int main(int argc, char *argv[])
{
using namespace Eigen;
using namespace std;
cout<<"Usage:"<<endl;
cout<<"[space] toggle showing input mesh, output mesh or slice "<<endl;
cout<<" through tet-mesh of convex hull."<<endl;
cout<<"'.'/',' push back/pull forward slicing plane."<<endl;
cout<<endl;
// Load mesh: (V,T) tet-mesh of convex hull, F contains facets of input
// surface mesh _after_ self-intersection resolution
igl::readMESH(TUTORIAL_SHARED_PATH "/big-sigcat.mesh",V,T,F);
// Compute barycenters of all tets
igl::barycenter(V,T,BC);
// Compute generalized winding number at all barycenters
cout<<"Computing winding number over all "<<T.rows()<<" tets..."<<endl;
igl::winding_number(V,F,BC,W);
// Extract interior tets
MatrixXi CT((W.array()>0.5).count(),4);
{
size_t k = 0;
for(size_t t = 0;t<T.rows();t++)
{
if(W(t)>0.5)
{
CT.row(k) = T.row(t);
k++;
}
}
}
// find bounary facets of interior tets
igl::boundary_facets(CT,G);
// boundary_facets seems to be reversed...
G = G.rowwise().reverse().eval();
// normalize
W = (W.array() - W.minCoeff())/(W.maxCoeff()-W.minCoeff());
// Plot the generated mesh
igl::opengl::glfw::Viewer viewer;
update_visualization(viewer);
viewer.callback_key_down = &key_down;
viewer.launch();
}
int calculate_column_width(const Eigen::VectorXd& x,
const std::string& name,
const int sig_figs,
std::ios_base::fmtflags& format) {
int padding = 2;
// Fixed Precision
size_t fixed_threshold = 8;
size_t max_fixed_width = 0;
for (int i = 0; i < x.size(); ++i) {
size_t width = compute_width(x[i], sig_figs);
max_fixed_width = width > max_fixed_width ? width : max_fixed_width;
}
if (max_fixed_width + padding < fixed_threshold) {
format = std::ios_base::fixed;
max_fixed_width = name.length() > max_fixed_width ? name.length() : max_fixed_width;
return max_fixed_width + padding;
}
// Scientific Notation
size_t scientific_width = sig_figs + 1 + 4; // Decimal place + exponent
if (x.minCoeff() < 0) ++scientific_width;
scientific_width = name.length() > scientific_width ? name.length() : scientific_width;
format = std::ios_base::scientific;
return scientific_width + padding;
}
TEST_F(Models_BasicDistributions_Triangle,
Test_Triangle) {
populate_chains();
Eigen::VectorXd y = chains->samples(chains->index("y"));
EXPECT_LE(y.minCoeff(), -0.9)
<< "expecting to get close to the corner";
EXPECT_GE(y.maxCoeff(), 0.9)
<< "expecting to get close to the corner";
}
int calculate_size(const Eigen::VectorXd& x,
const std::string& name,
const int digits,
std::ios_base::fmtflags& format) {
using std::max;
using std::ceil;
using std::log10;
double padding = 0;
if (digits > 0)
padding = digits + 1;
double fixed_size = 0.0;
if (x.maxCoeff() > 0)
fixed_size = ceil(log10(x.maxCoeff()+0.001)) + padding;
if (x.minCoeff() < 0)
fixed_size = max(fixed_size, ceil(log10(-x.minCoeff()+0.01))+(padding+1));
format = std::ios_base::fixed;
if (fixed_size < 7) {
return max(fixed_size,
max(name.length(), std::string("-0.0").length())+0.0);
}
double scientific_size = 0;
scientific_size += 4.0; // "-0.0" has four digits
scientific_size += 1.0; // e
double exponent_size = 0;
if (x.maxCoeff() > 0)
exponent_size = ceil(log10(log10(x.maxCoeff())));
if (x.minCoeff() < 0)
exponent_size = max(exponent_size,
ceil(log10(log10(-x.minCoeff()))));
scientific_size += fmin(exponent_size, 3);
format = std::ios_base::scientific;
return scientific_size;
}
void do_residuals_stats(const Eigen::VectorXd & r, Eigen::VectorXd &stats, std::string & file_hdr)
{
file_hdr = "% MAX_ABS_ERR MIN_ABS_ERR AVERAGE_ERR STD_DEV RMSE MEDIAN\n";
const size_t N = r.size();
stats.resize(6);
if (!N) return;
stats[0] = r.maxCoeff();
stats[1] = r.minCoeff();
mrpt::math::meanAndStd(r, stats[2], stats[3]);
// RMSE:
stats[4] = std::sqrt( r.array().square().sum() / N );
std::vector<double> v(N);
for (size_t i=0;i<N;i++) v[i]=r[i];
nth_element(v.begin(), v.begin()+(N/2), v.end());
stats[5] = v[N/2];
}
void IRLTest::generateData()
{
int num_active_features;
int num_data;
int num_samples_per_data;
double cost_noise_stddev;
num_features_ = 10;
num_active_features = 2;
num_data = 10;
num_samples_per_data = 10;
cost_noise_stddev = 0.1;
// generate random weights
real_weights_ = Eigen::VectorXd::Zero(num_features_);
real_weights_.head(num_active_features).setRandom();
data_.resize(num_data);
for (int i=0; i<num_data; ++i)
{
IRLData* d = new IRLData(num_features_);
Eigen::MatrixXd features = Eigen::MatrixXd::Zero(num_samples_per_data, num_features_);
Eigen::VectorXd target = Eigen::VectorXd::Zero(num_samples_per_data);
features.setRandom();
// compute w^t * phi
Eigen::VectorXd costs = features*real_weights_;
// add random noise to costs
costs += cost_noise_stddev * Eigen::VectorXd::Random(num_samples_per_data);
// set min cost target to 1
int min_index;
double min_cost = costs.minCoeff(&min_index);
target(min_index) = 1.0;
d->addSamples(features, target);
data_[i].reset(d);
}
real_weights_exist_ = true;
}
bool mrpt::vision::pnp::rpnp::compute_pose(Eigen::Ref<Eigen::Matrix3d> R_, Eigen::Ref<Eigen::Vector3d> t_)
{
// selecting an edge $P_{ i1 }P_{ i2 }$ by n random sampling
int i1 = 0, i2 = 1;
double lmin = Q(0, i1)*Q(0, i2) + Q(1, i1)*Q(1, i2) + Q(2, i1)*Q(2, i2);
Eigen::MatrixXi rij (n,2);
R_=Eigen::MatrixXd::Identity(3,3);
t_=Eigen::Vector3d::Zero();
for (int i = 0; i < n; i++)
for (int j = 0; j < 2; j++)
rij(i, j) = rand() % n;
for (int ii = 0; ii < n; ii++)
{
int i = rij(ii, 0), j = rij(ii,1);
if (i == j)
continue;
double l = Q(0, i)*Q(0, j) + Q(1, i)*Q(1, j) + Q(2, i)*Q(2, j);
if (l < lmin)
{
i1 = i;
i2 = j;
lmin = l;
}
}
// calculating the rotation matrix of $O_aX_aY_aZ_a$.
Eigen::Vector3d p1, p2, p0, x, y, z, dum_vec;
p1 = P.col(i1);
p2 = P.col(i2);
p0 = (p1 + p2) / 2;
x = p2 - p0; x /= x.norm();
if (abs(x(1)) < abs(x(2)) )
{
dum_vec << 0, 1, 0;
z = x.cross(dum_vec); z /= z.norm();
y = z.cross(x); y /= y.norm();
}
else
{
dum_vec << 0, 0, 1;
y = dum_vec.cross(x); y /= y.norm();
z = x.cross(y); x /= x.norm();
}
Eigen::Matrix3d R0;
R0.col(0) = x; R0.col(1) =y; R0.col(2) = z;
for (int i = 0; i < n; i++)
P.col(i) = R0.transpose() * (P.col(i) - p0);
// Dividing the n - point set into(n - 2) 3 - point subsets
// and setting up the P3P equations
Eigen::Vector3d v1 = Q.col(i1), v2 = Q.col(i2);
double cg1 = v1.dot(v2);
double sg1 = sqrt(1 - cg1*cg1);
double D1 = (P.col(i1) - P.col(i2)).norm();
Eigen::MatrixXd D4(n - 2, 5);
int j = 0;
Eigen::Vector3d vi;
Eigen::VectorXd rowvec(5);
for (int i = 0; i < n; i++)
{
if (i == i1 || i == i2)
continue;
vi = Q.col(i);
double cg2 = v1.dot(vi);
double cg3 = v2.dot(vi);
double sg2 = sqrt(1 - cg2*cg2);
double D2 = (P.col(i1) - P.col(i)).norm();
double D3 = (P.col(i) - P.col(i2)).norm();
// get the coefficients of the P3P equation from each subset.
rowvec = getp3p(cg1, cg2, cg3, sg1, sg2, D1, D2, D3);
D4.row(j) = rowvec;
j += 1;
if(j>n-3)
break;
}
Eigen::VectorXd D7(8), dumvec(8), dumvec1(5);
D7.setZero();
for (int i = 0; i < n-2; i++)
{
dumvec1 = D4.row(i);
dumvec= getpoly7(dumvec1);
D7 += dumvec;
}
Eigen::PolynomialSolver<double, 7> psolve(D7.reverse());
Eigen::VectorXcd comp_roots = psolve.roots().transpose();
Eigen::VectorXd real_comp, imag_comp;
real_comp = comp_roots.real();
imag_comp = comp_roots.imag();
Eigen::VectorXd::Index max_index;
double max_real= real_comp.cwiseAbs().maxCoeff(&max_index);
std::vector<double> act_roots_;
int cnt=0;
for (int i=0; i<imag_comp.size(); i++ )
{
if(abs(imag_comp(i))/max_real<0.001)
{
act_roots_.push_back(real_comp(i));
cnt++;
}
}
double* ptr = &act_roots_[0];
Eigen::Map<Eigen::VectorXd> act_roots(ptr, cnt);
if (cnt==0)
{
return false;
}
Eigen::VectorXd act_roots1(cnt);
act_roots1 << act_roots.segment(0,cnt);
std::vector<Eigen::Matrix3d> R_cum(cnt);
std::vector<Eigen::Vector3d> t_cum(cnt);
std::vector<double> err_cum(cnt);
for(int i=0; i<cnt; i++)
{
double root = act_roots(i);
// Compute the rotation matrix
double d2 = cg1 + root;
Eigen::Vector3d unitx, unity, unitz;
unitx << 1,0,0;
unity << 0,1,0;
unitz << 0,0,1;
x = v2*d2 -v1;
x/=x.norm();
if (abs(unity.dot(x)) < abs(unitz.dot(x)))
{
z = x.cross(unity);z/=z.norm();
y=z.cross(x); y/y.norm();
}
else
{
y=unitz.cross(x); y/=y.norm();
z = x.cross(y); z/=z.norm();
}
R.col(0)=x;
R.col(1)=y;
R.col(2)=z;
//calculating c, s, tx, ty, tz
Eigen::MatrixXd D(2 * n, 6);
D.setZero();
R0 = R.transpose();
Eigen::VectorXd r(Eigen::Map<Eigen::VectorXd>(R0.data(), R0.cols()*R0.rows()));
for (int j = 0; j<n; j++)
{
double ui = img_pts(j, 0), vi = img_pts(j, 1), xi = P(0, j), yi = P(1, j), zi = P(2, j);
D.row(2 * j) << -r(1)*yi + ui*(r(7)*yi + r(8)*zi) - r(2)*zi,
-r(2)*yi + ui*(r(8)*yi - r(7)*zi) + r(1)*zi,
-1,
0,
ui,
ui*r(6)*xi - r(0)*xi;
D.row(2 * j + 1) << -r(4)*yi + vi*(r(7)*yi + r(8)*zi) - r(5)*zi,
-r(5)*yi + vi*(r(8)*yi - r(7)*zi) + r(4)*zi,
0,
-1,
vi,
vi*r(6)*xi - r(3)*xi;
}
Eigen::MatrixXd DTD = D.transpose()*D;
Eigen::EigenSolver<Eigen::MatrixXd> es(DTD);
Eigen::VectorXd Diag = es.pseudoEigenvalueMatrix().diagonal();
Eigen::MatrixXd V_mat = es.pseudoEigenvectors();
Eigen::MatrixXd::Index min_index;
Diag.minCoeff(&min_index);
Eigen::VectorXd V = V_mat.col(min_index);
V /= V(5);
double c = V(0), s = V(1);
t << V(2), V(3), V(4);
//calculating the camera pose by 3d alignment
Eigen::VectorXd xi, yi, zi;
xi = P.row(0);
yi = P.row(1);
zi = P.row(2);
Eigen::MatrixXd XXcs(3, n), XXc(3,n);
XXc.setZero();
XXcs.row(0) = r(0)*xi + (r(1)*c + r(2)*s)*yi + (-r(1)*s + r(2)*c)*zi + t(0)*Eigen::VectorXd::Ones(n);
XXcs.row(1) = r(3)*xi + (r(4)*c + r(5)*s)*yi + (-r(4)*s + r(5)*c)*zi + t(1)*Eigen::VectorXd::Ones(n);
XXcs.row(2) = r(6)*xi + (r(7)*c + r(8)*s)*yi + (-r(7)*s + r(8)*c)*zi + t(2)*Eigen::VectorXd::Ones(n);
for (int ii = 0; ii < n; ii++)
XXc.col(ii) = Q.col(ii)*XXcs.col(ii).norm();
Eigen::Matrix3d R2;
Eigen::Vector3d t2;
Eigen::MatrixXd XXw = obj_pts.transpose();
calcampose(XXc, XXw, R2, t2);
R_cum[i] = R2;
t_cum[i] = t2;
for (int k = 0; k < n; k++)
XXc.col(k) = R2 * XXw.col(k) + t2;
Eigen::MatrixXd xxc(2, n);
xxc.row(0) = XXc.row(0).array() / XXc.row(2).array();
xxc.row(1) = XXc.row(1).array() / XXc.row(2).array();
double res = ((xxc.row(0) - img_pts.col(0).transpose()).norm() + (xxc.row(1) - img_pts.col(1).transpose()).norm()) / 2;
err_cum[i] = res;
}
int pos_cum = std::min_element(err_cum.begin(), err_cum.end()) - err_cum.begin();
R_ = R_cum[pos_cum];
t_ = t_cum[pos_cum];
return true;
}
int Lemke(const Eigen::MatrixXd& _M, const Eigen::VectorXd& _q,
Eigen::VectorXd* _z) {
int n = _q.size();
const double zer_tol = 1e-5;
const double piv_tol = 1e-8;
int maxiter = 1000;
int err = 0;
if (_q.minCoeff() > 0) {
// LOG(INFO) << "Trivial solution exists.";
*_z = Eigen::VectorXd::Zero(n);
return err;
}
*_z = Eigen::VectorXd::Zero(2 * n);
int iter = 0;
double theta = 0;
double ratio = 0;
int leaving = 0;
Eigen::VectorXd Be = Eigen::VectorXd::Constant(n, 1);
Eigen::VectorXd x = _q;
std::vector<int> bas;
std::vector<int> nonbas;
int t = 2 * n + 1;
int entering = t;
bas.clear();
nonbas.clear();
for (int i = 0; i < n; ++i) {
bas.push_back(i);
}
Eigen::MatrixXd B = -Eigen::MatrixXd::Identity(n, n);
for (int i = 0; i < bas.size(); ++i) {
B.col(bas[i]) = _M.col(bas[i]);
}
x = -B.partialPivLu().solve(_q);
Eigen::VectorXd minuxX = -x;
int lvindex;
double tval = minuxX.maxCoeff(&lvindex);
leaving = bas[lvindex];
bas[lvindex] = t;
Eigen::VectorXd U = Eigen::VectorXd::Zero(n);
for (int i = 0; i < n; ++i) {
if (x[i] < 0)
U[i] = 1;
}
Be = -(B * U);
x += tval * U;
x[lvindex] = tval;
B.col(lvindex) = Be;
for (iter = 0; iter < maxiter; ++iter) {
if (leaving == t) {
break;
} else if (leaving < n) {
entering = n + leaving;
Be = Eigen::VectorXd::Zero(n);
Be[leaving] = -1;
} else {
entering = leaving - n;
Be = _M.col(entering);
}
Eigen::VectorXd d = B.partialPivLu().solve(Be);
std::vector<int> j;
for (int i = 0; i < n; ++i) {
if (d[i] > piv_tol)
j.push_back(i);
}
if (j.empty()) {
// err = 2;
break;
}
int jSize = static_cast<int>(j.size());
Eigen::VectorXd minRatio(jSize);
for (int i = 0; i < jSize; ++i) {
minRatio[i] = (x[j[i]] + zer_tol) / d[j[i]];
}
double theta = minRatio.minCoeff();
std::vector<int> tmpJ;
std::vector<double> tmpMinRatio;
for (int i = 0; i < jSize; ++i) {
if (x[j[i]] / d[j[i]] <= theta) {
tmpJ.push_back(j[i]);
tmpMinRatio.push_back(minRatio[i]);
}
}
// if (tmpJ.empty())
// {
// LOG(WARNING) << "tmpJ should never be empty!!!";
// LOG(WARNING) << "dumping data:";
// LOG(WARNING) << "theta:" << theta;
// for (int i = 0; i < jSize; ++i)
// {
// LOG(WARNING) << "x(" << j[i] << "): " << x[j[i]] << "d: " << d[j[i]];
// }
// }
j = tmpJ;
jSize = static_cast<int>(j.size());
if (jSize == 0) {
err = 4;
break;
}
lvindex = -1;
for (int i = 0; i < jSize; ++i) {
if (bas[j[i]] == t)
lvindex = i;
}
if (lvindex != -1) {
lvindex = j[lvindex];
} else {
theta = tmpMinRatio[0];
lvindex = 0;
for (int i = 0; i < jSize; ++i) {
if (tmpMinRatio[i]-theta > piv_tol) {
theta = tmpMinRatio[i];
lvindex = i;
}
}
lvindex = j[lvindex];
}
leaving = bas[lvindex];
ratio = x[lvindex] / d[lvindex];
bool bDiverged = false;
for (int i = 0; i < n; ++i) {
if (isnan(x[i]) || isinf(x[i])) {
bDiverged = true;
break;
}
}
if (bDiverged) {
err = 4;
break;
}
x = x - ratio * d;
x[lvindex] = ratio;
B.col(lvindex) = Be;
bas[lvindex] = entering;
}
if (iter >= maxiter && leaving != t)
err = 1;
if (err == 0) {
for (size_t i = 0; i < bas.size(); ++i) {
if (bas[i] < _z->size()) {
(*_z)[bas[i]] = x[i];
}
}
Eigen::VectorXd realZ = _z->segment(0, n);
*_z = realZ;
if (!validate(_M, *_z, _q)) {
// _z = VectorXd::Zero(n);
err = 3;
}
} else {
*_z = Eigen::VectorXd::Zero(n); // solve failed, return a 0 vector
}
// if (err == 1)
// LOG(ERROR) << "LCP Solver: Iterations exceeded limit";
// else if (err == 2)
// LOG(ERROR) << "LCP Solver: Unbounded ray";
// else if (err == 3)
// LOG(ERROR) << "LCP Solver: Solver converged with numerical issues. "
// << "Validation failed.";
// else if (err == 4)
// LOG(ERROR) << "LCP Solver: Iteration diverged.";
return err;
}
//#define IGL_LINPROG_VERBOSE
IGL_INLINE bool igl::linprog(
const Eigen::VectorXd & c,
const Eigen::MatrixXd & _A,
const Eigen::VectorXd & b,
const int k,
Eigen::VectorXd & x)
{
// This is a very literal translation of
// http://www.mathworks.com/matlabcentral/fileexchange/2166-introduction-to-linear-algebra/content/strang/linprog.m
using namespace Eigen;
using namespace std;
bool success = true;
// number of constraints
const int m = _A.rows();
// number of original variables
const int n = _A.cols();
// number of iterations
int it = 0;
// maximum number of iterations
//const int MAXIT = 10*m;
const int MAXIT = 100*m;
// residual tolerance
const double tol = 1e-10;
const auto & sign = [](const Eigen::VectorXd & B) -> Eigen::VectorXd
{
Eigen::VectorXd Bsign(B.size());
for(int i = 0;i<B.size();i++)
{
Bsign(i) = B(i)>0?1:(B(i)<0?-1:0);
}
return Bsign;
};
// initial (inverse) basis matrix
VectorXd Dv = sign(sign(b).array()+0.5);
Dv.head(k).setConstant(1.);
MatrixXd D = Dv.asDiagonal();
// Incorporate slack variables
MatrixXd A(_A.rows(),_A.cols()+D.cols());
A<<_A,D;
// Initial basis
VectorXi B = igl::colon<int>(n,n+m-1);
// non-basis, may turn out that vector<> would be better here
VectorXi N = igl::colon<int>(0,n-1);
int j;
double bmin = b.minCoeff(&j);
int phase;
VectorXd xb;
VectorXd s;
VectorXi J;
if(k>0 && bmin<0)
{
phase = 1;
xb = VectorXd::Ones(m);
// super cost
s.resize(n+m+1);
s<<VectorXd::Zero(n+k),VectorXd::Ones(m-k+1);
N.resize(n+1);
N<<igl::colon<int>(0,n-1),B(j);
J.resize(B.size()-1);
// [0 1 2 3 4]
// ^
// [0 1]
// [3 4]
J.head(j) = B.head(j);
J.tail(B.size()-j-1) = B.tail(B.size()-j-1);
B(j) = n+m;
MatrixXd AJ;
igl::slice(A,J,2,AJ);
const VectorXd a = b - AJ.rowwise().sum();
{
MatrixXd old_A = A;
A.resize(A.rows(),A.cols()+a.cols());
A<<old_A,a;
}
D.col(j) = -a/a(j);
D(j,j) = 1./a(j);
}else if(k==m)
{
phase = 2;
xb = b;
s.resize(c.size()+m);
// cost function
s<<c,VectorXd::Zero(m);
}else //k = 0 or bmin >=0
{
phase = 1;
xb = b.array().abs();
s.resize(n+m);
// super cost
s<<VectorXd::Zero(n+k),VectorXd::Ones(m-k);
}
while(phase<3)
{
double df = -1;
int t = std::numeric_limits<int>::max();
// Lagrange mutipliers fro Ax=b
VectorXd yb = D.transpose() * igl::slice(s,B);
while(true)
{
if(MAXIT>0 && it>=MAXIT)
{
#ifdef IGL_LINPROG_VERBOSE
cerr<<"linprog: warning! maximum iterations without convergence."<<endl;
#endif
success = false;
break;
}
// no freedom for minimization
if(N.size() == 0)
{
break;
}
// reduced costs
VectorXd sN = igl::slice(s,N);
MatrixXd AN = igl::slice(A,N,2);
VectorXd r = sN - AN.transpose() * yb;
int q;
// determine new basic variable
double rmin = r.minCoeff(&q);
// optimal! infinity norm
if(rmin>=-tol*(sN.array().abs().maxCoeff()+1))
{
break;
}
// increment iteration count
it++;
// apply Bland's rule to avoid cycling
if(df>=0)
{
if(MAXIT == -1)
{
#ifdef IGL_LINPROG_VERBOSE
cerr<<"linprog: warning! degenerate vertex"<<endl;
#endif
success = false;
}
igl::find((r.array()<0).eval(),J);
double Nq = igl::slice(N,J).minCoeff();
// again seems like q is assumed to be a scalar though matlab code
// could produce a vector for multiple matches
(N.array()==Nq).cast<int>().maxCoeff(&q);
}
VectorXd d = D*A.col(N(q));
VectorXi I;
igl::find((d.array()>tol).eval(),I);
if(I.size() == 0)
{
#ifdef IGL_LINPROG_VERBOSE
cerr<<"linprog: warning! solution is unbounded"<<endl;
#endif
// This seems dubious:
it=-it;
success = false;
break;
}
VectorXd xbd = igl::slice(xb,I).array()/igl::slice(d,I).array();
// new use of r
int p;
{
double r;
r = xbd.minCoeff(&p);
p = I(p);
// apply Bland's rule to avoid cycling
if(df>=0)
{
igl::find((xbd.array()==r).eval(),J);
double Bp = igl::slice(B,igl::slice(I,J)).minCoeff();
// idiotic way of finding index in B of Bp
// code down the line seems to assume p is a scalar though the matlab
// code could find a vector of matches)
(B.array()==Bp).cast<int>().maxCoeff(&p);
}
// update x
xb -= r*d;
xb(p) = r;
// change in f
df = r*rmin;
}
// row vector
RowVectorXd v = D.row(p)/d(p);
yb += v.transpose() * (s(N(q)) - d.transpose()*igl::slice(s,B));
d(p)-=1;
// update inverse basis matrix
D = D - d*v;
t = B(p);
B(p) = N(q);
if(t>(n+k-1))
{
// remove qth entry from N
VectorXi old_N = N;
N.resize(N.size()-1);
N.head(q) = old_N.head(q);
N.head(q) = old_N.head(q);
N.tail(old_N.size()-q-1) = old_N.tail(old_N.size()-q-1);
}else
{
N(q) = t;
}
}
// iterative refinement
xb = (xb+D*(b-igl::slice(A,B,2)*xb)).eval();
// must be due to rounding
VectorXi I;
igl::find((xb.array()<0).eval(),I);
if(I.size()>0)
{
// so correct
VectorXd Z = VectorXd::Zero(I.size(),1);
igl::slice_into(Z,I,xb);
}
// B, xb,n,m,res=A(:,B)*xb-b
if(phase == 2 || it<0)
{
break;
}
if(xb.transpose()*igl::slice(s,B) > tol)
{
it = -it;
#ifdef IGL_LINPROG_VERBOSE
cerr<<"linprog: warning, no feasible solution"<<endl;
#endif
success = false;
break;
}
// re-initialize for Phase 2
phase = phase+1;
s*=1e6*c.array().abs().maxCoeff();
s.head(n) = c;
}
x.resize(std::max(B.maxCoeff()+1,n));
igl::slice_into(xb,B,x);
x = x.head(n).eval();
return success;
}
void propa_2d()
{
trace.beginBlock("2d propagation");
typedef DiscreteExteriorCalculus<2, 2, EigenLinearAlgebraBackend> Calculus;
typedef DiscreteExteriorCalculusFactory<EigenLinearAlgebraBackend> CalculusFactory;
{
trace.beginBlock("solving time dependent equation");
const Calculus::Scalar cc = 8; // px/s
trace.info() << "cc = " << cc << endl;
const Z2i::Domain domain(Z2i::Point(0,0), Z2i::Point(29,29));
const Calculus calculus = CalculusFactory::createFromDigitalSet(generateDiskSet(domain), false);
//! [time_laplace]
const Calculus::DualIdentity0 laplace = calculus.laplace<DUAL>() + 1e-8 * calculus.identity<0, DUAL>();
//! [time_laplace]
trace.info() << "laplace = " << laplace << endl;
trace.beginBlock("finding eigen pairs");
//! [time_eigen]
typedef Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> EigenSolverMatrix;
const EigenSolverMatrix eigen_solver(laplace.myContainer);
const Eigen::VectorXd eigen_values = eigen_solver.eigenvalues();
const Eigen::MatrixXd eigen_vectors = eigen_solver.eigenvectors();
//! [time_eigen]
trace.info() << "eigen_values_range = " << eigen_values.minCoeff() << "/" << eigen_values.maxCoeff() << endl;
trace.endBlock();
//! [time_omega]
const Eigen::VectorXd angular_frequencies = cc * eigen_values.array().sqrt();
//! [time_omega]
Eigen::VectorXcd initial_wave = Eigen::VectorXcd::Zero(calculus.kFormLength(0, DUAL));
//set_initial_phase_null(calculus, initial_wave);
set_initial_phase_dir_yy(calculus, initial_wave);
{
Board2D board;
board << domain;
board << CustomStyle("KForm", new KFormStyle2D(-1, 1));
board << Calculus::DualForm0(calculus, initial_wave.real());
board.saveSVG("propagation_time_wave_initial_coarse.svg");
}
//! [time_init_proj]
Eigen::VectorXcd initial_projections = eigen_vectors.transpose() * initial_wave;
//! [time_init_proj]
// low pass
//! [time_low_pass]
const Calculus::Scalar lambda_cutoff = 4.5;
const Calculus::Scalar angular_frequency_cutoff = 2*M_PI * cc / lambda_cutoff;
int cutted = 0;
for (int kk=0; kk<initial_projections.rows(); kk++)
{
const Calculus::Scalar angular_frequency = angular_frequencies(kk);
if (angular_frequency < angular_frequency_cutoff) continue;
initial_projections(kk) = 0;
cutted ++;
}
//! [time_low_pass]
trace.info() << "cutted = " << cutted << "/" << initial_projections.rows() << endl;
{
const Eigen::VectorXcd wave = eigen_vectors * initial_projections;
Board2D board;
board << domain;
board << CustomStyle("KForm", new KFormStyle2D(-1, 1));
board << Calculus::DualForm0(calculus, wave.real());
board.saveSVG("propagation_time_wave_initial_smoothed.svg");
}
const int kk_max = 60;
trace.progressBar(0,kk_max);
for (int kk=0; kk<kk_max; kk++)
{
//! [time_solve_time]
const Calculus::Scalar time = kk/20.;
const Eigen::VectorXcd current_projections = (angular_frequencies * std::complex<double>(0,time)).array().exp() * initial_projections.array();
const Eigen::VectorXcd current_wave = eigen_vectors * current_projections;
//! [time_solve_time]
std::stringstream ss;
ss << "propagation_time_wave_solution_" << std::setfill('0') << std::setw(3) << kk << ".svg";
Board2D board;
board << domain;
board << CustomStyle("KForm", new KFormStyle2D(-1, 1));
board << Calculus::DualForm0(calculus, current_wave.real());
board.saveSVG(ss.str().c_str());
trace.progressBar(kk+1,kk_max);
}
trace.info() << endl;
trace.endBlock();
}
{
trace.beginBlock("forced oscillations");
const Z2i::Domain domain(Z2i::Point(0,0), Z2i::Point(50,50));
const Calculus calculus = CalculusFactory::createFromDigitalSet(generateDiskSet(domain), false);
const Calculus::DualIdentity0 laplace = calculus.laplace<DUAL>();
trace.info() << "laplace = " << laplace << endl;
trace.beginBlock("finding eigen pairs");
typedef Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> EigenSolverMatrix;
const EigenSolverMatrix eigen_solver(laplace.myContainer);
const Eigen::VectorXd laplace_eigen_values = eigen_solver.eigenvalues();
const Eigen::MatrixXd laplace_eigen_vectors = eigen_solver.eigenvectors();
trace.info() << "eigen_values_range = " << laplace_eigen_values.minCoeff() << "/" << laplace_eigen_values.maxCoeff() << endl;
trace.endBlock();
Calculus::DualForm0 concentration(calculus);
{
const Z2i::Point point(25,25);
const Calculus::Cell cell = calculus.myKSpace.uSpel(point);
const Calculus::Index index = calculus.getCellIndex(cell);
concentration.myContainer(index) = 1;
}
{
Board2D board;
board << domain;
board << concentration;
board.saveSVG("propagation_forced_concentration.svg");
}
for (int ll=0; ll<6; ll++)
{
//! [forced_lambda]
const Calculus::Scalar lambda = 4*20.75/(1+2*ll);
//! [forced_lambda]
trace.info() << "lambda = " << lambda << endl;
//! [forced_dalembert_eigen]
const Eigen::VectorXd dalembert_eigen_values = (laplace_eigen_values.array() - (2*M_PI/lambda)*(2*M_PI/lambda)).array().inverse();
const Eigen::MatrixXd concentration_to_wave = laplace_eigen_vectors * dalembert_eigen_values.asDiagonal() * laplace_eigen_vectors.transpose();
//! [forced_dalembert_eigen]
//! [forced_wave]
Calculus::DualForm0 wave(calculus, concentration_to_wave * concentration.myContainer);
//! [forced_wave]
wave.myContainer /= wave.myContainer(calculus.getCellIndex(calculus.myKSpace.uSpel(Z2i::Point(25,25))));
{
trace.info() << "saving samples" << endl;
const Calculus::Properties properties = calculus.getProperties();
{
std::stringstream ss;
ss << "propagation_forced_samples_hv_" << ll << ".dat";
std::ofstream handle(ss.str().c_str());
for (int kk=0; kk<=50; kk++)
{
const Z2i::Point point_horizontal(kk,25);
const Z2i::Point point_vertical(25,kk);
const Calculus::Scalar xx = 2 * (kk/50. - .5);
handle << xx << " ";
handle << sample_dual_0_form<Calculus>(properties, wave, point_horizontal) << " ";
handle << sample_dual_0_form<Calculus>(properties, wave, point_vertical) << endl;
}
}
{
std::stringstream ss;
ss << "propagation_forced_samples_diag_" << ll << ".dat";
std::ofstream handle(ss.str().c_str());
for (int kk=0; kk<=50; kk++)
{
const Z2i::Point point_diag_pos(kk,kk);
const Z2i::Point point_diag_neg(kk,50-kk);
const Calculus::Scalar xx = 2 * sqrt(2) * (kk/50. - .5);
handle << xx << " ";
handle << sample_dual_0_form<Calculus>(properties, wave, point_diag_pos) << " ";
handle << sample_dual_0_form<Calculus>(properties, wave, point_diag_neg) << endl;
}
}
}
{
std::stringstream ss;
ss << "propagation_forced_wave_" << ll << ".svg";
Board2D board;
board << domain;
board << CustomStyle("KForm", new KFormStyle2D(-.5, 1));
board << wave;
board.saveSVG(ss.str().c_str());
}
}
trace.endBlock();
}
trace.endBlock();
}
int main(int argc, char * argv[])
{
using namespace Eigen;
using namespace std;
using namespace igl;
if(!readMESH("../shared/octopus-low.mesh",low.V,low.T,low.F))
{
cout<<"failed to load mesh"<<endl;
}
if(!readMESH("../shared/octopus-high.mesh",high.V,high.T,high.F))
{
cout<<"failed to load mesh"<<endl;
}
// Precomputation
{
Eigen::VectorXi b;
{
Eigen::VectorXi J = Eigen::VectorXi::LinSpaced(high.V.rows(),0,high.V.rows()-1);
Eigen::VectorXd sqrD;
Eigen::MatrixXd _2;
cout<<"Finding closest points..."<<endl;
igl::point_mesh_squared_distance(low.V,high.V,J,sqrD,b,_2);
assert(sqrD.minCoeff() < 1e-7 && "low.V should exist in high.V");
}
// force perfect positioning, rather have popping in low-res than high-res.
// The correct/elaborate thing to do is express original low.V in terms of
// linear interpolation (or extrapolation) via elements in (high.V,high.F)
igl::slice(high.V,b,1,low.V);
// list of points --> list of singleton lists
std::vector<std::vector<int> > S;
igl::matrix_to_list(b,S);
cout<<"Computing weights for "<<b.size()<<
" handles at "<<high.V.rows()<<" vertices..."<<endl;
// Technically k should equal 3 for smooth interpolation in 3d, but 2 is
// faster and looks OK
const int k = 2;
igl::biharmonic_coordinates(high.V,high.T,S,k,W);
cout<<"Reindexing..."<<endl;
// Throw away interior tet-vertices, keep weights and indices of boundary
VectorXi I,J;
igl::remove_unreferenced(high.V.rows(),high.F,I,J);
for_each(high.F.data(),high.F.data()+high.F.size(),[&I](int & a){a=I(a);});
for_each(b.data(),b.data()+b.size(),[&I](int & a){a=I(a);});
igl::slice(MatrixXd(high.V),J,1,high.V);
igl::slice(MatrixXd(W),J,1,W);
}
// Resize low res (high res will also be resized by affine precision of W)
low.V.rowwise() -= low.V.colwise().mean();
low.V /= (low.V.maxCoeff()-low.V.minCoeff());
low.V.rowwise() += RowVector3d(0,1,0);
low.U = low.V;
high.U = high.V;
arap_data.with_dynamics = true;
arap_data.max_iter = 10;
arap_data.energy = ARAP_ENERGY_TYPE_DEFAULT;
arap_data.h = 0.01;
arap_data.ym = 0.001;
if(!arap_precomputation(low.V,low.T,3,VectorXi(),arap_data))
{
cerr<<"arap_precomputation failed."<<endl;
return EXIT_FAILURE;
}
// Constant gravitational force
Eigen::SparseMatrix<double> M;
igl::massmatrix(low.V,low.T,igl::MASSMATRIX_TYPE_DEFAULT,M);
const size_t n = low.V.rows();
arap_data.f_ext = M * RowVector3d(0,-9.8,0).replicate(n,1);
// Random initial velocities to wiggle things
arap_data.vel = MatrixXd::Random(n,3);
igl::viewer::Viewer viewer;
// Create one huge mesh containing both meshes
igl::cat(1,low.U,high.U,scene.U);
igl::cat(1,low.F,MatrixXi(high.F.array()+low.V.rows()),scene.F);
// Color each mesh
viewer.data.set_mesh(scene.U,scene.F);
MatrixXd C(scene.F.rows(),3);
C<<
RowVector3d(0.8,0.5,0.2).replicate(low.F.rows(),1),
RowVector3d(0.3,0.4,1.0).replicate(high.F.rows(),1);
viewer.data.set_colors(C);
viewer.callback_key_pressed =
[&](igl::viewer::Viewer & viewer,unsigned int key,int mods)->bool
{
switch(key)
{
default:
return false;
case ' ':
viewer.core.is_animating = !viewer.core.is_animating;
return true;
case 'r':
low.U = low.V;
return true;
}
};
viewer.callback_pre_draw = [&](igl::viewer::Viewer & viewer)->bool
{
glEnable(GL_CULL_FACE);
if(viewer.core.is_animating)
{
arap_solve(MatrixXd(0,3),arap_data,low.U);
for(int v = 0;v<low.U.rows();v++)
{
// collide with y=0 plane
const int y = 1;
if(low.U(v,y) < 0)
{
low.U(v,y) = -low.U(v,y);
// ~ coefficient of restitution
const double cr = 1.1;
arap_data.vel(v,y) = - arap_data.vel(v,y) / cr;
}
}
scene.U.block(0,0,low.U.rows(),low.U.cols()) = low.U;
high.U = W * (low.U.rowwise() + RowVector3d(1,0,0));
scene.U.block(low.U.rows(),0,high.U.rows(),high.U.cols()) = high.U;
viewer.data.set_vertices(scene.U);
viewer.data.compute_normals();
}
return false;
};
viewer.core.show_lines = false;
viewer.core.is_animating = true;
viewer.core.animation_max_fps = 30.;
viewer.data.set_face_based(true);
cout<<R"(
[space] to toggle animation
'r' to reset positions
)";
viewer.core.rotation_type =
igl::viewer::ViewerCore::ROTATION_TYPE_TWO_AXIS_VALUATOR_FIXED_UP;
viewer.launch();
}
Eigen::RowVectorXd scaledValues(Eigen::VectorXd const& x)
{
return x.unaryExpr(scaledValue(x.minCoeff(),x.maxCoeff())).transpose();
}
