,j;SSKrSSKrSSKJrJr SSKJrJrJ r J r J r SSK J r SSKJr SSKJrJrJrJrJrJrJrJrJrJrJrJrJrJrJrJ r "SS 5r!g) N) batched_nmsbox_area)AnyDictListOptionalTuple)Sam) SamPredictor)MaskData area_from_rlebatch_iteratorbatched_mask_to_boxbox_xyxy_to_xywhbuild_all_layer_point_gridscalculate_stability_scorecoco_encode_rlegenerate_crop_boxesis_box_near_crop_edgemask_to_rle_pytorchremove_small_regions rle_to_maskuncrop_boxes_xyxy uncrop_masks uncrop_pointsc\rSrSrS#S\S\\S\S\S\S\S \S \S \S \S \S\\\ RS\S\ SS4Sjjr \ R"5S\ RS\\\ \44Sj5rS\ RS\4SjrS\ RS\\S\S\\S4S\4 SjrS\ RS\\S4S\\S\\S4S\4 Sjr\S\S\S \S\4S!j5rS"rg)$SamAutomaticMaskGenerator#Nmodelpoints_per_sidepoints_per_batchpred_iou_threshstability_score_threshstability_score_offsetbox_nms_thresh crop_n_layerscrop_nms_threshcrop_overlap_ratiocrop_n_points_downscale_factor point_gridsmin_mask_region_area output_modereturncnUSLU SL:wdS5eUb[UUU 5UlOU bXlO [S5eUS;d SUS35eUS:XaSS KJn U S:aSSKn[ U5UlX0lX@l XPl X`l Xpl Xl XlXlXlXlXlg) a Using a SAM model, generates masks for the entire image. Generates a grid of point prompts over the image, then filters low quality and duplicate masks. The default settings are chosen for SAM with a ViT-H backbone. Arguments: model (Sam): The SAM model to use for mask prediction. points_per_side (int or None): The number of points to be sampled along one side of the image. The total number of points is points_per_side**2. If None, 'point_grids' must provide explicit point sampling. points_per_batch (int): Sets the number of points run simultaneously by the model. Higher numbers may be faster but use more GPU memory. pred_iou_thresh (float): A filtering threshold in [0,1], using the model's predicted mask quality. stability_score_thresh (float): A filtering threshold in [0,1], using the stability of the mask under changes to the cutoff used to binarize the model's mask predictions. stability_score_offset (float): The amount to shift the cutoff when calculated the stability score. box_nms_thresh (float): The box IoU cutoff used by non-maximal suppression to filter duplicate masks. crops_n_layers (int): If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where each layer has 2**i_layer number of image crops. crops_nms_thresh (float): The box IoU cutoff used by non-maximal suppression to filter duplicate masks between different crops. crop_overlap_ratio (float): Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the image length. Later layers with more crops scale down this overlap. crop_n_points_downscale_factor (int): The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n. point_grids (list(np.ndarray) or None): A list over explicit grids of points used for sampling, normalized to [0,1]. The nth grid in the list is used in the nth crop layer. Exclusive with points_per_side. min_mask_region_area (int): If >0, postprocessing will be applied to remove disconnected regions and holes in masks with area smaller than min_mask_region_area. Requires opencv. output_mode (str): The form masks are returned in. Can be 'binary_mask', 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. For large resolutions, 'binary_mask' may consume large amounts of memory. Nz>Exactly one of points_per_side or point_grid must be provided.z7Can't have both points_per_side and point_grid be None.) binary_maskuncompressed_rlecoco_rlezUnknown output_mode .r2r)mask)rr+ ValueError pycocotoolsr4cv2r predictorr"r#r$r%r&r'r(r)r*r,r-)selfr r!r"r#r$r%r&r'r(r)r*r+r,r- mask_utilsr7s ڷC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\segment_anything/automatic_mask_generator.py__init__"SamAutomaticMaskGenerator.__init__$s| 4' 4   L K L   &:. D   $* VW W   1"+a 0  1 * $ 6 ! # %e, 0.&<#&<#,*."4.L+$8!&imagec <URU5nURS:a;URUUR[URUR 55nUR S:Xa#USVs/sHn[U5PM snUS'O;UR S:Xa#USVs/sHn[U5PM snUS'OUSUS'/n[[US55HnUSU[USU5[USU5R5USUR5USUR5/US UR5[US U5R5S .nURU5 M U$s snfs snf) a Generates masks for the given image. Arguments: image (np.ndarray): The image to generate masks for, in HWC uint8 format. Returns: list(dict(str, any)): A list over records for masks. Each record is a dict containing the following keys: segmentation (dict(str, any) or np.ndarray): The mask. If output_mode='binary_mask', is an array of shape HW. Otherwise, is a dictionary containing the RLE. bbox (list(float)): The box around the mask, in XYWH format. area (int): The area in pixels of the mask. predicted_iou (float): The model's own prediction of the mask's quality. This is filtered by the pred_iou_thresh parameter. point_coords (list(list(float))): The point coordinates input to the model to generate this mask. stability_score (float): A measure of the mask's quality. This is filtered on using the stability_score_thresh parameter. crop_box (list(float)): The crop of the image used to generate the mask, given in XYWH format. rr2rles segmentationsr0boxes iou_predspointsstability_score crop_boxes) segmentationareabbox predicted_iou point_coordsrFcrop_box)_generate_masksr,postprocess_small_regionsmaxr&r(r-rrrangelenrrtolistitemappend)r9r? mask_datarle curr_annsidxanns r;generate"SamAutomaticMaskGenerator.generates6((/   $ $q (66))D'')=)=>I   z )JSTZJ[)\J[3/#*>J[)\Io &    .FOPVFW)XFWs+c*:FW)XIo &)26):Io & Y789C )/ :3 ?%i&7&<=(7);C)@AHHJ!*;!7!#?#D#I#I#K,Y|-DS-IJQQSC   S !:)*])Xs 4F'Fc 6URSSn[X RUR5up4[ 5n[ X45H(upgUR XXr5nURU5 M* [U5S:aS[US5- n U RUSR5n [USR5U [R"[US55UR S9n UR#U 5 UR%5 U$)Nr rGrC iou_threshold)shaperr'r)r zip _process_cropcatrRrtodevicerfloattorchzerosr(filterto_numpy) r9r? orig_sizerG layer_idxsdatarM layer_idx crop_datascores keep_by_nmss r;rN)SamAutomaticMaskGenerator._generate_maskssKKO !4 ))4+B+B"  z#&z#> H**5IQI HHY $? z?Q $|"455FYYtG}334F%W ##% CW ./"22 K KK $  r>rMcrop_layer_idxrl.c 0UupVpxXU2XW2SS24n U RSSn URRU 5 [R"U 5SSSS24n UR UU -n [ 5n [URU 5H)unURXX$5nU RU5 AM+ URR5 [U SR5U S[R"[!U S55UR"S9nU R%U5 ['U SU5U S'[)U SU5U S'[R*"[-[!U S55Vs/sHnUPM sn5U S'U $s snf) Nr^rCrDr_rErArG)rar8 set_imagenparrayr+r rr"_process_batchrd reset_imagerrgrhrirRr&rjrrtensorrQ)r9r?rMrtrlx0y0x1y1 cropped_imcropped_im_size points_scalepoints_for_imagernrE batch_datarr_s r;rc'SamAutomaticMaskGenerator._process_crops"b5"%?+ $**2A.   ,xx0tt< ++N;lJz'(=(=?OPIV,,VhZJ HHZ Q ""$" M   !   KKDM* +--  K *$w-BW &tH~x@X"\\U3tF|CT=U*V=U8=U*VW\ +Ws; FrEim_sizec UupVURRRX5n[R"XpRR S9n[R "URS[RUR S9n URRUSS2SSS24U SS2S4SSS9upn [U RSS5U RSS5[R"URU RSSS95S9n A URS :a#U S UR:nU RU5 [U S URR R"UR$5U S 'UR&S :a#U S UR&:nU RU5 U S URR R":U S '[)U S 5U S '[+U S USSXe/5)n[R,"U5(dU RU5 [/U S X5U5U S '[1U S 5U S'U S U $)N)rfr)dtyperfT)multimask_output return_logitsr )axis)masksrDrErDrrFrCrA)r8 transform apply_coordsrh as_tensorrfonesraint predict_torchr flattenrepeatr#rjrr mask_thresholdr%r$rrallrr)r9rErrMrlorig_horig_wtransformed_points in_points in_labelsrrDrrn keep_masks r;rz(SamAutomaticMaskGenerator._process_batch s/#"^^55BB6SOO$6~~?T?TU JJyq19K[K[\ "nn:: aqj ! ag ! ; !--1%''1-??6==Qa=#HI     # %[)D,@,@@I KK "#< M4>>//>>@[@[#    & & ,./43N3NNI KK "W (<(<(K(KKW +DM:W +4=(Q6DZ[[ yy## KK "%T']HfMW *4=9V M r>rVmin_area nms_threshc [US5S:XaU$/n/nUSHn[U5n[XaSS9upgU(+n[XaSS9upgU=(a U(+nUR[R "U5R S55 UR[U55 M [R"USS9n [U 5n [U R5[R "U5[R"[U 55US9n U H>n XLS:XdM XR S5n [U 5SUSU 'XUS U 'M@ URU 5 U$) z Removes small disconnected regions and holes in masks, then reruns box NMS to remove any new duplicates. Edits mask_data in place. Requires open-cv as a dependency. rArholes)modeislands)dimr_rrC)rRrrrUrhr unsqueezergrdrrrirrj)rVrr new_masksrqrWr4changed unchangedrrCrri_mask mask_torchs r;rO3SamAutomaticMaskGenerator.postprocess_small_regionsCs[ y !Q &  V$Cs#D0gNMD# I0iPMD!1'kI   U__T2<) r&r'r*r(r)r,r-r+r"r#r8r%r$) @g)\(?gffffff?g?ffffff?rrgg?r Nrr0)__name__ __module__ __qualname____firstlineno__r rrrgrrxndarraystrr<rhno_gradrrr[r rNr rcrz staticmethodrO__static_attributes__r>r;rr#s*, "!%(,(+ #!$$../26$%(b'b'"#b' b'  b' !& b'!&b'b'b'b'"b'),b'd2::./b'"b'b' !b'H ]]_:bjj:T$sCx.-A::xRZZH8'zz's)' ' c? '  'R7 7sCx7s) 7 c? 7  7r00'*08=0 00r>r)"numpyrxrhtorchvision.ops.boxesrrtypingrrrrr modelingr r8r utils.amgr rrrrrrrrrrrrrrrrrr>r;rsA 733#(QQr>