UhґddlmZmZddlmZddlmZmZddlZ ddl m Z m Z m Z mZmZddlmZmZmZmZmZddlmZmZmZmZmZer ddlZdd l mZerddlZerddlZ erddl!mZ" dHd e jFd ee e$fd eee e$fd e jFfdZ%de jLdfd e jFde'dee de jPdeee$e fd e jFf dZ)dZ* dId ee jFddddfdee+dee$deee$e fd df dZ,dHd e-e.e.ffdZ/ dJde jFdee.e-e.e.fe0e.e-e.fde+d ee.deee$e fd e-f d!Z1 dKd e jFde-e.e.fd"d#d$ee.dee d%e+deee$e fd e jFfd&Z2 dLd e jFd'ee'ee'fd(ee'ee'fdee deee$e fd e jFf d)Z3 dLd e jFde-e.e.fdeee$e fdeee$e fd e jFf d*Z4dMd,Z5d+e jFd e jFfd-Z6dNd.Z7d+ed efd/Z8dOd1Z9d0e jFd e jFfd2Z:dPd3Z;d0ed efd4ZGd7d8eZ?e?jd9ddfd e jFd:ee.e-e.e.fee-e.e.ffd;e?dZB dLd e jFdee deee$e fd e jFfd?ZCd@ZDdAe0dd e-eEe-e.e.fe0dfeEe.e-e-e.e.fe.ffffdBZFdCeEe-e.e.fdfdDeEe.e-e.e.ffd e0dfdEZGGdFdGZHy)Q) CollectionIterable)ceil)OptionalUnionN)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_vision_availablerequires_backends)PILImageResamplingimage channel_diminput_channel_dimreturnct|tjstdt || t |}t |}||k(r|S|t jk(ratt|jdz |jdz |jdz |jdz gz}|j|}|S|t jk(ratt|jdz |jdz |jdz |jdz gz}|j|}|Std|)a Converts `image` to the channel dimension format specified by `channel_dim`. The input can have arbitrary number of leading dimensions. Only last three dimension will be permuted to format the `image`. 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 rz&Unsupported channel dimension format: ) isinstancenpndarray TypeErrortyper r FIRSTlistrangendim transposeLAST ValueError)rrrtarget_channel_dimaxess o/var/www/catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/image_transforms.pyto_channel_dimension_formatr05s** eRZZ (FtE{mTUU :5A)+6.. -333E%**q.)*ejj1nejj1nejj[\n-]]% L /44 4E%**q.)*ejj1nejj1nejj[\n-]]% LA+OPPscale 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$r%astypefloat64r0)rr2r3r4r5rescaled_images r/rescaler:`sk4 eRZZ (FtE{mTUU\\"**-5N4^[Rcd#**51N r1c|jtjk(rd}|Stj||j t rbtj d|krtj |dkrd}|Std|jd|jdtj d|krtj |dkrd}|Std |jd|jd) z Detects whether or not the image needs to be rescaled before being converted to a PIL image. The assumption is that if the image is of type `np.float` and all values are between 0 and 1, it needs to be rescaled. FrzZThe image to be converted to a PIL image contains values outside the range [0, 255], got [z, z%] which cannot be converted to uint8.rTzXThe image to be converted to a PIL image contains values outside the range [0, 1], got [) r4r"uint8allcloser7intallr,minmax)r do_rescales r/_rescale_for_pil_conversionrDs {{bhh  UELL- . 66!u* "&&#"6J  }Buyy{m3XZ  U uz 2   IIK=599;-/T V  r1zPIL.Image.Image torch.Tensor tf.Tensorz jnp.ndarrayrC image_modecttdgt|tjjr|St |s t |r|j}nRt|rtj|}n1t|tjstdt|t|tj |}|j"ddk(rtj$|dn|}| t'|n|}|r t)|d}|j+tj,}tjj/||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: raxisr<mode)r to_pil_imager!PILImagerrnumpyrr"arrayr#r,r%r0r r+shapesqueezerDr:r7r= fromarray)rrCrGr5s r/rOrOs2lXJ/%) ue!4  u  rzz *;DK=IJJ (/?/D/DFW XE+0++b/Q*>BJJu2 &EE8B7I,U3zJs# LL "E 99  u:  66r1c|\}}d}|Stt||f}tt||f}||z |z|kDr||z|z }tt |}||kr||k(s ||kr ||k(r||} }|| fS||kr0|} ||t||z|z }|| fSt||z|z }|| fS|}||t||z|z } || fSt||z|z } || fS)aC Computes the output image size given the input image size and the desired output size. Args: image_size (`Tuple[int, int]`): The input image size. size (`int`): The desired output size. max_size (`int`, *optional*): The maximum allowed output size. N)floatrArBr?round) image_sizesizemax_sizeheightwidthraw_sizemin_original_sizemax_original_sizeohows r/get_size_with_aspect_ratiordsFMFEH!#vuo"67!#vuo"67 0 04 7( B"336GGHuX'D%FdNETMB 8O    H$8X&./B 8OTF]U*+B 8O   H$8X%./B 8OTE\F*+B 8Or1 input_imager[default_to_squarer\ct|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 rrz7size 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!tupler'lenr,r r?) rer[rfr\r5r]r^shortlongrequested_new_short new_shortnew_longs r/get_resize_output_image_sizerosJ$ & 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)rprqrJrKrgp?)rresizerBILINEARrir,r r!rPrQrDrOr"rSr) expand_dimsr0r r+r:) rr[rprqr3rrr5rCr]r^ resized_images r/ruruBsBfxj)#/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. rNrrir ) r!r"r#rirTr4r=r7int32r?)colors r/ rgb_to_idrs%$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. rr|r.) r!r"r#copyrhr'rTzerosr=r(append)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. rrr zUnsupported format: )rr) r!r?rXrhrir,r r&r))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',jjAo&"6"  Reconstructs a list of images in the original order. rr)r(ri)rrrs r/reorder_imagesrasOs/01   -a0345I!5LQ5OP  s5c0eZdZdZdej fdZy) NumpyToTensorz4 Convert a numpy array to a PyTorch tensor. rcjtj|jdddjS)Nr rr)r from_numpyr* contiguous)selfrs r/__call__zNumpyToTensor.__call__rs+1a 89DDFFr1N)rrrrr"r#rrr1r/rrmsGbjjGr1rr)NNN)TNN)NNNTN)NN)rrErrE)rrFrrF)rrErrE)rrFrrF)Icollections.abcrrmathrtypingrrrRr" image_utilsr r r r r utilsrrrrrutils.import_utilsrrrrrrPrr tensorflowr jax.numpyjnpr#strr0rrXr4r:rDboolrOrhr?rdr'rorurrrrrrrrrrrrrrrrrrdictrrrrr1r/rs1"ZY/ AE( ::(',-( &6&; <=(ZZ (\/3jj@D # ::# #*+# 88 #  c+;&; <= # ZZ #L:"& $@D 37 . ]Z [3737 37 c+;&; <= 37  37l$5c?$V#"@D @O@O U38_d3is; <@O@Osm @O  c+;&; <= @O  @OL&*"&.2@DD ::D S/D#D3- D *+ D  D c+;&; <=DZZDV/3@D : ::: z%(( ): uj'' (:*+ :  c+;&; <= : ZZ :@;?@D Q ::Q S/Q%%5 567Q c+;&; <= Q ZZ Qh2::"**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 0  0 4c3hn!55 6S%cSVhY\H\B]=]8^ ^_0* 5c?N:; SWX[]bcfhkck]lXlSm  . 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