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| ''' | |
| Project: https://github.com/fabro66/GAST-Net-3DPoseEstimation | |
| ''' | |
| import numpy as np | |
| h36m_coco_order = [9, 11, 14, 12, 15, 13, 16, 4, 1, 5, 2, 6, 3] | |
| coco_order = [0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] | |
| spple_keypoints = [10, 8, 0, 7] | |
| scores_h36m_toe_oeder = [1, 2, 3, 5, 6, 7, 11, 13, 14, 15, 16, 17, 18] | |
| kpts_h36m_toe_order = [0, 1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18] | |
| scores_coco_order = [12, 14, 16, 11, 13, 15, 0, 5, 7, 9, 6, 8, 10] | |
| h36m_mpii_order = [3, 2, 1, 4, 5, 6, 0, 8, 9, 10, 16, 15, 14, 11, 12, 13] | |
| mpii_order = [i for i in range(16)] | |
| lr_hip_shouler = [2, 3, 12, 13] | |
| def coco_h36m(keypoints): | |
| temporal = keypoints.shape[0] | |
| keypoints_h36m = np.zeros_like(keypoints, dtype=np.float32) | |
| htps_keypoints = np.zeros((temporal, 4, 2), dtype=np.float32) | |
| # htps_keypoints: head, thorax, pelvis, spine | |
| htps_keypoints[:, 0, 0] = np.mean(keypoints[:, 1:5, 0], axis=1, dtype=np.float32) | |
| htps_keypoints[:, 0, 1] = np.sum(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1] | |
| htps_keypoints[:, 1, :] = np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32) | |
| htps_keypoints[:, 1, :] += (keypoints[:, 0, :] - htps_keypoints[:, 1, :]) / 3 | |
| htps_keypoints[:, 2, :] = np.mean(keypoints[:, 11:13, :], axis=1, dtype=np.float32) | |
| htps_keypoints[:, 3, :] = np.mean(keypoints[:, [5, 6, 11, 12], :], axis=1, dtype=np.float32) | |
| keypoints_h36m[:, spple_keypoints, :] = htps_keypoints | |
| keypoints_h36m[:, h36m_coco_order, :] = keypoints[:, coco_order, :] | |
| keypoints_h36m[:, 9, :] -= (keypoints_h36m[:, 9, :] - np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32)) / 4 | |
| keypoints_h36m[:, 7, 0] += 2*(keypoints_h36m[:, 7, 0] - np.mean(keypoints_h36m[:, [0, 8], 0], axis=1, dtype=np.float32)) | |
| keypoints_h36m[:, 8, 1] -= (np.mean(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1])*2/3 | |
| # half body: the joint of ankle and knee equal to hip | |
| # keypoints_h36m[:, [2, 3]] = keypoints_h36m[:, [1, 1]] | |
| # keypoints_h36m[:, [5, 6]] = keypoints_h36m[:, [4, 4]] | |
| valid_frames = np.where(np.sum(keypoints_h36m.reshape(-1, 34), axis=1) != 0)[0] | |
| return keypoints_h36m, valid_frames | |
| def mpii_h36m(keypoints): | |
| temporal = keypoints.shape[0] | |
| keypoints_h36m = np.zeros((temporal, 17, 2), dtype=np.float32) | |
| keypoints_h36m[:, h36m_mpii_order] = keypoints | |
| # keypoints_h36m[:, 7] = np.mean(keypoints[:, 6:8], axis=1, dtype=np.float32) | |
| keypoints_h36m[:, 7] = np.mean(keypoints[:, lr_hip_shouler], axis=1, dtype=np.float32) | |
| valid_frames = np.where(np.sum(keypoints_h36m.reshape(-1, 34), axis=1) != 0)[0] | |
| return keypoints_h36m, valid_frames | |
| def coco_h36m_toe_format(keypoints): | |
| assert len(keypoints.shape) == 3 | |
| temporal = keypoints.shape[0] | |
| new_kpts = np.zeros((temporal, 19, 2), dtype=np.float32) | |
| # convert body+foot keypoints | |
| coco_body_kpts = keypoints[:, :17].copy() | |
| h36m_body_kpts, _ = coco_h36m(coco_body_kpts) | |
| new_kpts[:, kpts_h36m_toe_order] = h36m_body_kpts | |
| new_kpts[:, 4] = np.mean(keypoints[:, [20, 21]], axis=1, dtype=np.float32) | |
| new_kpts[:, 8] = np.mean(keypoints[:, [17, 18]], axis=1, dtype=np.float32) | |
| valid_frames = np.where(np.sum(new_kpts.reshape(-1, 38), axis=-1) != 0)[0] | |
| return new_kpts, valid_frames | |