Olena-patches September 2010

olena-patches@lrde.epita.fr

8 participants
73 discussions

last-svn-commit-203-g876aaf5 More generic breadth-first-thinning-based skeleton transformations.

by Roland Levillain

* mln/topo/is_simple_cell.hh: Make this functor more generic. * mln/topo/detach: Rename as... * mln/topo/detach_cell.hh: ...this. (detach): Turn this function into... (detach_cell): ...a more generic functor. * mln/make/attachment.hh (mln::make::attachment) * mln/make/detachment.hh (mln::make::detachment): Make these routines more generic. * mln/topo/skeleton/breadth_first_thinning.hh (mln::topo::skeleton::breadth_first_thinning): Adjust. * headers.mk: Regen. --- milena/ChangeLog | 17 ++ milena/headers.mk | 2 +- milena/mln/make/attachment.hh | 48 ++++--- milena/mln/make/detachment.hh | 41 +++-- milena/mln/topo/detach.hh | 83 ---------- milena/mln/topo/detach_cell.hh | 157 ++++++++++++++++++++ milena/mln/topo/is_simple_cell.hh | 81 ++++++----- milena/mln/topo/skeleton/breadth_first_thinning.hh | 42 +++-- 8 files changed, 295 insertions(+), 176 deletions(-) delete mode 100644 milena/mln/topo/detach.hh create mode 100644 milena/mln/topo/detach_cell.hh diff --git a/milena/ChangeLog b/milena/ChangeLog index b95813a..b15ba2a 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,22 @@ 2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + More generic breadth-first-thinning-based skeleton transformations. + + * mln/topo/is_simple_cell.hh: Make this functor more generic. + * mln/topo/detach: Rename as... + * mln/topo/detach_cell.hh: ...this. + (detach): Turn this function into... + (detach_cell): ...a more generic functor. + * mln/make/attachment.hh (mln::make::attachment) + * mln/make/detachment.hh (mln::make::detachment): + Make these routines more generic. + * mln/topo/skeleton/breadth_first_thinning.hh + (mln::topo::skeleton::breadth_first_thinning): + Adjust. + * headers.mk: Regen. + +2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Introduce a helper to build an mln::mutable_extension_ima. * mln/core/routine/mutable_extend.hh: New. diff --git a/milena/headers.mk b/milena/headers.mk index 9a33d4e..c3aedc4 100644 --- a/milena/headers.mk +++ b/milena/headers.mk @@ -996,7 +996,7 @@ mln/topo/center_only_iter.hh \ mln/topo/centered_iter_adapter.hh \ mln/topo/complex.hh \ mln/topo/complex_iterators.hh \ -mln/topo/detach.hh \ +mln/topo/detach_cell.hh \ mln/topo/essential.hh \ mln/topo/face.hh \ mln/topo/face_data.hh \ diff --git a/milena/mln/make/attachment.hh b/milena/mln/make/attachment.hh index 088f9df..3af5332 100644 --- a/milena/mln/make/attachment.hh +++ b/milena/mln/make/attachment.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2009, 2010 EPITA Research and Development Laboratory (LRDE) // // This file is part of Olena. // @@ -43,52 +43,58 @@ namespace mln /** \brief Compute the attachment of the cell corresponding to the facet \a f to the image \a ima. + \param ima The input image to which the facet is attached. + \param f The psite corresponding to the attached facet. + \param nbh An adjacency relationship between faces + (should return the set of (n-1)- and (n+1)-faces + adjacent to an n-face). + + \return A set of faces containing the attachment. + + \pre ima is an image of Boolean values. \pre \a f is a facet (it does not belong to any face of higher dimension). - \pre ima is an image of Boolean values. - - \return a set of faces containing the attachment. We do not use the fomal definition of the attachment here (see couprie.08.pami). We use the following (equivalent) definition: an N-face F in CELL is in the attachment of CELL to IMA if it is adjacent to at least an (N-1)-face or an (N+1)-face that does not belong to CELL. */ - template <unsigned D, typename G, typename V> - p_set< complex_psite<D, G> > - attachment(const complex_psite<D, G>& f, - const complex_image<D, G, V>& ima); + template <typename I, typename N> + p_set<mln_psite(I)> + attachment(const Image& ima, const mln_psite(I)& f, + const Neighborhood<N>& nbh); # ifndef MLN_INCLUDE_ONLY - template <unsigned D, typename G, typename V> + template <typename I, typename N> inline - p_set< complex_psite<D, G> > - attachment(const complex_psite<D, G>& f, - const complex_image<D, G, V>& ima) + p_set<mln_psite(I)> + attachment(const Image& ima_, const mln_psite(I)& f, + const Neighborhood<N>& nbh_) { mln_precondition(topo::is_facet(f)); - mlc_equal(V, bool)::check(); + mlc_equal(mln_value(I), bool)::check(); + + I ima = exact(ima_); + N nbh = exact(nbh_); - typedef complex_psite<D, G> psite; - typedef p_set<psite> faces_t; + typedef p_set<mln_psite(I)> faces_t; faces_t f_hat = make::cell(f); - faces_t att_f; + faces_t attach_f; - typedef complex_lower_higher_neighborhood<D, G> adj_nbh_t; - adj_nbh_t adj_nbh; mln_piter(faces_t) g(f_hat); - mln_niter(adj_nbh_t) n(adj_nbh, g); + mln_niter(N) n(nbh, g); for_all(g) for_all(n) if (ima(n) && !f_hat.has(n)) { - att_f.insert(g); + attach_f.insert(g); break; } - return att_f; + return attach_f; } # endif // MLN_INCLUDE_ONLY diff --git a/milena/mln/make/detachment.hh b/milena/mln/make/detachment.hh index 16a8c92..6cd01ba 100644 --- a/milena/mln/make/detachment.hh +++ b/milena/mln/make/detachment.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2009, 2010 EPITA Research and Development Laboratory (LRDE) // // This file is part of Olena. // @@ -43,12 +43,19 @@ namespace mln /** \brief Compute the detachment of the cell corresponding to the facet \a f from the image \a ima. + \param ima The input image from which the facet is to be + detached. + \param f The psite corresponding to the facet to detach. + \param nbh An adjacency relationship between faces + (should return the set of (n-1)- and (n+1)-faces + adjacent to an n-face). + + \return A set of faces containing the detachment. + \pre \a f is a facet (it does not belong to any face of higher dimension). \pre \a ima is an image of Boolean values. - \return a set of faces containing the detachment. - We do not use the fomal definition of the detachment here (see couprie.08.pami). We use the following (equivalent) definition: an N-face F in CELL is not in the detachment of CELL from IMA if @@ -60,34 +67,34 @@ namespace mln the part that is removed, i.e., the detached part CELL - ATTACHMENT. It would be wise to rename this routine to something else. */ - template <unsigned D, typename G, typename V> - p_set< complex_psite<D, G> > - detachment(const complex_psite<D, G>& f, - const complex_image<D, G, V>& ima); + template <typename I, typename N> + p_set<mln_psite(I)> + detachment(const Image& ima, const mln_psite(I)& f, + const Neighborhood<N>& nbh); # ifndef MLN_INCLUDE_ONLY - template <unsigned D, typename G, typename V> + template <typename I, typename N> inline - p_set< complex_psite<D, G> > - detachment(const complex_psite<D, G>& f, - const complex_image<D, G, V>& ima) + p_set<mln_psite(I)> + detachment(const Image& ima_, const mln_psite(I)& f, + const Neighborhood<N>& nbh_) { mln_precondition(topo::is_facet(f)); - mlc_equal(V, bool)::check(); + mlc_equal(mln_value(I), bool)::check(); + + I ima = exact(ima_); + N nbh = exact(nbh_); - typedef complex_psite<D, G> psite; - typedef p_set<psite> faces_t; + typedef p_set<mln_psite(I)> faces_t; faces_t f_hat = make::cell(f); // Initialize DETACH_F to F_HAT. faces_t detach_f = f_hat; - typedef complex_lower_higher_neighborhood<D, G> adj_nbh_t; - adj_nbh_t adj_nbh; mln_piter(faces_t) g(f_hat); - mln_niter(adj_nbh_t) n(adj_nbh, g); + mln_niter(N) n(nbh, g); for_all(g) for_all(n) if (ima(n) && !f_hat.has(n)) diff --git a/milena/mln/topo/detach.hh b/milena/mln/topo/detach.hh deleted file mode 100644 index 6218739..0000000 --- a/milena/mln/topo/detach.hh +++ /dev/null @@ -1,83 +0,0 @@ -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) -// -// This file is part of Olena. -// -// Olena 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, version 2 of the License. -// -// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. -// -// As a special exception, you may use this file as part of a free -// software project without restriction. Specifically, if other files -// instantiate templates or use macros or inline functions from this -// file, or you compile this file and link it with other files to produce -// an executable, this file does not by itself cause the resulting -// executable to be covered by the GNU General Public License. This -// exception does not however invalidate any other reasons why the -// executable file might be covered by the GNU General Public License. - -#ifndef MLN_TOPO_DETACH_HH -# define MLN_TOPO_DETACH_HH - -/// \file -/// \brief Detaching a cell from a binary complex-based image. - -# include <mln/core/site_set/p_set.hh> -# include <mln/core/image/complex_image.hh> -# include <mln/make/detachment.hh> -# include <mln/topo/is_facet.hh> - -namespace mln -{ - - namespace topo - { - - /** Detach the cell corresponding to \a f from \a ima. - - \pre \a f is a facet (it does not belong to any face of higher - dimension). - \pre \a ima is an image of Boolean values. */ - template <unsigned D, typename G> - void - detach(const complex_psite<D, G>& f, complex_image<D, G, bool>& ima); - - -# ifndef MLN_INCLUDE_ONLY - - template <unsigned D, typename G> - inline - void - detach(const complex_psite<D, G>& f, complex_image<D, G, bool>& ima) - { - mln_precondition(topo::is_facet(f)); - - typedef complex_psite<D, G> psite; - typedef p_set<psite> faces_t; - - // Compute the detachment of P from IMA. - faces_t detach = make::detachment(f, ima); - // Detach all its faces from IMA. -# if 0 - // FIXME: Does not work yet? Check again. - data::fill(ima | detach, false); -# endif - mln_piter(faces_t) p(detach); - for_all(p) - ima(p) = false; - } - -# endif // MLN_INCLUDE_ONLY - - } // end of namespace mln::topo - -} // end of namespace mln - -#endif // ! MLN_TOPO_DETACH_HH diff --git a/milena/mln/topo/detach_cell.hh b/milena/mln/topo/detach_cell.hh new file mode 100644 index 0000000..8a828e4 --- /dev/null +++ b/milena/mln/topo/detach_cell.hh @@ -0,0 +1,157 @@ +// Copyright (C) 2009, 2010 EPITA Research and Development Laboratory (LRDE) +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#ifndef MLN_TOPO_DETACH_CELL_HH +# define MLN_TOPO_DETACH_CELL_HH + +/// \file +/// \brief Detaching a cell from a binary (probably complex-based) image. + +# include <mln/core/site_set/p_set.hh> +# include <mln/core/image/complex_image.hh> +# include <mln/make/detachment.hh> +# include <mln/topo/is_facet.hh> + +namespace mln +{ + + namespace topo + { + + /** A functor detaching a cell from a binary (probably + complex-based) image. + + \tparam I The type of the image. + \tparam N An neighborhood type corresponding to (directly) + adjacent faces (should return the set of (n-1)- and + (n+1)-faces adjacent to an n-face). */ + template <typename I, typename N> + class detach_cell + { + public: + /// Constructors. + /// \{ + /** Construct an mln::topo::detach_cell from a neighborhood. + + \param nbh An adjacency relationship between faces + (should return the set of (n-1)- and (n+1)-faces + adjacent to an n-face). */ + detach_cell(const Neighborhood<N>& nbh); + + /** Construct an mln::topo::detach_cell from an image and a + neighborhood. + + \pre \a ima is an image of Boolean values. + + \param ima The input image from which the facet is to be + detached. + \param nbh An adjacency relationship between faces + (should return the set of (n-1)- and (n+1)-faces + adjacent to an n-face). */ + detach_cell(mln::Image& ima, const Neighborhood<N>& nbh); + /// \} + + /* FIXME: Rename as init() or something like this? See how other + functors are written. */ + /** Set the underlying image. + + \pre \a ima is an image of Boolean values. */ + void set_image(mln::Image& ima); + + /** Detach the cell corresponding to \a f from \a ima. + + \pre \a f is a facet (it does not belong to any face of + higher dimension). + + \param f The psite corresponding to the facet to detach. + \param nbh An adjacency relationship between faces + (should return the set of (n-1)- and (n+1)-faces + adjacent to an n-face). */ + void + operator()(const mln_psite(I)& f); + + private: + I* ima_; + const N& nbh_; + }; + + + +# ifndef MLN_INCLUDE_ONLY + + template <typename I, typename N> + inline + detach_cell<I, N>::detach_cell(const Neighborhood<N>& nbh) + : ima_(0), nbh_(exact(nbh)) + { + } + + template <typename I, typename N> + inline + detach_cell<I, N>::detach_cell(mln::Image& ima, + const Neighborhood<N>& nbh) + : ima_(exact(&ima)), nbh_(exact(nbh)) + { + mlc_equal(mln_value(I), bool)::check(); + } + + template <typename I, typename N> + inline + void + detach_cell<I, N>::set_image(mln::Image& ima) + { + mlc_equal(mln_value(I), bool)::check(); + ima_ = exact(&ima); + } + + template <typename I, typename N> + inline + void + detach_cell<I, N>::operator()(const mln_psite(I)& f) + { + mln_precondition(ima_); + mln_precondition(topo::is_facet(f)); + + typedef p_set<mln_psite(I)> faces_t; + + // Compute the detachment of P from *IMA_. + faces_t detach = make::detachment(*ima_, f, nbh_); + // Detach all its faces from *IMA_. +# if 0 + // FIXME: Does not work yet? Check again. + data::fill(*ima_ | detach, false); +# endif + mln_piter(faces_t) p(detach); + for_all(p) + (*ima_)(p) = false; + } + +# endif // MLN_INCLUDE_ONLY + + } // end of namespace mln::topo + +} // end of namespace mln + +#endif // ! MLN_TOPO_DETACH_CELL_HH diff --git a/milena/mln/topo/is_simple_cell.hh b/milena/mln/topo/is_simple_cell.hh index a86a8fa..bf60c3d 100644 --- a/milena/mln/topo/is_simple_cell.hh +++ b/milena/mln/topo/is_simple_cell.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2009, 2010 EPITA Research and Development Laboratory (LRDE) // // This file is part of Olena. // @@ -27,7 +27,6 @@ # define MLN_TOPO_IS_SIMPLE_CELL_HH /// \file -/// /// \brief Testing whether a facet is a simple cell. # include <mln/core/concept/function.hh> @@ -48,22 +47,29 @@ namespace mln namespace topo { + /* FIXME: Maybe we could add traits to deduce N from NL and NH (or + the converse). Anyway, the part of the code where neighborhood + are used should be moved into another functor + (`is_collapsible', see below), thus pushing away this + issue. */ + /** \brief A predicate for the simplicity of a point based on the collapse property of the attachment. The functor does not actually take a cell as input, but a face - that is expected to be a D-facet. */ - template <typename I> - class is_simple_cell : public mln::Function_v2b< is_simple_cell > + that is expected to be a D-facet. + + \tparam I The type of the image. + \tparam N The neighborhood type returning the set of + (n-1)- and (n+1)-faces adjacent to a an n-face. + \tparam NL The neighborhood type returning the set of + (n-1)-faces adjacent to a an n-face. + \tparam NH The neighborhood type returning the set of + (n+1)-faces adjacent to a an n-face. */ + template <typename I, typename N, typename NL, typename NH> + class is_simple_cell : public mln::Function_v2b< is_simple_cell<I, N, NL, NH> > { public: - /// Dimension of the image (and therefore of the complex). - static const unsigned D = I::dim; - /// Geometry of the image. - typedef mln_geom(I) G; - /// Psite type. - typedef mln::complex_psite<D, G> psite; - /// Result type of the functor. typedef bool result; @@ -76,8 +82,11 @@ namespace mln void set_image(const mln::Image& ima); /// Based on the algorithm A2 from couprie.08.pami. - bool operator()(const mln::complex_psite<I::dim,mln_geom(I)>& p) const; - // Tech note: The argument type above is explicit to help g++-3.3. + /* FIXME: We probably broke the compatiblity with g++ 3.3, as it + seems this compiler does not like an indirect type like the + one of the following operator's argument. Check and possibly + improve this. */ + bool operator()(const mln_psite(I)& p) const; private: const I* ima_; @@ -87,44 +96,44 @@ namespace mln # ifndef MLN_INCLUDE_ONLY - template <typename I> + template <typename I, typename N, typename NL, typename NH> inline - is_simple_cell::is_simple_cell() + is_simple_cell<I, N, NL, NH>::is_simple_cell() : ima_(0) { } - template <typename I> + template <typename I, typename N, typename NL, typename NH> inline - is_simple_cell::is_simple_cell(const mln::Image& ima) - : ima_(mln::exact(&ima)) + is_simple_cell<I, N, NL, NH>::is_simple_cell(const mln::Image& ima) + : ima_(exact(&ima)) { } - template <typename I> + template <typename I, typename N, typename NL, typename NH> inline void - is_simple_cell::set_image(const mln::Image& ima) + is_simple_cell<I, N, NL, NH>::set_image(const mln::Image& ima) { - ima_ = mln::exact(&ima); + ima_ = exact(&ima); } - template <typename I> + template <typename I, typename N, typename NL, typename NH> inline bool - is_simple_cell::operator()(const mln::complex_psite<I::dim,mln_geom(I)>& p) const - // Tech note: The argument type above is explicit to help g++-3.3. + is_simple_cell<I, N, NL, NH>::operator()(const mln_psite(I)& p) const { mln_precondition(ima_); - typedef p_set<psite> faces_t; + typedef p_set<mln_psite(I)> faces_t; - // Compute the attachment of the cell corresponding to P to he + // Compute the attachment of the cell corresponding to P to the // domain of *IMA_. - faces_t att = make::attachment(p, *ima_); + N adj_nbh; + faces_t att = make::attachment(*ima_, p, adj_nbh); // A cell with an empty attachment is not simple. - /* FIXME: Why does p_set not provide an empty() predicate? */ + /* FIXME: Why p_set does not provide an empty() predicate? */ if (att.nsites() == 0) return false; @@ -134,10 +143,8 @@ namespace mln routine. */ // Try to collapse the attachment (to a single point). { - typedef complex_lower_neighborhood<D, G> lower_adj_nbh_t; - typedef complex_higher_neighborhood<D, G> higher_adj_nbh_t; - lower_adj_nbh_t lower_adj_nbh; - higher_adj_nbh_t higher_adj_nbh; + NL lower_adj_nbh; + NH higher_adj_nbh; while (att.nsites() > 1) { @@ -149,11 +156,11 @@ namespace mln for_all(g) /* G cannot have dimension 0, since we later look for an adjacent face H of lower dimension. */ - if (static_cast<psite>(g).n() > 0) + if (static_cast<mln_psite(I)>(g).n() > 0) { // Check whether G is a facet within ATT. bool g_is_facet = true; - mln_niter(higher_adj_nbh_t) f(higher_adj_nbh, g); + mln_niter(NH) f(higher_adj_nbh, g); for_all(f) if (att.has(f)) { @@ -165,13 +172,13 @@ namespace mln // Look for a face H stricly included in G. bool gh_is_simple_pair = false; - mln_niter(lower_adj_nbh_t) h(lower_adj_nbh, g); + mln_niter(NL) h(lower_adj_nbh, g); for_all(h) { bool h_strictly_in_g = true; if (att.has(h)) { - mln_niter(higher_adj_nbh_t) i(higher_adj_nbh, h); + mln_niter(NH) i(higher_adj_nbh, h); for_all(i) if (i != g && att.has(i)) { diff --git a/milena/mln/topo/skeleton/breadth_first_thinning.hh b/milena/mln/topo/skeleton/breadth_first_thinning.hh index f405166..eb1c212 100644 --- a/milena/mln/topo/skeleton/breadth_first_thinning.hh +++ b/milena/mln/topo/skeleton/breadth_first_thinning.hh @@ -55,15 +55,19 @@ namespace mln A generic implementation of the computation of a skeleton using a breadth-first thinning on a binary. - \param input The input image. - \param nbh The adjacency relation between triangles. - \param is_simple The predicate on the simplicity of points - (sites). This functor must provide a method - <tt>void set_image(const Image&)</tt>. + \param input The input image. + \param nbh The adjacency relation between triangles. + \param is_simple The predicate on the simplicity of points + (sites). This functor must provide a method + <tt>void set_image(const Image&)</tt>. \param detach A function used to detach a cell from \a input. - \param constraint A constraint on point (site); if it - returns \c false for a point, this point - will not be removed. */ + This functor must provide a method + <tt>void set_image(const Image&)</tt>. + \param constraint A constraint on point (site); if it + returns \c false for a point, this point + will not be removed. + + Keywords: skeletons, simple points. */ template <typename I, typename N, typename F, typename G, typename H> mln_concrete(I) breadth_first_thinning(const Image& input, @@ -78,13 +82,16 @@ namespace mln A generic implementation of the computation of a skeleton using a breadth-first thinning on a binary. - \param input The input image. - \param nbh The adjacency relation between triangles. - \param is_simple The predicate on the simplicity of points - (sites). This functor must provide a method - <tt>void set_image(const Image&)</tt>. - \param detach A function used to detach a cell from - \a input. */ + \param input The input image. + \param nbh The adjacency relation between triangles. + \param is_simple The predicate on the simplicity of points + (sites). This functor must provide a method + <tt>void set_image(const Image&)</tt>. + \param detach A function used to detach a cell from \a input. + This functor must provide a method + <tt>void set_image(const Image&)</tt>. + + Keywords: skeletons, simple points. */ template <typename I, typename N, typename F, typename G> mln_concrete(I) breadth_first_thinning(const Image& input, @@ -110,8 +117,9 @@ namespace mln const H& constraint = exact(constraint_); mln_concrete(I) output = duplicate(input); - // Attach the work image to IS_SIMPLE. + // Attach the work image to IS_SIMPLE and DETACH. is_simple.set_image(output); + detach.set_image(output); typedef mln_psite(I) psite; typedef p_set<psite> set_t; @@ -150,7 +158,7 @@ namespace mln given p during the thinning. */ if (constraint(p) && is_simple(p)) { - detach(p, output); + detach(p); mln_niter(N) n_(nbh, p); for_all(n_) { -- 1.5.6.5

13 years, 7 months

last-svn-commit-209-g643158a Introduce a helper to build an mln::mutable_extension_ima.

by Roland Levillain

* mln/core/routine/mutable_extend.hh: New. * headers.mk: Regen. --- milena/ChangeLog | 7 +++ milena/headers.mk | 1 + .../max_sli.hh => core/routine/mutable_extend.hh} | 47 ++++++++++---------- 3 files changed, 31 insertions(+), 24 deletions(-) copy milena/mln/{geom/max_sli.hh => core/routine/mutable_extend.hh} (59%) diff --git a/milena/ChangeLog b/milena/ChangeLog index 4711d11..eaf4b85 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,12 @@ 2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Introduce a helper to build an mln::mutable_extension_ima. + + * mln/core/routine/mutable_extend.hh: New. + * headers.mk: Regen. + +2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Introduce an extension-by-mutable-image morpher. * mln/core/image/dmorph/mutable_extension_ima.hh: New. diff --git a/milena/headers.mk b/milena/headers.mk index a08fbfa..6794868 100644 --- a/milena/headers.mk +++ b/milena/headers.mk @@ -371,6 +371,7 @@ mln/core/routine/extend.hh \ mln/core/routine/init.hh \ mln/core/routine/init.hxx \ mln/core/routine/initialize.hh \ +mln/core/routine/mutable_extend.hh \ mln/core/routine/ops.hh \ mln/core/routine/primary.hh \ mln/core/site_set/all.hh \ diff --git a/milena/mln/geom/max_sli.hh b/milena/mln/core/routine/mutable_extend.hh similarity index 59% copy from milena/mln/geom/max_sli.hh copy to milena/mln/core/routine/mutable_extend.hh index cf4fc51..73f2672 100644 --- a/milena/mln/geom/max_sli.hh +++ b/milena/mln/core/routine/mutable_extend.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2007, 2008, 2009 EPITA Research and Development +// Copyright (C) 2008, 2009, 2010 EPITA Research and Development // Laboratory (LRDE) // // This file is part of Olena. @@ -24,45 +24,44 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. -#ifndef MLN_GEOM_MAX_SLI_HH -# define MLN_GEOM_MAX_SLI_HH +#ifndef MLN_CORE_ROUTINE_MUTABLE_EXTEND_HH +# define MLN_CORE_ROUTINE_MUTABLE_EXTEND_HH /// \file /// -/// Give the maximum sli of an image. +/// Routine to extend a mutable image with a mutable extension. +/// +/// \todo Use the 'instant' mechanism. +/// \todo Deal with ambiguities. +/// \todo Check that there is no extension yet (except "mutable_extendable"). + +# include <mln/core/image/dmorph/mutable_extension_ima.hh> -# include <mln/core/concept/image.hh> -# include <mln/geom/bbox.hh> namespace mln { - namespace geom - { - - /// Give the maximum sli of an image. - template <typename I> - mln_deduce(I, site, coord) max_sli(const Image& ima); + /// Extend a mutable image with a mutable image. + template <typename I, typename J> + mutable_extension_ima<I, J> + mutable_extend(Image& ima, Image<J>& ext); # ifndef MLN_INCLUDE_ONLY - template <typename I> - inline - mln_deduce(I, site, coord) max_sli(const Image& ima) - { - mln_precondition(exact(ima).is_valid()); - mln_deduce(I, site, coord) maxsli = geom::bbox(ima).pmax().sli(); - - return maxsli; - } + template <typename I, typename J> + mutable_extension_ima<I, J> + mutable_extend(Image& ima, Image<J>& ext) + { + mlc_converts_to(mln_value(J), mln_value(I))::check(); + mutable_extension_ima<I, J> tmp(exact(ima), exact(ext)); + return tmp; + } # endif // ! MLN_INCLUDE_ONLY - } // end of namespace mln::geom - } // end of namespace mln -#endif // ! MLN_GEOM_MAX_SLI_HH +#endif // ! MLN_CORE_ROUTINE_MUTABLE_EXTEND_HH -- 1.5.6.5

13 years, 7 months

last-svn-commit-202-gc9a6f38 Introduce a helper to build an mln::mutable_extension_ima.

by Roland Levillain

* mln/core/routine/mutable_extend.hh: New. * headers.mk: Regen. --- milena/ChangeLog | 7 +++ milena/headers.mk | 1 + .../max_sli.hh => core/routine/mutable_extend.hh} | 47 ++++++++++---------- 3 files changed, 31 insertions(+), 24 deletions(-) copy milena/mln/{geom/max_sli.hh => core/routine/mutable_extend.hh} (59%) diff --git a/milena/ChangeLog b/milena/ChangeLog index eb4f2da..b95813a 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,12 @@ 2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Introduce a helper to build an mln::mutable_extension_ima. + + * mln/core/routine/mutable_extend.hh: New. + * headers.mk: Regen. + +2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Introduce an extension-by-mutable-image morpher. * mln/core/image/dmorph/mutable_extension_ima.hh: New. diff --git a/milena/headers.mk b/milena/headers.mk index 7929341..9a33d4e 100644 --- a/milena/headers.mk +++ b/milena/headers.mk @@ -371,6 +371,7 @@ mln/core/routine/extend.hh \ mln/core/routine/init.hh \ mln/core/routine/init.hxx \ mln/core/routine/initialize.hh \ +mln/core/routine/mutable_extend.hh \ mln/core/routine/ops.hh \ mln/core/routine/primary.hh \ mln/core/site_set/all.hh \ diff --git a/milena/mln/geom/max_sli.hh b/milena/mln/core/routine/mutable_extend.hh similarity index 59% copy from milena/mln/geom/max_sli.hh copy to milena/mln/core/routine/mutable_extend.hh index cf4fc51..73f2672 100644 --- a/milena/mln/geom/max_sli.hh +++ b/milena/mln/core/routine/mutable_extend.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2007, 2008, 2009 EPITA Research and Development +// Copyright (C) 2008, 2009, 2010 EPITA Research and Development // Laboratory (LRDE) // // This file is part of Olena. @@ -24,45 +24,44 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. -#ifndef MLN_GEOM_MAX_SLI_HH -# define MLN_GEOM_MAX_SLI_HH +#ifndef MLN_CORE_ROUTINE_MUTABLE_EXTEND_HH +# define MLN_CORE_ROUTINE_MUTABLE_EXTEND_HH /// \file /// -/// Give the maximum sli of an image. +/// Routine to extend a mutable image with a mutable extension. +/// +/// \todo Use the 'instant' mechanism. +/// \todo Deal with ambiguities. +/// \todo Check that there is no extension yet (except "mutable_extendable"). + +# include <mln/core/image/dmorph/mutable_extension_ima.hh> -# include <mln/core/concept/image.hh> -# include <mln/geom/bbox.hh> namespace mln { - namespace geom - { - - /// Give the maximum sli of an image. - template <typename I> - mln_deduce(I, site, coord) max_sli(const Image& ima); + /// Extend a mutable image with a mutable image. + template <typename I, typename J> + mutable_extension_ima<I, J> + mutable_extend(Image& ima, Image<J>& ext); # ifndef MLN_INCLUDE_ONLY - template <typename I> - inline - mln_deduce(I, site, coord) max_sli(const Image& ima) - { - mln_precondition(exact(ima).is_valid()); - mln_deduce(I, site, coord) maxsli = geom::bbox(ima).pmax().sli(); - - return maxsli; - } + template <typename I, typename J> + mutable_extension_ima<I, J> + mutable_extend(Image& ima, Image<J>& ext) + { + mlc_converts_to(mln_value(J), mln_value(I))::check(); + mutable_extension_ima<I, J> tmp(exact(ima), exact(ext)); + return tmp; + } # endif // ! MLN_INCLUDE_ONLY - } // end of namespace mln::geom - } // end of namespace mln -#endif // ! MLN_GEOM_MAX_SLI_HH +#endif // ! MLN_CORE_ROUTINE_MUTABLE_EXTEND_HH -- 1.5.6.5

13 years, 7 months

last-svn-commit-208-g0a6d80b Introduce an extension-by-mutable-image morpher.

by Roland Levillain

* mln/core/image/dmorph/mutable_extension_ima.hh: New. * headers.mk: Regen. --- milena/ChangeLog | 7 ++ milena/headers.mk | 1 + .../{extension_ima.hh => mutable_extension_ima.hh} | 92 +++++++++----------- 3 files changed, 51 insertions(+), 49 deletions(-) copy milena/mln/core/image/dmorph/{extension_ima.hh => mutable_extension_ima.hh} (68%) diff --git a/milena/ChangeLog b/milena/ChangeLog index da73aef..4711d11 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,3 +1,10 @@ +2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + + Introduce an extension-by-mutable-image morpher. + + * mln/core/image/dmorph/mutable_extension_ima.hh: New. + * headers.mk: Regen. + 2010-09-15 Roland Levillain <roland(a)lrde.epita.fr> Catch up with the renaming to detach_cell. diff --git a/milena/headers.mk b/milena/headers.mk index 90dd918..a08fbfa 100644 --- a/milena/headers.mk +++ b/milena/headers.mk @@ -277,6 +277,7 @@ mln/core/image/dmorph/hexa.hh \ mln/core/image/dmorph/hexa_piter.hh \ mln/core/image/dmorph/image2d_h.hh \ mln/core/image/dmorph/image_if.hh \ +mln/core/image/dmorph/mutable_extension_ima.hh \ mln/core/image/dmorph/p2p_image.hh \ mln/core/image/dmorph/slice_image.hh \ mln/core/image/dmorph/sub_image.hh \ diff --git a/milena/mln/core/image/dmorph/extension_ima.hh b/milena/mln/core/image/dmorph/mutable_extension_ima.hh similarity index 68% copy from milena/mln/core/image/dmorph/extension_ima.hh copy to milena/mln/core/image/dmorph/mutable_extension_ima.hh index a0bc9ca..f208581 100644 --- a/milena/mln/core/image/dmorph/extension_ima.hh +++ b/milena/mln/core/image/dmorph/mutable_extension_ima.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008, 2009 EPITA Research and Development Laboratory +// Copyright (C) 2008, 2009, 2010 EPITA Research and Development Laboratory // (LRDE) // // This file is part of Olena. @@ -24,13 +24,13 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. -#ifndef MLN_CORE_IMAGE_DMORPH_EXTENSION_IMA_HH -# define MLN_CORE_IMAGE_DMORPH_EXTENSION_IMA_HH +#ifndef MLN_CORE_IMAGE_DMORPH_MUTABLE_EXTENSION_IMA_HH +# define MLN_CORE_IMAGE_DMORPH_MUTABLE_EXTENSION_IMA_HH /// \file /// /// Definition of a morpher that extends the domain of an image -/// with an image. +/// with a readable and writable (rw) image. # include <mln/core/internal/image_identity.hh> @@ -40,17 +40,17 @@ namespace mln { // Forward declaration. - template <typename I, typename J> struct extension_ima; + template <typename I, typename J> struct mutable_extension_ima; namespace internal { - /// Data structure for \c mln::extension_ima<I, J>. + /// Data structure for \c mln::mutable_extension_ima<I, J>. template <typename I, typename J> - struct data< extension_ima<I, J> > + struct data< mutable_extension_ima<I, J> > { - data(I& ima, const J& ext); + data(I& ima, J& ext); I ima_; J ext_; @@ -63,7 +63,7 @@ namespace mln { template <typename I, typename J> - struct image_< extension_ima<I, J> > : image_ // Same as I except... + struct image_< mutable_extension_ima<I, J> > : image_ // Same as I except... { // ...these changes. typedef trait::image::category::identity_morpher category; @@ -77,11 +77,11 @@ namespace mln }; template <typename I, typename J, typename V> - struct ch_value< extension_ima<I, J>, V > + struct ch_value< mutable_extension_ima<I, J>, V > { typedef mlc_converts_to(mln_value(J), V) keep_ext; typedef mln_ch_value(I, V) Iv; - typedef extension_ima<Iv, J> Iv_ext; + typedef mutable_extension_ima<Iv, J> Iv_ext; typedef mlc_if(keep_ext, Iv_ext, Iv) ret; }; @@ -94,15 +94,15 @@ namespace mln /// \ingroup modimagedomainmorpher // template <typename I, typename J> - class extension_ima + class mutable_extension_ima - : public internal::image_identity< I, mln_domain(I), extension_ima<I, J> >, + : public internal::image_identity< I, mln_domain(I), mutable_extension_ima<I, J> >, private mlc_converts_to(mln_value(J), mln_value(I))::check_t { public: /// Skeleton. - typedef extension_ima< tag::image_, tag::ext_<J> > skeleton; + typedef mutable_extension_ima< tag::image_, tag::ext_<J> > skeleton; /// Image value type. typedef mln_value(I) value; @@ -111,14 +111,14 @@ namespace mln typedef mln_value(I) rvalue; /// Constructor without argument. - extension_ima(); + mutable_extension_ima(); /// Constructor from an image \p ima and a function \p ext. - extension_ima(I& ima, const J& ext); + mutable_extension_ima(I& ima, J& ext); /// Deferred initialization from an image \p ima and a function \p /// ext. - void init_(I& ima, const J& ext); + void init_(I& ima, J& ext); /// Test if \p p is valid. @@ -131,52 +131,52 @@ namespace mln mln_value(I) operator()(const mln_psite(I)& p) const; /// Read-write access to the image value located at site \p p. - mln_morpher_lvalue(I) operator()(const mln_psite(I)& p); + mln_lvalue(I) operator()(const mln_psite(I)& p); - /// Read-only access to the extension domain (image). - const J& extension() const; + /// Access to the extension domain (image). + J& extension() ; }; // init_ template <typename I, typename J, typename M> - void init_(tag::image_t, extension_ima<I,J>& target, const M& model); + void init_(tag::image_t, mutable_extension_ima<I,J>& target, const M& model); template <typename J, typename I> - void init_(tag::extension_t, J& target, const extension_ima<I,J>& model); + void init_(tag::extension_t, J& target, const mutable_extension_ima<I,J>& model); # ifndef MLN_INCLUDE_ONLY - // internal::data< extension_ima<I,S> > + // internal::data< mutable_extension_ima<I,S> > namespace internal { template <typename I, typename J> inline - data< extension_ima<I, J> >::data(I& ima, const J& ext) + data< mutable_extension_ima<I, J> >::data(I& ima, J& ext) : ima_(ima), - ext_(const_cast<J&>(ext)) + ext_(ext) { } } // end of namespace mln::internal - // extension_ima<I, J> + // mutable_extension_ima<I, J> template <typename I, typename J> inline - extension_ima<I, J>::extension_ima() + mutable_extension_ima<I, J>::mutable_extension_ima() { } template <typename I, typename J> inline - extension_ima<I, J>::extension_ima(I& ima, const J& ext) + mutable_extension_ima<I, J>::mutable_extension_ima(I& ima, J& ext) { init_(ima, ext); } @@ -184,16 +184,16 @@ namespace mln template <typename I, typename J> inline void - extension_ima<I, J>::init_(I& ima, const J& ext) + mutable_extension_ima<I, J>::init_(I& ima, J& ext) { - this->data_ = new internal::data< extension_ima<I, J> >(ima, ext); + this->data_ = new internal::data< mutable_extension_ima<I, J> >(ima, ext); } template <typename I, typename J> template <typename P> inline bool - extension_ima<I, J>::has(const P& p) const + mutable_extension_ima<I, J>::has(const P& p) const { mln_precondition(this->is_valid()); mln_precondition(this->data_->ext_.is_valid()); @@ -205,7 +205,7 @@ namespace mln template <typename I, typename J> inline mln_value(I) - extension_ima<I, J>::operator()(const mln_psite(I)& p) const + mutable_extension_ima<I, J>::operator()(const mln_psite(I)& p) const { mln_precondition(this->is_valid()); mln_precondition(has(p)); @@ -219,29 +219,23 @@ namespace mln template <typename I, typename J> inline - mln_morpher_lvalue(I) - extension_ima<I, J>::operator()(const mln_psite(I)& p) + mln_lvalue(I) + mutable_extension_ima<I, J>::operator()(const mln_psite(I)& p) { - static mln_value(I) cpy; mln_precondition(this->is_valid()); mln_precondition(has(p)); - // See the above comment about if-else v. ternary. + // if-else is preferred to the ternary op to allow for the + // function result to convert towards the expected return type. if (this->data_->ima_.domain().has(p)) return this->data_->ima_(p); else - { - // This hack makes this signature valid both in the image - // domain and in its extension. It works even if - // mln_morpher_lvalue(I) is a mutable reference. - cpy = this->data_->ext_(p); - return cpy; - } + return this->data_->ext_(p); } template <typename I, typename J> inline - const J& - extension_ima<I, J>::extension() const + J& + mutable_extension_ima<I, J>::extension() { mln_precondition(this->is_valid()); return this->data_->ext_; @@ -251,7 +245,7 @@ namespace mln template <typename I, typename J, typename M> inline - void init_(tag::image_t, extension_ima<I,J>& target, const M& model) + void init_(tag::image_t, mutable_extension_ima<I,J>& target, const M& model) { I ima; init_(tag::image, ima, model); @@ -262,10 +256,10 @@ namespace mln template <typename J, typename I> inline - void init_(tag::extension_t, J& target, const extension_ima<I,J>& model) + void init_(tag::extension_t, J& target, const mutable_extension_ima<I,J>& model) { typedef mlc_unconst(J) J_; - J_& ext_ = const_cast<J_&>(model.extension()); + J_& ext_ = const_cast< mutable_extension_ima<I,J>& >(model).extension(); J_& target_ = const_cast<J_&>(target); target_ = ext_; } @@ -275,4 +269,4 @@ namespace mln } // end of namespace mln -#endif // ! MLN_CORE_IMAGE_DMORPH_EXTENSION_IMA_HH +#endif // ! MLN_CORE_IMAGE_DMORPH_MUTABLE_EXTENSION_IMA_HH -- 1.5.6.5

13 years, 7 months

last-svn-commit-201-gd0945cb Introduce an extension-by-mutable-image morpher.

by Roland Levillain

* mln/core/image/dmorph/mutable_extension_ima.hh: New. * headers.mk: Regen. --- milena/ChangeLog | 7 ++ milena/headers.mk | 1 + .../{extension_ima.hh => mutable_extension_ima.hh} | 92 +++++++++----------- 3 files changed, 51 insertions(+), 49 deletions(-) copy milena/mln/core/image/dmorph/{extension_ima.hh => mutable_extension_ima.hh} (68%) diff --git a/milena/ChangeLog b/milena/ChangeLog index 18e4ada..eb4f2da 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,12 @@ 2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Introduce an extension-by-mutable-image morpher. + + * mln/core/image/dmorph/mutable_extension_ima.hh: New. + * headers.mk: Regen. + +2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + Make the 5th argument of breadth_first_thinning truly optional. * mln/topo/skeleton/breadth_first_thinning.hh diff --git a/milena/headers.mk b/milena/headers.mk index 110caa6..7929341 100644 --- a/milena/headers.mk +++ b/milena/headers.mk @@ -277,6 +277,7 @@ mln/core/image/dmorph/hexa.hh \ mln/core/image/dmorph/hexa_piter.hh \ mln/core/image/dmorph/image2d_h.hh \ mln/core/image/dmorph/image_if.hh \ +mln/core/image/dmorph/mutable_extension_ima.hh \ mln/core/image/dmorph/p2p_image.hh \ mln/core/image/dmorph/slice_image.hh \ mln/core/image/dmorph/sub_image.hh \ diff --git a/milena/mln/core/image/dmorph/extension_ima.hh b/milena/mln/core/image/dmorph/mutable_extension_ima.hh similarity index 68% copy from milena/mln/core/image/dmorph/extension_ima.hh copy to milena/mln/core/image/dmorph/mutable_extension_ima.hh index a0bc9ca..f208581 100644 --- a/milena/mln/core/image/dmorph/extension_ima.hh +++ b/milena/mln/core/image/dmorph/mutable_extension_ima.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008, 2009 EPITA Research and Development Laboratory +// Copyright (C) 2008, 2009, 2010 EPITA Research and Development Laboratory // (LRDE) // // This file is part of Olena. @@ -24,13 +24,13 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. -#ifndef MLN_CORE_IMAGE_DMORPH_EXTENSION_IMA_HH -# define MLN_CORE_IMAGE_DMORPH_EXTENSION_IMA_HH +#ifndef MLN_CORE_IMAGE_DMORPH_MUTABLE_EXTENSION_IMA_HH +# define MLN_CORE_IMAGE_DMORPH_MUTABLE_EXTENSION_IMA_HH /// \file /// /// Definition of a morpher that extends the domain of an image -/// with an image. +/// with a readable and writable (rw) image. # include <mln/core/internal/image_identity.hh> @@ -40,17 +40,17 @@ namespace mln { // Forward declaration. - template <typename I, typename J> struct extension_ima; + template <typename I, typename J> struct mutable_extension_ima; namespace internal { - /// Data structure for \c mln::extension_ima<I, J>. + /// Data structure for \c mln::mutable_extension_ima<I, J>. template <typename I, typename J> - struct data< extension_ima<I, J> > + struct data< mutable_extension_ima<I, J> > { - data(I& ima, const J& ext); + data(I& ima, J& ext); I ima_; J ext_; @@ -63,7 +63,7 @@ namespace mln { template <typename I, typename J> - struct image_< extension_ima<I, J> > : image_ // Same as I except... + struct image_< mutable_extension_ima<I, J> > : image_ // Same as I except... { // ...these changes. typedef trait::image::category::identity_morpher category; @@ -77,11 +77,11 @@ namespace mln }; template <typename I, typename J, typename V> - struct ch_value< extension_ima<I, J>, V > + struct ch_value< mutable_extension_ima<I, J>, V > { typedef mlc_converts_to(mln_value(J), V) keep_ext; typedef mln_ch_value(I, V) Iv; - typedef extension_ima<Iv, J> Iv_ext; + typedef mutable_extension_ima<Iv, J> Iv_ext; typedef mlc_if(keep_ext, Iv_ext, Iv) ret; }; @@ -94,15 +94,15 @@ namespace mln /// \ingroup modimagedomainmorpher // template <typename I, typename J> - class extension_ima + class mutable_extension_ima - : public internal::image_identity< I, mln_domain(I), extension_ima<I, J> >, + : public internal::image_identity< I, mln_domain(I), mutable_extension_ima<I, J> >, private mlc_converts_to(mln_value(J), mln_value(I))::check_t { public: /// Skeleton. - typedef extension_ima< tag::image_, tag::ext_<J> > skeleton; + typedef mutable_extension_ima< tag::image_, tag::ext_<J> > skeleton; /// Image value type. typedef mln_value(I) value; @@ -111,14 +111,14 @@ namespace mln typedef mln_value(I) rvalue; /// Constructor without argument. - extension_ima(); + mutable_extension_ima(); /// Constructor from an image \p ima and a function \p ext. - extension_ima(I& ima, const J& ext); + mutable_extension_ima(I& ima, J& ext); /// Deferred initialization from an image \p ima and a function \p /// ext. - void init_(I& ima, const J& ext); + void init_(I& ima, J& ext); /// Test if \p p is valid. @@ -131,52 +131,52 @@ namespace mln mln_value(I) operator()(const mln_psite(I)& p) const; /// Read-write access to the image value located at site \p p. - mln_morpher_lvalue(I) operator()(const mln_psite(I)& p); + mln_lvalue(I) operator()(const mln_psite(I)& p); - /// Read-only access to the extension domain (image). - const J& extension() const; + /// Access to the extension domain (image). + J& extension() ; }; // init_ template <typename I, typename J, typename M> - void init_(tag::image_t, extension_ima<I,J>& target, const M& model); + void init_(tag::image_t, mutable_extension_ima<I,J>& target, const M& model); template <typename J, typename I> - void init_(tag::extension_t, J& target, const extension_ima<I,J>& model); + void init_(tag::extension_t, J& target, const mutable_extension_ima<I,J>& model); # ifndef MLN_INCLUDE_ONLY - // internal::data< extension_ima<I,S> > + // internal::data< mutable_extension_ima<I,S> > namespace internal { template <typename I, typename J> inline - data< extension_ima<I, J> >::data(I& ima, const J& ext) + data< mutable_extension_ima<I, J> >::data(I& ima, J& ext) : ima_(ima), - ext_(const_cast<J&>(ext)) + ext_(ext) { } } // end of namespace mln::internal - // extension_ima<I, J> + // mutable_extension_ima<I, J> template <typename I, typename J> inline - extension_ima<I, J>::extension_ima() + mutable_extension_ima<I, J>::mutable_extension_ima() { } template <typename I, typename J> inline - extension_ima<I, J>::extension_ima(I& ima, const J& ext) + mutable_extension_ima<I, J>::mutable_extension_ima(I& ima, J& ext) { init_(ima, ext); } @@ -184,16 +184,16 @@ namespace mln template <typename I, typename J> inline void - extension_ima<I, J>::init_(I& ima, const J& ext) + mutable_extension_ima<I, J>::init_(I& ima, J& ext) { - this->data_ = new internal::data< extension_ima<I, J> >(ima, ext); + this->data_ = new internal::data< mutable_extension_ima<I, J> >(ima, ext); } template <typename I, typename J> template <typename P> inline bool - extension_ima<I, J>::has(const P& p) const + mutable_extension_ima<I, J>::has(const P& p) const { mln_precondition(this->is_valid()); mln_precondition(this->data_->ext_.is_valid()); @@ -205,7 +205,7 @@ namespace mln template <typename I, typename J> inline mln_value(I) - extension_ima<I, J>::operator()(const mln_psite(I)& p) const + mutable_extension_ima<I, J>::operator()(const mln_psite(I)& p) const { mln_precondition(this->is_valid()); mln_precondition(has(p)); @@ -219,29 +219,23 @@ namespace mln template <typename I, typename J> inline - mln_morpher_lvalue(I) - extension_ima<I, J>::operator()(const mln_psite(I)& p) + mln_lvalue(I) + mutable_extension_ima<I, J>::operator()(const mln_psite(I)& p) { - static mln_value(I) cpy; mln_precondition(this->is_valid()); mln_precondition(has(p)); - // See the above comment about if-else v. ternary. + // if-else is preferred to the ternary op to allow for the + // function result to convert towards the expected return type. if (this->data_->ima_.domain().has(p)) return this->data_->ima_(p); else - { - // This hack makes this signature valid both in the image - // domain and in its extension. It works even if - // mln_morpher_lvalue(I) is a mutable reference. - cpy = this->data_->ext_(p); - return cpy; - } + return this->data_->ext_(p); } template <typename I, typename J> inline - const J& - extension_ima<I, J>::extension() const + J& + mutable_extension_ima<I, J>::extension() { mln_precondition(this->is_valid()); return this->data_->ext_; @@ -251,7 +245,7 @@ namespace mln template <typename I, typename J, typename M> inline - void init_(tag::image_t, extension_ima<I,J>& target, const M& model) + void init_(tag::image_t, mutable_extension_ima<I,J>& target, const M& model) { I ima; init_(tag::image, ima, model); @@ -262,10 +256,10 @@ namespace mln template <typename J, typename I> inline - void init_(tag::extension_t, J& target, const extension_ima<I,J>& model) + void init_(tag::extension_t, J& target, const mutable_extension_ima<I,J>& model) { typedef mlc_unconst(J) J_; - J_& ext_ = const_cast<J_&>(model.extension()); + J_& ext_ = const_cast< mutable_extension_ima<I,J>& >(model).extension(); J_& target_ = const_cast<J_&>(target); target_ = ext_; } @@ -275,4 +269,4 @@ namespace mln } // end of namespace mln -#endif // ! MLN_CORE_IMAGE_DMORPH_EXTENSION_IMA_HH +#endif // ! MLN_CORE_IMAGE_DMORPH_MUTABLE_EXTENSION_IMA_HH -- 1.5.6.5

13 years, 7 months

last-svn-commit-200-gad49d7e Make the 5th argument of breadth_first_thinning truly optional.

by Roland Levillain

* mln/topo/skeleton/breadth_first_thinning.hh (mln::topo::skeleton::breadth_first_thinning): Disable the default value of the 5th argument, which cannot be used in practice, since it depends on the parameter H. Add a 4-argument overloading to make the `constraint' argument optional. --- milena/ChangeLog | 11 ++++++ milena/mln/topo/skeleton/breadth_first_thinning.hh | 36 ++++++++++++++++++- 2 files changed, 45 insertions(+), 2 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index ebcec24..18e4ada 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,3 +1,14 @@ +2010-09-09 Roland Levillain <roland(a)lrde.epita.fr> + + Make the 5th argument of breadth_first_thinning truly optional. + + * mln/topo/skeleton/breadth_first_thinning.hh + (mln::topo::skeleton::breadth_first_thinning): + Disable the default value of the 5th argument, which cannot be + used in practice, since it depends on the parameter H. + Add a 4-argument overloading to make the `constraint' argument + optional. + 2010-09-01 Roland Levillain <roland(a)lrde.epita.fr> Add more documentation on the detach procedure. diff --git a/milena/mln/topo/skeleton/breadth_first_thinning.hh b/milena/mln/topo/skeleton/breadth_first_thinning.hh index f9356f9..f405166 100644 --- a/milena/mln/topo/skeleton/breadth_first_thinning.hh +++ b/milena/mln/topo/skeleton/breadth_first_thinning.hh @@ -70,8 +70,27 @@ namespace mln const Neighborhood<N>& nbh, Function_v2b<F>& is_simple, G detach, - const Function_v2b<H>& constraint = - fun::p2b::tautology()); + const Function_v2b<H>& constraint); + + + /** \brief Skeleton by Breadth-First Thinning with no constraint. + + A generic implementation of the computation of a skeleton + using a breadth-first thinning on a binary. + + \param input The input image. + \param nbh The adjacency relation between triangles. + \param is_simple The predicate on the simplicity of points + (sites). This functor must provide a method + <tt>void set_image(const Image&)</tt>. + \param detach A function used to detach a cell from + \a input. */ + template <typename I, typename N, typename F, typename G> + mln_concrete(I) + breadth_first_thinning(const Image& input, + const Neighborhood<N>& nbh, + Function_v2b<F>& is_simple, + G detach); # ifndef MLN_INCLUDE_ONLY @@ -149,6 +168,19 @@ namespace mln return output; } + + template <typename I, typename N, typename F, typename G> + inline + mln_concrete(I) + breadth_first_thinning(const Image& input, + const Neighborhood<N>& nbh, + Function_v2b<F>& is_simple, + G detach) + { + return breadth_first_thinning(input, nbh, is_simple, detach, + fun::p2b::tautology()); + } + # endif // MLN_INCLUDE_ONLY } // end of namespace mln::topo::skeleton -- 1.5.6.5

13 years, 7 months

branch exp/scribo-z updated: last-svn-commit-496-g6c094d0

by git＠lrde.epita.fr

This is an automated email from the git hooks/post-receive script. It was generated because a ref change was pushed to the repository containing the project "Olena, a generic and efficient image processing platform". The branch exp/scribo-z has been updated via 6c094d01081e780f2f665042bb2b0786f926a117 (commit) via 471d53801c136f8930b6160500c68fc2010db0e0 (commit) from 4a2bb958c2a6159916a7e943a655f3b65184d3d3 (commit) Those revisions listed above that are new to this repository have not appeared on any other notification email; so we list those revisions in full, below. - Log ----------------------------------------------------------------- 6c094d0 Import annotating sources from milena green's sandbox. 471d538 Import regional maxima sources from milena green's sandbox. ----------------------------------------------------------------------- Summary of changes: scribo/sandbox/green/ChangeLog | 1147 ++++++++++++++++ scribo/sandbox/green/README.green | 851 +++++++++++- .../kmean1d => annotating/bic}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/bic/bic.cc | 122 ++ .../kmean1d => annotating/hsv}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/hsv/hsv.cc | 721 ++++++++++ .../kmean1d => 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scribo/sandbox/green/use/{accu/stat/histo1d => display/display_histo}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/display/display_histo/display_histo.cc hooks/post-receive -- Olena, a generic and efficient image processing platform

13 years, 7 months

branch exp/green updated: last-svn-commit-50-gf23ea9c

by git＠lrde.epita.fr

This is an automated email from the git hooks/post-receive script. It was generated because a ref change was pushed to the repository containing the project "Olena, a generic and efficient image processing platform". The branch exp/green has been updated via f23ea9c2f6d7fbdbc3a895d5ea6c6ce90da862a9 (commit) via 7eab14e181eab8694efdce70d4109abaacc70345 (commit) from 6db793f7e1bba8cde5c975d2a9fb6521265f551b (commit) Those revisions listed above that are new to this repository have not appeared on any other notification email; so we list those revisions in full, below. - Log ----------------------------------------------------------------- f23ea9c Import annotating sources from milena green's sandbox. 7eab14e Import regional maxima sources from milena green's sandbox. ----------------------------------------------------------------------- Summary of changes: scribo/sandbox/green/ChangeLog | 1147 ++++++++++++++++ scribo/sandbox/green/README.green | 851 +++++++++++- .../kmean1d => annotating/bic}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/bic/bic.cc | 122 ++ .../kmean1d => annotating/hsv}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/hsv/hsv.cc | 721 ++++++++++ .../kmean1d => 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.../sandbox/green/tools/labeling/histo/Makefile.am | 153 ++ scribo/sandbox/green/tools/labeling/histo/histo.cc | 204 +++ scribo/sandbox/green/tools/labeling/iz/Makefile.am | 153 ++ scribo/sandbox/green/tools/labeling/iz/iz.cc | 495 +++++++ .../green/tools/labeling/opening/Makefile.am | 153 ++ .../green/tools/labeling/opening/opening.cc | 174 +++ .../green/tools/labeling/regmax/Makefile.am | 153 ++ .../sandbox/green/tools/labeling/regmax/regmax.cc | 428 ++++++ .../histo1d => display/display_histo}/Makefile.am | 0 .../use/display/display_histo/display_histo.cc | 86 ++ 1032 files changed, 14401 insertions(+), 68 deletions(-) copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/bic}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/bic/bic.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/hsv/hsv.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => 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scribo/sandbox/green/exp/annotating/value}/text-only.txt (100%) create mode 100644 scribo/sandbox/green/exp/annotating/value/value.cc create mode 100644 scribo/sandbox/green/exp/regional_maxima/Makefile.am create mode 100644 scribo/sandbox/green/exp/regional_maxima/regional_maxima.cc copy {milena/sandbox/green/demo/labeling => scribo/sandbox/green/exp}/regional_maxima/thresholds.txt (100%) create mode 100644 scribo/sandbox/green/tools/labeling/histo/Makefile.am create mode 100644 scribo/sandbox/green/tools/labeling/histo/histo.cc create mode 100644 scribo/sandbox/green/tools/labeling/iz/Makefile.am create mode 100644 scribo/sandbox/green/tools/labeling/iz/iz.cc create mode 100644 scribo/sandbox/green/tools/labeling/opening/Makefile.am create mode 100644 scribo/sandbox/green/tools/labeling/opening/opening.cc create mode 100644 scribo/sandbox/green/tools/labeling/regmax/Makefile.am create mode 100644 scribo/sandbox/green/tools/labeling/regmax/regmax.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => display/display_histo}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/display/display_histo/display_histo.cc hooks/post-receive -- Olena, a generic and efficient image processing platform

13 years, 7 months

last-svn-commit-496-g6c094d0 Import annotating sources from milena green's sandbox.

by Yann Jacquelet

* README.green: New. * demo/annotating/bic/Makefile.am: New. * demo/annotating/bic/bic.cc: New. * demo/annotating/hsv/Makefile.am: New. * demo/annotating/hsv/hsv.cc: New. * demo/annotating/lep/Makefile.am: New. * demo/annotating/lep/lep.cc: New. * demo/annotating/nb_color/Makefile.am: New. * demo/annotating/nb_color/nb_color.cc: New. * demo/annotating/project/Makefile.am: New. * demo/annotating/project/project.cc: New. * demo/annotating/rgb_64/Makefile.am: New. * demo/annotating/rgb_64/rgb_64.cc: New. * demo/annotating/rgb_64_9/Makefile.am: New. * demo/annotating/rgb_64_9/rgb_64_9.cc: New. * demo/annotating/stddev_color/Makefile.am: New. * demo/annotating/stddev_color/stddev_color.cc: New. * demo/annotating/stddev_color_16/Makefile.am: New. * demo/annotating/stddev_color_16/stddev_color_16.cc: New. * exp/annotating/achromastism/Makefile.am: New. * exp/annotating/achromastism/achromastism.cc: New. * exp/annotating/achromastism/text-color.txt: New. * exp/annotating/achromastism/text-img.txt: New. * exp/annotating/achromastism/text-only.txt: New. * exp/annotating/bench/Makefile.am: New. * exp/annotating/bench/bench.cc: New. * exp/annotating/error/Makefile.am: New. * exp/annotating/error/error.cc: New. * exp/annotating/histo/Makefile.am: New. * exp/annotating/histo/histo.cc: New. * exp/annotating/hsv/Makefile.am: New. * exp/annotating/hsv/hsv.cc: New. * exp/annotating/hue/Makefile.am: New. * exp/annotating/hue/hue.cc: New. * exp/annotating/hue/text-color.txt: New. * exp/annotating/hue/text-img.txt: New. * exp/annotating/hue/text-only.txt: New. * exp/annotating/nb_color/Makefile.am: New. * exp/annotating/nb_color/nb_color.cc: New. * exp/annotating/saturation/Makefile.am: New. * exp/annotating/saturation/saturation.cc: New. * exp/annotating/saturation/text-color.txt: New. * exp/annotating/saturation/text-img.txt: New. * exp/annotating/saturation/text-only.txt: New. * exp/annotating/stddev_color/Makefile.am: New. * exp/annotating/stddev_color/stddev_color.cc: New. * exp/annotating/stddev_color_16/Makefile.am: New. * exp/annotating/stddev_color_16/stddev_color_16.cc: New. * exp/annotating/value/Makefile.am: New. * exp/annotating/value/text-color.txt: New. * exp/annotating/value/text-img.txt: New. * exp/annotating/value/text-only.txt: New. * exp/annotating/value/value.cc: New. * mln/clustering/kmean2d.hh: New. * mln/fun/p2b/achromatic.hh: New. * mln/fun/v2v/hue_concentration.hh: New. * mln/fun/v2v/rgb_to_achromatism_map.hh: New. * mln/fun/v2v/rgb_to_hsv.hh: New. --- scribo/sandbox/green/ChangeLog | 64 + scribo/sandbox/green/README.green | 507 +++++++- .../kmean1d => annotating/bic}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/bic/bic.cc | 122 ++ .../kmean1d => annotating/hsv}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/hsv/hsv.cc | 721 ++++++++++ .../kmean1d => annotating/lep}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/lep/lep.cc | 127 ++ .../kmean1d => annotating/nb_color}/Makefile.am | 0 .../green/demo/annotating/nb_color/nb_color.cc | 143 ++ .../kmean1d => annotating/project}/Makefile.am | 0 .../green/demo/annotating/project/project.cc | 275 ++++ .../kmean1d => annotating/rgb_64}/Makefile.am | 0 .../sandbox/green/demo/annotating/rgb_64/rgb_64.cc | 80 ++ .../kmean1d => annotating/rgb_64_9}/Makefile.am | 0 .../green/demo/annotating/rgb_64_9/rgb_64_9.cc | 132 ++ .../stddev_color}/Makefile.am | 0 .../demo/annotating/stddev_color/stddev_color.cc | 191 +++ .../stddev_color_16}/Makefile.am | 0 .../annotating/stddev_color_16/stddev_color_16.cc | 261 ++++ .../achromastism}/Makefile.am | 0 .../exp/annotating/achromastism/achromastism.cc | 179 +++ .../exp/annotating/achromastism/text-color.txt | 0 .../green/exp/annotating/achromastism/text-img.txt | 0 .../exp/annotating/achromastism/text-only.txt | 0 .../bench}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/bench/bench.cc | 1450 ++++++++++++++++++++ .../error}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/error/error.cc | 833 +++++++++++ .../histo}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/histo/histo.cc | 366 +++++ .../hsv}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/hsv/hsv.cc | 912 ++++++++++++ .../hue}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/hue/hue.cc | 402 ++++++ .../green/exp/annotating/hue}/text-color.txt | 0 .../sandbox/green/exp/annotating/hue}/text-img.txt | 0 .../green/exp/annotating/hue}/text-only.txt | 0 .../nb_color}/Makefile.am | 0 .../green/exp/annotating/nb_color/nb_color.cc | 171 +++ .../saturation}/Makefile.am | 0 .../green/exp/annotating/saturation/saturation.cc | 175 +++ .../exp/annotating/saturation}/text-color.txt | 0 .../green/exp/annotating/saturation}/text-img.txt | 0 .../green/exp/annotating/saturation}/text-only.txt | 0 .../stddev_color}/Makefile.am | 0 .../exp/annotating/stddev_color/stddev_color.cc | 216 +++ .../stddev_color_16}/Makefile.am | 0 .../annotating/stddev_color_16/stddev_color_16.cc | 277 ++++ .../value}/Makefile.am | 0 .../green/exp/annotating/value}/text-color.txt | 0 .../green/exp/annotating/value}/text-img.txt | 0 .../green/exp/annotating/value}/text-only.txt | 0 scribo/sandbox/green/exp/annotating/value/value.cc | 468 +++++++ scribo/sandbox/green/mln/clustering/kmean2d.hh | 4 +- scribo/sandbox/green/mln/fun/p2b/achromatic.hh | 20 +- .../sandbox/green/mln/fun/v2v/hue_concentration.hh | 10 + .../green/mln/fun/v2v/rgb_to_achromatism_map.hh | 5 + scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh | 15 +- 59 files changed, 8105 insertions(+), 21 deletions(-) copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/bic}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/bic/bic.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/hsv/hsv.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/lep}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/lep/lep.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/nb_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/project}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/project/project.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/rgb_64}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/rgb_64_9}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/stddev_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/stddev_color_16}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/achromastism}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-color.txt (100%) copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-img.txt (100%) copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/bench}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/bench/bench.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/error}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/error/error.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/histo}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/histo/histo.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/hsv/hsv.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/hue}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/hue/hue.cc copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/nb_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/saturation}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/saturation/saturation.cc copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/stddev_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/stddev_color_16}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/value}/Makefile.am (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-only.txt (100%) create mode 100644 scribo/sandbox/green/exp/annotating/value/value.cc diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 62a7aff..9963f53 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,67 @@ +2010-09-08 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Import annotating sources from milena green's sandbox. + + * README.green: New. + * demo/annotating/bic/Makefile.am: New. + * demo/annotating/bic/bic.cc: New. + * demo/annotating/hsv/Makefile.am: New. + * demo/annotating/hsv/hsv.cc: New. + * demo/annotating/lep/Makefile.am: New. + * demo/annotating/lep/lep.cc: New. + * demo/annotating/nb_color/Makefile.am: New. + * demo/annotating/nb_color/nb_color.cc: New. + * demo/annotating/project/Makefile.am: New. + * demo/annotating/project/project.cc: New. + * demo/annotating/rgb_64/Makefile.am: New. + * demo/annotating/rgb_64/rgb_64.cc: New. + * demo/annotating/rgb_64_9/Makefile.am: New. + * demo/annotating/rgb_64_9/rgb_64_9.cc: New. + * demo/annotating/stddev_color/Makefile.am: New. + * demo/annotating/stddev_color/stddev_color.cc: New. + * demo/annotating/stddev_color_16/Makefile.am: New. + * demo/annotating/stddev_color_16/stddev_color_16.cc: New. + * exp/annotating/achromastism/Makefile.am: New. + * exp/annotating/achromastism/achromastism.cc: New. + * exp/annotating/achromastism/text-color.txt: New. + * exp/annotating/achromastism/text-img.txt: New. + * exp/annotating/achromastism/text-only.txt: New. + * exp/annotating/bench/Makefile.am: New. + * exp/annotating/bench/bench.cc: New. + * exp/annotating/error/Makefile.am: New. + * exp/annotating/error/error.cc: New. + * exp/annotating/histo/Makefile.am: New. + * exp/annotating/histo/histo.cc: New. + * exp/annotating/hsv/Makefile.am: New. + * exp/annotating/hsv/hsv.cc: New. + * exp/annotating/hue/Makefile.am: New. + * exp/annotating/hue/hue.cc: New. + * exp/annotating/hue/text-color.txt: New. + * exp/annotating/hue/text-img.txt: New. + * exp/annotating/hue/text-only.txt: New. + * exp/annotating/nb_color/Makefile.am: New. + * exp/annotating/nb_color/nb_color.cc: New. + * exp/annotating/saturation/Makefile.am: New. + * exp/annotating/saturation/saturation.cc: New. + * exp/annotating/saturation/text-color.txt: New. + * exp/annotating/saturation/text-img.txt: New. + * exp/annotating/saturation/text-only.txt: New. + * exp/annotating/stddev_color/Makefile.am: New. + * exp/annotating/stddev_color/stddev_color.cc: New. + * exp/annotating/stddev_color_16/Makefile.am: New. + * exp/annotating/stddev_color_16/stddev_color_16.cc: New. + * exp/annotating/value/Makefile.am: New. + * exp/annotating/value/text-color.txt: New. + * exp/annotating/value/text-img.txt: New. + * exp/annotating/value/text-only.txt: New. + * exp/annotating/value/value.cc: New. + * mln/clustering/kmean2d.hh: New. + * mln/fun/p2b/achromatic.hh: New. + * mln/fun/v2v/hue_concentration.hh: New. + * mln/fun/v2v/rgb_to_achromatism_map.hh: New. + * mln/fun/v2v/rgb_to_hsv.hh: New. + + 2010-07-07 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Import regional maxima sources from milena green's sandbox. diff --git a/scribo/sandbox/green/README.green b/scribo/sandbox/green/README.green index 7886914..5d6e147 100644 --- a/scribo/sandbox/green/README.green +++ b/scribo/sandbox/green/README.green @@ -685,21 +685,6 @@ rechercher dans les derniers répertoires mp00042c et mp00307c. * doc/labeling/mp00307c: Test sur la representativité des couleurs trouvées. -Faire le changelog, il y a de l'aide dans git/doc/Changelog -... et commiter - - -XI ANNOTATING --------------- - -==> to do - - - -* doc/annotating - - - XI AUTRES ASPECTS DOCUMENTAIRES ------------------------------- @@ -732,3 +717,495 @@ l'effort. * doc/formulae: LaTex directory. * doc/quick_tour: LaTex directory. + +XII ANNOTATING +-------------- + +Tout d'abord, voici les notes documentaires qui ont été réalisées sur +la problématique d'annotation d'image. On trouvera un fichier +class.txt qui a pour but de poser quelques réflexions sur les types de +classes de document. Les informations ne sont pas abouties mais +permettent de défricher un peu le terrain. Le document +syntheseMillet2008 est un compte-rendu de lecture des parties +relatives à nos travaux dans la thèse de Millet. Pour bien comprendre +mon travail, il est impératif de lire les travaux de Millet, ou +simplement ce compte-rendu. Le dernier document est moins intéressant, +il s'agit d'une note de travail sur les indicateurs de Millet bruts de +fonderie (testMillet2008). Cette note n'est ni achevée, ni +aboutie. Elle conclue sur le fait que les indicateurs et les seuils +donnés par Millet dans sa thèse sont complètement à revoir pour nos +besoins. Notemment, des tests sur la détection des images par rapport +aux cliparts nous ont convaincu que certaines images AFP contiennent +des de grandes zones homogènes qui induisent des erreurs dans les +prédicteurs de Millet. Néanmoins, les cas particuliers qui +contredisent les aspects opérationnnels de Millet n'enlèvent pas sa +réflexion: Les images noir/blanc (ou monochromes) ont une très faible +saturation, et/ou une forte concentration de la teinte. Les cliparts +ont une forte concentration de niveaux de gris autour d'un pic. + +* doc/annotating: La documentation relative à l'annotation. + + +Après la lecture des descripteurs de Millet, un des premiers réflexe a +été d'en implémenter plusieurs pour voir ce qu'ils pouvaient ressortir +sur les images que nous avions. Le descripteur BIC sépare une image en +deux ensembles de pixels, les points intérieurs et les points +extérieurs. Les points intérieurs ont la propriété d'être de même +couleur que leur 4-voisins. Attention, la couleur est évaluée dans un +espace RGB à 3 bits. + +* demo/annotating/bic: Histogrammes des points intérieurs et extérieurs. + + +Le descripteur LEP, lui, propose de seuiller par sobel l'image et de +faire l'histogramme des configurations du voisinage des points +seuillés. Pour ce faire, on utilise une convolution un peu spéciale +qui va attribué un unique id en fonction du voisinage. + +* demo/annotating/lep: Histogramme des configurations des voisinages des pixels. + + +Un autre descripteur simple est le nombre de couleur dans une +image. Pour cela, il est possible de construire l'histogramme des +couleurs et de compter les cellules pleines. On peut éventuellement +appliquer une quantification sur l'espace des couleurs. La +compilation laisse place à d'étranges warnings sur une comparaison +entre entiers signés et non signés, mais je n'ai pas la main dans mon +code (ou je ne sais pas comment faire) pour enlever ces warnings. Ils +sont récents, je n'avais pas souvenir de les avoir eu. + +* demo/annotating/nb_color: Compte le nombre de couleurs dans une image. +* exp/annotating/nb_color: Adaptation pour fonctionner sur une base d'image. + + +L'histogramme RGB-64 est un descripteur simple qui quantifie les +couleurs sur 2 bits et réalise l'histogramme dans cet espace. C'est +bien sûr une classification gros grain, mais ajouté au reste ... La +version RGB-64-9 ajoute une phase de division de l'image en 9 sous +images. De cette manière, l'histogramme RGB-64 est construit sur les 9 +sous images. Pour former le descripteur final, on fusionne les neufs +histogrammes. + +* demo/annotating/rgb_64: Histogramme couleur dans l'espace RGB-64 (2 bits/axe). +* demo/annotating/rgb_64_9: Histogramme RGB-64 sur les 9 sous images. + + +Le descripteur de projection relaté par Millet est particulier. En +premier lieu l'image est sous échantillonnée pour réduire sa dimension +à 100 x 100 de manière à borner la taille des vecteurs obtenus au +final. Puis l'image est seuillée par sobel (threshold = 100). L'image +est d'abord divisée horizontalement en deux. Puis on établit la +projecton perpendiculairement à la séparation de manière à obtenir +deux vecteurs de 100 valeurs chacunes. On recommence l'opération en +divisant maintenant l'image verticalement. L'union des 4 vecteurs +forme le descripteur de projection. L'information condensée dans ces +vecteurs est simplement la répartition des contours de manière +horizontale ou verticale. + +* demo/annotating/project: Répartition des contours horizontaux et verticaux. + + +La reconnaissance des cliparts s'appuie sur une analyse d'histogramme +qui est fournie dans le code suivant. L'idée est de dire qu'une image +de type clipart va être reconnaissable surtout à l'aide de ces +contours. La couleur existe mais est très grossière. Le faitde +dessiner à la main implique de simplifier énormément, de caricaturer, +le remplissage. Du coup, une analyse en niveau de gris de +l'histogramme révèle très peu de nuances. Il peut cependant en avoir +un peu. Néanmoins, il existe des logiciels pour aider à la fabrication +des cliparts qui proposent l'usage de dégradé, ce qui nuit à cette +méthode de reconnaissance. Millet analyze l'histogramme normalisé +(histogramme divisé par son pic) et regarde si son energie ne serait +pas concentrée autour du pic (5 pixels de chaque côté +maximum). Parfois cette méthode ne fonctionne pas correctement sur des +photographies qui ont un cadre uniforme. La méthode trouve un pic (le +cadre) et vérifie alors qu'une proportion non négligeable des pixels +sont bien autour de ce pic (tout dépend de l'épaisseur du cadre). Pour +palier à cet inconvénient, Millet propose d'utiliser ce test, non plus +sur l'image entière, mais sur chacune des 16 sous images après un +découpage géométrique régulier. De facto, la contribution du cadre +diminue suffisemment pour repasser en dessous du seuil de +reconnaissance. + +* demo/annotating/stddev_color: Descripteur utilisé reconnaitre des cliparts. +* exp/annotating/stddev_color_16: Adaptation pour le travail sur base. +* demo/annotating/stddev_color_16: Descripteur pour cliparts avec 16 imagettes. +* exp/annotating/stddev_color: Adaptation pour le travail sur une base d'image. + + +A partir de maintenant, tous les morceaux de codes réalisés préparent +directement ou indirectement le résultat de la classification des +bases (exp/annotating/bench). + +Plus de temps pour faire le code use correspondant aux fichiers +librairies. Tout le code pour le faire est dans hsv. Rien de +compliqué, mais allons à l'essentiel. + +Le but du code HSV est d'effectuer les tests de Millet ou des +améliorations sur ces tests. Le premier test proposé par Millet est +l'achromaticité. Il s'agit de regarder s'il existe une faible +variation entre les trois canaux (R/G/B) pour un grand nombre de +pixels. Si c'est le cas, c'est que l'image est presque en niveau de +gris et peut être remplacée facilement par une image grisée sans trop +de distorsions au niveau de la couleur. Nous avons essayer de +généraliser un peu le test de manière à produire, non pas seulement +une réponse sur l'achromaticité de l'image, mais aussi avoir une vue +d'ensemble (sous forme d'image) des variations entre les cannaux pour +chaque pixel. Il n'est pas utile d'analyser les différences sur chacun +des canaux, prendre la différence absolue entre le canal min et le +canal max suffit pour définir le test d'achromaticité. + +Les autres tests de Millet sont la faible saturation et la dominance +de la teinte. Pour savoir si la saturation est faible, il faut +utiliser un histogramme du canal dédié à la saturation. Si 95% des +pixels sont en dessous de 100, alors l'image est faiblement saturée, +elle est en noir et blanc. De la même manière, on regarde la dominance +de la teinte. Pour ce faire, il faut voir si l'histogramme de la +teinte ne possède pas un pic avec une très faible variance (tout +rapproché autour du pic). Si c'est le cas, la dominance de la teinte +est avérée et l'on peut calculé la couleur dominante. L'image est en +niveau de gris, mais colorisé autour d'une couleur (par cepia, vert +...). + +Ce programme est très très sujet à changements, il m'a servit de test +et je ne peux pas juré qu'il est complètement sain au niveau du +traitement. D'autres versions sont potentiellement plus +stables. Notemment celle dans exp/annotating/hsv. + +* mln/fun/v2v/rgb_to_achromatism_map.hh : Distance pour l'achromaticité. +* mln/fun/v2v/achromatic.hh : Define the achromatic map. +* mln/fun/v2v/hue_concentration.hh : Define the distance hue/peak map. +* mln/fun/p2b/achromatic.hh : Say if a site is achromatic. +* demo/annotating/hsv: Code des différents tests de Millet. + + +Dans le répertoire exp/annotating/hue, on trouve 3 fichiers textes qui +rassemblent des classes d'images. Tout d'abord les images ICDAR +n'ayant que du texte et des traces de couleurs (lettrine de couleur, +trait, petit bandeau), un fichier où il n'y a que du texte noir & +blanc et un fichier contenant les images couleurs (avec photographies +ou dessins). Cette classification a été effectuée de manière manuelle. +Le code hue test la proportion de pixels étant autour du pic de +teinte. Il s'agit de savoir si la dominance d'une teinte est +avérée. Le code renvoit la proportion de pixels agglomérés autour du +pic. Le but est de généralisé les tests de millets pour qu'ils se +ressemblent le plus possible. + +* exp/annotating/hue: Implémentation de la généralisation du test de Millet. +* mln/fun/v2v/rgb_to_hue_map.hh : Construction de la map de teinte. + + +On retrouve les trois fichiers permettant de classifier la base ICDAR +en trois sous populations. Le test de saturation consiste simplement à +regarder si une certaine quantité de la population de l'histogramme de +saturation est en dessous d'un certain seuil. La généralisation du +test ne porte pas sur le test en lui-même, mais sur la forme dans +lequel le test est fait. + +* exp/annotating/saturation: Implémentation de la généralisation du test. +* mln/fun/v2v/rgb_to_saturation_map.hh : Construction de la map de saturation. + + +Le test de value a déjà été décrit précédemment dans +stddev_color. L'idée est toujours la même, mais cette fois il est +effectué dans l'espace des valeurs (HSV). Cela ne change pas grand +chose, puisqu'il était utilisé sur des images en niveau de gris. C'est +l'un des tests importants car il réagit à la différentiation entre une +image type photographie et une image plus stylisée comme un +clipart. Ce test a aussi des vertus pour la distinction entre du noir +& blanc et de la couleur. Il s'avère que les images type photographie +avec pleins de couleurs ont un histogramme moins sujet aux pics que +les histogrammes noir & blanc. De facto, l'énergie de l'histogramme +est distribué sur l'ensemble de la plage contrairement aux images noir +& blanc où il y a une concentration de chaque côté de l'histogramme +(bipolarité). + +* exp/annotating/value: Implémentation de la généralisation du test de Millet. +* mln/fun/v2v/rgb_to_value_map.hh : Transformation d'espace. + + +Le programme hsv reprend les tests préalablement élaborés auparavant +sur les plans H, S puis V. Il combine tout en un seul programme pour +avoir une vision plus synthétique de ce qui se passe sur les 3 espaces +simultanément. + +Les tests incorpore mes transformations. C'est à dire que l'on +effectue une série de test équivalent à ceux de Millet (au moins dans +l'idée et le plus souvent, il s'agit d'une réecriture sous une autre +forme) en partant des histogrammes normalisés. Un histogramme +normalisé est un histogramme de flottant qui contient l'histogramme +classique divisé par le nombre total de pixels. En faisant cela, on se +déplace dans l'espace des distributions. Si l'histogramme n'a qu'un +seul pic et que ce dernier contient tous les pixels (pic de dirac par +exemple), alors l'histogramme normalisé donnera 1 comme valeur de +proportion à ce pic. En fait, l'intégrale d'une distribution vaut +1. Le but des distributions est de s'affranchir des caractéristiques +de taille de l'image, ce qui permet de comparer les histogrammes entre +eux. On peut ainsi comparer les histogrammes d'une photo AFP petit +format avec celui d'une image grand format de la base ICDAR. Le but du +jeu est de garder les idées de Millet dans ce nouvel espace. Les tests +obtenus sont équivalents mais pas identiques. + +Pour la teinte, une fois l'histogramme normalisé obtenu, on cherche à +savoir s'il existe un pic avec une forte proportion de l'histogramme +autour de ce pic. Plutôt que le pic, nous prenons la moyenne qui est +un opérateur un peu plus robuste et nous calculons la proportion de +l'histogramme autour (un seuil donné en paramètre défini le périmètre +du calcul). L'expression sous forme de proportion plutôt que sous la +forme d'une variance rend le test homogène avec les autres tests. + +Pour la saturation, après normalisation, on regarde la proportion de +l'histogramme en dessous d'un certain seuil. Il n'y a pas de +changement significatif par rapport au test de Millet. Le seuil +initialement proposé par Millet était 100, il a été adapté car nos +bases sont différentes des siennes (le notre est à 25). + +Le test sur les valeurs (cliparts contre photos) a vraiment un intérêt +au delà de ce cas d'utilisation. Il nous renseigne sur la +concentration autour d'un pic de l'histogramme ou non. Nous préferrons +la mesure de similarité à l'équi-répartition des densités, mais l'idée +est exactement la même. Est-ce que notre histogramme est plat ou +inversement, est-ce que les contributions sont rassemblées autour d'un +pic? Si une image est noir et blanc, il existera un pic correspondant +au fond et la densité s'éloignera fortement de la distribution +équiprobable. Dans le cas maintenant d'une image couleur, la +répartition est plus homogène, couvrant un large spectre dans le +domaine de l'histogramme. Du coup, la distribution semblera davantage +equi-répartie. On notera que nous prenons le test à l'envers de ce que +propose Millet. Il essaye de voir si il y a un pic et calcule une +forme de contribution normalisée autour de ce pic. Nous au contraire, +on regarde l'absence de pic et on calcule la différence entre la +densité et cette absence de pic. Notre avantage par rapport à Millet +est démontré particulièrement dans les cas où il existe plusieurs +grosses distributions. A contrario, notre test souffre des cas où il +existe de nombreuses petites distributions. + + +Voici un premier retour sur les expériementations: +La discrimination entre la base AFP et la base ICDAR peut se faire en +étudiant la forme des densités des niveaux de gris. Les images +naturelles semblent avoir un spectre recouvrant en général les 256 +niveaux de gris alors que les images de documents ont une présence +importante du fond. Dans le cadre d'une densité, ce qui est alloué sur +le fond ne peut se retrouver ailleurs. Une comparaison avec la densité +équiprobable nous renseigne donc sur la nature des images. Il semble +néanmoins qu'un certain nombre d'images défient ce dispositif. Par +exemple des gros plans sur des zones mono-teintée (ski, voile,site +web). + +* exp/annotating/hsv: Code unifiant les trois tests sur chacun des plans HSV. +* mln/fun/v2v/rgb_to_hue_map.hh : Transformation d'espace. +* mln/fun/v2v/rgb_to_saturation_map.hh : Transformation d'espace. +* mln/fun/v2v/rgb_to_value_map.hh : Transformation d'espace. + + +Le test sur l'achromatisme des images est décrit dans le code +suivant. Il a été purement et simplement abandonné dans la mesure où +c'est un cas très particulier qui est repris par une saturation très +faible. La saturation s'exprime comme 1 - min(channel)/max(channel), +mais dans le cas où le min(channel) == max(channel), la saturation +vaut 0. Le problème vient plutôt du calcul de la teine qui ne peut pas +admettre que le min soit égal au max. Pour ce calcul, on se débrouille +pour gérer le cas et renvoyer une valeur standardisée, Millet +proposait -1. + +* exp/annotating/achromatism: Détection d'image couleur en niveau de gris. +* mln/fun/v2v/rgb_to_achromatism_map.hh : Transformation d'espace. + + +Ce programme a pour but de créer les histogrammes normalisés en rafale +pour les bases AFP et ICDAR. Il assure leur création dans les six +plans possibles R,G,B,H,S,V. De cette manière il est possible de +vérifier les corrélations éventuelles entre le canal B et G. Par +ailleurs, après visionnement de tous les histogrammes, on note des +spécificités dans les deux bases sur les plans S et V. Certaines +images ont des réactions très fortes sur le plan H, mais ce n'est pas +une caractéristique pour une base (seulement pour ces images en +question). Les résultats ont déjà été sauvegardés dans le répertoire +image du LRDE. Des infos sont notés à ce sujet dans README.result. Le +seul but de ce calcul est de maîtriser csujet dans README.result. Le +seul but de ce calcul est de maîtriser ce qui se passe dans ces +espaces, pas simplement de supposer ce qu'il pourrait s'y passer. La +comparaison des images est rendu possible quelque soit la base +d'origine par le fait qu'elles sont normalisées. Chaque fréquence des +histogrammes est divisée par la somme des fréquences. On obtient donc +une version discrète des densitées. La somme des nouvelles fréquences +vaut 1. On voit davantage si les densités se rapprochent d'une +équi-répartition ou non. La négative implique un ou plusieurs pics. Le +cas défavorable dans mon approche (qui existe pour certaines images) +est une multitude de tout petits pics autour de l'équi-répartition. Ce +n'est pas équi-répartie pour les calculs mais ce n'est clairement pas +la manisfestation d'une concentration de l'énergie quelque part. + +* exp/annotating/histo: Creation des histogrammes normalisés. +* mln/fun/v2v/rgb_to_hue_map.hh: Transformation d'espace. +* mln/fun/v2v/rgb_to_saturation_map.hh: Transformation d'espace. +* mln/fun/v2v/rgb_to_value_map.hh: Transformation d'espace. + + +Dans le programme erreur, on teste l'idée de jonathan. Une référence à +ce sujet peut être trouvée dans README.img. Les bases AFP et ICDAR +sont retravaillées à l'aide de ImageMagick et de Gimp pour diminuer le +nombre de couleurs initiales. On oblige à n'avoir que 30, 20 ou 10 +couleurs pour chaque base. Il y a donc 4 versions de chacune des bases +en comptant la version originale des images. Les algorithmes utilisés +par Gimp et ImageMagick sont très différents. L'idée de Jonathan était +de dire que lorsqu'une image est couleur, plus on réduit le nombre de +couleur, plus elle change vis-à-vis de l'image originale. Inversement, +si une image possède peu de couleurs (noir & blanc), sa dégradation ne +l'altérera pas tant que cela. Prenons une image d'un skieur de la base +AFP, en réduisant le nombre de couleurs, la combinaison marron va +devenir marron strictement homogène et la perception que nous en avons +est visuellement très altérée. A contrario, une image en noir & blanc +ne semble pas bouger d'un pouce. La comparaison avec la discrimination +sur la saturation ou la valeur montrera des résultats un peu meilleur +plus tard mais il impossible de préjuger a priori des résultats +futurs. L'erreur entre l'image initiale et l'image dégradée est +calculée avec le PNSNR (compression p278 Handbook Color). Le programme +calcule la dégradation et essaie de trouver automatiquement des seuils +de séparation pour les deux bases. 4 détection de seuils sont testées. +Ces seuils sont calculées avec deux bases représentatives des bases à +discriminer. En mode production, le seuil est vu comme une constante +pour le programme. Les deux premières classifications renvoient un +seuil calculée comme une moyenne pondérée des moyennes des populations +à discriminer. Le premier détecteur pondère par la déviation standard +et le second par la variance. Le troisième simule deux populations +gaussiennes de variances différentes et résoud l'équation de second +degré qui en résulte. Enfin, le dernier test ne préjuge pas des +distributions statistiques et réalise la minimisation de l'erreur de +classification. Pour cela, il suffit de compter le nombre d'images +bien classées et mal classées sur les bases d'apprentissage des +populations. On utilise pour cela la méthode Otsu. Le détecteur PNSRN +renvoie une valeur. Pour chaque population (AFP, ICDAR), on construit +l'histogramme de des valeurs obtenues. A noter, qu'il faut que les +valeurs du PNSNR soient ramenées entre 0 et 255. En pratique ce n'est +pas un problème, mais il faut le mettre en oeuvre, dans mon code, ce +n'est pas fait (les valeurs ne posaient pas de problème). Donc, pour +chaque seuil possible (de 0 à 256), on étudie la matrice de classement +(groupe 1 - détecté groupe 1, groupe 1 - détecté groupe 2, groupe 2 - +détecté groupe 1, groupe 2 - détecté groupe 2). L'erreur est +simplement la somme des quantités groupe x - détecté groupe y avec x +!= y. Finalement, pour chaque seuil, il est possible de connaitre +l'erreur. Le processus de minimisation de l'erreur revient à chercher +le seuil associé à l'erreur minimale. C'est ce dernier test que nous +préconisons. Il faut mettre en lumière que ces quatre tests renvoient +à peu près les mêmes seuils. La minimisation de l'erreur étant le +meilleur de tous puisqu'il minimise directement l'erreur de +classification et c'est ce que nous cherchions en fin de compte. Un +cinquième test a été fait avec une analyse de fisher sur l'histogramme +des populations mélangées, mais c'est une idée saugrenue car nous +disposons à ce niveau des populations séparées et évidemment les +résultats sont moins bons. + +Petit rappel. Un vieux problème a été mis à jour ici. Lorsqu'on +calcule des informations sur un histogramme, le type temporaire qui +contient les résultats ne doit pas être le type qui encode les valeurs +des résultats. Par exemple, un histogramme de byte, un calcul de +variance tout simple où il faut stocker la valeur des pixels au carré +multiplié par leur occurrence tiendra facilement dans un long mais pas +dans un byte. Le sucre avec les acesseurs des itérateurs tend à +mélanger les genres. Une solution simple est de stocker la valeur de +l'histogramme dans un type pouvant effectuer les calculs et ensuite +retravailler avec ce type. Nous avions déjà discuté de ce problème et +je ne sais pas si tu avais pu corriger le problème. Si la +classification renvoit n'importe quoi, il se peut que cela provienne +de ce problème. Le problème ne se voit pas sur des calculs de +moyennes, il ne s'observe que sur certains calculs de variance (dépend +de la formule utilisée). + +* exp/annotating/error: Test de l'idée de jonathan (dégradation des couleurs) + + +Le travail dans bench.cc reprend toute sorte de travaux déjà réalisés +dans d'autres fichiers. Il a pour but de comparer ensemble tous les +descripteurs pour la reconnaissance de base de données entre l'AFP et +l'ICDAR. + +Le travail commence avec un certain nombres de routines travaillant +sur les histogrammes permettant de trouver le pic, la moyenne, la +variance et d'autres éléments. Les routines étaient éparpillées dans +le code, du coup je les ai regroupées au même endroit. Il y a des +redondances de code correspondant à des copier/coller ou à différents +essais. Prendre les versions les plus génériques et en faire des +accumulateurs serait un riche idée. + +Huit détecteurs sont comparés ensembles: +- hue1, détection d'un pic de teinte très dense par la méthode de Millet. +- sat1, détection d'une densité importante dans les saturations basses (Millet). +- lvl0, comptage du nombre de niveau de gris (idée de Millet). +- hue0, détection d'un pic de teinte par la méthode des densités. +- sat0, détection d'une forte basse saturation par la méthode des densités. +- val0, détection d'un pic de niveau de gris par la méthode des densités. +- val1, détection d'un pic de niveau de gris par la méthode de Millet. +- err, PNSNR (idée de jonathan). + +FIXME: Attention, le PNSNR n'est pas borné, il faut le faire, il doit avoir +au maximum 255 comme valeur. + +Tous les détecteurs ont été expliqués en large, en long et en travers +dans les sources précédentes. LIRE le chapitre XII (ANNOTATING) de ce fichier +en entier. + +Ensuite vient les séparateurs de population statistiques qui ont été +déjà introduit dans le fichier error. Bien que tous soient présent, +c'est la minimisation de l'erreur qui est utilisée. + +Enfin vient le front end, l'un des plus complexes que j'ai écrit cette +année. Le main lui même, n'est pas la partie la plus complexe. Il +définit les actions fonctionnelles à réaliser et la structure de +donnée réalisée pour garder la trace de tous les résultats. +- File_name : Cette variable retient le nom des fichiers. C'est un tableau +de la taille du nombre d'images rencontrées toute base confondue. Comme on +ne parcourt qu'une fois les répertoires, il faut pouvoir retenir cette +information qui servira éventuellement pour la nomenclature des dumps +par la suite. +- Result : Cette variable contient toutes les informations du traitement. +C'est un tableau à deux dimensions, une pour les images et une autre pour +les descripteurs. L'index d'image est le même que pour file_name. +- Size : Cette variable contient le nombre d'images par database. +- Threshold : Cette variable va stocker les seuils calculés sur chacun +des descripteurs. Ces seuils sont sensés effectuer la séparation entre les +deux bases. +- Cxx : variables de comptage des images bien ou mal classées relativement +à la position des seuils et à l'appartenance d'origine des images. Cette +appartenance d'origine est calculée à l'aide Size qui contient le nombre +d'image par base de données. Comme les images sont vues dans l'ordre, les X +premières appartiennent à la base ICDAR et les Y suivantes à la base AFP. +- histo : Variable servant à effectuer par database et par descripteur un +histogramme utilisé ensuite lors de la discrimination des bases. + +La partie compute_descriptors a pour mission de passer une fois sur +toutes les images et de calculer les descripteurs associés. A l'issue +de cette passe file_name, result et size sont remplis et pourront être +utilisés par les autres routines. + +La partie dump_descriptors a pour but de réaliser un tracé en gnuplot, +avec une couleur différente pour chaque base. Le graphe montre les +valeurs utilisées par chaque descripteur pour toutes les images de la +base. + +La partie correction des descripteurs est optionnelle et force un +certain nombre de valeurs pour être inférieures à 256. Le but était de +pouvoir travaillé, même si les descripteurs n'étaient pas complètement +opérationnels. Elle sert donc pour le debug. + +La partie calcul des histogrammes sert à obtenir la distribution des +valeurs de chacun des descripteurs en fonction des bases +utilisées. C'est une étape préliminaire à l'analyse des populations et +au calcul des seuils. + +La partie calcul des seuils repose sur la minimisation des erreurs de +classification. Cette méthode ne fonctionne bien si la classe zéro à +une moyenne inférieure à la classe un. Du coup, il n'est pas possible +de déterminer de manière simple si la classe 0 correspond à l'AFP ou à +l'ICDAR. Pour chaque descripteur, les cartes sont rebattues. + +Enfin, tous les histogrammes sont sauvés pour pouvoir comprendre et +visualiser les résultats. + +Le main2 ne sert à rien, juste pour des essais. + +Attention, modification VAL0/VAL1, vérifiez que le bon descripteur est +au bon endroit dans le tableau. + +* exp/annotating/bench: Comparaison des détecteurs pour la classif. ICDAR/AFP. \ No newline at end of file diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/bic/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/bic/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/bic/bic.cc b/scribo/sandbox/green/demo/annotating/bic/bic.cc new file mode 100644 index 0000000..f63d260 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/bic/bic.cc @@ -0,0 +1,122 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet BIC descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It quantifies the three channels on 3 +/// bits, then builds two histograms with the interior pixels and the +/// exterior ones. The interior pixels are those where color value is +/// the same as their 4 neighbouring pixels. The descriptor is the +/// fusion of the two histograms. + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/image/image2d.hh> +#include <mln/core/alias/neighb2d.hh> +#include <mln/core/routine/initialize.hh> +#include <mln/core/var.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/transform.hh> +#include <mln/data/fill.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/colors.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Main entry. +/// +/// Load the images, compute the interior and the exterior with the +/// predicate, then compute the two histograms separately. +int main() +{ + typedef mln::value::rgb<3> t_rgb3; + typedef mln::value::rgb8 t_rgb8; + typedef mln::neighb2d t_neighb2d; + typedef mln::image2d<t_rgb3> t_image2d_rgb3; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<3> t_rgb8_to_rgb3; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb3 input_rgb3; + t_image2d_rgb3 interior_rgb3; + t_image2d_rgb3 exterior_rgb3; + t_histo3d histo_exterior; + t_histo3d histo_interior; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + input_rgb3 = mln::data::transform(input_rgb8, t_rgb8_to_rgb3()); + + mln::initialize(interior_rgb3, input_rgb3); + mln::initialize(exterior_rgb3, input_rgb3); + + mln::data::fill(interior_rgb3, mln::literal::black); + mln::data::fill(exterior_rgb3, mln::literal::black); + + mln_piter_(t_image2d_rgb3) p(input_rgb3.domain()); + mln_niter_(t_neighb2d) n(mln::c4(), p); + + for_all(p) + { + bool is_interior = true; + + for_all(n) + is_interior = is_interior && (input_rgb3(p) == input_rgb3(n)); + + if (is_interior) + interior_rgb3(p) = input_rgb3(p); + else + exterior_rgb3(p) = input_rgb3(p); + } + + histo_interior = mln::data::compute(t_histo3d_fun(), interior_rgb3); + histo_exterior = mln::data::compute(t_histo3d_fun(), exterior_rgb3); + + //mln::io::plot::save_image_sh(histo, "histo.sh"); + + // PRINTING PHASE + mln::debug::println(histo_interior); + mln::debug::println(histo_exterior); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/hsv/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/hsv/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/hsv/hsv.cc b/scribo/sandbox/green/demo/annotating/hsv/hsv.cc new file mode 100644 index 0000000..e87ab2d --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/hsv/hsv.cc @@ -0,0 +1,721 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet HSV operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to HSV +/// one. Formulae are classical, we can find them on the web. +/// +// Val = max(R,G,B). +// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +/// +/// \fixme: This code is unstable. It was using to make a lot of tests. +/// Don't use it, we have more stable version of it. +#include <iostream> +#include <fstream> + +#include <mln/accu/max_site.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> +#include <mln/core/concept/image.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> + +#include <mln/debug/println.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/fun/v2v/rgb_to_hsv.hh> +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> +#include <mln/fun/v2v/achromatism.hh> +#include <mln/fun/v2v/hue_concentration.hh> +#include <mln/fun/p2b/component_equals.hh> +#include <mln/fun/p2b/achromatic.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/img_path.hh> + +#include <mln/io/plot/save_image_sh.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/pw/cst.hh> +#include <mln/pw/value.hh> +//#include <mln/trace/quiet.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> +#include <mln/value/hsv.hh> + + +/// \brief Classify a rgb a value with a color of reference. +/// +/// \param[in] rgb a rgb8 value. +/// +/// \return a rgb8 initialize with the selecting literal. +/// +/// This classification of the RGB color space is based on the +/// Millet's work. It uses the hue wheel to do it and make new +/// distinction as brown/orange for instance. +mln::value::rgb8 label_color(const mln::value::rgb8 rgb) +{ + mln::value::hsv_f hsv = mln::fun::v2v::f_rgb_to_hsv_f(rgb); + + mln::value::rgb8 result; + + // Is it a gray level ? + if (0 == hsv.sat()) + { + // which result one ? + if (82 > hsv.sat()) + result = mln::literal::black; + else if (179 > hsv.sat()) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + } + // Is it a true result color ? + else if (14 > hsv.hue()) + result = mln::literal::red; + else if (29 > hsv.hue()) + { + // Is is brown or orange ? + unsigned dist_orange = mln::math::abs(hsv.sat() - 184) + + mln::math::abs(hsv.val() - 65); + + unsigned dist_brown = mln::math::abs(hsv.sat() - 255) + + mln::math::abs(hsv.val() - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else if (45 > hsv.hue()) + { + // Is it green or yellow ? + if (80 > hsv.val()) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else if (113 > hsv.hue()) + result = mln::literal::green; + else if (149 > hsv.hue()) + result = mln::literal::cyan; + else if (205 > hsv.hue()) + result = mln::literal::blue; + else if (235 > hsv.hue()) + result = mln::literal::violet; + else if (242 > hsv.hue()) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +// unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, +// const char *name_histo, +// const char *name_image) +// { +// typedef mln::point1d t_point1d; +// typedef mln::value::rgb8 t_rgb8; +// typedef mln::value::int_u8 t_int_u8; +// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +// typedef mln::image2d<t_int_u8> t_image2d_int_u8; +// typedef mln::image1d<unsigned> t_histo1d; +// typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; +// typedef mln::accu::meta::stat::histo1d t_histo1d_fun; +// typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + +// t_histo1d histo; + +// std::cout << "histo : " << name_histo << std::endl; +// std::cout << "image : " << name_image << std::endl; + +// histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + +// mln::io::pgm::save(input_int_u8, name_image); +// mln::io::plot::save_image_sh(histo, name_histo); +// mln::debug::println(histo); + +// // Find the peak of the histogram +// unsigned v_max = mln::opt::at(histo, 0); +// short p_max = 0; + +// mln_piter_(t_histo1d) p(histo.domain()); + +// for_all(p) +// { +// if (v_max < histo(p)) +// { +// v_max = histo(p); +// p_max = p.ind(); +// } +// } + +// // Compute the specific stddev + +// float stddev_low = 0.0; +// float stddev_up = 0.0; +// float stddev = 0.0; + +// if (250 > p_max) +// for (short i = p_max+1; i < p_max+6; ++i) +// stddev_up += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// if (5 < p_max) +// for (short i = p_max-1; i > p_max-6; --i) +// stddev_low += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : +// (stddev_low + stddev_up)/2; + +// std::cout << "max_site : " << p_max << std::endl; +// std::cout << "h(max_site) : " << v_max << std::endl; +// std::cout << "stddev_up : " << stddev_up << std::endl; +// std::cout << "stddev_low : " << stddev_low << std::endl; +// std::cout << "stddev : " << stddev << std::endl; + +// return 0; +// } + + +// ------------------------------------- +// input image <name>.ppm +// map <name>-<map>.pgm +// thresholded map <name>-<map>.pbm +// histogram <name>-<map>.sh +// decision <name>-<map>.txt +// ------------------------------------- +// Achromatism <name>-achromatism.pgm +// call achromatism(input_rgb8, 7, 99.0) + + +/// \brief Decide if an image is achromatic or not [Millet]. +/// +/// \param[in] input_rgb8 the input image. +/// \param[in] threshold the distance threshold used for equality. +/// \param[in] percentage the percentage of pixels that very the test. +/// +/// This is an improving of the Millet test. The original test +/// compares rgb values to each other and look at differences greater +/// than the threshold. If the number of pixel that pass the test are +/// greater than 99.0, then the image is declared achromatic. In fact, +/// there is few variations between the three channels, so we can say +/// that it is like a grey image. We can with no too distortions +/// replace the color image by the grey one. The improving is in first +/// creating a map of the difference. As we can't keep the free +/// differences between channels, we look at reducing the test and we +/// find an equivalent one based on the difference between minima +/// channel value and the maxima channel value. After the map is +/// ready, we make binarization with the threshold. Then we compute +/// the histogram 1d for every pixels of the map that are greater the +/// threshold. Then, we count pixels in the histogram and traduce the +/// count in percentage to compare to the second threshold. Details +/// are saved in files and printed in the screen. +void achromatism(mln::image2d<mln::value::rgb8> input_rgb8, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::int_u8> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + view = mln::data::stretch(mln::value::int_u8(), map); + mask = mln::binarization::threshold(map, threshold); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + map | (mln::pw::value(mask) == true)); + cnt1 = count_histo(histo); + cnt2 = mln::geom::nsites(input_rgb8); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + + std::ofstream txt_stream("achromatism.txt"); + txt_stream << "Achromatism" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + txt_stream << "Percentage : " << prop << std::endl; + txt_stream << "Image achromatique : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "achromatism.pgm"); + mln::io::plot::save_image_sh(histo, "achromatism.sh"); + mln::io::pbm::save(mask, "achromatism.pbm"); +} + + +/// \brief Decide if an image is low saturate or not and so b/w [Millet]. +/// +/// \param[in] input_hsvf the input image in the HSV space. +/// \param[in] achromatism_mask the mask to prevent computing bad saturation. +/// \param[in] threshold the distance threshold used for equality. +/// \param[in] percentage the percentage of pixels that very the test. +/// +/// As we are in the HSV space, we can just isolate saturation +/// channel. The original idea of Millet is to build histogram bound +/// to 256 for the saturation. If a great percentage (95%) of the +/// population are under a threshold (100), then the image has got low +/// saturation. So, it is b/w image. Low saturation is sometimes +/// redundant with the achromatism test but not every time. + +// call low_saturation(input_rgb8, achromatism_mask, 100, 95.0) +void low_saturation(mln::image2d<mln::value::hsv_f> input_hsvf, + mln::image2d<bool> achromatism_mask, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + + mln::image2d<t_sat> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_hsvf, t_component_s()); + view = mln::data::stretch(mln::value::int_u8(), map); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + view|(mln::pw::value(achromatism_mask) == true)); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0),mln::point1d(100))); + cnt2= mln::geom::nsites(achromatism_mask| + (mln::pw::value(achromatism_mask)==false)); + + std::ofstream txt_stream("saturation.txt"); + txt_stream << "Saturation" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre p faiblement saturés : " << cnt1 << std::endl; + txt_stream << "Pourcentage : " << prop << std::endl; + txt_stream << "Image faiblement saturé : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "saturation.pgm"); + mln::io::plot::save_image_sh(histo, "saturation.sh"); + mln::io::pbm::save(mask, "saturation.pbm"); +} + +/* This is a classification of the ICDAR base in 3 modalities. +// COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00032c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00042c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00076c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00142c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00215c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00228c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00234c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00248c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00252c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00253c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00255c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00259c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00271c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00290c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00293c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00304c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00307c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00376c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00411c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00419c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00447c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00498c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00510c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00550c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00573c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00589c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00592c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00597c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00599c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00600c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00031c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00034c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00043c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00063c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00065c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00072c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00081c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00083c_20p.ppm"); + +// BLACK AND WHITE + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00329c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00036c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00037c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00039c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00040c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00049c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00055c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00057c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00068c_20p.ppm"); + + +// A LITTLE BIT OF COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00262c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00263c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00311c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00319c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00440c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00608c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00630c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00631c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00028c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00046c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00073c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00089c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00090c_20p.ppm"); +*/ + + +/// \brief The main entry and the whole processing routine +/// +/// This is the routine which works on Millet test. First compute the +/// achromatic mask. Then build the transfer between RGB and HSV. Then +/// isolate each channel to work on it. Each separate channel is bound +/// between 0 and 255. Then keep only pixels low saturated (< 100 in S +/// canal) in a specefic map. Build the 3 histograms without the +/// achromatic pixels based on the 3 separated channels (H/S/V). Build the +/// 3 maps (hue_concentration, low_saturation and achromatism). Then do +/// the 3 tests of Millet with the thow thresolded pass (threshold,percentage). +int main() +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::h_type t_hue; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::value::hsv_f::v_type t_val; + typedef mln::image2d<t_hue> t_image2d_hue; + typedef mln::image2d<t_sat> t_image2d_sat; + typedef mln::image2d<t_val> t_image2d_val; + typedef mln::image2d<t_hsvf> t_image2d_hsvf; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<bool> t_mask; + typedef mln::fun::v2v::f_rgb_to_hsv_f_t t_rgb8_to_hsv; + typedef mln::accu::math::count<t_hsvf> t_count; + typedef mln::fun::v2v::component<t_hsvf,0> t_component_h; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + typedef mln::fun::v2v::component<t_hsvf,2> t_component_v; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,0> t_component_eq0; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,1> t_component_eq1; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,2> t_component_eq2; + typedef mln::fun::p2b::achromatic<t_rgb8> t_achromatic; + + t_image2d_rgb8 input_rgb8; + t_image2d_hsvf input_hsvf; + + t_mask achromatic; + t_mask low_saturation; + + t_image2d_float achromatism1; + t_image2d_int_u8 achromatism2; + t_image2d_float hue_concentration1; + t_image2d_int_u8 hue_concentration2; + + t_image2d_hue input_h; + t_image2d_hue input_h2; + t_image2d_sat input_s; + t_image2d_val input_v; + + t_image2d_int_u8 input_h8; + t_image2d_int_u8 input_s8; + t_image2d_int_u8 input_v8; + + t_histo1d histo_h; + t_histo1d histo_s; + t_histo1d histo_v; + + unsigned cnt1; + unsigned cnt2; + float percentage; + bool result; + + + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; +// mln::io::ppm::load(input_rgb8, ANNOTATING_1_BILL_IMG_PATH"/bill03.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + + +// achromatism(input_rgb8,7,99.0); +// exit(-1); + + // REPERAGE DES PIXELS ACHROMATICS + std::cout << "Init achromatic mask ..." << std::endl; + initialize(achromatic, input_rgb8); + mln::data::fill(achromatic, false); + mln::data::fill((achromatic | t_achromatic(input_rgb8, 0.03)).rw(), true); + + mln::io::pbm::save(achromatic, "achromatic.pbm"); + + std::cout << "Achieve canal forking ..." << std::endl; + input_hsvf = mln::data::transform(input_rgb8, t_rgb8_to_hsv()); + input_h = mln::data::transform(input_hsvf, t_component_h()); + input_s = mln::data::transform(input_hsvf, t_component_s()); + input_v = mln::data::transform(input_hsvf, t_component_v()); + + // quid of achromatic pixels ??? + input_h8 = mln::data::stretch(t_int_u8(), input_h); + input_s8 = mln::data::stretch(t_int_u8(), input_s); + input_v8 = mln::data::stretch(t_int_u8(), input_v); + + // IDENTIFY LOW SATURATED PIXELS + std::cout << "Init low saturation mask ..." << std::endl; + initialize(low_saturation, input_s8); + mln::data::fill(low_saturation, false); + mln::data::fill((low_saturation|(mln::pw::value(input_s8) < + mln::pw::cst(100u))).rw(), true); + + mln::io::pbm::save(low_saturation, "low_saturation.pbm"); + + std::cout << "Compute histograms ..." << std::endl; + histo_h = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_h8|(mln::pw::value(achromatic)==false)); + + histo_s = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_s8|(mln::pw::value(achromatic)==false)); + + histo_v = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_v8|(mln::pw::value(achromatic)==false)); + + + // Study the maps + hue_concentration1=mln::data::transform(input_h, + mln::fun::v2v::hue_concentration(histo_h)); + achromatism1=mln::data::transform(input_rgb8,mln::fun::v2v::achromatism()); + + hue_concentration2= mln::data::stretch(t_int_u8(), hue_concentration1); + achromatism2= mln::data::stretch(t_int_u8(), achromatism1); + + mln::io::pgm::save(achromatism2, "achromatism_map.pgm"); + mln::io::pgm::save(hue_concentration2, "hue_concentration_map.pgm"); + mln::io::pgm::save(input_s8, "saturation_map.pgm"); + +// cnt1 = mln::data::compute(t_count(), +// (input_hsvf|t_component_eq0(input_hsvf,-1)).rw()); + + + // (I) ACHROMATISM + std::cout << "Achromatism" << std::endl; + cnt1 = count_histo(histo_h); + cnt2 = mln::geom::nsites(input_h); + + percentage = (100.0 * (cnt2 - cnt1) / cnt2); + result = percentage > 99.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image achromatique : " << result << std::endl; + std::cout << std::endl; + + // (II) LOW SATURATION + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo_s | mln::box1d(mln::point1d(0),mln::point1d(100))); + + cnt2= mln::geom::nsites(achromatic | (mln::pw::value(achromatic)==false)); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre p faiblement saturés : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image faiblement saturé : " << result << std::endl; + std::cout << std::endl; + + // (III) HIGH HUE CONCENTRATION + mln::debug::println(histo_h); + unsigned peak = peak_histo(histo_h); + + cnt1 = count_histo(histo_h | mln::box1d(mln::point1d(peak-20), + mln::point1d(peak+20))); + + cnt2= count_histo(histo_h); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Position du pic : " << peak << std::endl; + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels proches pic : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image fortement teintée : " << result << std::endl; + std::cout << std::endl; + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/lep/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/lep/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/lep/lep.cc b/scribo/sandbox/green/demo/annotating/lep/lep.cc new file mode 100644 index 0000000..981a369 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/lep/lep.cc @@ -0,0 +1,127 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet LEP descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works on grey level image. First, +/// Sobel is applied on the image. Then a specific window is build to +/// be convolved on the image. The aim of the convolution is to mark +/// the configuration of the neighbouring for this thresholded image. +/// Histogram of the configuration is then created and that's all. + +#include <iostream> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/w_window2d_int.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/linear/ch_convolve.hh> +#include <mln/linear/convolve.hh> +#include <mln/linear/sobel_2d.hh> + +#include <mln/make/w_window2d.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> + +#include <mln/core/var.hh> + +/// \brief Main entry. +/// +/// Load the pgm image. Threshold it. Identifie the pixel +/// configuration with specific convolution filter. Build 1d histogram. + +int main() +{ + using namespace mln; + + typedef mln::w_window2d_int t_win2d; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::int_u<9> t_int_u9; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_int_u9> t_image2d_int_u9; + typedef mln::image2d<int> t_image2d_int; + typedef mln::image2d<unsigned> t_image2d_unsigned; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image2d<double> t_image2d_double; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + + t_image2d_int_u8 input_int_u8; + t_image2d_int input_int; + t_image2d_unsigned input_unsigned; + t_image2d_float sobel; + t_image2d_bool thresh_bool; + t_image2d_int_u8 thresh_int_u8; + t_image2d_float conf_float; + t_image2d_int_u9 conf_int_u9; + t_histo1d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::pgm::load(input_int_u8,ICDAR_50P_PGM_IMG_PATH"/mp00082c_50p.pgm"); + + sobel = mln::linear::sobel_2d_l1_norm(input_int_u8); + + //mln::io::plot::save_image_sh(sobel, "sobel.sh"); + thresh_bool = mln::binarization::threshold(sobel, 100); + thresh_int_u8 = mln::data::convert(mln::value::int_u8(), thresh_bool); + //mln::io::plot::save_image_sh(thresh, "thresh.sh"); + + int ws[] = { 1, 2, 4, + 8, 256, 16, + 32, 64, 128 }; + + t_win2d win2d = mln::make::w_window2d(ws); + conf_float = mln::linear::convolve(thresh_int_u8, win2d); + conf_int_u9 = mln::data::convert(t_int_u9(), conf_float); + histo = mln::data::compute(t_histo1d_fun(), conf_int_u9); + + mln::io::plot::save_image_sh(histo, "histo.sh"); + + // PRINTING PHASE + mln::debug::println(histo); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/nb_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/nb_color/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc b/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc new file mode 100644 index 0000000..acc64e9 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc @@ -0,0 +1,143 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Compute the number of colors in an image. +/// +/// Build histogram of colors and count the bins different from zero. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/math/sum.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo3d_rgb.hh> +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/variance.hh> + +#include <mln/algebra/vec.hh> + +#include <mln/arith/diff_abs.hh> + +#include <mln/core/macros.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/labeling/regional_maxima.hh> +#include <mln/labeling/mean_values.hh> +#include <mln/labeling/compute.hh> + +#include <mln/literal/colors.hh> + +#include <mln/morpho/opening/volume.hh> +#include <mln/morpho/elementary/dilation.hh> + +#include <mln/opt/at.hh> + +#include <mln/pw/cst.hh> + +#include <mln/util/array.hh> +#include <mln/util/timer.hh> + +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/rgb.hh> +#include <mln/value/int_u.hh> + +/// \brief Count the colors. +/// +/// \param[in] image the name of the image to process. +/// +/// \return the number of colors. +/// +/// Count the color by building histogram. Quantification is done to +/// reduce the size of the histogram. +/// +/// \fixme: Strange compilation warning, I don't know how to solve it! + +// n < 8, n is the degree of quantification +template <unsigned n> +unsigned count_image_color(const std::string& image) +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::meta::math::count t_count_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgbn input_rgbn; + t_image2d_rgbn output_rgbn; + t_histo3d histo; + t_histo3d opened; + + mln::io::ppm::load(input_rgb8, image.c_str()); + +// unsigned nb_pixel = input_rgb8.ncols() * input_rgb8.nrows(); +// unsigned min_volume = (unsigned)(nb_pixel * 0.054); + unsigned nb_color = 0; + + input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); + histo = mln::data::compute(t_histo3d_fun(), input_rgbn); + nb_color = mln::data::compute(t_count_fun(), + (histo | (mln::pw::value(histo) != 0)).rw()); + + return nb_color; +} + + +/// \brief Main entry. +/// +/// Print the number of colors. +int main() +{ + unsigned val=count_image_color<8>(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + + std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/project/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/project/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/project/project.cc b/scribo/sandbox/green/demo/annotating/project/project.cc new file mode 100644 index 0000000..4cab73c --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/project/project.cc @@ -0,0 +1,275 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet PROJECT descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works a grey level image. First, +/// we make subsampling of the image to the size 100x100. Then Sobel +/// is applied. After that, we divide the image in 2 sub-images with +/// horizontal or vertical splitting. Projection (summing along a +/// direction) is done in a way that preserves a vector of size +/// 100. Finally, it results 4 vector of size 100 which are concatened +/// to build the descriptor. + +#include <iostream> +#include <sstream> + +#include <mln/accu/image/init.hh> +#include <mln/accu/image/take.hh> +#include <mln/accu/image/to_result.hh> +#include <mln/accu/stat/mean.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/box2d.hh> +#include <mln/core/alias/point2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> +#include <mln/core/image/dmorph/unproject_image.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/paste.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/projection.hh> + +#include <mln/img_path.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/linear/sobel_2d.hh> + +#include <mln/opt/at.hh> + +/// \brief get the sub-image name. +/// +/// \param[in] base the common part of the name. +/// \param[in] i the column. +/// \param[in] j the line. +/// +/// \result the complete name of the sub-image. +/// +/// This function builds the complete name of a sub-image by making +/// explicit its position in the array. +const char *get_name(const char *base, const unsigned i, const unsigned j) +{ + std::ostringstream name; + + name << base; + name << i; + name << "_"; + name << j; + name << ".ppm"; + + return name.str().c_str(); +} + + +/// \brief Project row data. +/// +/// \param[in] img the binary image converted to u_int8. +/// +/// \ return a vector. +/// +/// This routine "eats" one dimension of the image by making a +/// projection. The projection sums the pixel along a specific +/// direction. It results a vector. +mln::image1d<mln::value::int_u8> +project_row(const mln::image2d<mln::value::int_u8>& img) +{ + typedef mln::accu::math::sum<mln::value::int_u8,mln::value::int_u8> t_sum; + typedef mln::fun::v2v::projection<mln::point2d,0> t_projection; + + mln::image1d<t_sum> img_accu(img.ncols()); + + mln::accu::image::init(img_accu); + + mln::accu::image::take(unproject(img_accu, + img.domain(), + t_projection()).rw(), + img); + + return mln::accu::image::to_result(img_accu); +} + + +/// \brief Project column data. +/// +/// \param[in] img the binary image converted to u_int8. +/// +/// \ return a vector. +/// +/// This routine "eats" one dimension of the image by making a +/// projection. The projection sums the pixel along a specific +/// direction. It results a vector. +mln::image1d<mln::value::int_u8> +project_col(const mln::image2d<mln::value::int_u8>& img) +{ + typedef mln::accu::math::sum<mln::value::int_u8,mln::value::int_u8> t_sum; + typedef mln::fun::v2v::projection<mln::point2d,1> t_projection; + + mln::image1d<t_sum> img_accu(img.nrows()); + + mln::accu::image::init(img_accu); + + mln::accu::image::take(unproject(img_accu, + img.domain(), + t_projection()).rw(), + img); + + return mln::accu::image::to_result(img_accu); +} + + +/// \brief The image processing routine. +/// +/// Describe the processing chain. First subsample the input image to +/// 100x100. This is done by splitting the image and then compute the +/// mean of the subimage. The result is a pixel of the subsample +/// image. Then apply Sobel and threshold. Then split in two +/// (vertically or horizontally), it results 4 images. Finally, make +/// the projection. +int main() +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::algebra::vec<3,unsigned> t_vec3u; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::accu::meta::stat::mean t_mean_fun; + typedef mln::accu::math::sum<t_int_u8,t_int_u8> t_sum; + typedef mln::image1d<t_int_u8> t_image1d_int_u8; + + t_image2d_int_u8 input_int_u8; + t_image2d_float sobel; + t_image2d_bool thresh_bool; + t_image2d_int_u8 thresh_int_u8; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::pgm::load(input_int_u8,ICDAR_50P_PGM_IMG_PATH"/mp00082c_50p.pgm"); + + + // IMAGE SPLITTING PHASE + t_image2d_int_u8 subimg_int_u8(mln::box2d(mln::point2d(0,0), + mln::point2d(100,100))); + + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 100; + unsigned sz_col = (pmax.col() - pmin.col())/ 100; + + std::cout << domain << std::endl; + + // Subsampling in 100x100 + // FIXME Test that is it a subsampling, not a upsampling! + for (unsigned i = 0; i < 100; ++i) + for (unsigned j = 0; j < 100; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_int_u8 input_part_int_u8(dom); + + mln::data::paste(input_int_u8 | dom, input_part_int_u8); + //mln::io::pgm::save(input_part_int_u8, get_name("output",i,j)); + float mean = mln::data::compute(t_mean_fun(), input_part_int_u8); + t_int_u8 val = static_cast<t_int_u8>(mean); + mln::opt::at(subimg_int_u8, i,j) = val; + } + + mln::io::pgm::save(subimg_int_u8, "subimg.pgm"); + + sobel = mln::linear::sobel_2d_l1_norm(subimg_int_u8); + thresh_bool = mln::binarization::threshold(sobel, 100); + thresh_int_u8 = mln::data::convert(t_int_u8(), thresh_bool); + + mln::io::pbm::save(thresh_bool, "subimg.pbm"); + + // Define img_up, img_down, img_left, img_right + t_image2d_int_u8 img_up(mln::box2d(mln::point2d(0,0), + mln::point2d(49,99))); + + mln::data::paste(thresh_int_u8 | img_up.domain(), img_up); + + + t_image2d_int_u8 img_down(mln::box2d(mln::point2d(50,0), + mln::point2d(99,99))); + + mln::data::paste(thresh_int_u8 | img_down.domain(), img_down); + + t_image2d_int_u8 img_left(mln::box2d(mln::point2d(0,0), + mln::point2d(99,49))); + + mln::data::paste(thresh_int_u8 | img_left.domain(), img_left); + + + t_image2d_int_u8 img_right(mln::box2d(mln::point2d(0,50), + mln::point2d(99,99))); + + mln::data::paste(thresh_int_u8 | img_right.domain(), img_right); + + + // Define project_up, project_down, project_left, project_right + t_image1d_int_u8 project_up = project_row(img_up); + t_image1d_int_u8 project_down = project_row(img_down); + t_image1d_int_u8 project_left = project_col(img_left); + t_image1d_int_u8 project_right = project_col(img_right); + + mln::io::pgm::save(img_up, "up.pgm"); + mln::io::pgm::save(img_down, "down.pgm"); + mln::io::pgm::save(img_left, "left.pgm"); + mln::io::pgm::save(img_right, "right.pgm"); + + // PRINTING PHASE + + std::cout << std::endl << "UP" << std::endl; + mln::debug::println(project_up); + + std::cout << std::endl << "DOWN" << std::endl; + mln::debug::println(project_down); + + std::cout << std::endl << "LEFT" << std::endl; + mln::debug::println(project_left); + + std::cout << std::endl << "RIGHT" << std::endl; + mln::debug::println(project_right); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/rgb_64/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/rgb_64/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc b/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc new file mode 100644 index 0000000..b49f979 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc @@ -0,0 +1,80 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet RGB-64 descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works by making 2 bits +/// quantification on each channel. It result a processing in RGB-64 +/// space. Then we build the color histogram. + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Main entry. +/// +/// Loading, Quantifiying in two bits each channel, then building +/// color histogram. +int main() +{ + typedef mln::fun::v2v::rgb8_to_rgbn<2> t_rgb8_to_rgb2; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<2> t_rgb2; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb2> t_image2d_rgb2; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb2 input_rgb2; + t_histo3d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + input_rgb2 = mln::data::transform(input_rgb8, t_rgb8_to_rgb2()); + histo = mln::data::compute(t_histo3d_fun(), input_rgb2); + + // PRINTING PHASE + mln::debug::println(histo); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/rgb_64_9/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/rgb_64_9/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc b/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc new file mode 100644 index 0000000..355cbfd --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc @@ -0,0 +1,132 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet RGB-64 descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works by making 2 bits +/// quantification on each channel. It result a processing in RGB-64 +/// space. Then we build the color histogram. + +#include <iostream> +#include <sstream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/alias/box2d.hh> +#include <mln/core/alias/point2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/compute.hh> +#include <mln/data/paste.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Get the sub image name. +/// +/// \return the name of the ith/jth sub image of the array. +const char *get_name(const char *base, const unsigned i, const unsigned j) +{ + std::ostringstream name; + + name << base; + name << i; + name << "_"; + name << j; + name << ".ppm"; + + return name.str().c_str(); +} + +/// \brief Same code as rgb-64 but divide the wall image in nine. +/// +/// Adding a splitting phase for image rgb-64 code reference. +int main() +{ + typedef mln::fun::v2v::rgb8_to_rgbn<2> t_rgb8_to_rgb2; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<2> t_rgb2; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb2> t_image2d_rgb2; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb2 input_rgb2; + t_histo3d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + + input_rgb2 = mln::data::transform(input_rgb8, t_rgb8_to_rgb2()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_rgb2.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 3; + unsigned sz_col = (pmax.col() - pmin.col())/ 3; + + std::cout << domain << std::endl; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 3; ++i) + for (unsigned j = 0; j < 3; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_rgb2 input_1o9_rgb2(dom); + + mln::data::paste(input_rgb2 | dom, input_1o9_rgb2); + mln::io::ppm::save(input_1o9_rgb2, get_name("output",i,j)); + + histo = mln::data::compute(t_histo3d_fun(), input_1o9_rgb2); + + // PRINTING PHASE + mln::debug::println(histo); + } + +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/stddev_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/stddev_color/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc b/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc new file mode 100644 index 0000000..549834f --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc @@ -0,0 +1,191 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +unsigned stddev_color(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + t_histo1d histo; + t_point1d max_site; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + mln::io::pgm::save(input_int_u8, "tmp.pgm"); + mln::io::plot::save_image_sh(histo, "histo.sh"); + mln::debug::println(histo); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + std::cout << "max_site : " << p_max << std::endl; + std::cout << "h(max_site) : " << v_max << std::endl; + std::cout << "stddev_up : " << stddev_up << std::endl; + std::cout << "stddev_low : " << stddev_low << std::endl; + std::cout << "stddev : " << stddev << std::endl; + + return 0; +} + + +/// \brief Main entry. +/// +/// Front end for image processing. +int main() +{ +// unsigned val = stdev_color(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + unsigned val = stddev_color(ANNOTATING_1_LOGO_IMG_PATH "/logo06.ppm"); + +// std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/stddev_color_16/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/stddev_color_16/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc b/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc new file mode 100644 index 0000000..98d94fd --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc @@ -0,0 +1,261 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. When photographies +/// has some large uniform border, the detection can fail. To improve +/// the method, Millet decide to subdivise the image in 16 sub images +/// on which it applies the test. +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> +#include <mln/data/paste.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] input_int_u8 the 8 bits input image to analyze. +/// \param[in] name_histo the name of the output histogram. +/// \param[in] name_image the name of the ouput sub image. +/// +/// \return the deviation, but at this time nothing.. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, + const char *name_histo, + const char *name_image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_histo1d histo; + + std::cout << "histo : " << name_histo << std::endl; + std::cout << "image : " << name_image << std::endl; + + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + mln::io::pgm::save(input_int_u8, name_image); + mln::io::plot::save_image_sh(histo, name_histo); + mln::debug::println(histo); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + std::cout << "max_site : " << p_max << std::endl; + std::cout << "h(max_site) : " << v_max << std::endl; + std::cout << "stddev_up : " << stddev_up << std::endl; + std::cout << "stddev_low : " << stddev_low << std::endl; + std::cout << "stddev : " << stddev << std::endl; + + return 0; +} + + +/// \brief Divide the image in 16 sub images. +/// +/// \param[in] image the input image. +/// +/// \result nothing. +/// +/// Divive the input image in 16 by uniform and geometrical +/// method. When a sub image is ready, call the stddev routine to show +/// stats on it. +unsigned stddev_color_16(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 4; + unsigned sz_col = (pmax.col() - pmin.col())/ 4; + + std::cout << domain << std::endl; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 4; ++i) + for (unsigned j = 0; j < 4; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_int_u8 input_1o16_int_u8(dom); + std::ostringstream name_histo(""); + std::ostringstream name_image(""); + + name_histo << "histo" << i << "_" << j << ".sh"; + name_image << "image" << i << "_" << j << ".ppm"; + + mln::data::paste(input_int_u8 | dom, input_1o16_int_u8); + + stddev_color(input_1o16_int_u8, + name_histo.str().c_str(), + name_image.str().c_str()); + } + + return 0; +} + + +/// \brief Main entry. +/// +/// Just a front end for image processing routine. +int main() +{ +// unsigned val = stddev_color_16(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + unsigned val = stddev_color_16(ANNOTATING_1_LOGO_IMG_PATH "/logo06.ppm"); + +// std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/achromastism/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/achromastism/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc b/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc new file mode 100644 index 0000000..3486b20 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc @@ -0,0 +1,179 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + + +/// \file +/// +/// \brief Implement the Millet achromatism operator [millet.phd.2008.pdf] +/// +/// An image is said to be achromatic when have the RGB channel which +/// are quite identicals. + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Decide if an image is achromatic or not [Millet]. +/// +/// \param[in] input the input image. +/// \param[in] output the histogram of the achromatic map. +/// \param[in] threshold the distance threshold used for equality. +/// +/// This is an improving of the Millet test. The original test +/// compares rgb values to each other and look at differences greater +/// than the threshold. If the number of pixel that pass the test are +/// greater than 99.0, then the image is declared achromatic. In fact, +/// there is few variations between the three channels, so we can say +/// that it is like a grey image. We can with no too distortions +/// replace the color image by the grey one. The improving is in first +/// creating a map of the difference. As we can't keep the free +/// differences between channels, we look at reducing the test and we +/// find an equivalent one based on the difference between minima +/// channel value and the maxima channel value. After the map is +/// ready, we make binarization with the threshold. Then we compute +/// the histogram 1d for every pixels of the map that are greater the +/// threshold. Then, we count pixels in the histogram and traduce the +/// count in percentage to compare to the second threshold. Details +/// are saved in files and printed in the screen. +float achromatism_test(const std::string input, + const std::string output, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + + return prop; +} + + +/// \brief The main entry. +/// +/// Deal with boost library for walking over image database +/// directories. Call the image processing routine and compute some +/// stats on the result. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + + prop = achromatism_test(dir_iter->path().string(), + output.string(), + 11); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/bench/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/bench/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/bench/bench.cc b/scribo/sandbox/green/exp/annotating/bench/bench.cc new file mode 100644 index 0000000..102a896 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/bench/bench.cc @@ -0,0 +1,1450 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// This file is the final work to differentiate between the two image +/// databases AFP and ICDAR. Several tests are used to show +/// improvement of detection. The jonathan's tests are put together to +/// compare them to the others (in fact, to the low saturation one). + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/algebra/vec.hh> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/times.hh> +#include <mln/arith/diff_abs.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/stretch.hh> +#include <mln/data/fill.hh> + +#include <mln/fun/v2v/component.hh> +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/ceil.hh> +#include <mln/math/floor.hh> + +#include <mln/opt/at.hh> + +#include <mln/trait/value_.hh> + +#include <mln/value/rgb8.hh> + + +#include <mln/value/int_u8.hh> + +/// Histogram part. +/// +/// We regroup here the routines that work on histogram, count_histo, +/// sum_frequency_histo, peak_histo, count_null_frequency_histo, +/// cmp_equi_frequency_histo and others (variance +/// operators). count_histo and sum_frequency_histo count the overall +/// frequency in the domain. peak_histo find the peak of the +/// histogram. count_null_frequency_histo compute the number of non +/// grey levels. cmp_equi_frequency_histo compare a histogram with +/// the one with the same frequency everywhere. +/// \{ +template <typename I> +mln_value(I) count_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) result = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + result += histo(p); + + return result; +} + + +template <typename I> +mln_value(I) sum_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += histo(p); + + return sum; +} + + +template <typename I> +mln_coord(mln_site_(I)) peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + mln_value(I) v_max = mln::opt::at(histo, mln::literal::zero); + mln_coord(mln_site_(I)) p_max = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +template <typename I> +mln_value(I) count_null_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) count = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + if (0 == histo(p)) + count++; + + return count; +} + +template <typename I> +float cmp_equi_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum ++; + var += mln::math::sqr(histo(p) - (1/256.0)); + } + + var = var / sum; + + return var; +} + +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + } + + return sum; +} + +template <typename I> +mln_value(I) sum_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + sum += pos*histo(p); + } + + return sum; +} + +template <typename I> +mln_value(I) avg_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += histo(p); + sum += pos*histo(p); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +template <typename I> +mln_value(I) var3_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + sum += (mln::math::sqr(p.ind()-mean)*histo(p)); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +template <typename I> +mln_value(I) var2_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_value(I) dlt = mln::literal::zero; + mln_value(I) mxt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + mxt += (histo(p)*pos*mean); + sqr += (histo(p)*mln::math::sqr(pos)); + dlt += (histo(p)*mln::math::sqr(pos - mean)); + + std::cout << "p = " << pos << std::endl; + std::cout << "p² = " << mln::math::sqr(pos) << std::endl; + std::cout << "p*mean = " << (pos*mean) << std::endl; + std::cout << "p-mean = " << (pos-mean) << std::endl; + std::cout << "(p-mean)² = " << mln::math::sqr(pos-mean) << std::endl; + std::cout << "histo(p) = " << histo(p) << std::endl; + std::cout << "histo(p)*p = " << (pos*histo(p)) << std::endl; + std::cout << "histo(p)*p²= " << (mln::math::sqr(pos)*histo(p)) + << std::endl; + std::cout << "cnt = " << cnt << std::endl; + std::cout << "sum = " << sum << std::endl; + std::cout << "sqr = " << sqr << std::endl; + std::cout << "dlt = " << dlt << std::endl; + std::cout << "mxt = " << mxt << std::endl; + std::cout << std::endl; + } + + std::cout << "sqr/cnt = " << (sqr/cnt) << std::endl; + std::cout << "sum/cnt = " << (sum/cnt) << std::endl; + std::cout << "(sum/cnt)² = " << mln::math::sqr(sum/cnt) << std::endl; + std::cout << "dlt/cnt = " << dlt/cnt << std::endl; + std::cout << "mxt/cnt = " << mxt/cnt << std::endl; + std::cout << std::endl; + + std::cout << "sqr = " + << (sqr) << std::endl; + std::cout << "dlt = " + << (dlt) << std::endl; + std::cout << "cnt*avg² = " + << (mln::math::sqr(sum/cnt)*cnt) << std::endl; + std::cout << "2*mxt = " + << (2*mxt) << std::endl; + std::cout << "sqr - cnt*avg² = " + << (sqr/cnt - mln::math::sqr(sum/cnt)) << std::endl; + std::cout << "(sqr -2*mxt + cnt*avg²) = " + << ((sqr - 2*mxt + mln::math::sqr(sum/cnt))/cnt) << std::endl; + std::cout << std::endl; + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +template <typename I> +mln_value(I) var_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + sqr += (histo(p)*mln::math::sqr(pos)); + } + + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +template <typename I> +mln_value(I) sqr_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += (mln::math::sqr(p.ind())*histo(p)); + + return sum; +} +/// \} + + +/// \brief Millet Hue detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold a way to adapt the test to the population. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test is used for discrimination between black and white pictures and +/// color ones. Some colored Black and white pictures have their energy near +/// the peak. +float hue1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + float cnt1; + float cnt2; + float prop; + short peak; + mln::point1d inf; + mln::point1d sup; + + peak = peak_histo(histo); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo|mln::box1d(inf,sup)); + cnt2 = count_histo(histo); + prop = ((255.0 * cnt1) / cnt2); + + return prop; +} + + +/// \brief Saturation detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold a way to adapt the test to the population. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test is used for discrimination between black and white pictures and +/// color ones. Black and white pictures have their energy in the low saturation +/// band. +float sat1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + float cnt1; + float cnt2; + float result; + + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = count_histo(histo); + result = ((255.0 * cnt1) / cnt2); + + return result; +} + + +/// \brief Counting grey levels descriptor. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the number of grey levels. +/// +/// This test aims at compute the number of grey levels. Photographies tends to +/// use all the levels or many of them. +float lvl0_descriptor(mln::image1d<unsigned> histo) +{ + float result; + + // FIXME 255 + result = 255-count_null_frequency_histo(histo); + + return result; +} + + +/// \brief Density hue detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float hue0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief Density saturation detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float sat0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief Density value detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float val0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// \param[in] peak the peak of the histogram. +/// \param[in] limit the threshold to compute the contribution. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief Millet value descriptor (cf. Millet.phd.2008) +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold the limit where to compute the contribution. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test aims at compute some deviation on the peak of the histogram of +/// the image. Large deviations mean lots of graduation in colors (such as +/// photos) and small ones mean something like cartoon. +float val1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + float result; + short peak; + + peak = peak_histo(histo); + result = stddev2(histo, peak, threshold); + + return result; +} + + +/// \brief Error detector (jonathan idea). +/// +/// \param[in] r_img_map the original red channel. +/// \param[in] g_img_map the original green channel. +/// \param[in] b_img_map the original blue channel. +/// \param[in] r_rdc_map the reduced (in color) red channel. +/// \param[in] g_rdc_map the reduced (in color) green channel. +/// \param[in] b_rdc_map the reduced (in color) blue channel. +/// +/// \return the PNSNR (cf compression p278 Handbook Color). +/// +/// \fixme the actual PNSNR is unbound, we need to fix its max to +/// 255. Two images which are the same produces an infinite PNSNR. +float err_descriptor(mln::image2d<mln::value::int_u8> r_img_map, + mln::image2d<mln::value::int_u8> g_img_map, + mln::image2d<mln::value::int_u8> b_img_map, + mln::image2d<mln::value::int_u8> r_rdc_map, + mln::image2d<mln::value::int_u8> g_rdc_map, + mln::image2d<mln::value::int_u8> b_rdc_map) + + +{ + typedef mln::accu::meta::stat::mean t_mean; + typedef mln::image2d<mln::value::int_u8> t_map; + typedef mln_trait_op_minus_(t_map,t_map) t_minus; + typedef mln_trait_op_times_(t_minus,t_minus) t_times; + + + t_minus minus_red; + t_minus minus_green; + t_minus minus_blue; + + t_times times_red; + t_times times_green; + t_times times_blue; + + float error_red; + float error_green; + float error_blue; + + float error; + + minus_red = (r_img_map - r_rdc_map); + times_red = minus_red * minus_red; + + minus_green = (g_img_map - g_rdc_map); + times_green = minus_green * minus_green; + + minus_blue = (b_img_map - b_rdc_map); + times_blue = minus_blue * minus_blue; + + error_red = mln::data::compute(t_mean(), times_red); + error_green = mln::data::compute(t_mean(), times_green); + error_blue = mln::data::compute(t_mean(), times_blue); + + error = (error_red + error_green + error_blue)/3.0; + error = mln::math::sqrt(error); + error = 20 * log(255/error); + +// FIXME: +// SAME IMAGE PRODUCE PNSNR GOING THROW THE INFINITY. +// DIFFERENT IMAGE PRODUCE PNSNR GOING NEAR ZERO. +// WE SHOULD KEEP THE PNSNR BELOW 255 FOR COMPARAISON. + + return error; +} + +/// Discriminant operators. +/// +/// We have some operators that compute the threshold which separate +/// two sub-populations in a histogram. +/// /{ + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*sqrt(v1)+m2*sqrt(v2))/(sqrt(v1)+sqrt(v2)). +float threshold_histo(float avg1, float var1, float avg2, float var2) +{ + float sigma1 = mln::math::sqrt(var1); + float sigma2 = mln::math::sqrt(var2); + float threshold = (avg1*sigma1+avg2*sigma2)/(sigma1+sigma2); + + return threshold; +} + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*var1+m2*var2)/(sqrt(v1)+sqrt(v2)). + +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Gaussian discriminant analysis in 1d is done. Compute the +/// discrimanation and solve the parabolic equation. +float threshold2_histo(float avg1, float var1, float avg2, float var2) +{ + float a = var2 - var1; + float b = -2 * (avg1 * var2 - avg2 * var1); + float c = var2 * mln::math::sqr(avg1) - var1 * mln::math::sqr(avg2); + float d = mln::math::sqr(b) - 4 * a * c; + + if (d < 0) + std::cout << "delta negatif" << std::endl; + + float x1 = (-b+mln::math::sqrt(d))/(2*a); + float x2 = (-b-mln::math::sqrt(d))/(2*a); + + std::cout << "a = " << a << std::endl; + std::cout << "b = " << b << std::endl; + std::cout << "c = " << c << std::endl; + std::cout << "d = " << d << std::endl; + std::cout << "x1 = " << x1 << std::endl; + std::cout << "x2 = " << x2 << std::endl; + + return x2; +} + +/// \brief Minimisation of the error. +/// +/// \param[in] histo_grp1 the histogram of the first population. +/// \param[in] histo_grp2 the histogram of the second population. +/// +/// \return the threshold. +/// +/// Computes the error and find the minimum error threshold. It's just +/// a counting of element in four categories (0 - but detects 1, 1 - +/// but detects 1, 1 but detects 0, 0 but detects 0). The error is the +/// sum of missclassifications for the classes 0 and 1. As Otsu done, +/// we can iterate on the threshold and compute the error associated +/// to it. The bes threshold is the one that minimize the error. +short min_error(const mln::image1d<float> histo_grp1, + const mln::image1d<float> histo_grp2, + float *_c00, float *_c10, float *_c01, float *_c11) +{ + float c00[256]; + float c10[256]; + float c01[256]; + float c11[256]; + float err[256]; + + for (short p = 0; p < 256; p++) + { + c00[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c10[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + + c01[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c11[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + } + + short threshold = 0; + float error = c01[0] + c01[0] + c00[0] + c11[0]; + + for(short p = 0; p < 256; p++) + { + err[p] = c10[p] + c01[p]; + +// std::cout << " p = " << p +// << ";c00 = " << c00[p] +// << ";c10 = " << c10[p] +// << ";c01 = " << c01[p] +// << ";c11 = " << c11[p] +// << std::endl; +// std::cout << "err[" < err[p]) + { + error = err[p]; + threshold = p; + } + } + + *_c00 = c00[threshold]; + *_c10 = c10[threshold]; + *_c01 = c01[threshold]; + *_c11 = c11[threshold]; + + return threshold; +} + + +/// \brief Fisher analysis. +/// +/// \param[in] histo the histogram of the mixed population. +/// +/// \return the threshold. +/// +/// This routine is less performant than the others because it starts +/// with a mixed population, but we know exactly the two separated +/// population. It was just a test. Don't take it in production +/// environement, it doesn't help you. +short fisher_analysis(const mln::image1d<float> histo) +{ + typedef mln::value::int_u8 t_int_u8; + + // FIXE ME SIZE const short a = mln_min(t_int_u8); + // float cnt1[a]; + + float cnt1[256]; + float sum1[256]; + float sqr1[256]; + float avg1[256]; + float var1[256]; + + float cnt2[256]; + float sum2[256]; + float sqr2[256]; + float avg2[256]; + float var2[256]; + + float cnt0[256]; // count of points + float sum0[256]; // sum of population + float sqr0[256]; // sqr of population + float avg0[256]; // average of the population + float v_in[256]; // variance with-in class + float v_bw[256]; // variance between class + float var0[256]; // variance of the population + short threshold; + float pos; + float min_var; + + // Assign the first elements + cnt1[0] = 0; + sum1[0] = 0; + sqr1[0] = 0; + avg1[0] = 0; + var1[0] = 0; + + // Compute the stats of the wall histogram + cnt2[0] = 0; + sum2[0] = 0; + sqr2[0] = 0; + + for(short p = 0; p < 256; p++) + { + pos = p; + cnt2[0] += mln::opt::at(histo,p); + sum2[0] += (pos * mln::opt::at(histo,p)); + sqr2[0] += (mln::math::sqr(pos) * mln::opt::at(histo,p)); + } + + avg2[0] = (0 == cnt2[0])? 0 : sum2[0] / cnt2[0]; + var2[0] = (0 == cnt2[0])? 0 : sqr2[0] / cnt2[0] - mln::math::sqr(avg2[0]); + + // watch the array limits + for (short p = 1; p < 256; p++) + { + pos = p; + + // Assign the statistics to the primary class + cnt1[p] = cnt1[p-1] + mln::opt::at(histo, p); + sum1[p] = sum1[p-1] + pos * mln::opt::at(histo, p); + sqr1[p] = sqr1[p-1] + mln::math::sqr(pos) * mln::opt::at(histo, p); + avg1[p] = (0 == cnt1[p])? 0 : (sum1[p] / cnt1[p]); + var1[p] = (0 == cnt1[p])? 0 : ((sqr1[p] / cnt1[p])-mln::math::sqr(avg1[p])); + + // Assign the statistics to the second class + cnt2[p] = cnt2[p-1] - mln::opt::at(histo, p);; + sum2[p] = sum2[p-1] - p * mln::opt::at(histo, p);; + sqr2[p] = sqr2[p-1] - mln::math::sqr(p) * mln::opt::at(histo, p);; + avg2[p] = (0 == cnt2[p])? 0 : (sum2[p] / cnt2[p]); + var2[p] = (0 == cnt2[p])? 0 : ((sqr2[p] / cnt2[p])-mln::math::sqr(avg2[p])); + + // Lets compute the invariants + cnt0[p] = cnt1[p] + cnt2[p]; + sum0[p] = sum1[p] + sum2[p]; + sqr0[p] = sqr1[p] + sqr2[p]; + avg0[p] = (cnt1[p] * avg1[p] + cnt2[p] * avg2[p])/cnt0[p]; + v_in[p] = (cnt1[p] * var1[p] + cnt2[p] * var2[p])/cnt0[p]; + v_bw[p] = (cnt1[p] * mln::math::sqr(avg1[p]-avg0[p]) + + cnt2[p] * mln::math::sqr(avg2[p]-avg0[p]))/cnt0[p]; + var0[p] = v_in[p] + v_bw[p]; + } + + // Find the threshold that minimizes the intra-class variance + min_var = cnt2[0]*var2[0]; + threshold = 0; + + for(short p = 0; p < 256; p++) + { + // Compute the intra-class variance + v_in[p] = cnt1[p]*var1[p] + cnt2[p]*var2[p]; +// std::cout << "var intra[" < v_in[p]) + { + min_var = v_in[p]; + threshold = p; + } + } + + return threshold; +} +/// /} + + +/// Launching part. +/// +/// In this part, we have the front end that walk over the image +/// databases and start to compute information that we pass to +/// previous routines. +/// \{ + +#define LVL0_DESCR 0 // Number of grey available +#define HUE0_DESCR 1 // Density histogram hue analysis +#define HUE1_DESCR 2 // Millet Hue analysis +#define SAT0_DESCR 3 // Density histogram saturation analysis +#define SAT1_DESCR 4 // Millet saturation analysis +#define VAL0_DESCR 5 // Density histogram grey level analysis +#define VAL1_DESCR 6 // Millet grey level analysis +#define GMP0_DESCR 7 // PNSNR with GIMP compression +#define GMP1_DESCR 8 // PNSNR with GIMP compression +#define GMP2_DESCR 9 // PNSNR with GIMP compression +#define MGK0_DESCR 9 // PNSNR with ImageMagick compression +#define MGK1_DESCR 10 // PNSNR with ImageMagick compression +#define MGK2_DESCR 11 // PNSNR with ImageMagick compression + +#define MGK_DESCR(version) (MGK0_DESCR + version) +#define GMP_DESCR(version) (GMP0_DESCR + version) + +#define NB_DESCR 12 // Number of descriptors +#define NB_DATABASE 2 // Number of image databases : AFP,ICDAR +#define NB_IMAGE 110 // Number of images +#define NB_VERSION 3 // Number of compression by GIMP or ImageMagick + +/// \brief Make sure that the result is between 0 and 255. +/// +/// \param[in] file_name the name of the analysis files. +/// \param[in] result the values for each image and each descriptors. +/// \param[in] size the number of image by each database. +/// +/// Correct the descriptors if there is overflow values. Just used in +/// debug mode. +void init_descriptors(std::string file_name[], + float result[][NB_DESCR], + int size[]) +{ + for (int i = 0; i < NB_IMAGE; i++) + { + file_name[i] = std::string("PGM"); + + for (int d = 0; d < NB_DESCR; d++) + result[i][d] = (i*d) % 256; + } + + size[0] = 62; + size[1] = 48; +} + + +/// \brief Dump gnuplot file for vizualisation of results. +/// +/// \param[in] file_name the name of the analysis files. +/// \param[in] result the values for each image and each descriptors. +/// \param[in] size the number of image by each database. +/// +/// This routine aimed at plotting the result with one graph. Each +/// database has got its color. We can see the values used by each +/// descriptors along the image of each database. +void dump_descriptors(const std::string file_name[], + const float result[][NB_DESCR], + const int size[]) +{ + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "plot '-' using 2 with point title 'ICDAR',\\" << std::endl; + std::cout << " '-' using 2 with point title 'AFP'" << std::endl; + + int num = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + for (int i = 0; i < size[db]; i++) + { + std::cout << result[num][LVL0_DESCR] << " "; + std::cout << result[num][HUE0_DESCR] << " "; + std::cout << result[num][HUE1_DESCR] << " "; + std::cout << result[num][SAT0_DESCR] << " "; + std::cout << result[num][SAT1_DESCR] << " "; + std::cout << result[num][VAL0_DESCR] << " "; + std::cout << result[num][VAL1_DESCR] << " "; + std::cout << result[num][GMP0_DESCR] << " "; + std::cout << result[num][GMP1_DESCR] << " "; + std::cout << result[num][GMP2_DESCR] << " "; + std::cout << result[num][MGK0_DESCR] << " "; + std::cout << result[num][MGK1_DESCR] << " "; + std::cout << result[num][MGK2_DESCR] << " "; + std::cout << " # " << file_name[num] << std::endl; + num++; + } + + std::cout << "e" << std::endl; + } +} + + +/// \brief Compute the descriptor histograms for each database. +/// +/// \param[in] result the result values of each descriptors. +/// \param[in] size the number of images by database. +/// \param[out] histo the computed histograms. +/// +/// This routine compute a histograms for each database and each descriptors. +/// So we can see the distribution of descriptor values over each database. +/// Result values are transform from float to int for histograms. +void compute_histo(const float result[][NB_DESCR], + const int size[], + mln::image1d<float> histo[][NB_DATABASE]) +{ + for (int i = 0; i < NB_DESCR; i++) + for (int db = 0; db < NB_DATABASE; db++) + { + histo[i][db].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + + mln::data::fill(histo[i][db], mln::literal::zero); + } + + short v; + int num = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + for (int i = 0; i < size[db]; i++) + { + v = (short)mln::math::floor(result[num][VAL0_DESCR]+0.4999); + mln::opt::at(histo[VAL0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][LVL0_DESCR]+0.4999); + mln::opt::at(histo[LVL0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][HUE0_DESCR]+0.4999); + mln::opt::at(histo[HUE0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][HUE1_DESCR]+0.4999); + mln::opt::at(histo[HUE1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][SAT0_DESCR]+0.4999); + mln::opt::at(histo[SAT0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][SAT1_DESCR]+0.4999); + mln::opt::at(histo[SAT1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][VAL1_DESCR]+0.4999); + mln::opt::at(histo[VAL1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP0_DESCR]+0.4999); + mln::opt::at(histo[GMP0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP1_DESCR]+0.4999); + mln::opt::at(histo[GMP1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP2_DESCR]+0.4999); + mln::opt::at(histo[GMP2_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK0_DESCR]+0.4999); + mln::opt::at(histo[MGK0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK1_DESCR]+0.4999); + mln::opt::at(histo[MGK1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK2_DESCR]+0.4999); + mln::opt::at(histo[MGK2_DESCR][db],v)++; + + num++; + } + } +} + + +/// Compute the threshold for discrimination between ICDAR and AFP. +/// +/// \param[in] histo the histogram for each descriptor and for each database. +/// \param[out] threshold computed values to discriminate between ICDAR/AFP. +/// \param[out] c00 say CLASS 1 but CLASS 1. +/// \param[out] c10 say CLASS 2 but CLASS 1. +/// \param[out] c01 say CLASS 1 but CLASS 2. +/// \param[out] c11 say CLASS 2 but CLASS 2. +/// +/// Compute the threshold by the minimum of error of +/// classification. Population are inverted in function of average, +/// technical solution to prevent min_error crashed. The bad thing is +/// now, the class 0 can be either ICDAR of AFP, depends on descriptors. +void compute_thresholds(const mln::image1d<float> histo[][NB_DATABASE], + short threshold[], + float c00[], + float c10[], + float c01[], + float c11[]) +{ + for (int i = 0; i < NB_DESCR; i++) + { + float avg0 = avg_histo(histo[i][0]); + float avg1 = avg_histo(histo[i][1]); + + if (avg0 < avg1) + { + threshold[i] = min_error(histo[i][0], histo[i][1], + &c00[i], &c10[i], &c01[i], &c11[i]); + } + else + { + threshold[i] = min_error(histo[i][1], histo[i][0], + &c00[i], &c10[i], &c01[i], &c11[i]); + } + + std::cerr << " i = " << i + << "; c00 = " << c00[i] + << "; c10 = " << c10[i] + << "; c01 = " << c01[i] + << "; c11 = " << c11[i] + << "; threshold " << threshold[i] + << std::endl; + + } +} + + +/// \brief Walk over the database directories. +/// +/// \param[out] file_name the array of the image name. +/// \param[out] result the descriptors values for each image. +/// \param[out] size the number of image for each database. +/// +/// This is the front end part to access to the database directories. We use +/// boost library to do it. First we explore ICDAR database en then the AFP one. +/// For each valid image, we compute all the descriptors. Histograms are +/// computed at this level because we can do that thing one time for every +/// density descriptors. Every parameters are updated to retrurn the values. +void compute_descriptors(std::string file_name[], + float result[][NB_DESCR], + int size[]) +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + typedef mln::image1d<unsigned> t_histo; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_map; + typedef mln::image2d<t_rgb8> t_input; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_2_hue; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_2_sat; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_2_val; + typedef mln::fun::v2v::component<t_rgb8,0> t_rgb_2_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_rgb_2_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_rgb_2_blue; + typedef mln::accu::meta::stat::histo1d t_accu_histo; + + + t_path img_path[2] = { ICDAR_20P_INPUT_IMG_PATH, AFP_PPM_IMG_PATH}; + t_path mgk_path[3][2] = {{ICDAR_20P_MGK30_IMG_PATH, AFP_MGK30_IMG_PATH}, + {ICDAR_20P_MGK20_IMG_PATH, AFP_MGK20_IMG_PATH}, + {ICDAR_20P_MGK10_IMG_PATH, AFP_MGK10_IMG_PATH}}; + t_path gmp_path[3][2] = {{ICDAR_20P_GMP30_IMG_PATH, AFP_GMP30_IMG_PATH}, + {ICDAR_20P_GMP20_IMG_PATH, AFP_GMP20_IMG_PATH}, + {ICDAR_20P_GMP10_IMG_PATH, AFP_GMP10_IMG_PATH}}; + + int num = 0; + int cnt = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + if (boost::filesystem::exists(img_path[db]) && + boost::filesystem::is_directory(img_path[db])) + { + boost::filesystem::system_complete(img_path[db]); + + const t_iter_path end_iter; + + cnt = 0; + + for (t_iter_path dir_iter(img_path[db]); end_iter != dir_iter; ++dir_iter) + { + t_path img_file = dir_iter->path().leaf(); + t_path dir_file = dir_iter->path(); + t_input img_input; + + mln::io::ppm::load(img_input, dir_file.string().c_str()); + + t_map h_img_map = mln::data::transform(img_input, t_rgb_2_hue()); + t_map s_img_map = mln::data::transform(img_input, t_rgb_2_sat()); + t_map v_img_map = mln::data::transform(img_input, t_rgb_2_val()); + t_map r_img_map = mln::data::transform(img_input, t_rgb_2_red()); + t_map g_img_map = mln::data::transform(img_input, t_rgb_2_green()); + t_map b_img_map = mln::data::transform(img_input, t_rgb_2_blue()); + t_histo h_img_hst = mln::data::compute(t_accu_histo(), h_img_map); + t_histo s_img_hst = mln::data::compute(t_accu_histo(), s_img_map); + t_histo v_img_hst = mln::data::compute(t_accu_histo(), v_img_map); + t_histo r_img_hst = mln::data::compute(t_accu_histo(), r_img_map); + t_histo g_img_hst = mln::data::compute(t_accu_histo(), g_img_map); + t_histo b_img_hst = mln::data::compute(t_accu_histo(), b_img_map); + + std::cerr << dir_iter->path() << std::endl; + + file_name[num] = img_file.string(); + + // descriptors + result[num][LVL0_DESCR] = lvl0_descriptor(v_img_hst); + result[num][HUE0_DESCR] = hue0_descriptor(h_img_hst); + result[num][HUE1_DESCR] = hue1_descriptor(h_img_hst,20); + result[num][SAT0_DESCR] = sat0_descriptor(s_img_hst); + result[num][SAT1_DESCR] = sat1_descriptor(s_img_hst,50); + result[num][VAL0_DESCR] = val0_descriptor(v_img_hst); + //result[num][VAL1_DESCR] = val1_descriptor(v_img_hst, 15); + result[num][VAL1_DESCR] = 0; + + // for gimp and magick + for (int v = 0; v < NB_VERSION; v++) + { + if (boost::filesystem::exists(mgk_path[v][db]) && + boost::filesystem::exists(gmp_path[v][db]) && + boost::filesystem::is_directory(mgk_path[v][db]) && + boost::filesystem::is_directory(gmp_path[v][db])) + { + t_path mgk_file = mgk_path[v][db] / img_file; + t_path gmp_file = gmp_path[v][db] / img_file; + t_input gmp_input; + + mln::io::ppm::load(gmp_input, gmp_file.string().c_str()); + + t_map r_gmp_map = mln::data::transform(gmp_input,t_rgb_2_red()); + t_map g_gmp_map = mln::data::transform(gmp_input,t_rgb_2_green()); + t_map b_gmp_map = mln::data::transform(gmp_input,t_rgb_2_blue()); + + result[num][GMP_DESCR(v)]= err_descriptor(r_img_map, + g_img_map, + b_img_map, + r_gmp_map, + g_gmp_map, + b_gmp_map); + + t_input mgk_input; + + mln::io::ppm::load(mgk_input, mgk_file.string().c_str()); + + t_map r_mgk_map = mln::data::transform(mgk_input,t_rgb_2_red()); + t_map g_mgk_map = mln::data::transform(mgk_input,t_rgb_2_green()); + t_map b_mgk_map = mln::data::transform(mgk_input,t_rgb_2_blue()); + + result[num][MGK_DESCR(v)]= err_descriptor(r_img_map, + g_img_map, + b_img_map, + r_mgk_map, + g_mgk_map, + b_mgk_map); + } + } + + num++; + cnt++; + } + } + + size[db] = cnt; + } +} + +/// \brief Just for debugging purpose and tests. +int main2() +{ + typedef mln::image1d<unsigned> t_histo; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_map; + typedef mln::image2d<t_rgb8> t_input; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_2_hue; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_2_sat; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_2_val; + typedef mln::fun::v2v::component<t_rgb8,0> t_rgb_2_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_rgb_2_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_rgb_2_blue; + typedef mln::accu::meta::stat::histo1d t_accu_histo; + + t_input img_input; + + mln::io::ppm::load(img_input, ICDAR_20P_INPUT_IMG_PATH"/mp00032c_20p.ppm"); + //mln::io::ppm::load(img_input, AFP_PPM_IMG_PATH"/000_Del218430.ppm"); + + + + t_map h_img_map = mln::data::transform(img_input, t_rgb_2_hue()); + t_map s_img_map = mln::data::transform(img_input, t_rgb_2_sat()); + t_map v_img_map = mln::data::transform(img_input, t_rgb_2_val()); + t_map r_img_map = mln::data::transform(img_input, t_rgb_2_red()); + t_map g_img_map = mln::data::transform(img_input, t_rgb_2_green()); + t_map b_img_map = mln::data::transform(img_input, t_rgb_2_blue()); + t_histo h_img_hst = mln::data::compute(t_accu_histo(), h_img_map); + t_histo s_img_hst = mln::data::compute(t_accu_histo(), s_img_map); + t_histo v_img_hst = mln::data::compute(t_accu_histo(), v_img_map); + t_histo r_img_hst = mln::data::compute(t_accu_histo(), r_img_map); + t_histo g_img_hst = mln::data::compute(t_accu_histo(), g_img_map); + t_histo b_img_hst = mln::data::compute(t_accu_histo(), b_img_map); + + + std::cout << "sat2 : " << sat0_descriptor(s_img_hst) << std::endl; + + return 0; +} + + +/// \brief Main entry. +/// +/// This is the front end for every routines. +/// - compute descriptors. +/// - dump descriptors. +/// - compute histograms. +/// - compute thresholds. +/// - save results. +/// +/// Global data used: +/// - file_name : all the initial images name, mixed every image database. +/// - result : all the result values for every descriptors and for every images. +/// - histo : the histograms ny database and by descriptors. +/// - size : the number of image by database. +/// - threshold : the computed histogram separation on each descriptors. +/// - cxx : the count of well/bad classified images function of threshold info. +int main() +{ + std::string file_name[NB_IMAGE]; + float result[NB_IMAGE][NB_DESCR]; + int size[NB_DATABASE]; + mln::image1d<float> histo[NB_DESCR][NB_DATABASE]; + short threshold[NB_DESCR]; + float c00[NB_DESCR]; + float c10[NB_DESCR]; + float c01[NB_DESCR]; + float c11[NB_DESCR]; + + std::cerr << "DESCRIPTORS" << std::endl; + compute_descriptors(file_name,result,size); +// std::cout << "DUMPING" << std::endl; +// init_descriptors(file_name,result,size); + dump_descriptors(file_name,result,size); + std::cerr << "HISTO" << std::endl; + compute_histo(result,size,histo); + std::cerr << "THRESHOLD" << std::endl; + compute_thresholds(histo,threshold,c00,c10,c01,c11); + + mln::io::plot::save_image_sh(histo[LVL0_DESCR][0], "lvl0_histo1.sh"); + mln::io::plot::save_image_sh(histo[HUE0_DESCR][0], "hue0_histo1.sh"); + mln::io::plot::save_image_sh(histo[HUE1_DESCR][0], "hue1_histo1.sh"); + mln::io::plot::save_image_sh(histo[SAT0_DESCR][0], "sat0_histo1.sh"); + mln::io::plot::save_image_sh(histo[SAT1_DESCR][0], "sat1_histo1.sh"); + mln::io::plot::save_image_sh(histo[VAL0_DESCR][0], "val0_histo1.sh"); + mln::io::plot::save_image_sh(histo[VAL1_DESCR][0], "val1_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP0_DESCR][0], "gmp0_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP1_DESCR][0], "gmp1_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP2_DESCR][0], "gmp2_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK0_DESCR][0], "mgk0_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK1_DESCR][0], "mgk1_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK2_DESCR][0], "mgk2_histo1.sh"); + + mln::io::plot::save_image_sh(histo[LVL0_DESCR][1], "lvl0_histo2.sh"); + mln::io::plot::save_image_sh(histo[HUE0_DESCR][1], "hue0_histo2.sh"); + mln::io::plot::save_image_sh(histo[HUE1_DESCR][1], "hue1_histo2.sh"); + mln::io::plot::save_image_sh(histo[SAT0_DESCR][1], "sat0_histo2.sh"); + mln::io::plot::save_image_sh(histo[SAT1_DESCR][1], "sat1_histo2.sh"); + mln::io::plot::save_image_sh(histo[VAL0_DESCR][1], "val0_histo2.sh"); + mln::io::plot::save_image_sh(histo[VAL1_DESCR][1], "val1_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP0_DESCR][1], "gmp0_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP1_DESCR][1], "gmp1_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP2_DESCR][1], "gmp2_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK0_DESCR][1], "mgk0_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK1_DESCR][1], "mgk1_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK2_DESCR][1], "mgk2_histo2.sh"); + + return 0; +} +/// \} + diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/error/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/error/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/error/error.cc b/scribo/sandbox/green/exp/annotating/error/error.cc new file mode 100644 index 0000000..949b51b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/error/error.cc @@ -0,0 +1,833 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// This file is a prelude just before using the bench source. It puts +/// the light on the Jonathan's idea which wants to compare ICDAR and +/// AFP image database with difference between original image and +/// color reducted one. The key point is to say that when a color +/// image is degraded, the visual quality fall quickly in function of +/// the number of colors left. A contrario, when an image is black and +/// white, this kind of degradation doesn't change the original visual +/// quality. The PNSNR detector is build and learning threshold for +/// automatic classification is done (four algorithms are tested). + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/algebra/vec.hh> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/times.hh> +#include <mln/arith/diff_abs.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/fill.hh> + +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/ceil.hh> +#include <mln/math/floor.hh> + +#include <mln/opt/at.hh> + +#include <mln/trait/value_.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> + +/// Work on histogram. +/// \brief Different routines are presented for computing mean, variance ... +/// \{ + +/// \brief Counting the number of pixels in the histogram. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the number of pixels in the histogram. +/// +/// Such routine is a generic and is generally applied on a portion of +/// the real domain. +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + } + + return sum; +} + +/// \brief Summing the values ponderated by their occurences. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the sum ponderated of values by their occurences. +/// +/// This method is not a production routine, it's just a test to debug +/// histogram problem. When we compute something over the histogram, +/// we need to take care of the intermediate type that contains the +/// temporary result. If not the case, the type used is the one of the +/// value of the histogram and we can go to some overflow +/// problems. The trick is to store the value returned by the iterator +/// in some temporary computing type (look at pos in the code). +template <typename I> +mln_value(I) sum_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + sum += pos*histo(p); + } + + return sum; +} + +/// \brief Summing the values ponderated by their occurences. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the sum ponderated of values by their occurences. +template <typename I> +mln_value(I) avg_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += histo(p); + sum += pos*histo(p); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// This computing is safe because the quantity of information stored +/// in sum is low. This formulae doesn't show the overflow problem. +template <typename I> +mln_value(I) var3_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + sum += (mln::math::sqr(p.ind()-mean)*histo(p)); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// This is the debugging code to observe overflow problem. +template <typename I> +mln_value(I) var2_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_value(I) dlt = mln::literal::zero; + mln_value(I) mxt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + mxt += (histo(p)*pos*mean); + sqr += (histo(p)*mln::math::sqr(pos)); + dlt += (histo(p)*mln::math::sqr(pos - mean)); + + std::cout << "p = " << pos << std::endl; + std::cout << "p² = " << mln::math::sqr(pos) << std::endl; + std::cout << "p*mean = " << (pos*mean) << std::endl; + std::cout << "p-mean = " << (pos-mean) << std::endl; + std::cout << "(p-mean)² = " << mln::math::sqr(pos-mean) << std::endl; + std::cout << "histo(p) = " << histo(p) << std::endl; + std::cout << "histo(p)*p = " << (pos*histo(p)) << std::endl; + std::cout << "histo(p)*p²= " << (mln::math::sqr(pos)*histo(p)) + << std::endl; + std::cout << "cnt = " << cnt << std::endl; + std::cout << "sum = " << sum << std::endl; + std::cout << "sqr = " << sqr << std::endl; + std::cout << "dlt = " << dlt << std::endl; + std::cout << "mxt = " << mxt << std::endl; + std::cout << std::endl; + } + + std::cout << "sqr/cnt = " << (sqr/cnt) << std::endl; + std::cout << "sum/cnt = " << (sum/cnt) << std::endl; + std::cout << "(sum/cnt)² = " << mln::math::sqr(sum/cnt) << std::endl; + std::cout << "dlt/cnt = " << dlt/cnt << std::endl; + std::cout << "mxt/cnt = " << mxt/cnt << std::endl; + std::cout << std::endl; + + std::cout << "sqr = " + << (sqr) << std::endl; + std::cout << "dlt = " + << (dlt) << std::endl; + std::cout << "cnt*avg² = " + << (mln::math::sqr(sum/cnt)*cnt) << std::endl; + std::cout << "2*mxt = " + << (2*mxt) << std::endl; + std::cout << "sqr - cnt*avg² = " + << (sqr/cnt - mln::math::sqr(sum/cnt)) << std::endl; + std::cout << "(sqr -2*mxt + cnt*avg²) = " + << ((sqr - 2*mxt + mln::math::sqr(sum/cnt))/cnt) << std::endl; + std::cout << std::endl; + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// My standard code for the variance. +template <typename I> +mln_value(I) var_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + sqr += (histo(p)*mln::math::sqr(pos)); + } + + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} +/// \} + + +/// Classify. +/// \brief Classification routines that compute threshold. +/// \{ + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*sqrt(v1)+m2*sqrt(v2))/(sqrt(v1)+sqrt(v2)). +float threshold_histo(float avg1, float var1, float avg2, float var2) +{ + float sigma1 = mln::math::sqrt(var1); + float sigma2 = mln::math::sqrt(var2); + float threshold = (avg1*sigma1+avg2*sigma2)/(sigma1+sigma2); + + return threshold; +} + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*var1+m2*var2)/(sqrt(v1)+sqrt(v2)). +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Gaussian discriminant analysis in 1d is done. Compute the +/// discrimanation and solve the parabolic equation. +float threshold2_histo(float avg1, float var1, float avg2, float var2) +{ + float a = var2 - var1; + float b = -2 * (avg1 * var2 - avg2 * var1); + float c = var2 * mln::math::sqr(avg1) - var1 * mln::math::sqr(avg2); + float d = mln::math::sqr(b) - 4 * a * c; + + if (d < 0) + std::cout << "delta negatif" << std::endl; + + float x1 = (-b+mln::math::sqrt(d))/(2*a); + float x2 = (-b-mln::math::sqrt(d))/(2*a); + + std::cout << "a = " << a << std::endl; + std::cout << "b = " << b << std::endl; + std::cout << "c = " << c << std::endl; + std::cout << "d = " << d << std::endl; + std::cout << "x1 = " << x1 << std::endl; + std::cout << "x2 = " << x2 << std::endl; + + return x2; +} + +/// \brief Compute the other proportion of histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the ponderated sum of the sqrt values. +/// +/// This is a test. Don't take this routine in production. +template <typename I> +mln_value(I) sqr_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += (mln::math::sqr(p.ind())*histo(p)); + + return sum; +} + +/// \brief Minimisation of the error. +/// +/// \param[in] histo_grp1 the histogram of the first population. +/// \param[in] histo_grp2 the histogram of the second population. +/// +/// \return the threshold. +/// +/// Computes the error and find the minimum error threshold. It's just +/// a counting of element in four categories (0 - but detects 1, 1 - +/// but detects 1, 1 but detects 0, 0 but detects 0). The error is the +/// sum of missclassifications for the classes 0 and 1. As Otsu done, +/// we can iterate on the threshold and compute the error associated +/// to it. The bes threshold is the one that minimize the error. +short min_error(const mln::image1d<float> histo_grp1, + const mln::image1d<float> histo_grp2) +{ + float c00[256]; + float c10[256]; + float c01[256]; + float c11[256]; + float err[256]; + + for (short p = 0; p < 256; p++) + { + c00[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c10[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + + c01[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c11[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + } + + short threshold = 0; + float error = c01[0] + c01[0] + c00[0] + c11[0]; + + for(short p = 0; p < 256; p++) + { + err[p] = c10[p] + c01[p]; + + std::cout << " p = " < err[p]) + { + error = err[p]; + threshold = p; + } + } + + return threshold; +} + +/// \brief Fisher analysis. +/// +/// \param[in] histo the histogram of the mixed population. +/// +/// \return the threshold. +/// +/// This routine is less performant than the others because it starts +/// with a mixed population, but we know exactly the two separated +/// population. It was just a test. Don't take it in production +/// environement, it doesn't help you. +short fisher_analysis(const mln::image1d<float> histo) +{ + typedef mln::value::int_u8 t_int_u8; + + // FIXE ME SIZE const short a = mln_min(t_int_u8); + // float cnt1[a]; + + float cnt1[256]; + float sum1[256]; + float sqr1[256]; + float avg1[256]; + float var1[256]; + + float cnt2[256]; + float sum2[256]; + float sqr2[256]; + float avg2[256]; + float var2[256]; + + float cnt0[256]; // count of points + float sum0[256]; // sum of population + float sqr0[256]; // sqr of population + float avg0[256]; // average of the population + float v_in[256]; // variance with-in class + float v_bw[256]; // variance between class + float var0[256]; // variance of the population + short threshold; + float pos; + float min_var; + + // Assign the first elements + cnt1[0] = 0; + sum1[0] = 0; + sqr1[0] = 0; + avg1[0] = 0; + var1[0] = 0; + + // Compute the stats of the wall histogram + cnt2[0] = 0; + sum2[0] = 0; + sqr2[0] = 0; + + for(short p = 0; p < 256; p++) + { + pos = p; + cnt2[0] += mln::opt::at(histo,p); + sum2[0] += (pos * mln::opt::at(histo,p)); + sqr2[0] += (mln::math::sqr(pos) * mln::opt::at(histo,p)); + } + + avg2[0] = (0 == cnt2[0])? 0 : sum2[0] / cnt2[0]; + var2[0] = (0 == cnt2[0])? 0 : sqr2[0] / cnt2[0] - mln::math::sqr(avg2[0]); + + // watch the array limits + for (short p = 1; p < 256; p++) + { + pos = p; + + // Assign the statistics to the primary class + cnt1[p] = cnt1[p-1] + mln::opt::at(histo, p); + sum1[p] = sum1[p-1] + pos * mln::opt::at(histo, p); + sqr1[p] = sqr1[p-1] + mln::math::sqr(pos) * mln::opt::at(histo, p); + avg1[p] = (0 == cnt1[p])? 0 : (sum1[p] / cnt1[p]); + var1[p] = (0 == cnt1[p])? 0 : ((sqr1[p] / cnt1[p])-mln::math::sqr(avg1[p])); + + // Assign the statistics to the second class + cnt2[p] = cnt2[p-1] - mln::opt::at(histo, p);; + sum2[p] = sum2[p-1] - p * mln::opt::at(histo, p);; + sqr2[p] = sqr2[p-1] - mln::math::sqr(p) * mln::opt::at(histo, p);; + avg2[p] = (0 == cnt2[p])? 0 : (sum2[p] / cnt2[p]); + var2[p] = (0 == cnt2[p])? 0 : ((sqr2[p] / cnt2[p])-mln::math::sqr(avg2[p])); + + // Lets compute the invariants + cnt0[p] = cnt1[p] + cnt2[p]; + sum0[p] = sum1[p] + sum2[p]; + sqr0[p] = sqr1[p] + sqr2[p]; + avg0[p] = (cnt1[p] * avg1[p] + cnt2[p] * avg2[p])/cnt0[p]; + v_in[p] = (cnt1[p] * var1[p] + cnt2[p] * var2[p])/cnt0[p]; + v_bw[p] = (cnt1[p] * mln::math::sqr(avg1[p]-avg0[p]) + + cnt2[p] * mln::math::sqr(avg2[p]-avg0[p]))/cnt0[p]; + var0[p] = v_in[p] + v_bw[p]; + } + + // Find the threshold that minimizes the intra-class variance + min_var = cnt2[0]*var2[0]; + threshold = 0; + + for(short p = 0; p < 256; p++) + { + // Compute the intra-class variance + v_in[p] = cnt1[p]*var1[p] + cnt2[p]*var2[p]; +// std::cout << "var intra[" < v_in[p]) + { + min_var = v_in[p]; + threshold = p; + } + } + + return threshold; +} +/// \} + +/// \brief ERROR (MSE, PNSNR) compression p278 Handbook Color. +/// \param[in] original the original image. +/// \param[in] reduced the image with a reduced number of colors. +/// +/// \return the PNSNR. +/// +/// Compute the PNSNR. First compute the square difference in each +/// channel. Then compute the mean of those square differences and get +/// the mean over the three channels. Then, get the sqrt of that to +/// have a distance. The PNSNR is a logarithmic quantity of that distance. +float error_test(const std::string original, + const std::string reduced) + +{ + + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::fun::v2v::component<t_rgb8,0> t_component_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_component_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_component_blue; + typedef mln::accu::meta::stat::mean t_mean; + typedef mln::accu::meta::stat::histo1d t_histo; + typedef mln::image2d<t_int_u8> t_img; + typedef mln_trait_op_minus_(t_img,t_img) t_minus; + typedef mln_trait_op_times_(t_minus,t_minus) t_times; + + mln::image2d<mln::value::rgb8> original_rgb8; + mln::image2d<mln::value::rgb8> reduced_rgb8; + + mln::image2d<mln::value::int_u8> original_red; + mln::image2d<mln::value::int_u8> original_green; + mln::image2d<mln::value::int_u8> original_blue; + + mln::image2d<mln::value::int_u8> reduced_red; + mln::image2d<mln::value::int_u8> reduced_green; + mln::image2d<mln::value::int_u8> reduced_blue; + +// mln::image2d<mln::value::int_u8> map_red; +// mln::image2d<mln::value::int_u8> map_green; +// mln::image2d<mln::value::int_u8> map_blue; + +// mln::image1d<unsigned> histo_red; +// mln::image1d<unsigned> histo_green; +// mln::image1d<unsigned> histo_blue; + + t_minus minus_red; + t_minus minus_green; + t_minus minus_blue; + + t_times times_red; + t_times times_green; + t_times times_blue; + + float error_red; + float error_green; + float error_blue; + + float error; + + + mln::io::ppm::load(original_rgb8, original.c_str()); + mln::io::ppm::load(reduced_rgb8, reduced.c_str()); + + original_red = mln::data::transform(original_rgb8, t_component_red()); + original_green = mln::data::transform(original_rgb8, t_component_green()); + original_blue = mln::data::transform(original_rgb8, t_component_blue()); + + reduced_red = mln::data::transform(reduced_rgb8, t_component_red()); + reduced_green = mln::data::transform(reduced_rgb8, t_component_green()); + reduced_blue = mln::data::transform(reduced_rgb8, t_component_blue()); + + minus_red = (original_red - reduced_red); + times_red = minus_red * minus_red; + + minus_green = (original_green - reduced_green); + times_green = minus_green * minus_green; + + minus_blue = (original_blue - reduced_blue); + times_blue = minus_blue * minus_blue; + + error_red = mln::data::compute(t_mean(), times_red); + error_green = mln::data::compute(t_mean(), times_green); + error_blue = mln::data::compute(t_mean(), times_blue); + +// map_red = mln::data::stretch(t_int_u8(), times_red); +// map_green = mln::data::stretch(t_int_u8(), times_blue); +// map_blue = mln::data::stretch(t_int_u8(), times_green); + +// histo_red = mln::data::compute(t_histo(), map_red); +// histo_green = mln::data::compute(t_histo(), map_green); +// histo_blue = mln::data::compute(t_histo(), map_blue); + +// mln::io::plot::save_image_sh(histo_red, "histo_red.sh"); +// mln::io::plot::save_image_sh(histo_green,"histo_green.sh"); +// mln::io::plot::save_image_sh(histo_blue, "histo_blue.sh"); + +// mln::io::pgm::save(map_red, "red.pgm"); +// mln::io::pgm::save(map_green,"green.pgm"); +// mln::io::pgm::save(map_blue, "blue.pgm"); + + error = (error_red + error_green + error_blue)/3.0; + error = mln::math::sqrt(error); + error = 20 * log(255/error); + +// Le PNSNR semble offrir plus d'espace pour la discrimination +// Si les images sont identiques ==> PNSNR = +inf +// Si les images sont très différentes ==> PNSNR = 0 + + return error; +} + + +/// \brief The main entry. +/// +/// This is the front end for error classification. This is the big +/// loop where we walk other image data bases. Computation of +/// automatic thresholds are called from here. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + mln::image1d<float> histo(256); + mln::image1d<float> histo_grp[2]; // histo by group + + histo_grp[0].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + histo_grp[1].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + + mln::data::fill(histo, mln::literal::zero); + mln::data::fill(histo_grp[0], mln::literal::zero); + mln::data::fill(histo_grp[1], mln::literal::zero); + + t_path original_path[] = {ICDAR_20P_INPUT_IMG_PATH, + AFP_PPM_IMG_PATH}; + +// t_path reduced1_path[] = {ICDAR_20P_MGK30_IMG_PATH, +// AFP_MGK30_IMG_PATH}; + +// t_path reduced1_path[] = {ICDAR_20P_MGK20_IMG_PATH, +// AFP_MGK20_IMG_PATH}; + + t_path reduced1_path[] = {ICDAR_20P_MGK10_IMG_PATH, + AFP_MGK10_IMG_PATH}; + +// t_path reduced2_path[] = {ICDAR_20P_GMP30_IMG_PATH, +// AFP_GMP30_IMG_PATH}; + +// t_path reduced2_path[] = {ICDAR_20P_GMP20_IMG_PATH, +// AFP_GMP20_IMG_PATH}; + + t_path reduced2_path[] = {ICDAR_20P_GMP10_IMG_PATH, + AFP_GMP10_IMG_PATH}; + + + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "ERROR" << std::endl; + std::cout << "plot '-' using 1 with point notitle,\\" << std::endl; + std::cout << " '-' using 1 with point notitle" << std::endl; + + for (int i = 0; i < 2; i++) + { + if (boost::filesystem::exists(original_path[i]) && + boost::filesystem::exists(reduced1_path[i]) && + boost::filesystem::exists(reduced2_path[i]) && + boost::filesystem::is_directory(original_path[i]) && + boost::filesystem::is_directory(reduced1_path[i]) && + boost::filesystem::is_directory(reduced2_path[i])) + { + boost::filesystem::system_complete(original_path[i]); + const t_iter_path end_iter; + float error1 = 0.0; + float error2 = 0.0; + t_path leaf; + t_path reduced1_file; + t_path reduced2_file; + + for (t_iter_path dir_iter(original_path[i]);end_iter!=dir_iter;++dir_iter) + { + leaf = dir_iter->path().leaf(); + reduced1_file = reduced1_path[i] / leaf; + reduced2_file = reduced2_path[i] / leaf; + + error1 = error_test(dir_iter->path().string(), reduced1_file.string()); + error2 = error_test(dir_iter->path().string(), reduced2_file.string()); + + float a1 = 1; + short v1 = (short)mln::math::floor(error2+0.4999); + mln::opt::at(histo,v1) += a1; + mln::opt::at(histo_grp[i],v1) += a1; + +// float a1 = error2 - mln::math::floor(error2); +// float a2 = mln::math::ceil(error2) - error2; +// short v1 = (short)mln::math::floor(error2); +// short v2 = (short)mln::math::ceil(error2); +// mln::opt::at(histo,v1) += a1; +// mln::opt::at(histo,v2) += a2; +// mln::opt::at(histo_grp[i],v1) += a1; +// mln::opt::at(histo_grp[i],v2) += a2; + + std::cout << error1 << " "; + std::cout << error2 << " "; + std::cout << "# " << dir_iter->path().leaf() << std::endl; + } + std::cout << "e" << std::endl; + } + } + + mln::io::plot::save_image_sh(histo, "histo.sh"); + mln::io::plot::save_image_sh(histo_grp[1], "histo_grp1.sh"); + mln::io::plot::save_image_sh(histo_grp[0], "histo_grp2.sh"); + + float threshold = fisher_analysis(histo); + float threshold2 = threshold_histo(avg_histo(histo_grp[1]), + var_histo(histo_grp[1]), + avg_histo(histo_grp[0]), + var_histo(histo_grp[0])); + float threshold3 = threshold2_histo(avg_histo(histo_grp[1]), + var_histo(histo_grp[1]), + avg_histo(histo_grp[0]), + var_histo(histo_grp[0])); + float threshold4 = min_error(histo_grp[1],histo_grp[0]); + + std::cout << "threshold = " << threshold << std::endl; + std::cout << "threshold2 = " << threshold2 << std::endl; + std::cout << "threshold3 = " << threshold3 << std::endl; + std::cout << "threshold4 = " << threshold4 << std::endl; + std::cout << "avg_grp1 = " << avg_histo(histo_grp[1]) << std::endl; + std::cout << "avg_grp2 = " << avg_histo(histo_grp[0]) << std::endl; + + // compute the classification matrix + // for each sub population + + float c00 = cnt_histo(histo_grp[1] | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + + float c10 = cnt_histo(histo_grp[1] | mln::box1d(mln::point1d(threshold+1), + mln::point1d(255))); + + float c01 = cnt_histo(histo_grp[0] | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + + float c11 = cnt_histo(histo_grp[0] | mln::box1d(mln::point1d(threshold+1), + mln::point1d(255))); + + + std::cout << "pop0 = " << cnt_histo(histo_grp[1]) << std::endl; + std::cout << "pop1 = " << cnt_histo(histo_grp[0]) << std::endl; + std::cout << std::endl; + + std::cout << "c00 = " << c00 << std::endl; + std::cout << "c10 = " << c10 << std::endl; + std::cout << "c01 = " << c01 << std::endl; + std::cout << "c11 = " << c11 << std::endl; + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/histo/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/histo/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/histo/histo.cc b/scribo/sandbox/green/exp/annotating/histo/histo.cc new file mode 100644 index 0000000..90b18e4 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/histo/histo.cc @@ -0,0 +1,366 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Build normalized histograms for visualization only. +/// +/// The goal of this code is to build every normalized histograms of +/// an image database for visual inspection. It enables the +/// comparaison of the densities of each image database and the +/// understanding of the differences in this space. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/convert.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/zero.hh> +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/math/sqrt.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +//============================================================================// +// HISTOGRAM +//============================================================================// + +/// \brief Count the number of pixel in the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the number of pixels in the domain. +/// +/// This is simple counting routine that compute the number of pixels +/// in the domain of the histogram. For each site, it sums the +/// frequencies associated to its site. +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + } + + return cnt; +} + +/// \brief The computing of the normalized histogram. +/// +/// \param[in] input the name of the input image. +/// \param[in] output_map the name of the specific map (hue,sat,val,R,G,B). +/// \param[in] output_histo the name of the output normalized histogram. +/// \param[in] space the name of the studied channel (R,G,B,H,S,V). +/// +/// Compute the normalized histogram in the given channel. First get +/// the channel from the RGB space or the HSV space and then compute +/// its normalized histogram. Normalization is done by dividing each +/// frequency by the sum of frequencies. It's a way to make the +/// histogram independant from the dimension of the input image and +/// then to compare different image databases together. +void histo(const std::string input, + const std::string output_map, + const std::string output_histo, + const char space) + +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_sat_map; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_val_map; + typedef mln::fun::v2v::component<t_rgb8,0> t_component_r; + typedef mln::fun::v2v::component<t_rgb8,1> t_component_g; + typedef mln::fun::v2v::component<t_rgb8,2> t_component_b; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float sum; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + switch(space) + { + case 'h': map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); break; + case 's': map = mln::data::transform(input_rgb8, t_rgb_to_sat_map()); break; + case 'v': map = mln::data::transform(input_rgb8, t_rgb_to_val_map()); break; + case 'r': map = mln::data::transform(input_rgb8, t_component_r()); break; + case 'g': map = mln::data::transform(input_rgb8, t_component_g()); break; + case 'b': map = mln::data::transform(input_rgb8, t_component_b()); break; + default: break;// crash + } + + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = cnt_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + + mln::io::pgm::save(map, output_map.c_str()); + mln::io::plot::save_image_sh(histo_float, output_histo.c_str()); +} + +//============================================================================// +// MAIN +//============================================================================// + + +/// \brief The main entry. +/// +/// This a front end to compute histogram with boost plumberies. There +/// is 2 uses cases, the computing over the icdar database and over +/// the afp database. We force the computing over the six channel +/// (H,S,V,R,G,B) in order to select the channel the most specific for +/// a database. It seems that saturation channel and value channel are +/// the most specific to describe each database. Take care to +/// coherence of the output directories for each source of images. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + +// t_path full_path[] = {t_path(ICDAR_20P_TEXT_ONLY_IMG_PATH), +// t_path(ICDAR_20P_TEXT_COLOR_IMG_PATH), +// t_path(ICDAR_20P_TEXT_PHOTO_IMG_PATH)}; + +// t_path full_path[] = {t_path(AFP_INPUT_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP30_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP20_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP10_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK30_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK20_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK10_IMG_PATH)}; + +// t_path full_path[] = {t_path(ICDAR_20P_INPUT_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_GMP30_IMG_PATH)}; + t_path full_path[] = {t_path(ICDAR_20P_GMP20_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_GMP10_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK30_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK20_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK10_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + t_path directory; + t_path leaf; + t_path output_map; + t_path output_histo; + + std::cerr << "entering " << full_path[i] << std::endl; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + std::cerr << dir_iter->path() << std::endl; + + leaf = dir_iter->path().leaf(); + +// directory = ANNOTATING_AFP_R_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_R_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_R_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_R_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_R_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'r'); + +// directory = ANNOTATING_AFP_G_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_G_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_G_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_G_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_G_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'g'); + +// directory = ANNOTATING_AFP_B_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_B_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_B_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_B_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_B_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'b'); + +// directory = ANNOTATING_AFP_H_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_H_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_H_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_H_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_H_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'h'); + +// directory = ANNOTATING_AFP_S_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_S_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_S_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_S_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_S_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 's'); + +// directory = ANNOTATING_AFP_V_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_V_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_V_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_V_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_V_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'v'); + } + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/hsv/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/hsv/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/hsv/hsv.cc b/scribo/sandbox/green/exp/annotating/hsv/hsv.cc new file mode 100644 index 0000000..507d07b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/hsv/hsv.cc @@ -0,0 +1,912 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet HSV operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Hue +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +/// +/// This operator try to integrate many processing in one shot, after +/// HSV transformation. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/convert.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/zero.hh> +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/math/sqrt.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +//============================================================================// +// HISTOGRAM +//============================================================================// + +/// Histogram +/// \{ +/// \brief Histogram processing methods. +/// +/// In this part, we define methods for histogram processing as average, +/// peak, variance etc ... + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +mln_value(I) count_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) result = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + result += histo(p); + + return result; +} + +/// \brief Sum the whole frequencies. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the number of pixels in the domain. +/// +/// By summing all the frequencies of the domain, we count in fact the +/// number of pixels in the image. +template <typename I> +mln_value(I) sum_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += histo(p); + + return sum; +} + +/// \brief Count the null frequencies. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the number of null frequencies. +/// +/// It's a way to access the number of distinct colors in the +/// histogram by comparing it with the histogram domain. +template <typename I> +mln_value(I) count_null_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) count = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + if (0 == histo(p)) + count++; + + return count; +} + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +mln_coord(mln_site_(I)) peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + mln_value(I) v_max = mln::opt::at(histo, mln::literal::zero); + mln_coord(mln_site_(I)) p_max = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Find the maximum frequency of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the maximum frequency of the histogram. +/// +/// Find the peak and return its value, not its position. We use that +/// function to normalize histograms. +template <typename I> +mln_value(I) max_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the value of the peak from the histogram + mln_value(I) max = mln::opt::at(histo, mln::literal::zero); + mln_piter(I) p(histo.domain()); + + for_all(p) + { + max = mln::math::max(histo(p),max); + } + + return max; +} + +/// \brief Find the mean of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the aveerage of the histogram. +/// +/// Compute the mean. +template <typename I> +float mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief Compare the histogram with the equi distributed histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the distance between the equi distributed histogram. +/// +/// Compute the square euclidian distance between histogram and the +/// equi distributed histogram. The equi distributed histogram is an +/// histogram in which each bin has the same frequency (id est 1/256 +/// for instance). The integral equals one for this two +/// histograms. This is a major contribution test to differentiate +/// image database. +template <typename I> +float cmp_equi_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + var += mln::math::sqr(histo(p) - (1/256.0)); + } + + var = var / sum; + + return var; +} + +/// \brief Compute the variance on the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the variance. +/// +/// Compute the variance by substracting the mean. +template <typename I> +float var_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + var += mln::math::sqr(p.ind() - mean) * histo(p); + } + + var = var / sum; + + return var; +} + +/// \brief Find the mean of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the aveerage of the histogram. +/// +/// Compute the mean. +template <typename I> +float mean_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum++; + mean += histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief Compute the standard deviation on the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the dstandard deviation. +/// +/// Compute the standard deviation by substracting the mean +template <typename I> +float stddev_frequency_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the var of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum++; + var += mln::math::sqr(histo(p)-mean); + } + + var = mln::math::sqrt(var / sum); + + return var; +} +/// \} + +//============================================================================// +// HUE TEST +//============================================================================// + +/// Hue test +/// \{ +/// \brief Hue test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the hue analysis. + + +/// \brief Label a grey value. +/// +/// \param[in] int_u8 the grey value. +/// +/// \return the reference color for this grey value. +/// +/// Classify grey value in three class, white, black and medium_gray. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the +/// primary colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + + if (10 > hue) + result = mln::literal::red; + else if (32 > hue) + result = mln::literal::orange; + else if (53 > hue) + result = mln::literal::yellow; + else if (74 > hue) + result = mln::literal::green; // chartreuse + else if (96 > hue) + result = mln::literal::green; + else if (116 > hue) + result = mln::literal::green;// turquoise, aigue-marine + else if (138 > hue) + result = mln::literal::green; // cyan + else if (159 > hue) + result = mln::literal::blue; // azur + else if (181 > hue) + result = mln::literal::blue; + else if (202 > hue) + result = mln::literal::violet; + else if (223 > hue) + result = mln::literal::pink; + else // if (244 > hue) + result = mln::literal::red; + + return result; +} + +/// \brief The hue test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// \param[in] threshold the limit to compute the histogram contribution. +/// +/// \return the proportion of pixels that pass the test. +/// +/// This is an augmented test. We first normalized the histogram to +/// obtain a discrete distribution. Then we compute the mean (or the +/// peak, i don't really know what is the best) and we look at the +/// proportion of the pixels around the mean or the peak. This +/// proportion tell us how histogram distribution is concentrated +/// around the modal value. If a hue modal value exist, we can say +/// that the image is colorized. +float hue_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float prop; + short peak; + mln::value::rgb8 color; + float sum; + mln::point1d inf; + mln::point1d sup; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + peak = mean_histo(histo); //peak_histo(histo); + color = label_hue(peak); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo_float|mln::box1d(inf,sup)); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); +// std::cout << "peak = " << peak << std::endl; +// std::cout << "color = " << color << std::endl; + + return prop; +} +/// \} + +//============================================================================// +// SATURATION TEST +//============================================================================// + +/// Saturation test +/// \{ +/// \brief Saturation test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the +/// saturation analysis. + + +/// \brief The saturation test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// \param[in] threshold for deciding which is low saturation or not. +/// +/// \return the proportion of pixels which pass the test. +/// +/// This is the augmented test, we normalized the histogram and then +/// we count the proportion of the histogram below the threshold. +float saturation_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_saturation_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float sum; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_saturation_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + cnt1 = count_histo(histo_float | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} +/// \} + +//============================================================================// +// VALUE TEST +//============================================================================// + +/// Value test +/// \{ +/// \brief Value test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the +/// value analysis. + + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief This is the Value test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of result histogram. +/// \param[in] tmp the name of the output value (HSV) map. +/// \param[in] threshold the range around the peak where R is computed. +/// +/// \return the proportion of pixels that pass the test. +/// +/// This is the augmented version of the Millet test. This code is not +/// stable. There is a lots of tests on. The Millet test is computed +/// with stddev2 call. We test simple counting around the pic as we +/// do for other tests but it's not really insteresting. The prop4 is +/// the best for me. We test the difference between the distribution and the +/// equi-distribution. +float value_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float prop; + float sum; + float prop4; + short peak; + mln::point1d inf; + mln::point1d sup; + + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_value_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + prop4 = cmp_equi_frequency_histo(histo_float); + peak = peak_histo(histo); // mean_histo(histo); + //prop = stddev2(histo, peak, threshold); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo_float|mln::box1d(inf,sup)); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + std::cerr << "peak = " << peak << std::endl; + std::cerr << "inf = " << inf << std::endl; + std::cerr << "sup = " << sup << std::endl; + std::cerr << "cnt1 = " << cnt1 << std::endl; + std::cerr << "cnt2 = " << cnt2 << std::endl; + std::cerr << "prop = " << prop << std::endl; + std::cerr << "prop4= " << prop4 << std::endl; + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + +//============================================================================// +// MAIN +//============================================================================// + + +/// \brief The main entry. +/// +/// This is a front end for launching image processing test. We deal +/// with boost library to walk on image database directories. Nothing +/// very interesting here, it is a lot of plumberies. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + +// t_path full_path[] = {t_path(ICDAR_20P_TEXT_ONLY_IMG_PATH), +// t_path(ICDAR_20P_TEXT_COLOR_IMG_PATH), +// t_path(ICDAR_20P_TEXT_PHOTO_IMG_PATH)}; + + t_path full_path[] = {t_path(AFP_PPM_IMG_PATH)}; + + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "#HUE - SATURATION - VALUE" << std::endl; + std::cout << "plot '-' using 1:2 with point notitle,\\" << std::endl; + std::cout << " '-' using 1:2 with point notitle,\\" << std::endl; + std::cout << " '-' using 1:2 with point notitle" << std::endl; + + for (int i = 0; i < 1; ++i) + { + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + t_path directory; + t_path leaf; + t_path output; + t_path tmp; + + std::cerr << "entering " << full_path[i] << std::endl; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + std::cerr << dir_iter->path() << std::endl; + // concatenation de chaine +// directory = (ANNOTATING_ICDAR_H_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_H_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = hue_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 20); + + std::cout << prop << " "; + +// directory = (ANNOTATING_ICDAR_S_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_S_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = saturation_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 25); + + std::cout << prop << " "; + +// directory = (ANNOTATING_ICDAR_V_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_V_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = value_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 15); + + std::cout << prop << " "; + std::cout << "# " << dir_iter->path().leaf() << std::endl; + } + std::cout << "e" << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/hue/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/hue/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/hue/hue.cc b/scribo/sandbox/green/exp/annotating/hue/hue.cc new file mode 100644 index 0000000..6e1195c --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/hue/hue.cc @@ -0,0 +1,402 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet hue operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Hue +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/value/rgb8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + + + +/// \brief Label a grey value. +/// +/// \param[in] int_u8 the grey value. +/// +/// \return the reference color for this grey value. +/// +/// Classify grey value in three class, white, black and medium_gray. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the +/// primary colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + + if (10 > hue) + result = mln::literal::red; + else if (32 > hue) + result = mln::literal::orange; + else if (53 > hue) + result = mln::literal::yellow; + else if (74 > hue) + result = mln::literal::green; // chartreuse + else if (96 > hue) + result = mln::literal::green; + else if (116 > hue) + result = mln::literal::green;// turquoise, aigue-marine + else if (138 > hue) + result = mln::literal::green; // cyan + else if (159 > hue) + result = mln::literal::blue; // azur + else if (181 > hue) + result = mln::literal::blue; + else if (202 > hue) + result = mln::literal::violet; + else if (223 > hue) + result = mln::literal::pink; + else // if (244 > hue) + result = mln::literal::red; + + +// if (14 > hue) +// result = mln::literal::red; +// else if (29 > hue) +// result = mln::literal::orange; +// else if (45 > hue) +// result = mln::literal::yellow; +// else if (113 > hue) +// result = mln::literal::green; +// else if (149 > hue) +// result = mln::literal::cyan; +// else if (205 > hue) +// result = mln::literal::blue; +// else if (235 > hue) +// result = mln::literal::violet; +// else if (242 > hue) +// result = mln::literal::pink; +// else +// result = mln::literal::red; + + return result; +} + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Compute the average of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the average of the histogram. +/// +/// Compute the mean of the histogram by summing the values of each +/// bin ponderated by its frequency. The result is divided by the sum +/// of the frequencies. +template <typename I> +unsigned mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + + +/// \brief The hue test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// +/// \return the proportion of pixels that pass the test. +/// +/// Load the input image, transform it to get the hue_map and compute +/// the histogram. Then count the number of pixels that are between +/// peak-threshold and peak +threshold. Print the peak and the +/// percentage of pixels around the pic. +float hue_test(const std::string input, + const std::string output, + const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + unsigned peak; + mln::value::rgb8 color; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + peak = mean_histo(histo); //peak_histo(histo); + color = label_hue(peak); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(peak-threshold), + mln::point1d(peak+threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + std::cout << "peak = " << peak << std::endl; + std::cout << "color = " << color << std::endl; + + return prop; +} + + +/// \brief Main entry. +/// +/// It is a front end which managing the iteration through one image +/// data base with boost. Save hue map and hue histogram. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_ICDAR_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = hue_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 20); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/hue/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/hue/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/hue/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/hue/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/hue/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/hue/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/nb_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/nb_color/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc b/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc new file mode 100644 index 0000000..2331c61 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc @@ -0,0 +1,171 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Compute the number of colors in an image. +/// +/// Build histogram of colors and count the bins different from zero. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +// #include <mln/morpho/opening/volume.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/pw/value.hh> +#include <mln/pw/cst.hh> + +#include <mln/util/timer.hh> + +#include <mln/value/rgb8.hh> +//#include <mln/value/rgb.hh> + + +/// \brief Count the colors. +/// +/// \param[in] image the name of the image to process. +/// +/// \return the number of colors. +/// +/// Count the color by building histogram. Strange filtering is +/// done. We use also the technique of regional maxima in +/// comment. Quantification is done to reduce the size of the +/// histogram as well. + +// n < 8, n is the degree of quantification +template <unsigned n> +unsigned count_image_color(const std::string& image) +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image3d<bool> t_histo3d_bool; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::meta::math::count t_count_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgbn input_rgbn; + t_image2d_rgbn output_rgbn; + t_histo3d histo; +// t_histo3d opened; + t_histo3d_bool filtered; + + mln::io::ppm::load(input_rgb8, image.c_str()); + + unsigned nb_pixel = input_rgb8.ncols() * input_rgb8.nrows(); + unsigned min_volume = (unsigned)(nb_pixel * 0.0001); + unsigned nb_color = 0; + +// input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); + histo = mln::data::compute(t_histo3d_fun(), input_rgb8); +// opened = mln::morpho::opening::volume(histo, mln::c6(), min_volume); + filtered = mln::binarization::threshold(histo, min_volume); + nb_color = mln::data::compute(t_count_fun(), + (filtered|(mln::pw::value(filtered)!=0)).rw()); + + return nb_color; +} + + +/// \brief Main entry. +/// +/// The main routine makes the base driver job. It looks after many +/// directories and lists every images in it. We use the boost library +/// to do it. Statistics are computed on the number of colors. The +/// first idea was to answer the question is this descriptor allow to +/// recognize a specific base. +int main() +{ + typedef boost::filesystem::path t_path; + //typedef boost::filesystem::initial_path<t_path()> t_init_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + unsigned count = 0; + unsigned sum1 = 0; + unsigned sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + unsigned val = count_image_color<8>(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/saturation/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/saturation/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/saturation/saturation.cc b/scribo/sandbox/green/exp/annotating/saturation/saturation.cc new file mode 100644 index 0000000..b5834a1 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/saturation/saturation.cc @@ -0,0 +1,175 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet saturation operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Sat +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_saturation_map.hh> + +#include <mln/io/ppm/load.hh> +//#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief The saturation test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] threshold for deciding which is low saturation or not. +/// +/// \return the proportion of pixels that pass the test. +/// +/// Load the input image, transform it to get the saturation_map and compute +/// the histogram. Then count the number of pixels that are under the threshold. +/// Then return the proportion of pixels. +float saturation_test(const std::string input, + const std::string output, +// const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_saturation_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_saturation_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); +// mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + + +/// \brief the main entry. +/// +/// This is a front end for image processing routine. It manages the +/// calling of every image in the database. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_ICDAR_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = saturation_test(dir_iter->path().string(), + output.string(), +// tmp.string(), + 25); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/saturation/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/saturation/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/saturation/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/stddev_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/stddev_color/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc b/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc new file mode 100644 index 0000000..365a9c3 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc @@ -0,0 +1,216 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +float stddev_color(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + t_histo1d histo; + t_point1d max_site; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + return stddev; +} + + +/// \brief Main entry. +/// +/// Front end for image processing. It is in the main routine where we +/// go through the image data base (using boost to do it). +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float count = 0; + float sum1 = 0; + float sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + float val = stddev_color(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/stddev_color_16/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/stddev_color_16/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc b/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc new file mode 100644 index 0000000..fc55547 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc @@ -0,0 +1,277 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. When photographies +/// has some large uniform border, the detection can fail. To improve +/// the method, Millet decide to subdivise the image in 16 sub images +/// on which it applies the test. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> +#include <mln/data/paste.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> +#include <mln/math/max.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] input_int_u8 the 8 bits input image to analyze. +/// \param[in] name_histo the name of the output histogram. +/// \param[in] name_image the name of the ouput sub image. +/// +/// \return the deviation, but at this time nothing.. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +float stddev_color(mln::image2d<mln::value::int_u8> input_int_u8) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_histo1d histo; + + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + return stddev; +} + +/// \brief Divide the image in 16 sub images. +/// +/// \param[in] image the input image. +/// +/// \result nothing. +/// +/// Divive the input image in 16 by uniform and geometrical +/// method. When a sub image is ready, call the stddev routine to show +/// stats on it. +float stddev_color_16(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 4; + unsigned sz_col = (pmax.col() - pmin.col())/ 4; + float stddev = 0.0; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 4; ++i) + for (unsigned j = 0; j < 4; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + // Save it + t_image2d_int_u8 input_1o16_int_u8(dom); + + mln::data::paste(input_int_u8 | dom, input_1o16_int_u8); + + stddev = mln::math::max(stddev, stddev_color(input_1o16_int_u8)); + } + + return stddev; +} + + +/// \brief Main entry. +/// +/// Just a front end for image processing routine. Use boost to go +/// through the image database. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float count = 0; + float sum1 = 0; + float sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + float val = stddev_color_16(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/value/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/value/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/value/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/value/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/value/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/value/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/value/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/value/text-only.txt diff --git a/scribo/sandbox/green/exp/annotating/value/value.cc b/scribo/sandbox/green/exp/annotating/value/value.cc new file mode 100644 index 0000000..cade88b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/value/value.cc @@ -0,0 +1,468 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet value operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to value +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +/// \brief Label the value. +/// +/// \param[in] val the value in [0..255]. +/// +/// \return the reference color. +/// +/// Segment the grey value in three classes. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +/// +/// Discriminate between orange and brown colors which are at the same +/// position on the hue wheel. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +/// +/// Segment between yellow and green because the discrimination is not +/// so easy based on the hue wheel. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the primary +/// colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + if (14 > hue) + result = mln::literal::red; + else if (29 > hue) + result = mln::literal::orange; + else if (45 > hue) + result = mln::literal::yellow; + else if (113 > hue) + result = mln::literal::green; + else if (149 > hue) + result = mln::literal::cyan; + else if (205 > hue) + result = mln::literal::blue; + else if (235 > hue) + result = mln::literal::violet; + else if (242 > hue) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Detect the peak of the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the position of the peak. +/// +/// This is a typical accumulator use case but as it needs the +/// position it couldn't be one of them. Look at the last maxima in +/// the curve and return its position. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Compute the average of the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the average of the histogram. +/// +/// This code should be an accumulator. It computes the mean of the +/// histogram, just stats in loop. Compute the sum, compute the +/// ponderate sum and divide the second by the first. +template <typename I> +unsigned mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// \param[in] peak the peak of the histogram. +/// \param[in] limit the threshold to compute the contribution. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief This is the main Millet test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of result histogram. +/// \param[in] tmp the name of the output value (HSV) map. +/// \param[in] threshold the range around the peak where R is computed. +/// +/// \return the proportion of pixels that pass the test. +float value_test(const std::string input, + const std::string output, + const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + unsigned peak; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_value_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + peak = peak_histo(histo); // mean_histo(histo); + prop = stddev2(histo, peak, threshold); +// cnt1 = count_histo(histo | mln::box1d(mln::point1d(peak-threshold), +// mln::point1d(peak+threshold))); +// cnt2 = mln::geom::nsites(input_rgb8); +// prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + + +/// \brief The main entry. +/// +/// This is the front end for using directories with boost. It calls +/// the true image processing routine. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = value_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 15); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/scribo/sandbox/green/mln/clustering/kmean2d.hh b/scribo/sandbox/green/mln/clustering/kmean2d.hh index 51aaf49..4539083 100644 --- a/scribo/sandbox/green/mln/clustering/kmean2d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean2d.hh @@ -150,7 +150,7 @@ namespace mln { /// Type definitions. /// \brief A few type definitions to limit the refactoring impact. - ///{ + /// \{ typedef value::rgb<8> t_rgb; typedef value::label<8> t_label; typedef value::rg<n> t_value; @@ -179,7 +179,7 @@ namespace mln typedef util::array<t_mean_set> t_mean_cnv; typedef image1d<t_result1d> t_variance_val; typedef util::array<t_variance_val> t_variance_cnv; - ///} + /// \} /// \brief Constructor. /// \param[in] point : the image as the population of pixels. diff --git a/scribo/sandbox/green/mln/fun/p2b/achromatic.hh b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh index 1f45b2d..91a2dc5 100644 --- a/scribo/sandbox/green/mln/fun/p2b/achromatic.hh +++ b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh @@ -44,15 +44,29 @@ namespace mln namespace p2b { - /// \brief Functor that compare the i-th component of a value. - // V is for the type of the value received - // i is the ith component to select + /// \brief Functor that compare the i-th component with a threshold. + /// + /// T_rgb is the kind of RGB we use. template <typename T_rgb> struct achromatic : public Function_v2b< achromatic<T_rgb> > { typedef bool result; + + /// \brief Operator that makes the threshold [millet.phd.2008.pdf] + /// + /// \param[in] p the selected site + /// + /// \return if the site is achromatic or not. + /// + /// This a try for opimized the call to the achromatic + /// routine. The goal is to decide if a site has got an + /// achromatic status or not. This is the true Millet test. bool operator()(const point2d& p) const; + /// \brief Cstor of the object. + /// + /// \param[in] img the RGB input image. + /// \param[in] threshold the value to compare with. achromatic(const image2d<T_rgb>& img, const float threshold); const float threshold_; diff --git a/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh index 84d26c8..e776c9a 100644 --- a/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh +++ b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh @@ -32,6 +32,10 @@ # include <mln/value/hsv.hh> # include <mln/value/rgb8.hh> +/// \file +/// +/// This is the code for building hue_concentration_map. + namespace mln { @@ -41,12 +45,18 @@ namespace mln namespace v2v { + /// \brief internal method for detecting the histogram peak. + /// + /// \param[in] hue_histo the histogram of hue. + /// + /// \return the seed of the peek. unsigned peak_histo(const mln::image1d<unsigned>& hue_histo); struct hue_concentration : public Function_v2v< hue_concentration > { typedef float result; + /// \brief Gibe the distance map between actual hue and the peak. float operator()(const float hue) const; hue_concentration(const mln::image1d<unsigned>& hue_histo) diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh index 6de63ec..126f9bd 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh @@ -49,6 +49,11 @@ namespace mln /// \brief Convert rgb value to achromatism map. /// + /// Convert rgb value to binary achromastism map using the + /// rebuilded Millet phd formulae [millet.phd.2008.pdf]. The + /// idea is to look at the minimum and maximum of the channels + /// and to return the difference. + /// /// \ingroup modfunv2v template <unsigned n> diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh index 3dd8eb0..a191095 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh @@ -34,6 +34,11 @@ #include <mln/value/hsv.hh> #include <mln/value/rgb.hh> +/// \fiie +/// +/// This is the millet [millet.phd.2008.pdf] transformation from RGB +/// space to HSV space. + namespace mln { @@ -57,7 +62,15 @@ namespace mln typedef T_hsv result; - /// HSV implementation from millet.2008.phd.pdf p67 + /// \brief HSV implementation from millet.2008.phd.pdf p67 + /// + /// \param[in] rgb the input rgb pixel. + /// + /// \return a HSV pixel. + /// + /// This is the Millet implementation of its transformation + /// operator to go from RGB space to HSV space. When pixels + /// are achromatic, hue equals -1. template <typename T_rgb> T_hsv operator()(const T_rgb& rgb) const; -- 1.5.6.5

13 years, 7 months

last-svn-commit-50-gf23ea9c Import annotating sources from milena green's sandbox.

by Yann Jacquelet

* README.green: New. * demo/annotating/bic/Makefile.am: New. * demo/annotating/bic/bic.cc: New. * demo/annotating/hsv/Makefile.am: New. * demo/annotating/hsv/hsv.cc: New. * demo/annotating/lep/Makefile.am: New. * demo/annotating/lep/lep.cc: New. * demo/annotating/nb_color/Makefile.am: New. * demo/annotating/nb_color/nb_color.cc: New. * demo/annotating/project/Makefile.am: New. * demo/annotating/project/project.cc: New. * demo/annotating/rgb_64/Makefile.am: New. * demo/annotating/rgb_64/rgb_64.cc: New. * demo/annotating/rgb_64_9/Makefile.am: New. * demo/annotating/rgb_64_9/rgb_64_9.cc: New. * demo/annotating/stddev_color/Makefile.am: New. * demo/annotating/stddev_color/stddev_color.cc: New. * demo/annotating/stddev_color_16/Makefile.am: New. * demo/annotating/stddev_color_16/stddev_color_16.cc: New. * exp/annotating/achromastism/Makefile.am: New. * exp/annotating/achromastism/achromastism.cc: New. * exp/annotating/achromastism/text-color.txt: New. * exp/annotating/achromastism/text-img.txt: New. * exp/annotating/achromastism/text-only.txt: New. * exp/annotating/bench/Makefile.am: New. * exp/annotating/bench/bench.cc: New. * exp/annotating/error/Makefile.am: New. * exp/annotating/error/error.cc: New. * exp/annotating/histo/Makefile.am: New. * exp/annotating/histo/histo.cc: New. * exp/annotating/hsv/Makefile.am: New. * exp/annotating/hsv/hsv.cc: New. * exp/annotating/hue/Makefile.am: New. * exp/annotating/hue/hue.cc: New. * exp/annotating/hue/text-color.txt: New. * exp/annotating/hue/text-img.txt: New. * exp/annotating/hue/text-only.txt: New. * exp/annotating/nb_color/Makefile.am: New. * exp/annotating/nb_color/nb_color.cc: New. * exp/annotating/saturation/Makefile.am: New. * exp/annotating/saturation/saturation.cc: New. * exp/annotating/saturation/text-color.txt: New. * exp/annotating/saturation/text-img.txt: New. * exp/annotating/saturation/text-only.txt: New. * exp/annotating/stddev_color/Makefile.am: New. * exp/annotating/stddev_color/stddev_color.cc: New. * exp/annotating/stddev_color_16/Makefile.am: New. * exp/annotating/stddev_color_16/stddev_color_16.cc: New. * exp/annotating/value/Makefile.am: New. * exp/annotating/value/text-color.txt: New. * exp/annotating/value/text-img.txt: New. * exp/annotating/value/text-only.txt: New. * exp/annotating/value/value.cc: New. * mln/clustering/kmean2d.hh: New. * mln/fun/p2b/achromatic.hh: New. * mln/fun/v2v/hue_concentration.hh: New. * mln/fun/v2v/rgb_to_achromatism_map.hh: New. * mln/fun/v2v/rgb_to_hsv.hh: New. --- scribo/sandbox/green/ChangeLog | 64 + scribo/sandbox/green/README.green | 507 +++++++- .../kmean1d => annotating/bic}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/bic/bic.cc | 122 ++ .../kmean1d => annotating/hsv}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/hsv/hsv.cc | 721 ++++++++++ .../kmean1d => annotating/lep}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/lep/lep.cc | 127 ++ .../kmean1d => annotating/nb_color}/Makefile.am | 0 .../green/demo/annotating/nb_color/nb_color.cc | 143 ++ .../kmean1d => annotating/project}/Makefile.am | 0 .../green/demo/annotating/project/project.cc | 275 ++++ .../kmean1d => annotating/rgb_64}/Makefile.am | 0 .../sandbox/green/demo/annotating/rgb_64/rgb_64.cc | 80 ++ .../kmean1d => annotating/rgb_64_9}/Makefile.am | 0 .../green/demo/annotating/rgb_64_9/rgb_64_9.cc | 132 ++ .../stddev_color}/Makefile.am | 0 .../demo/annotating/stddev_color/stddev_color.cc | 191 +++ .../stddev_color_16}/Makefile.am | 0 .../annotating/stddev_color_16/stddev_color_16.cc | 261 ++++ .../achromastism}/Makefile.am | 0 .../exp/annotating/achromastism/achromastism.cc | 179 +++ .../exp/annotating/achromastism/text-color.txt | 0 .../green/exp/annotating/achromastism/text-img.txt | 0 .../exp/annotating/achromastism/text-only.txt | 0 .../bench}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/bench/bench.cc | 1450 ++++++++++++++++++++ .../error}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/error/error.cc | 833 +++++++++++ .../histo}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/histo/histo.cc | 366 +++++ .../hsv}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/hsv/hsv.cc | 912 ++++++++++++ .../hue}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/hue/hue.cc | 402 ++++++ .../green/exp/annotating/hue}/text-color.txt | 0 .../sandbox/green/exp/annotating/hue}/text-img.txt | 0 .../green/exp/annotating/hue}/text-only.txt | 0 .../nb_color}/Makefile.am | 0 .../green/exp/annotating/nb_color/nb_color.cc | 171 +++ .../saturation}/Makefile.am | 0 .../green/exp/annotating/saturation/saturation.cc | 175 +++ .../exp/annotating/saturation}/text-color.txt | 0 .../green/exp/annotating/saturation}/text-img.txt | 0 .../green/exp/annotating/saturation}/text-only.txt | 0 .../stddev_color}/Makefile.am | 0 .../exp/annotating/stddev_color/stddev_color.cc | 216 +++ .../stddev_color_16}/Makefile.am | 0 .../annotating/stddev_color_16/stddev_color_16.cc | 277 ++++ .../value}/Makefile.am | 0 .../green/exp/annotating/value}/text-color.txt | 0 .../green/exp/annotating/value}/text-img.txt | 0 .../green/exp/annotating/value}/text-only.txt | 0 scribo/sandbox/green/exp/annotating/value/value.cc | 468 +++++++ scribo/sandbox/green/mln/clustering/kmean2d.hh | 4 +- scribo/sandbox/green/mln/fun/p2b/achromatic.hh | 20 +- .../sandbox/green/mln/fun/v2v/hue_concentration.hh | 10 + .../green/mln/fun/v2v/rgb_to_achromatism_map.hh | 5 + scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh | 15 +- 59 files changed, 8105 insertions(+), 21 deletions(-) copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/bic}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/bic/bic.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/hsv/hsv.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/lep}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/lep/lep.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/nb_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/project}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/project/project.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/rgb_64}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/rgb_64_9}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/stddev_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/stddev_color_16}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/achromastism}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-color.txt (100%) copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-img.txt (100%) copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/bench}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/bench/bench.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/error}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/error/error.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/histo}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/histo/histo.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/hsv/hsv.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/hue}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/hue/hue.cc copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/nb_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/saturation}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/saturation/saturation.cc copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/stddev_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/stddev_color_16}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/value}/Makefile.am (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-only.txt (100%) create mode 100644 scribo/sandbox/green/exp/annotating/value/value.cc diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 5e4cb27..6210a79 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,67 @@ +2010-09-08 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Import annotating sources from milena green's sandbox. + + * README.green: New. + * demo/annotating/bic/Makefile.am: New. + * demo/annotating/bic/bic.cc: New. + * demo/annotating/hsv/Makefile.am: New. + * demo/annotating/hsv/hsv.cc: New. + * demo/annotating/lep/Makefile.am: New. + * demo/annotating/lep/lep.cc: New. + * demo/annotating/nb_color/Makefile.am: New. + * demo/annotating/nb_color/nb_color.cc: New. + * demo/annotating/project/Makefile.am: New. + * demo/annotating/project/project.cc: New. + * demo/annotating/rgb_64/Makefile.am: New. + * demo/annotating/rgb_64/rgb_64.cc: New. + * demo/annotating/rgb_64_9/Makefile.am: New. + * demo/annotating/rgb_64_9/rgb_64_9.cc: New. + * demo/annotating/stddev_color/Makefile.am: New. + * demo/annotating/stddev_color/stddev_color.cc: New. + * demo/annotating/stddev_color_16/Makefile.am: New. + * demo/annotating/stddev_color_16/stddev_color_16.cc: New. + * exp/annotating/achromastism/Makefile.am: New. + * exp/annotating/achromastism/achromastism.cc: New. + * exp/annotating/achromastism/text-color.txt: New. + * exp/annotating/achromastism/text-img.txt: New. + * exp/annotating/achromastism/text-only.txt: New. + * exp/annotating/bench/Makefile.am: New. + * exp/annotating/bench/bench.cc: New. + * exp/annotating/error/Makefile.am: New. + * exp/annotating/error/error.cc: New. + * exp/annotating/histo/Makefile.am: New. + * exp/annotating/histo/histo.cc: New. + * exp/annotating/hsv/Makefile.am: New. + * exp/annotating/hsv/hsv.cc: New. + * exp/annotating/hue/Makefile.am: New. + * exp/annotating/hue/hue.cc: New. + * exp/annotating/hue/text-color.txt: New. + * exp/annotating/hue/text-img.txt: New. + * exp/annotating/hue/text-only.txt: New. + * exp/annotating/nb_color/Makefile.am: New. + * exp/annotating/nb_color/nb_color.cc: New. + * exp/annotating/saturation/Makefile.am: New. + * exp/annotating/saturation/saturation.cc: New. + * exp/annotating/saturation/text-color.txt: New. + * exp/annotating/saturation/text-img.txt: New. + * exp/annotating/saturation/text-only.txt: New. + * exp/annotating/stddev_color/Makefile.am: New. + * exp/annotating/stddev_color/stddev_color.cc: New. + * exp/annotating/stddev_color_16/Makefile.am: New. + * exp/annotating/stddev_color_16/stddev_color_16.cc: New. + * exp/annotating/value/Makefile.am: New. + * exp/annotating/value/text-color.txt: New. + * exp/annotating/value/text-img.txt: New. + * exp/annotating/value/text-only.txt: New. + * exp/annotating/value/value.cc: New. + * mln/clustering/kmean2d.hh: New. + * mln/fun/p2b/achromatic.hh: New. + * mln/fun/v2v/hue_concentration.hh: New. + * mln/fun/v2v/rgb_to_achromatism_map.hh: New. + * mln/fun/v2v/rgb_to_hsv.hh: New. + + 2010-07-07 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Import regional maxima sources from milena green's sandbox. diff --git a/scribo/sandbox/green/README.green b/scribo/sandbox/green/README.green index 7886914..5d6e147 100644 --- a/scribo/sandbox/green/README.green +++ b/scribo/sandbox/green/README.green @@ -685,21 +685,6 @@ rechercher dans les derniers répertoires mp00042c et mp00307c. * doc/labeling/mp00307c: Test sur la representativité des couleurs trouvées. -Faire le changelog, il y a de l'aide dans git/doc/Changelog -... et commiter - - -XI ANNOTATING --------------- - -==> to do - - - -* doc/annotating - - - XI AUTRES ASPECTS DOCUMENTAIRES ------------------------------- @@ -732,3 +717,495 @@ l'effort. * doc/formulae: LaTex directory. * doc/quick_tour: LaTex directory. + +XII ANNOTATING +-------------- + +Tout d'abord, voici les notes documentaires qui ont été réalisées sur +la problématique d'annotation d'image. On trouvera un fichier +class.txt qui a pour but de poser quelques réflexions sur les types de +classes de document. Les informations ne sont pas abouties mais +permettent de défricher un peu le terrain. Le document +syntheseMillet2008 est un compte-rendu de lecture des parties +relatives à nos travaux dans la thèse de Millet. Pour bien comprendre +mon travail, il est impératif de lire les travaux de Millet, ou +simplement ce compte-rendu. Le dernier document est moins intéressant, +il s'agit d'une note de travail sur les indicateurs de Millet bruts de +fonderie (testMillet2008). Cette note n'est ni achevée, ni +aboutie. Elle conclue sur le fait que les indicateurs et les seuils +donnés par Millet dans sa thèse sont complètement à revoir pour nos +besoins. Notemment, des tests sur la détection des images par rapport +aux cliparts nous ont convaincu que certaines images AFP contiennent +des de grandes zones homogènes qui induisent des erreurs dans les +prédicteurs de Millet. Néanmoins, les cas particuliers qui +contredisent les aspects opérationnnels de Millet n'enlèvent pas sa +réflexion: Les images noir/blanc (ou monochromes) ont une très faible +saturation, et/ou une forte concentration de la teinte. Les cliparts +ont une forte concentration de niveaux de gris autour d'un pic. + +* doc/annotating: La documentation relative à l'annotation. + + +Après la lecture des descripteurs de Millet, un des premiers réflexe a +été d'en implémenter plusieurs pour voir ce qu'ils pouvaient ressortir +sur les images que nous avions. Le descripteur BIC sépare une image en +deux ensembles de pixels, les points intérieurs et les points +extérieurs. Les points intérieurs ont la propriété d'être de même +couleur que leur 4-voisins. Attention, la couleur est évaluée dans un +espace RGB à 3 bits. + +* demo/annotating/bic: Histogrammes des points intérieurs et extérieurs. + + +Le descripteur LEP, lui, propose de seuiller par sobel l'image et de +faire l'histogramme des configurations du voisinage des points +seuillés. Pour ce faire, on utilise une convolution un peu spéciale +qui va attribué un unique id en fonction du voisinage. + +* demo/annotating/lep: Histogramme des configurations des voisinages des pixels. + + +Un autre descripteur simple est le nombre de couleur dans une +image. Pour cela, il est possible de construire l'histogramme des +couleurs et de compter les cellules pleines. On peut éventuellement +appliquer une quantification sur l'espace des couleurs. La +compilation laisse place à d'étranges warnings sur une comparaison +entre entiers signés et non signés, mais je n'ai pas la main dans mon +code (ou je ne sais pas comment faire) pour enlever ces warnings. Ils +sont récents, je n'avais pas souvenir de les avoir eu. + +* demo/annotating/nb_color: Compte le nombre de couleurs dans une image. +* exp/annotating/nb_color: Adaptation pour fonctionner sur une base d'image. + + +L'histogramme RGB-64 est un descripteur simple qui quantifie les +couleurs sur 2 bits et réalise l'histogramme dans cet espace. C'est +bien sûr une classification gros grain, mais ajouté au reste ... La +version RGB-64-9 ajoute une phase de division de l'image en 9 sous +images. De cette manière, l'histogramme RGB-64 est construit sur les 9 +sous images. Pour former le descripteur final, on fusionne les neufs +histogrammes. + +* demo/annotating/rgb_64: Histogramme couleur dans l'espace RGB-64 (2 bits/axe). +* demo/annotating/rgb_64_9: Histogramme RGB-64 sur les 9 sous images. + + +Le descripteur de projection relaté par Millet est particulier. En +premier lieu l'image est sous échantillonnée pour réduire sa dimension +à 100 x 100 de manière à borner la taille des vecteurs obtenus au +final. Puis l'image est seuillée par sobel (threshold = 100). L'image +est d'abord divisée horizontalement en deux. Puis on établit la +projecton perpendiculairement à la séparation de manière à obtenir +deux vecteurs de 100 valeurs chacunes. On recommence l'opération en +divisant maintenant l'image verticalement. L'union des 4 vecteurs +forme le descripteur de projection. L'information condensée dans ces +vecteurs est simplement la répartition des contours de manière +horizontale ou verticale. + +* demo/annotating/project: Répartition des contours horizontaux et verticaux. + + +La reconnaissance des cliparts s'appuie sur une analyse d'histogramme +qui est fournie dans le code suivant. L'idée est de dire qu'une image +de type clipart va être reconnaissable surtout à l'aide de ces +contours. La couleur existe mais est très grossière. Le faitde +dessiner à la main implique de simplifier énormément, de caricaturer, +le remplissage. Du coup, une analyse en niveau de gris de +l'histogramme révèle très peu de nuances. Il peut cependant en avoir +un peu. Néanmoins, il existe des logiciels pour aider à la fabrication +des cliparts qui proposent l'usage de dégradé, ce qui nuit à cette +méthode de reconnaissance. Millet analyze l'histogramme normalisé +(histogramme divisé par son pic) et regarde si son energie ne serait +pas concentrée autour du pic (5 pixels de chaque côté +maximum). Parfois cette méthode ne fonctionne pas correctement sur des +photographies qui ont un cadre uniforme. La méthode trouve un pic (le +cadre) et vérifie alors qu'une proportion non négligeable des pixels +sont bien autour de ce pic (tout dépend de l'épaisseur du cadre). Pour +palier à cet inconvénient, Millet propose d'utiliser ce test, non plus +sur l'image entière, mais sur chacune des 16 sous images après un +découpage géométrique régulier. De facto, la contribution du cadre +diminue suffisemment pour repasser en dessous du seuil de +reconnaissance. + +* demo/annotating/stddev_color: Descripteur utilisé reconnaitre des cliparts. +* exp/annotating/stddev_color_16: Adaptation pour le travail sur base. +* demo/annotating/stddev_color_16: Descripteur pour cliparts avec 16 imagettes. +* exp/annotating/stddev_color: Adaptation pour le travail sur une base d'image. + + +A partir de maintenant, tous les morceaux de codes réalisés préparent +directement ou indirectement le résultat de la classification des +bases (exp/annotating/bench). + +Plus de temps pour faire le code use correspondant aux fichiers +librairies. Tout le code pour le faire est dans hsv. Rien de +compliqué, mais allons à l'essentiel. + +Le but du code HSV est d'effectuer les tests de Millet ou des +améliorations sur ces tests. Le premier test proposé par Millet est +l'achromaticité. Il s'agit de regarder s'il existe une faible +variation entre les trois canaux (R/G/B) pour un grand nombre de +pixels. Si c'est le cas, c'est que l'image est presque en niveau de +gris et peut être remplacée facilement par une image grisée sans trop +de distorsions au niveau de la couleur. Nous avons essayer de +généraliser un peu le test de manière à produire, non pas seulement +une réponse sur l'achromaticité de l'image, mais aussi avoir une vue +d'ensemble (sous forme d'image) des variations entre les cannaux pour +chaque pixel. Il n'est pas utile d'analyser les différences sur chacun +des canaux, prendre la différence absolue entre le canal min et le +canal max suffit pour définir le test d'achromaticité. + +Les autres tests de Millet sont la faible saturation et la dominance +de la teinte. Pour savoir si la saturation est faible, il faut +utiliser un histogramme du canal dédié à la saturation. Si 95% des +pixels sont en dessous de 100, alors l'image est faiblement saturée, +elle est en noir et blanc. De la même manière, on regarde la dominance +de la teinte. Pour ce faire, il faut voir si l'histogramme de la +teinte ne possède pas un pic avec une très faible variance (tout +rapproché autour du pic). Si c'est le cas, la dominance de la teinte +est avérée et l'on peut calculé la couleur dominante. L'image est en +niveau de gris, mais colorisé autour d'une couleur (par cepia, vert +...). + +Ce programme est très très sujet à changements, il m'a servit de test +et je ne peux pas juré qu'il est complètement sain au niveau du +traitement. D'autres versions sont potentiellement plus +stables. Notemment celle dans exp/annotating/hsv. + +* mln/fun/v2v/rgb_to_achromatism_map.hh : Distance pour l'achromaticité. +* mln/fun/v2v/achromatic.hh : Define the achromatic map. +* mln/fun/v2v/hue_concentration.hh : Define the distance hue/peak map. +* mln/fun/p2b/achromatic.hh : Say if a site is achromatic. +* demo/annotating/hsv: Code des différents tests de Millet. + + +Dans le répertoire exp/annotating/hue, on trouve 3 fichiers textes qui +rassemblent des classes d'images. Tout d'abord les images ICDAR +n'ayant que du texte et des traces de couleurs (lettrine de couleur, +trait, petit bandeau), un fichier où il n'y a que du texte noir & +blanc et un fichier contenant les images couleurs (avec photographies +ou dessins). Cette classification a été effectuée de manière manuelle. +Le code hue test la proportion de pixels étant autour du pic de +teinte. Il s'agit de savoir si la dominance d'une teinte est +avérée. Le code renvoit la proportion de pixels agglomérés autour du +pic. Le but est de généralisé les tests de millets pour qu'ils se +ressemblent le plus possible. + +* exp/annotating/hue: Implémentation de la généralisation du test de Millet. +* mln/fun/v2v/rgb_to_hue_map.hh : Construction de la map de teinte. + + +On retrouve les trois fichiers permettant de classifier la base ICDAR +en trois sous populations. Le test de saturation consiste simplement à +regarder si une certaine quantité de la population de l'histogramme de +saturation est en dessous d'un certain seuil. La généralisation du +test ne porte pas sur le test en lui-même, mais sur la forme dans +lequel le test est fait. + +* exp/annotating/saturation: Implémentation de la généralisation du test. +* mln/fun/v2v/rgb_to_saturation_map.hh : Construction de la map de saturation. + + +Le test de value a déjà été décrit précédemment dans +stddev_color. L'idée est toujours la même, mais cette fois il est +effectué dans l'espace des valeurs (HSV). Cela ne change pas grand +chose, puisqu'il était utilisé sur des images en niveau de gris. C'est +l'un des tests importants car il réagit à la différentiation entre une +image type photographie et une image plus stylisée comme un +clipart. Ce test a aussi des vertus pour la distinction entre du noir +& blanc et de la couleur. Il s'avère que les images type photographie +avec pleins de couleurs ont un histogramme moins sujet aux pics que +les histogrammes noir & blanc. De facto, l'énergie de l'histogramme +est distribué sur l'ensemble de la plage contrairement aux images noir +& blanc où il y a une concentration de chaque côté de l'histogramme +(bipolarité). + +* exp/annotating/value: Implémentation de la généralisation du test de Millet. +* mln/fun/v2v/rgb_to_value_map.hh : Transformation d'espace. + + +Le programme hsv reprend les tests préalablement élaborés auparavant +sur les plans H, S puis V. Il combine tout en un seul programme pour +avoir une vision plus synthétique de ce qui se passe sur les 3 espaces +simultanément. + +Les tests incorpore mes transformations. C'est à dire que l'on +effectue une série de test équivalent à ceux de Millet (au moins dans +l'idée et le plus souvent, il s'agit d'une réecriture sous une autre +forme) en partant des histogrammes normalisés. Un histogramme +normalisé est un histogramme de flottant qui contient l'histogramme +classique divisé par le nombre total de pixels. En faisant cela, on se +déplace dans l'espace des distributions. Si l'histogramme n'a qu'un +seul pic et que ce dernier contient tous les pixels (pic de dirac par +exemple), alors l'histogramme normalisé donnera 1 comme valeur de +proportion à ce pic. En fait, l'intégrale d'une distribution vaut +1. Le but des distributions est de s'affranchir des caractéristiques +de taille de l'image, ce qui permet de comparer les histogrammes entre +eux. On peut ainsi comparer les histogrammes d'une photo AFP petit +format avec celui d'une image grand format de la base ICDAR. Le but du +jeu est de garder les idées de Millet dans ce nouvel espace. Les tests +obtenus sont équivalents mais pas identiques. + +Pour la teinte, une fois l'histogramme normalisé obtenu, on cherche à +savoir s'il existe un pic avec une forte proportion de l'histogramme +autour de ce pic. Plutôt que le pic, nous prenons la moyenne qui est +un opérateur un peu plus robuste et nous calculons la proportion de +l'histogramme autour (un seuil donné en paramètre défini le périmètre +du calcul). L'expression sous forme de proportion plutôt que sous la +forme d'une variance rend le test homogène avec les autres tests. + +Pour la saturation, après normalisation, on regarde la proportion de +l'histogramme en dessous d'un certain seuil. Il n'y a pas de +changement significatif par rapport au test de Millet. Le seuil +initialement proposé par Millet était 100, il a été adapté car nos +bases sont différentes des siennes (le notre est à 25). + +Le test sur les valeurs (cliparts contre photos) a vraiment un intérêt +au delà de ce cas d'utilisation. Il nous renseigne sur la +concentration autour d'un pic de l'histogramme ou non. Nous préferrons +la mesure de similarité à l'équi-répartition des densités, mais l'idée +est exactement la même. Est-ce que notre histogramme est plat ou +inversement, est-ce que les contributions sont rassemblées autour d'un +pic? Si une image est noir et blanc, il existera un pic correspondant +au fond et la densité s'éloignera fortement de la distribution +équiprobable. Dans le cas maintenant d'une image couleur, la +répartition est plus homogène, couvrant un large spectre dans le +domaine de l'histogramme. Du coup, la distribution semblera davantage +equi-répartie. On notera que nous prenons le test à l'envers de ce que +propose Millet. Il essaye de voir si il y a un pic et calcule une +forme de contribution normalisée autour de ce pic. Nous au contraire, +on regarde l'absence de pic et on calcule la différence entre la +densité et cette absence de pic. Notre avantage par rapport à Millet +est démontré particulièrement dans les cas où il existe plusieurs +grosses distributions. A contrario, notre test souffre des cas où il +existe de nombreuses petites distributions. + + +Voici un premier retour sur les expériementations: +La discrimination entre la base AFP et la base ICDAR peut se faire en +étudiant la forme des densités des niveaux de gris. Les images +naturelles semblent avoir un spectre recouvrant en général les 256 +niveaux de gris alors que les images de documents ont une présence +importante du fond. Dans le cadre d'une densité, ce qui est alloué sur +le fond ne peut se retrouver ailleurs. Une comparaison avec la densité +équiprobable nous renseigne donc sur la nature des images. Il semble +néanmoins qu'un certain nombre d'images défient ce dispositif. Par +exemple des gros plans sur des zones mono-teintée (ski, voile,site +web). + +* exp/annotating/hsv: Code unifiant les trois tests sur chacun des plans HSV. +* mln/fun/v2v/rgb_to_hue_map.hh : Transformation d'espace. +* mln/fun/v2v/rgb_to_saturation_map.hh : Transformation d'espace. +* mln/fun/v2v/rgb_to_value_map.hh : Transformation d'espace. + + +Le test sur l'achromatisme des images est décrit dans le code +suivant. Il a été purement et simplement abandonné dans la mesure où +c'est un cas très particulier qui est repris par une saturation très +faible. La saturation s'exprime comme 1 - min(channel)/max(channel), +mais dans le cas où le min(channel) == max(channel), la saturation +vaut 0. Le problème vient plutôt du calcul de la teine qui ne peut pas +admettre que le min soit égal au max. Pour ce calcul, on se débrouille +pour gérer le cas et renvoyer une valeur standardisée, Millet +proposait -1. + +* exp/annotating/achromatism: Détection d'image couleur en niveau de gris. +* mln/fun/v2v/rgb_to_achromatism_map.hh : Transformation d'espace. + + +Ce programme a pour but de créer les histogrammes normalisés en rafale +pour les bases AFP et ICDAR. Il assure leur création dans les six +plans possibles R,G,B,H,S,V. De cette manière il est possible de +vérifier les corrélations éventuelles entre le canal B et G. Par +ailleurs, après visionnement de tous les histogrammes, on note des +spécificités dans les deux bases sur les plans S et V. Certaines +images ont des réactions très fortes sur le plan H, mais ce n'est pas +une caractéristique pour une base (seulement pour ces images en +question). Les résultats ont déjà été sauvegardés dans le répertoire +image du LRDE. Des infos sont notés à ce sujet dans README.result. Le +seul but de ce calcul est de maîtriser csujet dans README.result. Le +seul but de ce calcul est de maîtriser ce qui se passe dans ces +espaces, pas simplement de supposer ce qu'il pourrait s'y passer. La +comparaison des images est rendu possible quelque soit la base +d'origine par le fait qu'elles sont normalisées. Chaque fréquence des +histogrammes est divisée par la somme des fréquences. On obtient donc +une version discrète des densitées. La somme des nouvelles fréquences +vaut 1. On voit davantage si les densités se rapprochent d'une +équi-répartition ou non. La négative implique un ou plusieurs pics. Le +cas défavorable dans mon approche (qui existe pour certaines images) +est une multitude de tout petits pics autour de l'équi-répartition. Ce +n'est pas équi-répartie pour les calculs mais ce n'est clairement pas +la manisfestation d'une concentration de l'énergie quelque part. + +* exp/annotating/histo: Creation des histogrammes normalisés. +* mln/fun/v2v/rgb_to_hue_map.hh: Transformation d'espace. +* mln/fun/v2v/rgb_to_saturation_map.hh: Transformation d'espace. +* mln/fun/v2v/rgb_to_value_map.hh: Transformation d'espace. + + +Dans le programme erreur, on teste l'idée de jonathan. Une référence à +ce sujet peut être trouvée dans README.img. Les bases AFP et ICDAR +sont retravaillées à l'aide de ImageMagick et de Gimp pour diminuer le +nombre de couleurs initiales. On oblige à n'avoir que 30, 20 ou 10 +couleurs pour chaque base. Il y a donc 4 versions de chacune des bases +en comptant la version originale des images. Les algorithmes utilisés +par Gimp et ImageMagick sont très différents. L'idée de Jonathan était +de dire que lorsqu'une image est couleur, plus on réduit le nombre de +couleur, plus elle change vis-à-vis de l'image originale. Inversement, +si une image possède peu de couleurs (noir & blanc), sa dégradation ne +l'altérera pas tant que cela. Prenons une image d'un skieur de la base +AFP, en réduisant le nombre de couleurs, la combinaison marron va +devenir marron strictement homogène et la perception que nous en avons +est visuellement très altérée. A contrario, une image en noir & blanc +ne semble pas bouger d'un pouce. La comparaison avec la discrimination +sur la saturation ou la valeur montrera des résultats un peu meilleur +plus tard mais il impossible de préjuger a priori des résultats +futurs. L'erreur entre l'image initiale et l'image dégradée est +calculée avec le PNSNR (compression p278 Handbook Color). Le programme +calcule la dégradation et essaie de trouver automatiquement des seuils +de séparation pour les deux bases. 4 détection de seuils sont testées. +Ces seuils sont calculées avec deux bases représentatives des bases à +discriminer. En mode production, le seuil est vu comme une constante +pour le programme. Les deux premières classifications renvoient un +seuil calculée comme une moyenne pondérée des moyennes des populations +à discriminer. Le premier détecteur pondère par la déviation standard +et le second par la variance. Le troisième simule deux populations +gaussiennes de variances différentes et résoud l'équation de second +degré qui en résulte. Enfin, le dernier test ne préjuge pas des +distributions statistiques et réalise la minimisation de l'erreur de +classification. Pour cela, il suffit de compter le nombre d'images +bien classées et mal classées sur les bases d'apprentissage des +populations. On utilise pour cela la méthode Otsu. Le détecteur PNSRN +renvoie une valeur. Pour chaque population (AFP, ICDAR), on construit +l'histogramme de des valeurs obtenues. A noter, qu'il faut que les +valeurs du PNSNR soient ramenées entre 0 et 255. En pratique ce n'est +pas un problème, mais il faut le mettre en oeuvre, dans mon code, ce +n'est pas fait (les valeurs ne posaient pas de problème). Donc, pour +chaque seuil possible (de 0 à 256), on étudie la matrice de classement +(groupe 1 - détecté groupe 1, groupe 1 - détecté groupe 2, groupe 2 - +détecté groupe 1, groupe 2 - détecté groupe 2). L'erreur est +simplement la somme des quantités groupe x - détecté groupe y avec x +!= y. Finalement, pour chaque seuil, il est possible de connaitre +l'erreur. Le processus de minimisation de l'erreur revient à chercher +le seuil associé à l'erreur minimale. C'est ce dernier test que nous +préconisons. Il faut mettre en lumière que ces quatre tests renvoient +à peu près les mêmes seuils. La minimisation de l'erreur étant le +meilleur de tous puisqu'il minimise directement l'erreur de +classification et c'est ce que nous cherchions en fin de compte. Un +cinquième test a été fait avec une analyse de fisher sur l'histogramme +des populations mélangées, mais c'est une idée saugrenue car nous +disposons à ce niveau des populations séparées et évidemment les +résultats sont moins bons. + +Petit rappel. Un vieux problème a été mis à jour ici. Lorsqu'on +calcule des informations sur un histogramme, le type temporaire qui +contient les résultats ne doit pas être le type qui encode les valeurs +des résultats. Par exemple, un histogramme de byte, un calcul de +variance tout simple où il faut stocker la valeur des pixels au carré +multiplié par leur occurrence tiendra facilement dans un long mais pas +dans un byte. Le sucre avec les acesseurs des itérateurs tend à +mélanger les genres. Une solution simple est de stocker la valeur de +l'histogramme dans un type pouvant effectuer les calculs et ensuite +retravailler avec ce type. Nous avions déjà discuté de ce problème et +je ne sais pas si tu avais pu corriger le problème. Si la +classification renvoit n'importe quoi, il se peut que cela provienne +de ce problème. Le problème ne se voit pas sur des calculs de +moyennes, il ne s'observe que sur certains calculs de variance (dépend +de la formule utilisée). + +* exp/annotating/error: Test de l'idée de jonathan (dégradation des couleurs) + + +Le travail dans bench.cc reprend toute sorte de travaux déjà réalisés +dans d'autres fichiers. Il a pour but de comparer ensemble tous les +descripteurs pour la reconnaissance de base de données entre l'AFP et +l'ICDAR. + +Le travail commence avec un certain nombres de routines travaillant +sur les histogrammes permettant de trouver le pic, la moyenne, la +variance et d'autres éléments. Les routines étaient éparpillées dans +le code, du coup je les ai regroupées au même endroit. Il y a des +redondances de code correspondant à des copier/coller ou à différents +essais. Prendre les versions les plus génériques et en faire des +accumulateurs serait un riche idée. + +Huit détecteurs sont comparés ensembles: +- hue1, détection d'un pic de teinte très dense par la méthode de Millet. +- sat1, détection d'une densité importante dans les saturations basses (Millet). +- lvl0, comptage du nombre de niveau de gris (idée de Millet). +- hue0, détection d'un pic de teinte par la méthode des densités. +- sat0, détection d'une forte basse saturation par la méthode des densités. +- val0, détection d'un pic de niveau de gris par la méthode des densités. +- val1, détection d'un pic de niveau de gris par la méthode de Millet. +- err, PNSNR (idée de jonathan). + +FIXME: Attention, le PNSNR n'est pas borné, il faut le faire, il doit avoir +au maximum 255 comme valeur. + +Tous les détecteurs ont été expliqués en large, en long et en travers +dans les sources précédentes. LIRE le chapitre XII (ANNOTATING) de ce fichier +en entier. + +Ensuite vient les séparateurs de population statistiques qui ont été +déjà introduit dans le fichier error. Bien que tous soient présent, +c'est la minimisation de l'erreur qui est utilisée. + +Enfin vient le front end, l'un des plus complexes que j'ai écrit cette +année. Le main lui même, n'est pas la partie la plus complexe. Il +définit les actions fonctionnelles à réaliser et la structure de +donnée réalisée pour garder la trace de tous les résultats. +- File_name : Cette variable retient le nom des fichiers. C'est un tableau +de la taille du nombre d'images rencontrées toute base confondue. Comme on +ne parcourt qu'une fois les répertoires, il faut pouvoir retenir cette +information qui servira éventuellement pour la nomenclature des dumps +par la suite. +- Result : Cette variable contient toutes les informations du traitement. +C'est un tableau à deux dimensions, une pour les images et une autre pour +les descripteurs. L'index d'image est le même que pour file_name. +- Size : Cette variable contient le nombre d'images par database. +- Threshold : Cette variable va stocker les seuils calculés sur chacun +des descripteurs. Ces seuils sont sensés effectuer la séparation entre les +deux bases. +- Cxx : variables de comptage des images bien ou mal classées relativement +à la position des seuils et à l'appartenance d'origine des images. Cette +appartenance d'origine est calculée à l'aide Size qui contient le nombre +d'image par base de données. Comme les images sont vues dans l'ordre, les X +premières appartiennent à la base ICDAR et les Y suivantes à la base AFP. +- histo : Variable servant à effectuer par database et par descripteur un +histogramme utilisé ensuite lors de la discrimination des bases. + +La partie compute_descriptors a pour mission de passer une fois sur +toutes les images et de calculer les descripteurs associés. A l'issue +de cette passe file_name, result et size sont remplis et pourront être +utilisés par les autres routines. + +La partie dump_descriptors a pour but de réaliser un tracé en gnuplot, +avec une couleur différente pour chaque base. Le graphe montre les +valeurs utilisées par chaque descripteur pour toutes les images de la +base. + +La partie correction des descripteurs est optionnelle et force un +certain nombre de valeurs pour être inférieures à 256. Le but était de +pouvoir travaillé, même si les descripteurs n'étaient pas complètement +opérationnels. Elle sert donc pour le debug. + +La partie calcul des histogrammes sert à obtenir la distribution des +valeurs de chacun des descripteurs en fonction des bases +utilisées. C'est une étape préliminaire à l'analyse des populations et +au calcul des seuils. + +La partie calcul des seuils repose sur la minimisation des erreurs de +classification. Cette méthode ne fonctionne bien si la classe zéro à +une moyenne inférieure à la classe un. Du coup, il n'est pas possible +de déterminer de manière simple si la classe 0 correspond à l'AFP ou à +l'ICDAR. Pour chaque descripteur, les cartes sont rebattues. + +Enfin, tous les histogrammes sont sauvés pour pouvoir comprendre et +visualiser les résultats. + +Le main2 ne sert à rien, juste pour des essais. + +Attention, modification VAL0/VAL1, vérifiez que le bon descripteur est +au bon endroit dans le tableau. + +* exp/annotating/bench: Comparaison des détecteurs pour la classif. ICDAR/AFP. \ No newline at end of file diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/bic/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/bic/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/bic/bic.cc b/scribo/sandbox/green/demo/annotating/bic/bic.cc new file mode 100644 index 0000000..f63d260 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/bic/bic.cc @@ -0,0 +1,122 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet BIC descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It quantifies the three channels on 3 +/// bits, then builds two histograms with the interior pixels and the +/// exterior ones. The interior pixels are those where color value is +/// the same as their 4 neighbouring pixels. The descriptor is the +/// fusion of the two histograms. + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/image/image2d.hh> +#include <mln/core/alias/neighb2d.hh> +#include <mln/core/routine/initialize.hh> +#include <mln/core/var.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/transform.hh> +#include <mln/data/fill.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/colors.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Main entry. +/// +/// Load the images, compute the interior and the exterior with the +/// predicate, then compute the two histograms separately. +int main() +{ + typedef mln::value::rgb<3> t_rgb3; + typedef mln::value::rgb8 t_rgb8; + typedef mln::neighb2d t_neighb2d; + typedef mln::image2d<t_rgb3> t_image2d_rgb3; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<3> t_rgb8_to_rgb3; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb3 input_rgb3; + t_image2d_rgb3 interior_rgb3; + t_image2d_rgb3 exterior_rgb3; + t_histo3d histo_exterior; + t_histo3d histo_interior; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + input_rgb3 = mln::data::transform(input_rgb8, t_rgb8_to_rgb3()); + + mln::initialize(interior_rgb3, input_rgb3); + mln::initialize(exterior_rgb3, input_rgb3); + + mln::data::fill(interior_rgb3, mln::literal::black); + mln::data::fill(exterior_rgb3, mln::literal::black); + + mln_piter_(t_image2d_rgb3) p(input_rgb3.domain()); + mln_niter_(t_neighb2d) n(mln::c4(), p); + + for_all(p) + { + bool is_interior = true; + + for_all(n) + is_interior = is_interior && (input_rgb3(p) == input_rgb3(n)); + + if (is_interior) + interior_rgb3(p) = input_rgb3(p); + else + exterior_rgb3(p) = input_rgb3(p); + } + + histo_interior = mln::data::compute(t_histo3d_fun(), interior_rgb3); + histo_exterior = mln::data::compute(t_histo3d_fun(), exterior_rgb3); + + //mln::io::plot::save_image_sh(histo, "histo.sh"); + + // PRINTING PHASE + mln::debug::println(histo_interior); + mln::debug::println(histo_exterior); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/hsv/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/hsv/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/hsv/hsv.cc b/scribo/sandbox/green/demo/annotating/hsv/hsv.cc new file mode 100644 index 0000000..e87ab2d --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/hsv/hsv.cc @@ -0,0 +1,721 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet HSV operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to HSV +/// one. Formulae are classical, we can find them on the web. +/// +// Val = max(R,G,B). +// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +/// +/// \fixme: This code is unstable. It was using to make a lot of tests. +/// Don't use it, we have more stable version of it. +#include <iostream> +#include <fstream> + +#include <mln/accu/max_site.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> +#include <mln/core/concept/image.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> + +#include <mln/debug/println.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/fun/v2v/rgb_to_hsv.hh> +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> +#include <mln/fun/v2v/achromatism.hh> +#include <mln/fun/v2v/hue_concentration.hh> +#include <mln/fun/p2b/component_equals.hh> +#include <mln/fun/p2b/achromatic.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/img_path.hh> + +#include <mln/io/plot/save_image_sh.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/pw/cst.hh> +#include <mln/pw/value.hh> +//#include <mln/trace/quiet.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> +#include <mln/value/hsv.hh> + + +/// \brief Classify a rgb a value with a color of reference. +/// +/// \param[in] rgb a rgb8 value. +/// +/// \return a rgb8 initialize with the selecting literal. +/// +/// This classification of the RGB color space is based on the +/// Millet's work. It uses the hue wheel to do it and make new +/// distinction as brown/orange for instance. +mln::value::rgb8 label_color(const mln::value::rgb8 rgb) +{ + mln::value::hsv_f hsv = mln::fun::v2v::f_rgb_to_hsv_f(rgb); + + mln::value::rgb8 result; + + // Is it a gray level ? + if (0 == hsv.sat()) + { + // which result one ? + if (82 > hsv.sat()) + result = mln::literal::black; + else if (179 > hsv.sat()) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + } + // Is it a true result color ? + else if (14 > hsv.hue()) + result = mln::literal::red; + else if (29 > hsv.hue()) + { + // Is is brown or orange ? + unsigned dist_orange = mln::math::abs(hsv.sat() - 184) + + mln::math::abs(hsv.val() - 65); + + unsigned dist_brown = mln::math::abs(hsv.sat() - 255) + + mln::math::abs(hsv.val() - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else if (45 > hsv.hue()) + { + // Is it green or yellow ? + if (80 > hsv.val()) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else if (113 > hsv.hue()) + result = mln::literal::green; + else if (149 > hsv.hue()) + result = mln::literal::cyan; + else if (205 > hsv.hue()) + result = mln::literal::blue; + else if (235 > hsv.hue()) + result = mln::literal::violet; + else if (242 > hsv.hue()) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +// unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, +// const char *name_histo, +// const char *name_image) +// { +// typedef mln::point1d t_point1d; +// typedef mln::value::rgb8 t_rgb8; +// typedef mln::value::int_u8 t_int_u8; +// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +// typedef mln::image2d<t_int_u8> t_image2d_int_u8; +// typedef mln::image1d<unsigned> t_histo1d; +// typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; +// typedef mln::accu::meta::stat::histo1d t_histo1d_fun; +// typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + +// t_histo1d histo; + +// std::cout << "histo : " << name_histo << std::endl; +// std::cout << "image : " << name_image << std::endl; + +// histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + +// mln::io::pgm::save(input_int_u8, name_image); +// mln::io::plot::save_image_sh(histo, name_histo); +// mln::debug::println(histo); + +// // Find the peak of the histogram +// unsigned v_max = mln::opt::at(histo, 0); +// short p_max = 0; + +// mln_piter_(t_histo1d) p(histo.domain()); + +// for_all(p) +// { +// if (v_max < histo(p)) +// { +// v_max = histo(p); +// p_max = p.ind(); +// } +// } + +// // Compute the specific stddev + +// float stddev_low = 0.0; +// float stddev_up = 0.0; +// float stddev = 0.0; + +// if (250 > p_max) +// for (short i = p_max+1; i < p_max+6; ++i) +// stddev_up += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// if (5 < p_max) +// for (short i = p_max-1; i > p_max-6; --i) +// stddev_low += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : +// (stddev_low + stddev_up)/2; + +// std::cout << "max_site : " << p_max << std::endl; +// std::cout << "h(max_site) : " << v_max << std::endl; +// std::cout << "stddev_up : " << stddev_up << std::endl; +// std::cout << "stddev_low : " << stddev_low << std::endl; +// std::cout << "stddev : " << stddev << std::endl; + +// return 0; +// } + + +// ------------------------------------- +// input image <name>.ppm +// map <name>-<map>.pgm +// thresholded map <name>-<map>.pbm +// histogram <name>-<map>.sh +// decision <name>-<map>.txt +// ------------------------------------- +// Achromatism <name>-achromatism.pgm +// call achromatism(input_rgb8, 7, 99.0) + + +/// \brief Decide if an image is achromatic or not [Millet]. +/// +/// \param[in] input_rgb8 the input image. +/// \param[in] threshold the distance threshold used for equality. +/// \param[in] percentage the percentage of pixels that very the test. +/// +/// This is an improving of the Millet test. The original test +/// compares rgb values to each other and look at differences greater +/// than the threshold. If the number of pixel that pass the test are +/// greater than 99.0, then the image is declared achromatic. In fact, +/// there is few variations between the three channels, so we can say +/// that it is like a grey image. We can with no too distortions +/// replace the color image by the grey one. The improving is in first +/// creating a map of the difference. As we can't keep the free +/// differences between channels, we look at reducing the test and we +/// find an equivalent one based on the difference between minima +/// channel value and the maxima channel value. After the map is +/// ready, we make binarization with the threshold. Then we compute +/// the histogram 1d for every pixels of the map that are greater the +/// threshold. Then, we count pixels in the histogram and traduce the +/// count in percentage to compare to the second threshold. Details +/// are saved in files and printed in the screen. +void achromatism(mln::image2d<mln::value::rgb8> input_rgb8, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::int_u8> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + view = mln::data::stretch(mln::value::int_u8(), map); + mask = mln::binarization::threshold(map, threshold); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + map | (mln::pw::value(mask) == true)); + cnt1 = count_histo(histo); + cnt2 = mln::geom::nsites(input_rgb8); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + + std::ofstream txt_stream("achromatism.txt"); + txt_stream << "Achromatism" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + txt_stream << "Percentage : " << prop << std::endl; + txt_stream << "Image achromatique : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "achromatism.pgm"); + mln::io::plot::save_image_sh(histo, "achromatism.sh"); + mln::io::pbm::save(mask, "achromatism.pbm"); +} + + +/// \brief Decide if an image is low saturate or not and so b/w [Millet]. +/// +/// \param[in] input_hsvf the input image in the HSV space. +/// \param[in] achromatism_mask the mask to prevent computing bad saturation. +/// \param[in] threshold the distance threshold used for equality. +/// \param[in] percentage the percentage of pixels that very the test. +/// +/// As we are in the HSV space, we can just isolate saturation +/// channel. The original idea of Millet is to build histogram bound +/// to 256 for the saturation. If a great percentage (95%) of the +/// population are under a threshold (100), then the image has got low +/// saturation. So, it is b/w image. Low saturation is sometimes +/// redundant with the achromatism test but not every time. + +// call low_saturation(input_rgb8, achromatism_mask, 100, 95.0) +void low_saturation(mln::image2d<mln::value::hsv_f> input_hsvf, + mln::image2d<bool> achromatism_mask, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + + mln::image2d<t_sat> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_hsvf, t_component_s()); + view = mln::data::stretch(mln::value::int_u8(), map); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + view|(mln::pw::value(achromatism_mask) == true)); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0),mln::point1d(100))); + cnt2= mln::geom::nsites(achromatism_mask| + (mln::pw::value(achromatism_mask)==false)); + + std::ofstream txt_stream("saturation.txt"); + txt_stream << "Saturation" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre p faiblement saturés : " << cnt1 << std::endl; + txt_stream << "Pourcentage : " << prop << std::endl; + txt_stream << "Image faiblement saturé : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "saturation.pgm"); + mln::io::plot::save_image_sh(histo, "saturation.sh"); + mln::io::pbm::save(mask, "saturation.pbm"); +} + +/* This is a classification of the ICDAR base in 3 modalities. +// COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00032c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00042c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00076c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00142c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00215c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00228c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00234c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00248c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00252c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00253c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00255c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00259c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00271c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00290c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00293c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00304c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00307c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00376c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00411c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00419c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00447c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00498c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00510c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00550c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00573c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00589c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00592c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00597c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00599c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00600c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00031c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00034c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00043c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00063c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00065c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00072c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00081c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00083c_20p.ppm"); + +// BLACK AND WHITE + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00329c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00036c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00037c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00039c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00040c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00049c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00055c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00057c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00068c_20p.ppm"); + + +// A LITTLE BIT OF COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00262c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00263c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00311c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00319c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00440c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00608c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00630c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00631c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00028c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00046c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00073c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00089c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00090c_20p.ppm"); +*/ + + +/// \brief The main entry and the whole processing routine +/// +/// This is the routine which works on Millet test. First compute the +/// achromatic mask. Then build the transfer between RGB and HSV. Then +/// isolate each channel to work on it. Each separate channel is bound +/// between 0 and 255. Then keep only pixels low saturated (< 100 in S +/// canal) in a specefic map. Build the 3 histograms without the +/// achromatic pixels based on the 3 separated channels (H/S/V). Build the +/// 3 maps (hue_concentration, low_saturation and achromatism). Then do +/// the 3 tests of Millet with the thow thresolded pass (threshold,percentage). +int main() +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::h_type t_hue; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::value::hsv_f::v_type t_val; + typedef mln::image2d<t_hue> t_image2d_hue; + typedef mln::image2d<t_sat> t_image2d_sat; + typedef mln::image2d<t_val> t_image2d_val; + typedef mln::image2d<t_hsvf> t_image2d_hsvf; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<bool> t_mask; + typedef mln::fun::v2v::f_rgb_to_hsv_f_t t_rgb8_to_hsv; + typedef mln::accu::math::count<t_hsvf> t_count; + typedef mln::fun::v2v::component<t_hsvf,0> t_component_h; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + typedef mln::fun::v2v::component<t_hsvf,2> t_component_v; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,0> t_component_eq0; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,1> t_component_eq1; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,2> t_component_eq2; + typedef mln::fun::p2b::achromatic<t_rgb8> t_achromatic; + + t_image2d_rgb8 input_rgb8; + t_image2d_hsvf input_hsvf; + + t_mask achromatic; + t_mask low_saturation; + + t_image2d_float achromatism1; + t_image2d_int_u8 achromatism2; + t_image2d_float hue_concentration1; + t_image2d_int_u8 hue_concentration2; + + t_image2d_hue input_h; + t_image2d_hue input_h2; + t_image2d_sat input_s; + t_image2d_val input_v; + + t_image2d_int_u8 input_h8; + t_image2d_int_u8 input_s8; + t_image2d_int_u8 input_v8; + + t_histo1d histo_h; + t_histo1d histo_s; + t_histo1d histo_v; + + unsigned cnt1; + unsigned cnt2; + float percentage; + bool result; + + + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; +// mln::io::ppm::load(input_rgb8, ANNOTATING_1_BILL_IMG_PATH"/bill03.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + + +// achromatism(input_rgb8,7,99.0); +// exit(-1); + + // REPERAGE DES PIXELS ACHROMATICS + std::cout << "Init achromatic mask ..." << std::endl; + initialize(achromatic, input_rgb8); + mln::data::fill(achromatic, false); + mln::data::fill((achromatic | t_achromatic(input_rgb8, 0.03)).rw(), true); + + mln::io::pbm::save(achromatic, "achromatic.pbm"); + + std::cout << "Achieve canal forking ..." << std::endl; + input_hsvf = mln::data::transform(input_rgb8, t_rgb8_to_hsv()); + input_h = mln::data::transform(input_hsvf, t_component_h()); + input_s = mln::data::transform(input_hsvf, t_component_s()); + input_v = mln::data::transform(input_hsvf, t_component_v()); + + // quid of achromatic pixels ??? + input_h8 = mln::data::stretch(t_int_u8(), input_h); + input_s8 = mln::data::stretch(t_int_u8(), input_s); + input_v8 = mln::data::stretch(t_int_u8(), input_v); + + // IDENTIFY LOW SATURATED PIXELS + std::cout << "Init low saturation mask ..." << std::endl; + initialize(low_saturation, input_s8); + mln::data::fill(low_saturation, false); + mln::data::fill((low_saturation|(mln::pw::value(input_s8) < + mln::pw::cst(100u))).rw(), true); + + mln::io::pbm::save(low_saturation, "low_saturation.pbm"); + + std::cout << "Compute histograms ..." << std::endl; + histo_h = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_h8|(mln::pw::value(achromatic)==false)); + + histo_s = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_s8|(mln::pw::value(achromatic)==false)); + + histo_v = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_v8|(mln::pw::value(achromatic)==false)); + + + // Study the maps + hue_concentration1=mln::data::transform(input_h, + mln::fun::v2v::hue_concentration(histo_h)); + achromatism1=mln::data::transform(input_rgb8,mln::fun::v2v::achromatism()); + + hue_concentration2= mln::data::stretch(t_int_u8(), hue_concentration1); + achromatism2= mln::data::stretch(t_int_u8(), achromatism1); + + mln::io::pgm::save(achromatism2, "achromatism_map.pgm"); + mln::io::pgm::save(hue_concentration2, "hue_concentration_map.pgm"); + mln::io::pgm::save(input_s8, "saturation_map.pgm"); + +// cnt1 = mln::data::compute(t_count(), +// (input_hsvf|t_component_eq0(input_hsvf,-1)).rw()); + + + // (I) ACHROMATISM + std::cout << "Achromatism" << std::endl; + cnt1 = count_histo(histo_h); + cnt2 = mln::geom::nsites(input_h); + + percentage = (100.0 * (cnt2 - cnt1) / cnt2); + result = percentage > 99.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image achromatique : " << result << std::endl; + std::cout << std::endl; + + // (II) LOW SATURATION + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo_s | mln::box1d(mln::point1d(0),mln::point1d(100))); + + cnt2= mln::geom::nsites(achromatic | (mln::pw::value(achromatic)==false)); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre p faiblement saturés : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image faiblement saturé : " << result << std::endl; + std::cout << std::endl; + + // (III) HIGH HUE CONCENTRATION + mln::debug::println(histo_h); + unsigned peak = peak_histo(histo_h); + + cnt1 = count_histo(histo_h | mln::box1d(mln::point1d(peak-20), + mln::point1d(peak+20))); + + cnt2= count_histo(histo_h); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Position du pic : " << peak << std::endl; + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels proches pic : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image fortement teintée : " << result << std::endl; + std::cout << std::endl; + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/lep/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/lep/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/lep/lep.cc b/scribo/sandbox/green/demo/annotating/lep/lep.cc new file mode 100644 index 0000000..981a369 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/lep/lep.cc @@ -0,0 +1,127 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet LEP descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works on grey level image. First, +/// Sobel is applied on the image. Then a specific window is build to +/// be convolved on the image. The aim of the convolution is to mark +/// the configuration of the neighbouring for this thresholded image. +/// Histogram of the configuration is then created and that's all. + +#include <iostream> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/w_window2d_int.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/linear/ch_convolve.hh> +#include <mln/linear/convolve.hh> +#include <mln/linear/sobel_2d.hh> + +#include <mln/make/w_window2d.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> + +#include <mln/core/var.hh> + +/// \brief Main entry. +/// +/// Load the pgm image. Threshold it. Identifie the pixel +/// configuration with specific convolution filter. Build 1d histogram. + +int main() +{ + using namespace mln; + + typedef mln::w_window2d_int t_win2d; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::int_u<9> t_int_u9; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_int_u9> t_image2d_int_u9; + typedef mln::image2d<int> t_image2d_int; + typedef mln::image2d<unsigned> t_image2d_unsigned; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image2d<double> t_image2d_double; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + + t_image2d_int_u8 input_int_u8; + t_image2d_int input_int; + t_image2d_unsigned input_unsigned; + t_image2d_float sobel; + t_image2d_bool thresh_bool; + t_image2d_int_u8 thresh_int_u8; + t_image2d_float conf_float; + t_image2d_int_u9 conf_int_u9; + t_histo1d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::pgm::load(input_int_u8,ICDAR_50P_PGM_IMG_PATH"/mp00082c_50p.pgm"); + + sobel = mln::linear::sobel_2d_l1_norm(input_int_u8); + + //mln::io::plot::save_image_sh(sobel, "sobel.sh"); + thresh_bool = mln::binarization::threshold(sobel, 100); + thresh_int_u8 = mln::data::convert(mln::value::int_u8(), thresh_bool); + //mln::io::plot::save_image_sh(thresh, "thresh.sh"); + + int ws[] = { 1, 2, 4, + 8, 256, 16, + 32, 64, 128 }; + + t_win2d win2d = mln::make::w_window2d(ws); + conf_float = mln::linear::convolve(thresh_int_u8, win2d); + conf_int_u9 = mln::data::convert(t_int_u9(), conf_float); + histo = mln::data::compute(t_histo1d_fun(), conf_int_u9); + + mln::io::plot::save_image_sh(histo, "histo.sh"); + + // PRINTING PHASE + mln::debug::println(histo); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/nb_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/nb_color/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc b/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc new file mode 100644 index 0000000..acc64e9 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc @@ -0,0 +1,143 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Compute the number of colors in an image. +/// +/// Build histogram of colors and count the bins different from zero. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/math/sum.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo3d_rgb.hh> +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/variance.hh> + +#include <mln/algebra/vec.hh> + +#include <mln/arith/diff_abs.hh> + +#include <mln/core/macros.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/labeling/regional_maxima.hh> +#include <mln/labeling/mean_values.hh> +#include <mln/labeling/compute.hh> + +#include <mln/literal/colors.hh> + +#include <mln/morpho/opening/volume.hh> +#include <mln/morpho/elementary/dilation.hh> + +#include <mln/opt/at.hh> + +#include <mln/pw/cst.hh> + +#include <mln/util/array.hh> +#include <mln/util/timer.hh> + +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/rgb.hh> +#include <mln/value/int_u.hh> + +/// \brief Count the colors. +/// +/// \param[in] image the name of the image to process. +/// +/// \return the number of colors. +/// +/// Count the color by building histogram. Quantification is done to +/// reduce the size of the histogram. +/// +/// \fixme: Strange compilation warning, I don't know how to solve it! + +// n < 8, n is the degree of quantification +template <unsigned n> +unsigned count_image_color(const std::string& image) +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::meta::math::count t_count_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgbn input_rgbn; + t_image2d_rgbn output_rgbn; + t_histo3d histo; + t_histo3d opened; + + mln::io::ppm::load(input_rgb8, image.c_str()); + +// unsigned nb_pixel = input_rgb8.ncols() * input_rgb8.nrows(); +// unsigned min_volume = (unsigned)(nb_pixel * 0.054); + unsigned nb_color = 0; + + input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); + histo = mln::data::compute(t_histo3d_fun(), input_rgbn); + nb_color = mln::data::compute(t_count_fun(), + (histo | (mln::pw::value(histo) != 0)).rw()); + + return nb_color; +} + + +/// \brief Main entry. +/// +/// Print the number of colors. +int main() +{ + unsigned val=count_image_color<8>(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + + std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/project/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/project/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/project/project.cc b/scribo/sandbox/green/demo/annotating/project/project.cc new file mode 100644 index 0000000..4cab73c --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/project/project.cc @@ -0,0 +1,275 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet PROJECT descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works a grey level image. First, +/// we make subsampling of the image to the size 100x100. Then Sobel +/// is applied. After that, we divide the image in 2 sub-images with +/// horizontal or vertical splitting. Projection (summing along a +/// direction) is done in a way that preserves a vector of size +/// 100. Finally, it results 4 vector of size 100 which are concatened +/// to build the descriptor. + +#include <iostream> +#include <sstream> + +#include <mln/accu/image/init.hh> +#include <mln/accu/image/take.hh> +#include <mln/accu/image/to_result.hh> +#include <mln/accu/stat/mean.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/box2d.hh> +#include <mln/core/alias/point2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> +#include <mln/core/image/dmorph/unproject_image.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/paste.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/projection.hh> + +#include <mln/img_path.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/linear/sobel_2d.hh> + +#include <mln/opt/at.hh> + +/// \brief get the sub-image name. +/// +/// \param[in] base the common part of the name. +/// \param[in] i the column. +/// \param[in] j the line. +/// +/// \result the complete name of the sub-image. +/// +/// This function builds the complete name of a sub-image by making +/// explicit its position in the array. +const char *get_name(const char *base, const unsigned i, const unsigned j) +{ + std::ostringstream name; + + name << base; + name << i; + name << "_"; + name << j; + name << ".ppm"; + + return name.str().c_str(); +} + + +/// \brief Project row data. +/// +/// \param[in] img the binary image converted to u_int8. +/// +/// \ return a vector. +/// +/// This routine "eats" one dimension of the image by making a +/// projection. The projection sums the pixel along a specific +/// direction. It results a vector. +mln::image1d<mln::value::int_u8> +project_row(const mln::image2d<mln::value::int_u8>& img) +{ + typedef mln::accu::math::sum<mln::value::int_u8,mln::value::int_u8> t_sum; + typedef mln::fun::v2v::projection<mln::point2d,0> t_projection; + + mln::image1d<t_sum> img_accu(img.ncols()); + + mln::accu::image::init(img_accu); + + mln::accu::image::take(unproject(img_accu, + img.domain(), + t_projection()).rw(), + img); + + return mln::accu::image::to_result(img_accu); +} + + +/// \brief Project column data. +/// +/// \param[in] img the binary image converted to u_int8. +/// +/// \ return a vector. +/// +/// This routine "eats" one dimension of the image by making a +/// projection. The projection sums the pixel along a specific +/// direction. It results a vector. +mln::image1d<mln::value::int_u8> +project_col(const mln::image2d<mln::value::int_u8>& img) +{ + typedef mln::accu::math::sum<mln::value::int_u8,mln::value::int_u8> t_sum; + typedef mln::fun::v2v::projection<mln::point2d,1> t_projection; + + mln::image1d<t_sum> img_accu(img.nrows()); + + mln::accu::image::init(img_accu); + + mln::accu::image::take(unproject(img_accu, + img.domain(), + t_projection()).rw(), + img); + + return mln::accu::image::to_result(img_accu); +} + + +/// \brief The image processing routine. +/// +/// Describe the processing chain. First subsample the input image to +/// 100x100. This is done by splitting the image and then compute the +/// mean of the subimage. The result is a pixel of the subsample +/// image. Then apply Sobel and threshold. Then split in two +/// (vertically or horizontally), it results 4 images. Finally, make +/// the projection. +int main() +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::algebra::vec<3,unsigned> t_vec3u; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::accu::meta::stat::mean t_mean_fun; + typedef mln::accu::math::sum<t_int_u8,t_int_u8> t_sum; + typedef mln::image1d<t_int_u8> t_image1d_int_u8; + + t_image2d_int_u8 input_int_u8; + t_image2d_float sobel; + t_image2d_bool thresh_bool; + t_image2d_int_u8 thresh_int_u8; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::pgm::load(input_int_u8,ICDAR_50P_PGM_IMG_PATH"/mp00082c_50p.pgm"); + + + // IMAGE SPLITTING PHASE + t_image2d_int_u8 subimg_int_u8(mln::box2d(mln::point2d(0,0), + mln::point2d(100,100))); + + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 100; + unsigned sz_col = (pmax.col() - pmin.col())/ 100; + + std::cout << domain << std::endl; + + // Subsampling in 100x100 + // FIXME Test that is it a subsampling, not a upsampling! + for (unsigned i = 0; i < 100; ++i) + for (unsigned j = 0; j < 100; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_int_u8 input_part_int_u8(dom); + + mln::data::paste(input_int_u8 | dom, input_part_int_u8); + //mln::io::pgm::save(input_part_int_u8, get_name("output",i,j)); + float mean = mln::data::compute(t_mean_fun(), input_part_int_u8); + t_int_u8 val = static_cast<t_int_u8>(mean); + mln::opt::at(subimg_int_u8, i,j) = val; + } + + mln::io::pgm::save(subimg_int_u8, "subimg.pgm"); + + sobel = mln::linear::sobel_2d_l1_norm(subimg_int_u8); + thresh_bool = mln::binarization::threshold(sobel, 100); + thresh_int_u8 = mln::data::convert(t_int_u8(), thresh_bool); + + mln::io::pbm::save(thresh_bool, "subimg.pbm"); + + // Define img_up, img_down, img_left, img_right + t_image2d_int_u8 img_up(mln::box2d(mln::point2d(0,0), + mln::point2d(49,99))); + + mln::data::paste(thresh_int_u8 | img_up.domain(), img_up); + + + t_image2d_int_u8 img_down(mln::box2d(mln::point2d(50,0), + mln::point2d(99,99))); + + mln::data::paste(thresh_int_u8 | img_down.domain(), img_down); + + t_image2d_int_u8 img_left(mln::box2d(mln::point2d(0,0), + mln::point2d(99,49))); + + mln::data::paste(thresh_int_u8 | img_left.domain(), img_left); + + + t_image2d_int_u8 img_right(mln::box2d(mln::point2d(0,50), + mln::point2d(99,99))); + + mln::data::paste(thresh_int_u8 | img_right.domain(), img_right); + + + // Define project_up, project_down, project_left, project_right + t_image1d_int_u8 project_up = project_row(img_up); + t_image1d_int_u8 project_down = project_row(img_down); + t_image1d_int_u8 project_left = project_col(img_left); + t_image1d_int_u8 project_right = project_col(img_right); + + mln::io::pgm::save(img_up, "up.pgm"); + mln::io::pgm::save(img_down, "down.pgm"); + mln::io::pgm::save(img_left, "left.pgm"); + mln::io::pgm::save(img_right, "right.pgm"); + + // PRINTING PHASE + + std::cout << std::endl << "UP" << std::endl; + mln::debug::println(project_up); + + std::cout << std::endl << "DOWN" << std::endl; + mln::debug::println(project_down); + + std::cout << std::endl << "LEFT" << std::endl; + mln::debug::println(project_left); + + std::cout << std::endl << "RIGHT" << std::endl; + mln::debug::println(project_right); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/rgb_64/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/rgb_64/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc b/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc new file mode 100644 index 0000000..b49f979 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc @@ -0,0 +1,80 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet RGB-64 descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works by making 2 bits +/// quantification on each channel. It result a processing in RGB-64 +/// space. Then we build the color histogram. + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Main entry. +/// +/// Loading, Quantifiying in two bits each channel, then building +/// color histogram. +int main() +{ + typedef mln::fun::v2v::rgb8_to_rgbn<2> t_rgb8_to_rgb2; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<2> t_rgb2; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb2> t_image2d_rgb2; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb2 input_rgb2; + t_histo3d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + input_rgb2 = mln::data::transform(input_rgb8, t_rgb8_to_rgb2()); + histo = mln::data::compute(t_histo3d_fun(), input_rgb2); + + // PRINTING PHASE + mln::debug::println(histo); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/rgb_64_9/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/rgb_64_9/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc b/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc new file mode 100644 index 0000000..355cbfd --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc @@ -0,0 +1,132 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet RGB-64 descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works by making 2 bits +/// quantification on each channel. It result a processing in RGB-64 +/// space. Then we build the color histogram. + +#include <iostream> +#include <sstream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/alias/box2d.hh> +#include <mln/core/alias/point2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/compute.hh> +#include <mln/data/paste.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Get the sub image name. +/// +/// \return the name of the ith/jth sub image of the array. +const char *get_name(const char *base, const unsigned i, const unsigned j) +{ + std::ostringstream name; + + name << base; + name << i; + name << "_"; + name << j; + name << ".ppm"; + + return name.str().c_str(); +} + +/// \brief Same code as rgb-64 but divide the wall image in nine. +/// +/// Adding a splitting phase for image rgb-64 code reference. +int main() +{ + typedef mln::fun::v2v::rgb8_to_rgbn<2> t_rgb8_to_rgb2; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<2> t_rgb2; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb2> t_image2d_rgb2; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb2 input_rgb2; + t_histo3d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + + input_rgb2 = mln::data::transform(input_rgb8, t_rgb8_to_rgb2()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_rgb2.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 3; + unsigned sz_col = (pmax.col() - pmin.col())/ 3; + + std::cout << domain << std::endl; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 3; ++i) + for (unsigned j = 0; j < 3; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_rgb2 input_1o9_rgb2(dom); + + mln::data::paste(input_rgb2 | dom, input_1o9_rgb2); + mln::io::ppm::save(input_1o9_rgb2, get_name("output",i,j)); + + histo = mln::data::compute(t_histo3d_fun(), input_1o9_rgb2); + + // PRINTING PHASE + mln::debug::println(histo); + } + +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/stddev_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/stddev_color/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc b/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc new file mode 100644 index 0000000..549834f --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc @@ -0,0 +1,191 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +unsigned stddev_color(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + t_histo1d histo; + t_point1d max_site; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + mln::io::pgm::save(input_int_u8, "tmp.pgm"); + mln::io::plot::save_image_sh(histo, "histo.sh"); + mln::debug::println(histo); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + std::cout << "max_site : " << p_max << std::endl; + std::cout << "h(max_site) : " << v_max << std::endl; + std::cout << "stddev_up : " << stddev_up << std::endl; + std::cout << "stddev_low : " << stddev_low << std::endl; + std::cout << "stddev : " << stddev << std::endl; + + return 0; +} + + +/// \brief Main entry. +/// +/// Front end for image processing. +int main() +{ +// unsigned val = stdev_color(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + unsigned val = stddev_color(ANNOTATING_1_LOGO_IMG_PATH "/logo06.ppm"); + +// std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/stddev_color_16/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/stddev_color_16/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc b/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc new file mode 100644 index 0000000..98d94fd --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc @@ -0,0 +1,261 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. When photographies +/// has some large uniform border, the detection can fail. To improve +/// the method, Millet decide to subdivise the image in 16 sub images +/// on which it applies the test. +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> +#include <mln/data/paste.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] input_int_u8 the 8 bits input image to analyze. +/// \param[in] name_histo the name of the output histogram. +/// \param[in] name_image the name of the ouput sub image. +/// +/// \return the deviation, but at this time nothing.. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, + const char *name_histo, + const char *name_image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_histo1d histo; + + std::cout << "histo : " << name_histo << std::endl; + std::cout << "image : " << name_image << std::endl; + + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + mln::io::pgm::save(input_int_u8, name_image); + mln::io::plot::save_image_sh(histo, name_histo); + mln::debug::println(histo); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + std::cout << "max_site : " << p_max << std::endl; + std::cout << "h(max_site) : " << v_max << std::endl; + std::cout << "stddev_up : " << stddev_up << std::endl; + std::cout << "stddev_low : " << stddev_low << std::endl; + std::cout << "stddev : " << stddev << std::endl; + + return 0; +} + + +/// \brief Divide the image in 16 sub images. +/// +/// \param[in] image the input image. +/// +/// \result nothing. +/// +/// Divive the input image in 16 by uniform and geometrical +/// method. When a sub image is ready, call the stddev routine to show +/// stats on it. +unsigned stddev_color_16(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 4; + unsigned sz_col = (pmax.col() - pmin.col())/ 4; + + std::cout << domain << std::endl; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 4; ++i) + for (unsigned j = 0; j < 4; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_int_u8 input_1o16_int_u8(dom); + std::ostringstream name_histo(""); + std::ostringstream name_image(""); + + name_histo << "histo" << i << "_" << j << ".sh"; + name_image << "image" << i << "_" << j << ".ppm"; + + mln::data::paste(input_int_u8 | dom, input_1o16_int_u8); + + stddev_color(input_1o16_int_u8, + name_histo.str().c_str(), + name_image.str().c_str()); + } + + return 0; +} + + +/// \brief Main entry. +/// +/// Just a front end for image processing routine. +int main() +{ +// unsigned val = stddev_color_16(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + unsigned val = stddev_color_16(ANNOTATING_1_LOGO_IMG_PATH "/logo06.ppm"); + +// std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/achromastism/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/achromastism/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc b/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc new file mode 100644 index 0000000..3486b20 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc @@ -0,0 +1,179 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + + +/// \file +/// +/// \brief Implement the Millet achromatism operator [millet.phd.2008.pdf] +/// +/// An image is said to be achromatic when have the RGB channel which +/// are quite identicals. + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Decide if an image is achromatic or not [Millet]. +/// +/// \param[in] input the input image. +/// \param[in] output the histogram of the achromatic map. +/// \param[in] threshold the distance threshold used for equality. +/// +/// This is an improving of the Millet test. The original test +/// compares rgb values to each other and look at differences greater +/// than the threshold. If the number of pixel that pass the test are +/// greater than 99.0, then the image is declared achromatic. In fact, +/// there is few variations between the three channels, so we can say +/// that it is like a grey image. We can with no too distortions +/// replace the color image by the grey one. The improving is in first +/// creating a map of the difference. As we can't keep the free +/// differences between channels, we look at reducing the test and we +/// find an equivalent one based on the difference between minima +/// channel value and the maxima channel value. After the map is +/// ready, we make binarization with the threshold. Then we compute +/// the histogram 1d for every pixels of the map that are greater the +/// threshold. Then, we count pixels in the histogram and traduce the +/// count in percentage to compare to the second threshold. Details +/// are saved in files and printed in the screen. +float achromatism_test(const std::string input, + const std::string output, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + + return prop; +} + + +/// \brief The main entry. +/// +/// Deal with boost library for walking over image database +/// directories. Call the image processing routine and compute some +/// stats on the result. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + + prop = achromatism_test(dir_iter->path().string(), + output.string(), + 11); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/bench/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/bench/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/bench/bench.cc b/scribo/sandbox/green/exp/annotating/bench/bench.cc new file mode 100644 index 0000000..102a896 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/bench/bench.cc @@ -0,0 +1,1450 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// This file is the final work to differentiate between the two image +/// databases AFP and ICDAR. Several tests are used to show +/// improvement of detection. The jonathan's tests are put together to +/// compare them to the others (in fact, to the low saturation one). + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/algebra/vec.hh> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/times.hh> +#include <mln/arith/diff_abs.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/stretch.hh> +#include <mln/data/fill.hh> + +#include <mln/fun/v2v/component.hh> +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/ceil.hh> +#include <mln/math/floor.hh> + +#include <mln/opt/at.hh> + +#include <mln/trait/value_.hh> + +#include <mln/value/rgb8.hh> + + +#include <mln/value/int_u8.hh> + +/// Histogram part. +/// +/// We regroup here the routines that work on histogram, count_histo, +/// sum_frequency_histo, peak_histo, count_null_frequency_histo, +/// cmp_equi_frequency_histo and others (variance +/// operators). count_histo and sum_frequency_histo count the overall +/// frequency in the domain. peak_histo find the peak of the +/// histogram. count_null_frequency_histo compute the number of non +/// grey levels. cmp_equi_frequency_histo compare a histogram with +/// the one with the same frequency everywhere. +/// \{ +template <typename I> +mln_value(I) count_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) result = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + result += histo(p); + + return result; +} + + +template <typename I> +mln_value(I) sum_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += histo(p); + + return sum; +} + + +template <typename I> +mln_coord(mln_site_(I)) peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + mln_value(I) v_max = mln::opt::at(histo, mln::literal::zero); + mln_coord(mln_site_(I)) p_max = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +template <typename I> +mln_value(I) count_null_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) count = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + if (0 == histo(p)) + count++; + + return count; +} + +template <typename I> +float cmp_equi_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum ++; + var += mln::math::sqr(histo(p) - (1/256.0)); + } + + var = var / sum; + + return var; +} + +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + } + + return sum; +} + +template <typename I> +mln_value(I) sum_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + sum += pos*histo(p); + } + + return sum; +} + +template <typename I> +mln_value(I) avg_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += histo(p); + sum += pos*histo(p); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +template <typename I> +mln_value(I) var3_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + sum += (mln::math::sqr(p.ind()-mean)*histo(p)); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +template <typename I> +mln_value(I) var2_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_value(I) dlt = mln::literal::zero; + mln_value(I) mxt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + mxt += (histo(p)*pos*mean); + sqr += (histo(p)*mln::math::sqr(pos)); + dlt += (histo(p)*mln::math::sqr(pos - mean)); + + std::cout << "p = " << pos << std::endl; + std::cout << "p² = " << mln::math::sqr(pos) << std::endl; + std::cout << "p*mean = " << (pos*mean) << std::endl; + std::cout << "p-mean = " << (pos-mean) << std::endl; + std::cout << "(p-mean)² = " << mln::math::sqr(pos-mean) << std::endl; + std::cout << "histo(p) = " << histo(p) << std::endl; + std::cout << "histo(p)*p = " << (pos*histo(p)) << std::endl; + std::cout << "histo(p)*p²= " << (mln::math::sqr(pos)*histo(p)) + << std::endl; + std::cout << "cnt = " << cnt << std::endl; + std::cout << "sum = " << sum << std::endl; + std::cout << "sqr = " << sqr << std::endl; + std::cout << "dlt = " << dlt << std::endl; + std::cout << "mxt = " << mxt << std::endl; + std::cout << std::endl; + } + + std::cout << "sqr/cnt = " << (sqr/cnt) << std::endl; + std::cout << "sum/cnt = " << (sum/cnt) << std::endl; + std::cout << "(sum/cnt)² = " << mln::math::sqr(sum/cnt) << std::endl; + std::cout << "dlt/cnt = " << dlt/cnt << std::endl; + std::cout << "mxt/cnt = " << mxt/cnt << std::endl; + std::cout << std::endl; + + std::cout << "sqr = " + << (sqr) << std::endl; + std::cout << "dlt = " + << (dlt) << std::endl; + std::cout << "cnt*avg² = " + << (mln::math::sqr(sum/cnt)*cnt) << std::endl; + std::cout << "2*mxt = " + << (2*mxt) << std::endl; + std::cout << "sqr - cnt*avg² = " + << (sqr/cnt - mln::math::sqr(sum/cnt)) << std::endl; + std::cout << "(sqr -2*mxt + cnt*avg²) = " + << ((sqr - 2*mxt + mln::math::sqr(sum/cnt))/cnt) << std::endl; + std::cout << std::endl; + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +template <typename I> +mln_value(I) var_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + sqr += (histo(p)*mln::math::sqr(pos)); + } + + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +template <typename I> +mln_value(I) sqr_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += (mln::math::sqr(p.ind())*histo(p)); + + return sum; +} +/// \} + + +/// \brief Millet Hue detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold a way to adapt the test to the population. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test is used for discrimination between black and white pictures and +/// color ones. Some colored Black and white pictures have their energy near +/// the peak. +float hue1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + float cnt1; + float cnt2; + float prop; + short peak; + mln::point1d inf; + mln::point1d sup; + + peak = peak_histo(histo); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo|mln::box1d(inf,sup)); + cnt2 = count_histo(histo); + prop = ((255.0 * cnt1) / cnt2); + + return prop; +} + + +/// \brief Saturation detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold a way to adapt the test to the population. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test is used for discrimination between black and white pictures and +/// color ones. Black and white pictures have their energy in the low saturation +/// band. +float sat1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + float cnt1; + float cnt2; + float result; + + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = count_histo(histo); + result = ((255.0 * cnt1) / cnt2); + + return result; +} + + +/// \brief Counting grey levels descriptor. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the number of grey levels. +/// +/// This test aims at compute the number of grey levels. Photographies tends to +/// use all the levels or many of them. +float lvl0_descriptor(mln::image1d<unsigned> histo) +{ + float result; + + // FIXME 255 + result = 255-count_null_frequency_histo(histo); + + return result; +} + + +/// \brief Density hue detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float hue0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief Density saturation detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float sat0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief Density value detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float val0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// \param[in] peak the peak of the histogram. +/// \param[in] limit the threshold to compute the contribution. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief Millet value descriptor (cf. Millet.phd.2008) +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold the limit where to compute the contribution. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test aims at compute some deviation on the peak of the histogram of +/// the image. Large deviations mean lots of graduation in colors (such as +/// photos) and small ones mean something like cartoon. +float val1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + float result; + short peak; + + peak = peak_histo(histo); + result = stddev2(histo, peak, threshold); + + return result; +} + + +/// \brief Error detector (jonathan idea). +/// +/// \param[in] r_img_map the original red channel. +/// \param[in] g_img_map the original green channel. +/// \param[in] b_img_map the original blue channel. +/// \param[in] r_rdc_map the reduced (in color) red channel. +/// \param[in] g_rdc_map the reduced (in color) green channel. +/// \param[in] b_rdc_map the reduced (in color) blue channel. +/// +/// \return the PNSNR (cf compression p278 Handbook Color). +/// +/// \fixme the actual PNSNR is unbound, we need to fix its max to +/// 255. Two images which are the same produces an infinite PNSNR. +float err_descriptor(mln::image2d<mln::value::int_u8> r_img_map, + mln::image2d<mln::value::int_u8> g_img_map, + mln::image2d<mln::value::int_u8> b_img_map, + mln::image2d<mln::value::int_u8> r_rdc_map, + mln::image2d<mln::value::int_u8> g_rdc_map, + mln::image2d<mln::value::int_u8> b_rdc_map) + + +{ + typedef mln::accu::meta::stat::mean t_mean; + typedef mln::image2d<mln::value::int_u8> t_map; + typedef mln_trait_op_minus_(t_map,t_map) t_minus; + typedef mln_trait_op_times_(t_minus,t_minus) t_times; + + + t_minus minus_red; + t_minus minus_green; + t_minus minus_blue; + + t_times times_red; + t_times times_green; + t_times times_blue; + + float error_red; + float error_green; + float error_blue; + + float error; + + minus_red = (r_img_map - r_rdc_map); + times_red = minus_red * minus_red; + + minus_green = (g_img_map - g_rdc_map); + times_green = minus_green * minus_green; + + minus_blue = (b_img_map - b_rdc_map); + times_blue = minus_blue * minus_blue; + + error_red = mln::data::compute(t_mean(), times_red); + error_green = mln::data::compute(t_mean(), times_green); + error_blue = mln::data::compute(t_mean(), times_blue); + + error = (error_red + error_green + error_blue)/3.0; + error = mln::math::sqrt(error); + error = 20 * log(255/error); + +// FIXME: +// SAME IMAGE PRODUCE PNSNR GOING THROW THE INFINITY. +// DIFFERENT IMAGE PRODUCE PNSNR GOING NEAR ZERO. +// WE SHOULD KEEP THE PNSNR BELOW 255 FOR COMPARAISON. + + return error; +} + +/// Discriminant operators. +/// +/// We have some operators that compute the threshold which separate +/// two sub-populations in a histogram. +/// /{ + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*sqrt(v1)+m2*sqrt(v2))/(sqrt(v1)+sqrt(v2)). +float threshold_histo(float avg1, float var1, float avg2, float var2) +{ + float sigma1 = mln::math::sqrt(var1); + float sigma2 = mln::math::sqrt(var2); + float threshold = (avg1*sigma1+avg2*sigma2)/(sigma1+sigma2); + + return threshold; +} + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*var1+m2*var2)/(sqrt(v1)+sqrt(v2)). + +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Gaussian discriminant analysis in 1d is done. Compute the +/// discrimanation and solve the parabolic equation. +float threshold2_histo(float avg1, float var1, float avg2, float var2) +{ + float a = var2 - var1; + float b = -2 * (avg1 * var2 - avg2 * var1); + float c = var2 * mln::math::sqr(avg1) - var1 * mln::math::sqr(avg2); + float d = mln::math::sqr(b) - 4 * a * c; + + if (d < 0) + std::cout << "delta negatif" << std::endl; + + float x1 = (-b+mln::math::sqrt(d))/(2*a); + float x2 = (-b-mln::math::sqrt(d))/(2*a); + + std::cout << "a = " << a << std::endl; + std::cout << "b = " << b << std::endl; + std::cout << "c = " << c << std::endl; + std::cout << "d = " << d << std::endl; + std::cout << "x1 = " << x1 << std::endl; + std::cout << "x2 = " << x2 << std::endl; + + return x2; +} + +/// \brief Minimisation of the error. +/// +/// \param[in] histo_grp1 the histogram of the first population. +/// \param[in] histo_grp2 the histogram of the second population. +/// +/// \return the threshold. +/// +/// Computes the error and find the minimum error threshold. It's just +/// a counting of element in four categories (0 - but detects 1, 1 - +/// but detects 1, 1 but detects 0, 0 but detects 0). The error is the +/// sum of missclassifications for the classes 0 and 1. As Otsu done, +/// we can iterate on the threshold and compute the error associated +/// to it. The bes threshold is the one that minimize the error. +short min_error(const mln::image1d<float> histo_grp1, + const mln::image1d<float> histo_grp2, + float *_c00, float *_c10, float *_c01, float *_c11) +{ + float c00[256]; + float c10[256]; + float c01[256]; + float c11[256]; + float err[256]; + + for (short p = 0; p < 256; p++) + { + c00[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c10[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + + c01[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c11[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + } + + short threshold = 0; + float error = c01[0] + c01[0] + c00[0] + c11[0]; + + for(short p = 0; p < 256; p++) + { + err[p] = c10[p] + c01[p]; + +// std::cout << " p = " << p +// << ";c00 = " << c00[p] +// << ";c10 = " << c10[p] +// << ";c01 = " << c01[p] +// << ";c11 = " << c11[p] +// << std::endl; +// std::cout << "err[" < err[p]) + { + error = err[p]; + threshold = p; + } + } + + *_c00 = c00[threshold]; + *_c10 = c10[threshold]; + *_c01 = c01[threshold]; + *_c11 = c11[threshold]; + + return threshold; +} + + +/// \brief Fisher analysis. +/// +/// \param[in] histo the histogram of the mixed population. +/// +/// \return the threshold. +/// +/// This routine is less performant than the others because it starts +/// with a mixed population, but we know exactly the two separated +/// population. It was just a test. Don't take it in production +/// environement, it doesn't help you. +short fisher_analysis(const mln::image1d<float> histo) +{ + typedef mln::value::int_u8 t_int_u8; + + // FIXE ME SIZE const short a = mln_min(t_int_u8); + // float cnt1[a]; + + float cnt1[256]; + float sum1[256]; + float sqr1[256]; + float avg1[256]; + float var1[256]; + + float cnt2[256]; + float sum2[256]; + float sqr2[256]; + float avg2[256]; + float var2[256]; + + float cnt0[256]; // count of points + float sum0[256]; // sum of population + float sqr0[256]; // sqr of population + float avg0[256]; // average of the population + float v_in[256]; // variance with-in class + float v_bw[256]; // variance between class + float var0[256]; // variance of the population + short threshold; + float pos; + float min_var; + + // Assign the first elements + cnt1[0] = 0; + sum1[0] = 0; + sqr1[0] = 0; + avg1[0] = 0; + var1[0] = 0; + + // Compute the stats of the wall histogram + cnt2[0] = 0; + sum2[0] = 0; + sqr2[0] = 0; + + for(short p = 0; p < 256; p++) + { + pos = p; + cnt2[0] += mln::opt::at(histo,p); + sum2[0] += (pos * mln::opt::at(histo,p)); + sqr2[0] += (mln::math::sqr(pos) * mln::opt::at(histo,p)); + } + + avg2[0] = (0 == cnt2[0])? 0 : sum2[0] / cnt2[0]; + var2[0] = (0 == cnt2[0])? 0 : sqr2[0] / cnt2[0] - mln::math::sqr(avg2[0]); + + // watch the array limits + for (short p = 1; p < 256; p++) + { + pos = p; + + // Assign the statistics to the primary class + cnt1[p] = cnt1[p-1] + mln::opt::at(histo, p); + sum1[p] = sum1[p-1] + pos * mln::opt::at(histo, p); + sqr1[p] = sqr1[p-1] + mln::math::sqr(pos) * mln::opt::at(histo, p); + avg1[p] = (0 == cnt1[p])? 0 : (sum1[p] / cnt1[p]); + var1[p] = (0 == cnt1[p])? 0 : ((sqr1[p] / cnt1[p])-mln::math::sqr(avg1[p])); + + // Assign the statistics to the second class + cnt2[p] = cnt2[p-1] - mln::opt::at(histo, p);; + sum2[p] = sum2[p-1] - p * mln::opt::at(histo, p);; + sqr2[p] = sqr2[p-1] - mln::math::sqr(p) * mln::opt::at(histo, p);; + avg2[p] = (0 == cnt2[p])? 0 : (sum2[p] / cnt2[p]); + var2[p] = (0 == cnt2[p])? 0 : ((sqr2[p] / cnt2[p])-mln::math::sqr(avg2[p])); + + // Lets compute the invariants + cnt0[p] = cnt1[p] + cnt2[p]; + sum0[p] = sum1[p] + sum2[p]; + sqr0[p] = sqr1[p] + sqr2[p]; + avg0[p] = (cnt1[p] * avg1[p] + cnt2[p] * avg2[p])/cnt0[p]; + v_in[p] = (cnt1[p] * var1[p] + cnt2[p] * var2[p])/cnt0[p]; + v_bw[p] = (cnt1[p] * mln::math::sqr(avg1[p]-avg0[p]) + + cnt2[p] * mln::math::sqr(avg2[p]-avg0[p]))/cnt0[p]; + var0[p] = v_in[p] + v_bw[p]; + } + + // Find the threshold that minimizes the intra-class variance + min_var = cnt2[0]*var2[0]; + threshold = 0; + + for(short p = 0; p < 256; p++) + { + // Compute the intra-class variance + v_in[p] = cnt1[p]*var1[p] + cnt2[p]*var2[p]; +// std::cout << "var intra[" < v_in[p]) + { + min_var = v_in[p]; + threshold = p; + } + } + + return threshold; +} +/// /} + + +/// Launching part. +/// +/// In this part, we have the front end that walk over the image +/// databases and start to compute information that we pass to +/// previous routines. +/// \{ + +#define LVL0_DESCR 0 // Number of grey available +#define HUE0_DESCR 1 // Density histogram hue analysis +#define HUE1_DESCR 2 // Millet Hue analysis +#define SAT0_DESCR 3 // Density histogram saturation analysis +#define SAT1_DESCR 4 // Millet saturation analysis +#define VAL0_DESCR 5 // Density histogram grey level analysis +#define VAL1_DESCR 6 // Millet grey level analysis +#define GMP0_DESCR 7 // PNSNR with GIMP compression +#define GMP1_DESCR 8 // PNSNR with GIMP compression +#define GMP2_DESCR 9 // PNSNR with GIMP compression +#define MGK0_DESCR 9 // PNSNR with ImageMagick compression +#define MGK1_DESCR 10 // PNSNR with ImageMagick compression +#define MGK2_DESCR 11 // PNSNR with ImageMagick compression + +#define MGK_DESCR(version) (MGK0_DESCR + version) +#define GMP_DESCR(version) (GMP0_DESCR + version) + +#define NB_DESCR 12 // Number of descriptors +#define NB_DATABASE 2 // Number of image databases : AFP,ICDAR +#define NB_IMAGE 110 // Number of images +#define NB_VERSION 3 // Number of compression by GIMP or ImageMagick + +/// \brief Make sure that the result is between 0 and 255. +/// +/// \param[in] file_name the name of the analysis files. +/// \param[in] result the values for each image and each descriptors. +/// \param[in] size the number of image by each database. +/// +/// Correct the descriptors if there is overflow values. Just used in +/// debug mode. +void init_descriptors(std::string file_name[], + float result[][NB_DESCR], + int size[]) +{ + for (int i = 0; i < NB_IMAGE; i++) + { + file_name[i] = std::string("PGM"); + + for (int d = 0; d < NB_DESCR; d++) + result[i][d] = (i*d) % 256; + } + + size[0] = 62; + size[1] = 48; +} + + +/// \brief Dump gnuplot file for vizualisation of results. +/// +/// \param[in] file_name the name of the analysis files. +/// \param[in] result the values for each image and each descriptors. +/// \param[in] size the number of image by each database. +/// +/// This routine aimed at plotting the result with one graph. Each +/// database has got its color. We can see the values used by each +/// descriptors along the image of each database. +void dump_descriptors(const std::string file_name[], + const float result[][NB_DESCR], + const int size[]) +{ + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "plot '-' using 2 with point title 'ICDAR',\\" << std::endl; + std::cout << " '-' using 2 with point title 'AFP'" << std::endl; + + int num = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + for (int i = 0; i < size[db]; i++) + { + std::cout << result[num][LVL0_DESCR] << " "; + std::cout << result[num][HUE0_DESCR] << " "; + std::cout << result[num][HUE1_DESCR] << " "; + std::cout << result[num][SAT0_DESCR] << " "; + std::cout << result[num][SAT1_DESCR] << " "; + std::cout << result[num][VAL0_DESCR] << " "; + std::cout << result[num][VAL1_DESCR] << " "; + std::cout << result[num][GMP0_DESCR] << " "; + std::cout << result[num][GMP1_DESCR] << " "; + std::cout << result[num][GMP2_DESCR] << " "; + std::cout << result[num][MGK0_DESCR] << " "; + std::cout << result[num][MGK1_DESCR] << " "; + std::cout << result[num][MGK2_DESCR] << " "; + std::cout << " # " << file_name[num] << std::endl; + num++; + } + + std::cout << "e" << std::endl; + } +} + + +/// \brief Compute the descriptor histograms for each database. +/// +/// \param[in] result the result values of each descriptors. +/// \param[in] size the number of images by database. +/// \param[out] histo the computed histograms. +/// +/// This routine compute a histograms for each database and each descriptors. +/// So we can see the distribution of descriptor values over each database. +/// Result values are transform from float to int for histograms. +void compute_histo(const float result[][NB_DESCR], + const int size[], + mln::image1d<float> histo[][NB_DATABASE]) +{ + for (int i = 0; i < NB_DESCR; i++) + for (int db = 0; db < NB_DATABASE; db++) + { + histo[i][db].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + + mln::data::fill(histo[i][db], mln::literal::zero); + } + + short v; + int num = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + for (int i = 0; i < size[db]; i++) + { + v = (short)mln::math::floor(result[num][VAL0_DESCR]+0.4999); + mln::opt::at(histo[VAL0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][LVL0_DESCR]+0.4999); + mln::opt::at(histo[LVL0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][HUE0_DESCR]+0.4999); + mln::opt::at(histo[HUE0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][HUE1_DESCR]+0.4999); + mln::opt::at(histo[HUE1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][SAT0_DESCR]+0.4999); + mln::opt::at(histo[SAT0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][SAT1_DESCR]+0.4999); + mln::opt::at(histo[SAT1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][VAL1_DESCR]+0.4999); + mln::opt::at(histo[VAL1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP0_DESCR]+0.4999); + mln::opt::at(histo[GMP0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP1_DESCR]+0.4999); + mln::opt::at(histo[GMP1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP2_DESCR]+0.4999); + mln::opt::at(histo[GMP2_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK0_DESCR]+0.4999); + mln::opt::at(histo[MGK0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK1_DESCR]+0.4999); + mln::opt::at(histo[MGK1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK2_DESCR]+0.4999); + mln::opt::at(histo[MGK2_DESCR][db],v)++; + + num++; + } + } +} + + +/// Compute the threshold for discrimination between ICDAR and AFP. +/// +/// \param[in] histo the histogram for each descriptor and for each database. +/// \param[out] threshold computed values to discriminate between ICDAR/AFP. +/// \param[out] c00 say CLASS 1 but CLASS 1. +/// \param[out] c10 say CLASS 2 but CLASS 1. +/// \param[out] c01 say CLASS 1 but CLASS 2. +/// \param[out] c11 say CLASS 2 but CLASS 2. +/// +/// Compute the threshold by the minimum of error of +/// classification. Population are inverted in function of average, +/// technical solution to prevent min_error crashed. The bad thing is +/// now, the class 0 can be either ICDAR of AFP, depends on descriptors. +void compute_thresholds(const mln::image1d<float> histo[][NB_DATABASE], + short threshold[], + float c00[], + float c10[], + float c01[], + float c11[]) +{ + for (int i = 0; i < NB_DESCR; i++) + { + float avg0 = avg_histo(histo[i][0]); + float avg1 = avg_histo(histo[i][1]); + + if (avg0 < avg1) + { + threshold[i] = min_error(histo[i][0], histo[i][1], + &c00[i], &c10[i], &c01[i], &c11[i]); + } + else + { + threshold[i] = min_error(histo[i][1], histo[i][0], + &c00[i], &c10[i], &c01[i], &c11[i]); + } + + std::cerr << " i = " << i + << "; c00 = " << c00[i] + << "; c10 = " << c10[i] + << "; c01 = " << c01[i] + << "; c11 = " << c11[i] + << "; threshold " << threshold[i] + << std::endl; + + } +} + + +/// \brief Walk over the database directories. +/// +/// \param[out] file_name the array of the image name. +/// \param[out] result the descriptors values for each image. +/// \param[out] size the number of image for each database. +/// +/// This is the front end part to access to the database directories. We use +/// boost library to do it. First we explore ICDAR database en then the AFP one. +/// For each valid image, we compute all the descriptors. Histograms are +/// computed at this level because we can do that thing one time for every +/// density descriptors. Every parameters are updated to retrurn the values. +void compute_descriptors(std::string file_name[], + float result[][NB_DESCR], + int size[]) +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + typedef mln::image1d<unsigned> t_histo; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_map; + typedef mln::image2d<t_rgb8> t_input; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_2_hue; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_2_sat; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_2_val; + typedef mln::fun::v2v::component<t_rgb8,0> t_rgb_2_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_rgb_2_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_rgb_2_blue; + typedef mln::accu::meta::stat::histo1d t_accu_histo; + + + t_path img_path[2] = { ICDAR_20P_INPUT_IMG_PATH, AFP_PPM_IMG_PATH}; + t_path mgk_path[3][2] = {{ICDAR_20P_MGK30_IMG_PATH, AFP_MGK30_IMG_PATH}, + {ICDAR_20P_MGK20_IMG_PATH, AFP_MGK20_IMG_PATH}, + {ICDAR_20P_MGK10_IMG_PATH, AFP_MGK10_IMG_PATH}}; + t_path gmp_path[3][2] = {{ICDAR_20P_GMP30_IMG_PATH, AFP_GMP30_IMG_PATH}, + {ICDAR_20P_GMP20_IMG_PATH, AFP_GMP20_IMG_PATH}, + {ICDAR_20P_GMP10_IMG_PATH, AFP_GMP10_IMG_PATH}}; + + int num = 0; + int cnt = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + if (boost::filesystem::exists(img_path[db]) && + boost::filesystem::is_directory(img_path[db])) + { + boost::filesystem::system_complete(img_path[db]); + + const t_iter_path end_iter; + + cnt = 0; + + for (t_iter_path dir_iter(img_path[db]); end_iter != dir_iter; ++dir_iter) + { + t_path img_file = dir_iter->path().leaf(); + t_path dir_file = dir_iter->path(); + t_input img_input; + + mln::io::ppm::load(img_input, dir_file.string().c_str()); + + t_map h_img_map = mln::data::transform(img_input, t_rgb_2_hue()); + t_map s_img_map = mln::data::transform(img_input, t_rgb_2_sat()); + t_map v_img_map = mln::data::transform(img_input, t_rgb_2_val()); + t_map r_img_map = mln::data::transform(img_input, t_rgb_2_red()); + t_map g_img_map = mln::data::transform(img_input, t_rgb_2_green()); + t_map b_img_map = mln::data::transform(img_input, t_rgb_2_blue()); + t_histo h_img_hst = mln::data::compute(t_accu_histo(), h_img_map); + t_histo s_img_hst = mln::data::compute(t_accu_histo(), s_img_map); + t_histo v_img_hst = mln::data::compute(t_accu_histo(), v_img_map); + t_histo r_img_hst = mln::data::compute(t_accu_histo(), r_img_map); + t_histo g_img_hst = mln::data::compute(t_accu_histo(), g_img_map); + t_histo b_img_hst = mln::data::compute(t_accu_histo(), b_img_map); + + std::cerr << dir_iter->path() << std::endl; + + file_name[num] = img_file.string(); + + // descriptors + result[num][LVL0_DESCR] = lvl0_descriptor(v_img_hst); + result[num][HUE0_DESCR] = hue0_descriptor(h_img_hst); + result[num][HUE1_DESCR] = hue1_descriptor(h_img_hst,20); + result[num][SAT0_DESCR] = sat0_descriptor(s_img_hst); + result[num][SAT1_DESCR] = sat1_descriptor(s_img_hst,50); + result[num][VAL0_DESCR] = val0_descriptor(v_img_hst); + //result[num][VAL1_DESCR] = val1_descriptor(v_img_hst, 15); + result[num][VAL1_DESCR] = 0; + + // for gimp and magick + for (int v = 0; v < NB_VERSION; v++) + { + if (boost::filesystem::exists(mgk_path[v][db]) && + boost::filesystem::exists(gmp_path[v][db]) && + boost::filesystem::is_directory(mgk_path[v][db]) && + boost::filesystem::is_directory(gmp_path[v][db])) + { + t_path mgk_file = mgk_path[v][db] / img_file; + t_path gmp_file = gmp_path[v][db] / img_file; + t_input gmp_input; + + mln::io::ppm::load(gmp_input, gmp_file.string().c_str()); + + t_map r_gmp_map = mln::data::transform(gmp_input,t_rgb_2_red()); + t_map g_gmp_map = mln::data::transform(gmp_input,t_rgb_2_green()); + t_map b_gmp_map = mln::data::transform(gmp_input,t_rgb_2_blue()); + + result[num][GMP_DESCR(v)]= err_descriptor(r_img_map, + g_img_map, + b_img_map, + r_gmp_map, + g_gmp_map, + b_gmp_map); + + t_input mgk_input; + + mln::io::ppm::load(mgk_input, mgk_file.string().c_str()); + + t_map r_mgk_map = mln::data::transform(mgk_input,t_rgb_2_red()); + t_map g_mgk_map = mln::data::transform(mgk_input,t_rgb_2_green()); + t_map b_mgk_map = mln::data::transform(mgk_input,t_rgb_2_blue()); + + result[num][MGK_DESCR(v)]= err_descriptor(r_img_map, + g_img_map, + b_img_map, + r_mgk_map, + g_mgk_map, + b_mgk_map); + } + } + + num++; + cnt++; + } + } + + size[db] = cnt; + } +} + +/// \brief Just for debugging purpose and tests. +int main2() +{ + typedef mln::image1d<unsigned> t_histo; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_map; + typedef mln::image2d<t_rgb8> t_input; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_2_hue; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_2_sat; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_2_val; + typedef mln::fun::v2v::component<t_rgb8,0> t_rgb_2_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_rgb_2_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_rgb_2_blue; + typedef mln::accu::meta::stat::histo1d t_accu_histo; + + t_input img_input; + + mln::io::ppm::load(img_input, ICDAR_20P_INPUT_IMG_PATH"/mp00032c_20p.ppm"); + //mln::io::ppm::load(img_input, AFP_PPM_IMG_PATH"/000_Del218430.ppm"); + + + + t_map h_img_map = mln::data::transform(img_input, t_rgb_2_hue()); + t_map s_img_map = mln::data::transform(img_input, t_rgb_2_sat()); + t_map v_img_map = mln::data::transform(img_input, t_rgb_2_val()); + t_map r_img_map = mln::data::transform(img_input, t_rgb_2_red()); + t_map g_img_map = mln::data::transform(img_input, t_rgb_2_green()); + t_map b_img_map = mln::data::transform(img_input, t_rgb_2_blue()); + t_histo h_img_hst = mln::data::compute(t_accu_histo(), h_img_map); + t_histo s_img_hst = mln::data::compute(t_accu_histo(), s_img_map); + t_histo v_img_hst = mln::data::compute(t_accu_histo(), v_img_map); + t_histo r_img_hst = mln::data::compute(t_accu_histo(), r_img_map); + t_histo g_img_hst = mln::data::compute(t_accu_histo(), g_img_map); + t_histo b_img_hst = mln::data::compute(t_accu_histo(), b_img_map); + + + std::cout << "sat2 : " << sat0_descriptor(s_img_hst) << std::endl; + + return 0; +} + + +/// \brief Main entry. +/// +/// This is the front end for every routines. +/// - compute descriptors. +/// - dump descriptors. +/// - compute histograms. +/// - compute thresholds. +/// - save results. +/// +/// Global data used: +/// - file_name : all the initial images name, mixed every image database. +/// - result : all the result values for every descriptors and for every images. +/// - histo : the histograms ny database and by descriptors. +/// - size : the number of image by database. +/// - threshold : the computed histogram separation on each descriptors. +/// - cxx : the count of well/bad classified images function of threshold info. +int main() +{ + std::string file_name[NB_IMAGE]; + float result[NB_IMAGE][NB_DESCR]; + int size[NB_DATABASE]; + mln::image1d<float> histo[NB_DESCR][NB_DATABASE]; + short threshold[NB_DESCR]; + float c00[NB_DESCR]; + float c10[NB_DESCR]; + float c01[NB_DESCR]; + float c11[NB_DESCR]; + + std::cerr << "DESCRIPTORS" << std::endl; + compute_descriptors(file_name,result,size); +// std::cout << "DUMPING" << std::endl; +// init_descriptors(file_name,result,size); + dump_descriptors(file_name,result,size); + std::cerr << "HISTO" << std::endl; + compute_histo(result,size,histo); + std::cerr << "THRESHOLD" << std::endl; + compute_thresholds(histo,threshold,c00,c10,c01,c11); + + mln::io::plot::save_image_sh(histo[LVL0_DESCR][0], "lvl0_histo1.sh"); + mln::io::plot::save_image_sh(histo[HUE0_DESCR][0], "hue0_histo1.sh"); + mln::io::plot::save_image_sh(histo[HUE1_DESCR][0], "hue1_histo1.sh"); + mln::io::plot::save_image_sh(histo[SAT0_DESCR][0], "sat0_histo1.sh"); + mln::io::plot::save_image_sh(histo[SAT1_DESCR][0], "sat1_histo1.sh"); + mln::io::plot::save_image_sh(histo[VAL0_DESCR][0], "val0_histo1.sh"); + mln::io::plot::save_image_sh(histo[VAL1_DESCR][0], "val1_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP0_DESCR][0], "gmp0_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP1_DESCR][0], "gmp1_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP2_DESCR][0], "gmp2_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK0_DESCR][0], "mgk0_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK1_DESCR][0], "mgk1_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK2_DESCR][0], "mgk2_histo1.sh"); + + mln::io::plot::save_image_sh(histo[LVL0_DESCR][1], "lvl0_histo2.sh"); + mln::io::plot::save_image_sh(histo[HUE0_DESCR][1], "hue0_histo2.sh"); + mln::io::plot::save_image_sh(histo[HUE1_DESCR][1], "hue1_histo2.sh"); + mln::io::plot::save_image_sh(histo[SAT0_DESCR][1], "sat0_histo2.sh"); + mln::io::plot::save_image_sh(histo[SAT1_DESCR][1], "sat1_histo2.sh"); + mln::io::plot::save_image_sh(histo[VAL0_DESCR][1], "val0_histo2.sh"); + mln::io::plot::save_image_sh(histo[VAL1_DESCR][1], "val1_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP0_DESCR][1], "gmp0_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP1_DESCR][1], "gmp1_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP2_DESCR][1], "gmp2_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK0_DESCR][1], "mgk0_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK1_DESCR][1], "mgk1_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK2_DESCR][1], "mgk2_histo2.sh"); + + return 0; +} +/// \} + diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/error/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/error/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/error/error.cc b/scribo/sandbox/green/exp/annotating/error/error.cc new file mode 100644 index 0000000..949b51b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/error/error.cc @@ -0,0 +1,833 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// This file is a prelude just before using the bench source. It puts +/// the light on the Jonathan's idea which wants to compare ICDAR and +/// AFP image database with difference between original image and +/// color reducted one. The key point is to say that when a color +/// image is degraded, the visual quality fall quickly in function of +/// the number of colors left. A contrario, when an image is black and +/// white, this kind of degradation doesn't change the original visual +/// quality. The PNSNR detector is build and learning threshold for +/// automatic classification is done (four algorithms are tested). + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/algebra/vec.hh> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/times.hh> +#include <mln/arith/diff_abs.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/fill.hh> + +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/ceil.hh> +#include <mln/math/floor.hh> + +#include <mln/opt/at.hh> + +#include <mln/trait/value_.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> + +/// Work on histogram. +/// \brief Different routines are presented for computing mean, variance ... +/// \{ + +/// \brief Counting the number of pixels in the histogram. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the number of pixels in the histogram. +/// +/// Such routine is a generic and is generally applied on a portion of +/// the real domain. +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + } + + return sum; +} + +/// \brief Summing the values ponderated by their occurences. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the sum ponderated of values by their occurences. +/// +/// This method is not a production routine, it's just a test to debug +/// histogram problem. When we compute something over the histogram, +/// we need to take care of the intermediate type that contains the +/// temporary result. If not the case, the type used is the one of the +/// value of the histogram and we can go to some overflow +/// problems. The trick is to store the value returned by the iterator +/// in some temporary computing type (look at pos in the code). +template <typename I> +mln_value(I) sum_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + sum += pos*histo(p); + } + + return sum; +} + +/// \brief Summing the values ponderated by their occurences. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the sum ponderated of values by their occurences. +template <typename I> +mln_value(I) avg_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += histo(p); + sum += pos*histo(p); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// This computing is safe because the quantity of information stored +/// in sum is low. This formulae doesn't show the overflow problem. +template <typename I> +mln_value(I) var3_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + sum += (mln::math::sqr(p.ind()-mean)*histo(p)); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// This is the debugging code to observe overflow problem. +template <typename I> +mln_value(I) var2_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_value(I) dlt = mln::literal::zero; + mln_value(I) mxt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + mxt += (histo(p)*pos*mean); + sqr += (histo(p)*mln::math::sqr(pos)); + dlt += (histo(p)*mln::math::sqr(pos - mean)); + + std::cout << "p = " << pos << std::endl; + std::cout << "p² = " << mln::math::sqr(pos) << std::endl; + std::cout << "p*mean = " << (pos*mean) << std::endl; + std::cout << "p-mean = " << (pos-mean) << std::endl; + std::cout << "(p-mean)² = " << mln::math::sqr(pos-mean) << std::endl; + std::cout << "histo(p) = " << histo(p) << std::endl; + std::cout << "histo(p)*p = " << (pos*histo(p)) << std::endl; + std::cout << "histo(p)*p²= " << (mln::math::sqr(pos)*histo(p)) + << std::endl; + std::cout << "cnt = " << cnt << std::endl; + std::cout << "sum = " << sum << std::endl; + std::cout << "sqr = " << sqr << std::endl; + std::cout << "dlt = " << dlt << std::endl; + std::cout << "mxt = " << mxt << std::endl; + std::cout << std::endl; + } + + std::cout << "sqr/cnt = " << (sqr/cnt) << std::endl; + std::cout << "sum/cnt = " << (sum/cnt) << std::endl; + std::cout << "(sum/cnt)² = " << mln::math::sqr(sum/cnt) << std::endl; + std::cout << "dlt/cnt = " << dlt/cnt << std::endl; + std::cout << "mxt/cnt = " << mxt/cnt << std::endl; + std::cout << std::endl; + + std::cout << "sqr = " + << (sqr) << std::endl; + std::cout << "dlt = " + << (dlt) << std::endl; + std::cout << "cnt*avg² = " + << (mln::math::sqr(sum/cnt)*cnt) << std::endl; + std::cout << "2*mxt = " + << (2*mxt) << std::endl; + std::cout << "sqr - cnt*avg² = " + << (sqr/cnt - mln::math::sqr(sum/cnt)) << std::endl; + std::cout << "(sqr -2*mxt + cnt*avg²) = " + << ((sqr - 2*mxt + mln::math::sqr(sum/cnt))/cnt) << std::endl; + std::cout << std::endl; + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// My standard code for the variance. +template <typename I> +mln_value(I) var_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + sqr += (histo(p)*mln::math::sqr(pos)); + } + + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} +/// \} + + +/// Classify. +/// \brief Classification routines that compute threshold. +/// \{ + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*sqrt(v1)+m2*sqrt(v2))/(sqrt(v1)+sqrt(v2)). +float threshold_histo(float avg1, float var1, float avg2, float var2) +{ + float sigma1 = mln::math::sqrt(var1); + float sigma2 = mln::math::sqrt(var2); + float threshold = (avg1*sigma1+avg2*sigma2)/(sigma1+sigma2); + + return threshold; +} + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*var1+m2*var2)/(sqrt(v1)+sqrt(v2)). +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Gaussian discriminant analysis in 1d is done. Compute the +/// discrimanation and solve the parabolic equation. +float threshold2_histo(float avg1, float var1, float avg2, float var2) +{ + float a = var2 - var1; + float b = -2 * (avg1 * var2 - avg2 * var1); + float c = var2 * mln::math::sqr(avg1) - var1 * mln::math::sqr(avg2); + float d = mln::math::sqr(b) - 4 * a * c; + + if (d < 0) + std::cout << "delta negatif" << std::endl; + + float x1 = (-b+mln::math::sqrt(d))/(2*a); + float x2 = (-b-mln::math::sqrt(d))/(2*a); + + std::cout << "a = " << a << std::endl; + std::cout << "b = " << b << std::endl; + std::cout << "c = " << c << std::endl; + std::cout << "d = " << d << std::endl; + std::cout << "x1 = " << x1 << std::endl; + std::cout << "x2 = " << x2 << std::endl; + + return x2; +} + +/// \brief Compute the other proportion of histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the ponderated sum of the sqrt values. +/// +/// This is a test. Don't take this routine in production. +template <typename I> +mln_value(I) sqr_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += (mln::math::sqr(p.ind())*histo(p)); + + return sum; +} + +/// \brief Minimisation of the error. +/// +/// \param[in] histo_grp1 the histogram of the first population. +/// \param[in] histo_grp2 the histogram of the second population. +/// +/// \return the threshold. +/// +/// Computes the error and find the minimum error threshold. It's just +/// a counting of element in four categories (0 - but detects 1, 1 - +/// but detects 1, 1 but detects 0, 0 but detects 0). The error is the +/// sum of missclassifications for the classes 0 and 1. As Otsu done, +/// we can iterate on the threshold and compute the error associated +/// to it. The bes threshold is the one that minimize the error. +short min_error(const mln::image1d<float> histo_grp1, + const mln::image1d<float> histo_grp2) +{ + float c00[256]; + float c10[256]; + float c01[256]; + float c11[256]; + float err[256]; + + for (short p = 0; p < 256; p++) + { + c00[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c10[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + + c01[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c11[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + } + + short threshold = 0; + float error = c01[0] + c01[0] + c00[0] + c11[0]; + + for(short p = 0; p < 256; p++) + { + err[p] = c10[p] + c01[p]; + + std::cout << " p = " < err[p]) + { + error = err[p]; + threshold = p; + } + } + + return threshold; +} + +/// \brief Fisher analysis. +/// +/// \param[in] histo the histogram of the mixed population. +/// +/// \return the threshold. +/// +/// This routine is less performant than the others because it starts +/// with a mixed population, but we know exactly the two separated +/// population. It was just a test. Don't take it in production +/// environement, it doesn't help you. +short fisher_analysis(const mln::image1d<float> histo) +{ + typedef mln::value::int_u8 t_int_u8; + + // FIXE ME SIZE const short a = mln_min(t_int_u8); + // float cnt1[a]; + + float cnt1[256]; + float sum1[256]; + float sqr1[256]; + float avg1[256]; + float var1[256]; + + float cnt2[256]; + float sum2[256]; + float sqr2[256]; + float avg2[256]; + float var2[256]; + + float cnt0[256]; // count of points + float sum0[256]; // sum of population + float sqr0[256]; // sqr of population + float avg0[256]; // average of the population + float v_in[256]; // variance with-in class + float v_bw[256]; // variance between class + float var0[256]; // variance of the population + short threshold; + float pos; + float min_var; + + // Assign the first elements + cnt1[0] = 0; + sum1[0] = 0; + sqr1[0] = 0; + avg1[0] = 0; + var1[0] = 0; + + // Compute the stats of the wall histogram + cnt2[0] = 0; + sum2[0] = 0; + sqr2[0] = 0; + + for(short p = 0; p < 256; p++) + { + pos = p; + cnt2[0] += mln::opt::at(histo,p); + sum2[0] += (pos * mln::opt::at(histo,p)); + sqr2[0] += (mln::math::sqr(pos) * mln::opt::at(histo,p)); + } + + avg2[0] = (0 == cnt2[0])? 0 : sum2[0] / cnt2[0]; + var2[0] = (0 == cnt2[0])? 0 : sqr2[0] / cnt2[0] - mln::math::sqr(avg2[0]); + + // watch the array limits + for (short p = 1; p < 256; p++) + { + pos = p; + + // Assign the statistics to the primary class + cnt1[p] = cnt1[p-1] + mln::opt::at(histo, p); + sum1[p] = sum1[p-1] + pos * mln::opt::at(histo, p); + sqr1[p] = sqr1[p-1] + mln::math::sqr(pos) * mln::opt::at(histo, p); + avg1[p] = (0 == cnt1[p])? 0 : (sum1[p] / cnt1[p]); + var1[p] = (0 == cnt1[p])? 0 : ((sqr1[p] / cnt1[p])-mln::math::sqr(avg1[p])); + + // Assign the statistics to the second class + cnt2[p] = cnt2[p-1] - mln::opt::at(histo, p);; + sum2[p] = sum2[p-1] - p * mln::opt::at(histo, p);; + sqr2[p] = sqr2[p-1] - mln::math::sqr(p) * mln::opt::at(histo, p);; + avg2[p] = (0 == cnt2[p])? 0 : (sum2[p] / cnt2[p]); + var2[p] = (0 == cnt2[p])? 0 : ((sqr2[p] / cnt2[p])-mln::math::sqr(avg2[p])); + + // Lets compute the invariants + cnt0[p] = cnt1[p] + cnt2[p]; + sum0[p] = sum1[p] + sum2[p]; + sqr0[p] = sqr1[p] + sqr2[p]; + avg0[p] = (cnt1[p] * avg1[p] + cnt2[p] * avg2[p])/cnt0[p]; + v_in[p] = (cnt1[p] * var1[p] + cnt2[p] * var2[p])/cnt0[p]; + v_bw[p] = (cnt1[p] * mln::math::sqr(avg1[p]-avg0[p]) + + cnt2[p] * mln::math::sqr(avg2[p]-avg0[p]))/cnt0[p]; + var0[p] = v_in[p] + v_bw[p]; + } + + // Find the threshold that minimizes the intra-class variance + min_var = cnt2[0]*var2[0]; + threshold = 0; + + for(short p = 0; p < 256; p++) + { + // Compute the intra-class variance + v_in[p] = cnt1[p]*var1[p] + cnt2[p]*var2[p]; +// std::cout << "var intra[" < v_in[p]) + { + min_var = v_in[p]; + threshold = p; + } + } + + return threshold; +} +/// \} + +/// \brief ERROR (MSE, PNSNR) compression p278 Handbook Color. +/// \param[in] original the original image. +/// \param[in] reduced the image with a reduced number of colors. +/// +/// \return the PNSNR. +/// +/// Compute the PNSNR. First compute the square difference in each +/// channel. Then compute the mean of those square differences and get +/// the mean over the three channels. Then, get the sqrt of that to +/// have a distance. The PNSNR is a logarithmic quantity of that distance. +float error_test(const std::string original, + const std::string reduced) + +{ + + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::fun::v2v::component<t_rgb8,0> t_component_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_component_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_component_blue; + typedef mln::accu::meta::stat::mean t_mean; + typedef mln::accu::meta::stat::histo1d t_histo; + typedef mln::image2d<t_int_u8> t_img; + typedef mln_trait_op_minus_(t_img,t_img) t_minus; + typedef mln_trait_op_times_(t_minus,t_minus) t_times; + + mln::image2d<mln::value::rgb8> original_rgb8; + mln::image2d<mln::value::rgb8> reduced_rgb8; + + mln::image2d<mln::value::int_u8> original_red; + mln::image2d<mln::value::int_u8> original_green; + mln::image2d<mln::value::int_u8> original_blue; + + mln::image2d<mln::value::int_u8> reduced_red; + mln::image2d<mln::value::int_u8> reduced_green; + mln::image2d<mln::value::int_u8> reduced_blue; + +// mln::image2d<mln::value::int_u8> map_red; +// mln::image2d<mln::value::int_u8> map_green; +// mln::image2d<mln::value::int_u8> map_blue; + +// mln::image1d<unsigned> histo_red; +// mln::image1d<unsigned> histo_green; +// mln::image1d<unsigned> histo_blue; + + t_minus minus_red; + t_minus minus_green; + t_minus minus_blue; + + t_times times_red; + t_times times_green; + t_times times_blue; + + float error_red; + float error_green; + float error_blue; + + float error; + + + mln::io::ppm::load(original_rgb8, original.c_str()); + mln::io::ppm::load(reduced_rgb8, reduced.c_str()); + + original_red = mln::data::transform(original_rgb8, t_component_red()); + original_green = mln::data::transform(original_rgb8, t_component_green()); + original_blue = mln::data::transform(original_rgb8, t_component_blue()); + + reduced_red = mln::data::transform(reduced_rgb8, t_component_red()); + reduced_green = mln::data::transform(reduced_rgb8, t_component_green()); + reduced_blue = mln::data::transform(reduced_rgb8, t_component_blue()); + + minus_red = (original_red - reduced_red); + times_red = minus_red * minus_red; + + minus_green = (original_green - reduced_green); + times_green = minus_green * minus_green; + + minus_blue = (original_blue - reduced_blue); + times_blue = minus_blue * minus_blue; + + error_red = mln::data::compute(t_mean(), times_red); + error_green = mln::data::compute(t_mean(), times_green); + error_blue = mln::data::compute(t_mean(), times_blue); + +// map_red = mln::data::stretch(t_int_u8(), times_red); +// map_green = mln::data::stretch(t_int_u8(), times_blue); +// map_blue = mln::data::stretch(t_int_u8(), times_green); + +// histo_red = mln::data::compute(t_histo(), map_red); +// histo_green = mln::data::compute(t_histo(), map_green); +// histo_blue = mln::data::compute(t_histo(), map_blue); + +// mln::io::plot::save_image_sh(histo_red, "histo_red.sh"); +// mln::io::plot::save_image_sh(histo_green,"histo_green.sh"); +// mln::io::plot::save_image_sh(histo_blue, "histo_blue.sh"); + +// mln::io::pgm::save(map_red, "red.pgm"); +// mln::io::pgm::save(map_green,"green.pgm"); +// mln::io::pgm::save(map_blue, "blue.pgm"); + + error = (error_red + error_green + error_blue)/3.0; + error = mln::math::sqrt(error); + error = 20 * log(255/error); + +// Le PNSNR semble offrir plus d'espace pour la discrimination +// Si les images sont identiques ==> PNSNR = +inf +// Si les images sont très différentes ==> PNSNR = 0 + + return error; +} + + +/// \brief The main entry. +/// +/// This is the front end for error classification. This is the big +/// loop where we walk other image data bases. Computation of +/// automatic thresholds are called from here. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + mln::image1d<float> histo(256); + mln::image1d<float> histo_grp[2]; // histo by group + + histo_grp[0].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + histo_grp[1].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + + mln::data::fill(histo, mln::literal::zero); + mln::data::fill(histo_grp[0], mln::literal::zero); + mln::data::fill(histo_grp[1], mln::literal::zero); + + t_path original_path[] = {ICDAR_20P_INPUT_IMG_PATH, + AFP_PPM_IMG_PATH}; + +// t_path reduced1_path[] = {ICDAR_20P_MGK30_IMG_PATH, +// AFP_MGK30_IMG_PATH}; + +// t_path reduced1_path[] = {ICDAR_20P_MGK20_IMG_PATH, +// AFP_MGK20_IMG_PATH}; + + t_path reduced1_path[] = {ICDAR_20P_MGK10_IMG_PATH, + AFP_MGK10_IMG_PATH}; + +// t_path reduced2_path[] = {ICDAR_20P_GMP30_IMG_PATH, +// AFP_GMP30_IMG_PATH}; + +// t_path reduced2_path[] = {ICDAR_20P_GMP20_IMG_PATH, +// AFP_GMP20_IMG_PATH}; + + t_path reduced2_path[] = {ICDAR_20P_GMP10_IMG_PATH, + AFP_GMP10_IMG_PATH}; + + + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "ERROR" << std::endl; + std::cout << "plot '-' using 1 with point notitle,\\" << std::endl; + std::cout << " '-' using 1 with point notitle" << std::endl; + + for (int i = 0; i < 2; i++) + { + if (boost::filesystem::exists(original_path[i]) && + boost::filesystem::exists(reduced1_path[i]) && + boost::filesystem::exists(reduced2_path[i]) && + boost::filesystem::is_directory(original_path[i]) && + boost::filesystem::is_directory(reduced1_path[i]) && + boost::filesystem::is_directory(reduced2_path[i])) + { + boost::filesystem::system_complete(original_path[i]); + const t_iter_path end_iter; + float error1 = 0.0; + float error2 = 0.0; + t_path leaf; + t_path reduced1_file; + t_path reduced2_file; + + for (t_iter_path dir_iter(original_path[i]);end_iter!=dir_iter;++dir_iter) + { + leaf = dir_iter->path().leaf(); + reduced1_file = reduced1_path[i] / leaf; + reduced2_file = reduced2_path[i] / leaf; + + error1 = error_test(dir_iter->path().string(), reduced1_file.string()); + error2 = error_test(dir_iter->path().string(), reduced2_file.string()); + + float a1 = 1; + short v1 = (short)mln::math::floor(error2+0.4999); + mln::opt::at(histo,v1) += a1; + mln::opt::at(histo_grp[i],v1) += a1; + +// float a1 = error2 - mln::math::floor(error2); +// float a2 = mln::math::ceil(error2) - error2; +// short v1 = (short)mln::math::floor(error2); +// short v2 = (short)mln::math::ceil(error2); +// mln::opt::at(histo,v1) += a1; +// mln::opt::at(histo,v2) += a2; +// mln::opt::at(histo_grp[i],v1) += a1; +// mln::opt::at(histo_grp[i],v2) += a2; + + std::cout << error1 << " "; + std::cout << error2 << " "; + std::cout << "# " << dir_iter->path().leaf() << std::endl; + } + std::cout << "e" << std::endl; + } + } + + mln::io::plot::save_image_sh(histo, "histo.sh"); + mln::io::plot::save_image_sh(histo_grp[1], "histo_grp1.sh"); + mln::io::plot::save_image_sh(histo_grp[0], "histo_grp2.sh"); + + float threshold = fisher_analysis(histo); + float threshold2 = threshold_histo(avg_histo(histo_grp[1]), + var_histo(histo_grp[1]), + avg_histo(histo_grp[0]), + var_histo(histo_grp[0])); + float threshold3 = threshold2_histo(avg_histo(histo_grp[1]), + var_histo(histo_grp[1]), + avg_histo(histo_grp[0]), + var_histo(histo_grp[0])); + float threshold4 = min_error(histo_grp[1],histo_grp[0]); + + std::cout << "threshold = " << threshold << std::endl; + std::cout << "threshold2 = " << threshold2 << std::endl; + std::cout << "threshold3 = " << threshold3 << std::endl; + std::cout << "threshold4 = " << threshold4 << std::endl; + std::cout << "avg_grp1 = " << avg_histo(histo_grp[1]) << std::endl; + std::cout << "avg_grp2 = " << avg_histo(histo_grp[0]) << std::endl; + + // compute the classification matrix + // for each sub population + + float c00 = cnt_histo(histo_grp[1] | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + + float c10 = cnt_histo(histo_grp[1] | mln::box1d(mln::point1d(threshold+1), + mln::point1d(255))); + + float c01 = cnt_histo(histo_grp[0] | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + + float c11 = cnt_histo(histo_grp[0] | mln::box1d(mln::point1d(threshold+1), + mln::point1d(255))); + + + std::cout << "pop0 = " << cnt_histo(histo_grp[1]) << std::endl; + std::cout << "pop1 = " << cnt_histo(histo_grp[0]) << std::endl; + std::cout << std::endl; + + std::cout << "c00 = " << c00 << std::endl; + std::cout << "c10 = " << c10 << std::endl; + std::cout << "c01 = " << c01 << std::endl; + std::cout << "c11 = " << c11 << std::endl; + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/histo/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/histo/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/histo/histo.cc b/scribo/sandbox/green/exp/annotating/histo/histo.cc new file mode 100644 index 0000000..90b18e4 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/histo/histo.cc @@ -0,0 +1,366 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Build normalized histograms for visualization only. +/// +/// The goal of this code is to build every normalized histograms of +/// an image database for visual inspection. It enables the +/// comparaison of the densities of each image database and the +/// understanding of the differences in this space. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/convert.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/zero.hh> +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/math/sqrt.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +//============================================================================// +// HISTOGRAM +//============================================================================// + +/// \brief Count the number of pixel in the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the number of pixels in the domain. +/// +/// This is simple counting routine that compute the number of pixels +/// in the domain of the histogram. For each site, it sums the +/// frequencies associated to its site. +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + } + + return cnt; +} + +/// \brief The computing of the normalized histogram. +/// +/// \param[in] input the name of the input image. +/// \param[in] output_map the name of the specific map (hue,sat,val,R,G,B). +/// \param[in] output_histo the name of the output normalized histogram. +/// \param[in] space the name of the studied channel (R,G,B,H,S,V). +/// +/// Compute the normalized histogram in the given channel. First get +/// the channel from the RGB space or the HSV space and then compute +/// its normalized histogram. Normalization is done by dividing each +/// frequency by the sum of frequencies. It's a way to make the +/// histogram independant from the dimension of the input image and +/// then to compare different image databases together. +void histo(const std::string input, + const std::string output_map, + const std::string output_histo, + const char space) + +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_sat_map; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_val_map; + typedef mln::fun::v2v::component<t_rgb8,0> t_component_r; + typedef mln::fun::v2v::component<t_rgb8,1> t_component_g; + typedef mln::fun::v2v::component<t_rgb8,2> t_component_b; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float sum; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + switch(space) + { + case 'h': map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); break; + case 's': map = mln::data::transform(input_rgb8, t_rgb_to_sat_map()); break; + case 'v': map = mln::data::transform(input_rgb8, t_rgb_to_val_map()); break; + case 'r': map = mln::data::transform(input_rgb8, t_component_r()); break; + case 'g': map = mln::data::transform(input_rgb8, t_component_g()); break; + case 'b': map = mln::data::transform(input_rgb8, t_component_b()); break; + default: break;// crash + } + + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = cnt_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + + mln::io::pgm::save(map, output_map.c_str()); + mln::io::plot::save_image_sh(histo_float, output_histo.c_str()); +} + +//============================================================================// +// MAIN +//============================================================================// + + +/// \brief The main entry. +/// +/// This a front end to compute histogram with boost plumberies. There +/// is 2 uses cases, the computing over the icdar database and over +/// the afp database. We force the computing over the six channel +/// (H,S,V,R,G,B) in order to select the channel the most specific for +/// a database. It seems that saturation channel and value channel are +/// the most specific to describe each database. Take care to +/// coherence of the output directories for each source of images. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + +// t_path full_path[] = {t_path(ICDAR_20P_TEXT_ONLY_IMG_PATH), +// t_path(ICDAR_20P_TEXT_COLOR_IMG_PATH), +// t_path(ICDAR_20P_TEXT_PHOTO_IMG_PATH)}; + +// t_path full_path[] = {t_path(AFP_INPUT_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP30_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP20_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP10_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK30_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK20_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK10_IMG_PATH)}; + +// t_path full_path[] = {t_path(ICDAR_20P_INPUT_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_GMP30_IMG_PATH)}; + t_path full_path[] = {t_path(ICDAR_20P_GMP20_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_GMP10_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK30_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK20_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK10_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + t_path directory; + t_path leaf; + t_path output_map; + t_path output_histo; + + std::cerr << "entering " << full_path[i] << std::endl; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + std::cerr << dir_iter->path() << std::endl; + + leaf = dir_iter->path().leaf(); + +// directory = ANNOTATING_AFP_R_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_R_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_R_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_R_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_R_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'r'); + +// directory = ANNOTATING_AFP_G_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_G_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_G_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_G_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_G_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'g'); + +// directory = ANNOTATING_AFP_B_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_B_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_B_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_B_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_B_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'b'); + +// directory = ANNOTATING_AFP_H_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_H_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_H_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_H_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_H_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'h'); + +// directory = ANNOTATING_AFP_S_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_S_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_S_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_S_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_S_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 's'); + +// directory = ANNOTATING_AFP_V_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_V_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_V_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_V_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_V_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'v'); + } + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/hsv/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/hsv/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/hsv/hsv.cc b/scribo/sandbox/green/exp/annotating/hsv/hsv.cc new file mode 100644 index 0000000..507d07b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/hsv/hsv.cc @@ -0,0 +1,912 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet HSV operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Hue +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +/// +/// This operator try to integrate many processing in one shot, after +/// HSV transformation. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/convert.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/zero.hh> +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/math/sqrt.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +//============================================================================// +// HISTOGRAM +//============================================================================// + +/// Histogram +/// \{ +/// \brief Histogram processing methods. +/// +/// In this part, we define methods for histogram processing as average, +/// peak, variance etc ... + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +mln_value(I) count_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) result = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + result += histo(p); + + return result; +} + +/// \brief Sum the whole frequencies. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the number of pixels in the domain. +/// +/// By summing all the frequencies of the domain, we count in fact the +/// number of pixels in the image. +template <typename I> +mln_value(I) sum_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += histo(p); + + return sum; +} + +/// \brief Count the null frequencies. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the number of null frequencies. +/// +/// It's a way to access the number of distinct colors in the +/// histogram by comparing it with the histogram domain. +template <typename I> +mln_value(I) count_null_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) count = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + if (0 == histo(p)) + count++; + + return count; +} + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +mln_coord(mln_site_(I)) peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + mln_value(I) v_max = mln::opt::at(histo, mln::literal::zero); + mln_coord(mln_site_(I)) p_max = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Find the maximum frequency of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the maximum frequency of the histogram. +/// +/// Find the peak and return its value, not its position. We use that +/// function to normalize histograms. +template <typename I> +mln_value(I) max_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the value of the peak from the histogram + mln_value(I) max = mln::opt::at(histo, mln::literal::zero); + mln_piter(I) p(histo.domain()); + + for_all(p) + { + max = mln::math::max(histo(p),max); + } + + return max; +} + +/// \brief Find the mean of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the aveerage of the histogram. +/// +/// Compute the mean. +template <typename I> +float mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief Compare the histogram with the equi distributed histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the distance between the equi distributed histogram. +/// +/// Compute the square euclidian distance between histogram and the +/// equi distributed histogram. The equi distributed histogram is an +/// histogram in which each bin has the same frequency (id est 1/256 +/// for instance). The integral equals one for this two +/// histograms. This is a major contribution test to differentiate +/// image database. +template <typename I> +float cmp_equi_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + var += mln::math::sqr(histo(p) - (1/256.0)); + } + + var = var / sum; + + return var; +} + +/// \brief Compute the variance on the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the variance. +/// +/// Compute the variance by substracting the mean. +template <typename I> +float var_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + var += mln::math::sqr(p.ind() - mean) * histo(p); + } + + var = var / sum; + + return var; +} + +/// \brief Find the mean of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the aveerage of the histogram. +/// +/// Compute the mean. +template <typename I> +float mean_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum++; + mean += histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief Compute the standard deviation on the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the dstandard deviation. +/// +/// Compute the standard deviation by substracting the mean +template <typename I> +float stddev_frequency_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the var of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum++; + var += mln::math::sqr(histo(p)-mean); + } + + var = mln::math::sqrt(var / sum); + + return var; +} +/// \} + +//============================================================================// +// HUE TEST +//============================================================================// + +/// Hue test +/// \{ +/// \brief Hue test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the hue analysis. + + +/// \brief Label a grey value. +/// +/// \param[in] int_u8 the grey value. +/// +/// \return the reference color for this grey value. +/// +/// Classify grey value in three class, white, black and medium_gray. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the +/// primary colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + + if (10 > hue) + result = mln::literal::red; + else if (32 > hue) + result = mln::literal::orange; + else if (53 > hue) + result = mln::literal::yellow; + else if (74 > hue) + result = mln::literal::green; // chartreuse + else if (96 > hue) + result = mln::literal::green; + else if (116 > hue) + result = mln::literal::green;// turquoise, aigue-marine + else if (138 > hue) + result = mln::literal::green; // cyan + else if (159 > hue) + result = mln::literal::blue; // azur + else if (181 > hue) + result = mln::literal::blue; + else if (202 > hue) + result = mln::literal::violet; + else if (223 > hue) + result = mln::literal::pink; + else // if (244 > hue) + result = mln::literal::red; + + return result; +} + +/// \brief The hue test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// \param[in] threshold the limit to compute the histogram contribution. +/// +/// \return the proportion of pixels that pass the test. +/// +/// This is an augmented test. We first normalized the histogram to +/// obtain a discrete distribution. Then we compute the mean (or the +/// peak, i don't really know what is the best) and we look at the +/// proportion of the pixels around the mean or the peak. This +/// proportion tell us how histogram distribution is concentrated +/// around the modal value. If a hue modal value exist, we can say +/// that the image is colorized. +float hue_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float prop; + short peak; + mln::value::rgb8 color; + float sum; + mln::point1d inf; + mln::point1d sup; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + peak = mean_histo(histo); //peak_histo(histo); + color = label_hue(peak); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo_float|mln::box1d(inf,sup)); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); +// std::cout << "peak = " << peak << std::endl; +// std::cout << "color = " << color << std::endl; + + return prop; +} +/// \} + +//============================================================================// +// SATURATION TEST +//============================================================================// + +/// Saturation test +/// \{ +/// \brief Saturation test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the +/// saturation analysis. + + +/// \brief The saturation test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// \param[in] threshold for deciding which is low saturation or not. +/// +/// \return the proportion of pixels which pass the test. +/// +/// This is the augmented test, we normalized the histogram and then +/// we count the proportion of the histogram below the threshold. +float saturation_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_saturation_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float sum; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_saturation_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + cnt1 = count_histo(histo_float | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} +/// \} + +//============================================================================// +// VALUE TEST +//============================================================================// + +/// Value test +/// \{ +/// \brief Value test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the +/// value analysis. + + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief This is the Value test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of result histogram. +/// \param[in] tmp the name of the output value (HSV) map. +/// \param[in] threshold the range around the peak where R is computed. +/// +/// \return the proportion of pixels that pass the test. +/// +/// This is the augmented version of the Millet test. This code is not +/// stable. There is a lots of tests on. The Millet test is computed +/// with stddev2 call. We test simple counting around the pic as we +/// do for other tests but it's not really insteresting. The prop4 is +/// the best for me. We test the difference between the distribution and the +/// equi-distribution. +float value_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float prop; + float sum; + float prop4; + short peak; + mln::point1d inf; + mln::point1d sup; + + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_value_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + prop4 = cmp_equi_frequency_histo(histo_float); + peak = peak_histo(histo); // mean_histo(histo); + //prop = stddev2(histo, peak, threshold); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo_float|mln::box1d(inf,sup)); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + std::cerr << "peak = " << peak << std::endl; + std::cerr << "inf = " << inf << std::endl; + std::cerr << "sup = " << sup << std::endl; + std::cerr << "cnt1 = " << cnt1 << std::endl; + std::cerr << "cnt2 = " << cnt2 << std::endl; + std::cerr << "prop = " << prop << std::endl; + std::cerr << "prop4= " << prop4 << std::endl; + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + +//============================================================================// +// MAIN +//============================================================================// + + +/// \brief The main entry. +/// +/// This is a front end for launching image processing test. We deal +/// with boost library to walk on image database directories. Nothing +/// very interesting here, it is a lot of plumberies. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + +// t_path full_path[] = {t_path(ICDAR_20P_TEXT_ONLY_IMG_PATH), +// t_path(ICDAR_20P_TEXT_COLOR_IMG_PATH), +// t_path(ICDAR_20P_TEXT_PHOTO_IMG_PATH)}; + + t_path full_path[] = {t_path(AFP_PPM_IMG_PATH)}; + + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "#HUE - SATURATION - VALUE" << std::endl; + std::cout << "plot '-' using 1:2 with point notitle,\\" << std::endl; + std::cout << " '-' using 1:2 with point notitle,\\" << std::endl; + std::cout << " '-' using 1:2 with point notitle" << std::endl; + + for (int i = 0; i < 1; ++i) + { + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + t_path directory; + t_path leaf; + t_path output; + t_path tmp; + + std::cerr << "entering " << full_path[i] << std::endl; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + std::cerr << dir_iter->path() << std::endl; + // concatenation de chaine +// directory = (ANNOTATING_ICDAR_H_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_H_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = hue_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 20); + + std::cout << prop << " "; + +// directory = (ANNOTATING_ICDAR_S_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_S_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = saturation_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 25); + + std::cout << prop << " "; + +// directory = (ANNOTATING_ICDAR_V_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_V_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = value_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 15); + + std::cout << prop << " "; + std::cout << "# " << dir_iter->path().leaf() << std::endl; + } + std::cout << "e" << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/hue/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/hue/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/hue/hue.cc b/scribo/sandbox/green/exp/annotating/hue/hue.cc new file mode 100644 index 0000000..6e1195c --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/hue/hue.cc @@ -0,0 +1,402 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet hue operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Hue +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/value/rgb8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + + + +/// \brief Label a grey value. +/// +/// \param[in] int_u8 the grey value. +/// +/// \return the reference color for this grey value. +/// +/// Classify grey value in three class, white, black and medium_gray. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the +/// primary colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + + if (10 > hue) + result = mln::literal::red; + else if (32 > hue) + result = mln::literal::orange; + else if (53 > hue) + result = mln::literal::yellow; + else if (74 > hue) + result = mln::literal::green; // chartreuse + else if (96 > hue) + result = mln::literal::green; + else if (116 > hue) + result = mln::literal::green;// turquoise, aigue-marine + else if (138 > hue) + result = mln::literal::green; // cyan + else if (159 > hue) + result = mln::literal::blue; // azur + else if (181 > hue) + result = mln::literal::blue; + else if (202 > hue) + result = mln::literal::violet; + else if (223 > hue) + result = mln::literal::pink; + else // if (244 > hue) + result = mln::literal::red; + + +// if (14 > hue) +// result = mln::literal::red; +// else if (29 > hue) +// result = mln::literal::orange; +// else if (45 > hue) +// result = mln::literal::yellow; +// else if (113 > hue) +// result = mln::literal::green; +// else if (149 > hue) +// result = mln::literal::cyan; +// else if (205 > hue) +// result = mln::literal::blue; +// else if (235 > hue) +// result = mln::literal::violet; +// else if (242 > hue) +// result = mln::literal::pink; +// else +// result = mln::literal::red; + + return result; +} + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Compute the average of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the average of the histogram. +/// +/// Compute the mean of the histogram by summing the values of each +/// bin ponderated by its frequency. The result is divided by the sum +/// of the frequencies. +template <typename I> +unsigned mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + + +/// \brief The hue test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// +/// \return the proportion of pixels that pass the test. +/// +/// Load the input image, transform it to get the hue_map and compute +/// the histogram. Then count the number of pixels that are between +/// peak-threshold and peak +threshold. Print the peak and the +/// percentage of pixels around the pic. +float hue_test(const std::string input, + const std::string output, + const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + unsigned peak; + mln::value::rgb8 color; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + peak = mean_histo(histo); //peak_histo(histo); + color = label_hue(peak); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(peak-threshold), + mln::point1d(peak+threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + std::cout << "peak = " << peak << std::endl; + std::cout << "color = " << color << std::endl; + + return prop; +} + + +/// \brief Main entry. +/// +/// It is a front end which managing the iteration through one image +/// data base with boost. Save hue map and hue histogram. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_ICDAR_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = hue_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 20); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/hue/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/hue/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/hue/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/hue/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/hue/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/hue/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/nb_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/nb_color/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc b/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc new file mode 100644 index 0000000..2331c61 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc @@ -0,0 +1,171 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Compute the number of colors in an image. +/// +/// Build histogram of colors and count the bins different from zero. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +// #include <mln/morpho/opening/volume.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/pw/value.hh> +#include <mln/pw/cst.hh> + +#include <mln/util/timer.hh> + +#include <mln/value/rgb8.hh> +//#include <mln/value/rgb.hh> + + +/// \brief Count the colors. +/// +/// \param[in] image the name of the image to process. +/// +/// \return the number of colors. +/// +/// Count the color by building histogram. Strange filtering is +/// done. We use also the technique of regional maxima in +/// comment. Quantification is done to reduce the size of the +/// histogram as well. + +// n < 8, n is the degree of quantification +template <unsigned n> +unsigned count_image_color(const std::string& image) +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image3d<bool> t_histo3d_bool; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::meta::math::count t_count_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgbn input_rgbn; + t_image2d_rgbn output_rgbn; + t_histo3d histo; +// t_histo3d opened; + t_histo3d_bool filtered; + + mln::io::ppm::load(input_rgb8, image.c_str()); + + unsigned nb_pixel = input_rgb8.ncols() * input_rgb8.nrows(); + unsigned min_volume = (unsigned)(nb_pixel * 0.0001); + unsigned nb_color = 0; + +// input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); + histo = mln::data::compute(t_histo3d_fun(), input_rgb8); +// opened = mln::morpho::opening::volume(histo, mln::c6(), min_volume); + filtered = mln::binarization::threshold(histo, min_volume); + nb_color = mln::data::compute(t_count_fun(), + (filtered|(mln::pw::value(filtered)!=0)).rw()); + + return nb_color; +} + + +/// \brief Main entry. +/// +/// The main routine makes the base driver job. It looks after many +/// directories and lists every images in it. We use the boost library +/// to do it. Statistics are computed on the number of colors. The +/// first idea was to answer the question is this descriptor allow to +/// recognize a specific base. +int main() +{ + typedef boost::filesystem::path t_path; + //typedef boost::filesystem::initial_path<t_path()> t_init_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + unsigned count = 0; + unsigned sum1 = 0; + unsigned sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + unsigned val = count_image_color<8>(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/saturation/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/saturation/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/saturation/saturation.cc b/scribo/sandbox/green/exp/annotating/saturation/saturation.cc new file mode 100644 index 0000000..b5834a1 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/saturation/saturation.cc @@ -0,0 +1,175 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet saturation operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Sat +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_saturation_map.hh> + +#include <mln/io/ppm/load.hh> +//#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief The saturation test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] threshold for deciding which is low saturation or not. +/// +/// \return the proportion of pixels that pass the test. +/// +/// Load the input image, transform it to get the saturation_map and compute +/// the histogram. Then count the number of pixels that are under the threshold. +/// Then return the proportion of pixels. +float saturation_test(const std::string input, + const std::string output, +// const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_saturation_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_saturation_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); +// mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + + +/// \brief the main entry. +/// +/// This is a front end for image processing routine. It manages the +/// calling of every image in the database. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_ICDAR_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = saturation_test(dir_iter->path().string(), + output.string(), +// tmp.string(), + 25); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/saturation/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/saturation/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/saturation/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/stddev_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/stddev_color/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc b/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc new file mode 100644 index 0000000..365a9c3 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc @@ -0,0 +1,216 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +float stddev_color(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + t_histo1d histo; + t_point1d max_site; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + return stddev; +} + + +/// \brief Main entry. +/// +/// Front end for image processing. It is in the main routine where we +/// go through the image data base (using boost to do it). +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float count = 0; + float sum1 = 0; + float sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + float val = stddev_color(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/stddev_color_16/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/stddev_color_16/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc b/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc new file mode 100644 index 0000000..fc55547 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc @@ -0,0 +1,277 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. When photographies +/// has some large uniform border, the detection can fail. To improve +/// the method, Millet decide to subdivise the image in 16 sub images +/// on which it applies the test. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> +#include <mln/data/paste.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> +#include <mln/math/max.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] input_int_u8 the 8 bits input image to analyze. +/// \param[in] name_histo the name of the output histogram. +/// \param[in] name_image the name of the ouput sub image. +/// +/// \return the deviation, but at this time nothing.. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +float stddev_color(mln::image2d<mln::value::int_u8> input_int_u8) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_histo1d histo; + + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + return stddev; +} + +/// \brief Divide the image in 16 sub images. +/// +/// \param[in] image the input image. +/// +/// \result nothing. +/// +/// Divive the input image in 16 by uniform and geometrical +/// method. When a sub image is ready, call the stddev routine to show +/// stats on it. +float stddev_color_16(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 4; + unsigned sz_col = (pmax.col() - pmin.col())/ 4; + float stddev = 0.0; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 4; ++i) + for (unsigned j = 0; j < 4; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + // Save it + t_image2d_int_u8 input_1o16_int_u8(dom); + + mln::data::paste(input_int_u8 | dom, input_1o16_int_u8); + + stddev = mln::math::max(stddev, stddev_color(input_1o16_int_u8)); + } + + return stddev; +} + + +/// \brief Main entry. +/// +/// Just a front end for image processing routine. Use boost to go +/// through the image database. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float count = 0; + float sum1 = 0; + float sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + float val = stddev_color_16(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/value/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/value/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/value/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/value/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/value/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/value/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/value/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/value/text-only.txt diff --git a/scribo/sandbox/green/exp/annotating/value/value.cc b/scribo/sandbox/green/exp/annotating/value/value.cc new file mode 100644 index 0000000..cade88b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/value/value.cc @@ -0,0 +1,468 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena 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, version 2 of the License. +// +// Olena 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 Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet value operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to value +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +/// \brief Label the value. +/// +/// \param[in] val the value in [0..255]. +/// +/// \return the reference color. +/// +/// Segment the grey value in three classes. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +/// +/// Discriminate between orange and brown colors which are at the same +/// position on the hue wheel. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +/// +/// Segment between yellow and green because the discrimination is not +/// so easy based on the hue wheel. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the primary +/// colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + if (14 > hue) + result = mln::literal::red; + else if (29 > hue) + result = mln::literal::orange; + else if (45 > hue) + result = mln::literal::yellow; + else if (113 > hue) + result = mln::literal::green; + else if (149 > hue) + result = mln::literal::cyan; + else if (205 > hue) + result = mln::literal::blue; + else if (235 > hue) + result = mln::literal::violet; + else if (242 > hue) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Detect the peak of the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the position of the peak. +/// +/// This is a typical accumulator use case but as it needs the +/// position it couldn't be one of them. Look at the last maxima in +/// the curve and return its position. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Compute the average of the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the average of the histogram. +/// +/// This code should be an accumulator. It computes the mean of the +/// histogram, just stats in loop. Compute the sum, compute the +/// ponderate sum and divide the second by the first. +template <typename I> +unsigned mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// \param[in] peak the peak of the histogram. +/// \param[in] limit the threshold to compute the contribution. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief This is the main Millet test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of result histogram. +/// \param[in] tmp the name of the output value (HSV) map. +/// \param[in] threshold the range around the peak where R is computed. +/// +/// \return the proportion of pixels that pass the test. +float value_test(const std::string input, + const std::string output, + const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + unsigned peak; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_value_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + peak = peak_histo(histo); // mean_histo(histo); + prop = stddev2(histo, peak, threshold); +// cnt1 = count_histo(histo | mln::box1d(mln::point1d(peak-threshold), +// mln::point1d(peak+threshold))); +// cnt2 = mln::geom::nsites(input_rgb8); +// prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + + +/// \brief The main entry. +/// +/// This is the front end for using directories with boost. It calls +/// the true image processing routine. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = value_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 15); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/scribo/sandbox/green/mln/clustering/kmean2d.hh b/scribo/sandbox/green/mln/clustering/kmean2d.hh index 51aaf49..4539083 100644 --- a/scribo/sandbox/green/mln/clustering/kmean2d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean2d.hh @@ -150,7 +150,7 @@ namespace mln { /// Type definitions. /// \brief A few type definitions to limit the refactoring impact. - ///{ + /// \{ typedef value::rgb<8> t_rgb; typedef value::label<8> t_label; typedef value::rg<n> t_value; @@ -179,7 +179,7 @@ namespace mln typedef util::array<t_mean_set> t_mean_cnv; typedef image1d<t_result1d> t_variance_val; typedef util::array<t_variance_val> t_variance_cnv; - ///} + /// \} /// \brief Constructor. /// \param[in] point : the image as the population of pixels. diff --git a/scribo/sandbox/green/mln/fun/p2b/achromatic.hh b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh index 1f45b2d..91a2dc5 100644 --- a/scribo/sandbox/green/mln/fun/p2b/achromatic.hh +++ b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh @@ -44,15 +44,29 @@ namespace mln namespace p2b { - /// \brief Functor that compare the i-th component of a value. - // V is for the type of the value received - // i is the ith component to select + /// \brief Functor that compare the i-th component with a threshold. + /// + /// T_rgb is the kind of RGB we use. template <typename T_rgb> struct achromatic : public Function_v2b< achromatic<T_rgb> > { typedef bool result; + + /// \brief Operator that makes the threshold [millet.phd.2008.pdf] + /// + /// \param[in] p the selected site + /// + /// \return if the site is achromatic or not. + /// + /// This a try for opimized the call to the achromatic + /// routine. The goal is to decide if a site has got an + /// achromatic status or not. This is the true Millet test. bool operator()(const point2d& p) const; + /// \brief Cstor of the object. + /// + /// \param[in] img the RGB input image. + /// \param[in] threshold the value to compare with. achromatic(const image2d<T_rgb>& img, const float threshold); const float threshold_; diff --git a/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh index 84d26c8..e776c9a 100644 --- a/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh +++ b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh @@ -32,6 +32,10 @@ # include <mln/value/hsv.hh> # include <mln/value/rgb8.hh> +/// \file +/// +/// This is the code for building hue_concentration_map. + namespace mln { @@ -41,12 +45,18 @@ namespace mln namespace v2v { + /// \brief internal method for detecting the histogram peak. + /// + /// \param[in] hue_histo the histogram of hue. + /// + /// \return the seed of the peek. unsigned peak_histo(const mln::image1d<unsigned>& hue_histo); struct hue_concentration : public Function_v2v< hue_concentration > { typedef float result; + /// \brief Gibe the distance map between actual hue and the peak. float operator()(const float hue) const; hue_concentration(const mln::image1d<unsigned>& hue_histo) diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh index 6de63ec..126f9bd 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh @@ -49,6 +49,11 @@ namespace mln /// \brief Convert rgb value to achromatism map. /// + /// Convert rgb value to binary achromastism map using the + /// rebuilded Millet phd formulae [millet.phd.2008.pdf]. The + /// idea is to look at the minimum and maximum of the channels + /// and to return the difference. + /// /// \ingroup modfunv2v template <unsigned n> diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh index 3dd8eb0..a191095 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh @@ -34,6 +34,11 @@ #include <mln/value/hsv.hh> #include <mln/value/rgb.hh> +/// \fiie +/// +/// This is the millet [millet.phd.2008.pdf] transformation from RGB +/// space to HSV space. + namespace mln { @@ -57,7 +62,15 @@ namespace mln typedef T_hsv result; - /// HSV implementation from millet.2008.phd.pdf p67 + /// \brief HSV implementation from millet.2008.phd.pdf p67 + /// + /// \param[in] rgb the input rgb pixel. + /// + /// \return a HSV pixel. + /// + /// This is the Millet implementation of its transformation + /// operator to go from RGB space to HSV space. When pixels + /// are achromatic, hue equals -1. template <typename T_rgb> T_hsv operator()(const T_rgb& rgb) const; -- 1.5.6.5

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Olena-patches September 2010