You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
4.6 KiB
4.6 KiB
2D Pose Tracking Demo
2D Top-Down Video Human Pose Tracking Demo
We provide a video demo to illustrate the pose tracking results.
Assume that you have already installed mmdet.
python demo/top_down_pose_tracking_demo_with_mmdet.py \
${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
--video-path ${VIDEO_FILE} \
--out-video-root ${OUTPUT_VIDEO_ROOT} \
[--show --device ${GPU_ID or CPU}] \
[--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]
[--use-oks-tracking --tracking-thr ${TRACKING_THR} --euro]
Examples:
python demo/top_down_pose_tracking_demo_with_mmdet.py \
demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192.py \
https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth \
--video-path demo/resources/demo.mp4 \
--out-video-root vis_results
2D Top-Down Video Human Pose Tracking Demo with MMTracking
MMTracking is an open source video perception toolbox based on PyTorch for tracking related tasks. Here we show how to utilize MMTracking and MMPose to achieve human pose tracking.
Assume that you have already installed mmtracking.
python demo/top_down_video_demo_with_mmtracking.py \
${MMTRACKING_CONFIG_FILE} \
${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
--video-path ${VIDEO_FILE} \
--out-video-root ${OUTPUT_VIDEO_ROOT} \
[--show --device ${GPU_ID or CPU}] \
[--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]
Examples:
python demo/top_down_pose_tracking_demo_with_mmtracking.py \
demo/mmtracking_cfg/tracktor_faster-rcnn_r50_fpn_4e_mot17-private.py \
configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192.py \
https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth \
--video-path demo/resources/demo.mp4 \
--out-video-root vis_results
2D Bottom-Up Video Human Pose Tracking Demo
We also provide a pose tracking demo with bottom-up pose estimation methods.
python demo/bottom_up_pose_tracking_demo.py \
${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
--video-path ${VIDEO_FILE} \
--out-video-root ${OUTPUT_VIDEO_ROOT} \
[--show --device ${GPU_ID or CPU}] \
[--kpt-thr ${KPT_SCORE_THR} --pose-nms-thr ${POSE_NMS_THR}]
[--use-oks-tracking --tracking-thr ${TRACKING_THR} --euro]
Examples:
python demo/bottom_up_pose_tracking_demo.py \
configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_w32_coco_512x512.py \
https://download.openmmlab.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth \
--video-path demo/resources/demo.mp4 \
--out-video-root vis_results
Speed Up Inference
Some tips to speed up MMPose inference:
For top-down models, try to edit the config file. For example,
- set
flip_test=False
in topdown-res50. - set
post_process='default'
in topdown-res50. - use faster human detector or human tracker, see MMDetection or MMTracking.
For bottom-up models, try to edit the config file. For example,