def _get_anns(self, ann_tokens, ref_pose_record):
"""
Get all annotations for a given sample
:param ann_tokens (list): list of annotation tokens
:param cam (dict): ca
"""
## TODO: add filtering based on num_radar/num_lidar points here
## TODO: for 'scene' samples, ref_pose_record stuff needs attention here
annotations = []
for i, token in enumerate(ann_tokens):
ann = self.get('sample_annotation', token)
this_ann = {}
try:
this_ann['category'] = self.cfg.CATEGORIES[
ann['category_name']]
except:
continue
this_ann['category_id'] = self.cfg.CAT_ID[this_ann['category']]
this_ann['num_lidar_pts'] = ann['num_lidar_pts']
this_ann['num_radar_pts'] = ann['num_radar_pts']
## Create Box object (boxes are in global coordinates)
box = Box(ann['translation'],
ann['size'],
Quaternion(ann['rotation']),
name=this_ann['category'],
token=ann['token'])
## Get distance to vehicle
box_dist = nsutils.get_box_dist(box, ref_pose_record)
this_ann['distance'] = box_dist
if self.cfg.MAX_BOX_DIST:
if box_dist > abs(self.cfg.MAX_BOX_DIST):
continue
## Take to the vehicle coordinate system
box = nsutils.global_to_vehicle(box, ref_pose_record)
## Calculate box velocity
if self.cfg.BOX_VELOCITY:
box.velocity = self.box_velocity(box.token)
this_ann['box_3d'] = box
annotations.append(this_ann)
return annotations
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.
:param token: KittiDB unique id.
:param filter_classes: List of Kitti classes to use or None to use all.
:param max_dist: Maximum distance in m to still draw a box.
:return: 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
