public JsonResult RecognizeByteArray(byte[] photo)
{
DataPath = Server.MapPath("~/App_Data/Faces");
FaceRecognizer.Load(DataPath);
var depthImage = new Image <Gray, byte>(photo.Rank, (photo.Length / photo.Rank))
{
Bytes = photo
};
var s = FaceRecognizer.Predict(depthImage).Label;
var person = PhotoContext.People.FirstOrDefault(x => x.Id == s);
return(Json(person, JsonRequestBehavior.AllowGet));
}
public void Load(string filename)
{
recognizer.Load(filename);
string path = Path.GetDirectoryName(filename);
FileStream labels = File.OpenRead(Path.Combine(path, "Labels.xml"));
names.Clear();
using (XmlReader reader = XmlTextReader.Create(labels))
{
while (reader.Read())
{
if (reader.IsStartElement())
{
switch (reader.Name)
{
case "name":
names.Add(reader.GetAttribute("name"));
break;
}
}
}
}
labels.Close();
IsTrained = true;
}
private void button2_Click(object sender, EventArgs e)
{
if (comboBoxAlgorithm.Text == "EigenFaces")
{
try
{
string dataDirectory = Directory.GetCurrentDirectory() + "\\TrainedFaces\\trainedDataEigen.dat";
eigenFaceRecognizer = new EigenFaceRecognizer(eigenTrainedImageCounter, 3000);
eigenFaceRecognizer.Load(dataDirectory);
richTextBox1.Text += "Trained Database Loaded.";
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
MessageBox.Show("Nothing in binary database, please add at least a face(Simply train the prototype with the Add Face Button).", "Triained faces load", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
}
else if (comboBoxAlgorithm.Text == "FisherFaces")
{
try
{
string dataDirectory = Directory.GetCurrentDirectory() + "\\TrainedFaces\\trainedDataFisher.dat";
fisherFaceRecognizer = new FisherFaceRecognizer(fisherTrainedImageCounter, 3000);
fisherFaceRecognizer.Load(dataDirectory);
richTextBox1.Text += "Trained Database Loaded.";
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
MessageBox.Show("Nothing in binary database, please add at least a face(Simply train the prototype with the Add Face Button).", "Triained faces load", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
}
else if (comboBoxAlgorithm.Text == "LBPHFaces")
{
try
{
string dataDirectory = Directory.GetCurrentDirectory() + "\\TrainedFaces\\trainedDataLBPH.dat";
lbphFaceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 400);
lbphFaceRecognizer.Load(dataDirectory);
richTextBox1.Text += "Trained Database Loaded.";
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
MessageBox.Show("Nothing in binary database, please add at least a face(Simply train the prototype with the Add Face Button).", "Triained faces load", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
}
}
public MainWindow()
{
InitializeComponent();
// Initialize detector and recognizer
detector = new CascadeClassifier(cascadePath);
recognizer = new LBPHFaceRecognizer();
recognizer.Load(trainedData);
// Load the labels
names = new Dictionary <int, string>();
var labels = System.IO.File.ReadAllLines(labelPath);
foreach (var line in labels)
{
var content = line.Split(',');
names.Add(int.Parse(content[0]), content[1]);
}
}
public void LoadRecognizerData()
{
_LBPHFaceRecognizer.Load(_recognizerFilePath);
//_faceRecognizer.Load(_recognizerFilePath);
}
/***
* 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.SourceFile || misk == MachineInputSourceKind.SourceNone || train)
{
speechRecognitionOnline = true;
}
else
{
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);
};
}
