ݘhddlZddlZddlmZddlmZddlmZddlm Z m Z ddl Z ddl Z ddlmZddlmZmZmZmZmZmZmZmZmZmZmZmZdd lmZmZm Z m!Z!m"Z"m#Z#erddl$Z%ddl&Z%ej'ej'e%j(j)ej'd kr e%j*j+Z,ne%j*Z,erbdd l-m.Z.e,j/ere.j0ne.j/e,j1e.j1e,j2e.j2e,j3e.j3e,j4e.j4e,j5e.j5iZ6niZ6erddl7Z7ej8e9Z:e d e j;d eGddeZ?GddeZ@eAeBe eCeBefd)ZT dXde j;d*e e e>eBfdeCfd+ZUdXde j;d,e>deSeCeCffd-ZVd.eSeCeCfd/eCd0eCdeSeCeCffd1ZWd2eAeBe ee e e\eeZdZejjZejjZdS)AnnotionFormatN)r!r"r#r*r-valuer.r&r'r(r0r0bs$%4:N$28MMMr'r0c\tot|tjjSN)r isinstancePILImageimgs r( is_pil_imager9js!  EZSY_%E%EEr'c"eZdZdZdZdZdZdZdS) ImageTypepillowtorchnumpy tensorflowjaxN)r!r"r#r5TORCHNUMPY TENSORFLOWJAXr&r'r(r;r;ns' C E EJ CCCr'r;cNt|r tjSt|r tjSt |r tjSt|r tjSt|r tj Stdt|)NzUnrecognized image type ) r9r;r5rrAr rBr rCr rD ValueErrortypeimages r(get_image_typerJvsE}ueE$##U} =U == > >>r'ct|p;t|p,t|pt|pt |Sr3)r9r rr r r7s r(is_valid_imagerLsG    vs 3 3 vs7K7K v|\_O`O` vdqrudvdvvr'imagesc8|otd|DS)Nc34K|]}t|VdSr3)rL.0rIs r( z*is_valid_list_of_images..s*DDE.//DDDDDDr'all)rMs r(is_valid_list_of_imagesrUs$  DcDDVDDDDDDr'c,t|dtr d|DSt|dtjrtj|dSt|dt jrt j|dSdS)Nrcg|] }|D]}| Sr&r&)rQsublistitems r( z$concatenate_list..s%CCC7CC4CCCCr'axis)dim)r4listnpndarray concatenater=Tensorcat) input_lists r(concatenate_listres*Q-&&,CCJCCCC JqM2: . .,~jq1111 JqM5< 0 0,y++++,,r'ct|ttfr|D]}t|sdSnt |sdSdS)NFT)r4r^tuple valid_imagesrL)imgsr8s r(rhrhsd$u &&  C$$ uu  D ! !u 4r'cht|ttfrt|dSdS)NrF)r4r^rgrLr7s r( is_batchedrks/#e}%%&c!f%%% 5r'rIreturnc|jtjkrdStj|dkotj|dkS)zV Checks to see whether the pixel values have already been rescaled to [0, 1]. Frr )dtyper_uint8minmaxrHs r(is_scaled_imagerrs? {bhu 6%==A  4"&--1"44r'expected_ndimsc Ft|r|St|r|gSt|rP|j|dzkrt |}n0|j|kr|g}n!t d|dzd|d|jd|St dt |d)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. r z%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 .)rkr9rLndimr^rFrGrMrts r(make_list_of_imagesrys& Fxf  ;.1, , ,&\\FF [N * *XFF.8J..P^..K...   0 $V  0 0 0  r'cRt|ttfr>td|Dr%td|Dr d|DSt|ttfrWt |rHt |ds|dj|kr|S|dj|dzkr d|DSt|r:t |s |j|kr|gS|j|dzkrt|Std|)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. c3NK|] }t|ttfV!dSr3r4r^rgrQimages_is r(rRz+make_flat_list_of_images..0KK 8dE]33KKKKKKr'c34K|]}t|VdSr3rUr}s r(rRz+make_flat_list_of_images..+IIh'11IIIIIIr'cg|] }|D]}| Sr&r&rQimg_listr8s r(rZz,make_flat_list_of_images..s%???h??s????r'rr cg|] }|D]}| Sr&r&rs r(rZz,make_flat_list_of_images..s%CCCH(CC3CCCCCr'z*Could not make a flat list of images from r4r^rgrTrUr9rwrLrFrxs r(make_flat_list_of_imagesrsW" 6D%=))@ KKFKKK K K@ II&III I I@ @?F????&4-((D-DV-L-LD q " " fQin&F&FM !9>^a/ / /CCCCC Cf    6;.#@#@8O ;.1, , ,<<  J&JJ K KKr'c>t|ttfr4td|Drtd|Dr|St|ttfrXt |rIt |ds|dj|kr|gS|dj|dzkr d|DSt|r.rr'c34K|]}t|VdSr3rr}s r(rRz-make_nested_list_of_images..rr'rr c,g|]}t|Sr&)r^rPs r(rZz.make_nested_list_of_images..s444EDKK444r'z]Invalid input type. Must be a single image, a list of images, or a list of batches of images.rrxs r(make_nested_list_of_imagesrsE 6D%=)) KKFKKK K K II&III I I  &4-((5-DV-L-L5 q " " fQin&F&F8O !9>^a/ / /44V444 4f"    6;.#@#@H:  ;.1, , ,LL> ! t u uur'ct|stdt|tr3t |t jjrtj|St|S)NzInvalid image type: ) rLrFrGrr4r5r6r_arrayrr7s r(to_numpy_arrayr&sl #  =;S ;;<<<C!A!Ax}} C==r' num_channels.c||nd}t|tr|fn|}|jdkrd\}}n9|jdkrd\}}n(|jdkrd\}}ntd|j|j||vr>|j||vr/t d |jd tjS|j||vr tjS|j||vr tj Std ) a[ 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. Nr rsrs)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) r4intrwrFshapeloggerwarningrr$r%)rIr first_dimlast_dims r(infer_channel_dimension_formatr/s/$0#;<<L&0s&C&CUL??L zQ" 88 q" 88 q" 88PEJPPQQQ {9--%+h2G<2W2W K5; K K K    %% Y < / /%% X , . .$$ ? @ @@r'input_data_formatc|t|}|tjkr |jdz S|tjkr |jdz St d|)a 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. Nrsr Unsupported data format: )rrr$rwr%rF)rIrs r(get_channel_dimension_axisrVsf :5AA,222zA~ .3 3 3zA~ D1BDD E EEr' channel_dimc|t|}|tjkr|jd|jdfS|tjkr|jd|jdfSt d|)a Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of. channel_dim (`ChannelDimension`, *optional*): Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. Returns: A tuple of the image's height and width. Nr)rrr$rr%rF)rIrs r(get_image_sizernsz4U;; &,,,{2 B// (- - -{2 B//B[BBCCCr' image_size max_height max_widthc|\}}||z }||z }t||}t||z}t||z} || fS)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. )rpr) rrrheightwidth height_scale width_scale min_scale new_height new_widths r(#get_image_size_for_max_height_widthrs_,MFE&Le#KL+..IVi'((JEI%&&I y  r' annotationct|trfd|vrbd|vr^t|dttfrWXY>Z\`3a3a /t 5r'ct|trjd|vrfd|vrbd|vr^t|dttfr.s+NN31#66NNNNNNr'rSrs r( valid_coco_detection_annotationsrs NN+NNN N NNr'c4td|DS)Nc34K|]}t|VdSr3)rrs r(rRz2valid_coco_panoptic_annotations..s+MM#055MMMMMMr'rSrs r(valid_coco_panoptic_annotationsrs MMMMM M MMr'timeoutcttdgt|tr[|ds|drGt jttj ||j }ntj |r t j|}n|dr|dd} t!j|}t jt|}nX#t&$r}t)d|d |d }~wwxYwt|t jjr|}nt+d t j|}|d }|S) a3 Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. timeout (`float`, *optional*): The timeout value in seconds for the URL request. Returns: `PIL.Image.Image`: A PIL Image. visionzhttp://zhttps://rz data:image/,r zIncorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got z. Failed with NzuIncorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image.RGB)r load_imager4str startswithr5r6openrrequestsgetcontentospathisfilesplitbase64 decodebytesencode ExceptionrF TypeErrorImageOpsexif_transposeconvert)rIrb64es r(rrsj8*---%   I & & %*:*::*F*F INN78<w+O+O+O+W#X#XYYEE W^^E " " INN5))EE .. , C((+ (88 ws||44    BjoBB@BB  E39? + +  D    L ' ' . .E MM% E LsAE!! F+FFct|ttfrMt|r0t|dttfrfd|DSfd|DSt |S)aLoads images, handling different levels of nesting. Args: images: A single image, a list of images, or a list of lists of images to load. timeout: Timeout for loading images. Returns: A single image, a list of images, a list of lists of images. rc,g|]}fd|DS)c2g|]}t|SrrrQrIrs r(rZz*load_images...s&QQQEZw777QQQr'r&)rQ image_grouprs r(rZzload_images..s/lllVaQQQQ[QQQlllr'c2g|]}t|Srrrs r(rZzload_images..s&KKK5Jug666KKKr'r)r4r^rgrr)rMrs `r( load_imagesrs&4-((3 v;; L:fQi$?? Llllleklll lKKKKFKKK K&'2222r' do_rescalerescale_factor do_normalize image_mean image_stddo_padsize_divisibilitydo_center_crop crop_size do_resizesizeresamplePILImageResampling interpolationrc |r|td|r|td|r||td|r|td| | td| r| | | tddSdSdS)a 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`.zzDepending on the model, `size_divisibility`, `size_divisor`, `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`.)rF) rrrrrrrrrrrrrs r(validate_preprocess_argumentsr s.Zn,XYYY  #+ I   m+y/@klllY)+WXXX X%9 p   l$*0D Hajkkkll0D0DHaHar'ceZdZdZdZddZdZdejde e e fdejfd Z dd Z d ZddZddZdZdZddZdS)ImageFeatureExtractionMixinzD Mixin that contain utilities for preparing image features. ct|tjjtjfs/t |s"t dt|ddSdS)Nz Got type zS which is not supported, only `PIL.Image.Image`, `np.array` and `torch.Tensor` are.)r4r5r6r_r`rrFrGselfrIs r(_ensure_format_supportedz4ImageFeatureExtractionMixin._ensure_format_supportedBsq%#)/2:!>?? X]H^H^ &DKK&&&     r'Nc||t|r|}t|tjr|%t|jdtj}|jdkr&|j ddvr| ddd}|r|dz}| tj }tj|S|S)a" Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`): The image to convert to the PIL Image format. rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type, `False` otherwise. Nrrsrr r)rrr>r4r_r`flatfloatingrwr transposeastyperor5r6 fromarray)rrIrescales r( to_pil_imagez(ImageFeatureExtractionMixin.to_pil_imageIs %%e,,, 5 ! ! "KKMME eRZ ( ( .$UZ]BK@@zQ5;q>V#;#;1a00 $ LL**E9&&u-- - r'c||t|tjjs|S|dS)z Converts `PIL.Image.Image` to RGB format. Args: image (`PIL.Image.Image`): The image to convert. r)rr4r5r6rrs r( convert_rgbz'ImageFeatureExtractionMixin.convert_rgbgsE %%e,,,%11 L}}U###r'rIscalerlc6||||zS)z7 Rescale a numpy image by scale amount )r)rrIrs r(r z#ImageFeatureExtractionMixin.rescaleus" %%e,,,u}r'Tc||t|tjjrt j|}t |r|}|%t|jdtj n|}|r3| | tj d}|r"|j dkr|ddd}|S)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. Nrp?rsrr )rr4r5r6r_rrr>rintegerr r float32rwr)rrIr  channel_firsts r(rz*ImageFeatureExtractionMixin.to_numpy_array|s %%e,,, eSY_ - - $HUOOE 5 ! ! "KKMME;B?*UZ]BJ777PW  FLLbj!9!99EEE  -UZ1__OOAq!,,E r'c||t|tjjr|St |r|d}nt j|d}|S)z Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to expand. rr[)rr4r5r6r unsqueezer_ expand_dimsrs r(rz'ImageFeatureExtractionMixin.expand_dimssq %%e,,, eSY_ - - L 5 ! ! 2OOA&&EEN5q111E r'Fc||t|tjjr||d}n|rt|t jr4||t j d}n7t|r(|| d}t|t jrt|t js,t j ||j }t|t js,t j ||j }nt|rddl}t||js;t|t jr|j|}n|j|}t||js;t|t jr|j|}n|j|}|jdkr-|jddvr||ddddfz |ddddfz S||z |z S)a  Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array if it's a PIL Image. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): The image to normalize. mean (`list[float]` or `np.ndarray` or `torch.Tensor`): The mean (per channel) to use for normalization. std (`list[float]` or `np.ndarray` or `torch.Tensor`): The standard deviation (per channel) to use for normalization. rescale (`bool`, *optional*, defaults to `False`): Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will happen automatically. T)r rrNrsr)rr4r5r6rr_r`r r rrfloatrrnr=rb from_numpytensorrwr)rrImeanstdr r=s r( normalizez%ImageFeatureExtractionMixin.normalizes6 %%e,,, eSY_ - - ?''t'<> eRZ ( ( ,dBJ// :x~~,,U[99c2:.. 8hsmm**5;77 U # # , LLLdEL11 .dBJ//.+5+D11DD'5<--Dc5<00 ,c2:..,*%*3//CC&%,s++C :??u{1~77DD$//3qqq$}3EE EDLC' 'r'c||n tj}||t|tjjs||}t|trt|}t|tst|dkr|r*t|tr||fn|d|df}n|j \}}||kr||fn||f\}} t|tr|n|d} || kr|S| t| | z|z } } |8|| krtd|d|| |krt|| z| z |} } ||kr| | fn| | f}| ||S)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`. Nr rz max_size = zN must be strictly greater than the requested size for the smaller edge size = )r)rBILINEARrr4r5r6r r^rgrrrrFresize) rrIrrdefault_to_squaremax_sizerrshortlongrequested_new_short new_shortnew_longs r(r"z"ImageFeatureExtractionMixin.resizes< (3889K9T %%e,,,%11 -%%e,,E dD ! ! ;;D dC  [CIINN  ['1$'<'<Td||47DQRGBT % v16&ufoovuo t.8s.C.C&Pdda#/// L&93?RUY?Y\a?a;b;b8 '#666(G(GG@DGG (**.1(Y2F2Q.R.RT\8 05 8,,hPYEZ||D8|444r'cV||t|ts||f}t|st|tjrN|jdkr||}|jddvr|jddn|jdd}n|j d|j df}|d|dz dz}||dz}|d|dz dz}||dz}t|tj j r| ||||fS|jddv}|sWt|tjr| ddd}t|r|ddd}|dkr-||dkr!|dkr||dkr|d||||fS|jddt|d|dt|d|dfz} t|tjrt j|| } n$t|r|| } | d|dz dz} | |dz} | d |dz dz} | |dz}|| d| | | |f<|| z }|| z }|| z }|| z }| dtd|t%| jd|td|t%| jd |f} | S) a Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result has the size asked). Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): The image to resize. size (`int` or `tuple[int, int]`): The size to which crop the image. Returns: new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, height, width). rrrr N.r)rr)rr4rgrr_r`rwrrrr5r6croprpermuterq zeros_like new_zerosrp)rrIr image_shapetopbottomleftrightr new_shape new_imagetop_pad bottom_padleft_pad right_pads r( center_cropz'ImageFeatureExtractionMixin.center_crop'sd %%e,,,$&& $1a7{1~- bMKN2q8{1~- AF #wz)8I+==> w'   Qs9?2#6???QPST]TcdfTginPoPoAo o r'c||t|tjjr||}|dddddddfS)a 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)rr4r5r6rrs r(flip_channel_orderz.ImageFeatureExtractionMixin.flip_channel_orderrs` %%e,,, eSY_ - - /''..ETTrT111aaaZ  r'rc||ntjj}||t |tjjs||}|||||||S)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`. N)rexpandcenter translate fillcolor)r5r6NEARESTrr4r rotate)rrIanglerr>r?r@rAs r(rCz"ImageFeatureExtractionMixin.rotates} (3889J %%e,,,%11 -%%e,,E|| HVFicl   r'r3)NT)F)NTN)NrNNN)r!r"r#__doc__rr rr_r`rrrr rrrr"r:r<rCr&r'r(rr=s< $ $ $RZeSj0Abj@(2(2(2(2(hA5A5A5A5FIIIV!!!"      r'rannotation_formatsupported_annotation_formatsc||vrtdtd||tjurt |std|tjurt |stddSdS)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*r-rr.r)rFrGrs r(validate_annotationsrJs  <<<q6qqSoqqrrr,;;;/ << B  ,:::.{;; M ;:  r'valid_processor_keyscaptured_kwargsct|t|}|r5d|}td|ddSdS)Nz, zUnused or unrecognized kwargs: rv)set differencejoinrr)rKrL unused_keysunused_key_strs r(validate_kwargsrSspo&&11#6J2K2KLLKL;//JJJJKKKKKLLr'T)frozenceZdZUdZdZeeed<dZeeed<dZ eeed<dZ eeed<dZ eeed<dZ eeed<d Z dS) SizeDictz> Hashable dictionary to store image size information. 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