* green/demo/labeling/regional_maxima/regional_maxima.cc: Add many modifications to reach the current software state. There is some nomenclatura, some refactoring and debugging. --- milena/sandbox/ChangeLog | 8 + .../labeling/regional_maxima/regional_maxima.cc | 354 ++++++++++++-------- 2 files changed, 214 insertions(+), 148 deletions(-) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index b94de66..e1a6235 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -75,6 +75,14 @@ * bin/pgm_to_pbm.cc, * bin/ppm_negate.cc: New. +2009-12-18 Yann Jacquelet&lt;jacquelet(a)lrde.epita.fr&gt; + + Fix the right behaviour of the regmax software. + + * green/demo/labeling/regional_maxima/regional_maxima.cc: Add + many modifications to reach the current software state. There is some + nomenclatura, some refactoring and debugging. + 2009-12-17 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; Fix bug in the direction of projection. 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 c832486..65b6608 100644 --- a/milena/sandbox/green/demo/labeling/regional_maxima/regional_maxima.cc +++ b/milena/sandbox/green/demo/labeling/regional_maxima/regional_maxima.cc @@ -27,7 +27,8 @@ // #include <mln/data/stretch.hh> #include <mln/data/transform.hh> -// #include <mln/display/display_histo.hh> +// #include <mln/debug/println.hh> +#include <mln/display/display_histo.hh> #include <mln/fun/v2v/rgb8_to_rgbn.hh> @@ -49,6 +50,7 @@ #include <mln/opt/at.hh> +#include <mln/transform/influence_zone_geodesic.hh> // #include <mln/pw/value.hh> #include <mln/util/array.hh> @@ -255,108 +257,6 @@ void print_count2(const mln::image2d<mln::value::rgb<n> >& input_rgbn, } } -template <unsigned n> -mln::image2d<mln::value::label_8> -compute_stats(const mln::image2d<mln::value::rgb8>& input_rgb8, - 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::rgb8 t_rgb8; - 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::image2d<t_lbl8> t_image2d_lbl8; - typedef mln::image2d<t_rgbn> t_image2d_rgbn; - typedef mln::image2d<t_rgb8> t_image2d_rgb8; - typedef mln::image2d<t_int_un> t_image2d_int_un; - typedef mln::util::array<unsigned> t_count_array; - typedef mln::util::array<t_vec3f> t_mean_array; - - 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) - { - abs[i] = 0.0; - rel[i] = 0.0; - } - - // COMPUTE THE SUM - t_count_array count = mln::labeling::compute(t_sum(), - histo, - label, - n_labels); - - // 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) - if (0 < count[i]) - { - abs[i] = ((float)count[i] / nb)*100.0; - rel[i] = ((float)count[i] / (nb - count[0]))*100.0; - } - - // COMPUTE THE MEAN - t_image2d_lbl8 label_img = mln::data::transform(input_rgbn, - t_labeling_rgbn<n>(label)); - - t_mean_array mean = mln::labeling::compute(t_mean(), - input_rgb8, - label_img, - n_labels); - - // CORRECT LABEL 0 STATS - rel[0] = 0; - mean[0][0] = 255.0; - mean[0][1] = 255.0; - mean[0][2] = 0.0; - - for (unsigned i = 0; i <= n_labels; ++i) - { - const t_vec3f& mean_v = mean[i]; - - std::cout << 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; - */ - } - - t_image2d_rgb8 mean_rgb8 = mln::data::transform(label_img, - t_mean_lbl8_with_rgb8(mean)); - - mln::io::ppm::save(mean_rgb8, "mean.ppm"); - - return label_img; -} void print_count(const mln::image3d<unsigned>& histo, const mln::image3d<mln::value::label_8>& label, @@ -561,9 +461,111 @@ int main2() return 0; } +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) +{ + 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::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) + { + abs[i] = 0.0; + rel[i] = 0.0; + } + + // COMPUTE THE SUM + t_count_array count = mln::labeling::compute(t_sum(), + h_histo_rgbn, + l_histo_lbl8, + n_labels); + + // 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) + if (0 < count[i]) + { + abs[i] = ((float)count[i] / nb)*100.0; + rel[i] = ((float)count[i] / (nb - count[0]))*100.0; + } + + // COMPUTE THE MEAN + + t_mean_array mean = mln::labeling::compute(t_mean(), + i_input_rgb8, + l_input_lbl8, + n_labels); + + // CORRECT 0 LABEL STATS + rel[0] = 0; + mean[0][0] = 255.0; + mean[0][1] = 255.0; + mean[0][2] = 0.0; + + // PRINT STATS + for (unsigned i = 0; i <= n_labels; ++i) + { + const t_vec3f& mean_v = mean[i]; + + std::cout << 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; + */ + } + + std::cout << std::endl << std::endl; + +} + +void save_histo(const mln::image2d<mln::value::rgb8>& input_rgb8, + const std::string& name) +{ + 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; + + t_histo3d histo = mln::data::compute(t_histo3d_fun(), input_rgb8); + t_image2d_int_u8 display = mln::display::display_histo3d_unsigned(histo); + mln::io::pgm::save(display, name); +} + + // n < 8, n is the degree of quantification template <unsigned n> -void demo(const std::string& image, const unsigned min_volume) +void demo(const std::string& image, const unsigned min_vol) { typedef mln::value::label_8 t_lbl8; typedef mln::value::int_u8 t_int_u8; @@ -580,45 +582,106 @@ void demo(const std::string& image, const unsigned min_volume) typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; // START OF IMAGE PROCESSING CHAIN - t_image2d_rgb8 input_rgb8; - t_image2d_rgbn input_rgbn; - t_image2d_rgb8 output_rgb8; - t_image2d_rgb8 mean_rgb8; - t_histo3d histo; - t_histo3d histo2; - // t_image2d_int_u8 projected; - // t_image2d_int_u8 filtered; - t_histo3d opened; - t_image3d_lbl8 label; - t_image2d_lbl8 label_img; - t_image3d_lbl8 dilated; - t_lbl8 n_labels; - - mln::io::ppm::load(input_rgb8, image.c_str()); - input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); - histo = mln::data::compute(t_histo3d_fun(), input_rgbn); - 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()); + 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 l3_histo; // label dilated + t_image2d_lbl8 l3_input; // label image + t_image2d_rgb8 i3_merge; // merge label + t_image2d_rgb8 i3_mean; // mean label + + 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_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); + l3_histo = mln::morpho::elementary::dilation(l2_histo, mln::c26()); + l4_histo = mln::transform::influence_zone_geodesic(l2_histo, mln::c26()); + l5_histo = mln::transform::influence_zone_geodesic(l2_histo, mln::c26(), 2); // END OF IMAGE PROCESSING CHAIN +// i2_mean = mln::data::transform(l0_input,t_mean_lbl8_with_rgb8(mean_array)); // BEGIN DUMPING - label_img = compute_stats(input_rgb8, input_rgbn, histo, label, n_labels); - // mean_rgb8 = mln::labeling::mean_values(input_rgb8, label_img, n_labels); - // mln::io::ppm::save(mean_rgb8, "mean.ppm"); + save_histo(i0_input, "h0_input.pgm"); + + // without anything + 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"); - output_rgb8 = mln::data::transform(input_rgb8, - t_merge_lbl8_with_rgbn<5>(label)); + // with dilation + l3_input = mln::data::transform(i1_input, t_labeling_rgbn<n>(l3_histo)); + i3_mean = mln::labeling::mean_values(i0_input, l3_input, n_lbl); + i3_merge = mln::data::transform(i0_input,t_merge_lbl8_with_rgbn<n>(l3_histo)); + compute_stats(i0_input, l3_input, h1_input, l3_histo, n_lbl); - mln::io::ppm::save(output_rgb8, "merge.ppm"); - mln::io::pgm::save(label_img, "label.pgm"); + mln::io::pgm::save(l3_input, "l3_input.pgm"); + mln::io::ppm::save(i3_mean, "i3_mean.ppm"); + mln::io::ppm::save(i3_merge, "i3_merge.ppm"); + save_histo(i3_mean, "h3_mean.pgm"); + save_histo(i3_merge, "h3_merge.pgm"); -// histo2 = mln::data::compute(t_histo3d_fun(), mean_rgbn); -// mln::debug::println(histo2); + // with geodesic influence + 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); + i4_merge = mln::data::transform(i0_input,t_merge_lbl8_with_rgbn<n>(l4_histo)); + + compute_stats(i0_input, l4_input, h1_input, l4_histo, n_lbl); + + mln::io::pgm::save(l4_input, "l4_input.pgm"); + mln::io::ppm::save(i4_mean, "i4_mean.ppm"); + mln::io::ppm::save(i4_merge, "i4_merge.ppm"); + + save_histo(i4_mean, "h4_mean.pgm"); + save_histo(i4_merge, "h4_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 /* @@ -685,25 +748,14 @@ void demo(const std::string& image, const unsigned min_volume) */ } -/* -int main_old() -{ - demo<2>(); // 2.26 s - demo<3>(); // 2.29 s - demo<4>(); // 2.29 s - demo<5>(); // 2.37 s - demo<6>(); // 3.19 s - demo<7>(); // 11.43 s - demo<8>(); // 96.19 s -} -*/ + void usage() { std::cout << std::endl; std::cout << "regional_maxima" << std::endl; std::cout << "image <name>" << std::endl; std::cout << "quantification {5,6}" << std::endl; - std::cout << "volume 1000" << std::endl; + std::cout << "min_volume 1000" << std::endl; // 3 paramètres en entré: // * l'image @@ -712,8 +764,14 @@ void usage() // sorties: // * fichier de stats [etiquette|couleur moyenne|% image|% classé] - // * couleur moyenne de l'étiquette + couleur specifique pour étiquette 0 - // * histo sur l'image d'étiquette + // * image de labels + // * image reconstruite avec la moyenne de chaque classe [i_mean] + // * image reconstruite avec les couleurs de chaque classe [i_merge] + // * histogramme original de l'image d'entrée [h_input] + // * histogramme projeté de mean [h_mean] + // * histogramme projeté de merge [h_merge] + + // * } int main(int argc, char* args[]) -- 1.5.6.5