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import numpy as np |
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import scipy |
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from scipy.spatial.distance import cdist |
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from ultralytics.utils.metrics import bbox_ioa, batch_probiou |
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try: |
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import lap |
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assert lap.__version__ |
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except (ImportError, AssertionError, AttributeError): |
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from ultralytics.utils.checks import check_requirements |
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check_requirements("lapx>=0.5.2") |
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import lap |
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def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = True) -> tuple: |
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""" |
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Perform linear assignment using scipy or lap.lapjv. |
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Args: |
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cost_matrix (np.ndarray): The matrix containing cost values for assignments. |
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thresh (float): Threshold for considering an assignment valid. |
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use_lap (bool, optional): Whether to use lap.lapjv. Defaults to True. |
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Returns: |
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Tuple with: |
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- matched indices |
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- unmatched indices from 'a' |
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- unmatched indices from 'b' |
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""" |
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if cost_matrix.size == 0: |
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return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1])) |
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if use_lap: |
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_, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh) |
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matches = [[ix, mx] for ix, mx in enumerate(x) if mx >= 0] |
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unmatched_a = np.where(x < 0)[0] |
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unmatched_b = np.where(y < 0)[0] |
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else: |
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x, y = scipy.optimize.linear_sum_assignment(cost_matrix) |
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matches = np.asarray([[x[i], y[i]] for i in range(len(x)) if cost_matrix[x[i], y[i]] <= thresh]) |
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if len(matches) == 0: |
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unmatched_a = list(np.arange(cost_matrix.shape[0])) |
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unmatched_b = list(np.arange(cost_matrix.shape[1])) |
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else: |
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unmatched_a = list(set(np.arange(cost_matrix.shape[0])) - set(matches[:, 0])) |
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unmatched_b = list(set(np.arange(cost_matrix.shape[1])) - set(matches[:, 1])) |
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return matches, unmatched_a, unmatched_b |
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def iou_distance(atracks: list, btracks: list) -> np.ndarray: |
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""" |
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Compute cost based on Intersection over Union (IoU) between tracks. |
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Args: |
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atracks (list[STrack] | list[np.ndarray]): List of tracks 'a' or bounding boxes. |
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btracks (list[STrack] | list[np.ndarray]): List of tracks 'b' or bounding boxes. |
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Returns: |
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(np.ndarray): Cost matrix computed based on IoU. |
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""" |
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if atracks and isinstance(atracks[0], np.ndarray) or btracks and isinstance(btracks[0], np.ndarray): |
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atlbrs = atracks |
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btlbrs = btracks |
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else: |
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atlbrs = [track.xywha if track.angle is not None else track.xyxy for track in atracks] |
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btlbrs = [track.xywha if track.angle is not None else track.xyxy for track in btracks] |
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ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32) |
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if len(atlbrs) and len(btlbrs): |
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if len(atlbrs[0]) == 5 and len(btlbrs[0]) == 5: |
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ious = batch_probiou( |
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np.ascontiguousarray(atlbrs, dtype=np.float32), |
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np.ascontiguousarray(btlbrs, dtype=np.float32), |
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).numpy() |
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else: |
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ious = bbox_ioa( |
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np.ascontiguousarray(atlbrs, dtype=np.float32), |
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np.ascontiguousarray(btlbrs, dtype=np.float32), |
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iou=True, |
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) |
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return 1 - ious |
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def embedding_distance(tracks: list, detections: list, metric: str = "cosine") -> np.ndarray: |
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""" |
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Compute distance between tracks and detections based on embeddings. |
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Args: |
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tracks (list[STrack]): List of tracks. |
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detections (list[BaseTrack]): List of detections. |
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metric (str, optional): Metric for distance computation. Defaults to 'cosine'. |
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Returns: |
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(np.ndarray): Cost matrix computed based on embeddings. |
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""" |
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cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32) |
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if cost_matrix.size == 0: |
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return cost_matrix |
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det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float32) |
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track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32) |
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cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) |
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return cost_matrix |
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def fuse_score(cost_matrix: np.ndarray, detections: list) -> np.ndarray: |
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""" |
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Fuses cost matrix with detection scores to produce a single similarity matrix. |
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Args: |
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cost_matrix (np.ndarray): The matrix containing cost values for assignments. |
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detections (list[BaseTrack]): List of detections with scores. |
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Returns: |
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(np.ndarray): Fused similarity matrix. |
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""" |
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if cost_matrix.size == 0: |
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return cost_matrix |
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iou_sim = 1 - cost_matrix |
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det_scores = np.array([det.score for det in detections]) |
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det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0) |
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fuse_sim = iou_sim * det_scores |
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return 1 - fuse_sim |
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