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_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
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)
# Decode the image
img = utils.decode_image(camera)