class_docstr: Constructs an OWL-ViT image processor.
  
  This image processor inherits from [`ImageProcessingMixin`] which contains most of the main methods. Users should
  refer to this superclass for more information regarding those methods.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the shorter edge of the input to a certain `size`.
      size (`Dict[str, int]`, *optional*, defaults to {"height": 768, "width": 768}):
          The size to use for resizing the image. Only has an effect if `do_resize` is set to `True`. If `size` is a
          sequence like (h, w), output size will be matched to this. If `size` is an int, then image will be resized
          to (size, size).
      resample (`int`, *optional*, defaults to `Resampling.BICUBIC`):
          An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
          `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
          `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
          to `True`.
      do_center_crop (`bool`, *optional*, defaults to `False`):
          Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
          image is padded with 0's and then center cropped.
      crop_size (`int`, *optional*, defaults to {"height": 768, "width": 768}):
          The size to use for center cropping the image. Only has an effect if `do_center_crop` is set to `True`.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Whether to rescale the input by a certain factor.
      rescale_factor (`float`, *optional*, defaults to `1/255`):
          The factor to use for rescaling the image. Only has an effect if `do_rescale` is set to `True`.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with `image_mean` and `image_std`. Desired output size when applying
          center-cropping. Only has an effect if `do_center_crop` is set to `True`.
      image_mean (`List[int]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
          The sequence of means for each channel, to be used when normalizing images.
      image_std (`List[int]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
          The sequence of standard deviations for each channel, to be used when normalizing images.
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docstr: Center crop an image to a certain size.
  
  Args:
      image (`np.ndarray`):
          Image to center crop.
      crop_size (`Dict[str, int]`):
          The size to center crop the image to. Must contain height and width keys.
      data_format (`str` or `ChannelDimension`, *optional*):
          The channel dimension format for the output image. If unset, the channel dimension format of the input
          image is used.
      input_data_format (`str` or `ChannelDimension`, *optional*):
          The channel dimension format of the input image. If not provided, it will be inferred.
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code: def center_crop(
      self,
      image: np.ndarray,
      crop_size: Dict[str, int],
      data_format: Optional[Union[str, ChannelDimension]] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
      **kwargs,
  ) -> np.ndarray:
      """
      Center crop an image to a certain size.
  
      Args:
          image (`np.ndarray`):
              Image to center crop.
          crop_size (`Dict[str, int]`):
              The size to center crop the image to. Must contain height and width keys.
          data_format (`str` or `ChannelDimension`, *optional*):
              The channel dimension format for the output image. If unset, the channel dimension format of the input
              image is used.
          input_data_format (`str` or `ChannelDimension`, *optional*):
              The channel dimension format of the input image. If not provided, it will be inferred.
      """
      crop_size = get_size_dict(crop_size, default_to_square=True)
      if "height" not in crop_size or "width" not in crop_size:
          raise ValueError(
              "crop_size dictionary must contain height and width keys"
          )
  
      return center_crop(
          image,
          (crop_size["height"], crop_size["width"]),
          data_format=data_format,
          input_data_format=input_data_format,
          **kwargs,
      )
  
imports: import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, center_to_corners_format, rescale, resize, to_channel_dimension_format
  from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 3 for calling the OwlViTImageProcessor obj's underlying member method code with example usage: obj.center_crop(image, crop_size, data_format, input_data_format) and returns: center_crop