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Animal Pose Estimation

We provide a demo script to test a single image or video with top-down pose estimators and animal detectors. Assume that you have already installed mmdet with version >= 3.0.

2D Animal Pose Image Demo

python demo/topdown_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_PATH} --det-cat-id ${DET_CAT_ID} \
    [--show] [--output-root ${OUTPUT_DIR}] [--save-predictions] \
    [--draw-heatmap ${DRAW_HEATMAP}] [--radius ${KPT_RADIUS}] \
    [--kpt-thr ${KPT_SCORE_THR}] [--bbox-thr ${BBOX_SCORE_THR}] \
    [--device ${GPU_ID or CPU}]

The pre-trained animal pose estimation model can be found from model zoo. Take animalpose model as an example:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
    configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
    https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
    --input tests/data/animalpose/ca110.jpeg \
    --show --draw-heatmap --det-cat-id=15

Visualization result:


If you use a heatmap-based model and set argument --draw-heatmap, the predicted heatmap will be visualized together with the keypoints.

The augement --det-cat-id=15 selected detected bounding boxes with label ‘cat’. 15 is the index of category ‘cat’ in COCO dataset, on which the detection model is trained.

COCO-animals In COCO dataset, there are 80 object categories, including 10 common animal categories (14: ‘bird’, 15: ‘cat’, 16: ‘dog’, 17: ‘horse’, 18: ‘sheep’, 19: ‘cow’, 20: ‘elephant’, 21: ‘bear’, 22: ‘zebra’, 23: ‘giraffe’).

For other animals, we have also provided some pre-trained animal detection models. Supported models can be found in detection model zoo.

To save visualized results on disk:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
    configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
    https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
    --input tests/data/animalpose/ca110.jpeg \
    --output-root vis_results --draw-heatmap --det-cat-id=15

To save predicted results on disk:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
    configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
    https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
    --input tests/data/animalpose/ca110.jpeg \
    --output-root vis_results --save-predictions --draw-heatmap --det-cat-id=15

To run demos on CPU:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_tiny_8xb32-300e_coco.py \
    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth \
    configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
    https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
    --input tests/data/animalpose/ca110.jpeg \
    --show --draw-heatmap --det-cat-id=15 --device cpu

2D Animal Pose Video Demo

Videos share the same interface with images. The difference is that the ${INPUT_PATH} for videos can be the local path or URL link to video file.

For example,

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_8xb32-300e_coco.py \
    https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth \
    configs/animal_2d_keypoint/topdown_heatmap/animalpose/td-hm_hrnet-w32_8xb64-210e_animalpose-256x256.py \
    https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth \
    --input demo/resources/<demo_dog.mp4> \
    --output-root vis_results --draw-heatmap --det-cat-id=16


The original video can be downloaded from Google Drive.

2D Animal Pose Demo with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/ap10k \
    --pose2d animal --vis-out-dir vis_results/ap10k

This command infers all images located in tests/data/ap10k and saves the visualization results in the vis_results/ap10k directory.

Image 1 Image 2

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

Speed Up Inference

Some tips to speed up MMPose inference:

  1. set model.test_cfg.flip_test=False in animalpose_hrnet-w32.

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

Face Keypoint Estimation

We provide a demo script to test a single image or video with face detectors and top-down pose estimators. Assume that you have already installed mmdet with version >= 3.0.

Face Bounding Box Model Preparation: The pre-trained face box estimation model can be found in mmdet model zoo.

2D Face Image Demo

python demo/topdown_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_PATH} [--output-root ${OUTPUT_DIR}] \
    [--show] [--device ${GPU_ID or CPU}] [--save-predictions] \
    [--draw-heatmap ${DRAW_HEATMAP}] [--radius ${KPT_RADIUS}] \
    [--kpt-thr ${KPT_SCORE_THR}] [--bbox-thr ${BBOX_SCORE_THR}]

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

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
    https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
    --input tests/data/cofw/001766.jpg \
    --show --draw-heatmap

Visualization result:


If you use a heatmap-based model and set argument --draw-heatmap, the predicted heatmap will be visualized together with the keypoints.

To save visualized results on disk:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
    https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
    --input tests/data/cofw/001766.jpg \
    --draw-heatmap --output-root vis_results

To save the predicted results on disk, please specify --save-predictions.

To run demos on CPU:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
    https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
    --input tests/data/cofw/001766.jpg \
    --show --draw-heatmap --device=cpu

2D Face Video Demo

Videos share the same interface with images. The difference is that the ${INPUT_PATH} for videos can be the local path or URL link to video file.

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/yolox-s_8xb8-300e_coco-face.py \
    https://download.openmmlab.com/mmpose/mmdet_pretrained/yolo-x_8xb8-300e_coco-face_13274d7c.pth \
    configs/face_2d_keypoint/rtmpose/face6/rtmpose-m_8xb256-120e_face6-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400_20230529.pth \
    --input demo/resources/<demo_face.mp4> \
    --show --output-root vis_results --radius 1


The original video can be downloaded from Google Drive.

2D Face Pose Demo with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/wflw \
    --pose2d face --vis-out-dir vis_results/wflw --radius 1

This command infers all images located in tests/data/wflw and saves the visualization results in the vis_results/wflw directory.

Image 1 Image 2

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

Speed Up Inference

For 2D face keypoint estimation models, try to edit the config file. For example, set model.test_cfg.flip_test=False in line 90 of aflw_hrnetv2.

Hand Keypoint Estimation

We provide a demo script to test a single image or video with hand detectors and top-down pose estimators. Assume that you have already installed mmdet with version >= 3.0.

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

2D Hand Image Demo

python demo/topdown_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_PATH} [--output-root ${OUTPUT_DIR}] \
    [--show] [--device ${GPU_ID or CPU}] [--save-predictions] \
    [--draw-heatmap ${DRAW_HEATMAP}] [--radius ${KPT_RADIUS}] \
    [--kpt-thr ${KPT_SCORE_THR}] [--bbox-thr ${BBOX_SCORE_THR}]

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

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_nano_320-8xb32_hand.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmdet_nano_8xb32-300e_hand-267f9c8f.pth \
    configs/hand_2d_keypoint/rtmpose/hand5/rtmpose-m_8xb256-210e_hand5-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-hand5_pt-aic-coco_210e-256x256-74fb594_20230320.pth \
    --input tests/data/onehand10k/9.jpg \
    --show --draw-heatmap

Visualization result:


If you use a heatmap-based model and set argument --draw-heatmap, the predicted heatmap will be visualized together with the keypoints.

To save visualized results on disk:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_nano_320-8xb32_hand.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmdet_nano_8xb32-300e_hand-267f9c8f.pth \
    configs/hand_2d_keypoint/rtmpose/hand5/rtmpose-m_8xb256-210e_hand5-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-hand5_pt-aic-coco_210e-256x256-74fb594_20230320.pth \
    --input tests/data/onehand10k/9.jpg \
    --output-root vis_results --show --draw-heatmap

To save the predicted results on disk, please specify --save-predictions.

To run demos on CPU:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_nano_320-8xb32_hand.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmdet_nano_8xb32-300e_hand-267f9c8f.pth \
    configs/hand_2d_keypoint/rtmpose/hand5/rtmpose-m_8xb256-210e_hand5-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-hand5_pt-aic-coco_210e-256x256-74fb594_20230320.pth \
    --input tests/data/onehand10k/9.jpg \
    --show --draw-heatmap  --device cpu

2D Hand Keypoints Video Demo

Videos share the same interface with images. The difference is that the ${INPUT_PATH} for videos can be the local path or URL link to video file.

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_nano_320-8xb32_hand.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmdet_nano_8xb32-300e_hand-267f9c8f.pth \
    configs/hand_2d_keypoint/rtmpose/hand5/rtmpose-m_8xb256-210e_hand5-256x256.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-hand5_pt-aic-coco_210e-256x256-74fb594_20230320.pth \
    --input data/tests_data_nvgesture_sk_color.avi \
    --output-root vis_results --kpt-thr 0.1


The original video can be downloaded from Github.

2D Hand Keypoints Demo with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/onehand10k \
    --pose2d hand --vis-out-dir vis_results/onehand10k \
    --bbox-thr 0.5 --kpt-thr 0.05

This command infers all images located in tests/data/onehand10k and saves the visualization results in the vis_results/onehand10k directory.

Image 1 Image 2 Image 3 Image 4

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

Speed Up Inference

For 2D hand keypoint estimation models, try to edit the config file. For example, set model.test_cfg.flip_test=False in onehand10k_hrnetv2.

Human Pose Estimation

We provide demo scripts to perform human pose estimation on images or videos.

2D Human Pose Top-Down Image Demo

Use full image as input

We provide a demo script to test a single image, using the full image as input bounding box.

python demo/image_demo.py \
    ${IMG_FILE} ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --out-file ${OUTPUT_FILE} \
    [--device ${GPU_ID or CPU}] \
    [--draw_heatmap]

If you use a heatmap-based model and set argument --draw-heatmap, the predicted heatmap will be visualized together with the keypoints.

The pre-trained human pose estimation models can be downloaded from model zoo. Take coco model as an example:

python demo/image_demo.py \
    tests/data/coco/000000000785.jpg \
    configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    --out-file vis_results.jpg \
    --draw-heatmap

To run this demo on CPU:

python demo/image_demo.py \
    tests/data/coco/000000000785.jpg \
    configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth \
    --out-file vis_results.jpg \
    --draw-heatmap \
    --device=cpu

Visualization result:


Use 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 with version >= 3.0.

python demo/topdown_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_PATH} \
    [--output-root ${OUTPUT_DIR}] [--save-predictions] \
    [--show] [--draw-heatmap] [--device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR}] [--kpt-thr ${KPT_SCORE_THR}]

Example:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
    configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth \
    --input tests/data/coco/000000197388.jpg --show --draw-heatmap \
    --output-root vis_results/

Visualization result:


To save the predicted results on disk, please specify --save-predictions.

2D Human Pose Top-Down Video Demo

The above demo script can also take video as input, and run mmdet for human detection, and mmpose for pose estimation. The difference is, the ${INPUT_PATH} for videos can be the local path or URL link to video file.

Assume that you have already installed mmdet with version >= 3.0.

Example:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
    configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth \
    --input tests/data/posetrack18/videos/000001_mpiinew_test/000001_mpiinew_test.mp4 \
    --output-root=vis_results/demo --show --draw-heatmap

2D Human Pose Bottom-up Image/Video Demo

We also provide a demo script using bottom-up models to estimate the human pose in an image or a video, which does not rely on human detectors.

python demo/bottomup_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_PATH} \
    [--output-root ${OUTPUT_DIR}] [--save-predictions] \
    [--show] [--device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Example:

python demo/bottomup_demo.py \
    configs/body_2d_keypoint/dekr/coco/dekr_hrnet-w32_8xb10-140e_coco-512x512.py \
    https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/dekr/coco/dekr_hrnet-w32_8xb10-140e_coco-512x512_ac7c17bf-20221228.pth \
    --input tests/data/coco/000000197388.jpg --output-root=vis_results \
    --show --save-predictions

Visualization result:


2D Human Pose Estimation with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py \
    tests/data/posetrack18/videos/000001_mpiinew_test/000001_mpiinew_test.mp4 \
    --pose2d human --vis-out-dir vis_results/posetrack18

This command infers the video and saves the visualization results in the vis_results/posetrack18 directory.

Image 1

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

Speed Up Inference

Some tips to speed up MMPose inference:

For top-down models, try to edit the config file. For example,

  1. set model.test_cfg.flip_test=False in topdown-res50.

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

Human Whole-Body Pose Estimation

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

Use full image as input

We provide a demo script to test a single image, using the full image as input bounding box.

python demo/image_demo.py \
    ${IMG_FILE} ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --out-file ${OUTPUT_FILE} \
    [--device ${GPU_ID or CPU}] \
    [--draw_heatmap]

The pre-trained hand pose estimation models can be downloaded from model zoo. Take coco-wholebody_vipnas_res50_dark model as an example:

python demo/image_demo.py \
    tests/data/coco/000000000785.jpg \
    configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_vipnas-res50_dark-8xb64-210e_coco-wholebody-256x192.py \
    https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_wholebody_256x192_dark-67c0ce35_20211112.pth \
    --out-file vis_results.jpg

To run demos on CPU:

python demo/image_demo.py \
    tests/data/coco/000000000785.jpg \
    configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_vipnas-res50_dark-8xb64-210e_coco-wholebody-256x192.py \
    https://download.openmmlab.com/mmpose/top_down/vipnas/vipnas_res50_wholebody_256x192_dark-67c0ce35_20211112.pth \
    --out-file vis_results.jpg \
    --device=cpu

Use 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 with version >= 3.0.

python demo/topdown_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_PATH} \
    [--output-root ${OUTPUT_DIR}] [--save-predictions] \
    [--show] [--draw-heatmap] [--device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR}] [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
    configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w48_dark-8xb32-210e_coco-wholebody-384x288.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \
    --input tests/data/coco/000000196141.jpg \
    --output-root vis_results/ --show

To save the predicted results on disk, please specify --save-predictions.

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

The above demo script can also take video as input, and run mmdet for human detection, and mmpose for pose estimation.

Assume that you have already installed mmdet.

Examples:

python demo/topdown_demo_with_mmdet.py \
    demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
    configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w48_dark-8xb32-210e_coco-wholebody-384x288.py \
    https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \
    --input https://user-images.githubusercontent.com/87690686/137440639-fb08603d-9a35-474e-b65f-46b5c06b68d6.mp4 \
    --output-root vis_results/ --show

Visualization result:


2D Human Whole-Body Pose Estimation with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/crowdpose \
    --pose2d wholebody --vis-out-dir vis_results/crowdpose

This command infers all images located in tests/data/crowdpose and saves the visualization results in the vis_results/crowdpose directory.

Image 1 Image 2

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

Speed Up Inference

Some tips to speed up MMPose inference:

For top-down models, try to edit the config file. For example,

  1. set model.test_cfg.flip_test=False in pose_hrnet_w48_dark+.

  2. 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/hand3d_internet_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --input ${INPUT_FILE} \
    --output-root ${OUTPUT_ROOT} \
    [--save-predictions] \
    [--gt-joints-file ${GT_JOINTS_FILE}]\
    [--disable-rebase-keypoint] \
    [--show] \
    [--device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_THR}] \
    [--show-kpt-idx] \
    [--show-interval] \
    [--radius ${RADIUS}] \
    [--thickness ${THICKNESS}]

The pre-trained hand pose estimation model can be downloaded from model zoo. Take internet model as an example:

python demo/hand3d_internet_demo.py \
    configs/hand_3d_keypoint/internet/interhand3d/internet_res50_4xb16-20e_interhand3d-256x256.py \
    https://download.openmmlab.com/mmpose/hand3d/internet/res50_intehand3dv1.0_all_256x256-42b7f2ac_20210702.pth \
    --input tests/data/interhand2.6m/image69148.jpg \
    --save-predictions \
    --output-root vis_results

3D Hand Pose Estimation with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/interhand2.6m/image29590.jpg --pose3d hand3d --vis-out-dir vis_results/hand3d

This command infers the image and saves the visualization results in the vis_results/hand3d directory.

Image 1

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

3D Human Pose Demo


3D Human Pose Two-stage Estimation 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_pose_lifter_demo.py  \
${MMDET_CONFIG_FILE} \
${MMDET_CHECKPOINT_FILE} \
${MMPOSE_CONFIG_FILE_2D} \
${MMPOSE_CHECKPOINT_FILE_2D} \
${MMPOSE_CONFIG_FILE_3D} \
${MMPOSE_CHECKPOINT_FILE_3D} \
--input ${VIDEO_PATH or IMAGE_PATH or 'webcam'} \
[--show] \
[--disable-rebase-keypoint] \
[--disable-norm-pose-2d] \
[--num-instances ${NUM_INSTANCES}] \
[--output-root ${OUT_VIDEO_ROOT}] \
[--save-predictions] \
[--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}] \
[--show-interval ${INTERVAL}] \
[--thickness ${THICKNESS}] \
[--radius ${RADIUS}] \
[--online]

Note that

  1. ${VIDEO_PATH} can be the local path or URL link to video file.

  2. If the [--online] option is set to True, future frame information can not be used when using multi frames for inference in the 2D pose detection stage.

Examples:

During 2D pose detection, for single-frame inference that do not rely on extra frames to get the final results of the current frame and save the prediction results, try this:

python demo/body3d_pose_lifter_demo.py  \
demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth \
configs/body_3d_keypoint/video_pose_lift/h36m/video-pose-lift_tcn-243frm-supv-cpn-ft_8xb128-200e_h36m.py \
https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth  \
--input https://user-images.githubusercontent.com/87690686/164970135-b14e424c-765a-4180-9bc8-fa8d6abc5510.mp4 \
--output-root  vis_results \
--save-predictions

During 2D pose detection, for multi-frame inference that rely on extra frames to get the final results of the current frame, try this:

python demo/body3d_pose_lifter_demo.py \
demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth \
configs/body_3d_keypoint/video_pose_lift/h36m/video-pose-lift_tcn-243frm-supv-cpn-ft_8xb128-200e_h36m.py \
https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth \
--input https://user-images.githubusercontent.com/87690686/164970135-b14e424c-765a-4180-9bc8-fa8d6abc5510.mp4 \
--output-root  vis_results  \
--online

3D Human Pose Demo with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/coco/000000000785.jpg \
    --pose3d human3d --vis-out-dir vis_results/human3d

This command infers the image and saves the visualization results in the vis_results/human3d directory.

Image 1

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.

Webcam Demo

The original Webcam API has been deprecated starting from version v1.1.0. Users now have the option to utilize either the Inferencer or the demo script for conducting pose estimation using webcam input.

Webcam Demo with Inferencer

Users can utilize the MMPose Inferencer to estimate human poses in webcam inputs by executing the following command:

python demo/inferencer_demo.py webcam --pose2d 'human'

For additional information about the arguments of Inferencer, please refer to the Inferencer Documentation.

Webcam Demo with Demo Script

All of the demo scripts, except for demo/image_demo.py, support webcam input.

Take demo/topdown_demo_with_mmdet.py as example, users can utilize this script with webcam input by specifying --input webcam in the command:

# inference with webcam
python demo/topdown_demo_with_mmdet.py \
    projects/rtmpose/rtmdet/person/rtmdet_nano_320-8xb32_coco-person.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_nano_8xb32-100e_coco-obj365-person-05d8511e.pth \
    projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py \
    https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth \
    --input webcam \
    --show
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