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from mmpose.apis import (inference_top_down_pose_model, init_pose_model, |
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process_mmdet_results, vis_pose_result) |
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from mmpose.datasets import DatasetInfo |
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from mmdet.apis import inference_detector, init_detector |
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det_model = init_detector( |
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"./external/faster_rcnn_r50_fpn_coco.py", |
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"./faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth", |
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device="cpu") |
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pose_model = init_pose_model( |
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"./external/hrnet_w48_coco_256x192.py", |
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"./hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth", |
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device="cpu") |
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dataset = pose_model.cfg.data['test']['type'] |
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dataset_info = pose_model.cfg.data['test'].get('dataset_info', None) |
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dataset_info = DatasetInfo(dataset_info) |
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def infer(image): |
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mmdet_results = inference_detector(det_model, image) |
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person_results = process_mmdet_results(mmdet_results, 1) |
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pose_results, returned_outputs = inference_top_down_pose_model( |
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pose_model, |
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image, |
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person_results, |
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bbox_thr=0.3, |
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format='xyxy', |
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dataset=dataset, |
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dataset_info=dataset_info, |
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return_heatmap=False, |
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outputs=None) |
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return pose_results, returned_outputs |
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def draw(image, results): |
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return vis_pose_result( |
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pose_model, |
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image, |
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results, |
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dataset=dataset, |
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dataset_info=dataset_info, |
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kpt_score_thr=0.3, |
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radius=4, |
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thickness=3, |
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show=False, |
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out_file=None) |
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