def pose_to_matrix(pose):
translation = pose["position"]
translation = np.asarray([translation[key] for key in translation])
rotation = pose["heading"]
rotation = np.asarray([rotation[key] for key in rotation])
rotation = quaternion_to_euler(*rotation)
Rt = to_transform_matrix(translation, rotation)
return Rt
def preprocess_data(dataset_dir):
"""
The function visualizes data from pandaset.
Arguments:
dataset_dir: directory with Pandaset data
"""
shift_lidar = [25, 50, 2.5]
# Get list of data samples
search_string = os.path.join(dataset_dir, "*")
seq_list = sorted(glob.glob(search_string))
for seq in tqdm(seq_list, desc="Process sequences", total=len(seq_list)):
search_string = os.path.join(seq, "lidar", "*.pkl.gz")
lidar_list = sorted(glob.glob(search_string))
lidar_pose_path = os.path.join(seq, "lidar", "poses.json")
lidar_pose = read_json(lidar_pose_path)
for idx, lidar_path in enumerate(lidar_list):
# Get pose of the lidar
translation = lidar_pose[idx]["position"]
translation = np.asarray([translation[key] for key in translation])
rotation = lidar_pose[idx]["heading"]
rotation = np.asarray([rotation[key] for key in rotation])
rotation = quaternion_to_euler(*rotation)
Rt = to_transform_matrix(translation, rotation)
# Get respective bboxes
bbox_path = lidar_path.split("/")
bbox_path[-2] = "annotations/cuboids"
bbox_path = os.path.join(*bbox_path)
# Load data
lidar = np.asarray(pd.read_pickle(lidar_path))
# Get only lidar 0 (there is also lidar 1)
lidar = lidar[lidar[:, -1] == 0]
intensity = lidar[:, 3]
lidar = transform_lidar_box_3d(lidar, Rt)
# add intensity
lidar = np.concatenate((lidar, intensity[:, None]), axis=-1)
# Load bboxes
bboxes = np.asarray(pd.read_pickle(bbox_path))
labels, bboxes = make_xzyhwly(bboxes)
corners_3d, orientation_3d = make_eight_points_boxes(bboxes)
corners_3d = np.asarray(
[transform_lidar_box_3d(box, Rt) for box in corners_3d])
orientation_3d = np.asarray(
[transform_lidar_box_3d(box, Rt) for box in orientation_3d])
labels, corners_3d, orientation_3d = filter_boxes(
labels, corners_3d, orientation_3d, lidar)
centroid, width, length, height, yaw = get_bboxes_parameters_from_points(
corners_3d)
boxes_new = np.concatenate(
(
centroid,
length[:, None],
width[:, None],
height[:, None],
yaw[:, None],
),
axis=-1,
)
lidar[:, :3] = lidar[:, :3] + shift_lidar
corners_3d, orientation_3d = make_eight_points_boxes(boxes_new)
corners_3d = corners_3d + shift_lidar
orientation_3d = orientation_3d + shift_lidar
figure = visualize_bboxes_3d(corners_3d, None, orientation_3d)
figure = visualize_lidar(lidar, figure)
mlab.show(1)
input()
mlab.close(figure)
def preprocess_data(dataset_dir):
"""
The function visualizes data from pandaset.
Arguments:
dataset_dir: directory with Pandaset data
"""
# Get list of data samples
search_string = os.path.join(dataset_dir, "*")
seq_list = sorted(glob.glob(search_string))
for seq in tqdm(seq_list, desc="Process sequences", total=len(seq_list)):
# Make output dirs for data
lidar_out_dir = os.path.join(seq, "lidar_processed")
bbox_out_dir = os.path.join(seq, "bbox_processed")
os.makedirs(lidar_out_dir, exist_ok=True)
os.makedirs(bbox_out_dir, exist_ok=True)
search_string = os.path.join(seq, "lidar", "*.pkl.gz")
lidar_list = sorted(glob.glob(search_string))
lidar_pose_path = os.path.join(seq, "lidar", "poses.json")
lidar_pose = read_json(lidar_pose_path)
for idx, lidar_path in enumerate(lidar_list):
sample_idx = os.path.splitext(os.path.basename(lidar_path))[0].split(".")[0]
# Get pose of the lidar
translation = lidar_pose[idx]["position"]
translation = np.asarray([translation[key] for key in translation])
rotation = lidar_pose[idx]["heading"]
rotation = np.asarray([rotation[key] for key in rotation])
rotation = quaternion_to_euler(*rotation)
Rt = to_transform_matrix(translation, rotation)
# Get respective bboxes
bbox_path = lidar_path.split("/")
bbox_path[-2] = "annotations/cuboids"
bbox_path = os.path.join(*bbox_path)
# Load data
lidar = np.asarray(pd.read_pickle(lidar_path))
# Get only lidar 0 (there is also lidar 1)
lidar = lidar[lidar[:, -1] == 0]
intensity = lidar[:, 3]
lidar = transform_lidar_box_3d(lidar, Rt)
# add intensity
lidar = np.concatenate((lidar, intensity[:, None]), axis=-1)
# Load bboxes
bboxes = np.asarray(pd.read_pickle(bbox_path))
labels, bboxes = make_xzyhwly(bboxes)
corners_3d, orientation_3d = make_eight_points_boxes(bboxes)
corners_3d = np.asarray(
[transform_lidar_box_3d(box, Rt) for box in corners_3d]
)
orientation_3d = np.asarray(
[transform_lidar_box_3d(box, Rt) for box in orientation_3d]
)
# filter boxes containing less then 20 lidar points inside
labels, corners_3d, orientation_3d = filter_boxes(
labels, corners_3d, orientation_3d, lidar
)
centroid, width, length, height, yaw = get_bboxes_parameters_from_points(
corners_3d
)
# Save data
lidar_filename = os.path.join(lidar_out_dir, sample_idx + ".bin")
save_lidar(lidar_filename, lidar.astype(np.float32))
box_filename = os.path.join(bbox_out_dir, sample_idx + ".txt")
save_bboxes_to_file(
box_filename, centroid, width, length, height, yaw, labels
)