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#!/usr/bin/env python3
# Copyright (c) 2017 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB), and the INTEL Visual Computing Lab.
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""Basic CARLA client to generate point cloud in PLY format that you
can visualize with MeshLab (meshlab.net) for instance. Please
refer to client_example.py for a simpler and more documented example."""
from __future__ import print_function
import argparse
import logging
import os
import random
import time
from carla.client import make_carla_client
from carla.sensor import Camera
from carla.settings import CarlaSettings
from carla.tcp import TCPConnectionError
from carla.util import print_over_same_line, StopWatch
from carla.image_converter import depth_to_local_point_cloud, to_rgb_array
from carla.transform import Transform
def run_carla_client(host, port, far):
# Here we will run a single episode with 300 frames.
number_of_frames = 3000
frame_step = 100 # Save one image every 100 frames
output_folder = '_out'
image_size = [800, 600]
camera_local_pos = [0.3, 0.0, 1.3] # [X, Y, Z]
camera_local_rotation = [0, 0, 0] # [pitch(Y), yaw(Z), roll(X)]
fov = 70
# Connect with the server
with make_carla_client(host, port) as client:
print('CarlaClient connected')
# Here we load the settings.
settings = CarlaSettings()
settings.set(
SynchronousMode=True,
SendNonPlayerAgentsInfo=False,
NumberOfVehicles=20,
NumberOfPedestrians=40,
WeatherId=random.choice([1, 3, 7, 8, 14]))
settings.randomize_seeds()
camera1 = Camera('CameraDepth', PostProcessing='Depth', FOV=fov)
camera1.set_image_size(*image_size)
camera1.set_position(*camera_local_pos)
camera1.set_rotation(*camera_local_rotation)
settings.add_sensor(camera1)
camera2 = Camera('CameraRGB', PostProcessing='SceneFinal', FOV=fov)
camera2.set_image_size(*image_size)
camera2.set_position(*camera_local_pos)
camera2.set_rotation(*camera_local_rotation)
settings.add_sensor(camera2)
client.load_settings(settings)
# Start at location index id '0'
client.start_episode(0)
# Compute the camera transform matrix
camera_to_car_transform = camera2.get_unreal_transform()
# Iterate every frame in the episode except for the first one.
for frame in range(1, number_of_frames):
# Read the data produced by the server this frame.
measurements, sensor_data = client.read_data()
# Save one image every 'frame_step' frames
if not frame % frame_step:
# Start transformations time mesure.
timer = StopWatch()
# RGB image [[[r,g,b],..[r,g,b]],..[[r,g,b],..[r,g,b]]]
image_RGB = to_rgb_array(sensor_data['CameraRGB'])
# 2d to (camera) local 3d
# We use the image_RGB to colorize each 3D point, this is optional.
# "max_depth" is used to keep only the points that are near to the
# camera, meaning 1.0 the farest points (sky)
point_cloud = depth_to_local_point_cloud(
sensor_data['CameraDepth'],
image_RGB,
max_depth=far
)
# (Camera) local 3d to world 3d.
# Get the transform from the player protobuf transformation.
world_transform = Transform(
measurements.player_measurements.transform
)
# Compute the final transformation matrix.
car_to_world_transform = world_transform * camera_to_car_transform
# Car to World transformation given the 3D points and the
# transformation matrix.
point_cloud.apply_transform(car_to_world_transform)
# End transformations time mesure.
timer.stop()
# Save PLY to disk
# This generates the PLY string with the 3D points and the RGB colors
# for each row of the file.
point_cloud.save_to_disk(os.path.join(
output_folder, '{:0>5}.ply'.format(frame))
)
print_message(timer.milliseconds(), len(point_cloud), frame)
client.send_control(
measurements.player_measurements.autopilot_control
)
def print_message(elapsed_time, point_n, frame):
message = ' '.join([
'Transformations took {:>3.0f} ms.',
'Saved {:>6} points to "{:0>5}.ply".'
]).format(elapsed_time, point_n, frame)
print_over_same_line(message)
def check_far(value):
fvalue = float(value)
if fvalue < 0.0 or fvalue > 1.0:
raise argparse.ArgumentTypeError(
"{} must be a float between 0.0 and 1.0")
return fvalue
def main():
argparser = argparse.ArgumentParser(description=__doc__)
argparser.add_argument(
'-v', '--verbose',
action='store_true',
dest='debug',
help='print debug information')
argparser.add_argument(
'--host',
metavar='H',
default='localhost',
help='IP of the host server (default: localhost)')
argparser.add_argument(
'-p', '--port',
metavar='P',
default=2000,
type=int,
help='TCP port to listen to (default: 2000)')
argparser.add_argument(
'-f', '--far',
default=0.2,
type=check_far,
help='The maximum save distance of camera-point '
'[0.0 (near), 1.0 (far)] (default: 0.2)')
args = argparser.parse_args()
log_level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)
logging.info('listening to server %s:%s', args.host, args.port)
while True:
try:
run_carla_client(host=args.host, port=args.port, far=args.far)
print('\nDone!')
return
except TCPConnectionError as error:
logging.error(error)
time.sleep(1)
if __name__ == '__main__':
try:
main()
except KeyboardInterrupt:
print('\nClient stoped by user.')