FPhmddlZddZddZy)Nc|\}}d||dddf|kdf<d||dddf|kdf<|||dddf||z kDdf<|||dddf||z kDdf<|S)a3 Adjust bounding boxes to stick to image border if they are within a certain threshold. Args: boxes (torch.Tensor): (n, 4) image_shape (tuple): (height, width) threshold (int): pixel threshold Returns: adjusted_boxes (torch.Tensor): adjusted bounding boxes rN)boxes image_shape thresholdhws kC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/models/fastsam/utils.pyadjust_bboxes_to_image_borderrs DAq)*E%1+ !1 $%()E%1+ !1 $%,-E%1+I %q (),-E%1+I %q () Lct||}tj|d|dddf}tj|d|dddf}tj|d|dddf}tj|d|dddf}||z j d||z j dz} |d|dz |d|dz z} |dddf|dddfz |dddf|dddfz z} | | z| z } | | z } |r| j dk(rdS| Stj | |kDjS)a Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes. Args: box1 (torch.Tensor): (4, ) boxes (torch.Tensor): (n, 4) iou_thres (float): IoU threshold image_shape (tuple): (height, width) raw_output (bool): If True, return the raw IoU values instead of the indices Returns: high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres rNrrr)rtorchmaxminclampnumelnonzeroflatten)box1r iou_thresr raw_outputx1y1x2y2 intersection box1_area box2_areaunionious r bbox_iour$sV *% =E 47E!Q$K (B 47E!Q$K (B 47E!Q$K (B 47E!Q$K (BG??1%b(::La47"tAwa'89Iq!tuQT{*uQT{U1a4[/HII  !L 0E  CIIK1$q-#- ==y ) 1 1 33r))g?)r&F)rrr$rrr r's 0%4r