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;
}
}
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();
}
/***
Function: public MainWindow(bool train, FaceIdentity fit, String nt, int cn, int fps, bool crd, int mdt, MachineInputSourceKind misk)
Parameter(s): bool train
Whether or not the machine is supposed to be training itself.
FaceIdentity fit
The classification of the person that the Machine is training itself to recognize (supposed to be equal to FaceIdentity.FaceNone during
normal execution).
String nt
The name of the person that the Machine is training itself to recognize (supposed to be equal to "" during normal execution).
int cn
The camera number to used by the 'capture' variable.
int fps
The frame rate of the camera (not used when processing video files).
bool crd
Whether or not the Machine should dispose of resources (could potentially be unstable).
int mdt
The maxDistanceThreshold to use when classifying faces.
MachineInputSourceKind misk
The input source from which the Machine will gather its input.
String t
The title of this window.
MachineStartPage msp
A reference to the main machine window to control.
Return Value: N/A (Constructor)
***/
public MainWindow(bool train, FaceIdentity fit, String nt, int cn, int fps, bool crd, int mdt,
MachineInputSourceKind misk, String t, MachineStartPage msp, UIKind uk)
{
machineStartPage = msp;
cameraResourceDisposal = crd;
maxDistanceThreshold = mdt;
cameraNumber = cn;
cameraFrameRate = fps;
machineInputSourceKind = misk;
if (misk == MachineInputSourceKind.SourceWebcam) {
speechRecognitionOnline = true;
userSpeech = "";
}
FileUtilities.DirectoryCreation();
FileUtilities.TrainingDirectoryCreation();
faceClassifier = new CascadeClassifier("assets\\haarcascade_frontalface_alt.xml");
uikind = uk;
if (uikind == UIKind.UIMachine) {
admin_focus = new Bitmap("assets\\machine\\admin_focus.jpg");
secondary_focus = new Bitmap("assets\\machine\\secondary_focus.jpg");
threat_focus = new Bitmap("assets\\machine\\threat_focus.jpg");
}
else
{
admin_focus = new Bitmap("assets\\samaritan\\deviant_focus.jpg");
secondary_focus = new Bitmap("assets\\samaritan\\irrelevant_focus.jpg");
threat_focus = new Bitmap("assets\\samaritan\\threat_focus.jpg");
}
machineIsTraining = train;
//Check if the CORE_IMAGE_DATA.dat file exists and read the predefined width and height.
if (File.Exists(FileUtilities.DirectoryName + "\\" + FileUtilities.CoreImageData))
{
String[] lines = File.ReadAllLines(FileUtilities.DirectoryName + "\\" + FileUtilities.CoreImageData);
if (lines.Length != 0)
{
String[] heightWidth = lines[0].Split('|');
facialRecognitionHeight = Int32.Parse(heightWidth[0]);
facialRecognitionWidth = Int32.Parse(heightWidth[1]);
}
}
FileUtilities.CoreImageDataCreation();
// Check if the Machine is being trained or not.
if (!train && fit == FaceIdentity.FaceNone && nt == "")
{
String[] lines2 = File.ReadAllLines(FileUtilities.DirectoryName + "\\" + FileUtilities.AssetIndexData);
List<FaceIdentity> faceIdentityList = new List<FaceIdentity>();
List<String> nList = new List<String>();
List<LBPHFaceRecognizer> lbphList = new List<LBPHFaceRecognizer>();
// Load the trained neural networks, list of names, and classifications.
foreach (String line in lines2)
{
String[] innerSplit = line.Split('^');
String name = innerSplit[0];
String identifier = innerSplit[1];
String file = innerSplit[2];
if (identifier == adminIdentifier)
faceIdentityList.Add(FaceIdentity.FaceAdmin);
else if (identifier == auxAdminIdentifier)
faceIdentityList.Add(FaceIdentity.FaceAuxAdmin);
else if (identifier == assetIdentifier)
faceIdentityList.Add(FaceIdentity.FaceAsset);
else
PanicAndTerminateProgram();
nList.Add(name);
LBPHFaceRecognizer lbph = new LBPHFaceRecognizer();
lbph.Load(file);
lbphList.Add(lbph);
}
focusKindDatabase = faceIdentityList.ToArray();
nameDatabase = nList.ToArray();
lbphFaceRecognizerList = lbphList.ToArray();
// Check to make sure the ANN, name, and classification database lengths are all equal.
if (focusKindDatabase.Length == nameDatabase.Length && nameDatabase.Length == lbphFaceRecognizerList.Length) ;
else
PanicAndTerminateProgram();
}
faceIdentityTraining = fit;
nameTraining = nt;
InitializeComponent();
this.Title = t;
Closed += (object sender, EventArgs args) =>
{
Environment.Exit(1);
};
}
