/// <summary>
/// Predict emotion on the basis of wrinkles and distances
/// </summary>
/// <param name="ImageMats">Input image on which emotion need to detect</param>
/// <returns>Business model containing images of different steps and output result</returns>
public EmotionBusinessModel DetectEmotionsDistanceAndWrinkledBased(Mat[] ImageMats, List <Rectangle> facess)
{
EmotionBusinessModel output = new EmotionBusinessModel();
EmotionBusinessModel.outputmsgs = new List <string>();
try
{
using (Logger logger = new Logger())
{
KeyValuePair <string, Color> keyPair = new KeyValuePair <string, Color>("smile", Color.Yellow);
keyPairs.Add(keyPair);
keyPair = new KeyValuePair <string, Color>("sad", Color.Blue);
keyPairs.Add(keyPair);
keyPair = new KeyValuePair <string, Color>("surprise", Color.SlateGray);
keyPairs.Add(keyPair);
keyPair = new KeyValuePair <string, Color>("anger", Color.Red);
keyPairs.Add(keyPair);
keyPair = new KeyValuePair <string, Color>("disgust", Color.Purple);
keyPairs.Add(keyPair);
keyPair = new KeyValuePair <string, Color>("fear", Color.Black);
keyPairs.Add(keyPair);
keyPair = new KeyValuePair <string, Color>("neutral", Color.Green);
keyPairs.Add(keyPair);
Mat ImageMat = ImageMats[0];
logger.LogIntoFile(Log.logType.INFO, (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionDetectionMessage));
List <Rectangle> faces = new List <Rectangle>();
faces = facess;
//faces = FDIPMAin.DetectFace(ImageMat);
output.faceRect = new List <Rectangle>();
foreach (Rectangle face in faces)
{
List <Rectangle> fcs = new List <Rectangle>();
fcs.Add(face);
output.outputResult = EDRIPDistanceAndWrinklesBasedFeatures.getInstance().FindEmotionDistanceAndWrinkleBased(ImageMat, fcs, 0);
EmotionBusinessModel.outputmsgs.Add(output.outputResult);
output.faceRect.Add(face);
}
imageModel img = new imageModel();
if (faces.Count > 0)
{
img.image = CvmlUtility.getInstance().annotate(ImageMat, faces[0], output.outputResult, Color.Blue);
}
//img.image=drawRect(ImageMat.ToImage<Bgr, byte>(), faces[0], output.outputResult);
img.label = "Final emotion detected image with rectangle on face";
output.images.Add(img);
output.outputMessage = "Emotion detected successfully in given image using wrinkled and distance based approach";
output.success = true;
}
}
catch (Exception e)
{
string text = ExceptionHandle.GetExceptionMessage(e);
output.success = false;
output.outputResult = "neutral";
output.e = e;
}
return(output);
}
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;
}
}
