\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.10.1 - Point Set Processing
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Point_set_processing_3/scale_estimation_2d_example.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/estimate_scale.h>
#include <CGAL/IO/read_xyz_points.h>
#include <CGAL/Random.h>
#include <vector>
#include <fstream>
#include <boost/tuple/tuple.hpp>
// Types
typedef Kernel::FT FT;
typedef Kernel::Point_2 Point_2;
int main (int, char**)
{
std::vector<Point_2> samples;
samples.reserve (100000);
// Generate circle with gradually variable noise
// - noise-free for points with x close to (-1)
// - noisy for points with x close to (+1)
for (std::size_t i = 0; i < 100000; ++ i)
{
FT theta = CGAL::get_default_random().get_double(0, 2. * CGAL_PI);
FT noise = 0.5 * (std::cos(theta) + 1.) * CGAL::get_default_random().get_double(0., 0.2);
int mult = (CGAL::get_default_random().get_bool() ? 1 : -1);
samples.push_back (Point_2 (std::cos(theta) * (1. + mult * noise * noise),
std::sin(theta) * (1. + mult * noise * noise)));
}
// Search for local scales on 3 different locations
std::vector<Point_2> queries;
queries.reserve (3);
queries.push_back (Point_2 (-1., 0.));
queries.push_back (Point_2 (0., 1.));
queries.push_back (Point_2 (1., 0.));
std::vector<std::size_t> k_scales;
CGAL::estimate_local_k_neighbor_scales (samples.begin(), samples.end(),
queries.begin(), queries.end(),
std::back_inserter (k_scales));
// Display results
std::cerr << "K-Scales found:" << std::endl
<< " - On noise-free region: " << k_scales[0] << std::endl // Should be small
<< " - On moderately noisy region: " << k_scales[1] << std::endl // Should be higher
<< " - On very noisy region: " << k_scales[2] << std::endl; // Should be even higher
return EXIT_SUCCESS;
}