//Matching caotured image with the TrainedFace
//--------------------------------------------
public void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
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));
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); //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
//the colour of the face label name
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("");
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
label6.Text = names;
/*auto();
* cc.con = new SqlConnection(cs.DBConn);
* cc.con.Open();
* string cb = "Update Student set StudentID=@d2,Name=@d3,Year=@d4,Term=@d5,Subject=@d6,PresentAbsent=@d7,Photo=@d8 where C_ID=@d1";
* cc.cmd = new SqlCommand(cb);
* cc.cmd.Connection = cc.con;
* cc.cmd.Parameters.AddWithValue("@d1", txtID.Text);
* cc.cmd.Parameters.AddWithValue("@d2", txtStudentID.Text);
* cc.cmd.Parameters.AddWithValue("@d3", name);
* cc.cmd.Parameters.AddWithValue("@d4", txtYear.Text);
* cc.cmd.Parameters.AddWithValue("@d5", txtTerm.Text);
* cc.cmd.Parameters.AddWithValue("@d6", txtSubject.Text);
* cc.cmd.Parameters.AddWithValue("@d7", atten);
* MemoryStream ms = new MemoryStream();
* Bitmap bmpImage = new Bitmap(Picture.Image);
* bmpImage.Save(ms, System.Drawing.Imaging.ImageFormat.Jpeg);
* byte[] data = ms.GetBuffer();
* SqlParameter p = new SqlParameter("@d8", SqlDbType.Image);
* p.Value = data;
* cc.cmd.Parameters.Add(p);
* cc.cmd.ExecuteReader();
* cc.con.Close();
* st1 = lblUser.Text;
* st2 = "updated the Student'" + name + "' having Student id '" + txtStudentID.Text + "'";
* cf.LogFunc(st1, System.DateTime.Now, st2);
* btnUpdate.Enabled = false;
* MessageBox.Show("Successfully updated", "Record", MessageBoxButtons.OK, MessageBoxIcon.Information);
*/
names = "";
NamePersons.Clear();
}
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);
//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();
}
//процедура по сверке лица из базы с камерой
private void FrameProcedure(object sender, EventArgs e)
{
//проверка на всякий случай
try
{
//добавляю новое лицо, не имеющее (пока что) имени
Users.Add("");
//создаю объект Frame, устанавливаю для него разрешение камеры (855х588)
Frame = camera.QueryFrame().Resize(cameraBox.Width, cameraBox.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//делаю изображение чёрно-белым
grayFace = Frame.Convert <Gray, Byte>();
MCvAvgComp[][] facesDetectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
//беру КАЖДОЕ фото из "базы лиц" и делаю для каждого лица следующее
foreach (MCvAvgComp f in facesDetectedNow[0])
{
//изменяю размер "лица", делаю его чёрно-белым
result = Frame.Copy(f.rect).Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//делаю обводку лица зелёного цвета в ширине линии = 3
Frame.Draw(f.rect, new Bgr(Color.Green), 3);
//если фото из базы совпадает, то....
if (trainingImames.ToArray().Length != 0)
{
MCvTermCriteria termCriterias = new MCvTermCriteria(Count, 0.001);
//распознаю лицо из базы (создаю объект, который распознаёт лицо)
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImames.ToArray(), labels.ToArray(), 1500, ref termCriterias);
//вывожу имя (name)
name = recognizer.Recognize(result);
//делаю подпись имени лица в зависимости от координат, в которых находится зелёный "квадрат" обводки лица
//т.е. отталкиваюсь от предыдущих координат лица, которые получены были выше
Frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Red));
}
//если ничего не нашёл, печатаю пустоту
Users.Add("");
}
//вывод найденого лица в label4
if (name == "")
{
metroLabel3.Text = "Лица нет в базе лиц";
metroLabel5.Text = "или";
metroLabel6.Text = "Лицо не найдено";
}
else
{
metroLabel3.Text = name;
metroLabel5.Text = "";
metroLabel6.Text = "";
}
//пытаюсьь, подключена ли камера. В случае чего, вывожу ошибку
try
{
cameraBox.Image = Frame;
name = "";
Users.Clear();
}
catch (Exception)
{
//вывод сообщения и закрытие приложения, если всё плохо
var camfail = MessageBox.Show("Похоже, что камера не была обнаружена. Вы уверены, что камера подключена и стабильно работает?", "Камера не обнаружена", MessageBoxButtons.YesNo, MessageBoxIcon.Question);
if (camfail == DialogResult.Yes)
{
cameraBox.Image = Frame;
name = "";
Users.Clear();
}
if (camfail == DialogResult.No)
{
MessageBox.Show("Попробуйте перезапустить компьютер. Если ошибка не исчезла, сообщите на почту: [email protected]", "Спасибо", MessageBoxButtons.OK, MessageBoxIcon.None);
Close();
Environment.Exit(0);
Application.Exit();
}
}
}
catch (Exception)
{
//вывод сообщения и закрытие приложения, если всё плохо
var camfail = MessageBox.Show("Похоже, что камера не была обнаружена. Вы уверены, что камера подключена и стабильно работает?", "Камера не обнаружена", MessageBoxButtons.YesNo, MessageBoxIcon.Question);
if (camfail == DialogResult.Yes)
{
}
if (camfail == DialogResult.No)
{
MessageBox.Show("Попробуйте перезапустить компьютер. Если ошибка не исчезла, сообщите на почту: [email protected]", "Спасибо", MessageBoxButtons.OK, MessageBoxIcon.None);
Close();
Environment.Exit(0);
Application.Exit();
}
}
}
void FrameGrabber(object sender, EventArgs e)
{
lblYuzSayisi2.Text = "0";
kisiAdlari.Add("");
kisiSoyAdlari.Add("");
kisiNumaralari.Add("");
kisiMailleri.Add("");
kisiBolumleri.Add("");
kisiSiniflari.Add("");
kisiCinsiyetleri.Add("");
kameraResmi = fotoCek.QueryFrame().Resize(300, 280, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = kameraResmi.Convert <Gray, Byte>();
MCvAvgComp[][] tanimliYuz = gray.DetectHaarCascade(
yuz,
1.1,
5,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty);
foreach (MCvAvgComp f in tanimliYuz[0])
{
t = t + 1;
sonuc = kameraResmi.Copy(f.rect).Convert <Gray, byte>().Resize(150, 150, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
kameraResmi.Draw(f.rect, new Bgr(Color.Red), 2);
if (kisiTanimla.ToArray().Length != 0)
{
MCvTermCriteria tc = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
adlari.ToArray(),
3000,
ref tc);
ad = rec.Recognize(sonuc);
kameraResmi.Draw(ad, ref kameraKisiAd, new Point(f.rect.X - 5, f.rect.Y - 5), new Bgr(Color.Lime));
if ((!lblGuvenlikDuvariDurum.Text.Equals("Kapalı") && ad != ""))
{
//baglan.Write("1");
//baglan.Write("5");
//baglan.Write("8");
}
MCvTermCriteria tc1 = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec1 = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
soyAdlari.ToArray(),
3000,
ref tc1);
soyAd = rec1.Recognize(sonuc);
MCvTermCriteria tc2 = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec2 = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
numaralari.ToArray(),
3000,
ref tc2);
numara = rec2.Recognize(sonuc);
MCvTermCriteria tc3 = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec3 = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
mailleri.ToArray(),
3000,
ref tc3);
mail = rec3.Recognize(sonuc);
MCvTermCriteria tc4 = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec4 = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
bolumleri.ToArray(),
3000,
ref tc4);
bolum = rec4.Recognize(sonuc);
MCvTermCriteria tc5 = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec5 = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
siniflari.ToArray(),
3000,
ref tc5);
sinif = rec5.Recognize(sonuc);
MCvTermCriteria tc6 = new MCvTermCriteria(kisiSayisi, 0.001);
EigenObjectRecognizer rec6 = new EigenObjectRecognizer(
kisiTanimla.ToArray(),
cinsiyetleri.ToArray(),
3000,
ref tc6);
cinsiyet = rec6.Recognize(sonuc);
}
kisiAdlari[t - 1] = ad;
kisiAdlari.Add("");
kisiSoyAdlari[t - 1] = soyAd;
kisiSoyAdlari.Add("");
kisiNumaralari[t - 1] = numara;
kisiNumaralari.Add("");
kisiMailleri[t - 1] = mail;
kisiMailleri.Add("");
kisiBolumleri[t - 1] = bolum;
kisiBolumleri.Add("");
kisiSiniflari[t - 1] = sinif;
kisiSiniflari.Add("");
kisiCinsiyetleri[t - 1] = cinsiyet;
kisiCinsiyetleri.Add("");
lblYuzSayisi2.Text = tanimliYuz[0].Length.ToString();
if ((adlar == "Alanda Kimse Yok" ||
adlar == "" ||
adlar == "örn: BuuM" ||
numaralar == "Alanda Kimse Yok" ||
mailler == "Alanda Kimse Yok" ||
soyAdlar == "Alanda Kimse Yok" ||
soyAdlar == "örn: BuuM" ||
soyAdlar == "" ||
bolumler == "Alanda Kimse Yok" ||
siniflar == "Alanda Kimse Yok" ||
cinsiyetler == "Alanda Kimse Yok") &&
lblGuvenlikDuvariDurum.Text.Equals("Açık"))
{
/*
* {
* baglan.Write("0");
* }
*/
//Göz Tarama
gray.ROI = f.rect;
}
MCvAvgComp[][] gozTarama = gray.DetectHaarCascade(
goz,
1.1,
2,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty);
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp g in gozTarama[0])
{
Rectangle gozKayit = g.rect;
gozKayit.Offset(f.rect.X, f.rect.Y);
kameraResmi.Draw(gozKayit, new Bgr(Color.Blue), 2);
}
//Burun Tarama
gray.ROI = f.rect;
MCvAvgComp[][] burunTarama = gray.DetectHaarCascade(
burun,
1.1,
2,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty);
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp b in burunTarama[0])
{
Rectangle burunKayit = b.rect;
burunKayit.Offset(f.rect.X, f.rect.Y);
kameraResmi.Draw(burunKayit, new Bgr(Color.Orange), 2);
}
//Yuz2 Tarama
gray.ROI = f.rect;
MCvAvgComp[][] yuzTarama2 = gray.DetectHaarCascade(
yuz2,
1.1,
3,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty);
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp y in yuzTarama2[0])
{
Rectangle yuzKayit2 = y.rect;
yuzKayit2.Offset(f.rect.X, f.rect.Y);
kameraResmi.Draw(yuzKayit2, new Bgr(Color.SkyBlue), 1);
}
}
t = 0;
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
adlar = kisiAdlari[i];
}
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
soyAdlar = kisiSoyAdlari[i];
}
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
numaralar = kisiNumaralari[i];
}
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
mailler = kisiMailleri[i];
}
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
bolumler = kisiBolumleri[i];
}
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
siniflar = kisiSiniflari[i];
}
for (int i = 0; i < tanimliYuz[0].Length; i++)
{
cinsiyetler = kisiCinsiyetleri[i];
}
imgBoxYuzuTara.Image = kameraResmi;
lblTanimliKisiAd.Text = adlar;
lblTanimliKisiSoyad.Text = soyAdlar;
lblTanimliKisiNumara.Text = numaralar;
lblTanimliKisiMail.Text = mailler;
lblTanimliKisiBolum.Text = bolumler;
lblTanimliKisiSinif.Text = siniflar;
lblTanimliKisiCinsiyet.Text = cinsiyetler;
adlar = "Alanda Kimse Yok";
numaralar = "Alanda Kimse Yok";
mailler = "Alanda Kimse Yok";
soyAdlar = "Alanda Kimse Yok";
bolumler = "Alanda Kimse Yok";
siniflar = "Alanda Kimse Yok";
cinsiyetler = "Alanda Kimse Yok";
kisiAdlari.Clear();
kisiSoyAdlari.Clear();
kisiNumaralari.Clear();
kisiMailleri.Clear();
kisiBolumleri.Clear();
kisiSiniflari.Clear();
kisiCinsiyetleri.Clear();
}
void FrameGrabber(object sender, EventArgs e)
{
//mengisi default
label3.Text = "0";
NamePersons.Add("");
//Mendapatkan jumlah frame dalam form kamera
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//Konversi ke abu abu
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));
//Perulangan mendeteksi wajah
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);
//membuat kotak wajah dengan warna kuning ke hijauan
currentFrame.Draw(f.rect, new Bgr(Color.GreenYellow), 2);
if (trainingImages.ToArray().Length != 0)
{
//TermCriteria for face recognition
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//Membuat label diatas kotak wajah yang terdeteksi
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
String tampil_dilayar = name;
currentFrame.Draw(tampil_dilayar, ref font, new Point(f.rect.X - 2, f.rect.Y - 7), new Bgr(Color.Yellow));
}
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Menampilkan nomor jumlah wajah yang terdeteksi dalam scene
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
try
{
//jika terdeteksi wajah
if (load == 1)
{
if (berapa < 100)
{
berapa = berapa + 1;
string nama_user = NamePersons[nnn];
string texts = System.IO.File.ReadAllText(Application.StartupPath + "/" + "setting.txt");
webBrowser1.Navigate(texts + "baca.php?nama=" + nama_user);
if (nama_user == "wajah belum terdaftar")
{
axWindowsMediaPlayer1.URL = (Application.StartupPath + "/belum.mp3");
}
else
{
axWindowsMediaPlayer1.URL = (Application.StartupPath + "/suara.mp3");
}
}
else
{
berapa = 0;
}
}
}
catch (Exception ex)
{
Debug.WriteLine(ex.Message);
}
}
//menampilkan nama wajah yang terdeteksi
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
NamePersons.Clear();
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
NamePersons.Add("");
currentFrame = grabber.QueryFrame().Resize(800, 500, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = currentFrame.Convert <Gray, Byte>();
grabber.FlipHorizontal = true;
//Funkcja wykrywająca twarz
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
3,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(100, 100));
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);
//rysuje kwadrat na wykrytej twarzy
currentFrame.Draw(f.rect, new Bgr(Color.Green), 3);
if (trainingImages.ToArray().Length != 0)
{
double threshold = trackBar1.Value;
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), threshold, ref termCrit);
name = recognizer.Recognize(result);
//Podpis
if (name == "")
{
currentFrame.Draw("Unknown", ref font, new Point(f.rect.X, f.rect.Y - 10), new Bgr(Color.Red));
}
else
{
currentFrame.Draw(name, ref font, new Point(f.rect.X, f.rect.Y - 10), new Bgr(Color.Yellow));
}
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Liczba wykrytych twarzy
label3.Text = facesDetected[0].Length.ToString();
}
if (wykryjOczyToolStripMenuItem.Checked)
{
MCvAvgComp[][] eyesDetected = gray.DetectHaarCascade(
eye,
1.2,
5,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(50, 50)
);
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp ey in eyesDetected[0])
{
Rectangle eyeRect = ey.rect;
currentFrame.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
t = 0;
//Names concatenation of persons recognized
for (int n = 0; n < facesDetected[0].Length; n++)
{
names = names + NamePersons[n] + ", ";
}
//label z imionami
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Odświerzanie
NamePersons.Clear();
}
// Hàm này thực hiện nhận dạng
void FrameGrabber(object sender, EventArgs e)
{
try
{
// khởi tạo 1 đối tượng là currentFrame có dạng là hình ảnh
imageBoxFrameGrabber.Image = currentFrame;
// khởi tạo currentFrame sẽ có khung hình rộng 350, cao 335
currentFrame = grabber.QueryFrame().Resize(350, 335, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
// khai báo đối tượng gray này để quy định cái ảnh mà mình nhận được nằm trong khung
gray = currentFrame.Convert <Gray, Byte>();
// facesDetected là khuôn hình ảnh nằm trong khung và sẽ có dạng theo quy định của gray
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(haar, 1.1, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
foreach (MCvAvgComp f in facesDetected[0])
{
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(), EmployeeID.ToArray(), 3000, ref termCrit);
name = recognizer.Recognize(result);
Check(name);
if (test == true)
{
double StopValue = Convert.ToDouble(System.Configuration.ConfigurationSettings.AppSettings["StopValue"]);
// cái này là hiển thị ở trên khung hình màu đỏ gồm có số ID, cái thứ 2 là font chữ, cái thứ 3 là vị trí hiển thị, cái cuối là màu sắc
currentFrame.Draw("Ma NV:" + name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.White));
dt1.Clear();
SqlCommand cmd = new SqlCommand();
cmd.Connection = con;
cmd.CommandType = CommandType.Text;
// truy vấn lấy thông tin của Employee trong csdl
cmd.CommandText = @"SELECT lastdate,fldEmployeeID as N'" + applicationConfiguration.getLanguagePackage().getMemberCodeAlias()
+ @"',fldFirstName as N'" + applicationConfiguration.getLanguagePackage().getLastNameAlias()
+ @"',fldLastName as N'" + applicationConfiguration.getLanguagePackage().getFirstNameAlias()
+ @"',
(Case fldSex when 'True' then N'" + applicationConfiguration.getLanguagePackage().getMaleAlias()
+ @"' else N'" + applicationConfiguration.getLanguagePackage().getFemaleAlias()
+ @"' end) as N'" + applicationConfiguration.getLanguagePackage().getSexAlias()
+ @"',
fldBirth as N'" + applicationConfiguration.getLanguagePackage().getBirthAlias()
+ @"', fldGroupName as N'" + applicationConfiguration.getLanguagePackage().getClassAlias()
+ @"'
FROM tblGroup INNER JOIN tblEmployee
ON tblGroup.fldGroupID = tblEmployee.fldGroupID
where fldEmployeeID= @EmployeeID";
cmd.Parameters.Add("@EmployeeID", SqlDbType.Int, 16).Value = Convert.ToInt16(name);
da.SelectCommand = cmd;
// Hiển thị thông tin của Employee vừa lấy được ở trên ra bảng
da.Fill(dt1);
/*grvdata.Columns[4].DefaultCellStyle.Format = "dd/MM/yyyy";
* txtmanv.DataBindings.Clear();
* txtmanv.DataBindings.Add("Text", dt1, applicationConfiguration.getLanguagePackage().getMemberCodeAlias());
* txtHo.DataBindings.Clear();
* txtHo.DataBindings.Add("Text", dt1, applicationConfiguration.getLanguagePackage().getLastNameAlias());
* txtTen.DataBindings.Clear();
* txtTen.DataBindings.Add("Text", dt1, applicationConfiguration.getLanguagePackage().getFirstNameAlias());
* txtPhong.DataBindings.Clear();
* txtPhong.DataBindings.Add("Text", dt1, applicationConfiguration.getLanguagePackage().getClassAlias());
* txtGioitinh.DataBindings.Clear();
* txtGioitinh.DataBindings.Add("Text", dt1, applicationConfiguration.getLanguagePackage().getSexAlias());
* dpBirth.DataBindings.Clear();
* dpBirth.DataBindings.Add("Text", dt1, applicationConfiguration.getLanguagePackage().getBirthAlias());*/
//btnCallTheRoll_Click(sender, e);
DateTime date = Convert.ToDateTime(dt1.Rows[0]["lastdate"]);
TimeSpan diff = DateTime.Now - date;
double seconds = diff.TotalSeconds;
if (seconds > StopValue)
{
SqlCommand cmd2 = new SqlCommand();
cmd2.Connection = con;
cmd2.CommandType = CommandType.Text;
// truy vấn lấy thông tin của Employee trong csdl
cmd2.CommandText = "update tblEmployee set lastdate=GETDATE() where fldEmployeeID=" + dt1.Rows[0][1].ToString();
cmd2.ExecuteNonQuery();
MessageBox.Show("Thông báo", "Quá thời gian quy định", MessageBoxButtons.OK, MessageBoxIcon.Warning);
}
}
else
{
/*name = "";
* txtmanv.Text = null;
* txtHo.Text = null;
* txtTen.Text = null;
* txtPhong.Text = null;
* txtGioitinh.Text = null;
* dpBirth.Value = Convert.ToDateTime("dd/MM/yyyy");*/
}
}
}
}
catch (Exception)
{
}
}
public void CapturedImageFaceDetect()
{
Captured = Original;
//Captured = Captured.Resize(imageBox2.Height, imageBox2.Width, INTER.CV_INTER_LINEAR);
CaptureClicked = false;
if (Captured != null)
{
Image <Gray, byte> capturedGrayFrame = Captured.Convert <Gray, byte>();
var capturedFaces = capturedGrayFrame.DetectHaarCascade(Haar, 1.2, 4, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(imageBox2.Height / 5, imageBox2.Width / 5))[0];
if (capturedFaces.Length > 0)
{
Bitmap bitmapInput = capturedGrayFrame.ToBitmap();
Bitmap extractedFace;
Graphics faceCanvas;
extractedFaces = new Bitmap[capturedFaces.Length];
if (capturedFaces.Length > 1)
{
btnNext.Enabled = true;
btnPrevious.Enabled = true;
}
foreach (var face in capturedFaces)
{
result = Captured.Copy(face.rect).Convert <Gray, byte>().Resize(100, 100, INTER.CV_INTER_CUBIC);
Captured.Draw(face.rect, new Bgr(Color.Yellow), 2);
MCvTermCriteria termCrit = new MCvTermCriteria(1000, 0.001);
//label2.Text = FacesDatabase.facesNamesFromDb.ToString();
try
{
EigenObjectRecognizer recongnizer = new EigenObjectRecognizer(FacesDatabase.facesFromDb.ToArray(), FacesDatabase.facesNamesFromDb.ToArray(), 2500, ref termCrit);
name = recongnizer.Recognize(result).Label;
Captured.Draw(name, ref font, new Point(face.rect.X - 2, face.rect.Y - 2), new Bgr(Color.LightGreen));
}
catch (Exception e) {
//MessageBox.Show("Ne postoji u bazi, greska: " + e.ToString());
//size of an empty box
extractedFace = new Bitmap(face.rect.Width, face.rect.Height);
//seting empty face image as canvas for painting
faceCanvas = Graphics.FromImage(extractedFace);
faceCanvas.DrawImage(bitmapInput, 0, 0, face.rect, GraphicsUnit.Pixel);
extractedFace = new Bitmap(extractedFace, new Size(100, 100));
extractedFaces[faceNumber] = extractedFace;
faceNumber++;
}
//size of an empty box
/* extractedFace = new Bitmap(face.rect.Width, face.rect.Height);
*
* //seting empty face image as canvas for painting
* faceCanvas = Graphics.FromImage(extractedFace);
*
* faceCanvas.DrawImage(bitmapInput, 0, 0, face.rect, GraphicsUnit.Pixel);
*
* extractedFace = new Bitmap(extractedFace, new Size(100, 100));
*
* extractedFaces[faceNumber] = extractedFace;
* faceNumber++; */
}
faceNumber = 0;
pbExtractedFace.Image = extractedFaces[faceNumber];
if (extractedFaces.Length == 1)
{
btnNext.Enabled = false;
btnPrevious.Enabled = false;
}
imageBox2.Image = Captured;
}
}
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
// Obtener el dispositivo de captura de forma de marco actual.
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
// Convertirlo en escala de grises.
gray = currentFrame.Convert <Gray, Byte>();
//Reconocimiento Facial
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
// Acción por cada elemento detectado.
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);
//dibujar la cara detectada en el canal 0 (gris) con color azul
currentFrame.Draw(f.rect, new Bgr(Color.Yellow), 2);
if (trainingImages.ToArray().Length != 0)
{
//TermCriteria para reconocimiento facial con números de imágenes entrenadas como maxIteration.
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Reconocedor de cara Eigen.
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//Dibujar la etiqueta para cada cara detectada y reconocida.
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("");
//Establecer el número de caras detectadas en la escena.
label3.Text = facesDetected[0].Length.ToString();
/*
* //Establecer la región de interés en las caras.
*
* 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;
//Concatenación de nombres de personas reconocidas.
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
//Mostrar las caras procesadas y reconocidas.
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Borrar la lista (vector) de nombres.
NamePersons.Clear();
}
/// <summary>
/// 人脸识别与检测
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//得到摄像头当前帧
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//转换格式
gray = currentFrame.Convert <Gray, Byte>();
// 人脸检测
// MCvAvgComp[] facesDetected1 = face.Detect(gray, 1.2, 10, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20), new Size(20, 20));
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foundPeople.Clear();
// 人脸识别
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);
// 圈出头像
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 识别器
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
5000, // 改为2500或者3000会更准确
ref termCrit);
name = recognizer.Recognize(result);
foundPeople[name] = f.rect;
// 显示识别出的头像标签
//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("");
// 显示探测出的头像数
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;
// 头像标签字符串拼接
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
// 显示识别出的头像标签
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
namess = names;
names = "";
// Clear the list(vector) of names
NamePersons.Clear();
}
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.Gray), 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);
textBoxMatricNumber.Text = name;
//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.Green));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
if (name == textBoxMatricNumber.Text)
{
AttendanceTable attendanceTable = new AttendanceTable();
string date = DateTime.Today.ToShortDateString();
string matricNumber = name;
if (attendanceTable.AlreadyTaken(date, matricNumber) == false)
{
attendanceTable.TakeAttendance(date, matricNumber);
labelAttendance.Text = "Last Attendance Taken: " +
new StudentTable().GetStudentName(matricNumber);
}
else
{
MessageBox.Show("Attendance Already Taken");
}
}
else
{
MessageBox.Show("User not found");
}
//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] + ", ";
talk.SelectVoiceByHints(VoiceGender.Female);
talk.Speak("Attendance Taken Successfully For " + names);
}
//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("");
//Get the current frame form capture device
// lấy khung hình capture, truyền vào thuật toán tích phân bật 3
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//chuyển sang khung gray
gray = currentFrame.Convert <Gray, Byte>();
//nhận diện khuôn mặt
// DetectHaarCascade: xữ lý nhận diện khuôn mặt bằng khoảng cách: 2 mắt, mũi, ...
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
// facesDetected[]: lưu khuôn mặt đang có trên camera
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 = "";
name = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
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,
1.1,
5,
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(),
0,
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();
//check if number of person >1 then show exeption "only one person at a time"
if (facesDetected[0].Length > 1)
{
MessageBox.Show("ERROR : Only One Person at a time !!");
}
}
t = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
//names = names + NamePersons[nnn] + ", ";
names = names + NamePersons[nnn];
nam = names;
}
//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)
{
try
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(imageBoxFrameGrabber.Width, imageBoxFrameGrabber.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//currentFrame = brightnessImage(currentFrame, fc.brightness);
currentFrame += fc.brightness;
currentFrame = currentFrame.Mul(fc.contrast / 4);
//currentFrame.SmoothMedian(5);
if (fc.checkqual.Checked)
{
currentFrame._EqualizeHist();
}
if (fc.checkRetinexSSR.Checked || fc.checkRetinexHSV.Checked || fc.checkRetinexColors.Checked)
{
if (fc.checkRetinexSSR.Checked)
{
currentFrame = SingleScaleRetinex(currentFrame, fc.gaussianKernelSize, fc.sigmaGaussian, (int)fc.numericUpDown1.Value, 1);
}
if (fc.checkRetinexHSV.Checked)
{
currentFrame = SingleScaleRetinex(currentFrame, fc.gaussianKernelSize, fc.sigmaGaussian, (int)fc.numericUpDown1.Value, 2);
}
if (fc.checkRetinexColors.Checked)
{
currentFrame = SingleScaleRetinex(currentFrame, fc.gaussianKernelSize, fc.sigmaGaussian, (int)fc.numericUpDown1.Value, 3);
}
}
if (fc.checkGaussian.Checked)
{
currentFrame._SmoothGaussian(5);
}
if (fc.checkMedian.Checked)
{
currentFrame.SmoothMedian(5);
}
if (fc.checkBinariz.Checked)
{
gray = currentFrame.Convert <Gray, Byte>();
gray._ThresholdBinary(new Gray(fc.MinthreasoldBinarisation), new Gray(fc.MaxthreasoldBinarisation));
CvInvoke.cvCvtColor(gray, currentFrame, COLOR_CONVERSION.CV_GRAY2BGR);
//currentFrame._ThresholdBinaryInv(new Bgr(fc.MinthreasoldBinarisation, fc.MinthreasoldBinarisation, fc.MinthreasoldBinarisation), new Bgr(fc.MaxthreasoldBinarisation, fc.MaxthreasoldBinarisation, fc.MaxthreasoldBinarisation));
}
if (fc.checkCanny.Checked)
{
gray = currentFrame.Convert <Gray, Byte>();
//gray.Canny(new Gray(80), new Gray(160));
CvInvoke.cvCvtColor(gray.Canny(new Gray(fc.MinthreasoldCanny), new Gray(fc.MaxthreasoldCanny)), currentFrame, COLOR_CONVERSION.CV_GRAY2BGR);
}
if (fc.checkSobel.Checked)
{
gray = currentFrame.Convert <Gray, Byte>();
//gray.Canny(new Gray(80), new Gray(160));
CvInvoke.cvCvtColor(gray.Canny(new Gray(fc.MinthreasoldCanny), new Gray(fc.MaxthreasoldCanny)), currentFrame, COLOR_CONVERSION.CV_GRAY2BGR); //.Add(gray.Sobel(1, 0, 3)).AbsDiff(new Gray(0.0))
}
//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.DarkGray), 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;
if (names == "")
{
names = "Noknow";
}
//textLogs.Clear();
if (names != "")
{
//DateTime date1 = new DateTime();
string s = DateTime.Now.ToString("dd MMMM yyyy | HH:mm:ss");
textLogs.AppendText(s + " Имя:" + names + "\n");
}
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
}
catch (Exception ex) {
Console.WriteLine(ex.Message);
}
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Pega o frame para captura
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//Converte em 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));
//Ação para cada elemnto detectado
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);
//Desenha o quadrado na cara
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);
//Escreve o nome para cada face
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("");
//Seta o numero de faces detectadas
label3.Text = facesDetected[0].Length.ToString();
/*
*
* //Le a quantidade de pessoas e atribui a variavel pessoas
* string Pessoas = facesDetected[0].Length.ToString();
* if (Pessoas != "0")
* {
* Application.Idle -= FrameGrabber;
* grabber.Dispose();
* this.Hide();
* Painel painel = new Painel();
* painel.label4.Text = names;
* painel.Closed += (s, args) => this.Close();
* painel.Show();
* }
*
* //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;
//Concatena o nome das pessoas
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn];
}
//Le a quantidade de pessoas e atribui a variavel pessoas
string Pessoas = facesDetected[0].Length.ToString();
if (Pessoas != "0")
{
Application.Idle -= FrameGrabber;
grabber.Dispose();
this.Hide();
Painel painel = new Painel();
//Faz a separação do nome pro nivel
char[] Separador = { '|' };
string NomeENivel = names;
string[] split = NomeENivel.Split(Separador, StringSplitOptions.None);
painel.label4.Text = split[0];
painel.label3.Text = split[1];
painel.Closed += (s, args) => this.Close();
painel.Show();
}
//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)
{
try
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
//Get the current frame form capture device
//currentFrame = grabber.QueryFrame().Rotate(90, new Bgr(255, 255, 255)).Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
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(),
5000,
ref termCrit);
name = recognizer.Recognize(result);
// PONER ACÁ EXCERCICE SERVICE.
//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));
_timesAppeared.Add(name);
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Set the number of faces detected on the scene
label3.Text = facesDetected[0].Length.ToString();
if (name != string.Empty)
{
var userId = int.Parse(name.Substring(0, name.IndexOf(".jpeg")));
_exerciseService.Start(userId);
}
/*
* //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] + ", ";
//if (NamePersons[nnn] != string.Empty)
//{
// var i = int.Parse(NamePersons[nnn].Substring(0, NamePersons[nnn].IndexOf(".jpeg")));
// exerciseService.Start(i);
// var index = exerciseService.Exercises.IndexOf(exerciseService.Exercises.Where(ee => ee.UserId == i).FirstOrDefault());
// exerciseService.Exercises[index].LastCheckTicks = DateTime.Now.Ticks;
//}
}
//Show the faces procesed and recognized
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
} catch (Exception ex)
{
var distinct = _timesAppeared.GroupBy(test => test)
.Select(grp => grp)
.ToList();
}
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
//label4.Text = "";
NamePersons.Add("");
// Obtener el dispositivo de captura de forma de marco actual.
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
// Convertirlo en escala de grises.
gray = currentFrame.Convert <Gray, Byte>();
//Reconocimiento Facial
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
// Acción por cada elemento detectado.
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);
//dibujar la cara detectada en el canal 0 (gris) con color azul
currentFrame.Draw(f.rect, new Bgr(Color.DarkRed), 2);
if (trainingImages.ToArray().Length != 0)
{
//TermCriteria para reconocimiento facial con números de imágenes entrenadas como maxIteration.
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Reconocedor de cara Eigen.
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
name = recognizer.Recognize(result);
//Dibujar la etiqueta para cada cara detectada y reconocida.
currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.DarkRed));
}
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] + ", ";
}
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
NamePersons.Clear();
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
NamePersons.Add("");
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = currentFrame.Convert <Gray, Byte>();
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);
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("");
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;
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
imageBoxFrameGrabber.Image = currentFrame;
label4.Text = names;
names = "";
NamePersons.Clear();
}
/// <summary>
/// Functon to process captured frame and detection of face and also recognizing
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void FrameProcedure(object sender, EventArgs e)
{
try
{
Users.Add("");
Frame = camera.QueryFrame().Resize(480, 360, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
grayFace = Frame.Convert <Gray, byte>();
/*
* Face and face feactures detection data, if you don't want to detect of face feacture comment the line.
* this portion is releted to Haarcascade
*/
MCvAvgComp[][] facesDetectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(10, 10));
//MCvAvgComp[][] eyesDetectedNow = grayFace.DetectHaarCascade(eyesDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(5, 5));
//MCvAvgComp[][] noseDetectedNow = grayFace.DetectHaarCascade(noseDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(5, 5));
//MCvAvgComp[][] mouthDetectedNow = grayFace.DetectHaarCascade(mouthDetected, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(5, 5));
foreach (MCvAvgComp f in facesDetectedNow[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.DarkViolet), 2);
if (trainingImages.ToArray().Length != 0)
{
MCvTermCriteria termCriteries = new MCvTermCriteria(Count, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), 1500, ref termCriteries);
name = recognizer.Recognize(result);
Frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green)); //drawing box around the detect face
if (name != "")
{
if (monitorMode)
{
imageBoxFaceDetected.Image = new Image <Gray, byte>(Application.StartupPath + $"/Faces/{name}.bmp");
Person person = DBConnecttion.getInstance().personInfo(name);
labelName.Text = person.Name;
labelUserName.Text = name;
labelAge.Text = person.Age;
}
}
}
Users.Add("");
/*
* foreach(MCvAvgComp j in eyesDetectedNow[0])
* {
*
* Frame.Draw(j.rect, new Bgr(Color.Red), 2);
*
* }
*/
/*
*
* foreach (MCvAvgComp j in noseDetectedNow[0])
* {
*
* Frame.Draw(j.rect, new Bgr(Color.Red), 2);
*
* }
*/
/*
* foreach (MCvAvgComp j in mouthDetectedNow[0])
* {
* Frame.Draw(j.rect, new Bgr(Color.Red), 2);
*
* }
*/
}
liveCameraView.Image = Frame;
names = "";
Users.Clear();
}
catch (Exception ex)
{
//MessageBox.Show(ex.Message);
}
}
// Per camera frame routine method
private static void FrameGrabber(object sender, EventArgs e)
{
if (!Started)
{
return;
}
// Get current frame from camera
currentFrame = grabber.QueryFrame().Resize(420, 340, INTER.CV_INTER_CUBIC);
// Convert to grayscale
gray = currentFrame.Convert <Gray, byte>();
// Do haar cascade classifier on camera frame
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
// Classify detected faces on frame
currentDectectionCount = facesDetected[0].Length;
foreach (MCvAvgComp f in facesDetected[0])
{
// Crop face image area and convert to gray
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, INTER.CV_INTER_CUBIC);
// Draw red rectangle on face area
currentFrame.Draw(f.rect, new Bgr(Color.Red), 2);
//
if (trainingImages.ToArray().Length != 0)
{
// Eigen algorithm term criteria for recognizing face
MCvTermCriteria termCrit = new MCvTermCriteria(trainingImages.Count, 0.001);
// Create eigen recognizer object
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
3000,
ref termCrit);
// Do recognize current face using eigen algorithm
currentDectection = recognizer.Recognize(result);
// Verbous diagnostic show name on recognizer
if (ShowLabel)
{
currentFrame.Draw(currentDectection, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));
}
}
}
// Update frame on UI
imageBoxFrameGrabber.Image = currentFrame;
// Update current recognizer state on UI
if (currentDectectionCount == 1 && !string.IsNullOrEmpty(currentDectection))
{
OnFaceRecognized?.Invoke(currentDectection);
}
}
public void FrameGrabberRecognition(object sender, EventArgs e)
{
try
{
NamePersons.Add("");
//Get the current frame form capture device
currentFrameRecognition = grabberRecognition.QueryFrame().Resize(501, 407, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//Convert it to Grayscale
gray = currentFrameRecognition.Convert <Gray, Byte>();
//Face Detector
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
faceRecognition,
1.2,
2,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new System.Drawing.Size(20, 20));
//Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = currentFrameRecognition.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
currentFrameRecognition.Draw(f.rect, new Bgr(Color.Red), 2);
//initialize result,t and gray if (trainingImages.ToArray().Length != 0)
{
//term criteria against each image to find a match with it, perform different iterations
MCvTermCriteria termCrit = new MCvTermCriteria(contTrain, 0.001);
//call class by creating object and pass parameters
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labelsRecognition.ToArray(),
3000,
ref termCrit);
//next step is to name find for recognize face
name = recognizer.Recognize(result);
//now show recognized person name so
currentFrameRecognition.Draw(name, ref font, new System.Drawing.Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));//initalize font for the name captured
}
if (!FacesAlreadyDetected.Contains(name))
{
SaveToDatabase(name, DateTime.Now);
FacesAlreadyDetected.Add(name);
}
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] + ", ";
//System.Windows.MessageBox.Show(NamePersons[nnn]);
string test = NamePersons[nnn] + ",";
}
//load haarclassifier and previous saved images to find matches
imgBox_Detected.Image = currentFrameRecognition;
TBx_DetectedStudent.Text = names;
names = "";
NamePersons.Clear();
}
catch (Exception ex)
{
MessageBox.Show("Press Ok to Continue");
}
}
/// <summary>
/// Loads the traing data given a (string) folder location
/// </summary>
/// <param name="Folder_location"></param>
/// <returns></returns>
private bool LoadTrainingData(string Folder_location)
{
if (File.Exists(Folder_location + "\\" + XmlVeriDosyasi))
{
try
{
//message_bar.Text = "";
Names_List.Clear();
trainingImages.Clear();
FileStream filestream = File.OpenRead(Folder_location + "\\" + XmlVeriDosyasi);
long filelength = filestream.Length;
byte[] xmlBytes = new byte[filelength];
filestream.Read(xmlBytes, 0, (int)filelength);
filestream.Close();
MemoryStream xmlStream = new MemoryStream(xmlBytes);
using (XmlReader xmlreader = XmlTextReader.Create(xmlStream))
{
while (xmlreader.Read())
{
if (xmlreader.IsStartElement())
{
switch (xmlreader.Name)
{
case "NAME":
if (xmlreader.Read())
{
Names_List.Add(xmlreader.Value.Trim());
NumLabels += 1;
}
break;
case "FILE":
if (xmlreader.Read())
{
//PROBLEM HERE IF TRAININGG MOVED
trainingImages.Add(new Image <Gray, byte>(Dizin + "\\" + xmlreader.Value.Trim()));
}
break;
}
}
}
}
ContTrain = NumLabels;
if (trainingImages.ToArray().Length != 0)
{
//Eigen face recognizer
recognizer = new EigenObjectRecognizer(trainingImages.ToArray(),
Names_List.ToArray(), 5000, ref termCrit); //5000 default
return(true);
}
else
{
return(false);
}
}
catch (Exception ex)
{
Error = ex.ToString();
return(false);
}
}
else
{
return(false);
}
}
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[][] facesDetectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10,
// Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
MCvAvgComp[][] facesDetectedNow = grayFace.DetectHaarCascade(
faceDetected,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetectedNow[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), 2);
if (trainningImages.ToArray().Length != 0)
{
MCvTermCriteria termCriteria = new MCvTermCriteria(count, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainningImages.ToArray(), labels.ToArray(),
2500, ref termCriteria);
name = recognizer.Recognize(result);
if (name.Length != 0)
{
Frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green));
}
else
{
Frame.Draw("Unknown", ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green));
// code สำหรับใช้จับหน้าเป็นชุดโดยผมตั้งไว้ให้เก็บหน้าไว้แค่ 20 ภาพ ต่อ ครั้ง
if (capture_mode == true && capture_count < 20)
{
count = 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(40, 40));
trainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
trainningImages.Add(trainedFace);
labels.Add(tbxName.Text);
File.WriteAllText(Application.StartupPath + "/Faces/Faces.txt", trainningImages.ToArray().Length.ToString() + ",");
capture_count++;
//lblFace.Text = "# of faces : " + capture_count;
for (int i = 1; i < trainningImages.ToArray().Length + 1; i++)
{
trainningImages.ToArray()[i - 1].Save(Application.StartupPath + "/Faces/face" + i + ".bmp");
File.AppendAllText(Application.StartupPath + "/Faces/Faces.txt", labels.ToArray()[i - 1] + ",");
}
}
else
{
capture_count = 0;
capture_mode = false;
//btnCapture.Text = "Start";
//lblFace.Text = "# of faces : 0";
}
}
}
else
{
Frame.Draw("Unknown", ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green));
// code สำหรับใช้จับหน้าเป็นชุดโดยผมตั้งไว้ให้เก็บหน้าไว้แค่ 20 ภาพ ต่อ ครั้ง
if (capture_mode == true && capture_count < 20)
{
count = 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));
MCvAvgComp[][] detectedFaces = grayFace.DetectHaarCascade(
faceDetected,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(40, 40));
trainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
trainningImages.Add(trainedFace);
labels.Add(tbxName.Text);
File.WriteAllText(Application.StartupPath + "/Faces/Faces.txt", trainningImages.ToArray().Length.ToString() + ",");
capture_count++;
//lblFace.Text = "# of faces : " + capture_count;
for (int i = 1; i < trainningImages.ToArray().Length + 1; i++)
{
trainningImages.ToArray()[i - 1].Save(Application.StartupPath + "/Faces/face" + i + ".bmp");
File.AppendAllText(Application.StartupPath + "/Faces/Faces.txt", labels.ToArray()[i - 1] + ",");
}
}
else
{
capture_count = 0;
capture_mode = false;
//btnCapture.Text = "Start";
//lblFace.Text = "# of faces : 0";
}
}
//Users[t - 1] = name;
Users.Add("");
}
cameraBox.Image = Frame;
names = "";
Users.Clear();
//Frame.Draw(font.rect, new Bgr(Color.Green), 3);
}
private void FrameGrabber(object sender, EventArgs e)
{
//enquanto nao é detectado o rosto dou o valor de 0
lblNumeroDetect.Text = "0";
//mesmo processo em nome das pessoas
NamePersons.Add("");
//vamos tratar o processo abaixo com try
try
{
currentFrame = grabber.QueryFrame().Resize(400, 300, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
currentFrame._Flip(FLIP.HORIZONTAL);
//Converte a escala para cinza
gray = currentFrame.Convert <Gray, Byte>();
//Aqui vai detectar os rostos no frame
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(face, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
//ação para o elemento detectado
foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, INTER.CV_INTER_CUBIC);
//Mostrar o quadro nos rostos ou no rosto e a propriedade de cor das bordas
currentFrame.Draw(f.rect, new Bgr(Color.Aquamarine), 1);
if (trainingImages.ToArray().Length != 0)
{
//Reconhecer o numero de rostos na imagem
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//reconhecer o rosto
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), ref termCrit);
var fa = new Image <Gray, byte> [trainingImages.Count];
name = recognizer.Recognize(result);
//Mostrar o nome em base de dados de cada rosto
currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Black));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Establecer quantidade de rostos
lblNumeroDetect.Text = facesDetected[0].Length.ToString();
label1.Text = name;
}
t = 0;
//Nomes concatenados
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
lblNadie = names + NamePersons[nnn] + ", ";
}
//rostos processados e identificados
imageBoxFrameGrabber.Image = currentFrame;
name = "";
//limpar a lista dos nomes
NamePersons.Clear();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
private void FrameProcedure2(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[][] facesDetectedNow = grayFace.DetectHaarCascade(faceDetected, 1.2, 10,
// Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
MCvAvgComp[][] facesDetectedNow = grayFace.DetectHaarCascade(
faceDetected,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetectedNow[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), 2);
if (trainningImages.ToArray().Length != 0)
{
MCvTermCriteria termCriteria = new MCvTermCriteria(count, 0.001);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainningImages.ToArray(), labels.ToArray(),
2500, ref termCriteria);
name = recognizer.Recognize(result);
if (name.Length != 0)
{
Frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green));
EmpID++;
//ใส่ตาราง
//ตั้งเวลาไว้ เมื่อ EmpID นับได้ถึง 20
if (EmpID == 20)
{
table.Rows.Add(int.Parse(name), DateTime.Now.ToString("HH:mm:ss"));
dataGridView1.DataSource = table;
//ส่งข้อมูลไปยังDB
string sqlDataSource = "Data Source=LAPTOP-3KQ0AE11\\MSSQLSERVER01;Initial Catalog=GUYs;Integrated Security=True;";
SqlDataReader myReader;
using (SqlConnection con = new SqlConnection(sqlDataSource))
{
for (int i = 0; i < dataGridView1.Rows.Count - 2; i++)
{
using (SqlCommand cmd = new SqlCommand(@"INSERT INTO dbo.UserCheck VALUES ( '" + dataGridView1.Rows[i].Cells[0].Value + "', CAST(GETDATE() AS DATE), '" + dataGridView1.Rows[i].Cells[1].Value + "','');", con))
{
con.Open();
myReader = cmd.ExecuteReader();
table.Load(myReader);
myReader.Close();
con.Close();
}
}
}
EmpID = 0;
}
}
else
{
Frame.Draw("Unknown", ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green));
// code สำหรับใช้จับหน้าเป็นชุดโดยผมตั้งไว้ให้เก็บหน้าไว้แค่ 20 ภาพ ต่อ ครั้ง
if (capture_mode == true && capture_count < 20)
{
count = 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(40, 40));
trainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
trainningImages.Add(trainedFace);
labels.Add(tbxName.Text);
File.WriteAllText(Application.StartupPath + "/Faces/Faces.txt", trainningImages.ToArray().Length.ToString() + ",");
capture_count++;
//lblFace.Text = "# of faces : " + capture_count;
for (int i = 1; i < trainningImages.ToArray().Length + 1; i++)
{
trainningImages.ToArray()[i - 1].Save(Application.StartupPath + "/Faces/face" + i + ".bmp");
File.AppendAllText(Application.StartupPath + "/Faces/Faces.txt", labels.ToArray()[i - 1] + ",");
}
}
else
{
capture_count = 0;
capture_mode = false;
//btnCapture.Text = "Start";
//lblFace.Text = "# of faces : 0";
}
}
}
else
{
Frame.Draw("Unknown", ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Green));
// code สำหรับใช้จับหน้าเป็นชุดโดยผมตั้งไว้ให้เก็บหน้าไว้แค่ 20 ภาพ ต่อ ครั้ง
if (capture_mode == true && capture_count < 20)
{
count = 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));
MCvAvgComp[][] detectedFaces = grayFace.DetectHaarCascade(
faceDetected,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(40, 40));
trainedFace = result.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
trainningImages.Add(trainedFace);
labels.Add(tbxName.Text);
File.WriteAllText(Application.StartupPath + "/Faces/Faces.txt", trainningImages.ToArray().Length.ToString() + ",");
capture_count++;
//lblFace.Text = "# of faces : " + capture_count;
for (int i = 1; i < trainningImages.ToArray().Length + 1; i++)
{
trainningImages.ToArray()[i - 1].Save(Application.StartupPath + "/Faces/face" + i + ".bmp");
File.AppendAllText(Application.StartupPath + "/Faces/Faces.txt", labels.ToArray()[i - 1] + ",");
}
}
else
{
capture_count = 0;
capture_mode = false;
//btnCapture.Text = "Start";
//lblFace.Text = "# of faces : 0";
}
}
//Users[t - 1] = name;
Users.Add("");
}
cameraBox.Image = Frame;
names = "";
Users.Clear();
//Frame.Draw(font.rect, new Bgr(Color.Green), 3);
}
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
NamePersons.Add("");
//Get the current frame form capture device
if (strPicVid == "Picture")
{
currentFrame = new Image <Bgr, Byte>(strBrowseFileName).Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
if (strPicVid == "Video")
{
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
//Convert it to Grayscale
gray = currentFrame.Convert <Gray, Byte>();
if (blnDetection == true)
{
//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);
//This gets the face so it can be added to the db
if (blnRecSetting == false)
{
if (blnSelect == true)
{
imageBox1.Image = imageBox1.Image;
//this stops the FrameGrabber so that detection can end cleanly until the face is added to the db
//or continue is selected
Application.Idle -= FrameGrabber;
if (strPicVid == "Video")
{
grabber.Dispose();
}
}
else
{
imageBox1.Image = result;
}
}
else
{
imageBox1.Image = null;
}
if (blnRecognition == true)
{
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)
{
//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.LightGreen));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
if (!name.Equals(""))
{
b += 1;
a = true;
}
}
t = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
}
//Show the faces procesed and recognized
imageBoxFrameGrabber.Image = currentFrame;
names = "";
//Clear the list(vector) of names
NamePersons.Clear();
if (a == true&&b==5) {
File.WriteAllText(@"C:\Users\kiat\Desktop\abc2.txt", "True");
System.Windows.Forms.Application.Exit();
}
}
public void FrameGrabber(object sender, EventArgs e)
{
if (checkBox2.Checked)
{
eyesfocus = true;
}
else
{
eyesfocus = false;
}
label3.Text = "0";
label4.Text = "";
NamePersons.Add("Khong x.dinh");
NamePersonsEyes.Add("Khong x.dinh");
//Get the current frame form capture device
//currentFrame = grabber.QueryFrame().Resize(550, 450, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
currentFrame = grabber.QueryFrame().Resize(600, 400, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
if (label11.Text != "00" || label11.Text != "1" || label11.Text != "-1")
{
currentFrame = new Image <Bgr, Byte>(AdjustBrightness(currentFrame.ToBitmap(), bri));
}
//Convert it to Grayscale
gray = currentFrame.Convert <Gray, Byte>();
MCvAvgComp[][] facesDetected;
//Face Detector
if (checkBox1.Checked)
{
facesDetected = gray.DetectHaarCascade(
face,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
}
else
{
facesDetected = gray.DetectHaarCascade(
face,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.FIND_BIGGEST_OBJECT,
new Size(20, 20));
}
//Action for each element detected
foreach (MCvAvgComp f in facesDetected[0])
{
imageBox3.Image = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
Int32 yCoordStartSearchEyes = f.rect.Top + (f.rect.Height * 3 / 11);
Point startingPointSearchEyes = new Point(f.rect.X, yCoordStartSearchEyes);
Size searchEyesAreaSize = new Size(f.rect.Width, (f.rect.Height * 3 / 11));
Rectangle possibleROI_eyes = new Rectangle(startingPointSearchEyes, searchEyesAreaSize);
t = t + 1;
tEyes++;
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
resultEyes = currentFrame.Copy(possibleROI_eyes).Convert <Gray, byte>().Resize(100, 30, 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), 1);
currentFrame.Draw(new CircleF(new PointF(f.rect.X, f.rect.Y), 1), new Bgr(Color.Yellow), 10);
currentFrame.Draw(new LineSegment2D(new Point(f.rect.X, f.rect.Y), new Point(f.rect.X - 80, f.rect.Y)), new Bgr(Color.Yellow), 2);
currentFrame.Draw(new LineSegment2D(new Point(f.rect.X - 80, f.rect.Y), new Point(f.rect.X - 80, f.rect.Y - 30)), new Bgr(Color.Yellow), 1);
currentFrame.Draw(new LineSegment2D(new Point(f.rect.X - 80, f.rect.Y - 30), new Point(f.rect.X - 150, f.rect.Y - 30)), new Bgr(Color.Yellow), 1);
currentFrame.Draw(new LineSegment2D(new Point(f.rect.X - 80, f.rect.Y - 30), new Point(f.rect.X - 70, f.rect.Y - 30)), new Bgr(Color.Yellow), 1);
currentFrame.Draw(possibleROI_eyes, new Bgr(Color.DeepPink), 1);
currentFrame.Draw(new CircleF(new PointF(possibleROI_eyes.X, possibleROI_eyes.Y + possibleROI_eyes.Height), 1), new Bgr(Color.DeepPink), 5);
currentFrame.Draw(new LineSegment2D(new Point(possibleROI_eyes.X, possibleROI_eyes.Y + possibleROI_eyes.Height), new Point(possibleROI_eyes.X - 40, possibleROI_eyes.Y + possibleROI_eyes.Height)), new Bgr(Color.DeepPink), 1);
currentFrame.Draw(new LineSegment2D(new Point(possibleROI_eyes.X - 40, possibleROI_eyes.Y + possibleROI_eyes.Height), new Point(possibleROI_eyes.X - 40, possibleROI_eyes.Y + possibleROI_eyes.Height + 120)), new Bgr(Color.DeepPink), 1);
currentFrame.Draw(new LineSegment2D(new Point(possibleROI_eyes.X - 40, possibleROI_eyes.Y + possibleROI_eyes.Height + 120), new Point(possibleROI_eyes.X, possibleROI_eyes.Y + possibleROI_eyes.Height + 120)), new Bgr(Color.DeepPink), 1);
currentFrame.Draw(new LineSegment2D(new Point(possibleROI_eyes.X - 40, possibleROI_eyes.Y + possibleROI_eyes.Height + 120), new Point(possibleROI_eyes.X - 80, possibleROI_eyes.Y + possibleROI_eyes.Height + 120)), new Bgr(Color.DeepPink), 1);
currentFrame.Draw("Eyes Area", ref font, new Point(possibleROI_eyes.X - 80, possibleROI_eyes.Y + possibleROI_eyes.Height + 130), new Bgr(Color.DeepPink));
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
if (trainingImagesEyes.ToArray().Length != 0)
{
MCvTermCriteria termCritEyes = new MCvTermCriteria(ContTrainEyes, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizerEyes = new EigenObjectRecognizer(
trainingImagesEyes.ToArray(),
labelsEyes.ToArray(),
3000,
ref termCritEyes);
nameEyes = recognizerEyes.Recognize(resultEyes);
if (name == nameEyes)
{
currentFrame.Draw(nameEyes, ref font, new Point(f.rect.X - 150, f.rect.Y - 35), new Bgr(Color.Red));
}
else
{
currentFrame.Draw("noname", ref font, new Point(f.rect.X - 150, f.rect.Y - 35), new Bgr(Color.Red));
}
}
}
else
{
currentFrame.Draw("noname", ref font, new Point(f.rect.X - 150, f.rect.Y - 35), new Bgr(Color.Red));
}
NamePersons[t - 1] = name;
NamePersonsEyes[t - 1] = nameEyes;
NamePersons.Add("");
NamePersonsEyes.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);
//var Arrayeyes = eyesDetected[0];
// Arrayeyes[0].rect.X
if (eyesfocus)
{
currentFrame.Draw(eyeRect, new Bgr(Color.Blue), 1);
if (eyeRect.X < f.rect.X + (f.rect.Width / 2))
{
currentFrame.Draw(new CircleF(new PointF(eyeRect.X, eyeRect.Y), 1), new Bgr(Color.Blue), 5);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X, eyeRect.Y), new Point(eyeRect.X - 100, eyeRect.Y)), new Bgr(Color.Blue), 1);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X - 100, eyeRect.Y), new Point(eyeRect.X - 100, eyeRect.Y + 15)), new Bgr(Color.Blue), 1);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X - 100, eyeRect.Y + 15), new Point(eyeRect.X - 170, eyeRect.Y + 15)), new Bgr(Color.Blue), 1);
currentFrame.Draw("right eye", ref font, new Point(eyeRect.X - 185, eyeRect.Y + 10), new Bgr(Color.Blue));
}
else
{
currentFrame.Draw(new CircleF(new PointF(eyeRect.X + eyeRect.Width, eyeRect.Y), 1), new Bgr(Color.Blue), 5);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X + eyeRect.Width, eyeRect.Y), new Point(eyeRect.X + eyeRect.Width, eyeRect.Y - 100)), new Bgr(Color.Blue), 1);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X + eyeRect.Width, eyeRect.Y - 100), new Point(eyeRect.X + eyeRect.Width + 40, eyeRect.Y - 100)), new Bgr(Color.Blue), 1);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X + eyeRect.Width + 40, eyeRect.Y - 100), new Point(eyeRect.X + eyeRect.Width + 40, eyeRect.Y - 90)), new Bgr(Color.Blue), 1);
currentFrame.Draw(new LineSegment2D(new Point(eyeRect.X + eyeRect.Width + 40, eyeRect.Y - 90), new Point(eyeRect.X + eyeRect.Width + 140, eyeRect.Y - 90)), new Bgr(Color.Blue), 1);
currentFrame.Draw("left eye", ref font, new Point(eyeRect.X + eyeRect.Width + 45, eyeRect.Y - 95), new Bgr(Color.Blue));
}
}
}
}
t = 0;
tEyes = 0;
//Names concatenation of persons recognized
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
namesEyes = namesEyes + NamePersonsEyes[nnn] + ", ";
}
//Show the faces procesed and recognized
// imageBoxFrameGrabber.Image = currentFrame.Flip(FLIP.HORIZONTAL);
if (camlive)
{
imageBoxFrameGrabber.Image = currentFrame;
}
label4.Text = names;
names = "";
nameEyes = "";
//Clear the list(vector) of names
NamePersons.Clear();
NamePersonsEyes.Clear();
}
private void FrameGrabber(object sender, EventArgs e)
{
lblCantidad.Text = "0";
NamePersons.Add("");
try
{
currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
//Convertir a escala de grises
gray = currentFrame.Convert <Gray, Byte>();
//Detector de Rostros
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(face, 1.5, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
//1.2
//Accion para cada elemento detectado
foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, INTER.CV_INTER_CUBIC);
//Dibujar el cuadro para el rostro
currentFrame.Draw(f.rect, new Bgr(Color.Blue), 1);
if (trainingImages.ToArray().Length != 0)
{
//Clase para reconocimiento con el nùmero de imagenes
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.88);
//0.001
//Clase Eigen para reconocimiento de rostro
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), labels.ToArray(), ref termCrit);
var fa = new Image <Gray, byte> [trainingImages.Count];
name = recognizer.Recognize(result);
//Dibujar el nombre para cada rostro detectado y reconocido
currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Blue));
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//Establecer el nùmero de rostros detectados
lblCantidad.Text = facesDetected[0].Length.ToString();
lblNombre.Text = name;
}
t = 0;
//Nombres concatenados de todos los rostros reconocidos
for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
//Mostrar los rostros procesados y reconocidos
imageBox1.Image = currentFrame;
//lblNombre.Text = names;
name = "";
//Borrar la lista de nombres
NamePersons.Clear();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
private void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";
NamePersons.Add("");
//Get the current frame form capture device
currentFrame = grabber.QueryFrame().Resize(320, 240, INTER.CV_INTER_CUBIC);
//Convert it to Grayscale
gray = currentFrame.Convert<Gray, byte>();
//Face Detector
var facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
//Action for each element detected
foreach (var f in facesDetected[0])
{
t = t + 1;
result = currentFrame.Copy(f.rect).Convert<Gray, byte>().Resize(100, 100, 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
var termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
var 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));
//Add prezent faces to label
if (!label8.Text.Contains(name))
{
label8.Text = label8.Text + " " + name;
}
}
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 (var 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)
{
NamePersons.Add("");
//now detect no. of faces in scene
label2.Text = "0";
//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,
1.3,
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])
{
DateTime horadetectada = DateTime.Now;
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);
//initialize result,t and gray if (trainingImages.ToArray().Length != 0)
{
//termcriteria against each image to find a match with it perform different iterations
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//call class by creating object and pass parameters
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
1000,
ref termCrit);
//next step is to name find for recognize face
name = recognizer.Recognize(result);
//now show recognized person name so
currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));//initalize font for the name captured
c.InsertarHoradetect(name, horadetectada);
}
NamePersons[t - 1] = name;
NamePersons.Add("");
//now we will check detected faces multiple or just one in next lecture uptill now we are done with recognition
label2.Text = facesDetected[0].Length.ToString();
if (label2.Text == "1")
{
c.InsertarHoradetect(name, horadetectada);
MessageBox.Show(" Te la inserto toda");
this.BackColor = System.Drawing.Color.Lime;
}
else
{
MessageBox.Show(" No se logro Insertar nada ctm");
this.BackColor = System.Drawing.Color.Red;
}
}
imageBox1.Image = currentFrame;
//load haarclassifier and previous save image in directory to find match
//hi now perform face recognitione
//first of all add eigen class to project
//i will upload in resource section so you can have it
//Check that trained faces are present to recognize face
//Done now run and test your program
//Done with this now i will upload complete face recognition sdk
//hope you learn program and enjoyed it
}
