基于深度学习的课堂行为识别和考试作弊检测系统的设计与实现（python源码）

资源内容介绍

课堂专注度及考试作弊系统、课堂动态点名，情绪识别、表情识别和人脸识别结合转头(probe)+低头(peep)+传递物品(passing)课堂专注度+表情识别侧面的传递物品识别**人脸识别**：dlib_face_recognition_resnet_model_v1.dat- detection_system/face_recog/weights**人脸对齐**：shape_predictor_68_face_landmarks.dat- detection_system/face_recog/weights**作弊动作分类器**：cheating_detector_rfc_kp.pkl## 使用### 运行setup.py安装必要内容## 使用### 运行setup.py安装必要内容```shellpython setup.py build develop```[windows上安装scipy1.1.0可能会遇到的问题](https://github.com/MVIG-SJTU/AlphaPose/issues/722)### 运行

## 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 EstimationResults 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.## InstallationPlease check out [docs/INSTALL.md](docs/INSTALL.md)## Model ZooPlease 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}#orpython 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 & FAQCheck 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!## ContributorsAlphaPose 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 APIWe would really appreciate if you can offer any help and be the [contributor](docs/contributors.md) of AlphaPose.## CitationPlease 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} }## LicenseAlphaPose 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.