pythonwebcoder
diff --git a/‎modules/features2d/include/opencv2/features2d.hpp
Lines changed: 15 additions & 6 deletions b/‎modules/features2d/include/opencv2/features2d.hpp
Lines changed: 15 additions & 6 deletions
diff --git a/‎modules/features2d/src/akaze.cpp
Lines changed: 5 additions & 18 deletions b/‎modules/features2d/src/akaze.cpp
Lines changed: 5 additions & 18 deletions
@@ -658,13 +658,22 @@ class CV_EXPORTS_W KAZE : public Feature2D
     CV_WRAP virtual int getDiffusivity() const = 0;
 };
 
-/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13 . :
+/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13.
 
-@note AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using *extract*
-and *detect* instead of *operator()* due to performance reasons. .. [ANB13] Fast Explicit Diffusion
-for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien
-Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
- */
+@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe.
+
+@note When you need descriptors use Feature2D::detectAndCompute, which
+provides better performance. When using Feature2D::detect followed by
+Feature2D::compute scale space pyramid is computed twice.
+
+@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm
+will use OpenCL.
+
+@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear
+Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In
+British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
+
+*/
 class CV_EXPORTS_W AKAZE : public Feature2D
 {
 public:
 
@@ -169,46 +169,33 @@ namespace cv
         {
             CV_INSTRUMENT_REGION()
 
-            Mat img = image.getMat();
-            if (img.channels() > 1)
-                cvtColor(image, img, COLOR_BGR2GRAY);
-
-            Mat img1_32;
-            if ( img.depth() == CV_32F )
-                img1_32 = img;
-            else if ( img.depth() == CV_8U )
-                img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
-            else if ( img.depth() == CV_16U )
-                img.convertTo(img1_32, CV_32F, 1.0 / 65535.0, 0);
-
-            CV_Assert( ! img1_32.empty() );
+            CV_Assert( ! image.empty() );
 
             AKAZEOptions options;
             options.descriptor = descriptor;
             options.descriptor_channels = descriptor_channels;
             options.descriptor_size = descriptor_size;
-            options.img_width = img.cols;
-            options.img_height = img.rows;
+            options.img_width = image.cols();
+            options.img_height = image.rows();
             options.dthreshold = threshold;
             options.omax = octaves;
             options.nsublevels = sublevels;
             options.diffusivity = diffusivity;
 
             AKAZEFeatures impl(options);
-            impl.Create_Nonlinear_Scale_Space(img1_32);
+            impl.Create_Nonlinear_Scale_Space(image);
 
             if (!useProvidedKeypoints)
             {
                 impl.Feature_Detection(keypoints);
-                impl.Compute_Keypoints_Orientation(keypoints);
             }
 
             if (!mask.empty())
             {
                 KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
             }
 
-            if( descriptors.needed() )
+            if(descriptors.needed())
             {
                 impl.Compute_Descriptors(keypoints, descriptors);