def display_next_frame(event=None):
global once
frame = next(datafile_iter)
# Get the top laser information
laser_name = dataset_pb2.LaserName.TOP
laser = utils.get(frame.lasers, laser_name)
laser_calibration = utils.get(frame.context.laser_calibrations, laser_name)
# Parse the top laser range image and get the associated projection.
ri, camera_projection, range_image_pose = utils.parse_range_image_and_camera_projection(
laser)
# Convert the range image to a point cloud.
pcl, pcl_attr = utils.project_to_pointcloud(frame, ri, camera_projection,
range_image_pose,
laser_calibration)
# Update the geometry and render the pointcloud
pcd.points = o3d.utility.Vector3dVector(pcl)
if once:
vis.add_geometry(pcd)
once = False
else:
vis.update_geometry(pcd)
def get_image(self, frame, idx):
'''Get image
'''
camera_name = dataset_pb2.CameraName.FRONT
camera_calibration = utils.get(frame.context.camera_calibrations, camera_name)
camera = utils.get(frame.images, camera_name)
vehicle_to_image = utils.get_image_transform(camera_calibration) # Transformation
img = utils.decode_image(camera)
self.image=img
return img
def get_lidar(self, frame, idx, all_points=False):
'''Get lidar pointcloud
TODO: Get all 4 lidar points appeneded together
:return pcl: (N, 3) points in (x,y,z)
'''
laser_name = dataset_pb2.LaserName.TOP # laser information
laser = utils.get(frame.lasers, laser_name)
laser_calibration = utils.get(frame.context.laser_calibrations, laser_name)
ri, camera_projection, range_image_pose = utils.parse_range_image_and_camera_projection(laser)
pcl, pcl_attr = utils.project_to_pointcloud(frame, ri, camera_projection, range_image_pose, laser_calibration)
return pcl
def get_calibration(
frame: dataset_pb2.Frame, camera_id: int
) -> Tuple[ImageSize, Intrinsics, Extrinsics, Extrinsics, Extrinsics]:
"""Load and decode calibration data of camera in frame."""
calib = utils.get(frame.context.camera_calibrations, camera_id)
image_size = ImageSize(height=calib.height, width=calib.width)
intrinsics = Intrinsics(
focal=(calib.intrinsic[0], calib.intrinsic[1]),
center=(calib.intrinsic[2], calib.intrinsic[3]),
)
cam2car_mat: NDArrayF64 = np.array(calib.extrinsic.transform,
dtype=np.float64).reshape(4, 4)
car2cam_mat = np.linalg.inv(cam2car_mat)
car2cam_mat = np.dot(waymo2kitti_RT, car2cam_mat)
cam2car_mat = np.linalg.inv(car2cam_mat)
car2global_mat: NDArrayF64 = np.array(frame.pose.transform,
dtype=np.float64).reshape(4, 4)
cam2global_mat = np.dot(car2global_mat, cam2car_mat)
cam2car = get_extrinsics_from_matrix(cam2car_mat)
car2global = get_extrinsics_from_matrix(car2global_mat)
cam2global = get_extrinsics_from_matrix(cam2global_mat)
return (
image_size,
intrinsics,
cam2car,
car2global,
cam2global,
)
def export_file(filename):
print('[{}] Porting: {}'.format(threading.get_ident(), filename))
head, tail = os.path.split(filename)
m = re.search('(.*)(_with_camera_labels)?\.tfrecord$', tail)
if m is None:
exit(1)
filename_without_tfrecord = m.group(1)
_start_time = time.time()
# waymo data file reader
datafile = WaymoDataFileReader(filename)
# Generate a table of the offset of all frame records in the file.
table = datafile.get_record_table()
print("There are %d frames in this file." % len(table))
# Loop through the whole file
## and display 3D labels.
frame_id = 0
for frame in datafile:
camera_name = dataset_pb2.CameraName.FRONT
camera_calibration = utils.get(frame.context.camera_calibrations,
camera_name)
camera = utils.get(frame.images, camera_name)
camera_labels = None
if len(frame.camera_labels) != 0:
camera_labels = utils.get(frame.camera_labels, camera_name)
camera_labels = camera_labels.labels
exporter.export(camera_calibration,
camera,
frame.laser_labels,
camera_labels,
frame_id=frame_id,
sequence_id=filename_without_tfrecord)
frame_id += 1
print('[{}] Processed file in {}'.format(threading.get_ident(),
time.time() - _start_time))
filename = sys.argv[1]
datafile = WaymoDataFileReader(filename)
# Generate a table of the offset of all frame records in the file.
table = datafile.get_record_table()
print("There are %d frames in this file." % len(table))
# Loop through the whole file
## and display 3D labels.
for frameno,frame in enumerate(datafile):
# Get the top laser information
laser_name = dataset_pb2.LaserName.TOP
laser = utils.get(frame.lasers, laser_name)
laser_calibration = utils.get(frame.context.laser_calibrations, laser_name)
# Parse the top laser range image and get the associated projection.
ri, camera_projection, range_image_pose = utils.parse_range_image_and_camera_projection(laser)
# Convert the range image to a point cloud.
pcl, pcl_attr = utils.project_to_pointcloud(frame, ri, camera_projection, range_image_pose, laser_calibration)
# Get the front camera information
camera_name = dataset_pb2.CameraName.FRONT
camera_calibration = utils.get(frame.context.camera_calibrations, camera_name)
camera = utils.get(frame.images, camera_name)
# Get the transformation matrix for the camera.
vehicle_to_image = utils.get_image_transform(camera_calibration)
frame_number = 0
for frame in frames:
num_points = 0
num_features = 7
point_cloud_list = []
for laser_id in range(1, 6):
laser_name = dataset_pb2.LaserName.Name.DESCRIPTOR.values_by_number[
laser_id].name
# Get the laser information
laser = utils.get(frame.lasers, laser_id)
laser_calibration = utils.get(frame.context.laser_calibrations,
laser_id)
# Parse the top laser range image and get the associated projection.
ri, camera_projection, range_image_pose = utils.parse_range_image_and_camera_projection(
laser)
# Convert the range image to a point cloud.
pcl, pcl_attr = utils.project_to_pointcloud(
frame, ri, camera_projection, range_image_pose,
laser_calibration)
pcl_with_attr = np.column_stack((pcl, pcl_attr))
num_points += len(pcl)
filenames = list(sorted(filenames))
datafile = WaymoDataFileReader(filenames[23])
# Generate a table of the offset of all frame records in the file.
table = datafile.get_record_table()
print("There are %d frames in this file." % len(table))
# Loop through the whole file
## and display 3D labels.
i = 0
for frame in datafile:
i += 1
if i == 1:
camera_name = dataset_pb2.CameraName.SIDE_RIGHT
camera_calibration = utils.get(frame.context.camera_calibrations,
camera_name)
camera = utils.get(frame.images, camera_name)
display_labels_on_image(camera_calibration, camera, frame.laser_labels,
10)
# Alternative: Displaying a single frame:
# # Jump to the frame 150
# datafile.seek(table[150])
#
# # Read and display this frame
# frame = datafile.read_record()
# display_labels_on_image(frame.context.camera_calibrations[0], frame.images[0], frame.laser_labels)
# Alternative: Displaying a 10 frames:
# # Jump to the frame 150
# datafile.seek(table[150])
def parse_frame(
frame: dataset_pb2.Frame,
frame_id: int,
output_dir: str,
save_images: bool = False,
use_lidar_labels: bool = False,
) -> Tuple[List[Frame], List[FrameGroup]]:
"""Parse information in single frame to Scalabel Frame per camera."""
frame_name = frame.context.name + f"_{frame_id:07d}.jpg"
attributes = parse_frame_attributes(frame, use_lidar_labels)
results, group_results = [], []
frame_names = []
sequence = frame.context.name
for camera_id, camera in cameras_id2name.items():
(
image_size,
intrinsics,
cam2car,
car2global,
cam2global,
) = get_calibration(frame, camera_id)
url = os.path.join(sequence, camera, frame_name)
img_filepath = os.path.join(output_dir, sequence, camera, frame_name)
img_name = (frame.context.name + "_" + camera.lower() +
f"_{frame_id:07d}.jpg")
if save_images and not os.path.exists(img_filepath):
if not os.path.exists(os.path.dirname(img_filepath)):
os.makedirs(os.path.dirname(img_filepath))
im_bytes = utils.get(frame.images, camera_id).image
with open(img_filepath, "wb") as fp:
fp.write(im_bytes)
if use_lidar_labels:
labels = parse_lidar_labels(frame, cam2car, camera, camera_id)
else:
labels = parse_camera_labels(frame, camera_id)
f = Frame(
name=img_name,
videoName=sequence,
frameIndex=frame_id,
url=url,
size=image_size,
extrinsics=cam2global,
intrinsics=intrinsics,
labels=labels,
attributes=attributes,
)
frame_names.append(img_name)
results.append(f)
url = f"segment-{frame.context.name}_with_camera_labels.tfrecord"
lidar2car_mat: NDArrayF64 = np.array(
frame.context.laser_calibrations[
lasers_name2id["TOP"]].extrinsic.transform,
dtype=np.float64,
).reshape(4, 4)
lidar2global_mat = np.dot(get_matrix_from_extrinsics(car2global),
lidar2car_mat)
lidar2car = get_extrinsics_from_matrix(lidar2car_mat)
lidar2global = get_extrinsics_from_matrix(lidar2global_mat)
group_results = [
FrameGroup(
name=frame_name,
videoName=sequence,
frameIndex=frame_id,
url=url,
extrinsics=lidar2global,
frames=frame_names,
labels=parse_lidar_labels(frame, lidar2car),
)
]
return results, group_results