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#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/Real_timer.h>
typedef CGAL::Simple_cartesian<double> Kernel;
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::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::Point_set_feature_generator<Kernel, Point_set, Pmap> Feature_generator;
int main (int argc, char** argv)
{
std::string filename = (argc > 1) ? argv[1] : CGAL::data_file_path("points_3/b9_training.ply");
std::cerr << "Reading input" << std::endl;
std::ifstream in (filename.c_str(), std::ios::binary);
Point_set pts;
in >> pts;
std::optional<Imap> label_map = pts.property_map<int> ("label");
if (!label_map.has_value())
{
std::cerr << "Error: \"label\" property not found in input file." << std::endl;
return EXIT_FAILURE;
}
Feature_set features;
std::cerr << "Generating features" << std::endl;
CGAL::Real_timer t;
t.start();
Feature_generator generator (pts, pts.point_map(),
5); // using 5 scales
features.begin_parallel_additions();
generator.generate_point_based_features (features);
features.end_parallel_additions();
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
// Add labels
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(pts.size(), -1);
std::cerr << "Using OpenCV Random Forest Classifier" << std::endl;
Classification::OpenCV::Random_forest_classifier classifier (labels, features);
std::cerr << "Training" << std::endl;
t.reset();
t.start();
classifier.train (pts.range(label_map.value()));
t.stop();
std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
t.reset();
t.start();
Classification::classify_with_graphcut<CGAL::Parallel_if_available_tag>
(pts, pts.point_map(), labels, classifier,
generator.neighborhood().k_neighbor_query(12),
0.2f, 1, label_indices);
t.stop();
std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl;
std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl;
Classification::Evaluation evaluation (labels, pts.range(label_map.value()), label_indices);
for (Label_handle l : labels)
{
std::cerr << " * " << l->name() << ": "
<< evaluation.precision(l) << " ; "
<< evaluation.recall(l) << " ; "
<< evaluation.f1_score(l) << " ; "
<< evaluation.intersection_over_union(l) << std::endl;
}
std::cerr << "Accuracy = " << evaluation.accuracy() << std::endl
<< "Mean F1 score = " << evaluation.mean_f1_score() << std::endl
<< "Mean IoU = " << evaluation.mean_intersection_over_union() << std::endl;
// Color point set according to class
UCmap red = pts.add_property_map<unsigned char>("red", 0).first;
UCmap green = pts.add_property_map<unsigned char>("green", 0).first;
UCmap blue = pts.add_property_map<unsigned char>("blue", 0).first;
for (std::size_t i = 0; i < label_indices.size(); ++ i)
{
label_map.value()[i] = label_indices[i]; // update label map with computed classification
Label_handle label = labels[label_indices[i]];
const CGAL::IO::Color& color = label->color();
red[i] = color.red();
green[i] = color.green();
blue[i] = color.blue();
}
// Write result
std::ofstream f ("classification_opencv_random_forest.ply");
f.precision(18);
f << pts;
std::cerr << "All done" << std::endl;
return EXIT_SUCCESS;
}
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