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add animal demos in readme (open-mmlab#561)
* add demos in readme * update readme * cat-id to det-cat-id * add demo in README * rename demo name
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| Original file line number | Diff line number | Diff line change |
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| ## 2D Animal Pose Demo | ||
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| ### 2D Animal Pose Image Demo | ||
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| #### Using gt hand bounding boxes as input | ||
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| We provide a demo script to test a single image, given gt json file. | ||
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| *Pose Model Preparation:* | ||
| The pre-trained pose estimation model can be downloaded from [model zoo](https://mmpose.readthedocs.io/en/latest/animal_models.html#). | ||
| Take [macaque model](https://download.openmmlab.com/mmpose/animal/resnet/res50_macaque_256x192-98f1dd3a_20210407.pth) as an example: | ||
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| ```shell | ||
| 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}] | ||
| ``` | ||
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| Examples: | ||
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| ```shell | ||
| python demo/top_down_img_demo.py \ | ||
| configs/animal/resnet/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 | ||
| ``` | ||
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| To run demos on CPU: | ||
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| ```shell | ||
| python demo/top_down_img_demo.py \ | ||
| configs/animal/resnet/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 | ||
| ``` | ||
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| ### 2D Animal Pose Video Demo | ||
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| We also provide video demos to illustrate the results. | ||
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| #### Using the full image as input | ||
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| 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). | ||
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| ```shell | ||
| 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}] | ||
| ``` | ||
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| Examples: | ||
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| ```shell | ||
| python demo/top_down_video_demo_full_frame_without_det.py \ | ||
| configs/animal/resnet/fly/res152_fly_192x192.py \ | ||
| https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth \ | ||
| --video-path demo_fly_video.avi \ | ||
| --out-video-root vis_results | ||
| ``` | ||
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| <img src="https://user-images.githubusercontent.com/11788150/114023530-944c8280-98a5-11eb-86b0-5f6d3e232af0.gif" height="140px" alt><br> | ||
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| #### Using MMDetection to detect animals | ||
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| Assume that you have already installed [mmdet](https://github.com/open-mmlab/mmdetection). | ||
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| **COCO-animals** | ||
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| In COCO dataset, there are 80 object categories, including 10 common `animal` categories (16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe') | ||
| For these COCO-animals, please download the COCO pre-trained detection model from [MMDetection Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html). | ||
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| ```shell | ||
| 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}] | ||
| ``` | ||
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| Examples: | ||
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| ```shell | ||
| python demo/top_down_video_demo_with_mmdet.py \ | ||
| demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py \ | ||
| http://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/resnet/horse10/res50_horse10_256x256-split1.py \ | ||
| https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth \ | ||
| --video-path demo_horse.mp4 \ | ||
| --out-video-root vis_results \ | ||
| --bbox-thr 0.1 \ | ||
| --kpt-thr 0.4 \ | ||
| --det-cat-id 19 | ||
| ``` | ||
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| <img src="https://user-images.githubusercontent.com/11788150/114201893-4446ec00-9989-11eb-808b-5718c47c7b23.gif" height="140px" alt><br> | ||
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| **Other Animals** | ||
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| 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](/demo/mmdet_modelzoo.md). | ||
| The pre-trained animal pose estimation model can be found in [pose model zoo](https://mmpose.readthedocs.io/en/latest/animal_models.html#). | ||
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| ```shell | ||
| 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}] | ||
| ``` | ||
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| Examples: | ||
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| ```shell | ||
| 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/resnet/macaque/res152_macaque_256x192.py \ | ||
| https://download.openmmlab.com/mmpose/animal/resnet/res152_macaque_256x192-c42abc02_20210407.pth \ | ||
| --video-path demo_macaque.mp4 \ | ||
| --out-video-root vis_results \ | ||
| --bbox-thr 0.5 \ | ||
| --kpt-thr 0.3 \ | ||
| ``` | ||
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| <img src="https://user-images.githubusercontent.com/11788150/114205282-b5d46980-998c-11eb-9d6b-85ba47f81252.gif" height="140px" alt><br> | ||
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| ### Speed Up Inference | ||
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| Some tips to speed up MMPose inference: | ||
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| For 2D animal pose estimation models, try to edit the config file. For example, | ||
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| 1. set `flip_test=False` in [macaque-res50](/configs/animal/resnet/macaque/res50_macaque_256x192.py#L51). | ||
| 1. set `post_process='default'` in [macaque-res50](/configs/animal/resnet/macaque/res50_macaque_256x192.py#L52). |
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