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