PyTorch implementation of openpose including Body and Hand Pose Estimation, and the PyTorch model is directly converted from openpose caffemodel by caffemodel2pytorch.
For example, export body25 pytorch model pose_iter_584000.caffemodel.pt from the caffe model pose_iter_584000.caffemodel:
cd caffemodel2pytorch
python -m caffemodel2pytorch pose_iter_584000.caffemodel
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.
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.
This repository is based on the pure python wrapper repository of openpose pytorch implementation , maybe it helps you to implement a standalone hand keypoint detector.
Don't be mean to star this repo and the reference repositories at the end if it helps your research. :)
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)
Install other requirements with pip
pip install -r requirements.txt
original pytorch-openpose models:
body25 models:
*.pth and *.pt 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:
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).
- convert caffemodel to pytorch.
- Body Pose Estimation.
- Hand Pose Estimation.
- Performance test.
- Speed up.
body25_model
body_coco_model and hand
body_coco_model + hand_model:
body25_model + hand_model:
Attribution: this video.
Attribution: this video.
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}
}
@code{pytorch-openpose,
author={Hzzone},
year={2021},
website={https://github.com/Hzzone/pytorch-openpose.git}
}
@code{caffemodel2pytorch,
author={vadimkantorov},
year={2021},
website={https://github.com/vadimkantorov/caffemodel2pytorch.git}
}
@code{pytorch_openpose_body_25,
author={beingjoey},
year={2022},
website={https://github.com/beingjoey/pytorch_openpose_body_25.git}
}
@code{openpose,
author={CMU-Perceptual-Computing-Lab},
year={2022},
website={https://github.com/CMU-Perceptual-Computing-Lab/openpose.git}
}
All the pytorch models used in this repo are converted from openpose released caffe models. OpenPose.PyTorch is freely available for free non-commercial use, and may be redistributed under some conditions, which is same as openpose. Please see the license of openpose for further details.