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 bool LoadTrainingData()
{
mydb = new DBConn();
imagelabel = mydb.getLabelNumList().ToArray();
imageStringlabel = mydb.getLabelList().ToArray();
trainingImages = mydb.getTrainedImageList();
Itrainingimage = trainingImages;
if (mydb.getImageCount() > 0)
{
if (trainingImages.Length != 0)
{
f_recognize = new FisherFaceRecognizer(0, 123.0);
f_recognize.Train(Itrainingimage, imagelabel);
return(true);
}
else
{
return(false);
}
}
else
{
return(false);
}
}
private bool CdmFaceTrain(bool minus = false)
{
try
{
if (minus && trainingImages.Count > 1)
{
trainingImages.RemoveAt(0);
Names_List_ID.RemoveAt(0);
}
else if (trainingImages.Count < 1)
{
UpdateStatus(string.Format(" {0} ,not trained.", trainingImages.Count));
return(true);
}
UpdateStatus(string.Format(" {0} photo for trainin.", trainingImages.Count));
recognizer.Train(trainingImages.ToArray(), Names_List_ID.ToArray());
UpdateStatus(string.Format("trained trained {0}", trainingImages.Count));
return(true);
}
catch (Exception ex)
{
UpdateStatus(string.Format("trained error {0}", ex));
return(false);
}
}
private static FaceRecognizer CreateRollCallRecognizer(int RollCallID)
{
//Tu rollcall ID, tao face recognizer, train
//FaceRecognizer FaceRec = new LBPHFaceRecognizer(1, 8, 8, 8, 75);
FaceRecognizer FaceRec = new FisherFaceRecognizer(80, RECOGNIZER_THREEHOLD);
List <int> StudentIDs = new List <int>();
List <Image <Gray, byte> > StudentImages = new List <Image <Gray, byte> >();
RollCallBusiness RollBO = new RollCallBusiness();
//Load danh sach student cua roll call
RollCall RollCall = RollBO.GetRollCallByID(RollCallID);
foreach (var Student in RollCall.Students)
{
foreach (var Image in Student.StudentImages)
{
//Load ID va anh de train cho bo recognizer
StudentIDs.Add(Image.StudentID);
String TrainingImagePath = TRAINING_FOLDER_PATH + "/" + Image.ImageLink;
Image <Gray, byte> TrainingImage = new Image <Gray, byte>(TrainingImagePath);
TrainingImage._EqualizeHist();
StudentImages.Add(TrainingImage);
}
}
FaceRec.Train(StudentImages.ToArray(), StudentIDs.ToArray());
return(FaceRec);
}
public void TrainImages()
{
string path = Application.StartupPath + @"/../../Images/";
string[] files = Directory.GetFiles(path, "*.jpg", SearchOption.AllDirectories);
// int[] labelsDb = _context.Labels.Select(_ => _.LabelNumber).ToArray();
List <int> labelsDb = new List <int>();
Mat[] matImages = new Mat[files.Length];
for (int i = 0; i < files.Length; i++)
{
matImages[i] = new Image <Gray, byte>(files[i]).Mat;
string[] strings = files[i].Split('-');
string number = strings[strings.Length - 1].Split('.')[0];
labelsDb.Add(int.Parse(number));
}
VectorOfMat images = new VectorOfMat(matImages);
VectorOfInt labels = new VectorOfInt(labelsDb.ToArray());
faceRecognizer.Train(images, labels);
faceRecognizer.Write(Application.StartupPath + @"/../../Images/eigenRecognizer.yml");
fisherRecognizer.Train(images, labels);
fisherRecognizer.Write(Application.StartupPath + @"/../../Images/fisherRecognizer.yml");
LBPHFaceRecognizer.Train(images, labels);
LBPHFaceRecognizer.Write(Application.StartupPath + @"/../../Images/lpbhRecognizer.yml");
isTrained = true;
}
/// <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 bool LoadTrainingData()
{
if (File.Exists(LabelsFile))
{
try
{
//Clearing Lists In Case of Retrain Operation Occoured.
ListOFNames.Clear();
ListOfIds.Clear();
trainingImages.Clear();
//Reading Xml File
FileStream filestream = File.OpenRead(LabelsFile);
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())
{
ListOfIds.Add(ListOFNames.Count);
ListOFNames.Add(xmlreader.Value.Trim());
NumLabels += 1;
}
break;
case "FILE":
if (xmlreader.Read())
{
//PROBLEM HERE IF TRAININGG MOVED
trainingImages.Add(new Image <Gray, byte>(Variables.DataSetFolder + "\\" + xmlreader.Value.Trim()));
}
break;
}
}
}
}
ContTrain = NumLabels;
if (trainingImages.ToArray().Length != 0)
{
eigen.Train(trainingImages.ToArray(), ListOfIds.ToArray());
fisher.Train(trainingImages.ToArray(), ListOfIds.ToArray());
Lp.Train(trainingImages.ToArray(), ListOfIds.ToArray());
Loaded = true;
return(true);
}
else
{
return(false);
}
}
catch (Exception Exp)
{
HasError = true;
Debug.WriteLine("Error In Loading Function : " + Exp.Message);
return(false);
}
}
else
{
return(false);
}
}
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);
}
}
}