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nanoflann_testRandom.cpp
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nanoflann_testRandom.cpp
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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 <ctime>
#include <cstdlib>
#include <iostream>
using namespace std;
using namespace nanoflann;
// This is an exampleof a custom data set class
template <typename T>
struct PointCloud
{
struct Point
{
T x,y,z;
};
std::vector<Point> pts;
// Must return the number of data points
inline size_t kdtree_get_point_count() const { return pts.size(); }
// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
{
const T d0=p1[0]-pts[idx_p2].x;
const T d1=p1[1]-pts[idx_p2].y;
const T d2=p1[2]-pts[idx_p2].z;
return d0*d0+d1*d1+d2*d2;
}
// Returns the dim'th component of the idx'th point in the class:
// Since this is inlined and the "dim" argument is typically an immediate value, the
// "if/else's" are actually solved at compile time.
inline T kdtree_get_pt(const size_t idx, int dim) const
{
if (dim==0) return pts[idx].x;
else if (dim==1) return pts[idx].y;
else return pts[idx].z;
}
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX& /* bb */) const { return false; }
};
template <typename T>
PointCloud<T> generateRandomPointCloud(const size_t N, const T max_range = 10)
{
PointCloud<T> point;
point.pts.resize(N);
for (size_t i=0;i<N;i++)
{
point.pts[i].x = max_range * (rand() % 1000) / T(1000);
point.pts[i].y = max_range * (rand() % 1000) / T(1000);
point.pts[i].z = max_range * (rand() % 1000) / T(1000);
}
return point;
}
template <typename num_t>
void kdtree_demo(const size_t N, double &buildTimer, double &queryTimer)
{
PointCloud<num_t> cloudS = generateRandomPointCloud<num_t>(N);
PointCloud<num_t> cloudT = generateRandomPointCloud<num_t>(N);
clock_t begin = clock();
// 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*/, cloudS, KDTreeSingleIndexAdaptorParams(10 /* max leaf */) );
index.buildIndex();
clock_t end = clock();
buildTimer += double(end - begin) / CLOCKS_PER_SEC;
{
double elapsed_secs=0;
for(size_t i=0;i<N;i++)
{
num_t query_pt[3] = {cloudT.pts[i].x, cloudT.pts[i].y, cloudT.pts[i].z};
// do a knn search
const size_t num_results = 1;
size_t ret_index;
num_t out_dist_sqr;
clock_t begin = clock();
nanoflann::KNNResultSet<num_t> resultSet(num_results);
resultSet.init(&ret_index, &out_dist_sqr);
index.findNeighbors(resultSet, query_pt, nanoflann::SearchParams(10));
clock_t end = clock();
elapsed_secs += double(end - begin);
}
elapsed_secs = elapsed_secs/CLOCKS_PER_SEC;
queryTimer += elapsed_secs/N;
}
}
int main(int argc, char *argv[])
{
size_t plotCount = 10;
size_t maxSize = 10000;
if(argc == 3)
{
srand(atoi(argv[2]));
maxSize = atoi(argv[1]);
}
else
{
cerr << "**Running Instructions:**\n ./nanoflann_testRandom numPoints seed\nExample:\n ./nanoflann_testRandom 10000 1" << endl;
return 0;
}
// buildTime : time required to build the kd-tree index
// queryTime : time required to find nearest neighbor for a single point in the kd-tree
vector<double> buildTime, queryTime;
for (size_t i=1;i<=plotCount;i++)
{
size_t currSize=((i*1.0)/plotCount)*maxSize;
std::cout<<currSize<<" ";
double buildTimer = 0, queryTimer = 0;
kdtree_demo<float>(currSize, buildTimer, queryTimer);
buildTime.push_back(buildTimer);
queryTime.push_back(queryTimer);
}
cout<<"\n";
for(size_t i=0;i<buildTime.size();i++)
std::cout<<buildTime[i]<<" ";
std::cout<<"\n";
for(size_t i=0;i<queryTime.size();i++)
std::cout<<queryTime[i]<<" ";
std::cout<<"\n";
return 0;
}