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/***************************************************************************
* Copyright (c) 2016 Werner Mayer <wmayer[at]users.sourceforge.net> *
* *
* This file is part of the FreeCAD CAx development system. *
* *
* This library is free software; you can redistribute it and/or *
* modify it under the terms of the GNU Library General Public *
* License as published by the Free Software Foundation; either *
* version 2 of the License, or (at your option) any later version. *
* *
* This library is distributed in the hope that it will be useful, *
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
* GNU Library General Public License for more details. *
* *
* You should have received a copy of the GNU Library General Public *
* License along with this library; see the file COPYING.LIB. If not, *
* write to the Free Software Foundation, Inc., 59 Temple Place, *
* Suite 330, Boston, MA 02111-1307, USA *
* *
***************************************************************************/
#include <boost/math/special_functions/fpclassify.hpp>
#include <Base/Exception.h>
#include <Mod/Points/App/Points.h>
#include "SampleConsensus.h"
#if defined(HAVE_PCL_SAMPLE_CONSENSUS)
# include <pcl/features/normal_3d.h>
# include <pcl/point_types.h>
# include <pcl/sample_consensus/ransac.h>
# include <pcl/sample_consensus/sac_model_cone.h>
# include <pcl/sample_consensus/sac_model_cylinder.h>
# include <pcl/sample_consensus/sac_model_plane.h>
# include <pcl/sample_consensus/sac_model_sphere.h>
using namespace std;
using namespace Reen;
using pcl::PointCloud;
using pcl::PointNormal;
using pcl::PointXYZ;
SampleConsensus::SampleConsensus(
SacModel sac,
const Points::PointKernel& pts,
const std::vector<Base::Vector3d>& nor
)
: mySac(sac)
, myPoints(pts)
, myNormals(nor)
{}
double SampleConsensus::perform(std::vector<float>& parameters, std::vector<int>& model)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
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(pcl::PointXYZ(it->x, it->y, it->z));
}
}
cloud->width = int(cloud->points.size());
cloud->height = 1;
cloud->is_dense = true;
pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>());
if (mySac == SACMODEL_CONE || mySac == SACMODEL_CYLINDER) {
# if 0
// Create search tree
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree;
tree.reset (new pcl::search::KdTree<PointXYZ> (false));
tree->setInputCloud (cloud);
// Normal estimation
int ksearch = 10;
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> n;
n.setInputCloud (cloud);
n.setSearchMethod (tree);
n.setKSearch (ksearch);
n.compute (*normals);
# else
normals->reserve(myNormals.size());
for (std::vector<Base::Vector3d>::const_iterator it = myNormals.begin();
it != myNormals.end();
++it) {
if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y)
&& !boost::math::isnan(it->z)) {
normals->push_back(pcl::Normal(it->x, it->y, it->z));
}
}
# endif
}
// created RandomSampleConsensus object and compute the appropriated model
pcl::SampleConsensusModel<pcl::PointXYZ>::Ptr model_p;
switch (mySac) {
case SACMODEL_PLANE: {
model_p.reset(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud));
break;
}
case SACMODEL_SPHERE: {
model_p.reset(new pcl::SampleConsensusModelSphere<pcl::PointXYZ>(cloud));
break;
}
case SACMODEL_CONE: {
pcl::SampleConsensusModelCone<pcl::PointXYZ, pcl::Normal>::Ptr model_c(
new pcl::SampleConsensusModelCone<pcl::PointXYZ, pcl::Normal>(cloud)
);
model_c->setInputNormals(normals);
model_p = model_c;
break;
}
case SACMODEL_CYLINDER: {
pcl::SampleConsensusModelCylinder<pcl::PointXYZ, pcl::Normal>::Ptr model_c(
new pcl::SampleConsensusModelCylinder<pcl::PointXYZ, pcl::Normal>(cloud)
);
model_c->setInputNormals(normals);
model_p = model_c;
break;
}
default:
throw Base::RuntimeError("Unsupported SAC model");
}
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_p);
ransac.setDistanceThreshold(.01);
ransac.computeModel();
ransac.getInliers(model);
// ransac.getModel (model);
Eigen::VectorXf model_p_coefficients;
ransac.getModelCoefficients(model_p_coefficients);
for (int i = 0; i < model_p_coefficients.size(); i++) {
parameters.push_back(model_p_coefficients[i]);
}
return ransac.getProbability();
}
#endif // HAVE_PCL_SAMPLE_CONSENSUS
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