def box_nuscenes_to_kitti(
box: Box,
velo_to_cam_rot: Quaternion,
velo_to_cam_trans: np.ndarray,
r0_rect: Quaternion,
kitti_to_nu_lidar_inv: Quaternion = Quaternion(axis=(0, 0, 1),
angle=np.pi /
2).inverse,
) -> Box:
"""Transform from nuScenes lidar frame to KITTI reference frame.
Args:
box: Instance in nuScenes lidar frame.
velo_to_cam_rot: Quaternion to rotate from lidar to camera frame.
velo_to_cam_trans: <np.float: 3>. Translate from lidar to camera frame.
r0_rect: Quaternion to rectify camera frame.
kitti_to_nu_lidar_inv: Quaternion to rotate nuScenes to KITTI LIDAR.
Returns: Box instance in KITTI reference frame.
"""
# Copy box to avoid side-effects.
box = box.copy()
# Rotate to KITTI lidar.
box.rotate(kitti_to_nu_lidar_inv)
# Transform to KITTI camera.
box.rotate(velo_to_cam_rot)
box.translate(velo_to_cam_trans)
# Rotate to KITTI rectified camera.
box.rotate(r0_rect)
# KITTI defines the box center as the bottom center of the object.
# We use the true center, so we need to adjust half height in y direction.
box.translate(np.array([0, box.wlh[2] / 2, 0]))
return box
def get_boxes(self,
token: str,
filter_classes: List[str] = None,
max_dist: float = None) -> List[Box]:
"""Load up all the boxes associated with a sample.
Boxes are in nuScenes lidar frame.
Args:
token: KittiDB unique id.
filter_classes: List of Kitti classes to use or None to use all.
max_dist: List of Kitti classes to use or None to use all.
Returns: Boxes in nuScenes lidar reference frame.
"""
# Get transforms for this sample
transforms = self.get_transforms(token, root=self.root)
boxes = []
if token.startswith("test_"):
# No boxes to return for the test set.
return boxes
with open(KittiDB.get_filepath(token, "label_2", root=self.root),
"r") as f:
for line in f:
# Parse this line into box information.
parsed_line = self.parse_label_line(line)
if parsed_line["name"] in {"DontCare", "Misc"}:
continue
center = parsed_line["xyz_camera"]
wlh = parsed_line["wlh"]
yaw_camera = parsed_line["yaw_camera"]
name = parsed_line["name"]
score = parsed_line["score"]
# Optional: Filter classes.
if filter_classes is not None and name not in filter_classes:
continue
# The Box class coord system is oriented the same way as as KITTI LIDAR: x forward, y left, z up.
# For orientation confer: http://www.cvlibs.net/datasets/kitti/setup.php.
# 1: Create box in Box coordinate system with center at origin.
# The second quaternion in yaw_box transforms the coordinate frame from the object frame
# to KITTI camera frame. The equivalent cannot be naively done afterwards, as it's a rotation
# around the local object coordinate frame, rather than the camera frame.
quat_box = Quaternion(axis=(0, 1, 0),
angle=yaw_camera) * Quaternion(
axis=(1, 0, 0), angle=np.pi / 2)
box = Box([0.0, 0.0, 0.0], wlh, quat_box, name=name)
# 2: Translate: KITTI defines the box center as the bottom center of the vehicle. We use true center,
# so we need to add half height in negative y direction, (since y points downwards), to adjust. The
# center is already given in camera coord system.
box.translate(center + np.array([0, -wlh[2] / 2, 0]))
# 3: Transform to KITTI LIDAR coord system. First transform from rectified camera to camera, then
# camera to KITTI lidar.
box.rotate(Quaternion(matrix=transforms["r0_rect"]).inverse)
box.translate(-transforms["velo_to_cam"]["T"])
box.rotate(
Quaternion(matrix=transforms["velo_to_cam"]["R"]).inverse)
# 4: Transform to nuScenes LIDAR coord system.
box.rotate(self.kitti_to_nu_lidar)
# Set score or NaN.
box.score = score
# Set dummy velocity.
box.velocity = np.array((0.0, 0.0, 0.0))
# Optional: Filter by max_dist
if max_dist is not None:
dist = np.sqrt(np.sum(box.center[:2]**2))
if dist > max_dist:
continue
boxes.append(box)
return boxes
