public static void Test()
{
using (Mat image = new Mat("testGamePic.jpg"))
{
using (Mat uimg = new Mat())
{
using (CascadeClassifier face = new CascadeClassifier("haarcascade_frontalface_default.xml"))
{
CvInvoke.CvtColor(image, uimg, ColorConversion.Bgr2Gray);
CvInvoke.EqualizeHist(uimg, uimg);
Rectangle[] facesDetected = face.DetectMultiScale(uimg, 1.1, 10, new Size(20, 20));
EigenFaceRecognizer efr = new EigenFaceRecognizer();
efr.Train(new VectorOfMat(uimg), new VectorOfInt(new int[] { 1 }));
var res = efr.Predict(uimg);
return;
}
}
}
}
public FormMain()
{
InitializeComponent();
_qz2018 = new Quiz2018();
_qz2018.Init(this);
cbTRFFaces.Items.AddRange(_qz2018.GetNames());
cbTFFaces.Items.AddRange(_qz2018.GetNames());
cbTFFaces.Items.Add("ALL");
cbTGGroups.Items.AddRange(_qz2018.GetGroups());
cbQNbr.Items.AddRange(_qz2018.GetQuestions());
cbQGrp.Items.AddRange(_qz2018.GetGroups());
FaceRecognition = new EigenFaceRecognizer(80, double.PositiveInfinity);
FaceDetection = new CascadeClassifier(System.IO.Path.GetFullPath(@"../../Algo/haarcascade_frontalface_default.xml"));
Frame = new Mat();
Faces = new List <Image <Gray, byte> >();
Ids = new List <int>();
Names = new List <string>();
//BeginWebcam();
}
public void TrainRecognizer()
{
// FaceRecognizer _faceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, double.PositiveInfinity);
FaceRecognizer _faceRecognizer = new EigenFaceRecognizer(80, double.PositiveInfinity);
DataStoreAccess _datastoreaccess = new DataStoreAccess();
var allFaces = _datastoreaccess.callfaces();
// MessageBox.Show(allFaces.Count.ToString());
if (allFaces != null)
{
var faceImages = new Image <Gray, byte> [allFaces.Count];
var faceLabels = new int[allFaces.Count];
for (int i = 0; i < allFaces.Count; i++)
{
Stream stream = new MemoryStream();
stream.Write(allFaces[i].Image, 0, allFaces[i].Image.Length);
var faceImage = new Image <Gray, byte>(new Bitmap(stream));
// faceImages[i] = faceImage.Resize(100, 100, Inter.Cubic);
faceImages[i] = faceImage.Resize(100, 100, Inter.Cubic);
faceLabels[i] = Convert.ToInt32(allFaces[i].Label);
}
string filepath = Application.StartupPath + "/traineddata";
// var filePath = Application.StartupPath + String.Format("/{0}.bmp", face);
_faceRecognizer.Train(faceImages, faceLabels);
_faceRecognizer.Save(filepath);
// MessageBox.Show(allFaces[0].Label);
}
else
{
//MessageBox.Show("adasfsf");
}
}
static OpenCvTrainer()
{
Subjects = new Dictionary <int, string>();
SubjectSamples = new List <Tuple <int, Image <Gray, byte> > >();
var haarPath = @"haarcascade_frontalface_default.xml";
DetectionClassifier = new CascadeClassifier(haarPath);
FaceRecognizerData = new EigenFaceRecognizer();
if (File.Exists(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "trainingFile.ocv")))
{
FaceRecognizerData.Load(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "trainingFile.ocv"));
}
if (File.Exists(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "trainingSubjects.txt")))
{
var lines = File.ReadAllLines((Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "trainingSubjects.txt")));
foreach (var line in lines)
{
if (!(string.IsNullOrEmpty(line)) && line.Contains(":"))
{
var items = line.Split(':');
Subjects.Add(int.Parse(items[0]), items[1]);
}
}
}
}
/// <summary>
/// Création d'un fichier d’entraînement
/// </summary>
/// <param name="labels">Dictionnaire Id de libellé / libellé</param>
/// <param name="files">Liste des couples fichier / Id de libellé</param>
/// <param name="labelsFileName">Nom du fichier des correspondances Id de libellé / Libellé</param>
/// <param name="recognizerFileName">Nom du fichier de reconnaissance</param>
public void CreateTrainingFaceRecognizerFile(Dictionary <int, string> labels, List <Tuple <string, int> > files, string labelsFileName, string recognizerFileName)
{
List <Image <Gray, byte> > images = new List <Image <Gray, byte> >();
List <int> labelIds = new List <int>();
try
{
foreach (var item in files)
{
images.Add(new Image <Bgr, byte>(item.Item1).Convert <Gray, byte>());
labelIds.Add(item.Item2);
}
using (EigenFaceRecognizer frz = new EigenFaceRecognizer(images.Count, 3000))
{
frz.Train(images.ToArray(), labelIds.ToArray());
frz.Write(recognizerFileName);
}
}
finally
{
images.ForEach(i => i.Dispose());
images.Clear();
}
StringBuilder sb = new StringBuilder();
foreach (var item in labels)
{
sb.AppendLine($"{item.Key}|{item.Value}");
}
File.WriteAllText(labelsFileName, sb.ToString());
}
private void EigenFaceRecognition(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)
{
eigenFaceRecognizer = new EigenFaceRecognizer(Count, double.PositiveInfinity);
eigenFaceRecognizer.Train(trainingImages.ToArray(), indexLabels.ToArray());
var result = eigenFaceRecognizer.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);
}
private void BtnPredict_Click(object sender, EventArgs e)
{
Frame = Capture.QueryFrame();
var imageFrame = Frame.ToImage <Gray, byte>();
if (imageFrame != null)
{
var faces = FaceDetection.DetectMultiScale(imageFrame, 1.3, 5);
var userDetected = new List <string>();
foreach (var face in faces)
{
var processImage = imageFrame.Copy(face).Resize(ProcessImageWidth, ProcessImageHeight, Inter.Cubic);
var resultEigen = EigenFaceRecognizer.Predict(processImage);
if (resultEigen.Label != -1)
{
userDetected.Add(DataStoreAccess.GetUsername(resultEigen.Label));
}
else
{
userDetected.Add("Unknown");
}
}
lbResult.Text = string.Join(",", userDetected);
}
}
private void DetectAndTrain_Click(object sender, EventArgs e)
{
using (var capture = new VideoCapture()) {
Thread.Sleep(PluginOptions.CameraDelayMs);
using (Image <Bgr, byte> imageFrame = capture.QueryFrame().ToImage <Bgr, byte>()) {
if (imageFrame == null)
{
return;
}
using (Image <Gray, byte> grayframe = imageFrame.Convert <Gray, byte>()) {
if (PluginOptions.UseImageCorrection)
{
grayframe._EqualizeHist();
}
Rectangle[] part1;
using (var classifier = new CascadeClassifier(PluginOptions.CascadesPath + "haarcascade_frontalface_default.xml")) {
part1 = classifier.DetectMultiScale(grayframe, 1.1, 10);
}
if (part1.Length > 0)
{
Rectangle face = part1[0]; //выбор первого лица
using (Image <Gray, byte> resultingImage = imageFrame.Copy(face).Convert <Gray, byte>().Resize(100, 100, Inter.Cubic)) {
imageFrame.Draw(face, new Bgr(Color.Blue), 2);
TestImage.Image = imageFrame;
DetectedGrayFace.Image = resultingImage;
#region Добавление лица
if (string.IsNullOrEmpty(FaceName.Text))
{
MessageBox.Show(
"Сначала введите имя распознанного лица", "Имя лица не указано", MessageBoxButtons.OK, MessageBoxIcon.Warning
);
return;
}
TrainedImages.Add(resultingImage);
PluginOptions.PeopleFaces.Add(
PluginOptions.PeopleFaces.Count + 1,
FaceName.Text
);
TrainedImages.Last().Save($"{PluginOptions.PluginPath}Faces\\face{TrainedImages.Count}.bmp");
#endregion
}
PluginOptions.SaveOptionsToXml();
using (FaceRecognizer recognizer = new EigenFaceRecognizer()) {
recognizer.Train(TrainedImages.ToArray(), PluginOptions.PeopleFaces.Keys.ToArray());
recognizer.Write(PluginOptions.PluginPath + "SavedCascade.xml");
}
MessageBox.Show("Лицо успешно добавлено", "Training OK", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
else
{
MessageBox.Show("лиц не найдено", "Training OK", MessageBoxButtons.OK, MessageBoxIcon.Warning);
}
}
}
}
}
private bool TrainImage()
{
int imageCount = 0;
double Threshold = -1;
Trained.Clear();
PersonLabel.Clear();
try
{
string path = Directory.GetCurrentDirectory() + @"\TrainedFaces";
string[] files = Directory.GetFiles(path, "*.jpg", SearchOption.AllDirectories);
foreach (var file in files)
{
Image <Gray, Byte> trainedImage = new Image <Gray, byte>(file);
Trained.Add(trainedImage);
PersonLabel.Add(imageCount);
PersonLabel.Add(imageCount);
imageCount++;
}
EigenFaceRecognizer recognizer = new EigenFaceRecognizer(imageCount, Threshold);
recognizer.Train(Trained.ToArray(), PersonLabel.ToArray());
isTrained = true;
Debug.WriteLine(imageCount);
Debug.WriteLine(isTrained);
return(isTrained = true);
}
catch (Exception ex)
{
isTrained = false;
MessageBox.Show("Erro" + ex.Message);
return(false);
}
}
public void TrainRecognizer()
{
var allFaces = new FRService().All();
if (allFaces.Count > 0)
{
var faceImages = new Image <Gray, byte> [allFaces.Count];
var faceLabels = new int[allFaces.Count];
for (int i = 0; i < allFaces.Count; i++)
{
Stream stream = new MemoryStream();
stream.Write(allFaces[i].Face, 0, allFaces[i].Face.Length);
var faceImage = new Image <Gray, byte>(new Bitmap(stream));
faceImages[i] = faceImage.Resize(100, 100, Inter.Cubic);
faceLabels[i] = (int)(allFaces[i].Id);
}
// can also try :LBPHFaceRecognizer
var fr = new EigenFaceRecognizer();
fr.Train(faceImages, faceLabels);
var retPath = ConfigurationManager.AppSettings["trainedPath"];
var savedFile = retPath + $"{DateTime.Now.ToString("yyyy-MM-dd HH-mm-ss")}_frModel";
fr.Save(savedFile);
MessageBox.Show($"Model trained successfully. saved into {savedFile}");
}
else
{
MessageBox.Show("No face found in db");
}
}
// Start training from the collected faces.
// The face recognition algorithm can be one of these and perhaps more, depending on your version of OpenCV, which must be atleast v2.4.1:
// "FaceRecognizer.Eigenfaces": Eigenfaces, also referred to as PCA (Turk and Pentland, 1991).
// "FaceRecognizer.Fisherfaces": Fisherfaces, also referred to as LDA (Belhumeur et al, 1997).
// "FaceRecognizer.LBPH": Local Binary Pattern Histograms (Ahonen et al, 2006).
public static BasicFaceRecognizer LearnCollectedFaces(List <Mat> preprocessedFaces, List <int> faceLabels, string facerecAlgorithm = "FaceRecognizer.Eigenfaces")
{
BasicFaceRecognizer model = null;
Debug.Log("Learning the collected faces using the [" + facerecAlgorithm + "] algorithm ...");
if (facerecAlgorithm == "FaceRecognizer.Fisherfaces")
{
model = FisherFaceRecognizer.create();
}
else if (facerecAlgorithm == "FaceRecognizer.Eigenfaces")
{
model = EigenFaceRecognizer.create();
}
if (model == null)
{
Debug.LogError("ERROR: The FaceRecognizer algorithm [" + facerecAlgorithm + "] is not available in your version of OpenCV. Please update to OpenCV v2.4.1 or newer.");
//exit(1);
}
// Do the actual training from the collected faces. Might take several seconds or minutes depending on input!
MatOfInt labels = new MatOfInt();
labels.fromList(faceLabels);
model.train(preprocessedFaces, labels);
return(model);
}
// private static readonly int threshold = 10000;
// higher threshold - more chances to recognize a face (sometimes incorrectly);
public static EigenFaceRecognizer NewEigen()
{
EigenFaceRecognizer eigenRec = new EigenFaceRecognizer(80, 4000);
eigenRec.Write(Application.StartupPath + "/data/recognizer.yaml");
return(eigenRec);
}
/// <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");
}
private void Run()
{
List <Mat> images = new List <Mat> ();
List <int> labelsList = new List <int> ();
MatOfInt labels = new MatOfInt();
images.Add(Imgcodecs.imread(image_0_filepath, 0));
images.Add(Imgcodecs.imread(image_1_filepath, 0));
labelsList.Add(0);
labelsList.Add(1);
labels.fromList(labelsList);
Mat testSampleMat = Imgcodecs.imread(sample_image_filepath, 0);
int testSampleLabel = 0;
// foreach (Mat item in images) {
// Debug.Log ("images.ToString " + item.ToString ());
// }
// foreach (int item in labelsList) {
// Debug.Log ("labels.ToString " + item.ToString ());
// }
int[] predictedLabel = new int[1];
double[] predictedConfidence = new double[1];
BasicFaceRecognizer faceRecognizer = EigenFaceRecognizer.create();
faceRecognizer.train(images, labels);
faceRecognizer.predict(testSampleMat, predictedLabel, predictedConfidence);
Debug.Log("Predicted class: " + predictedLabel [0] + " / " + "Actual class: " + testSampleLabel);
Debug.Log("Confidence: " + predictedConfidence [0]);
Mat predictedMat = images [predictedLabel [0]];
Mat baseMat = new Mat(testSampleMat.rows(), predictedMat.cols() + testSampleMat.cols(), CvType.CV_8UC1);
predictedMat.copyTo(baseMat.submat(new OpenCVForUnity.CoreModule.Rect(0, 0, predictedMat.cols(), predictedMat.rows())));
testSampleMat.copyTo(baseMat.submat(new OpenCVForUnity.CoreModule.Rect(predictedMat.cols(), 0, testSampleMat.cols(), testSampleMat.rows())));
Imgproc.putText(baseMat, "Predicted", new Point(10, 15), Imgproc.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "Confidence:", new Point(5, 25), Imgproc.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, " " + predictedConfidence [0], new Point(5, 33), Imgproc.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "TestSample", new Point(predictedMat.cols() + 10, 15), Imgproc.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Texture2D texture = new Texture2D(baseMat.cols(), baseMat.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(baseMat, texture);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
}
private void Window_Loaded(object sender, RoutedEventArgs e)
{
capture = new VideoCapture();
Frontface_Cascade = new CascadeClassifier(@"haarcascades/haarcascade_frontalface_default.xml");
var connectionstring = ConfigurationManager.ConnectionStrings["Test"].ConnectionString;
using (SqlConnection connection = new SqlConnection(connectionstring))
{
connection.Open();
string query = "SELECT * FROM Attendance.dbo.TrainingData Order by StudentID";
using (SqlCommand command = new SqlCommand(query, connection))
{
using (SqlDataReader reader = command.ExecuteReader())
{
while (reader.Read())
{
labels.Add(reader.GetInt32(0));
NamePersons.Add(reader.GetString(1));
byte[] blob = null;
blob = (byte[])reader.GetValue(2);
var image = ByteToImage(blob);
Image <Gray, Byte> newImage = new Image <Gray, Byte>(image);
trainingImages.Add(newImage);
}
}
}
}
recognizer = new EigenFaceRecognizer(1, 5000);
Mat[] faceImages = new Mat[trainingImages.Count];
int[] faceLabels = new int[labels.Count];
for (int i = 0; i < trainingImages.Count; i++)
{
var face = Frontface_Cascade.DetectMultiScale(trainingImages[i]);
foreach (var Tface in face)
{
trainingImages[i].ROI = Tface;
}
gray = trainingImages[i].Clone().Resize(200, 200, 0);
Mat x = gray.Mat;
faceImages[i] = x;
}
for (int i = 0; i < labels.Count; i++)
{
faceLabels = labels.ToArray();
}
recognizer.Train(faceImages, faceLabels);
logger.Info("Trained Face Recognizer");
timer = new DispatcherTimer();
timer.Tick += new EventHandler(timer_Tick);
timer.Interval = new TimeSpan(0, 0, 0, 0, 1);
timer.Start();
}
public static EigenFaceRecognizer NewEigen()
{
EigenFaceRecognizer eigen = new EigenFaceRecognizer(7, tre);
/*
* int.Parse(ConfigurationManager.AppSettings["RecognizerComponentsNum"]),
* int.Parse(ConfigurationManager.AppSettings["RecognizerThreshold"]));
*/
return(eigen);
}
/// <summary>
/// Reconnaît les visages dans une image
/// </summary>
/// <param name="fileName"></param>
/// <param name="labelsFileName"></param>
/// <param name="recognizerFileName"></param>
private void RecognizeFaces(string fileName, string labelsFileName, string recognizerFileName)
{
_faceLabels = GetLabels(labelsFileName);
using (EigenFaceRecognizer faceRecognizer = new EigenFaceRecognizer())
{
_currentFaceRecognizer = faceRecognizer;
faceRecognizer.Read(recognizerFileName);
RecognizeFaces(fileName);
}
}
public FaceClassifier()
{
_faceRecognition = new EigenFaceRecognizer(80, double.PositiveInfinity);
_faceDetection = new CascadeClassifier(Path.GetFullPath(@"haarcascades/haarcascade_frontalface_default.xml"));
_eyeDetection = new CascadeClassifier(Path.GetFullPath(@"haarcascades/haarcascade_eye.xml"));
Faces = new List <Face>();
_frame = new Mat();
StartCapture();
}
private void TrainerForm_FormClosing(object sender, FormClosingEventArgs e)
{
EigenFaceRecognizer eigen = Recognizer.NewEigen();
bool success = Recognizer.TrainRecognizer(eigen, ImageHandler.GetGrayFaceArrayFromFiles(), ImageHandler.GetLabelArrayFromFiles());
if (success == false)
{
MessageBox.Show("Corrupted data");
}
}
private void DetectFaces()
{
NamePersons.Add("");
Image <Gray, byte> grayframe = the_image_frame.Convert <Gray, byte>();
ImgCamera.Image = the_image_frame;
MCvAvgComp[][] facesDetected = grayframe.DetectHaarCascade(
face,
1.1,
2,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
lblTotalFacesDetected.Text = facesDetected[0].Length.ToString();
if (facesDetected[0].Length > 0)
{
foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = the_image_frame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
the_image_frame.Draw(f.rect, new Bgr(Color.Green), 3);
if (TrainingImages.ToArray().Length != 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.01);
EigenFaceRecognizer recognizer = new EigenFaceRecognizer(
TrainingImages.ToArray(),
labels.ToArray(),
5000,
ref termCrit
);
name = recognizer.Recognize(result);
if (string.IsNullOrEmpty(name) == true)
{
name = "UNKNOWN";
}
the_image_frame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.Red));
}
//Now draw the rectangle on the detected image
NamePersons[t - 1] = name;
NamePersons.Add("");
}
lblTotalFacesDetected.Text = facesDetected[0].Length.ToString();
}
t = 0;
ImgTrainedFaces.Image = result;
for (int namelabel = 0; namelabel < facesDetected[0].Length; namelabel++)
{
names = names + NamePersons[namelabel] + ",";
}
string names_without_comma_at_the_end = names.Remove(names.Length - 1);
lblNamesExtracted.Text = names_without_comma_at_the_end;
names = "";
NamePersons.Clear();
}
public Form1()
{
InitializeComponent();
FacialRecognition = new EigenFaceRecognizer(80, double.PositiveInfinity);
FaceDetection = new CascadeClassifier(System.IO.Path.GetFullPath(@"../../Data/haarcascade_frontalface_default.xml"));
EyeDetection = new CascadeClassifier(System.IO.Path.GetFullPath(@"../../Data/haarcascade_eye.xml"));
Frame = new Mat();
Faces = new List <Image <Gray, byte> >();
Ids = new List <int>();
BeginCamera();
}
public JAVSFacialRecognizer()
{
_faceRecognizer = new EigenFaceRecognizer();
try
{
_faceRecognizer.Load(Environment.GetFolderPath(Environment.SpecialFolder.UserProfile) + "\\Documents\\test-recognizer.yaml");
}
catch
{
var fail = "fail";
}
}
/// <summary>
/// Reconnaît les visages dans toutes les images d'un répertoire
/// </summary>
/// <param name="directoryName"></param>
/// <param name="searchPattern"></param>
/// <param name="recursive"></param>
public void RecognizeFacesInDirectory(string directoryName, string labelsFileName, string recognizerFileName, string searchPattern = "*.jpg", bool recursive = false)
{
_faceLabels = GetLabels(labelsFileName);
using (EigenFaceRecognizer faceRecognizer = new EigenFaceRecognizer())
{
_currentFaceRecognizer = faceRecognizer;
faceRecognizer.Read(recognizerFileName);
foreach (var item in Directory.GetFiles(directoryName, searchPattern, recursive ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly))
{
RecognizeFaces(item);
}
}
}
public int RecognizeFace(Image <Gray, byte> image)
{
EigenFaceRecognizer eigen = OldEigen();
FaceRecognizer.PredictionResult result = eigen.Predict(image);
Console.WriteLine("ID: " + result.Label + ", " + "Threshold: " + result.Distance);
if (result.Label != -1 && result.Distance < treshold)//int.Parse(ConfigurationManager.AppSettings["RecognizerThreshold"]))
{
return(result.Label);
}
return(0);
}
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 FindPersonOnImage()
{
InitializeComponent();
TrainedFaces.Clear();
FaceIDs.Clear();
PersonsNames.Clear();
using (ApplicationContext db = new ApplicationContext())
{
InitializeComponent();
db.Persons.Load();
Persons = db.Persons.Local.ToList();
}
int FaceCount = 0;
foreach (var person in Persons)
{
var img = new Bitmap(BitmapHelpers.byteArrayToImage(person.PersonPhoto));
Image <Gray, byte> imageForTrain = img.ToImage <Gray, byte>();
System.Drawing.Rectangle[] faces = faceCascadeClassifier.DetectMultiScale(imageForTrain, 1.1, 3, System.Drawing.Size.Empty, System.Drawing.Size.Empty);
foreach (var face in faces)
{
faceResult = imageForTrain;
faceResult.ROI = face;
CvInvoke.Resize(faceResult, faceResult, new System.Drawing.Size(200, 200), 0, 0, Inter.Cubic);
CvInvoke.EqualizeHist(faceResult, faceResult);
TrainedFaces.Add(faceResult.Mat);
}
FaceIDs.Add(FaceCount);
PersonsNames.Add(person.FirstName + " " + person.LastName);
FaceCount++;
Debug.WriteLine(FaceCount + ". " + person.FirstName + " " + person.LastName);
double Threshold = 2000;
if (TrainedFaces.Count > 0)
{
recognizer = new EigenFaceRecognizer(FaceCount, Threshold);
recognizer.Train(TrainedFaces.ToArray(), FaceIDs.ToArray());
}
InitializeComponent();
}
}
public MainWindowViewModel()
{
_dataStoreAccess = new DataStoreAccess(_databasePath);
if (File.Exists(_faceRecognizerPath))
{
_faceRecognizer = new EigenFaceRecognizer();
_faceRecognizer.Read(_faceRecognizerPath);
}
else
{
_faceRecognizer = new EigenFaceRecognizer();
}
InitializeFaceDetection();
}
//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);
}
}
public int RecognizeUser(Image <Gray, byte> userImage)
{
//creating new instatnce of facerecognizer
FaceRecognizer _faceRecognizer = new EigenFaceRecognizer(80, 1000);
// EigenObjectRecognizer recognizer = new EigenObjectRecognizer(trainingImages.ToArray(), ref termCrit);
// LBPHFaceRecognizer _faceRecognizer = new LBPHFaceRecognizer(1,8,8,8, double.PositiveInfinity);
string _recognizerFilePath = Application.StartupPath + "/traineddata";
_faceRecognizer.Load(_recognizerFilePath);
var result = _faceRecognizer.Predict(userImage.Resize(100, 100, Inter.Cubic));
return(result.Label);
}
public Form1()
{
InitializeComponent();
FaceRecognition = new EigenFaceRecognizer(80, 2500);
FaceDetection = new CascadeClassifier(System.IO.Path.GetFullPath(@"../../Algo/haarcascade_frontalface_default.xml"));
EyeDetection = new CascadeClassifier(System.IO.Path.GetFullPath(@"../../Algo/haarcascade_eye.xml"));
Frame = new Mat();
Faces = new List <Mat>();
IDs = new List <int>();
listOfNames = new List <string>();
listOfIds = new List <int>();
BeginWebcam();
}
