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RE: apply clang format

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This commit is contained in:
wmayer
2023-09-02 11:46:46 +02:00
committed by wwmayer
parent 7783e683c8
commit c6bc17ffc1
19 changed files with 1465 additions and 1237 deletions
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131
src/Mod/ReverseEngineering/App/RegionGrowing.cpp
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@@ -22,7 +22,7 @@
#include "PreCompiled.h"
#ifndef _PreComp_
# include <boost/math/special_functions/fpclassify.hpp>
#include <boost/math/special_functions/fpclassify.hpp>
#endif
#include <Mod/Points/App/Points.h>
@@ -31,69 +31,71 @@
#if defined(HAVE_PCL_FILTERS)
# include <pcl/filters/passthrough.h>
# include <pcl/point_types.h>
#include <pcl/filters/passthrough.h>
#include <pcl/point_types.h>
#endif
#if defined(HAVE_PCL_SEGMENTATION)
# include <pcl/search/search.h>
# include <pcl/search/kdtree.h>
# include <pcl/features/normal_3d.h>
# include <pcl/segmentation/region_growing.h>
# include <pcl/filters/extract_indices.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/search/kdtree.h>
#include <pcl/search/search.h>
#include <pcl/segmentation/region_growing.h>
using namespace std;
using namespace Reen;
using pcl::PointXYZ;
using pcl::PointNormal;
using pcl::PointCloud;
using pcl::PointNormal;
using pcl::PointXYZ;
RegionGrowing::RegionGrowing(const Points::PointKernel& pts, std::list<std::vector<int> >& clusters)
: myPoints(pts)
, myClusters(clusters)
{
}
RegionGrowing::RegionGrowing(const Points::PointKernel& pts, std::list<std::vector<int>>& clusters)
: myPoints(pts)
, myClusters(clusters)
{}
void RegionGrowing::perform(int ksearch)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
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))
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));
}
}
//normal estimation
// normal estimation
pcl::search::Search<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
pcl::PointCloud <pcl::Normal>::Ptr normals (new pcl::PointCloud <pcl::Normal>);
pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;
normal_estimator.setSearchMethod (tree);
normal_estimator.setInputCloud (cloud);
normal_estimator.setKSearch (ksearch);
normal_estimator.compute (*normals);
normal_estimator.setSearchMethod(tree);
normal_estimator.setInputCloud(cloud);
normal_estimator.setKSearch(ksearch);
normal_estimator.compute(*normals);
// pass through
pcl::IndicesPtr indices (new std::vector <int>);
pcl::IndicesPtr indices(new std::vector<int>);
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0.0, 1.0);
pass.filter (*indices);
pass.setInputCloud(cloud);
pass.setFilterFieldName("z");
pass.setFilterLimits(0.0, 1.0);
pass.filter(*indices);
pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;
reg.setMinClusterSize (50);
reg.setMaxClusterSize (1000000);
reg.setSearchMethod (tree);
reg.setNumberOfNeighbours (30);
reg.setInputCloud (cloud);
//reg.setIndices (indices);
reg.setInputNormals (normals);
reg.setSmoothnessThreshold (3.0 / 180.0 * M_PI);
reg.setCurvatureThreshold (1.0);
reg.setMinClusterSize(50);
reg.setMaxClusterSize(1000000);
reg.setSearchMethod(tree);
reg.setNumberOfNeighbours(30);
reg.setInputCloud(cloud);
// reg.setIndices (indices);
reg.setInputNormals(normals);
reg.setSmoothnessThreshold(3.0 / 180.0 * M_PI);
reg.setCurvatureThreshold(1.0);
std::vector <pcl::PointIndices> clusters;
reg.extract (clusters);
std::vector<pcl::PointIndices> clusters;
reg.extract(clusters);
for (std::vector<pcl::PointIndices>::iterator it = clusters.begin (); it != clusters.end (); ++it) {
for (std::vector<pcl::PointIndices>::iterator it = clusters.begin(); it != clusters.end();
++it) {
myClusters.push_back(std::vector<int>());
myClusters.back().swap(it->indices);
}
@@ -101,17 +103,18 @@ void RegionGrowing::perform(int ksearch)
void RegionGrowing::perform(const std::vector<Base::Vector3f>& myNormals)
{
if (myPoints.size() != myNormals.size())
if (myPoints.size() != myNormals.size()) {
throw Base::RuntimeError("Number of points doesn't match with number of normals");
}
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
cloud->reserve(myPoints.size());
pcl::PointCloud <pcl::Normal>::Ptr normals (new pcl::PointCloud <pcl::Normal>);
pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
normals->reserve(myNormals.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++) {
for (std::size_t index = 0; index < num_points; index++) {
const Base::Vector3f& p = points[index];
const Base::Vector3f& n = myNormals[index];
if (!boost::math::isnan(p.x) && !boost::math::isnan(p.y) && !boost::math::isnan(p.z)) {
@@ -121,35 +124,35 @@ void RegionGrowing::perform(const std::vector<Base::Vector3f>& myNormals)
}
pcl::search::Search<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (cloud);
tree->setInputCloud(cloud);
// pass through
pcl::IndicesPtr indices (new std::vector <int>);
pcl::IndicesPtr indices(new std::vector<int>);
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0.0, 1.0);
pass.filter (*indices);
pass.setInputCloud(cloud);
pass.setFilterFieldName("z");
pass.setFilterLimits(0.0, 1.0);
pass.filter(*indices);
pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;
reg.setMinClusterSize (50);
reg.setMaxClusterSize (1000000);
reg.setSearchMethod (tree);
reg.setNumberOfNeighbours (30);
reg.setInputCloud (cloud);
//reg.setIndices (indices);
reg.setInputNormals (normals);
reg.setSmoothnessThreshold (3.0 / 180.0 * M_PI);
reg.setCurvatureThreshold (1.0);
reg.setMinClusterSize(50);
reg.setMaxClusterSize(1000000);
reg.setSearchMethod(tree);
reg.setNumberOfNeighbours(30);
reg.setInputCloud(cloud);
// reg.setIndices (indices);
reg.setInputNormals(normals);
reg.setSmoothnessThreshold(3.0 / 180.0 * M_PI);
reg.setCurvatureThreshold(1.0);
std::vector <pcl::PointIndices> clusters;
reg.extract (clusters);
std::vector<pcl::PointIndices> clusters;
reg.extract(clusters);
for (std::vector<pcl::PointIndices>::iterator it = clusters.begin (); it != clusters.end (); ++it) {
for (std::vector<pcl::PointIndices>::iterator it = clusters.begin(); it != clusters.end();
++it) {
myClusters.push_back(std::vector<int>());
myClusters.back().swap(it->indices);
}
}
#endif // HAVE_PCL_SEGMENTATION
#endif// HAVE_PCL_SEGMENTATION