Olena-patches

olena-patches@lrde.epita.fr

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last-svn-commit-40-g2cfa144 Fix the way to convert unsigned to float.

by Yann Jacquelet

* mln/convert/from_to.hxx: New conversion utilities. * mln/convert/impl/from_unsigned_to_value.hh: New conversion utilities. --- milena/ChangeLog | 7 +++++++ milena/mln/convert/from_to.hxx | 4 ++++ milena/mln/convert/impl/from_unsigned_to_value.hh | 10 ++++++++++ 3 files changed, 21 insertions(+), 0 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index 10c5def..3804c90 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,3 +1,10 @@ +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Fix the way to convert unsigned to float. + + * mln/convert/from_to.hxx: New conversion utilities. + * mln/convert/impl/from_unsigned_to_value.hh: New conversion utilities. + 2009-12-02 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Fix the outside template use case for the convolve macros. diff --git a/milena/mln/convert/from_to.hxx b/milena/mln/convert/from_to.hxx index ebc30cd..8334b14 100644 --- a/milena/mln/convert/from_to.hxx +++ b/milena/mln/convert/from_to.hxx @@ -261,6 +261,10 @@ namespace mln void from_to_(const Value<F>& from, Value<T>& to); + // unsigned -> float + void + from_to_(const unsigned& from, float& to); + // double-> Value template <typename V> void diff --git a/milena/mln/convert/impl/from_unsigned_to_value.hh b/milena/mln/convert/impl/from_unsigned_to_value.hh index ad54cff..fe09a3e 100644 --- a/milena/mln/convert/impl/from_unsigned_to_value.hh +++ b/milena/mln/convert/impl/from_unsigned_to_value.hh @@ -118,6 +118,16 @@ namespace mln internal::from_unsigned_to_value_dispatch(from, to); } + + // Facades. + // unsigned-> float + void + from_to_(const unsigned& from, float& to) + { + to = from; + } + + } // end of namespace mln::convert::over_load -- 1.5.6.5

14 years, 2 months

last-svn-commit-37-gd4f2e49 Work on Millet saturation descriptor.

by Yann Jacquelet

* green/exp/annotating/hue: New directory. * green/exp/annotating/hue/Makefile.am: New Makefile. * green/exp/annotating/hue/hue.cc: New source file. * green/exp/annotating/hue/text-color.txt: New image class. * green/exp/annotating/hue/text-img.txt: New image class. * green/exp/annotating/hue/text-only.txt: New image class. --- milena/sandbox/ChangeLog | 11 + .../annotating/{achromastism => hue}/Makefile.am | 0 milena/sandbox/green/exp/annotating/hue/hue.cc | 291 ++++++++++++++++++++ .../{achromastism => hue}/text-color.txt | 0 .../annotating/{achromastism => hue}/text-img.txt | 0 .../annotating/{achromastism => hue}/text-only.txt | 0 6 files changed, 302 insertions(+), 0 deletions(-) copy milena/sandbox/green/exp/annotating/{achromastism => hue}/Makefile.am (100%) create mode 100644 milena/sandbox/green/exp/annotating/hue/hue.cc copy milena/sandbox/green/exp/annotating/{achromastism => hue}/text-color.txt (100%) copy milena/sandbox/green/exp/annotating/{achromastism => hue}/text-img.txt (100%) copy milena/sandbox/green/exp/annotating/{achromastism => hue}/text-only.txt (100%) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index eb301e5..2c3ec85 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -2,6 +2,17 @@ Work on Millet saturation descriptor. + * green/exp/annotating/hue: New directory. + * green/exp/annotating/hue/Makefile.am: New Makefile. + * green/exp/annotating/hue/hue.cc: New source file. + * green/exp/annotating/hue/text-color.txt: New image class. + * green/exp/annotating/hue/text-img.txt: New image class. + * green/exp/annotating/hue/text-only.txt: New image class. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Work on Millet saturation descriptor. + * green/exp/annotating/saturation: New directory. * green/exp/annotating/saturation/Makefile.am: New Makefile. * green/exp/annotating/saturation/saturation.cc: New source file. diff --git a/milena/sandbox/green/exp/annotating/achromastism/Makefile.am b/milena/sandbox/green/exp/annotating/hue/Makefile.am similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/Makefile.am copy to milena/sandbox/green/exp/annotating/hue/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/hue/hue.cc b/milena/sandbox/green/exp/annotating/hue/hue.cc new file mode 100644 index 0000000..883e1c6 --- /dev/null +++ b/milena/sandbox/green/exp/annotating/hue/hue.cc @@ -0,0 +1,291 @@ +// SATURATION TEST CF MILLET 2008 + +#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/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +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; +} + + +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; +} + +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; +} + +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; +} + +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; +} + +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; +} + +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; +} + + +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; +} + + +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_HUE_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/milena/sandbox/green/exp/annotating/hue/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to milena/sandbox/green/exp/annotating/hue/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/milena/sandbox/green/exp/annotating/hue/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to milena/sandbox/green/exp/annotating/hue/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/milena/sandbox/green/exp/annotating/hue/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to milena/sandbox/green/exp/annotating/hue/text-only.txt -- 1.5.6.5

14 years, 2 months

last-svn-commit-34-g1d23bfa Work on histograms view as density.

by Yann Jacquelet

* green/exp/annotating/histo: New directory. * green/exp/annotating/histo/histo.cc: New Makefile.am. * green/exp/annotating/histo/histo.cc: New source. --- .../annotating/{achromastism => histo}/Makefile.am | 0 milena/sandbox/green/exp/annotating/histo/histo.cc | 306 ++++++++++++++++++++ 2 files changed, 306 insertions(+), 0 deletions(-) copy milena/sandbox/green/exp/annotating/{achromastism => histo}/Makefile.am (100%) create mode 100644 milena/sandbox/green/exp/annotating/histo/histo.cc diff --git a/milena/sandbox/green/exp/annotating/achromastism/Makefile.am b/milena/sandbox/green/exp/annotating/histo/Makefile.am similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/Makefile.am copy to milena/sandbox/green/exp/annotating/histo/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/histo/histo.cc b/milena/sandbox/green/exp/annotating/histo/histo.cc new file mode 100644 index 0000000..2fb0131 --- /dev/null +++ b/milena/sandbox/green/exp/annotating/histo/histo.cc @@ -0,0 +1,306 @@ +// Build normalized histogram as density + +#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 +//============================================================================// + +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; +} + +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 +//============================================================================// + + +// in the directory of the images +// out the density directory +// 2 use-cases afp and icdar + +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; +} -- 1.5.6.5

14 years, 2 months

last-svn-commit-35-g485f8de Work on Millet hsv descriptors.

by Yann Jacquelet

* green/exp/annotating/hsv: New directory. * green/Exp/annotating/hsv/hsv.cc: New source file. --- milena/sandbox/ChangeLog | 24 + .../annotating/{achromastism => hsv}/Makefile.am | 0 milena/sandbox/green/exp/annotating/hsv/hsv.cc | 652 ++++++++++++++++++++ 3 files changed, 676 insertions(+), 0 deletions(-) copy milena/sandbox/green/exp/annotating/{achromastism => hsv}/Makefile.am (100%) create mode 100644 milena/sandbox/green/exp/annotating/hsv/hsv.cc diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index cc4634d..15be8a1 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,5 +1,28 @@ 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Work on Millet hsv descriptors. + + * green/exp/annotating/hsv: New directory. + * green/Exp/annotating/hsv/hsv.cc: New source file. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Work on histograms view as density. + + * green/exp/annotating/histo: New directory. + * green/exp/annotating/histo/histo.cc: New Makefile.am. + * green/exp/annotating/histo/histo.cc: New source. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Test error quantification as a color descriptor in our database. + + * green/exp/annotating/error: New directory. + * green/exp/annotating/error/Makefile.am: New Makefile. + * green/exp/annotating/error/error.cc: New source. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Benchmark few descriptors. * green/exp/annotating/bench: New directory. @@ -10,6 +33,7 @@ Test on image database the achromatism descriptor. + * green/exp/annotating/achromatism: New directory. * green/exp/annotating/achromatism/Makefile.am: New Makefile. * green/exp/annotating/achromatism/achromatism.am: New source. * green/exp/annotating/achromatism/text-color.txt: New image class. diff --git a/milena/sandbox/green/exp/annotating/achromastism/Makefile.am b/milena/sandbox/green/exp/annotating/hsv/Makefile.am similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/Makefile.am copy to milena/sandbox/green/exp/annotating/hsv/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/hsv/hsv.cc b/milena/sandbox/green/exp/annotating/hsv/hsv.cc new file mode 100644 index 0000000..2aa9113 --- /dev/null +++ b/milena/sandbox/green/exp/annotating/hsv/hsv.cc @@ -0,0 +1,652 @@ +// HSV TEST CF MILLET 2008 + +#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 +//============================================================================// + +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_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> +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) 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; +} + +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; +} + +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; +} + +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; +} + +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; +} + +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 +//============================================================================// + +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; +} + + +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; +} + +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; +} + +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; +} + +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 +//============================================================================// + +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 +//============================================================================// + +// 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> +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; +} + +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; +} + +// DOC: +// 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). + + + + +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 +//============================================================================// + + +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_HUE_RET_PATH); + directory = (ANNOTATING_AFP_HUE_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_SAT_RET_PATH); + directory = (ANNOTATING_AFP_SAT_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_VAL_RET_PATH); + directory = (ANNOTATING_AFP_VAL_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; +} -- 1.5.6.5

14 years, 2 months

last-svn-commit-36-ge9328a0 Work on Millet saturation descriptor.

by Yann Jacquelet

* green/exp/annotating/saturation: New directory. * green/exp/annotating/saturation/Makefile.am: New Makefile. * green/exp/annotating/saturation/saturation.cc: New source file. --- milena/sandbox/ChangeLog | 14 ++++++++- .../{achromastism => saturation}/Makefile.am | 0 .../achromastism.cc => saturation/saturation.cc} | 30 ++++++++++++-------- .../{achromastism => saturation}/text-color.txt | 0 .../{achromastism => saturation}/text-img.txt | 0 .../{achromastism => saturation}/text-only.txt | 0 6 files changed, 31 insertions(+), 13 deletions(-) copy milena/sandbox/green/exp/annotating/{achromastism => saturation}/Makefile.am (100%) copy milena/sandbox/green/exp/annotating/{achromastism/achromastism.cc => saturation/saturation.cc} (75%) copy milena/sandbox/green/exp/annotating/{achromastism => saturation}/text-color.txt (100%) copy milena/sandbox/green/exp/annotating/{achromastism => saturation}/text-img.txt (100%) copy milena/sandbox/green/exp/annotating/{achromastism => saturation}/text-only.txt (100%) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 15be8a1..eb301e5 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,9 +1,21 @@ 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Work on Millet saturation descriptor. + + * green/exp/annotating/saturation: New directory. + * green/exp/annotating/saturation/Makefile.am: New Makefile. + * green/exp/annotating/saturation/saturation.cc: New source file. + * green/exp/annotating/saturation/text-color.txt: New image class. + * green/exp/annotating/saturation/text-img.txt: New image class. + * green/exp/annotating/saturation/text-only.txt: New image class. + + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Work on Millet hsv descriptors. * green/exp/annotating/hsv: New directory. - * green/Exp/annotating/hsv/hsv.cc: New source file. + * green/exp/annotating/hsv/hsv.cc: New source file. 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> diff --git a/milena/sandbox/green/exp/annotating/achromastism/Makefile.am b/milena/sandbox/green/exp/annotating/saturation/Makefile.am similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/Makefile.am copy to milena/sandbox/green/exp/annotating/saturation/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/achromastism/achromastism.cc b/milena/sandbox/green/exp/annotating/saturation/saturation.cc similarity index 75% copy from milena/sandbox/green/exp/annotating/achromastism/achromastism.cc copy to milena/sandbox/green/exp/annotating/saturation/saturation.cc index fdb8e6d..3eb27a4 100644 --- a/milena/sandbox/green/exp/annotating/achromastism/achromastism.cc +++ b/milena/sandbox/green/exp/annotating/saturation/saturation.cc @@ -1,4 +1,4 @@ -// ACHROMATISM TEST CF MILLET 2008 +// SATURATION TEST CF MILLET 2008 #include <iostream> #include <sstream> @@ -21,9 +21,10 @@ #include <mln/geom/nsites.hh> -#include <mln/fun/v2v/rgb_to_achromatism_map.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> @@ -45,12 +46,14 @@ unsigned count_histo(const mln::Image& img_) return result; } -float achromatism_test(const std::string input, - const std::string output, - const unsigned threshold) + +float saturation_test(const std::string input, + const std::string output, +// const std::string tmp, + const unsigned threshold) { - typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + 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; @@ -61,7 +64,7 @@ float achromatism_test(const std::string input, mln::io::ppm::load(input_rgb8, input.c_str()); - map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + 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))); @@ -69,6 +72,7 @@ float achromatism_test(const std::string input, 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; } @@ -95,13 +99,15 @@ int main() for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) { // concatenation de chaine - t_path directory(ANNOTATING_ACHROMATISM_RET_PATH); - t_path leaf = dir_iter->path().leaf(); + t_path directory(ANNOTATING_SATURATION_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 = achromatism_test(dir_iter->path().string(), - output.string(), - 11); + prop = saturation_test(dir_iter->path().string(), + output.string(), +// tmp.string(), + 25); std::cout << output << " : " << prop << std::endl; std::cerr << output << " : " << prop << std::endl; diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/milena/sandbox/green/exp/annotating/saturation/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to milena/sandbox/green/exp/annotating/saturation/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/milena/sandbox/green/exp/annotating/saturation/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to milena/sandbox/green/exp/annotating/saturation/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/milena/sandbox/green/exp/annotating/saturation/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to milena/sandbox/green/exp/annotating/saturation/text-only.txt -- 1.5.6.5

14 years, 2 months

last-svn-commit-32-g174b917 Benchmark few descriptors.

by Yann Jacquelet

* green/exp/annotating/bench: New directory. * green/exp/annotating/bench/Makefile.am: New Makefile. * green/exp/annotating/bench/bench.cc: New source. --- milena/sandbox/ChangeLog | 8 + .../annotating/{achromastism => bench}/Makefile.am | 0 milena/sandbox/green/exp/annotating/bench/bench.cc | 1213 ++++++++++++++++++++ 3 files changed, 1221 insertions(+), 0 deletions(-) copy milena/sandbox/green/exp/annotating/{achromastism => bench}/Makefile.am (100%) create mode 100644 milena/sandbox/green/exp/annotating/bench/bench.cc diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index d86642a..cc4634d 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,5 +1,13 @@ 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Benchmark few descriptors. + + * green/exp/annotating/bench: New directory. + * green/exp/annotating/bench/Makefile.am: New Makefile. + * green/exp/annotating/bench/bench.cc: New source. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Test on image database the achromatism descriptor. * green/exp/annotating/achromatism/Makefile.am: New Makefile. diff --git a/milena/sandbox/green/exp/annotating/achromastism/Makefile.am b/milena/sandbox/green/exp/annotating/bench/Makefile.am similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/Makefile.am copy to milena/sandbox/green/exp/annotating/bench/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/bench/bench.cc b/milena/sandbox/green/exp/annotating/bench/bench.cc new file mode 100644 index 0000000..8e4525f --- /dev/null +++ b/milena/sandbox/green/exp/annotating/bench/bench.cc @@ -0,0 +1,1213 @@ +// BENCH TEST CF MILLET 2008 + +#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> + +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; +} + + +//============================================================================// +// MILLET HUE DESCRIPTOR +// +// 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; +} + + +//============================================================================// +// MILLET SATURATION DESCRIPTOR +// +// 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; + color = label_hue(peak);} + + +//============================================================================// +// MILLET DESCRIPTOR +// +// This test aims at compute the number of grey levels. Photographies tends to +// use all the levels or many of them. +//============================================================================// + +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; +} + + +float lvl0_descriptor(mln::image1d<unsigned> histo) +{ + float result; + + // FIXME 255 + result = 255-count_null_frequency_histo(histo); + + return result; +} + +//============================================================================// +// DENSITY DESCRIPTOR +// +// +//============================================================================// + +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; +} + +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; +} + + +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; +} + +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; +} + +//============================================================================// +// MILLET DESCRIPTOR +// +// 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. +//============================================================================// + + +// 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> +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; +} + +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; +} + +float var0_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; +} + + +//============================================================================// +// ERROR DESCRIPTOR +//============================================================================// + + +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); + +// 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 + // FIXME METTRE UN MAX A 255 + + return error; +} + + + +//============================================================================// +// CLASSIFICATION DE FISHER EN 2 CLASSES SUR UN HISTO 1D +//============================================================================// + +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); +} + +//============================================================================// +// CLASSIFIEUR +//============================================================================// + + +// Linear discriminant analysis in 1d +// With same variance, threshold = (m1+m2)/2 +// With different variance, (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; +} + +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + +// if gaussian without the same variance +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; +} + +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; +} + + +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; +} + +// return the threshold +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; +} + + + + +//============================================================================// +// MAIN +//============================================================================// + + + + + +#define LVL0_DESCR 0 +#define HUE0_DESCR 1 +#define HUE1_DESCR 2 +#define SAT0_DESCR 3 +#define SAT1_DESCR 4 +#define VAL0_DESCR 5 +#define VAL1_DESCR 6 +#define GMP0_DESCR 7 +#define GMP1_DESCR 8 +#define GMP2_DESCR 9 +#define MGK0_DESCR 9 +#define MGK1_DESCR 10 +#define MGK2_DESCR 11 + +#define MGK_DESCR(version) (MGK0_DESCR + version) +#define GMP_DESCR(version) (GMP0_DESCR + version) + +#define NB_DESCR 12 +#define NB_DATABASE 2 +#define NB_IMAGE 110 +#define NB_VERSION 3 + + +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; +} + + +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; + } +} + +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][VAR0_DESCR]+0.4999); + mln::opt::at(histo[VAR0_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][VAL0_DESCR]+0.4999); + mln::opt::at(histo[VAL0_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++; + } + } +} + +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; + + } +} + + +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] = var0_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; + } +} + + +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; +} + +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; +} + -- 1.5.6.5

14 years, 2 months

last-svn-commit-33-g2c26e60 Test error quantification as a color descriptor in our database.

by Yann Jacquelet

* green/exp/annotating/error: New directory. * green/exp/annotating/error/Makefile.am: New Makefile. * green/exp/annotating/error/error.cc: New source. --- .../annotating/{achromastism => error}/Makefile.am | 0 milena/sandbox/green/exp/annotating/error/error.cc | 700 ++++++++++++++++++++ 2 files changed, 700 insertions(+), 0 deletions(-) copy milena/sandbox/green/exp/annotating/{achromastism => error}/Makefile.am (100%) create mode 100644 milena/sandbox/green/exp/annotating/error/error.cc diff --git a/milena/sandbox/green/exp/annotating/achromastism/Makefile.am b/milena/sandbox/green/exp/annotating/error/Makefile.am similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/Makefile.am copy to milena/sandbox/green/exp/annotating/error/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/error/error.cc b/milena/sandbox/green/exp/annotating/error/error.cc new file mode 100644 index 0000000..5bc67ba --- /dev/null +++ b/milena/sandbox/green/exp/annotating/error/error.cc @@ -0,0 +1,700 @@ +// HSV TEST CF MILLET 2008 + +#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> + +//============================================================================// +// CLASSIFICATION DE FISHER EN 2 CLASSES SUR UN HISTO 1D +//============================================================================// + +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); +} + +//============================================================================// +// CLASSIFIEUR +//============================================================================// + + +// Linear discriminant analysis in 1d +// With same variance, threshold = (m1+m2)/2 +// With different variance, (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; +} + +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + +// if gaussian without the same variance +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; +} + +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; +} + + +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; +} + +// return the threshold +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; +} + + +//============================================================================// +// ERROR (MSE, PNSNR) compression p278 Handbook Color +//============================================================================// + +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; +} + + +//============================================================================// +// MAIN +//============================================================================// + + +int main2() +{ + float val = error_test(AFP_PPM_IMG_PATH"/000_APP2003011515775.ppm", + AFP_GMP10_IMG_PATH"/000_APP2003011515775.ppm"); + + std::cout << val << std::endl; + + return 0; +} + +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; +} -- 1.5.6.5

14 years, 2 months

last-svn-commit-31-gba99cc2 Test on image database the achromatism descriptor.

by Yann Jacquelet

* green/exp/annotating/achromatism/Makefile.am: New Makefile. * green/exp/annotating/achromatism/achromatism.am: New source. * green/exp/annotating/achromatism/text-color.txt: New image class. * green/exp/annotating/achromatism/text-img.txt: New image class. * green/exp/annotating/achromatism/text-only.txt: New image class. --- milena/sandbox/ChangeLog | 18 +++ .../{nb_color => achromastism}/Makefile.am | 4 +- .../exp/annotating/achromastism/achromastism.cc | 113 ++++++++++++++++++++ .../exp/annotating/achromastism/text-color.txt | 15 +++ .../green/exp/annotating/achromastism/text-img.txt | 40 +++++++ .../exp/annotating/achromastism/text-only.txt | 8 ++ 6 files changed, 197 insertions(+), 1 deletions(-) copy milena/sandbox/green/exp/annotating/{nb_color => achromastism}/Makefile.am (96%) create mode 100644 milena/sandbox/green/exp/annotating/achromastism/achromastism.cc create mode 100644 milena/sandbox/green/exp/annotating/achromastism/text-color.txt create mode 100644 milena/sandbox/green/exp/annotating/achromastism/text-img.txt create mode 100644 milena/sandbox/green/exp/annotating/achromastism/text-only.txt diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index 27c37db..d86642a 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,3 +1,21 @@ +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Test on image database the achromatism descriptor. + + * green/exp/annotating/achromatism/Makefile.am: New Makefile. + * green/exp/annotating/achromatism/achromatism.am: New source. + * green/exp/annotating/achromatism/text-color.txt: New image class. + * green/exp/annotating/achromatism/text-img.txt: New image class. + * green/exp/annotating/achromatism/text-only.txt: New image class. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Turn around Millet 2008 hsv descriptors. + + * green/demo/annotating/hsv: New directory. + * green/demo/annotating/hsv/Makefile.am: New Makefile. + + 2010-02-10 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Save Theo's exhaustive demonstration results. diff --git a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am b/milena/sandbox/green/exp/annotating/achromastism/Makefile.am similarity index 96% copy from milena/sandbox/green/exp/annotating/nb_color/Makefile.am copy to milena/sandbox/green/exp/annotating/achromastism/Makefile.am index 8e204c6..d33b94d 100644 --- a/milena/sandbox/green/exp/annotating/nb_color/Makefile.am +++ b/milena/sandbox/green/exp/annotating/achromastism/Makefile.am @@ -7,7 +7,9 @@ ######### LOADLIBES= -lboost_filesystem -INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +INCLUDES1= -I$(HOME)/git/olena/milena/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) diff --git a/milena/sandbox/green/exp/annotating/achromastism/achromastism.cc b/milena/sandbox/green/exp/annotating/achromastism/achromastism.cc new file mode 100644 index 0000000..fdb8e6d --- /dev/null +++ b/milena/sandbox/green/exp/annotating/achromastism/achromastism.cc @@ -0,0 +1,113 @@ +// ACHROMATISM TEST CF MILLET 2008 + +#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> + +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; +} + +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; +} + + +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_ACHROMATISM_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/milena/sandbox/green/exp/annotating/achromastism/text-color.txt new file mode 100644 index 0000000..4dfcbd3 --- /dev/null +++ b/milena/sandbox/green/exp/annotating/achromastism/text-color.txt @@ -0,0 +1,15 @@ +mp00262c_20p.ppm +mp00263c_20p.ppm +mp00319c_20p.ppm +mp00440c_20p.ppm +mp00608c_20p.ppm +mp00630c_20p.ppm +mp00631c_20p.ppm +ta00028c_20p.ppm +ta00037c_20p.ppm +ta00043c_20p.ppm +ta00046c_20p.ppm +ta00073c_20p.ppm +ta00081c_20p.ppm +ta00089c_20p.ppm +ta00090c_20p.ppm diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/milena/sandbox/green/exp/annotating/achromastism/text-img.txt new file mode 100644 index 0000000..4ecb7ca --- /dev/null +++ b/milena/sandbox/green/exp/annotating/achromastism/text-img.txt @@ -0,0 +1,40 @@ +mp00032c_20p.ppm +mp00042c_20p.ppm +mp00076c_20p.ppm +mp00082c_20p.ppm +mp00142c_20p.ppm +mp00215c_20p.ppm +mp00228c_20p.ppm +mp00234c_20p.ppm +mp00248c_20p.ppm +mp00252c_20p.ppm +mp00253c_20p.ppm +mp00255c_20p.ppm +mp00259c_20p.ppm +mp00271c_20p.ppm +mp00290c_20p.ppm +mp00293c_20p.ppm +mp00304c_20p.ppm +mp00307c_20p.ppm +mp00311c_20p.ppm +mp00376c_20p.ppm +mp00411c_20p.ppm +mp00419c_20p.ppm +mp00447c_20p.ppm +mp00498c_20p.ppm +mp00510c_20p.ppm +mp00550c_20p.ppm +mp00573c_20p.ppm +mp00589c_20p.ppm +mp00592c_20p.ppm +mp00597c_20p.ppm +mp00599c_20p.ppm +mp00600c_20p.ppm +ta00031c_20p.ppm +ta00034c_20p.ppm +ta00063c_20p.ppm +ta00065c_20p.ppm +ta00072c_20p.ppm +ta00081c_20p.ppm +ta00083c_20p.ppm + diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/milena/sandbox/green/exp/annotating/achromastism/text-only.txt new file mode 100644 index 0000000..0218a2a --- /dev/null +++ b/milena/sandbox/green/exp/annotating/achromastism/text-only.txt @@ -0,0 +1,8 @@ +mp00329c_20p.ppm +ta00036c_20p.ppm +ta00039c_20p.ppm +ta00040c_20p.ppm +ta00049c_20p.ppm +ta00055c_20p.ppm +ta00057c_20p.ppm +ta00068c_20p.ppm -- 1.5.6.5

14 years, 2 months

last-svn-commit-25-g1dff8cf Extend the histogram visualization tools for new projection concept.

by Yann Jacquelet

* green/mln/display/project_histo.hh (project2_histo): New functions that keep the max of the histogram or the class associate to it while projecting along a direction. * green/mln/display/project_histo.hh (project3_histo): New functions that keep the color of the class associate to the histogram maximum while projecting along a direction. * green/mln/display/display_histo.hh: New interface functions for project2_histo and project3_histo. --- milena/sandbox/ChangeLog | 13 + milena/sandbox/green/mln/display/display_histo.hh | 50 +++ milena/sandbox/green/mln/display/project_histo.hh | 344 +++++++++++++++++++++ 3 files changed, 407 insertions(+), 0 deletions(-) diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index f34508b..b9d40cb 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,5 +1,18 @@ 2010-01-05 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Extend the histogram visualization tools for new projection concept. + + * green/mln/display/project_histo.hh (project2_histo): New functions + that keep the max of the histogram or the class associate to it while + projecting along a direction. + * green/mln/display/project_histo.hh (project3_histo): New functions + that keep the color of the class associate to the histogram maximum + while projecting along a direction. + * green/mln/display/display_histo.hh: New interface functions for + project2_histo and project3_histo. + +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. diff --git a/milena/sandbox/green/mln/display/display_histo.hh b/milena/sandbox/green/mln/display/display_histo.hh index 1fd5da4..2ba0b61 100644 --- a/milena/sandbox/green/mln/display/display_histo.hh +++ b/milena/sandbox/green/mln/display/display_histo.hh @@ -33,6 +33,8 @@ # 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> /// \file @@ -55,6 +57,20 @@ namespace mln image2d<value::int_u8> display_histo3d_unsigned(const image3d<unsigned>& histo); + image2d<value::int_u8> + display2_histo3d_unsigned(const image3d<unsigned>& histo); + + image2d<value::label_8> + display2_histo3d_unsigned(const image3d<unsigned>& histo, + const image3d<value::label_8>& label); + + image2d<value::rgb8> + display3_histo3d_unsigned(const image3d<unsigned>& histo); + + image2d<value::rgb8> + display3_histo3d_unsigned(const image3d<unsigned>& histo, + const image3d<value::label_8>& label); + #ifndef MLN_INCLUDE_ONLY /// \brief Allow the visualization of a 3d histogram by projection. @@ -86,6 +102,40 @@ namespace mln return proj_int; } + image2d<value::int_u8> + display2_histo3d_unsigned(const image3d<unsigned>& histo) + { + image2d<value::int_u8> proj = project2_histo<0>(histo); + + return proj; + } + + image2d<value::label_8> + display2_histo3d_unsigned(const image3d<unsigned>& histo, + const image3d<value::label_8>& label) + { + image2d<value::label_8> proj = project2_histo<0>(histo, label); + + return proj; + } + + image2d<value::rgb8> + display3_histo3d_unsigned(const image3d<unsigned>& histo) + { + image2d<value::rgb8> proj = project3_histo<0>(histo); + + return proj; + } + + image2d<value::rgb8> + display3_histo3d_unsigned(const image3d<unsigned>& histo, + const image3d<value::label_8>& label) + { + image2d<value::rgb8> proj = project3_histo<0>(histo, label); + + 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 f0e6858..d842c70 100644 --- a/milena/sandbox/green/mln/display/project_histo.hh +++ b/milena/sandbox/green/mln/display/project_histo.hh @@ -37,6 +37,12 @@ # include <mln/accu/image/take.hh> # include <mln/accu/image/to_result.hh> +# include <mln/opt/at.hh> + +# include <mln/value/int_u8.hh> +# include <mln/value/rgb8.hh> +# include <mln/value/label_8.hh> + /// \file /// /// \brief Allow the visualization of 3d histogram. @@ -54,6 +60,10 @@ namespace mln image2d<mln_result(A)> project_histo(const image3d<V>& histo); + template <typename A, unsigned direction, typename V> + image2d<mln_result(A)> + project2_histo(const image3d<V>& histo); + # ifndef MLN_INCLUDE_ONLY /// \brief Allow the visualization of 3d histogram. @@ -86,6 +96,340 @@ namespace mln return accu::image::to_result(histo_accu); } + template <unsigned direction> + image2d<value::int_u8> + project2_histo(const image3d<unsigned>& histo) + { + image2d<value::int_u8> result; + + if (0 == direction) // blue + { + image2d<value::int_u8> arg_max(histo.ncols(), histo.nslices()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nslices(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nrows(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = pos; + } + + result = arg_max; + } + else if (1 == direction) // red + { + image2d<value::int_u8> arg_max(histo.nrows(), histo.nslices()); + + for (unsigned j = 0; j < histo.nslices(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.ncols(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = pos; + } + + result = arg_max; + } + else // 2 == direction // green + { + image2d<value::int_u8> arg_max(histo.nrows(), histo.ncols()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nslices(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = pos; + } + + result = arg_max; + } + + return result; + } + + template <unsigned direction> + image2d<value::label_8> + project2_histo(const image3d<unsigned>& histo, + const image3d<value::label_8>& label) + { + image2d<value::label_8> result; + + if (0 == direction) // blue + { + image2d<value::label_8> arg_max(histo.ncols(), histo.nslices()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nslices(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nrows(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = opt::at(label,i,j,pos); + } + + result = arg_max; + } + else if (1 == direction) // red + { + image2d<value::label_8> arg_max(histo.nrows(), histo.nslices()); + + for (unsigned j = 0; j < histo.nslices(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.ncols(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = opt::at(label,pos,i,j); + } + + result = arg_max; + } + else // 2 == direction // green + { + image2d<value::label_8> arg_max(histo.nrows(), histo.ncols()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nslices(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = opt::at(label,i,pos,j); + } + + result = arg_max; + } + + return result; + } + + + // 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) + { + image2d<value::rgb8> result; + + if (0 == direction) // blue + { + image2d<value::rgb8> arg_max(histo.ncols(), histo.nslices()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nslices(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nrows(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = value::rgb8(i,j,pos); + } + + result = arg_max; + } + else if (1 == direction) // red + { + image2d<value::rgb8> arg_max(histo.nrows(), histo.nslices()); + + for (unsigned j = 0; j < histo.nslices(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.ncols(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = value::rgb8(pos,i,j); + } + + result = arg_max; + } + else // 2 == direction // green + { + image2d<value::rgb8> arg_max(histo.nrows(), histo.ncols()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nslices(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + // FIXME ... how to fix the n of rgb + opt::at(arg_max,i,j) = value::rgb8(i,pos,j); + } + + result = arg_max; + } + + return result; + } + + template <unsigned direction> + image2d<value::rgb8> + project3_histo(const image3d<unsigned>& histo) + { + image2d<value::rgb8> result; + + if (0 == direction) // blue + { + image2d<value::rgb8> arg_max(histo.ncols(), histo.nslices()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nslices(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nrows(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = value::rgb8(i,j,pos); + } + + result = arg_max; + } + else if (1 == direction) // red + { + image2d<value::rgb8> arg_max(histo.nrows(), histo.nslices()); + + for (unsigned j = 0; j < histo.nslices(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.ncols(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + opt::at(arg_max,i,j) = value::rgb8(pos,i,j); + } + + result = arg_max; + } + else // 2 == direction // green + { + image2d<value::rgb8> arg_max(histo.nrows(), histo.ncols()); + + for (unsigned j = 0; j < histo.ncols(); ++j) + for (unsigned i = 0; i < histo.nrows(); ++i) + { + unsigned max = 0; // minimum as possible + signed pos = -1; + + for (unsigned k = 0; k < histo.nslices(); ++k) + { + if (max <= opt::at(histo,i,j,k)) + { + max = opt::at(histo,i,j,k); + pos = k; + } + } + + // FIXME ... how to fix the n of rgb + opt::at(arg_max,i,j) = value::rgb8(i,pos,j); + } + + result = arg_max; + } + + return result; + } + # endif // ! MLN_INCLUDE_ONLY -- 1.5.6.5

14 years, 2 months

last-svn-commit-26-g3849ff5 Fix last details in the image processing chain.

by Yann Jacquelet

* green/tools/annotating/histo/histo.cc: Manage new inputs/outputs. * green/tools/annotating/opening/opening.cc: Manage new inputs/outputs. * green/tools/annotating/iz/Makefile.am: New Makefile. * green/tools/annotating/iz/iz.cc: New file. * green/tools/annotating/regmax/regmax.cc: Manage new inputs/outputs. --- milena/sandbox/ChangeLog | 10 + .../sandbox/green/tools/annotating/histo/histo.cc | 84 +++-- .../tools/annotating/{histo => iz}/Makefile.am | 0 milena/sandbox/green/tools/annotating/iz/iz.cc | 373 ++++++++++++++++++++ .../green/tools/annotating/opening/opening.cc | 76 +++-- .../green/tools/annotating/regmax/regmax.cc | 253 ++++++++++++-- 6 files changed, 714 insertions(+), 82 deletions(-) copy milena/sandbox/green/tools/annotating/{histo => iz}/Makefile.am (100%) create mode 100644 milena/sandbox/green/tools/annotating/iz/iz.cc diff --git a/milena/sandbox/ChangeLog b/milena/sandbox/ChangeLog index b9d40cb..097ea5c 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -1,3 +1,13 @@ +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. + * green/tools/annotating/opening/opening.cc: Manage new inputs/outputs. + * green/tools/annotating/iz/Makefile.am: New Makefile. + * green/tools/annotating/iz/iz.cc: New file. + * green/tools/annotating/regmax/regmax.cc: Manage new inputs/outputs. + 2010-01-05 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Extend the histogram visualization tools for new projection concept. diff --git a/milena/sandbox/green/tools/annotating/histo/histo.cc b/milena/sandbox/green/tools/annotating/histo/histo.cc index ab0b8af..8037e1a 100644 --- a/milena/sandbox/green/tools/annotating/histo/histo.cc +++ b/milena/sandbox/green/tools/annotating/histo/histo.cc @@ -16,14 +16,14 @@ #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/dump/save.hh> +#include <mln/io/pgm/save.hh> #include <mln/io/ppm/save.hh> +#include <mln/literal/colors.hh> + #include <mln/opt/at.hh> #include <mln/pw/value.hh> @@ -33,15 +33,19 @@ template <unsigned n> -void mk_histo(const std::string& input, - const std::string& output, - const std::string& histo, - const std::string& mask) +void mk_histo(const std::string& input, // in + const std::string& quant, // in + const std::string& histo, // out + const std::string& proj1, // out + const std::string& proj2, // out + const std::string& mask) // [in] { typedef mln::value::int_u8 t_int_u8; + typedef mln::value::int_u<n> t_int_un; 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_int_un> t_image2d_int_un; typedef mln::image2d<t_rgb8> t_image2d_rgb8; typedef mln::image2d<t_rgbn> t_image2d_rgbn; typedef mln::image2d<bool> t_image2d_bool; @@ -54,7 +58,9 @@ void mk_histo(const std::string& input, 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 + t_image2d_int_u8 p1_histo1;// histo proj1 + t_image2d_rgbn p1_histo2;// histo proj2 + t_rgbn red(mln::literal::red); mln::io::ppm::load(i0_input, input.c_str()); i1_input = mln::data::transform(i0_input, t_rgb8_to_rgbn()); @@ -72,9 +78,13 @@ void mk_histo(const std::string& input, // END OF IMAGE PROCESSING CHAIN // BEGIN DUMPING - p1_histo = mln::display::display_histo3d_unsigned(h1_input); + p1_histo1 = mln::display::display_histo3d_unsigned(h1_input); + p1_histo2 = mln::display::display3_histo3d_unsigned(h1_input, red); + + mln::io::ppm::save(i1_input, quant.c_str()); mln::io::dump::save(h1_input, histo.c_str()); - mln::io::pgm::save(p1_histo, output.c_str()); + mln::io::pgm::save(p1_histo1, proj1.c_str()); + mln::io::ppm::save(p1_histo2, proj2.c_str()); // END DUMPING } @@ -82,35 +92,47 @@ void mk_histo(const std::string& input, 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 << "histo input.ppm q quant.ppm histo.dump proj.pgm" + << " proj.ppm [msk.pbm]" << std::endl; + std::cout << std::endl; + std::cout << "where :" << std::endl; + std::cout << "* [ in] input.ppm is the 8 bits color ppm image" << std::endl; + std::cout << "* [ in] q is the degree of quantification" + << " {2,3,4,5,6,7,8}" << std::endl; + std::cout << "* [out] quant.ppm is the q bits quantified input" + << " image" << std::endl; + std::cout << "* [out] histo.dump is the quantified color" + << " histogram" << std::endl; + std::cout << "* [out] proj.pgm is the r/g projection of the" + << " histogram (summing along the blue axe)" << std::endl; + std::cout << "* [out] proj.ppm is the r/g projection of the" + << " histogram with maxima plot on" << std::endl; + std::cout << "* [ in] msk.pbm is the mask which selects the" + << " pixels" << std::endl; std::cout << std::endl; } int main(int argc, char* args[]) { - if (5 == argc || 6 == argc) + if (7 == argc || 8 == 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] : ""); + const std::string input(args[1]); // in + const char q = args[2][0]; // in + const std::string quant(args[3]); // out + const std::string histo(args[4]); // out + const std::string proj1(args[5]); // out + const std::string proj2(args[6]); // out + const std::string mask(8 == argc? args[7] : ""); // [in] 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; + case '2': mk_histo<2>(input, quant, histo, proj1, proj2, mask); break; + case '3': mk_histo<3>(input, quant, histo, proj1, proj2, mask); break; + case '4': mk_histo<4>(input, quant, histo, proj1, proj2, mask); break; + case '5': mk_histo<5>(input, quant, histo, proj1, proj2, mask); break; + case '6': mk_histo<6>(input, quant, histo, proj1, proj2, mask); break; + case '7': mk_histo<7>(input, quant, histo, proj1, proj2, mask); break; + case '8': mk_histo<8>(input, quant, histo, proj1, proj2, mask); break; default: usage(); break; } } diff --git a/milena/sandbox/green/tools/annotating/histo/Makefile.am b/milena/sandbox/green/tools/annotating/iz/Makefile.am similarity index 100% copy from milena/sandbox/green/tools/annotating/histo/Makefile.am copy to milena/sandbox/green/tools/annotating/iz/Makefile.am diff --git a/milena/sandbox/green/tools/annotating/iz/iz.cc b/milena/sandbox/green/tools/annotating/iz/iz.cc new file mode 100644 index 0000000..07e5dd9 --- /dev/null +++ b/milena/sandbox/green/tools/annotating/iz/iz.cc @@ -0,0 +1,373 @@ +// TOOLS ==> influence zone transformation + +#include <iostream> +#include <fstream> +#include <boost/format.hpp> + +#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/ppm/load.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> + +#include <mln/literal/colors.hh> + +#include <mln/labeling/compute.hh> +#include <mln/labeling/mean_values.hh> + +#include <mln/transform/influence_zone_geodesic.hh> + +#include <mln/value/int_u8.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 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::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 + std::ofstream log_stream(log.c_str()); + + for (unsigned i = 0; i <= n_labels; ++i) + { + const t_vec3f& mean_v = mean[i]; + + 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; + } + + log_stream << std::endl << std::endl; + log_stream.flush(); + log_stream.close(); +} + +bool expect(std::istream& stream, const std::string expected) +{ + bool result; + std::string got; + + stream >> got; + + result = (got == expected); + + return result; +} + +std::istream& operator>>(std::istream& stream, + mln::algebra::vec<3,float>& color) +{ + unsigned lbl; + + stream >> lbl; + if (expect(stream, std::string("|")) && + expect(stream, std::string("r")) && + expect(stream, std::string("="))) + { + stream >> color[0]; + + if (expect(stream, std::string(",")) && + expect(stream, std::string("g")) && + expect(stream, std::string("="))) + { + stream >> color[1]; + + if (expect(stream, std::string(",")) && + expect(stream, std::string("b")) && + expect(stream, std::string("="))) + { + stream >> color[2]; + } + } + } + + return stream; +} + +void load(mln::util::array< mln::algebra::vec<3,float> >& m2_label, + const char *colormap) +{ + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::util::array<t_vec3f> t_mean_array; + + std::ifstream stream(colormap); + std::string buffer; + + getline(stream, buffer); + + while (0 < buffer.size()) + { + std::stringstream line(buffer); + t_vec3f mean_v; + + line >> mean_v; + + m2_label.append(mean_v); + + getline(stream, buffer); + } + + stream.close(); +} + +template<unsigned n> +void mk_iz(const std::string& labeled, // in + const unsigned d, // in + const mln::neighb3d& nbh, // in + const std::string& input, // in + const std::string& quant, // in + const std::string& histo, // in + const std::string& colormap,// in + const std::string& iz, // out + const std::string& proj, // out + const std::string& mean, // out + const std::string& stats) // [out] +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::label_8 t_lbl8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + 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::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image3d<t_lbl8> t_image3d_lbl8; + 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; + + // START OF IMAGE PROCESSING CHAIN + t_image2d_rgb8 i0_input; // input img + t_image2d_rgbn i1_input; // quant img + t_histo3d h1_input; // histo input + t_histo3d h2_input; // histo input + t_image2d_int_u8 p1_histo; // histo proj + t_image2d_rgb8 p3_histo; // histo proj + t_image3d_lbl8 l2_histo; // label histo + t_image3d_lbl8 l3_histo; // iz label hist + t_mean_array m2_label; // colormap + t_mean_array m3_label; // colormap + t_image2d_lbl8 l3_input; // label input + t_image2d_rgb8 i3_mean; // reconstructed + + t_lbl8 n_lbl; // nb labels + t_rgb8 red(mln::literal::red); + + mln::io::dump::load(l2_histo, labeled.c_str()); + mln::io::ppm::load(i0_input, input.c_str()); + mln::io::ppm::load(i1_input, quant.c_str()); + mln::io::dump::load(h1_input, histo.c_str()); + load(m2_label, colormap.c_str()); + + if (0 == d) + { + l3_histo = mln::transform::influence_zone_geodesic(l2_histo, nbh); + } + else + { + l3_histo = mln::transform::influence_zone_geodesic(l2_histo, nbh, d); + } + // END OF IMAGE PROCESSING CHAIN + + // BEGIN DUMPING + + n_lbl = (t_lbl8)(m2_label.size()-1); + + 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); + m3_label = mln::labeling::compute(t_mean(), i0_input, l3_input, n_lbl); + + // CORRECT 0 LABEL STATS + m3_label[0][0] = 255.0; + m3_label[0][1] = 255.0; + m3_label[0][2] = 0.0; + + p3_histo = mln::display::display3_histo3d_unsigned<n>(h1_input, + l3_histo, + m3_label, + red); + mln::io::dump::save(l3_input, iz.c_str()); + mln::io::ppm::save(p3_histo, proj.c_str()); + mln::io::ppm::save(i3_mean, mean.c_str()); + + if (0 < stats.size()) + compute_stats(i0_input, l3_input, h1_input, l3_histo, n_lbl, stats); + + // END DUMPING +} + + +void usage() +{ + std::cout << std::endl; + std::cout << "iz labeled.dump d nbh input.ppm q quant.ppm" + << " histo.dump colormap.txt iz.dump proj.ppm" + << " mean.ppm [stats.txt]" << std::endl; + std::cout << std::endl; + std::cout << "where :" << std::endl; + std::cout << "* [ in] labeled.dump is the labeled 3d histogram" << std::endl; + std::cout << "* [ in] d is the depth for the zone influence" + << " transformation (0 means infinite)" << std::endl; + std::cout << "* [ in] nbh is the 3d neighbourhood {6,18,26}" << std::endl; + std::cout << "* [ in] input.ppm is the 8 bits color ppm image" << std::endl; + std::cout << "* [ in] q is the degree of quantification" + << " {2,3,4,5,6,7,8}" << std::endl; + std::cout << "* [ in] quant.ppm is the q bits quantified input" + << " image" << std::endl; + std::cout << "* [ in] histo.dump is the quantified color" + << " histogram" << std::endl; + std::cout << "* [ in] colormap.txt is the colormap for labels" << std::endl; + std::cout << "* [out] iz.dump is the iz labeled 3d histogram" << std::endl; + std::cout << "* [out] proj.ppm is the r/g projection of the" + << " histogram with maxima label plot on" << std::endl; + std::cout << "* [out] mean.ppm is the mean reconstructed image" << std::endl; + std::cout << "* [out] stats.txt is the statistical label report"<< std::endl; + std::cout << std::endl; +} + +int main(int argc, char* args[]) +{ + if (12 == argc || 13 == argc) + { + const std::string labeled(args[1]); // in + const unsigned d = atoi(args[2]); // in + const char nbh = args[3][0]; // in + const std::string input(args[4]); // in + const char q = args[5][0]; // in + const std::string quant(args[6]); // in + const std::string histo(args[7]); // in + const std::string colormap(args[8]); // in + const std::string iz(args[9]); // out + const std::string proj(args[10]); // out + const std::string mean(args[11]); // out + const std::string stats(13 == argc? args[12] : ""); // [out] + + + mln::neighb3d neighbourhood; + + switch (nbh) + { + case '6': neighbourhood = mln::c6(); break; + case '1': neighbourhood = mln::c18(); break; + case '2': neighbourhood = mln::c26(); break; + default: usage(); return 0; // force usage and quit + } + + switch (q) + { + case '2' : mk_iz<2>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + case '3' : mk_iz<3>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + case '4' : mk_iz<4>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + case '5' : mk_iz<5>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + case '6' : mk_iz<6>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + case '7' : mk_iz<7>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + case '8' : mk_iz<8>(labeled,d,neighbourhood,input,quant, + histo,colormap,iz,proj,mean,stats);break; + default: usage(); break; + } + } + else + usage(); + + return 0; +} diff --git a/milena/sandbox/green/tools/annotating/opening/opening.cc b/milena/sandbox/green/tools/annotating/opening/opening.cc index 3e1dbf2..cdd37fb 100644 --- a/milena/sandbox/green/tools/annotating/opening/opening.cc +++ b/milena/sandbox/green/tools/annotating/opening/opening.cc @@ -15,36 +15,47 @@ #include <mln/io/dump/load.hh> #include <mln/io/dump/save.hh> -#include <mln/io/pgm/load.hh> +#include <mln/io/ppm/save.hh> #include <mln/io/pgm/save.hh> +#include <mln/literal/colors.hh> + #include <mln/morpho/opening/volume.hh> +#include <mln/value/rgb.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) +template <unsigned n> +void mk_opening(const std::string& histo, // in + const unsigned min_vol, // in + const std::string& opened, // out + const std::string& proj1, // out + const std::string& proj2) // out { typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb<n> t_rgbn; typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; 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 + t_image2d_int_u8 p1_histo1;// histo proj1 + t_image2d_rgbn p1_histo2;// histo proj2 + t_rgbn red(mln::literal::red); - mln::io::dump::load(h1_input, input.c_str()); + mln::io::dump::load(h1_input, histo.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); + p1_histo1 = mln::display::display_histo3d_unsigned(h2_input); + p1_histo2 = mln::display::display3_histo3d_unsigned(h2_input, red); mln::io::dump::save(h2_input, opened.c_str()); - mln::io::pgm::save(p1_histo, output.c_str()); + mln::io::pgm::save(p1_histo1, proj1.c_str()); + mln::io::ppm::save(p1_histo2, proj2.c_str()); // END DUMPING } @@ -52,25 +63,46 @@ void mk_opening(const std::string& input, 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 << "opening q histo.dump v opened.dump proj.pgm" + << " proj.ppm" << std::endl; + std::cout << std::endl; + std::cout << "where :" << std::endl; + std::cout << "* [ in] q is the degree of quantification" + << " {2,3,4,5,6,7,8}" << std::endl; + std::cout << "* [ in] histo.dump is the quantified color" + << " histogram" << std::endl; + std::cout << "* [ in] v is the minimum size (in pixels) of" + << " each composant" << std::endl; + std::cout << "* [out] opened.dump is the filtered histogram" << std::endl; + std::cout << "* [out] proj.pgm is the r/g projection of the" + << " histogram (summing along the blue axe)" << std::endl; + std::cout << "* [out] proj.ppm is the r/g projection of the" + << " histogram with maxima plot on" << std::endl; std::cout << std::endl; } int main(int argc, char* args[]) { - if (5 == argc) + if (7 == 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); + const char q = args[1][0]; // in + const std::string histo(args[2]); // in + const unsigned min_vol = atoi(args[3]); // in + const std::string opened(args[4]); // out + const std::string proj1(args[5]); // out + const std::string proj2(args[6]); // out + + switch(q) + { + case '2': mk_opening<2>(histo, min_vol, opened, proj1, proj2); break; + case '3': mk_opening<3>(histo, min_vol, opened, proj1, proj2); break; + case '4': mk_opening<4>(histo, min_vol, opened, proj1, proj2); break; + case '5': mk_opening<5>(histo, min_vol, opened, proj1, proj2); break; + case '6': mk_opening<6>(histo, min_vol, opened, proj1, proj2); break; + case '7': mk_opening<7>(histo, min_vol, opened, proj1, proj2); break; + case '8': mk_opening<8>(histo, min_vol, opened, proj1, proj2); break; + default: usage(); break; + } } else usage(); diff --git a/milena/sandbox/green/tools/annotating/regmax/regmax.cc b/milena/sandbox/green/tools/annotating/regmax/regmax.cc index 2079bc4..0ff4d2e 100644 --- a/milena/sandbox/green/tools/annotating/regmax/regmax.cc +++ b/milena/sandbox/green/tools/annotating/regmax/regmax.cc @@ -1,8 +1,13 @@ // TOOLS ==> regmax on histo #include <iostream> +#include <fstream> +#include <boost/format.hpp> #include <mln/accu/stat/histo3d_rgb.hh> +#include <mln/accu/stat/mean.hh> + +#include <mln/algebra/vec.hh> #include <mln/core/macros.hh> #include <mln/core/alias/neighb3d.hh> @@ -18,9 +23,14 @@ #include <mln/io/dump/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/labeling/regional_maxima.hh> +#include <mln/labeling/compute.hh> +#include <mln/labeling/mean_values.hh> + +#include <mln/literal/colors.hh> #include <mln/morpho/opening/volume.hh> @@ -28,6 +38,8 @@ #include <mln/value/int_u8.hh> #include <mln/value/rgb8.hh> +#include <mln/util/array.hh> + template <unsigned n> struct t_labeling_rgbn : mln::Function_v2v< t_labeling_rgbn<n> > { @@ -49,19 +61,134 @@ struct t_labeling_rgbn : mln::Function_v2v< t_labeling_rgbn<n> > } }; -void mk_regmax(const std::string& input, - const std::string& quant, - const std::string& histo, - const std::string& label, - const std::string& output) +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::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 + std::ofstream log_stream(log.c_str()); + + for (unsigned i = 0; i <= n_labels; ++i) + { + const t_vec3f& mean_v = mean[i]; + + 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; + } + + log_stream << std::endl << std::endl; + log_stream.flush(); + log_stream.close(); +} + +void save(mln::util::array< mln::algebra::vec<3,float> >& m2_label, + const char *colormap) +{ + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::util::array<t_vec3f> t_mean_array; + + std::ofstream stream(colormap); + + for (unsigned i = 0; i < m2_label.size(); ++i) + { + const t_vec3f& mean_v = m2_label[i]; + + stream << boost::format("%2i | r = %6.2f, g = %6.2f, b = %6.2f") + % i + % mean_v[0] + % mean_v[1] + % mean_v[2] + << std::endl; + } + + stream.flush(); + stream.close(); +} + +template <unsigned n> +void mk_regmax(const std::string& input, // in + const std::string& quant, // in + const std::string& histo, // in + const std::string& opened, // in + const mln::neighb3d& nbh, // in + const std::string& labeled, // out + const std::string& proj, // out + const std::string& colormap,// out + const std::string& mean, // out + const std::string& stats) // [out] { typedef mln::value::label_8 t_lbl8; typedef mln::value::rgb8 t_rgb8; - typedef mln::value::rgbn t_rgbn; + typedef mln::value::rgb<n> t_rgbn; typedef mln::value::int_u8 t_int_u8; + typedef mln::value::int_u<n> t_int_un; typedef mln::algebra::vec<3,float> t_v3f; typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_int_un> t_image2d_int_un; typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; typedef mln::image3d<t_lbl8> t_image3d_lbl8; typedef mln::image2d<t_lbl8> t_image2d_lbl8; typedef mln::image3d<unsigned> t_histo3d; @@ -71,60 +198,128 @@ void mk_regmax(const std::string& input, t_image2d_rgb8 i0_input; // input img t_image2d_rgbn i1_input; // quant img + t_histo3d h1_input; // input histo 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_image2d_rgb8 p2_label; // histo proj t_image3d_lbl8 l2_histo; // label histo - t_mean_array m2_label; // palette + t_image2d_lbl8 l2_input; // label input + t_mean_array m2_label; // colormap + t_image2d_rgb8 i2_mean; // reconstructed t_lbl8 n_lbl; // nb class + t_rgb8 red(mln::literal::red); + // 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()); + mln::io::dump::load(h1_input, histo.c_str()); + mln::io::dump::load(h2_input, opened.c_str()); // END LOADING // BEGIN IMAGE PROCESSING - l2_histo = mln::labeling::regional_maxima(h2_input, mln::c6(), n_lbl); + l2_histo = mln::labeling::regional_maxima(h2_input, nbh, 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); + i2_mean = mln::labeling::mean_values(i0_input, l2_input, n_lbl); + m2_label = mln::labeling::compute(t_mean(), i0_input, l2_input, n_lbl); + + // CORRECT 0 LABEL STATS + m2_label[0][0] = 255.0; + m2_label[0][1] = 255.0; + m2_label[0][2] = 0.0; + + p2_label =mln::display::display3_histo3d_unsigned<n>(h1_input, + l2_histo, + m2_label, + red); + + mln::io::ppm::save(p2_label, proj.c_str()); + save(m2_label, colormap.c_str()); + mln::io::ppm::save(i2_mean, mean.c_str()); + + if (0 < stats.size()) + compute_stats(i0_input, l2_input, h1_input, l2_histo, n_lbl, stats); -// 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 << "regmax input.ppm q quant.ppm histo.dump" + << " opened.dump nbh labeled.dump proj.ppm" + << " colormap.txt mean.ppm [stats.txt]" << std::endl; + std::cout << std::endl; + std::cout << "where :" << std::endl; + std::cout << "* [ in] input.ppm is the 8 bits color ppm image" << std::endl; + std::cout << "* [ in] q is the degree of quantification" + << " {2,3,4,5,6,7,8}" << std::endl; + std::cout << "* [ in] quant.ppm is the q bits quantified input" + << " image" << std::endl; + std::cout << "* [ in] histo.dump is the quantified color" + << " histogram" << std::endl; + std::cout << "* [ in] opened.dump is the filtered histogram" << std::endl; + std::cout << "* [ in] nbh is the 3d neighbourhood {6,18,26}" << std::endl; + std::cout << "* [out] labeled.dump is the labeled 3d histogram" << std::endl; + std::cout << "* [out] proj.ppm is the r/g projection of the" + << " histogram with maxima label plot on" << std::endl; + std::cout << "* [out] colormap.txt is the colormap for labels" << std::endl; + std::cout << "* [out] mean.ppm is the mean reconstructed image" << std::endl; + std::cout << "* [out] stats.txt is the statistical label report"<< std::endl; std::cout << std::endl; } int main(int argc, char* args[]) { - if (4 == argc) + if (11 == argc || 12 == argc) { - const std::string input(args[1]); - const std::string output(args[2]); - const std::string labeled(args[3]); + const std::string input(args[1]); // in + const char q = args[2][0]; // in + const std::string quant(args[3]); // in + const std::string histo(args[4]); // in + const std::string opened(args[5]); // in + const char nbh = args[6][0]; // in + const std::string labeled(args[7]); // out + const std::string proj(args[8]); // out + const std::string colormap(args[9]);// out + const std::string mean(args[10]); // out + const std::string stats(12 == argc? args[11] : ""); // [out] + + + mln::neighb3d neighbourhood; + + switch (nbh) + { + case '6': neighbourhood = mln::c6(); break; + case '1': neighbourhood = mln::c18(); break; + case '2': neighbourhood = mln::c26(); break; + default: usage(); return 0; // force usage and quit + } + + switch (q) + { - mk_regmax(input, output, labeled); + case '2': mk_regmax<2>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + case '3': mk_regmax<3>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + case '4': mk_regmax<4>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + case '5': mk_regmax<5>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + case '6': mk_regmax<6>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + case '7': mk_regmax<7>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + case '8': mk_regmax<8>(input,quant,histo,opened,neighbourhood, + labeled,proj,colormap,mean,stats); break; + default: usage(); break; + } } else usage(); -- 1.5.6.5

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