pyiSSKrSSKrSSKJr SSKJrJr SSKJr SSK J r J r SSK r SSK rSSKJrJrJrJrJrJrJrJrJr SSKJrJrJrJrJrJr SS K J!r! \"5(aSSK"r#SSK$r#\#RJRLr'\"5(aSS K(J)r)J*r* SS K+J,r, SS K-J.r. \'R^\,R`\'Rb\,Rb\'Rd\,Rd\'Rf\,Rf\'Rh\,Rh\'Rj\,Rj0r6\6Ro5VVs0sHupX_M snnr8O0r60r8\"5(aSSK9r9\Rt"\;5r<\ S \RzS\>S \>\Rz\>S4r?"SS\5r@"SS\5rA\B\C\D\C-\>\B-4rESrF"SS\5rGSrHSrIS\>4SjrJSrKSrLSrMS\RzS\N4SjrOSXS \DS\>\?4S!jjrPSXS\>\?\?-S \DS\?4S"jjrQSXS\>\?\?-S \DS\>\?4S#jjrRS\Rz4S$jrSSYS\RzS%\D\T\DS&4-S-S\@4S'jjrUSYS\RzS(\@\C-S-S\D4S)jjrVSYS\RzS*\@S-S\T\D\D44S+jjrWS,\T\D\D4S-\DS.\DS\T\D\D44S/jrXS0\\ S\>\ 4S1jrY\@R4S\>\ S\Rz4S(\C\@-S\>\D4S2jjr[S3\B\C\>\T-4S\N4S4jr\S3\B\C\>\T-4S\N4S5jr]S6\\B\C\>\T-4S\N4S7jr^S6\\B\C\>\T-4S\N4S8jr_SYS\ \CS 4S9\`S-SS 4S:jjra\!"S;S<9SYS\ \CS 4S9\`S-SS4S=jj5rbSYS\ \>\T\CS 4S9\`S-S\ S \>S \>\>S 44S>jjrcSZS?\NS-S@\`S-SA\NS-SB\`\>\`-S-SC\`\>\`-S-SD\NS-SE\B\C\D4\D-S-SF\NS-SG\B\C\D4S-SH\NS-SI\B\C\D4S-SJ\ SKSL\D4S-4SMjjrd"SNSO5reSP\ASQ\T\AS&4S6\>\BSS4SRjrfSS\>\CST\>\C4SUjrg\"5"SVSW55rhgs snnf)[N)Iterable) dataclassfields)BytesIO)AnyUnion) ExplicitEnumis_numpy_arrayis_torch_availableis_torch_tensoris_torchvision_availableis_vision_availableloggingrequires_backendsto_numpy)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDIMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDOPENAI_CLIP_MEANOPENAI_CLIP_STD)requires) ImageReadMode decode_image)InterpolationMode) pil_to_tensorzPIL.Image.Imagez torch.Tensorc\rSrSrSrSrSrg)ChannelDimensionUchannels_first channels_lastN)__name__ __module__ __qualname____firstlineno__FIRSTLAST__static_attributes__r#U/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/image_utils.pyrrUs E Dr+rc\rSrSrSrSrSrg)AnnotationFormatZcoco_detection coco_panopticr#N)r$r%r&r'COCO_DETECTION COCO_PANOPTICr*r#r+r,r.r.Zs %N#Mr+r.cl[5=(a$ [U[RR5$N)r isinstancePILImageimgs r, is_pil_imager;bs  EZSYY__%EEr+c \rSrSrSrSrSrSrg) ImageTypefpillowtorchnumpyr#N)r$r%r&r'r7TORCHNUMPYr*r#r+r,r=r=fs C E Er+r=c[U5(a[R$[U5(a[R$[ U5(a[R $[S[U535e)NzUnrecognized image type ) r;r=r7r rBr rC ValueErrortypeimages r,get_image_typerIlsXE}}ue /U }= >>r+c`[U5=(d [U5=(d [U5$r5)r;r r r9s r,is_valid_imagerKvs!   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Args: images (`ImageInput`): Image of images to turn into a list of images. expected_ndims (`int`, *optional*, defaults to 3): Expected number of dimensions for a single input image. If the input image has a different number of dimensions, an error is raised. r z%Invalid image shape. Expected either z or z dimensions, but got z dimensions.z]Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, or torch.Tensor, but got .)rir;rKndimrZrErF)rLrqs r,make_list_of_imagesrus& Fxf ;;.1, ,&\F [[N *XF  78J7K4P^O_`KK= .   ghlmshtguuvw r+c[U[[45(aK[SU55(a4[SU55(aUVVs/sH o"Ho3PM M snn$[U[[45(ak[ U5(a[[ US5(dUSR U:XaU$USR US-:XaUVVs/sH o"Ho3PM M snn$[U5(aA[ U5(dUR U:XaU/$UR US-:Xa [U5$[SU35es snnfs snnf)a Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1. If the input is a nested list of images, it is converted to a flat list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of images or a 4d array of images. c3N# UHn[U[[45v M g7fr5r6rZrerOimages_is r,rP+make_flat_list_of_images..KF 8dE]33F#%c3T# UHn[U5=(d U(+v M g7fr5rUrys r,rPr{"YRXh'1A\ARX&(rr z*Could not make a flat list of images from r6rZrerTrUr;rtrKrE)rLrqimg_listr:s r,make_flat_list_of_imagesrs(" 6D%=)) KFK K K YRXY Y Y$*?FhshF??&4-((-DV-L-L q " "fQinn&FM !9>>^a/ /(.CH(3C(CC Cf   6;;.#@8O ;;.1, ,<  A&J KK@ Ds E Ecj[U[[45(a0[SU55(a[SU55(aU$[U[[45(ak[ U5(a[[ US5(dUSR U:XaU/$USR US-:XaUVs/sHn[U5PM sn$[U5(aC[ U5(dUR U:XaU//$UR US-:Xa [U5/$[S5es snf)aO Ensure that the output is a nested list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of list of images or a list of 4d array of images. c3N# UHn[U[[45v M g7fr5rxrys r,rP-make_nested_list_of_images..r|r}c3T# UHn[U5=(d U(+v M g7fr5rrys r,rPrrrrr z]Invalid input type. Must be a single image, a list of images, or a list of batches of images.r)rLrqrHs r,make_nested_list_of_imagesrs  6D%=)) KFK K K YRXY Y Y &4-((-DV-L-L q " "fQinn&F8O !9>>^a/ /-34VEDKV4 4f   6;;.#@H:  ;;.1, ,L> ! t uu5s:D0c[U5(d[S[U535e[5(a?[ U[ R R 5(a[R"U5$[U5$)NzInvalid image type: ) rKrErFrr6r7r8r[arrayrr9s r,to_numpy_arrayrsY #  /S {;<<C!A!Axx} C=r+ num_channels.cFUbUOSn[U[5(aU4OUnURS:XaSup#OBURS:XaSup#O-URS:XaSup#O[SUR35eURUU;aFURUU;a3[ R SURS 35 [R$URUU;a[R$URUU;a[R$[S 5e) a7 Infers the channel dimension format of `image`. Args: image (`np.ndarray`): The image to infer the channel dimension of. num_channels (`int` or `tuple[int, ...]`, *optional*, defaults to `(1, 3)`): The number of channels of the image. Returns: The channel dimension of the image. r r)r)rrz(Unsupported number of image dimensions: z4The channel dimension is ambiguous. Got image shape z. Assuming channels are the first dimension. Use the [input_data_format](https://huggingface.co/docs/transformers/main/internal/image_processing_utils#transformers.image_transforms.rescale.input_data_format) parameter to assign the channel dimension.z(Unable to infer channel dimension format) r6intrtrEshapeloggerwarningrr(r))rHr first_dimlast_dims r,infer_channel_dimension_formatr s$0#;<L&0s&C&CL?L zzQ" 8 q" 8 q" 8CEJJ<PQQ {{9-%++h2G<2WB5;;-PJ K  %%% Y < /%%% X , .$$$ ? @@r+input_data_formatcUc [U5nU[R:XaURS- $U[R:XaURS- $[ SU35e)ar Returns the channel dimension axis of the image. Args: image (`np.ndarray`): The image to get the channel dimension axis of. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the image. If `None`, will infer the channel dimension from the image. Returns: The channel dimension axis of the image. rr Unsupported data format: )rrr(rtr)rE)rHrs r,get_channel_dimension_axisrGsd :5A,222zzA~ .33 3zzA~ 01B0CD EEr+ channel_dimcUc [U5nU[R:XaURSURS4$U[R:XaURSURS4$[ SU35e)a] Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of. channel_dim (`ChannelDimension`, *optional*): Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. Returns: A tuple of the image's height and width. r)rrr(rr)rE)rHrs r,get_image_sizer]s{4U; &,,,{{2 B// (-- -{{2 B//4[MBCCr+ image_size max_height max_widthcjUup4X- nX$- n[XV5n[X7-5n[XG-5n X4$)a Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio. Important, even if image_height < max_height and image_width < max_width, the image will be resized to at least one of the edges be equal to max_height or max_width. For example: - input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50) - input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400) Args: image_size (`tuple[int, int]`): The image to resize. max_height (`int`): The maximum allowed height. max_width (`int`): The maximum allowed width. )rnr) rrrheightwidth height_scale width_scale min_scale new_height new_widths r,#get_image_size_for_max_height_widthrusH,MF&L#KL.IV'(JE%&I   r+valuescP[U6Vs/sHn[U5PM sn$s snf)zG Return the maximum value across all indices of an iterable of values. )zipro)rvalues_is r,max_across_indicesrs$+.v, 7,hCM, 77 7s#c.U[R:Xa+[UVs/sHo"RPM sn5up4nXE4$U[R:Xa+[UVs/sHo"RPM sn5upEnXE4$[ SU35es snfs snf)z@ Get the maximum height and width across all images in a batch. z"Invalid channel dimension format: )rr(rrr)rE)rLrr:_rrs r,get_max_height_widthrs ,222#5F6SFSyyF6S#T y  "" .33 3#5F6SFSyyF6S#T q  ""=>O=PQRR 7T6Ss B B annotationc[U[5(aXSU;aRSU;aL[US[[45(a.[ US5S:Xd[USS[5(agg)Nimage_id annotationsrTFr6dictrZrelenrs r,"is_valid_annotation_coco_detectionrsi:t$$ * $ Z ' z-04- @ @  =) *a /:j>WXY>Z\`3a3a r+c[U[5(a^SU;aXSU;aRSU;aL[US[[45(a.[ US5S:Xd[USS[5(agg)Nr segments_info file_namerTFrrs r,!is_valid_annotation_coco_panopticrsq:t$$ * $ z ) : % z/2T5M B B  ?+ , 1Z ?@[\]@^`d5e5e r+rc&[SU55$)Nc38# UHn[U5v M g7fr5)rrOanns r,rP3valid_coco_detection_annotations..sN+31#66+rRrSrs r, valid_coco_detection_annotationsrs N+N NNr+c&[SU55$)Nc38# UHn[U5v M g7fr5)rrs r,rP2valid_coco_panoptic_annotations..sM#055rRrSrs r,valid_coco_panoptic_annotationsrs MM MMr+timeoutc [[S/5 [U[5(Ga.UR S5(dUR S5(aF[ R R[[R"XSS9R55nO[RRU5(a [ R RU5nOUR S5(aURS5Sn[ R""UR%55n[ R R[U55nO4[U[ R R 5(d [+S 5e[ R,R/U5nUR1S 5nU$![&an[)S US U35eS nAff=f)a Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionhttp://https://Trfollow_redirects data:image/,r Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got . Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager6str startswithr7r8openrhttpxgetcontentospathisfilesplitbase64 decodebytesencode ExceptionrE TypeErrorImageOpsexif_transposeconvert)rHrb64es r,rrs j8*-%   I & &%*:*::*F*FIINN7599U^b+c+k+k#lmE WW^^E " "IINN5)E .. C(+ ((8 ws|4 syy / / D   LL ' ' .E MM% E L  ijoipp~@~AB s A F<< GGG) torchvision)backendscSSKn[U[5(GaNURS5(dURS5(a[[R "XSS9R nUR"[U5URS9n[U[RS9$[RRU5(a[U[RS9$URS 5(aUR!S 5S n["R$"UR'55nUR"[U5URS9n[U[RS9$[U[,R.R.5(a9[,R0R3U5n[5UR7S55$[9S5e![(an[+S US U35eSnAff=f)aP Loads `image` directly to a `torch.Tensor` using torchvision. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `torch.Tensor`: A `[C, H, W]` uint8 tensor in RGB channel order. rNrrTr)rl)moderrr rrrz`Incorrect format used for image. Should be a URL, a local path, a base64 string, or a PIL image.)r@r6rrrrr frombuffer bytearrayrmrrrrrrrrrrrrEr7r8rrrrr)rHrr@rawbufrs r,load_image_as_tensorrs"%   I & &%*:*::*F*F))ETJRRC""9S>EC-*;*;< < WW^^E " "M,=,=> > .. C(+ ((8 ""9S>EC-*;*;< < E399?? + + ++E2U]]5122 n    ijoipp~@~AB s $G G6 G11G6c R[U[[45(at[U5(aJ[US[[45(a,UVVs/sHo"Vs/sH n[ X1S9PM snPM snn$UVs/sH n[ X1S9PM sn$[ XS9$s snfs snnfs snf)zLoads images, handling different levels of nesting. Args: images: A single image, a list of images, or a list of lists of images to load. timeout: Timeout for loading images. Returns: A single image, a list of images, a list of lists of images. r)r)r6rZrerr)rLr image_grouprHs r, load_imagesr,s&4-(( v;;:fQi$??eklekVa[Q[EZ7[Qekl lDJKF5Ju6FK K&22 RlKs BB +B:B$B do_rescalerescale_factor do_normalize image_mean image_stddo_padpad_sizedo_center_crop crop_size do_resizesizeresamplePILImageResamplingrc U(aUc [S5eU(aUc [S5eU(aUbUc [S5eU(aUc [S5eU (aU bU c [S5egg)ao Checks validity of typically used arguments in an `ImageProcessor` `preprocess` method. Raises `ValueError` if arguments incompatibility is caught. Many incompatibilities are model-specific. `do_pad` sometimes needs `size_divisor`, sometimes `size_divisibility`, and sometimes `size`. New models and processors added should follow existing arguments when possible. Nz=`rescale_factor` must be specified if `do_rescale` is `True`.zgDepending on the model, `size_divisor` or `pad_size` or `size` must be specified if `do_pad` is `True`.zP`image_mean` and `image_std` must both be specified if `do_normalize` is `True`.z<`crop_size` must be specified if `do_center_crop` is `True`.zA`size` and `resample` must be specified if `do_resize` is `True`.)rE) rrrrrrrrrrrr s r,validate_preprocess_argumentsr As,n,XYY (" u  +y/@kll)+WXX$*x/C\]]0Dyr+c\rSrSrSrSrSSjrSrS\RS\ \ -S \R4S jr SS jr S rSS jrSSjrSrSrSSjrSrg)ImageFeatureExtractionMixinioz< Mixin that contain utilities for preparing image features. c[U[RR[R45(d)[ U5(d[ S[U5S35egg)Nz Got type zU which is not supported, only `PIL.Image.Image`, `np.ndarray` and `torch.Tensor` are.)r6r7r8r[r\r rErFselfrHs r,_ensure_format_supported4ImageFeatureExtractionMixin._ensure_format_supportedtsW%#))//2::!>??X]H^H^DK=)&& I_?r+NcURU5 [U5(aUR5n[U[R 5(aUc'[UR S[R5nURS:Xa&URSS;aURSSS5nU(aUS-nUR[R5n[RRU5$U$)a Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`): The image to convert to the PIL Image format. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. rrrr r)rr rAr6r[r\flatfloatingrtr transposeastypermr7r8 fromarray)rrHrescales r, to_pil_image(ImageFeatureExtractionMixin.to_pil_image{s %%e, 5 ! !KKME eRZZ ( ($UZZ]BKK@zzQ5;;q>V#;1a0 LL*E99&&u- - r+cURU5 [U[RR5(dU$UR S5$)zo Converts `PIL.Image.Image` to RGB format. Args: image (`PIL.Image.Image`): The image to convert. r)rr6r7r8rrs r, convert_rgb'ImageFeatureExtractionMixin.convert_rgbs; %%e,%11L}}U##r+rHscalerjc,URU5 X-$)z' Rescale a numpy image by scale amount )r)rrHr!s r,r#ImageFeatureExtractionMixin.rescales %%e,}r+cURU5 [U[RR5(a[R "U5n[ U5(aUR5nUc'[URS[R5OUnU(a/URUR[R5S5nU(a#URS:XaURSSS5nU$)a{ Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first dimension. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to convert to a NumPy array. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise. channel_first (`bool`, *optional*, defaults to `True`): Whether or not to permute the dimensions of the image to put the channel dimension first. rp?rrr )rr6r7r8r[rr rArintegerrrfloat32rtr)rrHr channel_firsts r,r*ImageFeatureExtractionMixin.to_numpy_arrays %%e, eSYY__ - -HHUOE 5 ! !KKME;B?*UZZ]BJJ7PW LLbjj!99EE UZZ1_OOAq!,E r+cURU5 [U[RR5(aU$[ U5(aUR S5nU$[ R"USS9nU$)z Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to expand. rrW)rr6r7r8r unsqueezer[ expand_dimsrs r,r,'ImageFeatureExtractionMixin.expand_dimsse %%e, eSYY__ - -L 5 ! !OOA&E NN5q1E r+c>URU5 [U[RR5(aUR USS9nOU(a[U[ R 5(a0URUR[ R5S5nO0[U5(a URUR5S5n[U[ R 5(a[U[ R 5(d/[ R"U5RUR5n[U[ R 5(d/[ R"U5RUR5nO[U5(aSSKn[X%R5(dD[U[ R 5(aUR "U5nOUR""U5n[X5R5(dD[U[ R 5(aUR "U5nOUR""U5nUR$S:Xa*UR&SS;aXSS2SS4- USS2SS4- $X- U- $)a Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to normalize. mean (`list[float]` or `np.ndarray` or `torch.Tensor`): The mean (per channel) to use for normalization. std (`list[float]` or `np.ndarray` or `torch.Tensor`): The standard deviation (per channel) to use for normalization. rescale (`bool`, *optional*, defaults to `False`): Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically. T)rr%rNrr)rr6r7r8rr[r\rrr'r floatrrlr@r^ from_numpytensorrtr)rrHmeanstdrr@s r, normalize%ImageFeatureExtractionMixin.normalizes %%e, eSYY__ - -''t' eRZZ ( (dBJJ//xx~,,U[[9c2::..hhsm**5;;7 U # # dLL11dBJJ// ++D1D <<-Dc<<00c2::..**3/C,,s+C ::?u{{1~7D$//3q$}3EE ELC' 'r+cUbUO[RnURU5 [U[R R 5(dUR U5n[U[5(a [U5n[U[5(d[U5S:XaU(a#[U[5(aX"4O USUS4nOURupgXg::aXg4OXv4up[U[5(aUOUSn X:XaU$U [X-U- 5pUb,XZ::a[SUSU35eX:a[X[-U - 5UpXg::aX4OX4nURX#S9$)a Resizes `image`. Enforces conversion of input to PIL.Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`int` or `tuple[int, int]`): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*, defaults to `None`): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: image: A resized `PIL.Image.Image`. r rz max_size = zN must be strictly greater than the requested size for the smaller edge size = )r )r BILINEARrr6r7r8rrZrerrrrEresize) rrHrr default_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longs r,r8"ImageFeatureExtractionMixin.resizesb< (389K9T9T %%e,%11%%e,E dD ! !;D dC CIN '1$'<'<|47DQRGBT % 16uovo .8s.C.Cda#/ L&93?R?Y\a?a;b8'6()(4@@DvG *.1(2F2Q.RT\805 ,hEZ||D|44r+cURU5 [U[5(dX"4n[U5(d[U[R 5(aSUR S:XaURU5nURSS;aURSSOURSSnOURSURS4nUSUS- S-nXBS-nUSUS- S-nXbS-n[U[RR5(aURXdXu45$URSS;nU(dU[U[R 5(aURSSS5n[U5(aURSSS5nUS:a XSS::aUS:aXsS::a USXE2Xg24$URSS[USUS5[USUS54-n [U[R 5(a[R "XS9n O![U5(aUR#U 5n U SUS- S-n XS-n U S US- S-n XS-nUW SX2X24'XK- nX[- nXm- nX}- nU S[SU5[%U RSU52[SU5[%U RS U524n U $) a= Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). rrrr N.r)rr)rr6rer r[r\rtr,rrr7r8croprpermutero zeros_like new_zerosrn)rrHr image_shapetopbottomleftrightr( new_shape new_imagetop_pad bottom_padleft_pad right_pads r, center_crop'ImageFeatureExtractionMixin.center_cropYs %%e,$&&1a71~- bMKN2q81~- AF #w)8+==>    Qs9??2#6??QPST]TcTcdfTginPoAo o r+cURU5 [U[RR5(aUR U5nUSSS2SS2SS24$)ah Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first. Nr)rr6r7r8rrs r,flip_channel_order.ImageFeatureExtractionMixin.flip_channel_ordersL %%e, eSYY__ - -''.ETrT1aZ  r+c UbUO[RRnURU5 [ U[RR5(dUR U5nUR X#XEXgS9$)a Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees counter clockwise around its centre. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before rotating. Returns: image: A rotated `PIL.Image.Image`. )r expandcenter translate fillcolor)r7r8NEARESTrr6rrotate)rrHangler rWrXrYrZs r,r\"ImageFeatureExtractionMixin.rotatesj (389J9J %%e,%11%%e,E|| Vi  r+r#r5)NT)F)NTN)NrNNN)r$r%r&r'__doc__rrrr[r\r/rrrr,r4r8rQrTr\r*r#r+r,rrose< $RZZ  @(2(hA5FIV!" r+rannotation_formatsupported_annotation_formatscX;a[S[SU35eU[RLa[ U5(d [S5eU[R La[ U5(d [S5egg)NzUnsupported annotation format: z must be one of zInvalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.zInvalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.)rEformatr.r2rr3r)r`rars r,validate_annotationsrds <:6(BRSoRpqrr,;;;/ <<B  ,:::.{;;M <;r+valid_processor_keyscaptured_kwargsc[U5R[U55nU(a+SRU5n[R SUS35 gg)Nz, zUnused or unrecognized kwargs: rs)set differencejoinrr)rerf unused_keysunused_key_strs r,validate_kwargsrmsJo&11#6J2KLK;/88HJKr+c\rSrSr%SrSr\S-\S'Sr\S-\S'Sr \S-\S'Sr \S-\S'Sr \S-\S'Sr \S-\S 'S r SS jrS rS rSrSrSrSSjrS\4SjrSrg)SizeDictiz6 Hashable dictionary to store image size information. Nrr longest_edge shortest_edgerrcV[X5(a [X5$[SUS35e)NKey z not found in SizeDict.)hasattrgetattrKeyErrorrkeys r, __getitem__SizeDict.__getitem__s- 4  4% %cU"9:;;r+cT[X5(a[X5b [X5$U$r5rtru)rrxdefaults r,r SizeDict.gets' 4  '$"4"@4% %r+c## [U5H-n[XR5nUcMURU4v M/ g7fr5)rruname)rfvals r,__iter__SizeDict.__iter__s4A$'Cffck!s %AAc[URURURURUR UR 45$r5)hashrrrprqrr)rs r,__hash__SizeDict.__hash__ s<T[[$**d.?.?ASASUYUdUdfjftftuvvr+c@[X5=(a [X5SL$r5r|rws r, __contains__SizeDict.__contains__st!Dgd&8&DDr+cn[X5(d[SUS35e[RXU5 g)Nrsz" is not a valid field of SizeDict.)rtrvobject __setattr__)rrxvalues r, __setitem__SizeDict.__setitem__s2t!!T#&HIJ J4e,r+c^^[T[5(a[T5T:H$[T[5(a=[U4Sj[ T555[U4Sj[ T555:H$[ $)Nc3P># UHn[TUR5v M g7fr5rur)rOrrs r,rP"SizeDict.__eq__..sE 1qvv.. #&c3P># UHn[TUR5v M g7fr5r)rOrothers r,rPrs#O0<1qvv&& r)r6rrorerNotImplemented)rrs``r,__eq__SizeDict.__eq__sk eT " ":& & eX & &Et EEO06t OJ r+rjc[U[[-5(a0[U5nUR[U55 [S0UD6$[$)Nr#)r6rroupdaterrrmergeds r,__or__SizeDict.__or__!s@ eTH_ - -$ZF MM$u+ &%f% %r+c[U[5(a'[U5nUR[U55 U$[$r5)r6rrrrs r,__ror__SizeDict.__ror__(s3 eT " "%[F MM$t* %Mr+r#r5)rjro)r$r%r&r'r_rr__annotations__rrprqrrryrrrrrrrrrr*r#r+r,rorosFC$JE3:#L#*# $M3:$!Jd ! IsTz <  "wE- r+ro)rr5) NNNNNNNNNNNN)irrcollections.abcr dataclassesrriortypingrrrrAr[utilsr r r r rrrrrutils.constantsrrrrrrutils.import_utilsr PIL.Imager7 PIL.ImageOpsr8 Resamplingr torchvision.iorrtorchvision.transformsr!torchvision.transforms.functionalrr[ NEAREST_EXACTBOXr7HAMMINGBICUBICLANCZOSpil_torch_interpolation_mappingitemstorch_pil_interpolation_mappingr@ get_loggerr$rr\rZ ImageInputrr.rrrAnnotationTyper;r=rIrKrUrcrfriboolrprurrrrerrrrrr(rrrrrr/rrrr rrdrmro)kvs00r,rs $)    )--:8? ""$5$C$C 1 5 5##%6%?%?""$5$=$=""$5$=$=""$5$=$= '#9X8]8]8_&`8_qt8_&`#&(#&(#   H %rzz>48I3JDQSQ[Q[L\^bcq^rr | $|$ c39tDz112F  ?LEDE,  52::5$5##D8x}8c8brawaw # ~rzz12 3 #HKN^H^ # #Y # 4TE\8I3J t  $sD5L7H2I d  O(4TE\@Q;R2SOX\ON$sD5L?P:Q1RNW[N !* '' (* T\**Z #$!* '' (* T\* * %* \QU3 $s$55 63AF3 d#45tDAR