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Update cameras and sensors documentation

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Docs/cameras_and_sensors.md
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@ -1,194 +1,163 @@
<h1>Cameras and sensors</h1>
!!! important
This document still refers to the 0.8.X API (stable version), this API is
currently located under _"Deprecated/PythonClient"_. The proceedings stated
here may not apply to latest versions, 0.9.0 or later. Latest versions
introduced significant changes in the API, we are still working on
documenting everything, sorry for the inconvenience.
!!! important
Since version 0.8.0 the positions of the sensors are specified in meters
instead of centimeters. Always relative to the vehicle.
Cameras and sensors can be added to the player vehicle by defining them in the
settings sent by the client on every new episode. This can be done either by
filling a `CarlaSettings` Python class ([client_example.py][clientexamplelink])
or by loading an INI settings file ([CARLA Settings example][settingslink]).
This document describes the details of the different cameras/sensors currently
available as well as the resulting images produced by them.
Although we plan to extend the sensor suite of CARLA in the near future, at the
moment there are four different sensors available.
Sensors are one type of actor with the characteristic of having a listen
function, you can subscribe to the sensor by providing a callback function. This
callback is called each time a new measurement is received from the sensor.
* [Camera: Scene final](#camera-scene-final)
* [Camera: Depth map](#camera-depth-map)
* [Camera: Semantic segmentation](#camera-semantic-segmentation)
* [Ray-cast based lidar](#ray-cast-based-lidar)
You typically add a sensor to a vehicle with the following Python code, here we
are adding an HD camera
!!! note
The images are sent by the server as a BGRA array of bytes. The provided
Python client retrieves the images in this format, it's up to the users to
parse the images and convert them to the desired format. There are some
examples in the Deprecated/PythonClient folder showing how to parse the
images.
```py
# Find the blueprint of the sensor.
blueprint = world.get_blueprint_library().find('sensor.camera.rgb')
# Modify the attributes of the blueprint to set image resolution and field of view.
blueprint.set_attribute('image_size_x', '1920')
blueprint.set_attribute('image_size_y', '1080')
blueprint.set_attribute('fov', '100')
# Provide the position of the sensor relative to the vehicle.
transform = carla.Transform(carla.Location(x=0.8, z=1.7))
# Tell the world to spawn the sensor, don't forget to attach it to your vehicle actor.
sensor = world.spawn_actor(blueprint, transform, attach_to=my_vehicle)
# Subscribe to the sensor stream by providing a callback function, this function is
# called each time a new image is generated by the sensor.
sensor.listen(lambda image: do_something(image))
```
There is a fourth post-processing effect available for cameras, _None_, which
provides a view with of the scene with no effect, not even scene lighting; we
will skip this one in the following descriptions.
Note that each sensor has a different set of attributes and produces different
type of data. However, the data produced by a sensor comes always tagged with a
**frame number** and a **transform**. The frame number is used to identify the
frame at which the measurement took place, the transform gives you the
transformation in world coordinates of the sensor at that same frame.
We provide a tool to convert raw depth and semantic segmentation images in bulk
to a more human readable palette of colors. It can be found at
["Util/ImageConverter"][imgconvlink]. Alternatively, they can also be converted
using the functions at `carla.image_converter` Python module.
This is the list of sensors currently available in CARLA
Note that all the sensor data comes with a _frame number_ stamp, this _frame
number_ matches the one received in the measurements. This is especially useful
for running the simulator in asynchronous mode and synchronize sensor data on
the client side.
* [sensor.camera.rgb](#sensorcamerargb)
* [sensor.camera.depth](#sensorcameradepth)
* [sensor.camera.semantic_segmentation](#sensorcamerasemantic_segmentation)
* [sensor.lidar.ray_cast](#sensorlidarray_cast)
* [sensor.other.collision](#sensorothercollision)
* [sensor.other.lane_detector](#sensorotherlane_detector)
[clientexamplelink]: https://github.com/carla-simulator/carla/blob/master/Deprecated/PythonClient/client_example.py
[settingslink]: https://github.com/carla-simulator/carla/blob/master/Docs/Example.CarlaSettings.ini
[imgconvlink]: https://github.com/carla-simulator/carla/tree/master/Util/ImageConverter
sensor.camera.rgb
-----------------
Camera: Scene final
-------------------
![ImageRGB](img/capture_scenefinal.png)
![SceneFinal](img/capture_scenefinal.png)
The "RGB" camera acts as a regular camera capturing images from the scene.
The "scene final" camera provides a view of the scene after applying some
post-processing effects to create a more realistic feel. These are actually
stored in the Level, in an actor called [PostProcessVolume][postprolink] and not
in the Camera. We use the following post process effects:
| Blueprint attribute | Type | Default | Description |
| ------------------- | ---- | ------- | ----------- |
| `image_size_x` | int | 800 | Image width in pixels |
| `image_size_y` | int | 600 | Image height in pixels |
| `fov` | float | 90.0 | Field of view in degrees |
| `enable_postprocess_effects` | bool | True | Whether the post-process effect in the scene affect the image |
If `enable_postprocess_effects` is enabled, a set of post-process effects is
applied to the image to create a more realistic feel
* **Vignette** Darkens the border of the screen.
* **Grain jitter** Adds a bit of noise to the render.
* **Bloom** Intense lights burn the area around them.
* **Auto exposure** Modifies the image gamma to simulate the eye adaptation to darker or brighter areas.
* **Auto exposure** Modifies the image gamma to simulate the eye adaptation to
darker or brighter areas.
* **Lens flares** Simulates the reflection of bright objects on the lens.
* **Depth of field** Blurs objects near or very far away of the camera.
[postprolink]: https://docs.unrealengine.com/latest/INT/Engine/Rendering/PostProcessEffects/
This sensor produces `carla.Image` objects.
<h6>Python</h6>
| Sensor data attribute | Type | Description |
| --------------------- | ---- | ----------- |
| `frame_number` | int | Frame count when the measurement took place |
| `transform` | carla.Transform | Transform in world coordinates of the sensor at the time of the measurement |
| `width` | int | Image width in pixels |
| `height` | int | Image height in pixels |
| `fov` | float | Field of view in degrees |
| `raw_data` | bytes | Array of BGRA 32-bit pixels |
```py
camera = carla.sensor.Camera('MyCamera', PostProcessing='SceneFinal')
camera.set(FOV=90.0)
camera.set_image_size(800, 600)
camera.set_position(x=0.30, y=0, z=1.30)
camera.set_rotation(pitch=0, yaw=0, roll=0)
sensor.camera.depth
-------------------
carla_settings.add_sensor(camera)
![ImageDepth](img/capture_depth.png)
The "Depth" camera provides a view over the scene codifying the distance of each
pixel to the camera (also known as **depth buffer** or **z-buffer**).
| Blueprint attribute | Type | Default | Description |
| ------------------- | ---- | ------- | ----------- |
| `image_size_x` | int | 800 | Image width in pixels |
| `image_size_y` | int | 600 | Image height in pixels |
| `fov` | float | 90.0 | Field of view in degrees |
This sensor produces `carla.Image` objects.
| Sensor data attribute | Type | Description |
| --------------------- | ---- | ----------- |
| `frame_number` | int | Frame count when the measurement took place |
| `transform` | carla.Transform | Transform in world coordinates of the sensor at the time of the measurement |
| `width` | int | Image width in pixels |
| `height` | int | Image height in pixels |
| `fov` | float | Field of view in degrees |
| `raw_data` | bytes | Array of BGRA 32-bit pixels |
The image codifies the depth in 3 channels of the RGB color space, from less to
more significant bytes: R -> G -> B. The actual distance in meters can be
decoded with
```
normalized = (R + G * 256 + B * 256 * 256) / (256 * 256 * 256 - 1)
in_meters = 1000 * normalized
```
<h6>CarlaSettings.ini</h6>
sensor.camera.semantic_segmentation
-----------------------------------
```ini
[CARLA/Sensor/MyCamera]
SensorType=CAMERA
PostProcessing=SceneFinal
ImageSizeX=800
ImageSizeY=600
FOV=90
PositionX=0.30
PositionY=0
PositionZ=1.30
RotationPitch=0
RotationRoll=0
RotationYaw=0
```
![ImageSemanticSegmentation](img/capture_semseg.png)
Camera: Depth map
-----------------
![Depth](img/capture_depth.png)
The "depth map" camera provides an image with 24 bit floating precision point
codified in the 3 channels of the RGB color space. The order from less to more
significant bytes is R -> G -> B.
| R | G | B | int24 | |
|----------|----------|----------|----------|------------|
| 00000000 | 00000000 | 00000000 | 0 | min (near) |
| 11111111 | 11111111 | 11111111 | 16777215 | max (far) |
Our max render distance (far) is 1km.
1. To decodify our depth first we get the int24.
R + G*256 + B*256*256
2. Then normalize it in the range [0, 1].
Ans / ( 256*256*256 - 1 )
3. And finally multiply for the units that we want to get. We have set the far plane at 1000 metres.
Ans * far
The generated "depth map" images are usually converted to a logarithmic
grayscale for display. A point cloud can also be extracted from depth images as
seen in "Deprecated/PythonClient/point_cloud_example.py".
<h6>Python</h6>
```py
camera = carla.sensor.Camera('MyCamera', PostProcessing='Depth')
camera.set(FOV=90.0)
camera.set_image_size(800, 600)
camera.set_position(x=0.30, y=0, z=1.30)
camera.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(camera)
```
<h6>CarlaSettings.ini</h6>
```ini
[CARLA/Sensor/MyCamera]
SensorType=CAMERA
PostProcessing=Depth
ImageSizeX=800
ImageSizeY=600
FOV=90
PositionX=0.30
PositionY=0
PositionZ=1.30
RotationPitch=0
RotationRoll=0
RotationYaw=0
```
Camera: Semantic segmentation
-----------------------------
![SemanticSegmentation](img/capture_semseg.png)
The "semantic segmentation" camera classifies every object in the view by
The "Semantic Segmentation" camera classifies every object in the view by
displaying it in a different color according to the object class. E.g.,
pedestrians appear in a different color than vehicles.
| Blueprint attribute | Type | Default | Description |
| ------------------- | ---- | ------- | ----------- |
| `image_size_x` | int | 800 | Image width in pixels |
| `image_size_y` | int | 600 | Image height in pixels |
| `fov` | float | 90.0 | Field of view in degrees |
This sensor produces `carla.Image` objects.
| Sensor data attribute | Type | Description |
| --------------------- | ---- | ----------- |
| `frame_number` | int | Frame count when the measurement took place |
| `transform` | carla.Transform | Transform in world coordinates of the sensor at the time of the measurement |
| `width` | int | Image width in pixels |
| `height` | int | Image height in pixels |
| `fov` | float | Field of view in degrees |
| `raw_data` | bytes | Array of BGRA 32-bit pixels |
The server provides an image with the tag information **encoded in the red
channel**. A pixel with a red value of x displays an object with tag x. The
following tags are currently available
Value | Tag
-----:|:-----
0 | None
1 | Buildings
2 | Fences
3 | Other
4 | Pedestrians
5 | Poles
6 | RoadLines
7 | Roads
8 | Sidewalks
9 | Vegetation
10 | Vehicles
11 | Walls
12 | TrafficSigns
| Value | Tag | Converted color |
| -----:|:------------ | --------------- |
| 0 | Unlabeled | ( 0, 0, 0) |
| 1 | Building | ( 70, 70, 70) |
| 2 | Fence | (190, 153, 153) |
| 3 | Other | (250, 170, 160) |
| 4 | Pedestrian | (220, 20, 60) |
| 5 | Pole | (153, 153, 153) |
| 6 | Road line | (157, 234, 50) |
| 7 | Road | (128, 64, 128) |
| 8 | Sidewalk | (244, 35, 232) |
| 9 | Vegetation | (107, 142, 35) |
| 10 | Car | ( 0, 0, 142) |
| 11 | Wall | (102, 102, 156) |
| 12 | Traffic sign | (220, 220, 0) |
This is implemented by tagging every object in the scene before hand (either at
begin play or on spawn). The objects are classified by their relative file path
@ -202,91 +171,98 @@ _"Unreal/CarlaUE4/Content/Static/Pedestrians"_ folder it's tagged as pedestrian.
and its corresponding filepath check inside `GetLabelByFolderName()`
function in "Tagger.cpp".
<h6>Python</h6>
```py
camera = carla.sensor.Camera('MyCamera', PostProcessing='SemanticSegmentation')
camera.set(FOV=90.0)
camera.set_image_size(800, 600)
camera.set_position(x=0.30, y=0, z=1.30)
camera.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(camera)
```
<h6>CarlaSettings.ini</h6>
```ini
[CARLA/Sensor/MyCamera]
SensorType=CAMERA
PostProcessing=SemanticSegmentation
ImageSizeX=800
ImageSizeY=600
FOV=90
PositionX=0.30
PositionY=0
PositionZ=1.30
RotationPitch=0
RotationRoll=0
RotationYaw=0
```
Ray-cast based Lidar
--------------------
sensor.lidar.ray_cast
---------------------
![LidarPointCloud](img/lidar_point_cloud.gif)
A rotating Lidar implemented with ray-casting. The points are computed by adding
a laser for each channel distributed in the vertical FOV, then the rotation is
simulated computing the horizontal angle that the Lidar rotated this frame, and
doing a ray-cast for each point that each laser was supposed to generate this
frame; `PointsPerSecond / (FPS * Channels)`.
This sensor simulates a rotating Lidar implemented using ray-casting. The points
are computed by adding a laser for each channel distributed in the vertical FOV,
then the rotation is simulated computing the horizontal angle that the Lidar
rotated this frame, and doing a ray-cast for each point that each laser was
supposed to generate this frame; `PointsPerSecond / (FPS * Channels)`.
Each frame the server sends a packet with all the points generated during a
`1/FPS` interval. During the interval the physics wasn’t updated so all the
points in a packet reflect the same "static picture" of the scene.
| Blueprint attribute | Type | Default | Description |
| -------------------- | ---- | ------- | ----------- |
| `channels` | int | 32 | Number of lasers |
| `range` | float | 1000 | Maximum measurement distance in meters |
| `points_per_second` | int | 56000 | Points generated by all lasers per second |
| `rotation_frequency` | float | 10.0 | Lidar rotation frequency |
| `upper_fov` | float | 10.0 | Angle in degrees of the upper most laser |
| `lower_fov` | float | -30.0 | Angle in degrees of the lower most laser |
The received `LidarMeasurement` object contains the following information
This sensor produces `carla.LidarMeasurement` objects.
Key | Type | Description
-------------------------- | ---------- | ------------
horizontal_angle | float | Angle in XY plane of the lidar this frame (in degrees).
channels | uint32 | Number of channels (lasers) of the lidar.
point_count_by_channel | uint32 | Number of points per channel captured this frame.
point_cloud | PointCloud | Captured points this frame.
| Sensor data attribute | Type | Description |
| -------------------------- | ---------- | ----------- |
| `frame_number` | int | Frame count when the measurement took place |
| `transform` | carla.Transform | Transform in world coordinates of the sensor at the time of the measurement |
| `horizontal_angle` | float | Angle in XY plane of the lidar this frame (in degrees) |
| `channels` | int | Number of channels (lasers) of the lidar |
| `get_point_count(channel)` | int | Number of points per channel captured this frame |
| `raw_data` | bytes | Array of 32-bits floats (XYZ of each point) |
<h6>Python</h6>
The object also acts as a Python list of `carla.Location`
```py
lidar = carla.sensor.Lidar('MyLidar')
lidar.set(
Channels=32,
Range=50,
PointsPerSecond=100000,
RotationFrequency=10,
UpperFovLimit=10,
LowerFovLimit=-30)
lidar.set_position(x=0, y=0, z=1.40)
lidar.set_rotation(pitch=0, yaw=0, roll=0)
carla_settings.add_sensor(lidar)
for location in lidar_measurement:
print(location)
```
<h6>CarlaSettings.ini</h6>
A Lidar measurement contains a packet with all the points generated during a
`1/FPS` interval. During this interval the physics is not updated so all the
points in a measurement reflect the same "static picture" of the scene.
```ini
[CARLA/Sensor/MyLidar]
SensorType=LIDAR_RAY_CAST
Channels=32
Range=50
PointsPerSecond=100000
RotationFrequency=10
UpperFOVLimit=10
LowerFOVLimit=-30
PositionX=0
PositionY=0
PositionZ=1.40
RotationPitch=0
RotationYaw=0
RotationRoll=0
```
!!! tip
Running the simulator at
[fixed time-step](configuring_the_simulation.md#fixed-time-step) it is
possible to tune the horizontal angle of each measurement. By adjusting the
frame rate and the rotation frequency is possible, for instance, to get a
360 view each measurement.
sensor.other.collision
----------------------
This sensor, when attached to an actor, it registers an event each time the
actor collisions against something in the world. This sensor does not have any
configurable attribute.
This sensor produces a `carla.CollisionEvent` object for each collision
registered
| Sensor data attribute | Type | Description |
| ---------------------- | ----------- | ----------- |
| `frame_number` | int | Frame count when the measurement took place |
| `transform` | carla.Transform | Transform in world coordinates of the sensor at the time of the measurement |
| `actor` | carla.Actor | Actor that measured the collision ("self" actor) |
| `other_actor` | carla.Actor | Actor against whom we collide |
| `normal_impulse` | carla.Vector3D | Normal impulse result of the collision |
Note that several collision events might be registered during a single
simulation update.
sensor.other.lane_detector
--------------------------
> _This sensor is a work in progress, currently very limited._
This sensor, when attached to an actor, it registers an event each time the
actor crosses a lane marking. This sensor is somehow special as it works fully
on the client-side. The lane detector uses the road data of the active map to
determine whether a vehicle is invading another lane. This information is based
on the OpenDrive file provided by the map, therefore it is subject to the
fidelity of the OpenDrive description. In some places there might be
discrepancies between the lanes visible by the cameras and the lanes registered
by this sensor.
This sensor does not have any configurable attribute.
This sensor produces a `carla.LaneInvasionEvent` object for each lane marking
crossed by the actor
| Sensor data attribute | Type | Description |
| ----------------------- | ----------- | ----------- |
| `frame_number` | int | Frame count when the measurement took place |
| `transform` | carla.Transform | Transform in world coordinates of the sensor at the time of the measurement |
| `actor` | carla.Actor | Actor that invaded another lane ("self" actor) |
| `crossed_lane_markings` | carla.LaneMarking list | List of lane markings that have been crossed |

1
LibCarla/source/carla/image/CityScapesPalette.h
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@ -16,6 +16,7 @@ namespace detail {
#if __cplusplus >= 201703L // C++17
inline
#endif
// Please update documentation if you change this.
uint8_t CITYSCAPES_PALETTE_MAP[][3u] = {
{ 0u, 0u, 0u}, // unlabeled = 0u,
{ 70u, 70u, 70u}, // building = 1u,

2
Unreal/CarlaUE4/Plugins/Carla/Source/Carla/Actor/ActorBlueprintFunctionLibrary.cpp
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@ -306,7 +306,7 @@ void UActorBlueprintFunctionLibrary::MakeLidarDefinition(
FActorVariation Range;
Range.Id = TEXT("range");
Range.Type = EActorAttributeType::Float;
Range.RecommendedValues = { TEXT("5000.0") };
Range.RecommendedValues = { TEXT("1000.0") };
// Points per second.
FActorVariation PointsPerSecond;
PointsPerSecond.Id = TEXT("points_per_second");