RMatrixXf evalBoundary( const RMatrixXf & d, const std::vector<RMatrixXb> & bnd, int nthres, double max_r, const RMatrixXb & mask ) { RMatrixXf r( nthres, 5 ); for( int i=0; i<nthres; i++ ) { float t = 1.0 * i / nthres; r(i,0) = t; RMatrixXb tmp = d.array() > t; if ( t > 0 ) thinningGuoHall( tmp ); r.block(i,1,1,4) = evalBoundaryBinary( tmp, bnd, max_r, mask ).cast<float>().transpose(); } return r; }
VectorXf project1D( const RMatrixXf & Y, int * rep_label=NULL ) { // const int MAX_SAMPLE = 20000; const bool fast = true, very_fast = true; // Remove the DC (Y : N x M) RMatrixXf dY = Y.rowwise() - Y.colwise().mean(); // RMatrixXf sY = dY; // if( 0 < MAX_SAMPLE && MAX_SAMPLE < dY.rows() ) { // VectorXi samples = randomChoose( dY.rows(), MAX_SAMPLE ); // std::sort( samples.data(), samples.data()+samples.size() ); // sY = RMatrixXf( samples.size(), dY.cols() ); // for( int i=0; i<samples.size(); i++ ) // sY.row(i) = dY.row( samples[i] ); // } // ... and use (pc > 0) VectorXf lbl = VectorXf::Zero( Y.rows() ); // Find the largest PC of (dY.T * dY) and project onto it if( very_fast ) { // Find the largest PC using poweriterations VectorXf U = VectorXf::Random( dY.cols() ); U = U.array() / U.norm()+std::numeric_limits<float>::min(); for( int it=0; it<20; it++ ){ // Normalize VectorXf s = dY.transpose()*(dY*U); s.array() /= s.norm()+std::numeric_limits<float>::min(); if ( (U-s).norm() < 1e-6 ) break; U = s; } // Project onto the PC lbl = dY*U; } else if(fast) { // Compute the eigen values of the covariance (and project onto the largest eigenvector) MatrixXf cov = dY.transpose()*dY; SelfAdjointEigenSolver<MatrixXf> eigensolver(0.5*(cov+cov.transpose())); MatrixXf ev = eigensolver.eigenvectors(); lbl = dY * ev.col( ev.cols()-1 ); } else { // Use the SVD JacobiSVD<RMatrixXf> svd = dY.jacobiSvd(ComputeThinU | ComputeThinV ); // Project onto the largest PC lbl = svd.matrixU().col(0) * svd.singularValues()[0]; } // Find the representative label if( rep_label ) dY.array().square().rowwise().sum().minCoeff( rep_label ); return (lbl.array() < 0).cast<float>(); }
RMatrixXf SeedFeature::computeObjFeatures( const ImageOverSegmentation & ios ) { Image rgb_im = ios.image(); const RMatrixXs & s = ios.s(); const Edges & g = ios.edges(); const int Ns = ios.Ns(); RMatrixXf r = RMatrixXf::Zero( Ns, N_OBJ_F ); if( N_OBJ_F<=1 ) return r; VectorXf area = bin( s, 1, [&](int x, int y){ return 1.f; } ); VectorXf norm = (area.array()+1e-10).cwiseInverse(); r.col(0).setOnes(); int o = 1; if (N_OBJ_COL>=6) { Image lab_im; rgb2lab( lab_im, rgb_im ); r.middleCols(o,6) = norm.asDiagonal() * bin( s, 6, [&](int x, int y){ return makeArray<6>( lab_im(y,x,0), lab_im(y,x,1), lab_im(y,x,2), lab_im(y,x,0)*lab_im(y,x,0), lab_im(y,x,1)*lab_im(y,x,1), lab_im(y,x,2)*lab_im(y,x,2) ); } ); RMatrixXf col = r.middleCols(o,3); if( N_OBJ_COL >= 9) r.middleCols(o+6,3) = col.array().square(); o += N_OBJ_COL; // Add color difference features if( N_OBJ_COL_DIFF ) { RMatrixXf bcol = RMatrixXf::Ones( col.rows(), col.cols()+1 ); bcol.leftCols(3) = col; for( int it=0; it*3+2<N_OBJ_COL_DIFF; it++ ) { // Apply a box filter on the graph RMatrixXf tmp = bcol; for( const auto & e: g ) { tmp.row(e.a) += bcol.row(e.b); tmp.row(e.b) += bcol.row(e.a); } bcol = tmp.col(3).cwiseInverse().asDiagonal()*tmp; r.middleCols(o,3) = (bcol.leftCols(3)-col).array().abs(); o += 3; } } } if( N_OBJ_POS >= 2 ) { RMatrixXf xy = norm.asDiagonal() * bin( s, 2, [&](int x, int y){ return makeArray<2>( 1.0*x/(s.cols()-1)-0.5, 1.0*y/(s.rows()-1)-0.5 ); } ); r.middleCols(o,2) = xy; o+=2; if( N_OBJ_POS >=4 ) { r.middleCols(o,2) = xy.array().square(); o+=2; } } if( N_OBJ_EDGE ) { RMatrixXf edge_map = DirectedSobel().detect( rgb_im ); for( int j=0; j<s.rows(); j++ ) for( int i=0; i<s.cols(); i++ ) { const int id = s(j,i); int bin = edge_map(j,i)*N_OBJ_EDGE; if ( bin < 0 ) bin = 0; if ( bin >= N_OBJ_EDGE ) bin = N_OBJ_EDGE-1; r(id,o+bin) += norm[id]; } o += N_OBJ_EDGE; } const int N_BASIC = o-1; // Add in context features for( int i=0; i<N_OBJ_CONTEXT; i++ ) { const int o0 = o - N_BASIC; // Box filter the edges RMatrixXf f = RMatrixXf::Ones( Ns, N_BASIC+1 ), bf = RMatrixXf::Zero( Ns, N_BASIC+1 ); f.rightCols( N_BASIC ) = r.middleCols(o0,N_BASIC); for( Edge e: g ) { bf.row(e.a) += f.row(e.b); bf.row(e.b) += f.row(e.a); } r.middleCols(o,N_BASIC) = bf.col(0).array().max(1e-10f).inverse().matrix().asDiagonal() * bf.rightCols(N_BASIC); o += N_BASIC; } return r; }