3d_hand_demo.md

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.

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