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","''","."]
code: def val_processor(self, image_data):
      import groundingdino.datasets.transforms as T
  
      image_path = image_data[self.image_key]["filename"]
  
      image_pil = PIL.Image.open(image_path).convert("RGB")
  
      transform = T.Compose(
          [
              T.RandomResize([800], max_size=1333),
              T.ToTensor(),
              T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
          ]
      )
      image, _ = transform(image_pil, None)  # 3, h, w
      return image
  
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 10 for calling the OVDProcessor obj's underlying member method code with example usage: obj.val_processor(image_data) and returns: image