sgFddlZddlmZddlmZmZmZmZmZddl Z ddl m Z m Z mZmZmZddlmZmZmZmZmZddlmZmZmZmZmZmZmZer ddlZddl m Z erddl!Z!erddl"Z#erddl$m Z%erdd l&m'Z(n erdd l)m'Z( dFd e jTd ee e+fd eee e+fd e jTfdZ,de jZdfd e jTde.dee de j^deee+e fd e jTf dZ0dZ1 dGd ee jTddddfdee2dee+deee+e fd df dZ3 dHde jTdee4ee4e4fee4ee4fde2dee4deee+e fd e5f d Z6 dId e jTdee4e4fd!d"d#ee4dee d$e2deee+e fd e jTfd%Z7 dJd e jTd&ee.ee.fd'ee.ee.fdee deee+e fd e jTf d(Z8 dGd e jTdee4e4fdeee+e fdeee+e fd$ee2d e jTf d)Z9dKd+Z:d*e jTd e jTfd,Z;dLd-Zd/e jTd e jTfd1Z?dNd2Z@d/ed efd3ZAd4ZBd5ZCGd6d7eZDeDjd8ddfd e jTd9ee4ee4e4feee4e4ffd:eDd;ee.ee.fdeee+e fdeee+e fd e jTfd<ZFd e d e fd=ZG dJd e jTdee deee+e fd e jTfd>ZHd?ZIGd@dAZJGdBdCZKGdDdEZLy)ON)ceil)IterableListOptionalTupleUnion)ChannelDimension ImageInputget_channel_dimension_axisget_image_sizeinfer_channel_dimension_format) ExplicitEnum TensorType is_jax_tensor is_tf_tensoris_torch_tensor)is_flax_availableis_tf_availableis_torch_availableis_torchvision_availableis_torchvision_v2_availableis_vision_availablerequires_backends)PILImageResampling) functionalimage channel_diminput_channel_dimreturncnt|tjstdt || t |}t |}||k(r|S|t jk(r|jd}|S|t jk(r|jd}|Stdj|)a) Converts `image` to the channel dimension format specified by `channel_dim`. Args: image (`numpy.ndarray`): The image to have its channel dimension set. channel_dim (`ChannelDimension`): The channel dimension format to use. input_channel_dim (`ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. Returns: `np.ndarray`: The image with the channel dimension set to `channel_dim`. ,Input image must be of type np.ndarray, got )rr )r r#rz(Unsupported channel dimension format: {}) isinstancenpndarray TypeErrortyperr FIRST transposeLAST ValueErrorformat)rrrtarget_channel_dims P/var/www/html/venv/lib/python3.12/site-packages/transformers/image_transforms.pyto_channel_dimension_formatr0=s& eRZZ (FtE{mTUU :5A)+6.. -333 * L /44 4 * LCJJ;WXXscale data_formatdtypeinput_data_formatct|tjstdt ||j tj |z}| t|||}|j |}|S)a Rescales `image` by `scale`. Args: image (`np.ndarray`): The image to rescale. scale (`float`): The scale to use for rescaling the image. data_format (`ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. dtype (`np.dtype`, *optional*, defaults to `np.float32`): The dtype of the output image. Defaults to `np.float32`. Used for backwards compatibility with feature extractors. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. Returns: `np.ndarray`: The rescaled image. r")r$r%r&r'r(astypefloat64r0)rr2r3r4r5rescaled_images r/rescaler:dsk4 eRZZ (FtE{mTUU\\"**-5N4^[Rcd#**51N r1c|jtjk(rd}|Stj||j t rbtj d|krtj |dkrd}|Std|jd|jdtj d|krtj |dkrd}|Std |jd|jd) z Detects whether or not the image needs to be rescaled before being converted to a PIL image. The assumption is that if the image is of type `np.float` and all values are between 0 and 1, it needs to be rescaled. FrzZThe image to be converted to a PIL image contains values outside the range [0, 255], got [z, z%] which cannot be converted to uint8.r TzXThe image to be converted to a PIL image contains values outside the range [0, 1], got [) r4r%uint8allcloser7intallr,minmax)r do_rescales r/_rescale_for_pil_conversionrDs {{bhh  UELL- . 66!u* "&&#"6J  }Buyy{m3XZ  U uz 2   IIK=599;-/T V  r1zPIL.Image.Image torch.Tensor tf.Tensorz jnp.ndarrayrC image_modecttdgt|tjjr|St |s t |r|j}n^t|rtj|}n=t|tjs#tdjt|t|t j"|}|j$ddk(rtj&|dn|}| t)|n|}|r t+|d}|j-tj.}tjj1||S)a Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed. Args: image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor` or `tf.Tensor`): The image to convert to the `PIL.Image` format. do_rescale (`bool`, *optional*): Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type and casting to `int` would result in a loss of precision, and `False` otherwise. image_mode (`str`, *optional*): The mode to use for the PIL image. If unset, will use the default mode for the input image type. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `PIL.Image.Image`: The converted image. visionz"Input image type not supported: {}r axisr<mode)r to_pil_imager$PILImagerrnumpyrr%arrayr&r,r-r(r0r r+shapesqueezerDr:r7r= fromarray)rrCrGr5s r/rOrOs 2lXJ/%) ue!4  u  rzz *=DDT%[QRR (/?/D/DFW XE+0++b/Q*>BJJu2 &EE8B7I,U3zJs# LL "E 99  u:  66r1 input_imagesizedefault_to_squaremax_sizect|ttfr8t|dk(r t|St|dk(r|d}n t d|r||fSt ||\}}||kr||fn||f\}}|} | t | |z|z } } |.|| krt d|d|| |kDrt || z| z |} } ||kr| | fS| | fS)a Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: input_image (`np.ndarray`): The image to resize. size (`int` or `Tuple[int, int]` or List[int] or `Tuple[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). 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*): 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`. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `tuple`: The target (height, width) dimension of the output image after resizing. r#r rz7size must have 1 or 2 elements if it is a list or tuplez max_size = zN must be strictly greater than the requested size for the smaller edge size = )r$tuplelistlenr,r r?) rWrXrYrZr5heightwidthshortlongrequested_new_short new_shortnew_longs r/get_resize_output_image_sizerfsJ$ & t9>;  Y!^7DVW Wd|";0ABMFE%*f_5&/65/KE4-s3F3MPU3U/VxI * *hZ(4486;  h "%h&:X&E"FxI$)VOHi N)X9NNr1resampler reducing_gap return_numpyc2ttdg||ntj}t |dk(s t d| t |}||n|}d}t|tjjst|}t|||}|\}} |j| |f||} |ritj| } | jdk(rtj| dn| } t!| |t"j$ } |r t'| d n| } | S) a Resizes `image` to `(height, width)` specified by `size` using the PIL library. Args: image (`np.ndarray`): The image to resize. size (`Tuple[int, int]`): The size to use for resizing the image. resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. reducing_gap (`int`, *optional*): Apply optimization by resizing the image in two steps. The bigger `reducing_gap`, the closer the result to the fair resampling. See corresponding Pillow documentation for more details. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If unset, will use the inferred format from the input. return_numpy (`bool`, *optional*, defaults to `True`): Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is returned. input_data_format (`ChannelDimension`, *optional*): The channel dimension format of the input image. If unset, will use the inferred format from the input. Returns: `np.ndarray`: The resized image. rIr#zsize must have 2 elementsF)rCr5)rgrhrJrKrgp?)rresizerBILINEARr^r,rr$rPrQrDrOr%rSndim expand_dimsr0r r+r:) rrXrgrhr3rir5rCr_r` resized_images r/rlrlsBfxj)#/x5G5P5PH t9>455 :5A'2':# KJ eSYY__ -07 UzM^_MFELL%8R^L_M/ CPBTBTXYBY}2>_l 3 ;:J:O:O > NBJJ /.~>> n %+N;; .tN/C.DE FFr1cvt|tjrt|jdk(rh|j tj k(r|jtj}|dddddfd|dddddfzzd|dddddfzzSt|dd|dzzd|dzzS)z* Converts RGB color to unique ID. Nrr ir#) r$r%r&r^rTr4r=r7int32r?)colors r/ rgb_to_idrxs%$U[[)9Q)> ;;"(( "LL*EQ1W~eAq!Gn 44y5Aq>7QQQ uQx#a.(9uQx+?? @@r1ct|tjrx|j}t t |j dgz}tj|tj}tdD]}|dz|d|f<|dz}|Sg}tdD]}|j|dz|dz}|S)z* Converts unique ID to RGB color. rrtr.) r$r%r&copyr\r]rTzerosr=rangeappend)id_map id_map_copy rgb_shapergb_mapir_s r/ id_to_rgbrs&"**%kkm $v||,s23 ((9BHH5q A)C/GCFO C K  E 1X Vc\"3 Lr1c eZdZdZdZdZdZdZy) PaddingModezP Enum class for the different padding modes to use when padding images. constantreflect replicate symmetricN)__name__ __module__ __qualname____doc__CONSTANTREFLECT REPLICATE SYMMETRICr1r/rrsHGIIr1rgpaddingrNconstant_valuesc tfd}||}|tjk(r"||}tj|d|n|tj k(rtj|dnf|tj k(rtj|dn:|tjk(rtj|dntd||t|SS) a Pads the `image` with the specified (height, width) `padding` and `mode`. Args: image (`np.ndarray`): The image to pad. padding (`int` or `Tuple[int, int]` or `Iterable[Tuple[int, int]]`): Padding to apply to the edges of the height, width axes. Can be one of three formats: - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis. - `((before, after),)` yields same before and after pad for height and width. - `(pad,)` or int is a shortcut for before = after = pad width for all axes. mode (`PaddingMode`): The padding mode to use. Can be one of: - `"constant"`: pads with a constant value. - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis. - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array. constant_values (`float` or `Iterable[float]`, *optional*): The value to use for the padding if `mode` is `"constant"`. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use the inferred format of the input image. Returns: `np.ndarray`: The padded image. ct|ttfr ||f||ff}nt|tr#t |dk(r|d|df|d|dff}nxt|tr&t |dk(rt|dtr||f}nBt|tr$t |dk(rt|dtr|}nt d|t jk(rdg|ng|d}jdk(rdg|}|S|}|S)za Convert values to be in the format expected by np.pad based on the data format. r rr#zUnsupported format: )rr) r$r?floatr\r^r,r r)rn)valuesrr5s r/_expand_for_data_formatz$pad.._expand_for_data_formats fsEl +v&(89F  &3v;!+;ay&),vay&).DEF  &3v;!+; 6RS9VY@Zf%F  &3v;!+; 6RS9V[@\F3F8<= ='8;K;Q;Q&Q&"6"WhY_WhagWh"'q!f 7= r1r)rNrrrMedgerzInvalid padding mode: ) rrrr%padrrrr,r0)rrrNrr3r5rs` ` r/rrsV :5A,&g.G {###1/BugJX $$ $ugI6 && &ugF3 && &ugK81$899R]Ri '{? <<$n(<= r1ct|}tj||j|j|j S)N)r)rFr{rqrrrrrs r/__call__zFusedRescaleNormalize.__call__Bs-%e,{{5$))TXXt||LLr1N)rFrrE)rrrrrboolrrrr1r/rr8s % Mr1rc&eZdZdZddefdZddZy)RescalezM Rescale the input image by rescale factor: image *= rescale_factor. rc||_yrr)rrs r/rzRescale.__init__Ls ,r1c$||jz}|Srrrs r/rzRescale.__call__Os+++ r1N)rr)rrrrrrrrr1r/rrGs-u-r1rc0eZdZdZdej fdZy) NumpyToTensorz4 Convert a numpy array to a PyTorch tensor. rcjtj|jdddjS)Nr#rr )r from_numpyr* contiguousrs r/rzNumpyToTensor.__call__Ys+1a 89DDFFr1N)rrrrr%r&rrr1r/rrTsGbjjGr1rr)NNN)TNN)NNNTN)NN)rrEr rE)rrFr rF)rrEr rE)rrFr rF)MrmathrtypingrrrrrrRr% image_utilsr r r r rutilsrrrrrutils.import_utilsrrrrrrrrPrr tensorflowr jax.numpyjnptorchvision.transforms.v2rrtorchvision.transformsr&strr0rwrr4r:rDrrOr?r\rfrlr{r~rrrrrrrrrrrrrrrrrrrrr1r/rs 99ZY/ 96 AE$ ::$',-$ &6&; <=$ZZ $T/3jj@D # ::# #*+# 88 #  c+;&; <= # ZZ #L:"& $@D 37 . ]Z [3737 37 c+;&; <= 37  37t#"@D @O@O U38_d3is; <@O@Osm @O  c+;&; <= @O  @OL&*"&.2@DD ::D S/D#D3- D *+ D  D c+;&; <=DZZDV/3@D : ::: x& ': uhuo% &:*+ :  c+;&; <= : ZZ :@;?@D#' ` ::` S/`%%5 567` c+;&; <= ` 4. ` ZZ `F2::"**FJF:F*" BJJ 2::  GZGJG.A&,$,,58:>@D S ::S 3c3h%S/)BB CS S5(5/12 S %%5 567 S  c+;&; <= SZZSn*,/3@D# ::#*+# c+;&; <=#ZZ #L M M  GGr1