//-------------------------------------------------------------------------------------//
//<<<<<----------------FUNCTIONS USED TO TRAIN RECOGNIZER ON TRAINING SET----------->>>>
//-------------------------------------------------------------------------------------//
/// <summary>
/// Trains recognizer on fetched face-label pairs and saves the trained data to recognition variables
/// </summary>
public void TrainRecognizer()
{
MCvTermCriteria termCrit = new MCvTermCriteria(iMaxItereations, dEPS);
ImageInDatabase dbTrainingSet = new ImageInDatabase();
// This will fill the training images array AND labels array
dbTrainingSet.LoadCompleteTrainingSet();
recognizer = new EigenObjectRecognizer(dbTrainingSet.m_trainingImages, dbTrainingSet.m_TrainingLabels, dDistTreshHold, ref termCrit);
}
private void InitEigenObjectRecognizer() {
if (trainedImages.Count <= 0) { return; }
initEigen = true;
// TermCriteria for face recognition with numbers of trained images like maxIteration
termCrit = new MCvTermCriteria(trainedImages.Count, 0.001);
// Eigen face recognizer
recognizer = new EigenObjectRecognizer(trainedImages.ToArray(), trainedLabels.ToArray(), 5000, ref termCrit);
initEigen = false;
}
public EigenCardDetector(string foldername)
{
List<FileInfo> files = new List<FileInfo>(new DirectoryInfo(foldername).GetFiles());
List<Image<Gray, byte>> images = new List<Image<Gray, byte>>(files.Count);
foreach (FileInfo info in files)
{
Bitmap bit = new Bitmap(info.FullName);
images.Add(new Image<Gray, byte>(bit));
}
MCvTermCriteria crit = new MCvTermCriteria(0.05);
recog = new EigenObjectRecognizer(images.ToArray(), ref crit);
}
public FacialInfo()
{
StreamReader SR = new StreamReader("CVConfig.txt");
gFacedetection = new HaarCascade(@"haarcascade_frontalface_alt.xml");
gHanddetection = new HaarCascade(@"haarcascade_hand.xml");
gCompareBoxes = new List<Image<Gray, byte>>();
gRecognitionBoxes = new List<Image<Gray, byte>>();
Image<Gray, byte> Blank = new Image<Gray, byte>(128, 120, new Gray(0.5));
for (int x = 0; x < 6; x++)
{
gCompareBoxes.Add(Blank);
gRecognitionBoxes.Add(Blank);
}
try
{
ImageWidth = int.Parse(SR.ReadLine().Split(':')[1]);
ImageHeight = int.Parse(SR.ReadLine().Split(':')[1]);
Threshold = int.Parse(SR.ReadLine().Split(':')[1]);
termcrit = double.Parse(SR.ReadLine().Split(':')[1]);
}
catch
{
//default values
ImageWidth = 128;
ImageHeight = 120;
termcrit = 0.001;
Threshold = 4500;
}
SR.Close();
gUP = new UserProfile();
MCvTermCriteria termCrit = new MCvTermCriteria(gUP.LoadedUsers.Count, termcrit);
gEOR = new EigenObjectRecognizer(
gUP.getImages(),
gUP.getNumbers(),
10000,//4500
ref termCrit);
}
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();
}
/// <summary>
/// Jedyny konstruktor. Odczytuje twarze z bazy i inicjuje wewnętrzny obiekt odpowiedzialny za rozpoznawanie twarzy.
/// </summary>
public Recognizer()
{
readFiles();//odczyt twarzy z bazy
//ustawienie etykiet i dokładności z jaką ma być wykonywane ropoznawanie
MCvTermCriteria criteria = new MCvTermCriteria(labels.Length, 0.001);
//utworzenie nowego obiektu do rozpoznawania twarzy
//obiekt ten wylicza eigenvectors dla każdej twarzy w bazie
//oraz dla każdej sprawdzanej twarz, po czym wartości wyliczone
//dla sprawdzanej twarzy porównuje z wartościami wyliczonymi dla tych
//twarzy z bazy
eor = new EigenObjectRecognizer(
faces,//tablica twarzy
labels,//etykiety odpowiadające twarzom
3000,//poziom progowania pomiędzy poszczególnymi eigenvectors
ref criteria//kryterium
);
//utwórz wzorzec do wykrywania twarzy
haar = new HaarCascade("haarcascade_frontalface_default.xml");
}
public static EigenObjectRecognizer CreateRecognizerFromFotosInFolder(String folder, String pattern, double accuracy, int eigenDistanceThreshold)
{
List<Image<Gray, byte>> trainedImages = new List<Image<Gray, byte>>();
List<String> labels = new List<string>();
string[] subdirEntries = Directory.GetDirectories(folder);
foreach (var directory in subdirEntries)
{
string[] fileEntries = Directory.GetFiles(directory);
var label = directory.Remove(0, directory.LastIndexOf("\\")+1);
foreach (var file in fileEntries.Where(x=>x.Contains(pattern)))
{
Image<Gray, byte> image = new Image<Gray, byte>(file);
trainedImages.Add(image);
labels.Add(label);
}
}
MCvTermCriteria termCrit = new MCvTermCriteria(40, accuracy);
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainedImages.ToArray(),
labels.ToArray(),
eigenDistanceThreshold,
ref termCrit);
//int i = 1;
//Image<Gray, float>[] eigenfaces;
//Image<Gray, float> avg;
//EigenObjectRecognizer.CalcEigenObjects(trainedImages.ToArray(),ref termCrit,out eigenfaces, out avg);
//foreach(var eigenface in eigenfaces)
// {
// eigenface.Bitmap.Save(@"e:\data\phototest\eigen" + i + ".bmp");
// i++;
// }
return recognizer;
}
public CommandService()
{
userInfoManager = new UserInfoManager();
userInfoManager.connect();
foreach(var user in userInfoManager.ListUsers())
{
foreach (var photo in userInfoManager.ListUserPhotoData(user))
{
labels.Add(user.Name);
images.Add(photo.Image);
Console.WriteLine("Added image");
}
}
MCvTermCriteria crit = new MCvTermCriteria(1.0);
this.recognizer = new EigenObjectRecognizer(images.ToArray(),
labels.ToArray(),
2000,
ref crit);
}
public static void TrainRecognizer()
{
var trainingCases = GetTrainingCases();
if (trainingCases != null
&& trainingCases.Count > 0)
{
Image<Gray, byte>[] images = trainingCases.Select(item => item.Image).ToArray();
string[] labels = trainingCases.Select(item => item.Label).ToArray();
Recognizer = new EigenObjectRecognizer(images, labels, ref _criteria);
}
}
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];
}
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();
}
/// <summary>
/// Loads the traing data given a (string) folder location
/// </summary>
/// <param name="Folder_loacation"></param>
/// <returns></returns>
private bool LoadTrainingData(string Folder_loacation)
{
Names_List.Clear();
trainingImages.Clear();
//We should have 5 sets of trainig data
for (int i = 0; i < 5; ++i)
{
int id = i + 1;
for (int j = 0; j < 5; ++j)
{
if (File.Exists(Folder_loacation + "\\hand_" + i + "_"+j+".png"))
{
//using index of 0 so let's increment i by one
Names_List.Add("" + id);
Console.WriteLine("Opening data " + i + " for training");
trainingImages.Add(new Image<Gray, byte>(Folder_loacation + "hand_" + i + "_"+j+".png"));
}
else return false;
}
}
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;
}
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);
}
/// <summary>
/// load eigen value in EigenValueTags class
/// </summary>
/// <param name="eigenRec">
/// A <see cref="EigenObjectRecognizer"/>
/// </param>
/// <returns>
/// A <see cref="EigenValueTags"/>
/// </returns>
public EigenValueTags RecordEigenValue(EigenObjectRecognizer eigenRec)
{
//DELETEME
//FaceClassifier.WriteEigenValueFile(eigenRec,"","eigenvalue");
EigenValueTags eigenValueTags = new EigenValueTags();
const int MAX_EIGEN_LENGTH = 30;
int nums_train = eigenRec.Labels.Length;
float[][] eigenMatrix = new float[nums_train][];
int max_eigenvalueLength = Math.Min(MAX_EIGEN_LENGTH, 4 + nums_train/5);
if(nums_train < 5)
max_eigenvalueLength = nums_train;
for(int i=0;i<nums_train;i++){
Emgu.CV.Matrix<float> eigenValue = eigenRec.EigenValues[i];
float[] temp = new float[max_eigenvalueLength];
for(int k=0; k<max_eigenvalueLength; k++){
temp[k] = eigenValue.Data[k,0];
}
eigenValueTags.Add(new VTag(temp, eigenRec.Labels[i]));
}
Log.Debug("eigenVTags Length = "+ eigenValueTags.eigenTaglist.Count);
return eigenValueTags;
}
/// <summary>
/// Loads the traing data given a (string) folder location
/// </summary>
/// <param name="Folder_loacation"></param>
/// <returns></returns>
private bool LoadTrainingData(string Folder_loacation)
{
if (File.Exists(Folder_loacation +"\\TrainedLabels.xml"))
{
try
{
//message_bar.Text = "";
Names_List.Clear();
trainingImages.Clear();
FileStream filestream = File.OpenRead(Folder_loacation + "\\TrainedLabels.xml");
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>(Application.StartupPath + "\\TrainedFaces\\" + 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 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
}
public void UpdateRecognizer()
{
//TermCriteria for face recognition with numbers of trained images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);
//Eigen face recognizer
recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
threashold,
ref termCrit);
}
private bool LoadTrainingData()
{
mydb = new DBConn();
allname = mydb.getAllImageID();
string[] allname_st = allname.Select(x => x.ToString()).ToArray();
trainingImages = mydb.getTrainedImageList();
//trainingImages = mydb.getRawTrainedImageList();
if (mydb.getImageCount() > 0)
{
if (trainingImages.Length != 0)
{
//set round and ...
//termCrit = new MCvTermCriteria(mydb.getImageCount(), 0.001);
termCrit = new MCvTermCriteria(5000, 0.0001);
//Eigen face recognizer
recognizer = new EigenObjectRecognizer(trainingImages, allname_st, maxRecognizeTreshold, ref termCrit);
return true;
}
else
{
return false;
}
}
else
{
return false;
}
}
public void Dispose()
{
recognizer = null;
trainingImages = null;
allname = null;
Error = null;
GC.Collect();
}
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 = "";
}
}
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" ;
}
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));
}
}
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 = "";
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();
}
/// <summary>
/// Dispose of Class call Garbage Collector
/// </summary>
public void Dispose()
{
recognizer = null;
trainingImages = null;
Names_List = null;
Error = null;
GC.Collect();
}
public static void Train()
{
List < TrainingItem > items = TrainingItem.Load(trainingSetCSVFile);
Image<Gray, Byte>[] trainingImages = new Image<Gray, Byte>[items.Count];
String[] labels = new String[items.Count];
for (int index = 0; index < items.Count; index++)
{
trainingImages[index] = new Image<Gray, byte>(trainingSetDir + @"\" + items[index].FileName);
labels[index] = Path.GetFileNameWithoutExtension(items[index].Label);
}
MCvTermCriteria termCrit = new MCvTermCriteria(maxEigenFaces, 0.001);
// Initialize Eigen Recognizer Object
recognizer = new EigenObjectRecognizer(
trainingImages,
labels,
eigenDistanceThreshold,
ref termCrit);
// Remove all old eigen images
DirectoryInfo eigenDir = new DirectoryInfo(eigenImageDir);
FileInfo[] eigenFiles = eigenDir.GetFiles();
foreach (FileInfo file in eigenFiles)
file.Delete();
// Save the average Image
recognizer.AverageImage.Save(averageImageName);
// Save all eigen images
for (int index = 0; index < recognizer.EigenImages.Length; index++)
{
String fileName = eigenImageName + (index + 1) + imageExt;
recognizer.EigenImages[index].ToBitmap().Save(fileName);
}
// Print eigen values
Console.WriteLine(String.Format("Num eigen values: {0}", recognizer.EigenValues.Length));
Console.WriteLine(String.Format("Num rows in eigen values: {0}", recognizer.EigenValues[0].Rows));
Console.WriteLine(String.Format("Num cols in eigen values: {0}", recognizer.EigenValues[0].Cols));
}
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
}
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;
}
}
/// <summary>
/// Process PCA and save a serialized recognizer in specified savepath
/// </summary>
/// <param name="faces">
/// A <see cref="List<FaceTag>"/>
/// </param>
/// <param name="savepath">
/// A <see cref="System.String"/>
/// </param>
/// <param name="filename">
/// A <see cref="System.String"/>
/// </param>
//private void ProcessPCA(List<FaceTag> faces){
public EigenValueTags ProcessPCA(Face[] faces)
{
Log.Debug("ProcessPCA Started...");
int MINFACES = 3;
int numsFaces = faces.Length;
List<Image<Gray, Byte>> train_imagesList = new List<Image<Gray, byte>>();
List<string> train_labelsList = new List<string>();
// load faces from detected data
List<int> banList = new List<int>();
// filter too small number of faces
for(int i=0;i<faces.Length;i++){
uint cnt = 0;
for(int j=0;j<faces.Length;j++){
if(i==j || faces[i].Tag.Name.Equals(faces[j].Tag.Name)){
cnt++;
}
}
if(cnt < MINFACES)
banList.Add(i);
}
for(int k=0;k<faces.Length;k++){
if(faces[k].Tag == null || banList.Contains(k)){
//Log.Debug("null Tag :: id = {0}, name = {0}",faces[k].Id,faces[k].Name);
continue;
}
train_labelsList.Add(faces[k].Tag.Name);
train_imagesList.Add(ImageTypeConverter.ConvertPixbufToGrayCVImage(faces[k].iconPixbuf));
}
//FIXME
for(int k=0; k<train_imagesList.Count;k++){
train_imagesList[k] = train_imagesList[k].Resize(100,100);
}
string[] train_labels = train_labelsList.ToArray();
Image<Gray, Byte>[] train_images = train_imagesList.ToArray();
MCvTermCriteria crit = new MCvTermCriteria(0.0001);
EigenObjectRecognizer eigenRec = new EigenObjectRecognizer(train_images,train_labels,5000,ref crit);
string path = Path.Combine (FSpot.Global.BaseDirectory, "eigen.dat");
// Serialize
SerializeUtil.Serialize(path, eigenRec);
// save recognized data into file of eigen value and into EigenValueTags class
EigenValueTags eigenVtags = RecordEigenValue(eigenRec);
Log.Debug("ProcessPCA ended...");
return eigenVtags;
}
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();
}
/// <summary>
/// Given savepath and filename, create a csv file containing set of eigen values.
/// The csv is formatted according to WEKA classifer.
/// </summary>
/// <param name="eigenRec">
/// A <see cref="EigenObjectRecognizer"/>
/// </param>
/// <param name="savepath">
/// A <see cref="System.String"/>
/// </param>
/// <param name="filename">
/// A <see cref="System.String"/>
/// </param>
public static void WriteEigenValueFile(EigenObjectRecognizer eigenRec, string savepath, string filename)
{
// don't store eigen value more than this number
const int MAX_EIGEN_LENGTH = 30;
int nums_train = eigenRec.Labels.Length;
float[] eigenvalue_float = new float[nums_train];
float[][] eigenMatrix = new float[nums_train][];
TextWriter tw = new StreamWriter(savepath+filename+".csv");
int max_eigenvalueLength = Math.Min(MAX_EIGEN_LENGTH, 4 + nums_train/5);
if(nums_train < 5)
max_eigenvalueLength = nums_train;
// write header
for(int i=0;i<max_eigenvalueLength;i++){
tw.Write("a"+i+",");
}
tw.WriteLine("class");
for(int i=0;i<nums_train;i++){
Emgu.CV.Matrix<float> eigenValue = eigenRec.EigenValues[i];
for(int k=0; k<max_eigenvalueLength; k++)
tw.Write(eigenValue.Data[k,0]+",");
tw.WriteLine(eigenRec.Labels[i]);
}
tw.Close();
}
