def kitti_data_prep(root_path):
# compress info of image(path), velodyne(path), label (all info but dc removed),
# calib (all) into pkl file; DontCare has been removed
# root_path: "/mnt/proj50/zhengwu/KITTI/object"
#
# kitti_infos_train/trainval/val/test for each sample in train.txt/trainval.txt/val.txt/test.txt
# info{
# "point_cloud": {
# "num_featuers": 4,
# "velodyne_path": "/training/velodyne/000000.bin",
# }
# "image": {
# "image_idx": index, (eg. element in ImageSets/trainval.txt)
# "image_path": "/training/image_2/000000.png",
# "image_shape": [H(370, y), W(1224, x)], (eg. in image coord)
# }
# "calib": {
# "P0": P0,
# "P1": P1,
# "P2": P2,
# "P3": P3,
# "R0_rect": rect_4x4,
# "Tr_velo_to_cam": Tr_velo_to_cam,
# "Tr_imu_to_velo": Tr_imu_to_velo,
# }
# "annos": {
# "name": ,
# "truncated": ,
# "occluded": ,
# "alpha": ,
# "bbox": ,
# "dimensions": lhw, # including dc obj.
# "location": xyz, (eg. in cam coord)
# "rotation_y": ry,
# "index": array([0,1,2..num_non_dc_obj, -1, -1, -1]), # there are num_dc_obj of -1.
# "group_ids": array([0,1,2.., num_dc&non_dc_obj]),
# "difficulty": [d0, d1, ...num_all_obj], # di in [0 (easy), 1 (moderate), 2 (hard), -1].
# "num_points_in_gt": [n0, n1, n2, ..., -1, -1, -1], # dc gt are counted as -1.
# }
# }
#kitti_ds.create_kitti_info_file(root_path)
# all points outside of image_range are removed and kept as reduced point_cloud .bin file.
#kitti_ds.create_reduced_point_cloud(root_path)
# dbinfos_train.pkl
# all_db_infos['Car'] = [db_info_0, db_info_1, ...], grouped by class_names.
# db_info = {
# "name": names[i], # "Car"
# "path": "object/training/velodyne_reduced/xxxxx.bin", # saved with relative distance to the box center.
# "image_idx": image_idx,
# "gt_idx": i, # index in a single scene.
# "box3d_lidar": gt_boxes[i], # dc removed.
# "num_points_in_gt": gt_points.shape[0],
# "difficulty": difficulty[i] # todo: not accurate, all are set as 0.
# }
# save each gt box points separately and all gt info in a
create_groundtruth_database("KITTI", root_path, Path(root_path) / "kitti_infos_train.pkl", gt_aug_with_context=cfg.my_paras.gt_aug_with_context)
def nuscenes_data_prep(root_path, version, nsweeps=10, filter_zero=True):
nu_ds.create_nuscenes_infos(root_path, version=version, nsweeps=nsweeps, filter_zero=filter_zero)
create_groundtruth_database(
"NUSC",
root_path,
Path(root_path) / "infos_train_{:02d}sweeps_withvelo.pkl".format(nsweeps),
nsweeps=nsweeps,
)
def waymo_data_prep(root_path, split, nsweeps=1):
waymo_ds.create_waymo_infos(root_path, split=split, nsweeps=nsweeps)
if split == 'train':
create_groundtruth_database(
"WAYMO",
root_path,
Path(root_path) /
"infos_train_{:02d}sweeps_filter_zero_gt.pkl".format(nsweeps),
used_classes=['VEHICLE', 'CYCLIST', 'PEDESTRIAN'],
nsweeps=nsweeps)
def nuscenes_data_prep(root_path,
version,
nsweeps=10,
filter_zero=True,
virtual=False):
nu_ds.create_nuscenes_infos(root_path,
version=version,
nsweeps=nsweeps,
filter_zero=filter_zero)
if version == 'v1.0-trainval':
create_groundtruth_database(
"NUSC",
root_path,
Path(root_path) /
"infos_train_{:02d}sweeps_withvelo_filter_{}.pkl".format(
nsweeps, filter_zero),
nsweeps=nsweeps,
virtual=virtual)
def lyft_data_prep(root_path, version="trainval"):
lyft_ds.create_lyft_infos(root_path, version=version)
create_groundtruth_database("LYFT", root_path,
Path(root_path) / "lyft_info_train.pkl")
def kitti_data_prep(root_path):
# kitti_ds.create_kitti_info_file(root_path)
create_groundtruth_database("KITTI", root_path,
Path(root_path) / "kitti_infos_train.pkl")
def kitti_data_prep(root_path):
kitti_ds.create_kitti_info_file(root_path)
kitti_ds.create_reduced_point_cloud(root_path)
create_groundtruth_database("KITTI", root_path,
Path(root_path) / "kitti_infos_train.pkl")
def lvx_data_prep(root_path):
lvx_ds.create_lvx_info_file(root_path)
# lvx_ds.create_reduced_point_cloud(root_path)
create_groundtruth_database("LVX", root_path,
Path(root_path) / "lvx_infos_train.pkl")
