import argparse import os.path as osp import tools.data_converter.indoor_converter as indoor import tools.data_converter.kitti_converter as kitti import tools.data_converter.lyft_converter as lyft_converter import tools.data_converter.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) 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': 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 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) 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 == '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 == 'sunrgbd': sunrgbd_data_prep( root_path=args.root_path, info_prefix=args.extra_tag, out_dir=args.out_dir, workers=args.workers)