mirror of https://github.com/CGAL/cgal
123 lines
4.0 KiB
C++
123 lines
4.0 KiB
C++
#if defined (_MSC_VER) && !defined (_WIN64)
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#pragma warning(disable:4244) // boost::number_distance::distance()
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// converts 64 to 32 bits integers
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#endif
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#include <cstdlib>
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#include <fstream>
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#include <iostream>
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#include <string>
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#include <CGAL/Simple_cartesian.h>
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#include <CGAL/Classification.h>
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#include <CGAL/Point_set_3.h>
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#include <CGAL/Point_set_3/IO.h>
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#include <CGAL/Real_timer.h>
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typedef CGAL::Simple_cartesian<double> Kernel;
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typedef Kernel::Point_3 Point;
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typedef CGAL::Point_set_3<Point> Point_set;
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typedef Kernel::Iso_cuboid_3 Iso_cuboid_3;
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typedef Point_set::Point_map Pmap;
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typedef Point_set::Property_map<int> Imap;
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namespace Classification = CGAL::Classification;
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typedef Classification::Label_handle Label_handle;
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typedef Classification::Feature_handle Feature_handle;
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typedef Classification::Label_set Label_set;
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typedef Classification::Feature_set Feature_set;
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typedef Classification::Sum_of_weighted_features_classifier Classifier;
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typedef Classification::Point_set_feature_generator<Kernel, Point_set, Pmap> Feature_generator;
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int main (int argc, char** argv)
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{
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std::string filename (argc > 1 ? argv[1] : CGAL::data_file_path("points_3/b9_training.ply"));
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std::ifstream in (filename.c_str(), std::ios::binary);
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Point_set pts;
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std::cerr << "Reading input" << std::endl;
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in >> pts;
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std::optional<Imap> label_map = pts.property_map<int> ("label");
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if (!label_map.has_value())
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{
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std::cerr << "Error: \"label\" property not found in input file." << std::endl;
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return EXIT_FAILURE;
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}
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std::cerr << "Generating features" << std::endl;
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CGAL::Real_timer t;
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t.start();
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///////////////////////////////////////////////////////////////////
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//! [Generator]
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Feature_set features;
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std::size_t number_of_scales = 5;
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Feature_generator generator (pts, pts.point_map(), number_of_scales);
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features.begin_parallel_additions();
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generator.generate_point_based_features (features);
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features.end_parallel_additions();
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//! [Generator]
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///////////////////////////////////////////////////////////////////
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t.stop();
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std::cerr << features.size() << " feature(s) generated in " << t.time() << " second(s)" << std::endl;
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Label_set labels = { "ground", "vegetation", "roof" };
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Classifier classifier (labels, features);
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std::cerr << "Training" << std::endl;
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t.reset();
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t.start();
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classifier.train<CGAL::Parallel_if_available_tag> (pts.range(label_map.value()), 800);
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t.stop();
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std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
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t.reset();
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t.start();
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std::vector<int> label_indices(pts.size(), -1);
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Classification::classify_with_graphcut<CGAL::Parallel_if_available_tag>
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(pts, pts.point_map(), labels, classifier,
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generator.neighborhood().k_neighbor_query(12),
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0.2f, 10, label_indices);
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t.stop();
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std::cerr << "Classification with graphcut done in " << t.time() << " second(s)" << std::endl;
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std::cerr << "Precision, recall, F1 scores and IoU:" << std::endl;
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Classification::Evaluation evaluation (labels, pts.range(label_map.value()), label_indices);
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for (Label_handle l : labels)
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{
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std::cerr << " * " << l->name() << ": "
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<< evaluation.precision(l) << " ; "
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<< evaluation.recall(l) << " ; "
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<< evaluation.f1_score(l) << " ; "
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<< evaluation.intersection_over_union(l) << std::endl;
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}
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std::cerr << "Accuracy = " << evaluation.accuracy() << std::endl
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<< "Mean F1 score = " << evaluation.mean_f1_score() << std::endl
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<< "Mean IoU = " << evaluation.mean_intersection_over_union() << std::endl;
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/// Save the configuration to be able to reload it later
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std::ofstream fconfig ("config.xml");
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classifier.save_configuration (fconfig);
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fconfig.close();
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std::cerr << "All done" << std::endl;
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return EXIT_SUCCESS;
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}
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