autopilot/ai_controller/src-opencv-onnx/aiengineinferenceopencvonnx.cpp

#include <QDebug>
#include <QThread>
#include "aiengineinferenceopencvonnx.h"


AiEngineInferenceOpencvOnnx::AiEngineInferenceOpencvOnnx(QString modelPath, QObject *parent)
    : AiEngineInference{modelPath, parent},
      mInference(modelPath.toStdString(), cv::Size(640, 640), "classes.txt")
{
    //qDebug() << "TUOMAS test mModelPath=" << mModelPath;
    //mEngine = new InferenceEngine(modelPath.toStdString());
    //mInference = new Inference(modelPath.toStdString(), cv::Size(INFERENCE_SQUARE_WIDTH, INFERENCE_SQUARE_HEIGHT), "classes.txt");
}


void AiEngineInferenceOpencvOnnx::performInferenceSlot(cv::Mat frame)
{
    try {
        qDebug() << "performInferenceSlot() in thread: " << QThread::currentThreadId();

        mActive = true;

        cv::Mat scaledImage               = resizeAndPad(frame);
        //cv::imwrite("/tmp/frame.png", scaledImage);
        std::vector<Detection> detections = mInference.runInference(scaledImage);
        AiEngineInferenceResult result;

        qDebug() << "performInferenceSlot() found " << detections.size() << " objects";

        for (uint i = 0; i < detections.size(); ++i) {
            const Detection &detection = detections[i];

            // Add detected objects to the results
            AiEngineObject object;
            object.classId          = detection.class_id;
            object.propability      = detection.confidence;
            object.rectangle.top    = detection.box.y;
            object.rectangle.left   = detection.box.x;
            object.rectangle.bottom = detection.box.y + detection.box.height;
            object.rectangle.right  = detection.box.x + detection.box.width;
            result.objects.append(object);

            //qDebug() << "performInferenceSlot()" << object.rectangle.top << object.rectangle.left << "and" << object.rectangle.bottom << object.rectangle.right;
        }

        auto end = std::remove_if(detections.begin(), detections.end(), [](const Detection& detection) {
            return detection.class_id != 0;
        });
        detections.erase(end, detections.end());

        if (result.objects.empty() == false) {
            result.frame = mInference.drawLabels(scaledImage, detections);
            emit resultsReady(result);
        }

        mActive = false;
    }
    catch (const cv::Exception& e) {
        std::cerr << "performInferenceSlot() Error: " << e.what() << std::endl;
    }
}


void AiEngineInferenceOpencvOnnx::initialize(int number)
{
    (void)number;
}