_t& op, EngineSession& sess) { - return op(sess); + return op(sess); } size_t ProcessingEngineBase::get_ready_frames_count() const diff --git a/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.hpp b/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.hpp index 059ef963de46..54c6f4e2c485 100644 --- a/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.hpp +++ b/modules/gapi/src/streaming/onevpl/engine/processing_engine_base.hpp @@ -9,6 +9,7 @@ #include #include +#include #include "streaming/onevpl/engine/engine_session.hpp" #include "opencv2/gapi/own/exports.hpp" // GAPI_EXPORTS @@ -67,16 +68,25 @@ class GAPI_EXPORTS ProcessingEngineBase { std::vector pipeline; std::unique_ptr acceleration_policy; - +public: virtual ExecutionStatus execute_op(operation_t& op, EngineSession& sess); template void create_pipeline(Ops&&...ops) { - GAPI_DbgAssert(pipeline.empty() && "Pipeline must be empty"); std::vector({std::forward(ops)...}).swap(pipeline); } + template + void inject_pipeline_operations(size_t in_position, Ops&&...ops) + { + GAPI_Assert(pipeline.size() >= in_position && + "Invalid position to inject pipeline operation"); + auto it = pipeline.begin(); + std::advance(it, in_position); + pipeline.insert(it, {std::forward(ops)...}); + } + template std::shared_ptr register_session(mfxSession key, SessionArgs&& ...args) diff --git a/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_engine_legacy.cpp b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_engine_legacy.cpp new file mode 100644 index 000000000000..23703bf172ed --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_engine_legacy.cpp @@ -0,0 +1,469 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#ifdef HAVE_ONEVPL + +#include +#include + +#include +#include "streaming/onevpl/data_provider_defines.hpp" + +#include "streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp" +#include "streaming/onevpl/engine/transcode/transcode_session.hpp" +#include "streaming/onevpl/accelerators/accel_policy_interface.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/cfg_params_parser.hpp" +#include "streaming/onevpl/utils.hpp" +#include "logger.hpp" + +#define ALIGN16(value) (((value + 15) >> 4) << 4) + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +using vpp_param_storage = const std::map; +using vpp_param_storage_cit = typename vpp_param_storage::const_iterator; + +template +Type get_mfx_value(const vpp_param_storage_cit &cit); + +template<> +uint16_t get_mfx_value(const vpp_param_storage_cit& cit) { + return cit->second.Data.U16; +} + +template<> +uint32_t get_mfx_value(const vpp_param_storage_cit& cit) { + return cit->second.Data.U32; +} + +template +bool set_vpp_param(const char* name, Type& out_vpp_param, + const vpp_param_storage ¶ms_storage, + mfxSession session) { + auto it = params_storage.find(name); + if (it != params_storage.end()) { + auto value = get_mfx_value(it); + GAPI_LOG_INFO(nullptr, "[" << session << "] set \"" << name << + "\": " << value); + out_vpp_param = value; + return true; + } + return false; +} + +std::map + VPLLegacyTranscodeEngine::get_vpp_params(const std::vector &cfg_params) { + std::map ret; + static const char* vpp_param_prefix {"vpp."}; + for (const auto ¶m : cfg_params) { + const char *param_name_cptr = param.get_name().c_str(); + if (strstr(param_name_cptr, vpp_param_prefix) == param_name_cptr) { + ret.emplace(param.get_name(), cfg_param_to_mfx_variant(param)); + } + } + GAPI_LOG_INFO(nullptr, "Detected VPP params count: [" << ret.size() << + "/" << cfg_params.size() << "]"); + return ret; +} + +VPLLegacyTranscodeEngine::VPLLegacyTranscodeEngine(std::unique_ptr&& accel) + : VPLLegacyDecodeEngine(std::move(accel)) { + + GAPI_LOG_INFO(nullptr, "Create Legacy Transcode Engine"); + create_pipeline( + // 1) Read File + [this] (EngineSession& sess) -> ExecutionStatus + { + LegacyTranscodeSession &my_sess = static_cast(sess); + if (!my_sess.data_provider) { + my_sess.last_status = MFX_ERR_MORE_DATA; + return ExecutionStatus::Continue; + } + + my_sess.last_status = MFX_ERR_NONE; + if (!my_sess.data_provider->fetch_bitstream_data(my_sess.stream)) { + my_sess.last_status = MFX_ERR_MORE_DATA; + my_sess.data_provider.reset(); //close source + } + return ExecutionStatus::Continue; + }, + // 2) enqueue ASYNC decode operation + [this] (EngineSession& sess) -> ExecutionStatus + { + LegacyTranscodeSession &my_sess = static_cast(sess); + + // prepare sync object for new surface + LegacyTranscodeSession::op_handle_t sync_pair{}; + + // enqueue decode operation with current session surface + my_sess.last_status = + MFXVideoDECODE_DecodeFrameAsync(my_sess.session, + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), + &sync_pair.second, + &sync_pair.first); + + GAPI_LOG_DEBUG(nullptr, "START decode: " << + ", sync id: " << + sync_pair.first << + ", dec in surface: " << + my_sess.processing_surface_ptr.lock()->get_handle() << + ", dec out surface: " << sync_pair.second << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + + // process wait-like statuses in-place: + // It had better to use up all VPL decoding resources in pipeline + // as soon as possible. So waiting more free-surface or device free + while (my_sess.last_status == MFX_ERR_MORE_SURFACE || + my_sess.last_status == MFX_WRN_DEVICE_BUSY) { + try { + if (my_sess.last_status == MFX_ERR_MORE_SURFACE) { + my_sess.swap_decode_surface(*this); + } + my_sess.last_status = + MFXVideoDECODE_DecodeFrameAsync(my_sess.session, + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), + &sync_pair.second, + &sync_pair.first); + + } catch (const std::runtime_error& ex) { + // NB: not an error, yield CPU ticks to check + // surface availability at a next phase. + // But print WARNING to notify user about pipeline stuck + GAPI_LOG_WARNING(nullptr, "[" << my_sess.session << + "] has no surface, reason: " << + ex.what()); + break; + } + } + + if (my_sess.last_status == MFX_ERR_NONE) { + my_sess.sync_queue.emplace(sync_pair); + } else if (my_sess.last_status != MFX_ERR_MORE_DATA) /* suppress MFX_ERR_MORE_DATA warning */ { + GAPI_LOG_WARNING(nullptr, "decode pending ops count: " << + my_sess.sync_queue.size() << + ", sync id: " << sync_pair.first << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + } + return ExecutionStatus::Continue; + }, + // 3) transcode + [this] (EngineSession& sess) -> ExecutionStatus + { + LegacyTranscodeSession &my_sess = static_cast(sess); + + LegacyDecodeSession::op_handle_t last_op {}; + while (!my_sess.sync_queue.empty()) { + do { + if (!my_sess.vpp_surface_ptr.expired()) { + LegacyDecodeSession::op_handle_t pending_op = my_sess.sync_queue.front(); + GAPI_LOG_DEBUG(nullptr, "pending DEC ops count: " << + my_sess.sync_queue.size() << + ", sync id: " << + pending_op.first << + ", surface: " << + pending_op.second << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + + my_sess.sync_queue.pop(); + auto *dec_surface = pending_op.second; + auto *vpp_suface = my_sess.vpp_surface_ptr.lock()->get_handle(); + my_sess.last_status = MFXVideoVPP_RunFrameVPPAsync(my_sess.session, + dec_surface, + vpp_suface, + nullptr, &pending_op.first); + pending_op.second = vpp_suface; + + GAPI_LOG_DEBUG(nullptr, "START transcode ops count: " << + my_sess.vpp_queue.size() << + ", sync id: " << + pending_op.first << + ", dec surface: " << + dec_surface << + ", trans surface: " << pending_op.second << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + + if (my_sess.last_status == MFX_ERR_MORE_SURFACE || + my_sess.last_status == MFX_ERR_NONE) { + pending_op.second->Data.Locked++; // TODO -S- workaround + my_sess.vpp_queue.emplace(pending_op); + } + } + + try { + my_sess.swap_transcode_surface(*this); + } catch (const std::runtime_error& ex) { + // NB: not an error, yield CPU ticks to check + // surface availability at a next phase. + // But print WARNING to notify user about pipeline stuck + GAPI_LOG_WARNING(nullptr, "[" << my_sess.session << + "] has no VPP surface, reason: " << + ex.what()); + my_sess.vpp_surface_ptr.reset(); + break; + } + } while(my_sess.last_status == MFX_ERR_MORE_SURFACE); + + if (my_sess.vpp_surface_ptr.expired()) { + // TODO break main loop + break; + } + } + return ExecutionStatus::Continue; + }, + // 4) Wait for ASYNC decode result + [this] (EngineSession& sess) -> ExecutionStatus + { + LegacyTranscodeSession& my_sess = static_cast(sess); + do { + if (!my_sess.vpp_queue.empty()) { // FIFO: check the oldest async operation complete + LegacyDecodeSession::op_handle_t& pending_op = my_sess.vpp_queue.front(); + sess.last_status = MFXVideoCORE_SyncOperation(sess.session, pending_op.first, 0); + + GAPI_LOG_DEBUG(nullptr, "pending VPP ops count: " << + my_sess.vpp_queue.size() << + ", sync id: " << + pending_op.first << + ", surface: " << + pending_op.second << + ", status: " << + mfxstatus_to_string(my_sess.last_status)); + + // put frames in ready queue on success + if (MFX_ERR_NONE == sess.last_status) { + on_frame_ready(my_sess, pending_op.second); + } + } + } while (MFX_ERR_NONE == sess.last_status && !my_sess.vpp_queue.empty()); + return ExecutionStatus::Continue; + }, + // 5) Falls back on generic status processing + [this] (EngineSession& sess) -> ExecutionStatus + { + return this->process_error(sess.last_status, static_cast(sess)); + } + ); +} + +ProcessingEngineBase::session_ptr +VPLLegacyTranscodeEngine::initialize_session(mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider) { + // NB: obtain decoder params + VPLLegacyDecodeEngine::SessionParam decode_params = + prepare_session_param(mfx_session, cfg_params, provider); + + + // NB: create transcode params + const auto& mfxDecParams = decode_params.decoder_params.param; + + // NB: create transcode params: Out = In by default, In = initially decoded + mfxVideoParam mfxVPPParams{}; + memset(&mfxVPPParams, 0, sizeof(mfxVPPParams)); + mfxVPPParams.vpp.In = mfxDecParams.mfx.FrameInfo; + mfxVPPParams.vpp.Out = mfxVPPParams.vpp.In; + + std::map cfg_vpp_params = + VPLLegacyTranscodeEngine::get_vpp_params(cfg_params); + + // override some in-params + if (set_vpp_param(CfgParam::vpp_in_width_name(), mfxVPPParams.vpp.In.Width, + cfg_vpp_params, mfx_session)) { + mfxVPPParams.vpp.In.Width = ALIGN16(mfxVPPParams.vpp.In.Width); + } + if (set_vpp_param(CfgParam::vpp_in_height_name(), mfxVPPParams.vpp.In.Height, + cfg_vpp_params, mfx_session)) { + mfxVPPParams.vpp.In.Height = ALIGN16(mfxVPPParams.vpp.In.Height); + } + set_vpp_param(CfgParam::vpp_in_crop_x_name(), mfxVPPParams.vpp.In.CropX, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_in_crop_y_name(), mfxVPPParams.vpp.In.CropY, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_in_crop_w_name(), mfxVPPParams.vpp.In.CropW, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_in_crop_h_name(), mfxVPPParams.vpp.In.CropH, + cfg_vpp_params, mfx_session); + + // override out params + set_vpp_param(CfgParam::vpp_out_fourcc_name(), mfxVPPParams.vpp.Out.FourCC, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_chroma_format_name(), mfxVPPParams.vpp.Out.ChromaFormat, + cfg_vpp_params, mfx_session); + if (set_vpp_param(CfgParam::vpp_out_width_name(), mfxVPPParams.vpp.Out.Width, + cfg_vpp_params, mfx_session)) { + mfxVPPParams.vpp.Out.Width = ALIGN16(mfxVPPParams.vpp.Out.Width); + } + if (set_vpp_param(CfgParam::vpp_out_height_name(), mfxVPPParams.vpp.Out.Height, + cfg_vpp_params, mfx_session)) { + mfxVPPParams.vpp.Out.Height = ALIGN16(mfxVPPParams.vpp.Out.Height); + } + set_vpp_param(CfgParam::vpp_out_crop_x_name(), mfxVPPParams.vpp.Out.CropX, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_crop_y_name(), mfxVPPParams.vpp.Out.CropY, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_crop_w_name(), mfxVPPParams.vpp.Out.CropW, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_crop_h_name(), mfxVPPParams.vpp.Out.CropH, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_pic_struct_name(), mfxVPPParams.vpp.Out.PicStruct, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_framerate_n_name(), mfxVPPParams.vpp.Out.FrameRateExtN, + cfg_vpp_params, mfx_session); + set_vpp_param(CfgParam::vpp_out_framerate_d_name(), mfxVPPParams.vpp.Out.FrameRateExtD, + cfg_vpp_params, mfx_session); + + VPLLegacyTranscodeEngine::validate_vpp_param(mfxVPPParams); + + if (mfxDecParams.IOPattern == MFX_IOPATTERN_OUT_VIDEO_MEMORY) { + mfxVPPParams.IOPattern = MFX_IOPATTERN_IN_VIDEO_MEMORY | MFX_IOPATTERN_OUT_VIDEO_MEMORY; + } else { + mfxVPPParams.IOPattern = MFX_IOPATTERN_IN_SYSTEM_MEMORY | MFX_IOPATTERN_OUT_SYSTEM_MEMORY; + } + GAPI_LOG_INFO(nullptr, "Starting VPP initialization"); + + mfxFrameAllocRequest vppRequests[2]; + memset(&vppRequests, 0, sizeof(mfxFrameAllocRequest) * 2); + mfxStatus sts = MFXVideoVPP_QueryIOSurf(mfx_session, &mfxVPPParams, vppRequests); + if (MFX_ERR_NONE != sts) { + GAPI_LOG_WARNING(nullptr, "cannot execute MFXVideoVPP_QueryIOSurf"); + throw std::runtime_error("Cannot execute MFXVideoVPP_QueryIOSurf, error: " + + mfxstatus_to_string(sts)); + } + + // NB: override NumFrameSuggested preallocation size (how many frames we can hold) + // if you see bunch of WARNING about "cannot get free surface from pool" + // and have abundant RAM size then increase `CfgParam::vpp_frames_pool_size_name()` + // to keep more free surfaces in a round. Otherwise VPL decode pipeline will be waiting + // till application is freeing unusable surface on its side. + cv::optional preallocated_frames_count_cfg; + extract_optional_param_by_name(CfgParam::vpp_frames_pool_size_name(), + cfg_params, + preallocated_frames_count_cfg); + if (preallocated_frames_count_cfg.has_value()) { + GAPI_LOG_INFO(nullptr, "Try to use CfgParam \"" << CfgParam::vpp_frames_pool_size_name() << "\": " << + preallocated_frames_count_cfg.value() << ", for session: " << mfx_session); + try_modify_pool_size_request_param(CfgParam::vpp_frames_pool_size_name(), + preallocated_frames_count_cfg.value(), + vppRequests[1]); + + } + + // NB: Assign ID as upper limit descendant to distinguish specific VPP allocation + // from decode allocations witch started from 0: by local module convention + vppRequests[1].AllocId = std::numeric_limits::max(); + + vppRequests[1].Type |= MFX_MEMTYPE_FROM_VPPIN; + VPLAccelerationPolicy::pool_key_t vpp_out_pool_key = + acceleration_policy->create_surface_pool(vppRequests[1], mfxVPPParams.vpp.Out); + + GAPI_LOG_INFO(nullptr, "Initialize VPP for session: " << mfx_session << + ", out frame info: " << mfx_frame_info_to_string(mfxVPPParams.vpp.Out)); + sts = MFXVideoVPP_Init(mfx_session, &mfxVPPParams); + if (MFX_ERR_NONE != sts) { + GAPI_LOG_WARNING(nullptr, "cannot Init VPP"); + throw std::runtime_error("Cannot init VPP, error: " + + mfxstatus_to_string(sts)); + } + + // create engine session + TranscoderParams transcoder_param {mfxVPPParams}; + std::shared_ptr sess_ptr = + register_session(mfx_session, + std::move(decode_params.decoder_params), + std::move(transcoder_param), + provider); + + sess_ptr->init_surface_pool(decode_params.decode_pool_key); + sess_ptr->init_transcode_surface_pool(vpp_out_pool_key); + + // prepare working surfaces + sess_ptr->swap_decode_surface(*this); + sess_ptr->swap_transcode_surface(*this); + return sess_ptr; +} + +void VPLLegacyTranscodeEngine::validate_vpp_param(const mfxVideoParam& mfxVPPParams) { + GAPI_LOG_INFO(nullptr, "Starting VPP param validation"); + if (mfxVPPParams.vpp.In.Width < mfxVPPParams.vpp.In.CropW + mfxVPPParams.vpp.In.CropX) { + GAPI_LOG_WARNING(nullptr, "Invalid vonfiguration params: sum \"" << + CfgParam::vpp_in_crop_w_name() << + "\": " << mfxVPPParams.vpp.In.CropW << " and \"" << + CfgParam::vpp_in_crop_x_name() << + "\": " << mfxVPPParams.vpp.In.CropX << + " must be less or equal to \"" << + CfgParam::vpp_in_width_name() << "\": " << + mfxVPPParams.vpp.In.Width); + GAPI_Assert(false && "Invalid VPP params combination: Width & Crop"); + } + + if (mfxVPPParams.vpp.In.Height < mfxVPPParams.vpp.In.CropH + mfxVPPParams.vpp.In.CropY) { + GAPI_LOG_WARNING(nullptr, "Invalid vonfiguration params: sum \"" << + CfgParam::vpp_in_crop_h_name() << + "\": " << mfxVPPParams.vpp.In.CropH << " and \"" << + CfgParam::vpp_in_crop_y_name() << + "\": " << mfxVPPParams.vpp.In.CropY << + " must be less or equal to \"" << + CfgParam::vpp_in_height_name() << "\": " << + mfxVPPParams.vpp.In.Height); + GAPI_Assert(false && "Invalid VPP params combination: Height & Crop"); + } + + if (mfxVPPParams.vpp.Out.Width < mfxVPPParams.vpp.Out.CropW + mfxVPPParams.vpp.Out.CropX) { + GAPI_LOG_WARNING(nullptr, "Invalid vonfiguration params: sum \"" << + CfgParam::vpp_out_crop_w_name() << + "\": " << mfxVPPParams.vpp.Out.CropW << " and \"" << + CfgParam::vpp_out_crop_x_name() << + "\": " << mfxVPPParams.vpp.Out.CropX << + " must be less or equal to \"" << + CfgParam::vpp_out_width_name() << "\": " << + mfxVPPParams.vpp.Out.Width); + GAPI_Assert(false && "Invalid VPP params combination: Width & Crop"); + } + + if (mfxVPPParams.vpp.Out.Height < mfxVPPParams.vpp.Out.CropH + mfxVPPParams.vpp.Out.CropY) { + GAPI_LOG_WARNING(nullptr, "Invalid vonfiguration params: sum \"" << + CfgParam::vpp_out_crop_h_name() << + "\": " << mfxVPPParams.vpp.Out.CropH << " and \"" << + CfgParam::vpp_out_crop_y_name() << + "\": " << mfxVPPParams.vpp.Out.CropY << + " must be less or equal to \"" << + CfgParam::vpp_out_height_name() << "\": " << + mfxVPPParams.vpp.Out.Height); + GAPI_Assert(false && "Invalid VPP params combination: Height & Crop"); + } + + GAPI_LOG_INFO(nullptr, "Finished VPP param validation"); +} + +void VPLLegacyTranscodeEngine::on_frame_ready(LegacyTranscodeSession& sess, + mfxFrameSurface1* ready_surface) +{ + GAPI_LOG_DEBUG(nullptr, "[" << sess.session << "], frame ready"); + + // manage memory ownership rely on acceleration policy + ready_surface->Data.Locked--; // TODO -S- workaround + + VPLAccelerationPolicy::FrameConstructorArgs args{ready_surface, sess.session}; + auto frame_adapter = acceleration_policy->create_frame_adapter(sess.vpp_out_pool_id, + args); + ready_frames.emplace(cv::MediaFrame(std::move(frame_adapter)), sess.generate_frame_meta()); + + // pop away synced out object + sess.vpp_queue.pop(); +} +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp new file mode 100644 index 000000000000..d06b76a13d2b --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp @@ -0,0 +1,45 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#ifndef GAPI_STREAMING_ONVPL_TRANSCODE_ENGINE_LEGACY_HPP +#define GAPI_STREAMING_ONVPL_TRANSCODE_ENGINE_LEGACY_HPP +#include +#include + +#include "streaming/onevpl/engine/decode/decode_engine_legacy.hpp" + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/onevpl_export.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { + +class LegacyTranscodeSession; +struct IDataProvider; +struct VPLAccelerationPolicy; + +class GAPI_EXPORTS VPLLegacyTranscodeEngine : public VPLLegacyDecodeEngine { +public: + + VPLLegacyTranscodeEngine(std::unique_ptr&& accel); + session_ptr initialize_session(mfxSession mfx_session, + const std::vector& cfg_params, + std::shared_ptr provider) override; + + static std::map get_vpp_params(const std::vector &cfg_params); +private: + void on_frame_ready(LegacyTranscodeSession& sess, + mfxFrameSurface1* ready_surface); + void validate_vpp_param(const mfxVideoParam& mfxVPPParams); +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL +#endif // GAPI_STREAMING_ONVPL_DECODE_ENGINE_LEGACY_HPP diff --git a/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_session.cpp b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_session.cpp new file mode 100644 index 000000000000..8672a000adf4 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_session.cpp @@ -0,0 +1,55 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#ifdef HAVE_ONEVPL + +#include +#include + +#include "streaming/onevpl/engine/transcode/transcode_session.hpp" +#include "streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp" +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/utils.hpp" + +#include "logger.hpp" +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +LegacyTranscodeSession::LegacyTranscodeSession(mfxSession sess, + DecoderParams&& decoder_param, + TranscoderParams&& transcoder_param, + std::shared_ptr provider) : + LegacyDecodeSession(sess, std::move(decoder_param), std::move(provider)), + mfx_transcoder_param(std::move(transcoder_param.param)) +{ +} + +LegacyTranscodeSession::~LegacyTranscodeSession() +{ + GAPI_LOG_INFO(nullptr, "Close Transcode for session: " << session); + MFXVideoVPP_Close(session); +} + +void LegacyTranscodeSession::init_transcode_surface_pool(VPLAccelerationPolicy::pool_key_t key) { + GAPI_Assert(key && "Init transcode pull with empty key"); + vpp_out_pool_id = key; +} + +void LegacyTranscodeSession::swap_transcode_surface(VPLLegacyTranscodeEngine& engine) { + VPLAccelerationPolicy* acceleration_policy = engine.get_accel(); + GAPI_Assert(acceleration_policy && "Empty acceleration_policy"); + request_free_surface(session, vpp_out_pool_id, *acceleration_policy, vpp_surface_ptr); +} + +const mfxFrameInfo& LegacyTranscodeSession::get_video_param() const { + return mfx_transcoder_param.vpp.Out; +} +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL diff --git a/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_session.hpp b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_session.hpp new file mode 100644 index 000000000000..03df16375023 --- /dev/null +++ b/modules/gapi/src/streaming/onevpl/engine/transcode/transcode_session.hpp @@ -0,0 +1,43 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#ifndef GAPI_STREAMING_ONVPL_ENGINE_TRANSCODE_SESSION_HPP +#define GAPI_STREAMING_ONVPL_ENGINE_TRANSCODE_SESSION_HPP + +#ifdef HAVE_ONEVPL +#include "streaming/onevpl/engine/decode/decode_session.hpp" + +namespace cv { +namespace gapi { +namespace wip { +namespace onevpl { +class Surface; +class VPLLegacyTranscodeEngine; +class GAPI_EXPORTS LegacyTranscodeSession : public LegacyDecodeSession { +public: + friend class VPLLegacyTranscodeEngine; + + LegacyTranscodeSession(mfxSession sess, DecoderParams&& decoder_param, + TranscoderParams&& transcoder_param, + std::shared_ptr provider); + ~LegacyTranscodeSession(); + + void init_transcode_surface_pool(VPLAccelerationPolicy::pool_key_t key); + void swap_transcode_surface(VPLLegacyTranscodeEngine& engine); + const mfxFrameInfo& get_video_param() const override; +private: + mfxVideoParam mfx_transcoder_param; + VPLAccelerationPolicy::pool_key_t vpp_out_pool_id; + + std::weak_ptr vpp_surface_ptr; + std::queue vpp_queue; +}; +} // namespace onevpl +} // namespace wip +} // namespace gapi +} // namespace cv +#endif // HAVE_ONEVPL +#endif // GAPI_STREAMING_ONVPL_ENGINE_TRANSCODE_SESSION_HPP diff --git a/modules/gapi/src/streaming/onevpl/file_data_provider.hpp b/modules/gapi/src/streaming/onevpl/file_data_provider.hpp index cfa12459169f..10171999a0cc 100644 --- a/modules/gapi/src/streaming/onevpl/file_data_provider.hpp +++ b/modules/gapi/src/streaming/onevpl/file_data_provider.hpp @@ -18,7 +18,7 @@ namespace cv { namespace gapi { namespace wip { namespace onevpl { -struct FileDataProvider : public IDataProvider { +struct GAPI_EXPORTS FileDataProvider : public IDataProvider { using file_ptr = std::unique_ptr; FileDataProvider(const std::string& file_path, diff --git a/modules/gapi/src/streaming/onevpl/onevpl_export.hpp b/modules/gapi/src/streaming/onevpl/onevpl_export.hpp index 44970ee7beca..5e2dc0447870 100644 --- a/modules/gapi/src/streaming/onevpl/onevpl_export.hpp +++ b/modules/gapi/src/streaming/onevpl/onevpl_export.hpp @@ -10,12 +10,17 @@ #endif // defined(_MSC_VER) #ifdef HAVE_ONEVPL +#if defined(MFX_VERSION) #if (MFX_VERSION >= 2000) #include #endif // MFX_VERSION +#endif // defined(MFX_VERSION) #include #include + +extern mfxLoader mfx_handle; +extern int impl_number; #endif // HAVE_ONEVPL #if defined(_MSC_VER) diff --git a/modules/gapi/src/streaming/onevpl/source.cpp b/modules/gapi/src/streaming/onevpl/source.cpp index e5b045188d37..3bad463e41ec 100644 --- a/modules/gapi/src/streaming/onevpl/source.cpp +++ b/modules/gapi/src/streaming/onevpl/source.cpp @@ -33,6 +33,13 @@ GSource::GSource(const std::string& filePath, accel_ctx_ptr, cfg_params)) { } +GSource::GSource(const std::string& filePath, + const CfgParams& cfg_params, + const Device &device, const Context &ctx) : + GSource(filePath, cfg_params, + std::make_shared(device, ctx, cfg_params)) { +} + GSource::GSource(const std::string& filePath, const CfgParams& cfg_params, std::shared_ptr selector) : @@ -74,6 +81,10 @@ GSource::GSource(const std::string&, const CfgParams&, const std::string&, GAPI_Assert(false && "Unsupported: G-API compiled without `WITH_GAPI_ONEVPL=ON`"); } +GSource::GSource(const std::string&, const CfgParams&, const Device &, const Context &) { + GAPI_Assert(false && "Unsupported: G-API compiled without `WITH_GAPI_ONEVPL=ON`"); +} + GSource::GSource(const std::string&, const CfgParams&, std::shared_ptr) { GAPI_Assert(false && "Unsupported: G-API compiled without `WITH_GAPI_ONEVPL=ON`"); } diff --git a/modules/gapi/src/streaming/onevpl/source_priv.cpp b/modules/gapi/src/streaming/onevpl/source_priv.cpp index fd2a401957de..765bdd3b6491 100644 --- a/modules/gapi/src/streaming/onevpl/source_priv.cpp +++ b/modules/gapi/src/streaming/onevpl/source_priv.cpp @@ -8,8 +8,10 @@ #include #include "streaming/onevpl/engine/decode/decode_engine_legacy.hpp" +#include "streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp" #include "streaming/onevpl/accelerators/accel_policy_dx11.hpp" #include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" +#include "streaming/onevpl/accelerators/accel_policy_va_api.hpp" #include "streaming/onevpl/utils.hpp" #include "streaming/onevpl/cfg_params_parser.hpp" #include "streaming/onevpl/data_provider_defines.hpp" @@ -35,6 +37,10 @@ GMetaArg GSource::Priv::descr_of() const { #else // HAVE_ONEVPL +// TODO global variable move it into Source after CloneSession issue resolving +mfxLoader mfx_handle = MFXLoad(); +int impl_number = 0; + namespace cv { namespace gapi { namespace wip { @@ -46,7 +52,7 @@ enum { }; GSource::Priv::Priv() : - mfx_handle(MFXLoad()), +// mfx_handle(MFXLoad()), mfx_impl_description(), mfx_handle_configs(), cfg_params(), @@ -106,11 +112,25 @@ GSource::Priv::Priv(std::shared_ptr provider, GAPI_Assert(false && "MFXSetConfigFilterProperty failed"); } + mfx_param.Type = MFX_VARIANT_TYPE_U32; + mfx_param.Data.U32 = MFX_EXTBUFF_VPP_SCALING; + sts = MFXSetConfigFilterProperty(cfg_inst, + (mfxU8 *)"mfxImplDescription.mfxVPPDescription.filter.FilterFourCC", + mfx_param); + + if (sts != MFX_ERR_NONE ) + { + GAPI_LOG_WARNING(nullptr, "MFXSetConfigFilterProperty failed, error: " << + mfxstatus_to_string(sts) << + " - for \"mfxImplDescription.mfxVPPDescription.filter.FilterFourCC\""); + GAPI_Assert(false && "MFXSetConfigFilterProperty failed"); + } + ++cfg_param_it; } // collect optional-preferred input parameters from input params - // which may (optionally) or may not be used to choose the most preferrable + // which may (optionally) or may not be used to choose the most preferable // VPL implementation (for example, specific API version or Debug/Release VPL build) std::vector preferred_params; std::copy_if(cfg_params.begin(), cfg_params.end(), std::back_inserter(preferred_params), @@ -118,7 +138,7 @@ GSource::Priv::Priv(std::shared_ptr provider, std::sort(preferred_params.begin(), preferred_params.end()); GAPI_LOG_DEBUG(nullptr, "Find MFX better implementation from handle: " << mfx_handle << - " is satisfying preferrable params count: " << preferred_params.size()); + " is satisfying preferable params count: " << preferred_params.size()); int i = 0; mfxImplDescription *idesc = nullptr; std::vector available_impl_descriptions; @@ -143,7 +163,7 @@ GSource::Priv::Priv(std::shared_ptr provider, GAPI_LOG_INFO(nullptr, "Implementation index: " << i << "\n" << ss.str()); // Only one VPL implementation is required for GSource here. - // Let's find intersection params from available impl with preferrable input params + // Let's find intersection params from available impl with preferable input params // to find best match. // An available VPL implementation with max matching count std::vector impl_params = get_params_from_string(ss.str()); @@ -159,7 +179,7 @@ GSource::Priv::Priv(std::shared_ptr provider, // in case of no input preferrance we consider all params are matched // for the first available VPL implementation. It will be a chosen one matches_count.emplace(impl_params.size(), i++); - GAPI_LOG_DEBUG(nullptr, "No preferrable params, use the first one implementation"); + GAPI_LOG_DEBUG(nullptr, "No preferable params, use the first one implementation"); break; } else { GAPI_LOG_DEBUG(nullptr, "Equal param intersection count: " << matched_params.size()); @@ -172,7 +192,8 @@ GSource::Priv::Priv(std::shared_ptr provider, GAPI_Assert(max_match_it != matches_count.rend() && "Cannot find matched MFX implementation for requested configuration"); - int impl_number = max_match_it->second; + // TODO impl_number is global for now + impl_number = max_match_it->second; GAPI_LOG_INFO(nullptr, "Chosen implementation index: " << impl_number); // release unusable impl available_impl_descriptions @@ -204,7 +225,12 @@ GSource::Priv::Priv(std::shared_ptr provider, "GSource mfx_impl_description->ApiVersion.Major >= VPL_NEW_API_MAJOR_VERSION" " - is not implemented"); } else { - engine.reset(new VPLLegacyDecodeEngine(std::move(acceleration))); + const auto& transcode_params = VPLLegacyTranscodeEngine::get_vpp_params(preferred_params); + if (!transcode_params.empty()) { + engine.reset(new VPLLegacyTranscodeEngine(std::move(acceleration))); + } else { + engine.reset(new VPLLegacyDecodeEngine(std::move(acceleration))); + } } } @@ -212,13 +238,13 @@ GSource::Priv::Priv(std::shared_ptr provider, auto engine_session_ptr = engine->initialize_session(mfx_session, cfg_params, provider); - const mfxVideoParam& video_param = engine_session_ptr->get_video_param(); + const mfxFrameInfo& video_param = engine_session_ptr->get_video_param(); // set valid description description.size = cv::Size { - video_param.mfx.FrameInfo.Width, - video_param.mfx.FrameInfo.Height}; - switch(video_param.mfx.FrameInfo.FourCC) { + video_param.Width, + video_param.Height}; + switch(video_param.FourCC) { case MFX_FOURCC_I420: throw std::runtime_error("Cannot parse GMetaArg description: MediaFrame doesn't support I420 type"); case MFX_FOURCC_NV12: @@ -226,7 +252,7 @@ GSource::Priv::Priv(std::shared_ptr provider, break; default: throw std::runtime_error("Cannot parse GMetaArg description: MediaFrame unknown 'fmt' type: " + - std::to_string(video_param.mfx.FrameInfo.FourCC)); + std::to_string(video_param.FourCC)); } description_is_valid = true; @@ -241,7 +267,7 @@ GSource::Priv::~Priv() { GAPI_LOG_INFO(nullptr, "Unload MFX implementation description: " << mfx_impl_description); MFXDispReleaseImplDescription(mfx_handle, mfx_impl_description); GAPI_LOG_INFO(nullptr, "Unload MFX handle: " << mfx_handle); - MFXUnload(mfx_handle); + //MFXUnload(mfx_handle); } std::unique_ptr GSource::Priv::initializeHWAccel(std::shared_ptr selector) @@ -269,6 +295,12 @@ std::unique_ptr GSource::Priv::initializeHWAccel(std::sha ret = std::move(cand); break; } + case MFX_ACCEL_MODE_VIA_VAAPI: + { + std::unique_ptr cand(new VPLVAAPIAccelerationPolicy(selector)); + ret = std::move(cand); + break; + } case MFX_ACCEL_MODE_NA: { std::unique_ptr cand(new VPLCPUAccelerationPolicy(selector)); diff --git a/modules/gapi/src/streaming/onevpl/source_priv.hpp b/modules/gapi/src/streaming/onevpl/source_priv.hpp index b835850d3535..21248df7fb43 100644 --- a/modules/gapi/src/streaming/onevpl/source_priv.hpp +++ b/modules/gapi/src/streaming/onevpl/source_priv.hpp @@ -44,7 +44,8 @@ struct GSource::Priv Priv(); std::unique_ptr initializeHWAccel(std::shared_ptr selector); - mfxLoader mfx_handle; + // TODO not it is global variable. Waiting for FIX issue with CloneSession + // mfxLoader mfx_handle; mfxImplDescription *mfx_impl_description; std::vector mfx_handle_configs; std::vector cfg_params; diff --git a/modules/gapi/src/streaming/onevpl/utils.cpp b/modules/gapi/src/streaming/onevpl/utils.cpp index 3ec0dea8ae7e..efd1618b7136 100644 --- a/modules/gapi/src/streaming/onevpl/utils.cpp +++ b/modules/gapi/src/streaming/onevpl/utils.cpp @@ -25,6 +25,8 @@ #define APPEND_STRINGIFY_MASK_N_ERASE(value, pref, mask) \ if (value & mask) { ss << pref << #mask; value ^= mask; } +#define DUMP_MEMBER(stream, object, member) \ + stream << #member << ": " << object.member << "\n"; namespace cv { namespace gapi { @@ -355,15 +357,51 @@ std::string mfxstatus_to_string(mfxStatus err) { } std::string ret(""; + ret += std::to_string(static_cast(err)) + ">"; return ret; } +std::string mfx_frame_info_to_string(const mfxFrameInfo &info) { + std::stringstream ss; + DUMP_MEMBER(ss, info, FrameRateExtN) + DUMP_MEMBER(ss, info, FrameRateExtD) + DUMP_MEMBER(ss, info, AspectRatioW) + DUMP_MEMBER(ss, info, AspectRatioH) + DUMP_MEMBER(ss, info, CropX) + DUMP_MEMBER(ss, info, CropY) + DUMP_MEMBER(ss, info, CropW) + DUMP_MEMBER(ss, info, CropH) + DUMP_MEMBER(ss, info, ChannelId) + DUMP_MEMBER(ss, info, BitDepthLuma) + DUMP_MEMBER(ss, info, BitDepthChroma) + DUMP_MEMBER(ss, info, Shift) + DUMP_MEMBER(ss, info, FourCC) + DUMP_MEMBER(ss, info, Width) + DUMP_MEMBER(ss, info, Height) + DUMP_MEMBER(ss, info, BufferSize) + DUMP_MEMBER(ss, info, PicStruct) + DUMP_MEMBER(ss, info, ChromaFormat); + return ss.str(); +} + +static int compare(const mfxFrameInfo &lhs, const mfxFrameInfo &rhs) { + //NB: mfxFrameInfo is a `packed` struct declared in VPL + return memcmp(&lhs, &rhs, sizeof(mfxFrameInfo)); +} + +bool operator< (const mfxFrameInfo &lhs, const mfxFrameInfo &rhs) { + return (compare(lhs, rhs) < 0); +} + +bool operator== (const mfxFrameInfo &lhs, const mfxFrameInfo &rhs) { + return (compare(lhs, rhs) == 0); +} + std::string ext_mem_frame_type_to_cstr(int type) { std::stringstream ss; APPEND_STRINGIFY_MASK_N_ERASE(type, "|", MFX_MEMTYPE_DXVA2_DECODER_TARGET); APPEND_STRINGIFY_MASK_N_ERASE(type, "|", MFX_MEMTYPE_DXVA2_PROCESSOR_TARGET); - // NB: accoring to VPL source the commented MFX_* constane below are belong to the + // NB: according to VPL source the commented MFX_* constane below are belong to the // same actual integral value as condition abobe. So it is impossible // to distinct them in condition branch. Just put this comment and possible // constans here... diff --git a/modules/gapi/src/streaming/onevpl/utils.hpp b/modules/gapi/src/streaming/onevpl/utils.hpp index 36711bf9a0bc..459f6127437f 100644 --- a/modules/gapi/src/streaming/onevpl/utils.hpp +++ b/modules/gapi/src/streaming/onevpl/utils.hpp @@ -73,8 +73,12 @@ const char* mfx_codec_type_to_cstr(const mfxU32 fourcc, const mfxU32 type); mfxU32 cstr_to_mfx_version(const char* cstr); -std::string mfxstatus_to_string(int64_t err); -std::string mfxstatus_to_string(mfxStatus err); +std::string GAPI_EXPORTS mfxstatus_to_string(int64_t err); +std::string GAPI_EXPORTS mfxstatus_to_string(mfxStatus err); + +std::string mfx_frame_info_to_string(const mfxFrameInfo &info); +bool operator< (const mfxFrameInfo &lhs, const mfxFrameInfo &rhs); +bool operator== (const mfxFrameInfo &lhs, const mfxFrameInfo &rhs); std::ostream& operator<< (std::ostream& out, const mfxImplDescription& idesc); diff --git a/modules/gapi/src/utils/itt.hpp b/modules/gapi/src/utils/itt.hpp index 0b49af7226b9..4a9cd54d6553 100644 --- a/modules/gapi/src/utils/itt.hpp +++ b/modules/gapi/src/utils/itt.hpp @@ -13,7 +13,7 @@ #include -// NOTE: OPENCV_WITH_ITT is only defined if ITT dependecy is built by OpenCV infrastructure. +// NOTE: OPENCV_WITH_ITT is only defined if ITT dependency is built by OpenCV infrastructure. // There will not be such define in G-API standalone mode. // TODO: Consider using OpenCV's trace.hpp #if defined(OPENCV_WITH_ITT) diff --git a/modules/gapi/test/common/gapi_core_tests.hpp b/modules/gapi/test/common/gapi_core_tests.hpp index 0d8015eac09e..b5a46282122f 100644 --- a/modules/gapi/test/common/gapi_core_tests.hpp +++ b/modules/gapi/test/common/gapi_core_tests.hpp @@ -107,12 +107,6 @@ GAPI_TEST_FIXTURE(ThresholdOTTest, initMatrixRandU, FIXTURE_API(int), 1, tt) GAPI_TEST_FIXTURE(InRangeTest, initMatrixRandU, <>, 0) GAPI_TEST_FIXTURE(Split3Test, initMatrixRandU, <>, 0) GAPI_TEST_FIXTURE(Split4Test, initMatrixRandU, <>, 0) -GAPI_TEST_FIXTURE(ResizeTest, initNothing, FIXTURE_API(CompareMats,int,cv::Size), 3, - cmpF, interp, sz_out) -GAPI_TEST_FIXTURE(ResizePTest, initNothing, FIXTURE_API(CompareMats,int,cv::Size), 3, - cmpF, interp, sz_out) -GAPI_TEST_FIXTURE(ResizeTestFxFy, initNothing, FIXTURE_API(CompareMats,int,double,double), 4, - cmpF, interp, fx, fy) GAPI_TEST_FIXTURE(Merge3Test, initMatsRandU, <>, 0) GAPI_TEST_FIXTURE(Merge4Test, initMatsRandU, <>, 0) GAPI_TEST_FIXTURE(RemapTest, initMatrixRandU, <>, 0) diff --git a/modules/gapi/test/common/gapi_core_tests_inl.hpp b/modules/gapi/test/common/gapi_core_tests_inl.hpp index 09b61e28764a..11b6e066a64e 100644 --- a/modules/gapi/test/common/gapi_core_tests_inl.hpp +++ b/modules/gapi/test/common/gapi_core_tests_inl.hpp @@ -847,78 +847,6 @@ TEST_P(Split4Test, AccuracyTest) } } -static void ResizeAccuracyTest(const CompareMats& cmpF, int type, int interp, cv::Size sz_in, - cv::Size sz_out, double fx, double fy, cv::GCompileArgs&& compile_args) -{ - cv::Mat in_mat1 (sz_in, type ); - cv::Scalar mean = cv::Scalar::all(127); - cv::Scalar stddev = cv::Scalar::all(40.f); - - cv::randn(in_mat1, mean, stddev); - - auto out_mat_sz = sz_out.area() == 0 ? cv::Size(saturate_cast(sz_in.width *fx), - saturate_cast(sz_in.height*fy)) - : sz_out; - cv::Mat out_mat(out_mat_sz, type); - cv::Mat out_mat_ocv(out_mat_sz, type); - - // G-API code ////////////////////////////////////////////////////////////// - cv::GMat in; - auto out = cv::gapi::resize(in, sz_out, fx, fy, interp); - - cv::GComputation c(in, out); - c.apply(in_mat1, out_mat, std::move(compile_args)); - // OpenCV code ///////////////////////////////////////////////////////////// - { - cv::resize(in_mat1, out_mat_ocv, sz_out, fx, fy, interp); - } - // Comparison ////////////////////////////////////////////////////////////// - { - EXPECT_TRUE(cmpF(out_mat, out_mat_ocv)); - } -} - -TEST_P(ResizeTest, AccuracyTest) -{ - ResizeAccuracyTest(cmpF, type, interp, sz, sz_out, 0.0, 0.0, getCompileArgs()); -} - -TEST_P(ResizeTestFxFy, AccuracyTest) -{ - ResizeAccuracyTest(cmpF, type, interp, sz, cv::Size{0, 0}, fx, fy, getCompileArgs()); -} - -TEST_P(ResizePTest, AccuracyTest) -{ - constexpr int planeNum = 3; - cv::Size sz_in_p {sz.width, sz.height*planeNum}; - cv::Size sz_out_p{sz_out.width, sz_out.height*planeNum}; - - cv::Mat in_mat(sz_in_p, CV_8UC1); - cv::randn(in_mat, cv::Scalar::all(127.0f), cv::Scalar::all(40.f)); - - cv::Mat out_mat (sz_out_p, CV_8UC1); - cv::Mat out_mat_ocv_p(sz_out_p, CV_8UC1); - - cv::GMatP in; - auto out = cv::gapi::resizeP(in, sz_out, interp); - cv::GComputation c(cv::GIn(in), cv::GOut(out)); - - c.compile(cv::descr_of(in_mat).asPlanar(planeNum), getCompileArgs()) - (cv::gin(in_mat), cv::gout(out_mat)); - - for (int i = 0; i < planeNum; i++) { - const cv::Mat in_mat_roi = in_mat(cv::Rect(0, i*sz.height, sz.width, sz.height)); - cv::Mat out_mat_roi = out_mat_ocv_p(cv::Rect(0, i*sz_out.height, sz_out.width, sz_out.height)); - cv::resize(in_mat_roi, out_mat_roi, sz_out, 0, 0, interp); - } - - // Comparison ////////////////////////////////////////////////////////////// - { - EXPECT_TRUE(cmpF(out_mat, out_mat_ocv_p)); - } -} - TEST_P(Merge3Test, AccuracyTest) { cv::Mat in_mat3(sz, type); @@ -1773,6 +1701,25 @@ namespace { }; }; +namespace { + class TestMediaGray final : public cv::MediaFrame::IAdapter { + cv::Mat m_mat; + + public: + explicit TestMediaGray(cv::Mat m) + : m_mat(m) { + } + cv::GFrameDesc meta() const override { + return cv::GFrameDesc{ cv::MediaFormat::GRAY, cv::Size(m_mat.cols, m_mat.rows) }; + } + cv::MediaFrame::View access(cv::MediaFrame::Access) override { + cv::MediaFrame::View::Ptrs pp = { m_mat.ptr(), nullptr, nullptr, nullptr }; + cv::MediaFrame::View::Strides ss = { m_mat.step, 0u, 0u, 0u }; + return cv::MediaFrame::View(std::move(pp), std::move(ss)); + } + }; +}; + TEST_P(SizeMFTest, ParseTest) { cv::Size out_sz; @@ -1787,6 +1734,20 @@ TEST_P(SizeMFTest, ParseTest) EXPECT_EQ(sz, out_sz); } +TEST_P(SizeMFTest, ParseGrayTest) +{ + cv::Size out_sz; + cv::Mat gray = cv::Mat::eye(sz.height, sz.width, CV_8UC1); + cv::MediaFrame frame = cv::MediaFrame::Create(gray); + + cv::GFrame in; + auto out = cv::gapi::streaming::size(in); + cv::GComputation c(cv::GIn(in), cv::GOut(out)); + c.apply(cv::gin(frame), cv::gout(out_sz), getCompileArgs()); + + EXPECT_EQ(sz, out_sz); +} + } // opencv_test #endif //OPENCV_GAPI_CORE_TESTS_INL_HPP diff --git a/modules/gapi/test/common/gapi_imgproc_tests.hpp b/modules/gapi/test/common/gapi_imgproc_tests.hpp index 97d46943d5ad..a5663a4ce6d7 100644 --- a/modules/gapi/test/common/gapi_imgproc_tests.hpp +++ b/modules/gapi/test/common/gapi_imgproc_tests.hpp @@ -119,6 +119,12 @@ GAPI_TEST_FIXTURE(YUV2BGRTest, initMatrixRandN, FIXTURE_API(CompareMats), 1, cmp GAPI_TEST_FIXTURE(RGB2HSVTest, initMatrixRandN, FIXTURE_API(CompareMats), 1, cmpF) GAPI_TEST_FIXTURE(BayerGR2RGBTest, initMatrixRandN, FIXTURE_API(CompareMats), 1, cmpF) GAPI_TEST_FIXTURE(RGB2YUV422Test, initMatrixRandN, FIXTURE_API(CompareMats), 1, cmpF) +GAPI_TEST_FIXTURE(ResizeTest, initNothing, FIXTURE_API(CompareMats,int,cv::Size), 3, + cmpF, interp, sz_out) +GAPI_TEST_FIXTURE(ResizePTest, initNothing, FIXTURE_API(CompareMats,int,cv::Size), 3, + cmpF, interp, sz_out) +GAPI_TEST_FIXTURE(ResizeTestFxFy, initNothing, FIXTURE_API(CompareMats,int,double,double), 4, + cmpF, interp, fx, fy) } // opencv_test #endif //OPENCV_GAPI_IMGPROC_TESTS_HPP diff --git a/modules/gapi/test/common/gapi_imgproc_tests_inl.hpp b/modules/gapi/test/common/gapi_imgproc_tests_inl.hpp index 6500c7853d82..e9f4edfd66d3 100644 --- a/modules/gapi/test/common/gapi_imgproc_tests_inl.hpp +++ b/modules/gapi/test/common/gapi_imgproc_tests_inl.hpp @@ -1067,6 +1067,79 @@ TEST_P(RGB2YUV422Test, AccuracyTest) EXPECT_EQ(sz, out_mat_gapi.size()); } } + +static void ResizeAccuracyTest(const CompareMats& cmpF, int type, int interp, cv::Size sz_in, + cv::Size sz_out, double fx, double fy, cv::GCompileArgs&& compile_args) +{ + cv::Mat in_mat1 (sz_in, type ); + cv::Scalar mean = cv::Scalar::all(127); + cv::Scalar stddev = cv::Scalar::all(40.f); + + cv::randn(in_mat1, mean, stddev); + + auto out_mat_sz = sz_out.area() == 0 ? cv::Size(saturate_cast(sz_in.width *fx), + saturate_cast(sz_in.height*fy)) + : sz_out; + cv::Mat out_mat(out_mat_sz, type); + cv::Mat out_mat_ocv(out_mat_sz, type); + + // G-API code ////////////////////////////////////////////////////////////// + cv::GMat in; + auto out = cv::gapi::resize(in, sz_out, fx, fy, interp); + + cv::GComputation c(in, out); + c.apply(in_mat1, out_mat, std::move(compile_args)); + // OpenCV code ///////////////////////////////////////////////////////////// + { + cv::resize(in_mat1, out_mat_ocv, sz_out, fx, fy, interp); + } + // Comparison ////////////////////////////////////////////////////////////// + { + EXPECT_TRUE(cmpF(out_mat, out_mat_ocv)); + } +} + +TEST_P(ResizeTest, AccuracyTest) +{ + ResizeAccuracyTest(cmpF, type, interp, sz, sz_out, 0.0, 0.0, getCompileArgs()); +} + +TEST_P(ResizeTestFxFy, AccuracyTest) +{ + ResizeAccuracyTest(cmpF, type, interp, sz, cv::Size{0, 0}, fx, fy, getCompileArgs()); +} + +TEST_P(ResizePTest, AccuracyTest) +{ + constexpr int planeNum = 3; + cv::Size sz_in_p {sz.width, sz.height*planeNum}; + cv::Size sz_out_p{sz_out.width, sz_out.height*planeNum}; + + cv::Mat in_mat(sz_in_p, CV_8UC1); + cv::randn(in_mat, cv::Scalar::all(127.0f), cv::Scalar::all(40.f)); + + cv::Mat out_mat (sz_out_p, CV_8UC1); + cv::Mat out_mat_ocv_p(sz_out_p, CV_8UC1); + + cv::GMatP in; + auto out = cv::gapi::resizeP(in, sz_out, interp); + cv::GComputation c(cv::GIn(in), cv::GOut(out)); + + c.compile(cv::descr_of(in_mat).asPlanar(planeNum), getCompileArgs()) + (cv::gin(in_mat), cv::gout(out_mat)); + + for (int i = 0; i < planeNum; i++) { + const cv::Mat in_mat_roi = in_mat(cv::Rect(0, i*sz.height, sz.width, sz.height)); + cv::Mat out_mat_roi = out_mat_ocv_p(cv::Rect(0, i*sz_out.height, sz_out.width, sz_out.height)); + cv::resize(in_mat_roi, out_mat_roi, sz_out, 0, 0, interp); + } + + // Comparison ////////////////////////////////////////////////////////////// + { + EXPECT_TRUE(cmpF(out_mat, out_mat_ocv_p)); + } +} + } // opencv_test #endif //OPENCV_GAPI_IMGPROC_TESTS_INL_HPP diff --git a/modules/gapi/test/common/gapi_video_tests_inl.hpp b/modules/gapi/test/common/gapi_video_tests_inl.hpp index 34cbbf2731e5..d2157d4c2d37 100644 --- a/modules/gapi/test/common/gapi_video_tests_inl.hpp +++ b/modules/gapi/test/common/gapi_video_tests_inl.hpp @@ -78,7 +78,7 @@ TEST_P(BuildPyr_CalcOptFlow_PipelineTest, AccuracyTest) auto customKernel = gapi::kernels(); auto kernels = gapi::combine(customKernel, - params.compileArgs[0].get()); + params.compileArgs[0].get()); params.compileArgs = compile_args(kernels); OptFlowLKTestOutput outOCV { outPtsOCV, outStatusOCV, outErrOCV }; diff --git a/modules/gapi/test/cpu/gapi_core_tests_cpu.cpp b/modules/gapi/test/cpu/gapi_core_tests_cpu.cpp index 424cf1b0ad83..d8bf970bb8e3 100644 --- a/modules/gapi/test/cpu/gapi_core_tests_cpu.cpp +++ b/modules/gapi/test/cpu/gapi_core_tests_cpu.cpp @@ -12,6 +12,7 @@ namespace { #define CORE_CPU [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::core::cpu::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test @@ -20,9 +21,7 @@ namespace opencv_test // FIXME: Wut? See MulTestCPU/MathOpTest below (duplicate?) INSTANTIATE_TEST_CASE_P(AddTestCPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU), Values(ADD, MUL), @@ -32,9 +31,7 @@ INSTANTIATE_TEST_CASE_P(AddTestCPU, MathOpTest, INSTANTIATE_TEST_CASE_P(MulTestCPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU), Values(MUL), @@ -44,9 +41,7 @@ INSTANTIATE_TEST_CASE_P(MulTestCPU, MathOpTest, INSTANTIATE_TEST_CASE_P(SubTestCPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU), Values(SUB), @@ -56,9 +51,7 @@ INSTANTIATE_TEST_CASE_P(SubTestCPU, MathOpTest, INSTANTIATE_TEST_CASE_P(DivTestCPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU), Values(DIV), @@ -68,90 +61,68 @@ INSTANTIATE_TEST_CASE_P(DivTestCPU, MathOpTest, INSTANTIATE_TEST_CASE_P(MulTestCPU, MulDoubleTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(DivTestCPU, DivTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(DivCTestCPU, DivCTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(MeanTestCPU, MeanTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(MaskTestCPU, MaskTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(SelectTestCPU, SelectTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(Polar2CartCPU, Polar2CartTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_32FC1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(Cart2PolarCPU, Cart2PolarTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_32FC1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(PhaseCPU, PhaseTest, Combine(Values(CV_32F, CV_32FC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), /* angle_in_degrees */ testing::Bool())); INSTANTIATE_TEST_CASE_P(SqrtCPU, SqrtTest, Combine(Values(CV_32F, CV_32FC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(CompareTestCPU, CmpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U), Values(CORE_CPU), Values(CMP_EQ, CMP_GE, CMP_NE, CMP_GT, CMP_LT, CMP_LE), @@ -160,9 +131,7 @@ INSTANTIATE_TEST_CASE_P(CompareTestCPU, CmpTest, INSTANTIATE_TEST_CASE_P(BitwiseTestCPU, BitwiseTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AND, OR, XOR), @@ -170,33 +139,25 @@ INSTANTIATE_TEST_CASE_P(BitwiseTestCPU, BitwiseTest, INSTANTIATE_TEST_CASE_P(BitwiseNotTestCPU, NotTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(MinTestCPU, MinTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(MaxTestCPU, MaxTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(SumTestCPU, SumTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), //Values(1e-5), Values(CORE_CPU), @@ -204,43 +165,33 @@ INSTANTIATE_TEST_CASE_P(SumTestCPU, SumTest, INSTANTIATE_TEST_CASE_P(CountNonZeroTestCPU, CountNonZeroTest, Combine(Values( CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AbsToleranceScalar(1e-5).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(AbsDiffTestCPU, AbsDiffTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(AbsDiffCTestCPU, AbsDiffCTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(AddWeightedTestCPU, AddWeightedTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_CPU), Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NormTestCPU, NormTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AbsToleranceScalar(1e-5).to_compare_obj()), @@ -248,17 +199,13 @@ INSTANTIATE_TEST_CASE_P(NormTestCPU, NormTest, INSTANTIATE_TEST_CASE_P(IntegralTestCPU, IntegralTest, Combine(Values( CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(ThresholdTestCPU, ThresholdTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, @@ -269,9 +216,7 @@ INSTANTIATE_TEST_CASE_P(ThresholdTestCPU, ThresholdTest, INSTANTIATE_TEST_CASE_P(ThresholdTestCPU, ThresholdOTTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(cv::THRESH_OTSU, cv::THRESH_TRIANGLE))); @@ -279,135 +224,75 @@ INSTANTIATE_TEST_CASE_P(ThresholdTestCPU, ThresholdOTTest, INSTANTIATE_TEST_CASE_P(InRangeTestCPU, InRangeTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(Split3TestCPU, Split3Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(Split4TestCPU, Split4Test, Combine(Values(CV_8UC4), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_CPU))); -INSTANTIATE_TEST_CASE_P(ResizeTestCPU, ResizeTest, - Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_CPU), - Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values(cv::Size(64,64), - cv::Size(30,30)))); - -INSTANTIATE_TEST_CASE_P(ResizePTestCPU, ResizePTest, - Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_CPU), - Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), - Values(cv::INTER_LINEAR), - Values(cv::Size(64,64), - cv::Size(30,30)))); - -INSTANTIATE_TEST_CASE_P(ResizeTestCPU, ResizeTestFxFy, - Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_CPU), - Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values(0.5, 0.1), - Values(0.5, 0.1))); - INSTANTIATE_TEST_CASE_P(Merge3TestCPU, Merge3Test, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC3), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(Merge4TestCPU, Merge4Test, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC4), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(RemapTestCPU, RemapTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(FlipTestCPU, FlipTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(0,1,-1))); INSTANTIATE_TEST_CASE_P(CropTestCPU, CropTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(cv::Rect(10, 8, 20, 35), cv::Rect(4, 10, 37, 50)))); INSTANTIATE_TEST_CASE_P(CopyTestCPU, CopyTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(LUTTestCPU, LUTTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(LUTTestCustomCPU, LUTTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC3), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(ConvertToCPU, ConvertToTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U, CV_16U, CV_16S, CV_32F), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -416,40 +301,31 @@ INSTANTIATE_TEST_CASE_P(ConvertToCPU, ConvertToTest, INSTANTIATE_TEST_CASE_P(ConcatHorTestCPU, ConcatHorTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(ConcatVertTestCPU, ConcatVertTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(ConcatVertVecTestCPU, ConcatVertVecTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(ConcatHorVecTestCPU, ConcatHorVecTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); INSTANTIATE_TEST_CASE_P(WarpPerspectiveTestCPU, WarpPerspectiveTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -461,8 +337,7 @@ INSTANTIATE_TEST_CASE_P(WarpPerspectiveTestCPU, WarpPerspectiveTest, INSTANTIATE_TEST_CASE_P(WarpAffineTestCPU, WarpAffineTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -474,8 +349,7 @@ INSTANTIATE_TEST_CASE_P(WarpAffineTestCPU, WarpAffineTest, INSTANTIATE_TEST_CASE_P(NormalizeTestCPU, NormalizeTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -555,9 +429,7 @@ INSTANTIATE_TEST_CASE_P(TransposeTestCPU, TransposeTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1, CV_8UC2, CV_16UC2, CV_16SC2, CV_32FC2, CV_8UC3, CV_16UC3, CV_16SC3, CV_32FC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()))); diff --git a/modules/gapi/test/cpu/gapi_core_tests_fluid.cpp b/modules/gapi/test/cpu/gapi_core_tests_fluid.cpp index d7d4abc46aca..b61ff95147bd 100644 --- a/modules/gapi/test/cpu/gapi_core_tests_fluid.cpp +++ b/modules/gapi/test/cpu/gapi_core_tests_fluid.cpp @@ -11,6 +11,7 @@ namespace { #define CORE_FLUID [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::core::fluid::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test @@ -18,10 +19,7 @@ namespace opencv_test // FIXME: Windows accuracy problems after recent update! INSTANTIATE_TEST_CASE_P(MathOpTestFluid, MathOpTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1, CV_8U, CV_32F), Values(CORE_FLUID), Values(DIV, MUL), @@ -37,10 +35,7 @@ INSTANTIATE_TEST_CASE_P(MathOpTestFluid, MathOpTest, // Github ticket: https://github.com/opencv/opencv/issues/18373. INSTANTIATE_TEST_CASE_P(DISABLED_MathOpTestFluid, MathOpTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1, CV_8U, CV_32F), Values(CORE_FLUID), Values(ADD, SUB), @@ -54,9 +49,7 @@ INSTANTIATE_TEST_CASE_P(DISABLED_MathOpTestFluid, MathOpTest, // Github ticket: https://github.com/opencv/opencv/issues/18373. INSTANTIATE_TEST_CASE_P(DISABLED_SubTestFluid, MathOpTest, Combine(Values(CV_8UC1, CV_16SC1 , CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1, CV_8U, CV_32F), Values(CORE_FLUID), Values(SUB), @@ -66,41 +59,31 @@ INSTANTIATE_TEST_CASE_P(DISABLED_SubTestFluid, MathOpTest, INSTANTIATE_TEST_CASE_P(MulSTestFluid, MulDoubleTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), // FIXME: extend with more types Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(DivCTestFluid, DivCTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U, CV_32F), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(DISABLED_MeanTestFluid, MeanTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(MaskTestFluid, MaskTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(AbsDiffTestFluid, AbsDiffTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); @@ -108,18 +91,13 @@ INSTANTIATE_TEST_CASE_P(AbsDiffCTestFluid, AbsDiffCTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_8UC2, CV_16UC2, CV_16SC2, CV_8UC3, CV_16UC3, CV_16SC3, CV_8UC4, CV_16UC4, CV_16SC4), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(BitwiseTestFluid, BitwiseTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AND, OR, XOR), @@ -127,46 +105,32 @@ INSTANTIATE_TEST_CASE_P(BitwiseTestFluid, BitwiseTest, INSTANTIATE_TEST_CASE_P(BitwiseNotTestFluid, NotTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(MinTestFluid, MinTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(MaxTestFluid, MaxTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(DISABLED_SumTestFluid, SumTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsToleranceScalar(1e-5).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(CompareTestFluid, CmpTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U), Values(CORE_FLUID), Values(CMP_EQ, CMP_GE, CMP_NE, CMP_GT, CMP_LT, CMP_LE), @@ -176,10 +140,7 @@ INSTANTIATE_TEST_CASE_P(CompareTestFluid, CmpTest, // FIXME: solve comparison error to unite with the test above INSTANTIATE_TEST_CASE_P(CompareTestFluidScalar, CmpTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U), Values(CORE_FLUID), Values(CMP_EQ, CMP_GE, CMP_NE, CMP_GT, CMP_LT, CMP_LE), @@ -188,18 +149,14 @@ INSTANTIATE_TEST_CASE_P(CompareTestFluidScalar, CmpTest, INSTANTIATE_TEST_CASE_P(AddWeightedTestFluid, AddWeightedTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1, CV_8U, CV_32F), Values(CORE_FLUID), Values(Tolerance_FloatRel_IntAbs(1e-5, 2).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(DISABLED_NormTestFluid, NormTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsToleranceScalar(1e-5).to_compare_obj()), @@ -207,26 +164,19 @@ INSTANTIATE_TEST_CASE_P(DISABLED_NormTestFluid, NormTest, INSTANTIATE_TEST_CASE_P(DISABLED_IntegralTestFluid, IntegralTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(LUTTestFluid, LUTTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(ConvertToFluid, ConvertToTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U, CV_16U, CV_16S, CV_32F), Values(CORE_FLUID), Values(Tolerance_FloatRel_IntAbs(1e-5, 2).to_compare_obj()), @@ -235,128 +185,94 @@ INSTANTIATE_TEST_CASE_P(ConvertToFluid, ConvertToTest, INSTANTIATE_TEST_CASE_P(DISABLED_ConcatHorTestFluid, ConcatHorTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(DISABLED_ConcatVertTestFluid, ConcatVertTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(Split3TestFluid, Split3Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(Split4TestFluid, Split4Test, Combine(Values(CV_8UC4), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(Merge3TestFluid, Merge3Test, Combine(Values(CV_8UC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC3), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(Merge4TestFluid, Merge4Test, Combine(Values(CV_8UC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC4), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(DISABLED_RemapTestFluid, RemapTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(DISABLED_FlipTestFluid, FlipTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(0,1,-1))); INSTANTIATE_TEST_CASE_P(DISABLED_CropTestFluid, CropTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(cv::Rect(10, 8, 20, 35), cv::Rect(4, 10, 37, 50)))); INSTANTIATE_TEST_CASE_P(SelectTestFluid, SelectTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(Polar2CartFluid, Polar2CartTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_32FC1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(Cart2PolarFluid, Cart2PolarTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_32FC1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(PhaseFluid, PhaseTest, Combine(Values(CV_32F, CV_32FC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), /* angle_in_degrees */ testing::Bool())); INSTANTIATE_TEST_CASE_P(SqrtFluid, SqrtTest, Combine(Values(CV_32F, CV_32FC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); INSTANTIATE_TEST_CASE_P(ThresholdTestFluid, ThresholdTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(cv::THRESH_BINARY, cv::THRESH_BINARY_INV, @@ -368,53 +284,17 @@ INSTANTIATE_TEST_CASE_P(ThresholdTestFluid, ThresholdTest, INSTANTIATE_TEST_CASE_P(DISABLED_ThresholdTestFluid, ThresholdOTTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(cv::THRESH_OTSU, cv::THRESH_TRIANGLE))); INSTANTIATE_TEST_CASE_P(InRangeTestFluid, InRangeTest, Combine(Values(CV_8UC3, CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1920, 1080), - cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID))); -INSTANTIATE_TEST_CASE_P(ResizeTestFluid, ResizeTest, - Combine(Values(CV_8UC3/*CV_8UC1, CV_16UC1, CV_16SC1*/), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128), - cv::Size(64, 64), - cv::Size(30, 30)), - Values(-1), - Values(CORE_FLUID), - Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_obj()), - Values(/*cv::INTER_NEAREST,*/ cv::INTER_LINEAR/*, cv::INTER_AREA*/), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128), - cv::Size(64, 64), - cv::Size(30, 30)))); - -INSTANTIATE_TEST_CASE_P(ResizeTestFxFyFluid, ResizeTestFxFy, - Combine(Values(CV_8UC3/*CV_8UC1, CV_16UC1, CV_16SC1*/), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128), - cv::Size(64, 64), - cv::Size(30, 30)), - Values(-1), - Values(CORE_FLUID), - Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_obj()), - Values(/*cv::INTER_NEAREST,*/ cv::INTER_LINEAR/*, cv::INTER_AREA*/), - Values(0.5, 1, 2), - Values(0.5, 1, 2))); - INSTANTIATE_TEST_CASE_P(BackendOutputAllocationTestFluid, BackendOutputAllocationTest, Combine(Values(CV_8UC3, CV_16SC2, CV_32FC1), Values(cv::Size(50, 50)), diff --git a/modules/gapi/test/cpu/gapi_imgproc_tests_cpu.cpp b/modules/gapi/test/cpu/gapi_imgproc_tests_cpu.cpp index f3e70c0f9ab2..93df74e98f80 100644 --- a/modules/gapi/test/cpu/gapi_imgproc_tests_cpu.cpp +++ b/modules/gapi/test/cpu/gapi_imgproc_tests_cpu.cpp @@ -13,16 +13,45 @@ namespace { #define IMGPROC_CPU [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::imgproc::cpu::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test { +INSTANTIATE_TEST_CASE_P(ResizeTestCPU, ResizeTest, + Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + ValuesIn(in_sizes), + Values(-1), + Values(IMGPROC_CPU), + Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(cv::Size(64,64), + cv::Size(30,30)))); + +INSTANTIATE_TEST_CASE_P(ResizePTestCPU, ResizePTest, + Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + ValuesIn(in_sizes), + Values(-1), + Values(IMGPROC_CPU), + Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), + Values(cv::INTER_LINEAR), + Values(cv::Size(64,64), + cv::Size(30,30)))); + +INSTANTIATE_TEST_CASE_P(ResizeTestCPU, ResizeTestFxFy, + Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + ValuesIn(in_sizes), + Values(-1), + Values(IMGPROC_CPU), + Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(0.5, 0.1), + Values(0.5, 0.1))); + INSTANTIATE_TEST_CASE_P(Filter2DTestCPU, Filter2DTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1, CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -34,8 +63,7 @@ INSTANTIATE_TEST_CASE_P(Filter2DTestCPU, Filter2DTest, INSTANTIATE_TEST_CASE_P(BoxFilterTestCPU, BoxFilterTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_32F), Values(IMGPROC_CPU), Values(AbsTolerance(0).to_compare_obj()), @@ -44,8 +72,7 @@ INSTANTIATE_TEST_CASE_P(BoxFilterTestCPU, BoxFilterTest, INSTANTIATE_TEST_CASE_P(SepFilterTestCPU_8U, SepFilterTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -53,8 +80,7 @@ INSTANTIATE_TEST_CASE_P(SepFilterTestCPU_8U, SepFilterTest, INSTANTIATE_TEST_CASE_P(SepFilterTestCPU_other, SepFilterTest, Combine(Values(CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -62,8 +88,7 @@ INSTANTIATE_TEST_CASE_P(SepFilterTestCPU_other, SepFilterTest, INSTANTIATE_TEST_CASE_P(BlurTestCPU, BlurTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsTolerance(0.0).to_compare_obj()), @@ -72,8 +97,7 @@ INSTANTIATE_TEST_CASE_P(BlurTestCPU, BlurTest, INSTANTIATE_TEST_CASE_P(gaussBlurTestCPU, GaussianBlurTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -81,8 +105,7 @@ INSTANTIATE_TEST_CASE_P(gaussBlurTestCPU, GaussianBlurTest, INSTANTIATE_TEST_CASE_P(MedianBlurTestCPU, MedianBlurTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -90,8 +113,7 @@ INSTANTIATE_TEST_CASE_P(MedianBlurTestCPU, MedianBlurTest, INSTANTIATE_TEST_CASE_P(ErodeTestCPU, ErodeTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -102,8 +124,7 @@ INSTANTIATE_TEST_CASE_P(ErodeTestCPU, ErodeTest, INSTANTIATE_TEST_CASE_P(Erode3x3TestCPU, Erode3x3Test, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -111,8 +132,7 @@ INSTANTIATE_TEST_CASE_P(Erode3x3TestCPU, Erode3x3Test, INSTANTIATE_TEST_CASE_P(DilateTestCPU, DilateTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -123,8 +143,7 @@ INSTANTIATE_TEST_CASE_P(DilateTestCPU, DilateTest, INSTANTIATE_TEST_CASE_P(Dilate3x3TestCPU, Dilate3x3Test, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -132,8 +151,7 @@ INSTANTIATE_TEST_CASE_P(Dilate3x3TestCPU, Dilate3x3Test, INSTANTIATE_TEST_CASE_P(MorphologyExTestCPU, MorphologyExTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -147,8 +165,7 @@ INSTANTIATE_TEST_CASE_P(MorphologyExTestCPU, MorphologyExTest, INSTANTIATE_TEST_CASE_P(MorphologyExHitMissTestCPU, MorphologyExTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -156,8 +173,7 @@ INSTANTIATE_TEST_CASE_P(MorphologyExHitMissTestCPU, MorphologyExTest, INSTANTIATE_TEST_CASE_P(SobelTestCPU, SobelTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -167,8 +183,7 @@ INSTANTIATE_TEST_CASE_P(SobelTestCPU, SobelTest, INSTANTIATE_TEST_CASE_P(SobelTestCPU32F, SobelTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -178,8 +193,7 @@ INSTANTIATE_TEST_CASE_P(SobelTestCPU32F, SobelTest, INSTANTIATE_TEST_CASE_P(SobelXYTestCPU, SobelXYTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -190,8 +204,7 @@ INSTANTIATE_TEST_CASE_P(SobelXYTestCPU, SobelXYTest, INSTANTIATE_TEST_CASE_P(SobelXYTestCPU32F, SobelXYTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_32F), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -202,8 +215,7 @@ INSTANTIATE_TEST_CASE_P(SobelXYTestCPU32F, SobelXYTest, INSTANTIATE_TEST_CASE_P(LaplacianTestCPU, LaplacianTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -213,8 +225,7 @@ INSTANTIATE_TEST_CASE_P(LaplacianTestCPU, LaplacianTest, INSTANTIATE_TEST_CASE_P(BilateralFilterTestCPU, BilateralFilterTest, Combine(Values(CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()), @@ -225,16 +236,14 @@ INSTANTIATE_TEST_CASE_P(BilateralFilterTestCPU, BilateralFilterTest, INSTANTIATE_TEST_CASE_P(EqHistTestCPU, EqHistTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(CannyTestCPU, CannyTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsSimilarPoints(0, 0.05).to_compare_obj()), @@ -278,8 +287,7 @@ INSTANTIATE_TEST_CASE_P(FindContoursOffsetTestCPU, FindContoursOffsetTest, INSTANTIATE_TEST_CASE_P(FindContoursHNoOffsetTestCPU, FindContoursHNoOffsetTest, Combine(Values(IMGPROC_CPU), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(RETR_EXTERNAL, RETR_LIST, RETR_CCOMP, RETR_TREE), Values(CHAIN_APPROX_NONE, CHAIN_APPROX_SIMPLE, @@ -288,8 +296,7 @@ INSTANTIATE_TEST_CASE_P(FindContoursHNoOffsetTestCPU, FindContoursHNoOffsetTest, INSTANTIATE_TEST_CASE_P(FindContoursHNoOffset32STestCPU, FindContoursHNoOffsetTest, Combine(Values(IMGPROC_CPU), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_32SC1), Values(RETR_CCOMP, RETR_FLOODFILL), Values(CHAIN_APPROX_NONE, CHAIN_APPROX_SIMPLE, @@ -301,9 +308,7 @@ INSTANTIATE_TEST_CASE_P(FindContoursHOffsetTestCPU, FindContoursHOffsetTest, INSTANTIATE_TEST_CASE_P(BoundingRectMatTestCPU, BoundingRectMatTest, Combine(Values( CV_8UC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(IMGPROC_CPU), Values(IoUToleranceRect(0).to_compare_obj()), @@ -404,176 +409,154 @@ INSTANTIATE_TEST_CASE_P(FitLine3DVector64FTestCPU, FitLine3DVector64FTest, INSTANTIATE_TEST_CASE_P(BGR2RGBTestCPU, BGR2RGBTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2GrayTestCPU, RGB2GrayTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2GrayTestCPU, BGR2GrayTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2YUVTestCPU, RGB2YUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(YUV2RGBTestCPU, YUV2RGBTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2I420TestCPU, BGR2I420Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2I420TestCPU, RGB2I420Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(I4202BGRTestCPU, I4202BGRTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(I4202RGBTestCPU, I4202RGBTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NV12toRGBTestCPU, NV12toRGBTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NV12toBGRTestCPU, NV12toBGRTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NV12toGrayTestCPU, NV12toGrayTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NV12toRGBpTestCPU, NV12toRGBpTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NV12toBGRpTestCPU, NV12toBGRpTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2LabTestCPU, RGB2LabTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2LUVTestCPU, BGR2LUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(LUV2BGRTestCPU, LUV2BGRTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2YUVTestCPU, BGR2YUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(YUV2BGRTestCPU, YUV2BGRTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2HSVTestCPU, RGB2HSVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BayerGR2RGBTestCPU, BayerGR2RGBTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_CPU), Values(AbsExact().to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2YUV422TestCPU, RGB2YUV422Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC2), Values(IMGPROC_CPU), Values(AbsTolerance(1).to_compare_obj()))); diff --git a/modules/gapi/test/cpu/gapi_imgproc_tests_fluid.cpp b/modules/gapi/test/cpu/gapi_imgproc_tests_fluid.cpp index 4e847825f117..1b4c35123203 100644 --- a/modules/gapi/test/cpu/gapi_imgproc_tests_fluid.cpp +++ b/modules/gapi/test/cpu/gapi_imgproc_tests_fluid.cpp @@ -16,42 +16,59 @@ namespace namespace opencv_test { +INSTANTIATE_TEST_CASE_P(ResizeTestFluid, ResizeTest, + Combine(Values(CV_8UC3/*CV_8UC1, CV_16UC1, CV_16SC1*/), + Values(cv::Size(1280, 720), + cv::Size(30, 30)), + Values(-1), + Values(IMGPROC_FLUID), + Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_obj()), + Values(/*cv::INTER_NEAREST,*/ cv::INTER_LINEAR/*, cv::INTER_AREA*/), + Values(cv::Size(1280, 720), + cv::Size(30, 30)))); + +INSTANTIATE_TEST_CASE_P(ResizeTestFxFyFluid, ResizeTestFxFy, + Combine(Values(CV_8UC3/*CV_8UC1, CV_16UC1, CV_16SC1*/), + Values(cv::Size(1280, 720), + cv::Size(30, 30)), + Values(-1), + Values(IMGPROC_FLUID), + Values(Tolerance_FloatRel_IntAbs(1e-5, 1).to_compare_obj()), + Values(/*cv::INTER_NEAREST,*/ cv::INTER_LINEAR/*, cv::INTER_AREA*/), + Values(0.5, 1, 2), + Values(0.5, 1, 2))); + INSTANTIATE_TEST_CASE_P(RGB2GrayTestFluid, RGB2GrayTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_FLUID), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2GrayTestFluid, BGR2GrayTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_FLUID), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2YUVTestFluid, RGB2YUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_FLUID), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(YUV2RGBTestFluid, YUV2RGBTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_FLUID), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2LabTestFluid, RGB2LabTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_FLUID), Values(AbsSimilarPoints(1, 0.05).to_compare_obj()))); @@ -59,40 +76,35 @@ INSTANTIATE_TEST_CASE_P(RGB2LabTestFluid, RGB2LabTest, // FIXME: Not supported by Fluid yet (no kernel implemented) INSTANTIATE_TEST_CASE_P(BGR2LUVTestFluid, BGR2LUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_FLUID), Values(ToleranceColor(5e-3, 6).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2HSVTestFluid, RGB2HSVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_FLUID), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BayerGR2RGBTestFluid, BayerGR2RGBTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_FLUID), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2YUV422TestFluid, RGB2YUV422Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC2), Values(IMGPROC_FLUID), Values(AbsTolerance(1).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(blurTestFluid, BlurTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_FLUID), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -101,8 +113,7 @@ INSTANTIATE_TEST_CASE_P(blurTestFluid, BlurTest, INSTANTIATE_TEST_CASE_P(gaussBlurTestFluid, GaussianBlurTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_FLUID), Values(ToleranceFilter(1e-3f, 0.01).to_compare_obj()), @@ -110,8 +121,7 @@ INSTANTIATE_TEST_CASE_P(gaussBlurTestFluid, GaussianBlurTest, INSTANTIATE_TEST_CASE_P(medianBlurTestFluid, MedianBlurTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_FLUID), Values(AbsExact().to_compare_obj()), @@ -119,8 +129,7 @@ INSTANTIATE_TEST_CASE_P(medianBlurTestFluid, MedianBlurTest, INSTANTIATE_TEST_CASE_P(erodeTestFluid, ErodeTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_FLUID), Values(AbsExact().to_compare_obj()), @@ -131,8 +140,7 @@ INSTANTIATE_TEST_CASE_P(erodeTestFluid, ErodeTest, INSTANTIATE_TEST_CASE_P(dilateTestFluid, DilateTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_FLUID), Values(AbsExact().to_compare_obj()), @@ -143,8 +151,7 @@ INSTANTIATE_TEST_CASE_P(dilateTestFluid, DilateTest, INSTANTIATE_TEST_CASE_P(SobelTestFluid, SobelTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_FLUID), Values(AbsExact().to_compare_obj()), @@ -154,8 +161,7 @@ INSTANTIATE_TEST_CASE_P(SobelTestFluid, SobelTest, INSTANTIATE_TEST_CASE_P(SobelTestFluid32F, SobelTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_32F), Values(IMGPROC_FLUID), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -165,8 +171,7 @@ INSTANTIATE_TEST_CASE_P(SobelTestFluid32F, SobelTest, INSTANTIATE_TEST_CASE_P(SobelXYTestFluid, SobelXYTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_FLUID), Values(AbsExact().to_compare_obj()), @@ -177,8 +182,7 @@ INSTANTIATE_TEST_CASE_P(SobelXYTestFluid, SobelXYTest, INSTANTIATE_TEST_CASE_P(SobelXYTestFluid32F, SobelXYTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_32F), Values(IMGPROC_FLUID), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -189,8 +193,7 @@ INSTANTIATE_TEST_CASE_P(SobelXYTestFluid32F, SobelXYTest, INSTANTIATE_TEST_CASE_P(boxFilterTestFluid32, BoxFilterTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_32F), Values(IMGPROC_FLUID), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -199,8 +202,7 @@ INSTANTIATE_TEST_CASE_P(boxFilterTestFluid32, BoxFilterTest, INSTANTIATE_TEST_CASE_P(sepFilterTestFluid, SepFilterTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_32F), Values(IMGPROC_FLUID), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -209,7 +211,6 @@ INSTANTIATE_TEST_CASE_P(sepFilterTestFluid, SepFilterTest, INSTANTIATE_TEST_CASE_P(filter2DTestFluid, Filter2DTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), Values(cv::Size(1280, 720), - cv::Size(640, 480), cv::Size(128, 128)), Values(-1, CV_32F), Values(IMGPROC_FLUID), diff --git a/modules/gapi/test/cpu/gapi_ocv_stateful_kernel_tests.cpp b/modules/gapi/test/cpu/gapi_ocv_stateful_kernel_tests.cpp index 239afc38c456..b462e701f236 100644 --- a/modules/gapi/test/cpu/gapi_ocv_stateful_kernel_tests.cpp +++ b/modules/gapi/test/cpu/gapi_ocv_stateful_kernel_tests.cpp @@ -2,7 +2,7 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2020 Intel Corporation +// Copyright (C) 2020-2022 Intel Corporation #include "gapi_ocv_stateful_kernel_test_utils.hpp" #include @@ -14,6 +14,7 @@ #include #endif +#include // required by std::shared_ptr namespace opencv_test { @@ -21,6 +22,11 @@ namespace opencv_test { std::string method; }; + + struct CountStateSetupsParams + { + std::shared_ptr pSetupsCount; + }; } // namespace opencv_test namespace cv @@ -34,6 +40,14 @@ namespace cv return "org.opencv.test.background_substractor_state_params"; } }; + + template<> struct CompileArgTag + { + static const char* tag() + { + return "org.opencv.test.count_state_setups_params"; + } + }; } // namespace detail } // namespace cv @@ -127,8 +141,101 @@ namespace } }; #endif + + G_TYPED_KERNEL(GCountStateSetups, (GMat)>, + "org.opencv.test.count_state_setups") + { + static GOpaqueDesc outMeta(GMatDesc /* in */) { return empty_gopaque_desc(); } + }; + + GAPI_OCV_KERNEL_ST(GOCVCountStateSetups, GCountStateSetups, int) + { + static void setup(const cv::GMatDesc &, std::shared_ptr &, + const cv::GCompileArgs &compileArgs) + { + auto params = cv::gapi::getCompileArg(compileArgs) + .value_or(CountStateSetupsParams { }); + if (params.pSetupsCount != nullptr) { + (*params.pSetupsCount)++; + } + } + + static void run(const cv::Mat & , bool &out, int &) + { + out = true; + } + }; }; +TEST(StatefulKernel, StateInitOnceInRegularMode) +{ + cv::GMat in; + cv::GOpaque out = GCountStateSetups::on(in); + cv::GComputation c(cv::GIn(in), cv::GOut(out)); + + // Input mat: + cv::Mat inputData(1080, 1920, CV_8UC1); + cv::randu(inputData, cv::Scalar::all(1), cv::Scalar::all(128)); + + // variable to update when state is initialized in the kernel + CountStateSetupsParams params; + params.pSetupsCount.reset(new int(0)); + + // Testing for 100 frames + bool result { }; + for (int i = 0; i < 100; ++i) { + c.apply(cv::gin(inputData), cv::gout(result), + cv::compile_args(cv::gapi::kernels(), params)); + EXPECT_TRUE(result); + EXPECT_TRUE(params.pSetupsCount != nullptr); + EXPECT_EQ(1, *params.pSetupsCount); + } +}; + +struct StateInitOnce : public ::testing::TestWithParam{}; +TEST_P(StateInitOnce, StreamingCompiledWithMeta) +{ + bool compileWithMeta = GetParam(); + cv::GMat in; + cv::GOpaque out = GCountStateSetups::on(in); + cv::GComputation c(cv::GIn(in), cv::GOut(out)); + + // Input mat: + cv::Mat inputData(1080, 1920, CV_8UC1); + cv::randu(inputData, cv::Scalar::all(1), cv::Scalar::all(128)); + + // variable to update when state is initialized in the kernel + CountStateSetupsParams params; + params.pSetupsCount.reset(new int(0)); + + // Compilation & testing + auto ccomp = (compileWithMeta) + ? c.compileStreaming(cv::descr_of(inputData), + cv::compile_args(cv::gapi::kernels(), + params)) + : c.compileStreaming( + cv::compile_args(cv::gapi::kernels(), + params)); + + ccomp.setSource(cv::gin(inputData)); + + ccomp.start(); + EXPECT_TRUE(ccomp.running()); + + int counter { }; + bool result; + // Process mat 100 times + while (ccomp.pull(cv::gout(result)) && (counter++ < 100)) { + EXPECT_TRUE(params.pSetupsCount != nullptr); + EXPECT_EQ(1, *params.pSetupsCount); + } + + ccomp.stop(); + EXPECT_FALSE(ccomp.running()); +} + +INSTANTIATE_TEST_CASE_P(StatefulKernel, StateInitOnce, ::testing::Bool()); + TEST(StatefulKernel, StateIsMutableInRuntime) { constexpr int expectedCallsCount = 10; @@ -163,7 +270,43 @@ TEST(StatefulKernel, StateIsMutableInRuntime) } -TEST(StatefulKernel, StateIsAutoResetForNewStream) +TEST(StateIsResetOnNewStream, RegularMode) +{ + cv::GMat in; + cv::GOpaque out = GCountStateSetups::on(in); + cv::GComputation c(cv::GIn(in), cv::GOut(out)); + + // Input mat: + cv::Mat inputData(1080, 1920, CV_8UC1); + cv::randu(inputData, cv::Scalar::all(1), cv::Scalar::all(128)); + + // variable to update when state is initialized in the kernel + CountStateSetupsParams params; + params.pSetupsCount.reset(new int(0)); + + auto setupsCounter = c.compile(cv::descr_of(inputData), + cv::compile_args(cv::gapi::kernels(), + params)); + + bool result { }; + for (int i = 0; i < 2; ++i) { + setupsCounter(cv::gin(inputData), cv::gout(result)); + EXPECT_TRUE(params.pSetupsCount != nullptr); + EXPECT_EQ(1, *params.pSetupsCount); + } + + EXPECT_TRUE(params.pSetupsCount != nullptr); + EXPECT_EQ(1, *params.pSetupsCount); + setupsCounter.prepareForNewStream(); + + for (int i = 0; i < 2; ++i) { + setupsCounter(cv::gin(inputData), cv::gout(result)); + EXPECT_TRUE(params.pSetupsCount != nullptr); + EXPECT_EQ(2, *params.pSetupsCount); + } +} + +TEST(StateIsResetOnNewStream, StreamingMode) { cv::GMat in; cv::GOpaque out = GIsStateUpToDate::on(in); @@ -272,7 +415,7 @@ TEST(StatefulKernel, StateIsInitViaCompArgs) // Allowing 1% difference of all pixels between G-API and OpenCV results compareBackSubResults(gapiForeground, ocvForeground, 1); - // Additionally, test the case where state is resetted + // Additionally, test the case where state is reset gapiBackSub.prepareForNewStream(); gapiBackSub(cv::gin(frame), cv::gout(gapiForeground)); pOcvBackSub->apply(frame, ocvForeground); @@ -342,7 +485,118 @@ TEST(StatefulKernel, StateIsInitViaCompArgsInStreaming) // Allowing 5% difference of all pixels between G-API and reference OpenCV results testBackSubInStreaming(gapiBackSub, 5); } + +TEST(StatefulKernel, StateIsChangedViaCompArgsOnReshape) +{ + cv::GMat in; + cv::GComputation comp(in, GBackSub::on(in)); + + const auto pkg = cv::gapi::kernels(); + + // OpenCV reference substractor + auto pOCVBackSubKNN = createBackgroundSubtractorKNN(); + auto pOCVBackSubMOG2 = createBackgroundSubtractorMOG2(); + + const auto run = [&](const std::string& videoPath, const std::string& method) { + auto path = findDataFile(videoPath); + cv::gapi::wip::IStreamSource::Ptr source; + try { + source = gapi::wip::make_src(path); + } catch(...) { + throw SkipTestException("Video file can not be opened"); + } + cv::Mat inMat, gapiForeground, ocvForeground; + + for (int i = 0; i < 10; i++) { + cv::gapi::wip::Data inData; + source->pull(inData); + inMat = cv::util::get(inData); + comp.apply(inMat, gapiForeground, + cv::compile_args(pkg, BackSubStateParams{method})); + + if (method == "knn") { + pOCVBackSubKNN->apply(inMat, ocvForeground, -1); + // Allowing 1% difference among all pixels + compareBackSubResults(gapiForeground, ocvForeground, 1); + } else if (method == "mog2") { + pOCVBackSubMOG2->apply(inMat, ocvForeground, -1); + compareBackSubResults(gapiForeground, ocvForeground, 5); + } else { + CV_Assert(false && "Unknown BackSub method"); + } + } + }; + + run("cv/video/768x576.avi", "knn"); + run("cv/video/1920x1080.avi", "mog2"); +} + +TEST(StatefulKernel, StateIsResetOnceOnReshapeInStreaming) +{ + cv::GMat in; + cv::GOpaque out = GCountStateSetups::on(in); + cv::GComputation c(cv::GIn(in), cv::GOut(out)); + + // variable to update when state is initialized in the kernel + CountStateSetupsParams params; + params.pSetupsCount.reset(new int(0)); + + auto ccomp = c.compileStreaming( + cv::compile_args(cv::gapi::kernels(), params)); + + auto run = [&ccomp, ¶ms](const std::string& videoPath, int expectedSetupsCount) { + auto path = findDataFile(videoPath); + try { + ccomp.setSource(path); + } catch(...) { + throw SkipTestException("Video file can not be opened"); + } + ccomp.start(); + + int frames = 0; + bool result = false; + while (ccomp.pull(cv::gout(result)) && (frames++ < 10)) { + EXPECT_TRUE(result); + EXPECT_TRUE(params.pSetupsCount != nullptr); + EXPECT_EQ(expectedSetupsCount, *params.pSetupsCount); + } + ccomp.stop(); + }; + + run("cv/video/768x576.avi", 1); + // FIXME: it should be 2, not 3 for expectedSetupsCount here. + // With current implemention both GCPUExecutable reshape() and + // handleNewStream() call setupKernelStates() + run("cv/video/1920x1080.avi", 3); +} #endif + +TEST(StatefulKernel, StateIsAutoResetOnReshape) +{ + cv::GMat in; + cv::GOpaque up_to_date = GIsStateUpToDate::on(in); + cv::GOpaque calls_count = GCountCalls::on(in); + cv::GComputation comp(cv::GIn(in), cv::GOut(up_to_date, calls_count)); + + auto run = [&comp](const cv::Mat& in_mat) { + const auto pkg = cv::gapi::kernels(); + bool stateIsUpToDate = false; + int callsCount = 0; + for (int i = 0; i < 3; i++) { + comp.apply(cv::gin(in_mat), cv::gout(stateIsUpToDate, callsCount), + cv::compile_args(pkg)); + EXPECT_TRUE(stateIsUpToDate); + EXPECT_EQ(i+1, callsCount); + } + }; + + cv::Mat in_mat1(32, 32, CV_8UC1); + run(in_mat1); + + cv::Mat in_mat2(16, 16, CV_8UC1); + run(in_mat2); +} + //------------------------------------------------------------------------------------------------------------- diff --git a/modules/gapi/test/cpu/gapi_operators_tests_cpu.cpp b/modules/gapi/test/cpu/gapi_operators_tests_cpu.cpp index df74a046cdf9..5e070f8ce5f2 100644 --- a/modules/gapi/test/cpu/gapi_operators_tests_cpu.cpp +++ b/modules/gapi/test/cpu/gapi_operators_tests_cpu.cpp @@ -12,6 +12,7 @@ namespace { #define CORE_CPU [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::core::cpu::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test @@ -19,21 +20,17 @@ namespace opencv_test // FIXME: CPU test runs are disabled since Fluid is an exclusive plugin now! INSTANTIATE_TEST_CASE_P(MathOperatorTestCPU, MathOperatorMatMatTest, - Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_CPU), - Values(AbsExact().to_compare_obj()), - Values( ADD, SUB, DIV, + Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), + ValuesIn(in_sizes), + Values(-1), + Values(CORE_CPU), + Values(AbsExact().to_compare_obj()), + Values( ADD, SUB, DIV, GT, LT, GE, LE, EQ, NE))); INSTANTIATE_TEST_CASE_P(MathOperatorTestCPU, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -44,9 +41,7 @@ INSTANTIATE_TEST_CASE_P(MathOperatorTestCPU, MathOperatorMatScalarTest, INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestCPU, MathOperatorMatMatTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -54,9 +49,7 @@ INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestCPU, MathOperatorMatMatTest, INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestCPU, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU), Values(AbsExact().to_compare_obj()), @@ -65,9 +58,7 @@ INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestCPU, MathOperatorMatScalarTest, INSTANTIATE_TEST_CASE_P(BitwiseNotOperatorTestCPU, NotOperatorTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_CPU))); } diff --git a/modules/gapi/test/cpu/gapi_operators_tests_fluid.cpp b/modules/gapi/test/cpu/gapi_operators_tests_fluid.cpp index d076ecdd8e57..af0f2f136bf9 100644 --- a/modules/gapi/test/cpu/gapi_operators_tests_fluid.cpp +++ b/modules/gapi/test/cpu/gapi_operators_tests_fluid.cpp @@ -11,6 +11,7 @@ namespace { #define CORE_FLUID [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::core::fluid::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test @@ -18,9 +19,7 @@ namespace opencv_test INSTANTIATE_TEST_CASE_P(MathOperatorTestFluid, MathOperatorMatMatTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsExact().to_compare_obj()), @@ -29,9 +28,7 @@ INSTANTIATE_TEST_CASE_P(MathOperatorTestFluid, MathOperatorMatMatTest, INSTANTIATE_TEST_CASE_P(MathOperatorArithmeticTestFluid, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsExact().to_compare_obj()), @@ -41,9 +38,7 @@ INSTANTIATE_TEST_CASE_P(MathOperatorArithmeticTestFluid, MathOperatorMatScalarTe // FIXME: solve comparison error INSTANTIATE_TEST_CASE_P(MathOperatorCompareTestFluid, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsSimilarPoints(1, 0.01).to_compare_obj()), @@ -52,9 +47,7 @@ INSTANTIATE_TEST_CASE_P(MathOperatorCompareTestFluid, MathOperatorMatScalarTest, INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestFluid, MathOperatorMatMatTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsExact().to_compare_obj()), @@ -62,9 +55,7 @@ INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestFluid, MathOperatorMatMatTest, INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestFluid, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_FLUID), Values(AbsExact().to_compare_obj()), @@ -72,10 +63,8 @@ INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestFluid, MathOperatorMatScalarTest, ANDR, ORR, XORR ))); INSTANTIATE_TEST_CASE_P(BitwiseNotOperatorTestFluid, NotOperatorTest, - Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_FLUID))); + Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), + ValuesIn(in_sizes), + Values(-1), + Values(CORE_FLUID))); } diff --git a/modules/gapi/test/gapi_basic_hetero_tests.cpp b/modules/gapi/test/gapi_basic_hetero_tests.cpp index b4a05d7ebb50..aaa67ac71189 100644 --- a/modules/gapi/test/gapi_basic_hetero_tests.cpp +++ b/modules/gapi/test/gapi_basic_hetero_tests.cpp @@ -153,9 +153,9 @@ namespace struct GAPIHeteroTest: public ::testing::Test { cv::GComputation m_comp; - cv::gapi::GKernelPackage m_ocv_kernels; - cv::gapi::GKernelPackage m_fluid_kernels; - cv::gapi::GKernelPackage m_hetero_kernels; + cv::GKernelPackage m_ocv_kernels; + cv::GKernelPackage m_fluid_kernels; + cv::GKernelPackage m_hetero_kernels; cv::Mat m_in_mat; cv::Mat m_out_mat; @@ -210,7 +210,7 @@ TEST_F(GAPIHeteroTest, TestBoth) struct GAPIBigHeteroTest : public ::testing::TestWithParam> { cv::GComputation m_comp; - cv::gapi::GKernelPackage m_kernels; + cv::GKernelPackage m_kernels; cv::Mat m_in_mat; cv::Mat m_out_mat1; diff --git a/modules/gapi/test/gapi_compile_args_tests.cpp b/modules/gapi/test/gapi_compile_args_tests.cpp index bea1b9b8ef8e..07cfcec3b246 100644 --- a/modules/gapi/test/gapi_compile_args_tests.cpp +++ b/modules/gapi/test/gapi_compile_args_tests.cpp @@ -44,14 +44,14 @@ GAPI_OCV_KERNEL(GOCVTestOp, GTestOp) TEST(GetCompileArgTest, PredefinedArgs) { - cv::gapi::GKernelPackage pkg = cv::gapi::kernels(); + cv::GKernelPackage pkg = cv::gapi::kernels(); cv::GCompileArg arg0 { pkg }, arg1 { cv::gapi::use_only { pkg } }, arg2 { cv::graph_dump_path { "fake_path" } }; GCompileArgs compArgs { arg0, arg1, arg2 }; - auto kernelPkgOpt = cv::gapi::getCompileArg(compArgs); + auto kernelPkgOpt = cv::gapi::getCompileArg(compArgs); GAPI_Assert(kernelPkgOpt.has_value()); EXPECT_NO_THROW(kernelPkgOpt.value().lookup("org.opencv.test.test_op")); diff --git a/modules/gapi/test/gapi_fluid_resize_test.cpp b/modules/gapi/test/gapi_fluid_resize_test.cpp index 7bac668b7cc9..0ec00c8e0b51 100644 --- a/modules/gapi/test/gapi_fluid_resize_test.cpp +++ b/modules/gapi/test/gapi_fluid_resize_test.cpp @@ -41,7 +41,7 @@ GAPI_FLUID_KERNEL(FCopy, TCopy, false) } }; -GAPI_FLUID_KERNEL(FResizeNN1Lpi, cv::gapi::core::GResize, false) +GAPI_FLUID_KERNEL(FResizeNN1Lpi, cv::gapi::imgproc::GResize, false) { static const int Window = 1; static const auto Kind = GFluidKernel::Kind::Resize; @@ -203,7 +203,7 @@ struct Mapper } // namespace areaUpscale } // anonymous namespace -GAPI_FLUID_KERNEL(FResizeLinear1Lpi, cv::gapi::core::GResize, true) +GAPI_FLUID_KERNEL(FResizeLinear1Lpi, cv::gapi::imgproc::GResize, true) { static const int Window = 1; static const auto Kind = GFluidKernel::Kind::Resize; @@ -238,7 +238,7 @@ auto endInCoord = [](int outCoord, double ratio) { }; } // namespace -GAPI_FLUID_KERNEL(FResizeArea1Lpi, cv::gapi::core::GResize, false) +GAPI_FLUID_KERNEL(FResizeArea1Lpi, cv::gapi::imgproc::GResize, false) { static const int Window = 1; static const auto Kind = GFluidKernel::Kind::Resize; @@ -302,7 +302,7 @@ GAPI_FLUID_KERNEL(FResizeArea1Lpi, cv::gapi::core::GResize, false) } }; -GAPI_FLUID_KERNEL(FResizeAreaUpscale1Lpi, cv::gapi::core::GResize, true) +GAPI_FLUID_KERNEL(FResizeAreaUpscale1Lpi, cv::gapi::imgproc::GResize, true) { static const int Window = 1; static const auto Kind = GFluidKernel::Kind::Resize; @@ -326,7 +326,7 @@ GAPI_FLUID_KERNEL(FResizeAreaUpscale1Lpi, cv::gapi::core::GResize, true) #define ADD_RESIZE_KERNEL_WITH_LPI(interp, lpi, scratch) \ struct Resize##interp##lpi##LpiHelper : public FResize##interp##1Lpi { static const int LPI = lpi; }; \ -struct FResize##interp##lpi##Lpi : public cv::GFluidKernelImpl{}; +struct FResize##interp##lpi##Lpi : public cv::GFluidKernelImpl{}; ADD_RESIZE_KERNEL_WITH_LPI(NN, 2, false) ADD_RESIZE_KERNEL_WITH_LPI(NN, 3, false) @@ -364,7 +364,7 @@ static auto fluidResizeTestPackage = [](int interpolation, cv::Size szIn, cv::Si default: CV_Assert(false); \ } - GKernelPackage pkg; + cv::GKernelPackage pkg; switch (interpolation) { case INTER_NEAREST: RESIZE_SWITCH(NN); break; @@ -742,7 +742,7 @@ TEST_P(NV12PlusResizeTest, Test) auto out = cv::gapi::resize(rgb, out_sz, 0, 0, interp); cv::GComputation c(cv::GIn(y, uv), cv::GOut(out)); - auto pkg = cv::gapi::combine(fluidTestPackage, cv::gapi::core::fluid::kernels()); + auto pkg = cv::gapi::combine(fluidTestPackage, cv::gapi::imgproc::fluid::kernels()); c.apply(cv::gin(y_mat, uv_mat), cv::gout(out_mat) ,cv::compile_args(pkg, cv::GFluidOutputRois{{roi}})); diff --git a/modules/gapi/test/gapi_fluid_test.cpp b/modules/gapi/test/gapi_fluid_test.cpp index 29466987d943..03c98e3ef386 100644 --- a/modules/gapi/test/gapi_fluid_test.cpp +++ b/modules/gapi/test/gapi_fluid_test.cpp @@ -791,8 +791,15 @@ TEST(Fluid, UnusedNodeOutputCompileTest) TEST(Fluid, UnusedNodeOutputReshapeTest) { const auto test_size = cv::Size(8, 8); - const auto get_compile_args = - [] () { return cv::compile_args(cv::gapi::core::fluid::kernels()); }; + + const auto get_compile_args = [] () { + return cv::compile_args( + cv::gapi::combine( + cv::gapi::core::fluid::kernels(), + cv::gapi::imgproc::fluid::kernels() + ) + ); + }; cv::GMat in; cv::GMat a, b, c, d; diff --git a/modules/gapi/test/gapi_fluid_test_kernels.cpp b/modules/gapi/test/gapi_fluid_test_kernels.cpp index b298de39d00a..307e7549cfb0 100644 --- a/modules/gapi/test/gapi_fluid_test_kernels.cpp +++ b/modules/gapi/test/gapi_fluid_test_kernels.cpp @@ -602,7 +602,7 @@ GMat merge3_4lpi(const GMat& src1, const GMat& src2, const GMat& src3) return TMerge3_4lpi::on(src1, src2, src3); } -cv::gapi::GKernelPackage fluidTestPackage = cv::gapi::kernels +cv::GKernelPackage fluidTestPackage = cv::gapi::kernels (GMat)>, "test.ocv.calc_hist") GMat merge3_4lpi(const GMat& src1, const GMat& src2, const GMat& src3); std::tuple split3_4lpi(const GMat& src); -extern cv::gapi::GKernelPackage fluidTestPackage; +extern cv::GKernelPackage fluidTestPackage; } // namespace gapi_test_kernels } // namespace cv diff --git a/modules/gapi/test/gapi_frame_tests.cpp b/modules/gapi/test/gapi_frame_tests.cpp index 5911ef9d9ab4..76038b5168dd 100644 --- a/modules/gapi/test/gapi_frame_tests.cpp +++ b/modules/gapi/test/gapi_frame_tests.cpp @@ -29,6 +29,23 @@ GAPI_OCV_KERNEL(OCVBlurFrame, GBlurFrame) { } }; +G_API_OP(GBlurFrameGray, , "test.blur_frame_gray") { + static GMatDesc outMeta(GFrameDesc in) { + return cv::GMatDesc(CV_8U, 1, in.size); + } +}; + +GAPI_OCV_KERNEL(OCVBlurFrameGray, GBlurFrameGray) { + static void run(const cv::MediaFrame & in, cv::Mat & out) { + GAPI_Assert(in.desc().fmt == cv::MediaFormat::GRAY); + cv::MediaFrame::View view = in.access(cv::MediaFrame::Access::R); + cv::blur(cv::Mat(in.desc().size, CV_8UC1, view.ptr[0], view.stride[0]), + out, + cv::Size{ 3,3 }); + } +}; + + //////////////////////////////////////////////////////////////////////////////// // cv::MediaFrame tests namespace { @@ -70,6 +87,26 @@ class TestMediaNV12 final: public cv::MediaFrame::IAdapter { return cv::MediaFrame::View(std::move(pp), std::move(ss)); } }; + +class TestMediaGray final : public cv::MediaFrame::IAdapter { + cv::Mat m_mat; + using Cb = cv::MediaFrame::View::Callback; + Cb m_cb; + +public: + explicit TestMediaGray(cv::Mat m, Cb cb = []() {}) + : m_mat(m), m_cb(cb) { + } + cv::GFrameDesc meta() const override { + return cv::GFrameDesc{ cv::MediaFormat::GRAY, cv::Size(m_mat.cols, m_mat.rows) }; + } + cv::MediaFrame::View access(cv::MediaFrame::Access) override { + cv::MediaFrame::View::Ptrs pp = { m_mat.ptr(), nullptr, nullptr, nullptr }; + cv::MediaFrame::View::Strides ss = { m_mat.step, 0u, 0u, 0u }; + return cv::MediaFrame::View(std::move(pp), std::move(ss), Cb{ m_cb }); + } +}; + } // anonymous namespace struct MediaFrame_Test: public ::testing::Test { @@ -120,6 +157,49 @@ TEST_F(MediaFrame_BGR, Input) { EXPECT_EQ(0, cvtest::norm(out_mat_ocv, out_mat_gapi, NORM_INF)); } +struct MediaFrame_Gray : public MediaFrame_Test { + M gray; + MediaFrame_Gray() + : gray(M::eye(240, 320, CV_8UC1)) { + cv::randn(gray, cv::Scalar::all(127.0f), cv::Scalar::all(40.f)); + frame = MF::Create(gray); + } +}; + +TEST_F(MediaFrame_Gray, Meta) { + auto meta = frame.desc(); + EXPECT_EQ(cv::MediaFormat::GRAY, meta.fmt); + EXPECT_EQ(cv::Size(320, 240), meta.size); +} + +TEST_F(MediaFrame_Gray, Access) { + cv::MediaFrame::View view1 = frame.access(cv::MediaFrame::Access::R); + EXPECT_EQ(gray.ptr(), view1.ptr[0]); + EXPECT_EQ(gray.step, view1.stride[0]); + + cv::MediaFrame::View view2 = frame.access(cv::MediaFrame::Access::R); + EXPECT_EQ(gray.ptr(), view2.ptr[0]); + EXPECT_EQ(gray.step, view2.stride[0]); +} + +TEST_F(MediaFrame_Gray, Input) { + // Run the OpenCV code + cv::Mat out_mat_ocv, out_mat_gapi; + cv::blur(gray, out_mat_ocv, cv::Size{ 3,3 }); + + // Run the G-API code + cv::GFrame in; + cv::GMat out = GBlurFrameGray::on(in); + cv::GComputation(cv::GIn(in), cv::GOut(out)) + .apply(cv::gin(frame), + cv::gout(out_mat_gapi), + cv::compile_args(cv::gapi::kernels())); + + // Compare + EXPECT_EQ(0, cvtest::norm(out_mat_ocv, out_mat_gapi, NORM_INF)); +} + + struct MediaFrame_NV12: public MediaFrame_Test { cv::Size sz; cv::Mat buf, y, uv; diff --git a/modules/gapi/test/gapi_gpu_test.cpp b/modules/gapi/test/gapi_gpu_test.cpp index 6c4e10a1399a..ac2e1fd6a9ae 100644 --- a/modules/gapi/test/gapi_gpu_test.cpp +++ b/modules/gapi/test/gapi_gpu_test.cpp @@ -146,7 +146,7 @@ namespace cv } }; - cv::gapi::GKernelPackage gpuTestPackage = cv::gapi::kernels + cv::GKernelPackage gpuTestPackage = cv::gapi::kernels (); diff --git a/modules/gapi/test/gapi_sample_pipelines.cpp b/modules/gapi/test/gapi_sample_pipelines.cpp index b4d9f3e10068..da71cd0ab03b 100644 --- a/modules/gapi/test/gapi_sample_pipelines.cpp +++ b/modules/gapi/test/gapi_sample_pipelines.cpp @@ -49,7 +49,25 @@ namespace static GMatDesc outMeta(GMatDesc in) { return in; } }; - // These definitons test the correct macro work if the kernel has multiple output values + G_TYPED_KERNEL(GZeros, , "org.opencv.test.zeros") + { + static GMatDesc outMeta(GMatDesc /*in*/, GMatDesc user_desc) + { + return user_desc; + } + }; + + GAPI_OCV_KERNEL(GOCVZeros, GZeros) + { + static void run(const cv::Mat& /*in*/, + const cv::GMatDesc& /*desc*/, + cv::Mat& out) + { + out.setTo(0); + } + }; + + // These definitions test the correct macro work if the kernel has multiple output values G_TYPED_KERNEL(GRetGArrayTupleOfGMat2Kernel, >(GMat, Scalar)>, "org.opencv.test.retarrayoftupleofgmat2kernel") {}; G_TYPED_KERNEL(GRetGArraTupleyOfGMat3Kernel, >(GMat)>, "org.opencv.test.retarrayoftupleofgmat3kernel") {}; G_TYPED_KERNEL(GRetGArraTupleyOfGMat4Kernel, >(GMat)>, "org.opencv.test.retarrayoftupleofgmat4kernel") {}; @@ -430,4 +448,69 @@ TEST(GAPI_Pipeline, ReplaceDefaultByFunctor) EXPECT_TRUE(f.is_called); } +TEST(GAPI_Pipeline, GraphOutputIs1DMat) +{ + int dim = 100; + cv::Mat in_mat(1, 1, CV_8UC3); + cv::Mat out_mat; + + cv::GMat in; + auto cc = cv::GComputation(in, GZeros::on(in, cv::GMatDesc(CV_8U, {dim}))) + .compile(cv::descr_of(in_mat), cv::compile_args(cv::gapi::kernels())); + + // NB: Computation is able to write 1D output cv::Mat to empty out_mat. + ASSERT_NO_THROW(cc(cv::gin(in_mat), cv::gout(out_mat))); + ASSERT_EQ(1, out_mat.size.dims()); + ASSERT_EQ(dim, out_mat.size[0]); + + // NB: Computation is able to write 1D output cv::Mat + // to pre-allocated with the same meta out_mat. + ASSERT_NO_THROW(cc(cv::gin(in_mat), cv::gout(out_mat))); + ASSERT_EQ(1, out_mat.size.dims()); + ASSERT_EQ(dim, out_mat.size[0]); +} + +TEST(GAPI_Pipeline, 1DMatBetweenIslands) +{ + int dim = 100; + cv::Mat in_mat(1, 1, CV_8UC3); + cv::Mat out_mat; + + cv::Mat ref_mat({dim}, CV_8U); + ref_mat.dims = 1; + ref_mat.setTo(0); + + cv::GMat in; + auto out = cv::gapi::copy(GZeros::on(cv::gapi::copy(in), cv::GMatDesc(CV_8U, {dim}))); + auto cc = cv::GComputation(in, out) + .compile(cv::descr_of(in_mat), cv::compile_args(cv::gapi::kernels())); + + cc(cv::gin(in_mat), cv::gout(out_mat)); + + EXPECT_EQ(0, cv::norm(out_mat, ref_mat)); +} + +TEST(GAPI_Pipeline, 1DMatWithinSingleIsland) +{ + int dim = 100; + cv::Size blur_sz(3, 3); + cv::Mat in_mat(10, 10, CV_8UC3); + cv::randu(in_mat, 0, 255); + cv::Mat out_mat; + + cv::Mat ref_mat({dim}, CV_8U); + ref_mat.dims = 1; + ref_mat.setTo(0); + + cv::GMat in; + auto out = cv::gapi::blur( + GZeros::on(cv::gapi::blur(in, blur_sz), cv::GMatDesc(CV_8U, {dim})), blur_sz); + auto cc = cv::GComputation(in, out) + .compile(cv::descr_of(in_mat), cv::compile_args(cv::gapi::kernels())); + + cc(cv::gin(in_mat), cv::gout(out_mat)); + + EXPECT_EQ(0, cv::norm(out_mat, ref_mat)); +} + } // namespace opencv_test diff --git a/modules/gapi/test/gapi_transform_tests.cpp b/modules/gapi/test/gapi_transform_tests.cpp index 4077008f6891..fb691742ddd7 100644 --- a/modules/gapi/test/gapi_transform_tests.cpp +++ b/modules/gapi/test/gapi_transform_tests.cpp @@ -168,7 +168,7 @@ TEST(KernelPackageTransform, CreatePackage) TEST(KernelPackageTransform, Include) { - cv::gapi::GKernelPackage pkg; + cv::GKernelPackage pkg; pkg.include(); pkg.include(); pkg.include(); diff --git a/modules/gapi/test/gpu/gapi_core_tests_gpu.cpp b/modules/gapi/test/gpu/gapi_core_tests_gpu.cpp index 2cc859c5391b..f32f1adee90a 100644 --- a/modules/gapi/test/gpu/gapi_core_tests_gpu.cpp +++ b/modules/gapi/test/gpu/gapi_core_tests_gpu.cpp @@ -11,6 +11,7 @@ namespace { #define CORE_GPU [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::core::gpu::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test @@ -19,9 +20,7 @@ namespace opencv_test // FIXME: Wut? See MulTestGPU/MathOpTest below (duplicate?) INSTANTIATE_TEST_CASE_P(AddTestGPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU), Values(ADD, MUL), @@ -31,9 +30,7 @@ INSTANTIATE_TEST_CASE_P(AddTestGPU, MathOpTest, INSTANTIATE_TEST_CASE_P(MulTestGPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU), Values(MUL), @@ -43,9 +40,7 @@ INSTANTIATE_TEST_CASE_P(MulTestGPU, MathOpTest, INSTANTIATE_TEST_CASE_P(SubTestGPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU), Values(SUB), @@ -65,9 +60,7 @@ INSTANTIATE_TEST_CASE_P(SubTestGPU, MathOpTest, // Github ticket: https://github.com/opencv/opencv/issues/18373. INSTANTIATE_TEST_CASE_P(DISABLED_DivTestGPU, MathOpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU), Values(DIV), @@ -77,74 +70,56 @@ INSTANTIATE_TEST_CASE_P(DISABLED_DivTestGPU, MathOpTest, INSTANTIATE_TEST_CASE_P(MulTestGPU, MulDoubleTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(DivTestGPU, DivTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(DivCTestGPU, DivCTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(MeanTestGPU, MeanTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); //TODO: mask test doesn't work INSTANTIATE_TEST_CASE_P(DISABLED_MaskTestGPU, MaskTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(SelectTestGPU, SelectTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(Polar2CartGPU, Polar2CartTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_32FC1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(Cart2PolarGPU, Cart2PolarTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_32FC1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(CompareTestGPU, CmpTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U), Values(CORE_GPU), Values(CMP_EQ, CMP_GE, CMP_NE, CMP_GT, CMP_LT, CMP_LE), @@ -153,9 +128,7 @@ INSTANTIATE_TEST_CASE_P(CompareTestGPU, CmpTest, INSTANTIATE_TEST_CASE_P(BitwiseTestGPU, BitwiseTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AND, OR, XOR), @@ -163,76 +136,58 @@ INSTANTIATE_TEST_CASE_P(BitwiseTestGPU, BitwiseTest, INSTANTIATE_TEST_CASE_P(BitwiseNotTestGPU, NotTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(DISABLED_MinTestGPU, MinTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(DISABLED_MaxTestGPU, MaxTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(SumTestGPU, SumTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AbsToleranceScalar(1e-5).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(CountNonZeroTestGPU, CountNonZeroTest, Combine(Values( CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AbsToleranceScalar(1e-5).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(AbsDiffTestGPU, AbsDiffTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(AbsDiffCTestGPU, AbsDiffCTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(AddWeightedTestGPU, AddWeightedTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values( -1, CV_8U, CV_16U, CV_32F ), Values(CORE_GPU), Values(Tolerance_FloatRel_IntAbs(1e-6, 1).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(NormTestGPU, NormTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AbsToleranceScalar(1e-3).to_compare_obj()), //TODO: too relaxed? @@ -240,17 +195,13 @@ INSTANTIATE_TEST_CASE_P(NormTestGPU, NormTest, INSTANTIATE_TEST_CASE_P(IntegralTestGPU, IntegralTest, Combine(Values( CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(ThresholdTestGPU, ThresholdTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, @@ -261,9 +212,7 @@ INSTANTIATE_TEST_CASE_P(ThresholdTestGPU, ThresholdTest, INSTANTIATE_TEST_CASE_P(ThresholdTestGPU, ThresholdOTTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(cv::THRESH_OTSU, cv::THRESH_TRIANGLE))); @@ -271,115 +220,69 @@ INSTANTIATE_TEST_CASE_P(ThresholdTestGPU, ThresholdOTTest, INSTANTIATE_TEST_CASE_P(InRangeTestGPU, InRangeTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(Split3TestGPU, Split3Test, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(Split4TestGPU, Split4Test, Combine(Values(CV_8UC4), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_GPU))); -INSTANTIATE_TEST_CASE_P(ResizeTestGPU, ResizeTest, - Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_GPU), - Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values(cv::Size(64,64), - cv::Size(30,30)))); - -INSTANTIATE_TEST_CASE_P(ResizeTestGPU, ResizeTestFxFy, - Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), - Values(-1), - Values(CORE_GPU), - Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), - Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), - Values(0.5, 0.1), - Values(0.5, 0.1))); - INSTANTIATE_TEST_CASE_P(Merge3TestGPU, Merge3Test, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC3), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(Merge4TestGPU, Merge4Test, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC4), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(RemapTestGPU, RemapTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(FlipTestGPU, FlipTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(0,1,-1))); INSTANTIATE_TEST_CASE_P(CropTestGPU, CropTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(cv::Rect(10, 8, 20, 35), cv::Rect(4, 10, 37, 50)))); INSTANTIATE_TEST_CASE_P(LUTTestGPU, LUTTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(LUTTestCustomGPU, LUTTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8UC3), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(ConvertToGPU, ConvertToTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(CV_8U, CV_16U, CV_16S, CV_32F), Values(CORE_GPU), Values(AbsExact().to_compare_obj()), @@ -388,17 +291,13 @@ INSTANTIATE_TEST_CASE_P(ConvertToGPU, ConvertToTest, INSTANTIATE_TEST_CASE_P(ConcatHorTestGPU, ConcatHorTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(ConcatVertTestGPU, ConcatVertTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); @@ -406,9 +305,7 @@ INSTANTIATE_TEST_CASE_P(TransposeTestGPU, TransposeTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1, CV_8UC2, CV_16UC2, CV_16SC2, CV_32FC2, CV_8UC3, CV_16UC3, CV_16SC3, CV_32FC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AbsExact().to_compare_obj()))); @@ -439,17 +336,13 @@ INSTANTIATE_TEST_CASE_P(ReInitOutTestGPU, ReInitOutTest, //TODO: fix this backend to allow ConcatVertVec ConcatHorVec INSTANTIATE_TEST_CASE_P(DISABLED_ConcatVertVecTestGPU, ConcatVertVecTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); INSTANTIATE_TEST_CASE_P(DISABLED_ConcatHorVecTestGPU, ConcatHorVecTest, Combine(Values( CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1 ), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); } diff --git a/modules/gapi/test/gpu/gapi_imgproc_tests_gpu.cpp b/modules/gapi/test/gpu/gapi_imgproc_tests_gpu.cpp index 7d9bd761a161..bd9452a79588 100644 --- a/modules/gapi/test/gpu/gapi_imgproc_tests_gpu.cpp +++ b/modules/gapi/test/gpu/gapi_imgproc_tests_gpu.cpp @@ -12,16 +12,35 @@ namespace { #define IMGPROC_GPU [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::imgproc::gpu::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test { +INSTANTIATE_TEST_CASE_P(ResizeTestGPU, ResizeTest, + Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + ValuesIn(in_sizes), + Values(-1), + Values(IMGPROC_GPU), + Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(cv::Size(64,64), + cv::Size(30,30)))); + +INSTANTIATE_TEST_CASE_P(ResizeTestGPU, ResizeTestFxFy, + Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), + ValuesIn(in_sizes), + Values(-1), + Values(IMGPROC_GPU), + Values(AbsSimilarPoints(2, 0.05).to_compare_obj()), + Values(cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_AREA), + Values(0.5, 0.1), + Values(0.5, 0.1))); + INSTANTIATE_TEST_CASE_P(Filter2DTestGPU, Filter2DTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1, CV_32F), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-5, 2).to_compare_obj()), @@ -33,8 +52,7 @@ INSTANTIATE_TEST_CASE_P(Filter2DTestGPU, Filter2DTest, INSTANTIATE_TEST_CASE_P(BoxFilterTestGPU, BoxFilterTest, Combine(Values(/*CV_8UC1,*/ CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_32F), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-5, 2).to_compare_obj()), @@ -44,8 +62,7 @@ INSTANTIATE_TEST_CASE_P(BoxFilterTestGPU, BoxFilterTest, INSTANTIATE_TEST_CASE_P(SepFilterTestGPU_8U, SepFilterTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_GPU), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -53,8 +70,7 @@ INSTANTIATE_TEST_CASE_P(SepFilterTestGPU_8U, SepFilterTest, INSTANTIATE_TEST_CASE_P(SepFilterTestGPU_other, SepFilterTest, Combine(Values(CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_32F), Values(IMGPROC_GPU), Values(ToleranceFilter(1e-4f, 0.01).to_compare_obj()), @@ -62,8 +78,7 @@ INSTANTIATE_TEST_CASE_P(SepFilterTestGPU_other, SepFilterTest, INSTANTIATE_TEST_CASE_P(BlurTestGPU, BlurTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-4, 2).to_compare_obj()), @@ -72,8 +87,7 @@ INSTANTIATE_TEST_CASE_P(BlurTestGPU, BlurTest, INSTANTIATE_TEST_CASE_P(gaussBlurTestGPU, GaussianBlurTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(ToleranceFilter(1e-5f, 0.01).to_compare_obj()), @@ -81,8 +95,7 @@ INSTANTIATE_TEST_CASE_P(gaussBlurTestGPU, GaussianBlurTest, INSTANTIATE_TEST_CASE_P(MedianBlurTestGPU, MedianBlurTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(AbsExact().to_compare_obj()), @@ -90,8 +103,7 @@ INSTANTIATE_TEST_CASE_P(MedianBlurTestGPU, MedianBlurTest, INSTANTIATE_TEST_CASE_P(ErodeTestGPU, ErodeTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(AbsExact().to_compare_obj()), @@ -102,8 +114,7 @@ INSTANTIATE_TEST_CASE_P(ErodeTestGPU, ErodeTest, INSTANTIATE_TEST_CASE_P(Erode3x3TestGPU, Erode3x3Test, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(AbsExact().to_compare_obj()), @@ -111,8 +122,7 @@ INSTANTIATE_TEST_CASE_P(Erode3x3TestGPU, Erode3x3Test, INSTANTIATE_TEST_CASE_P(DilateTestGPU, DilateTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(AbsExact().to_compare_obj()), @@ -123,8 +133,7 @@ INSTANTIATE_TEST_CASE_P(DilateTestGPU, DilateTest, INSTANTIATE_TEST_CASE_P(Dilate3x3TestGPU, Dilate3x3Test, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(AbsExact().to_compare_obj()), @@ -132,8 +141,7 @@ INSTANTIATE_TEST_CASE_P(Dilate3x3TestGPU, Dilate3x3Test, INSTANTIATE_TEST_CASE_P(SobelTestGPU, SobelTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1, CV_16S, CV_32F), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-4, 2).to_compare_obj()), @@ -143,8 +151,7 @@ INSTANTIATE_TEST_CASE_P(SobelTestGPU, SobelTest, INSTANTIATE_TEST_CASE_P(SobelTestGPU32F, SobelTest, Combine(Values(CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_32F), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-4, 2).to_compare_obj()), @@ -154,8 +161,7 @@ INSTANTIATE_TEST_CASE_P(SobelTestGPU32F, SobelTest, INSTANTIATE_TEST_CASE_P(LaplacianTestGPU, LaplacianTest, Combine(Values(CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-4, 2).to_compare_obj()), @@ -165,8 +171,7 @@ INSTANTIATE_TEST_CASE_P(LaplacianTestGPU, LaplacianTest, INSTANTIATE_TEST_CASE_P(BilateralFilterTestGPU, BilateralFilterTest, Combine(Values(CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(Tolerance_FloatRel_IntAbs(1e-4, 2).to_compare_obj()), @@ -177,16 +182,14 @@ INSTANTIATE_TEST_CASE_P(BilateralFilterTestGPU, BilateralFilterTest, INSTANTIATE_TEST_CASE_P(EqHistTestGPU, EqHistTest, Combine(Values(CV_8UC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(-1), Values(IMGPROC_GPU), Values(AbsExact().to_compare_obj()))); // FIXIT Non reliable check INSTANTIATE_TEST_CASE_P(CannyTestGPU, CannyTest, Combine(Values(CV_8UC1, CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_GPU), Values(AbsSimilarPoints(0, 0.05).to_compare_obj()), @@ -197,72 +200,63 @@ INSTANTIATE_TEST_CASE_P(CannyTestGPU, CannyTest, INSTANTIATE_TEST_CASE_P(RGB2GrayTestGPU, RGB2GrayTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2GrayTestGPU, BGR2GrayTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC1), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2YUVTestGPU, RGB2YUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(YUV2RGBTestGPU, YUV2RGBTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(RGB2LabTestGPU, RGB2LabTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(AbsSimilarPoints(1, 0.05).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2LUVTestGPU, BGR2LUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(ToleranceColor(5e-3, 6).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(LUV2BGRTestGPU, LUV2BGRTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(BGR2YUVTestGPU, BGR2YUVTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); INSTANTIATE_TEST_CASE_P(YUV2BGRTestGPU, YUV2BGRTest, Combine(Values(CV_8UC3), - Values(cv::Size(1280, 720), - cv::Size(640, 480)), + Values(cv::Size(1280, 720)), Values(CV_8UC3), Values(IMGPROC_GPU), Values(ToleranceColor(1e-3).to_compare_obj()))); diff --git a/modules/gapi/test/gpu/gapi_operators_tests_gpu.cpp b/modules/gapi/test/gpu/gapi_operators_tests_gpu.cpp index 46a2155be495..8184ff8c4578 100644 --- a/modules/gapi/test/gpu/gapi_operators_tests_gpu.cpp +++ b/modules/gapi/test/gpu/gapi_operators_tests_gpu.cpp @@ -11,6 +11,7 @@ namespace { #define CORE_GPU [] () { return cv::compile_args(cv::gapi::use_only{cv::gapi::core::gpu::kernels()}); } + const std::vector in_sizes{ cv::Size(1280, 720), cv::Size(128, 128) }; } // anonymous namespace namespace opencv_test @@ -18,9 +19,7 @@ namespace opencv_test INSTANTIATE_TEST_CASE_P(MathOperatorTestGPU, MathOperatorMatMatTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(Tolerance_FloatRel_IntAbs(1e-5, 2).to_compare_obj()), @@ -29,9 +28,7 @@ INSTANTIATE_TEST_CASE_P(MathOperatorTestGPU, MathOperatorMatMatTest, INSTANTIATE_TEST_CASE_P(MathOperatorTestGPU, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16SC1, CV_32FC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(Tolerance_FloatRel_IntAbs(1e-4, 2).to_compare_obj()), @@ -42,9 +39,7 @@ INSTANTIATE_TEST_CASE_P(MathOperatorTestGPU, MathOperatorMatScalarTest, INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestGPU, MathOperatorMatMatTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AbsExact().to_compare_obj()), @@ -52,9 +47,7 @@ INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestGPU, MathOperatorMatMatTest, INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestGPU, MathOperatorMatScalarTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU), Values(AbsExact().to_compare_obj()), @@ -63,9 +56,7 @@ INSTANTIATE_TEST_CASE_P(BitwiseOperatorTestGPU, MathOperatorMatScalarTest, INSTANTIATE_TEST_CASE_P(BitwiseNotOperatorTestGPU, NotOperatorTest, Combine(Values(CV_8UC1, CV_16UC1, CV_16SC1), - Values(cv::Size(1280, 720), - cv::Size(640, 480), - cv::Size(128, 128)), + ValuesIn(in_sizes), Values(-1), Values(CORE_GPU))); } diff --git a/modules/gapi/test/infer/gapi_infer_ie_test.cpp b/modules/gapi/test/infer/gapi_infer_ie_test.cpp index 69ed80054caa..3741438373e2 100644 --- a/modules/gapi/test/infer/gapi_infer_ie_test.cpp +++ b/modules/gapi/test/infer/gapi_infer_ie_test.cpp @@ -29,6 +29,10 @@ #elif defined(_MSC_VER) #pragma warning(push) #pragma warning(disable : 4100) +# if _MSC_VER < 1910 +# pragma warning(disable:4268) // Disable warnings of ngraph. OpenVINO recommends to use MSVS 2019. +# pragma warning(disable:4800) +# endif #elif defined(__GNUC__) #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wunused-parameter" @@ -2911,6 +2915,47 @@ TEST(Infer, ModelWith2DInputs) #endif // HAVE_NGRAPH +TEST(TestAgeGender, ThrowBlobAndInputPrecisionMismatchStreaming) +{ + const std::string device = "MYRIAD"; + skipIfDeviceNotAvailable(device); + + initDLDTDataPath(); + + cv::gapi::ie::detail::ParamDesc params; + // NB: Precision for inputs is U8. + params.model_path = compileAgeGenderBlob(device); + params.device_id = device; + + // Configure & run G-API + using AGInfo = std::tuple; + G_API_NET(AgeGender, , "test-age-gender"); + + auto pp = cv::gapi::ie::Params { + params.model_path, params.device_id + }.cfgOutputLayers({ "age_conv3", "prob" }); + + cv::GMat in, age, gender; + std::tie(age, gender) = cv::gapi::infer(in); + auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(age, gender)) + .compileStreaming(cv::compile_args(cv::gapi::networks(pp))); + + cv::Mat in_mat(320, 240, CV_32FC3); + cv::randu(in_mat, 0, 1); + cv::Mat gapi_age, gapi_gender; + + pipeline.setSource(cv::gin(in_mat)); + pipeline.start(); + + // NB: Blob precision is U8, but user pass FP32 data, so exception will be thrown. + // Now exception comes directly from IE, but since G-API has information + // about data precision at the compile stage, consider the possibility of + // throwing exception from there. + for (int i = 0; i < 10; ++i) { + EXPECT_ANY_THROW(pipeline.pull(cv::gout(gapi_age, gapi_gender))); + } +} + } // namespace opencv_test #endif // HAVE_INF_ENGINE diff --git a/modules/gapi/test/internal/gapi_int_executor_tests.cpp b/modules/gapi/test/internal/gapi_int_executor_tests.cpp index 90a338a3d04c..b8f0e18e0ba7 100644 --- a/modules/gapi/test/internal/gapi_int_executor_tests.cpp +++ b/modules/gapi/test/internal/gapi_int_executor_tests.cpp @@ -2,12 +2,14 @@ // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // -// Copyright (C) 2018 Intel Corporation +// Copyright (C) 2018-2022 Intel Corporation #include "../test_precomp.hpp" #include "../gapi_mock_kernels.hpp" +#include + namespace opencv_test { @@ -152,7 +154,7 @@ struct GExecutorReshapeTest: public ::testing::Test GMockExecutable island2; std::shared_ptr backend_impl2; cv::gapi::GBackend backend2; - cv::gapi::GKernelPackage pkg; + cv::GKernelPackage pkg; cv::Mat in_mat1, in_mat2, out_mat;; }; @@ -294,6 +296,28 @@ TEST_F(GExecutorReshapeTest, ReshapeCallAllocate) EXPECT_EQ(1, island1.getReshapeCounter()); } +TEST_F(GExecutorReshapeTest, CPUBackendIsReshapable) +{ + comp = cv::GComputation([](){ + cv::GMat in; + cv::GMat foo = I::Foo::on(in); + cv::GMat out = cv::gapi::bitwise_not(cv::gapi::bitwise_not(in)); + return cv::GComputation(cv::GIn(in), cv::GOut(foo, out)); + }); + // NB: Initial state + EXPECT_EQ(0, island1.getReshapeCounter()); + + // NB: First compilation. + cv::Mat out_mat2; + comp.apply(cv::gin(in_mat1), cv::gout(out_mat, out_mat2), cv::compile_args(pkg)); + EXPECT_EQ(0, island1.getReshapeCounter()); + + // NB: The entire graph is reshapable, so it won't be recompiled, but reshaped. + comp.apply(cv::gin(in_mat2), cv::gout(out_mat, out_mat2), cv::compile_args(pkg)); + EXPECT_EQ(1, island1.getReshapeCounter()); + EXPECT_EQ(0, cvtest::norm(out_mat2, in_mat2, NORM_INF)); +} + // FIXME: Add explicit tests on GMat/GScalar/GArray being connectors // between executed islands diff --git a/modules/gapi/test/internal/gapi_int_pattern_matching_test.cpp b/modules/gapi/test/internal/gapi_int_pattern_matching_test.cpp index 17405f1a9bca..f4113de47431 100644 --- a/modules/gapi/test/internal/gapi_int_pattern_matching_test.cpp +++ b/modules/gapi/test/internal/gapi_int_pattern_matching_test.cpp @@ -318,7 +318,7 @@ TEST(PatternMatching, TestPrepResizeSplit3) r_nh, op1_nh, op2_nh}), nodes); - EXPECT_EQ(cv::gapi::core::GResize::id(), matching_test::opName(tgm, op1_nh)); + EXPECT_EQ(cv::gapi::imgproc::GResize::id(), matching_test::opName(tgm, op1_nh)); EXPECT_EQ(cv::gapi::core::GSplit3::id(), matching_test::opName(tgm, op2_nh)); EXPECT_EQ(1u, tmp_nh->outEdges().size()); @@ -385,7 +385,7 @@ TEST(PatternMatching, TestPrepResizeToNCHW) EXPECT_EQ(matching_test::S({bgr_nh, tmp_nh, plr_nh, op1_nh, op2_nh}), nodes); - EXPECT_EQ(cv::gapi::core::GResize::id(), matching_test::opName(tgm, op1_nh)); + EXPECT_EQ(cv::gapi::imgproc::GResize::id(), matching_test::opName(tgm, op1_nh)); EXPECT_EQ(GToNCHW::id(), matching_test::opName(tgm, op2_nh)); EXPECT_EQ(1u, tmp_nh->outEdges().size()); diff --git a/modules/gapi/test/internal/gapi_int_perform_substitution_test.cpp b/modules/gapi/test/internal/gapi_int_perform_substitution_test.cpp index f75503b1016d..8ac6307d7334 100644 --- a/modules/gapi/test/internal/gapi_int_perform_substitution_test.cpp +++ b/modules/gapi/test/internal/gapi_int_perform_substitution_test.cpp @@ -63,7 +63,7 @@ GAPI_OCV_KERNEL(MyNV12toBGRImpl, MyNV12toBGR) }; G_TYPED_KERNEL(MyPlanarResize, , "test.my_planar_resize") { static GMatDesc outMeta(GMatDesc in, Size sz, int interp) { - return cv::gapi::core::GResizeP::outMeta(in, sz, interp); + return cv::gapi::imgproc::GResizeP::outMeta(in, sz, interp); } }; GAPI_OCV_KERNEL(MyPlanarResizeImpl, MyPlanarResize) { @@ -83,7 +83,7 @@ GAPI_OCV_KERNEL(MyPlanarResizeImpl, MyPlanarResize) { }; G_TYPED_KERNEL(MyInterleavedResize, , "test.my_interleaved_resize") { static GMatDesc outMeta(GMatDesc in, Size sz, int interp) { - return cv::gapi::core::GResize::outMeta(in, sz, 0.0, 0.0, interp); + return cv::gapi::imgproc::GResize::outMeta(in, sz, 0.0, 0.0, interp); } }; GAPI_OCV_KERNEL(MyInterleavedResizeImpl, MyInterleavedResize) { diff --git a/modules/gapi/test/internal/gapi_int_recompilation_test.cpp b/modules/gapi/test/internal/gapi_int_recompilation_test.cpp index e4171c5df037..fe6a6143536a 100644 --- a/modules/gapi/test/internal/gapi_int_recompilation_test.cpp +++ b/modules/gapi/test/internal/gapi_int_recompilation_test.cpp @@ -104,7 +104,7 @@ TEST(GComputationCompile, FluidReshapeResizeDownScale) cv::randu(in_mat2, cv::Scalar::all(0), cv::Scalar::all(255)); cv::Mat out_mat1, out_mat2; - cc.apply(in_mat1, out_mat1, cv::compile_args(cv::gapi::core::fluid::kernels())); + cc.apply(in_mat1, out_mat1, cv::compile_args(cv::gapi::imgproc::fluid::kernels())); auto comp1 = cc.priv().m_lastCompiled; cc.apply(in_mat2, out_mat2); @@ -136,7 +136,7 @@ TEST(GComputationCompile, FluidReshapeSwitchToUpscaleFromDownscale) cv::randu(in_mat3, cv::Scalar::all(0), cv::Scalar::all(255)); cv::Mat out_mat1, out_mat2, out_mat3; - cc.apply(in_mat1, out_mat1, cv::compile_args(cv::gapi::core::fluid::kernels())); + cc.apply(in_mat1, out_mat1, cv::compile_args(cv::gapi::imgproc::fluid::kernels())); auto comp1 = cc.priv().m_lastCompiled; cc.apply(in_mat2, out_mat2); diff --git a/modules/gapi/test/oak/gapi_tests_oak.cpp b/modules/gapi/test/oak/gapi_tests_oak.cpp new file mode 100644 index 000000000000..c153333374c2 --- /dev/null +++ b/modules/gapi/test/oak/gapi_tests_oak.cpp @@ -0,0 +1,26 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2021 Intel Corporation + +#include "../test_precomp.hpp" + +#ifdef HAVE_OAK + +#include + +namespace opencv_test +{ + +// FIXME: consider a better solution +TEST(OAK, Available) +{ + cv::GFrame in; + auto out = cv::gapi::oak::encode(in, {}); + auto args = cv::compile_args(cv::gapi::oak::ColorCameraParams{}, cv::gapi::oak::kernels()); + auto pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out)).compileStreaming(std::move(args)); +} +} // opencv_test + +#endif // HAVE_OAK diff --git a/modules/gapi/test/rmat/rmat_integration_tests.cpp b/modules/gapi/test/rmat/rmat_integration_tests.cpp index 58168453ead9..e9d8dbf446ff 100644 --- a/modules/gapi/test/rmat/rmat_integration_tests.cpp +++ b/modules/gapi/test/rmat/rmat_integration_tests.cpp @@ -113,10 +113,10 @@ struct RMatIntTestStreaming : public RMatIntTestBase }; struct OcvKernels { - cv::gapi::GKernelPackage kernels() { return cv::gapi::imgproc::cpu::kernels(); } + cv::GKernelPackage kernels() { return cv::gapi::imgproc::cpu::kernels(); } }; struct FluidKernels { - cv::gapi::GKernelPackage kernels() { return cv::gapi::imgproc::fluid::kernels(); } + cv::GKernelPackage kernels() { return cv::gapi::imgproc::fluid::kernels(); } }; struct RMatIntTestCpuRef : public diff --git a/modules/gapi/test/rmat/rmat_view_tests.cpp b/modules/gapi/test/rmat/rmat_view_tests.cpp index d829b6c655be..f7458e8d94f1 100644 --- a/modules/gapi/test/rmat/rmat_view_tests.cpp +++ b/modules/gapi/test/rmat/rmat_view_tests.cpp @@ -132,7 +132,7 @@ TEST_P(RMatViewNDTest, StepFromView) { INSTANTIATE_TEST_CASE_P(Test, RMatViewNDTest, Combine(Values(CV_8U, CV_32F), // depth - Values(1,2,3,4,7))); // ndims + Values(1,2,3,4,5))); // ndims struct RMatViewNDTestNegative : public TestWithParam< std::tuple>{}; @@ -153,7 +153,7 @@ TEST_P(RMatViewNDTestNegative, DefaultStep) { INSTANTIATE_TEST_CASE_P(Test, RMatViewNDTestNegative, Combine(Values(CV_8U, CV_32F), // depth Values(1,2,3,4), // chan - Values(2,4,7))); // ndims + Values(2,4,5))); // ndims TEST_P(RMatViewTest, NonDefaultStepInput) { auto type = GetParam(); diff --git a/modules/gapi/test/s11n/gapi_s11n_tests.cpp b/modules/gapi/test/s11n/gapi_s11n_tests.cpp index 94b99f877a3e..22398ec92ccf 100644 --- a/modules/gapi/test/s11n/gapi_s11n_tests.cpp +++ b/modules/gapi/test/s11n/gapi_s11n_tests.cpp @@ -569,7 +569,6 @@ TEST_F(S11N_Basic, Test_Bind_RunArgs_MatScalar) { v[0] = cv::GRunArg{ mat }; v[1] = cv::GRunArg{ scalar }; GRunArgsP output = cv::gapi::bind(v); - unsigned int i = 0; for (auto it : output) { using T = cv::GRunArgP; @@ -591,7 +590,6 @@ TEST_F(S11N_Basic, Test_Bind_RunArgs_MatScalar) { GAPI_Assert(false && "This value type is not supported!"); // ...maybe because of STANDALONE mode. break; } - i++; } } diff --git a/modules/gapi/test/streaming/gapi_gstreamersource_tests.cpp b/modules/gapi/test/streaming/gapi_gstreamersource_tests.cpp index 0478d2dc1d96..7921eb71c201 100644 --- a/modules/gapi/test/streaming/gapi_gstreamersource_tests.cpp +++ b/modules/gapi/test/streaming/gapi_gstreamersource_tests.cpp @@ -29,6 +29,7 @@ namespace opencv_test struct GStreamerSourceTest : public TestWithParam> { }; + TEST_P(GStreamerSourceTest, AccuracyTest) { std::string pipeline; @@ -143,6 +144,16 @@ G_TYPED_KERNEL(GGstFrameCopyToNV12, (GFrame)>, } }; +G_TYPED_KERNEL(GGstFrameCopyToGRAY8, , + "org.opencv.test.gstframe_copy_to_gray8") +{ + static GMatDesc outMeta(GFrameDesc desc) { + GMatDesc y{ CV_8U, 1, desc.size, false }; + return y; + } +}; + + GAPI_OCV_KERNEL(GOCVGstFrameCopyToNV12, GGstFrameCopyToNV12) { static void run(const cv::MediaFrame& in, cv::Mat& y, cv::Mat& uv) @@ -156,21 +167,50 @@ GAPI_OCV_KERNEL(GOCVGstFrameCopyToNV12, GGstFrameCopyToNV12) } }; +GAPI_OCV_KERNEL(GOCVGstFrameCopyToGRAY8, GGstFrameCopyToGRAY8) +{ + static void run(const cv::MediaFrame & in, cv::Mat & y) + { + auto view = in.access(cv::MediaFrame::Access::R); + cv::Mat ly(y.size(), y.type(), view.ptr[0], view.stride[0]); + ly.copyTo(y); + } +}; + + TEST_P(GStreamerSourceTest, GFrameTest) { std::string pipeline; cv::Size expectedFrameSize; std::size_t streamLength { }; + bool isNV12 = false; std::tie(pipeline, expectedFrameSize, streamLength) = GetParam(); + //Check if pipline string contains NV12 sub-string + if (pipeline.find("NV12") != std::string::npos) { + isNV12 = true; + } + // Graph declaration: cv::GFrame in; cv::GMat copiedY, copiedUV; - std::tie(copiedY, copiedUV) = GGstFrameCopyToNV12::on(in); - cv::GComputation c(cv::GIn(in), cv::GOut(copiedY, copiedUV)); + if (isNV12) { + std::tie(copiedY, copiedUV) = GGstFrameCopyToNV12::on(in); + } + else { + copiedY = GGstFrameCopyToGRAY8::on(in); + } + + cv::GComputation c(cv::GIn(in), isNV12 ? cv::GOut(copiedY, copiedUV) : cv::GOut(copiedY)); // Graph compilation for streaming mode: - auto ccomp = c.compileStreaming(cv::compile_args(cv::gapi::kernels())); + cv::GStreamingCompiled ccomp; + if (isNV12) { + ccomp = c.compileStreaming(cv::compile_args(cv::gapi::kernels())); + } else { + ccomp = c.compileStreaming(cv::compile_args(cv::gapi::kernels())); + } + EXPECT_TRUE(ccomp); EXPECT_FALSE(ccomp.running()); @@ -186,29 +226,41 @@ TEST_P(GStreamerSourceTest, GFrameTest) // Streaming - pulling of frames until the end: cv::Mat y_mat, uv_mat; - EXPECT_TRUE(ccomp.pull(cv::gout(y_mat, uv_mat))); + EXPECT_TRUE(isNV12 ? ccomp.pull(cv::gout(y_mat, uv_mat)) : ccomp.pull(cv::gout(y_mat))); EXPECT_TRUE(!y_mat.empty()); - EXPECT_TRUE(!uv_mat.empty()); + if (isNV12) { + EXPECT_TRUE(!uv_mat.empty()); + } cv::Size expectedYSize = expectedFrameSize; cv::Size expectedUVSize = expectedFrameSize / 2; EXPECT_EQ(expectedYSize, y_mat.size()); - EXPECT_EQ(expectedUVSize, uv_mat.size()); + if (isNV12) { + EXPECT_EQ(expectedUVSize, uv_mat.size()); + } EXPECT_EQ(CV_8UC1, y_mat.type()); - EXPECT_EQ(CV_8UC2, uv_mat.type()); + if (isNV12) { + EXPECT_EQ(CV_8UC2, uv_mat.type()); + } std::size_t framesCount = 1UL; - while (ccomp.pull(cv::gout(y_mat, uv_mat))) { + while (isNV12 ? ccomp.pull(cv::gout(y_mat, uv_mat)) : ccomp.pull(cv::gout(y_mat))) { EXPECT_TRUE(!y_mat.empty()); - EXPECT_TRUE(!uv_mat.empty()); + if (isNV12) { + EXPECT_TRUE(!uv_mat.empty()); + } EXPECT_EQ(expectedYSize, y_mat.size()); - EXPECT_EQ(expectedUVSize, uv_mat.size()); + if (isNV12) { + EXPECT_EQ(expectedUVSize, uv_mat.size()); + } EXPECT_EQ(CV_8UC1, y_mat.type()); - EXPECT_EQ(CV_8UC2, uv_mat.type()); + if (isNV12) { + EXPECT_EQ(CV_8UC2, uv_mat.type()); + } framesCount++; } @@ -221,36 +273,56 @@ TEST_P(GStreamerSourceTest, GFrameTest) EXPECT_EQ(streamLength, framesCount); } + // FIXME: Need to launch with sudo. May be infrastructure problems. // TODO: It is needed to add tests for streaming from native KMB camera: kmbcamsrc // GStreamer element. INSTANTIATE_TEST_CASE_P(CameraEmulatingPipeline, GStreamerSourceTest, Combine(Values("videotestsrc is-live=true pattern=colors num-buffers=10 ! " "videorate ! videoscale ! " - "video/x-raw,width=1920,height=1080,framerate=3/1 ! " + "video/x-raw,format=NV12,width=1920,height=1080,framerate=3/1 ! " + "appsink", + "videotestsrc is-live=true pattern=colors num-buffers=10 ! " + "videorate ! videoscale ! " + "video/x-raw,format=GRAY8,width=1920,height=1080,framerate=3/1 ! " "appsink"), Values(cv::Size(1920, 1080)), Values(10UL))); + INSTANTIATE_TEST_CASE_P(FileEmulatingPipeline, GStreamerSourceTest, Combine(Values("videotestsrc pattern=colors num-buffers=10 ! " "videorate ! videoscale ! " - "video/x-raw,width=640,height=420,framerate=3/1 ! " + "video/x-raw,format=NV12,width=640,height=420,framerate=3/1 ! " + "appsink", + "videotestsrc pattern=colors num-buffers=10 ! " + "videorate ! videoscale ! " + "video/x-raw,format=GRAY8,width=640,height=420,framerate=3/1 ! " "appsink"), Values(cv::Size(640, 420)), Values(10UL))); + INSTANTIATE_TEST_CASE_P(MultipleLiveSources, GStreamerSourceTest, Combine(Values("videotestsrc is-live=true pattern=colors num-buffers=10 ! " - "videoscale ! video/x-raw,width=1280,height=720 ! appsink " + "videoscale ! video/x-raw,format=NV12,width=1280,height=720 ! appsink " + "videotestsrc is-live=true pattern=colors num-buffers=10 ! " + "fakesink", + "videotestsrc is-live=true pattern=colors num-buffers=10 ! " + "videoscale ! video/x-raw,format=GRAY8,width=1280,height=720 ! appsink " "videotestsrc is-live=true pattern=colors num-buffers=10 ! " "fakesink"), Values(cv::Size(1280, 720)), Values(10UL))); + INSTANTIATE_TEST_CASE_P(MultipleNotLiveSources, GStreamerSourceTest, Combine(Values("videotestsrc pattern=colors num-buffers=10 ! " - "videoscale ! video/x-raw,width=1280,height=720 ! appsink " + "videoscale ! video/x-raw,format=NV12,width=1280,height=720 ! appsink " + "videotestsrc pattern=colors num-buffers=10 ! " + "fakesink", + "videotestsrc pattern=colors num-buffers=10 ! " + "videoscale ! video/x-raw,format=GRAY8,width=1280,height=720 ! appsink " "videotestsrc pattern=colors num-buffers=10 ! " "fakesink"), Values(cv::Size(1280, 720)), @@ -308,11 +380,11 @@ TEST(GStreamerMultiSourceSmokeTest, Test) EXPECT_FALSE(ccomp.running()); } -struct GStreamerMultiSourceTest : +struct GStreamerMultiSourceTestNV12 : public TestWithParam> { }; -TEST_P(GStreamerMultiSourceTest, ImageDataTest) +TEST_P(GStreamerMultiSourceTestNV12, ImageDataTest) { std::string pathToLeftIm = findDataFile("cv/stereomatching/datasets/tsukuba/im6.png"); std::string pathToRightIm = findDataFile("cv/stereomatching/datasets/tsukuba/im2.png"); @@ -377,7 +449,96 @@ TEST_P(GStreamerMultiSourceTest, ImageDataTest) EXPECT_FALSE(compiled.running()); } -INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGMatsTest, GStreamerMultiSourceTest, +INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGMatsTest, GStreamerMultiSourceTestNV12, + Combine(Values(cv::GComputation([]() + { + cv::GMat in1, in2; + return cv::GComputation(cv::GIn(in1, in2), + cv::GOut(cv::gapi::copy(in1), + cv::gapi::copy(in2))); + })), + Values(cv::gapi::wip::GStreamerSource::OutputType::MAT))); + +INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGFramesTest, GStreamerMultiSourceTestNV12, + Combine(Values(cv::GComputation([]() + { + cv::GFrame in1, in2; + return cv::GComputation(cv::GIn(in1, in2), + cv::GOut(cv::gapi::streaming::BGR(in1), + cv::gapi::streaming::BGR(in2))); + })), + Values(cv::gapi::wip::GStreamerSource::OutputType::FRAME))); + +struct GStreamerMultiSourceTestGRAY8 : + public TestWithParam> +{ }; + +TEST_P(GStreamerMultiSourceTestGRAY8, ImageDataTest) +{ + std::string pathToLeftIm = findDataFile("cv/stereomatching/datasets/tsukuba/im6.png"); + std::string pathToRightIm = findDataFile("cv/stereomatching/datasets/tsukuba/im2.png"); + + std::string pipelineToReadImage("filesrc location=LOC ! pngdec ! videoconvert ! " + "videoscale ! video/x-raw,format=GRAY8 ! appsink"); + + cv::gapi::wip::GStreamerSource leftImageProvider( + std::regex_replace(pipelineToReadImage, std::regex("LOC"), pathToLeftIm)); + cv::gapi::wip::GStreamerSource rightImageProvider( + std::regex_replace(pipelineToReadImage, std::regex("LOC"), pathToRightIm)); + + cv::gapi::wip::Data leftImData, rightImData; + leftImageProvider.pull(leftImData); + rightImageProvider.pull(rightImData); + + cv::Mat leftRefMat = cv::util::get(leftImData); + cv::Mat rightRefMat = cv::util::get(rightImData); + + // Retrieve test parameters: + std::tuple params = GetParam(); + cv::GComputation extractImage = std::move(std::get<0>(params)); + cv::gapi::wip::GStreamerSource::OutputType outputType = std::get<1>(params); + + // Graph compilation for streaming mode: + auto compiled = + extractImage.compileStreaming(); + + EXPECT_TRUE(compiled); + EXPECT_FALSE(compiled.running()); + + cv::gapi::wip::GStreamerPipeline + pipeline(std::string("multifilesrc location=" + pathToLeftIm + " index=0 loop=true ! " + "pngdec ! videoconvert ! videoscale ! video/x-raw,format=GRAY8 ! " + "appsink name=sink1 ") + + std::string("multifilesrc location=" + pathToRightIm + " index=0 loop=true ! " + "pngdec ! videoconvert ! videoscale ! video/x-raw,format=GRAY8 ! " + "appsink name=sink2")); + + // GStreamer streaming sources configuration: + auto src1 = pipeline.getStreamingSource("sink1", outputType); + auto src2 = pipeline.getStreamingSource("sink2", outputType); + + compiled.setSource(cv::gin(src1, src2)); + + // Start of streaming: + compiled.start(); + EXPECT_TRUE(compiled.running()); + + // Streaming - pulling of frames: + cv::Mat in_mat1, in_mat2; + + std::size_t counter { }, limit { 10 }; + while(compiled.pull(cv::gout(in_mat1, in_mat2)) && (counter < limit)) { + EXPECT_EQ(0, cv::norm(in_mat1, leftRefMat, cv::NORM_INF)); + EXPECT_EQ(0, cv::norm(in_mat2, rightRefMat, cv::NORM_INF)); + ++counter; + } + + compiled.stop(); + + EXPECT_FALSE(compiled.running()); +} + +INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGMatsTest, GStreamerMultiSourceTestGRAY8, Combine(Values(cv::GComputation([]() { cv::GMat in1, in2; @@ -387,7 +548,7 @@ INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGMatsTest, GStreamerMultiSourceTe })), Values(cv::gapi::wip::GStreamerSource::OutputType::MAT))); -INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGFramesTest, GStreamerMultiSourceTest, +INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGFramesTest, GStreamerMultiSourceTestGRAY8, Combine(Values(cv::GComputation([]() { cv::GFrame in1, in2; @@ -396,6 +557,7 @@ INSTANTIATE_TEST_CASE_P(GStreamerMultiSourceViaGFramesTest, GStreamerMultiSource cv::gapi::streaming::BGR(in2))); })), Values(cv::gapi::wip::GStreamerSource::OutputType::FRAME))); + } // namespace opencv_test #endif // HAVE_GSTREAMER diff --git a/modules/gapi/test/streaming/gapi_streaming_tests.cpp b/modules/gapi/test/streaming/gapi_streaming_tests.cpp index 8cef807cd2cf..5cac4df84581 100644 --- a/modules/gapi/test/streaming/gapi_streaming_tests.cpp +++ b/modules/gapi/test/streaming/gapi_streaming_tests.cpp @@ -67,7 +67,7 @@ struct GAPI_Streaming: public ::testing::TestWithParam cap; }; @@ -164,6 +164,26 @@ class TestMediaNV12 final: public cv::MediaFrame::IAdapter { } }; +class TestMediaGRAY final : public cv::MediaFrame::IAdapter { + cv::Mat m_mat; + using Cb = cv::MediaFrame::View::Callback; + Cb m_cb; + +public: + explicit TestMediaGRAY(cv::Mat m, Cb cb = []() {}) + : m_mat(m), m_cb(cb) { + } + cv::GFrameDesc meta() const override { + return cv::GFrameDesc{ cv::MediaFormat::GRAY, cv::Size(m_mat.cols, m_mat.rows) }; + } + cv::MediaFrame::View access(cv::MediaFrame::Access) override { + cv::MediaFrame::View::Ptrs pp = { m_mat.ptr(), nullptr, nullptr, nullptr }; + cv::MediaFrame::View::Strides ss = { m_mat.step, 0u, 0u, 0u }; + return cv::MediaFrame::View(std::move(pp), std::move(ss), Cb{ m_cb }); + } +}; + + class BGRSource : public cv::gapi::wip::GCaptureSource { public: explicit BGRSource(const std::string& pipeline) @@ -230,6 +250,31 @@ class NV12Source : public cv::gapi::wip::GCaptureSource { } }; +class GRAYSource : public cv::gapi::wip::GCaptureSource { +public: + explicit GRAYSource(const std::string& pipeline) + : cv::gapi::wip::GCaptureSource(pipeline) { + } + + bool pull(cv::gapi::wip::Data& data) { + if (cv::gapi::wip::GCaptureSource::pull(data)) { + cv::Mat bgr = cv::util::get(data); + cv::Mat gray; + cvtColor(bgr, gray, cv::COLOR_BGR2GRAY); + data = cv::MediaFrame::Create(gray); + return true; + } + return false; + } + + GMetaArg descr_of() const override { + return cv::GMetaArg{ cv::GFrameDesc{cv::MediaFormat::GRAY, + cv::util::get( + cv::gapi::wip::GCaptureSource::descr_of()).size} }; + } +}; + + void checkPullOverload(const cv::Mat& ref, const bool has_output, cv::util::variant& args) { @@ -259,6 +304,66 @@ void checkPullOverload(const cv::Mat& ref, EXPECT_EQ(0., cv::norm(ref, out_mat, cv::NORM_INF)); } +class InvalidSource : public cv::gapi::wip::IStreamSource { +public: + InvalidSource(const size_t throw_every_nth_frame, + const size_t num_frames) + : m_throw_every_nth_frame(throw_every_nth_frame), + m_curr_frame_id(0u), + m_num_frames(num_frames), + m_mat(1, 1, CV_8U) { + } + + static std::string exception_msg() + { + return "InvalidSource sucessfuly failed!"; + } + + bool pull(cv::gapi::wip::Data& d) { + ++m_curr_frame_id; + if (m_curr_frame_id > m_num_frames) { + return false; + } + + if (m_curr_frame_id % m_throw_every_nth_frame == 0) { + throw std::logic_error(InvalidSource::exception_msg()); + return true; + } else { + d = cv::Mat(m_mat); + } + + return true; + } + + cv::GMetaArg descr_of() const override { + return cv::GMetaArg{cv::descr_of(m_mat)}; + } + +private: + size_t m_throw_every_nth_frame; + size_t m_curr_frame_id; + size_t m_num_frames; + cv::Mat m_mat; +}; + +G_TYPED_KERNEL(GThrowExceptionOp, , "org.opencv.test.throw_error_op") +{ + static GMatDesc outMeta(GMatDesc in) { return in; } +}; + +GAPI_OCV_KERNEL(GThrowExceptionKernel, GThrowExceptionOp) +{ + static std::string exception_msg() + { + return "GThrowExceptionKernel sucessfuly failed"; + } + + static void run(const cv::Mat&, cv::Mat&) + { + throw std::logic_error(GThrowExceptionKernel::exception_msg()); + } +}; + } // anonymous namespace TEST_P(GAPI_Streaming, SmokeTest_ConstInput_GMat) @@ -1062,7 +1167,7 @@ struct GAPI_Streaming_TemplateTypes: ::testing::Test { cv::GMat blur; cv::GArray vec; cv::GOpaque opq; - cv::gapi::GKernelPackage pkg; + cv::GKernelPackage pkg; cv::Mat in_mat; }; @@ -1789,6 +1894,46 @@ TEST(GAPI_Streaming, CopyFrame) } } +TEST(GAPI_Streaming, CopyFrameGray) +{ + std::string filepath = findDataFile("cv/video/768x576.avi"); + + cv::GFrame in; + auto out = cv::gapi::copy(in); + + cv::GComputation comp(cv::GIn(in), cv::GOut(out)); + + auto cc = comp.compileStreaming(); + try { + cc.setSource(filepath); + } + catch (...) { + throw SkipTestException("Video file can not be opened"); + } + + cv::VideoCapture cap; + cap.open(filepath); + if (!cap.isOpened()) + throw SkipTestException("Video file can not be opened"); + + cv::MediaFrame frame; + cv::Mat ocv_mat; + std::size_t num_frames = 0u; + std::size_t max_frames = 10u; + + cc.start(); + while (cc.pull(cv::gout(frame)) && num_frames < max_frames) + { + auto view = frame.access(cv::MediaFrame::Access::R); + cv::Mat gapi_mat(frame.desc().size, CV_8UC1, view.ptr[0]); + num_frames++; + cap >> ocv_mat; + cv::Mat gray; + cvtColor(ocv_mat, gray, cv::COLOR_BGR2GRAY); + EXPECT_EQ(0, cvtest::norm(gray, gapi_mat, NORM_INF)); + } +} + TEST(GAPI_Streaming, CopyMat) { std::string filepath = findDataFile("cv/video/768x576.avi"); @@ -1892,23 +2037,97 @@ TEST(GAPI_Streaming, Reshape) } } +TEST(GAPI_Streaming, ReshapeGray) +{ + std::string filepath = findDataFile("cv/video/768x576.avi"); + + cv::GFrame in; + auto out = cv::gapi::copy(in); + + cv::GComputation comp(cv::GIn(in), cv::GOut(out)); + + auto cc = comp.compileStreaming(); + try { + cc.setSource(filepath); + } + catch (...) { + throw SkipTestException("Video file can not be opened"); + } + + cv::VideoCapture cap; + cap.open(filepath); + if (!cap.isOpened()) + throw SkipTestException("Video file can not be opened"); + + cv::MediaFrame frame; + cv::Mat ocv_mat; + std::size_t num_frames = 0u; + std::size_t max_frames = 10u; + + cc.start(); + while (cc.pull(cv::gout(frame)) && num_frames < max_frames) + { + auto view = frame.access(cv::MediaFrame::Access::R); + cv::Mat gapi_mat(frame.desc().size, CV_8UC1, view.ptr[0]); + num_frames++; + cap >> ocv_mat; + cv::Mat gray; + cvtColor(ocv_mat, gray, cv::COLOR_BGR2GRAY); + EXPECT_EQ(0, cvtest::norm(gray, gapi_mat, NORM_INF)); + } + + // Reshape the graph meta + filepath = findDataFile("cv/video/1920x1080.avi"); + cc.stop(); + try { + cc.setSource(filepath); + } + catch (...) { + throw SkipTestException("Video file can not be opened"); + } + + cap.open(filepath); + if (!cap.isOpened()) + throw SkipTestException("Video file can not be opened"); + + cv::MediaFrame frame2; + cv::Mat ocv_mat2; + + num_frames = 0u; + + cc.start(); + while (cc.pull(cv::gout(frame2)) && num_frames < max_frames) + { + auto view = frame2.access(cv::MediaFrame::Access::R); + cv::Mat gapi_mat(frame2.desc().size, CV_8UC1, view.ptr[0]); + num_frames++; + cap >> ocv_mat2; + cv::Mat gray; + cvtColor(ocv_mat2, gray, cv::COLOR_BGR2GRAY); + EXPECT_EQ(0, cvtest::norm(gray, gapi_mat, NORM_INF)); + } +} + + namespace { enum class TestSourceType { BGR, - NV12 + NV12, + GRAY }; std::ostream& operator<<(std::ostream& os, TestSourceType a) { os << "Source:"; switch (a) { case TestSourceType::BGR: return os << "BGR"; case TestSourceType::NV12: return os << "NV12"; + case TestSourceType::GRAY: return os << "GRAY"; default: CV_Assert(false && "unknown TestSourceType"); } } cv::gapi::wip::IStreamSource::Ptr createTestSource(TestSourceType sourceType, const std::string& pipeline) { - assert(sourceType == TestSourceType::BGR || sourceType == TestSourceType::NV12); + assert(sourceType == TestSourceType::BGR || sourceType == TestSourceType::NV12 || sourceType == TestSourceType::GRAY); cv::gapi::wip::IStreamSource::Ptr ptr { }; @@ -1933,6 +2152,16 @@ namespace { } break; } + case TestSourceType::GRAY: { + try { + ptr = cv::gapi::wip::make_src(pipeline); + } + catch (...) { + throw SkipTestException(std::string("GRAYSource for '") + pipeline + + "' couldn't be created!"); + } + break; + } default: { throw SkipTestException("Incorrect type of source! " "Something went wrong in the test!"); @@ -2000,6 +2229,25 @@ namespace { cvtBGR2NV12(bgr, y, uv); return uv; } }, + { std::make_pair(TestSourceType::GRAY, TestAccessType::BGR), + [](const cv::Mat& bgr) { + cv::Mat gray; + cv::cvtColor(bgr, gray, cv::COLOR_BGR2GRAY); + cv::Mat out_bgr; + cv::cvtColor(gray, out_bgr, cv::COLOR_GRAY2BGR); + return out_bgr; + } }, + { std::make_pair(TestSourceType::GRAY, TestAccessType::Y), + [](const cv::Mat& bgr) { + cv::Mat gray; + cv::cvtColor(bgr, gray, cv::COLOR_BGR2GRAY); + return gray; + } }, + { std::make_pair(TestSourceType::GRAY, TestAccessType::UV), + [](const cv::Mat& bgr) { + cv::Mat uv(bgr.size() / 2, CV_8UC2, cv::Scalar::all(127)); + return uv; + } }, }; } // anonymous namespace @@ -2007,6 +2255,7 @@ struct GAPI_Accessors_In_Streaming : public TestWithParam< std::tuple> { }; + TEST_P(GAPI_Accessors_In_Streaming, AccuracyTest) { std::string filepath{}; @@ -2050,10 +2299,11 @@ TEST_P(GAPI_Accessors_In_Streaming, AccuracyTest) INSTANTIATE_TEST_CASE_P(TestAccessor, GAPI_Accessors_In_Streaming, Combine(Values("cv/video/768x576.avi"), - Values(TestSourceType::BGR, TestSourceType::NV12), + Values(TestSourceType::BGR, TestSourceType::NV12, TestSourceType::GRAY), Values(TestAccessType::BGR, TestAccessType::Y, TestAccessType::UV) )); + struct GAPI_Accessors_Meta_In_Streaming : public TestWithParam< std::tuple> { }; @@ -2120,7 +2370,7 @@ TEST_P(GAPI_Accessors_Meta_In_Streaming, AccuracyTest) INSTANTIATE_TEST_CASE_P(AccessorMeta, GAPI_Accessors_Meta_In_Streaming, Combine(Values("cv/video/768x576.avi"), - Values(TestSourceType::BGR, TestSourceType::NV12), + Values(TestSourceType::BGR, TestSourceType::NV12, TestSourceType::GRAY), Values(TestAccessType::BGR, TestAccessType::Y, TestAccessType::UV) )); @@ -2190,7 +2440,11 @@ TEST(OneVPL_Source, Init) std::vector src_params; src_params.push_back(CfgParam::create_implementation(MFX_IMPL_TYPE_HARDWARE)); +#ifdef __WIN32 src_params.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); +#elif defined(__linux__) + src_params.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_VAAPI)); +#endif src_params.push_back(CfgParam::create_decoder_id(MFX_CODEC_HEVC)); std::stringstream stream(std::ios_base::in | std::ios_base::out | std::ios_base::binary); @@ -2232,7 +2486,7 @@ TEST(GAPI_Streaming, TestDesyncRMat) { cv::optional out_desync; cv::optional out_rmat; while (true) { - // Initially it throwed "bad variant access" since there was + // Initially it threw "bad variant access" since there was // no RMat handling in wrap_opt_arg EXPECT_NO_THROW(pipe.pull(cv::gout(out_desync, out_rmat))); if (out_rmat) break; @@ -2273,11 +2527,158 @@ TEST(GAPI_Streaming, TestDesyncMediaFrame) { cv::optional out_desync; cv::optional out_frame; while (true) { - // Initially it throwed "bad variant access" since there was + // Initially it threw "bad variant access" since there was + // no MediaFrame handling in wrap_opt_arg + EXPECT_NO_THROW(pipe.pull(cv::gout(out_desync, out_frame))); + if (out_frame) break; + } +} + +G_API_OP(GTestBlurGray, , "test.blur_gray") { + static GFrameDesc outMeta(GFrameDesc d) { return d; } +}; +GAPI_OCV_KERNEL(GOcvTestBlurGray, GTestBlurGray) { + static void run(const cv::MediaFrame & in, cv::MediaFrame & out) { + auto d = in.desc(); + GAPI_Assert(d.fmt == cv::MediaFormat::GRAY); + auto view = in.access(cv::MediaFrame::Access::R); + cv::Mat mat(d.size, CV_8UC1, view.ptr[0]); + cv::Mat blurred; + cv::blur(mat, blurred, cv::Size{ 3,3 }); + out = cv::MediaFrame::Create(blurred); + } +}; + +TEST(GAPI_Streaming, TestDesyncMediaFrameGray) { + cv::GFrame in; + auto blurred = GTestBlurGray::on(in); + auto desynced = cv::gapi::streaming::desync(blurred); + auto out = GTestBlurGray::on(blurred); + auto pipe = cv::GComputation(cv::GIn(in), cv::GOut(desynced, out)) + .compileStreaming(cv::compile_args(cv::gapi::kernels())); + + std::string filepath = findDataFile("cv/video/768x576.avi"); + try { + pipe.setSource(filepath); + } + catch (...) { + throw SkipTestException("Video file can not be opened"); + } + pipe.start(); + + cv::optional out_desync; + cv::optional out_frame; + while (true) { + // Initially it threw "bad variant access" since there was // no MediaFrame handling in wrap_opt_arg EXPECT_NO_THROW(pipe.pull(cv::gout(out_desync, out_frame))); if (out_frame) break; } } +TEST(GAPI_Streaming_Exception, SingleKernelThrow) { + cv::GMat in; + auto pipeline = cv::GComputation(in, GThrowExceptionOp::on(in)) + .compileStreaming(cv::compile_args(cv::gapi::kernels())); + + cv::Mat in_mat(cv::Size(300, 300), CV_8UC3); + cv::randu(in_mat, cv::Scalar::all(0), cv::Scalar::all(255)); + pipeline.setSource(cv::gin(in_mat)); + pipeline.start(); + + EXPECT_THROW( + try { + cv::Mat out_mat; + pipeline.pull(cv::gout(out_mat)); + } catch (const std::logic_error& e) { + EXPECT_EQ(GThrowExceptionKernel::exception_msg(), e.what()); + throw; + }, std::logic_error); +} + +TEST(GAPI_Streaming_Exception, StreamingBackendExceptionAsInput) { + cv::GMat in; + auto pipeline = cv::GComputation(in, + cv::gapi::copy(GThrowExceptionOp::on(in))) + .compileStreaming(cv::compile_args(cv::gapi::kernels())); + + cv::Mat in_mat(cv::Size(300, 300), CV_8UC3); + cv::randu(in_mat, cv::Scalar::all(0), cv::Scalar::all(255)); + pipeline.setSource(cv::gin(in_mat)); + pipeline.start(); + + EXPECT_THROW( + try { + cv::Mat out_mat; + pipeline.pull(cv::gout(out_mat)); + } catch (const std::logic_error& e) { + EXPECT_EQ(GThrowExceptionKernel::exception_msg(), e.what()); + throw; + }, std::logic_error); +} + +TEST(GAPI_Streaming_Exception, RegularBacckendsExceptionAsInput) { + cv::GMat in; + auto pipeline = cv::GComputation(in, + cv::gapi::add(GThrowExceptionOp::on(in), GThrowExceptionOp::on(in))) + .compileStreaming(cv::compile_args(cv::gapi::kernels())); + + cv::Mat in_mat(cv::Size(300, 300), CV_8UC3); + cv::randu(in_mat, cv::Scalar::all(0), cv::Scalar::all(255)); + pipeline.setSource(cv::gin(in_mat)); + pipeline.start(); + + EXPECT_THROW( + try { + cv::Mat out_mat; + pipeline.pull(cv::gout(out_mat)); + } catch (const std::logic_error& e) { + EXPECT_EQ(GThrowExceptionKernel::exception_msg(), e.what()); + throw; + }, std::logic_error); +} + +TEST(GAPI_Streaming_Exception, SourceThrow) { + cv::GMat in; + auto pipeline = cv::GComputation(in, cv::gapi::copy(in)).compileStreaming(); + + pipeline.setSource(std::make_shared(1u, 1u)); + pipeline.start(); + + EXPECT_THROW( + try { + cv::Mat out_mat; + pipeline.pull(cv::gout(out_mat)); + } catch (const std::logic_error& e) { + EXPECT_EQ(InvalidSource::exception_msg(), e.what()); + throw; + }, std::logic_error); +} + +TEST(GAPI_Streaming_Exception, SourceThrowEverySecondFrame) { + constexpr size_t throw_every_nth_frame = 2u; + constexpr size_t num_frames = 10u; + size_t curr_frame = 0; + bool has_frame = true; + cv::Mat out_mat; + + cv::GMat in; + auto pipeline = cv::GComputation(in, cv::gapi::copy(in)).compileStreaming(); + + pipeline.setSource(std::make_shared(throw_every_nth_frame, num_frames)); + pipeline.start(); + while (has_frame) { + ++curr_frame; + try { + has_frame = pipeline.pull(cv::gout(out_mat)); + } catch (const std::exception& e) { + EXPECT_TRUE(curr_frame % throw_every_nth_frame == 0); + EXPECT_EQ(InvalidSource::exception_msg(), e.what()); + } + } + + // NB: Pull was called num_frames + 1(stop). + EXPECT_EQ(num_frames, curr_frame - 1); +} + } // namespace opencv_test diff --git a/modules/gapi/test/streaming/gapi_streaming_vpl_core_test.cpp b/modules/gapi/test/streaming/gapi_streaming_vpl_core_test.cpp index c62f58eecf69..d83b09d7d370 100644 --- a/modules/gapi/test/streaming/gapi_streaming_vpl_core_test.cpp +++ b/modules/gapi/test/streaming/gapi_streaming_vpl_core_test.cpp @@ -29,16 +29,25 @@ #ifdef HAVE_ONEVPL #include +#include "streaming/onevpl/file_data_provider.hpp" #include "streaming/onevpl/cfg_param_device_selector.hpp" #include "streaming/onevpl/accelerators/surface/surface.hpp" #include "streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp" #include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" #include "streaming/onevpl/accelerators/accel_policy_dx11.hpp" +#include "streaming/onevpl/accelerators/accel_policy_va_api.hpp" #include "streaming/onevpl/accelerators/dx11_alloc_resource.hpp" #include "streaming/onevpl/accelerators/utils/shared_lock.hpp" -#include "streaming/onevpl/engine/processing_engine_base.hpp" -#include "streaming/onevpl/engine/engine_session.hpp" +#define private public +#define protected public +#include "streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp" +#include "streaming/onevpl/engine/transcode/transcode_session.hpp" +#undef protected +#undef private +#include "logger.hpp" + +#define ALIGN16(value) (((value + 15) >> 4) << 4) namespace opencv_test { @@ -60,18 +69,18 @@ struct EmptyDataProvider : public cv::gapi::wip::onevpl::IDataProvider { struct TestProcessingSession : public cv::gapi::wip::onevpl::EngineSession { TestProcessingSession(mfxSession mfx_session) : - EngineSession(mfx_session, {}) { + EngineSession(mfx_session) { } - const mfxVideoParam& get_video_param() const override { + const mfxFrameInfo& get_video_param() const override { static mfxVideoParam empty; - return empty; + return empty.mfx.FrameInfo; } }; struct TestProcessingEngine: public cv::gapi::wip::onevpl::ProcessingEngineBase { - size_t pipeline_stage_num = 0; + int pipeline_stage_num = 0; TestProcessingEngine(std::unique_ptr&& accel) : cv::gapi::wip::onevpl::ProcessingEngineBase(std::move(accel)) { @@ -146,7 +155,7 @@ public : mfxFrameAllocator m_allocator; }; template -std::map TestLockableAllocator::lock_processor_table {}; +std::map TestLockableAllocator::lock_processor_table {}; template std::map TestLockableAllocator::unlock_processor_table {}; @@ -186,11 +195,11 @@ TEST(OneVPL_Source_Surface, InitSurface) // check self consistency EXPECT_EQ(reinterpret_cast(surf->get_handle()), reinterpret_cast(mfx_core_handle)); - EXPECT_EQ(0, surf->get_locks_count()); - EXPECT_EQ(0, surf->obtain_lock()); - EXPECT_EQ(1, surf->get_locks_count()); - EXPECT_EQ(1, surf->release_lock()); - EXPECT_EQ(0, surf->get_locks_count()); + EXPECT_TRUE(0 == surf->get_locks_count()); + EXPECT_TRUE(0 == surf->obtain_lock()); + EXPECT_TRUE(1 == surf->get_locks_count()); + EXPECT_TRUE(1 == surf->release_lock()); + EXPECT_TRUE(0 == surf->get_locks_count()); } TEST(OneVPL_Source_Surface, ConcurrentLock) @@ -205,7 +214,7 @@ TEST(OneVPL_Source_Surface, ConcurrentLock) auto surf = Surface::create_surface(std::move(handle), associated_memory); // check self consistency - EXPECT_EQ(0, surf->get_locks_count()); + EXPECT_TRUE(0 == surf->get_locks_count()); // MFX internal limitation: do not exceede U16 range // so I16 is using here @@ -230,7 +239,7 @@ TEST(OneVPL_Source_Surface, ConcurrentLock) } worker_thread.join(); - EXPECT_EQ(lock_counter * 2, surf->get_locks_count()); + EXPECT_TRUE(static_cast(lock_counter * 2) == surf->get_locks_count()); } TEST(OneVPL_Source_Surface, MemoryLifeTime) @@ -263,7 +272,7 @@ TEST(OneVPL_Source_Surface, MemoryLifeTime) } // workspace memory must be alive - EXPECT_EQ(0, surfaces.size()); + EXPECT_TRUE(0 == surfaces.size()); EXPECT_TRUE(associated_memory != nullptr); EXPECT_TRUE(preallocated_memory_ptr.get() != nullptr); @@ -285,7 +294,7 @@ TEST(OneVPL_Source_Surface, MemoryLifeTime) associated_memory.reset(); // workspace memory must be still alive - EXPECT_EQ(0, surfaces.size()); + EXPECT_TRUE(0 == surfaces.size()); EXPECT_TRUE(associated_memory == nullptr); EXPECT_TRUE(preallocated_memory_ptr.get() != nullptr); @@ -308,13 +317,14 @@ TEST(OneVPL_Source_CPU_FrameAdapter, InitFrameAdapter) auto surf = Surface::create_surface(std::move(handle), associated_memory); // check consistency - EXPECT_EQ(0, surf->get_locks_count()); + EXPECT_TRUE(0 == surf->get_locks_count()); { - VPLMediaFrameCPUAdapter adapter(surf); - EXPECT_EQ(1, surf->get_locks_count()); + mfxSession stub_session = reinterpret_cast(0x1); + VPLMediaFrameCPUAdapter adapter(surf, stub_session); + EXPECT_TRUE(1 == surf->get_locks_count()); } - EXPECT_EQ(0, surf->get_locks_count()); + EXPECT_TRUE(0 == surf->get_locks_count()); } TEST(OneVPL_Source_CPU_Accelerator, InitDestroy) @@ -376,13 +386,13 @@ TEST(OneVPL_Source_CPU_Accelerator, PoolProduceConsume) for (size_t i = 0; i < surface_count; i++) { std::shared_ptr surf = acceleration_policy->get_free_surface(key).lock(); EXPECT_TRUE(surf.get() != nullptr); - EXPECT_EQ(0, surf->obtain_lock()); + EXPECT_TRUE(0 == surf->obtain_lock()); surfaces.push_back(std::move(surf)); } // check consistency (no free surfaces) EXPECT_EQ(acceleration_policy->get_surface_count(key), surface_count); - EXPECT_EQ(0, acceleration_policy->get_free_surface_count(key)); + EXPECT_TRUE(0 == acceleration_policy->get_free_surface_count(key)); // fail consume non-free surfaces for (size_t i = 0; i < surface_count; i++) { @@ -391,7 +401,7 @@ TEST(OneVPL_Source_CPU_Accelerator, PoolProduceConsume) // release surfaces for (auto& surf : surfaces) { - EXPECT_EQ(1, surf->release_lock()); + EXPECT_TRUE(1 == surf->release_lock()); } surfaces.clear(); @@ -403,7 +413,7 @@ TEST(OneVPL_Source_CPU_Accelerator, PoolProduceConsume) for (size_t i = 0; i < surface_count; i++) { std::shared_ptr surf = acceleration_policy->get_free_surface(key).lock(); EXPECT_TRUE(surf.get() != nullptr); - EXPECT_EQ(0, surf->obtain_lock()); + EXPECT_TRUE(0 == surf->obtain_lock()); } } @@ -435,7 +445,7 @@ TEST(OneVPL_Source_CPU_Accelerator, PoolProduceConcurrentConsume) for (size_t i = 0; i < surface_count; i++) { std::shared_ptr surf = acceleration_policy->get_free_surface(key).lock(); EXPECT_TRUE(surf.get() != nullptr); - EXPECT_EQ(0, surf->obtain_lock()); + EXPECT_TRUE(0 == surf->obtain_lock()); surfaces.push_back(std::move(surf)); } @@ -449,7 +459,7 @@ TEST(OneVPL_Source_CPU_Accelerator, PoolProduceConcurrentConsume) // concurrent release surfaces size_t surfaces_count = surfaces.size(); for (auto& surf : surfaces) { - EXPECT_EQ(1, surf->release_lock()); + EXPECT_TRUE(1 == surf->release_lock()); std::this_thread::sleep_for(std::chrono::seconds(1)); } surfaces.clear(); @@ -483,7 +493,7 @@ TEST(OneVPL_Source_ProcessingEngine, Init) mfxSession mfx_session{}; engine.initialize_session(mfx_session, {}, std::shared_ptr{}); - EXPECT_EQ(0, engine.get_ready_frames_count()); + EXPECT_TRUE(0 == engine.get_ready_frames_count()); ProcessingEngineBase::ExecutionStatus ret = engine.process(mfx_session); EXPECT_EQ(ret, ProcessingEngineBase::ExecutionStatus::Continue); EXPECT_EQ(0, engine.pipeline_stage_num); @@ -499,12 +509,12 @@ TEST(OneVPL_Source_ProcessingEngine, Init) ret = engine.process(mfx_session); EXPECT_EQ(ret, ProcessingEngineBase::ExecutionStatus::Processed); EXPECT_EQ(3, engine.pipeline_stage_num); - EXPECT_EQ(1, engine.get_ready_frames_count()); + EXPECT_TRUE(1 == engine.get_ready_frames_count()); ret = engine.process(mfx_session); EXPECT_EQ(ret, ProcessingEngineBase::ExecutionStatus::SessionNotFound); EXPECT_EQ(3, engine.pipeline_stage_num); - EXPECT_EQ(1, engine.get_ready_frames_count()); + EXPECT_TRUE(1 == engine.get_ready_frames_count()); cv::gapi::wip::Data frame; engine.get_frame(frame); @@ -520,9 +530,9 @@ TEST(OneVPL_Source_DX11_Accel, Init) cfg_params_w_dx11.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); VPLDX11AccelerationPolicy accel(std::make_shared(cfg_params_w_dx11)); - mfxLoader mfx_handle = MFXLoad(); + mfxLoader test_mfx_handle = MFXLoad(); - mfxConfig cfg_inst_0 = MFXCreateConfig(mfx_handle); + mfxConfig cfg_inst_0 = MFXCreateConfig(test_mfx_handle); EXPECT_TRUE(cfg_inst_0); mfxVariant mfx_param_0; mfx_param_0.Type = MFX_VARIANT_TYPE_U32; @@ -530,7 +540,7 @@ TEST(OneVPL_Source_DX11_Accel, Init) EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_0,(mfxU8 *)CfgParam::implementation_name(), mfx_param_0), MFX_ERR_NONE); - mfxConfig cfg_inst_1 = MFXCreateConfig(mfx_handle); + mfxConfig cfg_inst_1 = MFXCreateConfig(test_mfx_handle); EXPECT_TRUE(cfg_inst_1); mfxVariant mfx_param_1; mfx_param_1.Type = MFX_VARIANT_TYPE_U32; @@ -538,7 +548,7 @@ TEST(OneVPL_Source_DX11_Accel, Init) EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_1,(mfxU8 *)CfgParam::acceleration_mode_name(), mfx_param_1), MFX_ERR_NONE); - mfxConfig cfg_inst_2 = MFXCreateConfig(mfx_handle); + mfxConfig cfg_inst_2 = MFXCreateConfig(test_mfx_handle); EXPECT_TRUE(cfg_inst_2); mfxVariant mfx_param_2; mfx_param_2.Type = MFX_VARIANT_TYPE_U32; @@ -548,7 +558,7 @@ TEST(OneVPL_Source_DX11_Accel, Init) // create session mfxSession mfx_session{}; - mfxStatus sts = MFXCreateSession(mfx_handle, 0, &mfx_session); + mfxStatus sts = MFXCreateSession(test_mfx_handle, 0, &mfx_session); EXPECT_EQ(MFX_ERR_NONE, sts); // assign acceleration @@ -581,7 +591,7 @@ TEST(OneVPL_Source_DX11_Accel, Init) // Allocate surfaces for decoder VPLAccelerationPolicy::pool_key_t key = accel.create_surface_pool(request, - mfxDecParams); + mfxDecParams.mfx.FrameInfo); auto cand_surface = accel.get_free_surface(key).lock(); sts = MFXVideoDECODE_Init(mfx_session, &mfxDecParams); @@ -592,11 +602,521 @@ TEST(OneVPL_Source_DX11_Accel, Init) EXPECT_NO_THROW(accel.deinit(mfx_session)); MFXClose(mfx_session); - MFXUnload(mfx_handle); + MFXUnload(test_mfx_handle); } #endif // HAVE_DIRECTX #endif // HAVE_D3D11 +#ifdef __linux__ +#if defined(HAVE_VA) || defined(HAVE_VA_INTEL) +TEST(OneVPL_Source_VAAPI_Accel, Init) +{ + using namespace cv::gapi::wip::onevpl; + + std::vector cfg_params_w_vaapi; + cfg_params_w_vaapi.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_VAAPI)); + VPLVAAPIAccelerationPolicy accel(std::make_shared(cfg_params_w_vaapi)); + + mfxLoader test_mfx_handle = MFXLoad(); + + mfxConfig cfg_inst_0 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_0); + mfxVariant mfx_param_0; + mfx_param_0.Type = MFX_VARIANT_TYPE_U32; + mfx_param_0.Data.U32 = MFX_IMPL_TYPE_HARDWARE; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_0,(mfxU8 *)CfgParam::implementation_name(), + mfx_param_0), MFX_ERR_NONE); + + mfxConfig cfg_inst_1 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_1); + mfxVariant mfx_param_1; + mfx_param_1.Type = MFX_VARIANT_TYPE_U32; + mfx_param_1.Data.U32 = MFX_ACCEL_MODE_VIA_VAAPI; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_1,(mfxU8 *)CfgParam::acceleration_mode_name(), + mfx_param_1), MFX_ERR_NONE); + + mfxConfig cfg_inst_2 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_2); + mfxVariant mfx_param_2; + mfx_param_2.Type = MFX_VARIANT_TYPE_U32; + mfx_param_2.Data.U32 = MFX_CODEC_HEVC; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_2,(mfxU8 *)CfgParam::decoder_id_name(), + mfx_param_2), MFX_ERR_NONE); + + // create session + mfxSession mfx_session{}; + mfxStatus sts = MFXCreateSession(test_mfx_handle, 0, &mfx_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // assign acceleration + EXPECT_NO_THROW(accel.init(mfx_session)); + + // create proper bitstream + mfxBitstream bitstream{}; + const int BITSTREAM_BUFFER_SIZE = 2000000; + bitstream.MaxLength = BITSTREAM_BUFFER_SIZE; + bitstream.Data = (mfxU8 *)calloc(bitstream.MaxLength, sizeof(mfxU8)); + EXPECT_TRUE(bitstream.Data); + + // simulate read stream + bitstream.DataOffset = 0; + bitstream.DataLength = sizeof(streaming::onevpl::hevc_header) * sizeof(streaming::onevpl::hevc_header[0]); + memcpy(bitstream.Data, streaming::onevpl::hevc_header, bitstream.DataLength); + bitstream.CodecId = MFX_CODEC_HEVC; + + // prepare dec params + mfxVideoParam mfxDecParams {}; + mfxDecParams.mfx.CodecId = bitstream.CodecId; + mfxDecParams.IOPattern = MFX_IOPATTERN_OUT_SYSTEM_MEMORY; + sts = MFXVideoDECODE_DecodeHeader(mfx_session, &bitstream, &mfxDecParams); + EXPECT_EQ(MFX_ERR_NONE, sts); + + mfxFrameAllocRequest request{}; + memset(&request, 0, sizeof(request)); + sts = MFXVideoDECODE_QueryIOSurf(mfx_session, &mfxDecParams, &request); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // Allocate surfaces for decoder + VPLAccelerationPolicy::pool_key_t key = accel.create_surface_pool(request, + mfxDecParams.mfx.FrameInfo); + auto cand_surface = accel.get_free_surface(key).lock(); + + sts = MFXVideoDECODE_Init(mfx_session, &mfxDecParams); + EXPECT_EQ(MFX_ERR_NONE, sts); + + MFXVideoDECODE_Close(mfx_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + EXPECT_NO_THROW(accel.deinit(mfx_session)); + MFXClose(mfx_session); + MFXUnload(test_mfx_handle); +} +#endif // defined(HAVE_VA) || defined(HAVE_VA_INTEL) +#endif // __linux__ + +#ifdef HAVE_DIRECTX +#ifdef HAVE_D3D11 +TEST(OneVPL_Source_DX11_Accel_VPL, Init) +{ + using namespace cv::gapi::wip::onevpl; + + std::vector cfg_params_w_dx11; + cfg_params_w_dx11.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); + std::unique_ptr acceleration_policy (new VPLDX11AccelerationPolicy(std::make_shared(cfg_params_w_dx11))); + + mfxLoader test_mfx_handle = MFXLoad(); + + mfxConfig cfg_inst_0 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_0); + mfxVariant mfx_param_0; + mfx_param_0.Type = MFX_VARIANT_TYPE_U32; + mfx_param_0.Data.U32 = MFX_IMPL_TYPE_HARDWARE; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_0,(mfxU8 *)CfgParam::implementation_name(), + mfx_param_0), MFX_ERR_NONE); + + mfxConfig cfg_inst_1 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_1); + mfxVariant mfx_param_1; + mfx_param_1.Type = MFX_VARIANT_TYPE_U32; + mfx_param_1.Data.U32 = MFX_ACCEL_MODE_VIA_D3D11; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_1,(mfxU8 *)CfgParam::acceleration_mode_name(), + mfx_param_1), MFX_ERR_NONE); + + mfxConfig cfg_inst_2 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_2); + mfxVariant mfx_param_2; + mfx_param_2.Type = MFX_VARIANT_TYPE_U32; + mfx_param_2.Data.U32 = MFX_CODEC_HEVC; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_2,(mfxU8 *)CfgParam::decoder_id_name(), + mfx_param_2), MFX_ERR_NONE); + + mfxConfig cfg_inst_3 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_3); + mfxVariant mfx_param_3; + mfx_param_3.Type = MFX_VARIANT_TYPE_U32; + mfx_param_3.Data.U32 = MFX_EXTBUFF_VPP_SCALING; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_3, + (mfxU8 *)"mfxImplDescription.mfxVPPDescription.filter.FilterFourCC", + mfx_param_3), MFX_ERR_NONE); + // create session + mfxSession mfx_session{}; + mfxStatus sts = MFXCreateSession(test_mfx_handle, 0, &mfx_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // assign acceleration + EXPECT_NO_THROW(acceleration_policy->init(mfx_session)); + + // create proper bitstream + std::string file_path = findDataFile("highgui/video/big_buck_bunny.h265"); + std::shared_ptr data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(MFX_CODEC_HEVC)})); + IDataProvider::mfx_codec_id_type decoder_id_name = data_provider->get_mfx_codec_id(); + + // Prepare video param + mfxVideoParam mfxDecParams {}; + mfxDecParams.mfx.CodecId = decoder_id_name; + mfxDecParams.IOPattern = MFX_IOPATTERN_OUT_VIDEO_MEMORY; + + // try fetch & decode input data + sts = MFX_ERR_NONE; + std::shared_ptr bitstream{}; + do { + EXPECT_TRUE(data_provider->fetch_bitstream_data(bitstream)); + sts = MFXVideoDECODE_DecodeHeader(mfx_session, bitstream.get(), &mfxDecParams); + EXPECT_TRUE(MFX_ERR_NONE == sts || MFX_ERR_MORE_DATA == sts); + } while (sts == MFX_ERR_MORE_DATA && !data_provider->empty()); + + EXPECT_EQ(MFX_ERR_NONE, sts); + + mfxFrameAllocRequest request{}; + memset(&request, 0, sizeof(request)); + sts = MFXVideoDECODE_QueryIOSurf(mfx_session, &mfxDecParams, &request); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // Allocate surfaces for decoder + request.Type |= MFX_MEMTYPE_EXTERNAL_FRAME | MFX_MEMTYPE_FROM_DECODE | MFX_MEMTYPE_FROM_VPPIN; + VPLAccelerationPolicy::pool_key_t decode_pool_key = acceleration_policy->create_surface_pool(request, + mfxDecParams.mfx.FrameInfo); + sts = MFXVideoDECODE_Init(mfx_session, &mfxDecParams); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // initialize VPLL + mfxU16 vppOutImgWidth = 672; + mfxU16 vppOutImgHeight = 382; + + mfxVideoParam mfxVPPParams{0}; + mfxVPPParams.vpp.In = mfxDecParams.mfx.FrameInfo; + + mfxVPPParams.vpp.Out.FourCC = MFX_FOURCC_NV12; + mfxVPPParams.vpp.Out.ChromaFormat = MFX_CHROMAFORMAT_YUV420; + mfxVPPParams.vpp.Out.Width = ALIGN16(vppOutImgWidth); + mfxVPPParams.vpp.Out.Height = ALIGN16(vppOutImgHeight); + mfxVPPParams.vpp.Out.CropX = 0; + mfxVPPParams.vpp.Out.CropY = 0; + mfxVPPParams.vpp.Out.CropW = vppOutImgWidth; + mfxVPPParams.vpp.Out.CropH = vppOutImgHeight; + mfxVPPParams.vpp.Out.PicStruct = MFX_PICSTRUCT_PROGRESSIVE; + mfxVPPParams.vpp.Out.FrameRateExtN = 30; + mfxVPPParams.vpp.Out.FrameRateExtD = 1; + + mfxVPPParams.IOPattern = MFX_IOPATTERN_IN_VIDEO_MEMORY | MFX_IOPATTERN_OUT_VIDEO_MEMORY; + + mfxFrameAllocRequest vppRequests[2]; + memset(&vppRequests, 0, sizeof(mfxFrameAllocRequest) * 2); + EXPECT_EQ(MFXVideoVPP_QueryIOSurf(mfx_session, &mfxVPPParams, vppRequests), MFX_ERR_NONE); + + vppRequests[1].AllocId = 666; + VPLAccelerationPolicy::pool_key_t vpp_out_pool_key = + acceleration_policy->create_surface_pool(vppRequests[1], mfxVPPParams.vpp.Out); + EXPECT_EQ(MFXVideoVPP_Init(mfx_session, &mfxVPPParams), MFX_ERR_NONE); + + // finalize session creation + DecoderParams d_param{bitstream, mfxDecParams}; + TranscoderParams t_param{mfxVPPParams}; + VPLLegacyTranscodeEngine engine(std::move(acceleration_policy)); + std::shared_ptr sess_ptr = + engine.register_session( + mfx_session, + std::move(d_param), + std::move(t_param), + data_provider); + + sess_ptr->init_surface_pool(decode_pool_key); + sess_ptr->init_transcode_surface_pool(vpp_out_pool_key); + + // prepare working surfaces + sess_ptr->swap_decode_surface(engine); + sess_ptr->swap_transcode_surface(engine); + + // launch pipeline + LegacyTranscodeSession & my_sess = *sess_ptr; + { + if (!my_sess.data_provider) { + my_sess.last_status = MFX_ERR_MORE_DATA; + } else { + my_sess.last_status = MFX_ERR_NONE; + if (!my_sess.data_provider->fetch_bitstream_data(my_sess.stream)) { + my_sess.last_status = MFX_ERR_MORE_DATA; + my_sess.data_provider.reset(); //close source + } + } + + // 2) enqueue ASYNC decode operation + // prepare sync object for new surface + LegacyTranscodeSession::op_handle_t sync_pair{}; + + // enqueue decode operation with current session surface + { + my_sess.last_status = + MFXVideoDECODE_DecodeFrameAsync(my_sess.session, + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), + &sync_pair.second, + &sync_pair.first); + + // process wait-like statuses in-place: + // It had better to use up all VPL decoding resources in pipeline + // as soon as possible. So waiting more free-surface or device free + while (my_sess.last_status == MFX_ERR_MORE_SURFACE || + my_sess.last_status == MFX_WRN_DEVICE_BUSY) { + try { + if (my_sess.last_status == MFX_ERR_MORE_SURFACE) { + my_sess.swap_decode_surface(engine); + } + my_sess.last_status = + MFXVideoDECODE_DecodeFrameAsync(my_sess.session, + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), + &sync_pair.second, + &sync_pair.first); + + } catch (const std::runtime_error&) { + // NB: not an error, yield CPU ticks to check + // surface availability at a next phase. + break; + } + } + } + // 4) transcode + { + auto *dec_surface = sync_pair.second; + if(my_sess.vpp_surface_ptr.lock()) + { + mfxFrameSurface1* out_surf = my_sess.vpp_surface_ptr.lock()->get_handle(); + my_sess.last_status = MFXVideoVPP_RunFrameVPPAsync(my_sess.session, dec_surface, + out_surf, + nullptr, &sync_pair.first); + sync_pair.second = out_surf; + + my_sess.last_status = MFXVideoCORE_SyncOperation(my_sess.session, sync_pair.first, 11000); + } + try { + my_sess.swap_transcode_surface(engine); + } catch (... ) { + my_sess.vpp_surface_ptr.reset(); + } + } + } +} + +TEST(OneVPL_Source_DX11_Accel_VPL, preproc) +{ + using namespace cv::gapi::wip::onevpl; + + std::vector cfg_params_w_dx11; + cfg_params_w_dx11.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); + std::unique_ptr acceleration_policy (new VPLDX11AccelerationPolicy(std::make_shared(cfg_params_w_dx11))); + + mfxLoader test_mfx_handle = MFXLoad(); + + mfxConfig cfg_inst_0 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_0); + mfxVariant mfx_param_0; + mfx_param_0.Type = MFX_VARIANT_TYPE_U32; + mfx_param_0.Data.U32 = MFX_IMPL_TYPE_HARDWARE; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_0,(mfxU8 *)CfgParam::implementation_name(), + mfx_param_0), MFX_ERR_NONE); + + mfxConfig cfg_inst_1 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_1); + mfxVariant mfx_param_1; + mfx_param_1.Type = MFX_VARIANT_TYPE_U32; + mfx_param_1.Data.U32 = MFX_ACCEL_MODE_VIA_D3D11; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_1,(mfxU8 *)CfgParam::acceleration_mode_name(), + mfx_param_1), MFX_ERR_NONE); + + mfxConfig cfg_inst_2 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_2); + mfxVariant mfx_param_2; + mfx_param_2.Type = MFX_VARIANT_TYPE_U32; + mfx_param_2.Data.U32 = MFX_CODEC_HEVC; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_2,(mfxU8 *)CfgParam::decoder_id_name(), + mfx_param_2), MFX_ERR_NONE); + + mfxConfig cfg_inst_3 = MFXCreateConfig(test_mfx_handle); + EXPECT_TRUE(cfg_inst_3); + mfxVariant mfx_param_3; + mfx_param_3.Type = MFX_VARIANT_TYPE_U32; + mfx_param_3.Data.U32 = MFX_EXTBUFF_VPP_SCALING; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_3, + (mfxU8 *)"mfxImplDescription.mfxVPPDescription.filter.FilterFourCC", + mfx_param_3), MFX_ERR_NONE); + // create session + mfxSession mfx_decode_session{}; + mfxStatus sts = MFXCreateSession(test_mfx_handle, 0, &mfx_decode_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // assign acceleration + EXPECT_NO_THROW(acceleration_policy->init(mfx_decode_session)); + + // create proper bitstream + std::string file_path = findDataFile("highgui/video/big_buck_bunny.h265"); + std::shared_ptr data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(MFX_CODEC_HEVC)})); + IDataProvider::mfx_codec_id_type decoder_id_name = data_provider->get_mfx_codec_id(); + + // Prepare video param + mfxVideoParam mfxDecParams {}; + mfxDecParams.mfx.CodecId = decoder_id_name; + mfxDecParams.IOPattern = MFX_IOPATTERN_OUT_VIDEO_MEMORY; + + // try fetch & decode input data + sts = MFX_ERR_NONE; + std::shared_ptr bitstream{}; + do { + EXPECT_TRUE(data_provider->fetch_bitstream_data(bitstream)); + sts = MFXVideoDECODE_DecodeHeader(mfx_decode_session, bitstream.get(), &mfxDecParams); + EXPECT_TRUE(MFX_ERR_NONE == sts || MFX_ERR_MORE_DATA == sts); + } while (sts == MFX_ERR_MORE_DATA && !data_provider->empty()); + + EXPECT_EQ(MFX_ERR_NONE, sts); + + mfxFrameAllocRequest request{}; + memset(&request, 0, sizeof(request)); + sts = MFXVideoDECODE_QueryIOSurf(mfx_decode_session, &mfxDecParams, &request); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // Allocate surfaces for decoder + request.Type |= MFX_MEMTYPE_EXTERNAL_FRAME | MFX_MEMTYPE_FROM_DECODE | MFX_MEMTYPE_FROM_VPPIN; + VPLAccelerationPolicy::pool_key_t decode_pool_key = acceleration_policy->create_surface_pool(request, + mfxDecParams.mfx.FrameInfo); + sts = MFXVideoDECODE_Init(mfx_decode_session, &mfxDecParams); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // initialize VPL session + mfxSession mfx_vpl_session{}; + sts = MFXCreateSession(test_mfx_handle, 0, &mfx_vpl_session); + // assign acceleration + EXPECT_NO_THROW(acceleration_policy->init(mfx_vpl_session)); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // request VPL surface + mfxU16 vppOutImgWidth = 672; + mfxU16 vppOutImgHeight = 382; + + mfxVideoParam mfxVPPParams{0}; + mfxVPPParams.vpp.In = mfxDecParams.mfx.FrameInfo; + + mfxVPPParams.vpp.Out.FourCC = MFX_FOURCC_NV12; + mfxVPPParams.vpp.Out.ChromaFormat = MFX_CHROMAFORMAT_YUV420; + mfxVPPParams.vpp.Out.Width = ALIGN16(vppOutImgWidth); + mfxVPPParams.vpp.Out.Height = ALIGN16(vppOutImgHeight); + mfxVPPParams.vpp.Out.CropX = 0; + mfxVPPParams.vpp.Out.CropY = 0; + mfxVPPParams.vpp.Out.CropW = vppOutImgWidth; + mfxVPPParams.vpp.Out.CropH = vppOutImgHeight; + mfxVPPParams.vpp.Out.PicStruct = MFX_PICSTRUCT_PROGRESSIVE; + mfxVPPParams.vpp.Out.FrameRateExtN = 30; + mfxVPPParams.vpp.Out.FrameRateExtD = 1; + + mfxVPPParams.IOPattern = MFX_IOPATTERN_IN_VIDEO_MEMORY | MFX_IOPATTERN_OUT_VIDEO_MEMORY; + + mfxFrameAllocRequest vppRequests[2]; + memset(&vppRequests, 0, sizeof(mfxFrameAllocRequest) * 2); + EXPECT_EQ(MFXVideoVPP_QueryIOSurf(mfx_vpl_session, &mfxVPPParams, vppRequests), MFX_ERR_NONE); + + vppRequests[1].AllocId = 666; + VPLAccelerationPolicy::pool_key_t vpp_out_pool_key = + acceleration_policy->create_surface_pool(vppRequests[1], mfxVPPParams.vpp.Out); + EXPECT_EQ(MFXVideoVPP_Init(mfx_vpl_session, &mfxVPPParams), MFX_ERR_NONE); + + // finalize session creation + DecoderParams d_param{bitstream, mfxDecParams}; + TranscoderParams t_param{mfxVPPParams}; + VPLLegacyDecodeEngine engine(std::move(acceleration_policy)); + std::shared_ptr sess_ptr = + engine.register_session( + mfx_decode_session, + std::move(d_param), + data_provider); + + sess_ptr->init_surface_pool(decode_pool_key); + + // prepare working surfaces + sess_ptr->swap_decode_surface(engine); + + // launch pipeline + LegacyDecodeSession &my_sess = *sess_ptr; + + size_t min_available_frames_count = + std::min(engine.get_accel()->get_surface_count(decode_pool_key), + engine.get_accel()->get_surface_count(vpp_out_pool_key)); + size_t frame_num = 0; + do { + if (!my_sess.data_provider) { + my_sess.last_status = MFX_ERR_MORE_DATA; + } else { + my_sess.last_status = MFX_ERR_NONE; + if (!my_sess.data_provider->fetch_bitstream_data(my_sess.stream)) { + my_sess.last_status = MFX_ERR_MORE_DATA; + my_sess.data_provider.reset(); //close source + } + } + + // 2) enqueue ASYNC decode operation + // prepare sync object for new surface + LegacyTranscodeSession::op_handle_t sync_pair{}; + + // enqueue decode operation with current session surface + { + my_sess.last_status = + MFXVideoDECODE_DecodeFrameAsync(my_sess.session, + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), + &sync_pair.second, + &sync_pair.first); + + // process wait-like statuses in-place: + // It had better to use up all VPL decoding resources in pipeline + // as soon as possible. So waiting more free-surface or device free + while (my_sess.last_status == MFX_ERR_MORE_SURFACE || + my_sess.last_status == MFX_WRN_DEVICE_BUSY) { + try { + if (my_sess.last_status == MFX_ERR_MORE_SURFACE) { + my_sess.swap_decode_surface(engine); + } + my_sess.last_status = + MFXVideoDECODE_DecodeFrameAsync(my_sess.session, + my_sess.get_mfx_bitstream_ptr(), + my_sess.processing_surface_ptr.lock()->get_handle(), + &sync_pair.second, + &sync_pair.first); + + } catch (const std::runtime_error&) { + // NB: not an error, yield CPU ticks to check + // surface availability at a next phase. + EXPECT_TRUE(false); + } + } + } + { + do { + my_sess.last_status = MFXVideoCORE_SyncOperation(my_sess.session, sync_pair.first, 0); + // put frames in ready queue on success + if (MFX_ERR_NONE == my_sess.last_status) { + break; + } + } while (MFX_WRN_IN_EXECUTION == my_sess.last_status); + EXPECT_EQ(my_sess.last_status, MFX_ERR_NONE); + } + + // perform VPP operation on decoder synchronized surface + + auto vpp_out = engine.get_accel()->get_free_surface(vpp_out_pool_key).lock(); + EXPECT_TRUE(vpp_out.get()); + my_sess.last_status = MFXVideoVPP_RunFrameVPPAsync(mfx_vpl_session, + sync_pair.second, + vpp_out->get_handle(), + nullptr, &sync_pair.first); + if (my_sess.last_status == MFX_ERR_MORE_SURFACE || + my_sess.last_status == MFX_ERR_NONE) { + my_sess.last_status = MFXVideoCORE_SyncOperation(mfx_vpl_session, sync_pair.first, INFINITE); + EXPECT_EQ(my_sess.last_status, MFX_ERR_NONE); + frame_num++; + } + } while(frame_num < min_available_frames_count); +} + TEST(OneVPL_Source_DX11_FrameLockable, LockUnlock_without_Adaptee) { using namespace cv::gapi::wip::onevpl; @@ -684,6 +1204,8 @@ TEST(OneVPL_Source_DX11_FrameLockable, LockUnlock_with_Adaptee) EXPECT_EQ(w_lock_counter, exec_count); EXPECT_EQ(w_unlock_counter, exec_count); } +#endif // HAVE_DIRECTX +#endif // HAVE_D3D11 } } // namespace opencv_test #endif // HAVE_ONEVPL diff --git a/modules/gapi/test/streaming/gapi_streaming_vpl_data_provider.cpp b/modules/gapi/test/streaming/gapi_streaming_vpl_data_provider.cpp index c8c27fa6a425..a76d6537f9f6 100644 --- a/modules/gapi/test/streaming/gapi_streaming_vpl_data_provider.cpp +++ b/modules/gapi/test/streaming/gapi_streaming_vpl_data_provider.cpp @@ -16,6 +16,7 @@ #include "streaming/onevpl/demux/async_mfp_demux_data_provider.hpp" #include "streaming/onevpl/source_priv.hpp" +#ifdef _WIN32 namespace opencv_test { namespace @@ -73,9 +74,9 @@ TEST_P(OneVPL_Source_MFPAsyncDispatcherTest, open_and_decode_file) EXPECT_TRUE(dd_result); // initialize MFX - mfxLoader mfx_handle = MFXLoad(); + mfxLoader mfx = MFXLoad(); - mfxConfig cfg_inst_0 = MFXCreateConfig(mfx_handle); + mfxConfig cfg_inst_0 = MFXCreateConfig(mfx); EXPECT_TRUE(cfg_inst_0); mfxVariant mfx_param_0; mfx_param_0.Type = MFX_VARIANT_TYPE_U32; @@ -85,7 +86,7 @@ TEST_P(OneVPL_Source_MFPAsyncDispatcherTest, open_and_decode_file) // create MFX session mfxSession mfx_session{}; - mfxStatus sts = MFXCreateSession(mfx_handle, 0, &mfx_session); + mfxStatus sts = MFXCreateSession(mfx, 0, &mfx_session); EXPECT_EQ(MFX_ERR_NONE, sts); // create proper bitstream @@ -112,7 +113,7 @@ TEST_P(OneVPL_Source_MFPAsyncDispatcherTest, open_and_decode_file) MFXVideoDECODE_Close(mfx_session); MFXClose(mfx_session); - MFXUnload(mfx_handle); + MFXUnload(mfx); } @@ -299,4 +300,5 @@ TEST(OneVPL_Source_MFPAsyncDemux, produce_consume) { } } // namespace opencv_test +#endif // _WIN32 #endif // HAVE_ONEVPL diff --git a/modules/gapi/test/streaming/gapi_streaming_vpl_device_selector.cpp b/modules/gapi/test/streaming/gapi_streaming_vpl_device_selector.cpp index d484dcec7589..ee1be9f433da 100644 --- a/modules/gapi/test/streaming/gapi_streaming_vpl_device_selector.cpp +++ b/modules/gapi/test/streaming/gapi_streaming_vpl_device_selector.cpp @@ -69,11 +69,11 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, DefaultDevice) using namespace cv::gapi::wip::onevpl; CfgParamDeviceSelector selector; IDeviceSelector::DeviceScoreTable devs = selector.select_devices(); - EXPECT_EQ(devs.size(), 1); + EXPECT_TRUE(devs.size() == 1); test_host_dev_eq(*devs.begin(), IDeviceSelector::Score::MaxActivePriority); IDeviceSelector::DeviceContexts ctxs = selector.select_context(); - EXPECT_EQ(ctxs.size(), 1); + EXPECT_TRUE(ctxs.size() == 1); test_host_ctx_eq(*ctxs.begin()); } @@ -83,10 +83,10 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, DefaultDeviceWithEmptyCfgParam) std::vector empty_params; CfgParamDeviceSelector selector(empty_params); IDeviceSelector::DeviceScoreTable devs = selector.select_devices(); - EXPECT_EQ(devs.size(), 1); + EXPECT_TRUE(devs.size() == 1); test_host_dev_eq(*devs.begin(), IDeviceSelector::Score::MaxActivePriority); IDeviceSelector::DeviceContexts ctxs = selector.select_context(); - EXPECT_EQ(ctxs.size(), 1); + EXPECT_TRUE(ctxs.size() == 1); test_host_ctx_eq(*ctxs.begin()); } @@ -97,11 +97,11 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, DefaultDeviceWithAccelNACfgParam) cfg_params_w_no_accel.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_NA)); CfgParamDeviceSelector selector(cfg_params_w_no_accel); IDeviceSelector::DeviceScoreTable devs = selector.select_devices(); - EXPECT_EQ(devs.size(), 1); + EXPECT_TRUE(devs.size() == 1); test_host_dev_eq(*devs.begin(), IDeviceSelector::Score::MaxActivePriority); IDeviceSelector::DeviceContexts ctxs = selector.select_context(); - EXPECT_EQ(ctxs.size(), 1); + EXPECT_TRUE(ctxs.size() == 1); test_host_ctx_eq(*ctxs.begin()); } @@ -113,11 +113,11 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, DefaultDeviceWithEmptyCfgParam_DX11 std::vector empty_params; CfgParamDeviceSelector selector(empty_params); IDeviceSelector::DeviceScoreTable devs = selector.select_devices(); - EXPECT_EQ(devs.size(), 1); + EXPECT_TRUE(devs.size() == 1); test_host_dev_eq(*devs.begin(), IDeviceSelector::Score::MaxActivePriority); IDeviceSelector::DeviceContexts ctxs = selector.select_context(); - EXPECT_EQ(ctxs.size(), 1); + EXPECT_TRUE(ctxs.size() == 1); test_host_ctx_eq(*ctxs.begin()); } @@ -130,13 +130,13 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, DefaultDeviceWithDX11AccelCfgParam_ EXPECT_NO_THROW(selector_ptr.reset(new CfgParamDeviceSelector(cfg_params_w_dx11))); IDeviceSelector::DeviceScoreTable devs = selector_ptr->select_devices(); - EXPECT_EQ(devs.size(), 1); + EXPECT_TRUE(devs.size() == 1); test_dev_eq(*devs.begin(), IDeviceSelector::Score::MaxActivePriority, AccelType::DX11, std::get<1>(*devs.begin()).get_ptr() /* compare just type */); IDeviceSelector::DeviceContexts ctxs = selector_ptr->select_context(); - EXPECT_EQ(ctxs.size(), 1); + EXPECT_TRUE(ctxs.size() == 1); EXPECT_TRUE(ctxs.begin()->get_ptr()); } @@ -182,12 +182,12 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, ExternalDeviceWithDX11AccelCfgParam cfg_params_w_dx11))); IDeviceSelector::DeviceScoreTable devs = selector_ptr->select_devices(); - EXPECT_EQ(devs.size(), 1); + EXPECT_TRUE(devs.size() == 1); test_dev_eq(*devs.begin(), IDeviceSelector::Score::MaxActivePriority, AccelType::DX11, device); IDeviceSelector::DeviceContexts ctxs = selector_ptr->select_context(); - EXPECT_EQ(ctxs.size(), 1); + EXPECT_TRUE(ctxs.size() == 1); EXPECT_EQ(reinterpret_cast(ctxs.begin()->get_ptr()), device_context); } @@ -201,7 +201,7 @@ TEST(OneVPL_Source_Device_Selector_CfgParam, DX11DeviceFromCfgParamWithDX11Disab { using namespace cv::gapi::wip::onevpl; std::vector cfg_params_w_non_existed_dx11; - cfg_params_w_not_existed_dx11.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); + cfg_params_w_non_existed_dx11.push_back(CfgParam::create_acceleration_mode(MFX_ACCEL_MODE_VIA_D3D11)); EXPECT_THROW(CfgParamDeviceSelector{cfg_params_w_non_existed_dx11}, std::logic_error); } diff --git a/modules/gapi/test/streaming/gapi_streaming_vpp_preproc_test.cpp b/modules/gapi/test/streaming/gapi_streaming_vpp_preproc_test.cpp new file mode 100644 index 000000000000..9da9221c8f42 --- /dev/null +++ b/modules/gapi/test/streaming/gapi_streaming_vpp_preproc_test.cpp @@ -0,0 +1,736 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. +// +// Copyright (C) 2022 Intel Corporation + + +#include "../test_precomp.hpp" + +#include "../common/gapi_tests_common.hpp" +#include "../common/gapi_streaming_tests_common.hpp" + +#include +#include +#include + +#include +#include +#include + +#include +#include +#include + +#include +#include + +#include +#include +#include + +#ifdef HAVE_ONEVPL + +#include +#include "streaming/onevpl/file_data_provider.hpp" +#include "streaming/onevpl/cfg_param_device_selector.hpp" + +#include "streaming/onevpl/accelerators/surface/surface.hpp" +#include "streaming/onevpl/accelerators/surface/cpu_frame_adapter.hpp" +#include "streaming/onevpl/accelerators/surface/dx11_frame_adapter.hpp" +#include "streaming/onevpl/accelerators/accel_policy_cpu.hpp" +#include "streaming/onevpl/accelerators/accel_policy_dx11.hpp" +#include "streaming/onevpl/accelerators/accel_policy_va_api.hpp" +#include "streaming/onevpl/accelerators/dx11_alloc_resource.hpp" +#include "streaming/onevpl/accelerators/utils/shared_lock.hpp" +#define private public +#define protected public +#include "streaming/onevpl/engine/decode/decode_engine_legacy.hpp" +#include "streaming/onevpl/engine/decode/decode_session.hpp" + +#include "streaming/onevpl/engine/preproc/preproc_engine.hpp" +#include "streaming/onevpl/engine/preproc/preproc_session.hpp" +#include "streaming/onevpl/engine/preproc/preproc_dispatcher.hpp" + +#include "streaming/onevpl/engine/transcode/transcode_engine_legacy.hpp" +#include "streaming/onevpl/engine/transcode/transcode_session.hpp" +#undef protected +#undef private +#include "logger.hpp" + +#define ALIGN16(value) (((value + 15) >> 4) << 4) + +namespace opencv_test +{ +namespace +{ +template +cv::MediaFrame extract_decoded_frame(mfxSession sessId, ProcessingEngine& engine) { + using namespace cv::gapi::wip::onevpl; + ProcessingEngineBase::ExecutionStatus status = ProcessingEngineBase::ExecutionStatus::Continue; + while (0 == engine.get_ready_frames_count() && + status == ProcessingEngineBase::ExecutionStatus::Continue) { + status = engine.process(sessId); + } + + if (engine.get_ready_frames_count() == 0) { + GAPI_LOG_WARNING(nullptr, "failed: cannot obtain preprocessed frames, last status: " << + ProcessingEngineBase::status_to_string(status)); + throw std::runtime_error("cannot finalize VPP preprocessing operation"); + } + cv::gapi::wip::Data data; + engine.get_frame(data); + return cv::util::get(data); +} + +std::tuple prepare_mfx(int mfx_codec, int mfx_accel_mode) { + using namespace cv::gapi::wip::onevpl; + mfxLoader mfx = MFXLoad(); + mfxConfig cfg_inst_0 = MFXCreateConfig(mfx); + EXPECT_TRUE(cfg_inst_0); + mfxVariant mfx_param_0; + mfx_param_0.Type = MFX_VARIANT_TYPE_U32; + mfx_param_0.Data.U32 = MFX_IMPL_TYPE_HARDWARE; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_0,(mfxU8 *)CfgParam::implementation_name(), + mfx_param_0), MFX_ERR_NONE); + + mfxConfig cfg_inst_1 = MFXCreateConfig(mfx); + EXPECT_TRUE(cfg_inst_1); + mfxVariant mfx_param_1; + mfx_param_1.Type = MFX_VARIANT_TYPE_U32; + mfx_param_1.Data.U32 = mfx_accel_mode; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_1,(mfxU8 *)CfgParam::acceleration_mode_name(), + mfx_param_1), MFX_ERR_NONE); + + mfxConfig cfg_inst_2 = MFXCreateConfig(mfx); + EXPECT_TRUE(cfg_inst_2); + mfxVariant mfx_param_2; + mfx_param_2.Type = MFX_VARIANT_TYPE_U32; + mfx_param_2.Data.U32 = mfx_codec; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_2,(mfxU8 *)CfgParam::decoder_id_name(), + mfx_param_2), MFX_ERR_NONE); + + mfxConfig cfg_inst_3 = MFXCreateConfig(mfx); + EXPECT_TRUE(cfg_inst_3); + mfxVariant mfx_param_3; + mfx_param_3.Type = MFX_VARIANT_TYPE_U32; + mfx_param_3.Data.U32 = MFX_EXTBUFF_VPP_SCALING; + EXPECT_EQ(MFXSetConfigFilterProperty(cfg_inst_3, + (mfxU8 *)"mfxImplDescription.mfxVPPDescription.filter.FilterFourCC", + mfx_param_3), MFX_ERR_NONE); + return std::make_tuple(mfx, cfg_inst_3); +} + +static std::unique_ptr +create_accel_policy_from_int(int accel, + std::shared_ptr selector) { + using namespace cv::gapi::wip::onevpl; + std::unique_ptr decode_accel_policy; + if (accel == MFX_ACCEL_MODE_VIA_D3D11) { + decode_accel_policy.reset (new VPLDX11AccelerationPolicy(selector)); + } else if (accel == MFX_ACCEL_MODE_VIA_VAAPI) { + decode_accel_policy.reset (new VPLVAAPIAccelerationPolicy(selector)); + } + EXPECT_TRUE(decode_accel_policy.get()); + return decode_accel_policy; +} + +static std::unique_ptr +create_accel_policy_from_int(int &accel, + std::vector &out_cfg_params) { + using namespace cv::gapi::wip::onevpl; + out_cfg_params.push_back(CfgParam::create_acceleration_mode(accel)); + return create_accel_policy_from_int(accel, std::make_shared(out_cfg_params)); +} + +class SafeQueue { +public: + void push(cv::MediaFrame&& f) { + std::unique_lock lock(mutex); + queue.push(std::move(f)); + cv.notify_all(); + } + + cv::MediaFrame pop() { + cv::MediaFrame ret; + std::unique_lock lock(mutex); + cv.wait(lock, [this] () { + return !queue.empty(); + }); + ret = queue.front(); + queue.pop(); + return ret; + } + + void push_stop() { + push(cv::MediaFrame::Create()); + } + + static bool is_stop(const cv::MediaFrame &f) { + try { + return f.get(); + } catch(...) {} + return false; + } + +private: + struct IStopAdapter final : public cv::MediaFrame::IAdapter { + ~IStopAdapter() {} + cv::GFrameDesc meta() const { return {}; }; + MediaFrame::View access(MediaFrame::Access) { return {{}, {}}; }; + }; +private: + std::condition_variable cv; + std::mutex mutex; + std::queue queue; +}; + +struct EmptyDataProvider : public cv::gapi::wip::onevpl::IDataProvider { + + bool empty() const override { + return true; + } + mfx_codec_id_type get_mfx_codec_id() const override { + return std::numeric_limits::max(); + } + bool fetch_bitstream_data(std::shared_ptr &) override { + return false; + } +}; +} + +using source_t = std::string; +using decoder_t = int; +using acceleration_t = int; +using out_frame_info_t = cv::GFrameDesc; +using preproc_args_t = std::tuple; + +static cv::util::optional empty_roi; + +class VPPPreprocParams : public ::testing::TestWithParam {}; + +#if defined(HAVE_DIRECTX) && defined(HAVE_D3D11) + #define UT_ACCEL_TYPE MFX_ACCEL_MODE_VIA_D3D11 +#elif __linux__ + #define UT_ACCEL_TYPE MFX_ACCEL_MODE_VIA_VAAPI +#else + #define UT_ACCEL_TYPE -1 +#endif + +preproc_args_t files[] = { + preproc_args_t {"highgui/video/big_buck_bunny.h264", + MFX_CODEC_AVC, UT_ACCEL_TYPE, + cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1080}}}, + preproc_args_t {"highgui/video/big_buck_bunny.h265", + MFX_CODEC_HEVC, UT_ACCEL_TYPE, + cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1280}}} +}; + +class OneVPL_PreproEngineTest : public ::testing::TestWithParam {}; +TEST_P(OneVPL_PreproEngineTest, functional_single_thread) +{ + using namespace cv::gapi::wip::onevpl; + using namespace cv::gapi::wip; + + int accel_type = GetParam(); + std::vector cfg_params_w_accel; + std::unique_ptr decode_accel_policy = create_accel_policy_from_int(accel_type, cfg_params_w_accel); + + // create file data provider + std::string file_path = findDataFile("highgui/video/big_buck_bunny.h265"); + std::shared_ptr data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(MFX_CODEC_HEVC)})); + + mfxLoader mfx{}; + mfxConfig mfx_cfg{}; + std::tie(mfx, mfx_cfg) = prepare_mfx(MFX_CODEC_HEVC, accel_type); + + // create decode session + mfxSession mfx_decode_session{}; + mfxStatus sts = MFXCreateSession(mfx, 0, &mfx_decode_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // create decode engine + auto device_selector = decode_accel_policy->get_device_selector(); + VPLLegacyDecodeEngine decode_engine(std::move(decode_accel_policy)); + auto sess_ptr = decode_engine.initialize_session(mfx_decode_session, + cfg_params_w_accel, + data_provider); + + // simulate net info + cv::GFrameDesc required_frame_param {cv::MediaFormat::NV12, + {1920, 1080}}; + + // create VPP preproc engine + VPPPreprocEngine preproc_engine(create_accel_policy_from_int(accel_type, device_selector)); + + // launch pipeline + // 1) decode frame + cv::MediaFrame first_decoded_frame; + ASSERT_NO_THROW(first_decoded_frame = extract_decoded_frame(sess_ptr->session, decode_engine)); + cv::GFrameDesc first_frame_decoded_desc = first_decoded_frame.desc(); + + // 1.5) create preproc session based on frame description & network info + cv::util::optional first_pp_params = preproc_engine.is_applicable(first_decoded_frame); + ASSERT_TRUE(first_pp_params.has_value()); + pp_session first_pp_sess = preproc_engine.initialize_preproc(first_pp_params.value(), + required_frame_param); + + // 2) make preproc using incoming decoded frame & preproc session + cv::MediaFrame first_pp_frame = preproc_engine.run_sync(first_pp_sess, + first_decoded_frame, + empty_roi); + cv::GFrameDesc first_outcome_pp_desc = first_pp_frame.desc(); + ASSERT_FALSE(first_frame_decoded_desc == first_outcome_pp_desc); + + // do not hold media frames because they share limited DX11 surface pool resources + first_decoded_frame = cv::MediaFrame(); + first_pp_frame = cv::MediaFrame(); + + // make test in loop + bool in_progress = false; + int frames_processed_count = 1; + const auto &first_pp_param_value_impl = + cv::util::get(first_pp_params.value().value); + try { + while(true) { + cv::MediaFrame decoded_frame = extract_decoded_frame(sess_ptr->session, decode_engine); + in_progress = true; + ASSERT_EQ(decoded_frame.desc(), first_frame_decoded_desc); + + cv::util::optional params = preproc_engine.is_applicable(decoded_frame); + ASSERT_TRUE(params.has_value()); + const auto &cur_pp_param_value_impl = + cv::util::get(params.value().value); + + ASSERT_EQ(first_pp_param_value_impl.handle, cur_pp_param_value_impl.handle); + ASSERT_TRUE(FrameInfoComparator::equal_to(first_pp_param_value_impl.info, cur_pp_param_value_impl.info)); + + pp_session pp_sess = preproc_engine.initialize_preproc(params.value(), + required_frame_param); + ASSERT_EQ(pp_sess.get().handle.get(), + first_pp_sess.get().handle.get()); + + cv::MediaFrame pp_frame = preproc_engine.run_sync(pp_sess, + decoded_frame, + empty_roi); + cv::GFrameDesc pp_desc = pp_frame.desc(); + ASSERT_TRUE(pp_desc == first_outcome_pp_desc); + in_progress = false; + frames_processed_count++; + } + } catch (...) {} + + // test if interruption has happened + ASSERT_FALSE(in_progress); + ASSERT_NE(frames_processed_count, 1); +} + +INSTANTIATE_TEST_CASE_P(OneVPL_Source_PreprocEngine, OneVPL_PreproEngineTest, + testing::Values(UT_ACCEL_TYPE)); + +static void decode_function(cv::gapi::wip::onevpl::VPLLegacyDecodeEngine &decode_engine, + cv::gapi::wip::onevpl::ProcessingEngineBase::session_ptr sess_ptr, + SafeQueue &queue, int &decoded_number) { + // decode first frame + { + cv::MediaFrame decoded_frame; + ASSERT_NO_THROW(decoded_frame = extract_decoded_frame(sess_ptr->session, decode_engine)); + queue.push(std::move(decoded_frame)); + } + + // launch pipeline + try { + while(true) { + queue.push(extract_decoded_frame(sess_ptr->session, decode_engine)); + decoded_number++; + } + } catch (...) {} + + // send stop + queue.push_stop(); +} + +static void preproc_function(cv::gapi::wip::IPreprocEngine &preproc_engine, SafeQueue&queue, + int &preproc_number, const out_frame_info_t &required_frame_param, + const cv::util::optional &roi_rect = {}) { + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + // create preproc session based on frame description & network info + cv::MediaFrame first_decoded_frame = queue.pop(); + cv::util::optional first_pp_params = preproc_engine.is_applicable(first_decoded_frame); + ASSERT_TRUE(first_pp_params.has_value()); + pp_session first_pp_sess = + preproc_engine.initialize_preproc(first_pp_params.value(), + required_frame_param); + + // make preproc using incoming decoded frame & preproc session + cv::MediaFrame first_pp_frame = preproc_engine.run_sync(first_pp_sess, + first_decoded_frame, + roi_rect); + cv::GFrameDesc first_outcome_pp_desc = first_pp_frame.desc(); + + // do not hold media frames because they share limited DX11 surface pool resources + first_decoded_frame = cv::MediaFrame(); + first_pp_frame = cv::MediaFrame(); + + // launch pipeline + bool in_progress = false; + // let's allow counting of preprocessed frames to check this value later: + // Currently, it looks redundant to implement any kind of graceful shutdown logic + // in this test - so let's apply agreement that media source is processed + // successfully when preproc_number != 1 in result. + // Specific validation logic which adhere to explicit counter value may be implemented + // in particular test scope + preproc_number = 1; + try { + while(true) { + cv::MediaFrame decoded_frame = queue.pop(); + if (SafeQueue::is_stop(decoded_frame)) { + break; + } + in_progress = true; + + cv::util::optional params = preproc_engine.is_applicable(decoded_frame); + ASSERT_TRUE(params.has_value()); + const auto &vpp_params = params.value().get(); + const auto &first_vpp_params = first_pp_params.value().get(); + ASSERT_EQ(vpp_params.handle, first_vpp_params.handle); + ASSERT_TRUE(0 == memcmp(&vpp_params.info, &first_vpp_params.info, sizeof(mfxFrameInfo))); + + pp_session pp_sess = preproc_engine.initialize_preproc(params.value(), + required_frame_param); + ASSERT_EQ(pp_sess.get().handle.get(), + first_pp_sess.get().handle.get()); + + cv::MediaFrame pp_frame = preproc_engine.run_sync(pp_sess, decoded_frame, empty_roi); + cv::GFrameDesc pp_desc = pp_frame.desc(); + ASSERT_TRUE(pp_desc == first_outcome_pp_desc); + in_progress = false; + preproc_number++; + } + } catch (...) {} + + // test if interruption has happened + ASSERT_FALSE(in_progress); + ASSERT_NE(preproc_number, 1); +} + +#ifdef __WIN32__ +static void multi_source_preproc_function(size_t source_num, + cv::gapi::wip::IPreprocEngine &preproc_engine, SafeQueue&queue, + int &preproc_number, const out_frame_info_t &required_frame_param, + const cv::util::optional &roi_rect = {}) { + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + // create preproc session based on frame description & network info + cv::MediaFrame first_decoded_frame = queue.pop(); + cv::util::optional first_pp_params = preproc_engine.is_applicable(first_decoded_frame); + ASSERT_TRUE(first_pp_params.has_value()); + pp_session first_pp_sess = + preproc_engine.initialize_preproc(first_pp_params.value(), + required_frame_param); + + // make preproc using incoming decoded frame & preproc session + cv::MediaFrame first_pp_frame = preproc_engine.run_sync(first_pp_sess, + first_decoded_frame, + roi_rect); + cv::GFrameDesc first_outcome_pp_desc = first_pp_frame.desc(); + + // do not hold media frames because they share limited DX11 surface pool resources + first_decoded_frame = cv::MediaFrame(); + first_pp_frame = cv::MediaFrame(); + + // launch pipeline + bool in_progress = false; + preproc_number = 1; + size_t received_stop_count = 0; + try { + while(received_stop_count != source_num) { + cv::MediaFrame decoded_frame = queue.pop(); + if (SafeQueue::is_stop(decoded_frame)) { + ++received_stop_count; + continue; + } + in_progress = true; + + cv::util::optional params = preproc_engine.is_applicable(decoded_frame); + ASSERT_TRUE(params.has_value()); + + pp_session pp_sess = preproc_engine.initialize_preproc(params.value(), + required_frame_param); + cv::MediaFrame pp_frame = preproc_engine.run_sync(pp_sess, decoded_frame, empty_roi); + cv::GFrameDesc pp_desc = pp_frame.desc(); + ASSERT_TRUE(pp_desc == first_outcome_pp_desc); + in_progress = false; + decoded_frame = cv::MediaFrame(); + preproc_number++; + } + } catch (const std::exception& ex) { + GAPI_LOG_WARNING(nullptr, "Caught exception in preproc worker: " << ex.what()); + } + + // test if interruption has happened + if (in_progress) { + while (true) { + cv::MediaFrame decoded_frame = queue.pop(); + if (SafeQueue::is_stop(decoded_frame)) { + break; + } + } + } + ASSERT_FALSE(in_progress); + ASSERT_NE(preproc_number, 1); +} +#endif // __WIN32__ + +using roi_t = cv::util::optional; +using preproc_roi_args_t = decltype(std::tuple_cat(std::declval(), + std::declval>())); +class VPPPreprocROIParams : public ::testing::TestWithParam {}; +TEST_P(VPPPreprocROIParams, functional_roi_different_threads) +{ + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + source_t file_path; + decoder_t decoder_id; + acceleration_t accel; + out_frame_info_t required_frame_param; + roi_t opt_roi; + std::tie(file_path, decoder_id, accel, required_frame_param, opt_roi) = GetParam(); + + file_path = findDataFile(file_path); + + std::vector cfg_params_w_accel; + std::unique_ptr decode_accel_policy = create_accel_policy_from_int(accel, cfg_params_w_accel); + + // create file data provider + std::shared_ptr data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(decoder_id)})); + + mfxLoader mfx{}; + mfxConfig mfx_cfg{}; + std::tie(mfx, mfx_cfg) = prepare_mfx(decoder_id, accel); + + // create decode session + mfxSession mfx_decode_session{}; + mfxStatus sts = MFXCreateSession(mfx, 0, &mfx_decode_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // create decode engine + auto device_selector = decode_accel_policy->get_device_selector(); + VPLLegacyDecodeEngine decode_engine(std::move(decode_accel_policy)); + auto sess_ptr = decode_engine.initialize_session(mfx_decode_session, + cfg_params_w_accel, + data_provider); + + // create VPP preproc engine + VPPPreprocEngine preproc_engine(create_accel_policy_from_int(accel, device_selector)); + + // launch threads + SafeQueue queue; + int decoded_number = 1; + int preproc_number = 0; + + std::thread decode_thread(decode_function, std::ref(decode_engine), sess_ptr, + std::ref(queue), std::ref(decoded_number)); + std::thread preproc_thread(preproc_function, std::ref(preproc_engine), + std::ref(queue), std::ref(preproc_number), + std::cref(required_frame_param), + std::cref(opt_roi)); + + decode_thread.join(); + preproc_thread.join(); + ASSERT_EQ(preproc_number, decoded_number); +} + +preproc_roi_args_t files_w_roi[] = { + preproc_roi_args_t {"highgui/video/big_buck_bunny.h264", + MFX_CODEC_AVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1080}}}, + roi_t{cv::Rect{0,0,50,50}}}, + preproc_roi_args_t {"highgui/video/big_buck_bunny.h264", + MFX_CODEC_AVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1080}}}, + roi_t{}}, + preproc_roi_args_t {"highgui/video/big_buck_bunny.h264", + MFX_CODEC_AVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1080}}}, + roi_t{cv::Rect{0,0,100,100}}}, + preproc_roi_args_t {"highgui/video/big_buck_bunny.h264", + MFX_CODEC_AVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1080}}}, + roi_t{cv::Rect{100,100,200,200}}}, + preproc_roi_args_t {"highgui/video/big_buck_bunny.h265", + MFX_CODEC_HEVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1280}}}, + roi_t{cv::Rect{0,0,100,100}}}, + preproc_roi_args_t {"highgui/video/big_buck_bunny.h265", + MFX_CODEC_HEVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1280}}}, + roi_t{}}, + preproc_roi_args_t {"highgui/video/big_buck_bunny.h265", + MFX_CODEC_HEVC, UT_ACCEL_TYPE, + out_frame_info_t{cv::GFrameDesc {cv::MediaFormat::NV12, {1920, 1280}}}, + roi_t{cv::Rect{100,100,200,200}}} +}; + +INSTANTIATE_TEST_CASE_P(OneVPL_Source_PreprocEngineROI, VPPPreprocROIParams, + testing::ValuesIn(files_w_roi)); + + +using VPPInnerPreprocParams = VPPPreprocParams; +TEST_P(VPPInnerPreprocParams, functional_inner_preproc_size) +{ + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + source_t file_path; + decoder_t decoder_id; + acceleration_t accel; + out_frame_info_t required_frame_param; + std::tie(file_path, decoder_id, accel, required_frame_param) = GetParam(); + + file_path = findDataFile(file_path); + + std::vector cfg_params_w_accel_vpp; + + // create accel policy + std::unique_ptr accel_policy = create_accel_policy_from_int(accel, cfg_params_w_accel_vpp); + + // create file data provider + std::shared_ptr data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(decoder_id)})); + + // create decode session + mfxLoader mfx{}; + mfxConfig mfx_cfg{}; + std::tie(mfx, mfx_cfg) = prepare_mfx(decoder_id, accel); + + mfxSession mfx_decode_session{}; + mfxStatus sts = MFXCreateSession(mfx, 0, &mfx_decode_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // fill vpp params beforehand: resolution + cfg_params_w_accel_vpp.push_back(CfgParam::create_vpp_out_width( + static_cast(required_frame_param.size.width))); + cfg_params_w_accel_vpp.push_back(CfgParam::create_vpp_out_height( + static_cast(required_frame_param.size.height))); + + // create transcode engine + auto device_selector = accel_policy->get_device_selector(); + VPLLegacyTranscodeEngine engine(std::move(accel_policy)); + auto sess_ptr = engine.initialize_session(mfx_decode_session, + cfg_params_w_accel_vpp, + data_provider); + // make test in loop + bool in_progress = false; + int frames_processed_count = 1; + try { + while(true) { + cv::MediaFrame decoded_frame = extract_decoded_frame(sess_ptr->session, engine); + in_progress = true; + ASSERT_EQ(decoded_frame.desc().size.width, + ALIGN16(required_frame_param.size.width)); + ASSERT_EQ(decoded_frame.desc().size.height, + ALIGN16(required_frame_param.size.height)); + ASSERT_EQ(decoded_frame.desc().fmt, required_frame_param.fmt); + frames_processed_count++; + in_progress = false; + } + } catch (...) {} + + // test if interruption has happened + ASSERT_FALSE(in_progress); + ASSERT_NE(frames_processed_count, 1); +} + +INSTANTIATE_TEST_CASE_P(OneVPL_Source_PreprocInner, VPPInnerPreprocParams, + testing::ValuesIn(files)); + +// enable only for WIN32 because there are not CPU processing on Linux by default +#ifdef __WIN32__ +class VPPPreprocDispatcherROIParams : public ::testing::TestWithParam {}; +TEST_P(VPPPreprocDispatcherROIParams, functional_roi_different_threads) +{ + using namespace cv::gapi::wip; + using namespace cv::gapi::wip::onevpl; + source_t file_path; + decoder_t decoder_id; + acceleration_t accel = 0; + out_frame_info_t required_frame_param; + roi_t opt_roi; + std::tie(file_path, decoder_id, accel, required_frame_param, opt_roi) = GetParam(); + + file_path = findDataFile(file_path); + + std::vector cfg_params_w_accel; + std::unique_ptr decode_accel_policy = create_accel_policy_from_int(accel, cfg_params_w_accel); + + // create file data provider + std::shared_ptr data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(decoder_id)})); + std::shared_ptr cpu_data_provider(new FileDataProvider(file_path, + {CfgParam::create_decoder_id(decoder_id)})); + + mfxLoader mfx{}; + mfxConfig mfx_cfg{}; + std::tie(mfx, mfx_cfg) = prepare_mfx(decoder_id, accel); + + // create decode session + mfxSession mfx_decode_session{}; + mfxStatus sts = MFXCreateSession(mfx, 0, &mfx_decode_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + mfxSession mfx_cpu_decode_session{}; + sts = MFXCreateSession(mfx, 0, &mfx_cpu_decode_session); + EXPECT_EQ(MFX_ERR_NONE, sts); + + // create decode engines + auto device_selector = decode_accel_policy->get_device_selector(); + VPLLegacyDecodeEngine decode_engine(std::move(decode_accel_policy)); + auto sess_ptr = decode_engine.initialize_session(mfx_decode_session, + cfg_params_w_accel, + data_provider); + std::vector cfg_params_cpu; + auto cpu_device_selector = std::make_shared(cfg_params_cpu); + VPLLegacyDecodeEngine cpu_decode_engine(std::unique_ptr{ + new VPLCPUAccelerationPolicy(cpu_device_selector)}); + auto cpu_sess_ptr = cpu_decode_engine.initialize_session(mfx_cpu_decode_session, + cfg_params_cpu, + cpu_data_provider); + + // create VPP preproc engines + VPPPreprocDispatcher preproc_dispatcher; + preproc_dispatcher.insert_worker(create_accel_policy_from_int(accel, device_selector)); + preproc_dispatcher.insert_worker(std::unique_ptr{ + new VPLCPUAccelerationPolicy(cpu_device_selector)}); + + // launch threads + SafeQueue queue; + int decoded_number = 1; + int cpu_decoded_number = 1; + int preproc_number = 0; + + std::thread decode_thread(decode_function, std::ref(decode_engine), sess_ptr, + std::ref(queue), std::ref(decoded_number)); + std::thread cpu_decode_thread(decode_function, std::ref(cpu_decode_engine), cpu_sess_ptr, + std::ref(queue), std::ref(cpu_decoded_number)); + std::thread preproc_thread(multi_source_preproc_function, + preproc_dispatcher.size(), + std::ref(preproc_dispatcher), + std::ref(queue), std::ref(preproc_number), + std::cref(required_frame_param), + std::cref(opt_roi)); + + decode_thread.join(); + cpu_decode_thread.join(); + preproc_thread.join(); + ASSERT_EQ(preproc_number, decoded_number + cpu_decoded_number); +} + +INSTANTIATE_TEST_CASE_P(OneVPL_Source_PreprocDispatcherROI, VPPPreprocDispatcherROIParams, + testing::ValuesIn(files_w_roi)); + +#endif // __WIN32__ +} // namespace opencv_test +#endif // HAVE_ONEVPL diff --git a/modules/highgui/CMakeLists.txt b/modules/highgui/CMakeLists.txt index 0d63d42fea56..65d24e0ab051 100644 --- a/modules/highgui/CMakeLists.txt +++ b/modules/highgui/CMakeLists.txt @@ -84,6 +84,9 @@ if(HAVE_QT) list(APPEND qt_deps OpenGLWidgets) endif() list(APPEND qt_deps OpenGL) + if(OPENGL_LIBRARIES) + list(APPEND HIGHGUI_LIBRARIES "${OPENGL_LIBRARIES}") + endif() endif() foreach(dt_dep ${qt_deps}) @@ -93,8 +96,11 @@ if(HAVE_QT) endforeach() else() ocv_assert(QT_VERSION_MAJOR EQUAL 4) - if (HAVE_QT_OPENGL) + if(HAVE_QT_OPENGL) set(QT_USE_QTOPENGL TRUE) + if(OPENGL_LIBRARIES) + list(APPEND HIGHGUI_LIBRARIES "${OPENGL_LIBRARIES}") + endif() endif() include(${QT_USE_FILE}) @@ -157,6 +163,9 @@ if(TARGET ocv.3rdparty.win32ui) set(OPENCV_HIGHGUI_BUILTIN_BACKEND "WIN32UI") list(APPEND highgui_srcs ${CMAKE_CURRENT_LIST_DIR}/src/window_w32.cpp) list(APPEND tgts ocv.3rdparty.win32ui) + if(HAVE_OPENGL AND OPENGL_LIBRARIES) + list(APPEND tgts "${OPENGL_LIBRARIES}") + endif() endif() endif() @@ -271,10 +280,6 @@ if(APPLE) add_apple_compiler_options(${the_module}) endif() -if(OPENCV_HIGHGUI_BUILTIN_BACKEND STREQUAL "WIN32UI" AND HAVE_OPENGL AND OPENGL_LIBRARIES) - ocv_target_link_libraries(${the_module} PRIVATE "${OPENGL_LIBRARIES}") -endif() - if(MSVC AND NOT BUILD_SHARED_LIBS AND BUILD_WITH_STATIC_CRT) set_target_properties(${the_module} PROPERTIES LINK_FLAGS "/NODEFAULTLIB:atlthunk.lib /NODEFAULTLIB:atlsd.lib /NODEFAULTLIB:libcmt.lib /DEBUG") endif() diff --git a/modules/highgui/src/window.cpp b/modules/highgui/src/window.cpp index 481fee9fbda8..81d205a69a42 100644 --- a/modules/highgui/src/window.cpp +++ b/modules/highgui/src/window.cpp @@ -963,6 +963,8 @@ void cv::imshow( const String& winname, InputArray _img ) { CV_TRACE_FUNCTION(); + const Size size = _img.size(); + CV_Assert(size.width>0 && size.height>0); { cv::AutoLock lock(cv::getWindowMutex()); cleanupClosedWindows_(); @@ -995,9 +997,7 @@ void cv::imshow( const String& winname, InputArray _img ) } } - const Size size = _img.size(); #ifndef HAVE_OPENGL - CV_Assert(size.width>0 && size.height>0); { Mat img = _img.getMat(); CvMat c_img = cvMat(img); @@ -1005,7 +1005,6 @@ void cv::imshow( const String& winname, InputArray _img ) } #else const double useGl = getWindowProperty(winname, WND_PROP_OPENGL); - CV_Assert(size.width>0 && size.height>0); if (useGl <= 0) { diff --git a/modules/highgui/src/window_QT.cpp b/modules/highgui/src/window_QT.cpp index f6ba44b425fc..eba3599a0db5 100644 --- a/modules/highgui/src/window_QT.cpp +++ b/modules/highgui/src/window_QT.cpp @@ -529,6 +529,9 @@ static int icvInitSystem(int* c, char** v) //"For any GUI application using Qt, there is precisely one QApplication object" if (!QApplication::instance()) { +#if QT_VERSION >= QT_VERSION_CHECK(5, 6, 0) + QCoreApplication::setAttribute(Qt::AA_EnableHighDpiScaling, true); +#endif new QApplication(*c, v); setlocale(LC_NUMERIC,"C"); @@ -2195,23 +2198,58 @@ void CvWindow::displayPropertiesWin() global_control_panel->hide(); } +static bool isTranslatableKey(Qt::Key key) +{ + // https://github.com/opencv/opencv/issues/21899 + // https://doc.qt.io/qt-5/qt.html#Key-enum + // https://doc.qt.io/qt-6/qt.html#Key-enum + // https://github.com/qt/qtbase/blob/dev/src/testlib/qasciikey.cpp + + bool ret = false; + + switch ( key ) + { + // Special keys + case Qt::Key_Escape: + case Qt::Key_Tab: + case Qt::Key_Backtab: + case Qt::Key_Backspace: + case Qt::Key_Enter: + case Qt::Key_Return: + ret = true; + break; + + // latin-1 keys. + default: + ret = ( + ( ( Qt::Key_Space <= key ) && ( key <= Qt::Key_AsciiTilde ) ) // 0x20--0x7e + || + ( ( Qt::Key_nobreakspace <= key ) && ( key <= Qt::Key_ssharp ) ) // 0x0a0--0x0de + || + ( key == Qt::Key_division ) // 0x0f7 + || + ( key == Qt::Key_ydiaeresis ) // 0x0ff + ); + break; + } + + return ret; +} //Need more test here ! void CvWindow::keyPressEvent(QKeyEvent *evnt) { - //see http://doc.trolltech.com/4.6/qt.html#Key-enum int key = evnt->key(); + const Qt::Key qtkey = static_cast(key); - Qt::Key qtkey = static_cast(key); - char asciiCode = QTest::keyToAscii(qtkey); - if (asciiCode != 0) - key = static_cast(asciiCode); - else - key = evnt->nativeVirtualKey(); //same codes as returned by GTK-based backend + if ( isTranslatableKey( qtkey ) ) + key = static_cast( QTest::keyToAscii( qtkey ) ); + else + key = evnt->nativeVirtualKey(); //same codes as returned by GTK-based backend //control plus (Z, +, -, up, down, left, right) are used for zoom/panning functions - if (evnt->modifiers() != Qt::ControlModifier) - { + if (evnt->modifiers() != Qt::ControlModifier) + { mutexKey.lock(); last_key = key; mutexKey.unlock(); diff --git a/modules/highgui/src/window_cocoa.mm b/modules/highgui/src/window_cocoa.mm index 97a6831f1a15..e9cf5a3c1bfa 100644 --- a/modules/highgui/src/window_cocoa.mm +++ b/modules/highgui/src/window_cocoa.mm @@ -92,6 +92,7 @@ - (void)setImageData:(CvArr *)arr; @interface CVSlider : NSView { NSSlider *slider; NSTextField *name; + NSString *initialName; int *value; void *userData; CvTrackbarCallback callback; @@ -99,6 +100,7 @@ @interface CVSlider : NSView { } @property(retain) NSSlider *slider; @property(retain) NSTextField *name; +@property(retain) NSString *initialName; @property(assign) int *value; @property(assign) void *userData; @property(assign) CvTrackbarCallback callback; @@ -107,6 +109,7 @@ @interface CVSlider : NSView { @interface CVWindow : NSWindow { NSMutableDictionary *sliders; + NSMutableArray *slidersKeys; CvMouseCallback mouseCallback; void *mouseParam; BOOL autosize; @@ -121,6 +124,7 @@ @interface CVWindow : NSWindow { @property(assign) int x0; @property(assign) int y0; @property(retain) NSMutableDictionary *sliders; +@property(retain) NSMutableArray *slidersKeys; @property(readwrite) int status; - (CVView *)contentView; - (void)cvSendMouseEvent:(NSEvent *)event type:(int)type flags:(int)flags; @@ -842,6 +846,7 @@ @implementation CVWindow @synthesize x0; @synthesize y0; @synthesize sliders; +@synthesize slidersKeys; @synthesize status; - (void)cvSendMouseEvent:(NSEvent *)event type:(int)type flags:(int)flags { @@ -933,6 +938,9 @@ - (void)createSliderWithName:(const char *)name maxValue:(int)max value:(int *)v if(sliders == nil) sliders = [[NSMutableDictionary alloc] init]; + if(slidersKeys == nil) + slidersKeys = [[NSMutableArray alloc] init]; + NSString *cvname = [NSString stringWithFormat:@"%s", name]; // Avoid overwriting slider @@ -942,18 +950,23 @@ - (void)createSliderWithName:(const char *)name maxValue:(int)max value:(int *)v // Create slider CVSlider *slider = [[CVSlider alloc] init]; [[slider name] setStringValue:cvname]; + slider.initialName = [NSString stringWithFormat:@"%s", name]; [[slider slider] setMaxValue:max]; [[slider slider] setMinValue:0]; if(value) { [[slider slider] setIntValue:*value]; [slider setValue:value]; + NSString *temp = [slider initialName]; + NSString *text = [NSString stringWithFormat:@"%@ %d", temp, *value]; + [[slider name] setStringValue: text]; } if(callback) [slider setCallback:callback]; // Save slider [sliders setValue:slider forKey:cvname]; + [slidersKeys addObject:cvname]; [[self contentView] addSubview:slider]; @@ -1092,7 +1105,7 @@ - (void)setFrameSize:(NSSize)size { CVWindow *cvwindow = (CVWindow *)[self window]; if ([cvwindow respondsToSelector:@selector(sliders)]) { - for(NSString *key in [cvwindow sliders]) { + for(NSString *key in [cvwindow slidersKeys]) { CVSlider *slider = [[cvwindow sliders] valueForKey:key]; NSRect r = [slider frame]; r.origin.y = height - r.size.height; @@ -1144,6 +1157,7 @@ @implementation CVSlider @synthesize slider; @synthesize name; +@synthesize initialName; @synthesize value; @synthesize userData; @synthesize callback; @@ -1186,6 +1200,9 @@ - (id)init { - (void)sliderChanged:(NSNotification *)notification { (void)notification; int pos = [slider intValue]; + NSString *temp = [self initialName]; + NSString *text = [NSString stringWithFormat:@"%@ %d", temp, *value]; + [name setStringValue: text]; if(value) *value = pos; if(callback) diff --git a/modules/highgui/src/window_gtk.cpp b/modules/highgui/src/window_gtk.cpp index 260166f33bef..98698cbadefe 100644 --- a/modules/highgui/src/window_gtk.cpp +++ b/modules/highgui/src/window_gtk.cpp @@ -1973,7 +1973,7 @@ static gboolean icvOnMouse( GtkWidget *widget, GdkEvent *event, gpointer user_da #if defined(GTK_VERSION3_4) // NOTE: in current implementation doesn't possible to put into callback function delta_x and delta_y separately double delta = (event->scroll.delta_x + event->scroll.delta_y); - cv_event = (event->scroll.delta_y!=0) ? CV_EVENT_MOUSEHWHEEL : CV_EVENT_MOUSEWHEEL; + cv_event = (event->scroll.delta_x==0) ? CV_EVENT_MOUSEWHEEL : CV_EVENT_MOUSEHWHEEL; #else cv_event = CV_EVENT_MOUSEWHEEL; #endif //GTK_VERSION3_4 diff --git a/modules/imgcodecs/include/opencv2/imgcodecs.hpp b/modules/imgcodecs/include/opencv2/imgcodecs.hpp index d7ff9a178d32..c70ef6313dd6 100644 --- a/modules/imgcodecs/include/opencv2/imgcodecs.hpp +++ b/modules/imgcodecs/include/opencv2/imgcodecs.hpp @@ -88,8 +88,8 @@ enum ImwriteFlags { IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. - IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. - IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. + IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is -1 - don't use. + IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is -1 - don't use. IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting). IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE. IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. @@ -98,17 +98,17 @@ enum ImwriteFlags { IMWRITE_EXR_COMPRESSION = (3 << 4) + 1, /* 49 */ //!< override EXR compression type (ZIP_COMPRESSION = 3 is default) IMWRITE_WEBP_QUALITY = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. IMWRITE_PAM_TUPLETYPE = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format - IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values - IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI - IMWRITE_TIFF_YDPI = 258, //!< For TIFF, use to specify the Y direction DPI - IMWRITE_TIFF_COMPRESSION = 259, //!< For TIFF, use to specify the image compression scheme. See libtiff for integer constants corresponding to compression formats. Note, for images whose depth is CV_32F, only libtiff's SGILOG compression scheme is used. For other supported depths, the compression scheme can be specified by this flag; LZW compression is the default. + IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values + IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI + IMWRITE_TIFF_YDPI = 258,//!< For TIFF, use to specify the Y direction DPI + IMWRITE_TIFF_COMPRESSION = 259,//!< For TIFF, use to specify the image compression scheme. See libtiff for integer constants corresponding to compression formats. Note, for images whose depth is CV_32F, only libtiff's SGILOG compression scheme is used. For other supported depths, the compression scheme can be specified by this flag; LZW compression is the default. IMWRITE_JPEG2000_COMPRESSION_X1000 = 272 //!< For JPEG2000, use to specify the target compression rate (multiplied by 1000). The value can be from 0 to 1000. Default is 1000. }; enum ImwriteEXRTypeFlags { /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */ - IMWRITE_EXR_TYPE_HALF = 1, //!< store as HALF (FP16) - IMWRITE_EXR_TYPE_FLOAT = 2 //!< store as FP32 (default) + IMWRITE_EXR_TYPE_HALF = 1, //!< store as HALF (FP16) + IMWRITE_EXR_TYPE_FLOAT = 2 //!< store as FP32 (default) }; enum ImwriteEXRCompressionFlags { @@ -140,14 +140,14 @@ enum ImwritePNGFlags { IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. }; -//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file. +//! Imwrite PAM specific tupletype flags used to define the 'TUPLETYPE' field of a PAM file. enum ImwritePAMFlags { - IMWRITE_PAM_FORMAT_NULL = 0, - IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, - IMWRITE_PAM_FORMAT_GRAYSCALE = 2, + IMWRITE_PAM_FORMAT_NULL = 0, + IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, + IMWRITE_PAM_FORMAT_GRAYSCALE = 2, IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, - IMWRITE_PAM_FORMAT_RGB = 4, - IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, + IMWRITE_PAM_FORMAT_RGB = 4, + IMWRITE_PAM_FORMAT_RGB_ALPHA = 5 }; //! @} imgcodecs_flags @@ -209,8 +209,8 @@ CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. @param filename Name of file to be loaded. +@param mats A vector of Mat objects holding each page. @param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. -@param mats A vector of Mat objects holding each page, if more than one. @sa cv::imread */ CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& mats, int flags = IMREAD_ANYCOLOR); @@ -219,10 +219,10 @@ CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& m The function imreadmulti loads a specified range from a multi-page image from the specified file into a vector of Mat objects. @param filename Name of file to be loaded. +@param mats A vector of Mat objects holding each page. @param start Start index of the image to load @param count Count number of images to load @param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. -@param mats A vector of Mat objects holding each page, if more than one. @sa cv::imread */ CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& mats, int start, int count, int flags = IMREAD_ANYCOLOR); @@ -301,7 +301,7 @@ CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); The function imencode compresses the image and stores it in the memory buffer that is resized to fit the result. See cv::imwrite for the list of supported formats and flags description. -@param ext File extension that defines the output format. +@param ext File extension that defines the output format. Must include a leading period. @param img Image to be written. @param buf Output buffer resized to fit the compressed image. @param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. diff --git a/modules/imgcodecs/src/grfmt_exr.cpp b/modules/imgcodecs/src/grfmt_exr.cpp index 960f5da3d319..0585035202fd 100644 --- a/modules/imgcodecs/src/grfmt_exr.cpp +++ b/modules/imgcodecs/src/grfmt_exr.cpp @@ -637,7 +637,7 @@ bool ExrEncoder::write( const Mat& img, const std::vector& params ) for( size_t i = 0; i < params.size(); i += 2 ) { - if( params[i] == CV_IMWRITE_EXR_TYPE ) + if( params[i] == IMWRITE_EXR_TYPE ) { switch( params[i+1] ) { diff --git a/modules/imgcodecs/src/grfmt_jpeg.cpp b/modules/imgcodecs/src/grfmt_jpeg.cpp index 3dd9d68771d1..b200c35a316a 100644 --- a/modules/imgcodecs/src/grfmt_jpeg.cpp +++ b/modules/imgcodecs/src/grfmt_jpeg.cpp @@ -602,9 +602,9 @@ bool JpegEncoder::write( const Mat& img, const std::vector& params ) JpegErrorMgr jerr; JpegDestination dest; - jpeg_create_compress(&cinfo); cinfo.err = jpeg_std_error(&jerr.pub); jerr.pub.error_exit = error_exit; + jpeg_create_compress(&cinfo); if( !m_buf ) { @@ -643,23 +643,23 @@ bool JpegEncoder::write( const Mat& img, const std::vector& params ) for( size_t i = 0; i < params.size(); i += 2 ) { - if( params[i] == CV_IMWRITE_JPEG_QUALITY ) + if( params[i] == IMWRITE_JPEG_QUALITY ) { quality = params[i+1]; quality = MIN(MAX(quality, 0), 100); } - if( params[i] == CV_IMWRITE_JPEG_PROGRESSIVE ) + if( params[i] == IMWRITE_JPEG_PROGRESSIVE ) { progressive = params[i+1]; } - if( params[i] == CV_IMWRITE_JPEG_OPTIMIZE ) + if( params[i] == IMWRITE_JPEG_OPTIMIZE ) { optimize = params[i+1]; } - if( params[i] == CV_IMWRITE_JPEG_LUMA_QUALITY ) + if( params[i] == IMWRITE_JPEG_LUMA_QUALITY ) { if (params[i+1] >= 0) { @@ -674,7 +674,7 @@ bool JpegEncoder::write( const Mat& img, const std::vector& params ) } } - if( params[i] == CV_IMWRITE_JPEG_CHROMA_QUALITY ) + if( params[i] == IMWRITE_JPEG_CHROMA_QUALITY ) { if (params[i+1] >= 0) { @@ -682,7 +682,7 @@ bool JpegEncoder::write( const Mat& img, const std::vector& params ) } } - if( params[i] == CV_IMWRITE_JPEG_RST_INTERVAL ) + if( params[i] == IMWRITE_JPEG_RST_INTERVAL ) { rst_interval = params[i+1]; rst_interval = MIN(MAX(rst_interval, 0), 65535L); diff --git a/modules/imgcodecs/src/grfmt_jpeg2000_openjpeg.cpp b/modules/imgcodecs/src/grfmt_jpeg2000_openjpeg.cpp index 73d49282d7aa..c5b1a292cc44 100644 --- a/modules/imgcodecs/src/grfmt_jpeg2000_openjpeg.cpp +++ b/modules/imgcodecs/src/grfmt_jpeg2000_openjpeg.cpp @@ -545,7 +545,7 @@ bool Jpeg2KOpjDecoderBase::readHeader() */ bool hasAlpha = false; const int numcomps = image_->numcomps; - CV_Assert(numcomps >= 1); + CV_Check(numcomps, numcomps >= 1 && numcomps <= 4, "Unsupported number of components"); for (int i = 0; i < numcomps; i++) { const opj_image_comp_t& comp = image_->comps[i]; diff --git a/modules/imgcodecs/src/grfmt_pam.cpp b/modules/imgcodecs/src/grfmt_pam.cpp index 4db595055ed1..1c8f8476a5f5 100644 --- a/modules/imgcodecs/src/grfmt_pam.cpp +++ b/modules/imgcodecs/src/grfmt_pam.cpp @@ -111,12 +111,12 @@ static bool rgb_convert (void *src, void *target, int width, int target_channels int target_depth); const static struct pam_format formats[] = { - {CV_IMWRITE_PAM_FORMAT_NULL, "", NULL, {0, 0, 0, 0} }, - {CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE, "BLACKANDWHITE", NULL, {0, 0, 0, 0} }, - {CV_IMWRITE_PAM_FORMAT_GRAYSCALE, "GRAYSCALE", NULL, {0, 0, 0, 0} }, - {CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA, "GRAYSCALE_ALPHA", NULL, {0, 0, 0, 0} }, - {CV_IMWRITE_PAM_FORMAT_RGB, "RGB", rgb_convert, {0, 1, 2, 0} }, - {CV_IMWRITE_PAM_FORMAT_RGB_ALPHA, "RGB_ALPHA", NULL, {0, 1, 2, 0} }, + {IMWRITE_PAM_FORMAT_NULL, "", NULL, {0, 0, 0, 0} }, + {IMWRITE_PAM_FORMAT_BLACKANDWHITE, "BLACKANDWHITE", NULL, {0, 0, 0, 0} }, + {IMWRITE_PAM_FORMAT_GRAYSCALE, "GRAYSCALE", NULL, {0, 0, 0, 0} }, + {IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA, "GRAYSCALE_ALPHA", NULL, {0, 0, 0, 0} }, + {IMWRITE_PAM_FORMAT_RGB, "RGB", rgb_convert, {0, 1, 2, 0} }, + {IMWRITE_PAM_FORMAT_RGB_ALPHA, "RGB_ALPHA", NULL, {0, 1, 2, 0} }, }; #define PAM_FORMATS_NO (sizeof (fields) / sizeof ((fields)[0])) @@ -341,7 +341,7 @@ PAMDecoder::PAMDecoder() m_offset = -1; m_buf_supported = true; bit_mode = false; - selected_fmt = CV_IMWRITE_PAM_FORMAT_NULL; + selected_fmt = IMWRITE_PAM_FORMAT_NULL; m_maxval = 0; m_channels = 0; m_sampledepth = 0; @@ -462,15 +462,19 @@ bool PAMDecoder::readHeader() if (flds_endhdr && flds_height && flds_width && flds_depth && flds_maxval) { - if (selected_fmt == CV_IMWRITE_PAM_FORMAT_NULL) + if (selected_fmt == IMWRITE_PAM_FORMAT_NULL) { if (m_channels == 1 && m_maxval == 1) - selected_fmt = CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE; + selected_fmt = IMWRITE_PAM_FORMAT_BLACKANDWHITE; else if (m_channels == 1 && m_maxval < 256) - selected_fmt = CV_IMWRITE_PAM_FORMAT_GRAYSCALE; + selected_fmt = IMWRITE_PAM_FORMAT_GRAYSCALE; else if (m_channels == 3 && m_maxval < 256) - selected_fmt = CV_IMWRITE_PAM_FORMAT_RGB; + selected_fmt = IMWRITE_PAM_FORMAT_RGB; + else + CV_Error(Error::StsError, "Can't determine selected_fmt (IMWRITE_PAM_FORMAT_NULL)"); } + CV_CheckDepth(m_sampledepth, m_sampledepth == CV_8U || m_sampledepth == CV_16U, ""); + CV_Check(m_channels, m_channels >= 1 && m_channels <= 4, "Unsupported number of channels"); m_type = CV_MAKETYPE(m_sampledepth, m_channels); m_offset = m_strm.getPos(); @@ -512,7 +516,7 @@ bool PAMDecoder::readData(Mat& img) if( m_offset < 0 || !m_strm.isOpened()) return false; - if (selected_fmt != CV_IMWRITE_PAM_FORMAT_NULL) + if (selected_fmt != IMWRITE_PAM_FORMAT_NULL) fmt = &formats[selected_fmt]; else { /* default layout handling */ @@ -567,6 +571,10 @@ bool PAMDecoder::readData(Mat& img) FillColorRow1( data, src, m_width, palette ); } } + else + { + CV_Error(Error::StsError, cv::format("Unsupported value of target_channels: %d", target_channels)); + } } else { for (int y = 0; y < m_height; y++, data += imp_stride) { @@ -662,8 +670,8 @@ bool PAMEncoder::write( const Mat& img, const std::vector& params ) /* parse save file type */ for( size_t i = 0; i < params.size(); i += 2 ) - if( params[i] == CV_IMWRITE_PAM_TUPLETYPE ) { - if ( params[i+1] > CV_IMWRITE_PAM_FORMAT_NULL && + if( params[i] == IMWRITE_PAM_TUPLETYPE ) { + if ( params[i+1] > IMWRITE_PAM_FORMAT_NULL && params[i+1] < (int) PAM_FORMATS_NO) fmt = &formats[params[i+1]]; } diff --git a/modules/imgcodecs/src/grfmt_tiff.cpp b/modules/imgcodecs/src/grfmt_tiff.cpp index 5e7523b2039d..a2b55ea2ffbe 100644 --- a/modules/imgcodecs/src/grfmt_tiff.cpp +++ b/modules/imgcodecs/src/grfmt_tiff.cpp @@ -112,6 +112,8 @@ static bool cv_tiffSetErrorHandler() static const char fmtSignTiffII[] = "II\x2a\x00"; static const char fmtSignTiffMM[] = "MM\x00\x2a"; +static const char fmtSignBigTiffII[] = "II\x2b\x00"; +static const char fmtSignBigTiffMM[] = "MM\x00\x2b"; TiffDecoder::TiffDecoder() { @@ -140,13 +142,15 @@ bool TiffDecoder::checkSignature( const String& signature ) const { return signature.size() >= 4 && (memcmp(signature.c_str(), fmtSignTiffII, 4) == 0 || - memcmp(signature.c_str(), fmtSignTiffMM, 4) == 0); + memcmp(signature.c_str(), fmtSignTiffMM, 4) == 0 || + memcmp(signature.c_str(), fmtSignBigTiffII, 4) == 0 || + memcmp(signature.c_str(), fmtSignBigTiffMM, 4) == 0); } int TiffDecoder::normalizeChannelsNumber(int channels) const { - CV_Assert(channels <= 4); - return channels > 4 ? 4 : channels; + CV_Check(channels, channels >= 1 && channels <= 4, "Unsupported number of channels"); + return channels; } ImageDecoder TiffDecoder::newDecoder() const @@ -295,36 +299,58 @@ bool TiffDecoder::readHeader() (ncn != 1 && ncn != 3 && ncn != 4))) bpp = 8; + uint16 sample_format = SAMPLEFORMAT_UINT; + TIFFGetField(tif, TIFFTAG_SAMPLEFORMAT, &sample_format); int wanted_channels = normalizeChannelsNumber(ncn); - switch(bpp) + switch (bpp) { - case 1: - m_type = CV_MAKETYPE(CV_8U, !isGrayScale ? wanted_channels : 1); - result = true; - break; - case 8: - //Palette color, the value of the component is used as an index into the red, - //green and blue curves in the ColorMap field to retrieve an RGB triplet that defines the color. - if(photometric == PHOTOMETRIC_PALETTE) - m_type = CV_MAKETYPE(CV_8U, 3); - else - m_type = CV_MAKETYPE(CV_8U, !isGrayScale ? wanted_channels : 1); - result = true; - break; - case 16: - m_type = CV_MAKETYPE(CV_16U, !isGrayScale ? wanted_channels : 1); - result = true; - break; - case 32: - m_type = CV_MAKETYPE(CV_32F, wanted_channels); - result = true; - break; - case 64: - m_type = CV_MAKETYPE(CV_64F, wanted_channels); - result = true; - break; + case 1: + { + CV_Check((int)sample_format, sample_format == SAMPLEFORMAT_UINT || sample_format == SAMPLEFORMAT_INT, ""); + int depth = sample_format == SAMPLEFORMAT_INT ? CV_8S : CV_8U; + m_type = CV_MAKETYPE(depth, !isGrayScale ? wanted_channels : 1); + result = true; + break; + } + case 8: + { + //Palette color, the value of the component is used as an index into the red, + //green and blue curves in the ColorMap field to retrieve an RGB triplet that defines the color. + CV_Check((int)sample_format, sample_format == SAMPLEFORMAT_UINT || sample_format == SAMPLEFORMAT_INT, ""); + int depth = sample_format == SAMPLEFORMAT_INT ? CV_8S : CV_8U; + if (photometric == PHOTOMETRIC_PALETTE) + m_type = CV_MAKETYPE(depth, 3); + else + m_type = CV_MAKETYPE(depth, !isGrayScale ? wanted_channels : 1); + result = true; + break; + } + case 10: + case 12: + case 14: + case 16: + { + CV_Check((int)sample_format, sample_format == SAMPLEFORMAT_UINT || sample_format == SAMPLEFORMAT_INT, ""); + int depth = sample_format == SAMPLEFORMAT_INT ? CV_16S : CV_16U; + m_type = CV_MAKETYPE(depth, !isGrayScale ? wanted_channels : 1); + result = true; + break; + } + case 32: + { + CV_Check((int)sample_format, sample_format == SAMPLEFORMAT_IEEEFP || sample_format == SAMPLEFORMAT_INT, ""); + int depth = sample_format == SAMPLEFORMAT_IEEEFP ? CV_32F : CV_32S; + m_type = CV_MAKETYPE(depth, wanted_channels); + result = true; + break; + } + case 64: + CV_CheckEQ((int)sample_format, SAMPLEFORMAT_IEEEFP, ""); + m_type = CV_MAKETYPE(CV_64F, wanted_channels); + result = true; + break; default: - CV_Error(cv::Error::StsError, "Invalid bitsperpixel value read from TIFF header! Must be 1, 8, 16, 32 or 64."); + CV_Error(cv::Error::StsError, "Invalid bitsperpixel value read from TIFF header! Must be 1, 8, 10, 12, 14, 16, 32 or 64."); } } } @@ -414,6 +440,147 @@ static void fixOrientation(Mat &img, uint16 orientation, int dst_bpp) } } +static void _unpack10To16(const uchar* src, const uchar* srcEnd, ushort* dst, ushort* dstEnd, size_t expectedDstElements) +{ + //5*8b=4*10b : 5 src for 4 dst + constexpr const size_t packedBitsCount = 10; + constexpr const size_t packedBitsMask = ((1<(srcEnd-src)/srcElementsPerPacket), + (static_cast(dstEnd-dst)/dstElementsPerPacket) + }); + union { + uint64_t u64; + uint8_t u8[8]; + } buf = {0}; + for(size_t i = 0 ; i(buf.u64 & packedBitsMask); + buf.u64 >>= packedBitsCount; + } + dst += dstElementsPerPacket; + } + size_t remainingDstElements = std::min( + expectedDstElements-fullPacketsCount*dstElementsPerPacket, + static_cast(dstEnd-dst) + ); + bool stop = !remainingDstElements; + while(!stop) + { + for(size_t j = 0 ; j((buf.u64 >> (bitsPerPacket-(j+1)*packedBitsCount)) & packedBitsMask); + } + }//end while(!stop) +} +//end _unpack10To16() + +static void _unpack12To16(const uchar* src, const uchar* srcEnd, ushort* dst, ushort* dstEnd, size_t expectedDstElements) +{ + //3*8b=2*12b : 3 src for 2 dst + constexpr const size_t packedBitsCount = 12; + constexpr const size_t packedBitsMask = ((1<(srcEnd-src)/srcElementsPerPacket), + (static_cast(dstEnd-dst)/dstElementsPerPacket) + }); + union { + uint32_t u32; + uint8_t u8[4]; + } buf = {0}; + for(size_t i = 0 ; i(buf.u32 & packedBitsMask); + buf.u32 >>= packedBitsCount; + } + dst += dstElementsPerPacket; + } + size_t remainingDstElements = std::min( + expectedDstElements-fullPacketsCount*dstElementsPerPacket, + static_cast(dstEnd-dst) + ); + bool stop = !remainingDstElements; + while(!stop) + { + for(size_t j = 0 ; j((buf.u32 >> (bitsPerPacket-(j+1)*packedBitsCount)) & packedBitsMask); + } + }//end while(!stop) +} +//end _unpack12To16() + +static void _unpack14To16(const uchar* src, const uchar* srcEnd, ushort* dst, ushort* dstEnd, size_t expectedDstElements) +{ + //7*8b=4*14b : 7 src for 4 dst + constexpr const size_t packedBitsCount = 14; + constexpr const size_t packedBitsMask = ((1<(srcEnd-src)/srcElementsPerPacket), + (static_cast(dstEnd-dst)/dstElementsPerPacket) + }); + union { + uint64_t u64; + uint8_t u8[8]; + } buf = {0}; + for(size_t i = 0 ; i(buf.u64 & packedBitsMask); + buf.u64 >>= packedBitsCount; + } + dst += dstElementsPerPacket; + } + size_t remainingDstElements = std::min( + expectedDstElements-fullPacketsCount*dstElementsPerPacket, + static_cast(dstEnd-dst) + ); + bool stop = !remainingDstElements; + while(!stop) + { + for(size_t j = 0 ; j((buf.u64 >> (bitsPerPacket-(j+1)*packedBitsCount)) & packedBitsMask); + } + }//end while(!stop) +} +//end _unpack14To16() + bool TiffDecoder::readData( Mat& img ) { int type = img.type(); @@ -432,7 +599,7 @@ bool TiffDecoder::readData( Mat& img ) bool color = img.channels() > 1; - CV_CheckType(type, depth == CV_8U || depth == CV_16U || depth == CV_32F || depth == CV_64F, ""); + CV_CheckType(type, depth == CV_8U || depth == CV_8S || depth == CV_16U || depth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64F, ""); if (m_width && m_height) { @@ -447,7 +614,7 @@ bool TiffDecoder::readData( Mat& img ) CV_TIFF_CHECK_CALL_DEBUG(TIFFGetField(tif, TIFFTAG_SAMPLESPERPIXEL, &ncn)); uint16 img_orientation = ORIENTATION_TOPLEFT; CV_TIFF_CHECK_CALL_DEBUG(TIFFGetField(tif, TIFFTAG_ORIENTATION, &img_orientation)); - const int bitsPerByte = 8; + constexpr const int bitsPerByte = 8; int dst_bpp = (int)(img.elemSize1() * bitsPerByte); bool vert_flip = dst_bpp == 8 && (img_orientation == ORIENTATION_BOTRIGHT || img_orientation == ORIENTATION_RIGHTBOT || @@ -506,10 +673,15 @@ bool TiffDecoder::readData( Mat& img ) CV_Assert(ncn == img.channels()); CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_SAMPLEFORMAT, SAMPLEFORMAT_IEEEFP)); } - const size_t buffer_size = (bpp / bitsPerByte) * ncn * tile_height0 * tile_width0; - AutoBuffer _buffer(buffer_size); - uchar* buffer = _buffer.data(); - ushort* buffer16 = (ushort*)buffer; + const size_t src_buffer_bytes_per_row = divUp(static_cast(ncn * tile_width0 * bpp), static_cast(bitsPerByte)); + const size_t src_buffer_size = tile_height0 * src_buffer_bytes_per_row; + const size_t src_buffer_unpacked_bytes_per_row = divUp(static_cast(ncn * tile_width0 * dst_bpp), static_cast(bitsPerByte)); + const size_t src_buffer_unpacked_size = tile_height0 * src_buffer_unpacked_bytes_per_row; + const bool needsUnpacking = (bpp < dst_bpp); + AutoBuffer _src_buffer(src_buffer_size); + uchar* src_buffer = _src_buffer.data(); + AutoBuffer _src_buffer_unpacked(needsUnpacking ? src_buffer_unpacked_size : 0); + uchar* src_buffer_unpacked = needsUnpacking ? _src_buffer_unpacked.data() : nullptr; int tileidx = 0; for (int y = 0; y < m_height; y += (int)tile_height0) @@ -526,14 +698,14 @@ bool TiffDecoder::readData( Mat& img ) { case 8: { - uchar* bstart = buffer; + uchar* bstart = src_buffer; if (!is_tiled) { - CV_TIFF_CHECK_CALL(TIFFReadRGBAStrip(tif, y, (uint32*)buffer)); + CV_TIFF_CHECK_CALL(TIFFReadRGBAStrip(tif, y, (uint32*)src_buffer)); } else { - CV_TIFF_CHECK_CALL(TIFFReadRGBATile(tif, x, y, (uint32*)buffer)); + CV_TIFF_CHECK_CALL(TIFFReadRGBATile(tif, x, y, (uint32*)src_buffer)); // Tiles fill the buffer from the bottom up bstart += (tile_height0 - tile_height) * tile_width0 * 4; } @@ -571,28 +743,48 @@ bool TiffDecoder::readData( Mat& img ) { if (!is_tiled) { - CV_TIFF_CHECK_CALL((int)TIFFReadEncodedStrip(tif, tileidx, (uint32*)buffer, buffer_size) >= 0); + CV_TIFF_CHECK_CALL((int)TIFFReadEncodedStrip(tif, tileidx, (uint32*)src_buffer, src_buffer_size) >= 0); } else { - CV_TIFF_CHECK_CALL((int)TIFFReadEncodedTile(tif, tileidx, (uint32*)buffer, buffer_size) >= 0); + CV_TIFF_CHECK_CALL((int)TIFFReadEncodedTile(tif, tileidx, (uint32*)src_buffer, src_buffer_size) >= 0); } for (int i = 0; i < tile_height; i++) { + ushort* buffer16 = (ushort*)(src_buffer+i*src_buffer_bytes_per_row); + if (needsUnpacking) + { + const uchar* src_packed = src_buffer+i*src_buffer_bytes_per_row; + uchar* dst_unpacked = src_buffer_unpacked+i*src_buffer_unpacked_bytes_per_row; + if (bpp == 10) + _unpack10To16(src_packed, src_packed+src_buffer_bytes_per_row, + (ushort*)dst_unpacked, (ushort*)(dst_unpacked+src_buffer_unpacked_bytes_per_row), + ncn * tile_width0); + else if (bpp == 12) + _unpack12To16(src_packed, src_packed+src_buffer_bytes_per_row, + (ushort*)dst_unpacked, (ushort*)(dst_unpacked+src_buffer_unpacked_bytes_per_row), + ncn * tile_width0); + else if (bpp == 14) + _unpack14To16(src_packed, src_packed+src_buffer_bytes_per_row, + (ushort*)dst_unpacked, (ushort*)(dst_unpacked+src_buffer_unpacked_bytes_per_row), + ncn * tile_width0); + buffer16 = (ushort*)dst_unpacked; + } + if (color) { if (ncn == 1) { CV_CheckEQ(wanted_channels, 3, ""); - icvCvt_Gray2BGR_16u_C1C3R(buffer16 + i*tile_width0*ncn, 0, + icvCvt_Gray2BGR_16u_C1C3R(buffer16, 0, img.ptr(img_y + i, x), 0, Size(tile_width, 1)); } else if (ncn == 3) { CV_CheckEQ(wanted_channels, 3, ""); - icvCvt_RGB2BGR_16u_C3R(buffer16 + i*tile_width0*ncn, 0, + icvCvt_RGB2BGR_16u_C3R(buffer16, 0, img.ptr(img_y + i, x), 0, Size(tile_width, 1)); } @@ -600,14 +792,14 @@ bool TiffDecoder::readData( Mat& img ) { if (wanted_channels == 4) { - icvCvt_BGRA2RGBA_16u_C4R(buffer16 + i*tile_width0*ncn, 0, + icvCvt_BGRA2RGBA_16u_C4R(buffer16, 0, img.ptr(img_y + i, x), 0, Size(tile_width, 1)); } else { CV_CheckEQ(wanted_channels, 3, "TIFF-16bpp: BGR/BGRA images are supported only"); - icvCvt_BGRA2BGR_16u_C4C3R(buffer16 + i*tile_width0*ncn, 0, + icvCvt_BGRA2BGR_16u_C4C3R(buffer16, 0, img.ptr(img_y + i, x), 0, Size(tile_width, 1), 2); } @@ -623,12 +815,12 @@ bool TiffDecoder::readData( Mat& img ) if( ncn == 1 ) { memcpy(img.ptr(img_y + i, x), - buffer16 + i*tile_width0*ncn, + buffer16, tile_width*sizeof(ushort)); } else { - icvCvt_BGRA2Gray_16u_CnC1R(buffer16 + i*tile_width0*ncn, 0, + icvCvt_BGRA2Gray_16u_CnC1R(buffer16, 0, img.ptr(img_y + i, x), 0, Size(tile_width, 1), ncn, 2); } @@ -642,14 +834,14 @@ bool TiffDecoder::readData( Mat& img ) { if( !is_tiled ) { - CV_TIFF_CHECK_CALL((int)TIFFReadEncodedStrip(tif, tileidx, buffer, buffer_size) >= 0); + CV_TIFF_CHECK_CALL((int)TIFFReadEncodedStrip(tif, tileidx, src_buffer, src_buffer_size) >= 0); } else { - CV_TIFF_CHECK_CALL((int)TIFFReadEncodedTile(tif, tileidx, buffer, buffer_size) >= 0); + CV_TIFF_CHECK_CALL((int)TIFFReadEncodedTile(tif, tileidx, src_buffer, src_buffer_size) >= 0); } - Mat m_tile(Size(tile_width0, tile_height0), CV_MAKETYPE((dst_bpp == 32) ? CV_32F : CV_64F, ncn), buffer); + Mat m_tile(Size(tile_width0, tile_height0), CV_MAKETYPE((dst_bpp == 32) ? (depth == CV_32S ? CV_32S : CV_32F) : CV_64F, ncn), src_buffer); Rect roi_tile(0, 0, tile_width, tile_height); Rect roi_img(x, img_y, tile_width, tile_height); if (!m_hdr && ncn == 3) @@ -668,6 +860,8 @@ bool TiffDecoder::readData( Mat& img ) } // for x } // for y } + if (bpp < dst_bpp) + img *= (1<<(dst_bpp-bpp)); fixOrientation(img, img_orientation, dst_bpp); } @@ -698,7 +892,7 @@ ImageEncoder TiffEncoder::newEncoder() const bool TiffEncoder::isFormatSupported( int depth ) const { - return depth == CV_8U || depth == CV_16U || depth == CV_32F || depth == CV_64F; + return depth == CV_8U || depth == CV_8S || depth == CV_16U || depth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64F; } void TiffEncoder::writeTag( WLByteStream& strm, TiffTag tag, @@ -842,7 +1036,7 @@ bool TiffEncoder::writeLibTiff( const std::vector& img_vec, const std::vect int width = img.cols, height = img.rows; int type = img.type(); int depth = CV_MAT_DEPTH(type); - CV_CheckType(type, depth == CV_8U || depth == CV_16U || depth == CV_32F || depth == CV_64F, ""); + CV_CheckType(type, depth == CV_8U || depth == CV_8S || depth == CV_16U || depth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64F, ""); CV_CheckType(type, channels >= 1 && channels <= 4, ""); CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_IMAGEWIDTH, width)); @@ -865,19 +1059,31 @@ bool TiffEncoder::writeLibTiff( const std::vector& img_vec, const std::vect int page_compression = compression; int bitsPerChannel = -1; + uint16 sample_format = SAMPLEFORMAT_INT; switch (depth) { case CV_8U: + sample_format = SAMPLEFORMAT_UINT; + /* FALLTHRU */ + case CV_8S: { bitsPerChannel = 8; break; } + case CV_16U: + sample_format = SAMPLEFORMAT_UINT; + /* FALLTHRU */ + case CV_16S: { bitsPerChannel = 16; break; } + case CV_32F: + sample_format = SAMPLEFORMAT_IEEEFP; + /* FALLTHRU */ + case CV_32S: { bitsPerChannel = 32; page_compression = COMPRESSION_NONE; @@ -887,6 +1093,7 @@ bool TiffEncoder::writeLibTiff( const std::vector& img_vec, const std::vect { bitsPerChannel = 64; page_compression = COMPRESSION_NONE; + sample_format = SAMPLEFORMAT_IEEEFP; break; } default: @@ -912,9 +1119,9 @@ bool TiffEncoder::writeLibTiff( const std::vector& img_vec, const std::vect CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_PLANARCONFIG, PLANARCONFIG_CONTIG)); CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_ROWSPERSTRIP, rowsPerStrip)); - CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_SAMPLEFORMAT, depth >= CV_32F ? SAMPLEFORMAT_IEEEFP : SAMPLEFORMAT_UINT)); + CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_SAMPLEFORMAT, sample_format)); - if (page_compression != COMPRESSION_NONE) + if (page_compression == COMPRESSION_LZW || page_compression == COMPRESSION_ADOBE_DEFLATE || page_compression == COMPRESSION_DEFLATE) { CV_TIFF_CHECK_CALL(TIFFSetField(tif, TIFFTAG_PREDICTOR, predictor)); } @@ -1011,7 +1218,7 @@ bool TiffEncoder::write( const Mat& img, const std::vector& params) int type = img.type(); int depth = CV_MAT_DEPTH(type); - CV_CheckType(type, depth == CV_8U || depth == CV_16U || depth == CV_32F || depth == CV_64F, ""); + CV_CheckType(type, depth == CV_8U || depth == CV_8S || depth == CV_16U || depth == CV_16S || depth == CV_32S || depth == CV_32F || depth == CV_64F, ""); std::vector img_vec; img_vec.push_back(img); diff --git a/modules/imgcodecs/src/grfmt_webp.cpp b/modules/imgcodecs/src/grfmt_webp.cpp index e137b8734db9..3860abb64e50 100644 --- a/modules/imgcodecs/src/grfmt_webp.cpp +++ b/modules/imgcodecs/src/grfmt_webp.cpp @@ -243,7 +243,7 @@ bool WebPEncoder::write(const Mat& img, const std::vector& params) if (params.size() > 1) { - if (params[0] == CV_IMWRITE_WEBP_QUALITY) + if (params[0] == IMWRITE_WEBP_QUALITY) { comp_lossless = false; quality = static_cast(params[1]); diff --git a/modules/imgcodecs/src/loadsave.cpp b/modules/imgcodecs/src/loadsave.cpp index 91f30cfe9806..e9b6d0517c51 100644 --- a/modules/imgcodecs/src/loadsave.cpp +++ b/modules/imgcodecs/src/loadsave.cpp @@ -562,7 +562,7 @@ imreadmulti_(const String& filename, int flags, std::vector& mats, int star if ((flags & IMREAD_ANYDEPTH) == 0) type = CV_MAKETYPE(CV_8U, CV_MAT_CN(type)); - if ((flags & CV_LOAD_IMAGE_COLOR) != 0 || + if ((flags & IMREAD_COLOR) != 0 || ((flags & IMREAD_ANYCOLOR) != 0 && CV_MAT_CN(type) > 1)) type = CV_MAKETYPE(CV_MAT_DEPTH(type), 3); else diff --git a/modules/imgcodecs/src/precomp.hpp b/modules/imgcodecs/src/precomp.hpp index aa2a999f6381..70cc1e71059a 100644 --- a/modules/imgcodecs/src/precomp.hpp +++ b/modules/imgcodecs/src/precomp.hpp @@ -43,11 +43,8 @@ #define __IMGCODECS_H_ #include "opencv2/imgcodecs.hpp" -#include "opencv2/imgcodecs/legacy/constants_c.h" - #include "opencv2/core/utility.hpp" #include "opencv2/core/private.hpp" - #include "opencv2/imgproc.hpp" #include diff --git a/modules/imgcodecs/test/test_tiff.cpp b/modules/imgcodecs/test/test_tiff.cpp index a2f9655c73be..b7b6b95d83a8 100644 --- a/modules/imgcodecs/test/test_tiff.cpp +++ b/modules/imgcodecs/test/test_tiff.cpp @@ -117,6 +117,116 @@ TEST(Imgcodecs_Tiff, decode_tile_remainder) // What about 32, 64 bit? } +TEST(Imgcodecs_Tiff, decode_10_12_14) +{ + /* see issue #21700 + */ + const string root = cvtest::TS::ptr()->get_data_path(); + + const double maxDiff = 256;//samples do not have the exact same values because of the tool that created them + cv::Mat tmp; + double diff = 0; + + cv::Mat img8UC1 = imread(root + "readwrite/pattern_8uc1.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img8UC1.empty()); + ASSERT_EQ(img8UC1.type(), CV_8UC1); + + cv::Mat img8UC3 = imread(root + "readwrite/pattern_8uc3.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img8UC3.empty()); + ASSERT_EQ(img8UC3.type(), CV_8UC3); + + cv::Mat img8UC4 = imread(root + "readwrite/pattern_8uc4.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img8UC4.empty()); + ASSERT_EQ(img8UC4.type(), CV_8UC4); + + cv::Mat img16UC1 = imread(root + "readwrite/pattern_16uc1.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img16UC1.empty()); + ASSERT_EQ(img16UC1.type(), CV_16UC1); + ASSERT_EQ(img8UC1.size(), img16UC1.size()); + img8UC1.convertTo(tmp, img16UC1.type(), (1U<<(16-8))); + diff = cv::norm(tmp.reshape(1), img16UC1.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img16UC3 = imread(root + "readwrite/pattern_16uc3.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img16UC3.empty()); + ASSERT_EQ(img16UC3.type(), CV_16UC3); + ASSERT_EQ(img8UC3.size(), img16UC3.size()); + img8UC3.convertTo(tmp, img16UC3.type(), (1U<<(16-8))); + diff = cv::norm(tmp.reshape(1), img16UC3.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img16UC4 = imread(root + "readwrite/pattern_16uc4.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img16UC4.empty()); + ASSERT_EQ(img16UC4.type(), CV_16UC4); + ASSERT_EQ(img8UC4.size(), img16UC4.size()); + img8UC4.convertTo(tmp, img16UC4.type(), (1U<<(16-8))); + diff = cv::norm(tmp.reshape(1), img16UC4.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img10UC1 = imread(root + "readwrite/pattern_10uc1.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img10UC1.empty()); + ASSERT_EQ(img10UC1.type(), CV_16UC1); + ASSERT_EQ(img10UC1.size(), img16UC1.size()); + diff = cv::norm(img10UC1.reshape(1), img16UC1.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img10UC3 = imread(root + "readwrite/pattern_10uc3.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img10UC3.empty()); + ASSERT_EQ(img10UC3.type(), CV_16UC3); + ASSERT_EQ(img10UC3.size(), img16UC3.size()); + diff = cv::norm(img10UC3.reshape(1), img16UC3.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img10UC4 = imread(root + "readwrite/pattern_10uc4.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img10UC4.empty()); + ASSERT_EQ(img10UC4.type(), CV_16UC4); + ASSERT_EQ(img10UC4.size(), img16UC4.size()); + diff = cv::norm(img10UC4.reshape(1), img16UC4.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img12UC1 = imread(root + "readwrite/pattern_12uc1.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img12UC1.empty()); + ASSERT_EQ(img12UC1.type(), CV_16UC1); + ASSERT_EQ(img12UC1.size(), img16UC1.size()); + diff = cv::norm(img12UC1.reshape(1), img16UC1.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img12UC3 = imread(root + "readwrite/pattern_12uc3.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img12UC3.empty()); + ASSERT_EQ(img12UC3.type(), CV_16UC3); + ASSERT_EQ(img12UC3.size(), img16UC3.size()); + diff = cv::norm(img12UC3.reshape(1), img16UC3.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img12UC4 = imread(root + "readwrite/pattern_12uc4.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img12UC4.empty()); + ASSERT_EQ(img12UC4.type(), CV_16UC4); + ASSERT_EQ(img12UC4.size(), img16UC4.size()); + diff = cv::norm(img12UC4.reshape(1), img16UC4.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img14UC1 = imread(root + "readwrite/pattern_14uc1.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img14UC1.empty()); + ASSERT_EQ(img14UC1.type(), CV_16UC1); + ASSERT_EQ(img14UC1.size(), img16UC1.size()); + diff = cv::norm(img14UC1.reshape(1), img16UC1.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img14UC3 = imread(root + "readwrite/pattern_14uc3.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img14UC3.empty()); + ASSERT_EQ(img14UC3.type(), CV_16UC3); + ASSERT_EQ(img14UC3.size(), img16UC3.size()); + diff = cv::norm(img14UC3.reshape(1), img16UC3.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); + + cv::Mat img14UC4 = imread(root + "readwrite/pattern_14uc4.tif", cv::IMREAD_UNCHANGED); + ASSERT_FALSE(img14UC4.empty()); + ASSERT_EQ(img14UC4.type(), CV_16UC4); + ASSERT_EQ(img14UC4.size(), img16UC4.size()); + diff = cv::norm(img14UC4.reshape(1), img16UC4.reshape(1), cv::NORM_INF); + ASSERT_LE(diff, maxDiff); +} + TEST(Imgcodecs_Tiff, decode_infinite_rowsperstrip) { const uchar sample_data[142] = { @@ -147,6 +257,26 @@ TEST(Imgcodecs_Tiff, decode_infinite_rowsperstrip) EXPECT_EQ(0, remove(filename.c_str())); } +TEST(Imgcodecs_Tiff, readWrite_unsigned) +{ + const string root = cvtest::TS::ptr()->get_data_path(); + const string filenameInput = root + "readwrite/gray_8u.tif"; + const string filenameOutput = cv::tempfile(".tiff"); + const Mat img = cv::imread(filenameInput, IMREAD_UNCHANGED); + ASSERT_FALSE(img.empty()); + ASSERT_EQ(CV_8UC1, img.type()); + + Mat matS8; + img.convertTo(matS8, CV_8SC1); + + ASSERT_TRUE(cv::imwrite(filenameOutput, matS8)); + const Mat img2 = cv::imread(filenameOutput, IMREAD_UNCHANGED); + ASSERT_EQ(img2.type(), matS8.type()); + ASSERT_EQ(img2.size(), matS8.size()); + EXPECT_LE(cvtest::norm(matS8, img2, NORM_INF | NORM_RELATIVE), 1e-3); + EXPECT_EQ(0, remove(filenameOutput.c_str())); +} + TEST(Imgcodecs_Tiff, readWrite_32FC1) { const string root = cvtest::TS::ptr()->get_data_path(); @@ -229,6 +359,49 @@ TEST(Imgcodecs_Tiff, read_palette_color_image) ASSERT_EQ(CV_8UC3, img.type()); } +TEST(Imgcodecs_Tiff, readWrite_predictor) +{ + /* see issue #21871 + */ + const uchar sample_data[160] = { + 0xff, 0xff, 0xff, 0xff, 0x88, 0x88, 0xff, 0xff, 0x88, 0x88, 0xff, 0xff, 0xff, 0xff, 0xff, 0x88, + 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x00, 0xff, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, + 0xff, 0x00, 0x00, 0x44, 0xff, 0xff, 0x88, 0xff, 0x33, 0x00, 0x66, 0xff, 0xff, 0x88, 0x00, 0x44, + 0x88, 0x00, 0x44, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x44, 0xff, 0xff, 0x11, 0x00, 0xff, + 0x11, 0x00, 0x88, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0xff, 0xff, 0x00, 0x00, 0xff, + 0x11, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x33, 0x00, 0x88, 0xff, 0x00, 0x66, 0xff, + 0x11, 0x00, 0x66, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x44, 0x33, 0x00, 0xff, 0xff, + 0x88, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0xff, 0xff, + 0xff, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0x33, 0x00, 0x00, 0x66, 0xff, 0xff, + 0xff, 0xff, 0x88, 0x00, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0xff, 0xff, 0xff + }; + + cv::Mat mat(10, 16, CV_8UC1, (void*)sample_data); + int methods[] = { + COMPRESSION_NONE, COMPRESSION_LZW, + COMPRESSION_PACKBITS, COMPRESSION_DEFLATE, COMPRESSION_ADOBE_DEFLATE + }; + for (size_t i = 0; i < sizeof(methods) / sizeof(int); i++) + { + string out = cv::tempfile(".tif"); + + std::vector params; + params.push_back(TIFFTAG_COMPRESSION); + params.push_back(methods[i]); + params.push_back(TIFFTAG_PREDICTOR); + params.push_back(PREDICTOR_HORIZONTAL); + + EXPECT_NO_THROW(cv::imwrite(out, mat, params)); + + const Mat img = cv::imread(out, IMREAD_UNCHANGED); + ASSERT_FALSE(img.empty()); + + ASSERT_EQ(0, cv::norm(mat, img, cv::NORM_INF)); + + EXPECT_EQ(0, remove(out.c_str())); + } +} + //================================================================================================== @@ -455,6 +628,29 @@ TEST(Imgcodecs_Tiff, read_multipage_indexed) } } +TEST(Imgcodecs_Tiff, read_bigtiff_images) +{ + const string root = cvtest::TS::ptr()->get_data_path(); + const string filenamesInput[] = { + "readwrite/BigTIFF.tif", + "readwrite/BigTIFFMotorola.tif", + "readwrite/BigTIFFLong.tif", + "readwrite/BigTIFFLong8.tif", + "readwrite/BigTIFFMotorolaLongStrips.tif", + "readwrite/BigTIFFLong8Tiles.tif", + "readwrite/BigTIFFSubIFD4.tif", + "readwrite/BigTIFFSubIFD8.tif" + }; + + for (int i = 0; i < 8; i++) + { + const Mat bigtiff_img = imread(root + filenamesInput[i], IMREAD_UNCHANGED); + ASSERT_FALSE(bigtiff_img.empty()); + EXPECT_EQ(64, bigtiff_img.cols); + EXPECT_EQ(64, bigtiff_img.rows); + ASSERT_EQ(CV_8UC3, bigtiff_img.type()); + } +} #endif diff --git a/modules/imgproc/include/opencv2/imgproc.hpp b/modules/imgproc/include/opencv2/imgproc.hpp index eacec6119039..3e0180ab6a64 100644 --- a/modules/imgproc/include/opencv2/imgproc.hpp +++ b/modules/imgproc/include/opencv2/imgproc.hpp @@ -118,7 +118,7 @@ sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_ where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See -resize for details. +#resize for details. @note The geometrical transformations do not work with `CV_8S` or `CV_32S` images. @@ -1576,7 +1576,7 @@ CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. -The unnormalized square box filter can be useful in computing local image statistics such as the the local +The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel. @param src input image @@ -2345,7 +2345,7 @@ If you want to decimate the image by factor of 2 in each direction, you can call resize(src, dst, Size(), 0.5, 0.5, interpolation); @endcode To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to -enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR +enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR (faster but still looks OK). @param src input image. @@ -2437,7 +2437,7 @@ The function remap transforms the source image using the specified map: where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in -\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to +\f$map_1\f$, or fixed-point maps created by using #convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. @@ -2447,7 +2447,7 @@ This function cannot operate in-place. @param src Source image. @param dst Destination image. It has the same size as map1 and the same type as src . @param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , -CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point +CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point representation to fixed-point for speed. @param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map if map1 is (x,y) points), respectively. @@ -2472,7 +2472,7 @@ options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2. supported: - \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the -most frequently used conversion operation, in which the original floating-point maps (see remap ) +most frequently used conversion operation, in which the original floating-point maps (see #remap) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables. @@ -3559,10 +3559,11 @@ a mask and then extract the contour, or copy the region to another image, and so function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below. @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels -taller than image. Since this is both an input and output parameter, you must take responsibility -of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, +taller than image. If an empty Mat is passed it will be created automatically. Since this is both an +input and output parameter, you must take responsibility of initializing it. +Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the -mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags +mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap. @@ -4832,13 +4833,11 @@ CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace, const int pixelHeight, const int thickness = 1); -/** @brief Line iterator +/** @brief Class for iterating over all pixels on a raster line segment. -The class is used to iterate over all the pixels on the raster line -segment connecting two specified points. - -The class LineIterator is used to get each pixel of a raster line. It -can be treated as versatile implementation of the Bresenham algorithm +The class LineIterator is used to get each pixel of a raster line connecting +two specified points. +It can be treated as a versatile implementation of the Bresenham algorithm where you can stop at each pixel and do some extra processing, for example, grab pixel values along the line or draw a line with an effect (for example, with XOR operation). @@ -4867,14 +4866,19 @@ for(int i = 0; i < it2.count; i++, ++it2) class CV_EXPORTS LineIterator { public: - /** @brief initializes the iterator - - creates iterators for the line connecting pt1 and pt2 - the line will be clipped on the image boundaries - the line is 8-connected or 4-connected - If leftToRight=true, then the iteration is always done - from the left-most point to the right most, - not to depend on the ordering of pt1 and pt2 parameters; + /** @brief Initializes iterator object for the given line and image. + + The returned iterator can be used to traverse all pixels on a line that + connects the given two points. + The line will be clipped on the image boundaries. + + @param img Underlying image. + @param pt1 First endpoint of the line. + @param pt2 The other endpoint of the line. + @param connectivity Pixel connectivity of the iterator. Valid values are 4 (iterator can move + up, down, left and right) and 8 (iterator can also move diagonally). + @param leftToRight If true, the line is traversed from the leftmost endpoint to the rightmost + endpoint. Otherwise, the line is traversed from \p pt1 to \p pt2. */ LineIterator( const Mat& img, Point pt1, Point pt2, int connectivity = 8, bool leftToRight = false ) @@ -4907,16 +4911,23 @@ class CV_EXPORTS LineIterator } void init(const Mat* img, Rect boundingAreaRect, Point pt1, Point pt2, int connectivity, bool leftToRight); - /** @brief returns pointer to the current pixel + /** @brief Returns pointer to the current pixel. */ uchar* operator *(); - /** @brief prefix increment operator (++it). shifts iterator to the next pixel + + /** @brief Moves iterator to the next pixel on the line. + + This is the prefix version (++it). */ LineIterator& operator ++(); - /** @brief postfix increment operator (it++). shifts iterator to the next pixel + + /** @brief Moves iterator to the next pixel on the line. + + This is the postfix version (it++). */ LineIterator operator ++(int); - /** @brief returns coordinates of the current pixel + + /** @brief Returns coordinates of the current pixel. */ Point pos() const; diff --git a/modules/imgproc/include/opencv2/imgproc/segmentation.hpp b/modules/imgproc/include/opencv2/imgproc/segmentation.hpp index 26882f444e7f..c40d5011ee3a 100644 --- a/modules/imgproc/include/opencv2/imgproc/segmentation.hpp +++ b/modules/imgproc/include/opencv2/imgproc/segmentation.hpp @@ -92,7 +92,7 @@ class CV_EXPORTS_W_SIMPLE IntelligentScissorsMB CV_WRAP IntelligentScissorsMB& applyImage(InputArray image); - /** @brief Specify custom features of imput image + /** @brief Specify custom features of input image * * Customized advanced variant of applyImage() call. * diff --git a/modules/imgproc/src/color_hsv.simd.hpp b/modules/imgproc/src/color_hsv.simd.hpp index b1eb50d7a438..9837af4acb79 100644 --- a/modules/imgproc/src/color_hsv.simd.hpp +++ b/modules/imgproc/src/color_hsv.simd.hpp @@ -39,36 +39,51 @@ struct RGB2HSV_b : srccn(_srccn), blueIdx(_blueIdx), hrange(_hrange) { CV_Assert( hrange == 180 || hrange == 256 ); + + const TablesSingleton& global_tables = TablesSingleton::getInstance(); + hdiv_table_ = hrange == 180 ? global_tables.hdiv_table180 : global_tables.hdiv_table256; + sdiv_table_ = global_tables.sdiv_table; } - void operator()(const uchar* src, uchar* dst, int n) const + struct TablesSingleton { - CV_INSTRUMENT_REGION(); - - int i, bidx = blueIdx, scn = srccn; - const int hsv_shift = 12; + int sdiv_table[256]; + int hdiv_table180[256]; + int hdiv_table256[256]; - static int sdiv_table[256]; - static int hdiv_table180[256]; - static int hdiv_table256[256]; - static volatile bool initialized = false; - - int hr = hrange; - const int* hdiv_table = hr == 180 ? hdiv_table180 : hdiv_table256; - - if( !initialized ) + protected: + TablesSingleton() { + const int hsv_shift = 12; + sdiv_table[0] = hdiv_table180[0] = hdiv_table256[0] = 0; - for( i = 1; i < 256; i++ ) + for (int i = 1; i < 256; i++) { sdiv_table[i] = saturate_cast((255 << hsv_shift)/(1.*i)); hdiv_table180[i] = saturate_cast((180 << hsv_shift)/(6.*i)); hdiv_table256[i] = saturate_cast((256 << hsv_shift)/(6.*i)); } - initialized = true; } + public: + static TablesSingleton& getInstance() + { + static TablesSingleton g_tables; + return g_tables; + } + }; + + void operator()(const uchar* src, uchar* dst, int n) const + { + CV_INSTRUMENT_REGION(); - i = 0; + int bidx = blueIdx, scn = srccn; + const int hsv_shift = 12; + + int hr = hrange; + const int* hdiv_table/*[256]*/ = hdiv_table_; + const int* sdiv_table/*[256]*/ = sdiv_table_; + + int i = 0; #if CV_SIMD const int vsize = v_uint8::nlanes; @@ -231,6 +246,9 @@ struct RGB2HSV_b } int srccn, blueIdx, hrange; + + const int* hdiv_table_/*[256]*/; + const int* sdiv_table_/*[256]*/; }; diff --git a/modules/imgproc/src/colormap.cpp b/modules/imgproc/src/colormap.cpp index 26371edad69e..966e39eaab92 100644 --- a/modules/imgproc/src/colormap.cpp +++ b/modules/imgproc/src/colormap.cpp @@ -734,12 +734,57 @@ namespace colormap Mat src = _src.getMat(); if(src.type() != CV_8UC1 && src.type() != CV_8UC3) CV_Error(Error::StsBadArg, "cv::ColorMap only supports source images of type CV_8UC1 or CV_8UC3"); - // Turn into a BGR matrix into its grayscale representation. - if(src.type() == CV_8UC3) - cvtColor(src.clone(), src, COLOR_BGR2GRAY); - cvtColor(src.clone(), src, COLOR_GRAY2BGR); - // Apply the ColorMap. - LUT(src, _lut, _dst); + + CV_CheckEQ(src.dims, 2, "Not supported"); + + CV_Assert(_lut.isContinuous()); + const int lut_type = _lut.type(); + CV_CheckType(lut_type, (lut_type == CV_8UC1) || (lut_type == CV_8UC3), + "Only CV_8UC1 and CV_8UC3 LUT are supported"); + + Mat srcGray; + if (src.channels() == 1) + srcGray = src; + else + cv::cvtColor(src, srcGray, cv::COLOR_BGR2GRAY);//BGR because of historical cv::LUT() usage + + _dst.create(src.size(), lut_type); + Mat dstMat = _dst.getMat(); + + //we do not use cv::LUT() which requires src.channels() == dst.channels() + const int rows = srcGray.rows; + const int cols = srcGray.cols; + const int minimalPixelsPerPacket = 1<<12; + const int rowsPerPacket = std::max(1, minimalPixelsPerPacket/cols); + const int rowsPacketsCount = (rows+rowsPerPacket-1)/rowsPerPacket; + const Range all(0, rows); + + if (lut_type == CV_8UC1) { + typedef unsigned char lut_pixel_t; + const lut_pixel_t* srcLUT = _lut.ptr(0); + auto body = [&, cols](const Range& range) -> void { + for(int row = range.start ; row(row); + lut_pixel_t* dstRow = dstMat.ptr(row); + for(int col = 0 ; col(0); + auto body = [&, cols](const Range& range) -> void { + for(int row = range.start ; row(row); + lut_pixel_t* dstRow = dstMat.ptr(row); + for(int col = 0 ; col 0 #define CONDITION_X img_row[c] > 0 -#define ACTION_1 // nothing to do +#define ACTION_1 img_labels_row[c] = 0; #define ACTION_2 img_labels_row[c] = label; \ P_[label] = label; \ label = label + 1; @@ -1831,7 +1831,7 @@ namespace cv{ std::vector P_(Plength, 0); LabelT* P = P_.data(); - //P[0] = 0; + P[0] = 0; LabelT lunique = 1; // First scan @@ -1851,7 +1851,7 @@ namespace cv{ #define CONDITION_S img_row[c - 1] > 0 #define CONDITION_X img_row[c] > 0 -#define ACTION_1 // nothing to do +#define ACTION_1 img_labels_row[c] = 0; #define ACTION_2 img_labels_row[c] = lunique; \ P[lunique] = lunique; \ lunique = lunique + 1; // new label @@ -4339,8 +4339,8 @@ namespace cv{ // without going outside the image limits. #define condition_b c-1>=0 && r-2>=0 && img_row_prev_prev[c-1]>0 #define condition_c r-2>=0 && img_row_prev_prev[c]>0 - #define condition_d c+1=0 && img_row_prev_prev[c+1]>0 - #define condition_e c+2=0 && img_row_prev[c-1]>0 + #define condition_d c+1=0 && img_row_prev_prev[c+1]>0 + #define condition_e c+2=0 && img_row_prev_prev[c+2]>0 #define condition_g c-2>=0 && r-1>=0 && img_row_prev[c-2]>0 #define condition_h c-1>=0 && r-1>=0 && img_row_prev[c-1]>0 diff --git a/modules/imgproc/src/drawing.cpp b/modules/imgproc/src/drawing.cpp index 59047247e87c..5e31482dfac2 100644 --- a/modules/imgproc/src/drawing.cpp +++ b/modules/imgproc/src/drawing.cpp @@ -673,7 +673,7 @@ Line2( Mat& img, Point2l pt1, Point2l pt2, const void* color) pt1.y ^= pt2.y & j; x_step = XY_ONE; - y_step = (dy << XY_SHIFT) / (ax | 1); + y_step = dy * (1 << XY_SHIFT) / (ax | 1); ecount = (int)((pt2.x - pt1.x) >> XY_SHIFT); } else @@ -686,7 +686,7 @@ Line2( Mat& img, Point2l pt1, Point2l pt2, const void* color) pt2.y ^= pt1.y & i; pt1.y ^= pt2.y & i; - x_step = (dx << XY_SHIFT) / (ay | 1); + x_step = dx * (1 << XY_SHIFT) / (ay | 1); y_step = XY_ONE; ecount = (int)((pt2.y - pt1.y) >> XY_SHIFT); } diff --git a/modules/imgproc/src/floodfill.cpp b/modules/imgproc/src/floodfill.cpp index 2816795bc60d..8595011d483a 100644 --- a/modules/imgproc/src/floodfill.cpp +++ b/modules/imgproc/src/floodfill.cpp @@ -477,11 +477,10 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask, nv_buf._[0] = nv_buf._[1] = nv_buf._[2] = nv_buf._[3] = 0; struct { Vec3b b; Vec3i i; Vec3f f; } ld_buf, ud_buf; + Mat img = _image.getMat(), mask; - if( !_mask.empty() ) - mask = _mask.getMat(); - Size size = img.size(); + Size size = img.size(); int type = img.type(); int depth = img.depth(); int cn = img.channels(); @@ -495,6 +494,20 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask, if( connectivity != 0 && connectivity != 4 && connectivity != 8 ) CV_Error( CV_StsBadFlag, "Connectivity must be 4, 0(=4) or 8" ); + if( _mask.empty() ) + { + _mask.create( size.height + 2, size.width + 2, CV_8UC1 ); + _mask.setTo(0); + } + + mask = _mask.getMat(); + CV_CheckTypeEQ( mask.type(), CV_8U, "" ); + CV_CheckEQ( mask.rows, size.height + 2, "" ); + CV_CheckEQ( mask.cols, size.width + 2, "" ); + + Mat mask_inner = mask( Rect(1, 1, mask.cols - 2, mask.rows - 2) ); + copyMakeBorder( mask_inner, mask, 1, 1, 1, 1, BORDER_ISOLATED | BORDER_CONSTANT, Scalar(1) ); + bool is_simple = mask.empty() && (flags & FLOODFILL_MASK_ONLY) == 0; for( i = 0; i < cn; i++ ) @@ -544,26 +557,6 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask, } } - if( mask.empty() ) - { - Mat tempMask( size.height + 2, size.width + 2, CV_8UC1 ); - tempMask.setTo(Scalar::all(0)); - mask = tempMask; - } - else - { - CV_Assert( mask.rows == size.height+2 && mask.cols == size.width+2 ); - CV_Assert( mask.type() == CV_8U ); - } - - memset( mask.ptr(), 1, mask.cols ); - memset( mask.ptr(mask.rows-1), 1, mask.cols ); - - for( i = 1; i <= size.height; i++ ) - { - mask.at(i, 0) = mask.at(i, mask.cols-1) = (uchar)1; - } - if( depth == CV_8U ) for( i = 0; i < cn; i++ ) { @@ -632,7 +625,8 @@ int cv::floodFill( InputOutputArray _image, Point seedPoint, { CV_INSTRUMENT_REGION(); - return floodFill(_image, Mat(), seedPoint, newVal, rect, loDiff, upDiff, flags); + Mat mask; + return floodFill(_image, mask, seedPoint, newVal, rect, loDiff, upDiff, flags); } diff --git a/modules/imgproc/src/histogram.cpp b/modules/imgproc/src/histogram.cpp index 35923b1e47dc..84a2734eae4d 100644 --- a/modules/imgproc/src/histogram.cpp +++ b/modules/imgproc/src/histogram.cpp @@ -909,7 +909,8 @@ static bool ipp_calchist(const Mat &image, Mat &hist, int histSize, const float* #endif // IPP_DISABLE_HISTOGRAM - https://github.com/opencv/opencv/issues/11544 - if (uniform && (ranges[0][1] - ranges[0][0]) != histSize) + // and https://github.com/opencv/opencv/issues/21595 + if ((uniform && (ranges[0][1] - ranges[0][0]) != histSize) || abs(ranges[0][0]) != cvFloor(ranges[0][0])) return false; Mat ihist = hist; diff --git a/modules/imgproc/src/intelligent_scissors.cpp b/modules/imgproc/src/intelligent_scissors.cpp index 38acfd79e3ab..1b7e3dd16375 100644 --- a/modules/imgproc/src/intelligent_scissors.cpp +++ b/modules/imgproc/src/intelligent_scissors.cpp @@ -429,13 +429,13 @@ struct IntelligentScissorsMB::Impl gradient_direction.create(src_size); for (int y = 0; y < src_size.height; y++) { - const float* magnutude_row = image_magnitude_.ptr(y); + const float* magnitude_row = image_magnitude_.ptr(y); const float* Ix_row = Ix_.ptr(y); const float* Iy_row = Iy_.ptr(y); Point2f* gradient_direction_row = gradient_direction.ptr(y); for (int x = 0; x < src_size.width; x++) { - const float m = magnutude_row[x]; + const float m = magnitude_row[x]; if (m > FLT_EPSILON) { float m_inv = 1.0f / m; diff --git a/modules/imgproc/src/intersection.cpp b/modules/imgproc/src/intersection.cpp index 47d3f3f457b5..b9659f666e3e 100644 --- a/modules/imgproc/src/intersection.cpp +++ b/modules/imgproc/src/intersection.cpp @@ -51,15 +51,15 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate { CV_INSTRUMENT_REGION(); - // L2 metric - const float samePointEps = std::max(1e-16f, 1e-6f * (float)std::max(rect1.size.area(), rect2.size.area())); - Point2f vec1[4], vec2[4]; Point2f pts1[4], pts2[4]; rect1.points(pts1); rect2.points(pts2); + // L2 metric + float samePointEps = 1e-6f * (float)std::max(rect1.size.area(), rect2.size.area()); + int ret = INTERSECT_FULL; // Specical case of rect1 == rect2 @@ -99,14 +99,22 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate vec2[i].y = pts2[(i+1)%4].y - pts2[i].y; } + //we adapt the epsilon to the smallest dimension of the rects + for( int i = 0; i < 4; i++ ) + { + samePointEps = std::min(samePointEps, std::sqrt(vec1[i].x*vec1[i].x+vec1[i].y*vec1[i].y)); + samePointEps = std::min(samePointEps, std::sqrt(vec2[i].x*vec2[i].x+vec2[i].y*vec2[i].y)); + } + samePointEps = std::max(1e-16f, samePointEps); + // Line test - test all line combos for intersection for( int i = 0; i < 4; i++ ) { for( int j = 0; j < 4; j++ ) { // Solve for 2x2 Ax=b - float x21 = pts2[j].x - pts1[i].x; - float y21 = pts2[j].y - pts1[i].y; + const float x21 = pts2[j].x - pts1[i].x; + const float y21 = pts2[j].y - pts1[i].y; float vx1 = vec1[i].x; float vy1 = vec1[i].y; @@ -114,10 +122,22 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate float vx2 = vec2[j].x; float vy2 = vec2[j].y; - float det = vx2*vy1 - vx1*vy2; + float normalizationScale = std::min(vx1*vx1+vy1*vy1, vx2*vx2+vy2*vy2);//sum of squares : this is >= 0 + //normalizationScale is a square, and we usually limit accuracy around 1e-6, so normalizationScale should be rather limited by ((1e-6)^2)=1e-12 + normalizationScale = (normalizationScale < 1e-12f) ? 1.f : 1.f/normalizationScale; + + vx1 *= normalizationScale; + vy1 *= normalizationScale; + vx2 *= normalizationScale; + vy2 *= normalizationScale; + + const float det = vx2*vy1 - vx1*vy2; + if (std::abs(det) < 1e-12)//like normalizationScale, we consider accuracy around 1e-6, i.e. 1e-12 when squared + continue; + const float detInvScaled = normalizationScale/det; - float t1 = (vx2*y21 - vy2*x21) / det; - float t2 = (vx1*y21 - vy1*x21) / det; + const float t1 = (vx2*y21 - vy2*x21)*detInvScaled; + const float t2 = (vx1*y21 - vy1*x21)*detInvScaled; // This takes care of parallel lines if( cvIsInf(t1) || cvIsInf(t2) || cvIsNaN(t1) || cvIsNaN(t2) ) @@ -127,8 +147,8 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate if( t1 >= 0.0f && t1 <= 1.0f && t2 >= 0.0f && t2 <= 1.0f ) { - float xi = pts1[i].x + vec1[i].x*t1; - float yi = pts1[i].y + vec1[i].y*t1; + const float xi = pts1[i].x + vec1[i].x*t1; + const float yi = pts1[i].y + vec1[i].y*t1; intersection.push_back(Point2f(xi,yi)); } @@ -149,18 +169,20 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate int posSign = 0; int negSign = 0; - float x = pts1[i].x; - float y = pts1[i].y; + const float x = pts1[i].x; + const float y = pts1[i].y; for( int j = 0; j < 4; j++ ) { + float normalizationScale = vec2[j].x*vec2[j].x+vec2[j].y*vec2[j].y; + normalizationScale = (normalizationScale < 1e-12f) ? 1.f : 1.f/normalizationScale; // line equation: Ax + By + C = 0 // see which side of the line this point is at - float A = -vec2[j].y; - float B = vec2[j].x; - float C = -(A*pts2[j].x + B*pts2[j].y); + const float A = -vec2[j].y*normalizationScale ; + const float B = vec2[j].x*normalizationScale ; + const float C = -(A*pts2[j].x + B*pts2[j].y); - float s = A*x+ B*y+ C; + const float s = A*x + B*y + C; if( s >= 0 ) { @@ -187,18 +209,22 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate int posSign = 0; int negSign = 0; - float x = pts2[i].x; - float y = pts2[i].y; + const float x = pts2[i].x; + const float y = pts2[i].y; for( int j = 0; j < 4; j++ ) { // line equation: Ax + By + C = 0 // see which side of the line this point is at - float A = -vec1[j].y; - float B = vec1[j].x; - float C = -(A*pts1[j].x + B*pts1[j].y); + float normalizationScale = vec2[j].x*vec2[j].x+vec2[j].y*vec2[j].y; + normalizationScale = (normalizationScale < 1e-12f) ? 1.f : 1.f/normalizationScale; + if (std::isinf(normalizationScale )) + normalizationScale = 1.f; + const float A = -vec1[j].y*normalizationScale ; + const float B = vec1[j].x*normalizationScale ; + const float C = -(A*pts1[j].x + B*pts1[j].y); - float s = A*x + B*y + C; + const float s = A*x + B*y + C; if( s >= 0 ) { @@ -223,7 +249,7 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate } // Get rid of duplicated points - int Nstride = N; + const int Nstride = N; cv::AutoBuffer distPt(N * N); cv::AutoBuffer ptDistRemap(N); for (int i = 0; i < N; ++i) @@ -233,7 +259,7 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate for (int j = i + 1; j < N; ) { const Point2f pt1 = intersection[j]; - float d2 = normL2Sqr(pt1 - pt0); + const float d2 = normL2Sqr(pt1 - pt0); if(d2 <= samePointEps) { if (j < N - 1) @@ -252,10 +278,10 @@ static int _rotatedRectangleIntersection( const RotatedRect& rect1, const Rotate float minD = distPt[1]; for (int i = 0; i < N - 1; ++i) { - float* pDist = distPt.data() + Nstride * ptDistRemap[i]; + const float* pDist = distPt.data() + Nstride * ptDistRemap[i]; for (int j = i + 1; j < N; ++j) { - float d = pDist[ptDistRemap[j]]; + const float d = pDist[ptDistRemap[j]]; if (d < minD) { minD = d; diff --git a/modules/imgproc/src/opencl/medianFilter.cl b/modules/imgproc/src/opencl/medianFilter.cl index f9a6c9e8f4a5..b517a3805906 100644 --- a/modules/imgproc/src/opencl/medianFilter.cl +++ b/modules/imgproc/src/opencl/medianFilter.cl @@ -43,7 +43,7 @@ #ifdef USE_4OPT -//Utility macros for for 1,2,4 channel images: +//Utility macros for 1,2,4 channel images: // - LOAD4/STORE4 - load/store 4-pixel groups from/to global memory // - SHUFFLE4_3/SHUFFLE4_5 - rearrange scattered border/central pixels into regular 4-pixel variables diff --git a/modules/imgproc/src/precomp.hpp b/modules/imgproc/src/precomp.hpp index e178eecc8c97..a72d2a4d2aa7 100644 --- a/modules/imgproc/src/precomp.hpp +++ b/modules/imgproc/src/precomp.hpp @@ -115,4 +115,12 @@ inline bool isStorageOrMat(void * arr) CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" ); } -#endif /*__OPENCV_CV_INTERNAL_H_*/ + +namespace cv { + +CV_EXPORTS +cv::Mutex& getInitializationMutex(); // defined in core module + +} // namespace cv + +#endif /*__OPENCV_PRECOMP_H__*/ diff --git a/modules/imgproc/src/smooth.dispatch.cpp b/modules/imgproc/src/smooth.dispatch.cpp index 69d07580f2f1..b001a37f8932 100644 --- a/modules/imgproc/src/smooth.dispatch.cpp +++ b/modules/imgproc/src/smooth.dispatch.cpp @@ -566,7 +566,7 @@ static bool ipp_GaussianBlur(InputArray _src, OutputArray _dst, Size ksize, if (IPP_DISABLE_GAUSSIAN_BLUR_32FC4_1TH && (threads == 1 && src.type() == CV_32FC4)) return false; - if(IPP_GAUSSIANBLUR_PARALLEL && threads > 1) { + if(IPP_GAUSSIANBLUR_PARALLEL && threads > 1 && iwSrc.m_size.height/(threads * 4) >= ksize.height/2) { bool ok; ipp_gaussianBlurParallel invoker(iwSrc, iwDst, ksize.width, (float) sigma1, ippBorder, &ok); diff --git a/modules/imgproc/test/test_approxpoly.cpp b/modules/imgproc/test/test_approxpoly.cpp index aace416b9705..f09475c9fcda 100644 --- a/modules/imgproc/test/test_approxpoly.cpp +++ b/modules/imgproc/test/test_approxpoly.cpp @@ -220,6 +220,7 @@ int CV_ApproxPolyTest::check_slice( CvPoint StartPt, CvPoint EndPt, *_j = j; + (void) TotalErrors; // To avoid -Wunused-but-set-variable warning //return TotalErrors; return 0; } diff --git a/modules/imgproc/test/test_connectedcomponents.cpp b/modules/imgproc/test/test_connectedcomponents.cpp index ed11ea6fdad1..e1a6b761c76f 100644 --- a/modules/imgproc/test/test_connectedcomponents.cpp +++ b/modules/imgproc/test/test_connectedcomponents.cpp @@ -789,5 +789,16 @@ TEST(Imgproc_ConnectedComponents, single_column) } +TEST(Imgproc_ConnectedComponents, 4conn_regression_21366) +{ + Mat src = Mat::zeros(Size(10, 10), CV_8UC1); + { + Mat labels, stats, centroids; + EXPECT_NO_THROW(cv::connectedComponentsWithStats(src, labels, stats, centroids, 4)); + } +} + + + } } // namespace diff --git a/modules/imgproc/test/test_convhull.cpp b/modules/imgproc/test/test_convhull.cpp index ab1b464eb1d3..14f560b97e39 100644 --- a/modules/imgproc/test/test_convhull.cpp +++ b/modules/imgproc/test/test_convhull.cpp @@ -1015,7 +1015,7 @@ int CV_MinCircleTest::validate_test_results( int test_case_idx ) if( point_count >= 2 && (j < 2 || (j == 2 && cvTsDist(v[0],v[1]) < (radius-1)*2/eps)) ) { ts->printf( cvtest::TS::LOG, - "There should be at at least 3 points near the circle boundary or 2 points on the diameter\n" ); + "There should be at least 3 points near the circle boundary or 2 points on the diameter\n" ); code = cvtest::TS::FAIL_BAD_ACCURACY; goto _exit_; } diff --git a/modules/imgproc/test/test_filter.cpp b/modules/imgproc/test/test_filter.cpp index 37e86d33221f..89b56b3892bd 100644 --- a/modules/imgproc/test/test_filter.cpp +++ b/modules/imgproc/test/test_filter.cpp @@ -2355,5 +2355,16 @@ TEST(Imgproc, filter_empty_src_16857) EXPECT_TRUE(dst2.empty()); } +TEST(Imgproc_GaussianBlur, regression_11303) +{ + cv::Mat dst; + int width = 2115; + int height = 211; + double sigma = 8.64421; + cv::Mat src(cv::Size(width, height), CV_32F, 1); + cv::GaussianBlur(src, dst, cv::Size(), sigma, sigma); + EXPECT_LE(cv::norm(src, dst, NORM_L2), 1e-3); +} + }} // namespace diff --git a/modules/imgproc/test/test_floodfill.cpp b/modules/imgproc/test/test_floodfill.cpp index b880c4ee3746..934e421fba1c 100644 --- a/modules/imgproc/test/test_floodfill.cpp +++ b/modules/imgproc/test/test_floodfill.cpp @@ -531,11 +531,11 @@ TEST(Imgproc_FloodFill, maskValue) { const int n = 50; Mat img = Mat::zeros(n, n, CV_8U); - Mat mask = Mat::zeros(n + 2, n + 2, CV_8U); + Mat mask; circle(img, Point(n/2, n/2), 20, Scalar(100), 4); - int flags = 4 + CV_FLOODFILL_MASK_ONLY; + int flags = 4 + FLOODFILL_MASK_ONLY; floodFill(img, mask, Point(n/2 + 13, n/2), Scalar(100), NULL, Scalar(), Scalar(), flags); ASSERT_EQ(1, cvtest::norm(mask.rowRange(1, n-1).colRange(1, n-1), NORM_INF)); diff --git a/modules/imgproc/test/test_histograms.cpp b/modules/imgproc/test/test_histograms.cpp index a6c75a318da7..b57af774f2b4 100644 --- a/modules/imgproc/test/test_histograms.cpp +++ b/modules/imgproc/test/test_histograms.cpp @@ -1993,6 +1993,38 @@ TEST(Imgproc_Hist_Calc, badarg) EXPECT_NO_THROW(cv::calcBackProject(&img, 1, channels, hist, backProj, NULL, 1, true)); } +TEST(Imgproc_Hist_Calc, IPP_ranges_with_equal_exponent_21595) +{ + const int channels[] = { 0 }; + float range1[] = { -0.5f, 1.5f }; + const float* ranges[] = { range1 }; + const int hist_size[] = { 2 }; + + uint8_t m[1][6] = { { 0, 1, 0, 1 , 1, 1 } }; + cv::Mat images_u = Mat(1, 6, CV_8UC1, m); + cv::Mat histogram_u; + cv::calcHist(&images_u, 1, channels, noArray(), histogram_u, 1, hist_size, ranges); + + ASSERT_EQ(histogram_u.at(0), 2.f) << "0 not counts correctly, res: " << histogram_u.at(0); + ASSERT_EQ(histogram_u.at(1), 4.f) << "1 not counts correctly, res: " << histogram_u.at(0); +} + +TEST(Imgproc_Hist_Calc, IPP_ranges_with_nonequal_exponent_21595) +{ + const int channels[] = { 0 }; + float range1[] = { -1.3f, 1.5f }; + const float* ranges[] = { range1 }; + const int hist_size[] = { 3 }; + + uint8_t m[1][6] = { { 0, 1, 0, 1 , 1, 1 } }; + cv::Mat images_u = Mat(1, 6, CV_8UC1, m); + cv::Mat histogram_u; + cv::calcHist(&images_u, 1, channels, noArray(), histogram_u, 1, hist_size, ranges); + + ASSERT_EQ(histogram_u.at(0), 0.f) << "not equal to zero, res: " << histogram_u.at(0); + ASSERT_EQ(histogram_u.at(1), 2.f) << "0 not counts correctly, res: " << histogram_u.at(1); + ASSERT_EQ(histogram_u.at(2), 4.f) << "1 not counts correctly, res: " << histogram_u.at(2); +} }} // namespace /* End Of File */ diff --git a/modules/imgproc/test/test_intersection.cpp b/modules/imgproc/test/test_intersection.cpp index c455c439fce1..9ba3bf812500 100644 --- a/modules/imgproc/test/test_intersection.cpp +++ b/modules/imgproc/test/test_intersection.cpp @@ -366,6 +366,84 @@ TEST(Imgproc_RotatedRectangleIntersection, regression_12221_2) EXPECT_LE(intersections.size(), (size_t)8); } +TEST(Imgproc_RotatedRectangleIntersection, accuracy_21659) +{ + float scaleFactor = 1000;//to challenge the normalizationScale in the algorithm + cv::RectanglesIntersectTypes intersectionResult = cv::RectanglesIntersectTypes::INTERSECT_NONE; + std::vector intersection; + double intersectionArea = 0; + cv::RotatedRect r1 = cv::RotatedRect(cv::Point2f(.5f, .5f)*scaleFactor, cv::Size2f(1.f, 1.f)*scaleFactor, 0); + cv::RotatedRect r2; + + r2 = cv::RotatedRect(cv::Point2f(-2.f, -2.f)*scaleFactor, cv::Size2f(1.f, 1.f)*scaleFactor, 0); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_NONE, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-0), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(1.5f, .5f)*scaleFactor, cv::Size2f(1.f, 2.f)*scaleFactor, 0); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_PARTIAL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-0), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(1.5f, 1.5f)*scaleFactor, cv::Size2f(1.f, 1.f)*scaleFactor, 0); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_PARTIAL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-0), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(.5f, .5f)*scaleFactor, cv::Size2f(1.f, 1.f)*scaleFactor, 0); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_FULL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-r2.size.area()), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(.5f, .5f)*scaleFactor, cv::Size2f(.5f, .5f)*scaleFactor, 0); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_FULL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-r2.size.area()), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(.5f, .5f)*scaleFactor, cv::Size2f(2.f, .5f)*scaleFactor, 0); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_PARTIAL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-500000), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(.5f, .5f)*scaleFactor, cv::Size2f(1.f, 1.f)*scaleFactor, 45); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_PARTIAL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-828427), 1e-1); + + r2 = cv::RotatedRect(cv::Point2f(1.f, 1.f)*scaleFactor, cv::Size2f(1.f, 1.f)*scaleFactor, 45); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_PARTIAL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-250000), 1e-1); + + //see #21659 + r1 = cv::RotatedRect(cv::Point2f(4.48589373f, 12.5545063f), cv::Size2f(4.0f, 4.0f), 0.0347290039f); + r2 = cv::RotatedRect(cv::Point2f(4.48589373f, 12.5545235f), cv::Size2f(4.0f, 4.0f), 0.0347290039f); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_EQ(cv::RectanglesIntersectTypes::INTERSECT_PARTIAL, intersectionResult); + ASSERT_LE(std::abs(intersectionArea-r1.size.area()), 1e-3); + + r1 = cv::RotatedRect(cv::Point2f(4.48589373f, 12.5545063f + 0.01f), cv::Size2f(4.0f, 4.0f), 0.0347290039f); + r2 = cv::RotatedRect(cv::Point2f(4.48589373f, 12.5545235f), cv::Size2f(4.0f, 4.0f), 0.0347290039f); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_LE(std::abs(intersectionArea-r1.size.area()), 1e-1); + + r1 = cv::RotatedRect(cv::Point2f(45.0715866f, 39.8825722f), cv::Size2f(3.0f, 3.0f), 0.10067749f); + r2 = cv::RotatedRect(cv::Point2f(45.0715866f, 39.8825874f), cv::Size2f(3.0f, 3.0f), 0.10067749f); + intersectionResult = (cv::RectanglesIntersectTypes) cv::rotatedRectangleIntersection(r1, r2, intersection); + intersectionArea = (intersection.size() <= 2) ? 0. : cv::contourArea(intersection); + ASSERT_LE(std::abs(intersectionArea-r1.size.area()), 1e-3); +} + TEST(Imgproc_RotatedRectangleIntersection, regression_18520) { RotatedRect rr_empty( diff --git a/modules/java/generator/android/java/org/opencv/android/CameraBridgeViewBase.java b/modules/java/generator/android/java/org/opencv/android/CameraBridgeViewBase.java index 07c059b7d97d..1993cf1407a0 100644 --- a/modules/java/generator/android/java/org/opencv/android/CameraBridgeViewBase.java +++ b/modules/java/generator/android/java/org/opencv/android/CameraBridgeViewBase.java @@ -376,7 +376,7 @@ private void onEnterStartedState() { if (!connectCamera(getWidth(), getHeight())) { AlertDialog ad = new AlertDialog.Builder(getContext()).create(); ad.setCancelable(false); // This blocks the 'BACK' button - ad.setMessage("It seems that you device does not support camera (or it is locked). Application will be closed."); + ad.setMessage("It seems that your device does not support camera (or it is locked). Application will be closed."); ad.setButton(DialogInterface.BUTTON_NEUTRAL, "OK", new DialogInterface.OnClickListener() { public void onClick(DialogInterface dialog, int which) { dialog.dismiss(); diff --git a/modules/js/test/test_imgproc.js b/modules/js/test/test_imgproc.js index ada315b3908e..a0d2d937cc0a 100644 --- a/modules/js/test/test_imgproc.js +++ b/modules/js/test/test_imgproc.js @@ -948,7 +948,7 @@ QUnit.test('test_filter', function(assert) { cv.rotate(src, dst, cv.ROTATE_90_CLOCKWISE); - size = dst.size(); + let size = dst.size(); assert.equal(size.height, 2, "ROTATE_HEIGHT"); assert.equal(size.width, 3, "ROTATE_WIGTH"); diff --git a/modules/ml/src/em.cpp b/modules/ml/src/em.cpp index ec73bfd1b517..3e0eeb560a49 100644 --- a/modules/ml/src/em.cpp +++ b/modules/ml/src/em.cpp @@ -656,7 +656,7 @@ class CV_EXPORTS EMImpl CV_FINAL : public EM // Update weights // not normalized first - reduce(trainProbs, weights, 0, CV_REDUCE_SUM); + reduce(trainProbs, weights, 0, REDUCE_SUM); // Update means means.create(nclusters, dim, CV_64FC1); diff --git a/modules/ml/src/kdtree.cpp b/modules/ml/src/kdtree.cpp index a80e12964a60..8cdab98f7350 100644 --- a/modules/ml/src/kdtree.cpp +++ b/modules/ml/src/kdtree.cpp @@ -120,16 +120,13 @@ medianPartition( size_t* ofs, int a, int b, const float* vals ) } float pivot = vals[ofs[middle]]; - int less = 0, more = 0; for( k = a0; k < middle; k++ ) { CV_Assert(vals[ofs[k]] <= pivot); - less += vals[ofs[k]] < pivot; } for( k = b0; k > middle; k-- ) { CV_Assert(vals[ofs[k]] >= pivot); - more += vals[ofs[k]] > pivot; } return vals[ofs[middle]]; diff --git a/modules/ml/src/lr.cpp b/modules/ml/src/lr.cpp index ed4fb4c7201a..b43e1040454d 100644 --- a/modules/ml/src/lr.cpp +++ b/modules/ml/src/lr.cpp @@ -126,7 +126,7 @@ bool LogisticRegressionImpl::train(const Ptr& trainData, int) int num_classes = (int) this->forward_mapper.size(); if(num_classes < 2) { - CV_Error( CV_StsBadArg, "data should have atleast 2 classes" ); + CV_Error( CV_StsBadArg, "data should have at least 2 classes" ); } // add a column of ones to the data (bias/intercept term) diff --git a/modules/ml/test/test_precomp.hpp b/modules/ml/test/test_precomp.hpp index e2d36d2c2d8a..380e6126169a 100644 --- a/modules/ml/test/test_precomp.hpp +++ b/modules/ml/test/test_precomp.hpp @@ -4,7 +4,6 @@ #include "opencv2/ts.hpp" #include // EXPECT_MAT_NEAR #include "opencv2/ml.hpp" -#include "opencv2/core/core_c.h" #include using std::ifstream; diff --git a/modules/objc/generator/gen_objc.py b/modules/objc/generator/gen_objc.py index 3e3ff8a2b05d..83029312b9ef 100755 --- a/modules/objc/generator/gen_objc.py +++ b/modules/objc/generator/gen_objc.py @@ -82,6 +82,9 @@ def copy_tree(src, dst): # { class : { func : { arg_name : {"ctype" : ctype, "attrib" : [attrib]} } } } func_arg_fix = {} +# { class : { func : { prolog : "", epilog : "" } } } +header_fix = {} + # { class : { enum: fixed_enum } } enum_fix = {} @@ -479,6 +482,9 @@ def __init__(self, decl, module, namespaces=[]): # [ funcname, return_ctype, [mo self.ctype = re.sub(r"^CvTermCriteria", "TermCriteria", decl[1] or "") self.args = [] func_fix_map = func_arg_fix.get(self.classname or module, {}).get(self.objc_name, {}) + header_fixes = header_fix.get(self.classname or module, {}).get(self.objc_name, {}) + self.prolog = header_fixes.get('prolog', None) + self.epilog = header_fixes.get('epilog', None) for a in decl[3]: arg = a[:] arg_fix_map = func_fix_map.get(arg[1], {}) @@ -1170,6 +1176,9 @@ def gen_func(self, ci, fi, extension_implementations, extension_signatures): objc_name = fi.objc_name if not constructor else ("init" + ("With" + (args[0].name[0].upper() + args[0].name[1:]) if len(args) > 0 else "")) ) + if fi.prolog is not None: + method_declarations.write("\n%s\n\n" % fi.prolog) + method_declarations.write( Template( """$prototype$swift_name$deprecation_decl; @@ -1181,6 +1190,9 @@ def gen_func(self, ci, fi, extension_implementations, extension_signatures): ) ) + if fi.epilog is not None: + method_declarations.write("%s\n\n" % fi.epilog) + method_implementations.write( Template( """$prototype {$prologue $ret_val$obj_deref$cv_name($cv_args)$tail;$epilogue$ret @@ -1646,6 +1658,7 @@ def sanitize_documentation_string(doc, type): AdditionalImports[module] = gen_type_dict.get("AdditionalImports", {}) ManualFuncs.update(gen_type_dict.get("ManualFuncs", {})) func_arg_fix.update(gen_type_dict.get("func_arg_fix", {})) + header_fix.update(gen_type_dict.get("header_fix", {})) enum_fix.update(gen_type_dict.get("enum_fix", {})) const_fix.update(gen_type_dict.get("const_fix", {})) namespaces_dict.update(gen_type_dict.get("namespaces_dict", {})) diff --git a/modules/objdetect/CMakeLists.txt b/modules/objdetect/CMakeLists.txt index f4d5b22b74f8..305211ca0ab3 100644 --- a/modules/objdetect/CMakeLists.txt +++ b/modules/objdetect/CMakeLists.txt @@ -1,5 +1,16 @@ set(the_description "Object Detection") -ocv_define_module(objdetect opencv_core opencv_imgproc opencv_calib3d opencv_dnn WRAP java objc python js) +ocv_define_module(objdetect + opencv_core + opencv_imgproc + opencv_calib3d + OPTIONAL + opencv_dnn + WRAP + python + java + objc + js +) if(HAVE_QUIRC) get_property(QUIRC_INCLUDE GLOBAL PROPERTY QUIRC_INCLUDE_DIR) diff --git a/modules/objdetect/include/opencv2/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect.hpp index 35922557911a..13271cebf484 100644 --- a/modules/objdetect/include/opencv2/objdetect.hpp +++ b/modules/objdetect/include/opencv2/objdetect.hpp @@ -49,8 +49,8 @@ /** @defgroup objdetect Object Detection -Haar Feature-based Cascade Classifier for Object Detection ----------------------------------------------------------- +@{ + @defgroup objdetect_cascade_classifier Cascade Classifier for Object Detection The object detector described below has been initially proposed by Paul Viola @cite Viola01 and improved by Rainer Lienhart @cite Lienhart02 . @@ -90,8 +90,7 @@ middle) and the sum of the image pixels under the black stripe multiplied by 3 i compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the integral description). -To see the object detector at work, have a look at the facedetect demo: - +Check @ref tutorial_cascade_classifier "the corresponding tutorial" for more details. The following reference is for the detection part only. There is a separate application called opencv_traincascade that can train a cascade of boosted classifiers from a set of samples. @@ -99,10 +98,13 @@ opencv_traincascade that can train a cascade of boosted classifiers from a set o @note In the new C++ interface it is also possible to use LBP (local binary pattern) features in addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at - + -@{ - @defgroup objdetect_c C API + @defgroup objdetect_hog HOG (Histogram of Oriented Gradients) descriptor and object detector + @defgroup objdetect_qrcode QRCode detection and encoding + @defgroup objdetect_dnn_face DNN-based face detection and recognition +Check @ref tutorial_dnn_face "the corresponding tutorial" for more details. + @defgroup objdetect_common Common functions and classes @} */ @@ -111,13 +113,15 @@ typedef struct CvHaarClassifierCascade CvHaarClassifierCascade; namespace cv { -//! @addtogroup objdetect +//! @addtogroup objdetect_common //! @{ ///////////////////////////// Object Detection //////////////////////////// -//! class for grouping object candidates, detected by Cascade Classifier, HOG etc. -//! instance of the class is to be passed to cv::partition (see cxoperations.hpp) +/** @brief This class is used for grouping object candidates detected by Cascade Classifier, HOG etc. + +instance of the class is to be passed to cv::partition + */ class CV_EXPORTS SimilarRects { public: @@ -162,6 +166,10 @@ CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, std::vector& foundScales, double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); +//! @} + +//! @addtogroup objdetect_cascade_classifier +//! @{ template<> struct DefaultDeleter{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; }; @@ -243,7 +251,7 @@ class CV_EXPORTS_W CascadeClassifier CV_WRAP bool load( const String& filename ); /** @brief Reads a classifier from a FileStorage node. - @note The file may contain a new cascade classifier (trained traincascade application) only. + @note The file may contain a new cascade classifier (trained by the traincascade application) only. */ CV_WRAP bool read( const FileNode& node ); @@ -260,12 +268,6 @@ class CV_EXPORTS_W CascadeClassifier cvHaarDetectObjects. It is not used for a new cascade. @param minSize Minimum possible object size. Objects smaller than that are ignored. @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. - - The function is parallelized with the TBB library. - - @note - - (Python) A face detection example using cascade classifiers can be found at - opencv_source_code/samples/python/facedetect.py */ CV_WRAP void detectMultiScale( InputArray image, CV_OUT std::vector& objects, @@ -338,7 +340,10 @@ class CV_EXPORTS_W CascadeClassifier }; CV_EXPORTS Ptr createFaceDetectionMaskGenerator(); +//! @} +//! @addtogroup objdetect_hog +//! @{ //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// //! struct for detection region of interest (ROI) @@ -378,7 +383,7 @@ struct CV_EXPORTS_W HOGDescriptor }; enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW }; - /**@brief Creates the HOG descriptor and detector with default params. + /**@brief Creates the HOG descriptor and detector with default parameters. aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 ) */ @@ -414,6 +419,8 @@ struct CV_EXPORTS_W HOGDescriptor {} /** @overload + + Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file. @param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier. */ CV_WRAP HOGDescriptor(const String& filename) @@ -452,19 +459,19 @@ struct CV_EXPORTS_W HOGDescriptor */ CV_WRAP virtual void setSVMDetector(InputArray svmdetector); - /** @brief Reads HOGDescriptor parameters from a cv::FileNode. + /** @brief Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node. @param fn File node */ virtual bool read(FileNode& fn); - /** @brief Stores HOGDescriptor parameters in a cv::FileStorage. + /** @brief Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage. @param fs File storage @param objname Object name */ virtual void write(FileStorage& fs, const String& objname) const; - /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file. - @param filename Path of the file to read. + /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file + @param filename Name of the file to read. @param objname The optional name of the node to read (if empty, the first top-level node will be used). */ CV_WRAP virtual bool load(const String& filename, const String& objname = String()); @@ -537,13 +544,14 @@ struct CV_EXPORTS_W HOGDescriptor @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @param scale Coefficient of the detection window increase. - @param finalThreshold Final threshold + @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered + by many rectangles. 0 means not to perform grouping. @param useMeanshiftGrouping indicates grouping algorithm */ CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, CV_OUT std::vector& foundWeights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), double scale = 1.05, - double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const; + double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const; /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. @@ -555,13 +563,14 @@ struct CV_EXPORTS_W HOGDescriptor @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @param scale Coefficient of the detection window increase. - @param finalThreshold Final threshold + @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered + by many rectangles. 0 means not to perform grouping. @param useMeanshiftGrouping indicates grouping algorithm */ virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), double scale = 1.05, - double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; + double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const; /** @brief Computes gradients and quantized gradient orientations. @param img Matrix contains the image to be computed @@ -666,6 +675,10 @@ struct CV_EXPORTS_W HOGDescriptor */ void groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const; }; +//! @} + +//! @addtogroup objdetect_qrcode +//! @{ class CV_EXPORTS_W QRCodeEncoder { protected: @@ -827,7 +840,7 @@ class CV_EXPORTS_W QRCodeDetector Ptr p; }; -//! @} objdetect +//! @} } #include "opencv2/objdetect/detection_based_tracker.hpp" diff --git a/modules/objdetect/include/opencv2/objdetect/detection_based_tracker.hpp b/modules/objdetect/include/opencv2/objdetect/detection_based_tracker.hpp index 18cde13eabe7..fb96c668a5a4 100644 --- a/modules/objdetect/include/opencv2/objdetect/detection_based_tracker.hpp +++ b/modules/objdetect/include/opencv2/objdetect/detection_based_tracker.hpp @@ -51,7 +51,7 @@ namespace cv { -//! @addtogroup objdetect +//! @addtogroup objdetect_cascade_classifier //! @{ class CV_EXPORTS DetectionBasedTracker @@ -215,7 +215,7 @@ class CV_EXPORTS DetectionBasedTracker void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector& detectedObjectsInRegions); }; -//! @} objdetect +//! @} } //end of cv namespace diff --git a/modules/objdetect/include/opencv2/objdetect/face.hpp b/modules/objdetect/include/opencv2/objdetect/face.hpp index f2429c5f31fa..1b3681c65213 100644 --- a/modules/objdetect/include/opencv2/objdetect/face.hpp +++ b/modules/objdetect/include/opencv2/objdetect/face.hpp @@ -7,13 +7,15 @@ #include -/** @defgroup dnn_face DNN-based face detection and recognition - */ - namespace cv { -/** @brief DNN-based face detector, model download link: https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx. +//! @addtogroup objdetect_dnn_face +//! @{ + +/** @brief DNN-based face detector + +model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet */ class CV_EXPORTS_W FaceDetectorYN { @@ -80,7 +82,9 @@ class CV_EXPORTS_W FaceDetectorYN int target_id = 0); }; -/** @brief DNN-based face recognizer, model download link: https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view. +/** @brief DNN-based face recognizer + +model download link: https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface */ class CV_EXPORTS_W FaceRecognizerSF { @@ -105,11 +109,11 @@ class CV_EXPORTS_W FaceRecognizerSF CV_WRAP virtual void feature(InputArray aligned_img, OutputArray face_feature) = 0; /** @brief Calculating the distance between two face features - * @param _face_feature1 the first input feature - * @param _face_feature2 the second input feature of the same size and the same type as _face_feature1 + * @param face_feature1 the first input feature + * @param face_feature2 the second input feature of the same size and the same type as face_feature1 * @param dis_type defining the similarity with optional values "FR_OSINE" or "FR_NORM_L2" */ - CV_WRAP virtual double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type = FaceRecognizerSF::FR_COSINE) const = 0; + CV_WRAP virtual double match(InputArray face_feature1, InputArray face_feature2, int dis_type = FaceRecognizerSF::FR_COSINE) const = 0; /** @brief Creates an instance of this class with given parameters * @param model the path of the onnx model used for face recognition @@ -120,6 +124,7 @@ class CV_EXPORTS_W FaceRecognizerSF CV_WRAP static Ptr create(const String& model, const String& config, int backend_id = 0, int target_id = 0); }; +//! @} } // namespace cv #endif diff --git a/modules/objdetect/perf/perf_qrcode_pipeline.cpp b/modules/objdetect/perf/perf_qrcode_pipeline.cpp index 716eb2d77960..9e7960d819cc 100644 --- a/modules/objdetect/perf/perf_qrcode_pipeline.cpp +++ b/modules/objdetect/perf/perf_qrcode_pipeline.cpp @@ -106,10 +106,11 @@ PERF_TEST_P_(Perf_Objdetect_QRCode_Multi, decodeMulti) INSTANTIATE_TEST_CASE_P(/*nothing*/, Perf_Objdetect_QRCode, ::testing::Values( "version_1_down.jpg", "version_1_left.jpg", "version_1_right.jpg", "version_1_up.jpg", "version_1_top.jpg", - "version_5_down.jpg", "version_5_left.jpg", "version_5_right.jpg", "version_5_up.jpg", "version_5_top.jpg", + "version_5_down.jpg", "version_5_left.jpg",/*version_5_right.jpg*/ "version_5_up.jpg", "version_5_top.jpg", "russian.jpg", "kanji.jpg", "link_github_ocv.jpg", "link_ocv.jpg", "link_wiki_cv.jpg" ) ); +// version_5_right.jpg DISABLED after tile fix, PR #22025 INSTANTIATE_TEST_CASE_P(/*nothing*/, Perf_Objdetect_QRCode_Multi, ::testing::Values( diff --git a/modules/objdetect/src/face_detect.cpp b/modules/objdetect/src/face_detect.cpp index a9ca2d8957ce..10259a32e66f 100644 --- a/modules/objdetect/src/face_detect.cpp +++ b/modules/objdetect/src/face_detect.cpp @@ -6,13 +6,16 @@ #include "opencv2/imgproc.hpp" #include "opencv2/core.hpp" +#ifdef HAVE_OPENCV_DNN #include "opencv2/dnn.hpp" +#endif #include namespace cv { +#ifdef HAVE_OPENCV_DNN class FaceDetectorYNImpl : public FaceDetectorYN { public: @@ -273,6 +276,7 @@ class FaceDetectorYNImpl : public FaceDetectorYN std::vector priors; }; +#endif Ptr FaceDetectorYN::create(const String& model, const String& config, @@ -283,7 +287,12 @@ Ptr FaceDetectorYN::create(const String& model, const int backend_id, const int target_id) { +#ifdef HAVE_OPENCV_DNN return makePtr(model, config, input_size, score_threshold, nms_threshold, top_k, backend_id, target_id); +#else + CV_UNUSED(model); CV_UNUSED(config); CV_UNUSED(input_size); CV_UNUSED(score_threshold); CV_UNUSED(nms_threshold); CV_UNUSED(top_k); CV_UNUSED(backend_id); CV_UNUSED(target_id); + CV_Error(cv::Error::StsNotImplemented, "cv::FaceDetectorYN requires enabled 'dnn' module."); +#endif } } // namespace cv diff --git a/modules/objdetect/src/face_recognize.cpp b/modules/objdetect/src/face_recognize.cpp index 66271068b2c5..497303e42b05 100644 --- a/modules/objdetect/src/face_recognize.cpp +++ b/modules/objdetect/src/face_recognize.cpp @@ -4,13 +4,17 @@ #include "precomp.hpp" +#include "opencv2/core.hpp" +#ifdef HAVE_OPENCV_DNN #include "opencv2/dnn.hpp" +#endif #include namespace cv { +#ifdef HAVE_OPENCV_DNN class FaceRecognizerSFImpl : public FaceRecognizerSF { public: @@ -173,10 +177,16 @@ class FaceRecognizerSFImpl : public FaceRecognizerSF private: dnn::Net net; }; +#endif Ptr FaceRecognizerSF::create(const String& model, const String& config, int backend_id, int target_id) { +#ifdef HAVE_OPENCV_DNN return makePtr(model, config, backend_id, target_id); +#else + CV_UNUSED(model); CV_UNUSED(config); CV_UNUSED(backend_id); CV_UNUSED(target_id); + CV_Error(cv::Error::StsNotImplemented, "cv::FaceRecognizerSF requires enabled 'dnn' module"); +#endif } } // namespace cv diff --git a/modules/objdetect/src/hog.cpp b/modules/objdetect/src/hog.cpp index 281b00955861..b57e92ff9a30 100644 --- a/modules/objdetect/src/hog.cpp +++ b/modules/objdetect/src/hog.cpp @@ -42,7 +42,6 @@ #include "precomp.hpp" #include "cascadedetect.hpp" -#include "opencv2/core/core_c.h" #include "opencv2/core/hal/intrin.hpp" #include "opencl_kernels_objdetect.hpp" @@ -1887,7 +1886,7 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector &found_locat void HOGDescriptor::detectMultiScale( InputArray _img, std::vector& foundLocations, std::vector& foundWeights, double hitThreshold, Size winStride, Size padding, - double scale0, double finalThreshold, bool useMeanshiftGrouping) const + double scale0, double groupThreshold, bool useMeanshiftGrouping) const { CV_INSTRUMENT_REGION(); @@ -1913,7 +1912,7 @@ void HOGDescriptor::detectMultiScale( CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 && winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(), - ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector, + ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, groupThreshold, oclSvmDetector, blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient)); std::vector allCandidates; @@ -1934,21 +1933,21 @@ void HOGDescriptor::detectMultiScale( std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights)); if ( useMeanshiftGrouping ) - groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize); + groupRectangles_meanshift(foundLocations, foundWeights, foundScales, groupThreshold, winSize); else - groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2); + groupRectangles(foundLocations, foundWeights, (int)groupThreshold, 0.2); clipObjects(imgSize, foundLocations, 0, &foundWeights); } void HOGDescriptor::detectMultiScale(InputArray img, std::vector& foundLocations, double hitThreshold, Size winStride, Size padding, - double scale0, double finalThreshold, bool useMeanshiftGrouping) const + double scale0, double groupThreshold, bool useMeanshiftGrouping) const { CV_INSTRUMENT_REGION(); std::vector foundWeights; detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride, - padding, scale0, finalThreshold, useMeanshiftGrouping); + padding, scale0, groupThreshold, useMeanshiftGrouping); } std::vector HOGDescriptor::getDefaultPeopleDetector() diff --git a/modules/objdetect/src/qrcode.cpp b/modules/objdetect/src/qrcode.cpp index a7c3aeadf103..ef6b47373c9a 100644 --- a/modules/objdetect/src/qrcode.cpp +++ b/modules/objdetect/src/qrcode.cpp @@ -1061,6 +1061,15 @@ class QRDecode }; }; +float static getMinSideLen(const vector &points) { + CV_Assert(points.size() == 4ull); + double res = norm(points[1]-points[0]); + for (size_t i = 1ull; i < points.size(); i++) { + res = min(res, norm(points[i]-points[(i+1ull) % points.size()])); + } + return static_cast(res); +} + void QRDecode::init(const Mat &src, const vector &points) { CV_TRACE_FUNCTION(); @@ -1072,7 +1081,7 @@ void QRDecode::init(const Mat &src, const vector &points) original_points = bbox; version = 0; version_size = 0; - test_perspective_size = 251; + test_perspective_size = max(getMinSideLen(points)+1.f, 251.f); result_info = ""; } @@ -2088,7 +2097,7 @@ bool QRDecode::straightenQRCodeInParts() { return false; } - float perspective_curved_size = 251.0; + float perspective_curved_size = max(getMinSideLen(original_points)+1.f, 251.f);; const Size temporary_size(cvRound(perspective_curved_size), cvRound(perspective_curved_size)); float dist = perspective_curved_size / (number_pnts_to_cut - 1); @@ -2359,9 +2368,9 @@ bool QRDecode::versionDefinition() bool QRDecode::samplingForVersion() { CV_TRACE_FUNCTION(); - const double multiplyingFactor = (version < 3) ? 1 : - (version == 3) ? 1.5 : - version * (version + 1); + const double multiplyingFactor = (version < 3) ? 1. : + (version == 3) ? 2. : + 3.; const Size newFactorSize( cvRound(no_border_intermediate.size().width * multiplyingFactor), cvRound(no_border_intermediate.size().height * multiplyingFactor)); @@ -2370,45 +2379,38 @@ bool QRDecode::samplingForVersion() const int delta_rows = cvRound((postIntermediate.rows * 1.0) / version_size); const int delta_cols = cvRound((postIntermediate.cols * 1.0) / version_size); + // number of elements in the tail + const int skipped_rows = postIntermediate.rows - delta_rows * version_size; + const int skipped_cols = postIntermediate.cols - delta_cols * version_size; - vector listFrequencyElem; - for (int r = 0; r < postIntermediate.rows; r += delta_rows) - { - for (int c = 0; c < postIntermediate.cols; c += delta_cols) - { + vector deltas_rows(version_size, delta_rows); + vector deltas_cols(version_size, delta_cols); + + for (int i = 0; i < abs(skipped_rows); i++) { + // fix deltas_rows at each skip_step + const double skip_step = static_cast(version_size)/abs(skipped_rows); + const int corrected_index = static_cast(i*skip_step + skip_step/2); + deltas_rows[corrected_index] += skipped_rows > 0 ? 1 : -1; + } + for (int i = 0; i < abs(skipped_cols); i++) { + // fix deltas_cols at each skip_step + const double skip_step = static_cast(version_size)/abs(skipped_cols); + const int corrected_index = static_cast(i*skip_step + skip_step/2); + deltas_cols[corrected_index] += skipped_cols > 0 ? 1 : -1; + } + + const double totalFrequencyElem = countNonZero(postIntermediate) / static_cast(postIntermediate.total()); + straight = Mat(Size(version_size, version_size), CV_8UC1, Scalar(0)); + + for (int r = 0, i = 0; i < version_size; r += deltas_rows[i], i++) { + for (int c = 0, j = 0; j < version_size; c += deltas_cols[j], j++) { Mat tile = postIntermediate( Range(r, min(r + delta_rows, postIntermediate.rows)), Range(c, min(c + delta_cols, postIntermediate.cols))); const double frequencyElem = (countNonZero(tile) * 1.0) / tile.total(); - listFrequencyElem.push_back(frequencyElem); + straight.ptr(i)[j] = (frequencyElem < totalFrequencyElem) ? 0 : 255; } } - - double dispersionEFE = std::numeric_limits::max(); - double experimentalFrequencyElem = 0; - for (double expVal = 0; expVal < 1; expVal+=0.001) - { - double testDispersionEFE = 0.0; - for (size_t i = 0; i < listFrequencyElem.size(); i++) - { - testDispersionEFE += (listFrequencyElem[i] - expVal) * - (listFrequencyElem[i] - expVal); - } - testDispersionEFE /= (listFrequencyElem.size() - 1); - if (dispersionEFE > testDispersionEFE) - { - dispersionEFE = testDispersionEFE; - experimentalFrequencyElem = expVal; - } - } - - straight = Mat(Size(version_size, version_size), CV_8UC1, Scalar(0)); - for (int r = 0; r < version_size * version_size; r++) - { - int i = r / straight.cols; - int j = r % straight.cols; - straight.ptr(i)[j] = (listFrequencyElem[r] < experimentalFrequencyElem) ? 0 : 255; - } return true; } diff --git a/modules/objdetect/src/qrcode_encoder.cpp b/modules/objdetect/src/qrcode_encoder.cpp index 2b363b607d07..1016a17936ef 100644 --- a/modules/objdetect/src/qrcode_encoder.cpp +++ b/modules/objdetect/src/qrcode_encoder.cpp @@ -975,7 +975,7 @@ void QRCodeEncoderImpl::writeReservedArea() original.at(x, y) = INVALID_REGION_VALUE; if (version_level >= 7) { - for (int i = 0; i <= 6; i++) + for (int i = 0; i <= 5; i++) { for (int j = version_size - 11; j <= version_size - 8; j++) { diff --git a/modules/objdetect/test/test_face.cpp b/modules/objdetect/test/test_face.cpp index 2e944c50df0a..d33032fa2f62 100644 --- a/modules/objdetect/test/test_face.cpp +++ b/modules/objdetect/test/test_face.cpp @@ -78,7 +78,7 @@ TEST(Objdetect_face_detection, regression) // } // Initialize detector - std::string model = findDataFile("dnn/onnx/models/yunet-202109.onnx", false); + std::string model = findDataFile("dnn/onnx/models/yunet-202202.onnx", false); Ptr faceDetector = FaceDetectorYN::create(model, "", Size(300, 300)); faceDetector->setScoreThreshold(0.7f); @@ -178,7 +178,7 @@ TEST(Objdetect_face_recognition, regression) } // Initialize detector - std::string detect_model = findDataFile("dnn/onnx/models/yunet-202109.onnx", false); + std::string detect_model = findDataFile("dnn/onnx/models/yunet-202202.onnx", false); Ptr faceDetector = FaceDetectorYN::create(detect_model, "", Size(150, 150), score_thresh, nms_thresh); std::string recog_model = findDataFile("dnn/onnx/models/face_recognizer_fast.onnx", false); diff --git a/modules/objdetect/test/test_qrcode.cpp b/modules/objdetect/test/test_qrcode.cpp index 19a9f762600f..b5680387cbd0 100644 --- a/modules/objdetect/test/test_qrcode.cpp +++ b/modules/objdetect/test/test_qrcode.cpp @@ -11,8 +11,9 @@ std::string qrcode_images_name[] = { "version_2_down.jpg", "version_2_left.jpg", "version_2_right.jpg", "version_2_up.jpg", "version_2_top.jpg", "version_3_down.jpg", "version_3_left.jpg", "version_3_right.jpg", "version_3_up.jpg", "version_3_top.jpg", "version_4_down.jpg", "version_4_left.jpg", "version_4_right.jpg", "version_4_up.jpg", "version_4_top.jpg", - "version_5_down.jpg", "version_5_left.jpg", "version_5_right.jpg", "version_5_up.jpg", "version_5_top.jpg", + "version_5_down.jpg", "version_5_left.jpg"/*"version_5_right.jpg"*/, "russian.jpg", "kanji.jpg", "link_github_ocv.jpg", "link_ocv.jpg", "link_wiki_cv.jpg" +// version_5_right.jpg DISABLED after tile fix, PR #22025 }; std::string qrcode_images_close[] = { @@ -22,8 +23,9 @@ std::string qrcode_images_monitor[] = { "monitor_1.png", "monitor_2.png", "monitor_3.png", "monitor_4.png", "monitor_5.png" }; std::string qrcode_images_curved[] = { - "curved_1.jpg", "curved_2.jpg", "curved_3.jpg", "curved_4.jpg", "curved_5.jpg", "curved_6.jpg", "curved_7.jpg", "curved_8.jpg" + "curved_1.jpg", "curved_2.jpg", "curved_3.jpg", /*"curved_4.jpg",*/ "curved_5.jpg", /*"curved_6.jpg",*/ "curved_7.jpg", "curved_8.jpg" }; +// curved_4.jpg, curved_6.jpg DISABLED after tile fix, PR #22025 std::string qrcode_images_multiple[] = { "2_qrcodes.png", "3_close_qrcodes.png", "3_qrcodes.png", "4_qrcodes.png", "5_qrcodes.png", "6_qrcodes.png", "7_qrcodes.png", "8_close_qrcodes.png" @@ -683,7 +685,78 @@ TEST(Objdetect_QRCode_basic, not_found_qrcode) #endif } +TEST(Objdetect_QRCode_detect, detect_regression_21287) +{ + const std::string name_current_image = "issue_21287.png"; + const std::string root = "qrcode/"; + + std::string image_path = findDataFile(root + name_current_image); + Mat src = imread(image_path); + ASSERT_FALSE(src.empty()) << "Can't read image: " << image_path; + + QRCodeDetector qrcode; + std::vector corners; + Mat straight_barcode; + cv::String decoded_info; + EXPECT_TRUE(qrcode.detect(src, corners)); + EXPECT_TRUE(!corners.empty()); +#ifdef HAVE_QUIRC + EXPECT_NO_THROW(qrcode.decode(src, corners, straight_barcode)); +#endif +} + +// @author Kumataro, https://github.com/Kumataro +TEST(Objdetect_QRCode_decode, decode_regression_21929) +{ + const cv::String expect_msg = "OpenCV"; + Mat qrImg; + QRCodeEncoder::Params params; + params.version = 8; // 49x49 + Ptr qrcode_enc = cv::QRCodeEncoder::create(params);; + qrcode_enc->encode(expect_msg, qrImg); + + Mat src; + cv::resize(qrImg, src, Size(200,200), 1.0, 1.0, INTER_NEAREST); + + QRCodeDetector qrcode; + std::vector corners; + Mat straight_barcode; + + EXPECT_TRUE(qrcode.detect(src, corners)); + EXPECT_TRUE(!corners.empty()); +#ifdef HAVE_QUIRC + cv::String decoded_msg; + EXPECT_NO_THROW(decoded_msg = qrcode.decode(src, corners, straight_barcode)); + ASSERT_FALSE(straight_barcode.empty()) << "Can't decode qrimage."; + EXPECT_EQ(expect_msg, decoded_msg); +#endif +} + +TEST(Objdetect_QRCode_decode, decode_regression_version_25) +{ + const cv::String expect_msg = "OpenCV"; + Mat qrImg; + QRCodeEncoder::Params params; + params.version = 25; // 117x117 + Ptr qrcode_enc = cv::QRCodeEncoder::create(params);; + qrcode_enc->encode(expect_msg, qrImg); + Mat src; + cv::resize(qrImg, src, qrImg.size()*3, 1.0, 1.0, INTER_NEAREST); + + QRCodeDetector qrcode; + std::vector corners; + Mat straight_barcode; + + EXPECT_TRUE(qrcode.detect(src, corners)); + EXPECT_TRUE(!corners.empty()); +#ifdef HAVE_QUIRC + cv::String decoded_msg; + EXPECT_NO_THROW(decoded_msg = qrcode.decode(src, corners, straight_barcode)); + ASSERT_FALSE(straight_barcode.empty()) << "Can't decode qrimage."; + EXPECT_EQ(expect_msg, decoded_msg); +#endif +} #endif // UPDATE_QRCODE_TEST_DATA diff --git a/modules/objdetect/test/test_qrcode_encode.cpp b/modules/objdetect/test/test_qrcode_encode.cpp index e2d6b5fc628c..fe4c9fc954bd 100644 --- a/modules/objdetect/test/test_qrcode_encode.cpp +++ b/modules/objdetect/test/test_qrcode_encode.cpp @@ -433,4 +433,82 @@ TEST(Objdetect_QRCode_Encode_Decode_Structured_Append, DISABLED_regression) #endif // UPDATE_QRCODE_TEST_DATA +TEST(Objdetect_QRCode_Encode_Decode, regression_issue22029) +{ + const cv::String msg = "OpenCV"; + const int min_version = 1; + const int max_version = 40; + + for ( int v = min_version ; v <= max_version ; v++ ) + { + SCOPED_TRACE(cv::format("version=%d",v)); + + Mat qrimg; + QRCodeEncoder::Params params; + params.version = v; + Ptr qrcode_enc = cv::QRCodeEncoder::create(params); + qrcode_enc->encode(msg, qrimg); + + const int white_margin = 2; + const int finder_width = 7; + + const int timing_pos = white_margin + 6; + int i; + + // Horizontal Check + // (1) White margin(Left) + for(i = 0; i < white_margin ; i++ ) + { + ASSERT_EQ((uint8_t)255, qrimg.at(i, timing_pos)) << "i=" << i; + } + // (2) Finder pattern(Left) + for( ; i < white_margin + finder_width ; i++ ) + { + ASSERT_EQ((uint8_t)0, qrimg.at(i, timing_pos)) << "i=" << i; + } + // (3) Timing pattern + for( ; i < qrimg.rows - finder_width - white_margin; i++ ) + { + ASSERT_EQ((uint8_t)(i % 2 == 0)?0:255, qrimg.at(i, timing_pos)) << "i=" << i; + } + // (4) Finder pattern(Right) + for( ; i < qrimg.rows - white_margin; i++ ) + { + ASSERT_EQ((uint8_t)0, qrimg.at(i, timing_pos)) << "i=" << i; + } + // (5) White margin(Right) + for( ; i < qrimg.rows ; i++ ) + { + ASSERT_EQ((uint8_t)255, qrimg.at(i, timing_pos)) << "i=" << i; + } + + // Vertical Check + // (1) White margin(Top) + for(i = 0; i < white_margin ; i++ ) + { + ASSERT_EQ((uint8_t)255, qrimg.at(timing_pos, i)) << "i=" << i; + } + // (2) Finder pattern(Top) + for( ; i < white_margin + finder_width ; i++ ) + { + ASSERT_EQ((uint8_t)0, qrimg.at(timing_pos, i)) << "i=" << i; + } + // (3) Timing pattern + for( ; i < qrimg.rows - finder_width - white_margin; i++ ) + { + ASSERT_EQ((uint8_t)(i % 2 == 0)?0:255, qrimg.at(timing_pos, i)) << "i=" << i; + } + // (4) Finder pattern(Bottom) + for( ; i < qrimg.rows - white_margin; i++ ) + { + ASSERT_EQ((uint8_t)0, qrimg.at(timing_pos, i)) << "i=" << i; + } + // (5) White margin(Bottom) + for( ; i < qrimg.rows ; i++ ) + { + ASSERT_EQ((uint8_t)255, qrimg.at(timing_pos, i)) << "i=" << i; + } + } +} + }} // namespace diff --git a/modules/photo/src/seamless_cloning.hpp b/modules/photo/src/seamless_cloning.hpp index 92b24e7b097a..4d43970d2d94 100644 --- a/modules/photo/src/seamless_cloning.hpp +++ b/modules/photo/src/seamless_cloning.hpp @@ -53,7 +53,7 @@ namespace cv class Cloning { public: - void normalClone(const cv::Mat& destination, const cv::Mat &mask, const cv::Mat &wmask, cv::Mat &cloned, int flag); + void normalClone(const cv::Mat& destination, const cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, int flag); void illuminationChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float alpha, float beta); void localColorChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float red_mul, float green_mul, float blue_mul); void textureFlatten(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, float low_threshold, float high_threhold, int kernel_size, cv::Mat &cloned); @@ -61,10 +61,10 @@ namespace cv protected: void initVariables(const cv::Mat &destination, const cv::Mat &binaryMask); - void computeDerivatives(const cv::Mat &destination, const cv::Mat &patch, const cv::Mat &binaryMask); + void computeDerivatives(const cv::Mat &destination, const cv::Mat &patch, cv::Mat &binaryMask); void scalarProduct(cv::Mat mat, float r, float g, float b); void poisson(const cv::Mat &destination); - void evaluate(const cv::Mat &I, const cv::Mat &wmask, const cv::Mat &cloned); + void evaluate(const cv::Mat &I, cv::Mat &wmask, const cv::Mat &cloned); void dst(const Mat& src, Mat& dest, bool invert = false); void solve(const Mat &img, Mat& mod_diff, Mat &result); diff --git a/modules/photo/src/seamless_cloning_impl.cpp b/modules/photo/src/seamless_cloning_impl.cpp index 8fd4bc7865d4..4b3258a1d902 100644 --- a/modules/photo/src/seamless_cloning_impl.cpp +++ b/modules/photo/src/seamless_cloning_impl.cpp @@ -246,7 +246,7 @@ void Cloning::initVariables(const Mat &destination, const Mat &binaryMask) filter_Y[j] = 2.0f * (float)std::cos(scale * (j + 1)); } -void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, const Mat &binaryMask) +void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, Mat &binaryMask) { initVariables(destination, binaryMask); @@ -306,7 +306,7 @@ void Cloning::poisson(const Mat &destination) } } -void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned) +void Cloning::evaluate(const Mat &I, Mat &wmask, const Mat &cloned) { bitwise_not(wmask,wmask); @@ -320,7 +320,7 @@ void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned) merge(output,cloned); } -void Cloning::normalClone(const Mat &destination, const Mat &patch, const Mat &binaryMask, Mat &cloned, int flag) +void Cloning::normalClone(const Mat &destination, const Mat &patch, Mat &binaryMask, Mat &cloned, int flag) { const int w = destination.cols; const int h = destination.rows; diff --git a/modules/python/package/setup.py b/modules/python/package/setup.py index 191ab4e77d23..9736e78db891 100644 --- a/modules/python/package/setup.py +++ b/modules/python/package/setup.py @@ -9,7 +9,7 @@ def main(): os.chdir(SCRIPT_DIR) package_name = 'opencv' - package_version = os.environ.get('OPENCV_VERSION', '4.5.5') # TODO + package_version = os.environ.get('OPENCV_VERSION', '4.6.0') # TODO long_description = 'Open Source Computer Vision Library Python bindings' # TODO diff --git a/modules/python/src2/cv2.cpp b/modules/python/src2/cv2.cpp index b39db34fcb7a..5d952412f36b 100644 --- a/modules/python/src2/cv2.cpp +++ b/modules/python/src2/cv2.cpp @@ -79,9 +79,9 @@ static int convert_to_char(PyObject *o, char *dst, const ArgInfo& info) #include "pyopencv_generated_enums.h" #ifdef CVPY_DYNAMIC_INIT -#define CVPY_TYPE(WNAME, NAME, STORAGE, SNAME, _1, _2) CVPY_TYPE_DECLARE_DYNAMIC(WNAME, NAME, STORAGE, SNAME) +#define CVPY_TYPE(EXPORT_NAME, CLASS_ID, STORAGE, SNAME, _1, _2, SCOPE) CVPY_TYPE_DECLARE_DYNAMIC(EXPORT_NAME, CLASS_ID, STORAGE, SNAME, SCOPE) #else -#define CVPY_TYPE(WNAME, NAME, STORAGE, SNAME, _1, _2) CVPY_TYPE_DECLARE(WNAME, NAME, STORAGE, SNAME) +#define CVPY_TYPE(EXPORT_NAME, CLASS_ID, STORAGE, SNAME, _1, _2, SCOPE) CVPY_TYPE_DECLARE(EXPORT_NAME, CLASS_ID, STORAGE, SNAME, SCOPE) #endif #include "pyopencv_generated_types.h" #undef CVPY_TYPE @@ -130,45 +130,338 @@ struct ConstDef long long val; }; -static void init_submodule(PyObject * root, const char * name, PyMethodDef * methods, ConstDef * consts) +static inline bool strStartsWith(const std::string& str, const std::string& prefix) { + return prefix.empty() || \ + (str.size() >= prefix.size() && std::memcmp(str.data(), prefix.data(), prefix.size()) == 0); +} + +static inline bool strEndsWith(const std::string& str, char symbol) { + return !str.empty() && str[str.size() - 1] == symbol; +} + +/** + * \brief Creates a submodule of the `root`. Missing parents submodules + * are created as needed. If name equals to parent module name than + * borrowed reference to parent module is returned (no reference counting + * are done). + * Submodule lifetime is managed by the parent module. + * If nested submodules are created than the lifetime is managed by the + * predecessor submodule in a list. + * + * \param parent_module Parent module object. + * \param name Submodule name. + * \return borrowed reference to the created submodule. + * If any of submodules can't be created than NULL is returned. + */ +static PyObject* createSubmodule(PyObject* parent_module, const std::string& name) { - // traverse and create nested submodules - std::string s = name; - size_t i = s.find('.'); - while (i < s.length() && i != std::string::npos) - { - size_t j = s.find('.', i); - if (j == std::string::npos) - j = s.length(); - std::string short_name = s.substr(i, j-i); - std::string full_name = s.substr(0, j); - i = j+1; - - PyObject * d = PyModule_GetDict(root); - PyObject * submod = PyDict_GetItemString(d, short_name.c_str()); - if (submod == NULL) + if (!parent_module) + { + return PyErr_Format(PyExc_ImportError, + "Bindings generation error. " + "Parent module is NULL during the submodule '%s' creation", + name.c_str() + ); + } + if (strEndsWith(name, '.')) + { + return PyErr_Format(PyExc_ImportError, + "Bindings generation error. " + "Submodule can't end with a dot. Got: %s", name.c_str() + ); + } + + const std::string parent_name = PyModule_GetName(parent_module); + + /// Special case handling when caller tries to register a submodule of the parent module with + /// the same name + if (name == parent_name) { + return parent_module; + } + + if (!strStartsWith(name, parent_name)) + { + return PyErr_Format(PyExc_ImportError, + "Bindings generation error. " + "Submodule name should always start with a parent module name. " + "Parent name: %s. Submodule name: %s", parent_name.c_str(), + name.c_str() + ); + } + + size_t submodule_name_end = name.find('.', parent_name.size() + 1); + /// There is no intermediate submodules in the provided name + if (submodule_name_end == std::string::npos) { - submod = PyImport_AddModule(full_name.c_str()); - PyDict_SetItemString(d, short_name.c_str(), submod); + submodule_name_end = name.size(); } - if (short_name != "") - root = submod; - } - - // populate module's dict - PyObject * d = PyModule_GetDict(root); - for (PyMethodDef * m = methods; m->ml_name != NULL; ++m) - { - PyObject * method_obj = PyCFunction_NewEx(m, NULL, NULL); - PyDict_SetItemString(d, m->ml_name, method_obj); - Py_DECREF(method_obj); - } - for (ConstDef * c = consts; c->name != NULL; ++c) - { - PyDict_SetItemString(d, c->name, PyLong_FromLongLong(c->val)); - } + PyObject* submodule = parent_module; + for (size_t submodule_name_start = parent_name.size() + 1; + submodule_name_start < name.size(); ) + { + const std::string submodule_name = name.substr(submodule_name_start, + submodule_name_end - submodule_name_start); + + const std::string full_submodule_name = name.substr(0, submodule_name_end); + + + PyObject* parent_module_dict = PyModule_GetDict(submodule); + /// If submodule already exists it can be found in the parent module dictionary, + /// otherwise it should be added to it. + submodule = PyDict_GetItemString(parent_module_dict, + submodule_name.c_str()); + if (!submodule) + { + /// Populates global modules dictionary and returns borrowed reference to it + submodule = PyImport_AddModule(full_submodule_name.c_str()); + if (!submodule) + { + /// Return `PyImport_AddModule` NULL with an exception set on failure. + return NULL; + } + /// Populates parent module dictionary. Submodule lifetime should be managed + /// by the global modules dictionary and parent module dictionary, so Py_DECREF after + /// successfull call to the `PyDict_SetItemString` is redundant. + if (PyDict_SetItemString(parent_module_dict, submodule_name.c_str(), submodule) < 0) { + return PyErr_Format(PyExc_ImportError, + "Can't register a submodule '%s' (full name: '%s')", + submodule_name.c_str(), full_submodule_name.c_str() + ); + } + } + + submodule_name_start = submodule_name_end + 1; + + submodule_name_end = name.find('.', submodule_name_start); + if (submodule_name_end == std::string::npos) { + submodule_name_end = name.size(); + } + } + return submodule; +} + +static bool init_submodule(PyObject * root, const char * name, PyMethodDef * methods, ConstDef * consts) +{ + // traverse and create nested submodules + PyObject* submodule = createSubmodule(root, name); + if (!submodule) + { + return false; + } + // populate module's dict + PyObject * d = PyModule_GetDict(submodule); + for (PyMethodDef * m = methods; m->ml_name != NULL; ++m) + { + PyObject * method_obj = PyCFunction_NewEx(m, NULL, NULL); + if (PyDict_SetItemString(d, m->ml_name, method_obj) < 0) + { + PyErr_Format(PyExc_ImportError, + "Can't register function %s in module: %s", m->ml_name, name + ); + Py_CLEAR(method_obj); + return false; + } + Py_DECREF(method_obj); + } + for (ConstDef * c = consts; c->name != NULL; ++c) + { + PyObject* const_obj = PyLong_FromLongLong(c->val); + if (PyDict_SetItemString(d, c->name, const_obj) < 0) + { + PyErr_Format(PyExc_ImportError, + "Can't register constant %s in module %s", c->name, name + ); + Py_CLEAR(const_obj); + return false; + } + Py_DECREF(const_obj); + } + return true; +} + +static inline +bool registerTypeInModuleScope(PyObject* module, const char* type_name, PyObject* type_obj) +{ + if (PyModule_AddObject(module, type_name, type_obj) < 0) + { + PyErr_Format(PyExc_ImportError, + "Failed to register type '%s' in module scope '%s'", + type_name, PyModule_GetName(module) + ); + Py_DECREF(type_obj); + return false; + } + return true; +} + +static inline +bool registerTypeInClassScope(PyObject* cls, const char* type_name, PyObject* type_obj) +{ + if (!PyType_CheckExact(cls)) { + PyErr_Format(PyExc_ImportError, + "Failed to register type '%s' in class scope. " + "Scope class object has a wrong type", type_name + ); + return false; + } + if (PyObject_SetAttrString(cls, type_name, type_obj) < 0) + { + #ifndef Py_LIMITED_API + PyObject* cls_dict = reinterpret_cast(cls)->tp_dict; + if (PyDict_SetItemString(cls_dict, type_name, type_obj) >= 0) { + /// Clearing the error set by PyObject_SetAttrString: + /// TypeError: can't set attributes of built-in/extension type NAME + PyErr_Clear(); + return true; + } + #endif + const std::string cls_name = getPyObjectNameAttr(cls); + PyErr_Format(PyExc_ImportError, + "Failed to register type '%s' in '%s' class scope. Can't update scope dictionary", + type_name, cls_name.c_str() + ); + return false; + } + return true; +} + +static inline +PyObject* getScopeFromTypeObject(PyObject* obj, const std::string& scope_name) +{ + if (!PyType_CheckExact(obj)) { + const std::string type_name = getPyObjectNameAttr(obj); + return PyErr_Format(PyExc_ImportError, + "Failed to get scope from type '%s' " + "Scope class object has a wrong type", type_name.c_str() + ); + } + /// When using LIMITED API all classes are registered in the heap +#if defined(Py_LIMITED_API) + return PyObject_GetAttrString(obj, scope_name.c_str()); +#else + /// Otherwise classes may be registed on the stack or heap + PyObject* type_dict = reinterpret_cast(obj)->tp_dict; + if (!type_dict) { + const std::string type_name = getPyObjectNameAttr(obj); + return PyErr_Format(PyExc_ImportError, + "Failed to get scope from type '%s' " + "Type dictionary is not available", type_name.c_str() + ); + } + return PyDict_GetItemString(type_dict, scope_name.c_str()); +#endif // Py_LIMITED_API +} + +static inline +PyObject* findTypeScope(PyObject* root_module, const std::string& scope_name) +{ + PyObject* scope = root_module; + if (scope_name.empty()) + { + return scope; + } + /// Starting with 1 to omit leading dot in the scope name + size_t name_end = scope_name.find('.', 1); + if (name_end == std::string::npos) + { + name_end = scope_name.size(); + } + for (size_t name_start = 1; name_start < scope_name.size() && scope; ) + { + const std::string current_scope_name = scope_name.substr(name_start, + name_end - name_start); + + if (PyModule_CheckExact(scope)) + { + PyObject* scope_dict = PyModule_GetDict(scope); + if (!scope_dict) + { + return PyErr_Format(PyExc_ImportError, + "Scope '%s' dictionary is not available during the search for " + " the '%s' scope object", current_scope_name.c_str(), + scope_name.c_str() + ); + } + + scope = PyDict_GetItemString(scope_dict, current_scope_name.c_str()); + } + else if (PyType_CheckExact(scope)) + { + scope = getScopeFromTypeObject(scope, current_scope_name); + } + else + { + return PyErr_Format(PyExc_ImportError, + "Can't find scope '%s'. '%s' doesn't reference a module or a class", + scope_name.c_str(), current_scope_name.c_str() + ); + } + + + name_start = name_end + 1; + name_end = scope_name.find('.', name_start); + if (name_end == std::string::npos) + { + name_end = scope_name.size(); + } + } + if (!scope) + { + return PyErr_Format(PyExc_ImportError, + "Module or class with name '%s' can't be found in '%s' module", + scope_name.c_str(), PyModule_GetName(root_module) + ); + } + return scope; +} + +static bool registerNewType(PyObject* root_module, const char* type_name, + PyObject* type_obj, const std::string& scope_name) +{ + PyObject* scope = findTypeScope(root_module, scope_name); + + /// If scope can't be found it means that there is an error during + /// bindings generation + if (!scope) { + return false; + } + + if (PyModule_CheckExact(scope)) + { + if (!registerTypeInModuleScope(scope, type_name, type_obj)) + { + return false; + } + } + else + { + /// In Python 2 it is disallowed to register an inner classes + /// via modifing dictionary of the built-in type. + if (!registerTypeInClassScope(scope, type_name, type_obj)) + { + return false; + } + } + + /// Expose all classes that are defined in the submodules as aliases in the + /// root module for backward compatibility + /// If submodule and root module are same than no aliases registration are + /// required + if (scope != root_module) + { + std::string type_name_str(type_name); + + std::string alias_name; + alias_name.reserve(scope_name.size() + type_name_str.size()); + std::replace_copy(scope_name.begin() + 1, scope_name.end(), std::back_inserter(alias_name), '.', '_'); + alias_name += '_'; + alias_name += type_name_str; + + return registerTypeInModuleScope(root_module, alias_name.c_str(), type_obj); + } + return true; } #include "pyopencv_generated_modules_content.h" @@ -176,15 +469,18 @@ static void init_submodule(PyObject * root, const char * name, PyMethodDef * met static bool init_body(PyObject * m) { #define CVPY_MODULE(NAMESTR, NAME) \ - init_submodule(m, MODULESTR NAMESTR, methods_##NAME, consts_##NAME) + if (!init_submodule(m, MODULESTR NAMESTR, methods_##NAME, consts_##NAME)) \ + { \ + return false; \ + } #include "pyopencv_generated_modules.h" #undef CVPY_MODULE #ifdef CVPY_DYNAMIC_INIT -#define CVPY_TYPE(WNAME, NAME, _1, _2, BASE, CONSTRUCTOR) CVPY_TYPE_INIT_DYNAMIC(WNAME, NAME, return false, BASE, CONSTRUCTOR) +#define CVPY_TYPE(EXPORT_NAME, CLASS_ID, _1, _2, BASE, CONSTRUCTOR, SCOPE) CVPY_TYPE_INIT_DYNAMIC(EXPORT_NAME, CLASS_ID, return false, BASE, CONSTRUCTOR, SCOPE) PyObject * pyopencv_NoBase_TypePtr = NULL; #else -#define CVPY_TYPE(WNAME, NAME, _1, _2, BASE, CONSTRUCTOR) CVPY_TYPE_INIT_STATIC(WNAME, NAME, return false, BASE, CONSTRUCTOR) +#define CVPY_TYPE(EXPORT_NAME, CLASS_ID, _1, _2, BASE, CONSTRUCTOR, SCOPE) CVPY_TYPE_INIT_STATIC(EXPORT_NAME, CLASS_ID, return false, BASE, CONSTRUCTOR, SCOPE) PyTypeObject * pyopencv_NoBase_TypePtr = NULL; #endif #include "pyopencv_generated_types.h" @@ -193,7 +489,13 @@ static bool init_body(PyObject * m) PyObject* d = PyModule_GetDict(m); - PyDict_SetItemString(d, "__version__", PyString_FromString(CV_VERSION)); + PyObject* version_obj = PyString_FromString(CV_VERSION); + if (PyDict_SetItemString(d, "__version__", version_obj) < 0) { + PyErr_SetString(PyExc_ImportError, "Can't update module version"); + Py_CLEAR(version_obj); + return false; + } + Py_DECREF(version_obj); PyObject *opencv_error_dict = PyDict_New(); PyDict_SetItemString(opencv_error_dict, "file", Py_None); @@ -207,7 +509,18 @@ static bool init_body(PyObject * m) PyDict_SetItemString(d, "error", opencv_error); -#define PUBLISH(I) PyDict_SetItemString(d, #I, PyInt_FromLong(I)) +#define PUBLISH_(I, var_name, type_obj) \ + PyObject* type_obj = PyInt_FromLong(I); \ + if (PyDict_SetItemString(d, var_name, type_obj) < 0) \ + { \ + PyErr_SetString(PyExc_ImportError, "Can't register " var_name " constant"); \ + Py_CLEAR(type_obj); \ + return false; \ + } \ + Py_DECREF(type_obj); + +#define PUBLISH(I) PUBLISH_(I, #I, I ## _obj) + PUBLISH(CV_8U); PUBLISH(CV_8UC1); PUBLISH(CV_8UC2); @@ -243,6 +556,7 @@ static bool init_body(PyObject * m) PUBLISH(CV_64FC2); PUBLISH(CV_64FC3); PUBLISH(CV_64FC4); +#undef PUBLISH_ #undef PUBLISH return true; diff --git a/modules/python/src2/gen2.py b/modules/python/src2/gen2.py index fbdf5677c4b7..14ef90425165 100755 --- a/modules/python/src2/gen2.py +++ b/modules/python/src2/gen2.py @@ -245,10 +245,20 @@ def __init__(self, decl): self.readonly = False class ClassInfo(object): - def __init__(self, name, decl=None): + def __init__(self, name, decl=None, codegen=None): + # Scope name can be a module or other class e.g. cv::SimpleBlobDetector::Params + scope_name, self.original_name = name.rsplit(".", 1) + + # In case scope refer the outer class exported with different name + if codegen: + scope_name = codegen.get_export_scope_name(scope_name) + self.scope_name = re.sub(r"^cv\.?", "", scope_name) + + self.export_name = self.original_name + + self.class_id = normalize_class_name(name) + self.cname = name.replace(".", "::") - self.name = self.wname = normalize_class_name(name) - self.sname = name[name.rfind('.') + 1:] self.ismap = False self.issimple = False self.isalgorithm = False @@ -258,12 +268,11 @@ def __init__(self, name, decl=None): self.consts = {} self.base = None self.constructor = None - customname = False if decl: bases = decl[1].split()[1:] if len(bases) > 1: - print("Note: Class %s has more than 1 base class (not supported by Python C extensions)" % (self.name,)) + print("Note: Class %s has more than 1 base class (not supported by Python C extensions)" % (self.cname,)) print(" Bases: ", " ".join(bases)) print(" Only the first base class will be used") #return sys.exit(-1) @@ -277,21 +286,43 @@ def __init__(self, name, decl=None): for m in decl[2]: if m.startswith("="): - wname = m[1:] - npos = name.rfind('.') - if npos >= 0: - self.wname = normalize_class_name(name[:npos] + '.' + wname) - else: - self.wname = wname - customname = True + # Aliasing only affects the exported class name, not class identifier + self.export_name = m[1:] elif m == "/Map": self.ismap = True elif m == "/Simple": self.issimple = True self.props = [ClassProp(p) for p in decl[3]] - if not customname and self.wname.startswith("Cv"): - self.wname = self.wname[2:] + if not self.has_export_alias and self.original_name.startswith("Cv"): + self.export_name = self.export_name[2:] + + @property + def wname(self): + if len(self.scope_name) > 0: + return self.scope_name.replace(".", "_") + "_" + self.export_name + + return self.export_name + + @property + def name(self): + return self.class_id + + @property + def full_scope_name(self): + return "cv." + self.scope_name if len(self.scope_name) else "cv" + + @property + def full_export_name(self): + return self.full_scope_name + "." + self.export_name + + @property + def full_original_name(self): + return self.full_scope_name + "." + self.original_name + + @property + def has_export_alias(self): + return self.export_name != self.original_name def gen_map_code(self, codegen): all_classes = codegen.classes @@ -345,9 +376,11 @@ def gen_code(self, codegen): methods_code.write(m.gen_code(codegen)) methods_inits.write(m.get_tab_entry()) - code = gen_template_type_impl.substitute(name=self.name, wname=self.wname, cname=self.cname, - getset_code=getset_code.getvalue(), getset_inits=getset_inits.getvalue(), - methods_code=methods_code.getvalue(), methods_inits=methods_inits.getvalue()) + code = gen_template_type_impl.substitute(name=self.name, + getset_code=getset_code.getvalue(), + getset_inits=getset_inits.getvalue(), + methods_code=methods_code.getvalue(), + methods_inits=methods_inits.getvalue()) return code @@ -361,13 +394,15 @@ def gen_def(self, codegen): if self.constructor is not None: constructor_name = self.constructor.get_wrapper_name() - return "CVPY_TYPE({}, {}, {}, {}, {}, {});\n".format( - self.wname, - self.name, + return 'CVPY_TYPE({}, {}, {}, {}, {}, {}, "{}");\n'.format( + self.export_name, + self.class_id, self.cname if self.issimple else "Ptr<{}>".format(self.cname), - self.sname if self.issimple else "Ptr", + self.original_name if self.issimple else "Ptr", baseptr, - constructor_name + constructor_name, + # Leading dot is required to provide correct class naming + "." + self.scope_name if len(self.scope_name) > 0 else self.scope_name ) @@ -823,12 +858,12 @@ def gen_code(self, codegen): classinfo = all_classes[self.classname] #if dump: pprint(vars(classinfo)) if self.isconstructor: - py_name = 'cv.' + classinfo.wname - elif self.is_static: - py_name = '.'.join([self.namespace, classinfo.sname + '_' + self.variants[0].wname]) + py_name = classinfo.full_export_name else: + py_name = classinfo.full_export_name + "." + self.variants[0].wname + + if not self.is_static: cname = classinfo.cname + '::' + cname - py_name = 'cv.' + classinfo.wname + '.' + self.variants[0].wname else: py_name = '.'.join([self.namespace, self.variants[0].wname]) #if dump: print(cname + " => " + py_name) @@ -870,7 +905,7 @@ def clear(self): self.class_idx = 0 def add_class(self, stype, name, decl): - classinfo = ClassInfo(name, decl) + classinfo = ClassInfo(name, decl, self) classinfo.decl_idx = self.class_idx self.class_idx += 1 @@ -880,16 +915,30 @@ def add_class(self, stype, name, decl): sys.exit(-1) self.classes[classinfo.name] = classinfo - # Add Class to json file. - namespace, classes, name = self.split_decl_name(name) + namespace, _, _ = self.split_decl_name(name) namespace = '.'.join(namespace) - name = '_'.join(classes+[name]) + # Registering a namespace if it is not already handled or + # doesn't have anything except classes defined in it + self.namespaces.setdefault(namespace, Namespace()) - py_name = 'cv.' + classinfo.wname # use wrapper name + # Add Class to json file. + py_name = classinfo.full_export_name # use wrapper name py_signatures = self.py_signatures.setdefault(classinfo.cname, []) py_signatures.append(dict(name=py_name)) #print('class: ' + classinfo.cname + " => " + py_name) + def get_export_scope_name(self, original_scope_name): + # Outer classes should be registered before their content - inner classes in this case + class_scope = self.classes.get(normalize_class_name(original_scope_name), None) + + if class_scope: + return class_scope.full_export_name + + # Otherwise it is a namespace. + # If something is messed up at this point - it will be revelead during + # library import + return original_scope_name + def split_decl_name(self, name): chunks = name.split('.') namespace = chunks[:-1] @@ -979,6 +1028,7 @@ def add_func(self, decl): w_classes.append(w_classname) g_wname = "_".join(w_classes+[name]) func_map = self.namespaces.setdefault(namespace_str, Namespace()).funcs + # Exports static function with internal name (backward compatibility) func = func_map.setdefault(g_name, FuncInfo("", g_name, cname, isconstructor, namespace_str, False)) func.add_variant(decl, isphantom) if g_wname != g_name: # TODO OpenCV 5.0 @@ -1142,10 +1192,25 @@ def process_isalgorithm(classinfo): classlist1 = [(classinfo.decl_idx, name, classinfo) for name, classinfo in classlist] classlist1.sort() + published_types = set() # ensure toposort with base classes for decl_idx, name, classinfo in classlist1: if classinfo.ismap: continue - self.code_type_publish.write(classinfo.gen_def(self)) + def _registerType(classinfo): + if classinfo.decl_idx in published_types: + #print(classinfo.decl_idx, classinfo.name, ' - already published') + return + published_types.add(classinfo.decl_idx) + + if classinfo.base and classinfo.base in self.classes: + base_classinfo = self.classes[classinfo.base] + #print(classinfo.decl_idx, classinfo.name, ' - request publishing of base type ', base_classinfo.decl_idx, base_classinfo.name) + _registerType(base_classinfo) + + #print(classinfo.decl_idx, classinfo.name, ' - published!') + self.code_type_publish.write(classinfo.gen_def(self)) + + _registerType(classinfo) # step 3: generate the code for all the global functions diff --git a/modules/python/src2/hdr_parser.py b/modules/python/src2/hdr_parser.py index 0c3360fcbc7d..ebe13f05c72e 100755 --- a/modules/python/src2/hdr_parser.py +++ b/modules/python/src2/hdr_parser.py @@ -1002,7 +1002,7 @@ def parse(self, hname, wmode=True): docstring = "" if stmt_type == "namespace": chunks = [block[1] for block in self.block_stack if block[0] == 'namespace'] + [name] - self.namespaces.add('.'.join(chunks)) + self.namespaces.add('.'.join(filter(lambda c: len(c)> 0, chunks))) else: stmt_type, name, parse_flag = "block", "", False diff --git a/modules/python/src2/pycompat.hpp b/modules/python/src2/pycompat.hpp index 03379ec956d5..18336d429565 100644 --- a/modules/python/src2/pycompat.hpp +++ b/modules/python/src2/pycompat.hpp @@ -62,6 +62,10 @@ #endif // PY_MAJOR >=3 +#ifndef PyType_CheckExact +#define PyType_CheckExact(obj) (Py_TYPE(op) == &PyType_Type) +#endif // !PyType_CheckExact + static inline bool getUnicodeString(PyObject * obj, std::string &str) { bool res = false; @@ -93,6 +97,26 @@ static inline bool getUnicodeString(PyObject * obj, std::string &str) return res; } +static inline +std::string getPyObjectNameAttr(PyObject* obj) +{ + std::string obj_name; + PyObject* cls_name_obj = PyObject_GetAttrString(obj, "__name__"); + if (cls_name_obj && !getUnicodeString(cls_name_obj, obj_name)) { + obj_name.clear(); + } + #ifndef Py_LIMITED_API + if (PyType_CheckExact(obj) && obj_name.empty()) + { + obj_name = reinterpret_cast(obj)->tp_name; + } + #endif + if (obj_name.empty()) { + obj_name = ""; + } + return obj_name; +} + //================================================================================================== #define CV_PY_FN_WITH_KW_(fn, flags) (PyCFunction)(void*)(PyCFunctionWithKeywords)(fn), (flags) | METH_VARARGS | METH_KEYWORDS @@ -174,102 +198,106 @@ PyObject* pyopencv_from(const TYPE& src) #endif -#define CVPY_TYPE_DECLARE(WNAME, NAME, STORAGE, SNAME) \ - struct pyopencv_##NAME##_t \ +#define CVPY_TYPE_DECLARE(EXPORT_NAME, CLASS_ID, STORAGE, SNAME, SCOPE) \ + struct pyopencv_##CLASS_ID##_t \ { \ PyObject_HEAD \ STORAGE v; \ }; \ - static PyTypeObject pyopencv_##NAME##_TypeXXX = \ + static PyTypeObject pyopencv_##CLASS_ID##_TypeXXX = \ { \ CVPY_TYPE_HEAD \ - MODULESTR"."#WNAME, \ - sizeof(pyopencv_##NAME##_t), \ + MODULESTR SCOPE"."#EXPORT_NAME, \ + sizeof(pyopencv_##CLASS_ID##_t), \ }; \ - static PyTypeObject * pyopencv_##NAME##_TypePtr = &pyopencv_##NAME##_TypeXXX; \ - static bool pyopencv_##NAME##_getp(PyObject * self, STORAGE * & dst) \ + static PyTypeObject * pyopencv_##CLASS_ID##_TypePtr = &pyopencv_##CLASS_ID##_TypeXXX; \ + static bool pyopencv_##CLASS_ID##_getp(PyObject * self, STORAGE * & dst) \ { \ - if (PyObject_TypeCheck(self, pyopencv_##NAME##_TypePtr)) \ + if (PyObject_TypeCheck(self, pyopencv_##CLASS_ID##_TypePtr)) \ { \ - dst = &(((pyopencv_##NAME##_t*)self)->v); \ + dst = &(((pyopencv_##CLASS_ID##_t*)self)->v); \ return true; \ } \ return false; \ } \ - static PyObject * pyopencv_##NAME##_Instance(const STORAGE &r) \ + static PyObject * pyopencv_##CLASS_ID##_Instance(const STORAGE &r) \ { \ - pyopencv_##NAME##_t *m = PyObject_NEW(pyopencv_##NAME##_t, pyopencv_##NAME##_TypePtr); \ + pyopencv_##CLASS_ID##_t *m = PyObject_NEW(pyopencv_##CLASS_ID##_t, pyopencv_##CLASS_ID##_TypePtr); \ new (&(m->v)) STORAGE(r); \ return (PyObject*)m; \ } \ - static void pyopencv_##NAME##_dealloc(PyObject* self) \ + static void pyopencv_##CLASS_ID##_dealloc(PyObject* self) \ { \ - ((pyopencv_##NAME##_t*)self)->v.STORAGE::~SNAME(); \ + ((pyopencv_##CLASS_ID##_t*)self)->v.STORAGE::~SNAME(); \ PyObject_Del(self); \ } \ - static PyObject* pyopencv_##NAME##_repr(PyObject* self) \ + static PyObject* pyopencv_##CLASS_ID##_repr(PyObject* self) \ { \ char str[1000]; \ - sprintf(str, "<"#WNAME" %p>", self); \ + sprintf(str, "< " MODULESTR SCOPE"."#EXPORT_NAME" %p>", self); \ return PyString_FromString(str); \ } -#define CVPY_TYPE_INIT_STATIC(WNAME, NAME, ERROR_HANDLER, BASE, CONSTRUCTOR) \ +#define CVPY_TYPE_INIT_STATIC(EXPORT_NAME, CLASS_ID, ERROR_HANDLER, BASE, CONSTRUCTOR, SCOPE) \ { \ - pyopencv_##NAME##_TypePtr->tp_base = pyopencv_##BASE##_TypePtr; \ - pyopencv_##NAME##_TypePtr->tp_dealloc = pyopencv_##NAME##_dealloc; \ - pyopencv_##NAME##_TypePtr->tp_repr = pyopencv_##NAME##_repr; \ - pyopencv_##NAME##_TypePtr->tp_getset = pyopencv_##NAME##_getseters; \ - pyopencv_##NAME##_TypePtr->tp_init = (initproc) CONSTRUCTOR; \ - pyopencv_##NAME##_TypePtr->tp_methods = pyopencv_##NAME##_methods; \ - pyopencv_##NAME##_TypePtr->tp_alloc = PyType_GenericAlloc; \ - pyopencv_##NAME##_TypePtr->tp_new = PyType_GenericNew; \ - pyopencv_##NAME##_TypePtr->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE; \ - if (PyType_Ready(pyopencv_##NAME##_TypePtr) != 0) \ + pyopencv_##CLASS_ID##_TypePtr->tp_base = pyopencv_##BASE##_TypePtr; \ + pyopencv_##CLASS_ID##_TypePtr->tp_dealloc = pyopencv_##CLASS_ID##_dealloc; \ + pyopencv_##CLASS_ID##_TypePtr->tp_repr = pyopencv_##CLASS_ID##_repr; \ + pyopencv_##CLASS_ID##_TypePtr->tp_getset = pyopencv_##CLASS_ID##_getseters; \ + pyopencv_##CLASS_ID##_TypePtr->tp_init = (initproc) CONSTRUCTOR; \ + pyopencv_##CLASS_ID##_TypePtr->tp_methods = pyopencv_##CLASS_ID##_methods; \ + pyopencv_##CLASS_ID##_TypePtr->tp_alloc = PyType_GenericAlloc; \ + pyopencv_##CLASS_ID##_TypePtr->tp_new = PyType_GenericNew; \ + pyopencv_##CLASS_ID##_TypePtr->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE; \ + if (PyType_Ready(pyopencv_##CLASS_ID##_TypePtr) != 0) \ + { \ + ERROR_HANDLER; \ + } \ + CVPY_TYPE_INCREF(pyopencv_##CLASS_ID##_TypePtr); \ + if (!registerNewType(m, #EXPORT_NAME, (PyObject*)pyopencv_##CLASS_ID##_TypePtr, SCOPE)) \ { \ + printf("Failed to register a new type: " #EXPORT_NAME ", base (" #BASE ") in " SCOPE " \n"); \ ERROR_HANDLER; \ } \ - CVPY_TYPE_INCREF(pyopencv_##NAME##_TypePtr); \ - PyModule_AddObject(m, #WNAME, (PyObject *)pyopencv_##NAME##_TypePtr); \ } //================================================================================================== -#define CVPY_TYPE_DECLARE_DYNAMIC(WNAME, NAME, STORAGE, SNAME) \ - struct pyopencv_##NAME##_t \ +#define CVPY_TYPE_DECLARE_DYNAMIC(EXPORT_NAME, CLASS_ID, STORAGE, SNAME, SCOPE) \ + struct pyopencv_##CLASS_ID##_t \ { \ PyObject_HEAD \ STORAGE v; \ }; \ - static PyObject * pyopencv_##NAME##_TypePtr = 0; \ - static bool pyopencv_##NAME##_getp(PyObject * self, STORAGE * & dst) \ + static PyObject * pyopencv_##CLASS_ID##_TypePtr = 0; \ + static bool pyopencv_##CLASS_ID##_getp(PyObject * self, STORAGE * & dst) \ { \ - if (PyObject_TypeCheck(self, (PyTypeObject*)pyopencv_##NAME##_TypePtr)) \ + if (PyObject_TypeCheck(self, (PyTypeObject*)pyopencv_##CLASS_ID##_TypePtr)) \ { \ - dst = &(((pyopencv_##NAME##_t*)self)->v); \ + dst = &(((pyopencv_##CLASS_ID##_t*)self)->v); \ return true; \ } \ return false; \ } \ - static PyObject * pyopencv_##NAME##_Instance(const STORAGE &r) \ + static PyObject * pyopencv_##CLASS_ID##_Instance(const STORAGE &r) \ { \ - pyopencv_##NAME##_t *m = PyObject_New(pyopencv_##NAME##_t, (PyTypeObject*)pyopencv_##NAME##_TypePtr); \ + pyopencv_##CLASS_ID##_t *m = PyObject_New(pyopencv_##CLASS_ID##_t, (PyTypeObject*)pyopencv_##CLASS_ID##_TypePtr); \ new (&(m->v)) STORAGE(r); \ return (PyObject*)m; \ } \ - static void pyopencv_##NAME##_dealloc(PyObject* self) \ + static void pyopencv_##CLASS_ID##_dealloc(PyObject* self) \ { \ - ((pyopencv_##NAME##_t*)self)->v.STORAGE::~SNAME(); \ + ((pyopencv_##CLASS_ID##_t*)self)->v.STORAGE::~SNAME(); \ PyObject_Del(self); \ } \ - static PyObject* pyopencv_##NAME##_repr(PyObject* self) \ + static PyObject* pyopencv_##CLASS_ID##_repr(PyObject* self) \ { \ char str[1000]; \ - sprintf(str, "<"#WNAME" %p>", self); \ + sprintf(str, "< " MODULESTR SCOPE"."#EXPORT_NAME" %p>", self); \ return PyString_FromString(str); \ } \ - static PyType_Slot pyopencv_##NAME##_Slots[] = \ + static PyType_Slot pyopencv_##CLASS_ID##_Slots[] = \ { \ {Py_tp_dealloc, 0}, \ {Py_tp_repr, 0}, \ @@ -280,34 +308,38 @@ PyObject* pyopencv_from(const TYPE& src) {Py_tp_new, 0}, \ {0, 0} \ }; \ - static PyType_Spec pyopencv_##NAME##_Spec = \ + static PyType_Spec pyopencv_##CLASS_ID##_Spec = \ { \ - MODULESTR"."#WNAME, \ - sizeof(pyopencv_##NAME##_t), \ + MODULESTR SCOPE"."#EXPORT_NAME, \ + sizeof(pyopencv_##CLASS_ID##_t), \ 0, \ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, \ - pyopencv_##NAME##_Slots \ + pyopencv_##CLASS_ID##_Slots \ }; -#define CVPY_TYPE_INIT_DYNAMIC(WNAME, NAME, ERROR_HANDLER, BASE, CONSTRUCTOR) \ +#define CVPY_TYPE_INIT_DYNAMIC(EXPORT_NAME, CLASS_ID, ERROR_HANDLER, BASE, CONSTRUCTOR, SCOPE) \ { \ - pyopencv_##NAME##_Slots[0].pfunc /*tp_dealloc*/ = (void*)pyopencv_##NAME##_dealloc; \ - pyopencv_##NAME##_Slots[1].pfunc /*tp_repr*/ = (void*)pyopencv_##NAME##_repr; \ - pyopencv_##NAME##_Slots[2].pfunc /*tp_getset*/ = (void*)pyopencv_##NAME##_getseters; \ - pyopencv_##NAME##_Slots[3].pfunc /*tp_init*/ = (void*) CONSTRUCTOR; \ - pyopencv_##NAME##_Slots[4].pfunc /*tp_methods*/ = pyopencv_##NAME##_methods; \ - pyopencv_##NAME##_Slots[5].pfunc /*tp_alloc*/ = (void*)PyType_GenericAlloc; \ - pyopencv_##NAME##_Slots[6].pfunc /*tp_new*/ = (void*)PyType_GenericNew; \ + pyopencv_##CLASS_ID##_Slots[0].pfunc /*tp_dealloc*/ = (void*)pyopencv_##CLASS_ID##_dealloc; \ + pyopencv_##CLASS_ID##_Slots[1].pfunc /*tp_repr*/ = (void*)pyopencv_##CLASS_ID##_repr; \ + pyopencv_##CLASS_ID##_Slots[2].pfunc /*tp_getset*/ = (void*)pyopencv_##CLASS_ID##_getseters; \ + pyopencv_##CLASS_ID##_Slots[3].pfunc /*tp_init*/ = (void*) CONSTRUCTOR; \ + pyopencv_##CLASS_ID##_Slots[4].pfunc /*tp_methods*/ = pyopencv_##CLASS_ID##_methods; \ + pyopencv_##CLASS_ID##_Slots[5].pfunc /*tp_alloc*/ = (void*)PyType_GenericAlloc; \ + pyopencv_##CLASS_ID##_Slots[6].pfunc /*tp_new*/ = (void*)PyType_GenericNew; \ PyObject * bases = 0; \ if (pyopencv_##BASE##_TypePtr) \ bases = PyTuple_Pack(1, pyopencv_##BASE##_TypePtr); \ - pyopencv_##NAME##_TypePtr = PyType_FromSpecWithBases(&pyopencv_##NAME##_Spec, bases); \ - if (!pyopencv_##NAME##_TypePtr) \ + pyopencv_##CLASS_ID##_TypePtr = PyType_FromSpecWithBases(&pyopencv_##CLASS_ID##_Spec, bases); \ + if (!pyopencv_##CLASS_ID##_TypePtr) \ + { \ + printf("Failed to create type from spec: " #CLASS_ID ", base (" #BASE ")\n"); \ + ERROR_HANDLER; \ + } \ + if (!registerNewType(m, #EXPORT_NAME, (PyObject*)pyopencv_##CLASS_ID##_TypePtr, SCOPE)) \ { \ - printf("Failed to init: " #WNAME ", base (" #BASE ")" "\n"); \ + printf("Failed to register a new type: " #EXPORT_NAME ", base (" #BASE ") in " SCOPE " \n"); \ ERROR_HANDLER; \ } \ - PyModule_AddObject(m, #NAME, (PyObject *)pyopencv_##NAME##_TypePtr); \ } // Debug module load: diff --git a/modules/python/test/test_cuda.py b/modules/python/test/test_cuda.py index 4b3fc7d27815..a5f3fae847d9 100644 --- a/modules/python/test/test_cuda.py +++ b/modules/python/test/test_cuda.py @@ -45,5 +45,24 @@ def test_cuda_interop(self): asyncstream = cv.cuda_Stream(1) # cudaStreamNonBlocking self.assertTrue(asyncstream.cudaPtr() != 0) + def test_cuda_buffer_pool(self): + cv.cuda.setBufferPoolUsage(True) + cv.cuda.setBufferPoolConfig(cv.cuda.getDevice(), 1024 * 1024 * 64, 2) + stream_a = cv.cuda.Stream() + pool_a = cv.cuda.BufferPool(stream_a) + cuMat = pool_a.getBuffer(1024, 1024, cv.CV_8UC3) + cv.cuda.setBufferPoolUsage(False) + self.assertEqual(cuMat.size(), (1024, 1024)) + self.assertEqual(cuMat.type(), cv.CV_8UC3) + + def test_cuda_release(self): + npMat = (np.random.random((128, 128, 3)) * 255).astype(np.uint8) + cuMat = cv.cuda_GpuMat() + cuMat.upload(npMat) + cuMat.release() + self.assertTrue(cuMat.cudaPtr() == 0) + self.assertTrue(cuMat.step == 0) + self.assertTrue(cuMat.size() == (0, 0)) + if __name__ == '__main__': NewOpenCVTests.bootstrap() diff --git a/modules/python/test/test_misc.py b/modules/python/test/test_misc.py index 051ac33ac9e8..fd21656d83c8 100644 --- a/modules/python/test/test_misc.py +++ b/modules/python/test/test_misc.py @@ -1,6 +1,7 @@ #!/usr/bin/env python from __future__ import print_function +import sys import ctypes from functools import partial from collections import namedtuple @@ -607,6 +608,110 @@ def test_result_rotated_rect_issue_20930(self): self.assertTrue(isinstance(rr, tuple), msg=type(rrv)) self.assertEqual(len(rr), 3) + def test_nested_function_availability(self): + self.assertTrue(hasattr(cv.utils, "nested"), + msg="Module is not generated for nested namespace") + self.assertTrue(hasattr(cv.utils.nested, "testEchoBooleanFunction"), + msg="Function in nested module is not available") + + if sys.version_info[0] < 3: + # Nested submodule is managed only by the global submodules dictionary + # and parent native module + expected_ref_count = 2 + else: + # Nested submodule is managed by the global submodules dictionary, + # parent native module and Python part of the submodule + expected_ref_count = 3 + + # `getrefcount` temporary increases reference counter by 1 + actual_ref_count = sys.getrefcount(cv.utils.nested) - 1 + + self.assertEqual(actual_ref_count, expected_ref_count, + msg="Nested submodule reference counter has wrong value\n" + "Expected: {}. Actual: {}".format(expected_ref_count, actual_ref_count)) + for flag in (True, False): + self.assertEqual(flag, cv.utils.nested.testEchoBooleanFunction(flag), + msg="Function in nested module returns wrong result") + + def test_class_from_submodule_has_global_alias(self): + self.assertTrue(hasattr(cv.ml, "Boost"), + msg="Class is not registered in the submodule") + self.assertTrue(hasattr(cv, "ml_Boost"), + msg="Class from submodule doesn't have alias in the " + "global module") + self.assertEqual(cv.ml.Boost, cv.ml_Boost, + msg="Classes from submodules and global module don't refer " + "to the same type") + + def test_class_from_submodule_has_global_alias(self): + self.assertTrue(hasattr(cv.ml, "Boost"), + msg="Class is not registered in the submodule") + self.assertTrue(hasattr(cv, "ml_Boost"), + msg="Class from submodule doesn't have alias in the " + "global module") + self.assertEqual(cv.ml.Boost, cv.ml_Boost, + msg="Classes from submodules and global module don't refer " + "to the same type") + + def test_inner_class_has_global_alias(self): + self.assertTrue(hasattr(cv.SimpleBlobDetector, "Params"), + msg="Class is not registered as inner class") + self.assertTrue(hasattr(cv, "SimpleBlobDetector_Params"), + msg="Inner class doesn't have alias in the global module") + self.assertEqual(cv.SimpleBlobDetector.Params, cv.SimpleBlobDetector_Params, + msg="Inner class and class in global module don't refer " + "to the same type") + self.assertTrue(hasattr(cv, "SimpleBlobDetector_Params"), + msg="Inner class doesn't have alias in the global module") + + def test_export_class_with_different_name(self): + self.assertTrue(hasattr(cv.utils.nested, "ExportClassName"), + msg="Class with export alias is not registered in the submodule") + self.assertTrue(hasattr(cv, "utils_nested_ExportClassName"), + msg="Class with export alias doesn't have alias in the " + "global module") + self.assertEqual(cv.utils.nested.ExportClassName.originalName(), "OriginalClassName") + + instance = cv.utils.nested.ExportClassName.create() + self.assertTrue(isinstance(instance, cv.utils.nested.ExportClassName), + msg="Factory function returns wrong class instance: {}".format(type(instance))) + self.assertTrue(hasattr(cv.utils.nested, "ExportClassName_create"), + msg="Factory function should have alias in the same module as the class") + # self.assertFalse(hasattr(cv.utils.nested, "OriginalClassName_create"), + # msg="Factory function should not be registered with original class name, "\ + # "when class has different export name") + + def test_export_inner_class_of_class_exported_with_different_name(self): + if not hasattr(cv.utils.nested, "ExportClassName"): + raise unittest.SkipTest("Outer class with export alias is not registered in the submodule") + + self.assertTrue(hasattr(cv.utils.nested.ExportClassName, "Params"), + msg="Inner class with export alias is not registered in " + "the outer class") + self.assertTrue(hasattr(cv, "utils_nested_ExportClassName_Params"), + msg="Inner class with export alias is not registered in " + "global module") + params = cv.utils.nested.ExportClassName.Params() + params.int_value = 45 + params.float_value = 4.5 + + instance = cv.utils.nested.ExportClassName.create(params) + self.assertTrue(isinstance(instance, cv.utils.nested.ExportClassName), + msg="Factory function returns wrong class instance: {}".format(type(instance))) + self.assertEqual( + params.int_value, instance.getIntParam(), + msg="Class initialized with wrong integer parameter. Expected: {}. Actual: {}".format( + params.int_value, instance.getIntParam() + )) + self.assertEqual( + params.float_value, instance.getFloatParam(), + msg="Class initialized with wrong integer parameter. Expected: {}. Actual: {}".format( + params.float_value, instance.getFloatParam() + )) + + + + class CanUsePurePythonModuleFunction(NewOpenCVTests): def test_can_get_ocv_version(self): import sys diff --git a/modules/stitching/include/opencv2/stitching.hpp b/modules/stitching/include/opencv2/stitching.hpp index fb0ebe929de3..3d1dbbd04431 100644 --- a/modules/stitching/include/opencv2/stitching.hpp +++ b/modules/stitching/include/opencv2/stitching.hpp @@ -205,12 +205,12 @@ class CV_EXPORTS_W Stitcher void setWaveCorrectKind(detail::WaveCorrectKind kind) { wave_correct_kind_ = kind; } Ptr featuresFinder() { return features_finder_; } - const Ptr featuresFinder() const { return features_finder_; } + Ptr featuresFinder() const { return features_finder_; } void setFeaturesFinder(Ptr features_finder) { features_finder_ = features_finder; } Ptr featuresMatcher() { return features_matcher_; } - const Ptr featuresMatcher() const { return features_matcher_; } + Ptr featuresMatcher() const { return features_matcher_; } void setFeaturesMatcher(Ptr features_matcher) { features_matcher_ = features_matcher; } diff --git a/modules/stitching/include/opencv2/stitching/detail/matchers.hpp b/modules/stitching/include/opencv2/stitching/detail/matchers.hpp index cd9749ca8b0e..1b7d7d689746 100644 --- a/modules/stitching/include/opencv2/stitching/detail/matchers.hpp +++ b/modules/stitching/include/opencv2/stitching/detail/matchers.hpp @@ -104,8 +104,8 @@ struct CV_EXPORTS_W_SIMPLE MatchesInfo CV_PROP_RW int src_img_idx; CV_PROP_RW int dst_img_idx; //!< Images indices (optional) - std::vector matches; - std::vector inliers_mask; //!< Geometrically consistent matches mask + CV_PROP_RW std::vector matches; + CV_PROP_RW std::vector inliers_mask; //!< Geometrically consistent matches mask CV_PROP_RW int num_inliers; //!< Number of geometrically consistent matches CV_PROP_RW Mat H; //!< Estimated transformation CV_PROP_RW double confidence; //!< Confidence two images are from the same panorama diff --git a/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp b/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp index ad21ee12778c..95919ea00955 100644 --- a/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp +++ b/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp @@ -133,7 +133,7 @@ class CV_EXPORTS_W AffineBasedEstimator : public Estimator class CV_EXPORTS_W BundleAdjusterBase : public Estimator { public: - CV_WRAP const Mat refinementMask() const { return refinement_mask_.clone(); } + CV_WRAP Mat refinementMask() const { return refinement_mask_.clone(); } CV_WRAP void setRefinementMask(const Mat &mask) { CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3)); diff --git a/modules/stitching/misc/python/test/test_stitching.py b/modules/stitching/misc/python/test/test_stitching.py index 0d66182fb875..2e7b2b581835 100644 --- a/modules/stitching/misc/python/test/test_stitching.py +++ b/modules/stitching/misc/python/test/test_stitching.py @@ -118,5 +118,22 @@ def test_simple(self): assert result == 0 +class stitching_matches_info_test(NewOpenCVTests): + + def test_simple(self): + finder = cv.ORB.create() + img1 = self.get_sample('stitching/a1.png') + img2 = self.get_sample('stitching/a2.png') + + img_feat1 = cv.detail.computeImageFeatures2(finder, img1) + img_feat2 = cv.detail.computeImageFeatures2(finder, img2) + + matcher = cv.detail.BestOf2NearestMatcher_create() + matches_info = matcher.apply(img_feat1, img_feat2) + + self.assertIsNotNone(matches_info.matches) + self.assertIsNotNone(matches_info.inliers_mask) + + if __name__ == '__main__': NewOpenCVTests.bootstrap() diff --git a/modules/stitching/src/motion_estimators.cpp b/modules/stitching/src/motion_estimators.cpp index c0b46b101dac..29616a4915b6 100644 --- a/modules/stitching/src/motion_estimators.cpp +++ b/modules/stitching/src/motion_estimators.cpp @@ -262,7 +262,9 @@ bool BundleAdjusterBase::estimate(const std::vector &features, CvMat matParams = cvMat(cam_params_); cvCopy(&matParams, solver.param); +#if ENABLE_LOG int iter = 0; +#endif for(;;) { const CvMat* _param = 0; @@ -287,7 +289,9 @@ bool BundleAdjusterBase::estimate(const std::vector &features, { calcError(err); LOG_CHAT("."); +#if ENABLE_LOG iter++; +#endif CvMat tmp = cvMat(err); cvCopy(&tmp, _err); } diff --git a/modules/stitching/src/seam_finders.cpp b/modules/stitching/src/seam_finders.cpp index c5e4cb04ff65..0e0c7d1967c1 100644 --- a/modules/stitching/src/seam_finders.cpp +++ b/modules/stitching/src/seam_finders.cpp @@ -587,8 +587,8 @@ void DpSeamFinder::computeGradients(const Mat &image1, const Mat &image2) bool DpSeamFinder::hasOnlyOneNeighbor(int comp) { std::set >::iterator begin, end; - begin = lower_bound(edges_.begin(), edges_.end(), std::make_pair(comp, std::numeric_limits::min())); - end = upper_bound(edges_.begin(), edges_.end(), std::make_pair(comp, std::numeric_limits::max())); + begin = edges_.lower_bound(std::make_pair(comp, std::numeric_limits::min())); + end = edges_.upper_bound(std::make_pair(comp, std::numeric_limits::max())); return ++begin == end; } diff --git a/modules/ts/include/opencv2/ts/cuda_test.hpp b/modules/ts/include/opencv2/ts/cuda_test.hpp index 53bdbc8a4f10..f1851c5f8f0c 100644 --- a/modules/ts/include/opencv2/ts/cuda_test.hpp +++ b/modules/ts/include/opencv2/ts/cuda_test.hpp @@ -63,6 +63,7 @@ namespace cvtest // GpuMat create cv::cuda::GpuMat createMat(cv::Size size, int type, bool useRoi = false); + cv::cuda::GpuMat createMat(cv::Size size, int type, cv::Size& size0, cv::Point& ofs, bool useRoi = false); cv::cuda::GpuMat loadMat(const cv::Mat& m, bool useRoi = false); ////////////////////////////////////////////////////////////////////// diff --git a/modules/ts/include/opencv2/ts/ocl_test.hpp b/modules/ts/include/opencv2/ts/ocl_test.hpp index 11572e9f4899..717eb7b14ca9 100644 --- a/modules/ts/include/opencv2/ts/ocl_test.hpp +++ b/modules/ts/include/opencv2/ts/ocl_test.hpp @@ -89,7 +89,7 @@ extern int test_loop_times; #define EXPECT_MAT_NORM(mat, eps) \ do \ { \ - EXPECT_LE(TestUtils::checkNorm1(mat), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNorm1(mat), eps) \ } while ((void)0, 0) #undef EXPECT_MAT_NEAR @@ -98,7 +98,7 @@ do \ { \ ASSERT_EQ(mat1.type(), mat2.type()); \ ASSERT_EQ(mat1.size(), mat2.size()); \ - EXPECT_LE(TestUtils::checkNorm2(mat1, mat2), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNorm2(mat1, mat2), eps) \ << "Size: " << mat1.size() << std::endl; \ } while ((void)0, 0) @@ -107,7 +107,7 @@ do \ { \ ASSERT_EQ((mat1).type(), (mat2).type()); \ ASSERT_EQ((mat1).size(), (mat2).size()); \ - EXPECT_LE(TestUtils::checkNormRelative((mat1), (mat2)), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNormRelative((mat1), (mat2)), eps) \ << "Size: " << (mat1).size() << std::endl; \ } while ((void)0, 0) @@ -146,7 +146,7 @@ do \ { \ ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \ ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \ - EXPECT_LE(TestUtils::checkNorm2(name ## _roi, u ## name ## _roi), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNorm2(name ## _roi, u ## name ## _roi), eps) \ << "Size: " << name ## _roi.size() << std::endl; \ Point _offset; \ Size _wholeSize; \ @@ -155,7 +155,7 @@ do \ _mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \ ASSERT_EQ(name.type(), u ## name.type()); \ ASSERT_EQ(name.size(), u ## name.size()); \ - EXPECT_LE(TestUtils::checkNorm2(name, u ## name, _mask), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNorm2(name, u ## name, _mask), eps) \ << "Size: " << name ## _roi.size() << std::endl; \ } while ((void)0, 0) @@ -183,7 +183,7 @@ do \ { \ ASSERT_EQ(name ## _roi.type(), u ## name ## _roi.type()); \ ASSERT_EQ(name ## _roi.size(), u ## name ## _roi.size()); \ - EXPECT_LE(TestUtils::checkNormRelativeSparse(name ## _roi, u ## name ## _roi), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNormRelativeSparse(name ## _roi, u ## name ## _roi), eps) \ << "Size: " << name ## _roi.size() << std::endl; \ Point _offset; \ Size _wholeSize; \ @@ -192,7 +192,7 @@ do \ _mask(Rect(_offset, name ## _roi.size())).setTo(Scalar::all(0)); \ ASSERT_EQ(name.type(), u ## name.type()); \ ASSERT_EQ(name.size(), u ## name.size()); \ - EXPECT_LE(TestUtils::checkNormRelativeSparse(name, u ## name, _mask), eps) \ + EXPECT_LE(cvtest::ocl::TestUtils::checkNormRelativeSparse(name, u ## name, _mask), eps) \ << "Size: " << name ## _roi.size() << std::endl; \ } while ((void)0, 0) diff --git a/modules/ts/src/cuda_test.cpp b/modules/ts/src/cuda_test.cpp index 3870415f0579..a50f2cc3ce3e 100644 --- a/modules/ts/src/cuda_test.cpp +++ b/modules/ts/src/cuda_test.cpp @@ -91,7 +91,13 @@ namespace cvtest GpuMat createMat(Size size, int type, bool useRoi) { - Size size0 = size; + Size size0; Point ofs; + return createMat(size, type, size0, ofs, useRoi); + } + + GpuMat createMat(Size size, int type, Size& size0, Point& ofs, bool useRoi) + { + size0 = size; if (useRoi) { @@ -100,9 +106,10 @@ namespace cvtest } GpuMat d_m(size0, type); - - if (size0 != size) - d_m = d_m(Rect((size0.width - size.width) / 2, (size0.height - size.height) / 2, size.width, size.height)); + if (size0 != size) { + ofs = Point((size0.width - size.width) / 2, (size0.height - size.height) / 2); + d_m = d_m(Rect(ofs, size)); + } return d_m; } diff --git a/modules/ts/src/ts_gtest.cpp b/modules/ts/src/ts_gtest.cpp index b3debd54d2ed..d3752a5fe4b6 100644 --- a/modules/ts/src/ts_gtest.cpp +++ b/modules/ts/src/ts_gtest.cpp @@ -8718,7 +8718,7 @@ static void StackLowerThanAddress(const void* ptr, bool* result) { // Make sure AddressSanitizer does not tamper with the stack here. GTEST_ATTRIBUTE_NO_SANITIZE_ADDRESS_ static bool StackGrowsDown() { - int dummy; + int dummy = 0; bool result; StackLowerThanAddress(&dummy, &result); return result; diff --git a/modules/video/src/bgfg_KNN.cpp b/modules/video/src/bgfg_KNN.cpp index 1ddf1b7d519d..5ec2266921cf 100644 --- a/modules/video/src/bgfg_KNN.cpp +++ b/modules/video/src/bgfg_KNN.cpp @@ -214,6 +214,8 @@ class BackgroundSubtractorKNNImpl CV_FINAL : public BackgroundSubtractorKNN } } + virtual String getDefaultName() const CV_OVERRIDE { return "BackgroundSubtractor_KNN"; } + virtual int getHistory() const CV_OVERRIDE { return history; } virtual void setHistory(int _nframes) CV_OVERRIDE { history = _nframes; } diff --git a/modules/video/src/bgfg_gaussmix2.cpp b/modules/video/src/bgfg_gaussmix2.cpp index 69e4baf657b3..f7b26ef06bc3 100644 --- a/modules/video/src/bgfg_gaussmix2.cpp +++ b/modules/video/src/bgfg_gaussmix2.cpp @@ -236,6 +236,8 @@ class BackgroundSubtractorMOG2Impl CV_FINAL : public BackgroundSubtractorMOG2 } } + virtual String getDefaultName() const CV_OVERRIDE { return "BackgroundSubtractor_MOG2"; } + virtual int getHistory() const CV_OVERRIDE { return history; } virtual void setHistory(int _nframes) CV_OVERRIDE { history = _nframes; } diff --git a/modules/video/src/kalman.cpp b/modules/video/src/kalman.cpp index f90f9f7b9e7d..10e7ffbe0261 100644 --- a/modules/video/src/kalman.cpp +++ b/modules/video/src/kalman.cpp @@ -96,7 +96,7 @@ const Mat& KalmanFilter::predict(const Mat& control) // P'(k) = temp1*At + Q gemm(temp1, transitionMatrix, 1, processNoiseCov, 1, errorCovPre, GEMM_2_T); - // handle the case when there will be measurement before the next predict. + // handle the case when there will be no measurement before the next predict. statePre.copyTo(statePost); errorCovPre.copyTo(errorCovPost); diff --git a/modules/video/src/tracking/tracker_dasiamrpn.cpp b/modules/video/src/tracking/tracker_dasiamrpn.cpp index 72e0aa1f3a28..debe4e9c4507 100644 --- a/modules/video/src/tracking/tracker_dasiamrpn.cpp +++ b/modules/video/src/tracking/tracker_dasiamrpn.cpp @@ -160,7 +160,7 @@ void TrackerDaSiamRPNImpl::trackerInit(Mat img) dnn::blobFromImage(zCrop, blob, 1.0, Size(trackState.exemplarSize, trackState.exemplarSize), Scalar(), trackState.swapRB, false, CV_32F); siamRPN.setInput(blob); Mat out1; - siamRPN.forward(out1, "63"); + siamRPN.forward(out1, "onnx_node_output_0!63"); siamKernelCL1.setInput(out1); siamKernelR1.setInput(out1); @@ -169,8 +169,8 @@ void TrackerDaSiamRPNImpl::trackerInit(Mat img) Mat r1 = siamKernelR1.forward(); std::vector r1_shape = { 20, 256, 4, 4 }, cls1_shape = { 10, 256, 4, 4 }; - siamRPN.setParam(siamRPN.getLayerId("65"), 0, r1.reshape(0, r1_shape)); - siamRPN.setParam(siamRPN.getLayerId("68"), 0, cls1.reshape(0, cls1_shape)); + siamRPN.setParam(siamRPN.getLayerId("onnx_node_output_0!65"), 0, r1.reshape(0, r1_shape)); + siamRPN.setParam(siamRPN.getLayerId("onnx_node_output_0!68"), 0, cls1.reshape(0, cls1_shape)); } bool TrackerDaSiamRPNImpl::update(InputArray image, Rect& boundingBox) diff --git a/modules/video/test/test_optflowpyrlk.cpp b/modules/video/test/test_optflowpyrlk.cpp index 905cfebafa0f..a79a0ff4e401 100644 --- a/modules/video/test/test_optflowpyrlk.cpp +++ b/modules/video/test/test_optflowpyrlk.cpp @@ -64,10 +64,9 @@ void CV_OptFlowPyrLKTest::run( int ) const int bad_points_max = 8; /* test parameters */ - double max_err = 0., sum_err = 0; - int pt_cmpd = 0; + double max_err = 0.; int pt_exceed = 0; - int merr_i = 0, merr_j = 0, merr_k = 0, merr_nan = 0; + int merr_i = 0, merr_nan = 0; char filename[1000]; cv::Point2f *v = 0, *v2 = 0; @@ -155,7 +154,6 @@ void CV_OptFlowPyrLKTest::run( int ) double err; if( cvIsNaN(v[i].x) || cvIsNaN(v[i].y) ) { - merr_j++; continue; } @@ -173,15 +171,12 @@ void CV_OptFlowPyrLKTest::run( int ) } pt_exceed += err > success_error_level; - sum_err += err; - pt_cmpd++; } else { if( !cvIsNaN( v[i].x )) { merr_i = i; - merr_k++; ts->printf( cvtest::TS::LOG, "The algorithm lost the point #%d\n", i ); code = cvtest::TS::FAIL_BAD_ACCURACY; break; diff --git a/modules/video/test/test_trackers.impl.hpp b/modules/video/test/test_trackers.impl.hpp index 5ae98d1eb8e1..fc2315ced085 100644 --- a/modules/video/test/test_trackers.impl.hpp +++ b/modules/video/test/test_trackers.impl.hpp @@ -336,7 +336,6 @@ void TrackerTest::checkDataTest() gt2.open(gtFile.c_str()); ASSERT_TRUE(gt2.is_open()) << gtFile; string line2; - int bbCounter2 = 0; while (getline(gt2, line2)) { vector tokens = splitString(line2, ","); @@ -344,7 +343,6 @@ void TrackerTest::checkDataTest() ASSERT_EQ((size_t)4, tokens.size()) << "Incorrect ground truth file " << gtFile; bbs.push_back(bb); - bbCounter2++; } gt2.close(); diff --git a/modules/videoio/cmake/detect_aravis.cmake b/modules/videoio/cmake/detect_aravis.cmake index e7b382899343..cf8429e5dcfb 100644 --- a/modules/videoio/cmake/detect_aravis.cmake +++ b/modules/videoio/cmake/detect_aravis.cmake @@ -1,6 +1,6 @@ # --- Aravis SDK --- if(NOT HAVE_ARAVIS_API AND PKG_CONFIG_FOUND) - ocv_check_modules(ARAVIS aravis-0.6 QUIET) + ocv_check_modules(ARAVIS aravis-0.8 QUIET) if(ARAVIS_FOUND) set(HAVE_ARAVIS_API TRUE) endif() @@ -9,9 +9,9 @@ endif() if(NOT HAVE_ARAVIS_API) find_path(ARAVIS_INCLUDE "arv.h" PATHS "${ARAVIS_ROOT}" ENV ARAVIS_ROOT - PATH_SUFFIXES "include/aravis-0.6" + PATH_SUFFIXES "include/aravis-0.8" NO_DEFAULT_PATH) - find_library(ARAVIS_LIBRARY "aravis-0.6" + find_library(ARAVIS_LIBRARY "aravis-0.8" PATHS "${ARAVIS_ROOT}" ENV ARAVIS_ROOT PATH_SUFFIXES "lib" NO_DEFAULT_PATH) diff --git a/modules/videoio/cmake/detect_gstreamer.cmake b/modules/videoio/cmake/detect_gstreamer.cmake index fc6c34738328..b2ab06060d19 100644 --- a/modules/videoio/cmake/detect_gstreamer.cmake +++ b/modules/videoio/cmake/detect_gstreamer.cmake @@ -44,6 +44,10 @@ if(NOT HAVE_GSTREAMER AND WIN32) NAMES gstvideo gstvideo-1.0 PATHS ${env_paths} PATH_SUFFIXES "lib") + find_library(GSTREAMER_audio_LIBRARY + NAMES gstaudio gstaudio-1.0 + PATHS ${env_paths} + PATH_SUFFIXES "lib") find_library(GSTREAMER_glib_LIBRARY NAMES glib-2.0 @@ -63,6 +67,7 @@ if(NOT HAVE_GSTREAMER AND WIN32) AND GSTREAMER_pbutils_LIBRARY AND GSTREAMER_riff_LIBRARY AND GSTREAMER_video_LIBRARY + AND GSTREAMER_audio_LIBRARY AND GSTREAMER_glib_LIBRARY AND GSTREAMER_gobject_LIBRARY) file(STRINGS "${GSTREAMER_gst_INCLUDE_DIR}/gst/gstversion.h" ver_strings REGEX "#define +GST_VERSION_(MAJOR|MINOR|MICRO|NANO).*") @@ -77,6 +82,7 @@ if(NOT HAVE_GSTREAMER AND WIN32) ${GSTREAMER_app_LIBRARY} ${GSTREAMER_riff_LIBRARY} ${GSTREAMER_video_LIBRARY} + ${GSTREAMER_audio_LIBRARY} ${GSTREAMER_pbutils_LIBRARY} ${GSTREAMER_glib_LIBRARY} ${GSTREAMER_gobject_LIBRARY}) diff --git a/modules/videoio/include/opencv2/videoio.hpp b/modules/videoio/include/opencv2/videoio.hpp index 93ea8cdddcf5..35491a0b5ee0 100644 --- a/modules/videoio/include/opencv2/videoio.hpp +++ b/modules/videoio/include/opencv2/videoio.hpp @@ -751,7 +751,7 @@ class CV_EXPORTS_W VideoCapture */ CV_WRAP virtual bool open(const String& filename, int apiPreference = CAP_ANY); - /** @brief Opens a camera for video capturing + /** @brief Opens a video file or a capturing device or an IP video stream for video capturing with API Preference and parameters @overload @@ -775,7 +775,7 @@ class CV_EXPORTS_W VideoCapture */ CV_WRAP virtual bool open(int index, int apiPreference = CAP_ANY); - /** @brief Returns true if video capturing has been initialized already. + /** @brief Opens a camera for video capturing with API Preference and parameters @overload diff --git a/modules/videoio/src/cap_aravis.cpp b/modules/videoio/src/cap_aravis.cpp index 1f0e21eb336a..49f7789f802e 100644 --- a/modules/videoio/src/cap_aravis.cpp +++ b/modules/videoio/src/cap_aravis.cpp @@ -51,8 +51,8 @@ #include // -// This file provides wrapper for using Aravis SDK library to access GigE Vision cameras. -// Aravis library (version 0.4 or 0.6) shall be installed else this code will not be included in build. +// This file provides wrapper for using Aravis SDK library to access GigE and USB 3 Vision cameras. +// Aravis library (version 0.8) shall be installed else this code will not be included in build. // // To include this module invoke cmake with -DWITH_ARAVIS=ON // @@ -151,10 +151,6 @@ class CvCaptureCAM_Aravis : public CvCapture bool softwareTriggered; // Flag if the camera is software triggered bool allowAutoTrigger; // Flag that user allowed to trigger software triggered cameras automatically - gint64 *pixelFormats; - guint pixelFormatsCnt; - - int num_buffers; // number of payload transmission buffers ArvPixelFormat pixelFormat; // pixel format @@ -225,7 +221,7 @@ bool CvCaptureCAM_Aravis::create( int index ) if(!getDeviceNameById(index, deviceName)) return false; - return NULL != (camera = arv_camera_new(deviceName.c_str())); + return NULL != (camera = arv_camera_new(deviceName.c_str(), NULL)); } bool CvCaptureCAM_Aravis::init_buffers() @@ -234,7 +230,7 @@ bool CvCaptureCAM_Aravis::init_buffers() g_object_unref(stream); stream = NULL; } - if( (stream = arv_camera_create_stream(camera, NULL, NULL)) ) { + if( (stream = arv_camera_create_stream(camera, NULL, NULL, NULL)) ) { if( arv_camera_is_gv_device(camera) ) { g_object_set(stream, "socket-buffer", ARV_GV_STREAM_SOCKET_BUFFER_AUTO, @@ -245,7 +241,7 @@ bool CvCaptureCAM_Aravis::init_buffers() "packet-timeout", (unsigned) 40000, "frame-retention", (unsigned) 200000, NULL); } - payload = arv_camera_get_payload (camera); + payload = arv_camera_get_payload (camera, NULL); for (int i = 0; i < num_buffers; i++) arv_stream_push_buffer(stream, arv_buffer_new(payload, NULL)); @@ -260,25 +256,23 @@ bool CvCaptureCAM_Aravis::open( int index ) { if(create(index)) { // fetch properties bounds - pixelFormats = arv_camera_get_available_pixel_formats(camera, &pixelFormatsCnt); - - arv_camera_get_width_bounds(camera, &widthMin, &widthMax); - arv_camera_get_height_bounds(camera, &heightMin, &heightMax); - arv_camera_set_region(camera, 0, 0, widthMax, heightMax); + arv_camera_get_width_bounds(camera, &widthMin, &widthMax, NULL); + arv_camera_get_height_bounds(camera, &heightMin, &heightMax, NULL); + arv_camera_set_region(camera, 0, 0, widthMax, heightMax, NULL); - if( (fpsAvailable = arv_camera_is_frame_rate_available(camera)) ) - arv_camera_get_frame_rate_bounds(camera, &fpsMin, &fpsMax); - if( (gainAvailable = arv_camera_is_gain_available(camera)) ) - arv_camera_get_gain_bounds (camera, &gainMin, &gainMax); - if( (exposureAvailable = arv_camera_is_exposure_time_available(camera)) ) - arv_camera_get_exposure_time_bounds (camera, &exposureMin, &exposureMax); + if( (fpsAvailable = arv_camera_is_frame_rate_available(camera, NULL)) ) + arv_camera_get_frame_rate_bounds(camera, &fpsMin, &fpsMax, NULL); + if( (gainAvailable = arv_camera_is_gain_available(camera, NULL)) ) + arv_camera_get_gain_bounds (camera, &gainMin, &gainMax, NULL); + if( (exposureAvailable = arv_camera_is_exposure_time_available(camera, NULL)) ) + arv_camera_get_exposure_time_bounds (camera, &exposureMin, &exposureMax, NULL); // get initial values - pixelFormat = arv_camera_get_pixel_format(camera); - exposure = exposureAvailable ? arv_camera_get_exposure_time(camera) : 0; - gain = gainAvailable ? arv_camera_get_gain(camera) : 0; - fps = arv_camera_get_frame_rate(camera); - softwareTriggered = (strcmp(arv_camera_get_trigger_source(camera), "Software") == 0); + pixelFormat = arv_camera_get_pixel_format(camera, NULL); + exposure = exposureAvailable ? arv_camera_get_exposure_time(camera, NULL) : 0; + gain = gainAvailable ? arv_camera_get_gain(camera, NULL) : 0; + fps = arv_camera_get_frame_rate(camera, NULL); + softwareTriggered = (strcmp(arv_camera_get_trigger_source(camera, NULL), "Software") == 0); return startCapture(); } @@ -295,7 +289,7 @@ bool CvCaptureCAM_Aravis::grabFrame() int max_tries = 10; int tries = 0; if (softwareTriggered && allowAutoTrigger) { - arv_camera_software_trigger (camera); + arv_camera_software_trigger (camera, NULL); } for(; tries < max_tries; tries ++) { arv_buffer = arv_stream_timeout_pop_buffer (stream, 200000); @@ -402,7 +396,7 @@ void CvCaptureCAM_Aravis::autoExposureControl(IplImage* image) if( ng < gain ) { // priority 1 - reduce gain - arv_camera_set_gain(camera, (gain = ng)); + arv_camera_set_gain(camera, (gain = ng), NULL); return; } } @@ -411,7 +405,7 @@ void CvCaptureCAM_Aravis::autoExposureControl(IplImage* image) // priority 2 - control of exposure time if(std::fabs(exposure - ne) > 2) { // we have not yet reach the max-e level - arv_camera_set_exposure_time(camera, (exposure = ne) ); + arv_camera_set_exposure_time(camera, (exposure = ne), NULL); return; } } @@ -420,12 +414,12 @@ void CvCaptureCAM_Aravis::autoExposureControl(IplImage* image) if(exposureAvailable) { // exposure at maximum - increase gain if possible if(ng > gain && ng < gainMax && ne >= maxe) { - arv_camera_set_gain(camera, (gain = ng)); + arv_camera_set_gain(camera, (gain = ng), NULL); return; } } else { // priority 3 - increase gain - arv_camera_set_gain(camera, (gain = ng)); + arv_camera_set_gain(camera, (gain = ng), NULL); return; } } @@ -435,7 +429,7 @@ void CvCaptureCAM_Aravis::autoExposureControl(IplImage* image) if(gainAvailable && autoGain && exposureAvailable) { if(gain > gainMin && exposure < maxe) { exposure = CLIP( ne * 1.05, exposureMin, maxe); - arv_camera_set_exposure_time(camera, exposure ); + arv_camera_set_exposure_time(camera, exposure, NULL); } } } @@ -461,25 +455,25 @@ double CvCaptureCAM_Aravis::getProperty( int property_id ) const case CV_CAP_PROP_EXPOSURE: if(exposureAvailable) { /* exposure time in seconds, like 1/100 s */ - return arv_camera_get_exposure_time(camera) / 1e6; + return arv_camera_get_exposure_time(camera, NULL) / 1e6; } break; case CV_CAP_PROP_FPS: if(fpsAvailable) { - return arv_camera_get_frame_rate(camera); + return arv_camera_get_frame_rate(camera, NULL); } break; case CV_CAP_PROP_GAIN: if(gainAvailable) { - return arv_camera_get_gain(camera); + return arv_camera_get_gain(camera, NULL); } break; case CV_CAP_PROP_FOURCC: { - ArvPixelFormat currFormat = arv_camera_get_pixel_format(camera); + ArvPixelFormat currFormat = arv_camera_get_pixel_format(camera, NULL); switch( currFormat ) { case ARV_PIXEL_FORMAT_MONO_8: return MODE_Y800; @@ -517,8 +511,8 @@ bool CvCaptureCAM_Aravis::setProperty( int property_id, double value ) case CV_CAP_PROP_AUTO_EXPOSURE: if(exposureAvailable || gainAvailable) { if( (controlExposure = (bool)(int)value) ) { - exposure = exposureAvailable ? arv_camera_get_exposure_time(camera) : 0; - gain = gainAvailable ? arv_camera_get_gain(camera) : 0; + exposure = exposureAvailable ? arv_camera_get_exposure_time(camera, NULL) : 0; + gain = gainAvailable ? arv_camera_get_gain(camera, NULL) : 0; } } break; @@ -531,13 +525,13 @@ bool CvCaptureCAM_Aravis::setProperty( int property_id, double value ) /* exposure time in seconds, like 1/100 s */ value *= 1e6; // -> from s to us - arv_camera_set_exposure_time(camera, exposure = CLIP(value, exposureMin, exposureMax)); + arv_camera_set_exposure_time(camera, exposure = CLIP(value, exposureMin, exposureMax), NULL); break; } else return false; case CV_CAP_PROP_FPS: if(fpsAvailable) { - arv_camera_set_frame_rate(camera, fps = CLIP(value, fpsMin, fpsMax)); + arv_camera_set_frame_rate(camera, fps = CLIP(value, fpsMin, fpsMax), NULL); break; } else return false; @@ -546,7 +540,7 @@ bool CvCaptureCAM_Aravis::setProperty( int property_id, double value ) if ( (autoGain = (-1 == value) ) ) break; - arv_camera_set_gain(camera, gain = CLIP(value, gainMin, gainMax)); + arv_camera_set_gain(camera, gain = CLIP(value, gainMin, gainMax), NULL); break; } else return false; @@ -574,7 +568,7 @@ bool CvCaptureCAM_Aravis::setProperty( int property_id, double value ) } if(newFormat != pixelFormat) { stopCapture(); - arv_camera_set_pixel_format(camera, pixelFormat = newFormat); + arv_camera_set_pixel_format(camera, pixelFormat = newFormat, NULL); startCapture(); } } @@ -606,7 +600,7 @@ bool CvCaptureCAM_Aravis::setProperty( int property_id, double value ) void CvCaptureCAM_Aravis::stopCapture() { - arv_camera_stop_acquisition(camera); + arv_camera_stop_acquisition(camera, NULL); if(stream) { g_object_unref(stream); @@ -617,8 +611,8 @@ void CvCaptureCAM_Aravis::stopCapture() bool CvCaptureCAM_Aravis::startCapture() { if(init_buffers() ) { - arv_camera_set_acquisition_mode(camera, ARV_ACQUISITION_MODE_CONTINUOUS); - arv_camera_start_acquisition(camera); + arv_camera_set_acquisition_mode(camera, ARV_ACQUISITION_MODE_CONTINUOUS, NULL); + arv_camera_start_acquisition(camera, NULL); return true; } diff --git a/modules/videoio/src/cap_ffmpeg_impl.hpp b/modules/videoio/src/cap_ffmpeg_impl.hpp index 2188c25444cf..b7fa0b745c1e 100644 --- a/modules/videoio/src/cap_ffmpeg_impl.hpp +++ b/modules/videoio/src/cap_ffmpeg_impl.hpp @@ -41,6 +41,8 @@ //M*/ #include "cap_ffmpeg_legacy_api.hpp" +#include "opencv2/core/utils/logger.hpp" +#include "cap_interface.hpp" using namespace cv; @@ -49,6 +51,7 @@ using namespace cv; #endif #include #include +#include #ifndef __OPENCV_BUILD #define CV_FOURCC(c1, c2, c3, c4) (((c1) & 255) + (((c2) & 255) << 8) + (((c3) & 255) << 16) + (((c4) & 255) << 24)) @@ -79,6 +82,7 @@ extern "C" { #include #include +#include #if LIBAVUTIL_BUILD >= (LIBAVUTIL_VERSION_MICRO >= 100 \ ? CALC_FFMPEG_VERSION(51, 63, 100) : CALC_FFMPEG_VERSION(54, 6, 0)) @@ -88,6 +92,62 @@ extern "C" { #include #include +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L602-L605 +#if LIBAVFORMAT_BUILD < CALC_FFMPEG_VERSION(58, 9, 100) +# define CV_FFMPEG_REGISTER +#endif + +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L654-L657 +#if LIBAVCODEC_BUILD < CALC_FFMPEG_VERSION(58, 9, 100) +# define CV_FFMPEG_LOCKMGR +#endif + +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L390-L392 +#if LIBAVCODEC_BUILD >= CALC_FFMPEG_VERSION(58, 87, 100) +#include +#endif + +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L208-L210 +#if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(59, 0, 100) +# define CV_FFMPEG_FMT_CONST const +#else +# define CV_FFMPEG_FMT_CONST +#endif + +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L623-L624 +#if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(58, 7, 100) +# define CV_FFMPEG_URL +#endif + +// AVStream.codec deprecated in favor of AVStream.codecpar +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L1039-L1040 +#if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(59, 16, 100) +//#if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(57, 33, 100) +# define CV_FFMPEG_CODECPAR +# define CV_FFMPEG_CODEC_FIELD codecpar +#else +# define CV_FFMPEG_CODEC_FIELD codec +#endif + +#if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(59, 16, 100) +# define CV_FFMPEG_PTS_FIELD pts +#else +# define CV_FFMPEG_PTS_FIELD pkt_pts +#endif + +// https://github.com/FFmpeg/FFmpeg/blob/b6af56c034759b81985f8ea094e41cbd5f7fecfb/doc/APIchanges#L1757-L1758 +#if LIBAVUTIL_BUILD < CALC_FFMPEG_VERSION(52, 63, 100) +inline static AVRational av_make_q(int num, int den) +{ + AVRational res; + res.num = num; + res.den = den; + return res; +} +#endif + + + #ifdef __cplusplus } #endif @@ -471,6 +531,15 @@ static AVRational _opencv_ffmpeg_get_sample_aspect_ratio(AVStream *stream) #endif } +inline static std::string _opencv_ffmpeg_get_error_string(int error_code) +{ + char buf[255] = {0}; + const int err = av_strerror(error_code, buf, 254); + if (err == 0) + return std::string(buf); + else + return std::string("Unknown error"); +} struct CvCapture_FFMPEG { @@ -502,6 +571,7 @@ struct CvCapture_FFMPEG AVFormatContext * ic; AVCodec * avcodec; + AVCodecContext * context; int video_stream; AVStream * video_st; AVFrame * picture; @@ -565,6 +635,7 @@ void CvCapture_FFMPEG::init() img_convert_ctx = 0; avcodec = 0; + context = 0; frame_number = 0; eps_zero = 0.000025; @@ -617,10 +688,19 @@ void CvCapture_FFMPEG::close() if( video_st ) { - avcodec_close( video_st->codec ); +#ifdef CV_FFMPEG_CODECPAR + avcodec_close( context ); +#endif video_st = NULL; } + if (context) + { +#ifdef CV_FFMPEG_CODECPAR + avcodec_free_context(&context); +#endif + } + if( ic ) { avformat_close_input(&ic); @@ -798,8 +878,10 @@ class AutoLock }; #endif + static ImplMutex _mutex; +#ifdef CV_FFMPEG_LOCKMGR static int LockCallBack(void **mutex, AVLockOp op) { ImplMutex* localMutex = reinterpret_cast(*mutex); @@ -830,7 +912,7 @@ static int LockCallBack(void **mutex, AVLockOp op) } return 0; } - +#endif static void ffmpeg_log_callback(void *ptr, int level, const char *fmt, va_list vargs) { @@ -881,19 +963,59 @@ class InternalFFMpegRegister { avformat_network_init(); +#ifdef CV_FFMPEG_REGISTER /* register all codecs, demux and protocols */ av_register_all(); +#endif +#ifdef CV_FFMPEG_LOCKMGR /* register a callback function for synchronization */ av_lockmgr_register(&LockCallBack); +#endif } ~InternalFFMpegRegister() { +#ifdef CV_FFMPEG_LOCKMGR av_lockmgr_register(NULL); +#endif av_log_set_callback(NULL); } }; +inline void fill_codec_context(AVCodecContext * enc, AVDictionary * dict) +{ +//#ifdef FF_API_THREAD_INIT +// avcodec_thread_init(enc, get_number_of_cpus()); +//#else + enc->thread_count = get_number_of_cpus(); +//#endif + + AVDictionaryEntry* avdiscard_entry = av_dict_get(dict, "avdiscard", NULL, 0); + + if (avdiscard_entry) + { + if(strcmp(avdiscard_entry->value, "all") == 0) + enc->skip_frame = AVDISCARD_ALL; + else if (strcmp(avdiscard_entry->value, "bidir") == 0) + enc->skip_frame = AVDISCARD_BIDIR; + else if (strcmp(avdiscard_entry->value, "default") == 0) + enc->skip_frame = AVDISCARD_DEFAULT; + else if (strcmp(avdiscard_entry->value, "none") == 0) + enc->skip_frame = AVDISCARD_NONE; + // NONINTRA flag was introduced with version bump at revision: + // https://github.com/FFmpeg/FFmpeg/commit/b152152df3b778d0a86dcda5d4f5d065b4175a7b + // This key is supported only for FFMPEG version +#if LIBAVCODEC_VERSION_MICRO >= 100 && LIBAVCODEC_BUILD >= CALC_FFMPEG_VERSION(55, 67, 100) + else if (strcmp(avdiscard_entry->value, "nonintra") == 0) + enc->skip_frame = AVDISCARD_NONINTRA; +#endif + else if (strcmp(avdiscard_entry->value, "nonkey") == 0) + enc->skip_frame = AVDISCARD_NONKEY; + else if (strcmp(avdiscard_entry->value, "nonref") == 0) + enc->skip_frame = AVDISCARD_NONREF; + } +} + bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& params) { InternalFFMpegRegister::init(); @@ -980,7 +1102,11 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& char* options = getenv("OPENCV_FFMPEG_CAPTURE_OPTIONS"); if(options == NULL) { +#if LIBAVFORMAT_VERSION_MICRO >= 100 && LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(55, 48, 100) + av_dict_set(&dict, "rtsp_flags", "prefer_tcp", 0); +#else av_dict_set(&dict, "rtsp_transport", "tcp", 0); +#endif } else { @@ -993,7 +1119,7 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& #else av_dict_set(&dict, "rtsp_transport", "tcp", 0); #endif - AVInputFormat* input_format = NULL; + CV_FFMPEG_FMT_CONST AVInputFormat* input_format = NULL; AVDictionaryEntry* entry = av_dict_get(dict, "input_format", NULL, 0); if (entry != 0) { @@ -1011,60 +1137,44 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& err = avformat_find_stream_info(ic, NULL); if (err < 0) { - CV_WARN("Could not find codec parameters"); + CV_LOG_WARNING(NULL, "Unable to read codec parameters from stream (" << _opencv_ffmpeg_get_error_string(err) << ")"); goto exit_func; } for(i = 0; i < ic->nb_streams; i++) { - AVCodecContext* enc = ic->streams[i]->codec; - -//#ifdef FF_API_THREAD_INIT -// avcodec_thread_init(enc, get_number_of_cpus()); -//#else - enc->thread_count = get_number_of_cpus(); -//#endif - - AVDictionaryEntry* avdiscard_entry = av_dict_get(dict, "avdiscard", NULL, 0); - - if (avdiscard_entry) { - if(strcmp(avdiscard_entry->value, "all") == 0) - enc->skip_frame = AVDISCARD_ALL; - else if (strcmp(avdiscard_entry->value, "bidir") == 0) - enc->skip_frame = AVDISCARD_BIDIR; - else if (strcmp(avdiscard_entry->value, "default") == 0) - enc->skip_frame = AVDISCARD_DEFAULT; - else if (strcmp(avdiscard_entry->value, "none") == 0) - enc->skip_frame = AVDISCARD_NONE; - // NONINTRA flag was introduced with version bump at revision: - // https://github.com/FFmpeg/FFmpeg/commit/b152152df3b778d0a86dcda5d4f5d065b4175a7b - // This key is supported only for FFMPEG version -#if LIBAVCODEC_VERSION_MICRO >= 100 && LIBAVCODEC_BUILD >= CALC_FFMPEG_VERSION(55, 67, 100) - else if (strcmp(avdiscard_entry->value, "nonintra") == 0) - enc->skip_frame = AVDISCARD_NONINTRA; +#ifndef CV_FFMPEG_CODECPAR + context = ic->streams[i]->codec; + AVCodecID codec_id = context->codec_id; + AVMediaType codec_type = context->codec_type; +#else + AVCodecParameters* par = ic->streams[i]->codecpar; + AVCodecID codec_id = par->codec_id; + AVMediaType codec_type = par->codec_type; #endif - else if (strcmp(avdiscard_entry->value, "nonkey") == 0) - enc->skip_frame = AVDISCARD_NONKEY; - else if (strcmp(avdiscard_entry->value, "nonref") == 0) - enc->skip_frame = AVDISCARD_NONREF; - } - if( AVMEDIA_TYPE_VIDEO == enc->codec_type && video_stream < 0) + if( AVMEDIA_TYPE_VIDEO == codec_type && video_stream < 0) { - CV_LOG_DEBUG(NULL, "FFMPEG: stream[" << i << "] is video stream with codecID=" << (int)enc->codec_id - << " width=" << enc->width - << " height=" << enc->height + // backup encoder' width/height +#ifndef CV_FFMPEG_CODECPAR + int enc_width = context->width; + int enc_height = context->height; +#else + int enc_width = par->width; + int enc_height = par->height; +#endif + + CV_LOG_DEBUG(NULL, "FFMPEG: stream[" << i << "] is video stream with codecID=" << (int)codec_id + << " width=" << enc_width + << " height=" << enc_height ); - // backup encoder' width/height - int enc_width = enc->width; - int enc_height = enc->height; #if !USE_AV_HW_CODECS va_type = VIDEO_ACCELERATION_NONE; #endif // find and open decoder, try HW acceleration types specified in 'hw_acceleration' list (in order) - AVCodec *codec = NULL; + const AVCodec *codec = NULL; err = -1; #if USE_AV_HW_CODECS HWAccelIterator accel_iter(va_type, false/*isEncoder*/, dict); @@ -1076,21 +1186,27 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& #if USE_AV_HW_CODECS accel_iter.parse_next(); AVHWDeviceType hw_type = accel_iter.hw_type(); - enc->get_format = avcodec_default_get_format; - if (enc->hw_device_ctx) { - av_buffer_unref(&enc->hw_device_ctx); - } if (hw_type != AV_HWDEVICE_TYPE_NONE) { CV_LOG_DEBUG(NULL, "FFMPEG: trying to configure H/W acceleration: '" << accel_iter.hw_type_device_string() << "'"); AVPixelFormat hw_pix_fmt = AV_PIX_FMT_NONE; - codec = hw_find_codec(enc->codec_id, hw_type, av_codec_is_decoder, accel_iter.disabled_codecs().c_str(), &hw_pix_fmt); - if (codec) { + codec = hw_find_codec(codec_id, hw_type, av_codec_is_decoder, accel_iter.disabled_codecs().c_str(), &hw_pix_fmt); + if (codec) + { +#ifdef CV_FFMPEG_CODECPAR + context = avcodec_alloc_context3(codec); +#endif + CV_Assert(context); + context->get_format = avcodec_default_get_format; + if (context->hw_device_ctx) { + av_buffer_unref(&context->hw_device_ctx); + } if (hw_pix_fmt != AV_PIX_FMT_NONE) - enc->get_format = hw_get_format_callback; // set callback to select HW pixel format, not SW format - enc->hw_device_ctx = hw_create_device(hw_type, hw_device, accel_iter.device_subname(), use_opencl != 0); - if (!enc->hw_device_ctx) + context->get_format = hw_get_format_callback; // set callback to select HW pixel format, not SW format + context->hw_device_ctx = hw_create_device(hw_type, hw_device, accel_iter.device_subname(), use_opencl != 0); + if (!context->hw_device_ctx) { + context->get_format = avcodec_default_get_format; CV_LOG_DEBUG(NULL, "FFMPEG: ... can't create H/W device: '" << accel_iter.hw_type_device_string() << "'"); codec = NULL; } @@ -1102,10 +1218,10 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& AVDictionaryEntry* video_codec_param = av_dict_get(dict, "video_codec", NULL, 0); if (video_codec_param == NULL) { - codec = avcodec_find_decoder(enc->codec_id); + codec = avcodec_find_decoder(codec_id); if (!codec) { - CV_LOG_ERROR(NULL, "Could not find decoder for codec_id=" << (int)enc->codec_id); + CV_LOG_ERROR(NULL, "Could not find decoder for codec_id=" << (int)codec_id); } } else @@ -1117,10 +1233,26 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& CV_LOG_ERROR(NULL, "Could not find decoder '" << video_codec_param->value << "'"); } } + if (codec) + { +#ifdef CV_FFMPEG_CODECPAR + context = avcodec_alloc_context3(codec); +#endif + CV_Assert(context); + } } if (!codec) + { +#ifdef CV_FFMPEG_CODECPAR + avcodec_free_context(&context); +#endif continue; - err = avcodec_open2(enc, codec, NULL); + } + fill_codec_context(context, dict); +#ifdef CV_FFMPEG_CODECPAR + avcodec_parameters_to_context(context, par); +#endif + err = avcodec_open2(context, codec, NULL); if (err >= 0) { #if USE_AV_HW_CODECS va_type = hw_type_to_va_type(hw_type); @@ -1142,10 +1274,10 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& } // checking width/height (since decoder can sometimes alter it, eg. vp6f) - if (enc_width && (enc->width != enc_width)) - enc->width = enc_width; - if (enc_height && (enc->height != enc_height)) - enc->height = enc_height; + if (enc_width && (context->width != enc_width)) + context->width = enc_width; + if (enc_height && (context->height != enc_height)) + context->height = enc_height; video_stream = i; video_st = ic->streams[i]; @@ -1156,8 +1288,8 @@ bool CvCapture_FFMPEG::open(const char* _filename, const VideoCaptureParameters& picture = avcodec_alloc_frame(); #endif - frame.width = enc->width; - frame.height = enc->height; + frame.width = context->width; + frame.height = context->height; frame.cn = 3; frame.step = 0; frame.data = NULL; @@ -1302,7 +1434,7 @@ bool CvCapture_FFMPEG::grabFrame() int count_errs = 0; const int max_number_of_attempts = 1 << 9; - if( !ic || !video_st ) return false; + if( !ic || !video_st || !context ) return false; if( ic->streams[video_stream]->nb_frames > 0 && frame_number > ic->streams[video_stream]->nb_frames ) @@ -1318,7 +1450,7 @@ bool CvCapture_FFMPEG::grabFrame() #if USE_AV_SEND_FRAME_API // check if we can receive frame from previously decoded packet - valid = avcodec_receive_frame(video_st->codec, picture) >= 0; + valid = avcodec_receive_frame(context, picture) >= 0; #endif // get the next frame @@ -1368,19 +1500,19 @@ bool CvCapture_FFMPEG::grabFrame() // Decode video frame #if USE_AV_SEND_FRAME_API - if (avcodec_send_packet(video_st->codec, &packet) < 0) { + if (avcodec_send_packet(context, &packet) < 0) { break; } - ret = avcodec_receive_frame(video_st->codec, picture); + ret = avcodec_receive_frame(context, picture); #else int got_picture = 0; - avcodec_decode_video2(video_st->codec, picture, &got_picture, &packet); + avcodec_decode_video2(context, picture, &got_picture, &packet); ret = got_picture ? 0 : -1; #endif if (ret >= 0) { //picture_pts = picture->best_effort_timestamp; if( picture_pts == AV_NOPTS_VALUE_ ) - picture_pts = picture->pkt_pts != AV_NOPTS_VALUE_ && picture->pkt_pts != 0 ? picture->pkt_pts : picture->pkt_dts; + picture_pts = picture->CV_FFMPEG_PTS_FIELD != AV_NOPTS_VALUE_ && picture->CV_FFMPEG_PTS_FIELD != 0 ? picture->CV_FFMPEG_PTS_FIELD : picture->pkt_dts; valid = true; } else if (ret == AVERROR(EAGAIN)) { @@ -1411,7 +1543,7 @@ bool CvCapture_FFMPEG::grabFrame() bool CvCapture_FFMPEG::retrieveFrame(int flag, unsigned char** data, int* step, int* width, int* height, int* cn) { - if (!video_st) + if (!video_st || !context) return false; if (rawMode || flag == extraDataIdx) @@ -1424,8 +1556,8 @@ bool CvCapture_FFMPEG::retrieveFrame(int flag, unsigned char** data, int* step, ret = p.data != NULL; } else if (flag == extraDataIdx) { - *data = ic->streams[video_stream]->codec->extradata; - *step = ic->streams[video_stream]->codec->extradata_size; + *data = ic->streams[video_stream]->CV_FFMPEG_CODEC_FIELD->extradata; + *step = ic->streams[video_stream]->CV_FFMPEG_CODEC_FIELD->extradata_size; } *width = *step; *height = 1; @@ -1450,13 +1582,13 @@ bool CvCapture_FFMPEG::retrieveFrame(int flag, unsigned char** data, int* step, return false; if( img_convert_ctx == NULL || - frame.width != video_st->codec->width || - frame.height != video_st->codec->height || + frame.width != video_st->CV_FFMPEG_CODEC_FIELD->width || + frame.height != video_st->CV_FFMPEG_CODEC_FIELD->height || frame.data == NULL ) { // Some sws_scale optimizations have some assumptions about alignment of data/step/width/height // Also we use coded_width/height to workaround problem with legacy ffmpeg versions (like n0.8) - int buffer_width = video_st->codec->coded_width, buffer_height = video_st->codec->coded_height; + int buffer_width = context->coded_width, buffer_height = context->coded_height; img_convert_ctx = sws_getCachedContext( img_convert_ctx, @@ -1490,8 +1622,8 @@ bool CvCapture_FFMPEG::retrieveFrame(int flag, unsigned char** data, int* step, _opencv_ffmpeg_av_image_fill_arrays(&rgb_picture, rgb_picture.data[0], AV_PIX_FMT_BGR24, buffer_width, buffer_height ); #endif - frame.width = video_st->codec->width; - frame.height = video_st->codec->height; + frame.width = video_st->CV_FFMPEG_CODEC_FIELD->width; + frame.height = video_st->CV_FFMPEG_CODEC_FIELD->height; frame.cn = 3; frame.data = rgb_picture.data[0]; frame.step = rgb_picture.linesize[0]; @@ -1501,7 +1633,7 @@ bool CvCapture_FFMPEG::retrieveFrame(int flag, unsigned char** data, int* step, img_convert_ctx, sw_picture->data, sw_picture->linesize, - 0, video_st->codec->coded_height, + 0, context->coded_height, rgb_picture.data, rgb_picture.linesize ); @@ -1515,7 +1647,7 @@ bool CvCapture_FFMPEG::retrieveFrame(int flag, unsigned char** data, int* step, #if USE_AV_HW_CODECS if (sw_picture != picture) { - av_frame_unref(sw_picture); + av_frame_free(&sw_picture); } #endif return true; @@ -1525,12 +1657,12 @@ bool CvCapture_FFMPEG::retrieveHWFrame(cv::OutputArray output) { #if USE_AV_HW_CODECS // check that we have HW frame in GPU memory - if (!picture || !picture->hw_frames_ctx) { + if (!picture || !picture->hw_frames_ctx || !context) { return false; } // GPU color conversion NV12->BGRA, from GPU media buffer to GPU OpenCL buffer - return hw_copy_frame_to_umat(video_st->codec->hw_device_ctx, picture, output); + return hw_copy_frame_to_umat(context->hw_device_ctx, picture, output); #else CV_UNUSED(output); return false; @@ -1539,7 +1671,7 @@ bool CvCapture_FFMPEG::retrieveHWFrame(cv::OutputArray output) double CvCapture_FFMPEG::getProperty( int property_id ) const { - if( !video_st ) return 0; + if( !video_st || !context ) return 0; double codec_tag = 0; CV_CODEC_ID codec_id = AV_CODEC_ID_NONE; @@ -1566,8 +1698,8 @@ double CvCapture_FFMPEG::getProperty( int property_id ) const case CAP_PROP_FPS: return get_fps(); case CAP_PROP_FOURCC: - codec_id = video_st->codec->codec_id; - codec_tag = (double) video_st->codec->codec_tag; + codec_id = video_st->CV_FFMPEG_CODEC_FIELD->codec_id; + codec_tag = (double) video_st->CV_FFMPEG_CODEC_FIELD->codec_tag; if(codec_tag || codec_id == AV_CODEC_ID_NONE) { @@ -1587,7 +1719,11 @@ double CvCapture_FFMPEG::getProperty( int property_id ) const return _opencv_ffmpeg_get_sample_aspect_ratio(ic->streams[video_stream]).den; case CAP_PROP_CODEC_PIXEL_FORMAT: { +#ifdef CV_FFMPEG_CODECPAR + AVPixelFormat pix_fmt = (AVPixelFormat)video_st->codecpar->format; +#else AVPixelFormat pix_fmt = video_st->codec->pix_fmt; +#endif unsigned int fourcc_tag = avcodec_pix_fmt_to_codec_tag(pix_fmt); return (fourcc_tag == 0) ? (double)-1 : (double)fourcc_tag; } @@ -1667,7 +1803,7 @@ double CvCapture_FFMPEG::get_fps() const if (fps < eps_zero) { - fps = 1.0 / r2d(ic->streams[video_stream]->codec->time_base); + fps = 1.0 / r2d(ic->streams[video_stream]->time_base); } #endif return fps; @@ -1699,7 +1835,16 @@ double CvCapture_FFMPEG::dts_to_sec(int64_t dts) const void CvCapture_FFMPEG::get_rotation_angle() { rotation_angle = 0; -#if LIBAVUTIL_BUILD >= CALC_FFMPEG_VERSION(52, 94, 100) +#if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(57, 68, 100) + const uint8_t *data = 0; + data = av_stream_get_side_data(video_st, AV_PKT_DATA_DISPLAYMATRIX, NULL); + if (data) + { + rotation_angle = cvRound(av_display_rotation_get((const int32_t*)data)); + if (rotation_angle < 0) + rotation_angle += 360; + } +#elif LIBAVUTIL_BUILD >= CALC_FFMPEG_VERSION(52, 94, 100) AVDictionaryEntry *rotate_tag = av_dict_get(video_st->metadata, "rotate", NULL, 0); if (rotate_tag != NULL) rotation_angle = atoi(rotate_tag->value); @@ -1708,6 +1853,7 @@ void CvCapture_FFMPEG::get_rotation_angle() void CvCapture_FFMPEG::seek(int64_t _frame_number) { + CV_Assert(context); _frame_number = std::min(_frame_number, get_total_frames()); int delta = 16; @@ -1724,7 +1870,7 @@ void CvCapture_FFMPEG::seek(int64_t _frame_number) double time_base = r2d(ic->streams[video_stream]->time_base); time_stamp += (int64_t)(sec / time_base + 0.5); if (get_total_frames() > 1) av_seek_frame(ic, video_stream, time_stamp, AVSEEK_FLAG_BACKWARD); - avcodec_flush_buffers(ic->streams[video_stream]->codec); + avcodec_flush_buffers(context); if( _frame_number > 0 ) { grabFrame(); @@ -1829,7 +1975,7 @@ struct CvVideoWriter_FFMPEG void init(); - AVOutputFormat * fmt; + CV_FFMPEG_FMT_CONST AVOutputFormat * fmt; AVFormatContext * oc; uint8_t * outbuf; uint32_t outbuf_size; @@ -1838,6 +1984,7 @@ struct CvVideoWriter_FFMPEG AVFrame * input_picture; uint8_t * picbuf; AVStream * video_st; + AVCodecContext * context; AVPixelFormat input_pix_fmt; unsigned char * aligned_input; size_t aligned_input_size; @@ -1902,6 +2049,7 @@ void CvVideoWriter_FFMPEG::init() input_picture = 0; picbuf = 0; video_st = 0; + context = 0; input_pix_fmt = AV_PIX_FMT_NONE; aligned_input = NULL; aligned_input_size = 0; @@ -1953,23 +2101,32 @@ static AVFrame * icv_alloc_picture_FFMPEG(int pix_fmt, int width, int height, bo } /* configure video stream */ -static bool icv_configure_video_stream_FFMPEG(AVFormatContext *oc, +static AVCodecContext * icv_configure_video_stream_FFMPEG(AVFormatContext *oc, AVStream *st, const AVCodec* codec, int w, int h, int bitrate, - double fps, AVPixelFormat pixel_format) + double fps, AVPixelFormat pixel_format, int fourcc) { +#ifdef CV_FFMPEG_CODECPAR + AVCodecContext *c = avcodec_alloc_context3(codec); +#else AVCodecContext *c = st->codec; +#endif + CV_Assert(c); + int frame_rate, frame_rate_base; c->codec_id = codec->id; c->codec_type = AVMEDIA_TYPE_VIDEO; + c->codec_tag = fourcc; +#ifndef CV_FFMPEG_CODECPAR // Set per-codec defaults CV_CODEC_ID c_id = c->codec_id; avcodec_get_context_defaults3(c, codec); // avcodec_get_context_defaults3 erases codec_id for some reason c->codec_id = c_id; +#endif /* put sample parameters */ int64_t lbit_rate = (int64_t)bitrate; @@ -2012,7 +2169,12 @@ static bool icv_configure_video_stream_FFMPEG(AVFormatContext *oc, } } if (best == NULL) - return false; + { +#ifdef CV_FFMPEG_CODECPAR + avcodec_free_context(&c); +#endif + return NULL; + } c->time_base.den= best->num; c->time_base.num= best->den; } @@ -2055,26 +2217,20 @@ static bool icv_configure_video_stream_FFMPEG(AVFormatContext *oc, #endif } -#if defined(_MSC_VER) - AVRational avg_frame_rate = {frame_rate, frame_rate_base}; - st->avg_frame_rate = avg_frame_rate; -#else - st->avg_frame_rate = (AVRational){frame_rate, frame_rate_base}; -#endif + st->avg_frame_rate = av_make_q(frame_rate, frame_rate_base); #if LIBAVFORMAT_BUILD >= CALC_FFMPEG_VERSION(55, 20, 0) st->time_base = c->time_base; #endif - return true; + return c; } static const int OPENCV_NO_FRAMES_WRITTEN_CODE = 1000; -static int icv_av_write_frame_FFMPEG( AVFormatContext * oc, AVStream * video_st, +static int icv_av_write_frame_FFMPEG( AVFormatContext * oc, AVStream * video_st, AVCodecContext * c, uint8_t *, uint32_t, AVFrame * picture, int frame_idx) { - AVCodecContext* c = video_st->codec; int ret = OPENCV_NO_FRAMES_WRITTEN_CODE; #if LIBAVFORMAT_BUILD < CALC_FFMPEG_VERSION(57, 0, 0) @@ -2172,9 +2328,6 @@ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int width = frame_width; height = frame_height; - // typecast from opaque data type to implemented struct - AVCodecContext* c = video_st->codec; - // FFmpeg contains SIMD optimizations which can sometimes read data past // the supplied input buffer. // Related info: https://trac.ffmpeg.org/ticket/6763 @@ -2211,10 +2364,10 @@ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int step = aligned_step; } - AVPixelFormat sw_pix_fmt = c->pix_fmt; + AVPixelFormat sw_pix_fmt = context->pix_fmt; #if USE_AV_HW_CODECS - if (c->hw_frames_ctx) - sw_pix_fmt = ((AVHWFramesContext*)c->hw_frames_ctx->data)->sw_format; + if (context->hw_frames_ctx) + sw_pix_fmt = ((AVHWFramesContext*)context->hw_frames_ctx->data)->sw_format; #endif if ( sw_pix_fmt != input_pix_fmt ) { CV_Assert( input_picture ); @@ -2228,8 +2381,8 @@ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int img_convert_ctx = sws_getContext(width, height, (AVPixelFormat)input_pix_fmt, - c->width, - c->height, + context->width, + context->height, sw_pix_fmt, SWS_BICUBIC, NULL, NULL, NULL); @@ -2251,14 +2404,14 @@ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int bool ret; #if USE_AV_HW_CODECS - if (video_st->codec->hw_device_ctx) { + if (context->hw_device_ctx) { // copy data to HW frame AVFrame* hw_frame = av_frame_alloc(); if (!hw_frame) { CV_LOG_ERROR(NULL, "Error allocating AVFrame (av_frame_alloc)"); return false; } - if (av_hwframe_get_buffer(video_st->codec->hw_frames_ctx, hw_frame, 0) < 0) { + if (av_hwframe_get_buffer(context->hw_frames_ctx, hw_frame, 0) < 0) { CV_LOG_ERROR(NULL, "Error obtaining HW frame (av_hwframe_get_buffer)"); av_frame_free(&hw_frame); return false; @@ -2269,14 +2422,14 @@ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int return false; } hw_frame->pts = frame_idx; - int ret_write = icv_av_write_frame_FFMPEG(oc, video_st, outbuf, outbuf_size, hw_frame, frame_idx); + int ret_write = icv_av_write_frame_FFMPEG(oc, video_st, context, outbuf, outbuf_size, hw_frame, frame_idx); ret = ret_write >= 0 ? true : false; av_frame_free(&hw_frame); } else #endif { picture->pts = frame_idx; - int ret_write = icv_av_write_frame_FFMPEG(oc, video_st, outbuf, outbuf_size, picture, frame_idx); + int ret_write = icv_av_write_frame_FFMPEG(oc, video_st, context, outbuf, outbuf_size, picture, frame_idx); ret = ret_write >= 0 ? true : false; } @@ -2287,7 +2440,7 @@ bool CvVideoWriter_FFMPEG::writeFrame( const unsigned char* data, int step, int bool CvVideoWriter_FFMPEG::writeHWFrame(cv::InputArray input) { #if USE_AV_HW_CODECS - if (!video_st->codec->hw_frames_ctx) + if (!video_st || !context || !context->hw_frames_ctx || !context->hw_device_ctx) return false; // Get hardware frame from frame pool @@ -2295,20 +2448,20 @@ bool CvVideoWriter_FFMPEG::writeHWFrame(cv::InputArray input) { if (!hw_frame) { return false; } - if (av_hwframe_get_buffer(video_st->codec->hw_frames_ctx, hw_frame, 0) < 0) { + if (av_hwframe_get_buffer(context->hw_frames_ctx, hw_frame, 0) < 0) { av_frame_free(&hw_frame); return false; } // GPU to GPU copy - if (!hw_copy_umat_to_frame(video_st->codec->hw_device_ctx, input, hw_frame)) { + if (!hw_copy_umat_to_frame(context->hw_device_ctx, input, hw_frame)) { av_frame_free(&hw_frame); return false; } // encode hw_frame->pts = frame_idx; - icv_av_write_frame_FFMPEG( oc, video_st, outbuf, outbuf_size, hw_frame, frame_idx); + icv_av_write_frame_FFMPEG( oc, video_st, context, outbuf, outbuf_size, hw_frame, frame_idx); frame_idx++; av_frame_free(&hw_frame); @@ -2343,17 +2496,13 @@ double CvVideoWriter_FFMPEG::getProperty(int propId) const /// close video output stream and free associated memory void CvVideoWriter_FFMPEG::close() { - // nothing to do if already released - if ( !picture ) - return; - /* no more frame to compress. The codec has a latency of a few frames if using B frames, so we get the last frames by passing the same picture again */ // TODO -- do we need to account for latency here? /* write the trailer, if any */ - if(ok && oc) + if (picture && ok && oc) { #if LIBAVFORMAT_BUILD < CALC_FFMPEG_VERSION(57, 0, 0) if (!(oc->oformat->flags & AVFMT_RAWPICTURE)) @@ -2361,7 +2510,7 @@ void CvVideoWriter_FFMPEG::close() { for(;;) { - int ret = icv_av_write_frame_FFMPEG( oc, video_st, outbuf, outbuf_size, NULL, frame_idx); + int ret = icv_av_write_frame_FFMPEG( oc, video_st, context, outbuf, outbuf_size, NULL, frame_idx); if( ret == OPENCV_NO_FRAMES_WRITTEN_CODE || ret < 0 ) break; } @@ -2376,7 +2525,7 @@ void CvVideoWriter_FFMPEG::close() } // free pictures - if( video_st->codec->pix_fmt != input_pix_fmt) + if (picture && context && context->pix_fmt != input_pix_fmt) { if(picture->data[0]) free(picture->data[0]); @@ -2387,8 +2536,14 @@ void CvVideoWriter_FFMPEG::close() if (input_picture) av_free(input_picture); +#ifdef CV_FFMPEG_CODECPAR + avcodec_free_context(&context); +#else /* close codec */ - avcodec_close(video_st->codec); + if (context) // fixed after https://github.com/FFmpeg/FFmpeg/commit/3e1f507f3e8f16b716aa115552d243b48ae809bd + avcodec_close(context); + context = NULL; +#endif av_free(outbuf); @@ -2595,8 +2750,15 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, /* set file name */ oc->oformat = fmt; +#ifndef CV_FFMPEG_URL snprintf(oc->filename, sizeof(oc->filename), "%s", filename); - +#else + size_t name_len = strlen(filename); + oc->url = (char*)av_malloc(name_len + 1); + CV_Assert(oc->url); + memcpy((void*)oc->url, filename, name_len + 1); + oc->url[name_len] = '\0'; +#endif /* set some options */ oc->max_delay = (int)(0.7*AV_TIME_BASE); /* This reduces buffer underrun warnings with MPEG */ @@ -2711,7 +2873,7 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, double bitrate = std::min(bitrate_scale*fps*width*height, (double)INT_MAX/2); if (codec_id == AV_CODEC_ID_NONE) { - codec_id = av_guess_codec(oc->oformat, NULL, oc->filename, NULL, AVMEDIA_TYPE_VIDEO); + codec_id = av_guess_codec(oc->oformat, NULL, filename, NULL, AVMEDIA_TYPE_VIDEO); } // Add video stream to output file @@ -2729,11 +2891,9 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, } #endif - AVCodecContext *c = video_st->codec; - // find and open encoder, try HW acceleration types specified in 'hw_acceleration' list (in order) int err = -1; - AVCodec* codec = NULL; + const AVCodec* codec = NULL; #if USE_AV_HW_CODECS AVBufferRef* hw_device_ctx = NULL; HWAccelIterator accel_iter(va_type, true/*isEncoder*/, dict); @@ -2776,9 +2936,14 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, AVPixelFormat format = codec_pix_fmt; #endif - if (!icv_configure_video_stream_FFMPEG(oc, video_st, codec, - width, height, (int) (bitrate + 0.5), - fps, format)) { +#ifdef CV_FFMPEG_CODECPAR + avcodec_free_context(&context); +#endif + context = icv_configure_video_stream_FFMPEG(oc, video_st, codec, + width, height, (int) (bitrate + 0.5), + fps, format, fourcc); + if (!context) + { continue; } @@ -2790,27 +2955,25 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, #endif #endif - c->codec_tag = fourcc; - #if USE_AV_HW_CODECS if (hw_device_ctx) { - c->hw_device_ctx = av_buffer_ref(hw_device_ctx); + context->hw_device_ctx = av_buffer_ref(hw_device_ctx); if (hw_format != AV_PIX_FMT_NONE) { - c->hw_frames_ctx = hw_create_frames(NULL, hw_device_ctx, width, height, hw_format); - if (!c->hw_frames_ctx) + context->hw_frames_ctx = hw_create_frames(NULL, hw_device_ctx, width, height, hw_format); + if (!context->hw_frames_ctx) continue; } } #endif - int64_t lbit_rate = (int64_t) c->bit_rate; + int64_t lbit_rate = (int64_t) context->bit_rate; lbit_rate += (int64_t)(bitrate / 2); lbit_rate = std::min(lbit_rate, (int64_t) INT_MAX); - c->bit_rate_tolerance = (int) lbit_rate; - c->bit_rate = (int) lbit_rate; + context->bit_rate_tolerance = (int) lbit_rate; + context->bit_rate = (int) lbit_rate; /* open the codec */ - err = avcodec_open2(c, codec, NULL); + err = avcodec_open2(context, codec, NULL); if (err >= 0) { #if USE_AV_HW_CODECS va_type = hw_type_to_va_type(hw_type); @@ -2819,7 +2982,7 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, #endif break; } else { - CV_LOG_ERROR(NULL, "Could not open codec " << codec->name << ", error: " << icvFFMPEGErrStr(err)); + CV_LOG_ERROR(NULL, "Could not open codec " << codec->name << ", error: " << icvFFMPEGErrStr(err) << " (" << err << ")"); } #if USE_AV_HW_CODECS } // while (accel_iter.good()) @@ -2840,6 +3003,12 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, return false; } +#ifdef CV_FFMPEG_CODECPAR + // Copy all to codecpar... + // !!! https://stackoverflow.com/questions/15897849/c-ffmpeg-not-writing-avcc-box-information + avcodec_parameters_from_context(video_st->codecpar, context); +#endif + outbuf = NULL; @@ -2854,16 +3023,16 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, } bool need_color_convert; - AVPixelFormat sw_pix_fmt = c->pix_fmt; + AVPixelFormat sw_pix_fmt = context->pix_fmt; #if USE_AV_HW_CODECS - if (c->hw_frames_ctx) - sw_pix_fmt = ((AVHWFramesContext*)c->hw_frames_ctx->data)->sw_format; + if (context->hw_frames_ctx) + sw_pix_fmt = ((AVHWFramesContext*)context->hw_frames_ctx->data)->sw_format; #endif need_color_convert = (sw_pix_fmt != input_pix_fmt); /* allocate the encoded raw picture */ - picture = icv_alloc_picture_FFMPEG(sw_pix_fmt, c->width, c->height, need_color_convert); + picture = icv_alloc_picture_FFMPEG(sw_pix_fmt, context->width, context->height, need_color_convert); if (!picture) { return false; } @@ -2873,7 +3042,7 @@ bool CvVideoWriter_FFMPEG::open( const char * filename, int fourcc, to the required output format */ input_picture = NULL; if ( need_color_convert ) { - input_picture = icv_alloc_picture_FFMPEG(input_pix_fmt, c->width, c->height, false); + input_picture = icv_alloc_picture_FFMPEG(input_pix_fmt, context->width, context->height, false); if (!input_picture) { return false; } diff --git a/modules/videoio/src/cap_gstreamer.cpp b/modules/videoio/src/cap_gstreamer.cpp index 16b09cc5903d..cdf8d47249d5 100644 --- a/modules/videoio/src/cap_gstreamer.cpp +++ b/modules/videoio/src/cap_gstreamer.cpp @@ -475,7 +475,7 @@ bool GStreamerCapture::setAudioProperties(const cv::VideoCaptureParameters& para /*! * \brief CvCapture_GStreamer::grabFrame * \return - * Grabs a sample from the pipeline, awaiting consumation by retreiveFrame. + * Grabs a sample from the pipeline, awaiting consumation by retrieveFrame. * The pipeline is started if it was not running yet */ bool GStreamerCapture::grabFrame() diff --git a/modules/videoio/src/cap_msmf.cpp b/modules/videoio/src/cap_msmf.cpp index d78236913b80..d3002a51518a 100644 --- a/modules/videoio/src/cap_msmf.cpp +++ b/modules/videoio/src/cap_msmf.cpp @@ -536,7 +536,7 @@ class SourceReaderCB : public IMFSourceReaderCallback // Destructor is private. Caller should call Release. virtual ~SourceReaderCB() { - CV_LOG_WARNING(NULL, "terminating async callback"); + CV_LOG_INFO(NULL, "terminating async callback"); } public: diff --git a/modules/videoio/src/cap_v4l.cpp b/modules/videoio/src/cap_v4l.cpp index a9f68e19a5b9..2fc41ce05e7e 100644 --- a/modules/videoio/src/cap_v4l.cpp +++ b/modules/videoio/src/cap_v4l.cpp @@ -268,6 +268,13 @@ typedef uint32_t __u32; #define V4L2_PIX_FMT_Y12 v4l2_fourcc('Y', '1', '2', ' ') #endif +#ifndef V4L2_PIX_FMT_ABGR32 +#define V4L2_PIX_FMT_ABGR32 v4l2_fourcc('A', 'R', '2', '4') +#endif +#ifndef V4L2_PIX_FMT_XBGR32 +#define V4L2_PIX_FMT_XBGR32 v4l2_fourcc('X', 'R', '2', '4') +#endif + /* Defaults - If your board can do better, set it here. Set for the most common type inputs. */ #define DEFAULT_V4L_WIDTH 640 #define DEFAULT_V4L_HEIGHT 480 @@ -564,6 +571,8 @@ bool CvCaptureCAM_V4L::autosetup_capture_mode_v4l2() V4L2_PIX_FMT_NV21, V4L2_PIX_FMT_SBGGR8, V4L2_PIX_FMT_SGBRG8, + V4L2_PIX_FMT_XBGR32, + V4L2_PIX_FMT_ABGR32, V4L2_PIX_FMT_SN9C10X, #ifdef HAVE_JPEG V4L2_PIX_FMT_MJPEG, @@ -632,6 +641,8 @@ bool CvCaptureCAM_V4L::convertableToRgb() const case V4L2_PIX_FMT_Y10: case V4L2_PIX_FMT_GREY: case V4L2_PIX_FMT_BGR24: + case V4L2_PIX_FMT_XBGR32: + case V4L2_PIX_FMT_ABGR32: return true; default: break; @@ -651,6 +662,8 @@ void CvCaptureCAM_V4L::v4l2_create_frame() switch (palette) { case V4L2_PIX_FMT_BGR24: case V4L2_PIX_FMT_RGB24: + case V4L2_PIX_FMT_XBGR32: + case V4L2_PIX_FMT_ABGR32: break; case V4L2_PIX_FMT_YUYV: case V4L2_PIX_FMT_UYVY: @@ -1613,6 +1626,10 @@ void CvCaptureCAM_V4L::convertToRgb(const Buffer ¤tBuffer) case V4L2_PIX_FMT_GREY: cv::cvtColor(cv::Mat(imageSize, CV_8UC1, currentBuffer.start), destination, COLOR_GRAY2BGR); break; + case V4L2_PIX_FMT_XBGR32: + case V4L2_PIX_FMT_ABGR32: + cv::cvtColor(cv::Mat(imageSize, CV_8UC4, currentBuffer.start), destination, COLOR_BGRA2BGR); + break; case V4L2_PIX_FMT_BGR24: default: memcpy((char *)frame.imageData, (char *)currentBuffer.start, diff --git a/modules/videoio/test/test_audio.cpp b/modules/videoio/test/test_audio.cpp index b1eb0ed4b79c..e077b5bff9fe 100644 --- a/modules/videoio/test/test_audio.cpp +++ b/modules/videoio/test/test_audio.cpp @@ -39,6 +39,10 @@ class AudioBaseTest ASSERT_EQ(expectedNumAudioCh, (int)audioData.size()); for (unsigned int nCh = 0; nCh < audioData.size(); nCh++) { +#ifdef _WIN32 + if (audioData[nCh].size() == 132924 && numberOfSamples == 131819 && fileName == "test_audio.mp4") + throw SkipTestException("Detected failure observed on legacy Windows versions. SKIP"); +#endif ASSERT_EQ(numberOfSamples, audioData[nCh].size()) << "nCh=" << nCh; for (unsigned int i = 0; i < numberOfSamples; i++) { diff --git a/modules/videoio/test/test_ffmpeg.cpp b/modules/videoio/test/test_ffmpeg.cpp index 87e25bbd3dbb..40ef586fd644 100644 --- a/modules/videoio/test/test_ffmpeg.cpp +++ b/modules/videoio/test/test_ffmpeg.cpp @@ -235,8 +235,8 @@ static void generateFrame(Mat &frame, unsigned int i, const Point ¢er, const frame = Scalar::all(i % 255); stringstream buf(ios::out); buf << "frame #" << i; - putText(frame, buf.str(), Point(50, center.y), FONT_HERSHEY_SIMPLEX, 5.0, color, 5, CV_AA); - circle(frame, center, i + 2, color, 2, CV_AA); + putText(frame, buf.str(), Point(50, center.y), FONT_HERSHEY_SIMPLEX, 5.0, color, 5, LINE_AA); + circle(frame, center, i + 2, color, 2, LINE_AA); } TEST(videoio_ffmpeg, parallel) @@ -536,5 +536,17 @@ TEST(videoio_ffmpeg, create_with_property_badarg) EXPECT_FALSE(cap.isOpened()); } +// related issue: https://github.com/opencv/opencv/issues/16821 +TEST(videoio_ffmpeg, DISABLED_open_from_web) +{ + if (!videoio_registry::hasBackend(CAP_FFMPEG)) + throw SkipTestException("FFmpeg backend was not found"); + + string video_file = "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4"; + VideoCapture cap(video_file, CAP_FFMPEG); + int n_frames = -1; + EXPECT_NO_THROW(n_frames = (int)cap.get(CAP_PROP_FRAME_COUNT)); + EXPECT_EQ((int)14315, n_frames); +} }} // namespace diff --git a/modules/videoio/test/test_precomp.hpp b/modules/videoio/test/test_precomp.hpp index 5bc2ccdf95b6..cffdf2bef45a 100644 --- a/modules/videoio/test/test_precomp.hpp +++ b/modules/videoio/test/test_precomp.hpp @@ -9,8 +9,6 @@ #include "opencv2/ts.hpp" #include "opencv2/videoio.hpp" #include "opencv2/videoio/registry.hpp" -#include "opencv2/imgproc/imgproc_c.h" - #include "opencv2/core/private.hpp" namespace cv { diff --git a/modules/videoio/test/test_video_io.cpp b/modules/videoio/test/test_video_io.cpp index 8b6b16e16e31..6661b13c5ad7 100644 --- a/modules/videoio/test/test_video_io.cpp +++ b/modules/videoio/test/test_video_io.cpp @@ -1,47 +1,8 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. #include "test_precomp.hpp" -#include "opencv2/videoio/videoio_c.h" namespace opencv_test { @@ -263,7 +224,7 @@ class videoio_bunny : public Videoio_Test_Base, public testing::TestWithParam 0) std::cout << "i = " << i << ": timestamp = " << timestamp << std::endl; const double frame_period = 1000.f/bunny_param.getFps(); - // NOTE: eps == frame_period, because videoCapture returns frame begining timestamp or frame end + // NOTE: eps == frame_period, because videoCapture returns frame beginning timestamp or frame end // timestamp depending on codec and back-end. So the first frame has timestamp 0 or frame_period. EXPECT_NEAR(timestamp, i*frame_period, frame_period) << "i=" << i; } @@ -752,6 +713,13 @@ TEST_P(videocapture_acceleration, read) if (filename == "sample_322x242_15frames.yuv420p.libaom-av1.mp4") throw SkipTestException("Unable to read the first frame with AV1 codec (missing support)"); } +#ifdef _WIN32 + if (!read_umat_result && i == 1) + { + if (filename == "sample_322x242_15frames.yuv420p.libvpx-vp9.mp4") + throw SkipTestException("Unable to read the second frame with VP9 codec (media stack misconfiguration / outdated MSMF version)"); + } +#endif EXPECT_TRUE(read_umat_result); ASSERT_FALSE(umat.empty()); umat.copyTo(frame); @@ -767,6 +735,13 @@ TEST_P(videocapture_acceleration, read) if (filename == "sample_322x242_15frames.yuv420p.libaom-av1.mp4") throw SkipTestException("Unable to read the first frame with AV1 codec (missing support)"); } +#ifdef _WIN32 + if (!read_result && i == 1) + { + if (filename == "sample_322x242_15frames.yuv420p.libvpx-vp9.mp4") + throw SkipTestException("Unable to read the second frame with VP9 codec (media stack misconfiguration / outdated MSMF version)"); + } +#endif EXPECT_TRUE(read_result); } ASSERT_FALSE(frame.empty()); diff --git a/platforms/apple/build_xcframework.py b/platforms/apple/build_xcframework.py index afea5e469138..49878435d089 100755 --- a/platforms/apple/build_xcframework.py +++ b/platforms/apple/build_xcframework.py @@ -58,7 +58,7 @@ macos_archs = "x86_64,arm64" print('Using MacOS ARCHS={}'.format(macos_archs)) - catalyst_archs = args.macos_archs + catalyst_archs = args.catalyst_archs if not catalyst_archs and not args.build_only_specified_archs: # Supply defaults catalyst_archs = "x86_64,arm64" diff --git a/platforms/linux/riscv64-clang.toolchain.cmake b/platforms/linux/riscv64-clang.toolchain.cmake index f19c244f7be6..62d9e293d275 100644 --- a/platforms/linux/riscv64-clang.toolchain.cmake +++ b/platforms/linux/riscv64-clang.toolchain.cmake @@ -17,8 +17,8 @@ set(CMAKE_ASM_COMPILER_TARGET ${CLANG_TARGET_TRIPLE}) # Don't run the linker on compiler check set(CMAKE_TRY_COMPILE_TARGET_TYPE STATIC_LIBRARY) -set(CMAKE_C_FLAGS "-march=rv64gcv0p10 -menable-experimental-extensions --gcc-toolchain=${RISCV_GCC_INSTALL_ROOT} -w ${CMAKE_C_FLAGS}") -set(CMAKE_CXX_FLAGS "-march=rv64gcv0p10 -menable-experimental-extensions --gcc-toolchain=${RISCV_GCC_INSTALL_ROOT} -w ${CXX_FLAGS}") +set(CMAKE_C_FLAGS "-march=rv64gcv --gcc-toolchain=${RISCV_GCC_INSTALL_ROOT} -w ${CMAKE_C_FLAGS}") +set(CMAKE_CXX_FLAGS "-march=rv64gcv --gcc-toolchain=${RISCV_GCC_INSTALL_ROOT} -w ${CXX_FLAGS}") set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -O2") set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O2") diff --git a/platforms/maven/opencv-it/pom.xml b/platforms/maven/opencv-it/pom.xml index a7dc090f3822..1e66e6979abe 100644 --- a/platforms/maven/opencv-it/pom.xml +++ b/platforms/maven/opencv-it/pom.xml @@ -4,7 +4,7 @@ org.opencv opencv-parent - 4.5.5 + 4.6.0 org.opencv opencv-it diff --git a/platforms/maven/opencv/pom.xml b/platforms/maven/opencv/pom.xml index 9b081e4d1f1c..c135d4b75f71 100644 --- a/platforms/maven/opencv/pom.xml +++ b/platforms/maven/opencv/pom.xml @@ -4,7 +4,7 @@ org.opencv opencv-parent - 4.5.5 + 4.6.0 org.opencv opencv diff --git a/platforms/maven/pom.xml b/platforms/maven/pom.xml index 453b79f4ecfd..de42826a07bc 100644 --- a/platforms/maven/pom.xml +++ b/platforms/maven/pom.xml @@ -3,7 +3,7 @@ 4.0.0 org.opencv opencv-parent - 4.5.5 + 4.6.0 pom OpenCV Parent POM diff --git a/platforms/winpack_dldt/2021.4.2/20220118-dldt-fix-msvs-compilation-21469.patch b/platforms/winpack_dldt/2021.4.2/20220118-dldt-fix-msvs-compilation-21469.patch new file mode 100644 index 000000000000..411d5cbd5ca1 --- /dev/null +++ b/platforms/winpack_dldt/2021.4.2/20220118-dldt-fix-msvs-compilation-21469.patch @@ -0,0 +1,12 @@ +diff --git a/inference-engine/src/plugin_api/caseless.hpp b/inference-engine/src/plugin_api/caseless.hpp +index d8ce739..0dd8886 100644 +--- a/inference-engine/src/plugin_api/caseless.hpp ++++ b/inference-engine/src/plugin_api/caseless.hpp +@@ -12,6 +12,7 @@ + #include + #include + #include ++#include + #include + #include + #include diff --git a/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake new file mode 100644 index 000000000000..bc449d05cdd7 --- /dev/null +++ b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake @@ -0,0 +1,29 @@ +# Copyright (C) 2018-2021 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# + +set(PACKAGE_VERSION_MAJOR 2021) +set(PACKAGE_VERSION_MINOR 4) +set(PACKAGE_VERSION_PATCH 2) +set(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}") + +set(PACKAGE_VERSION_EXACT False) +set(PACKAGE_VERSION_COMPATIBLE False) + +# Compatibility with old versioning for 2.x +if(PACKAGE_FIND_VERSION_MAJOR VERSION_EQUAL 2) + set(PACKAGE_VERSION_COMPATIBLE True) + if(${CMAKE_FIND_PACKAGE_NAME}_FIND_REQUIRED) + message(WARNING "Inference Engine versioning has changed. Use ${PACKAGE_VERSION} instead of ${PACKAGE_FIND_VERSION}") + endif() +endif() + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT True) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() + +if(PACKAGE_FIND_VERSION_MAJOR EQUAL PACKAGE_VERSION_MAJOR AND + PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION) + set(PACKAGE_VERSION_COMPATIBLE True) +endif() diff --git a/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake new file mode 100644 index 000000000000..d9c9a12de684 --- /dev/null +++ b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake @@ -0,0 +1,31 @@ +# Inference Engine CMake config for OpenCV windows package + +get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) +get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH) + +set(InferenceEngine_LIBRARIES IE::inference_engine) +add_library(IE::inference_engine SHARED IMPORTED) + +set_target_properties(IE::inference_engine PROPERTIES + INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/deployment_tools/inference_engine/include" +) + +# Import target "IE::inference_engine" for configuration "Debug" +set_property(TARGET IE::inference_engine APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG) +set_target_properties(IE::inference_engine PROPERTIES + IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/deployment_tools/inference_engine/lib/intel64/inference_engined.lib" + IMPORTED_LINK_DEPENDENT_LIBRARIES_DEBUG "" + IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/inference_engined.dll" + ) + +# Import target "IE::inference_engine" for configuration "Release" +set_property(TARGET IE::inference_engine APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(IE::inference_engine PROPERTIES + IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/deployment_tools/inference_engine/lib/intel64/inference_engine.lib" + IMPORTED_LINK_DEPENDENT_LIBRARIES_RELEASE "" + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/inference_engine.dll" + ) + +set(InferenceEngine_FOUND ON) diff --git a/platforms/winpack_dldt/2021.4.2/patch.config.py b/platforms/winpack_dldt/2021.4.2/patch.config.py index 7f8715aae2da..bd31af236fd0 100644 --- a/platforms/winpack_dldt/2021.4.2/patch.config.py +++ b/platforms/winpack_dldt/2021.4.2/patch.config.py @@ -2,3 +2,4 @@ applyPatch('20210630-dldt-pdb.patch') applyPatch('20210630-dldt-disable-multidevice-autoplugin.patch') applyPatch('20210630-dldt-vs-version.patch') +applyPatch('20220118-dldt-fix-msvs-compilation-21469.patch') diff --git a/platforms/winpack_dldt/2021.4.2/sysroot.config.py b/platforms/winpack_dldt/2021.4.2/sysroot.config.py index fa4281107d23..f11e99f84360 100644 --- a/platforms/winpack_dldt/2021.4.2/sysroot.config.py +++ b/platforms/winpack_dldt/2021.4.2/sysroot.config.py @@ -1,3 +1,5 @@ +copytree(self.cpath / 'cmake', self.sysrootdir / 'deployment_tools' / 'inference_engine' / 'cmake') + sysroot_bin_dir = prepare_dir(self.sysrootdir / 'bin') copytree(self.build_dir / 'install', self.sysrootdir / 'ngraph') #rm_one(self.sysrootdir / 'ngraph' / 'lib' / 'ngraph.dll') diff --git a/platforms/winpack_dldt/build_package.py b/platforms/winpack_dldt/build_package.py index 88154bafb559..277a13c232ae 100644 --- a/platforms/winpack_dldt/build_package.py +++ b/platforms/winpack_dldt/build_package.py @@ -388,10 +388,9 @@ def build(self, builderDLDT): if self.config.dldt_release: cmake_vars['INF_ENGINE_RELEASE'] = str(self.config.dldt_release) - cmake_vars['INF_ENGINE_LIB_DIRS:PATH'] = str(builderDLDT.sysrootdir / 'deployment_tools/inference_engine/lib/intel64') - assert os.path.exists(cmake_vars['INF_ENGINE_LIB_DIRS:PATH']), cmake_vars['INF_ENGINE_LIB_DIRS:PATH'] - cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH'] = str(builderDLDT.sysrootdir / 'deployment_tools/inference_engine/include') - assert os.path.exists(cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH']), cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH'] + InferenceEngine_DIR = str(builderDLDT.sysrootdir / 'deployment_tools' / 'inference_engine' / 'cmake') + assert os.path.exists(InferenceEngine_DIR), InferenceEngine_DIR + cmake_vars['InferenceEngine_DIR:PATH'] = InferenceEngine_DIR ngraph_DIR = str(builderDLDT.sysrootdir / 'ngraph/cmake') if not os.path.exists(ngraph_DIR): diff --git a/samples/_winpack_run_python_sample.cmd b/samples/_winpack_run_python_sample.cmd index 9c791735be15..275891c16142 100644 --- a/samples/_winpack_run_python_sample.cmd +++ b/samples/_winpack_run_python_sample.cmd @@ -23,6 +23,8 @@ IF %ERRORLEVEL% EQU 0 ( GOTO :PYTHON_FOUND ) +CALL :QUERY_PYTHON 3.10 +IF %ERRORLEVEL% EQU 0 GOTO :PYTHON_FOUND CALL :QUERY_PYTHON 3.9 IF %ERRORLEVEL% EQU 0 GOTO :PYTHON_FOUND CALL :QUERY_PYTHON 3.8 diff --git a/samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp b/samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp index 0abcab89b383..5f672cd59312 100644 --- a/samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp +++ b/samples/cpp/tutorial_code/gapi/doc_snippets/api_ref_snippets.cpp @@ -211,7 +211,7 @@ int main(int argc, char *argv[]) //! [graph_decl_apply] //! [apply_with_param] - cv::gapi::GKernelPackage kernels = cv::gapi::combine + cv::GKernelPackage kernels = cv::gapi::combine (cv::gapi::core::fluid::kernels(), cv::gapi::imgproc::fluid::kernels()); sobelEdge.apply(input, output, cv::compile_args(kernels)); @@ -235,7 +235,7 @@ int main(int argc, char *argv[]) cv::imwrite(argv[2], output); //! [kernels_snippet] - cv::gapi::GKernelPackage pkg = cv::gapi::kernels + cv::GKernelPackage pkg = cv::gapi::kernels < CustomAdd , CustomFilter2D , CustomRGB2YUV diff --git a/samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp b/samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp index cb8022c42f59..a6e6a372e5e9 100644 --- a/samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp +++ b/samples/cpp/tutorial_code/gapi/doc_snippets/dynamic_graph_snippets.cpp @@ -1,6 +1,6 @@ #include -#include -#include +#include +#include int main(int argc, char *argv[]) { @@ -57,7 +57,7 @@ int main(int argc, char *argv[]) out_vector += cv::gout(out_mat2); // ! [GRunArgsP usage] - auto stream = cc.compileStreaming(cv::compile_args(cv::gapi::core::cpu::kernels())); + auto stream = cc.compileStreaming(cv::compile_args(cv::gapi::imgproc::cpu::kernels())); stream.setSource(std::move(in_vector)); stream.start(); diff --git a/samples/cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi_fluid.cpp b/samples/cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi_fluid.cpp index 2d528aa412ab..d37ac174911d 100644 --- a/samples/cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi_fluid.cpp +++ b/samples/cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi_fluid.cpp @@ -63,7 +63,7 @@ int main() //! [kernel_pkg_proper] //! [kernel_pkg] // Prepare the kernel package and run the graph - cv::gapi::GKernelPackage fluid_kernels = cv::gapi::combine // Define a custom kernel package: + cv::GKernelPackage fluid_kernels = cv::gapi::combine // Define a custom kernel package: (cv::gapi::core::fluid::kernels(), // ...with Fluid Core kernels cv::gapi::imgproc::fluid::kernels()); // ...and Fluid ImgProc kernels //! [kernel_pkg] diff --git a/samples/dnn/face_detect.cpp b/samples/dnn/face_detect.cpp index 161940cb4a09..d1e63149694f 100644 --- a/samples/dnn/face_detect.cpp +++ b/samples/dnn/face_detect.cpp @@ -44,8 +44,8 @@ int main(int argc, char** argv) "{image2 i2 | | Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm}" "{video v | 0 | Path to the input video}" "{scale sc | 1.0 | Scale factor used to resize input video frames}" - "{fd_model fd | yunet.onnx | Path to the model. Download yunet.onnx in https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx }" - "{fr_model fr | face_recognizer_fast.onnx | Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view}" + "{fd_model fd | face_detection_yunet_2021dec.onnx| Path to the model. Download yunet.onnx in https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet}" + "{fr_model fr | face_recognition_sface_2021dec.onnx | Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface}" "{score_threshold | 0.9 | Filter out faces of score < score_threshold}" "{nms_threshold | 0.3 | Suppress bounding boxes of iou >= nms_threshold}" "{top_k | 5000 | Keep top_k bounding boxes before NMS}" @@ -65,6 +65,7 @@ int main(int argc, char** argv) int topK = parser.get("top_k"); bool save = parser.get("save"); + float scale = parser.get("scale"); double cosine_similar_thresh = 0.363; double l2norm_similar_thresh = 1.128; @@ -87,6 +88,9 @@ int main(int argc, char** argv) return 2; } + int imageWidth = int(image1.cols * scale); + int imageHeight = int(image1.rows * scale); + resize(image1, image1, Size(imageWidth, imageHeight)); tm.start(); //! [inference] @@ -199,7 +203,6 @@ int main(int argc, char** argv) else { int frameWidth, frameHeight; - float scale = parser.get("scale"); VideoCapture capture; std::string video = parser.get("video"); if (video.size() == 1 && isdigit(video[0])) diff --git a/samples/dnn/face_detect.py b/samples/dnn/face_detect.py index 8900a7f7adda..9cf38b5d5f43 100644 --- a/samples/dnn/face_detect.py +++ b/samples/dnn/face_detect.py @@ -16,8 +16,8 @@ def str2bool(v): parser.add_argument('--image2', '-i2', type=str, help='Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm.') parser.add_argument('--video', '-v', type=str, help='Path to the input video.') parser.add_argument('--scale', '-sc', type=float, default=1.0, help='Scale factor used to resize input video frames.') -parser.add_argument('--face_detection_model', '-fd', type=str, default='yunet.onnx', help='Path to the face detection model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.') -parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognizer_fast.onnx', help='Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.') +parser.add_argument('--face_detection_model', '-fd', type=str, default='face_detection_yunet_2021dec.onnx', help='Path to the face detection model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet') +parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognition_sface_2021dec.onnx', help='Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface') parser.add_argument('--score_threshold', type=float, default=0.9, help='Filtering out faces of score < score_threshold.') parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.') parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.') @@ -56,11 +56,15 @@ def visualize(input, faces, fps, thickness=2): # If input is an image if args.image1 is not None: img1 = cv.imread(cv.samples.findFile(args.image1)) + img1Width = int(img1.shape[1]*args.scale) + img1Height = int(img1.shape[0]*args.scale) + img1 = cv.resize(img1, (img1Width, img1Height)) tm.start() + ## [inference] # Set input size before inference - detector.setInputSize((img1.shape[1], img1.shape[0])) + detector.setInputSize((img1Width, img1Height)) faces1 = detector.detect(img1) ## [inference] diff --git a/samples/dnn/speech_recognition.cpp b/samples/dnn/speech_recognition.cpp new file mode 100644 index 000000000000..ff461c50f5b3 --- /dev/null +++ b/samples/dnn/speech_recognition.cpp @@ -0,0 +1,587 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +using namespace cv; +using namespace std; + +class FilterbankFeatures { + +// Initializes pre-processing class. Default values are the values used by the Jasper +// architecture for pre-processing. For more details, refer to the paper here: +// https://arxiv.org/abs/1904.03288 + +private: + int sample_rate = 16000; + double window_size = 0.02; + double window_stride = 0.01; + int win_length = static_cast(sample_rate * window_size); // Number of samples in window + int hop_length = static_cast(sample_rate * window_stride); // Number of steps to advance between frames + int n_fft = 512; // Size of window for STFT + + // Parameters for filterbanks calculation + int n_filt = 64; + double lowfreq = 0.; + double highfreq = sample_rate / 2; + +public: + // Mel filterbanks preparation + double hz_to_mel(double frequencies) + { + //Converts frequencies from hz to mel scale + // Fill in the linear scale + double f_min = 0.0; + double f_sp = 200.0 / 3; + double mels = (frequencies - f_min) / f_sp; + // Fill in the log-scale part + double min_log_hz = 1000.0; // beginning of log region (Hz) + double min_log_mel = (min_log_hz - f_min) / f_sp; // same (Mels) + double logstep = std::log(6.4) / 27.0; // step size for log region + + if (frequencies >= min_log_hz) + { + mels = min_log_mel + std::log(frequencies / min_log_hz) / logstep; + } + return mels; + } + + vector mel_to_hz(vector& mels) + { + // Converts frequencies from mel to hz scale + + // Fill in the linear scale + double f_min = 0.0; + double f_sp = 200.0 / 3; + vector freqs; + for (size_t i = 0; i < mels.size(); i++) + { + freqs.push_back(f_min + f_sp * mels[i]); + } + + // And now the nonlinear scale + double min_log_hz = 1000.0; // beginning of log region (Hz) + double min_log_mel = (min_log_hz - f_min) / f_sp; // same (Mels) + double logstep = std::log(6.4) / 27.0; // step size for log region + + for(size_t i = 0; i < mels.size(); i++) + { + if (mels[i] >= min_log_mel) + { + freqs[i] = min_log_hz * exp(logstep * (mels[i] - min_log_mel)); + } + } + return freqs; + } + + vector mel_frequencies(int n_mels, double fmin, double fmax) + { + // Calculates n mel frequencies between 2 frequencies + double min_mel = hz_to_mel(fmin); + double max_mel = hz_to_mel(fmax); + + vector mels; + double step = (max_mel - min_mel) / (n_mels - 1); + for(double i = min_mel; i < max_mel; i += step) + { + mels.push_back(i); + } + mels.push_back(max_mel); + + vector res = mel_to_hz(mels); + return res; + } + + vector> mel(int n_mels, double fmin, double fmax) + { + // Generates mel filterbank matrix + + double num = 1 + n_fft / 2; + vector> weights(n_mels, vector(static_cast(num), 0.)); + + // Center freqs of each FFT bin + vector fftfreqs; + double step = (sample_rate / 2) / (num - 1); + for(double i = 0; i <= sample_rate / 2; i += step) + { + fftfreqs.push_back(i); + } + // 'Center freqs' of mel bands - uniformly spaced between limits + vector mel_f = mel_frequencies(n_mels + 2, fmin, fmax); + + vector fdiff; + for(size_t i = 1; i < mel_f.size(); ++i) + { + fdiff.push_back(mel_f[i]- mel_f[i - 1]); + } + + vector> ramps(mel_f.size(), vector(fftfreqs.size())); + for (size_t i = 0; i < mel_f.size(); ++i) + { + for (size_t j = 0; j < fftfreqs.size(); ++j) + { + ramps[i][j] = mel_f[i] - fftfreqs[j]; + } + } + + double lower, upper, enorm; + for (int i = 0; i < n_mels; ++i) + { + // using Slaney-style mel which is scaled to be approx constant energy per channel + enorm = 2./(mel_f[i + 2] - mel_f[i]); + + for (int j = 0; j < static_cast(num); ++j) + { + // lower and upper slopes for all bins + lower = (-1) * ramps[i][j] / fdiff[i]; + upper = ramps[i + 2][j] / fdiff[i + 1]; + + weights[i][j] = max(0., min(lower, upper)) * enorm; + } + } + return weights; + } + + // STFT preparation + vector pad_window_center(vector&data, int size) + { + // Pad the window out to n_fft size + int n = static_cast(data.size()); + int lpad = static_cast((size - n) / 2); + vector pad_array; + + for(int i = 0; i < lpad; ++i) + { + pad_array.push_back(0.); + } + + for(size_t i = 0; i < data.size(); ++i) + { + pad_array.push_back(data[i]); + } + + for(int i = 0; i < lpad; ++i) + { + pad_array.push_back(0.); + } + return pad_array; + } + + vector> frame(vector& x) + { + // Slices a data array into overlapping frames. + int n_frames = static_cast(1 + (x.size() - n_fft) / hop_length); + vector> new_x(n_fft, vector(n_frames)); + + for (int i = 0; i < n_fft; ++i) + { + for (int j = 0; j < n_frames; ++j) + { + new_x[i][j] = x[i + j * hop_length]; + } + } + return new_x; + } + + vector hanning() + { + // https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows + vector window_tensor; + for (int j = 1 - win_length; j < win_length; j+=2) + { + window_tensor.push_back(1 - (0.5 * (1 - cos(CV_PI * j / (win_length - 1))))); + } + return window_tensor; + } + + vector> stft_power(vector& y) + { + // Short Time Fourier Transform. The STFT represents a signal in the time-frequency + // domain by computing discrete Fourier transforms (DFT) over short overlapping windows. + // https://en.wikipedia.org/wiki/Short-time_Fourier_transform + + // Pad the time series so that frames are centered + vector new_y; + int num = int(n_fft / 2); + + for (int i = 0; i < num; ++i) + { + new_y.push_back(y[num - i]); + } + for (size_t i = 0; i < y.size(); ++i) + { + new_y.push_back(y[i]); + } + for (size_t i = y.size() - 2; i >= y.size() - num - 1; --i) + { + new_y.push_back(y[i]); + } + + // Compute a window function + vector window_tensor = hanning(); + + // Pad the window out to n_fft size + vector fft_window = pad_window_center(window_tensor, n_fft); + + // Window the time series + vector> y_frames = frame(new_y); + + // Multiply on fft_window + for (size_t i = 0; i < y_frames.size(); ++i) + { + for (size_t j = 0; j < y_frames[0].size(); ++j) + { + y_frames[i][j] *= fft_window[i]; + } + } + + // Transpose frames for computing stft + vector> y_frames_transpose(y_frames[0].size(), vector(y_frames.size())); + for (size_t i = 0; i < y_frames[0].size(); ++i) + { + for (size_t j = 0; j < y_frames.size(); ++j) + { + y_frames_transpose[i][j] = y_frames[j][i]; + } + } + + // Short Time Fourier Transform + // and get power of spectrum + vector> spectrum_power(y_frames_transpose[0].size() / 2 + 1 ); + for (size_t i = 0; i < y_frames_transpose.size(); ++i) + { + Mat dstMat; + dft(y_frames_transpose[i], dstMat, DFT_COMPLEX_OUTPUT); + + // we need only the first part of the spectrum, the second part is symmetrical + for (int j = 0; j < static_cast(y_frames_transpose[0].size()) / 2 + 1; ++j) + { + double power_re = dstMat.at(2 * j) * dstMat.at(2 * j); + double power_im = dstMat.at(2 * j + 1) * dstMat.at(2 * j + 1); + spectrum_power[j].push_back(power_re + power_im); + } + } + return spectrum_power; + } + + Mat calculate_features(vector& x) + { + // Calculates filterbank features matrix. + + // Do preemphasis + std::default_random_engine generator; + std::normal_distribution normal_distr(0, 1); + double dither = 1e-5; + for(size_t i = 0; i < x.size(); ++i) + { + x[i] += dither * static_cast(normal_distr(generator)); + } + double preemph = 0.97; + for (size_t i = x.size() - 1; i > 0; --i) + { + x[i] -= preemph * x[i-1]; + } + + // Calculate Short Time Fourier Transform and get power of spectrum + auto spectrum_power = stft_power(x); + + vector> filterbanks = mel(n_filt, lowfreq, highfreq); + + // Calculate log of multiplication of filterbanks matrix on spectrum_power matrix + vector> x_stft(filterbanks.size(), vector(spectrum_power[0].size(), 0)); + + for (size_t i = 0; i < filterbanks.size(); ++i) + { + for (size_t j = 0; j < filterbanks[0].size(); ++j) + { + for (size_t k = 0; k < spectrum_power[0].size(); ++k) + { + x_stft[i][k] += filterbanks[i][j] * spectrum_power[j][k]; + } + } + for (size_t k = 0; k < spectrum_power[0].size(); ++k) + { + x_stft[i][k] = std::log(x_stft[i][k] + 1e-20); + } + } + + // normalize data + auto elments_num = x_stft[0].size(); + for(size_t i = 0; i < x_stft.size(); ++i) + { + double x_mean = std::accumulate(x_stft[i].begin(), x_stft[i].end(), 0.) / elments_num; // arithmetic mean + double x_std = 0; // standard deviation + for(size_t j = 0; j < elments_num; ++j) + { + double subtract = x_stft[i][j] - x_mean; + x_std += subtract * subtract; + } + x_std /= elments_num; + x_std = sqrt(x_std) + 1e-10; // make sure x_std is not zero + + for(size_t j = 0; j < elments_num; ++j) + { + x_stft[i][j] = (x_stft[i][j] - x_mean) / x_std; // standard score + } + } + + Mat calculate_features(static_cast(x_stft.size()), static_cast(x_stft[0].size()), CV_32F); + for(int i = 0; i < calculate_features.size[0]; ++i) + { + for(int j = 0; j < calculate_features.size[1]; ++j) + { + calculate_features.at(i, j) = static_cast(x_stft[i][j]); + } + } + return calculate_features; + } +}; + +class Decoder { + // Used for decoding the output of jasper model +private: + unordered_map labels_map = fillMap(); + int blank_id = 28; + +public: + unordered_map fillMap() + { + vector labels={' ','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p' + ,'q','r','s','t','u','v','w','x','y','z','\''}; + unordered_map map; + for(int i = 0; i < static_cast(labels.size()); ++i) + { + map[i] = labels[i]; + } + return map; + } + + string decode(Mat& x) + { + // Takes output of Jasper model and performs ctc decoding algorithm to + // remove duplicates and special symbol. Returns prediction + + vector prediction; + for(int i = 0; i < x.size[1]; ++i) + { + double maxEl = -1e10; + int ind = 0; + for(int j = 0; j < x.size[2]; ++j) + { + if (maxEl <= x.at(0, i, j)) + { + maxEl = x.at(0, i, j); + ind = j; + } + } + prediction.push_back(ind); + } + // CTC decoding procedure + vector decoded_prediction = {}; + int previous = blank_id; + + for(int i = 0; i < static_cast(prediction.size()); ++i) + { + if (( prediction[i] != previous || previous == blank_id) && prediction[i] != blank_id) + { + decoded_prediction.push_back(prediction[i]); + } + previous = prediction[i]; + } + + string hypotheses = {}; + for(size_t i = 0; i < decoded_prediction.size(); ++i) + { + auto it = labels_map.find(static_cast(decoded_prediction[i])); + if (it != labels_map.end()) + hypotheses.push_back(it->second); + } + return hypotheses; + } + +}; + +static string predict(Mat& features, dnn::Net net, Decoder decoder) +{ + // Passes the features through the Jasper model and decodes the output to english transcripts. + + // expand 2d features matrix to 3d + vector sizes = {1, static_cast(features.size[0]), + static_cast(features.size[1])}; + features = features.reshape(0, sizes); + + // make prediction + net.setInput(features); + Mat output = net.forward(); + + // decode output to transcript + auto prediction = decoder.decode(output); + return prediction; +} + +static int readAudioFile(vector& inputAudio, string file, int audioStream) +{ + VideoCapture cap; + int samplingRate = 16000; + vector params { CAP_PROP_AUDIO_STREAM, audioStream, + CAP_PROP_VIDEO_STREAM, -1, + CAP_PROP_AUDIO_DATA_DEPTH, CV_32F, + CAP_PROP_AUDIO_SAMPLES_PER_SECOND, samplingRate + }; + cap.open(file, CAP_ANY, params); + if (!cap.isOpened()) + { + cerr << "Error : Can't read audio file: '" << file << "' with audioStream = " << audioStream << endl; + return -1; + } + const int audioBaseIndex = (int)cap.get(CAP_PROP_AUDIO_BASE_INDEX); + vector frameVec; + Mat frame; + for (;;) + { + if (cap.grab()) + { + cap.retrieve(frame, audioBaseIndex); + frameVec = frame; + inputAudio.insert(inputAudio.end(), frameVec.begin(), frameVec.end()); + } + else + { + break; + } + } + return samplingRate; +} + +static int readAudioMicrophone(vector& inputAudio, int microTime) +{ + VideoCapture cap; + int samplingRate = 16000; + vector params { CAP_PROP_AUDIO_STREAM, 0, + CAP_PROP_VIDEO_STREAM, -1, + CAP_PROP_AUDIO_DATA_DEPTH, CV_32F, + CAP_PROP_AUDIO_SAMPLES_PER_SECOND, samplingRate + }; + cap.open(0, CAP_ANY, params); + if (!cap.isOpened()) + { + cerr << "Error: Can't open microphone" << endl; + return -1; + } + + const int audioBaseIndex = (int)cap.get(CAP_PROP_AUDIO_BASE_INDEX); + vector frameVec; + Mat frame; + if (microTime <= 0) + { + cerr << "Error: Duration of audio chunk must be > 0" << endl; + return -1; + } + size_t sizeOfData = static_cast(microTime * samplingRate); + while (inputAudio.size() < sizeOfData) + { + if (cap.grab()) + { + cap.retrieve(frame, audioBaseIndex); + frameVec = frame; + inputAudio.insert(inputAudio.end(), frameVec.begin(), frameVec.end()); + } + else + { + cerr << "Error: Grab error" << endl; + break; + } + } + return samplingRate; +} + +int main(int argc, char** argv) +{ + const String keys = + "{help h usage ? | | This script runs Jasper Speech recognition model }" + "{input_file i | | Path to input audio file. If not specified, microphone input will be used }" + "{audio_duration t | 15 | Duration of audio chunk to be captured from microphone }" + "{audio_stream a | 0 | CAP_PROP_AUDIO_STREAM value }" + "{show_spectrogram s | false | Show a spectrogram of the input audio: true / false / 1 / 0 }" + "{model m | jasper.onnx | Path to the onnx file of Jasper. You can download the converted onnx model " + "from https://drive.google.com/drive/folders/1wLtxyao4ItAg8tt4Sb63zt6qXzhcQoR6?usp=sharing}" + "{backend b | dnn::DNN_BACKEND_DEFAULT | Select a computation backend: " + "dnn::DNN_BACKEND_DEFAULT, " + "dnn::DNN_BACKEND_INFERENCE_ENGINE, " + "dnn::DNN_BACKEND_OPENCV }" + "{target t | dnn::DNN_TARGET_CPU | Select a target device: " + "dnn::DNN_TARGET_CPU, " + "dnn::DNN_TARGET_OPENCL, " + "dnn::DNN_TARGET_OPENCL_FP16 }" + ; + CommandLineParser parser(argc, argv, keys); + if (parser.has("help")) + { + parser.printMessage(); + return 0; + } + + // Load Network + dnn::Net net = dnn::readNetFromONNX(parser.get("model")); + net.setPreferableBackend(parser.get("backend")); + net.setPreferableTarget(parser.get("target")); + + // Get audio + vectorinputAudio = {}; + int samplingRate = 0; + if (parser.has("input_file")) + { + string audio = samples::findFile(parser.get("input_file")); + samplingRate = readAudioFile(inputAudio, audio, parser.get("audio_stream")); + } + else + { + samplingRate = readAudioMicrophone(inputAudio, parser.get("audio_duration")); + } + + if ((inputAudio.size() == 0) || samplingRate <= 0) + { + cerr << "Error: problems with audio reading, check input arguments" << endl; + return -1; + } + + if (inputAudio.size() / samplingRate < 6) + { + cout << "Warning: For predictable network performance duration of audio must exceed 6 sec." + " Audio will be extended with zero samples" << endl; + for(int i = static_cast(inputAudio.size()) - 1; i < samplingRate * 6; ++i) + { + inputAudio.push_back(0); + } + } + + // Calculate features + FilterbankFeatures filter; + auto calculated_features = filter.calculate_features(inputAudio); + + // Show spectogram if required + if (parser.get("show_spectrogram") == true) + { + Mat spectogram; + normalize(calculated_features, spectogram, 0, 255, NORM_MINMAX, CV_8U); + applyColorMap(spectogram, spectogram, COLORMAP_INFERNO); + imshow("spectogram", spectogram); + waitKey(0); + } + + Decoder decoder; + string prediction = predict(calculated_features, net, decoder); + for( auto &transcript: prediction) + { + cout << transcript; + } + + return 0; +} diff --git a/samples/dnn/speech_recognition.py b/samples/dnn/speech_recognition.py index 7bc424b37c6b..da2ce11521fd 100644 --- a/samples/dnn/speech_recognition.py +++ b/samples/dnn/speech_recognition.py @@ -44,7 +44,7 @@ model.graph.initializer.insert(i,init) ``` - 6. Add an additional reshape node to handle the inconsistant input from python and c++ of openCV. + 6. Add an additional reshape node to handle the inconsistent input from python and c++ of openCV. see https://github.com/opencv/opencv/issues/19091 Make & insert a new node with 'Reshape' operation & required initializer ``` @@ -256,7 +256,7 @@ def mel(self, sr, n_fft, n_mels=128, fmin=0.0, fmax=None, dtype=np.float32): weights *= enorm[:, np.newaxis] return weights - # STFT preperation + # STFT preparation def pad_window_center(self, data, size, axis=-1, **kwargs): ''' Centers the data and pads. @@ -329,7 +329,7 @@ def stft(self, y, n_fft, hop_length=None, win_length=None, fft_window=None, pad_ then padded with zeros to match n_fft fft_window : a vector or array of length `n_fft` having values computed by a window function - pad_mode : mode while padding the singnal + pad_mode : mode while padding the signal return_complex : returns array with complex data type if `True` return : Matrix of short-term Fourier transform coefficients. ''' diff --git a/samples/dnn/text_detection.py b/samples/dnn/text_detection.py index 6fb1e9090171..db0ea197bd3e 100644 --- a/samples/dnn/text_detection.py +++ b/samples/dnn/text_detection.py @@ -195,7 +195,7 @@ def main(): indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold, nmsThreshold) for i in indices: # get 4 corners of the rotated rect - vertices = cv.boxPoints(boxes[i[0]]) + vertices = cv.boxPoints(boxes[i]) # scale the bounding box coordinates based on the respective ratios for j in range(4): vertices[j][0] *= rW diff --git a/samples/gdb/mat_pretty_printer.py b/samples/gdb/mat_pretty_printer.py index e6ad2cbde212..54afd5e1d321 100644 --- a/samples/gdb/mat_pretty_printer.py +++ b/samples/gdb/mat_pretty_printer.py @@ -122,28 +122,38 @@ def __init__(self, m, size, flags): (dtype, ctype) = flags.dtype() elsize = np.dtype(dtype).itemsize - ptr = m['data'] - dataptr = int(ptr) - length = (int(m['dataend']) - dataptr) // elsize - start = (int(m['datastart']) - dataptr) // elsize + shape = size.to_numpy() + steps = np.asarray([int(m['step']['p'][i]) for i in range(len(shape))], dtype=np.int64) - if length == 0: + ptr = m['data'] + # either we are default-constructed or sizes are zero + if int(ptr) == 0 or np.prod(shape * steps) == 0: self.mat = np.array([]) self.view = self.mat return + # we don't want to show excess brackets + if flags.channels() != 1: + shape = np.append(shape, flags.channels()) + steps = np.append(steps, elsize) + + # get the length of contiguous array from data to the last element of the matrix + length = 1 + np.sum((shape - 1) * steps) // elsize + if dtype != np.float16: + # read all elements into self.mat ctype = gdb.lookup_type(ctype) ptr = ptr.cast(ctype.array(length - 1).pointer()).dereference() self.mat = np.array([ptr[i] for i in range(length)], dtype=dtype) else: + # read as uint16_t and then reinterpret the bytes as float16 u16 = gdb.lookup_type('uint16_t') ptr = ptr.cast(u16.array(length - 1).pointer()).dereference() self.mat = np.array([ptr[i] for i in range(length)], dtype=np.uint16) self.mat = self.mat.view(np.float16) - steps = np.asarray([int(m['step']['p'][i]) for i in range(size.dims())], dtype=np.int64) - self.view = np.lib.stride_tricks.as_strided(self.mat[start:], shape=size.to_numpy(), strides=steps) + # numpy will do the heavy lifting of strided access + self.view = np.lib.stride_tricks.as_strided(self.mat, shape=shape, strides=steps) def __iter__(self): return iter({'data': stri(self.view)}.items()) diff --git a/samples/python/camera_calibration_show_extrinsics.py b/samples/python/camera_calibration_show_extrinsics.py index d676691f15d7..0ee2a19b6877 100755 --- a/samples/python/camera_calibration_show_extrinsics.py +++ b/samples/python/camera_calibration_show_extrinsics.py @@ -1,5 +1,18 @@ #!/usr/bin/env python -# -*- coding: utf-8 -*- + +''' +Plot camera calibration extrinsics. + +usage: + camera_calibration_show_extrinsics.py [--calibration ] [--cam_width] [--cam_height] [--scale_focal] [--patternCentric ] + +default values: + --calibration : left_intrinsics.yml + --cam_width : 0.064/2 + --cam_height : 0.048/2 + --scale_focal : 40 + --patternCentric : True +''' # Python 2/3 compatibility from __future__ import print_function diff --git a/samples/python/common.py b/samples/python/common.py index 85cda62cd425..e7ad478b885f 100755 --- a/samples/python/common.py +++ b/samples/python/common.py @@ -222,7 +222,7 @@ def mosaic(w, imgs): pad = np.zeros_like(img0) imgs = it.chain([img0], imgs) rows = grouper(w, imgs, pad) - return np.vstack(map(np.hstack, rows)) + return np.vstack(list(map(np.hstack, rows))) def getsize(img): h, w = img.shape[:2] diff --git a/samples/python/digits.py b/samples/python/digits.py index e5d8ceb59a59..25db411f942c 100755 --- a/samples/python/digits.py +++ b/samples/python/digits.py @@ -191,3 +191,4 @@ def preprocess_hog(digits): model.save('digits_svm.dat') cv.waitKey(0) + cv.destroyAllWindows() diff --git a/samples/python/digits_video.py b/samples/python/digits_video.py index 692da91219b1..17f44c333dda 100755 --- a/samples/python/digits_video.py +++ b/samples/python/digits_video.py @@ -29,7 +29,7 @@ def main(): src = sys.argv[1] except: src = 0 - cap = video.create_capture(src) + cap = video.create_capture(src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('sudoku.png'))) classifier_fn = 'digits_svm.dat' if not os.path.exists(classifier_fn): diff --git a/samples/python/facedetect.py b/samples/python/facedetect.py index 488c92d5e5d7..248206a7cd9f 100755 --- a/samples/python/facedetect.py +++ b/samples/python/facedetect.py @@ -39,13 +39,13 @@ def main(): except: video_src = 0 args = dict(args) - cascade_fn = args.get('--cascade', "data/haarcascades/haarcascade_frontalface_alt.xml") - nested_fn = args.get('--nested-cascade', "data/haarcascades/haarcascade_eye.xml") + cascade_fn = args.get('--cascade', "haarcascades/haarcascade_frontalface_alt.xml") + nested_fn = args.get('--nested-cascade', "haarcascades/haarcascade_eye.xml") cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn)) nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn)) - cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('samples/data/lena.jpg'))) + cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('lena.jpg'))) while True: _ret, img = cam.read() diff --git a/samples/python/qrcode.py b/samples/python/qrcode.py index b3253f96c642..21b1a5907312 100644 --- a/samples/python/qrcode.py +++ b/samples/python/qrcode.py @@ -245,4 +245,6 @@ def main(): if __name__ == '__main__': + print(__doc__) main() + cv.destroyAllWindows() diff --git a/samples/python/stitching_detailed.py b/samples/python/stitching_detailed.py index 6dcfe14f0504..56d6965733b0 100644 --- a/samples/python/stitching_detailed.py +++ b/samples/python/stitching_detailed.py @@ -246,9 +246,9 @@ def get_matcher(args): if matcher_type == "affine": matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf) elif range_width == -1: - matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf) + matcher = cv.detail_BestOf2NearestMatcher(try_cuda, match_conf) else: - matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf) + matcher = cv.detail_BestOf2NearestRangeMatcher(range_width, try_cuda, match_conf) return matcher @@ -324,7 +324,10 @@ def main(): is_work_scale_set = True img = cv.resize(src=full_img, dsize=None, fx=work_scale, fy=work_scale, interpolation=cv.INTER_LINEAR_EXACT) if is_seam_scale_set is False: - seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0] * full_img.shape[1]))) + if seam_megapix > 0: + seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0] * full_img.shape[1]))) + else: + seam_scale = 1.0 seam_work_aspect = seam_scale / work_scale is_seam_scale_set = True img_feat = cv.detail.computeImageFeatures2(finder, img) @@ -345,9 +348,9 @@ def main(): img_names_subset = [] full_img_sizes_subset = [] for i in range(len(indices)): - img_names_subset.append(img_names[indices[i, 0]]) - img_subset.append(images[indices[i, 0]]) - full_img_sizes_subset.append(full_img_sizes[indices[i, 0]]) + img_names_subset.append(img_names[indices[i]]) + img_subset.append(images[indices[i]]) + full_img_sizes_subset.append(full_img_sizes[indices[i]]) images = img_subset img_names = img_names_subset full_img_sizes = full_img_sizes_subset @@ -479,7 +482,7 @@ def main(): blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO) elif blend_type == "multiband": blender = cv.detail_MultiBandBlender() - blender.setNumBands((np.log(blend_width) / np.log(2.) - 1.).astype(np.int)) + blender.setNumBands((np.log(blend_width) / np.log(2.) - 1.).astype(np.int32)) elif blend_type == "feather": blender = cv.detail_FeatherBlender() blender.setSharpness(1. / blend_width) @@ -513,6 +516,5 @@ def main(): if __name__ == '__main__': - print(__doc__) main() cv.destroyAllWindows() diff --git a/samples/python/text_skewness_correction.py b/samples/python/text_skewness_correction.py index c8ee33b39d6b..c3e97a333be5 100644 --- a/samples/python/text_skewness_correction.py +++ b/samples/python/text_skewness_correction.py @@ -15,7 +15,7 @@ def main(): parser = argparse.ArgumentParser() - parser.add_argument("-i", "--image", required=True, help="path to input image file") + parser.add_argument("-i", "--image", default="imageTextR.png", help="path to input image file") args = vars(parser.parse_args()) # load the image from disk @@ -37,9 +37,9 @@ def main(): coords = cv.findNonZero(thresh) angle = cv.minAreaRect(coords)[-1] # the `cv.minAreaRect` function returns values in the - # range [-90, 0) if the angle is less than -45 we need to add 90 to it - if angle < -45: - angle = (90 + angle) + # range [0, 90) if the angle is more than 45 we need to subtract 90 from it + if angle > 45: + angle = (angle - 90) (h, w) = image.shape[:2] center = (w // 2, h // 2) @@ -55,4 +55,6 @@ def main(): if __name__ == "__main__": + print(__doc__) main() + cv.destroyAllWindows() diff --git a/samples/python/tracker.py b/samples/python/tracker.py index 753e166ad896..3b04c57e8a42 100644 --- a/samples/python/tracker.py +++ b/samples/python/tracker.py @@ -1,5 +1,4 @@ #!/usr/bin/env python - ''' Tracker demo @@ -36,43 +35,49 @@ class App(object): def __init__(self, args): self.args = args - - def initializeTracker(self, image, trackerAlgorithm): + self.trackerAlgorithm = args.tracker_algo + self.tracker = self.createTracker() + + def createTracker(self): + if self.trackerAlgorithm == 'mil': + tracker = cv.TrackerMIL_create() + elif self.trackerAlgorithm == 'goturn': + params = cv.TrackerGOTURN_Params() + params.modelTxt = self.args.goturn + params.modelBin = self.args.goturn_model + tracker = cv.TrackerGOTURN_create(params) + elif self.trackerAlgorithm == 'dasiamrpn': + params = cv.TrackerDaSiamRPN_Params() + params.model = self.args.dasiamrpn_net + params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1 + params.kernel_r1 = self.args.dasiamrpn_kernel_r1 + tracker = cv.TrackerDaSiamRPN_create(params) + else: + sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn.".format(self.trackerAlgorithm)) + return tracker + + def initializeTracker(self, image): while True: - if trackerAlgorithm == 'mil': - tracker = cv.TrackerMIL_create() - elif trackerAlgorithm == 'goturn': - params = cv.TrackerGOTURN_Params() - params.modelTxt = self.args.goturn - params.modelBin = self.args.goturn_model - tracker = cv.TrackerGOTURN_create(params) - elif trackerAlgorithm == 'dasiamrpn': - params = cv.TrackerDaSiamRPN_Params() - params.model = self.args.dasiamrpn_net - params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1 - params.kernel_r1 = self.args.dasiamrpn_kernel_r1 - tracker = cv.TrackerDaSiamRPN_create(params) - else: - sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn.".format(trackerAlgorithm)) - print('==> Select object ROI for tracker ...') bbox = cv.selectROI('tracking', image) print('ROI: {}'.format(bbox)) + if bbox[2] <= 0 or bbox[3] <= 0: + sys.exit("ROI selection cancelled. Exiting...") try: - tracker.init(image, bbox) + self.tracker.init(image, bbox) except Exception as e: print('Unable to initialize tracker with requested bounding box. Is there any object?') print(e) print('Try again ...') continue - return tracker + return def run(self): videoPath = self.args.input - trackerAlgorithm = self.args.tracker_algo - camera = create_capture(videoPath, presets['cube']) + print('Using video: {}'.format(videoPath)) + camera = create_capture(cv.samples.findFileOrKeep(videoPath), presets['cube']) if not camera.isOpened(): sys.exit("Can't open video stream: {}".format(videoPath)) @@ -82,7 +87,7 @@ def run(self): assert image is not None cv.namedWindow('tracking') - tracker = self.initializeTracker(image, trackerAlgorithm) + self.initializeTracker(image) print("==> Tracking is started. Press 'SPACE' to re-initialize tracker or 'ESC' for exit...") @@ -92,7 +97,7 @@ def run(self): print("Can't read frame") break - ok, newbox = tracker.update(image) + ok, newbox = self.tracker.update(image) #print(ok, newbox) if ok: @@ -101,7 +106,7 @@ def run(self): cv.imshow("tracking", image) k = cv.waitKey(1) if k == 32: # SPACE - tracker = self.initializeTracker(image) + self.initializeTracker(image) if k == 27: # ESC break @@ -112,22 +117,13 @@ def run(self): print(__doc__) parser = argparse.ArgumentParser(description="Run tracker") parser.add_argument("--input", type=str, default="vtest.avi", help="Path to video source") - parser.add_argument("--tracker_algo", type=str, default="mil", help="One of three available tracking algorithms: mil, goturn, dasiamrpn") + parser.add_argument("--tracker_algo", type=str, default="mil", help="One of available tracking algorithms: mil, goturn, dasiamrpn") parser.add_argument("--goturn", type=str, default="goturn.prototxt", help="Path to GOTURN architecture") parser.add_argument("--goturn_model", type=str, default="goturn.caffemodel", help="Path to GOTERN model") parser.add_argument("--dasiamrpn_net", type=str, default="dasiamrpn_model.onnx", help="Path to onnx model of DaSiamRPN net") parser.add_argument("--dasiamrpn_kernel_r1", type=str, default="dasiamrpn_kernel_r1.onnx", help="Path to onnx model of DaSiamRPN kernel_r1") parser.add_argument("--dasiamrpn_kernel_cls1", type=str, default="dasiamrpn_kernel_cls1.onnx", help="Path to onnx model of DaSiamRPN kernel_cls1") - parser.add_argument("--dasiamrpn_backend", type=int, default=0, help="Choose one of computation backends:\ - 0: automatically (by default),\ - 1: Halide language (http://halide-lang.org/),\ - 2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit),\ - 3: OpenCV implementation") - parser.add_argument("--dasiamrpn_target", type=int, default=0, help="Choose one of target computation devices:\ - 0: CPU target (by default),\ - 1: OpenCL,\ - 2: OpenCL fp16 (half-float precision),\ - 3: VPU") + args = parser.parse_args() App(args).run() cv.destroyAllWindows()