\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.13.2 - Classification
Classification/example_cluster_classification.cpp
#if defined (_MSC_VER) && !defined (_WIN64)
#pragma warning(disable:4244) // boost::number_distance::distance()
// converts 64 to 32 bits integers
#endif
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <string>
#include <CGAL/Simple_cartesian.h>
#include <CGAL/Classification.h>
#include <CGAL/Point_set_3.h>
#include <CGAL/Point_set_3/IO.h>
#include <CGAL/jet_estimate_normals.h>
#include <CGAL/Real_timer.h>
#ifdef CGAL_LINKED_WITH_TBB
typedef CGAL::Parallel_tag Concurrency_tag;
#else
typedef CGAL::Sequential_tag Concurrency_tag;
#endif
typedef Kernel::Point_3 Point;
typedef CGAL::Point_set_3<Point> Point_set;
typedef Kernel::Iso_cuboid_3 Iso_cuboid_3;
typedef Point_set::Point_map Pmap;
typedef Point_set::Vector_map Vmap;
typedef Point_set::Property_map<int> Imap;
typedef Point_set::Property_map<unsigned char> UCmap;
namespace Classification = CGAL::Classification;
typedef Classification::Label_handle Label_handle;
typedef Classification::Feature_handle Feature_handle;
typedef Classification::Label_set Label_set;
typedef Classification::Feature_set Feature_set;
typedef Classification::Local_eigen_analysis Local_eigen_analysis;
typedef Classification::Point_set_feature_generator<Kernel, Point_set, Pmap> Feature_generator;
typedef Classification::Cluster<Point_set, Pmap> Cluster;
int main (int argc, char** argv)
{
std::string filename = "data/b9.ply";
std::string filename_config = "data/b9_clusters_config.gz";
if (argc > 1)
filename = argv[1];
if (argc > 2)
filename_config = argv[2];
std::ifstream in (filename.c_str(), std::ios::binary);
Point_set pts;
std::cerr << "Reading input" << std::endl;
in >> pts;
std::cerr << "Estimating normals" << std::endl;
CGAL::Real_timer t;
t.start();
pts.add_normal_map();
CGAL::jet_estimate_normals<Concurrency_tag> (pts, 12);
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
t.reset();
Feature_set pointwise_features;
std::cerr << "Generating pointwise features" << std::endl;
t.start();
Feature_generator generator (pts, pts.point_map(),
5); // using 5 scales
#ifdef CGAL_LINKED_WITH_TBB
pointwise_features.begin_parallel_additions();
#endif
generator.generate_point_based_features (pointwise_features);
generator.generate_normal_based_features (pointwise_features, pts.normal_map());
#ifdef CGAL_LINKED_WITH_TBB
pointwise_features.end_parallel_additions();
#endif
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
std::cerr << "Detecting planes" << std::endl;
t.start();
Region_growing::Parameters parameters;
parameters.min_points = 1;
parameters.epsilon = 1.0;
parameters.cluster_epsilon = 1.0;
parameters.normal_threshold = 0.9;
Region_growing region_growing;
region_growing.set_input (pts, pts.point_map(), pts.normal_map());
region_growing.add_shape_factory<Plane>();
region_growing.detect (parameters);
t.stop();
std::cerr << region_growing.shapes().end() - region_growing.shapes().begin() << " planes detected in "
<< t.time() << " second(s)" << std::endl;
t.reset();
std::cerr << "Creating clusters" << std::endl;
t.start();
std::vector<Cluster> clusters;
(pts, region_growing.planes()),
clusters);
t.stop();
std::cerr << clusters.size() << " clusters created in "
<< t.time() << " second(s)" << std::endl;
t.reset();
std::cerr << "Computing cluster features" << std::endl;
Local_eigen_analysis eigen = Local_eigen_analysis::create_from_point_clusters (clusters);
t.start();
Feature_set features;
#ifdef CGAL_LINKED_WITH_TBB
features.begin_parallel_additions();
#endif
// First compute means of features
for (std::size_t i = 0; i < pointwise_features.size(); ++ i)
features.add<Classification::Feature::Cluster_mean_of_feature> (clusters, pointwise_features[i]);
#ifdef CGAL_LINKED_WITH_TBB
features.end_parallel_additions();
features.begin_parallel_additions();
#endif
// Then compute variances of features (and remaining cluster features)
for (std::size_t i = 0; i < pointwise_features.size(); ++ i)
features.add<Classification::Feature::Cluster_variance_of_feature> (clusters,
pointwise_features[i], // i^th feature
features[i]); // mean of i^th feature
features.add<Classification::Feature::Cluster_size> (clusters);
features.add<Classification::Feature::Cluster_vertical_extent> (clusters);
for (std::size_t i = 0; i < 3; ++ i)
features.add<Classification::Feature::Eigenvalue> (clusters, eigen, (unsigned int)(i));
#ifdef CGAL_LINKED_WITH_TBB
features.end_parallel_additions();
#endif
t.stop();
// Add types
Label_set labels;
Label_handle ground = labels.add ("ground");
Label_handle vegetation = labels.add ("vegetation");
Label_handle roof = labels.add ("roof");
std::vector<int> label_indices(clusters.size(), -1);
std::cerr << "Using ETHZ Random Forest Classifier" << std::endl;
Classification::ETHZ_random_forest_classifier classifier (labels, features);
std::cerr << "Loading configuration" << std::endl;
std::ifstream in_config (filename_config, std::ios_base::in | std::ios_base::binary);
classifier.load_configuration (in_config);
std::cerr << "Classifying" << std::endl;
t.reset();
t.start();
Classification::classify<Concurrency_tag> (clusters, labels, classifier, label_indices);
t.stop();
std::cerr << "Classification done in " << t.time() << " second(s)" << std::endl;
return EXIT_SUCCESS;
}