How to use the carla.ColorConverter function in carla
To help you get started, we’ve selected a few carla examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
layssi / Carla_Ray_Rlib / test_code / synchronous_mode.py View on Github 
# Create a synchronous mode context.
with CarlaSyncMode(world, camera_rgb, camera_semseg, fps=50) as sync_mode:
while True:
if should_quit():
return
clock.tick()
t = time.time()
# Advance the simulation and wait for the data.
snapshot, image_rgb, image_semseg = sync_mode.tick(timeout=2.0)
# Choose the next waypoint and update the car location.
waypoint = random.choice(waypoint.next(1.5))
vehicle.set_transform(waypoint.transform)
image_semseg.convert(carla.ColorConverter.CityScapesPalette)
fps = round(1.0 / snapshot.timestamp.delta_seconds)
# Draw the display.
draw_image(display, image_rgb)
draw_image(display, image_semseg, blend=True)
display.blit(
font.render('% 5d FPS (real)' % clock.get_fps(), True, (255, 255, 255)),
(8, 10))
display.blit(
font.render('% 5d FPS (simulated)' % fps, True, (255, 255, 255)),
(8, 28))
pygame.display.flip()
elapsed = time.time() - t
print("Elapsed (ms):{:0.2f}".format(elapsed * 1000))
finally:def __init__(self, parent_actor, hud):
self.sensor = None
self.surface = None
self._parent = parent_actor
self.hud = hud
self.recording = False
self._camera_transforms = [
carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15)),
carla.Transform(carla.Location(x=1.6, z=1.7))]
self.transform_index = 1
self.sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB'],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)'],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)'],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)'],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)'],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette,
'Camera Semantic Segmentation (CityScapes Palette)'],
['sensor.lidar.ray_cast', None, 'Lidar (Ray-Cast)']]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self.sensors:
bp = bp_library.find(item[0])
if item[0].startswith('sensor.camera'):
bp.set_attribute('image_size_x', str(hud.dim[0]))
bp.set_attribute('image_size_y', str(hud.dim[1]))
elif item[0].startswith('sensor.lidar'):
bp.set_attribute('range', '5000')self._agent_view = None
self._current_frame = 0
self._agent_view_internal_1 = None
self._agent_view_internal_2 = None
self._agent_view_internal_3 = None
self._parent = parent_actor
self._hud = hud
self._recording = False
self._camera_transforms = [
carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15)),
carla.Transform(carla.Location(x=1.6, z=1.7))]
self._transform_index = 1
self._sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB'],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)'],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)'],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)'],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)'],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette,
'Camera Semantic Segmentation (CityScapes Palette)'],
['sensor.lidar.ray_cast', None, 'Lidar (Ray-Cast)']]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self._sensors:
bp = bp_library.find(item[0])
if item[0].startswith('sensor.camera'):
bp.set_attribute('image_size_x', str(hud.dim[0]))
bp.set_attribute('image_size_y', str(hud.dim[1]))
item.append(bp)
self._index = Nonedef __init__(self, parent_actor, hud):
self.sensor = None
self._surface = None
self._parent = parent_actor
self._hud = hud
self._recording = False
self._camera_transforms = [
carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15)),
carla.Transform(carla.Location(x=1.6, z=1.7))]
self._transform_index = 1
self._sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB'],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)'],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)'],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)'],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)'],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette, 'Camera Semantic Segmentation (CityScapes Palette)'],
['sensor.lidar.ray_cast', None, 'Lidar (Ray-Cast)']]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self._sensors:
bp = bp_library.find(item[0])
if item[0].startswith('sensor.camera'):
bp.set_attribute('image_size_x', str(hud.dim[0]))
bp.set_attribute('image_size_y', str(hud.dim[1]))
elif item[0].startswith('sensor.lidar'):
bp.set_attribute('range', '5000')
bp.set_attribute('role_name', 'view')
item.append(bp)self.sensor = None
self.surface = None
self._parent = parent_actor
self.hud = hud
self.recording = False
bound_y = 0.5 + self._parent.bounding_box.extent.y
Attachment = carla.AttachmentType
self._camera_transforms = [
(carla.Transform(carla.Location(x=-5.5, z=2.5), carla.Rotation(pitch=8.0)), Attachment.SpringArm),
(carla.Transform(carla.Location(x=1.6, z=1.7)), Attachment.Rigid),
(carla.Transform(carla.Location(x=5.5, y=1.5, z=1.5)), Attachment.SpringArm),
(carla.Transform(carla.Location(x=-8.0, z=6.0), carla.Rotation(pitch=6.0)), Attachment.SpringArm),
(carla.Transform(carla.Location(x=-1, y=-bound_y, z=0.5)), Attachment.Rigid)]
self.transform_index = 1
self.sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB'],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)'],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)'],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)'],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)'],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette,
'Camera Semantic Segmentation (CityScapes Palette)'],
['sensor.lidar.ray_cast', None, 'Lidar (Ray-Cast)']]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self.sensors:
bp = bp_library.find(item[0])
if item[0].startswith('sensor.camera'):
bp.set_attribute('image_size_x', str(hud.dim[0]))
bp.set_attribute('image_size_y', str(hud.dim[1]))
elif item[0].startswith('sensor.lidar'):
bp.set_attribute('range', '5000')def __init__(self, parent_actor, hud):
self.sensor = None
self._surface = None
self._parent = parent_actor
self._hud = hud
self._recording = False
self._camera_transforms = [
carla.Transform(carla.Location(x=1.6, z=1.7)),
carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15))]
self._transform_index = 1
self._sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB'],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)'],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)'],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)'],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)'],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette, 'Camera Semantic Segmentation (CityScapes Palette)']]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self._sensors:
bp = bp_library.find(item[0])
bp.set_attribute('image_size_x', str(hud.dim[0]))
bp.set_attribute('image_size_y', str(hud.dim[1]))
item.append(bp)
self._index = None
self._server_clock = pygame.time.Clock()self.sensor = None
self.surface = None
self._parent = parent_actor
self.hud = hud
self.recording = False
bound_y = 0.5 + self._parent.bounding_box.extent.y
Attachment = carla.AttachmentType
self._camera_transforms = [
(carla.Transform(carla.Location(x=-5.5, z=2.5), carla.Rotation(pitch=8.0)), Attachment.SpringArm),
(carla.Transform(carla.Location(x=1.6, z=1.7)), Attachment.Rigid),
(carla.Transform(carla.Location(x=5.5, y=1.5, z=1.5)), Attachment.SpringArm),
(carla.Transform(carla.Location(x=-8.0, z=6.0), carla.Rotation(pitch=6.0)), Attachment.SpringArm),
(carla.Transform(carla.Location(x=-1, y=-bound_y, z=0.5)), Attachment.Rigid)]
self.transform_index = 1
self.sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB', {}],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)', {}],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)', {}],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)', {}],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)', {}],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette,
'Camera Semantic Segmentation (CityScapes Palette)', {}],
['sensor.lidar.ray_cast', None, 'Lidar (Ray-Cast)', {}],
['sensor.camera.rgb', cc.Raw, 'Camera RGB Distorted',
{'lens_circle_multiplier': '3.0',
'lens_circle_falloff': '3.0',
'chromatic_aberration_intensity': '0.5',
'chromatic_aberration_offset': '0'}]]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self.sensors:
bp = bp_library.find(item[0])),
(
carla.Transform(
carla.Location(x=-8.0, z=6.0), carla.Rotation(pitch=6.0)
),
attachment.SpringArm,
),
(
carla.Transform(carla.Location(x=-1, y=-bound_y, z=0.5)),
attachment.Rigid,
),
]
self.transform_index = 1
self.sensors = [
["sensor.camera.rgb", cc.Raw, "Camera RGB"],
["sensor.camera.depth", cc.Raw, "Camera Depth (Raw)"],
["sensor.camera.depth", cc.Depth, "Camera Depth (Gray Scale)"],
[
"sensor.camera.depth",
cc.LogarithmicDepth,
"Camera Depth (Logarithmic Gray Scale)",
],
[
"sensor.camera.semantic_segmentation",
cc.Raw,
"Camera Semantic Segmentation (Raw)",
],
[
"sensor.camera.semantic_segmentation",
cc.CityScapesPalette,
"Camera Semantic Segmentation (CityScapes Palette)",
],def process_img(image):
image.convert(cc.Raw)
image.save_to_disk('_out/{:08d}.png'.format(image.frame_number))
_last_transform = image.transform
print('process_img', image.frame_number, _last_transform)
# image.save_to_disk('_out/%08d' % image.frame_number)self._agent_view_internal_1 = None
self._agent_view_internal_2 = None
self._agent_view_internal_3 = None
self._parent = parent_actor
self._hud = hud
self._recording = False
self._camera_transforms = [
carla.Transform(carla.Location(x=-5.5, z=2.8), carla.Rotation(pitch=-15)),
carla.Transform(carla.Location(x=1.6, z=1.7))]
self._transform_index = 1
self._sensors = [
['sensor.camera.rgb', cc.Raw, 'Camera RGB'],
['sensor.camera.depth', cc.Raw, 'Camera Depth (Raw)'],
['sensor.camera.depth', cc.Depth, 'Camera Depth (Gray Scale)'],
['sensor.camera.depth', cc.LogarithmicDepth, 'Camera Depth (Logarithmic Gray Scale)'],
['sensor.camera.semantic_segmentation', cc.Raw, 'Camera Semantic Segmentation (Raw)'],
['sensor.camera.semantic_segmentation', cc.CityScapesPalette,
'Camera Semantic Segmentation (CityScapes Palette)'],
['sensor.lidar.ray_cast', None, 'Lidar (Ray-Cast)']]
world = self._parent.get_world()
bp_library = world.get_blueprint_library()
for item in self._sensors:
bp = bp_library.find(item[0])
if item[0].startswith('sensor.camera'):
bp.set_attribute('image_size_x', str(hud.dim[0]))
bp.set_attribute('image_size_y', str(hud.dim[1]))
item.append(bp)
self._index = Nonecarla
Python API for communicating with the CARLA server.
Package Health Score
68 / 100Popular carla functions
- carla.Client
- carla.client.make_carla_client
- carla.Color
- carla.ColorConverter
- carla.driving_benchmark.experiment.Experiment
- carla.driving_benchmark.experiment_suites.experiment_suite.ExperimentSuite
- carla.LaneType
- carla.Location
- carla.Rotation
- carla.sensor.Camera
- carla.settings.CarlaSettings
- carla.TrafficLightState
- carla.TrafficLightState.Green
- carla.Transform
- carla.util.print_over_same_line
- carla.Vector3D
- carla.VehicleControl
- carla.VehicleLightState
- carla.WalkerControl
- carla.WeatherParameters