songsenand
/
cgal
mirror of https://github.com/CGAL/cgal
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

use dedicated namespace

This commit is contained in:
Sébastien Loriot 2025-03-24 18:43:05 +01:00
parent fc36a26c90
commit 0f8440da4e
1 changed files with 17 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
Point_set_processing_3/include/CGAL/poisson_eliminate.h
View File

@ -28,7 +28,7 @@
namespace CGAL {
namespace internal {
namespace poisson_eliminate_impl {
double get_maximum_radius(std::size_t dimension, std::size_t sample_size, double domain_size) {
double sampleArea = domain_size / double(sample_size);
@ -103,7 +103,7 @@ public:
if (range.size() > k)
return get(point_map, *(range.begin() + k));
else
return tiling_points[k - range.size()];
return tiling_points.at(k - range.size());
}
friend reference get(const Indexed_extended_point_map& ppmap, key_type k) {
@ -111,7 +111,7 @@ public:
if ((k < ppmap.range.size()))
return get(ppmap.point_map, *(ppmap.range.begin() + k));
else
return ppmap.tiling_points[k - ppmap.range.size()];
return ppmap.tiling_points.at(k - ppmap.range.size());
}
};
@ -281,19 +281,19 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
using Point = typename boost::property_traits<PointMap>::value_type;
using GeomTraits = typename NP_helper::Geom_traits;
using FT = typename GeomTraits::FT;
using IPM = internal::Indexed_extended_point_map<PointRange, PointMap>;
using IPM = poisson_eliminate_impl::Indexed_extended_point_map<PointRange, PointMap>;
PointMap point_map = NP_helper::get_const_point_map(points, np);
const unsigned int ambient_dimension = CGAL::Ambient_dimension<Point>::value;
using Search_base = internal::Search_traits<Point>;
using Search_base = poisson_eliminate_impl::Search_traits<Point>;
using Search_traits = CGAL::Search_traits_adapter<std::size_t, IPM, Search_base>;
using Splitter = CGAL::Sliding_midpoint<Search_traits>;
using Fuzzy_sphere = CGAL::Fuzzy_sphere<Search_traits>;
using Tree = CGAL::Kd_tree<Search_traits, Splitter, CGAL::Tag_true, CGAL::Tag_true>;
internal::Compute_squared_distance<Point> squared_distance;
poisson_eliminate_impl::Compute_squared_distance<Point> squared_distance;
// hard coded parameters alpha, beta and gamma with values proposed in publication
const double alpha = 8;
@ -329,10 +329,10 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
domain_size *= CGAL::to_double(upper[i] - lower[i]);
// named parameter for r_max
double r_max = CGAL::to_double(parameters::choose_parameter(parameters::get_parameter(np, internal_np::maximum_radius), 2 * internal::get_maximum_radius(dimension, number_of_points, domain_size)));
double r_min = CGAL::to_double(weight_limiting ? internal::get_minimum_radius(points.size(), number_of_points, beta, gamma, r_max) : 0);
double r_max = CGAL::to_double(parameters::choose_parameter(parameters::get_parameter(np, internal_np::maximum_radius), 2 * poisson_eliminate_impl::get_maximum_radius(dimension, number_of_points, domain_size)));
double r_min = CGAL::to_double(weight_limiting ? poisson_eliminate_impl::get_minimum_radius(points.size(), number_of_points, beta, gamma, r_max) : 0);
auto weight_functor = parameters::choose_parameter(parameters::get_parameter(np, internal_np::weight_function), internal::Weight_functor<Point>(r_min, alpha));
auto weight_functor = parameters::choose_parameter(parameters::get_parameter(np, internal_np::weight_function), poisson_eliminate_impl::Weight_functor<Point>(r_min, alpha));
std::size_t heap_size = points.size();
std::vector<Point> tiling_points;
@ -346,7 +346,7 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
if (lower[int(dim)] > (*(it + dim) - r_max)) {
FT v = 2 * lower[int(dim)] - (*(it + dim));
Point p2;
internal::copy_and_replace(p, p2, dim, v);
poisson_eliminate_impl::copy_and_replace(p, p2, dim, v);
tiling_points.emplace_back(p2);
if (dim + 1 < CGAL::Ambient_dimension<Point>::value)
self(self, tiling_points.back(), dim + 1);
@ -355,7 +355,7 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
if (upper[int(dim)] < (*(it + dim) + r_max)) {
FT v = 2 * upper[int(dim)]- (*(it + dim));
Point p2;
internal::copy_and_replace(p, p2, dim, v);
poisson_eliminate_impl::copy_and_replace(p, p2, dim, v);
tiling_points.emplace_back(p2);
if (dim + 1 < CGAL::Ambient_dimension<Point>::value)
self(self, tiling_points.back(), dim + 1);
@ -373,7 +373,9 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
}
// Tiling requires either to copy the point range or to use a special Index_property_map with a second array of points to keep the input range const.
Tree tree(boost::counting_iterator<std::size_t>(0), boost::counting_iterator<std::size_t>(points.size() + tiling_points.size()), Splitter(), Search_traits(ipm));
Tree tree(boost::counting_iterator<std::size_t>(0),
boost::counting_iterator<std::size_t>(points.size() + tiling_points.size()),
Splitter(), Search_traits(ipm));
tree.build();
std::vector<std::size_t> heap(boost::counting_iterator<std::size_t>(0), boost::counting_iterator<std::size_t>(points.size()));
@ -385,7 +387,7 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
// Computing weights
std::vector<double> weights(points.size(), 0);
std::vector<std::size_t> res;
for (std::size_t i = 0; i < heap_size; i++) {
for (std::size_t i = 0; i < heap_size; ++i) {
const Point& p = get(ipm, heap[i]);
res.clear();
tree.search(std::back_inserter(res), Fuzzy_sphere(p, r_max, 0, Search_traits(ipm)));
@ -413,7 +415,7 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
while (heap_size > target_points) {
std::size_t i = heap[0];
internal::pop_heap(heap, heap_pos, heap_size, weights);
poisson_eliminate_impl::pop_heap(heap, heap_pos, heap_size, weights);
// Adjust weights of neighbors
res.clear();
@ -429,7 +431,7 @@ void poisson_eliminate(const PointRange &points, std::size_t number_of_points, O
weights[res[n]] -= weight_functor(p2, p, d2, r_max);
internal::move_down(heap, heap_pos, heap_size, weights, heap_pos[res[n]]);
poisson_eliminate_impl::move_down(heap, heap_pos, heap_size, weights, heap_pos[res[n]]);
}
}