June 2010 - Olena-patches

last-svn-commit-218-g241c246 Test error quantification as a color descriptor in our database.
by Yann Jacquelet 21 Jun '10

21 Jun '10

* 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

1 0

last-svn-commit-33-g20909f4 Test error quantification as a color descriptor in our database.
by Yann Jacquelet 21 Jun '10

21 Jun '10

* 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

1 0

last-svn-commit-217-gf5c0ae5 Benchmark few descriptors.
by Yann Jacquelet 21 Jun '10

21 Jun '10

* 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 8f900b8..ec9743c 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -77,6 +77,14 @@ 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

1 0

last-svn-commit-32-g49aaec0 Benchmark few descriptors.
by Yann Jacquelet 21 Jun '10

21 Jun '10

* 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

1 0

last-svn-commit-216-g7e1a2f8 Test on image database the achromatism descriptor.
by Yann Jacquelet 21 Jun '10

21 Jun '10

* 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 | 17 +++ .../{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, 196 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 44903c6..8f900b8 100644 --- a/milena/sandbox/ChangeLog +++ b/milena/sandbox/ChangeLog @@ -75,6 +75,23 @@ * bin/pgm_to_pbm.cc, * bin/ppm_negate.cc: New. +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

1 0

last-svn-commit-31-g1106562 Test on image database the achromatism descriptor.
by Yann Jacquelet 21 Jun '10

21 Jun '10

* 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

1 0

last-svn-commit-215-g31925d2 Turn around Millet 2008 hsv descriptors.
by Yann Jacquelet 21 Jun '10

21 Jun '10

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

1 0

last-svn-commit-30-gc920529 Turn around Millet 2008 hsv descriptors.
by Yann Jacquelet 21 Jun '10

21 Jun '10

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

1 0

branch exp/scribo-z updated: last-svn-commit-162-gf89278c
by lazzara＠lrde.epita.fr 18 Jun '10

18 Jun '10

This is an automated email from the git hooks/post-receive script. It was generated because a ref change was pushed to the repository containing the project "Olena, a generic and efficient image processing platform". The branch exp/scribo-z has been updated via f89278cf5b12300abe45d7bf8eb464c7bdb7fba7 (commit) via 3ca05868e71d080d12c18329019b2addbf83bec9 (commit) from f819edfc8980df439b1920cadc3893a2f8b4eee6 (commit) Those revisions listed above that are new to this repository have not appeared on any other notification email; so we list those revisions in full, below. - Log ----------------------------------------------------------------- f89278c Fix a bug in conversions from vec to point. 3ca0586 Add a first draft of a white space finder. ----------------------------------------------------------------------- Summary of changes: milena/ChangeLog | 10 + milena/mln/core/concept/gpoint.hh | 2 +- milena/mln/core/image/image1d.hh | 6 +- milena/mln/core/point.hh | 28 +- scribo/sandbox/white_spaces/white_spaces.cc | 792 +++++++++++++++++++++++++++ 5 files changed, 827 insertions(+), 11 deletions(-) create mode 100644 scribo/sandbox/white_spaces/white_spaces.cc hooks/post-receive -- Olena, a generic and efficient image processing platform

1 0

last-svn-commit-162-gf89278c Fix a bug in conversions from vec to point.
by Guillaume Lazzara 18 Jun '10

18 Jun '10

* milena/mln/core/concept/gpoint.hh: Fix comment. * milena/mln/core/image/image1d.hh: Share a dpoint value. * milena/mln/core/point.hh: Handle point1d. --- milena/ChangeLog | 10 ++++++++++ milena/mln/core/concept/gpoint.hh | 2 +- milena/mln/core/image/image1d.hh | 6 ++++-- milena/mln/core/point.hh | 28 ++++++++++++++++++++-------- 4 files changed, 35 insertions(+), 11 deletions(-) diff --git a/milena/ChangeLog b/milena/ChangeLog index 15a34bc..a3b5d0a 100644 --- a/milena/ChangeLog +++ b/milena/ChangeLog @@ -1,3 +1,13 @@ +2010-06-18 Guillaume Lazzara <z(a)lrde.epita.fr> + + Fix a bug in conversions from vec to point. + + * milena/mln/core/concept/gpoint.hh: Fix comment. + + * milena/mln/core/image/image1d.hh: Share a dpoint value. + + * milena/mln/core/point.hh: Handle point1d. + 2010-06-15 Guillaume Lazzara <z(a)lrde.epita.fr> Introduce labeling::value_and_compute. diff --git a/milena/mln/core/concept/gpoint.hh b/milena/mln/core/concept/gpoint.hh index f0e094b..f9bba11 100644 --- a/milena/mln/core/concept/gpoint.hh +++ b/milena/mln/core/concept/gpoint.hh @@ -182,7 +182,7 @@ namespace mln * topology and with the same type of coordinates; otherwise this * test does not compile. * - * \post The result, \p dp, is such as \p lhs == \p rhs + \p dp. + * \post The result, \p dp, is such as \p lhs == \p rhs + \p dp. * * \return A delta point (temporary object). * diff --git a/milena/mln/core/image/image1d.hh b/milena/mln/core/image/image1d.hh index e416a9e..89e93e7 100644 --- a/milena/mln/core/image/image1d.hh +++ b/milena/mln/core/image/image1d.hh @@ -304,8 +304,10 @@ namespace mln void data< image1d<T> >::update_vb_() { - vb_.pmin() = b_.pmin() - dpoint1d(all_to(bdr_)); - vb_.pmax() = b_.pmax() + dpoint1d(all_to(bdr_)); + dpoint1d dp(all_to(bdr_)); + + vb_.pmin() = b_.pmin() - dp; + vb_.pmax() = b_.pmax() + dp; } template <typename T> diff --git a/milena/mln/core/point.hh b/milena/mln/core/point.hh index 8da15b6..50a0f6b 100644 --- a/milena/mln/core/point.hh +++ b/milena/mln/core/point.hh @@ -353,10 +353,16 @@ namespace mln point<G,C>::point(const algebra::vec<dim,double>& v) { unsigned j = 0; - for (unsigned i = dim - 2; i < dim; ++i) - coord_[i] = round(v[j++]); - for (unsigned i = 2; i < dim; ++i, ++j) - coord_[i-j] = round(v[j]); + //FIXME: to be improved while adding a conversion routine. + if (dim < 3) + coord_ = v; + else + { + for (unsigned i = dim - 2; i < dim; ++i) + coord_[i] = round(v[j++]); + for (unsigned i = 2; i < dim; ++i, ++j) + coord_[i-j] = round(v[j]); + } } template <typename G, typename C> @@ -364,10 +370,16 @@ namespace mln point<G,C>::point(const algebra::vec<dim,float>& v) { unsigned j = 0; - for (unsigned i = dim - 2; i < dim; ++i) - coord_[i] = round(v[j++]); - for (unsigned i = 2; i < dim; ++i, ++j) - coord_[i-j] = round(v[j]); + //FIXME: to be improved while adding a conversion routine. + if (dim < 3) + coord_ = v; + else + { + for (unsigned i = dim - 2; i < dim; ++i) + coord_[i] = round(v[j++]); + for (unsigned i = 2; i < dim; ++i, ++j) + coord_[i-j] = round(v[j]); + } } -- 1.5.6.5

1 0