/// <summary>
/// Predict emotion on the basis of wrinkles and distances
/// </summary>
/// <param name="bmp">Input image on which emotion need to detect</param>
/// <returns>Business model containing images of different steps and output result</returns>
public EmotionBusinessModel DetectEmotionsDistanceAndWrinkledBased(Bitmap bmp)
{
Image <Bgr, Byte> img = new Image <Bgr, Byte>(bmp);
Mat[] mats = new Mat[1];
mats[0] = img.Mat;
return(DetectEmotionsDistanceAndWrinkledBased(mats, FDIPMAin.DetectFace(mats[0])));
}
/// <summary>
/// Train system using multiple files and then call ML algorithm to train
/// </summary>
/// <param name="files">List of file names to train machiene</param>
/// <returns>Returns a model of ML layer</returns>
public List <EmotionTrainingMLModel> TrainSystemForEmotion(string[] files)
{
using (Logger logger = new Logger())
{
List <EmotionTrainingMLModel> emotionList = new List <EmotionTrainingMLModel>();
int index = 0;
foreach (string file in files)
{
logger.LogIntoFile(Log.logType.INFO, (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionTrainingMessage));
Mat image = new Mat(file);
List <Rectangle> faces = new List <Rectangle>();
faces = FDIPMAin.DetectFace(image);
if (faces.Count > 0)
{
EDRFeatureExtraction featureExtracter = new EDRFeatureExtraction();
Mat shortImage = new Mat(image, faces[0]);
CvInvoke.Resize(shortImage, shortImage, new Size(320, 240), 0, 0, Inter.Linear);
faces = new List <Rectangle>();
faces.Add(new Rectangle(0, 0, shortImage.Width, shortImage.Height));
List <double> distances = featureExtracter.findDistances(shortImage, faces, index);
EmotionTrainingMLModel dataModel = new EmotionTrainingMLModel();
for (int i = 0; i < 14; i++)
{
var value = distances.ElementAt(i);
PropertyInfo propertyInfo = dataModel.GetType().GetProperty(listOfDistances[i]);
propertyInfo.SetValue(dataModel, value, null);
}
dataModel.noseWrinkles = featureExtracter.findWrinkles(shortImage, featureExtracter.getOnNose());
dataModel.betweenEyesWrinkles = featureExtracter.findWrinkles(shortImage, featureExtracter.getBetweenEyes());
dataModel.Label = Path.GetFileName(Path.GetDirectoryName(file));
emotionList.Add(dataModel);
}
index++;
}
return(emotionList);
}
}
private void ProcessFrame(object sender, EventArgs arg)
{
#region Face Detection Region
if (captureInProgress)
{
//FileInfo file = new FileInfo("E:\\training2\\1.jpg");
// Mat ImageMat = new Mat(file.FullName);
Mat ImageMat = capt.QueryFrame();
imageList.Images.Add(ImageMat.ToImage <Bgr, byte>().ToBitmap());
for (int j = 0; j < imageList.Images.Count; j++)
{
ListViewItem item = new ListViewItem();
item.ImageIndex = j;
imageListView.Items.Add(item);
}
string emotions = "";
if (Methodology.SelectedIndex == 1)
{
emotions = EDRIPMainDistanceAndWrinkledBased.getInstance().DetectEmotionsDistanceAndWrinkledBased(new Mat[] { ImageMat }, FDIPMAin.DetectFace(ImageMat)).outputResult;
}
else if (Methodology.SelectedIndex == 0)
{
emotions = EDRIPMainDistanceBased.getInstance().DetectEmotionsDistanceBased(new Mat[] { ImageMat }).outputResult;
}
if (emotions != null)
{
List <double> dfs = distributionsEmotions.dists;
foreach (var series in chart2.Series)
{
series.Points.Clear();
}
chart2.Series["Conf"].Points.AddXY("anger", dfs[0]);
chart2.Series["Conf"].Points.AddXY("smile", dfs[1]);
chart2.Series["Conf"].Points.AddXY("sad", dfs[2]);
chart2.Series["Conf"].Points.AddXY("surprise", dfs[3]);
chart2.Series["Conf"].Points.AddXY("neutral", dfs[4]);
chart2.Series["Conf"].Points.AddXY("fear", dfs[5]);
chart2.Series["Conf"].Points.AddXY("disgust", dfs[6]);
}
List <Rectangle> faces = new List <Rectangle>();
faces = FDIPMAin.DetectFace(ImageMat);
Mat shortImage = faces.Count > 0 ? new Mat(ImageMat, faces[0]) : ImageMat;
CvInvoke.Resize(shortImage, shortImage, new Size(320, 240), 0, 0, Inter.Linear);
shortImage = ImageMat;
//if(faces.Count>0&&i%3==0)
//{
// ImageMat.Save("E:\\dataset\\smile\\" + i/3 + ".jpg");
//}
//CvInvoke.Imshow("Orignal Face", shortImage);
faces = new List <Rectangle>();
faces.Add(new Rectangle(0, 0, shortImage.Width, shortImage.Height));
Image <Bgr, byte> I2 = shortImage.ToImage <Bgr, byte>();
GC.Collect();
Logger log = new Logger();
log.LogIntoFile(Log.logType.INFO, "message to be written");
//I2.Draw(emotions, new Point(10, 30), FontFace.HersheyPlain, 2.0, new Bgr(10, 0, 255));
imageBox1.Image = I2;
i++;
}
#endregion
else
{
imageBox1.Image = capt.QueryFrame();
}
if (FrameCount <= TotalFrames && Video.Checked)
{
trackBar1.Value = FrameCount;
FrameCount++;
}
else if (Video.Checked)
{
FrameCount = 0;
trackBar1.Value = FrameCount;
Play.Text = "Play";
Application.Idle -= ProcessFrame;
}
}
private void Upload_Click(object sender, EventArgs e)
{
Stream fileStream = null;
UploadImage.Multiselect = true;
if ((UploadImage.ShowDialog() == DialogResult.OK) && (fileStream = UploadImage.OpenFile()) != null)
{
string fileName = UploadImage.FileName;
FileInfo file = new FileInfo(UploadImage.FileName);
Mat ImageMat = new Mat(file.FullName);
string emotions = "";
EmotionBusinessModel mod = new EmotionBusinessModel();
if (Methodology.SelectedIndex == 1)
{
mod = EDRIPMainDistanceAndWrinkledBased.getInstance().DetectEmotionsDistanceAndWrinkledBased(new Mat[] { ImageMat }, FDIPMAin.DetectFace(ImageMat));
}
else if (Methodology.SelectedIndex == 2)
{
mod = EDRIPMainReducedDimensionsBased.getInstance().DetectEmotionsReducedDimentionsBased(new Mat[] { ImageMat });
}
// emotions = EDRIPMainReducedDimensionsBased.getInstance().DetectEmotionsReducedDimentionsBased(new Mat[] { ImageMat }).outputResult;
else if (Methodology.SelectedIndex == 3)
{
OpticalflowView(UploadImage);
OpticalFlow.Visible = true;
}
else if (Methodology.SelectedIndex == 4)
{
EDRIPHybridMethodologyBasedMain.getInstance().testSystemForEmotionHybrid(UploadImage.FileNames);
}
emotions = mod.outputMessage;
CvInvoke.Resize(ImageMat, ImageMat, new Size(320, 240), 0, 0, Inter.Linear);
Image <Bgr, byte> I2 = mod.images[0].image.ToImage <Bgr, byte>();
GC.Collect();
Logger log = new Logger();
log.LogIntoFile(Log.logType.INFO, "message to be written");
// I2.Draw(emotions, new Point(10, 30), FontFace.HersheyPlain, 2.0, new Bgr(10, 0, 255));
imageBox1.Image = I2;
}
}