void Start()
{
DontDestroyOnLoad(transform.gameObject);
this.Client = GameObject.Find("Network").GetComponent <Client>();
this.Client.MessageScoreUpdateEvent += Client_MessageScoreUpdateEvent;
this.Client.MessageBuildingConstructionEvent += Client_MessageBuildingConstructionEvent;
this.Client.MessageBuildingDestructionEvent += Client_MessageBuildingDestructionEvent;
//this.Client.MessageTrophyWonEvent += Client_MessageTrophyWonEvent;
this.Client.MessageChallengeFinalSuccessRateEvent += Client_MessageChallengeFinalSuccessRateEvent;
this.TrophyManager = GameObject.Find("Trophies").GetComponent <TrophyManager>();
this.TrophyManager.TrophyObtained += TrophyManager_TrophyObtained;
this.ScoreCount = 0;
this.filePath = ConstantsLoader.getConstant(TypeConstant.fileScores);
this.scores = new List <Tuple <int, string, int, int, int, float> >();
this.ChallengeSuccessRate = new List <float>();
for (int i = 0; i < 4; i++)
{
this.ChallengeSuccessRate.Add(0);
}
loadPreviousScores();
this.BuildingCount = 0;
this.MedalCount = 0;
}
/// <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);
}
void Awake()
{
if (!ConstantsLoader.loadData())
{
Debug.LogWarning("Could not load all constants!!!");
}
}
/// <summary>
/// Train machiene based on wrinkles and distances
/// </summary>
/// <param name="emotionDistances">Distances and wrinkles features to train emotion</param>
public void trainSystemForEmotionDistanceAndWrinkleBased(List <EmotionTrainingMLModel> emotionDistances)
{
db.saveToDbDistanceAndWrinkleBased(EDRMLUtil.getInstance().ConvertToDataModels(emotionDistances));
List <EmotionDataModel> emotionsDistances = db.getDataFromDatabaseDistanceAndWrinkleBased();
createWekaFileDistanceAndWrinkleBased(emotionsDistances);
MLMain.getInstance().trainMachineForEmotionUsingWeka((string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Arff), (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Model));
}
private static weka.classifiers.functions.MultilayerPerceptron loadModel(String name, java.io.File path)
{
// weka.classifiers.bayes.HMM .HMMEstimator.packageManagement.Package p=new ;
weka.classifiers.functions.MultilayerPerceptron classifier;
FileInputStream fis = new FileInputStream((string)ConstantsLoader.getInstance().getValue(EnumConstant.libsPath) + "\\models\\" + "models" + name + ".model");
ObjectInputStream ois = new ObjectInputStream(fis);
classifier = (weka.classifiers.functions.MultilayerPerceptron)ois.readObject();
ois.close();
return(classifier);
}
void Start()
{
sendingPort = Int32.Parse(ConstantsLoader.getConstant(TypeConstant.networkSendingPort));
listeningPort = Int32.Parse(ConstantsLoader.getConstant(TypeConstant.networkListeningPort));
serverIP = ConstantsLoader.getConstant(TypeConstant.networkServerIP);
_client = new UdpClient();
_client.Connect(this.serverIP, this.sendingPort);
UnityEngine.Debug.Log("Starting client...");
_continue = true;
_thListener = new Thread(new ThreadStart(ThreadListener));
_thListener.Start();
StartCoroutine(sendingProcess());
}
public Form2()
{
InitializeComponent();
CvmlUtility.getInstance().setDllSearchDirectory((string)ConstantsLoader.getInstance().getValue(EnumConstant.libsPath));
EDRIPMain.getInstance().loadPredictor();
DsDevice[] captureDevices;
// Get the set of directshow devices that are video inputs.
captureDevices = DsDevice.GetDevicesOfCat(FilterCategory.VideoInputDevice);
cameraList.DisplayMember = "Text";
cameraList.ValueMember = "Value";
for (int idx = 0; idx < captureDevices.Length; idx++)
{
cameraList.Items.Add(new { Text = idx + ":" + captureDevices.ElementAt(idx).Name, Value = idx });// Do something with the device here...
}
}
/// <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);
}
}
void Awake()
{
if (this.name == "verticalBackground") //doing it just one time
{
verticalTuto.resourcesTuto = GameObject.Find("ResourcesTuto");
verticalTuto.resourcesTuto.SetActive(false);
verticalTuto.islandsTuto = GameObject.Find("IslandsTuto");
verticalTuto.islandsTuto.SetActive(false);
verticalTuto.timeTuto = GameObject.Find("TimeTuto");
verticalTuto.timeTuto.SetActive(false);
verticalTuto.trophiesTuto = GameObject.Find("TrophiesTuto");
verticalTuto.trophiesTuto.SetActive(false);
verticalTuto.challengesTuto = GameObject.Find("ChallengesTuto");
verticalTuto.challengesTuto.SetActive(false);
verticalTuto.notificationsTuto = GameObject.Find("NotificationsTuto");
verticalTuto.notificationsTuto.SetActive(false);
verticalTuto.resourcesTutoText = GameObject.Find("resourcesTutoText");
verticalTuto.resourcesTutoText.SetActive(false);
verticalTuto.islandsTutoText = GameObject.Find("islandsTutoText");
verticalTuto.islandsTutoText.SetActive(false);
verticalTuto.timeTutoText = GameObject.Find("timeTutoText");
verticalTuto.timeTutoText.SetActive(false);
verticalTuto.trophiesTutoText = GameObject.Find("trophiesTutoText");
verticalTuto.trophiesTutoText.SetActive(false);
verticalTuto.challengesTutoText = GameObject.Find("challengesTutoText");
verticalTuto.challengesTutoText.SetActive(false);
verticalTuto.notificationsTutoText = GameObject.Find("notificationsTutoText");
verticalTuto.notificationsTutoText.SetActive(false);
verticalTuto.tutoTermine = GameObject.Find("tutoTermine");
verticalTuto.tutoTermine.SetActive(false);
//Reload constants for the next game
ConstantsLoader.loadData();
}
this.Client = GameObject.Find("Network").GetComponent <Client>();
}
/// <summary>
/// Create weka file of wrinkles and distances
/// </summary>
public void createWekaFileDistanceAndWrinkleBased(List <EmotionDataModel> emotionsDistances)
{
List <string> features = new List <string>();
foreach (EmotionDataModel obj in emotionsDistances)
{
string wekaData = obj.LEB1_CPLE1
+ "," + obj.LEB2_CPLE2
+ "," + obj.REB1_CPRE1
+ "," + obj.REB2_CPRE2
+ "," + obj.OPEN_LE1
+ "," + obj.OPEN_LE2
+ "," + obj.OPEN_RE1
+ "," + obj.OPEN_RE2
+ "," + obj.EXP_MO
+ "," + obj.OPEN_MO1
+ "," + obj.OPEN_MO2
+ "," + obj.OPEN_MO3
+ "," + obj.NS_LPLIP
+ "," + obj.NS_RPLIP
+ "," + obj.betweenEyesWrinkles
+ "," + obj.noseWrinkles
+ "," + obj.Label;
features.Add(wekaData);
}
// string[] features = { "1,4.5,34,apple", "2,56,3.4,banana", "3.5,6.5,34,orange" };
string[] lOD = { "LEB1_CPLE1 numeric", "LEB2_CPLE2 numeric", "REB1_CPRE1 numeric", "REB2_CPRE2 numeric", "OPEN_LE1 numeric", "OPEN_LE2 numeric", "OPEN_RE1 numeric", "OPEN_RE2 numeric", "EXP_MO numeric", "OPEN_MO1 numeric", "OPEN_MO2 numeric", "OPEN_MO3 numeric", "NS_LPLIP numeric", "NS_RPLIP numeric", "betweenEyesWrinkles numeric", "noseWrinkles numeric" };
List <string> attributes = lOD.ToList <string>();
string classHeader = "label {anger, smile, sad, surprise, neutral, fear, disgust}";
attributes.Add(classHeader);
MLMain.getInstance().createWekaFile("%% weka file for methodology2 emotion", "Emotion", attributes, features, (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Arff));
}
/// <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)));
}
void Start()
{
this.listeningPort = Int32.Parse(ConstantsLoader.getConstant(TypeConstant.networkListeningPort));
}