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 * Software License Agreement (BSD License)
 *
 * Copyright 2011-2022 Jose Luis Blanco (joseluisblancoc@gmail.com).
 *   All rights reserved.
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 *************************************************************************/

#include <cstdlib>
#include <ctime>
#include <iostream>
#include <nanoflann.hpp>
#include <type_traits>

#include "utils.h"

using num_t = double;

template <typename _DistanceType, typename _IndexType = size_t>
class MyCustomResultSet
{
   public:
    using DistanceType = _DistanceType;
    using IndexType    = _IndexType;

   public:
    const DistanceType radius;

    std::vector<nanoflann::ResultItem<IndexType, DistanceType>>&
        m_indices_dists;

    explicit MyCustomResultSet(
        DistanceType radius_,
        std::vector<nanoflann::ResultItem<IndexType, DistanceType>>&
            indices_dists)
        : radius(radius_), m_indices_dists(indices_dists)
    {
        init();
    }

    void init() { clear(); }
    void clear() { m_indices_dists.clear(); }

    size_t size() const { return m_indices_dists.size(); }
    size_t empty() const { return m_indices_dists.empty(); }

    bool full() const { return true; }

    /**
     * Called during search to add an element matching the criteria.
     * @return true if the search should be continued, false if the results are
     * sufficient
     */
    bool addPoint(DistanceType dist, IndexType index)
    {
        printf(
            "addPoint() called: dist=%f index=%u\n", dist,
            static_cast<unsigned int>(index));

        if (dist < radius) m_indices_dists.emplace_back(index, dist);
        return true;
    }

    DistanceType worstDist() const { return radius; }
};

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 */
        >;

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

    {
        // radius search:
        const num_t                                       squaredRadius = 1;
        std::vector<nanoflann::ResultItem<size_t, num_t>> indices_dists;

        MyCustomResultSet<num_t, size_t> resultSet(
            squaredRadius, indices_dists);

        index.findNeighbors(resultSet, query_pt);

        std::cout << "Found: " << indices_dists.size() << " NN points."
                  << std::endl;
    }
}

int main()
{
    // Randomize Seed
    srand(static_cast<unsigned int>(time(nullptr)));
    kdtree_demo(10000);
    return 0;
}
