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2D Animal Pose Demo

2D Animal Pose Image Demo

Using gt hand bounding boxes as input

We provide a demo script to test a single image, given gt json file.

Pose Model Preparation: The pre-trained pose estimation model can be downloaded from model zoo. Take macaque model as an example:

python demo/top_down_img_demo.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --img-root ${IMG_ROOT} --json-file ${JSON_FILE} \
    --out-img-root ${OUTPUT_DIR} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_img_demo.py \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res50_macaque_256x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth \
    --img-root tests/data/macaque/ --json-file tests/data/macaque/test_macaque.json \
    --out-img-root vis_results

To run demos on CPU:

python demo/top_down_img_demo.py \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res50_macaque_256x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth \
    --img-root tests/data/macaque/ --json-file tests/data/macaque/test_macaque.json \
    --out-img-root vis_results \
    --device=cpu

2D Animal Pose Video Demo

We also provide video demos to illustrate the results.

Using the full image as input

If the video is cropped with the object centered in the screen, we can simply use the full image as the model input (without object detection).

python demo/top_down_video_demo_full_frame_without_det.py \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --video-path ${VIDEO_FILE} \
    --out-video-root ${OUTPUT_VIDEO_ROOT} \
    [--show --device ${GPU_ID or CPU}] \
    [--kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_video_demo_full_frame_without_det.py \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth \
    --video-path demo/resources/<demo_fly_video.avi> \
    --out-video-root vis_results


Using MMDetection to detect animals

Assume that you have already installed mmdet.

COCO-animals

In COCO dataset, there are 80 object categories, including 10 common animal categories (15: 'bird', 16: 'cat', 17: 'dog', 18: 'horse', 19: 'sheep', 20: 'cow', 21: 'elephant', 22: 'bear', 23: 'zebra', 24: 'giraffe') For these COCO-animals, please download the COCO pre-trained detection model from MMDetection Model Zoo.

python demo/top_down_video_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --video-path ${VIDEO_FILE} \
    --out-video-root ${OUTPUT_VIDEO_ROOT} \
    --det-cat-id ${CATEGORY_ID}
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_video_demo_with_mmdet.py \
    demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \
    https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split1.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth \
    --video-path demo/resources/<demo_horse.mp4> \
    --out-video-root vis_results \
    --bbox-thr 0.1 \
    --kpt-thr 0.4 \
    --det-cat-id 18


Other Animals

For other animals, we have also provided some pre-trained animal detection models (1-class models). Supported models can be found in det model zoo. The pre-trained animal pose estimation model can be found in pose model zoo.

python demo/top_down_video_demo_with_mmdet.py \
    ${MMDET_CONFIG_FILE} ${MMDET_CHECKPOINT_FILE} \
    ${MMPOSE_CONFIG_FILE} ${MMPOSE_CHECKPOINT_FILE} \
    --video-path ${VIDEO_FILE} \
    --out-video-root ${OUTPUT_VIDEO_ROOT} \
    [--det-cat-id ${CATEGORY_ID}]
    [--show --device ${GPU_ID or CPU}] \
    [--bbox-thr ${BBOX_SCORE_THR} --kpt-thr ${KPT_SCORE_THR}]

Examples:

python demo/top_down_video_demo_with_mmdet.py \
    demo/mmdetection_cfg/cascade_rcnn_x101_64x4d_fpn_1class.py \
    https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/mmdet_pretrained/cascade_rcnn_x101_64x4d_fpn_20e_macaque-e45e36f5_20210409.pth \
    configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/res152_macaque_256x192.py \
    https://download.openmmlab.com/mmpose/animal/resnet/res152_macaque_256x192-c42abc02_20210407.pth \
    --video-path demo/resources/<demo_macaque.mp4> \
    --out-video-root vis_results \
    --bbox-thr 0.5 \
    --kpt-thr 0.3 \


Speed Up Inference

Some tips to speed up MMPose inference:

For 2D animal pose estimation models, try to edit the config file. For example,

  1. set flip_test=False in macaque-res50.
  2. set post_process='default' in macaque-res50.