关键点检测系列模型

#COCO DataSet
CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml

#MPII DataSet
CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml

#COCO DataSet
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml

#MPII DataSet
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml

#COCO DataSet
CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml

#MPII DataSet
CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml

#当只需要保存评估预测的结果时，可以通过设置save_prediction_only参数实现，评估预测结果默认保存在output/keypoints_results.json文件中
CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml --save_prediction_only

CUDA_VISIBLE_DEVICES=0 python3 tools/infer.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=./output/higherhrnet_hrnet_w32_512/model_final.pdparams --infer_dir=../images/ --draw_threshold=0.5 --save_txt=True

#导出检测模型
python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams

#导出关键点模型
python tools/export_model.py -c configs/keypoint/hrnet/hrnet_w32_256x192.yml -o weights=https://paddledet.bj.bcebos.com/models/keypoint/hrnet_w32_256x192.pdparams

#detector 检测 + keypoint top-down模型联合部署（联合推理只支持top-down方式）
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_384x288/ --video_file=../video/xxx.mp4  --device=gpu

#导出模型
python tools/export_model.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml -o weights=output/higherhrnet_hrnet_w32_512/model_final.pdparams

#部署推理
python deploy/python/keypoint_infer.py --model_dir=output_inference/higherhrnet_hrnet_w32_512/ --image_file=./demo/000000014439_640x640.jpg --device=gpu --threshold=0.5

#导出FairMOT跟踪模型
python tools/export_model.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams

#用导出的跟踪和关键点模型Python联合预测
python deploy/python/mot_keypoint_unite_infer.py --mot_model_dir=output_inference/fairmot_dla34_30e_1088x608/ --keypoint_model_dir=output_inference/higherhrnet_hrnet_w32_512/ --video_file={your video name}.mp4 --device=GPU

num_joints: &num_joints 17    #自定义数据的关键点数量
train_height: &train_height 256   #训练图片尺寸-高度h
train_width: &train_width 192   #训练图片尺寸-宽度w
hmsize: &hmsize [48, 64]  #对应训练尺寸的输出尺寸，这里是输入[w,h]的1/4
flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]] #关键点定义中左右对称的关键点，用于flip增强。若没有对称结构在 TrainReader 的 RandomFlipHalfBodyTransform 一栏中 flip_pairs 后面加一行 "flip: False"（注意缩紧对齐）
num_joints_half_body: 8   #半身关键点数量，用于半身增强
prob_half_body: 0.3   #半身增强实现概率，若不需要则修改为0
upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]    #上半身对应关键点id，用于半身增强中获取上半身对应的关键点。

# 使用Python推理
python deploy/python/det_keypoint_unite_infer.py \
          --det_model_dir output_inference/picodet_s_320 \
          --keypoint_model_dir output_inference/tinypose_256x192 \
          --video_file test_video.mp4 --device gpu --smooth True

# 使用CPP推理
./deploy/cpp/build/main --det_model_dir output_inference/picodet_s_320 \
          --keypoint_model_dir output_inference/tinypose_256x192 \
          --video_file test_video.mp4 --device gpu --smooth True

@inproceedings{cheng2020bottom,
  title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
  author={Bowen Cheng and Bin Xiao and Jingdong Wang and Honghui Shi and Thomas S. Huang and Lei Zhang},
  booktitle={CVPR},
  year={2020}
}

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{wang2019deep,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Wang, Jingdong and Sun, Ke and Cheng, Tianheng and Jiang, Borui and Deng, Chaorui and Zhao, Yang and Liu, Dong and Mu, Yadong and Tan, Mingkui and Wang, Xinggang and Liu, Wenyu and Xiao, Bin},
  journal={TPAMI},
  year={2019}
}

@InProceedings{Zhang_2020_CVPR,
    author = {Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
    title = {Distribution-Aware Coordinate Representation for Human Pose Estimation},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}

@inproceedings{Yulitehrnet21,
  title={Lite-HRNet: A Lightweight High-Resolution Network},
  author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
  booktitle={CVPR},
  year={2021}
}