iSSKrSSKrSSKJr SSKJr SSKJr SSKJ r J r SSK r SSK r SSKJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJrJrJrJ r \"5(aSSK!r"SSK#r"\"RHRJr&\"5(aSS K'J(r( \&RR\(RT\&RV\(RV\&RX\(RX\&RZ\(RZ\&R\\(R\\&R^\(R^0r0O0r0\"5(aSSK1r1\Rd"\35r4\ S \ RjS \6S \6\ Rj\6S 4r7"S S \5r8"SS\5r9"SS\5r:\;\<\ \=\<\6\;44r>Sr?"SS\5r@SrASrBS\64SjrCSrDSrESrFS\ RjS\G4SjrHSTS\=S\6\74S jjrISTS\ \6\7\74S\=S\74S!jjrJSTS\ \6\7\74S\=S\6\74S"jjrKS\ Rj4S#jrLSUS\ RjS$\ \ \=\M\=S%44S\84S&jjrNSUS\ RjS'\ \ \8\<4S\=4S(jjrOSUS\ RjS)\ \8S\M\=\=44S*jjrPS+\M\=\=4S,\=S-\=S\M\=\=44S.jrQS/\;\<\ \6\M44S\G4S0jrRS/\;\<\ \6\M44S\G4S1jrSS2\\;\<\ \6\M44S\G4S3jrTS2\\;\<\ \6\M44S\G4S4jrUSUS\ \\ \ \;\<\=4\=4S?\ \GS@\ \;\<\=4SA\ \GSB\ \;\<\=4SC\ SDSE\ SF4SGjjrY"SHSI5rZSJ\9SK\M\9S%4S2\6\;SS4SLjr[SM\6\<SN\6\<4SOjr\\"SPSQ9"SRSS55r]g)WN)Iterable) dataclass)BytesIO)OptionalUnion) ExplicitEnum is_jax_tensoris_numpy_array is_tf_tensoris_torch_availableis_torch_tensoris_torchvision_availableis_vision_availableloggingrequires_backendsto_numpy)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDIMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDOPENAI_CLIP_MEANOPENAI_CLIP_STD)InterpolationModezPIL.Image.Imagez torch.Tensorc\rSrSrSrSrSrg)ChannelDimensionQchannels_first channels_lastN)__name__ __module__ __qualname____firstlineno__FIRSTLAST__static_attributes__r h/home/dmtnaga/Documents/work/airagagent/rag_env/lib/python3.13/site-packages/transformers/image_utils.pyrrQs E Dr(rc\rSrSrSrSrSrg)AnnotationFormatVcoco_detection coco_panopticr N)r!r"r#r$COCO_DETECTION COCO_PANOPTICr'r r(r)r+r+Vs %N#Mr(r+cl\rSrSr\R R r\RR rSrg)AnnotionFormat[r N) r!r"r#r$r+r/valuer0r'r r(r)r2r2[s$%44::N$2288Mr(r2cl[5=(a$ [U[RR5$N)r isinstancePILImageimgs r) is_pil_imager<cs  EZSYY__%EEr(c(\rSrSrSrSrSrSrSrSr g) ImageTypegpillowtorchnumpy tensorflowjaxr N) r!r"r#r$r8TORCHNUMPY TENSORFLOWJAXr'r r(r)r>r>gs C E EJ Cr(r>cp[U5(a[R$[U5(a[R$[ U5(a[R $[U5(a[R$[U5(a[R$[S[U535e)NzUnrecognized image type ) r<r>r8rrEr rFr rGr rH ValueErrortypeimages r)get_image_typerNosE}}ueE###U}} /U }= >>r(c[U5=(dA [U5=(d/ [U5=(d [U5=(d [ U5$r6)r<r rr r r:s r)is_valid_imagerP}s8   vs 3 vs7K v|\_O` vdqrudvvr(imagesc8U=(a [SU55$)Nc38# UHn[U5v M g7fr6)rP).0rMs r) *is_valid_list_of_images..sDVE.//Vall)rQs r)is_valid_list_of_imagesrZs  DcDVDDDr(cV[US[5(aUVVs/sH oHo"PM M snn$[US[R5(a[R"USS9$[US[ R 5(a[ R"USS9$gs snnf)Nraxis)dim)r7listnpndarray concatenaterATensorcat) input_listsublistitems r)concatenate_listrhs*Q-&&$.CJ747JCC JqM2:: . .~~jq11 JqM5<< 0 0yy++ 1DsB%c[U[[45(aUHn[U5(aM g g[ U5(dgg)NFT)r7r_tuple valid_imagesrP)imgsr;s r)rkrksC$u &&C$$ D ! ! r(cV[U[[45(a[US5$g)NrF)r7r_rjrPr:s r) is_batchedrns%#e}%%c!f%% r(rMreturncUR[R:Xag[R"U5S:=(a [R"U5S:*$)zN Checks to see whether the pixel values have already been rescaled to [0, 1]. Frr)dtyper`uint8minmaxrLs r)is_scaled_imagerus> {{bhh 66%=A  4"&&-1"44r(expected_ndimsc J[U5(aU$[U5(aU/$[U5(aWURUS-:Xa [ U5nU$URU:XaU/nU$[ SUS-SUSURS35e[ S[ U5S35e)a Ensure that the output is a list of images. If the input is a single image, it is converted to a list of length 1. If the input is a batch of images, it is converted to a list of images. 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. rz%Invalid image shape. Expected either z or z dimensions, but got z dimensions.ztInvalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray, but got .)rnr<rPndimr_rJrK)rQrvs r)make_list_of_imagesrzs& Fxf ;;.1, ,&\F [[N *XF  78J7K4P^O_`KK= .   $V ~Q 0 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_rjrTimages_is r)rU+make_flat_list_of_images..KF 8dE]33F#%c3T# UHn[U5=(d U(+v M g7fr6rZr~s r)rUr"YRXh'1A\ARX&(rrz*Could not make a flat list of images from r7r_rjrYrZr<ryrPrJ)rQrvimg_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 g7fr6r}r~s r)rU-make_nested_list_of_images.. rrc3T# UHn[U5=(d U(+v M g7fr6rr~s r)rUr rrrrz]Invalid input type. Must be a single image, a list of images, or a list of batches of images.r)rQrvrMs 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: ) rPrJrKrr7r8r9r`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. rr)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) r7intryrJshapeloggerwarningrr%r&)rMr 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- $[ SU35e)ar Returns the channel dimension axis of the image. Args: image (`np.ndarray`): The image to get the channel dimension axis of. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the image. If `None`, will infer the channel dimension from the image. Returns: The channel dimension axis of the image. rrUnsupported data format: )rrr%ryr&rJ)rMrs r)get_channel_dimension_axisrOsd :5A,222zzA~ .33 3zzA~ 01B0CD EEr( channel_dimcUc [U5nU[R:XaURSURS4$U[R:XaURSURS4$[ SU35e)a] Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of. channel_dim (`ChannelDimension`, *optional*): Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. Returns: A tuple of the image's height and width. r)rrr%rr&rJ)rMrs r)get_image_sizergs{4U; &,,,{{2 B// (-- -{{2 B//4[MBCCr( image_size max_height max_widthcjUup4X- nX$- 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. )rsr) rrrheightwidth height_scale width_scale min_scale new_height new_widths r)#get_image_size_for_max_height_widthrsH,MF&L#KL.IV'(JE%&I   r( annotationc[U[5(aXSU;aRSU;aL[US[[45(a.[ US5S:Xd[USS[5(agg)Nimage_id annotationsrTFr7dictr_rjlenrs 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$)Nc38# UHn[U5v M g7fr6)rrTanns r)rU3valid_coco_detection_annotations..sN+31#66+rWrXrs r) valid_coco_detection_annotationsrs N+N NNr(c&[SU55$)Nc38# UHn[U5v M g7fr6)rrs r)rU2valid_coco_panoptic_annotations..sM#055rWrXrs r)valid_coco_panoptic_annotationsrs MM MMr(timeoutc [[S/5 [U[5(Ga-UR S5(dUR S5(aE[ R R[[R"XS9R55nO[RRU5(a [ R RU5nOUR S5(aURS5Sn[ R""UR%55n[ R R[U55nO4[U[ R R 5(d [+S 5e[ R,R/U5nUR1S 5nU$![&an[)SUS U35eS nAff=f) a Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionzhttp://zhttps://rz data:image/,rzIncorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got z. Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager7str startswithr8r9openrrequestsgetcontentospathisfilesplitbase64 decodebytesencode ExceptionrJ TypeErrorImageOpsexif_transposeconvert)rMrb64es r)rrsj8*-%   I & &%*:*::*F*FIINN78<<+O+W+W#XYE WW^^E " "IINN5)E .. C(+ ((8 ws|4 syy / / D   LL ' ' .E MM% E L  ijoipp~@~AB sA F;; GGGc R[U[[45(at[U5(aJ[US[[45(a,UVVs/sHo"Vs/sH n[ X1S9PM snPM snn$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. rr)r7r_rjrr)rQr image_grouprMs r) load_imagesrs&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_resizesizeresamplePILImageResampling interpolationrc U(aUc [S5eU(aUc [S5eU(aUbUc [S5eU(aUc [S5eU bU b [S5eU (aU bU cU c [S5eggg)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`.zbOnly one of `interpolation` and `resample` should be specified, depending on image processor type.zO`size` and `resample/interpolation` must be specified if `do_resize` is `True`.)rJ) rrrrrrrrrrrrrs r)validate_preprocess_argumentsrs.n,XYY (" u  +y/@kll)+WXX X%9 p  $*0D HajkkIb0Dyr(c\rSrSrSrSrSSjrSrS\RS\ \ \ 4S \R4S jr SS jrS rSS jrSSjrSrSrSSjrSrg)ImageFeatureExtractionMixini9z< Mixin that contain utilities for preparing image features. c[U[RR[R45(d)[ U5(d[ S[U5S35egg)Nz Got type zU which is not supported, only `PIL.Image.Image`, `np.ndarray` and `torch.Tensor` are.)r7r8r9r`rarrJrKselfrMs r)_ensure_format_supported4ImageFeatureExtractionMixin._ensure_format_supported>sW%#))//2::!>??X]H^H^DK=)&& I_?r(NcURU5 [U5(aUR5n[U[R 5(aUc'[UR S[R5nURS:Xa&URSS;aURSSS5nU(aUS-nUR[R5n[RRU5$U$)a Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`): The image to convert to the PIL Image format. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. rrrrr)rrrBr7r`raflatfloatingryr transposeastyperrr8r9 fromarray)rrMrescales r) to_pil_image(ImageFeatureExtractionMixin.to_pil_imageEs %%e, 5 ! !KKME eRZZ ( ($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_rgbcs; %%e,%11L}}U##r(rMscaleroc,URU5 X-$)z' Rescale a numpy image by scale amount )r)rrMrs r)r #ImageFeatureExtractionMixin.rescaleqs %%e,}r(cURU5 [U[RR5(a[R "U5n[ U5(aUR5nUc'[URS[R5OUnU(a/URUR[R5S5nU(a#URS:XaURSSS5nU$)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`rrrBrintegerr rfloat32ryr)rrMr  channel_firsts r)r*ImageFeatureExtractionMixin.to_numpy_arrayxs %%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. rr\)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- $)a Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to normalize. mean (`list[float]` or `np.ndarray` or `torch.Tensor`): The mean (per channel) to use for normalization. std (`list[float]` or `np.ndarray` or `torch.Tensor`): The standard deviation (per channel) to use for normalization. rescale (`bool`, *optional*, defaults to `False`): Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically. T)r rrNrr)rr7r8r9rr`rar rrrfloatrrqrArc from_numpytensorryr)rrMmeanstdr rAs r) normalize%ImageFeatureExtractionMixin.normalizes %%e, eSYY__ - -''t' eRZZ ( (dBJJ//xx~,,U[[9c2::..hhsm**5;;7 U # # dLL11dBJJ// ++D1D <<-Dc<<00c2::..**3/C,,s+C ::?u{{1~7D$//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$)a Resizes `image`. Enforces conversion of input to PIL.Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to resize. size (`int` or `tuple[int, int]`): The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square (`size`,`size`). If set to `False`, will replicate [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) with support for resizing only the smallest edge and providing an optional `max_size`. max_size (`int`, *optional*, defaults to `None`): The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than `max_size` after being resized according to `size`, then the image is resized again so that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter than `size`. Only used if `default_to_square` is `False`. Returns: image: A resized `PIL.Image.Image`. rrz max_size = zN must be strictly greater than the requested size for the smaller edge size = )r)rBILINEARrr7r8r9r r_rjrrrrJresize) rrMrrdefault_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longs r)r'"ImageFeatureExtractionMixin.resizesb< (389K9T9T %%e,%11%%e,E dD ! !;D dC CIN '1$'<'<|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 $) a= Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). rrrrN.r)rr)rr7rjrr`raryrrrr8r9croprpermutert zeros_like new_zerosrs)rrMr image_shapetopbottomleftrightr new_shape new_imagetop_pad bottom_padleft_pad right_pads r) center_crop'ImageFeatureExtractionMixin.center_crop#s %%e,$&&1a71~- bMKN2q81~- AF #w)8+==>    Qs9??2#6??QPST]TcTcdfTginPoAo o r(cURU5 [U[RR5(aUR U5nUSSS2SS2SS24$)ah Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should be first. Nr)rr7r8r9rrs r)flip_channel_order.ImageFeatureExtractionMixin.flip_channel_ordernsL %%e, eSYY__ - -''.ETrT1aZ  r(c UbUO[RRnURU5 [ U[RR5(dUR U5nUR X#XEXgS9$)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`. )rexpandcenter translate fillcolor)r8r9NEARESTrr7r rotate)rrManglerrFrGrHrIs r)rK"ImageFeatureExtractionMixin.rotatesj (389J9J %%e,%11%%e,E|| Vi  r(r r6)NT)F)NTN)NrNNN)r!r"r#r$__doc__rr rr`rarrrr rrr#r'r@rCrKr'r r(r)rr9sj< $RZZeSj0Abjj@(2(hA5FIV!" r(rannotation_formatsupported_annotation_formatscX;a[S[SU35eU[RLa[ U5(d [S5eU[R La[ U5(d [S5egg)NzUnsupported annotation format: z must be one of zInvalid COCO detection annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id` and `annotations`, with the latter being a list of annotations in the COCO format.zInvalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts (batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with the latter being a list of annotations in the COCO format.)rJformatr+r/rr0r)rOrPrs r)validate_annotationsrSs <:6(BRSoRpqrr,;;;/ <<B  ,:::.{;;M <;r(valid_processor_keyscaptured_kwargsc[U5R[U55nU(a+SRU5n[R SUS35 gg)Nz, zUnused or unrecognized kwargs: rx)set differencejoinrr)rTrU unused_keysunused_key_strs r)validate_kwargsr\sJo&11#6J2KLK;/88HJKr(T)frozenc\rSrSr%SrSr\\\S'Sr \\\S'Sr \\\S'Sr \\\S'Sr \\\S'Sr \\\S 'S rS rg) SizeDictiz6 Hashable dictionary to store image size information. Nrr longest_edge shortest_edgerrcV[X5(a [X5$[SUS35e)NzKey z not found in SizeDict.)hasattrgetattrKeyError)rkeys r) __getitem__SizeDict.__getitem__s- 4  4% %cU"9:;;r(r )r!r"r#r$rNrrr__annotations__rr`rarrrgr'r r(r)r_r_sb!FHSM E8C="&L(3-&#'M8C=' $J $#Ix}#rNrPrZrhrkrnboolrurzrrrrjrrrrrrrrrrrrrrSr\r_r r(r)r~sH $!"    --!!<  & &(9(G(G  " "$5$9$9  ' '):)C)C  & &(9(A(A  & &(9(A(A  & &(9(A(A + '+-'   H %rzz>48I3JDQSQ[Q[L\^bcq^rr | $|$ 9\9 c5c4:!5667F  ?wEDE,  52::5$5$$D 4U4;=O8O3P UY  $sE$+ "1l+,1l/01lj\ \ ~ '"'(8#(=">d 2L$s)Ld3iL $<<