mirror of https://github.com/CGAL/cgal
149 lines
4.8 KiB
C++
149 lines
4.8 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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typedef Point_set::Property_map<unsigned char> UCmap;
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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::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 = CGAL::data_file_path("points_3/b9_training.ply");
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if (argc > 1)
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filename = argv[1];
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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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Feature_set features;
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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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Feature_generator generator (pts, pts.point_map(),
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5); // using 5 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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t.stop();
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std::cerr << "Done in " << t.time() << " second(s)" << std::endl;
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// Add labels
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Label_set labels;
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Label_handle ground = labels.add ("ground");
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Label_handle vegetation = labels.add ("vegetation");
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Label_handle roof = labels.add ("roof");
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// Check if ground truth is valid for this label set
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if (!labels.is_valid_ground_truth (pts.range(label_map.value()), true))
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return EXIT_FAILURE;
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std::vector<int> label_indices(pts.size(), -1);
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std::cerr << "Using ETHZ Random Forest Classifier" << std::endl;
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Classification::ETHZ::Random_forest_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 (pts.range(label_map.value()));
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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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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, 1, 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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// Color point set according to class
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UCmap red = pts.add_property_map<unsigned char>("red", 0).first;
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UCmap green = pts.add_property_map<unsigned char>("green", 0).first;
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UCmap blue = pts.add_property_map<unsigned char>("blue", 0).first;
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for (std::size_t i = 0; i < label_indices.size(); ++ i)
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{
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label_map.value()[i] = label_indices[i]; // update label map with computed classification
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Label_handle label = labels[label_indices[i]];
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const CGAL::IO::Color& color = label->color();
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red[i] = color.red();
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green[i] = color.green();
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blue[i] = color.blue();
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}
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// Save configuration for later use
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std::ofstream fconfig ("ethz_random_forest.bin", std::ios_base::binary);
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classifier.save_configuration(fconfig);
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// Write result
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std::ofstream f ("classification_ethz_random_forest.ply");
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f.precision(18);
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f << pts;
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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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