code: def __init__(
      self,
      dataset_dir,
      cut_height,
      list_path,
      split="train",
      data_fields=["image"],
      video_file=None,
      frame_rate=-1,
  ):
      super(CULaneDataSet, self).__init__(
          dataset_dir=dataset_dir,
          cut_height=cut_height,
          split=split,
          data_fields=data_fields,
      )
      self.dataset_dir = dataset_dir
      self.list_path = osp.join(dataset_dir, list_path)
      self.cut_height = cut_height
      self.data_fields = data_fields
      self.split = split
      self.training = "train" in split
      self.data_infos = []
      self.video_file = video_file
      self.frame_rate = frame_rate
      self._imid2path = {}
      self.predict_dir = None
  
imports: from ppdet.core.workspace import register, serializable
  import cv2
  import os
  import tarfile
  import numpy
  import os.path
  from ppdet.data.source.dataset import DetDataset
  from imgaug.augmentables.lines import LineStringsOnImage
  from imgaug.augmentables.segmaps import SegmentationMapsOnImage
  from ppdet.data.culane_utils import lane_to_linestrings
  import pickle
  from ppdet.utils.logger import setup_logger
  from .dataset import DetDataset, _make_dataset, _is_valid_file
  from ppdet.utils.download import download_dataset
label_desc: Use image node_id 1 to create a new CULaneDataSet object from inherited base classes: DetDataset with example: obj = CULaneDataSet(dataset_dir, cut_height, list_path, split, data_fields, video_file, frame_rate)