class_docstr: Prepare images for detection.
  
  Args:
      attrbox(Callable): Method to get detection attributes of images.
      data_dir(str): Image dataset path.
      category_to_id_map(dict): Mapping relations of category and id for images.
  
  Returns:
      imgs(dict): The input for detection model, it is a dict.
class_docstr_tok: ["Prepare","images","for","detection",".","Args",":","attrbox","(","Callable",")",":","Method","to","get","detection","attributes","of","images",".","data_dir","(","str",")",":","Image","dataset","path",".","category_to_id_map","(","dict",")",":","Mapping","relations","of","category","and","id","for","images",".","Returns",":","imgs","(","dict",")",":","The","input","for","detection","model",",","it","is","a","dict","."]
code: def __call__(self):
      image_ids = self.attrbox.getImgIds()
      image_ids.sort()
      imgs = copy.deepcopy(self.attrbox.loadImgs(image_ids))
  
      for img in imgs:
          img["image"] = os.path.join(self.img_dir, img["file_name"])
          assert os.path.exists(
              img["image"]
          ), "image {} not found.".format(img["image"])
          box_num = 50
          img["gt_boxes"] = np.zeros((box_num, 4), dtype=np.float32)
          img["gt_labels"] = np.zeros((box_num), dtype=np.int32)
          img = self.parse_gt_annotations(img)
      return imgs
  
imports: import os
  import copy
  from typing import Callable
  import paddle
  import numpy
  from paddlehub.env import DATA_HOME
  from pycocotools.coco import COCO
label_desc: Use image node_id 2 for calling the ParseImages obj's underlying member method code with example usage: obj.__call__() and returns: imgs