| import json |
| import numpy as np |
| import os |
|
|
| 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] |
|
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|
|
| 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[:, 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 |
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| |
| |
| |
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|
| valid_frames = np.where(np.sum(keypoints_h36m.reshape(-1, 34), axis=1) != 0)[0] |
| |
| return keypoints_h36m, valid_frames |
|
|
|
|
| def h36m_coco_format(keypoints, scores): |
| assert len(keypoints.shape) == 4 and len(scores.shape) == 3 |
|
|
| h36m_kpts = [] |
| h36m_scores = [] |
| valid_frames = [] |
|
|
| for i in range(keypoints.shape[0]): |
| kpts = keypoints[i] |
| score = scores[i] |
|
|
| new_score = np.zeros_like(score, dtype=np.float32) |
|
|
| if np.sum(kpts) != 0.: |
| kpts, valid_frame = coco_h36m(kpts) |
| h36m_kpts.append(kpts) |
| valid_frames.append(valid_frame) |
|
|
| new_score[:, h36m_coco_order] = score[:, coco_order] |
| new_score[:, 0] = np.mean(score[:, [11, 12]], axis=1, dtype=np.float32) |
| new_score[:, 8] = np.mean(score[:, [5, 6]], axis=1, dtype=np.float32) |
| new_score[:, 7] = np.mean(new_score[:, [0, 8]], axis=1, dtype=np.float32) |
| new_score[:, 10] = np.mean(score[:, [1, 2, 3, 4]], axis=1, dtype=np.float32) |
|
|
| h36m_scores.append(new_score) |
|
|
| h36m_kpts = np.asarray(h36m_kpts, dtype=np.float32) |
| h36m_scores = np.asarray(h36m_scores, dtype=np.float32) |
|
|
| return h36m_kpts, h36m_scores, valid_frames |
|
|
|
|
| def revise_kpts(h36m_kpts, h36m_scores, valid_frames): |
|
|
| new_h36m_kpts = np.zeros_like(h36m_kpts) |
| for index, frames in enumerate(valid_frames): |
| kpts = h36m_kpts[index, frames] |
| score = h36m_scores[index, frames] |
|
|
| index_frame = np.where(np.sum(score < 0.3, axis=1) > 0)[0] |
|
|
| for frame in index_frame: |
| less_threshold_joints = np.where(score[frame] < 0.3)[0] |
|
|
| intersect = [i for i in [2, 3, 5, 6] if i in less_threshold_joints] |
|
|
| if [2, 3, 5, 6] == intersect: |
| kpts[frame, [2, 3, 5, 6]] = kpts[frame, [1, 1, 4, 4]] |
| elif [2, 3, 6] == intersect: |
| kpts[frame, [2, 3, 6]] = kpts[frame, [1, 1, 5]] |
| elif [3, 5, 6] == intersect: |
| kpts[frame, [3, 5, 6]] = kpts[frame, [2, 4, 4]] |
| elif [3, 6] == intersect: |
| kpts[frame, [3, 6]] = kpts[frame, [2, 5]] |
| elif [3] == intersect: |
| kpts[frame, 3] = kpts[frame, 2] |
| elif [6] == intersect: |
| kpts[frame, 6] = kpts[frame, 5] |
| else: |
| continue |
|
|
| new_h36m_kpts[index, frames] = kpts |
|
|
| return new_h36m_kpts |
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