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| import cv2 | |
| import numpy as np | |
| import lap | |
| from scipy.spatial.distance import cdist | |
| from cython_bbox import bbox_overlaps as bbox_ious | |
| from yolox.motdt_tracker import kalman_filter | |
| def _indices_to_matches(cost_matrix, indices, thresh): | |
| matched_cost = cost_matrix[tuple(zip(*indices))] | |
| matched_mask = (matched_cost <= thresh) | |
| matches = indices[matched_mask] | |
| unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0])) | |
| unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1])) | |
| return matches, unmatched_a, unmatched_b | |
| def linear_assignment(cost_matrix, thresh): | |
| if cost_matrix.size == 0: | |
| return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1])) | |
| matches, unmatched_a, unmatched_b = [], [], [] | |
| cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh) | |
| for ix, mx in enumerate(x): | |
| if mx >= 0: | |
| matches.append([ix, mx]) | |
| unmatched_a = np.where(x < 0)[0] | |
| unmatched_b = np.where(y < 0)[0] | |
| matches = np.asarray(matches) | |
| return matches, unmatched_a, unmatched_b | |
| def ious(atlbrs, btlbrs): | |
| """ | |
| Compute cost based on IoU | |
| :type atlbrs: list[tlbr] | np.ndarray | |
| :type atlbrs: list[tlbr] | np.ndarray | |
| :rtype ious np.ndarray | |
| """ | |
| ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float) | |
| if ious.size == 0: | |
| return ious | |
| ious = bbox_ious( | |
| np.ascontiguousarray(atlbrs, dtype=np.float), | |
| np.ascontiguousarray(btlbrs, dtype=np.float) | |
| ) | |
| return ious | |
| def iou_distance(atracks, btracks): | |
| """ | |
| Compute cost based on IoU | |
| :type atracks: list[STrack] | |
| :type btracks: list[STrack] | |
| :rtype cost_matrix np.ndarray | |
| """ | |
| atlbrs = [track.tlbr for track in atracks] | |
| btlbrs = [track.tlbr for track in btracks] | |
| _ious = ious(atlbrs, btlbrs) | |
| cost_matrix = 1 - _ious | |
| return cost_matrix | |
| def nearest_reid_distance(tracks, detections, metric='cosine'): | |
| """ | |
| Compute cost based on ReID features | |
| :type tracks: list[STrack] | |
| :type detections: list[BaseTrack] | |
| :rtype cost_matrix np.ndarray | |
| """ | |
| cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float) | |
| if cost_matrix.size == 0: | |
| return cost_matrix | |
| det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32) | |
| for i, track in enumerate(tracks): | |
| cost_matrix[i, :] = np.maximum(0.0, cdist(track.features, det_features, metric).min(axis=0)) | |
| return cost_matrix | |
| def mean_reid_distance(tracks, detections, metric='cosine'): | |
| """ | |
| Compute cost based on ReID features | |
| :type tracks: list[STrack] | |
| :type detections: list[BaseTrack] | |
| :type metric: str | |
| :rtype cost_matrix np.ndarray | |
| """ | |
| cost_matrix = np.empty((len(tracks), len(detections)), dtype=np.float) | |
| if cost_matrix.size == 0: | |
| return cost_matrix | |
| track_features = np.asarray([track.curr_feature for track in tracks], dtype=np.float32) | |
| det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32) | |
| cost_matrix = cdist(track_features, det_features, metric) | |
| return cost_matrix | |
| def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False): | |
| if cost_matrix.size == 0: | |
| return cost_matrix | |
| gating_dim = 2 if only_position else 4 | |
| gating_threshold = kalman_filter.chi2inv95[gating_dim] | |
| measurements = np.asarray([det.to_xyah() for det in detections]) | |
| for row, track in enumerate(tracks): | |
| gating_distance = kf.gating_distance( | |
| track.mean, track.covariance, measurements, only_position) | |
| cost_matrix[row, gating_distance > gating_threshold] = np.inf | |
| return cost_matrix |