public EmguFaceDetector(String preloadedTraining = "")
{
// model = new LBPHFaceRecognizer();
// model = new EigenFaceRecognizer();
model = new FisherFaceRecognizer(2, 3000);
if (preloadedTraining == "")
{
images = new List <Image <Gray, byte> >();
labels = new List <int>();
prepareTrainingData();
model.Train(images.ToArray(), labels.ToArray());
model.Save("Default");
}
else
{
if (preloadedTraining == "Default")
{
model.Load(preloadedTraining);
}
else
{
model.Load(preloadedTraining);
}
}
cascadeFace = new CascadeClassifier(AppDomain.CurrentDomain.BaseDirectory + "haarcascades\\haarcascade_frontalface_default.xml");
cascadeEyes = new CascadeClassifier(AppDomain.CurrentDomain.BaseDirectory + "haarcascades\\haarcascade_eye.xml");
}
public static void InitializeFaceRecognizer()
{
_faceRecognizer?.Dispose();
_faceRecognizer = new LBPHFaceRecognizer(Properties.Settings.Default.RecognitionRadius, Properties.Settings.Default.RecognitionNeighbours, 8, 8, Properties.Settings.Default.RecognitionThreshold);
if (!File.Exists(Properties.Settings.Default.RecognitionTrainFile))
{
try
{
Directory.CreateDirectory(Path.GetDirectoryName(Properties.Settings.Default.RecognitionTrainFile));
File.Create(Properties.Settings.Default.RecognitionTrainFile).Close();
}
catch (Exception ex)
{
Debug.WriteLine("Could not create recognition file: " + ex);
}
}
else
{
try
{
_faceRecognizer.Load(Properties.Settings.Default.RecognitionTrainFile);
_trained = true;
}
catch (Exception ex)
{
Debug.WriteLine("Could not load recognition file: " + ex);
}
}
}
public int RecognizeUser(Image <Gray, byte> userImage)
{
faceRecognizer.Load(recognizerPath);
var res = faceRecognizer.Predict(userImage.Convert <Gray, byte>().Resize(100, 100, Inter.Cubic));
return(res.Label);
}
/// <summary>
/// Routine to train face recognizer with sample images
/// </summary>
/*private void TrainRecognizer(string root)
* {
* // This one was actually used to train the recognizer. I didn't push much effort and satisfied once it
* // distinguished all detected faces on the sample image, for the real-world application you might want to
* // refer to the following documentation:
* // OpenCV documentation and samples: http://docs.opencv.org/3.0-beta/modules/face/doc/facerec/tutorial/facerec_video_recognition.html
* // Training sets overview: https://www.kairos.com/blog/60-facial-recognition-databases
* // Another OpenCV doc: http://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html#face-database
*
* int id = 0;
* var ids = new List<int>();
* var mats = new List<Mat>();
* var namesList = new List<string>();
*
* foreach (string dir in Directory.GetDirectories(root))
* {
* string name = System.IO.Path.GetFileNameWithoutExtension(dir);
* if (name.StartsWith("-"))
* continue;
*
* namesList.Add(name);
* UnityEngine.Debug.LogFormat("{0} = {1}", id, name);
*
* foreach (string file in Directory.GetFiles(dir))
* {
* var bytes = File.ReadAllBytes(file);
* var texture = new UnityEngine.Texture2D(2, 2);
* texture.LoadImage(bytes); // <--- this one has changed in Unity 2017 API and on that version must be changed
*
* ids.Add(id);
*
* // each loaded texture is converted to OpenCV Mat, turned to grayscale (assuming we have RGB source) and resized
* var mat = Unity.TextureToMat(texture);
* mat = mat.CvtColor(ColorConversionCodes.BGR2GRAY);
* if (requiredSize.Width > 0 && requiredSize.Height > 0)
* mat = mat.Resize(requiredSize);
* mats.Add(mat);
* }
* id++;
* }
*
* names = namesList.ToArray();
*
* // train recognizer and save result for the future re-use, while this isn't quite necessary on small training sets, on a bigger set it should
* // give serious performance boost
* recognizer.Train(mats, ids);
* recognizer.Save(root + "/face-recognizer.xml");
* }*/
#endregion
/// <summary>
/// Initializes scene
/// </summary>
protected virtual void Awake()
{
// classifier
FileStorage storageFaces = new FileStorage(faces.text, FileStorage.Mode.Read | FileStorage.Mode.Memory);
cascadeFaces = new CascadeClassifier();
if (!cascadeFaces.Read(storageFaces.GetFirstTopLevelNode()))
{
throw new System.Exception("FaceProcessor.Initialize: Failed to load faces cascade classifier");
}
// recognizer
// There are three available face recognition algorithms in current version of the OpenCV library (please, refer to the OpenCV documentation for details)
// Our particular training set was trained and saved with FisherFaceRecognizer() and shuld not work with others, however, you can refer to the "TrainRecognizer"
// method defined above to instructions and sample code regarding training your own recognizer from the scratch
//recognizer = FaceRecognizer.CreateLBPHFaceRecognizer();
//recognizer = FaceRecognizer.CreateEigenFaceRecognizer();
recognizer = FaceRecognizer.CreateFisherFaceRecognizer();
// This pre-trained set was quite tiny and contained only those 5 persons that are detected and recognized on the image. We took 5 photos for each person from
// public images on Google, for a real-world application you will need much more sample data for each persona, for more info refer to the OpenCV documentation
// (there are some links in the "TrainRecognizer" sample function
recognizer.Load(new FileStorage(recognizerXml.text, FileStorage.Mode.Read | FileStorage.Mode.Memory));
// label names
names = new string[] { "Cooper", "DeGeneres", "Nyongo", "Pitt", "Roberts", "Spacey" };
}
/// <summary>
/// Method which recognizes the user from the given image.
/// </summary>
/// <param name="userImage"></param>
/// <returns></returns>
public FaceRecognizer.PredictionResult RecognizeUser(Image <Gray, byte> userImage)
{
_faceRecognizer.Load(_recognizerFilePath);
////normalize brightness
//userImage._EqualizeHist();
var result = _faceRecognizer.Predict(userImage.Resize(100, 100, Inter.Cubic));
return(result);
}
private void BtnLoad_Click(object sender, System.EventArgs e)
{
faceRecognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100.0);
if (File.Exists("trainedData.xml"))
{
modeltrained = true;
faceRecognizer.Load("trainedData.xml");
}
else
{
MessageBox.Show("you didnt train the model. Press train button!");
}
}
public int Recognize(Image <Gray, byte> userImage)
{
faceRecognizer_.Load(recognizerFilePath_);
var result = faceRecognizer_.Predict(userImage.Resize(100, 100, Inter.Cubic));
int id = 0;
if (result.Distance <= 150)
{
id = result.Label;
}
return(id);
}
public void loadTrainingFileOfCourseCode(string crsCode)
{
var configFile = ConfigurationManager.OpenExeConfiguration(ConfigurationUserLevel.None);
var settings = configFile.AppSettings.Settings;
string trainingFilePath = configFile.AppSettings.Settings["TrainingFilesPath"].Value;
String trainFile1 = trainingFilePath + crsCode + "-1";
String trainFile2 = trainingFilePath + crsCode + "-2";
String trainFile3 = trainingFilePath + crsCode + "-3";
fr1.Load(trainFile1); //loading the training data
fr2.Load(trainFile2); //loading the training data
fr3.Load(trainFile3); //loading the training data
}
public Form1()
{
InitializeComponent();
recognizer = new LBPHFaceRecognizer(1, 8, 8, 9, 65);
classifier = new CascadeClassifier(haarcascade);
GPU_classifier = new GpuCascadeClassifier(haarcascade_cuda);
font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5, 0.5);
if (File.Exists(@"traningdata.xml"))
{
recognizer.Load(@"traningdata.xml");
}
else
{
foreach (var file in Directory.GetFiles(Application.StartupPath + @"\Traning Faces\"))
{
try { temp = new Image <Gray, Byte>(file); }
catch { continue; }
temp._EqualizeHist();
var detectedFaces = classifier.DetectMultiScale(temp, 1.1, 15, new Size(24, 24), Size.Empty);
if (detectedFaces.Length == 0)
{
continue;
}
temp.ROI = detectedFaces[0];
temp = temp.Copy();
temp = temp.Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
imagesList.Add(temp);
imagesLabels.Add(Path.GetFileNameWithoutExtension(file));
}
for (int i = 0; i < imagesList.Count; i++)
{
imagesLabels_indices.Add(i);
}
try { recognizer.Train(imagesList.ToArray(), imagesLabels_indices.ToArray()); }
catch (Exception ex)
{
MessageBox.Show(ex.Message);
Environment.Exit(0);
}
}
}
// Attempt to recognize the user using the parameterized image & the images in the database.
public User RecognizeUser(Image <Gray, byte> userImage)
{
_faceRecognizer.Load(_recognizerFilePath);
var faceData = _faceRecognizer.Predict(userImage.Resize(100, 100, Inter.Cubic));
// Threshold value set to 87 after thorough testing & evaluation with a database of 50+ images from the internet.
if (faceData.Distance < 87)
{
var user = _dataStoreAccess.GetUserByFaceId(faceData.Label);
user.Distance = faceData.Distance;
return(user);
}
else
{
return new User {
UserName = Config.UnrecognizedUserName, Distance = faceData.Distance
}
};
}
}
private void BtnLoad_Click(object sender, System.EventArgs e)
{
if (faceRecognizer == null)
{
MessageBox.Show("FaceRecognizer is null???");
return;
}
if (File.Exists(trainingFilePath))
{
Modeltrained = true;
faceRecognizer.Load(trainingFilePath);
StreamReader streamReader = new StreamReader(namesPath);
while (!streamReader.EndOfStream)
{
listOfFileNames.Add(streamReader.ReadLine());
}
streamReader.Close();
}
else
{
MessageBox.Show("you didnt train the model. Press train button!");
}
}
//Eğer biz yeni bir eğitim dosyası yüklemek istersek aşağıdaki kod bloğu çalışır.
// Belirtilen dosya yolundaki eğitim setini yükler.
public void FnkEgitimDosyasiniYukle(string filename) // filename trainedfaces klasörünün yolu
{
string dosyauzantisi = Path.GetExtension(filename); //belirtilen yolun uzantısını verir.
switch (dosyauzantisi) //seçilen dosyanın uzantısına göre tanıma algoritması seçilir.
{
case (".LBPH"):
TanimaTuru = "EMGU.CV.LBPHFaceRecognizer";
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100); //50
break;
case (".FFR"):
TanimaTuru = "EMGU.CV.FisherFaceRecognizer";
recognizer = new FisherFaceRecognizer(0, 3500); //4000
break;
case (".EFR"):
TanimaTuru = "EMGU.CV.EigenFaceRecognizer";
recognizer = new EigenFaceRecognizer(80, double.PositiveInfinity);
break;
}
recognizer.Load(filename);//seçilen dosya yüklenir.(******)
//Şimdide Etiketleri yükleme işlemi gerçekleştirilir.
string filepath = Path.GetDirectoryName(filename);//eğitim dosyasının bulunduğu dizini verir.(sadece dizin.)
AdSoyadList.Clear();
if (File.Exists(filepath + "/Labels.xml"))//belirtilen dizinde xml dosyasının olup olmadığının kontrolü yapılır.(*******)
{
FileStream FSKisiBilgileri = File.OpenRead(filepath + "/Labels.xml");
long filelength = FSKisiBilgileri.Length;//dosyanın uzunluğunu aldık.
byte[] xmlBytes = new byte[filelength];
FSKisiBilgileri.Read(xmlBytes, 0, (int)filelength);
FSKisiBilgileri.Close();
//xml'deki verileri her seferinde dosyaya ulaşıp alamk yerine
//memorystream kullanarak bellekte tutarak uygulamanın performansını arttırmayı sağladım.(******)
MemoryStream xmlStream = new MemoryStream(xmlBytes);
using (XmlReader xmlreader = XmlTextReader.Create(xmlStream))
{
while (xmlreader.Read())
{
if (xmlreader.IsStartElement())
{
switch (xmlreader.Name)
{
case "NAME":
if (xmlreader.Read())
{
AdSoyadList.Add(xmlreader.Value.Trim()); //xml dosyasından okuduğu isimleri AdSoyadList listesine ekliyoruz.
}
break;
}
}
}
}
ContTrain = NumLabels;
}
_DizinKontrol = true;
}
public void Train()
{
List <Image <Gray, byte> > images = new List <Image <Gray, byte> >();
//List<int> ids = new List<int>();
List <int> idsTrainned = new List <int>();
for (int i = 1; i <= 40; i++)
{
// ************************* //
String extFacesPath = "E:\\faces\\s" + i;
DirectoryInfo dInfo = new DirectoryInfo(extFacesPath);
System.IO.Directory.CreateDirectory("c:\\img\\s" + i);
var allImages = dInfo.GetFiles("*.bmp"); //get from this directory all files contain ".bmp"
int j = 1;
foreach (var image in allImages)
{
if (j > 10)
{
break;
}
MessageBox.Show("here" + j);
string photoPath = extFacesPath + "\\" + image;
Image <Gray, byte> img = new Image <Gray, byte>(photoPath).Resize(64, 64, Inter.Cubic);
img.ToBitmap().Save("C:\\img\\s" + i + "\\" + j + ".bmp");
//img._EqualizeHist();
img.Save(photoPath);
images.Add(img);
idsTrainned.Add(i);
j++;
}
}
var configFile = ConfigurationManager.OpenExeConfiguration(ConfigurationUserLevel.None);
var settings = configFile.AppSettings.Settings;
string h1Path = configFile.AppSettings.Settings["TrainingFilesPath"].Value + "\\" + "h1_";
string h2Path = configFile.AppSettings.Settings["TrainingFilesPath"].Value + "\\" + "h2_";
string h3Path = configFile.AppSettings.Settings["TrainingFilesPath"].Value + "\\" + "h3_";
if (images.Count > 0)
{
fr1.Train(images.ToArray(), idsTrainned.ToArray()); //this line is self explanatory
fr1.Save(h1Path); //saving the trainig
fr2.Train(images.ToArray(), idsTrainned.ToArray()); //this line is self explanatory
fr2.Save(h2Path); //saving the trainig
fr3.Train(images.ToArray(), idsTrainned.ToArray());
fr3.Save(h3Path); //saving the trainig
fr1.Load(h1Path); // Loading the training data of file 1
fr2.Load(h2Path); // Loading the training data of file 2
fr3.Load(h3Path); // Loading the training data of file 3
MessageBox.Show("dsdsds");
for (int i = 1; i <= 40; i++)
{
// ************************* //
String extFacesPath = "E:\\courses gp\\faces\\s" + i;
DirectoryInfo dInfo = new DirectoryInfo(extFacesPath);
var allImages = dInfo.GetFiles("*.bmp"); //get from this directory all files contain ".bmp"
int j = 1;
foreach (var face in allImages)
{
if (j <= 10)
{
string photoPath = extFacesPath + "\\" + face;
Image <Gray, byte> img = new Image <Gray, byte>(photoPath).Resize(200, 200, Inter.Cubic);
//This is used to get the result from testing
FaceRecognizer.PredictionResult result = new FaceRecognizer.PredictionResult();
result = fr1.Predict(img); //receiving the result
if (result.Distance <= 8000)
{
int testResult = result.Label;
if (i == testResult)
{
counter1++;
}
}
result = fr2.Predict(img); //receiving the result
if (result.Distance <= 3100)
{
int testResult = result.Label;
if (i == testResult)
{
counter2++;
}
}
result = fr3.Predict(img); //receiving the result
if (result.Distance <= 100)
{
int testResult = result.Label;
if (i == testResult)
{
counter3++;
}
}
}
j++;
}
}
}
MessageBox.Show("dsdsds");
int accurracy1, accurracy2, accurracy3;
accurracy1 = counter1 / 40;
accurracy2 = counter2 / 40;
accurracy3 = counter3 / 40;
MessageBox.Show(accurracy1 + " " + accurracy2 + " " + accurracy3);
//FinishAddStudent fas = new FinishAddStudent();
//fas.Tag = this;
//fas.Show(this);
Hide();
}
/*
* SK: Metóda, ktorá sa zavolá, pre vygenerovanie otázky
* ENG: Method, which is called for generating question
*/
void GenerateQuestion()
{
if (QnA.Count > 0)
{
currentQuestion = Random.Range(0, QnA.Count);
QuestionText.text = QnA[currentQuestion].Questiion;
Image.texture = QnA[currentQuestion].sample;
Mat image = Unity.TextureToMat(QnA[currentQuestion].sample);
// Deteguje tváre
var gray = image.CvtColor(ColorConversionCodes.BGR2GRAY);
Cv2.EqualizeHist(gray, gray);
// deteguje zhodné regióny (Faces bounding)
FileStorage storageFaces = new FileStorage(Faces.text, FileStorage.Mode.Read | FileStorage.Mode.Memory);
cascadeFaces = new CascadeClassifier();
if (!cascadeFaces.Read(storageFaces.GetFirstTopLevelNode()))
{
throw new System.Exception("FaceProcessor.Initialize: Failed to load Faces cascade classifier");
}
recognizer = FaceRecognizer.CreateFisherFaceRecognizer();
recognizer.Load(new FileStorage(RecognizerXml.text, FileStorage.Mode.Read | FileStorage.Mode.Memory));
// popisky (labels)
names = new string[] { "Cooper", "DeGeneres", "Nyongo", "Pitt", "Roberts", "Spacey" };
OpenCvSharp.Rect[] rawFaces = cascadeFaces.DetectMultiScale(gray, 1.1, 6);
foreach (var faceRect in rawFaces)
{
var grayFace = new Mat(gray, faceRect);
if (requiredSize.Width > 0 && requiredSize.Height > 0)
{
grayFace = grayFace.Resize(requiredSize);
}
int label = -1;
/*SK:
* pokús sa rozpoznať tvár:
* confidence" je v princípe vzdialenosť od vzorky k najbližšej známej tvári
* 0 je nejaká „ideálna zhoda“
*/
/*ENG:
* now try to recognize the face:
* confidence" here is actually a misguide. in fact, it's "distance from the sample to the closest known face".
* 0 being some "ideal match"
*/
double confidence = 0.0;
recognizer.Predict(grayFace, out label, out confidence);
faceName = names[label];
int line = 0;
const int textPadding = 2;
const double textScale = 2.0;
string messge = String.Format("{0}", names[label], (int)confidence);
var textSize = Cv2.GetTextSize(messge, HersheyFonts.HersheyPlain, textScale, 1, out line);
var textBox = new OpenCvSharp.Rect(
faceRect.X + (faceRect.Width - textSize.Width) / 2 - textPadding,
faceRect.Bottom,
textSize.Width + textPadding * 2,
textSize.Height + textPadding * 2
);
faceName = names[label];
Debug.Log(faceName);
}
// Priradenie obrázku textúre Image komponentu na scéne
// Asign image to the texture on Image component
var texture = Unity.MatToTexture(image);
var rawImage = Image;
rawImage.texture = texture;
var transform = Image.GetComponent <RectTransform>();
transform.sizeDelta = new Vector2(image.Width, image.Height);
for (int i = 0; i < Options.Length; i++)
{
Options[i].transform.GetChild(0).GetComponent <Text>().text = QnA[currentQuestion].Answers[i];
if (faceName == Options[i].transform.GetChild(0).GetComponent <Text>().text)
{
Options[i].GetComponent <AnswerScript>().isCorrect = true;
}
else
{
Options[i].GetComponent <AnswerScript>().isCorrect = false;
}
}
}
else
{
GameOver();
}
}
public void Load(string filePath)
{
_recognizer.Load(filePath);
}
private void loadToolStripMenuItem_Click(object sender, EventArgs e)
{
recognizer.Load("traningdata.xml");
}
public void LoadRecognizerData()
{
_faceRecognizer.Load(_recognizerFilePath);
}
public void LoadRecognizerData()
{
faceRecognizer.Load(recognizeFilePath);
}
/// <summary>
/// Loads the trained Eigen Recogniser from specified location
/// </summary>
/// <param name="filename"></param>
public void Load_Eigen_Recogniser(string filename)
{
//Lets get the recogniser type from the file extension
string ext = Path.GetExtension(filename);
switch (ext)
{
case (".LBPH"):
Recognizer_Type = "EMGU.CV.LBPHFaceRecognizer";
recognizer = new LBPHFaceRecognizer(1, 8, 8, 8, 100);//50
break;
case (".FFR"):
Recognizer_Type = "EMGU.CV.FisherFaceRecognizer";
recognizer = new FisherFaceRecognizer(0, 3500);//4000
break;
case (".EFR"):
Recognizer_Type = "EMGU.CV.EigenFaceRecognizer";
recognizer = new EigenFaceRecognizer(80, double.PositiveInfinity);
break;
}
//introduce error checking
recognizer.Load(filename);
//Now load the labels
string direct = Path.GetDirectoryName(filename);
Names_List.Clear();
if (File.Exists(direct + "/Labels.xml"))
{
FileStream filestream = File.OpenRead(direct + "/Labels.xml");
long filelength = filestream.Length;
byte[] xmlBytes = new byte[filelength];
filestream.Read(xmlBytes, 0, (int)filelength);
filestream.Close();
MemoryStream xmlStream = new MemoryStream(xmlBytes);
using (XmlReader xmlreader = XmlTextReader.Create(xmlStream))
{
while (xmlreader.Read())
{
if (xmlreader.IsStartElement())
{
switch (xmlreader.Name)
{
case "NAME":
if (xmlreader.Read())
{
Names_List.Add(xmlreader.Value.Trim());
}
break;
}
}
}
}
ContTrain = NumLabels;
}
_IsTrained = true;
}
/// <summary>
/// Loads the trained Eigen Recogniser from specified location
/// </summary>
/// <param name="filename"></param>
public void Load_Eigen_Recogniser(string filename)
{
//Lets get the recogniser type from the file extension
string ext = Path.GetExtension(filename);
switch (ext)
{
case (".LBPH"):
trainParameters.RecognizerType = "EMGU.CV.LBPHFaceRecognizer";
recognizer = new LBPHFaceRecognizer(
trainParameters.LBPH.Radius,
trainParameters.LBPH.Neighbors,
trainParameters.LBPH.GridX,
trainParameters.LBPH.GridY,
trainParameters.LBPH.Treshold); // 1,8,8,8,100
break;
case (".FFR"):
trainParameters.RecognizerType = "EMGU.CV.FisherFaceRecognizer";
recognizer = new FisherFaceRecognizer(
trainParameters.Fisher.Components,
trainParameters.Fisher.Treshold); // 0,3500
break;
case (".EFR"):
trainParameters.RecognizerType = "EMGU.CV.EigenFaceRecognizer";
recognizer = new EigenFaceRecognizer(
trainParameters.Eigen.Components,
trainParameters.Eigen.Treshold); // 80,double.PositiveInfinity
break;
}
//introduce error checking
recognizer.Load(filename);
//Now load the labels
string direct = Path.GetDirectoryName(filename);
Names_List.Clear();
if (File.Exists(direct + LABELSXML))
{
FileStream filestream = File.OpenRead(direct + LABELSXML);
long filelength = filestream.Length;
byte[] xmlBytes = new byte[filelength];
filestream.Read(xmlBytes, 0, (int)filelength);
filestream.Close();
MemoryStream xmlStream = new MemoryStream(xmlBytes);
using (XmlReader xmlreader = XmlTextReader.Create(xmlStream))
{
while (xmlreader.Read())
{
if (xmlreader.IsStartElement())
{
switch (xmlreader.Name)
{
case "NAME":
if (xmlreader.Read())
{
Names_List.Add(xmlreader.Value.Trim());
}
break;
}
}
}
}
contTrain = numLabels;
}
_IsTrained = true;
}
