static void Main(string[] args)
{
Image<Gray, Byte>[] trainingImages = new Image<Gray, Byte>[2];
trainingImages[0] = new Image<Gray, byte>("C:\\Image\\Romy.jpg");
trainingImages[1] = new Image<Gray, byte>("C:\\Image\\Stevie.jpg");
int[] labels = new int[] { 0 , 1 };
MCvTermCriteria termCrit = new MCvTermCriteria(16, 0.001);
EigenFaceRecognizer recognizer = new EigenFaceRecognizer(0,0.2);
Image<Gray, Byte> testImage = new Image<Gray, Byte>("C:\\Image\\Stevie.jpg");
recognizer.Train(trainingImages,labels);
EigenFaceRecognizer.PredictionResult result = recognizer.Predict(testImage);
Console.WriteLine(result.Label);
Console.WriteLine(result.Label);
Console.ReadKey();
}
/// <summary>
/// Trains recognizer on fetched face-label pairs and saves the trained data to recognition variables
/// </summary>
public void TrainRecognizer()
{
recog = new EigenFaceRecognizer();
recog.Train<Gray, byte>(imgs.ToArray(),ints);
MessageBox.Show("aww yes");
recog.Save("trainingset/test.frl");
MessageBox.Show("tuwid na daan o");
}
public RecognizerEngine(String databasePath, String recognizerFilePath)
{
this.recognizerFilePath = recognizerFilePath;
dbAccess = new DBAccess(databasePath);
faceRecognizer = new EigenFaceRecognizer(80, double.PositiveInfinity);
}
public string saveEmployee(Image newImage, string name, string middleName, string lastName, string email, FaceRecognizerMethode faceRecognizerMethode)
{
unitOfWork = new GenericUnitOfWork();
GenericRepository<Employee> employeeRepo = unitOfWork.GetRepoInstance<Employee>();
Employee employee = null;
try
{
employee = (employeeRepo.GetAllRecords().Where<Employee>(e => e.email == email)).First<Employee>();
}
catch
{
Debug.WriteLine("Nuevo usuario");
}
//Add Employee if not exist. The email is unique
if (employee == null)
{
employee = new Employee { name = name, middleName = middleName, lastName = lastName, email = email };
employeeRepo.Add(employee);
unitOfWork.SaveChanges();
}
//I save the image with a guid as a name
GenericRepository<DistanceResult> distanceResultRepo = unitOfWork.GetRepoInstance<DistanceResult>();
Guid guid = Guid.NewGuid();
var inputImage = new Image<Bgr, Byte>(new Bitmap(newImage));
Rectangle[] rectangleFace = detection(inputImage, pathXMLHaarcascade);
//The function detection(..) can extract N faces
if (rectangleFace.Length <= 0)
{
return Result.NoDetected.ToString();
}
else if (rectangleFace.Length > 1)
{
return Result.MultipleFacesDetected.ToString();
}
else
{
Image<Gray, byte> grayFrame = toGrayEqualizeFrame(inputImage);
Image<Gray, Byte> faceEMGUCV = formatRectangleFaces(grayFrame.ToBitmap(), rectangleFace[0]);
faceEMGUCV._EqualizeHist();
faceEMGUCV.Save(pathImg + @"\" + guid.ToString()+".Jpeg");
FaceRecognizer faceRecognition;
switch (faceRecognizerMethode.ToString())
{
case "EigenFaceRecognizerMethode": faceRecognition = new EigenFaceRecognizer(numComponentsEigen, thresholdEigen); //try catch aca
break;
case "FisherFaceRecognizerMethode": faceRecognition = new FisherFaceRecognizer(numComponentsFisher, thresholdFisher);
break;
case "LBPHFaceRecognizerMethode": faceRecognition = new LBPHFaceRecognizer(radiusLBPH, neighborsLBPH, gridXLBPH, gridYLBPH, thresholdLBPH);
break;
default: return null;
};
double distance = 2;
//Save register
DistanceResult dist = new DistanceResult();
dist.algorithm = RecognizeBLL.FaceRecognizerMethode.FisherFaceRecognizerMethode.ToString();
dist.employeeId = employee.employeeId;
dist.photoName = guid.ToString();
dist.distance = distance;
distanceResultRepo.Add(dist);
unitOfWork.SaveChanges();
int lengthArrays = distanceResultRepo.GetAllRecords().Count();
imagesDB = new Image<Gray, Byte>[lengthArrays];
labels = new int[lengthArrays];
int i = 0;
foreach (DistanceResult di in distanceResultRepo.GetAllRecords())
{
//This is to recalculate the faceRecognition and save it, but I think is not necesari declare imageDB and labels as global
imagesDB[i] = new Image<Gray, Byte>(pathImg + @"\" + di.photoName + ".Jpeg");
labels[i] = di.employeeId;
i++;
}
if (employeeRepo.GetAllRecords().Count() > 1)
{
faceRecognition.Train(imagesDB, labels);
faceRecognition.Save(pathImg + @"\" + "TrainingSet");
}
return Result.Saved.ToString();
}
//return Result.Error.ToString();
}
public EmployeeStructure[] recognizeMultipleFaces(Image newImage, FaceRecognizerMethode faceRecognizerMethode)
{
var inputImage = new Image<Bgr, Byte>(new Bitmap(newImage));
Rectangle[] rectangleFace = detection(inputImage, this.pathXMLHaarcascade);
EmployeeStructure[] employeeStructure;
if (rectangleFace.Length <= 0)
{
employeeStructure = new EmployeeStructure[0];
employeeStructure[0].result = Result.NoDetected.ToString();
return employeeStructure;
}
else
{
Image<Gray, byte> grayFrame = toGrayEqualizeFrame(inputImage);
employeeStructure = new EmployeeStructure[rectangleFace.Length];
FaceRecognizer faceRecognition;
switch (faceRecognizerMethode.ToString())
{
case "EigenFaceRecognizerMethode": faceRecognition = new EigenFaceRecognizer(numComponentsEigen, thresholdEigen); //try catch aca
break;
case "FisherFaceRecognizerMethode": faceRecognition = new FisherFaceRecognizer(numComponentsFisher, thresholdFisher);
break;
case "LBPHFaceRecognizerMethode": faceRecognition = new LBPHFaceRecognizer(radiusLBPH, neighborsLBPH, gridXLBPH, gridYLBPH, thresholdLBPH);
break;
default: faceRecognition = new EigenFaceRecognizer(numComponentsEigen, thresholdEigen);
break;
};
faceRecognition.Load(pathImg + @"\" + "TrainingSet");
Parallel.For(0, rectangleFace.Length, i =>
{
Image<Gray, byte> faceEMGUCV = formatRectangleFaces(grayFrame.ToBitmap(), rectangleFace[i]);
FaceRecognizer.PredictionResult ER = faceRecognition.Predict(faceEMGUCV);
if (ER.Label != -1 /*&& ER.Distance > thresholdEigen*/)
{
int label = ER.Label;
GenericRepository<Employee> emplyeeRepo = unitOfWork.GetRepoInstance<Employee>();
Employee em = emplyeeRepo.GetFirstOrDefault(label);
employeeStructure[i] = new EmployeeStructure(Result.Recognized.ToString(), em.name, em.middleName, em.lastName, em.email, rectangleFace[0].X, rectangleFace[0].Y, rectangleFace[0].Width, rectangleFace[0].Height);
}
employeeStructure[i].result = Result.Unknown.ToString();
});
return employeeStructure;
}
}
//0: Default, 1:to Accuracy 2: Middium, 3: Imprecise, 4:Ambiguous
//tmpNumComponentsFisher. If you leave this at the default (0), set it to a value less than 0, or greater than the number of your training inputs, it will be set to the correct number (your training inputs - 1) automatically
public void estimateParametersFisher(Image<Gray, Byte> imagesInput, int accuracy)
{
int tmpNumComponentsFisher;
double tmpThresholdFisher;
FaceRecognizer faceRecognition;
for (tmpThresholdFisher = 1000; tmpThresholdFisher < 10000; tmpThresholdFisher += 100)
{
for (tmpNumComponentsFisher = 50; tmpNumComponentsFisher < 100; tmpNumComponentsFisher += 10)
{
faceRecognition = new EigenFaceRecognizer(tmpNumComponentsFisher, tmpThresholdFisher);
GenericRepository<DistanceResult> distanceResultRepo = unitOfWork.GetRepoInstance<DistanceResult>();
int lengthArrays = distanceResultRepo.GetAllRecords().Count();
imagesDB = new Image<Gray, Byte>[lengthArrays];
labels = new int[lengthArrays];
int i = 0;
foreach (DistanceResult di in distanceResultRepo.GetAllRecords())
{
//This is to recalculate the faceRecognition and save it, but I think is not necesari declare imageDB and labels as global
imagesDB[i] = new Image<Gray, Byte>(pathImg + @"\" + di.photoName + ".Jpeg");
labels[i] = di.employeeId;
i++;
}
faceRecognition.Train(imagesDB, labels);
//faceRecognition.Load(pathImg + @"\" + "TrainingSet");
FaceRecognizer.PredictionResult ER = faceRecognition.Predict(imagesInput);
if (ER.Label != -1)
{
if (accuracy == 1)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher;
return;
}
else if (accuracy == 2)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher + 300;
return;
}
else if (accuracy == 3)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher + 600;
return;
}
else if (accuracy == 4)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher + 900;
return;
}
else if (accuracy > 4)
{
thresholdEigen = Double.PositiveInfinity;
}
else return;
}
faceRecognition.Dispose();
}
}
}
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);
}
