Olena-patches

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last-svn-commit-27-g18d1a42 Fix bugs in the histogram visualization tools.

by Yann Jacquelet

* green/mln/display/display_histo.cc: Add new vizualisations. * green/mln/display/project_histo.cc: Add new color projections. --- milena/sandbox/ChangeLog | 7 + milena/sandbox/green/mln/display/display_histo.hh | 75 ++++-- milena/sandbox/green/mln/display/project_histo.hh | 341 +++++++++++++-------- 3 files changed, 279 insertions(+), 144 deletions(-) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 097ea5c..d53313f 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,5 +1,12 @@ 2010-02-10 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Fix bugs in the histogram visualization tools. + + * green/mln/display/display_histo.cc: Add new vizualisations. + * green/mln/display/project_histo.cc: Add new color projections. + +2010-02-10 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Fix last details in the image processing chain. * green/tools/annotating/histo/histo.cc: Manage new inputs/outputs. diff --git a/milena/sandbox/green/mln/display/display_histo.hh b/milena/sandbox/green/mln/display/display_histo.hh index 2ba0b61..ef47182 100644 --- a/milena/sandbox/green/mln/display/display_histo.hh +++ b/milena/sandbox/green/mln/display/display_histo.hh @@ -29,12 +29,14 @@ # define MLN_DISPLAY_DISPLAY_HISTO_HH # include <mln/accu/math/sum.hh> +# include <mln/algebra/vec.hh> # include <mln/data/stretch.hh> # include <mln/display/project_histo.hh> # include <mln/fun/v2v/log.hh> # include <mln/value/int_u8.hh> # include <mln/value/rgb8.hh> # include <mln/value/label_8.hh> +# include <mln/util/array.hh> /// \file @@ -57,19 +59,28 @@ namespace mln image2d<value::int_u8> display_histo3d_unsigned(const image3d<unsigned>& histo); - image2d<value::int_u8> - display2_histo3d_unsigned(const image3d<unsigned>& histo); + template <unsigned n> + image2d< value::int_u<n> > + display2_histo3d_unsigned(const image3d<unsigned>& histo, + const value::int_u<n> ambiguous_color); + template <unsigned n> image2d<value::label_8> display2_histo3d_unsigned(const image3d<unsigned>& histo, - const image3d<value::label_8>& label); + const image3d<value::label_8>& label, + const value::label_8 ambiguous_label); - image2d<value::rgb8> - display3_histo3d_unsigned(const image3d<unsigned>& histo); + template <unsigned n> + image2d< value::rgb<n> > + display3_histo3d_unsigned(const image3d<unsigned>& histo, + const value::rgb<n> ambiguous_color); - image2d<value::rgb8> - display3_histo3d_unsigned(const image3d<unsigned>& histo, - const image3d<value::label_8>& label); + template <unsigned n> + image2d< value::rgb8 > + display3_histo3d_unsigned(const image3d<unsigned>& histo, + const image3d<value::label_8>& label, + const util::array< algebra::vec<3,float> >& pal, + const value::rgb8 ambiguous_color); #ifndef MLN_INCLUDE_ONLY @@ -87,7 +98,7 @@ namespace mln /// \parameter[in] histo the histogram in 3d. /// \result return a equivalent 2d image. - + // FIXME : display_shape [in int_u8] image2d<value::int_u8> display_histo3d_unsigned(const image3d<unsigned>& histo) { @@ -102,40 +113,62 @@ namespace mln return proj_int; } - image2d<value::int_u8> - display2_histo3d_unsigned(const image3d<unsigned>& histo) + // FIXME : display_color [in int_un] + template <unsigned n> + image2d< value::int_u<n> > + display2_histo3d_unsigned(const image3d<unsigned>& histo, + const value::int_u<n> ambiguous_color) { - image2d<value::int_u8> proj = project2_histo<0>(histo); + image2d< value::int_u<n> > proj = project2_histo<n,0>(histo, + ambiguous_color); return proj; } + // FIXME : display_label [in label] + template <unsigned n> image2d<value::label_8> display2_histo3d_unsigned(const image3d<unsigned>& histo, - const image3d<value::label_8>& label) + const image3d<value::label_8>& label, + const value::label_8 ambiguous_label) { - image2d<value::label_8> proj = project2_histo<0>(histo, label); + image2d<value::label_8> proj = project2_histo<n,0>(histo, + label, + ambiguous_label); return proj; } - image2d<value::rgb8> - display3_histo3d_unsigned(const image3d<unsigned>& histo) + // FIXME : display_color [in color] + template <unsigned n> + image2d< value::rgb<n> > + display3_histo3d_unsigned(const image3d<unsigned>& histo, + const value::rgb<n> ambiguous_color) { - image2d<value::rgb8> proj = project3_histo<0>(histo); + image2d< value::rgb<n> > proj = project3_histo<n,0>(histo, + ambiguous_color); return proj; } - image2d<value::rgb8> - display3_histo3d_unsigned(const image3d<unsigned>& histo, - const image3d<value::label_8>& label) + + // FIXME : display_label [in color] + template <unsigned n> + image2d< value::rgb8 > + display3_histo3d_unsigned(const image3d<unsigned>& histo, + const image3d<value::label_8>& label, + const util::array<algebra::vec<3,float> >& pal, + const value::rgb8 ambiguous_color) { - image2d<value::rgb8> proj = project3_histo<0>(histo, label); + image2d< value::rgb8 > proj = project3_histo<n,0>(histo, + label, + pal, + ambiguous_color); return proj; } + #endif // ! MLN_INCLUDE_ONLY diff --git a/milena/sandbox/green/mln/display/project_histo.hh b/milena/sandbox/green/mln/display/project_histo.hh index d842c70..30bcd6d 100644 --- a/milena/sandbox/green/mln/display/project_histo.hh +++ b/milena/sandbox/green/mln/display/project_histo.hh @@ -37,12 +37,16 @@ # include <mln/accu/image/take.hh> # include <mln/accu/image/to_result.hh> +# include <mln/algebra/vec.hh> + # include <mln/opt/at.hh> # include <mln/value/int_u8.hh> # include <mln/value/rgb8.hh> # include <mln/value/label_8.hh> +# include <mln/util/array.hh> + /// \file /// /// \brief Allow the visualization of 3d histogram. @@ -60,9 +64,30 @@ namespace mln image2d<mln_result(A)> project_histo(const image3d<V>& histo); - template <typename A, unsigned direction, typename V> + template <typename A, unsigned n, unsigned direction, typename V> image2d<mln_result(A)> - project2_histo(const image3d<V>& histo); + project2_histo(const image3d<V>& histo, + const value::int_u<n>& ambiguous_color); + + template <unsigned n, unsigned direction, typename V> + image2d<V> + project2_histo(const image3d<unsigned>& histo, + const image3d<V>& label); + + template <unsigned n, unsigned direction> + image2d< value::rgb<n> > + project3_histo(const image3d<unsigned>& histo, + const value::rgb<n> ambiguous_color); + + template <unsigned n, unsigned direction> + image2d< value::rgb8 > + project3_histo(const image3d<unsigned>& histo, + const image3d<value::label_8>& label, + const util::array<algebra::vec<3, float> >& pal, + const value::rgb8 ambiguous_color); + // FIXME ==> palette must be 1d-image not an array !! + + # ifndef MLN_INCLUDE_ONLY @@ -96,80 +121,96 @@ namespace mln return accu::image::to_result(histo_accu); } - template <unsigned direction> - image2d<value::int_u8> - project2_histo(const image3d<unsigned>& histo) + // 0 ==> blue + // 1 ==> red + // 2 ==> green + + // mln::opt::at(histo, blue, red, green) + + template <unsigned n, unsigned direction> + image2d< value::int_u<n> > + project2_histo(const image3d<unsigned>& histo, + const value::int_u<n>& ambiguous_color) { - image2d<value::int_u8> result; + image2d< value::int_u<n> > result; if (0 == direction) // blue { - image2d<value::int_u8> arg_max(histo.ncols(), histo.nslices()); + image2d< value::int_u<n> > arg_max(histo.nrows(), histo.ncols()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nslices(); ++i) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - unsigned max = 0; // minimum as possible - signed pos = -1; + unsigned max = 0; // minimum as possible + def::coord pos = -1; - for (unsigned k = 0; k < histo.nrows(); ++k) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = blue; } } - opt::at(arg_max,i,j) = pos; + if (-1 == pos) + opt::at(arg_max,red,green) = ambiguous_color; + else + opt::at(arg_max,red,green) = pos; } result = arg_max; } else if (1 == direction) // red { - image2d<value::int_u8> arg_max(histo.nrows(), histo.nslices()); + image2d< value::int_u<n> > arg_max(histo.ncols(), histo.nslices()); - for (unsigned j = 0; j < histo.nslices(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.ncols(); ++k) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = red; } } - opt::at(arg_max,i,j) = pos; + if (-1 == pos) + opt::at(arg_max,green,blue) = ambiguous_color; + else + opt::at(arg_max,green,blue) = pos; } result = arg_max; } else // 2 == direction // green { - image2d<value::int_u8> arg_max(histo.nrows(), histo.ncols()); + image2d< value::int_u<n> > arg_max(histo.nrows(), histo.nslices()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.nslices(); ++k) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = green; } } - opt::at(arg_max,i,j) = pos; + if (-1 == pos) + opt::at(arg_max,red,blue) = ambiguous_color; + else + opt::at(arg_max,red,blue) = pos; } result = arg_max; @@ -178,81 +219,91 @@ namespace mln return result; } - template <unsigned direction> + template <unsigned n, unsigned direction> image2d<value::label_8> project2_histo(const image3d<unsigned>& histo, - const image3d<value::label_8>& label) + const image3d<value::label_8>& label, + const value::label_8 ambiguous_label) { image2d<value::label_8> result; if (0 == direction) // blue { - image2d<value::label_8> arg_max(histo.ncols(), histo.nslices()); + image2d<value::label_8> arg_max(histo.nrows(), histo.ncols()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nslices(); ++i) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - unsigned max = 0; // minimum as possible - signed pos = -1; + unsigned max = 0; // minimum as possible + def::coord pos = -1; - for (unsigned k = 0; k < histo.nrows(); ++k) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = blue; } } - opt::at(arg_max,i,j) = opt::at(label,i,j,pos); + if (-1 == pos) + opt::at(arg_max,red,green) = ambiguous_label; + else + opt::at(arg_max,red,green) = opt::at(label, pos, red, green); } result = arg_max; } else if (1 == direction) // red { - image2d<value::label_8> arg_max(histo.nrows(), histo.nslices()); + image2d<value::label_8> arg_max(histo.ncols(), histo.nslices()); - for (unsigned j = 0; j < histo.nslices(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.ncols(); ++k) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = red; } } - opt::at(arg_max,i,j) = opt::at(label,pos,i,j); + if (-1 == pos) + opt::at(arg_max,green,blue) = ambiguous_label; + else + opt::at(arg_max,green,blue) = opt::at(label, blue, pos, green); } result = arg_max; } else // 2 == direction // green { - image2d<value::label_8> arg_max(histo.nrows(), histo.ncols()); + image2d<value::label_8> arg_max(histo.nrows(), histo.nslices()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.nslices(); ++k) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = green; } } - opt::at(arg_max,i,j) = opt::at(label,i,pos,j); + if (-1 == pos) + opt::at(arg_max,red,blue) = ambiguous_label; + else + opt::at(arg_max,red,blue) = opt::at(label, blue, red, pos); } result = arg_max; @@ -262,83 +313,117 @@ namespace mln } + + // FIXME ... determine the color of each class. - template <unsigned direction> - image2d<value::rgb8> - project3_histo(const image3d<unsigned>& histo, - const image3d<value::label_8>& label) + // FIXME la palette est supposée en 8 bits + template <unsigned n, unsigned direction> + image2d< value::rgb8 > + project3_histo(const image3d<unsigned>& histo, + const image3d<value::label_8>& label, + const util::array<algebra::vec<3,float> >& pal, + const value::rgb8 ambiguous_color) { - image2d<value::rgb8> result; + image2d< value::rgb8 > result; if (0 == direction) // blue { - image2d<value::rgb8> arg_max(histo.ncols(), histo.nslices()); + image2d< value::rgb8 > arg_max(histo.nrows(), histo.ncols()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nslices(); ++i) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - unsigned max = 0; // minimum as possible - signed pos = -1; + unsigned max = 0; // minimum as possible + def::coord pos = -1; - for (unsigned k = 0; k < histo.nrows(); ++k) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = blue; } } - opt::at(arg_max,i,j) = value::rgb8(i,j,pos); + if (-1 == pos) + opt::at(arg_max,red,green) = ambiguous_color; + else + { + value::int_u8 r = pal[opt::at(label,pos,red,green)][0]; + value::int_u8 g = pal[opt::at(label,pos,red,green)][1]; + value::int_u8 b = pal[opt::at(label,pos,red,green)][2]; + value::rgb8 color(r,g,b); + + opt::at(arg_max,red,green) = color; + } } result = arg_max; } else if (1 == direction) // red { - image2d<value::rgb8> arg_max(histo.nrows(), histo.nslices()); + image2d< value::rgb8 > arg_max(histo.ncols(), histo.nslices()); - for (unsigned j = 0; j < histo.nslices(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.ncols(); ++k) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = red; } } - opt::at(arg_max,i,j) = value::rgb8(pos,i,j); + if (-1 == pos) + opt::at(arg_max,green,blue) = ambiguous_color; + else + { + value::int_u8 r = pal[opt::at(label,blue,pos,green)][0]; + value::int_u8 g = pal[opt::at(label,blue,pos,green)][1]; + value::int_u8 b = pal[opt::at(label,blue,pos,green)][2]; + value::rgb8 color(r,g,b); + + opt::at(arg_max,green,blue) = color; + } } result = arg_max; } else // 2 == direction // green { - image2d<value::rgb8> arg_max(histo.nrows(), histo.ncols()); + image2d< value::rgb8 > arg_max(histo.nrows(), histo.nslices()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.nslices(); ++k) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = green; } } - // FIXME ... how to fix the n of rgb - opt::at(arg_max,i,j) = value::rgb8(i,pos,j); + if (-1 == pos) + opt::at(arg_max,red,blue) = ambiguous_color; + else + { + value::int_u8 r = pal[opt::at(label,blue,red,pos)][0]; + value::int_u8 g = pal[opt::at(label,blue,red,pos)][1]; + value::int_u8 b = pal[opt::at(label,blue,red,pos)][2]; + value::rgb8 color(r,g,b); + + opt::at(arg_max,red,blue) = color; + } } result = arg_max; @@ -347,81 +432,91 @@ namespace mln return result; } - template <unsigned direction> - image2d<value::rgb8> - project3_histo(const image3d<unsigned>& histo) + + template <unsigned n, unsigned direction> + image2d< value::rgb<n> > + project3_histo(const image3d<unsigned>& histo, + const value::rgb<n> ambiguous_color) { - image2d<value::rgb8> result; + image2d< value::rgb<n> > result; if (0 == direction) // blue { - image2d<value::rgb8> arg_max(histo.ncols(), histo.nslices()); + image2d< value::rgb<n> > arg_max(histo.nrows(), histo.ncols()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nslices(); ++i) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - unsigned max = 0; // minimum as possible - signed pos = -1; + unsigned max = 0; // minimum as possible + def::coord pos = -1; - for (unsigned k = 0; k < histo.nrows(); ++k) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = blue; } } - opt::at(arg_max,i,j) = value::rgb8(i,j,pos); + if (-1 == pos) + opt::at(arg_max,red,green) = ambiguous_color; + else + opt::at(arg_max,red,green) = value::rgb<n>(red,green,pos); } result = arg_max; } else if (1 == direction) // red { - image2d<value::rgb8> arg_max(histo.nrows(), histo.nslices()); + image2d< value::rgb<n> > arg_max(histo.ncols(), histo.nslices()); - for (unsigned j = 0; j < histo.nslices(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.ncols(); ++k) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = red; } } - opt::at(arg_max,i,j) = value::rgb8(pos,i,j); + if (-1 == pos) + opt::at(arg_max,green,blue) = ambiguous_color; + else + opt::at(arg_max,green,blue) = value::rgb<n>(pos,green,blue);; } result = arg_max; } else // 2 == direction // green { - image2d<value::rgb8> arg_max(histo.nrows(), histo.ncols()); + image2d< value::rgb<n> > arg_max(histo.nrows(), histo.nslices()); - for (unsigned j = 0; j < histo.ncols(); ++j) - for (unsigned i = 0; i < histo.nrows(); ++i) + for (def::coord blue = 0; blue < (signed)histo.nslices(); ++blue) + for (def::coord red = 0; red < (signed)histo.nrows(); ++red) { unsigned max = 0; // minimum as possible signed pos = -1; - for (unsigned k = 0; k < histo.nslices(); ++k) + for (def::coord green = 0; green < (signed)histo.ncols(); ++green) { - if (max <= opt::at(histo,i,j,k)) + if (max < opt::at(histo,blue,red,green)) { - max = opt::at(histo,i,j,k); - pos = k; + max = opt::at(histo,blue,red,green); + pos = green; } } - // FIXME ... how to fix the n of rgb - opt::at(arg_max,i,j) = value::rgb8(i,pos,j); + if (-1 == pos) + opt::at(arg_max,red,blue) = ambiguous_color; + else + opt::at(arg_max,red,blue) = value::rgb<n>(red,pos,blue); } result = arg_max; -- 1.5.6.5

14 years, 2 months

last-svn-commit-29-ga85f46a Delete BUG image file in milena/img.

by Yann Jacquelet

* milena/img/BUG_lean_ascii.pgm.gz: Delete this file. --- milena/img/BUG_lean_ascii.pgm.gz | Bin 75726 -> 0 bytes 1 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 milena/img/BUG_lean_ascii.pgm.gz diff --git a/milena/img/BUG_lean_ascii.pgm.gz b/milena/img/BUG_lean_ascii.pgm.gz deleted file mode 100644 index 7ba3e9d1b02e3a41704448faf72a6c7afe2e8259..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 75726 zcmV)EK)}BriwFqaeZols1430tUu<PzZeL+@V`*tFaA$1*r2R{8CDHQ)2<_`rSYiX4 zne%?k4k5%2Mk6$0J3=GD1`r@VKV2syGUJr}-^_1uWw+byzP`6kR%T>8{`>O$uYUe_ z|HJ?OfBS#_Km8B?-OvB!fBg9$|L6br|L6by|NMXa)c+=@|LN!FfB2vOfB)T2F8^)W z|Ltx+|Lgz$|M-9S`G5US|KI=X|LrILH|2lxls<m`J^p>{Pp<PP*YWRB@p1ioG&~9( z|GrlCzmM7eT&Df`d@cPv_7}?@?cX=Fe?O>ad$fPw)ZVXG`=R;YpY!_p+3DwJ{d<h| z?@@l9pOOE4vVVT&zmK!x@$WC^!pHINvGK9v>jNL{dmj77f8Vq!9?L$y|J!~){`=wk z-#_~Ivb>+?RQ`SZ_w3U9^?4poPV@XlPRYOc{-d1UAF%Owzo(rCe}Mg)dOm;b3kiQw z{V(im?>Bb*Ipgz+kKO<L*#GeU1^>75bN}G%&nTyXf0+D%Unk2yuK31;-yQ!bXZiJ( 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14 years, 2 months

last-svn-commit-30-gacb2f3d Turn around Millet 2008 hsv descriptors.

by Yann Jacquelet

* green/demo/annotating/hsv: New directory. * green/demo/annotating/hsv/Makefile.am: New Makefile. --- .../regional_maxima => annotating/hsv}/Makefile.am | 6 +- milena/sandbox/green/demo/annotating/hsv/hsv.cc | 607 ++++++++++++++++++++ .../sandbox/green/ChangeLog | 0 3 files changed, 611 insertions(+), 2 deletions(-) copy milena/sandbox/green/demo/{labeling/regional_maxima => annotating/hsv}/Makefile.am (94%) create mode 100644 milena/sandbox/green/demo/annotating/hsv/hsv.cc copy milena/doc/outputs/accu-right-instanciation.txt => scribo/sandbox/green/ChangeLog (100%) diff --git a/milena/sandbox/green/demo/labeling/regional_maxima/Makefile.am b/milena/sandbox/green/demo/annotating/hsv/Makefile.am similarity index 94% copy from milena/sandbox/green/demo/labeling/regional_maxima/Makefile.am copy to milena/sandbox/green/demo/annotating/hsv/Makefile.am index 1dd1cfb..a5d4fff 100644 --- a/milena/sandbox/green/demo/labeling/regional_maxima/Makefile.am +++ b/milena/sandbox/green/demo/annotating/hsv/Makefile.am @@ -6,8 +6,10 @@ # TOOLS # ######### -INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green -CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +INCLUDES1= -I$(HOME)/git/olena/milena/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -DNDEBUG -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) #CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) #CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) ECHO= echo diff --git a/milena/sandbox/green/demo/annotating/hsv/hsv.cc b/milena/sandbox/green/demo/annotating/hsv/hsv.cc new file mode 100644 index 0000000..a61a5de --- /dev/null +++ b/milena/sandbox/green/demo/annotating/hsv/hsv.cc @@ -0,0 +1,607 @@ +// Test de l'opérateur de Millet TSVal (HSV) +// +// Val = max(R,G,B) +// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B) +// si R = max(R,G,B) alors Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))] +// si G = max(R,G,B) alors Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))] +// si B = max(R,G,B) alors Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))] + + +#include <iostream> +#include <fstream> + +#include <mln/accu/max_site.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> +#include <mln/core/concept/image.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> + +#include <mln/debug/println.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/fun/v2v/rgb_to_hsv.hh> +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> +#include <mln/fun/v2v/achromatism.hh> +#include <mln/fun/v2v/hue_concentration.hh> +#include <mln/fun/p2b/component_equals.hh> +#include <mln/fun/p2b/achromatic.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/img_path.hh> + +#include <mln/io/plot/save_image_sh.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/pw/cst.hh> +#include <mln/pw/value.hh> +//#include <mln/trace/quiet.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> +#include <mln/value/hsv.hh> + + +mln::value::rgb8 label_color(const mln::value::rgb8 rgb) +{ + mln::value::hsv_f hsv = mln::fun::v2v::f_rgb_to_hsv_f(rgb); + + mln::value::rgb8 result; + + // Is it a gray level ? + if (0 == hsv.sat()) + { + // which result one ? + if (82 > hsv.sat()) + result = mln::literal::black; + else if (179 > hsv.sat()) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + } + // Is it a true result color ? + else if (14 > hsv.hue()) + result = mln::literal::red; + else if (29 > hsv.hue()) + { + // Is is brown or orange ? + unsigned dist_orange = mln::math::abs(hsv.sat() - 184) + + mln::math::abs(hsv.val() - 65); + + unsigned dist_brown = mln::math::abs(hsv.sat() - 255) + + mln::math::abs(hsv.val() - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else if (45 > hsv.hue()) + { + // Is it green or yellow ? + if (80 > hsv.val()) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else if (113 > hsv.hue()) + result = mln::literal::green; + else if (149 > hsv.hue()) + result = mln::literal::cyan; + else if (205 > hsv.hue()) + result = mln::literal::blue; + else if (235 > hsv.hue()) + result = mln::literal::violet; + else if (242 > hsv.hue()) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +//unsigned count_histo(const mln::image1d<unsigned>& img) +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +// calcul de contribution +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + + +// unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, +// const char *name_histo, +// const char *name_image) +// { +// typedef mln::point1d t_point1d; +// typedef mln::value::rgb8 t_rgb8; +// typedef mln::value::int_u8 t_int_u8; +// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +// typedef mln::image2d<t_int_u8> t_image2d_int_u8; +// typedef mln::image1d<unsigned> t_histo1d; +// typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; +// typedef mln::accu::meta::stat::histo1d t_histo1d_fun; +// typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + +// t_histo1d histo; + +// std::cout << "histo : " << name_histo << std::endl; +// std::cout << "image : " << name_image << std::endl; + +// histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + +// mln::io::pgm::save(input_int_u8, name_image); +// mln::io::plot::save_image_sh(histo, name_histo); +// mln::debug::println(histo); + +// // Find the peak of the histogram +// unsigned v_max = mln::opt::at(histo, 0); +// short p_max = 0; + +// mln_piter_(t_histo1d) p(histo.domain()); + +// for_all(p) +// { +// if (v_max < histo(p)) +// { +// v_max = histo(p); +// p_max = p.ind(); +// } +// } + +// // Compute the specific stddev + +// float stddev_low = 0.0; +// float stddev_up = 0.0; +// float stddev = 0.0; + +// if (250 > p_max) +// for (short i = p_max+1; i < p_max+6; ++i) +// stddev_up += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// if (5 < p_max) +// for (short i = p_max-1; i > p_max-6; --i) +// stddev_low += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : +// (stddev_low + stddev_up)/2; + +// std::cout << "max_site : " << p_max << std::endl; +// std::cout << "h(max_site) : " << v_max << std::endl; +// std::cout << "stddev_up : " << stddev_up << std::endl; +// std::cout << "stddev_low : " << stddev_low << std::endl; +// std::cout << "stddev : " << stddev << std::endl; + +// return 0; +// } + + +// ------------------------------------- +// input image <name>.ppm +// map <name>-<map>.pgm +// thresholded map <name>-<map>.pbm +// histogram <name>-<map>.sh +// decision <name>-<map>.txt +// ------------------------------------- + +// Achromatism <name>-achromatism.pgm + +// call achromatism(input_rgb8, 7, 99.0) +void achromatism(mln::image2d<mln::value::rgb8> input_rgb8, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::int_u8> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + view = mln::data::stretch(mln::value::int_u8(), map); + mask = mln::binarization::threshold(map, threshold); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + map | (mln::pw::value(mask) == true)); + cnt1 = count_histo(histo); + cnt2 = mln::geom::nsites(input_rgb8); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + + std::ofstream txt_stream("achromatism.txt"); + txt_stream << "Achromatism" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + txt_stream << "Percentage : " << prop << std::endl; + txt_stream << "Image achromatique : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "achromatism.pgm"); + mln::io::plot::save_image_sh(histo, "achromatism.sh"); + mln::io::pbm::save(mask, "achromatism.pbm"); +} + +// call low_saturation(input_rgb8, achromatism_mask, 100, 95.0) +void low_saturation(mln::image2d<mln::value::hsv_f> input_hsvf, + mln::image2d<bool> achromatism_mask, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + + mln::image2d<t_sat> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_hsvf, t_component_s()); + view = mln::data::stretch(mln::value::int_u8(), map); +// where is histo ?? + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo_s | mln::box1d(mln::point1d(0),mln::point1d(100))); + + cnt2= mln::geom::nsites(achromatic | (mln::pw::value(achromatic)==false)); + + + + std::ofstream txt_stream("achromatism.txt"); + txt_stream << "Saturation" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre p faiblement saturés : " << cnt1 << std::endl; + txt_stream << "Pourcentage : " << prop << std::endl; + txt_stream << "Image faiblement saturé : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "achromatism.pgm"); + mln::io::plot::save_image_sh(histo, "achromatism.sh"); + mln::io::pbm::save(mask, "achromatism.pbm"); +} + +/* +// COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00032c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00042c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00076c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00142c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00215c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00228c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00234c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00248c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00252c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00253c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00255c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00259c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00271c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00290c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00293c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00304c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00307c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00376c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00411c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00419c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00447c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00498c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00510c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00550c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00573c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00589c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00592c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00597c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00599c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00600c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00031c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00034c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00043c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00063c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00065c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00072c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00081c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00083c_20p.ppm"); + +// BLACK AND WHITE + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00329c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00036c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00037c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00039c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00040c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00049c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00055c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00057c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00068c_20p.ppm"); + + +// A LITTLE BIT OF COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00262c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00263c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00311c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00319c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00440c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00608c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00630c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00631c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00028c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00046c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00073c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00089c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00090c_20p.ppm"); +*/ + +// To DO mettre le seuil d'achromaticité en paramètre +// TO DO inverser les couleurs sur les masques +int main() +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::h_type t_hue; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::value::hsv_f::v_type t_val; + typedef mln::image2d<t_hue> t_image2d_hue; + typedef mln::image2d<t_sat> t_image2d_sat; + typedef mln::image2d<t_val> t_image2d_val; + typedef mln::image2d<t_hsvf> t_image2d_hsvf; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<bool> t_mask; + typedef mln::fun::v2v::f_rgb_to_hsv_f_t t_rgb8_to_hsv; + typedef mln::accu::math::count<t_hsvf> t_count; + typedef mln::fun::v2v::component<t_hsvf,0> t_component_h; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + typedef mln::fun::v2v::component<t_hsvf,2> t_component_v; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,0> t_component_eq0; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,1> t_component_eq1; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,2> t_component_eq2; + typedef mln::fun::p2b::achromatic<t_rgb8> t_achromatic; + + t_image2d_rgb8 input_rgb8; + t_image2d_hsvf input_hsvf; + + t_mask achromatic; + t_mask low_saturation; + + t_image2d_float achromatism1; + t_image2d_int_u8 achromatism2; + t_image2d_float hue_concentration1; + t_image2d_int_u8 hue_concentration2; + + t_image2d_hue input_h; + t_image2d_hue input_h2; + t_image2d_sat input_s; + t_image2d_val input_v; + + t_image2d_int_u8 input_h8; + t_image2d_int_u8 input_s8; + t_image2d_int_u8 input_v8; + + t_histo1d histo_h; + t_histo1d histo_s; + t_histo1d histo_v; + + unsigned cnt1; + unsigned cnt2; + float percentage; + bool result; + + + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; +// mln::io::ppm::load(input_rgb8, ANNOTATING_1_BILL_IMG_PATH"/bill03.ppm"); +// mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + + + achromatism(input_rgb8,7,99.0); + exit(-1); + // REPERAGE DES PIXELS ACHROMATICS + std::cout << "Init achromatic mask ..." << std::endl; + initialize(achromatic, input_rgb8); + mln::data::fill(achromatic, false); + mln::data::fill((achromatic | t_achromatic(input_rgb8, 0.03)).rw(), true); + + mln::io::pbm::save(achromatic, "achromatic.pbm"); + + std::cout << "Achieve canal forking ..." << std::endl; + input_hsvf = mln::data::transform(input_rgb8, t_rgb8_to_hsv()); + + input_h = mln::data::transform(input_hsvf, t_component_h()); + input_s = mln::data::transform(input_hsvf, t_component_s()); + input_v = mln::data::transform(input_hsvf, t_component_v()); + + // quid des achromatiques ??? + input_h8 = mln::data::stretch(t_int_u8(), input_h); + input_s8 = mln::data::stretch(t_int_u8(), input_s); + input_v8 = mln::data::stretch(t_int_u8(), input_v); + + // REPERAGE DES PIXELS ACHROMATICS + std::cout << "Init low saturation mask ..." << std::endl; + initialize(low_saturation, input_s8); + mln::data::fill(low_saturation, false); + mln::data::fill((low_saturation|(mln::pw::value(input_s8) < + mln::pw::cst(100u))).rw(), true); + + mln::io::pbm::save(low_saturation, "low_saturation.pbm"); + + std::cout << "Compute histograms ..." << std::endl; + histo_h = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_h8|(mln::pw::value(achromatic)==false)); + + histo_s = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_s8|(mln::pw::value(achromatic)==false)); + + histo_v = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_v8|(mln::pw::value(achromatic)==false)); + + + // etude des cartes + + hue_concentration1=mln::data::transform(input_h, + mln::fun::v2v::hue_concentration(histo_h)); + achromatism1=mln::data::transform(input_rgb8,mln::fun::v2v::achromatism()); + + hue_concentration2= mln::data::stretch(t_int_u8(), hue_concentration1); + achromatism2= mln::data::stretch(t_int_u8(), achromatism1); + + mln::io::pgm::save(achromatism2, "achromatism_map.pgm"); + mln::io::pgm::save(hue_concentration2, "hue_concentration_map.pgm"); + mln::io::pgm::save(input_s8, "saturation_map.pgm"); + +// cnt1 = mln::data::compute(t_count(), +// (input_hsvf|t_component_eq0(input_hsvf,-1)).rw()); + + + // (I) ACHROMATISME + std::cout << "Achromatism" << std::endl; + cnt1 = count_histo(histo_h); + cnt2 = mln::geom::nsites(input_h); + + percentage = (100.0 * (cnt2 - cnt1) / cnt2); + result = percentage > 99.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image achromatique : " << result << std::endl; + std::cout << std::endl; + + // (II) FAIBLE SATURATION + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo_s | mln::box1d(mln::point1d(0),mln::point1d(100))); + + cnt2= mln::geom::nsites(achromatic | (mln::pw::value(achromatic)==false)); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre p faiblement saturés : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image faiblement saturé : " << result << std::endl; + std::cout << std::endl; + + // (III) DOMINANCE DE LA TEINTE + // et peut être 50% des pixels faiblement saturées + + mln::debug::println(histo_h); + unsigned peak = peak_histo(histo_h); + + cnt1 = count_histo(histo_h | mln::box1d(mln::point1d(peak-20), + mln::point1d(peak+20))); + + cnt2= count_histo(histo_h); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Position du pic : " << peak << std::endl; + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels proches pic : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image fortement teintée : " << result << std::endl; + std::cout << std::endl; + + + + // Autre possibilité + // calculer le maximum de la teinte et regarder si le pourcentage pixels dont + // la distance est inférieure à 20 > 95% + // alors +} + +// 3 cartes +// 1) carte d'achromaticité d = max(|r-g|,|r-b|,|g-b|) +// 2) carte de saturation +// 3) carte d'éloignement par rapport au pic de teinte + + +// QUELS SONT LES CHARACTERISTIQUES HSV DE LA BASE ICDAR ? +// FAIBLE SATURATION DES IMAGES ? +// DOMINANCE DES TEINTES ? +// ACHROMATISME ? diff --git a/milena/doc/outputs/accu-right-instanciation.txt b/scribo/sandbox/green/ChangeLog similarity index 100% copy from milena/doc/outputs/accu-right-instanciation.txt copy to scribo/sandbox/green/ChangeLog -- 1.5.6.5

14 years, 2 months

last-svn-commit-48-g6ada0c4 Import annotating sources from milena green's sandbox.

by Yann Jacquelet

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

14 years, 2 months

last-svn-commit-22-ga3cbabf Write the opening volume thresholds for the scribo image mp00082c.ppm.

by Yann Jacquelet

* green/demo/labeling/regional_maxima/thresholds.txt: New documentation. --- milena/sandbox/ChangeLog | 6 ++++ .../demo/labeling/regional_maxima/thresholds.txt | 27 ++++++++++++++++++++ 2 files changed, 33 insertions(+), 0 deletions(-) create mode 100644 milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 48be011..0947048 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,5 +1,11 @@ 2009-12-23 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Write the opening volume thresholds for the scribo image mp00082c.ppm. + + * green/demo/labeling/regional_maxima/thresholds.txt: New documentation. + +2009-12-23 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Experiment various quantifications on regional maxima labeling. * green/doc/regional_maxima/cmp_quant/h0_input.pgm.gz: New histogram. diff --git a/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt b/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt new file mode 100644 index 0000000..ddf5ca7 --- /dev/null +++ b/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt @@ -0,0 +1,27 @@ +image = 1169 x 1567 = 1831823 + + +% image | min_volume +----------------------- + 0.05 % | 1000.00 + 1.00 % | 18318.23 + 5.00 % | 91591.15 + 10.00 % | 183182.30 + 15.00 % | 274773.45 + 20.00 % | 366364.60 + 25.00 % | 457955.75 + 30.00 % | 549546.90 + 35.00 % | 641138.05 + 40.00 % | 732729.20 + 45.00 % | 824320.35 + 50.00 % | 915911.50 + 55.00 % | 1007502.65 + 60.00 % | 1099093.80 + 65.00 % | 1190684.95 + 70.00 % | 1282276.10 + 75.00 % | 1373867.25 + 80.00 % | 1465458.40 + 85.00 % | 1557049.55 + 90.00 % | 1648640.70 + 95.00 % | 1740231.85 +100.00 % | 1831823.00 -- 1.5.6.5

14 years, 2 months

last-svn-commit-23-gfdf0f76 Split the regional maxima binary in small atomic binaries.

by Yann Jacquelet

* green/tools/annotating/histo: New directory. * green/tools/annotating/histo/Makefile.am: New Makefile. * green/tools/annotating/histo/histo.cc: New source file. * green/tools/annotating/opening: New directory. * green/tools/annotating/opening/Makefile.am: New Makefile. * green/tools/annotating/opening/opening.cc: New source file. * green/tools/annotating/regmax: New directory. * green/tools/annotating/regmax/Makefile.am: New Makefile. * green/tools/annotating/regmax/regmax.cc: New source file. --- milena/sandbox/ChangeLog | 14 ++ .../annotating/histo}/Makefile.am | 9 +- .../sandbox/green/tools/annotating/histo/histo.cc | 121 ++++++++++++++++++ .../annotating/opening}/Makefile.am | 9 +- .../green/tools/annotating/opening/opening.cc | 79 ++++++++++++ .../annotating/regmax}/Makefile.am | 9 +- .../green/tools/annotating/regmax/regmax.cc | 133 ++++++++++++++++++++ 7 files changed, 359 insertions(+), 15 deletions(-) copy milena/sandbox/green/{exp/annotating/nb_color => tools/annotating/histo}/Makefile.am (96%) create mode 100644 milena/sandbox/green/tools/annotating/histo/histo.cc copy milena/sandbox/green/{exp/annotating/nb_color => tools/annotating/opening}/Makefile.am (96%) create mode 100644 milena/sandbox/green/tools/annotating/opening/opening.cc copy milena/sandbox/green/{exp/annotating/nb_color => tools/annotating/regmax}/Makefile.am (96%) create mode 100644 milena/sandbox/green/tools/annotating/regmax/regmax.cc diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 0947048..cbd9cdc 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,3 +1,17 @@ +2010-01-05 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Split the regional maxima binary in small atomic binaries. + + * green/tools/annotating/histo: New directory. + * green/tools/annotating/histo/Makefile.am: New Makefile. + * green/tools/annotating/histo/histo.cc: New source file. + * green/tools/annotating/opening: New directory. + * green/tools/annotating/opening/Makefile.am: New Makefile. + * green/tools/annotating/opening/opening.cc: New source file. + * green/tools/annotating/regmax: New directory. + * green/tools/annotating/regmax/Makefile.am: New Makefile. + * green/tools/annotating/regmax/regmax.cc: New source file. + 2009-12-23 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Write the opening volume thresholds for the scribo image mp00082c.ppm. diff --git a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am b/milena/sandbox/green/tools/annotating/histo/Makefile.am similarity index 96% copy from milena/sandbox/green/exp/annotating/nb_color/Makefile.am copy to milena/sandbox/green/tools/annotating/histo/Makefile.am index 8e204c6..8cd7511 100644 --- a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am +++ b/milena/sandbox/green/tools/annotating/histo/Makefile.am @@ -6,7 +6,6 @@ # TOOLS # ######### -LOADLIBES= -lboost_filesystem INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green #CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) #CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) @@ -16,17 +15,17 @@ RM= rm MKDIR= mkdir -p CP= cp -SOURCE_PATTERN= green/exp -BUILD__PATTERN= green/build/exp +SOURCE_PATTERN= green/tools +BUILD__PATTERN= green/build/tools ifeq ($(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)/ +BUILD__DIR= $(PWD) else # Case where make is done from source directory. -SOURCE_DIR= $(PWD)/ +SOURCE_DIR= $(PWD) BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) endif diff --git a/milena/sandbox/green/tools/annotating/histo/histo.cc b/milena/sandbox/green/tools/annotating/histo/histo.cc new file mode 100644 index 0000000..ab0b8af --- /dev/null +++ b/milena/sandbox/green/tools/annotating/histo/histo.cc @@ -0,0 +1,121 @@ +// TOOLS ==> Color histogram + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/display/display_histo.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/io/dump/save.hh> +#include <mln/io/pbm/load.hh> +#include <mln/io/pbm/save.hh> +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/opt/at.hh> + +#include <mln/pw/value.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/rgb.hh> + + +template <unsigned n> +void mk_histo(const std::string& input, + const std::string& output, + const std::string& histo, + const std::string& mask) +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + // START OF IMAGE PROCESSING CHAIN + t_image2d_rgb8 i0_input; // input rgb8 + t_image2d_rgbn i1_input; // input rgbn + t_image2d_bool m0_input; // mask input + t_histo3d h1_input; // histo input + t_image2d_int_u8 p1_histo; // histo proj + + mln::io::ppm::load(i0_input, input.c_str()); + i1_input = mln::data::transform(i0_input, t_rgb8_to_rgbn()); + + if (0 < mask.size()) + { + mln::io::pbm::load(m0_input, mask.c_str()); + h1_input = mln::data::compute(t_histo3d_fun(), + (i1_input | mln::pw::value(m0_input)).rw()); + } + else + { + h1_input = mln::data::compute(t_histo3d_fun(), i1_input); + } + // END OF IMAGE PROCESSING CHAIN + + // BEGIN DUMPING + p1_histo = mln::display::display_histo3d_unsigned(h1_input); + mln::io::dump::save(h1_input, histo.c_str()); + mln::io::pgm::save(p1_histo, output.c_str()); + // END DUMPING +} + + +void usage() +{ + std::cout << std::endl; + std::cout << "histo input.ppm q out.ppm histo.dump [msk.pbm]" << std::endl; + std::cout << "where" << std::endl; + std::cout << "input.ppm is the 8 bits color ppm image" << std::endl; + std::cout << "q is the degree of quanification {2,3,4,5,6,7,8}" << std::endl; + std::cout << "out.pgm is the r/g projection of the histogram" << std::endl; + std::cout << "out.dump is the quantified color histogram" << std::endl; + std::cout << "msk.pbm is the mask which select the pixels" << std::endl; + std::cout << std::endl; +} + +int main(int argc, char* args[]) +{ + if (5 == argc || 6 == argc) + { + const std::string input(args[1]); + const char q = args[2][0]; + const std::string output(args[3]); + const std::string histo(args[4]); + const std::string mask(6 == argc? args[5] : ""); + + switch(q) + { + case '2': mk_histo<2>(input, output, histo, mask); break; + case '3': mk_histo<3>(input, output, histo, mask); break; + case '4': mk_histo<4>(input, output, histo, mask); break; + case '5': mk_histo<5>(input, output, histo, mask); break; + case '6': mk_histo<6>(input, output, histo, mask); break; + case '7': mk_histo<7>(input, output, histo, mask); break; + case '8': mk_histo<8>(input, output, histo, mask); break; + default: usage(); break; + } + } + else + usage(); + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am b/milena/sandbox/green/tools/annotating/opening/Makefile.am similarity index 96% copy from milena/sandbox/green/exp/annotating/nb_color/Makefile.am copy to milena/sandbox/green/tools/annotating/opening/Makefile.am index 8e204c6..8cd7511 100644 --- a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am +++ b/milena/sandbox/green/tools/annotating/opening/Makefile.am @@ -6,7 +6,6 @@ # TOOLS # ######### -LOADLIBES= -lboost_filesystem INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green #CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) #CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) @@ -16,17 +15,17 @@ RM= rm MKDIR= mkdir -p CP= cp -SOURCE_PATTERN= green/exp -BUILD__PATTERN= green/build/exp +SOURCE_PATTERN= green/tools +BUILD__PATTERN= green/build/tools ifeq ($(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)/ +BUILD__DIR= $(PWD) else # Case where make is done from source directory. -SOURCE_DIR= $(PWD)/ +SOURCE_DIR= $(PWD) BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) endif diff --git a/milena/sandbox/green/tools/annotating/opening/opening.cc b/milena/sandbox/green/tools/annotating/opening/opening.cc new file mode 100644 index 0000000..3e1dbf2 --- /dev/null +++ b/milena/sandbox/green/tools/annotating/opening/opening.cc @@ -0,0 +1,79 @@ +// TOOLS ==> histogram filtering + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/macros.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/data/compute.hh> + +#include <mln/display/display_histo.hh> + +#include <mln/io/dump/load.hh> +#include <mln/io/dump/save.hh> +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> + +#include <mln/morpho/opening/volume.hh> + +#include <mln/value/int_u8.hh> + +void mk_opening(const std::string& input, + const unsigned min_vol, + const std::string& output, + const std::string& opened) +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + // START OF IMAGE PROCESSING CHAIN + t_histo3d h1_input; // histo input + t_histo3d h2_input; // histo input + t_image2d_int_u8 p1_histo; // histo proj + + mln::io::dump::load(h1_input, input.c_str()); + h2_input = mln::morpho::opening::volume(h1_input, mln::c6(), min_vol); + // END OF IMAGE PROCESSING CHAIN + + // BEGIN DUMPING + p1_histo = mln::display::display_histo3d_unsigned(h2_input); + mln::io::dump::save(h2_input, opened.c_str()); + mln::io::pgm::save(p1_histo, output.c_str()); + // END DUMPING +} + + +void usage() +{ + std::cout << std::endl; + std::cout << "opening input.dump v out.dump out.ppm" << std::endl; + std::cout << "where" << std::endl; + std::cout << "input.dump is the 3d color input histo" << std::endl; + std::cout << "v is the minimum size of each composant" << std::endl; + std::cout << "out.pgm is the r/g proj of the opened histogram" << std::endl; + std::cout << "out.dump is the opened histogram" << std::endl; + std::cout << std::endl; +} + +int main(int argc, char* args[]) +{ + if (5 == argc) + { + const std::string input(args[1]); + const unsigned min_vol = atoi(args[2]); + const std::string output(args[3]); + const std::string opened(args[4]); + + mk_opening(input, min_vol, output, opened); + } + else + usage(); + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am b/milena/sandbox/green/tools/annotating/regmax/Makefile.am similarity index 96% copy from milena/sandbox/green/exp/annotating/nb_color/Makefile.am copy to milena/sandbox/green/tools/annotating/regmax/Makefile.am index 8e204c6..8cd7511 100644 --- a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am +++ b/milena/sandbox/green/tools/annotating/regmax/Makefile.am @@ -6,7 +6,6 @@ # TOOLS # ######### -LOADLIBES= -lboost_filesystem INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green #CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) #CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) @@ -16,17 +15,17 @@ RM= rm MKDIR= mkdir -p CP= cp -SOURCE_PATTERN= green/exp -BUILD__PATTERN= green/build/exp +SOURCE_PATTERN= green/tools +BUILD__PATTERN= green/build/tools ifeq ($(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)/ +BUILD__DIR= $(PWD) else # Case where make is done from source directory. -SOURCE_DIR= $(PWD)/ +SOURCE_DIR= $(PWD) BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) endif diff --git a/milena/sandbox/green/tools/annotating/regmax/regmax.cc b/milena/sandbox/green/tools/annotating/regmax/regmax.cc new file mode 100644 index 0000000..2079bc4 --- /dev/null +++ b/milena/sandbox/green/tools/annotating/regmax/regmax.cc @@ -0,0 +1,133 @@ +// TOOLS ==> regmax on histo + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/macros.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/data/compute.hh> + +#include <mln/debug/println.hh> +#include <mln/display/display_histo.hh> + +#include <mln/io/dump/load.hh> +#include <mln/io/dump/save.hh> +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/labeling/regional_maxima.hh> + +#include <mln/morpho/opening/volume.hh> + +#include <mln/value/label_8.hh> +#include <mln/value/int_u8.hh> +#include <mln/value/rgb8.hh> + +template <unsigned n> +struct t_labeling_rgbn : mln::Function_v2v< t_labeling_rgbn<n> > +{ + typedef mln::value::rgb<n> t_rgbn; + typedef mln::value::label_8 t_lbl8; + typedef t_rgbn argument; + typedef t_lbl8 result; + typedef mln::image3d<t_lbl8> t_label; + + const t_label& _label; + + t_labeling_rgbn(const t_label& label) : _label(label) {} + + result operator()(const argument& c) const + { + t_lbl8 tmp = mln::opt::at(_label, c.blue(), c.red(), c.green()); + + return tmp; + } +}; + +void mk_regmax(const std::string& input, + const std::string& quant, + const std::string& histo, + const std::string& label, + const std::string& output) +{ + typedef mln::value::label_8 t_lbl8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgbn t_rgbn; + typedef mln::value::int_u8 t_int_u8; + typedef mln::algebra::vec<3,float> t_v3f; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image3d<t_lbl8> t_image3d_lbl8; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::stat::mean<t_v3f,t_v3f,t_v3f> t_mean; + typedef mln::util::array<t_v3f> t_mean_array; + + t_image2d_rgb8 i0_input; // input img + t_image2d_rgbn i1_input; // quant img + t_histo3d h2_input; // opened histo +// t_image2d_int_u8 p2_label; // histo proj + t_image2d_lbl8 p2_label; // histo proj +// t_image2d_rgb8 p2_label; // histo proj + t_image3d_lbl8 l2_histo; // label histo + t_mean_array m2_label; // palette + + t_lbl8 n_lbl; // nb class + + // BEGIN LOADING + mln::io::ppm::load(i0_input, input.c_str()); + mln::io::ppm::load(i1_input, quant.c_str()); + mln::io::dump::load(h2_input, histo.c_str()); + // END LOADING + + // BEGIN IMAGE PROCESSING + l2_histo = mln::labeling::regional_maxima(h2_input, mln::c6(), n_lbl); + // END IMAGE PROCESSING + + // BEGIN SAVING + mln::debug::println(h2_input); + mln::io::dump::save(l2_histo, labeled.c_str()); + + l2_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l2_histo)); + m2_label = mln::labeling::compute(t_mean(), i0_input, l2_input, n_labels); + p2_label =mln::display::display3_histo3d_unsigned(h2_input,l2_histo,m2_label); + +// mln::io::pgm::save(p2_label, output.c_str()); + mln::io::ppm::save(p2_label, output.c_str()); + std::cout << "Nb classes : " << n_lbl << std::endl; + // END SAVING +} + + +void usage() +{ + std::cout << std::endl; + std::cout << "regmax input.dump out.dump out.ppm" << std::endl; + std::cout << "where" << std::endl; + std::cout << "input.dump is opened histo" << std::endl; + std::cout << "out.pgm is the r/g proj of the opened histogram" << std::endl; + std::cout << "out.dump is the labeled histogram" << std::endl; + std::cout << std::endl; +} + +int main(int argc, char* args[]) +{ + if (4 == argc) + { + const std::string input(args[1]); + const std::string output(args[2]); + const std::string labeled(args[3]); + + mk_regmax(input, output, labeled); + } + else + usage(); + + return 0; +} -- 1.5.6.5

14 years, 2 months

last-svn-commit-24-g40063b8 Build translation table between number of pixels and percentage of pixels in image for the scribo database.

by Yann Jacquelet

* green/demo/labeling/regional_maxima/threshold.txt: New translation table. --- milena/sandbox/ChangeLog | 8 ++++++++ .../demo/labeling/regional_maxima/thresholds.txt | 15 +++++++++++++++ 2 files changed, 23 insertions(+), 0 deletions(-) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index cbd9cdc..f34508b 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,5 +1,13 @@ 2010-01-05 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Build translation table between number of pixels and percentage of + pixels in image for the scribo database. + + * green/demo/labeling/regional_maxima/threshold.txt: New translation + table. + +2010-01-05 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Split the regional maxima binary in small atomic binaries. * green/tools/annotating/histo: New directory. diff --git a/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt b/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt index ddf5ca7..58f3e6a 100644 --- a/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt +++ b/milena/sandbox/green/demo/labeling/regional_maxima/thresholds.txt @@ -5,9 +5,24 @@ image = 1169 x 1567 = 1831823 ----------------------- 0.05 % | 1000.00 1.00 % | 18318.23 + 2.00 % | 36636.46 + 3.00 % | 54954.69 + 4.00 % | 73272.92 5.00 % | 91591.15 + 6.00 % | 109909.38 + 7.00 % | 128227.61 + 8.00 % | 146545.84 + 9.00 % | 164864.07 10.00 % | 183182.30 + 11.00 % | 201500.53 + 12.00 % | 219818.76 + 13.00 % | 238136.99 + 14.00 % | 256455.22 15.00 % | 274773.45 + 16.00 % | 293091.68 + 17.00 % | 311409.91 + 18.00 % | 329728.14 + 19.00 % | 348046.37 20.00 % | 366364.60 25.00 % | 457955.75 30.00 % | 549546.90 -- 1.5.6.5

14 years, 2 months

last-svn-commit-45-g400f1e8 Implement the kmean algorithh and start to optimize it.

by Yann Jacquelet

Implement the first version with vectors and matrices. * mln/clustering/k_mean.hh: New library component. * use/clustering/k_mean: New directory. * use/clustering/k_mean/Makefile.am: New makefile. * use/clustering/k_mean/k_mean.cc: New source file. * tests/clustering/k_mean: New directory. * tests/clustering/k_mean/Makefile.am: New makefile. * tests/clustering/k_mean/k_mean.cc: New source file. Implement the second version with image and working in 1d. * mln/clustering/kmean1d.hh: New library component. * use/clustering/kmean1d: New directory. * use/clustering/kmean1d/Makefile.am: New makefile. * use/clustering/kmean1d/kmean1d.cc: New source file. * demo/clustering/kmean1d: New directory. * demo/clustering/kmean1d/Makefile.am: New makefile. * demo/clustering/kmean1d/kmean1d.cc: New source file. Implement transformation between RG space and RGB space. * mln/fun/v2v/rg_to_rgb.hh: New library component. * use/fun/v2v/rg_to_rgb: New directory. * use/fun/v2v/rg_to_rgb/Makefile.am: New makefile. * use/fun/v2v/rg_to_rgb/rg_to_rgb.cc: New source file. Implement the third version working in 2d (r/g). * mln/clustering/kmean2d.hh: New library component. * use/clustering/kmean2d: New directory. * use/clustering/kmean2d/Makefile.am: New makefile. * use/clustering/kmean2d/kmean2d.cc: New source file. * demo/clustering/kmean2d: New directory. * demo/clustering/kmean2d/Makefile.am: New makefile. * demo/clustering/kmean2d/kmean2d.cc: New source file. Implement the fourth version working in 3d (rgb). * mln/clustering/kmean3d.hh: New library component. * use/clustering/kmean3d: New directory. * use/clustering/kmean3d/Makefile.am: New makefile. * use/clustering/kmean3d/kmean3d.cc: New source file. * demo/clustering/kmean3d: New directory. * demo/clustering/kmean3d/Makefile.am: New makefile. * demo/clustering/kmean3d/kmean3d.cc: New source file. Implement the fith version as a function (working in rgb space). * mln/clustering/kmean_rgb.hh: New library component. * use/clustering/kmean_rgb: New directory. * use/clustering/kmean_rgb/Makefile.am: New makefile. * use/clustering/kmean_rgb/kmean_rgb.cc: New source file. * demo/clustering/kmean_rgb: New directory. * demo/clustering/kmean_rgb/Makefile.am: New makefile. * demo/clustering/kmean_rgb/kmean_rgb.cc: New source file. Benchmark distance algorithm for the kmean algorithm. * bench/clustering/distance: New directory. * bench/clustering/distance/Makefile.am: New makefile. * bench/clustering/distance/distance.cc: New source file. --- scribo/sandbox/green/ChangeLog | 77 ++ scribo/sandbox/green/README.green | 150 ++++- .../green/bench/clustering/distance/Makefile.am | 153 ++++ .../green/bench/clustering/distance/distance.cc | 842 ++++++++++++++++++++ .../green/demo/clustering/kmean1d/Makefile.am | 153 ++++ .../green/demo/clustering/kmean1d/kmean1d.cc | 258 ++++++ .../green/demo/clustering/kmean2d/Makefile.am | 153 ++++ .../green/demo/clustering/kmean2d/kmean2d.cc | 278 +++++++ .../green/demo/clustering/kmean3d/Makefile.am | 153 ++++ .../green/demo/clustering/kmean3d/kmean3d.cc | 265 ++++++ .../green/demo/clustering/kmean_rgb/Makefile.am | 153 ++++ .../green/demo/clustering/kmean_rgb/kmean_rgb.cc | 239 ++++++ scribo/sandbox/green/mln/clustering/k_mean.hh | 365 ++++++--- scribo/sandbox/green/mln/clustering/kmean1d.hh | 194 ++--- scribo/sandbox/green/mln/clustering/kmean2d.hh | 329 ++++---- scribo/sandbox/green/mln/clustering/kmean3d.hh | 246 +++--- scribo/sandbox/green/mln/clustering/kmean_rgb.hh | 87 ++- scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh | 59 ++- .../green/tests/clustering/k_mean/Makefile.am | 13 +- .../green/tests/clustering/k_mean/k_mean.cc | 395 ++++++---- .../stat/histo1d => clustering/k_mean}/Makefile.am | 0 .../sandbox/green/use/clustering/k_mean/k_mean.cc | 55 ++ .../histo1d => clustering/kmean1d}/Makefile.am | 0 .../green/use/clustering/kmean1d/kmean1d.cc | 50 ++ .../histo1d => clustering/kmean2d}/Makefile.am | 0 .../green/use/clustering/kmean2d/kmean2d.cc | 61 ++ .../histo1d => clustering/kmean3d}/Makefile.am | 0 .../green/use/clustering/kmean3d/kmean3d.cc | 63 ++ .../histo1d => clustering/kmean_rgb}/Makefile.am | 0 .../green/use/clustering/kmean_rgb/kmean_rgb.cc | 63 ++ .../stat/histo1d => fun/v2v/rg_to_rgb}/Makefile.am | 0 .../green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc | 68 ++ 32 files changed, 4224 insertions(+), 698 deletions(-) create mode 100644 scribo/sandbox/green/bench/clustering/distance/Makefile.am create mode 100644 scribo/sandbox/green/bench/clustering/distance/distance.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/k_mean}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/k_mean/k_mean.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean1d}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean2d}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean3d}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean_rgb}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => fun/v2v/rg_to_rgb}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 7bb6646..4ddaa54 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,80 @@ +2010-06-28 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Implement the kmean algorithh and start to optimize it. + + Implement the first version with vectors and matrices. + + * mln/clustering/k_mean.hh: New library component. + + * use/clustering/k_mean: New directory. + * use/clustering/k_mean/Makefile.am: New makefile. + * use/clustering/k_mean/k_mean.cc: New source file. + + * tests/clustering/k_mean: New directory. + * tests/clustering/k_mean/Makefile.am: New makefile. + * tests/clustering/k_mean/k_mean.cc: New source file. + + Implement the second version with image and working in 1d. + + * mln/clustering/kmean1d.hh: New library component. + + * use/clustering/kmean1d: New directory. + * use/clustering/kmean1d/Makefile.am: New makefile. + * use/clustering/kmean1d/kmean1d.cc: New source file. + + * demo/clustering/kmean1d: New directory. + * demo/clustering/kmean1d/Makefile.am: New makefile. + * demo/clustering/kmean1d/kmean1d.cc: New source file. + + Implement transformation between RG space and RGB space. + + * mln/fun/v2v/rg_to_rgb.hh: New library component. + * use/fun/v2v/rg_to_rgb: New directory. + * use/fun/v2v/rg_to_rgb/Makefile.am: New makefile. + * use/fun/v2v/rg_to_rgb/rg_to_rgb.cc: New source file. + + Implement the third version working in 2d (r/g). + + * mln/clustering/kmean2d.hh: New library component. + + * use/clustering/kmean2d: New directory. + * use/clustering/kmean2d/Makefile.am: New makefile. + * use/clustering/kmean2d/kmean2d.cc: New source file. + + * demo/clustering/kmean2d: New directory. + * demo/clustering/kmean2d/Makefile.am: New makefile. + * demo/clustering/kmean2d/kmean2d.cc: New source file. + + Implement the fourth version working in 3d (rgb). + + * mln/clustering/kmean3d.hh: New library component. + + * use/clustering/kmean3d: New directory. + * use/clustering/kmean3d/Makefile.am: New makefile. + * use/clustering/kmean3d/kmean3d.cc: New source file. + + * demo/clustering/kmean3d: New directory. + * demo/clustering/kmean3d/Makefile.am: New makefile. + * demo/clustering/kmean3d/kmean3d.cc: New source file. + + Implement the fith version as a function (working in rgb space). + + * mln/clustering/kmean_rgb.hh: New library component. + + * use/clustering/kmean_rgb: New directory. + * use/clustering/kmean_rgb/Makefile.am: New makefile. + * use/clustering/kmean_rgb/kmean_rgb.cc: New source file. + + * demo/clustering/kmean_rgb: New directory. + * demo/clustering/kmean_rgb/Makefile.am: New makefile. + * demo/clustering/kmean_rgb/kmean_rgb.cc: New source file. + + Benchmark distance algorithm for the kmean algorithm. + + * bench/clustering/distance: New directory. + * bench/clustering/distance/Makefile.am: New makefile. + * bench/clustering/distance/distance.cc: New source file. + 2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Define documentation files. diff --git a/scribo/sandbox/green/README.green b/scribo/sandbox/green/README.green index 3f3c9a9..02d8654 100644 --- a/scribo/sandbox/green/README.green +++ b/scribo/sandbox/green/README.green @@ -33,8 +33,7 @@ n'y a rien à copier. Rendons-nous dans le répertoire de compilation et lançon le makefile. #:~/git/olena/scribo/sandbox/green/demo/annotating/hsv$ -cd ../../../build/demo/annotating/hsv - +#:cd ../../../build/demo/annotating/hsv #:~/git/olena/scribo/sandbox/green/build/demo/annotating/hsv$ make clean all L'exécutable est généré par le makefile, il porte le nom du @@ -168,6 +167,14 @@ de comptage puisqu'il est décrit sous la forme de triplets de float contient une séquence d'appels pour les routines permettant de considérer la dimension de la teinte comme circulaire. +Après réflexion, le code des histogrammes et tous les accumulateurs +qui en découlent devraient être rangés dans l'espace de nommage +mln::accu::histo. Cela permettrait de ne pas parasiter mln::accu::stat +avec tous les éléments propres aux histogrammes et de ne pas faire un +espace de nommage à rallonge en introduisant histo dans stat. Donc +mln::accu::stat semble être une bonne postion pour le rangement final +du code relatif aux histogrammes. + a) version 1d @@ -198,7 +205,6 @@ c) version 3d RGB * use/accu/stat/histo3_rgb: Code minimal utilisant un histogramme 3d RGB. - d) version 3d HSL * mln/accu/stat/histo3d_hsl.hh: Accumulateur histogramme image3d HSL. @@ -245,23 +251,145 @@ IX KMEANS Ce travail m'avait été demandé par théo. Je le laisse inachevé, quelque part perdu pendant l'optimisation du code et sa transformation en canevas. + +a) Première implémentation avec matrices et vecteurs + +Cette version est bien documentée et permet de mieux comprendre les autres +versions. Par ailleurs, elle n'est pas spécialement optimisée ce qui fait +qu'elle colle davantage au modèle de l'algorithme kmean traditionnel. + * mln/clustering/k_mean.hh: Première implémentation avec matrices et vecteurs. +* use/clustering/k_mean: Code minimal utilisant cette première implémentation. +* tests/clustering/k_mean: Tests unitaires sur la permière version. + + +b) Seconde implémentation avec image en 1d + +Cette seconde version intègre une optimisation testée par théo il y a +longtemps. Je me demande si ce n'étais pas pendant sa thèse qu'il +avait travaillé sur cette version. Bref, dans la mesure où +l'optimisation passe par la création d'un histogramme, cette version +devient dépendante des histogrammes réalisés plutôt (il est aussi +dépendant de la sauvegarde au format gnuplot shell). L'idée générale +de l'optimisation est de ne plus raisonner sur l'image, mais sur +l'histogramme. Car à un moment donné, la classification ne tient +compte que de la valeur du pixel en intensité (pas de ses coordonnées +dans l'image). Donc tout pixel de même intensité sera classé de +manière identique. D'où l'intérêt de travailler avec les +histogrammes. Les limites de cette optimisation se trouvent dans la +taille des histogrammes. L'optimisation marche à merveille pour de +faibles quantifications (8 bits c'est parfait). Par contre, lorsque +l'on passe à un histogramme couleur, la taille de l'histogramme +devient problématique et peut dépasser la taille de l'image. Cette +optimisation n'a alors plus aucun sens. + * mln/clustering/kmean1d.hh: Implémentation 1d avec des images. +* use/clustering/kmean1d : Code minimal utilisant cette seconde implémentation. +* demo/clustering/kmean1d : Demonstrateur. + +La visualisation de la convergence des moyennes, générée par le +démonstrateur, n'est pas très lisible. La lecture textuelle du fichier +gnuplot shell (mean_cnv.sh) permet d'interpréter ce qui se passe, par +contre le graphique est à refaire. + + +c) kmean2d + +Cette troisième version est identique à la seconde, sauf qu'elle +permet de faire la classification dans un espace à deux +dimensions. Malheureusement, un tel travail dans cet espace coûte +beaucoup plus cher à l'exécution. + +* mln/fun/v2v/rg_to_rgb.hh: Transformation de l'espace RG vers l'espace RGB. +* use/fun/v2v/rg_to_rgb: Exemple de code pour l'utilisation de rg_to_rgb. + * mln/clustering/kmean2d.hh: Implémentation 2d avec des images. -* mln/clustering/kmean3d.hh: Implémentation 3d avec des images. -* mln/clustering/kmean_rgb.hh: Impl. 3d aplatie et en cours de mise en canevas. +* use/clustering/kmean2d : Code minimal utilisant cette seconde implémentation. +* demo/clustering/kmean2d : Demonstrateur. -* tests/clustering/k_mean: Tests unitaires sur la permière version. -* tests/clustering/kmean1d: Tests unitaire sur la version 1d. +La visualisation de la convergence des moyennes est cette fois-ci +complètement aboutie. Le fichier semble avoir quelques soucis, il +manque des lignes dans le splot initial, en remettre pour qu'il y en +ait autant que les blocs de données. L'espace des couleurs étant le +bicanal r/g, on peut visualiser sur une troisième dimension +l'évolution des centres dans l'espace initial. L'effet est très réussi +et aussi très parlant. Chaque run, correspond alors à une trajectoire +particulière. L'affichage des variations des variances a été rénové et +est lui aussi beaucoup plus lisible. + + +d) kmean3d + +Cette quatrième version est la copie conforme de la version +précédente. Les problèmes de visualisation sur les fichiés générés +sont les mêmes. L'affichage des convergences est identique. Le recours +à une dégradation de l'image d'entrée est impérative pour avoir des +temps encore acceptables. -* demo/clustering/kmean1d: Utilisation de la version 1d. -* demo/clustering/kmean2d: Utilisation de la version 2d. -* demo/clustering/kmean3d: Utilisation de la version 3d. +* mln/clustering/kmean3d.hh: Implémentation 3d avec des images. +* use/clustering/kmean3d : Code minimal utilisant cette quatrième impl. +* demo/clustering/kmean3d : Demonstrateur. + + +e) kmean aplati + +Cette cinquième version est très spéciale. Elle doit permettre de +gagner du temps d'exécution. Le concept est simple au départ, tout +écrire d'un seul tenant. L'exercice est fastidieux et difficile +(intellectuellement). Une fois fait, il faut réfléchir à ce qui peut +être mis sous forme de canevas. Par exemple, la manière de calculer +les distances peut être une information paramétrable. Cela pemettrait +de faire un benchmark in situ. La transcription actuelle compile. Elle +n'intègre pas tous les outils de debuggage que nous pouvions avoir sur +les autres versions. Par ailleurs, comme le code est réuni en une +seule fonction, nous avons pour l'instant une seule sortie, l'image de +labels de la classification réalisée. A partir de cette image, nous +pouvons en reconstruire d'autres. Il manque quand même la possibilité +d'observer les convergences. Le travail était en cours, à prendre donc +avec beaucoup de pincettes. La dernière exécution house.ppm, 3 +centres, 10 itérations et 10 runs fonctionne parfaitement sur les +premiers runs puis l'affichage s'emballe sans que l'on y comprenne +rien. Un dump dans un fichier révèle le non appariement de certaines +traces (entering/exiting). Une piste à suivre est la sortie anticipée +d'une de mes boucles sans pour autant fermer une trace ... ??? + +* mln/clustering/kmean_rgb.hh: Implémentation 3d avec des images. +* use/clustering/kmean_rgb : Code minimal utilisant cette quatrième impl. * demo/clustering/kmean_rgb: Utilisation de la version aplatie. + +f) optimisation possible + +Le calcul des distances entre les points et les différents centres +peut être réalisé par des transformées. Certes, les distances ne seront +pas les mêmes que la distance euclidienne, mais elles s'en approchent +et cela constitue très certainement une très bonne approximation pour +l'objectif que nous cherchons à atteindre. Le but de ce benchmark est +de regarder quel type de transformation est le plus rapide pour +arriver à nos fins en fonction des données d'entrée. Cette +optimisation n'a pas encore été intégrée dans le code, et reste une +piste à exploiter. + * bench/clustering/distance: Comparaison algorithmes d'évaluation des distances. -==> to do +Une routine du benchmark ne compile plus. Il semble qu'il y ait un +mauvais appel à la fonction at_ dans la routine +influence_zone_geodesic.hh ligne 127. Le but du benchmark est de +tester les distances en 2d et 3d pour des voisinages différents (c04, +c08, c06, c18, c26) sur les routines distance euclidienne classique, +zone d'influence geodesique et zone d'influence "front". La première +serie de test vise à garder une taille d' image constante et +d'augmenter le nombre de centres pour voir comment se comporte les +algorithmes et la seconde serie, vise à garder un nombre constant de +centres et à agrandir progressivement l'image. Attention, le benchmark +essaye d'être assez exhaustif et donc se paye par un temps d'execution +assez long. Les différents fichiers générés reprennent les différents +tests effectués et montrent l'évolution du temps de calcul suivant la +progression du paramètre observé (taille de l'image ou nombre de +centres). + +==> to do : le changelog + commit + give it to scribo-z branch + X REGIONAL MAXIMA ----------------- diff --git a/scribo/sandbox/green/bench/clustering/distance/Makefile.am b/scribo/sandbox/green/bench/clustering/distance/Makefile.am new file mode 100644 index 0000000..ca5e187 --- /dev/null +++ b/scribo/sandbox/green/bench/clustering/distance/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +#CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/bench +BUILD__PATTERN= green/build/bench + + +ifeq ($(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/scribo/sandbox/green/bench/clustering/distance/distance.cc b/scribo/sandbox/green/bench/clustering/distance/distance.cc new file mode 100644 index 0000000..4daea44 --- /dev/null +++ b/scribo/sandbox/green/bench/clustering/distance/distance.cc @@ -0,0 +1,842 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#include <mln/core/alias/box3d.hh> +#include <mln/core/alias/neighb2d.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/alias/point3d.hh> +#include <mln/core/alias/w_window2d_int.hh> +#include <mln/core/alias/w_window3d_int.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/fill.hh> + +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/make/w_window2d_int.hh> +#include <mln/make/w_window3d_int.hh> + +#include <mln/opt/at.hh> + +#include <mln/transform/influence_zone_geodesic.hh> +#include <mln/transform/influence_zone_front.hh> + +#include <mln/util/array.hh> +#include <mln/util/timer.hh> + +#include <mln/value/label_8.hh> + + +/// Build inputs, build seeds, count distance. +/// \{ +/// \brief Build 2d/3d input image and 2d/3d seed image. +/// +/// Building seeds means making a rectangular grid over a virtual image with +/// a specific size and collects together in a array each node of this grid. +/// In the context of the kmean algorithm, seeds are equavalent to centers. +/// Building input means creating a label image and associates to each seed +/// a unique label id. +/// Distance are square euclidian distance in 2d and in 3d. +mln::image2d<mln::value::label_8> build_input2d(unsigned nb_seed, unsigned size) +{ + typedef mln::value::label_8 t_lbl; + mln::image2d<t_lbl> input(mln::box2d(mln::point2d(0,0), + mln::point2d(size,size))); + + mln::data::fill(input, mln::literal::zero); + + unsigned top = size / nb_seed; + unsigned lbl = 0; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + input(mln::point2d(i,j)) = ++lbl; + + //mln::io::pgm::save(input, "input.pgm"); + + return input; +} + +mln::image3d<mln::value::label_8> build_input3d(unsigned nb_seed, unsigned size) +{ + typedef mln::value::label_8 t_lbl; + mln::image3d<t_lbl> input(mln::box3d(mln::point3d(0,0,0), + mln::point3d(size,size,size))); + + mln::data::fill(input, mln::literal::zero); + + unsigned top = size / nb_seed; + unsigned lbl = 0; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + for (unsigned k = top/2; k < size; k += top) + input(mln::point3d(k,i,j)) = ++lbl; + + //mln::io::pgm::save(input, "input.pgm"); + + return input; +} + +mln::util::array<mln::point3d> build_seed3d(unsigned nb_seed, unsigned size) +{ + mln::util::array<mln::point3d> result; + unsigned top = size / nb_seed; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + for (unsigned k = top/2; k < size; k += top) + result.append(mln::point3d(k,i,j)); + + // std::cout << result << std::endl; + + return result; +} + +mln::util::array<mln::point2d> build_seed2d(unsigned nb_seed, unsigned size) +{ + mln::util::array<mln::point2d> result; + unsigned top = size / nb_seed; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + result.append(mln::point2d(j,i)); + + // std::cout << result << std::endl; + + return result; +} + +unsigned distance(mln::point3d p1, mln::point3d p2) +{ + unsigned row2 = (p1.row() - p2.row())*(p1.row() - p2.row()); + unsigned col2 = (p1.col() - p2.col())*(p1.col() - p2.col()); + unsigned sli2 = (p1.sli() - p2.sli())*(p1.sli() - p2.sli()); + unsigned res = row2 + col2 + sli2; + + return res; +} + +unsigned distance(mln::point2d p1, mln::point2d p2) +{ + unsigned row2 = (p1.row() - p2.row())*(p1.row() - p2.row()); + unsigned col2 = (p1.col() - p2.col())*(p1.col() - p2.col()); + unsigned res = row2 + col2; + + return res; +} +/// \} + +/// Benchmark geodesic distance. +/// \{ +/// \brief The geodesic material for the benchmark process. +/// +/// This part of the file contains all we need to benchmarck the +/// geodesic distance. First of all, we have the calling functions +/// that encapsulate the real call to geodesic routine. They bring us +/// a clean place where starting and stopping the timer without any +/// other code interference. Printing results could be done at this step. +/// At least, we have the test routine that call the previous functions +/// by passing arguments like different size of inputs and different +/// neighbouring. The test routines save their results in gnuplot script shell +/// file that enable to show charts. +void influence_zone_geodesic_2d(mln::util::timer& timer, + const mln::image2d<mln::value::label_8>& input, + const mln::neighb2d & neighb) +{ + mln::image2d<mln::value::label_8> output; + + timer.start(); + output = mln::transform::influence_zone_geodesic(input, neighb); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + +/// \fixme influence_zone_geodesic_3d doesn't compile! +void influence_zone_geodesic_3d(mln::util::timer& timer, + const mln::image3d<mln::value::label_8>& input, + const mln::neighb3d & neighb) +{ + mln::image3d<mln::value::label_8> output; + + timer.start(); + // FIXME : Problem with the influence_zone_geodesic in 3d (bad call to at_). + // output = mln::transform::influence_zone_geodesic(input, neighb); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +int test_influence_zone_geodesic_2d() +{ + mln::util::timer timer; + +// Test the label c4 + mln::image1d<float> chart_seed_c04(16*16+2); + + mln::data::fill(chart_seed_c04, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_geodesic_2d(timer,build_input2d(i,256),mln::c4()); + std::cout << "c04|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c04, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c04, "geodesic.c04.2d.seed.sh"); + + // Test the label c8 + mln::image1d<float> chart_seed_c08(16*16+2); + + mln::data::fill(chart_seed_c08, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_geodesic_2d(timer,build_input2d(i,256),mln::c8()); + std::cout << "c08|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c08, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c08, "geodesic.c08.2d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c04(256+1); + + mln::data::fill(chart_size_c04, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_geodesic_2d(timer, build_input2d(4,i),mln::c4()); + std::cout << "c04|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c04, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c04, "geodesic.c04.2d.size.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c08(256+1); + + mln::data::fill(chart_size_c08, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_geodesic_2d(timer, build_input2d(4,i),mln::c8()); + std::cout << "c08|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c08, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c08, "geodesic.c08.2d.size.sh"); + + + return 0; +} + + +void test_influence_zone_geodesic_3d() +{ + mln::util::timer timer; + + // Test the number of labels c06 + mln::image1d<float> chart_seed_c06(5*5*5+2); + + mln::data::fill(chart_seed_c06, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(i,128), mln::c6()); + std::cout << "c06|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c06, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c06, "geodesic.c06.3d.seed.sh"); + + // Test the number of labels c18 + mln::image1d<float> chart_seed_c18(5*5*5+2); + + mln::data::fill(chart_seed_c18, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(i,128), mln::c18()); + std::cout << "c18|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c18, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c18, "geodesic.c18.3d.seed.sh"); + + // Test the number of labels c26 + mln::image1d<float> chart_seed_c26(5*5*5+2); + + mln::data::fill(chart_seed_c26, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(i,128), mln::c26()); + std::cout << "c26|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c26, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c26, "geodesic.c26.3d.seed.sh"); + + // Test the size of the image c06 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c06(128+1); + + mln::data::fill(chart_size_c06, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(2,i), mln::c6()); + std::cout << "c06|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c06, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c06, "geodesic.c06.3d.size.sh"); + + // Test the size of the image c18 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c18(128+1); + + mln::data::fill(chart_size_c18, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(2,i), mln::c18()); + std::cout << "c18|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c18, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c18, "geodesic.c18.3d.size.sh"); + + // Test the size of the image c26 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c26(128+1); + + mln::data::fill(chart_size_c26, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(2,i), mln::c26()); + std::cout << "c26|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c26, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c26, "geodesic.c26.3d.size.sh"); +} +/// \} + +/// Benchmark front distance. +/// \{ +/// \brief The front material for the benchmark process. +/// +/// This part of the file contains all we need to benchmarck the +/// front distance. First of all, we have the calling functions +/// that encapsulate the real call to geodesic routine. They bring us +/// a clean place where starting and stopping the timer without any +/// other code interference. Printing results could be done at this step. +/// At least, we have the test routine that call the previous functions +/// by passing arguments like different size of inputs and different +/// neighbouring. The test routines save their results in gnuplot script shell +/// file that enable to show charts. +void influence_zone_front_2d(mln::util::timer& timer, + const mln::image2d<mln::value::label_8>& input, + const mln::neighb2d& neighb, + const mln::w_window2d_int& w2d) +{ + mln::image2d<mln::value::label_8> output; + + timer.start(); + output = mln::transform::influence_zone_front(input, neighb, w2d); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +void influence_zone_front_3d(mln::util::timer& timer, + const mln::image3d<mln::value::label_8>& input, + const mln::neighb3d& neighb, + const mln::w_window3d_int& w3d) +{ + mln::image3d<mln::value::label_8> output; + + timer.start(); + output = mln::transform::influence_zone_front(input, neighb, w3d); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +void test_influence_zone_front_3d() +{ + mln::util::timer timer; + + int ws_c06[] = { + // Internal slice + 0, 0, 0, + 0, 2, 0, + 0, 0, 0, + // Middle slice + 0, 2, 0, + 2, 0, 2, + 0, 2, 0, + // External slice + 0, 0, 0, + 0, 2, 0, + 0, 0, 0 }; + + int ws_c18[] = { + // Internal slice + 0, 6, 0, + 6, 4, 6, + 0, 6, 0, + // Middle slice + 6, 4, 6, + 4, 0, 4, + 6, 4, 6, + // External slice + 0, 6, 0, + 6, 4, 6, + 0, 6, 0 }; + + int ws_c26[] = { + // Internal slice + 7, 6, 7, + 6, 4, 6, + 7, 6, 7, + // Middle slice + 6, 4, 6, + 4, 0, 4, + 6, 4, 6, + // External slice + 7, 6, 7, + 6, 4, 6, + 7, 6, 7 }; + + mln::w_window3d_int w3d_c06 = mln::make::w_window3d_int(ws_c06); + mln::w_window3d_int w3d_c18 = mln::make::w_window3d_int(ws_c18); + mln::w_window3d_int w3d_c26 = mln::make::w_window3d_int(ws_c26); + + // Test the number of labels c06 + mln::image1d<float> chart_seed_c06(5*5*5+2); + + mln::data::fill(chart_seed_c06, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(i,128), mln::c6(), w3d_c06); + std::cout << "c06|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c06, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c06, "front.c06.3d.seed.sh"); + + // Test the number of labels c18 + mln::image1d<float> chart_seed_c18(5*5*5+2); + + mln::data::fill(chart_seed_c18, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(i,128), mln::c18(), w3d_c18); + std::cout << "c18|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c18, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c18, "front.c18.3d.seed.sh"); + + // Test the number of labels c26 + mln::image1d<float> chart_seed_c26(5*5*5+2); + + mln::data::fill(chart_seed_c26, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(i,128), mln::c26(), w3d_c26); + std::cout << "c26|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c26, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c26, "front.c26.3d.seed.sh"); + + // Test the size of the image c06 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c06(128+1); + + mln::data::fill(chart_size_c06, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(2,i), mln::c6(), w3d_c06); + std::cout << "c06|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c06, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c06, "front.c06.3d.size.sh"); + + // Test the size of the image c18 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c18(128+1); + + mln::data::fill(chart_size_c18, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(2,i), mln::c18(), w3d_c18); + std::cout << "c18|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c18, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c18, "front.c18.3d.size.sh"); + + // Test the size of the image c26 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c26(128+1); + + mln::data::fill(chart_size_c26, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(2,i), mln::c26(), w3d_c26); + std::cout << "c26|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c26, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c26, "front.c26.3d.size.sh"); +} + +void test_influence_zone_front_2d() +{ + mln::util::timer timer; + + int ws_c04[] = { + 0, 2, 0, + 2, 0, 2, + 0, 2, 0}; + + int ws_c08[] = { + 6, 4, 6, + 4, 0, 4, + 6, 4, 6}; + + mln::w_window2d_int w2d_c04 = mln::make::w_window2d_int(ws_c04); + mln::w_window2d_int w2d_c08 = mln::make::w_window2d_int(ws_c08); + + // Test the label c4 + mln::image1d<float> chart_seed_c04(16*16+2); + + mln::data::fill(chart_seed_c04, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_front_2d(timer,build_input2d(i,256),mln::c4(),w2d_c04); + std::cout << "c04|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c04, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c04, "front.c04.2d.seed.sh"); + + // Test the label c8 + mln::image1d<float> chart_seed_c08(16*16+2); + + mln::data::fill(chart_seed_c08, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_front_2d(timer,build_input2d(i,256),mln::c8(),w2d_c08); + std::cout << "c08|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c08, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c08, "front.c08.2d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c04(256+1); + + mln::data::fill(chart_size_c04, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_front_2d(timer, build_input2d(4,i),mln::c4(),w2d_c04); + std::cout << "c04|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c04, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c04, "front.c04.2d.size.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c08(256+1); + + mln::data::fill(chart_size_c08, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_front_2d(timer, build_input2d(4,i),mln::c8(),w2d_c08); + std::cout << "c08|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c08, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c08, "front.c08.2d.size.sh"); +} +/// \} + + + +/// Benchmark euclidian distance. +/// \{ +/// \brief The euclidian material for the benchmark process. +/// +/// This part of the file contains all we need to benchmarck the +/// euclidian distance. First of all, we have the calling functions +/// that encapsulate the real call to geodesic routine. They bring us +/// a clean place where starting and stopping the timer without any +/// other code interference. Printing results could be done at this step. +/// At least, we have the test routine that call the previous functions +/// by passing arguments like different size of inputs and different +/// neighbouring. The test routines save their results in gnuplot script shell +/// file that enable to show charts. +void influence_zone_euclidian_2d(mln::util::timer& timer, + const mln::image2d<mln::value::label_8>& input, + const mln::util::array<mln::point2d>& seed) +{ + mln::image2d<mln::value::label_8> output; + + timer.start(); + + mln::initialize(output, input); + + mln_piter_(mln::image2d<mln::value::label_8>) p(output.domain()); + mln_eiter_(mln::util::array<mln::point2d>) e(seed); + + for_all(p) + { + unsigned d = 0; + unsigned min_d = mln_max(unsigned); + unsigned min_l = 0; + + for_all(e) + { + d = distance(p, seed(e.index_())); + + if (min_d > d) + { + min_d = d; + min_l = input(e); + } + } + + output(p) = min_l; + } + + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +void influence_zone_euclidian_3d(mln::util::timer& timer, + const mln::image3d<mln::value::label_8>& input, + const mln::util::array<mln::point3d>& seed) +{ + mln::image3d<mln::value::label_8> output; + + timer.start(); + + mln::initialize(output, input); + + mln_piter_(mln::image3d<mln::value::label_8>) p(output.domain()); + mln_eiter_(mln::util::array<mln::point3d>) e(seed); + + for_all(p) + { + unsigned d = 0; + unsigned min_d = mln_max(unsigned); + unsigned min_l = 0; + + for_all(e) + { + d = distance(p, seed(e.index_())); + + if (min_d > d) + { + min_d = d; + min_l = input(e); + } + } + + output(p) = min_l; + } + + timer.stop(); +} + +void test_influence_zone_euclidian_2d() +{ + mln::util::timer timer; +/* + // Global test + mln::image2d<float> chart(mln::box2d(mln::point2d(0,0), + mln::point2d(16*16+2,256+1))); + + mln::data::fill(chart, mln::literal::zero); + + for (unsigned i = 1; i < 256+1; ++i) // size + for (unsigned j = 1; j < i*i && j < 16+1; ++j) // seed + { + influence_zone_euclidian_2d(timer,build_input2d(j,i),build_seed2d(j,i)); + std::cout << "xxx|" << i << "x" << i << "|" << j << " = " + << timer.read() << std::endl; + mln::opt::at(chart, j*j,i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart, "chart.sh"); +*/ + + // Test the number of labels + mln::image1d<float> chart_seed(16*16+2); + + mln::data::fill(chart_seed, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_euclidian_2d(timer,build_input2d(i,256),build_seed2d(i,256)); + std::cout << "xxx|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed, "euclidian.2d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size(256+1); + + mln::data::fill(chart_size, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_euclidian_2d(timer, build_input2d(4,i), build_seed2d(4,i)); + std::cout << "xxx|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size, "euclidian.2d.size.sh"); +} + +void test_influence_zone_euclidian_3d() +{ + mln::util::timer timer; + + // Test the number of labels + mln::image1d<float> chart_seed(5*5*5+2); + + mln::data::fill(chart_seed, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_euclidian_3d(timer,build_input3d(i,128),build_seed3d(i,128)); + std::cout << "xxx|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed, "euclidian.3d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size(128+1); + + mln::data::fill(chart_size, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_euclidian_3d(timer, build_input3d(2,i), build_seed3d(2,i)); + std::cout << "xxx|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size, "euclidian.3d.size.sh"); +} +/// \} + +//------------------------------------------------------------------------------ +// Main +// +// Point de fonctionnement du système [image de taille entre 1 et 256] +// Moins de 16 labels +// +//------------------------------------------------------------------------------ + +/// \brief The main routine that call all the benchmark tests. +/// +/// This benchmark aim at comparing different kind of distance. It +/// looks after the influence geodesic, the influence front and +/// euclidian distance. Many files are generated. Some rules exist to +/// build the name of the file: +/// distance.neighbouring.dimension.(size|seed). The distance could be +/// the front distance (front), the geodesic distance (geodesic) or +/// the euclidian distance (euclidian). The neighbouring could be c04 +/// or c08 in 2d, c06, c18 or c26 in 3d. The dimension could be 2d or 3d. +/// Finally, the flag size or seed precises the nature of that test, increasing +/// gradualy the size of the image or increasing gradualy the number of seeds. +/// The file contains the time benchmark of the distance studied. +/// +/// \fixme test_influence_zone_geodesic_3d doesn't compile. +int main() +{ + test_influence_zone_euclidian_2d(); + test_influence_zone_front_2d(); + test_influence_zone_geodesic_2d(); + + // FIXME : It doesn't compile because of a bad calling function at_ in + // FIXME : the influence_zone_geodesic.hh line 127. + // test_influence_zone_geodesic_3d(); + test_influence_zone_front_3d(); + test_influence_zone_euclidian_3d(); + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -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/scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc b/scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..22eae91 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,258 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean1d. +/// +/// The demonstrator produces five kind of results. The file +/// "color.ppm" allow us to localize the different center in the +/// image. There is an equivalent file "label.pgm" that's do the same +/// thing but inplace of a specific color, it gives the label of each +/// center. It's quit easy with xv, to transform the label image in +/// the color one. The file "mean.pgm" replaces each label by the mean +/// of the points that contributate to their center. It is a kind of +/// abstraction of the reality of the image, of its complexity. In +/// file "variance_cnv.sh" abscissa axis stands for the convergence +/// evolution and the ordinate axis for each runs. The "mean_cnv.sh" +/// file shows the evolution of the centers, whatever the run. The +/// x-axis represents the id of the center, the y-axis the iteration +/// in the convergence process and the z-axis the id of the particular +/// run. The graphic is very bad, but textual interpretation is still +/// available. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/clustering/kmean1d.hh> + +#include <mln/core/image/image2d.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> + +#include <mln/value/int_u8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the 8 bits greylevel image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() + +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> t_kmean; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + + t_image2d_int_u8 img; + + mln::io::pgm::load(img, image.c_str()); + + t_kmean kmean(img, + k_center, + watch_dog, + n_times); + + //mln::trace::quiet = false; + + kmean.launch_n_times(); + + // FIXME : Not safe because we don't test kmean.is_valid() + + 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 var_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.ppm"); + 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(var_cnv, "variance_cnv.sh"); +} + + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the 8 bits greylevel image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; + + result = !arg_stream.fail(); + } + + return result; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; + + return !arg_stream.fail(); + } + + return result; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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 [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 watch_dog (convergence loop)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the 8 bits greylevel image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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); + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -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/scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc new file mode 100644 index 0000000..2538dce --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc @@ -0,0 +1,278 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean2d. +/// +/// The demonstrator produces five kind of results. The file +/// "color.ppm" allow us to localize the different center in the +/// image. There is an equivalent file "label.pgm" that's do the same +/// thing but inplace of a specific color, it gives the label of each +/// center. It's quit easy with xv, to transform the label image in +/// the color one. The file "mean.pgm" replaces each label by the mean +/// of the points that contributate to their center. It is a kind of +/// abstraction of the reality of the image, of its complexity. The +/// "variance_cnv.sh" changes its representation and becomes more +/// readable. The "mean_cnv.sh" have got a generation problem. We can +/// bypass it by adding printing lines in the 'splot' +/// instruction. However, the graphic is correct and mainly shows the +/// convergence of the center value. Finally, as the work takes place in +/// the r/g space, the "point.ppm" file shows us the exact input space. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/clustering/kmean2d.hh> + +#include <mln/core/image/image2d.hh> + +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/fun/v2v/rg_to_rgb.hh> + +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) +{ + typedef mln::clustering::kmean2d<double,8> t_kmean; + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb8 point_img_rgb8; + t_image2d_rgb8 mean_img_rgb8; + t_image2d_rgb8 mean_dbg_rgb8; + t_image2d_rg8 img_rg8; + + // Read input. + mln::io::ppm::load(img_rgb8, image.c_str()); + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + + // Call kmean. + t_kmean kmean(img_rg8, k_center, watch_dog, n_times); + + mln::trace::quiet = false; + kmean.launch_n_times(); + + // FIXME : Not safe because we don't test kmean.is_valid() + + // Get outputs. + t_kmean::t_point_img point_img = kmean.get_point(); + 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(); + + // Convert outputs. + point_img_rgb8 = mln::data::transform(point_img, t_rg_to_rgb()); + mean_img_rgb8 = mln::data::transform(mean_img, t_rg_to_rgb()); + + mln::io::ppm::save(mean_img_rgb8, "mean.ppm"); + mln::io::ppm::save(color_img, "color.ppm"); + mln::io::pgm::save(label_img, "label.pgm"); + mln::io::ppm::save(point_img_rgb8, "point.ppm"); + + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +} + + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +void demo(const std::string& image = OLENA_IMG_PATH"/house.ppm", + 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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; + + result = !arg_stream.fail(); + } + + return result; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; + + return !arg_stream.fail(); + } + + return result; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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: kmean2d [image [k_center [n_times [watch_dog]]]]" + << std::endl; + std::cout << "ppm 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 watch_dog (convergence loop)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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); + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -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/scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc b/scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc new file mode 100644 index 0000000..0e2b16e --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc @@ -0,0 +1,265 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean3d. +/// +/// The demonstrator produces five kind of results. The file +/// "color.ppm" allow us to localize the different center in the +/// image. There is an equivalent file "label.pgm" that's do the same +/// thing but inplace of a specific color, it gives the label of each +/// center. It's quit easy with xv, to transform the label image in +/// the color one. The file "mean.pgm" replaces each label by the mean +/// of the points that contributate to their center. It is a kind of +/// abstraction of the reality of the image, of its complexity. The +/// "variance_cnv.sh" changes its representation and becomes more +/// readable. The "mean_cnv.sh" have got a generation problem. We can +/// bypass it by adding printing lines in the 'splot' +/// instruction. However, the graphic is correct and mainly shows the +/// convergence of the center value. + +#include <iostream> +#include <sstream> + +#include <mln/clustering/kmean3d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.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/io/plot/save_image_sh.hh> + +#include <mln/img_path.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) +{ + typedef mln::clustering::kmean3d<double,5> t_kmean; + typedef mln::value::label_8 t_label_8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<5> t_rgb5; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb5> t_image2d_rgb5; + + t_image2d_rgb8 house_rgb8; + t_image2d_rgb5 house_rgb5; + + mln::io::ppm::load(house_rgb8, image.c_str()); + house_rgb5=mln::data::transform(house_rgb8,mln::fun::v2v::rgb8_to_rgbn<5>()); + + t_kmean kmean(house_rgb5, k_center, watch_dog, n_times); + + mln::trace::quiet = false; + + kmean.launch_n_times(); + + // FIXME : Not safe because we don't test kmean.is_valid() + + 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::ppm::save(mean_img, "mean.ppm"); + mln::io::ppm::save(color_img, "color.ppm"); + mln::io::pgm::save(label_img, "label.pgm"); + + mln::io::plot::save_image_sh(mean_img, "mean.sh"); + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +} + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +void demo(const std::string& image = OLENA_IMG_PATH"/house.ppm", + 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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; + + result = !arg_stream.fail(); + } + + return result; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; + + return !arg_stream.fail(); + } + + return result; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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 [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 watch_dog (convergence loop)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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); + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -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/scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc b/scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc new file mode 100644 index 0000000..d02c497 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc @@ -0,0 +1,239 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean_rgb. +/// +/// The code is provided as is. It is an alpha version, so very very +/// experimental. Take care !! + +#include <iostream> +#include <sstream> + +#include <mln/clustering/kmean_rgb.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.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/io/plot/save_image_sh.hh> + +#include <mln/img_path.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) +{ + typedef mln::value::label_8 t_lbl8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<5> t_rgb5; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb5> t_image2d_rgb5; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb5 img_rgb5; + t_image2d_lbl8 img_lbl8; + + mln::io::ppm::load(img_rgb8, image.c_str()); + img_rgb5 = mln::data::transform(img_rgb8,mln::fun::v2v::rgb8_to_rgbn<5>()); + img_lbl8 = mln::clustering::kmean_rgb<double,5>(img_rgb5, + k_center, + watch_dog, + n_times); + + mln::io::pgm::save(img_lbl8, "label.pgm"); +} + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +void demo(const std::string& image = OLENA_IMG_PATH"/house.ppm", + const unsigned k_center = 3, + const unsigned n_times = 3, + const unsigned watch_dog = 3) +{ + 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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; + + result = !arg_stream.fail(); + } + + return result; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; + + return !arg_stream.fail(); + } + + return result; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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 [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 watch_dog (convergence loop)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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); + + return 0; +} diff --git a/scribo/sandbox/green/mln/clustering/k_mean.hh b/scribo/sandbox/green/mln/clustering/k_mean.hh index db3f34c..74acda7 100644 --- a/scribo/sandbox/green/mln/clustering/k_mean.hh +++ b/scribo/sandbox/green/mln/clustering/k_mean.hh @@ -1,6 +1,4 @@ -// Copyright (C) 2007 EPITA Research and Development Laboratory (LRDE) -// Copyright (C) 2008 EPITA Research and Development Laboratory (LRDE) -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -26,43 +24,89 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_K_MEAN_HH -#define MLN_CLUSTERING_K_MEAN_HH +# define MLN_CLUSTERING_K_MEAN_HH /// \file /// -/// \brief Implements the K MEAN algorithm. -/// DO -/// - ASSIGNEMENT STEP -/// - UPDATE STEP -/// LOOP UNTIL CONVERGENCE +/// \brief Implement the K MEAN algorithm with matrix and vectors. /// -/// ASSIGNEMENT STEP +/// This is the first impementation of the kmean algorithm. There is no +/// specific optimisation but every part of the algorithm is here and +/// it works. As it used vectors and matrices, it is not very convenient to +/// work with this version. +/// +/// \verbatim +/// This is the main lines of the kmean algorithm: +/// +/// PROCEDURE MAIN +/// BEGIN +/// DO +/// - CALL ASSIGNEMENT STEP +/// - CALL UPDATE STEP +/// LOOP UNTIL CONVERGENCE +/// END +/// +/// PROCEDURE ASSIGNEMENT STEP /// BEGIN -/// - COMPUTE THE DISTANCE MATRIX FROM POINTS TO CENTERS -/// - COMPUTE GROUP MATRIX WHERE EACH POINT IS ASSIGNED TO NEAREST CENTER +/// - COMPUTE THE DISTANCE MATRIX FROM POINTS TO CENTERS +/// - COMPUTE GROUP MATRIX WHERE EACH POINT IS ASSIGNED TO THE NEAREST CENTER /// END /// -/// UPDATE STEP +/// PROCEDURE UPDATE STEP /// BEGIN -/// - COMPUTE THE MEAN OF THE GROUPS WHICH ARE THE NEW CENTERS +/// - COMPUTE THE MEAN OF THE GROUPS WHICH ARE NOW THE NEW CENTERS /// END +/// \endvarbitim +/// +/// The following sample is the standard use of this first +/// implementation. The number of centers is a decision left to the +/// final user. As the representation uses vectors and matices, size +/// of each objects must be known at compile time, and so NB_POINT must be +/// a constant expression (mln::geom::nsites couldn't be called). +/// +/// #include <mln/clustering/k_mean.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/trait/value_.hh> +/// #include <mln/value/rgb8.hh> +/// +/// #define NB_CENTER 9 +/// #define NB_POINT (128*128) +/// #define POINT_SIZE mln_dim(mln::value::rgb8) +/// #define POINT_TYPE double +/// +/// int main() +/// { +/// typedef mln::value::rgb8 t_rgb8; +/// mln::image2d<t_rgb8> img_rgb8; +/// +/// mln::io::ppm::load(img_ref, OLENA_IMG_PATH"/house.ppm"); +/// mln::clustering::k_mean<NB_POINT,NB_CENTER,POINT_SIZE,POINT_TYPE> kmean; +/// +/// kmean.init_point(img_rgb8); +/// kmean.loop(img_rgb8); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> -#include <limits.h> -#include <iostream> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/algebra/mat.hh> +# include <mln/algebra/vec.hh> -#include <mln/core/contract.hh> -#include <mln/trait/value_.hh> +# include <mln/core/concept/image.hh> +# include <mln/core/contract.hh> +# include <mln/core/macros.hh> -#include <mln/algebra/mat.hh> -#include <mln/algebra/vec.hh> +# include <mln/math/min.hh> +# include <mln/norm/l2.hh> -#include <mln/math/min.hh> -#include <mln/norm/l2.hh> +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> -#include <mln/core/concept/image.hh> -#include <mln/core/macros.hh> +# include <mln/trait/value_.hh> namespace mln { @@ -78,149 +122,221 @@ namespace mln namespace clustering { - - // - // PARAM : DIM VECTOR, NUMBER OF CLASSES - // INPUT : MATRIX 1..N (SAMPLE) - // WORK - // - - // n is the size of the sample. - // k is the number of centers. - // p is the number of attributes for a point. - // T is the type for computations. + + /// \brief Implements the K MEAN algorithm with matrix and vectors. + /// + /// Param n is the size of the sample (the number of point to be + /// classified). Param k is the number of centers, at the end, + /// each point is link to one the nearest center. Param p is the + /// number of attributes of a point, in fact its dimension (int_u8 + /// = 1, rgb8 = 3, etc ...). Param T is the type used for + /// computations, the type of the point, typiccaly float or + /// double. + /// + /// \ingroup modclustering template <unsigned n, unsigned k, unsigned p, typename T> struct k_mean { - //------------------------------------------------------------------------ - // Constructor and destructor - //------------------------------------------------------------------------ - + /// Constructors and destructors. + /// \{ + /// \brief Allocate or deallocate the data structure. + /// + /// Allocate or deallocate the _point, _distance, _group, + /// _center, _variance data structure with the correct size. k_mean(); ~k_mean(); + /// \} - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - + /// Accessors. + /// \{ + /// \brief Hack to view temporary results of the kmean algorithm. + /// + /// These methods are necessary for unitary testing and debugging. algebra::mat<n, p, T>& get_point(); algebra::mat<k, p, T>& get_center(); algebra::mat<n, k, T>& get_distance(); algebra::mat<n, k, T>& get_group(); algebra::vec<k, T>& get_variance(); + /// \} - - //------------------------------------------------------------------------ - // Tools - //------------------------------------------------------------------------ + /// Tools. + /// \{ + /// \brief Define elementary function that are not available from milena. + /// + /// These methods are not interesting from the kmean'point of + /// view but are required for the gluing process. - /// \brief Return the euclidian distance beetween vector x and vector y. + /// \brief Return the euclidian distance beetween two vectors x and y. /// /// Compute the f$\sqrt(\sum_{i=0}^P ((x_i - y_i)^2)$f /// - /// \param[in] x a vector of dimension 1xP. - /// \param[in] y a second vector of dimension 1xP. + /// \param[in] x a vector of dimension p. + /// \param[in] y a second vector of dimension p. /// \return the distance between x and y. - mln_sum_product(T,T) euclidian_distance(const algebra::vec<p,T>& x, const algebra::vec<p,T>& y) const; - /// \brief Return the ith column. - /// - /// \param[in] m a matrix of dimension {N or K}xP. - /// \return the ith column as a vector of dimension 1xP. - - /// \brief Return the ith column. + /// \brief Return a stack copy of the ith column. /// - /// \param[in] m a matrix of dimension {N or K}xP. - /// \return the ith column as a vector of dimension 1xP. + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _col the index of the selected column. + /// \return a stack copy of the ith column as a vector of dimension q. template <unsigned q, unsigned r> algebra::vec<q,T> col(const algebra::mat<r, q, T>& m, const unsigned _col) const; + + /// \brief Return a heap copy of the ith col. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _col the index of the selected column. + /// \return a heap copy of the ith column as a vector of dimension q. template <unsigned q, unsigned r> algebra::vec<q,T>* ptr_col(const algebra::mat<r, q, T>& m, const unsigned _col) const; + + /// \brief Return a stack copy of the ith row. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _row the index of the selected row. + /// \return a stack copy of the ith row as a vector of dimension q. template <unsigned q, unsigned r> algebra::vec<r,T> row(const algebra::mat<r, q, T>& m, const unsigned _row) const; + + /// \brief Divide inplace a column of the matrix by a scalar. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _col the index of the selected column. + /// \param[in] value the scalar by which dividing the column. template <unsigned q, unsigned r> void div_col(algebra::mat<r, q, T>& m, const unsigned _col, const T value); + + /// \brief Sum all the elements of one row. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _row the index of the selected row. + /// \return the sum of every attributes of the row. template <unsigned q, unsigned r> mln_sum(T) sum_row(const algebra::mat<r, q, T>& m, const unsigned _row) const; + + /// \brief Return the minimum of a vector. + /// + /// \param[in] x a vector of dimension q. + /// \return the mininum of that input vector. template <unsigned q, typename M> M min_col(const algebra::vec<q, M>& x) const; + /// \} - //------------------------------------------------------------------------ - // Initializations of points and centers - //------------------------------------------------------------------------ - - /// \brief Initialize the matrix _point with the pixels of an image. + /// Initializations. + /// \{ + /// \brief Initialize the _point and _center data structure. /// - /// \param[in] input an image which contains the data points. + /// The _point data structure is fed with the value of the input + /// image. The _center data structure is fed with random points + /// taken from the input image. template <typename I> void init_point(const Image& _input); void init_center(); + /// \} - - //------------------------------------------------------------------------ - // Computations of distance, group, center, within variance - //------------------------------------------------------------------------ + /// Computations. + /// \{ + /// \brief Define the core routines of the kmean algorithm. + /// + /// The update_distance, update_group, update_center and update_variance + /// define the core kmean routines. If some optimization muste be done, + /// one may looks here. + + + /// \brief Update the _distance data structure. + /// + /// Compute the square distance between each point of the data + /// cloud and each center. void update_distance(); + + /// \brief Update the _group data structure. + /// + /// For each point of the data cloud, find the group which has + /// the minimum distance from it. void update_group(); - void update_center(); - T update_variance(); + /// \brief Update the _center data structure. + /// + /// For each group, compute the center of mass in therm of + /// points of the data cloud. + void update_center(); - //------------------------------------------------------------------------ - // Main loop - //------------------------------------------------------------------------ + /// \brief Update the _variance. + /// + /// Compute the within variance. Sum every shortest distance. + T update_variance(); + /// \} + /// \brief Define the orchestration of the kmean core routines. + /// + /// \param[in] _input the initial image which initialize the data cloud. + /// + /// The orchestration is in a fact a loop which call one after + /// each other the core routines. Ending the loop means managing + /// the statistical convergence or having some clues of + /// divergence (watch_dog). template <typename I> void loop(const Image& _input); - //void loop(); private: + /// \brief watch_dog define the maximum of iterations that can be done. + /// + /// That constant help us to decide if divergence has been + /// occurred or not. static const unsigned watch_dog = 100; - + + /// \brief _points contains the concatenation of every data points. /// - /// One data point is a vector 1xP where P is the number of attributes. - /// _points is a matrix NxP where N is the number of data points. + /// One data point is a vector 1 x p where p is the number of attributes. + /// So _points is a matrix n x P where n is the number of data points. algebra::mat<n, p, T>* _point; + /// \brief _distance contains all the euclidian distances between points /// and the centers. /// - /// _distance is a matrix NxK where N is the number of data points and - /// K the number of centers. + /// _distance is a matrix n x k where n is the number of data points and + /// k the number of centers. algebra::mat<n, k, mln_sum_product(T,T)>* _distance; + /// \brief _group contains the point assignement to one center. /// - /// _group is a boolean matrix NxK where N is the number of data points - /// and K the number of centers. + /// _group is a boolean matrix n x k where n is the number of data points + /// and k the number of centers. algebra::mat<n, k, T>* _group; - /// \brief _center contains the coordonnate of the K centers. + + /// \brief _center contains the coordonnate of the k centers. /// - /// _center is a matrix KxP where K is the number of centers and P is the - /// number of attributes. + /// _center is a matrix k x p where k is the number of centers + /// and p is the number of attributes. algebra::mat<k, p, T>* _center; + + /// \brief _variance contains the variance of each group. + /// + /// _variance is a vector 1 x k where k is the number of centers. algebra::vec<k, T>* _variance; }; @@ -234,7 +350,7 @@ namespace mln inline k_mean<n,k,p,T>::k_mean() { - trace::entering("mln::clustering::k_mean::k_mean"); + trace::entering("mln::clustering::k_mean::cstor"); _point = new algebra::mat<n, p, T>(); _distance = new algebra::mat<n, k, mln_sum_product(T,T)>(); @@ -248,14 +364,14 @@ namespace mln mln_postcondition(_center != 0); mln_postcondition(_variance != 0); - trace::exiting("mln::clustering::k_mean::k_mean"); + trace::exiting("mln::clustering::k_mean::cstor"); } template <unsigned n, unsigned k, unsigned p, typename T> inline k_mean<n,k,p,T>::~k_mean() { - trace::entering("mln::clustering::k_mean::~k_mean"); + trace::entering("mln::clustering::k_mean::dstor"); delete _point; delete _distance; @@ -263,7 +379,7 @@ namespace mln delete _center; delete _variance; - trace::exiting("mln::clustering::k_mean::~k_mean"); + trace::exiting("mln::clustering::k_mean::dstor"); } //-------------------------------------------------------------------------- @@ -350,7 +466,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - algebra::vec<q,T> k_mean<n,k,p,T>::col(const algebra::mat<r, q, T>& m, + algebra::vec<q,T> k_mean<n,k,p,T>::col(const algebra::mat<r, q, T>& m, const unsigned _col) const { trace::entering("mln::clustering::k_mean::col"); @@ -368,7 +484,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - algebra::vec<q,T>* k_mean<n,k,p,T>::ptr_col(const algebra::mat<r, q, T>& m, + algebra::vec<q,T>* k_mean<n,k,p,T>::ptr_col(const algebra::mat<r, q, T>& m, const unsigned _col) const { trace::entering("mln::clustering::k_mean::ptr_col"); @@ -386,7 +502,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - mln_sum(T) k_mean<n,k,p,T>::sum_row(const algebra::mat<r, q, T>& m, + mln_sum(T) k_mean<n,k,p,T>::sum_row(const algebra::mat<r, q, T>& m, const unsigned _row) const { trace::entering("mln::clustering::k_mean::sum_row"); @@ -404,7 +520,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - void k_mean<n,k,p,T>::div_col(algebra::mat<r, q, T>& m, + void k_mean<n,k,p,T>::div_col(algebra::mat<r, q, T>& m, const unsigned _col, const T value) { @@ -422,21 +538,21 @@ namespace mln inline M k_mean<n,k,p,T>::min_col(const algebra::vec<q, M>& x) const { - trace::entering("mln::clustering::k_mean<n,k,p,T>::min_col"); - + trace::entering("mln::clustering::k_mean::min_col"); + M result = x[0]; for (unsigned i = 1; i < q; ++i) result = math::min(result, x[i]); - trace::exiting("mln::clustering::k_mean<n,k,p,T>::min_col"); + trace::exiting("mln::clustering::k_mean::min_col"); return result; } template <unsigned n, unsigned k, unsigned p, typename T> inline mln_sum_product(T,T) - k_mean<n,k,p,T>::euclidian_distance(const algebra::vec<p, T>& x, + k_mean<n,k,p,T>::euclidian_distance(const algebra::vec<p, T>& x, const algebra::vec<p, T>& y) const { trace::entering("mln::clustering::k_mean::euclidian_distance"); @@ -458,8 +574,8 @@ namespace mln inline void k_mean<n,k,p,T>::init_point(const Image& _input) { - trace::entering("mln::clustering::k_mean<n,k,p,T>::init"); - + trace::entering("mln::clustering::k_mean::init"); + const I& input = exact(_input); algebra::mat<n, p, T>& point = *_point; @@ -474,28 +590,28 @@ namespace mln for_all(pi) { //std::cout << pi << std::endl; - + ++i; for (unsigned j = 0; j < p; ++j) { - + point(i,j) = input(pi).comp(j); //point(i,j) = input(pi); } } - trace::exiting("mln::clustering::k_mean<n,k,p,T>::init"); + trace::exiting("mln::clustering::k_mean::init"); } template <unsigned n, unsigned k, unsigned p, typename T> inline void k_mean<n,k,p,T>::init_center() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::init_center"); - + trace::entering("mln::clustering::k_mean::init_center"); + algebra::mat<n, p, T>& point = *_point; algebra::mat<k, p, T>& center = *_center; - + // A random point is choosen to be the initial value for a center. for (unsigned i = 0; i < k; ++i) { @@ -503,16 +619,16 @@ namespace mln for (unsigned j = 0; j < p; ++j) { - center(i,j) = point(random, j); + center(i,j) = point(random, j); } //std::cout << "center(" <::init_center"); + trace::exiting("mln::clustering::k_mean::init_center"); } - + //-------------------------------------------------------------------------- // Computations of distance, group, center, within variance //-------------------------------------------------------------------------- @@ -528,7 +644,7 @@ namespace mln algebra::mat<n, p, T>& point = *_point; algebra::mat<n, k, T>& distance = *_distance; algebra::mat<k, p, T>& center = *_center; - + // for all points in the data cloud. for (unsigned i = 0; i < n; ++i) { @@ -546,7 +662,7 @@ namespace mln inline void k_mean<n,k,p,T>::update_group() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::update_group"); + trace::entering("mln::clustering::k_mean::update_group"); algebra::mat<n, k, mln_sum_product(T,T)>& distance = *_distance; algebra::mat<n, k, T>& group = *_group; @@ -563,14 +679,14 @@ namespace mln } // mln_postcondition(sum(col(distance(i,j)) == 1) Vi - trace::exiting("mln::clustering::k_mean<n,k,p,T>::update_group"); + trace::exiting("mln::clustering::k_mean::update_group"); } template <unsigned n, unsigned k, unsigned p, typename T> inline void k_mean<n,k,p,T>::update_center() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::update_center"); + trace::entering("mln::clustering::k_mean::update_center"); algebra::mat<n, p, T>& point = *_point; algebra::mat<k, p, T>& center = *_center; @@ -592,14 +708,14 @@ namespace mln for (unsigned i = 0; i < k; ++i) div_col(center, i, sum_row(group, i)); - trace::exiting("mln::clustering::k_mean<n,k,p,T>::update_center"); + trace::exiting("mln::clustering::k_mean::update_center"); } template <unsigned n, unsigned k, unsigned p, typename T> inline T k_mean<n,k,p,T>::update_variance() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::update_variance"); + trace::entering("mln::clustering::k_mean::update_variance"); algebra::vec<k, T>& variance = *_variance; algebra::mat<n, k, T>& distance = *_distance; @@ -613,10 +729,10 @@ namespace mln result += variance[i]; } - trace::exiting("mln::clustering::k_mean<n,k,p,T>::update_variance"); + trace::exiting("mln::clustering::k_mean::update_variance"); return result; - } - + } + //-------------------------------------------------------------------------- // Main loop @@ -626,9 +742,8 @@ namespace mln template <typename I> inline void k_mean<n,k,p,T>::loop(const Image& _input) - //void k_mean<n,k,p,T>::loop() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::loop"); + trace::entering("mln::clustering::k_mean::loop"); T within_variance = INT_MAX-1; T old_variance = INT_MAX; @@ -658,12 +773,10 @@ namespace mln std::cout <::loop"); - } - - + trace::exiting("mln::clustering::k_mean::loop"); + } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean1d.hh b/scribo/sandbox/green/mln/clustering/kmean1d.hh index c3c1add..57d246b 100644 --- a/scribo/sandbox/green/mln/clustering/kmean1d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean1d.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -24,7 +24,7 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_KMEAN1D_HH -#define MLN_CLUSTERING_KMEAN1D_HH +# define MLN_CLUSTERING_KMEAN1D_HH /// \file /// @@ -34,36 +34,63 @@ /// the greylevel attribute inspite of the pixel attribute. The /// algorithm is independant from the image dimension. But, we have 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 +/// by a fix cost in the complexity. This version is very adapted to /// images with small quantification. +/// +/// The following sample is a typical use of the new kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean1d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/pgm/load.hh> +/// #include <mln/value/int_u8.hh> +/// +/// int main() +/// { +/// typedef mln::value::int_u8 t_int_u8; +/// typedef mln::image2d<t_int_u8> t_image2d_int_u8; +/// t_image2d_int_u8 img_int_u8; +/// +/// mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/house.pgm"); +/// mln::clustering::kmean1d<double, 8> kmean(img_int_u8, 3); +/// +/// kmean.launch_n_times(); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> -#include <limits.h> -#include <iostream> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/accu/stat/histo1d.hh> -#include <mln/core/contract.hh> -#include <mln/trait/value_.hh> -#include <mln/accu/stat/histo1d.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/macros.hh> -#include <mln/math/min.hh> -#include <mln/math/sqr.hh> -#include <mln/norm/l2.hh> +# include <mln/data/compute.hh> +# include <mln/data/fill.hh> +# include <mln/debug/println.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/io/ppm/save.hh> +# include <mln/io/pgm/save.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/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> +# include <mln/math/min.hh> +# include <mln/math/sqr.hh> +# include <mln/norm/l2.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/trait/value_.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> namespace mln { @@ -85,8 +112,8 @@ namespace mln /// 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. + /// Param T is the type used for computations (float or double). + /// Param n is the quantification for the image grayscale. template <typename T, unsigned n> struct kmean1d { @@ -117,32 +144,22 @@ namespace mln ///} - //------------------------------------------------------------------------ - // Constructor - //------------------------------------------------------------------------ - - /// \brief Constructor + /// \brief Constructor. /// \param[in] point : the image as the population of pixels. /// \param[in] k_center : the number of centers. /// \param[in] watch_dog : the limit to observe the convergence (10). /// \param[in] n_times : the number of times that we executed kmean(10). - kmean1d(const t_point_img& point, const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - - /// Mutator and accessors. + /// Mutators 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); @@ -173,31 +190,21 @@ namespace mln /// \} - //------------------------------------------------------------------------ - // Initializations of centers - //------------------------------------------------------------------------ - /// Initialization of centers. /// \{ - /// \brief Two ways: Regular greylevel tick or random greylevel value or. + /// \brief Initialize centers by regular tick or random position. /// /// 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. @@ -206,36 +213,27 @@ namespace mln /// first compute. Then we assign the pixels to their nearest center. /// The new location of each center can then update. Finaly, hommogeneity /// in group is observed by the within variance. - void update_distance(); void update_group(); void update_mean(); void update_variance(); - /// \} - //------------------------------------------------------------------------ - // Main loop - //------------------------------------------------------------------------ /// kmean main loop /// \{ /// \brief User interface to launch the kmean process. /// - /// There are two ways to launch the kmean process. The first one allow to - /// run it one time until convergence. As the process is a descent, it - /// depends on the initial center locations. The second call allow us to - /// rerun the process many times and to keep the best result (the one - /// with the smallest within variance). - + /// There are two ways to launch the kmean process. The first + /// one allows to run it one time until convergence. As the + /// process is a descent, it depends on the initial center + /// location. The second call allow us to rerun the process + /// many times and to keep the best result (the one with the + /// smallest within variance). void launch_one_time(); void launch_n_times(); - /// \} - //------------------------------------------------------------------------ - // Checking the validiy of the results - //------------------------------------------------------------------------ /// Checking the validity of the results. /// \{ @@ -244,15 +242,10 @@ namespace mln /// After each launching the kmean process one time, we need to know if /// the descent was successfull or not. The method is_valid_descent do it /// for us. The method looks for a bad center (a class with no greylevel - /// associate to it) and a failure in the convergence. - + /// associate to it) or a failure in the convergence. bool is_descent_valid(); - /// \} - //------------------------------------------------------------------------ - // Debugging tools - //------------------------------------------------------------------------ /// Debugging tools /// \{ @@ -264,12 +257,10 @@ namespace mln /// greylevel image. The update_cnv and finalize_cnv methods are used to /// trace the convergence. They fill two images with the mean info and /// the within variance info along the convergence and the tries. - void build_label_dbg(); void build_all_dbg(); void update_cnv(); void finalize_cnv(); - /// \} @@ -279,20 +270,19 @@ 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 + /// In fact, watch_dog limits 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 _k_center; unsigned _watch_dog; unsigned _n_times; - /// \} + /// Convergence information. /// \{ /// \brief This information is used to follow the convergence. @@ -300,26 +290,25 @@ namespace mln /// The within_variance is the homogeniety 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. The current_step info explicit where we are in the - /// kmean iteration. The last information, current_launching, - /// traces the progress while iterates kmean loop again and - /// again. The flag is_number_valid is set when a empty class - /// appears. This flag inhibit the process and force to restart - /// a try. - + /// is just the sum of all ponderated variances 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 current_step info explicit + /// where we are in the kmean iteration. The last information, + /// current_launching, traces the progress while iterates kmean + /// loop again and again. The flag is_number_valid is set when a + /// empty class appears. This flag inhibit the process and + /// force to restart a try. t_result _within_variance; unsigned _current_step; unsigned _current_launching; bool _is_number_valid; static const unsigned _N_TRIES = 3; - /// \} + /// Result of the kmean process. /// \{ /// \brief The center location are the major results of the kmean process. @@ -327,23 +316,23 @@ namespace mln /// 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. + /// \brief The inputs are the distribution of 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. @@ -354,13 +343,12 @@ namespace mln /// 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. @@ -369,15 +357,14 @@ namespace mln /// 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. + /// \brief Some information exports 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 @@ -385,14 +372,13 @@ namespace mln /// 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. + + /// Variance and center evolutions. /// \{ /// \brief Trace the variance and the center evolution. /// @@ -401,10 +387,8 @@ namespace mln /// 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; - /// \} }; @@ -803,8 +787,6 @@ namespace mln { 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 @@ -872,7 +854,7 @@ namespace mln for_all(g) { - if (l.ind() == _group(g)) + if (static_cast<t_label>(l.ind()) == _group(g)) _variance(l) += _distance(point2d(g.ind(), l.ind())); } @@ -1091,7 +1073,7 @@ namespace mln trace::exiting("mln::clustering::kmean1d::launch_n_times"); } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean2d.hh b/scribo/sandbox/green/mln/clustering/kmean2d.hh index e4918db..51aaf49 100644 --- a/scribo/sandbox/green/mln/clustering/kmean2d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean2d.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -24,52 +24,102 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_KMEAN2D_HH -#define MLN_CLUSTERING_KMEAN2D_HH +# define MLN_CLUSTERING_KMEAN2D_HH /// \file /// -/// \brief Implements the optimized kmean algorithm. +/// \brief Implements the optimized kmean algorithm in 2d. /// /// This algorithm is optimized in the way it proceeds directly with /// the greylevel attribute inspite of the pixel attribute. The /// algorithm is independant from the image dimension. But, we have 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 -/// images 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/histo2d.hh> - -#include <mln/math/min.hh> -#include <mln/math/sqr.hh> -#include <mln/norm/l2.hh> - -#include <mln/core/image/image2d.hh> -#include <mln/core/concept/image.hh> -#include <mln/value/int_u.hh> -#include <mln/value/rgb8.hh> -#include <mln/value/rg.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/literal/one.hh> -#include <mln/labeling/colorize.hh> -#include <mln/labeling/mean_values.hh> - -#include <mln/io/ppm/save.hh> -#include <mln/io/pgm/save.hh> - -#include <mln/util/array.hh> -#include <mln/algebra/vec.hh> +/// by a fix cost in the complexity. This version is very adapted to +/// images with small quantification. But, in 2d, the execution +/// becomes very slow. It's just normal because the quantification is +/// 8 bits per axis. So the actual histogram is bigger than the image. +/// +/// Take care to the following point: The within variance is still a +/// scalar value because we take the distance between two points and +/// the result is a scalar from the geometrical point of view. An +/// alternative implementation could study the variance/covariance +/// matrix of each sub data clouds and works with the trace of the +/// within variance matrix (as we do for the fisher criteria in N-d). +/// +/// The following sample is a typical use of the new kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rg8 img_rg8; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// +/// mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); +/// +/// kmean.launch_n_times(); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> + +# include <mln/accu/stat/histo2d.hh> + +# include <mln/algebra/vec.hh> + +# include <mln/core/concept/image.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/macros.hh> + +# include <mln/data/compute.hh> +# include <mln/data/fill.hh> + +# include <mln/debug/println.hh> + +# include <mln/io/pgm/save.hh> +# include <mln/io/ppm/save.hh> + +# include <mln/labeling/colorize.hh> +# include <mln/labeling/mean_values.hh> + +# include <mln/literal/one.hh> +# include <mln/literal/zero.hh> + +# include <mln/math/min.hh> +# include <mln/math/sqr.hh> +# include <mln/norm/l2.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/trait/value_.hh> + +# include <mln/util/array.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> +# include <mln/value/rg.hh> namespace mln { @@ -85,21 +135,22 @@ namespace mln 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. + /// This version of the kmean algorithm uses a 2-channels + /// (red/green channel) 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. + /// Param T is the type used for computations (float or double). + /// Param n is the quantification for the image grayscale. template <typename T, unsigned n> struct kmean2d { /// Type definitions. /// \brief A few type definitions to limit the refactoring impact. ///{ - typedef value::rgb<8> t_rgb; typedef value::label<8> t_label; typedef value::rg<n> t_value; @@ -108,7 +159,7 @@ namespace mln typedef T t_result1d; typedef algebra::vec<2,T> t_result2d; - /// FIXME t_point n'est pas forcément une image 2d, bien au contraire. + /// \fixme t_point_img is not an image2d ... but it works like this ... typedef image2d<t_value> t_point_img; typedef image2d<unsigned> t_histo_img; typedef util::array<t_result1d> t_number_img; @@ -128,35 +179,24 @@ namespace mln typedef util::array<t_mean_set> t_mean_cnv; typedef image1d<t_result1d> t_variance_val; typedef util::array<t_variance_val> t_variance_cnv; - ///} - //------------------------------------------------------------------------ - // Constructor - //------------------------------------------------------------------------ - - /// \brief Constructor + /// \brief Constructor. /// \param[in] point : the image as the population of pixels. /// \param[in] k_center : the number of centers. /// \param[in] watch_dog : the limit to observe the convergence (10). /// \param[in] n_times : the number of times that we executed kmean(10). - kmean2d(const t_point_img& point, const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - - /// Mutator and accessors. + /// Mutators 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); @@ -187,10 +227,10 @@ namespace mln /// \} - //------------------------------------------------------------------------ - // Printing temporary results - //------------------------------------------------------------------------ - + /// Show temporary results. + /// \{ + /// \brief Formating temporary outputs for debugging. + /// void print_mean(); void print_number(); void print_variance(); @@ -198,32 +238,18 @@ namespace mln void print_group(); void print_point(); void print_histo(); - - //------------------------------------------------------------------------ - // Initializations of centers - //------------------------------------------------------------------------ + /// \} /// Initialization of centers. /// \{ - /// \brief Two ways: Regular greylevel tick or random greylevel value or. + /// \brief Initialize centers by random position. /// - /// 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. - + /// Assign a random position in the 2-channel space to each center. 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. @@ -232,36 +258,27 @@ namespace mln /// first compute. Then we assign the pixels to their nearest center. /// The new location of each center can then update. Finaly, hommogeneity /// in group is observed by the within variance. - void update_distance(); void update_group(); void update_mean(); void update_variance(); - /// \} - //------------------------------------------------------------------------ - // Main loop - //------------------------------------------------------------------------ /// kmean main loop /// \{ /// \brief User interface to launch the kmean process. /// - /// There are two ways to launch the kmean process. The first one allow to - /// run it one time until convergence. As the process is a descent, it - /// depends on the initial center locations. The second call allow us to - /// rerun the process many times and to keep the best result (the one - /// with the smallest within variance). - + /// There are two ways to launch the kmean process. The first + /// one allows to run it one time until convergence. As the + /// process is a descent, it depends on the initial center + /// location. The second call allow us to rerun the process + /// many times and to keep the best result (the one with the + /// smallest within variance). void launch_one_time(); void launch_n_times(); - /// \} - //------------------------------------------------------------------------ - // Checking the validiy of the results - //------------------------------------------------------------------------ /// Checking the validity of the results. /// \{ @@ -269,16 +286,11 @@ namespace mln /// /// After each launching the kmean process one time, we need to know if /// the descent was successfull or not. The method is_valid_descent do it - /// for us. The method looks for a bad center (a class with no greylevel - /// associate to it) and a failure in the convergence. - + /// for us. The method looks for a bad center (a class with no r/g color + /// associate to it) or a failure in the convergence. bool is_descent_valid(); - /// \} - //------------------------------------------------------------------------ - // Debugging tools - //------------------------------------------------------------------------ /// Debugging tools /// \{ @@ -290,13 +302,11 @@ namespace mln /// greylevel image. The update_cnv and finalize_cnv methods are used to /// trace the convergence. They fill two images with the mean info and /// the within variance info along the convergence and the tries. - void build_label_dbg(); void build_mean_dbg(); void build_all_dbg(); void update_cnv(); void finalize_cnv(); - /// \} @@ -313,13 +323,12 @@ namespace mln /// 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 _k_center; unsigned _watch_dog; unsigned _n_times; - /// \} + /// Convergence information. /// \{ /// \brief This information is used to follow the convergence. @@ -337,16 +346,15 @@ namespace mln /// again. The flag is_number_valid is set when a empty class /// appears. This flag inhibit the process and force to restart /// a try. - t_result1d _within_variance; unsigned _current_step; unsigned _current_launching; bool _is_number_valid; static const unsigned _N_TRIES = 3; - /// \} + /// Result of the kmean process. /// \{ /// \brief The center location are the major results of the kmean process. @@ -354,10 +362,11 @@ namespace mln /// 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_result1d _variance_min; t_mean_img _mean_min; + /// \} + /// Inputs. /// \{ @@ -365,12 +374,11 @@ namespace mln /// /// 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. @@ -381,27 +389,25 @@ namespace mln /// 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_mean_img _mean; // k x 2 t_variance_img _variance; // k x 1 within group - /// \} + /// Greylevels description. /// \{ - /// \brief The information are concerned with the greylevel input image. + /// \brief The information are concerned with the 2-channel input image. /// - /// The group image allow us to decide which greylevel (and of course + /// The group image allow us to decide which r/g value (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 - + /// clue on how a r/g value could contribute to a center. The summation + /// over the r/g values of a center give us the within variance. + t_group_img _group; // g x 2 because dim(t_value) = 2 + t_distance_img _distance; // label x r/g value /// \} + /// Debugging, calibrating and testing results. /// \{ /// \brief Some exports information to control the results. @@ -412,14 +418,13 @@ namespace mln /// 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. + + /// Variance and center evolutions. /// \{ /// \brief Trace the variance and the center evolution. /// @@ -428,10 +433,8 @@ namespace mln /// 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; - /// \} }; @@ -448,7 +451,7 @@ namespace mln const unsigned watch_dog, const unsigned n_times) { - trace::entering("mln::clustering::kmean2d::kmean2d"); + trace::entering("mln::clustering::kmean2d::cstor"); mln_precondition(point.is_valid()); _k_center = k_center; @@ -516,7 +519,7 @@ namespace mln _mean_cnv.append(mean_set); } - trace::exiting("mln::clustering::kmean2d::kmean2d"); + trace::exiting("mln::clustering::kmean2d::cstor"); } //-------------------------------------------------------------------------- @@ -796,15 +799,15 @@ namespace mln { trace::entering("mln::clustering::kmean2d::print_histo"); - mln_piter(t_histo_img) rgb(_histo.domain()); + mln_piter(t_histo_img) rg(_histo.domain()); - for_all(rgb) + for_all(rg) { - if (0 < _histo(rgb)) + if (0 < _histo(rg)) { - std::cout << "histo(r=" << rgb.row(); - std::cout << ", g=" << rgb.col(); - std::cout << ")= " << _histo(rgb); + std::cout << "histo(r=" << rg.row(); + std::cout << ", g=" << rg.col(); + std::cout << ")= " << _histo(rg); std::cout << std::endl; } } @@ -818,15 +821,15 @@ namespace mln { trace::entering("mln::clustering::kmean2d::print_group"); - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { - if (0 < _histo(rgb)) + if (0 < _histo(rg)) { - std::cout << "group(r=" << rgb.row(); - std::cout << ", g=" << rgb.col(); - std::cout << ")= " << _group(rgb); + std::cout << "group(r=" << rg.row(); + std::cout << ", g=" << rg.col(); + std::cout << ")= " << _group(rg); std::cout << std::endl; } } @@ -844,16 +847,16 @@ namespace mln for_all(l) { - mln_piter(t_distance_val) rgb(_distance[l.index_()].domain()); + mln_piter(t_distance_val) rg(_distance[l.index_()].domain()); - for_all(rgb) + for_all(rg) { - if (0 < _histo(rgb)) + if (0 < _histo(rg)) { std::cout << "distance(l=" << l.index_(); - std::cout << ",r=" << rgb.row(); - std::cout << ", g=" << rgb.col(); - std::cout << ")= " << _distance[l.index_()](rgb); + std::cout << ",r=" << rg.row(); + std::cout << ", g=" << rg.col(); + std::cout << ")= " << _distance[l.index_()](rg); std::cout << std::endl; } } @@ -960,9 +963,9 @@ namespace mln { trace::entering("mln::clustering::kmean2d::update_group"); - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { mln_eiter(t_distance_img) l(_distance); t_result1d min = mln_max(t_result1d); @@ -972,9 +975,9 @@ namespace mln for_all(l) { - if (min > _distance[l.index_()](rgb)) + if (min > _distance[l.index_()](rg)) { - min = _distance[l.index_()](rgb); + min = _distance[l.index_()](rg); label = l.index_(); } @@ -984,7 +987,7 @@ namespace mln //std::cout << "g = " << g << std::endl; - _group(rgb) = label; + _group(rg) = label; //std::cout << "group = " << _group(g) << std::endl; //std::cout << "-----------" << std::endl; } @@ -998,8 +1001,6 @@ namespace mln { trace::entering("mln::clustering::kmean2d::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 @@ -1021,14 +1022,14 @@ namespace mln _mean[em.index_()] = literal::zero; } - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { // peut être faut-il le decomposer par composantes - _mean[_group(rgb)][0] += rgb.row() * _histo(rgb); - _mean[_group(rgb)][1] += rgb.col() * _histo(rgb); - _number(_group(rgb)) += _histo(rgb); + _mean[_group(rg)][0] += rg.row() * _histo(rg); + _mean[_group(rg)][1] += rg.col() * _histo(rg); + _number(_group(rg)) += _histo(rg); } mln_eiter(t_mean_img) l(_mean); @@ -1072,12 +1073,12 @@ namespace mln { _variance[l.index_()] = literal::zero; - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { - if (l.index_() == _group(rgb)) - _variance[l.index_()] += _distance[l.index_()](rgb); + if (l.index_() == _group(rg)) + _variance[l.index_()] += _distance[l.index_()](rg); } _within_variance += _variance[l.index_()]; @@ -1334,7 +1335,7 @@ namespace mln trace::exiting("mln::clustering::kmean2d::launch_n_times"); } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean3d.hh b/scribo/sandbox/green/mln/clustering/kmean3d.hh index fb1a8df..c35d2a7 100644 --- a/scribo/sandbox/green/mln/clustering/kmean3d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean3d.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -24,51 +24,102 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_KMEAN3D_HH -#define MLN_CLUSTERING_KMEAN3D_HH +# define MLN_CLUSTERING_KMEAN3D_HH /// \file /// -/// \brief Implements the optimized kmean algorithm. +/// \brief Implements the optimized kmean algorithm in 3d. /// /// This algorithm is optimized in the way it proceeds directly with -/// the rgb values inspite of the pixel attribute. The +/// the rgb value inspite of the pixel attribute. The /// algorithm is independant from the image dimension. But, we have to /// compute one time the histogram. In fact, we move a recurrent cost -/// to a fix cost in the complexity. This version is 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/histo3d_rgb.hh> - -#include <mln/math/min.hh> -#include <mln/math/sqr.hh> -#include <mln/norm/l2.hh> - -#include <mln/core/image/image2d.hh> -#include <mln/core/concept/image.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/literal/one.hh> -#include <mln/labeling/colorize.hh> -#include <mln/labeling/mean_values.hh> - -#include <mln/io/ppm/save.hh> -#include <mln/io/pgm/save.hh> - -#include <mln/util/array.hh> -#include <mln/algebra/vec.hh> +/// by a fix cost in the complexity. This version is very adapted to +/// images with small quantification. But, in 3d, the execution +/// becomes very slow. It's just normal because the quantification is +/// n bits per axis. So the actual histogram may be bigger than the image. +/// +/// Take care to the following point: The within variance is still a +/// scalar value because we take the distance between two points and +/// the result is a scalar from the geometrical point of view. An +/// alternative implementation could study the variance/covariance +/// matrix of each sub data clouds and works with the trace of the +/// within variance matrix (as we do for the fisher criteria in N-d). +/// +/// The following sample is a typical use of the new kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean3d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb8_to_rgbn.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rgb.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::clustering::kmean3d<double,5> t_kmean; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::value::rgb<5> t_rgb5; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<t_rgb5> t_image2d_rgb5; +/// typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rgb5 img_rgb5; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rgb5=mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); +/// +/// t_kmean kmean(img_rgb5, 3); +/// +/// kmean.launch_n_times(); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> + +# include <mln/accu/stat/histo3d_rgb.hh> + +# include <mln/algebra/vec.hh> + +# include <mln/math/min.hh> +# include <mln/math/sqr.hh> +# include <mln/norm/l2.hh> + +# include <mln/core/concept/image.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/macros.hh> + +# include <mln/data/compute.hh> +# include <mln/data/fill.hh> +# include <mln/debug/println.hh> + +# include <mln/labeling/colorize.hh> +# include <mln/labeling/mean_values.hh> + +# include <mln/literal/one.hh> +# include <mln/literal/zero.hh> + +# include <mln/io/ppm/save.hh> +# include <mln/io/pgm/save.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/trait/value_.hh> + +# include <mln/util/array.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> + namespace mln { @@ -98,7 +149,6 @@ namespace mln /// Type definitions. /// \brief A few type definitions to limit the refactoring impact. ///{ - typedef value::rgb<8> t_rgb; typedef value::label<8> t_label; typedef value::rgb<n> t_value; @@ -108,7 +158,7 @@ namespace mln typedef T t_result1d; typedef algebra::vec<3,T> t_result3d; - /// FIXME t_point n'est pas forcément une image 2d, bien au contraire. + /// \fixme t_point is not an image2d, it may be something else ... typedef image2d<t_value> t_point_img; typedef image3d<unsigned> t_histo_img; typedef util::array<t_result1d> t_number_img; @@ -128,35 +178,26 @@ namespace mln typedef util::array<t_mean_set> t_mean_cnv; typedef image1d<t_result1d> t_variance_val; typedef util::array<t_variance_val> t_variance_cnv; - ///} - //------------------------------------------------------------------------ - // Constructor - //------------------------------------------------------------------------ - /// \brief Constructor + /// \brief Constructor. + /// /// \param[in] point : the image as the population of pixels. /// \param[in] k_center : the number of centers. /// \param[in] watch_dog : the limit to observe the convergence (10). /// \param[in] n_times : the number of times that we executed kmean(10). - kmean3d(const t_point_img& point, const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - - /// Mutator and accessors. + /// Mutators 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); @@ -184,13 +225,13 @@ namespace mln // Normal outputs t_mean_img& to_result(); - /// \} - //------------------------------------------------------------------------ - // Printing temporary results - //------------------------------------------------------------------------ + /// Show temporary results. + /// \{ + /// \brief Formating temporary outputs for debugging. + /// void print_mean(); void print_number(); void print_variance(); @@ -198,32 +239,19 @@ namespace mln void print_group(); void print_point(); void print_histo(); + /// \} - //------------------------------------------------------------------------ - // Initializations of centers - //------------------------------------------------------------------------ /// Initialization of centers. /// \{ - /// \brief Two ways: Regular greylevel tick or random greylevel value or. + /// \brief Initialize centers by random position. /// - /// There is two way to proceed the initialization. First of all, we - /// divide the rgb space 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. - + /// Assign a random position in the rgb space to each center. 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. @@ -232,36 +260,27 @@ namespace mln /// first compute. Then we assign the pixels to their nearest center. /// The new location of each center can then update. Finaly, hommogeneity /// in group is observed by the within variance. - void update_distance(); void update_group(); void update_mean(); void update_variance(); - /// \} - //------------------------------------------------------------------------ - // Main loop - //------------------------------------------------------------------------ /// kmean main loop /// \{ /// \brief User interface to launch the kmean process. /// - /// There are two ways to launch the kmean process. The first one allow to - /// run it one time until convergence. As the process is a descent, it - /// depends on the initial center locations. The second call allow us to - /// rerun the process many times and to keep the best result (the one - /// with the smallest within variance). - + /// There are two ways to launch the kmean process. The first + /// one allows to run it one time until convergence. As the + /// process is a descent, it depends on the initial center + /// location. The second call allow us to rerun the process + /// many times and to keep the best result (the one with the + /// smallest within variance). void launch_one_time(); void launch_n_times(); - /// \} - //------------------------------------------------------------------------ - // Checking the validiy of the results - //------------------------------------------------------------------------ /// Checking the validity of the results. /// \{ @@ -271,32 +290,22 @@ namespace mln /// the descent was successfull or not. The method is_valid_descent do it /// for us. The method looks for a bad center (a class with no greylevel /// associate to it) and a failure in the convergence. - bool is_descent_valid(); - /// \} - //------------------------------------------------------------------------ - // Debugging tools - //------------------------------------------------------------------------ - - /// Debugging tools + /// Checking the validity of the results. /// \{ - /// \brief These methods help to interpret results. + /// \brief These methods help us to determine the validity of the 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. The update_cnv and finalize_cnv methods are used to - /// trace the convergence. They fill two images with the mean info and - /// the within variance info along the convergence and the tries. - + /// After each launching the kmean process one time, we need to know if + /// the descent was successfull or not. The method is_valid_descent do it + /// for us. The method looks for a bad center (a class with no color + /// associate to it) or a failure in the convergence. void build_label_dbg(); void build_mean_dbg(); void build_all_dbg(); void update_cnv(); void finalize_cnv(); - /// \} @@ -313,13 +322,12 @@ namespace mln /// 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 _k_center; unsigned _watch_dog; unsigned _n_times; - /// \} + /// Convergence information. /// \{ /// \brief This information is used to follow the convergence. @@ -337,14 +345,12 @@ namespace mln /// again. The flag is_number_valid is set when a empty class /// appears. This flag inhibit the process and force to restart /// a try. - t_result1d _within_variance; unsigned _current_step; unsigned _current_launching; bool _is_number_valid; static const unsigned _N_TRIES = 3; - /// \} /// Result of the kmean process. @@ -354,10 +360,10 @@ namespace mln /// 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_result1d _variance_min; t_mean_img _mean_min; + /// \} /// Inputs. /// \{ @@ -365,12 +371,11 @@ namespace mln /// /// 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. @@ -381,11 +386,9 @@ namespace mln /// 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_mean_img _mean; // k x 3 t_variance_img _variance; // k x 1 within group - /// \} /// rgb image description. @@ -396,12 +399,11 @@ namespace mln /// 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 rgb space of a center give us the within variance. - t_group_img _group; // g x 3 because dim(t_value) = 3 t_distance_img _distance; // label x rgb space - /// \} + /// Debugging, calibrating and testing results. /// \{ /// \brief Some exports information to control the results. @@ -412,14 +414,12 @@ namespace mln /// 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. + /// Variance and center evolutions. /// \{ /// \brief Trace the variance and the center evolution. /// @@ -428,10 +428,8 @@ namespace mln /// 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; - /// \} }; @@ -448,7 +446,7 @@ namespace mln const unsigned watch_dog, const unsigned n_times) { - trace::entering("mln::clustering::kmean3d::kmean3d"); + trace::entering("mln::clustering::kmean3d::cstor"); mln_precondition(point.is_valid()); _k_center = k_center; @@ -520,7 +518,7 @@ namespace mln _mean_cnv.append(mean_set); } - trace::exiting("mln::clustering::kmean3d::kmean3d"); + trace::exiting("mln::clustering::kmean3d::cstor"); } //-------------------------------------------------------------------------- @@ -1338,7 +1336,7 @@ namespace mln trace::exiting("mln::clustering::kmean3d::launch_n_times"); } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean_rgb.hh b/scribo/sandbox/green/mln/clustering/kmean_rgb.hh index 253745b..544066b 100644 --- a/scribo/sandbox/green/mln/clustering/kmean_rgb.hh +++ b/scribo/sandbox/green/mln/clustering/kmean_rgb.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -28,16 +28,68 @@ /// \file /// -/// \brief Implements the optimized kmean algorithm. +/// \brief Implements the optimized kmean algorithm in the 3d-RGB space. /// /// This algorithm is optimized in the way it proceeds directly with -/// the rgb values inspite of the pixel attribute. The +/// the rgb value inspite of the pixel attribute. The /// algorithm is independant from the image dimension. But, we have to /// compute one time the histogram. In fact, we move a recurrent cost -/// to a fix cost in the complexity. This version is adapted to -/// image with small quantification. - -/// APLATISSEMENT DES KMEAN3D +/// by a fix cost in the complexity. This version is very adapted to +/// images with small quantification. But, in 3d, the execution +/// becomes very slow. It's just normal because the quantification is +/// n bits per axis. So the actual histogram may be bigger than the image. +/// +/// Take care to the following point: The within variance is still a +/// scalar value because we take the distance between two points and +/// the result is a scalar from the geometrical point of view. An +/// alternative implementation could study the variance/covariance +/// matrix of each sub data clouds and works with the trace of the +/// within variance matrix (as we do for the fisher criteria in N-d). +/// +/// The following sample is a typical use of the functional (versus +/// object) kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean_rgb.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb8_to_rgbn.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/label_8.hh> +/// #include <mln/value/rgb.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::label_8 t_lbl8; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::value::rgb<5> t_rgb5; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<t_rgb5> t_image2d_rgb5; +/// typedef mln::image2d<t_lbl8> t_image2d_lbl8; +/// typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rgb5 img_rgb5; +/// t_image2d_lbl8 img_lbl8; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rgb5 = mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); +/// img_lbl8 = mln::clustering::kmean_rgb<double,5>(img_rgb5, 3); +/// +/// return 0; +/// } +/// +/// \fixme The execution shows a bug in printing outputs and it seems severe. +/// +/// The last execution with the following set of parameters +/// {house.ppm,3,10,10} shows that if the binary starts correctly, it +/// ends before returning the label image and with disturbing outputs. +/// Dumping the outputs in a file reveals that the number of +/// trace::entering differs from the number of trace::exiting. May the +/// program exit from a loop without ending a trace ??? # include <limits.h> # include <iostream> @@ -86,7 +138,7 @@ //-------------------------------------------------------------------------- -// CODE APLATI +// FUNCTIONAL CODE //-------------------------------------------------------------------------- @@ -95,7 +147,24 @@ namespace mln namespace clustering { - + /// \brief Implements the functional kmean algorithm. + /// + /// This functional version of the kmean is very specific. All the + /// code is view as a one-block function. This code is provided as + /// is. I (YJ) don't know where i stopped this version. It may not + /// work. Debugging tools are not yet provided. This code is just + /// the functional transcription of the kmean3d version. The code + /// has the very experimental status. + /// + /// T is the type used for computations (float or double). + /// n is the quantification for the rgb image. + /// + /// \param[in] point : the image as the population of pixels. + /// \param[in] k_center : the number of centers. + /// \param[in] watch_dog : the limit to observe the convergence (10). + /// \param[in] n_times : the number of times that we executed kmean(10). + /// + /// \return an image which represents the pixel classification. template <typename T, unsigned n, typename I> inline image2d<value::label_8> diff --git a/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh b/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh index ca7ba51..e89edad 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh @@ -1,6 +1,4 @@ -// Copyright (C) 2007 EPITA Research and Development Laboratory (LRDE) -// Copyright (C) 2008 EPITA Research and Development Laboratory (LRDE) -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -34,7 +32,51 @@ /// \file /// -/// \brief Convert rg value to rgb +/// \brief Convert a rg value to a rgb value. +/// +/// This source implements the conversion between rg space and rgb space. +/// +/// The following sample is a typical use of the rg/rgb conversion function. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/fun/v2v/rg_to_rgb.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rgb8 point_img_rgb8; +/// t_image2d_rg8 img_rg8; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// +/// mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); +/// +/// kmean.launch_n_times(); +/// +/// mln::clustering::kmean2d<double,8>::t_point_img point_img = +/// kmean.get_point(); +/// +/// point_img_rgb8 = mln::data::transform(point_img, t_rg_to_rgb()); +/// +/// return 0; +/// } namespace mln { @@ -47,14 +89,21 @@ namespace mln /// \brief Convert rg value to rgb. /// + /// Param n defines the quantification used for rgb space and rg space. + /// /// \ingroup modfunv2v - template <unsigned n> struct rg_to_rgb : Function_v2v< rg_to_rgb<n> > { typedef value::rg<n> argument; typedef value::rgb<n> result; + /// \brief Convert rg value to rgb value. + /// + /// \param[in] v the rg value to convert. + /// + /// Conversion is done by calling the rgb constructor and fill + /// the empty attirbute by 127. result operator()(const argument& v) const { return value::rgb<n>(v.red(), v.green(), 127); diff --git a/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am b/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am index 5f00678..77f9015 100644 --- a/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am +++ b/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am @@ -6,8 +6,13 @@ # TOOLS # ######### -INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) ECHO= echo RM= rm MKDIR= mkdir -p @@ -20,10 +25,10 @@ BUILD__PATTERN= green/build/tests ifeq ($(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) +BUILD__DIR= $(PWD)/ else # Case where make is done from source directory. -SOURCE_DIR= $(PWD) +SOURCE_DIR= $(PWD)/ BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) endif @@ -89,7 +94,7 @@ $(OBJ_F_PATH):$(SRC_F_PATH) ######### # Force every time the deletion -clean: print clean_target clean_obj clean_dst clean_old #clean_make +clean: clean_target clean_obj clean_dst clean_old #clean_make clean_target: diff --git a/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc b/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc index 27751fd..a839ece 100644 --- a/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc +++ b/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc @@ -1,34 +1,68 @@ -// UNARY TESTS ON K_MEAN - -#include <mln/clustering/k_mean.hh> +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This source manages unitary testing on the kmean implementation. +/// +/// Tests are performed from 2 bits up to 8 bits quantification. The goal +/// is to go through each programmatic flow to verify the conformity of the +/// code. It sounds like a theoritic model of unitary testing for milena. +/// The last test enables statistic computations on that component. #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/clustering/k_mean.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/data/transform.hh> + +#include <mln/img_path.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/data/transform.hh> - #include <mln/trait/value/print.hh> #include <mln/trait/image/print.hh> +#include <mln/pw/value.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/rgb8.hh> + #define SIZE_IMAGE 512 #define SIZE_SAMPLE1 (512*512) #define SIZE_SAMPLE2 4 @@ -44,20 +78,18 @@ #define MAT_GROUP2 mln::algebra::mat<SIZE_SAMPLE2, NB_CENTER, TYPE_POINT> #define VEC_VAR mln::algebra::vec<NB_CENTER, TYPE_POINT> - -void test_instantiation() -{ - mln::clustering::k_mean<SIZE_SAMPLE2,NB_CENTER, DIM_POINT, TYPE_POINT> kmean; - - // test the compilation - - std::cout << "test instantiation : ok" << std::endl; -} +/// Tools. +/// \{ +/// \brief Define some tools which are used in unitary testing. +/// +/// This kind of tools help to fill an image with only 4 tons or +/// computing a distance between two colors. Just keep an eye, nothing +/// really difficult in this code. 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; @@ -78,11 +110,12 @@ struct rgb8_to_4colors : mln::Function_v2v<rgb8_to_4colors> void print_color(const mln::value::rgb8& color) { - std::cout << "{r=" << color.red() << ", "; + 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; @@ -98,6 +131,140 @@ void fill_image_with_4colors(mln::image2d<mln::value::rgb8>& img) //print_color(purple); } + +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; +} + +/// \} + + +/// External mutators. +/// \{ +/// \brief Replace the kmean data structure values. +/// +/// This is a hack that provides low level routines to access key data +/// structure like point, center, distance and group. + +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 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 set_distance(MAT_DISTANCE2& distance, + const unsigned index, + const double d1, + const double d2) +{ + distance(index,0) = d1; + distance(index,1) = d2; +} + +void set_group(MAT_GROUP2& group, + const unsigned index, + const unsigned min) +{ + group(index, min) = 1.0; + group(index, 1-min) = 0.0; +} + +/// \} + +/// Fake states. +/// \{ +/// \brief Help to build from scratch temporary states for the kmean algorithm. +/// +/// This hack allow us to build temporary results used in the previous step to +/// make us very the behaviour of the current step. There is a fake state for +/// every key data structure (point, group, center and distance). + +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); +} + + +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 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); +} + + + + +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(" << i << "," << j << ") = " << distance(i,j) <<std::endl; + */ +} + +/// \} + +/// Equivalence. +/// \{ +/// \brief Test equivalence between point and image, center and color. +/// +/// Two kinds of equivalence are defined here. The first one tests the +/// equality between the data cloud and the initial image, while the +/// second one tests the equality between a center and a color. + bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, const MAT_POINT1& point) { @@ -118,11 +285,11 @@ bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, if (!test) { std::cout << pi; - std::cout << "{r=" << img(pi).red() << ", "; + 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 << "[r=" << point(index,0) << ", "; std::cout << "g=" << point(index,1) << ", "; std::cout << "b=" << point(index,2) << "]" << std::endl; @@ -135,6 +302,44 @@ bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, return result; } +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; +} + +/// \} + + +/// kmean unitary testing +/// \{ +/// \brief This part of the code manages the unitary testing. +/// +/// Many tests are performed and new kind of unitary testing +/// appears. In fact, we must simulate the previous steps of the line +/// currently testing and so making some fake steps. Bad and deep +/// hacking in data structure are required. We test the instantiation +/// of the kmean object, its initialization (points and centers), its +/// core routines (update_center, update_group, update_distance, +/// update_variance) and the final loop. + +void test_instantiation() +{ + mln::clustering::k_mean<SIZE_SAMPLE2,NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + // test the compilation + + std::cout << "test instantiation : ok" << std::endl; +} + + void test_init_point() { typedef mln::value::rgb8 rgb8; @@ -147,45 +352,12 @@ void test_init_point() 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() { @@ -195,10 +367,10 @@ void test_init_center() 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) || @@ -212,34 +384,6 @@ void test_init_center() 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() { @@ -269,34 +413,6 @@ void test_update_distance() 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(" << i << "," << j << ") = " << distance(i,j) <<std::endl; - */ -} void test_update_group() { @@ -331,26 +447,6 @@ void test_update_group() 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() { @@ -374,13 +470,14 @@ void test_update_center() 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; @@ -405,13 +502,15 @@ void test_update_variance() 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; } + +/// \fixme this procedure tests actually nothing. void test_loop() { typedef mln::value::rgb8 rgb8; @@ -426,24 +525,22 @@ void test_loop() kmean.init_point(img_ref); kmean.loop(img_ref); - - // std::cout << "Test update variance: ok" << std::endl; + // \FIXME: Which assertion must we define ? + // std::cout << "Test loop : ok" << std::endl; } +/// \} int main() { - //test_instantiation(); - //test_init_point(); - //test_init_center(); - //test_update_distance(); - //test_update_group(); - //test_update_center(); - //test_update_variance(); - - // mln::trace::quiet = false; - + test_instantiation(); + test_init_point(); + test_init_center(); + test_update_distance(); + test_update_group(); + test_update_center(); + test_update_variance(); test_loop(); return 0; diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/k_mean/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/k_mean/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/k_mean/k_mean.cc b/scribo/sandbox/green/use/clustering/k_mean/k_mean.cc new file mode 100644 index 0000000..9b00037 --- /dev/null +++ b/scribo/sandbox/green/use/clustering/k_mean/k_mean.cc @@ -0,0 +1,55 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the first implementation of the kmean algorithm. +/// + +#include <mln/clustering/k_mean.hh> +#include <mln/core/image/image2d.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/trait/value_.hh> +#include <mln/value/rgb8.hh> + +#define NB_CENTER 9 +#define NB_POINT (128*128) +#define POINT_SIZE mln_dim(mln::value::rgb8) +#define POINT_TYPE double + +int main() +{ + typedef mln::value::rgb8 t_rgb8; + mln::image2d<t_rgb8> img_rgb8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + mln::clustering::k_mean<NB_POINT, NB_CENTER, POINT_SIZE, POINT_TYPE> kmean; + + kmean.init_point(img_rgb8); + kmean.loop(img_rgb8); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean1d/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean1d/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc b/scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..82b13c9 --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,50 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the second implementation of the kmean algorithm. +/// + +#include <iostream> +#include <mln/clustering/kmean1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/img_path.hh> +#include <mln/io/pgm/load.hh> +#include <mln/value/int_u8.hh> + +int main() +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + t_image2d_int_u8 img_int_u8; + + mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/house.pgm"); + mln::clustering::kmean1d<double, 8> kmean(img_int_u8, 3); + + kmean.launch_n_times(); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean2d/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean2d/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc new file mode 100644 index 0000000..d6bf05b --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc @@ -0,0 +1,61 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the optimized version of the kmean2d algorithm. +/// + +#include <iostream> +#include <mln/clustering/kmean2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + +int main() +{ + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + + t_image2d_rgb8 img_rgb8; + t_image2d_rg8 img_rg8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + + mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); + + kmean.launch_n_times(); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean3d/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean3d/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc b/scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc new file mode 100644 index 0000000..c57d48a --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc @@ -0,0 +1,63 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the optimized version of the kmean3d algorithm. +/// + +#include <iostream> +#include <mln/clustering/kmean3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + + +int main() +{ + typedef mln::clustering::kmean3d<double,5> t_kmean; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<5> t_rgb5; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb5> t_image2d_rgb5; + typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb5 img_rgb5; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rgb5=mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); + + t_kmean kmean(img_rgb5, 3); + + kmean.launch_n_times(); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean_rgb/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean_rgb/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc b/scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc new file mode 100644 index 0000000..3bf2241 --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc @@ -0,0 +1,63 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the the functional (versus object) kmean code. +/// + +#include <iostream> +#include <mln/clustering/kmean_rgb.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + + +int main() +{ + typedef mln::value::label_8 t_lbl8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<5> t_rgb5; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb5> t_image2d_rgb5; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb5 img_rgb5; + t_image2d_lbl8 img_lbl8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rgb5 = mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); + img_lbl8 = mln::clustering::kmean_rgb<double,5>(img_rgb5, 3, 10, 10); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/fun/v2v/rg_to_rgb/Makefile.am diff --git a/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc b/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc new file mode 100644 index 0000000..84ae33f --- /dev/null +++ b/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc @@ -0,0 +1,68 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Example of using rg_t_rgb transformation used with kmean2d. +/// + +#include <iostream> +#include <mln/clustering/kmean2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/fun/v2v/rg_to_rgb.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + +int main() +{ + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb8 point_img_rgb8; + t_image2d_rg8 img_rg8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + + mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); + + kmean.launch_n_times(); + + mln::clustering::kmean2d<double,8>::t_point_img point_img = kmean.get_point(); + + point_img_rgb8 = mln::data::transform(point_img, t_rg_to_rgb()); + + return 0; +} -- 1.5.6.5

14 years, 2 months

last-svn-commit-46-gba64aaf Add some futur materials on regional maxima.

by Yann Jacquelet

* test_labelling_2.cc: New file. * test_labelling_3.cc: New file. --- scribo/sandbox/green/ChangeLog | 7 + .../sandbox/green/test_labelling_2.cc | 318 +++++++------------- .../sandbox/green/test_labelling_3.cc | 25 ++- 3 files changed, 141 insertions(+), 209 deletions(-) copy milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc => scribo/sandbox/green/test_labelling_2.cc (64%) copy milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc => scribo/sandbox/green/test_labelling_3.cc (92%) diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 4ddaa54..9e9b7d6 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,10 @@ +2010-06-30 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Add some futur materials on regional maxima. + + * test_labelling_2.cc: New file. + * test_labelling_3.cc: New file. + 2010-06-28 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Implement the kmean algorithh and start to optimize it. diff --git a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/test_labelling_2.cc similarity index 64% copy from milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc copy to scribo/sandbox/green/test_labelling_2.cc index 30e68b1..28129f1 100644 --- a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc +++ b/scribo/sandbox/green/test_labelling_2.cc @@ -1,4 +1,29 @@ -// DEMO ON KMEAN2D +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file #include <mln/clustering/kmean2d.hh> @@ -53,10 +78,10 @@ // Ok pour la convergence. // demander à Théo pour le temps (45 sec pour chaque launching [house,3 cl.]) // -void do_demo_kmean(const std::string& image, - const unsigned k_center, - const unsigned n_times, - const unsigned watch_dog) +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) { typedef mln::clustering::kmean2d<double,8> t_kmean; typedef mln::value::rg<8> t_rg8; @@ -104,6 +129,21 @@ void do_demo_kmean(const std::string& image, } +void demo(const std::string& image = OLENA_IMG_PATH"/house.ppm", + 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); +} mln::image2d<mln::value::rgb<8> > merge(const mln::image2d<mln::value::rg<8> >& input, @@ -134,124 +174,91 @@ merge(const mln::image2d<mln::value::rg<8> >& input, } -// -// Watershed image processing chain. -// -void do_demo_watershed(const std::string& image, - const unsigned k_center, - const unsigned n_times, - const unsigned watch_dog) -{ - typedef mln::value::label_8 t_lbl8; - typedef mln::value::rg<8> t_rg8; - typedef mln::value::rgb8 t_rgb8; - typedef mln::value::int_u8 t_int_u8; - typedef mln::image2d<t_lbl8> t_image2d_lbl8; - typedef mln::image2d<t_rgb8> t_image2d_rgb8; - typedef mln::image2d<t_int_u8> t_image2d_int_u8; - typedef mln::image2d<t_rg8> t_image2d_rg8; - typedef mln::image2d<unsigned> t_histo2d; - typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; - typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; - - t_image2d_rgb8 img_rgb8; - t_image2d_rgb8 output; - t_image2d_rgb8 img_cast; - t_image2d_rg8 img_rg8; - t_histo2d histo; - t_image2d_lbl8 label; - t_lbl8 n_labels; - t_histo2d reverted; - - // IMAGE LOADING PHASE - mln::io::ppm::load(house_rgb8, image.c_str()); - img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); - img_cast = mln::data::transform(img_rg8, t_rg_to_rgb()); - mln::io::ppm::save(img_cast, "red_green.ppm"); - - // HISTO COMPUTING AND FILTERING PHASE - histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); - mln::io::plot::save_image_sh(histo, "histo.sh"); - - //histo = mln::morpho::elementary::opening(histo, mln::c4()); - - mln::io::plot::save_image_sh(histo, "histo.sh"); - - // LABELING PHASE - reverted = mln::arith::revert(histo); - label = mln::morpho::watershed::flooding(reverted, mln::c4(), n_labels); - mln::io::pgm::save(label, "label.pgm"); +void usage(const int argc, const char *args[]) +{ + std::cout << "----------------------------------------" << std::endl; + std::cout << "argc : " << argc << std::endl; - // OUTPUT PHASE - output = merge(house_rg8, label); + for (int i = 0; i < argc; ++i) + std::cout << "args[" << i << "] : " << args[i] << std::endl; - mln::io::ppm::save(output, "merge.ppm"); + std::cout << "----------------------------------------" << std::endl; + std::cout << "usage: kmean2d [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 watch_dog (convergence loop)" << std::endl; + std::cout << "----------------------------------------" << std::endl; } - -// -// Regional maxima image processing chain. -// -void do_demo_regional1(const std::string& image, - const unsigned k_center, - const unsigned n_times, - const unsigned watch_dog) +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) { - typedef mln::value::label_8 t_lbl8; - typedef mln::value::rg<8> t_rg8; - typedef mln::value::rgb8 t_rgb8; - typedef mln::value::int_u8 t_int_u8; - typedef mln::image2d<t_lbl8> t_image2d_lbl8; - typedef mln::image2d<t_rgb8> t_image2d_rgb8; - typedef mln::image2d<t_int_u8> t_image2d_int_u8; - typedef mln::image2d<t_rg8> t_image2d_rg8; - typedef mln::image2d<unsigned> t_histo2d; - typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; - typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + bool result = false; - t_image2d_rgb8 img_rgb8; - t_image2d_rgb8 output; - t_image2d_rgb8 img_cast; - t_image2d_rg8 img_rg8; - t_histo2d histo; - t_histo2d opened; - t_image2d_lbl8 label; - t_image2d_lbl8 dilated; - t_lbl8 n_labels; + if (status) + { + std::istringstream arg_stream(arg); + arg_stream >> val; - // IMAGE LOADING PHASE - mln::io::ppm::load(img_rgb8, image.c_str()); - img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); - img_cast = mln::data::transform(img_rg8, t_rg_to_rgb()); - mln::io::ppm::save(img_cast, "red_green.ppm"); + result = !arg_stream.fail(); + } + return result; +} - // HISTO COMPUTING PHASE - histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); - mln::io::plot::save_image_sh(histo, "histo.sh"); +bool char_to_string(const bool status, const char *arg, std::string& val) +{ + bool result = false; + if (status) + { + std::istringstream arg_stream(arg); - // HISTO FILTERING PHASE - opened = mln::morpho::elementary::opening(histo, mln::c8()); - mln::io::plot::save_image_sh(opened, "opened.sh"); + arg_stream >> val; + return !arg_stream.fail(); + } - // HISTO LABELING PHASE - label = mln::labeling::regional_maxima(opened, mln::c8(), n_labels); - mln::io::pgm::save(label, "label.pgm"); + return result; +} +int main(const int argc, const char *args[]) +{ + std::string image("top"); + unsigned k_center; + unsigned watch_dog; + unsigned n_times; + bool status = true; - // HISTO FUZZY PHASE - dilated = mln::morpho::elementary::dilation(label, mln::c8()); - mln::io::pgm::save(dilated, "dilated.pgm"); + 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); - // OUTPUT PHASE - output = merge(img_rg8, dilated); - mln::io::ppm::save(output, "merge.ppm"); + return 0; } // @@ -333,108 +340,3 @@ void do_demo_regional2(const std::string& image, -void demo(const std::string& image = SCRIBO_PPM_IMG_PATH"/mp00082c_50p.ppm", - //const std::string& image = OLENA_IMG_PATH"/house.ppm", - const unsigned k_center = 2, - //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_kmean(image, k_center, n_times, watch_dog); -// do_demo_watershed(image, k_center, n_times, watch_dog); - do_demo_regional1(image, k_center, n_times, watch_dog); - //do_demo_regional2(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 << "----------------------------------------" << std::endl; - std::cout << "usage: kmean2d [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 watch_dog (convergence loop)" << std::endl; - std::cout << "----------------------------------------" << std::endl; -} - -bool char_to_unsigned(const bool status, const char *arg, unsigned& val) -{ - bool result = false; - - if (status) - { - 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; - - return !arg_stream.fail(); - } - - return result; -} - -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); - - return 0; -} diff --git a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/test_labelling_3.cc similarity index 92% copy from milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc copy to scribo/sandbox/green/test_labelling_3.cc index 30e68b1..317832b 100644 --- a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc +++ b/scribo/sandbox/green/test_labelling_3.cc @@ -1,4 +1,27 @@ -// DEMO ON KMEAN2D +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. #include <mln/clustering/kmean2d.hh> -- 1.5.6.5

14 years, 2 months

last-svn-commit-44-g7155a61 Define documentation files.

by Yann Jacquelet

* README.green: New index for source files in scribo/sandbox/green. * README.img: New index for image database. * README.result: New index for histogram database. --- scribo/sandbox/green/ChangeLog | 11 ++- scribo/sandbox/green/{README => README.green} | 58 ++++++++-- scribo/sandbox/green/README.img | 124 +++++++++++++++++++++ scribo/sandbox/green/README.result | 143 +++++++++++++++++++++++++ 4 files changed, 324 insertions(+), 12 deletions(-) rename scribo/sandbox/green/{README => README.green} (89%) create mode 100644 scribo/sandbox/green/README.img create mode 100644 scribo/sandbox/green/README.result diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 5a7ceaa..7bb6646 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,4 +1,13 @@ -2010-06-23 green <jacquelet(a)lrde.epita.fr> +2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Define documentation files. + + * README: Delete this file and replace it by README.green. + * README.green: New index for source files in scribo/sandbox/green. + * README.img: New index for image database. + * README.result: New index for histogram database. + +2010-06-23 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Define gnuplot shell export format. diff --git a/scribo/sandbox/green/README b/scribo/sandbox/green/README.green similarity index 89% rename from scribo/sandbox/green/README rename to scribo/sandbox/green/README.green index 03106d3..3f3c9a9 100644 --- a/scribo/sandbox/green/README +++ b/scribo/sandbox/green/README.green @@ -227,26 +227,62 @@ n'importe quel algorithme type huffman (tous les archiveurs en possède un). -VI MLN ------ +VIII VISUALISATION HISTOGRAMMES 3D +---------------------------------- -a) La sauvegarde des images au format gnuplot shell +==> to do -* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. -to do ... +* demo/accu/stat/histo2d +* mln/display/dispay_histo.hh +* mln/display/project_histo.hh +IX KMEANS +--------- + +Ce travail m'avait été demandé par théo. Je le laisse inachevé, quelque part +perdu pendant l'optimisation du code et sa transformation en canevas. + +* mln/clustering/k_mean.hh: Première implémentation avec matrices et vecteurs. +* mln/clustering/kmean1d.hh: Implémentation 1d avec des images. +* mln/clustering/kmean2d.hh: Implémentation 2d avec des images. +* mln/clustering/kmean3d.hh: Implémentation 3d avec des images. +* mln/clustering/kmean_rgb.hh: Impl. 3d aplatie et en cours de mise en canevas. + +* tests/clustering/k_mean: Tests unitaires sur la permière version. +* tests/clustering/kmean1d: Tests unitaire sur la version 1d. + +* demo/clustering/kmean1d: Utilisation de la version 1d. +* demo/clustering/kmean2d: Utilisation de la version 2d. +* demo/clustering/kmean3d: Utilisation de la version 3d. +* demo/clustering/kmean_rgb: Utilisation de la version aplatie. + +* bench/clustering/distance: Comparaison algorithmes d'évaluation des distances. + +==> to do + +X REGIONAL MAXIMA +----------------- + +==> to do + + +XI ANNOTATING +------------- + +==> to do + + +a) La sauvegarde des images au format gnuplot shell + +* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. + +to do ... -VII VISUALISATION HISTOGRAMMES 3D ---------------------------------- -c) La visualisation des histogrammes 3d -* demo/accu/stat/histo2d -* mln/display/dispay_histo.hh -* mln/displayproject_histo.hh diff --git a/scribo/sandbox/green/README.img b/scribo/sandbox/green/README.img new file mode 100644 index 0000000..c606a93 --- /dev/null +++ b/scribo/sandbox/green/README.img @@ -0,0 +1,124 @@ +I DESCRIPTION DES BASES D'IMAGES +-------------------------------- + +img ==> Répertoire des bases d'images +img/afp ==> Extrait de la base AFP (50 photos avec texte) +img/annotating-1 ==> Base diversifiée internet trop faible qualité +img/annotating-2 ==> Base diversifiée internet meilleur qualité +img/icdar ==> Base ICDAR en 20p/50p/100p (+quelques ajouts) +img/inim ==> Base complète du cours inim +img/olena ==> Images de tests d'olena (+quelques ajouts) + + +II DESCRIPTION DE CHAQUE BASE +----------------------------- + +A) BASE AFP + +img/afp/magick-pal-30-gen.sh ==> Génération base AFP 30 couleurs (imageMagick) +img/afp/magick-pal-20-gen.sh ==> Génération base AFP 20 couleurs (imageMagick) +img/afp/magick-pal-10-gen.sh ==> Génération base AFP 10 couleurs (imageMagick) + +img/afp/gimp-pal-30-gen.sh ==> Génération base AFP 30 couleurs (Gimp) +img/afp/gimp-pal-20-gen.sh ==> Génération base AFP 20 couleurs (Gimp) +img/afp/gimp-pal-10-gen.sh ==> Génération base AFP 10 couleurs (Gimp) + +Ne pas oublier le script de réduction des couleurs pour Gimp. + +$ more ~/.gimp-2.6/scripts/reduce-color.scm +(define (reduce-color filein fileout nbcol) + (let* ((image (car (gimp-file-load RUN-NONINTERACTIVE filein filein))) + (drawable (car (gimp-image-get-active-layer image))) + ) + (gimp-image-convert-indexed image NO-DITHER MAKE-PALETTE nbcol FALSE FALSE "") + (gimp-image-convert-rgb image) + (gimp-file-save RUN-NONINTERACTIVE image drawable fileout fileout) + (gimp-image-delete image) + )) + +img/afp/magick-pal-30 ==> Base AFP 30 couleurs (imageMagick) +img/afp/magick-pal-20 ==> Base AFP 20 couleurs (imageMagick) +img/afp/magick-pal-10 ==> Base AFP 10 couleurs (imageMagick) + +img/afp/gimp-pal-30 ==> Base AFP 30 couleurs (Gimp) +img/afp/gimp-pal-20 ==> Base AFP 20 couleurs (Gimp) +img/afp/gimp-pal-10 ==> Base AFP 10 couleurs (Gimp) + +img/afp/ppm ==> Base AFP originale convertie en ppm +img/afp/jpg ==> Base AFP originale + + +B) BASE ANNOTATING-1 + +img/annotating-1/screenshot ==> Fond d'écran, capture d'écran +img/annotating-1/slide ==> Transparents commerciaux ou conférences +img/annotating-1/handwritten ==> Ecritures manuscrites +img/annotating-1/map ==> Cartes marines, de régions, de villes +img/annotating-1/photo ==> Photo AFP +img/annotating-1/logo ==> Logos de toute sorte, de toute taille +img/annotating-1/typed ==> Documents textuels impimés +img/annotating-1/fax ==> Faxs +img/annotating-1/bill ==> Factures téléphones, eau ... + + +C) BASE ANNOTATING-2 + +img/annotating-2/slide ==> Transparents commerciaux ou conférences +img/annotating-2/handwritten ==> Ecritures manuscrites +img/annotating-2/logo ==> Logos de toute sorte, de toute taille +img/annotating-2/typed ==> Documents textuels impimés +img/annotating-1/bill ==> Factures téléphones, eau ... + + +D) BASE ICDAR + +img/icdar/100p/pbm ==> Base ICDAR 100% en noir & blanc +img/icdar/100p/pgm ==> Base ICDAR 100% en niveaux de gris +img/icdar/100p/ppm ==> Base ICDAR 100% en couleur +img/icdar/100p/gradient-thin ==> Masques de gradient thin sur base ICDAR 100% +img/icdar/100p/gradient-thick ==> Masques de gradient thick sur base ICDAR 100% + +img/icdar/50p/pbm ==> Base ICDAR 50% en noir & blanc +img/icdar/50p/pgm ==> Base ICDAR 50% en niveaux de gris +img/icdar/50p/ppm ==> Base ICDAR 50% en couleur + +img/icdar/20p/pbm ==> Base ICDAR 20% en noir & blanc +img/icdar/20p/pgm ==> Base ICDAR 20% en niveaux de gris +img/icdar/20p/ppm ==> Base ICDAR 20% en couleur + +img/icdar/20p/crop ==> Plein de crops sur la base ICDAR 20% (couleur) + +img/icdar/magick-pal-30-gen.sh => Génération base ICDAR 30 couleurs(imageMagick) +img/icdar/magick-pal-20-gen.sh => Génération base ICDAR 20 couleurs(imageMagick) +img/icdar/magick-pal-10-gen.sh => Génération base ICDAR 10 couleurs(imageMagick) + +img/icdar/gimp-pal-30-gen.sh ==> Génération base ICDAR 30 couleurs (Gimp) +img/icdar/gimp-pal-20-gen.sh ==> Génération base ICDAR 20 couleurs (Gimp) +img/icdar/gimp-pal-10-gen.sh ==> Génération base ICDAR 10 couleurs (Gimp) + +img/icdar/20p/magick-pal-30 ==> Base ICDAR 20% en noir & blanc +img/icdar/20p/magick-pal-20 ==> Base ICDAR 20% en niveaux de gris +img/icdar/20p/magick-pal-10 ==> Base ICDAR 20% en couleur + +img/icdar/20p/gimp-pal-30 ==> Base ICDAR 20% en noir & blanc +img/icdar/20p/gimp-pal-20 ==> Base ICDAR 20% en niveaux de gris +img/icdar/20p/gimp-pal-10 ==> Base ICDAR 20% en couleur + +img/icdar/20p/text-only ==> Images ICDAR avec textes uniquement +img/icdar/20p/text-color ==> Images ICDAR avec textes et couleurs +img/icdar/20p/text-photo ==> Images ICDAR avec textes et photos + + +E) BASE INIM + +img/inim/bg ==> Arrière plan (ppm) +img/inim/fg ==> Avant plan (ppm) +img/inim/in ==> Images d'entrées (ppm) + + +F) BASE OLENA + +Des images générales provenant d'Olena et d'autres pour tester, pour debugger +dans différents formats. + + diff --git a/scribo/sandbox/green/README.result b/scribo/sandbox/green/README.result new file mode 100644 index 0000000..4038a5b --- /dev/null +++ b/scribo/sandbox/green/README.result @@ -0,0 +1,143 @@ +I DESCRIPTION DES BASES D'IMAGES +-------------------------------- + +result ==> Répertoire des bases d'images +result/annotating/afp ==> Analyse de la base AFP +result/annotating/icdar ==> Analyse de la base ICDAR + + +II DESCRIPTION DES TYPES D'IMAGES +--------------------------------- + +Les deux bases d'images contiennent toutes les deux la même +structure. Les résultats sont donc générés sur les images d'origine +(input) et les images dont on a réduit le nombre de couleurs à +30/20/10 soit par gimp, soit par imageMagick. + +result/annotating/afp/input ==> Résultats base originale AFP +result/annotating/afp/magick-pal-30 ==> Résultats AFP 30 couleurs (imageMagick) +result/annotating/afp/magick-pal-20 ==> Résultats AFP 20 couleurs (imageMagick) +result/annotating/afp/magick-pal-10 ==> Résultats AFP 10 couleurs (imageMagick) +result/annotating/afp/gimp-pal-30 ==> Résultats AFP 30 couleurs (Gimp) +result/annotating/afp/gimp-pal-20 ==> Résultats AFP 20 couleurs (Gimp) +result/annotating/afp/gimp-pal-10 ==> Résultats AFP 10 couleurs (Gimp) + +result/annotating/icdar/input ==> Résultats base originale ICDAR +result/annotating/icdar/magick-pal-30 => Résultats ICDAR 30 clrs (imageMagick) +result/annotating/icdar/magick-pal-20 => Résultats ICDAR 20 clrs (imageMagick) +result/annotating/icdar/magick-pal-10 => Résultats ICDAR 10 clrs (imageMagick) +result/annotating/icdar/gimp-pal-30 ==> Résultats ICDAR 30 couleurs (Gimp) +result/annotating/icdar/gimp-pal-20 ==> Résultats ICDAR 20 couleurs (Gimp) +result/annotating/icdar/gimp-pal-10 ==> Résultats ICDAR 10 couleurs (Gimp) + + +II DESCRIPTION DES COMPOSANTES COULEURS +--------------------------------------- + +Pour chaque type d'image, on extraira ensuite les différents composantes +rouge (r), verte (v), bleue (b), teinte (h), saturation (s) et valeur (v). + +result/annotating/afp/input/r ==> comp. rouge base AFP +result/annotating/afp/input/g ==> comp. verte base AFP +result/annotating/afp/input/b ==> comp. bleue base AFP +result/annotating/afp/input/h ==> comp. teinte base AFP +result/annotating/afp/input/s ==> comp. saturation base AFP +result/annotating/afp/input/v ==> comp. valeur base AFP + +result/annotating/afp/magick-pal-30/r ==> comp. rouge AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/g ==> comp. verte AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/b ==> comp. bleue AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/h ==> comp. teinte AFP 30 c.(imageMagick) +result/annotating/afp/magick-pal-30/s ==> comp. sat. AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/v ==> comp. val. AFP 30 clr.(imageMagick) + +result/annotating/afp/magick-pal-20/r ==> comp. rouge AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/g ==> comp. verte AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/b ==> comp. bleue AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/h ==> comp. teinte AFP 20 c.(imageMagick) +result/annotating/afp/magick-pal-20/s ==> comp. sat. AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/v ==> comp. val. AFP 20 clr.(imageMagick) + +result/annotating/afp/magick-pal-10/r ==> comp. rouge AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/g ==> comp. verte AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/b ==> comp. bleue AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/h ==> comp. teinte AFP 10 c.(imageMagick) +result/annotating/afp/magick-pal-10/s ==> comp. sat. AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/v ==> comp. val. AFP 10 clr.(imageMagick) + +result/annotating/afp/gimp-pal-30/r ==> comp. rouge AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/g ==> comp. verte AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/b ==> comp. bleue AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/h ==> comp. teinte AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/s ==> comp. saturation AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/v ==> comp. valeur AFP 30 clr.(Gimp) + +result/annotating/afp/gimp-pal-20/r ==> comp. rouge AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/g ==> comp. verte AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/b ==> comp. bleue AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/h ==> comp. teinte AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/s ==> comp. saturation AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/v ==> comp. valeur AFP 20 clr.(Gimp) + +result/annotating/icdar/input/r ==> comp. rouge base ICDAR +result/annotating/icdar/input/g ==> comp. verte base ICDAR +result/annotating/icdar/input/b ==> comp. bleue base ICDAR +result/annotating/icdar/input/h ==> comp. teinte base ICDAR +result/annotating/icdar/input/s ==> comp. saturation base ICDAR +result/annotating/icdar/input/v ==> comp. valeur base ICDAR + +result/annotating/icdar/magick-pal-30/r => comp. rouge ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/g => comp. verte ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/b => comp. bleue ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/h => comp. teinte ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/s => comp. sat. ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/v => comp. val. ICDAR 30 c.(imageMagick) + +result/annotating/icdar/magick-pal-20/r => comp. rouge ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/g => comp. verte ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/b => comp. bleue ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/h => comp. teinte ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/s => comp. sat. ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/v => comp. val. ICDAR 20 c.(imageMagick) + +result/annotating/icdar/magick-pal-10/r => comp. rouge ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/g => comp. verte ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/b => comp. bleue ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/h => comp. teinte ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/s => comp. sat. ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/v => comp. val. ICDAR 10 c.(imageMagick) + +result/annotating/icdar/gimp-pal-30/r => comp. rouge ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/g => comp. verte ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/b => comp. bleue ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/h => comp. teinte ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/s => comp. saturation ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/v => comp. valeur ICDAR 30 c.(Gimp) + +result/annotating/icdar/gimp-pal-20/r => comp. rouge ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/g => comp. verte ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/b => comp. bleue ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/h => comp. teinte ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/s => comp. saturation ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/v => comp. valeur ICDAR 20 c.(Gimp) + +result/annotating/icdar/gimp-pal-10/r => comp. rouge ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/g => comp. verte ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/b => comp. bleue ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/h => comp. teinte ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/s => comp. saturation ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/v => comp. valeur ICDAR 10 c.(Gimp) + + +III DESCRIPTION DES TYPES DE RESULTAT +------------------------------------- + +Pour chaque composante et pour chaque image de la base, on obtient une +image en niveau de gris correspondant à la composante (pgm) et son +histogramme normalisé (gnuplot shell). L'histogramme normalisé est un +histogramme dont l'aire vaut 1 (c'est une densité en fait, autrement +dit chaque fréquence a été divisée par la somme des fréquences). Un +script add_range.sh, a permis de rajouter les bornes des axes pour la +visualisation pour chacun des histogrammes générés. Cette +représentation des histogrammes des composantes sous forme de densité +permet de comparer visuellement les images des différentes classes. -- 1.5.6.5

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