This is a third-party tensorflow implementation for BlazePose.
The original paper is "BlazePose: On-device Real-time Body Pose tracking" by Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, and Matthias Grundmann. Available on arXiv.
Since I do not have the full settings provided by the original author. There might be something different from the original paper. Please forgive me if I write something wrong.
Works are in process. The current version does not stand for the full functions.
It is highly recommented to run this code on Ubuntu 20.04 with Anaconda environment.
python >= 3.8.5
tensorflow >= 2.3
numpy
matplotlib
- Modify training settings in
config.py.
For pre-train of heatmap, set train_mode = 0. Or fine-tune, set train_mode = 1.
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Run
python3 train.py. -
If you are the first time to run this code, LSP dataset will be downloaded. Especially, if you are using Microsoft Windows 10, please download and unzip the dataset manually.
- Modify training settings in
config.py.
If you want to see the heatmaps, set train_mode = 0.
For skeleton joint results, set train_mode = 1.
- If you are the first time to test, open terminal:
mkdir result- Run
python3 test.py.
The tested images will be written in result dictionary.
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Basic code for network model BlazePose.
- Implementation of Channel Attention layer.
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Dataset and preprocess.
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LSP dataset train and validation.
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LSPET dataset.
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Custom dataset.
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Two stage training (pre-train and fine-tune).
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Online camera demo.
If the original paper helps your research, you can cite this paper in the LaTex file with:
@article{Bazarevsky2020BlazePoseOR,
title={BlazePose: On-device Real-time Body Pose tracking},
author={Valentin Bazarevsky and I. Grishchenko and K. Raveendran and Tyler Lixuan Zhu and Fangfang Zhang and M. Grundmann},
journal={ArXiv},
year={2020},
volume={abs/2006.10204}
}Please feel free to submit an issue or pull a request.