def _load_frame_and_sensor(
segment: FusionSegment,
annotation_info: Dict[str, Any],
subset_path: str,
is_test: bool,
) -> None:
frame = Frame()
for data_frames in annotation_info["frame_data"].values():
for data_frame in data_frames:
calibrated_sensor_info = annotation_info["calibrated_sensors"][
data_frame["calibrated_sensor_token"]]
common_sensor = annotation_info["sensor"][
calibrated_sensor_info["sensor_token"]]
sensor_name = common_sensor["channel"]
if sensor_name not in segment.sensors:
segment.sensors.add(
get_sensor("camera", sensor_name, calibrated_sensor_info))
data = Data(
os.path.join(subset_path, data_frame["filename"]),
timestamp=data_frame["timestamp"] / 10**6,
)
if not is_test:
data.label.box2d, data.label.rle = _get_labels(
data_frame["token"], annotation_info)
frame[sensor_name] = data
segment.append(frame)
def _load_frame_and_sensor(
segment: FusionSegment,
current_frame_token: str,
annotation_info: Dict[str, Any],
subset_path: str,
is_test: bool,
) -> None:
frame = Frame()
for sensor_frame in annotation_info["frame_data"][current_frame_token]:
calibrated_sensor_info = annotation_info["calibrated_sensors"][
sensor_frame["calibrated_sensor_token"]
]
common_sensor = annotation_info["sensor"][calibrated_sensor_info["sensor_token"]]
sensor_name = common_sensor["channel"]
sensor_type = common_sensor["modality"]
if sensor_name not in segment.sensors:
segment.sensors.add(get_sensor(sensor_type, sensor_name, calibrated_sensor_info))
data = Data(
os.path.join(subset_path, sensor_frame["filename"]),
timestamp=sensor_frame["timestamp"] / 10**6,
)
if not is_test and sensor_type == "lidar":
data.label.box3d = _get_labels(
current_frame_token,
annotation_info["ego_poses"][sensor_frame["ego_pose_token"]],
segment.sensors[sensor_name].extrinsics,
annotation_info,
)
frame[sensor_name] = data
segment.append(frame)