## Reminder
ST-GCN has transferred to [MMSkeleton](https://github.com/open-mmlab/mmskeleton),
and keep on developing as an flexible open source toolbox for skeleton-based human understanding.
You are welcome to migrate to new MMSkeleton.
Custom networks, data loaders and checkpoints of old st-gcn are compatible with MMSkeleton.
If you want to use old ST-GCN, please refer to [OLD_README.md](./OLD_README.md).
This code base will soon be not maintained and exists as a historical artifact to supplement our AAAI papers on:
> **Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition**, Sijie Yan, Yuanjun Xiong and Dahua Lin, AAAI 2018. [[Arxiv Preprint]](https://arxiv.org/abs/1801.07455)
For more recent works please checkout MMSkeleton.
---------------------------
2020 毕业设计使用
改了一些代码，主要是修改图结构添加了一些策略。
基于时空图卷积（ST-GCN）的骨骼动作识别（python源码.zip （91个子文件） 
基于时空图卷积（ST-GCN）的骨骼动作识别（python源码+项目说明） logData 
AddEdgeWeight_2.txt 502KB
OLD_README.md 9KB tools __init__.py 19B get_models.sh 624B utils __init__.py 70B visualization.py 5KB openpose.py 1KB video.py 2KB __pycache__ visualization.cpython-37.pyc 4KB video.cpython-37.pyc 2KB __init__.cpython-37.pyc 235B openpose.cpython-37.pyc 1KB ntu_read_skeleton.py 2KB ntu_gendata.py 4KB __pycache__ __init__.cpython-37.pyc 162B kinetics_gendata.py 2KB feeder __init__.py 19B feeder.py 2KB feeder_kinetics.py 6KB tools.py 6KB __pycache__ feeder_kinetics.cpython-37.pyc 5KB feeder.cpython-37.pyc 2KB tools.cpython-37.pyc 5KB __init__.cpython-37.pyc 163B processor demo_offline.py 11KB demo_old.py 5KB recognition.py 8KB io.py 4KB demo_realtime.py 11KB processor.py 8KB main.py 943B LICENSE 1KB resource demo_asset 
pose_estimation.png 6KB attention+prediction.png 8KB original_video.png 6KB attention+rgb.png 6KB media 
ta_chi.mp4 134KB clean_and_jerk.mp4 212KB skateboarding.mp4 1.44MB kinetics_skeleton label_name.txt 6KB kinetics-motion.txt 408B reference_model.txt 57B NTU-RGB-D samples_with_missing_skeletons.txt 6KB info 
S002C001P010R001A017_w.gif 673KB pipeline.png 1.13MB S001C001P001R001A051_w.gif 408KB tai_chi_w.gif 1.75MB hammer_throw_w.gif 1.13MB S003C001P008R001A008_w.gif 436KB S003C001P008R001A002_w.gif 504KB S001C001P001R001A044_w.gif 355KB juggling_balls_w.gif 1.96MB demo_video.gif 5.2MB pull_ups_w.gif 2.5MB clean_and_jerk_w.gif 2.18MB DrawLine.py 2KB net utils graph.py 12KB tgcn.py 3KB st_gcn.py 8KB st_gcn_twostream.py 789B ISSUE_TEMPLATE.md 167B torchlight __init__.py 204B setup.py 197B gpu.py 750B io.py 7KB __pycache__ io.cpython-37.pyc 7KB __init__.cpython-37.pyc 338B gpu.cpython-37.pyc 996B requirements.txt 79B models OriginSTGCN.pt 12.17MB AddEdgeSTGCN12345.pt 11.33MB kinetics-st_gcn.pt 12.17MB pose coco pose_deploy_linevec.prototxt 45KB work_dir recognition kinetics_skeleton ST_GCN log_1.txt 424KB log.txt 9KB config.yaml 1KB tmp log.txt 645KB config.yaml 1014B JustTest.py 3KB README.md 900B config st_gcn.twostream ntu-xview train.yaml 674B ntu-xsub train.yaml 668B st_gcn kinetics-skeleton train.yaml 748B demo_offline.yaml 252B demo_old.yaml 238B test.yaml 441B demo_realtime.yaml 252B ntu-xview train.yaml 653B test.yaml 436B ntu-xsub train.yaml 648B test.yaml 433B资源评论
