j"SSKrSSKrSSKJr SSKJrJrJr SSKJ r SSK J r J r SSK r SSKrSSKJrJrJrJrJrJrJrJrJr SSKJrJrJrJrJrJ r SS K!J"r" \"5(aSSK#r$SSK%r$\$RLRNr(\"5(aSS K)J*r*J+r+ SS K,J-r- SS K.J/r/ \(R`\-Rb\(Rd\-Rd\(Rf\-Rf\(Rh\-Rh\(Rj\-Rj\(Rl\-Rl0r7\7Rq5VVs0sHupX_M snnr9O0r70r9\"5(aSSK:r:\Rv"\<5r=\ S \R|S\?S \?\R|\?S4r@"SS\5rA"SS\5rB\C\D\E\D-\?\C-4rFSrG"SS\5rHSrISrJS\?4SjrKSrLSrMSrNS\R|S\O4SjrPSXS \ES\?\@4S!jjrQSXS\?\@\@-S \ES\@4S"jjrRSXS\?\@\@-S \ES\?\@4S#jjrSS\R|4S$jrTSYS\R|S%\E\U\ES&4-S-S\A4S'jjrVSYS\R|S(\A\D-S-S\E4S)jjrWSYS\ \R|S 4S*\AS-S\U\E\E44S+jjrXS,\U\E\E4S-\ES.\ES\U\E\E44S/jrYS0\\ S\?\ 4S1jrZ\AR4S\?\ S\R|4S(\D\A-S\?\E4S2jjr\S3\C\D\?\U-4S\O4S4jr]S3\C\D\?\U-4S\O4S5jr^S6\\C\D\?\U-4S\O4S7jr_S6\\C\D\?\U-4S\O4S8jr`SYS\ \DS 4S9\aS-SS 4S:jjrb\""S;S<9SYS\ \DS 4S9\aS-SS4S=jj5rcSYS\ \?\U\DS 4S9\aS-S\ S \?S \?\?S 44S>jjrdSZS?\OS-S@\aS-SA\OS-SB\a\?\a-S-SC\a\?\a-S-SD\OS-SE\C\D\E4\E-S-SF\OS-SG\C\D\E4S-SH\OS-SI\C\D\E4S-SJ\ SKSL\E4S-4SMjjre"SNSO5rfSP\BSQ\U\BS&4S6\?\CSS4SRjrgSS\?\DST\?\D4SUjrh\"5"SVSW55rigs snnf)[N)Iterable)astuple dataclassfields)BytesIO)AnyUnion) ExplicitEnumis_numpy_arrayis_torch_availableis_torch_tensoris_torchvision_availableis_vision_availableloggingrequires_backendsto_numpy)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDIMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDOPENAI_CLIP_MEANOPENAI_CLIP_STD)requires) ImageReadMode decode_image)InterpolationMode) pil_to_tensorzPIL.Image.Imagez torch.Tensorc\rSrSrSrSrSrg)ChannelDimensionUchannels_first channels_lastN)__name__ __module__ __qualname____firstlineno__FIRSTLAST__static_attributes__r$ڦC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/image_utils.pyr r Us E Dr,r c\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(r8TORCHNUMPYr+r$r,r-r>r>fs C E Er,r>c[U5(a[R$[U5(a[R$[ U5(a[R $[S[U535e)NzUnrecognized image type ) r<r>r8rrCr rD ValueErrortypeimages r-get_image_typerJlsXE}}ue /U }= >>r,c`[U5=(d [U5=(d [U5$r6)r<r rr:s r-is_valid_imagerLvs!   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Args: images (`ImageInput`): Image or batch 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 .)rjr<rLndimr[rFrG)rMrrs r-make_list_of_imagesrvs& 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$[SU35es snnfs snnf)a Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1. If the input is a nested list of images, it is converted to a flat list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of images or a 4d array of images. c3N# UHn[U[[45v M g7fr6r7r[rfrPimages_is r-rQ+make_flat_list_of_images..KF 8dE]33F#%c3T# UHn[U5=(d U(+v M g7fr6rVrzs r-rQr|"YRXh'1A\ARX&(rr z*Could not make a flat list of images from r7r[rfrUrVr<rurLrF)rMrrimg_listr;s r-make_flat_list_of_imagesrs(" 6D%=)) KFK K K YRXY Y Y$*?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/$[S5es snf)aO Ensure that the output is a nested list of images. Args: images (`Union[list[ImageInput], ImageInput]`): The input image. expected_ndims (`int`, *optional*, defaults to 3): The expected number of dimensions for a single input image. Returns: list: A list of list of images or a list of 4d array of images. c3N# UHn[U[[45v M g7fr6ryrzs r-rQ-make_nested_list_of_images..r}r~c3T# UHn[U5=(d U(+v M g7fr6rrzs r-rQrrrrr z]Invalid input type. Must be a single image, a list of images, or a list of batches of images.r)rMrrrIs r-make_nested_list_of_imagesrs  6D%=)) KFK K K YRXY Y Y &4-((-DV-L-L q " "fQinn&F8O !9>>^a/ /-34VEDKV4 4f   6;;.#@H:  ;;.1, ,L> ! t uu5s:D0c[U5(d[S[U535e[5(a?[ U[ R R 5(a[R"U5$[U5$)NzInvalid image type: ) rLrFrGrr7r8r9r\arrayrr:s r-to_numpy_arrayrsY #  /S {;<<C!A!Axx} C=r, num_channels.cFUbUOSn[U[5(aU4OUnURS:XaSup#OBURS:XaSup#O-URS:XaSup#O[SUR35eURUU;aFURUU;a3[ R SURS 35 [R$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) r7intrurFshapeloggerwarningr r)r*)rIr 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: )rr r)rur*rF)rIrs r-get_channel_dimension_axisrGsd :5A,222zzA~ .33 3zzA~ 01B0CD EEr, channel_dimcl[U[RR5(a UR$Uc [ U5nU[ R :XaURSURS4$U[ R:XaURSURS4$[SU35e)ao Returns the (height, width) dimensions of the image. Args: image (`np.ndarray | PIL.Image.Image`): 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) r7r8r9sizerr r)rr*rF)rIrs r-get_image_sizer]s%))zz4U; &,,,{{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. )ror) rrrheightwidth height_scale width_scale min_scale new_height new_widths r-#get_image_size_for_max_height_widthrzsH,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. )ziprp)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: )r r)rrr*rF)rMrr;_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 annotationsrTFr7dictr[rflenrs 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$)Nc38# UHn[U5v M g7fr6)rrPanns r-rQ3valid_coco_detection_annotations..sN+31#66+rSrTrs r- valid_coco_detection_annotationsrs N+N NNr,c&[SU55$)Nc38# UHn[U5v M g7fr6)rrs r-rQ2valid_coco_panoptic_annotations..sM#055rSrTrs 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$![&an[)S US U35eS nAff=f)a Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionhttp://https://Trfollow_redirects data:image/,r Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got . Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager7str startswithr8r9openrhttpxgetcontentospathisfilesplitbase64 decodebytesencode ExceptionrF TypeErrorImageOpsexif_transposeconvert)rIrb64es r-rrs j8*-%   I & &%*:*::*F*FIINN7599U^b+c+k+k#lmE WW^^E " "IINN5)E .. C(+ ((8 ws|4 syy / / D   LL ' ' .E MM% E L  ijoipp~@~AB s A F<< GGG) torchvision)backendscSSKn[U[5(GaNURS5(dURS5(a[[R "XSS9R nUR"[U5URS9n[U[RS9$[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)rm)moderrr rrrz`Incorrect format used for image. Should be a URL, a local path, a base64 string, or a PIL image.)rAr7rrrrr frombuffer bytearrayrnrrrrrrrrrrrrFr8r9rrrrr)rIrrArawbufrs 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)r7r[rfrr)rMr image_grouprIs r- load_imagesr1s&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_resizerresamplePILImageResamplingrc 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`.)rF) rrrrrrrrrr rr s r-validate_preprocess_argumentsr Fs,n,XYY (" u  +y/@kll)+WXX$*x/C\]]0Dyr,c\rSrSrSrSrSSjrSrS\RS\ \ -S \R4S jr SS jr S rSS jrSSjrSrSrSSjrSrg)ImageFeatureExtractionMixinitz< 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.)r7r8r9r\r]rrFrGselfrIs r-_ensure_format_supported4ImageFeatureExtractionMixin._ensure_format_supportedysW%#))//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)rrrBr7r\r]flatfloatingrur transposeastypernr8r9 fromarray)rrIrescales r- to_pil_image(ImageFeatureExtractionMixin.to_pil_images %%e, 5 ! !KKME eRZZ ( ($UZZ]BKK@zzQ5;;q>V#;1a0 LL*E99&&u- - r,cURU5 [U[RR5(dU$UR S5$)zo Converts `PIL.Image.Image` to RGB format. Args: image (`PIL.Image.Image`): The image to convert. r)rr7r8r9rrs r- convert_rgb'ImageFeatureExtractionMixin.convert_rgbs; %%e,%11L}}U##r,rIscalerkc,URU5 X-$)z' Rescale a numpy image by scale amount )r)rrIr"s r-r#ImageFeatureExtractionMixin.rescales %%e,}r,cURU5 [U[RR5(a[R "U5n[ U5(aUR5nUc'[URS[R5OUnU(a/URUR[R5S5nU(a#URS:XaURSSS5nU$)a{ Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first dimension. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to convert to a NumPy array. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise. channel_first (`bool`, *optional*, defaults to `True`): Whether or not to permute the dimensions of the image to put the channel dimension first. rp?rrr )rr7r8r9r\rrrBrintegerrrfloat32rur)rrIr channel_firsts r-r*ImageFeatureExtractionMixin.to_numpy_arrays %%e, eSYY__ - -HHUOE 5 ! !KKME;B?*UZZ]BJJ7PW LLbjj!99EE UZZ1_OOAq!,E r,cURU5 [U[RR5(aU$[ 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. rrX)rr7r8r9r 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- $)a Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to normalize. mean (`list[float]` or `np.ndarray` or `torch.Tensor`): The mean (per channel) to use for normalization. std (`list[float]` or `np.ndarray` or `torch.Tensor`): The standard deviation (per channel) to use for normalization. rescale (`bool`, *optional*, defaults to `False`): Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically. T)rr&rNrr)rr7r8r9rr\r]rrr(rfloatrrmrAr_ from_numpytensorrur)rrImeanstdrrAs r- normalize%ImageFeatureExtractionMixin.normalizes %%e, eSYY__ - -''t' eRZZ ( (dBJJ//xx~,,U[[9c2::..hhsm**5;;7 U # # dLL11dBJJ// ++D1D <<-Dc<<00c2::..**3/C,,s+C ::?u{{1~7D$//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$)a Resizes `image`. Enforces conversion of input to PIL.Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`int` or `tuple[int, int]`): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*, defaults to `None`): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: image: A resized `PIL.Image.Image`. r rz max_size = zN must be strictly greater than the requested size for the smaller edge size = )r )r BILINEARrr7r8r9rr[rfrrrrFresize) rrIrr default_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longs r-r9"ImageFeatureExtractionMixin.resizesb< (389K9T9T %%e,%11%%e,E dD ! !;D dC CIN '1$'<'<|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 $) a= Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). rrrr N.r)rr)rr7rfrr\r]rur-rrr8r9croprpermuterp zeros_like new_zerosro)rrIr image_shapetopbottomleftrightr) new_shape new_imagetop_pad bottom_padleft_pad right_pads r- center_crop'ImageFeatureExtractionMixin.center_crop^s %%e,$&&1a71~- bMKN2q81~- AF #w)8+==>    Qs9??2#6??QPST]TcTcdfTginPoAo o r,cURU5 [U[RR5(aUR U5nUSSS2SS2SS24$)ah Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first. Nr)rr7r8r9rrs r-flip_channel_order.ImageFeatureExtractionMixin.flip_channel_ordersL %%e, eSYY__ - -''.ETrT1aZ  r,c UbUO[RRnURU5 [ U[RR5(dUR U5nUR X#XEXgS9$)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)r8r9NEARESTrr7rrotate)rrIangler rXrYrZr[s r-r]"ImageFeatureExtractionMixin.rotatesj (389J9J %%e,%11%%e,E|| Vi  r,r$r6)NT)F)NTN)NrNNN)r%r&r'r(__doc__rrr r\r]r0rrrr-r5r9rRrUr]r+r$r,r-rrtse< $RZZ  @(2(hA5FIV!" r,rannotation_formatsupported_annotation_formatscX;a[S[SU35eU[RLa[ U5(d [S5eU[R La[ U5(d [S5egg)NzUnsupported annotation format: z must be one of zInvalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.zInvalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.)rFformatr/r3rr4r)rarbrs r-validate_annotationsres <:6(BRSoRpqrr,;;;/ <<B  ,:::.{;;M <;r,valid_processor_keyscaptured_kwargsc[U5R[U55nU(a+SRU5n[R SUS35 gg)Nz, zUnused or unrecognized kwargs: rt)set differencejoinrr)rfrg unused_keysunused_key_strs r-validate_kwargsrnsJo&11#6J2KLK;/88HJKr,c\rSrSr%SrSr\S-\S'Sr\S-\S'Sr \S-\S'Sr \S-\S'Sr \S-\S'Sr \S-\S 'Sr \S-\S 'Sr\S-\S 'S rSS jrSrSrSrSrSrSSjrS\4SjrSrg)SizeDictiz6 Hashable dictionary to store image size information. Nrr longest_edge shortest_edgerr min_pixels max_pixelscV[X5(a [X5$[SUS35e)NKey z not found in SizeDict.)hasattrgetattrKeyErrorrkeys r- __getitem__SizeDict.__getitem__s- 4  4% %cU"9:;;r,cT[X5(a[X5b [X5$U$r6rwrx)rr{defaults r-r SizeDict.gets' 4  '$"4"@4% %r,c## [U5H-n[XR5nUcMURU4v M/ g7fr6)rrxname)rfvals r-__iter__SizeDict.__iter__ s4A$'Cffck!s %AAc*[[U55$r6)hashr)rs r-__hash__SizeDict.__hash__sGDM""r,c@[X5=(a [X5SL$r6rrzs r- __contains__SizeDict.__contains__st!Dgd&8&DDr,cn[X5(d[SUS35e[RXU5 g)Nrvz" is not a valid field of SizeDict.)rwryobject __setattr__)rr{values r- __setitem__SizeDict.__setitem__s2t!!T#&HIJ J4e,r,c^^[T[5(a[T5T:H$[T[5(a=[U4Sj[ T555[U4Sj[ T555:H$[ $)Nc3P># UHn[TUR5v M g7fr6rxr)rPrrs r-rQ"SizeDict.__eq__..#sE 1qvv.. #&c3P># UHn[TUR5v M g7fr6r)rProthers r-rQr#s#O0<1qvv&& r)r7rrprfrNotImplemented)rrs``r-__eq__SizeDict.__eq__sk eT " ":& & eX & &Et EEO06t OJ r,rkc[U[[-5(a0[U5nUR[U55 [S0UD6$[$)Nr$)r7rrpupdaterrrmergeds r-__or__SizeDict.__or__(s@ eTH_ - -$ZF MM$u+ &%f% %r,c[U[5(a'[U5nUR[U55 U$[$r6)r7rrrrs r-__ror__SizeDict.__ror__/s3 eT " "%[F MM$t* %Mr,r$r6)rkrp)r%r&r'r(r`rr__annotations__rrqrrrrrsrtr|rrrrrrrrrr+r$r,r-rprpsFC$JE3:#L#*# $M3:$!Jd ! IsTz !Jd !!Jd !<  "#E- r,rp)rr6) NNNNNNNNNNNN)jrrcollections.abcr dataclassesrrriortypingrr rrBr\utilsr r r rrrrrrutils.constantsrrrrrrutils.import_utilsr PIL.Imager8 PIL.ImageOpsr9 Resamplingr torchvision.iorrtorchvision.transformsr!torchvision.transforms.functionalrr\ NEAREST_EXACTBOXr8HAMMINGBICUBICLANCZOSpil_torch_interpolation_mappingitemstorch_pil_interpolation_mappingrA get_loggerr%rr]r[ ImageInputr r/rrrAnnotationTyper<r>rJrLrVrdrgrjboolrqrvrrrrfrrrrrr)rrrrrr0rrrr rrernrp)kvs00r-rs $22    )--: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##DNQU>UD 38_D:!c3h!!! 38_ !>8x}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