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);
}
// 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 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);
}
}
public ImageService()
{
_fisherFaceRecognizer = new FisherFaceRecognizer();
//_fisherFaceRecognizer = new FisherFaceRecognizer(FaceRecognizerNumberOfComponents, FaceRecognizerThreshold);
_lbphFaceRecognizer = new LBPHFaceRecognizer();
//_lbphFaceRecognizer = new LBPHFaceRecognizer(1, 8, 32, 32, 256);
_cascadeClassifier = new CascadeClassifier(CascadeClassifierConfigPath);
if (!Directory.Exists(DataFolder))
{
Directory.CreateDirectory(DataFolder);
}
if (File.Exists(FisherTrainingDataPath))
{
_fisherFaceRecognizer.Read(FisherTrainingDataPath);
}
if (File.Exists(LBPHTrainingDataPath))
{
_lbphFaceRecognizer.Read(LBPHTrainingDataPath);
}
}
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 Dispose()
{
f_recognize = null;
trainingImages = null;
imagelabel = null;
imageStringlabel = null;
GC.Collect();
}
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);
}
}
}
/// <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);
}
static void Main(string[] args)
{
var images = new List <ImageInfo>();
var imageId = 0;
foreach (var dir in new DirectoryInfo(@"..\..\Images").GetDirectories())
{
var groupId = int.Parse(dir.Name.Replace("s", string.Empty)) - 1;
foreach (var imageFile in dir.GetFiles("*.pgm"))
{
images.Add(new ImageInfo
{
Image = new Mat(imageFile.FullName, ImreadModes.Grayscale),
ImageId = imageId++,
ImageGroupId = groupId
});
}
}
var model = FisherFaceRecognizer.Create();
model.Train(images.Select(x => x.Image), images.Select(x => x.ImageGroupId));
var rnd = new Random();
var randomImageId = rnd.Next(0, images.Count - 1);
var testSample = images[randomImageId];
Console.WriteLine("Actual group: {0}", testSample.ImageGroupId);
Cv2.ImShow("actual", testSample.Image);
var predictedGroupId = model.Predict(testSample.Image);
Console.WriteLine("Predicted group: {0}", predictedGroupId);
Cv2.WaitKey(0);
}
/// <summary>
/// Raises the load button click event.
/// </summary>
public void LoadModel()
{
string loadDirectoryPath = Path.Combine(Application.persistentDataPath, saveDirectoryName);
if (!Directory.Exists(loadDirectoryPath))
{
Debug.Log("load failure. saved train data file does not exist.");
return;
}
// Restart everything!
dispose();
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.");
m_mode = R_MODES.MODE_DETECTION;
return;
}
// load the train data.
model.read(Path.Combine(loadDirectoryPath, "traindata.yml"));
int maxLabel = (int)Core.minMaxLoc(model.getLabels()).maxVal;
if (maxLabel < 0)
{
Debug.Log("load failure.");
model.Dispose();
return;
}
// Restore the save data.
#if UNITY_WEBGL && !UNITY_EDITOR
string format = "jpg";
#else
string format = "png";
#endif
m_numPersons = maxLabel + 1;
personsNames = new string[m_numPersons];
for (int i = 0; i < m_numPersons; ++i)
{
personsNames[i] = GameManager.instance.personsNames[i];
m_latestFaces.Add(i);
preprocessedFaces.Add(Imgcodecs.imread(Path.Combine(loadDirectoryPath, "preprocessedface" + i + "." + format), 0));
if (preprocessedFaces[i].total() == 0)
{
preprocessedFaces[i] = new Mat(faceHeight, faceWidth, CvType.CV_8UC1, new Scalar(128));
}
faceLabels.Add(i);
}
// go to the recognition mode!
m_mode = R_MODES.MODE_RECOGNITION;
}
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);
}
}
}
