CGAL 6.2 - Classification

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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 <boost/iterator/function_output_iterator.hpp>
#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/Shape_detection/Region_growing.h>
#include <CGAL/Real_timer.h>
typedef CGAL::Simple_cartesian<double> Kernel;
typedef Kernel::Point_3 Point;
typedef Kernel::Iso_cuboid_3 Iso_cuboid_3;
typedef CGAL::Point_set_3<Point> Point_set;
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;
typedef CGAL::Shape_detection::Point_set::Sphere_neighbor_query_for_point_set<Point_set> Neighbor_query;
typedef CGAL::Shape_detection::Point_set::Least_squares_plane_fit_region_for_point_set<Point_set> Region_type;
typedef CGAL::Shape_detection::Region_growing<Neighbor_query, Region_type> Region_growing;
namespace Classification = CGAL::Classification;
namespace Feature = CGAL::Classification::Feature;
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 = CGAL::data_file_path("points_3/b9.ply");
std::string filename_config = "data/b9_clusters_config.bin";
if (argc > 1)
filename = argv[1];
if (argc > 2)
filename_config = argv[2];
std::cerr << "Reading input" << std::endl;
Point_set pts;
if(!(CGAL::IO::read_point_set(filename, pts,
// the PLY reader expects a binary file by default
CGAL::parameters::use_binary_mode(true))))
{
std::cerr << "Error: cannot read " << filename << std::endl;
return EXIT_FAILURE;
}
std::cerr << "Estimating normals" << std::endl;
CGAL::Real_timer t;
t.start();
pts.add_normal_map();
CGAL::jet_estimate_normals<CGAL::Parallel_if_available_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 should only be used with variables defined at the scope
// of the generator object, thus we instantiate the normal map
// outside of the function
Vmap normal_map = pts.normal_map();
generator.generate_normal_based_features (pointwise_features, 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 and creating clusters" << std::endl;
t.start();
const double search_sphere_radius = 1.0;
const double max_distance_to_plane = 1.0;
const double max_accepted_angle = 25.0;
const std::size_t min_region_size = 10;
Neighbor_query neighbor_query = CGAL::Shape_detection::Point_set::make_sphere_neighbor_query (
pts, CGAL::parameters::sphere_radius(search_sphere_radius)
.point_map(pts.point_map()));
Region_type region_type = CGAL::Shape_detection::Point_set::make_least_squares_plane_fit_region(
pts, CGAL::parameters::maximum_distance(max_distance_to_plane)
.maximum_angle(max_accepted_angle)
.minimum_region_size(min_region_size));
Region_growing region_growing (
pts, neighbor_query, region_type);
std::vector<Cluster> clusters;
region_growing.detect
(boost::make_function_output_iterator
([&](const std::pair<Kernel::Plane_3, std::vector<Point_set::Index>>& region) -> void {
// Create a new cluster.
Classification::Cluster<Point_set, Pmap> cluster (pts, pts.point_map());
for (Point_set::Index idx : region.second)
cluster.insert(idx);
clusters.push_back(cluster);
}));
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;
// First, compute means of features.
features.begin_parallel_additions();
for (std::size_t i = 0; i < pointwise_features.size(); ++ i)
features.add<Feature::Cluster_mean_of_feature> (clusters, pointwise_features[i]);
features.end_parallel_additions();
// Then, compute variances of features (and remaining cluster features).
features.begin_parallel_additions();
for (std::size_t i = 0; i < pointwise_features.size(); ++ i)
features.add<Feature::Cluster_variance_of_feature> (clusters,
pointwise_features[i], // i^th feature
features[i]); // mean of i^th feature
features.add<Feature::Cluster_size> (clusters);
features.add<Feature::Cluster_vertical_extent> (clusters);
for (std::size_t i = 0; i < 3; ++ i)
features.add<Feature::Eigenvalue> (clusters, eigen, (unsigned int)(i));
features.end_parallel_additions();
t.stop();
Label_set labels = { "ground", "vegetation", "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<CGAL::Parallel_if_available_tag> (clusters, labels, classifier, label_indices);
t.stop();
std::cerr << "Classification done in " << t.time() << " second(s)" << std::endl;
return EXIT_SUCCESS;
}
Region_growing.h
CGAL::Shape_detection::Region_growing
Kernel::Plane_3
Kernel::Point_3
CGAL::Classification::Feature
Definition: Cluster_mean_of_feature.h:26
CGAL::Classification
Definition: Classifier.h:5
Kernel
CGAL::Simple_cartesian