mirror of https://github.com/CGAL/cgal
132 lines
4.0 KiB
C++
132 lines
4.0 KiB
C++
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#define CGAL_TRACE_STREAM std::cerr
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#include "util.h"
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#include <CGAL/Exact_predicates_exact_constructions_kernel.h>
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#include <CGAL/Octree.h>
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#include <CGAL/Search_traits_3.h>
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#include <CGAL/Orthogonal_k_neighbor_search.h>
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#include <iostream>
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#include <chrono>
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using std::chrono::high_resolution_clock;
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using std::chrono::duration_cast;
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using std::chrono::microseconds;
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using Kernel = CGAL::Exact_predicates_inexact_constructions_kernel;
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using Point = Kernel::Point_3;
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using Point_set = CGAL::Point_set_3<Point>;
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using Point_map = Point_set::Point_map;
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using Octree = CGAL::Octree<Kernel, Point_set, Point_map>;
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using Kd_tree_traits = CGAL::Search_traits_3<Kernel>;
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using Kd_tree_search = CGAL::Orthogonal_k_neighbor_search<Kd_tree_traits>;
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using Kdtree = Kd_tree_search::Tree;
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int main(int argc, char **argv) {
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int num_runs = 100;
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size_t k = 10;
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// Set output file
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std::ofstream file;
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file.open((argc > 1) ? argv[1] : "../nearest_neighbor_benchmark.csv");
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// Add header for CSV
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file << "Number of Points,Octree,kDTree \n";
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// Perform tests for various dataset sizes
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for (size_t num_points = 100; num_points < 100000; num_points *= 1.05) {
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// We want the average of several runs for each point count, for cleaner results
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float octreeAverage = 0;
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float kdtreeAverage = 0;
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float naiveAverage = 0;
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// Repeat the tests, generating a new point set for each run
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for (int i = 0; i < num_runs; ++i) {
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// Create a collection of the right number of points
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auto points = generate<Kernel>(num_points);
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// Create a search point
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auto search_point = *(generate<Kernel>().points().end() - 1);
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// Build the kd tree from the point set
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Kdtree kdtree(points.points().begin(), points.points().end());
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kdtree.build();
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// Time how long it takes to find neighbors using the kd tree
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auto kdtreeTime = bench<microseconds>(
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[&] {
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Kd_tree_search search(kdtree, search_point, k);
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}
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);
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// Time how long it takes to find neighbors using a naive approach
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// auto naiveTime = bench<microseconds>(
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// [&] {
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//
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// std::vector<Point> nearest_neighbors;
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//
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// // Iterate over every point
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// for (auto &point : points.points()) {
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//
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// // Find out how this point ranks in comparison with other points we've saved
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// auto iter = nearest_neighbors.begin();
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// for (; iter < nearest_neighbors.end() &&
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// CGAL::squared_distance(point, search_point) <
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// CGAL::squared_distance(*iter, search_point);
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// iter++) {}
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//
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// // Add the point to the list (it'll usually be at the end)
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// nearest_neighbors.insert(iter, point);
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//
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// // Never keep more than k neighbors
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// if (nearest_neighbors.size() > k)
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// nearest_neighbors.resize(k);
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//
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// }
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//
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// }
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// );
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// Build the octree from points (this had to be done second because it rearranges the point set)
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Octree octree(points, points.point_map());
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octree.refine();
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// Time how long it takes to find neighbors using the octree
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auto octreeTime = bench<microseconds>(
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[&] {
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std::vector<Point_set::Index> nearest_neighbors;
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octree.nearest_k_neighbors(search_point, k, std::back_inserter(nearest_neighbors));
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}
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);
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// Incorporate our results into the average
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octreeAverage += (float) octreeTime.count() / (float) num_runs;
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kdtreeAverage += (float) kdtreeTime.count() / (float) num_runs;
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// naiveAverage += (float) naiveTime.count() / (float) num_runs;
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// A simple progress indication
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std::cout << ".";
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}
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file << num_points << ",";
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file << octreeAverage << ",";
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file << kdtreeAverage << ",";
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// file << naiveAverage << ",";
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file << "\n";
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std::cout << num_points << std::endl;
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}
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file.close();
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return 0;
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}
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