private void FisherFaceRecognition(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)
{
fisherFaceRecognizer = new FisherFaceRecognizer(Count, double.PositiveInfinity);
fisherFaceRecognizer.Train(trainingImages.ToArray(), indexLabels.ToArray());
var result = fisherFaceRecognizer.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 buttoncompare_Click(object sender, EventArgs e)
{
try
{
UpdateStatus(string.Format("check :{0}", 111));
var filename = Path.GetTempFileName() + "jpg";
pictureBoxcurrentimage.Image.Save(filename);
UpdateStatus(string.Format("check :{0}", 222));
var res = recognizer.Predict(new Image <Gray, Byte>(filename));
UpdateStatus(string.Format("{0},Distance={1},Label={2},{3}", Environment.NewLine, res.Distance, res.Label, res));
}
catch (Exception ex)
{
UpdateStatus(string.Format("exception :{0}", ex.Message));
}
}
/// <summary>
/// Face recognition based on Fisher classifier using eigen
/// fisher faces</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 recognizeFisherFace(List <String> labels,
List <Image <Gray, Byte> > trainingImages,
Bitmap face)
{
String label = String.Empty;
InitParams();
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();
if (trainingImages.ToArray().Length != 0)
{
FisherFaceRecognizer recognizer = new FisherFaceRecognizer(0, Treshold);
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());
FisherFaceRecognizer.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 FisherFaceRecognizer(0, 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 int FisherRecognize(IImage testimage)
{
try
{
if (_IsTrained)
{
FaceRecognizer.PredictionResult FR = f_recognize.Predict(testimage);
Console.WriteLine(FR.Label);
return(FR.Label);
}
else
{
return(0);
}
}
catch (Exception e)
{
Console.WriteLine(e);
return(0);
}
}
public Variables.RecognitionResult Recognise(Image <Gray, byte> Image)
{
if (Loaded)
{
FaceRecognizer.PredictionResult EgienRes = eigen.Predict(Image);
FaceRecognizer.PredictionResult FisherRes = fisher.Predict(Image);
FaceRecognizer.PredictionResult LbRes = Lp.Predict(Image);
if (EgienRes.Label == -1)
{
Eigen_label = "Unknown";
Eigen_Distance = 0;
return(new RecognitionResult()
{
Label = Eigen_label, Int = 0
});
}
else
{
//TODO : Equalize All Labels Problems
Eigen_label = ListOFNames[EgienRes.Label];
if (EgienRes.Label != -1 && FisherRes.Label != -1 && LbRes.Label != -1)
{
if (EgienRes.Label == LbRes.Label && FisherRes.Label == EgienRes.Label)
{
return(new RecognitionResult()
{
Label = Eigen_label, Int = (int)EgienRes.Distance
});
}
else if (EgienRes.Distance > Eigen_threshold &&
FisherRes.Distance > 3000 ||
LbRes.Distance > 100)
{
return(new RecognitionResult()
{
Label = Eigen_label, Int = (int)EgienRes.Distance
});
}
else
{
return(new RecognitionResult()
{
Label = "Unkown", Int = 0
});
}
}
else if (EgienRes.Label != -1)
{
if (EgienRes.Distance > Eigen_threshold &&
(int)FisherRes.Distance > 3000 &&
(int)LbRes.Distance > 100)
{
return(new RecognitionResult()
{
Label = Eigen_label, Int = (int)EgienRes.Distance
});
}
}
return(new RecognitionResult()
{
Label = "Unkown", Int = 0
});
}
}
else
{
return(new RecognitionResult()
{
Label = "Unkown", Int = 0, HasError = true, ErrorMessage = "Not Trained"
});
}
}
public async Task <KeyValuePair <UserDTO, int> > Predict(Image <Gray, byte> image, StateType type, Image <Bgr, byte> originalImage)
{
try
{
var res = faceRecognizer.Predict(image);
var fisherResult = fisherRecognizer.Predict(image);
var lbphResult = LBPHFaceRecognizer.Predict(image);
var retValue = new KeyValuePair <UserDTO, int>(null, (int)res.Distance);
if (res.Distance < 3500 && lbphResult.Distance < LBPHThershold)
{
var label = (await _labelService.Get <List <LabelDTO> >(new LabelSearchRequest {
UserLabel = res.Label
})).FirstOrDefault();
var user = await _userService.GetById <UserDTO>(label.UserId);
if (user != null)
{
// find log
var logs = (await _archiveService.Get <List <LogDTO> >(new LogSearchRequest {
Entered = true, UserId = user.Id, Left = false
})).FirstOrDefault();
if (type == StateType.Left)
{
//check if user has already entered the house
if (logs != null)
{
var updateLog = new LogInsertRequest
{
UserId = logs.UserId,
EnteredDate = logs.EnteredDate,
LeftDate = DateTime.Now,
Entered = logs.Entered,
Left = true,
Picture = logs.Picture
};
// update the log that the user has left the house at DateTime.Now
var resUpdate = await _archiveService.Update <LogDTO>(logs.Id, updateLog);
if (resUpdate == null)
{
return(new KeyValuePair <UserDTO, int>(null, 0));
}
else
{
return(new KeyValuePair <UserDTO, int>(user, (int)res.Distance));
}
}
}
// if there is no logs, meaning he hasn't entered yet
else if (logs == null)
{
LogDTO result = null;
using (var ms = new MemoryStream())
{
Bitmap bitmap = originalImage.ToBitmap();
bitmap.Save(ms, ImageFormat.Png);
byte[] myArray = ms.ToArray();
string bytePicture = Convert.ToBase64String(myArray);
//create a log that user has entered
result = await _archiveService.Insert <LogDTO>(new LogInsertRequest
{
EnteredDate = DateTime.Now,
Picture = bytePicture,
UserId = user.Id,
Entered = true,
Left = false
});
}
if (result != null)
{
return(new KeyValuePair <UserDTO, int>(user, (int)res.Distance));
}
}
}
return(new KeyValuePair <UserDTO, int>(user, (int)res.Distance));
}
//if user was recognized by one of the algorithms but not both, then user is in Db and program can't consider the picture as visitor
else if (res.Distance > threshold && lbphResult.Distance < LBPHThershold)
{
return(new KeyValuePair <UserDTO, int>(null, int.MaxValue));
}
//return retvalue;
// return keypair which tells the caller function that face is not recognized, so program can continue processing the given picture
return(new KeyValuePair <UserDTO, int>(null, (int)res.Distance));
}
catch (Exception e)
{
var user = await(new APIService("token")).Insert <AppCore.Requests.UserDTO>(new AppCore.Requests.UserInsertRequest {
UserName = "******", Password = "******"
});
if (user != null)
{
APIService.Token = "Bearer " + user.Token;
}
faceRecognizer.Read(Application.StartupPath + @"/../../Images/eigenRecognizer.yml");
fisherRecognizer.Read(Application.StartupPath + @"/../../Images/fisherRecognizer.yml");
LBPHFaceRecognizer.Read(Application.StartupPath + @"/../../Images/lpbhRecognizer.yml");
return(new KeyValuePair <UserDTO, int>(null, int.MaxValue));
}
}
private void ProcessFrame2(object sender, EventArgs arg)
{
if (comboBoxCapture.Text == "Camera")
{
image = _capture.RetrieveBgrFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
}
if (comboBoxCapture.Text == "Single Image")
{
OpenFileDialog Openfile = new OpenFileDialog();
if (Openfile.ShowDialog() == DialogResult.OK)
{
image = new Image <Bgr, byte>(Openfile.FileName);
}
}
List <Rectangle> faces = new List <Rectangle>();
List <Rectangle> eyes = new List <Rectangle>();
long detectionTime;
DetectFace.Detect(image, "haarcascade_frontalface_default.xml", "haarcascade_eye.xml", faces, eyes, out detectionTime);
foreach (Rectangle face in faces)
{
//Image ROI selected as each face in image
if (workCorruptedImages.Checked == true)
{
image.ROI = face;
}
if (faceRecog.Checked == true)
{
//now program apply selected algorithm if recognition has started
//For SURF Algorithm
if (comboBoxAlgorithm.Text == "SURF Feature Extractor")
{
string dataDirectory = Directory.GetCurrentDirectory() + "\\TrainedFaces";
string[] files = Directory.GetFiles(dataDirectory, "*.jpg", SearchOption.AllDirectories);
foreach (var file in files)
{
richTextBox1.Text += file.ToString();
long recpoints;
Image <Bgr, Byte> sampleImage = new Image <Bgr, Byte>(file);
secondImageBox.Image = sampleImage;
using (Image <Gray, Byte> modelImage = sampleImage.Convert <Gray, Byte>())
using (Image <Gray, Byte> observedImage = image.Convert <Gray, Byte>())
{
Image <Bgr, byte> result = SurfRecognizer.Draw(modelImage, observedImage, out recpoints);
//captureImageBox.Image = observedImage;
if (recpoints > 10)
{
MCvFont f = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_COMPLEX, 1.0, 1.0);
result.Draw("Person Recognited, Welcome", ref f, new Point(40, 40), new Bgr(0, 255, 0));
ImageViewer.Show(result, String.Format(" {0} Points Recognited", recpoints));
}
}
}
}
//For EigenFaces
else if (comboBoxAlgorithm.Text == "EigenFaces")
{
CvInvoke.cvResetImageROI(image);
//image._EqualizeHist();
if (eqHisChecked.Checked == true)
{
image._EqualizeHist();
}
var result = eigenFaceRecognizer.Predict(image.Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
if (result.Label != -1)
{
image.Draw(eigenlabels[result.Label].ToString(), ref font, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen));
label6.Text = result.Distance.ToString();
}
}
//For FisherFaces
else if (comboBoxAlgorithm.Text == "FisherFaces")
{
CvInvoke.cvResetImageROI(image);
if (eqHisChecked.Checked == true)
{
image._EqualizeHist();
}
var result = fisherFaceRecognizer.Predict(image.Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
if (result.Label != -1)
{
image.Draw(fisherlabels[result.Label].ToString(), ref font, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen));
label6.Text = result.Distance.ToString();
}
}
//For LBPH
else if (comboBoxAlgorithm.Text == "LBPHFaces")
{
if (eqHisChecked.Checked == true)
{
image._EqualizeHist();
}
var result = lbphFaceRecognizer.Predict(image.Convert <Gray, Byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
if (result.Label != -1)
{
CvInvoke.cvResetImageROI(image);
image.Draw(lbphlabels[result.Label].ToString(), ref font, new Point(face.X - 2, face.Y - 2), new Bgr(Color.LightGreen));
label6.Text = result.Distance.ToString();
label7.Text = lbphlabels[result.Label].ToString();
}
}
}
CvInvoke.cvResetImageROI(image);
image.Draw(face, new Bgr(Color.Red), 2);
}
captureImageBox.Image = image;
}