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+ add algorithm to estimate normals of points

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This commit is contained in:
wmayer
2016-03-09 14:46:19 +01:00
parent 9af03d9b68
commit 6da5c23990
3 changed files with 144 additions and 6 deletions
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69
src/Mod/ReverseEngineering/App/Segmentation.cpp
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@@ -27,16 +27,23 @@
#include <Mod/Points/App/Points.h>
#include <Base/Exception.h>
#if defined(HAVE_PCL_FILTERS)
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/passthrough.h>
#include <pcl/features/normal_3d.h>
#endif
#if defined(HAVE_PCL_SAMPLE_CONSENSUS)
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#endif
#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>
#endif
using namespace std;
using namespace Reen;
@@ -44,6 +51,7 @@ using pcl::PointXYZ;
using pcl::PointNormal;
using pcl::PointCloud;
#if defined(HAVE_PCL_SEGMENTATION)
Segmentation::Segmentation(const Points::PointKernel& pts, std::list<std::vector<int> >& clusters)
: myPoints(pts)
, myClusters(clusters)
@@ -87,7 +95,7 @@ void Segmentation::perform(int ksearch)
// Estimate point normals
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
ne.setKSearch (50);
ne.setKSearch (ksearch);
ne.compute (*cloud_normals);
// Create the segmentation object for the planar model and set all the parameters
@@ -146,3 +154,52 @@ void Segmentation::perform(int ksearch)
#endif // HAVE_PCL_SEGMENTATION
// ----------------------------------------------------------------------------
#if defined (HAVE_PCL_FILTERS)
NormalEstimation::NormalEstimation(const Points::PointKernel& pts)
: myPoints(pts)
, kSearch(0)
, searchRadius(0)
{
}
void NormalEstimation::perform(std::vector<Base::Vector3d>& normals)
{
// Copy the points
pcl::PointCloud<PointXYZ>::Ptr cloud (new pcl::PointCloud<PointXYZ>);
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
pcl::PointCloud<PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<PointXYZ>);
pcl::PassThrough<PointXYZ> pass;
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0, 1.5);
pass.filter (*cloud_filtered);
// Estimate point normals
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
pcl::search::KdTree<PointXYZ>::Ptr tree (new pcl::search::KdTree<PointXYZ> ());
pcl::NormalEstimation<PointXYZ, pcl::Normal> ne;
ne.setSearchMethod (tree);
ne.setInputCloud (cloud_filtered);
if (kSearch > 0)
ne.setKSearch (kSearch);
if (searchRadius > 0)
ne.setRadiusSearch (searchRadius);
ne.compute (*cloud_normals);
normals.reserve(cloud_normals->size());
for (pcl::PointCloud<pcl::Normal>::const_iterator it = cloud_normals->begin(); it != cloud_normals->end(); ++it) {
normals.push_back(Base::Vector3d(it->normal_x, it->normal_y, it->normal_z));
}
}
#endif // HAVE_PCL_FILTERS