private void button8_Click(object sender, EventArgs e)
{
if (int.Parse(((Control)sender).Name.Substring(6)) < 9)
{
bmp = bmp0;
}
else
{
bmp = (Bitmap)this.picBox_Original.Image;
}
this.picBox_Original.Image = ImageProcessing.ImagePreProcessing(bmp);
}
public bool CatchFace(string label, PictureBox pcb)
{
try
{
bool detected = false;
//Trained face counter
//Get a gray frame from capture device
gray = grabber.QueryGrayFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//Face Detector
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
//Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
TrainedFace = currentFrame.Copy(f.rect).Convert <Gray, byte>();
detected = true;
break;
}
if (!detected)
{
return(false);
}
//resize face detected image for force to compare the same size with the
//test image with cubic interpolation type method
TrainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
TrainedFace = new Image <Gray, byte>(ImageProcessing.ImagePreProcessing(TrainedFace.ToBitmap()));
//Show face added in gray scale
pcb.Image = TrainedFace.ToBitmap();
UpdateRecognizer();
MessageBox.Show(label + "´s face detected and added :)", "Training OK", MessageBoxButtons.OK, MessageBoxIcon.Information);
return(true);
}
catch
{
MessageBox.Show("Enable the face detection first", "Training Fail", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
return(false);
}
}
public string SaveString(string inputpath, string label, ref int index)
{
try
{
ContTrain = ContTrain + 1;
bool detected = false;
gray = new Image <Gray, byte>(inputpath);
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetected[0])
{
TrainedFace = gray.Copy(f.rect).Convert <Gray, byte>();
detected = true;
break;
}
if (!detected)
{
return(string.Empty);
}
TrainedFace = TrainedFace.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
TrainedFace = new Image <Gray, byte>(ImageProcessing.ImagePreProcessing(TrainedFace.ToBitmap()));
trainingImages.Add(TrainedFace);
labels.Add(label);
UpdateRecognizer();
return(BasicOperations.SaveImage(TrainedFace.ToBitmap(), ref index));;
}
catch
{
return(string.Empty);
}
}
public void FrameGrabber(object sender, EventArgs e)
{
lbl3 = "0";
lbl4 = "";
NamePersons.Add("");
//Get the current frame form capture device
try
{
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
catch { }
//Convert it to Grayscale
gray = currentFrame.Convert <Gray, Byte>();
//Face Detector
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
//Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//draw the face detected in the 0th (gray) channel with blue color
currentFrame.Draw(f.rect, new Bgr(Color.Red), 2);
if (trainingImages.ToArray().Length != 0)
{
//UpdateRecognizer();
name = recognizer.Recognize(new Image <Gray, byte>(ImageProcessing.ImagePreProcessing(result.ToBitmap())));
//Draw the label for each face detected and recognized
currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Set the number of faces detected on the scene
lbl3 = facesDetected[0].Length.ToString();
}
t = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
//Show the faces procesed and recognized
pictureBoxFrameGrabber.Image = currentFrame.ToBitmap();
lbl3 = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
