Program.cs

using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Dnn;
using Emgu.CV.Structure;
using FaceDetection;
using System.Drawing;
using System.Drawing.Imaging;
using System.Xml.Linq;

var useWebcam = true;
var pathToVideo = "examples/dancers.mp4";
var renderConfidence = true; 
var folderPath = "C:\\Users\\felen\\Downloads\\FacesForCalibration";
var imageFiles = Directory.GetFiles(folderPath, "*.jpg"); // Adjust the file extension filter as needed

foreach (var imagePath in imageFiles)
{
    Mat frame2;
    var saved = false;
    while (CvInvoke.WaitKey(1) == -1)
    {


     frame2 = CvInvoke.Imread(imagePath, ImreadModes.Color);
        //sing var capture2 = new VideoCapture(0);
        var detector = new FaceRectangleDetector(
            new Size(frame2.Width, frame2.Height));
        // using var frame2 = capture2.QueryFrame();
        if (frame2 == null) break;
        // Optional: draw rectangles for visual feedback
        var faces = detector.GetFaceRectangles(frame2.ToBitmap());
        foreach (var rect in faces)
        {
            CvInvoke.Rectangle(frame2, rect, new MCvScalar(0, 255, 0), 1);
            var score = new FaceRecognitionScoring.FaceQualityScorer().ScoreFaceImage(imagePath, faces.ToArray());
            DrawText("Score: " +score.ToString(), frame2);
        }

        if (!saved)
        {
            saved = true;
            var bitmap = frame2.ToBitmap();
            bitmap.Save(Path.Combine(folderPath+"\\Processed\\", "Processed_" + Path.GetFileName(imagePath)), ImageFormat.Jpeg);
        }

        CvInvoke.Imshow("Faces", frame2);

    }

    
    
}

var windowName = "Face detection (Press any key to close)";
    CvInvoke.NamedWindow(windowName);

//    using var capture = useWebcam
//        ? new VideoCapture(camIndex: 0)
//        : new VideoCapture(fileName: pathToVideo);
////using var model = InitializeFaceDetectionModel(new Size(201, 251));
//    using var model = InitializeFaceDetectionModel(new Size(capture.Width, capture.Height));

    
//        var frame = capture.QueryFrame();
//        MatToBitmap(frame);
//        if (frame is null)
//        {
//            break;
//        }

        
//        frame = CvInvoke.Imread(imagePath, ImreadModes.Color);
//        //var mat = BitmapExtension.ToMat(bitmap);
//        var faces = new Mat();
//        model.Detect(frame, faces);
//        DrawDetectedFaces(frame, faces, renderConfidence);
//        new FaceRecognitionScoring.FaceQualityScorer().ScoreFaceImage(imagePath);
//        CvInvoke.Imshow(windowName, frame);
    


FaceDetectorYN InitializeFaceDetectionModel(Size inputSize) => new FaceDetectorYN(
    model: "face_detection_yunet_2022mar.onnx",
    config: string.Empty,
    inputSize: inputSize,
    scoreThreshold: 0.9f,
    nmsThreshold: 0.3f,
    topK: 5000,
    backendId: Emgu.CV.Dnn.Backend.Default,
    targetId: Target.Cpu);


void DrawText(string text, Mat frame2)
{
    
    int margin = 10;
    int fontScale = 1;
    int thickness = 2;
    var  baseline = 0;
    var textSize = CvInvoke.GetTextSize(text, FontFace.HersheySimplex, fontScale, thickness, ref  baseline );
    var textPoint = new Point(margin, frame2.Height - margin);

    CvInvoke.PutText(
        frame2,
        text,
        textPoint,
        FontFace.HersheySimplex,
        fontScale,
        new MCvScalar(0, 255, 0),
        thickness);
}

void DrawDetectedFaces(Mat frame, Mat faces, bool renderConfidence)
{
    if (faces.Rows <= 0)
    {
        return;
    }

    // facesData is multidimensional array.
    // The first dimension is the index of the face, the second dimension is the data for that face.
    // The data for each face is 15 elements long:
    //  - the first 4 elements are the bounding box of the face (x, y, width, height)
    //  - the next 10 elements are the x and y coordinates of 5 facial landmarks:
    //      right eye, left eye, nose tip, right mouth corner, left mouth corner
    //  - the last element is the confidence score
    var facesData = (float[,])faces.GetData(jagged: true);

    for (var i = 0; i < facesData.GetLength(0); i++)
    {
        DrawFaceRectangle(frame, (int)facesData[i, 0], (int)facesData[i, 1], (int)facesData[i, 2], (int)facesData[i, 3]);
        DrawFaceLandMarks(frame, i, facesData);

        if (renderConfidence)
        {
            DrawConfidenceText(frame, (int)facesData[i, 0], (int)facesData[i, 1] - 5, facesData[i, 14]);
        }
    }
}

void DrawFaceRectangle(Mat frame, int x, int y, int width, int height)
{
    var faceRectangle = new Rectangle(x, y, width, height);
    CvInvoke.Rectangle(frame, faceRectangle, new MCvScalar(0, 255, 0), 1);
}

void DrawFaceLandMarks(Mat frame, int faceIndex, float[,] facesData)
{
    var landMarkColors = new MCvScalar[]
    {
        new MCvScalar(255, 0, 0),   // right eye
        new MCvScalar(0, 0, 255),   // left eye
        new MCvScalar(0, 255, 0),   // nose tip
        new MCvScalar(255, 0, 255), // right mouth corner
        new MCvScalar(0, 255, 255)  // left mouth corner
    };

    for (var landMark = 0; landMark < 5; landMark++)
    {
        var x = (int)facesData[faceIndex, 4 + landMark * 2];
        var y = (int)facesData[faceIndex, 4 + landMark * 2 + 1];
        CvInvoke.Circle(frame, new Point(x, y), 2, landMarkColors[landMark], -1);
    }
}

void DrawConfidenceText(Mat frame, int x, int y, float confidence)
{
    CvInvoke.PutText(frame, $"{confidence:N4}", new Point(x, y), FontFace.HersheyComplex, 0.3, new MCvScalar(0, 0, 255), 1);
}

Bitmap MatToBitmap(Mat mat)
{
    var image = mat.ToImage<Bgr, byte>();
    var bitmap = image.ToBitmap();
    return bitmap;
}