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Fallback mode using map if vector is too large (useful for sparse scenes)

This commit is contained in:
Simon Giraudot 2017-10-24 10:41:06 +02:00
parent 1b455ff6ec
commit c63df7a304
4 changed files with 81 additions and 58 deletions
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15
Classification/include/CGAL/Classification/Feature/Echo_scatter.h
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@ -81,14 +81,15 @@ public:
Image_float Scatter(grid.width(), grid.height());
for (std::size_t j = 0; j < grid.height(); j++)
for (std::size_t i = 0; i < grid.width(); i++)
Scatter(i,j)=0;
if (grid.has_points(i,j))
Scatter(i,j)=0;
std::size_t square = (std::size_t)(0.5 * radius_neighbors / grid.resolution()) + 1;
for (std::size_t j = 0; j < grid.height(); j++){
for (std::size_t i = 0; i < grid.width(); i++){
if(grid.has_points(i,j)){
for (std::size_t j = 0; j < grid.height(); j++)
for (std::size_t i = 0; i < grid.width(); i++)
if(grid.has_points(i,j))
{
std::size_t squareXmin = (i < square ? 0 : i - square);
std::size_t squareXmax = (std::min) (grid.width()-1, i + square);
@ -121,12 +122,8 @@ public:
}
Scatter(i,j)=(float)NB_echo_sup/NB_echo_total;
}
}
}
for(std::size_t i = 0; i < input.size(); i++){
std::size_t I= grid.x(i);
std::size_t J= grid.y(i);

83
Classification/include/CGAL/Classification/Feature/Elevation.h
View File

@ -99,43 +99,43 @@ public:
for (std::size_t j = 0; j < grid.height(); ++ j)
for (std::size_t i = 0; i < grid.width(); ++ i)
{
float mean = 0.;
std::size_t nb = 0;
typename Grid::iterator end = grid.indices_end(i,j);
for (typename Grid::iterator it = grid.indices_begin(i,j); it != end; ++ it)
if (grid.has_points(i,j))
{
mean += float(get(point_map, *(input.begin()+(*it))).z());
++ nb;
float mean = 0.;
std::size_t nb = 0;
typename Grid::iterator end = grid.indices_end(i,j);
for (typename Grid::iterator it = grid.indices_begin(i,j); it != end; ++ it)
{
mean += float(get(point_map, *(input.begin()+(*it))).z());
++ nb;
}
if (nb == 0)
continue;
mean /= nb;
dem(i,j) = mean;
}
if (nb == 0)
continue;
mean /= nb;
dem(i,j) = mean;
}
std::size_t square = (std::size_t)(0.5 * radius_dtm / grid.resolution()) + 1;
Image_float dtm_x(grid.width(),grid.height());
for (std::size_t j = 0; j < grid.height(); ++ j)
{
for (std::size_t i = 0; i < grid.width(); ++ i)
{
std::size_t squareXmin = (i < square ? 0 : i - square);
std::size_t squareXmax = (std::min)(grid.width() - 1, i + square);
if (grid.has_points(i,j))
{
std::size_t squareXmin = (i < square ? 0 : i - square);
std::size_t squareXmax = (std::min)(grid.width() - 1, i + square);
std::vector<float> z;
z.reserve(squareXmax - squareXmin +1 );
for(std::size_t k = squareXmin; k <= squareXmax; k++)
if (dem(k,j) != 0.)
z.push_back (dem(k,j));
if (z.empty())
continue;
std::nth_element (z.begin(), z.begin() + (z.size() / 10), z.end());
dtm_x(i,j) = z[z.size() / 10];
}
}
std::vector<float> z;
z.reserve(squareXmax - squareXmin +1 );
for(std::size_t k = squareXmin; k <= squareXmax; k++)
if (dem(k,j) != 0.)
z.push_back (dem(k,j));
if (z.empty())
continue;
std::nth_element (z.begin(), z.begin() + (z.size() / 10), z.end());
dtm_x(i,j) = z[z.size() / 10];
}
dem.free();
#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES
@ -145,22 +145,21 @@ public:
#endif
for (std::size_t i = 0; i < grid.width(); ++ i)
{
for (std::size_t j = 0; j < grid.height(); ++ j)
{
std::size_t squareYmin = (j < square ? 0 : j - square);
std::size_t squareYmax = (std::min)(grid.height() - 1, j + square);
std::vector<float> z;
z.reserve(squareYmax - squareYmin +1 );
for(std::size_t l = squareYmin; l <= squareYmax; l++)
if (dtm_x(i,l) != 0.)
z.push_back (dtm_x(i,l));
if (z.empty())
continue;
std::nth_element (z.begin(), z.begin() + (z.size() / 10), z.end());
dtm(i,j) = z[z.size() / 10];
}
}
if (grid.has_points(i,j))
{
std::size_t squareYmin = (j < square ? 0 : j - square);
std::size_t squareYmax = (std::min)(grid.height() - 1, j + square);
std::vector<float> z;
z.reserve(squareYmax - squareYmin +1 );
for(std::size_t l = squareYmin; l <= squareYmax; l++)
if (dtm_x(i,l) != 0.)
z.push_back (dtm_x(i,l));
if (z.empty())
continue;
std::nth_element (z.begin(), z.begin() + (z.size() / 10), z.end());
dtm(i,j) = z[z.size() / 10];
}
dtm_x.free();
#ifdef CGAL_CLASSIFICATION_PRECOMPUTE_FEATURES

3
Classification/include/CGAL/Classification/Feature/Vertical_dispersion.h
View File

@ -99,7 +99,8 @@ public:
for (std::size_t j = 0; j < grid.height(); j++)
for (std::size_t i = 0; i < grid.width(); i++)
Dispersion(i,j)=0;
if (grid.has_points(i,j))
Dispersion(i,j)=0;
std::size_t square = (std::size_t)(0.5 * radius_neighbors / grid.resolution()) + 1;
typename GeomTraits::Vector_3 verti (0., 0., 1.);

36
Classification/include/CGAL/Classification/Image.h
View File

@ -25,6 +25,8 @@
#include <boost/shared_ptr.hpp>
#define CGAL_CLASSIFICATION_IMAGE_SIZE_LIMIT 10000000
namespace CGAL {
namespace Classification {
@ -33,9 +35,14 @@ namespace Classification {
template <typename Type>
class Image
{
typedef std::vector<Type> Vector;
typedef std::map<std::size_t, Type> Map;
std::size_t m_width;
std::size_t m_height;
boost::shared_ptr<std::vector<Type> > m_raw;
boost::shared_ptr<Vector> m_raw;
boost::shared_ptr<Map> m_sparse;
Type m_default;
// Forbid using copy constructor
Image (const Image&)
@ -53,7 +60,12 @@ public:
m_height (height)
{
if (m_width * m_height > 0)
m_raw = boost::shared_ptr<std::vector<Type> > (new std::vector<Type>(m_width * m_height));
{
if (m_width * m_height < CGAL_CLASSIFICATION_IMAGE_SIZE_LIMIT)
m_raw = boost::shared_ptr<Vector> (new Vector(m_width * m_height));
else
m_sparse = boost::shared_ptr<Map> (new Map());
}
}
~Image ()
@ -62,12 +74,14 @@ public:
void free()
{
m_raw = boost::shared_ptr<std::vector<Type> >();
m_raw = boost::shared_ptr<Vector>();
m_sparse = boost::shared_ptr<Map>();
}
Image& operator= (const Image& other)
{
m_raw = other.m_raw;
m_sparse = other.m_sparse;
m_width = other.width();
m_height = other.height();
return *this;
@ -78,12 +92,24 @@ public:
Type& operator() (const std::size_t& x, const std::size_t& y)
{
// return m_raw[y * m_width + x];
if (m_raw == boost::shared_ptr<Vector>()) // sparse case
{
typename Map::iterator inserted = m_sparse->insert (std::make_pair (x * m_height + y, Type())).first;
return inserted->second;
}
return (*m_raw)[x * m_height + y];
}
const Type& operator() (const std::size_t& x, const std::size_t& y) const
{
// return m_raw[y * m_width + x];
if (m_raw == boost::shared_ptr<Vector>()) // sparse case
{
typename Map::iterator found = m_sparse->find (x * m_height + y);
if (found != m_sparse->end())
return found->second;
return m_default;
}
return (*m_raw)[x * m_height + y];
}