nanoflann-knn/examples/example_with_pkgconfig/pointcloud_example.cpp

/***********************************************************************
 * 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 <cstdlib>
#include <ctime>
#include <iostream>
#include <nanoflann.hpp>

#include "../utils.h"

template <typename num_t>
void kdtree_demo(const size_t N)
{
    PointCloud<num_t> 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<num_t, PointCloud<num_t>>,
        PointCloud<num_t>, 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<num_t> resultSet(num_results);
        resultSet.init(&ret_index, &out_dist_sqr);
        index.findNeighbors(
            resultSet, &query_pt[0], nanoflann::SearchParams(10));

        std::cout << "knnSearch(nn=" << num_results << "): \n";
        std::cout << "ret_index=" << ret_index
                  << " out_dist_sqr=" << out_dist_sqr << std::endl;
    }
    {
        // Unsorted radius search:
        const num_t                               radius = 1;
        std::vector<std::pair<size_t, num_t>>     indices_dists;
        nanoflann::RadiusResultSet<num_t, size_t> resultSet(
            radius, indices_dists);

        index.findNeighbors(resultSet, query_pt, nanoflann::SearchParams());

        // Get worst (furthest) point, without sorting:
        std::pair<size_t, num_t> worst_pair = resultSet.worst_item();
        std::cout << "Worst pair: idx=" << worst_pair.first
                  << " dist=" << worst_pair.second << std::endl;
    }
}

int main()
{
    // Randomize Seed
    srand(static_cast<unsigned int>(time(nullptr)));
    kdtree_demo<float>(1000000);
    kdtree_demo<double>(1000000);
    return 0;
}