static void Main(string[] args)
{
Image<Gray, Byte>[] trainingImages = new Image<Gray, Byte>[2];
trainingImages[0] = new Image<Gray, byte>("C:\\Image\\Romy.jpg");
trainingImages[1] = new Image<Gray, byte>("C:\\Image\\Stevie.jpg");
int[] labels = new int[] { 0 , 1 };
MCvTermCriteria termCrit = new MCvTermCriteria(16, 0.001);
EigenFaceRecognizer recognizer = new EigenFaceRecognizer(0,0.2);
Image<Gray, Byte> testImage = new Image<Gray, Byte>("C:\\Image\\Stevie.jpg");
recognizer.Train(trainingImages,labels);
EigenFaceRecognizer.PredictionResult result = recognizer.Predict(testImage);
Console.WriteLine(result.Label);
Console.WriteLine(result.Label);
Console.ReadKey();
}
/// <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");
}
//0: Default, 1:to Accuracy 2: Middium, 3: Imprecise, 4:Ambiguous
//tmpNumComponentsFisher. If you leave this at the default (0), set it to a value less than 0, or greater than the number of your training inputs, it will be set to the correct number (your training inputs - 1) automatically
public void estimateParametersFisher(Image<Gray, Byte> imagesInput, int accuracy)
{
int tmpNumComponentsFisher;
double tmpThresholdFisher;
FaceRecognizer faceRecognition;
for (tmpThresholdFisher = 1000; tmpThresholdFisher < 10000; tmpThresholdFisher += 100)
{
for (tmpNumComponentsFisher = 50; tmpNumComponentsFisher < 100; tmpNumComponentsFisher += 10)
{
faceRecognition = new EigenFaceRecognizer(tmpNumComponentsFisher, tmpThresholdFisher);
GenericRepository<DistanceResult> distanceResultRepo = unitOfWork.GetRepoInstance<DistanceResult>();
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++;
}
faceRecognition.Train(imagesDB, labels);
//faceRecognition.Load(pathImg + @"\" + "TrainingSet");
FaceRecognizer.PredictionResult ER = faceRecognition.Predict(imagesInput);
if (ER.Label != -1)
{
if (accuracy == 1)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher;
return;
}
else if (accuracy == 2)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher + 300;
return;
}
else if (accuracy == 3)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher + 600;
return;
}
else if (accuracy == 4)
{
numComponentsEigen = tmpNumComponentsFisher;
thresholdEigen = tmpThresholdFisher + 900;
return;
}
else if (accuracy > 4)
{
thresholdEigen = Double.PositiveInfinity;
}
else return;
}
faceRecognition.Dispose();
}
}
}
private void button_Click(object sender, RoutedEventArgs e)
{
OpenFileDialog openFileDialog = new OpenFileDialog();
openFileDialog.ShowDialog();
var filePath = openFileDialog.FileName;
Image<Bgr, Byte> image = new Image<Bgr, byte>(filePath); //Read the files as an 8-bit Bgr image
List<System.Drawing.Rectangle> faces = new List<System.Drawing.Rectangle>();
List<System.Drawing.Rectangle> eyes = new List<System.Drawing.Rectangle>();
Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes);
foreach (System.Drawing.Rectangle face in faces)
image.Draw(face, new Bgr(System.Drawing.Color.Red), 2);
foreach (System.Drawing.Rectangle eye in eyes)
image.Draw(eye, new Bgr(System.Drawing.Color.Blue), 2);
ImageViewer.Show(image);
File.WriteAllBytes("test.jpg", image.ToJpegData());
Image<Gray, Byte> smileImage = new Image<Gray, byte>("happy.jpg"); //Read the files as an 8-bit Bgr image
Image<Gray, Byte> sadImage = new Image<Gray, byte>("sad.jpg"); //Read the files as an 8-bit Bgr image
List<Image<Gray, Byte>> trainingList = new List<Image<Gray, byte>>();
trainingList.Add(smileImage);
trainingList.Add(sadImage);
List<string> labelList = new List<string>();
labelList.Add("happy");
labelList.Add("sad");
// labelList.Add(2);
MCvTermCriteria termCrit = new MCvTermCriteria(10, 0.001);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingList.ToArray(),
labelList.ToArray(),
5000,
ref termCrit);
Image<Gray, Byte> inputImage = new Image<Gray, byte>(filePath); //Read the files as an 8-bit Bgr image
var resizedImage = inputImage.Resize(smileImage.Width, smileImage.Height, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
var name = recognizer.Recognize(resizedImage).Label;
List<int> temp = new List<int>();
temp.Add(1);
temp.Add(2);
EigenFaceRecognizer recogizer2 = new EigenFaceRecognizer(80, double.PositiveInfinity);
recogizer2.Train(trainingList.ToArray(), temp.ToArray());
var dd = recogizer2.Predict(resizedImage);
ImageViewer.Show(resizedImage);
}