void ConjGradSolverImpl::minimizeOnTheLine(Ptr<MinProblemSolver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,
Mat_<double>& buf2){
double sigma=INITIAL_SEC_METHOD_SIGMA;
buf1=0.0;
buf2=0.0;
dprintf(("before minimizeOnTheLine\n"));
dprintf(("x:\n"));
print_matrix(x);
dprintf(("d:\n"));
print_matrix(d);
for(int i=0;i<SEC_METHOD_ITERATIONS;i++){
_f->getGradient((double*)x.data,(double*)buf1.data);
dprintf(("buf1:\n"));
print_matrix(buf1);
x=x+sigma*d;
_f->getGradient((double*)x.data,(double*)buf2.data);
dprintf(("buf2:\n"));
print_matrix(buf2);
double d1=buf1.dot(d), d2=buf2.dot(d);
if((d1-d2)==0){
break;
}
double alpha=-sigma*d1/(d2-d1);
dprintf(("(buf2.dot(d)-buf1.dot(d))=%f\nalpha=%f\n",(buf2.dot(d)-buf1.dot(d)),alpha));
x=x+(alpha-sigma)*d;
sigma=-alpha;
}
dprintf(("after minimizeOnTheLine\n"));
print_matrix(x);
}
double ConjGradSolverImpl::minimize(InputOutputArray x){
CV_Assert(_Function.empty()==false);
dprintf(("termcrit:\n\ttype: %d\n\tmaxCount: %d\n\tEPS: %g\n",_termcrit.type,_termcrit.maxCount,_termcrit.epsilon));
Mat x_mat=x.getMat();
CV_Assert(MIN(x_mat.rows,x_mat.cols)==1);
int ndim=MAX(x_mat.rows,x_mat.cols);
CV_Assert(x_mat.type()==CV_64FC1);
if(d.cols!=ndim){
d.create(1,ndim);
r.create(1,ndim);
r_old.create(1,ndim);
minimizeOnTheLine_buf1.create(1,ndim);
minimizeOnTheLine_buf2.create(1,ndim);
}
Mat_<double> proxy_x;
if(x_mat.rows>1){
buf_x.create(1,ndim);
Mat_<double> proxy(ndim,1,buf_x.ptr<double>());
x_mat.copyTo(proxy);
proxy_x=buf_x;
}else{
proxy_x=x_mat;
}
_Function->getGradient(proxy_x.ptr<double>(),d.ptr<double>());
d*=-1.0;
d.copyTo(r);
//here everything goes. check that everything is setted properly
dprintf(("proxy_x\n"));print_matrix(proxy_x);
dprintf(("d first time\n"));print_matrix(d);
dprintf(("r\n"));print_matrix(r);
for(int count=0;count<_termcrit.maxCount;count++){
minimizeOnTheLine(_Function,proxy_x,d,minimizeOnTheLine_buf1,minimizeOnTheLine_buf2);
r.copyTo(r_old);
_Function->getGradient(proxy_x.ptr<double>(),r.ptr<double>());
r*=-1.0;
double r_norm_sq=norm(r);
if(_termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && r_norm_sq<_termcrit.epsilon){
break;
}
r_norm_sq=r_norm_sq*r_norm_sq;
double beta=MAX(0.0,(r_norm_sq-r.dot(r_old))/r_norm_sq);
d=r+beta*d;
}
if(x_mat.rows>1){
Mat(ndim, 1, CV_64F, proxy_x.ptr<double>()).copyTo(x);
}
return _Function->calc(proxy_x.ptr<double>());
}
/**
* @author JIA Pei
* @version 2010-04-03
* @brief Calculate modified steepest descent image for template face - project out appearance variation
* @return void
*/
void VO_AAMInverseIA::VO_CalcModifiedSDI()
{
//project out appearance variation i.e. modify the steepest descent image
this->m_MatSteepestDescentImages = Mat_<float>::zeros(this->m_iNbOfTextures, this->m_iNbOfShapeEigens+4);
this->m_MatModifiedSteepestDescentImages = Mat_<float>::zeros(this->m_iNbOfTextures, this->m_iNbOfShapeEigens+4);
for (unsigned int i = 0; i < this->m_iNbOfTextures; i++)
{
// AAM Revisited (63)
for (unsigned int j = 0; j < 4; j++)
{
this->m_MatSteepestDescentImages(i, j) = this->m_MatSteepestDescentImages4GlobalShapeNormalization(i, j);
}
// AAM Revisited (64)
for (unsigned int j = 0; j < this->m_iNbOfShapeEigens; j++)
{
this->m_MatSteepestDescentImages(i, 4+j) = this->m_MatSteepestDescentImages4ShapeModel(i, j);
}
}
Mat_<float> oneCol = Mat_<float>::zeros(this->m_iNbOfTextures, 1);
Mat_<float> spanedsum = Mat_<float>::zeros(this->m_iNbOfTextures, 1);
Mat_<float> modifiedoneCol = Mat_<float>::zeros(this->m_iNbOfTextures, 1);
Mat_<float> oneSpanRowTranspose = Mat_<float>::zeros(this->m_iNbOfTextures, 1);
for (unsigned int i = 0; i < this->m_MatSteepestDescentImages.cols; i++)
{
spanedsum = Mat_<float>::zeros(this->m_iNbOfTextures, 1);
oneCol = this->m_MatSteepestDescentImages.col(i);
for (unsigned int j = 0; j < this->m_iNbOfTextureEigens; j++)
{
oneSpanRowTranspose = this->m_PCANormalizedTexture.eigenvectors.row(j).t();
double weight = oneSpanRowTranspose.dot(oneCol);
// dst(I)=src1(I)*alpha+src2(I)*beta+gamma
cv::addWeighted( spanedsum, 1.0, oneSpanRowTranspose, weight, 0.0, spanedsum );
}
cv::subtract(oneCol, spanedsum, modifiedoneCol);
Mat_<float> tmpCol = this->m_MatModifiedSteepestDescentImages.col(i);
modifiedoneCol.copyTo(tmpCol);
}
}