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// Copyright (c) 2020 GeometryFactory Sarl (France). // All rights reserved. // // This file is part of CGAL (www.cgal.org). // // $URL: https://github.com/CGAL/cgal/blob/v6.1/Point_set_processing_3/include/CGAL/cluster_point_set.h $ // $Id: include/CGAL/cluster_point_set.h b26b07a1242 $ // SPDX-License-Identifier: GPL-3.0-or-later OR LicenseRef-Commercial // // Author(s) : Simon Giraudot #ifndef CGAL_CLUSTER_POINT_SET_H #define CGAL_CLUSTER_POINT_SET_H #include #include #include #include #include #include #include #include #include namespace CGAL { /// \cond SKIP_IN_MANUAL namespace Point_set_processing_3 { namespace internal { // Trick to both compile version with Emptyset_iterator and with // user-provided OutputIterator. Many output iterators (such as // `std::back_insert_iterator`) cannot be default constructed, which // makes the mechanism `choose_param(get_param(...),Default())` fails. template OutputIterator get_adjacencies (const NamedParameters& np, OutputIterator*) { return CGAL::parameters::get_parameter(np, internal_np::adjacencies); } template CGAL::Emptyset_iterator get_adjacencies (const NamedParameters&, CGAL::Emptyset_iterator*) { return CGAL::Emptyset_iterator(); } } // namespace internal } // namespace Point_set_processing_3 /// \endcond // ---------------------------------------------------------------------------- // Public section // ---------------------------------------------------------------------------- /** \ingroup PkgPointSetProcessing3Algorithms Identifies connected components on a nearest neighbor graph built using a query sphere of fixed radius centered on each point. \tparam PointRange is a model of `Range`. The value type of its iterator is the key type of the named parameter `point_map`. \tparam ClusterMap is a model of `ReadWritePropertyMap` with value type `std::size_t`. \param points input point range \param cluster_map maps each point to the index of the cluster it belongs to. \param np optional sequence of \ref bgl_namedparameters "Named Parameters" among the ones listed below. \cgalNamedParamsBegin \cgalParamNBegin{point_map} \cgalParamDescription{a property map associating points to the elements of the point set `points`} \cgalParamType{a model of `ReadablePropertyMap` whose key type is the value type of the iterator of `PointRange` and whose value type is `geom_traits::Point_3`} \cgalParamDefault{`CGAL::Identity_property_map`} \cgalParamNEnd \cgalParamNBegin{callback} \cgalParamDescription{a mechanism to get feedback on the advancement of the algorithm while it's running and to interrupt it if needed} \cgalParamType{an instance of `std::function`.} \cgalParamDefault{unused} \cgalParamExtra{It is called regularly when the algorithm is running: the current advancement (between 0. and 1.) is passed as parameter. If it returns `true`, then the algorithm continues its execution normally; if it returns `false`, the algorithm is stopped and the number of already computed clusters is returned.} \cgalParamExtra{The callback will be copied and therefore needs to be lightweight.} \cgalParamNEnd \cgalParamNBegin{neighbor_radius} \cgalParamDescription{the spherical neighborhood radius} \cgalParamType{floating scalar value} \cgalParamDefault{`1` percent of the bounding box diagonal} \cgalParamNEnd \cgalParamNBegin{attraction_factor} \cgalParamDescription{used to compute adjacencies between clusters. Adjacencies are computed using a nearest neighbor graph built similarly to the one used for clustering, using `attraction_factor * neighbor_radius` as parameter.} \cgalParamType{floating scalar value} \cgalParamDefault{`2`} \cgalParamNEnd \cgalParamNBegin{adjacencies} \cgalParamDescription{an output iterator used to output pairs containing the indices of two adjacent clusters.} \cgalParamType{a model of `OutputIterator` that accepts objects of type `std::pair`} \cgalParamDefault{`CGAL::Emptyset_iterator`} \cgalParamExtra{If this parameter is not used, adjacencies are not computed at all.} \cgalParamNEnd \cgalParamNBegin{geom_traits} \cgalParamDescription{an instance of a geometric traits class} \cgalParamType{a model of `Kernel`} \cgalParamDefault{a \cgal Kernel deduced from the point type, using `CGAL::Kernel_traits`} \cgalParamNEnd \cgalNamedParamsEnd \return the number of clusters identified. */ template std::size_t cluster_point_set (PointRange& points, ClusterMap cluster_map, const NamedParameters& np = parameters::default_values()) { using parameters::choose_parameter; using parameters::get_parameter; // basic geometric types typedef typename PointRange::iterator iterator; typedef typename iterator::value_type value_type; typedef typename boost::property_traits::value_type Cluster_index_t; typedef Point_set_processing_3_np_helper NP_helper; typedef typename NP_helper::Point_map PointMap; typedef typename NP_helper::Geom_traits Kernel; typedef typename Point_set_processing_3::GetAdjacencies::type Adjacencies; static_assert(!(std::is_same::type, typename GetSvdTraits::NoTraits>::value), "Error: no SVD traits"); PointMap point_map = NP_helper::get_point_map(points, np); typename Kernel::FT neighbor_radius = choose_parameter(get_parameter(np, internal_np::neighbor_radius), typename Kernel::FT(-1)); typename Kernel::FT factor = choose_parameter(get_parameter(np, internal_np::attraction_factor), typename Kernel::FT(2)); const std::function& callback = choose_parameter(get_parameter(np, internal_np::callback), std::function()); double callback_factor = 1.; if (!std::is_same::Empty>::value) callback_factor = 0.5; // types for K nearest neighbors search structure typedef Point_set_processing_3::internal::Neighbor_query Neighbor_query; // precondition: at least one element in the container. // to fix: should have at least three distinct points // but this is costly to check CGAL_precondition(points.begin() != points.end()); // If no radius is given, init with 1% of bbox diagonal if (neighbor_radius < 0) neighbor_radius = 0.01 * Point_set_processing_3::internal::bbox_diagonal (points, point_map); // Init cluster map with -1 for (const value_type& p : points) put (cluster_map, p, Cluster_index_t(-1)); Neighbor_query neighbor_query (points, point_map); std::queue todo; std::size_t nb_clusters = 0; // Flooding algorithm from each point std::size_t done = 0; std::size_t size = points.size(); for (iterator it = points.begin(); it != points.end(); ++ it) { const value_type& p = *it; if (get (cluster_map, p) != Cluster_index_t(-1)) continue; todo.push (it); while (!todo.empty()) { iterator current = todo.front(); todo.pop(); if (get (cluster_map, *current) != Cluster_index_t(-1)) continue; put (cluster_map, *current, Cluster_index_t(nb_clusters)); ++ done; if (callback && !callback (callback_factor * (done + 1) / double(size))) return (nb_clusters + 1); neighbor_query.get_iterators (get (point_map, *current), 0, neighbor_radius, boost::make_function_output_iterator ([&](const iterator& it) { todo.push(it); }), true); } ++ nb_clusters; } if (!std::is_same::Empty>::value) { Adjacencies adjacencies = Point_set_processing_3::internal::get_adjacencies(np, (Adjacencies*)(nullptr)); neighbor_radius *= factor; std::vector neighbors; std::vector > adj; done = 0; for (const value_type& p : points) { std::size_t c0 = std::size_t(get (cluster_map, p)); neighbors.clear(); neighbor_query.get_iterators (get (point_map, p), 0, neighbor_radius, std::back_inserter (neighbors), 0); for (const iterator& it : neighbors) { std::size_t c1 = std::size_t(get (cluster_map, *it)); if (c0 < c1) adj.push_back (std::make_pair (c0, c1)); else if (c0 > c1) adj.push_back (std::make_pair (c1, c0)); // else c0 == c1, ignore } ++ done; if (callback && !callback (callback_factor + callback_factor * (done + 1) / double(size))) return nb_clusters; } std::sort (adj.begin(), adj.end()); auto last = std::unique (adj.begin(), adj.end()); std::copy (adj.begin(), last, adjacencies); } return nb_clusters; } } // namespace CGAL #endif // CGAL_CLUSTER_POINT_SET_H