dust3d/thirdparty/cgal/CGAL-5.1/include/CGAL/cluster_point_set.h

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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/v5.1/Point_set_processing_3/include/CGAL/cluster_point_set.h $
// $Id: cluster_point_set.h 93f1cd9 2020-07-16T09:53:31+02:00 Mael Rouxel-Labbé
// 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 <CGAL/license/Point_set_processing_3.h>
#include <CGAL/squared_distance_3.h>
#include <CGAL/Point_set_processing_3/internal/Neighbor_query.h>
#include <CGAL/Point_set_processing_3/internal/Callback_wrapper.h>
#include <CGAL/Point_set_processing_3/internal/bbox_diagonal.h>
#include <CGAL/for_each.h>
#include <CGAL/boost/graph/Named_function_parameters.h>
#include <CGAL/boost/graph/named_params_helper.h>
#include <queue>
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 <typename NamedParameters, typename OutputIterator>
OutputIterator get_adjacencies (const NamedParameters& np, OutputIterator*)
{
return CGAL::parameters::get_parameter(np, internal_np::adjacencies);
}
template <typename NamedParameters>
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<geom_traits::Point_3>`}
\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<bool(double)>`.}
\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<std::size_t, std::size_t>`}
\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 <typename PointRange, typename ClusterMap, typename NamedParameters>
std::size_t cluster_point_set (PointRange& points,
ClusterMap cluster_map,
const NamedParameters& np)
{
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 CGAL::GetPointMap<PointRange, NamedParameters>::type PointMap;
typedef typename Point_set_processing_3::GetK<PointRange, NamedParameters>::Kernel Kernel;
typedef typename Point_set_processing_3::GetAdjacencies<PointRange, NamedParameters>::type Adjacencies;
CGAL_static_assertion_msg(!(boost::is_same<typename GetSvdTraits<NamedParameters>::type,
typename GetSvdTraits<NamedParameters>::NoTraits>::value),
"Error: no SVD traits");
PointMap point_map = choose_parameter(get_parameter(np, internal_np::point_map), PointMap());
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<bool(double)>& callback = choose_parameter(get_parameter(np, internal_np::callback),
std::function<bool(double)>());
double callback_factor = 1.;
if (!std::is_same<Adjacencies,
typename Point_set_processing_3::GetAdjacencies<PointRange, NamedParameters>::Empty>::value)
callback_factor = 0.5;
// types for K nearest neighbors search structure
typedef Point_set_processing_3::internal::Neighbor_query<Kernel, PointRange&, PointMap> 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_point_set_processing_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, -1);
Neighbor_query neighbor_query (points, point_map);
std::queue<iterator> 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 (int(get (cluster_map, p)) != -1)
continue;
todo.push (it);
while (!todo.empty())
{
iterator current = todo.front();
todo.pop();
if (int(get (cluster_map, *current)) != -1)
continue;
put (cluster_map, *current, 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<Adjacencies,
typename Point_set_processing_3::GetAdjacencies<PointRange, NamedParameters>::Empty>::value)
{
Adjacencies adjacencies = Point_set_processing_3::internal::get_adjacencies(np, (Adjacencies*)(nullptr));
neighbor_radius *= factor;
std::vector<iterator> neighbors;
std::vector<std::pair<std::size_t, std::size_t> > adj;
done = 0;
for (const value_type& p : points)
{
std::size_t c0 = 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 = 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;
}
/// \cond SKIP_IN_MANUAL
// overload with default NP
template <typename PointRange, typename ClusterMap>
std::size_t cluster_point_set (PointRange& points,
ClusterMap cluster_map,
unsigned int k)
{
return cluster_point_set (points, cluster_map, k,
CGAL::Point_set_processing_3::parameters::all_default(points));
}
/// \endcond
} // namespace CGAL
#endif // CGAL_CLUSTER_POINT_SET_H