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/* 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): Clement Jamin
*
* Copyright (C) 2016 INRIA
*
* 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 <http://www.gnu.org/licenses/>.
*/
#ifndef GUDHI_SPATIAL_TREE_DS_H_
#define GUDHI_SPATIAL_TREE_DS_H_
#include <CGAL/Orthogonal_k_neighbor_search.h>
#include <CGAL/Orthogonal_incremental_neighbor_search.h>
#include <CGAL/Search_traits.h>
#include <CGAL/Search_traits_adapter.h>
#include <boost/iterator/counting_iterator.hpp>
#include <cstddef>
#include <vector>
namespace Gudhi {
namespace spatial_searching {
/**
* \class Spatial_tree_data_structure Spatial_tree_data_structure.h gudhi/Spatial_tree_data_structure.h
* \brief Spatial tree data structure to perform (approximate) nearest neighbor search.
*
* \ingroup spatial_searching
*
* \details
* The class Spatial_tree_data_structure represents a tree-based data structure, based on .
* <a target="_blank" href="http://doc.cgal.org/latest/Spatial_searching/index.html">CGAL dD spatial searching data structures</a>.
* It provides a simplified API to perform (approximate) nearest neighbor searches. Contrary to CGAL default behavior, the tree
* does not store the points themselves, but stores indices.
*
* \tparam K requires a <a target="_blank"
* href="http://doc.cgal.org/latest/Kernel_d/classCGAL_1_1Epick__d.html">CGAL::Epick_d</a> class, which
* can be static if you know the ambiant dimension at compile-time, or dynamic if you don't.
* \tparam Point_container_ is the type of the container where points are stored (on the user side).
* It must provide random-access via `operator[]` and the points should be stored contiguously in memory.
* `std::vector` is a good candidate.
*/
template <typename K, typename Point_container_>
class Spatial_tree_data_structure
{
public:
typedef typename Point_container_::value_type Point;
typedef K Kernel;
typedef typename Kernel::FT FT;
typedef CGAL::Search_traits<
FT, Point,
typename Kernel::Cartesian_const_iterator_d,
typename Kernel::Construct_cartesian_const_iterator_d> Traits_base;
// using a pointer as a special property map type
typedef CGAL::Search_traits_adapter<
std::ptrdiff_t, Point*, Traits_base> STraits;
typedef CGAL::Orthogonal_k_neighbor_search<STraits> K_neighbor_search;
typedef typename K_neighbor_search::Tree Tree;
typedef typename K_neighbor_search::Distance Distance;
typedef typename K_neighbor_search::iterator KNS_iterator;
typedef K_neighbor_search KNS_range;
typedef CGAL::Orthogonal_incremental_neighbor_search<
STraits, Distance, CGAL::Sliding_midpoint<STraits>, Tree>
Incremental_neighbor_search;
typedef typename Incremental_neighbor_search::iterator INS_iterator;
typedef Incremental_neighbor_search INS_range;
/// Constructor
Spatial_tree_data_structure(Point_container_ const& points)
: m_points(points),
m_tree(boost::counting_iterator<std::ptrdiff_t>(0),
boost::counting_iterator<std::ptrdiff_t>(points.size()),
typename Tree::Splitter(),
STraits((Point*)&(points[0])) )
{
// Build the tree now (we don't want to wait for the first query)
m_tree.build();
}
/// Constructor
template <typename Point_indices_range>
Spatial_tree_data_structure(
Point_container_ const& points,
Point_indices_range const& only_these_points)
: m_points(points),
m_tree(
only_these_points.begin(), only_these_points.end(),
typename Tree::Splitter(),
STraits((Point*)&(points[0])))
{
// Build the tree now (we don't want to wait for the first query)
m_tree.build();
}
/// Constructor
Spatial_tree_data_structure(
Point_container_ const& points,
std::size_t begin_idx, std::size_t past_the_end_idx)
: m_points(points),
m_tree(
boost::counting_iterator<std::ptrdiff_t>(begin_idx),
boost::counting_iterator<std::ptrdiff_t>(past_the_end_idx),
typename Tree::Splitter(),
STraits((Point*)&(points[0])) )
{
// Build the tree now (we don't want to wait for the first query)
m_tree.build();
}
/*Point_container_ &points()
{
return m_points;
}
const Point_container_ &points() const
{
return m_points;
}*/
// Be careful, this function invalidates the tree,
// which will be recomputed at the next query
void insert(std::ptrdiff_t point_idx)
{
m_tree.insert(point_idx);
}
KNS_range query_ANN(const
Point &sp,
unsigned int k,
bool sorted = true,
FT eps = FT(0)) const
{
// Initialize the search structure, and search all N points
// Note that we need to pass the Distance explicitly since it needs to
// know the property map
K_neighbor_search search(
m_tree,
sp,
k,
eps,
true,
CGAL::Distance_adapter<std::ptrdiff_t,Point*,CGAL::Euclidean_distance<Traits_base> >(
(Point*)&(m_points[0])),
sorted);
return search;
}
INS_range query_incremental_ANN(const Point &sp, FT eps = FT(0)) const
{
// Initialize the search structure, and search all N points
// Note that we need to pass the Distance explicitly since it needs to
// know the property map
Incremental_neighbor_search search(
m_tree,
sp,
eps,
true,
CGAL::Distance_adapter<std::ptrdiff_t, Point*, CGAL::Euclidean_distance<Traits_base> >(
(Point*)&(m_points[0])) );
return search;
}
protected:
Point_container_ const& m_points;
Tree m_tree;
};
} // namespace spatial_searching
} // namespace Gudhi
#endif // GUDHI_SPATIAL_TREE_DS_H_
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