pytorch-openpose:openpose的pytorch实现，包括手和身体姿势估计

## pytorch-openpose pytorch implementation of [openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) including **Body and Hand Pose Estimation**, and the pytorch model is directly converted from [openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) caffemodel by [caffemodel2pytorch](https://github.com/vadimkantorov/caffemodel2pytorch). You could implement face keypoint detection in the same way if you are interested in. Pay attention to that the face keypoint detector was trained using the procedure described in [Simon et al. 2017] for hands. openpose detects hand by the result of body pose estimation, please refer to the code of [handDetector.cpp](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp). In the paper, it states as: ``` This is an important detail: to use the keypoint detector in any practical situation, we need a way to generate this bounding box. We directly use the body pose estimation models from [29] and [4], and use the wrist and elbow position to approximate the hand location, assuming the hand extends 0.15 times the length of the forearm in the same direction. ``` If anybody wants a pure python wrapper, please refer to my [pytorch implementation](https://github.com/Hzzone/pytorch-openpose) of openpose, maybe it helps you to implement a standalone hand keypoint detector. Don't be mean to star this repo if it helps your research. ### Getting Started #### Install Requriements Create a python 3.7 environement, eg: conda create -n pytorch-openpose python=3.7 conda activate pytorch-openpose Install pytorch by following the quick start guide here (use pip) https://download.pytorch.org/whl/torch_stable.html Install other requirements with pip pip install -r requirements.txt #### Download the Models * [dropbox](https://www.dropbox.com/sh/7xbup2qsn7vvjxo/AABWFksdlgOMXR_r5v3RwKRYa?dl=0) * [baiduyun](https://pan.baidu.com/s/1IlkvuSi0ocNckwbnUe7j-g) `*.pth` files are pytorch model, you could also download caffemodel file if you want to use caffe as backend. Download the pytorch models and put them in a directory named `model` in the project root directory #### Run the Demo Run: python demo_camera.py to run a demo with a feed from your webcam or run python demo.py to use a image from the images folder or run python demo_video.py <video-file> to process a video file (requires [ffmpeg-python][ffmpeg]). [ffmpeg]: https://pypi.org/project/ffmpeg-python/ ### Todo list - [x] convert caffemodel to pytorch. - [x] Body Pose Estimation. - [x] Hand Pose Estimation. - [ ] Performance test. - [ ] Speed up. ### Demo #### Skeleton  #### Body Pose Estimation  #### Hand Pose Estimation  #### Body + Hand  #### Video Body  Attribution: [this video](https://www.youtube.com/watch?v=kc-e129SBb4). #### Video Hand  Attribution: [this video](https://www.youtube.com/watch?v=yOAmYSW3WyU). ### Citation Please cite these papers in your publications if it helps your research (the face keypoint detector was trained using the procedure described in [Simon et al. 2017] for hands): ``` @inproceedings{cao2017realtime, author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh}, booktitle = {CVPR}, title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields}, year = {2017} } @inproceedings{simon2017hand, author = {Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh}, booktitle = {CVPR}, title = {Hand Keypoint Detection in Single Images using Multiview Bootstrapping}, year = {2017} } @inproceedings{wei2016cpm, author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh}, booktitle = {CVPR}, title = {Convolutional pose machines}, year = {2016} } ```