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;
}
}
