public dynamicDetection(String s)
{
this.recog = new recognizer(s);
//this.file = new System.IO.StreamWriter("C:\\Users\\workshop\\Desktop\\11122.csv");
this.iter = 0;
this.frame = 0;
this.feature = new _feature(0.0);
//this.featureSet = new double[20];
this.wDist = new double[4];
this.wDistLeg = new double[4];
this.prevAccel = new double[4];
this.prevAccelLeg = new double[4];
this.prevSpeed = new double[4];
this.prevSpeedLeg = new double[4];
this.totJI = new double[4];
this.wprevLeg = new _qbit[4];
this.wprev = new _qbit[4];
this.featureList = new List<double[]>();
refreshVars();
}
public confidence(String filename = "confidenceNet.dat")
{
this.emoCategory = 10;
this.emoThreshold = 0.1;
this.ConfidenceLowThreshold = 0.4;
this.ConfidenceHighThreshold = 0.6;
this.confiRecog = new recognizer(filename);
}
private double sendToANN(double[] feat, recognizer r)
{
try
{
double[] output;// = new double[1];
double val = 0;
output = r.recognizeEmotion(feat);
if (r.net.Output.Length == 2)
{
if (Math.Round(output[0]) == 1 && Math.Round(output[1]) == 1)
val = 100;
else if (Math.Round(output[0]) == 1 && Math.Round(output[1]) == 0)
val = 75;
else if (Math.Round(output[0]) == 0 && Math.Round(output[1]) == 1)
val = 50;
else if (Math.Round(output[0]) == 0 && Math.Round(output[1]) == 0)
val = 25;
Console.WriteLine("output: " + output[0] + ", " + output[1]);
}
else
{
if (output[0] == double.NaN || output[0] == double.NegativeInfinity || output[0] == double.PositiveInfinity)
val = -1.0;
else
val = (output[0]) * 100;
Console.WriteLine("output: " + output[0]);
}
return val;
}
catch
{
System.Windows.MessageBox.Show("Detection Module: --" + r.name + "-- has failed for some reason. Most probably wrong Network file selected.",
"detection error", MessageBoxButton.OK, MessageBoxImage.Error);
return -2;
}
}
public int pollFeatures(SkeletonData s, userContext poseofUser)
{
iter++; //frame++;
double[][] ans = globalVars.fExtract.getRawDataStream(s);
/* if something goes wrong with the feature extractor*/
if (ans[0] == null || ans[1] == null)
{
//System.Windows.MessageBox.Show("Ans 0 " + ans[0] + " ans 1 "+ans[1], "feature extractor error", MessageBoxButton.OK, MessageBoxImage.Error);
return -3;
}
if (movement == null)
movement = new double[ans[0].Length];
for (int i = 0; i < ans[0].Length; i++)
movement[i] += ans[0][i];
movementTick++;
if (position == null)
position = new double[ans[1].Length];
for (int i = 0; i < ans[1].Length; i++)
position[i] += ans[1][i];
positionTick++;
//System.Windows.MessageBox.Show("iter: "+iter, "ANN init error", MessageBoxButton.OK, MessageBoxImage.Error);
/* Here the detection starts */
if (iter % 30 == 0 && iter != 0)
{
/*this awesome part automates the recognizer choice for Sitting or Standing context*/
if (poseofUser == userContext.Sitting)
{
mRecog = arousalSittingRecognizer;
posRecog = valenceSittingRecognizer;
}
else if (poseofUser == userContext.Standing)
{
mRecog = arousalStandingRecognizer;
posRecog = valenceStandingRecognizer;
}
else
{
System.Windows.MessageBox.Show("there is no saved network for this pose. Please try standing or sitting only.",
"detection error", MessageBoxButton.OK, MessageBoxImage.Error);
return -1;
}
double[] temp1, temp2;
temp1 = new double[ans[0].Length];
temp2 = new double[ans[1].Length];
/*movement and position average*/
/*releasing movement and position variables*/
for (int i = 0; i < movement.Length; i++)
{
temp1[i] = movement[i] / movementTick;
movement[i] = 0;
Console.Write(temp1[i] + " ");
}
movementTick = 0;
Console.WriteLine();
for (int i = 0; i < position.Length; i++)
{
temp2[i] = position[i] / positionTick;
//Console.Write(temp2[i] + " ");
position[i] = 0;
}
positionTick = 0;
Console.WriteLine();
if (slidingWindow == true)
{
/*adding them in a list for sliding window*/
movementFeatureList.Add(temp1);
positionFeatureList.Add(temp2);
double[] finalFeature = new double[movement.Length];
for (int i = 0; i < movement.Length; i++)
{
foreach (double[] d in this.movementFeatureList)
{
finalFeature[i] += d[i];
//Console.Write(d[i]+", ");
}
finalFeature[i] /= movementFeatureList.Count;
//Console.WriteLine("ffm: "+finalFeature[i]);
}
double[] finalFeature1 = new double[position.Length];
for (int i = 0; i < position.Length; i++)
{
foreach (double[] d in this.positionFeatureList)
{
finalFeature1[i] += d[i];
//Console.Write(d[i]+", ");
}
finalFeature1[i] /= positionFeatureList.Count;
//Console.WriteLine("ffp: " + finalFeature1[i]);
}
//Console.WriteLine(featureList.Count);
//Here the slidingwindow length is determined. ehich can be controlled with the variable: "interval".
if (movementFeatureList.Count == 6)
{
movementFeatureList.RemoveAt(0);
positionFeatureList.RemoveAt(0);
}
detect(finalFeature, finalFeature1);
}
else
{
detect(temp1, temp2);
}
}
return iter;
}
public newDynamicDetection(String s, String s1)
{
try
{
mRecog = new recognizer(s);
//posRecog = new recognizer(Environment.GetFolderPath(Environment.SpecialFolder.Desktop) + "\\" + "positionANN.dat");
posRecog = new recognizer(s1);
arousalSittingRecognizer = new recognizer(s);
arousalStandingRecognizer = new recognizer(s);
valenceSittingRecognizer = new recognizer(s1);
valenceStandingRecognizer = new recognizer(s1); ;
this.iter = 0;
movementFeatureList = new List<double[]>();
positionFeatureList = new List<double[]>();
globalVars.detectorOn = true;
globalVars.fExtract.frames = 0;
}
catch
{
System.Windows.MessageBox.Show("Some problem with the Saved Networks. CHeck your files and try again.", "ANN init error", MessageBoxButton.OK, MessageBoxImage.Error);
this.stopDetection();
globalVars.detectorOn = false;
}
//this.feature = featureExtractor();
}
