Pose Estimation Demo

This demo performs human bounding box and keypoint detection, and visualizes results.

Instruction

Get started

Launch the demo from the mmpose root directory:

python tools/webcam/run_webcam.py --config tools/webcam/configs/examples/pose_estimation.py

Hotkeys

Hotkey	Function
v	Toggle the pose visualization on/off.
h	Show help information.
m	Show the monitoring information.
q	Exit.

Note that the demo will automatically save the output video into a file record.mp4.

Configuration

Choose a detection model

Users can choose detection models from the MMDetection Model Zoo. Just set the model_config and model_checkpoint in the detector node accordingly, and the model will be automatically downloaded and loaded.

# 'DetectorNode':
    # This node performs object detection from the frame image using an
    # MMDetection model.
dict(
    type='DetectorNode',
    name='Detector',
    model_config='demo/mmdetection_cfg/'
    'ssdlite_mobilenetv2_scratch_600e_coco.py',
    model_checkpoint='https://download.openmmlab.com'
    '/mmdetection/v2.0/ssd/'
    'ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_'
    'scratch_600e_coco_20210629_110627-974d9307.pth',
    input_buffer='_input_',
    output_buffer='det_result')

Choose a or more pose models

In this demo we use two top-down pose estimation models for humans and animals respectively. Users can choose models from the MMPose Model Zoo. To apply different pose models on different instance types, you can add multiple pose estimator nodes with cls_names set accordingly.

# 'TopDownPoseEstimatorNode':
# This node performs keypoint detection from the frame image using an
# MMPose top-down model. Detection results is needed.
dict(
    type='TopDownPoseEstimatorNode',
    name='Human Pose Estimator',
    model_config='configs/wholebody/2d_kpt_sview_rgb_img/'
    'topdown_heatmap/coco-wholebody/'
    'vipnas_mbv3_coco_wholebody_256x192_dark.py',
    model_checkpoint='https://openmmlab-share.oss-cn-hangz'
    'hou.aliyuncs.com/mmpose/top_down/vipnas/vipnas_mbv3_co'
    'co_wholebody_256x192_dark-e2158108_20211205.pth',
    cls_names=['person'],
    input_buffer='det_result',
    output_buffer='human_pose'),
dict(
    type='TopDownPoseEstimatorNode',
    name='Animal Pose Estimator',
    model_config='configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap'
    '/animalpose/hrnet_w32_animalpose_256x256.py',
    model_checkpoint='https://download.openmmlab.com/mmpose/animal/'
    'hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth',
    cls_names=['cat', 'dog', 'horse', 'sheep', 'cow'],
    input_buffer='human_pose',
    output_buffer='animal_pose')

Run the demo without GPU

If you don't have GPU and CUDA in your device, the demo can run with only CPU by setting device='cpu' in all model nodes. For example:

dict(
    type='DetectorNode',
    name='Detector',
    model_config='demo/mmdetection_cfg/'
    'ssdlite_mobilenetv2_scratch_600e_coco.py',
    model_checkpoint='https://download.openmmlab.com'
    '/mmdetection/v2.0/ssd/'
    'ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_'
    'scratch_600e_coco_20210629_110627-974d9307.pth',
    device='cpu',
    input_buffer='_input_',
    output_buffer='det_result')

Debug webcam and display

You can lanch the webcam runner with a debug config:

python tools/webcam/run_webcam.py --config tools/webcam/configs/examples/test_camera.py