コード例 #1
0
int main(int argc, char** argv) {
	google::InitGoogleLogging(argv[0]);

	// The variable to solve for with its initial value. It will be
	// mutated in place by the solver.
	double x = 0.5;
	const double initial_x = x;

	// Build the problem.
	Problem problem;

	// Set up the only cost function (also known as residual). This uses
	// auto-differentiation to obtain the derivative (jacobian).
	CostFunction* cost_function =
		new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
	problem.AddResidualBlock(cost_function, NULL, &x);

	// Run the solver!
	Solver::Options options;
	options.minimizer_progress_to_stdout = true;
	Solver::Summary summary;
	Solve(options, &problem, &summary);

	std::cout << summary.BriefReport() << "\n";
	std::cout << "x : " << initial_x
		<< " -> " << x << "\n";
	return 0;
}
コード例 #2
0
int main(int argc, char** argv){

	std::string help = 
		"Locates the 3D position of a sound source\n"
		"Arguments: <timestamp1> <timestamp2> <timestamp3> <timestamp4>\n"
		"Note: \n\ttimestamps must be in the correct order to obtain meaningful result\n";
	
	if(std::strcmp(argv[1], "-h") == 0){
		std::cout << help << std::endl;
	} else if(argc < 5){
		std::cout << "Usage:\n" << argv[0] 
			<< " <timestamp1> <timestamp2> <timestamp3> <timestamp4>"
			<< std::endl;
	}

	double ts1 = std::atof(argv[1]);
	double ts2 = std::atof(argv[2]);
	double ts3 = std::atof(argv[3]);
	double ts4 = std::atof(argv[4]);

	const double initial_x = 10;
	const double initial_y = 0;
	const double initial_z = 0;
	const double initial_t = ts1;
	double x = initial_x;
	double y = initial_y;
	double z = initial_z;
	double t = initial_t;

	Problem problem;
	CostFunction* h1cost = new AutoDiffCostFunction<Hydrophone1Cost ,1 ,1, 1, 1, 1>(new Hydrophone1Cost(ts1));
	CostFunction* h2cost = new AutoDiffCostFunction<Hydrophone2Cost ,1 ,1, 1, 1, 1>(new Hydrophone2Cost(ts2));
	CostFunction* h3cost = new AutoDiffCostFunction<Hydrophone3Cost ,1 ,1, 1, 1, 1>(new Hydrophone3Cost(ts3));
	CostFunction* h4cost = new AutoDiffCostFunction<Hydrophone4Cost ,1 ,1, 1, 1, 1>(new Hydrophone4Cost(ts4));
	problem.AddResidualBlock(h1cost, NULL, &x, &y, &z, &t);
	problem.AddResidualBlock(h2cost, NULL, &x, &y, &z, &t);
	problem.AddResidualBlock(h3cost, NULL, &x, &y, &z, &t);
	problem.AddResidualBlock(h4cost, NULL, &x, &y, &z, &t);

	Solver::Options options;
	options.max_num_iterations = 100;
	options.linear_solver_type = ceres::DENSE_QR;
	options.minimizer_progress_to_stdout = true;
	std::cout << "Initial x = " << x
	          << ", y = " << y
	          << ", z = " << z
	          << ", t = " << t
	          << "\n";
	// Run the solver!
	Solver::Summary summary;
	Solve(options, &problem, &summary);
	std::cout << summary.FullReport() << "\n";
	std::cout << "Final x = " << x
	          << ", y = " << y
	          << ", z = " << z
	          << ", t = " << t
	          << "\n";
	return 0;
}
コード例 #3
0
ファイル: BALOptimizer.cpp プロジェクト: josetascon/mop
// ================================================================================================
// =============================== FUNCTIONS of CLASS BALOptimizer ================================
// ================================================================================================
void BALOptimizer::runBAL()
{
    int num_cams = visibility->rows();
    int num_features = visibility->cols();
    int step_tr = translation_and_intrinsics->rows();
    int step_st = structure->rows();
    double cost;
    quaternion_vector2eigen_vector( *quaternion, q_vector );
    
    Problem problem;
    ceres::LossFunction* loss_function = new ceres::HuberLoss(1.0);
    
    Solver::Options options;
    options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
    options.minimizer_progress_to_stdout = true;
    options.gradient_tolerance = 1e-16;
    options.function_tolerance = 1e-16;
    options.num_threads = 8;
    options.max_num_iterations = 50;
    
    for (register int cam = 0; cam < num_cams; ++cam)
    {
        for (register int ft = 0; ft < num_features ; ++ft)
        {
	  if( (*visibility)(cam,ft) == true )
	  {
	      CostFunction* cost_function = new AutoDiffCostFunction<Snavely_RE_KDQTS, 2, 4, 6, 3>( 
		new Snavely_RE_KDQTS( (*coordinates)(cam,ft)(0), (*coordinates)(cam,ft)(1)) );
	      problem.AddResidualBlock(cost_function, loss_function, q_vector[cam].data(), 
				(translation_and_intrinsics->data()+step_tr*cam), (structure->data()+step_st*ft) );
	  }
        }
    }
    
    cost = 0;
    problem.Evaluate(Problem::EvaluateOptions(), &cost, NULL, NULL, NULL);
    std::cout << "Initial RMS Reprojection Error is : " << std::sqrt(double(cost/num_features)) << "\n";
    
    Solver::Summary summary;
    Solve(options, &problem, &summary);
    std::cout << summary.BriefReport() << "\n";
    
    cost = 0;
    problem.Evaluate(Problem::EvaluateOptions(), &cost, NULL, NULL, NULL);
    std::cout << "RMS Reprojection Error is : " << std::sqrt(double(cost/num_features)) << "\n\n";
    
    update(); // update quaternion; normaliza translation 1
    return;
}
コード例 #4
0
ファイル: ops_test.cpp プロジェクト: bzcheeseman/bunfit
int main(int argc, char *argv[]) {
  FLAGS_log_dir = "logs/";
  google::InitGoogleLogging(argv[0]);
  google::ParseCommandLineFlags(&argc, &argv, true);

  dataset::dataSet<double> data;
  data.residual_type = "quadratic";
  data.numPoints = 100;
  data.range["begin"] = -5.0;
  data.range["end"] = 5.0;

  double A = 1.0;
  double B = 0.0;
  double C = 0.0;
  double D = 1.0;
  double E = 0.0;

  dataset::makeSet(&data, {&A, &B, &C});
  plot::plotData(&data);

  double Ap = 3.45;

  Problem problem;

  for (int i = 0; i < 100; i++){
    double x = data.xdata[i];
    double y = data.ydata[i];
    double r = 0.0;

    CostFunction* cost = residual<double>::Create(x, y, "quadratic");
    problem.AddResidualBlock(cost, NULL, &Ap, &B, &C);

  }

  Solver::Options options;

  Solver::Summary summary;
  Solve(options, &problem, &summary);

  std::cout << summary.FullReport() << std::endl;

  return 0;
}
コード例 #5
0
ファイル: CeresSolverBase.cpp プロジェクト: billbliss3/Opt
double CeresSolverBase::launchProfiledSolveAndSummary(const std::unique_ptr<ceres::Solver::Options>& options, ceres::Problem* problem, bool profileSolve, std::vector<SolverIteration>& iters) {
    Solver::Summary summary;
    double elapsedTime;
    {
        ml::Timer timer;
        Solve(*options, problem, &summary);
        elapsedTime = timer.getElapsedTimeMS();
    }

    cout << "Solver used: " << summary.linear_solver_type_used << endl;
    cout << "Minimizer iters: " << summary.iterations.size() << endl;
    cout << "Total time: " << elapsedTime << "ms" << endl;

    double iterationTotalTime = 0.0;
    int totalLinearItereations = 0;
    for (auto &i : summary.iterations)
    {
        iterationTotalTime += i.iteration_time_in_seconds;
        totalLinearItereations += i.linear_solver_iterations;
        cout << "Iteration: " << i.linear_solver_iterations << " " << i.iteration_time_in_seconds * 1000.0 << "ms," << " cost: " << i.cost << endl;
    }
    if (profileSolve) {
        for (auto &i : summary.iterations) {
            iters.push_back(SolverIteration(i.cost, i.iteration_time_in_seconds * 1000.0));
        }
    }


    cout << "Total iteration time: " << iterationTotalTime << endl;
    cout << "Cost per linear solver iteration: " << iterationTotalTime * 1000.0 / totalLinearItereations << "ms" << endl;

    double cost = -1.0;
    problem->Evaluate(Problem::EvaluateOptions(), &cost, nullptr, nullptr, nullptr);
    cout << "Cost end: " << cost << endl;
    cout << summary.FullReport() << endl;
    return cost;
}
コード例 #6
0
bool solve_translations_problem_l2_chordal
(
  const int* edges,
  const double* poses,
  const double* weights,
  int num_edges,
  double loss_width,
  double* X,
  double function_tolerance,
  double parameter_tolerance,
  int max_iterations
)
{
  // seed the random number generator
  std::srand( std::time( NULL ) );

  // re index the edges to be a sequential set
  std::vector<int> reindexed_edges(edges, edges+2*num_edges);
  std::vector<int> reindexed_lookup;
  reindex_problem(&reindexed_edges[0], num_edges, reindexed_lookup);
  const int num_nodes = reindexed_lookup.size();

  // Init with a random guess solution
  std::vector<double> x(3*num_nodes);
  for (int i=0; i<3*num_nodes; ++i)
    x[i] = (double)rand() / RAND_MAX;

  // add the parameter blocks (a 3-vector for each node)
  Problem problem;
  for (int i=0; i<num_nodes; ++i)
    problem.AddParameterBlock(&x[3*i], 3);

  // set the residual function (chordal distance for each edge)
  for (int i=0; i<num_edges; ++i) {
    CostFunction* cost_function =
      new AutoDiffCostFunction<ChordFunctor, 3, 3, 3>(
      new ChordFunctor(poses+3*i, weights[i]));

    if (loss_width == 0.0) {
      // No robust loss function
      problem.AddResidualBlock(cost_function, NULL, &x[3*reindexed_edges[2*i+0]], &x[3*reindexed_edges[2*i+1]]);
    } else {
      problem.AddResidualBlock(cost_function, new ceres::HuberLoss(loss_width), &x[3*reindexed_edges[2*i+0]], &x[3*reindexed_edges[2*i+1]]);
    }
  }

  // Fix first camera in {0,0,0}: fix the translation ambiguity
  x[0] = x[1] = x[2] = 0.0;
  problem.SetParameterBlockConstant(&x[0]);

  // solve
  Solver::Options options;
#ifdef OPENMVG_USE_OPENMP
  options.num_threads = omp_get_max_threads();
  options.num_linear_solver_threads = omp_get_max_threads();
#endif // OPENMVG_USE_OPENMP
  options.minimizer_progress_to_stdout = false;
  options.logging_type = ceres::SILENT;
  options.max_num_iterations = max_iterations;
  options.function_tolerance = function_tolerance;
  options.parameter_tolerance = parameter_tolerance;
  
  // Since the problem is sparse, use a sparse solver iff available
  if (ceres::IsSparseLinearAlgebraLibraryTypeAvailable(ceres::SUITE_SPARSE))
  {
    options.sparse_linear_algebra_library_type = ceres::SUITE_SPARSE;
    options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
  }
  else if (ceres::IsSparseLinearAlgebraLibraryTypeAvailable(ceres::CX_SPARSE))
  {
    options.sparse_linear_algebra_library_type = ceres::CX_SPARSE;
    options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
  }
  else if (ceres::IsSparseLinearAlgebraLibraryTypeAvailable(ceres::EIGEN_SPARSE))
  {
    options.sparse_linear_algebra_library_type = ceres::EIGEN_SPARSE;
    options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
  }
  else
  {
    options.linear_solver_type = ceres::DENSE_NORMAL_CHOLESKY;
  }

  Solver::Summary summary;
  Solve(options, &problem, &summary);

  std::cout << summary.FullReport() << "\n";

  if (summary.IsSolutionUsable())
  {
    // undo the re indexing
    for (int i=0; i<num_nodes; ++i) {
      const int j = reindexed_lookup[i];
      X[3*j+0] = x[3*i+0];
      X[3*j+1] = x[3*i+1];
      X[3*j+2] = x[3*i+2];
    }
  }
  return summary.IsSolutionUsable();
}
コード例 #7
0
void lidarBoostEngine::build_superresolution(short coeff)
{
    std::cout<< "Num of clouds : " << Y.size() << std::endl;

//    std::cout << Y[0] << std::endl;
    beta = coeff;
    std::vector < MatrixXd > optflow = lk_optical_flow( Y[2], Y[4], 10 );
    MatrixXd D( beta*n, beta*m ); //, X( beta*n, beta*m );
//    SparseMatrix<double> W( beta*n, beta*m ), T( beta*n, beta*m );

    D = apply_optical_flow(Y[2], optflow);
    T = check_unreliable_samples(intensityMap[2], 0.0001);

    MatrixXd up_D = nearest_neigh_upsampling(D);

////    Display and Debug
    cv::Mat M(n, m, CV_32FC1);
//    MatrixXd diff1(n, m);
//    diff1 = MatrixXd::Ones(n, m) - Y[0];
    cv::eigen2cv(Y[2], M);

    cv::Mat M1(n, m, CV_32FC1);
    cv::eigen2cv(Y[4], M1);

//    MatrixXd diff(beta*n, beta*m);
//    diff = MatrixXd::Ones(beta*n, beta*m) - up_D;
    cv::Mat M2(beta*n, beta*m, CV_32FC1);
    cv::eigen2cv(up_D, M2);

    cv::namedWindow("check", cv::WINDOW_AUTOSIZE );
    cv::imshow("check", M);

    cv::namedWindow("check1", cv::WINDOW_AUTOSIZE );
    cv::imshow("check1", M1);

    cv::namedWindow("check2", cv::WINDOW_AUTOSIZE );
    cv::imshow("check2", M2);

////  Solve example equation with eigen
//    Eigen::VectorXd x(2);
//    x(0) = 10.0;
//    x(1) = 25.0;
//    std::cout << "x: " << x << std::endl;

//    my_functor functor;
//    Eigen::NumericalDiff<my_functor> numDiff(functor);
//    Eigen::LevenbergMarquardt<Eigen::NumericalDiff<my_functor>,double> lm(numDiff);
//    lm.parameters.maxfev = 2000;
//    lm.parameters.xtol = 1.0e-10;
//    std::cout << lm.parameters.maxfev << std::endl;

//    int ret = lm.minimize(x);
//    std::cout << lm.iter << std::endl;
//    std::cout << ret << std::endl;

//    std::cout << "x that minimizes the function: " << x << std::endl;

//////    Try to solve lidarboost with Eigen
//      my_functor functor;
//      Eigen::NumericalDiff<my_functor> numDiff(functor);
//      Eigen::LevenbergMarquardt<Eigen::NumericalDiff<my_functor>,double> lm(numDiff);
//      lm.parameters.maxfev = 2000;
//      lm.parameters.xtol = 1.0e-10;
//      std::cout << lm.parameters.maxfev << std::endl;

//    VectorXd val(2);
//    for(int i = 0; i < X.rows(); i++)
//    {
//        for(int j = 0; j < X.cols(); j++)
//        {
//            val = X(i, j);
//            int ret = lm.minimize(val);
//        }
//    }

//    std::cout << lm.iter << std::endl;
//    std::cout << ret << std::endl;

//    std::cout << "x that minimizes the function: " << X << std::endl;

////  Solve example using ceres

//         The variable to solve for with its initial value.
//        double initial_x = 5.0;
//        double x = initial_x;

        MatrixXd X(beta*n, beta*m);// init_X(beta*n, beta*m);
//        X = MatrixXd::Zero(beta*n,beta*m);
        X = up_D;
//        MatrixXd init_X( beta*n, beta*m );
//        init_X = X;
//        int M[2][2], M2[2][2];
//        M[0][0] = 5;
//        M[1][0] = 10;
//        M[0][1] = 20;
//        M[1][1] = 30;

//        M2 = *M;

        // Build the problem.
        Problem problem;

        // Set up the only cost function (also known as residual). This uses
        // auto-differentiation to obtain the derivative (jacobian).

        double val, w, t, d;

        Solver::Options options;
        options.linear_solver_type = ceres::DENSE_QR;
        options.minimizer_progress_to_stdout = false;
        Solver::Summary summary;

        for(int i = 0; i < X.rows(); i++)
        {
            for(int j = 0; j < X.cols(); j++)
            {

                val = X(i, j);
                w = W(i, j);
                t = T(i, j);
                d = up_D(i, j);

                std::cout << "i = " << i << "; j = " << j << std::endl;
                std::cout << "w = " << w << "; t = " << t << "; d = " << d << std::endl;
                CostFunction* cost_function =
                    new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor(w, t, d));

                problem.AddResidualBlock(cost_function, NULL, &val);
                // Run the solver
                Solve(options, &problem, &summary);
                X(i, j) = val;
            }
        }




        std::cout << summary.BriefReport() << "\n";
//        std::cout << "x : " << init_X
//                  << " -> " << X << "\n";

        cv::Mat M3(beta*n, beta*m, CV_32FC1);
        cv::eigen2cv(X, M3);
        cv::namedWindow("check3", cv::WINDOW_AUTOSIZE );
        cv::imshow("check3", M3);
}