class SimpleArgoverseTrackingDataLoader:
"""
Simple abstraction for retrieving log data, given a path to the dataset.
"""
def __init__(self, data_dir: str, labels_dir: str) -> None:
"""
Args:
data_dir: str, representing path to raw Argoverse data
labels_dir: strrepresenting path to Argoverse data labels
"""
self.data_dir = data_dir
self.labels_dir = labels_dir
self.sdb = SynchronizationDB(data_dir)
def get_city_name(self, log_id: str) -> str:
"""
Args:
log_id: str
Returns:
city_name: str
"""
city_info_fpath = f"{self.data_dir}/{log_id}/city_info.json"
city_info = read_json_file(city_info_fpath)
city_name = city_info["city_name"]
assert isinstance(city_name, str)
return city_name
def get_log_calibration_data(self, log_id: str) -> Mapping[str, Any]:
"""
Args:
log_id: str
Returns:
log_calib_data: dictionary
"""
calib_fpath = f"{self.data_dir}/{log_id}/vehicle_calibration_info.json"
log_calib_data = read_json_file(calib_fpath)
assert isinstance(log_calib_data, dict)
return log_calib_data
def get_city_to_egovehicle_se3(self, log_id: str,
timestamp: int) -> Optional[SE3]:
"""
Args:
log_id: str, unique ID of vehicle log
timestamp: int, timestamp of sensor observation, in nanoseconds
Returns:
city_to_egovehicle_se3: SE3 transformation to bring egovehicle frame point into city frame.
"""
pose_fpath = f"{self.data_dir}/{log_id}/poses/city_SE3_egovehicle_{timestamp}.json"
if not Path(pose_fpath).exists():
return None
pose_data = read_json_file(pose_fpath)
rotation = np.array(pose_data["rotation"])
translation = np.array(pose_data["translation"])
city_to_egovehicle_se3 = SE3(rotation=quat2rotmat(rotation),
translation=translation)
return city_to_egovehicle_se3
def get_closest_im_fpath(self, log_id: str, camera_name: str,
lidar_timestamp: int) -> Optional[str]:
"""
Args:
log_id: str, unique ID of vehicle log
camera_name: str
lidar_timestamp: int, timestamp of LiDAR sweep capture, in nanoseconds
Returns:
im_fpath, string representing path to image, or else None.
"""
cam_timestamp = self.sdb.get_closest_cam_channel_timestamp(
lidar_timestamp, camera_name, log_id)
if cam_timestamp is None:
return None
im_dir = f"{self.data_dir}/{log_id}/{camera_name}"
im_fname = f"{camera_name}_{cam_timestamp}.jpg"
im_fpath = f"{im_dir}/{im_fname}"
return im_fpath
def get_closest_lidar_fpath(self, log_id: str,
cam_timestamp: int) -> Optional[str]:
"""
Args:
log_id: str, unique ID of vehicle log
cam_timestamp: int, timestamp of image capture, in nanoseconds
Returns:
ply_fpath: str, string representing path to PLY file, or else None.
"""
lidar_timestamp = self.sdb.get_closest_lidar_timestamp(
cam_timestamp, log_id)
if lidar_timestamp is None:
return None
lidar_dir = f"{self.data_dir}/{log_id}/lidar"
ply_fname = f"PC_{lidar_timestamp}.ply"
ply_fpath = f"{lidar_dir}/{ply_fname}"
return ply_fpath
def get_ordered_log_ply_fpaths(self, log_id: str) -> List[str]:
"""
Args:
log_id: str, unique ID of vehicle log
Returns:
ply_fpaths: List of strings, representing paths to ply files in this log
"""
ply_fpaths = sorted(
glob.glob(f"{self.data_dir}/{log_id}/lidar/PC_*.ply"))
return ply_fpaths
def get_ordered_log_cam_fpaths(self, log_id: str,
camera_name: str) -> List[str]:
"""
Args
log_id: str, unique ID of vehicle log
Returns
cam_img_fpaths: List of strings, representing paths to JPEG files in this log,
for a specific camera
"""
cam_img_fpaths = sorted(
glob.glob(
f"{self.data_dir}/{log_id}/{camera_name}/{camera_name}_*.jpg"))
return cam_img_fpaths
def get_labels_at_lidar_timestamp(
self, log_id: str,
lidar_timestamp: int) -> Optional[List[Mapping[str, Any]]]:
"""
Args:
log_id: str, unique ID of vehicle log
lidar_timestamp: int, timestamp of LiDAR sweep capture, in nanoseconds
Returns:
labels: dictionary
"""
timestamp_track_label_fpath = (
f"{self.labels_dir}/{log_id}/per_sweep_annotations_amodal/tracked_object_labels_{lidar_timestamp}.json"
)
if not Path(timestamp_track_label_fpath).exists():
return None
labels = read_json_file(timestamp_track_label_fpath)
assert isinstance(labels, list), labels
return labels
class SimpleArgoverseTrackingDataLoader:
"""
Simple abstraction for retrieving log data, given a path to the dataset.
"""
def __init__(self, data_dir: str, labels_dir: str) -> None:
"""
Args:
data_dir: str, representing path to raw Argoverse data
labels_dir: str representing path to Argoverse data labels (e.g. labels or estimated detections/tracks)
"""
self.data_dir = data_dir
self.labels_dir = labels_dir
self.sdb = SynchronizationDB(data_dir)
def get_city_name(self, log_id: str) -> str:
"""
Args:
log_id: str
Returns:
city_name: str
"""
city_info_fpath = f"{self.data_dir}/{log_id}/city_info.json"
city_name = read_city_name(city_info_fpath)
assert isinstance(city_name, str)
return city_name
def get_log_calibration_data(self, log_id: str) -> Mapping[str, Any]:
"""
Args:
log_id: str
Returns:
log_calib_data: dictionary
"""
calib_fpath = f"{self.data_dir}/{log_id}/vehicle_calibration_info.json"
log_calib_data = read_json_file(calib_fpath)
assert isinstance(log_calib_data, dict)
return log_calib_data
def get_city_to_egovehicle_se3(self, log_id: str,
timestamp: int) -> Optional[SE3]:
"""Deprecated version of get_city_SE3_egovehicle() below, as does not follow standard naming convention
Args:
log_id: str, unique ID of vehicle log
timestamp: int, timestamp of sensor observation, in nanoseconds
Returns:
city_SE3_egovehicle: SE3 transformation to bring points in egovehicle frame into city frame.
"""
return self.get_city_SE3_egovehicle(log_id, timestamp)
def get_city_SE3_egovehicle(self, log_id: str,
timestamp: int) -> Optional[SE3]:
"""
Args:
log_id: str, unique ID of vehicle log
timestamp: int, timestamp of sensor observation, in nanoseconds
Returns:
city_SE3_egovehicle: SE3 transformation to bring points in egovehicle frame into city frame.
"""
return get_city_SE3_egovehicle_at_sensor_t(timestamp, self.data_dir,
log_id)
def get_closest_im_fpath(self, log_id: str, camera_name: str,
lidar_timestamp: int) -> Optional[str]:
"""
Args:
log_id: str, unique ID of vehicle log
camera_name: str
lidar_timestamp: int, timestamp of LiDAR sweep capture, in nanoseconds
Returns:
im_fpath, string representing path to image, or else None.
"""
cam_timestamp = self.sdb.get_closest_cam_channel_timestamp(
lidar_timestamp, camera_name, log_id)
if cam_timestamp is None:
return None
im_dir = f"{self.data_dir}/{log_id}/{camera_name}"
im_fname = f"{camera_name}_{cam_timestamp}.jpg"
im_fpath = f"{im_dir}/{im_fname}"
return im_fpath
def get_closest_lidar_fpath(self, log_id: str,
cam_timestamp: int) -> Optional[str]:
"""
Args:
log_id: str, unique ID of vehicle log
cam_timestamp: int, timestamp of image capture, in nanoseconds
Returns:
ply_fpath: str, string representing path to PLY file, or else None.
"""
lidar_timestamp = self.sdb.get_closest_lidar_timestamp(
cam_timestamp, log_id)
if lidar_timestamp is None:
return None
lidar_dir = f"{self.data_dir}/{log_id}/lidar"
ply_fname = f"PC_{lidar_timestamp}.ply"
ply_fpath = f"{lidar_dir}/{ply_fname}"
return ply_fpath
def get_ordered_log_ply_fpaths(self, log_id: str) -> List[str]:
"""
Args:
log_id: str, unique ID of vehicle log
Returns:
ply_fpaths: List of strings, representing paths to chronologically ordered ply files in this log
File paths are strings are of the same length ending with a nanosecond timestamp, thus
sorted() will place them in numerical order.
"""
ply_fpaths = sorted(
glob.glob(f"{self.data_dir}/{log_id}/lidar/PC_*.ply"))
return ply_fpaths
def get_ordered_log_cam_fpaths(self, log_id: str,
camera_name: str) -> List[str]:
"""
Args
log_id: str, unique ID of vehicle log
Returns
cam_img_fpaths: List of strings, representing paths to ordered JPEG files in this log,
for a specific camera
"""
cam_img_fpaths = sorted(
glob.glob(
f"{self.data_dir}/{log_id}/{camera_name}/{camera_name}_*.jpg"))
return cam_img_fpaths
def get_labels_at_lidar_timestamp(
self, log_id: str,
lidar_timestamp: int) -> Optional[List[Mapping[str, Any]]]:
"""
Args:
log_id: str, unique ID of vehicle log
lidar_timestamp: int, timestamp of LiDAR sweep capture, in nanoseconds
Returns:
labels: dictionary
"""
timestamp_track_label_fpath = (
f"{self.labels_dir}/{log_id}/per_sweep_annotations_amodal/tracked_object_labels_{lidar_timestamp}.json"
)
if not Path(timestamp_track_label_fpath).exists():
return None
labels = read_json_file(timestamp_track_label_fpath)
assert isinstance(labels, list), labels
return labels