template <typename PointInT, typename IntensityT> void
pcl::tracking::PyramidalKLTTracker<PointInT, IntensityT>::mismatchVector (const Eigen::ArrayXXf& prev,
const Eigen::ArrayXXf& prev_grad_x,
const Eigen::ArrayXXf& prev_grad_y,
const FloatImage& next,
const Eigen::Array2i& location,
const Eigen::Array4f& weight,
Eigen::Array2f &b) const
{
const int step = next.width;
b.setZero ();
for (int y = 0; y < track_height_; y++)
{
const float* next_ptr = &(next.points[0]) + (y + location[1])*step + location[0];
const float* prev_ptr = prev.data () + y*prev.cols ();
const float* prev_grad_x_ptr = prev_grad_x.data () + y*prev_grad_x.cols ();
const float* prev_grad_y_ptr = prev_grad_y.data () + y*prev_grad_y.cols ();
for (int x = 0; x < track_width_; ++x, ++prev_grad_y_ptr, ++prev_grad_x_ptr)
{
float diff = next_ptr[x]*weight[0] + next_ptr[x+1]*weight[1]
+ next_ptr[x+step]*weight[2] + next_ptr[x+step+1]*weight[3] - prev_ptr[x];
b[0] += *prev_grad_x_ptr * diff;
b[1] += *prev_grad_y_ptr * diff;
}
}
}
template <typename PointInT, typename IntensityT> void
pcl::tracking::PyramidalKLTTracker<PointInT, IntensityT>::spatialGradient (const FloatImage& img,
const FloatImage& grad_x,
const FloatImage& grad_y,
const Eigen::Array2i& location,
const Eigen::Array4f& weight,
Eigen::ArrayXXf& win,
Eigen::ArrayXXf& grad_x_win,
Eigen::ArrayXXf& grad_y_win,
Eigen::Array3f &covariance) const
{
const int step = img.width;
covariance.setZero ();
for (int y = 0; y < track_height_; y++)
{
const float* img_ptr = &(img.points[0]) + (y + location[1])*step + location[0];
const float* grad_x_ptr = &(grad_x.points[0]) + (y + location[1])*step + location[0];
const float* grad_y_ptr = &(grad_y.points[0]) + (y + location[1])*step + location[0];
float* win_ptr = win.data () + y*win.cols ();
float* grad_x_win_ptr = grad_x_win.data () + y*grad_x_win.cols ();
float* grad_y_win_ptr = grad_y_win.data () + y*grad_y_win.cols ();
for (int x =0; x < track_width_; ++x, ++grad_x_ptr, ++grad_y_ptr)
{
*win_ptr++ = img_ptr[x]*weight[0] + img_ptr[x+1]*weight[1] + img_ptr[x+step]*weight[2] + img_ptr[x+step+1]*weight[3];
float ixval = grad_x_ptr[0]*weight[0] + grad_x_ptr[1]*weight[1] + grad_x_ptr[step]*weight[2] + grad_x_ptr[step+1]*weight[3];
float iyval = grad_y_ptr[0]*weight[0] + grad_y_ptr[1]*weight[1] + grad_y_ptr[step]*weight[2] + grad_y_ptr[step+1]*weight[3];
//!!! store those
*grad_x_win_ptr++ = ixval;
*grad_y_win_ptr++ = iyval;
//covariance components
covariance[0] += ixval*ixval;
covariance[1] += ixval*iyval;
covariance[2] += iyval*iyval;
}
}
}
RepSupernmotif::RepSupernmotif(Eigen::ArrayXXf matALLMotifWeigthed,int nbSupNmotifs,int p_outputOption) {
// TODO Auto-generated constructor stub
if((p_outputOption==0)||(p_outputOption==1))//Compute the super n-motifs representation
{
int maxDim;
if(matALLMotifWeigthed.rows()>matALLMotifWeigthed.cols())
{maxDim=matALLMotifWeigthed.cols();}
else{maxDim=matALLMotifWeigthed.rows();}
if (nbSupNmotifs>maxDim)
{
cerr<<"warning: the number of super n-motifs selected (-k) is greater than the maximum number of computed super n-motifs. -k is set to the maximum number of computed super n-motifs."<<endl;
nbSupNmotifs=maxDim;
}
//compute SVD//
Eigen::JacobiSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU);
//Eigen::BDCSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU | Eigen::ComputeThinV);
singularVal=svd.singularValues();
//Brocken stick model
if(nbSupNmotifs==0)
{
Eigen::RowVectorXf singularVal_precent=singularVal/singularVal.sum();
Eigen::RowVectorXf brModel=singularVal;
float tempbrModel;
for(int i=0; i<brModel.size(); i++)
{
tempbrModel=0;
for(int j=i; j<brModel.size(); j++)
{
tempbrModel+=1/float(j+1);
}
brModel(i)=tempbrModel/brModel.size();
}
int nbSupNmotifs2=0;
while(singularVal_precent(nbSupNmotifs2)>brModel(nbSupNmotifs2))
{nbSupNmotifs2++;}
nbSupNmotifs=nbSupNmotifs2;
if (nbSupNmotifs<2)
{
nbSupNmotifs=2;
}
}
matS_supernmotifs=svd.matrixU().leftCols(nbSupNmotifs);
//matS_supernmotifs=randomizedSVD.matrixU().leftCols(nbSupNmotifs);//Resvd Test
for(int i=0;i<nbSupNmotifs;i++)
{matS_supernmotifs.col(i)=matS_supernmotifs.col(i)*(singularVal(i));}
//Compute the SS dissimilarities using the cosine dissimilarity
//if (p_outputOption==0)
//{matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matS_supernmotifs);}
}
else if(p_outputOption==2)
{
int maxDim;
if(matALLMotifWeigthed.rows()>matALLMotifWeigthed.cols())
{maxDim=matALLMotifWeigthed.cols();}
else{maxDim=matALLMotifWeigthed.rows();}
if (nbSupNmotifs>maxDim)
{
cerr<<"warning: the number of super n-motifs selected (-k) is greater than the maximum number of computed super n-motifs. -k is set to the maximum number of computed super n-motifs."<<endl;
nbSupNmotifs=maxDim;
}
//*******compute SVD*************************//
Eigen::JacobiSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU | Eigen::ComputeThinV);
singularValFull=svd.singularValues();
//Brocken stick model
if(nbSupNmotifs==0)
{
Eigen::RowVectorXf singularVal_precent=singularValFull/singularValFull.sum();
Eigen::RowVectorXf brModel=singularValFull;
float tempbrModel;
for(int i=0; i<brModel.size(); i++)
{
tempbrModel=0;
for(int j=i; j<brModel.size(); j++)
{
tempbrModel+=1/float(j+1);
}
brModel(i)=tempbrModel/brModel.size();
}
int nbSupNmotifs2=0;
while(singularVal_precent(nbSupNmotifs2)>brModel(nbSupNmotifs2))
{nbSupNmotifs2++;}
nbSupNmotifs=nbSupNmotifs2;
if (nbSupNmotifs<2)
{
nbSupNmotifs=2;
}
}
singularVal=singularValFull.head(nbSupNmotifs);
matS_supernmotifs=svd.matrixU().leftCols(nbSupNmotifs);
matNM_supernmotifs=svd.matrixV().leftCols(nbSupNmotifs);
for(int i=0;i<nbSupNmotifs;i++)
{
matS_supernmotifs.col(i)=matS_supernmotifs.col(i)*(singularVal(i));
matNM_supernmotifs.col(i)=matNM_supernmotifs.col(i)*(singularVal(i));
}
//Compute the SS dissimilarities using the cosine dissimilarity
//matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matS_supernmotifs);
matDissimS2NM=repSupermotifComputeMatrixDissimCosS2NM(matS_supernmotifs,matNM_supernmotifs);
}
else if(p_outputOption==3)//Compute the secondary structure dissimilarities based on the n-motifs representation
{
cout<<"No super n-motifs computed (-p 3) ..."<<endl;
//matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matALLMotifWeigthed);
}
else if ((p_outputOption==4))
{
cout<<"No super n-motifs computed (-p 4) ..."<<endl;
}
else if (p_outputOption==5)
{
int maxDim;
if(matALLMotifWeigthed.rows()>matALLMotifWeigthed.cols())
{maxDim=matALLMotifWeigthed.cols();}
else{maxDim=matALLMotifWeigthed.rows();}
//compute SVD
if (nbSupNmotifs>maxDim)
{
cerr<<"warning: the number of super n-motifs selected (-k) is greater than the maximum number of computed super n-motifs. -k is set to the maximum number of computed super n-motifs."<<endl;
nbSupNmotifs=maxDim;
}
//compute SVD//
Eigen::JacobiSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU | Eigen::ComputeThinV);
singularValFull=svd.singularValues();
//Brocken stick model
if(nbSupNmotifs==0)
{
Eigen::RowVectorXf singularVal_precent=singularValFull/singularValFull.sum();
Eigen::RowVectorXf brModel=singularValFull;
float tempbrModel;
for(int i=0; i<brModel.size(); i++)
{
tempbrModel=0;
for(int j=i; j<brModel.size(); j++)
{
tempbrModel+=1/float(j+1);
}
brModel(i)=tempbrModel/brModel.size();
}
int nbSupNmotifs2=0;
while(singularVal_precent(nbSupNmotifs2)>brModel(nbSupNmotifs2))
{nbSupNmotifs2++;}
nbSupNmotifs=nbSupNmotifs2;
if (nbSupNmotifs<2)
{
nbSupNmotifs=2;
}
}
matS_supernmotifsFull=svd.matrixU();
matNM_supernmotifsFull=svd.matrixV();
singularVal=singularValFull.head(nbSupNmotifs);
matS_supernmotifs=matS_supernmotifsFull.leftCols(nbSupNmotifs);
matNM_supernmotifs=matNM_supernmotifsFull.leftCols(nbSupNmotifs);;
//Compute super n-motifs representation of n-motifs
for(int i=0;i<nbSupNmotifs;i++)
{
matS_supernmotifs.col(i)=matS_supernmotifs.col(i)*(singularVal(i));
matNM_supernmotifs.col(i)=matNM_supernmotifs.col(i)*(singularVal(i));
}
//Compute the dissimilarities between SS and n-motifs in the super n-motif space using the cosine dissimilarity
matDissimS2NM=repSupermotifComputeMatrixDissimCosS2NM(matS_supernmotifs,matNM_supernmotifs);
//Compute the SS dissimilarities using the cosine dissimilarity
//matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matS_supernmotifs);
}
}