//generate training set for given viewport (training purposes)
private ArrayList generateTrainingSet(BaseTypes.viewport vprt, Bitmap bmp)
{
int smallShift;
int largeShift;
int CoordX,CoordY, Radius = 0,Width,Height;
Point TL = new Point(0,0);
double[] temporaryDoubleVector; //training set element
ByteVector temporaryByteVector; //reqd to obtain double vector
ArrayList trainingSet=new ArrayList();
Rectangle crop;
switch (vprt)
{
case BaseTypes.viewport.XY:
{
smallShift=xyRadius+(int)(0.5*xyRadius);
largeShift=xyRadius*2;//+(int)(0.5*xyRadius);
CoordX=xyCoordX;
CoordY=xyCoordY;
Radius=xyRadius;
break;
}
case BaseTypes.viewport.XZ:
{
smallShift=xzRadius+(int)(0.5*xzRadius);
largeShift=xzRadius*2;//+(int)(0.5*xzRadius);
CoordX=xzCoordX;
CoordY=xzCoordZ;
Radius=xzRadius;
break;
}
case BaseTypes.viewport.YZ:
{
smallShift=yzRadius+(int)(0.5*yzRadius);
largeShift=yzRadius*2;//+(int)(0.5*yzRadius);
CoordX=yzCoordY;
CoordY=yzCoordZ;
Radius=yzRadius;
break;
}
default :
{
smallShift=xyRadius+(int)(0.5*xyRadius);
largeShift=xyRadius*2;//+(int)(0.5*xyRadius);
CoordX=xyCoordX;
CoordY=xyCoordY;
Radius=xyRadius;
break;
}
}
smallShift=Radius;
largeShift=Radius+(int)(0.5*xyRadius);
Width=Height=Radius*2;
//add sixteen wrong answers (image shifted by 50% and 75%)
for (int x=-1; x<2; x++)
for (int y=-1; y<2; y++)
{
if (!((x==0) && (y==0))) //To bylaby poprawna dana
{
int a = (int)x*(int)smallShift;
TL.X=(CoordX-Radius)+a;
a =(int)y*(int)smallShift;
TL.Y=(CoordY-Radius)+a;
crop = new Rectangle(TL.X,TL.Y,Width,Height);
temporaryByteVector=new ByteVector(BitmapFilters.CropBitmap(bmp, crop));
temporaryDoubleVector=temporaryByteVector.GetDoubleVector();
trainingSet.Add(temporaryDoubleVector);
a = (int)x*(int)largeShift;
TL.X=(CoordX-Radius)+a;
a =(int)y*(int)largeShift;
TL.Y=(CoordY-Radius)+a;
crop = new Rectangle(TL.X,TL.Y,Width,Height);
temporaryByteVector=new ByteVector(BitmapFilters.CropBitmap(bmp, crop));
temporaryDoubleVector=temporaryByteVector.GetDoubleVector();
trainingSet.Add(temporaryDoubleVector);
}
}
//the following code caused not really good results during precise tracking, but still helps
//add eight correct answers (genuine image shifted by 1)
for (int x=-1; x<2; x++)
for (int y=-1; y<2; y++)
{
if (!((x==0) && (y==0))) //To bylaby poprawna dana
{
int a = (int)x*(int)1;
TL.X=(CoordX-Radius)+a;
a =(int)y*(int)1;
TL.Y=(CoordY-Radius)+a;
crop = new Rectangle(TL.X,TL.Y,Width,Height);
temporaryByteVector=new ByteVector(BitmapFilters.CropBitmap(bmp, crop));
temporaryDoubleVector=temporaryByteVector.GetDoubleVector();
trainingSet.Add(temporaryDoubleVector);
}
}
//add eight answer vectors from genuine image
for (int x=0; x<8; x++)
{
crop = new Rectangle((CoordX-Radius),(CoordY-Radius),Width,Height);
temporaryByteVector=new ByteVector(BitmapFilters.CropBitmap(bmp, crop));
temporaryDoubleVector=temporaryByteVector.GetDoubleVector();
trainingSet.Add(temporaryDoubleVector);
}
return trainingSet;
}
// The thread procedure performs the task
public void ThreadProc()
{
ArrayList pointsFound = new ArrayList();
Point TL = new Point(0,0);
Point BR = new Point(0,0);
Rectangle crop;
double[] answer=new double[4];
double[] answerYes=new double[4]{1,1,1,1};
double threshold=acceptanceThreshold;
threshold/=100;
int xAvg=0,yAvg=0;
for (int i=scanArea.Left; i<scanArea.Width-(Radius*2); i++)
{
for (int j=scanArea.Top;j<scanArea.Height-(Radius*2); j++)
{
TL.X=scanArea.Left+((i+1)-scanArea.Left);
TL.Y=scanArea.Top+((j+1)-scanArea.Top);
BR.X=Radius*2;
BR.Y=Radius*2;
crop=new Rectangle(TL.X,TL.Y,BR.X,BR.Y);
ByteVector tempByteVector= new ByteVector(BitmapFilters.CropBitmap(bmp,crop));
answer=Ann.Calculate(tempByteVector.GetDoubleVector());
if
(
(answer[0]>answerYes[0]*threshold) &&
(answer[1]>answerYes[1]*threshold) &&
(answer[2]>answerYes[2]*threshold) &&
(answer[3]>answerYes[3]*threshold)
)
{
pointsFound.Add(new Point(crop.X+Radius, crop.Y+Radius));
break;
}
}
}
bool tracked=true;
if (pointsFound.Count>0)
{
foreach (Point pt in pointsFound)
{
xAvg+=pt.X;
yAvg+=pt.Y;
}
xAvg=(int)(xAvg/pointsFound.Count);
yAvg=(int)(yAvg/pointsFound.Count);
}
else
{
MessageBox.Show("Alert! Point not found!!! Viewport: " + Convert.ToString(this.vprt),"Point tracking failed!",MessageBoxButtons.OK,MessageBoxIcon.Warning);
xAvg=(int)(scanArea.Left);
yAvg=(int)(scanArea.Top);
tracked=false;
}
if (callback != null)
callback(xAvg, yAvg,vprt,tracked);
}
//train neural networks with given tracking point images
public void initAndTrainANN(int trainingIterationsCount, double trainingRate, int outputVectorSize,
int hiddenLayersCount, int hiddenLayersSize, int minimumWeight, int maximumWeight,
Bitmap xy1stFrame, Bitmap xz1stFrame, Bitmap yz1stFrame)
{
//define and initialize activationFunction for Ann's
iActivationFunction activationFunction = new LogisticCurve();
//generate ByteVectors for each viewport
xyVector=new ByteVector(xyTrackPointImage);
xzVector=new ByteVector(xzTrackPointImage);
yzVector=new ByteVector(yzTrackPointImage);
//train points in each viewport (to be threaded ;))
trainPoint(BaseTypes.viewport.XY,xy1stFrame,ref xyAnn,
hiddenLayersSize, outputVectorSize, hiddenLayersCount,
activationFunction, trainingRate, trainingIterationsCount,
minimumWeight, maximumWeight);
trainPoint(BaseTypes.viewport.XZ,xy1stFrame,ref xzAnn,
hiddenLayersSize, outputVectorSize, hiddenLayersCount,
activationFunction, trainingRate, trainingIterationsCount,
minimumWeight, maximumWeight);
trainPoint(BaseTypes.viewport.YZ,xy1stFrame,ref yzAnn,
hiddenLayersSize, outputVectorSize, hiddenLayersCount,
activationFunction, trainingRate, trainingIterationsCount,
minimumWeight, maximumWeight);
Trained=true;
}
