Whitehat-Carla/class-215.py at master · bunnypranav/Whitehat-Carla · GitHub

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import glob
import os
import sys
import time
import numpy as np
import threading
from mayavi import mlab

try:
    sys.path.append(
        glob.glob(
            "../carla/dist/carla-*%d.%d-%s.egg"
            % (
                sys.version_info.major,
                sys.version_info.minor,
                "win-amd64" if os.name == "nt" else "linux-x86_64",
            )
        )[0]
    )
except IndexError:
    pass

import carla

actor_list = []


def generate_lidar_blueprint(blueprint_library):
    lidar_blueprint = blueprint_library.find("sensor.lidar.ray_cast_semantic")
    lidar_blueprint.set_attribute("channels", str(64))
    lidar_blueprint.set_attribute("points_per_second", str(56000 * 10))
    lidar_blueprint.set_attribute("rotation_frequency", str(40))
    lidar_blueprint.set_attribute("range", str(100.0 * 200))
    lidar_blueprint.set_attribute("upper_fov", str(15))
    lidar_blueprint.set_attribute("lower_fov", str(-30))
    return lidar_blueprint


def semantic_lidar_data(point_cloud, lidar_point_cloud_buffer):
    """Prepares a point cloud with semantic segmentation colors"""
    matrix_representational_data = np.frombuffer(
        point_cloud.raw_data,
        dtype=np.dtype(
            [
                ("x", np.float32),
                ("y", np.float32),
                ("z", np.float32),
                ("CosAngle", np.float32),
                ("ObjIdx", np.uint32),
                ("ObjTag", np.uint32),
            ]
        ),
    )
    lidar_points = np.array(
        [
            matrix_representational_data["x"],
            -matrix_representational_data["y"],
            matrix_representational_data["z"],
        ]
    ).T
    # define object tag from matrix_representational_data and store in labels variable
    labels = np.array(matrix_representational_data["ObjTag"], dtype=np.float32)
    # store lidar points into lidar_point_cloud_buffer['pts']
    lidar_point_cloud_buffer["pts"] = lidar_points
    # store lables into lidar_point_cloud_buffer['intensity']
    lidar_point_cloud_buffer["intensity"] = labels


def carlaThreadingLoop(world):
    frame = 0
    while True:
        time.sleep(0.005)
        world.tick()
        frame += 1


try:
    client = carla.Client("127.0.0.1", 2000)
    client.set_timeout(10.0)
    world = client.get_world()

    get_blueprint_of_world = world.get_blueprint_library()
    car_model = get_blueprint_of_world.filter("model3")[0]
    spawn_point = world.get_map().get_spawn_points()[1]
    dropped_vehicle = world.spawn_actor(car_model, spawn_point)
    dropped_vehicle.set_autopilot()

    simulator_camera_location_rotation = carla.Transform(
        spawn_point.location, spawn_point.rotation
    )
    simulator_camera_location_rotation.location += spawn_point.get_forward_vector() * 30
    simulator_camera_location_rotation.rotation.yaw += 180
    simulator_camera_view = world.get_spectator()
    simulator_camera_view.set_transform(simulator_camera_location_rotation)
    actor_list.append(dropped_vehicle)

    lidar_sensor = generate_lidar_blueprint(get_blueprint_of_world)
    sensor_lidar_spawn_point = carla.Transform(carla.Location(x=-0.5, z=1.8))
    sensor = world.spawn_actor(
        lidar_sensor, sensor_lidar_spawn_point, attach_to=dropped_vehicle
    )

    lidar_figure = mlab.figure(size=(960, 540), bgcolor=(0.05, 0.05, 0.05))
    visualise_lidar_using_mayavi = mlab.points3d(
        0, 0, 0, 0, mode="point", figure=lidar_figure
    )
    mlab.view(distance=25)
    lidar_point_cloud_buffer = {"pts": np.zeros((1, 3)), "intensity": np.zeros(1)}

    def anim():
        i = 0
        while True:
            visualise_lidar_using_mayavi.mlab_source.reset(
                x=lidar_point_cloud_buffer["pts"][:, 0],
                y=lidar_point_cloud_buffer["pts"][:, 1],
                z=lidar_point_cloud_buffer["pts"][:, 2],
                scalars=lidar_point_cloud_buffer["intensity"],
            )
            mlab.savefig(f"output/{i}.png", figure=lidar_figure)
            time.sleep(0.1)
            i += 1

    # define sensor.listen() function
    sensor.listen(lambda data: semantic_lidar_data(data, lidar_point_cloud_buffer))

    loopThread = threading.Thread(
        target=carlaThreadingLoop, args=[world], daemon=True
    ).start()
    anim()

    actor_list.append(sensor)

    time.sleep(1000)
finally:
    print("destroying actors")
    for actor in actor_list:
        actor.destroy()
    print("done.")