def pcl_from_range_image(frame, lidar_name):
# extract lidar data and range image
lidar = waymo_utils.get(frame.lasers, lidar_name)
range_image, camera_projection, range_image_pose = waymo_utils.parse_range_image_and_camera_projection(lidar) # Parse the top laser range image and get the associated projection.
# Convert the range image to a point cloud
lidar_calib = waymo_utils.get(frame.context.laser_calibrations, lidar_name)
pcl, pcl_attr = project_to_pointcloud(frame, range_image, camera_projection, range_image_pose, lidar_calib)
# stack point cloud and lidar intensity
points_all = np.column_stack((pcl, pcl_attr[:, 1]))
return points_all
def extract_front_camera_image(frame):
# extract camera and calibration from frame
camera_name = dataset_pb2.CameraName.FRONT
camera = waymo_utils.get(frame.images, camera_name)
# get image and convert tom RGB
image = waymo_utils.decode_image(camera)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def show_range_image(frame):
####### ID_S1_EX1 START #######
#######
print("student task ID_S1_EX1")
# step 1 : extract lidar data and range image for the roof-mounted lidar == Done
lidar_data = waymo_utils.get(frame.lasers, dataset_pb2.LaserName.TOP)
range_image, camera_projection, range_image_pose = waymo_utils.parse_range_image_and_camera_projection(
lidar_data)
# step 2 : extract the range and the intensity channel from the range image == Done
range_image = range_image[:, :, :2]
# step 3 : set values <0 to zero == Done
range_image[range_image < 0] = 0
# step 4 : map the range channel onto an 8-bit scale and make sure that the full range of values is appropriately considered == Done
range_channel = range_image[:, :, 0]
range_channel = (range_channel / range_channel.max() * 255).astype(
np.uint8)
# step 5 : map the intensity channel onto an 8-bit scale and normalize with the difference between the 1- and 99-percentile to mitigate the influence of outliers == Done
intensity_channel = range_image[:, :, 1]
per1, per99 = np.percentile(intensity_channel, [1, 99])
intensity_channel[intensity_channel > per99] = per99
intensity_channel = (intensity_channel / (per99 - per1) * 255).astype(
np.uint8)
# step 6 : stack the range and intensity image vertically using np.vstack and convert the result to an unsigned 8-bit integer == Done
img_range_intensity = np.vstack([range_channel, intensity_channel])
h, w = img_range_intensity.shape
img_range_intensity = img_range_intensity[:,
int((w / 4)):int((w * (3 / 4)))]
#######
####### ID_S1_EX1 END #######
return img_range_intensity
cnt_frame = cnt_frame + 1
continue
elif cnt_frame > show_only_frames[1]:
print('reached end of selected frames')
break
print('------------------------------')
print('processing frame #' + str(cnt_frame))
#################################
## Perform 3D object detection
## Extract calibration data and front camera image from frame
lidar_name = dataset_pb2.LaserName.TOP
camera_name = dataset_pb2.CameraName.FRONT
lidar_calibration = waymo_utils.get(frame.context.laser_calibrations,
lidar_name)
camera_calibration = waymo_utils.get(frame.context.camera_calibrations,
camera_name)
if 'load_image' in exec_list:
image = tools.extract_front_camera_image(frame)
## Compute lidar point-cloud from range image
if 'pcl_from_rangeimage' in exec_list:
print('computing point-cloud from lidar range image')
lidar_pcl = tools.pcl_from_range_image(frame, lidar_name)
else:
print('loading lidar point-cloud from result file')
lidar_pcl = load_object_from_file(results_fullpath, data_filename,
'lidar_pcl', cnt_frame)
## Compute lidar birds-eye view (bev)