\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 5.0.4 - Shape Detection
Shape_detection/efficient_RANSAC_with_point_access.cpp
#include <fstream>
#include <iostream>
#include <CGAL/Timer.h>
#include <CGAL/number_utils.h>
#include <CGAL/property_map.h>
#include <CGAL/IO/read_xyz_points.h>
#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Shape_detection/Efficient_RANSAC.h>
// 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;
<Kernel, Pwn_vector, Point_map, Normal_map> Traits;
int main(int argc, char** argv) {
// Points with normals.
Pwn_vector points;
// Load point set from a file.
std::ifstream stream((argc > 1) ? argv[1] : "data/cube.pwn");
if (!stream ||
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;
}
// Instantiate shape detection engine.
Efficient_ransac ransac;
// Provide input data.
ransac.set_input(points);
// Register detection of planes.
ransac.add_shape_factory<Plane>();
// Measure time before setting up the shape detection.
CGAL::Timer time;
time.start();
// Build internal data structures.
ransac.preprocess();
// Measure time after preprocessing.
time.stop();
std::cout << "preprocessing took: " << time.time() * 1000 << "ms" << std::endl;
// Perform detection several times and choose result with the highest coverage.
Efficient_ransac::Shape_range shapes = ransac.shapes();
FT best_coverage = 0;
for (std::size_t i = 0; i < 3; ++i) {
// Reset timer.
time.reset();
time.start();
// Detect shapes.
ransac.detect();
// Measure time after detection.
time.stop();
// Compute coverage, i.e. ratio of the points assigned to a shape.
FT coverage =
FT(points.size() - ransac.number_of_unassigned_points()) / FT(points.size());
// Print number of assigned shapes and unassigned points.
std::cout << "time: " << time.time() * 1000 << "ms" << std::endl;
std::cout << ransac.shapes().end() - ransac.shapes().begin()
<< " primitives, " << coverage << " coverage" << std::endl;
// Choose result with the highest coverage.
if (coverage > best_coverage) {
best_coverage = coverage;
// Efficient_ransac::shapes() provides
// an iterator range to the detected shapes.
shapes = ransac.shapes();
}
}
Efficient_ransac::Shape_range::iterator it = shapes.begin();
while (it != shapes.end()) {
boost::shared_ptr<Efficient_ransac::Shape> shape = *it;
// Use Shape_base::info() to print the parameters of the detected shape.
std::cout << (*it)->info();
// Sums distances of points to the detected shapes.
FT sum_distances = 0;
// Iterate through point indices assigned to each detected shape.
std::vector<std::size_t>::const_iterator
index_it = (*it)->indices_of_assigned_points().begin();
while (index_it != (*it)->indices_of_assigned_points().end()) {
// Retrieve point.
const Point_with_normal& p = *(points.begin() + (*index_it));
// Adds Euclidean distance between point and shape.
sum_distances += CGAL::sqrt((*it)->squared_distance(p.first));
// Proceed with the next point.
index_it++;
}
// Compute and print the average distance.
FT average_distance = sum_distances / shape->indices_of_assigned_points().size();
std::cout << " average distance: " << average_distance << std::endl;
// Proceed with the next detected shape.
it++;
}
return EXIT_SUCCESS;
}