# AlphaPose

**Repository Path**: james9/AlphaPose

## Basic Information

- **Project Name**: AlphaPose
- **Description**: Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-10-27
- **Last Updated**: 2024-05-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


<div align="center">
    <img src="docs/logo.jpg", width="400">
</div>


## News!
- Aug 2020: [**v0.4.0** version](https://github.com/MVIG-SJTU/AlphaPose) of AlphaPose is released! Stronger tracking! Include whole body(face,hand,foot) keypoints! [Colab](https://colab.research.google.com/drive/14Zgotr2_F0LfvcpRi03uQdMvUbLQSgok?usp=sharing) now available.
  2020 年 8 月：发布 v0.4.0 版本的 AlphaPose！更强的追踪！包括全身（脸、手、脚）关键点！ Colab 现已推出。
- Dec 2019: [**v0.3.0** version](https://github.com/MVIG-SJTU/AlphaPose) of AlphaPose is released! Smaller model, higher accuracy!
  2019 年 12 月：发布 v0.3.0 版本的 AlphaPose！更小的模型，更高的精度！
- Apr 2019: [**MXNet** version](https://github.com/MVIG-SJTU/AlphaPose/tree/mxnet) of AlphaPose is released! It runs at **23 fps** on COCO validation set.
  2019 年 4 月：AlphaPose 的 MXNet 版本发布！它在 COCO 验证集上以 23 fps 的速度运行。
- Feb 2019: [CrowdPose](https://github.com/MVIG-SJTU/AlphaPose/docs/CrowdPose.md) is integrated into AlphaPose Now!
  2019 年 2 月：CrowdPose 现已集成到 AlphaPose 中！
- Dec 2018: [General version](https://github.com/MVIG-SJTU/AlphaPose/PoseFlow) of PoseFlow is released! 3X Faster and support pose tracking results visualization!
  2018 年 12 月：发布 PoseFlow 通用版！速度提升 3 倍，支持姿势追踪结果可视化！
- Sep 2018: [**v0.2.0** version](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch) of AlphaPose is released! It runs at **20 fps** on COCO validation set (4.6 people per image on average) and achieves 71 mAP!
  2018 年 9 月：发布 v0.2.0 版本的 AlphaPose！它在 COCO 验证集上以 20 fps 的速度运行（平均每张图像 4.6 人）并达到 71 mAP！


## AlphaPose
[AlphaPose](http://www.mvig.org/research/alphapose.html) is an accurate multi-person pose estimator, which is the **first open-source system that achieves 70+ mAP (75 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset.** 
To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the **first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.**
AlphaPose 是一个精确的多人姿态估计器，它是第一个在 COCO 数据集上实现 70+ mAP（75 mAP）和在 MPII 数据集上实现 80+ mAP（82.1 mAP）的开源系统。为了匹配跨帧对应于同一个人的姿势，我们还提供了一种称为 Pose Flow 的高效在线姿势跟踪器。它是第一个在 PoseTrack Challenge 数据集上同时实现 60+ mAP (66.5 mAP) 和 50+ MOTA (58.3 MOTA) 的开源在线姿势跟踪器。 
AlphaPose supports both Linux and **Windows!**

<div align="center">
    <img src="docs/alphapose_17.gif", width="400" alt><br>
    COCO 17 keypoints
</div>
<div align="center">
    <img src="docs/alphapose_26.gif", width="400" alt><br>
    <b><a href="https://github.com/Fang-Haoshu/Halpe-FullBody">Halpe 26 keypoints</a></b> + tracking
</div>
<div align="center">
    <img src="docs/alphapose_136.gif", width="400"alt><br>
    <b><a href="https://github.com/Fang-Haoshu/Halpe-FullBody">Halpe 136 keypoints</a></b> + tracking
</div>


## Results
### Pose Estimation（姿态估计）
Results on COCO test-dev 2015:
COCO test-dev 2015 的结果： 
<center>

| Method | AP @0.5:0.95 | AP @0.5 | AP @0.75 | AP medium | AP large |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|
| OpenPose (CMU-Pose) | 61.8 | 84.9 | 67.5 | 57.1 | 68.2 |
| Detectron (Mask R-CNN) | 67.0 | 88.0 | 73.1 | 62.2 | 75.6 |
| **AlphaPose** | **73.3** | **89.2** | **79.1** | **69.0** | **78.6** |

</center>

Results on MPII full test set（MPII 完整测试集的结果 ）:
<center>

| Method | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Ave |
|:-------|:-----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| OpenPose (CMU-Pose) | 91.2 | 87.6 | 77.7 | 66.8 | 75.4 | 68.9 | 61.7 | 75.6 |
| Newell & Deng | **92.1** | 89.3 | 78.9 | 69.8 | 76.2 | 71.6 | 64.7 | 77.5 |
| **AlphaPose** | 91.3 | **90.5** | **84.0** | **76.4** | **80.3** | **79.9** | **72.4** | **82.1** |

</center>

More results and models are available in the [docs/MODEL_ZOO.md](docs/MODEL_ZOO.md).
[docs/MODEL_ZOO.md](docs/MODEL_ZOO.md) 中提供了更多结果和模型。 
### Pose Tracking（姿势追踪 ）

<p align='center'>
    <img src="docs/posetrack.gif", width="360">
    <img src="docs/posetrack2.gif", width="344">
</p>

Please read [trackers/README.md](trackers/) for details.

### CrowdPose
<p align='center'>
    <img src="docs/crowdpose.gif", width="360">
</p>

Please read [docs/CrowdPose.md](docs/CrowdPose.md) for details.


## Installation
Please check out [docs/INSTALL.md](docs/INSTALL.md)

## Model Zoo
Please check out [docs/MODEL_ZOO.md](docs/MODEL_ZOO.md)

## Quick Start
- **Colab**: We provide a [colab example](https://colab.research.google.com/drive/14Zgotr2_F0LfvcpRi03uQdMvUbLQSgok?usp=sharing) for your quick start.

- **Inference**: Inference demo
``` bash
./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional
```
For high level API, please refer to `./scripts/demo_api.py`

- **Training**: Train from scratch
``` bash
./scripts/train.sh ${CONFIG} ${EXP_ID}
```

- **Validation**: Validate your model on MSCOCO val2017
``` bash
./scripts/validate.sh ${CONFIG} ${CHECKPOINT}
```

Examples:

Demo using `FastPose` model.
``` bash
./scripts/inference.sh configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml pretrained_models/fast_res50_256x192.pth ${VIDEO_NAME}
#or
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --indir examples/demo/
```

Train `FastPose` on mscoco dataset.
``` bash
./scripts/train.sh ./configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml exp_fastpose
```

More detailed inference options and examples, please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md)


## Common issue & FAQ
Check out [faq.md](docs/faq.md) for faq. If it can not solve your problems or if you find any bugs, don't hesitate to comment on GitHub or make a pull request!

## Contributors
AlphaPose is based on RMPE(ICCV'17), authored by [Hao-Shu Fang](https://fang-haoshu.github.io/), Shuqin Xie, [Yu-Wing Tai](https://scholar.google.com/citations?user=nFhLmFkAAAAJ&hl=en) and [Cewu Lu](http://www.mvig.org/), [Cewu Lu](http://mvig.sjtu.edu.cn/) is the corresponding author. Currently, it is maintained by [Jiefeng Li\*](http://jeff-leaf.site/), [Hao-shu Fang\*](https://fang-haoshu.github.io/),  [Yuliang Xiu](http://xiuyuliang.cn/about/) and [Chao Xu](http://www.isdas.cn/). 

The main contributors are listed in [doc/contributors.md](docs/contributors.md).

## TODO
- [x] Multi-GPU/CPU inference
- [ ] 3D pose
- [x] add tracking flag
- [ ] PyTorch C++ version
- [ ] Add MPII and AIC data
- [ ] dense support
- [x] small box easy filter
- [x] Crowdpose support
- [ ] Speed up PoseFlow
- [ ] Add stronger/light detectors and the [mobile pose](https://github.com/YuliangXiu/MobilePose-pytorch)
- [x] High level API

We would really appreciate if you can offer any help and be the [contributor](docs/contributors.md) of AlphaPose.


## Citation
Please cite these papers in your publications if it helps your research:

    @inproceedings{fang2017rmpe,
      title={{RMPE}: Regional Multi-person Pose Estimation},
      author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
      booktitle={ICCV},
      year={2017}
    }

    @article{li2018crowdpose,
      title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
      author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
      journal={arXiv preprint arXiv:1812.00324},
      year={2018}
    }

    @inproceedings{xiu2018poseflow,
      author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
      title = {{Pose Flow}: Efficient Online Pose Tracking},
      booktitle={BMVC},
      year = {2018}
    }



## License
AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail at mvig.alphapose[at]gmail[dot]com and cc lucewu[[at]sjtu[dot]edu[dot]cn. We will send the detail agreement to you.