nanoflann-knn/pointcloud_kdd_radius.cpp

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

#include "utils.h"

using namespace std;

const long long pointNum = 10000000; //节点数；
const int knn = 100; //k近邻；
long long queryNum = 10000;
double maxRange = 1000;

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

    PointCloud<num_t> cloud;
    
    // Generate points:
    generateRandomPointCloud<double>(cloud, N, maxRange);
    
    // 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 */
        >;
    auto insertStart = std::chrono::high_resolution_clock::now();
    my_kd_tree_t index(3 /*dim*/, cloud, {10 /* max leaf */});

    auto insertEnd = std::chrono::high_resolution_clock::now();
    std::chrono::duration<double> duration = insertEnd - insertStart;
    double insertTime = duration.count();
    cout<< "插入数据花费的时间为"<< insertTime << "秒"<<"("<<pointNum<<")"<<endl;
#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
    // ----------------------------------------------------------------
    {
        auto queryStart = std::chrono::high_resolution_clock::now();
        size_t                num_results = knn;
        std::vector<uint32_t> ret_index(num_results);
        std::vector<num_t>    out_dist_sqr(num_results);
        for (size_t i = 0; i < queryNum; i++)
        {
            num_t query_pt[3] ;
            query_pt[0] =  maxRange * (rand() % 1000) / 1000;
            query_pt[1] =  maxRange * (rand() % 1000) / 1000;
            query_pt[2] =  maxRange * (rand() % 1000) / 1000;
            num_results = index.knnSearch(
            &query_pt[0], num_results, &ret_index[0], &out_dist_sqr[0]);
                ret_index.resize(num_results);
                out_dist_sqr.resize(num_results);
        }
        auto queryEnd = std::chrono::high_resolution_clock::now();
        std::chrono::duration<double> duration2 = queryEnd - queryStart;
        double queryTime = duration2.count();
        cout<< "查询数据花费的时间为"<< queryTime << "秒"<<"("<<queryNum<<")"<<endl;
        // In case of less points in the tree than requested:
        
    /*
        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";
    }
    */
}

int main()
{
    // Randomize Seed
    cout<<"knn="<<knn<<endl;
    cout<<"pointNum="<<pointNum<<endl;
    srand(static_cast<unsigned int>(time(nullptr)));
    //kdtree_demo<float>(4);
    kdtree_demo<double>(pointNum);
    return 0;
}