[Olena-patches] 4564: Prepare kmean demo for theo. Work in progress.

Fix bugs, add hooks for tracing convergence. * green/mln/clustering/kmean1d.hh: Evict k parameter from template and replace it by a constructor parameter. Add printing for debugging. * green/mln/clustering/kmean1d.hh (update_variance): Fix bugs. * green/mln/clustering/kmean1d.hh (build_all_dbg, build_mean_dbg, update_cnv, finalize_cnv): New methods. * green/mln/clustering/kmean1d.hh (launch_one_time): Fix bugs. * green/mln/clustering/kmean1d.hh (launch_n_times): Add hooks. Do some works for managing shell parameters while executing the binary. * green/demo/clustering/kmean1d/kmean1d.cc (main, char_to_string, char_to_unsigned, demo, do_demo): New methods. --- trunk/milena/sandbox/ChangeLog | 19 ++ .../green/demo/clustering/kmean1d/kmean1d.cc | 118 +++++-- .../milena/sandbox/green/mln/clustering/kmean1d.hh | 342 ++++++++++++------- 3 files changed, 325 insertions(+), 154 deletions(-) diff --git a/trunk/milena/sandbox/ChangeLog b/trunk/milena/sandbox/ChangeLog index 4700515..53f1385 100644 --- a/trunk/milena/sandbox/ChangeLog +++ b/trunk/milena/sandbox/ChangeLog @@ -20,6 +20,25 @@ * fabien/tests/core/image/tiled2d.cc: Update performance benchmark for different types of image. +2009-09-29 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; + + Prepare kmean demo for theo. Work in progress. + + Fix bugs, add hooks for tracing convergence. + + * green/mln/clustering/kmean1d.hh: Evict k parameter from template + and replace it by a constructor parameter. Add printing for debugging. + * green/mln/clustering/kmean1d.hh (update_variance): Fix bugs. + * green/mln/clustering/kmean1d.hh + (build_all_dbg, build_mean_dbg, update_cnv, finalize_cnv): New methods. + * green/mln/clustering/kmean1d.hh (launch_one_time): Fix bugs. + * green/mln/clustering/kmean1d.hh (launch_n_times): Add hooks. + + Do some works for managing shell parameters while executing the binary. + + * green/demo/clustering/kmean1d/kmean1d.cc + (main, char_to_string, char_to_unsigned, demo, do_demo): New methods. + 2009-09-28 Yann Jacquelet &lt;jacquelet(a)lrde.epita.fr&gt; Prepare kmean demo for theo. Work in progress. diff --git a/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc index cc04149..f13f783 100644 --- a/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc +++ b/trunk/milena/sandbox/green/demo/clustering/kmean1d/kmean1d.cc @@ -21,20 +21,25 @@ #include <mln/io/ppm/save.hh> #include <mln/io/plot/save_image_sh.hh> -void demo(const unsigned watch_dog, const unsigned n_times) +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) { - typedef mln::clustering::kmean1d<double,8,3> t_kmean; - typedef mln::value::label8 t_label8; + typedef mln::clustering::kmean1d<double,8> t_kmean; + typedef mln::value::label_8 t_label_8; 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; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; t_image2d_int_u8 house; - mln::io::pgm::load(house, OLENA_IMG_PATH"/house.pgm"); + mln::io::pgm::load(house, image.c_str()); - t_kmean kmean(house, watch_dog, n_times); + t_kmean kmean(house, k_center, watch_dog, n_times); + + //mln::trace::quiet = false; kmean.launch_n_times(); @@ -52,43 +57,100 @@ void demo(const unsigned watch_dog, const unsigned n_times) mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); } +void demo(const std::string& image = OLENA_IMG_PATH"/house.pgm", + 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 << "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 << "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 k (number of centers)" << std::endl; - // std::cout << "pbm image (points to work with)" << std::endl; + std::cout << "unsigned watch_dog (convergence loop)" << std::endl; std::cout << "----------------------------------------" << std::endl; } -int main(const int argc, const char *args[]) +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) { - if (3 == argc) + bool result = false; + + if (status) { - 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; + 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; - arg1 >> watch_dog; - arg2 >> n_times; + return !arg_stream.fail(); + } + + return result; +} - if (!arg1.fail() && !arg2.fail()) - demo(watch_dog, n_times); - else - usage(argc, args); +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); diff --git a/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh index 05947c7..9a8c1ea 100644 --- a/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh +++ b/trunk/milena/sandbox/green/mln/clustering/kmean1d.hh @@ -74,7 +74,7 @@ namespace mln { // Forward declaration. - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> struct kmean1d; } // end of namespace mln::clustering @@ -89,8 +89,7 @@ namespace mln /// /// 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> + template <typename T, unsigned n> struct kmean1d { /// Type definitions. @@ -115,7 +114,7 @@ namespace mln typedef image2d<t_rgb> t_color_dbg; typedef image2d<t_value> t_mean_dbg; - typedef image3d<t_value> t_mean_cnv; + typedef image3d<t_result> t_mean_cnv; typedef image2d<t_result> t_variance_cnv; ///} @@ -130,6 +129,7 @@ namespace mln /// \param[in] n_times : the number of times that we executed it (10). kmean1d(const t_point_img& point, + const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); @@ -231,11 +231,24 @@ namespace mln bool is_descent_valid(); //------------------------------------------------------------------------ - // Tools + // Debugging tools //------------------------------------------------------------------------ - void build(); - void to_result2(); + /// 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. + + void build_label_dbg(); + void build_all_dbg(); + void update_cnv(); + void finalize_cnv(); + + /// \} private: @@ -243,6 +256,7 @@ namespace mln /// \{ /// \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 @@ -251,6 +265,7 @@ namespace mln /// 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; @@ -266,9 +281,12 @@ namespace mln /// 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 last information, current_launching, traces the progress while + /// iterates kmean loop again and again. t_result _within_variance; unsigned _current_step; + unsigned _current_launching; /// \} @@ -366,30 +384,32 @@ namespace mln // Constructor //-------------------------------------------------------------------------- + /// FIXME k must be a parameter, not a static compilation paramater. - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - kmean1d<T,n,k>::kmean1d(const image2d< t_value >& point, - const unsigned watch_dog, - const unsigned n_times) + kmean1d<T,n>::kmean1d(const t_point_img& point, + const unsigned k_center, + const unsigned watch_dog, + const unsigned n_times) { trace::entering("mln::clustering::kmean1d::kmean1d"); 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::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))); + _number.init_(box1d(point1d(0),point1d(_k_center-1))); + _mean.init_(box1d(point1d(0),point1d(_k_center-1))); + _variance.init_(box1d(point1d(0),point1d(_k_center-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))); + point2d(mln_max(t_value), _k_center-1))); // Debugging, calibrating and testing initialize(_label_dbg, _point); @@ -397,12 +417,16 @@ namespace mln initialize(_mean_dbg, _point); // Observing the convergence + _variance_cnv.init_(box2d(point2d(0, 0), - point2d(_watch_dog-1, _n_times-1))); + point2d(_n_times-1, _watch_dog-1))); + + data::fill(_variance_cnv, literal::zero); _mean_cnv.init_(box3d(point3d(0, 0, 0), - point3d(k-1, _watch_dog-1, _n_times-1))); + point3d(_n_times-1, _k_center-1, _watch_dog-1))); + data::fill(_mean_cnv, literal::zero); trace::exiting("mln::clustering::kmean1d::kmean1d"); } @@ -411,9 +435,9 @@ namespace mln // Mutators and accessors //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_point(t_point_img& point) + void kmean1d<T,n>::set_point(t_point_img& point) { trace::entering("mln::clustering::kmean1d::set_point"); @@ -422,9 +446,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_point"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_histo(t_histo_img& histo) + void kmean1d<T,n>::set_histo(t_histo_img& histo) { trace::entering("mln::clustering::kmean1d::set_histo"); @@ -433,9 +457,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_histo"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_number(t_number_img& number) + void kmean1d<T,n>::set_number(t_number_img& number) { trace::entering("mln::clustering::kmean1d::set_number"); @@ -444,9 +468,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_number"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_mean(t_mean_img& mean) + void kmean1d<T,n>::set_mean(t_mean_img& mean) { trace::entering("mln::clustering::kmean1d::set_mean"); @@ -455,9 +479,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_mean"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_variance(t_variance_img& variance) + void kmean1d<T,n>::set_variance(t_variance_img& variance) { trace::entering("mln::clustering::kmean1d::set_variance"); @@ -466,9 +490,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_variance"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_group(t_group_img& group) + void kmean1d<T,n>::set_group(t_group_img& group) { trace::entering("mln::clustering::kmean1d::set_group"); @@ -477,9 +501,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_group"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::set_distance(t_distance_img& distance) + void kmean1d<T,n>::set_distance(t_distance_img& distance) { trace::entering("mln::clustering::kmean1d::set_distance"); @@ -488,9 +512,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::set_distance"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_point_img& kmean1d<T,n,k>::get_point() + typename kmean1d<T,n>::t_point_img& kmean1d<T,n>::get_point() { trace::entering("mln::clustering::kmean1d::get_point"); @@ -498,9 +522,9 @@ namespace mln return _point; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_histo_img& kmean1d<T,n,k>::get_histo() + typename kmean1d<T,n>::t_histo_img& kmean1d<T,n>::get_histo() { trace::entering("mln::clustering::kmean1d::get_histo"); @@ -508,9 +532,9 @@ namespace mln return _histo; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_number_img& kmean1d<T,n,k>::get_number() + typename kmean1d<T,n>::t_number_img& kmean1d<T,n>::get_number() { trace::entering("mln::clustering::kmean1d::get_number"); @@ -518,9 +542,9 @@ namespace mln return _number; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::get_mean() + typename kmean1d<T,n>::t_mean_img& kmean1d<T,n>::get_mean() { trace::entering("mln::clustering::kmean1d::get_mean"); @@ -528,9 +552,9 @@ namespace mln return _mean; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_variance_img& kmean1d<T,n,k>::get_variance() + typename kmean1d<T,n>::t_variance_img& kmean1d<T,n>::get_variance() { trace::entering("mln::clustering::kmean1d::get_variance"); @@ -538,9 +562,9 @@ namespace mln return _variance; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_group_img& kmean1d<T,n,k>::get_group() + typename kmean1d<T,n>::t_group_img& kmean1d<T,n>::get_group() { trace::entering("mln::clustering::kmean1d::get_group"); @@ -548,9 +572,9 @@ namespace mln return _group; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_distance_img& kmean1d<T,n,k>::get_distance() + typename kmean1d<T,n>::t_distance_img& kmean1d<T,n>::get_distance() { trace::entering("mln::clustering::kmean1d::get_distance"); @@ -558,9 +582,9 @@ namespace mln return _distance; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_color_dbg& kmean1d<T,n,k>::get_color_dbg() + typename kmean1d<T,n>::t_color_dbg& kmean1d<T,n>::get_color_dbg() { trace::entering("mln::clustering::kmean1d::get_color_dbg"); @@ -568,9 +592,9 @@ namespace mln return _color_dbg; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_dbg& kmean1d<T,n,k>::get_mean_dbg() + typename kmean1d<T,n>::t_mean_dbg& kmean1d<T,n>::get_mean_dbg() { trace::entering("mln::clustering::kmean1d::get_mean_dbg"); @@ -578,9 +602,9 @@ namespace mln return _mean_dbg; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_label_dbg& kmean1d<T,n,k>::get_label_dbg() + typename kmean1d<T,n>::t_label_dbg& kmean1d<T,n>::get_label_dbg() { trace::entering("mln::clustering::kmean1d::get_label_dbg"); @@ -588,9 +612,9 @@ namespace mln return _label_dbg; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_cnv& kmean1d<T,n,k>::get_mean_cnv() + typename kmean1d<T,n>::t_mean_cnv& kmean1d<T,n>::get_mean_cnv() { trace::entering("mln::clustering::kmean1d::get_mean_cnv"); @@ -598,9 +622,9 @@ namespace mln return _mean_cnv; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_variance_cnv& kmean1d<T,n,k>::get_variance_cnv() + typename kmean1d<T,n>::t_variance_cnv& kmean1d<T,n>::get_variance_cnv() { trace::entering("mln::clustering::kmean1d::get_variance_cnv"); @@ -608,9 +632,9 @@ namespace mln return _variance_cnv; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - typename kmean1d<T,n,k>::t_mean_img& kmean1d<T,n,k>::to_result() + typename kmean1d<T,n>::t_mean_img& kmean1d<T,n>::to_result() { trace::entering("mln::clustering::kmean1d::to_result"); @@ -623,12 +647,12 @@ namespace mln // Initialization of centers //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::init_mean_regular() + void kmean1d<T,n>::init_mean_regular() { trace::entering("mln::clustering::kmean1d::init_mean_regular"); - T step = (mln_max(t_value) - mln_min(t_value)) / (k-1); + T step = (mln_max(t_value) - mln_min(t_value)) / (_k_center-1); mln_piter(image1d<t_value>) l(_mean.domain()); for_all(l) @@ -640,11 +664,11 @@ namespace mln } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::init_mean_random() + void kmean1d<T,n>::init_mean_random() { - trace::exiting("mln::clustering::kmean1d::init_mean_random"); + trace::entering("mln::clustering::kmean1d::init_mean_random"); t_value min = mln_min(t_value); t_value max = mln_max(t_value); @@ -653,14 +677,16 @@ namespace mln for_all(l) { _mean(l) = (rand() % (max-min)) + min; + + std::cout << "mean" << l << " : " << _mean(l) << std::endl; } trace::exiting("mln::clustering::kmean1d::init_mean_random"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::init_mean() + void kmean1d<T,n>::init_mean() { trace::entering("mln::clustering::kmean1d::init_mean"); @@ -673,9 +699,9 @@ namespace mln // Computations of distance, group, center, within variance //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_distance() + void kmean1d<T,n>::update_distance() { trace::entering("mln::clustering::kmean1d::update_distance"); @@ -684,22 +710,24 @@ namespace mln for_all(d) { // the square distance - _distance(d) = math::sqr(d.row() - _mean(point1d(d.col()))); + _distance(d) = _histo(point1d(d.row())) + * 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; + 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::kmean1d::update_distance"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_group() + void kmean1d<T,n>::update_group() { trace::entering("mln::clustering::kmean1d::update_group"); @@ -708,7 +736,7 @@ namespace mln for_all(g) { mln_piter(t_mean_img) l(_mean.domain()); - T min = mln_max(T); + t_result min = mln_max(t_result); t_label label = mln_max(t_label); //std::cout << "g = " << g << std::endl; @@ -721,7 +749,7 @@ namespace mln label = l.ind(); } - //std::cout << "d(" << l << ") = " << + //std::cout << "d" << l << " = " << // _distance(point2d(g.ind(), l.ind())) << std::endl; } @@ -735,9 +763,9 @@ namespace mln trace::exiting("mln::clustering::kmean1d::update_group"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_mean() + void kmean1d<T,n>::update_mean() { trace::entering("mln::clustering::kmean1d::update_mean"); @@ -774,23 +802,22 @@ namespace mln std::cout << "n(" << l << ") = " << _number(l) << std::endl; } */ - for_all(l) { _mean(l) /= _number(l); - //std::cout << "c(" << l << ") = " << _mean(l) << std::endl; + std::cout << "c" << l << " = " << _mean(l) << std::endl; } trace::exiting("mln::clustering::kmean1d::update_mean"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::update_variance() + void kmean1d<T,n>::update_variance() { trace::entering("mln::clustering::kmean1d::update_variance"); - T _within_variance = literal::zero; + _within_variance = literal::zero; mln_piter(t_variance_img) l(_variance.domain()); for_all(l) @@ -801,11 +828,12 @@ namespace mln for_all(g) { - _variance(l) += _distance(point2d(g.ind(), l.ind())); + if (l.ind() == _group(g)) + _variance(l) += _distance(point2d(g.ind(), l.ind())); } _within_variance += _variance(l); - //std::cout << "v(" << l << ") = " << _variance(l) << std::endl; + std::cout << "v(" << l << ") = " << _variance(l) << std::endl; } //std::cout << "result" << result << std::endl; @@ -814,14 +842,14 @@ namespace mln } //-------------------------------------------------------------------------- - // Tools + // Debugging tools //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::build() + void kmean1d<T,n>::build_label_dbg() { - trace::entering("build"); + trace::entering("mln::clustering::kmean1d::build_mean_dbg"); mln_piter(t_point_img) pi(_point.domain()); mln_piter(t_label_dbg) po(_label_dbg.domain()); @@ -834,26 +862,64 @@ namespace mln _label_dbg(po) = grp; } - trace::exiting("build"); + trace::exiting("mln::clustering::kmean1d::build_mean_dbg"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::to_result2() + void kmean1d<T,n>::build_all_dbg() { - typedef value::rgb8 rgb8; - std::cout << "BUILDING OUTPUT" << std::endl; + trace::entering("mln::clustering::kmean1d::build_all_dbg"); + build_label_dbg(); + + _mean_dbg = labeling::mean_values(_point, _label_dbg, _k_center); + _color_dbg = labeling::colorize(value::rgb8(), _label_dbg); + + trace::exiting("mln::clustering::kmean1d::build_all_dbg"); + } + + template <typename T, unsigned n> + inline + void kmean1d<T,n>::update_cnv() + { + trace::entering("mln::clustering::kmean1d::update_cnv"); + + _variance_cnv(point2d(_current_launching, + _current_step)) = _within_variance; + + mln_piter(t_mean_img) l(_mean.domain()); + + for_all(l) + { + _mean_cnv(point3d(_current_launching, + l.ind(), + _current_step)) = _mean(l); + } + + trace::exiting("mln::clustering::kmean1d::update_cnv"); + } - build(); + template <typename T, unsigned n> + inline + void kmean1d<T,n>::finalize_cnv() + { + trace::entering("mln::clustering::kmean1d::finalize_cnv"); - std::cout << "COLORING OUTPUT" << std::endl; + // saturate the curv with the within variance + for (unsigned i = _current_step; i < _watch_dog; ++i) + _variance_cnv(point2d(_current_launching, i)) = _within_variance; + + for (unsigned i = _current_step; i < _watch_dog; ++i) + { + mln_piter(t_mean_img) l(_mean.domain()); - _mean_dbg = labeling::mean_values(_point, _label_dbg, k); - _color_dbg = labeling::colorize(rgb8(), _label_dbg); + for_all(l) + { + _mean_cnv(point3d(_current_launching, l.ind(), i)) = _mean(l); + } + } - mln::io::pgm::save(_mean_dbg, "mean.pgm"); - mln::io::ppm::save(_color_dbg, "rgb.pgm"); - mln::io::pgm::save(_label_dbg, "label.pgm"); + trace::exiting("mln::clustering::kmean1d::finalize_cnv"); } @@ -861,9 +927,9 @@ namespace mln // Checking the validity of the results //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - bool kmean1d<T,n,k>::is_descent_valid() + bool kmean1d<T,n>::is_descent_valid() { trace::entering("mln::clustering::kmean1d::is_descent_valid"); bool result = true; @@ -881,9 +947,9 @@ namespace mln return result; } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - bool kmean1d<T,n,k>::is_valid() + bool kmean1d<T,n>::is_valid() { trace::entering("mln::clustering::kmean1d::is_valid"); bool result = true; @@ -905,61 +971,85 @@ namespace mln //-------------------------------------------------------------------------- - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::launch_one_time() + void kmean1d<T,n>::launch_one_time() { trace::entering("mln::clustering::kmean1d::launch_one_time"); + std::cout << "----------------------------------------" << std::endl; + // Initialization to start the descent - _within_variance = mln_max(t_result)/2; t_result old_variance = mln_max(t_result); - // Choose random points + // Choose random points and compute within variance init_mean(); + update_distance(); + update_group(); + update_variance(); // update _within_variance + + std::cout << "first_variance : " << _within_variance << std::endl; // Execute the descent - for (unsigned i = 0; i<_watch_dog && _within_variance<old_variance; ++i) + for (_current_step = 0; + _current_step < _watch_dog && _within_variance < old_variance; + ++_current_step) { + old_variance = _within_variance; + + update_mean(); update_distance(); update_group(); - update_mean(); + update_variance(); // update _within_variance - old_variance = _within_variance; + // debugging code + update_cnv(); - update_variance(); // update _within_variance + std::cout << "_current_step : " << _current_step << std::endl; + std::cout << "_within_variance : " << _within_variance << std::endl; } + std::cout << "----------------------------------------" << std::endl; + + finalize_cnv(); + trace::exiting("mln::clustering::kmean1d::launch_one_time"); } - template <typename T, unsigned n, unsigned k> + template <typename T, unsigned n> inline - void kmean1d<T,n,k>::launch_n_times() + void kmean1d<T,n>::launch_n_times() { trace::entering("mln::clustering::kmean1d::launch_n_times"); - _variance_min = mln_max(t_result); + std::cout << "watch_dog : " << _watch_dog << std::endl; + std::cout << "n_times : " << _n_times << std::endl; + + _variance_min = mln_max(t_result); + _current_launching = 0; // Execute the descent n times - for (unsigned i = 0; i < _n_times; ++i) + while (_current_launching < _n_times) { launch_one_time(); - if (!is_descent_valid()) - --i; - else + if (is_descent_valid()) { if (_within_variance < _variance_min) { _variance_min = _within_variance; _mean_min = _mean; - _launching_min = i; + _launching_min = _current_launching; } - } + + std::cout << "_current_launching : " << _current_launching + << std::endl; + + std::cout << "within_variance[" << _current_launching << "] = " + << _within_variance << std::endl; - std::cout << "within_variance[" << i << "] = " - << _within_variance << std::endl; + ++_current_launching; + } } trace::exiting("mln::clustering::kmean1d::launch_n_times"); -- 1.5.6.5