class_docstr: Constructs a DPT image processor.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the image's (height, width) dimensions. Can be overidden by `do_resize` in `preprocess`.
      size (`Dict[str, int]` *optional*, defaults to `{"height": 384, "width": 384}`):
          Size of the image after resizing. Can be overidden by `size` in `preprocess`.
      resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
          Defines the resampling filter to use if resizing the image. Can be overidden by `resample` in `preprocess`.
      keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
          If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can
          be overidden by `keep_aspect_ratio` in `preprocess`.
      ensure_multiple_of (`int`, *optional*, defaults to 1):
          If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overidden
          by `ensure_multiple_of` in `preprocess`.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Whether to rescale the image by the specified scale `rescale_factor`. Can be overidden by `do_rescale` in
          `preprocess`.
      rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
          Scale factor to use if rescaling the image. Can be overidden by `rescale_factor` in `preprocess`.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
          method.
      image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
          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 `IMAGENET_STANDARD_STD`):
          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.
      do_pad (`bool`, *optional*, defaults to `False`):
          Whether to apply center padding. This was introduced in the DINOv2 paper, which uses the model in
          combination with DPT.
      size_divisor (`int`, *optional*):
          If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the
          DINOv2 paper, which uses the model in combination with DPT.
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docstr: 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 reszing. If `keep_aspect_ratio` is `True`, the image is resized to the largest
          possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is set, the image is
          resized to a size that is a multiple of this value.
      keep_aspect_ratio (`bool`, *optional*, defaults to `self.keep_aspect_ratio`):
          Whether to keep the aspect ratio of the image. If False, the image will be resized to (size, size). If
          True, the image will be resized to keep the aspect ratio and the size will be the maximum possible.
      ensure_multiple_of (`int`, *optional*, defaults to `self.ensure_multiple_of`):
          Ensure that the image size is a multiple of this value.
      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_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
          Whether to rescale the image values between [0 - 1].
      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.
      image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
          Image standard deviation.
      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:
              - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
              - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
      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.
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code: def preprocess(
      self,
      images: ImageInput,
      do_resize: bool = None,
      size: int = None,
      keep_aspect_ratio: bool = None,
      ensure_multiple_of: int = None,
      resample: PILImageResampling = None,
      do_rescale: bool = None,
      rescale_factor: float = None,
      do_normalize: bool = None,
      image_mean: Optional[Union[float, List[float]]] = None,
      image_std: Optional[Union[float, List[float]]] = None,
      do_pad: bool = None,
      size_divisor: int = None,
      return_tensors: Optional[Union[str, TensorType]] = None,
      data_format: ChannelDimension = ChannelDimension.FIRST,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
      **kwargs,
  ) -> PIL.Image.Image:
      """
      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 reszing. If `keep_aspect_ratio` is `True`, the image is resized to the largest
              possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is set, the image is
              resized to a size that is a multiple of this value.
          keep_aspect_ratio (`bool`, *optional*, defaults to `self.keep_aspect_ratio`):
              Whether to keep the aspect ratio of the image. If False, the image will be resized to (size, size). If
              True, the image will be resized to keep the aspect ratio and the size will be the maximum possible.
          ensure_multiple_of (`int`, *optional*, defaults to `self.ensure_multiple_of`):
              Ensure that the image size is a multiple of this value.
          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_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
              Whether to rescale the image values between [0 - 1].
          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.
          image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
              Image standard deviation.
          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:
                  - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                  - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
          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.
      """
      do_resize = do_resize if do_resize is not None else self.do_resize
      size = size if size is not None else self.size
      size = get_size_dict(size)
      keep_aspect_ratio = (
          keep_aspect_ratio
          if keep_aspect_ratio is not None
          else self.keep_aspect_ratio
      )
      ensure_multiple_of = (
          ensure_multiple_of
          if ensure_multiple_of is not None
          else self.ensure_multiple_of
      )
      resample = resample if resample is not None else self.resample
      do_rescale = (
          do_rescale if do_rescale is not None else self.do_rescale
      )
      rescale_factor = (
          rescale_factor
          if rescale_factor is not None
          else self.rescale_factor
      )
      do_normalize = (
          do_normalize
          if do_normalize is not None
          else self.do_normalize
      )
      image_mean = (
          image_mean if image_mean is not None else self.image_mean
      )
      image_std = image_std if image_std is not None else self.image_std
      do_pad = do_pad if do_pad is not None else self.do_pad
      size_divisor = (
          size_divisor
          if size_divisor is not None
          else self.size_divisor
      )
  
      images = make_list_of_images(images)
  
      if not valid_images(images):
          raise ValueError(
              "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
              "torch.Tensor, tf.Tensor or jax.ndarray."
          )
  
      if do_resize and size is None or resample is None:
          raise ValueError(
              "Size and resample must be specified if do_resize is True."
          )
  
      if do_rescale and rescale_factor is None:
          raise ValueError(
              "Rescale factor must be specified if do_rescale is True."
          )
  
      if do_normalize and (image_mean is None or image_std is None):
          raise ValueError(
              "Image mean and std must be specified if do_normalize is True."
          )
  
      if do_pad and size_divisor is None:
          raise ValueError(
              "Size divisibility must be specified if do_pad is True."
          )
  
      # All transformations expect numpy arrays.
      images = [to_numpy_array(image) for image in images]
  
      if is_scaled_image(images[0]) and do_rescale:
          logger.warning_once(
              "It 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."
          )
  
      if input_data_format is None:
          # We assume that all images have the same channel dimension format.
          input_data_format = infer_channel_dimension_format(images[0])
  
      if do_resize:
          images = [
              self.resize(
                  image=image,
                  size=size,
                  resample=resample,
                  keep_aspect_ratio=keep_aspect_ratio,
                  ensure_multiple_of=ensure_multiple_of,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      if do_rescale:
          images = [
              self.rescale(
                  image=image,
                  scale=rescale_factor,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      if do_normalize:
          images = [
              self.normalize(
                  image=image,
                  mean=image_mean,
                  std=image_std,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      if do_pad:
          images = [
              self.pad_image(
                  image=image,
                  size_divisor=size_divisor,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      images = [
          to_channel_dimension_format(
              image, data_format, input_channel_dim=input_data_format
          )
          for image in images
      ]
  
      data = {"pixel_values": images}
      return BatchFeature(data=data, tensor_type=return_tensors)
  
file_path: transformers/src/transformers/models/dpt/image_processing_dpt.py
imports: import math
  from typing import Dict, Iterable, List, Optional, Tuple, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import pad, resize, to_channel_dimension_format
  from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, is_torch_available, is_torch_tensor, make_list_of_images, to_numpy_array, valid_images
  from ...utils import TensorType, is_vision_available, logging
label_desc: Use image node_id 4 for calling the DPTImageProcessor obj's underlying member method code with example usage: obj.preprocess(images, do_resize, size, keep_aspect_ratio, ensure_multiple_of, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, do_pad, size_divisor, return_tensors, data_format, input_data_format) and returns: BatchFeature