Please read general API doc first if you are not familiar with AirSim APIs.
Here's a sample code to get a single image from camera named "0". The returned value is bytes of png format image. To get uncompressed and other format as well as available cameras please see next sections.
import cosysairsim as airsim
# for car use CarClient()
client = airsim.MultirotorClient()
png_image = client.simGetImage("0", airsim.ImageType.Scene)
# do something with image#include "vehicles/multirotor/api/MultirotorRpcLibClient.hpp"
int getOneImage()
{
using namespace msr::airlib;
// for car use CarRpcLibClient
MultirotorRpcLibClient client;
std::vector<uint8_t> png_image = client.simGetImage("0", VehicleCameraBase::ImageType::Scene);
// do something with images
}The simGetImages API which is slightly more complex to use than simGetImage API, for example, you can get left camera view, right camera view and depth image from left camera in a single API call. The simGetImages API also allows you to get uncompressed images as well as floating point single channel images (instead of 3 channel (RGB), each 8 bit).
import cosysairsim as airsim
# for car use CarClient()
client = airsim.MultirotorClient()
responses = client.simGetImages([
# png format
airsim.ImageRequest(0, airsim.ImageType.Scene),
# uncompressed RGB array bytes
airsim.ImageRequest(1, airsim.ImageType.Scene, False, False),
# floating point uncompressed image
airsim.ImageRequest(1, airsim.ImageType.DepthPlanar, True)])
# do something with response which contains image data, pose, timestamp etcIf you plan to use numpy for image manipulation, you should get uncompressed RGB image and then convert to numpy like this:
responses = client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.Scene, False, False)])
response = responses[0]
# get numpy array
img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8)
# reshape array to 4 channel image array H X W X 4
img_rgb = img1d.reshape(response.height, response.width, 3)
# original image is fliped vertically
img_rgb = np.flipud(img_rgb)
# write to png
airsim.write_png(os.path.normpath(filename + '.png'), img_rgb) -
The API
simGetImagereturnsbinary string literalwhich means you can simply dump it in binary file to create a .png file. However if you want to process it in any other way than you can handy functionairsim.string_to_uint8_array. This converts binary string literal to NumPy uint8 array. -
The API
simGetImagescan accept request for multiple image types from any cameras in single call. You can specify if image is png compressed, RGB uncompressed or float array. For png compressed images, you getbinary string literal. For float array you get Python list of float64. You can convert this float array to NumPy 2D array usingairsim.list_to_2d_float_array(response.image_data_float, response.width, response.height)You can also save float array to .pfm file (Portable Float Map format) using
airsim.write_pfm()function. -
If you are looking to query position and orientation information in sync with a call to one of the image APIs, you can use
client.simPause(True)andclient.simPause(False)to pause the simulation while calling the image API and querying the desired physics state, ensuring that the physics state remains the same immediately after the image API call.
int getStereoAndDepthImages()
{
using namespace msr::airlib;
typedef VehicleCameraBase::ImageRequest ImageRequest;
typedef VehicleCameraBase::ImageResponse ImageResponse;
typedef VehicleCameraBase::ImageType ImageType;
// for car use
// CarRpcLibClient client;
MultirotorRpcLibClient client;
// get right, left and depth images. First two as png, second as float16.
std::vector<ImageRequest> request = {
//png format
ImageRequest("0", ImageType::Scene),
//uncompressed RGB array bytes
ImageRequest("1", ImageType::Scene, false, false),
//floating point uncompressed image
ImageRequest("1", ImageType::DepthPlanar, true)
};
const std::vector<ImageResponse>& response = client.simGetImages(request);
// do something with response which contains image data, pose, timestamp etc
}For a more complete ready to run sample code please see sample code in HelloDrone project for multirotors or HelloCar project.
See also other example code that generates specified number of stereo images along with ground truth depth and disparity and saving it to pfm format.
These are the default cameras already available in each vehicle. Apart from these, you can add more cameras to the vehicles or make them are not attached to any vehicle by setting them as external.
The cameras on car can be accessed by following names in API calls: front_center, front_right, front_left, fpv and back_center. Here FPV camera is driver's head position in the car.
The cameras on the drone can be accessed by following names in API calls: front_center, front_right, front_left, bottom_center and back_center.
Camera names are same as in multirotor.
Before AirSim v1.2, cameras were accessed using ID numbers instead of names. For backward compatibility you can still use following ID numbers for above camera names in same order as above: "0", "1", "2", "3", "4". In addition, camera name "" is also available to access the default camera which is generally the camera "0".
You can use AirSim in so-called "Computer Vision" mode. In this mode, physics engine is disabled and there is no vehicle, just cameras (If you want to have the vehicle but without its kinematics, you can use the Multirotor mode with the Physics Engine ExternalPhysicsEngine). You can move around using keyboard (use F1 to see help on keys). You can press Record button to continuously generate images. Or you can call APIs to move cameras around and take images. You can use AirSim in so-called "Computer Vision" mode. In this mode, physics engine is disabled. It has a standard set of cameras and can have any sensor added similar to other vehicles. You can move around using keyboard (use F1 to see help on keys, additionally use left shift to go faster and spacebar to hold in place (handy for when moving camera manually). You can press Record button to continuously generate images. Or you can call APIs to move cameras around and take images.
To active this mode, edit settings.json that you can find in your Documents\AirSim folder (or ~/Documents/AirSim on Linux) and make sure following values exist at root level:
{
"SettingsVersion": 2.0,
"SimMode": "ComputerVision"
}This mode was inspired from UnrealCV project.
To move around the environment using APIs you can use simSetVehiclePose API. This API takes position and orientation and sets that on the invisible vehicle where the front-center camera is located. All rest of the cameras move along keeping the relative position. If you don't want to change position (or orientation) then just set components of position (or orientation) to floating point nan values. The simGetVehiclePose allows to retrieve the current pose. You can also use simGetGroundTruthKinematics to get the quantities kinematics quantities for the movement. Many other non-vehicle specific APIs are also available such as segmentation APIs, collision APIs and camera APIs.
The simGetCameraInfo returns the FOV(in degrees), projection matrix of a camera as well as the pose which can be:
- Default: The pose of the camera in the vehicle frame.
- External: If set to
Externalthe coordinates will be in either Unreal NED whenExternalLocalisfalseor Local NED (from starting position from vehicle) whenExternalLocalistrue.
Note that if MoveWorldOrigin in the settings.json is set to true the Unreal coordinates will be moved to be the same origin as the player start location and as such this may effect where the sensor will spawn and which coordinates are returned when ExternalLocal is false.
The simSetCameraPose sets the pose for the specified camera while taking an input pose as a combination of relative position and a quaternion in NED frame. The handy airsim.to_quaternion() function allows to convert pitch, roll, yaw to quaternion. For example, to set camera-0 to 15-degree pitch while maintaining the same position, you can use:
camera_pose = airsim.Pose(airsim.Vector3r(0, 0, 0), airsim.to_quaternion(0.261799, 0, 0)) #PRY in radians
client.simSetCameraPose(0, camera_pose);
simSetCameraFovallows changing the Field-of-View of the camera at runtime.simSetDistortionParams,simGetDistortionParamsallow setting and fetching the distortion parameters K1, K2, K3, P1, P2
All Camera APIs take in 3 common parameters apart from the API-specific ones, camera_name(str), vehicle_name(str). Camera and vehicle name is used to get the specific camera on the specific vehicle.
You can set stabilization for pitch, roll or yaw for any camera using settings.
To change resolution, FOV etc, you can use settings.json. For example, below addition in settings.json sets parameters for scene capture and uses "Computer Vision" mode described above. If you omit any setting then below default values will be used. For more information see settings doc. If you are using stereo camera, currently the distance between left and right is fixed at 25 cm.
{
"SettingsVersion": 2.0,
"CameraDefaults": {
"CaptureSettings": [
{
"ImageType": 0,
"Width": 256,
"Height": 144,
"FOV_Degrees": 90,
"AutoExposureBias": 1.3,
"AutoExposureMaxBrightness": 0.64,
"AutoExposureMinBrightness": 0.03,
"MotionBlurAmount": 1,
"MotionBlurMax": 10,
"ChromaticAberrationScale": 2,
"LumenGIEnable": true,
"LumenReflectionEnable": true,
"LumenFinalQuality": 1,
"LumenSceneDetail": 1,
"LumenSceneLightningDetail": 1
}
]
},
"SimMode": "ComputerVision"
} Scene = 0,
DepthPlanar = 1,
DepthPerspective = 2,
DepthVis = 3,
DisparityNormalized = 4,
Segmentation = 5,
SurfaceNormals = 6,
Infrared = 7,
OpticalFlow = 8,
OpticalFlowVis = 9
Annotation = 10You normally want to retrieve the depth image as float (i.e. set pixels_as_float = true) and specify ImageType = DepthPlanar or ImageType = DepthPerspective in ImageRequest. For ImageType = DepthPlanar, you get depth in camera plane, i.e., all points that are plane-parallel to the camera have same depth. For ImageType = DepthPerspective, you get depth from camera using a projection ray that hits that pixel. Depending on your use case, planner depth or perspective depth may be the ground truth image that you want. For example, you may be able to feed perspective depth to ROS package such as depth_image_proc to generate a point cloud. Or planner depth may be more compatible with estimated depth image generated by stereo algorithms such as SGM.
When you specify ImageType = DepthVis in ImageRequest, you get an image that helps depth visualization. In this case, each pixel value is interpolated from black to white depending on depth in camera plane in meters. The pixels with pure white means depth of 100m or more while pure black means depth of 0 meters.
You normally want to retrieve disparity image as float (i.e. set pixels_as_float = true and specify ImageType = DisparityNormalized in ImageRequest) in which case each pixel is (Xl - Xr)/Xmax, which is thereby normalized to values between 0 to 1.
When you specify ImageType = Segmentation in ImageRequest, you get an image that gives you ground truth instance segmentation of the scene. At the startup, AirSim assigns a random color index to each mesh available in environment. You can disable this by setting the main parameter InitialInstanceSegmentation to false in the settings.json file. The RGB values for each color index ID can be retrieved from the API.
You can assign a specific value to a specific mesh using APIs. For example, below Python code sets the object ID for the mesh called "Ground" to 20 in Blocks environment and hence changes its color in Segmentation view to the 20th color of the instance segmentation colormap: Note that this will not do a check if this color is already assigned to a different object!
success = client.simSetSegmentationObjectID("Ground", 20)The return value is a boolean type that lets you know if the mesh was found.
Notice that typical Unreal environments, like Blocks, usually have many other meshes that comprises of same object, for example, "Ground_2", "Ground_3" and so on. As it is tedious to set object ID for all of these meshes, AirSim also supports regular expressions. For example, the code below sets all meshes which have names starting with "ground" (ignoring case) to 21 with just one line:
success = client.simSetSegmentationObjectID("ground[\w]*", 21, True)The return value is true if at least one mesh was found using regular expression matching.
When wanting to retrieve the segmentation image through the API, it is recommended that you request uncompressed image using this API to ensure you get precise RGB values for segmentation image:
responses = client.simGetImages([airsim.ImageRequest( "front_center", airsim.ImageType.Segmentation, False, False)])
img_rgb_string = responses[0].image_data_uint8
rgbarray = np.frombuffer(img_rgb_string, np.uint8)
rgbarray_shaped = rgbarray.reshape((540,960,3))
rgbarray_shaped = rgbarray_shaped
img = Image.fromarray(rgbarray_shaped, 'RGB')
img.show()To retrieve the color map to know which color is assign to each color index you can use:
colorMap = client.simGetSegmentationColorMap()An example can be found in segmentation_test.py (Cosys-AirSim/PythonClient/segmentation/segmentation_test.py). For a script that generates a full list of objects and their associated color, please see the script segmentation_generate_list.py (Cosys-AirSim/PythonClient/segmentation/segmentation_generate_list.py).
To get desired ground truth segmentation you will need to know the names of the meshes in your Unreal environment. To do this, you can use the API:
currentObjectList = client.simListInstanceSegmentationObjects()This will use an understandable naming depending on the hierarchy the object belong to in the Unreal World (example box_2_fullpalletspawner_5_pallet_4 or door_window_door_38 ).
Note that this provides a different result from simListSceneObjects() as this one will make a simple list of all Unreal Actors in the scene, without keeping the hierarchy in mind.
An extension to simListInstanceSegmentationObjects() is simListInstanceSegmentationPoses(ned=True, only_visible=True) which will retrieve the 3D object pose of each element in the same order as the first mentioned function. only_visible allows you to only get the objects that are physically visible in the scene.
Once you decide on the meshes you are interested, note down their names and use above API to set their object IDs. T
At present the color for each object ID is fixed as in this pallet. We will be adding ability to change colors for object IDs to desired values shortly. In the meantime you can open the segmentation image in your favorite image editor and get the RGB values you are interested in.
At the start, AirSim assigns color indexes to each object found in environment of type UStaticMeshComponent or USkinnedMeshComponent. It then makes an understandable naming depending on the hierarchy the object belong to in the Unreal World (example box_2_fullpalletspawner_5_pallet_4 or door_window_door_38 ).
The simGetSegmentationObjectID API allows you get object ID for given mesh name.
Please see the instance segmentation documentation for some more information on the segmentation system created by Cosys-Lab.
Currently, this is just a map from object ID to grey scale 0-255. So any mesh with object ID 42 shows up with color (42, 42, 42). Please see segmentation section for more details on how to set object IDs. Typically noise setting can be applied for this image type to get slightly more realistic effect. We are still working on adding other infrared artifacts and any contributions are welcome.
These image types return information about motion perceived by the point of view of the camera. OpticalFlow returns a 2-channel image where the channels correspond to vx and vy respectively. OpticalFlowVis is similar to OpticalFlow but converts flow data to RGB for a more 'visual' output.
This feature lets you generate object detection using existing cameras in AirSim, find more info here.
The annotation system allows you to choose different groundtruth labeling techniques to create more data from your simulation. Find more info here. When enabling annotation layers, one can choose to render images as well from these layers. The image type if set to annotation does usually require to also supply the name of the annotation layer as defined in the settings.
For example with Python, you can use the following examples for RGB and greyscale annotation layers.
responses = client.simGetImages([airsim.ImageRequest( "front_center", airsim.ImageType.Annotation, False, False, "RGBTest")])
img_rgb_string = responses[0].image_data_uint8
rgbarray = np.frombuffer(img_rgb_string, np.uint8)
rgbarray_shaped = rgbarray.reshape((540,960,3))
img = Image.fromarray(rgbarray_shaped, 'RGB')
img.show()
responses = client.simGetImages([airsim.ImageRequest( "front_center", airsim.ImageType.Annotation, False, False, "GreyscaleTest")])
img_rgb_string = responses[0].image_data_uint8
rgbarray = np.frombuffer(img_rgb_string, np.uint8)
rgbarray_shaped = rgbarray.reshape((540,960,3))
greyscale_values = np.divide(rgbarray_shaped[:,:,0], 255)
img = Image.fromarray(rgbarray_shaped[:,:,0])
img.show()Unreal 5 introduces Lumen lightning. Due to the cameras using scene capture components enabling Lumen for them can be costly on performance. Settings have been added specfically for the scene camera to customize the usage of Lumen for Global Illumination and Reflections.
The LumenGIEnable and LumenReflectionEnable settings enable or disable Lumen for the camera. The LumenFinalQuality(0.25-2) setting determines the quality of the final image. The LumenSceneDetail(0.25-4) setting determines the quality of the scene. The LumenSceneLightningDetail(0.25-2) setting determines the quality of the lightning in the scene.