\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.12.1 - Point Set Shape Detection
Point_set_shape_detection_3/efficient_RANSAC_parameters.cpp
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/IO/read_xyz_points.h>
#include <CGAL/Point_with_normal_3.h>
#include <CGAL/property_map.h>
#include <iostream>
#include <fstream>
// Type declarations
typedef Kernel::FT FT;
typedef std::pair<Kernel::Point_3, Kernel::Vector_3> Point_with_normal;
typedef std::vector<Point_with_normal> Pwn_vector;
// In Shape_detection_traits the basic types, i.e., Point and Vector types
// as well as iterator type and property maps, are defined.
Pwn_vector, Point_map, Normal_map> Traits;
int main()
{
// Points with normals.
Pwn_vector points;
// Loads point set from a file.
// read_xyz_points_and_normals takes an OutputIterator for storing the points
// and a property map to store the normal vector with each point.
std::ifstream stream("data/cube.pwn");
if (!stream ||
std::back_inserter(points),
CGAL::parameters::point_map(Point_map()).
normal_map(Normal_map())))
{
std::cerr << "Error: cannot read file cube.pwn" << std::endl;
return EXIT_FAILURE;
}
std::cout << points.size() << " points" << std::endl;
// Instantiates shape detection engine.
Efficient_ransac ransac;
// Provides the input data.
ransac.set_input(points);
// Register shapes for detection
ransac.add_shape_factory<Plane>();
ransac.add_shape_factory<Sphere>();
ransac.add_shape_factory<Cylinder>();
ransac.add_shape_factory<Cone>();
ransac.add_shape_factory<Torus>();
// Sets parameters for shape detection.
Efficient_ransac::Parameters parameters;
// Sets probability to miss the largest primitive at each iteration.
parameters.probability = 0.05;
// Detect shapes with at least 500 points.
parameters.min_points = 200;
// Sets maximum Euclidean distance between a point and a shape.
parameters.epsilon = 0.002;
// Sets maximum Euclidean distance between points to be clustered.
parameters.cluster_epsilon = 0.01;
// Sets maximum normal deviation.
// 0.9 < dot(surface_normal, point_normal);
parameters.normal_threshold = 0.9;
// Detects shapes
ransac.detect(parameters);
// Prints number of detected shapes and unassigned points.
std::cout << ransac.shapes().end() - ransac.shapes().begin() << " detected shapes, "
<< ransac.number_of_unassigned_points()
<< " unassigned points." << std::endl;
// Efficient_ransac::shapes() provides
// an iterator range to the detected shapes.
Efficient_ransac::Shape_range shapes = ransac.shapes();
Efficient_ransac::Shape_range::iterator it = shapes.begin();
while (it != shapes.end()) {
// Get specific parameters depending on detected shape.
if (Plane* plane = dynamic_cast<Plane*>(it->get()))
{
Kernel::Vector_3 normal = plane->plane_normal();
std::cout << "Plane with normal " << normal
<< std::endl;
// Plane shape can also be converted to Kernel::Plane_3
std::cout << "Kernel::Plane_3: " << static_cast<Kernel::Plane_3>(*plane) << std::endl;
}
else if (Cylinder* cyl = dynamic_cast<Cylinder*>(it->get()))
{
Kernel::Line_3 axis = cyl->axis();
FT radius = cyl->radius();
std::cout << "Cylinder with axis " << axis
<< " and radius " << radius
<< std::endl;
}
else
{
// Prints the parameters of the detected shape.
// This function is available for any type of shape.
std::cout << (*it)->info() << std::endl;
}
// Proceeds with next detected shape.
it++;
}
return EXIT_SUCCESS;
}