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WholeBody
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<h1>WholeBody</h1>
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<section class="tex2jax_ignore mathjax_ignore" id="wholebody">
<h1>WholeBody<a class="headerlink" href="#wholebody" title="Permalink to this headline">#</a></h1>
<div style="text-align: right"> by <a href="https://www.linkedin.com/in/duncan-zauss/">Duncan Zauss</a>, 07/05/2021</div> <br />
This is an extension to OpenPifPaf to detect body, foot, face and hand keypoints, which sum up to 133 keypoints per person. Thus, this plugin is especially useful if fine-grained face, hand or foot keypoints are required. The annotations for these keypoints are taken from the <a href="https://github.com/jin-s13/COCO-WholeBody">COCO WholeBody dataset</a>.<section id="prediction">
<h2>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">#</a></h2>
<p>We provide two pretrained models for predicting the 133 keypoints of the COCO WholeBody dataset. The models can be called with <code class="docutils literal notranslate"><span class="pre">--checkpoint=shufflenetv2k16-wholebody</span></code> or <code class="docutils literal notranslate"><span class="pre">--checkpoint=shufflenetv2k30-wholebody</span></code>. Below an example prediction is shown.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%</span><span class="k">bash</span>
python -m openpifpaf.predict wholebody/soccer.jpeg \
--checkpoint=shufflenetv2k30-wholebody --line-width=2 --image-output
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO:__main__:neural network device: cpu (CUDA available: False, count: 0)
INFO:openpifpaf.decoder.factory:No specific decoder requested. Using the first one from:
--decoder=cifcaf:0
--decoder=posesimilarity:0
Use any of the above arguments to select one or multiple decoders and to suppress this message.
INFO:openpifpaf.predictor:neural network device: cpu (CUDA available: False, count: 0)
INFO:openpifpaf.decoder.cifcaf:annotations 2: [127, 128]
INFO:openpifpaf.predictor:batch 0: wholebody/soccer.jpeg
src/openpifpaf/csrc/src/cif_hr.cpp:102: UserInfo: resizing cifhr buffer
src/openpifpaf/csrc/src/occupancy.cpp:53: UserInfo: resizing occupancy buffer
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">IPython</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">Image</span><span class="p">(</span><span class="s1">'wholebody/soccer.jpeg.predictions.jpeg'</span><span class="p">)</span>
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<p>Image credit: <a class="reference external" href="https://de.wikipedia.org/wiki/Kamil_Vacek#/media/Datei:Kamil_Vacek_20200627.jpg">Photo</a> by <a class="reference external" href="https://commons.wikimedia.org/wiki/User:Lokomotive74">Lokomotive74</a> which is licensed under <a class="reference external" href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a>.</p>
</section>
<section id="visualization-of-the-additional-keypoints">
<h2>Visualization of the additional keypoints<a class="headerlink" href="#visualization-of-the-additional-keypoints" title="Permalink to this headline">#</a></h2>
<p>Original MS COCO skeleton / COCO WholeBody skeleton</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># HIDE CODE</span>
<span class="c1"># first make an annotation</span>
<span class="n">ann_coco</span> <span class="o">=</span> <span class="n">openpifpaf</span><span class="o">.</span><span class="n">Annotation</span><span class="o">.</span><span class="n">from_cif_meta</span><span class="p">(</span>
<span class="n">openpifpaf</span><span class="o">.</span><span class="n">plugins</span><span class="o">.</span><span class="n">coco</span><span class="o">.</span><span class="n">CocoKp</span><span class="p">()</span><span class="o">.</span><span class="n">head_metas</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">ann_wholebody</span> <span class="o">=</span> <span class="n">openpifpaf</span><span class="o">.</span><span class="n">Annotation</span><span class="o">.</span><span class="n">from_cif_meta</span><span class="p">(</span>
<span class="n">openpifpaf</span><span class="o">.</span><span class="n">plugins</span><span class="o">.</span><span class="n">wholebody</span><span class="o">.</span><span class="n">Wholebody</span><span class="p">()</span><span class="o">.</span><span class="n">head_metas</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># visualize the annotation</span>
<span class="n">openpifpaf</span><span class="o">.</span><span class="n">show</span><span class="o">.</span><span class="n">KeypointPainter</span><span class="o">.</span><span class="n">show_joint_scales</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">openpifpaf</span><span class="o">.</span><span class="n">show</span><span class="o">.</span><span class="n">KeypointPainter</span><span class="o">.</span><span class="n">line_width</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">keypoint_painter</span> <span class="o">=</span> <span class="n">openpifpaf</span><span class="o">.</span><span class="n">show</span><span class="o">.</span><span class="n">KeypointPainter</span><span class="p">()</span>
<span class="k">with</span> <span class="n">openpifpaf</span><span class="o">.</span><span class="n">show</span><span class="o">.</span><span class="n">Canvas</span><span class="o">.</span><span class="n">annotation</span><span class="p">(</span><span class="n">ann_wholebody</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="k">as</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">):</span>
<span class="n">keypoint_painter</span><span class="o">.</span><span class="n">annotation</span><span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ann_coco</span><span class="p">)</span>
<span class="n">keypoint_painter</span><span class="o">.</span><span class="n">annotation</span><span class="p">(</span><span class="n">ax2</span><span class="p">,</span> <span class="n">ann_wholebody</span><span class="p">)</span>
</pre></div>
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<img alt="_images/plugins_wholebody_7_0.png" src="_images/plugins_wholebody_7_0.png" />
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</section>
<section id="train">
<h2>Train<a class="headerlink" href="#train" title="Permalink to this headline">#</a></h2>
<p>If you don’t want to use the pre-trained model, you can train a model from scratch.
To train you first need to download the wholebody into your MS COCO dataset folder:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wget</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">DuncanZauss</span><span class="o">/</span><span class="n">openpifpaf_assets</span><span class="o">/</span><span class="n">releases</span><span class="o">/</span><span class="n">download</span><span class="o">/</span><span class="n">v0</span><span class="mf">.1.0</span><span class="o">/</span><span class="n">person_keypoints_train2017_wholebody_pifpaf_style</span><span class="o">.</span><span class="n">json</span> <span class="o">-</span><span class="n">O</span> <span class="o">/<</span><span class="n">PathToYourMSCOCO</span><span class="o">>/</span><span class="n">data</span><span class="o">-</span><span class="n">mscoco</span><span class="o">/</span><span class="n">annotations</span>
<span class="n">wget</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">DuncanZauss</span><span class="o">/</span><span class="n">openpifpaf_assets</span><span class="o">/</span><span class="n">releases</span><span class="o">/</span><span class="n">download</span><span class="o">/</span><span class="n">v0</span><span class="mf">.1.0</span><span class="o">/</span><span class="n">person_keypoints_val2017_wholebody_pifpaf_style</span><span class="o">.</span><span class="n">json</span> <span class="o">-</span><span class="n">O</span> <span class="o">/<</span><span class="n">PathToYourMSCOCO</span><span class="o">>/</span><span class="n">data</span><span class="o">-</span><span class="n">mscoco</span><span class="o">/</span><span class="n">annotations</span>
</pre></div>
</div>
<p>Note: The pifpaf style annotation files were create with <a class="reference external" href="https://github.com/openpifpaf/openpifpaf/blob/main/openpifpaf/plugins/wholebody/helper_scripts/Get_annotations_from_coco_wholebody.py">Get_annotations_from_coco_wholebody.py</a>. If you want to create your own annotation files from coco wholebody, you need to download the original files from the <a class="reference external" href="https://github.com/jin-s13/COCO-WholeBody#download">COCO WholeBody page</a> and then create the pifpaf readable json files with <a class="reference external" href="https://github.com/openpifpaf/openpifpaf/blob/main/openpifpaf/plugins/wholebody/helper_scripts/Get_annotations_from_coco_wholebody.py">Get_annotations_from_coco_wholebody.py</a>. This can be useful if you for example only want to use a subset of images for training.</p>
<p>Finally you can train the model (Note: This can take several days, even on the good GPUs):<br/></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">openpifpaf</span><span class="o">.</span><span class="n">train</span> <span class="o">--</span><span class="n">lr</span><span class="o">=</span><span class="mf">0.0001</span> <span class="o">--</span><span class="n">momentum</span><span class="o">=</span><span class="mf">0.95</span> <span class="o">--</span><span class="n">b</span><span class="o">-</span><span class="n">scale</span><span class="o">=</span><span class="mf">3.0</span> <span class="o">--</span><span class="n">epochs</span><span class="o">=</span><span class="mi">150</span> <span class="o">--</span><span class="n">lr</span><span class="o">-</span><span class="n">decay</span> <span class="mi">130</span> <span class="mi">140</span> <span class="o">--</span><span class="n">lr</span><span class="o">-</span><span class="n">decay</span><span class="o">-</span><span class="n">epochs</span><span class="o">=</span><span class="mi">10</span> <span class="o">--</span><span class="n">batch</span><span class="o">-</span><span class="n">size</span><span class="o">=</span><span class="mi">16</span> <span class="o">--</span><span class="n">weight</span><span class="o">-</span><span class="n">decay</span><span class="o">=</span><span class="mf">1e-5</span> <span class="o">--</span><span class="n">dataset</span><span class="o">=</span><span class="n">wholebody</span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">upsample</span><span class="o">=</span><span class="mi">2</span> <span class="o">--</span><span class="n">basenet</span><span class="o">=</span><span class="n">shufflenetv2k16</span> <span class="o">--</span><span class="n">loader</span><span class="o">-</span><span class="n">workers</span><span class="o">=</span><span class="mi">16</span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">train</span><span class="o">-</span><span class="n">annotations</span><span class="o">=<</span><span class="n">PathToYourMSCOCO</span><span class="o">>/</span><span class="n">data</span><span class="o">-</span><span class="n">mscoco</span><span class="o">/</span><span class="n">annotations</span><span class="o">/</span><span class="n">person_keypoints_train2017_wholebody_pifpaf_style</span><span class="o">.</span><span class="n">json</span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">val</span><span class="o">-</span><span class="n">annotations</span><span class="o">=<</span><span class="n">PathToYourMSCOCO</span><span class="o">>/</span><span class="n">data</span><span class="o">-</span><span class="n">mscoco</span><span class="o">/</span><span class="n">annotations</span><span class="o">/</span><span class="n">person_keypoints_val2017_wholebody_pifpaf_style</span><span class="o">.</span><span class="n">json</span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">train</span><span class="o">-</span><span class="n">image</span><span class="o">-</span><span class="nb">dir</span><span class="o">=<</span><span class="n">COCO_train_image_dir</span><span class="o">></span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">val</span><span class="o">-</span><span class="n">image</span><span class="o">-</span><span class="nb">dir</span><span class="o">=<</span><span class="n">COCO_val_image_dir</span><span class="o">></span>
</pre></div>
</div>
</section>
<section id="evaluation">
<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline">#</a></h2>
<p>To evaluate your network you can use the following command. Important note: For evaluation you will need the original <a class="reference external" href="https://drive.google.com/file/d/1N6VgwKnj8DeyGXCvp1eYgNbRmw6jdfrb/view">validation annotation file from COCO WholeBody</a>, which has a different format than the files that are used for training. We use this different annotation format as we use the <a class="reference external" href="https://github.com/jin-s13/xtcocoapi">extended pycocotools</a> for evaluation as proposed by the authors of COCO WholeBody. You can run the evaluation with:<br/></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">openpifpaf</span><span class="o">.</span><span class="n">eval</span> <span class="o">--</span><span class="n">dataset</span><span class="o">=</span><span class="n">wholebody</span> <span class="o">--</span><span class="n">checkpoint</span><span class="o">=</span><span class="n">shufflenetv2k16</span><span class="o">-</span><span class="n">wholebody</span> <span class="o">--</span><span class="n">force</span><span class="o">-</span><span class="n">complete</span><span class="o">-</span><span class="n">pose</span> <span class="o">--</span><span class="n">seed</span><span class="o">-</span><span class="n">threshold</span><span class="o">=</span><span class="mf">0.2</span> <span class="o">--</span><span class="n">loader</span><span class="o">-</span><span class="n">workers</span><span class="o">=</span><span class="mi">16</span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">val</span><span class="o">-</span><span class="n">annotations</span><span class="o">=<</span><span class="n">PathToTheOriginalCOCOWholeBodyAnnotations</span><span class="o">>/</span><span class="n">coco_wholebody_val_v1</span><span class="mf">.0</span><span class="o">.</span><span class="n">json</span> <span class="o">--</span><span class="n">wholebody</span><span class="o">-</span><span class="n">val</span><span class="o">-</span><span class="n">image</span><span class="o">-</span><span class="nb">dir</span><span class="o">=<</span><span class="n">COCO_val_image_dir</span><span class="o">></span>
</pre></div>
</div>
</section>
<section id="using-only-a-subset-of-keypoints">
<h2>Using only a subset of keypoints<a class="headerlink" href="#using-only-a-subset-of-keypoints" title="Permalink to this headline">#</a></h2>
<p>If you only want to train on a subset of keypoints, e.g. if you do not need the facial keypoints and only want to train on the body, foot and hand keypoints, it should be fairly easy to just train on this subset. You will need to:</p>
<ul class="simple">
<li><p>Download the original annotation files from the <a class="reference external" href="https://github.com/jin-s13/COCO-WholeBody#download">Coco Whole body page</a>. Create a new annotations file with <a class="reference external" href="https://github.com/openpifpaf/openpifpaf/blob/main/openpifpaf/plugins/wholebody/helper_scripts/Get_annotations_from_coco_wholebody.py">Get_annotations_from_coco_wholebody.py</a>. Set <code class="docutils literal notranslate"><span class="pre">ann_types</span></code>to the keypoints that you would like to use and create the train and val json file. You can use <a class="reference external" href="https://github.com/openpifpaf/openpifpaf/blob/main/openpifpaf/plugins/wholebody/helper_scripts/Visualize_annotations.py.py">Visualize_annotations.py</a> to verify that the json file was created correctly.</p></li>
<li><p>In the <a class="reference external" href="https://github.com/openpifpaf/openpifpaf/blob/main/openpifpaf/plugins/wholebody/constants.py">constants.py</a> file comment out all the parts of the skeleton, pose, HFLIP, SIGMA and keypoint names that you do not need. All these constants are already split up in the body parts. The numbering of the joints may now be different (e.g. when you discard the face kpts, but keep the hand kpts), so you need to adjust the numbers in the skeleton definitions to be consisten with the new numbering of the joints.</p></li>
<li><p>That’s it! You can train the model with a subset of keypoints.</p></li>
</ul>
</section>
<section id="links">
<h2>Links<a class="headerlink" href="#links" title="Permalink to this headline">#</a></h2>
<p><a class="reference external" href="https://huggingface.co/spaces/akhaliq/Keypoint_Communities">HuggingFace Demo</a>, <a class="reference external" href="https://paperswithcode.com/paper/keypoint-communities">PapersWithCode</a>.</p>
</section>
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