* gaussian.sh: New gnuplot shell file. * guassian2d.sh: New gnuplot shell file. * test_labelling.cc: New source. * tests/clustering/k_mean/Makefile.am: New makefile. * tests/clustering/k_mean/k_mean.cc: New source. * tests/clustering/kmean1d/Makefile.am: New makefile. * tests/clustering/kmean1d/kmean1d.cc: New source. * tests/io/plot/save_image_sh/Makefile.am: New makefile. * tests/io/plot/save_image_sh/save_image_sh.cc: New source. --- scribo/sandbox/green/ChangeLog | 14 + .../histo1d => scribo/sandbox/green}/gaussian.sh | 0 .../histo2d => scribo/sandbox/green}/gaussian2d.sh | 0 scribo/sandbox/green/test_labelling.cc | 336 ++++++++++++++++++++ .../green/tests/clustering/k_mean/Makefile.am | 0 .../green/tests/clustering/k_mean/k_mean.cc | 0 .../green/tests/clustering/kmean1d/Makefile.am | 0 .../green/tests/clustering/kmean1d/kmean1d.cc | 0 .../green/tests/io/plot/save_image_sh}/Makefile.am | 0 .../tests/io/plot/save_image_sh/save_image_sh.cc | 0 10 files changed, 350 insertions(+), 0 deletions(-) copy {milena/sandbox/green/tests/accu/stat/histo1d => scribo/sandbox/green}/gaussian.sh (100%) copy {milena/sandbox/green/tests/accu/stat/histo2d => scribo/sandbox/green}/gaussian2d.sh (100%) create mode 100644 scribo/sandbox/green/test_labelling.cc copy {milena => scribo}/sandbox/green/tests/clustering/k_mean/Makefile.am (100%) copy {milena => scribo}/sandbox/green/tests/clustering/k_mean/k_mean.cc (100%) copy {milena => scribo}/sandbox/green/tests/clustering/kmean1d/Makefile.am (100%) copy {milena => scribo}/sandbox/green/tests/clustering/kmean1d/kmean1d.cc (100%) copy {milena/sandbox/green/doc/examples/frac => scribo/sandbox/green/tests/io/plot/save_image_sh}/Makefile.am (100%) copy {milena => scribo}/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc (100%) diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 8f5101b..db589a1 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -2,6 +2,20 @@ Import files from milena/sandbox/green. + * gaussian.sh: New gnuplot shell file. + * guassian2d.sh: New gnuplot shell file. + * test_labelling.cc: New source. + * tests/clustering/k_mean/Makefile.am: New makefile. + * tests/clustering/k_mean/k_mean.cc: New source. + * tests/clustering/kmean1d/Makefile.am: New makefile. + * tests/clustering/kmean1d/kmean1d.cc: New source. + * tests/io/plot/save_image_sh/Makefile.am: New makefile. + * tests/io/plot/save_image_sh/save_image_sh.cc: New source. + +2010-06-24 Yann Jacquelet <jacquelet@lrde.epita.fr> + + Import files from milena/sandbox/green. + * mln/accu/stat/histo1d.hh: New header file. * mln/accu/stat/histo2d.hh: New header file. * mln/accu/stat/histo3d_hsl.hh: New header file. diff --git a/milena/sandbox/green/tests/accu/stat/histo1d/gaussian.sh b/scribo/sandbox/green/gaussian.sh similarity index 100% copy from milena/sandbox/green/tests/accu/stat/histo1d/gaussian.sh copy to scribo/sandbox/green/gaussian.sh diff --git a/milena/sandbox/green/tests/accu/stat/histo2d/gaussian2d.sh b/scribo/sandbox/green/gaussian2d.sh similarity index 100% copy from milena/sandbox/green/tests/accu/stat/histo2d/gaussian2d.sh copy to scribo/sandbox/green/gaussian2d.sh diff --git a/scribo/sandbox/green/test_labelling.cc b/scribo/sandbox/green/test_labelling.cc new file mode 100644 index 0000000..6238d2b --- /dev/null +++ b/scribo/sandbox/green/test_labelling.cc @@ -0,0 +1,336 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + + +#include <mln/io/plot/save_histo_sh.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/label_8.hh> +#include <mln/core/alias/neighb1d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/labeling/colorize.hh> +#include <mln/labeling/mean_values.hh> +#include <mln/labeling/regional_maxima.hh> +#include <mln/make/w_window1d.hh> +#include <mln/math/pi.hh> +#include <mln/morpho/watershed/flooding.hh> +#include <mln/morpho/elementary/dilation.hh> +#include <mln/morpho/elementary/closing.hh> +#include <mln/pw/value.hh> +#include <mln/linear/convolve.hh> +#include <mln/linear/gaussian.hh> + + +/// ... to complete + +/// \brief Compute the gaussian probability to obtain a given value. +/// +/// \param[in] x the value from which we want the probability. +/// \param[in] mean the mean parameter of the gaussian distribution. +/// \param[in] sigma the sqrt(variance) parameter of the gaussian. +/// \return the probability to obtain the x value. +/// +/// Implements the standard equation of the gaussian. + +double gaussian_distribution(const double x, + const double mean, + const double sigma) +{ + double num = exp(-0.5*mln::math::sqr((x - mean)/sigma)); + double denom = sigma*mln::math::sqrt(2*mln::math::pi); + double result = num/denom; + + return result; +} + +/// \brief Make a 1d gaussian filter. +/// +/// \param[out] ws the window (filter) where to put the probability values. +/// \param[in] size the size of the filter. +/// \param[in] sigma the gaussian parameter for the standard deviation. +/// \return the probability to obtain the x value. +/// +/// Approach the gaussian distribution for a few discrete sites, and +/// then normilize the results to be sure that the sum obtain 1. + +void gaussian_filter(double ws[], const unsigned size, const double sigma) +{ + int h = size/2; + + for (int i = -h; i <= h; ++i) + { + ws[i+h] = gaussian_distribution(i, 0.0, sigma); + } + + double sum = 0.0; + + for (unsigned i = 0; i < size; ++i) + sum += ws[i]; + + for (unsigned i = 0; i < size; ++i) + ws[i] /= sum; +} + +/// \brief Make a 1d gaussian filter. +/// +/// \param[out] ws the window (filter) where to put the probability values. +/// \param[in] size the size of the filter. +/// \param[in] sigma the gaussian parameter for the standard deviation. +/// \return the probability to obtain the x value. +/// +/// Approach the gaussian distribution for a few discrete sites, and +/// then normilize the results to be sure that the sum obtain 1. + +/// The aim of this function is to rebuild an label image2d from the segmenting +/// image of the histogram (label) and the original image (input). +/// label_image2d / for each grey tone, associate its label. + +mln::image2d<mln::value::label_8> +build_8bits(const mln::image2d<mln::value::int_u8>& input, + const mln::image1d<mln::value::label_8>& label) +{ + mln::trace::entering("build_8bits"); + mln_precondition(label.is_valid()); + mln_precondition(input.is_valid()); + + mln::image2d<mln::value::label_8> output; + + mln::initialize(output, input); + + mln_piter_(mln::image2d<mln::value::int_u8>) pi(input.domain()); + mln_piter_(mln::image2d<mln::value::label_8>) po(output.domain()); + + for_all_2(pi, po) + { + mln::value::int_u8 val = input(pi); + unsigned grp = label(mln::point1d(val)); + + output(po) = grp; + } + + mln::trace::exiting("build_8bits"); + return output; +} + + +void test_8bits_classifying() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::value::label_8 label_8; + typedef mln::value::rgb8 rgb8; + typedef mln::accu::stat::mean<double> mean; + + mln::image2d<int_u8> img_ref; + mln::image2d<int_u8> img_out; + mln::image2d<rgb8> img_rgb; + mln::image1d<unsigned> img_res; + mln::image1d<double> img_smooth; + mln::image1d<label_8> labels; + label_8 nlabels; + + //----------------------------------------------------- + // Loading the scribo image and computing its histogram + //----------------------------------------------------- + + std::cout << "(08 bits) LOADING HISTOGRAM" << std::endl; + + // mln::io::pgm::load(img_ref, OLENA_IMG_PATH"/lena.pgm"); + mln::io::pgm::load(img_ref, SCRIBO_IMG_PATH"/mp00082c_50p_8bits.pgm"); + img_res = mln::data::compute(mln::accu::stat::histo1d<int_u8>(), img_ref); + mln::io::plot::save_histo_sh(img_res, "histo0_8bits.sh"); + + + //----------------------------------------------------- + // Smoothing the histogram with a gaussian filter + //----------------------------------------------------- + + std::cout << "(08 bits) SMOOTHING HISTOGRAM" << std::endl; + + double ws[41]; + gaussian_filter(ws, 41, 6.0); + img_smooth = mln::linear::convolve(img_res, mln::make::w_window1d(ws)); + mln::io::plot::save_histo_sh(img_smooth, "histo1_8bits.sh"); + + + //----------------------------------------------------- + // Segmenting the histogram with the watershed method + //----------------------------------------------------- + + std::cout << "SEGMENTING HISTOGRAM" << std::endl; + + /* + labels = mln::labeling::regional_maxima(img_smooth, mln::c2(), nlabels); + std::cout << "N labels : " << nlabels << std::endl; + mln::io::plot::save_histo_sh(labels, "histo2_8bits.sh"); + */ + + // need to revert the histogram + labels = mln::morpho::watershed::flooding(img_smooth, mln::c2(), nlabels); + std::cout << "N labels : " << nlabels << std::endl; + mln::io::plot::save_histo_sh(labels, "histo2_8bits.sh"); + + + //----------------------------------------------------- + // Rebuilding the image with the mean of each region + //----------------------------------------------------- + + std::cout << "(08 bits) BUILDING OUTPUT" << std::endl; + + mln::image2d<label_8>img_label = build_8bits(img_ref, labels); + + std::cout << "(08 bits) COLORING OUTPUT" << std::endl; + + img_out = mln::labeling::mean_values(img_ref, img_label, nlabels); + img_rgb = mln::labeling::colorize(rgb8(), img_label); + + mln::io::pgm::save(img_out, "out_8bits.pgm"); + mln::io::ppm::save(img_rgb, "color_8bits.pgm"); + + //labels = mln::morpho::elementary::dilation(labels, mln::c2()); + //mln::io::plot::save_histo_sh(labels, "histo3.sh"); + //mln::io::plot::save(labels, "labelized.data"); +} + + +/// The aim of this function is to rebuild an label image2d from the segmenting +/// image of the histogram (label) and the original image (input). +/// label_image2d / for each grey tone, associate its label. + +mln::image2d<mln::value::label_8> +build_14bits(const mln::image2d<mln::value::int_u<14> >& input, + const mln::image1d<mln::value::label_8>& label) +{ + mln::trace::entering("build_14bits"); + mln_precondition(label.is_valid()); + mln_precondition(input.is_valid()); + + mln::image2d<mln::value::label_8> output; + + mln::initialize(output, input); + + mln_piter_(mln::image2d<mln::value::int_u<14> >) pi(input.domain()); + mln_piter_(mln::image2d<mln::value::label_8>) po(output.domain()); + + for_all_2(pi, po) + { + mln::value::int_u<14> val = input(pi); + unsigned grp = label(mln::point1d(val)); + + output(po) = grp; + } + + mln::trace::exiting("build_14bits"); + return output; +} + + +void test_14bits_classifying() +{ + typedef mln::value::int_u16 int_u16; + typedef mln::value::int_u<14> int_u14; + typedef mln::value::label_8 label_8; + typedef mln::value::rgb8 rgb8; + typedef mln::accu::stat::mean<double> mean; + + mln::image2d<int_u16> img_fst; + mln::image2d<int_u14> img_ref; + mln::image2d<int_u14> img_out; + mln::image2d<rgb8> img_rgb; + mln::image1d<unsigned> img_res; + mln::image1d<double> img_smooth; + mln::image1d<label_8> labels; + label_8 nlabels; + + //----------------------------------------------------- + // Loading the scribo image and computing its histogram + //----------------------------------------------------- + + std::cout << "(14 bits) LOADING HISTOGRAM" << std::endl; + + //mln::io::pgm::load(img_fst, OLENA_IMG_PATH"/lena_16.pgm"); + mln::io::pgm::load(img_fst, SCRIBO_IMG_PATH"/mp00082c_50p_16bits.pgm"); + img_ref = mln::data::transform(img_fst, mln::fun::v2v::int_u16_to_int_u14()); + img_res = mln::data::compute(mln::accu::stat::histo1d<int_u14>(), img_ref); + mln::io::plot::save_histo_sh(img_res, "histo0_14bits.sh"); + + + //----------------------------------------------------- + // Smoothing the histogram with a gaussian filter + //----------------------------------------------------- + + std::cout << "(14 bits) SMOOTHING HISTOGRAM" << std::endl; + + double ws[401]; + gaussian_filter(ws, 401, 50.0); + img_smooth = mln::linear::convolve(img_res, mln::make::w_window1d(ws)); + mln::io::plot::save_histo_sh(img_smooth, "histo1_14bits.sh"); + + + //----------------------------------------------------- + // Segmenting the histogram with the watershed method + //----------------------------------------------------- + + std::cout << "(14 bits) SEGMENTING HISTOGRAM" << std::endl; + + /* + labels = mln::labeling::regional_maxima(img_smooth, mln::c2(), nlabels); + std::cout << "N labels : " << nlabels << std::endl; + mln::io::plot::save_histo_sh(labels, "histo2.sh"); + */ + + + labels = mln::morpho::watershed::flooding(img_smooth, mln::c2(), nlabels); + std::cout << "N labels : " << nlabels << std::endl; + mln::io::plot::save_histo_sh(labels, "histo2_14bits.sh"); + + + //----------------------------------------------------- + // Rebuilding the image with the mean of each region + //----------------------------------------------------- + + std::cout << "(14 bits) BUILDING OUTPUT" << std::endl; + + mln::image2d<label_8>img_label = build_14bits(img_ref, labels); + + std::cout << "(14 bits) COLORING OUTPUT" << std::endl; + + img_out = mln::labeling::mean_values(img_ref, img_label, nlabels); + img_rgb = mln::labeling::colorize(rgb8(), img_label); + + mln::io::pgm::save(img_out, "out_14bits.pgm"); + mln::io::ppm::save(img_rgb, "color_14bits.pgm"); + + //labels = mln::morpho::elementary::dilation(labels, mln::c2()); + //mln::io::plot::save_histo_sh(labels, "histo3.sh"); + //mln::io::plot::save(labels, "labelized.data"); +} + +int main() +{ + test_8bits_classifying(); + + test_14bits_classifying(); + + return 0; +} diff --git a/milena/sandbox/green/tests/clustering/k_mean/Makefile.am b/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am similarity index 100% copy from milena/sandbox/green/tests/clustering/k_mean/Makefile.am copy to scribo/sandbox/green/tests/clustering/k_mean/Makefile.am diff --git a/milena/sandbox/green/tests/clustering/k_mean/k_mean.cc b/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc similarity index 100% copy from milena/sandbox/green/tests/clustering/k_mean/k_mean.cc copy to scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc diff --git a/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/tests/clustering/kmean1d/Makefile.am similarity index 100% copy from milena/sandbox/green/tests/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/tests/clustering/kmean1d/Makefile.am diff --git a/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc b/scribo/sandbox/green/tests/clustering/kmean1d/kmean1d.cc similarity index 100% copy from milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc copy to scribo/sandbox/green/tests/clustering/kmean1d/kmean1d.cc diff --git a/milena/sandbox/green/doc/examples/frac/Makefile.am b/scribo/sandbox/green/tests/io/plot/save_image_sh/Makefile.am similarity index 100% copy from milena/sandbox/green/doc/examples/frac/Makefile.am copy to scribo/sandbox/green/tests/io/plot/save_image_sh/Makefile.am diff --git a/milena/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc b/scribo/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc similarity index 100% copy from milena/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc copy to scribo/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc -- 1.5.6.5