private void button2_Click(object sender, EventArgs e)
{
try
{
ContTrain = ContTrain + 1;
//take the 320x240 picture from the cemera and make it 20x20 for TrainedFace
gray = grabber.QueryGrayFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//TestImageBox.Image = gray;
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20)); // new size of pic only face
foreach (MCvAvgComp f in facesDetected[0])
{
TrainedFace = currentFrame.Copy(f.rect).Convert <Gray, byte>(); //converting the pic to gray and save it to TranedFace
break;
}
//Trained image is been save to "Trainedface" variable
TrainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
trainingImages.Add(TrainedFace);
//adding text box train name to label
labels.Add(textBox1.Text);
imageBox2.Image = TrainedFace;
File.WriteAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt", trainingImages.ToArray().Length.ToString() + "%");
for (int i = 1; i < trainingImages.ToArray().Length + 1; i++)
{
trainingImages.ToArray()[i - 1].Save(Application.StartupPath + "/TrainedFaces/face" + i + ".bmp");
File.AppendAllText(Application.StartupPath + "/TrainedFaces/TrainedLabels.txt", labels.ToArray()[i - 1] + "%");
}
MessageBox.Show(textBox1.Text + "´s face detected and added :)", "Training OK", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
catch
{
MessageBox.Show("Enable the face detection first", "Training Fail", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
}//////////// face detection /////////////
private void prosesframe(object sender, EventArgs arg)
{
Image <Bgr, Byte> imageframe = capture.QueryFrame();
imageBox1.Image = imageframe;
{
Image <Gray, Byte> grayframe = imageframe.Convert <Gray, Byte>();
var face = grayframe.DetectHaarCascade(Haar, 1.4, 4, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20))[0];
foreach (var faces in face)
{
imageframe.Draw(faces.rect, new Bgr(Color.Red), 3);
}
}
imageBox1.Image = imageframe;
}
private void button2_Click(object sender, EventArgs e)
{
count += 1;
grayFace = camera.QueryGrayFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
MCvAvgComp[][] Detectedfaces = grayFace.DetectHaarCascade(facedetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
foreach (MCvAvgComp f in Detectedfaces[0])
{
TrainedFace = Frame.Copy(f.rect).Convert <Gray, byte>();
break;
}
TrainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
trainningimages.Add(TrainedFace);
File.WriteAllText(Application.StartupPath + "/Faces/Faces.txt", trainningimages.ToArray().Length.ToString() + ",");
for (int i = 1; i < trainningimages.ToArray().Length + 1; i++)
{
trainningimages.ToArray()[i - 1].Save(Application.StartupPath + "/Faces/faces" + i + ".bmp");
File.AppendAllText(Application.StartupPath + "/Faces/Faces.txt", lables.ToArray()[i - 1] + ",");
}
}
private void timer1_Tick(object sender, EventArgs e)
{
using (Image nextframe = cap.QueryFrame())
{
if (nextframe != null)
{
if (isTrack == false)
{
Image grayframe = nextframe.Convert();
grayframe._EqualizeHist();
var faces = grayframe.DetectHaarCascade(haar, 1.4, 4, HAAR_DETECTION_TYPE.FIND_BIGGEST_OBJECT | HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(40, 40))[0];
hsv = new Image(grayframe.Width, grayframe.Height);
hsv = nextframe.Convert();
hsv._EqualizeHist();
hue = new Image(grayframe.Width, grayframe.Height);
mask = new Image(grayframe.Width, grayframe.Height);
backproject = new Image(grayframe.Width, grayframe.Height);
Emgu.CV.CvInvoke.cvInRangeS(hsv, new MCvScalar(0, 30, Math.Min(10, 255), 0), new MCvScalar(180, 256, Math.Max(10, 255), 0), mask);
Emgu.CV.CvInvoke.cvSplit(hsv, hue, IntPtr.Zero, IntPtr.Zero, IntPtr.Zero);
picHue.Image = hue.ToBitmap();
foreach (var face in faces)
{
// Rectangle roi = new Rectangle(face.rect.X + face.rect.Width / 4, face.rect.Y + face.rect.Height / 4, face.rect.Width / 2, face.rect.Height / 2);
// Rectangle roi = new Rectangle(face.rect.X, face.rect.Y, face.rect.Width / 2, face.rect.Height / 2);
Emgu.CV.CvInvoke.cvSetImageROI(hue, face.rect);
Emgu.CV.CvInvoke.cvSetImageROI(mask, face.rect);
nextframe.Draw(face.rect, new Bgr(0, double.MaxValue, 1), 2);
picMask.Image = mask.ToBitmap();
trackwin = face.rect;
}
img = new IntPtr[1]
{
hue
};
Emgu.CV.CvInvoke.cvCalcHist(img, hist, false, mask);
Emgu.CV.CvInvoke.cvResetImageROI(hue);
Emgu.CV.CvInvoke.cvResetImageROI(mask);
CapImg.Image = nextframe.ToBitmap();
isTrack = true;
// isTrack = true;
}
else
{
if (trackwin != null)
{
hsv = nextframe.Convert();
Emgu.CV.CvInvoke.cvInRangeS(hsv, new MCvScalar(0, 30, 10, 0), new MCvScalar(180, 256, 256, 0), mask);
Emgu.CV.CvInvoke.cvSplit(hsv, hue, IntPtr.Zero, IntPtr.Zero, IntPtr.Zero);
picMask.Image = mask.ToBitmap();
picHue.Image = hue.ToBitmap();
}
img = new IntPtr[1]
{
hue
};
Emgu.CV.CvInvoke.cvCalcBackProject(img, backproject, hist);
Emgu.CV.CvInvoke.cvAnd(backproject, mask, backproject, IntPtr.Zero);
Image grayframe = nextframe.Convert();
grayframe._EqualizeHist();
var faces = grayframe.DetectHaarCascade(haar, 1.4, 4, HAAR_DETECTION_TYPE.FIND_BIGGEST_OBJECT | HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(40, 40))[0];
foreach (var face in faces)
{
nextframe.Draw(face.rect, new Bgr(Color.Black), 2);
}
if (trackwin.Width == 0)
{
trackwin.Width = 40;
}
if (trackwin.Height == 0)
{
trackwin.Height = 40;
}
Emgu.CV.CvInvoke.cvCamShift(backproject, trackwin, new MCvTermCriteria(10, 0.1), out trackcomp, out trackbox);
trackwin = trackcomp.rect;
// CvInvoke.cvEllipseBox(nextframe, trackbox, new MCvScalar(0, 255, 0), 2, LINE_TYPE.CV_AA, 0);
nextframe.Draw(trackwin, new Bgr(Color.Blue), 3);
CapImg.Image = nextframe.ToBitmap();
faceS = nextframe.Copy(trackwin);
picFace.Image = faceS.ToBitmap();
}
}
}
}
public void FrameGrabber(object sender, EventArgs e)
{
NamePersons.Add("");
// capture a frame form device both face and all things on the image
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = currentFrame.Convert <Gray, Byte>();
//(TestImageBox.Image = currentFrame);
//Result of haarCascade will be on the "MCvAvgComp"-facedetected (is it face or not )
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(face, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20)); //face
MCvAvgComp[][] StoreEyes = gray.DetectHaarCascade(eye, 1.2, 2, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(1, 1)); //eye
MCvAvgComp[][] Mouthdetection = gray.DetectHaarCascade(mouth, 3, 3, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(1, 1));
foreach (MCvAvgComp mouths in Mouthdetection[0]) // loop over all the detected MOUTHs
{
//currentFrame.Draw(mouths.rect, new Bgr(Color.Blue), 1);
// draw a recangle for each mouth and put it on the display
int mouthLableX = mouths.rect.X; // find the X top-left corner of the mouth for labling
int mouthLableY = mouths.rect.Y; // find the Y top-left corner of the mouth for labling
Point p = new Point(mouthLableX, mouthLableY); // create a point to attach the "MOUTH" lable to it
}
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);
currentFrame.Draw(f.rect, new Bgr(Color.Green), 2); //Frame detect colour is 'read'
if (trainingImages.ToArray().Length != 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), 3000, ref termCrit);
name = recognizer.Recognize(result); // detected name of the face is been saved to the 'name'-variable
if (name == null)
{
currentFrame.Draw(f.rect, new Bgr(Color.Green), 2); //Frame detect colour is 'read'
}
//the colour of the face label name
}
NamePersons[t - 1] = name;
NamePersons.Add("");
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn];
}
label3.Text = names;
names = "";
NamePersons.Clear();
NamesPersons.Add("");
foreach (MCvAvgComp eyes in StoreEyes[0]) // more than one eye may be detected so for each eye there will be a rectangle
{
tem = tem + 1;
results = currentFrame.Copy(eyes.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
currentFrame.Draw(eyes.rect, new Bgr(Color.Red), 2);
// for each detecte eye in the image a rectangle will be drawn on the image
int eyeLableX = eyes.rect.X; // find the X top-left corner of the eye for labeling
int eyeLableY = eyes.rect.Y; // find the Y top-left corneer of the eye for lableing
Point p = new Point(eyeLableX, eyeLableY); // create a point p to attach the lable "EYE" to it
//currentFrame.Draw(name, ref font, p, new Bgr(Color.Red)); // add a string "EYE" to the image captured from webcam just to be distingushable
if (trainingImages.ToArray().Length != 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), 3000, ref termCrit);
eyename = recognizer.Recognize(results); // detected name of the face is been saved to the 'name'-variable
}
NamesPersons[tem - 1] = eyename;
NamesPersons.Add("");
label10.Text = StoreEyes[0].Length.ToString();
}
tem = 0;
for (int ee = 0; ee < StoreEyes[0].Length; ee++)
{
eyenames = eyenames + NamesPersons[ee];
}
label10.Text = eyenames;
eyenames = "";
NamesPersons.Clear();
imageBox1.Image = currentFrame;
}
