Zj8ZSrSSKrSSKJr SSKJrJr SSKrSSK r SSK J r SSK J r SSKJrJr SS KJrJrJrJrJrJrJrJr SS KJrJr SS KJrJrJ r \(aS S K!J"r" SSK#J$r% "SS\SS9r&SSSS\'\\'-S\(S\'S\)\-S-S\*\'\'44 Sjjr+\"SS\ 55r,S/r-g)z#Image processor class for ZoeDepth.N)Iterable) TYPE_CHECKINGUnion)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingSizeDictget_image_sizepil_torch_interpolation_mapping) ImagesKwargsUnpack) TensorTypeauto_docstringrequires_backends)ZoeDepthDepthEstimatorOutput) functionalc.\rSrSr%Sr\\S'\\S'Srg)ZoeDepthImageProcessorKwargs.a keep_aspect_ratio (`bool`, *optional*, defaults to `self.keep_aspect_ratio`): If `True`, the image is resized by choosing the smaller of the height and width scaling factors and using it for both dimensions. This ensures that the image is scaled down as little as possible while still fitting within the desired output size. In case `ensure_multiple_of` is also set, the image is further resized to a size that is a multiple of this value by flooring the height and width to the nearest multiple of this value. Can be overridden by `keep_aspect_ratio` in `preprocess`. ensure_multiple_of (`int`, *optional*, defaults to `self.ensure_multiple_of`): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Works by flooring the height and width to the nearest multiple of this value. Works both with and without `keep_aspect_ratio` being set to `True`. Can be overridden by `ensure_multiple_of` in `preprocess`. keep_aspect_ratioensure_multiple_ofN) __name__ __module__ __qualname____firstlineno____doc__bool__annotations__int__static_attributes__r ڇ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/zoedepth/image_processing_zoedepth.pyrr.s r*rF)total input_imageztorch.Tensor | np.ndarray output_sizermultipleinput_data_formatreturncSSjn[U[5(aX4OUn[X5upgUupX- n X- n U(a#[SU - 5[SU - 5:aU n OU n U"X-US9n U"X-US9n X4$)Nc[R"X- 5U-R[5nX2:a[R "X- 5U-nU$N)nproundastyper(mathceil)valr/min_valxs r+constrain_to_multiple_of>get_resize_output_image_size..constrain_to_multiple_ofHsA XXcn % 0 8 8 = ; #.)H4Ar*r)r/)r) isinstancer(rabs)r-r.rr/r0r= input_height input_width output_height output_width scale_height scale_width new_height new_widths r+get_resize_output_image_sizerIAs1;;0L0L;,R]K .{ NL"-M!/L,K q; #a,&6"7 7&L'K),*EPXYJ()BXVI  ""r*c^\rSrSr\rSrSrSr\ r \ r Sr SSS.r\R rSrSrS\\SS 4U4S jjr\S \S\\S\4U4S jj5rS(S S S\S\S\SSSS 4 SjjrS)SjrS*S \S S\S\S\S -S\S -SSS\S\S\ S -S\S\ \\ -S -S\ \\ -S -S\S -S\!\"-S -S\4Sjjr#S+SS S!\"\\$\\4-S -S -S"\"\\$\\4-S -S -S#\%S$S -S%\S -S -S\\&\!\"44 S&jjr'S'r(U=r)$),ZoeDepthImageProcessorhTii)heightwidthg?kwargsr1Nc &>[TU]"S0UD6 g)Nr )super__init__)selfrO __class__s r+rRZoeDepthImageProcessor.__init__vs "6"r*imagesc &>[TU]"U40UD6$r4)rQ preprocess)rSrVrOrTs r+rX!ZoeDepthImageProcessor.preprocessysw!&3F33r* torch.Tensorsizerrresamplez7PILImageResampling | tvF.InterpolationMode | int | NonecUR(aUR(d[SU35e[UURUR4UU[R S9nUupx[ U[R5(d [Un [RRRU[U5[U54W RSS9n U $)a Resize an image or batched images to target size `(size.height, size.width)`. If `keep_aspect_ratio` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is set, the image is resized to a size that is a multiple of this value. Args: images (`torch.Tensor`): Images to resize. size (`SizeDict`): Target size of the output image. keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. ensure_multiple_of (`int`, *optional*, defaults to 1): The image is resized to a size that is a multiple of this value. resample (`tvF.InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): Defines the resampling filter to use if resizing the image. Otherwise, the image is resized to size specified in `size`. zDThe size dictionary must contain the keys 'height' and 'width'. Got )r.rr/r0T)mode align_corners)rMrN ValueErrorrIr FIRSTr?tvFInterpolationModertorchnnr interpolater(value) rSrVr[rrr\r.rMrN interpolationresized_imagess r+resizeZoeDepthImageProcessor.resize}s4{{$**cdhcijk k2 djj1/'.44  $ (C$9$9::;HEM,,88 S[#e*-M4G4GW[9 r*c[U[RS9up#[[R "US- 5S-5n[[R "US- 5S-5n[ R"XU4SS9$)z: Args: images (`torch.Tensor`): Image to pad. ) channel_dimrreflect)padding padding_mode)rr rar(r5sqrtrbpad)rSrVrMrN pad_height pad_widths r+ _pad_images"ZoeDepthImageProcessor._pad_imagessg'v;K;Q;QR !,q01  *Q./ wwv:'>YWWr* do_resizedo_pad do_rescalerescale_factor do_normalize image_mean image_stddisable_groupingreturn_tensorsc P[XS9unn0nUR5HmunnU(aURUU 5nU(aURUS9nU(aUR UX4XV5nU (aUR UX5nUUU'Mo [ UU5n[SU0US9$)N)r)rV pixel_values)data tensor_type)r itemsrescalervrj normalizer r)rSrVrxr[rrr\ryrzr{r|r}r~rrrOgrouped_imagesgrouped_images_indexresized_images_groupedshapestacked_imagesprocessed_imagess r+ _preprocess"ZoeDepthImageProcessor._preprocesss&0EV/o,,!#%3%9%9%; !E>!%nn!M!%!1!1!1!H!%^TVh!s!% !V,: "5 )&<**@BVW.2B!CQ_``r*outputsr source_sizes target_sizesoutputs_flipped)rNdo_remove_paddingc[US5 URnUb/URURR:wa [S5eUb#[ U5[ U5:wa [S5eUc UR nUcU(a [S5eUb#[ U5[ U5:wa [S5eUb&U[ R"URS/S9-S - nURS 5nS =px/n UcS/[ U5-OUnUcS/[ U5-OUn[XcU5GH/upn U bS =pU(aP[[R"U S S - 5U-5n [[R"U S S - 5U-5n[R"U U S S U --U S S U--/[RR S S9n U S :aU SS2X*2SS24n US :aU SS2SS2X*24n U b9U S U S /n [R"U U [RR S S9n U R#S 5n U R%SU 05 GM2 U $)u Converts the raw output of [`ZoeDepthDepthEstimatorOutput`] into final depth predictions and depth PIL images. Only supports PyTorch. Args: outputs ([`ZoeDepthDepthEstimatorOutput`]): Raw outputs of the model. source_sizes (`TensorType` or `list[tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the source size (height, width) of each image in the batch before preprocessing. This argument should be dealt as "required" unless the user passes `do_remove_padding=False` as input to this function. 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. outputs_flipped ([`ZoeDepthDepthEstimatorOutput`], *optional*): Raw outputs of the model from flipped input (averaged out in the end). do_remove_padding (`bool`, *optional*): By default ZoeDepth adds padding equal to `int(√(height / 2) * 3)` (and similarly for width) to fix the boundary artifacts in the output depth map, so we need remove this padding during post_processing. The parameter exists here in case the user changed the image preprocessing to not include padding. Returns: `list[dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions. rdNzBMake sure that `outputs` and `outputs_flipped` have the same shapez]Make sure that you pass in as many target sizes as the batch dimension of the predicted depthzXEither `source_sizes` should be passed in, or `do_remove_padding` should be set to FalsezZMake sure that you pass in as many source image sizes as the batch dimension of the logits)dimsrnrrrF)r[rh antialiaspredicted_depth)rrrr`lenryrdflip unsqueezezipr(r5rrrbrjrcBICUBICsqueezeappend)rSrrrrrrpadding_factor_hpadding_factor_wresultsdepth target_size source_sizepad_hpad_ws r+post_process_depth_estimation4ZoeDepthImageProcessor.post_process_depth_estimationsB $(!11  'o.C.CGfGfGlGl.lab b  $3+?3|CT+To   $ $   $5j   $3+?3|CT+Tl   &.O<[<[cebf1ggkllO)33A6/0/8D8LvO 44R^ 8D8LvO 44R^ /2?R^/_ +E & !!$ A(: ;>N NOE A(: ;>N NOE %a.1u94k!nq5y6PQ"%"7"7"?"?# 19!!U6\1"45E19!!Qf "45E&*1~{1~>  $"%"7"7"?"?#  MM!$E NN-u5 6?0`Br*r )FrN)rVrZr4)NNNN)*r!r"r#r$r valid_kwargsryrzr|r r}r r~rxr[rBILINEARr\rrrrRrrrrXrr&r(rjrvlistfloatstrrrtuplerdictrr) __classcell__)rTs@r+rKrKhs/L FJL'J%IIC (D!**H#(D!E#$#44v>Z7[4`l44#("#NR --- -  - L - -^XX>37!a^$!a!a !a  $; !a  $J !aL!a!a!a !a!aDK'$.!a4;&-!a+!aj(4/!a" #!aLJNIMNR04 k/k!4c3h#884?$Fk!4c3h#884?$F k CDtK k  $;- k d3 ?# $kkr*rKr4).r%r8collections.abcrtypingrrnumpyr5rdimage_processing_backendsrimage_processing_utilsrimage_transformsr r image_utilsr r r rrrrrprocessing_utilsrrutilsrrrmodeling_zoedepthrtorchvision.transforms.v2rrbrr(r&rrrIrK__all__r r*r+rs* $' ;2E   5BB?7