def process_segmentation_images(msg,
camera_setup,
ego_vehicle,
speed,
csv,
dump=False):
print("Received a message for the time: {}".format(msg.timestamp))
# If we are in distance to the destination, stop and exit with success.
if ego_vehicle.get_location().distance(VEHICLE_DESTINATION) <= 5:
ego_vehicle.set_velocity(carla.Vector3D())
CLEANUP_FUNCTION()
sys.exit(0)
# Compute the segmentation mIOU.
frame = SegmentedFrame.from_simulator_image(msg, camera_setup)
compute_and_log_miou(frame, msg.timestamp, csv)
# Visualize the run.
if dump:
frame.save(int(msg.timestamp * 1000), './_out/', 'seg')
# Move the ego_vehicle according to the given speed.
ego_vehicle.set_velocity(carla.Vector3D(x=-speed))
# Move the simulator forward.
ego_vehicle.get_world().tick()
def on_segmented_msg(simulator_image):
global SEGMENTED_FRAME
transform = pylot.utils.Transform.from_simulator_transform(
simulator_image.transform)
camera_setup = SegmentedCameraSetup("segmented_camera", FLAGS.frame_width,
FLAGS.frame_height, transform,
FLAGS.camera_fov)
SEGMENTED_FRAME = SegmentedFrame.from_simulator_image(
simulator_image, camera_setup)
def process_images(self, simulator_image):
""" Invoked when an image is received from the simulator.
Args:
simulator_image: a carla.Image.
"""
game_time = int(simulator_image.timestamp * 1000)
timestamp = erdos.Timestamp(coordinates=[game_time])
watermark_msg = erdos.WatermarkMessage(timestamp)
with erdos.profile(self.config.name + '.process_images',
self,
event_data={'timestamp': str(timestamp)}):
# Ensure that the code executes serially
with self._lock:
msg = None
if self._camera_setup.camera_type == 'sensor.camera.rgb':
msg = FrameMessage(
timestamp,
CameraFrame.from_simulator_frame(
simulator_image, self._camera_setup))
elif self._camera_setup.camera_type == 'sensor.camera.depth':
# Include the transform relative to the vehicle.
# simulator_image.transform returns the world transform,
# but we do not use it directly.
msg = DepthFrameMessage(
timestamp,
DepthFrame.from_simulator_frame(
simulator_image,
self._camera_setup,
save_original_frame=self._flags.
visualize_depth_camera))
elif (self._camera_setup.camera_type ==
'sensor.camera.semantic_segmentation'):
msg = SegmentedFrameMessage(
timestamp,
SegmentedFrame.from_simulator_image(
simulator_image, self._camera_setup))
if self._release_data:
self._camera_stream.send(msg)
self._camera_stream.send(watermark_msg)
else:
# Pickle the data, and release it upon release msg receipt.
pickled_msg = pickle.dumps(
msg, protocol=pickle.HIGHEST_PROTOCOL)
with self._pickle_lock:
self._pickled_messages[msg.timestamp] = pickled_msg
self._notify_reading_stream.send(watermark_msg)
def process_depth_images(msg,
depth_camera_setup,
ego_vehicle,
speed,
csv,
surface,
visualize=False):
print("Received a message for the time: {}".format(msg.timestamp))
# If we are in distance to the destination, stop and exit with success.
if ego_vehicle.get_location().distance(VEHICLE_DESTINATION) <= 5:
ego_vehicle.set_velocity(carla.Vector3D())
CLEANUP_FUNCTION()
sys.exit(0)
# Get the RGB image corresponding to the given depth image timestamp.
rgb_image = retrieve_rgb_image(msg.timestamp)
# Get the semantic image corresponding to the given depth image timestamp.
semantic_image = retrieve_semantic_image(msg.timestamp)
semantic_frame = SegmentedFrame.from_simulator_image(semantic_image,
depth_frame.camera_setup)
# Visualize the image and the bounding boxes if needed.
if visualize:
draw_image(rgb_image, surface)
# Transform people into obstacles relative to the current frame.
bb_surface = None
resolution = (depth_camera_setup.width, depth_camera_setup.height)
if visualize:
bb_surface = pygame.Surface(resolution)
bb_surface.set_colorkey((0, 0, 0))
vehicle_transform = Transform.from_simulator_transform(
ego_vehicle.get_transform())
depth_frame = DepthFrame.from_simulator_frame(msg, depth_camera_setup)
# Transform the static camera setup with respect to the location of the
# vehicle in the world.
depth_frame.camera_setup.set_transform(vehicle_transform *
depth_frame.camera_setup.transform)
detected_people = []
for person in ego_vehicle.get_world().get_actors().filter('walker.*'):
obstacle = Obstacle.from_simulator_actor(person)
if obstacle._distance(vehicle_transform) > 125:
bbox = None
else:
bbox = obstacle.populate_bounding_box_2D(depth_frame,
semantic_frame.frame)
if bbox is not None:
detected_people.append(bbox)
if visualize:
draw_bounding_box(bbox, bb_surface)
# We have drawn all the bounding boxes on the bb_surface, now put it on
# the RGB image surface.
if visualize:
surface.blit(bb_surface, (0, 0))
pygame.display.flip()
# Compute the mAP.
print("We detected a total of {} people.".format(len(detected_people)))
compute_and_log_map(detected_people, msg.timestamp, csv)
# Move the ego_vehicle according to the given speed.
ego_vehicle.set_velocity(carla.Vector3D(x=-speed))
ego_vehicle.get_world().tick()
