Video6.cs

using Emgu.CV; 
using Emgu.CV.Structure; 
using Emgu.CV.Util; 
using Emgu.CV.Dnn; 

namespace ourNamespace;
public class Video6 
{
  static void Main(string[] args)
  {

    var net = Emgu.CV.Dnn.DnnInvoke.ReadNetFromDarknet("./detection/yolov3.cfg", "./detection/yolov3.weights"); 
    var classLabels = File.ReadAllLines("./detection/coco.names"); 

    net.SetPreferableBackend(Emgu.CV.Dnn.Backend.OpenCV); 
    net.SetPreferableTarget(Emgu.CV.Dnn.Target.Cpu); 

    var vc = new VideoCapture(0, VideoCapture.API.DShow); 

    Mat frame = new(); 
    VectorOfMat output = new(); 

    VectorOfRect boxes = new(); 
    VectorOfFloat scores = new(); 
    VectorOfInt indices = new(); 

    while(true) 
    {
      vc.Read(frame); 

      CvInvoke.Resize(frame, frame, new System.Drawing.Size(0,0), .4, .4); 

      boxes = new(); 
      indices = new(); 
      scores = new(); 

      var image = frame.ToImage<Bgr, byte>(); 

      var input = DnnInvoke.BlobFromImage(image, 1/255.0, swapRB: true);

      net.SetInput(input); 

      net.Forward(output, net.UnconnectedOutLayersNames); 

      for(int i = 0; i < output.Size; i++)
      {
        var mat = output[i]; 
        var data = (float[,]) mat.GetData(); 

        for (int j = 0; j < data.GetLength(0); j++)
        {
          float[] row = Enumerable.Range(0, data.GetLength(1))
                        .Select(x => data[j, x])
                        .ToArray(); 

          var rowScore = row.Skip(5).ToArray(); 
          var classId = rowScore.ToList().IndexOf(rowScore.Max()); 
          var confidence = rowScore[classId]; 

          if (confidence > 0.8f) 
          {
              var centerX = (int) (row[0] * frame.Width); 
              var centerY = (int) (row[1] * frame.Height); 
              var boxWidth = (int) (row[2] * frame.Width); 
              var boxHeight = (int) (row[3] * frame.Height); 

              var x = (int)(centerX - (boxWidth / 2)); 
              var y = (int)(centerY - (boxHeight / 2)); 

              boxes.Push(new System.Drawing.Rectangle[] { new System.Drawing.Rectangle(x, y, boxWidth, boxHeight)});
              indices.Push( new int[] {classId}); 
              scores.Push( new float[] { confidence});  
          }

        }

      }

      var bestIndex = DnnInvoke.NMSBoxes(boxes.ToArray(), scores.ToArray(), .8f, .8f); 

      var frameOut = frame.ToImage<Bgr, byte>(); 

      for (int i = 0; i < bestIndex.Length; i++) 
      {
          int index = bestIndex[i]; 
          var box = boxes[index]; 
          CvInvoke.Rectangle(frameOut, box, new MCvScalar(0, 255, 0), 2); 
          CvInvoke.PutText(frameOut, classLabels[indices[index]], new System.Drawing.Point(box.X, box.Y - 20),
          Emgu.CV.CvEnum.FontFace.HersheyPlain, 1.0, new MCvScalar(0, 0, 255), 2); 

      }

      CvInvoke.Resize(frameOut, frameOut, new System.Drawing.Size(0,0), 4, 4); 
      CvInvoke.Imshow("output", frameOut); 

      if (CvInvoke.WaitKey(1) == 27)
        break; 

    }


    /*var faceCascade = new CascadeClassifier("./detection/haarcascade_frontalface_default.xml"); 
    var vc = new VideoCapture(0, Emgu.CV.VideoCapture.API.DShow); 

    Mat frame = new(); 
    Mat frameGray = new(); 

    while(true)
    {
        vc.Read(frame); 

        CvInvoke.CvtColor(frame, frameGray, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray); 

        var faces = faceCascade.DetectMultiScale(frameGray, 1.3, 5); 

        if (faces is not null && faces.Length > 0) 
          CvInvoke.Rectangle(frame, faces[0], new MCvScalar(0, 255, 0), 2);

        CvInvoke.Imshow("face detection", frame); 

        if (CvInvoke.WaitKey(1) == 27)
        {
          break;
        }

    }
    */
  }
}