128 lines
4.6 KiB
Python
Executable File
128 lines
4.6 KiB
Python
Executable File
#!/usr/bin/env python
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# Copyright (c) 2017 Computer Vision Center (CVC) at the Universitat Autonoma de
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# Barcelona (UAB).
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#
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# This work is licensed under the terms of the MIT license.
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# For a copy, see <https://opensource.org/licenses/MIT>.
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import glob
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import os
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import sys
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try:
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sys.path.append(glob.glob('**/*%d.%d-%s.egg' % (
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sys.version_info.major,
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sys.version_info.minor,
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'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
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except IndexError:
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pass
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import carla
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import random
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import time
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def main():
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actor_list = []
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# In this tutorial script, we are going to add a vehicle to the simulation
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# and let it drive in autopilot. We will also create a camera attached to
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# that vehicle, and save all the images generated by the camera to disk.
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try:
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# First of all, we need to create the client that will send the requests
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# to the simulator. Here we'll assume the simulator is accepting
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# requests in the localhost at port 2000.
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client = carla.Client('localhost', 2000)
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client.set_timeout(2.0)
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# Once we have a client we can retrieve the world that is currently
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# running.
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world = client.get_world()
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# The world contains the list blueprints that we can use for adding new
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# actors into the simulation.
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blueprint_library = world.get_blueprint_library()
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# Now let's filter all the blueprints of type 'vehicle' and choose one
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# at random.
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bp = random.choice(blueprint_library.filter('vehicle'))
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# A blueprint contains the list of attributes that define a vehicle
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# instance, we can read them and modify some of them. For instance,
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# let's randomize its color.
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color = random.choice(bp.get_attribute('color').recommended_values)
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bp.set_attribute('color', color)
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# Now we need to give an initial transform to the vehicle. We choose a
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# random transform from the list of recommended spawn points of the map.
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transform = random.choice(world.get_map().get_spawn_points())
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# So let's tell the world to spawn the vehicle.
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vehicle = world.spawn_actor(bp, transform)
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# It is important to note that the actors we create won't be destroyed
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# unless we call their "destroy" function. If we fail to call "destroy"
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# they will stay in the simulation even after we quit the Python script.
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# For that reason, we are storing all the actors we create so we can
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# destroy them afterwards.
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actor_list.append(vehicle)
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print('created %s' % vehicle.type_id)
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# Let's put the vehicle to drive around.
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vehicle.set_autopilot(True)
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# Let's add now a "depth" camera attached to the vehicle. Note that the
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# transform we give here is now relative to the vehicle.
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camera_bp = blueprint_library.find('sensor.camera.depth')
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camera_transform = carla.Transform(carla.Location(x=1.5, z=2.4))
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camera = world.spawn_actor(camera_bp, camera_transform, attach_to=vehicle)
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actor_list.append(camera)
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print('created %s' % camera.type_id)
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# Now we register the function that will be called each time the sensor
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# receives an image. In this example we are saving the image to disk
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# converting the pixels to gray-scale.
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cc = carla.ColorConverter.LogarithmicDepth
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camera.listen(lambda image: image.save_to_disk('_out/%06d.png' % image.frame_number, cc))
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# Oh wait, I don't like the location we gave to the vehicle, I'm going
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# to move it a bit forward.
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location = vehicle.get_location()
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location.x += 40
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vehicle.set_location(location)
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print('moved vehicle to %s' % location)
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# But the city now is probably quite empty, let's add a few more
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# vehicles.
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transform.location += carla.Location(x=40, y=-3.2)
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transform.rotation.yaw = -180.0
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for x in range(0, 10):
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transform.location.x += 8.0
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bp = random.choice(blueprint_library.filter('vehicle'))
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# This time we are using try_spawn_actor. If the spot is already
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# occupied by another object, the function will return None.
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npc = world.try_spawn_actor(bp, transform)
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if npc is not None:
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actor_list.append(npc)
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npc.set_autopilot()
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print('created %s' % npc.type_id)
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time.sleep(5)
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finally:
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print('destroying actors')
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for actor in actor_list:
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actor.destroy()
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print('done.')
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if __name__ == '__main__':
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main()
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