Qt Edge AI C++ Classes

Detailed Description

The Qt Edge AI C++ classes describe an inference pipeline as a flow of steps that exchange data through typed ports, and provide the backend interfaces that plugins implement to run the pipeline.

To use these classes in your application, add the following include statement:

#include <QtEdgeAi/QtEdgeAi>

Example

The following builds a minimal flow: a camera feeds a vision inference step, whose output is parsed into JSON.

QCamera camera;
QMediaCaptureSession session;
session.setCamera(&camera);
camera.start();

auto *videoSource = new QInferenceVideoSource(&session);
auto *detection = new QVisionInference("libgstedgeimpulse.so", QString(), "edgeimpulse");
auto *parser = new QInferenceResultParser;

QInferenceFlow *flow = QInferenceFlow::fromSteps({ videoSource, detection, parser });
QObject::connect(parser, &QInferenceStep::dataAvailable,
                  [](const QVariant &data, const QString &) { qDebug() << data; });
flow->start();

libgstedgeimpulse.so above is not shipped with the module. Two sources are common: a model already optimized for the target hardware, such as a .tflite model from the Qualcomm Intelligent Multimedia SDK's model zoo, or a model trained on your own data through Edge Impulse Studio, which exports it as a compiled GStreamer plugin like the one above. Either way, QVisionInference::model() takes the path to the resulting file, and the model's format decides which backend step provider loads it: .so for Edge Impulse, .tflite for the Qualcomm IM SDK.

Classes