5.1 KiB
DatasetCapture
DatasetCapture
tracks egos, sensors, annotations, and metrics, combining them into a unified JSON-based dataset on disk. It also controls the simulation time elapsed per frame to accommodate the active sensors.
Sensor scheduling
While sensors are registered, DatasetCapture
ensures that frame timing is deterministic and run at the appropriate simulation times to let each sensor render and capture at its own rate.
Using Time.captureDeltaTime, it also decouples wall clock time from simulation time, allowing the simulation to run as fast as possible.
Custom sensors
You can register custom sensors using DatasetCapture.RegisterSensor()
. The simulationDeltaTime
you pass in at registration time is used as Time.captureDeltaTime
and determines how often (in simulation time) frames should be simulated for the sensor to run. This and the framesBetweenCaptures
value determine at which exact times the sensor should capture the simulated frames. The decoupling of simulation delta time and capture frequency based on frames simulated allows you to render frames in-between captures. If no in-between frames are desired, you can set framesBetweenCaptures
to 0. When it is time to capture, the ShouldCaptureThisFrame
check of the SensorHandle
returns true. SensorHandle.ReportCapture
should then be called in each of these frames to report the state of the sensor to populate the dataset.
Time.captureDeltaTime
is set at every frame in order to precisely fall on the next sensor that requires simulation, and this includes multi-sensor simulations. For instance, if one sensor has a simulationDeltaTime
of 2 and another 3, the first five values for Time.captureDeltaTime
will be 2, 1, 1, 2, and 3, meaning simulation will happen on the timestamps 0, 2, 3, 4, 6, and 9.
Custom annotations and metrics
In addition to the common annotations and metrics produced by PerceptionCamera, scripts can produce their own via DatasetCapture
. You must first register annotation and metric definitions using DatasetCapture.RegisterAnnotationDefinition()
or DatasetCapture.RegisterMetricDefinition()
. These return AnnotationDefinition
and MetricDefinition
instances which you can then use to report values during runtime.
Annotations and metrics are always associated with the frame they are reported in. They may also be associated with a specific sensor by using the Report*
methods on SensorHandle
.
Example
using System;
using UnityEngine;
using UnityEngine.Perception.GroundTruth;
[RequireComponent(typeof(PerceptionCamera))]
public class CustomAnnotationAndMetricReporter : MonoBehaviour
{
public GameObject targetLight;
public GameObject target;
MetricDefinition lightMetricDefinition;
AnnotationDefinition boundingBoxAnnotationDefinition;
SensorHandle cameraSensorHandle;
public void Start()
{
//Metrics and annotations are registered up-front
lightMetricDefinition = DatasetCapture.RegisterMetricDefinition(
"Light position",
"The world-space position of the light",
Guid.Parse("1F6BFF46-F884-4CC5-A878-DB987278FE35"));
boundingBoxAnnotationDefinition = DatasetCapture.RegisterAnnotationDefinition(
"Target bounding box",
"The position of the target in the camera's local space",
id: Guid.Parse("C0B4A22C-0420-4D9F-BAFC-954B8F7B35A7"));
}
public void Update()
{
//Report the light's position by manually creating the json array string.
var lightPos = targetLight.transform.position;
DatasetCapture.ReportMetric(lightMetricDefinition,
$@"[{{ ""x"": {lightPos.x}, ""y"": {lightPos.y}, ""z"": {lightPos.z} }}]");
//compute the location of the object in the camera's local space
Vector3 targetPos = transform.worldToLocalMatrix * target.transform.position;
//Report using the PerceptionCamera's SensorHandle if scheduled this frame
var sensorHandle = GetComponent<PerceptionCamera>().SensorHandle;
if (sensorHandle.ShouldCaptureThisFrame)
{
sensorHandle.ReportAnnotationValues(
boundingBoxAnnotationDefinition,
new[] { targetPos });
}
}
}
// Example metric that is added each frame in the dataset:
// {
// "capture_id": null,
// "annotation_id": null,
// "sequence_id": "9768671e-acea-4c9e-a670-0f2dba5afe12",
// "step": 1,
// "metric_definition": "1f6bff46-f884-4cc5-a878-db987278fe35",
// "values": [{ "x": 96.1856, "y": 192.676, "z": -193.8386 }]
// },
// Example annotation that is added to each capture in the dataset:
// {
// "id": "33f5a3aa-3e5e-48f1-8339-6cbd64ed4562",
// "annotation_definition": "c0b4a22c-0420-4d9f-bafc-954b8f7b35a7",
// "values": [
// [
// -1.03097284,
// 0.07265166,
// -6.318692
// ]
// ]
// }