- Olena-patches

[PATCH 07/22] Miscellaneous corrections to complex_window_base.
by Roland Levillain 30 Sep '09

30 Sep '09

* mln/core/internal/complex_window_base.hh: Add missing inline keywords. (complex_window_base<D, G, F, B, E>::geom): New typedef. (complex_window_base<D, G, F, B, E>::is_valid): Improve documentation. --- milena/ChangeLog | 10 ++++++++++ milena/mln/core/internal/complex_window_base.hh | 8 +++++++- 2 files changed, 17 insertions(+), 1 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index 3daaaa6..43d281c 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,3 +1,13 @@ +2009-09-28 Roland Levillain <roland(a)lrde.epita.fr> + + Miscellaneous corrections to complex_window_base. + + * mln/core/internal/complex_window_base.hh: Add missing inline + keywords. + (complex_window_base<D, G, F, B, E>::geom): New typedef. + (complex_window_base<D, G, F, B, E>::is_valid): Improve + documentation. + 2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> Add non-centered complex windows. diff --git a/milena/mln/core/internal/complex_window_base.hh b/milena/mln/core/internal/complex_window_base.hh index 559c9de..df08e6d 100644 --- a/milena/mln/core/internal/complex_window_base.hh +++ b/milena/mln/core/internal/complex_window_base.hh @@ -92,6 +92,8 @@ namespace mln public: /// Associated types. /// \{ + /// The geometry of the complex. + typedef G geom; /// The type of psite corresponding to the window. typedef complex_psite<D, G> psite; /// The type of site corresponding to the window. @@ -130,7 +132,7 @@ namespace mln /// Is this window centered? bool is_centered() const; - /// Return true by default. + /// Is this window valid ? (Return true by default.) bool is_valid() const; /// \} @@ -143,6 +145,7 @@ namespace mln # ifndef MLN_INCLUDE_ONLY template <unsigned D, typename G, typename F, typename B, typename E> + inline complex_window_base<D, G, F, B, E>::complex_window_base(bool is_centered) : is_centered_(is_centered) { @@ -150,6 +153,7 @@ namespace mln template <unsigned D, typename G, typename F, typename B, typename E> + inline bool complex_window_base<D, G, F, B, E>::is_empty() const { @@ -157,6 +161,7 @@ namespace mln } template <unsigned D, typename G, typename F, typename B, typename E> + inline bool complex_window_base<D, G, F, B, E>::is_centered() const { @@ -164,6 +169,7 @@ namespace mln } template <unsigned D, typename G, typename F, typename B, typename E> + inline bool complex_window_base<D, G, F, B, E>::is_valid() const { -- 1.6.3.1

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[PATCH 06/22] Add non-centered complex windows.
by Roland Levillain 30 Sep '09

30 Sep '09

* mln/core/image/complex_windows.hh (mln_gen_complex_window): New (generator) macro. (complex_lower_window) (complex_higher_window) (complex_lower_higher_window) (complex_lower_dim_connected_n_face_window) (complex_higher_dim_connected_n_face_window) (complex_m_face_window): New classes. --- milena/ChangeLog | 14 ++++++ milena/mln/core/image/complex_windows.hh | 69 ++++++++++++++++++++++++++++- 2 files changed, 80 insertions(+), 3 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index 2da5700..3daaaa6 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,19 @@ 2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Add non-centered complex windows. + + * mln/core/image/complex_windows.hh (mln_gen_complex_window): + New (generator) macro. + (complex_lower_window) + (complex_higher_window) + (complex_lower_higher_window) + (complex_lower_dim_connected_n_face_window) + (complex_higher_dim_connected_n_face_window) + (complex_m_face_window): + New classes. + +2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Prepare the factoring of complex windows generators. * mln/core/image/complex_windows.hh diff --git a/milena/mln/core/image/complex_windows.hh b/milena/mln/core/image/complex_windows.hh index c2c21fb..b409282 100644 --- a/milena/mln/core/image/complex_windows.hh +++ b/milena/mln/core/image/complex_windows.hh @@ -29,11 +29,10 @@ /// \file /// \brief Definitions of some windows of complex images. +# include <mln/core/internal/complex_window_base.hh> # include <mln/core/internal/complex_window_p_base.hh> -# include <mln/topo/adj_lower_face_iter.hh> -# include <mln/topo/adj_higher_face_iter.hh> -# include <mln/topo/adj_lower_higher_face_iter.hh> +# include <mln/topo/complex_iterators.hh> /** \brief Generate a complex window class, as well as its traits, @@ -77,6 +76,28 @@ } +/** \brief Generate a (non-centered) complex window class, as well as + its traits. + + \param Win The name of the class to generate. + \param Fwd_Complex_Iter The underlying forward iterator type. + \param Bkd_Complex_Iter The underlying backward iterator type. + + The generated class inherits from + + mln::internal::complex_window_base< D, G, + Fwd_Complex_Iter<D>, + Bkd_Complex_Iter<D>, + Win <D, G> > + + where D and G are template parameters of the generated Win class. */ +# define mln_gen_complex_window(Win, Fwd_Complex_Iter, Bkd_Complex_Iter) \ + mln_gen_complex_window_with_base(Win, \ + Fwd_Complex_Iter, \ + Bkd_Complex_Iter, \ + mln::internal::complex_window_base) + + /** \brief Generate a centered complex window class, as well as its traits. \param Win The name of the class to generate. @@ -102,6 +123,47 @@ namespace mln { + /*-----------------------. + | Non-centered windows. | + `-----------------------*/ + + /// \brief Window centered on an n-face of complex returning its + /// adjacent (n-1)-faces. + mln_gen_complex_window(complex_lower_window, + topo::adj_lower_face_fwd_iter, + topo::adj_lower_face_bkd_iter); + + /// \brief Window centered on an n-face of complex returning its + /// adjacent (n+1)-faces. + mln_gen_complex_window(complex_higher_window, + topo::adj_higher_face_fwd_iter, + topo::adj_higher_face_bkd_iter); + + /// \brief Window centered on an n-face of complex returning its + /// adjacent (n-1)-faces and (n+1)-faces. + mln_gen_complex_window(complex_lower_higher_window, + topo::adj_lower_higher_face_fwd_iter, + topo::adj_lower_higher_face_bkd_iter); + + /// \brief Window centered on an n-face of complex returning the + /// n-faces sharing an (n-1)-face with the center n-face. + mln_gen_complex_window(complex_lower_dim_connected_n_face_window, + topo::adj_lower_dim_connected_n_face_fwd_iter, + topo::adj_lower_dim_connected_n_face_bkd_iter); + + /// \brief Window centered on an n-face of complex returning the + /// n-faces sharing an (n+1)-face with the center n-face. + mln_gen_complex_window(complex_higher_dim_connected_n_face_window, + topo::adj_higher_dim_connected_n_face_fwd_iter, + topo::adj_higher_dim_connected_n_face_bkd_iter); + + /// \brief Window centered on an n-face of complex returning + /// the m-faces transitively adjacent to this center n-face. + mln_gen_complex_window(complex_m_face_window, + topo::adj_m_face_fwd_iter, + topo::adj_m_face_bkd_iter); + + /*-------------------. | Centered windows. | `-------------------*/ @@ -149,6 +211,7 @@ namespace mln } // end of namespace mln # undef mln_gen_complex_window_p +# undef mln_gen_complex_window # undef mln_gen_complex_window_from_base #endif // ! MLN_CORE_IMAGE_COMPLEX_WINDOWS_HH -- 1.6.3.1

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[PATCH 05/22] Prepare the factoring of complex windows generators.
by Roland Levillain 30 Sep '09

30 Sep '09

* mln/core/image/complex_windows.hh (mln_gen_complex_window_with_base): New macro. Use it to factor... (mln_gen_complex_window_p): ...this macro. --- milena/ChangeLog | 9 +++++ milena/mln/core/image/complex_windows.hh | 51 +++++++++++++++++++++-------- 2 files changed, 46 insertions(+), 14 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index 5d91401..2da5700 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,14 @@ 2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Prepare the factoring of complex windows generators. + + * mln/core/image/complex_windows.hh + (mln_gen_complex_window_with_base): New macro. + Use it to factor... + (mln_gen_complex_window_p): ...this macro. + +2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Rename mln::complex_m_face_window as mln::complex_m_face_window_p. * mln/core/image/complex_windows.hh: Here. diff --git a/milena/mln/core/image/complex_windows.hh b/milena/mln/core/image/complex_windows.hh index 30dcadc..c2c21fb 100644 --- a/milena/mln/core/image/complex_windows.hh +++ b/milena/mln/core/image/complex_windows.hh @@ -35,21 +35,21 @@ # include <mln/topo/adj_higher_face_iter.hh> # include <mln/topo/adj_lower_higher_face_iter.hh> -/** \brief Generate a centered complex window class, as well as its traits. + +/** \brief Generate a complex window class, as well as its traits, + using a given base class. \param Win The name of the class to generate. \param Fwd_Complex_Iter The underlying forward iterator type. \param Bkd_Complex_Iter The underlying backward iterator type. + \param Base The name of the base class, with no parameter. The generated class inherits from - mln:: internal::complex_window_p_base< D, G, - Fwd_Complex_Iter<D>, - Bkd_Complex_Iter<D>, - Win <D, G> > + Base < D, G, Fwd_Complex_Iter<D>, Bkd_Complex_Iter<D>, Win <D, G> > where D and G are template parameters of the generated Win class. */ -# define mln_gen_complex_window_p(Win, Fwd_Complex_Iter, Bkd_Complex_Iter) \ +# define mln_gen_complex_window_with_base(Win, Fwd_Complex_Iter, Bkd_Complex_Iter, Base) \ /* Forward declaration. */ \ template <unsigned D, typename G> struct Win; \ \ @@ -58,10 +58,10 @@ { \ template <unsigned D, typename G> \ struct window_< Win <D, G> > \ - : window_< mln::internal::complex_window_p_base< D, G, \ - Fwd_Complex_Iter<D>, \ - Bkd_Complex_Iter <D>, \ - Win <D, G> > > \ + : window_< Base < D, G, \ + Fwd_Complex_Iter<D>, \ + Bkd_Complex_Iter <D>, \ + Win <D, G> > > \ { \ }; \ } \ @@ -69,14 +69,36 @@ /* Definition. */ \ template <unsigned D, typename G> \ struct Win \ - : internal::complex_window_p_base< D, G, \ - Fwd_Complex_Iter <D>, \ - Bkd_Complex_Iter <D>, \ - Win <D, G> > \ + : Base < D, G, \ + Fwd_Complex_Iter <D>, \ + Bkd_Complex_Iter <D>, \ + Win <D, G> > \ { \ } +/** \brief Generate a centered complex window class, as well as its traits. + + \param Win The name of the class to generate. + \param Fwd_Complex_Iter The underlying forward iterator type. + \param Bkd_Complex_Iter The underlying backward iterator type. + + The generated class inherits from + + mln::internal::complex_window_p_base< D, G, + Fwd_Complex_Iter<D>, + Bkd_Complex_Iter<D>, + Win <D, G> > + + where D and G are template parameters of the generated Win class. */ +# define mln_gen_complex_window_p(Win, Fwd_Complex_Iter, Bkd_Complex_Iter) \ + mln_gen_complex_window_with_base(Win, \ + Fwd_Complex_Iter, \ + Bkd_Complex_Iter, \ + mln::internal::complex_window_p_base) + + + namespace mln { @@ -127,5 +149,6 @@ namespace mln } // end of namespace mln # undef mln_gen_complex_window_p +# undef mln_gen_complex_window_from_base #endif // ! MLN_CORE_IMAGE_COMPLEX_WINDOWS_HH -- 1.6.3.1

1 0

[PATCH 04/22] Rename mln::complex_m_face_window as mln::complex_m_face_window_p.
by Roland Levillain 30 Sep '09

30 Sep '09

* mln/core/image/complex_windows.hh: Here. --- milena/ChangeLog | 6 ++++++ milena/mln/core/image/complex_windows.hh | 7 ++++--- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index c8c8fca..5d91401 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,11 @@ 2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Rename mln::complex_m_face_window as mln::complex_m_face_window_p. + + * mln/core/image/complex_windows.hh: Here. + +2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Refactor complex_window_p_base using complex_window_base. * mln/core/internal/complex_window_p_base.hh diff --git a/milena/mln/core/image/complex_windows.hh b/milena/mln/core/image/complex_windows.hh index 906ac16..30dcadc 100644 --- a/milena/mln/core/image/complex_windows.hh +++ b/milena/mln/core/image/complex_windows.hh @@ -117,9 +117,10 @@ namespace mln topo::adj_higher_dim_connected_n_face_fwd_iter, topo::adj_higher_dim_connected_n_face_bkd_iter); - /// \brief Window centered on an n-face of complex returning - /// the m-faces transitively adjacent to this center n-face. - mln_gen_complex_window_p(complex_m_face_window, + /// \brief Window centered on an n-face of complex returning the + /// m-faces transitively adjacent to this center n-face, as well as + /// this center n-face. + mln_gen_complex_window_p(complex_m_face_window_p, topo::adj_m_face_fwd_iter, topo::adj_m_face_bkd_iter); -- 1.6.3.1

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[PATCH 03/22] Refactor complex_window_p_base using complex_window_base.
by Roland Levillain 30 Sep '09

30 Sep '09

* mln/core/internal/complex_window_p_base.hh (complex_window_p_base): Inherit from complex_window_p_base. (trait::window_< mln::internal::complex_window_p_base<D, G, F, B, E>) Adjust traits. Remove all typedefs and methods. (complex_window_p_base::complex_window_p_base): New ctor. --- milena/ChangeLog | 15 +++ milena/mln/core/image/complex_windows.hh | 46 +++++---- milena/mln/core/internal/complex_window_p_base.hh | 103 ++++----------------- 3 files changed, 59 insertions(+), 105 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index aa0ac48..c8c8fca 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,20 @@ 2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Refactor complex_window_p_base using complex_window_base. + + * mln/core/internal/complex_window_p_base.hh + (complex_window_p_base): Inherit from complex_window_p_base. + (trait::window_< mln::internal::complex_window_p_base<D, G, F, B, E>) + Adjust traits. + Remove all typedefs and methods. + (complex_window_p_base::complex_window_p_base): New ctor. + * mln/core/image/complex_windows.hh (mln_gen_complex_window): + Rename (internal) macro as.. + (mln_gen_complex_window_p): ...this. + Adjust. + +2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Introduce a base class for general complex windows. * mln/core/internal/complex_window_base.hh: New. diff --git a/milena/mln/core/image/complex_windows.hh b/milena/mln/core/image/complex_windows.hh index 2f22470..906ac16 100644 --- a/milena/mln/core/image/complex_windows.hh +++ b/milena/mln/core/image/complex_windows.hh @@ -35,7 +35,7 @@ # include <mln/topo/adj_higher_face_iter.hh> # include <mln/topo/adj_lower_higher_face_iter.hh> -/** \brief Generate a complex window class, as well as its traits. +/** \brief Generate a centered complex window class, as well as its traits. \param Win The name of the class to generate. \param Fwd_Complex_Iter The underlying forward iterator type. @@ -49,7 +49,7 @@ Win <D, G> > where D and G are template parameters of the generated Win class. */ -# define mln_gen_complex_window(Win, Fwd_Complex_Iter, Bkd_Complex_Iter) \ +# define mln_gen_complex_window_p(Win, Fwd_Complex_Iter, Bkd_Complex_Iter) \ /* Forward declaration. */ \ template <unsigned D, typename G> struct Win; \ \ @@ -80,47 +80,51 @@ namespace mln { + /*-------------------. + | Centered windows. | + `-------------------*/ + /// \brief Window centered on an n-face of complex returning its /// adjacent (n-1)-faces as well as the center n-face. - mln_gen_complex_window(complex_lower_window_p, - topo::adj_lower_face_fwd_iter, - topo::adj_lower_face_bkd_iter); + mln_gen_complex_window_p(complex_lower_window_p, + topo::adj_lower_face_fwd_iter, + topo::adj_lower_face_bkd_iter); /// \brief Window centered on an n-face of complex returning its /// adjacent (n+1)-faces as well as the center n-face. - mln_gen_complex_window(complex_higher_window_p, - topo::adj_higher_face_fwd_iter, - topo::adj_higher_face_bkd_iter); + mln_gen_complex_window_p(complex_higher_window_p, + topo::adj_higher_face_fwd_iter, + topo::adj_higher_face_bkd_iter); /// \brief Window centered on an n-face of complex returning its /// adjacent (n-1)-faces and (n+1)-faces as well as the center /// n-face. - mln_gen_complex_window(complex_lower_higher_window_p, - topo::adj_lower_higher_face_fwd_iter, - topo::adj_lower_higher_face_bkd_iter); + mln_gen_complex_window_p(complex_lower_higher_window_p, + topo::adj_lower_higher_face_fwd_iter, + topo::adj_lower_higher_face_bkd_iter); /// \brief Window centered on an n-face of complex returning the /// n-faces sharing an (n-1)-face with the center n-face, as well as /// this center n-face. - mln_gen_complex_window(complex_lower_dim_connected_n_face_window_p, - topo::adj_lower_dim_connected_n_face_fwd_iter, - topo::adj_lower_dim_connected_n_face_bkd_iter); + mln_gen_complex_window_p(complex_lower_dim_connected_n_face_window_p, + topo::adj_lower_dim_connected_n_face_fwd_iter, + topo::adj_lower_dim_connected_n_face_bkd_iter); /// \brief Window centered on an n-face of complex returning the /// n-faces sharing an (n+1)-face with the center n-face, as well as /// this center n-face. - mln_gen_complex_window(complex_higher_dim_connected_n_face_window_p, - topo::adj_higher_dim_connected_n_face_fwd_iter, - topo::adj_higher_dim_connected_n_face_bkd_iter); + mln_gen_complex_window_p(complex_higher_dim_connected_n_face_window_p, + topo::adj_higher_dim_connected_n_face_fwd_iter, + topo::adj_higher_dim_connected_n_face_bkd_iter); /// \brief Window centered on an n-face of complex returning /// the m-faces transitively adjacent to this center n-face. - mln_gen_complex_window(complex_m_face_window, - topo::adj_m_face_fwd_iter, - topo::adj_m_face_bkd_iter); + mln_gen_complex_window_p(complex_m_face_window, + topo::adj_m_face_fwd_iter, + topo::adj_m_face_bkd_iter); } // end of namespace mln -# undef mln_gen_complex_window +# undef mln_gen_complex_window_p #endif // ! MLN_CORE_IMAGE_COMPLEX_WINDOWS_HH diff --git a/milena/mln/core/internal/complex_window_p_base.hh b/milena/mln/core/internal/complex_window_p_base.hh index 3f04a6f..65e79e9 100644 --- a/milena/mln/core/internal/complex_window_p_base.hh +++ b/milena/mln/core/internal/complex_window_p_base.hh @@ -31,22 +31,14 @@ /// of a complex, based on a pair of (forward and backward) complex /// iterators. The center (site) is part of the window. -# include <mln/core/concept/window.hh> -# include <mln/core/site_set/complex_psite.hh> -# include <mln/core/image/complex_window_piter.hh> +# include <mln/core/internal/complex_window_base.hh> # include <mln/topo/centered_iter_adapter.hh> -// FIXME: Factor with mln::internal::complex_neighborhood_base. namespace mln { - // Forward declarations. - template <typename I, typename G, typename W> - class complex_window_fwd_piter; - template <typename I, typename G, typename W> - class complex_window_bkd_piter; namespace internal { @@ -60,10 +52,12 @@ namespace mln template <unsigned D, typename G, typename F, typename B, typename E> struct window_< mln::internal::complex_window_p_base<D, G, F, B, E> > + : window_< mln::internal::complex_window_base + < D, G, + mln::topo::centered_fwd_iter_adapter<D, F>, + mln::topo::centered_bkd_iter_adapter<D, B>, + E > > { - typedef trait::window::size::unknown size; - typedef trait::window::support::irregular support; - typedef trait::window::definition::varying definition; }; } // end of namespace mln::trait @@ -81,64 +75,20 @@ namespace mln \tparam B The underlying backward iterator type. \tparam E The exact type. */ template <unsigned D, typename G, typename F, typename B, typename E> - class complex_window_p_base : public Window<E> + class complex_window_p_base + : public complex_window_base + < D, G, + mln::topo::centered_fwd_iter_adapter<D, F>, + mln::topo::centered_bkd_iter_adapter<D, B>, + E > { - /// The complex iterators on the adjacent faces only - /// (without the center point). - /// \{ - typedef F adj_only_fwd_iter_; - typedef B adj_only_bkd_iter_; - /// \} + typedef complex_window_base< D, G, + topo::centered_fwd_iter_adapter<D, F>, + topo::centered_bkd_iter_adapter<D, B>, + E > super; public: - /// The associated complex iterators. - /// \{ - typedef topo::centered_fwd_iter_adapter<D, adj_only_fwd_iter_> - complex_fwd_iter; - - typedef topo::centered_bkd_iter_adapter<D, adj_only_bkd_iter_> - complex_bkd_iter; - /// \} - - public: - /// Associated types. - /// \{ - /// The type of psite corresponding to the window. - typedef complex_psite<D, G> psite; - /// The type of site corresponding to the window. - typedef mln_site(psite) site; - - // FIXME: This is a dummy value. - typedef void dpsite; - - /// \brief Site_Iterator type to browse the psites of the window - /// w.r.t. the ordering of vertices. - typedef - complex_window_fwd_piter<complex_fwd_iter, G, E> fwd_qiter; - - /// \brief Site_Iterator type to browse the psites of the window - /// w.r.t. the reverse ordering of vertices. - typedef - complex_window_bkd_piter<complex_bkd_iter, G, E> bkd_qiter; - - /// The default qiter type. - typedef fwd_qiter qiter; - /// \} - - public: - /// Services. - /// \{ - /* FIXME: mln::morpho::dilation requires these method from models - of concept Window, but Window does not list them in its - requirements. Who's guilty: morpho::dilation or Window? */ - /// Is this window empty? (Always returns \c false). - bool is_empty() const; - /// Is this window centered? (Always returns \c true). - bool is_centered() const; - - /// Return true by default. - bool is_valid() const; - /// \} + complex_window_p_base(); }; @@ -146,24 +96,9 @@ namespace mln # ifndef MLN_INCLUDE_ONLY template <unsigned D, typename G, typename F, typename B, typename E> - bool - complex_window_p_base<D, G, F, B, E>::is_empty() const - { - return false; - } - - template <unsigned D, typename G, typename F, typename B, typename E> - bool - complex_window_p_base<D, G, F, B, E>::is_centered() const - { - return true; - } - - template <unsigned D, typename G, typename F, typename B, typename E> - bool - complex_window_p_base<D, G, F, B, E>::is_valid() const + complex_window_p_base<D, G, F, B, E>::complex_window_p_base() + : super(true) { - return true; } # endif // ! MLN_INCLUDE_ONLY -- 1.6.3.1

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[PATCH 02/22] Introduce a base class for general complex windows.
by Roland Levillain 30 Sep '09

30 Sep '09

* mln/core/internal/complex_window_base.hh: New. --- milena/ChangeLog | 6 + milena/mln/core/internal/complex_window_base.hh | 179 +++++++++++++++++++++++ 2 files changed, 185 insertions(+), 0 deletions(-) create mode 100644 milena/mln/core/internal/complex_window_base.hh diff --git a/milena/ChangeLog b/milena/ChangeLog index c439a18..aa0ac48 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,5 +1,11 @@ 2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Introduce a base class for general complex windows. + + * mln/core/internal/complex_window_base.hh: New. + +2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + Replace ad hoc code by Milena's in apps/graph-morpho. * apps/graph-morpho/morpho.hh diff --git a/milena/mln/core/internal/complex_window_base.hh b/milena/mln/core/internal/complex_window_base.hh new file mode 100644 index 0000000..559c9de --- /dev/null +++ b/milena/mln/core/internal/complex_window_base.hh @@ -0,0 +1,179 @@ +// Copyright (C) 2008, 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_CORE_INTERNAL_COMPLEX_WINDOW_BASE_HH +# define MLN_CORE_INTERNAL_COMPLEX_WINDOW_BASE_HH + +/// \file +/// \brief Definition of a generic window of the face of a complex, +/// based on a pair of (forward and backward) complex iterators. + +# include <mln/core/concept/window.hh> +# include <mln/core/site_set/complex_psite.hh> +# include <mln/core/image/complex_window_piter.hh> + +# include <mln/topo/centered_iter_adapter.hh> + +// FIXME: Factor with mln::internal::complex_neighborhood_base. + + +namespace mln +{ + // Forward declarations. + template <typename I, typename G, typename W> + class complex_window_fwd_piter; + template <typename I, typename G, typename W> + class complex_window_bkd_piter; + + namespace internal + { + template <unsigned D, typename G, typename F, typename B, typename E> + class complex_window_base; + } + + + namespace trait + { + + template <unsigned D, typename G, typename F, typename B, typename E> + struct window_< mln::internal::complex_window_base<D, G, F, B, E> > + { + typedef trait::window::size::unknown size; + typedef trait::window::support::irregular support; + typedef trait::window::definition::varying definition; + }; + + } // end of namespace mln::trait + + + namespace internal + { + /** \brief Generic window centered on the face of a complex, based + on an pair of (forward and backward) complex iterators. The + center (site) is part of the window. + + \tparam D The dimension of the complex. + \tparam G The type of the geometry functor of the complex. + \tparam F The underlying forward iterator type. + \tparam B The underlying backward iterator type. + \tparam E The exact type. */ + template <unsigned D, typename G, typename F, typename B, typename E> + class complex_window_base : public Window<E> + { + public: + /// The associated complex iterators. + /// \{ + typedef F complex_fwd_iter; + typedef B complex_bkd_iter; + /// \} + + public: + /// Associated types. + /// \{ + /// The type of psite corresponding to the window. + typedef complex_psite<D, G> psite; + /// The type of site corresponding to the window. + typedef mln_site(psite) site; + + // FIXME: This is a dummy value. + typedef void dpsite; + + /* FIXME: Ideally, the `is_centered' information should be + fetched from the iterators, but that's not an straighforward + task. */ + complex_window_base(bool is_centered = false); + + /// \brief Site_Iterator type to browse the psites of the window + /// w.r.t. the ordering of vertices. + typedef + complex_window_fwd_piter<complex_fwd_iter, G, E> fwd_qiter; + + /// \brief Site_Iterator type to browse the psites of the window + /// w.r.t. the reverse ordering of vertices. + typedef + complex_window_bkd_piter<complex_bkd_iter, G, E> bkd_qiter; + + /// The default qiter type. + typedef fwd_qiter qiter; + /// \} + + public: + /// Services. + /// \{ + /* FIXME: mln::morpho::dilation requires these method from models + of concept Window, but Window does not list them in its + requirements. Who's guilty: morpho::dilation or Window? */ + /// Is this window empty? (Always returns \c false). + bool is_empty() const; + /// Is this window centered? + bool is_centered() const; + + /// Return true by default. + bool is_valid() const; + /// \} + + private: + bool is_centered_; + }; + + + +# ifndef MLN_INCLUDE_ONLY + + template <unsigned D, typename G, typename F, typename B, typename E> + complex_window_base<D, G, F, B, E>::complex_window_base(bool is_centered) + : is_centered_(is_centered) + { + } + + + template <unsigned D, typename G, typename F, typename B, typename E> + bool + complex_window_base<D, G, F, B, E>::is_empty() const + { + return false; + } + + template <unsigned D, typename G, typename F, typename B, typename E> + bool + complex_window_base<D, G, F, B, E>::is_centered() const + { + return is_centered_; + } + + template <unsigned D, typename G, typename F, typename B, typename E> + bool + complex_window_base<D, G, F, B, E>::is_valid() const + { + return true; + } + +# endif // ! MLN_INCLUDE_ONLY + + } // end of namespace mln::internal + +} // end of namespace mln + +#endif // ! MLN_CORE_INTERNAL_COMPLEX_WINDOW_BASE_HH -- 1.6.3.1

1 0

[PATCH 01/22] Replace ad hoc code by Milena's in apps/graph-morpho.
by Roland Levillain 30 Sep '09

30 Sep '09

* apps/graph-morpho/morpho.hh (dilation(const mln::Image&, const mln::Neighborhood<N>&)) (erosion(const mln::Image&, const mln::Neighborhood<N>&)): Remove functions. (dilation_e2v(const mln::Image&)) (erosion_v2e(const mln::Image&)) (erosion_e2v(const mln::Image&)) (dilation_v2e(const mln::Image&)): Use mln::dilation and mln::erosion instead of these ad hoc dilation and erosion routines. Use mln::image_if and mln::extend instead of the ad hoc image_if_large. --- milena/ChangeLog | 17 ++ milena/apps/graph-morpho/image_if_large.hh | 307 ---------------------------- milena/apps/graph-morpho/morpho.hh | 107 +++------- 3 files changed, 44 insertions(+), 387 deletions(-) delete mode 100644 milena/apps/graph-morpho/image_if_large.hh diff --git a/milena/ChangeLog b/milena/ChangeLog index 3ddf82a..c439a18 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,3 +1,20 @@ +2009-09-25 Roland Levillain <roland(a)lrde.epita.fr> + + Replace ad hoc code by Milena's in apps/graph-morpho. + + * apps/graph-morpho/morpho.hh + (dilation(const mln::Image&, const mln::Neighborhood<N>&)) + (erosion(const mln::Image&, const mln::Neighborhood<N>&)): + Remove functions. + (dilation_e2v(const mln::Image&)) + (erosion_v2e(const mln::Image&)) + (erosion_e2v(const mln::Image&)) + (dilation_v2e(const mln::Image&)): + Use mln::dilation and mln::erosion instead of these ad hoc + dilation and erosion routines. + Use mln::image_if and mln::extend instead of the ad hoc + image_if_large. + 2009-09-25 Edwin Carlinet <carlinet(a)lrde.epita.fr> Add test for dual input and fix p_transformed test. diff --git a/milena/apps/graph-morpho/image_if_large.hh b/milena/apps/graph-morpho/image_if_large.hh deleted file mode 100644 index 37c2d63..0000000 --- a/milena/apps/graph-morpho/image_if_large.hh +++ /dev/null @@ -1,307 +0,0 @@ -// Copyright (C) 2007, 2008, 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 APPS_GRAPH_MORPHO_IMAGE_IF_LARGE_HH -# define APPS_GRAPH_MORPHO_IMAGE_IF_LARGE_HH - -/// \file -/// -/// \brief Definition of a image which domain is restricted by a -/// function 'site -> Boolean', but where routine has() returns true -/// for sites of the underlying (morphed) image even if they are not -/// validating the predicate. - -// FIXME: This file is a workaround for the lack of ``tolerant'' -// image_if, but there must be a better way. - -# include <mln/core/internal/image_domain_morpher.hh> -# include <mln/core/site_set/p_if.hh> -# include <mln/pw/all.hh> - - -namespace mln -{ - - // Forward declaration. - template <typename I, typename F> struct image_if_large; - - - namespace internal - { - - /// Data structure for \c mln::image_if_large<I,F>. - template <typename I, typename F> - struct data< image_if_large<I,F> > - { - data(I& ima, const F& f); - - I ima_; - p_if<mln_domain(I), F> domain_; - }; - - } // end of namespace mln::internal - - - namespace trait - { - - template <typename I, typename F> - struct image_< image_if_large<I,F> > - : default_image_morpher< I, - mln_value(I), - image_if_large<I,F> > - { - typedef trait::image::category::domain_morpher category; - - // No extension of domain. - typedef trait::image::ext_domain::none ext_domain; - typedef trait::image::ext_value::irrelevant ext_value; - typedef trait::image::ext_io::irrelevant ext_io; - - typedef trait::image::vw_io::none vw_io; - typedef trait::image::vw_set::none vw_set; - typedef trait::image::value_alignment::not_aligned value_alignment; - typedef trait::image::value_storage::disrupted value_storage; - }; - - } // end of namespace mln::trait - - - - /// Image which domain is restricted by a function 'site -> - /// Boolean', but where routine has() returns true for sites of the - /// underlying (morphed) image even if they are not validating the - /// predicate. - /// - /// \ingroup modimagedomainmorpher - // - template <typename I, typename F> - struct image_if_large - : public mln::internal::image_domain_morpher< I, - p_if<mln_domain(I), F>, - image_if_large<I, F> > - { - typedef image_if_large<I, F> self; - typedef mln_psite(self) psite; - - /// Skeleton. - typedef image_if_large< tag::image_, tag::function_<F> > skeleton; - - /// Constructor without argument. - image_if_large(); - - /// Constructor from an image \p ima and a predicate \p f. - image_if_large(I& ima, const F& f); - - void init_(I& ima, const F& f); - - /// \brief Redefined psite membership method, returning true for - /// sites of the underlying (morphed) image even if they are not - /// validating the predicate. - /// - /// This is the only difference with mln::image_if. - bool has(const psite& p) const; - - /// Give the definition domain. - const p_if<mln_domain(I), F>& domain() const; - - /// Const promotion via conversion. - operator image_if_large<const I, F>() const; - }; - - - // Image || Function_v2b. - - /// ima || f creates an image_if_large with the image ima and the function - /// f. - // - template <typename I, typename F> - image_if_large<I,F> - operator || (Image& ima, const Function_v2b<F>& f); - - /// ima || f creates an image_if_large with the image ima and the function - /// f. - // - template <typename I, typename F> - image_if_large<const I,F> - operator || (const Image& ima, const Function_v2b<F>& f); - - - - template <typename I, typename A> - image_if_large< const I, fun::C<bool(*)(A)> > - operator || (const Image& ima, bool (*f)(A) ); - - template <typename I, typename A> - image_if_large< I, fun::C<bool(*)(A)> > - operator || (Image& ima, bool (*f)(A) ); - -// // Construction helpers. - -// template <typename I, typename A> -// image_if_large< const I, fun::C<bool(*)(A)> > -// make_image_if_large(const Image& ima, bool (*f)(A) ); - -// template <typename I, typename A> -// image_if_large< I, fun::C<bool(*)(A)> > -// make_image_if_large(Image& ima, bool (*f)(A) ); - - - -# ifndef MLN_INCLUDE_ONLY - - // init_. - - template <typename I, typename F> - void init_(tag::function_t, F& f, const image_if_large<I,F>& model) - { - f = model.domain().predicate(); - } - - template <typename I, typename F, typename J> - void init_(tag::image_t, image_if_large<I,F>& target, const J& model) - { - I ima; - init_(tag::image, ima, exact(model)); - F f; - init_(tag::function, f, exact(model)); - target.init_(ima, f); - } - - // internal::data< image_if_large<I,F> > - - namespace internal - { - - template <typename I, typename F> - inline - data< image_if_large<I,F> >::data(I& ima, const F& f) - : ima_(ima), - domain_(ima.domain() | f) - { - } - - } - - - // image_if_large<I,F> - - template <typename I, typename F> - inline - image_if_large<I,F>::image_if_large() - { - } - - template <typename I, typename F> - inline - image_if_large<I,F>::image_if_large(I& ima, const F& f) - { - init_(ima, f); - } - - template <typename I, typename F> - inline - void - image_if_large<I,F>::init_(I& ima, const F& f) - { - mln_precondition(! this->is_valid()); - this->data_ = new internal::data< image_if_large<I,F> >(ima, f); - } - - template <typename I, typename F> - inline - bool - image_if_large<I,F>::has(const typename image_if_large<I,F>::psite& p) const - { - mln_precondition(exact(this)->is_valid()); - // This method is more tolerant than mln::image_if's one (which - // returns this->domain().has(p)). - return this->data_->ima_.has(p); - } - - - template <typename I, typename F> - inline - const p_if<mln_domain(I), F>& - image_if_large<I,F>::domain() const - { - mln_precondition(this->is_valid()); - return this->data_->domain_; - } - - template <typename I, typename F> - inline - image_if_large<I,F>::operator image_if_large<const I,F>() const - { - mln_precondition(this->is_valid()); - image_if_large<const I,F> tmp(this->data_->ima_, - this->data_->domain_.predicate()); - return tmp; - } - - - // Operators. - - template <typename I, typename F> - inline - image_if_large<I,F> - operator || (Image& ima, const Function_v2b<F>& f) - { - image_if_large<I,F> tmp(exact(ima), exact(f)); - return tmp; - } - - template <typename I, typename F> - inline - image_if_large<const I, F> - operator || (const Image& ima, const Function_v2b<F>& f) - { - image_if_large<const I, F> tmp(exact(ima), exact(f)); - return tmp; - } - - - template <typename I, typename A> - image_if_large< const I, fun::C<bool(*)(A)> > - operator || (const Image& ima, bool (*f)(A) ) - { - return exact(ima) || convert::to_fun(f); - } - - template <typename I, typename A> - image_if_large< I, fun::C<bool(*)(A)> > - operator || (Image& ima, bool (*f)(A) ) - { - return exact(ima) || convert::to_fun(f); - } - - -# endif // ! MLN_INCLUDE_ONLY - -} // end of namespace mln - - -#endif // ! APPS_GRAPH_MORPHO_IMAGE_IF_LARGE_HH diff --git a/milena/apps/graph-morpho/morpho.hh b/milena/apps/graph-morpho/morpho.hh index 9a786da..bd883a0 100644 --- a/milena/apps/graph-morpho/morpho.hh +++ b/milena/apps/graph-morpho/morpho.hh @@ -41,6 +41,7 @@ # include <mln/core/image/dmorph/image_if.hh> +# include <mln/core/routine/extend.hh> # include <mln/core/routine/duplicate.hh> # include <mln/core/site_set/p_n_faces_piter.hh> @@ -53,7 +54,8 @@ # include <mln/data/paste.hh> -# include "apps/graph-morpho/image_if_large.hh" +# include <mln/morpho/dilation.hh> +# include <mln/morpho/erosion.hh> // FIXME: Instead of providing several implementation, move specific // parts (neighborhoods, etc.) to a graph_traits class, and write @@ -136,72 +138,6 @@ combine(const mln::Image& vertices, const mln::Image& edges) | Dilations and erosions. | `-------------------------*/ -/// A neighborhood-aware and graph-friendly version of the binary dilation. -template <typename I, typename N> -mln_concrete(I) -dilation(const mln::Image& input_, const mln::Neighborhood<N>& nbh_) -{ - using namespace mln; - - typedef mln_concrete(I) O; - const I& input = exact(input_); - const N& nbh = exact(nbh_); - - O output; - initialize(output, input); - - mln_piter(I) p(input.domain()); - mln_niter(N) n(nbh, p); - for_all(p) - { - /* There is a slight difference with Milena's classical binary - dilation here: instead of initializing OUTPUT with INPUT, we - fill it with default (here, false) values. */ - output(p) = false; - for_all(n) if (input.has(n)) - if (input(n) == true) - { - output(p) = true; - break; - } - } - return output; -} - - -/// A neighborhood-aware and graph-friendly version of the binary erosion. -template <typename I, typename N> -mln_concrete(I) -erosion(const mln::Image& input_, const mln::Neighborhood<N>& nbh_) -{ - using namespace mln; - - typedef mln_concrete(I) O; - const I& input = exact(input_); - const N& nbh = exact(nbh_); - - O output; - initialize(output, input); - - mln_piter(I) p(input.domain()); - mln_niter(N) n(nbh, p); - for_all(p) - { - /* There is a slight difference with Milena's classical erosion - here: instead of initializing OUTPUT with INPUT, we fill it - with default (here, true) values. */ - output(p) = true; - for_all(n) if (input.has(n)) - if (input(n) == false) - { - output(p) = false; - break; - } - } - return output; -} - - namespace impl { // ------------------------------------------ // @@ -341,11 +277,6 @@ namespace impl // Implementations on (mln::image2d-based) cubical 2-complexes. // // ------------------------------------------------------------- // - /* Note the operator `||' in the following routines: we have to use - our own ``tolerant'' version of mln::image_if (namely - mln::image_if_large) for these dilations and erosions to - work. */ - /// Dilation from edges to vertices (\f$\delta^\bullet\f$) on /// an mln::image2d<T>-based cubical complex. template <typename T> @@ -353,10 +284,14 @@ namespace impl mln::image2d<T> dilation_e2v(const mln::image2d<T>& input) { + using mln::world::inter_pixel::dim2::is_pixel; + using mln::world::inter_pixel::v2e; + mln::image2d<T> output(input.domain()); mln::data::fill(output, false); - mln::data::paste(dilation(input || mln::world::inter_pixel::dim2::is_pixel(), - mln::world::inter_pixel::v2e()), + mln::data::paste(mln::morpho::dilation(mln::extend(input | is_pixel(), + input), + v2e().win()), output); return output; } @@ -368,10 +303,14 @@ namespace impl mln::image2d<T> erosion_v2e(const mln::image2d<T>& input) { + using mln::world::inter_pixel::dim2::is_edge; + using mln::world::inter_pixel::e2v; + mln::image2d<T> output(input.domain()); mln::data::fill(output, false); - mln::data::paste(erosion(input || mln::world::inter_pixel::dim2::is_edge(), - mln::world::inter_pixel::e2v()), + mln::data::paste(mln::morpho::erosion(mln::extend(input | is_edge(), + input), + e2v().win()), output); return output; } @@ -383,10 +322,14 @@ namespace impl mln::image2d<T> erosion_e2v(const mln::image2d<T>& input) { + using mln::world::inter_pixel::dim2::is_pixel; + using mln::world::inter_pixel::v2e; + mln::image2d<T> output(input.domain()); mln::data::fill(output, false); - mln::data::paste(erosion(input || mln::world::inter_pixel::dim2::is_pixel(), - mln::world::inter_pixel::v2e()), + mln::data::paste(mln::morpho::erosion(mln::extend(input | is_pixel(), + input), + v2e().win()), output); return output; } @@ -398,10 +341,14 @@ namespace impl mln::image2d<T> dilation_v2e(const mln::image2d<T>& input) { + using mln::world::inter_pixel::dim2::is_edge; + using mln::world::inter_pixel::e2v; + mln::image2d<T> output(input.domain()); mln::data::fill(output, false); - mln::data::paste(dilation(input || mln::world::inter_pixel::dim2::is_edge(), - mln::world::inter_pixel::e2v()), + mln::data::paste(mln::morpho::dilation(mln::extend(input | is_edge(), + input), + e2v().win()), output); return output; } -- 1.6.3.1

1 0

4564: Prepare kmean demo for theo. Work in progress.
by Yann Jacquelet 29 Sep '09

29 Sep '09

Fix bugs, add hooks for tracing convergence. * green/mln/clustering/kmean1d.hh: Evict k parameter from template and replace it by a constructor parameter. Add printing for debugging. * green/mln/clustering/kmean1d.hh (update_variance): Fix bugs. * green/mln/clustering/kmean1d.hh (build_all_dbg, build_mean_dbg, update_cnv, finalize_cnv): New methods. * green/mln/clustering/kmean1d.hh (launch_one_time): Fix bugs. * green/mln/clustering/kmean1d.hh (launch_n_times): Add hooks. Do some works for managing shell parameters while executing the binary. * green/demo/clustering/kmean1d/kmean1d.cc (main, char_to_string, char_to_unsigned, demo, do_demo): New methods. --- trunk/milena/sandbox/ChangeLog | 19 ++ .../green/demo/clustering/kmean1d/kmean1d.cc | 118 +++++-- .../milena/sandbox/green/mln/clustering/kmean1d.hh | 342 ++++++++++++------- 3 files changed, 325 insertions(+), 154 deletions(-) diff --git a/trunk/milena/sandbox/ChangeLog b/trunk/milena/sandbox/ChangeLog index 4700515..53f1385 100644 --- a/trunk/milena/sandbox/ChangeLog +++ b/trunk/milena/sandbox/ChangeLog @@ -20,6 +20,25 @@ * fabien/tests/core/image/tiled2d.cc: Update performance benchmark for different types of image. +2009-09-29 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Prepare kmean demo for theo. Work in progress. + + Fix bugs, add hooks for tracing convergence. + + * green/mln/clustering/kmean1d.hh: Evict k parameter from template + and replace it by a constructor parameter. Add printing for debugging. + * green/mln/clustering/kmean1d.hh (update_variance): Fix bugs. + * green/mln/clustering/kmean1d.hh + (build_all_dbg, build_mean_dbg, update_cnv, finalize_cnv): New methods. + * green/mln/clustering/kmean1d.hh (launch_one_time): Fix bugs. + * green/mln/clustering/kmean1d.hh (launch_n_times): Add hooks. + + Do some works for managing shell parameters while executing the binary. + + * green/demo/clustering/kmean1d/kmean1d.cc + (main, char_to_string, char_to_unsigned, demo, do_demo): New methods. + 2009-09-28 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Prepare kmean demo for theo. Work in progress. diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc index cc04149..f13f783 100644 --- a/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc +++ b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc @@ -21,20 +21,25 @@ #include <mln/io/ppm/save.hh> #include <mln/io/plot/save_image_sh.hh> -void demo(const unsigned watch_dog, const unsigned n_times) +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) { - typedef mln::clustering::kmean1d<double,8,3> t_kmean; - typedef mln::value::label8 t_label8; + typedef mln::clustering::kmean1d<double,8> t_kmean; + typedef mln::value::label_8 t_label_8; typedef mln::value::rgb8 t_rgb8; typedef mln::value::int_u8 t_int_u8; - typedef mln::image2d<int_u8> t_image2d_int_u8; - typedef mln::image2d<rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; t_image2d_int_u8 house; - mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + mln::io::pgm::load(house, image.c_str()); - t_kmean kmean(house, watch_dog, n_times); + t_kmean kmean(house, k_center, watch_dog, n_times); + + //mln::trace::quiet = false; kmean.launch_n_times(); @@ -52,43 +57,100 @@ void demo(const unsigned watch_dog, const unsigned n_times) mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); } +void demo(const std::string& image = OLENA_IMG_PATH"/house.pgm", + const unsigned k_center = 3, + const unsigned n_times = 10, + const unsigned watch_dog = 10) +{ + std::cout << "----------------------------------------" << std::endl; + std::cout << "Launching the demo with these parameters" << std::endl; + std::cout << "image : " << image << std::endl; + std::cout << "k_center : " << k_center << std::endl; + std::cout << "n_times : " << n_times << std::endl; + std::cout << "watch_dog : " << watch_dog << std::endl; + std::cout << "----------------------------------------" << std::endl; + + do_demo(image, k_center, n_times, watch_dog); +} + void usage(const int argc, const char *args[]) { std::cout << "----------------------------------------" << std::endl; std::cout << "argc : " << argc << std::endl; for (int i = 0; i < argc; ++i) - std::cout << "args[i] : " << args[i] << std::endl; + std::cout << "args[" << i << "] : " << args[i] << std::endl; std::cout << "----------------------------------------" << std::endl; - std::cout << "usage: kmean1d watch_dog n_times k image" << std::endl; - std::cout << "unsigned watch_dog (convergence loop)" << std::endl; + std::cout << "usage: kmean1d [image [k_center [n_times [watch_dog]]]]" + << std::endl; + std::cout << "pbm image (points to work with)" << std::endl; + std::cout << "unsigned k_center (number of centers)" << std::endl; std::cout << "unsigned n_times (number of launching)" << std::endl; - // std::cout << "unsigned k (number of centers)" << std::endl; - // std::cout << "pbm image (points to work with)" << std::endl; + std::cout << "unsigned watch_dog (convergence loop)" << std::endl; std::cout << "----------------------------------------" << std::endl; } -int main(const int argc, const char *args[]) +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) { - if (3 == argc) + bool result = false; + + if (status) { - std::istringstream arg1(args[1]); - std::istringstream arg2(args[2]); - // std::istringstream arg3(args[3]); - // std::istringstream arg3(args[4]); - unsigned watch_dog; - unsigned n_times; - // const char *image; - // unsigned k; + std::istringstream arg_stream(arg); + + arg_stream >> val; + + result = !arg_stream.fail(); + } + + return result; +} + +bool char_to_string(const bool status, const char *arg, std::string& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; - arg1 >> watch_dog; - arg2 >> n_times; + return !arg_stream.fail(); + } + + return result; +} - if (!arg1.fail() && !arg2.fail()) - demo(watch_dog, n_times); - else - usage(argc, args); +int main(const int argc, const char *args[]) +{ + std::string image("top"); + unsigned k_center; + unsigned watch_dog; + unsigned n_times; + bool status = true; + + switch (argc) + { + case 5: status = char_to_unsigned(status, args[4], watch_dog); + case 4: status = char_to_unsigned(status, args[3], n_times); + case 3: status = char_to_unsigned(status, args[2], k_center); + case 2: status = char_to_string(status, args[1], image); break; + case 1: status = true; break; + default: status = false; + } + + if (status) + { + switch (argc) + { + case 1: demo(); break; + case 2: demo(image); break; + case 3: demo(image, k_center); break; + case 4: demo(image, k_center, n_times); break; + case 5: demo(image, k_center, n_times, watch_dog); break; + } } else usage(argc, args); diff --git a/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh index 05947c7..9a8c1ea 100644 --- a/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh +++ b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh @@ -74,7 +74,7 @@ namespace mln { // Forward declaration. - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> struct kmean1d; } // end of namespace mln::clustering @@ -89,8 +89,7 @@ namespace mln /// /// T is the type used for computations (float or double). /// n is the quantification for the image grayscale. - /// k is the number of classes. - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> struct kmean1d { /// Type definitions. @@ -115,7 +114,7 @@ namespace mln typedef image2d<t_rgb> t_color_dbg; typedef image2d<t_value> t_mean_dbg; - typedef image3d<t_value> t_mean_cnv; + typedef image3d<t_result> t_mean_cnv; typedef image2d<t_result> t_variance_cnv; ///} @@ -130,6 +129,7 @@ namespace mln /// \param[in] n_times : the number of times that we executed it (10). kmean1d(const t_point_img& point, + const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); @@ -231,11 +231,24 @@ namespace mln bool is_descent_valid(); //------------------------------------------------------------------------ - // Tools + // Debugging tools //------------------------------------------------------------------------ - void build(); - void to_result2(); + /// Debugging tools + /// \{ + /// \brief These methods help to interpret results. + /// + /// The methods build_label_dbg and build_all_dbg work in the input data + /// space. The first one build the 2d image of labels. The second call the + /// first one and then builds the colorize label' image and the mean + /// greylevel image. + + void build_label_dbg(); + void build_all_dbg(); + void update_cnv(); + void finalize_cnv(); + + /// \} private: @@ -243,6 +256,7 @@ namespace mln /// \{ /// \brief These parameters control the convergence of the process. /// + /// The first parameter, k_center, defines the number of center for kmean. /// In fact, watch_dog limit the number of iteration that a simple kmean /// loop can do. If the process reaches the watch_dog limit, it means /// that the process will not converge at all. The second parameter @@ -251,6 +265,7 @@ namespace mln /// from different location will confort us in that we found a global /// minima, not just a local one. + unsigned _k_center; unsigned _watch_dog; unsigned _n_times; @@ -266,9 +281,12 @@ namespace mln /// around the centers. The current_step variable allows us to remember /// the current iteration in the kmean loop. This information is needed /// by is_descent_valid routine which decide if convergence occurs or not. + /// The last information, current_launching, traces the progress while + /// iterates kmean loop again and again. t_result _within_variance; unsigned _current_step; + unsigned _current_launching; /// \} @@ -366,30 +384,32 @@ namespace mln // Constructor //-------------------------------------------------------------------------- + /// FIXME k must be a parameter, not a static compilation paramater. - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - kmean1d<T,n,k>::kmean1d(const image2d< t_value >& point, - const unsigned watch_dog, - const unsigned n_times) + kmean1d<T,n>::kmean1d(const t_point_img& point, + const unsigned k_center, + const unsigned watch_dog, + const unsigned n_times) { trace::entering("mln::clustering::kmean1d::kmean1d"); mln_precondition(point.is_valid()); - + _k_center = k_center; _watch_dog = watch_dog; _n_times = n_times; _point = point; _histo = data::compute(accu::meta::stat::histo1d(), _point); - _number.init_(box1d(point1d(0),point1d(k-1))); - _mean.init_(box1d(point1d(0),point1d(k-1))); - _variance.init_(box1d(point1d(0),point1d(k-1))); + _number.init_(box1d(point1d(0),point1d(_k_center-1))); + _mean.init_(box1d(point1d(0),point1d(_k_center-1))); + _variance.init_(box1d(point1d(0),point1d(_k_center-1))); _group.init_(box1d(point1d(mln_min(t_value)), point1d(mln_max(t_value)))); _distance.init_(box2d(point2d(mln_min(t_value), 0), - point2d(mln_max(t_value), k-1))); + point2d(mln_max(t_value), _k_center-1))); // Debugging, calibrating and testing initialize(_label_dbg, _point); @@ -397,12 +417,16 @@ namespace mln initialize(_mean_dbg, _point); // Observing the convergence + _variance_cnv.init_(box2d(point2d(0, 0), - point2d(_watch_dog-1, _n_times-1))); + point2d(_n_times-1, _watch_dog-1))); + + data::fill(_variance_cnv, literal::zero); _mean_cnv.init_(box3d(point3d(0, 0, 0), - point3d(k-1, _watch_dog-1, _n_times-1))); + point3d(_n_times-1, _k_center-1, _watch_dog-1))); + data::fill(_mean_cnv, literal::zero); trace::exiting("mln::clustering::kmean1d::kmean1d"); } @@ -411,9 +435,9 @@ namespace mln // Mutators and accessors //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_point(t_point_img& point) + void kmean1d<T,n>::set_point(t_point_img& point) { trace::entering("mln::clustering::kmean1d::set_point"); @@ -422,9 +446,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_point"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_histo(t_histo_img& histo) + void kmean1d<T,n>::set_histo(t_histo_img& histo) { trace::entering("mln::clustering::kmean1d::set_histo"); @@ -433,9 +457,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_histo"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_number(t_number_img& number) + void kmean1d<T,n>::set_number(t_number_img& number) { trace::entering("mln::clustering::kmean1d::set_number"); @@ -444,9 +468,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_number"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_mean(t_mean_img& mean) + void kmean1d<T,n>::set_mean(t_mean_img& mean) { trace::entering("mln::clustering::kmean1d::set_mean"); @@ -455,9 +479,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_mean"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_variance(t_variance_img& variance) + void kmean1d<T,n>::set_variance(t_variance_img& variance) { trace::entering("mln::clustering::kmean1d::set_variance"); @@ -466,9 +490,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_variance"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_group(t_group_img& group) + void kmean1d<T,n>::set_group(t_group_img& group) { trace::entering("mln::clustering::kmean1d::set_group"); @@ -477,9 +501,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_group"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_distance(t_distance_img& distance) + void kmean1d<T,n>::set_distance(t_distance_img& distance) { trace::entering("mln::clustering::kmean1d::set_distance"); @@ -488,9 +512,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_distance"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_point_img& kmean1d<T,n,k>::get_point() + typename kmean1d<T,n>::t_point_img& kmean1d<T,n>::get_point() { trace::entering("mln::clustering::kmean1d::get_point"); @@ -498,9 +522,9 @@ namespace mln return _point; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_histo_img& kmean1d<T,n,k>::get_histo() + typename kmean1d<T,n>::t_histo_img& kmean1d<T,n>::get_histo() { trace::entering("mln::clustering::kmean1d::get_histo"); @@ -508,9 +532,9 @@ namespace mln return _histo; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_number_img& kmean1d<T,n,k>::get_number() + typename kmean1d<T,n>::t_number_img& kmean1d<T,n>::get_number() { trace::entering("mln::clustering::kmean1d::get_number"); @@ -518,9 +542,9 @@ namespace mln return _number; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::get_mean() + typename kmean1d<T,n>::t_mean_img& kmean1d<T,n>::get_mean() { trace::entering("mln::clustering::kmean1d::get_mean"); @@ -528,9 +552,9 @@ namespace mln return _mean; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_variance_img& kmean1d<T,n,k>::get_variance() + typename kmean1d<T,n>::t_variance_img& kmean1d<T,n>::get_variance() { trace::entering("mln::clustering::kmean1d::get_variance"); @@ -538,9 +562,9 @@ namespace mln return _variance; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_group_img& kmean1d<T,n,k>::get_group() + typename kmean1d<T,n>::t_group_img& kmean1d<T,n>::get_group() { trace::entering("mln::clustering::kmean1d::get_group"); @@ -548,9 +572,9 @@ namespace mln return _group; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_distance_img& kmean1d<T,n,k>::get_distance() + typename kmean1d<T,n>::t_distance_img& kmean1d<T,n>::get_distance() { trace::entering("mln::clustering::kmean1d::get_distance"); @@ -558,9 +582,9 @@ namespace mln return _distance; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_color_dbg& kmean1d<T,n,k>::get_color_dbg() + typename kmean1d<T,n>::t_color_dbg& kmean1d<T,n>::get_color_dbg() { trace::entering("mln::clustering::kmean1d::get_color_dbg"); @@ -568,9 +592,9 @@ namespace mln return _color_dbg; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_dbg& kmean1d<T,n,k>::get_mean_dbg() + typename kmean1d<T,n>::t_mean_dbg& kmean1d<T,n>::get_mean_dbg() { trace::entering("mln::clustering::kmean1d::get_mean_dbg"); @@ -578,9 +602,9 @@ namespace mln return _mean_dbg; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_label_dbg& kmean1d<T,n,k>::get_label_dbg() + typename kmean1d<T,n>::t_label_dbg& kmean1d<T,n>::get_label_dbg() { trace::entering("mln::clustering::kmean1d::get_label_dbg"); @@ -588,9 +612,9 @@ namespace mln return _label_dbg; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_cnv& kmean1d<T,n,k>::get_mean_cnv() + typename kmean1d<T,n>::t_mean_cnv& kmean1d<T,n>::get_mean_cnv() { trace::entering("mln::clustering::kmean1d::get_mean_cnv"); @@ -598,9 +622,9 @@ namespace mln return _mean_cnv; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_variance_cnv& kmean1d<T,n,k>::get_variance_cnv() + typename kmean1d<T,n>::t_variance_cnv& kmean1d<T,n>::get_variance_cnv() { trace::entering("mln::clustering::kmean1d::get_variance_cnv"); @@ -608,9 +632,9 @@ namespace mln return _variance_cnv; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::to_result() + typename kmean1d<T,n>::t_mean_img& kmean1d<T,n>::to_result() { trace::entering("mln::clustering::kmean1d::to_result"); @@ -623,12 +647,12 @@ namespace mln // Initialization of centers //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::init_mean_regular() + void kmean1d<T,n>::init_mean_regular() { trace::entering("mln::clustering::kmean1d::init_mean_regular"); - T step = (mln_max(t_value) - mln_min(t_value)) / (k-1); + T step = (mln_max(t_value) - mln_min(t_value)) / (_k_center-1); mln_piter(image1d<t_value>) l(_mean.domain()); for_all(l) @@ -640,11 +664,11 @@ namespace mln } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::init_mean_random() + void kmean1d<T,n>::init_mean_random() { - trace::exiting("mln::clustering::kmean1d::init_mean_random"); + trace::entering("mln::clustering::kmean1d::init_mean_random"); t_value min = mln_min(t_value); t_value max = mln_max(t_value); @@ -653,14 +677,16 @@ namespace mln for_all(l) { _mean(l) = (rand() % (max-min)) + min; + + std::cout << "mean" << l << " : " << _mean(l) << std::endl; } trace::exiting("mln::clustering::kmean1d::init_mean_random"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::init_mean() + void kmean1d<T,n>::init_mean() { trace::entering("mln::clustering::kmean1d::init_mean"); @@ -673,9 +699,9 @@ namespace mln // Computations of distance, group, center, within variance //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_distance() + void kmean1d<T,n>::update_distance() { trace::entering("mln::clustering::kmean1d::update_distance"); @@ -684,22 +710,24 @@ namespace mln for_all(d) { // the square distance - _distance(d) = math::sqr(d.row() - _mean(point1d(d.col()))); + _distance(d) = _histo(point1d(d.row())) + * math::sqr(d.row() - _mean(point1d(d.col()))); /* - std::cout << "row : " << d.row() << std::endl; - std::cout << "col : " << d.col() << std::endl; - std::cout << "center : " << _mean(point1d(d.col())) << std::endl; - std::cout << "distance : " << _distance(d) << std::endl; - std::cout << "--------------------------------------" << std::endl; + std::cout << "row : " << d.row() << std::endl; + std::cout << "col : " << d.col() << std::endl; + std::cout << "histo : " << _histo(point1d(d.row())) << std::endl; + std::cout << "center : " << _mean(point1d(d.col())) << std::endl; + std::cout << "distance : " << _distance(d) << std::endl; + std::cout << "--------------------------------------" << std::endl; */ } trace::exiting("mln::clustering::kmean1d::update_distance"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_group() + void kmean1d<T,n>::update_group() { trace::entering("mln::clustering::kmean1d::update_group"); @@ -708,7 +736,7 @@ namespace mln for_all(g) { mln_piter(t_mean_img) l(_mean.domain()); - T min = mln_max(T); + t_result min = mln_max(t_result); t_label label = mln_max(t_label); //std::cout << "g = " << g << std::endl; @@ -721,7 +749,7 @@ namespace mln label = l.ind(); } - //std::cout << "d(" << l << ") = " << + //std::cout << "d" << l << " = " << // _distance(point2d(g.ind(), l.ind())) << std::endl; } @@ -735,9 +763,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::update_group"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_mean() + void kmean1d<T,n>::update_mean() { trace::entering("mln::clustering::kmean1d::update_mean"); @@ -774,23 +802,22 @@ namespace mln std::cout << "n(" << l << ") = " << _number(l) << std::endl; } */ - for_all(l) { _mean(l) /= _number(l); - //std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + std::cout << "c" << l << " = " << _mean(l) << std::endl; } trace::exiting("mln::clustering::kmean1d::update_mean"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_variance() + void kmean1d<T,n>::update_variance() { trace::entering("mln::clustering::kmean1d::update_variance"); - T _within_variance = literal::zero; + _within_variance = literal::zero; mln_piter(t_variance_img) l(_variance.domain()); for_all(l) @@ -801,11 +828,12 @@ namespace mln for_all(g) { - _variance(l) += _distance(point2d(g.ind(), l.ind())); + if (l.ind() == _group(g)) + _variance(l) += _distance(point2d(g.ind(), l.ind())); } _within_variance += _variance(l); - //std::cout << "v(" << l << ") = " << _variance(l) << std::endl; + std::cout << "v(" << l << ") = " << _variance(l) << std::endl; } //std::cout << "result" << result << std::endl; @@ -814,14 +842,14 @@ namespace mln } //-------------------------------------------------------------------------- - // Tools + // Debugging tools //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::build() + void kmean1d<T,n>::build_label_dbg() { - trace::entering("build"); + trace::entering("mln::clustering::kmean1d::build_mean_dbg"); mln_piter(t_point_img) pi(_point.domain()); mln_piter(t_label_dbg) po(_label_dbg.domain()); @@ -834,26 +862,64 @@ namespace mln _label_dbg(po) = grp; } - trace::exiting("build"); + trace::exiting("mln::clustering::kmean1d::build_mean_dbg"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::to_result2() + void kmean1d<T,n>::build_all_dbg() { - typedef value::rgb8 rgb8; - std::cout << "BUILDING OUTPUT" << std::endl; + trace::entering("mln::clustering::kmean1d::build_all_dbg"); + build_label_dbg(); + + _mean_dbg = labeling::mean_values(_point, _label_dbg, _k_center); + _color_dbg = labeling::colorize(value::rgb8(), _label_dbg); + + trace::exiting("mln::clustering::kmean1d::build_all_dbg"); + } + + template <typename T, unsigned n> + inline + void kmean1d<T,n>::update_cnv() + { + trace::entering("mln::clustering::kmean1d::update_cnv"); + + _variance_cnv(point2d(_current_launching, + _current_step)) = _within_variance; + + mln_piter(t_mean_img) l(_mean.domain()); + + for_all(l) + { + _mean_cnv(point3d(_current_launching, + l.ind(), + _current_step)) = _mean(l); + } + + trace::exiting("mln::clustering::kmean1d::update_cnv"); + } - build(); + template <typename T, unsigned n> + inline + void kmean1d<T,n>::finalize_cnv() + { + trace::entering("mln::clustering::kmean1d::finalize_cnv"); - std::cout << "COLORING OUTPUT" << std::endl; + // saturate the curv with the within variance + for (unsigned i = _current_step; i < _watch_dog; ++i) + _variance_cnv(point2d(_current_launching, i)) = _within_variance; + + for (unsigned i = _current_step; i < _watch_dog; ++i) + { + mln_piter(t_mean_img) l(_mean.domain()); - _mean_dbg = labeling::mean_values(_point, _label_dbg, k); - _color_dbg = labeling::colorize(rgb8(), _label_dbg); + for_all(l) + { + _mean_cnv(point3d(_current_launching, l.ind(), i)) = _mean(l); + } + } - mln::io::pgm::save(_mean_dbg, "mean.pgm"); - mln::io::ppm::save(_color_dbg, "rgb.pgm"); - mln::io::pgm::save(_label_dbg, "label.pgm"); + trace::exiting("mln::clustering::kmean1d::finalize_cnv"); } @@ -861,9 +927,9 @@ namespace mln // Checking the validity of the results //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - bool kmean1d<T,n,k>::is_descent_valid() + bool kmean1d<T,n>::is_descent_valid() { trace::entering("mln::clustering::kmean1d::is_descent_valid"); bool result = true; @@ -881,9 +947,9 @@ namespace mln return result; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - bool kmean1d<T,n,k>::is_valid() + bool kmean1d<T,n>::is_valid() { trace::entering("mln::clustering::kmean1d::is_valid"); bool result = true; @@ -905,61 +971,85 @@ namespace mln //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::launch_one_time() + void kmean1d<T,n>::launch_one_time() { trace::entering("mln::clustering::kmean1d::launch_one_time"); + std::cout << "----------------------------------------" << std::endl; + // Initialization to start the descent - _within_variance = mln_max(t_result)/2; t_result old_variance = mln_max(t_result); - // Choose random points + // Choose random points and compute within variance init_mean(); + update_distance(); + update_group(); + update_variance(); // update _within_variance + + std::cout << "first_variance : " << _within_variance << std::endl; // Execute the descent - for (unsigned i = 0; i<_watch_dog && _within_variance<old_variance; ++i) + for (_current_step = 0; + _current_step < _watch_dog && _within_variance < old_variance; + ++_current_step) { + old_variance = _within_variance; + + update_mean(); update_distance(); update_group(); - update_mean(); + update_variance(); // update _within_variance - old_variance = _within_variance; + // debugging code + update_cnv(); - update_variance(); // update _within_variance + std::cout << "_current_step : " << _current_step << std::endl; + std::cout << "_within_variance : " << _within_variance << std::endl; } + std::cout << "----------------------------------------" << std::endl; + + finalize_cnv(); + trace::exiting("mln::clustering::kmean1d::launch_one_time"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::launch_n_times() + void kmean1d<T,n>::launch_n_times() { trace::entering("mln::clustering::kmean1d::launch_n_times"); - _variance_min = mln_max(t_result); + std::cout << "watch_dog : " << _watch_dog << std::endl; + std::cout << "n_times : " << _n_times << std::endl; + + _variance_min = mln_max(t_result); + _current_launching = 0; // Execute the descent n times - for (unsigned i = 0; i < _n_times; ++i) + while (_current_launching < _n_times) { launch_one_time(); - if (!is_descent_valid()) - --i; - else + if (is_descent_valid()) { if (_within_variance < _variance_min) { _variance_min = _within_variance; _mean_min = _mean; - _launching_min = i; + _launching_min = _current_launching; } - } + + std::cout << "_current_launching : " << _current_launching + << std::endl; + + std::cout << "within_variance[" << _current_launching << "] = " + << _within_variance << std::endl; - std::cout << "within_variance[" << i << "] = " - << _within_variance << std::endl; + ++_current_launching; + } } trace::exiting("mln::clustering::kmean1d::launch_n_times"); -- 1.5.6.5

1 0

r4563: Update benchmark for types of image
by Fabien Freling 29 Sep '09

29 Sep '09

URL: https://svn.lrde.epita.fr/svn/oln/trunk/milena/sandbox ChangeLog: 2009-09-29 Fabien Freling <fabien.freling(a)lrde.epita.fr> Update benchmark for types of image. * fabien/README: Explain hierarchy of the sandbox. * fabien/TODO: Remove. * fabien/bin/dumpl16_to_colorize.cc: New bin tool. * fabien/bin/dumpl8_to_colorize.cc: New bin tool. * fabien/igr/color/find_color.cc: Update color detection. * fabien/magick/tiled_image.cc: Add draft of tiled image using ImageMagick. * fabien/mln/test/conversion.hh: Update conversion test. * fabien/tests/core/image/Makefile, * fabien/tests/core/image/bench.hh, * fabien/tests/core/image/bench.sh, * fabien/tests/core/image/disk_ima.cc, * fabien/tests/core/image/ima2d.cc, * fabien/tests/core/image/tiled2d.cc: Update performance benchmark for different types of image. --- README | 114 +++++++++++++++++++++++++++++++++++++++++++ bin/dumpl16_to_colorize.cc | 64 ++++++++++++++++++++++++ bin/dumpl8_to_colorize.cc | 62 +++++++++++++++++++++++ igr/color/find_color.cc | 62 ++++++++++++++++------- magick/tiled_image.cc | 38 ++++++++++++++ mln/test/conversion.hh | 14 +++-- tests/core/image/Makefile | 3 + tests/core/image/bench.hh | 19 +++++++ tests/core/image/bench.sh | 5 + tests/core/image/disk_ima.cc | 33 +----------- tests/core/image/ima2d.cc | 31 +++++++++++ tests/core/image/tiled2d.cc | 29 +--------- 12 files changed, 396 insertions(+), 78 deletions(-) Index: trunk/milena/sandbox/fabien/TODO (deleted) =================================================================== Index: trunk/milena/sandbox/fabien/igr/color/find_color.cc =================================================================== --- trunk/milena/sandbox/fabien/igr/color/find_color.cc (revision 4562) +++ trunk/milena/sandbox/fabien/igr/color/find_color.cc (revision 4563) @@ -10,42 +10,50 @@ #include <mln/convert/from_to.hh> #include <mln/data/convert.hh> #include <mln/math/diff_abs.hh> +#include <mln/math/min.hh> using namespace mln; using value::rgb8; using value::hsl_; using value::int_u8; +#define GREEN 120.f +#define BLUE 240.f + inline -bool -is_green(hsl_<float, float, float> value) +float +hue_truth(hsl_<float, float, float> value, float hue, float range) { - if (value.hue() > 75 && value.hue() < 170 && - value.sat() > 0.5 && - value.lum() > 0.3 && value.lum() < 0.7) - return true; + float truth = 0.0; - return false; + if (value.hue() > (hue - range) && value.hue() < (hue + range)) + { + truth += 1.f - ((math::diff_abs(hue, value.hue()) / 100.0) / (range / 100)); + } + + if (truth > 1.f) + truth = 1.f; + if (truth < 0.f) + truth = 0.f; + return truth; } inline -int_u8 -how_green(hsl_<float, float, float> value) +float +sat_truth(hsl_<float, float, float> value) { - float proba = 0.0; + return value.sat(); +} - if (value.hue() > 60 && value.hue() < 180) +inline +float +lum_truth(hsl_<float, float, float> value) { - proba += 1.f - ((math::diff_abs(120.f, value.hue()) / 100.0) / 0.6); - proba *= value.sat(); - proba *= 1.f - (math::diff_abs(0.5f, value.lum())); + return 1.f - 2 * (math::diff_abs(0.5f, value.lum())); } - if (proba > 1.f) - proba = 1.f; - return (int_u8) (proba * 255); -} + int main(int argc, char* argv[]) { @@ -58,17 +66,33 @@ image2d<rgb8> input; io::ppm::load(input, argv[1]); image2d<int_u8> output; + initialize(output, input); + image2d<int_u8> hue_ima; + initialize(hue_ima, input); + image2d<int_u8> sl_p_ima; + initialize(sl_p_ima, input); + image2d<int_u8> sl_m_ima; + initialize(sl_m_ima, input); typedef image2d<hsl_<float, float, float> > H; H ima_hsl = data::convert(hsl_<float, float, float>(), input); mln_piter_(H) p(ima_hsl.domain()); for_all(p) { - output(p) = how_green(ima_hsl(p)); + float truth = 0.0; + truth = hue_truth(ima_hsl(p), GREEN, 60.f) * sat_truth(ima_hsl(p)) * lum_truth(ima_hsl(p)); + output(p) = (int_u8) (truth * 255); + + hue_ima(p) = (int_u8) (hue_truth(ima_hsl(p), GREEN, 60.f) * 255); + sl_p_ima(p) = (int_u8) (sat_truth(ima_hsl(p)) * lum_truth(ima_hsl(p)) * 255); + sl_m_ima(p) = (int_u8) (math::min(sat_truth(ima_hsl(p)), lum_truth(ima_hsl(p))) * 255); } io::pgm::save(output, argv[2]); + io::pgm::save(hue_ima, "hue.pgm"); + io::pgm::save(sl_p_ima, "sl_p.pgm"); + io::pgm::save(sl_m_ima, "sl_m.pgm"); return 0; } Index: trunk/milena/sandbox/fabien/tests/core/image/ima2d.cc =================================================================== --- trunk/milena/sandbox/fabien/tests/core/image/ima2d.cc (revision 0) +++ trunk/milena/sandbox/fabien/tests/core/image/ima2d.cc (revision 4563) @@ -0,0 +1,31 @@ +#include <mln/core/image/image2d.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/literal/colors.hh> +#include <mln/value/rgb8.hh> + +#include <mln/debug/quiet.hh> + +#include "bench.hh" + + +using namespace mln; +using value::rgb8; + + +int main(int argc, char* argv[]) +{ + if (argc != 2) + { + std::cout << "Usage: " << argv[0] << " input" << std::endl; + return 1; + } + + image2d<rgb8> ima; + + io::ppm::load(ima, argv[1]); + + bench_disk_perf(ima); + + return 0; +} Index: trunk/milena/sandbox/fabien/tests/core/image/disk_ima.cc =================================================================== --- trunk/milena/sandbox/fabien/tests/core/image/disk_ima.cc (revision 4562) +++ trunk/milena/sandbox/fabien/tests/core/image/disk_ima.cc (revision 4563) @@ -7,6 +7,8 @@ #include <mln/debug/quiet.hh> +#include "bench.hh" + using namespace mln; using value::rgb8; @@ -20,36 +22,11 @@ return 1; } - //image2d<rgb8> ima; - disk_ima<rgb8> tiled_ima; - - //io::ppm::load(ima, argv[1]); - io::ppm::load(tiled_ima, argv[1]); - - //std::cout << "bbox: " << tiled_ima.bbox() << std::endl; - //std::cout << "file: " << tiled_ima.file_() << std::endl; - - /*point2d pt0(0, 0); - mln_assertion(tiled_ima(pt0) == ima(pt0)); + disk_ima<rgb8> ima; - point2d pt(0, 1); - mln_assertion(tiled_ima(pt) == ima(pt)); + io::ppm::load(ima, argv[1]); - point2d pt2(1, 0); - mln_assertion(tiled_ima(pt2) == ima(pt2)); - - point2d pt3(1, 1); - mln_assertion(tiled_ima(pt3) == ima(pt3));*/ - - mln_piter_(disk_ima<rgb8>) p(tiled_ima.domain()); - for_all(p) - if (p.row() % 16 == 0) - { - //std::cout << tiled_ima(p); - tiled_ima(p) = literal::green; - //std::cout << " -> " << tiled_ima(p) << std::endl; - //mln_assertion(tiled_ima(p) == ima(p)); - } + bench_disk_perf(ima); return 0; } Index: trunk/milena/sandbox/fabien/tests/core/image/bench.sh =================================================================== --- trunk/milena/sandbox/fabien/tests/core/image/bench.sh (revision 4562) +++ trunk/milena/sandbox/fabien/tests/core/image/bench.sh (revision 4563) @@ -2,6 +2,7 @@ make disk_ima make tiled2d +make ima2d echo "---" echo "Disk Image Performance" @@ -10,3 +11,7 @@ echo "---" echo "Tiled Image Performance" time ./tiled2d $1 + +echo "---" +echo "Image 2D Performance" +time ./ima2d $1 Index: trunk/milena/sandbox/fabien/tests/core/image/tiled2d.cc =================================================================== --- trunk/milena/sandbox/fabien/tests/core/image/tiled2d.cc (revision 4562) +++ trunk/milena/sandbox/fabien/tests/core/image/tiled2d.cc (revision 4563) @@ -7,6 +7,8 @@ #include <mln/debug/quiet.hh> +#include "bench.hh" + using namespace mln; using value::rgb8; @@ -20,36 +22,11 @@ return 1; } - //image2d<rgb8> ima; tiled2d<rgb8> tiled_ima; - //io::ppm::load(ima, argv[1]); io::ppm::load(tiled_ima, argv[1]); - //std::cout << "bbox: " << tiled_ima.bbox() << std::endl; - //std::cout << "file: " << tiled_ima.file_() << std::endl; - - /*point2d pt0(0, 0); - mln_assertion(tiled_ima(pt0) == ima(pt0)); - - point2d pt(0, 1); - mln_assertion(tiled_ima(pt) == ima(pt)); - - point2d pt2(1, 0); - mln_assertion(tiled_ima(pt2) == ima(pt2)); - - point2d pt3(1, 1); - mln_assertion(tiled_ima(pt3) == ima(pt3));*/ - - mln_piter_(tiled2d<rgb8>) p(tiled_ima.domain()); - for_all(p) - if (p.row() % 16 == 0) - { - //std::cout << tiled_ima(p); - tiled_ima(p) = literal::green; - //std::cout << " -> " << tiled_ima(p) << std::endl; - //mln_assertion(tiled_ima(p) == ima(p)); - } + bench_disk_perf(tiled_ima); return 0; } Index: trunk/milena/sandbox/fabien/tests/core/image/Makefile =================================================================== --- trunk/milena/sandbox/fabien/tests/core/image/Makefile (revision 4562) +++ trunk/milena/sandbox/fabien/tests/core/image/Makefile (revision 4563) @@ -10,3 +10,6 @@ disk_ima: disk_ima.cc ${CXX} ${CXXFLAGS} ${INC} $^ -o disk_ima + +ima2d: ima2d.cc + ${CXX} ${CXXFLAGS} ${INC} $^ -o ima2d Index: trunk/milena/sandbox/fabien/tests/core/image/bench.hh =================================================================== --- trunk/milena/sandbox/fabien/tests/core/image/bench.hh (revision 0) +++ trunk/milena/sandbox/fabien/tests/core/image/bench.hh (revision 4563) @@ -0,0 +1,19 @@ +#include <mln/core/concept/image.hh> +#include <mln/literal/colors.hh> + +using namespace mln; + +template <typename I> +inline +void +bench_disk_perf(Image& ima_) +{ + I& ima = exact(ima_); + + mln_piter(I) p(ima.domain()); + for_all(p) + if (p.row() % 16 == 0) + { + ima(p) = literal::green; + } +} Index: trunk/milena/sandbox/fabien/mln/test/conversion.hh =================================================================== --- trunk/milena/sandbox/fabien/mln/test/conversion.hh (revision 4562) +++ trunk/milena/sandbox/fabien/mln/test/conversion.hh (revision 4563) @@ -33,6 +33,8 @@ # include <mln/core/concept/image.hh> # include <mln/data/convert.hh> # include <mln/math/diff_abs.hh> +# include <mln/math/sqr.hh> +# include <mln/math/sqrt.hh> # include <mln/value/rgb8.hh> @@ -60,6 +62,8 @@ namespace impl { + /// Distance between two color values. + /// We have to specialized it for each type of color. template <typename V> inline float color_distance(const V& color1, const V& color2) @@ -67,6 +71,8 @@ return -1.0; // FIXME } + /// Distance between two RGB values. + /// \return Value is normalized ([0..1]). template <> inline float color_distance<value::rgb8>(const value::rgb8& color1, const value::rgb8& color2) @@ -82,8 +88,7 @@ return dist; } - /// \return The percentage of points which value is not preserved. - /// The return value is normalized ([0..1]). + /// \return Root mean square. template <typename I, typename V> inline float conversion_(const I& ima, const V& value) @@ -97,12 +102,11 @@ mln_piter(I) p(ima.domain()); for_all(p) { - delta += color_distance(ima(p), output(p)); - //if (ima(p) != output(p)) - // delta += 1.0; + delta += math::sqr(color_distance(ima(p), output(p))); } delta /= ima.nelements(); + delta = math::sqrt(delta); return delta; } Index: trunk/milena/sandbox/fabien/bin/dumpl16_to_colorize.cc =================================================================== --- trunk/milena/sandbox/fabien/bin/dumpl16_to_colorize.cc (revision 0) +++ trunk/milena/sandbox/fabien/bin/dumpl16_to_colorize.cc (revision 4563) @@ -0,0 +1,64 @@ +#include <mln/core/image/image2d.hh> +#include <mln/make/image3d.hh> +#include <mln/debug/slices_2d.hh> +#include <mln/debug/colorize.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/int_u12.hh> +#include <mln/value/label_16.hh> +#include <mln/value/label_32.hh> +#include <mln/value/rgb8.hh> +#include <mln/core/concept/literal.hh> + +#include <mln/io/dump/load.hh> +#include <mln/io/ppm/save.hh> + + +int usage(char* argv[]) +{ + std::cerr << "usage: " << argv[0] << " input.dump dim nbasins output.ppm" << std::endl; + return 1; +} + + + +int main(int argc, char* argv[]) +{ + using namespace mln; + using value::int_u8; + using value::int_u12; + using value::label_16; + using value::label_32; + using value::rgb8; + typedef label_16 L; + + if (argc != 5) + return usage(argv); + + unsigned dim = atoi(argv[2]); + unsigned nbasins = atoi(argv[3]); + if (dim != 2 && dim != 3) + { + std::cout << "<dimensions> invalid" << std::endl; + return 1; + } + + if (dim == 2) + { + image2d<L> ima2d; + io::dump::load(ima2d, argv[1]); + image2d<rgb8> ima_rgb = debug::colorize(rgb8(), ima2d, nbasins); + io::ppm::save(ima_rgb, argv[4]); + } + else + { + image3d<L> ima3d; + io::dump::load(ima3d, argv[1]); + image3d<rgb8> ima_rgb = debug::colorize(rgb8(), ima3d, nbasins); + + image2d<rgb8> ima_result = debug::slices_2d(ima_rgb, 1.f, literal::black); + io::ppm::save(ima_result, argv[4]); + } + + return 0; +} Index: trunk/milena/sandbox/fabien/bin/dumpl8_to_colorize.cc =================================================================== --- trunk/milena/sandbox/fabien/bin/dumpl8_to_colorize.cc (revision 0) +++ trunk/milena/sandbox/fabien/bin/dumpl8_to_colorize.cc (revision 4563) @@ -0,0 +1,62 @@ +#include <mln/core/image/image2d.hh> +#include <mln/make/image3d.hh> +#include <mln/debug/slices_2d.hh> +#include <mln/labeling/colorize.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/int_u12.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> +#include <mln/core/concept/literal.hh> + +#include <mln/io/dump/load.hh> +#include <mln/io/ppm/save.hh> + + +int usage(char* argv[]) +{ + std::cerr << "usage: " << argv[0] << " input.dump dim nbasins output.ppm" << std::endl; + return 1; +} + + + +int main(int argc, char* argv[]) +{ + using namespace mln; + using value::int_u8; + using value::int_u12; + using value::label_8; + using value::rgb8; + typedef label_8 L; + + if (argc != 5) + return usage(argv); + + unsigned dim = atoi(argv[2]); + unsigned nbasins = atoi(argv[3]); + if (dim != 2 && dim != 3) + { + std::cout << "<dimensions> invalid" << std::endl; + return 1; + } + + if (dim == 2) + { + image2d<L> ima2d; + io::dump::load(ima2d, argv[1]); + image2d<rgb8> ima_rgb = labeling::colorize(rgb8(), ima2d, nbasins); + io::ppm::save(ima_rgb, argv[4]); + } + else + { + image3d<L> ima3d; + io::dump::load(ima3d, argv[1]); + image3d<rgb8> ima_rgb = labeling::colorize(rgb8(), ima3d, nbasins); + + image2d<rgb8> ima_result = debug::slices_2d(ima_rgb, 1.f, literal::black); + io::ppm::save(ima_result, argv[4]); + } + + return 0; +} Index: trunk/milena/sandbox/fabien/magick/tiled_image.cc =================================================================== --- trunk/milena/sandbox/fabien/magick/tiled_image.cc (revision 0) +++ trunk/milena/sandbox/fabien/magick/tiled_image.cc (revision 4563) @@ -0,0 +1,38 @@ +#include <Magick++.h> +#include <iostream> + + +int main(int argc, char* argv[]) +{ + if (argc != 2) + { + std::cout << "Usage: " << argv[0] << " filename" << std::endl; + return 1; + } + + std::cout << "Loading image..." << std::endl; + Magick::Image image(argv[1]); + std::cout << "Loading done." << std::endl; + + std::cout << "columns: " << image.columns() << std::endl; + std::cout << "rows: " << image.rows() << std::endl; + + unsigned ncols = image.columns(); + unsigned nrows = image.rows(); + const Magick::PixelPacket *pixel_cache; + unsigned count = 0; + for (unsigned row = 0; row < nrows; row += 1024) + { + std::cout << "Processing row #" << row << std::endl; + pixel_cache = image.getConstPixels(0, 0, ncols / 128, row); + for (unsigned col = 0; col < ncols / 128; col += 256) + { + const Magick::PixelPacket *pixel = pixel_cache + col; + ++count; + } + } + + std::cout << "nbr pixels: " << count << std::endl; + + return 0; +} Index: trunk/milena/sandbox/fabien/README =================================================================== --- trunk/milena/sandbox/fabien/README (revision 0) +++ trunk/milena/sandbox/fabien/README (revision 4563) @@ -0,0 +1,114 @@ + ________ +< README > + -------- + \ ^__^ + \ (oo)\_______ + (__)\ )\/\ + ||----w | + || || + + + + +./bin/ +====== +Divers binaires pour convertir des images. +Par exemple, `dumpi12_to_pgm' convertit un dump en int_u12 en une image PGM. + +./binarization/ (Deprecated) +=============== + +./dcmtk/ +======== +Support incomplet de la bibliotheque DCMTK. +Abandonne au profit de GDCM (bibliotheque pour le format DICOM). + +./igr/ +====== + + ./igr/color + =========== + + ./igr/fixed_seg + =============== + + ./igr/mat2mln + ============= + + ./igr/plot_points + ================= + + ./igr/point_filtering + ===================== + + ./igr/smooth_fixed_seg + ====================== + + ./igr/space_smooth + ================== + + ./igr/time_smooth + ================= + + +./magick/ (Deprecated) +========= +Premiers tests pour support ImageMagick. +Ce support a ete deplace dans mln/io/magick + +./mln/ +====== + + ./mln/canvas/browsing/ + ====================== + Brouillon pour support du `snake' en n dimensions. + Peut etre ignore. + + ./mln/core/image/ + ================= + Nouveaux types d'images. + disk_ima = image avec access direct au disque dur + tiled2d = image 2D avec cache + cache = utilise par `tiled2d', gere la politique de cache + page = utilise par `cache', gere la forme d'un bloc de donnees + magick_tiled2d = brouillon pour image avec cache gere par ImageMagick + + ./mln/data/ + =========== + Brouillon pour support du `fast_median' avec un autre canvas. + Peut etre ignore. + + ./mln/debug/ + ============ + int2rgb : convertit un int en une valeur RGB afin de pouvoir l'afficher + + ./mln/display/ + ============== + display_region : cree une image avec les lignes de watershed remplacees + par une color precise afin d'exhiber les regions + + ./mln/io/pnm/ + ============= + Support de la lecture de PNM pour la creation d'image mappees sur disque + (`disk_ima'). + + ./mln/test/ + =========== + Teste la conversion d'un type de valeur de couleur dans un autre espace + de couleur. Renvoie le "root mean square". + + ./mln/upsampling/ + ================= + Implementations des HQx. + + ./mln/world/inter_pixel/ + ======================== + display_region : cree une image pour aider a la visualization, a ete remplace + par la demo d'inter_pixel + is_zero_face : implement zero-face + + + +./tests/ +======== +Tests pour les sources situees dans ./mln/

1 0

4562: Prepare kmean demo for theo. Work in progress.
by Yann Jacquelet 28 Sep '09

28 Sep '09

* green/mln/clustering/kmean1d.hh: New library file. * green/demo/clustering/kmean1d: New directory. * green/demo/clustering/kmean1d/Makefile.am: New makefile. * green/demo/clustering/kmean1d/kmean1d.cc: New demo. * green/tests/clustering/kmean1d: New directory. * green/tests/clustering/kmean1d/Makefile.am: New makefile. * green/tests/clustering/kmean1d/kmean1d.cc: New unitary tests. --- trunk/milena/sandbox/ChangeLog | 14 + .../green/demo/clustering/kmean1d/Makefile.am | 148 +++ .../green/demo/clustering/kmean1d/kmean1d.cc | 97 ++ .../milena/sandbox/green/mln/clustering/kmean1d.hh | 974 ++++++++++++++++++++ .../green/tests/clustering/kmean1d/Makefile.am | 148 +++ .../green/tests/clustering/kmean1d/kmean1d.cc | 483 ++++++++++ 6 files changed, 1864 insertions(+), 0 deletions(-) create mode 100644 trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am create mode 100644 trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc create mode 100644 trunk/milena/sandbox/green/mln/clustering/kmean1d.hh create mode 100644 trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am create mode 100644 trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc diff --git a/trunk/milena/sandbox/ChangeLog b/trunk/milena/sandbox/ChangeLog index 797fa7b..2f2684f 100644 --- a/trunk/milena/sandbox/ChangeLog +++ b/trunk/milena/sandbox/ChangeLog @@ -1,3 +1,17 @@ +2009-09-28 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Prepare kmean demo for theo. Work in progress. + + * green/mln/clustering/kmean1d.hh: New library file. + + * green/demo/clustering/kmean1d: New directory. + * green/demo/clustering/kmean1d/Makefile.am: New makefile. + * green/demo/clustering/kmean1d/kmean1d.cc: New demo. + + * green/tests/clustering/kmean1d: New directory. + * green/tests/clustering/kmean1d/Makefile.am: New makefile. + * green/tests/clustering/kmean1d/kmean1d.cc: New unitary tests. + 2009-09-24 Fabien Freling <fabien.freling(a)lrde.epita.fr> Add benchmark for disk-mapped image and work for color types. diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am b/trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am new file mode 100644 index 0000000..91230b6 --- /dev/null +++ b/trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am @@ -0,0 +1,148 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo + + +ifeq ($(findstring $(BUILD__PATTERN),$(PWD)), $(BUILD__PATTERN)) +# Case where make is done from build directory. +SOURCE_DIR= $(subst $(BUILD__PATTERN),$(SOURCE_PATTERN),$(PWD)) +BUILD__DIR= $(PWD)/ +else +# Case where make is done from source directory. +SOURCE_DIR= $(PWD)/ +BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) +endif + +SRC= $(notdir $(wildcard $(SOURCE_DIR)/*.cc)) +OLD= $(notdir $(wildcard $(SOURCE_DIR)/*~)) +OBJ= $(patsubst %.cc,%.o,$(SRC)) +SOURCE_MAKEFILE=Makefile.am +BUILD__MAKEFILE=Makefile +TARGET_FILE= $(notdir $(PWD)) +SOURCE_FILES= $(notdir $(wildcard $(SOURCE_DIR)/*.*)) +BUILD__FILES= $(filter-out $(SRC) $(SOURCE_MAKEFILE), $(SOURCE_FILES)) + +BUILD__F_PATH= $(addprefix $(BUILD__DIR)/,$(BUILD__FILES)) +SOURCE_F_PATH= $(addprefix $(SOURCE_DIR)/,$(SOURCE_FILES)) + +BUILD__M_PATH= $(addprefix $(BUILD__DIR)/,$(BUILD__MAKEFILE)) +SOURCE_M_PATH= $(addprefix $(SOURCE_DIR)/,$(SOURCE_MAKEFILE)) + +TARGET_F_PATH= $(addprefix $(BUILD__DIR)/,$(TARGET_FILE)) +OBJ_F_PATH= $(addprefix $(BUILD__DIR)/,$(OBJ)) +SRC_F_PATH= $(addprefix $(SOURCE_DIR)/,$(SRC)) +OLD_F_PATH= $(addprefix $(SOURCE_DIR)/,$(OLD)) + +############# +# BOOTSTRAP # +############# + + +bootstrap: $(BUILD__DIR) $(BUILD__F_PATH) $(BUILD__M_PATH) + +# Create, if nessary, the destination directory +$(BUILD__DIR): + $(MKDIR) $(BUILD__DIR) + +# Copy, if nessary, all the files, except the Makefile.am +$(BUILD__F_PATH): $(SOURCE_F_PATH) + $(CP) $(addprefix $(SOURCE_DIR),$(@F)) $@ + +# Copy if nessary, the Makefile.am into Makefile +$(BUILD__M_PATH): $(SOURCE_M_PATH) + $(CP) $(SOURCE_M_PATH) $(BUILD__M_PATH) + + +####### +# ALL # +####### + +# We assume that the call is done from the build directory. +# With the directive vpath, hidden files are found in the source directory. + +all: $(TARGET_F_PATH) + + +$(TARGET_F_PATH): $(OBJ_F_PATH) + $(LINK.cc) $< $(LOADLIBES) $(LDLIBS) -o $@ + +$(OBJ_F_PATH):$(SRC_F_PATH) + $(COMPILE.cc) $(OUTPUT_OPTION) $< + + +######### +# CLEAN # +######### + +# Force every time the deletion +clean: clean_target clean_obj clean_dst clean_old #clean_make + + +clean_target: + -@$(RM) $(TARGET_F_PATH) &> /dev/null + +clean_obj: + -@$(RM) $(OBJ_F_PATH) &> /dev/null + +clean_dst: + -@$(RM) $(BUILD_F_PATH) &> /dev/null + +clean_make: + -@$(RM) $(BUILD_M_PATH) &> /dev/null + +clean_old: + -@$(RM) $(OLD_F_PATH) &> /dev/null + + +######### +# PRINT # +######### + +print: print_tools print_bootstrap + +print_tools: + @$(ECHO) "HOME = $(HOME)" + @$(ECHO) "INCLUDES = $(INCLUDES)" + @$(ECHO) "CXXFLAGS = $(CXXFLAGS)" + @$(ECHO) "ECHO = $(ECHO)" + @$(ECHO) "RM = $(RM)" + @$(ECHO) "MKDIR = $(MKDIR)" + @$(ECHO) "CP = $(CP)" + @$(ECHO) + +print_bootstrap: + @$(ECHO) "PWD = $(PWD)" + @$(ECHO) "SOURCE_PATTERN = $(SOURCE_PATTERN)" + @$(ECHO) "BUILD__PATTERN = $(BUILD__PATTERN)" + @$(ECHO) "SOURCE_DIR = $(SOURCE_DIR)" + @$(ECHO) "BUILD__DIR = $(BUILD__DIR)" + @$(ECHO) "SOURCE_MAKEFILE = $(SOURCE_MAKEFILE)" + @$(ECHO) "BUILD__MAKEFILE = $(BUILD__MAKEFILE)" + @$(ECHO) "TARGET_FILE = $(TARGET_FILE)" + @$(ECHO) "SOURCE_FILES = $(SOURCE_FILES)" + @$(ECHO) "SOURCE_F_PATH = $(SOURCE_F_PATH)" + @$(ECHO) "BUILD__FILES = $(BUILD__FILES)" + @$(ECHO) "BUILD__F_PATH = $(BUILD__F_PATH)" + @$(ECHO) "BUILD__M_PATH = $(BUILD__M_PATH)" + @$(ECHO) "SOURCE_M_PATH = $(SOURCE_M_PATH)" + @$(ECHO) "SRC = $(SRC)" + @$(ECHO) "OBJ = $(OBJ)" + @$(ECHO) "OLD = $(OLD)" + @$(ECHO) "SRC_F_PATH = $(SRC_F_PATH)" + @$(ECHO) "OBJ_F_PATH = $(OBJ_F_PATH)" + @$(ECHO) "OLD_F_PATH = $(OLD_F_PATH)" + @$(ECHO) diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..cc04149 --- /dev/null +++ b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,97 @@ +// DEMO ON KMEAN1D + +#include <mln/clustering/kmean1d.hh> + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> +#include <mln/pw/value.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +void demo(const unsigned watch_dog, const unsigned n_times) +{ + typedef mln::clustering::kmean1d<double,8,3> t_kmean; + typedef mln::value::label8 t_label8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<int_u8> t_image2d_int_u8; + typedef mln::image2d<rgb8> t_image2d_rgb8; + + t_image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + t_kmean kmean(house, watch_dog, n_times); + + kmean.launch_n_times(); + + t_kmean::t_color_dbg color_img = kmean.get_color_dbg(); + t_kmean::t_mean_dbg mean_img = kmean.get_mean_dbg(); + t_kmean::t_label_dbg label_img = kmean.get_label_dbg(); + t_kmean::t_variance_cnv variance_cnv = kmean.get_variance_cnv(); + t_kmean::t_mean_cnv mean_cnv = kmean.get_mean_cnv(); + + mln::io::pgm::save(mean_img, "mean.pgm"); + mln::io::ppm::save(color_img, "color.pgm"); + mln::io::pgm::save(label_img, "label.pgm"); + + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +} + +void usage(const int argc, const char *args[]) +{ + std::cout << "----------------------------------------" << std::endl; + std::cout << "argc : " << argc << std::endl; + + for (int i = 0; i < argc; ++i) + std::cout << "args[i] : " << args[i] << std::endl; + + std::cout << "----------------------------------------" << std::endl; + std::cout << "usage: kmean1d watch_dog n_times k image" << std::endl; + std::cout << "unsigned watch_dog (convergence loop)" << std::endl; + std::cout << "unsigned n_times (number of launching)" << std::endl; + // std::cout << "unsigned k (number of centers)" << std::endl; + // std::cout << "pbm image (points to work with)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + +int main(const int argc, const char *args[]) +{ + if (3 == argc) + { + std::istringstream arg1(args[1]); + std::istringstream arg2(args[2]); + // std::istringstream arg3(args[3]); + // std::istringstream arg3(args[4]); + unsigned watch_dog; + unsigned n_times; + // const char *image; + // unsigned k; + + arg1 >> watch_dog; + arg2 >> n_times; + + if (!arg1.fail() && !arg2.fail()) + demo(watch_dog, n_times); + else + usage(argc, args); + } + else + usage(argc, args); + + return 0; +} diff --git a/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh new file mode 100644 index 0000000..05947c7 --- /dev/null +++ b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh @@ -0,0 +1,974 @@ +// Copyright (C) 2007 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2008 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// +// 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_CLUSTERING_KMEAN1D_HH +#define MLN_CLUSTERING_KMEAN1D_HH + +/// \file +/// +/// \brief Implements the optimized kmean algorithm. +/// +/// This algorithm is optimized in the way it proceeds directly with the +/// greylevel attribute inspite of pixel attribute. The algorithm is +/// independant from the image dimension. But, we have to use to compute +/// one time the histogram. In fact, we move a recurrent cost to a fix cost +/// in the complexity. This version is very adapted to image with small +/// quantification. + +#include <limits.h> +#include <iostream> +#include <mln/trace/entering.hh> +#include <mln/trace/exiting.hh> + +#include <mln/core/contract.hh> +#include <mln/trait/value_.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/norm/l2.hh> + +#include <mln/core/image/image2d.hh> +#include <mln/value/int_u.hh> +#include <mln/value/rgb8.hh> +#include <mln/core/macros.hh> + +#include <mln/data/compute.hh> +#include <mln/debug/println.hh> +#include <mln/data/fill.hh> +#include <mln/literal/zero.hh> +#include <mln/labeling/colorize.hh> +#include <mln/labeling/mean_values.hh> + +#include <mln/io/ppm/save.hh> +#include <mln/io/pgm/save.hh> + +namespace mln +{ + + namespace clustering + { + + // Forward declaration. + template <typename T, unsigned n, unsigned k> + struct kmean1d; + + } // end of namespace mln::clustering + + namespace clustering + { + /// \brief Implements the kmean algorithm in a specific way. + /// + /// This version of the kmean algorithm uses a greyscale image as input, + /// temporary images for computations and produces images as result. Images + /// play the role of matrix or vector in standard statistic algoritm. + /// + /// T is the type used for computations (float or double). + /// n is the quantification for the image grayscale. + /// k is the number of classes. + template <typename T, unsigned n, unsigned k> + struct kmean1d + { + /// Type definitions. + /// \brief A few type definitions to limit the refactoring impact. + ///{ + + typedef value::rgb<8> t_rgb; // just for debugging + typedef value::label<8> t_label; // in fact label, where k <= 2^p + typedef value::int_u<n> t_value; + typedef T t_result; + + typedef image2d<t_value> t_point_img; + typedef image1d<unsigned> t_histo_img; + typedef image1d<t_result> t_number_img; + typedef image1d<t_result> t_mean_img; + typedef image1d<t_result> t_variance_img; + + typedef image1d<t_label> t_group_img; + typedef image2d<t_result> t_distance_img; + + typedef image2d<t_label> t_label_dbg; + typedef image2d<t_rgb> t_color_dbg; + typedef image2d<t_value> t_mean_dbg; + + typedef image3d<t_value> t_mean_cnv; + typedef image2d<t_result> t_variance_cnv; + + ///} + + //------------------------------------------------------------------------ + // Constructor + //------------------------------------------------------------------------ + + /// \brief Constructor + /// \param[in] point : the image as the population of pixels. + /// \param[in] watch_dog : the limit to observe the convergence (10). + /// \param[in] n_times : the number of times that we executed it (10). + + kmean1d(const t_point_img& point, + const unsigned watch_dog = 10, + const unsigned n_times = 10); + + //------------------------------------------------------------------------ + // Accessors + //------------------------------------------------------------------------ + + /// Mutator and accessors. + /// \{ + /// \brief Mutator and accessors are required for debugging and testing. + /// + /// Testing needs to hack the kmean loop process in order to verify each + /// step of the algorithm. + + void set_point(t_point_img& point); + void set_histo(t_histo_img& histo); + void set_number(t_number_img& number); + void set_mean(t_mean_img& mean); + void set_variance(t_variance_img& variance); + void set_group(t_group_img& group); + void set_distance(t_distance_img& distance); + + // Hacking outputs + t_point_img& get_point(); + t_histo_img& get_histo(); + t_number_img& get_number(); + t_mean_img& get_mean(); + t_variance_img& get_variance(); + t_group_img& get_group(); + t_distance_img& get_distance(); + + // Testing outputs + t_color_dbg& get_color_dbg(); + t_mean_dbg& get_mean_dbg(); + t_label_dbg& get_label_dbg(); + + t_mean_cnv& get_mean_cnv(); + t_variance_cnv& get_variance_cnv(); + + // Normal outputs + t_mean_img& to_result(); + + /// \} + + //------------------------------------------------------------------------ + // Initializations of centers + //------------------------------------------------------------------------ + + /// Initialization of centers. + /// \{ + /// \brief Two ways: Regular greylevel tick or random greylevel value or. + /// + /// There is two way to proceed the initialization. First of all, we + /// divide the greyscale in regular tick and we assigne them to the mean + /// of the centers. Finaly, we can ask random initialization along the + /// greyscale axis. The second process is needed to launch_n_times the + /// kmean and converge to the best descent. + + void init_mean(); + void init_mean_regular(); + void init_mean_random(); + + /// \} + + + //------------------------------------------------------------------------ + // Computations of distance, group, center, within variance + //------------------------------------------------------------------------ + + /// Computations of distance, group, center, within variance. + /// \{ + /// \brief Update the statistical information needed by the kmean process. + /// + /// The kmean process is a loop where distance from centers to pixels are + /// first compute. Then we assign the pixels to their nearest center. + /// The new location of each center can then update. Finaly, hommogenity + /// in group is observed by the within variance. + + void update_distance(); + void update_group(); + void update_mean(); + void update_variance(); + + /// \} + + //------------------------------------------------------------------------ + // Main loop + //------------------------------------------------------------------------ + + void launch_one_time(); + void launch_n_times(); + + + //------------------------------------------------------------------------ + // Checking the validiy of the results + //------------------------------------------------------------------------ + + bool is_valid(); + bool is_descent_valid(); + + //------------------------------------------------------------------------ + // Tools + //------------------------------------------------------------------------ + + void build(); + void to_result2(); + + + private: + /// Parameters. + /// \{ + /// \brief These parameters control the convergence of the process. + /// + /// In fact, watch_dog limit the number of iteration that a simple kmean + /// loop can do. If the process reaches the watch_dog limit, it means + /// that the process will not converge at all. The second parameter + /// n_times is the number of times we launch again and again the simple + /// kmean loop. As the kmean process is a descent, restarting the process + /// from different location will confort us in that we found a global + /// minima, not just a local one. + + unsigned _watch_dog; + unsigned _n_times; + + /// \} + + /// Convergence information. + /// \{ + /// \brief This information is used to follow the convergence. + /// + /// The within_variance is the homogenity indicator for the kmean process. + /// The ideal situation is to find the center with the minimum variance + /// around them. The within variance is just the sum of all variance + /// around the centers. The current_step variable allows us to remember + /// the current iteration in the kmean loop. This information is needed + /// by is_descent_valid routine which decide if convergence occurs or not. + + t_result _within_variance; + unsigned _current_step; + + /// \} + + /// Result of the kmean process. + /// \{ + /// \brief The center location are the major results of the kmean process. + /// + /// The kmean process result is composed by three information. The best + /// launching iteration, the best within variance obtained and the + /// location of the centers associated. + + unsigned _launching_min; + t_result _variance_min; + t_mean_img _mean_min; + + /// Inputs. + /// \{ + /// \brief The inputs are the distribution of the values and the values. + /// + /// The point image is a saving data for the real inputs. In fact, we use + /// the histogram information in the optimized kmean process. + + t_point_img _point; + t_histo_img _histo; + + ///\} + + /// Centers description. + /// \{ + /// \brief Centers are described by the first moment of their group. + /// + /// Centers are describe by their group attributes like the number of + /// pixels wich are relying on, the mass center of the group and the + /// homogeneity of the group. The variance is used as indicator for the + /// convergence. The number of pixels is used as integrity indicator. + /// A center with no point is a non sense. Theses informations are updated + /// after each kmean iteration. + + t_number_img _number; // k x 1 + t_mean_img _mean; // k x 1 + t_variance_img _variance; // k x 1 within group + + /// \} + + /// Greylevels description. + /// \{ + /// \brief The information are concerned with the greylevel input image. + /// + /// The group image allow us to decide which greylevel (and of course + /// which pixel) is assigned to a center. The distance image give us a + /// clue on how a greylevel could contribute to a center. The summation + /// over the greylevels of a center give us the within variance. + + t_group_img _group; // g x 1 because dim(t_value) = 1 + t_distance_img _distance; // label x graylevel + + /// \} + + /// Debugging, calibrating and testing results. + /// \{ + /// \brief Some exports information to control the results. + /// + /// We come back in the input space. Label is the image which associates + /// each pixel to its center. Color is the image which gives a random rgb + /// color to each label. We can then visualize the regions of the same + /// label (assigned to the same center) in the image. The mean image + /// associate the mean of each pixel center to each pixel. We obtain thus + /// a rebuilded image. + + t_label_dbg _label_dbg; + t_color_dbg _color_dbg; + t_mean_dbg _mean_dbg; + + /// \} + + /// Debugging, calibrating and testing convergence. + /// \{ + /// \brief Trace the variance and the center evolution. + /// + /// The mean_cnv image is a trace of the evolution of the mean' signal. + /// We can observe the variation of the mean of each center along the + /// kmean loop or along the different launch. The variance_cnv is a trace + /// of the within variance along the kmean loop or along the different + /// launch. + + t_mean_cnv _mean_cnv; + t_variance_cnv _variance_cnv; + + /// \} + }; + +#ifndef MLN_INCLUDE_ONLY + + //-------------------------------------------------------------------------- + // Constructor + //-------------------------------------------------------------------------- + + + template <typename T, unsigned n, unsigned k> + inline + kmean1d<T,n,k>::kmean1d(const image2d< t_value >& point, + const unsigned watch_dog, + const unsigned n_times) + { + trace::entering("mln::clustering::kmean1d::kmean1d"); + mln_precondition(point.is_valid()); + + + _watch_dog = watch_dog; + _n_times = n_times; + + _point = point; + _histo = data::compute(accu::meta::stat::histo1d(), _point); + + _number.init_(box1d(point1d(0),point1d(k-1))); + _mean.init_(box1d(point1d(0),point1d(k-1))); + _variance.init_(box1d(point1d(0),point1d(k-1))); + + _group.init_(box1d(point1d(mln_min(t_value)), point1d(mln_max(t_value)))); + _distance.init_(box2d(point2d(mln_min(t_value), 0), + point2d(mln_max(t_value), k-1))); + + // Debugging, calibrating and testing + initialize(_label_dbg, _point); + initialize(_color_dbg, _point); + initialize(_mean_dbg, _point); + + // Observing the convergence + _variance_cnv.init_(box2d(point2d(0, 0), + point2d(_watch_dog-1, _n_times-1))); + + _mean_cnv.init_(box3d(point3d(0, 0, 0), + point3d(k-1, _watch_dog-1, _n_times-1))); + + + trace::exiting("mln::clustering::kmean1d::kmean1d"); + } + + //-------------------------------------------------------------------------- + // Mutators and accessors + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_point(t_point_img& point) + { + trace::entering("mln::clustering::kmean1d::set_point"); + + _point = point; + + trace::exiting("mln::clustering::kmean1d::set_point"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_histo(t_histo_img& histo) + { + trace::entering("mln::clustering::kmean1d::set_histo"); + + _histo = histo; + + trace::exiting("mln::clustering::kmean1d::set_histo"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_number(t_number_img& number) + { + trace::entering("mln::clustering::kmean1d::set_number"); + + _number = number; + + trace::exiting("mln::clustering::kmean1d::set_number"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_mean(t_mean_img& mean) + { + trace::entering("mln::clustering::kmean1d::set_mean"); + + _mean = mean; + + trace::exiting("mln::clustering::kmean1d::set_mean"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_variance(t_variance_img& variance) + { + trace::entering("mln::clustering::kmean1d::set_variance"); + + _variance = variance; + + trace::exiting("mln::clustering::kmean1d::set_variance"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_group(t_group_img& group) + { + trace::entering("mln::clustering::kmean1d::set_group"); + + _group = group; + + trace::exiting("mln::clustering::kmean1d::set_group"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_distance(t_distance_img& distance) + { + trace::entering("mln::clustering::kmean1d::set_distance"); + + _distance = distance; + + trace::exiting("mln::clustering::kmean1d::set_distance"); + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_point_img& kmean1d<T,n,k>::get_point() + { + trace::entering("mln::clustering::kmean1d::get_point"); + + trace::exiting("mln::clustering::kmean1d::get_point"); + return _point; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_histo_img& kmean1d<T,n,k>::get_histo() + { + trace::entering("mln::clustering::kmean1d::get_histo"); + + trace::exiting("mln::clustering::kmean1d::get_histo"); + return _histo; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_number_img& kmean1d<T,n,k>::get_number() + { + trace::entering("mln::clustering::kmean1d::get_number"); + + trace::exiting("mln::clustering::kmean1d::get_number"); + return _number; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::get_mean() + { + trace::entering("mln::clustering::kmean1d::get_mean"); + + trace::exiting("mln::clustering::kmean1d::get_mean"); + return _mean; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_variance_img& kmean1d<T,n,k>::get_variance() + { + trace::entering("mln::clustering::kmean1d::get_variance"); + + trace::exiting("mln::clustering::kmean1d::get_variance"); + return _variance; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_group_img& kmean1d<T,n,k>::get_group() + { + trace::entering("mln::clustering::kmean1d::get_group"); + + trace::exiting("mln::clustering::kmean1d::get_group"); + return _group; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_distance_img& kmean1d<T,n,k>::get_distance() + { + trace::entering("mln::clustering::kmean1d::get_distance"); + + trace::exiting("mln::clustering::kmean1d::get_distance"); + return _distance; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_color_dbg& kmean1d<T,n,k>::get_color_dbg() + { + trace::entering("mln::clustering::kmean1d::get_color_dbg"); + + trace::exiting("mln::clustering::kmean1d::get_color_dbg"); + return _color_dbg; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_dbg& kmean1d<T,n,k>::get_mean_dbg() + { + trace::entering("mln::clustering::kmean1d::get_mean_dbg"); + + trace::exiting("mln::clustering::kmean1d::get_mean_dbg"); + return _mean_dbg; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_label_dbg& kmean1d<T,n,k>::get_label_dbg() + { + trace::entering("mln::clustering::kmean1d::get_label_dbg"); + + trace::exiting("mln::clustering::kmean1d::get_label_dbg"); + return _label_dbg; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_cnv& kmean1d<T,n,k>::get_mean_cnv() + { + trace::entering("mln::clustering::kmean1d::get_mean_cnv"); + + trace::exiting("mln::clustering::kmean1d::get_mean_cnv"); + return _mean_cnv; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_variance_cnv& kmean1d<T,n,k>::get_variance_cnv() + { + trace::entering("mln::clustering::kmean1d::get_variance_cnv"); + + trace::exiting("mln::clustering::kmean1d::get_variance_cnv"); + return _variance_cnv; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::to_result() + { + trace::entering("mln::clustering::kmean1d::to_result"); + + trace::exiting("mln::clustering::kmean1d::to_result"); + return _mean_min; + } + + + //-------------------------------------------------------------------------- + // Initialization of centers + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::init_mean_regular() + { + trace::entering("mln::clustering::kmean1d::init_mean_regular"); + T step = (mln_max(t_value) - mln_min(t_value)) / (k-1); + mln_piter(image1d<t_value>) l(_mean.domain()); + + for_all(l) + { + _mean(l) = (l.ind()*step) + mln_min(t_value); + } + + trace::exiting("mln::clustering::kmean1d<T,n,k>::init_mean_regular"); + } + + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::init_mean_random() + { + trace::exiting("mln::clustering::kmean1d::init_mean_random"); + + t_value min = mln_min(t_value); + t_value max = mln_max(t_value); + mln_piter(image1d<t_value>) l(_mean.domain()); + + for_all(l) + { + _mean(l) = (rand() % (max-min)) + min; + } + + trace::exiting("mln::clustering::kmean1d::init_mean_random"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::init_mean() + { + trace::entering("mln::clustering::kmean1d::init_mean"); + + init_mean_random(); + + trace::exiting("mln::clustering::kmean1d::init_mean"); + } + + //-------------------------------------------------------------------------- + // Computations of distance, group, center, within variance + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_distance() + { + trace::entering("mln::clustering::kmean1d::update_distance"); + + mln_piter(t_distance_img) d(_distance.domain()); + + for_all(d) + { + // the square distance + _distance(d) = math::sqr(d.row() - _mean(point1d(d.col()))); + /* + std::cout << "row : " << d.row() << std::endl; + std::cout << "col : " << d.col() << std::endl; + std::cout << "center : " << _mean(point1d(d.col())) << std::endl; + std::cout << "distance : " << _distance(d) << std::endl; + std::cout << "--------------------------------------" << std::endl; + */ + } + + trace::exiting("mln::clustering::kmean1d::update_distance"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_group() + { + trace::entering("mln::clustering::kmean1d::update_group"); + + mln_piter(t_group_img) g(_group.domain()); + + for_all(g) + { + mln_piter(t_mean_img) l(_mean.domain()); + T min = mln_max(T); + t_label label = mln_max(t_label); + + //std::cout << "g = " << g << std::endl; + + for_all(l) + { + if (min > _distance(point2d(g.ind(),l.ind()))) + { + min = _distance(point2d(g.ind(),l.ind())); + label = l.ind(); + } + + //std::cout << "d(" << l << ") = " << + // _distance(point2d(g.ind(), l.ind())) << std::endl; + } + + //std::cout << "g = " << g << std::endl; + + _group(g) = label; + //std::cout << "group = " << _group(g) << std::endl; + //std::cout << "-----------" << std::endl; + } + + trace::exiting("mln::clustering::kmean1d::update_group"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_mean() + { + trace::entering("mln::clustering::kmean1d::update_mean"); + + /// FIXME VERIFIER QUE L'ON PEUT OBTENIR UNE IMAGE EN NDG SIGNE + + // avec g le niveau de gris (signed or not signed) + // w[g] la classe de g sous forme d'image + // h[g] l'histogramme de l'image sous forme d'image + // + // Vg in [0..255] + // si w[g] = l + // c[l] += g + // n[l] += h(g) + // + // c[l] /= n + + data::fill(_mean, literal::zero); + data::fill(_number, literal::zero); + + mln_piter(t_group_img) g(_group.domain()); + + for_all(g) + { + _mean(point1d(_group(g))) += _histo(g) * g.ind(); + _number(point1d(_group(g))) += _histo(g); + } + + mln_piter(t_mean_img) l(_mean.domain()); + + /* + for_all(l) + { + std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + std::cout << "n(" << l << ") = " << _number(l) << std::endl; + } + */ + + for_all(l) + { + _mean(l) /= _number(l); + //std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + } + + trace::exiting("mln::clustering::kmean1d::update_mean"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_variance() + { + trace::entering("mln::clustering::kmean1d::update_variance"); + + T _within_variance = literal::zero; + mln_piter(t_variance_img) l(_variance.domain()); + + for_all(l) + { + _variance(l) = literal::zero; + + mln_piter(t_group_img) g(_group.domain()); + + for_all(g) + { + _variance(l) += _distance(point2d(g.ind(), l.ind())); + } + + _within_variance += _variance(l); + //std::cout << "v(" << l << ") = " << _variance(l) << std::endl; + } + + //std::cout << "result" << result << std::endl; + + trace::exiting("mln::clustering::kmean1d::update_variance"); + } + + //-------------------------------------------------------------------------- + // Tools + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::build() + { + trace::entering("build"); + + mln_piter(t_point_img) pi(_point.domain()); + mln_piter(t_label_dbg) po(_label_dbg.domain()); + + for_all_2(pi, po) + { + t_value val = _point(pi); + t_label grp = _group(point1d(val)); + + _label_dbg(po) = grp; + } + + trace::exiting("build"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::to_result2() + { + typedef value::rgb8 rgb8; + std::cout << "BUILDING OUTPUT" << std::endl; + + build(); + + std::cout << "COLORING OUTPUT" << std::endl; + + _mean_dbg = labeling::mean_values(_point, _label_dbg, k); + _color_dbg = labeling::colorize(rgb8(), _label_dbg); + + mln::io::pgm::save(_mean_dbg, "mean.pgm"); + mln::io::ppm::save(_color_dbg, "rgb.pgm"); + mln::io::pgm::save(_label_dbg, "label.pgm"); + } + + + //-------------------------------------------------------------------------- + // Checking the validity of the results + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + bool kmean1d<T,n,k>::is_descent_valid() + { + trace::entering("mln::clustering::kmean1d::is_descent_valid"); + bool result = true; + + mln_piter(t_number_img) l(_number.domain()); + + for_all(l) + { + result = result && (_number(l) != 0.0); + } + + result = result && (_current_step < _watch_dog); + + trace::exiting("mln::clustering::kmean1d::is_descent_valid"); + return result; + } + + template <typename T, unsigned n, unsigned k> + inline + bool kmean1d<T,n,k>::is_valid() + { + trace::entering("mln::clustering::kmean1d::is_valid"); + bool result = true; + + mln_piter(t_number_img) l(_number.domain()); + + for_all(l) + { + result = result && (_number(l) != 0.0); + } + + trace::exiting("mln::clustering::kmean1d::is_valid"); + return result; + } + + + //-------------------------------------------------------------------------- + // Main loop + //-------------------------------------------------------------------------- + + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::launch_one_time() + { + trace::entering("mln::clustering::kmean1d::launch_one_time"); + + // Initialization to start the descent + _within_variance = mln_max(t_result)/2; + t_result old_variance = mln_max(t_result); + + // Choose random points + init_mean(); + + // Execute the descent + for (unsigned i = 0; i<_watch_dog && _within_variance<old_variance; ++i) + { + update_distance(); + update_group(); + update_mean(); + + old_variance = _within_variance; + + update_variance(); // update _within_variance + } + + trace::exiting("mln::clustering::kmean1d::launch_one_time"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::launch_n_times() + { + trace::entering("mln::clustering::kmean1d::launch_n_times"); + + _variance_min = mln_max(t_result); + + // Execute the descent n times + for (unsigned i = 0; i < _n_times; ++i) + { + launch_one_time(); + + if (!is_descent_valid()) + --i; + else + { + if (_within_variance < _variance_min) + { + _variance_min = _within_variance; + _mean_min = _mean; + _launching_min = i; + } + } + + std::cout << "within_variance[" << i << "] = " + << _within_variance << std::endl; + } + + trace::exiting("mln::clustering::kmean1d::launch_n_times"); + } + +#endif // ! MLN_INCLUDE_ONLY + + } // end of namespace mln::clustering + +} // end of namespace mln + +#endif // ! MLN_CLUSTERING_KMEAN1D_HH diff --git a/trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am b/trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am new file mode 100644 index 0000000..cdf3021 --- /dev/null +++ b/trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am @@ -0,0 +1,148 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/tests +BUILD__PATTERN= green/build/tests + + +ifeq ($(findstring $(BUILD__PATTERN),$(PWD)), $(BUILD__PATTERN)) +# Case where make is done from build directory. +SOURCE_DIR= $(subst $(BUILD__PATTERN),$(SOURCE_PATTERN),$(PWD)) +BUILD__DIR= $(PWD)/ +else +# Case where make is done from source directory. +SOURCE_DIR= $(PWD)/ +BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) +endif + +SRC= $(notdir $(wildcard $(SOURCE_DIR)/*.cc)) +OLD= $(notdir $(wildcard $(SOURCE_DIR)/*~)) +OBJ= $(patsubst %.cc,%.o,$(SRC)) +SOURCE_MAKEFILE=Makefile.am +BUILD__MAKEFILE=Makefile +TARGET_FILE= $(notdir $(PWD)) +SOURCE_FILES= $(notdir $(wildcard $(SOURCE_DIR)/*.*)) +BUILD__FILES= $(filter-out $(SRC) $(SOURCE_MAKEFILE), $(SOURCE_FILES)) + +BUILD__F_PATH= $(addprefix $(BUILD__DIR)/,$(BUILD__FILES)) +SOURCE_F_PATH= $(addprefix $(SOURCE_DIR)/,$(SOURCE_FILES)) + +BUILD__M_PATH= $(addprefix $(BUILD__DIR)/,$(BUILD__MAKEFILE)) +SOURCE_M_PATH= $(addprefix $(SOURCE_DIR)/,$(SOURCE_MAKEFILE)) + +TARGET_F_PATH= $(addprefix $(BUILD__DIR)/,$(TARGET_FILE)) +OBJ_F_PATH= $(addprefix $(BUILD__DIR)/,$(OBJ)) +SRC_F_PATH= $(addprefix $(SOURCE_DIR)/,$(SRC)) +OLD_F_PATH= $(addprefix $(SOURCE_DIR)/,$(OLD)) + +############# +# BOOTSTRAP # +############# + + +bootstrap: $(BUILD__DIR) $(BUILD__F_PATH) $(BUILD__M_PATH) + +# Create, if nessary, the destination directory +$(BUILD__DIR): + $(MKDIR) $(BUILD__DIR) + +# Copy, if nessary, all the files, except the Makefile.am +$(BUILD__F_PATH): $(SOURCE_F_PATH) + $(CP) $(addprefix $(SOURCE_DIR),$(@F)) $@ + +# Copy if nessary, the Makefile.am into Makefile +$(BUILD__M_PATH): $(SOURCE_M_PATH) + $(CP) $(SOURCE_M_PATH) $(BUILD__M_PATH) + + +####### +# ALL # +####### + +# We assume that the call is done from the build directory. +# With the directive vpath, hidden files are found in the source directory. + +all: $(TARGET_F_PATH) + + +$(TARGET_F_PATH): $(OBJ_F_PATH) + $(LINK.cc) $< $(LOADLIBES) $(LDLIBS) -o $@ + +$(OBJ_F_PATH):$(SRC_F_PATH) + $(COMPILE.cc) $(OUTPUT_OPTION) $< + + +######### +# CLEAN # +######### + +# Force every time the deletion +clean: clean_target clean_obj clean_dst clean_old #clean_make + + +clean_target: + -@$(RM) $(TARGET_F_PATH) &> /dev/null + +clean_obj: + -@$(RM) $(OBJ_F_PATH) &> /dev/null + +clean_dst: + -@$(RM) $(BUILD_F_PATH) &> /dev/null + +clean_make: + -@$(RM) $(BUILD_M_PATH) &> /dev/null + +clean_old: + -@$(RM) $(OLD_F_PATH) &> /dev/null + + +######### +# PRINT # +######### + +print: print_tools print_bootstrap + +print_tools: + @$(ECHO) "HOME = $(HOME)" + @$(ECHO) "INCLUDES = $(INCLUDES)" + @$(ECHO) "CXXFLAGS = $(CXXFLAGS)" + @$(ECHO) "ECHO = $(ECHO)" + @$(ECHO) "RM = $(RM)" + @$(ECHO) "MKDIR = $(MKDIR)" + @$(ECHO) "CP = $(CP)" + @$(ECHO) + +print_bootstrap: + @$(ECHO) "PWD = $(PWD)" + @$(ECHO) "SOURCE_PATTERN = $(SOURCE_PATTERN)" + @$(ECHO) "BUILD__PATTERN = $(BUILD__PATTERN)" + @$(ECHO) "SOURCE_DIR = $(SOURCE_DIR)" + @$(ECHO) "BUILD__DIR = $(BUILD__DIR)" + @$(ECHO) "SOURCE_MAKEFILE = $(SOURCE_MAKEFILE)" + @$(ECHO) "BUILD__MAKEFILE = $(BUILD__MAKEFILE)" + @$(ECHO) "TARGET_FILE = $(TARGET_FILE)" + @$(ECHO) "SOURCE_FILES = $(SOURCE_FILES)" + @$(ECHO) "SOURCE_F_PATH = $(SOURCE_F_PATH)" + @$(ECHO) "BUILD__FILES = $(BUILD__FILES)" + @$(ECHO) "BUILD__F_PATH = $(BUILD__F_PATH)" + @$(ECHO) "BUILD__M_PATH = $(BUILD__M_PATH)" + @$(ECHO) "SOURCE_M_PATH = $(SOURCE_M_PATH)" + @$(ECHO) "SRC = $(SRC)" + @$(ECHO) "OBJ = $(OBJ)" + @$(ECHO) "OLD = $(OLD)" + @$(ECHO) "SRC_F_PATH = $(SRC_F_PATH)" + @$(ECHO) "OBJ_F_PATH = $(OBJ_F_PATH)" + @$(ECHO) "OLD_F_PATH = $(OLD_F_PATH)" + @$(ECHO) diff --git a/trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc b/trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..fe5b6cf --- /dev/null +++ b/trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,483 @@ +// UNARY TESTS ON KMEAN1D + +#include <mln/clustering/kmean1d.hh> + +#include <iostream> + +#include <mln/pw/value.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/rgb8.hh> + +#include <mln/literal/colors.hh> + +#include <mln/algebra/vec.hh> +#include <mln/algebra/mat.hh> + +#include <mln/core/macros.hh> +#include <mln/core/contract.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/img_path.hh> + +#include <mln/data/transform.hh> + +#include <mln/trait/value/print.hh> +#include <mln/trait/image/print.hh> + +#define SIZE_IMAGE 512 +#define SIZE_SAMPLE1 (512*512) +#define SIZE_SAMPLE2 4 +#define NB_CENTER 2 +#define DIM_POINT 3 +#define TYPE_POINT double + +/* +void test_instantiation() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::image2d<int_u8> image2d_int_u8; + image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + mln::clustering::kmean1d<double, 8, 3> kmean(house); + + // test the compilation + std::cout << "test instantiation : ok" << std::endl; +} + +void test_init_center() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::image2d<int_u8> image2d_int_u8; + image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + mln::clustering::kmean1d<double, 8, 3> kmean(house); + + kmean.init_center(); + + std::cout << "test init center : ok" << std::endl; +} +*/ + +void test_init_loop() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::image2d<int_u8> image2d_int_u8; + image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + mln::clustering::kmean1d<double, 8, 3> kmean(house); + + kmean.launch_n_times(); + + std::cout << "test loop : ok" << std::endl; +} + + + +/* +struct rgb8_to_4colors : mln::Function_v2v<rgb8_to_4colors> +{ + typedef mln::value::rgb8 result; + + mln::value::rgb8 operator()(const mln::value::rgb8& color) const + { + mln::value::rgb8 result; + unsigned hash = (color.red() + color.green() + color.blue()) % 4; + + switch (hash) + { + case 0: result = mln::literal::lime; break; + case 1: result = mln::literal::brown; break; + case 2: result = mln::literal::teal; break; + case 3: result = mln::literal::purple; break; + } + + return result; + } +}; + + +void print_color(const mln::value::rgb8& color) +{ + std::cout << "{r=" << color.red() << ", "; + std::cout << "g=" << color.green() << ", "; + std::cout << "b=" << color.blue() << "}" << std::endl; +} + +void fill_image_with_4colors(mln::image2d<mln::value::rgb8>& img) +{ + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + + img = mln::data::transform(img, rgb8_to_4colors()); + + //print_color(lime); + //print_color(brown); + //print_color(teal); + //print_color(purple); +} + +bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, + const MAT_POINT1& point) +{ + mln_piter_(mln::image2d<mln::value::rgb8>) pi(img.domain()); + bool result = true; + unsigned index = -1; + + for_all(pi) + { + bool test = true; + + ++index; + + test = test && (point(index,0) == img(pi).red()); + test = test && (point(index,1) == img(pi).green()); + test = test && (point(index,2) == img(pi).blue()); + + if (!test) + { + std::cout << pi; + std::cout << "{r=" << img(pi).red() << ", "; + std::cout << "g=" << img(pi).green() << ", "; + std::cout << "b=" << img(pi).blue() << "}"; + std::cout << index; + std::cout << "[r=" << point(index,0) << ", "; + std::cout << "g=" << point(index,1) << ", "; + std::cout << "b=" << point(index,2) << "]" << std::endl; + + mln_assertion(test); + } + + result &= test; + } + + return result; +} + +void test_init_point() +{ + typedef mln::value::rgb8 rgb8; + mln::image2d<rgb8> img_ref; + + mln::clustering::k_mean<SIZE_SAMPLE1,NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + mln::io::ppm::load(img_ref, "/usr/local/share/olena/images/lena.ppm"); + //mln::io::ppm::save(img_ref, "verif.ppm"); + + fill_image_with_4colors(img_ref); + kmean.init_point(img_ref); + + mln_assertion(true == is_equivalent(img_ref, kmean.get_point())); + + std::cout << "Test init point : ok" << std::endl; +} + +void set_point(MAT_POINT2& point, + const unsigned index, + const mln::value::rgb8& color) +{ + point(index,0) = color.red(); + point(index,1) = color.green(); + point(index,2) = color.blue(); +} + +void fake_init_point(MAT_POINT2& point, + const mln::value::rgb8& point1, + const mln::value::rgb8& point2, + const mln::value::rgb8& point3, + const mln::value::rgb8& point4) +{ + set_point(point, 0, point1); + set_point(point, 1, point2); + set_point(point, 2, point3); + set_point(point, 3, point4); +} + +bool is_equal(const mln::value::rgb8& ref, + const MAT_CENTER& center, + const unsigned i) +{ + bool result = true; + + result = result && (center(i, 0) - ref.red() < 1.0); + result = result && (center(i, 1) - ref.green() < 1.0); + result = result && (center(i, 2) - ref.blue() < 1.0); + + return result; +} + +void test_init_center() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + kmean.init_center(); + + mln_assertion(is_equal(lime, kmean.get_center(), 0) || + is_equal(brown, kmean.get_center(), 0) || + is_equal(teal, kmean.get_center(), 0) || + is_equal(purple, kmean.get_center(), 0)); + + mln_assertion(is_equal(lime, kmean.get_center(), 1) || + is_equal(brown, kmean.get_center(), 1) || + is_equal(teal, kmean.get_center(), 1) || + is_equal(purple, kmean.get_center(), 1)); + + std::cout << "Test init center : ok" << std::endl; +} + +void set_center(MAT_CENTER& center, + const unsigned index, + const mln::value::rgb8& color) +{ + center(index,0) = color.red(); + center(index,1) = color.green(); + center(index,2) = color.blue(); +} + +void fake_init_center(MAT_CENTER& center, + const mln::value::rgb8 center1, + const mln::value::rgb8 center2) +{ + + set_center(center, 0, center1); + set_center(center, 1, center2); +} + + +double dist(mln::value::rgb8 color1, mln::value::rgb8 color2) +{ + double red = color1.red() - color2.red(); + double green = color1.green() - color2.green(); + double blue = color1.blue() - color2.blue(); + double result= red * red + green * green + blue * blue; + + return result; +} + +void test_update_distance() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const MAT_DISTANCE2& dist_v = kmean.get_distance(); + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + kmean.update_distance(); + + mln_assertion(dist(lime, c1) == dist_v(0,0)); + mln_assertion(dist(lime, c2) == dist_v(0,1)); + mln_assertion(dist(brown, c1) == dist_v(1,0)); + mln_assertion(dist(brown, c2) == dist_v(1,1)); + mln_assertion(dist(teal, c1) == dist_v(2,0)); + mln_assertion(dist(teal, c2) == dist_v(2,1)); + mln_assertion(dist(purple, c1) == dist_v(3,0)); + mln_assertion(dist(purple, c2) == dist_v(3,1)); + + std::cout << "Test update distance : ok" << std::endl; +} + +void set_distance(MAT_DISTANCE2& distance, + const unsigned index, + const double d1, + const double d2) +{ + distance(index,0) = d1; + distance(index,1) = d2; +} + +void fake_update_distance(MAT_DISTANCE2& distance, + const mln::value::rgb8& point1, + const mln::value::rgb8& point2, + const mln::value::rgb8& point3, + const mln::value::rgb8& point4, + const mln::value::rgb8& center1, + const mln::value::rgb8& center2) +{ + set_distance(distance, 0, dist(point1, center1), dist(point1, center2)); + set_distance(distance, 1, dist(point2, center1), dist(point2, center2)); + set_distance(distance, 2, dist(point3, center1), dist(point3, center2)); + set_distance(distance, 3, dist(point4, center1), dist(point4, center2)); + + + // for (int i = 0; i < SIZE_SAMPLE2; ++i) + //for (int j = 0; j < NB_CENTER; ++j) + // std::cout << "d(" < kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const unsigned point1_min= 0; // lime near lime (c1) + const unsigned point2_min= 1; // brown near purple (c2) + const unsigned point3_min= 1; // teal near purple (c2) + const unsigned point4_min= 1; // purple near purple (c2) + const MAT_GROUP2& group = kmean.get_group(); + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + fake_update_distance(kmean.get_distance(), lime, brown, teal, purple, c1, c2); + kmean.update_group(); + + mln_assertion(0.0 == group(0, 1 - point1_min)); + mln_assertion(1.0 == group(0, point1_min)); + mln_assertion(0.0 == group(1, 1 - point2_min)); + mln_assertion(1.0 == group(1, point2_min)); + mln_assertion(0.0 == group(2, 1 - point3_min)); + mln_assertion(1.0 == group(2, point3_min)); + mln_assertion(0.0 == group(3, 1 - point4_min)); + mln_assertion(1.0 == group(3, point4_min)); + + std::cout << "Test update group : ok" << std::endl; +} + +void set_group(MAT_GROUP2& group, + const unsigned index, + const unsigned min) +{ + group(index, min) = 1.0; + group(index, 1-min) = 0.0; +} + + +void fake_update_group(MAT_GROUP2& group, + const unsigned& point1_min, + const unsigned& point2_min, + const unsigned& point3_min, + const unsigned& point4_min) +{ + set_group(group, 0, point1_min); + set_group(group, 1, point2_min); + set_group(group, 2, point3_min); + set_group(group, 3, point4_min); +} + +void test_update_center() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const unsigned pt1_min = 0; // lime near lime (c1) + const unsigned pt2_min = 1; // brown near purple (c2) + const unsigned pt3_min = 1; // teal near purple (c2) + const unsigned pt4_min = 1; // purple near purple (c2) + const mln::value::rgb8 mean_c1 = lime; + const mln::value::rgb8 mean_c2 = (brown+teal+purple)/3; + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + fake_update_distance(kmean.get_distance(), lime, brown, teal, purple, c1, c2); + fake_update_group(kmean.get_group(), pt1_min, pt2_min, pt3_min, pt4_min); + kmean.update_center(); + + mln_assertion(is_equal(mean_c1, kmean.get_center(), 0)); + mln_assertion(is_equal(mean_c2, kmean.get_center(), 1)); + + std::cout << "Test update center : ok" << std::endl; +} + +void test_update_variance() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const unsigned pt1_min = 0; // lime near lime (c1) + const unsigned pt2_min = 1; // brown near purple (c2) + const unsigned pt3_min = 1; // teal near purple (c2) + const unsigned pt4_min = 1; // purple near purple (c2) + const double v1 = 0; + const double v2 = dist(purple, brown) + dist(purple, teal); + const mln::value::rgb8 mean_c2 = (brown+teal+purple)/3; + const VEC_VAR& var = kmean.get_variance(); + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + fake_update_distance(kmean.get_distance(), lime, brown, teal, purple, c1, c2); + fake_update_group(kmean.get_group(), pt1_min, pt2_min, pt3_min, pt4_min); + kmean.update_variance(); + + mln_assertion(v1 == var[0]); + mln_assertion(v2 == var[1]); + + std::cout << "Test update variance : ok" << std::endl; +} + +void test_loop() +{ + typedef mln::value::rgb8 rgb8; + mln::image2d<rgb8> img_ref; + + mln::clustering::k_mean<SIZE_SAMPLE1, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + mln::io::ppm::load(img_ref, "/usr/local/share/olena/images/lena.ppm"); + //mln::io::ppm::save(img_ref, "verif.ppm"); + + fill_image_with_4colors(img_ref); + kmean.init_point(img_ref); + + kmean.loop(img_ref); + + + // std::cout << "Test update variance: ok" << std::endl; +} +*/ + +int main() +{ + //test_instantiation(); + //test_init_point(); + // test_init_center(); + test_init_loop(); + //test_update_distance(); + //test_update_group(); + //test_update_center(); + //test_update_variance(); + + // mln::trace::quiet = false; + + //test_loop(); + + return 0; +} -- 1.5.6.5

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