From 9899ae167f281d10b1684dfcd02c6838c5bf28df Mon Sep 17 00:00:00 2001 From: Gard Spreemann Date: Fri, 2 Feb 2018 13:51:45 +0100 Subject: GUDHI 2.1.0 as released by upstream in a tarball. --- .../cech_complex_cgal_mini_sphere_3d.cpp | 221 +++++++++++++++++++++ 1 file changed, 221 insertions(+) create mode 100644 example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp (limited to 'example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp') diff --git a/example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp b/example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp new file mode 100644 index 00000000..9bd51106 --- /dev/null +++ b/example/Simplex_tree/cech_complex_cgal_mini_sphere_3d.cpp @@ -0,0 +1,221 @@ +/* This file is part of the Gudhi Library. The Gudhi library + * (Geometric Understanding in Higher Dimensions) is a generic C++ + * library for computational topology. + * + * Author(s): Clément Maria + * + * Copyright (C) 2014 INRIA Sophia Antipolis-Méditerranée (France) + * + * This program is free software: you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program. If not, see . + */ + +#include +#include +#include +#include + +#include +#include +#include + +#include + +#include +#include +#include // infinity +#include // for pair +#include + +// ------------------------------------------------------------------------------- +// cech_complex_cgal_mini_sphere_3d is an example of each step that is required to +// build a Cech over a Simplex_tree. Please refer to cech_persistence to see +// how to do the same thing with the Cech_complex wrapper for less detailed +// steps. +// ------------------------------------------------------------------------------- + +// Types definition +using Simplex_tree = Gudhi::Simplex_tree<>; +using Vertex_handle = Simplex_tree::Vertex_handle; +using Simplex_handle = Simplex_tree::Simplex_handle; +using Filtration_value = Simplex_tree::Filtration_value; +using Siblings = Simplex_tree::Siblings; +using Graph_t = boost::adjacency_list, + boost::property >; +using Edge_t = std::pair; + +using Kernel = CGAL::Epick_d >; +using Point = Kernel::Point_d; +using Traits = CGAL::Min_sphere_of_points_d_traits_d; +using Min_sphere = CGAL::Min_sphere_of_spheres_d; + +using Points_off_reader = Gudhi::Points_off_reader; + +class Cech_blocker { + public: + bool operator()(Simplex_handle sh) { + std::vector points; +#if DEBUG_TRACES + std::cout << "Cech_blocker on ["; +#endif // DEBUG_TRACES + for (auto vertex : simplex_tree_.simplex_vertex_range(sh)) { + points.push_back(point_cloud_[vertex]); +#if DEBUG_TRACES + std::cout << vertex << ", "; +#endif // DEBUG_TRACES + } + Min_sphere ms(points.begin(), points.end()); + Filtration_value radius = ms.radius(); +#if DEBUG_TRACES + std::cout << "] - radius = " << radius << " - returns " << (radius > threshold_) << std::endl; +#endif // DEBUG_TRACES + simplex_tree_.assign_filtration(sh, radius); + return (radius > threshold_); + } + Cech_blocker(Simplex_tree& simplex_tree, Filtration_value threshold, const std::vector& point_cloud) + : simplex_tree_(simplex_tree), threshold_(threshold), point_cloud_(point_cloud) {} + + private: + Simplex_tree simplex_tree_; + Filtration_value threshold_; + std::vector point_cloud_; +}; + +template +Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold); + +void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max); + +int main(int argc, char* argv[]) { + std::string off_file_points; + Filtration_value threshold; + int dim_max; + + program_options(argc, argv, off_file_points, threshold, dim_max); + + // Extract the points from the file filepoints + Points_off_reader off_reader(off_file_points); + + // Compute the proximity graph of the points + Graph_t prox_graph = compute_proximity_graph(off_reader.get_point_cloud(), threshold); + + // Min_sphere sph1(off_reader.get_point_cloud()[0], off_reader.get_point_cloud()[1], off_reader.get_point_cloud()[2]); + // Construct the Rips complex in a Simplex Tree + Simplex_tree st; + // insert the proximity graph in the simplex tree + st.insert_graph(prox_graph); + // expand the graph until dimension dim_max + st.expansion_with_blockers(dim_max, Cech_blocker(st, threshold, off_reader.get_point_cloud())); + + std::cout << "The complex contains " << st.num_simplices() << " simplices \n"; + std::cout << " and has dimension " << st.dimension() << " \n"; + + // Sort the simplices in the order of the filtration + st.initialize_filtration(); + +#if DEBUG_TRACES + std::cout << "********************************************************************\n"; + // Display the Simplex_tree - Can not be done in the middle of 2 inserts + std::cout << "* The complex contains " << st.num_simplices() << " simplices - dimension=" << st.dimension() << "\n"; + std::cout << "* Iterator on Simplices in the filtration, with [filtration value]:\n"; + for (auto f_simplex : st.filtration_simplex_range()) { + std::cout << " " + << "[" << st.filtration(f_simplex) << "] "; + for (auto vertex : st.simplex_vertex_range(f_simplex)) { + std::cout << static_cast(vertex) << " "; + } + std::cout << std::endl; + } +#endif // DEBUG_TRACES + return 0; +} + +void program_options(int argc, char* argv[], std::string& off_file_points, Filtration_value& threshold, int& dim_max) { + namespace po = boost::program_options; + po::options_description hidden("Hidden options"); + hidden.add_options()("input-file", po::value(&off_file_points), + "Name of an OFF file containing a 3d point set.\n"); + + po::options_description visible("Allowed options", 100); + visible.add_options()("help,h", "produce help message")( + "max-edge-length,r", + po::value(&threshold)->default_value(std::numeric_limits::infinity()), + "Maximal length of an edge for the Cech complex construction.")( + "cpx-dimension,d", po::value(&dim_max)->default_value(1), + "Maximal dimension of the Cech complex we want to compute."); + + po::positional_options_description pos; + pos.add("input-file", 1); + + po::options_description all; + all.add(visible).add(hidden); + + po::variables_map vm; + po::store(po::command_line_parser(argc, argv).options(all).positional(pos).run(), vm); + po::notify(vm); + + if (vm.count("help") || !vm.count("input-file")) { + std::cout << std::endl; + std::cout << "Construct a Cech complex defined on a set of input points.\n \n"; + + std::cout << "Usage: " << argv[0] << " [options] input-file" << std::endl << std::endl; + std::cout << visible << std::endl; + std::abort(); + } +} + +/** Output the proximity graph of the points. + * + * If points contains n elements, the proximity graph is the graph + * with n vertices, and an edge [u,v] iff the distance function between + * points u and v is smaller than threshold. + * + * The type PointCloud furnishes .begin() and .end() methods, that return + * iterators with value_type Point. + */ +template +Graph_t compute_proximity_graph(InputPointRange& points, Filtration_value threshold) { + std::vector edges; + std::vector edges_fil; + + Kernel k; + Vertex_handle idx_u, idx_v; + Filtration_value fil; + idx_u = 0; + for (auto it_u = points.begin(); it_u != points.end(); ++it_u) { + idx_v = idx_u + 1; + for (auto it_v = it_u + 1; it_v != points.end(); ++it_v, ++idx_v) { + fil = k.squared_distance_d_object()(*it_u, *it_v); + // For Cech Complex, threshold is a radius (distance /2) + fil = std::sqrt(fil) / 2.; + if (fil <= threshold) { + edges.emplace_back(idx_u, idx_v); + edges_fil.push_back(fil); + } + } + ++idx_u; + } + + Graph_t skel_graph(edges.begin(), edges.end(), edges_fil.begin(), + idx_u); // number of points labeled from 0 to idx_u-1 + + auto vertex_prop = boost::get(Gudhi::vertex_filtration_t(), skel_graph); + + boost::graph_traits::vertex_iterator vi, vi_end; + for (std::tie(vi, vi_end) = boost::vertices(skel_graph); vi != vi_end; ++vi) { + boost::put(vertex_prop, *vi, 0.); + } + + return skel_graph; +} -- cgit v1.2.3