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.
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/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.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/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.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/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
--input tests/data/cofw/001766.jpg \
--show --draw-heatmap --device=cpu
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/topdown_heatmap/aflw/td-hm_hrnetv2-w18_8xb64-60e_aflw-256x256.py \
https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_aflw_256x256-f2bbc62b_20210125.pth \
--input demo/resources/<demo_face.mp4> \
--show --draw-heatmap --output-root vis_results
The original video can be downloaded from Google Drive.
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.
In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.
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.