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import numpy as np |
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from human_pose_estimator.modules.pose import Pose |
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def rect(estimator, img: np.ndarray, height_size=512): |
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num_keypoints = Pose.num_kpts |
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_, pose_entries, all_keypoints = estimator.get_poses(img, height_size) |
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rects = [] |
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for n in range(len(pose_entries)): |
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if len(pose_entries[n]) == 0: |
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continue |
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pose_keypoints = np.ones((num_keypoints, 2), dtype=np.int32) * -1 |
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valid_keypoints = [] |
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for kpt_id in range(num_keypoints): |
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if pose_entries[n][kpt_id] != -1.0: |
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pose_keypoints[kpt_id, 0] = int(all_keypoints[int(pose_entries[n][kpt_id]), 0]) |
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pose_keypoints[kpt_id, 1] = int(all_keypoints[int(pose_entries[n][kpt_id]), 1]) |
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valid_keypoints.append([pose_keypoints[kpt_id, 0], pose_keypoints[kpt_id, 1]]) |
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valid_keypoints = np.array(valid_keypoints) |
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if pose_entries[n][10] != -1.0 or pose_entries[n][13] != -1.0: |
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pmin = valid_keypoints.min(0) |
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pmax = valid_keypoints.max(0) |
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center = (0.5 * (pmax[:2] + pmin[:2])).astype(np.int32) |
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radius = int(0.65 * max(pmax[0] - pmin[0], pmax[1] - pmin[1])) |
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elif pose_entries[n][10] == -1.0 and pose_entries[n][13] == -1.0 and pose_entries[n][8] != -1.0 and \ |
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pose_entries[n][11] != -1.0: |
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center = (0.5 * (pose_keypoints[8] + pose_keypoints[11])).astype(np.int32) |
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radius = int(1.45 * np.sqrt(((center[None, :] - valid_keypoints) ** 2).sum(1)).max(0)) |
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center[1] += int(0.05 * radius) |
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else: |
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center = np.array([img.shape[1] // 2, img.shape[0] // 2]) |
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radius = max(img.shape[1] // 2, img.shape[0] // 2) |
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x1 = center[0] - radius |
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y1 = center[1] - radius |
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rects.append([x1, y1, 2 * radius, 2 * radius]) |
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return img, rects |
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