<div align="center">
<img src="docs/logo.jpg", width="400">
</div>
## News!
- Nov 2022: [**AlphaPose paper**](http://arxiv.org/abs/2211.03375) is released! Checkout the paper for more details about this project.
- Sep 2022: [**Jittor** version](https://github.com/tycoer/AlphaPose_jittor) of AlphaPose is released! It achieves 1.45x speed up with resnet50 backbone on the training stage.
- July 2022: [**v0.6.0** version](https://github.com/MVIG-SJTU/AlphaPose) of AlphaPose is released! [HybrIK](https://github.com/Jeff-sjtu/HybrIK) for 3D pose and shape estimation is supported!
- Jan 2022: [**v0.5.0** version](https://github.com/MVIG-SJTU/AlphaPose) of AlphaPose is released! Stronger whole body(face,hand,foot) keypoints! More models are availabel. Checkout [docs/MODEL_ZOO.md](docs/MODEL_ZOO.md)
- 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/1c7xb_7U61HmeJp55xjXs24hf1GUtHmPs?usp=sharing) now available.
- Dec 2019: [**v0.3.0** version](https://github.com/MVIG-SJTU/AlphaPose) of AlphaPose is released! Smaller model, higher accuracy!
- 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.
- Feb 2019: [CrowdPose](https://github.com/MVIG-SJTU/AlphaPose/docs/CrowdPose.md) is integrated into AlphaPose Now!
- Dec 2018: [General version](https://github.com/MVIG-SJTU/AlphaPose/trackers/PoseFlow) of PoseFlow is released! 3X Faster and support pose tracking results visualization!
- 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!
## 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 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
<b><a href="https://youtu.be/uze6chg-YeU">YouTube link</a></b><br>
</div>
<div align="center">
<img src="docs/alphapose_hybrik_smpl.gif", width="400"alt><br>
<b><a href="https://github.com/Jeff-sjtu/HybrIK">SMPL</a></b> + tracking
</div>
## Results
### Pose Estimation
Results on COCO test-dev 2015:
<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:
<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).
### Pose Tracking
<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/1_3Wxi4H3QGVC28snL3rHIoeMAwI2otMR?usp=sharing) for your quick start.
- **Inference**: Inference demo
``` bash
./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional
```
Inference SMPL (Download the SMPL model `basicModel_neutral_lbs_10_207_0_v1.0.0.pkl` from [here](https://smpl.is.tue.mpg.de/) and put it in `model_files/`).
``` bash
./scripts/inference_3d.sh ./configs/smpl/256x192_adam_lr1e-3-res34_smpl_24_3d_base_2x_mix.yaml ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional
```
For high level API, please refer to `./scripts/demo_api.py`. To enable tracking, please refer to [this page](./trackers).
- **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/
#or if you want to use yolox-x as the detector
python scripts/demo_inference.py --detector yolox-x --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/), [Haoyi Zhu](https://github.com/HaoyiZhu), [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
- [x] 3D pose
- [x] add tracking flag
- [ ] PyTorch C++ version
- [x] Add model trained on mixture dataset (Check the model zoo)
- [ ] dense support
- [x] small box easy filter
- [x] Crowdpose support
- [ ] Speed up PoseFlow
- [x] Add stronger/light detectors (yolox is now supported)
- [x] High level API (check the scripts/demo_api.py)
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:
@article{alphapose,
author = {Fang
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基于Alphapose的课堂行为检测工具,并提供齐全的安装配置环境方案,免去长时间配置的烦恼 推荐使用Miniconda 官网https://docs.conda.io/en/latest/miniconda.html 完整版Anaconda https://www.anaconda.com/ 清华镜像https://repo.anaconda.com/archive/ 默认使用halpe136_fast_res50_256x192.pth数据集,config已经在放在同文件夹了 https://github.com/Fang-Haoshu/Halpe-FullBody 更多数据集,注意要放置config文件,config文件夹中已经附带了 https://github.com/MVIG-SJTU/AlphaPose/blob/master/docs/MODEL_ZOO.md 请将文件放置到C盘的根目录,以便操作,如果想要改到其他位置,可手动更改脚本中的地址 警告: 如果使用英伟达GPU进行运行实时监测,由于CUDAtoolkit的缘故,请确保有15G以上的存储空间。如果进行视
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基于Alphapose的课堂行为检测工具源码+环境配置方案.zip (369个子文件)
psroi_pooling_cuda.c 3KB
yolov3.cfg 10KB
yolov3-spp.cfg 9KB
yolov3.cfg 9KB
yolo-voc.cfg 3KB
yolo.cfg 3KB
tiny-yolo-voc.cfg 2KB
setup.cfg 68B
NvidiaGPUCUDASetup.cmd 360B
NvidiaGPUSetup.cmd 354B
OnlyCPUSetup.cmd 321B
RunWithOnlyCPU.cmd 306B
RunWithGPUCUDA.cmd 306B
RunWithGPU.cmd 283B
Uninstall.cmd 101B
soft_nms_cpu.cpp 359KB
deform_conv_cuda.cpp 30KB
cocoeval.cpp 21KB
deform_pool_cuda.cpp 4KB
roi_align_cuda.cpp 3KB
nms_cpu.cpp 2KB
nms_cuda.cpp 598B
deform_conv_cuda_kernel.cu 42KB
deform_pool_cuda_kernel.cu 16KB
roi_align_kernel.cu 12KB
psroi_pooling_kernel.cu 8KB
nms_kernel.cu 5KB
nms_kernel.cu 5KB
alphapose_hybrik_smpl.gif 10.07MB
alphapose_17.gif 7.93MB
alphapose_26.gif 6.74MB
alphapose_136.gif 6.7MB
posetrack1.gif 3.9MB
posetrack.gif 3.9MB
posetrack2.gif 3.13MB
posetrack2.gif 3.13MB
pose.gif 2.14MB
crowdpose.gif 1.61MB
.gitignore 1KB
.gitignore 28B
cocoeval.h 4KB
psroi_pooling_kernel.h 856B
psroi_pooling_cuda.h 494B
gpu_nms.hpp 148B
logo.jpg 438KB
step4.jpg 352KB
1.jpg 193KB
2.jpg 148KB
worlds-largest-selfie.jpg 136KB
step3.jpg 102KB
step2.jpg 70KB
3.jpg 41KB
step1.jpg 41KB
alpha-pose-results-sample.json 5KB
ccmcpe.json 630B
LICENSE 34KB
hrnet_w32_256x192.log 41KB
fast_dcn_res50_256x192.log 40KB
fast_421_res152_256x192.log 40KB
fast_421_res50-shuffle_256x192.log 40KB
fast_res50_256x192.log 40KB
simple_res50_256x192.log 30KB
MODEL_ZOO.md 15KB
README.md 10KB
INSTALL.md 7KB
README.md 4KB
output.md 3KB
GETTING_STARTED.md 3KB
run.md 3KB
CrowdPose.md 3KB
README.md 3KB
faq.md 2KB
README.md 2KB
win_install.md 1KB
contributors.md 833B
speed_up.md 475B
README.md 427B
README.md 188B
README.md 165B
README.md 154B
README.md 122B
README.md 120B
J_regressor_h36m.npy 915KB
smpl_faces.npy 323KB
h36m_mean_beta.npy 208B
pallete 908B
posetrack_data 48B
poseval 41B
lbs.py 49KB
vis.py 44KB
transforms.py 30KB
pPose_nms.py 29KB
utils.py 28KB
basetransforms.py 24KB
yolo_head.py 23KB
simple_transform_3d_smpl.py 23KB
hardnet.py 20KB
utils.py 20KB
utils.py 20KB
darknet.py 19KB
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