* green/demo/labeling/regional_maxima/regional_maxima.cc (compute_stats): New function. * green/demo/labeling/regional_maxima/regional_maxima.cc (demo,main): Update functions to map Theo's view. --- milena/sandbox/ChangeLog | 10 + .../labeling/regional_maxima/regional_maxima.cc | 261 ++++++++++++++++---- 2 files changed, 221 insertions(+), 50 deletions(-) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 2f5eae8..b180063 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,3 +1,13 @@ +2009-12-16 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; + + Fix bugs in regional_maxima routine, work with rgb8 for stats. + + * green/demo/labeling/regional_maxima/regional_maxima.cc + (compute_stats): New function. + + * green/demo/labeling/regional_maxima/regional_maxima.cc + (demo,main): Update functions to map Theo's view. + 2009-12-16 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; Test the local deviation operator (Millet2008) on the annotating base. 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 266fbfe..c832486 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 <boost/format.hpp> #include <mln/img_path.hh> @@ -106,30 +107,31 @@ struct t_labeling_rgbn : mln::Function_v2v< t_labeling_rgbn<n> > } }; +// Version optimisée de mean_values -// version non optimisée de label - -template <unsigned n> -mln::image2d<mln::value::label_8> -label_image(const mln::image2d< mln::value::rgb<n> >& input, - const mln::image3d< mln::value::label_8>& label) +struct t_mean_lbl8_with_rgb8 : mln::Function_v2v< t_mean_lbl8_with_rgb8 > { - mln::image2d<mln::value::label_8> output; + typedef mln::value::rgb8 t_rgb8; + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::value::label_8 t_lbl8; + typedef t_lbl8 argument; + typedef t_rgb8 result; + typedef mln::image3d<t_lbl8> t_label; + typedef mln::util::array<t_vec3f> t_mean_array; - initialize(output, input); + const t_mean_array& _mean_array; - mln_piter(mln::image2d< mln::value::label_8 >) po(output.domain()); - mln_piter(mln::image2d< mln::value::rgb<n> >) pi(input.domain()); + t_mean_lbl8_with_rgb8(const t_mean_array& mean_array) : + _mean_array(mean_array) {} - for_all_2(po, pi) + result operator()(const argument& c) const { - const mln::value::rgb<n>& vi = input(pi); + t_rgb8 tmp = _mean_array[c]; - output(po) = mln::opt::at(label, vi.blue(), vi.red(), vi.green()); + return tmp; } +}; - return output; -} template <unsigned n> unsigned unquant(const float& value) @@ -253,6 +255,109 @@ 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, const unsigned n_labels) @@ -320,26 +425,32 @@ void print_count(const mln::image3d<unsigned>& histo, template <unsigned n> struct t_merge_lbl8_with_rgbn : mln::Function_v2v< t_merge_lbl8_with_rgbn<n> > { - typedef mln::value::rgb<n> t_rgbn; - typedef mln::value::label_8 t_lbl8; - typedef t_rgbn argument; - typedef t_rgbn result; - typedef mln::image3d<t_lbl8> t_label; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::label_8 t_lbl8; + typedef t_rgb8 argument; + typedef t_rgb8 result; + typedef mln::image3d<t_lbl8> t_label; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; - const t_label& _label; + const t_label& _label; + const t_rgb8_to_rgbn _convert; t_merge_lbl8_with_rgbn(const t_label& label) : _label(label) {} result operator()(const argument& c) const { - t_rgbn tmp = c; + t_rgb8 tmp_rgb8 = c; + t_rgbn tmp_rgbn = _convert(c); - if (0 == mln::opt::at(_label, c.blue(), c.red(), c.green())) + if (0==mln::opt::at(_label,tmp_rgbn.blue(),tmp_rgbn.red(),tmp_rgbn.green())) { - tmp = mln::literal::black; + // tmp = mln::literal::black; + // tmp = mln::literal::yellow; + tmp_rgb8 = mln::literal::red; } - return tmp; + return tmp_rgb8; } }; @@ -452,13 +563,8 @@ int main2() // n < 8, n is the degree of quantification template <unsigned n> -void demo() +void demo(const std::string& image, const unsigned min_volume) { - 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::int_u8 t_int_u8; typedef mln::value::rgb8 t_rgb8; @@ -473,16 +579,13 @@ void demo() typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; - mln::util::timer timer; - // START OF IMAGE PROCESSING CHAIN - timer.start(); - t_image2d_rgb8 input_rgb8; t_image2d_rgbn input_rgbn; - t_image2d_rgbn output_rgbn; - // t_image2d_rgbn mean_rgb5; + 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; @@ -490,18 +593,35 @@ void demo() t_image2d_lbl8 label_img; t_image3d_lbl8 dilated; t_lbl8 n_labels; - // t_rgbn value_rgbn; 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()); + // dilated = mln::morpho::elementary::dilation(label, mln::c26()); - timer.stop(); // END OF IMAGE PROCESSING CHAIN + + // 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"); + + output_rgb8 = mln::data::transform(input_rgb8, + t_merge_lbl8_with_rgbn<5>(label)); + + + mln::io::ppm::save(output_rgb8, "merge.ppm"); + mln::io::pgm::save(label_img, "label.pgm"); + + +// histo2 = mln::data::compute(t_histo3d_fun(), mean_rgbn); +// mln::debug::println(histo2); + // END DUMPING + +/* std::ostringstream name; std::ostringstream name2; std::ostringstream name3; @@ -510,17 +630,11 @@ void demo() name2 << "output_rgb" << n << ".ppm"; name3 << "label_img" << n << ".pgm"; - std::cout << "Done in : " << timer.read() << " s" << std::endl; std::cout << "Labels : " << n_labels << std::endl; std::cout << "Name : " << name.str() << std::endl; - mln::io::ppm::save(input_rgbn, name.str()); - - mln::util::timer timer2; +// mln::io::ppm::save(input_rgbn, name.str()); - timer2.start(); - print_count2(input_rgbn, histo, dilated, n_labels); - timer2.stop(); std::cout << "timer2 : " << timer2.read() << std::endl; output_rgbn = mln::data::transform(input_rgbn, t_merge_lbl8_with_rgbn<n>(label)); @@ -531,7 +645,7 @@ void demo() 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) @@ -571,8 +685,8 @@ void demo() */ } - -int main() +/* +int main_old() { demo<2>(); // 2.26 s demo<3>(); // 2.29 s @@ -582,3 +696,50 @@ int main() 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; + + // 3 paramètres en entré: + // * l'image + // * la quantification utilisée + // * le volume minimale laissé pour les pics de l'histo + + // 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 +} + +int main(int argc, char* args[]) +{ + if (argc != 3) + { + // 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]); + + switch(args[2][0]) + { + 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; + // default: usage(); break; + } + } + else + usage(); +} -- 1.5.6.5