mirror of
https://github.com/jlblancoc/nanoflann.git
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111 lines
4.1 KiB
C++
111 lines
4.1 KiB
C++
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2011-2025 Jose Luis Blanco (joseluisblancoc@gmail.com).
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#include <cstdlib>
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#include <ctime>
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#include <iostream>
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#include <nanoflann.hpp>
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#include "utils.h"
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template <typename num_t>
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void kdtree_demo(const size_t N)
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{
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using std::cout;
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using std::endl;
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PointCloud<num_t> cloud;
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// Generate points:
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generateRandomPointCloud(cloud, N);
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// construct a kd-tree index:
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using my_kd_tree_t = nanoflann::KDTreeSingleIndexAdaptor<
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nanoflann::L2_Simple_Adaptor<num_t, PointCloud<num_t>>, PointCloud<num_t>, 3 /* dim */
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>;
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my_kd_tree_t index(3 /*dim*/, cloud, {10 /* max leaf */});
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#if 0
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// Test resize of dataset and rebuild of index:
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cloud.pts.resize(cloud.pts.size()*0.5);
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index.buildIndex();
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#endif
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const num_t query_pt[3] = {0.5, 0.5, 0.5};
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// ----------------------------------------------------------------
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// knnSearch(): Perform a search for the N closest points
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// ----------------------------------------------------------------
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{
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size_t num_results = 5;
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std::vector<uint32_t> ret_index(num_results);
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std::vector<num_t> out_dist_sqr(num_results);
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num_results = index.knnSearch(&query_pt[0], num_results, &ret_index[0], &out_dist_sqr[0]);
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// In case of less points in the tree than requested:
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ret_index.resize(num_results);
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out_dist_sqr.resize(num_results);
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cout << "knnSearch(): num_results=" << num_results << "\n";
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for (size_t i = 0; i < num_results; i++)
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cout << "idx[" << i << "]=" << ret_index[i] << " dist[" << i << "]=" << out_dist_sqr[i]
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<< endl;
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cout << "\n";
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}
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// ----------------------------------------------------------------
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// radiusSearch(): Perform a search for the points within search_radius
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// ----------------------------------------------------------------
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{
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const num_t search_radius = static_cast<num_t>(0.1);
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std::vector<nanoflann::ResultItem<uint32_t, num_t>> ret_matches;
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// nanoflanSearchParamsameters params;
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// params.sorted = false;
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const size_t nMatches = index.radiusSearch(&query_pt[0], search_radius, ret_matches);
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cout << "radiusSearch(): radius=" << search_radius << " -> " << nMatches << " matches\n";
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for (size_t i = 0; i < nMatches; i++)
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cout << "idx[" << i << "]=" << ret_matches[i].first << " dist[" << i
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<< "]=" << ret_matches[i].second << endl;
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cout << "\n";
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}
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}
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int main()
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{
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// Randomize Seed
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srand(static_cast<unsigned int>(time(nullptr)));
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kdtree_demo<float>(4);
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kdtree_demo<double>(100000);
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return 0;
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}
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