def __getitem__(self, idx):
self._curr_counter = idx
# Get the next dataset if frame number is more than table count
if self._frame_counter == -1 or not len(self._dataset_nums) or self._frame_counter >= self._dataset_nums[self._file_counter]-1:
self.current_file = self.__file_names.pop() # get one filename
dataset = WaymoDataFileReader(self.current_file) # get Dataloader
self._sample_list = dataset.get_record_table() # get number of record table
self._dataset_itr = iter(dataset) # Get next record iterator
if self._frame_counter == -1:
self._file_counter +=1
self._dataset_nums.append(len(self._sample_list))
self._frame_counter = 1
else:
self._frame_counter+=1
self._num_frames+=1
self._idx_to_frame.append((self._file_counter, self._frame_counter))
return next(self.dataset_itr) # Send next frame from record
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))
def parse_record(
output_dir: str,
save_images: bool,
use_lidar_labels: bool,
record_name: str,
) -> Tuple[List[Frame], List[FrameGroup]]:
"""Parse data into List of Scalabel format annotations."""
datafile = WaymoDataFileReader(record_name)
table = datafile.get_record_table()
frames, groups = [], []
for frame_id, offset in enumerate(table):
# jump to correct place, read frame
datafile.seek(offset)
frame = datafile.read_record()
# add images and annotations to coco
frame, group = parse_frame(frame, frame_id, output_dir, save_images,
use_lidar_labels)
frames.extend(frame)
groups.extend(group)
return frames, groups
# Draw a circle for each point.
for i in range(proj_pcl.shape[0]):
cv2.circle(img, (int(proj_pcl[i,0]),int(proj_pcl[i,1])), 1, coloured_intensity[i])
if len(sys.argv) != 2:
print("""Usage: python display_laser_on_image.py <datafile>
Display the groundtruth 3D bounding boxes and LIDAR points on the front camera video stream.""")
sys.exit(0)
# Open a .tfrecord
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)