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;
}
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;
}
// ================================================================================================
// =============================== 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;
}
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;
}
void Odometry::computeTransformation()
{
assert(observationVec.size() == cloud.size());
assert(observationVec.size() == inlierMask.size());
Problem problem;
for (unsigned int i = 0; i < cloud.size(); i++)
{
if (not inlierMask[i]) continue;
CostFunction * costFunc = new OdometryError(cloud[i],
observationVec[i], TbaseCam, camera);
problem.AddResidualBlock(costFunc, NULL,
TorigBase.transData(), TorigBase.rotData());
}
Solver::Options options;
options.linear_solver_type = ceres::DENSE_SCHUR;
Solver::Summary summary;
Solve(options, &problem, &summary);
}
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();
}
double CeresSolverPoissonImageEditing::solve(const NamedParameters& solverParameters, const NamedParameters& problemParameters, bool profileSolve, std::vector<SolverIteration>& iters)
{
Problem problem;
auto getPixel = [=](int x, int y) {
return y * m_dims[0] + x;
};
const int pixelCount = m_dims[0] * m_dims[1];
std::vector<float4> h_unknownFloat(pixelCount);
std::vector<float4> h_target(pixelCount);
std::vector<float> h_mask(pixelCount);
findAndCopyArrayToCPU("X", h_unknownFloat, problemParameters);
findAndCopyArrayToCPU("T", h_target, problemParameters);
findAndCopyArrayToCPU("M", h_mask, problemParameters);
std::vector<double4> h_unknownDouble(pixelCount);
for (int i = 0; i < pixelCount; i++)
{
h_unknownDouble[i].x = h_unknownFloat[i].x;
h_unknownDouble[i].y = h_unknownFloat[i].y;
h_unknownDouble[i].z = h_unknownFloat[i].z;
h_unknownDouble[i].w = h_unknownFloat[i].w;
}
vector< pair<int, int> > edges;
for (int y = 0; y < m_dims[1] - 1; y++)
{
for (int x = 0; x < m_dims[0] - 1; x++)
{
int pixel00 = getPixel(x + 0, y + 0);
int pixel10 = getPixel(x + 1, y + 0);
int pixel01 = getPixel(x + 0, y + 1);
int pixel11 = getPixel(x + 1, y + 1);
edges.push_back(make_pair(pixel00, pixel10));
edges.push_back(make_pair(pixel00, pixel01));
edges.push_back(make_pair(pixel11, pixel10));
edges.push_back(make_pair(pixel11, pixel01));
}
}
cout << "Edges: " << edges.size() << endl;
int edgesAdded = 0;
// add all edge constraints
for (auto &e : edges)
{
const float mask = h_mask[e.first];
if (mask == 0.0f)
{
const vec4f targetA = toVec(h_target[e.first]);
const vec4f targetB = toVec(h_target[e.second]);
vec4f targetDelta = targetA - targetB;
ceres::CostFunction* costFunction = EdgeTerm::Create(targetA - targetB, 1.0f);
double4 *varStartA = h_unknownDouble.data() + e.first;
double4 *varStartB = h_unknownDouble.data() + e.second;
problem.AddResidualBlock(costFunction, NULL, (double*)varStartA, (double*)varStartB);
edgesAdded++;
}
}
cout << "Edges added: " << edgesAdded << endl;
// add all fit constraints
set<int> addedEdges;
for (auto &e : edges)
{
const float mask = h_mask[e.first];
if (mask != 0.0f && addedEdges.count(e.first) == 0)
{
addedEdges.insert(e.first);
const vec4f target = toVec(h_unknownFloat[e.first]);
ceres::CostFunction* costFunction = FitTerm::Create(target, 1.0f);
double4 *varStart = h_unknownDouble.data() + e.first;
problem.AddResidualBlock(costFunction, NULL, (double*)varStart);
edgesAdded++;
}
}
cout << "Solving..." << endl;
Solver::Summary summary;
unique_ptr<Solver::Options> options = initializeOptions(solverParameters);
options->function_tolerance = 0.01;
options->gradient_tolerance = 1e-4 * options->function_tolerance;
double cost = launchProfiledSolveAndSummary(options, &problem, profileSolve, iters);
m_finalCost = cost;
for (int i = 0; i < pixelCount; i++)
{
h_unknownFloat[i].x = (float)h_unknownDouble[i].x;
h_unknownFloat[i].y = (float)h_unknownDouble[i].y;
h_unknownFloat[i].z = (float)h_unknownDouble[i].z;
h_unknownFloat[i].w = (float)h_unknownDouble[i].w;
}
findAndCopyToArrayFromCPU("X", h_unknownFloat, problemParameters);
return m_finalCost;
}
int main( int argc, char** argv )
{
google::InitGoogleLogging(argv[0]);
if ((argc < 3) || (argc > 4)) {
std::cerr << "usage: gettruth <measurement_file> <camera file> [<output file>]\n";
return 1;
}
FILE* file;
if (argc == 3) {
file = stdout;
} else {
file = fopen(argv[3], "w");
}
// get camera model
calibu::Rig<double> rig;
calibu::LoadRig( std::string(argv[2]), &rig );
if (rig.cameras_.size() == 0) {
fprintf(stderr, "No cameras in this rig or no camera file provided\n");
exit(0);
}
calibu::CameraInterface<double>* cam = rig.cameras_[0];
LocalizationProblem ctx;
if (!ctx.LoadFile(argv[1])) {
std::cerr << "ERROR: unable to open file " << argv[1] << "\n";
return 1;
}
// Build the problem.
Problem problem;
for (int i = 0; i < ctx.num_observations(); ++i) {
const Vector2d& measurement = ctx.observation(i);
const Vector3d& landmark = ctx.landmark_for_observation(i);
ceres::CostFunction* cost_function = ProjectionCost( landmark, measurement, cam );
problem.AddResidualBlock( cost_function, NULL, ctx.pose_data_for_observation(i) );
}
fprintf(stdout, "\n\nThe problem has been set up with %d residuals and %d residual blocks\n", problem.NumResiduals(), problem.NumResidualBlocks());
// Make Ceres automatically detect the bundle structure. Note that the
// standard solver, SPARSE_NORMAL_CHOLESKY, also works fine but it is slower
// for standard bundle adjustment problems.
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_SCHUR;
options.minimizer_progress_to_stdout = true;
ceres::Solver::Summary summary;
ceres::Solve(options, &problem, &summary);
for (int ii = 0; ii < ctx.num_observations(); ii++) {
fprintf(file, "%f, %f, %f, %f, %f, %f\n", ii + 1, ctx.pose_for_observation(ii)(0), ctx.pose_for_observation(ii)(1),
ctx.pose_for_observation(ii)(2), ctx.pose_for_observation(ii)(3), ctx.pose_for_observation(ii)(4), ctx.pose_for_observation(ii)(5));
}
fclose(file);
return 0;
}
void Odometry::Ransac()
{
assert(observationVec.size() == cloud.size());
int numPoints = observationVec.size();
inlierMask.resize(numPoints);
const int numIterMax = 25;
const Transformation<double> initialPose = TorigBase;
int bestInliers = 0;
//TODO add a termination criterion
for (unsigned int iteration = 0; iteration < numIterMax; iteration++)
{
Transformation<double> pose = initialPose;
int maxIdx = observationVec.size();
//choose three points at random
int idx1m = rand() % maxIdx;
int idx2m, idx3m;
do
{
idx2m = rand() % maxIdx;
} while (idx2m == idx1m);
do
{
idx3m = rand() % maxIdx;
} while (idx3m == idx1m or idx3m == idx2m);
//solve an optimization problem
Problem problem;
for (auto i : {idx1m, idx2m, idx3m})
{
CostFunction * costFunc = new OdometryError(cloud[i],
observationVec[i], TbaseCam, camera);
problem.AddResidualBlock(costFunc, NULL,
pose.transData(), pose.rotData());
}
Solver::Options options;
options.linear_solver_type = ceres::DENSE_SCHUR;
Solver::Summary summary;
options.max_num_iterations = 5;
Solve(options, &problem, &summary);
//count inliers
vector<Vector3d> XcamVec(numPoints);
Transformation<double> TorigCam = pose.compose(TbaseCam);
TorigCam.inverseTransform(cloud, XcamVec);
vector<Vector2d> projVec(numPoints);
camera.projectPointCloud(XcamVec, projVec);
vector<bool> currentInlierMask(numPoints, false);
int countInliers = 0;
for (unsigned int i = 0; i < numPoints; i++)
{
Vector2d err = observationVec[i] - projVec[i];
if (err.norm() < 2)
{
currentInlierMask[i] = true;
countInliers++;
}
}
//keep the best hypothesis
if (countInliers > bestInliers)
{
//TODO copy in a bettegit lor way
inlierMask = currentInlierMask;
bestInliers = countInliers;
TorigBase = pose;
}
}
}
bool TargetLocatorService::executeCallBack( target_locater::Request &req, target_locater::Response &res)
{
ros::NodeHandle nh;
CameraObservations camera_observations;
ROSCameraObserver camera_observer(image_topic_);
camera_observer.clearObservations();
camera_observer.clearTargets();
// set the roi to the requested
Roi roi;
roi.x_min = req.roi.x_offset;
roi.y_min = req.roi.y_offset;
roi.x_max = req.roi.x_offset + req.roi.width;
roi.y_max = req.roi.y_offset + req.roi.height;
industrial_extrinsic_cal::Cost_function cost_type;
camera_observer.clearTargets();
camera_observer.clearObservations();
camera_observer.addTarget(target_, roi, cost_type);
camera_observer.triggerCamera();
while (!camera_observer.observationsDone()) ;
camera_observer.getObservations(camera_observations);
int num_observations = (int) camera_observations.size();
if(num_observations != target_rows_* target_cols_){
ROS_ERROR("Target Locator could not find target %d", num_observations);
return(false);
}
// set initial conditions
target_->pose_.setQuaternion(req.initial_pose.orientation.x, req.initial_pose.orientation.y, req.initial_pose.orientation.z, req.initial_pose.orientation.w );
target_->pose_.setOrigin(req.initial_pose.position.x, req.initial_pose.position.y, req.initial_pose.position.z );
Problem problem;
for(int i=0; i<num_observations; i++){
double image_x = camera_observations[i].image_loc_x;
double image_y = camera_observations[i].image_loc_y;
Point3d point = target_->pts_[i]; // assume the correct ordering
CostFunction* cost_function =
industrial_extrinsic_cal::CameraReprjErrorPK::Create(image_x, image_y, focal_length_x_, focal_length_y_,center_x_, center_y_, point);
problem.AddResidualBlock(cost_function, NULL , target_->pose_.pb_pose);
}
Solver::Options options;
Solver::Summary summary;
options.linear_solver_type = ceres::DENSE_SCHUR;
options.minimizer_progress_to_stdout = false;
options.max_num_iterations = 1000;
ceres::Solve(options, &problem, &summary);
if(summary.termination_type == ceres::USER_SUCCESS
|| summary.termination_type == ceres::FUNCTION_TOLERANCE
|| summary.termination_type == ceres::GRADIENT_TOLERANCE
|| summary.termination_type == ceres::PARAMETER_TOLERANCE
){
double error_per_observation = summary.final_cost/num_observations;
if(error_per_observation <= req.allowable_cost_per_observation){
res.final_pose.position.x = target_->pose_.x;
res.final_pose.position.y = target_->pose_.y;
res.final_pose.position.z = target_->pose_.z;
res.final_cost_per_observation = error_per_observation;
target_->pose_.getQuaternion(res.final_pose.orientation.x, res.final_pose.orientation.y, res.final_pose.orientation.z, res.final_pose.orientation.w);
return true;
}
else{
res.final_cost_per_observation = error_per_observation;
ROS_ERROR("allowable cost exceeded %f > %f", error_per_observation, req.allowable_cost_per_observation);
return(false);
}
}
}
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);
}
