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)
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
def draw_ground_pts_in_image(
sdb: SynchronizationDB,
lidar_points: np.ndarray,
city_to_egovehicle_se3: SE3,
dataset_map: ArgoverseMap,
log_id: str,
lidar_timestamp: int,
city_name: str,
dataset_dir: str,
experiment_prefix: str,
plot_ground: bool = True,
motion_compensate: bool = False,
camera: Optional[str] = None,
) -> Union[None, np.ndarray]:
"""Write an image to disk with rendered ground points for every camera.
Args:
sdb: instance of SynchronizationDB
lidar_points: Numpy array of shape (N,3) in egovehicle frame
city_to_egovehicle_se3: SE3 instance which takes a point in egovehicle frame and brings it into city frame
dataset_map: Map dataset instance
log_id: ID of the log
city_name: A city's name (e.g. 'MIA' or 'PIT')
motion_compensate: Whether to bring lidar points from world frame @ lidar timestamp, to world frame @ camera
timestamp
camera: camera name, if specified will return image of that specific camera, if None, will save all camera to
disk and return None
"""
# put into city coords, then prune away ground and non-RoI points
lidar_points = city_to_egovehicle_se3.transform_point_cloud(lidar_points)
lidar_points = dataset_map.remove_non_driveable_area_points(
lidar_points, city_name)
_, not_ground_logicals = dataset_map.remove_ground_surface(
copy.deepcopy(lidar_points), city_name, return_logicals=True)
lidar_points = lidar_points[np.logical_not(not_ground_logicals)
if plot_ground else not_ground_logicals]
# put back into ego-vehicle coords
lidar_points = city_to_egovehicle_se3.inverse_transform_point_cloud(
lidar_points)
calib_fpath = f"{dataset_dir}/{log_id}/vehicle_calibration_info.json"
calib_data = read_json_file(calib_fpath)
# repeat green to red colormap every 50 m.
colors_arr = np.array([
[color_obj.rgb]
for color_obj in Color("red").range_to(Color("green"), NUM_RANGE_BINS)
]).squeeze()
np.fliplr(colors_arr)
for cam_idx, camera_name in enumerate(RING_CAMERA_LIST +
STEREO_CAMERA_LIST):
im_dir = f"{dataset_dir}/{log_id}/{camera_name}"
# load images, e.g. 'image_raw_ring_front_center_000000486.jpg'
cam_timestamp = sdb.get_closest_cam_channel_timestamp(
lidar_timestamp, camera_name, log_id)
if cam_timestamp is None:
continue
im_fname = f"{camera_name}_{cam_timestamp}.jpg"
im_fpath = f"{im_dir}/{im_fname}"
# Swap channel order as OpenCV expects it -- BGR not RGB
# must make a copy to make memory contiguous
img = imageio.imread(im_fpath)[:, :, ::-1].copy()
points_h = point_cloud_to_homogeneous(copy.deepcopy(lidar_points)).T
if motion_compensate:
uv, uv_cam, valid_pts_bool = project_lidar_to_img_motion_compensated(
points_h, # these are recorded at lidar_time
copy.deepcopy(calib_data),
camera_name,
cam_timestamp,
lidar_timestamp,
dataset_dir,
log_id,
False,
)
else:
uv, uv_cam, valid_pts_bool = project_lidar_to_img(
points_h, copy.deepcopy(calib_data), camera_name, False)
if valid_pts_bool is None or uv is None or uv_cam is None:
continue
if valid_pts_bool.sum() == 0:
continue
uv = np.round(uv[valid_pts_bool]).astype(np.int32)
uv_cam = uv_cam.T[valid_pts_bool]
pt_ranges = np.linalg.norm(uv_cam[:, :3], axis=1)
rgb_bins = np.round(pt_ranges).astype(np.int32)
# account for moving past 100 meters, loop around again
rgb_bins = rgb_bins % NUM_RANGE_BINS
uv_colors = (255 * colors_arr[rgb_bins]).astype(np.int32)
img = draw_point_cloud_in_img_cv2(img, uv, np.fliplr(uv_colors))
if not Path(f"{experiment_prefix}_ground_viz/{log_id}/{camera_name}"
).exists():
os.makedirs(
f"{experiment_prefix}_ground_viz/{log_id}/{camera_name}")
save_dir = f"{experiment_prefix}_ground_viz/{log_id}/{camera_name}"
cv2.imwrite(f"{save_dir}/{camera_name}_{lidar_timestamp}.jpg", img)
if camera == camera_name:
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return None
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