void F4Res::makeMatrix()
{
// std::cout << "entering makeMatrix()" << std::endl;
auto& myframe = mFrame.level(mThisLevel);
long r = 0;
long comp = 0;
for (auto it = myframe.begin(); it != myframe.end(); ++it)
{
if (it->mDegree == mThisDegree)
{
mSPairs.push_back(Row());
mSPairComponents.push_back(comp);
Row& row = mSPairs[r];
r++;
row.mLeadTerm = it->mMonom;
loadRow(row);
if (M2_gbTrace >= 4)
if (r % 5000 == 0)
std::cout << "makeMatrix sp: " << r
<< " #rows = " << mColumns.size() << std::endl;
}
comp++;
}
// Now we process all monomials in the columns array
while (mNextReducerToProcess < mColumns.size())
{
// Warning: mReducers is being appended to during 'loadRow', and
// since we act on the Row directly, it might get moved on us!
// (actually, it did get moved, which prompted this fix)
Row thisrow;
std::swap(mReducers[mNextReducerToProcess], thisrow);
loadRow(thisrow);
std::swap(mReducers[mNextReducerToProcess], thisrow);
mNextReducerToProcess++;
if (M2_gbTrace >= 4)
if (mNextReducerToProcess % 5000 == 0)
std::cout << "makeMatrix red: " << mNextReducerToProcess
<< " #rows = " << mReducers.size() << std::endl;
}
reorderColumns();
#if 0
debugOutputReducers();
debugOutputColumns();
std :: cout << "-- reducer matrix --" << std::endl;
debugOutputMatrix(mReducers);
debugOutputMatrixSparse(mReducers);
std :: cout << "-- spair matrix --" << std::endl;
debugOutputMatrix(mSPairs);
debugOutputMatrixSparse(mSPairs);
#endif
}
cv::Mat
WavefrontSensor::WavefrontSensing(const std::vector<cv::Mat>& d, const double& meanPowerNoise)
{
unsigned int numberOfZernikes = 20; //total number of zernikes to be considered
int M = numberOfZernikes;
int K = d.size();
cv::Mat Q2;
//We introduce here the lineal relationship between parameter phases of each optical path
partlyKnownDifferencesInPhaseConstraints(M, K, Q2);
std::vector<cv::Mat> Q2_v = {Q2, cv::Mat::zeros(Q2.size(), Q2.type())};
cv::Mat LEC; //Linear equality constraints
cv::merge(Q2_v, LEC); //Build also the complex version of Q2
//process each patch independently
cv::Mat dd;
std::vector<cv::Mat> d_w;
std::vector<Metric> mtrc_v;
std::vector<std::pair<cv::Range,cv::Range> > rngs;
unsigned int pixelsBetweenTiles = (int)(d.front().cols);
unsigned int tileSize = 34;
OpticalSetup tsettings( tileSize );
std::shared_ptr<Zernike> zrnk = std::make_shared<Zernike>(tsettings.pupilRadiousPixels(), tileSize, numberOfZernikes);
divideIntoTiles(d.front().size(), pixelsBetweenTiles, tileSize, rngs);
//Random row selector: Pick incoherent measurements
cv::Mat eye_nn = cv::Mat::eye(K*tileSize*tileSize, K*tileSize*tileSize, cv::DataType<double>::type);
unsigned int a = 400; //number of incoheren measurements
cv::Mat shuffle_eye;
shuffleRows(eye_nn, shuffle_eye);
//Split 'a' into rngs.size() pieces
std::vector<cv::Mat> A_v = {shuffle_eye(cv::Range(0, a), cv::Range::all()), cv::Mat::zeros(a, K*tileSize*tileSize, cv::DataType<double>::type)};
cv::Mat A;
cv::merge(A_v, A);
std::cout << "Number of anisoplanatic patches to annalize at once: " << rngs.size() << std::endl;
for(auto rng_i : rngs)
{
cv::Mat d_col;
//get ready dataset format
std::vector<cv::Mat> D;
std::vector<cv::Mat> d_col_v;
for(cv::Mat di : d)
{
cv::Mat Di;
cv::dft(di(rng_i.first, rng_i.second), Di, cv::DFT_COMPLEX_OUTPUT + cv::DFT_SCALE);
fftShift(Di);
D.push_back(Di);
cv::Mat Di_t(Di.t());
d_col_v.push_back(Di_t.reshape(0, Di_t.total() ));
}
cv::vconcat(d_col_v, d_col);
cv::gemm(A, d_col, 1.0, cv::Mat(), 1.0, d_col); //Picks rows randomly
d_w.push_back( d_col );
mtrc_v.push_back( Metric(D, zrnk, meanPowerNoise) );
}
cv::vconcat(d_w, dd);
//-----------------------BY MEANS OF CONVEX OPTIMIZATION:
//Objective function and gradient of the objective function
if(false)
{
for(auto mtrc : mtrc_v)
{
std::function<double(cv::Mat)> func = std::bind(&Metric::objective, &mtrc, std::placeholders::_1);
std::function<cv::Mat(cv::Mat)> dfunc = std::bind(&Metric::gradient, &mtrc, std::placeholders::_1);
ConvexOptimization minimizationKit;
cv::Mat x0_conv = cv::Mat::zeros(M*K, 1, cv::DataType<double>::type); //reset starting point
//Lambda function that turn minimize function + constraints problem into minimize function lower dimension problem
auto F_constrained = [] (cv::Mat x, std::function<double(cv::Mat)> func, const cv::Mat& Q2) -> double
{
return func(Q2*x);
};
auto DF_constrained = [] (cv::Mat x, std::function<cv::Mat(cv::Mat)> dfunc, const cv::Mat& Q2) -> cv::Mat
{
return Q2.t() * dfunc(Q2*x);
};
std::function<double(cv::Mat)> f_constrained = std::bind(F_constrained, std::placeholders::_1, func, Q2);
std::function<cv::Mat(cv::Mat)> df_constrained = std::bind(DF_constrained, std::placeholders::_1, dfunc, Q2);
//Define a new starting point with lower dimensions after reduction with contraints
cv::Mat p_constrained = Q2.t() * x0_conv;
ConvexOptimization min;
min.perform_BFGS(p_constrained, f_constrained, df_constrained);
x0_conv = Q2 * p_constrained; //Go back to original dimensional
std::cout << "mimumum: " << x0_conv.t() << std::endl;
}
std::cout << "END OF CONVEX OPTIMIZATION" << std::endl;
}
//-----------------------BY MEANS OF SPARSE RECOVERY:
//Create phase_div bias: only for the case of two diversity images!!
// cv::Mat phase_div = cv::Mat::zeros(rngs.size()*M*K, 1, cv::DataType<double>::type);
// phase_div.at<double>(M + 3, 0) = tsettings.k() * 3.141592/(2.0*std::sqrt(3.0));
cv::Mat x0 = cv::Mat::zeros(rngs.size()*M*K, 1, cv::DataType<double>::type); //Starting point
std::vector<double> gamma_v(M*K, 1.0);
for(unsigned int count=0;count<600;++count)
{
std::vector<cv::Mat> x0_vvv;
cv::split(x0, x0_vvv);
x0_vvv.at(0).copyTo(x0);
cv::Mat_<std::complex<double> > blockMatrix_M;
std::vector<cv::Mat> De_v;
for(unsigned int t=0; t < rngs.size(); ++t)
{
cv::Mat jacob_i;
mtrc_v.at(t).jacobian( x0(cv::Range(t*M*K, (t*M*K) + (M*K)), cv::Range::all()), jacob_i );
cv::gemm(A, jacob_i, 1.0, cv::Mat(), 1.0, jacob_i); //Picks rows randomly
cv::gemm(jacob_i, LEC, 1.0, cv::Mat(), 1.0, jacob_i); //Apply constraints LECs
cv::copyMakeBorder(blockMatrix_M, blockMatrix_M, 0, jacob_i.size().height, 0, jacob_i.size().width, cv::BORDER_CONSTANT, cv::Scalar(0.0, 0.0) );
cv::Rect rect(cv::Point(t*jacob_i.size().width, t*jacob_i.size().height), jacob_i.size() );
jacob_i.copyTo(blockMatrix_M( rect ));
cv::Mat De_i;
mtrc_v.at(t).phi( x0(cv::Range(t*M*K, (t*M*K) + (M*K)), cv::Range::all()), De_i );
cv::gemm(A, De_i, 1.0, cv::Mat(), 1.0, De_i); //Picks rows randomly
De_v.push_back( De_i );
}
cv::Mat De;
cv::vconcat(De_v, De);
std::vector<cv::Mat> x0_v = {x0, cv::Mat::zeros(x0.size(), x0.type())};
cv::merge(x0_v, x0);
//Apply algorithm to get solution
unsigned int blkLen = rngs.size();
cv::Mat blockMatrix_M_r;
reorderColumns(blockMatrix_M, M, blockMatrix_M_r); //reorder columns so correlated data form a single block
gamma_v = std::vector<double>(M*K, 1.0);
//cv::Mat coeffs = perform_BSBL(blockMatrix_M_r, dd - De, NoiseLevel::Noiseless, gamma_v, blkLen); //Noiseless, LittleNoise
//cv::Mat coeffs = perform_SBL(blockMatrix_M_r, dd - De, NoiseLevel::Noiseless, gamma_v); //Noiseless, LittleNoise
cv::Mat coeffs = perform_projection(blockMatrix_M_r, dd - De); //Noiseless, LittleNoise
cv::Mat coeffs_r;
reorderColumns(coeffs.t(), blockMatrix_M.cols/M, coeffs_r);
cv::Mat coeffs_r_n(coeffs_r.t());
//Undo constraints
cv::Mat sol = cv::Mat::zeros(x0.size(), cv::DataType<std::complex<double> >::type);
for(unsigned int t=0; t < rngs.size(); ++t)
{
cv::Mat sol_i;
cv::gemm(LEC, coeffs_r_n(cv::Range(t*LEC.cols, (t*LEC.cols) + (LEC.cols)), cv::Range::all()), 1.0, cv::Mat(), 1.0, sol_i);
sol_i.copyTo(sol(cv::Range(t*M*K, (t*M*K) + (M*K)), cv::Range::all()));
}
std::cout << "cv::norm(sol): " << cv::norm(sol) << std::endl;
if(cv::norm(sol) < 1e-4 ) {std::cout << "Solution found" << std::endl; break;}
x0 = x0 - sol;
std::cout << "Solution number: " << count << std::endl;
std::cout << "x0: " << x0.t() << std::endl;
}
return cv::Mat(); //mtrc.F();
}