MCvFont font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_TRIPLEX, 0.6d, 0.6d); code example
Example: MCvFont font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_TRIPLEX,0.6d,0.6d); not working
public partial class Biometric : Form{ MCvFont font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_TRIPLEX, 0.6d, 0.6d); HaarCascade faceDetected; Image<Bgr, Byte> frame; Capture camera; Image<Gray, byte> result; Image<Gray, byte> trainedFace = null; Image<Gray, byte> grayFace = null; List<Image<Gray, byte>> trainingImage = new List<Image<Gray, byte>>(); List<string> labels = new List<string>(); List<string> users = new List<string>(); int count, numLabeles, t; string name, names = null; public Biometric() { InitializeComponent(); student = LoadDetails(Register.StudentId); StartWebcam(); } private void StartWebcam() { try { faceDetected = new HaarCascade("haarcascade_frontalface_default.xml"); camera = new Capture(); camera.QueryFrame(); Application.Idle += new EventHandler(FrameProcedure); } catch (Exception ex) { MessageBox.Show(ex.Message); } } private void FrameProcedure(object sender, EventArgs e) { users.Add(""); frame = camera.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC); grayFace = frame.Convert<Gray, Byte>(); MCvAvgComp[][] faceDetectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20)); if (faceDetectedNow[0].Count() > 1) { AbortRegistraction(student.StudentId); LogEvent(student.StudentId, "Two person detected. registration aborted"); MessageBox.Show("Two person detected. registration aborted"); Login login = new Login(); login.Show(); this.Close(); } foreach (MCvAvgComp f in faceDetectedNow[0]) { result = frame.Copy(f.rect).Convert<Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC); frame.Draw(f.rect, new Bgr(Color.Green), 3); if (trainingImage.ToArray().Length != 0) { MCvTermCriteria termCriterias = new MCvTermCriteria(count, 0.001); EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImage.ToArray(), labels.ToArray(), 1500, ref termCriterias); name = recognizer.Recognize(result); frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Red)); } users.Add(""); } cameraBox.Image = frame; name = ""; users.Clear(); }}