h lSSKrSSKrSSKJr SSKJrJr SSKr\R(deSS\RS\S\4S jjrS \S \S \R4S jrSS\S\S\S \R4SjjrS\RS\S \R4Sjrg!\ \ \ 4a SSK Jr \"S5 SSKrNf=f)N)cdist) batch_probioubbox_ioa)check_requirementsz lap>=0.5.12 cost_matrixthreshuse_lapc URS:XaZ[R"S[S9[ [ UR S55[ [ UR S554$U(a{[R"USUS9up4n[U5VVs/sHupgUS:dM Xg/PM nnn[R"US:5Sn [R"US:5Sn GOh[RRU5upE[R"[ [U55V s/sHoXKX[4U::dMXKX[/PM sn 5n[U5S:XaY[![R""UR S55n [![R""UR S55n O[![%[R""UR S55[%USS2S45- 5n [![%[R""UR S55[%USS2S45- 5n XU 4$s snnfs sn f)ac 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_limitN)sizenpemptyinttuplerangeshapelaplapjv enumeratewherescipyoptimizelinear_sum_assignmentasarraylenlistarange frozenset) rrr _xyixmxmatches unmatched_a unmatched_bis ]/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/trackers/utils/matching.pylinear_assignmentr.s&1xxc*E% 8I8I!8L2M,NPUV[\g\m\mno\pVqPrrr))KTfMa*3A,B,"'8B8,Bhhq1uoa( hhq1uoa( ~~33K@**E#a&MgMqQRQUWXW[Q[E\`fEflqtQTlMgh w<1 ryy):):1)=>?Kryy):):1)=>?Ky;3D3DQ3G)HIIV]^_ab^bVcLddeKy;3D3DQ3G)HIIV]^_ab^bVcLddeK  ,,Chs I)I4I IatracksbtracksreturncU(a"[US[R5(d)U(a'[US[R5(aUnUnOhUVs/sH'oDRb URO UR PM) nnUVs/sH'oDRb URO UR PM) nn[R "[U5[U54[RS9n[U5(a[U5(a[US5S:Xas[US5S:Xaa[[R"U[RS9[R"U[RS95R5nSU- $[[R"U[RS9[R"U[RS9SS9nSU- $s snfs snf)ac 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) rr T)iour) isinstancerndarrayanglexywhaxyxyzerosr float32rascontiguousarraynumpyr)r/r0atlbrsbtlbrstrackiouss r- iou_distancerB@sz":gaj"**55ZPWXYPZ\^\f\fEgEgV]^V]U!8%++ejjHV]^V]^V]U!8%++ejjHV]^ 88S[#f+.bjj AD 6{{s6{{ vay>Q 3vay>Q#6 $$V2::>$$V2::>eg  t8O $$V2::>$$V2::>D t8O!_^s .G..G3tracks detectionsmetricc[R"[U5[U54[RS9nURS:XaU$[R "UVs/sHoDR PM sn[RS9n[R "UVs/sHoDRPM sn[RS9n[R"S[XeU55nU$s snfs snf)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 rg) rr:r r;rr curr_feat smooth_featmaximumr)rCrDrErr@ det_featurestrack_featuress r-embedding_distancerLhs&((CKZ9LK1::JGJ5JGrzzZLZZ Gu!2!2 GrzzZN**S%f"MNK  H!Hs C'C,c URS:XaU$SU- n[R"UVs/sHo3RPM sn5n[R"USS9R UR SSS9nX$-nSU- $s snf)ac Fuse cost matrix with detection scores to produce a single similarity matrix. Args: cost_matrix (np.ndarray): The matrix containing cost values for assignments, with shape (N, M). detections (list[BaseTrack]): List of detections, each containing a score attribute. Returns: (np.ndarray): Fused similarity matrix with shape (N, M). Examples: Fuse a cost matrix with detection scores >>> cost_matrix = np.random.rand(5, 10) # 5 tracks and 10 detections >>> detections = [BaseTrack(score=np.random.rand()) for _ in range(10)] >>> fused_matrix = fuse_score(cost_matrix, detections) rr)axis)rrarrayscore expand_dimsrepeatr)rrDiou_simdet det_scoresfuse_sims r- fuse_scorerWs"1+oG ; 99 ;res (= ???)-2::)-u)-t)-X%$%%"**%PtsRTR\R\<BJJDRZZs ^^4;}% sBB32B3