Close_Loop_Evaluation/Carla_Simulation/PythonAPI/examples/open3d_lidar.py

#!/usr/bin/env python

# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.

"""Open3D Lidar visuialization example for CARLA"""

import glob
import os
import sys
import argparse
import time
from datetime import datetime
import random
import numpy as np
from matplotlib import cm
import open3d as o3d

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

VIRIDIS = np.array(cm.get_cmap('plasma').colors)
VID_RANGE = np.linspace(0.0, 1.0, VIRIDIS.shape[0])
LABEL_COLORS = np.array([
    (255, 255, 255), # None
    (70, 70, 70),    # Building
    (100, 40, 40),   # Fences
    (55, 90, 80),    # Other
    (220, 20, 60),   # Pedestrian
    (153, 153, 153), # Pole
    (157, 234, 50),  # RoadLines
    (128, 64, 128),  # Road
    (244, 35, 232),  # Sidewalk
    (107, 142, 35),  # Vegetation
    (0, 0, 142),     # Vehicle
    (102, 102, 156), # Wall
    (220, 220, 0),   # TrafficSign
    (70, 130, 180),  # Sky
    (81, 0, 81),     # Ground
    (150, 100, 100), # Bridge
    (230, 150, 140), # RailTrack
    (180, 165, 180), # GuardRail
    (250, 170, 30),  # TrafficLight
    (110, 190, 160), # Static
    (170, 120, 50),  # Dynamic
    (45, 60, 150),   # Water
    (145, 170, 100), # Terrain
]) / 255.0 # normalize each channel [0-1] since is what Open3D uses


def lidar_callback(point_cloud, point_list):
    """Prepares a point cloud with intensity
    colors ready to be consumed by Open3D"""
    data = np.copy(np.frombuffer(point_cloud.raw_data, dtype=np.dtype('f4')))
    data = np.reshape(data, (int(data.shape[0] / 4), 4))

    # Isolate the intensity and compute a color for it
    intensity = data[:, -1]
    intensity_col = 1.0 - np.log(intensity) / np.log(np.exp(-0.004 * 100))
    int_color = np.c_[
        np.interp(intensity_col, VID_RANGE, VIRIDIS[:, 0]),
        np.interp(intensity_col, VID_RANGE, VIRIDIS[:, 1]),
        np.interp(intensity_col, VID_RANGE, VIRIDIS[:, 2])]

    # Isolate the 3D data
    points = data[:, :-1]

    # We're negating the y to correclty visualize a world that matches
    # what we see in Unreal since Open3D uses a right-handed coordinate system
    points[:, :1] = -points[:, :1]

    # # An example of converting points from sensor to vehicle space if we had
    # # a carla.Transform variable named "tran":
    # points = np.append(points, np.ones((points.shape[0], 1)), axis=1)
    # points = np.dot(tran.get_matrix(), points.T).T
    # points = points[:, :-1]

    point_list.points = o3d.utility.Vector3dVector(points)
    point_list.colors = o3d.utility.Vector3dVector(int_color)


def semantic_lidar_callback(point_cloud, point_list):
    """Prepares a point cloud with semantic segmentation
    colors ready to be consumed by Open3D"""
    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)]))

    # We're negating the y to correclty visualize a world that matches
    # what we see in Unreal since Open3D uses a right-handed coordinate system
    points = np.array([data['x'], -data['y'], data['z']]).T

    # # An example of adding some noise to our data if needed:
    # points += np.random.uniform(-0.05, 0.05, size=points.shape)

    # Colorize the pointcloud based on the CityScapes color palette
    labels = np.array(data['ObjTag'])
    int_color = LABEL_COLORS[labels]

    # # In case you want to make the color intensity depending
    # # of the incident ray angle, you can use:
    # int_color *= np.array(data['CosAngle'])[:, None]

    point_list.points = o3d.utility.Vector3dVector(points)
    point_list.colors = o3d.utility.Vector3dVector(int_color)


def generate_lidar_bp(arg, world, blueprint_library, delta):
    """Generates a CARLA blueprint based on the script parameters"""
    if arg.semantic:
        lidar_bp = world.get_blueprint_library().find('sensor.lidar.ray_cast_semantic')
    else:
        lidar_bp = blueprint_library.find('sensor.lidar.ray_cast')
        if arg.no_noise:
            lidar_bp.set_attribute('dropoff_general_rate', '0.0')
            lidar_bp.set_attribute('dropoff_intensity_limit', '1.0')
            lidar_bp.set_attribute('dropoff_zero_intensity', '0.0')
        else:
            lidar_bp.set_attribute('noise_stddev', '0.2')

    lidar_bp.set_attribute('upper_fov', str(arg.upper_fov))
    lidar_bp.set_attribute('lower_fov', str(arg.lower_fov))
    lidar_bp.set_attribute('channels', str(arg.channels))
    lidar_bp.set_attribute('range', str(arg.range))
    lidar_bp.set_attribute('rotation_frequency', str(1.0 / delta))
    lidar_bp.set_attribute('points_per_second', str(arg.points_per_second))
    return lidar_bp


def add_open3d_axis(vis):
    """Add a small 3D axis on Open3D Visualizer"""
    axis = o3d.geometry.LineSet()
    axis.points = o3d.utility.Vector3dVector(np.array([
        [0.0, 0.0, 0.0],
        [1.0, 0.0, 0.0],
        [0.0, 1.0, 0.0],
        [0.0, 0.0, 1.0]]))
    axis.lines = o3d.utility.Vector2iVector(np.array([
        [0, 1],
        [0, 2],
        [0, 3]]))
    axis.colors = o3d.utility.Vector3dVector(np.array([
        [1.0, 0.0, 0.0],
        [0.0, 1.0, 0.0],
        [0.0, 0.0, 1.0]]))
    vis.add_geometry(axis)


def main(arg):
    """Main function of the script"""
    client = carla.Client(arg.host, arg.port)
    client.set_timeout(2.0)
    world = client.get_world()

    try:
        original_settings = world.get_settings()
        settings = world.get_settings()
        traffic_manager = client.get_trafficmanager(8000)
        traffic_manager.set_synchronous_mode(True)

        delta = 0.05

        settings.fixed_delta_seconds = delta
        settings.synchronous_mode = True
        settings.no_rendering_mode = arg.no_rendering
        world.apply_settings(settings)

        blueprint_library = world.get_blueprint_library()
        vehicle_bp = blueprint_library.filter(arg.filter)[0]
        vehicle_transform = random.choice(world.get_map().get_spawn_points())
        vehicle = world.spawn_actor(vehicle_bp, vehicle_transform)
        vehicle.set_autopilot(arg.no_autopilot)

        lidar_bp = generate_lidar_bp(arg, world, blueprint_library, delta)

        user_offset = carla.Location(arg.x, arg.y, arg.z)
        lidar_transform = carla.Transform(carla.Location(x=-0.5, z=1.8) + user_offset)

        lidar = world.spawn_actor(lidar_bp, lidar_transform, attach_to=vehicle)

        point_list = o3d.geometry.PointCloud()
        if arg.semantic:
            lidar.listen(lambda data: semantic_lidar_callback(data, point_list))
        else:
            lidar.listen(lambda data: lidar_callback(data, point_list))

        vis = o3d.visualization.Visualizer()
        vis.create_window(
            window_name='Carla Lidar',
            width=960,
            height=540,
            left=480,
            top=270)
        vis.get_render_option().background_color = [0.05, 0.05, 0.05]
        vis.get_render_option().point_size = 1
        vis.get_render_option().show_coordinate_frame = True

        if arg.show_axis:
            add_open3d_axis(vis)

        frame = 0
        dt0 = datetime.now()
        while True:
            if frame == 2:
                vis.add_geometry(point_list)
            vis.update_geometry(point_list)

            vis.poll_events()
            vis.update_renderer()
            # # This can fix Open3D jittering issues:
            time.sleep(0.005)
            world.tick()

            process_time = datetime.now() - dt0
            sys.stdout.write('\r' + 'FPS: ' + str(1.0 / process_time.total_seconds()))
            sys.stdout.flush()
            dt0 = datetime.now()
            frame += 1

    finally:
        world.apply_settings(original_settings)
        traffic_manager.set_synchronous_mode(False)

        vehicle.destroy()
        lidar.destroy()
        vis.destroy_window()


if __name__ == "__main__":
    argparser = argparse.ArgumentParser(
        description=__doc__)
    argparser.add_argument(
        '--host',
        metavar='H',
        default='localhost',
        help='IP of the host CARLA Simulator (default: localhost)')
    argparser.add_argument(
        '-p', '--port',
        metavar='P',
        default=2000,
        type=int,
        help='TCP port of CARLA Simulator (default: 2000)')
    argparser.add_argument(
        '--no-rendering',
        action='store_true',
        help='use the no-rendering mode which will provide some extra'
        ' performance but you will lose the articulated objects in the'
        ' lidar, such as pedestrians')
    argparser.add_argument(
        '--semantic',
        action='store_true',
        help='use the semantic lidar instead, which provides ground truth'
        ' information')
    argparser.add_argument(
        '--no-noise',
        action='store_true',
        help='remove the drop off and noise from the normal (non-semantic) lidar')
    argparser.add_argument(
        '--no-autopilot',
        action='store_false',
        help='disables the autopilot so the vehicle will remain stopped')
    argparser.add_argument(
        '--show-axis',
        action='store_true',
        help='show the cartesian coordinates axis')
    argparser.add_argument(
        '--filter',
        metavar='PATTERN',
        default='model3',
        help='actor filter (default: "vehicle.*")')
    argparser.add_argument(
        '--upper-fov',
        default=15.0,
        type=float,
        help='lidar\'s upper field of view in degrees (default: 15.0)')
    argparser.add_argument(
        '--lower-fov',
        default=-25.0,
        type=float,
        help='lidar\'s lower field of view in degrees (default: -25.0)')
    argparser.add_argument(
        '--channels',
        default=64.0,
        type=float,
        help='lidar\'s channel count (default: 64)')
    argparser.add_argument(
        '--range',
        default=100.0,
        type=float,
        help='lidar\'s maximum range in meters (default: 100.0)')
    argparser.add_argument(
        '--points-per-second',
        default=500000,
        type=int,
        help='lidar\'s points per second (default: 500000)')
    argparser.add_argument(
        '-x',
        default=0.0,
        type=float,
        help='offset in the sensor position in the X-axis in meters (default: 0.0)')
    argparser.add_argument(
        '-y',
        default=0.0,
        type=float,
        help='offset in the sensor position in the Y-axis in meters (default: 0.0)')
    argparser.add_argument(
        '-z',
        default=0.0,
        type=float,
        help='offset in the sensor position in the Z-axis in meters (default: 0.0)')
    args = argparser.parse_args()

    try:
        main(args)
    except KeyboardInterrupt:
        print(' - Exited by user.')