text: import argparse
  from os import path as osp
  
  from tools.data_converter import indoor_converter as indoor
  from tools.data_converter import kitti_converter as kitti
  from tools.data_converter import lyft_converter as lyft_converter
  from tools.data_converter import nuscenes_converter as nuscenes_converter
  from tools.data_converter.create_gt_database import create_groundtruth_database
  
  
  def kitti_data_prep(root_path, info_prefix, version, out_dir):
      """Prepare data related to Kitti dataset.
  
      Related data consists of '.pkl' files recording basic infos,
      2D annotations and groundtruth database.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          version (str): Dataset version.
          out_dir (str): Output directory of the groundtruth database info.
      """
      kitti.create_kitti_info_file(root_path, info_prefix)
      kitti.create_reduced_point_cloud(root_path, info_prefix)
  
      info_train_path = osp.join(root_path, f'{info_prefix}_infos_train.pkl')
      info_val_path = osp.join(root_path, f'{info_prefix}_infos_val.pkl')
      info_trainval_path = osp.join(root_path,
                                    f'{info_prefix}_infos_trainval.pkl')
      info_test_path = osp.join(root_path, f'{info_prefix}_infos_test.pkl')
      kitti.export_2d_annotation(root_path, info_train_path)
      kitti.export_2d_annotation(root_path, info_val_path)
      kitti.export_2d_annotation(root_path, info_trainval_path)
      kitti.export_2d_annotation(root_path, info_test_path)
  
      create_groundtruth_database(
          'KittiDataset',
          root_path,
          info_prefix,
          f'{out_dir}/{info_prefix}_infos_train.pkl',
          relative_path=False,
          mask_anno_path='instances_train.json',
          with_mask=(version == 'mask'))
  
  
  def nuscenes_data_prep(root_path,
                         info_prefix,
                         version,
                         dataset_name,
                         out_dir,
                         max_sweeps=10):
      """Prepare data related to nuScenes dataset.
  
      Related data consists of '.pkl' files recording basic infos,
      2D annotations and groundtruth database.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          version (str): Dataset version.
          dataset_name (str): The dataset class name.
          out_dir (str): Output directory of the groundtruth database info.
          max_sweeps (int): Number of input consecutive frames. Default: 10
      """
      nuscenes_converter.create_nuscenes_infos(
          root_path, info_prefix, version=version, max_sweeps=max_sweeps)
  
      if version == 'v1.0-test':
          info_test_path = osp.join(root_path, f'{info_prefix}_infos_test.pkl')
          nuscenes_converter.export_2d_annotation(
              root_path, info_test_path, version=version)
          return
  
      info_train_path = osp.join(root_path, f'{info_prefix}_infos_train.pkl')
      info_val_path = osp.join(root_path, f'{info_prefix}_infos_val.pkl')
      nuscenes_converter.export_2d_annotation(
          root_path, info_train_path, version=version)
      nuscenes_converter.export_2d_annotation(
          root_path, info_val_path, version=version)
      create_groundtruth_database(dataset_name, root_path, info_prefix,
                                  f'{out_dir}/{info_prefix}_infos_train.pkl')
  
  
  def lyft_data_prep(root_path,
                     info_prefix,
                     version,
                     dataset_name,
                     out_dir,
                     max_sweeps=10):
      """Prepare data related to Lyft dataset.
  
      Related data consists of '.pkl' files recording basic infos,
      and 2D annotations.
      Although the ground truth database is not used in Lyft, it can also be
      generated like nuScenes.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          version (str): Dataset version.
          dataset_name (str): The dataset class name.
          out_dir (str): Output directory of the groundtruth database info.
              Not used here if the groundtruth database is not generated.
          max_sweeps (int): Number of input consecutive frames. Default: 10
      """
      lyft_converter.create_lyft_infos(
          root_path, info_prefix, version=version, max_sweeps=max_sweeps)
  
      if version == 'v1.01-test':
          return
  
      train_info_name = f'{info_prefix}_infos_train'
      val_info_name = f'{info_prefix}_infos_val'
  
      info_train_path = osp.join(root_path, f'{train_info_name}.pkl')
      info_val_path = osp.join(root_path, f'{val_info_name}.pkl')
  
      lyft_converter.export_2d_annotation(
          root_path, info_train_path, version=version)
      lyft_converter.export_2d_annotation(
          root_path, info_val_path, version=version)
  
  
  def scannet_data_prep(root_path, info_prefix, out_dir, workers):
      """Prepare the info file for scannet dataset.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          out_dir (str): Output directory of the generated info file.
          workers (int): Number of threads to be used.
      """
      indoor.create_indoor_info_file(
          root_path, info_prefix, out_dir, workers=workers)
  
  
  def s3dis_data_prep(root_path, info_prefix, out_dir, workers):
      """Prepare the info file for s3dis dataset.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          out_dir (str): Output directory of the generated info file.
          workers (int): Number of threads to be used.
      """
      indoor.create_indoor_info_file(
          root_path, info_prefix, out_dir, workers=workers)
  
  
  def sunrgbd_data_prep(root_path, info_prefix, out_dir, workers):
      """Prepare the info file for sunrgbd dataset.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          out_dir (str): Output directory of the generated info file.
          workers (int): Number of threads to be used.
      """
      indoor.create_indoor_info_file(
          root_path, info_prefix, out_dir, workers=workers)
  
  
  def waymo_data_prep(root_path,
                      info_prefix,
                      version,
                      out_dir,
                      workers,
                      max_sweeps=5):
      """Prepare the info file for waymo dataset.
  
      Args:
          root_path (str): Path of dataset root.
          info_prefix (str): The prefix of info filenames.
          out_dir (str): Output directory of the generated info file.
          workers (int): Number of threads to be used.
          max_sweeps (int): Number of input consecutive frames. Default: 5 \
              Here we store pose information of these frames for later use.
      """
      from tools.data_converter import waymo_converter as waymo
  
      splits = ['training', 'validation', 'testing']
      for i, split in enumerate(splits):
          load_dir = osp.join(root_path, 'waymo_format', split)
          if split == 'validation':
              save_dir = osp.join(out_dir, 'kitti_format', 'training')
          else:
              save_dir = osp.join(out_dir, 'kitti_format', split)
          converter = waymo.Waymo2KITTI(
              load_dir,
              save_dir,
              prefix=str(i),
              workers=workers,
              test_mode=(split == 'test'))
          converter.convert()
      # Generate waymo infos
      out_dir = osp.join(out_dir, 'kitti_format')
      kitti.create_waymo_info_file(out_dir, info_prefix, max_sweeps=max_sweeps)
      create_groundtruth_database(
          'WaymoDataset',
          out_dir,
          info_prefix,
          f'{out_dir}/{info_prefix}_infos_train.pkl',
          relative_path=False,
          with_mask=False)
  
  
  parser = argparse.ArgumentParser(description='Data converter arg parser')
  parser.add_argument('dataset', metavar='kitti', help='name of the dataset')
  parser.add_argument(
      '--root-path',
      type=str,
      default='./data/kitti',
      help='specify the root path of dataset')
  parser.add_argument(
      '--version',
      type=str,
      default='v1.0',
      required=False,
      help='specify the dataset version, no need for kitti')
  parser.add_argument(
      '--max-sweeps',
      type=int,
      default=10,
      required=False,
      help='specify sweeps of lidar per example')
  parser.add_argument(
      '--out-dir',
      type=str,
      default='./data/kitti',
      required=False,
      help='name of info pkl')
  parser.add_argument('--extra-tag', type=str, default='kitti')
  parser.add_argument(
      '--workers', type=int, default=4, help='number of threads to be used')
  args = parser.parse_args()
  
  if __name__ == '__main__':
      if args.dataset == 'kitti':
          kitti_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=args.version,
              out_dir=args.out_dir)
      elif args.dataset == 'nuscenes' and args.version != 'v1.0-mini':
          train_version = f'{args.version}-trainval'
          nuscenes_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=train_version,
              dataset_name='NuScenesDataset',
              out_dir=args.out_dir,
              max_sweeps=args.max_sweeps)
          test_version = f'{args.version}-test'
          nuscenes_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=test_version,
              dataset_name='NuScenesDataset',
              out_dir=args.out_dir,
              max_sweeps=args.max_sweeps)
      elif args.dataset == 'nuscenes' and args.version == 'v1.0-mini':
          train_version = f'{args.version}'
          nuscenes_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=train_version,
              dataset_name='NuScenesDataset',
              out_dir=args.out_dir,
              max_sweeps=args.max_sweeps)
      elif args.dataset == 'lyft':
          train_version = f'{args.version}-train'
          lyft_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=train_version,
              dataset_name='LyftDataset',
              out_dir=args.out_dir,
              max_sweeps=args.max_sweeps)
          test_version = f'{args.version}-test'
          lyft_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=test_version,
              dataset_name='LyftDataset',
              out_dir=args.out_dir,
              max_sweeps=args.max_sweeps)
      elif args.dataset == 'waymo':
          waymo_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              version=args.version,
              out_dir=args.out_dir,
              workers=args.workers,
              max_sweeps=args.max_sweeps)
      elif args.dataset == 'scannet':
          scannet_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              out_dir=args.out_dir,
              workers=args.workers)
      elif args.dataset == 's3dis':
          s3dis_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              out_dir=args.out_dir,
              workers=args.workers)
      elif args.dataset == 'sunrgbd':
          sunrgbd_data_prep(
              root_path=args.root_path,
              info_prefix=args.extra_tag,
              out_dir=args.out_dir,
              workers=args.workers)
  