void boundary_dirichlet:: specify_boundary_conditions(flcuda E_fi_upper, flcuda E_fi_left, flcuda E_fi_right, flcuda fi_upper_wall)
{
int i=0, k=0;
int n_grid1=cyl_geom->n_grid_1;
int n_grid2 = cyl_geom->n_grid_2;
// setazimuthal component electric field initial value
/////////////////////////////////////////////
for (i=0;i<(n_grid1);i++)
{
e_f->e2[i][0]=E_fi_left;
e_f->e2[i][n_grid2-1]=E_fi_right;
}
for(k=0;k<n_grid2;k++)
{
e_f->e2[n_grid1-1][k]=E_fi_upper;
}
/////////////////////////////////////////////
////////////////////////////////////////////
//set potential initial value in z wall
for(k=0;k<n_grid2;k++)
{
e_f->fi[n_grid1-1][k]=fi_upper_wall;
}
///////////////////////////////////////////
// calculate poisson equation with Neumann boundary conditions
Poisson* solve = new Poisson_dirichlet(cyl_geom);
solve->poisson_solve(e_f,rho);
solve->test_poisson_equation(e_f,rho);
/////////////////////////////////////////
}
TEST (PCL, Poisson)
{
Poisson<PointNormal> poisson;
poisson.setInputCloud (cloud_with_normals);
PolygonMesh mesh;
poisson.reconstruct (mesh);
// io::saveVTKFile ("bunny_poisson.vtk", mesh);
ASSERT_EQ (mesh.polygons.size (), 1051);
// All polygons should be triangles
for (size_t i = 0; i < mesh.polygons.size (); ++i)
EXPECT_EQ (mesh.polygons[i].vertices.size (), 3);
EXPECT_EQ (mesh.polygons[10].vertices[0], 121);
EXPECT_EQ (mesh.polygons[10].vertices[1], 120);
EXPECT_EQ (mesh.polygons[10].vertices[2], 23);
EXPECT_EQ (mesh.polygons[200].vertices[0], 130);
EXPECT_EQ (mesh.polygons[200].vertices[1], 119);
EXPECT_EQ (mesh.polygons[200].vertices[2], 131);
EXPECT_EQ (mesh.polygons[1000].vertices[0], 521);
EXPECT_EQ (mesh.polygons[1000].vertices[1], 516);
EXPECT_EQ (mesh.polygons[1000].vertices[2], 517);
}
int main (int argc, char **argv)
{
/* if (argc != 1)
{
PCL_ERROR ("Syntax: %s input.pcd output.ply\n", argv[0]);
return -1;
}*/
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ> ());
io::loadPCDFile (argv[1], *cloud);
MovingLeastSquares<PointXYZ, PointXYZ> mls;
mls.setInputCloud (cloud);
mls.setSearchRadius (4);
mls.setPolynomialFit (true);
mls.setPolynomialOrder (1);
mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointXYZ>::SAMPLE_LOCAL_PLANE);
mls.setUpsamplingRadius (1);
mls.setUpsamplingStepSize (0.03);
PointCloud<PointXYZ>::Ptr cloud_smoothed (new PointCloud<PointXYZ> ());
mls.process (*cloud_smoothed);
NormalEstimationOMP<PointXYZ, Normal> ne;
ne.setNumberOfThreads (8);
ne.setInputCloud (cloud_smoothed);
ne.setRadiusSearch (0.8);
Eigen::Vector4f centroid;
compute3DCentroid (*cloud_smoothed, centroid);
ne.setViewPoint (centroid[0], centroid[1], centroid[2]);
PointCloud<Normal>::Ptr cloud_normals (new PointCloud<Normal> ());
ne.compute (*cloud_normals);
for (size_t i = 0; i < cloud_normals->size (); ++i)
{
cloud_normals->points[i].normal_x *= -1;
cloud_normals->points[i].normal_y *= -1;
cloud_normals->points[i].normal_z *= -1;
}
PointCloud<PointNormal>::Ptr cloud_smoothed_normals (new PointCloud<PointNormal> ());
concatenateFields (*cloud_smoothed, *cloud_normals, *cloud_smoothed_normals);
Poisson<pcl::PointNormal> poisson;
poisson.setDepth (9);
poisson.setInputCloud (cloud_smoothed_normals);
PolygonMesh mesh_poisson;
poisson.reconstruct (mesh_poisson);
pcl::io::savePLYFile("mesh.ply", mesh_poisson);
return 0;
}
void PoissonReconstruction::perform(int ksearch)
{
PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
search::KdTree<PointXYZ>::Ptr tree;
search::KdTree<PointNormal>::Ptr tree2;
cloud->reserve(myPoints.size());
for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
cloud->push_back(PointXYZ(it->x, it->y, it->z));
}
// Create search tree
tree.reset (new search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
NormalEstimation<PointXYZ, Normal> n;
PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
n.setInputCloud (cloud);
//n.setIndices (indices[B);
n.setSearchMethod (tree);
n.setKSearch (ksearch);
n.compute (*normals);
// Concatenate XYZ and normal information
pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
// Create search tree
tree2.reset (new search::KdTree<PointNormal>);
tree2->setInputCloud (cloud_with_normals);
// Init objects
Poisson<PointNormal> poisson;
// Set parameters
poisson.setInputCloud (cloud_with_normals);
poisson.setSearchMethod (tree2);
if (depth >= 1)
poisson.setDepth(depth);
if (solverDivide >= 1)
poisson.setSolverDivide(solverDivide);
if (samplesPerNode >= 1.0f)
poisson.setSamplesPerNode(samplesPerNode);
// Reconstruct
PolygonMesh mesh;
poisson.reconstruct (mesh);
MeshConversion::convert(mesh, myMesh);
}
void PoissonReconstruction::perform(const std::vector<Base::Vector3f>& normals)
{
if (myPoints.size() != normals.size())
throw Base::RuntimeError("Number of points doesn't match with number of normals");
PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
search::KdTree<PointNormal>::Ptr tree;
cloud_with_normals->reserve(myPoints.size());
std::size_t num_points = myPoints.size();
const std::vector<Base::Vector3f>& points = myPoints.getBasicPoints();
for (std::size_t index=0; index<num_points; index++) {
const Base::Vector3f& p = points[index];
const Base::Vector3f& n = normals[index];
if (!boost::math::isnan(p.x) && !boost::math::isnan(p.y) && !boost::math::isnan(p.z)) {
PointNormal pn;
pn.x = p.x;
pn.y = p.y;
pn.z = p.z;
pn.normal_x = n.x;
pn.normal_y = n.y;
pn.normal_z = n.z;
cloud_with_normals->push_back(pn);
}
}
// Create search tree
tree.reset (new search::KdTree<PointNormal>);
tree->setInputCloud (cloud_with_normals);
// Init objects
Poisson<PointNormal> poisson;
// Set parameters
poisson.setInputCloud (cloud_with_normals);
poisson.setSearchMethod (tree);
if (depth >= 1)
poisson.setDepth(depth);
if (solverDivide >= 1)
poisson.setSolverDivide(solverDivide);
if (samplesPerNode >= 1.0f)
poisson.setSamplesPerNode(samplesPerNode);
// Reconstruct
PolygonMesh mesh;
poisson.reconstruct (mesh);
MeshConversion::convert(mesh, myMesh);
}
void test_poisson2(int w, int h, int radius) {
sf::RenderWindow window(sf::VideoMode(w, h), "Wave Test");
Random r;
Poisson p;
p.generate(r, w, h, radius, 40);
window.clear(sf::Color::Black);
sf::CircleShape cs(radius, 8);
for (vector<double>::const_iterator xit = p.get_x().begin(), yit = p.get_y().begin();
xit != p.get_x().end() && yit != p.get_y().end(); xit++, yit++) {
cs.setPosition(*xit, *yit);
window.draw(cs);
}
window.display();
cerr << p.get_x().size() << endl;
window_loop(window);
}
void test_poisson() {
sf::RenderWindow window(sf::VideoMode(200, 200), "Wave Test");
Random r;
Poisson p;
p.generate(r, 200, 200, 30, 10, 100);
window.clear(sf::Color::Black);
sf::CircleShape cs(2, 4);
for (vector<double>::const_iterator xit = p.get_x().begin(), yit = p.get_y().begin();
xit != p.get_x().end() && yit != p.get_y().end(); xit++, yit++) {
cs.setPosition(*xit, *yit);
window.draw(cs);
}
window.display();
window_loop(window);
}
void
compute (const sensor_msgs::PointCloud2::ConstPtr &input, PolygonMesh &output,
int depth, int solver_divide, int iso_divide)
{
PointCloud<PointNormal>::Ptr xyz_cloud (new pcl::PointCloud<PointNormal> ());
fromROSMsg (*input, *xyz_cloud);
Poisson<PointNormal> poisson;
poisson.setDepth (depth);
poisson.setSolverDivide (solver_divide);
poisson.setIsoDivide (iso_divide);
poisson.setInputCloud (xyz_cloud);
TicToc tt;
tt.tic ();
print_highlight ("Computing ");
poisson.reconstruct (output);
print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms]\n");
}
void FSModel::convertPointCloudToSurfaceMesh3()
{
/*pcl::MovingLeastSquares<PointXYZRGB, PointXYZ> mls;
mls.setInputCloud (pointCloud);
mls.setSearchRadius (0.01);
mls.setPolynomialFit (true);
mls.setPolynomialOrder (2);
mls.setUpsamplingMethod (pcl::MovingLeastSquares<PointXYZRGB, PointXYZ>::SAMPLE_LOCAL_PLANE);
mls.setUpsamplingRadius (0.005);
mls.setUpsamplingStepSize (0.003);
pcl::PointCloud<PointXYZ>::Ptr cloud_smoothed (new pcl::PointCloud<PointXYZ> ());
mls.process (*cloud_smoothed);*/
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
cloud->points.resize(pointCloud->size());
for (size_t i = 0; i < pointCloud->points.size(); i++) {
cloud->points[i].x = pointCloud->points[i].x;
cloud->points[i].y = pointCloud->points[i].y;
cloud->points[i].z = pointCloud->points[i].z;
}
pcl::NormalEstimation<pcl::PointXYZ, Normal> ne;
//ne.setNumberOfThreads(8);
ne.setInputCloud (cloud);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
ne.setSearchMethod (tree);
ne.setRadiusSearch (0.01);
Eigen::Vector4f centroid;
compute3DCentroid (*cloud, centroid);
ne.setViewPoint (centroid[0], centroid[1], centroid[2]);
qDebug() << "Centroid:"<<centroid[0] <<centroid[1]<<centroid[2];
PointCloud<Normal>::Ptr cloud_normals (new PointCloud<Normal> ());
ne.compute (*cloud_normals);
//invert all normals, primarly they all point to the centroid
for (size_t i = 0; i < cloud_normals->size (); ++i) {
cloud_normals->points[i].normal_x *= -1;
cloud_normals->points[i].normal_y *= -1;
cloud_normals->points[i].normal_z *= -1;
}
PointCloud<PointNormal>::Ptr cloud_smoothed_normals (new PointCloud<PointNormal> ());
concatenateFields (*cloud, *cloud_normals, *cloud_smoothed_normals);
//concatenateFields (*cloud_smoothed, *cloud_normals, *cloud_smoothed_normals);*/
Poisson<PointNormal> poisson;
poisson.setScale(1.0);
poisson.setDepth (9);
poisson.setDegree(2);
poisson.setSamplesPerNode(3);
poisson.setIsoDivide(8);
poisson.setConfidence(0);
poisson.setManifold(0);
poisson.setOutputPolygons(0);
poisson.setSolverDivide(8);
poisson.setInputCloud(cloud_smoothed_normals);
poisson.reconstruct(surfaceMeshPoisson);
//pcl::io::savePLYFile("meshPoisson.ply", surfaceMeshPoisson);
FSController::getInstance()->meshComputed=true;
}
