MatrixX3f Surface::compute_normals(const MatrixX3f& rr, const MatrixX3i& tris)
{
printf("\tcomputing normals\n");
// first, compute triangle normals
MatrixX3f r1(tris.rows(),3); MatrixX3f r2(tris.rows(),3); MatrixX3f r3(tris.rows(),3);
for(qint32 i = 0; i < tris.rows(); ++i)
{
r1.row(i) = rr.row(tris(i, 0));
r2.row(i) = rr.row(tris(i, 1));
r3.row(i) = rr.row(tris(i, 2));
}
MatrixX3f x = r2 - r1;
MatrixX3f y = r3 - r1;
MatrixX3f tri_nn(x.rows(),y.cols());
tri_nn.col(0) = x.col(1).cwiseProduct(y.col(2)) - x.col(2).cwiseProduct(y.col(1));
tri_nn.col(1) = x.col(2).cwiseProduct(y.col(0)) - x.col(0).cwiseProduct(y.col(2));
tri_nn.col(2) = x.col(0).cwiseProduct(y.col(1)) - x.col(1).cwiseProduct(y.col(0));
// Triangle normals and areas
MatrixX3f tmp = tri_nn.cwiseProduct(tri_nn);
VectorXf normSize = tmp.rowwise().sum();
normSize = normSize.cwiseSqrt();
for(qint32 i = 0; i < normSize.size(); ++i)
if(normSize(i) != 0)
tri_nn.row(i) /= normSize(i);
MatrixX3f nn = MatrixX3f::Zero(rr.rows(), 3);
for(qint32 p = 0; p < tris.rows(); ++p)
{
Vector3i verts = tris.row(p);
for(qint32 j = 0; j < verts.size(); ++j)
nn.row(verts(j)) = tri_nn.row(p);
}
tmp = nn.cwiseProduct(nn);
normSize = tmp.rowwise().sum();
normSize = normSize.cwiseSqrt();
for(qint32 i = 0; i < normSize.size(); ++i)
if(normSize(i) != 0)
nn.row(i) /= normSize(i);
return nn;
}
Image smooth_detector(const Image& source, Interpolation level, int r) {
Image output(source.rows(), source.columns(), 1, numeric_limits<float>::max());
const MatrixXf reg_matrix = ComputeRegMatrix(level, r);
const LDLT<MatrixXf> solver = (reg_matrix.transpose() * reg_matrix).ldlt();
for (int pr = 0; pr <= source.rows() - r; ++pr) {
for (int pc = 0; pc <= source.columns() - r; ++pc) {
VectorXf dist = VectorXf::Zero(r * r);
for (int ch = 0; ch < source.channels(); ++ch) {
EigenImage y = ExtractPatch(source, r, pr, pc, ch);
VectorXf reg_surf = solver.solve(reg_matrix.transpose() * y.asvector());
dist += (reg_matrix * reg_surf - y.asvector()).cwiseAbs2();
}
dist = dist.cwiseSqrt();
for (int row = pr; row < min(output.rows(), pr + r); ++row) {
for (int col = pc; col < min(output.columns(), pc + r); ++col) {
output.val(col, row) = min(output.val(col, row), dist((row - pr) * r + col - pc));
}
}
}
}
return output;
}
