diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..dd84ea7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,38 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**Smartphone (please complete the following information):** + - Device: [e.g. iPhone6] + - OS: [e.g. iOS8.1] + - Browser [e.g. stock browser, safari] + - Version [e.g. 22] + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/custom.md new file mode 100644 index 0000000..48d5f81 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/custom.md @@ -0,0 +1,10 @@ +--- +name: Custom issue template +about: Describe this issue template's purpose here. +title: '' +labels: '' +assignees: '' + +--- + + diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..bbcbbe7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/blank.yml b/.github/workflows/blank.yml new file mode 100644 index 0000000..f937a06 --- /dev/null +++ b/.github/workflows/blank.yml @@ -0,0 +1,36 @@ +# This is a basic workflow to help you get started with Actions + +name: CI + +# Controls when the workflow will run +on: + # Triggers the workflow on push or pull request events but only for the master branch + push: + branches: [ master ] + pull_request: + branches: [ master ] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "build" + build: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v2 + + # Runs a single command using the runners shell + - name: Run a one-line script + run: echo Hello, world! + + # Runs a set of commands using the runners shell + - name: Run a multi-line script + run: | + echo Add other actions to build, + echo test, and deploy your project. diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..527699b --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +# Sphinx documentation +/docs/build/ +/docs/examples/data/ +/docs/source/auto_examples/ +!/docs/source/auto_examples/*.ipynb +!/docs/source/auto_examples/*.py.md5 +!/docs/source/auto_examples/*.rst +!/docs/source/auto_examples/images + +# Editors +.vscode/ +.idea/ + diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 0e40fe8..0000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ - -# Default ignored files -/workspace.xml \ No newline at end of file diff --git a/.idea/TensorFlowObjectDetectionTutorial.iml b/.idea/TensorFlowObjectDetectionTutorial.iml deleted file mode 100644 index 9b05612..0000000 --- a/.idea/TensorFlowObjectDetectionTutorial.iml +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - - - - - \ No newline at end of file diff --git a/.idea/codeStyles/codeStyleConfig.xml b/.idea/codeStyles/codeStyleConfig.xml deleted file mode 100644 index a55e7a1..0000000 --- a/.idea/codeStyles/codeStyleConfig.xml +++ /dev/null @@ -1,5 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2d..0000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index 971634a..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml deleted file mode 100644 index 9570b9b..0000000 --- a/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index 94a25f7..0000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index fb3df05..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "python.pythonPath": "C:\\Users\\sglvladi\\Anaconda3\\envs\\sphinx\\python.exe" -} \ No newline at end of file diff --git a/README.md b/README.md index 948cc64..9e8042c 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,22 @@ -# TensorFlowObjectDetectionTutorial [![Documentation Status](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/badge/?version=latest)](http://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/?badge=latest) +# TensorFlowObjectDetectionTutorial This is a "short" (rather lengthy) tutorial that started off as self notes on how to set-up and get going with the TensorFlow Object Detection API. There currently exist several versions of the tutorial, corresponding to the various different versions of TensorFlow. The two major versions are outlined below. -## TensorFlow 2 Object Detection API tutorial [![Documentation Status](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/badge/?version=latest)](http://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/?badge=latest) +## TensorFlow 2 Object Detection API tutorial + +[![TensorFlow 2.5](https://img.shields.io/badge/TensorFlow-2.5-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.5.0) [![Documentation Status](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/badge/?version=latest)](http://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/?badge=latest) Since July 10, 2020 TensorFlow [announced that the Object Detection API officially supports TensorFlow 2](https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html). Therefore, an updated version of the tutorial was created to cover TensorFlow 2. To read the tutorial, visit [http://tensorflow-object-detection-api-tutorial.readthedocs.io](http://tensorflow-object-detection-api-tutorial.readthedocs.io). -## TensorFlow Object Detection API tutorial [![Documentation Status](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/badge/?version=1.14.2)](http://tensorflow-object-detection-api-tutorial.readthedocs.io/en/1.14.2/?badge=1.14.2) +## TensorFlow Object Detection API tutorial + +[![TensorFlow 1.14](https://img.shields.io/badge/TensorFlow-1.14-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v1.14.0) [![Documentation Status](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/badge/?version=tensorflow-1.14)](http://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/?badge=tensorflow-1.14) Originally, the tutorial was written with TensorFlow 1 in mind. Since it is possible that many users will still be interested in working with TensorFlow 1, a version of this tutorial will also be maintained for the foreseeable future. -To read the tutorial, visit [https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/1.14.2/](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/1.14.2/). +To read the tutorial, visit [https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/). diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..a311fd9 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,184 @@ +# TensorFlow Object Detection API +[![TensorFlow 2.2](https://img.shields.io/badge/TensorFlow-2.2-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0) +[![TensorFlow 1.15](https://img.shields.io/badge/TensorFlow-1.15-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v1.15.0) +[![Python 3.6](https://img.shields.io/badge/Python-3.6-3776AB)](https://www.python.org/downloads/release/python-360/) + +Creating accurate machine learning models capable of localizing and identifying +multiple objects in a single image remains a core challenge in computer vision. +The TensorFlow Object Detection API is an open source framework built on top of +TensorFlow that makes it easy to construct, train and deploy object detection +models. At Google we’ve certainly found this codebase to be useful for our +computer vision needs, and we hope that you will as well.

+

+Contributions to the codebase are welcome and we would love to hear back from +you if you find this API useful. Finally if you use the TensorFlow Object +Detection API for a research publication, please consider citing: + +``` +"Speed/accuracy trade-offs for modern convolutional object detectors." +Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, +Song Y, Guadarrama S, Murphy K, CVPR 2017 +``` + +\[[link](https://arxiv.org/abs/1611.10012)\]\[[bibtex](https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAWUIIlnPZ_L9jxvPwcC49kDlELtaeIyU-&scisf=4&ct=citation&cd=-1&hl=en&scfhb=1)\] + +

+ +

+ +## Support for TensorFlow 2 and 1 +The TensorFlow Object Detection API supports both TensorFlow 2 (TF2) and +TensorFlow 1 (TF1). A majority of the modules in the library are both TF1 and +TF2 compatible. In cases where they are not, we provide two versions. + +Although we will continue to maintain the TF1 models and provide support, we +encourage users to try the Object Detection API with TF2 for the following +reasons: + +* We provide new architectures supported in TF2 only and we will continue to + develop in TF2 going forward. + +* The popular models we ported from TF1 to TF2 achieve the same performance. + +* A single training and evaluation binary now supports both GPU and TPU + distribution strategies making it possible to train models with synchronous + SGD by default. + +* Eager execution with new binaries makes debugging easy! + +Finally, if are an existing user of the Object Detection API we have retained +the same config language you are familiar with and ensured that the +TF2 training/eval binary takes the same arguments as our TF1 binaries. + +Note: The models we provide in [TF2 Zoo](g3doc/tf2_detection_zoo.md) and +[TF1 Zoo](g3doc/tf1_detection_zoo.md) are specific to the TensorFlow major +version and are not interoperable. + +Please select one of the links below for TensorFlow version-specific +documentation of the Object Detection API: + + +### Tensorflow 2.x + * + Object Detection API TensorFlow 2
+ * + TensorFlow 2 Model Zoo
+ +### Tensorflow 1.x + * + Object Detection API TensorFlow 1
+ * + TensorFlow 1 Model Zoo
+ + +## Whats New + +### TensorFlow 2 Support + +We are happy to announce that the TF OD API officially supports TF2! Our release +includes: + +* New binaries for train/eval/export that are designed to run in eager mode. +* A suite of TF2 compatible (Keras-based) models; this includes migrations of + our most popular TF1.x models (e.g., SSD with MobileNet, RetinaNet, + Faster R-CNN, Mask R-CNN), as well as a few new architectures for which we + will only maintain TF2 implementations: + + 1. CenterNet - a simple and effective anchor-free architecture based on + the recent [Objects as Points](https://arxiv.org/abs/1904.07850) paper by + Zhou et al. + 2. [EfficientDet](https://arxiv.org/abs/1911.09070) - a recent family of + SOTA models discovered with the help of Neural Architecture Search. + +* COCO pre-trained weights for all of the models provided as TF2 style + object-based checkpoints. +* Access to [Distribution Strategies](https://www.tensorflow.org/guide/distributed_training) + for distributed training --- our model are designed to be trainable using sync + multi-GPU and TPU platforms. +* Colabs demo’ing eager mode training and inference. + +See our release blogpost [here](https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html). +If you are an existing user of the TF OD API using TF 1.x, don’t worry, we’ve +got you covered. + +**Thanks to contributors**: Akhil Chinnakotla, Allen Lavoie, Anirudh Vegesana, +Anjali Sridhar, Austin Myers, Dan Kondratyuk, David Ross, Derek Chow, Jaeyoun +Kim, Jing Li, Jonathan Huang, Jordi Pont-Tuset, Karmel Allison, Kathy Ruan, +Kaushik Shivakumar, Lu He, Mingxing Tan, Pengchong Jin, Ronny Votel, Sara Beery, +Sergi Caelles Prat, Shan Yang, Sudheendra Vijayanarasimhan, Tina Tian, Tomer +Kaftan, Vighnesh Birodkar, Vishnu Banna, Vivek Rathod, Yanhui Liang, Yiming Shi, +Yixin Shi, Yu-hui Chen, Zhichao Lu. + +### MobileDet GPU + +We have released SSDLite with MobileDet GPU backbone, which achieves 17% mAP +higher than the MobileNetV2 SSDLite (27.5 mAP vs 23.5 mAP) on a NVIDIA Jetson +Xavier at comparable latency (3.2ms vs 3.3ms). + +Along with the model definition, we are also releasing model checkpoints trained +on the COCO dataset. + +Thanks to contributors: Yongzhe Wang, Bo Chen, Hanxiao Liu, Le An +(NVIDIA), Yu-Te Cheng (NVIDIA), Oliver Knieps (NVIDIA), and Josh Park (NVIDIA). + +### Context R-CNN + +We have released [Context R-CNN](https://arxiv.org/abs/1912.03538), a model that +uses attention to incorporate contextual information images (e.g. from +temporally nearby frames taken by a static camera) in order to improve accuracy. +Importantly, these contextual images need not be labeled. + +* When applied to a challenging wildlife detection dataset + ([Snapshot Serengeti](http://lila.science/datasets/snapshot-serengeti)), + Context R-CNN with context from up to a month of images outperforms a + single-frame baseline by 17.9% mAP, and outperforms S3D (a 3d convolution + based baseline) by 11.2% mAP. +* Context R-CNN leverages temporal context from the unlabeled frames of a + novel camera deployment to improve performance at that camera, boosting + model generalizeability. + +Read about Context R-CNN on the Google AI blog +[here](https://ai.googleblog.com/2020/06/leveraging-temporal-context-for-object.html). + +We have provided code for generating data with associated context +[here](g3doc/context_rcnn.md), and a sample config for a Context R-CNN model +[here](samples/configs/context_rcnn_resnet101_snapshot_serengeti_sync.config). + +Snapshot Serengeti-trained Faster R-CNN and Context R-CNN models can be found in +the +[model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md#snapshot-serengeti-camera-trap-trained-models). + +A colab demonstrating Context R-CNN is provided +[here](colab_tutorials/context_rcnn_tutorial.ipynb). + +Thanks to contributors: Sara Beery, Jonathan Huang, Guanhang Wu, Vivek +Rathod, Ronny Votel, Zhichao Lu, David Ross, Pietro Perona, Tanya Birch, and the +Wildlife Insights AI Team. + +## Release Notes +See [notes](g3doc/release_notes.md) for all past releases. + +## Getting Help + +To get help with issues you may encounter using the TensorFlow Object Detection +API, create a new question on [StackOverflow](https://stackoverflow.com/) with +the tags "tensorflow" and "object-detection". + +Please report bugs (actually broken code, not usage questions) to the +tensorflow/models GitHub +[issue tracker](https://github.com/tensorflow/models/issues), prefixing the +issue name with "object_detection". + +Please check the [FAQ](g3doc/faq.md) for frequently asked questions before +reporting an issue. + +## Maintainers + +* Jonathan Huang ([@GitHub jch1](https://github.com/jch1)) +* Vivek Rathod ([@GitHub tombstone](https://github.com/tombstone)) +* Vighnesh Birodkar ([@GitHub vighneshbirodkar](https://github.com/vighneshbirodkar)) +* Austin Myers ([@GitHub austin-myers](https://github.com/austin-myers)) +* Zhichao Lu ([@GitHub pkulzc](https://github.com/pkulzc)) +* Ronny Votel ([@GitHub ronnyvotel](https://github.com/ronnyvotel)) +* Yu-hui Chen ([@GitHub yuhuichen1015](https://github.com/yuhuichen1015)) +* Derek Chow ([@GitHub derekjchow](https://github.com/derekjchow)) diff --git a/docs/build/.buildinfo b/docs/build/.buildinfo deleted file mode 100644 index 9336572..0000000 --- a/docs/build/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: fe7be69e6c8f8e32eecf964d57e6c7b9 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/.doctrees/auto_examples/index.doctree b/docs/build/.doctrees/auto_examples/index.doctree deleted file mode 100644 index 5891021..0000000 Binary files a/docs/build/.doctrees/auto_examples/index.doctree and /dev/null differ diff --git a/docs/build/.doctrees/auto_examples/object_detection_camera.doctree b/docs/build/.doctrees/auto_examples/object_detection_camera.doctree deleted file mode 100644 index 410d1c3..0000000 Binary files a/docs/build/.doctrees/auto_examples/object_detection_camera.doctree and /dev/null differ diff --git a/docs/build/.doctrees/auto_examples/plot_object_detection_simple.doctree b/docs/build/.doctrees/auto_examples/plot_object_detection_simple.doctree deleted file mode 100644 index 33b2a13..0000000 Binary files a/docs/build/.doctrees/auto_examples/plot_object_detection_simple.doctree and /dev/null differ diff --git a/docs/build/.doctrees/auto_examples/sg_execution_times.doctree b/docs/build/.doctrees/auto_examples/sg_execution_times.doctree deleted file mode 100644 index b346783..0000000 Binary files a/docs/build/.doctrees/auto_examples/sg_execution_times.doctree and /dev/null differ diff --git a/docs/build/.doctrees/environment.pickle b/docs/build/.doctrees/environment.pickle deleted file mode 100644 index 21ec2c2..0000000 Binary files a/docs/build/.doctrees/environment.pickle and /dev/null differ diff --git a/docs/build/.doctrees/index.doctree b/docs/build/.doctrees/index.doctree deleted file mode 100644 index 147c944..0000000 Binary files a/docs/build/.doctrees/index.doctree and /dev/null differ diff --git a/docs/build/.doctrees/install.doctree b/docs/build/.doctrees/install.doctree deleted file mode 100644 index f0de252..0000000 Binary files a/docs/build/.doctrees/install.doctree and /dev/null differ diff --git a/docs/build/.doctrees/issues.doctree b/docs/build/.doctrees/issues.doctree deleted file mode 100644 index a387eb3..0000000 Binary files a/docs/build/.doctrees/issues.doctree and /dev/null differ diff --git a/docs/build/.doctrees/training.doctree b/docs/build/.doctrees/training.doctree deleted file mode 100644 index a3c9efa..0000000 Binary files a/docs/build/.doctrees/training.doctree and /dev/null differ diff --git a/docs/build/_downloads/1a3be7f9b5153f8f6d44c96a51032716/plot_object_detection_simple.ipynb b/docs/build/_downloads/1a3be7f9b5153f8f6d44c96a51032716/plot_object_detection_simple.ipynb deleted file mode 100644 index c18284c..0000000 --- a/docs/build/_downloads/1a3be7f9b5153f8f6d44c96a51032716/plot_object_detection_simple.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nObject Detection Test\n=====================\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This demo will take you through the steps of running an \"out-of-the-box\" detection model on a\ncollection of images.\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the data directory\n~~~~~~~~~~~~~~~~~~~~~~~~~\nThe snippet shown below will create the ``data`` directory where all our data will be stored. The\ncode will create a directory structure as shown bellow:\n\n.. code-block:: bash\n\n data\n \u251c\u2500\u2500 images\n \u2514\u2500\u2500 models\n\nwhere the ``images`` folder will contain the downlaoded test images, while ``models`` will\ncontain the downloaded models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n\nDATA_DIR = os.path.join(os.getcwd(), 'data')\nIMAGES_DIR = os.path.join(DATA_DIR, 'images')\nMODELS_DIR = os.path.join(DATA_DIR, 'models')\nfor dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]:\n if not os.path.exists(dir):\n os.mkdir(dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the test images\n~~~~~~~~~~~~~~~~~~~~~~~~\nFirst we will download the images that we will use throughout this tutorial. The code snippet\nshown bellow will download the test images from the `TensorFlow Model Garden `_\nand save them inside the ``data/images`` folder.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import urllib.request\n\nIMAGE_FILENAMES = ['image1.jpg', 'image2.jpg']\nIMAGES_DOWNLOAD_BASE = \\\n 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'\n\nfor image_filename in IMAGE_FILENAMES:\n\n image_path = os.path.join(IMAGES_DIR, image_filename)\n\n # Download image\n if not os.path.exists(image_path):\n print('Downloading {}... '.format(image_filename), end='')\n urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path)\n print('Done')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the model\n~~~~~~~~~~~~~~~~~~\nThe code snippet shown below is used to download the object detection model checkpoint file,\nas well as the labels file (.pbtxt) which contains a list of strings used to add the correct\nlabel to each detection (e.g. person). Once downloaded the files will be stored under the\n``data/models`` folder.\n\nThe particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More\nmodels can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz``\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import tarfile\n\n# Download and extract model\nMODEL_DATE = '20200711'\nMODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'\nMODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz'\nMODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/'\nMODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME\nPATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME)\nPATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/'))\nPATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config'))\nif not os.path.exists(PATH_TO_CKPT):\n print('Downloading model. This may take a while... ', end='')\n urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR)\n tar_file = tarfile.open(PATH_TO_MODEL_TAR)\n tar_file.extractall(MODELS_DIR)\n tar_file.close()\n os.remove(PATH_TO_MODEL_TAR)\n print('Done')\n\n# Download labels file\nLABEL_FILENAME = 'mscoco_label_map.pbtxt'\nLABELS_DOWNLOAD_BASE = \\\n 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'\nPATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME))\nif not os.path.exists(PATH_TO_LABELS):\n print('Downloading label file... ', end='')\n urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS)\n print('Done')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the model\n~~~~~~~~~~~~~~\nNext we load the downloaded model\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)\nimport tensorflow as tf\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import config_util\nfrom object_detection.utils import visualization_utils as viz_utils\nfrom object_detection.builders import model_builder\n\ntf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n\n# Enable GPU dynamic memory allocation\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n\n# Load pipeline config and build a detection model\nconfigs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\nmodel_config = configs['model']\ndetection_model = model_builder.build(model_config=model_config, is_training=False)\n\n# Restore checkpoint\nckpt = tf.compat.v2.train.Checkpoint(\n model=detection_model)\nckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()\n\n@tf.function\ndef detect_fn(image):\n \"\"\"Detect objects in image.\"\"\"\n\n image, shapes = detection_model.preprocess(image)\n prediction_dict = detection_model.predict(image, shapes)\n detections = detection_model.postprocess(prediction_dict, shapes)\n\n return detections, prediction_dict, tf.reshape(shapes, [-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load label map data (for plotting)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n use_display_name=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Putting everything together\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nfrom six import BytesIO\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore') # Suppress Matplotlib warnings\n\ndef load_image_into_numpy_array(path):\n \"\"\"Load an image from file into a numpy array.\n\n Puts image into numpy array to feed into tensorflow graph.\n Note that by convention we put it into a numpy array with shape\n (height, width, channels), where channels=3 for RGB.\n\n Args:\n path: the file path to the image\n\n Returns:\n uint8 numpy array with shape (img_height, img_width, 3)\n \"\"\"\n img_data = tf.io.gfile.GFile(path, 'rb').read()\n image = Image.open(BytesIO(img_data))\n (im_width, im_height) = image.size\n return np.array(image.getdata()).reshape(\n (im_height, im_width, 3)).astype(np.uint8)\n\n\nfor image_filename in IMAGE_FILENAMES:\n\n print('Running inference for {}... '.format(image_filename), end='')\n\n image_path = os.path.join(IMAGES_DIR, image_filename)\n image_np = load_image_into_numpy_array(image_path)\n\n # Things to try:\n # Flip horizontally\n # image_np = np.fliplr(image_np).copy()\n\n # Convert image to grayscale\n # image_np = np.tile(\n # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n\n input_tensor = tf.convert_to_tensor(\n np.expand_dims(image_np, 0), dtype=tf.float32)\n detections, predictions_dict, shapes = detect_fn(input_tensor)\n\n label_id_offset = 1\n image_np_with_detections = image_np.copy()\n\n viz_utils.visualize_boxes_and_labels_on_image_array(\n image_np_with_detections,\n detections['detection_boxes'][0].numpy(),\n (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),\n detections['detection_scores'][0].numpy(),\n category_index,\n use_normalized_coordinates=True,\n max_boxes_to_draw=200,\n min_score_thresh=.30,\n agnostic_mode=False)\n\n plt.figure()\n plt.imshow(image_np_with_detections)\n print('Done')\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 2" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/build/_downloads/20476501054a7e18cf729f8808e7672e/auto_examples_python.zip b/docs/build/_downloads/20476501054a7e18cf729f8808e7672e/auto_examples_python.zip deleted file mode 100644 index a7052fc..0000000 Binary files a/docs/build/_downloads/20476501054a7e18cf729f8808e7672e/auto_examples_python.zip and /dev/null differ diff --git a/docs/build/_downloads/29dd112360879edd9c41f5e05a1e5bae/object_detection_camera.py b/docs/build/_downloads/29dd112360879edd9c41f5e05a1e5bae/object_detection_camera.py deleted file mode 100644 index cd4443e..0000000 --- a/docs/build/_downloads/29dd112360879edd9c41f5e05a1e5bae/object_detection_camera.py +++ /dev/null @@ -1,196 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 -""" -Detect Objects Using Your Webcam -================================ -""" - -# %% -# This demo will take you through the steps of running an "out-of-the-box" detection model to -# detect objects in the video stream extracted from your camera. - -# %% -# Create the data directory -# ~~~~~~~~~~~~~~~~~~~~~~~~~ -# The snippet shown below will create the ``data`` directory where all our data will be stored. The -# code will create a directory structure as shown bellow: -# -# .. code-block:: bash -# -# data -# └── models -# -# where the ``models`` folder will will contain the downloaded models. -import os - -DATA_DIR = os.path.join(os.getcwd(), 'data') -MODELS_DIR = os.path.join(DATA_DIR, 'models') -for dir in [DATA_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) - -# %% -# Download the model -# ~~~~~~~~~~~~~~~~~~ -# The code snippet shown below is used to download the object detection model checkpoint file, -# as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -# label to each detection (e.g. person). -# -# The particular detection algorithm we will use is the `SSD ResNet101 V1 FPN 640x640`. More -# models can be found in the `TensorFlow 2 Detection Model Zoo `_. -# To use a different model you will need the URL name of the specific model. This can be done as -# follows: -# -# 1. Right click on the `Model name` of the model you would like to use; -# 2. Click on `Copy link address` to copy the download link of the model; -# 3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``; -# 4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; -# 5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below. -# -# For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz`` -import tarfile -import urllib.request - -# Download and extract model -MODEL_DATE = '20200711' -MODEL_NAME = 'ssd_resnet101_v1_fpn_640x640_coco17_tpu-8' -MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' -MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' -MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME -PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) -PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) -PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) -if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') - -# Download labels file -LABEL_FILENAME = 'mscoco_label_map.pbtxt' -LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' -PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) -if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') - -# %% -# Load the model -# ~~~~~~~~~~~~~~ -# Next we load the downloaded model - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging -import tensorflow as tf -from object_detection.utils import label_map_util -from object_detection.utils import config_util -from object_detection.utils import visualization_utils as viz_utils -from object_detection.builders import model_builder - -tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) - -# Enable GPU dynamic memory allocation -gpus = tf.config.experimental.list_physical_devices('GPU') -for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) - -# Load pipeline config and build a detection model -configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) -model_config = configs['model'] -detection_model = model_builder.build(model_config=model_config, is_training=False) - -# Restore checkpoint -ckpt = tf.compat.v2.train.Checkpoint(model=detection_model) -ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() - -@tf.function -def detect_fn(image): - """Detect objects in image.""" - - image, shapes = detection_model.preprocess(image) - prediction_dict = detection_model.predict(image, shapes) - detections = detection_model.postprocess(prediction_dict, shapes) - - return detections, prediction_dict, tf.reshape(shapes, [-1]) - - -# %% -# Load label map data (for plotting) -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# Label maps correspond index numbers to category names, so that when our convolution network -# predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility -# functions, but anything that returns a dictionary mapping integers to appropriate string labels -# would be fine. -category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, - use_display_name=True) - -# %% -# Define the video stream -# ~~~~~~~~~~~~~~~~~~~~~~~ -# We will use `OpenCV `_ to capture the video stream -# generated by our webcam. For more information you can refer to the `OpenCV-Python Tutorials `_ -import cv2 - -cap = cv2.VideoCapture(0) - -# %% -# Putting everything together -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# The code shown below loads an image, runs it through the detection model and visualizes the -# detection results, including the keypoints. -# -# Note that this will take a long time (several minutes) the first time you run this code due to -# tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be -# faster. -# -# Here are some simple things to try out if you are curious: -# -# * Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). -# * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). -# * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. -import numpy as np - -while True: - # Read frame from camera - ret, image_np = cap.read() - - # Expand dimensions since the model expects images to have shape: [1, None, None, 3] - image_np_expanded = np.expand_dims(image_np, axis=0) - - # Things to try: - # Flip horizontally - # image_np = np.fliplr(image_np).copy() - - # Convert image to grayscale - # image_np = np.tile( - # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - - input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) - - label_id_offset = 1 - image_np_with_detections = image_np.copy() - - viz_utils.visualize_boxes_and_labels_on_image_array( - image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), - category_index, - use_normalized_coordinates=True, - max_boxes_to_draw=200, - min_score_thresh=.30, - agnostic_mode=False) - - # Display output - cv2.imshow('object detection', cv2.resize(image_np_with_detections, (800, 600))) - - if cv2.waitKey(25) & 0xFF == ord('q'): - break - -cap.release() -cv2.destroyAllWindows() \ No newline at end of file diff --git a/docs/build/_downloads/a2a6b5af38efde6fafcb44df9762f22d/object_detection_camera.ipynb b/docs/build/_downloads/a2a6b5af38efde6fafcb44df9762f22d/object_detection_camera.ipynb deleted file mode 100644 index 7c8c384..0000000 --- a/docs/build/_downloads/a2a6b5af38efde6fafcb44df9762f22d/object_detection_camera.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nDetect Objects Using Your Webcam\n================================\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This demo will take you through the steps of running an \"out-of-the-box\" detection model to\ndetect objects in the video stream extracted from your camera.\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the data directory\n~~~~~~~~~~~~~~~~~~~~~~~~~\nThe snippet shown below will create the ``data`` directory where all our data will be stored. The\ncode will create a directory structure as shown bellow:\n\n.. code-block:: bash\n\n data\n \u2514\u2500\u2500 models\n\nwhere the ``models`` folder will will contain the downloaded models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n\nDATA_DIR = os.path.join(os.getcwd(), 'data')\nMODELS_DIR = os.path.join(DATA_DIR, 'models')\nfor dir in [DATA_DIR, MODELS_DIR]:\n if not os.path.exists(dir):\n os.mkdir(dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the model\n~~~~~~~~~~~~~~~~~~\nThe code snippet shown below is used to download the object detection model checkpoint file,\nas well as the labels file (.pbtxt) which contains a list of strings used to add the correct\nlabel to each detection (e.g. person).\n\nThe particular detection algorithm we will use is the `SSD ResNet101 V1 FPN 640x640`. More\nmodels can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz``\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import tarfile\nimport urllib.request\n\n# Download and extract model\nMODEL_DATE = '20200711'\nMODEL_NAME = 'ssd_resnet101_v1_fpn_640x640_coco17_tpu-8'\nMODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz'\nMODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/'\nMODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME\nPATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME)\nPATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/'))\nPATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config'))\nif not os.path.exists(PATH_TO_CKPT):\n print('Downloading model. This may take a while... ', end='')\n urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR)\n tar_file = tarfile.open(PATH_TO_MODEL_TAR)\n tar_file.extractall(MODELS_DIR)\n tar_file.close()\n os.remove(PATH_TO_MODEL_TAR)\n print('Done')\n\n# Download labels file\nLABEL_FILENAME = 'mscoco_label_map.pbtxt'\nLABELS_DOWNLOAD_BASE = \\\n 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'\nPATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME))\nif not os.path.exists(PATH_TO_LABELS):\n print('Downloading label file... ', end='')\n urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS)\n print('Done')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the model\n~~~~~~~~~~~~~~\nNext we load the downloaded model\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging\nimport tensorflow as tf\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import config_util\nfrom object_detection.utils import visualization_utils as viz_utils\nfrom object_detection.builders import model_builder\n\ntf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n\n# Enable GPU dynamic memory allocation\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n\n# Load pipeline config and build a detection model\nconfigs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\nmodel_config = configs['model']\ndetection_model = model_builder.build(model_config=model_config, is_training=False)\n\n# Restore checkpoint\nckpt = tf.compat.v2.train.Checkpoint(model=detection_model)\nckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()\n\n@tf.function\ndef detect_fn(image):\n \"\"\"Detect objects in image.\"\"\"\n\n image, shapes = detection_model.preprocess(image)\n prediction_dict = detection_model.predict(image, shapes)\n detections = detection_model.postprocess(prediction_dict, shapes)\n\n return detections, prediction_dict, tf.reshape(shapes, [-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load label map data (for plotting)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n use_display_name=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define the video stream\n~~~~~~~~~~~~~~~~~~~~~~~\nWe will use `OpenCV `_ to capture the video stream\ngenerated by our webcam. For more information you can refer to the `OpenCV-Python Tutorials `_\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import cv2\n\ncap = cv2.VideoCapture(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Putting everything together\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n\nwhile True:\n # Read frame from camera\n ret, image_np = cap.read()\n\n # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n image_np_expanded = np.expand_dims(image_np, axis=0)\n\n # Things to try:\n # Flip horizontally\n # image_np = np.fliplr(image_np).copy()\n\n # Convert image to grayscale\n # image_np = np.tile(\n # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n\n input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n detections, predictions_dict, shapes = detect_fn(input_tensor)\n\n label_id_offset = 1\n image_np_with_detections = image_np.copy()\n\n viz_utils.visualize_boxes_and_labels_on_image_array(\n image_np_with_detections,\n detections['detection_boxes'][0].numpy(),\n (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),\n detections['detection_scores'][0].numpy(),\n category_index,\n use_normalized_coordinates=True,\n max_boxes_to_draw=200,\n min_score_thresh=.30,\n agnostic_mode=False)\n\n # Display output\n cv2.imshow('object detection', cv2.resize(image_np_with_detections, (800, 600)))\n\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/build/_downloads/c257975512db4c0870a09ff2e222a88d/auto_examples_jupyter.zip b/docs/build/_downloads/c257975512db4c0870a09ff2e222a88d/auto_examples_jupyter.zip deleted file mode 100644 index ee48b1c..0000000 Binary files a/docs/build/_downloads/c257975512db4c0870a09ff2e222a88d/auto_examples_jupyter.zip and /dev/null differ diff --git a/docs/build/_downloads/cbc63cc7463d82d360a24cb612cedafe/plot_object_detection_simple.py b/docs/build/_downloads/cbc63cc7463d82d360a24cb612cedafe/plot_object_detection_simple.py deleted file mode 100644 index a262fc5..0000000 --- a/docs/build/_downloads/cbc63cc7463d82d360a24cb612cedafe/plot_object_detection_simple.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 -""" -Object Detection Test -===================== -""" - -# %% -# This demo will take you through the steps of running an "out-of-the-box" detection model on a -# collection of images. - -# %% -# Create the data directory -# ~~~~~~~~~~~~~~~~~~~~~~~~~ -# The snippet shown below will create the ``data`` directory where all our data will be stored. The -# code will create a directory structure as shown bellow: -# -# .. code-block:: bash -# -# data -# ├── images -# └── models -# -# where the ``images`` folder will contain the downlaoded test images, while ``models`` will -# contain the downloaded models. -import os - -DATA_DIR = os.path.join(os.getcwd(), 'data') -IMAGES_DIR = os.path.join(DATA_DIR, 'images') -MODELS_DIR = os.path.join(DATA_DIR, 'models') -for dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) - -# %% -# Download the test images -# ~~~~~~~~~~~~~~~~~~~~~~~~ -# First we will download the images that we will use throughout this tutorial. The code snippet -# shown bellow will download the test images from the `TensorFlow Model Garden `_ -# and save them inside the ``data/images`` folder. -import urllib.request - -IMAGE_FILENAMES = ['image1.jpg', 'image2.jpg'] -IMAGES_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' - -for image_filename in IMAGE_FILENAMES: - - image_path = os.path.join(IMAGES_DIR, image_filename) - - # Download image - if not os.path.exists(image_path): - print('Downloading {}... '.format(image_filename), end='') - urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path) - print('Done') - - -# %% -# Download the model -# ~~~~~~~~~~~~~~~~~~ -# The code snippet shown below is used to download the object detection model checkpoint file, -# as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -# label to each detection (e.g. person). Once downloaded the files will be stored under the -# ``data/models`` folder. -# -# The particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More -# models can be found in the `TensorFlow 2 Detection Model Zoo `_. -# To use a different model you will need the URL name of the specific model. This can be done as -# follows: -# -# 1. Right click on the `Model name` of the model you would like to use; -# 2. Click on `Copy link address` to copy the download link of the model; -# 3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``; -# 4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; -# 5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below. -# -# For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz`` - -import tarfile - -# Download and extract model -MODEL_DATE = '20200711' -MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32' -MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' -MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' -MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME -PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) -PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) -PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) -if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') - -# Download labels file -LABEL_FILENAME = 'mscoco_label_map.pbtxt' -LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' -PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) -if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') - -# %% -# Load the model -# ~~~~~~~~~~~~~~ -# Next we load the downloaded model -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) -import tensorflow as tf -from object_detection.utils import label_map_util -from object_detection.utils import config_util -from object_detection.utils import visualization_utils as viz_utils -from object_detection.builders import model_builder - -tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) - -# Enable GPU dynamic memory allocation -gpus = tf.config.experimental.list_physical_devices('GPU') -for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) - -# Load pipeline config and build a detection model -configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) -model_config = configs['model'] -detection_model = model_builder.build(model_config=model_config, is_training=False) - -# Restore checkpoint -ckpt = tf.compat.v2.train.Checkpoint( - model=detection_model) -ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() - -@tf.function -def detect_fn(image): - """Detect objects in image.""" - - image, shapes = detection_model.preprocess(image) - prediction_dict = detection_model.predict(image, shapes) - detections = detection_model.postprocess(prediction_dict, shapes) - - return detections, prediction_dict, tf.reshape(shapes, [-1]) - - -# %% -# Load label map data (for plotting) -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# Label maps correspond index numbers to category names, so that when our convolution network -# predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility -# functions, but anything that returns a dictionary mapping integers to appropriate string labels -# would be fine. - -category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, - use_display_name=True) - -# %% -# Putting everything together -# ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# The code shown below loads an image, runs it through the detection model and visualizes the -# detection results, including the keypoints. -# -# Note that this will take a long time (several minutes) the first time you run this code due to -# tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be -# faster. -# -# Here are some simple things to try out if you are curious: -# -# * Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). -# * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). -# * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. -import numpy as np -from six import BytesIO -from PIL import Image -import matplotlib.pyplot as plt -import warnings -warnings.filterwarnings('ignore') # Suppress Matplotlib warnings - -def load_image_into_numpy_array(path): - """Load an image from file into a numpy array. - - Puts image into numpy array to feed into tensorflow graph. - Note that by convention we put it into a numpy array with shape - (height, width, channels), where channels=3 for RGB. - - Args: - path: the file path to the image - - Returns: - uint8 numpy array with shape (img_height, img_width, 3) - """ - img_data = tf.io.gfile.GFile(path, 'rb').read() - image = Image.open(BytesIO(img_data)) - (im_width, im_height) = image.size - return np.array(image.getdata()).reshape( - (im_height, im_width, 3)).astype(np.uint8) - - -for image_filename in IMAGE_FILENAMES: - - print('Running inference for {}... '.format(image_filename), end='') - - image_path = os.path.join(IMAGES_DIR, image_filename) - image_np = load_image_into_numpy_array(image_path) - - # Things to try: - # Flip horizontally - # image_np = np.fliplr(image_np).copy() - - # Convert image to grayscale - # image_np = np.tile( - # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - - input_tensor = tf.convert_to_tensor( - np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) - - label_id_offset = 1 - image_np_with_detections = image_np.copy() - - viz_utils.visualize_boxes_and_labels_on_image_array( - image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), - category_index, - use_normalized_coordinates=True, - max_boxes_to_draw=200, - min_score_thresh=.30, - agnostic_mode=False) - - plt.figure() - plt.imshow(image_np_with_detections) - print('Done') -plt.show() - -# sphinx_gallery_thumbnail_number = 2 \ No newline at end of file diff --git a/docs/build/_downloads/d0e545609c5f7f49f39abc7b6a38cec3/partition_dataset.py b/docs/build/_downloads/d0e545609c5f7f49f39abc7b6a38cec3/partition_dataset.py deleted file mode 100644 index a9d0c11..0000000 --- a/docs/build/_downloads/d0e545609c5f7f49f39abc7b6a38cec3/partition_dataset.py +++ /dev/null @@ -1,98 +0,0 @@ -""" usage: partition_dataset.py [-h] [-i IMAGEDIR] [-o OUTPUTDIR] [-r RATIO] [-x] - -Partition dataset of images into training and testing sets - -optional arguments: - -h, --help show this help message and exit - -i IMAGEDIR, --imageDir IMAGEDIR - Path to the folder where the image dataset is stored. If not specified, the CWD will be used. - -o OUTPUTDIR, --outputDir OUTPUTDIR - Path to the output folder where the train and test dirs should be created. Defaults to the same directory as IMAGEDIR. - -r RATIO, --ratio RATIO - The ratio of the number of test images over the total number of images. The default is 0.1. - -x, --xml Set this flag if you want the xml annotation files to be processed and copied over. -""" -import os -import re -from shutil import copyfile -import argparse -import math -import random - - -def iterate_dir(source, dest, ratio, copy_xml): - source = source.replace('\\', '/') - dest = dest.replace('\\', '/') - train_dir = os.path.join(dest, 'train') - test_dir = os.path.join(dest, 'test') - - if not os.path.exists(train_dir): - os.makedirs(train_dir) - if not os.path.exists(test_dir): - os.makedirs(test_dir) - - images = [f for f in os.listdir(source) - if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(.jpg|.jpeg|.png)$', f)] - - num_images = len(images) - num_test_images = math.ceil(ratio*num_images) - - for i in range(num_test_images): - idx = random.randint(0, len(images)-1) - filename = images[idx] - copyfile(os.path.join(source, filename), - os.path.join(test_dir, filename)) - if copy_xml: - xml_filename = os.path.splitext(filename)[0]+'.xml' - copyfile(os.path.join(source, xml_filename), - os.path.join(test_dir,xml_filename)) - images.remove(images[idx]) - - for filename in images: - copyfile(os.path.join(source, filename), - os.path.join(train_dir, filename)) - if copy_xml: - xml_filename = os.path.splitext(filename)[0]+'.xml' - copyfile(os.path.join(source, xml_filename), - os.path.join(train_dir, xml_filename)) - - -def main(): - - # Initiate argument parser - parser = argparse.ArgumentParser(description="Partition dataset of images into training and testing sets", - formatter_class=argparse.RawTextHelpFormatter) - parser.add_argument( - '-i', '--imageDir', - help='Path to the folder where the image dataset is stored. If not specified, the CWD will be used.', - type=str, - default=os.getcwd() - ) - parser.add_argument( - '-o', '--outputDir', - help='Path to the output folder where the train and test dirs should be created. ' - 'Defaults to the same directory as IMAGEDIR.', - type=str, - default=None - ) - parser.add_argument( - '-r', '--ratio', - help='The ratio of the number of test images over the total number of images. The default is 0.1.', - default=0.1, - type=float) - parser.add_argument( - '-x', '--xml', - help='Set this flag if you want the xml annotation files to be processed and copied over.', - action='store_true' - ) - args = parser.parse_args() - - if args.outputDir is None: - args.outputDir = args.imageDir - - # Now we are ready to start the iteration - iterate_dir(args.imageDir, args.outputDir, args.ratio, args.xml) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/docs/build/_downloads/da4babe668a8afb093cc7776d7e630f3/generate_tfrecord.py b/docs/build/_downloads/da4babe668a8afb093cc7776d7e630f3/generate_tfrecord.py deleted file mode 100644 index caad456..0000000 --- a/docs/build/_downloads/da4babe668a8afb093cc7776d7e630f3/generate_tfrecord.py +++ /dev/null @@ -1,168 +0,0 @@ -""" Sample TensorFlow XML-to-TFRecord converter - -usage: generate_tfrecord.py [-h] [-x XML_DIR] [-l LABELS_PATH] [-o OUTPUT_PATH] [-i IMAGE_DIR] [-c CSV_PATH] - -optional arguments: - -h, --help show this help message and exit - -x XML_DIR, --xml_dir XML_DIR - Path to the folder where the input .xml files are stored. - -l LABELS_PATH, --labels_path LABELS_PATH - Path to the labels (.pbtxt) file. - -o OUTPUT_PATH, --output_path OUTPUT_PATH - Path of output TFRecord (.record) file. - -i IMAGE_DIR, --image_dir IMAGE_DIR - Path to the folder where the input image files are stored. Defaults to the same directory as XML_DIR. - -c CSV_PATH, --csv_path CSV_PATH - Path of output .csv file. If none provided, then no file will be written. -""" - -import os -import glob -import pandas as pd -import io -import xml.etree.ElementTree as ET -import argparse - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) -import tensorflow.compat.v1 as tf -from PIL import Image -from object_detection.utils import dataset_util, label_map_util -from collections import namedtuple - -# Initiate argument parser -parser = argparse.ArgumentParser( - description="Sample TensorFlow XML-to-TFRecord converter") -parser.add_argument("-x", - "--xml_dir", - help="Path to the folder where the input .xml files are stored.", - type=str) -parser.add_argument("-l", - "--labels_path", - help="Path to the labels (.pbtxt) file.", type=str) -parser.add_argument("-o", - "--output_path", - help="Path of output TFRecord (.record) file.", type=str) -parser.add_argument("-i", - "--image_dir", - help="Path to the folder where the input image files are stored. " - "Defaults to the same directory as XML_DIR.", - type=str, default=None) -parser.add_argument("-c", - "--csv_path", - help="Path of output .csv file. If none provided, then no file will be " - "written.", - type=str, default=None) - -args = parser.parse_args() - -if args.image_dir is None: - args.image_dir = args.xml_dir - -label_map = label_map_util.load_labelmap(args.labels_path) -label_map_dict = label_map_util.get_label_map_dict(label_map) - - -def xml_to_csv(path): - """Iterates through all .xml files (generated by labelImg) in a given directory and combines - them in a single Pandas dataframe. - - Parameters: - ---------- - path : str - The path containing the .xml files - Returns - ------- - Pandas DataFrame - The produced dataframe - """ - - xml_list = [] - for xml_file in glob.glob(path + '/*.xml'): - tree = ET.parse(xml_file) - root = tree.getroot() - for member in root.findall('object'): - value = (root.find('filename').text, - int(root.find('size')[0].text), - int(root.find('size')[1].text), - member[0].text, - int(member[4][0].text), - int(member[4][1].text), - int(member[4][2].text), - int(member[4][3].text) - ) - xml_list.append(value) - column_name = ['filename', 'width', 'height', - 'class', 'xmin', 'ymin', 'xmax', 'ymax'] - xml_df = pd.DataFrame(xml_list, columns=column_name) - return xml_df - - -def class_text_to_int(row_label): - return label_map_dict[row_label] - - -def split(df, group): - data = namedtuple('data', ['filename', 'object']) - gb = df.groupby(group) - return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)] - - -def create_tf_example(group, path): - with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid: - encoded_jpg = fid.read() - encoded_jpg_io = io.BytesIO(encoded_jpg) - image = Image.open(encoded_jpg_io) - width, height = image.size - - filename = group.filename.encode('utf8') - image_format = b'jpg' - xmins = [] - xmaxs = [] - ymins = [] - ymaxs = [] - classes_text = [] - classes = [] - - for index, row in group.object.iterrows(): - xmins.append(row['xmin'] / width) - xmaxs.append(row['xmax'] / width) - ymins.append(row['ymin'] / height) - ymaxs.append(row['ymax'] / height) - classes_text.append(row['class'].encode('utf8')) - classes.append(class_text_to_int(row['class'])) - - tf_example = tf.train.Example(features=tf.train.Features(feature={ - 'image/height': dataset_util.int64_feature(height), - 'image/width': dataset_util.int64_feature(width), - 'image/filename': dataset_util.bytes_feature(filename), - 'image/source_id': dataset_util.bytes_feature(filename), - 'image/encoded': dataset_util.bytes_feature(encoded_jpg), - 'image/format': dataset_util.bytes_feature(image_format), - 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), - 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), - 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), - 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), - 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), - 'image/object/class/label': dataset_util.int64_list_feature(classes), - })) - return tf_example - - -def main(_): - - writer = tf.python_io.TFRecordWriter(args.output_path) - path = os.path.join(args.image_dir) - examples = xml_to_csv(args.xml_dir) - grouped = split(examples, 'filename') - for group in grouped: - tf_example = create_tf_example(group, path) - writer.write(tf_example.SerializeToString()) - writer.close() - print('Successfully created the TFRecord file: {}'.format(args.output_path)) - if args.csv_path is not None: - examples.to_csv(args.csv_path, index=None) - print('Successfully created the CSV file: {}'.format(args.csv_path)) - - -if __name__ == '__main__': - tf.app.run() diff --git a/docs/build/_images/TensorBoard.JPG b/docs/build/_images/TensorBoard.JPG deleted file mode 100644 index 8493d2f..0000000 Binary files a/docs/build/_images/TensorBoard.JPG and /dev/null differ diff --git a/docs/build/_images/labelImg.JPG b/docs/build/_images/labelImg.JPG deleted file mode 100644 index c62009e..0000000 Binary files a/docs/build/_images/labelImg.JPG and /dev/null differ diff --git a/docs/build/_images/sphx_glr_object_detection_camera_thumb.png b/docs/build/_images/sphx_glr_object_detection_camera_thumb.png deleted file mode 100644 index 233f8e6..0000000 Binary files a/docs/build/_images/sphx_glr_object_detection_camera_thumb.png and /dev/null differ diff --git a/docs/build/_images/sphx_glr_plot_object_detection_simple_001.png b/docs/build/_images/sphx_glr_plot_object_detection_simple_001.png deleted file mode 100644 index 15505e3..0000000 Binary files a/docs/build/_images/sphx_glr_plot_object_detection_simple_001.png and /dev/null differ diff --git a/docs/build/_images/sphx_glr_plot_object_detection_simple_002.png b/docs/build/_images/sphx_glr_plot_object_detection_simple_002.png deleted file mode 100644 index 6193835..0000000 Binary files a/docs/build/_images/sphx_glr_plot_object_detection_simple_002.png and /dev/null differ diff --git a/docs/build/_images/sphx_glr_plot_object_detection_simple_thumb.png b/docs/build/_images/sphx_glr_plot_object_detection_simple_thumb.png deleted file mode 100644 index 40fcf69..0000000 Binary files a/docs/build/_images/sphx_glr_plot_object_detection_simple_thumb.png and /dev/null differ diff --git a/docs/build/_sources/auto_examples/index.rst.txt b/docs/build/_sources/auto_examples/index.rst.txt deleted file mode 100644 index d835a74..0000000 --- a/docs/build/_sources/auto_examples/index.rst.txt +++ /dev/null @@ -1,83 +0,0 @@ -:orphan: - - - -.. _sphx_glr_auto_examples: - -.. _examples: - -Examples -======== - -Below is a gallery of examples - - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_object_detection_camera_thumb.png - :alt: Detect Objects Using Your Webcam - - :ref:`sphx_glr_auto_examples_object_detection_camera.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/object_detection_camera - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_object_detection_simple_thumb.png - :alt: Object Detection Test - - :ref:`sphx_glr_auto_examples_plot_object_detection_simple.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_object_detection_simple -.. raw:: html - -
- - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-gallery - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: auto_examples_python.zip ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/build/_sources/auto_examples/object_detection_camera.rst.txt b/docs/build/_sources/auto_examples/object_detection_camera.rst.txt deleted file mode 100644 index 655a07a..0000000 --- a/docs/build/_sources/auto_examples/object_detection_camera.rst.txt +++ /dev/null @@ -1,257 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_object_detection_camera.py: - - -Detect Objects Using Your Webcam -================================ - -This demo will take you through the steps of running an "out-of-the-box" detection model to -detect objects in the video stream extracted from your camera. - -Create the data directory -~~~~~~~~~~~~~~~~~~~~~~~~~ -The snippet shown below will create the ``data`` directory where all our data will be stored. The -code will create a directory structure as shown bellow: - -.. code-block:: bash - - data - └── models - -where the ``models`` folder will will contain the downloaded models. - - -.. code-block:: default - - import os - - DATA_DIR = os.path.join(os.getcwd(), 'data') - MODELS_DIR = os.path.join(DATA_DIR, 'models') - for dir in [DATA_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) - - -Download the model -~~~~~~~~~~~~~~~~~~ -The code snippet shown below is used to download the object detection model checkpoint file, -as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -label to each detection (e.g. person). - -The particular detection algorithm we will use is the `SSD ResNet101 V1 FPN 640x640`. More -models can be found in the `TensorFlow 2 Detection Model Zoo `_. -To use a different model you will need the URL name of the specific model. This can be done as -follows: - -1. Right click on the `Model name` of the model you would like to use; -2. Click on `Copy link address` to copy the download link of the model; -3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``; -4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; -5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below. - -For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz`` - - -.. code-block:: default - - import tarfile - import urllib.request - - # Download and extract model - MODEL_DATE = '20200711' - MODEL_NAME = 'ssd_resnet101_v1_fpn_640x640_coco17_tpu-8' - MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' - MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' - MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME - PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) - PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) - PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) - if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') - - # Download labels file - LABEL_FILENAME = 'mscoco_label_map.pbtxt' - LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' - PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) - if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') - - -Load the model -~~~~~~~~~~~~~~ -Next we load the downloaded model - - -.. code-block:: default - - - os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging - import tensorflow as tf - from object_detection.utils import label_map_util - from object_detection.utils import config_util - from object_detection.utils import visualization_utils as viz_utils - from object_detection.builders import model_builder - - tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) - - # Enable GPU dynamic memory allocation - gpus = tf.config.experimental.list_physical_devices('GPU') - for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) - - # Load pipeline config and build a detection model - configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) - model_config = configs['model'] - detection_model = model_builder.build(model_config=model_config, is_training=False) - - # Restore checkpoint - ckpt = tf.compat.v2.train.Checkpoint(model=detection_model) - ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() - - @tf.function - def detect_fn(image): - """Detect objects in image.""" - - image, shapes = detection_model.preprocess(image) - prediction_dict = detection_model.predict(image, shapes) - detections = detection_model.postprocess(prediction_dict, shapes) - - return detections, prediction_dict, tf.reshape(shapes, [-1]) - - - -Load label map data (for plotting) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Label maps correspond index numbers to category names, so that when our convolution network -predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility -functions, but anything that returns a dictionary mapping integers to appropriate string labels -would be fine. - - -.. code-block:: default - - category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, - use_display_name=True) - - -Define the video stream -~~~~~~~~~~~~~~~~~~~~~~~ -We will use `OpenCV `_ to capture the video stream -generated by our webcam. For more information you can refer to the `OpenCV-Python Tutorials `_ - - -.. code-block:: default - - import cv2 - - cap = cv2.VideoCapture(0) - - -Putting everything together -~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The code shown below loads an image, runs it through the detection model and visualizes the -detection results, including the keypoints. - -Note that this will take a long time (several minutes) the first time you run this code due to -tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be -faster. - -Here are some simple things to try out if you are curious: - -* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). -* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). -* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. - - -.. code-block:: default - - import numpy as np - - while True: - # Read frame from camera - ret, image_np = cap.read() - - # Expand dimensions since the model expects images to have shape: [1, None, None, 3] - image_np_expanded = np.expand_dims(image_np, axis=0) - - # Things to try: - # Flip horizontally - # image_np = np.fliplr(image_np).copy() - - # Convert image to grayscale - # image_np = np.tile( - # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - - input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) - - label_id_offset = 1 - image_np_with_detections = image_np.copy() - - viz_utils.visualize_boxes_and_labels_on_image_array( - image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), - category_index, - use_normalized_coordinates=True, - max_boxes_to_draw=200, - min_score_thresh=.30, - agnostic_mode=False) - - # Display output - cv2.imshow('object detection', cv2.resize(image_np_with_detections, (800, 600))) - - if cv2.waitKey(25) & 0xFF == ord('q'): - break - - cap.release() - cv2.destroyAllWindows() - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.000 seconds) - - -.. _sphx_glr_download_auto_examples_object_detection_camera.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: object_detection_camera.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: object_detection_camera.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/build/_sources/auto_examples/sg_execution_times.rst.txt b/docs/build/_sources/auto_examples/sg_execution_times.rst.txt deleted file mode 100644 index d6c8b1f..0000000 --- a/docs/build/_sources/auto_examples/sg_execution_times.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ - -:orphan: - -.. _sphx_glr_auto_examples_sg_execution_times: - -Computation times -================= -**02:29.261** total execution time for **auto_examples** files: - -+-----------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_object_detection_simple.py` (``plot_object_detection_simple.py``) | 02:29.261 | 0.0 MB | -+-----------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_object_detection_camera.py` (``object_detection_camera.py``) | 00:00.000 | 0.0 MB | -+-----------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/docs/build/_sources/index.rst.txt b/docs/build/_sources/index.rst.txt deleted file mode 100644 index 7dd7df2..0000000 --- a/docs/build/_sources/index.rst.txt +++ /dev/null @@ -1,49 +0,0 @@ -.. TensorFlow setup documentation master file, created by - sphinx-quickstart on Wed Mar 21 19:03:08 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -TensorFlow 2 Object Detection API tutorial -========================================== - -.. important:: This tutorial is intended for TensorFlow 2.2, which (at the time of writing this tutorial) is the latest stable version of TensorFlow 2.x. - - A version for TensorFlow 1.14 can be found `here `_. - -This is a step-by-step tutorial/guide to setting up and using TensorFlow's Object Detection API to perform, namely, object detection in images/video. - -The software tools which we shall use throughout this tutorial are listed in the table below: - -+---------------------------------------------+ -| Target Software versions | -+==============+==============================+ -| OS | Windows, Linux | -+--------------+------------------------------+ -| Python | 3.8 | -+--------------+------------------------------+ -| TensorFlow | 2.2.0 | -+--------------+------------------------------+ -| CUDA Toolkit | 10.1 | -+--------------+------------------------------+ -| CuDNN | 7.6.5 | -+--------------+------------------------------+ -| Anaconda | Python 3.7 (Optional) | -+--------------+------------------------------+ - -.. toctree:: - :maxdepth: 4 - :caption: Contents: - - install - training - auto_examples/index - issues - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/build/_sources/install.rst.txt b/docs/build/_sources/install.rst.txt deleted file mode 100644 index ab1c61e..0000000 --- a/docs/build/_sources/install.rst.txt +++ /dev/null @@ -1,591 +0,0 @@ -Installation -============ - -General Remarks ---------------- - -- In contrast to TensorFlow 1.x, where different Python packages needed to be installed for one to run TensorFlow on either their CPU or GPU (namely ``tensorflow`` and ``tensorflow-gpu``), TensorFlow 2.x only requires that the ``tensorflow`` package is installed and automatically checks to see if a GPU can be successfully registered. - - -Anaconda Python 3.7 (Optional) ------------------------------- - -Although having Anaconda is not a requirement in order to install and use TensorFlow, I suggest doing so, due to it's intuitive way of managing packages and setting up new virtual environments. Anaconda is a pretty useful tool, not only for working with TensorFlow, but in general for anyone working in Python, so if you haven't had a chance to work with it, now is a good chance. - -Install Anaconda Python 3.7 -*************************** - -.. tabs:: - - .. tab:: Windows - - - Go to ``_ and click the "Download" button - - Download the `Python 3.7 64-Bit Graphical Installer `_ or the `32-Bit Graphical Installer `_ installer, per your system requirements - - Run the downloaded executable (``.exe``) file to begin the installation. See `here `_ for more details - - (Optional) In the next step, check the box "Add Anaconda3 to my PATH environment variable". This will make Anaconda your default Python distribution, which should ensure that you have the same default Python distribution across all editors. - - .. tab:: Linux - - - Go to ``_ and click the "Download" button - - Download the `Python 3.7 64-Bit (x86) Installer `_ - - Run the downloaded bash script (``.sh``) file to begin the installation. See `here `_ for more details. - - When prompted with the question "Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home//.bashrc ?", answer "Yes". If you enter "No", you must manually add the path to Anaconda or conda will not work. - -Create a new Anaconda virtual environment -***************************************** -- Open a new `Terminal` window -- Type the following command: - - .. code-block:: posh - - conda create -n tensorflow pip python=3.8 - -- The above will create a new virtual environment with name ``tensorflow`` - -.. important:: The term `Terminal` will be used to refer to the Terminal of your choice (e.g. Command Prompt, Powershell, etc.) - -Activate the Anaconda virtual environment -***************************************** -- Activating the newly created virtual environment is achieved by running the following in the `Terminal` window: - - .. code-block:: posh - - conda activate tensorflow - -- Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beggining of your cmd path specifier, e.g.: - - .. code-block:: ps1con - - (tensorflow) C:\Users\sglvladi> - -.. important:: - - Throughout the rest of the tutorial, execution of any commands in a `Terminal` window should be done after the Anaconda virtual environment has been activated! - -.. _tf_install: - -TensorFlow Installation ------------------------ - -Getting setup with an installation of TensorFlow can be done in 3 simple steps. - -Install the TensorFlow PIP package -********************************** -- Run the following command in a `Terminal` window: - - .. code-block:: posh - - pip install --ignore-installed --upgrade tensorflow==2.2.0 - -Verify your Installation -************************ -- Run the following command in a `Terminal` window: - - .. code-block:: posh - - python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" - -- Once the above is run, you should see a print-out similar to the one bellow: - - .. code-block:: posh - - 2020-06-22 19:20:32.614181: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found - 2020-06-22 19:20:32.620571: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. - 2020-06-22 19:20:35.027232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll - 2020-06-22 19:20:35.060549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: - pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1 - coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s - 2020-06-22 19:20:35.074967: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found - 2020-06-22 19:20:35.084458: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cublas64_10.dll'; dlerror: cublas64_10.dll not found - 2020-06-22 19:20:35.094112: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found - 2020-06-22 19:20:35.103571: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found - 2020-06-22 19:20:35.113102: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found - 2020-06-22 19:20:35.123242: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusparse64_10.dll'; dlerror: cusparse64_10.dll not found - 2020-06-22 19:20:35.140987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll - 2020-06-22 19:20:35.146285: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1598] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. - Skipping registering GPU devices... - 2020-06-22 19:20:35.162173: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 - 2020-06-22 19:20:35.178588: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x15140db6390 initialized for platform Host (this does not guarantee that XLA will be used). Devices: - 2020-06-22 19:20:35.185082: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version - 2020-06-22 19:20:35.191117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: - 2020-06-22 19:20:35.196815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] - tf.Tensor(1620.5817, shape=(), dtype=float32) - -.. _tensorflow_gpu: - -GPU Support (Optional) -********************** - -Although using a GPU to run TensorFlow is not necessary, the computational gains are substantial. -Therefore, if your machine is equipped with a compatible CUDA-enabled GPU, it is recommended that -you follow the steps listed below to install the relevant libraries necessary to enable TensorFlow -to make use of your GPU. - -By default, when TensorFlow is run it will attempt to register compatible GPU devices. If this -fails, TensorFlow will resort to running on the platform's CPU. This can also be observed in the -printout shown in the previous section, under the "Verify the install" bullet-point, where there -are a number of messages which report missing library files (e.g. ``Could not load dynamic library -'cudart64_101.dll'; dlerror: cudart64_101.dll not found``). - -In order for TensorFlow to run on your GPU, the following requirements must be met: - -+-------------------------------------+ -| Prerequisites | -+=====================================+ -| Nvidia GPU (GTX 650 or newer) | -+-------------------------------------+ -| CUDA Toolkit v10.1 | -+-------------------------------------+ -| CuDNN 7.6.5 | -+-------------------------------------+ - -.. _cuda_install: - -Install CUDA Toolkit -~~~~~~~~~~~~~~~~~~~~ -.. tabs:: - - .. tab:: Windows - - - Follow this `link `_ to download and install CUDA Toolkit 10.1 - - Installation instructions can be found `here `_ - - .. tab:: Linux - - - Follow this `link `_ to download and install CUDA Toolkit 10.1 for your Linux distribution. - - Installation instructions can be found `here `_ - - -.. _cudnn_install: - -Install CUDNN -~~~~~~~~~~~~~ -.. tabs:: - - .. tab:: Windows - - - Go to ``_ - - Create a user profile if needed and log in - - Select `cuDNN v7.6.5 (Nov 5, 2019), for CUDA 10.1 `_ - - Download `cuDNN v7.6.5 Library for Windows 10 `_ - - Extract the contents of the zip file (i.e. the folder named ``cuda``) inside ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\``, where ```` points to the installation directory specified during the installation of the CUDA Toolkit. By default ```` = ``C:\Program Files``. - - .. tab:: Linux - - - Go to ``_ - - Create a user profile if needed and log in - - Select `cuDNN v7.6.5 (Nov 5, 2019), for CUDA 10.1 `_ - - Download `cuDNN v7.6.5 Library for Linux `_ - - Follow the instructions under Section 2.3.1 of the `CuDNN Installation Guide `_ to install CuDNN. - -.. _set_env: - -Environment Setup -~~~~~~~~~~~~~~~~~ -.. tabs:: - - .. tab:: Windows - - - Go to `Start` and Search "environment variables" - - Click "Edit the system environment variables". This should open the "System Properties" window - - In the opened window, click the "Environment Variables..." button to open the "Environment Variables" window. - - Under "System variables", search for and click on the ``Path`` system variable, then click "Edit..." - - Add the following paths, then click "OK" to save the changes: - - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin`` - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp`` - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\libx64`` - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\cuda\bin`` - - .. tab:: Linux - - As per Section 7.1.1 of the `CUDA Installation Guide for Linux `_, append the following lines to ``~/.bashrc``: - - .. code-block:: bash - - # CUDA related exports - export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}} - export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} - -Update your GPU drivers (Optional) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If during the installation of the CUDA Toolkit (see :ref:`cuda_install`) you selected the `Express Installation` option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. These drivers are typically NOT the latest drivers and, thus, you may wish to updte your drivers. - -- Go to ``_ -- Select your GPU version to download -- Install the driver for your chosen OS - -Verify the installation -~~~~~~~~~~~~~~~~~~~~~~~ - -- Run the following command in a **NEW** `Terminal` window: - - .. code-block:: posh - - python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" - - .. important:: - - A new terminal window must be opened for the changes to the Environmental variables to take effect!! - -- Once the above is run, you should see a print-out similar to the one bellow: - - .. code-block:: posh - :emphasize-lines: 1,2,6,7,8,9,10,11,12,20,21,22,23,24,25,26,31 - - 2020-06-22 20:24:31.355541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll - 2020-06-22 20:24:33.650692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll - 2020-06-22 20:24:33.686846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: - pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1 - coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s - 2020-06-22 20:24:33.697234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll - 2020-06-22 20:24:33.747540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll - 2020-06-22 20:24:33.787573: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll - 2020-06-22 20:24:33.810063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll - 2020-06-22 20:24:33.841474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll - 2020-06-22 20:24:33.862787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll - 2020-06-22 20:24:33.907318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll - 2020-06-22 20:24:33.913612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 - 2020-06-22 20:24:33.918093: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 - 2020-06-22 20:24:33.932784: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2382acc1c40 initialized for platform Host (this does not guarantee that XLA will be used). Devices: - 2020-06-22 20:24:33.939473: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version - 2020-06-22 20:24:33.944570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: - pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1 - coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s - 2020-06-22 20:24:33.953910: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll - 2020-06-22 20:24:33.958772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll - 2020-06-22 20:24:33.963656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll - 2020-06-22 20:24:33.968210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll - 2020-06-22 20:24:33.973389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll - 2020-06-22 20:24:33.978058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll - 2020-06-22 20:24:33.983547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll - 2020-06-22 20:24:33.990380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 - 2020-06-22 20:24:35.338596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: - 2020-06-22 20:24:35.344643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 - 2020-06-22 20:24:35.348795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N - 2020-06-22 20:24:35.353853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6284 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1) - 2020-06-22 20:24:35.369758: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2384aa9f820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: - 2020-06-22 20:24:35.376320: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1070 Ti, Compute Capability 6.1 - tf.Tensor(122.478485, shape=(), dtype=float32) - -- Notice from the lines highlighted above that the library files are now ``Successfully opened`` and a debugging message is presented to confirm that TensorFlow has successfully ``Created TensorFlow device``. - - -.. _tf_models_install: - -TensorFlow Object Detection API Installation --------------------------------------------- - -Now that you have installed TensorFlow, it is time to install the TensorFlow Object Detection API. - -Downloading the TensorFlow Model Garden -*************************************** - -- Create a new folder under a path of your choice and name it ``TensorFlow``. (e.g. ``C:\Users\sglvladi\Documents\TensorFlow``). -- From your `Terminal` ``cd`` into the ``TensorFlow`` directory. -- To download the models you can either use `Git `_ to clone the `TensorFlow Models repository `_ inside the ``TensorFlow`` folder, or you can simply download it as a `ZIP `_ and extract its contents inside the ``TensorFlow`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``models-master`` to ``models``. -- You should now have a single folder named ``models`` under your ``TensorFlow`` folder, which contains another 4 folders as such: - -.. code-block:: bash - - TensorFlow/ - └─ models/ - ├─ community/ - ├─ official/ - ├─ orbit/ - ├─ research/ - └── ... - -Protobuf Installation/Compilation -********************************* - -The Tensorflow Object Detection API uses Protobufs to configure model and -training parameters. Before the framework can be used, the Protobuf libraries -must be downloaded and compiled. - -This should be done as follows: - -- Head to the `protoc releases page `_ -- Download the latest ``protoc-*-*.zip`` release (e.g. ``protoc-3.12.3-win64.zip`` for 64-bit Windows) -- Extract the contents of the downloaded ``protoc-*-*.zip`` in a directory ```` of your choice (e.g. ``C:\Program Files\Google Protobuf``) -- Add ```` to your ``Path`` environment variable (see :ref:`set_env`) -- In a new `Terminal` [#]_, ``cd`` into ``TensorFlow/models/research/`` directory and run the following command: - - .. code-block:: python - - # From within TensorFlow/models/research/ - protoc object_detection/protos/*.proto --python_out=. - -.. important:: - - If you are on Windows and using Protobuf 3.5 or later, the multi-file selection wildcard (i.e ``*.proto``) may not work but you can do one of the following: - - .. tabs:: - - .. tab:: Windows Powershell - - .. code-block:: - - # From within TensorFlow/models/research/ - Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.} - - - .. tab:: Command Prompt - - .. code-block:: - - # From within TensorFlow/models/research/ - for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=. - - -.. [#] NOTE: You MUST open a new `Terminal` for the changes in the environment variables to take effect. - - -.. _tf_models_install_coco: - -COCO API installation -********************* - -As of TensorFlow 2.x, the ``pycocotools`` package is listed as `a dependency of the Object Detection API `_. Ideally, this package should get installed when installing the Object Detection API as documented in the :ref:`tf_models_install_object_detection` section below, however the installation can fail for various reasons and therefore it is simpler to just install the package beforehand, in which case later installation will be skipped. - -.. tabs:: - - .. tab:: Windows - - Run the following command to install ``pycocotools`` with Windows support: - - .. code-block:: bash - - pip install cython - pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI - - - Note that, according to the `package's instructions `_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here `_. - - .. tab:: Linux - - Download `cocoapi `_ to a directory of your choice, then ``make`` and copy the pycocotools subfolder to the ``Tensorflow/models/research`` directory, as such: - - .. code-block:: bash - - git clone https://github.com/cocodataset/cocoapi.git - cd cocoapi/PythonAPI - make - cp -r pycocotools /TensorFlow/models/research/ - -.. note:: The default metrics are based on those used in Pascal VOC evaluation. - - - To use the COCO object detection metrics add ``metrics_set: "coco_detection_metrics"`` to the ``eval_config`` message in the config file. - - - To use the COCO instance segmentation metrics add ``metrics_set: "coco_mask_metrics"`` to the ``eval_config`` message in the config file. - - -.. _tf_models_install_object_detection: - -Install the Object Detection API -******************************** -Installation of the Object Detection API is achieved by installing the ``object_detection`` package. This is done by running the following commands from within ``Tensorflow\models\research``: - -.. code-block:: - - # From within TensorFlow/models/research/ - cp object_detection/packages/tf2/setup.py . - python -m pip install . - -.. note:: - - During the above installation, you may observe the following error: - - .. code-block:: - - ERROR: Command errored out with exit status 1: - command: 'C:\Users\sglvladi\Anaconda3\envs\tf2\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"'; __file__='"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\sglvladi\AppData\Local\Temp\pip-record-wpn7b6qo\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\sglvladi\Anaconda3\envs\tf2\Include\pycocotools' - cwd: C:\Users\sglvladi\AppData\Local\Temp\pip-install-yn46ecei\pycocotools\ - Complete output (14 lines): - running install - running build - running build_py - creating build - creating build\lib.win-amd64-3.8 - creating build\lib.win-amd64-3.8\pycocotools - copying pycocotools\coco.py -> build\lib.win-amd64-3.8\pycocotools - copying pycocotools\cocoeval.py -> build\lib.win-amd64-3.8\pycocotools - copying pycocotools\mask.py -> build\lib.win-amd64-3.8\pycocotools - copying pycocotools\__init__.py -> build\lib.win-amd64-3.8\pycocotools - running build_ext - skipping 'pycocotools\_mask.c' Cython extension (up-to-date) - building 'pycocotools._mask' extension - error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/ - ---------------------------------------- - ERROR: Command errored out with exit status 1: 'C:\Users\sglvladi\Anaconda3\envs\tf2\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"'; __file__='"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\sglvladi\AppData\Local\Temp\pip-record-wpn7b6qo\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\sglvladi\Anaconda3\envs\tf2\Include\pycocotools' Check the logs for full command output. - - This is caused because installation of the ``pycocotools`` package has failed. To fix this have a look at the :ref:`tf_models_install_coco` section and rerun the above commands. - - -.. _test_tf_models: - -Test your Installation -********************** - -To test the installation, run the following command from within ``Tensorflow\models\research``: - -.. code-block:: - - # From within TensorFlow/models/research/ - python object_detection/builders/model_builder_tf2_test.py - -Once the above is run, allow some time for the test to complete and once done you should observe a -printout similar to the one below: - -.. code-block:: - - ... - [ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config - [ RUN ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update - [ OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update - [ RUN ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold - [ OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold - [ RUN ] ModelBuilderTF2Test.test_invalid_model_config_proto - [ OK ] ModelBuilderTF2Test.test_invalid_model_config_proto - [ RUN ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size - [ OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size - [ RUN ] ModelBuilderTF2Test.test_session - [ SKIPPED ] ModelBuilderTF2Test.test_session - [ RUN ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor - [ OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor - [ RUN ] ModelBuilderTF2Test.test_unknown_meta_architecture - [ OK ] ModelBuilderTF2Test.test_unknown_meta_architecture - [ RUN ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor - [ OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor - ---------------------------------------------------------------------- - Ran 20 tests in 68.510s - - OK (skipped=1) - -Try out the examples -******************** -If the previous step completed successfully it means you have successfully installed all the -components necessary to perform object detection using pre-trained models. - -If you want to play around with some examples to see how this can be done, now would be a good -time to have a look at the :ref:`examples` section. - - -.. _labelImg_install: - -LabelImg Installation ---------------------- - -There exist several ways to install ``labelImg``. Below are 3 of the most common. - -Get from PyPI (Recommended) -*************************** -1. Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) -2. Run the following command to install ``labelImg``: - -.. code-block:: bash - - pip install labelImg - -3. ``labelImg`` can then be run as follows: - -.. code-block:: bash - - labelImg - # or - labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] - -Use precompiled binaries (Easy) -******************************* -Precompiled binaries for both Windows and Linux can be found `here `_ . - -Installation is the done in three simple steps: - -1. Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it. - -2. Download the latest binary for your OS from `here `_. and extract its contents under ``Tensorflow/addons/labelImg``. - -3. You should now have a single folder named ``addons/labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such: - -.. code-block:: bash - - TensorFlow/ - ├─ addons/ - │ └─ labelImg/ - └─ models/ - ├─ community/ - ├─ official/ - ├─ orbit/ - ├─ research/ - └─ ... - -4. ``labelImg`` can then be run as follows: - -.. code-block:: bash - - # From within Tensorflow/addons/labelImg - labelImg - # or - labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] - -Build from source (Hard) -************************ -The steps for installing from source follow below. - -**1. Download labelImg** - -- Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it. -- To download the package you can either use `Git `_ to clone the `labelImg repo `_ inside the ``TensorFlow\addons`` folder, or you can simply download it as a `ZIP `_ and extract it's contents inside the ``TensorFlow\addons`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``labelImg-master`` to ``labelImg``. [#]_ -- You should now have a single folder named ``addons\labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such: - -.. code-block:: bash - - TensorFlow/ - ├─ addons - │ └─ labelImg/ - └─ models/ - ├─ community/ - ├─ official/ - ├─ orbit/ - ├─ research/ - └─ ... - -.. [#] The latest repo commit when writing this tutorial is `8d1bd68 `_. - -**2. Install dependencies and compiling package** - -- Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) -- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following commands: - - .. tabs:: - - .. tab:: Windows - - .. code-block:: bash - - conda install pyqt=5 - pyrcc5 -o libs/resources.py resources.qrc - - .. tab:: Linux - - .. code-block:: bash - - sudo apt-get install pyqt5-dev-tools - sudo pip install -r requirements/requirements-linux-python3.txt - make qt5py3 - - -**3. Test your installation** - -- Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) -- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following command: - - .. code-block:: posh - - # From within Tensorflow/addons/labelImg - python labelImg.py - # or - python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] - - - diff --git a/docs/build/_sources/issues.rst.txt b/docs/build/_sources/issues.rst.txt deleted file mode 100644 index 6138799..0000000 --- a/docs/build/_sources/issues.rst.txt +++ /dev/null @@ -1,120 +0,0 @@ -.. _issues: - -Common issues -============= - -Below is a list of common issues encountered while using TensorFlow for objects detection. - -Python crashes - TensorFlow GPU -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If you are using :ref:`tensorflow_gpu` and when you try to run some Python object detection script (e.g. :ref:`test_tf_models`), after a few seconds, Windows reports that Python has crashed then have a look at the `Anaconda/Command Prompt` window you used to run the script and check for a line similar (maybe identical) to the one below: - - .. code-block:: python - - 2018-03-22 03:07:54.623130: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:378] Loaded runtime CuDNN library: 7101 (compatibility version 7100) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration. - -If the above line is present in the printed debugging, it means that you have not installed the correct version of the cuDNN libraries. In this case make sure you re-do the :ref:`cudnn_install` step, making sure you instal cuDNN v7.0.5. - -Cleaning up Nvidia containers (TensorFlow GPU) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Sometimes, when terminating a TensorFlow training process, the Nvidia containers associated to the process are not cleanly terminated. This can lead to bogus errors when we try to run a new TensorFlow process. - -Some known issues caused by the above are presented below: - -- Failure to restart training of a model. Look for the following errors in the debugging: - - .. code-block:: python - - 2018-03-23 03:03:10.326902: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED - 2018-03-23 03:03:10.330475: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM - 2018-03-23 03:03:10.333797: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow/stream_executor/stream.h:1983] attempting to perform DNN operation using StreamExecutor without DNN support - 2018-03-23 03:03:10.333807: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\stream.cc:1851] stream 00000216F05CB660 did not wait for stream: 00000216F05CA6E0 - 2018-03-23 03:03:10.340765: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\stream.cc:4637] stream 00000216F05CB660 did not memcpy host-to-device; source: 000000020DB37B00 - 2018-03-23 03:03:10.343752: F C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_util.cc:343] CPU->GPU Memcpy failed - -To solve such issues in Windows, open a `Task Manager` windows, look for Tasks with name ``NVIDIA Container`` and kill them by selecting them and clicking the `End Task` button at the bottom left corner of the window. - -If the issue persists, then you're probably running out of memory. Try closing down anything else that might be eating up your GPU memory (e.g. Youtube videos, webpages etc.) - -"WARNING:tensorflow:Entity ``>`` could not be transformed ..." -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -In some versions of Tensorflow, you may see errors that look similar to the ones below: - -.. code-block:: python - - ... - WARNING:tensorflow:Entity > could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting >: AssertionError: Bad argument number for Name: 3, expecting 4 - WARNING:tensorflow:Entity > could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting >: AssertionError: Bad argument number for Name: 3, expecting 4 - ... - -These warnings appear to be harmless form my experience, however they can saturate the console with unnecessary messages, which makes it hard to scroll through the output of the training/evaluation process. - -As reported `here `_, this issue seems to -be caused by a mismatched version of `gast `_. Simply -downgrading gast to version ``0.2.2`` seems to remove the warnings. This can be done by running: - -.. code-block:: bash - - pip install gast==0.2.2 - -"AttributeError: module 'google.protobuf.descriptor' has no attribute '_internal_create_key" -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -It is possible that when executing ``from object_detection.utils import label_map_util`` you may -get the above error. As per the discussion is in `this Stack Overflow thread `_, -upgrading the Python protobuf version seems to solve this issue: - -.. code-block:: - - pip install --upgrade protobuf - -.. _export_error: - -"TypeError: Expected Operation, Variable, or Tensor, got level_5" -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -When trying to export oyu trained model using the ``exporter_main_v2.py`` script, you may come -across an error that looks like this: - -.. code-block:: - :linenos: - :emphasize-lines: 9 - - Traceback (most recent call last): - File ".\exporter_main_v2.py", line 126, in - app.run(main) - File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\absl\app.py", line 299, in run - _run_main(main, args) - ... - File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1627, in get_losses_for - reachable = tf_utils.get_reachable_from_inputs(inputs, losses) - File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py", line 140, in get_reachable_from_inputs - raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x)) - TypeError: Expected Operation, Variable, or Tensor, got level_5 - -This error seems to come from TensorFlow itself and a discussion on the issue can be found -`here `_. As discussed there, a fix to the above -issue can be achieved by opening the ``tf_utils.py`` file and adding a line of code. Below is a -summary of how this can be done: - -- Look at the line that corresponds to line 9 (highlighted) in the above error print out. -- Copy the path to the ``tf_utils.py`` file; in my case this was ``C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py`` -- Open the file and replace line 140 of the file as follows: - - - Change: - - .. code-block:: python - - raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x)) - - to: - - .. code-block:: python - - if not isinstance(x, str): - raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x)) - -At the time of writting this tutorial, a fix to the issue had not been implemented in the version -of TensorFlow installed using ``pip``. It is possible that this will get incorporated at some later -point. \ No newline at end of file diff --git a/docs/build/_sources/training.rst.txt b/docs/build/_sources/training.rst.txt deleted file mode 100644 index 79d7e17..0000000 --- a/docs/build/_sources/training.rst.txt +++ /dev/null @@ -1,526 +0,0 @@ -Training Custom Object Detector -=============================== - -So, up to now you should have done the following: - -- Installed TensorFlow (See :ref:`tf_install`) -- Installed TensorFlow Object Detection API (See :ref:`tf_models_install`) -- Installed labelImg (See :ref:`labelImg_install`) - -Now that we have done all the above, we can start doing some cool stuff. Here we will see how you can train your own object detector, and since it is not as simple as it sounds, we will have a look at: - -1. How to organise your workspace/training files -2. How to prepare/annotate image datasets -3. How to generate tf records from such datasets -4. How to configure a simple training pipeline -5. How to train a model and monitor it's progress -6. How to export the resulting model and use it to detect objects. - -Preparing the Workspace ------------------------ - -1. If you have followed the tutorial, you should by now have a folder ``Tensorflow``, placed under ```` (e.g. ``C:/Users/sglvladi/Documents``), with the following directory tree: - - .. code-block:: bash - - TensorFlow/ - ├─ addons/ (Optional) - │ └─ labelImg/ - └─ models/ - ├─ community/ - ├─ official/ - ├─ orbit/ - ├─ research/ - └─ ... - -2. Now create a new folder under ``TensorFlow`` and call it ``workspace``. It is within the ``workspace`` that we will store all our training set-ups. Now let's go under workspace and create another folder named ``training_demo``. Now our directory structure should be as so: - - .. code-block:: bash - - TensorFlow/ - ├─ addons/ (Optional) - │ └─ labelImg/ - ├─ models/ - │ ├─ community/ - │ ├─ official/ - │ ├─ orbit/ - │ ├─ research/ - │ └─ ... - └─ workspace/ - └─ training_demo/ - -3. The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below. - - .. code-block:: bash - - training_demo/ - ├─ annotations/ - ├─ images/ - │ ├─ test/ - │ └─ train/ - ├─ models/ - ├─ pre-trained-models/ - └─ README.md - -Here's an explanation for each of the folders/filer shown in the above tree: - -- ``annotations``: This folder will be used to store all ``*.csv`` files and the respective TensorFlow ``*.record`` files, which contain the list of annotations for our dataset images. -- ``images``: This folder contains a copy of all the images in our dataset, as well as the respective ``*.xml`` files produced for each one, once ``labelImg`` is used to annotate objects. - - * ``images/train``: This folder contains a copy of all images, and the respective ``*.xml`` files, which will be used to train our model. - * ``images/test``: This folder contains a copy of all images, and the respective ``*.xml`` files, which will be used to test our model. - -- ``models``: This folder will contain a sub-folder for each of training job. Each subfolder will contain the training pipeline configuration file ``*.config``, as well as all files generated during the training and evaluation of our model. -- ``pre-trained-models``: This folder will contain the downloaded pre-trained models, which shall be used as a starting checkpoint for our training jobs. -- ``README.md``: This is an optional file which provides some general information regarding the training conditions of our model. It is not used by TensorFlow in any way, but it generally helps when you have a few training folders and/or you are revisiting a trained model after some time. - -If you do not understand most of the things mentioned above, no need to worry, as we'll see how all the files are generated further down. - - -Preparing the Dataset ---------------------- - -Annotate Images -~~~~~~~~~~~~~~~ - -To annotate images we will be using the `labelImg `_ package. If you haven't installed the package yet, then have a look at :ref:`labelImg_install`. - -- Once you have collected all the images to be used to test your model (ideally more than 100 per class), place them inside the folder ``training_demo/images``. -- Open a new `Anaconda/Command Prompt` window and ``cd`` into ``Tensorflow/addons/labelImg``. -- If (as suggested in :ref:`labelImg_install`) you created a separate Conda environment for ``labelImg`` then go ahead and activate it by running: - - .. code-block:: bash - - activate labelImg - -- Next go ahead and start ``labelImg``, pointing it to your ``training_demo/images`` folder. - - .. code-block:: bash - - python labelImg.py ../../workspace/training_demo/images - -- A File Explorer Dialog windows should open, which points to the ``training_demo/images`` folder. -- Press the "Select Folder" button, to start annotating your images. - -Once open, you should see a window similar to the one below: - -.. image:: ./_static/labelImg.JPG - :width: 90% - :alt: alternate text - :align: center - -I won't be covering a tutorial on how to use ``labelImg``, but you can have a look at `labelImg's repo `_ for more details. A nice Youtube video demonstrating how to use ``labelImg`` is also available `here `_. What is important is that once you annotate all your images, a set of new ``*.xml`` files, one for each image, should be generated inside your ``training_demo/images`` folder. - -.. _image_partitioning_sec: - -Partition the Dataset -~~~~~~~~~~~~~~~~~~~~~ - -Once you have finished annotating your image dataset, it is a general convention to use only part of it for training, and the rest is used for evaluation purposes (e.g. as discussed in :ref:`evaluation_sec`). - -Typically, the ratio is 90%/10%, i.e. 90% of the images are used for training and the rest 10% is maintained for testing, but you can chose whatever ratio suits your needs. - -Once you have decided how you will be splitting your dataset, copy all training images, together with their corresponding ``*.xml`` files, and place them inside the ``training_demo/images/train`` folder. Similarly, copy all testing images, with their ``*.xml`` files, and paste them inside ``training_demo/images/test``. - -For lazy people like myself, who cannot be bothered to do the above, I have put tugether a simple script that automates the above process: - -.. literalinclude:: scripts/partition_dataset.py - -- Click :download:`here ` to download the above script and save it inside ``TensorFlow/scripts/preprocessing``. -- Then, ``cd`` into ``TensorFlow/scripts/preprocessing`` and run: - - .. code-block:: - - python partition_dataset.py -x -i [PATH_TO_IMAGES_FOLDER] -r 0.1 - - # For example - # python partition_dataset.py -x -i C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images -r 0.1 - -Once the script has finished, two new folders should have been created under ``training_demo/images``, -namely ``training_demo/images/train`` and ``training_demo/images/test``, containing 90% and 10% of -the images (and ``*.xml`` files), respectively. To avoid loss of any files, the script will not -delete the images under ``training_demo/images``. Once you have checked that your images have been -safely copied over, you can delete the images under ``training_demo/images`` manually. - - -Create Label Map -~~~~~~~~~~~~~~~~ - -TensorFlow requires a label map, which namely maps each of the used labels to an integer values. This label map is used both by the training and detection processes. - -Below we show an example label map (e.g ``label_map.pbtxt``), assuming that our dataset containes 2 labels, ``dogs`` and ``cats``: - -.. code-block:: json - - item { - id: 1 - name: 'cat' - } - - item { - id: 2 - name: 'dog' - } - -Label map files have the extention ``.pbtxt`` and should be placed inside the ``training_demo/annotations`` folder. - -Create TensorFlow Records -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Now that we have generated our annotations and split our dataset into the desired training and -testing subsets, it is time to convert our annotations into the so called ``TFRecord`` format. - -Before we proceed to describe the above steps, let's create a directory where we can store some -scripts. Under the ``TensorFlow`` folder, create a new folder ``TensorFlow/scripts``, which we can -use to store some useful scripts. To make things even tidier, let's create a new folder -``TensorFlow/scripts/preprocessing``, where we shall store scripts that we can use to preprocess -our training inputs. Below is out ``TensorFlow`` directory tree structure, up to now: - -.. code-block:: bash - - TensorFlow/ - ├─ addons/ (Optional) - │ └─ labelImg/ - ├─ models/ - │ ├─ community/ - │ ├─ official/ - │ ├─ orbit/ - │ ├─ research/ - │ └─ ... - ├─ scripts/ - │ └─ preprocessing/ - └─ workspace/ - └─ training_demo/ - - -Convert ``*.xml`` to ``*.record`` -********************************* - -To do this we can write a simple script that iterates through all ``*.xml`` files in the ``training_demo/images/train`` and ``training_demo/images/test`` folders, and generates a ``*.record`` file for each of the two. - -Here is an example script that allows us to do just that: - -.. literalinclude:: scripts/generate_tfrecord.py - - -- Click :download:`here ` to download the above script and save it inside ``TensorFlow/scripts/preprocessing``. -- Install the ``pandas`` package: - - .. code-block:: - - conda install pandas # Anaconda - # or - pip install pandas # pip - -- Finally, ``cd`` into ``TensorFlow/scripts/preprocessing`` and run: - - .. code-block:: - - # Create train data: - python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/train -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/train.record - - # Create test data: - python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/test -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/test.record - - # For example - # python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/train -l C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/train.record - # python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/test -l C:/Users/sglvladi/Documents/Tensorflow2/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/test.record - -Once the above is done, there should be 2 new files under the ``training_demo/annotations`` folder, named ``test.record`` and ``train.record``, respectively. - - -.. _config_training_pipeline_sec: - -Configuring a Training Job --------------------------- - -For the purposes of this tutorial we will not be creating a training job from scratch, but rather -we will reuse one of the pre-trained models provided by TensorFlow. If you would like to train an -entirely new model, you can have a look at `TensorFlow's tutorial `_. - -The model we shall be using in our examples is the `SSD ResNet50 V1 FPN 640x640 `_ -model, since it provides a relatively good trade-off between performance and speed. However, there -exist a number of other models you can use, all of which are listed in `TensorFlow 2 Detection Model Zoo `_. - -Download Pre-Trained Model -~~~~~~~~~~~~~~~~~~~~~~~~~~ -To begin with, we need to download the latest pre-trained network for the model we wish to use. -This can be done by simply clicking on the name of the desired model in the table found in -`TensorFlow 2 Detection Model Zoo `_. -Clicking on the name of your model should initiate a download for a ``*.tar.gz`` file. - -Once the ``*.tar.gz`` file has been downloaded, open it using a decompression program of your -choice (e.g. 7zip, WinZIP, etc.). Next, open the ``*.tar`` folder that you see when the compressed -folder is opened, and extract its contents inside the folder ``training_demo/pre-trained-models``. -Since we downloaded the `SSD ResNet50 V1 FPN 640x640 `_ -model, our ``training_demo`` directory should now look as follows: - - .. code-block:: bash - - training_demo/ - ├─ ... - ├─ pre-trained-models/ - | └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/ - | ├─ checkpoint/ - │ ├─ saved_model/ - | └─ pipeline.config - └─ ... - -Note that the above process can be repeated for all other pre-trained models you wish to experiment -with. For example, if you wanted to also configure a training job for the `EfficientDet D1 640x640 `_ -model, you can download the model and after extracting its context the demo directory will be: - - .. code-block:: bash - - training_demo/ - ├─ ... - ├─ pre-trained-models/ - │ ├─ efficientdet_d1_coco17_tpu-32/ - │ │ ├─ checkpoint/ - │ │ ├─ saved_model/ - | │ └─ pipeline.config - │ └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/ - | ├─ checkpoint/ - │ ├─ saved_model/ - | └─ pipeline.config - └─ ... - -Configure the Training Pipeline -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Now that we have downloaded and extracted our pre-trained model, let's create a directory for our -training job. Under the ``training_demo/models`` create a new directory named ``my_ssd_resnet50_v1_fpn`` -and copy the ``training_demo/pre-trained-models/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/pipeline.config`` -file inside the newly created directory. Our ``training_demo/models`` directory should now look -like this: - - .. code-block:: bash - - training_demo/ - ├─ ... - ├─ models/ - │ └─ my_ssd_resnet50_v1_fpn/ - | └─ pipeline.config - └─ ... - -Now, let's have a look at the changes that we shall need to apply to the ``pipeline.config`` file -(highlighted in yellow): - -.. literalinclude:: scripts/pipeline.config - :linenos: - :emphasize-lines: 3,131,161,167,168,172,174,178,179,182,186 - -It is worth noting here that the changes to lines ``178`` to ``179`` above are optional. These -should only be used if you installed the COCO evaluation tools, as outlined in the -:ref:`tf_models_install_coco` section, and you intend to run evaluation (see :ref:`evaluation_sec`). - -Once the above changes have been applied to our config file, go ahead and save it. - -.. _training_sec: - -Training the Model ------------------- -Before we begin training our model, let's go and copy the ``TensorFlow/models/research/object_detection/model_main_tf2.py`` -script and paste it straight into our ``training_demo`` folder. We will need this script in order -to train our model. - -Now, to initiate a new training job, open a new `Terminal`, ``cd`` inside the ``training_demo`` -folder and run the following command: - -.. code-block:: - - python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config - -Once the training process has been initiated, you should see a series of print outs similar to the -one below (plus/minus some warnings): - -.. code-block:: - - ... - WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma - W0716 05:24:19.105542 1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma - WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.beta - W0716 05:24:19.106541 1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.beta - WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_mean - W0716 05:24:19.107540 1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_mean - WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_variance - W0716 05:24:19.108539 1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_variance - WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. - W0716 05:24:19.108539 1364 util.py:151] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. - WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards. - INFO:tensorflow:Step 100 per-step time 1.153s loss=0.761 - I0716 05:26:55.879558 1364 model_lib_v2.py:632] Step 100 per-step time 1.153s loss=0.761 - ... - -.. important:: - - The output will normally look like it has "frozen", but DO NOT rush to cancel the process. The - training outputs logs only every 100 steps by default, therefore if you wait for a while, you - should see a log for the loss at step 100. - - The time you should wait can vary greatly, depending on whether you are using a GPU and the - chosen value for ``batch_size`` in the config file, so be patient. - -If you ARE observing a similar output to the above, then CONGRATULATIONS, you have successfully -started your first training job. Now you may very well treat yourself to a cold beer, as waiting -on the training to finish is likely to take a while. Following what people have said online, it -seems that it is advisable to allow you model to reach a ``TotalLoss`` of at least 2 (ideally 1 -and lower) if you want to achieve "fair" detection results. Obviously, lower ``TotalLoss`` is -better, however very low ``TotalLoss`` should be avoided, as the model may end up overfitting the -dataset, meaning that it will perform poorly when applied to images outside the dataset. To -monitor ``TotalLoss``, as well as a number of other metrics, while your model is training, have a -look at :ref:`tensorboard_sec`. - -If you ARE NOT seeing a print-out similar to that shown above, and/or the training job crashes -after a few seconds, then have a look at the issues and proposed solutions, under the -:ref:`issues` section, to see if you can find a solution. Alternatively, you can try the issues -section of the official `Tensorflow Models repo `_. - -.. note:: - Training times can be affected by a number of factors such as: - - - The computational power of you hardware (either CPU or GPU): Obviously, the more powerful your PC is, the faster the training process. - - Whether you are using the TensorFlow CPU or GPU variant: In general, even when compared to the best CPUs, almost any GPU graphics card will yield much faster training and detection speeds. As a matter of fact, when I first started I was running TensorFlow on my `Intel i7-5930k` (6/12 cores @ 4GHz, 32GB RAM) and was getting step times of around `12 sec/step`, after which I installed TensorFlow GPU and training the very same model -using the same dataset and config files- on a `EVGA GTX-770` (1536 CUDA-cores @ 1GHz, 2GB VRAM) I was down to `0.9 sec/step`!!! A 12-fold increase in speed, using a "low/mid-end" graphics card, when compared to a "mid/high-end" CPU. - - The complexity of the objects you are trying to detect: Obviously, if your objective is to track a black ball over a white background, the model will converge to satisfactory levels of detection pretty quickly. If on the other hand, for example, you wish to detect ships in ports, using Pan-Tilt-Zoom cameras, then training will be a much more challenging and time-consuming process, due to the high variability of the shape and size of ships, combined with a highly dynamic background. - - And many, many, many, more.... - -.. _evaluation_sec: - -Evaluating the Model (Optional) -------------------------------- - -By default, the training process logs some basic measures of training performance. These seem to -change depending on the installed version of Tensorflow. - -As you will have seen in various parts of this tutorial, we have mentioned a few times the -optional utilisation of the COCO evaluation metrics. Also, under section -:ref:`_image_partitioning_sec` we partitioned our dataset in two parts, where one was to be used -for training and the other for evaluation. In this section we will look at how we can use these -metrics, along with the test images, to get a sense of the performance achieved by our model as it -is being trained. - -Firstly, let's start with a brief explanation of what the evaluation process does. While the -training process runs, it will occasionally create checkpoint files inside the -``training_demo/training`` folder, which correspond to snapshots of the model at given steps. When -a set of such new checkpoint files is generated, the evaluation process uses these files and -evaluates how well the model performs in detecting objects in the test dataset. The results of -this evaluation are summarised in the form of some metrics, which can be examined over time. - -The steps to run the evaluation are outlined below: - -1. Firstly we need to download and install the metrics we want to use. - - For a description of the supported object detection evaluation metrics, see `here `_. - - The process of installing the COCO evaluation metrics is described in :ref:`tf_models_install_coco`. -2. Secondly, we must modify the configuration pipeline (``*.config`` script). - - See lines 178 and 181 of the script in :ref:`config_training_pipeline_sec`. -3. The third step is to actually run the evaluation. To do so, open a new `Terminal`, ``cd`` inside the ``training_demo`` folder and run the following command: - - .. code-block:: - - python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config --checkpoint_dir=models/my_ssd_resnet50_v1_fpn - - Once the above is run, you should see a checkpoint similar to the one below (plus/minus some warnings): - - .. code-block:: - - ... - WARNING:tensorflow:From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version. - Instructions for updating: - Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead. - W0716 05:44:10.059399 17144 deprecation.py:317] From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version. - Instructions for updating: - Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead. - WARNING:tensorflow:From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:259: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. - Instructions for updating: - Use `tf.cast` instead. - W0716 05:44:12.383937 17144 deprecation.py:317] From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:259: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. - Instructions for updating: - Use `tf.cast` instead. - INFO:tensorflow:Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpn - I0716 05:44:22.779590 17144 checkpoint_utils.py:125] Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpn - INFO:tensorflow:Found new checkpoint at models/my_ssd_resnet50_v1_fpn\ckpt-2 - I0716 05:44:22.882485 17144 checkpoint_utils.py:134] Found new checkpoint at models/my_ssd_resnet50_v1_fpn\ckpt-2 - -While the evaluation process is running, it will periodically check (every 300 sec by default) and -use the latest ``models/my_ssd_resnet50_v1_fpn/ckpt-*`` checkpoint files to evaluate the performance -of the model. The results are stored in the form of tf event files (``events.out.tfevents.*``) -inside ``models/my_ssd_resnet50_v1_fpn/eval_0``. These files can then be used to monitor the -computed metrics, using the process described by the next section. - -.. _tensorboard_sec: - -Monitor Training Job Progress using TensorBoard ------------------------------------------------ - -A very nice feature of TensorFlow, is that it allows you to coninuously monitor and visualise a -number of different training/evaluation metrics, while your model is being trained. The specific -tool that allows us to do all that is `Tensorboard `_. - -To start a new TensorBoard server, we follow the following steps: - -- Open a new `Anaconda/Command Prompt` -- Activate your TensorFlow conda environment (if you have one), e.g.: - - .. code-block:: bash - - activate tensorflow_gpu - -- ``cd`` into the ``training_demo`` folder. -- Run the following command: - - .. code-block:: bash - - tensorboard --logdir=models/my_ssd_resnet50_v1_fpn - -The above command will start a new TensorBoard server, which (by default) listens to port 6006 of -your machine. Assuming that everything went well, you should see a print-out similar to the one -below (plus/minus some warnings): - - .. code-block:: bash - - ... - TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit) - -Once this is done, go to your browser and type ``http://localhost:6006/`` in your address bar, -following which you should be presented with a dashboard similar to the one shown below -(maybe less populated if your model has just started training): - -.. image:: ./_static/TensorBoard.JPG - :width: 90% - :alt: alternate text - :align: center - - - -Exporting a Trained Inference Graph ------------------------------------ - -Once your training job is complete, you need to extract the newly trained inference graph, which -will be later used to perform the object detection. This can be done as follows: - -- Copy the ``TensorFlow/models/research/object_detection/exporter_main_v2.py`` script and paste it straight into your ``training_demo`` folder. -- Now, open a `Terminal`, ``cd`` inside your ``training_demo`` folder, and run the following command: - -.. code-block:: bash - - python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_efficientdet_d1\pipeline.config --trained_checkpoint_dir .\models\my_efficientdet_d1\ --output_directory .\trained-inference-graphs\output - -.. note:: - - You may get the following error when trying to export your model: - - .. code-block:: - - Traceback (most recent call last): - File ".\exporter_main_v2.py", line 126, in - app.run(main) - File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\absl\app.py", line 299, in run - _run_main(main, args) - ... - File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1627, in 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singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/docs/build/_static/documentation_options.js b/docs/build/_static/documentation_options.js deleted file mode 100644 index 2fa8c97..0000000 --- a/docs/build/_static/documentation_options.js +++ /dev/null @@ -1,12 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false -}; \ No newline at end of file diff --git a/docs/build/_static/file.png b/docs/build/_static/file.png deleted file mode 100644 index a858a41..0000000 Binary files a/docs/build/_static/file.png and /dev/null differ diff --git a/docs/build/_static/fonts/FontAwesome.otf b/docs/build/_static/fonts/FontAwesome.otf deleted file mode 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All rights reserved. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/build/_static/fonts/fontawesome-webfont.ttf b/docs/build/_static/fonts/fontawesome-webfont.ttf deleted file mode 100644 index 35acda2..0000000 Binary files a/docs/build/_static/fonts/fontawesome-webfont.ttf and /dev/null differ diff --git a/docs/build/_static/fonts/fontawesome-webfont.woff b/docs/build/_static/fonts/fontawesome-webfont.woff deleted file mode 100644 index 400014a..0000000 Binary files a/docs/build/_static/fonts/fontawesome-webfont.woff and /dev/null differ diff --git a/docs/build/_static/fonts/fontawesome-webfont.woff2 b/docs/build/_static/fonts/fontawesome-webfont.woff2 deleted file mode 100644 index 4d13fc6..0000000 Binary files a/docs/build/_static/fonts/fontawesome-webfont.woff2 and /dev/null differ diff --git a/docs/build/_static/fonts/lato-bold-italic.woff b/docs/build/_static/fonts/lato-bold-italic.woff deleted file mode 100644 index 88ad05b..0000000 Binary files a/docs/build/_static/fonts/lato-bold-italic.woff and /dev/null differ diff --git a/docs/build/_static/fonts/lato-bold-italic.woff2 b/docs/build/_static/fonts/lato-bold-italic.woff2 deleted file mode 100644 index c4e3d80..0000000 Binary files a/docs/build/_static/fonts/lato-bold-italic.woff2 and /dev/null differ diff --git a/docs/build/_static/fonts/lato-bold.woff b/docs/build/_static/fonts/lato-bold.woff deleted file mode 100644 index c6dff51..0000000 Binary files a/docs/build/_static/fonts/lato-bold.woff and /dev/null differ diff --git a/docs/build/_static/fonts/lato-bold.woff2 b/docs/build/_static/fonts/lato-bold.woff2 deleted file mode 100644 index bb19504..0000000 Binary files a/docs/build/_static/fonts/lato-bold.woff2 and /dev/null differ diff --git a/docs/build/_static/fonts/lato-normal-italic.woff b/docs/build/_static/fonts/lato-normal-italic.woff deleted file mode 100644 index 76114bc..0000000 Binary files a/docs/build/_static/fonts/lato-normal-italic.woff and /dev/null differ diff --git a/docs/build/_static/fonts/lato-normal-italic.woff2 b/docs/build/_static/fonts/lato-normal-italic.woff2 deleted file mode 100644 index 3404f37..0000000 Binary files a/docs/build/_static/fonts/lato-normal-italic.woff2 and /dev/null differ diff --git a/docs/build/_static/fonts/lato-normal.woff b/docs/build/_static/fonts/lato-normal.woff deleted file mode 100644 index ae1307f..0000000 Binary files a/docs/build/_static/fonts/lato-normal.woff and /dev/null differ diff --git a/docs/build/_static/fonts/lato-normal.woff2 b/docs/build/_static/fonts/lato-normal.woff2 deleted file mode 100644 index 3bf9843..0000000 Binary files a/docs/build/_static/fonts/lato-normal.woff2 and /dev/null differ diff --git a/docs/build/_static/gallery-binder.css b/docs/build/_static/gallery-binder.css deleted file mode 100644 index a33aa42..0000000 --- a/docs/build/_static/gallery-binder.css +++ /dev/null @@ -1,6 +0,0 @@ -/* CSS for binder integration */ - -div.binder-badge { - margin: 1em auto; - vertical-align: middle; -} diff --git a/docs/build/_static/gallery-dataframe.css b/docs/build/_static/gallery-dataframe.css deleted file mode 100644 index f01e6b3..0000000 --- a/docs/build/_static/gallery-dataframe.css +++ /dev/null @@ -1,36 +0,0 @@ -/* Pandas dataframe css */ -/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */ - -table.dataframe { - border: none !important; - border-collapse: collapse; - border-spacing: 0; - border-color: transparent; - color: black; - font-size: 12px; - table-layout: fixed; -} -table.dataframe thead { - border-bottom: 1px solid black; - vertical-align: bottom; -} -table.dataframe tr, -table.dataframe th, -table.dataframe td { - text-align: right; - vertical-align: middle; - padding: 0.5em 0.5em; - line-height: normal; - white-space: normal; - max-width: none; - border: none; -} -table.dataframe th { - font-weight: bold; -} -table.dataframe tbody tr:nth-child(odd) { - background: #f5f5f5; -} -table.dataframe tbody tr:hover { - background: rgba(66, 165, 245, 0.2); -} diff --git a/docs/build/_static/gallery.css b/docs/build/_static/gallery.css deleted file mode 100644 index 848774f..0000000 --- a/docs/build/_static/gallery.css +++ /dev/null @@ -1,204 +0,0 @@ -/* -Sphinx-Gallery has compatible CSS to fix default sphinx themes -Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc, -scrolls, agogo, traditional, nature, haiku, pyramid -Tested for Read the Docs theme 0.1.7 */ -.sphx-glr-thumbcontainer { - background: #fff; - border: solid #fff 1px; - -moz-border-radius: 5px; - -webkit-border-radius: 5px; - border-radius: 5px; - box-shadow: none; - float: left; - margin: 5px; - min-height: 230px; - padding-top: 5px; - position: relative; -} -.sphx-glr-thumbcontainer:hover { - border: solid #b4ddfc 1px; - box-shadow: 0 0 15px rgba(142, 176, 202, 0.5); -} -.sphx-glr-thumbcontainer a.internal { - bottom: 0; - display: block; - left: 0; - padding: 150px 10px 0; - position: absolute; - right: 0; - top: 0; -} -/* Next one is to avoid Sphinx traditional theme to cover all the -thumbnail with its default link Background color */ -.sphx-glr-thumbcontainer a.internal:hover { - background-color: transparent; -} - -.sphx-glr-thumbcontainer p { - margin: 0 0 .1em 0; -} -.sphx-glr-thumbcontainer .figure { - margin: 10px; - width: 160px; -} -.sphx-glr-thumbcontainer img { - display: inline; - max-height: 112px; - max-width: 160px; -} -.sphx-glr-thumbcontainer[tooltip]:hover:after { - background: rgba(0, 0, 0, 0.8); - -webkit-border-radius: 5px; - -moz-border-radius: 5px; - border-radius: 5px; - color: #fff; - content: attr(tooltip); - left: 95%; - padding: 5px 15px; - position: absolute; - z-index: 98; - width: 220px; - bottom: 52%; -} -.sphx-glr-thumbcontainer[tooltip]:hover:before { - border: solid; - border-color: #333 transparent; - border-width: 18px 0 0 20px; - bottom: 58%; - content: ''; - left: 85%; - position: absolute; - z-index: 99; -} - -.sphx-glr-script-out { - color: #888; - margin: 0; -} -p.sphx-glr-script-out { - padding-top: 0.7em; -} -.sphx-glr-script-out .highlight { - background-color: transparent; - margin-left: 2.5em; - margin-top: -2.1em; -} -.sphx-glr-script-out .highlight pre { - background-color: #fafae2; - border: 0; - max-height: 30em; - overflow: auto; - padding-left: 1ex; - margin: 0px; - word-break: break-word; -} -.sphx-glr-script-out + p { - margin-top: 1.8em; -} -blockquote.sphx-glr-script-out { - margin-left: 0pt; -} -.sphx-glr-script-out.highlight-pytb .highlight pre { - color: #000; - background-color: #ffe4e4; - border: 1px solid #f66; - margin-top: 10px; - padding: 7px; -} - -div.sphx-glr-footer { - text-align: center; -} - -div.sphx-glr-download { - margin: 1em auto; - vertical-align: middle; -} - -div.sphx-glr-download a { - background-color: #ffc; - background-image: linear-gradient(to bottom, #FFC, #d5d57e); - border-radius: 4px; - border: 1px solid #c2c22d; - color: #000; - display: inline-block; - font-weight: bold; - padding: 1ex; - text-align: center; -} - -div.sphx-glr-download code.download { - display: inline-block; - white-space: normal; - word-break: normal; - overflow-wrap: break-word; - /* border and background are given by the enclosing 'a' */ - border: none; - background: none; -} - -div.sphx-glr-download a:hover { - box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 5px rgba(0,0,0,.25); - text-decoration: none; - background-image: none; - background-color: #d5d57e; -} - -.sphx-glr-example-title > :target::before { - display: block; - content: ""; - margin-top: -50px; - height: 50px; - visibility: hidden; -} - -ul.sphx-glr-horizontal { - list-style: none; - padding: 0; -} -ul.sphx-glr-horizontal li { - display: inline; -} -ul.sphx-glr-horizontal img { - height: auto !important; -} - -.sphx-glr-single-img { - margin: auto; - display: block; - max-width: 100%; -} - -.sphx-glr-multi-img { - max-width: 42%; - height: auto; -} - -div.sphx-glr-animation { - margin: auto; - display: block; - max-width: 100%; -} -div.sphx-glr-animation .animation{ - display: block; -} - -p.sphx-glr-signature a.reference.external { - -moz-border-radius: 5px; - -webkit-border-radius: 5px; - border-radius: 5px; - padding: 3px; - font-size: 75%; - text-align: right; - margin-left: auto; - display: table; -} - -.sphx-glr-clear{ - clear: both; -} - -a.sphx-glr-backref-instance { - text-decoration: none; -} diff --git a/docs/build/_static/jquery-3.5.1.js b/docs/build/_static/jquery-3.5.1.js deleted file mode 100644 index 5093733..0000000 --- a/docs/build/_static/jquery-3.5.1.js +++ /dev/null @@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.ontimeout = - xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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-
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- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/auto_examples/object_detection_camera.html b/docs/build/auto_examples/object_detection_camera.html deleted file mode 100644 index e9ff0d7..0000000 --- a/docs/build/auto_examples/object_detection_camera.html +++ /dev/null @@ -1,436 +0,0 @@ - - - - - - - - - - Detect Objects Using Your Webcam — TensorFlow 2 Object Detection API tutorial documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

Note

-

Click here to download the full example code

-
-
-

Detect Objects Using Your Webcam

-

This demo will take you through the steps of running an “out-of-the-box” detection model to -detect objects in the video stream extracted from your camera.

-
-

Create the data directory

-

The snippet shown below will create the data directory where all our data will be stored. The -code will create a directory structure as shown bellow:

-
data
-└── models
-
-
-

where the models folder will will contain the downloaded models.

-
import os
-
-DATA_DIR = os.path.join(os.getcwd(), 'data')
-MODELS_DIR = os.path.join(DATA_DIR, 'models')
-for dir in [DATA_DIR, MODELS_DIR]:
-    if not os.path.exists(dir):
-        os.mkdir(dir)
-
-
-
-
-

Download the model

-

The code snippet shown below is used to download the object detection model checkpoint file, -as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -label to each detection (e.g. person).

-

The particular detection algorithm we will use is the SSD ResNet101 V1 FPN 640x640. More -models can be found in the TensorFlow 2 Detection Model Zoo. -To use a different model you will need the URL name of the specific model. This can be done as -follows:

-
    -

  1. Right click on the Model name of the model you would like to use;

    2. -

  2. Click on Copy link address to copy the download link of the model;

    3. -

  3. Paste the link in a text editor of your choice. You should observe a link similar to download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz;

    4. -

  4. Copy the XXXXXXXXX part of the link and use it to replace the value of the MODEL_NAME variable in the code shown below;

    5. -

  5. Copy the YYYYYYYY part of the link and use it to replace the value of the MODEL_DATE variable in the code shown below.

    6. -
-

For example, the download link for the model used below is: download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz

-
import tarfile
-import urllib.request
-
-# Download and extract model
-MODEL_DATE = '20200711'
-MODEL_NAME = 'ssd_resnet101_v1_fpn_640x640_coco17_tpu-8'
-MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz'
-MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/'
-MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME
-PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME)
-PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/'))
-PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config'))
-if not os.path.exists(PATH_TO_CKPT):
-    print('Downloading model. This may take a while... ', end='')
-    urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR)
-    tar_file = tarfile.open(PATH_TO_MODEL_TAR)
-    tar_file.extractall(MODELS_DIR)
-    tar_file.close()
-    os.remove(PATH_TO_MODEL_TAR)
-    print('Done')
-
-# Download labels file
-LABEL_FILENAME = 'mscoco_label_map.pbtxt'
-LABELS_DOWNLOAD_BASE = \
-    'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'
-PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME))
-if not os.path.exists(PATH_TO_LABELS):
-    print('Downloading label file... ', end='')
-    urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS)
-    print('Done')
-
-
-
-
-

Load the model

-

Next we load the downloaded model

-
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging
-import tensorflow as tf
-from object_detection.utils import label_map_util
-from object_detection.utils import config_util
-from object_detection.utils import visualization_utils as viz_utils
-from object_detection.builders import model_builder
-
-tf.get_logger().setLevel('ERROR')           # Suppress TensorFlow logging (2)
-
-# Enable GPU dynamic memory allocation
-gpus = tf.config.experimental.list_physical_devices('GPU')
-for gpu in gpus:
-    tf.config.experimental.set_memory_growth(gpu, True)
-
-# Load pipeline config and build a detection model
-configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)
-model_config = configs['model']
-detection_model = model_builder.build(model_config=model_config, is_training=False)
-
-# Restore checkpoint
-ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
-ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()
-
-@tf.function
-def detect_fn(image):
-    """Detect objects in image."""
-
-    image, shapes = detection_model.preprocess(image)
-    prediction_dict = detection_model.predict(image, shapes)
-    detections = detection_model.postprocess(prediction_dict, shapes)
-
-    return detections, prediction_dict, tf.reshape(shapes, [-1])
-
-
-
-
-

Load label map data (for plotting)

-

Label maps correspond index numbers to category names, so that when our convolution network -predicts 5, we know that this corresponds to airplane. Here we use internal utility -functions, but anything that returns a dictionary mapping integers to appropriate string labels -would be fine.

-
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,
-                                                                    use_display_name=True)
-
-
-
-
-

Define the video stream

-

We will use OpenCV to capture the video stream -generated by our webcam. For more information you can refer to the OpenCV-Python Tutorials

-
import cv2
-
-cap = cv2.VideoCapture(0)
-
-
-
-
-

Putting everything together

-

The code shown below loads an image, runs it through the detection model and visualizes the -detection results, including the keypoints.

-

Note that this will take a long time (several minutes) the first time you run this code due to -tf.function’s trace-compilation — on subsequent runs (e.g. on new images), things will be -faster.

-

Here are some simple things to try out if you are curious:

-
    -

  • Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).

    • -

  • Print out detections[‘detection_boxes’] and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).

    • -

  • Set min_score_thresh to other values (between 0 and 1) to allow more detections in or to filter out more detections.

    • -
-
import numpy as np
-
-while True:
-    # Read frame from camera
-    ret, image_np = cap.read()
-
-    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
-    image_np_expanded = np.expand_dims(image_np, axis=0)
-
-    # Things to try:
-    # Flip horizontally
-    # image_np = np.fliplr(image_np).copy()
-
-    # Convert image to grayscale
-    # image_np = np.tile(
-    #     np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
-
-    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
-    detections, predictions_dict, shapes = detect_fn(input_tensor)
-
-    label_id_offset = 1
-    image_np_with_detections = image_np.copy()
-
-    viz_utils.visualize_boxes_and_labels_on_image_array(
-          image_np_with_detections,
-          detections['detection_boxes'][0].numpy(),
-          (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),
-          detections['detection_scores'][0].numpy(),
-          category_index,
-          use_normalized_coordinates=True,
-          max_boxes_to_draw=200,
-          min_score_thresh=.30,
-          agnostic_mode=False)
-
-    # Display output
-    cv2.imshow('object detection', cv2.resize(image_np_with_detections, (800, 600)))
-
-    if cv2.waitKey(25) & 0xFF == ord('q'):
-        break
-
-cap.release()
-cv2.destroyAllWindows()
-
-
-

Total running time of the script: ( 0 minutes 0.000 seconds)

- -

Gallery generated by Sphinx-Gallery

-
-
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- -
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- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/auto_examples/plot_object_detection_simple.html b/docs/build/auto_examples/plot_object_detection_simple.html deleted file mode 100644 index 6d8365a..0000000 --- a/docs/build/auto_examples/plot_object_detection_simple.html +++ /dev/null @@ -1,498 +0,0 @@ - - - - - - - - - - Object Detection Test — TensorFlow 2 Object Detection API tutorial documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

Note

-

Click here to download the full example code

-
-
-

Object Detection Test

-

This demo will take you through the steps of running an “out-of-the-box” detection model on a -collection of images.

-
-

Create the data directory

-

The snippet shown below will create the data directory where all our data will be stored. The -code will create a directory structure as shown bellow:

-
data
-├── images
-└── models
-
-
-

where the images folder will contain the downlaoded test images, while models will -contain the downloaded models.

-
import os
-
-DATA_DIR = os.path.join(os.getcwd(), 'data')
-IMAGES_DIR = os.path.join(DATA_DIR, 'images')
-MODELS_DIR = os.path.join(DATA_DIR, 'models')
-for dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]:
-    if not os.path.exists(dir):
-        os.mkdir(dir)
-
-
-
-
-

Download the test images

-

First we will download the images that we will use throughout this tutorial. The code snippet -shown bellow will download the test images from the TensorFlow Model Garden -and save them inside the data/images folder.

-
import urllib.request
-
-IMAGE_FILENAMES = ['image1.jpg', 'image2.jpg']
-IMAGES_DOWNLOAD_BASE = \
-    'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'
-
-for image_filename in IMAGE_FILENAMES:
-
-    image_path = os.path.join(IMAGES_DIR, image_filename)
-
-    # Download image
-    if not os.path.exists(image_path):
-        print('Downloading {}... '.format(image_filename), end='')
-        urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path)
-        print('Done')
-
-
-

Out:

-
Downloading image1.jpg... Done
-Downloading image2.jpg... Done
-
-
-
-
-

Download the model

-

The code snippet shown below is used to download the object detection model checkpoint file, -as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -label to each detection (e.g. person). Once downloaded the files will be stored under the -data/models folder.

-

The particular detection algorithm we will use is the CenterNet HourGlass104 1024x1024. More -models can be found in the TensorFlow 2 Detection Model Zoo. -To use a different model you will need the URL name of the specific model. This can be done as -follows:

-
    -

  1. Right click on the Model name of the model you would like to use;

    2. -

  2. Click on Copy link address to copy the download link of the model;

    3. -

  3. Paste the link in a text editor of your choice. You should observe a link similar to download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz;

    4. -

  4. Copy the XXXXXXXXX part of the link and use it to replace the value of the MODEL_NAME variable in the code shown below;

    5. -

  5. Copy the YYYYYYYY part of the link and use it to replace the value of the MODEL_DATE variable in the code shown below.

    6. -
-

For example, the download link for the model used below is: download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz

-
import tarfile
-
-# Download and extract model
-MODEL_DATE = '20200711'
-MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'
-MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz'
-MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/'
-MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME
-PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME)
-PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/'))
-PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config'))
-if not os.path.exists(PATH_TO_CKPT):
-    print('Downloading model. This may take a while... ', end='')
-    urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR)
-    tar_file = tarfile.open(PATH_TO_MODEL_TAR)
-    tar_file.extractall(MODELS_DIR)
-    tar_file.close()
-    os.remove(PATH_TO_MODEL_TAR)
-    print('Done')
-
-# Download labels file
-LABEL_FILENAME = 'mscoco_label_map.pbtxt'
-LABELS_DOWNLOAD_BASE = \
-    'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'
-PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME))
-if not os.path.exists(PATH_TO_LABELS):
-    print('Downloading label file... ', end='')
-    urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS)
-    print('Done')
-
-
-

Out:

-
Downloading model. This may take a while... Done
-Downloading label file... Done
-
-
-
-
-

Load the model

-

Next we load the downloaded model

-
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)
-import tensorflow as tf
-from object_detection.utils import label_map_util
-from object_detection.utils import config_util
-from object_detection.utils import visualization_utils as viz_utils
-from object_detection.builders import model_builder
-
-tf.get_logger().setLevel('ERROR')           # Suppress TensorFlow logging (2)
-
-# Enable GPU dynamic memory allocation
-gpus = tf.config.experimental.list_physical_devices('GPU')
-for gpu in gpus:
-    tf.config.experimental.set_memory_growth(gpu, True)
-
-# Load pipeline config and build a detection model
-configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)
-model_config = configs['model']
-detection_model = model_builder.build(model_config=model_config, is_training=False)
-
-# Restore checkpoint
-ckpt = tf.compat.v2.train.Checkpoint(
-      model=detection_model)
-ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()
-
-@tf.function
-def detect_fn(image):
-    """Detect objects in image."""
-
-    image, shapes = detection_model.preprocess(image)
-    prediction_dict = detection_model.predict(image, shapes)
-    detections = detection_model.postprocess(prediction_dict, shapes)
-
-    return detections, prediction_dict, tf.reshape(shapes, [-1])
-
-
-
-
-

Load label map data (for plotting)

-

Label maps correspond index numbers to category names, so that when our convolution network -predicts 5, we know that this corresponds to airplane. Here we use internal utility -functions, but anything that returns a dictionary mapping integers to appropriate string labels -would be fine.

-
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,
-                                                                    use_display_name=True)
-
-
-
-
-

Putting everything together

-

The code shown below loads an image, runs it through the detection model and visualizes the -detection results, including the keypoints.

-

Note that this will take a long time (several minutes) the first time you run this code due to -tf.function’s trace-compilation — on subsequent runs (e.g. on new images), things will be -faster.

-

Here are some simple things to try out if you are curious:

-
    -

  • Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).

    • -

  • Print out detections[‘detection_boxes’] and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).

    • -

  • Set min_score_thresh to other values (between 0 and 1) to allow more detections in or to filter out more detections.

    • -
-
import numpy as np
-from six import BytesIO
-from PIL import Image
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings
-
-def load_image_into_numpy_array(path):
-    """Load an image from file into a numpy array.
-
-    Puts image into numpy array to feed into tensorflow graph.
-    Note that by convention we put it into a numpy array with shape
-    (height, width, channels), where channels=3 for RGB.
-
-    Args:
-      path: the file path to the image
-
-    Returns:
-      uint8 numpy array with shape (img_height, img_width, 3)
-    """
-    img_data = tf.io.gfile.GFile(path, 'rb').read()
-    image = Image.open(BytesIO(img_data))
-    (im_width, im_height) = image.size
-    return np.array(image.getdata()).reshape(
-        (im_height, im_width, 3)).astype(np.uint8)
-
-
-for image_filename in IMAGE_FILENAMES:
-
-    print('Running inference for {}... '.format(image_filename), end='')
-
-    image_path = os.path.join(IMAGES_DIR, image_filename)
-    image_np = load_image_into_numpy_array(image_path)
-
-    # Things to try:
-    # Flip horizontally
-    # image_np = np.fliplr(image_np).copy()
-
-    # Convert image to grayscale
-    # image_np = np.tile(
-    #     np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
-
-    input_tensor = tf.convert_to_tensor(
-        np.expand_dims(image_np, 0), dtype=tf.float32)
-    detections, predictions_dict, shapes = detect_fn(input_tensor)
-
-    label_id_offset = 1
-    image_np_with_detections = image_np.copy()
-
-    viz_utils.visualize_boxes_and_labels_on_image_array(
-          image_np_with_detections,
-          detections['detection_boxes'][0].numpy(),
-          (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),
-          detections['detection_scores'][0].numpy(),
-          category_index,
-          use_normalized_coordinates=True,
-          max_boxes_to_draw=200,
-          min_score_thresh=.30,
-          agnostic_mode=False)
-
-    plt.figure()
-    plt.imshow(image_np_with_detections)
-    print('Done')
-plt.show()
-
-# sphinx_gallery_thumbnail_number = 2
-
-
-
    -
  • plot object detection simple -
    • -
  • plot object detection simple -
    • -
-

Out:

-
Running inference for image1.jpg... Done
-Running inference for image2.jpg... Done
-
-
-

Total running time of the script: ( 2 minutes 29.261 seconds)

- -

Gallery generated by Sphinx-Gallery

-
-
- - -
- -
- - -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/auto_examples/sg_execution_times.html b/docs/build/auto_examples/sg_execution_times.html deleted file mode 100644 index 431d749..0000000 --- a/docs/build/auto_examples/sg_execution_times.html +++ /dev/null @@ -1,227 +0,0 @@ - - - - - - - - - - Computation times — TensorFlow 2 Object Detection API tutorial documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Computation times

-

02:29.261 total execution time for auto_examples files:

- ----- - - - - - - - - - - -

Object Detection Test (plot_object_detection_simple.py)

02:29.261

0.0 MB

Detect Objects Using Your Webcam (object_detection_camera.py)

00:00.000

0.0 MB

-
- - -
- -
-
- - -
- -
-

- - © Copyright 2020, Lyudmil Vladimirov - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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- - © Copyright 2020, Lyudmil Vladimirov - -

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- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
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    • - - -
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    • - -
- - -
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- -
-

TensorFlow 2 Object Detection API tutorial

-
-

Important

-

This tutorial is intended for TensorFlow 2.2, which (at the time of writing this tutorial) is the latest stable version of TensorFlow 2.x.

-

A version for TensorFlow 1.14 can be found here.

-
-

This is a step-by-step tutorial/guide to setting up and using TensorFlow’s Object Detection API to perform, namely, object detection in images/video.

-

The software tools which we shall use throughout this tutorial are listed in the table below:

- ---- - - - - - - - - - - - - - - - - - - - - - - - - -

Target Software versions

OS

Windows, Linux

Python

3.8

TensorFlow

2.2.0

CUDA Toolkit

10.1

CuDNN

7.6.5

Anaconda

Python 3.7 (Optional)

-
-

Contents:

- -
-
-
-

Indices and tables

- -
- - -
- -
-
- - - - -
- -
-

- - © Copyright 2020, Lyudmil Vladimirov - -

-
- - - - Built with Sphinx using a - - theme - - provided by Read the Docs. - -
- -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/build/install.html b/docs/build/install.html deleted file mode 100644 index 99c07b6..0000000 --- a/docs/build/install.html +++ /dev/null @@ -1,932 +0,0 @@ - - - - - - - - - - Installation — TensorFlow 2 Object Detection API tutorial documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

Installation

-
-

General Remarks

-
    -

  • In contrast to TensorFlow 1.x, where different Python packages needed to be installed for one to run TensorFlow on either their CPU or GPU (namely tensorflow and tensorflow-gpu), TensorFlow 2.x only requires that the tensorflow package is installed and automatically checks to see if a GPU can be successfully registered.

    • -
-
-
-

Anaconda Python 3.7 (Optional)

-

Although having Anaconda is not a requirement in order to install and use TensorFlow, I suggest doing so, due to it’s intuitive way of managing packages and setting up new virtual environments. Anaconda is a pretty useful tool, not only for working with TensorFlow, but in general for anyone working in Python, so if you haven’t had a chance to work with it, now is a good chance.

-
-

Install Anaconda Python 3.7

-
-
-
-
-

Windows

-
-
-
-
-

Linux

-
-
-
-
-
    -

  • Go to https://www.anaconda.com/products/individual and click the “Download” button

    • -

  • Download the Python 3.7 64-Bit Graphical Installer or the 32-Bit Graphical Installer installer, per your system requirements

    • -

  • Run the downloaded executable (.exe) file to begin the installation. See here for more details

    • -

  • (Optional) In the next step, check the box “Add Anaconda3 to my PATH environment variable”. This will make Anaconda your default Python distribution, which should ensure that you have the same default Python distribution across all editors.

    • -
-
-
-
    -

  • Go to https://www.anaconda.com/products/individual and click the “Download” button

    • -

  • Download the Python 3.7 64-Bit (x86) Installer

    • -

  • Run the downloaded bash script (.sh) file to begin the installation. See here for more details.

    • -

  • When prompted with the question “Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home/<user>/.bashrc ?”, answer “Yes”. If you enter “No”, you must manually add the path to Anaconda or conda will not work.

    • -
-
-
-
-
-

Create a new Anaconda virtual environment

-
    -

  • Open a new Terminal window

    • -

  • Type the following command:

    -
    -
    conda create -n tensorflow pip python=3.8
-
    -
    -
    -
    • -

  • The above will create a new virtual environment with name tensorflow

    • -
-
-

Important

-

The term Terminal will be used to refer to the Terminal of your choice (e.g. Command Prompt, Powershell, etc.)

-
-
-
-

Activate the Anaconda virtual environment

-
    -

  • Activating the newly created virtual environment is achieved by running the following in the Terminal window:

    -
    -
    conda activate tensorflow
-
    -
    -
    -
    • -

  • Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beggining of your cmd path specifier, e.g.:

    -
    -
    (tensorflow) C:\Users\sglvladi>
-
    -
    -
    -
    • -
-
-

Important

-

Throughout the rest of the tutorial, execution of any commands in a Terminal window should be done after the Anaconda virtual environment has been activated!

-
-
-
-
-

TensorFlow Installation

-

Getting setup with an installation of TensorFlow can be done in 3 simple steps.

-
-

Install the TensorFlow PIP package

-
    -

  • Run the following command in a Terminal window:

    -
    -
    pip install --ignore-installed --upgrade tensorflow==2.2.0
-
    -
    -
    -
    • -
-
-
-

Verify your Installation

-
    -

  • Run the following command in a Terminal window:

    -
    -
    python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
-
    -
    -
    -
    • -

  • Once the above is run, you should see a print-out similar to the one bellow:

    -
    -
    2020-06-22 19:20:32.614181: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
-2020-06-22 19:20:32.620571: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
-2020-06-22 19:20:35.027232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
-2020-06-22 19:20:35.060549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
-pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
-coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
-2020-06-22 19:20:35.074967: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
-2020-06-22 19:20:35.084458: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cublas64_10.dll'; dlerror: cublas64_10.dll not found
-2020-06-22 19:20:35.094112: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
-2020-06-22 19:20:35.103571: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
-2020-06-22 19:20:35.113102: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
-2020-06-22 19:20:35.123242: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusparse64_10.dll'; dlerror: cusparse64_10.dll not found
-2020-06-22 19:20:35.140987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
-2020-06-22 19:20:35.146285: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1598] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
-Skipping registering GPU devices...
-2020-06-22 19:20:35.162173: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
-2020-06-22 19:20:35.178588: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x15140db6390 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
-2020-06-22 19:20:35.185082: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
-2020-06-22 19:20:35.191117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
-2020-06-22 19:20:35.196815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]
-tf.Tensor(1620.5817, shape=(), dtype=float32)
-
    -
    -
    -
    • -
-
-
-

GPU Support (Optional)

-

Although using a GPU to run TensorFlow is not necessary, the computational gains are substantial. -Therefore, if your machine is equipped with a compatible CUDA-enabled GPU, it is recommended that -you follow the steps listed below to install the relevant libraries necessary to enable TensorFlow -to make use of your GPU.

-

By default, when TensorFlow is run it will attempt to register compatible GPU devices. If this -fails, TensorFlow will resort to running on the platform’s CPU. This can also be observed in the -printout shown in the previous section, under the “Verify the install” bullet-point, where there -are a number of messages which report missing library files (e.g. Could not load dynamic library -'cudart64_101.dll'; dlerror: cudart64_101.dll not found).

-

In order for TensorFlow to run on your GPU, the following requirements must be met:

- --- - - - - - - - - - - - - -

Prerequisites

Nvidia GPU (GTX 650 or newer)

CUDA Toolkit v10.1

CuDNN 7.6.5

-
-

Install CUDA Toolkit

-
-
-
-
-

Windows

-
-
-
-
-

Linux

-
-
-
-
-
    -

  • Follow this link to download and install CUDA Toolkit 10.1

    • -

  • Installation instructions can be found here

    • -
-
-
-
    -

  • Follow this link to download and install CUDA Toolkit 10.1 for your Linux distribution.

    • -

  • Installation instructions can be found here

    • -
-
-
-
-
-

Install CUDNN

-
-
-
-
-

Windows

-
-
-
-
-

Linux

-
-
-
-
- -
-
- -
-
-
-
-

Environment Setup

-
-
-
-
-

Windows

-
-
-
-
-

Linux

-
-
-
-
-
    -

  • Go to Start and Search “environment variables”

    • -

  • Click “Edit the system environment variables”. This should open the “System Properties” window

    • -

  • In the opened window, click the “Environment Variables…” button to open the “Environment Variables” window.

    • -

  • Under “System variables”, search for and click on the Path system variable, then click “Edit…”

    • -

  • Add the following paths, then click “OK” to save the changes:

    -
    -
      -

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin

      • -

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp

      • -

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\libx64

      • -

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\cuda\bin

      • -
    -
    -
    • -
-
-
-

As per Section 7.1.1 of the CUDA Installation Guide for Linux, append the following lines to ~/.bashrc:

-
# CUDA related exports
-export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
-export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
-
-
-
-
-
-
-

Update your GPU drivers (Optional)

-

If during the installation of the CUDA Toolkit (see Install CUDA Toolkit) you selected the Express Installation option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. These drivers are typically NOT the latest drivers and, thus, you may wish to updte your drivers.

- -
-
-

Verify the installation

-
    -

  • Run the following command in a NEW Terminal window:

    -
    -
    python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
-
    -
    -
    -

    Important

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    A new terminal window must be opened for the changes to the Environmental variables to take effect!!

    -
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    • -

  • Once the above is run, you should see a print-out similar to the one bellow:

    -
    -
    2020-06-22 20:24:31.355541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
-2020-06-22 20:24:33.650692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
-2020-06-22 20:24:33.686846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
-pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
-coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
-2020-06-22 20:24:33.697234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
-2020-06-22 20:24:33.747540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
-2020-06-22 20:24:33.787573: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
-2020-06-22 20:24:33.810063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
-2020-06-22 20:24:33.841474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
-2020-06-22 20:24:33.862787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
-2020-06-22 20:24:33.907318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
-2020-06-22 20:24:33.913612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
-2020-06-22 20:24:33.918093: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
-2020-06-22 20:24:33.932784: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2382acc1c40 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
-2020-06-22 20:24:33.939473: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
-2020-06-22 20:24:33.944570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
-pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
-coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
-2020-06-22 20:24:33.953910: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
-2020-06-22 20:24:33.958772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
-2020-06-22 20:24:33.963656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
-2020-06-22 20:24:33.968210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
-2020-06-22 20:24:33.973389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
-2020-06-22 20:24:33.978058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
-2020-06-22 20:24:33.983547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
-2020-06-22 20:24:33.990380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
-2020-06-22 20:24:35.338596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
-2020-06-22 20:24:35.344643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0
-2020-06-22 20:24:35.348795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N
-2020-06-22 20:24:35.353853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6284 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
-2020-06-22 20:24:35.369758: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2384aa9f820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
-2020-06-22 20:24:35.376320: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070 Ti, Compute Capability 6.1
-tf.Tensor(122.478485, shape=(), dtype=float32)
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TensorFlow Object Detection API Installation

-

Now that you have installed TensorFlow, it is time to install the TensorFlow Object Detection API.

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Downloading the TensorFlow Model Garden

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  • Create a new folder under a path of your choice and name it TensorFlow. (e.g. C:\Users\sglvladi\Documents\TensorFlow).

    • -

  • From your Terminal cd into the TensorFlow directory.

    • -

  • To download the models you can either use Git to clone the TensorFlow Models repository inside the TensorFlow folder, or you can simply download it as a ZIP and extract its contents inside the TensorFlow folder. To keep things consistent, in the latter case you will have to rename the extracted folder models-master to models.

    • -

  • You should now have a single folder named models under your TensorFlow folder, which contains another 4 folders as such:

    • -
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TensorFlow/
-└─ models/
-   ├─ community/
-   ├─ official/
-   ├─ orbit/
-   ├─ research/
-   └── ...
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-
-
-
-

Protobuf Installation/Compilation

-

The Tensorflow Object Detection API uses Protobufs to configure model and -training parameters. Before the framework can be used, the Protobuf libraries -must be downloaded and compiled.

-

This should be done as follows:

-
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  • Head to the protoc releases page

    • -

  • Download the latest protoc-*-*.zip release (e.g. protoc-3.12.3-win64.zip for 64-bit Windows)

    • -

  • Extract the contents of the downloaded protoc-*-*.zip in a directory <PATH_TO_PB> of your choice (e.g. C:\Program Files\Google Protobuf)

    • -

  • Add <PATH_TO_PB> to your Path environment variable (see Environment Setup)

    • -

  • In a new Terminal 1, cd into TensorFlow/models/research/ directory and run the following command:

    -
    -
    # From within TensorFlow/models/research/
-protoc object_detection/protos/*.proto --python_out=.
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    -
    • -
-
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Important

-

If you are on Windows and using Protobuf 3.5 or later, the multi-file selection wildcard (i.e *.proto) may not work but you can do one of the following:

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Windows Powershell

-
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Command Prompt

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# From within TensorFlow/models/research/
-Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}
-
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# From within TensorFlow/models/research/
-for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
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-
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1
-

NOTE: You MUST open a new Terminal for the changes in the environment variables to take effect.

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COCO API installation

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As of TensorFlow 2.x, the pycocotools package is listed as a dependency of the Object Detection API. Ideally, this package should get installed when installing the Object Detection API as documented in the Install the Object Detection API section below, however the installation can fail for various reasons and therefore it is simpler to just install the package beforehand, in which case later installation will be skipped.

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Windows

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Linux

-
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Run the following command to install pycocotools with Windows support:

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pip install cython
-pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
-
-
-

Note that, according to the package’s instructions, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from here.

-
-
-

Download cocoapi to a directory of your choice, then make and copy the pycocotools subfolder to the Tensorflow/models/research directory, as such:

-
git clone https://github.com/cocodataset/cocoapi.git
-cd cocoapi/PythonAPI
-make
-cp -r pycocotools <PATH_TO_TF>/TensorFlow/models/research/
-
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-
-
-

Note

-

The default metrics are based on those used in Pascal VOC evaluation.

-
    -

  • To use the COCO object detection metrics add metrics_set: "coco_detection_metrics" to the eval_config message in the config file.

    • -

  • To use the COCO instance segmentation metrics add metrics_set: "coco_mask_metrics" to the eval_config message in the config file.

    • -
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Install the Object Detection API

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Installation of the Object Detection API is achieved by installing the object_detection package. This is done by running the following commands from within Tensorflow\models\research:

-
# From within TensorFlow/models/research/
-cp object_detection/packages/tf2/setup.py .
-python -m pip install .
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-
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Note

-

During the above installation, you may observe the following error:

-
-
ERROR: Command errored out with exit status 1:
-     command: 'C:\Users\sglvladi\Anaconda3\envs\tf2\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"'; __file__='"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\sglvladi\AppData\Local\Temp\pip-record-wpn7b6qo\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\sglvladi\Anaconda3\envs\tf2\Include\pycocotools'
-         cwd: C:\Users\sglvladi\AppData\Local\Temp\pip-install-yn46ecei\pycocotools\
-    Complete output (14 lines):
-    running install
-    running build
-    running build_py
-    creating build
-    creating build\lib.win-amd64-3.8
-    creating build\lib.win-amd64-3.8\pycocotools
-    copying pycocotools\coco.py -> build\lib.win-amd64-3.8\pycocotools
-    copying pycocotools\cocoeval.py -> build\lib.win-amd64-3.8\pycocotools
-    copying pycocotools\mask.py -> build\lib.win-amd64-3.8\pycocotools
-    copying pycocotools\__init__.py -> build\lib.win-amd64-3.8\pycocotools
-    running build_ext
-    skipping 'pycocotools\_mask.c' Cython extension (up-to-date)
-    building 'pycocotools._mask' extension
-    error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
-    ----------------------------------------
-ERROR: Command errored out with exit status 1: 'C:\Users\sglvladi\Anaconda3\envs\tf2\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"'; __file__='"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\sglvladi\AppData\Local\Temp\pip-record-wpn7b6qo\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\sglvladi\Anaconda3\envs\tf2\Include\pycocotools' Check the logs for full command output.
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This is caused because installation of the pycocotools package has failed. To fix this have a look at the COCO API installation section and rerun the above commands.

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Test your Installation

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To test the installation, run the following command from within Tensorflow\models\research:

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# From within TensorFlow/models/research/
-python object_detection/builders/model_builder_tf2_test.py
-
-
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Once the above is run, allow some time for the test to complete and once done you should observe a -printout similar to the one below:

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...
-[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
-[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
-[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
-[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
-[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
-[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
-[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
-[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
-[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
-[ RUN      ] ModelBuilderTF2Test.test_session
-[  SKIPPED ] ModelBuilderTF2Test.test_session
-[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
-[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
-[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
-[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
-[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
-[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
-----------------------------------------------------------------------
-Ran 20 tests in 68.510s
-
-OK (skipped=1)
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Try out the examples

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If the previous step completed successfully it means you have successfully installed all the -components necessary to perform object detection using pre-trained models.

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If you want to play around with some examples to see how this can be done, now would be a good -time to have a look at the Examples section.

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LabelImg Installation

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There exist several ways to install labelImg. Below are 3 of the most common.

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Use precompiled binaries (Easy)

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Precompiled binaries for both Windows and Linux can be found here .

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Installation is the done in three simple steps:

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  1. Inside you TensorFlow folder, create a new directory, name it addons and then cd into it.

    2. -

  2. Download the latest binary for your OS from here. and extract its contents under Tensorflow/addons/labelImg.

    3. -

  3. You should now have a single folder named addons/labelImg under your TensorFlow folder, which contains another 4 folders as such:

    4. -
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TensorFlow/
-├─ addons/
-│  └─ labelImg/
-└─ models/
-   ├─ community/
-   ├─ official/
-   ├─ orbit/
-   ├─ research/
-   └─ ...
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  1. labelImg can then be run as follows:

    2. -
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# From within Tensorflow/addons/labelImg
-labelImg
-# or
-labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Build from source (Hard)

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The steps for installing from source follow below.

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1. Download labelImg

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  • Inside you TensorFlow folder, create a new directory, name it addons and then cd into it.

    • -

  • To download the package you can either use Git to clone the labelImg repo inside the TensorFlow\addons folder, or you can simply download it as a ZIP and extract it’s contents inside the TensorFlow\addons folder. To keep things consistent, in the latter case you will have to rename the extracted folder labelImg-master to labelImg. 2

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  • You should now have a single folder named addons\labelImg under your TensorFlow folder, which contains another 4 folders as such:

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TensorFlow/
-├─ addons
-│  └─ labelImg/
-└─ models/
-   ├─ community/
-   ├─ official/
-   ├─ orbit/
-   ├─ research/
-   └─ ...
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The latest repo commit when writing this tutorial is 8d1bd68.

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2. Install dependencies and compiling package

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  • Open a new Terminal window and activate the tensorflow_gpu environment (if you have not done so already)

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  • cd into TensorFlow/addons/labelImg and run the following commands:

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-pyrcc5 -o libs/resources.py resources.qrc
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-sudo pip install -r requirements/requirements-linux-python3.txt
-make qt5py3
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  • cd into TensorFlow/addons/labelImg and run the following command:

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    # From within Tensorflow/addons/labelImg
-python labelImg.py
-# or
-python  labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Common issues

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Below is a list of common issues encountered while using TensorFlow for objects detection.

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Python crashes - TensorFlow GPU

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If you are using GPU Support (Optional) and when you try to run some Python object detection script (e.g. Test your Installation), after a few seconds, Windows reports that Python has crashed then have a look at the Anaconda/Command Prompt window you used to run the script and check for a line similar (maybe identical) to the one below:

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2018-03-22 03:07:54.623130: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:378] Loaded runtime CuDNN library: 7101 (compatibility version 7100) but source was compiled with 7003 (compatibility version 7000).  If using a binary install, upgrade your CuDNN library to match.  If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
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If the above line is present in the printed debugging, it means that you have not installed the correct version of the cuDNN libraries. In this case make sure you re-do the Install CUDNN step, making sure you instal cuDNN v7.0.5.

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Cleaning up Nvidia containers (TensorFlow GPU)

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Sometimes, when terminating a TensorFlow training process, the Nvidia containers associated to the process are not cleanly terminated. This can lead to bogus errors when we try to run a new TensorFlow process.

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Some known issues caused by the above are presented below:

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  • Failure to restart training of a model. Look for the following errors in the debugging:

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    2018-03-23 03:03:10.326902: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED
-2018-03-23 03:03:10.330475: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
-2018-03-23 03:03:10.333797: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow/stream_executor/stream.h:1983] attempting to perform DNN operation using StreamExecutor without DNN support
-2018-03-23 03:03:10.333807: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\stream.cc:1851] stream 00000216F05CB660 did not wait for stream: 00000216F05CA6E0
-2018-03-23 03:03:10.340765: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\stream.cc:4637] stream 00000216F05CB660 did not memcpy host-to-device; source: 000000020DB37B00
-2018-03-23 03:03:10.343752: F C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_util.cc:343] CPU->GPU Memcpy failed
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To solve such issues in Windows, open a Task Manager windows, look for Tasks with name NVIDIA Container and kill them by selecting them and clicking the End Task button at the bottom left corner of the window.

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If the issue persists, then you’re probably running out of memory. Try closing down anything else that might be eating up your GPU memory (e.g. Youtube videos, webpages etc.)

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“WARNING:tensorflow:Entity <bound method X of <Y>> could not be transformed …”

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In some versions of Tensorflow, you may see errors that look similar to the ones below:

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...
-WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x000001E92103EDD8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x000001E92103EDD8>>: AssertionError: Bad argument number for Name: 3, expecting 4
-WARNING:tensorflow:Entity <bound method BatchNormalization.call of <tensorflow.python.layers.normalization.BatchNormalization object at 0x000001E9225EBA90>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method BatchNormalization.call of <tensorflow.python.layers.normalization.BatchNormalization object at 0x000001E9225EBA90>>: AssertionError: Bad argument number for Name: 3, expecting 4
-...
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These warnings appear to be harmless form my experience, however they can saturate the console with unnecessary messages, which makes it hard to scroll through the output of the training/evaluation process.

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As reported here, this issue seems to -be caused by a mismatched version of gast. Simply -downgrading gast to version 0.2.2 seems to remove the warnings. This can be done by running:

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pip install gast==0.2.2
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“AttributeError: module ‘google.protobuf.descriptor’ has no attribute ‘_internal_create_key”

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It is possible that when executing from object_detection.utils import label_map_util you may -get the above error. As per the discussion is in this Stack Overflow thread, -upgrading the Python protobuf version seems to solve this issue:

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pip install --upgrade protobuf
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“TypeError: Expected Operation, Variable, or Tensor, got level_5”

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When trying to export oyu trained model using the exporter_main_v2.py script, you may come -across an error that looks like this:

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Traceback (most recent call last):
-  File ".\exporter_main_v2.py", line 126, in <module>
-    app.run(main)
-  File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\absl\app.py", line 299, in run
-    _run_main(main, args)
-  ...
-  File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1627, in get_losses_for
-    reachable = tf_utils.get_reachable_from_inputs(inputs, losses)
-  File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py", line 140, in get_reachable_from_inputs
-    raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x))
-TypeError: Expected Operation, Variable, or Tensor, got level_5
-
-
-

This error seems to come from TensorFlow itself and a discussion on the issue can be found -here. As discussed there, a fix to the above -issue can be achieved by opening the tf_utils.py file and adding a line of code. Below is a -summary of how this can be done:

-
    -

  • Look at the line that corresponds to line 9 (highlighted) in the above error print out.

    • -

  • Copy the path to the tf_utils.py file; in my case this was C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py

    • -

  • Open the file and replace line 140 of the file as follows:

    -
      -

    • Change:

      -
      raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x))
-
      -
      -

      to:

      -
      if not isinstance(x, str):
-    raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x))
-
      -
      -
      • -
    -
    • -
-

At the time of writting this tutorial, a fix to the issue had not been implemented in the version -of TensorFlow installed using pip. It is possible that this will get incorporated at some later -point.

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- - © Copyright 2020, Lyudmil Vladimirov - -

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- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

Training Custom Object Detector

-

So, up to now you should have done the following:

- -

Now that we have done all the above, we can start doing some cool stuff. Here we will see how you can train your own object detector, and since it is not as simple as it sounds, we will have a look at:

-
    -

  1. How to organise your workspace/training files

    2. -

  2. How to prepare/annotate image datasets

    3. -

  3. How to generate tf records from such datasets

    4. -

  4. How to configure a simple training pipeline

    5. -

  5. How to train a model and monitor it’s progress

    6. -

  6. How to export the resulting model and use it to detect objects.

    7. -
-
-

Preparing the Workspace

-
    -

  1. If you have followed the tutorial, you should by now have a folder Tensorflow, placed under <PATH_TO_TF> (e.g. C:/Users/sglvladi/Documents), with the following directory tree:

    -
    -
    TensorFlow/
-├─ addons/ (Optional)
-│  └─ labelImg/
-└─ models/
-   ├─ community/
-   ├─ official/
-   ├─ orbit/
-   ├─ research/
-   └─ ...
-
    -
    -
    -
    2. -

  2. Now create a new folder under TensorFlow and call it workspace. It is within the workspace that we will store all our training set-ups. Now let’s go under workspace and create another folder named training_demo. Now our directory structure should be as so:

    -
    -
    TensorFlow/
-├─ addons/ (Optional)
-│  └─ labelImg/
-├─ models/
-│  ├─ community/
-│  ├─ official/
-│  ├─ orbit/
-│  ├─ research/
-│  └─ ...
-└─ workspace/
-   └─ training_demo/
-
    -
    -
    -
    3. -

  3. The training_demo folder shall be our training folder, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below.

    -
    -
    training_demo/
-├─ annotations/
-├─ images/
-│  ├─ test/
-│  └─ train/
-├─ models/
-├─ pre-trained-models/
-└─ README.md
-
    -
    -
    -
    4. -
-

Here’s an explanation for each of the folders/filer shown in the above tree:

-
    -

  • annotations: This folder will be used to store all *.csv files and the respective TensorFlow *.record files, which contain the list of annotations for our dataset images.

    • -

  • images: This folder contains a copy of all the images in our dataset, as well as the respective *.xml files produced for each one, once labelImg is used to annotate objects.

    -
    -
      -

    • images/train: This folder contains a copy of all images, and the respective *.xml files, which will be used to train our model.

      • -

    • images/test: This folder contains a copy of all images, and the respective *.xml files, which will be used to test our model.

      • -
    -
    -
    • -

  • models: This folder will contain a sub-folder for each of training job. Each subfolder will contain the training pipeline configuration file *.config, as well as all files generated during the training and evaluation of our model.

    • -

  • pre-trained-models: This folder will contain the downloaded pre-trained models, which shall be used as a starting checkpoint for our training jobs.

    • -

  • README.md: This is an optional file which provides some general information regarding the training conditions of our model. It is not used by TensorFlow in any way, but it generally helps when you have a few training folders and/or you are revisiting a trained model after some time.

    • -
-

If you do not understand most of the things mentioned above, no need to worry, as we’ll see how all the files are generated further down.

-
-
-

Preparing the Dataset

-
-

Annotate Images

-

To annotate images we will be using the labelImg package. If you haven’t installed the package yet, then have a look at LabelImg Installation.

-
    -

  • Once you have collected all the images to be used to test your model (ideally more than 100 per class), place them inside the folder training_demo/images.

    • -

  • Open a new Anaconda/Command Prompt window and cd into Tensorflow/addons/labelImg.

    • -

  • If (as suggested in LabelImg Installation) you created a separate Conda environment for labelImg then go ahead and activate it by running:

    -
    -
    activate labelImg
-
    -
    -
    -
    • -

  • Next go ahead and start labelImg, pointing it to your training_demo/images folder.

    -
    -
    python labelImg.py ../../workspace/training_demo/images
-
    -
    -
    -
    • -

  • A File Explorer Dialog windows should open, which points to the training_demo/images folder.

    • -

  • Press the “Select Folder” button, to start annotating your images.

    • -
-

Once open, you should see a window similar to the one below:

-alternate text -

I won’t be covering a tutorial on how to use labelImg, but you can have a look at labelImg’s repo for more details. A nice Youtube video demonstrating how to use labelImg is also available here. What is important is that once you annotate all your images, a set of new *.xml files, one for each image, should be generated inside your training_demo/images folder.

-
-
-

Partition the Dataset

-

Once you have finished annotating your image dataset, it is a general convention to use only part of it for training, and the rest is used for evaluation purposes (e.g. as discussed in Evaluating the Model (Optional)).

-

Typically, the ratio is 90%/10%, i.e. 90% of the images are used for training and the rest 10% is maintained for testing, but you can chose whatever ratio suits your needs.

-

Once you have decided how you will be splitting your dataset, copy all training images, together with their corresponding *.xml files, and place them inside the training_demo/images/train folder. Similarly, copy all testing images, with their *.xml files, and paste them inside training_demo/images/test.

-

For lazy people like myself, who cannot be bothered to do the above, I have put tugether a simple script that automates the above process:

-
""" usage: partition_dataset.py [-h] [-i IMAGEDIR] [-o OUTPUTDIR] [-r RATIO] [-x]
-
-Partition dataset of images into training and testing sets
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -i IMAGEDIR, --imageDir IMAGEDIR
-                        Path to the folder where the image dataset is stored. If not specified, the CWD will be used.
-  -o OUTPUTDIR, --outputDir OUTPUTDIR
-                        Path to the output folder where the train and test dirs should be created. Defaults to the same directory as IMAGEDIR.
-  -r RATIO, --ratio RATIO
-                        The ratio of the number of test images over the total number of images. The default is 0.1.
-  -x, --xml             Set this flag if you want the xml annotation files to be processed and copied over.
-"""
-import os
-import re
-from shutil import copyfile
-import argparse
-import math
-import random
-
-
-def iterate_dir(source, dest, ratio, copy_xml):
-    source = source.replace('\\', '/')
-    dest = dest.replace('\\', '/')
-    train_dir = os.path.join(dest, 'train')
-    test_dir = os.path.join(dest, 'test')
-
-    if not os.path.exists(train_dir):
-        os.makedirs(train_dir)
-    if not os.path.exists(test_dir):
-        os.makedirs(test_dir)
-
-    images = [f for f in os.listdir(source)
-              if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(.jpg|.jpeg|.png)$', f)]
-
-    num_images = len(images)
-    num_test_images = math.ceil(ratio*num_images)
-
-    for i in range(num_test_images):
-        idx = random.randint(0, len(images)-1)
-        filename = images[idx]
-        copyfile(os.path.join(source, filename),
-                 os.path.join(test_dir, filename))
-        if copy_xml:
-            xml_filename = os.path.splitext(filename)[0]+'.xml'
-            copyfile(os.path.join(source, xml_filename),
-                     os.path.join(test_dir,xml_filename))
-        images.remove(images[idx])
-
-    for filename in images:
-        copyfile(os.path.join(source, filename),
-                 os.path.join(train_dir, filename))
-        if copy_xml:
-            xml_filename = os.path.splitext(filename)[0]+'.xml'
-            copyfile(os.path.join(source, xml_filename),
-                     os.path.join(train_dir, xml_filename))
-
-
-def main():
-
-    # Initiate argument parser
-    parser = argparse.ArgumentParser(description="Partition dataset of images into training and testing sets",
-                                     formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument(
-        '-i', '--imageDir',
-        help='Path to the folder where the image dataset is stored. If not specified, the CWD will be used.',
-        type=str,
-        default=os.getcwd()
-    )
-    parser.add_argument(
-        '-o', '--outputDir',
-        help='Path to the output folder where the train and test dirs should be created. '
-             'Defaults to the same directory as IMAGEDIR.',
-        type=str,
-        default=None
-    )
-    parser.add_argument(
-        '-r', '--ratio',
-        help='The ratio of the number of test images over the total number of images. The default is 0.1.',
-        default=0.1,
-        type=float)
-    parser.add_argument(
-        '-x', '--xml',
-        help='Set this flag if you want the xml annotation files to be processed and copied over.',
-        action='store_true'
-    )
-    args = parser.parse_args()
-
-    if args.outputDir is None:
-        args.outputDir = args.imageDir
-
-    # Now we are ready to start the iteration
-    iterate_dir(args.imageDir, args.outputDir, args.ratio, args.xml)
-
-
-if __name__ == '__main__':
-    main()
-
-
-
    -

  • Click here to download the above script and save it inside TensorFlow/scripts/preprocessing.

    • -

  • Then, cd into TensorFlow/scripts/preprocessing and run:

    -
    -
    python partition_dataset.py -x -i [PATH_TO_IMAGES_FOLDER] -r 0.1
-
-# For example
-# python partition_dataset.py -x -i C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images -r 0.1
-
    -
    -
    -
    • -
-

Once the script has finished, two new folders should have been created under training_demo/images, -namely training_demo/images/train and training_demo/images/test, containing 90% and 10% of -the images (and *.xml files), respectively. To avoid loss of any files, the script will not -delete the images under training_demo/images. Once you have checked that your images have been -safely copied over, you can delete the images under training_demo/images manually.

-
-
-

Create Label Map

-

TensorFlow requires a label map, which namely maps each of the used labels to an integer values. This label map is used both by the training and detection processes.

-

Below we show an example label map (e.g label_map.pbtxt), assuming that our dataset containes 2 labels, dogs and cats:

-
item {
-    id: 1
-    name: 'cat'
-}
-
-item {
-    id: 2
-    name: 'dog'
-}
-
-
-

Label map files have the extention .pbtxt and should be placed inside the training_demo/annotations folder.

-
-
-

Create TensorFlow Records

-

Now that we have generated our annotations and split our dataset into the desired training and -testing subsets, it is time to convert our annotations into the so called TFRecord format.

-

Before we proceed to describe the above steps, let’s create a directory where we can store some -scripts. Under the TensorFlow folder, create a new folder TensorFlow/scripts, which we can -use to store some useful scripts. To make things even tidier, let’s create a new folder -TensorFlow/scripts/preprocessing, where we shall store scripts that we can use to preprocess -our training inputs. Below is out TensorFlow directory tree structure, up to now:

-
TensorFlow/
-├─ addons/ (Optional)
-│  └─ labelImg/
-├─ models/
-│  ├─ community/
-│  ├─ official/
-│  ├─ orbit/
-│  ├─ research/
-│  └─ ...
-├─ scripts/
-│  └─ preprocessing/
-└─ workspace/
-   └─ training_demo/
-
-
-
-

Convert *.xml to *.record

-

To do this we can write a simple script that iterates through all *.xml files in the training_demo/images/train and training_demo/images/test folders, and generates a *.record file for each of the two.

-

Here is an example script that allows us to do just that:

-
""" Sample TensorFlow XML-to-TFRecord converter
-
-usage: generate_tfrecord.py [-h] [-x XML_DIR] [-l LABELS_PATH] [-o OUTPUT_PATH] [-i IMAGE_DIR] [-c CSV_PATH]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -x XML_DIR, --xml_dir XML_DIR
-                        Path to the folder where the input .xml files are stored.
-  -l LABELS_PATH, --labels_path LABELS_PATH
-                        Path to the labels (.pbtxt) file.
-  -o OUTPUT_PATH, --output_path OUTPUT_PATH
-                        Path of output TFRecord (.record) file.
-  -i IMAGE_DIR, --image_dir IMAGE_DIR
-                        Path to the folder where the input image files are stored. Defaults to the same directory as XML_DIR.
-  -c CSV_PATH, --csv_path CSV_PATH
-                        Path of output .csv file. If none provided, then no file will be written.
-"""
-
-import os
-import glob
-import pandas as pd
-import io
-import xml.etree.ElementTree as ET
-import argparse
-
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)
-import tensorflow.compat.v1 as tf
-from PIL import Image
-from object_detection.utils import dataset_util, label_map_util
-from collections import namedtuple
-
-# Initiate argument parser
-parser = argparse.ArgumentParser(
-    description="Sample TensorFlow XML-to-TFRecord converter")
-parser.add_argument("-x",
-                    "--xml_dir",
-                    help="Path to the folder where the input .xml files are stored.",
-                    type=str)
-parser.add_argument("-l",
-                    "--labels_path",
-                    help="Path to the labels (.pbtxt) file.", type=str)
-parser.add_argument("-o",
-                    "--output_path",
-                    help="Path of output TFRecord (.record) file.", type=str)
-parser.add_argument("-i",
-                    "--image_dir",
-                    help="Path to the folder where the input image files are stored. "
-                         "Defaults to the same directory as XML_DIR.",
-                    type=str, default=None)
-parser.add_argument("-c",
-                    "--csv_path",
-                    help="Path of output .csv file. If none provided, then no file will be "
-                         "written.",
-                    type=str, default=None)
-
-args = parser.parse_args()
-
-if args.image_dir is None:
-    args.image_dir = args.xml_dir
-
-label_map = label_map_util.load_labelmap(args.labels_path)
-label_map_dict = label_map_util.get_label_map_dict(label_map)
-
-
-def xml_to_csv(path):
-    """Iterates through all .xml files (generated by labelImg) in a given directory and combines
-    them in a single Pandas dataframe.
-
-    Parameters:
-    ----------
-    path : str
-        The path containing the .xml files
-    Returns
-    -------
-    Pandas DataFrame
-        The produced dataframe
-    """
-
-    xml_list = []
-    for xml_file in glob.glob(path + '/*.xml'):
-        tree = ET.parse(xml_file)
-        root = tree.getroot()
-        for member in root.findall('object'):
-            value = (root.find('filename').text,
-                     int(root.find('size')[0].text),
-                     int(root.find('size')[1].text),
-                     member[0].text,
-                     int(member[4][0].text),
-                     int(member[4][1].text),
-                     int(member[4][2].text),
-                     int(member[4][3].text)
-                     )
-            xml_list.append(value)
-    column_name = ['filename', 'width', 'height',
-                   'class', 'xmin', 'ymin', 'xmax', 'ymax']
-    xml_df = pd.DataFrame(xml_list, columns=column_name)
-    return xml_df
-
-
-def class_text_to_int(row_label):
-    return label_map_dict[row_label]
-
-
-def split(df, group):
-    data = namedtuple('data', ['filename', 'object'])
-    gb = df.groupby(group)
-    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
-
-
-def create_tf_example(group, path):
-    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
-        encoded_jpg = fid.read()
-    encoded_jpg_io = io.BytesIO(encoded_jpg)
-    image = Image.open(encoded_jpg_io)
-    width, height = image.size
-
-    filename = group.filename.encode('utf8')
-    image_format = b'jpg'
-    xmins = []
-    xmaxs = []
-    ymins = []
-    ymaxs = []
-    classes_text = []
-    classes = []
-
-    for index, row in group.object.iterrows():
-        xmins.append(row['xmin'] / width)
-        xmaxs.append(row['xmax'] / width)
-        ymins.append(row['ymin'] / height)
-        ymaxs.append(row['ymax'] / height)
-        classes_text.append(row['class'].encode('utf8'))
-        classes.append(class_text_to_int(row['class']))
-
-    tf_example = tf.train.Example(features=tf.train.Features(feature={
-        'image/height': dataset_util.int64_feature(height),
-        'image/width': dataset_util.int64_feature(width),
-        'image/filename': dataset_util.bytes_feature(filename),
-        'image/source_id': dataset_util.bytes_feature(filename),
-        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
-        'image/format': dataset_util.bytes_feature(image_format),
-        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
-        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
-        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
-        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
-        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
-        'image/object/class/label': dataset_util.int64_list_feature(classes),
-    }))
-    return tf_example
-
-
-def main(_):
-
-    writer = tf.python_io.TFRecordWriter(args.output_path)
-    path = os.path.join(args.image_dir)
-    examples = xml_to_csv(args.xml_dir)
-    grouped = split(examples, 'filename')
-    for group in grouped:
-        tf_example = create_tf_example(group, path)
-        writer.write(tf_example.SerializeToString())
-    writer.close()
-    print('Successfully created the TFRecord file: {}'.format(args.output_path))
-    if args.csv_path is not None:
-        examples.to_csv(args.csv_path, index=None)
-        print('Successfully created the CSV file: {}'.format(args.csv_path))
-
-
-if __name__ == '__main__':
-    tf.app.run()
-
-
-
    -

  • Click here to download the above script and save it inside TensorFlow/scripts/preprocessing.

    • -

  • Install the pandas package:

    -
    -
    conda install pandas # Anaconda
-                     # or
-pip install pandas   # pip
-
    -
    -
    -
    • -

  • Finally, cd into TensorFlow/scripts/preprocessing and run:

    -
    -
    # Create train data:
-python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/train -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/train.record
-
-# Create test data:
-python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/test -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/test.record
-
-# For example
-# python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/train -l C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/train.record
-# python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/test -l C:/Users/sglvladi/Documents/Tensorflow2/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/test.record
-
    -
    -
    -
    • -
-

Once the above is done, there should be 2 new files under the training_demo/annotations folder, named test.record and train.record, respectively.

-
-
-
-
-

Configuring a Training Job

-

For the purposes of this tutorial we will not be creating a training job from scratch, but rather -we will reuse one of the pre-trained models provided by TensorFlow. If you would like to train an -entirely new model, you can have a look at TensorFlow’s tutorial.

-

The model we shall be using in our examples is the SSD ResNet50 V1 FPN 640x640 -model, since it provides a relatively good trade-off between performance and speed. However, there -exist a number of other models you can use, all of which are listed in TensorFlow 2 Detection Model Zoo.

-
-

Download Pre-Trained Model

-

To begin with, we need to download the latest pre-trained network for the model we wish to use. -This can be done by simply clicking on the name of the desired model in the table found in -TensorFlow 2 Detection Model Zoo. -Clicking on the name of your model should initiate a download for a *.tar.gz file.

-

Once the *.tar.gz file has been downloaded, open it using a decompression program of your -choice (e.g. 7zip, WinZIP, etc.). Next, open the *.tar folder that you see when the compressed -folder is opened, and extract its contents inside the folder training_demo/pre-trained-models. -Since we downloaded the SSD ResNet50 V1 FPN 640x640 -model, our training_demo directory should now look as follows:

-
-
training_demo/
-├─ ...
-├─ pre-trained-models/
-|  └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/
-|     ├─ checkpoint/
-│     ├─ saved_model/
-|     └─ pipeline.config
-└─ ...
-
-
-
-

Note that the above process can be repeated for all other pre-trained models you wish to experiment -with. For example, if you wanted to also configure a training job for the EfficientDet D1 640x640 -model, you can download the model and after extracting its context the demo directory will be:

-
-
training_demo/
-├─ ...
-├─ pre-trained-models/
-│  ├─ efficientdet_d1_coco17_tpu-32/
-│  │  ├─ checkpoint/
-│  │  ├─ saved_model/
-|  │  └─ pipeline.config
-│  └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/
-|     ├─ checkpoint/
-│     ├─ saved_model/
-|     └─ pipeline.config
-└─ ...
-
-
-
-
-
-

Configure the Training Pipeline

-

Now that we have downloaded and extracted our pre-trained model, let’s create a directory for our -training job. Under the training_demo/models create a new directory named my_ssd_resnet50_v1_fpn -and copy the training_demo/pre-trained-models/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/pipeline.config -file inside the newly created directory. Our training_demo/models directory should now look -like this:

-
-
training_demo/
-├─ ...
-├─ models/
-│  └─ my_ssd_resnet50_v1_fpn/
-|     └─ pipeline.config
-└─ ...
-
-
-
-

Now, let’s have a look at the changes that we shall need to apply to the pipeline.config file -(highlighted in yellow):

-
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model {
-  ssd {
-    num_classes: 1 # Set this to the number of different label classes
-    image_resizer {
-      fixed_shape_resizer {
-        height: 640
-        width: 640
-      }
-    }
-    feature_extractor {
-      type: "ssd_resnet50_v1_fpn_keras"
-      depth_multiplier: 1.0
-      min_depth: 16
-      conv_hyperparams {
-        regularizer {
-          l2_regularizer {
-            weight: 0.00039999998989515007
-          }
-        }
-        initializer {
-          truncated_normal_initializer {
-            mean: 0.0
-            stddev: 0.029999999329447746
-          }
-        }
-        activation: RELU_6
-        batch_norm {
-          decay: 0.996999979019165
-          scale: true
-          epsilon: 0.0010000000474974513
-        }
-      }
-      override_base_feature_extractor_hyperparams: true
-      fpn {
-        min_level: 3
-        max_level: 7
-      }
-    }
-    box_coder {
-      faster_rcnn_box_coder {
-        y_scale: 10.0
-        x_scale: 10.0
-        height_scale: 5.0
-        width_scale: 5.0
-      }
-    }
-    matcher {
-      argmax_matcher {
-        matched_threshold: 0.5
-        unmatched_threshold: 0.5
-        ignore_thresholds: false
-        negatives_lower_than_unmatched: true
-        force_match_for_each_row: true
-        use_matmul_gather: true
-      }
-    }
-    similarity_calculator {
-      iou_similarity {
-      }
-    }
-    box_predictor {
-      weight_shared_convolutional_box_predictor {
-        conv_hyperparams {
-          regularizer {
-            l2_regularizer {
-              weight: 0.00039999998989515007
-            }
-          }
-          initializer {
-            random_normal_initializer {
-              mean: 0.0
-              stddev: 0.009999999776482582
-            }
-          }
-          activation: RELU_6
-          batch_norm {
-            decay: 0.996999979019165
-            scale: true
-            epsilon: 0.0010000000474974513
-          }
-        }
-        depth: 256
-        num_layers_before_predictor: 4
-        kernel_size: 3
-        class_prediction_bias_init: -4.599999904632568
-      }
-    }
-    anchor_generator {
-      multiscale_anchor_generator {
-        min_level: 3
-        max_level: 7
-        anchor_scale: 4.0
-        aspect_ratios: 1.0
-        aspect_ratios: 2.0
-        aspect_ratios: 0.5
-        scales_per_octave: 2
-      }
-    }
-    post_processing {
-      batch_non_max_suppression {
-        score_threshold: 9.99999993922529e-09
-        iou_threshold: 0.6000000238418579
-        max_detections_per_class: 100
-        max_total_detections: 100
-        use_static_shapes: false
-      }
-      score_converter: SIGMOID
-    }
-    normalize_loss_by_num_matches: true
-    loss {
-      localization_loss {
-        weighted_smooth_l1 {
-        }
-      }
-      classification_loss {
-        weighted_sigmoid_focal {
-          gamma: 2.0
-          alpha: 0.25
-        }
-      }
-      classification_weight: 1.0
-      localization_weight: 1.0
-    }
-    encode_background_as_zeros: true
-    normalize_loc_loss_by_codesize: true
-    inplace_batchnorm_update: true
-    freeze_batchnorm: false
-  }
-}
-train_config {
-  batch_size: 8 # Increase/Decrease this value depending on the available memory (Higher values require more memory and vice-versa)
-  data_augmentation_options {
-    random_horizontal_flip {
-    }
-  }
-  data_augmentation_options {
-    random_crop_image {
-      min_object_covered: 0.0
-      min_aspect_ratio: 0.75
-      max_aspect_ratio: 3.0
-      min_area: 0.75
-      max_area: 1.0
-      overlap_thresh: 0.0
-    }
-  }
-  sync_replicas: true
-  optimizer {
-    momentum_optimizer {
-      learning_rate {
-        cosine_decay_learning_rate {
-          learning_rate_base: 0.03999999910593033
-          total_steps: 25000
-          warmup_learning_rate: 0.013333000242710114
-          warmup_steps: 2000
-        }
-      }
-      momentum_optimizer_value: 0.8999999761581421
-    }
-    use_moving_average: false
-  }
-  fine_tune_checkpoint: "pre-trained-models/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0" # Path to checkpoint of pre-trained model
-  num_steps: 25000
-  startup_delay_steps: 0.0
-  replicas_to_aggregate: 8
-  max_number_of_boxes: 100
-  unpad_groundtruth_tensors: false
-  fine_tune_checkpoint_type: "detection" # Set this to "detection" since we want to be training the full detection model
-  use_bfloat16: false # Set this to false if you are not training on a TPU
-  fine_tune_checkpoint_version: V2
-}
-train_input_reader {
-  label_map_path: "annotations/label_map.pbtxt" # Path to label map file
-  tf_record_input_reader {
-    input_path: "annotations/train.record" # Path to training TFRecord file
-  }
-}
-eval_config {
-  metrics_set: "coco_detection_metrics"
-  use_moving_averages: false
-}
-eval_input_reader {
-  label_map_path: "annotations/label_map.pbtxt" # Path to label map file
-  shuffle: false
-  num_epochs: 1
-  tf_record_input_reader {
-    input_path: "annotations/test.record" # Path to testing TFRecord
-  }
-}
-
-
-

It is worth noting here that the changes to lines 178 to 179 above are optional. These -should only be used if you installed the COCO evaluation tools, as outlined in the -COCO API installation section, and you intend to run evaluation (see Evaluating the Model (Optional)).

-

Once the above changes have been applied to our config file, go ahead and save it.

-
-
-
-

Training the Model

-

Before we begin training our model, let’s go and copy the TensorFlow/models/research/object_detection/model_main_tf2.py -script and paste it straight into our training_demo folder. We will need this script in order -to train our model.

-

Now, to initiate a new training job, open a new Terminal, cd inside the training_demo -folder and run the following command:

-
python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config
-
-
-

Once the training process has been initiated, you should see a series of print outs similar to the -one below (plus/minus some warnings):

-
...
-WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma
-W0716 05:24:19.105542  1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma
-WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.beta
-W0716 05:24:19.106541  1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.beta
-WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_mean
-W0716 05:24:19.107540  1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_mean
-WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_variance
-W0716 05:24:19.108539  1364 util.py:143] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.moving_variance
-WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
-W0716 05:24:19.108539  1364 util.py:151] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
-WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
-INFO:tensorflow:Step 100 per-step time 1.153s loss=0.761
-I0716 05:26:55.879558  1364 model_lib_v2.py:632] Step 100 per-step time 1.153s loss=0.761
-...
-
-
-
-

Important

-

The output will normally look like it has “frozen”, but DO NOT rush to cancel the process. The -training outputs logs only every 100 steps by default, therefore if you wait for a while, you -should see a log for the loss at step 100.

-

The time you should wait can vary greatly, depending on whether you are using a GPU and the -chosen value for batch_size in the config file, so be patient.

-
-

If you ARE observing a similar output to the above, then CONGRATULATIONS, you have successfully -started your first training job. Now you may very well treat yourself to a cold beer, as waiting -on the training to finish is likely to take a while. Following what people have said online, it -seems that it is advisable to allow you model to reach a TotalLoss of at least 2 (ideally 1 -and lower) if you want to achieve “fair” detection results. Obviously, lower TotalLoss is -better, however very low TotalLoss should be avoided, as the model may end up overfitting the -dataset, meaning that it will perform poorly when applied to images outside the dataset. To -monitor TotalLoss, as well as a number of other metrics, while your model is training, have a -look at Monitor Training Job Progress using TensorBoard.

-

If you ARE NOT seeing a print-out similar to that shown above, and/or the training job crashes -after a few seconds, then have a look at the issues and proposed solutions, under the -Common issues section, to see if you can find a solution. Alternatively, you can try the issues -section of the official Tensorflow Models repo.

-
-

Note

-

Training times can be affected by a number of factors such as:

-
    -

  • The computational power of you hardware (either CPU or GPU): Obviously, the more powerful your PC is, the faster the training process.

    • -

  • Whether you are using the TensorFlow CPU or GPU variant: In general, even when compared to the best CPUs, almost any GPU graphics card will yield much faster training and detection speeds. As a matter of fact, when I first started I was running TensorFlow on my Intel i7-5930k (6/12 cores @ 4GHz, 32GB RAM) and was getting step times of around 12 sec/step, after which I installed TensorFlow GPU and training the very same model -using the same dataset and config files- on a EVGA GTX-770 (1536 CUDA-cores @ 1GHz, 2GB VRAM) I was down to 0.9 sec/step!!! A 12-fold increase in speed, using a “low/mid-end” graphics card, when compared to a “mid/high-end” CPU.

    • -

  • The complexity of the objects you are trying to detect: Obviously, if your objective is to track a black ball over a white background, the model will converge to satisfactory levels of detection pretty quickly. If on the other hand, for example, you wish to detect ships in ports, using Pan-Tilt-Zoom cameras, then training will be a much more challenging and time-consuming process, due to the high variability of the shape and size of ships, combined with a highly dynamic background.

    • -

  • And many, many, many, more….

    • -
-
-
-
-

Evaluating the Model (Optional)

-

By default, the training process logs some basic measures of training performance. These seem to -change depending on the installed version of Tensorflow.

-

As you will have seen in various parts of this tutorial, we have mentioned a few times the -optional utilisation of the COCO evaluation metrics. Also, under section -_image_partitioning_sec we partitioned our dataset in two parts, where one was to be used -for training and the other for evaluation. In this section we will look at how we can use these -metrics, along with the test images, to get a sense of the performance achieved by our model as it -is being trained.

-

Firstly, let’s start with a brief explanation of what the evaluation process does. While the -training process runs, it will occasionally create checkpoint files inside the -training_demo/training folder, which correspond to snapshots of the model at given steps. When -a set of such new checkpoint files is generated, the evaluation process uses these files and -evaluates how well the model performs in detecting objects in the test dataset. The results of -this evaluation are summarised in the form of some metrics, which can be examined over time.

-

The steps to run the evaluation are outlined below:

-
    -

  1. Firstly we need to download and install the metrics we want to use.

    2. -
-
-
    -

  • For a description of the supported object detection evaluation metrics, see here.

    • -

  • The process of installing the COCO evaluation metrics is described in COCO API installation.

    • -
-
-
    -

  1. Secondly, we must modify the configuration pipeline (*.config script).

    2. -
-
-
-
-
    -

  1. The third step is to actually run the evaluation. To do so, open a new Terminal, cd inside the training_demo folder and run the following command:

    -
    -
    python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config --checkpoint_dir=models/my_ssd_resnet50_v1_fpn
-
    -
    -

    Once the above is run, you should see a checkpoint similar to the one below (plus/minus some warnings):

    -
    ...
-WARNING:tensorflow:From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
-Instructions for updating:
-Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
-W0716 05:44:10.059399 17144 deprecation.py:317] From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
-Instructions for updating:
-Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
-WARNING:tensorflow:From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:259: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
-Instructions for updating:
-Use `tf.cast` instead.
-W0716 05:44:12.383937 17144 deprecation.py:317] From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:259: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
-Instructions for updating:
-Use `tf.cast` instead.
-INFO:tensorflow:Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpn
-I0716 05:44:22.779590 17144 checkpoint_utils.py:125] Waiting for new checkpoint at models/my_ssd_resnet50_v1_fpn
-INFO:tensorflow:Found new checkpoint at models/my_ssd_resnet50_v1_fpn\ckpt-2
-I0716 05:44:22.882485 17144 checkpoint_utils.py:134] Found new checkpoint at models/my_ssd_resnet50_v1_fpn\ckpt-2
-
    -
    -
    -
    2. -
-

While the evaluation process is running, it will periodically check (every 300 sec by default) and -use the latest models/my_ssd_resnet50_v1_fpn/ckpt-* checkpoint files to evaluate the performance -of the model. The results are stored in the form of tf event files (events.out.tfevents.*) -inside models/my_ssd_resnet50_v1_fpn/eval_0. These files can then be used to monitor the -computed metrics, using the process described by the next section.

-
-
-

Monitor Training Job Progress using TensorBoard

-

A very nice feature of TensorFlow, is that it allows you to coninuously monitor and visualise a -number of different training/evaluation metrics, while your model is being trained. The specific -tool that allows us to do all that is Tensorboard.

-

To start a new TensorBoard server, we follow the following steps:

-
    -

  • Open a new Anaconda/Command Prompt

    • -

  • Activate your TensorFlow conda environment (if you have one), e.g.:

    -
    -
    activate tensorflow_gpu
-
    -
    -
    -
    • -

  • cd into the training_demo folder.

    • -

  • Run the following command:

    -
    -
    tensorboard --logdir=models/my_ssd_resnet50_v1_fpn
-
    -
    -
    -
    • -
-

The above command will start a new TensorBoard server, which (by default) listens to port 6006 of -your machine. Assuming that everything went well, you should see a print-out similar to the one -below (plus/minus some warnings):

-
-
...
-TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit)
-
-
-
-

Once this is done, go to your browser and type http://localhost:6006/ in your address bar, -following which you should be presented with a dashboard similar to the one shown below -(maybe less populated if your model has just started training):

-alternate text -
-
-

Exporting a Trained Inference Graph

-

Once your training job is complete, you need to extract the newly trained inference graph, which -will be later used to perform the object detection. This can be done as follows:

-
    -

  • Copy the TensorFlow/models/research/object_detection/exporter_main_v2.py script and paste it straight into your training_demo folder.

    • -

  • Now, open a Terminal, cd inside your training_demo folder, and run the following command:

    • -
-
python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_efficientdet_d1\pipeline.config --trained_checkpoint_dir .\models\my_efficientdet_d1\ --output_directory .\trained-inference-graphs\output
-
-
-
-

Note

-

You may get the following error when trying to export your model:

-
Traceback (most recent call last):
-  File ".\exporter_main_v2.py", line 126, in <module>
-    app.run(main)
-  File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\absl\app.py", line 299, in run
-    _run_main(main, args)
-  ...
-  File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1627, in get_losses_for
-    reachable = tf_utils.get_reachable_from_inputs(inputs, losses)
-  File "C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py", line 140, in get_reachable_from_inputs
-    raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x))
-TypeError: Expected Operation, Variable, or Tensor, got level_5
-
-
-

If this happens, have a look at the “TypeError: Expected Operation, Variable, or Tensor, got level_5” issue section for a potential solution.

-
-
-
- - -
- -
- - -
-
- -
- -
- - - - - - - - - - - \ No newline at end of file diff --git a/docs/examples/plot_object_detection_simple.py b/docs/examples/plot_object_detection_checkpoint.py similarity index 56% rename from docs/examples/plot_object_detection_simple.py rename to docs/examples/plot_object_detection_checkpoint.py index a262fc5..0137603 100644 --- a/docs/examples/plot_object_detection_simple.py +++ b/docs/examples/plot_object_detection_checkpoint.py @@ -1,36 +1,14 @@ #!/usr/bin/env python # coding: utf-8 """ -Object Detection Test -===================== +Object Detection From TF2 Checkpoint +==================================== """ # %% -# This demo will take you through the steps of running an "out-of-the-box" detection model on a -# collection of images. - -# %% -# Create the data directory -# ~~~~~~~~~~~~~~~~~~~~~~~~~ -# The snippet shown below will create the ``data`` directory where all our data will be stored. The -# code will create a directory structure as shown bellow: -# -# .. code-block:: bash -# -# data -# ├── images -# └── models -# -# where the ``images`` folder will contain the downlaoded test images, while ``models`` will -# contain the downloaded models. -import os - -DATA_DIR = os.path.join(os.getcwd(), 'data') -IMAGES_DIR = os.path.join(DATA_DIR, 'images') -MODELS_DIR = os.path.join(DATA_DIR, 'models') -for dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) +# This demo will take you through the steps of running an "out-of-the-box" TensorFlow 2 compatible +# detection model on a collection of images. More specifically, in this example we will be using +# the `Checkpoint Format `__ to load the model. # %% # Download the test images @@ -38,33 +16,39 @@ # First we will download the images that we will use throughout this tutorial. The code snippet # shown bellow will download the test images from the `TensorFlow Model Garden `_ # and save them inside the ``data/images`` folder. -import urllib.request +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) +import pathlib +import tensorflow as tf -IMAGE_FILENAMES = ['image1.jpg', 'image2.jpg'] -IMAGES_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' +tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) -for image_filename in IMAGE_FILENAMES: +# Enable GPU dynamic memory allocation +gpus = tf.config.experimental.list_physical_devices('GPU') +for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) - image_path = os.path.join(IMAGES_DIR, image_filename) +def download_images(): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' + filenames = ['image1.jpg', 'image2.jpg'] + image_paths = [] + for filename in filenames: + image_path = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + image_path = pathlib.Path(image_path) + image_paths.append(str(image_path)) + return image_paths - # Download image - if not os.path.exists(image_path): - print('Downloading {}... '.format(image_filename), end='') - urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path) - print('Done') +IMAGE_PATHS = download_images() # %% # Download the model # ~~~~~~~~~~~~~~~~~~ -# The code snippet shown below is used to download the object detection model checkpoint file, -# as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -# label to each detection (e.g. person). Once downloaded the files will be stored under the -# ``data/models`` folder. -# -# The particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More -# models can be found in the `TensorFlow 2 Detection Model Zoo `_. +# The code snippet shown below is used to download the pre-trained object detection model we shall +# use to perform inference. The particular detection algorithm we will use is the +# `CenterNet HourGlass104 1024x1024`. More models can be found in the `TensorFlow 2 Detection Model Zoo `_. # To use a different model you will need the URL name of the specific model. This can be done as # follows: # @@ -76,53 +60,55 @@ # # For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz`` -import tarfile - # Download and extract model +def download_model(model_name, model_date): + base_url = 'http://download.tensorflow.org/models/object_detection/tf2/' + model_file = model_name + '.tar.gz' + model_dir = tf.keras.utils.get_file(fname=model_name, + origin=base_url + model_date + '/' + model_file, + untar=True) + return str(model_dir) + MODEL_DATE = '20200711' MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32' -MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' -MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' -MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME -PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) -PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) -PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) -if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') +PATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE) + +# %% +# Download the labels +# ~~~~~~~~~~~~~~~~~~~ +# The coode snippet shown below is used to download the labels file (.pbtxt) which contains a list +# of strings used to add the correct label to each detection (e.g. person). Since the pre-trained +# model we will use has been trained on the COCO dataset, we will need to download the labels file +# corresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files +# included in the TensorFlow Models Garden can be found `here `__. # Download labels file +def download_labels(filename): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + label_dir = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + label_dir = pathlib.Path(label_dir) + return str(label_dir) + LABEL_FILENAME = 'mscoco_label_map.pbtxt' -LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' -PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) -if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') +PATH_TO_LABELS = download_labels(LABEL_FILENAME) # %% # Load the model # ~~~~~~~~~~~~~~ # Next we load the downloaded model -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) -import tensorflow as tf +import time from object_detection.utils import label_map_util from object_detection.utils import config_util from object_detection.utils import visualization_utils as viz_utils from object_detection.builders import model_builder -tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) +PATH_TO_CFG = PATH_TO_MODEL_DIR + "/pipeline.config" +PATH_TO_CKPT = PATH_TO_MODEL_DIR + "/checkpoint" -# Enable GPU dynamic memory allocation -gpus = tf.config.experimental.list_physical_devices('GPU') -for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) +print('Loading model... ', end='') +start_time = time.time() # Load pipeline config and build a detection model configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) @@ -130,8 +116,7 @@ detection_model = model_builder.build(model_config=model_config, is_training=False) # Restore checkpoint -ckpt = tf.compat.v2.train.Checkpoint( - model=detection_model) +ckpt = tf.compat.v2.train.Checkpoint(model=detection_model) ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() @tf.function @@ -142,8 +127,11 @@ def detect_fn(image): prediction_dict = detection_model.predict(image, shapes) detections = detection_model.postprocess(prediction_dict, shapes) - return detections, prediction_dict, tf.reshape(shapes, [-1]) + return detections +end_time = time.time() +elapsed_time = end_time - start_time +print('Done! Took {} seconds'.format(elapsed_time)) # %% # Load label map data (for plotting) @@ -172,7 +160,6 @@ def detect_fn(image): # * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). # * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. import numpy as np -from six import BytesIO from PIL import Image import matplotlib.pyplot as plt import warnings @@ -191,18 +178,13 @@ def load_image_into_numpy_array(path): Returns: uint8 numpy array with shape (img_height, img_width, 3) """ - img_data = tf.io.gfile.GFile(path, 'rb').read() - image = Image.open(BytesIO(img_data)) - (im_width, im_height) = image.size - return np.array(image.getdata()).reshape( - (im_height, im_width, 3)).astype(np.uint8) + return np.array(Image.open(path)) -for image_filename in IMAGE_FILENAMES: +for image_path in IMAGE_PATHS: - print('Running inference for {}... '.format(image_filename), end='') + print('Running inference for {}... '.format(image_path), end='') - image_path = os.path.join(IMAGES_DIR, image_filename) image_np = load_image_into_numpy_array(image_path) # Things to try: @@ -213,23 +195,34 @@ def load_image_into_numpy_array(path): # image_np = np.tile( # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - input_tensor = tf.convert_to_tensor( - np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) + input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) + + detections = detect_fn(input_tensor) + + # All outputs are batches tensors. + # Convert to numpy arrays, and take index [0] to remove the batch dimension. + # We're only interested in the first num_detections. + num_detections = int(detections.pop('num_detections')) + detections = {key: value[0, :num_detections].numpy() + for key, value in detections.items()} + detections['num_detections'] = num_detections + + # detection_classes should be ints. + detections['detection_classes'] = detections['detection_classes'].astype(np.int64) label_id_offset = 1 image_np_with_detections = image_np.copy() viz_utils.visualize_boxes_and_labels_on_image_array( - image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), - category_index, - use_normalized_coordinates=True, - max_boxes_to_draw=200, - min_score_thresh=.30, - agnostic_mode=False) + image_np_with_detections, + detections['detection_boxes'], + detections['detection_classes']+label_id_offset, + detections['detection_scores'], + category_index, + use_normalized_coordinates=True, + max_boxes_to_draw=200, + min_score_thresh=.30, + agnostic_mode=False) plt.figure() plt.imshow(image_np_with_detections) diff --git a/docs/source/auto_examples/plot_object_detection_simple.py b/docs/examples/plot_object_detection_saved_model.py similarity index 52% rename from docs/source/auto_examples/plot_object_detection_simple.py rename to docs/examples/plot_object_detection_saved_model.py index a262fc5..2e79b05 100644 --- a/docs/source/auto_examples/plot_object_detection_simple.py +++ b/docs/examples/plot_object_detection_saved_model.py @@ -1,36 +1,14 @@ #!/usr/bin/env python # coding: utf-8 """ -Object Detection Test -===================== +Object Detection From TF2 Saved Model +===================================== """ # %% -# This demo will take you through the steps of running an "out-of-the-box" detection model on a -# collection of images. - -# %% -# Create the data directory -# ~~~~~~~~~~~~~~~~~~~~~~~~~ -# The snippet shown below will create the ``data`` directory where all our data will be stored. The -# code will create a directory structure as shown bellow: -# -# .. code-block:: bash -# -# data -# ├── images -# └── models -# -# where the ``images`` folder will contain the downlaoded test images, while ``models`` will -# contain the downloaded models. -import os - -DATA_DIR = os.path.join(os.getcwd(), 'data') -IMAGES_DIR = os.path.join(DATA_DIR, 'images') -MODELS_DIR = os.path.join(DATA_DIR, 'models') -for dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) +# This demo will take you through the steps of running an "out-of-the-box" TensorFlow 2 compatible +# detection model on a collection of images. More specifically, in this example we will be using +# the `Saved Model Format `__ to load the model. # %% # Download the test images @@ -38,33 +16,39 @@ # First we will download the images that we will use throughout this tutorial. The code snippet # shown bellow will download the test images from the `TensorFlow Model Garden `_ # and save them inside the ``data/images`` folder. -import urllib.request +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) +import pathlib +import tensorflow as tf -IMAGE_FILENAMES = ['image1.jpg', 'image2.jpg'] -IMAGES_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' +tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) -for image_filename in IMAGE_FILENAMES: +# Enable GPU dynamic memory allocation +gpus = tf.config.experimental.list_physical_devices('GPU') +for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) - image_path = os.path.join(IMAGES_DIR, image_filename) +def download_images(): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' + filenames = ['image1.jpg', 'image2.jpg'] + image_paths = [] + for filename in filenames: + image_path = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + image_path = pathlib.Path(image_path) + image_paths.append(str(image_path)) + return image_paths - # Download image - if not os.path.exists(image_path): - print('Downloading {}... '.format(image_filename), end='') - urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path) - print('Done') +IMAGE_PATHS = download_images() # %% # Download the model # ~~~~~~~~~~~~~~~~~~ -# The code snippet shown below is used to download the object detection model checkpoint file, -# as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -# label to each detection (e.g. person). Once downloaded the files will be stored under the -# ``data/models`` folder. -# -# The particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More -# models can be found in the `TensorFlow 2 Detection Model Zoo `_. +# The code snippet shown below is used to download the pre-trained object detection model we shall +# use to perform inference. The particular detection algorithm we will use is the +# `CenterNet HourGlass104 1024x1024`. More models can be found in the `TensorFlow 2 Detection Model Zoo `_. # To use a different model you will need the URL name of the specific model. This can be done as # follows: # @@ -76,74 +60,59 @@ # # For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz`` -import tarfile - # Download and extract model +def download_model(model_name, model_date): + base_url = 'http://download.tensorflow.org/models/object_detection/tf2/' + model_file = model_name + '.tar.gz' + model_dir = tf.keras.utils.get_file(fname=model_name, + origin=base_url + model_date + '/' + model_file, + untar=True) + return str(model_dir) + MODEL_DATE = '20200711' MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32' -MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' -MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' -MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME -PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) -PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) -PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) -if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') +PATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE) + +# %% +# Download the labels +# ~~~~~~~~~~~~~~~~~~~ +# The coode snippet shown below is used to download the labels file (.pbtxt) which contains a list +# of strings used to add the correct label to each detection (e.g. person). Since the pre-trained +# model we will use has been trained on the COCO dataset, we will need to download the labels file +# corresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files +# included in the TensorFlow Models Garden can be found `here `__. # Download labels file +def download_labels(filename): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + label_dir = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + label_dir = pathlib.Path(label_dir) + return str(label_dir) + LABEL_FILENAME = 'mscoco_label_map.pbtxt' -LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' -PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) -if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') +PATH_TO_LABELS = download_labels(LABEL_FILENAME) # %% # Load the model # ~~~~~~~~~~~~~~ # Next we load the downloaded model -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) -import tensorflow as tf +import time from object_detection.utils import label_map_util -from object_detection.utils import config_util from object_detection.utils import visualization_utils as viz_utils -from object_detection.builders import model_builder - -tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) - -# Enable GPU dynamic memory allocation -gpus = tf.config.experimental.list_physical_devices('GPU') -for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) - -# Load pipeline config and build a detection model -configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) -model_config = configs['model'] -detection_model = model_builder.build(model_config=model_config, is_training=False) -# Restore checkpoint -ckpt = tf.compat.v2.train.Checkpoint( - model=detection_model) -ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() +PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + "/saved_model" -@tf.function -def detect_fn(image): - """Detect objects in image.""" +print('Loading model...', end='') +start_time = time.time() - image, shapes = detection_model.preprocess(image) - prediction_dict = detection_model.predict(image, shapes) - detections = detection_model.postprocess(prediction_dict, shapes) - - return detections, prediction_dict, tf.reshape(shapes, [-1]) +# Load saved model and build the detection function +detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL) +end_time = time.time() +elapsed_time = end_time - start_time +print('Done! Took {} seconds'.format(elapsed_time)) # %% # Load label map data (for plotting) @@ -172,7 +141,6 @@ def detect_fn(image): # * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). # * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. import numpy as np -from six import BytesIO from PIL import Image import matplotlib.pyplot as plt import warnings @@ -191,18 +159,13 @@ def load_image_into_numpy_array(path): Returns: uint8 numpy array with shape (img_height, img_width, 3) """ - img_data = tf.io.gfile.GFile(path, 'rb').read() - image = Image.open(BytesIO(img_data)) - (im_width, im_height) = image.size - return np.array(image.getdata()).reshape( - (im_height, im_width, 3)).astype(np.uint8) + return np.array(Image.open(path)) -for image_filename in IMAGE_FILENAMES: +for image_path in IMAGE_PATHS: - print('Running inference for {}... '.format(image_filename), end='') + print('Running inference for {}... '.format(image_path), end='') - image_path = os.path.join(IMAGES_DIR, image_filename) image_np = load_image_into_numpy_array(image_path) # Things to try: @@ -213,18 +176,32 @@ def load_image_into_numpy_array(path): # image_np = np.tile( # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - input_tensor = tf.convert_to_tensor( - np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) + # The input needs to be a tensor, convert it using `tf.convert_to_tensor`. + input_tensor = tf.convert_to_tensor(image_np) + # The model expects a batch of images, so add an axis with `tf.newaxis`. + input_tensor = input_tensor[tf.newaxis, ...] + + # input_tensor = np.expand_dims(image_np, 0) + detections = detect_fn(input_tensor) + + # All outputs are batches tensors. + # Convert to numpy arrays, and take index [0] to remove the batch dimension. + # We're only interested in the first num_detections. + num_detections = int(detections.pop('num_detections')) + detections = {key: value[0, :num_detections].numpy() + for key, value in detections.items()} + detections['num_detections'] = num_detections + + # detection_classes should be ints. + detections['detection_classes'] = detections['detection_classes'].astype(np.int64) - label_id_offset = 1 image_np_with_detections = image_np.copy() viz_utils.visualize_boxes_and_labels_on_image_array( image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), + detections['detection_boxes'], + detections['detection_classes'], + detections['detection_scores'], category_index, use_normalized_coordinates=True, max_boxes_to_draw=200, diff --git a/docs/examples/plot_object_detection_saved_model_tf1.py b/docs/examples/plot_object_detection_saved_model_tf1.py new file mode 100644 index 0000000..0da1a74 --- /dev/null +++ b/docs/examples/plot_object_detection_saved_model_tf1.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python +# coding: utf-8 +""" +Object Detection From TF1 Saved Model +===================================== +""" + +# %% +# This demo will take you through the steps of running an "out-of-the-box" TensorFlow 1 compatible +# detection model on a collection of images. More specifically, in this example we will be using +# the `Saved Model Format `__ to load the model. + +# %% +# Download the test images +# ~~~~~~~~~~~~~~~~~~~~~~~~ +# First we will download the images that we will use throughout this tutorial. The code snippet +# shown bellow will download the test images from the `TensorFlow Model Garden `_ +# and save them inside the ``data/images`` folder. +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) +import pathlib +import tensorflow as tf + +tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) + +# Enable GPU dynamic memory allocation +gpus = tf.config.experimental.list_physical_devices('GPU') +for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + +def download_images(): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' + filenames = ['image1.jpg', 'image2.jpg'] + image_paths = [] + for filename in filenames: + image_path = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + image_path = pathlib.Path(image_path) + image_paths.append(str(image_path)) + return image_paths + +IMAGE_PATHS = download_images() + + +# %% +# Download the model +# ~~~~~~~~~~~~~~~~~~ +# The code snippet shown below is used to download the pre-trained object detection model we shall +# use to perform inference. The particular detection algorithm we will use is the +# `SSD MobileNet v2`. More models can be found in the `TensorFlow 1 Detection Model Zoo `_. +# To use a different model you will need the URL name of the specific model. This can be done as +# follows: +# +# 1. Right click on the `Model name` of the model you would like to use; +# 2. Click on `Copy link address` to copy the download link of the model; +# 3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/XXXXXXXXX.tar.gz``; +# 4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; +# +# For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz`` + +# Download and extract model +def download_model(model_name): + base_url = 'http://download.tensorflow.org/models/object_detection/' + model_file = model_name + '.tar.gz' + model_dir = tf.keras.utils.get_file(fname=model_name, + origin=base_url + model_file, + untar=True) + return str(model_dir) + +MODEL_NAME = 'ssd_mobilenet_v2_coco_2018_03_29' +PATH_TO_MODEL_DIR = download_model(MODEL_NAME) + +# %% +# Download the labels +# ~~~~~~~~~~~~~~~~~~~ +# The coode snippet shown below is used to download the labels file (.pbtxt) which contains a list +# of strings used to add the correct label to each detection (e.g. person). Since the pre-trained +# model we will use has been trained on the COCO dataset, we will need to download the labels file +# corresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files +# included in the TensorFlow Models Garden can be found `here `__. + +# Download labels file +def download_labels(filename): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + label_dir = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + label_dir = pathlib.Path(label_dir) + return str(label_dir) + +LABEL_FILENAME = 'mscoco_label_map.pbtxt' +PATH_TO_LABELS = download_labels(LABEL_FILENAME) + +# %% +# Load the model +# ~~~~~~~~~~~~~~ +# Next we load the downloaded model +import time +from object_detection.utils import label_map_util +from object_detection.utils import visualization_utils as viz_utils + +PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + "/saved_model" + +print('Loading model...', end='') +start_time = time.time() + +# Load saved model and build the detection function +model = tf.saved_model.load(PATH_TO_SAVED_MODEL) +detect_fn = model.signatures['serving_default'] + +end_time = time.time() +elapsed_time = end_time - start_time +print('Done! Took {} seconds'.format(elapsed_time)) + +# %% +# Load label map data (for plotting) +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Label maps correspond index numbers to category names, so that when our convolution network +# predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility +# functions, but anything that returns a dictionary mapping integers to appropriate string labels +# would be fine. + +category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, + use_display_name=True) + +# %% +# Putting everything together +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# The code shown below loads an image, runs it through the detection model and visualizes the +# detection results, including the keypoints. +# +# Note that this will take a long time (several minutes) the first time you run this code due to +# tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be +# faster. +# +# Here are some simple things to try out if you are curious: +# +# * Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). +# * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). +# * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt +import warnings +warnings.filterwarnings('ignore') # Suppress Matplotlib warnings + +def load_image_into_numpy_array(path): + """Load an image from file into a numpy array. + + Puts image into numpy array to feed into tensorflow graph. + Note that by convention we put it into a numpy array with shape + (height, width, channels), where channels=3 for RGB. + + Args: + path: the file path to the image + + Returns: + uint8 numpy array with shape (img_height, img_width, 3) + """ + return np.array(Image.open(path)) + + +for image_path in IMAGE_PATHS: + + print('Running inference for {}... '.format(image_path), end='') + + image_np = load_image_into_numpy_array(image_path) + + # Things to try: + # Flip horizontally + # image_np = np.fliplr(image_np).copy() + + # Convert image to grayscale + # image_np = np.tile( + # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) + + # The input needs to be a tensor, convert it using `tf.convert_to_tensor`. + input_tensor = tf.convert_to_tensor(image_np) + # The model expects a batch of images, so add an axis with `tf.newaxis`. + input_tensor = input_tensor[tf.newaxis, ...] + + detections = detect_fn(input_tensor) + + # All outputs are batches tensors. + # Convert to numpy arrays, and take index [0] to remove the batch dimension. + # We're only interested in the first num_detections. + num_detections = int(detections.pop('num_detections')) + detections = {key: value[0, :num_detections].numpy() + for key, value in detections.items()} + detections['num_detections'] = num_detections + + # detection_classes should be ints. + detections['detection_classes'] = detections['detection_classes'].astype(np.int64) + + image_np_with_detections = image_np.copy() + + viz_utils.visualize_boxes_and_labels_on_image_array( + image_np_with_detections, + detections['detection_boxes'], + detections['detection_classes'], + detections['detection_scores'], + category_index, + use_normalized_coordinates=True, + max_boxes_to_draw=200, + min_score_thresh=.30, + agnostic_mode=False) + + plt.figure() + plt.imshow(image_np_with_detections) + print('Done') +plt.show() + +# sphinx_gallery_thumbnail_number = 2 \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip deleted file mode 100644 index ee48b1c..0000000 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and /dev/null differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip deleted file mode 100644 index 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- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_object_detection_camera_thumb.png - :alt: Detect Objects Using Your Webcam - - :ref:`sphx_glr_auto_examples_object_detection_camera.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/object_detection_camera - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_object_detection_simple_thumb.png - :alt: Object Detection Test - - :ref:`sphx_glr_auto_examples_plot_object_detection_simple.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_object_detection_simple -.. raw:: html - -
- - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-gallery - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: auto_examples_python.zip ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/object_detection_camera.ipynb b/docs/source/auto_examples/object_detection_camera.ipynb index 7c8c384..35c2292 100644 --- a/docs/source/auto_examples/object_detection_camera.ipynb +++ b/docs/source/auto_examples/object_detection_camera.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\nDetect Objects Using Your Webcam\n================================\n" + "\n# Detect Objects Using Your Webcam\n" ] }, { @@ -29,7 +29,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create the data directory\n~~~~~~~~~~~~~~~~~~~~~~~~~\nThe snippet shown below will create the ``data`` directory where all our data will be stored. The\ncode will create a directory structure as shown bellow:\n\n.. code-block:: bash\n\n data\n \u2514\u2500\u2500 models\n\nwhere the ``models`` folder will will contain the downloaded models.\n\n" + "## Create the data directory\nThe snippet shown below will create the ``data`` directory where all our data will be stored. The\ncode will create a directory structure as shown bellow:\n\n.. code-block:: bash\n\n data\n \u2514\u2500\u2500 models\n\nwhere the ``models`` folder will will contain the downloaded models.\n\n" ] }, { @@ -47,7 +47,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Download the model\n~~~~~~~~~~~~~~~~~~\nThe code snippet shown below is used to download the object detection model checkpoint file,\nas well as the labels file (.pbtxt) which contains a list of strings used to add the correct\nlabel to each detection (e.g. person).\n\nThe particular detection algorithm we will use is the `SSD ResNet101 V1 FPN 640x640`. More\nmodels can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz``\n\n" + "## Download the model\nThe code snippet shown below is used to download the object detection model checkpoint file,\nas well as the labels file (.pbtxt) which contains a list of strings used to add the correct\nlabel to each detection (e.g. person).\n\nThe particular detection algorithm we will use is the `SSD ResNet101 V1 FPN 640x640`. More\nmodels can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz``\n\n" ] }, { @@ -65,7 +65,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load the model\n~~~~~~~~~~~~~~\nNext we load the downloaded model\n\n" + "## Load the model\nNext we load the downloaded model\n\n" ] }, { @@ -83,7 +83,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load label map data (for plotting)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" + "## Load label map data (for plotting)\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" ] }, { @@ -101,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Define the video stream\n~~~~~~~~~~~~~~~~~~~~~~~\nWe will use `OpenCV `_ to capture the video stream\ngenerated by our webcam. For more information you can refer to the `OpenCV-Python Tutorials `_\n\n" + "## Define the video stream\nWe will use `OpenCV `_ to capture the video stream\ngenerated by our webcam. For more information you can refer to the `OpenCV-Python Tutorials `_\n\n" ] }, { @@ -119,7 +119,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Putting everything together\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" + "## Putting everything together\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" ] }, { @@ -150,7 +150,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.9.5" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/object_detection_camera.rst b/docs/source/auto_examples/object_detection_camera.rst index 655a07a..b3bf6df 100644 --- a/docs/source/auto_examples/object_detection_camera.rst +++ b/docs/source/auto_examples/object_detection_camera.rst @@ -1,20 +1,33 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples\object_detection_camera.py" +.. LINE NUMBERS ARE GIVEN BELOW. + .. only:: html .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title + Click :ref:`here ` + to download the full example code + +.. rst-class:: sphx-glr-example-title - .. _sphx_glr_auto_examples_object_detection_camera.py: +.. _sphx_glr_auto_examples_object_detection_camera.py: Detect Objects Using Your Webcam ================================ +.. GENERATED FROM PYTHON SOURCE LINES 9-11 + This demo will take you through the steps of running an "out-of-the-box" detection model to detect objects in the video stream extracted from your camera. +.. GENERATED FROM PYTHON SOURCE LINES 13-24 + Create the data directory ~~~~~~~~~~~~~~~~~~~~~~~~~ The snippet shown below will create the ``data`` directory where all our data will be stored. The @@ -27,6 +40,7 @@ code will create a directory structure as shown bellow: where the ``models`` folder will will contain the downloaded models. +.. GENERATED FROM PYTHON SOURCE LINES 24-32 .. code-block:: default @@ -39,6 +53,8 @@ where the ``models`` folder will will contain the downloaded models. os.mkdir(dir) +.. GENERATED FROM PYTHON SOURCE LINES 33-51 + Download the model ~~~~~~~~~~~~~~~~~~ The code snippet shown below is used to download the object detection model checkpoint file, @@ -58,6 +74,7 @@ follows: For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz`` +.. GENERATED FROM PYTHON SOURCE LINES 51-82 .. code-block:: default @@ -93,10 +110,13 @@ For example, the download link for the model used below is: ``download.tensorflo print('Done') +.. GENERATED FROM PYTHON SOURCE LINES 83-86 + Load the model ~~~~~~~~~~~~~~ Next we load the downloaded model +.. GENERATED FROM PYTHON SOURCE LINES 86-121 .. code-block:: default @@ -136,6 +156,8 @@ Next we load the downloaded model +.. GENERATED FROM PYTHON SOURCE LINES 122-128 + Load label map data (for plotting) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Label maps correspond index numbers to category names, so that when our convolution network @@ -143,6 +165,7 @@ predicts `5`, we know that this corresponds to `airplane`. Here we use internal functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine. +.. GENERATED FROM PYTHON SOURCE LINES 128-131 .. code-block:: default @@ -150,11 +173,14 @@ would be fine. use_display_name=True) +.. GENERATED FROM PYTHON SOURCE LINES 132-136 + Define the video stream ~~~~~~~~~~~~~~~~~~~~~~~ We will use `OpenCV `_ to capture the video stream generated by our webcam. For more information you can refer to the `OpenCV-Python Tutorials `_ +.. GENERATED FROM PYTHON SOURCE LINES 136-140 .. code-block:: default @@ -163,6 +189,8 @@ generated by our webcam. For more information you can refer to the `OpenCV-Pytho cap = cv2.VideoCapture(0) +.. GENERATED FROM PYTHON SOURCE LINES 141-155 + Putting everything together ~~~~~~~~~~~~~~~~~~~~~~~~~~~ The code shown below loads an image, runs it through the detection model and visualizes the @@ -178,6 +206,7 @@ Here are some simple things to try out if you are curious: * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. +.. GENERATED FROM PYTHON SOURCE LINES 155-196 .. code-block:: default diff --git a/docs/source/auto_examples/object_detection_camera_codeobj.pickle b/docs/source/auto_examples/object_detection_camera_codeobj.pickle deleted file mode 100644 index 304af12..0000000 Binary files a/docs/source/auto_examples/object_detection_camera_codeobj.pickle and /dev/null differ diff --git a/docs/source/auto_examples/plot_object_detection_checkpoint.ipynb b/docs/source/auto_examples/plot_object_detection_checkpoint.ipynb new file mode 100644 index 0000000..1dd0d04 --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_checkpoint.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Object Detection From TF2 Checkpoint\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This demo will take you through the steps of running an \"out-of-the-box\" TensorFlow 2 compatible\ndetection model on a collection of images. More specifically, in this example we will be using\nthe `Checkpoint Format `__ to load the model.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the test images\nFirst we will download the images that we will use throughout this tutorial. The code snippet\nshown bellow will download the test images from the `TensorFlow Model Garden `_\nand save them inside the ``data/images`` folder.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)\nimport pathlib\nimport tensorflow as tf\n\ntf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n\n# Enable GPU dynamic memory allocation\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n\ndef download_images():\n base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'\n filenames = ['image1.jpg', 'image2.jpg']\n image_paths = []\n for filename in filenames:\n image_path = tf.keras.utils.get_file(fname=filename,\n origin=base_url + filename,\n untar=False)\n image_path = pathlib.Path(image_path)\n image_paths.append(str(image_path))\n return image_paths\n\nIMAGE_PATHS = download_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the model\nThe code snippet shown below is used to download the pre-trained object detection model we shall\nuse to perform inference. The particular detection algorithm we will use is the\n`CenterNet HourGlass104 1024x1024`. More models can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz``\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download and extract model\ndef download_model(model_name, model_date):\n base_url = 'http://download.tensorflow.org/models/object_detection/tf2/'\n model_file = model_name + '.tar.gz'\n model_dir = tf.keras.utils.get_file(fname=model_name,\n origin=base_url + model_date + '/' + model_file,\n untar=True)\n return str(model_dir)\n\nMODEL_DATE = '20200711'\nMODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'\nPATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the labels\nThe coode snippet shown below is used to download the labels file (.pbtxt) which contains a list\nof strings used to add the correct label to each detection (e.g. person). Since the pre-trained\nmodel we will use has been trained on the COCO dataset, we will need to download the labels file\ncorresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files\nincluded in the TensorFlow Models Garden can be found `here `__.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download labels file\ndef download_labels(filename):\n base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'\n label_dir = tf.keras.utils.get_file(fname=filename,\n origin=base_url + filename,\n untar=False)\n label_dir = pathlib.Path(label_dir)\n return str(label_dir)\n\nLABEL_FILENAME = 'mscoco_label_map.pbtxt'\nPATH_TO_LABELS = download_labels(LABEL_FILENAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the model\nNext we load the downloaded model\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import time\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import config_util\nfrom object_detection.utils import visualization_utils as viz_utils\nfrom object_detection.builders import model_builder\n\nPATH_TO_CFG = PATH_TO_MODEL_DIR + \"/pipeline.config\"\nPATH_TO_CKPT = PATH_TO_MODEL_DIR + \"/checkpoint\"\n\nprint('Loading model... ', end='')\nstart_time = time.time()\n\n# Load pipeline config and build a detection model\nconfigs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\nmodel_config = configs['model']\ndetection_model = model_builder.build(model_config=model_config, is_training=False)\n\n# Restore checkpoint\nckpt = tf.compat.v2.train.Checkpoint(model=detection_model)\nckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()\n\n@tf.function\ndef detect_fn(image):\n \"\"\"Detect objects in image.\"\"\"\n\n image, shapes = detection_model.preprocess(image)\n prediction_dict = detection_model.predict(image, shapes)\n detections = detection_model.postprocess(prediction_dict, shapes)\n\n return detections\n\nend_time = time.time()\nelapsed_time = end_time - start_time\nprint('Done! Took {} seconds'.format(elapsed_time))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load label map data (for plotting)\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n use_display_name=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Putting everything together\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore') # Suppress Matplotlib warnings\n\ndef load_image_into_numpy_array(path):\n \"\"\"Load an image from file into a numpy array.\n\n Puts image into numpy array to feed into tensorflow graph.\n Note that by convention we put it into a numpy array with shape\n (height, width, channels), where channels=3 for RGB.\n\n Args:\n path: the file path to the image\n\n Returns:\n uint8 numpy array with shape (img_height, img_width, 3)\n \"\"\"\n return np.array(Image.open(path))\n\n\nfor image_path in IMAGE_PATHS:\n\n print('Running inference for {}... '.format(image_path), end='')\n\n image_np = load_image_into_numpy_array(image_path)\n\n # Things to try:\n # Flip horizontally\n # image_np = np.fliplr(image_np).copy()\n\n # Convert image to grayscale\n # image_np = np.tile(\n # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n\n input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n\n detections = detect_fn(input_tensor)\n\n # All outputs are batches tensors.\n # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n # We're only interested in the first num_detections.\n num_detections = int(detections.pop('num_detections'))\n detections = {key: value[0, :num_detections].numpy()\n for key, value in detections.items()}\n detections['num_detections'] = num_detections\n\n # detection_classes should be ints.\n detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n\n label_id_offset = 1\n image_np_with_detections = image_np.copy()\n\n viz_utils.visualize_boxes_and_labels_on_image_array(\n image_np_with_detections,\n detections['detection_boxes'],\n detections['detection_classes']+label_id_offset,\n detections['detection_scores'],\n category_index,\n use_normalized_coordinates=True,\n max_boxes_to_draw=200,\n min_score_thresh=.30,\n agnostic_mode=False)\n\n plt.figure()\n plt.imshow(image_np_with_detections)\n print('Done')\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_checkpoint.py.md5 b/docs/source/auto_examples/plot_object_detection_checkpoint.py.md5 new file mode 100644 index 0000000..c4e77cd --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_checkpoint.py.md5 @@ -0,0 +1 @@ +4fa911c47518a43cb2c1ac76cbb58d37 \ No newline at end of file diff --git a/docs/build/_sources/auto_examples/plot_object_detection_simple.rst.txt b/docs/source/auto_examples/plot_object_detection_checkpoint.rst similarity index 53% rename from docs/build/_sources/auto_examples/plot_object_detection_simple.rst.txt rename to docs/source/auto_examples/plot_object_detection_checkpoint.rst index aaa0514..af03822 100644 --- a/docs/build/_sources/auto_examples/plot_object_detection_simple.rst.txt +++ b/docs/source/auto_examples/plot_object_detection_checkpoint.rst @@ -1,52 +1,33 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples\plot_object_detection_checkpoint.py" +.. LINE NUMBERS ARE GIVEN BELOW. + .. only:: html .. note:: :class: sphx-glr-download-link-note - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_object_detection_simple.py: - - -Object Detection Test -===================== - -This demo will take you through the steps of running an "out-of-the-box" detection model on a -collection of images. - -Create the data directory -~~~~~~~~~~~~~~~~~~~~~~~~~ -The snippet shown below will create the ``data`` directory where all our data will be stored. The -code will create a directory structure as shown bellow: - -.. code-block:: bash - - data - ├── images - └── models - -where the ``images`` folder will contain the downlaoded test images, while ``models`` will -contain the downloaded models. - - -.. code-block:: default - - import os - - DATA_DIR = os.path.join(os.getcwd(), 'data') - IMAGES_DIR = os.path.join(DATA_DIR, 'images') - MODELS_DIR = os.path.join(DATA_DIR, 'models') - for dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) + Click :ref:`here ` + to download the full example code +.. rst-class:: sphx-glr-example-title +.. _sphx_glr_auto_examples_plot_object_detection_checkpoint.py: +Object Detection From TF2 Checkpoint +==================================== +.. GENERATED FROM PYTHON SOURCE LINES 9-12 +This demo will take you through the steps of running an "out-of-the-box" TensorFlow 2 compatible +detection model on a collection of images. More specifically, in this example we will be using +the `Checkpoint Format `__ to load the model. +.. GENERATED FROM PYTHON SOURCE LINES 14-19 Download the test images ~~~~~~~~~~~~~~~~~~~~~~~~ @@ -54,51 +35,51 @@ First we will download the images that we will use throughout this tutorial. The shown bellow will download the test images from the `TensorFlow Model Garden `_ and save them inside the ``data/images`` folder. +.. GENERATED FROM PYTHON SOURCE LINES 19-46 .. code-block:: default - import urllib.request - - IMAGE_FILENAMES = ['image1.jpg', 'image2.jpg'] - IMAGES_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' - - for image_filename in IMAGE_FILENAMES: - - image_path = os.path.join(IMAGES_DIR, image_filename) + import os + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) + import pathlib + import tensorflow as tf - # Download image - if not os.path.exists(image_path): - print('Downloading {}... '.format(image_filename), end='') - urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path) - print('Done') + tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) + # Enable GPU dynamic memory allocation + gpus = tf.config.experimental.list_physical_devices('GPU') + for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + def download_images(): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' + filenames = ['image1.jpg', 'image2.jpg'] + image_paths = [] + for filename in filenames: + image_path = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + image_path = pathlib.Path(image_path) + image_paths.append(str(image_path)) + return image_paths + IMAGE_PATHS = download_images() -.. rst-class:: sphx-glr-script-out - Out: - .. code-block:: none - Downloading image1.jpg... Done - Downloading image2.jpg... Done +.. GENERATED FROM PYTHON SOURCE LINES 47-62 Download the model ~~~~~~~~~~~~~~~~~~ -The code snippet shown below is used to download the object detection model checkpoint file, -as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -label to each detection (e.g. person). Once downloaded the files will be stored under the -``data/models`` folder. - -The particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More -models can be found in the `TensorFlow 2 Detection Model Zoo `_. +The code snippet shown below is used to download the pre-trained object detection model we shall +use to perform inference. The particular detection algorithm we will use is the +`CenterNet HourGlass104 1024x1024`. More models can be found in the `TensorFlow 2 Detection Model Zoo `_. To use a different model you will need the URL name of the specific model. This can be done as follows: @@ -110,76 +91,86 @@ follows: For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz`` +.. GENERATED FROM PYTHON SOURCE LINES 62-76 .. code-block:: default - import tarfile - # Download and extract model + def download_model(model_name, model_date): + base_url = 'http://download.tensorflow.org/models/object_detection/tf2/' + model_file = model_name + '.tar.gz' + model_dir = tf.keras.utils.get_file(fname=model_name, + origin=base_url + model_date + '/' + model_file, + untar=True) + return str(model_dir) + MODEL_DATE = '20200711' MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32' - MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' - MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' - MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME - PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) - PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) - PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) - if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') + PATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE) - # Download labels file - LABEL_FILENAME = 'mscoco_label_map.pbtxt' - LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' - PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) - if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') -.. rst-class:: sphx-glr-script-out - Out: - .. code-block:: none +.. GENERATED FROM PYTHON SOURCE LINES 77-84 + +Download the labels +~~~~~~~~~~~~~~~~~~~ +The coode snippet shown below is used to download the labels file (.pbtxt) which contains a list +of strings used to add the correct label to each detection (e.g. person). Since the pre-trained +model we will use has been trained on the COCO dataset, we will need to download the labels file +corresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files +included in the TensorFlow Models Garden can be found `here `__. + +.. GENERATED FROM PYTHON SOURCE LINES 84-97 + +.. code-block:: default + + + # Download labels file + def download_labels(filename): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + label_dir = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + label_dir = pathlib.Path(label_dir) + return str(label_dir) + + LABEL_FILENAME = 'mscoco_label_map.pbtxt' + PATH_TO_LABELS = download_labels(LABEL_FILENAME) + - Downloading model. This may take a while... Done - Downloading label file... Done + + +.. GENERATED FROM PYTHON SOURCE LINES 98-101 + Load the model ~~~~~~~~~~~~~~ Next we load the downloaded model +.. GENERATED FROM PYTHON SOURCE LINES 101-136 .. code-block:: default - os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) - import tensorflow as tf + import time from object_detection.utils import label_map_util from object_detection.utils import config_util from object_detection.utils import visualization_utils as viz_utils from object_detection.builders import model_builder - tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) + PATH_TO_CFG = PATH_TO_MODEL_DIR + "/pipeline.config" + PATH_TO_CKPT = PATH_TO_MODEL_DIR + "/checkpoint" - # Enable GPU dynamic memory allocation - gpus = tf.config.experimental.list_physical_devices('GPU') - for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) + print('Loading model... ', end='') + start_time = time.time() # Load pipeline config and build a detection model configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) @@ -187,8 +178,7 @@ Next we load the downloaded model detection_model = model_builder.build(model_config=model_config, is_training=False) # Restore checkpoint - ckpt = tf.compat.v2.train.Checkpoint( - model=detection_model) + ckpt = tf.compat.v2.train.Checkpoint(model=detection_model) ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() @tf.function @@ -199,15 +189,28 @@ Next we load the downloaded model prediction_dict = detection_model.predict(image, shapes) detections = detection_model.postprocess(prediction_dict, shapes) - return detections, prediction_dict, tf.reshape(shapes, [-1]) + return detections + + end_time = time.time() + elapsed_time = end_time - start_time + print('Done! Took {} seconds'.format(elapsed_time)) + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + Loading model... Done! Took 0.506481409072876 seconds +.. GENERATED FROM PYTHON SOURCE LINES 137-143 Load label map data (for plotting) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -216,6 +219,7 @@ predicts `5`, we know that this corresponds to `airplane`. Here we use internal functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine. +.. GENERATED FROM PYTHON SOURCE LINES 143-147 .. code-block:: default @@ -230,6 +234,8 @@ would be fine. +.. GENERATED FROM PYTHON SOURCE LINES 148-162 + Putting everything together ~~~~~~~~~~~~~~~~~~~~~~~~~~~ The code shown below loads an image, runs it through the detection model and visualizes the @@ -245,11 +251,11 @@ Here are some simple things to try out if you are curious: * Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). * Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. +.. GENERATED FROM PYTHON SOURCE LINES 162-232 .. code-block:: default import numpy as np - from six import BytesIO from PIL import Image import matplotlib.pyplot as plt import warnings @@ -268,18 +274,13 @@ Here are some simple things to try out if you are curious: Returns: uint8 numpy array with shape (img_height, img_width, 3) """ - img_data = tf.io.gfile.GFile(path, 'rb').read() - image = Image.open(BytesIO(img_data)) - (im_width, im_height) = image.size - return np.array(image.getdata()).reshape( - (im_height, im_width, 3)).astype(np.uint8) + return np.array(Image.open(path)) - for image_filename in IMAGE_FILENAMES: + for image_path in IMAGE_PATHS: - print('Running inference for {}... '.format(image_filename), end='') + print('Running inference for {}... '.format(image_path), end='') - image_path = os.path.join(IMAGES_DIR, image_filename) image_np = load_image_into_numpy_array(image_path) # Things to try: @@ -290,23 +291,34 @@ Here are some simple things to try out if you are curious: # image_np = np.tile( # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - input_tensor = tf.convert_to_tensor( - np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) + input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) + + detections = detect_fn(input_tensor) + + # All outputs are batches tensors. + # Convert to numpy arrays, and take index [0] to remove the batch dimension. + # We're only interested in the first num_detections. + num_detections = int(detections.pop('num_detections')) + detections = {key: value[0, :num_detections].numpy() + for key, value in detections.items()} + detections['num_detections'] = num_detections + + # detection_classes should be ints. + detections['detection_classes'] = detections['detection_classes'].astype(np.int64) label_id_offset = 1 image_np_with_detections = image_np.copy() viz_utils.visualize_boxes_and_labels_on_image_array( - image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), - category_index, - use_normalized_coordinates=True, - max_boxes_to_draw=200, - min_score_thresh=.30, - agnostic_mode=False) + image_np_with_detections, + detections['detection_boxes'], + detections['detection_classes']+label_id_offset, + detections['detection_scores'], + category_index, + use_normalized_coordinates=True, + max_boxes_to_draw=200, + min_score_thresh=.30, + agnostic_mode=False) plt.figure() plt.imshow(image_np_with_detections) @@ -321,14 +333,14 @@ Here are some simple things to try out if you are curious: * - .. image:: /auto_examples/images/sphx_glr_plot_object_detection_simple_001.png - :alt: plot object detection simple + .. image:: /auto_examples/images/sphx_glr_plot_object_detection_checkpoint_001.png + :alt: plot object detection checkpoint :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_object_detection_simple_002.png - :alt: plot object detection simple + .. image:: /auto_examples/images/sphx_glr_plot_object_detection_checkpoint_002.png + :alt: plot object detection checkpoint :class: sphx-glr-multi-img @@ -338,8 +350,8 @@ Here are some simple things to try out if you are curious: .. code-block:: none - Running inference for image1.jpg... Done - Running inference for image2.jpg... Done + Running inference for C:\Users\sglvladi\.keras\datasets\image1.jpg... Done + Running inference for C:\Users\sglvladi\.keras\datasets\image2.jpg... Done @@ -347,10 +359,10 @@ Here are some simple things to try out if you are curious: .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 2 minutes 29.261 seconds) + **Total running time of the script:** ( 0 minutes 20.819 seconds) -.. _sphx_glr_download_auto_examples_plot_object_detection_simple.py: +.. _sphx_glr_download_auto_examples_plot_object_detection_checkpoint.py: .. only :: html @@ -362,13 +374,13 @@ Here are some simple things to try out if you are curious: .. container:: sphx-glr-download sphx-glr-download-python - :download:`Download Python source code: plot_object_detection_simple.py ` + :download:`Download Python source code: plot_object_detection_checkpoint.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter - :download:`Download Jupyter notebook: plot_object_detection_simple.ipynb ` + :download:`Download Jupyter notebook: plot_object_detection_checkpoint.ipynb ` .. only:: html diff --git a/docs/source/auto_examples/plot_object_detection_saved_model.ipynb b/docs/source/auto_examples/plot_object_detection_saved_model.ipynb new file mode 100644 index 0000000..9a13130 --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_saved_model.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Object Detection From TF2 Saved Model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This demo will take you through the steps of running an \"out-of-the-box\" TensorFlow 2 compatible\ndetection model on a collection of images. More specifically, in this example we will be using\nthe `Saved Model Format `__ to load the model.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the test images\nFirst we will download the images that we will use throughout this tutorial. The code snippet\nshown bellow will download the test images from the `TensorFlow Model Garden `_\nand save them inside the ``data/images`` folder.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)\nimport pathlib\nimport tensorflow as tf\n\ntf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n\n# Enable GPU dynamic memory allocation\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n\ndef download_images():\n base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'\n filenames = ['image1.jpg', 'image2.jpg']\n image_paths = []\n for filename in filenames:\n image_path = tf.keras.utils.get_file(fname=filename,\n origin=base_url + filename,\n untar=False)\n image_path = pathlib.Path(image_path)\n image_paths.append(str(image_path))\n return image_paths\n\nIMAGE_PATHS = download_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the model\nThe code snippet shown below is used to download the pre-trained object detection model we shall\nuse to perform inference. The particular detection algorithm we will use is the\n`CenterNet HourGlass104 1024x1024`. More models can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz``\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download and extract model\ndef download_model(model_name, model_date):\n base_url = 'http://download.tensorflow.org/models/object_detection/tf2/'\n model_file = model_name + '.tar.gz'\n model_dir = tf.keras.utils.get_file(fname=model_name,\n origin=base_url + model_date + '/' + model_file,\n untar=True)\n return str(model_dir)\n\nMODEL_DATE = '20200711'\nMODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'\nPATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the labels\nThe coode snippet shown below is used to download the labels file (.pbtxt) which contains a list\nof strings used to add the correct label to each detection (e.g. person). Since the pre-trained\nmodel we will use has been trained on the COCO dataset, we will need to download the labels file\ncorresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files\nincluded in the TensorFlow Models Garden can be found `here `__.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download labels file\ndef download_labels(filename):\n base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'\n label_dir = tf.keras.utils.get_file(fname=filename,\n origin=base_url + filename,\n untar=False)\n label_dir = pathlib.Path(label_dir)\n return str(label_dir)\n\nLABEL_FILENAME = 'mscoco_label_map.pbtxt'\nPATH_TO_LABELS = download_labels(LABEL_FILENAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the model\nNext we load the downloaded model\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import time\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as viz_utils\n\nPATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + \"/saved_model\"\n\nprint('Loading model...', end='')\nstart_time = time.time()\n\n# Load saved model and build the detection function\ndetect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)\n\nend_time = time.time()\nelapsed_time = end_time - start_time\nprint('Done! Took {} seconds'.format(elapsed_time))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load label map data (for plotting)\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n use_display_name=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Putting everything together\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore') # Suppress Matplotlib warnings\n\ndef load_image_into_numpy_array(path):\n \"\"\"Load an image from file into a numpy array.\n\n Puts image into numpy array to feed into tensorflow graph.\n Note that by convention we put it into a numpy array with shape\n (height, width, channels), where channels=3 for RGB.\n\n Args:\n path: the file path to the image\n\n Returns:\n uint8 numpy array with shape (img_height, img_width, 3)\n \"\"\"\n return np.array(Image.open(path))\n\n\nfor image_path in IMAGE_PATHS:\n\n print('Running inference for {}... '.format(image_path), end='')\n\n image_np = load_image_into_numpy_array(image_path)\n\n # Things to try:\n # Flip horizontally\n # image_np = np.fliplr(image_np).copy()\n\n # Convert image to grayscale\n # image_np = np.tile(\n # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n\n # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n input_tensor = tf.convert_to_tensor(image_np)\n # The model expects a batch of images, so add an axis with `tf.newaxis`.\n input_tensor = input_tensor[tf.newaxis, ...]\n\n # input_tensor = np.expand_dims(image_np, 0)\n detections = detect_fn(input_tensor)\n\n # All outputs are batches tensors.\n # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n # We're only interested in the first num_detections.\n num_detections = int(detections.pop('num_detections'))\n detections = {key: value[0, :num_detections].numpy()\n for key, value in detections.items()}\n detections['num_detections'] = num_detections\n\n # detection_classes should be ints.\n detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n\n image_np_with_detections = image_np.copy()\n\n viz_utils.visualize_boxes_and_labels_on_image_array(\n image_np_with_detections,\n detections['detection_boxes'],\n detections['detection_classes'],\n detections['detection_scores'],\n category_index,\n use_normalized_coordinates=True,\n max_boxes_to_draw=200,\n min_score_thresh=.30,\n agnostic_mode=False)\n\n plt.figure()\n plt.imshow(image_np_with_detections)\n print('Done')\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_saved_model.py.md5 b/docs/source/auto_examples/plot_object_detection_saved_model.py.md5 new file mode 100644 index 0000000..ed819f8 --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_saved_model.py.md5 @@ -0,0 +1 @@ +55ed283987df75b48eaa5899b94fddb6 \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_saved_model.rst b/docs/source/auto_examples/plot_object_detection_saved_model.rst new file mode 100644 index 0000000..80b0b9b --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_saved_model.rst @@ -0,0 +1,7442 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples\plot_object_detection_saved_model.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_plot_object_detection_saved_model.py: + + +Object Detection From TF2 Saved Model +===================================== + +.. GENERATED FROM PYTHON SOURCE LINES 9-12 + +This demo will take you through the steps of running an "out-of-the-box" TensorFlow 2 compatible +detection model on a collection of images. More specifically, in this example we will be using +the `Saved Model Format `__ to load the model. + +.. GENERATED FROM PYTHON SOURCE LINES 14-19 + +Download the test images +~~~~~~~~~~~~~~~~~~~~~~~~ +First we will download the images that we will use throughout this tutorial. The code snippet +shown bellow will download the test images from the `TensorFlow Model Garden `_ +and save them inside the ``data/images`` folder. + +.. GENERATED FROM PYTHON SOURCE LINES 19-46 + +.. code-block:: default + + import os + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) + import pathlib + import tensorflow as tf + + tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) + + # Enable GPU dynamic memory allocation + gpus = tf.config.experimental.list_physical_devices('GPU') + for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + + def download_images(): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' + filenames = ['image1.jpg', 'image2.jpg'] + image_paths = [] + for filename in filenames: + image_path = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + image_path = pathlib.Path(image_path) + image_paths.append(str(image_path)) + return image_paths + + IMAGE_PATHS = download_images() + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 47-62 + +Download the model +~~~~~~~~~~~~~~~~~~ +The code snippet shown below is used to download the pre-trained object detection model we shall +use to perform inference. The particular detection algorithm we will use is the +`CenterNet HourGlass104 1024x1024`. More models can be found in the `TensorFlow 2 Detection Model Zoo `_. +To use a different model you will need the URL name of the specific model. This can be done as +follows: + +1. Right click on the `Model name` of the model you would like to use; +2. Click on `Copy link address` to copy the download link of the model; +3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``; +4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; +5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below. + +For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz`` + +.. GENERATED FROM PYTHON SOURCE LINES 62-76 + +.. code-block:: default + + + # Download and extract model + def download_model(model_name, model_date): + base_url = 'http://download.tensorflow.org/models/object_detection/tf2/' + model_file = model_name + '.tar.gz' + model_dir = tf.keras.utils.get_file(fname=model_name, + origin=base_url + model_date + '/' + model_file, + untar=True) + return str(model_dir) + + MODEL_DATE = '20200711' + MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32' + PATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Downloading data from http://download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz + + 8192/1426460092 [..............................] - ETA: 0s + 24576/1426460092 [..............................] - ETA: 49:17 + 49152/1426460092 [..............................] - ETA: 1:16:38 + 81920/1426460092 [..............................] - 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ETA: 6:09 + 38223872/1426460092 [..............................] - ETA: 6:14 + 38535168/1426460092 [..............................] - ETA: 6:13 + 38699008/1426460092 [..............................] - ETA: 6:13 + 38772736/1426460092 [..............................] - ETA: 6:18 + 38936576/1426460092 [..............................] - ETA: 6:18 + 39075840/1426460092 [..............................] - ETA: 6:21 + 39239680/1426460092 [..............................] - ETA: 6:21 + 39370752/1426460092 [..............................] - ETA: 6:22 + 39747584/1426460092 [..............................] - ETA: 6:20 + 40124416/1426460092 [..............................] - ETA: 6:19 + 40337408/1426460092 [..............................] - ETA: 6:18 + 40607744/1426460092 [..............................] - ETA: 6:19 + 40861696/1426460092 [..............................] - ETA: 6:19 + 41172992/1426460092 [..............................] - ETA: 6:18 + 41345024/1426460092 [..............................] - ETA: 6:18 + 41631744/1426460092 [..............................] - ETA: 6:17 + 41910272/1426460092 [..............................] - ETA: 6:16 + 41951232/1426460092 [..............................] - ETA: 6:20 + 42237952/1426460092 [..............................] - ETA: 6:19 + 42516480/1426460092 [..............................] - ETA: 6:18 + 42942464/1426460092 [..............................] - ETA: 6:16 + 43089920/1426460092 [..............................] - ETA: 6:17 + 43393024/1426460092 [..............................] - ETA: 6:16 + 43728896/1426460092 [..............................] - ETA: 6:14 + 44048384/1426460092 [..............................] - ETA: 6:13 + 44376064/1426460092 [..............................] - ETA: 6:12 + 44703744/1426460092 [..............................] - ETA: 6:11 + 44744704/1426460092 [..............................] - ETA: 6:12 + 44802048/1426460092 [..............................] - ETA: 6:14 + 45154304/1426460092 [..............................] - ETA: 6:12 + 45424640/1426460092 [..............................] - ETA: 6:11 + 45785088/1426460092 [..............................] - ETA: 6:10 + 46145536/1426460092 [..............................] - ETA: 6:09 + 46374912/1426460092 [..............................] - ETA: 6:08 + 46686208/1426460092 [..............................] - ETA: 6:07 + 47095808/1426460092 [..............................] - ETA: 6:06 + 47374336/1426460092 [..............................] - ETA: 6:05 + 47726592/1426460092 [>.............................] - ETA: 6:04 + 48013312/1426460092 [>.............................] - ETA: 6:03 + 48439296/1426460092 [>.............................] - ETA: 6:01 + 48791552/1426460092 [>.............................] - ETA: 6:00 + 48947200/1426460092 [>.............................] - ETA: 6:00 + 49160192/1426460092 [>.............................] - ETA: 6:00 + 49274880/1426460092 [>.............................] - ETA: 6:00 + 49627136/1426460092 [>.............................] - ETA: 5:59 + 50012160/1426460092 [>.............................] - ETA: 5:58 + 50282496/1426460092 [>.............................] - ETA: 5:57 + 50642944/1426460092 [>.............................] - ETA: 5:56 + 50913280/1426460092 [>.............................] - ETA: 5:55 + 51142656/1426460092 [>.............................] - ETA: 5:55 + 51322880/1426460092 [>.............................] - ETA: 5:55 + 51576832/1426460092 [>.............................] - ETA: 5:57 + 51585024/1426460092 [>.............................] - ETA: 5:59 + 51978240/1426460092 [>.............................] - ETA: 5:58 + 52379648/1426460092 [>.............................] - ETA: 5:56 + 52699136/1426460092 [>.............................] - ETA: 5:56 + 52789248/1426460092 [>.............................] - ETA: 5:58 + 53116928/1426460092 [>.............................] - ETA: 5:57 + 53460992/1426460092 [>.............................] - ETA: 5:56 + 53764096/1426460092 [>.............................] - ETA: 5:55 + 53927936/1426460092 [>.............................] - ETA: 5:55 + 54026240/1426460092 [>.............................] - ETA: 6:00 + 55435264/1426460092 [>.............................] - ETA: 5:52 + 55721984/1426460092 [>.............................] - ETA: 5:52 + 56041472/1426460092 [>.............................] - ETA: 5:51 + 56221696/1426460092 [>.............................] - ETA: 5:51 + 56573952/1426460092 [>.............................] - ETA: 5:50 + 56877056/1426460092 [>.............................] - ETA: 5:49 + 57204736/1426460092 [>.............................] - ETA: 5:49 + 57483264/1426460092 [>.............................] - ETA: 5:48 + 57704448/1426460092 [>.............................] - ETA: 5:48 + 58122240/1426460092 [>.............................] - ETA: 5:47 + 58359808/1426460092 [>.............................] - ETA: 5:46 + 58769408/1426460092 [>.............................] - ETA: 5:45 + 59006976/1426460092 [>.............................] - 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ETA: 5:40 + 71925760/1426460092 [>.............................] - ETA: 5:40 + 72073216/1426460092 [>.............................] - ETA: 5:40 + 72228864/1426460092 [>.............................] - ETA: 5:40 + 72433664/1426460092 [>.............................] - ETA: 5:43 + 72974336/1426460092 [>.............................] - ETA: 5:41 + 72998912/1426460092 [>.............................] - ETA: 5:42 + 73244672/1426460092 [>.............................] - ETA: 5:42 + 73474048/1426460092 [>.............................] - ETA: 5:42 + 73777152/1426460092 [>.............................] - ETA: 5:41 + 73883648/1426460092 [>.............................] - ETA: 5:42 + 74276864/1426460092 [>.............................] - ETA: 5:41 + 74514432/1426460092 [>.............................] - ETA: 5:41 + 74817536/1426460092 [>.............................] - ETA: 5:40 + 74907648/1426460092 [>.............................] - ETA: 5:41 + 74981376/1426460092 [>.............................] - ETA: 5:42 + 75284480/1426460092 [>.............................] - ETA: 5:41 + 75423744/1426460092 [>.............................] - ETA: 5:42 + 75776000/1426460092 [>.............................] - ETA: 5:41 + 75923456/1426460092 [>.............................] - ETA: 5:41 + 76292096/1426460092 [>.............................] - ETA: 5:41 + 76595200/1426460092 [>.............................] - ETA: 5:40 + 76881920/1426460092 [>.............................] - ETA: 5:40 + 77160448/1426460092 [>.............................] - ETA: 5:40 + 77561856/1426460092 [>.............................] - ETA: 5:39 + 77922304/1426460092 [>.............................] - ETA: 5:38 + 78135296/1426460092 [>.............................] - ETA: 5:38 + 78528512/1426460092 [>.............................] - ETA: 5:37 + 78757888/1426460092 [>.............................] - ETA: 5:37 + 78962688/1426460092 [>.............................] - ETA: 5:37 + 79159296/1426460092 [>.............................] - 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ETA: 5:37 + 86073344/1426460092 [>.............................] - ETA: 5:36 + 86351872/1426460092 [>.............................] - ETA: 5:36 + 86622208/1426460092 [>.............................] - ETA: 5:35 + 86908928/1426460092 [>.............................] - ETA: 5:35 + 87203840/1426460092 [>.............................] - ETA: 5:35 + 87400448/1426460092 [>.............................] - ETA: 5:35 + 87687168/1426460092 [>.............................] - ETA: 5:35 + 87777280/1426460092 [>.............................] - ETA: 5:36 + 87818240/1426460092 [>.............................] - ETA: 5:36 + 87957504/1426460092 [>.............................] - ETA: 5:36 + 88039424/1426460092 [>.............................] - ETA: 5:37 + 88285184/1426460092 [>.............................] - ETA: 5:37 + 88539136/1426460092 [>.............................] - ETA: 5:37 + 88768512/1426460092 [>.............................] - ETA: 5:37 + 88997888/1426460092 [>.............................] - 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ETA: 5:37 + 92618752/1426460092 [>.............................] - ETA: 5:36 + 92913664/1426460092 [>.............................] - ETA: 5:36 + 93208576/1426460092 [>.............................] - ETA: 5:35 + 93454336/1426460092 [>.............................] - ETA: 5:35 + 93642752/1426460092 [>.............................] - ETA: 5:35 + 93937664/1426460092 [>.............................] - ETA: 5:35 + 94052352/1426460092 [>.............................] - ETA: 5:36 + 94298112/1426460092 [>.............................] - ETA: 5:36 + 94633984/1426460092 [>.............................] - ETA: 5:35 + 94920704/1426460092 [>.............................] - ETA: 5:35 + 95215616/1426460092 [=>............................] - ETA: 5:34 + 95494144/1426460092 [=>............................] - ETA: 5:34 + 95649792/1426460092 [=>............................] - ETA: 5:34 + 95657984/1426460092 [=>............................] - ETA: 5:35 + 95936512/1426460092 [=>............................] - ETA: 5:35 + 96165888/1426460092 [=>............................] - ETA: 5:35 + 96436224/1426460092 [=>............................] - ETA: 5:35 + 96493568/1426460092 [=>............................] - ETA: 5:35 + 96722944/1426460092 [=>............................] - ETA: 5:35 + 96919552/1426460092 [=>............................] - ETA: 5:35 + 97378304/1426460092 [=>............................] - ETA: 5:34 + 97804288/1426460092 [=>............................] - ETA: 5:34 + 98041856/1426460092 [=>............................] - ETA: 5:33 + 98320384/1426460092 [=>............................] - ETA: 5:33 + 98410496/1426460092 [=>............................] - ETA: 5:33 + 98738176/1426460092 [=>............................] - ETA: 5:33 + 98951168/1426460092 [=>............................] - ETA: 5:33 + 99311616/1426460092 [=>............................] - ETA: 5:33 + 99442688/1426460092 [=>............................] - ETA: 5:33 + 99696640/1426460092 [=>............................] - 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ETA: 5:38 + 106536960/1426460092 [=>............................] - ETA: 5:38 + 106741760/1426460092 [=>............................] - ETA: 5:38 + 106938368/1426460092 [=>............................] - ETA: 5:38 + 107184128/1426460092 [=>............................] - ETA: 5:38 + 107429888/1426460092 [=>............................] - ETA: 5:38 + 107692032/1426460092 [=>............................] - ETA: 5:38 + 107913216/1426460092 [=>............................] - ETA: 5:37 + 108052480/1426460092 [=>............................] - ETA: 5:38 + 108560384/1426460092 [=>............................] - ETA: 5:37 + 108740608/1426460092 [=>............................] - ETA: 5:37 + 109142016/1426460092 [=>............................] - ETA: 5:36 + 109559808/1426460092 [=>............................] - ETA: 5:35 + 109690880/1426460092 [=>............................] - ETA: 5:36 + 109699072/1426460092 [=>............................] - ETA: 5:37 + 110100480/1426460092 [=>............................] - 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ETA: 5:55 + 241893376/1426460092 [====>.........................] - ETA: 5:56 + 242163712/1426460092 [====>.........................] - ETA: 5:56 + 242442240/1426460092 [====>.........................] - ETA: 5:56 + 242597888/1426460092 [====>.........................] - ETA: 5:56 + 243228672/1426460092 [====>.........................] - ETA: 5:55 + 243539968/1426460092 [====>.........................] - ETA: 5:55 + 243646464/1426460092 [====>.........................] - ETA: 5:55 + 243793920/1426460092 [====>.........................] - ETA: 5:55 + 244039680/1426460092 [====>.........................] - ETA: 5:55 + 244318208/1426460092 [====>.........................] - ETA: 5:54 + 244342784/1426460092 [====>.........................] - ETA: 5:55 + 244580352/1426460092 [====>.........................] - ETA: 5:54 + 244834304/1426460092 [====>.........................] - ETA: 5:54 + 244973568/1426460092 [====>.........................] - ETA: 5:54 + 245194752/1426460092 [====>.........................] - ETA: 5:54 + 245358592/1426460092 [====>.........................] - ETA: 5:54 + 245547008/1426460092 [====>.........................] - ETA: 5:54 + 245825536/1426460092 [====>.........................] - ETA: 5:54 + 246022144/1426460092 [====>.........................] - ETA: 5:54 + 246161408/1426460092 [====>.........................] - ETA: 5:54 + 246300672/1426460092 [====>.........................] - ETA: 5:54 + 246497280/1426460092 [====>.........................] - ETA: 5:54 + 246677504/1426460092 [====>.........................] - ETA: 5:54 + 246874112/1426460092 [====>.........................] - ETA: 5:54 + 246956032/1426460092 [====>.........................] - ETA: 5:54 + 247095296/1426460092 [====>.........................] - ETA: 5:54 + 247201792/1426460092 [====>.........................] - ETA: 5:54 + 247357440/1426460092 [====>.........................] - ETA: 5:54 + 247488512/1426460092 [====>.........................] - ETA: 5:54 + 247586816/1426460092 [====>.........................] - ETA: 5:54 + 247742464/1426460092 [====>.........................] - ETA: 5:54 + 247922688/1426460092 [====>.........................] - ETA: 5:54 + 248020992/1426460092 [====>.........................] - ETA: 5:54 + 248193024/1426460092 [====>.........................] - ETA: 5:55 + 248471552/1426460092 [====>.........................] - ETA: 5:55 + 248782848/1426460092 [====>.........................] - ETA: 5:54 + 248938496/1426460092 [====>.........................] - ETA: 5:54 + 249184256/1426460092 [====>.........................] - ETA: 5:54 + 249323520/1426460092 [====>.........................] - ETA: 5:55 + 249413632/1426460092 [====>.........................] - ETA: 5:55 + 249503744/1426460092 [====>.........................] - ETA: 5:55 + 249520128/1426460092 [====>.........................] - ETA: 5:56 + 250036224/1426460092 [====>.........................] - ETA: 5:55 + 250380288/1426460092 [====>.........................] - ETA: 5:55 + 250560512/1426460092 [====>.........................] - ETA: 5:54 + 250814464/1426460092 [====>.........................] - ETA: 5:54 + 250978304/1426460092 [====>.........................] - ETA: 5:54 + 251289600/1426460092 [====>.........................] - ETA: 5:54 + 251584512/1426460092 [====>.........................] - ETA: 5:54 + 251813888/1426460092 [====>.........................] - ETA: 5:54 + 251994112/1426460092 [====>.........................] - ETA: 5:54 + 252198912/1426460092 [====>.........................] - ETA: 5:54 + 252436480/1426460092 [====>.........................] - ETA: 5:53 + 252690432/1426460092 [====>.........................] - ETA: 5:53 + 252895232/1426460092 [====>.........................] - ETA: 5:53 + 253083648/1426460092 [====>.........................] - ETA: 5:53 + 253378560/1426460092 [====>.........................] - ETA: 5:53 + 253648896/1426460092 [====>.........................] - ETA: 5:52 + 254017536/1426460092 [====>.........................] - ETA: 5:53 + 254353408/1426460092 [====>.........................] - ETA: 5:52 + 254599168/1426460092 [====>.........................] - ETA: 5:52 + 254877696/1426460092 [====>.........................] - ETA: 5:52 + 255197184/1426460092 [====>.........................] - ETA: 5:52 + 255467520/1426460092 [====>.........................] - ETA: 5:52 + 255795200/1426460092 [====>.........................] - ETA: 5:51 + 256163840/1426460092 [====>.........................] - ETA: 5:51 + 256516096/1426460092 [====>.........................] - ETA: 5:51 + 256884736/1426460092 [====>.........................] - ETA: 5:50 + 257024000/1426460092 [====>.........................] - ETA: 5:51 + 257261568/1426460092 [====>.........................] - ETA: 5:50 + 257515520/1426460092 [====>.........................] - ETA: 5:50 + 257974272/1426460092 [====>.........................] - ETA: 5:50 + 258260992/1426460092 [====>.........................] - ETA: 5:50 + 258605056/1426460092 [====>.........................] - ETA: 5:49 + 258834432/1426460092 [====>.........................] - ETA: 5:49 + 259063808/1426460092 [====>.........................] - ETA: 5:49 + 259448832/1426460092 [====>.........................] - ETA: 5:49 + 259645440/1426460092 [====>.........................] - ETA: 5:49 + 259825664/1426460092 [====>.........................] - ETA: 5:49 + 260128768/1426460092 [====>.........................] - ETA: 5:48 + 260292608/1426460092 [====>.........................] - ETA: 5:48 + 260571136/1426460092 [====>.........................] - ETA: 5:48 + 260694016/1426460092 [====>.........................] - ETA: 5:48 + 260808704/1426460092 [====>.........................] - ETA: 5:49 + 261169152/1426460092 [====>.........................] - ETA: 5:49 + 261406720/1426460092 [====>.........................] - ETA: 5:49 + 261873664/1426460092 [====>.........................] - ETA: 5:49 + 262111232/1426460092 [====>.........................] - ETA: 5:48 + 262340608/1426460092 [====>.........................] - ETA: 5:48 + 262692864/1426460092 [====>.........................] - ETA: 5:48 + 262946816/1426460092 [====>.........................] - ETA: 5:48 + 263151616/1426460092 [====>.........................] - ETA: 5:48 + 263372800/1426460092 [====>.........................] - ETA: 5:48 + 263798784/1426460092 [====>.........................] - ETA: 5:47 + 264085504/1426460092 [====>.........................] - ETA: 5:47 + 264200192/1426460092 [====>.........................] - ETA: 5:47 + 264486912/1426460092 [====>.........................] - ETA: 5:47 + 264765440/1426460092 [====>.........................] - ETA: 5:47 + 264888320/1426460092 [====>.........................] - ETA: 5:47 + 265191424/1426460092 [====>.........................] - ETA: 5:46 + 265437184/1426460092 [====>.........................] - ETA: 5:46 + 265551872/1426460092 [====>.........................] - ETA: 5:46 + 265822208/1426460092 [====>.........................] - ETA: 5:46 + 266043392/1426460092 [====>.........................] - ETA: 5:46 + 266199040/1426460092 [====>.........................] - ETA: 5:46 + 266444800/1426460092 [====>.........................] - ETA: 5:46 + 266641408/1426460092 [====>.........................] - ETA: 5:46 + 266936320/1426460092 [====>.........................] - ETA: 5:46 + 267182080/1426460092 [====>.........................] - ETA: 5:45 + 267362304/1426460092 [====>.........................] - ETA: 5:45 + 267558912/1426460092 [====>.........................] - ETA: 5:45 + 267821056/1426460092 [====>.........................] - ETA: 5:45 + 268066816/1426460092 [====>.........................] - ETA: 5:45 + 268312576/1426460092 [====>.........................] - ETA: 5:45 + 268460032/1426460092 [====>.........................] - ETA: 5:45 + 268648448/1426460092 [====>.........................] - ETA: 5:45 + 268869632/1426460092 [====>.........................] - ETA: 5:45 + 269090816/1426460092 [====>.........................] - ETA: 5:44 + 269312000/1426460092 [====>.........................] - ETA: 5:44 + 269549568/1426460092 [====>.........................] - ETA: 5:44 + 269697024/1426460092 [====>.........................] - ETA: 5:44 + 269877248/1426460092 [====>.........................] - ETA: 5:44 + 270163968/1426460092 [====>.........................] - ETA: 5:44 + 270368768/1426460092 [====>.........................] - ETA: 5:44 + 270598144/1426460092 [====>.........................] - ETA: 5:44 + 270729216/1426460092 [====>.........................] - ETA: 5:44 + 270835712/1426460092 [====>.........................] - ETA: 5:44 + 270893056/1426460092 [====>.........................] - ETA: 5:45 + 271048704/1426460092 [====>.........................] - ETA: 5:45 + 271245312/1426460092 [====>.........................] - ETA: 5:45 + 271400960/1426460092 [====>.........................] - ETA: 5:45 + 271663104/1426460092 [====>.........................] - ETA: 5:44 + 271835136/1426460092 [====>.........................] - ETA: 5:44 + 272048128/1426460092 [====>.........................] - ETA: 5:44 + 272285696/1426460092 [====>.........................] - ETA: 5:44 + 272547840/1426460092 [====>.........................] - ETA: 5:44 + 272670720/1426460092 [====>.........................] - ETA: 5:44 + 273080320/1426460092 [====>.........................] - ETA: 5:44 + 273309696/1426460092 [====>.........................] - ETA: 5:44 + 273612800/1426460092 [====>.........................] - ETA: 5:43 + 273907712/1426460092 [====>.........................] - ETA: 5:43 + 274251776/1426460092 [====>.........................] - ETA: 5:43 + 274472960/1426460092 [====>.........................] - ETA: 5:43 + 274849792/1426460092 [====>.........................] - ETA: 5:42 + 275120128/1426460092 [====>.........................] - ETA: 5:42 + 275439616/1426460092 [====>.........................] - ETA: 5:42 + 275628032/1426460092 [====>.........................] - ETA: 5:42 + 275963904/1426460092 [====>.........................] - ETA: 5:41 + 276291584/1426460092 [====>.........................] - ETA: 5:41 + 276652032/1426460092 [====>.........................] - ETA: 5:41 + 276660224/1426460092 [====>.........................] - ETA: 5:41 + 277159936/1426460092 [====>.........................] - ETA: 5:41 + 277422080/1426460092 [====>.........................] - ETA: 5:41 + 277831680/1426460092 [====>.........................] - ETA: 5:40 + 278110208/1426460092 [====>.........................] - ETA: 5:40 + 278446080/1426460092 [====>.........................] - ETA: 5:40 + 278806528/1426460092 [====>.........................] - ETA: 5:40 + 279126016/1426460092 [====>.........................] - ETA: 5:39 + 279240704/1426460092 [====>.........................] - ETA: 5:40 + 279642112/1426460092 [====>.........................] - ETA: 5:39 + 280035328/1426460092 [====>.........................] - ETA: 5:39 + 280444928/1426460092 [====>.........................] - ETA: 5:38 + 280592384/1426460092 [====>.........................] - ETA: 5:38 + 280952832/1426460092 [====>.........................] - ETA: 5:38 + 281223168/1426460092 [====>.........................] - ETA: 5:38 + 281649152/1426460092 [====>.........................] - ETA: 5:37 + 281886720/1426460092 [====>.........................] - ETA: 5:37 + 282279936/1426460092 [====>.........................] - ETA: 5:37 + 282558464/1426460092 [====>.........................] - ETA: 5:37 + 282902528/1426460092 [====>.........................] - ETA: 5:37 + 283197440/1426460092 [====>.........................] - ETA: 5:37 + 283451392/1426460092 [====>.........................] - ETA: 5:37 + 283770880/1426460092 [====>.........................] - ETA: 5:36 + 284024832/1426460092 [====>.........................] - ETA: 5:36 + 284139520/1426460092 [====>.........................] - ETA: 5:36 + 284377088/1426460092 [====>.........................] - ETA: 5:36 + 284614656/1426460092 [====>.........................] - ETA: 5:36 + 284770304/1426460092 [====>.........................] - ETA: 5:36 + 285089792/1426460092 [====>.........................] - ETA: 5:36 + 285335552/1426460092 [=====>........................] - ETA: 5:36 + 285597696/1426460092 [=====>........................] - ETA: 5:36 + 285966336/1426460092 [=====>........................] - ETA: 5:35 + 286179328/1426460092 [=====>........................] - ETA: 5:35 + 286449664/1426460092 [=====>........................] - ETA: 5:35 + 286523392/1426460092 [=====>........................] - ETA: 5:35 + 286777344/1426460092 [=====>........................] - ETA: 5:35 + 287129600/1426460092 [=====>........................] - ETA: 5:35 + 287244288/1426460092 [=====>........................] - ETA: 5:35 + 287522816/1426460092 [=====>........................] - ETA: 5:35 + 287735808/1426460092 [=====>........................] - ETA: 5:35 + 287858688/1426460092 [=====>........................] - ETA: 5:35 + 288030720/1426460092 [=====>........................] - ETA: 5:35 + 288186368/1426460092 [=====>........................] - ETA: 5:35 + 288350208/1426460092 [=====>........................] - ETA: 5:35 + 288530432/1426460092 [=====>........................] - ETA: 5:35 + 288874496/1426460092 [=====>........................] - ETA: 5:34 + 289038336/1426460092 [=====>........................] - ETA: 5:34 + 289210368/1426460092 [=====>........................] - ETA: 5:34 + 289439744/1426460092 [=====>........................] - ETA: 5:34 + 289693696/1426460092 [=====>........................] - ETA: 5:34 + 289906688/1426460092 [=====>........................] - ETA: 5:34 + 290111488/1426460092 [=====>........................] - ETA: 5:34 + 290332672/1426460092 [=====>........................] - ETA: 5:34 + 290439168/1426460092 [=====>........................] - ETA: 5:34 + 290660352/1426460092 [=====>........................] - ETA: 5:34 + 290914304/1426460092 [=====>........................] - ETA: 5:34 + 290996224/1426460092 [=====>........................] - ETA: 5:34 + 291348480/1426460092 [=====>........................] - ETA: 5:34 + 291651584/1426460092 [=====>........................] - ETA: 5:34 + 291954688/1426460092 [=====>........................] - ETA: 5:34 + 292200448/1426460092 [=====>........................] - ETA: 5:34 + 292290560/1426460092 [=====>........................] - ETA: 5:34 + 292339712/1426460092 [=====>........................] - ETA: 5:34 + 292372480/1426460092 [=====>........................] - ETA: 5:34 + 292511744/1426460092 [=====>........................] - ETA: 5:34 + 292708352/1426460092 [=====>........................] - ETA: 5:34 + 292921344/1426460092 [=====>........................] - ETA: 5:34 + 293142528/1426460092 [=====>........................] - ETA: 5:34 + 293380096/1426460092 [=====>........................] - ETA: 5:34 + 293568512/1426460092 [=====>........................] - ETA: 5:34 + 293691392/1426460092 [=====>........................] - ETA: 5:34 + 293855232/1426460092 [=====>........................] - ETA: 5:34 + 294068224/1426460092 [=====>........................] - ETA: 5:34 + 294289408/1426460092 [=====>........................] - ETA: 5:34 + 294559744/1426460092 [=====>........................] - ETA: 5:34 + 294748160/1426460092 [=====>........................] - ETA: 5:34 + 295002112/1426460092 [=====>........................] - ETA: 5:34 + 295165952/1426460092 [=====>........................] - ETA: 5:34 + 295395328/1426460092 [=====>........................] - ETA: 5:34 + 295518208/1426460092 [=====>........................] - ETA: 5:34 + 295583744/1426460092 [=====>........................] - ETA: 5:34 + 295919616/1426460092 [=====>........................] - ETA: 5:34 + 296091648/1426460092 [=====>........................] - ETA: 5:34 + 296288256/1426460092 [=====>........................] - ETA: 5:34 + 296534016/1426460092 [=====>........................] - ETA: 5:34 + 296624128/1426460092 [=====>........................] - ETA: 5:34 + 296828928/1426460092 [=====>........................] - ETA: 5:34 + 297099264/1426460092 [=====>........................] - ETA: 5:33 + 297295872/1426460092 [=====>........................] - ETA: 5:33 + 297336832/1426460092 [=====>........................] - ETA: 5:33 + 297541632/1426460092 [=====>........................] - ETA: 5:33 + 297721856/1426460092 [=====>........................] - ETA: 5:33 + 297943040/1426460092 [=====>........................] - ETA: 5:33 + 298065920/1426460092 [=====>........................] - ETA: 5:33 + 298278912/1426460092 [=====>........................] - ETA: 5:33 + 298500096/1426460092 [=====>........................] - ETA: 5:33 + 298713088/1426460092 [=====>........................] - ETA: 5:33 + 299089920/1426460092 [=====>........................] - ETA: 5:33 + 299278336/1426460092 [=====>........................] - ETA: 5:33 + 299458560/1426460092 [=====>........................] - ETA: 5:33 + 299638784/1426460092 [=====>........................] - ETA: 5:32 + 299917312/1426460092 [=====>........................] - ETA: 5:32 + 299925504/1426460092 [=====>........................] - ETA: 5:33 + 300064768/1426460092 [=====>........................] - ETA: 5:33 + 300220416/1426460092 [=====>........................] - ETA: 5:33 + 300384256/1426460092 [=====>........................] - ETA: 5:33 + 300498944/1426460092 [=====>........................] - ETA: 5:33 + 300720128/1426460092 [=====>........................] - ETA: 5:33 + 300826624/1426460092 [=====>........................] - ETA: 5:33 + 300990464/1426460092 [=====>........................] - ETA: 5:33 + 301260800/1426460092 [=====>........................] - ETA: 5:33 + 301457408/1426460092 [=====>........................] - ETA: 5:33 + 301596672/1426460092 [=====>........................] - ETA: 5:33 + 301703168/1426460092 [=====>........................] - ETA: 5:33 + 301768704/1426460092 [=====>........................] - ETA: 5:33 + 301858816/1426460092 [=====>........................] - ETA: 5:33 + 302047232/1426460092 [=====>........................] - ETA: 5:33 + 302194688/1426460092 [=====>........................] - ETA: 5:33 + 302391296/1426460092 [=====>........................] - ETA: 5:33 + 302604288/1426460092 [=====>........................] - ETA: 5:33 + 302817280/1426460092 [=====>........................] - ETA: 5:33 + 303046656/1426460092 [=====>........................] - ETA: 5:33 + 303210496/1426460092 [=====>........................] - ETA: 5:33 + 303464448/1426460092 [=====>........................] - ETA: 5:32 + 303644672/1426460092 [=====>........................] - ETA: 5:33 + 303783936/1426460092 [=====>........................] - ETA: 5:33 + 303988736/1426460092 [=====>........................] - ETA: 5:32 + 304226304/1426460092 [=====>........................] - ETA: 5:32 + 304447488/1426460092 [=====>........................] - ETA: 5:32 + 304594944/1426460092 [=====>........................] - ETA: 5:32 + 304791552/1426460092 [=====>........................] - ETA: 5:32 + 304979968/1426460092 [=====>........................] - ETA: 5:32 + 305111040/1426460092 [=====>........................] - ETA: 5:32 + 305299456/1426460092 [=====>........................] - ETA: 5:32 + 305512448/1426460092 [=====>........................] - 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GENERATED FROM PYTHON SOURCE LINES 77-84 + +Download the labels +~~~~~~~~~~~~~~~~~~~ +The code snippet shown below is used to download the labels file (.pbtxt) which contains a list +of strings used to add the correct label to each detection (e.g. person). Since the pre-trained +model we will use has been trained on the COCO dataset, we will need to download the labels file +corresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files +included in the TensorFlow Models Garden can be found `here `__. + +.. GENERATED FROM PYTHON SOURCE LINES 84-97 + +.. code-block:: default + + + # Download labels file + def download_labels(filename): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + label_dir = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + label_dir = pathlib.Path(label_dir) + return str(label_dir) + + LABEL_FILENAME = 'mscoco_label_map.pbtxt' + PATH_TO_LABELS = download_labels(LABEL_FILENAME) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 98-101 + +Load the model +~~~~~~~~~~~~~~ +Next we load the downloaded model + +.. GENERATED FROM PYTHON SOURCE LINES 101-117 + +.. code-block:: default + + import time + from object_detection.utils import label_map_util + from object_detection.utils import visualization_utils as viz_utils + + PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + "/saved_model" + + print('Loading model...', end='') + start_time = time.time() + + # Load saved model and build the detection function + detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL) + + end_time = time.time() + elapsed_time = end_time - start_time + print('Done! Took {} seconds'.format(elapsed_time)) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Loading model...WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_53414) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_35984) with ops with custom gradients. 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Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_32825) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_encoder_decoder_block_layer_call_and_return_conditional_losses_60787) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211300) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212980) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_50223) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219340) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_217420) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216460) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_27250) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_27924) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_51825) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_47318) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_24593) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_47102) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_21936) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211540) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_204580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_31916) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_214540) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_encoder_decoder_block_5_layer_call_and_return_conditional_losses_71112) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_convolutional_block_36_layer_call_and_return_conditional_losses_64774) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_encoder_decoder_block_layer_call_and_return_conditional_losses_188192) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219100) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_29030) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_201940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_input_downsample_block_layer_call_and_return_conditional_losses_181590) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_51380) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_206860) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_217660) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207340) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_201700) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212260) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_215380) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_204220) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_43987) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_202180) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_215740) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_19495) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211780) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_208180) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218980) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_29475) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216100) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_23042) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_201820) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211060) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_34643) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_204940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_208660) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_38444) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_209140) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_205780) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_22381) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_31255) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_53166) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_44216) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_215860) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_52257) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_42665) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_24148) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_214660) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_209500) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218140) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_30136) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_210220) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_49098) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_41775) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_36893) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_40885) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_50916) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_39563) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_30365) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_217180) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_29691) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_convolutional_block_71_layer_call_and_return_conditional_losses_200410) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_26373) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_35304) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_213460) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_203860) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212860) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_217060) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_19279) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_53859) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_31471) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207820) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_22152) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_20601) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_213340) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_hourglass_network_layer_call_and_return_conditional_losses_155730) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_54304) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_center_net_hourglass_feature_extractor_layer_call_and_return_conditional_losses_136884) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_214780) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_220180) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_214060) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_39995) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_39779) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_52721) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_convolutional_block_36_layer_call_and_return_conditional_losses_200274) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_208420) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_33041) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_25928) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_44432) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_21707) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_214180) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_20385) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212620) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212380) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_28585) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_33950) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_45983) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218860) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_46212) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_25254) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_209020) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_19050) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_213100) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_20830) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_202660) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_205060) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_33270) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_213220) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_206500) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212740) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219220) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_41991) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_220060) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_convolutional_block_72_layer_call_and_return_conditional_losses_200543) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216220) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_206260) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218020) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_217780) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_46873) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_52505) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_213580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_43326) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219700) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207700) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_204460) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_210820) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_220300) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_29920) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_41114) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211180) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_38673) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_39334) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_27695) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_input_downsample_block_layer_call_and_return_conditional_losses_54872) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_215260) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_20156) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207220) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_41559) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_210340) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_44661) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218380) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_28369) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_hourglass_network_layer_call_and_return_conditional_losses_174576) with ops with custom gradients. 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Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_hourglass_network_layer_call_and_return_conditional_losses_93754) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_202420) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_23703) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_25699) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_37567) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_40669) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_32361) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211900) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_34859) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218500) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_51596) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_25483) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_204820) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212020) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211660) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_32145) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_28814) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_36677) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_203140) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_210460) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_207100) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_209260) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_53643) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_206740) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_208540) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_201580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_212140) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_36213) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_206980) with ops with custom gradients. 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Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_203620) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_203380) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_31700) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_center_net_hourglass_feature_extractor_layer_call_and_return_conditional_losses_118038) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_30581) with ops with custom gradients. 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Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_35520) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_211420) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_210940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_21262) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216340) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_19940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_35768) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_215140) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_218260) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_204100) with ops with custom gradients. 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Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_43110) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_42881) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_206620) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219460) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216700) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_26144) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_209620) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_202540) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_31026) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_216940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_208780) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_28140) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_residual_block_69_layer_call_and_return_conditional_losses_200881) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_217300) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_213940) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_24822) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_208060) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_22826) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_37338) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_23487) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_47547) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_35088) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_21491) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_29246) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_219820) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_40224) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_203980) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_39118) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_37783) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_24377) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_convolutional_block_71_layer_call_and_return_conditional_losses_75099) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_210580) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_202900) with ops with custom gradients. Will likely fail if a gradient is requested. + WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_203260) with ops with custom gradients. Will likely fail if a gradient is requested. + Done! Took 179.16713190078735 seconds + + + + +.. GENERATED FROM PYTHON SOURCE LINES 118-124 + +Load label map data (for plotting) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Label maps correspond index numbers to category names, so that when our convolution network +predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility +functions, but anything that returns a dictionary mapping integers to appropriate string labels +would be fine. + +.. GENERATED FROM PYTHON SOURCE LINES 124-128 + +.. code-block:: default + + + category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, + use_display_name=True) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 129-143 + +Putting everything together +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The code shown below loads an image, runs it through the detection model and visualizes the +detection results, including the keypoints. + +Note that this will take a long time (several minutes) the first time you run this code due to +tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be +faster. + +Here are some simple things to try out if you are curious: + +* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). +* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). +* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. + +.. GENERATED FROM PYTHON SOURCE LINES 143-216 + +.. code-block:: default + + import numpy as np + from PIL import Image + import matplotlib.pyplot as plt + import warnings + warnings.filterwarnings('ignore') # Suppress Matplotlib warnings + + def load_image_into_numpy_array(path): + """Load an image from file into a numpy array. + + Puts image into numpy array to feed into tensorflow graph. + Note that by convention we put it into a numpy array with shape + (height, width, channels), where channels=3 for RGB. + + Args: + path: the file path to the image + + Returns: + uint8 numpy array with shape (img_height, img_width, 3) + """ + return np.array(Image.open(path)) + + + for image_path in IMAGE_PATHS: + + print('Running inference for {}... '.format(image_path), end='') + + image_np = load_image_into_numpy_array(image_path) + + # Things to try: + # Flip horizontally + # image_np = np.fliplr(image_np).copy() + + # Convert image to grayscale + # image_np = np.tile( + # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) + + # The input needs to be a tensor, convert it using `tf.convert_to_tensor`. + input_tensor = tf.convert_to_tensor(image_np) + # The model expects a batch of images, so add an axis with `tf.newaxis`. + input_tensor = input_tensor[tf.newaxis, ...] + + # input_tensor = np.expand_dims(image_np, 0) + detections = detect_fn(input_tensor) + + # All outputs are batches tensors. + # Convert to numpy arrays, and take index [0] to remove the batch dimension. + # We're only interested in the first num_detections. + num_detections = int(detections.pop('num_detections')) + detections = {key: value[0, :num_detections].numpy() + for key, value in detections.items()} + detections['num_detections'] = num_detections + + # detection_classes should be ints. + detections['detection_classes'] = detections['detection_classes'].astype(np.int64) + + image_np_with_detections = image_np.copy() + + viz_utils.visualize_boxes_and_labels_on_image_array( + image_np_with_detections, + detections['detection_boxes'], + detections['detection_classes'], + detections['detection_scores'], + category_index, + use_normalized_coordinates=True, + max_boxes_to_draw=200, + min_score_thresh=.30, + agnostic_mode=False) + + plt.figure() + plt.imshow(image_np_with_detections) + print('Done') + plt.show() + + # sphinx_gallery_thumbnail_number = 2 + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_object_detection_saved_model_001.png + :alt: plot object detection saved model + :class: sphx-glr-multi-img + + * + + .. image:: /auto_examples/images/sphx_glr_plot_object_detection_saved_model_002.png + :alt: plot object detection saved model + :class: sphx-glr-multi-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Running inference for C:\Users\sglvladi\.keras\datasets\image1.jpg... Done + Running inference for C:\Users\sglvladi\.keras\datasets\image2.jpg... Done + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 10 minutes 37.757 seconds) + + +.. _sphx_glr_download_auto_examples_plot_object_detection_saved_model.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_object_detection_saved_model.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_object_detection_saved_model.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_object_detection_saved_model_tf1.ipynb b/docs/source/auto_examples/plot_object_detection_saved_model_tf1.ipynb new file mode 100644 index 0000000..54dd74a --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_saved_model_tf1.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Object Detection From TF1 Saved Model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This demo will take you through the steps of running an \"out-of-the-box\" TensorFlow 1 compatible\ndetection model on a collection of images. More specifically, in this example we will be using\nthe `Saved Model Format `__ to load the model.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the test images\nFirst we will download the images that we will use throughout this tutorial. The code snippet\nshown bellow will download the test images from the `TensorFlow Model Garden `_\nand save them inside the ``data/images`` folder.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)\nimport pathlib\nimport tensorflow as tf\n\ntf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n\n# Enable GPU dynamic memory allocation\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n\ndef download_images():\n base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'\n filenames = ['image1.jpg', 'image2.jpg']\n image_paths = []\n for filename in filenames:\n image_path = tf.keras.utils.get_file(fname=filename,\n origin=base_url + filename,\n untar=False)\n image_path = pathlib.Path(image_path)\n image_paths.append(str(image_path))\n return image_paths\n\nIMAGE_PATHS = download_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the model\nThe code snippet shown below is used to download the pre-trained object detection model we shall\nuse to perform inference. The particular detection algorithm we will use is the\n`SSD MobileNet v2`. More models can be found in the `TensorFlow 1 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz``\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download and extract model\ndef download_model(model_name):\n base_url = 'http://download.tensorflow.org/models/object_detection/'\n model_file = model_name + '.tar.gz'\n model_dir = tf.keras.utils.get_file(fname=model_name,\n origin=base_url + model_file,\n untar=True)\n return str(model_dir)\n\nMODEL_NAME = 'ssd_mobilenet_v2_coco_2018_03_29'\nPATH_TO_MODEL_DIR = download_model(MODEL_NAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the labels\nThe coode snippet shown below is used to download the labels file (.pbtxt) which contains a list\nof strings used to add the correct label to each detection (e.g. person). Since the pre-trained\nmodel we will use has been trained on the COCO dataset, we will need to download the labels file\ncorresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files\nincluded in the TensorFlow Models Garden can be found `here `__.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download labels file\ndef download_labels(filename):\n base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'\n label_dir = tf.keras.utils.get_file(fname=filename,\n origin=base_url + filename,\n untar=False)\n label_dir = pathlib.Path(label_dir)\n return str(label_dir)\n\nLABEL_FILENAME = 'mscoco_label_map.pbtxt'\nPATH_TO_LABELS = download_labels(LABEL_FILENAME)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the model\nNext we load the downloaded model\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import time\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as viz_utils\n\nPATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + \"/saved_model\"\n\nprint('Loading model...', end='')\nstart_time = time.time()\n\n# Load saved model and build the detection function\nmodel = tf.saved_model.load(PATH_TO_SAVED_MODEL)\ndetect_fn = model.signatures['serving_default']\n\nend_time = time.time()\nelapsed_time = end_time - start_time\nprint('Done! Took {} seconds'.format(elapsed_time))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load label map data (for plotting)\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n use_display_name=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Putting everything together\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore') # Suppress Matplotlib warnings\n\ndef load_image_into_numpy_array(path):\n \"\"\"Load an image from file into a numpy array.\n\n Puts image into numpy array to feed into tensorflow graph.\n Note that by convention we put it into a numpy array with shape\n (height, width, channels), where channels=3 for RGB.\n\n Args:\n path: the file path to the image\n\n Returns:\n uint8 numpy array with shape (img_height, img_width, 3)\n \"\"\"\n return np.array(Image.open(path))\n\n\nfor image_path in IMAGE_PATHS:\n\n print('Running inference for {}... '.format(image_path), end='')\n\n image_np = load_image_into_numpy_array(image_path)\n\n # Things to try:\n # Flip horizontally\n # image_np = np.fliplr(image_np).copy()\n\n # Convert image to grayscale\n # image_np = np.tile(\n # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n\n # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n input_tensor = tf.convert_to_tensor(image_np)\n # The model expects a batch of images, so add an axis with `tf.newaxis`.\n input_tensor = input_tensor[tf.newaxis, ...]\n\n detections = detect_fn(input_tensor)\n\n # All outputs are batches tensors.\n # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n # We're only interested in the first num_detections.\n num_detections = int(detections.pop('num_detections'))\n detections = {key: value[0, :num_detections].numpy()\n for key, value in detections.items()}\n detections['num_detections'] = num_detections\n\n # detection_classes should be ints.\n detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n\n image_np_with_detections = image_np.copy()\n\n viz_utils.visualize_boxes_and_labels_on_image_array(\n image_np_with_detections,\n detections['detection_boxes'],\n detections['detection_classes'],\n detections['detection_scores'],\n category_index,\n use_normalized_coordinates=True,\n max_boxes_to_draw=200,\n min_score_thresh=.30,\n agnostic_mode=False)\n\n plt.figure()\n plt.imshow(image_np_with_detections)\n print('Done')\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_saved_model_tf1.py.md5 b/docs/source/auto_examples/plot_object_detection_saved_model_tf1.py.md5 new file mode 100644 index 0000000..8e04e79 --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_saved_model_tf1.py.md5 @@ -0,0 +1 @@ +5bc3e063e2cdf821df654da64d7758a7 \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_saved_model_tf1.rst b/docs/source/auto_examples/plot_object_detection_saved_model_tf1.rst new file mode 100644 index 0000000..834b899 --- /dev/null +++ b/docs/source/auto_examples/plot_object_detection_saved_model_tf1.rst @@ -0,0 +1,1206 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "auto_examples\plot_object_detection_saved_model_tf1.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` + to download the full example code + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_auto_examples_plot_object_detection_saved_model_tf1.py: + + +Object Detection From TF1 Saved Model +===================================== + +.. GENERATED FROM PYTHON SOURCE LINES 9-12 + +This demo will take you through the steps of running an "out-of-the-box" TensorFlow 1 compatible +detection model on a collection of images. More specifically, in this example we will be using +the `Saved Model Format `__ to load the model. + +.. GENERATED FROM PYTHON SOURCE LINES 14-19 + +Download the test images +~~~~~~~~~~~~~~~~~~~~~~~~ +First we will download the images that we will use throughout this tutorial. The code snippet +shown bellow will download the test images from the `TensorFlow Model Garden `_ +and save them inside the ``data/images`` folder. + +.. GENERATED FROM PYTHON SOURCE LINES 19-46 + +.. code-block:: default + + import os + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) + import pathlib + import tensorflow as tf + + tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) + + # Enable GPU dynamic memory allocation + gpus = tf.config.experimental.list_physical_devices('GPU') + for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + + def download_images(): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' + filenames = ['image1.jpg', 'image2.jpg'] + image_paths = [] + for filename in filenames: + image_path = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + image_path = pathlib.Path(image_path) + image_paths.append(str(image_path)) + return image_paths + + IMAGE_PATHS = download_images() + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 47-61 + +Download the model +~~~~~~~~~~~~~~~~~~ +The code snippet shown below is used to download the pre-trained object detection model we shall +use to perform inference. The particular detection algorithm we will use is the +`SSD MobileNet v2`. More models can be found in the `TensorFlow 1 Detection Model Zoo `_. +To use a different model you will need the URL name of the specific model. This can be done as +follows: + +1. Right click on the `Model name` of the model you would like to use; +2. Click on `Copy link address` to copy the download link of the model; +3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/XXXXXXXXX.tar.gz``; +4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; + +For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz`` + +.. GENERATED FROM PYTHON SOURCE LINES 61-74 + +.. code-block:: default + + + # Download and extract model + def download_model(model_name): + base_url = 'http://download.tensorflow.org/models/object_detection/' + model_file = model_name + '.tar.gz' + model_dir = tf.keras.utils.get_file(fname=model_name, + origin=base_url + model_file, + untar=True) + return str(model_dir) + + MODEL_NAME = 'ssd_mobilenet_v2_coco_2018_03_29' + PATH_TO_MODEL_DIR = download_model(MODEL_NAME) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Downloading data from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz + + 8192/187925923 [..............................] - ETA: 0s + 16384/187925923 [..............................] - ETA: 41:09 + 32768/187925923 [..............................] - ETA: 25:46 + 49152/187925923 [..............................] - ETA: 24:11 + 90112/187925923 [..............................] - ETA: 15:23 + 172032/187925923 [..............................] - 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GENERATED FROM PYTHON SOURCE LINES 75-82 + +Download the labels +~~~~~~~~~~~~~~~~~~~ +The coode snippet shown below is used to download the labels file (.pbtxt) which contains a list +of strings used to add the correct label to each detection (e.g. person). Since the pre-trained +model we will use has been trained on the COCO dataset, we will need to download the labels file +corresponding to this dataset, named ``mscoco_label_map.pbtxt``. A full list of the labels files +included in the TensorFlow Models Garden can be found `here `__. + +.. GENERATED FROM PYTHON SOURCE LINES 82-95 + +.. code-block:: default + + + # Download labels file + def download_labels(filename): + base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' + label_dir = tf.keras.utils.get_file(fname=filename, + origin=base_url + filename, + untar=False) + label_dir = pathlib.Path(label_dir) + return str(label_dir) + + LABEL_FILENAME = 'mscoco_label_map.pbtxt' + PATH_TO_LABELS = download_labels(LABEL_FILENAME) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Downloading data from https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt + + 8192/5056 [================================================] - 0s 0us/step + + + + +.. GENERATED FROM PYTHON SOURCE LINES 96-99 + +Load the model +~~~~~~~~~~~~~~ +Next we load the downloaded model + +.. GENERATED FROM PYTHON SOURCE LINES 99-116 + +.. code-block:: default + + import time + from object_detection.utils import label_map_util + from object_detection.utils import visualization_utils as viz_utils + + PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + "/saved_model" + + print('Loading model...', end='') + start_time = time.time() + + # Load saved model and build the detection function + model = tf.saved_model.load(PATH_TO_SAVED_MODEL) + detect_fn = model.signatures['serving_default'] + + end_time = time.time() + elapsed_time = end_time - start_time + print('Done! Took {} seconds'.format(elapsed_time)) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Loading model...Done! Took 17.68789768218994 seconds + + + + +.. GENERATED FROM PYTHON SOURCE LINES 117-123 + +Load label map data (for plotting) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Label maps correspond index numbers to category names, so that when our convolution network +predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility +functions, but anything that returns a dictionary mapping integers to appropriate string labels +would be fine. + +.. GENERATED FROM PYTHON SOURCE LINES 123-127 + +.. code-block:: default + + + category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, + use_display_name=True) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 128-142 + +Putting everything together +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The code shown below loads an image, runs it through the detection model and visualizes the +detection results, including the keypoints. + +Note that this will take a long time (several minutes) the first time you run this code due to +tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be +faster. + +Here are some simple things to try out if you are curious: + +* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). +* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). +* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. + +.. GENERATED FROM PYTHON SOURCE LINES 142-214 + +.. code-block:: default + + import numpy as np + from PIL import Image + import matplotlib.pyplot as plt + import warnings + warnings.filterwarnings('ignore') # Suppress Matplotlib warnings + + def load_image_into_numpy_array(path): + """Load an image from file into a numpy array. + + Puts image into numpy array to feed into tensorflow graph. + Note that by convention we put it into a numpy array with shape + (height, width, channels), where channels=3 for RGB. + + Args: + path: the file path to the image + + Returns: + uint8 numpy array with shape (img_height, img_width, 3) + """ + return np.array(Image.open(path)) + + + for image_path in IMAGE_PATHS: + + print('Running inference for {}... '.format(image_path), end='') + + image_np = load_image_into_numpy_array(image_path) + + # Things to try: + # Flip horizontally + # image_np = np.fliplr(image_np).copy() + + # Convert image to grayscale + # image_np = np.tile( + # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) + + # The input needs to be a tensor, convert it using `tf.convert_to_tensor`. + input_tensor = tf.convert_to_tensor(image_np) + # The model expects a batch of images, so add an axis with `tf.newaxis`. + input_tensor = input_tensor[tf.newaxis, ...] + + detections = detect_fn(input_tensor) + + # All outputs are batches tensors. + # Convert to numpy arrays, and take index [0] to remove the batch dimension. + # We're only interested in the first num_detections. + num_detections = int(detections.pop('num_detections')) + detections = {key: value[0, :num_detections].numpy() + for key, value in detections.items()} + detections['num_detections'] = num_detections + + # detection_classes should be ints. + detections['detection_classes'] = detections['detection_classes'].astype(np.int64) + + image_np_with_detections = image_np.copy() + + viz_utils.visualize_boxes_and_labels_on_image_array( + image_np_with_detections, + detections['detection_boxes'], + detections['detection_classes'], + detections['detection_scores'], + category_index, + use_normalized_coordinates=True, + max_boxes_to_draw=200, + min_score_thresh=.30, + agnostic_mode=False) + + plt.figure() + plt.imshow(image_np_with_detections) + print('Done') + plt.show() + + # sphinx_gallery_thumbnail_number = 2 + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_object_detection_saved_model_tf1_001.png + :alt: plot object detection saved model tf1 + :class: sphx-glr-multi-img + + * + + .. image:: /auto_examples/images/sphx_glr_plot_object_detection_saved_model_tf1_002.png + :alt: plot object detection saved model tf1 + :class: sphx-glr-multi-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Running inference for C:\Users\sglvladi\.keras\datasets\image1.jpg... Done + Running inference for C:\Users\sglvladi\.keras\datasets\image2.jpg... Done + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 1 minutes 29.283 seconds) + + +.. _sphx_glr_download_auto_examples_plot_object_detection_saved_model_tf1.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_object_detection_saved_model_tf1.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_object_detection_saved_model_tf1.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_object_detection_simple.ipynb b/docs/source/auto_examples/plot_object_detection_simple.ipynb deleted file mode 100644 index c18284c..0000000 --- a/docs/source/auto_examples/plot_object_detection_simple.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\nObject Detection Test\n=====================\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This demo will take you through the steps of running an \"out-of-the-box\" detection model on a\ncollection of images.\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the data directory\n~~~~~~~~~~~~~~~~~~~~~~~~~\nThe snippet shown below will create the ``data`` directory where all our data will be stored. The\ncode will create a directory structure as shown bellow:\n\n.. code-block:: bash\n\n data\n \u251c\u2500\u2500 images\n \u2514\u2500\u2500 models\n\nwhere the ``images`` folder will contain the downlaoded test images, while ``models`` will\ncontain the downloaded models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n\nDATA_DIR = os.path.join(os.getcwd(), 'data')\nIMAGES_DIR = os.path.join(DATA_DIR, 'images')\nMODELS_DIR = os.path.join(DATA_DIR, 'models')\nfor dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]:\n if not os.path.exists(dir):\n os.mkdir(dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the test images\n~~~~~~~~~~~~~~~~~~~~~~~~\nFirst we will download the images that we will use throughout this tutorial. The code snippet\nshown bellow will download the test images from the `TensorFlow Model Garden `_\nand save them inside the ``data/images`` folder.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import urllib.request\n\nIMAGE_FILENAMES = ['image1.jpg', 'image2.jpg']\nIMAGES_DOWNLOAD_BASE = \\\n 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'\n\nfor image_filename in IMAGE_FILENAMES:\n\n image_path = os.path.join(IMAGES_DIR, image_filename)\n\n # Download image\n if not os.path.exists(image_path):\n print('Downloading {}... '.format(image_filename), end='')\n urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path)\n print('Done')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the model\n~~~~~~~~~~~~~~~~~~\nThe code snippet shown below is used to download the object detection model checkpoint file,\nas well as the labels file (.pbtxt) which contains a list of strings used to add the correct\nlabel to each detection (e.g. person). Once downloaded the files will be stored under the\n``data/models`` folder.\n\nThe particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More\nmodels can be found in the `TensorFlow 2 Detection Model Zoo `_.\nTo use a different model you will need the URL name of the specific model. This can be done as\nfollows:\n\n1. Right click on the `Model name` of the model you would like to use;\n2. Click on `Copy link address` to copy the download link of the model;\n3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``;\n4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below;\n5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below.\n\nFor example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz``\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import tarfile\n\n# Download and extract model\nMODEL_DATE = '20200711'\nMODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'\nMODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz'\nMODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/'\nMODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME\nPATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME)\nPATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/'))\nPATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config'))\nif not os.path.exists(PATH_TO_CKPT):\n print('Downloading model. This may take a while... ', end='')\n urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR)\n tar_file = tarfile.open(PATH_TO_MODEL_TAR)\n tar_file.extractall(MODELS_DIR)\n tar_file.close()\n os.remove(PATH_TO_MODEL_TAR)\n print('Done')\n\n# Download labels file\nLABEL_FILENAME = 'mscoco_label_map.pbtxt'\nLABELS_DOWNLOAD_BASE = \\\n 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'\nPATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME))\nif not os.path.exists(PATH_TO_LABELS):\n print('Downloading label file... ', end='')\n urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS)\n print('Done')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the model\n~~~~~~~~~~~~~~\nNext we load the downloaded model\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)\nimport tensorflow as tf\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import config_util\nfrom object_detection.utils import visualization_utils as viz_utils\nfrom object_detection.builders import model_builder\n\ntf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n\n# Enable GPU dynamic memory allocation\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n\n# Load pipeline config and build a detection model\nconfigs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)\nmodel_config = configs['model']\ndetection_model = model_builder.build(model_config=model_config, is_training=False)\n\n# Restore checkpoint\nckpt = tf.compat.v2.train.Checkpoint(\n model=detection_model)\nckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()\n\n@tf.function\ndef detect_fn(image):\n \"\"\"Detect objects in image.\"\"\"\n\n image, shapes = detection_model.preprocess(image)\n prediction_dict = detection_model.predict(image, shapes)\n detections = detection_model.postprocess(prediction_dict, shapes)\n\n return detections, prediction_dict, tf.reshape(shapes, [-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load label map data (for plotting)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nLabel maps correspond index numbers to category names, so that when our convolution network\npredicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\nfunctions, but anything that returns a dictionary mapping integers to appropriate string labels\nwould be fine.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n use_display_name=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Putting everything together\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe code shown below loads an image, runs it through the detection model and visualizes the\ndetection results, including the keypoints.\n\nNote that this will take a long time (several minutes) the first time you run this code due to\ntf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\nfaster.\n\nHere are some simple things to try out if you are curious:\n\n* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nfrom six import BytesIO\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore') # Suppress Matplotlib warnings\n\ndef load_image_into_numpy_array(path):\n \"\"\"Load an image from file into a numpy array.\n\n Puts image into numpy array to feed into tensorflow graph.\n Note that by convention we put it into a numpy array with shape\n (height, width, channels), where channels=3 for RGB.\n\n Args:\n path: the file path to the image\n\n Returns:\n uint8 numpy array with shape (img_height, img_width, 3)\n \"\"\"\n img_data = tf.io.gfile.GFile(path, 'rb').read()\n image = Image.open(BytesIO(img_data))\n (im_width, im_height) = image.size\n return np.array(image.getdata()).reshape(\n (im_height, im_width, 3)).astype(np.uint8)\n\n\nfor image_filename in IMAGE_FILENAMES:\n\n print('Running inference for {}... '.format(image_filename), end='')\n\n image_path = os.path.join(IMAGES_DIR, image_filename)\n image_np = load_image_into_numpy_array(image_path)\n\n # Things to try:\n # Flip horizontally\n # image_np = np.fliplr(image_np).copy()\n\n # Convert image to grayscale\n # image_np = np.tile(\n # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n\n input_tensor = tf.convert_to_tensor(\n np.expand_dims(image_np, 0), dtype=tf.float32)\n detections, predictions_dict, shapes = detect_fn(input_tensor)\n\n label_id_offset = 1\n image_np_with_detections = image_np.copy()\n\n viz_utils.visualize_boxes_and_labels_on_image_array(\n image_np_with_detections,\n detections['detection_boxes'][0].numpy(),\n (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),\n detections['detection_scores'][0].numpy(),\n category_index,\n use_normalized_coordinates=True,\n max_boxes_to_draw=200,\n min_score_thresh=.30,\n agnostic_mode=False)\n\n plt.figure()\n plt.imshow(image_np_with_detections)\n print('Done')\nplt.show()\n\n# sphinx_gallery_thumbnail_number = 2" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_simple.py.md5 b/docs/source/auto_examples/plot_object_detection_simple.py.md5 deleted file mode 100644 index af06899..0000000 --- a/docs/source/auto_examples/plot_object_detection_simple.py.md5 +++ /dev/null @@ -1 +0,0 @@ -b87b60278a02bdaad1f3a32379487fec \ No newline at end of file diff --git a/docs/source/auto_examples/plot_object_detection_simple.rst b/docs/source/auto_examples/plot_object_detection_simple.rst deleted file mode 100644 index aaa0514..0000000 --- a/docs/source/auto_examples/plot_object_detection_simple.rst +++ /dev/null @@ -1,378 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_object_detection_simple.py: - - -Object Detection Test -===================== - -This demo will take you through the steps of running an "out-of-the-box" detection model on a -collection of images. - -Create the data directory -~~~~~~~~~~~~~~~~~~~~~~~~~ -The snippet shown below will create the ``data`` directory where all our data will be stored. The -code will create a directory structure as shown bellow: - -.. code-block:: bash - - data - ├── images - └── models - -where the ``images`` folder will contain the downlaoded test images, while ``models`` will -contain the downloaded models. - - -.. code-block:: default - - import os - - DATA_DIR = os.path.join(os.getcwd(), 'data') - IMAGES_DIR = os.path.join(DATA_DIR, 'images') - MODELS_DIR = os.path.join(DATA_DIR, 'models') - for dir in [DATA_DIR, IMAGES_DIR, MODELS_DIR]: - if not os.path.exists(dir): - os.mkdir(dir) - - - - - - - - -Download the test images -~~~~~~~~~~~~~~~~~~~~~~~~ -First we will download the images that we will use throughout this tutorial. The code snippet -shown bellow will download the test images from the `TensorFlow Model Garden `_ -and save them inside the ``data/images`` folder. - - -.. code-block:: default - - import urllib.request - - IMAGE_FILENAMES = ['image1.jpg', 'image2.jpg'] - IMAGES_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/' - - for image_filename in IMAGE_FILENAMES: - - image_path = os.path.join(IMAGES_DIR, image_filename) - - # Download image - if not os.path.exists(image_path): - print('Downloading {}... '.format(image_filename), end='') - urllib.request.urlretrieve(IMAGES_DOWNLOAD_BASE + image_filename, image_path) - print('Done') - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Downloading image1.jpg... Done - Downloading image2.jpg... Done - - - - -Download the model -~~~~~~~~~~~~~~~~~~ -The code snippet shown below is used to download the object detection model checkpoint file, -as well as the labels file (.pbtxt) which contains a list of strings used to add the correct -label to each detection (e.g. person). Once downloaded the files will be stored under the -``data/models`` folder. - -The particular detection algorithm we will use is the `CenterNet HourGlass104 1024x1024`. More -models can be found in the `TensorFlow 2 Detection Model Zoo `_. -To use a different model you will need the URL name of the specific model. This can be done as -follows: - -1. Right click on the `Model name` of the model you would like to use; -2. Click on `Copy link address` to copy the download link of the model; -3. Paste the link in a text editor of your choice. You should observe a link similar to ``download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz``; -4. Copy the ``XXXXXXXXX`` part of the link and use it to replace the value of the ``MODEL_NAME`` variable in the code shown below; -5. Copy the ``YYYYYYYY`` part of the link and use it to replace the value of the ``MODEL_DATE`` variable in the code shown below. - -For example, the download link for the model used below is: ``download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz`` - - -.. code-block:: default - - - import tarfile - - # Download and extract model - MODEL_DATE = '20200711' - MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32' - MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz' - MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/' - MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME - PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME) - PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/')) - PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config')) - if not os.path.exists(PATH_TO_CKPT): - print('Downloading model. This may take a while... ', end='') - urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR) - tar_file = tarfile.open(PATH_TO_MODEL_TAR) - tar_file.extractall(MODELS_DIR) - tar_file.close() - os.remove(PATH_TO_MODEL_TAR) - print('Done') - - # Download labels file - LABEL_FILENAME = 'mscoco_label_map.pbtxt' - LABELS_DOWNLOAD_BASE = \ - 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/' - PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME)) - if not os.path.exists(PATH_TO_LABELS): - print('Downloading label file... ', end='') - urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS) - print('Done') - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Downloading model. This may take a while... Done - Downloading label file... Done - - - - -Load the model -~~~~~~~~~~~~~~ -Next we load the downloaded model - - -.. code-block:: default - - os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1) - import tensorflow as tf - from object_detection.utils import label_map_util - from object_detection.utils import config_util - from object_detection.utils import visualization_utils as viz_utils - from object_detection.builders import model_builder - - tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2) - - # Enable GPU dynamic memory allocation - gpus = tf.config.experimental.list_physical_devices('GPU') - for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) - - # Load pipeline config and build a detection model - configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG) - model_config = configs['model'] - detection_model = model_builder.build(model_config=model_config, is_training=False) - - # Restore checkpoint - ckpt = tf.compat.v2.train.Checkpoint( - model=detection_model) - ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial() - - @tf.function - def detect_fn(image): - """Detect objects in image.""" - - image, shapes = detection_model.preprocess(image) - prediction_dict = detection_model.predict(image, shapes) - detections = detection_model.postprocess(prediction_dict, shapes) - - return detections, prediction_dict, tf.reshape(shapes, [-1]) - - - - - - - - - -Load label map data (for plotting) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Label maps correspond index numbers to category names, so that when our convolution network -predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility -functions, but anything that returns a dictionary mapping integers to appropriate string labels -would be fine. - - -.. code-block:: default - - - category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, - use_display_name=True) - - - - - - - - -Putting everything together -~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The code shown below loads an image, runs it through the detection model and visualizes the -detection results, including the keypoints. - -Note that this will take a long time (several minutes) the first time you run this code due to -tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be -faster. - -Here are some simple things to try out if you are curious: - -* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels). -* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]). -* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections. - - -.. code-block:: default - - import numpy as np - from six import BytesIO - from PIL import Image - import matplotlib.pyplot as plt - import warnings - warnings.filterwarnings('ignore') # Suppress Matplotlib warnings - - def load_image_into_numpy_array(path): - """Load an image from file into a numpy array. - - Puts image into numpy array to feed into tensorflow graph. - Note that by convention we put it into a numpy array with shape - (height, width, channels), where channels=3 for RGB. - - Args: - path: the file path to the image - - Returns: - uint8 numpy array with shape (img_height, img_width, 3) - """ - img_data = tf.io.gfile.GFile(path, 'rb').read() - image = Image.open(BytesIO(img_data)) - (im_width, im_height) = image.size - return np.array(image.getdata()).reshape( - (im_height, im_width, 3)).astype(np.uint8) - - - for image_filename in IMAGE_FILENAMES: - - print('Running inference for {}... '.format(image_filename), end='') - - image_path = os.path.join(IMAGES_DIR, image_filename) - image_np = load_image_into_numpy_array(image_path) - - # Things to try: - # Flip horizontally - # image_np = np.fliplr(image_np).copy() - - # Convert image to grayscale - # image_np = np.tile( - # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8) - - input_tensor = tf.convert_to_tensor( - np.expand_dims(image_np, 0), dtype=tf.float32) - detections, predictions_dict, shapes = detect_fn(input_tensor) - - label_id_offset = 1 - image_np_with_detections = image_np.copy() - - viz_utils.visualize_boxes_and_labels_on_image_array( - image_np_with_detections, - detections['detection_boxes'][0].numpy(), - (detections['detection_classes'][0].numpy() + label_id_offset).astype(int), - detections['detection_scores'][0].numpy(), - category_index, - use_normalized_coordinates=True, - max_boxes_to_draw=200, - min_score_thresh=.30, - agnostic_mode=False) - - plt.figure() - plt.imshow(image_np_with_detections) - print('Done') - plt.show() - - # sphinx_gallery_thumbnail_number = 2 - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_object_detection_simple_001.png - :alt: plot object detection simple - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_object_detection_simple_002.png - :alt: plot object detection simple - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Running inference for image1.jpg... Done - Running inference for image2.jpg... Done - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 2 minutes 29.261 seconds) - - -.. _sphx_glr_download_auto_examples_plot_object_detection_simple.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_object_detection_simple.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_object_detection_simple.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_object_detection_simple_codeobj.pickle b/docs/source/auto_examples/plot_object_detection_simple_codeobj.pickle deleted file mode 100644 index 845165d..0000000 Binary files a/docs/source/auto_examples/plot_object_detection_simple_codeobj.pickle and /dev/null differ diff --git a/docs/source/auto_examples/sg_execution_times.rst b/docs/source/auto_examples/sg_execution_times.rst deleted file mode 100644 index d6c8b1f..0000000 --- a/docs/source/auto_examples/sg_execution_times.rst +++ /dev/null @@ -1,14 +0,0 @@ - -:orphan: - -.. _sphx_glr_auto_examples_sg_execution_times: - -Computation times -================= -**02:29.261** total execution time for **auto_examples** files: - -+-----------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_object_detection_simple.py` (``plot_object_detection_simple.py``) | 02:29.261 | 0.0 MB | -+-----------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_object_detection_camera.py` (``object_detection_camera.py``) | 00:00.000 | 0.0 MB | -+-----------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/docs/source/index.rst b/docs/source/index.rst index 7dd7df2..3a945f5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,9 +6,11 @@ TensorFlow 2 Object Detection API tutorial ========================================== -.. important:: This tutorial is intended for TensorFlow 2.2, which (at the time of writing this tutorial) is the latest stable version of TensorFlow 2.x. +.. important:: This tutorial is intended for TensorFlow 2.5, which (at the time of writing this tutorial) is the latest stable version of TensorFlow 2.x. - A version for TensorFlow 1.14 can be found `here `_. + A version for TensorFlow 2.2 can be found `here `_. + + A version for TensorFlow 1.14 can be found `here `_. This is a step-by-step tutorial/guide to setting up and using TensorFlow's Object Detection API to perform, namely, object detection in images/video. @@ -19,31 +21,25 @@ The software tools which we shall use throughout this tutorial are listed in the +==============+==============================+ | OS | Windows, Linux | +--------------+------------------------------+ -| Python | 3.8 | +| Python | 3.9 [#]_ | +--------------+------------------------------+ -| TensorFlow | 2.2.0 | +| TensorFlow | 2.5.0 | +--------------+------------------------------+ -| CUDA Toolkit | 10.1 | +| CUDA Toolkit | 11.2 | +--------------+------------------------------+ -| CuDNN | 7.6.5 | +| CuDNN | 8.1.0 | +--------------+------------------------------+ -| Anaconda | Python 3.7 (Optional) | +| Anaconda | Python 3.8 (Optional) | +--------------+------------------------------+ +.. [#] Python 3.9 is not a strict requirement. Any Python 3.x version should work, although this has not been tested. + + .. toctree:: :maxdepth: 4 - :caption: Contents: + :caption: Contents install training auto_examples/index issues - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/install.rst b/docs/source/install.rst index ab1c61e..066d30d 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -4,15 +4,15 @@ Installation General Remarks --------------- -- In contrast to TensorFlow 1.x, where different Python packages needed to be installed for one to run TensorFlow on either their CPU or GPU (namely ``tensorflow`` and ``tensorflow-gpu``), TensorFlow 2.x only requires that the ``tensorflow`` package is installed and automatically checks to see if a GPU can be successfully registered. +In contrast to TensorFlow 1.x, where different Python packages needed to be installed for one to run TensorFlow on either their CPU or GPU (namely ``tensorflow`` and ``tensorflow-gpu``), TensorFlow 2.x only requires that the ``tensorflow`` package is installed and automatically checks to see if a GPU can be successfully registered. -Anaconda Python 3.7 (Optional) +Anaconda Python 3.8 (Optional) ------------------------------ Although having Anaconda is not a requirement in order to install and use TensorFlow, I suggest doing so, due to it's intuitive way of managing packages and setting up new virtual environments. Anaconda is a pretty useful tool, not only for working with TensorFlow, but in general for anyone working in Python, so if you haven't had a chance to work with it, now is a good chance. -Install Anaconda Python 3.7 +Install Anaconda Python 3.8 *************************** .. tabs:: @@ -20,15 +20,15 @@ Install Anaconda Python 3.7 .. tab:: Windows - Go to ``_ and click the "Download" button - - Download the `Python 3.7 64-Bit Graphical Installer `_ or the `32-Bit Graphical Installer `_ installer, per your system requirements - - Run the downloaded executable (``.exe``) file to begin the installation. See `here `_ for more details + - Download the `Python 3.8 64-Bit Graphical Installer `_ or the `32-Bit Graphical Installer `_ installer, per your system requirements + - Run the downloaded executable (``.exe``) file to begin the installation. See `here `__ for more details - (Optional) In the next step, check the box "Add Anaconda3 to my PATH environment variable". This will make Anaconda your default Python distribution, which should ensure that you have the same default Python distribution across all editors. .. tab:: Linux - Go to ``_ and click the "Download" button - - Download the `Python 3.7 64-Bit (x86) Installer `_ - - Run the downloaded bash script (``.sh``) file to begin the installation. See `here `_ for more details. + - Download the `Python 3.8 64-Bit (x86) Installer `_ + - Run the downloaded bash script (``.sh``) file to begin the installation. See `here `__ for more details. - When prompted with the question "Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home//.bashrc ?", answer "Yes". If you enter "No", you must manually add the path to Anaconda or conda will not work. Create a new Anaconda virtual environment @@ -36,9 +36,9 @@ Create a new Anaconda virtual environment - Open a new `Terminal` window - Type the following command: - .. code-block:: posh + .. code-block:: default - conda create -n tensorflow pip python=3.8 + conda create -n tensorflow pip python=3.9 - The above will create a new virtual environment with name ``tensorflow`` @@ -48,7 +48,7 @@ Activate the Anaconda virtual environment ***************************************** - Activating the newly created virtual environment is achieved by running the following in the `Terminal` window: - .. code-block:: posh + .. code-block:: default conda activate tensorflow @@ -73,21 +73,21 @@ Install the TensorFlow PIP package ********************************** - Run the following command in a `Terminal` window: - .. code-block:: posh + .. code-block:: default - pip install --ignore-installed --upgrade tensorflow==2.2.0 + pip install --ignore-installed --upgrade tensorflow==2.5.0 Verify your Installation ************************ - Run the following command in a `Terminal` window: - .. code-block:: posh + .. code-block:: default python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" - Once the above is run, you should see a print-out similar to the one bellow: - .. code-block:: posh + .. code-block:: default 2020-06-22 19:20:32.614181: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found 2020-06-22 19:20:32.620571: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. @@ -134,9 +134,9 @@ In order for TensorFlow to run on your GPU, the following requirements must be m +=====================================+ | Nvidia GPU (GTX 650 or newer) | +-------------------------------------+ -| CUDA Toolkit v10.1 | +| CUDA Toolkit v11.2 | +-------------------------------------+ -| CuDNN 7.6.5 | +| CuDNN 8.1.0 | +-------------------------------------+ .. _cuda_install: @@ -147,13 +147,13 @@ Install CUDA Toolkit .. tab:: Windows - - Follow this `link `_ to download and install CUDA Toolkit 10.1 - - Installation instructions can be found `here `_ + - Follow this `link `__ to download and install CUDA Toolkit 11.2 + - Installation instructions can be found `here `__ .. tab:: Linux - - Follow this `link `_ to download and install CUDA Toolkit 10.1 for your Linux distribution. - - Installation instructions can be found `here `_ + - Follow this `link `__ to download and install CUDA Toolkit 11.2 for your Linux distribution. + - Installation instructions can be found `here `__ .. _cudnn_install: @@ -166,16 +166,16 @@ Install CUDNN - Go to ``_ - Create a user profile if needed and log in - - Select `cuDNN v7.6.5 (Nov 5, 2019), for CUDA 10.1 `_ - - Download `cuDNN v7.6.5 Library for Windows 10 `_ - - Extract the contents of the zip file (i.e. the folder named ``cuda``) inside ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\``, where ```` points to the installation directory specified during the installation of the CUDA Toolkit. By default ```` = ``C:\Program Files``. + - Select `Download cuDNN v8.1.0 (January 26th, 2021), for CUDA 11.0,11.1 and 11.2 `_ + - Download `cuDNN Library for Windows (x86) `_ + - Extract the contents of the zip file (i.e. the folder named ``cuda``) inside ``\NVIDIA GPU Computing Toolkit\CUDA\v11.2\``, where ```` points to the installation directory specified during the installation of the CUDA Toolkit. By default ```` = ``C:\Program Files``. .. tab:: Linux - Go to ``_ - Create a user profile if needed and log in - - Select `cuDNN v7.6.5 (Nov 5, 2019), for CUDA 10.1 `_ - - Download `cuDNN v7.6.5 Library for Linux `_ + - Select `Download cuDNN v8.1.0 (January 26th, 2021), for CUDA 11.0,11.1 and 11.2 `_ + - Download `cuDNN Library for Linux (x86_64) `_ - Follow the instructions under Section 2.3.1 of the `CuDNN Installation Guide `_ to install CuDNN. .. _set_env: @@ -192,24 +192,25 @@ Environment Setup - Under "System variables", search for and click on the ``Path`` system variable, then click "Edit..." - Add the following paths, then click "OK" to save the changes: - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin`` - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp`` - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\libx64`` - - ``\NVIDIA GPU Computing Toolkit\CUDA\v10.1\cuda\bin`` + - ``\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin`` + - ``\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp`` + - ``\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include`` + - ``\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64`` + - ``\NVIDIA GPU Computing Toolkit\CUDA\v11.2\cuda\bin`` .. tab:: Linux As per Section 7.1.1 of the `CUDA Installation Guide for Linux `_, append the following lines to ``~/.bashrc``: - .. code-block:: bash + .. code-block:: default # CUDA related exports - export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}} - export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} + export PATH=/usr/local/cuda-11.2/bin${PATH:+:${PATH}} + export LD_LIBRARY_PATH=/usr/local/cuda-11.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} Update your GPU drivers (Optional) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If during the installation of the CUDA Toolkit (see :ref:`cuda_install`) you selected the `Express Installation` option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. These drivers are typically NOT the latest drivers and, thus, you may wish to updte your drivers. +If during the installation of the CUDA Toolkit (see :ref:`cuda_install`) you selected the `Express Installation` option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. These drivers are typically NOT the latest drivers and, thus, you may wish to update your drivers. - Go to ``_ - Select your GPU version to download @@ -220,7 +221,7 @@ Verify the installation - Run the following command in a **NEW** `Terminal` window: - .. code-block:: posh + .. code-block:: default python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" @@ -230,43 +231,34 @@ Verify the installation - Once the above is run, you should see a print-out similar to the one bellow: - .. code-block:: posh - :emphasize-lines: 1,2,6,7,8,9,10,11,12,20,21,22,23,24,25,26,31 + .. code-block:: default + :emphasize-lines: 1,2,6,7,8,9,10,11,12,13,24 - 2020-06-22 20:24:31.355541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll - 2020-06-22 20:24:33.650692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll - 2020-06-22 20:24:33.686846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: + 2021-06-08 18:28:38.452128: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll + 2021-06-08 18:28:40.948968: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library nvcuda.dll + 2021-06-08 18:28:40.973992: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1 coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s - 2020-06-22 20:24:33.697234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll - 2020-06-22 20:24:33.747540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll - 2020-06-22 20:24:33.787573: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll - 2020-06-22 20:24:33.810063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll - 2020-06-22 20:24:33.841474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll - 2020-06-22 20:24:33.862787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll - 2020-06-22 20:24:33.907318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll - 2020-06-22 20:24:33.913612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 - 2020-06-22 20:24:33.918093: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 - 2020-06-22 20:24:33.932784: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2382acc1c40 initialized for platform Host (this does not guarantee that XLA will be used). Devices: - 2020-06-22 20:24:33.939473: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version - 2020-06-22 20:24:33.944570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: + 2021-06-08 18:28:40.974115: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudart64_110.dll + 2021-06-08 18:28:40.982483: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublas64_11.dll + 2021-06-08 18:28:40.982588: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cublasLt64_11.dll + 2021-06-08 18:28:40.986795: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cufft64_10.dll + 2021-06-08 18:28:40.988451: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library curand64_10.dll + 2021-06-08 18:28:40.994115: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusolver64_11.dll + 2021-06-08 18:28:40.998408: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cusparse64_11.dll + 2021-06-08 18:28:41.000573: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library cudnn64_8.dll + 2021-06-08 18:28:41.001094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 + 2021-06-08 18:28:41.001651: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 + To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. + 2021-06-08 18:28:41.003095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1 coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s - 2020-06-22 20:24:33.953910: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll - 2020-06-22 20:24:33.958772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll - 2020-06-22 20:24:33.963656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll - 2020-06-22 20:24:33.968210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll - 2020-06-22 20:24:33.973389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll - 2020-06-22 20:24:33.978058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll - 2020-06-22 20:24:33.983547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll - 2020-06-22 20:24:33.990380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 - 2020-06-22 20:24:35.338596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: - 2020-06-22 20:24:35.344643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 - 2020-06-22 20:24:35.348795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N - 2020-06-22 20:24:35.353853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6284 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1) - 2020-06-22 20:24:35.369758: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2384aa9f820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: - 2020-06-22 20:24:35.376320: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1070 Ti, Compute Capability 6.1 - tf.Tensor(122.478485, shape=(), dtype=float32) + 2021-06-08 18:28:41.003244: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 + 2021-06-08 18:28:42.072538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: + 2021-06-08 18:28:42.072630: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 + 2021-06-08 18:28:42.072886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N + 2021-06-08 18:28:42.075566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6613 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1) + tf.Tensor(641.5694, shape=(), dtype=float32) - Notice from the lines highlighted above that the library files are now ``Successfully opened`` and a debugging message is presented to confirm that TensorFlow has successfully ``Created TensorFlow device``. @@ -286,7 +278,7 @@ Downloading the TensorFlow Model Garden - To download the models you can either use `Git `_ to clone the `TensorFlow Models repository `_ inside the ``TensorFlow`` folder, or you can simply download it as a `ZIP `_ and extract its contents inside the ``TensorFlow`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``models-master`` to ``models``. - You should now have a single folder named ``models`` under your ``TensorFlow`` folder, which contains another 4 folders as such: -.. code-block:: bash +.. code-block:: default TensorFlow/ └─ models/ @@ -308,10 +300,10 @@ This should be done as follows: - Head to the `protoc releases page `_ - Download the latest ``protoc-*-*.zip`` release (e.g. ``protoc-3.12.3-win64.zip`` for 64-bit Windows) - Extract the contents of the downloaded ``protoc-*-*.zip`` in a directory ```` of your choice (e.g. ``C:\Program Files\Google Protobuf``) -- Add ```` to your ``Path`` environment variable (see :ref:`set_env`) +- Add ``\bin`` to your ``Path`` environment variable (see :ref:`set_env`) - In a new `Terminal` [#]_, ``cd`` into ``TensorFlow/models/research/`` directory and run the following command: - .. code-block:: python + .. code-block:: default # From within TensorFlow/models/research/ protoc object_detection/protos/*.proto --python_out=. @@ -324,7 +316,7 @@ This should be done as follows: .. tab:: Windows Powershell - .. code-block:: + .. code-block:: default # From within TensorFlow/models/research/ Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.} @@ -332,7 +324,7 @@ This should be done as follows: .. tab:: Command Prompt - .. code-block:: + .. code-block:: default # From within TensorFlow/models/research/ for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=. @@ -354,19 +346,19 @@ As of TensorFlow 2.x, the ``pycocotools`` package is listed as `a dependency of Run the following command to install ``pycocotools`` with Windows support: - .. code-block:: bash + .. code-block:: default pip install cython pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI - Note that, according to the `package's instructions `_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here `_. + Note that, according to the `package's instructions `_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here `__. .. tab:: Linux Download `cocoapi `_ to a directory of your choice, then ``make`` and copy the pycocotools subfolder to the ``Tensorflow/models/research`` directory, as such: - .. code-block:: bash + .. code-block:: default git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI @@ -386,17 +378,17 @@ Install the Object Detection API ******************************** Installation of the Object Detection API is achieved by installing the ``object_detection`` package. This is done by running the following commands from within ``Tensorflow\models\research``: -.. code-block:: +.. code-block:: default # From within TensorFlow/models/research/ cp object_detection/packages/tf2/setup.py . - python -m pip install . + python -m pip install --use-feature=2020-resolver . .. note:: During the above installation, you may observe the following error: - .. code-block:: + .. code-block:: default ERROR: Command errored out with exit status 1: command: 'C:\Users\sglvladi\Anaconda3\envs\tf2\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"'; __file__='"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\sglvladi\AppData\Local\Temp\pip-record-wpn7b6qo\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\sglvladi\Anaconda3\envs\tf2\Include\pycocotools' @@ -429,7 +421,7 @@ Test your Installation To test the installation, run the following command from within ``Tensorflow\models\research``: -.. code-block:: +.. code-block:: default # From within TensorFlow/models/research/ python object_detection/builders/model_builder_tf2_test.py @@ -437,28 +429,42 @@ To test the installation, run the following command from within ``Tensorflow\mod Once the above is run, allow some time for the test to complete and once done you should observe a printout similar to the one below: -.. code-block:: +.. code-block:: default ... [ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config [ RUN ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s + I0608 18:49:13.183754 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update [ RUN ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s + I0608 18:49:13.186750 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold [ RUN ] ModelBuilderTF2Test.test_invalid_model_config_proto + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s + I0608 18:49:13.188250 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_model_config_proto [ RUN ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s + I0608 18:49:13.190746 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size [ RUN ] ModelBuilderTF2Test.test_session [ SKIPPED ] ModelBuilderTF2Test.test_session [ RUN ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s + I0608 18:49:13.193742 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor [ RUN ] ModelBuilderTF2Test.test_unknown_meta_architecture + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s + I0608 18:49:13.195241 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_meta_architecture [ RUN ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor + INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s + I0608 18:49:13.197239 29296 test_util.py:2102] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor ---------------------------------------------------------------------- - Ran 20 tests in 68.510s + Ran 24 tests in 29.980s OK (skipped=1) @@ -471,121 +477,4 @@ If you want to play around with some examples to see how this can be done, now w time to have a look at the :ref:`examples` section. -.. _labelImg_install: - -LabelImg Installation ---------------------- - -There exist several ways to install ``labelImg``. Below are 3 of the most common. - -Get from PyPI (Recommended) -*************************** -1. Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) -2. Run the following command to install ``labelImg``: - -.. code-block:: bash - - pip install labelImg - -3. ``labelImg`` can then be run as follows: - -.. code-block:: bash - - labelImg - # or - labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] - -Use precompiled binaries (Easy) -******************************* -Precompiled binaries for both Windows and Linux can be found `here `_ . - -Installation is the done in three simple steps: - -1. Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it. - -2. Download the latest binary for your OS from `here `_. and extract its contents under ``Tensorflow/addons/labelImg``. - -3. You should now have a single folder named ``addons/labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such: - -.. code-block:: bash - - TensorFlow/ - ├─ addons/ - │ └─ labelImg/ - └─ models/ - ├─ community/ - ├─ official/ - ├─ orbit/ - ├─ research/ - └─ ... - -4. ``labelImg`` can then be run as follows: - -.. code-block:: bash - - # From within Tensorflow/addons/labelImg - labelImg - # or - labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] - -Build from source (Hard) -************************ -The steps for installing from source follow below. - -**1. Download labelImg** - -- Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it. -- To download the package you can either use `Git `_ to clone the `labelImg repo `_ inside the ``TensorFlow\addons`` folder, or you can simply download it as a `ZIP `_ and extract it's contents inside the ``TensorFlow\addons`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``labelImg-master`` to ``labelImg``. [#]_ -- You should now have a single folder named ``addons\labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such: - -.. code-block:: bash - - TensorFlow/ - ├─ addons - │ └─ labelImg/ - └─ models/ - ├─ community/ - ├─ official/ - ├─ orbit/ - ├─ research/ - └─ ... - -.. [#] The latest repo commit when writing this tutorial is `8d1bd68 `_. - -**2. Install dependencies and compiling package** - -- Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) -- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following commands: - - .. tabs:: - - .. tab:: Windows - - .. code-block:: bash - - conda install pyqt=5 - pyrcc5 -o libs/resources.py resources.qrc - - .. tab:: Linux - - .. code-block:: bash - - sudo apt-get install pyqt5-dev-tools - sudo pip install -r requirements/requirements-linux-python3.txt - make qt5py3 - - -**3. Test your installation** - -- Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) -- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following command: - - .. code-block:: posh - - # From within Tensorflow/addons/labelImg - python labelImg.py - # or - python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] - - diff --git a/docs/source/issues.rst b/docs/source/issues.rst index 6138799..9f97ebd 100644 --- a/docs/source/issues.rst +++ b/docs/source/issues.rst @@ -10,7 +10,7 @@ Python crashes - TensorFlow GPU If you are using :ref:`tensorflow_gpu` and when you try to run some Python object detection script (e.g. :ref:`test_tf_models`), after a few seconds, Windows reports that Python has crashed then have a look at the `Anaconda/Command Prompt` window you used to run the script and check for a line similar (maybe identical) to the one below: - .. code-block:: python + .. code-block:: default 2018-03-22 03:07:54.623130: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:378] Loaded runtime CuDNN library: 7101 (compatibility version 7100) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration. @@ -25,7 +25,7 @@ Some known issues caused by the above are presented below: - Failure to restart training of a model. Look for the following errors in the debugging: - .. code-block:: python + .. code-block:: default 2018-03-23 03:03:10.326902: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED 2018-03-23 03:03:10.330475: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM @@ -43,7 +43,7 @@ If the issue persists, then you're probably running out of memory. Try closing d In some versions of Tensorflow, you may see errors that look similar to the ones below: -.. code-block:: python +.. code-block:: default ... WARNING:tensorflow:Entity > could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting >: AssertionError: Bad argument number for Name: 3, expecting 4 @@ -52,11 +52,11 @@ In some versions of Tensorflow, you may see errors that look similar to the ones These warnings appear to be harmless form my experience, however they can saturate the console with unnecessary messages, which makes it hard to scroll through the output of the training/evaluation process. -As reported `here `_, this issue seems to +As reported `here `__, this issue seems to be caused by a mismatched version of `gast `_. Simply downgrading gast to version ``0.2.2`` seems to remove the warnings. This can be done by running: -.. code-block:: bash +.. code-block:: default pip install gast==0.2.2 @@ -66,7 +66,7 @@ It is possible that when executing ``from object_detection.utils import label_ma get the above error. As per the discussion is in `this Stack Overflow thread `_, upgrading the Python protobuf version seems to solve this issue: -.. code-block:: +.. code-block:: default pip install --upgrade protobuf @@ -77,7 +77,7 @@ upgrading the Python protobuf version seems to solve this issue: When trying to export oyu trained model using the ``exporter_main_v2.py`` script, you may come across an error that looks like this: -.. code-block:: +.. code-block:: default :linenos: :emphasize-lines: 9 @@ -104,13 +104,13 @@ summary of how this can be done: - Change: - .. code-block:: python + .. code-block:: default raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x)) to: - .. code-block:: python + .. code-block:: default if not isinstance(x, str): raise TypeError('Expected Operation, Variable, or Tensor, got ' + str(x)) diff --git a/docs/source/scripts/generate_tfrecord.py b/docs/source/scripts/generate_tfrecord.py index caad456..944b9ec 100644 --- a/docs/source/scripts/generate_tfrecord.py +++ b/docs/source/scripts/generate_tfrecord.py @@ -80,15 +80,19 @@ def xml_to_csv(path): for xml_file in glob.glob(path + '/*.xml'): tree = ET.parse(xml_file) root = tree.getroot() + filename = root.find('filename').text + width = int(root.find('size').find('width').text) + height = int(root.find('size').find('height').text) for member in root.findall('object'): - value = (root.find('filename').text, - int(root.find('size')[0].text), - int(root.find('size')[1].text), - member[0].text, - int(member[4][0].text), - int(member[4][1].text), - int(member[4][2].text), - int(member[4][3].text) + bndbox = member.find('bndbox') + value = (filename, + width, + height, + member.find('name').text, + int(bndbox.find('xmin').text), + int(bndbox.find('ymin').text), + int(bndbox.find('xmax').text), + int(bndbox.find('ymax').text), ) xml_list.append(value) column_name = ['filename', 'width', 'height', diff --git a/docs/source/scripts/partition_dataset.py b/docs/source/scripts/partition_dataset.py index a9d0c11..697d1a5 100644 --- a/docs/source/scripts/partition_dataset.py +++ b/docs/source/scripts/partition_dataset.py @@ -32,7 +32,7 @@ def iterate_dir(source, dest, ratio, copy_xml): os.makedirs(test_dir) images = [f for f in os.listdir(source) - if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(.jpg|.jpeg|.png)$', f)] + if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(?i)(.jpg|.jpeg|.png)$', f)] num_images = len(images) num_test_images = math.ceil(ratio*num_images) @@ -95,4 +95,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/docs/source/training.rst b/docs/source/training.rst index 79d7e17..79fd0ba 100644 --- a/docs/source/training.rst +++ b/docs/source/training.rst @@ -5,7 +5,6 @@ So, up to now you should have done the following: - Installed TensorFlow (See :ref:`tf_install`) - Installed TensorFlow Object Detection API (See :ref:`tf_models_install`) -- Installed labelImg (See :ref:`labelImg_install`) Now that we have done all the above, we can start doing some cool stuff. Here we will see how you can train your own object detector, and since it is not as simple as it sounds, we will have a look at: @@ -21,7 +20,7 @@ Preparing the Workspace 1. If you have followed the tutorial, you should by now have a folder ``Tensorflow``, placed under ```` (e.g. ``C:/Users/sglvladi/Documents``), with the following directory tree: - .. code-block:: bash + .. code-block:: default TensorFlow/ ├─ addons/ (Optional) @@ -35,7 +34,7 @@ Preparing the Workspace 2. Now create a new folder under ``TensorFlow`` and call it ``workspace``. It is within the ``workspace`` that we will store all our training set-ups. Now let's go under workspace and create another folder named ``training_demo``. Now our directory structure should be as so: - .. code-block:: bash + .. code-block:: default TensorFlow/ ├─ addons/ (Optional) @@ -49,12 +48,13 @@ Preparing the Workspace └─ workspace/ └─ training_demo/ -3. The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train a different model. The typical structure for training folders is shown below. +3. The ``training_demo`` folder shall be our `training folder`, which will contain all files related to our model training. It is advisable to create a separate training folder each time we wish to train on a different dataset. The typical structure for training folders is shown below. - .. code-block:: bash + .. code-block:: default training_demo/ ├─ annotations/ + ├─ exported-models/ ├─ images/ │ ├─ test/ │ └─ train/ @@ -65,6 +65,7 @@ Preparing the Workspace Here's an explanation for each of the folders/filer shown in the above tree: - ``annotations``: This folder will be used to store all ``*.csv`` files and the respective TensorFlow ``*.record`` files, which contain the list of annotations for our dataset images. +- ``exported-models``: This folder will be used to store exported versions of our trained model(s). - ``images``: This folder contains a copy of all the images in our dataset, as well as the respective ``*.xml`` files produced for each one, once ``labelImg`` is used to annotate objects. * ``images/train``: This folder contains a copy of all images, and the respective ``*.xml`` files, which will be used to train our model. @@ -80,23 +81,146 @@ If you do not understand most of the things mentioned above, no need to worry, a Preparing the Dataset --------------------- +Annotate the Dataset +******************** + +.. _labelImg_install: + +Install LabelImg +~~~~~~~~~~~~~~~~ + +There exist several ways to install ``labelImg``. Below are 3 of the most common. + +.. _labelImg_install_pip: + +Using PIP (Recommended) +####################### +1. Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) +2. Run the following command to install ``labelImg``: + +.. code-block:: default + + pip install labelImg + +3. ``labelImg`` can then be run as follows: + +.. code-block:: default + + labelImg + # or + labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] + +Use precompiled binaries (Easy) +############################### +Precompiled binaries for both Windows and Linux can be found `here `__ . + +Installation is the done in three simple steps: + +1. Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it. + +2. Download the latest binary for your OS from `here `__. and extract its contents under ``Tensorflow/addons/labelImg``. + +3. You should now have a single folder named ``addons/labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such: + +.. code-block:: default + + TensorFlow/ + ├─ addons/ + │ └─ labelImg/ + └─ models/ + ├─ community/ + ├─ official/ + ├─ orbit/ + ├─ research/ + └─ ... + +4. ``labelImg`` can then be run as follows: + +.. code-block:: default + + # From within Tensorflow/addons/labelImg + labelImg + # or + labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] + +Build from source (Hard) +######################## +The steps for installing from source follow below. + +**1. Download labelImg** + +- Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it. +- To download the package you can either use `Git `_ to clone the `labelImg repo `_ inside the ``TensorFlow\addons`` folder, or you can simply download it as a `ZIP `_ and extract it's contents inside the ``TensorFlow\addons`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``labelImg-master`` to ``labelImg``. [#]_ +- You should now have a single folder named ``addons\labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such: + +.. code-block:: default + + TensorFlow/ + ├─ addons + │ └─ labelImg/ + └─ models/ + ├─ community/ + ├─ official/ + ├─ orbit/ + ├─ research/ + └─ ... + +.. [#] The latest repo commit when writing this tutorial is `8d1bd68 `_. + +**2. Install dependencies and compiling package** + +- Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) +- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following commands: + + .. tabs:: + + .. tab:: Windows + + .. code-block:: default + + conda install pyqt=5 + pyrcc5 -o libs/resources.py resources.qrc + + .. tab:: Linux + + .. code-block:: default + + sudo apt-get install pyqt5-dev-tools + sudo pip install -r requirements/requirements-linux-python3.txt + make qt5py3 + + +**3. Test your installation** + +- Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already) +- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following command: + + .. code-block:: default + + # From within Tensorflow/addons/labelImg + python labelImg.py + # or + python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] + + Annotate Images ~~~~~~~~~~~~~~~ -To annotate images we will be using the `labelImg `_ package. If you haven't installed the package yet, then have a look at :ref:`labelImg_install`. - - Once you have collected all the images to be used to test your model (ideally more than 100 per class), place them inside the folder ``training_demo/images``. -- Open a new `Anaconda/Command Prompt` window and ``cd`` into ``Tensorflow/addons/labelImg``. -- If (as suggested in :ref:`labelImg_install`) you created a separate Conda environment for ``labelImg`` then go ahead and activate it by running: +- Open a new `Terminal` window. +- Next go ahead and start ``labelImg``, pointing it to your ``training_demo/images`` folder. - .. code-block:: bash + - If you installed ``labelImg`` :ref:`labelImg_install_pip`: - activate labelImg + .. code-block:: default -- Next go ahead and start ``labelImg``, pointing it to your ``training_demo/images`` folder. + labelImg /TensorFlow/workspace/training_demo/images + + - Othewise, ``cd`` into ``Tensorflow/addons/labelImg`` and run: - .. code-block:: bash + .. code-block:: default + # From within Tensorflow/addons/labelImg python labelImg.py ../../workspace/training_demo/images - A File Explorer Dialog windows should open, which points to the ``training_demo/images`` folder. @@ -109,27 +233,54 @@ Once open, you should see a window similar to the one below: :alt: alternate text :align: center -I won't be covering a tutorial on how to use ``labelImg``, but you can have a look at `labelImg's repo `_ for more details. A nice Youtube video demonstrating how to use ``labelImg`` is also available `here `_. What is important is that once you annotate all your images, a set of new ``*.xml`` files, one for each image, should be generated inside your ``training_demo/images`` folder. +I won't be covering a tutorial on how to use ``labelImg``, but you can have a look at `labelImg's repo `_ for more details. A nice Youtube video demonstrating how to use ``labelImg`` is also available `here `__. What is important is that once you annotate all your images, a set of new ``*.xml`` files, one for each image, should be generated inside your ``training_demo/images`` folder. + .. _image_partitioning_sec: Partition the Dataset -~~~~~~~~~~~~~~~~~~~~~ +********************* -Once you have finished annotating your image dataset, it is a general convention to use only part of it for training, and the rest is used for evaluation purposes (e.g. as discussed in :ref:`evaluation_sec`). +Once you have finished annotating your image dataset, it is a general convention to use only part +of it for training, and the rest is used for evaluation purposes (e.g. as discussed in +:ref:`evaluation_sec`). -Typically, the ratio is 90%/10%, i.e. 90% of the images are used for training and the rest 10% is maintained for testing, but you can chose whatever ratio suits your needs. +Typically, the ratio is 9:1, i.e. 90% of the images are used for training and the rest 10% is +maintained for testing, but you can chose whatever ratio suits your needs. -Once you have decided how you will be splitting your dataset, copy all training images, together with their corresponding ``*.xml`` files, and place them inside the ``training_demo/images/train`` folder. Similarly, copy all testing images, with their ``*.xml`` files, and paste them inside ``training_demo/images/test``. +Once you have decided how you will be splitting your dataset, copy all training images, together +with their corresponding ``*.xml`` files, and place them inside the ``training_demo/images/train`` +folder. Similarly, copy all testing images, with their ``*.xml`` files, and paste them inside +``training_demo/images/test``. -For lazy people like myself, who cannot be bothered to do the above, I have put tugether a simple script that automates the above process: +For lazy people like myself, who cannot be bothered to do the above, I have put together a simple +script that automates the above process: .. literalinclude:: scripts/partition_dataset.py +- Under the ``TensorFlow`` folder, create a new folder ``TensorFlow/scripts``, which we can use to store some useful scripts. +- To make things even tidier, let's create a new folder ``TensorFlow/scripts/preprocessing``, where we shall store scripts that we can use to preprocess our training inputs. Below is out ``TensorFlow`` directory tree structure, up to now: + + .. code-block:: default + + TensorFlow/ + ├─ addons/ (Optional) + │ └─ labelImg/ + ├─ models/ + │ ├─ community/ + │ ├─ official/ + │ ├─ orbit/ + │ ├─ research/ + │ └─ ... + ├─ scripts/ + │ └─ preprocessing/ + └─ workspace/ + └─ training_demo/ + - Click :download:`here ` to download the above script and save it inside ``TensorFlow/scripts/preprocessing``. - Then, ``cd`` into ``TensorFlow/scripts/preprocessing`` and run: - .. code-block:: + .. code-block:: default python partition_dataset.py -x -i [PATH_TO_IMAGES_FOLDER] -r 0.1 @@ -144,13 +295,13 @@ safely copied over, you can delete the images under ``training_demo/images`` man Create Label Map -~~~~~~~~~~~~~~~~ +**************** TensorFlow requires a label map, which namely maps each of the used labels to an integer values. This label map is used both by the training and detection processes. Below we show an example label map (e.g ``label_map.pbtxt``), assuming that our dataset containes 2 labels, ``dogs`` and ``cats``: -.. code-block:: json +.. code-block:: default item { id: 1 @@ -165,40 +316,18 @@ Below we show an example label map (e.g ``label_map.pbtxt``), assuming that our Label map files have the extention ``.pbtxt`` and should be placed inside the ``training_demo/annotations`` folder. Create TensorFlow Records -~~~~~~~~~~~~~~~~~~~~~~~~~ +************************* Now that we have generated our annotations and split our dataset into the desired training and testing subsets, it is time to convert our annotations into the so called ``TFRecord`` format. -Before we proceed to describe the above steps, let's create a directory where we can store some -scripts. Under the ``TensorFlow`` folder, create a new folder ``TensorFlow/scripts``, which we can -use to store some useful scripts. To make things even tidier, let's create a new folder -``TensorFlow/scripts/preprocessing``, where we shall store scripts that we can use to preprocess -our training inputs. Below is out ``TensorFlow`` directory tree structure, up to now: - -.. code-block:: bash - - TensorFlow/ - ├─ addons/ (Optional) - │ └─ labelImg/ - ├─ models/ - │ ├─ community/ - │ ├─ official/ - │ ├─ orbit/ - │ ├─ research/ - │ └─ ... - ├─ scripts/ - │ └─ preprocessing/ - └─ workspace/ - └─ training_demo/ - Convert ``*.xml`` to ``*.record`` -********************************* - -To do this we can write a simple script that iterates through all ``*.xml`` files in the ``training_demo/images/train`` and ``training_demo/images/test`` folders, and generates a ``*.record`` file for each of the two. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Here is an example script that allows us to do just that: +To do this we can write a simple script that iterates through all ``*.xml`` files in the +``training_demo/images/train`` and ``training_demo/images/test`` folders, and generates a +``*.record`` file for each of the two. Here is an example script that allows us to do just that: .. literalinclude:: scripts/generate_tfrecord.py @@ -206,7 +335,7 @@ Here is an example script that allows us to do just that: - Click :download:`here ` to download the above script and save it inside ``TensorFlow/scripts/preprocessing``. - Install the ``pandas`` package: - .. code-block:: + .. code-block:: default conda install pandas # Anaconda # or @@ -214,7 +343,7 @@ Here is an example script that allows us to do just that: - Finally, ``cd`` into ``TensorFlow/scripts/preprocessing`` and run: - .. code-block:: + .. code-block:: default # Create train data: python generate_tfrecord.py -x [PATH_TO_IMAGES_FOLDER]/train -l [PATH_TO_ANNOTATIONS_FOLDER]/label_map.pbtxt -o [PATH_TO_ANNOTATIONS_FOLDER]/train.record @@ -243,7 +372,7 @@ model, since it provides a relatively good trade-off between performance and spe exist a number of other models you can use, all of which are listed in `TensorFlow 2 Detection Model Zoo `_. Download Pre-Trained Model -~~~~~~~~~~~~~~~~~~~~~~~~~~ +************************** To begin with, we need to download the latest pre-trained network for the model we wish to use. This can be done by simply clicking on the name of the desired model in the table found in `TensorFlow 2 Detection Model Zoo `_. @@ -255,22 +384,22 @@ folder is opened, and extract its contents inside the folder ``training_demo/pre Since we downloaded the `SSD ResNet50 V1 FPN 640x640 `_ model, our ``training_demo`` directory should now look as follows: - .. code-block:: bash + .. code-block:: default training_demo/ ├─ ... ├─ pre-trained-models/ - | └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/ - | ├─ checkpoint/ + │ └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/ + │ ├─ checkpoint/ │ ├─ saved_model/ - | └─ pipeline.config + │ └─ pipeline.config └─ ... Note that the above process can be repeated for all other pre-trained models you wish to experiment with. For example, if you wanted to also configure a training job for the `EfficientDet D1 640x640 `_ model, you can download the model and after extracting its context the demo directory will be: - .. code-block:: bash + .. code-block:: default training_demo/ ├─ ... @@ -278,28 +407,30 @@ model, you can download the model and after extracting its context the demo dire │ ├─ efficientdet_d1_coco17_tpu-32/ │ │ ├─ checkpoint/ │ │ ├─ saved_model/ - | │ └─ pipeline.config + │ │ └─ pipeline.config │ └─ ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/ - | ├─ checkpoint/ + │ ├─ checkpoint/ │ ├─ saved_model/ - | └─ pipeline.config + │ └─ pipeline.config └─ ... +.. _training_pipeline_conf: + Configure the Training Pipeline -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +******************************* Now that we have downloaded and extracted our pre-trained model, let's create a directory for our training job. Under the ``training_demo/models`` create a new directory named ``my_ssd_resnet50_v1_fpn`` and copy the ``training_demo/pre-trained-models/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/pipeline.config`` file inside the newly created directory. Our ``training_demo/models`` directory should now look like this: - .. code-block:: bash + .. code-block:: default training_demo/ ├─ ... ├─ models/ │ └─ my_ssd_resnet50_v1_fpn/ - | └─ pipeline.config + │ └─ pipeline.config └─ ... Now, let's have a look at the changes that we shall need to apply to the ``pipeline.config`` file @@ -326,14 +457,14 @@ to train our model. Now, to initiate a new training job, open a new `Terminal`, ``cd`` inside the ``training_demo`` folder and run the following command: -.. code-block:: +.. code-block:: default python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config Once the training process has been initiated, you should see a series of print outs similar to the one below (plus/minus some warnings): -.. code-block:: +.. code-block:: default ... WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10.gamma @@ -393,7 +524,7 @@ change depending on the installed version of Tensorflow. As you will have seen in various parts of this tutorial, we have mentioned a few times the optional utilisation of the COCO evaluation metrics. Also, under section -:ref:`_image_partitioning_sec` we partitioned our dataset in two parts, where one was to be used +:ref:`image_partitioning_sec` we partitioned our dataset in two parts, where one was to be used for training and the other for evaluation. In this section we will look at how we can use these metrics, along with the test images, to get a sense of the performance achieved by our model as it is being trained. @@ -407,20 +538,24 @@ this evaluation are summarised in the form of some metrics, which can be examine The steps to run the evaluation are outlined below: -1. Firstly we need to download and install the metrics we want to use. - - For a description of the supported object detection evaluation metrics, see `here `_. - - The process of installing the COCO evaluation metrics is described in :ref:`tf_models_install_coco`. -2. Secondly, we must modify the configuration pipeline (``*.config`` script). - - See lines 178 and 181 of the script in :ref:`config_training_pipeline_sec`. -3. The third step is to actually run the evaluation. To do so, open a new `Terminal`, ``cd`` inside the ``training_demo`` folder and run the following command: +#. Firstly we need to download and install the metrics we want to use. + + - For a description of the supported object detection evaluation metrics, see `here `__. + - The process of installing the COCO evaluation metrics is described in :ref:`tf_models_install_coco`. + +#. Secondly, we must modify the configuration pipeline (``*.config`` script). + + - See lines 178-179 of the script in :ref:`training_pipeline_conf`. - .. code-block:: +#. The third step is to actually run the evaluation. To do so, open a new `Terminal`, ``cd`` inside the ``training_demo`` folder and run the following command: + + .. code-block:: default python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config --checkpoint_dir=models/my_ssd_resnet50_v1_fpn Once the above is run, you should see a checkpoint similar to the one below (plus/minus some warnings): - .. code-block:: + .. code-block:: default ... WARNING:tensorflow:From C:\Users\sglvladi\Anaconda3\envs\tf2\lib\site-packages\object_detection\inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version. @@ -460,14 +595,14 @@ To start a new TensorBoard server, we follow the following steps: - Open a new `Anaconda/Command Prompt` - Activate your TensorFlow conda environment (if you have one), e.g.: - .. code-block:: bash + .. code-block:: default activate tensorflow_gpu - ``cd`` into the ``training_demo`` folder. - Run the following command: - .. code-block:: bash + .. code-block:: default tensorboard --logdir=models/my_ssd_resnet50_v1_fpn @@ -475,10 +610,10 @@ The above command will start a new TensorBoard server, which (by default) listen your machine. Assuming that everything went well, you should see a print-out similar to the one below (plus/minus some warnings): - .. code-block:: bash +.. code-block:: default - ... - TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit) + ... + TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit) Once this is done, go to your browser and type ``http://localhost:6006/`` in your address bar, following which you should be presented with a dashboard similar to the one shown below @@ -491,8 +626,8 @@ following which you should be presented with a dashboard similar to the one show -Exporting a Trained Inference Graph ------------------------------------ +Exporting a Trained Model +------------------------- Once your training job is complete, you need to extract the newly trained inference graph, which will be later used to perform the object detection. This can be done as follows: @@ -500,15 +635,31 @@ will be later used to perform the object detection. This can be done as follows: - Copy the ``TensorFlow/models/research/object_detection/exporter_main_v2.py`` script and paste it straight into your ``training_demo`` folder. - Now, open a `Terminal`, ``cd`` inside your ``training_demo`` folder, and run the following command: -.. code-block:: bash +.. code-block:: default - python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_efficientdet_d1\pipeline.config --trained_checkpoint_dir .\models\my_efficientdet_d1\ --output_directory .\trained-inference-graphs\output + python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_efficientdet_d1\pipeline.config --trained_checkpoint_dir .\models\my_efficientdet_d1\ --output_directory .\exported-models\my_model + +After the above process has completed, you should find a new folder ``my_model`` under the +``training_demo/exported-models``, that has the following structure: + + .. code-block:: default + + training_demo/ + ├─ ... + ├─ exported-models/ + │ └─ my_model/ + │ ├─ checkpoint/ + │ ├─ saved_model/ + │ └─ pipeline.config + └─ ... + +This model can then be used to perform inference. .. note:: You may get the following error when trying to export your model: - .. code-block:: + .. code-block:: default Traceback (most recent call last): File ".\exporter_main_v2.py", line 126, in