/* This fuction traveres the table and add vint and time to the trajectory. */
int recursiveMove(std::vector<classPoint2T> &traj, int t, int d, int vend_index, int v0_index, std::vector<std::vector<fSet> >& G, std::vector<std::vector<int> > &T, std::vector<std::vector<int> > &D ){
// std::cout<<"t and d: "<<t<< " - "<<d<<std::endl;
if(t <= 0.1 && d <= 0.1){
// std::cout<<" End recursive move!"<<std::endl;
return 1;
}
// print T
std::cout<<"T table in recursive:"<<std::endl;
for(int i=0; i<26; i++){
for(int j = 0; j<26; j++)
std::cout<<T[i][j]<<"\t";
std::cout<<"\n";
}
for(int vint_index=0; vint_index<M; vint_index++){
int delta_d = d - D[v0_index][vint_index];
if(delta_d >=0){
int delta_t = t - T[v0_index][vint_index];
classPoint2T point(delta_t, vend_index);
if(delta_t>=0 && point.inSet(G[vint_index][delta_d]) >=0){
/* add point to the trajectory */
std::cout<<"v0_index: "<<v0_index<<" vint_index: "<<vint_index<<" T[][]: "<<T[v0_index][vint_index]<<" D[][]: "<<D[v0_index][vint_index]<< " t:" <<t<<" d:"<<d<<std::endl;
classPoint2T intPoint(T[v0_index][vint_index] + traj.back().time, vint_index);
traj.push_back(intPoint);
// std::cout<<"\t recursiveMove: obtained an intermediate point:"<< intPoint.time <<"-"<<intPoint.vel<<std::endl;
// continue recursiveMove() with new ending time, ending distance and starting velocity
recursiveMove(traj, delta_t, delta_d, vend_index, vint_index, G, T, D);
break;
}
}
}
}
void BtCircleTrain::train2DGaussian(BFImage& bfImage, BFImage& bfHist, const BFCircle& circle, BfGaussian2DPixelClassifier& classifier) {
assert(bfImage.getColorMode() == BF_LAB);
std::vector<BFCoordinate<int> > innerPoints;
BFCircle::getInnerPoints(circle, innerPoints);
int nPoints = innerPoints.size();
Eigen::MatrixXd xT(nPoints,2);
cv::Mat histImg = cv::Mat::zeros(histSize,histSize,CV_8U);
for(int i=0; i<nPoints; i++) {
BFCoordinate<unsigned int> intPoint(static_cast<int>(innerPoints[i].getX()), static_cast<int>(innerPoints[i].getY()));
BFColor color = bfImage.getColor(intPoint);
int aValue = color.getChannel(1);
int bValue = color.getChannel(2);
assert(aValue+abRange >= 0 && aValue+abRange < 2*abRange);
assert(bValue+abRange >= 0 && bValue+abRange < 2*abRange);
histImg.datastart[(bValue+abRange)*histSize + aValue+abRange]++;
xT(i,0) = static_cast<double>(aValue);
xT(i,1) = static_cast<double>(bValue);
}
double meanA = xT.col(0).mean();
double meanB = xT.col(1).mean();
Eigen::VectorXd meanVecA;
Eigen::VectorXd meanVecB;
meanVecA.setConstant(nPoints, meanA);
meanVecB.setConstant(nPoints, meanB);
xT.col(0) -= meanVecA;
xT.col(1) -= meanVecB;
Eigen::Matrix2d C = xT.transpose()*xT/static_cast<double>(nPoints-1);
Eigen::Vector2d mu;
mu << meanA, meanB;
bfHist.setColorMode(BF_GRAYSCALE);
cv::Mat& histNorm = bfHist.getImageMat();
// just for visualization
cv::normalize(histImg,histNorm,255.0,0.0,cv::NORM_INF,-1);
classifier.setC(C);
classifier.setMu(mu);
classifier.calculateEllipse();
}
TEyeXMaybeValue<FVector2D> FEyeXUtils::VirtualDesktopPixelToViewportPixel(FVector2D Point)
{
auto viewport = GetViewport();
if (viewport == nullptr)
{
return TEyeXMaybeValue<FVector2D>(FVector2D::ZeroVector, false);
}
FIntPoint intPoint((int32)Point.X, (int32)Point.Y);
FVector2D viewportPoint = viewport->VirtualDesktopPixelToViewport(intPoint);
auto viewportSize = viewport->GetSizeXY();
FVector2D viewportPixels(viewportPoint.X * viewportSize.X, viewportPoint.Y * viewportSize.Y);
return TEyeXMaybeValue<FVector2D>(viewportPixels);
}
