a yf @sddlZddlZddlmZddlmZmZzddlZej s@JWn4e e e fyvddl mZedddlZYn0dejeeeddd Zeeejd d d ZdeeeejdddZejeejdddZdS)N)cdist) batch_probioubbox_ioa)check_requirementsz lapx>=0.5.2T) cost_matrixthreshuse_lapreturncsZjdkr:tjdtdttjdttjdfS|rtjdd\}ddt D}t dkd}t dkd}nt j \tfd dttD}t|dkrttjd}ttjd}nXtttjdt|d d df}tttjdt|d d df}|||fS) a 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: (tuple): A tuple containing: - 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) r)rZdtypeT)Z extend_costZ cost_limitcSs g|]\}}|dkr||gqS)r).0ZixZmxr r _/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/trackers/utils/matching.py /z%linear_assignment..cs4g|],}||fkr||gqSr r )rirrxyr rr6rN)sizenpemptyinttuplerangeshapelapZlapjv enumeratewherescipyoptimizeZlinear_sum_assignmentasarraylenlistZarangeset)rrr_matchesZ unmatched_aZ unmatched_br rrlinear_assignments 0& ,,r()atracksbtracksr cCs|rt|dtjs(|r2t|dtjr2|}|}ndd|D}dd|D}tjt|t|ftjd}t|rt|rt|ddkrt|ddkrttj|tjdtj|tjd}n&t tj|tjdtj|tjddd}d |S) ak 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) rcSs"g|]}|jdur|jn|jqSNZangleZxywhaZxyxyrtrackr r rrVrz iou_distance..cSs"g|]}|jdur|jn|jqSr+r,r-r r rrWrr T)Ziour ) isinstancerndarrayzerosr#float32rZascontiguousarraynumpyr)r)r*ZatlbrsZbtlbrsZiousr r r iou_distanceAs$(  r5cosine)tracks detectionsmetricr cCsvtjt|t|ftjd}|jdkr*|Stjdd|Dtjd}tjdd|Dtjd}tdt|||}|S)a 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") r rcSsg|] }|jqSr )Z curr_featr-r r rrrz&embedding_distance..cSsg|] }|jqSr )Z smooth_featr-r r rrrg)rr2r#r3rr"maximumr)r7r8r9rZ det_featuresZtrack_featuresr r rembedding_distanceis r;)rr8r cCsX|jdkr|Sd|}tdd|D}tj|ddj|jddd}||}d|S)a 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) rr cSsg|] }|jqSr )Zscore)rZdetr r rrrzfuse_score..)Zaxis)rrarrayZ expand_dimsrepeatr)rr8Ziou_simZ det_scoresZfuse_simr r r fuse_scores r>)T)r6)r4rr Zscipy.spatial.distancerZultralytics.utils.metricsrrr __version__ ImportErrorAssertionErrorAttributeErrorZultralytics.utils.checksrr1floatboolrr(r$r5strr;r>r r r rs  -(