^js2.ddlmZddlZddlmZddlmZm Z ddl m Z  ddZ d ddZ e ejdd d di  d dd Z dd Z ddZddZ ddZ ddZ d ddZy)) annotationsN) TargetMode deprecated) box_iou_batchxyxyctj|}|\}}|ddddgfjd||ddddgf<|ddddgfjd||ddddgf<|S)a Clips bounding boxes coordinates to fit within the frame resolution. Args: xyxy: A numpy array of shape `(N, 4)` where each row corresponds to a bounding box in the format `(x_min, y_min, x_max, y_max)`. resolution_wh: A tuple of the form `(width, height)` representing the resolution of the frame. Returns: A numpy array of shape `(N, 4)` where each row corresponds to a bounding box with coordinates clipped to fit within the frame resolution. Examples: ```pycon >>> import numpy as np >>> import supervision as sv >>> xyxy = np.array([ ... [10, 20, 300, 200], ... [15, 25, 350, 450], ... [-10, -20, 30, 40] ... ]) >>> sv.clip_boxes(xyxy=xyxy, resolution_wh=(320, 240)) array([[ 10, 20, 300, 200], [ 15, 25, 320, 240], [ 0, 0, 30, 40]]) ``` Nr)npcopyclip)r resolution_whresultwidthheights l/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/supervision/detection/utils/boxes.py clip_boxesr sxF&(WWT]F!ME6q1a&y)..q%8F1q!f9q1a&y)..q&9F1q!f9 Mc||}|j}|ddddgfxx||gzcc<|ddddgfxx||gz cc<|S)a Pads bounding boxes coordinates with a constant padding. Args: xyxy: A numpy array of shape `(N, 4)` where each row corresponds to a bounding box in the format `(x_min, y_min, x_max, y_max)`. px: The padding value to be added to both the left and right sides of each bounding box. py: The padding value to be added to both the top and bottom sides of each bounding box. If not provided, `px` will be used for both dimensions. Returns: A numpy array of shape `(N, 4)` where each row corresponds to a bounding box with coordinates padded according to the provided padding values. Examples: ```pycon >>> import numpy as np >>> import supervision as sv >>> xyxy = np.array([ ... [10, 20, 30, 40], ... [15, 25, 35, 45] ... ]) >>> sv.pad_boxes(xyxy=xyxy, px=5, py=10) array([[ 5, 10, 35, 50], [10, 15, 40, 55]]) ``` Nrr r r r )rpxpyrs r pad_boxesr4sWJ z  YY[F 1q!f9"b! 1q!f9"b! Mrz0.27.0z0.30.0normalized_xyxy)target deprecated_in remove_in args_mappingc|\}}|j}|ddddgf|z|z |ddddgf<|ddddgf|z|z |ddddgf<|S)a Convert normalized bounding box coordinates to absolute pixel coordinates. Multiplies each bounding box coordinate by image size and divides by `normalization_factor`, mapping values from normalized `[0, normalization_factor]` to absolute pixel values for a given resolution. Args: xyxy: Normalized bounding boxes of shape `(N, 4)`, where each row is `(x_min, y_min, x_max, y_max)`, values in `[0, normalization_factor]`. resolution_wh: Target image resolution as `(width, height)`. normalization_factor: Maximum value of input coordinate range. Defaults to `1.0`. Returns: Array of shape `(N, 4)` with absolute coordinates in `(x_min, y_min, x_max, y_max)` format. Examples: ```pycon >>> import numpy as np >>> import supervision as sv >>> xyxy = np.array([ ... [0.1, 0.2, 0.5, 0.6], ... [0.3, 0.4, 0.7, 0.8], ... [0.2, 0.1, 0.6, 0.5] ... ]) >>> sv.denormalize_boxes(xyxy, (1280, 720)) array([[128., 144., 640., 432.], [384., 288., 896., 576.], [256., 72., 768., 360.]]) ``` ```pycon >>> xyxy = np.array([ ... [256., 128., 768., 640.] ... ]) >>> sv.denormalize_boxes(xyxy, (1280, 720), normalization_factor=1024.0) array([[320., 90., 960., 450.]]) ``` Nrr r r r)rrnormalization_factorrrrrs rdenormalize_boxesr"csrp"ME6 YY[FAq6 *U26JJF1q!f9Aq6 *V37KKF1q!f9 Mrc6|tj||gzS)a Args: xyxy: An array of shape `(n, 4)` containing the bounding boxes coordinates in format `[x1, y1, x2, y2]` offset: An array of shape `(2,)` containing offset values in format is `[dx, dy]`. Returns: Repositioned bounding boxes. Examples: ```pycon >>> import numpy as np >>> import supervision as sv >>> xyxy = np.array([ ... [10, 10, 20, 20], ... [30, 30, 40, 40] ... ]) >>> offset = np.array([5, 5]) >>> sv.move_boxes(xyxy=xyxy, offset=offset) array([[15, 15, 25, 25], [35, 35, 45, 45]]) ``` )r hstack)roffsets r move_boxesr&s8 "))VV,- --rc ||zS)a Args: xyxyxyxy: An array of shape `(n, 4, 2)` containing the oriented bounding boxes coordinates in format `[[x1, y1], [x2, y2], [x3, y3], [x3, y3]]` offset: An array of shape `(2,)` containing offset values in format is `[dx, dy]`. Returns: Repositioned bounding boxes. Examples: ```pycon >>> import numpy as np >>> from supervision.detection.utils.boxes import move_oriented_boxes >>> xyxyxyxy = np.array([ ... [ ... [20, 10], ... [10, 20], ... [20, 30], ... [30, 20] ... ], ... [ ... [30, 30], ... [20, 40], ... [30, 50], ... [40, 40] ... ] ... ]) >>> offset = np.array([5, 5]) >>> move_oriented_boxes(xyxyxyxy=xyxyxyxy, offset=offset) array([[[25, 15], [15, 25], [25, 35], [35, 25]], [[35, 35], [25, 45], [35, 55], [45, 45]]]) ``` )xyxyxyxyr%s rmove_oriented_boxesr*s\ f rctj|}|jdk7s|jdddk7rt d|j|dj tj d}|d j tj d}|tj|d d z|tj|d d zz }d tjtj|d zS) aBCompute the area of N oriented bounding boxes using the shoelace formula. Args: corners: OBB corner coordinates with shape `(N, 4, 2)`. Returns: Area of each box as a 1-D float64 array of shape `(N,)`. Raises: ValueError: If `corners` does not have shape `(N, 4, 2)`. Examples: >>> import numpy as np >>> from supervision.detection.utils.boxes import obb_polygon_area >>> corners = np.array([[[0, 5], [5, 10], [10, 5], [5, 0]]], dtype=np.float32) >>> obb_polygon_area(corners) array([50.]) r Nr z'corners must have shape (N, 4, 2); got .rFr.r axisg?) r asarrayndimshape ValueErrorastypefloat64rollabssum)cornersxycrosss robb_polygon_arearAs&jj!G||qGMM"#.&8B7==/RSSrzz6Arzz6A 2B' '!bgga".E*E EE u2./ //rc|tj|}|jdk7s|jdddk7rt d|j|dj d}|d j d}|dj d}|d j d}tj||||gdS) adConvert oriented bounding box corners to axis-aligned bounding boxes. Args: xyxyxyxy: OBB corner coordinates with shape `(N, 4, 2)` where each box is represented as `[[x1, y1], [x2, y2], [x3, y3], [x4, y4]]`. Returns: Axis-aligned bounding boxes as an array of shape `(N, 4)` in `(x_min, y_min, x_max, y_max)` format. Raises: ValueError: If `xyxyxyxy` does not have shape `(N, 4, 2)`. Examples: ```pycon >>> import numpy as np >>> import supervision as sv >>> corners = np.array([ ... [[0, 0], [10, 0], [10, 5], [0, 5]], ... [[5, 5], [15, 5], [15, 10], [5, 10]], ... ], dtype=np.float32) >>> sv.xyxyxyxy_to_xyxy(corners) array([[ 0., 0., 10., 5.], [ 5., 5., 15., 10.]], dtype=float32) ``` r r,Nr-z(xyxyxyxy must have shape (N, 4, 2); got r/r1r2r0)r r4r5r6r7minmaxstack)r)x_miny_minx_maxy_maxs rxyxyxyxy_to_xyxyrJs<zz(#H}}X^^BC0F:CHNNCSTUU V  b )E V  b )E V  b )E V  b )E 88UE5%0r ::rc|ddddf|ddddfzdz }|ddddf|ddddfz |z}tj||dz z ||dz zfdS)a Scale the dimensions of bounding boxes. Args: xyxy: An array of shape `(n, 4)` containing the bounding boxes coordinates in format `[x1, y1, x2, y2]` factor: A float value representing the factor by which the box dimensions are scaled. A factor greater than 1 enlarges the boxes, while a factor less than 1 shrinks them. Returns: Scaled bounding boxes. Examples: ```pycon >>> import numpy as np >>> import supervision as sv >>> xyxy = np.array([ ... [10, 10, 20, 20], ... [30, 30, 40, 40] ... ]) >>> sv.scale_boxes(xyxy=xyxy, factor=1.5) array([[ 7.5, 7.5, 22.5, 22.5], [27.5, 27.5, 42.5, 42.5]]) ``` Nr r r2)r concatenate)rfactorcenters new_sizess r scale_boxesrP8sw<ArrE{T!QR%[(A-GaetArrE{*f4I >>7Y]2Gi!m4KLST UUrct|dk(r|St|d}t|D]}t||}t j |dt j |dk(rni|dkD}|ddddf|ddddfzdz }|ddtjddf|tjddddfz }||ddddtjfz}t j|d}tjj|dd} t j|| t j|| dk7 } t j|d} | ddtjf| z} | d z} d| | dkD| dkz<d | | dk| d kDz<| jt} |ddddgfxx| z cc<|dddd gfxx| z cc<t|d S)a Spread out boxes that overlap with each other. Args: xyxy: Numpy array of shape (N, 4) where N is the number of boxes. max_iterations: Maximum number of iterations to run the algorithm for. Example: ```pycon >>> import numpy as np >>> from supervision.detection.utils.boxes import spread_out_boxes >>> xyxy = np.array([ ... [10, 10, 20, 20], ... [12, 12, 22, 22] ... ]) >>> spread_out = spread_out_boxes(xyxy=xyxy, max_iterations=10) >>> # The boxes should be moved apart >>> bool(spread_out[0, 0] < 10 and spread_out[0, 1] < 10) True >>> bool(spread_out[1, 0] > 12 and spread_out[1, 1] > 12) True ``` rr )rNr r2T)r3keepdims)outwhere r,r r1)lenrrangerr fill_diagonalallnewaxisr<linalgnormdivide zeros_liker8int) rmax_iterations xyxy_padded_iou overlap_maskrN delta_centers delta_sumdelta_magnitudedirection_vectors force_vectorss rspread_out_boxesrj[s8 4yA~ DQ'K > "$0K5 a 66#(  Qw q"1"u% AqrE(::a? 2::q 01GBJJ1?@DF }q(]R-?@A%,,S1 A1vI-/A1vI-/I$0L [R ((r)rnpt.NDArray[np.number]rtuple[int, int]returnrk)N)rrkrr_rz int | Nonermrk)g?N) rrkrrlr!floatrznpt.NDArray[np.number] | Nonermrk)rnpt.NDArray[np.float64]r%npt.NDArray[np.int32]rmro)r)ror%rprmro)r=z npt.NDArrayrmro)r)rkrmrk)rrorMrnrmro)d)rrkr`r_rmrk) __future__rnumpyr numpy.typingtypingnpt deprecaterr'supervision.detection.utils.iou_and_nmsrrr ARGS_REMAPr"r&r*rArJrPrjr(rrrzsj",A' '"''Z, , , , ,^  #V, #&59 8 8"8 83 8  8  8v. !.+@..>.%./D..b08%;$%;%;P V ! 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