[Olena-patches] 4625: Add kmean2d demo for theo, work in progress.

* green/demo/clustering/kmean2d/Makefile.am: New makefile. * green/demo/clustering/kmean2d/kmean2d.cc: New demo code. * green/mln/clustering/kmean2d.hh: New library file. --- trunk/milena/sandbox/ChangeLog | 8 + .../green/demo/clustering/kmean2d/Makefile.am | 148 +++ .../green/demo/clustering/kmean2d/kmean2d.cc | 237 ++++ .../milena/sandbox/green/mln/clustering/kmean2d.hh | 1340 ++++++++++++++++++++ 4 files changed, 1733 insertions(+), 0 deletions(-) create mode 100644 trunk/milena/sandbox/green/demo/clustering/kmean2d/Makefile.am create mode 100644 trunk/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc create mode 100644 trunk/milena/sandbox/green/mln/clustering/kmean2d.hh diff --git a/trunk/milena/sandbox/ChangeLog b/trunk/milena/sandbox/ChangeLog index f3f91d6..d7bce42 100644 --- a/trunk/milena/sandbox/ChangeLog +++ b/trunk/milena/sandbox/ChangeLog @@ -1,5 +1,13 @@ 2009-10-08 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; + Add kmean2d demo for theo, work in progress. + + * green/demo/clustering/kmean2d/Makefile.am: New makefile. + * green/demo/clustering/kmean2d/kmean2d.cc: New demo code. + * green/mln/clustering/kmean2d.hh: New library file. + +2009-10-08 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; + Fix LRDE norm fault in rgb8_to_rgn.hh * green/mln/fun/v2v/rg8_to_rgn.hh: Correct space location. diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean2d/Makefile.am b/trunk/milena/sandbox/green/demo/clustering/kmean2d/Makefile.am new file mode 100644 index 0000000..91230b6 --- /dev/null +++ b/trunk/milena/sandbox/green/demo/clustering/kmean2d/Makefile.am @@ -0,0 +1,148 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo + + +ifeq ($(findstring $(BUILD__PATTERN),$(PWD)), $(BUILD__PATTERN)) +# Case where make is done from build directory. +SOURCE_DIR= $(subst $(BUILD__PATTERN),$(SOURCE_PATTERN),$(PWD)) +BUILD__DIR= $(PWD)/ +else +# Case where make is done from source directory. +SOURCE_DIR= $(PWD)/ +BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) +endif + +SRC= $(notdir $(wildcard $(SOURCE_DIR)/*.cc)) +OLD= $(notdir $(wildcard $(SOURCE_DIR)/*~)) +OBJ= $(patsubst %.cc,%.o,$(SRC)) +SOURCE_MAKEFILE=Makefile.am +BUILD__MAKEFILE=Makefile +TARGET_FILE= $(notdir $(PWD)) +SOURCE_FILES= $(notdir $(wildcard $(SOURCE_DIR)/*.*)) +BUILD__FILES= $(filter-out $(SRC) $(SOURCE_MAKEFILE), $(SOURCE_FILES)) + +BUILD__F_PATH= $(addprefix $(BUILD__DIR)/,$(BUILD__FILES)) +SOURCE_F_PATH= $(addprefix $(SOURCE_DIR)/,$(SOURCE_FILES)) + +BUILD__M_PATH= $(addprefix $(BUILD__DIR)/,$(BUILD__MAKEFILE)) +SOURCE_M_PATH= $(addprefix $(SOURCE_DIR)/,$(SOURCE_MAKEFILE)) + +TARGET_F_PATH= $(addprefix $(BUILD__DIR)/,$(TARGET_FILE)) +OBJ_F_PATH= $(addprefix $(BUILD__DIR)/,$(OBJ)) +SRC_F_PATH= $(addprefix $(SOURCE_DIR)/,$(SRC)) +OLD_F_PATH= $(addprefix $(SOURCE_DIR)/,$(OLD)) + +############# +# BOOTSTRAP # +############# + + +bootstrap: $(BUILD__DIR) $(BUILD__F_PATH) $(BUILD__M_PATH) + +# Create, if nessary, the destination directory +$(BUILD__DIR): + $(MKDIR) $(BUILD__DIR) + +# Copy, if nessary, all the files, except the Makefile.am +$(BUILD__F_PATH): $(SOURCE_F_PATH) + $(CP) $(addprefix $(SOURCE_DIR),$(@F)) $@ + +# Copy if nessary, the Makefile.am into Makefile +$(BUILD__M_PATH): $(SOURCE_M_PATH) + $(CP) $(SOURCE_M_PATH) $(BUILD__M_PATH) + + +####### +# ALL # +####### + +# We assume that the call is done from the build directory. +# With the directive vpath, hidden files are found in the source directory. + +all: $(TARGET_F_PATH) + + +$(TARGET_F_PATH): $(OBJ_F_PATH) + $(LINK.cc) $< $(LOADLIBES) $(LDLIBS) -o $@ + +$(OBJ_F_PATH):$(SRC_F_PATH) + $(COMPILE.cc) $(OUTPUT_OPTION) $< + + +######### +# CLEAN # +######### + +# Force every time the deletion +clean: clean_target clean_obj clean_dst clean_old #clean_make + + +clean_target: + -@$(RM) $(TARGET_F_PATH) &> /dev/null + +clean_obj: + -@$(RM) $(OBJ_F_PATH) &> /dev/null + +clean_dst: + -@$(RM) $(BUILD_F_PATH) &> /dev/null + +clean_make: + -@$(RM) $(BUILD_M_PATH) &> /dev/null + +clean_old: + -@$(RM) $(OLD_F_PATH) &> /dev/null + + +######### +# PRINT # +######### + +print: print_tools print_bootstrap + +print_tools: + @$(ECHO) "HOME = $(HOME)" + @$(ECHO) "INCLUDES = $(INCLUDES)" + @$(ECHO) "CXXFLAGS = $(CXXFLAGS)" + @$(ECHO) "ECHO = $(ECHO)" + @$(ECHO) "RM = $(RM)" + @$(ECHO) "MKDIR = $(MKDIR)" + @$(ECHO) "CP = $(CP)" + @$(ECHO) + +print_bootstrap: + @$(ECHO) "PWD = $(PWD)" + @$(ECHO) "SOURCE_PATTERN = $(SOURCE_PATTERN)" + @$(ECHO) "BUILD__PATTERN = $(BUILD__PATTERN)" + @$(ECHO) "SOURCE_DIR = $(SOURCE_DIR)" + @$(ECHO) "BUILD__DIR = $(BUILD__DIR)" + @$(ECHO) "SOURCE_MAKEFILE = $(SOURCE_MAKEFILE)" + @$(ECHO) "BUILD__MAKEFILE = $(BUILD__MAKEFILE)" + @$(ECHO) "TARGET_FILE = $(TARGET_FILE)" + @$(ECHO) "SOURCE_FILES = $(SOURCE_FILES)" + @$(ECHO) "SOURCE_F_PATH = $(SOURCE_F_PATH)" + @$(ECHO) "BUILD__FILES = $(BUILD__FILES)" + @$(ECHO) "BUILD__F_PATH = $(BUILD__F_PATH)" + @$(ECHO) "BUILD__M_PATH = $(BUILD__M_PATH)" + @$(ECHO) "SOURCE_M_PATH = $(SOURCE_M_PATH)" + @$(ECHO) "SRC = $(SRC)" + @$(ECHO) "OBJ = $(OBJ)" + @$(ECHO) "OLD = $(OLD)" + @$(ECHO) "SRC_F_PATH = $(SRC_F_PATH)" + @$(ECHO) "OBJ_F_PATH = $(OBJ_F_PATH)" + @$(ECHO) "OLD_F_PATH = $(OLD_F_PATH)" + @$(ECHO) diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/trunk/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc new file mode 100644 index 0000000..ebb5981 --- /dev/null +++ b/trunk/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc @@ -0,0 +1,237 @@ +// DEMO ON KMEAN2D + +#include <mln/clustering/kmean2d.hh> + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> +#include <mln/pw/value.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/value_array.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rg.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/fun/v2v/rg_to_rgb.hh> + +#include <mln/accu/stat/histo2d.hh> +#include <mln/data/compute.hh> + + + +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) +{ + typedef mln::clustering::kmean2d<double,8> t_kmean; + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::algebra::vec<2,t_int_u8> t_vec_rg8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::fun::v2v::component<t_rgb8, 0> t_comp_red; + typedef mln::fun::v2v::component<t_rgb8, 1> t_comp_green; + typedef mln::fun::v2v::component<t_rgb8, 2> t_comp_blue; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + typedef mln::image2d<t_vec_rg8> t_image2d_vec_rg8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::image2d<unsigned> t_histo2d; + + t_image2d_rgb8 house_rgb8; + t_image2d_int_u8 house_red; + t_image2d_int_u8 house_blue; + t_image2d_int_u8 house_green; + //t_image2d_vec_rg8 house_rg8; + t_image2d_rgb8 house_cast; + t_image2d_rg8 house_rg8; + t_histo2d house_histo; + + + mln::io::ppm::load(house_rgb8, image.c_str()); + house_red = mln::data::transform(house_rgb8, t_comp_red()); + house_green = mln::data::transform(house_rgb8, t_comp_green()); + house_blue = mln::data::transform(house_rgb8, t_comp_blue()); + + house_rg8 = mln::data::transform(house_rgb8, t_rgb_to_rg()); + house_histo = mln::data::compute(mln::accu::meta::stat::histo2d(), house_rg8); + + house_cast = mln::data::transform(house_rg8, t_rg_to_rgb()); + + t_kmean kmean(house_rg8, k_center, watch_dog, n_times); + + mln::io::pgm::save(house_red, "red.pgm"); + mln::io::pgm::save(house_green, "green.pgm"); + mln::io::pgm::save(house_blue, "blue.pgm"); + mln::io::ppm::save(house_cast, "red_green.ppm"); + mln::io::pgm::save(house_histo, "histo.pgm"); + mln::io::plot::save_image_sh(house_histo, "histo.sh"); + +/* + mln::io::plot::save_image_sh(mean_img, "mean.sh"); + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +*/ +} +/* +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) +{ + typedef mln::clustering::kmean3d<double,5> t_kmean; + typedef mln::value::label_8 t_label_8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<5> t_rgb5; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb5> t_image2d_rgb5; + + t_image2d_rgb8 house_rgb8; + t_image2d_rgb5 house_rgb5; + + mln::io::ppm::load(house_rgb8, image.c_str()); + house_rgb5=mln::data::transform(house_rgb8,mln::fun::v2v::rgb8_to_rgbn<5>()); + + t_kmean kmean(house_rgb5, k_center, watch_dog, n_times); + + mln::trace::quiet = false; + + //kmean.launch_one_time(); + kmean.launch_n_times(); + + // Not safe because we don't test kmean.is_valid() + + t_kmean::t_color_dbg color_img = kmean.get_color_dbg(); + t_kmean::t_mean_dbg mean_img = kmean.get_mean_dbg(); + t_kmean::t_label_dbg label_img = kmean.get_label_dbg(); + t_kmean::t_variance_cnv variance_cnv = kmean.get_variance_cnv(); + t_kmean::t_mean_cnv mean_cnv = kmean.get_mean_cnv(); + + mln::io::ppm::save(mean_img, "mean.ppm"); + mln::io::ppm::save(color_img, "color.ppm"); + mln::io::pgm::save(label_img, "label.pgm"); + + mln::io::plot::save_image_sh(mean_img, "mean.sh"); + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +} +*/ +void demo(const std::string& image = OLENA_IMG_PATH"/house.ppm", + const unsigned k_center = 3, + const unsigned n_times = 10, + const unsigned watch_dog = 10) +{ + std::cout << "----------------------------------------" << std::endl; + std::cout << "Launching the demo with these parameters" << std::endl; + std::cout << "image : " << image << std::endl; + std::cout << "k_center : " << k_center << std::endl; + std::cout << "n_times : " << n_times << std::endl; + std::cout << "watch_dog : " << watch_dog << std::endl; + std::cout << "----------------------------------------" << std::endl; + + do_demo(image, k_center, n_times, watch_dog); +} + +void usage(const int argc, const char *args[]) +{ + std::cout << "----------------------------------------" << std::endl; + std::cout << "argc : " << argc << std::endl; + + for (int i = 0; i < argc; ++i) + std::cout << "args[" << i << "] : " << args[i] << std::endl; + + std::cout << "----------------------------------------" << std::endl; + std::cout << "usage: kmean1d [image [k_center [n_times [watch_dog]]]]" + << std::endl; + std::cout << "pbm image (points to work with)" << std::endl; + std::cout << "unsigned k_center (number of centers)" << std::endl; + std::cout << "unsigned n_times (number of launching)" << std::endl; + std::cout << "unsigned watch_dog (convergence loop)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; + + result = !arg_stream.fail(); + } + + return result; +} + +bool char_to_string(const bool status, const char *arg, std::string& val) +{ + bool result = false; + + if (status) + { + std::istringstream arg_stream(arg); + + arg_stream >> val; + + return !arg_stream.fail(); + } + + return result; +} + +int main(const int argc, const char *args[]) +{ + std::string image("top"); + unsigned k_center; + unsigned watch_dog; + unsigned n_times; + bool status = true; + + switch (argc) + { + case 5: status = char_to_unsigned(status, args[4], watch_dog); + case 4: status = char_to_unsigned(status, args[3], n_times); + case 3: status = char_to_unsigned(status, args[2], k_center); + case 2: status = char_to_string(status, args[1], image); break; + case 1: status = true; break; + default: status = false; + } + + if (status) + { + switch (argc) + { + case 1: demo(); break; + case 2: demo(image); break; + case 3: demo(image, k_center); break; + case 4: demo(image, k_center, n_times); break; + case 5: demo(image, k_center, n_times, watch_dog); break; + } + } + else + usage(argc, args); + + return 0; +} diff --git a/trunk/milena/sandbox/green/mln/clustering/kmean2d.hh b/trunk/milena/sandbox/green/mln/clustering/kmean2d.hh new file mode 100644 index 0000000..4ff2e6d --- /dev/null +++ b/trunk/milena/sandbox/green/mln/clustering/kmean2d.hh @@ -0,0 +1,1340 @@ +// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (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. + +#ifndef MLN_CLUSTERING_KMEAN2D_HH +#define MLN_CLUSTERING_KMEAN2D_HH + +/// \file +/// +/// \brief Implements the optimized kmean algorithm. +/// +/// This algorithm is optimized in the way it proceeds directly with +/// the greylevel attribute inspite of the pixel attribute. The +/// algorithm is independant from the image dimension. But, we have to +/// compute one time the histogram. In fact, we move a recurrent cost +/// to a fix cost in the complexity. This version is very adapted to +/// images with small quantification. + +#include <limits.h> +#include <iostream> +#include <mln/trace/entering.hh> +#include <mln/trace/exiting.hh> + +#include <mln/core/contract.hh> +#include <mln/trait/value_.hh> +#include <mln/accu/stat/histo2d.hh> + +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/norm/l2.hh> + +#include <mln/core/image/image2d.hh> +#include <mln/core/concept/image.hh> +#include <mln/value/int_u.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/rg.hh> +#include <mln/core/macros.hh> + +#include <mln/data/compute.hh> +#include <mln/debug/println.hh> +#include <mln/data/fill.hh> +#include <mln/literal/zero.hh> +#include <mln/literal/one.hh> +#include <mln/labeling/colorize.hh> +#include <mln/labeling/mean_values.hh> + +#include <mln/io/ppm/save.hh> +#include <mln/io/pgm/save.hh> + +#include <mln/util/array.hh> +#include <mln/algebra/vec.hh> + +namespace mln +{ + + namespace clustering + { + + // Forward declaration. + template <typename T, unsigned n> + struct kmean2d; + + } // end of namespace mln::clustering + + namespace clustering + { + /// \brief Implements the kmean algorithm in a specific way. + /// + /// This version of the kmean algorithm uses a greyscale image as input, + /// temporary images for computations and produces images as result. Images + /// play the role of matrix or vector in standard statistic algoritm. + /// + /// T is the type used for computations (float or double). + /// n is the quantification for the image grayscale. + template <typename T, unsigned n> + struct kmean2d + { + /// Type definitions. + /// \brief A few type definitions to limit the refactoring impact. + ///{ + + typedef value::rgb<8> t_rgb; + typedef value::label<8> t_label; + typedef value::rg<n> t_value; + typedef mln_trait_value_comp(t_value,0) t_value_comp0; + typedef mln_trait_value_comp(t_value,1) t_value_comp1; + typedef T t_result1d; + typedef algebra::vec<2,T> t_result2d; + + /// FIXME t_point n'est pas forcément une image 2d, bien au contraire. + typedef image2d<t_value> t_point_img; + typedef image2d<unsigned> t_histo_img; + typedef util::array<t_result1d> t_number_img; + typedef util::array<t_result2d> t_mean_img; + typedef util::array<t_result1d> t_variance_img; + + typedef image2d<t_label> t_group_img; + typedef image2d<t_result1d> t_distance_val; + typedef util::array<t_distance_val> t_distance_img; + + typedef image2d<t_label> t_label_dbg; + typedef image2d<t_rgb> t_color_dbg; + typedef image2d<t_value> t_mean_dbg; + + typedef image1d<t_result2d> t_mean_val; + typedef util::array<t_mean_val> t_mean_set; + typedef util::array<t_mean_set> t_mean_cnv; + typedef image1d<t_result1d> t_variance_val; + typedef util::array<t_variance_val> t_variance_cnv; + + ///} + + //------------------------------------------------------------------------ + // Constructor + //------------------------------------------------------------------------ + + /// \brief Constructor + /// \param[in] point : the image as the population of pixels. + /// \param[in] k_center : the number of centers. + /// \param[in] watch_dog : the limit to observe the convergence (10). + /// \param[in] n_times : the number of times that we executed kmean(10). + + kmean2d(const t_point_img& point, + const unsigned k_center, + const unsigned watch_dog = 10, + const unsigned n_times = 10); + + //------------------------------------------------------------------------ + // Accessors + //------------------------------------------------------------------------ + + /// Mutator and accessors. + /// \{ + /// \brief Mutator and accessors are required for debugging and testing. + /// + /// Testing needs to hack the kmean loop process in order to verify each + /// step of the algorithm. + + void set_point(t_point_img& point); + void set_histo(t_histo_img& histo); + void set_number(t_number_img& number); + void set_mean(t_mean_img& mean); + void set_variance(t_variance_img& variance); + void set_group(t_group_img& group); + void set_distance(t_distance_img& distance); + + // Hacking outputs + t_point_img& get_point(); + t_histo_img& get_histo(); + t_number_img& get_number(); + t_mean_img& get_mean(); + t_variance_img& get_variance(); + t_group_img& get_group(); + t_distance_img& get_distance(); + + // Testing outputs + t_color_dbg& get_color_dbg(); + t_mean_dbg& get_mean_dbg(); + t_label_dbg& get_label_dbg(); + + t_mean_cnv& get_mean_cnv(); + t_variance_cnv& get_variance_cnv(); + + // Normal outputs + t_mean_img& to_result(); + + /// \} + + //------------------------------------------------------------------------ + // Printing temporary results + //------------------------------------------------------------------------ + + void print_mean(); + void print_number(); + void print_variance(); + void print_distance(); + void print_group(); + void print_point(); + void print_histo(); + + //------------------------------------------------------------------------ + // Initializations of centers + //------------------------------------------------------------------------ + + /// Initialization of centers. + /// \{ + /// \brief Two ways: Regular greylevel tick or random greylevel value or. + /// + /// There is two way to proceed the initialization. First of all, we + /// divide the greyscale in regular tick and we assigne them to the mean + /// of the centers. Finaly, we can ask random initialization along the + /// greyscale axis. The second process is needed to launch_n_times the + /// kmean and converge to the best descent. + + void init_mean(); + void init_mean_regular(); + void init_mean_random(); + + /// \} + + + //------------------------------------------------------------------------ + // Computations of distance, group, center, within variance + //------------------------------------------------------------------------ + + /// Computations of distance, group, center, within variance. + /// \{ + /// \brief Update the statistical information needed by the kmean process. + /// + /// The kmean process is a loop where distance from centers to pixels are + /// first compute. Then we assign the pixels to their nearest center. + /// The new location of each center can then update. Finaly, hommogeneity + /// in group is observed by the within variance. + + void update_distance(); + void update_group(); + void update_mean(); + void update_variance(); + + /// \} + + //------------------------------------------------------------------------ + // Main loop + //------------------------------------------------------------------------ + + /// kmean main loop + /// \{ + /// \brief User interface to launch the kmean process. + /// + /// There are two ways to launch the kmean process. The first one allow to + /// run it one time until convergence. As the process is a descent, it + /// depends on the initial center locations. The second call allow us to + /// rerun the process many times and to keep the best result (the one + /// with the smallest within variance). + + void launch_one_time(); + void launch_n_times(); + + /// \} + + //------------------------------------------------------------------------ + // Checking the validiy of the results + //------------------------------------------------------------------------ + + /// Checking the validity of the results. + /// \{ + /// \brief These methods help us to determine the validity of the results. + /// + /// After each launching the kmean process one time, we need to know if + /// the descent was successfull or not. The method is_valid_descent do it + /// for us. The method looks for a bad center (a class with no greylevel + /// associate to it) and a failure in the convergence. + + bool is_descent_valid(); + + /// \} + + //------------------------------------------------------------------------ + // Debugging tools + //------------------------------------------------------------------------ + + /// Debugging tools + /// \{ + /// \brief These methods help to interpret results. + /// + /// The methods build_label_dbg and build_all_dbg work in the input data + /// space. The first one build the 2d image of labels. The second call the + /// first one and then builds the colorize label' image and the mean + /// greylevel image. The update_cnv and finalize_cnv methods are used to + /// trace the convergence. They fill two images with the mean info and + /// the within variance info along the convergence and the tries. + + void build_label_dbg(); + void build_mean_dbg(); + void build_all_dbg(); + void update_cnv(); + void finalize_cnv(); + + /// \} + + + private: + /// Parameters. + /// \{ + /// \brief These parameters control the convergence of the process. + /// + /// The first parameter, k_center, defines the number of center for kmean. + /// In fact, watch_dog limit the number of iteration that a simple kmean + /// loop can do. If the process reaches the watch_dog limit, it means + /// that the process will not converge at all. The second parameter + /// n_times is the number of times we launch again and again the simple + /// kmean loop. As the kmean process is a descent, restarting the process + /// from different location will confort us in that we found a global + /// minima, not just a local one. + + unsigned _k_center; + unsigned _watch_dog; + unsigned _n_times; + + /// \} + + /// Convergence information. + /// \{ + /// \brief This information is used to follow the convergence. + /// + /// The within_variance is the homogeniety indicator for the + /// kmean process. The ideal situation is to find the center + /// with the minimum variance around them. The within variance + /// is just the sum of all variance around the centers. The + /// current_step variable allows us to remember the current + /// iteration in the kmean loop. This information is needed by + /// is_descent_valid routine which decide if convergence occurs + /// or not. The current_step info explicit where we are in the + /// kmean iteration. The last information, current_launching, + /// traces the progress while iterates kmean loop again and + /// again. The flag is_number_valid is set when a empty class + /// appears. This flag inhibit the process and force to restart + /// a try. + + t_result1d _within_variance; + unsigned _current_step; + unsigned _current_launching; + bool _is_number_valid; + + static const unsigned _N_TRIES = 3; + + /// \} + + /// Result of the kmean process. + /// \{ + /// \brief The center location are the major results of the kmean process. + /// + /// The kmean process result is composed by three information. The best + /// launching iteration, the best within variance obtained and the + /// location of the centers associated. + + unsigned _launching_min; + t_result1d _variance_min; + t_mean_img _mean_min; + + /// Inputs. + /// \{ + /// \brief The inputs are the distribution of the values and the values. + /// + /// The point image is a saving data for the real inputs. In fact, we use + /// the histogram information in the optimized kmean process. + + t_point_img _point; + t_histo_img _histo; + + ///\} + + /// Centers description. + /// \{ + /// \brief Centers are described by the first moment of their group. + /// + /// Centers are describe by their group attributes like the number of + /// pixels wich are relying on, the mass center of the group and the + /// homogeneity of the group. The variance is used as indicator for the + /// convergence. The number of pixels is used as integrity indicator. + /// A center with no point is a non sense. Theses informations are updated + /// after each kmean iteration. + + t_number_img _number; // k x 1 + t_mean_img _mean; // k x 1 + t_variance_img _variance; // k x 1 within group + + /// \} + + /// Greylevels description. + /// \{ + /// \brief The information are concerned with the greylevel input image. + /// + /// The group image allow us to decide which greylevel (and of course + /// which pixel) is assigned to a center. The distance image give us a + /// clue on how a greylevel could contribute to a center. The summation + /// over the greylevels of a center give us the within variance. + + t_group_img _group; // g x 1 because dim(t_value) = 1 + t_distance_img _distance; // label x graylevel + + /// \} + + /// Debugging, calibrating and testing results. + /// \{ + /// \brief Some exports information to control the results. + /// + /// We come back in the input space. Label is the image which associates + /// each pixel to its center. Color is the image which gives a random rgb + /// color to each label. We can then visualize the regions of the same + /// label (assigned to the same center) in the image. The mean image + /// associate the mean of each pixel center to each pixel. We obtain thus + /// a rebuilded image. + + t_label_dbg _label_dbg; + t_color_dbg _color_dbg; + t_mean_dbg _mean_dbg; + + /// \} + + /// Debugging, calibrating and testing convergence. + /// \{ + /// \brief Trace the variance and the center evolution. + /// + /// The mean_cnv image is a trace of the evolution of the mean' signal. + /// We can observe the variation of the mean of each center along the + /// kmean loop or along the different launch. The variance_cnv is a trace + /// of the within variance along the kmean loop or along the different + /// launch. + + t_mean_cnv _mean_cnv; + t_variance_cnv _variance_cnv; + + /// \} + }; + +#ifndef MLN_INCLUDE_ONLY + + //-------------------------------------------------------------------------- + // Constructor + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + kmean2d<T,n>::kmean2d(const t_point_img& point, + const unsigned k_center, + const unsigned watch_dog, + const unsigned n_times) + { + trace::entering("mln::clustering::kmean2d::kmean2d"); + mln_precondition(point.is_valid()); + + _k_center = k_center; + _watch_dog = watch_dog; + _n_times = n_times; + _point = point; + _histo = data::compute(accu::meta::stat::histo2d(),_point); + + // Results aren't valid since they aren't available + _is_number_valid = false; + _current_step = 0; + _current_launching = 0; + + for (unsigned i = 0; i < _k_center; ++i) + { + _number.append(literal::zero); + _mean.append(literal::zero); + _variance.append(literal::zero); + } + + _group.init_(box2d(point2d(mln_min(t_value_comp0), + mln_min(t_value_comp1)), + point2d(mln_max(t_value_comp0), + mln_max(t_value_comp1)))); + + for (unsigned i = 0; i < _k_center; ++i) + { + t_distance_val img(box2d(point2d(mln_min(t_value_comp0), + mln_min(t_value_comp1)), + point2d(mln_max(t_value_comp0), + mln_max(t_value_comp1)))); + + _distance.append(img); + } + + // Debugging, calibrating and testing + initialize(_label_dbg, _point); + initialize(_color_dbg, _point); + initialize(_mean_dbg, _point); + + // Observing the convergence + + for (unsigned i = 0; i < _n_times; ++i) + { + t_variance_val img(box1d(point1d(0), point1d(_watch_dog-1))); + + data::fill(img, literal::zero); + + _variance_cnv.append(img); + } + + for (unsigned i = 0; i < _k_center; ++i) + { + t_mean_set mean_set; + + for (unsigned j = 0; j < _n_times; ++j) + { + t_mean_val img(box1d(point1d(0), point1d(_watch_dog-1))); + + data::fill(img, literal::zero); + + mean_set.append(img); + } + + _mean_cnv.append(mean_set); + } + + trace::exiting("mln::clustering::kmean2d::kmean2d"); + } + + //-------------------------------------------------------------------------- + // Mutators and accessors + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_point(t_point_img& point) + { + trace::entering("mln::clustering::kmean2d::set_point"); + + _point = point; + + trace::exiting("mln::clustering::kmean2d::set_point"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_histo(t_histo_img& histo) + { + trace::entering("mln::clustering::kmean2d::set_histo"); + + _histo = histo; + + trace::exiting("mln::clustering::kmean2d::set_histo"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_number(t_number_img& number) + { + trace::entering("mln::clustering::kmean2d::set_number"); + + _number = number; + + trace::exiting("mln::clustering::kmean2d::set_number"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_mean(t_mean_img& mean) + { + trace::entering("mln::clustering::kmean2d::set_mean"); + + _mean = mean; + + trace::exiting("mln::clustering::kmean2d::set_mean"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_variance(t_variance_img& variance) + { + trace::entering("mln::clustering::kmean2d::set_variance"); + + _variance = variance; + + trace::exiting("mln::clustering::kmean2d::set_variance"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_group(t_group_img& group) + { + trace::entering("mln::clustering::kmean2d::set_group"); + + _group = group; + + trace::exiting("mln::clustering::kmean2d::set_group"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::set_distance(t_distance_img& distance) + { + trace::entering("mln::clustering::kmean2d::set_distance"); + + _distance = distance; + + trace::exiting("mln::clustering::kmean2d::set_distance"); + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_point_img& kmean2d<T,n>::get_point() + { + trace::entering("mln::clustering::kmean2d::get_point"); + + trace::exiting("mln::clustering::kmean2d::get_point"); + return _point; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_histo_img& kmean2d<T,n>::get_histo() + { + trace::entering("mln::clustering::kmean2d::get_histo"); + + trace::exiting("mln::clustering::kmean2d::get_histo"); + return _histo; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_number_img& kmean2d<T,n>::get_number() + { + trace::entering("mln::clustering::kmean2d::get_number"); + + trace::exiting("mln::clustering::kmean2d::get_number"); + return _number; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_mean_img& kmean2d<T,n>::get_mean() + { + trace::entering("mln::clustering::kmean2d::get_mean"); + + trace::exiting("mln::clustering::kmean2d::get_mean"); + return _mean; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_variance_img& kmean2d<T,n>::get_variance() + { + trace::entering("mln::clustering::kmean2d::get_variance"); + + trace::exiting("mln::clustering::kmean2d::get_variance"); + return _variance; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_group_img& kmean2d<T,n>::get_group() + { + trace::entering("mln::clustering::kmean2d::get_group"); + + trace::exiting("mln::clustering::kmean2d::get_group"); + return _group; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_distance_img& kmean2d<T,n>::get_distance() + { + trace::entering("mln::clustering::kmean2d::get_distance"); + + trace::exiting("mln::clustering::kmean2d::get_distance"); + return _distance; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_color_dbg& kmean2d<T,n>::get_color_dbg() + { + trace::entering("mln::clustering::kmean2d::get_color_dbg"); + + trace::exiting("mln::clustering::kmean2d::get_color_dbg"); + return _color_dbg; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_mean_dbg& kmean2d<T,n>::get_mean_dbg() + { + trace::entering("mln::clustering::kmean2d::get_mean_dbg"); + + trace::exiting("mln::clustering::kmean2d::get_mean_dbg"); + return _mean_dbg; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_label_dbg& kmean2d<T,n>::get_label_dbg() + { + trace::entering("mln::clustering::kmean2d::get_label_dbg"); + + trace::exiting("mln::clustering::kmean2d::get_label_dbg"); + return _label_dbg; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_mean_cnv& kmean2d<T,n>::get_mean_cnv() + { + trace::entering("mln::clustering::kmean2d::get_mean_cnv"); + + trace::exiting("mln::clustering::kmean2d::get_mean_cnv"); + return _mean_cnv; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_variance_cnv& kmean2d<T,n>::get_variance_cnv() + { + trace::entering("mln::clustering::kmean2d::get_variance_cnv"); + + trace::exiting("mln::clustering::kmean2d::get_variance_cnv"); + return _variance_cnv; + } + + template <typename T, unsigned n> + inline + typename kmean2d<T,n>::t_mean_img& kmean2d<T,n>::to_result() + { + trace::entering("mln::clustering::kmean2d::to_result"); + + trace::exiting("mln::clustering::kmean2d::to_result"); + return _mean_min; + } + + //-------------------------------------------------------------------------- + // Printing temporary results + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_mean() + { + trace::entering("mln::clustering::kmean2d::print_mean"); + + mln_eiter(t_mean_img) l(_mean); + + for_all(l) + { + std::cout << "mean(" << l.index_(); + std::cout << ") = [r=" << _mean[l.index_()][0]; + std::cout << ", g=" << _mean[l.index_()][1]; + std::cout << ", b=" << _mean[l.index_()][2]; + std::cout << "]" << std::endl; + } + + trace::exiting("mln::clustering::kmean2d::print_mean"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_number() + { + trace::entering("mln::clustering::kmean2d::print_number"); + + mln_eiter(t_number_img) l(_number); + + for_all(l) + { + std::cout << "number(" << l.index_(); + std::cout << ") = " << _number[l.index_()]; + std::cout << std::endl; + } + + trace::exiting("mln::clustering::kmean2d::print_number"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_variance() + { + trace::entering("mln::clustering::kmean2d::print_variance"); + + mln_eiter(t_variance_img) l(_number); + + for_all(l) + { + std::cout << "variance(" << l.index_(); + std::cout << ") = " << _variance[l.index_()]; + std::cout << std::endl; + } + + trace::exiting("mln::clustering::kmean2d::print_variance"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_histo() + { + trace::entering("mln::clustering::kmean2d::print_histo"); + + mln_piter(t_histo_img) rgb(_histo.domain()); + + for_all(rgb) + { + if (0 < _histo(rgb)) + { + std::cout << "histo(r=" << rgb.row(); + std::cout << ", g=" << rgb.col(); + std::cout << ")= " << _histo(rgb); + std::cout << std::endl; + } + } + + trace::exiting("mln::clustering::kmean2d::print_histo"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_group() + { + trace::entering("mln::clustering::kmean2d::print_group"); + + mln_piter(t_group_img) rgb(_group.domain()); + + for_all(rgb) + { + if (0 < _histo(rgb)) + { + std::cout << "group(r=" << rgb.row(); + std::cout << ", g=" << rgb.col(); + std::cout << ")= " << _group(rgb); + std::cout << std::endl; + } + } + + trace::exiting("mln::clustering::kmean2d::print_group"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_distance() + { + trace::entering("mln::clustering::kmean2d::print_distance"); + + mln_eiter(t_distance_img) l(_distance); + + for_all(l) + { + mln_piter(t_distance_val) rgb(_distance[l.index_()].domain()); + + for_all(rgb) + { + if (0 < _histo(rgb)) + { + std::cout << "distance(l=" << l.index_(); + std::cout << ",r=" << rgb.row(); + std::cout << ", g=" << rgb.col(); + std::cout << ")= " << _distance[l.index_()](rgb); + std::cout << std::endl; + } + } + } + + trace::exiting("mln::clustering::kmean2d::print_distance"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::print_point() + { + trace::entering("mln::clustering::kmean2d::print_point"); + + mln_piter(t_point_img) p(_point.domain()); + + for_all(p) + { + std::cout << "point(r=" << p.row(); + std::cout << ", c=" << p.col(); + std::cout << ")= " << _point(p); + std::cout << std::endl; + } + + trace::exiting("mln::clustering::kmean2d::print_point"); + } + + + + //-------------------------------------------------------------------------- + // Initialization of centers + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::init_mean_random() + { + trace::entering("mln::clustering::kmean2d::init_mean_random"); + + t_value_comp0 min_comp0 = mln_min(t_value_comp0); + t_value_comp0 max_comp0 = mln_max(t_value_comp0); + t_value_comp1 min_comp1 = mln_min(t_value_comp1); + t_value_comp1 max_comp1 = mln_max(t_value_comp1); + + mln_eiter(t_mean_img) l(_mean); + + for_all(l) + { + _mean[l.index_()][0] = (rand() % (max_comp0-min_comp0)) + min_comp0; + _mean[l.index_()][1] = (rand() % (max_comp1-min_comp1)) + min_comp1; + } + + trace::exiting("mln::clustering::kmean2d::init_mean_random"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::init_mean() + { + trace::entering("mln::clustering::kmean2d::init_mean"); + + init_mean_random(); + + trace::exiting("mln::clustering::kmean2d::init_mean"); + } + + //-------------------------------------------------------------------------- + // Computations of distance, group, center, within variance + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::update_distance() + { + trace::entering("mln::clustering::kmean2d::update_distance"); + + for (unsigned i = 0; i < _k_center; ++i) + { + mln_piter(t_distance_val) d(_distance[i].domain()); + + for_all(d) + { + // the square distance + t_result1d diff2_row = math::sqr(d.row() - _mean[i][0]); + t_result1d diff2_col = math::sqr(d.col() - _mean[i][1]); + t_result1d diff2_sli = math::sqr(d.sli() - _mean[i][2]); + _distance[i](d) = _histo(d)*(diff2_row + diff2_col + diff2_sli); + /* + std::cout << "row : " << d.row() << std::endl; + std::cout << "col : " << d.col() << std::endl; + std::cout << "histo : " << _histo(point1d(d.row())) << std::endl; + std::cout << "center : " << _mean(point1d(d.col())) << std::endl; + std::cout << "distance : " << _distance(d) << std::endl; + std::cout << "--------------------------------------" << std::endl; + */ + } + } + + trace::exiting("mln::clustering::kmean2d::update_distance"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::update_group() + { + trace::entering("mln::clustering::kmean2d::update_group"); + + mln_piter(t_group_img) rgb(_group.domain()); + + for_all(rgb) + { + mln_eiter(t_distance_img) l(_distance); + t_result1d min = mln_max(t_result1d); + t_label label = mln_max(t_label); + + //std::cout << "g = " << g << std::endl; + + for_all(l) + { + if (min > _distance[l.index_()](rgb)) + { + min = _distance[l.index_()](rgb); + label = l.index_(); + } + + //std::cout << "d" << l << " = " << + // _distance(point2d(g.ind(), l.ind())) << std::endl; + } + + //std::cout << "g = " << g << std::endl; + + _group(rgb) = label; + //std::cout << "group = " << _group(g) << std::endl; + //std::cout << "-----------" << std::endl; + } + + trace::exiting("mln::clustering::kmean2d::update_group"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::update_mean() + { + trace::entering("mln::clustering::kmean2d::update_mean"); + + /// FIXME VERIFIER QUE L'ON PEUT OBTENIR UNE IMAGE EN NDG SIGNE + + // avec g le niveau de gris (signed or not signed) + // w[g] la classe de g sous forme d'image + // h[g] l'histogramme de l'image sous forme d'image + // + // Vg in [0..255] + // si w[g] = l + // c[l] += h(g) * g + // n[l] += h(g) + // + // c[l] /= n + + + mln_eiter(t_number_img) en(_number); + mln_eiter(t_mean_img) em(_mean); + + for_all_2(en,em) + { + _number[en.index_()] = literal::zero; + _mean[em.index_()] = literal::zero; + } + + mln_piter(t_group_img) rgb(_group.domain()); + + for_all(rgb) + { + // peut être faut-il le decomposer par composantes + _mean[_group(rgb)][0] += rgb.row() * _histo(rgb); + _mean[_group(rgb)][1] += rgb.col() * _histo(rgb); + _number(_group(rgb)) += _histo(rgb); + } + + mln_eiter(t_mean_img) l(_mean); + + /* + for_all(l) + { + std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + std::cout << "n(" << l << ") = " << _number(l) << std::endl; + } + */ + for_all(l) + { + // State the stopping propagation Nan flag + _is_number_valid = (0 != _number[l.index_()]); + + // Emergency exit + if (!_is_number_valid) + break; + + // Compute the mean + _mean[l.index_()] /= _number[l.index_()]; + + // Debugging + //std::cout << "c" << l << " = " << _mean(l) << std::endl; + } + + trace::exiting("mln::clustering::kmean2d::update_mean"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::update_variance() + { + trace::entering("mln::clustering::kmean2d::update_variance"); + + _within_variance = literal::zero; + mln_eiter(t_variance_img) l(_variance); + + for_all(l) + { + _variance[l.index_()] = literal::zero; + + mln_piter(t_group_img) rgb(_group.domain()); + + for_all(rgb) + { + if (l.index_() == _group(rgb)) + _variance[l.index_()] += _distance[l.index_()](rgb); + } + + _within_variance += _variance[l.index_()]; + //std::cout << "v(" << l << ") = " << _variance(l) << std::endl; + } + + //std::cout << "result" << result << std::endl; + + trace::exiting("mln::clustering::kmean2d::update_variance"); + } + + //-------------------------------------------------------------------------- + // Debugging tools + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::build_label_dbg() + { + trace::entering("mln::clustering::kmean2d::build_label_dbg"); + + mln_piter(t_point_img) pi(_point.domain()); + mln_piter(t_label_dbg) po(_label_dbg.domain()); + + for_all_2(pi, po) + { + t_value val = _point(pi); + t_label grp = _group(point2d(val.red(), val.green())); + + // As label zero has got a particular semantic, the first label is one + _label_dbg(po) = ++grp; + } + + trace::exiting("mln::clustering::kmean2d::build_label_dbg"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::build_mean_dbg() + { + trace::entering("mln::clustering::kmean2d::build_mean_dbg"); + + mln_piter(t_mean_dbg) p(_mean_dbg.domain()); + + for_all(p) + { + _mean_dbg(p).red() = static_cast<unsigned>(_mean[_label_dbg(p)][0]); + _mean_dbg(p).green() = static_cast<unsigned>(_mean[_label_dbg(p)][1]); + _mean_dbg(p).blue() = static_cast<unsigned>(_mean[_label_dbg(p)][2]); + } + + trace::exiting("mln::clustering::kmean2d::build_mean_dbg"); + } + + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::build_all_dbg() + { + trace::entering("mln::clustering::kmean2d::build_all_dbg"); + build_label_dbg(); + //build_mean_dbg(); + _mean_dbg = labeling::mean_values(_point, _label_dbg, _k_center); + _color_dbg = labeling::colorize(value::rgb8(), _label_dbg); + + trace::exiting("mln::clustering::kmean2d::build_all_dbg"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::update_cnv() + { + trace::entering("mln::clustering::kmean2d::update_cnv"); + + _variance_cnv[_current_launching](point1d(_current_step)) + = _within_variance; + + mln_eiter(t_mean_img) l(_mean); + + for_all(l) + { + _mean_cnv[l.index_()][_current_launching](point1d(_current_step)) + = _mean[l.index_()]; + } + + trace::exiting("mln::clustering::kmean2d::update_cnv"); + } + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::finalize_cnv() + { + trace::entering("mln::clustering::kmean2d::finalize_cnv"); + + // saturate the curv with the within variance + for (unsigned i = _current_step; i < _watch_dog; ++i) + _variance_cnv[_current_launching](point1d(i)) = _within_variance; + + for (unsigned i = _current_step; i < _watch_dog; ++i) + { + mln_eiter(t_mean_img) l(_mean); + + for_all(l) + { + _mean_cnv[l.index_()][_current_launching](point1d(i)) + = _mean[l.index_()]; + } + } + + trace::exiting("mln::clustering::kmean2d::finalize_cnv"); + } + + + //-------------------------------------------------------------------------- + // Checking the validity of the results + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + bool kmean2d<T,n>::is_descent_valid() + { + trace::entering("mln::clustering::kmean2d::is_descent_valid"); + + bool result = _is_number_valid && (_current_step < _watch_dog); + + trace::exiting("mln::clustering::kmean2d::is_descent_valid"); + return result; + } + + + //-------------------------------------------------------------------------- + // Main loop + //-------------------------------------------------------------------------- + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::launch_one_time() + { + trace::entering("mln::clustering::kmean2d::launch_one_time"); + + //std::cout << "----------------------------------------" << std::endl; + + // Initialization to start the descent + t_result1d old_variance = mln_max(t_result1d); + _within_variance = mln_max(t_result1d); + _current_step = 0; + + // Build the first group and evaluate its caharacteristics + init_mean(); + update_distance(); + + //print_point(); + //print_histo(); + //print_mean(); + //print_distance(); + + //std::cout << "first_variance : " << old_variance << std::endl; + + // Execute the descent + do + { + old_variance = _within_variance; + + update_group(); + + //print_group(); + + update_mean(); // update _is_number_valid + + //print_mean(); + + // Stopping Nan propagation + if (!_is_number_valid) + break; + + update_distance(); + + //print_distance(); + + update_variance(); // update _within_variance + + //print_variance(); + + // Debugging code + update_cnv(); + + std::cout << "_current_step : " << _current_step << std::endl; + std::cout << "_within_variance : " << _within_variance << std::endl; + + ++_current_step; + } + while (_current_step < _watch_dog && _within_variance < old_variance); + + //std::cout << "----------------------------------------" << std::endl; + + finalize_cnv(); + //build_all_dbg(); + + trace::exiting("mln::clustering::kmean2d::launch_one_time"); + } + + + template <typename T, unsigned n> + inline + void kmean2d<T,n>::launch_n_times() + { + trace::entering("mln::clustering::kmean2d::launch_n_times"); + + //std::cout << "watch_dog : " << _watch_dog << std::endl; + //std::cout << "n_times : " << _n_times << std::endl; + + // number of times we reexecute launch_one_time without any success + unsigned tries = 0; + + _variance_min = mln_max(t_result1d); + _current_launching = 0; + + // Execute the descent n times + while (_current_launching < _n_times) + { + launch_one_time(); + + if (is_descent_valid() || _N_TRIES < tries) + { + if (_within_variance < _variance_min) + { + _variance_min = _within_variance; + _mean_min = _mean; + _launching_min = _current_launching; + } + + // Reinitialize the number of echecs possible + tries = 0; + + //std::cout << "_current_launching : " << _current_launching + // << std::endl; + + //std::cout << "within_variance[" << _current_launching << "] = " + // << _within_variance << std::endl; + + ++_current_launching; + } + else + ++tries; + } + + //Debugging code + build_all_dbg(); + + trace::exiting("mln::clustering::kmean2d::launch_n_times"); + } + +#endif // ! MLN_INCLUDE_ONLY + + } // end of namespace mln::clustering + +} // end of namespace mln + +#endif // ! MLN_CLUSTERING_KMEAN2D_HH -- 1.5.6.5