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;
}