\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL - dD Spatial Searching
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Spatial_searching/searching_with_point_with_info_pmap.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Search_traits_3.h>
#include <CGAL/Search_traits_adapter.h>
#include <CGAL/point_generators_3.h>
#include <CGAL/Orthogonal_k_neighbor_search.h>
#include <boost/iterator/counting_iterator.hpp>
#include <utility>
typedef Kernel::Point_3 Point_3;
typedef boost::const_associative_property_map<std::map<std::size_t,Point_3> > My_point_property_map;
typedef CGAL::Random_points_in_cube_3<Point_3> Random_points_iterator;
typedef CGAL::Search_traits_3<Kernel> Traits_base;
typedef K_neighbor_search::Tree Tree;
typedef Tree::Splitter Splitter;
typedef K_neighbor_search::Distance Distance;
int main() {
const unsigned int K = 5;
// generator for random data points in the cube ( (-1,-1,-1), (1,1,1) )
Random_points_iterator rpit( 1.0);
std::map<std::size_t,Point_3> points;
points[0]=Point_3(*rpit++);
points[1]=Point_3(*rpit++);
points[2]=Point_3(*rpit++);
points[3]=Point_3(*rpit++);
points[4]=Point_3(*rpit++);
points[5]=Point_3(*rpit++);
points[6]=Point_3(*rpit++);
My_point_property_map ppmap(points);
// Insert number_of_data_points in the tree
Tree tree(
boost::counting_iterator<std::size_t>(0),
boost::counting_iterator<std::size_t>(points.size()),
Traits(ppmap)
);
Point_3 query(0.0, 0.0, 0.0);
Distance tr_dist(ppmap);
// search K nearest neighbours
K_neighbor_search search(tree, query, K,0,true,tr_dist);
for(K_neighbor_search::iterator it = search.begin(); it != search.end(); it++){
std::cout << " d(q, nearest neighbor)= "
<< tr_dist.inverse_of_transformed_distance(it->second) << " "
<< points[it->first] << " " << it->first << std::endl;
}
return 0;
}