class_docstr: Prepare data for open vocabulary detection models specified by "prefix".
class_docstr_tok: ["Prepare","data","for","open","vocabulary","detection","models","specified","by","``","prefix","''","."]
docstr: Obtain one sample's images and customized them for a specific model.
  
  Parameters
  ----------
  images
      Images of one sample.
  feature_modalities
      The modality of the feature columns.
  is_training
      Whether to process images in the training mode.
  
  Returns
  -------
  A dictionary containing one sample's processed images and their number.
docstr_tok: ["Obtain","one","sample","'s","images","and","customized","them","for","a","specific","model",".","Parameters","--","--","--","--","--","images","Images","of","one","sample",".","feature_modalities","The","modality","of","the","feature","columns",".","is_training","Whether","to","process","images","in","the","training","mode",".","Returns","--","--","--","-","A","dictionary","containing","one","sample","'s","processed","images","and","their","number","."]
code: def __call__(
      self,
      texts_and_images: Dict[str, Union[str, List[str]]],
      feature_modalities: Dict[str, Union[int, float, list]],
      is_training: bool,
  ) -> Dict:
      """
      Obtain one sample's images and customized them for a specific model.
  
      Parameters
      ----------
      images
          Images of one sample.
      feature_modalities
          The modality of the feature columns.
      is_training
          Whether to process images in the training mode.
  
      Returns
      -------
      A dictionary containing one sample's processed images and their number.
      """
      texts_and_images = {
          k: [v] if isinstance(v, str) else v
          for k, v in texts_and_images.items()
      }
  
      return self.process_one_sample(
          texts_and_images, feature_modalities, is_training
      )
  
imports: import logging
  import warnings
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  import PIL
  from PIL import ImageFile
  from torch import nn
  from ..constants import COLUMN, IMAGE, IMAGE_META, IMAGE_VALID_NUM, PROMPT, TEXT
  from .collator import ListCollator, PadCollator
  from .utils import is_rois_input
label_desc: Use image node_id 11 for calling the OVDProcessor obj's underlying member method code with example usage: obj.__call__(texts_and_images, feature_modalities, is_training) and returns: self