qyix ~SSKrSSKJrJr SSKrSSKJr SSKJr SSKJ r SSK J r SSK JrJr SS KJrJrJr SS KJrJrJrJrJrJrJrJrJrJrJrJ r SS K!J"r"J#r# SS K$J%r%J&r& "S S\"SS9r'\RP\RR4r*S\+S\+S\RXS\RZ4Sjr.S(S\/S\\0-S-4Sjjr1S\RZS\RZ4Sjr2Sr3S)S\RZS\4S\0\Rj-S\/S\\0-S\44 S jjr6S*S!\7\+\+4S"\+S#\+S-S$\+S\7\+\+44 S%jjr8\&"S&S'\ 55r9S'/r:g)+N)AnyOptional)nn) read_image) functional)TorchvisionBackend) BatchFeature get_size_dict)center_to_corners_formatcorners_to_center_format safe_squeeze) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDAnnotationFormatAnnotationTypeChannelDimension ImageInputPILImageResamplingSizeDictget_image_size#get_image_size_for_max_height_widthget_max_height_widthvalidate_annotations) ImagesKwargsUnpack) TensorTypeauto_docstringc4\rSrSr%Sr\\-\S'\\S'Sr g)YolosImageProcessorKwargs$a format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`): Data format of the annotations. One of "coco_detection" or "coco_panoptic". do_convert_annotations (`bool`, *optional*, defaults to `True`): Controls whether to convert the annotations to the format expected by the YOLOS model. Converts the bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. formatdo_convert_annotationsN) __name__ __module__ __qualname____firstlineno____doc__strr__annotations__bool__static_attributes__r$m/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/models/yolos/image_processing_yolos.pyr r $s " ""  r.r F)totalheightwidthdevicereturncSSKJn /nUHnURXaU5nUR U5n[ UR 5S:aUSn[R"U[RUS9n[R"USS9nURU5 M U(a[R"USS9nU$[R"SX4[RUS9nU$![a [S5ef=f) a  Convert a COCO polygon annotation to a mask. Args: segmentations (`list[list[float]]`): List of polygons, each polygon represented by a list of x-y coordinates. height (`int`): Height of the mask. width (`int`): Width of the mask. r)maskz1Pycocotools is not installed in your environment.r).Ndtyper3)axis) pycocotoolsr6 ImportError frPyObjectsdecodelenshapetorch as_tensoruint8anyappendstackzeros) segmentationsr1r2r3 coco_maskmaskspolygonsrlesr6s r/convert_coco_poly_to_maskrM6sO1 E!$$Xu=% tzz?Q  ?Dt5;;vFyyA& T"  E* L Q.ekk&Q L# OMNNOs C$$C:return_segmentation_masksinput_data_formatc $UR5SSupEUSn[R"U/[RURS9nUSn/n/n /n /n UHln SU ;d U SS:XdMUR U S5 U R U S 5 U R U S 5 S U ;dMXU R U S 5 Mn [R"U[RURS9n[R"U [R URS9n [R"U[RURS9n [R"U [R URS9RS S 5n U SS2SS24==U SS2SS24- ss'U SS2SSS24RSUS9U SS2SSS24'U SS2SSS24RSUS9U SS2SSS24'U SS2S4U SS2S4:U SS2S4U SS2S4:-nUXXXX[R"[U5[U5/[RURS9S.nU (a_[R"U [R URS9n Xn U RSnU(aU RS5OU n XS 'U(a1UV s/sHoSPM nn [UXEURS9nUUUS'U$s sn f)zF Convert the target in COCO format into the format expected by YOLOS. Nimage_idr7 annotationsiscrowdr category_idareabbox keypointsr9)minmaxr)rR class_labelsboxesrVrT orig_size)rYr segmentationr3rJ) sizerArBint64r3rEfloat32 zeros_likereshapeclipintr@rM)imagetargetrNrO image_height image_widthrRrSclassesrVr_rXobjrTkeep new_target num_keypointssegmentation_masksrJs r/!prepare_coco_detection_annotationrtYs!& RS 1Lj!HzU\\RH'KG D EI C 3y>Q#6 NN3}- . KKF $ LLV %c!  [!12 oogU[[NG ??4u}}U\\ JDwekk%,,OG OOEu|| L T TUWYZ [E !QR%LE!RaR%L L1add7^((QK(@E!QTT'N1add7^((QL(AE!QTT'N !Q$K%1+ %%1+ad *C DD  =__c,&7[9I%JRWR]R]fkfrfrs JOOIU]]5<<X O !* 2?I%%g.Y "+; =HI[c.1[I)*>! .DA U__Q* *F KK Q , 0 0 4Q 7E E;;q>5<<ELL+QRWWX]XcXcdeXfhjkooprstuv U__Q* *F KK Q , 0 0 4Q 7E E;;q>5<<ELL+QRWWX]XcXcdeXfhjkooprstuv  ;;e3Q 77r.c[U[R5(a[UR5S:XaiUR [R :XaUR[R5nUSS2SS2S4SUSS2SS2S4--SUSS2SS2S4--$[USSUS--SUS--5$)z" Converts RGB color to unique ID. rNrr]ir9) isinstancerATensorr?r@r8rCtoint32ri)colors r/ rgb_to_idrs%&&3u{{+;q+@ ;;%++ %HHU[[)EQ1W~eAq!Gn 44y5Aq>7QQQ uQx#a.(9uQx+?? @@r.rjrk masks_path return_masksc [XS9upV[R"U5US- n0n[R"SU;aUSOUS/[R UR S9US'[R"XV/[R UR S9US'[R"XV/[R UR S9US'SU;Ga[U5RS S S 5R[RUR S9n [U 5n [R"USV s/sHoSPM sn UR S 9n XS S 2S S 4:Hn U R[R5n U(aXS'[U 5US'[R"USV s/sHoSPM sn [R UR S9US'[R"USV s/sHoSPM sn [R UR S9US'[R"USV s/sHoSPM sn [RUR S9US'U$s sn fs sn fs sn fs sn f)z/ Prepare a coco panoptic annotation for YOLOS. ) channel_dim file_namerRidr7rcr` segments_infor]r9rrbNrJr_rUr^rTrV)rpathlibPathrArBrdr3rpermuterrrr,rre) rjrkrrrOrlrmannotation_pathrqrJ segment_infoidss r/ prepare_coco_panoptic_annotationrsG!/u TLll:. 1DDOJ"__)V3 FekkbgbnbnJz,)DEKK`e`l`lmJv#oo|.IQVQ\Q\ejeqeqrJ{& ?+33Aq!<??ekkZ_ZfZf?g% oof_F]^F]lD1F]^glgsgstQd]++$ "'w ,U3 7%*__=CO=T U=T\- (=T U++<<& >" !&9?9P Q9P) $9P Q++<<! 9 #__6<_6M N6Ml& !6M N--<< 6 -_ V R Os9I22I79I<J image_sizercmax_sizemod_sizecUupESnUbP[[XE455n[[XE455nX- U-U:aX'-U- n[[ U55nXT:a*Un UbUb[Xd-U- 5n OQ[X-U- 5n O@XE::aXA:Xd XT::aXQ:XaXEpO)Un UbUb[Xe-U- 5n O[X-U- 5n UbXU-- n XU-- n X4$)a_ Computes the output image size given the input image size and the desired output size, while ensuring that both height and width are multiples of `mod_size`. This mirrors the YOLOS-specific behavior used in the torch/fast backends and is required so that all YOLOS image processing backends (PIL, torchvision, fast) produce identical output shapes. N)floatr[r\riround) rrcrrr1r2raw_sizemin_original_sizemax_original_sizeowohs r/ get_size_with_aspect_ratio_yolosrs MFH!#vo"67!#vo"67  04 7( B36GGHuX'D ~   H$8X&./BT]U*+B /fn%/emB   H$8X%./BT\F*+B = ! = ! 8Or.c$^\rSrSr\r\Rr\ r \ r \ RrSrSrSrSrSSS.rSrSS/rS \\S S 4U4S jjrS:S \R4S\S\ S -S\S -S\\R>-S -S\\ -S -S \4Sjjr!S;S \R4S\"S\#SS \R44U4Sjjjr$S\RJ4S\\\&4S\'\(\(4S\'\(\(4S\)S\#S4 U4Sjjjr*S\S\'\(\(4S \4Sjr+S\S \'\(\(4S!\'\(\(4S \4S"jr,S-S -S \\S \24 U4S)jjj5r3S'\1S*S(\0\1\0-S -S\S\\R>-S -S+\S\"SS,S-\S.\)S/\S0\S1\)\1\)-S -S2\)\1\)-S -S3\S4\"S -S\\ -S -S5\\4-S -S \24$S6jr5S>S\)S7\4\1\'-4S8jjr6S9r7U=r8$)?YolosImageProcessori#T 5 shortest_edge longest_edgeF pixel_values pixel_maskkwargsr4Nc >URSURSUR55 URSS5nUcSOURSS5nUbUOSSS.n[X#SS 9US'UR S 5nUR S 5nUc$[ US S5cUbUO UR Ul[TU]$"S 0UD6 g) Ndo_padpad_and_return_pixel_maskrcrrrrF)rdefault_to_squarer# do_normalizer$) setdefaultpoprr getgetattrrr#super__init__)selfrrcrr#r __class__s r/rYolosImageProcessor.__init__2s(FJJ/JDKK$XYzz&$'<4VZZ D-I'tsTX-Y&tRWXv"(,D!Ezz.1 ! )gdUR(a@UR(a/[URSSURUR5nOUR(a@UR (a/[ URSSURUR 5nOJUR(a*UR(aURUR4nO[SUS35e[TU],"U4[USUSS9US.UD6nU$) a Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an int, smaller edge of the image will be matched to this number. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Size of the image's `(height, width)` dimensions after resizing. Available options are: - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. Do NOT keep the aspect ratio. - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge less or equal to `longest_edge`. - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to `max_width`. resample (`PILImageResampling | tvF.InterpolationMode | int`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use if resizing the image. rQNz\Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got .rr]r1r2rcr) rrrr@ max_height max_widthrr1r2rrresizer)rrjrcrrnew_sizers r/rYolosImageProcessor.resizecs6   $"3"38 BC8H$J\J\^b^o^opH __:5;;rs;KT__^b^l^lmH [[TZZ TZZ0Hnosnttuv     8A;GRZ ^d  r.? annotationr` target_size thresholdc ~>[X25VVs/sH upgXg- PM snnup0n X:S'UR5HupU S:Xa;U n U [R"XX/[RU R S9-nXS'MFU S:XaU nXU--nUU S'M\U S:XaU SS2S4nUVs/sH$n[ [U]#U[USUSS 9US 9PM& nn[R"U5R[R5nUSS2S4U:nUU S'MU S:XaX:S'MXU 'M U $s snnfs snf) ak Resizes an annotation to a target size. Args: annotation (`dict[str, Any]`): The annotation dictionary. orig_size (`tuple[int, int]`): The original size of the input image. target_size (`tuple[int, int]`): The target size of the image, as returned by the preprocessing `resize` step. threshold (`float`, *optional*, defaults to 0.5): The threshold used to binarize the segmentation masks. resample (`PILImageResampling | tvF.InterpolationMode | int`, defaults to `tvF.InterpolationMode.NEAREST_EXACT`): The resampling filter to use when resizing the masks. rcr_r7rVrJNrr]rr) zipitemsrArBrer3rrrrrFr)rrr`rrrrkorig ratio_height ratio_widthnew_annotationkeyvaluer_ scaled_boxesrV scaled_arearJr6rs r/resize_annotation%YolosImageProcessor.resize_annotations}.HK;Gb$cGb|vV]Gb$c! !,v$**,JCg~$u JRWR_R_hmhtht(  +7w'"L&@A )4v&ag !& !&-t;8;q>UV#Xck<!&   E*--emm<ad i/*/w')4v&&+s#3-6A%d$s D4++D:rcUup40nUR5HUupgUS:XaFUn[U5nU[R"XCXC/[RUR S9-nXU'MQXuU'MW U$)Nr_r7)rr rArBrer3) rrrrlrmnorm_annotationrrr_s r/normalize_annotation(YolosImageProcessor.normalize_annotations$.! $**,JCg~07 JRWR_R_hmhtht(-$',$-r.input_image_sizeoutput_image_sizec^0nX6S'S[X255upxUR5H~upU S:Xa*U n [R"U USS9n [ U S5n XS'M5U S:Xa3U(a,U n U [ R "XX/U RS9-n XS'MnU S:XaX6S'MzXU 'M U$) z+ Update the annotation for a padded image. rcc3.# UH upX!- v M g7fNr$).0outputinputs r/ JYolosImageProcessor._update_annotation_for_padded_image..s$rIq U^IqsrJrfillr]r_rb)rrtvFpadrrArBr3) rrrrpadding update_bboxesrrrrrrJr_s r/#_update_annotation_for_padded_image7YolosImageProcessor._update_annotation_for_padded_images!2v$rM^Iq$r! $**,JCg~ %UA.*/w'M+[)_hmhthtuu*/w'):v&&+s##-$r. padded_sizerrcUR5SSnUSUS- nUSUS- nUS:dUS:a[SUSUS35eXb:wa0SSX/n [R"XUS9nUbUR X6X)U5n[ R "U[ RURS9n SU SUS2SUS24'XU4$) NrQrr]zzPadding dimensions are negative. Please make sure that the padded size is larger than the original size. Got padded size: z, original size: rrr7) rcrrrrrArGrdr3) rrjrrrr original_sizepadding_bottom padding_rightrrs r/rYolosImageProcessor.pads RS) $Q-*::#Aq)99 A !233>-?PQ^P__`b   '!];GGGE6E%!EE{] [[EKK U => %]1%%'9q)9'99:*,,r.imagesrSc (>[TU]"XX440UD6$)a annotations (`AnnotationType` or `list[AnnotationType]`, *optional*): Annotations to transform according to the padding that is applied to the images. return_segmentation_masks (`bool`, *optional*, defaults to `self.return_segmentation_masks`): Whether to return segmentation masks. masks_path (`str` or `pathlib.Path`, *optional*): Path to the directory containing the segmentation masks. )r preprocess)rrrSrNrrrs r/rYolosImageProcessor.preprocesss"w!&7Pg`fggr.z torch.Tensor do_resizez7PILImageResampling | tvF.InterpolationMode | int | None do_rescalerescale_factorrr# image_mean image_stdrpad_sizereturn_tensorsc Ub[U[5(aU/nUb<[U5[U5:wa$[S[U5S[U5S35e[ U5nUb[ U[ U5 UbQU[R:Xa=[U[R[45(d[S[U5S35e0n/n/n/n[XbUOS/[U5-5HunnUb"URUUUUU[RS9nU(aHUR!UXgS9nUb3UR#UUR%5S SUR%5S SS 9nUnUR'UXXU 5nU (a-Ub*UR)U[+U[R55nUR-U5 UR-U5 M UnUbUOSnU(GaBUbUR.UR04nO [3U5n/n/n[XbUOS/[U5-5HunnUUR%5S S:XaaUR-U5 UR-[4R6"U[4R8UR:S 95 UR-U5 M~UR=UUUU S 9unnnUR-U5 UR-U5 UR-U5 M UnUbUOSnUR?S [4R@"USS905 UR?S[4R@"USS905 [CUUS9nUbUVs/sH n[CUUS9PM snUS'U$s snf)zO Preprocess an image or a batch of images so that it can be used by the model. NzThe number of images (z) and annotations (z) do not match.zxThe path to the directory containing the mask PNG files should be provided as a `pathlib.Path` or string object, but is z instead.)rNrrOrrQ)r`rr7)rrrrdimr) tensor_typelabels)"rdictr?rrrSUPPORTED_ANNOTATION_FORMATSrrrr*typerrrFIRSTrrrcrescale_and_normalizerrrEr1r2rrAonesrdr3rupdaterFr )rrrSrNrrrcrrrrr#rr rr r"r rdataprocessed_imagesprocessed_annotations pixel_masksrjr resized_imager padded_imagespadded_annotationsrencoded_inputss r/ _preprocessYolosImageProcessor._preprocess,s0  "z+t'D'D&-K  "s6{c+6F'F(V 5H[IYHZZij "&)  " )E{ S  "*888zGLL#+>??<<@RWYiYoYoCpq  # #E * ! ( ( 47"o8"/:/F+D #'? 26: M!# %(@W^b]cfijpfq]q%r!z%**,rs"33!((/&&uzz+U[[Y^YeYe'fg&--j904;:Mc191-z:$$U+"))*5"":.&s#F0;0G,TK KKu{{;A'FG H ^U[[Q%?@A%dG  "Wb(Wb Z^DWb(N8 $(s%N target_sizescURURpTUb#[U5[U5:wa [S5e[R R US5nUSSS24RS5upx[U5n Ub[U[5(aS[R"UV s/sHoSPM sn 5n [R"UV s/sHoSPM sn 5n OURS5up[R"XX/SS9RU R 5n XSS2SSS24-n /n[#XxU 5H1unnnXU:nUX:nUX:nUR%UUUS.5 M3 U$s sn fs sn f) a! Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. Args: outputs ([`YolosObjectDetectionOutput`]): Raw outputs of the model. threshold (`float`, *optional*): Score threshold to keep object detection predictions. target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the batch. If unset, predictions will not be resized. Returns: `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image in the batch as predicted by the model. NzTMake sure that you pass in as many target sizes as the batch dimension of the logitsrY.rr]r )scoresrr_)logits pred_boxesr?rrrsoftmaxr\r rlistrArunbindrFrr3rrE)routputsrr" out_logitsout_bboxprobr$rr_iimg_himg_w scale_fctresultsslbscorelabelboxs r/post_process_object_detection1YolosImageProcessor.post_process_object_detections}& '~~w/A/AH  #:#l"33 j}}$$Z4c3B3h++B/)2  #,-- L%ALqdL%AB L%ALqdL%AB+2215  U5$@aHKKELLYIaqj11E651GAq!)m$Eam$EAM"C NNeusK L 2 &B%As /FF )r#)NNNNr)NTr)NNN)rN)9r%r&r'r(r valid_kwargsrBILINEARrrrrr rrr"rrrrrcrmodel_input_namesrrrArrr,r*rrrrrrrNEARESTrtuplerirrrrrrrrr(r rrr r9r- __classcell__)rs@r/rr#s*,L!**H&J$I  , ,FIJL F $ 7D'6#(A!B#t#*+/1504;?||!4'  $($;  ',,&- !11D8 HRV +||++MN +  ++dQcQkQk 7cN7c?738_ 7  7 MN 77r t sCx UY !! S/!!c? ! !N-1" -||-38_-cNT) -  -  -<EI1504 hh$d>&::TAh$($; h ',,&- h 23 h hh$r^$r$d>&::TAr$( r ',,&- r  rrLrrrr!%rDK'$.r4;&-rr T/!r"&&-#r$j(4/%r( )rjY]3"'3=G$u+=U33r.r)FN)TN)N);rtypingrrrArtorchvision.iortorchvision.transforms.v2rrimage_processing_backendsr image_processing_utilsr r image_transformsr r r image_utilsrrrrrrrrrrrrprocessing_utilsrrutilsrrr rrrrir3rrMr,r*rtrrrrrr?rr__all__r$r.r/rLs %7;A``    5/ ! E !!1 ? ?AQA_A_`Sell_d_k_kL',7; = $=(#-4 =@8%,,85<<8JA04 . <<. .gll". . (#- .  .dZ\&c3h&'*&69Dj&SV& 38_&Ro,ood ! !r.