qyiC <SSKrSSKJr SSKJrJr SSKrSSKJs J r SSK J r SSK Jr SSKJr SSKJrJr SS KJrJrJrJrJrJr SS KJrJr SS KJrJ r J!r!J"r" \(aSS K#J$r$ "S S\SS9r%SSS\&\\&-S\'S\&S\4 Sjr(\ "SS\55r)S/r*g)N)Iterable) TYPE_CHECKINGUnion) functional)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingSizeDict) ImagesKwargsUnpack) TensorTypeauto_docstringis_torch_availablerequires_backends)DepthEstimatorOutputcB\rSrSr%Sr\\S'\\S'\\S'\\S'Srg) DPTImageProcessorKwargs1a ensure_multiple_of (`int`, *optional*, defaults to 1): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overridden by `ensure_multiple_of` in `preprocess`. keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can be overridden by `keep_aspect_ratio` in `preprocess`. do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. ensure_multiple_of size_divisorkeep_aspect_ratiodo_reduce_labelsN) __name__ __module__ __qualname____firstlineno____doc__int__annotations__bool__static_attributes__r i/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/models/dpt/image_processing_dpt.pyrr1s! r*rF)total input_image torch.Tensor output_sizermultiplereturncSSjnURSSupVUupxXu- n X- n U(a#[SU - 5[SU - 5:aU n OU n U"X-US9n U"X-US9n [XS9$)Nc[X- 5U-nUb XC:a[R"X- 5U-nXB:a[R"X- 5U-nU$N)roundmathfloorceil)valr0min_valmax_valxs r+constrain_to_multiple_of>get_resize_output_image_size..constrain_to_multiple_ofKsQ #. !H ,  1; 3>*X5A ; #.)H4Ar*)r0heightwidth)rN)shapeabsr) r-r/rr0r= input_height input_width output_height output_width scale_height scale_width new_height new_widths r+get_resize_output_image_sizerNEs !, 1 1"# 6L"-M!/L,K q; #a,&6"7 7&L'K),*EPXYJ()BXVI : 77r*c$^\rSrSrSr\r\Rr \ r \ r SSS.rSrSrSrSrSrSrSrSrSrS rSrS \\4U4S jjr\S5S \S \S-S \\S\4U4Sjjj5rS5S \S \S-S\ S\!S\"\#-S-S\$\"S4S-S\4Sjjr%S\&SS\&S4Sjr'S \&SS\ S\ S\(SSS\ S\(S \ S!\)S"\ S#\)\&\)-S-S$\)\&\)-S-S%\ S&\*S-S'\ S(\*S-S)\ S-S\4$S*jr+S5S+\&\,S-4S,jjr-S6S-SS\(SSS.\ S&\*S-S%\ SS4U4S/jjjr.S7S-SS(\*SS4S0jjr/S5S1S2S+\#\&\,\*\*4-S-S-S\&\0\"\#44S3jjr1S4r2U=r3$)8DPTImageProcessorlz.PIL backend for DPT with reduce_label support.irATNFgp?r@kwargsc &>[TU]"S0UD6 g)Nr )super__init__)selfrR __class__s r+rUDPTImageProcessor.__init__s "6"r*imagessegmentation_mapsr1c &>[TU]"X40UD6$)zX segmentation_maps (`ImageInput`, *optional*): The segmentation maps to preprocess. )rT preprocess)rVrYrZrRrWs r+r\DPTImageProcessor.preprocesssw!&FvFFr*do_convert_rgbinput_data_formatreturn_tensorsdevicez torch.devicec URXXFS9nUR5nSUS'0n UR"U40UD6U S'UbURUSS[RS9n UR5n U R SSS.5 UR"S SU 0U D6n U V s/sH1n U R S 5R[R5PM3 n n XS '[XS 9$s sn f) z"Handle extra inputs beyond images.)rYr^r_raFr pixel_values)rYexpected_ndimsr^r_) do_normalize do_rescalerYrlabels)data tensor_typer ) _prepare_image_like_inputscopy _preprocessrFIRSTupdatesqueezetotorchint64r ) rVrYrZr^r_r`rarR images_kwargsriprocessed_segmentation_mapssegmentation_maps_kwargsprocessed_segmentation_maps r+_preprocess_image_like_inputs/DPTImageProcessor._preprocess_image_like_inputss!00L]1  ,1 ()#//H-H^  (*.*I*I( $"2"8"8 +J+ '(.{{} $ $ + +URW,X Y*.*:*:+2+6N+ '3N+2M.+221588E2M (+9NBB +s8C$rhr.c b[[U55HnXn[R"US:H[R"SUR UR S9U5nUS- n[R"US:H[R"SUR UR S9U5nX1U'M U$)z/Reduce label values by 1, replacing 0 with 255.r)dtyperar@)rangelenrrwheretensorr|ra)rVrhidxlabels r+ reduce_labelDPTImageProcessor.reduce_labelsV%CKEKK ELLEKKX]XdXd,eglmEAIEKK ell3ekkZ_ZfZf.ginoE3K &  r*r do_resizesizeresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonedo_center_crop crop_sizergrescale_factorrf image_mean image_stdrrdo_padrdisable_groupingc U(aURU5n[UUS9unn0nUR5H%unnU(aURUUUUU S9nUUU'M' [ UU5n[UUS9unn0nUR5HQunnU(aUR UU5nUR UXXU 5nU(aURUU5nUUU'MS [ UU5nU$)zCustom preprocessing for DPT.)r)imagerrrr)rr itemsresizer center_croprescale_and_normalize pad_image)rVrYrrrrrrrgrrfrrrrrrrrRgrouped_imagesgrouped_images_indexresized_images_groupedrDstacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess r+rmDPTImageProcessor._preprocesss', &&v.F0EV^n/o,,!#%3%9%9%; !E>!%(%'9&7 "-"-; "5 )&<((>@TU0E^fv/w,,#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N!% !M.< $U +&<**BDXYr* target_sizescL[5(d [S5eURnUb[U5[U5:wa [ S5e[ U[ R5(aUR5n/n[[U55HNn[R"X5RSS9X%SSS9nUSRSS9nURU5 MP U$URSS9n[URS5Vs/sHoUPM nnU$s snf) a Converts the output of [`DPTForSemanticSegmentation`] into semantic segmentation maps. Args: outputs ([`DPTForSemanticSegmentation`]): Raw outputs of the model. target_sizes (`list[Tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. Returns: semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id. z:PyTorch is required for post_process_semantic_segmentationzTMake sure that you pass in as many target sizes as the batch dimension of the logitsr)dimbilinearFrmode align_cornersr@)r ImportErrorlogitsr ValueError isinstancerrTensornumpyr~F interpolate unsqueezeargmaxappendrD) rVoutputsrrsemantic_segmentationrresized_logits semantic_mapis r+"post_process_semantic_segmentation4DPTImageProcessor.post_process_semantic_segmentations+ "##Z[ [  #6{c,// j, 55+113 $& !S[)!"K))a)0|7Hzin" .a077A7> %,,\: *%$%+MMaM$8 !GLMbMhMhijMkGl$mGl!1%=Gl !$m$$%nsD!r antialiasc>UR(aUR(d[SUR535e[ UURUR4UUS9n[ TU]XX4S9$)a Resize an image to `(size["height"], size["width"])`. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`. antialias (`bool`, *optional*, defaults to `True`): Whether to use antialiasing when resizing the image ensure_multiple_of (`int`, *optional*): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, and `do_resize` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Returns: `torch.Tensor`: The resized image. zDThe size dictionary must contain the keys 'height' and 'width'. Got )r/rr0)rr)rBrCrkeysrNrTr) rVrrrrrrr/rWs r+rDPTImageProcessor.resize1sh:{{$**cdhdmdmdocpqr r2 djj1/'  w~e8~YYr*cURSSup4SnU"X25upgU"XB5upXX4n [R"X5$)aL Center pad a batch of images to be a multiple of `size_divisor`. Args: image (`torch.Tensor`): Image to pad. Can be a batch of images of dimensions (N, C, H, W) or a single image of dimensions (C, H, W). size_divisor (`int`): The width and height of the image will be padded to a multiple of this number. r?NcX[R"X- 5U-nX - nUS-nX4- nXE4$)Nrd)r6r8)rrnew_sizepad_size pad_size_leftpad_size_rights r+_get_pad-DPTImageProcessor.pad_image.._get_padis9yy!45 DHH$MM%5N 0 0r*)rDtvFpad) rVrrrBrCrpad_top pad_bottompad_left pad_rightpaddings r+rDPTImageProcessor.pad_imageYsP BC(  1'v<&u;i<wwu&&r*rrc[US5 URnUb#[U5[U5:wa [S5e/nUcS/[U5-OUn[ X25HqupVUbV[ R RRURS5RS5USSS9R5nURS U05 Ms U$) aj Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. Only supports PyTorch. Args: outputs ([`DepthEstimatorOutput`]): Raw outputs of the model. target_sizes (`TensorType` 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 left to None, predictions will not be resized. Returns: `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions. rrNz]Make sure that you pass in as many target sizes as the batch dimension of the predicted depthrr@bicubicFrpredicted_depth) rrrrziprrnnrrrrpr)rVrrrresultsdepth target_sizes r+post_process_depth_estimation/DPTImageProcessor.post_process_depth_estimationus( $(!11  $3+?3|CT+To 8D8LvO 44R^ "%o"D E&++77OOA&003+Iej8') NN-u5 6 #Er*r r4)Tr@F)r@)4r!r"r#r$r%r valid_kwargsrBICUBICrr rr rrdefault_to_squarerrrrgrfrrrrrrrUrrr r\r(rstrrrrxlistrrfloatr&rmtuplerrrdictrr) __classcell__)rWs@r+rPrPls=8*L!))H'J%IC (DIINJL FN#(?!@#04 G G&, G01 G  G G&59*C*C&,*C *C , *C j(4/ *Cc>)*T1*C *CX4#7DJN'''!4c3h#884?$F' d3 ?# $ ''r*rP)+r6collections.abcrtypingrrrrtorch.nn.functionalrrrtorchvision.transforms.v2rimage_processing_backendsrimage_processing_baser image_transformsr r image_utilsr r rrrrprocessing_utilsrrutilsrrrrmodeling_outputsrrr&r(rNrP__all__r r*r+rs, $' 7;1E5VV8l%($8$8x}$$8$8 $8  $8No*ood  r*