|
public Recognize ( Byte>.Image |
||
| image | Byte>.Image | The image to be recognized |
| Результат | RecognitionResult | |
private void btnDetect_Click(object sender, EventArgs e)
{
current = new Image<Bgr, byte>(filePath).Resize(300, 250, INTER.CV_INTER_CUBIC);
Image<Gray, byte> grayScale = current.Convert<Gray, byte>();
MCvAvgComp[][] detected = grayScale.DetectHaarCascade(face, scale, minNeighbors, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
foreach (MCvAvgComp d in detected[0])
{
current.Draw(d.rect, new Bgr(Color.LawnGreen), 2);
if (trainingImgs.Count > 0)
{
Image<Gray, byte> dFace = current.Copy(d.rect).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
MCvTermCriteria criteria = new MCvTermCriteria(trainingImgs.Count, epsilon); //count, epsilon value
EigenObjectRecognizer recognize = new EigenObjectRecognizer(trainingImgs.ToArray(), trainingNames.ToArray(), 0, ref criteria);
MCvFont font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 1, 1);
string name = recognize.Recognize(dFace);
current.Draw(name, ref font, new Point(d.rect.X - 2, d.rect.Y - 20), new Bgr(Color.Red));
}
}
picWebCam.Image = current.ToBitmap();
}
public void FrameGrabber(object sender, EventArgs e)
{
_lastInfo = new List<HeadInformation>();
CountOfFacesLabel.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//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.Red));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Set the number of faces detected on the scene
CountOfFacesLabel.Text = facesDetected[0].Length.ToString();
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
eye,
1.9,
5,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp ey in eyesDetected[0])
{
Rectangle eyeRect = ey.rect;
eyeRect.Inflate(-7, -7);
eyeRect.Offset(f.rect.X, f.rect.Y);
currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
}
//gray.ROI = f.rect;
//MCvAvgComp[][] mouthDetected = gray.DetectHaarCascade(
// mouth,
// 1.1,
// 37,
// Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
// new Size(20, 20));
//gray.ROI = Rectangle.Empty;
//foreach (MCvAvgComp ey in mouthDetected[0])
//{
// Rectangle mouthRect = ey.rect;
// mouthRect.Offset(f.rect.X, f.rect.Y);
// currentFrame.Draw(mouthRect, new Bgr(Color.Black), 2);
//}
gray.ROI = f.rect;
MCvAvgComp[][] smileDetected = gray.DetectHaarCascade(
smile,
2,
20,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
HeadInformation hi = new HeadInformation();
hi.IsSmile = false;
foreach (MCvAvgComp ey in smileDetected[0])
{
Rectangle smileRect = ey.rect;
smileRect.Offset(f.rect.X, f.rect.Y);
currentFrame.Draw(smileRect, new Bgr(Color.Black), 2);
currentFrame.Draw("smile", ref font, new Point(smileRect.X, smileRect.Y), new Bgr(Color.Red));
hi.IsSmile = true;
}
hi.Head = f.rect;
if (eyesDetected[0] != null && eyesDetected[0].Length > 0)
{
if (eyesDetected[0].Length == 1)
{
hi.Eye1 = eyesDetected[0][0].rect;
}
if (eyesDetected[0].Length == 2)
{
hi.Eye1 = eyesDetected[0][0].rect;
hi.Eye2 = eyesDetected[0][1].rect;
}
}
hi.Name = name;
_lastInfo.Add(hi);
}
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
imageBoxFrameGrabber.Image = currentFrame.Resize(800, 600, INTER.CV_INTER_CUBIC);
ListOfUserLabel.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
public User RecognizeFace(Image<Gray, byte> face)
{
using (var context = new FaceRecognitionContext())
{
var faces = new List<Image<Gray, byte>>();
var ids = new List<string>();
foreach (var user in context.Users)
{
var reconogizedFace = new Image<Gray, byte>(user.Face.GetBitmap());
var id = user.Id.ToString(CultureInfo.InvariantCulture);
faces.Add(reconogizedFace);
ids.Add(id);
}
if (ids.Any())
{
var termCrit = new MCvTermCriteria(ids.Count(), 0.001);
var recognizedFaces = new EigenObjectRecognizer(faces.ToArray(), ids.ToArray(), 2500, ref termCrit);
var label = recognizedFaces.Recognize(face);
if (!String.IsNullOrEmpty(label))
{
var id = int.Parse(label);
return context.Users.SingleOrDefault(x => x.Id == id);
}
}
return null;
}
}
private void CompositionTarget_Rendering(object sender, EventArgs e)
{
_status.Fill = _rd;
#region Recognition
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = currentFrame.Convert<Gray, Byte>();
var size = new System.Drawing.Size(20, 20);
var window = new System.Drawing.Size(grabber.Width, grabber.Height);
_rects = _faceClassifier.DetectMultiScale(gray, 1.2, 10, size, window);
foreach (var f in _rects)
{
result = currentFrame.Copy(f).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
_status.Fill = new SolidColorBrush(Colors.Green);
currentFrame.Draw(f, new Bgr(System.Drawing.Color.Red), 2);
//if we have already trained
if (CommonData.TrainingImages.Count > 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
recognizer = new EigenObjectRecognizer(
CommonData.TrainingImages.ToArray(),
CommonData.Names.ToArray(),
3000,
ref termCrit);
string name = recognizer.Recognize(result);
currentFrame.Draw(name, ref font, new System.Drawing.Point(f.X - 2, f.Y - 2),
new Bgr(System.Drawing.Color.LightGreen));
}
//finally draw the source
_imgCamera.Source = ImageHelper.ToBitmapSource(currentFrame);
}
#endregion
}
void FrameGrabber(object sender, EventArgs e)
{
currentFrame = grabber.QueryFrame();
if (currentFrame != null)
{
if (counter >= 1)
{
label5.Text = "Selected character found in frame";
labcounter.Text = ":" + counter + "times.";
}
//label3.Text = "0";
//label4.Text = "";
//NamePersons.Add("");
try
{
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
catch (Exception ex)
{
Application.Idle += new EventHandler(checkoutput);
MessageBox.Show("Video was Ended", "Frame Query Exit", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
//Convert it to Grayscale
gray = currentFrame.Convert <Gray, Byte>();
//Face Detector
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
pro = 0;
//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);
imageBox2.Image = result;
if (FaceImgaes.ToArray().Length != 0)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
Emgu.CV.EigenObjectRecognizer recognizer = new Emgu.CV.EigenObjectRecognizer(
FaceImgaes.ToArray(),
labels.ToArray(),
2000,
ref termCrit);
name = recognizer.Recognize(result);
if (pro == 0)
{
if (name != "")
{
counter++;
currentFrame.Draw(f.rect, new Bgr(Color.Green), 2);
pro++;
}
//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));
}
}
else
{
imageBox2.Image = currentFrame;
}
//Set the number of faces detected on the scene
label4.Text = facesDetected[0].Length.ToString();
/*
*
* //Set the region of interest on the faces
*
* gray.ROI = f.rect;
* MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
* eye,
* 1.1,
* 10,
* Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
* new Size(20, 20));
* gray.ROI = Rectangle.Empty;
*
* foreach (MCvAvgComp ey in eyesDetected[0])
* {
* Rectangle eyeRect = ey.rect;
* eyeRect.Offset(f.rect.X, f.rect.Y);
* currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
* }
*/
}
t = 0;
//Show the faces procesed and recognized
imageBoxFrameGrabber.Image = currentFrame;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
else
{
btn_decrec.Enabled = true;
label16.Text = "";
}
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(473, 355, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//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.Yellow), 2);
if (trainingImages.ToArray().Length != 0)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
string index = recognizer.RecognizeIndex(result);
if (index != string.Empty && name != String.Empty)
{
imageBox1.Image = trainingImages[Convert.ToInt32(index)];
}
else {
imageBox1.Image = null;
}
LabelNameNId = name.Split('|');
//Draw the label for each face detected and recognized
currentFrame.Draw(LabelNameNId[0].ToString(), ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Yellow));
btnLogInNLogOut.Enabled = true;
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Set the number of faces detected on the scene
label3.Text = facesDetected[0].Length.ToString();
/*
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
eye,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp ey in eyesDetected[0])
{
Rectangle eyeRect = ey.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
}
*/
}
t = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
if (nnn < facesDetected[0].Length - 1)
{
names = names + NamePersons[nnn] + ", ";
}
else
{
names = names + NamePersons[nnn];
}
}
//Show the faces procesed and recognized
imageBoxFrameGrabber.Image = currentFrame;
if (imageBox1.Image==null)
pictureBox2.Visible = true;
else
pictureBox2.Visible = false;
if (names != String.Empty)
pictureBox1.Visible = true;
else
pictureBox1.Visible = false;
if (names == String.Empty)
{
imageBox1.Image = null;
btnLogInNLogOut.Enabled = false;
}
else
{
btnLogInNLogOut.Enabled = true;
}
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
public void TestEigenObjects()
{
String[] fileNames = new string[] { "stuff.jpg", "squares.gif", "lena.jpg" };
int width = 100, height = 100;
MCvTermCriteria termCrit = new MCvTermCriteria(3, 0.001);
#region using batch method
Image<Gray, Byte>[] imgs = Array.ConvertAll<String, Image<Gray, Byte>>(fileNames,
delegate(String file)
{
return new Image<Gray, Byte>(file).Resize(width, height, CvEnum.INTER.CV_INTER_LINEAR);
});
EigenObjectRecognizer imgRecognizer1 = new EigenObjectRecognizer(imgs, ref termCrit);
for (int i = 0; i < imgs.Length; i++)
{
Assert.AreEqual(i.ToString(), imgRecognizer1.Recognize(imgs[i]));
}
XmlDocument xDoc = Toolbox.XmlSerialize<EigenObjectRecognizer>(imgRecognizer1);
EigenObjectRecognizer imgRecognizer2 = Toolbox.XmlDeserialize<EigenObjectRecognizer>(xDoc);
for (int i = 0; i < imgs.Length; i++)
{
Assert.AreEqual(i.ToString(), imgRecognizer2.Recognize(imgs[i]));
}
System.Runtime.Serialization.Formatters.Binary.BinaryFormatter
formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
Byte[] bytes;
using (MemoryStream ms = new MemoryStream())
{
formatter.Serialize(ms, imgRecognizer1);
bytes = ms.GetBuffer();
}
using (MemoryStream ms2 = new MemoryStream(bytes))
{
EigenObjectRecognizer imgRecognizer3 = (EigenObjectRecognizer)formatter.Deserialize(ms2);
for (int i = 0; i < imgs.Length; i++)
{
Assert.AreEqual(i.ToString(), imgRecognizer3.Recognize(imgs[i]));
}
}
#endregion
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//mengambil queryFrame dari gambar
DateTime StarTime = DateTime.Now;
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//konversi
gray = currentFrame.Convert<Gray, Byte>();
//yang terdeteksi
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
//Aksi untuk setiap elemen terdeteksi
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.Red), 2);
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);
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("");
//jumlah yang terdeteksi
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;
//nama yang terdeteksi
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
//tampilan pada imageboxframeGrabber
imageBoxFrameGrabber.Image = currentFrame;
DateTime endTime = DateTime.Now;
textBox2.Text = (endTime - StarTime).ToString();
label4.Text = names;
names = "";
NamePersons.Clear();
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
label4.Text = "Tidak Ada";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//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
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
//names = names + NamePersons[nnn] + ", ";
names = names + NamePersons[nnn];
}
//Show the faces procesed and recognized
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
if (label4.Text != "")
{
Label_Nama.Text = label4.Text;
//Label_NIP.Text = "test";
DBConnect koneksi_db = new DBConnect();
MySqlConnection db = new MySqlConnection(koneksi_db.koneksi());
db.Open();
MySqlCommand dbcmd = db.CreateCommand();
string sql = "select nim from data_peg where nama like'%" + label4.Text + "%'";
dbcmd.CommandText = sql;
MySqlDataReader reader = dbcmd.ExecuteReader();
while (reader.Read())
{
Label_NIP.Text = reader.GetString(0).ToString();
}
db.Close();
//button_SimpanAbsen.Enabled = true;
//label11.Visible = false;
}
else
{
//button_SimpanAbsen.Enabled = false;
//label11.Visible = true;
}
names = "";
//ketemu = true;
//Clear the list(vector) of names
NamePersons.Clear();
}
public void FrameGrabber2(object sender, EventArgs e)
{
NamePersons.Add("");
face = new HaarCascade("haarcascade_frontalface_default.xml");
//Utility UTl = new Utility();
//Get the current frame form capture device
//Image<Bgr, Byte> currentFrame = UTl.ImageToBgrByte(Image);
try
{
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
catch (Exception exp)
{
grabber = new Capture("video002.mp4");
}
//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 element detected
try
{
MCvAvgComp f = facesDetected[0][0];
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.White), 2);
}
catch (Exception ex)
{
//MessageBox.Show("Camera Error: Empty frames arrived" + ex.Message.ToString(), "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
if (trainingImages.ToArray().Length != 0)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//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("");
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
emguImgFace.Image = currentFrame;
lblCandidateID.Text = name;
name = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
private Image<Bgr, byte> findFaces(Image<Bgr, byte> img)
{
name = "";
//Convert it to Grayscale
Image<Gray, byte> gray = img.Convert<Gray, Byte>();
//Equalization step
gray._EqualizeHist();
try
{
//Face Detector
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
scaleFactor,minNeighbors,
//Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.FIND_BIGGEST_OBJECT,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(minsize, minsize));
Image<Gray, byte> result;
foreach (Control ct in flowLayoutPanel1.Controls)
{
(ct as PictureBox).Image = null;
ct.Dispose();
}
flowLayoutPanel1.Controls.Clear();
int ContTrain = 0;
//Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
//result = gray.Copy(f.rect).Convert<Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
result = gray.Convert<Gray, byte>().Resize(500, 500, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//draw the face detected in the 0th (gray) channel with blue color
img.Draw(f.rect, new Bgr(Color.Red), 2);
if (trainedFaces.Count != 0 && !skipname.Checked)
{
ContTrain = trainedFaces.Count;
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain*2, 0.001);
try
{
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainedFaces.OrderBy(x => x.path).Select(x => x.img).ToArray(), trainedFaces.OrderBy(x => x.path).Select(x => x.name).ToList<string>().ToArray(), 4000, ref termCrit);
name = recognizer.Recognize(result);
//Draw the label for each face detected and recognized
img.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));
}
catch (Exception ex) { errorLabel.Text = ex.Message + "\n" + ex.StackTrace; }
}
addToFlow(img, f, name);
}
}
catch (Exception ex) { errorLabel.Text = ex.Message + "\n" + ex.StackTrace; }
GC.Collect();
return img;
}
void FrameGrabber(object sender, EventArgs e)
{
NamePersons.Add("");
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//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 System.Drawing.Size(20, 20));
Console.WriteLine(facesDetected[0].Length);
//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(System.Drawing.Color.Red), 2);
if (trainingImages.ToArray().Length != 0)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//Draw the label for each face detected and recognized
currentFrame.Draw(name, ref font, new System.Drawing.Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(System.Drawing.Color.LightGreen));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
label3.Text = facesDetected[0].Length.ToString();
if (result != null)
{
dispatcherTimer.Stop();
break;
}
}
t = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
imageBoxFrameGrabber.Source = ConvertImage(currentFrame.ToBitmap());
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
private Image<Bgr, byte> findFaces(Image<Bgr, byte> img)
{
//Convert it to Grayscale
Image<Gray, byte> gray = img.Convert<Gray, Byte>();
//Equalization step
gray._EqualizeHist();
try
{
//Face Detector
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
scaleFactor, minNeighbors,
//Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.FIND_BIGGEST_OBJECT,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(minsize, minsize));
Image<Bgr, byte> result;
int ContTrain = 0;
//Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
result = img.Copy(f.rect);
//gray.Copy(f.rect).Convert<Gray, byte>().Resize(f.rect.Width, f.rect.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//result = gray.Convert<Gray, byte>().Resize(f.rect.Width, f.rect.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//draw the face detected in the 0th (gray) channel with blue color
img.Draw(f.rect, new Bgr(Color.Red), 2);
if(addFacesToolStripMenuItem.Checked)
addTrainedFace(result, f.rect);
if (trainedFaces.Count != 0 && matchFaceToolStripMenuItem.Checked)
{
ContTrain = trainedFaces.Count;
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain * 2, 0.001);
try
{
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainedFaces.OrderBy(x => x.path).Select(x => x.img.Convert<Gray, Byte>().Resize(200, 200, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC)).ToArray(), trainedFaces.OrderBy(x => x.path).Select(x => x.name).ToList<string>().ToArray(), 4000, ref termCrit);
this.Text = recognizer.Recognize(result.Convert<Gray, Byte>().Resize(200, 200, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
//Draw the label for each face detected and recognized
img.Draw(this.Text, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));
}
catch (Exception ex) { this.Text = ex.Message + "\n" + ex.StackTrace; }
}
}
}
catch (Exception ex) { this.Text = ex.Message + "\n" + ex.StackTrace; }
GC.Collect();
return img;
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
if (doc != null)
{
//Console.WriteLine(getImageString(doc));
//added by PFS
//www.codeproject.com/Articles/257502/Creating-Your-First-EMGU-Image-Processing-Project
//img = Base64ToImage(getImageString(doc)); //1-
}
else
{
img = Image.FromFile(String.Format("{0}/loading.jpg", curDir));
}
//Get the current frame form capture device
//currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
currentFrame = new Image<Bgr, byte>(new Bitmap(img)).Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//End addition by PFS
//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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//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
label3.Text = facesDetected[0].Length.ToString();
/*
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
eye,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp ey in eyesDetected[0])
{
Rectangle eyeRect = ey.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
}
*/
}
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
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
private string recognizerall(MCvAvgComp f)
{
string[] ten = new string[5];
ten[0] = "";
if (trainingImages.ToArray().Length != 0)
{
// /Term Criteria for face recognition with numbers of trained images like max Iteration,eps > =>chinh xac
MCvTermCriteria termCrit = new MCvTermCriteria(tong, 0.6);
MCvTermCriteria termCritn = new MCvTermCriteria(tong, 0.7);
MCvTermCriteria termCritm = new MCvTermCriteria(tong, 0.7);
MCvTermCriteria termCriteL = new MCvTermCriteria(tong, 0.7);
MCvTermCriteria termCriteR = new MCvTermCriteria(tong, 0.7);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
2000,
ref termCrit);
ten[0] = recognizer.Recognize(resultface);
/*
///////////////////////////////////////////////////kiem tra nose/
if (resultnose != null)
{
EigenObjectRecognizer recognizernose = new EigenObjectRecognizer(
trainingImagenose.ToArray(),
labels.ToArray(),
1000,
ref termCritn);
ten[1] = recognizernose.Recognize(resultnose);
currentFrame.Draw("nose: "+ten[1], ref font, new Point(f.rect.X - 2, f.rect.Y - 15), new Bgr(Color.DarkBlue));
}
//////////////////////////////////////////////////////////
if (resultmouth != null)
{
EigenObjectRecognizer recognizermouth = new EigenObjectRecognizer(
trainingImagemouth.ToArray(),
labels.ToArray(),
1000,
ref termCritm);
ten[2] = recognizermouth.Recognize(resultmouth);
currentFrame.Draw("mouth: "+ten[2], ref font, new Point(f.rect.X - 2, f.rect.Y - 30), new Bgr(Color.LightGreen));
}
if (resulteyeL != null)
{
EigenObjectRecognizer recognizereyeL = new EigenObjectRecognizer(
trainingImageneyeL.ToArray(),
labels.ToArray(),
1000,
ref termCriteL);
ten[3] = recognizereyeL.Recognize(resulteyeL);
currentFrame.Draw("eyes: "+ten[3], ref font, new Point(f.rect.X - 45, f.rect.Y - 45), new Bgr(Color.LightGreen));
}
if (resulteyeR != null)
{
EigenObjectRecognizer recognizereyeR = new EigenObjectRecognizer(
trainingImageneyeR.ToArray(),
labels.ToArray(),
600,
ref termCriteR);
ten[4] = recognizereyeR.Recognize(resulteyeR);
currentFrame.Draw(ten[4], ref font, new Point(f.rect.X +65, f.rect.Y - 45), new Bgr(Color.LightGreen));
}
}
int tam = 0;
string name="";
for (int i = 1; i < 5; i++)
{
if (ten[0] == ten[i]) tam++;
if (tam > 2&&ten[0]!=null) { name = ten[0]; break; } else name = "";
}
*/
}
return ten[0];
}
internal string getFaceTag(Bitmap sourceBmp)
{
//Get the current frame form capture device
currentFrame = new Image<Bgr, byte>(sourceBmp).Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
if (currentFrame != null)
{
gray_frame = currentFrame.Convert<Gray, Byte>();
//Face Detector
MCvAvgComp[][] facesDetected = gray_frame.DetectHaarCascade(
Face,
1.2,
1,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new System.Drawing.Size(20, 20));
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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result) ;
if (!name.Equals("")&&name!=null)
{
return name;
}
}
}
}
return "Sanmeet" ;
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
if (!emailSent)
{
emailSent = true;
System.Diagnostics.Debug.WriteLine("sending Email");
email_send(currentFrame);
}
//************* THIS WORKS!! *************************//
if (name == "" && intruderFrameCount < 20 && sentSMS == false)
{
intruderFrameCount++;
}
else if (intruderFrameCount == 20 && sentSMS == false)
{
sentSMS = true;
System.Diagnostics.Debug.WriteLine("sending Email");
email_send(currentFrame);
// var request = (HttpWebRequest)WebRequest.Create("http://gb4.site40.net/HeardFood/sendSms.php?body=intruder&to=4105043967");
// var response = (HttpWebResponse)request.GetResponse();
}
// intruderFrameCount = name == "" ? intruderFrameCount + 1 : 0;
if (f.rect.X > 180 || f.rect.X < 80)
{
currentFrame.Draw(name + " is distracted", ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Red));
}
else
{
currentFrame.Draw(name + " is alert", ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Cyan));
//Draw the label for each face detected and recognized
}
}
NamePersons[t-1] = name;
NamePersons.Add("");
//Set the number of faces detected on the scene
label3.Text = facesDetected[0].Length.ToString();
/*
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
eye,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp ey in eyesDetected[0])
{
Rectangle eyeRect = ey.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
}
*/
}
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
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
public void TestEigenObjectRecognizer()
{
Image<Gray, Byte>[] images = new Image<Gray, byte>[20];
for (int i = 0; i < images.Length; i++)
{
images[i] = new Image<Gray, byte>(200, 200);
images[i].SetRandUniform(new MCvScalar(0), new MCvScalar(255));
}
MCvTermCriteria termCrit = new MCvTermCriteria(10, 1.0e-6);
EigenObjectRecognizer rec = new EigenObjectRecognizer(images, ref termCrit);
foreach (Image<Gray, Byte> img in images)
{
rec.Recognize(img);
//Trace.WriteLine(rec.Recognize(img));
}
}
private void button_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog openFileDialog = new OpenFileDialog();
openFileDialog.ShowDialog();
var filePath = openFileDialog.FileName;
Image<Bgr, Byte> image = new Image<Bgr, byte>(filePath); //Read the files as an 8-bit Bgr image
List<System.Drawing.Rectangle> faces = new List<System.Drawing.Rectangle>();
List<System.Drawing.Rectangle> eyes = new List<System.Drawing.Rectangle>();
Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes);
foreach (System.Drawing.Rectangle face in faces)
image.Draw(face, new Bgr(System.Drawing.Color.Red), 2);
foreach (System.Drawing.Rectangle eye in eyes)
image.Draw(eye, new Bgr(System.Drawing.Color.Blue), 2);
ImageViewer.Show(image);
File.WriteAllBytes("test.jpg", image.ToJpegData());
Image<Gray, Byte> smileImage = new Image<Gray, byte>("happy.jpg"); //Read the files as an 8-bit Bgr image
Image<Gray, Byte> sadImage = new Image<Gray, byte>("sad.jpg"); //Read the files as an 8-bit Bgr image
List<Image<Gray, Byte>> trainingList = new List<Image<Gray, byte>>();
trainingList.Add(smileImage);
trainingList.Add(sadImage);
List<string> labelList = new List<string>();
labelList.Add("happy");
labelList.Add("sad");
// labelList.Add(2);
MCvTermCriteria termCrit = new MCvTermCriteria(10, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingList.ToArray(),
labelList.ToArray(),
5000,
ref termCrit);
Image<Gray, Byte> inputImage = new Image<Gray, byte>(filePath); //Read the files as an 8-bit Bgr image
var resizedImage = inputImage.Resize(smileImage.Width, smileImage.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
var name = recognizer.Recognize(resizedImage).Label;
List<int> temp = new List<int>();
temp.Add(1);
temp.Add(2);
EigenFaceRecognizer recogizer2 = new EigenFaceRecognizer(80, double.PositiveInfinity);
recogizer2.Train(trainingList.ToArray(), temp.ToArray());
var dd = recogizer2.Predict(resizedImage);
ImageViewer.Show(resizedImage);
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//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));
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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//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
label3.Text = 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
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
if (mc.isconnect()&& useNao && !useWebcam)
{
while (!mc.updatedUpper)
{ }
img = Image.FromStream(new MemoryStream(mc.getByte(0)));
currentFrame = new Image<Bgr, byte>(new Bitmap(img)).Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
else if(!useNao && useWebcam)
{
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
else
{
img = Image.FromFile(String.Format("{0}/loading.jpg", curDir));
currentFrame = new Image<Bgr, byte>(new Bitmap(img)).Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
//End addition by PFS
//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));
try
{
//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)
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.00005);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
string[] faceName = new string[faceIteration];
//Addition by PS
for (int i = 0; i < faceIteration; i++)
{
termCrit.epsilon = i * 0.00005;
faceName[i]=recognizer.Recognize(result);
Console.WriteLine(faceName[i]);
}
name = faceName.GroupBy(v => v)
.OrderByDescending(g => g.Count())
.First()
.Key;
//End of Addition by PS
// name = recognizer.Recognize(result);
if (name != "" && useNao )//&& !calledName.Any(x=> x==name) )
{
mc.sendVoice(name);
calledName.Add(name);
calledNumber++;
}
else if (useNao )
{
called = true;
//mc.sendVoice("");
}
//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
label3.Text = facesDetected[0].Length.ToString();
/*
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
eye,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp ey in eyesDetected[0])
{
Rectangle eyeRect = ey.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 2);
}
*/
}
}
catch (Exception ex)
{
MessageBox.Show(ex.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
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
