[Olena-patches] 4562: Prepare kmean demo for theo. Work in progress.

* green/mln/clustering/kmean1d.hh: New library file. * green/demo/clustering/kmean1d: New directory. * green/demo/clustering/kmean1d/Makefile.am: New makefile. * green/demo/clustering/kmean1d/kmean1d.cc: New demo. * green/tests/clustering/kmean1d: New directory. * green/tests/clustering/kmean1d/Makefile.am: New makefile. * green/tests/clustering/kmean1d/kmean1d.cc: New unitary tests. --- trunk/milena/sandbox/ChangeLog | 14 + .../green/demo/clustering/kmean1d/Makefile.am | 148 +++ .../green/demo/clustering/kmean1d/kmean1d.cc | 97 ++ .../milena/sandbox/green/mln/clustering/kmean1d.hh | 974 ++++++++++++++++++++ .../green/tests/clustering/kmean1d/Makefile.am | 148 +++ .../green/tests/clustering/kmean1d/kmean1d.cc | 483 ++++++++++ 6 files changed, 1864 insertions(+), 0 deletions(-) create mode 100644 trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am create mode 100644 trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc create mode 100644 trunk/milena/sandbox/green/mln/clustering/kmean1d.hh create mode 100644 trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am create mode 100644 trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc diff --git a/trunk/milena/sandbox/ChangeLog b/trunk/milena/sandbox/ChangeLog index 797fa7b..2f2684f 100644 --- a/trunk/milena/sandbox/ChangeLog +++ b/trunk/milena/sandbox/ChangeLog @@ -1,3 +1,17 @@ +2009-09-28 Yann Jacquelet <jacquelet@lrde.epita.fr> + + Prepare kmean demo for theo. Work in progress. + + * green/mln/clustering/kmean1d.hh: New library file. + + * green/demo/clustering/kmean1d: New directory. + * green/demo/clustering/kmean1d/Makefile.am: New makefile. + * green/demo/clustering/kmean1d/kmean1d.cc: New demo. + + * green/tests/clustering/kmean1d: New directory. + * green/tests/clustering/kmean1d/Makefile.am: New makefile. + * green/tests/clustering/kmean1d/kmean1d.cc: New unitary tests. + 2009-09-24 Fabien Freling <fabien.freling@lrde.epita.fr> Add benchmark for disk-mapped image and work for color types. diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am b/trunk/milena/sandbox/green/demo/clustering/kmean1d/Makefile.am new file mode 100644 index 0000000..91230b6 --- /dev/null +++ b/trunk/milena/sandbox/green/demo/clustering/kmean1d/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/kmean1d/kmean1d.cc b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..cc04149 --- /dev/null +++ b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,97 @@ +// DEMO ON KMEAN1D + +#include <mln/clustering/kmean1d.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/core/macros.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.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> + +void demo(const unsigned watch_dog, const unsigned n_times) +{ + typedef mln::clustering::kmean1d<double,8,3> t_kmean; + typedef mln::value::label8 t_label8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<int_u8> t_image2d_int_u8; + typedef mln::image2d<rgb8> t_image2d_rgb8; + + t_image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + t_kmean kmean(house, watch_dog, n_times); + + kmean.launch_n_times(); + + 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::pgm::save(mean_img, "mean.pgm"); + mln::io::ppm::save(color_img, "color.pgm"); + mln::io::pgm::save(label_img, "label.pgm"); + + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +} + +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 watch_dog n_times k image" << std::endl; + std::cout << "unsigned watch_dog (convergence loop)" << std::endl; + std::cout << "unsigned n_times (number of launching)" << std::endl; + // std::cout << "unsigned k (number of centers)" << std::endl; + // std::cout << "pbm image (points to work with)" << std::endl; + std::cout << "----------------------------------------" << std::endl; +} + +int main(const int argc, const char *args[]) +{ + if (3 == argc) + { + std::istringstream arg1(args[1]); + std::istringstream arg2(args[2]); + // std::istringstream arg3(args[3]); + // std::istringstream arg3(args[4]); + unsigned watch_dog; + unsigned n_times; + // const char *image; + // unsigned k; + + arg1 >> watch_dog; + arg2 >> n_times; + + if (!arg1.fail() && !arg2.fail()) + demo(watch_dog, n_times); + else + usage(argc, args); + } + else + usage(argc, args); + + return 0; +} diff --git a/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh new file mode 100644 index 0000000..05947c7 --- /dev/null +++ b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh @@ -0,0 +1,974 @@ +// Copyright (C) 2007 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2008 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 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_KMEAN1D_HH +#define MLN_CLUSTERING_KMEAN1D_HH + +/// \file +/// +/// \brief Implements the optimized kmean algorithm. +/// +/// This algorithm is optimized in the way it proceeds directly with the +/// greylevel attribute inspite of pixel attribute. The algorithm is +/// independant from the image dimension. But, we have to use 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 image 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/histo1d.hh> + +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/norm/l2.hh> + +#include <mln/core/image/image2d.hh> +#include <mln/value/int_u.hh> +#include <mln/value/rgb8.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/labeling/colorize.hh> +#include <mln/labeling/mean_values.hh> + +#include <mln/io/ppm/save.hh> +#include <mln/io/pgm/save.hh> + +namespace mln +{ + + namespace clustering + { + + // Forward declaration. + template <typename T, unsigned n, unsigned k> + struct kmean1d; + + } // 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. + /// k is the number of classes. + template <typename T, unsigned n, unsigned k> + struct kmean1d + { + /// Type definitions. + /// \brief A few type definitions to limit the refactoring impact. + ///{ + + typedef value::rgb<8> t_rgb; // just for debugging + typedef value::label<8> t_label; // in fact label<p>, where k <= 2^p + typedef value::int_u<n> t_value; + typedef T t_result; + + typedef image2d<t_value> t_point_img; + typedef image1d<unsigned> t_histo_img; + typedef image1d<t_result> t_number_img; + typedef image1d<t_result> t_mean_img; + typedef image1d<t_result> t_variance_img; + + typedef image1d<t_label> t_group_img; + typedef image2d<t_result> 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 image3d<t_value> t_mean_cnv; + typedef image2d<t_result> t_variance_cnv; + + ///} + + //------------------------------------------------------------------------ + // Constructor + //------------------------------------------------------------------------ + + /// \brief Constructor + /// \param[in] point : the image as the population of pixels. + /// \param[in] watch_dog : the limit to observe the convergence (10). + /// \param[in] n_times : the number of times that we executed it (10). + + kmean1d(const t_point_img& point, + 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(); + + /// \} + + //------------------------------------------------------------------------ + // 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, hommogenity + /// in group is observed by the within variance. + + void update_distance(); + void update_group(); + void update_mean(); + void update_variance(); + + /// \} + + //------------------------------------------------------------------------ + // Main loop + //------------------------------------------------------------------------ + + void launch_one_time(); + void launch_n_times(); + + + //------------------------------------------------------------------------ + // Checking the validiy of the results + //------------------------------------------------------------------------ + + bool is_valid(); + bool is_descent_valid(); + + //------------------------------------------------------------------------ + // Tools + //------------------------------------------------------------------------ + + void build(); + void to_result2(); + + + private: + /// Parameters. + /// \{ + /// \brief These parameters control the convergence of the process. + /// + /// 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 _watch_dog; + unsigned _n_times; + + /// \} + + /// Convergence information. + /// \{ + /// \brief This information is used to follow the convergence. + /// + /// The within_variance is the homogenity 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. + + t_result _within_variance; + unsigned _current_step; + + /// \} + + /// 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_result _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, unsigned k> + inline + kmean1d<T,n,k>::kmean1d(const image2d< t_value >& point, + const unsigned watch_dog, + const unsigned n_times) + { + trace::entering("mln::clustering::kmean1d::kmean1d"); + mln_precondition(point.is_valid()); + + + _watch_dog = watch_dog; + _n_times = n_times; + + _point = point; + _histo = data::compute(accu::meta::stat::histo1d(), _point); + + _number.init_(box1d(point1d(0),point1d(k-1))); + _mean.init_(box1d(point1d(0),point1d(k-1))); + _variance.init_(box1d(point1d(0),point1d(k-1))); + + _group.init_(box1d(point1d(mln_min(t_value)), point1d(mln_max(t_value)))); + _distance.init_(box2d(point2d(mln_min(t_value), 0), + point2d(mln_max(t_value), k-1))); + + // Debugging, calibrating and testing + initialize(_label_dbg, _point); + initialize(_color_dbg, _point); + initialize(_mean_dbg, _point); + + // Observing the convergence + _variance_cnv.init_(box2d(point2d(0, 0), + point2d(_watch_dog-1, _n_times-1))); + + _mean_cnv.init_(box3d(point3d(0, 0, 0), + point3d(k-1, _watch_dog-1, _n_times-1))); + + + trace::exiting("mln::clustering::kmean1d::kmean1d"); + } + + //-------------------------------------------------------------------------- + // Mutators and accessors + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_point(t_point_img& point) + { + trace::entering("mln::clustering::kmean1d::set_point"); + + _point = point; + + trace::exiting("mln::clustering::kmean1d::set_point"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_histo(t_histo_img& histo) + { + trace::entering("mln::clustering::kmean1d::set_histo"); + + _histo = histo; + + trace::exiting("mln::clustering::kmean1d::set_histo"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_number(t_number_img& number) + { + trace::entering("mln::clustering::kmean1d::set_number"); + + _number = number; + + trace::exiting("mln::clustering::kmean1d::set_number"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_mean(t_mean_img& mean) + { + trace::entering("mln::clustering::kmean1d::set_mean"); + + _mean = mean; + + trace::exiting("mln::clustering::kmean1d::set_mean"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_variance(t_variance_img& variance) + { + trace::entering("mln::clustering::kmean1d::set_variance"); + + _variance = variance; + + trace::exiting("mln::clustering::kmean1d::set_variance"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_group(t_group_img& group) + { + trace::entering("mln::clustering::kmean1d::set_group"); + + _group = group; + + trace::exiting("mln::clustering::kmean1d::set_group"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::set_distance(t_distance_img& distance) + { + trace::entering("mln::clustering::kmean1d::set_distance"); + + _distance = distance; + + trace::exiting("mln::clustering::kmean1d::set_distance"); + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_point_img& kmean1d<T,n,k>::get_point() + { + trace::entering("mln::clustering::kmean1d::get_point"); + + trace::exiting("mln::clustering::kmean1d::get_point"); + return _point; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_histo_img& kmean1d<T,n,k>::get_histo() + { + trace::entering("mln::clustering::kmean1d::get_histo"); + + trace::exiting("mln::clustering::kmean1d::get_histo"); + return _histo; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_number_img& kmean1d<T,n,k>::get_number() + { + trace::entering("mln::clustering::kmean1d::get_number"); + + trace::exiting("mln::clustering::kmean1d::get_number"); + return _number; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::get_mean() + { + trace::entering("mln::clustering::kmean1d::get_mean"); + + trace::exiting("mln::clustering::kmean1d::get_mean"); + return _mean; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_variance_img& kmean1d<T,n,k>::get_variance() + { + trace::entering("mln::clustering::kmean1d::get_variance"); + + trace::exiting("mln::clustering::kmean1d::get_variance"); + return _variance; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_group_img& kmean1d<T,n,k>::get_group() + { + trace::entering("mln::clustering::kmean1d::get_group"); + + trace::exiting("mln::clustering::kmean1d::get_group"); + return _group; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_distance_img& kmean1d<T,n,k>::get_distance() + { + trace::entering("mln::clustering::kmean1d::get_distance"); + + trace::exiting("mln::clustering::kmean1d::get_distance"); + return _distance; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_color_dbg& kmean1d<T,n,k>::get_color_dbg() + { + trace::entering("mln::clustering::kmean1d::get_color_dbg"); + + trace::exiting("mln::clustering::kmean1d::get_color_dbg"); + return _color_dbg; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_dbg& kmean1d<T,n,k>::get_mean_dbg() + { + trace::entering("mln::clustering::kmean1d::get_mean_dbg"); + + trace::exiting("mln::clustering::kmean1d::get_mean_dbg"); + return _mean_dbg; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_label_dbg& kmean1d<T,n,k>::get_label_dbg() + { + trace::entering("mln::clustering::kmean1d::get_label_dbg"); + + trace::exiting("mln::clustering::kmean1d::get_label_dbg"); + return _label_dbg; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_cnv& kmean1d<T,n,k>::get_mean_cnv() + { + trace::entering("mln::clustering::kmean1d::get_mean_cnv"); + + trace::exiting("mln::clustering::kmean1d::get_mean_cnv"); + return _mean_cnv; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_variance_cnv& kmean1d<T,n,k>::get_variance_cnv() + { + trace::entering("mln::clustering::kmean1d::get_variance_cnv"); + + trace::exiting("mln::clustering::kmean1d::get_variance_cnv"); + return _variance_cnv; + } + + template <typename T, unsigned n, unsigned k> + inline + typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::to_result() + { + trace::entering("mln::clustering::kmean1d::to_result"); + + trace::exiting("mln::clustering::kmean1d::to_result"); + return _mean_min; + } + + + //-------------------------------------------------------------------------- + // Initialization of centers + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::init_mean_regular() + { + trace::entering("mln::clustering::kmean1d::init_mean_regular"); + T step = (mln_max(t_value) - mln_min(t_value)) / (k-1); + mln_piter(image1d<t_value>) l(_mean.domain()); + + for_all(l) + { + _mean(l) = (l.ind()*step) + mln_min(t_value); + } + + trace::exiting("mln::clustering::kmean1d<T,n,k>::init_mean_regular"); + } + + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::init_mean_random() + { + trace::exiting("mln::clustering::kmean1d::init_mean_random"); + + t_value min = mln_min(t_value); + t_value max = mln_max(t_value); + mln_piter(image1d<t_value>) l(_mean.domain()); + + for_all(l) + { + _mean(l) = (rand() % (max-min)) + min; + } + + trace::exiting("mln::clustering::kmean1d::init_mean_random"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::init_mean() + { + trace::entering("mln::clustering::kmean1d::init_mean"); + + init_mean_random(); + + trace::exiting("mln::clustering::kmean1d::init_mean"); + } + + //-------------------------------------------------------------------------- + // Computations of distance, group, center, within variance + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_distance() + { + trace::entering("mln::clustering::kmean1d::update_distance"); + + mln_piter(t_distance_img) d(_distance.domain()); + + for_all(d) + { + // the square distance + _distance(d) = math::sqr(d.row() - _mean(point1d(d.col()))); + /* + std::cout << "row : " << d.row() << std::endl; + std::cout << "col : " << d.col() << 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::kmean1d::update_distance"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_group() + { + trace::entering("mln::clustering::kmean1d::update_group"); + + mln_piter(t_group_img) g(_group.domain()); + + for_all(g) + { + mln_piter(t_mean_img) l(_mean.domain()); + T min = mln_max(T); + t_label label = mln_max(t_label); + + //std::cout << "g = " << g << std::endl; + + for_all(l) + { + if (min > _distance(point2d(g.ind(),l.ind()))) + { + min = _distance(point2d(g.ind(),l.ind())); + label = l.ind(); + } + + //std::cout << "d(" << l << ") = " << + // _distance(point2d(g.ind(), l.ind())) << std::endl; + } + + //std::cout << "g = " << g << std::endl; + + _group(g) = label; + //std::cout << "group = " << _group(g) << std::endl; + //std::cout << "-----------" << std::endl; + } + + trace::exiting("mln::clustering::kmean1d::update_group"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_mean() + { + trace::entering("mln::clustering::kmean1d::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] += g + // n[l] += h(g) + // + // c[l] /= n + + data::fill(_mean, literal::zero); + data::fill(_number, literal::zero); + + mln_piter(t_group_img) g(_group.domain()); + + for_all(g) + { + _mean(point1d(_group(g))) += _histo(g) * g.ind(); + _number(point1d(_group(g))) += _histo(g); + } + + mln_piter(t_mean_img) l(_mean.domain()); + + /* + for_all(l) + { + std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + std::cout << "n(" << l << ") = " << _number(l) << std::endl; + } + */ + + for_all(l) + { + _mean(l) /= _number(l); + //std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + } + + trace::exiting("mln::clustering::kmean1d::update_mean"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::update_variance() + { + trace::entering("mln::clustering::kmean1d::update_variance"); + + T _within_variance = literal::zero; + mln_piter(t_variance_img) l(_variance.domain()); + + for_all(l) + { + _variance(l) = literal::zero; + + mln_piter(t_group_img) g(_group.domain()); + + for_all(g) + { + _variance(l) += _distance(point2d(g.ind(), l.ind())); + } + + _within_variance += _variance(l); + //std::cout << "v(" << l << ") = " << _variance(l) << std::endl; + } + + //std::cout << "result" << result << std::endl; + + trace::exiting("mln::clustering::kmean1d::update_variance"); + } + + //-------------------------------------------------------------------------- + // Tools + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::build() + { + trace::entering("build"); + + 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(point1d(val)); + + _label_dbg(po) = grp; + } + + trace::exiting("build"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::to_result2() + { + typedef value::rgb8 rgb8; + std::cout << "BUILDING OUTPUT" << std::endl; + + build(); + + std::cout << "COLORING OUTPUT" << std::endl; + + _mean_dbg = labeling::mean_values(_point, _label_dbg, k); + _color_dbg = labeling::colorize(rgb8(), _label_dbg); + + mln::io::pgm::save(_mean_dbg, "mean.pgm"); + mln::io::ppm::save(_color_dbg, "rgb.pgm"); + mln::io::pgm::save(_label_dbg, "label.pgm"); + } + + + //-------------------------------------------------------------------------- + // Checking the validity of the results + //-------------------------------------------------------------------------- + + template <typename T, unsigned n, unsigned k> + inline + bool kmean1d<T,n,k>::is_descent_valid() + { + trace::entering("mln::clustering::kmean1d::is_descent_valid"); + bool result = true; + + mln_piter(t_number_img) l(_number.domain()); + + for_all(l) + { + result = result && (_number(l) != 0.0); + } + + result = result && (_current_step < _watch_dog); + + trace::exiting("mln::clustering::kmean1d::is_descent_valid"); + return result; + } + + template <typename T, unsigned n, unsigned k> + inline + bool kmean1d<T,n,k>::is_valid() + { + trace::entering("mln::clustering::kmean1d::is_valid"); + bool result = true; + + mln_piter(t_number_img) l(_number.domain()); + + for_all(l) + { + result = result && (_number(l) != 0.0); + } + + trace::exiting("mln::clustering::kmean1d::is_valid"); + return result; + } + + + //-------------------------------------------------------------------------- + // Main loop + //-------------------------------------------------------------------------- + + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::launch_one_time() + { + trace::entering("mln::clustering::kmean1d::launch_one_time"); + + // Initialization to start the descent + _within_variance = mln_max(t_result)/2; + t_result old_variance = mln_max(t_result); + + // Choose random points + init_mean(); + + // Execute the descent + for (unsigned i = 0; i<_watch_dog && _within_variance<old_variance; ++i) + { + update_distance(); + update_group(); + update_mean(); + + old_variance = _within_variance; + + update_variance(); // update _within_variance + } + + trace::exiting("mln::clustering::kmean1d::launch_one_time"); + } + + template <typename T, unsigned n, unsigned k> + inline + void kmean1d<T,n,k>::launch_n_times() + { + trace::entering("mln::clustering::kmean1d::launch_n_times"); + + _variance_min = mln_max(t_result); + + // Execute the descent n times + for (unsigned i = 0; i < _n_times; ++i) + { + launch_one_time(); + + if (!is_descent_valid()) + --i; + else + { + if (_within_variance < _variance_min) + { + _variance_min = _within_variance; + _mean_min = _mean; + _launching_min = i; + } + } + + std::cout << "within_variance[" << i << "] = " + << _within_variance << std::endl; + } + + trace::exiting("mln::clustering::kmean1d::launch_n_times"); + } + +#endif // ! MLN_INCLUDE_ONLY + + } // end of namespace mln::clustering + +} // end of namespace mln + +#endif // ! MLN_CLUSTERING_KMEAN1D_HH diff --git a/trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am b/trunk/milena/sandbox/green/tests/clustering/kmean1d/Makefile.am new file mode 100644 index 0000000..cdf3021 --- /dev/null +++ b/trunk/milena/sandbox/green/tests/clustering/kmean1d/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 $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/tests +BUILD__PATTERN= green/build/tests + + +ifeq ($(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/tests/clustering/kmean1d/kmean1d.cc b/trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..fe5b6cf --- /dev/null +++ b/trunk/milena/sandbox/green/tests/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,483 @@ +// UNARY TESTS ON KMEAN1D + +#include <mln/clustering/kmean1d.hh> + +#include <iostream> + +#include <mln/pw/value.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/rgb8.hh> + +#include <mln/literal/colors.hh> + +#include <mln/algebra/vec.hh> +#include <mln/algebra/mat.hh> + +#include <mln/core/macros.hh> +#include <mln/core/contract.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.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/img_path.hh> + +#include <mln/data/transform.hh> + +#include <mln/trait/value/print.hh> +#include <mln/trait/image/print.hh> + +#define SIZE_IMAGE 512 +#define SIZE_SAMPLE1 (512*512) +#define SIZE_SAMPLE2 4 +#define NB_CENTER 2 +#define DIM_POINT 3 +#define TYPE_POINT double + +/* +void test_instantiation() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::image2d<int_u8> image2d_int_u8; + image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + mln::clustering::kmean1d<double, 8, 3> kmean(house); + + // test the compilation + std::cout << "test instantiation : ok" << std::endl; +} + +void test_init_center() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::image2d<int_u8> image2d_int_u8; + image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + mln::clustering::kmean1d<double, 8, 3> kmean(house); + + kmean.init_center(); + + std::cout << "test init center : ok" << std::endl; +} +*/ + +void test_init_loop() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::image2d<int_u8> image2d_int_u8; + image2d_int_u8 house; + + mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + + mln::clustering::kmean1d<double, 8, 3> kmean(house); + + kmean.launch_n_times(); + + std::cout << "test loop : ok" << std::endl; +} + + + +/* +struct rgb8_to_4colors : mln::Function_v2v<rgb8_to_4colors> +{ + typedef mln::value::rgb8 result; + + mln::value::rgb8 operator()(const mln::value::rgb8& color) const + { + mln::value::rgb8 result; + unsigned hash = (color.red() + color.green() + color.blue()) % 4; + + switch (hash) + { + case 0: result = mln::literal::lime; break; + case 1: result = mln::literal::brown; break; + case 2: result = mln::literal::teal; break; + case 3: result = mln::literal::purple; break; + } + + return result; + } +}; + + +void print_color(const mln::value::rgb8& color) +{ + std::cout << "{r=" << color.red() << ", "; + std::cout << "g=" << color.green() << ", "; + std::cout << "b=" << color.blue() << "}" << std::endl; +} + +void fill_image_with_4colors(mln::image2d<mln::value::rgb8>& img) +{ + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + + img = mln::data::transform(img, rgb8_to_4colors()); + + //print_color(lime); + //print_color(brown); + //print_color(teal); + //print_color(purple); +} + +bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, + const MAT_POINT1& point) +{ + mln_piter_(mln::image2d<mln::value::rgb8>) pi(img.domain()); + bool result = true; + unsigned index = -1; + + for_all(pi) + { + bool test = true; + + ++index; + + test = test && (point(index,0) == img(pi).red()); + test = test && (point(index,1) == img(pi).green()); + test = test && (point(index,2) == img(pi).blue()); + + if (!test) + { + std::cout << pi; + std::cout << "{r=" << img(pi).red() << ", "; + std::cout << "g=" << img(pi).green() << ", "; + std::cout << "b=" << img(pi).blue() << "}"; + std::cout << index; + std::cout << "[r=" << point(index,0) << ", "; + std::cout << "g=" << point(index,1) << ", "; + std::cout << "b=" << point(index,2) << "]" << std::endl; + + mln_assertion(test); + } + + result &= test; + } + + return result; +} + +void test_init_point() +{ + typedef mln::value::rgb8 rgb8; + mln::image2d<rgb8> img_ref; + + mln::clustering::k_mean<SIZE_SAMPLE1,NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + mln::io::ppm::load(img_ref, "/usr/local/share/olena/images/lena.ppm"); + //mln::io::ppm::save(img_ref, "verif.ppm"); + + fill_image_with_4colors(img_ref); + kmean.init_point(img_ref); + + mln_assertion(true == is_equivalent(img_ref, kmean.get_point())); + + std::cout << "Test init point : ok" << std::endl; +} + +void set_point(MAT_POINT2& point, + const unsigned index, + const mln::value::rgb8& color) +{ + point(index,0) = color.red(); + point(index,1) = color.green(); + point(index,2) = color.blue(); +} + +void fake_init_point(MAT_POINT2& point, + const mln::value::rgb8& point1, + const mln::value::rgb8& point2, + const mln::value::rgb8& point3, + const mln::value::rgb8& point4) +{ + set_point(point, 0, point1); + set_point(point, 1, point2); + set_point(point, 2, point3); + set_point(point, 3, point4); +} + +bool is_equal(const mln::value::rgb8& ref, + const MAT_CENTER& center, + const unsigned i) +{ + bool result = true; + + result = result && (center(i, 0) - ref.red() < 1.0); + result = result && (center(i, 1) - ref.green() < 1.0); + result = result && (center(i, 2) - ref.blue() < 1.0); + + return result; +} + +void test_init_center() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + kmean.init_center(); + + mln_assertion(is_equal(lime, kmean.get_center(), 0) || + is_equal(brown, kmean.get_center(), 0) || + is_equal(teal, kmean.get_center(), 0) || + is_equal(purple, kmean.get_center(), 0)); + + mln_assertion(is_equal(lime, kmean.get_center(), 1) || + is_equal(brown, kmean.get_center(), 1) || + is_equal(teal, kmean.get_center(), 1) || + is_equal(purple, kmean.get_center(), 1)); + + std::cout << "Test init center : ok" << std::endl; +} + +void set_center(MAT_CENTER& center, + const unsigned index, + const mln::value::rgb8& color) +{ + center(index,0) = color.red(); + center(index,1) = color.green(); + center(index,2) = color.blue(); +} + +void fake_init_center(MAT_CENTER& center, + const mln::value::rgb8 center1, + const mln::value::rgb8 center2) +{ + + set_center(center, 0, center1); + set_center(center, 1, center2); +} + + +double dist(mln::value::rgb8 color1, mln::value::rgb8 color2) +{ + double red = color1.red() - color2.red(); + double green = color1.green() - color2.green(); + double blue = color1.blue() - color2.blue(); + double result= red * red + green * green + blue * blue; + + return result; +} + +void test_update_distance() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const MAT_DISTANCE2& dist_v = kmean.get_distance(); + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + kmean.update_distance(); + + mln_assertion(dist(lime, c1) == dist_v(0,0)); + mln_assertion(dist(lime, c2) == dist_v(0,1)); + mln_assertion(dist(brown, c1) == dist_v(1,0)); + mln_assertion(dist(brown, c2) == dist_v(1,1)); + mln_assertion(dist(teal, c1) == dist_v(2,0)); + mln_assertion(dist(teal, c2) == dist_v(2,1)); + mln_assertion(dist(purple, c1) == dist_v(3,0)); + mln_assertion(dist(purple, c2) == dist_v(3,1)); + + std::cout << "Test update distance : ok" << std::endl; +} + +void set_distance(MAT_DISTANCE2& distance, + const unsigned index, + const double d1, + const double d2) +{ + distance(index,0) = d1; + distance(index,1) = d2; +} + +void fake_update_distance(MAT_DISTANCE2& distance, + const mln::value::rgb8& point1, + const mln::value::rgb8& point2, + const mln::value::rgb8& point3, + const mln::value::rgb8& point4, + const mln::value::rgb8& center1, + const mln::value::rgb8& center2) +{ + set_distance(distance, 0, dist(point1, center1), dist(point1, center2)); + set_distance(distance, 1, dist(point2, center1), dist(point2, center2)); + set_distance(distance, 2, dist(point3, center1), dist(point3, center2)); + set_distance(distance, 3, dist(point4, center1), dist(point4, center2)); + + + // for (int i = 0; i < SIZE_SAMPLE2; ++i) + //for (int j = 0; j < NB_CENTER; ++j) + // std::cout << "d(" << i << "," << j << ") = " << distance(i,j) <<std::endl; +} + +void test_update_group() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const unsigned point1_min= 0; // lime near lime (c1) + const unsigned point2_min= 1; // brown near purple (c2) + const unsigned point3_min= 1; // teal near purple (c2) + const unsigned point4_min= 1; // purple near purple (c2) + const MAT_GROUP2& group = kmean.get_group(); + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + fake_update_distance(kmean.get_distance(), lime, brown, teal, purple, c1, c2); + kmean.update_group(); + + mln_assertion(0.0 == group(0, 1 - point1_min)); + mln_assertion(1.0 == group(0, point1_min)); + mln_assertion(0.0 == group(1, 1 - point2_min)); + mln_assertion(1.0 == group(1, point2_min)); + mln_assertion(0.0 == group(2, 1 - point3_min)); + mln_assertion(1.0 == group(2, point3_min)); + mln_assertion(0.0 == group(3, 1 - point4_min)); + mln_assertion(1.0 == group(3, point4_min)); + + std::cout << "Test update group : ok" << std::endl; +} + +void set_group(MAT_GROUP2& group, + const unsigned index, + const unsigned min) +{ + group(index, min) = 1.0; + group(index, 1-min) = 0.0; +} + + +void fake_update_group(MAT_GROUP2& group, + const unsigned& point1_min, + const unsigned& point2_min, + const unsigned& point3_min, + const unsigned& point4_min) +{ + set_group(group, 0, point1_min); + set_group(group, 1, point2_min); + set_group(group, 2, point3_min); + set_group(group, 3, point4_min); +} + +void test_update_center() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const unsigned pt1_min = 0; // lime near lime (c1) + const unsigned pt2_min = 1; // brown near purple (c2) + const unsigned pt3_min = 1; // teal near purple (c2) + const unsigned pt4_min = 1; // purple near purple (c2) + const mln::value::rgb8 mean_c1 = lime; + const mln::value::rgb8 mean_c2 = (brown+teal+purple)/3; + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + fake_update_distance(kmean.get_distance(), lime, brown, teal, purple, c1, c2); + fake_update_group(kmean.get_group(), pt1_min, pt2_min, pt3_min, pt4_min); + kmean.update_center(); + + mln_assertion(is_equal(mean_c1, kmean.get_center(), 0)); + mln_assertion(is_equal(mean_c2, kmean.get_center(), 1)); + + std::cout << "Test update center : ok" << std::endl; +} + +void test_update_variance() +{ + mln::clustering::k_mean<SIZE_SAMPLE2, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + const mln::value::rgb8 lime = mln::literal::lime; + const mln::value::rgb8 brown = mln::literal::brown; + const mln::value::rgb8 teal = mln::literal::teal; + const mln::value::rgb8 purple = mln::literal::purple; + const mln::value::rgb8 c1 = lime; + const mln::value::rgb8 c2 = purple; + const unsigned pt1_min = 0; // lime near lime (c1) + const unsigned pt2_min = 1; // brown near purple (c2) + const unsigned pt3_min = 1; // teal near purple (c2) + const unsigned pt4_min = 1; // purple near purple (c2) + const double v1 = 0; + const double v2 = dist(purple, brown) + dist(purple, teal); + const mln::value::rgb8 mean_c2 = (brown+teal+purple)/3; + const VEC_VAR& var = kmean.get_variance(); + + fake_init_point(kmean.get_point(), lime, brown, teal, purple); + fake_init_center(kmean.get_center(), c1, c2); + fake_update_distance(kmean.get_distance(), lime, brown, teal, purple, c1, c2); + fake_update_group(kmean.get_group(), pt1_min, pt2_min, pt3_min, pt4_min); + kmean.update_variance(); + + mln_assertion(v1 == var[0]); + mln_assertion(v2 == var[1]); + + std::cout << "Test update variance : ok" << std::endl; +} + +void test_loop() +{ + typedef mln::value::rgb8 rgb8; + mln::image2d<rgb8> img_ref; + + mln::clustering::k_mean<SIZE_SAMPLE1, NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + mln::io::ppm::load(img_ref, "/usr/local/share/olena/images/lena.ppm"); + //mln::io::ppm::save(img_ref, "verif.ppm"); + + fill_image_with_4colors(img_ref); + kmean.init_point(img_ref); + + kmean.loop(img_ref); + + + // std::cout << "Test update variance: ok" << std::endl; +} +*/ + +int main() +{ + //test_instantiation(); + //test_init_point(); + // test_init_center(); + test_init_loop(); + //test_update_distance(); + //test_update_group(); + //test_update_center(); + //test_update_variance(); + + // mln::trace::quiet = false; + + //test_loop(); + + return 0; +} -- 1.5.6.5