Ponca basic CPU

This is an example of how to use Ponca to compute the GLS Geometric variation on random data.

/*
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
*/
 
 
#include <cmath>
#include <algorithm>
#include <iostream>
 
#include <Ponca/src/Fitting/basket.h>
#include <Ponca/src/Fitting/gls.h>
#include <Ponca/src/Fitting/orientedSphereFit.h>
#include <Ponca/src/Fitting/unorientedSphereFit.h>
#include <Ponca/src/Fitting/sphereFit.h>
#include <Ponca/src/Fitting/weightFunc.h>
#include <Ponca/src/Fitting/weightKernel.h>
#include <Ponca/src/Fitting/curvatureEstimation.h>
#include <Ponca/src/Fitting/curvature.h>
 
#include <Ponca/SpatialPartitioning>
#include <Ponca/src/Common/pointTypes.h>
#include <Ponca/src/Common/pointGeneration.h>
 
#include <vector>
 
using namespace std;
using namespace Ponca;
 
using MyPoint = PointPositionNormal<double, 3>;
typedef MyPoint::Scalar Scalar;
typedef MyPoint::VectorType VectorType;
 
// Define related structure
typedef DistWeightFunc<MyPoint,SmoothWeightKernel<Scalar> > WeightFunc;
using Fit1 = Basket<MyPoint,WeightFunc,OrientedSphereFit,   GLSParam>;
using Fit2 = Basket<MyPoint,WeightFunc,UnorientedSphereFit, GLSParam>;
using Fit3 = BasketDiff< Fit1, FitSpaceDer, OrientedSphereDer, GLSDer, CurvatureEstimatorBase, NormalDerivativesCurvatureEstimator>;
using Fit4 = Basket<MyPoint,WeightFunc,SphereFit, GLSParam>;
 
template<typename Fit>
void test_fit(Fit& _fit, const KdTree<MyPoint>& tree, const VectorType& _p)
{
  Scalar tmax = 100.0;
 
  // Set a weighting function instance
  _fit.setNeighborFilter({_p, tmax});
 
  _fit.init();
 
  // Iterate over samples and _fit the primitive
  for(int i : tree.rangeNeighbors(_p, tmax) )
  {
      _fit.addNeighbor( tree.points()[i] );
  }
 
  //finalize fitting
  _fit.finalize();
 
  //Test if the fitting ended without errors
  if(_fit.isStable())
  {
    cout << "Center: [" << _fit.center().transpose() << "] ;  radius: " << _fit.radius() << endl;
 
    cout << "Pratt normalization"
      << (_fit.applyPrattNorm() ? " is now done." : " has already been applied.") << endl;
 
    // Play with fitting output
    cout << "Value of the scalar field at the initial point: "
      << _p.transpose()
      << " is equal to " << _fit.potential(_p)
      << endl;
 
    cout << "It's gradient at this place is equal to: "
      << _fit.primitiveGradient(_p).transpose()
      << endl;
 
    cout << "Fitted Sphere: " << endl
      << "\t Tau  : "      << _fit.tau()             << endl
      << "\t Eta  : "      << _fit.eta().transpose() << endl
      << "\t Kappa: "      << _fit.kappa()           << endl;
 
    cout << "The initial point " << _p.transpose()              << endl
      << "Is projected at   " << _fit.project(_p).transpose() << endl;
  }
 
}
 
int main()
{
  // set evaluation point and scale
  VectorType p = VectorType::Random();
 
  // init input data
  int n = 10000;
  vector<MyPoint> vecs (n);
  std::generate(vecs.begin(), vecs.end(), getRandomPoint<MyPoint>);
 
  p = vecs.at(0).pos();
 
  KdTreeDense<MyPoint> tree {vecs};
 
  std::cout << "====================\nOrientedSphereFit:\n";
  Fit1 fit1;
  test_fit(fit1, tree, p);
 
  std::cout << "\n\n====================\nUnorientedSphereFit:\n";
  Fit2 fit2;
  test_fit(fit2, tree, p);
 
  std::cout << "\n\n====================\nUnorientedSphereFit:\n";
  Fit3 fit3;
  test_fit(fit3, tree, p);
 
  if(fit3.isStable())
  {
    cout << "eigen values: "<< endl;
    cout << fit3.kmin() << endl;
    cout << fit3.kmax() << endl;
    cout << "eigen vectors: "<< endl;
    cout << fit3.kminDirection() << endl << endl;
    cout << fit3.kmaxDirection() << endl;
  }
 
  std::cout << "\n\n====================\nSphereFit:\n";
  Fit4 fit4;
  test_fit(fit4, tree, p);
 
  return 0;
}