智慧教室基于深度学习实现课堂专注度分析及考试作弊检测系统python源码+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.