cCiCESrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r Jr SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJr \R@"\!5r"\"5(aSSK#r#"S S \5r$S /r%g) z"Image processor class for TextNet.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbget_resize_output_image_sizeresizeto_channel_dimension_format) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_kwargsvalidate_preprocess_arguments) TensorTypeis_vision_availableloggingc$^\rSrSrSrS/rSSS\RSSSSS\\ S4 S \ S \ \ \ \4S \S \S \ S\ \ \ \4S\ S\\\4S\ S\ \\\\4S\ \\\\4S\ SS4U4Sjjjr\RSS4S\R(S \ \ \4S \S\ \\ \4S\ \\ \4S\R(4 SjjrSSSSSSSSSSSSS\R.S4S\S \ \ S \ \ \ \4S \ \S \ \S \ \ S\ \S\ \ S\ \S\ \ S\ \\\\4S\ \\\\4S\ \ S\ \\ \4S\ \S\ \\ \4S\R6R64"SjjrSrU=r$)TextNetImageProcessor3a Constructs a TextNet image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 640}`): Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` method. size_divisor (`int`, *optional*, defaults to 32): Ensures height and width are rounded to a multiple of this value after resizing. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `False`): Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method. crop_size (`dict[str, int]` *optional*, defaults to 224): Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `[0.485, 0.456, 0.406]`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `list[float]`, *optional*, defaults to `[0.229, 0.224, 0.225]`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. pixel_valuesTN Fgp? do_resizesize size_divisorresampledo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbreturnc  >>[TU]"S 0U D6 UbUOSS0n[USS9nUbUOSSS.n[USS9nXlX lX0lX@lXPlX`lXpl Xl Xl U bU O[Ul U bU O[UlXl/S QUlg) N shortest_edgeiF)default_to_square)heightwidthr&) param_name)imagesr!r"r#r$r%r&r'r(r)r*r+r,return_tensors data_formatinput_data_format)super__init__rr!r"r#r$r%r&r'r(r)r r*rr+r,_valid_processor_keys)selfr!r"r#r$r%r&r'r(r)r*r+r,kwargs __class__s n/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/textnet/image_processing_textnet.pyr;TextNetImageProcessor.__init__^s "6"'tos-CTU;!*!6IsUX*DY&/&;AU,& "imager7r8c XSU;aUSnO"SU;aSU;a USUS4nO [S5e[XUSS9upxXpR-S:waXpRXpR-- - nXR-S:waXRXR-- - n[U4Xx4UUUS.UD6$) a? Resize an image. The shortest edge of the image is resized to size["shortest_edge"] , with the longest edge resized to keep the input aspect ratio. Both the height and width are resized to be divisible by 32. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. size_divisor (`int`, *optional*, defaults to `32`): Ensures height and width are rounded to a multiple of this value after resizing. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. default_to_square (`bool`, *optional*, defaults to `False`): The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`. r/r2r3zASize must contain either 'shortest_edge' or 'height' and 'width'.F)r"r8r0r)r"r$r7r8) ValueErrorr r#r ) r=rCr"r$r7r8r>r2r3s r@r TextNetImageProcessor.resizes> d "(D  'T/NDM2D`a a4 0AUZ  %% % * ''64E4E+EF FF $$ $ ) &&%2C2C*CD DE  #/     rBr5r6c UbUO URnUbUO URn[USSS9nUbUO URnUbUO URnUbUO UR nUbUO UR n[USSS9nUbUO URnU bU O URn U bU O URn U bU O URn U bU O URn U bU O URn [UR5URS9 [!U5n[#U5(d [%S5e['UU U U U UUUUUS9 U (aUVs/sHn[)U5PM nnUVs/sHn[+U5PM nn[-US 5(aU(a[.R1S 5 Uc[3US 5n/nUHtnU(aUR5UX5US 9nU(aUR7UUUS 9nU(aUR9UU US 9nU (aUR;UXUS9nUR=U5 Mv UVs/sHn[?UUUS9PM nnSU0n[AUUS9$s snfs snfs snf)a  Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. size_divisor (`int`, *optional*, defaults to `32`): Ensures height and width are rounded to a multiple of this value after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the center crop. Only has an effect if `do_center_crop` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): 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. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. r"F)r4r0r&T)captured_kwargsvalid_processor_keyszkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.) r'r(r)r*r+r%r&r!r"r$rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)rCr"r$r8)rCr"r8)rCscaler8)rCmeanstdr8)input_channel_dimr)data tensor_type)!r!r"rr#r$r%r&r'r(r)r*r+r,rkeysr<rrrErr rrlogger warning_oncerr center_croprescale normalizeappendr r)r=r5r!r"r#r$r%r&r'r(r)r*r+r,r6r7r8r>rC all_imagesrNs r@ preprocess TextNetImageProcessor.preprocesssR"+!6IDNN 'tTYYTfN'3'?|TEVEV '38+9+E4K^K^!*!6IDNN !) 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