o V h*+ @sddlZddlmZddlmZmZmZmZddlZ ddl Z d0de j dee dee de d e j f d d Zd e d eeeddffd dZde j de de d e j fddZde d e jfddZde de de d ee jfddZdee dfde de d eeee ee ffddZde j dee d e j fd d!Zd"e j dee d e j fd#d$Zde j dee d%e d&e d e j f d'd(Zd)e jd*e d+ed ee jeffd,d-Zde j d e j fd.d/ZdS)1N)product)Any GeneratorListTuple4@boxescrop_boxorig_boxatolreturncCstj|tj|jd}tj|tj|jd}t||}tj||dddf|dd}tj||dddf|dd}t||}tj|ddS)zeDetermines if bounding boxes are near the edge of a cropped image region using a specified tolerance.)dtypedeviceNr)r rtoldim)torch as_tensorfloatruncrop_boxes_xyxyisclose logical_andany)rr r r crop_box_torchorig_box_torchnear_crop_edgenear_image_edgerN/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/models/sam/amg.pyis_box_near_crop_edge sr batch_sizec'strtfddDsJdtdttddk}t|D] fddDVq*dS)a Yield batches of data from input arguments with specified batch size for efficient processing. This function takes a batch size and any number of iterables, then yields batches of elements from those iterables. All input iterables must have the same length. Args: batch_size (int): Size of each batch to yield. *args (Any): Variable length input iterables to batch. All iterables must have the same length. Yields: (List[Any]): A list of batched elements from each input iterable. Examples: >>> data = [1, 2, 3, 4, 5] >>> labels = ["a", "b", "c", "d", "e"] >>> for batch in batch_iterator(2, data, labels): ... print(batch) [[1, 2], ['a', 'b']] [[3, 4], ['c', 'd']] [[5], ['e']] c3s$|] }t|tdkVqdS)rN)len).0a)argsrr /s"z!batch_iterator..z-Batched iteration must have same-size inputs.rcs$g|]}|dqSrr)r#arg)br!rr 2s$z"batch_iterator..N)allr"intrange)r!r% n_batchesr)r%r)r!rbatch_iterators "( r/masksmask_thresholdthreshold_offsetcCsP|||kjdtjdjdtjd}|||kjdtjdjdtjd}||S)a Computes the stability score for a batch of masks. The stability score is the IoU between binary masks obtained by thresholding the predicted mask logits at high and low values. Args: masks (torch.Tensor): Batch of predicted mask logits. mask_threshold (float): Threshold value for creating binary masks. threshold_offset (float): Offset applied to the threshold for creating high and low binary masks. Returns: (torch.Tensor): Stability scores for each mask in the batch. Notes: - One mask is always contained inside the other. - Memory is saved by preventing unnecessary cast to torch.int64. Examples: >>> masks = torch.rand(10, 256, 256) # Batch of 10 masks >>> mask_threshold = 0.5 >>> threshold_offset = 0.1 >>> stability_scores = calculate_stability_score(masks, mask_threshold, threshold_offset) )r )sumrint16int32)r0r1r2 intersectionsunionsrrrcalculate_stability_score5s$$r9 n_per_sidecCspdd|}t|d||}t|dddf|df}t|dddfd|f}tj||gddddS)zaGenerate a 2D grid of evenly spaced points in the range [0,1]x[0,1] for image segmentation tasks.rNr3)axis)nplinspacetilestackreshape)r:offsetpoints_one_sidepoints_xpoints_yrrrbuild_point_gridSs rFn_layersscale_per_layercsfddt|dDS)zQGenerates point grids for multiple crop layers with varying scales and densities.cs g|] }tt|qSr)rFr,r#ir:rHrrr*^ z/build_all_layer_point_grids..r)r-)r:rGrHrrKrbuild_all_layer_point_grids\srMim_size. overlap_ratiocs gg}}|\}}t||}|dd||g|ddd}t|D]\} d| d} t||d| ||| ||| fddt| D} fddt| D} t| | D] \} }| |t| |t||g}|||| dq_q$||fS) a Generates crop boxes of varying sizes for multiscale image processing, with layered overlapping regions. Args: im_size (Tuple[int, ...]): Height and width of the input image. n_layers (int): Number of layers to generate crop boxes for. overlap_ratio (float): Ratio of overlap between adjacent crop boxes. Returns: (List[List[int]]): List of crop boxes in [x0, y0, x1, y1] format. (List[int]): List of layer indices corresponding to each crop box. Examples: >>> im_size = (800, 1200) # Height, width >>> n_layers = 3 >>> overlap_ratio = 0.25 >>> crop_boxes, layer_idxs = generate_crop_boxes(im_size, n_layers, overlap_ratio) rcSstt||d||S)z[Calculates the length of each crop given the original length, number of crops, and overlap.r)r,mathceil)orig_lenn_cropsoverlaprrrcrop_len~sz%generate_crop_boxes..crop_lenr;rcg|] }t|qSrr,rI)crop_wrTrrr*z'generate_crop_boxes..crVrrWrI)crop_hrTrrr*rY)minappendr-r,r)rNrGrO crop_boxes layer_idxsim_him_w short_siderUi_layern_crops_per_side crop_box_x0 crop_box_y0x0y0boxr)rZrXrTrgenerate_crop_boxesas&         ricCsF|\}}}}tj||||gg|jd}t|jdkr|d}||S)zIUncrop bounding boxes by adding the crop box offset to their coordinates.rrrtensorrr"shape unsqueeze)rr rfrg_rBrrrrs  rpointscCsB|\}}}}tj||gg|jd}t|jdkr|d}||S)zAUncrop points by adding the crop box offset to their coordinates.rjrkrrl)rqr rfrgrprBrrr uncrop_pointss  rrorig_horig_wc Csr|\}}}}|dkr|dkr||kr||kr|S||||||}} ||||| |f} tjjj|| ddS)z]Uncrop masks by padding them to the original image size, handling coordinate transformations.r)value)rnn functionalpad) r0r rsrtrfrgx1y1pad_xpad_yrxrrr uncrop_maskss  r}mask area_threshmodec sddl}|dvsJd|d|dk}||Atj}||d\}}}} |dddfd d} fd d t| D} | sC|d fSdg| |s_fd d t|Dp^tt| d gt |}|dfS)a Removes small disconnected regions or holes in a mask based on area threshold and mode. Args: mask (np.ndarray): Binary mask to process. area_thresh (float): Area threshold below which regions will be removed. mode (str): Processing mode, either 'holes' to fill small holes or 'islands' to remove small disconnected regions. Returns: (np.ndarray): Processed binary mask with small regions removed. (bool): Whether any regions were modified. Examples: >>> mask = np.zeros((100, 100), dtype=np.bool_) >>> mask[40:60, 40:60] = True # Create a square >>> mask[45:55, 45:55] = False # Create a hole >>> processed_mask, modified = remove_small_regions(mask, 50, "holes") rN>holesislandszProvided mode z is invalidrr3rcs g|] \}}|kr|dqSr'r)r#rJs)rrrr*rLz(remove_small_regions..Fcsg|]}|vr|qSrrrI) fill_labelsrrr*sT) cv2astyper=uint8connectedComponentsWithStats enumerater-r,argmaxisin) r~rrr correct_holes working_maskn_labelsregionsstatsrpsizes small_regionsr)rrrremove_small_regionss * rcCst|dkrtjg|jdddRd|jiS|j}|dd\}}t|dkr1|ddn|d}tj|dd \}}|tj ||jd dddf}tj|dd \}}|||}tj |dd \}}tj|dd \} }| tj || jd dddf} tj| dd \} }| || } tj | dd \} }| | k||kB} tj | || |gdd }|| d}t|dkr|j g|dddRS|dS) a Calculates bounding boxes in XYXY format around binary masks. Args: masks (torch.Tensor): Binary masks with shape (B, H, W) or (B, C, H, W). Returns: (torch.Tensor): Bounding boxes in XYXY format with shape (B, 4) or (B, C, 4). Notes: - Handles empty masks by returning zero boxes. - Preserves input tensor dimensions in the output. rNrr;r3rrj) rnumelzerosrnrr"flattenromaxaranger[r@rA)r0rnhw in_heightrpin_height_coords bottom_edges top_edgesin_widthin_width_coords right_edges left_edges empty_filteroutrrrbatched_mask_to_boxs&&"  0r)r)rP itertoolsrtypingrrrrnumpyr=rTensorr,rr r/r9ndarrayrFrMrirrrr}strboolrrrrrrsF     4 & & %