def render_kitti(self, render_2d: bool) -> None:
"""
Renders the annotations in the KITTI dataset from a lidar and a camera view.
:param render_2d: Whether to render 2d boxes (only works for camera data).
"""
if render_2d:
print('Rendering 2d boxes from KITTI format')
else:
print('Rendering 3d boxes projected from 3d KITTI format')
# Load the KITTI dataset.
kitti = KittiDB(root=self.nusc_kitti_dir, splits=(self.split, ))
# Create output folder.
render_dir = os.path.join(self.nusc_kitti_dir, 'render')
if not os.path.isdir(render_dir):
os.mkdir(render_dir)
# Render each image.
for token in kitti.tokens[:self.image_count]:
for sensor in ['lidar', 'camera']:
out_path = os.path.join(render_dir,
'%s_%s.png' % (token, sensor))
print('Rendering file to disk: %s' % out_path)
kitti.render_sample_data(token,
sensor_modality=sensor,
out_path=out_path,
render_2d=render_2d)
plt.close(
) # Close the windows to avoid a warning of too many open windows.
def render_kitti(self) -> None:
"""
Renders the annotations in the KITTI dataset from a lidar and a camera view.
"""
# Load the KITTI dataset.
kitti = KittiDB(root=self.nusc_kitti_dir, splits=(self.split, ))
# Create output folder.
render_dir = os.path.join(self.nusc_kitti_dir, 'render')
if not os.path.isdir(render_dir):
os.mkdir(render_dir)
# Render each image.
for token in kitti.tokens[:self.image_count]:
for sensor in ['lidar', 'camera']:
out_path = os.path.join(render_dir,
'%s_%s.png' % (token, sensor))
print('Rendering file to disk: %s' % out_path)
kitti.render_sample_data(token,
sensor_modality=sensor,
out_path=out_path)
plt.close(
) # Close the windows to avoid a warning of too many open windows.
def render_kitti(self, render_2d: bool = False) -> None:
"""
Renders the annotations in the KITTI dataset from a lidar and a camera view.
:param render_2d: Whether to render 2d boxes (only works for camera data).
"""
if render_2d:
print('Rendering 2d boxes from KITTI format')
else:
print('Rendering 3d boxes projected from 3d KITTI format')
# Load the KITTI dataset.
kitti = KittiDB(root=self.nusc_kitti_dir, splits=[''])
def get_transforms(token: str, root: str) -> dict:
calib_filename = KittiDB.get_filepath(token, 'calib', root=root)
lines = [line.rstrip() for line in open(calib_filename)]
velo_to_cam = np.array(lines[5].strip().split(' ')[1:],
dtype=np.float64)
velo_to_cam.resize((3, 4))
r0_rect = np.array(lines[4].strip().split(' ')[1:],
dtype=np.float64)
r0_rect.resize((3, 3))
p_left = np.array(lines[2].strip().split(' ')[1:],
dtype=np.float64)
p_left.resize((3, 4))
# Merge rectification and projection into one matrix.
p_combined = np.eye(4)
p_combined[:3, :3] = r0_rect
p_combined = np.dot(p_left, p_combined)
return {
'velo_to_cam': {
'R': velo_to_cam[:, :3],
'T': velo_to_cam[:, 3]
},
'r0_rect': r0_rect,
'p_left': p_left,
'p_combined': p_combined,
}
kitti.get_transforms = get_transforms # monkeypatching np.float32 -> 64
# Create output folder.
render_dir = os.path.join(self.nusc_kitti_dir, 'render')
if not os.path.isdir(render_dir):
os.mkdir(render_dir)
# Render each image.
tokens = kitti.tokens
if self.samples_count is not None:
tokens = tokens[:self.samples_count]
# currently supports only single thread processing
for token in tqdm(tokens):
for sensor in ['lidar', 'camera']:
out_path = os.path.join(render_dir,
'%s_%s.png' % (token, sensor))
kitti.render_sample_data(token,
sensor_modality=sensor,
out_path=out_path,
render_2d=render_2d)
# Close the windows to avoid a warning of too many open windows.
plt.close()