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;
}
}
static void train(string trainingSheetPath, string outMatrixPath)
{
try
{
string genderFilesdirectory = Path.GetDirectoryName(trainingSheetPath);
int imageSize = 64;
using (StreamReader sr = new StreamReader(trainingSheetPath))
{
string line = sr.ReadLine();
while (line != null)
{
string[] data = line.Split(';');
string filePath = genderFilesdirectory + data[0];
imageList.Add(new Image<Gray, byte>(filePath).Resize(imageSize, imageSize, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC));
labelList.Add(int.Parse(data[1]));
line = sr.ReadLine();
}
}
faceRecognizer = new FisherFaceRecognizer(imageList.Count, imageList.Count + 75);
faceRecognizer.Train(imageList.ToArray(), labelList.ToArray());
faceRecognizer.Save(outMatrixPath);
}
catch (Exception ex)
{
throw ex;
}
}
public override int FaceRecognition()
{
FaceDetect(this.userImage);
foreach (CImage tempImage in lDatabaseImages)
{
FaceDetect(tempImage);
}
int ContTrain = lDatabaseImages.Count; // Количество изображений для тренировки.
//Image<Gray, byte> result;
foreach (MCvAvgComp f in aDetectedFaces[0])
{
if (this.lDatabaseImages.ToArray().Length != 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.0001);
FisherFaceRecognizer modelRecognition = new FisherFaceRecognizer(0, 3500);
Image<Gray, Byte>[] im = this.lDatabaseImages.Select(w => w.grayImage).ToArray();
modelRecognition.Train(this.lDatabaseImages.Select(w => w.grayImage).ToArray(), lIntDatabaseImagesLabels.ToArray());
FaceRecognizer.PredictionResult resultRecognition = new FaceRecognizer.PredictionResult();
resultRecognition = modelRecognition.Predict(userImage.grayImage);
// Зависимость степени похожести от разности изображений приблизительно имет вид
// 10 -> 99%; 100 -> 90%; 750 -> 50%, 10000 -> 1%
// тогда необходимо ввести шкалу (например, линейную):
// Алгоритм расчёта степени похожести.
float threshold = 750; // пороговое значение, равное 50% похожести изображений (установленно экспериментально)
float thresholdMismatch = 10000; // пороговое значение несовпадения изоюражений (равное 1%, установлено экспериментально)
if (resultRecognition.Distance < threshold)
similarityDegree = (100 - (resultRecognition.Distance * 50.0 / threshold)).ToString();
else
similarityDegree = (50 - (resultRecognition.Distance * 50 / thresholdMismatch)).ToString();
}
}
return 0;
}
private void faceAnalysis()
{
//Data for face emotion when data base is not ready.
Image<Gray, byte>[] imageList = new Image<Gray, Byte>[70];
imageList[0] = new Image<Gray, byte>(@"..\..\emotionData\netural1.jpg");
imageList[1] = new Image<Gray, byte>(@"..\..\emotionData\sad1.jpg");
imageList[2] = new Image<Gray, byte>(@"..\..\emotionData\angry1.jpg");
imageList[3] = new Image<Gray, byte>(@"..\..\emotionData\happy1.jpg");
imageList[4] = new Image<Gray, byte>(@"..\..\emotionData\happy2.jpg");
imageList[5] = new Image<Gray, byte>(@"..\..\emotionData\happy3.jpg");
imageList[6] = new Image<Gray, byte>(@"..\..\emotionData\happy4.jpg");
imageList[7] = new Image<Gray, byte>(@"..\..\emotionData\happy5.jpg");
imageList[8] = new Image<Gray, byte>(@"..\..\emotionData\happy6.jpg");
imageList[9] = new Image<Gray, byte>(@"..\..\emotionData\happy7.jpg");
imageList[10] = new Image<Gray, byte>(@"..\..\emotionData\375.jpg");
imageList[11] = new Image<Gray, byte>(@"..\..\emotionData\376.jpg");
imageList[12] = new Image<Gray, byte>(@"..\..\emotionData\377.jpg");
imageList[13] = new Image<Gray, byte>(@"..\..\emotionData\378.jpg");
imageList[14] = new Image<Gray, byte>(@"..\..\emotionData\379.jpg");
imageList[15] = new Image<Gray, byte>(@"..\..\emotionData\380.jpg");
imageList[16] = new Image<Gray, byte>(@"..\..\emotionData\381.jpg");
imageList[17] = new Image<Gray, byte>(@"..\..\emotionData\382.jpg");
imageList[18] = new Image<Gray, byte>(@"..\..\emotionData\383.jpg");
imageList[19] = new Image<Gray, byte>(@"..\..\emotionData\384.jpg");
imageList[20] = new Image<Gray, byte>(@"..\..\emotionData\385.jpg");
imageList[21] = new Image<Gray, byte>(@"..\..\emotionData\386.jpg");
imageList[22] = new Image<Gray, byte>(@"..\..\emotionData\387.jpg");
imageList[23] = new Image<Gray, byte>(@"..\..\emotionData\388.jpg");
imageList[24] = new Image<Gray, byte>(@"..\..\emotionData\389.jpg");
imageList[25] = new Image<Gray, byte>(@"..\..\emotionData\390.jpg");
imageList[26] = new Image<Gray, byte>(@"..\..\emotionData\391.jpg");
imageList[27] = new Image<Gray, byte>(@"..\..\emotionData\392.jpg");
imageList[28] = new Image<Gray, byte>(@"..\..\emotionData\393.jpg");
imageList[29] = new Image<Gray, byte>(@"..\..\emotionData\394.jpg");
imageList[30] = new Image<Gray, byte>(@"..\..\emotionData\395.jpg");
imageList[31] = new Image<Gray, byte>(@"..\..\emotionData\396.jpg");
imageList[32] = new Image<Gray, byte>(@"..\..\emotionData\545.jpg");
imageList[33] = new Image<Gray, byte>(@"..\..\emotionData\546.jpg");
imageList[34] = new Image<Gray, byte>(@"..\..\emotionData\547.jpg");
imageList[35] = new Image<Gray, byte>(@"..\..\emotionData\548.jpg");
imageList[36] = new Image<Gray, byte>(@"..\..\emotionData\579.jpg");
imageList[37] = new Image<Gray, byte>(@"..\..\emotionData\580.jpg");
imageList[38] = new Image<Gray, byte>(@"..\..\emotionData\581.jpg");
imageList[39] = new Image<Gray, byte>(@"..\..\emotionData\582.jpg");
imageList[40] = new Image<Gray, byte>(@"..\..\emotionData\583.jpg");
imageList[41] = new Image<Gray, byte>(@"..\..\emotionData\584.jpg");
imageList[42] = new Image<Gray, byte>(@"..\..\emotionData\585.jpg");
imageList[43] = new Image<Gray, byte>(@"..\..\emotionData\586.jpg");
imageList[44] = new Image<Gray, byte>(@"..\..\emotionData\587.jpg");
imageList[45] = new Image<Gray, byte>(@"..\..\emotionData\588.jpg");
imageList[46] = new Image<Gray, byte>(@"..\..\emotionData\589.jpg");
imageList[47] = new Image<Gray, byte>(@"..\..\emotionData\590.jpg");
imageList[48] = new Image<Gray, byte>(@"..\..\emotionData\557.jpg");
imageList[49] = new Image<Gray, byte>(@"..\..\emotionData\558.jpg");
imageList[50] = new Image<Gray, byte>(@"..\..\emotionData\559.jpg");
imageList[51] = new Image<Gray, byte>(@"..\..\emotionData\560.jpg");
imageList[52] = new Image<Gray, byte>(@"..\..\emotionData\561.jpg");
imageList[53] = new Image<Gray, byte>(@"..\..\emotionData\562.jpg");
imageList[54] = new Image<Gray, byte>(@"..\..\emotionData\563.jpg");
imageList[55] = new Image<Gray, byte>(@"..\..\emotionData\564.jpg");
imageList[56] = new Image<Gray, byte>(@"..\..\emotionData\565.jpg");
imageList[57] = new Image<Gray, byte>(@"..\..\emotionData\566.jpg");
imageList[58] = new Image<Gray, byte>(@"..\..\emotionData\567.jpg");
imageList[59] = new Image<Gray, byte>(@"..\..\emotionData\568.jpg");
imageList[60] = new Image<Gray, byte>(@"..\..\emotionData\569.jpg");
imageList[61] = new Image<Gray, byte>(@"..\..\emotionData\570.jpg");
imageList[62] = new Image<Gray, byte>(@"..\..\emotionData\571.jpg");
imageList[63] = new Image<Gray, byte>(@"..\..\emotionData\572.jpg");
imageList[64] = new Image<Gray, byte>(@"..\..\emotionData\573.jpg");
imageList[65] = new Image<Gray, byte>(@"..\..\emotionData\574.jpg");
imageList[66] = new Image<Gray, byte>(@"..\..\emotionData\575.jpg");
imageList[67] = new Image<Gray, byte>(@"..\..\emotionData\576.jpg");
imageList[68] = new Image<Gray, byte>(@"..\..\emotionData\577.jpg");
imageList[69] = new Image<Gray, byte>(@"..\..\emotionData\578.jpg");
String[] emoList = new String[70];
emoList[0] = "Netural";
emoList[1] = "Sad";
emoList[2] = "Angry";
emoList[3] = "Smile";
emoList[4] = "Smile";
emoList[5] = "Smile";
emoList[6] = "Smile";
emoList[7] = "Smile";
emoList[8] = "Smile";
emoList[9] = "Smile";
emoList[10] = "Angry";
emoList[11] = "Angry";
emoList[12] = "Angry";
emoList[13] = "Angry";
emoList[14] = "Angry";
emoList[15] = "Angry";
emoList[16] = "Angry";
emoList[17] = "Angry";
emoList[18] = "Angry";
emoList[19] = "Angry";
emoList[20] = "Angry";
emoList[21] = "Angry";
emoList[22] = "Angry";
emoList[23] = "Angry";
emoList[24] = "Angry";
emoList[25] = "Angry";
emoList[26] = "Angry";
emoList[27] = "Angry";
emoList[28] = "Angry";
emoList[29] = "Angry";
emoList[30] = "Angry";
emoList[31] = "Angry";
emoList[32] = "Angry";
emoList[33] = "Angry";
emoList[34] = "Angry";
emoList[35] = "Angry";
emoList[36] = "Angry";
emoList[37] = "Angry";
emoList[38] = "Angry";
emoList[39] = "Angry";
emoList[40] = "Angry";
emoList[41] = "Angry";
emoList[42] = "Angry";
emoList[43] = "Angry";
emoList[44] = "Angry";
emoList[45] = "Angry";
emoList[46] = "Angry";
emoList[47] = "Angry";
emoList[48] = "Angry";
emoList[49] = "Angry";
emoList[50] = "Angry";
emoList[51] = "Angry";
emoList[52] = "Angry";
emoList[53] = "Angry";
emoList[54] = "Angry";
emoList[55] = "Angry";
emoList[56] = "Angry";
emoList[57] = "Angry";
emoList[58] = "Angry";
emoList[59] = "Angry";
emoList[60] = "Angry";
emoList[61] = "Angry";
emoList[62] = "Angry";
emoList[63] = "Angry";
emoList[64] = "Angry";
emoList[65] = "Angry";
emoList[66] = "Angry";
emoList[67] = "Angry";
emoList[68] = "Angry";
emoList[69] = "Angry";
int[] label = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 };
//Use the variable to detect the number of times these emotion appear in the video by frames;
int smile = 0;
int angry = 0;
int netural = 0;
int sad = 0;
int noDetect = 0;
totalFrame = grayFrameList.Count;
List<Image<Gray, byte>> trainfaceList = new List<Image<Gray, byte>>();
Rectangle[] trainFace;
for (int i = 0; i < imageList.Length; i++)
{
trainFace = faceHaar.DetectMultiScale(imageList[i], 1.1, 20, new Size(20, 20), Size.Empty);
foreach (Rectangle f in trainFace)
{
trainfaceList.Add(imageList[i].Copy(f).Resize(64, 64, INTER.CV_INTER_CUBIC));
}
}
//Detect the face in the frames and do emotion detect using fisher eigen face recognizer
double maxDistance = 1000; //-> Range from 1000 to 5000
FisherFaceRecognizer fisher = new FisherFaceRecognizer(imageList.Length, maxDistance);
fisher.Train(trainfaceList.ToArray(), label);
FaceRecognizer.PredictionResult result = new FaceRecognizer.PredictionResult();
for (int i = 0; i < totalFrame; i++)
{
result = fisher.Predict(grayFrameList.ElementAt(i));
lbmsg.Text = totalFrame.ToString();
int num = result.Label;
if (num == -1)
{
faceEmotions.Add("No Detect");
}
else
{
faceEmotions.Add(emoList[num].ToString());
}
if (num == -1)
{
noDetect++;
}
else if (emoList[num].Equals("Smile"))
{
smile++;
}
else if (emoList[num] == "Sad")
{
sad++;
}
else if (emoList[num] == "Angry")
{
angry++;
}
else if (emoList[num] == "Netural")
{
netural++;
}
else
{
// it should not come here .
}
}
if (angry > smile && angry > sad && angry > netural)
{
lbEmotionConclusion2.Text = "There is a risk of child abuse";
}
//get total sum of the 5 emotion data.
int total = smile + sad + angry + netural + noDetect;
//put them into the percentage (emotion/frames * 100)
double smilePer = (Convert.ToDouble(smile) / totalFrame) * 100;
double sadPer = (Convert.ToDouble(sad) / totalFrame) * 100;
double angryPer = (Convert.ToDouble(angry) / totalFrame) * 100;
double neturalPer = (Convert.ToDouble(netural) / totalFrame) * 100;
double noDetectPer = (Convert.ToDouble(noDetect) / totalFrame) * 100;
lbmsg.Text = "Smile Count:" + smile.ToString() + " ,Sad Count:" + sad.ToString() + " ,Angry Count:" + angry.ToString() + " ,Netural Count:" + netural.ToString() + " ,No Detect Count:" + noDetect.ToString();
String[] emotionList = { "Smile", "Sad", "Angry", "Netural", "No Detect" };
double[] emotionPer = { smilePer, sadPer, angryPer, neturalPer, noDetectPer };
//Designing the Pie Chart
emotionChart.Series.Add("Emotion");
emotionChart.Series["Emotion"].ChartType = System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Pie; // set the chart to pie
emotionChart.Series["Emotion"].ChartArea = "ChartArea1";
//Add data to pie chart
for (int i = 0; i < emotionList.Count(); i++)
{
emotionChart.Series["Emotion"].Points.AddXY(emotionList[i], emotionPer[i]);
}
emotionChart.Series["Emotion"].BorderWidth = 1;
emotionChart.Series["Emotion"].BorderColor = System.Drawing.Color.FromArgb(26, 59, 105);
emotionChart.Series["Emotion"].Points[0].Color = System.Drawing.Color.LightGreen;
emotionChart.Series["Emotion"].Points[1].Color = System.Drawing.Color.Blue;
emotionChart.Series["Emotion"].Points[2].Color = System.Drawing.Color.Red;
emotionChart.Series["Emotion"].Points[3].Color = System.Drawing.Color.Gray;
emotionChart.Series["Emotion"].Points[4].Color = System.Drawing.Color.Black;
emotionChart.Titles.Add("Anaylsis of Face Expression (Frame By Frame)");
emotionChart.Series["Emotion"]["PieLabelStyle"] = "Disabled";
//chart1.Series["Emotion"].Label = "#VALX";
emotionChart.Legends.Add("Legend1");
emotionChart.Legends["Legend1"].Enabled = true;
emotionChart.Legends["Legend1"].Docking = Docking.Bottom;
emotionChart.Legends["Legend1"].Alignment = System.Drawing.StringAlignment.Center;
emotionChart.Series["Emotion"].LegendText = "#VALX (#PERCENT{P2}) ";
emotionChart.DataManipulator.Sort(PointSortOrder.Ascending, emotionChart.Series["Emotion"]);
}
public void Dispose()
{
f_recognize = null;
trainingImages = null;
imagelabel = null;
imageStringlabel = null;
GC.Collect();
}
public string saveEmployee(Image newImage, string name, string middleName, string lastName, string email, FaceRecognizerMethode faceRecognizerMethode)
{
unitOfWork = new GenericUnitOfWork();
GenericRepository<Employee> employeeRepo = unitOfWork.GetRepoInstance<Employee>();
Employee employee = null;
try
{
employee = (employeeRepo.GetAllRecords().Where<Employee>(e => e.email == email)).First<Employee>();
}
catch
{
Debug.WriteLine("Nuevo usuario");
}
//Add Employee if not exist. The email is unique
if (employee == null)
{
employee = new Employee { name = name, middleName = middleName, lastName = lastName, email = email };
employeeRepo.Add(employee);
unitOfWork.SaveChanges();
}
//I save the image with a guid as a name
GenericRepository<DistanceResult> distanceResultRepo = unitOfWork.GetRepoInstance<DistanceResult>();
Guid guid = Guid.NewGuid();
var inputImage = new Image<Bgr, Byte>(new Bitmap(newImage));
Rectangle[] rectangleFace = detection(inputImage, pathXMLHaarcascade);
//The function detection(..) can extract N faces
if (rectangleFace.Length <= 0)
{
return Result.NoDetected.ToString();
}
else if (rectangleFace.Length > 1)
{
return Result.MultipleFacesDetected.ToString();
}
else
{
Image<Gray, byte> grayFrame = toGrayEqualizeFrame(inputImage);
Image<Gray, Byte> faceEMGUCV = formatRectangleFaces(grayFrame.ToBitmap(), rectangleFace[0]);
faceEMGUCV._EqualizeHist();
faceEMGUCV.Save(pathImg + @"\" + guid.ToString()+".Jpeg");
FaceRecognizer faceRecognition;
switch (faceRecognizerMethode.ToString())
{
case "EigenFaceRecognizerMethode": faceRecognition = new EigenFaceRecognizer(numComponentsEigen, thresholdEigen); //try catch aca
break;
case "FisherFaceRecognizerMethode": faceRecognition = new FisherFaceRecognizer(numComponentsFisher, thresholdFisher);
break;
case "LBPHFaceRecognizerMethode": faceRecognition = new LBPHFaceRecognizer(radiusLBPH, neighborsLBPH, gridXLBPH, gridYLBPH, thresholdLBPH);
break;
default: return null;
};
double distance = 2;
//Save register
DistanceResult dist = new DistanceResult();
dist.algorithm = RecognizeBLL.FaceRecognizerMethode.FisherFaceRecognizerMethode.ToString();
dist.employeeId = employee.employeeId;
dist.photoName = guid.ToString();
dist.distance = distance;
distanceResultRepo.Add(dist);
unitOfWork.SaveChanges();
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++;
}
if (employeeRepo.GetAllRecords().Count() > 1)
{
faceRecognition.Train(imagesDB, labels);
faceRecognition.Save(pathImg + @"\" + "TrainingSet");
}
return Result.Saved.ToString();
}
//return Result.Error.ToString();
}
public EmployeeStructure[] recognizeMultipleFaces(Image newImage, FaceRecognizerMethode faceRecognizerMethode)
{
var inputImage = new Image<Bgr, Byte>(new Bitmap(newImage));
Rectangle[] rectangleFace = detection(inputImage, this.pathXMLHaarcascade);
EmployeeStructure[] employeeStructure;
if (rectangleFace.Length <= 0)
{
employeeStructure = new EmployeeStructure[0];
employeeStructure[0].result = Result.NoDetected.ToString();
return employeeStructure;
}
else
{
Image<Gray, byte> grayFrame = toGrayEqualizeFrame(inputImage);
employeeStructure = new EmployeeStructure[rectangleFace.Length];
FaceRecognizer faceRecognition;
switch (faceRecognizerMethode.ToString())
{
case "EigenFaceRecognizerMethode": faceRecognition = new EigenFaceRecognizer(numComponentsEigen, thresholdEigen); //try catch aca
break;
case "FisherFaceRecognizerMethode": faceRecognition = new FisherFaceRecognizer(numComponentsFisher, thresholdFisher);
break;
case "LBPHFaceRecognizerMethode": faceRecognition = new LBPHFaceRecognizer(radiusLBPH, neighborsLBPH, gridXLBPH, gridYLBPH, thresholdLBPH);
break;
default: faceRecognition = new EigenFaceRecognizer(numComponentsEigen, thresholdEigen);
break;
};
faceRecognition.Load(pathImg + @"\" + "TrainingSet");
Parallel.For(0, rectangleFace.Length, i =>
{
Image<Gray, byte> faceEMGUCV = formatRectangleFaces(grayFrame.ToBitmap(), rectangleFace[i]);
FaceRecognizer.PredictionResult ER = faceRecognition.Predict(faceEMGUCV);
if (ER.Label != -1 /*&& ER.Distance > thresholdEigen*/)
{
int label = ER.Label;
GenericRepository<Employee> emplyeeRepo = unitOfWork.GetRepoInstance<Employee>();
Employee em = emplyeeRepo.GetFirstOrDefault(label);
employeeStructure[i] = new EmployeeStructure(Result.Recognized.ToString(), em.name, em.middleName, em.lastName, em.email, rectangleFace[0].X, rectangleFace[0].Y, rectangleFace[0].Width, rectangleFace[0].Height);
}
employeeStructure[i].result = Result.Unknown.ToString();
});
return employeeStructure;
}
}
private void faceAnalysis()
{
//Data for face emotion when data base is not ready.
Image<Gray, byte>[] imageList = new Image<Gray, Byte>[70];
imageList[0] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/netural1.jpg");
imageList[1] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/sad1.jpg");
imageList[2] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/angry1.jpg");
imageList[3] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy1.jpg");
imageList[4] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy2.jpg");
imageList[5] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy3.jpg");
imageList[6] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy4.jpg");
imageList[7] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy5.jpg");
imageList[8] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy6.jpg");
imageList[9] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/happy7.jpg");
imageList[10] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/375.jpg");
imageList[11] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/376.jpg");
imageList[12] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/377.jpg");
imageList[13] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/378.jpg");
imageList[14] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/379.jpg");
imageList[15] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/380.jpg");
imageList[16] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/381.jpg");
imageList[17] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/382.jpg");
imageList[18] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/383.jpg");
imageList[19] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/384.jpg");
imageList[20] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/385.jpg");
imageList[21] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/386.jpg");
imageList[22] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/387.jpg");
imageList[23] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/388.jpg");
imageList[24] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/389.jpg");
imageList[25] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/390.jpg");
imageList[26] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/391.jpg");
imageList[27] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/392.jpg");
imageList[28] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/393.jpg");
imageList[29] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/394.jpg");
imageList[30] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/395.jpg");
imageList[31] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/396.jpg");
imageList[32] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/545.jpg");
imageList[33] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/546.jpg");
imageList[34] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/547.jpg");
imageList[35] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/548.jpg");
imageList[36] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/579.jpg");
imageList[37] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/580.jpg");
imageList[38] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/581.jpg");
imageList[39] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/582.jpg");
imageList[40] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/583.jpg");
imageList[41] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/584.jpg");
imageList[42] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/585.jpg");
imageList[43] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/586.jpg");
imageList[44] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/587.jpg");
imageList[45] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/588.jpg");
imageList[46] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/589.jpg");
imageList[47] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/590.jpg");
imageList[48] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/557.jpg");
imageList[49] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/558.jpg");
imageList[50] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/559.jpg");
imageList[51] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/560.jpg");
imageList[52] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/561.jpg");
imageList[53] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/562.jpg");
imageList[54] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/563.jpg");
imageList[55] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/564.jpg");
imageList[56] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/565.jpg");
imageList[57] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/566.jpg");
imageList[58] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/567.jpg");
imageList[59] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/568.jpg");
imageList[60] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/569.jpg");
imageList[61] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/570.jpg");
imageList[62] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/571.jpg");
imageList[63] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/572.jpg");
imageList[64] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/573.jpg");
imageList[65] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/574.jpg");
imageList[66] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/575.jpg");
imageList[67] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/576.jpg");
imageList[68] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/577.jpg");
imageList[69] = new Image<Gray, byte>("C:/Users/L33506/Desktop/emotionData/578.jpg");
String[] emoList = new String[70];
emoList[0] = "Netural";
emoList[1] = "Sad";
emoList[2] = "Angry";
emoList[3] = "Smile";
emoList[4] = "Smile";
emoList[5] = "Smile";
emoList[6] = "Smile";
emoList[7] = "Smile";
emoList[8] = "Smile";
emoList[9] = "Smile";
emoList[10] = "Angry";
emoList[11] = "Angry";
emoList[12] = "Angry";
emoList[13] = "Angry";
emoList[14] = "Angry";
emoList[15] = "Angry";
emoList[16] = "Angry";
emoList[17] = "Angry";
emoList[18] = "Angry";
emoList[19] = "Angry";
emoList[20] = "Angry";
emoList[21] = "Angry";
emoList[22] = "Angry";
emoList[23] = "Angry";
emoList[24] = "Angry";
emoList[25] = "Angry";
emoList[26] = "Angry";
emoList[27] = "Angry";
emoList[28] = "Angry";
emoList[29] = "Angry";
emoList[30] = "Angry";
emoList[31] = "Angry";
emoList[32] = "Angry";
emoList[33] = "Angry";
emoList[34] = "Angry";
emoList[35] = "Angry";
emoList[36] = "Angry";
emoList[37] = "Angry";
emoList[38] = "Angry";
emoList[39] = "Angry";
emoList[40] = "Angry";
emoList[41] = "Angry";
emoList[42] = "Angry";
emoList[43] = "Angry";
emoList[44] = "Angry";
emoList[45] = "Angry";
emoList[46] = "Angry";
emoList[47] = "Angry";
emoList[48] = "Angry";
emoList[49] = "Angry";
emoList[50] = "Angry";
emoList[51] = "Angry";
emoList[52] = "Angry";
emoList[53] = "Angry";
emoList[54] = "Angry";
emoList[55] = "Angry";
emoList[56] = "Angry";
emoList[57] = "Angry";
emoList[58] = "Angry";
emoList[59] = "Angry";
emoList[60] = "Angry";
emoList[61] = "Angry";
emoList[62] = "Angry";
emoList[63] = "Angry";
emoList[64] = "Angry";
emoList[65] = "Angry";
emoList[66] = "Angry";
emoList[67] = "Angry";
emoList[68] = "Angry";
emoList[69] = "Angry";
int[] label = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69 };
//Use the variable to detect the number of times these emotion appear in the video by frames;
noDetect = 0;
totalFrame = grayFrameList.Count;
List<Image<Gray, byte>> trainfaceList = new List<Image<Gray, byte>>();
Rectangle[] trainFace;
for (int i = 0; i < imageList.Length; i++)
{
trainFace = faceHaar.DetectMultiScale(imageList[i], 1.1, 20, new Size(20, 20), Size.Empty);
foreach (Rectangle f in trainFace)
{
trainfaceList.Add(imageList[i].Copy(f).Resize(64, 64, INTER.CV_INTER_CUBIC));
}
}
//Detect the face in the frames and do emotion detect using fisher eigen face recognizer
double maxDistance = 1000; //-> Range from 1000 to 5000
FisherFaceRecognizer fisher = new FisherFaceRecognizer(imageList.Length, maxDistance);
fisher.Train(trainfaceList.ToArray(), label);
FaceRecognizer.PredictionResult result = new FaceRecognizer.PredictionResult();
for (int i = 0; i < totalFrame; i++)
{
result = fisher.Predict(grayFrameList.ElementAt(i));
int num = result.Label;
if (num == -1)
{
faceEmotions.Add("No Detect");
}
else
{
faceEmotions.Add(emoList[num].ToString());
}
if (num == -1)
{
noDetect++;
}
else if (emoList[num].Equals("Smile"))
{
smile++;
}
else if (emoList[num] == "Sad")
{
sad++;
}
else if (emoList[num] == "Angry")
{
angry++;
}
else if (emoList[num] == "Netural")
{
neutral++;
}
else
{
// it should not come here .
}
}
//get total sum of the 5 emotion data.
int total = smile + sad + angry + neutral + noDetect;
//put them into the percentage (emotion/frames * 100)
double smilePer = (Convert.ToDouble(smile) / totalFrame) * 100;
double sadPer = (Convert.ToDouble(sad) / totalFrame) * 100;
double angryPer = (Convert.ToDouble(angry) / totalFrame) * 100;
double neturalPer = (Convert.ToDouble(neutral) / totalFrame) * 100;
double noDetectPer = (Convert.ToDouble(noDetect) / totalFrame) * 100;
//lbmsg.Text = "Smile Count:" + smile.ToString() + " ,Sad Count:" + sad.ToString() + " ,Angry Count:" + angry.ToString() + " ,Netural Count:" + netural.ToString() + " ,No Detect Count:" + noDetect.ToString();
String[] emotionList = { "Smile", "Sad", "Angry", "Netural", "No Detect" };
double[] emotionPer = { smilePer, sadPer, angryPer, neturalPer, noDetectPer };
}