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+ add segmentation algorithm

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This commit is contained in:
wmayer 2016-03-03 18:07:17 +01:00
parent b825dcd6bb
commit 816b54bd25
4 changed files with 235 additions and 2 deletions
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35
src/Mod/ReverseEngineering/App/AppReverseEngineering.cpp
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@ -45,6 +45,7 @@
#include "BSplineFitting.h"
#include "SurfaceTriangulation.h"
#include "RegionGrowing.h"
#include "Segmentation.h"
#include "SampleConsensus.h"
#if defined(HAVE_PCL_FILTERS)
#include <pcl/filters/passthrough.h>
@ -99,8 +100,11 @@ public:
add_keyword_method("regionGrowingSegmentation",&Module::regionGrowingSegmentation,
"regionGrowingSegmentation()."
);
add_keyword_method("featureSegmentation",&Module::featureSegmentation,
"featureSegmentation()."
);
#endif
#if defined(HAVE_PCL_SEGMENTATION)
#if defined(HAVE_PCL_SAMPLE_CONSENSUS)
add_keyword_method("sampleConsensus",&Module::sampleConsensus,
"sampleConsensus()."
);
@ -625,10 +629,37 @@ Mesh.show(m)
lists.append(tuple);
}
return lists;
}
Py::Object featureSegmentation(const Py::Tuple& args, const Py::Dict& kwds)
{
PyObject *pts;
int ksearch=5;
static char* kwds_segment[] = {"Points", "KSearch", NULL};
if (!PyArg_ParseTupleAndKeywords(args.ptr(), kwds.ptr(), "O!|i", kwds_segment,
&(Points::PointsPy::Type), &pts, &ksearch))
throw Py::Exception();
Points::PointKernel* points = static_cast<Points::PointsPy*>(pts)->getPointKernelPtr();
std::list<std::vector<int> > clusters;
Segmentation segm(*points, clusters);
segm.perform(ksearch);
Py::List lists;
for (std::list<std::vector<int> >::iterator it = clusters.begin(); it != clusters.end(); ++it) {
Py::Tuple tuple(it->size());
for (std::size_t i = 0; i < it->size(); i++) {
tuple.setItem(i, Py::Long((*it)[i]));
}
lists.append(tuple);
}
return lists;
}
#endif
#if defined(HAVE_PCL_SEGMENTATION)
#if defined(HAVE_PCL_SAMPLE_CONSENSUS)
Py::Object sampleConsensus(const Py::Tuple& args, const Py::Dict& kwds)
{
PyObject *pts;

2
src/Mod/ReverseEngineering/App/CMakeLists.txt
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@ -65,6 +65,8 @@ SET(Reen_SRCS
RegionGrowing.h
SampleConsensus.cpp
SampleConsensus.h
Segmentation.cpp
Segmentation.h
SurfaceTriangulation.cpp
SurfaceTriangulation.h
PreCompiled.cpp

148
src/Mod/ReverseEngineering/App/Segmentation.cpp Normal file
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@ -0,0 +1,148 @@
/***************************************************************************
* 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 "PreCompiled.h"
#include "Segmentation.h"
#include <Mod/Points/App/Points.h>
#include <Base/Exception.h>
#if defined(HAVE_PCL_SEGMENTATION)
#include <pcl/ModelCoefficients.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/segmentation/sac_segmentation.h>
using namespace std;
using namespace Reen;
using pcl::PointXYZ;
using pcl::PointNormal;
using pcl::PointCloud;
Segmentation::Segmentation(const Points::PointKernel& pts, std::list<std::vector<int> >& clusters)
: myPoints(pts)
, myClusters(clusters)
{
}
void Segmentation::perform(int ksearch)
{
// All the objects needed
pcl::PassThrough<PointXYZ> pass;
pcl::NormalEstimation<PointXYZ, pcl::Normal> ne;
pcl::SACSegmentationFromNormals<PointXYZ, pcl::Normal> seg;
pcl::ExtractIndices<PointXYZ> extract;
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::search::KdTree<PointXYZ>::Ptr tree (new pcl::search::KdTree<PointXYZ> ());
// Datasets
pcl::PointCloud<PointXYZ>::Ptr cloud (new pcl::PointCloud<PointXYZ>);
pcl::PointCloud<PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<PointXYZ>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::PointCloud<PointXYZ>::Ptr cloud_filtered2 (new pcl::PointCloud<PointXYZ>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals2 (new pcl::PointCloud<pcl::Normal>);
pcl::ModelCoefficients::Ptr coefficients_plane (new pcl::ModelCoefficients), coefficients_cylinder (new pcl::ModelCoefficients);
pcl::PointIndices::Ptr inliers_plane (new pcl::PointIndices), inliers_cylinder (new pcl::PointIndices);
// Copy the points
cloud->reserve(myPoints.size());
for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
cloud->push_back(pcl::PointXYZ(it->x, it->y, it->z));
}
cloud->width = int (cloud->points.size ());
cloud->height = 1;
// Build a passthrough filter to remove spurious NaNs
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 1.5);
pass.filter (*cloud_filtered);
// Estimate point normals
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
ne.setKSearch (50);
ne.compute (*cloud_normals);
// Create the segmentation object for the planar model and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_NORMAL_PLANE);
seg.setNormalDistanceWeight (0.1);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.03);
seg.setInputCloud (cloud_filtered);
seg.setInputNormals (cloud_normals);
// Obtain the plane inliers and coefficients
seg.segment (*inliers_plane, *coefficients_plane);
myClusters.push_back(inliers_plane->indices);
// Extract the planar inliers from the input cloud
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers_plane);
extract.setNegative (false);
// Write the planar inliers to disk
pcl::PointCloud<PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<PointXYZ> ());
extract.filter (*cloud_plane);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_filtered2);
extract_normals.setNegative (true);
extract_normals.setInputCloud (cloud_normals);
extract_normals.setIndices (inliers_plane);
extract_normals.filter (*cloud_normals2);
// Create the segmentation object for cylinder segmentation and set all the parameters
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_CYLINDER);
seg.setNormalDistanceWeight (0.1);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (10000);
seg.setDistanceThreshold (0.05);
seg.setRadiusLimits (0, 0.1);
seg.setInputCloud (cloud_filtered2);
seg.setInputNormals (cloud_normals2);
// Obtain the cylinder inliers and coefficients
seg.segment (*inliers_cylinder, *coefficients_cylinder);
myClusters.push_back(inliers_cylinder->indices);
// Write the cylinder inliers to disk
extract.setInputCloud (cloud_filtered2);
extract.setIndices (inliers_cylinder);
extract.setNegative (false);
pcl::PointCloud<PointXYZ>::Ptr cloud_cylinder (new pcl::PointCloud<PointXYZ> ());
extract.filter (*cloud_cylinder);
}
#endif // HAVE_PCL_SEGMENTATION

52
src/Mod/ReverseEngineering/App/Segmentation.h Normal file
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@ -0,0 +1,52 @@
/***************************************************************************
* 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 *
* *
***************************************************************************/
#ifndef REEN_SEGMENTATION_H
#define REEN_SEGMENTATION_H
#include <Base/Vector3D.h>
#include <vector>
#include <list>
namespace Points {class PointKernel;}
namespace Reen {
class Segmentation
{
public:
Segmentation(const Points::PointKernel&, std::list<std::vector<int> >& clusters);
/** \brief Set the number of k nearest neighbors to use for the normal estimation.
* \param[in] k the number of k-nearest neighbors
*/
void perform(int ksearch);
private:
const Points::PointKernel& myPoints;
std::list<std::vector<int> >& myClusters;
};
} // namespace Reen
#endif // REEN_SEGMENTATION_H