class_docstr: Dataset for MOTChallenge.
  
  Args:
      visibility_thr (float, optional): The minimum visibility
          for the objects during training. Default to -1.
docstr: Parse raw annotation to target format. The difference between this
  function and the one in ``BaseVideoDataset`` is that the parsing here
  adds ``visibility`` and ``mot_conf``.
  
  Args:
      raw_data_info (dict): Raw data information load from ``ann_file``
  
  Returns:
      Union[dict, List[dict]]: Parsed annotation.
docstr_tok: ["Parse","raw","annotation","to","target","format",".","The","difference","between","this","function","and","the","one","in","``","BaseVideoDataset","``","is","that","the","parsing","here","adds","``","visibility","``","and","``","mot_conf","``",".","Args",":","raw_data_info","(","dict",")",":","Raw","data","information","load","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. The difference between this
      function and the one in ``BaseVideoDataset`` is that the parsing here
      adds ``visibility`` and ``mot_conf``.
  
      Args:
          raw_data_info (dict): Raw data information load 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 = {}
  
      data_info.update(img_info)
      if self.data_prefix.get("img_path", None) is not None:
          img_path = osp.join(
              self.data_prefix["img_path"], img_info["file_name"]
          )
      else:
          img_path = img_info["file_name"]
      data_info["img_path"] = img_path
  
      instances = []
      for i, ann in enumerate(ann_info):
          instance = {}
  
          if (not self.test_mode) and (
              ann["visibility"] < self.visibility_thr
          ):
              continue
          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_flag"] = 1
          else:
              instance["ignore_flag"] = 0
          instance["bbox"] = bbox
          instance["bbox_label"] = self.cat2label[ann["category_id"]]
          instance["instance_id"] = ann["instance_id"]
          instance["category_id"] = ann["category_id"]
          instance["mot_conf"] = ann["mot_conf"]
          instance["visibility"] = ann["visibility"]
          if len(instance) > 0:
              instances.append(instance)
      if not self.test_mode:
          assert len(instances) > 0, (
              f"No valid instances found in "
              f'image {data_info["img_path"]}!'
          )
      data_info["instances"] = instances
      return data_info
  
file_path: mmdetection/mmdet/datasets/mot_challenge_dataset.py
imports: import os.path
  from typing import List, Union
  from mmdet.registry import DATASETS
  from .base_video_dataset import BaseVideoDataset
label_desc: Use image node_id 2 for calling the MOTChallengeDataset obj's underlying member method code with example usage: obj.parse_data_info(raw_data_info) and returns: data_info