def extract_data_in_pylot_format(actor_list):
"""Extracts actor information in pylot format from an actor list.
Args:
actor_list (carla.ActorList): An actor list object with all the
simulation actors.
Returns:
A tuple that contains objects for all different types of actors.
"""
# Note: the output will include the ego vehicle as well.
vec_actors = actor_list.filter('vehicle.*')
vehicles = [
Obstacle.from_carla_actor(vec_actor) for vec_actor in vec_actors
]
person_actors = actor_list.filter('walker.pedestrian.*')
people = [
Obstacle.from_carla_actor(ped_actor) for ped_actor in person_actors
]
tl_actors = actor_list.filter('traffic.traffic_light*')
traffic_lights = [
TrafficLight.from_carla_actor(tl_actor) for tl_actor in tl_actors
]
speed_limit_actors = actor_list.filter('traffic.speed_limit*')
speed_limits = [
SpeedLimitSign.from_carla_actor(ts_actor)
for ts_actor in speed_limit_actors
]
traffic_stop_actors = actor_list.filter('traffic.stop')
traffic_stops = [
StopSign.from_carla_actor(ts_actor) for ts_actor in traffic_stop_actors
]
return (vehicles, people, traffic_lights, speed_limits, traffic_stops)
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_carla_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_carla_transform(
ego_vehicle.get_transform())
depth_frame = DepthFrame.from_carla_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_carla_actor(person)
if obstacle.distance(vehicle_transform) > 125:
bbox = None
else:
bbox = obstacle.to_camera_view(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()
