System.Drawing.Size face_size = new System.Drawing.Size(7, 7); // units in pixels
public FacialRecognition(dynamic mainWindow)
{
this.mainWindow = mainWindow;
this.commHelper = this.mainWindow.communicationHelper;
this.systemData = this.commHelper.systemData;
classifier_path = System.IO.Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location) + "/opencv/data/haarcascades/haarcascade_frontalface_default.xml";
database_path = "../../../../public/";
people_path = database_path + "people/";
activations_path = activations_path + "activations/";
recognized_threshold = 105;
add_new_training_threshold = 75;
num_trained = 0;
input_height = 240;
input_width = 426;
face_outline_pen = new System.Windows.Media.Pen(System.Windows.Media.Brushes.LightBlue, 3);
face_label_brush = Brushes.Black;
face_label_font = new Typeface("Georgia");
face_label_font_size = 10;
label_to_int = new Dictionary <string, int>();
training_images = new List <Image <Gray, byte> >();
training_int_labels = new List <int>();
training_labels = new List <string>();
face_finder = new CascadeClassifier(classifier_path);
face_recognizer = new LBPHFaceRecognizer();
load_database();
}
private void DeleteFaces_Click(object sender, RoutedEventArgs e)
{
if (MessageBox.Show("Вы действительно собираетесь удалить базу лиц?", "Подтверждение",
MessageBoxButton.YesNo, MessageBoxImage.Warning) == MessageBoxResult.Yes)
{
FaceItemsPlace.Children.Clear();
foreach (var file in PeopleData.Face)
{
if (File.Exists(Environment.CurrentDirectory + file))
{
File.Delete(Environment.CurrentDirectory + file);
}
}
PeopleData = new PeopleData();
if (File.Exists(Environment.CurrentDirectory + ConfigurationManager.AppSettings["JsonData"]))
{
File.Delete(Environment.CurrentDirectory + ConfigurationManager.AppSettings["JsonData"]);
}
if (File.Exists(ConfigurationManager.AppSettings["FaceRecognizerData"]))
{
File.Delete(ConfigurationManager.AppSettings["FaceRecognizerData"]);
}
FaceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
MessageBox.Show("Данные обучения удалены");
}
}
public void FaceLearn()
{
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 80);
Image <Gray, Byte>[] images = new Image <Gray, Byte> [30];
Image <Gray, Byte> image;
// image = getFace("123.jpg");
int[] labels = new int[30];
open_image(ref images, ref labels);
for (int i = 25; i < 30; i++)
{
images[i] = getFace((i - 24) + ".jpg");
labels[i] = 6;
}
recognizer.Train <Gray, Byte>(images, labels);
Console.WriteLine("Training iss over");
FaceRecognizer.PredictionResult r;
Console.WriteLine("!");
//for (int i = 0; i < 5; i++)
//{
// r = recognizer.Predict(getFace("yalefaces/subject1" + (i + 1) + ".centerlight"));
// Console.WriteLine("labels:" + r.Label + " and distance:" + r.Distance);
//}
//r = recognizer.Predict(image);
//Console.WriteLine("labels:" + r.Label + " and distance:" + r.Distance);
// Console.ReadKey();
}
private void LockScreen_Load(object sender, EventArgs e)
{
this.TopMost = true;
this.BackgroundImageLayout = ImageLayout.Stretch;
FormBorderStyle = FormBorderStyle.None;
WindowState = FormWindowState.Maximized;
this.Show();
this.Activate();
for (int i = 0; i < Properties.Settings.Default.numberOfProfiles; i++)
{
FaceRecognizer recogniser = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
recogniser.Read(Application.StartupPath + $"\\Profiles\\profile{i + 1}.txt");
recognisers.Add(recogniser);
}
//set the lockscreen form to fullscreen on-top of all windows
//setup the video capture device
filter = new FilterInfoCollection(FilterCategory.VideoInputDevice);
foreach (FilterInfo device in filter)
{
if (device.Name == Properties.Settings.Default.webcam)
{
cam = new VideoCaptureDevice(device.MonikerString);
}
}
cam.Start();
cam.NewFrame += Cam_NewFrame;
}
DetectService()
{
detectFaceTrain = new CascadeClassifier(haarLikePath);
detectFaceRollCall = new CascadeClassifier(haarLikePath);
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 90);
trainData = new List <TrainModel>();
}
// Trains a recognizer using the labeled images in inputDir and saves the result
// to outputPath
static void TrainRecognizer(string inputDir, string outputPath)
{
var imageFiles = Directory.EnumerateFiles(inputDir).ToList();
var images = new Image <Gray, byte> [imageFiles.Count];
var labels = new int[imageFiles.Count];
// Load each file and it's label
Console.WriteLine("Loading data...");
int i = 0;
foreach (var imageFile in imageFiles)
{
var label = Path.GetFileNameWithoutExtension(imageFile).Split('_')[0];
labels[i] = int.Parse(label);
images[i] = CvInvoke.Imread(imageFile, Emgu.CV.CvEnum.LoadImageType.Grayscale).ToImage <Gray, byte>();
i++;
}
Console.WriteLine("Loading complete.");
// Train the recognizer and save the result
Console.WriteLine("Training...");
LBPHFaceRecognizer recognizer = new LBPHFaceRecognizer();
recognizer.Train(images, labels);
Console.WriteLine("Training done. Saving results...");
recognizer.Save(outputPath);
}
private void LBPHFaceRecognition(object sender, EventArgs e)
{
Frame = _capture.QueryFrame().ToImage <Bgr, byte>();
var frame = Frame.Resize(frameW, frameH, Inter.Cubic);
grayFrame = frame.Convert <Gray, Byte>();
var faces = cascadeClassifier.DetectMultiScale(grayFrame, 1.1, 10, Size.Empty);
foreach (var f in faces)
{
lbphFaceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 400);
lbphFaceRecognizer.Train(trainingImages.ToArray(), indexLabels.ToArray());
var result = lbphFaceRecognizer.Predict(frame.Copy(f).Convert <Gray, Byte>().Resize(100, 100, Inter.Cubic));
if (result.Label == -1)
{
frame.Draw(f, new Bgr(Color.Red), 2);
frame.Draw("Unknown", new Point(f.X, f.Y - 10), font, 0.8, new Bgr(Color.Blue), 2, new LineType(), false);
}
else
{
frame.Draw(f, new Bgr(Color.Green), 2);
frame.Draw(nameLabels[result.Label], new Point(f.X, f.Y - 10), font, 0.8, new Bgr(Color.Blue), 2, new LineType(), false);
}
alertMessage.Text = (alert + "เริ่มการ Face Recognition ด้วยวิธีการ " + RecognitionType.Text + " แล้ว \r\n" + "Distance " + result.Distance + "\r\n Faces " + faces.Length.ToString());
}
imgFrame.Image = frame.Resize(imgBoxW, imgBoxH, Inter.Cubic);
}
public FaceRecognitionService(String databasePath, String recognizerFilePath)
{
_recognizerFilePath = recognizerFilePath;
_dataStoreAccess = new DataStoreAccess(databasePath);
_faceRecognizer = new EigenFaceRecognizer(80, 1.1);
_LBPHFaceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
}
public void TrainAndPredict()
{
using (var image = Image("lenna.png"))
using (var grayImage = image.CvtColor(ColorConversionCodes.BGR2GRAY))
using (var model = LBPHFaceRecognizer.Create())
using (var cascade = new CascadeClassifier("_data/text/haarcascade_frontalface_default.xml"))
{
var rects = cascade.DetectMultiScale(image);
model.Train(new[] { grayImage }, new[] { 1 });
foreach (Rect rect in rects)
{
using (Mat face = grayImage[rect].Clone())
{
Cv2.Resize(face, face, new Size(256, 256));
model.Predict(face, out int label, out double confidence);
Console.WriteLine($"{label} ({confidence})");
Assert.Equal(1, label);
Assert.NotEqual(0, confidence, 9);
Window.ShowImages(face);
}
}
}
}
public ImageService()
{
_fisherFaceRecognizer = new FisherFaceRecognizer();
//_fisherFaceRecognizer = new FisherFaceRecognizer(FaceRecognizerNumberOfComponents, FaceRecognizerThreshold);
_lbphFaceRecognizer = new LBPHFaceRecognizer();
//_lbphFaceRecognizer = new LBPHFaceRecognizer(1, 8, 32, 32, 256);
_cascadeClassifier = new CascadeClassifier(CascadeClassifierConfigPath);
if (!Directory.Exists(DataFolder))
{
Directory.CreateDirectory(DataFolder);
}
if (File.Exists(FisherTrainingDataPath))
{
_fisherFaceRecognizer.Read(FisherTrainingDataPath);
}
if (File.Exists(LBPHTrainingDataPath))
{
_lbphFaceRecognizer.Read(LBPHTrainingDataPath);
}
}
private FaceRecognizer LoadFaceRecognizer()
{
if (File.Exists(ConfigurationManager.AppSettings["FaceRecognizerData"]))
{
FaceRecognizer faceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
faceRecognizer.Read(ConfigurationManager.AppSettings["FaceRecognizerData"]);
return(faceRecognizer);
}
return(new LBPHFaceRecognizer(1, 8, 8, 8, 100));
}
public Form1( )
{
//reader = new AVIReader();
//reader.Open("123.avi");
//Capture
faceRecog = new FaceRecognition();
InitializeComponent( );
faceCascade = new CascadeClassifier("haarcascade_frontalface_default.xml");
// button2.Enabled = false;
// button3.Enabled = false;
// buttonLearn.Enabled = false;
LBPHFaceRecognizer recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 123);
camera1FpsLabel.Text = string.Empty;
videoSourcePlayer1.NewFrame += new VideoSourcePlayer.NewFrameHandler(this.playerControl_NewFrame);
// show device list
try
{
// enumerate video devices
videoDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
if (videoDevices.Count == 0)
{
throw new Exception( );
}
for (int i = 1, n = videoDevices.Count; i <= n; i++)
{
string cameraName = i + " : " + videoDevices[i - 1].Name;
camera1Combo.Items.Add(cameraName);
}
//camera1Combo.Items.Add("2 : WebCam GG-321");
//camera1Combo.Items.Add("3 : WebCam SC-01DCL32219N");
// check cameras count
camera1Combo.SelectedIndex = 0;
}
catch
{
startButton.Enabled = false;
camera1Combo.Items.Add("No cameras found");
camera1Combo.SelectedIndex = 0;
camera1Combo.Enabled = false;
}
}
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);
}
}
}
//4: train hình //sử dụng các hình ảnh đã lưu từ bước 3
private bool TrainImagesFromDir()
{
int ImagesCount = 0;
double Threshold = 7000;
TrainedFaces.Clear();
PersonsLabes.Clear();
PersonsNames.Clear();
try
{
string path = Directory.GetCurrentDirectory() + @"\TrainedImages";
string[] files = Directory.GetFiles(path, "*.jpg", SearchOption.AllDirectories);
foreach (var file in files)
{
Image <Gray, byte> trainedImage = new Image <Gray, byte>(file).Resize(200, 200, Inter.Cubic);
CvInvoke.EqualizeHist(trainedImage, trainedImage);
TrainedFaces.Add(trainedImage);
PersonsLabes.Add(ImagesCount);
string name = file.Split('\\').Last().Split('_')[0];
PersonsNames.Add(name);
ImagesCount++;
Debug.WriteLine(ImagesCount + ". " + name);
}
if (TrainedFaces.Count() > 0)
{
// recognizer = new EigenFaceRecognizer(ImagesCount,Threshold);
recognizer = new EigenFaceRecognizer(ImagesCount, Threshold);
recognizer.Train(TrainedFaces.ToArray(), PersonsLabes.ToArray());
recognizerLBPH = new LBPHFaceRecognizer(ImagesCount, 10, 10, 10, Threshold);
recognizerLBPH.Train(TrainedFaces.ToArray(), PersonsLabes.ToArray());
isTrained = true;
//Debug.WriteLine(ImagesCount);
//Debug.WriteLine(isTrained);
return(true);
}
else
{
isTrained = false;
return(false);
}
}
catch (Exception ex)
{
isTrained = false;
MessageBox.Show("Error in Train Images: " + ex.Message);
return(false);
}
}
/// <summary>
/// Face recognition based on Local Binary Pattern Histogram
/// (LBPH) classifier </summary>
/// <param name="labels">The set of labels in the training set</param>
/// <param name="trainingImages">The set of images(faces) in the
/// training set</param>
/// <param name="face">The face detected in gray scale
/// to be recognized. The dimension of the image must be
/// equal to the dimension of the images in the training set</param>
/// <returns>A string representing the label of the face recognized
/// or an empty string if no matches were found</returns>
public String recognizeLBPHFace(List <String> labels,
List <Image <Gray, Byte> > trainingImages,
Bitmap face)
{
String label = String.Empty;
Image <Bgr, Byte> imageEmgu = new Image <Bgr, Byte>(face);
Image <Gray, Byte> extractedFace = imageEmgu.Convert <Gray, Byte>().Copy().Resize(
100, 100, INTER.CV_INTER_CUBIC);
extractedFace._EqualizeHist();
InitParams();
if (trainingImages.ToArray().Length != 0)
{
LBPHFaceRecognizer recognizer = new LBPHFaceRecognizer(
1, 8, 8, 8, LBPHTreshold);
int[] labelsInt = new int[labels.ToArray().Length];
for (int i = 0; i < labels.ToArray().Length; i++)
{
labelsInt[i] = i;
}
recognizer.Train(trainingImages.ToArray(), labelsInt.ToArray());
LBPHFaceRecognizer.PredictionResult pr;
pr = recognizer.Predict(extractedFace);
if (pr.Label != -1)
{
label = labels[pr.Label];
MostSimilarFace = trainingImages[pr.Label];
MostSimilarFaceIndex = pr.Label;
MostSimilarFaceDistance = (float)pr.Distance;
MostSimilarFaceLabel = labels[pr.Label];
}
else
{
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 10000);
recognizer.Train(trainingImages.ToArray(), labelsInt.ToArray());
pr = recognizer.Predict(extractedFace);
MostSimilarFace = trainingImages[pr.Label];
MostSimilarFaceIndex = pr.Label;
MostSimilarFaceDistance = (float)pr.Distance;
MostSimilarFaceLabel = labels[pr.Label];
}
}
return(label);
}
public bool Train()
{
lock (_sync) {
if (_images.Count <= 1)
{
return(false);
}
if (_faceRecognizer != null)
{
_faceRecognizer.Dispose();
}
_faceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
_faceRecognizer.Train(_images.ToArray(), _images.Select((c, i) => i).ToArray());
_shouldTrain = false;
return(true);
}
}
public void TrainFaceRecognizer()
{
if (PeopleData.Face.Count() == 0)
{
return;
}
Image <Gray, Byte>[] trainFaces = new Image <Gray, Byte> [PeopleData.Face.Count()];
for (int i = 0; i < PeopleData.Face.Count(); i++)
{
trainFaces[i] = new Image <Gray, byte>(Environment.CurrentDirectory + PeopleData.Face[i]);
}
FaceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
FaceRecognizer.Train(trainFaces, PeopleData.NameId.ToArray());
SaveFaceRecognizer();
}
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 IIdentifier GetIdentity(IFaceImage img)
{
IIdentifier answ = new Identifier(int.MinValue);
var labels = _labelRepo.GetAll();
foreach (var label in labels)
{
IEnumerable <IFaceImage> batch = label.Images;
List <Image <Gray, Byte> > compBatch = new List <Image <Gray, Byte> >();
List <int> trainingLabels = new List <int>();
int enumerator = 0;
foreach (var current in batch)
{
compBatch.Add(new Image <Gray, Byte>(current.ImageOfFace));
trainingLabels.Add(enumerator++);
}
FaceRecognizer recognizer = new LBPHFaceRecognizer(_recognizerRadius, _recognizerNeighbors,
_recognizerGridX, _recognizerGridY, _recognizerThreshold);
recognizer.Train(compBatch.ToArray(), trainingLabels.ToArray());
PredictionResult result = recognizer.Predict(new Image <Gray, Byte>(img.ImageOfFace));
if (result.Distance < _minDistanse)
{
_minDistanse = result.Distance;
answ = label.Id;
}
}
if (_minDistanse < _requiredDistance)
{
return(answ);
}
else
{
return(new Identifier(-1));
}
}
public Form1()
{
InitializeComponent();
faceRecognition = new LBPHFaceRecognizer(2, 10, 8, 8, 85);
if (File.Exists(YMLPath))
{
faceRecognition.Read(YMLPath);
}
else
{
PredictButton.Enabled = false;
}
faceDetection = new CascadeClassifier(Path.GetFullPath(@"../../data/haarcascades/haarcascade_frontalface_default.xml"));
eyeDetection = new CascadeClassifier(Path.GetFullPath(@"../../data/haarcascades/haarcascade_eye.xml"));
frame = new Mat();
Faces = new List <Image <Gray, byte> >();
IDs = new List <int>();
BeginWebCam();
}
public FaceRecognition()
{
faceCascade = new CascadeClassifier("haarcascade_frontalface_default.xml");
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 130);
}
/***
* Function: private void TrainMachine(FaceIdentity faceIdentity, String name)
* Parameter(s): FaceIdentity faceIdentity
* Privilege of the face that is being trained to store in the ASSET_INDEX.dat file.
* String name
* The name of the individual that is being trained; currently, it is not used, but it exists so that in the next version,
* the machine can be more customized.
* Return Value: void
***/
private void TrainMachine(FaceIdentity faceIdentity, String name)
{
// Notify the user that training has begun.
MessageBox.Show(trainingBeginning, this.Title, MessageBoxButton.OK);
String[] fileList = Directory.GetFiles(FileUtilities.TrainingDirectoryName);
List <Mat> matList = new List <Mat>();
foreach (String file in fileList)
{
matList.Add(new Mat(file, Emgu.CV.CvEnum.LoadImageType.Unchanged));
}
List <Image <Gray, Byte> > list = new List <Image <Gray, Byte> >();
// Detect each face in each image.
foreach (Mat mat in matList)
{
Image <Gray, Byte> image = mat.ToImage <Gray, Byte>().Resize(1 / (double)scale_factor, Emgu.CV.CvEnum.Inter.Cubic);
Rectangle[] faceList = faceClassifier.DetectMultiScale(image);
foreach (Rectangle rect in faceList)
{
list.Add(image.Copy(rect).Convert <Gray, Byte>());
}
}
// Make sure that there is at least one face to train.
if (list.Count() == 0)
{
PanicAndTerminateProgram();
}
// If a height exists in the CORE_IMAGE_DATA.dat file, resize to that, useful for future training.
int height = facialRecognitionHeight == 0 ? list[0].Height * scale_factor : facialRecognitionHeight;
int width = facialRecognitionWidth == 0 ? list[0].Width * scale_factor : facialRecognitionWidth;
if (facialRecognitionHeight == 0 || facialRecognitionWidth == 0)
{
List <String> lines = new List <String>();
lines.Add(height + "|" + width);
File.WriteAllLines(FileUtilities.DirectoryName + "\\" + FileUtilities.CoreImageData, lines.ToArray());
}
List <Image <Gray, Byte> > listFinal = new List <Image <Gray, Byte> >();
foreach (Image <Gray, Byte> image in list)
{
listFinal.Add(image.Resize(width, height, Emgu.CV.CvEnum.Inter.Cubic));
}
List <int> labelList = new List <int>();
int integer = 0;
String prefix = "";
String ident = "";
if (faceIdentity == FaceIdentity.FaceAdmin)
{
integer = 0;
prefix = FileUtilities.AdminTrainedPrefix;
ident = adminIdentifier;
}
else if (faceIdentity == FaceIdentity.FaceAsset)
{
integer = 2;
prefix = FileUtilities.AssetTrainedPrefix;
ident = auxAdminIdentifier;
}
else if (faceIdentity == FaceIdentity.FaceAuxAdmin)
{
integer = 1;
prefix = FileUtilities.AuxAdminTrainedPrefix;
ident = assetIdentifier;
}
else
{
PanicAndTerminateProgram();
}
for (int i = 0; i < list.Count(); i++)
{
labelList.Add(integer);
}
// Train the machine and write its trained state to a file.
LBPHFaceRecognizer lbphFaceRecognizer = new LBPHFaceRecognizer();
lbphFaceRecognizer.Train <Gray, Byte>(listFinal.ToArray(), labelList.ToArray());
Directory.Delete(FileUtilities.TrainingDirectoryName, true);
String temp = categories[integer];
String fname = FileUtilities.DirectoryName + "\\" + prefix + temp.ToUpper().Replace(' ', '_') + FileUtilities.FileExtension;
lbphFaceRecognizer.Save(fname);
// Write everything to the ASSET_INDEX.dat file.
FileUtilities.TrainingDirectoryDeletion();
List <String> aboutTraining = new List <String>();
aboutTraining.Add(name + "^" + ident + "^" + fname);
File.AppendAllLines(FileUtilities.DirectoryName + "\\" + FileUtilities.AssetIndexData, aboutTraining.ToArray());
// Notify the used that training has ended.
MessageBox.Show(trainingEnded, this.Title, MessageBoxButton.OK);
}
static LoadData()
{
try
{
Console.WriteLine("====================================LOAD DATA========================================");
myconnection.ConnectionString = Properties.Settings.Default.ConnectionString;
myconnection.Open();
string query = "select employee.userid,employeename,image from employee,image where image.userid=employee.userid";
OracleCommand cmd = new OracleCommand(query, myconnection);
OracleDataReader dr = cmd.ExecuteReader();
while (dr.Read())
{
Labels_ID.Add(Convert.ToInt32(dr["userid"].ToString()));
Console.WriteLine("id: " + Convert.ToInt32(dr["userid"].ToString()));
Labels_Name.Add(dr["employeename"].ToString());
Console.WriteLine("name: " + dr["employeename"].ToString());
string img_name = "image.bmp";
FileStream FS = new FileStream(temp_images + img_name, FileMode.Create);
byte[] blob = (byte[])dr["IMAGE"];
//byte[] blob = Convert.FromBase64String(dr["IMAGE"].ToString()); //for clob
FS.Write(blob, 0, blob.Length);
FS.Close();
FS = null;
Bitmap masterImage;
using (var fs = new System.IO.FileStream(temp_images + img_name, System.IO.FileMode.Open))
{
masterImage = new Bitmap(fs);
}
// Normalizing it to grayscale
Image <Gray, Byte> normalizedMasterImage = new Image <Gray, Byte>(masterImage);
normalizedMasterImage = normalizedMasterImage.Resize(320, 244, Emgu.CV.CvEnum.Inter.Cubic);
_traineImages.Add(normalizedMasterImage);
//---------------------delete file after being added----------------------------
var filePath = temp_images + img_name;
if (File.Exists(filePath))
{
File.Delete(filePath);
}
}
myconnection.Close();
Console.WriteLine("====================================LOAD DATA END========================================");
//converting data
faceImages = new Mat[_traineImages.Count];
for (int i = 0; i < _traineImages.Count; i++)
{
faceImages[i] = _traineImages[i].Mat;
}
//display faceImages somewhere
Console.WriteLine("==============================Training model=======================================");
recog = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
//Training model
if (_traineImages.ToArray().Length != 0)
{
recog.Train(faceImages, Labels_ID.ToArray());
}
Console.WriteLine("==============================Training model end=======================================");
}
catch (Exception e)
{
MessageBox.Show(e.ToString());
Console.Write(e.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);
}
}
private void button1_Click(object sender, EventArgs e)
{
if (comboBoxAlgorithm.Text == "EigenFaces")
{
try
{
string dataDirectory = Directory.GetCurrentDirectory() + "\\TrainedFaces";
string[] files = Directory.GetFiles(dataDirectory, "*.jpg", SearchOption.AllDirectories);
eigenTrainedImageCounter = 0;
foreach (var file in files)
{
Image <Bgr, Byte> TrainedImage = new Image <Bgr, Byte>(file);
if (eqHisChecked.Checked == true)
{
TrainedImage._EqualizeHist();
}
eigenTrainingImages.Add(TrainedImage.Convert <Gray, Byte>());
eigenlabels.Add(fileName(file));
eigenIntlabels.Add(eigenTrainedImageCounter);
eigenTrainedImageCounter++;
richTextBox1.Text += fileName(file) + "\n";
}
/*
* //TermCriteria for face recognition with numbers of trained images like maxIteration
* MCvTermCriteria termCrit = new MCvTermCriteria(eigenTrainedImageCounter, 0.001);
*
* //Eigen face recognizer
* eigenObjRecognizer=new EigenObjectRecognizer(
* eigenTrainingImages.ToArray(),
* eigenlabels.ToArray(),
* 3000,
* ref termCrit);
*/
eigenFaceRecognizer = new EigenFaceRecognizer(eigenTrainedImageCounter, 2000);
eigenFaceRecognizer.Train(eigenTrainingImages.ToArray(), eigenIntlabels.ToArray());
//eigenFaceRecognizer.Save(dataDirectory + "\\trainedDataEigen.dat");
}
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";
string[] files = Directory.GetFiles(dataDirectory, "*.jpg", SearchOption.AllDirectories);
fisherTrainedImageCounter = 0;
foreach (var file in files)
{
Image <Bgr, Byte> TrainedImage = new Image <Bgr, Byte>(file);
fisherTrainingImages.Add(TrainedImage.Convert <Gray, Byte>());
if (eqHisChecked.Checked == true)
{
TrainedImage._EqualizeHist();
}
fisherlabels.Add(fileName(file));
fisherIntlabels.Add(fisherTrainedImageCounter);
fisherTrainedImageCounter++;
richTextBox1.Text += fileName(file) + "\n";
}
fisherFaceRecognizer = new FisherFaceRecognizer(fisherTrainedImageCounter, 2000);
fisherFaceRecognizer.Train(fisherTrainingImages.ToArray(), fisherIntlabels.ToArray());
//fisherFaceRecognizer.Save(dataDirectory + "\\trainedDataFisher.dat");
}
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";
string[] files = Directory.GetFiles(dataDirectory, "*.jpg", SearchOption.AllDirectories);
lbphTrainedImageCounter = 0;
foreach (var file in files)
{
Image <Bgr, Byte> TrainedImage = new Image <Bgr, Byte>(file);
if (eqHisChecked.Checked == true)
{
TrainedImage._EqualizeHist();
}
lbphTrainingImages.Add(TrainedImage.Convert <Gray, Byte>());
lbphlabels.Add(fileName(file));
lbphIntlabels.Add(lbphTrainedImageCounter);
lbphTrainedImageCounter++;
richTextBox1.Text += fileName(file) + "\n";
}
lbphFaceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 400);
lbphFaceRecognizer.Train(lbphTrainingImages.ToArray(), lbphIntlabels.ToArray());
lbphFaceRecognizer.Save(dataDirectory + "\\trainedDataLBPH.dat");
}
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);
}
}
}
/***
* 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);
};
}
public void CreateAndDispose()
{
var recognizer = LBPHFaceRecognizer.Create(1, 8, 8, 8, 123);
recognizer.Dispose();
}
private bool Train(string folder)
{
string facesPath = Path.Combine(folder, "faces.xml");
if (!File.Exists(facesPath))
{
return(false);
}
try
{
names.Clear();
faces.Clear();
List <int> tmp = new List <int>();
FileStream facesInfo = File.OpenRead(facesPath);
using (XmlReader reader = XmlTextReader.Create(facesInfo))
{
while (reader.Read())
{
if (reader.IsStartElement())
{
switch (reader.Name)
{
case "name":
if (reader.Read())
{
tmp.Add(names.Count);
names.Add(reader.Value.Trim());
}
break;
case "file":
if (reader.Read())
{
faces.Add(new Image <Gray, byte>(Path.Combine(Application.StartupPath,
"TrainedFaces",
reader.Value.Trim())));
}
break;
}
}
}
}
facesInfo.Close();
if (faces.Count == 0)
{
return(false);
}
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);
recognizer.Train(faces.ToArray(), tmp.ToArray());
return(true);
}
catch
{
return(false);
}
}
