def parse_range_image_and_camera_projection(self, frame):
"""Parse range images and camera projections given a frame.
Args:
frame: open dataset frame proto
Returns:
range_images: A dict of {laser_name,
[range_image_first_return, range_image_second_return]}.
range_image_top_pose: range image pixel pose for top lidar.
"""
self.__range_images = {}
for laser in frame.lasers:
if len(laser.ri_return1.range_image_compressed) > 0:
range_image_str_tensor = tf.decode_compressed(
laser.ri_return1.range_image_compressed, 'ZLIB')
ri = open_dataset.MatrixFloat()
ri.ParseFromString(bytearray(range_image_str_tensor.numpy()))
self.__range_images[laser.name] = [ri]
if laser.name == open_dataset.LaserName.TOP:
range_image_top_pose_str_tensor = tf.decode_compressed(
laser.ri_return1.range_image_pose_compressed, 'ZLIB')
range_image_top_pose = open_dataset.MatrixFloat()
range_image_top_pose.ParseFromString(
bytearray(range_image_top_pose_str_tensor.numpy()))
if len(laser.ri_return2.range_image_compressed) > 0:
range_image_str_tensor = tf.decode_compressed(
laser.ri_return2.range_image_compressed, 'ZLIB')
ri = open_dataset.MatrixFloat()
ri.ParseFromString(bytearray(range_image_str_tensor.numpy()))
self.__range_images[laser.name].append(ri)
return self.__range_images, range_image_top_pose
def parse_range_image_and_camera_projection(frame):
"""Parse range images and camera projections given a frame.
Args:
frame: open dataset frame proto
Returns:
range_images: A dict of {laser_name,
[range_image_first_return, range_image_second_return]}.
camera_projections: A dict of {laser_name,
[camera_projection_from_first_return,
camera_projection_from_second_return]}.
range_image_top_pose: range image pixel pose for top lidar.
"""
range_images = {}
camera_projections = {}
range_image_top_pose = None
for laser in frame.lasers:
if len(laser.ri_return1.range_image_compressed) > 0:
# use tf.io.decode_compressed() if TF 2.0
range_image_str_tensor = tf.decode_compressed(
laser.ri_return1.range_image_compressed, 'ZLIB')
ri = open_dataset.MatrixFloat()
ri.ParseFromString(bytearray(range_image_str_tensor.numpy()))
range_images[laser.name] = [ri]
if laser.name == open_dataset.LaserName.TOP:
# use tf.io.decode_compressed() if TF 2.0
range_image_top_pose_str_tensor = tf.decode_compressed(
laser.ri_return1.range_image_pose_compressed, 'ZLIB')
range_image_top_pose = open_dataset.MatrixFloat()
range_image_top_pose.ParseFromString(
bytearray(range_image_top_pose_str_tensor.numpy()))
# use tf.io.decode_compressed() if TF 2.0
camera_projection_str_tensor = tf.decode_compressed(
laser.ri_return1.camera_projection_compressed, 'ZLIB')
cp = open_dataset.MatrixInt32()
cp.ParseFromString(bytearray(camera_projection_str_tensor.numpy()))
camera_projections[laser.name] = [cp]
if len(laser.ri_return2.range_image_compressed) > 0:
# use tf.io.decode_compressed() if TF 2.0
range_image_str_tensor = tf.decode_compressed(
laser.ri_return2.range_image_compressed, 'ZLIB')
ri = open_dataset.MatrixFloat()
ri.ParseFromString(bytearray(range_image_str_tensor.numpy()))
range_images[laser.name].append(ri)
# use tf.io.decode_compressed() if TF 2.0
camera_projection_str_tensor = tf.decode_compressed(
laser.ri_return2.camera_projection_compressed, 'ZLIB')
cp = open_dataset.MatrixInt32()
cp.ParseFromString(bytearray(camera_projection_str_tensor.numpy()))
camera_projections[laser.name].append(cp)
return range_images, camera_projections, range_image_top_pose
