o W h @sddlZddlZddlmZddlmZmZz ddlZej s JWne e e fy;ddl mZedddlZYnwddejded ed efd d Zd eded ejfddZddededed ejfddZdejded ejfddZdS)N)cdist) batch_probioubbox_ioa)check_requirementsz lap>=0.5.12T cost_matrixthreshuse_lapreturncsZjdkrtjdtdttjdttjdfS|rFtjdd\}ddt D}t dkd}t dkd}nbt j \tfd dttD}t|dkr|ttjd}ttjd}n,tttjdt|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: 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)rdtypeT) extend_cost cost_limitcSs g|] \}}|dkr||gqS)r).0ixmxrrW/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/trackers/utils/matching.py .s z%linear_assignment..cs4g|]}||fkr||gqSrr)rirrxyrrr5s4N)sizenpemptyinttuplerangeshapelaplapjv enumeratewherescipyoptimizelinear_sum_assignmentasarraylenlistarange frozenset)rrr_matches unmatched_a unmatched_brrrlinear_assignments 0& ,, r1atracksbtrackscCs|r t|dtjs|rt|dtjr|}|}ndd|D}dd|D}tjt|t|ftjd}t|rwt|rwt|ddkrdt|ddkrdttj|tjdtj|tjd}d |St tj|tjdtj|tjddd}d |S) a 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 with shape (len(atracks), len(btracks)). 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) rcS"g|] }|jdur |jn|jqSNanglexywhaxyxyrtrackrrrrU"z iou_distance..cSr4r5r6r:rrrrVr<r T)iour ) isinstancerndarrayzerosr)float32rascontiguousarraynumpyr)r2r3atlbrsbtlbrsiousrrr iou_distance@s*(  rHcosinetracks detectionsmetriccCsvtjt|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 rcSg|]}|jqSr) curr_featr:rrrr~z&embedding_distance..cSrMr) smooth_featr:rrrrrOg)rrAr)rBrr(maximumr)rJrKrLr det_featurestrack_featuresrrrembedding_distancehs rTcCsX|jdkr|Sd|}tdd|D}tj|ddj|jddd}||}d|S)a Fuse 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 cSrMr)score)rdetrrrrrOzfuse_score..)axis)rrarray expand_dimsrepeatr )rrKiou_sim det_scoresfuse_simrrr fuse_scores r^)T)rI)rDrr%scipy.spatial.distancerultralytics.utils.metricsrrr! __version__ ImportErrorAssertionErrorAttributeErrorultralytics.utils.checksrr@floatboolrr1r*rHstrrTr^rrrrs    ,(