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 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 createwaymo_info(root_path,
info_prefix,
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.
"""
# Generate waymo infos
out_dir = osp.join(out_dir, 'kitti_format')
# Create ImageSets, train test split
create_trainvaltestsplitfile(out_dir)
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)
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']
splits = ['training', 'validation', 'test']
for i, split in enumerate(splits):
#load_dir = osp.join(root_path, 'waymo_format', split)
load_dir = osp.join(root_path, 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)
def carla_data_prep(root_path,
info_prefix,
version,
dataset_name,
out_dir,
max_prev_samples=10):
carla_data_converter.create_carla_infos(root_path,
info_prefix,
max_prev_samples=max_prev_samples,
balance=None)
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")
create_groundtruth_database(dataset_name, root_path, info_prefix,
f"{out_dir}/{info_prefix}_infos_train.pkl")
import pathlib
info_train_path = osp.join(root_path, f"{info_prefix}_infos_train.pkl")
train_set_folder = pathlib.Path(root_path).joinpath(
carla_data_converter.train_set_folder_name)
carla_data_converter.export_2d_annotation(train_set_folder,
info_train_path)
info_val_path = osp.join(root_path, f"{info_prefix}_infos_val.pkl")
val_set_folder = pathlib.Path(root_path).joinpath(
carla_data_converter.val_set_folder_name)
carla_data_converter.export_2d_annotation(val_set_folder, info_val_path)
info_test_path = osp.join(root_path, f"{info_prefix}_infos_test.pkl")
test_set_folder = pathlib.Path(root_path).joinpath(
carla_data_converter.test_set_folder_name)
carla_data_converter.export_2d_annotation(test_set_folder, info_test_path)
# add extensions
info_easy_test_path = osp.join(root_path,
f"{info_prefix}_infos_easy_test.pkl")
easy_test_set_folder = pathlib.Path(root_path).joinpath(
carla_data_converter.easy_test_set_folder_name)
carla_data_converter.export_2d_annotation(easy_test_set_folder,
info_easy_test_path)
def aiodrive_data_prep(root_path, info_prefix, out_dir):
"""Prepare data related to AIODrive dataset.
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.
"""
# aiodrive.create_aiodrive_info_file(root_path, info_prefix, out_dir)
create_groundtruth_database(
'AIODriveDataset',
root_path,
info_prefix,
# f'{out_dir}/{info_prefix}_infos_train.pkl',
f'{out_dir}/aiodrive_velodyne_infos_train.pkl', # TODO: Acutally, the info files are shared between different point types.
database_save_path=osp.join(out_dir, f'{info_prefix}_gt_database'),
db_info_save_path=osp.join(out_dir,
f'{info_prefix}_dbinfos_train.pkl'),
relative_path=False,
with_mask=False)
def astyx_data_prep(root_path, info_prefix, pc_type, out_dir):
"""Prepare data related to Astyx 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.
"""
info_prefix += pc_type
astyx_converter.create_astyx_info_file(root_path, info_prefix, pc_type)
astyx_converter.create_reduced_point_cloud(root_path, info_prefix)
create_groundtruth_database('AstyxDataset',
root_path,
info_prefix,
f'{out_dir}/{info_prefix}_infos_train.pkl',
relative_path=False,
mask_anno_path='instances_train.json',
pc_type=pc_type)
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 extended_nuscenes_data_prep(root_path,
info_prefix,
version,
dataset_name,
out_dir,
max_prev_samples=10):
"""Prepare data related to own nuScenes dataset with different api.
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_prev_samples (int): Number of input consecutive frames. Default: 10
"""
# extended_nuscenes_converter.create_nuscenes_infos(
# root_path, info_prefix, version=version, max_prev_samples=max_prev_samples
# )
# 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")
# extended_nuscenes_converter.export_2d_annotation(
# root_path, info_train_path, version=version
# )
# extended_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 deeproute_data_prep(root_path, info_prefix, out_dir):
deeproute_converter.create_deeproute_info_file(root_path, info_prefix)
create_groundtruth_database('DeeprouteDataset', root_path, info_prefix,
f'{out_dir}/{info_prefix}_infos_train.pkl')
