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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2011-2016 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 <nanoflann.hpp>
#include "utils.h"
#include <ctime>
#include <cstdlib>
#include <iostream>
using namespace std;
using namespace nanoflann;
template <typename num_t>
void kdtree_demo(const size_t N)
{
PointCloud<num_t> cloud;
// Generate points:
generateRandomPointCloud(cloud, N);
// construct a kd-tree index:
typedef KDTreeSingleIndexAdaptor<
L2_Simple_Adaptor<num_t, PointCloud<num_t> > ,
PointCloud<num_t>,
3 /* dim */
> my_kd_tree_t;
my_kd_tree_t index(3 /*dim*/, cloud, KDTreeSingleIndexAdaptorParams(10 /* max leaf */) );
index.buildIndex();
#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<size_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<std::pair<size_t,num_t> > ret_matches;
nanoflann::SearchParams params;
//params.sorted = false;
const size_t nMatches = index.radiusSearch(&query_pt[0], search_radius, ret_matches, params);
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
srand(static_cast<unsigned int>(time(nullptr)));
kdtree_demo<float>(4);
kdtree_demo<double>(100000);
return 0;
}