课堂专注度及考试作弊系统课堂动态点名情绪识别表情识别姿态识别和人脸识别结合源码+运行说明+模型.zip

<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. - 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/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 </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/14Zgotr2_F0LfvcpRi03uQdMvUbLQSgok?usp=sharing) for your quick start. - **Inference**: Inference demo ``` bash ./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional ``` - **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.