carla/PythonAPI/tutorial.py

128 lines
4.6 KiB
Python
Executable File

#!/usr/bin/env python
# Copyright (c) 2017 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>.
import glob
import os
import sys
try:
sys.path.append(glob.glob('**/*%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
import random
import time
def main():
actor_list = []
# In this tutorial script, we are going to add a vehicle to the simulation
# and let it drive in autopilot. We will also create a camera attached to
# that vehicle, and save all the images generated by the camera to disk.
try:
# First of all, we need to create the client that will send the requests
# to the simulator. Here we'll assume the simulator is accepting
# requests in the localhost at port 2000.
client = carla.Client('localhost', 2000)
client.set_timeout(2.0)
# Once we have a client we can retrieve the world that is currently
# running.
world = client.get_world()
# The world contains the list blueprints that we can use for adding new
# actors into the simulation.
blueprint_library = world.get_blueprint_library()
# Now let's filter all the blueprints of type 'vehicle' and choose one
# at random.
bp = random.choice(blueprint_library.filter('vehicle'))
# A blueprint contains the list of attributes that define a vehicle
# instance, we can read them and modify some of them. For instance,
# let's randomize its color.
color = random.choice(bp.get_attribute('color').recommended_values)
bp.set_attribute('color', color)
# Now we need to give an initial transform to the vehicle. We choose a
# random transform from the list of recommended spawn points of the map.
transform = random.choice(world.get_map().get_spawn_points())
# So let's tell the world to spawn the vehicle.
vehicle = world.spawn_actor(bp, transform)
# It is important to note that the actors we create won't be destroyed
# unless we call their "destroy" function. If we fail to call "destroy"
# they will stay in the simulation even after we quit the Python script.
# For that reason, we are storing all the actors we create so we can
# destroy them afterwards.
actor_list.append(vehicle)
print('created %s' % vehicle.type_id)
# Let's put the vehicle to drive around.
vehicle.set_autopilot(True)
# Let's add now a "depth" camera attached to the vehicle. Note that the
# transform we give here is now relative to the vehicle.
camera_bp = blueprint_library.find('sensor.camera.depth')
camera_transform = carla.Transform(carla.Location(x=1.5, z=2.4))
camera = world.spawn_actor(camera_bp, camera_transform, attach_to=vehicle)
actor_list.append(camera)
print('created %s' % camera.type_id)
# Now we register the function that will be called each time the sensor
# receives an image. In this example we are saving the image to disk
# converting the pixels to gray-scale.
cc = carla.ColorConverter.LogarithmicDepth
camera.listen(lambda image: image.save_to_disk('_out/%06d.png' % image.frame_number, cc))
# Oh wait, I don't like the location we gave to the vehicle, I'm going
# to move it a bit forward.
location = vehicle.get_location()
location.x += 40
vehicle.set_location(location)
print('moved vehicle to %s' % location)
# But the city now is probably quite empty, let's add a few more
# vehicles.
transform.location += carla.Location(x=40, y=-3.2)
transform.rotation.yaw = -180.0
for x in range(0, 10):
transform.location.x += 8.0
bp = random.choice(blueprint_library.filter('vehicle'))
# This time we are using try_spawn_actor. If the spot is already
# occupied by another object, the function will return None.
npc = world.try_spawn_actor(bp, transform)
if npc is not None:
actor_list.append(npc)
npc.set_autopilot()
print('created %s' % npc.type_id)
time.sleep(5)
finally:
print('destroying actors')
for actor in actor_list:
actor.destroy()
print('done.')
if __name__ == '__main__':
main()