def get_split_cam_configs_and_sync_data(argoverse_tracking_root_dir, split_dir, camera_list, source_views_indexes):
"""
Returns a dictionary giving the intrinsics/extrinsics/resolution of given cameras for each sequence.
Parameters
----------
split_dir: pathlib Path
argoverse-tracking split root dir, e.g.: path_to_data/argoverse-tracking/train1
camera_list: list
list of camera for which to load the parameters. Must be in Argoverse's CAMERA_LIST
Returns
-------
dict
A dictionary where the key is the log string and the value a dict corresponding to the cameras' parameters
"""
camera_configs = {}
synchronized_data = []
db = SynchronizationDB(str(split_dir))
valid_logs = list(db.get_valid_logs()) # log_ids founds under split_dir
split = split_dir.stem # e.g., train1
for log in valid_logs:
camera_configs[log] = load_camera_config(str(split_dir / log / VEHICLE_CALIBRATION_INFO_FILENAME),
camera_list)
synchronized_data += get_synchronized_data(argoverse_tracking_root_dir, db, split, log, camera_list,
source_views_indexes)
return camera_configs, synchronized_data
def __init__(
self, dataset_dir: str, experiment_prefix: str, use_existing_files: bool = True, log_id: str = None
) -> None:
"""We will cache the accumulated trajectories per city, per log, and per frame
for the tracking benchmark.
"""
self.plot_lane_tangent_arrows = True
self.plot_lidar_bev = True
self.plot_lidar_in_img = False
self.experiment_prefix = experiment_prefix
self.dataset_dir = dataset_dir
self.labels_dir = dataset_dir
self.sdb = SynchronizationDB(self.dataset_dir)
if log_id is None:
tmp_dir = tempfile.gettempdir()
per_city_traj_dict_fpath = f"{tmp_dir}/per_city_traj_dict_{experiment_prefix}.pkl"
log_egopose_dict_fpath = f"{tmp_dir}/log_egopose_dict_{experiment_prefix}.pkl"
log_timestamp_dict_fpath = f"{tmp_dir}/log_timestamp_dict_{experiment_prefix}.pkl"
if not use_existing_files:
# write the accumulate data dictionaries to disk
PerFrameLabelAccumulator(dataset_dir, dataset_dir, experiment_prefix)
self.per_city_traj_dict = load_pkl_dictionary(per_city_traj_dict_fpath)
self.log_egopose_dict = load_pkl_dictionary(log_egopose_dict_fpath)
self.log_timestamp_dict = load_pkl_dictionary(log_timestamp_dict_fpath)
else:
pfa = PerFrameLabelAccumulator(dataset_dir, dataset_dir, experiment_prefix, save=False)
pfa.accumulate_per_log_data(log_id=log_id)
self.per_city_traj_dict = pfa.per_city_traj_dict
self.log_egopose_dict = pfa.log_egopose_dict
self.log_timestamp_dict = pfa.log_timestamp_dict
def visualize_ground_lidar_pts(log_id: str, dataset_dir: str,
experiment_prefix: str):
"""Process a log by drawing the LiDAR returns that are classified as belonging
to the ground surface in a red to green colormap in the image.
Args:
log_id: The ID of a log
dataset_dir: Where the dataset is stored
experiment_prefix: Output prefix
"""
sdb = SynchronizationDB(dataset_dir, collect_single_log_id=log_id)
city_info_fpath = f"{dataset_dir}/{log_id}/city_info.json"
city_info = read_json_file(city_info_fpath)
city_name = city_info["city_name"]
avm = ArgoverseMap()
ply_fpaths = sorted(glob.glob(f"{dataset_dir}/{log_id}/lidar/PC_*.ply"))
for i, ply_fpath in enumerate(ply_fpaths):
if i % 500 == 0:
print(f"\tOn file {i} of {log_id}")
lidar_timestamp_ns = ply_fpath.split("/")[-1].split(".")[0].split(
"_")[-1]
pose_fpath = f"{dataset_dir}/{log_id}/poses/city_SE3_egovehicle_{lidar_timestamp_ns}.json"
if not Path(pose_fpath).exists():
continue
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)
lidar_pts = load_ply(ply_fpath)
lidar_timestamp_ns = int(lidar_timestamp_ns)
draw_ground_pts_in_image(
sdb,
lidar_pts,
city_to_egovehicle_se3,
avm,
log_id,
lidar_timestamp_ns,
city_name,
dataset_dir,
experiment_prefix,
)
for camera_name in CAMERA_LIST:
if "stereo" in camera_name:
fps = 5
else:
fps = 10
cmd = f"ffmpeg -r {fps} -f image2 -i '{experiment_prefix}_ground_viz/{log_id}/{camera_name}/%*.jpg' {experiment_prefix}_ground_viz/{experiment_prefix}_{log_id}_{camera_name}_{fps}fps.mp4"
print(cmd)
run_command(cmd)
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 __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 __init__(self, root_dir: str) -> None:
# initialize class member
self.CAMERA_LIST = CAMERA_LIST
self._log_list: Optional[List[str]] = [None]
self._image_list: Optional[Dict[str, Dict[str, List[str]]]] = None
self._image_list_sync: Optional[Dict[str,
Dict[str,
List[np.ndarray]]]] = None
self._lidar_list: Optional[Dict[str, List[str]]] = None
self._image_timestamp_list: Optional[Dict[str, Dict[str,
List[int]]]] = None
self._timestamp_image_dict: Optional[Dict[str, Dict[str,
Dict[int,
str]]]] = None
self._image_timestamp_list_sync: Optional[Dict[str,
Dict[str,
List[int]]]] = None
self._lidar_timestamp_list: Optional[Dict[str, List[int]]] = None
self._timestamp_lidar_dict: Optional[Dict[str, Dict[int, str]]] = None
self._label_list: Optional[Dict[str, List[str]]] = None
self._calib: Optional[Dict[str, Dict[
str,
Calibration]]] = None # { log_name: { camera_name: Calibration } }
self._city_name = None
self.counter: int = 0
self.image_count: int = 0
self.lidar_count: int = 0
self.root_dir: str = root_dir
self.current_log = self.log_list[self.counter]
assert self.image_list is not None
assert self.lidar_list is not None
assert self.label_list is not None
# load calibration file
self.calib_filename: str = os.path.join(
self.root_dir, self.current_log, "vehicle_calibration_info.json")
# lidar @10hz, ring camera @30hz, stereo camera @5hz
self.num_lidar_frame: int = len(self.lidar_timestamp_list)
self.num_ring_camera_frame: int = len(
self.image_timestamp_list[RING_CAMERA_LIST[0]])
self.num_stereo_camera_frame: int = len(
self.image_timestamp_list[STEREO_CAMERA_LIST[0]])
self.sync: SynchronizationDB = SynchronizationDB(root_dir)
assert self.image_list_sync is not None
assert self.calib is not None
def __init__(self, dataset_dir: str, experiment_prefix: str,
tf_record_prefix: str) -> None:
"""We will cache the accumulated trajectories per city, per log, and per frame
for the tracking benchmark.
"""
self.experiment_prefix = experiment_prefix
self.tf_record_prefix = tf_record_prefix
self.dataset_dir = dataset_dir
self.labels_dir = dataset_dir
self.sdb = SynchronizationDB(self.dataset_dir)
self.per_city_traj_dict = None
self.log_egopose_dict = None
self.log_timestamp_dict = None
self.timestamps = None
def main(argo_tracking_root_dir, output_dir, cameras, acc_sweeps, ip_basic):
print('Preprocessing data....')
print("INPUT DIR: ", argo_tracking_root_dir)
print("OUTPUT DIR: ", output_dir)
if cameras is None:
cameras = CAMERA_LIST
else:
cameras = validate_camera_option(cameras)
print(cameras)
argo_tracking_root_dir = Path(argo_tracking_root_dir).expanduser()
output_dir = Path(output_dir).expanduser()
split_namedirs = ["train1", "train2", "train3", "train4", "test", "val"]
split_dirs = [
argo_tracking_root_dir / split_namedir
for split_namedir in split_namedirs
]
for split_dir in split_dirs:
db = SynchronizationDB(str(split_dir))
log_dirs = sorted(list(split_dir.iterdir()))
for log_dir in log_dirs:
lidar_dir = log_dir / "lidar"
lidar_filepaths = [f for f in sorted(list(lidar_dir.iterdir()))]
total = len(lidar_filepaths)
calib_filepath = str(log_dir / "vehicle_calibration_info.json")
output_base_path = output_dir / split_dir.stem / log_dir.stem
with open(calib_filepath, "r") as f:
calib_data = json.load(f)
args = (output_base_path, calib_data, cameras, db, acc_sweeps,
ip_basic)
with tqdm(lidar_filepaths,
desc=f"{split_dir.stem} | log {log_dir.stem} ",
total=total) as progress:
for lidar_filepath in progress:
project_and_save(lidar_filepath, *args)
print('Preprocessing of LiDAR data Finished.')
def __init__(
self,
dataset_dir: str,
labels_dir: str,
experiment_prefix: str,
bboxes_3d: bool = False,
save: bool = True,
) -> None:
"""Initialize PerFrameLabelAccumulator object for use with tracking benchmark data.
Args:
dataset_dir (str): Dataset directory.
labels_dir (str): Labels directory.
experiment_prefix (str): Prefix for experimint to use.
bboxes_3d (bool, optional): to use 3d bounding boxes (True) or 2d bounding boxes (False).
"""
self.bboxes_3d = bboxes_3d
self.dataset_dir = dataset_dir
self.labels_dir = labels_dir
tmp_dir = tempfile.gettempdir()
per_city_traj_dict_fpath = f"{tmp_dir}/per_city_traj_dict_{experiment_prefix}.pkl"
log_egopose_dict_fpath = f"{tmp_dir}/log_egopose_dict_{experiment_prefix}.pkl"
log_timestamp_dict_fpath = f"{tmp_dir}/log_timestamp_dict_{experiment_prefix}.pkl"
# coordinate system is the map world frame
self.per_city_traj_dict: Dict[str, List[Tuple[np.ndarray, str]]] = {
"MIA": [],
"PIT": [],
} # all the trajectories for these 2 cities
self.log_egopose_dict: Dict[str, Dict[int, Dict[str, np.ndarray]]] = {}
self.log_timestamp_dict: Dict[str, Dict[int, List[FrameRecord]]] = {}
self.sdb = SynchronizationDB(self.dataset_dir)
if save:
self.accumulate_per_log_data()
save_pkl_dictionary(per_city_traj_dict_fpath,
self.per_city_traj_dict)
save_pkl_dictionary(log_egopose_dict_fpath, self.log_egopose_dict)
save_pkl_dictionary(log_timestamp_dict_fpath,
self.log_timestamp_dict)
def generate_vehicle_bev(dataset_dir="", log_id="", output_dir=""):
argoverse_loader = ArgoverseTrackingLoader(dataset_dir)
argoverse_data = argoverse_loader.get(log_id)
camera = argoverse_loader.CAMERA_LIST[7]
calib = argoverse_data.get_calibration(camera)
sdb = SynchronizationDB(dataset_dir, collect_single_log_id=log_id)
calib_path = f"{dataset_dir}/{log_id}/vehicle_calibration_info.json"
calib_data = read_json_file(calib_path)
ind = 0
for i in range(9):
if calib_data['camera_data_'][i]['key'] == \
'image_raw_stereo_front_left':
ind = i
break
rotation = np.array(calib_data['camera_data_'][ind]['value']
['vehicle_SE3_camera_']['rotation']['coefficients'])
translation = np.array(calib_data['camera_data_'][ind]['value']
['vehicle_SE3_camera_']['translation'])
egovehicle_SE3_cam = SE3(rotation=quat2rotmat(rotation),
translation=translation)
if not os.path.exists(os.path.join(output_dir, log_id, "car_bev_gt")):
os.makedirs(os.path.join(output_dir, log_id, "car_bev_gt"))
lidar_dir = os.path.join(dataset_dir, log_id, "lidar")
ply_list = os.listdir(lidar_dir)
pfa = PerFrameLabelAccumulator(dataset_dir,
dataset_dir,
"argoverse_bev_viz",
save=False,
bboxes_3d=True)
pfa.accumulate_per_log_data(log_id)
log_timestamp_dict = pfa.log_timestamp_dict
for i, ply_name in enumerate(ply_list):
lidar_timestamp = ply_name.split('.')[0].split('_')[1]
lidar_timestamp = int(lidar_timestamp)
cam_timestamp = sdb.get_closest_cam_channel_timestamp(
lidar_timestamp, "stereo_front_left", str(log_id))
image_path = os.path.join(
output_dir, str(log_id), "car_bev_gt",
"stereo_front_left_" + str(cam_timestamp) + ".jpg")
objects = log_timestamp_dict[log_id][lidar_timestamp]
top_view = np.zeros((256, 256))
all_occluded = True
for frame_rec in objects:
if frame_rec.occlusion_val != IS_OCCLUDED_FLAG:
all_occluded = False
if not all_occluded:
for i, frame_rec in enumerate(objects):
bbox_ego_frame = frame_rec.bbox_ego_frame
uv = calib.project_ego_to_image(bbox_ego_frame).T
idx_ = np.all(
np.logical_and(
np.logical_and(
np.logical_and(uv[0, :] >= 0.0,
uv[0, :] < size[1] - 1.0),
np.logical_and(uv[1, :] >= 0.0,
uv[1, :] < size[0] - 1.0)),
uv[2, :] > 0))
if not idx_:
continue
bbox_cam_fr = egovehicle_SE3_cam.inverse().\
transform_point_cloud(bbox_ego_frame)
X = bbox_cam_fr[:, 0]
Z = bbox_cam_fr[:, 2]
if (frame_rec.occlusion_val != IS_OCCLUDED_FLAG
and frame_rec.obj_class_str == "VEHICLE"):
y_img = (-Z / res).astype(np.int32)
x_img = (X / res).astype(np.int32)
x_img -= int(np.floor(-20 / res))
y_img += int(np.floor(40 / res))
box = np.array([x_img[2], y_img[2]])
box = np.vstack((box, [x_img[6], y_img[6]]))
box = np.vstack((box, [x_img[7], y_img[7]]))
box = np.vstack((box, [x_img[3], y_img[3]]))
cv2.drawContours(top_view, [box], 0, 255, -1)
cv2.imwrite(image_path, top_view)
def generate_road_bev(dataset_dir="", log_id="", output_dir=""):
argoverse_loader = ArgoverseTrackingLoader(dataset_dir)
argoverse_data = argoverse_loader.get(log_id)
city_name = argoverse_data.city_name
avm = ArgoverseMap()
sdb = SynchronizationDB(dataset_dir, collect_single_log_id=log_id)
try:
path = os.path.join(output_dir, log_id, 'road_gt')
command = "rm -r " + path
os.system(command)
except BaseException:
pass
try:
os.makedirs(os.path.join(output_dir, log_id, 'road_gt'))
except BaseException:
pass
ply_fpath = os.path.join(dataset_dir, log_id, 'lidar')
ply_locs = []
for idx, ply_name in enumerate(os.listdir(ply_fpath)):
ply_loc = np.array([idx, int(ply_name.split('.')[0].split('_')[-1])])
ply_locs.append(ply_loc)
ply_locs = np.array(ply_locs)
lidar_timestamps = sorted(ply_locs[:, 1])
calib_path = f"{dataset_dir}/{log_id}/vehicle_calibration_info.json"
calib_data = read_json_file(calib_path)
ind = 0
for i in range(9):
if calib_data['camera_data_'][i]['key'] ==\
'image_raw_stereo_front_left':
ind = i
break
rotation = np.array(calib_data['camera_data_'][ind]['value']
['vehicle_SE3_camera_']['rotation']['coefficients'])
translation = np.array(calib_data['camera_data_'][ind]['value']
['vehicle_SE3_camera_']['translation'])
egovehicle_SE3_cam = SE3(rotation=quat2rotmat(rotation),
translation=translation)
for idx in range(len(lidar_timestamps)):
lidar_timestamp = lidar_timestamps[idx]
cam_timestamp = sdb.get_closest_cam_channel_timestamp(
lidar_timestamp, "stereo_front_left", str(log_id))
occupancy_map = np.zeros((256, 256))
pose_fpath = os.path.join(
dataset_dir, log_id, "poses",
"city_SE3_egovehicle_" + str(lidar_timestamp) + ".json")
if not Path(pose_fpath).exists():
continue
pose_data = read_json_file(pose_fpath)
rotation = np.array(pose_data["rotation"])
translation = np.array(pose_data["translation"])
xcenter = translation[0]
ycenter = translation[1]
city_to_egovehicle_se3 = SE3(rotation=quat2rotmat(rotation),
translation=translation)
ego_car_nearby_lane_ids = avm.get_lane_ids_in_xy_bbox(
xcenter, ycenter, city_name, 50.0)
occupancy_map = get_lane_bev(ego_car_nearby_lane_ids, avm, city_name,
city_to_egovehicle_se3,
egovehicle_SE3_cam, occupancy_map, res,
255)
output_loc = os.path.join(
output_dir, log_id, 'road_gt',
'stereo_front_left_' + str(cam_timestamp) + '.png')
cv2.imwrite(output_loc, occupancy_map)