* ok/mln/img_path.hh: New header file.
* ok/mln/accu/histo/histo1d.hh: New header file.
* ok/mln/accu/histo/histo2d.hh: New header file.
* ok/mln/accu/histo/histo3d.hh: New header file.
* ok/test/accu/histo/gaussian.sh: New gnuplot script file.
* ok/test/accu/histo/histo1d.cc: New source file.
---
scribo/sandbox/green/ChangeLog | 12 +
scribo/sandbox/green/README | 32 +
.../sandbox/green/ok/mln/accu/histo}/histo1d.hh | 100 ++--
.../sandbox/green/ok/mln/accu/histo}/histo2d.hh | 0
.../sandbox/green/ok/mln/accu/histo/histo3d.hh | 0
.../sandbox/green/ok}/mln/img_path.hh | 0
.../sandbox/green/ok/test/accu/histo}/gaussian.sh | 0
scribo/sandbox/green/ok/test/accu/histo/histo1d | Bin 0 -> 802920 bytes
scribo/sandbox/green/ok/test/accu/histo/histo1d.cc | 726 ++++++++++++++++++++
9 files changed, 813 insertions(+), 57 deletions(-)
create mode 100644 scribo/sandbox/green/README
copy {milena/sandbox/green/mln/accu/stat =>
scribo/sandbox/green/ok/mln/accu/histo}/histo1d.hh (74%)
copy {milena/sandbox/green/mln/accu/stat =>
scribo/sandbox/green/ok/mln/accu/histo}/histo2d.hh (100%)
copy milena/sandbox/green/mln/accu/stat/histo3d_rgb.hh =>
scribo/sandbox/green/ok/mln/accu/histo/histo3d.hh (100%)
copy {milena/sandbox/green => scribo/sandbox/green/ok}/mln/img_path.hh (100%)
copy {milena/sandbox/green/tests/accu/stat/histo1d =>
scribo/sandbox/green/ok/test/accu/histo}/gaussian.sh (100%)
create mode 100755 scribo/sandbox/green/ok/test/accu/histo/histo1d
create mode 100644 scribo/sandbox/green/ok/test/accu/histo/histo1d.cc
diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog
index e69de29..ed399c5 100644
--- a/scribo/sandbox/green/ChangeLog
+++ b/scribo/sandbox/green/ChangeLog
@@ -0,0 +1,12 @@
+2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr>
+
+ Simple integration test.
+
+ * README: New text file.
+ * ok/mln/img_path.hh: New header file.
+ * ok/mln/accu/histo/histo1d.hh: New header file.
+ * ok/mln/accu/histo/histo2d.hh: New header file.
+ * ok/mln/accu/histo/histo3d.hh: New header file.
+ * ok/test/accu/histo/gaussian.sh: New gnuplot script file.
+ * ok/test/accu/histo/histo1d.cc: New source file.
+
diff --git a/scribo/sandbox/green/README b/scribo/sandbox/green/README
new file mode 100644
index 0000000..4eb71b7
--- /dev/null
+++ b/scribo/sandbox/green/README
@@ -0,0 +1,32 @@
+Le travail est classé dans deux répertoires ok et ko.
+Le répertoire ok correspond aux sources qui ont été reprises et retestées.
+Le répertoire ko correspond aux sources qui ont été juste transférées.
+
+La première brique logicielle importante sur laquelle s'appuie mes travaux est
+la création d'histogrammes sous forme d'image.
+
+
+CREATION DES HISTOGRAMMES
+-------------------------
+
+ko/mln/accu/stat/histo1d.hh: histogramme image valeur scalaire.
+ko/mln/accu/stat/histo2d.hh: histogramme image valeur vectorielle 2d.
+ko/mln/accu/stat/histo3d_hsl.hh: histogramme image valeur vectorielle 3d (HSL).
+ko/mln/accu/stat/histo3d_rgb.hh: histogramme image valeur vectorielle 3d (RGB).
+
+
+
+
+ok/mln/accu/histo/histo1d.hh: histogramme image valeur scalaire entière.
+ok/mln/accu/histo/histo2d.hh: histogramme image valeur vectorielle 2d entière.
+ok/mln/accu/histo/histo3d.hh: histogramme image valeur vectorielle 3d entière.
+
+
+SAUVEGARDE DES HISTOGRAMMES
+---------------------------
+
+
+
+
+VISUALISATION DES HISTOGRAMMES
+------------------------------
\ No newline at end of file
diff --git a/milena/sandbox/green/mln/accu/stat/histo1d.hh
b/scribo/sandbox/green/ok/mln/accu/histo/histo1d.hh
similarity index 74%
copy from milena/sandbox/green/mln/accu/stat/histo1d.hh
copy to scribo/sandbox/green/ok/mln/accu/histo/histo1d.hh
index 57d6103..139cf39 100644
--- a/milena/sandbox/green/mln/accu/stat/histo1d.hh
+++ b/scribo/sandbox/green/ok/mln/accu/histo/histo1d.hh
@@ -1,6 +1,5 @@
-// Copyright (C) 2007 EPITA Research and Development Laboratory (LRDE)
-// Copyright (C) 2008 EPITA Research and Development Laboratory (LRDE)
-// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE)
+// Copyright (C) 2007,2008,2009,2010 EPITA Research and Development
+// Laboratory (LRDE)
//
// This file is part of Olena.
//
@@ -25,35 +24,41 @@
// exception does not however invalidate any other reasons why the
// executable file might be covered by the GNU General Public License.
-#ifndef MLN_ACCU_STAT_HISTO1D_HH
-#define MLN_ACCU_STAT_HISTO1D_HH
+#ifndef MLN_ACCU_HISTO_HISTO1D_HH
+#define MLN_ACCU_HISTO_HISTO1D_HH
/// \file
///
-/// \brief Define a histogram as an accumulator which returns an image1d .
+/// \brief Define a histogram as accumulator which returns an image1d.
+///
+/// This source implements the discrete histogram version. It was
+/// created for images which the values are derived from integer
+/// type. The number of beans is directly infered from the cardinality
+/// of the image value. So the int_u8 image has got 256 bins, the
+/// int_u<14> image has got 16384 bins. But, this code doesn't work
+/// for that quantification because of the coord limitation (signed
+/// short, as it is defined in mln/core/def/coord.hh).
///
-/// This source implements the discrete histogram version. The number of beans
-/// is infer from the number of graylevels. A typical int_u8 image has got
-/// 256 bins. An int_u<14> image has got 16384 bins.
/// The following sample is a typical use of the histogram.
///
-/// #include <mln/value/int_u8.hh>
+/// #include <mln/accu/histo/histo1d.hh>
+/// #include <mln/data/compute.hh>
/// #include <mln/core/image/image1d.hh>
/// #include <mln/core/image/image2d.hh>
+/// #include <mln/img_path.hh>
/// #include <mln/io/pgm/load.hh>
-/// #include <mln/accu/stat/histo1d.hh>
-/// #include <mln/data/compute.hh>
-///
-/// #define OLENA_LENA "/usr/local/share/olena/images/lena.pgm"
+/// #include <mln/value/int_u8.hh>
///
-/// void test()
+/// int main()
/// {
-/// typedef mln::value::int_u8 int_u8;
-/// mln::image2d<int_u8> img_ref;
-/// mln::image1d<unsigned> img_res;
+/// typedef mln::value::int_u8 t_int_u8;
+/// mln::image2d<t_int_u8> img;
+/// mln::image1d<unsigned> histo;
+///
+/// mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm");
+/// histo = mln::data::compute(mln::accu::meta::histo::histo1d(), img);
///
-/// mln::io::pgm::load(img_ref, OLENA_LENA);
-/// img_res = mln::data::compute(mln::accu::meta::stat::histo1d(), img_ref);
+/// return 0;
/// }
@@ -80,20 +85,20 @@ namespace mln
namespace accu
{
- namespace stat
+ namespace histo
{
// Forward declaration
template <typename V>
struct histo1d;
- } // end of namespace mln::accu::stat
+ } // end of namespace mln::accu::histo
namespace meta
{
- namespace stat
+ namespace histo
{
struct histo1d : public Meta_Accumulator<histo1d>
@@ -101,11 +106,11 @@ namespace mln
template <typename V>
struct with
{
- typedef accu::stat::histo1d<V> ret;
+ typedef accu::histo::histo1d<V> ret;
};
};
- } // end of namespace mln::accu::meta::stat
+ } // end of namespace mln::accu::meta::histo
} // end of namespace mln::accu::meta
@@ -115,7 +120,7 @@ namespace mln
{
template <typename V>
- struct accumulator_< mln::accu::stat::histo1d<V> >
+ struct accumulator_< mln::accu::histo::histo1d<V> >
{
typedef accumulator::has_untake::no has_untake;
typedef accumulator::has_set_value::no has_set_value;
@@ -125,8 +130,8 @@ namespace mln
template <typename V>
struct set_precise_binary_<op::eq,
- accu::stat::histo1d<V>,
- accu::stat::histo1d<V> >
+ accu::histo::histo1d<V>,
+ accu::histo::histo1d<V> >
{
typedef bool ret;
};
@@ -136,7 +141,7 @@ namespace mln
namespace accu
{
- namespace stat
+ namespace histo
{
/// \brief Define an histogram which returns an image1d .
@@ -152,7 +157,7 @@ namespace mln
template <typename V>
struct histo1d :
- public mln::accu::internal::base<image1d<unsigned>, histo1d<V> >
+ public mln::accu::internal::base< image1d<unsigned>,histo1d<V> >
{
typedef V argument;
typedef image1d<unsigned> result;
@@ -230,7 +235,7 @@ namespace mln
inline
histo1d<V>::histo1d()
{
- trace::entering("mln::accu::stat::histo1d<V>::histo1d");
+ trace::entering("mln::accu::histo::histo1d::cstor");
typedef mln_trait_value_comp(V,0) comp;
typedef point<grid::tick, V> v_point1d;
typedef box<v_point1d> v_box1d;
@@ -242,33 +247,26 @@ namespace mln
count_.init_(box1d(point1d(mln_min(comp)),
point1d(mln_max(comp))));
-// std::cout << "min : " << mln_min(comp) << std::endl;
-// std::cout << "max : " << mln_max(comp) << std::endl;
+ // std::cout << "min : " << mln_min(comp) << std::endl;
+ // std::cout << "max : " << mln_max(comp) << std::endl;
- trace::exiting("mln::accu::stat::histo1d<V>::histo1d");
+ trace::exiting("mln::accu::histo::histo1d::cstor");
}
template <typename V>
inline
void histo1d<V>::init()
{
- trace::entering("mln::accu::stat::histo1d<V>::init");
-
data::fill(count_, 0);
- trace::exiting("mln::accu::stat::histo1d<V>::init");
}
template <typename V>
inline
void histo1d<V>::take(const argument& t)
{
- trace::entering("mln::accu::stat::histo1d<V>::take");
-
// Just convert a graylevel value (int_u8 like) to a position for an
// iterator on the resulting image.
++count_(point1d(t));
-
- trace::exiting("mln::accu::stat::histo1d<V>::take");
}
@@ -276,20 +274,13 @@ namespace mln
inline
void histo1d<V>::take(const histo1d<V>& other)
{
- trace::entering("mln::accu::stat::histo1d<V>::take");
-
count_ += other.count_;
-
- trace::exiting("mln::accu::stat::histo1d<V>::take");
}
template <typename V>
inline
typename histo1d<V>::result histo1d<V>::to_result() const
{
- trace::entering("mln::accu::stat::histo1d<V>::to_result");
-
- trace::exiting("mln::accu::stat::histo1d<V>::to_result");
return count_;
}
@@ -297,9 +288,6 @@ namespace mln
inline
histo1d<V>::operator result() const
{
- trace::entering("mln::accu::stat::histo1d<V>::operator result");
-
- trace::exiting("mln::accu::stat::histo1d<V>::operator result");
return count_;
}
@@ -307,17 +295,15 @@ namespace mln
inline
bool histo1d<V>::is_valid() const
{
- trace::entering("mln::accu::stat::histo1d<V>::is_valid");
bool result = count_.is_valid();
- trace::exiting("mln::accu::stat::histo1d<V>::is_valid");
return result;
}
template <typename V>
bool operator==(const histo1d<V>& histo1, const histo1d<V>&
histo2)
{
- trace::entering("mln::accu::stat::operator==");
+ trace::entering("mln::accu::histo::histo1d::operator==");
bool result = true;
const image1d<unsigned>& res1 = histo1.to_result();
@@ -332,17 +318,17 @@ namespace mln
for_all_2(p1, p2)
result &= (res1(p1) == res2(p2));
- trace::exiting("mln::accu::stat::operator==");
+ trace::exiting("mln::accu::histo::histo1d::operator==");
return result;
}
#endif // ! MLN_INCLUDE_ONLY
- } // end of namespace mln::accu::stat
+ } // end of namespace mln::accu::histo
} // end of namespace mln::accu
} // end of namespace mln
-#endif // ! MLN_ACCU_STAT_HISTO1D_HH
+#endif // ! MLN_ACCU_HISTO_HISTO1D_HH
diff --git a/milena/sandbox/green/mln/accu/stat/histo2d.hh
b/scribo/sandbox/green/ok/mln/accu/histo/histo2d.hh
similarity index 100%
copy from milena/sandbox/green/mln/accu/stat/histo2d.hh
copy to scribo/sandbox/green/ok/mln/accu/histo/histo2d.hh
diff --git a/milena/sandbox/green/mln/accu/stat/histo3d_rgb.hh
b/scribo/sandbox/green/ok/mln/accu/histo/histo3d.hh
similarity index 100%
copy from milena/sandbox/green/mln/accu/stat/histo3d_rgb.hh
copy to scribo/sandbox/green/ok/mln/accu/histo/histo3d.hh
diff --git a/milena/sandbox/green/mln/img_path.hh
b/scribo/sandbox/green/ok/mln/img_path.hh
similarity index 100%
copy from milena/sandbox/green/mln/img_path.hh
copy to scribo/sandbox/green/ok/mln/img_path.hh
diff --git a/milena/sandbox/green/tests/accu/stat/histo1d/gaussian.sh
b/scribo/sandbox/green/ok/test/accu/histo/gaussian.sh
similarity index 100%
copy from milena/sandbox/green/tests/accu/stat/histo1d/gaussian.sh
copy to scribo/sandbox/green/ok/test/accu/histo/gaussian.sh
diff --git a/scribo/sandbox/green/ok/test/accu/histo/histo1d
b/scribo/sandbox/green/ok/test/accu/histo/histo1d
new file mode 100755
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HcmV?d00001
diff --git a/scribo/sandbox/green/ok/test/accu/histo/histo1d.cc
b/scribo/sandbox/green/ok/test/accu/histo/histo1d.cc
new file mode 100644
index 0000000..3d542cd
--- /dev/null
+++ b/scribo/sandbox/green/ok/test/accu/histo/histo1d.cc
@@ -0,0 +1,726 @@
+/// TEST HISTO1D
+
+#include <mln/accu/histo/histo1d.hh>
+#include <mln/data/compute.hh>
+#include <mln/core/image/image1d.hh>
+#include <mln/core/image/image2d.hh>
+#include <mln/img_path.hh>
+#include <mln/io/pgm/load.hh>
+#include <mln/value/int_u8.hh>
+
+int main()
+{
+ typedef mln::value::int_u8 t_int_u8;
+ mln::image2d<t_int_u8> img;
+ mln::image1d<unsigned> histo;
+
+ mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm");
+ histo = mln::data::compute(mln::accu::meta::histo::histo1d(), img);
+
+ return 0;
+}
+
+// #include <mln/img_path.hh>
+
+// #include <mln/io/plot/save_histo_sh.hh>
+// #include <mln/accu/stat/histo1d.hh>
+// #include <mln/fun/v2v/int_u16_to_int_u14.hh>
+
+// #include <mln/io/pgm/load.hh>
+// #include <mln/io/pgm/save.hh>
+// #include <mln/io/ppm/save.hh>
+// #include <mln/io/plot/save.hh>
+
+// #include <mln/data/compute.hh>
+// #include <mln/data/transform.hh>
+// #include <mln/data/fill.hh>
+
+// #include <mln/value/int_u8.hh>
+// #include <mln/value/int_u16.hh>
+// #include <mln/value/int_u.hh>
+// #include <mln/value/label_8.hh>
+// #include <mln/value/rgb8.hh>
+
+// #include <mln/core/alias/neighb1d.hh>
+// #include <mln/core/alias/box1d.hh>
+// #include <mln/core/alias/point1d.hh>
+
+// #include <mln/core/routine/initialize.hh>
+// #include <mln/core/contract.hh>
+// #include <mln/core/grids.hh>
+
+// #include <mln/accu/math/sum.hh>
+// #include <mln/accu/math/count.hh>
+// #include <mln/accu/stat/mean.hh>
+// #include <mln/accu/stat/variance.hh>
+
+// #include <mln/morpho/watershed/flooding.hh>
+// #include <mln/morpho/elementary/dilation.hh>
+// #include <mln/morpho/elementary/closing.hh>
+
+// #include <mln/linear/convolve.hh>
+// #include <mln/linear/gaussian.hh>
+// #include <mln/labeling/regional_maxima.hh>
+// #include <mln/labeling/colorize.hh>
+// #include <mln/labeling/mean_values.hh>
+
+// #include <mln/make/w_window1d.hh>
+
+// #include <mln/math/sqr.hh>
+// #include <mln/math/pi.hh>
+// #include <mln/math/abs.hh>
+
+// #include <mln/core/image/dmorph/image_if.hh>
+// #include <mln/pw/value.hh>
+
+// //------------------------------------------------------------------------------
+// // TEST IN 8 BITS
+// //------------------------------------------------------------------------------
+
+
+// void test_8bits_operator_equal()
+// {
+// using namespace mln::accu::stat;
+
+// typedef mln::value::int_u8 int_u8;
+// mln::accu::stat::histo1d<int_u8> histo1;
+// mln::accu::stat::histo1d<int_u8> histo2;
+// int_u8 val = 3;
+
+// histo1.init();
+// histo2.init();
+
+// mln_assertion(histo1 == histo2);
+
+// histo1.take(val);
+
+// mln_assertion(histo1 != histo2);
+
+// histo2.take(val);
+
+// mln_assertion(histo1 == histo2);
+
+// std::cout << "(08 bits) histo == histo : ok" <<
std::endl;
+// }
+
+// void test_8bits_instantiation_without_argument()
+// {
+// typedef mln::value::int_u8 int_u8;
+// const mln::accu::stat::histo1d<int_u8> histo;
+// const mln::image1d<unsigned>& res = histo.to_result();
+// const mln::point1d& min = mln::point1d(mln_min(int_u8));
+// const mln::point1d& max = mln::point1d(mln_max(int_u8));
+// const mln::box1d& ref = mln::box1d(min, max);
+
+// mln_assertion(ref == res.domain());
+// mln_assertion(res.is_valid());
+
+// std::cout << "(08 bits) histo<T> histo : ok" <<
std::endl;
+// }
+
+
+// void test_8bits_initialization()
+// {
+// typedef mln::value::int_u8 int_u8;
+// mln::accu::stat::histo1d<int_u8> histo;
+// mln::image1d<unsigned> img_res = histo.to_result();
+// mln::image1d<unsigned> img_ref;
+
+// mln::initialize(img_ref, img_res);
+// mln::data::fill(img_ref, 0);
+// histo.init();
+
+// unsigned res = mln::data::compute(mln::accu::math::sum<int_u8>(), img_res);
+// unsigned ref = mln::data::compute(mln::accu::math::sum<int_u8>(), img_ref);
+
+// mln_assertion(ref == res);
+
+// std::cout << "(08 bits) histo.init() : ok" <<
std::endl;
+// }
+
+
+// void test_8bits_take_argument()
+// {
+// typedef mln::value::int_u8 int_u8;
+// mln::accu::stat::histo1d<int_u8> histo1;
+// mln::accu::stat::histo1d<int_u8> histo2;
+// int_u8 val = 3;
+
+// histo1.init();
+// histo2.init();
+// histo1.take(val);
+
+// const mln::image1d<unsigned> img1 = histo1.to_result();
+// const mln::image1d<unsigned> img2 = histo2.to_result();
+
+// const unsigned res = mln::data::compute(mln::accu::math::sum<int_u8>(),
img1);
+// const unsigned ref = mln::data::compute(mln::accu::math::sum<int_u8>(),
img2);
+
+// mln_assertion(ref == res-1);
+// mln_assertion(1 == img1(mln::point1d(val)));
+
+// std::cout << "(08 bits) histo.take(argument) : ok" <<
std::endl;
+// }
+
+
+// void test_8bits_take_other()
+// {
+// typedef mln::value::int_u8 int_u8;
+// mln::accu::stat::histo1d<int_u8> histo1;
+// mln::accu::stat::histo1d<int_u8> histo2;
+// mln::accu::stat::histo1d<int_u8> histo3;
+// int_u8 val = 3;
+
+// histo1.init();
+// histo2.init();
+// histo3.init();
+
+// histo1.take(val);
+// histo1.take(val);
+// histo3.take(val);
+// histo2.take(val);
+// histo2.take(histo3);
+
+// mln_assertion(histo1 == histo2);
+
+// std::cout << "(08 bits) histo.take(other) : ok" <<
std::endl;
+// }
+
+// double count_histo(mln::image1d<unsigned> img)
+// {
+// mln_precondition(img.is_valid());
+
+// double result = 0.0;
+// mln_piter_(mln::image1d<unsigned>) p(img.domain());
+
+// for_all(p)
+// result += img(p);
+
+// return result;
+// }
+
+// double mean_histo(mln::image1d<unsigned> img)
+// {
+// mln_precondition(img.is_valid());
+
+// double count = 0.0;
+// double sum = 0.0;
+// double result = 0.0;
+// mln_piter_(mln::image1d<unsigned>) p(img.domain());
+
+// for_all(p)
+// {
+// count += img(p);
+// sum += p[0] * img(p);
+// }
+
+// result = sum / count;
+
+// return result;
+// }
+
+
+// double var_histo(mln::image1d<unsigned> img)
+// {
+// mln_precondition(img.is_valid());
+
+// double count = 0.0;
+// double sum = 0.0;
+// double sum2 = 0.0;
+// double result = 0.0;
+// mln_piter_(mln::image1d<unsigned>) p(img.domain());
+
+// for_all(p)
+// {
+// count += img(p);
+// sum += p[0] * img(p);
+// sum2 += p[0] * p[0] * img(p);
+// }
+
+// result = sum2 / count - (sum / count) * (sum / count);
+
+// return result;
+// }
+
+// double var_histo2(mln::image1d<unsigned> img)
+// {
+// mln_precondition(img.is_valid());
+
+// double count = count_histo(img);
+// double mean = mean_histo(img);
+// double result = 0.0;
+// mln_piter_(mln::image1d<unsigned>) p(img.domain());
+
+// for_all(p)
+// {
+// result += img(p) * (p[0] - mean) * (p[0] - mean);
+// }
+
+// result /= count;
+
+// return result;
+// }
+
+// void test_8bits_integration()
+// {
+// typedef mln::value::int_u8 int_u8;
+// typedef mln::accu::math::count<double> count;
+// typedef mln::accu::math::sum<double> sum;
+// typedef mln::accu::stat::mean<double> mean;
+// typedef mln::accu::stat::variance<double> variance;
+
+// mln::image2d<int_u8> img_ref;
+// mln::image1d<unsigned> img_res;
+
+// mln::io::pgm::load(img_ref, OLENA_IMG_PATH"/lena.pgm");
+
+// const double count_ref = mln::data::compute(count(), img_ref);
+// const double mean_ref = mln::data::compute(mean(), img_ref);
+// const double var_ref = mln::data::compute(variance(), img_ref);
+
+// img_res = mln::data::compute(mln::accu::meta::stat::histo1d(), img_ref);
+// // img_res = mln::data::compute(mln::accu::stat::histo1d<int_u8>(),
img_ref);
+
+// const double count_res = count_histo(img_res);
+// const double mean_res = mean_histo(img_res);
+// const double var_res = var_histo(img_res);
+
+// mln_assertion(count_ref == count_res);
+// mln_assertion( mean_ref == mean_res );
+// mln_assertion(0.0001 > abs(var_ref - var_res));
+
+// std::cout << "(08 bits) test integration : ok" <<
std::endl;
+// }
+
+// double gaussian_distribution(double x, double mean, double sigma)
+// {
+// double num = exp(-0.5*mln::math::sqr((x - mean)/sigma));
+// double denom = sigma*mln::math::sqrt(2*mln::math::pi);
+// double result = num/denom;
+
+// return result;
+// }
+
+// /// To calibrate with gnuplot (see gaussian.sh)
+// /// square(x) = x*x
+// /// gaussian(x,mean,sigma)=exp(-0.5*square((x-mean)/sigma))/(sigma*sqrt(2*pi))
+// /// plot [-20:20] gaussian(x,0,6.0)
+
+// void gaussian_filter(double ws[], unsigned size, double sigma)
+// {
+// int h = size/2;
+
+// for (int i = -h; i <= h; ++i)
+// {
+// ws[i+h] = gaussian_distribution(i, 0.0, sigma);
+// }
+
+// double sum = 0.0;
+
+// for (unsigned i = 0; i < size; ++i)
+// sum += ws[i];
+
+// for (unsigned i = 0; i < size; ++i)
+// ws[i] /= sum;
+// }
+
+// /// The aim of this function is to rebuild an label image2d from the segmenting
+// /// image of the histogram (label) and the original image (input).
+// /// label_image2d / for each grey tone, associate its label.
+
+// mln::image2d<mln::value::label_8>
+// build_8bits(const mln::image2d<mln::value::int_u8>& input,
+// const mln::image1d<mln::value::label_8>& label)
+// {
+// mln::trace::entering("build_8bits");
+// mln_precondition(label.is_valid());
+// mln_precondition(input.is_valid());
+
+// mln::image2d<mln::value::label_8> output;
+
+// mln::initialize(output, input);
+
+// mln_piter_(mln::image2d<mln::value::int_u8>) pi(input.domain());
+// mln_piter_(mln::image2d<mln::value::label_8>) po(output.domain());
+
+// for_all_2(pi, po)
+// {
+// mln::value::int_u8 val = input(pi);
+// unsigned grp = label(mln::point1d(val));
+
+// output(po) = grp;
+// }
+
+// mln::trace::exiting("build_8bits");
+// return output;
+// }
+
+
+// void test_8bits_classifying()
+// {
+// typedef mln::value::int_u8 int_u8;
+// typedef mln::value::label_8 label_8;
+// typedef mln::value::rgb8 rgb8;
+// typedef mln::accu::stat::mean<double> mean;
+
+// mln::image2d<int_u8> img_ref;
+// mln::image2d<int_u8> img_out;
+// mln::image2d<rgb8> img_rgb;
+// mln::image1d<unsigned> img_res;
+// mln::image1d<double> img_smooth;
+// mln::image1d<label_8> labels;
+// label_8 nlabels;
+
+// //-----------------------------------------------------
+// // Loading the scribo image and computing its histogram
+// //-----------------------------------------------------
+
+// std::cout << "(08 bits) LOADING HISTOGRAM" << std::endl;
+
+// // mln::io::pgm::load(img_ref, OLENA_IMG_PATH"/lena.pgm");
+// mln::io::pgm::load(img_ref, SCRIBO_IMG_PATH"/mp00082c_50p_8bits.pgm");
+// img_res = mln::data::compute(mln::accu::stat::histo1d<int_u8>(), img_ref);
+// mln::io::plot::save_histo_sh(img_res, "histo0_8bits.sh");
+
+
+// //-----------------------------------------------------
+// // Smoothing the histogram with a gaussian filter
+// //-----------------------------------------------------
+
+// std::cout << "(08 bits) SMOOTHING HISTOGRAM" << std::endl;
+
+// double ws[41];
+// gaussian_filter(ws, 41, 6.0);
+// img_smooth = mln::linear::convolve(img_res, mln::make::w_window1d(ws));
+// mln::io::plot::save_histo_sh(img_smooth, "histo1_8bits.sh");
+
+
+// //-----------------------------------------------------
+// // Segmenting the histogram with the watershed method
+// //-----------------------------------------------------
+
+// std::cout << "SEGMENTING HISTOGRAM" << std::endl;
+
+// /*
+// labels = mln::labeling::regional_maxima(img_smooth, mln::c2(), nlabels);
+// std::cout << "N labels : " << nlabels << std::endl;
+// mln::io::plot::save_histo_sh(labels, "histo2_8bits.sh");
+// */
+
+
+// labels = mln::morpho::watershed::flooding(img_smooth, mln::c2(), nlabels);
+// std::cout << "N labels : " << nlabels << std::endl;
+// mln::io::plot::save_histo_sh(labels, "histo2_8bits.sh");
+
+
+// //-----------------------------------------------------
+// // Rebuilding the image with the mean of each region
+// //-----------------------------------------------------
+
+// std::cout << "(08 bits) BUILDING OUTPUT" << std::endl;
+
+// mln::image2d<label_8>img_label = build_8bits(img_ref, labels);
+
+// std::cout << "(08 bits) COLORING OUTPUT" << std::endl;
+
+// img_out = mln::labeling::mean_values(img_ref, img_label, nlabels);
+// img_rgb = mln::labeling::colorize(rgb8(), img_label);
+
+// mln::io::pgm::save(img_out, "out_8bits.pgm");
+// mln::io::ppm::save(img_rgb, "color_8bits.pgm");
+
+// //labels = mln::morpho::elementary::dilation(labels, mln::c2());
+// //mln::io::plot::save_histo_sh(labels, "histo3.sh");
+// //mln::io::plot::save(labels, "labelized.data");
+// }
+
+// //------------------------------------------------------------------------------
+// // TEST IN 15 BITS
+// //------------------------------------------------------------------------------
+
+// void test_14bits_operator_equal()
+// {
+// using namespace mln::accu::stat;
+
+// typedef mln::value::int_u<14> int_u14;
+// mln::accu::stat::histo1d<int_u14> histo1;
+// mln::accu::stat::histo1d<int_u14> histo2;
+// int_u14 val = 3;
+
+// histo1.init();
+// histo2.init();
+
+// mln_assertion(histo1 == histo2);
+
+// histo1.take(val);
+
+// /// FIXME mln_assertion(histo1 != histo2); doesn't work!!
+// mln_assertion(!(histo1 == histo2));
+
+// histo2.take(val);
+
+// mln_assertion(histo1 == histo2);
+
+// std::cout << "(14 bits) histo == histo : ok" <<
std::endl;
+// }
+
+// void test_14bits_instantiation_without_argument()
+// {
+// typedef mln::value::int_u<14> int_u14;
+// const mln::accu::stat::histo1d<int_u14> histo;
+// const mln::image1d<unsigned>& res = histo.to_result();
+// const mln::point1d& min =mln::point1d(mln_min(int_u14));
+// const mln::point1d& max =mln::point1d(mln_max(int_u14));
+// const mln::box1d& ref = mln::box1d(min, max);
+
+// mln_assertion(ref == res.domain());
+// mln_assertion(res.is_valid());
+
+// std::cout << "(14 bits) histo<T> histo : ok" <<
std::endl;
+// }
+
+
+// void test_14bits_initialization()
+// {
+// typedef mln::value::int_u<14> int_u14;
+// mln::accu::stat::histo1d<int_u14> histo;
+// mln::image1d<unsigned> img_res = histo.to_result();
+// mln::image1d<unsigned> img_ref;
+
+// mln::initialize(img_ref, img_res);
+// mln::data::fill(img_ref, 0);
+// histo.init();
+
+// unsigned res = mln::data::compute(mln::accu::math::sum<int_u14>(), img_res);
+// unsigned ref = mln::data::compute(mln::accu::math::sum<int_u14>(), img_ref);
+
+// mln_assertion(ref == res);
+
+// std::cout << "(14 bits) histo.init() : ok" <<
std::endl;
+// }
+
+
+// void test_14bits_take_argument()
+// {
+// typedef mln::value::int_u<14> int_u14;
+// mln::accu::stat::histo1d<int_u14> histo1;
+// mln::accu::stat::histo1d<int_u14> histo2;
+// int_u14 val = 3;
+
+// histo1.init();
+// histo2.init();
+// histo1.take(val);
+
+// const mln::image1d<unsigned> img1 = histo1.to_result();
+// const mln::image1d<unsigned> img2 = histo2.to_result();
+
+// const unsigned res =
mln::data::compute(mln::accu::math::sum<int_u14>(),img1);
+// const unsigned ref =
mln::data::compute(mln::accu::math::sum<int_u14>(),img2);
+
+// mln_assertion(ref == res-1);
+// mln_assertion(1 == img1(mln::point1d(val)));
+
+// std::cout << "(14 bits) histo.take(argument) : ok" <<
std::endl;
+// }
+
+
+// void test_14bits_take_other()
+// {
+// typedef mln::value::int_u<14> int_u14;
+// mln::accu::stat::histo1d<int_u14> histo1;
+// mln::accu::stat::histo1d<int_u14> histo2;
+// mln::accu::stat::histo1d<int_u14> histo3;
+// int_u14 val = 3;
+
+// histo1.init();
+// histo2.init();
+// histo3.init();
+
+// histo1.take(val);
+// histo1.take(val);
+// histo3.take(val);
+// histo2.take(val);
+// histo2.take(histo3);
+
+// mln_assertion(histo1 == histo2);
+
+// std::cout << "(14 bits) histo.take(other) : ok" <<
std::endl;
+// }
+
+// void test_14bits_integration()
+// {
+// typedef mln::value::int_u16 int_u16;
+// typedef mln::value::int_u<14> int_u14;
+// typedef mln::accu::math::count<double> count;
+// typedef mln::accu::math::sum<double> sum;
+// typedef mln::accu::stat::mean<double> mean;
+// typedef mln::accu::stat::variance<double> variance;
+
+// mln::image2d<int_u16> img_fst;
+// mln::image2d<int_u14> img_ref;
+// mln::image1d<unsigned> img_res;
+
+// mln::io::pgm::load(img_fst, OLENA_IMG_PATH"/lena_16.pgm");
+// img_ref = mln::data::transform(img_fst, mln::fun::v2v::int_u16_to_int_u14());
+
+// const double count_ref = mln::data::compute(count(), img_ref);
+// const double mean_ref = mln::data::compute(mean(), img_ref);
+// const double var_ref = mln::data::compute(variance(), img_ref);
+
+// img_res = mln::data::compute(mln::accu::stat::histo1d<int_u14>(), img_ref);
+
+// const double count_res = count_histo(img_res);
+// const double mean_res = mean_histo(img_res);
+// const double var_res = var_histo2(img_res);
+
+// //std::cout << "var_res : " << var_res << std::endl;
+// //std::cout << "var_ref : " << var_ref << std::endl;
+
+// mln_assertion(count_ref == count_res);
+// mln_assertion( mean_ref == mean_res );
+// mln_assertion(0.0001 > abs(var_ref - var_res));
+
+// std::cout << "(14 bits) test integration : ok" <<
std::endl;
+// }
+
+// /// The aim of this function is to rebuild an label image2d from the segmenting
+// /// image of the histogram (label) and the original image (input).
+// /// label_image2d / for each grey tone, associate its label.
+
+// mln::image2d<mln::value::label_8>
+// build_14bits(const mln::image2d<mln::value::int_u<14> >& input,
+// const mln::image1d<mln::value::label_8>& label)
+// {
+// mln::trace::entering("build_14bits");
+// mln_precondition(label.is_valid());
+// mln_precondition(input.is_valid());
+
+// mln::image2d<mln::value::label_8> output;
+
+// mln::initialize(output, input);
+
+// mln_piter_(mln::image2d<mln::value::int_u<14> >) pi(input.domain());
+// mln_piter_(mln::image2d<mln::value::label_8>) po(output.domain());
+
+// for_all_2(pi, po)
+// {
+// mln::value::int_u<14> val = input(pi);
+// unsigned grp = label(mln::point1d(val));
+
+// output(po) = grp;
+// }
+
+// mln::trace::exiting("build_14bits");
+// return output;
+// }
+
+
+// void test_14bits_classifying()
+// {
+// typedef mln::value::int_u16 int_u16;
+// typedef mln::value::int_u<14> int_u14;
+// typedef mln::value::label_8 label_8;
+// typedef mln::value::rgb8 rgb8;
+// typedef mln::accu::stat::mean<double> mean;
+
+// mln::image2d<int_u16> img_fst;
+// mln::image2d<int_u14> img_ref;
+// mln::image2d<int_u14> img_out;
+// mln::image2d<rgb8> img_rgb;
+// mln::image1d<unsigned> img_res;
+// mln::image1d<double> img_smooth;
+// mln::image1d<label_8> labels;
+// label_8 nlabels;
+
+// //-----------------------------------------------------
+// // Loading the scribo image and computing its histogram
+// //-----------------------------------------------------
+
+// std::cout << "(14 bits) LOADING HISTOGRAM" << std::endl;
+
+// //mln::io::pgm::load(img_fst, OLENA_IMG_PATH"/lena_16.pgm");
+// mln::io::pgm::load(img_fst, SCRIBO_IMG_PATH"/mp00082c_50p_16bits.pgm");
+// img_ref = mln::data::transform(img_fst, mln::fun::v2v::int_u16_to_int_u14());
+// img_res = mln::data::compute(mln::accu::stat::histo1d<int_u14>(), img_ref);
+// mln::io::plot::save_histo_sh(img_res, "histo0_14bits.sh");
+
+
+// //-----------------------------------------------------
+// // Smoothing the histogram with a gaussian filter
+// //-----------------------------------------------------
+
+// std::cout << "(14 bits) SMOOTHING HISTOGRAM" << std::endl;
+
+// double ws[401];
+// gaussian_filter(ws, 401, 50.0);
+// img_smooth = mln::linear::convolve(img_res, mln::make::w_window1d(ws));
+// mln::io::plot::save_histo_sh(img_smooth, "histo1_14bits.sh");
+
+
+// //-----------------------------------------------------
+// // Segmenting the histogram with the watershed method
+// //-----------------------------------------------------
+
+// std::cout << "(14 bits) SEGMENTING HISTOGRAM" << std::endl;
+
+// /*
+// labels = mln::labeling::regional_maxima(img_smooth, mln::c2(), nlabels);
+// std::cout << "N labels : " << nlabels << std::endl;
+// mln::io::plot::save_histo_sh(labels, "histo2.sh");
+// */
+
+
+// labels = mln::morpho::watershed::flooding(img_smooth, mln::c2(), nlabels);
+// std::cout << "N labels : " << nlabels << std::endl;
+// mln::io::plot::save_histo_sh(labels, "histo2_14bits.sh");
+
+
+// //-----------------------------------------------------
+// // Rebuilding the image with the mean of each region
+// //-----------------------------------------------------
+
+// std::cout << "(14 bits) BUILDING OUTPUT" << std::endl;
+
+// mln::image2d<label_8>img_label = build_14bits(img_ref, labels);
+
+// std::cout << "(14 bits) COLORING OUTPUT" << std::endl;
+
+// img_out = mln::labeling::mean_values(img_ref, img_label, nlabels);
+// img_rgb = mln::labeling::colorize(rgb8(), img_label);
+
+// mln::io::pgm::save(img_out, "out_14bits.pgm");
+// mln::io::ppm::save(img_rgb, "color_14bits.pgm");
+
+// //labels = mln::morpho::elementary::dilation(labels, mln::c2());
+// //mln::io::plot::save_histo_sh(labels, "histo3.sh");
+// //mln::io::plot::save(labels, "labelized.data");
+// }
+
+// int main()
+// {
+// test_8bits_operator_equal();
+// /*
+// test_8bits_instantiation_without_argument();
+// test_8bits_initialization();
+// test_8bits_take_argument();
+// test_8bits_take_other();
+// test_8bits_operator_equal();
+// */
+// //test_8bits_integration();
+
+// //test_8bits_classifying();
+
+// // PROBLEME AVEC LES COORDONNEES PAR DEFAUT QUI SONT EN SIGNED SHORT
+// // SEE mln/core/def/coord.hh
+// /*
+// test_14bits_instantiation_without_argument();
+// test_14bits_initialization();
+// test_14bits_take_argument();
+// test_14bits_take_other();
+// test_14bits_operator_equal();
+// test_14bits_integration();
+// */
+// //test_14bits_classifying();
+
+// return 0;
+// }
--
1.5.6.5