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 * Copyright 2011-2025 Jose Luis Blanco (joseluisblancoc@gmail.com).
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#include <cstdlib>
#include <ctime>
#include <iostream>
#include <nanoflann.hpp>

#include "utils.h"

namespace
{

template <typename num_t>
void kdtree_demo(const size_t N)
{
    using std::cout;
    using std::endl;

    PointCloud<num_t> cloud;

    // Generate points:
    generateRandomPointCloud(cloud, N);

    // 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 */
        >;

    my_kd_tree_t index(3 /*dim*/, cloud, {10 /* max leaf */});

#if 0
	// Test resize of dataset and rebuild of index:
	cloud.pts.resize(cloud.pts.size()*0.5);
	index.buildIndex();
#endif

    const num_t query_pt[3] = {0.5, 0.5, 0.5};

    // ----------------------------------------------------------------
    // knnSearch():  Perform a search for the N closest points
    // ----------------------------------------------------------------
    {
        size_t                num_results = 5;
        std::vector<uint32_t> ret_index(num_results);
        std::vector<num_t>    out_dist_sqr(num_results);

        num_results = index.knnSearch(&query_pt[0], num_results, &ret_index[0], &out_dist_sqr[0]);

        // In case of less points in the tree than requested:
        ret_index.resize(num_results);
        out_dist_sqr.resize(num_results);

        cout << "knnSearch(): num_results=" << num_results << "\n";
        for (size_t i = 0; i < num_results; i++)
            cout << "idx[" << i << "]=" << ret_index[i] << " dist[" << i << "]=" << out_dist_sqr[i]
                 << endl;
        cout << "\n";
    }

    // ----------------------------------------------------------------
    // radiusSearch(): Perform a search for the points within search_radius
    // ----------------------------------------------------------------
    {
        const num_t                                         search_radius = static_cast<num_t>(0.1);
        std::vector<nanoflann::ResultItem<uint32_t, num_t>> ret_matches;

        // nanoflanSearchParamsameters params;
        // params.sorted = false;

        const size_t nMatches = index.radiusSearch(&query_pt[0], search_radius, ret_matches);

        cout << "radiusSearch(): radius=" << search_radius << " -> " << nMatches << " matches\n";
        for (size_t i = 0; i < nMatches; i++)
            cout << "idx[" << i << "]=" << ret_matches[i].first << " dist[" << i
                 << "]=" << ret_matches[i].second << endl;
        cout << "\n";
    }
}
}  // namespace

int main()
{
    try
    {
        // Randomize Seed
        srand(static_cast<unsigned int>(time(nullptr)));
        kdtree_demo<float>(4);
        kdtree_demo<double>(100000);
        return 0;
    }
    catch (const std::exception& e)
    {
        std::cerr << e.what() << "\n";
        return 1;
    }
}
