class_docstr: Dataset for text detection while ann_file in Adelai's coco format.
  
  Args:
      ann_file (str): Annotation file path. Defaults to ''.
      metainfo (dict, optional): Meta information for dataset, such as class
          information. Defaults to None.
      data_root (str): The root directory for ``data_prefix`` and
          ``ann_file``. Defaults to ''.
      data_prefix (dict): Prefix for training data. Defaults to
          dict(img_path='').
      filter_cfg (dict, optional): Config for filter data. Defaults to None.
      indices (int or Sequence[int], optional): Support using first few
          data in annotation file to facilitate training/testing on a smaller
          dataset. Defaults to None which means using all ``data_infos``.
      serialize_data (bool, optional): Whether to hold memory using
          serialized objects, when enabled, data loader workers can use
          shared RAM from master process instead of making a copy. Defaults
          to True.
      pipeline (list, optional): Processing pipeline. Defaults to [].
      test_mode (bool, optional): ``test_mode=True`` means in test phase.
          Defaults to False.
      lazy_init (bool, optional): Whether to load annotation during
          instantiation. In some cases, such as visualization, only the meta
          information of the dataset is needed, which is not necessary to
          load annotation file. ``Basedataset`` can skip load annotations to
          save time by set ``lazy_init=False``. Defaults to False.
      max_refetch (int, optional): If ``Basedataset.prepare_data`` get a
          None img. The maximum extra number of cycles to get a valid
          image. Defaults to 1000.
docstr: Parse raw annotation to target format.
  
  Args:
      raw_data_info (dict): Raw data information loaded from ``ann_file``
  
  Returns:
      Union[dict, List[dict]]: Parsed annotation.
docstr_tok: ["Parse","raw","annotation","to","target","format",".","Args",":","raw_data_info","(","dict",")",":","Raw","data","information","loaded","from","``","ann_file","``","Returns",":","Union","[","dict",",","List","[","dict","]","]",":","Parsed","annotation","."]
code: def parse_data_info(
      self, raw_data_info: dict
  ) -> Union[dict, List[dict]]:
      """Parse raw annotation to target format.
  
      Args:
          raw_data_info (dict): Raw data information loaded from ``ann_file``
  
      Returns:
          Union[dict, List[dict]]: Parsed annotation.
      """
      img_info = raw_data_info["raw_img_info"]
      ann_info = raw_data_info["raw_ann_info"]
  
      data_info = {}
  
      img_path = osp.join(
          self.data_prefix["img_path"], img_info["file_name"]
      )
      data_info["img_path"] = img_path
      data_info["img_id"] = img_info["img_id"]
      data_info["height"] = img_info["height"]
      data_info["width"] = img_info["width"]
  
      instances = []
      for ann in ann_info:
          instance = {}
  
          if ann.get("ignore", False):
              continue
          x1, y1, w, h = ann["bbox"]
          inter_w = max(0, min(x1 + w, img_info["width"]) - max(x1, 0))
          inter_h = max(0, min(y1 + h, img_info["height"]) - max(y1, 0))
          if inter_w * inter_h == 0:
              continue
          if ann["area"] <= 0 or w < 1 or h < 1:
              continue
          if ann["category_id"] not in self.cat_ids:
              continue
          bbox = [x1, y1, x1 + w, y1 + h]
  
          if ann.get("iscrowd", False):
              instance["ignore"] = 1
          else:
              instance["ignore"] = 0
          instance["bbox"] = bbox
          instance["bbox_label"] = self.cat2label[ann["category_id"]]
          # instance['polygon'] = bezier2poly(
          #     np.array(ann['bezier_pts'], dtype=np.float32))
          instance["beziers"] = np.array(
              ann["bezier_pts"], dtype=np.float32
          )
          instance["text"] = ann["rec"]
  
          instances.append(instance)
      data_info["instances"] = instances
      return data_info
  
imports: import os.path
  from typing import List, Union
  import numpy
  from mmdet.datasets.coco import CocoDataset
  from mmocr.registry import DATASETS
label_desc: Use image node_id 1 for calling the AdelDataset obj's underlying member method code with example usage: obj.parse_data_info(raw_data_info) and returns: data_info