[Olena-patches] last-svn-commit-20-gd8a9be8 Experiment variation on regional maxima labeling.

* green/demo/labeling/regional_maxima/regional_maxima.cc (demo_17_12_2009): New function that describes the experimentation. --- milena/sandbox/ChangeLog | 7 + .../labeling/regional_maxima/regional_maxima.cc | 702 ++++++++++---------- 2 files changed, 367 insertions(+), 342 deletions(-) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 3a7ffb7..8a92218 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,3 +1,10 @@ +2009-12-23 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; + + Experiment variation on regional maxima labeling. + + * green/demo/labeling/regional_maxima/regional_maxima.cc + (demo_17_12_2009): New function that describes the experimentation. + 2009-12-18 Yann Jacquelet&lt;jacquelet(a)lrde.epita.fr&gt; Fix the right behaviour of the regmax software. diff --git a/milena/sandbox/green/demo/labeling/regional_maxima/regional_maxima.cc b/milena/sandbox/green/demo/labeling/regional_maxima/regional_maxima.cc index 65b6608..566c698 100644 --- a/milena/sandbox/green/demo/labeling/regional_maxima/regional_maxima.cc +++ b/milena/sandbox/green/demo/labeling/regional_maxima/regional_maxima.cc @@ -2,6 +2,7 @@ #include <iostream> #include <sstream> +#include <fstream> #include <boost/format.hpp> #include <mln/img_path.hh> @@ -144,182 +145,6 @@ unsigned unquant(const float& value) return result; } -template <unsigned n> -void print_count2(const mln::image2d<mln::value::rgb<n> >& input_rgbn, - const mln::image3d<unsigned>& histo, - const mln::image3d<mln::value::label_8>& label, - const unsigned n_labels) -{ - typedef mln::value::label_8 t_lbl8; - typedef mln::value::rgb<n> t_rgbn; - typedef mln::value::int_u<n> t_int_un; - typedef mln::algebra::vec<3,float> t_vec3f; - typedef mln::accu::math::sum<unsigned,unsigned> t_sum; - typedef mln::accu::stat::mean<t_vec3f,t_vec3f,t_vec3f> t_mean; - typedef mln::accu::math::sum<t_vec3f,t_vec3f> t_diff; - typedef mln::accu::stat::variance<float,float,float> t_var; - typedef mln::image2d<t_lbl8> t_image2d_lbl8; - typedef mln::image2d<t_rgbn> t_image2d_rgbn; - typedef mln::image2d<t_int_un> t_image2d_int_un; - - mln::util::array<t_mean> mean((unsigned)(n_labels)+1); -// mln::util::array<t_diff> diff((unsigned)(n_labels)+1); - mln::util::array<t_var> var_red((unsigned)(n_labels)+1); - mln::util::array<t_var> var_green((unsigned)(n_labels)+1); - mln::util::array<t_var> var_blue((unsigned)(n_labels)+1); - mln::util::array<t_sum> count((unsigned)(n_labels)+1); - mln::util::array<float> abs((unsigned)(n_labels)+1); - mln::util::array<float> rel((unsigned)(n_labels)+1); - unsigned nb = 0; - - for (unsigned i = 0; i <= n_labels; ++i) - { - count(i).init(); - mean(i).init(); -// diff(i).init(); - var_red(i).init(); - var_green(i).init(); - var_blue(i).init(); - abs[i] = 0.0; - rel[i] = 0.0; - } - - mln::labeling::compute(count, histo, label, n_labels); - - for (unsigned i = 0; i <= n_labels; ++i) - { - unsigned c = count[i]; - nb += c; - } - - for (unsigned i = 0; i <= n_labels; ++i) - if (0 < count[i]) - { - abs[i] = ((float)count[i] / nb)*100.0; - rel[i] = ((float)count[i] / (nb - count[0]))*100.0; - } - - t_image2d_lbl8 label_img = mln::data::transform(input_rgbn, - t_labeling_rgbn<n>(label)); - - mln::labeling::compute(mean, input_rgbn, label_img, n_labels); - - -// t_image2d_rgbn mean_rgbn = mln::labeling::mean_values(input_rgbn, -// label_img, -// n_labels); - -// t_image2d_int_un mean_red =mln::data::transform(mean_rgbn,t_channel<n>()); -// t_image2d_int_un mean_green=mln::data::transform(mean_rgbn,t_channel<n>()); -// t_image2d_int_un mean_blue =mln::data::transform(mean_rgbn,t_channel<n>()); - - t_image2d_int_un input_red =mln::data::transform(input_rgbn, - t_channel<n,0>()); - t_image2d_int_un input_green=mln::data::transform(input_rgbn, - t_channel<n,1>()); - t_image2d_int_un input_blue =mln::data::transform(input_rgbn, - t_channel<n,2>()); - -// FIXME VARIANCE NEGATIVE DANS LES RESULTATS !! - -// mln::labeling::compute(var_red, input_rgbn, label_img, n_labels); -// mln::labeling::compute(var_green, input_rgbn, label_img, n_labels); -// mln::labeling::compute(var_blue, input_rgbn, label_img, n_labels); - -// t_image2d_rgbn diff_rgbn = mln::arith::diff_abs(input_rgbn, mean_rgbn); - - std::cout << mln::labeling::compute(var_red, input_red, label_img, n_labels)(29) << std::endl; -// mln::labeling::compute(t_var(), input_red, label_img, n_labels); - mln::labeling::compute(var_green, input_green, label_img, n_labels); - mln::labeling::compute(var_blue, input_blue, label_img, n_labels); - - for (unsigned i = 0; i <= n_labels; ++i) - { - if (5.0 < abs[i] && 10.0 < rel[i]) - { - const t_vec3f& mean_v = mean[i]; -// const t_vec3f& diff_v = diff[i]; - - std::cout << i << " :[" << unquant<n>(mean_v[0]) -// << "(" << diff_v[0] - << "(" << var_red[i] - << ")," << unquant<n>(mean_v[1]) -// << "(" << diff_v[1] - << "(" << var_green[i] - << ")," << unquant<n>(mean_v[2]) -// << "(" << diff_v[2] - << "(" << var_blue[i] - << ")]- " << count[i] - << " - " << abs[i] - << " - " << rel[i] - << std::endl; - } - } -} - - -void print_count(const mln::image3d<unsigned>& histo, - const mln::image3d<mln::value::label_8>& label, - const unsigned n_labels) -{ - unsigned count[255]; - unsigned red[255]; - unsigned green[255]; - unsigned blue[255]; - unsigned nb = 0; - unsigned tmp = 0; - - for (unsigned i = 0; i <= n_labels; ++i) - { - count[i] = 0; - red[i] = 0; - green[i] = 0; - blue[i] = 0; - } - - mln_piter_(mln::image3d<unsigned>) p(histo.domain()); - - for_all(p) - { - count[label(p)] += histo(p); - red[label(p)] += histo(p) * p.row(); - green[label(p)] += histo(p) * p.col(); - blue[label(p)] += histo(p) * p.sli(); - nb += histo(p); - ++tmp; - } - - std::cout << std::endl; - - std::cout << "nb : " << nb << std::endl; - std::cout << "tmp : " << tmp << std::endl; - - std::cout << std::endl; - - for (unsigned i = 0; i <= n_labels; ++i) - if (0 < count[i]) - { - float percentage_abs = ((float)count[i] / nb)*100.0; - float percentage_rel = ((float)count[i] / (nb - count[0]))*100.0; - - red[i] = red[i] / count[i]; - green[i] = green[i] / count[i]; - blue[i] = blue[i] / count[i]; - - std::cout << "count[" << i << "](" - << red[i] << ", " << green[i] << ", " - << blue[i] << ") = " << count[i] << "(" - << percentage_abs << "%)"; - - if (0 < i) - std::cout << "[" << percentage_rel << "%]"; - - std::cout << std::endl; - } - - std::cout << std::endl; -} - // Version optimisée de merge template <unsigned n> @@ -354,111 +179,98 @@ struct t_merge_lbl8_with_rgbn : mln::Function_v2v< t_merge_lbl8_with_rgbn<n> > } }; -// version non optimisée de merge -template <unsigned n> -mln::image2d< mln::value::rgb<n> > -merge(const mln::image2d< mln::value::rgb<n> >& input, - const mln::image3d< mln::value::label_8 >& label) -{ - mln::image2d<mln::value::rgb<n> > output; - mln::initialize(output, input); - // mln::data::fill(output, mln::literal::green); +void compute_stats(const mln::image2d<mln::value::rgb8>& i_input_rgb8, + const mln::image2d<mln::value::label_8>& l_input_lbl8, + const mln::image3d<unsigned>& h_histo_rgbn, + const mln::image3d<mln::value::label_8>& l_histo_lbl8, + const mln::value::label_8& n_labels, + const std::string& log) +{ + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::accu::math::sum<unsigned,unsigned> t_sum; + typedef mln::accu::stat::mean<t_vec3f,t_vec3f,t_vec3f> t_mean; + typedef mln::util::array<unsigned> t_count_array; + typedef mln::util::array<t_vec3f> t_mean_array; - mln_piter(mln::image2d< mln::value::rgb<n> >) pi(input.domain()); - mln_piter(mln::image2d< mln::value::rgb<n> >) po(output.domain()); + mln::util::array<float> abs((unsigned)(n_labels)+1); + mln::util::array<float> rel((unsigned)(n_labels)+1); + unsigned nb = 0; - for_all_2(pi, po) + for (unsigned i = 0; i <= n_labels; ++i) { - const mln::value::rgb<n>& vi = input(pi); - mln::value::rgb<n>& vo = output(po); - - if (0 < mln::opt::at(label,vi.blue(),vi.red(),vi.green())) - { - vo.red() = vi.red(); - vo.green() = vi.green(); - vo.blue() = vi.blue(); - } - else - vo = mln::literal::black; + abs[i] = 0.0; + rel[i] = 0.0; } - return output; -} - - -// -// Theo regional maxima image processing chain. -// - -// FIXME C'est la dilatation qui fait apparaître des classes < min_volume. -// Une couleur se dilate au détriment du fond et des autres couleurs. - -int main2() -{ - const unsigned min_volume = 1000; - //const std::string& image = OLENA_IMG_PATH"/fly.ppm"; - const std::string& image = SCRIBO_PPM_IMG_PATH"/mp00082c_50p.ppm"; - //const std::string& image = OLENA_IMG_PATH"/tiny.ppm"; - - 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::image3d<t_lbl8> t_image3d_lbl8; - typedef mln::image3d<unsigned> t_image3d_unsigned; - typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; - typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; - typedef mln::accu::math::sum<unsigned,unsigned> t_sum; - - mln::util::timer timer; - - // START IMAGE PROCESSING CHAIN - timer.start(); - - t_image2d_rgb8 input_rgb8; - t_image2d_rgb5 input_rgb5; - t_image3d_unsigned histo; - t_image3d_unsigned opened; - t_image3d_lbl8 label; - t_image3d_lbl8 dilated; - t_lbl8 n_labels; - - - mln::io::ppm::load(input_rgb8, image.c_str()); - input_rgb5 = mln::data::transform(input_rgb8, t_rgb8_to_rgb5()); - histo = mln::data::compute(t_histo3d_fun(), input_rgb5); - opened = mln::morpho::opening::volume(histo, mln::c6(), min_volume); - label = mln::labeling::regional_maxima(opened, mln::c6(), n_labels); - dilated = mln::morpho::elementary::dilation(label, mln::c26()); + // COMPUTE THE SUM + t_count_array count = mln::labeling::compute(t_sum(), + h_histo_rgbn, + l_histo_lbl8, + n_labels); - mln::util::array<t_sum> length((unsigned)(n_labels)+1); + // COMPUTE THE TOTAL + for (unsigned i = 0; i <= n_labels; ++i) + { + unsigned c = count[i]; + nb += c; + } + // COMPUTE THE PERCENTAGES for (unsigned i = 0; i <= n_labels; ++i) - length(i).init(); + if (0 < count[i]) + { + abs[i] = ((float)count[i] / nb)*100.0; + rel[i] = ((float)count[i] / (nb - count[0]))*100.0; + } - mln::labeling::compute(length, histo, dilated, n_labels); + // COMPUTE THE MEAN + + t_mean_array mean = mln::labeling::compute(t_mean(), + i_input_rgb8, + l_input_lbl8, + n_labels); - timer.stop(); - // STOP IMAGE PROCESSING CHAIN + // CORRECT 0 LABEL STATS + rel[0] = 0; + mean[0][0] = 255.0; + mean[0][1] = 255.0; + mean[0][2] = 0.0; - std::cout << "Done in " << timer.read() << " ms" << std::endl; - std::cout << "n_labels : " << n_labels << std::endl; + // PRINT STATS + std::ofstream log_stream(log.c_str()); for (unsigned i = 0; i <= n_labels; ++i) { - std::cout << "count[" << i << "] = " << length[i] << std::endl; - } - - print_count(histo,label,n_labels); + const t_vec3f& mean_v = mean[i]; - //mln::io::plot::save_image_sh(histo, "histo.sh"); - //mln::io::plot::save_image_sh(histo, "opened.sh"); - //mln::io::plot::save_image_sh(label, "label.sh"); + log_stream << boost::format("%2i|" + "r = %6.2f, g = %6.2f, b = %6.2f |" + "c = %7i, %%i = %5.2f, %%c = %5.2f") + % i + % mean_v[0] + % mean_v[1] + % mean_v[2] + % count[i] + % abs[i] + % rel[i] + << std::endl; + /* + std::cout << i << "|(" + << "r = " << unquant<n>(mean_v[0]) << ", " + << "g = " << unquant<n>(mean_v[1]) << ", " + << "b = " << unquant<n>(mean_v[2]) << ")|(" + << "c = " << count[i] << ", " + << "%i= " << abs[i] << "%, " + << "%c= " << rel[i] << "%)" + << std::endl; + */ + } - return 0; + log_stream << std::endl << std::endl; + log_stream.flush(); + log_stream.close(); } void compute_stats(const mln::image2d<mln::value::rgb8>& i_input_rgb8, @@ -546,7 +358,6 @@ void compute_stats(const mln::image2d<mln::value::rgb8>& i_input_rgb8, } std::cout << std::endl << std::endl; - } void save_histo(const mln::image2d<mln::value::rgb8>& input_rgb8, @@ -565,7 +376,7 @@ void save_histo(const mln::image2d<mln::value::rgb8>& input_rgb8, // n < 8, n is the degree of quantification template <unsigned n> -void demo(const std::string& image, const unsigned min_vol) +void demo_17_12_2009(const std::string& image, const unsigned min_vol) { typedef mln::value::label_8 t_lbl8; typedef mln::value::int_u8 t_int_u8; @@ -622,8 +433,6 @@ void demo(const std::string& image, const unsigned min_vol) // END OF IMAGE PROCESSING CHAIN -// i2_mean = mln::data::transform(l0_input,t_mean_lbl8_with_rgb8(mean_array)); - // BEGIN DUMPING save_histo(i0_input, "h0_input.pgm"); @@ -683,69 +492,235 @@ void demo(const std::string& image, const unsigned min_vol) save_histo(i5_mean, "h5_mean.pgm"); save_histo(i5_merge, "h5_merge.pgm"); // END DUMPING +} -/* - std::ostringstream name; - std::ostringstream name2; - std::ostringstream name3; - - name << "input_rgb" << n << ".ppm"; - name2 << "output_rgb" << n << ".ppm"; - name3 << "label_img" << n << ".pgm"; - - std::cout << "Labels : " << n_labels << std::endl; - std::cout << "Name : " << name.str() << std::endl; - -// mln::io::ppm::save(input_rgbn, name.str()); - - std::cout << "timer2 : " << timer2.read() << std::endl; - output_rgbn = mln::data::transform(input_rgbn, - t_merge_lbl8_with_rgbn<n>(label)); - // output_rgbn = merge<n>(input_rgbn, dilated); - mln::io::ppm::save(output_rgbn, name2.str()); - - label_img = mln::data::transform(input_rgbn, - t_labeling_rgbn<n>(label)); - // label_img = label_image<n>(input_rgbn, dilated); - mln::io::pgm::save(label_img, name3.str()); -*/ - - // localiser les couleurs sur l'image (fond en black, le reste) - - // La dilatation englobe beaucoup plus de couleur, mais celles-ci ne - // sont pas forcément présentes dans l'image. Du coup, les classes ne - // bougent pas démeusurément. - -// mln::io::ppm::save(input_rgb5, "input_rgb5.ppm"); -// mln::io::plot::save_image_sh(input_rgb8, "input_rgb8.sh"); -// mln::io::plot::save_image_sh(input_rgb5, "input_rgb5.sh"); -// projected = mln::display::display_histo3d_unsigned(histo); -// mln::io::pgm::save(projected, "histo.pgm"); -// mln::io::plot::save_image_sh(histo, "histo.sh"); -// mln::io::plot::save_image_sh(opened, "opened.sh"); -// filtered = mln::display::display_histo3d_unsigned(opened); -// mln::io::pgm::save(filtered, "filtered.pgm"); -// mln::io::plot::save_image_sh(label, "label.sh"); - // mln::io::plot::save_image_sh(dilated, "dilated.sh"); - - /* - // OUTPUT PHASE - std::cout << "Output phase ..." << std::endl; - output_rgb5 = merge(input_rgb5, dilated); - //mln::io::ppm::save(output_rgb5, "output_rgb5.ppm"); - mln::io::plot::save_image_sh(output_rgb5, "output_rgb5.sh"); - - - // LABELING IMG OUTPUT - std::cout << "Labeling img output phase ..." << std::endl; - label_img = label_image(input_rgb5, dilated); - mln::io::pgm::save(label_img, "label_img.pgm"); - - // BUILDING MEAN VALUES - std::cout << "Building mean values phase ..." << std::endl; - mean_rgb5 = mln::labeling::mean_values(input_rgb5, label_img, n_labels); - mln::io::ppm::save(mean_rgb5, "mean.ppm"); - */ +// n < 8, n is the degree of quantification +template <unsigned n> +void demo_21_12_2009(const std::string& image, const unsigned min_vol) +{ + typedef mln::value::label_8 t_lbl8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image3d<t_lbl8> t_image3d_lbl8; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image2d<unsigned> t_histo2d; + 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_histo3d h0_input; // histo input + + t_image2d_rgbn i1_input; // input rgbn + t_histo3d h1_input; // histo input + + t_histo3d h2_input; // histo opened + t_image3d_lbl8 l2_histo; // label histo + t_image2d_lbl8 l2_input; // label image + t_image2d_rgb8 i2_merge; // merge label + t_image2d_rgb8 i2_mean; // mean label + + t_image3d_lbl8 l5_histo; // label iz 2 + t_image2d_lbl8 l5_input; // label image + t_image2d_rgb8 i5_merge; // merge label + t_image2d_rgb8 i5_mean; // mean label + + t_lbl8 n_lbl; // nb class + + mln::io::ppm::load(i0_input, image.c_str()); + i1_input = mln::data::transform(i0_input, t_rgb8_to_rgbn()); + h1_input = mln::data::compute(t_histo3d_fun(), i1_input); + h2_input = mln::morpho::opening::volume(h1_input, mln::c6(), min_vol); + l2_histo = mln::labeling::regional_maxima(h2_input, mln::c6(), n_lbl); + l5_histo = mln::transform::influence_zone_geodesic(l2_histo, mln::c26(), 2); + // END OF IMAGE PROCESSING CHAIN + + // BEGIN DUMPING + save_histo(i0_input, "h0_input.pgm"); + + // without nothing + l2_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l2_histo)); + i2_mean = mln::labeling::mean_values(i0_input, l2_input, n_lbl); + i2_merge = mln::data::transform(i0_input,t_merge_lbl8_with_rgbn<n>(l2_histo)); + + compute_stats(i0_input, l2_input, h1_input, l2_histo, n_lbl); + + mln::io::pgm::save(l2_input, "l2_input.pgm"); + mln::io::ppm::save(i2_mean, "i2_mean.ppm"); + mln::io::ppm::save(i2_merge, "i2_merge.ppm"); + + save_histo(i2_mean, "h2_mean.pgm"); + save_histo(i2_merge, "h2_merge.pgm"); + + // with restricted geodesic influence + l5_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l5_histo)); + i5_mean = mln::labeling::mean_values(i0_input, l5_input, n_lbl); + i5_merge = mln::data::transform(i0_input,t_merge_lbl8_with_rgbn<n>(l5_histo)); + + compute_stats(i0_input, l5_input, h1_input, l5_histo, n_lbl); + + mln::io::pgm::save(l5_input, "l5_input.pgm"); + mln::io::ppm::save(i5_mean, "i5_mean.ppm"); + mln::io::ppm::save(i5_merge, "i5_merge.ppm"); + + save_histo(i5_mean, "h5_mean.pgm"); + save_histo(i5_merge, "h5_merge.pgm"); + // END DUMPING +} + + +// EXE THEO +// a.out input.ppm q v output.ppm [log.txt] +// input.ppm ==> l'image 8 bits couleur format ppm +// q ==> la quantification [2,3,4,5,6,7,8] +// v ==> l'effectif minimal de chaque classe [0 = pas de filtrage] +// output.ppm ==> l'image où l'étiquette est remplacée par la couleur moyenne +// log.txt ==> le fichier de statistiques [optionnel] + +// n < 8, n is the degree of quantification +template <unsigned n> +void exe_theo_22_12_2009(const std::string& input, + const unsigned min_vol, + const std::string& output, + const std::string& log) +{ + typedef mln::value::label_8 t_lbl8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image3d<t_lbl8> t_image3d_lbl8; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image2d<unsigned> t_histo2d; + 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_histo3d h0_input; // histo input + + t_image2d_rgbn i1_input; // input rgbn + t_histo3d h1_input; // histo input + + t_histo3d h2_input; // histo opened + t_image3d_lbl8 l2_histo; // label histo + + t_image3d_lbl8 l4_histo; // label iz + t_image2d_lbl8 l4_input; // label image + t_image2d_rgb8 i4_merge; // merge label + t_image2d_rgb8 i4_mean; // mean label + + t_lbl8 n_lbl; // nb class + + mln::io::ppm::load(i0_input, input.c_str()); + i1_input = mln::data::transform(i0_input, t_rgb8_to_rgbn()); + h1_input = mln::data::compute(t_histo3d_fun(), i1_input); + + if (0 < min_vol) + h2_input = mln::morpho::opening::volume(h1_input, mln::c6(), min_vol); + else + h2_input = h1_input; // no filtering + + l2_histo = mln::labeling::regional_maxima(h2_input, mln::c6(), n_lbl); + l4_histo = mln::transform::influence_zone_geodesic(l2_histo, mln::c26()); + // END OF IMAGE PROCESSING CHAIN + + // BEGIN DUMPING + l4_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l4_histo)); + i4_mean = mln::labeling::mean_values(i0_input, l4_input, n_lbl); + + if (0 < log.size()) + compute_stats(i0_input, l4_input, h1_input, l4_histo, n_lbl, log); + + mln::io::ppm::save(i4_mean, output.c_str()); + // END DUMPING +} + +// n < 8, n is the degree of quantification +template <unsigned n> +void demo_22_12_2009(const std::string& image, const unsigned min_vol) +{ + typedef mln::value::label_8 t_lbl8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image3d<t_lbl8> t_image3d_lbl8; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image2d<unsigned> t_histo2d; + 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_histo3d h0_input; // histo input + + t_image2d_rgbn i1_input; // input rgbn + t_histo3d h1_input; // histo input + + t_histo3d h2_input; // histo opened + t_image3d_lbl8 l2_histo; // label histo + t_image2d_lbl8 l2_input; // label image + t_image2d_rgb8 i2_merge; // merge label + t_image2d_rgb8 i2_mean; // mean label + + t_image3d_lbl8 l5_histo; // label iz 2 + t_image2d_lbl8 l5_input; // label image + t_image2d_rgb8 i5_merge; // merge label + t_image2d_rgb8 i5_mean; // mean label + + t_lbl8 n_lbl; // nb class + + mln::io::ppm::load(i0_input, image.c_str()); + i1_input = mln::data::transform(i0_input, t_rgb8_to_rgbn()); + h1_input = mln::data::compute(t_histo3d_fun(), i1_input); + h2_input = mln::morpho::opening::volume(h1_input, mln::c6(), min_vol); + l2_histo = mln::labeling::regional_maxima(h2_input, mln::c6(), n_lbl); + l5_histo = mln::transform::influence_zone_geodesic(l2_histo, mln::c26(), 2); + // END OF IMAGE PROCESSING CHAIN + + // BEGIN DUMPING + save_histo(i0_input, "h0_input.pgm"); + + // without nothing + l2_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l2_histo)); + i2_mean = mln::labeling::mean_values(i0_input, l2_input, n_lbl); + i2_merge = mln::data::transform(i0_input,t_merge_lbl8_with_rgbn<n>(l2_histo)); + + compute_stats(i0_input, l2_input, h1_input, l2_histo, n_lbl); + + mln::io::pgm::save(l2_input, "l2_input.pgm"); + mln::io::ppm::save(i2_mean, "i2_mean.ppm"); + mln::io::ppm::save(i2_merge, "i2_merge.ppm"); + + save_histo(i2_mean, "h2_mean.pgm"); + save_histo(i2_merge, "h2_merge.pgm"); + + // with restricted geodesic influence + l5_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l5_histo)); + i5_mean = mln::labeling::mean_values(i0_input, l5_input, n_lbl); + i5_merge = mln::data::transform(i0_input,t_merge_lbl8_with_rgbn<n>(l5_histo)); + + compute_stats(i0_input, l5_input, h1_input, l5_histo, n_lbl); + + mln::io::pgm::save(l5_input, "l5_input.pgm"); + mln::io::ppm::save(i5_mean, "i5_mean.ppm"); + mln::io::ppm::save(i5_merge, "i5_merge.ppm"); + + save_histo(i5_mean, "h5_mean.pgm"); + save_histo(i5_merge, "h5_merge.pgm"); + // END DUMPING } @@ -774,30 +749,73 @@ void usage() // * } -int main(int argc, char* args[]) +void usage_exe_theo_22_12_2009() { - if (argc != 3) + std::cout << std::endl; + std::cout << "a.out input.ppm q v [log.txt]" << 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 << "v is the minimal size for the histogram classes" << std::endl; + std::cout << "log.txt is the statistical file for classes" << std::endl; + std::cout << std::endl; +} + +int main_21_12_2009(int argc, char* args[]) +{ + if (argc == 2) { // const std::string& image = OLENA_IMG_PATH"/fly.ppm"; const std::string& image = SCRIBO_PPM_IMG_PATH"/mp00082c_50p.ppm"; // const std::string& image = OLENA_IMG_PATH"/tiny.ppm"; // const std::string image = OLENA_IMG_PATH"/tiny.ppm"; - const unsigned min_volume = 1000; - // const unsigned min_volume = atoi(args[2]); + // const unsigned min_volume = 1000; + const unsigned min_volume = atoi(args[1]); - switch(args[2][0]) +// switch(args[1][0]) + switch(5) { - case '2': demo<2>(image, min_volume); break; - case '3': demo<3>(image, min_volume); break; - case '4': demo<4>(image, min_volume); break; - case '5': demo<5>(image, min_volume); break; - case '6': demo<6>(image, min_volume); break; - case '7': demo<7>(image, min_volume); break; - case '8': demo<8>(image, min_volume); break; - default: demo<5>(image, min_volume); break; + case '2': demo_21_12_2009<2>(image, min_volume); break; + case '3': demo_21_12_2009<3>(image, min_volume); break; + case '4': demo_21_12_2009<4>(image, min_volume); break; + case '5': demo_21_12_2009<5>(image, min_volume); break; + case '6': demo_21_12_2009<6>(image, min_volume); break; + case '7': demo_21_12_2009<7>(image, min_volume); break; + case '8': demo_21_12_2009<8>(image, min_volume); break; + default: demo_21_12_2009<5>(image, min_volume); break; // default: usage(); break; } } else usage(); + + return 0; +} + +int main(int argc, char* args[]) +{ + if (5 == argc || 6 == argc) + { + const std::string input(args[1]); + const char q = args[2][0]; + const unsigned v = atoi(args[3]); + const std::string output(args[4]); + const std::string log(6 == argc? args[5] : ""); + + switch(q) + { + case '2': exe_theo_22_12_2009<2>(input, v, output, log); break; + case '3': exe_theo_22_12_2009<3>(input, v, output, log); break; + case '4': exe_theo_22_12_2009<4>(input, v, output, log); break; + case '5': exe_theo_22_12_2009<5>(input, v, output, log); break; + case '6': exe_theo_22_12_2009<6>(input, v, output, log); break; + case '7': exe_theo_22_12_2009<7>(input, v, output, log); break; + case '8': exe_theo_22_12_2009<8>(input, v, output, log); break; + default: usage_exe_theo_22_12_2009(); break; + } + } + else + usage_exe_theo_22_12_2009(); + + return 0; } -- 1.5.6.5