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2D Animal Pose Demo

2D Animal Pose Image Demo

Using gt hand bounding boxes as input

We provide a demo script to test a single image, given gt json file.

Pose Model Preparation: The pre-trained pose estimation model can be downloaded from model zoo. Take macaque model as an example:

python demo/top_down_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --json-file ${JSON_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_demo.py \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res50_macaque_256x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth \
    --img-root tests/data/macaque/ --json-file tests/data/macaque/test_macaque.json \
    --out-img-root vis_results

To run demos on CPU:

python demo/top_down_img_demo.py \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res50_macaque_256x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth \
    --img-root tests/data/macaque/ --json-file tests/data/macaque/test_macaque.json \
    --out-img-root vis_results \
    --device=cpu

2D Animal Pose Video Demo

We also provide video demos to illustrate the results.

Using the full image as input

If the video is cropped with the object centered in the screen, we can simply use the full image as the model input (without object detection).

python demo/top_down_video_demo_full_frame_without_det.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}]

Examples:

python demo/top_down_video_demo_full_frame_without_det.py \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth \
    --video-path demo/resources/<demo_fly_video.avi> \
    --out-video-root vis_results


Using MMDetection to detect animals

Assume that you have already installed mmdet.

COCO-animals

In COCO dataset, there are 80 object categories, including 10 common animal categories (15: ‘bird’, 16: ‘cat’, 17: ‘dog’, 18: ‘horse’, 19: ‘sheep’, 20: ‘cow’, 21: ‘elephant’, 22: ‘bear’, 23: ‘zebra’, 24: ‘giraffe’) For these COCO-animals, please download the COCO pre-trained detection model from MMDetection Model Zoo.

python demo/top_down_video_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} \
    --det-cat-id ${CATEGORY_ID}
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_video_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_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split1.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth \
    --video-path demo/resources/<demo_horse.mp4> \
    --out-video-root vis_results \
    --bbox-thr 0.1 \
    --kpt-thr 0.4 \
    --det-cat-id 18


Other Animals

For other animals, we have also provided some pre-trained animal detection models (1-class models). Supported models can be found in det model zoo. The pre-trained animal pose estimation model can be found in pose model zoo.

python demo/top_down_video_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} \
    [--det-cat-id ${CATEGORY_ID}]
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_video_demo_with_mmdet.py \
    demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \
    https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_macaque-e45e36f5_20210409.pth \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res152_macaque_256x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res152_macaque_256x192-c42abc02_20210407.pth \
    --video-path demo/resources/<demo_macaque.mp4> \
    --out-video-root vis_results \
    --bbox-thr 0.5 \
    --kpt-thr 0.3 \


Speed Up Inference

Some tips to speed up MMPose inference:

For 2D animal pose estimation models, try to edit the config file. For example,

  1. set flip_test=False in macaque-res50.

  2. set post_process='default' in macaque-res50.

2D Face Keypoint Demo


2D Face Image Demo

Using gt face bounding boxes as input

We provide a demo script to test a single image, given gt json file.

Face Keypoint Model Preparation: The pre-trained face keypoint estimation model can be found from model zoo. Take aflw model as an example:

python demo/top_down_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --json-file ${JSON_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_demo.py \
    configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \
    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
    --img-root tests/data/aflw/ --json-file tests/data/aflw/test_aflw.json \
    --out-img-root vis_results

To run demos on CPU:

python demo/top_down_img_demo.py \
    configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \
    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
    --img-root tests/data/aflw/ --json-file tests/data/aflw/test_aflw.json \
    --out-img-root vis_results \
    --device=cpu

Using face bounding box detectors

We provide a demo script to run face detection and face keypoint estimation.

Please install face_recognition before running the demo, by pip install face_recognition. For more details, please refer to https://github.com/ageitgey/face_recognition.

python demo/face_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --img ${IMG_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]
python demo/face_img_demo.py \
    configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \
    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
    --img-root tests/data/aflw/ \
    --img image04476.jpg \
    --out-img-root vis_results

2D Face Video Demo

We also provide a video demo to illustrate the results.

Please install face_recognition before running the demo, by pip install face_recognition. For more details, please refer to https://github.com/ageitgey/face_recognition.

python demo/face_video_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}]

Examples:

python demo/face_video_demo.py \
    configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_w18_aflw_256x256.py \
    https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
    --video-path https://user-images.githubusercontent.com/87690686/137441355-ec4da09c-3a8f-421b-bee9-b8b26f8c2dd0.mp4 \
    --out-video-root vis_results

Speed Up Inference

Some tips to speed up MMPose inference:

For 2D face keypoint estimation models, try to edit the config file. For example,

  1. set flip_test=False in face-hrnetv2_w18.

  2. set post_process='default' in face-hrnetv2_w18.

2D Hand Keypoint Demo


2D Hand Image Demo

Using gt hand bounding boxes as input

We provide a demo script to test a single image, given gt json file.

Hand Pose Model Preparation: The pre-trained hand pose estimation model can be downloaded from model zoo. Take onehand10k model as an example:

python demo/top_down_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --json-file ${JSON_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_demo.py \
    configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \
    https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \
    --img-root tests/data/onehand10k/ --json-file tests/data/onehand10k/test_onehand10k.json \
    --out-img-root vis_results

To run demos on CPU:

python demo/top_down_img_demo.py \
    configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \
    https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \
    --img-root tests/data/onehand10k/ --json-file tests/data/onehand10k/test_onehand10k.json \
    --out-img-root vis_results \
    --device=cpu

Using mmdet for hand bounding box detection

We provide a demo script to run mmdet for hand detection, and mmpose for hand pose estimation.

Assume that you have already installed mmdet.

Hand Box Model Preparation: The pre-trained hand box estimation model can be found in det model zoo.

Hand Pose Model Preparation: The pre-trained hand pose estimation model can be downloaded from pose model zoo.

python demo/top_down_img_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --img ${IMG_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]
python demo/top_down_img_demo_with_mmdet.py demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \
    https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth \
    configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \
    https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \
    --img-root tests/data/onehand10k/ \
    --img 9.jpg \
    --out-img-root vis_results

2D Hand Video Demo

We also provide a video demo to illustrate the results.

Assume that you have already installed mmdet.

Hand Box Model Preparation: The pre-trained hand box estimation model can be found in det model zoo.

Hand Pose Model Preparation: The pre-trained hand pose estimation model can be found in pose model zoo.

python demo/top_down_video_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}]

Examples:

python demo/top_down_video_demo_with_mmdet.py demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \
    https://download.openmmlab.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_onehand10k-dac19597_20201030.pth \
    configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/res50_onehand10k_256x256.py \
    https://download.openmmlab.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth \
    --video-path https://user-images.githubusercontent.com/87690686/137441388-3ea93d26-5445-4184-829e-bf7011def9e4.mp4 \
    --out-video-root vis_results

Speed Up Inference

Some tips to speed up MMPose inference:

For 2D hand pose estimation models, try to edit the config file. For example,

  1. set flip_test=False in hand-res50.

  2. set post_process='default' in hand-res50.

2D Human Pose Demo


2D Human Pose Top-Down Image Demo

Using gt human bounding boxes as input

We provide a demo script to test a single image, given gt json file.

python demo/top_down_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --json-file ${JSON_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_demo.py \
    configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    --img-root tests/data/coco/ --json-file tests/data/coco/test_coco.json \
    --out-img-root vis_results

To run demos on CPU:

python demo/top_down_img_demo.py \
    configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    --img-root tests/data/coco/ --json-file tests/data/coco/test_coco.json \
    --out-img-root vis_results \
    --device=cpu

Using mmdet for human bounding box detection

We provide a demo script to run mmdet for human detection, and mmpose for pose estimation.

Assume that you have already installed mmdet.

python demo/top_down_img_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --img ${IMG_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_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/hrnet_w48_coco_256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    --img-root tests/data/coco/ \
    --img 000000196141.jpg \
    --out-img-root vis_results

2D Human Pose Top-Down Video Demo

We also provide a video demo to illustrate the results.

Assume that you have already installed mmdet.

python demo/top_down_video_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}]

Examples:

python demo/top_down_video_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/hrnet_w48_coco_256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    --video-path demo/resources/demo.mp4 \
    --out-video-root vis_results

2D Human Pose Bottom-Up Image Demo

We provide a demo script to test a single image.

python demo/bottom_up_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-path ${IMG_PATH}\
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR} --pose-nms-thr ${POSE_NMS_THR}]

Examples:

python demo/bottom_up_img_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 \
    --img-path tests/data/coco/ \
    --out-img-root vis_results

2D Human Pose Bottom-Up Video Demo

We also provide a video demo to illustrate the results.

python demo/bottom_up_video_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}]

Examples:

python demo/bottom_up_video_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,

  1. set flip_test=False in topdown-res50.

  2. set post_process='default' in topdown-res50.

  3. use faster human bounding box detector, see MMDetection.

For bottom-up models, try to edit the config file. For example,

  1. set flip_test=False in AE-res50.

  2. set adjust=False in AE-res50.

  3. set refine=False in AE-res50.

  4. use smaller input image size in AE-res50.

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,

  1. set flip_test=False in topdown-res50.

  2. set post_process='default' in topdown-res50.

  3. use faster human detector or human tracker, see MMDetection or MMTracking.

For bottom-up models, try to edit the config file. For example,

  1. set flip_test=False in AE-res50.

  2. set adjust=False in AE-res50.

  3. set refine=False in AE-res50.

  4. use smaller input image size in AE-res50.

2D Human Whole-Body Pose Demo


2D Human Whole-Body Pose Top-Down Image Demo

Using gt human bounding boxes as input

We provide a demo script to test a single image, given gt json file.

python demo/top_down_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --json-file ${JSON_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_demo.py \
    configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \
    --img-root tests/data/coco/ --json-file tests/data/coco/test_coco.json \
    --out-img-root vis_results

To run demos on CPU:

python demo/top_down_img_demo.py \
    configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \
    --img-root tests/data/coco/ --json-file tests/data/coco/test_coco.json \
    --out-img-root vis_results \
    --device=cpu

Using mmdet for human bounding box detection

We provide a demo script to run mmdet for human detection, and mmpose for pose estimation.

Assume that you have already installed mmdet.

python demo/top_down_img_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --img ${IMG_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_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/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \
    --img-root tests/data/coco/ \
    --img 000000196141.jpg \
    --out-img-root vis_results

2D Human Whole-Body Pose Top-Down Video Demo

We also provide a video demo to illustrate the results.

Assume that you have already installed mmdet.

python demo/top_down_video_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}]

Examples:

python demo/top_down_video_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/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark_plus.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \
    --video-path https://user-images.githubusercontent.com/87690686/137440639-fb08603d-9a35-474e-b65f-46b5c06b68d6.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,

  1. set flip_test=False in pose_hrnet_w48_dark+.

  2. set post_process='default' in pose_hrnet_w48_dark+.

  3. use faster human bounding box detector, see MMDetection.

3D Hand Demo


3D Hand Estimation Image Demo

Using gt hand bounding boxes as input

We provide a demo script to test a single image, given gt json file.

python demo/interhand3d_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --json-file ${JSON_FILE} \
    --img-root ${IMG_ROOT} \
    [--show] \
    [--device ${GPU_ID or CPU}] \
    [--out-img-root ${OUTPUT_DIR}]

Example:

python demo/mesh_img_demo.py \
    configs/body/3d_mesh_sview_rgb_img/hmr/mixed/res50_mixed_224x224.py \
    https://download.openmmlab.com/mmpose/mesh/hmr/hmr_mesh_224x224-c21e8229_20201015.pth \
    --json-file tests/data/h36m/h36m_coco.json \
    --img-root tests/data/h36m \
    --out-img-root vis_results

3D Hand Demo


3D Hand Estimation Image Demo

Using gt hand bounding boxes as input

We provide a demo script to test a single image, given gt json file.

python demo/interhand3d_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --json-file ${JSON_FILE} \
    --img-root ${IMG_ROOT} \
    [--camera-param-file ${CAMERA_PARAM_FILE}] \
    [--gt-joints-file ${GT_JOINTS_FILE}]\
    [--show] \
    [--device ${GPU_ID or CPU}] \
    [--out-img-root ${OUTPUT_DIR}] \
    [--rebase-keypoint-height] \
    [--show-ground-truth]

Example with gt keypoints and camera parameters:

python demo/interhand3d_img_demo.py \
    configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py \
    https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3d_all_256x256-b9c1cf4c_20210506.pth \
    --json-file tests/data/interhand2.6m/test_interhand2.6m_data.json \
    --img-root tests/data/interhand2.6m \
    --camera-param-file tests/data/interhand2.6m/test_interhand2.6m_camera.json \
    --gt-joints-file tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json \
    --out-img-root vis_results \
    --rebase-keypoint-height \
    --show-ground-truth

Example without gt keypoints and camera parameters:

python demo/interhand3d_img_demo.py \
    configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/res50_interhand3d_all_256x256.py \
    https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3d_all_256x256-b9c1cf4c_20210506.pth \
    --json-file tests/data/interhand2.6m/test_interhand2.6m_data.json \
    --img-root tests/data/interhand2.6m \
    --out-img-root vis_results \
    --rebase-keypoint-height

3D Human Pose Demo


3D Human Pose Two-stage Estimation Image Demo

Using ground truth 2D poses as the 1st stage (pose detection) result, and inference the 2nd stage (2D-to-3D lifting)

We provide a demo script to test on single images with a given ground-truth Json file.

python demo/body3d_two_stage_img_demo.py \
    ${MMPOSE_CONFIG_FILE_3D} \
    ${MMPOSE_CHECKPOINT_FILE_3D} \
    --json-file ${JSON_FILE} \
    --img-root ${IMG_ROOT} \
    --only-second-stage \
    [--show] \
    [--device ${GPU_ID or CPU}] \
    [--out-img-root ${OUTPUT_DIR}] \
    [--rebase-keypoint-height] \
    [--show-ground-truth]

Example:

python demo/body3d_two_stage_img_demo.py \
    configs/body/3d_kpt_sview_rgb_img/pose_lift/h36m/simplebaseline3d_h36m.py \
    https://download.openmmlab.com/mmpose/body3d/simple_baseline/simple3Dbaseline_h36m-f0ad73a4_20210419.pth \
    --json-file tests/data/h36m/h36m_coco.json \
    --img-root tests/data/h36m \
    --camera-param-file tests/data/h36m/cameras.pkl \
    --only-second-stage \
    --out-img-root vis_results \
    --rebase-keypoint-height \
    --show-ground-truth

3D Human Pose Two-stage Estimation Video Demo

Using mmdet for human bounding box detection and top-down model for the 1st stage (2D pose detection), and inference the 2nd stage (2D-to-3D lifting)

Assume that you have already installed mmdet.

python demo/body3d_two_stage_video_demo.py \
    ${MMDET_CONFIG_FILE} \
    ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE_2D} \
    ${MMPOSE_CHECKPOINT_FILE_2D} \
    ${MMPOSE_CONFIG_FILE_3D} \
    ${MMPOSE_CHECKPOINT_FILE_3D} \
    --video-path ${VIDEO_PATH} \
    [--rebase-keypoint-height] \
    [--norm-pose-2d] \
    [--num-poses-vis NUM_POSES_VIS] \
    [--show] \
    [--out-video-root ${OUT_VIDEO_ROOT}] \
    [--device ${GPU_ID or CPU}] \
    [--det-cat-id DET_CAT_ID] \
    [--bbox-thr BBOX_THR] \
    [--kpt-thr KPT_THR] \
    [--use-oks-tracking] \
    [--tracking-thr TRACKING_THR] \
    [--euro] \
    [--radius RADIUS] \
    [--thickness THICKNESS]

Example:

python demo/body3d_two_stage_video_demo.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/hrnet_w48_coco_256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/h36m/videopose3d_h36m_243frames_fullconv_supervised_cpn_ft.py \
    https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth \
    --video-path demo/resources/<demo_body3d>.mp4 \
    --out-video-root vis_results \
    --rebase-keypoint-height

Webcam Demo

We provide a webcam demo tool which integrartes detection and 2D pose estimation for humans and animals. You can simply run the following command:

python demo/webcam_demo.py

It will launch a window to display the webcam video steam with detection and pose estimation results:


Usage Tips

  • Which model is used in the demo tool?

    Please check the following default arguments in the script. You can also choose other models from the MMDetection Model Zoo and MMPose Model Zoo or use your own models.

Model Arguments
Detection --det-config, --det-checkpoint
Human Pose --human-pose-config, --human-pose-checkpoint
Animal Pose --animal-pose-config, --animal-pose-checkpoint
  • Can this tool run without GPU?

    Yes, you can set --device=cpu and the model inference will be performed on CPU. Of course, this may cause a low inference FPS compared to using GPU devices.

  • Why there is time delay between the pose visualization and the video?

    The video I/O and model inference are running asynchronously and the latter usually takes more time for a single frame. To allevidate the time delay, you can:

    1. set --display-delay=MILLISECONDS to defer the video stream, according to the inference delay shown at the top left corner. Or,

    2. set --synchronous-mode to force video stream being aligned with inference results. This may reduce the video display FPS.

  • Can this tool process video files?

    Yes. You can set --cam-id=VIDEO_FILE_PATH to run the demo tool in offline mode on a video file. Note that --synchronous-mode should be set in this case.

  • How to enable/disable the special effects?

    The special effects can be enabled/disabled at launch time by setting arguments like --bugeye, --sunglasses, etc. You can also toggle the effects by keyboard shortcuts like b, s when the tool starts.

  • What if my computer doesn’t have a camera?

    You can use a smart phone as a webcam with apps like Camo or DroidCam.

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