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convert.py
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# Convert pandaset to semantic kitti format
# Usage: python convert.py <path_to_pandaset> <path_to_output>
import os
import sys
import quaternion
import numpy as np
from pandaset import DataSet
print("Usage: python convert.py <path_to_pandaset> <path_to_output>")
dataset_path = sys.argv[1]
output_path = sys.argv[2]
# load Pandaset
dataset = DataSet(dataset_path)
# label mapping from pandaset to semantic kitti
# TODO: specify mapping
label_map = {
0: 0,
1: 1,
2: 1,
3: 1,
4: 1,
5: 70,
6: 72,
7: 40,
8: 60,
9: 60,
10: 49,
11: 48,
12: 49,
13: 10,
14: 18,
15: 18,
16: 18,
17: 20,
18: 15,
19: 20,
20: 20,
21: 20,
22: 20,
23: 13,
24: 20,
25: 15,
26: 11,
27: 16,
28: 20,
29: 20,
30: 30,
31: 30,
32: 0,
33: 0,
34: 99,
35: 99,
36: 81,
37: 99,
38: 81,
39: 99,
40: 99,
41: 50,
42: 99
}
print(f"Sequences found: {len(dataset.sequences())}")
for scene_id in dataset.sequences():
print(f'Processing scene: {scene_id}')
# extract Sequence object
scene = dataset[scene_id]
# skip if sequence does not contain annotations
if scene.semseg is None:
print('Does not contain annotations')
continue
scene.load_lidar()
scene.load_semseg()
# Create the SemanticKITTI label directory
output_dir = os.path.join(output_path, "sequences", scene_id)
os.makedirs(output_dir, exist_ok=True)
os.makedirs(os.path.join(output_dir, "velodyne"), exist_ok=True)
os.makedirs(os.path.join(output_dir, "labels"), exist_ok=True)
# use only 360° lidar
scene.lidar.set_sensor(0)
lidar_data = scene.lidar.data
# retrieve sensor poses
poses = scene.lidar.poses
num_points = lidar_data[0].shape[0]
# Convert each LiDAR frame in the scene (data is a list of dataframes)
for index, lidar in enumerate(lidar_data):
# Load LiDAR points and labels
points = lidar.to_numpy()[:, :3]
labels = scene.semseg[index].to_numpy()[:points.shape[0]]
# print(points.shape)
# print(labels.shape)
assert points.shape[0] == labels.shape[0]
# in case normalize z coordinates in kitti format
# z_coords = points[:, 2]
# Calculate the minimum and maximum values of the X-coordinate
# z_min = np.min(z_coords)
# z_max = np.max(z_coords)
# Normalize the Z-coordinate values to the range (-3, 24)
# normalized_z_coords = -6 + (z_coords - z_min) * (24 / (z_max - z_min))
# Update the normalized X-coordinate values in the original array
# points[:, 2] = normalized_z_coords
# convert coordinates wrt LiDAR
pose = poses[index]
position = np.array([pose['position']['x'], pose['position']['y'], pose['position']['z']])
orientation = list(pose['heading'].values())
R = quaternion.as_rotation_matrix(np.quaternion(*orientation))
# translate point cloud
points -= position
# apply transformation
point_cloud = np.dot(points, R.T)
# add intensity values
point_cloud = np.column_stack((point_cloud, lidar.to_numpy()[:, 3]))
# Convert Pandaset labels to SemanticKITTI labels
semantic_labels = np.array([label_map[label[0]] for label in labels])
# Save LiDAR points and labels in SemanticKITTI format
output_file = os.path.join(output_dir, "velodyne", "{0:02d}".format(index) + ".bin")
point_cloud.astype(np.float32).tofile(output_file)
semantic_labels.astype(np.uint32).tofile(os.path.join(output_dir, "labels", "{0:02d}".format(index) + ".label"))
# unload scene
dataset.unload(scene_id)