/*********************************************************************** * Software License Agreement (BSD License) * * Copyright 2011-2024 Jose Luis Blanco (joseluisblancoc@gmail.com). * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. *************************************************************************/ #include #include #include #include #include #include "utils.h" template void kdtree_demo(const size_t N) { static_assert( std::is_standard_layout>::value, "Unexpected memory layout for nanoflann::ResultItem"); PointCloud cloud; // Generate points: generateRandomPointCloud(cloud, N); num_t query_pt[3] = {0.5, 0.5, 0.5}; // construct a kd-tree index: using my_kd_tree_t = nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Simple_Adaptor>, PointCloud, 3 /* dim */ >; dump_mem_usage(); my_kd_tree_t index(3 /*dim*/, cloud, {10 /* max leaf */}); dump_mem_usage(); { // do a knn search const size_t num_results = 1; size_t ret_index; num_t out_dist_sqr; nanoflann::KNNResultSet resultSet(num_results); resultSet.init(&ret_index, &out_dist_sqr); index.findNeighbors(resultSet, &query_pt[0]); std::cout << "knnSearch(nn=" << num_results << "): \n"; std::cout << "ret_index=" << ret_index << " out_dist_sqr=" << out_dist_sqr << std::endl; } { // radius search: const num_t squaredRadius = 1; std::vector> indices_dists; nanoflann::RadiusResultSet resultSet( squaredRadius, indices_dists); index.findNeighbors(resultSet, query_pt); // Get worst (furthest) point, without sorting: nanoflann::ResultItem worst_pair = resultSet.worst_item(); std::cout << "Worst pair: idx=" << worst_pair.first << " squaredDist=" << worst_pair.second << std::endl; } } int main() { // Randomize Seed srand(static_cast(time(nullptr))); kdtree_demo(1000000); kdtree_demo(1000000); return 0; }