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Just stores indices in the search tree

This commit is contained in:
Clement Jamin 2014-09-12 17:00:00 +02:00
parent 6037445c50
commit 43c8a1d0ba
2 changed files with 47 additions and 26 deletions
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6
Tangential_complex/include/CGAL/Tangential_complex.h
View File

@ -358,9 +358,8 @@ private:
const int NUM_NEIGHBORS = 150;
std::size_t nearest_nb[NUM_NEIGHBORS];
FT squared_distance[NUM_NEIGHBORS];
m_points_ds.query_ANN(
center_pt, NUM_NEIGHBORS, nearest_nb, squared_distance);
center_pt, NUM_NEIGHBORS, nearest_nb);
/*const int NUM_NEIGHBORS = 150;
std::size_t nearest_nb[NUM_NEIGHBORS];
for (int ii = 0 ; ii < NUM_NEIGHBORS ; ++ii)
@ -509,9 +508,8 @@ private:
Kernel::Difference_of_vectors_d diff_vec = m_k.difference_of_vectors_d_object();
std::size_t neighbor_indices[NUM_POINTS_FOR_PCA];
FT squared_distance[NUM_POINTS_FOR_PCA];
m_points_ds.query_ANN(
p, NUM_POINTS_FOR_PCA, neighbor_indices, squared_distance);
p, NUM_POINTS_FOR_PCA, neighbor_indices);
//******************************* PCA *************************************

67
Tangential_complex/include/CGAL/Tangential_complex/Point_cloud.h
View File

@ -225,17 +225,16 @@ class Point_cloud_data_structure
{
public:
typedef typename Point_container_::value_type Point;
typedef typename CGAL::Kernel_traits<Point>::type Kernel;
typedef typename Kernel::FT FT;
typedef typename CGAL::Kernel_traits<Point>::type Kernel;
typedef typename Kernel::FT FT;
typedef boost::tuple<Point, std::size_t> Point_and_idx;
typedef CGAL::Search_traits<
FT, Point,
FT, Point,
typename Kernel::Cartesian_const_iterator_d,
typename Kernel::Construct_cartesian_const_iterator_d> Traits_base;
typedef CGAL::Search_traits_adapter<Point_and_idx,
CGAL::Nth_of_tuple_property_map<0, Point_and_idx>,
Traits_base> STraits;
// 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;
@ -244,15 +243,13 @@ public:
static const int AMB_DIM = Ambient_dimension<Point>::value;
/// Constructor
Point_cloud_data_structure(Point_container_ &points, Kernel const& k)
Point_cloud_data_structure(Point_container_ const& points, Kernel const& k)
: m_points(points),
m_tree(
boost::make_zip_iterator(boost::make_tuple(
points.begin(),
boost::counting_iterator<std::size_t>(0))),
boost::make_zip_iterator(boost::make_tuple(
points.end(),
boost::counting_iterator<std::size_t>(points.size()))) )
boost::counting_iterator<std::ptrdiff_t>(0),
boost::counting_iterator<std::ptrdiff_t>(points.size()),
Tree::Splitter(),
STraits((Point*)&(points[0])) )
{
}
@ -265,22 +262,48 @@ public:
{
return m_points;
}*/
template<typename IndexOutputIt>
void query_ANN(const Point &sp,
unsigned int k,
size_t *neighbor_indices,
FT *squared_distance) const
IndexOutputIt indices_out) const
{
// Initialize the search structure, and search all N points
K_neighbor_search search(m_tree, sp, k);
// 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, FT(0.1), true,
Distance_adapter<std::ptrdiff_t, Point*, Euclidean_distance<Traits_base> >(
(Point*)&(m_points[0])) );
// report the N nearest neighbors and their distance
// This should sort all N points by increasing distance from origin
int i = 0;
for(K_neighbor_search::iterator it = search.begin();
it != search.end(); ++it, ++i)
it != search.end(); ++it)
{
neighbor_indices[i] = boost::get<1>(it->first);
squared_distance[i] = it->second;
*indices_out++ = it->first;
}
}
template<typename IndexOutputIt, typename DistanceOutputIt>
void query_ANN(const Point &sp,
unsigned int k,
IndexOutputIt indices_out,
DistanceOutputIt distances_out) 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, FT(0.1), true,
Distance_adapter<std::ptrdiff_t, Point*, Euclidean_distance<Traits_base> >(
(Point*)&(m_points[0])) );
// report the N nearest neighbors and their distance
// This should sort all N points by increasing distance from origin
for(K_neighbor_search::iterator it = search.begin();
it != search.end(); ++it)
{
*indices_out++ = it->first;
*distances_out++ = it->second;
}
}