create_data.py 10.8 KB
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from os import path as osp
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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
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from tools.data_converter.create_gt_database import (
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    GTDatabaseCreater, create_groundtruth_database)
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def kitti_data_prep(root_path,
                    info_prefix,
                    version,
                    out_dir,
                    with_plane=False):
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    """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.
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        with_plane (bool, optional): Whether to use plane information.
            Default: False.
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    """
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    kitti.create_kitti_info_file(root_path, info_prefix, with_plane)
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    kitti.create_reduced_point_cloud(root_path, info_prefix)
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    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)

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    create_groundtruth_database(
        'KittiDataset',
        root_path,
        info_prefix,
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        f'{out_dir}/{info_prefix}_infos_train.pkl',
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        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):
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    """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.
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        max_sweeps (int, optional): Number of input consecutive frames.
            Default: 10
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    """
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    nuscenes_converter.create_nuscenes_infos(
        root_path, info_prefix, version=version, max_sweeps=max_sweeps)

    if version == 'v1.0-test':
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        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)
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        return

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    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')
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    nuscenes_converter.export_2d_annotation(
        root_path, info_train_path, version=version)
    nuscenes_converter.export_2d_annotation(
        root_path, info_val_path, version=version)
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    create_groundtruth_database(dataset_name, root_path, info_prefix,
                                f'{out_dir}/{info_prefix}_infos_train.pkl')


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def lyft_data_prep(root_path, info_prefix, version, max_sweeps=10):
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    """Prepare data related to Lyft dataset.

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    Related data consists of '.pkl' files recording basic infos.
    Although the ground truth database and 2D annotations are not used in
    Lyft, it can also be generated like nuScenes.
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    Args:
        root_path (str): Path of dataset root.
        info_prefix (str): The prefix of info filenames.
        version (str): Dataset version.
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        max_sweeps (int, optional): Number of input consecutive frames.
            Defaults to 10.
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    """
    lyft_converter.create_lyft_infos(
        root_path, info_prefix, version=version, max_sweeps=max_sweeps)

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def scannet_data_prep(root_path, info_prefix, out_dir, workers):
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    """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.
    """
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    indoor.create_indoor_info_file(
        root_path, info_prefix, out_dir, workers=workers)
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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)


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def sunrgbd_data_prep(root_path, info_prefix, out_dir, workers):
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    """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.
    """
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    indoor.create_indoor_info_file(
        root_path, info_prefix, out_dir, workers=workers)
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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.
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        max_sweeps (int, optional): Number of input consecutive frames.
            Default: 5. Here we store pose information of these frames
            for later use.
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    """
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    from tools.data_converter import waymo_converter as waymo

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    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,
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            test_mode=(split == 'testing'))
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        converter.convert()
    # Generate waymo infos
    out_dir = osp.join(out_dir, 'kitti_format')
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    kitti.create_waymo_info_file(
        out_dir, info_prefix, max_sweeps=max_sweeps, workers=workers)
    GTDatabaseCreater(
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        'WaymoDataset',
        out_dir,
        info_prefix,
        f'{out_dir}/{info_prefix}_infos_train.pkl',
        relative_path=False,
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        with_mask=False,
        num_worker=workers).create()
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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')
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parser.add_argument(
    '--with-plane',
    action='store_true',
    help='Whether to use plane information for kitti.')
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parser.add_argument(
    '--out-dir',
    type=str,
    default='./data/kitti',
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    required=False,
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    help='name of info pkl')
parser.add_argument('--extra-tag', type=str, default='kitti')
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parser.add_argument(
    '--workers', type=int, default=4, help='number of threads to be used')
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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,
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            out_dir=args.out_dir,
            with_plane=args.with_plane)
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    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)
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    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,
            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,
            max_sweeps=args.max_sweeps)
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    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)
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    elif args.dataset == 'scannet':
        scannet_data_prep(
            root_path=args.root_path,
            info_prefix=args.extra_tag,
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            out_dir=args.out_dir,
            workers=args.workers)
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    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)
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    elif args.dataset == 'sunrgbd':
        sunrgbd_data_prep(
            root_path=args.root_path,
            info_prefix=args.extra_tag,
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            out_dir=args.out_dir,
            workers=args.workers)