/// <summary>
/// Get emotion features using distances and wrinkles
/// </summary>
/// <param name="ImageMat">Image on which distances need to calculate</param>
/// <param name="faces">Rectangles of faces</param>
/// <param name="index">Index of images in case of multiple images</param>
/// <returns>Returns the list of distances</returns>
public EmotionTrainingMLModel FindEmotions(Mat ImageMat, List <Rectangle> faces, int index)
{
EmotionTrainingMLModel dataModel = new EmotionTrainingMLModel();
if (faces.Count > 0)
{
Mat shortImage = new Mat(ImageMat, 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 = findDistances(shortImage, faces, index);
double noseWrinkles = findWrinkles(shortImage, onNose);
double betweenEyesWrinkles = findWrinkles(shortImage, betweenEyes);
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 = noseWrinkles;
dataModel.betweenEyesWrinkles = betweenEyesWrinkles;
dataModel.Label = "";
//emotions= EDRMLMain.getInstance().getCalculatedEmotions(dataModel);
}
return(dataModel);
}
/// <summary>
/// Find features of face for distance and wrinkles based
/// </summary>
/// <param name="ImageMat">Input image for features detection</param>
/// <param name="faces">List of facse rectangles onm face</param>
/// <param name="index">Index of image in case of multiple images</param>
/// <returns>Return the predicted emotions based on distances and wrinkles</returns>
public string FindEmotionDistanceAndWrinkleBased(Mat ImageMat, List <Rectangle> faces, int index)
{
EmotionTrainingMLModel dataModel = new EmotionTrainingMLModel();
EDRFeatureExtraction obj = new EDRFeatureExtraction();
dataModel = obj.FindEmotions(ImageMat, faces, index);
return(EDRMLMainDistanceAndWrinkleBased.getInstance().getCalculatedEmotionsDistanceAndWrinkleBased(dataModel));
}
/// <summary>
/// Get calculated emotion after applying machiene learning algorithm based on wrinkles and distances
/// </summary>
/// <param name="emotionDistances">Distances and wrinkles features to test emotion probilities</param>
/// <returns>Returns a array probilities of emotion </returns>
public List <double> getCalculatedEmotionsProbilitiesDistanceAndWrinkleBased(EmotionTrainingMLModel emotionDistances)
{
string[] attributeArray = { "LEB1_CPLE1", "LEB2_CPLE2", "REB1_CPRE1", "REB2_CPRE2", "OPEN_LE1", "OPEN_LE2", "OPEN_RE1", "OPEN_RE2", "EXP_MO", "OPEN_MO1", "OPEN_MO2", "OPEN_MO3", "NS_LPLIP", "NS_RPLIP", "betweenEyesWrinkles", "noseWrinkles" };
string[] classNames = { "anger", "smile", "sad", "surprise", "neutral", "fear", "disgust" };
double[] dataValues = { emotionDistances.LEB1_CPLE1, emotionDistances.LEB2_CPLE2, emotionDistances.REB1_CPRE1, emotionDistances.REB2_CPRE2, emotionDistances.OPEN_LE1, emotionDistances.OPEN_LE2, emotionDistances.OPEN_RE1, emotionDistances.OPEN_RE2, emotionDistances.EXP_MO, emotionDistances.OPEN_MO1, emotionDistances.OPEN_MO2, emotionDistances.OPEN_MO3, emotionDistances.NS_LPLIP, emotionDistances.NS_RPLIP, emotionDistances.betweenEyesWrinkles, emotionDistances.noseWrinkles };
string classHeader = "label";
string defaultclass = "neutral";
return(MLMain.getInstance().testMLPPredictionsUsingWeka(attributeArray, classNames, dataValues, classHeader, defaultclass, (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Model)));
}
/// <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);
}
}
/// <summary>
/// Convert from business model to ML model
/// </summary>
/// <param name="emotion">IP layer model</param>
/// <returns>ML layer model</returns>
public EmotionTrainingMLModel ConvertFromBusinessObject(EmotionTrainingDataModel emotion)
{
EmotionTrainingMLModel E = new EmotionTrainingMLModel(emotion.LEB1_CPLE1, emotion.LEB2_CPLE2, emotion.REB1_CPRE1, emotion.REB2_CPRE2, emotion.OPEN_LE1, emotion.OPEN_LE2, emotion.OPEN_RE1, emotion.OPEN_RE2, emotion.OPEN_MO1, emotion.OPEN_MO2, emotion.OPEN_MO3, emotion.EXP_MO, emotion.NS_LPLIP, emotion.NS_RPLIP, emotion.noseWrinkles, emotion.betweenEyesWrinkles, emotion.Label);
return(E);
}
