## 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)
* [google drive](https://drive.google.com/drive/folders/1JsvI4M4ZTg98fmnCZLFM-3TeovnCRElG?usp=sharing)
`*.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/
### Demo
#### Skeleton
![](images/skeleton.jpg)
#### Body Pose Estimation
![](images/body_preview.jpg)
#### Hand Pose Estimation
![](images/hand_preview.png)
#### Body + Hand
![](images/demo_preview.png)
#### Video Body
![](images/kc-e129SBb4-sample.processed.gif)
#### Video Hand
![](images/yOAmYSW3WyU-sample.small.processed.gif)
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OpenPose_基于Pytorch实现OpenPose算法_支持手部和人体姿态估计_附流程教程+项目源码_优质项目分享
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OpenPose_基于Pytorch实现OpenPose算法_支持手部和人体姿态估计_附流程教程+项目源码_优质项目分享.zip (31个子文件)
OpenPose_基于Pytorch实现OpenPose算法_支持手部和人体姿态估计_附流程教程+项目源码_优质项目分享
demo_camera.py 1KB
src
__init__.py 0B
hand_model_outputsize.py 355B
util.py 9KB
body.py 11KB
model.py 9KB
hand_model_output_size.json 14KB
hand.py 3KB
demo_video.py 4KB
model
.gitkeep 0B
requirements.txt 54B
images
yOAmYSW3WyU-sample.small.processed.gif 5.7MB
ski.jpg 299KB
body_preview_estimation.jpg 156KB
hand_preview.png 74KB
hand.jpg 9KB
demo_preview.png 150KB
skeleton.jpg 156KB
detect_hand_preview.jpg 59KB
demo.jpg 16KB
keypoints_pose_18.png 11KB
body_preview_keypoints.jpg 137KB
hand_preview_estimation.png 55KB
kc-e129SBb4-sample.processed.gif 10.75MB
keypoints_hand.png 181KB
body_preview.jpg 1.03MB
demo.py 1KB
README.md 3KB
notebooks
detectHand.ipynb 154KB
network_graph.ipynb 74KB
hand.ipynb 680KB
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