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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| import numpy as np | |
| import scipy | |
| from scipy.spatial.distance import cdist | |
| from ultralytics.utils.metrics import batch_probiou, bbox_ioa | |
| try: | |
| import lap # for linear_assignment | |
| assert lap.__version__ # verify package is not directory | |
| except (ImportError, AssertionError, AttributeError): | |
| from ultralytics.utils.checks import check_requirements | |
| check_requirements("lap>=0.5.12") # https://github.com/gatagat/lap | |
| import lap | |
| def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = True) -> tuple: | |
| """ | |
| Perform linear assignment using either the scipy or lap.lapjv method. | |
| Args: | |
| cost_matrix (np.ndarray): The matrix containing cost values for assignments, with shape (N, M). | |
| thresh (float): Threshold for considering an assignment valid. | |
| use_lap (bool): Use lap.lapjv for the assignment. If False, scipy.optimize.linear_sum_assignment is used. | |
| Returns: | |
| matched_indices (np.ndarray): Array of matched indices of shape (K, 2), where K is the number of matches. | |
| unmatched_a (np.ndarray): Array of unmatched indices from the first set, with shape (L,). | |
| unmatched_b (np.ndarray): Array of unmatched indices from the second set, with shape (M,). | |
| Examples: | |
| >>> cost_matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) | |
| >>> thresh = 5.0 | |
| >>> matched_indices, unmatched_a, unmatched_b = linear_assignment(cost_matrix, thresh, use_lap=True) | |
| """ | |
| if cost_matrix.size == 0: | |
| return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1])) | |
| if use_lap: | |
| # Use lap.lapjv | |
| # https://github.com/gatagat/lap | |
| _, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh) | |
| matches = [[ix, mx] for ix, mx in enumerate(x) if mx >= 0] | |
| unmatched_a = np.where(x < 0)[0] | |
| unmatched_b = np.where(y < 0)[0] | |
| else: | |
| # Use scipy.optimize.linear_sum_assignment | |
| # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html | |
| x, y = scipy.optimize.linear_sum_assignment(cost_matrix) # row x, col y | |
| matches = np.asarray([[x[i], y[i]] for i in range(len(x)) if cost_matrix[x[i], y[i]] <= thresh]) | |
| if len(matches) == 0: | |
| unmatched_a = list(np.arange(cost_matrix.shape[0])) | |
| unmatched_b = list(np.arange(cost_matrix.shape[1])) | |
| else: | |
| unmatched_a = list(set(np.arange(cost_matrix.shape[0])) - set(matches[:, 0])) | |
| unmatched_b = list(set(np.arange(cost_matrix.shape[1])) - set(matches[:, 1])) | |
| return matches, unmatched_a, unmatched_b | |
| def iou_distance(atracks: list, btracks: list) -> np.ndarray: | |
| """ | |
| Compute cost based on Intersection over Union (IoU) between tracks. | |
| Args: | |
| atracks (list[STrack] | list[np.ndarray]): List of tracks 'a' or bounding boxes. | |
| btracks (list[STrack] | list[np.ndarray]): List of tracks 'b' or bounding boxes. | |
| Returns: | |
| (np.ndarray): Cost matrix computed based on IoU. | |
| Examples: | |
| Compute IoU distance between two sets of tracks | |
| >>> atracks = [np.array([0, 0, 10, 10]), np.array([20, 20, 30, 30])] | |
| >>> btracks = [np.array([5, 5, 15, 15]), np.array([25, 25, 35, 35])] | |
| >>> cost_matrix = iou_distance(atracks, btracks) | |
| """ | |
| if atracks and isinstance(atracks[0], np.ndarray) or btracks and isinstance(btracks[0], np.ndarray): | |
| atlbrs = atracks | |
| btlbrs = btracks | |
| else: | |
| atlbrs = [track.xywha if track.angle is not None else track.xyxy for track in atracks] | |
| btlbrs = [track.xywha if track.angle is not None else track.xyxy for track in btracks] | |
| ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32) | |
| if len(atlbrs) and len(btlbrs): | |
| if len(atlbrs[0]) == 5 and len(btlbrs[0]) == 5: | |
| ious = batch_probiou( | |
| np.ascontiguousarray(atlbrs, dtype=np.float32), | |
| np.ascontiguousarray(btlbrs, dtype=np.float32), | |
| ).numpy() | |
| else: | |
| ious = bbox_ioa( | |
| np.ascontiguousarray(atlbrs, dtype=np.float32), | |
| np.ascontiguousarray(btlbrs, dtype=np.float32), | |
| iou=True, | |
| ) | |
| return 1 - ious # cost matrix | |
| def embedding_distance(tracks: list, detections: list, metric: str = "cosine") -> np.ndarray: | |
| """ | |
| Compute distance between tracks and detections based on embeddings. | |
| Args: | |
| tracks (list[STrack]): List of tracks, where each track contains embedding features. | |
| detections (list[BaseTrack]): List of detections, where each detection contains embedding features. | |
| metric (str): Metric for distance computation. Supported metrics include 'cosine', 'euclidean', etc. | |
| Returns: | |
| (np.ndarray): Cost matrix computed based on embeddings with shape (N, M), where N is the number of tracks | |
| and M is the number of detections. | |
| Examples: | |
| Compute the embedding distance between tracks and detections using cosine metric | |
| >>> tracks = [STrack(...), STrack(...)] # List of track objects with embedding features | |
| >>> detections = [BaseTrack(...), BaseTrack(...)] # List of detection objects with embedding features | |
| >>> cost_matrix = embedding_distance(tracks, detections, metric="cosine") | |
| """ | |
| cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32) | |
| if cost_matrix.size == 0: | |
| return cost_matrix | |
| det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float32) | |
| # for i, track in enumerate(tracks): | |
| # cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric)) | |
| track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32) | |
| cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Normalized features | |
| return cost_matrix | |
| def fuse_score(cost_matrix: np.ndarray, detections: list) -> np.ndarray: | |
| """ | |
| Fuses cost matrix with detection scores to produce a single similarity matrix. | |
| Args: | |
| cost_matrix (np.ndarray): The matrix containing cost values for assignments, with shape (N, M). | |
| detections (list[BaseTrack]): List of detections, each containing a score attribute. | |
| Returns: | |
| (np.ndarray): Fused similarity matrix with shape (N, M). | |
| Examples: | |
| Fuse a cost matrix with detection scores | |
| >>> cost_matrix = np.random.rand(5, 10) # 5 tracks and 10 detections | |
| >>> detections = [BaseTrack(score=np.random.rand()) for _ in range(10)] | |
| >>> fused_matrix = fuse_score(cost_matrix, detections) | |
| """ | |
| if cost_matrix.size == 0: | |
| return cost_matrix | |
| iou_sim = 1 - cost_matrix | |
| det_scores = np.array([det.score for det in detections]) | |
| det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0) | |
| fuse_sim = iou_sim * det_scores | |
| return 1 - fuse_sim # fuse_cost | |