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2D Human Whole-Body Pose Demo

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.