Olena-patches November 2010

olena-patches@lrde.epita.fr

7 participants
370 discussions

last-svn-commit-566-ge222518 Build Libiberty as a shared library.

by Roland Levillain

* libiberty/configure.gnu: New. Pass `--enable-shared' to configure, unless told otherwise. * libiberty.mk (EXTRA_DIST): Add libiberty/configure.gnu. * src/Makefile.am (libextatica_la_LIBADD): Replace $(top_builddir)/libiberty/libiberty.a with $(top_builddir)/libiberty/pic/libiberty.a. --- extatica/ChangeLog | 11 +++++++++++ extatica/libiberty.mk | 3 +++ extatica/{ => libiberty}/configure.gnu | 19 +++++++++---------- extatica/src/Makefile.am | 2 +- 4 files changed, 24 insertions(+), 11 deletions(-) copy extatica/{ => libiberty}/configure.gnu (65%) mode change 100644 => 100755 diff --git a/extatica/ChangeLog b/extatica/ChangeLog index 2a0e684..ca5bca6 100644 --- a/extatica/ChangeLog +++ b/extatica/ChangeLog @@ -1,3 +1,14 @@ +2010-11-15 Roland Levillain <roland(a)lrde.epita.fr> + + Build Libiberty as a shared library. + + * libiberty/configure.gnu: New. + Pass `--enable-shared' to configure, unless told otherwise. + * libiberty.mk (EXTRA_DIST): Add libiberty/configure.gnu. + * src/Makefile.am (libextatica_la_LIBADD): Replace + $(top_builddir)/libiberty/libiberty.a with + $(top_builddir)/libiberty/pic/libiberty.a. + 2010-11-09 Roland Levillain <roland(a)lrde.epita.fr> Mention Libiberty in Extatica's AUTHORS, NEWS and README. diff --git a/extatica/libiberty.mk b/extatica/libiberty.mk index 8f8b0be..c803e96 100644 --- a/extatica/libiberty.mk +++ b/extatica/libiberty.mk @@ -126,3 +126,6 @@ EXTRA_DIST += \ # These scripts are required by Libiberty's build system. EXTRA_DIST += mkinstalldirs move-if-change + +# `configure' wrapper used in recursive configuration. +EXTRA_DIST += libiberty/configure.gnu diff --git a/extatica/configure.gnu b/extatica/libiberty/configure.gnu old mode 100644 new mode 100755 similarity index 65% copy from extatica/configure.gnu copy to extatica/libiberty/configure.gnu index 09a9463..a463862 --- a/extatica/configure.gnu +++ b/extatica/libiberty/configure.gnu @@ -11,15 +11,15 @@ # Is option checking disabled? no_option_checking_p=false -# Is ``--with-milena' not provided? -no_milena_p=true +# Is `--disable-shared' (or `--enable-shared=no') not provided? +no_disable_shared_p=true for i; do case "$i" in # First try to get SRCDIR from a possible `--srcdir' argument. --srcdir=*) srcdir=`echo "$i" | sed 's/^--srcdir=//'`;; --disable-option-checking) no_option_checking_p=true;; - --with-milena*) no_milena_p=false;; + --disable-shared|--enable-shared=no) no_disable_shared_p=false;; esac done @@ -27,13 +27,12 @@ done test x"${srcdir+set}" != xset && srcdir=`dirname $0` # Disabled option checking is a hint that this `configure.gnu' was -# called from a parent `configure'. If so, and if no `--with-milena' -# option was provided, pass our own `--with-milena' option to the -# recursive call to the actual `configure', carrying the path to -# Milena's source directory. -if $no_option_checking_p && $no_milena_p; then - milena_dir=`cd "$srcdir/../milena" && pwd` - exec "$srcdir/configure" ${1+"$@"} "--with-milena=$milena_dir" +# called from a parent `configure'. If so, and if no +# `--disable-shared' (or `--enable-shared=no') option was provided, +# pass our own `--enable-shared' option to the recursive call to the +# actual `configure'. +if $no_option_checking_p && $no_disable_shared_p; then + exec "$srcdir/configure" ${1+"$@"} --enable-shared else exec "$srcdir/configure" ${1+"$@"} fi diff --git a/extatica/src/Makefile.am b/extatica/src/Makefile.am index ed6830a..00bac51 100644 --- a/extatica/src/Makefile.am +++ b/extatica/src/Makefile.am @@ -82,7 +82,7 @@ libextatica_la_LIBADD = \ $(RUBY_LIBRUBYARG_SHARED) \ $(LIBLTDL) \ $(top_builddir)/libmd5/libmd5.la \ - $(top_builddir)/libiberty/libiberty.a \ + $(top_builddir)/libiberty/pic/libiberty.a \ $(BOOST_FILESYSTEM_LIBS) libextatica_la_DEPENDENCIES = $(LTDLDEPS) -- 1.5.6.5

14 years, 1 month

last-svn-commit-566-g07fe77d Build Libiberty as a shared library.

by Roland Levillain

* libiberty/configure.gnu: New. Pass `--enable-shared' to configure, unless told otherwise. * libiberty.mk (EXTRA_DIST): Add libiberty/configure.gnu. * src/Makefile.am (libextatica_la_LIBADD): Replace $(top_builddir)/libiberty/libiberty.a with $(top_builddir)/libiberty/pic/libiberty.a. --- extatica/ChangeLog | 11 +++++++++++ extatica/libiberty.mk | 3 +++ extatica/{ => libiberty}/configure.gnu | 19 +++++++++---------- extatica/src/Makefile.am | 2 +- 4 files changed, 24 insertions(+), 11 deletions(-) copy extatica/{ => libiberty}/configure.gnu (65%) mode change 100644 => 100755 diff --git a/extatica/ChangeLog b/extatica/ChangeLog index 90696c0..83850fc 100644 --- a/extatica/ChangeLog +++ b/extatica/ChangeLog @@ -1,3 +1,14 @@ +2010-11-15 Roland Levillain <roland(a)lrde.epita.fr> + + Build Libiberty as a shared library. + + * libiberty/configure.gnu: New. + Pass `--enable-shared' to configure, unless told otherwise. + * libiberty.mk (EXTRA_DIST): Add libiberty/configure.gnu. + * src/Makefile.am (libextatica_la_LIBADD): Replace + $(top_builddir)/libiberty/libiberty.a with + $(top_builddir)/libiberty/pic/libiberty.a. + 2010-11-09 Roland Levillain <roland(a)lrde.epita.fr> Mention Libiberty in Extatica's AUTHORS, NEWS and README. diff --git a/extatica/libiberty.mk b/extatica/libiberty.mk index 8f8b0be..c803e96 100644 --- a/extatica/libiberty.mk +++ b/extatica/libiberty.mk @@ -126,3 +126,6 @@ EXTRA_DIST += \ # These scripts are required by Libiberty's build system. EXTRA_DIST += mkinstalldirs move-if-change + +# `configure' wrapper used in recursive configuration. +EXTRA_DIST += libiberty/configure.gnu diff --git a/extatica/configure.gnu b/extatica/libiberty/configure.gnu old mode 100644 new mode 100755 similarity index 65% copy from extatica/configure.gnu copy to extatica/libiberty/configure.gnu index 09a9463..a463862 --- a/extatica/configure.gnu +++ b/extatica/libiberty/configure.gnu @@ -11,15 +11,15 @@ # Is option checking disabled? no_option_checking_p=false -# Is ``--with-milena' not provided? -no_milena_p=true +# Is `--disable-shared' (or `--enable-shared=no') not provided? +no_disable_shared_p=true for i; do case "$i" in # First try to get SRCDIR from a possible `--srcdir' argument. --srcdir=*) srcdir=`echo "$i" | sed 's/^--srcdir=//'`;; --disable-option-checking) no_option_checking_p=true;; - --with-milena*) no_milena_p=false;; + --disable-shared|--enable-shared=no) no_disable_shared_p=false;; esac done @@ -27,13 +27,12 @@ done test x"${srcdir+set}" != xset && srcdir=`dirname $0` # Disabled option checking is a hint that this `configure.gnu' was -# called from a parent `configure'. If so, and if no `--with-milena' -# option was provided, pass our own `--with-milena' option to the -# recursive call to the actual `configure', carrying the path to -# Milena's source directory. -if $no_option_checking_p && $no_milena_p; then - milena_dir=`cd "$srcdir/../milena" && pwd` - exec "$srcdir/configure" ${1+"$@"} "--with-milena=$milena_dir" +# called from a parent `configure'. If so, and if no +# `--disable-shared' (or `--enable-shared=no') option was provided, +# pass our own `--enable-shared' option to the recursive call to the +# actual `configure'. +if $no_option_checking_p && $no_disable_shared_p; then + exec "$srcdir/configure" ${1+"$@"} --enable-shared else exec "$srcdir/configure" ${1+"$@"} fi diff --git a/extatica/src/Makefile.am b/extatica/src/Makefile.am index ed6830a..00bac51 100644 --- a/extatica/src/Makefile.am +++ b/extatica/src/Makefile.am @@ -82,7 +82,7 @@ libextatica_la_LIBADD = \ $(RUBY_LIBRUBYARG_SHARED) \ $(LIBLTDL) \ $(top_builddir)/libmd5/libmd5.la \ - $(top_builddir)/libiberty/libiberty.a \ + $(top_builddir)/libiberty/pic/libiberty.a \ $(BOOST_FILESYSTEM_LIBS) libextatica_la_DEPENDENCIES = $(LTDLDEPS) -- 1.5.6.5

14 years, 1 month

last-svn-commit-48-gd652519 Import annotating sources from milena green's sandbox.

by Yann Jacquelet

* README.green: New. * demo/annotating/bic/Makefile.am: New. * demo/annotating/bic/bic.cc: New. * demo/annotating/hsv/Makefile.am: New. * demo/annotating/hsv/hsv.cc: New. * demo/annotating/lep/Makefile.am: New. * demo/annotating/lep/lep.cc: New. * demo/annotating/nb_color/Makefile.am: New. * demo/annotating/nb_color/nb_color.cc: New. * demo/annotating/project/Makefile.am: New. * demo/annotating/project/project.cc: New. * demo/annotating/rgb_64/Makefile.am: New. * demo/annotating/rgb_64/rgb_64.cc: New. * demo/annotating/rgb_64_9/Makefile.am: New. * demo/annotating/rgb_64_9/rgb_64_9.cc: New. * demo/annotating/stddev_color/Makefile.am: New. * demo/annotating/stddev_color/stddev_color.cc: New. * demo/annotating/stddev_color_16/Makefile.am: New. * demo/annotating/stddev_color_16/stddev_color_16.cc: New. * exp/annotating/achromastism/Makefile.am: New. * exp/annotating/achromastism/achromastism.cc: New. * exp/annotating/achromastism/text-color.txt: New. * exp/annotating/achromastism/text-img.txt: New. * exp/annotating/achromastism/text-only.txt: New. * exp/annotating/bench/Makefile.am: New. * exp/annotating/bench/bench.cc: New. * exp/annotating/error/Makefile.am: New. * exp/annotating/error/error.cc: New. * exp/annotating/histo/Makefile.am: New. * exp/annotating/histo/histo.cc: New. * exp/annotating/hsv/Makefile.am: New. * exp/annotating/hsv/hsv.cc: New. * exp/annotating/hue/Makefile.am: New. * exp/annotating/hue/hue.cc: New. * exp/annotating/hue/text-color.txt: New. * exp/annotating/hue/text-img.txt: New. * exp/annotating/hue/text-only.txt: New. * exp/annotating/nb_color/Makefile.am: New. * exp/annotating/nb_color/nb_color.cc: New. * exp/annotating/saturation/Makefile.am: New. * exp/annotating/saturation/saturation.cc: New. * exp/annotating/saturation/text-color.txt: New. * exp/annotating/saturation/text-img.txt: New. * exp/annotating/saturation/text-only.txt: New. * exp/annotating/stddev_color/Makefile.am: New. * exp/annotating/stddev_color/stddev_color.cc: New. * exp/annotating/stddev_color_16/Makefile.am: New. * exp/annotating/stddev_color_16/stddev_color_16.cc: New. * exp/annotating/value/Makefile.am: New. * exp/annotating/value/text-color.txt: New. * exp/annotating/value/text-img.txt: New. * exp/annotating/value/text-only.txt: New. * exp/annotating/value/value.cc: New. * mln/clustering/kmean2d.hh: New. * mln/fun/p2b/achromatic.hh: New. * mln/fun/v2v/hue_concentration.hh: New. * mln/fun/v2v/rgb_to_achromatism_map.hh: New. * mln/fun/v2v/rgb_to_hsv.hh: New. --- scribo/sandbox/green/ChangeLog | 64 + scribo/sandbox/green/README.green | 507 +++++++- .../kmean1d => annotating/bic}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/bic/bic.cc | 122 ++ .../kmean1d => annotating/hsv}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/hsv/hsv.cc | 721 ++++++++++ .../kmean1d => annotating/lep}/Makefile.am | 0 scribo/sandbox/green/demo/annotating/lep/lep.cc | 127 ++ .../kmean1d => annotating/nb_color}/Makefile.am | 0 .../green/demo/annotating/nb_color/nb_color.cc | 143 ++ .../kmean1d => annotating/project}/Makefile.am | 0 .../green/demo/annotating/project/project.cc | 275 ++++ .../kmean1d => annotating/rgb_64}/Makefile.am | 0 .../sandbox/green/demo/annotating/rgb_64/rgb_64.cc | 80 ++ .../kmean1d => annotating/rgb_64_9}/Makefile.am | 0 .../green/demo/annotating/rgb_64_9/rgb_64_9.cc | 132 ++ .../stddev_color}/Makefile.am | 0 .../demo/annotating/stddev_color/stddev_color.cc | 191 +++ .../stddev_color_16}/Makefile.am | 0 .../annotating/stddev_color_16/stddev_color_16.cc | 261 ++++ .../achromastism}/Makefile.am | 0 .../exp/annotating/achromastism/achromastism.cc | 179 +++ .../exp/annotating/achromastism/text-color.txt | 0 .../green/exp/annotating/achromastism/text-img.txt | 0 .../exp/annotating/achromastism/text-only.txt | 0 .../bench}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/bench/bench.cc | 1450 ++++++++++++++++++++ .../error}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/error/error.cc | 833 +++++++++++ .../histo}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/histo/histo.cc | 366 +++++ .../hsv}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/hsv/hsv.cc | 912 ++++++++++++ .../hue}/Makefile.am | 0 scribo/sandbox/green/exp/annotating/hue/hue.cc | 402 ++++++ .../green/exp/annotating/hue}/text-color.txt | 0 .../sandbox/green/exp/annotating/hue}/text-img.txt | 0 .../green/exp/annotating/hue}/text-only.txt | 0 .../nb_color}/Makefile.am | 0 .../green/exp/annotating/nb_color/nb_color.cc | 171 +++ .../saturation}/Makefile.am | 0 .../green/exp/annotating/saturation/saturation.cc | 175 +++ .../exp/annotating/saturation}/text-color.txt | 0 .../green/exp/annotating/saturation}/text-img.txt | 0 .../green/exp/annotating/saturation}/text-only.txt | 0 .../stddev_color}/Makefile.am | 0 .../exp/annotating/stddev_color/stddev_color.cc | 216 +++ .../stddev_color_16}/Makefile.am | 0 .../annotating/stddev_color_16/stddev_color_16.cc | 277 ++++ .../value}/Makefile.am | 0 .../green/exp/annotating/value}/text-color.txt | 0 .../green/exp/annotating/value}/text-img.txt | 0 .../green/exp/annotating/value}/text-only.txt | 0 scribo/sandbox/green/exp/annotating/value/value.cc | 468 +++++++ scribo/sandbox/green/mln/clustering/kmean2d.hh | 4 +- scribo/sandbox/green/mln/fun/p2b/achromatic.hh | 20 +- .../sandbox/green/mln/fun/v2v/hue_concentration.hh | 10 + .../green/mln/fun/v2v/rgb_to_achromatism_map.hh | 5 + scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh | 15 +- 59 files changed, 8105 insertions(+), 21 deletions(-) copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/bic}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/bic/bic.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/hsv/hsv.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/lep}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/lep/lep.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/nb_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/project}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/project/project.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/rgb_64}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/rgb_64_9}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/stddev_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc copy scribo/sandbox/green/demo/{clustering/kmean1d => annotating/stddev_color_16}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/achromastism}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-color.txt (100%) copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-img.txt (100%) copy {milena => scribo}/sandbox/green/exp/annotating/achromastism/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/bench}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/bench/bench.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/error}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/error/error.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/histo}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/histo/histo.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/hsv}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/hsv/hsv.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/hue}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/hue/hue.cc copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/hue}/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/nb_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/saturation}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/saturation/saturation.cc copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/saturation}/text-only.txt (100%) copy scribo/sandbox/green/exp/{regional_maxima => annotating/stddev_color}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/stddev_color_16}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc copy scribo/sandbox/green/exp/{regional_maxima => annotating/value}/Makefile.am (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-color.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-img.txt (100%) copy {milena/sandbox/green/exp/annotating/achromastism => scribo/sandbox/green/exp/annotating/value}/text-only.txt (100%) create mode 100644 scribo/sandbox/green/exp/annotating/value/value.cc diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 5e4cb27..6210a79 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,67 @@ +2010-09-08 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Import annotating sources from milena green's sandbox. + + * README.green: New. + * demo/annotating/bic/Makefile.am: New. + * demo/annotating/bic/bic.cc: New. + * demo/annotating/hsv/Makefile.am: New. + * demo/annotating/hsv/hsv.cc: New. + * demo/annotating/lep/Makefile.am: New. + * demo/annotating/lep/lep.cc: New. + * demo/annotating/nb_color/Makefile.am: New. + * demo/annotating/nb_color/nb_color.cc: New. + * demo/annotating/project/Makefile.am: New. + * demo/annotating/project/project.cc: New. + * demo/annotating/rgb_64/Makefile.am: New. + * demo/annotating/rgb_64/rgb_64.cc: New. + * demo/annotating/rgb_64_9/Makefile.am: New. + * demo/annotating/rgb_64_9/rgb_64_9.cc: New. + * demo/annotating/stddev_color/Makefile.am: New. + * demo/annotating/stddev_color/stddev_color.cc: New. + * demo/annotating/stddev_color_16/Makefile.am: New. + * demo/annotating/stddev_color_16/stddev_color_16.cc: New. + * exp/annotating/achromastism/Makefile.am: New. + * exp/annotating/achromastism/achromastism.cc: New. + * exp/annotating/achromastism/text-color.txt: New. + * exp/annotating/achromastism/text-img.txt: New. + * exp/annotating/achromastism/text-only.txt: New. + * exp/annotating/bench/Makefile.am: New. + * exp/annotating/bench/bench.cc: New. + * exp/annotating/error/Makefile.am: New. + * exp/annotating/error/error.cc: New. + * exp/annotating/histo/Makefile.am: New. + * exp/annotating/histo/histo.cc: New. + * exp/annotating/hsv/Makefile.am: New. + * exp/annotating/hsv/hsv.cc: New. + * exp/annotating/hue/Makefile.am: New. + * exp/annotating/hue/hue.cc: New. + * exp/annotating/hue/text-color.txt: New. + * exp/annotating/hue/text-img.txt: New. + * exp/annotating/hue/text-only.txt: New. + * exp/annotating/nb_color/Makefile.am: New. + * exp/annotating/nb_color/nb_color.cc: New. + * exp/annotating/saturation/Makefile.am: New. + * exp/annotating/saturation/saturation.cc: New. + * exp/annotating/saturation/text-color.txt: New. + * exp/annotating/saturation/text-img.txt: New. + * exp/annotating/saturation/text-only.txt: New. + * exp/annotating/stddev_color/Makefile.am: New. + * exp/annotating/stddev_color/stddev_color.cc: New. + * exp/annotating/stddev_color_16/Makefile.am: New. + * exp/annotating/stddev_color_16/stddev_color_16.cc: New. + * exp/annotating/value/Makefile.am: New. + * exp/annotating/value/text-color.txt: New. + * exp/annotating/value/text-img.txt: New. + * exp/annotating/value/text-only.txt: New. + * exp/annotating/value/value.cc: New. + * mln/clustering/kmean2d.hh: New. + * mln/fun/p2b/achromatic.hh: New. + * mln/fun/v2v/hue_concentration.hh: New. + * mln/fun/v2v/rgb_to_achromatism_map.hh: New. + * mln/fun/v2v/rgb_to_hsv.hh: New. + + 2010-07-07 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Import regional maxima sources from milena green's sandbox. diff --git a/scribo/sandbox/green/README.green b/scribo/sandbox/green/README.green index 7886914..5d6e147 100644 --- a/scribo/sandbox/green/README.green +++ b/scribo/sandbox/green/README.green @@ -685,21 +685,6 @@ rechercher dans les derniers répertoires mp00042c et mp00307c. * doc/labeling/mp00307c: Test sur la representativité des couleurs trouvées. -Faire le changelog, il y a de l'aide dans git/doc/Changelog -... et commiter - - -XI ANNOTATING --------------- - -==> to do - - - -* doc/annotating - - - XI AUTRES ASPECTS DOCUMENTAIRES ------------------------------- @@ -732,3 +717,495 @@ l'effort. * doc/formulae: LaTex directory. * doc/quick_tour: LaTex directory. + +XII ANNOTATING +-------------- + +Tout d'abord, voici les notes documentaires qui ont été réalisées sur +la problématique d'annotation d'image. On trouvera un fichier +class.txt qui a pour but de poser quelques réflexions sur les types de +classes de document. Les informations ne sont pas abouties mais +permettent de défricher un peu le terrain. Le document +syntheseMillet2008 est un compte-rendu de lecture des parties +relatives à nos travaux dans la thèse de Millet. Pour bien comprendre +mon travail, il est impératif de lire les travaux de Millet, ou +simplement ce compte-rendu. Le dernier document est moins intéressant, +il s'agit d'une note de travail sur les indicateurs de Millet bruts de +fonderie (testMillet2008). Cette note n'est ni achevée, ni +aboutie. Elle conclue sur le fait que les indicateurs et les seuils +donnés par Millet dans sa thèse sont complètement à revoir pour nos +besoins. Notemment, des tests sur la détection des images par rapport +aux cliparts nous ont convaincu que certaines images AFP contiennent +des de grandes zones homogènes qui induisent des erreurs dans les +prédicteurs de Millet. Néanmoins, les cas particuliers qui +contredisent les aspects opérationnnels de Millet n'enlèvent pas sa +réflexion: Les images noir/blanc (ou monochromes) ont une très faible +saturation, et/ou une forte concentration de la teinte. Les cliparts +ont une forte concentration de niveaux de gris autour d'un pic. + +* doc/annotating: La documentation relative à l'annotation. + + +Après la lecture des descripteurs de Millet, un des premiers réflexe a +été d'en implémenter plusieurs pour voir ce qu'ils pouvaient ressortir +sur les images que nous avions. Le descripteur BIC sépare une image en +deux ensembles de pixels, les points intérieurs et les points +extérieurs. Les points intérieurs ont la propriété d'être de même +couleur que leur 4-voisins. Attention, la couleur est évaluée dans un +espace RGB à 3 bits. + +* demo/annotating/bic: Histogrammes des points intérieurs et extérieurs. + + +Le descripteur LEP, lui, propose de seuiller par sobel l'image et de +faire l'histogramme des configurations du voisinage des points +seuillés. Pour ce faire, on utilise une convolution un peu spéciale +qui va attribué un unique id en fonction du voisinage. + +* demo/annotating/lep: Histogramme des configurations des voisinages des pixels. + + +Un autre descripteur simple est le nombre de couleur dans une +image. Pour cela, il est possible de construire l'histogramme des +couleurs et de compter les cellules pleines. On peut éventuellement +appliquer une quantification sur l'espace des couleurs. La +compilation laisse place à d'étranges warnings sur une comparaison +entre entiers signés et non signés, mais je n'ai pas la main dans mon +code (ou je ne sais pas comment faire) pour enlever ces warnings. Ils +sont récents, je n'avais pas souvenir de les avoir eu. + +* demo/annotating/nb_color: Compte le nombre de couleurs dans une image. +* exp/annotating/nb_color: Adaptation pour fonctionner sur une base d'image. + + +L'histogramme RGB-64 est un descripteur simple qui quantifie les +couleurs sur 2 bits et réalise l'histogramme dans cet espace. C'est +bien sûr une classification gros grain, mais ajouté au reste ... La +version RGB-64-9 ajoute une phase de division de l'image en 9 sous +images. De cette manière, l'histogramme RGB-64 est construit sur les 9 +sous images. Pour former le descripteur final, on fusionne les neufs +histogrammes. + +* demo/annotating/rgb_64: Histogramme couleur dans l'espace RGB-64 (2 bits/axe). +* demo/annotating/rgb_64_9: Histogramme RGB-64 sur les 9 sous images. + + +Le descripteur de projection relaté par Millet est particulier. En +premier lieu l'image est sous échantillonnée pour réduire sa dimension +à 100 x 100 de manière à borner la taille des vecteurs obtenus au +final. Puis l'image est seuillée par sobel (threshold = 100). L'image +est d'abord divisée horizontalement en deux. Puis on établit la +projecton perpendiculairement à la séparation de manière à obtenir +deux vecteurs de 100 valeurs chacunes. On recommence l'opération en +divisant maintenant l'image verticalement. L'union des 4 vecteurs +forme le descripteur de projection. L'information condensée dans ces +vecteurs est simplement la répartition des contours de manière +horizontale ou verticale. + +* demo/annotating/project: Répartition des contours horizontaux et verticaux. + + +La reconnaissance des cliparts s'appuie sur une analyse d'histogramme +qui est fournie dans le code suivant. L'idée est de dire qu'une image +de type clipart va être reconnaissable surtout à l'aide de ces +contours. La couleur existe mais est très grossière. Le faitde +dessiner à la main implique de simplifier énormément, de caricaturer, +le remplissage. Du coup, une analyse en niveau de gris de +l'histogramme révèle très peu de nuances. Il peut cependant en avoir +un peu. Néanmoins, il existe des logiciels pour aider à la fabrication +des cliparts qui proposent l'usage de dégradé, ce qui nuit à cette +méthode de reconnaissance. Millet analyze l'histogramme normalisé +(histogramme divisé par son pic) et regarde si son energie ne serait +pas concentrée autour du pic (5 pixels de chaque côté +maximum). Parfois cette méthode ne fonctionne pas correctement sur des +photographies qui ont un cadre uniforme. La méthode trouve un pic (le +cadre) et vérifie alors qu'une proportion non négligeable des pixels +sont bien autour de ce pic (tout dépend de l'épaisseur du cadre). Pour +palier à cet inconvénient, Millet propose d'utiliser ce test, non plus +sur l'image entière, mais sur chacune des 16 sous images après un +découpage géométrique régulier. De facto, la contribution du cadre +diminue suffisemment pour repasser en dessous du seuil de +reconnaissance. + +* demo/annotating/stddev_color: Descripteur utilisé reconnaitre des cliparts. +* exp/annotating/stddev_color_16: Adaptation pour le travail sur base. +* demo/annotating/stddev_color_16: Descripteur pour cliparts avec 16 imagettes. +* exp/annotating/stddev_color: Adaptation pour le travail sur une base d'image. + + +A partir de maintenant, tous les morceaux de codes réalisés préparent +directement ou indirectement le résultat de la classification des +bases (exp/annotating/bench). + +Plus de temps pour faire le code use correspondant aux fichiers +librairies. Tout le code pour le faire est dans hsv. Rien de +compliqué, mais allons à l'essentiel. + +Le but du code HSV est d'effectuer les tests de Millet ou des +améliorations sur ces tests. Le premier test proposé par Millet est +l'achromaticité. Il s'agit de regarder s'il existe une faible +variation entre les trois canaux (R/G/B) pour un grand nombre de +pixels. Si c'est le cas, c'est que l'image est presque en niveau de +gris et peut être remplacée facilement par une image grisée sans trop +de distorsions au niveau de la couleur. Nous avons essayer de +généraliser un peu le test de manière à produire, non pas seulement +une réponse sur l'achromaticité de l'image, mais aussi avoir une vue +d'ensemble (sous forme d'image) des variations entre les cannaux pour +chaque pixel. Il n'est pas utile d'analyser les différences sur chacun +des canaux, prendre la différence absolue entre le canal min et le +canal max suffit pour définir le test d'achromaticité. + +Les autres tests de Millet sont la faible saturation et la dominance +de la teinte. Pour savoir si la saturation est faible, il faut +utiliser un histogramme du canal dédié à la saturation. Si 95% des +pixels sont en dessous de 100, alors l'image est faiblement saturée, +elle est en noir et blanc. De la même manière, on regarde la dominance +de la teinte. Pour ce faire, il faut voir si l'histogramme de la +teinte ne possède pas un pic avec une très faible variance (tout +rapproché autour du pic). Si c'est le cas, la dominance de la teinte +est avérée et l'on peut calculé la couleur dominante. L'image est en +niveau de gris, mais colorisé autour d'une couleur (par cepia, vert +...). + +Ce programme est très très sujet à changements, il m'a servit de test +et je ne peux pas juré qu'il est complètement sain au niveau du +traitement. D'autres versions sont potentiellement plus +stables. Notemment celle dans exp/annotating/hsv. + +* mln/fun/v2v/rgb_to_achromatism_map.hh : Distance pour l'achromaticité. +* mln/fun/v2v/achromatic.hh : Define the achromatic map. +* mln/fun/v2v/hue_concentration.hh : Define the distance hue/peak map. +* mln/fun/p2b/achromatic.hh : Say if a site is achromatic. +* demo/annotating/hsv: Code des différents tests de Millet. + + +Dans le répertoire exp/annotating/hue, on trouve 3 fichiers textes qui +rassemblent des classes d'images. Tout d'abord les images ICDAR +n'ayant que du texte et des traces de couleurs (lettrine de couleur, +trait, petit bandeau), un fichier où il n'y a que du texte noir & +blanc et un fichier contenant les images couleurs (avec photographies +ou dessins). Cette classification a été effectuée de manière manuelle. +Le code hue test la proportion de pixels étant autour du pic de +teinte. Il s'agit de savoir si la dominance d'une teinte est +avérée. Le code renvoit la proportion de pixels agglomérés autour du +pic. Le but est de généralisé les tests de millets pour qu'ils se +ressemblent le plus possible. + +* exp/annotating/hue: Implémentation de la généralisation du test de Millet. +* mln/fun/v2v/rgb_to_hue_map.hh : Construction de la map de teinte. + + +On retrouve les trois fichiers permettant de classifier la base ICDAR +en trois sous populations. Le test de saturation consiste simplement à +regarder si une certaine quantité de la population de l'histogramme de +saturation est en dessous d'un certain seuil. La généralisation du +test ne porte pas sur le test en lui-même, mais sur la forme dans +lequel le test est fait. + +* exp/annotating/saturation: Implémentation de la généralisation du test. +* mln/fun/v2v/rgb_to_saturation_map.hh : Construction de la map de saturation. + + +Le test de value a déjà été décrit précédemment dans +stddev_color. L'idée est toujours la même, mais cette fois il est +effectué dans l'espace des valeurs (HSV). Cela ne change pas grand +chose, puisqu'il était utilisé sur des images en niveau de gris. C'est +l'un des tests importants car il réagit à la différentiation entre une +image type photographie et une image plus stylisée comme un +clipart. Ce test a aussi des vertus pour la distinction entre du noir +& blanc et de la couleur. Il s'avère que les images type photographie +avec pleins de couleurs ont un histogramme moins sujet aux pics que +les histogrammes noir & blanc. De facto, l'énergie de l'histogramme +est distribué sur l'ensemble de la plage contrairement aux images noir +& blanc où il y a une concentration de chaque côté de l'histogramme +(bipolarité). + +* exp/annotating/value: Implémentation de la généralisation du test de Millet. +* mln/fun/v2v/rgb_to_value_map.hh : Transformation d'espace. + + +Le programme hsv reprend les tests préalablement élaborés auparavant +sur les plans H, S puis V. Il combine tout en un seul programme pour +avoir une vision plus synthétique de ce qui se passe sur les 3 espaces +simultanément. + +Les tests incorpore mes transformations. C'est à dire que l'on +effectue une série de test équivalent à ceux de Millet (au moins dans +l'idée et le plus souvent, il s'agit d'une réecriture sous une autre +forme) en partant des histogrammes normalisés. Un histogramme +normalisé est un histogramme de flottant qui contient l'histogramme +classique divisé par le nombre total de pixels. En faisant cela, on se +déplace dans l'espace des distributions. Si l'histogramme n'a qu'un +seul pic et que ce dernier contient tous les pixels (pic de dirac par +exemple), alors l'histogramme normalisé donnera 1 comme valeur de +proportion à ce pic. En fait, l'intégrale d'une distribution vaut +1. Le but des distributions est de s'affranchir des caractéristiques +de taille de l'image, ce qui permet de comparer les histogrammes entre +eux. On peut ainsi comparer les histogrammes d'une photo AFP petit +format avec celui d'une image grand format de la base ICDAR. Le but du +jeu est de garder les idées de Millet dans ce nouvel espace. Les tests +obtenus sont équivalents mais pas identiques. + +Pour la teinte, une fois l'histogramme normalisé obtenu, on cherche à +savoir s'il existe un pic avec une forte proportion de l'histogramme +autour de ce pic. Plutôt que le pic, nous prenons la moyenne qui est +un opérateur un peu plus robuste et nous calculons la proportion de +l'histogramme autour (un seuil donné en paramètre défini le périmètre +du calcul). L'expression sous forme de proportion plutôt que sous la +forme d'une variance rend le test homogène avec les autres tests. + +Pour la saturation, après normalisation, on regarde la proportion de +l'histogramme en dessous d'un certain seuil. Il n'y a pas de +changement significatif par rapport au test de Millet. Le seuil +initialement proposé par Millet était 100, il a été adapté car nos +bases sont différentes des siennes (le notre est à 25). + +Le test sur les valeurs (cliparts contre photos) a vraiment un intérêt +au delà de ce cas d'utilisation. Il nous renseigne sur la +concentration autour d'un pic de l'histogramme ou non. Nous préferrons +la mesure de similarité à l'équi-répartition des densités, mais l'idée +est exactement la même. Est-ce que notre histogramme est plat ou +inversement, est-ce que les contributions sont rassemblées autour d'un +pic? Si une image est noir et blanc, il existera un pic correspondant +au fond et la densité s'éloignera fortement de la distribution +équiprobable. Dans le cas maintenant d'une image couleur, la +répartition est plus homogène, couvrant un large spectre dans le +domaine de l'histogramme. Du coup, la distribution semblera davantage +equi-répartie. On notera que nous prenons le test à l'envers de ce que +propose Millet. Il essaye de voir si il y a un pic et calcule une +forme de contribution normalisée autour de ce pic. Nous au contraire, +on regarde l'absence de pic et on calcule la différence entre la +densité et cette absence de pic. Notre avantage par rapport à Millet +est démontré particulièrement dans les cas où il existe plusieurs +grosses distributions. A contrario, notre test souffre des cas où il +existe de nombreuses petites distributions. + + +Voici un premier retour sur les expériementations: +La discrimination entre la base AFP et la base ICDAR peut se faire en +étudiant la forme des densités des niveaux de gris. Les images +naturelles semblent avoir un spectre recouvrant en général les 256 +niveaux de gris alors que les images de documents ont une présence +importante du fond. Dans le cadre d'une densité, ce qui est alloué sur +le fond ne peut se retrouver ailleurs. Une comparaison avec la densité +équiprobable nous renseigne donc sur la nature des images. Il semble +néanmoins qu'un certain nombre d'images défient ce dispositif. Par +exemple des gros plans sur des zones mono-teintée (ski, voile,site +web). + +* exp/annotating/hsv: Code unifiant les trois tests sur chacun des plans HSV. +* mln/fun/v2v/rgb_to_hue_map.hh : Transformation d'espace. +* mln/fun/v2v/rgb_to_saturation_map.hh : Transformation d'espace. +* mln/fun/v2v/rgb_to_value_map.hh : Transformation d'espace. + + +Le test sur l'achromatisme des images est décrit dans le code +suivant. Il a été purement et simplement abandonné dans la mesure où +c'est un cas très particulier qui est repris par une saturation très +faible. La saturation s'exprime comme 1 - min(channel)/max(channel), +mais dans le cas où le min(channel) == max(channel), la saturation +vaut 0. Le problème vient plutôt du calcul de la teine qui ne peut pas +admettre que le min soit égal au max. Pour ce calcul, on se débrouille +pour gérer le cas et renvoyer une valeur standardisée, Millet +proposait -1. + +* exp/annotating/achromatism: Détection d'image couleur en niveau de gris. +* mln/fun/v2v/rgb_to_achromatism_map.hh : Transformation d'espace. + + +Ce programme a pour but de créer les histogrammes normalisés en rafale +pour les bases AFP et ICDAR. Il assure leur création dans les six +plans possibles R,G,B,H,S,V. De cette manière il est possible de +vérifier les corrélations éventuelles entre le canal B et G. Par +ailleurs, après visionnement de tous les histogrammes, on note des +spécificités dans les deux bases sur les plans S et V. Certaines +images ont des réactions très fortes sur le plan H, mais ce n'est pas +une caractéristique pour une base (seulement pour ces images en +question). Les résultats ont déjà été sauvegardés dans le répertoire +image du LRDE. Des infos sont notés à ce sujet dans README.result. Le +seul but de ce calcul est de maîtriser csujet dans README.result. Le +seul but de ce calcul est de maîtriser ce qui se passe dans ces +espaces, pas simplement de supposer ce qu'il pourrait s'y passer. La +comparaison des images est rendu possible quelque soit la base +d'origine par le fait qu'elles sont normalisées. Chaque fréquence des +histogrammes est divisée par la somme des fréquences. On obtient donc +une version discrète des densitées. La somme des nouvelles fréquences +vaut 1. On voit davantage si les densités se rapprochent d'une +équi-répartition ou non. La négative implique un ou plusieurs pics. Le +cas défavorable dans mon approche (qui existe pour certaines images) +est une multitude de tout petits pics autour de l'équi-répartition. Ce +n'est pas équi-répartie pour les calculs mais ce n'est clairement pas +la manisfestation d'une concentration de l'énergie quelque part. + +* exp/annotating/histo: Creation des histogrammes normalisés. +* mln/fun/v2v/rgb_to_hue_map.hh: Transformation d'espace. +* mln/fun/v2v/rgb_to_saturation_map.hh: Transformation d'espace. +* mln/fun/v2v/rgb_to_value_map.hh: Transformation d'espace. + + +Dans le programme erreur, on teste l'idée de jonathan. Une référence à +ce sujet peut être trouvée dans README.img. Les bases AFP et ICDAR +sont retravaillées à l'aide de ImageMagick et de Gimp pour diminuer le +nombre de couleurs initiales. On oblige à n'avoir que 30, 20 ou 10 +couleurs pour chaque base. Il y a donc 4 versions de chacune des bases +en comptant la version originale des images. Les algorithmes utilisés +par Gimp et ImageMagick sont très différents. L'idée de Jonathan était +de dire que lorsqu'une image est couleur, plus on réduit le nombre de +couleur, plus elle change vis-à-vis de l'image originale. Inversement, +si une image possède peu de couleurs (noir & blanc), sa dégradation ne +l'altérera pas tant que cela. Prenons une image d'un skieur de la base +AFP, en réduisant le nombre de couleurs, la combinaison marron va +devenir marron strictement homogène et la perception que nous en avons +est visuellement très altérée. A contrario, une image en noir & blanc +ne semble pas bouger d'un pouce. La comparaison avec la discrimination +sur la saturation ou la valeur montrera des résultats un peu meilleur +plus tard mais il impossible de préjuger a priori des résultats +futurs. L'erreur entre l'image initiale et l'image dégradée est +calculée avec le PNSNR (compression p278 Handbook Color). Le programme +calcule la dégradation et essaie de trouver automatiquement des seuils +de séparation pour les deux bases. 4 détection de seuils sont testées. +Ces seuils sont calculées avec deux bases représentatives des bases à +discriminer. En mode production, le seuil est vu comme une constante +pour le programme. Les deux premières classifications renvoient un +seuil calculée comme une moyenne pondérée des moyennes des populations +à discriminer. Le premier détecteur pondère par la déviation standard +et le second par la variance. Le troisième simule deux populations +gaussiennes de variances différentes et résoud l'équation de second +degré qui en résulte. Enfin, le dernier test ne préjuge pas des +distributions statistiques et réalise la minimisation de l'erreur de +classification. Pour cela, il suffit de compter le nombre d'images +bien classées et mal classées sur les bases d'apprentissage des +populations. On utilise pour cela la méthode Otsu. Le détecteur PNSRN +renvoie une valeur. Pour chaque population (AFP, ICDAR), on construit +l'histogramme de des valeurs obtenues. A noter, qu'il faut que les +valeurs du PNSNR soient ramenées entre 0 et 255. En pratique ce n'est +pas un problème, mais il faut le mettre en oeuvre, dans mon code, ce +n'est pas fait (les valeurs ne posaient pas de problème). Donc, pour +chaque seuil possible (de 0 à 256), on étudie la matrice de classement +(groupe 1 - détecté groupe 1, groupe 1 - détecté groupe 2, groupe 2 - +détecté groupe 1, groupe 2 - détecté groupe 2). L'erreur est +simplement la somme des quantités groupe x - détecté groupe y avec x +!= y. Finalement, pour chaque seuil, il est possible de connaitre +l'erreur. Le processus de minimisation de l'erreur revient à chercher +le seuil associé à l'erreur minimale. C'est ce dernier test que nous +préconisons. Il faut mettre en lumière que ces quatre tests renvoient +à peu près les mêmes seuils. La minimisation de l'erreur étant le +meilleur de tous puisqu'il minimise directement l'erreur de +classification et c'est ce que nous cherchions en fin de compte. Un +cinquième test a été fait avec une analyse de fisher sur l'histogramme +des populations mélangées, mais c'est une idée saugrenue car nous +disposons à ce niveau des populations séparées et évidemment les +résultats sont moins bons. + +Petit rappel. Un vieux problème a été mis à jour ici. Lorsqu'on +calcule des informations sur un histogramme, le type temporaire qui +contient les résultats ne doit pas être le type qui encode les valeurs +des résultats. Par exemple, un histogramme de byte, un calcul de +variance tout simple où il faut stocker la valeur des pixels au carré +multiplié par leur occurrence tiendra facilement dans un long mais pas +dans un byte. Le sucre avec les acesseurs des itérateurs tend à +mélanger les genres. Une solution simple est de stocker la valeur de +l'histogramme dans un type pouvant effectuer les calculs et ensuite +retravailler avec ce type. Nous avions déjà discuté de ce problème et +je ne sais pas si tu avais pu corriger le problème. Si la +classification renvoit n'importe quoi, il se peut que cela provienne +de ce problème. Le problème ne se voit pas sur des calculs de +moyennes, il ne s'observe que sur certains calculs de variance (dépend +de la formule utilisée). + +* exp/annotating/error: Test de l'idée de jonathan (dégradation des couleurs) + + +Le travail dans bench.cc reprend toute sorte de travaux déjà réalisés +dans d'autres fichiers. Il a pour but de comparer ensemble tous les +descripteurs pour la reconnaissance de base de données entre l'AFP et +l'ICDAR. + +Le travail commence avec un certain nombres de routines travaillant +sur les histogrammes permettant de trouver le pic, la moyenne, la +variance et d'autres éléments. Les routines étaient éparpillées dans +le code, du coup je les ai regroupées au même endroit. Il y a des +redondances de code correspondant à des copier/coller ou à différents +essais. Prendre les versions les plus génériques et en faire des +accumulateurs serait un riche idée. + +Huit détecteurs sont comparés ensembles: +- hue1, détection d'un pic de teinte très dense par la méthode de Millet. +- sat1, détection d'une densité importante dans les saturations basses (Millet). +- lvl0, comptage du nombre de niveau de gris (idée de Millet). +- hue0, détection d'un pic de teinte par la méthode des densités. +- sat0, détection d'une forte basse saturation par la méthode des densités. +- val0, détection d'un pic de niveau de gris par la méthode des densités. +- val1, détection d'un pic de niveau de gris par la méthode de Millet. +- err, PNSNR (idée de jonathan). + +FIXME: Attention, le PNSNR n'est pas borné, il faut le faire, il doit avoir +au maximum 255 comme valeur. + +Tous les détecteurs ont été expliqués en large, en long et en travers +dans les sources précédentes. LIRE le chapitre XII (ANNOTATING) de ce fichier +en entier. + +Ensuite vient les séparateurs de population statistiques qui ont été +déjà introduit dans le fichier error. Bien que tous soient présent, +c'est la minimisation de l'erreur qui est utilisée. + +Enfin vient le front end, l'un des plus complexes que j'ai écrit cette +année. Le main lui même, n'est pas la partie la plus complexe. Il +définit les actions fonctionnelles à réaliser et la structure de +donnée réalisée pour garder la trace de tous les résultats. +- File_name : Cette variable retient le nom des fichiers. C'est un tableau +de la taille du nombre d'images rencontrées toute base confondue. Comme on +ne parcourt qu'une fois les répertoires, il faut pouvoir retenir cette +information qui servira éventuellement pour la nomenclature des dumps +par la suite. +- Result : Cette variable contient toutes les informations du traitement. +C'est un tableau à deux dimensions, une pour les images et une autre pour +les descripteurs. L'index d'image est le même que pour file_name. +- Size : Cette variable contient le nombre d'images par database. +- Threshold : Cette variable va stocker les seuils calculés sur chacun +des descripteurs. Ces seuils sont sensés effectuer la séparation entre les +deux bases. +- Cxx : variables de comptage des images bien ou mal classées relativement +à la position des seuils et à l'appartenance d'origine des images. Cette +appartenance d'origine est calculée à l'aide Size qui contient le nombre +d'image par base de données. Comme les images sont vues dans l'ordre, les X +premières appartiennent à la base ICDAR et les Y suivantes à la base AFP. +- histo : Variable servant à effectuer par database et par descripteur un +histogramme utilisé ensuite lors de la discrimination des bases. + +La partie compute_descriptors a pour mission de passer une fois sur +toutes les images et de calculer les descripteurs associés. A l'issue +de cette passe file_name, result et size sont remplis et pourront être +utilisés par les autres routines. + +La partie dump_descriptors a pour but de réaliser un tracé en gnuplot, +avec une couleur différente pour chaque base. Le graphe montre les +valeurs utilisées par chaque descripteur pour toutes les images de la +base. + +La partie correction des descripteurs est optionnelle et force un +certain nombre de valeurs pour être inférieures à 256. Le but était de +pouvoir travaillé, même si les descripteurs n'étaient pas complètement +opérationnels. Elle sert donc pour le debug. + +La partie calcul des histogrammes sert à obtenir la distribution des +valeurs de chacun des descripteurs en fonction des bases +utilisées. C'est une étape préliminaire à l'analyse des populations et +au calcul des seuils. + +La partie calcul des seuils repose sur la minimisation des erreurs de +classification. Cette méthode ne fonctionne bien si la classe zéro à +une moyenne inférieure à la classe un. Du coup, il n'est pas possible +de déterminer de manière simple si la classe 0 correspond à l'AFP ou à +l'ICDAR. Pour chaque descripteur, les cartes sont rebattues. + +Enfin, tous les histogrammes sont sauvés pour pouvoir comprendre et +visualiser les résultats. + +Le main2 ne sert à rien, juste pour des essais. + +Attention, modification VAL0/VAL1, vérifiez que le bon descripteur est +au bon endroit dans le tableau. + +* exp/annotating/bench: Comparaison des détecteurs pour la classif. ICDAR/AFP. \ No newline at end of file diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/bic/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/bic/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/bic/bic.cc b/scribo/sandbox/green/demo/annotating/bic/bic.cc new file mode 100644 index 0000000..f63d260 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/bic/bic.cc @@ -0,0 +1,122 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet BIC descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It quantifies the three channels on 3 +/// bits, then builds two histograms with the interior pixels and the +/// exterior ones. The interior pixels are those where color value is +/// the same as their 4 neighbouring pixels. The descriptor is the +/// fusion of the two histograms. + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/image/image2d.hh> +#include <mln/core/alias/neighb2d.hh> +#include <mln/core/routine/initialize.hh> +#include <mln/core/var.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/transform.hh> +#include <mln/data/fill.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/colors.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Main entry. +/// +/// Load the images, compute the interior and the exterior with the +/// predicate, then compute the two histograms separately. +int main() +{ + typedef mln::value::rgb<3> t_rgb3; + typedef mln::value::rgb8 t_rgb8; + typedef mln::neighb2d t_neighb2d; + typedef mln::image2d<t_rgb3> t_image2d_rgb3; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<3> t_rgb8_to_rgb3; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb3 input_rgb3; + t_image2d_rgb3 interior_rgb3; + t_image2d_rgb3 exterior_rgb3; + t_histo3d histo_exterior; + t_histo3d histo_interior; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + input_rgb3 = mln::data::transform(input_rgb8, t_rgb8_to_rgb3()); + + mln::initialize(interior_rgb3, input_rgb3); + mln::initialize(exterior_rgb3, input_rgb3); + + mln::data::fill(interior_rgb3, mln::literal::black); + mln::data::fill(exterior_rgb3, mln::literal::black); + + mln_piter_(t_image2d_rgb3) p(input_rgb3.domain()); + mln_niter_(t_neighb2d) n(mln::c4(), p); + + for_all(p) + { + bool is_interior = true; + + for_all(n) + is_interior = is_interior && (input_rgb3(p) == input_rgb3(n)); + + if (is_interior) + interior_rgb3(p) = input_rgb3(p); + else + exterior_rgb3(p) = input_rgb3(p); + } + + histo_interior = mln::data::compute(t_histo3d_fun(), interior_rgb3); + histo_exterior = mln::data::compute(t_histo3d_fun(), exterior_rgb3); + + //mln::io::plot::save_image_sh(histo, "histo.sh"); + + // PRINTING PHASE + mln::debug::println(histo_interior); + mln::debug::println(histo_exterior); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/hsv/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/hsv/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/hsv/hsv.cc b/scribo/sandbox/green/demo/annotating/hsv/hsv.cc new file mode 100644 index 0000000..e87ab2d --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/hsv/hsv.cc @@ -0,0 +1,721 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet HSV operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to HSV +/// one. Formulae are classical, we can find them on the web. +/// +// Val = max(R,G,B). +// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +/// +/// \fixme: This code is unstable. It was using to make a lot of tests. +/// Don't use it, we have more stable version of it. +#include <iostream> +#include <fstream> + +#include <mln/accu/max_site.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> +#include <mln/core/concept/image.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> + +#include <mln/debug/println.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/fun/v2v/rgb_to_hsv.hh> +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> +#include <mln/fun/v2v/achromatism.hh> +#include <mln/fun/v2v/hue_concentration.hh> +#include <mln/fun/p2b/component_equals.hh> +#include <mln/fun/p2b/achromatic.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/img_path.hh> + +#include <mln/io/plot/save_image_sh.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/pw/cst.hh> +#include <mln/pw/value.hh> +//#include <mln/trace/quiet.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> +#include <mln/value/hsv.hh> + + +/// \brief Classify a rgb a value with a color of reference. +/// +/// \param[in] rgb a rgb8 value. +/// +/// \return a rgb8 initialize with the selecting literal. +/// +/// This classification of the RGB color space is based on the +/// Millet's work. It uses the hue wheel to do it and make new +/// distinction as brown/orange for instance. +mln::value::rgb8 label_color(const mln::value::rgb8 rgb) +{ + mln::value::hsv_f hsv = mln::fun::v2v::f_rgb_to_hsv_f(rgb); + + mln::value::rgb8 result; + + // Is it a gray level ? + if (0 == hsv.sat()) + { + // which result one ? + if (82 > hsv.sat()) + result = mln::literal::black; + else if (179 > hsv.sat()) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + } + // Is it a true result color ? + else if (14 > hsv.hue()) + result = mln::literal::red; + else if (29 > hsv.hue()) + { + // Is is brown or orange ? + unsigned dist_orange = mln::math::abs(hsv.sat() - 184) + + mln::math::abs(hsv.val() - 65); + + unsigned dist_brown = mln::math::abs(hsv.sat() - 255) + + mln::math::abs(hsv.val() - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else if (45 > hsv.hue()) + { + // Is it green or yellow ? + if (80 > hsv.val()) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else if (113 > hsv.hue()) + result = mln::literal::green; + else if (149 > hsv.hue()) + result = mln::literal::cyan; + else if (205 > hsv.hue()) + result = mln::literal::blue; + else if (235 > hsv.hue()) + result = mln::literal::violet; + else if (242 > hsv.hue()) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +// unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, +// const char *name_histo, +// const char *name_image) +// { +// typedef mln::point1d t_point1d; +// typedef mln::value::rgb8 t_rgb8; +// typedef mln::value::int_u8 t_int_u8; +// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +// typedef mln::image2d<t_int_u8> t_image2d_int_u8; +// typedef mln::image1d<unsigned> t_histo1d; +// typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; +// typedef mln::accu::meta::stat::histo1d t_histo1d_fun; +// typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + +// t_histo1d histo; + +// std::cout << "histo : " << name_histo << std::endl; +// std::cout << "image : " << name_image << std::endl; + +// histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + +// mln::io::pgm::save(input_int_u8, name_image); +// mln::io::plot::save_image_sh(histo, name_histo); +// mln::debug::println(histo); + +// // Find the peak of the histogram +// unsigned v_max = mln::opt::at(histo, 0); +// short p_max = 0; + +// mln_piter_(t_histo1d) p(histo.domain()); + +// for_all(p) +// { +// if (v_max < histo(p)) +// { +// v_max = histo(p); +// p_max = p.ind(); +// } +// } + +// // Compute the specific stddev + +// float stddev_low = 0.0; +// float stddev_up = 0.0; +// float stddev = 0.0; + +// if (250 > p_max) +// for (short i = p_max+1; i < p_max+6; ++i) +// stddev_up += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// if (5 < p_max) +// for (short i = p_max-1; i > p_max-6; --i) +// stddev_low += r(p_max, mln::opt::at(histo,p_max), +// i, mln::opt::at(histo,i)); + +// stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : +// (stddev_low + stddev_up)/2; + +// std::cout << "max_site : " << p_max << std::endl; +// std::cout << "h(max_site) : " << v_max << std::endl; +// std::cout << "stddev_up : " << stddev_up << std::endl; +// std::cout << "stddev_low : " << stddev_low << std::endl; +// std::cout << "stddev : " << stddev << std::endl; + +// return 0; +// } + + +// ------------------------------------- +// input image <name>.ppm +// map <name>-<map>.pgm +// thresholded map <name>-<map>.pbm +// histogram <name>-<map>.sh +// decision <name>-<map>.txt +// ------------------------------------- +// Achromatism <name>-achromatism.pgm +// call achromatism(input_rgb8, 7, 99.0) + + +/// \brief Decide if an image is achromatic or not [Millet]. +/// +/// \param[in] input_rgb8 the input image. +/// \param[in] threshold the distance threshold used for equality. +/// \param[in] percentage the percentage of pixels that very the test. +/// +/// This is an improving of the Millet test. The original test +/// compares rgb values to each other and look at differences greater +/// than the threshold. If the number of pixel that pass the test are +/// greater than 99.0, then the image is declared achromatic. In fact, +/// there is few variations between the three channels, so we can say +/// that it is like a grey image. We can with no too distortions +/// replace the color image by the grey one. The improving is in first +/// creating a map of the difference. As we can't keep the free +/// differences between channels, we look at reducing the test and we +/// find an equivalent one based on the difference between minima +/// channel value and the maxima channel value. After the map is +/// ready, we make binarization with the threshold. Then we compute +/// the histogram 1d for every pixels of the map that are greater the +/// threshold. Then, we count pixels in the histogram and traduce the +/// count in percentage to compare to the second threshold. Details +/// are saved in files and printed in the screen. +void achromatism(mln::image2d<mln::value::rgb8> input_rgb8, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::int_u8> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + view = mln::data::stretch(mln::value::int_u8(), map); + mask = mln::binarization::threshold(map, threshold); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + map | (mln::pw::value(mask) == true)); + cnt1 = count_histo(histo); + cnt2 = mln::geom::nsites(input_rgb8); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + + std::ofstream txt_stream("achromatism.txt"); + txt_stream << "Achromatism" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + txt_stream << "Percentage : " << prop << std::endl; + txt_stream << "Image achromatique : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "achromatism.pgm"); + mln::io::plot::save_image_sh(histo, "achromatism.sh"); + mln::io::pbm::save(mask, "achromatism.pbm"); +} + + +/// \brief Decide if an image is low saturate or not and so b/w [Millet]. +/// +/// \param[in] input_hsvf the input image in the HSV space. +/// \param[in] achromatism_mask the mask to prevent computing bad saturation. +/// \param[in] threshold the distance threshold used for equality. +/// \param[in] percentage the percentage of pixels that very the test. +/// +/// As we are in the HSV space, we can just isolate saturation +/// channel. The original idea of Millet is to build histogram bound +/// to 256 for the saturation. If a great percentage (95%) of the +/// population are under a threshold (100), then the image has got low +/// saturation. So, it is b/w image. Low saturation is sometimes +/// redundant with the achromatism test but not every time. + +// call low_saturation(input_rgb8, achromatism_mask, 100, 95.0) +void low_saturation(mln::image2d<mln::value::hsv_f> input_hsvf, + mln::image2d<bool> achromatism_mask, + mln::value::int_u8 threshold, + float percentage) +{ + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + + mln::image2d<t_sat> map; + mln::image2d<mln::value::int_u8> view; + mln::image2d<bool> mask; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + bool result; + + + map = mln::data::transform(input_hsvf, t_component_s()); + view = mln::data::stretch(mln::value::int_u8(), map); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), + view|(mln::pw::value(achromatism_mask) == true)); + prop = (100.0 * (cnt2 - cnt1) / cnt2); + result = (prop > percentage); + + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0),mln::point1d(100))); + cnt2= mln::geom::nsites(achromatism_mask| + (mln::pw::value(achromatism_mask)==false)); + + std::ofstream txt_stream("saturation.txt"); + txt_stream << "Saturation" << std::endl; + + txt_stream << "Nbre pixels : " << cnt2 << std::endl; + txt_stream << "Nbre p faiblement saturés : " << cnt1 << std::endl; + txt_stream << "Pourcentage : " << prop << std::endl; + txt_stream << "Image faiblement saturé : " << result << std::endl; + txt_stream << std::endl; + + txt_stream.flush(); + txt_stream.close(); + + mln::io::pgm::save(view, "saturation.pgm"); + mln::io::plot::save_image_sh(histo, "saturation.sh"); + mln::io::pbm::save(mask, "saturation.pbm"); +} + +/* This is a classification of the ICDAR base in 3 modalities. +// COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00032c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00042c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00076c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00142c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00215c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00228c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00234c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00248c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00252c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00253c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00255c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00259c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00271c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00290c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00293c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00304c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00307c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00376c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00411c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00419c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00447c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00498c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00510c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00550c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00573c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00589c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00592c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00597c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00599c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00600c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00031c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00034c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00043c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00063c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00065c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00072c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00081c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00083c_20p.ppm"); + +// BLACK AND WHITE + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00329c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00036c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00037c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00039c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00040c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00049c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00055c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00057c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00068c_20p.ppm"); + + +// A LITTLE BIT OF COLOR + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00262c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00263c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00311c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00319c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00440c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00608c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00630c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00631c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00028c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00046c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00073c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00089c_20p.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/ta00090c_20p.ppm"); +*/ + + +/// \brief The main entry and the whole processing routine +/// +/// This is the routine which works on Millet test. First compute the +/// achromatic mask. Then build the transfer between RGB and HSV. Then +/// isolate each channel to work on it. Each separate channel is bound +/// between 0 and 255. Then keep only pixels low saturated (< 100 in S +/// canal) in a specefic map. Build the 3 histograms without the +/// achromatic pixels based on the 3 separated channels (H/S/V). Build the +/// 3 maps (hue_concentration, low_saturation and achromatism). Then do +/// the 3 tests of Millet with the thow thresolded pass (threshold,percentage). +int main() +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::hsv_f t_hsvf; + typedef mln::value::hsv_f::h_type t_hue; + typedef mln::value::hsv_f::s_type t_sat; + typedef mln::value::hsv_f::v_type t_val; + typedef mln::image2d<t_hue> t_image2d_hue; + typedef mln::image2d<t_sat> t_image2d_sat; + typedef mln::image2d<t_val> t_image2d_val; + typedef mln::image2d<t_hsvf> t_image2d_hsvf; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<bool> t_mask; + typedef mln::fun::v2v::f_rgb_to_hsv_f_t t_rgb8_to_hsv; + typedef mln::accu::math::count<t_hsvf> t_count; + typedef mln::fun::v2v::component<t_hsvf,0> t_component_h; + typedef mln::fun::v2v::component<t_hsvf,1> t_component_s; + typedef mln::fun::v2v::component<t_hsvf,2> t_component_v; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,0> t_component_eq0; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,1> t_component_eq1; + typedef mln::fun::p2b::component_equals<t_image2d_hsvf,2> t_component_eq2; + typedef mln::fun::p2b::achromatic<t_rgb8> t_achromatic; + + t_image2d_rgb8 input_rgb8; + t_image2d_hsvf input_hsvf; + + t_mask achromatic; + t_mask low_saturation; + + t_image2d_float achromatism1; + t_image2d_int_u8 achromatism2; + t_image2d_float hue_concentration1; + t_image2d_int_u8 hue_concentration2; + + t_image2d_hue input_h; + t_image2d_hue input_h2; + t_image2d_sat input_s; + t_image2d_val input_v; + + t_image2d_int_u8 input_h8; + t_image2d_int_u8 input_s8; + t_image2d_int_u8 input_v8; + + t_histo1d histo_h; + t_histo1d histo_s; + t_histo1d histo_v; + + unsigned cnt1; + unsigned cnt2; + float percentage; + bool result; + + + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; +// mln::io::ppm::load(input_rgb8, ANNOTATING_1_BILL_IMG_PATH"/bill03.ppm"); + mln::io::ppm::load(input_rgb8, ICDAR_20P_PPM_IMG_PATH"/mp00082c_20p.ppm"); + + +// achromatism(input_rgb8,7,99.0); +// exit(-1); + + // REPERAGE DES PIXELS ACHROMATICS + std::cout << "Init achromatic mask ..." << std::endl; + initialize(achromatic, input_rgb8); + mln::data::fill(achromatic, false); + mln::data::fill((achromatic | t_achromatic(input_rgb8, 0.03)).rw(), true); + + mln::io::pbm::save(achromatic, "achromatic.pbm"); + + std::cout << "Achieve canal forking ..." << std::endl; + input_hsvf = mln::data::transform(input_rgb8, t_rgb8_to_hsv()); + input_h = mln::data::transform(input_hsvf, t_component_h()); + input_s = mln::data::transform(input_hsvf, t_component_s()); + input_v = mln::data::transform(input_hsvf, t_component_v()); + + // quid of achromatic pixels ??? + input_h8 = mln::data::stretch(t_int_u8(), input_h); + input_s8 = mln::data::stretch(t_int_u8(), input_s); + input_v8 = mln::data::stretch(t_int_u8(), input_v); + + // IDENTIFY LOW SATURATED PIXELS + std::cout << "Init low saturation mask ..." << std::endl; + initialize(low_saturation, input_s8); + mln::data::fill(low_saturation, false); + mln::data::fill((low_saturation|(mln::pw::value(input_s8) < + mln::pw::cst(100u))).rw(), true); + + mln::io::pbm::save(low_saturation, "low_saturation.pbm"); + + std::cout << "Compute histograms ..." << std::endl; + histo_h = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_h8|(mln::pw::value(achromatic)==false)); + + histo_s = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_s8|(mln::pw::value(achromatic)==false)); + + histo_v = mln::data::compute(mln::accu::meta::stat::histo1d(), + input_v8|(mln::pw::value(achromatic)==false)); + + + // Study the maps + hue_concentration1=mln::data::transform(input_h, + mln::fun::v2v::hue_concentration(histo_h)); + achromatism1=mln::data::transform(input_rgb8,mln::fun::v2v::achromatism()); + + hue_concentration2= mln::data::stretch(t_int_u8(), hue_concentration1); + achromatism2= mln::data::stretch(t_int_u8(), achromatism1); + + mln::io::pgm::save(achromatism2, "achromatism_map.pgm"); + mln::io::pgm::save(hue_concentration2, "hue_concentration_map.pgm"); + mln::io::pgm::save(input_s8, "saturation_map.pgm"); + +// cnt1 = mln::data::compute(t_count(), +// (input_hsvf|t_component_eq0(input_hsvf,-1)).rw()); + + + // (I) ACHROMATISM + std::cout << "Achromatism" << std::endl; + cnt1 = count_histo(histo_h); + cnt2 = mln::geom::nsites(input_h); + + percentage = (100.0 * (cnt2 - cnt1) / cnt2); + result = percentage > 99.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels achromatiques : " << (cnt2-cnt1)<< std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image achromatique : " << result << std::endl; + std::cout << std::endl; + + // (II) LOW SATURATION + std::cout << "Saturation" << std::endl; + + cnt1 = count_histo(histo_s | mln::box1d(mln::point1d(0),mln::point1d(100))); + + cnt2= mln::geom::nsites(achromatic | (mln::pw::value(achromatic)==false)); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre p faiblement saturés : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image faiblement saturé : " << result << std::endl; + std::cout << std::endl; + + // (III) HIGH HUE CONCENTRATION + mln::debug::println(histo_h); + unsigned peak = peak_histo(histo_h); + + cnt1 = count_histo(histo_h | mln::box1d(mln::point1d(peak-20), + mln::point1d(peak+20))); + + cnt2= count_histo(histo_h); + + percentage = (100.0 * cnt1 / cnt2); + result = percentage > 95.0; + + std::cout << "Position du pic : " << peak << std::endl; + std::cout << "Nbre pixels : " << cnt2 << std::endl; + std::cout << "Nbre pixels proches pic : " << cnt1 << std::endl; + std::cout << "Percentage : " << percentage << std::endl; + std::cout << "Image fortement teintée : " << result << std::endl; + std::cout << std::endl; + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/lep/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/lep/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/lep/lep.cc b/scribo/sandbox/green/demo/annotating/lep/lep.cc new file mode 100644 index 0000000..981a369 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/lep/lep.cc @@ -0,0 +1,127 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet LEP descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works on grey level image. First, +/// Sobel is applied on the image. Then a specific window is build to +/// be convolved on the image. The aim of the convolution is to mark +/// the configuration of the neighbouring for this thresholded image. +/// Histogram of the configuration is then created and that's all. + +#include <iostream> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/w_window2d_int.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/linear/ch_convolve.hh> +#include <mln/linear/convolve.hh> +#include <mln/linear/sobel_2d.hh> + +#include <mln/make/w_window2d.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> + +#include <mln/core/var.hh> + +/// \brief Main entry. +/// +/// Load the pgm image. Threshold it. Identifie the pixel +/// configuration with specific convolution filter. Build 1d histogram. + +int main() +{ + using namespace mln; + + typedef mln::w_window2d_int t_win2d; + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::int_u<9> t_int_u9; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image2d<t_int_u9> t_image2d_int_u9; + typedef mln::image2d<int> t_image2d_int; + typedef mln::image2d<unsigned> t_image2d_unsigned; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image2d<double> t_image2d_double; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + + t_image2d_int_u8 input_int_u8; + t_image2d_int input_int; + t_image2d_unsigned input_unsigned; + t_image2d_float sobel; + t_image2d_bool thresh_bool; + t_image2d_int_u8 thresh_int_u8; + t_image2d_float conf_float; + t_image2d_int_u9 conf_int_u9; + t_histo1d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::pgm::load(input_int_u8,ICDAR_50P_PGM_IMG_PATH"/mp00082c_50p.pgm"); + + sobel = mln::linear::sobel_2d_l1_norm(input_int_u8); + + //mln::io::plot::save_image_sh(sobel, "sobel.sh"); + thresh_bool = mln::binarization::threshold(sobel, 100); + thresh_int_u8 = mln::data::convert(mln::value::int_u8(), thresh_bool); + //mln::io::plot::save_image_sh(thresh, "thresh.sh"); + + int ws[] = { 1, 2, 4, + 8, 256, 16, + 32, 64, 128 }; + + t_win2d win2d = mln::make::w_window2d(ws); + conf_float = mln::linear::convolve(thresh_int_u8, win2d); + conf_int_u9 = mln::data::convert(t_int_u9(), conf_float); + histo = mln::data::compute(t_histo1d_fun(), conf_int_u9); + + mln::io::plot::save_image_sh(histo, "histo.sh"); + + // PRINTING PHASE + mln::debug::println(histo); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/nb_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/nb_color/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc b/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc new file mode 100644 index 0000000..acc64e9 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/nb_color/nb_color.cc @@ -0,0 +1,143 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Compute the number of colors in an image. +/// +/// Build histogram of colors and count the bins different from zero. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/math/sum.hh> +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo3d_rgb.hh> +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/variance.hh> + +#include <mln/algebra/vec.hh> + +#include <mln/arith/diff_abs.hh> + +#include <mln/core/macros.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/labeling/regional_maxima.hh> +#include <mln/labeling/mean_values.hh> +#include <mln/labeling/compute.hh> + +#include <mln/literal/colors.hh> + +#include <mln/morpho/opening/volume.hh> +#include <mln/morpho/elementary/dilation.hh> + +#include <mln/opt/at.hh> + +#include <mln/pw/cst.hh> + +#include <mln/util/array.hh> +#include <mln/util/timer.hh> + +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> +#include <mln/value/rgb.hh> +#include <mln/value/int_u.hh> + +/// \brief Count the colors. +/// +/// \param[in] image the name of the image to process. +/// +/// \return the number of colors. +/// +/// Count the color by building histogram. Quantification is done to +/// reduce the size of the histogram. +/// +/// \fixme: Strange compilation warning, I don't know how to solve it! + +// n < 8, n is the degree of quantification +template <unsigned n> +unsigned count_image_color(const std::string& image) +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::meta::math::count t_count_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgbn input_rgbn; + t_image2d_rgbn output_rgbn; + t_histo3d histo; + t_histo3d opened; + + mln::io::ppm::load(input_rgb8, image.c_str()); + +// unsigned nb_pixel = input_rgb8.ncols() * input_rgb8.nrows(); +// unsigned min_volume = (unsigned)(nb_pixel * 0.054); + unsigned nb_color = 0; + + input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); + histo = mln::data::compute(t_histo3d_fun(), input_rgbn); + nb_color = mln::data::compute(t_count_fun(), + (histo | (mln::pw::value(histo) != 0)).rw()); + + return nb_color; +} + + +/// \brief Main entry. +/// +/// Print the number of colors. +int main() +{ + unsigned val=count_image_color<8>(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + + std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/project/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/project/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/project/project.cc b/scribo/sandbox/green/demo/annotating/project/project.cc new file mode 100644 index 0000000..4cab73c --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/project/project.cc @@ -0,0 +1,275 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet PROJECT descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works a grey level image. First, +/// we make subsampling of the image to the size 100x100. Then Sobel +/// is applied. After that, we divide the image in 2 sub-images with +/// horizontal or vertical splitting. Projection (summing along a +/// direction) is done in a way that preserves a vector of size +/// 100. Finally, it results 4 vector of size 100 which are concatened +/// to build the descriptor. + +#include <iostream> +#include <sstream> + +#include <mln/accu/image/init.hh> +#include <mln/accu/image/take.hh> +#include <mln/accu/image/to_result.hh> +#include <mln/accu/stat/mean.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/box2d.hh> +#include <mln/core/alias/point2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> +#include <mln/core/image/dmorph/unproject_image.hh> + +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/paste.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/projection.hh> + +#include <mln/img_path.hh> + +#include <mln/io/pgm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/pbm/save.hh> + +#include <mln/linear/sobel_2d.hh> + +#include <mln/opt/at.hh> + +/// \brief get the sub-image name. +/// +/// \param[in] base the common part of the name. +/// \param[in] i the column. +/// \param[in] j the line. +/// +/// \result the complete name of the sub-image. +/// +/// This function builds the complete name of a sub-image by making +/// explicit its position in the array. +const char *get_name(const char *base, const unsigned i, const unsigned j) +{ + std::ostringstream name; + + name << base; + name << i; + name << "_"; + name << j; + name << ".ppm"; + + return name.str().c_str(); +} + + +/// \brief Project row data. +/// +/// \param[in] img the binary image converted to u_int8. +/// +/// \ return a vector. +/// +/// This routine "eats" one dimension of the image by making a +/// projection. The projection sums the pixel along a specific +/// direction. It results a vector. +mln::image1d<mln::value::int_u8> +project_row(const mln::image2d<mln::value::int_u8>& img) +{ + typedef mln::accu::math::sum<mln::value::int_u8,mln::value::int_u8> t_sum; + typedef mln::fun::v2v::projection<mln::point2d,0> t_projection; + + mln::image1d<t_sum> img_accu(img.ncols()); + + mln::accu::image::init(img_accu); + + mln::accu::image::take(unproject(img_accu, + img.domain(), + t_projection()).rw(), + img); + + return mln::accu::image::to_result(img_accu); +} + + +/// \brief Project column data. +/// +/// \param[in] img the binary image converted to u_int8. +/// +/// \ return a vector. +/// +/// This routine "eats" one dimension of the image by making a +/// projection. The projection sums the pixel along a specific +/// direction. It results a vector. +mln::image1d<mln::value::int_u8> +project_col(const mln::image2d<mln::value::int_u8>& img) +{ + typedef mln::accu::math::sum<mln::value::int_u8,mln::value::int_u8> t_sum; + typedef mln::fun::v2v::projection<mln::point2d,1> t_projection; + + mln::image1d<t_sum> img_accu(img.nrows()); + + mln::accu::image::init(img_accu); + + mln::accu::image::take(unproject(img_accu, + img.domain(), + t_projection()).rw(), + img); + + return mln::accu::image::to_result(img_accu); +} + + +/// \brief The image processing routine. +/// +/// Describe the processing chain. First subsample the input image to +/// 100x100. This is done by splitting the image and then compute the +/// mean of the subimage. The result is a pixel of the subsample +/// image. Then apply Sobel and threshold. Then split in two +/// (vertically or horizontally), it results 4 images. Finally, make +/// the projection. +int main() +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::algebra::vec<3,float> t_vec3f; + typedef mln::algebra::vec<3,unsigned> t_vec3u; + typedef mln::image2d<float> t_image2d_float; + typedef mln::image2d<bool> t_image2d_bool; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::accu::meta::stat::mean t_mean_fun; + typedef mln::accu::math::sum<t_int_u8,t_int_u8> t_sum; + typedef mln::image1d<t_int_u8> t_image1d_int_u8; + + t_image2d_int_u8 input_int_u8; + t_image2d_float sobel; + t_image2d_bool thresh_bool; + t_image2d_int_u8 thresh_int_u8; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::pgm::load(input_int_u8,ICDAR_50P_PGM_IMG_PATH"/mp00082c_50p.pgm"); + + + // IMAGE SPLITTING PHASE + t_image2d_int_u8 subimg_int_u8(mln::box2d(mln::point2d(0,0), + mln::point2d(100,100))); + + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 100; + unsigned sz_col = (pmax.col() - pmin.col())/ 100; + + std::cout << domain << std::endl; + + // Subsampling in 100x100 + // FIXME Test that is it a subsampling, not a upsampling! + for (unsigned i = 0; i < 100; ++i) + for (unsigned j = 0; j < 100; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_int_u8 input_part_int_u8(dom); + + mln::data::paste(input_int_u8 | dom, input_part_int_u8); + //mln::io::pgm::save(input_part_int_u8, get_name("output",i,j)); + float mean = mln::data::compute(t_mean_fun(), input_part_int_u8); + t_int_u8 val = static_cast<t_int_u8>(mean); + mln::opt::at(subimg_int_u8, i,j) = val; + } + + mln::io::pgm::save(subimg_int_u8, "subimg.pgm"); + + sobel = mln::linear::sobel_2d_l1_norm(subimg_int_u8); + thresh_bool = mln::binarization::threshold(sobel, 100); + thresh_int_u8 = mln::data::convert(t_int_u8(), thresh_bool); + + mln::io::pbm::save(thresh_bool, "subimg.pbm"); + + // Define img_up, img_down, img_left, img_right + t_image2d_int_u8 img_up(mln::box2d(mln::point2d(0,0), + mln::point2d(49,99))); + + mln::data::paste(thresh_int_u8 | img_up.domain(), img_up); + + + t_image2d_int_u8 img_down(mln::box2d(mln::point2d(50,0), + mln::point2d(99,99))); + + mln::data::paste(thresh_int_u8 | img_down.domain(), img_down); + + t_image2d_int_u8 img_left(mln::box2d(mln::point2d(0,0), + mln::point2d(99,49))); + + mln::data::paste(thresh_int_u8 | img_left.domain(), img_left); + + + t_image2d_int_u8 img_right(mln::box2d(mln::point2d(0,50), + mln::point2d(99,99))); + + mln::data::paste(thresh_int_u8 | img_right.domain(), img_right); + + + // Define project_up, project_down, project_left, project_right + t_image1d_int_u8 project_up = project_row(img_up); + t_image1d_int_u8 project_down = project_row(img_down); + t_image1d_int_u8 project_left = project_col(img_left); + t_image1d_int_u8 project_right = project_col(img_right); + + mln::io::pgm::save(img_up, "up.pgm"); + mln::io::pgm::save(img_down, "down.pgm"); + mln::io::pgm::save(img_left, "left.pgm"); + mln::io::pgm::save(img_right, "right.pgm"); + + // PRINTING PHASE + + std::cout << std::endl << "UP" << std::endl; + mln::debug::println(project_up); + + std::cout << std::endl << "DOWN" << std::endl; + mln::debug::println(project_down); + + std::cout << std::endl << "LEFT" << std::endl; + mln::debug::println(project_left); + + std::cout << std::endl << "RIGHT" << std::endl; + mln::debug::println(project_right); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/rgb_64/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/rgb_64/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc b/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc new file mode 100644 index 0000000..b49f979 --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/rgb_64/rgb_64.cc @@ -0,0 +1,80 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet RGB-64 descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works by making 2 bits +/// quantification on each channel. It result a processing in RGB-64 +/// space. Then we build the color histogram. + +#include <iostream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Main entry. +/// +/// Loading, Quantifiying in two bits each channel, then building +/// color histogram. +int main() +{ + typedef mln::fun::v2v::rgb8_to_rgbn<2> t_rgb8_to_rgb2; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<2> t_rgb2; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb2> t_image2d_rgb2; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb2 input_rgb2; + t_histo3d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + input_rgb2 = mln::data::transform(input_rgb8, t_rgb8_to_rgb2()); + histo = mln::data::compute(t_histo3d_fun(), input_rgb2); + + // PRINTING PHASE + mln::debug::println(histo); +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/rgb_64_9/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/rgb_64_9/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc b/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc new file mode 100644 index 0000000..355cbfd --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/rgb_64_9/rgb_64_9.cc @@ -0,0 +1,132 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet RGB-64 descriptor [millet.phd.2008.pdf] +/// +/// This is an image descriptor. It works by making 2 bits +/// quantification on each channel. It result a processing in RGB-64 +/// space. Then we build the color histogram. + +#include <iostream> +#include <sstream> + +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/core/alias/box2d.hh> +#include <mln/core/alias/point2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/compute.hh> +#include <mln/data/paste.hh> +#include <mln/data/transform.hh> + +#include <mln/debug/println.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> + +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + +/// \brief Get the sub image name. +/// +/// \return the name of the ith/jth sub image of the array. +const char *get_name(const char *base, const unsigned i, const unsigned j) +{ + std::ostringstream name; + + name << base; + name << i; + name << "_"; + name << j; + name << ".ppm"; + + return name.str().c_str(); +} + +/// \brief Same code as rgb-64 but divide the wall image in nine. +/// +/// Adding a splitting phase for image rgb-64 code reference. +int main() +{ + typedef mln::fun::v2v::rgb8_to_rgbn<2> t_rgb8_to_rgb2; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<2> t_rgb2; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgb2> t_image2d_rgb2; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgb2 input_rgb2; + t_histo3d histo; + + // IMAGE LOADING PHASE + std::cout << "Image loading phase ..." << std::endl; + mln::io::ppm::load(input_rgb8, ICDAR_50P_PPM_IMG_PATH"/mp00082c_50p.ppm"); + + input_rgb2 = mln::data::transform(input_rgb8, t_rgb8_to_rgb2()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_rgb2.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 3; + unsigned sz_col = (pmax.col() - pmin.col())/ 3; + + std::cout << domain << std::endl; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 3; ++i) + for (unsigned j = 0; j < 3; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_rgb2 input_1o9_rgb2(dom); + + mln::data::paste(input_rgb2 | dom, input_1o9_rgb2); + mln::io::ppm::save(input_1o9_rgb2, get_name("output",i,j)); + + histo = mln::data::compute(t_histo3d_fun(), input_1o9_rgb2); + + // PRINTING PHASE + mln::debug::println(histo); + } + +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/stddev_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/stddev_color/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc b/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc new file mode 100644 index 0000000..549834f --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/stddev_color/stddev_color.cc @@ -0,0 +1,191 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +unsigned stddev_color(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + t_histo1d histo; + t_point1d max_site; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + mln::io::pgm::save(input_int_u8, "tmp.pgm"); + mln::io::plot::save_image_sh(histo, "histo.sh"); + mln::debug::println(histo); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + std::cout << "max_site : " << p_max << std::endl; + std::cout << "h(max_site) : " << v_max << std::endl; + std::cout << "stddev_up : " << stddev_up << std::endl; + std::cout << "stddev_low : " << stddev_low << std::endl; + std::cout << "stddev : " << stddev << std::endl; + + return 0; +} + + +/// \brief Main entry. +/// +/// Front end for image processing. +int main() +{ +// unsigned val = stdev_color(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + unsigned val = stddev_color(ANNOTATING_1_LOGO_IMG_PATH "/logo06.ppm"); + +// std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/annotating/stddev_color_16/Makefile.am similarity index 100% copy from scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am copy to scribo/sandbox/green/demo/annotating/stddev_color_16/Makefile.am diff --git a/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc b/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc new file mode 100644 index 0000000..98d94fd --- /dev/null +++ b/scribo/sandbox/green/demo/annotating/stddev_color_16/stddev_color_16.cc @@ -0,0 +1,261 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. When photographies +/// has some large uniform border, the detection can fail. To improve +/// the method, Millet decide to subdivise the image in 16 sub images +/// on which it applies the test. +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> +#include <mln/data/paste.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] input_int_u8 the 8 bits input image to analyze. +/// \param[in] name_histo the name of the output histogram. +/// \param[in] name_image the name of the ouput sub image. +/// +/// \return the deviation, but at this time nothing.. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +unsigned stddev_color(mln::image2d<mln::value::int_u8> input_int_u8, + const char *name_histo, + const char *name_image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_histo1d histo; + + std::cout << "histo : " << name_histo << std::endl; + std::cout << "image : " << name_image << std::endl; + + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + mln::io::pgm::save(input_int_u8, name_image); + mln::io::plot::save_image_sh(histo, name_histo); + mln::debug::println(histo); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + std::cout << "max_site : " << p_max << std::endl; + std::cout << "h(max_site) : " << v_max << std::endl; + std::cout << "stddev_up : " << stddev_up << std::endl; + std::cout << "stddev_low : " << stddev_low << std::endl; + std::cout << "stddev : " << stddev << std::endl; + + return 0; +} + + +/// \brief Divide the image in 16 sub images. +/// +/// \param[in] image the input image. +/// +/// \result nothing. +/// +/// Divive the input image in 16 by uniform and geometrical +/// method. When a sub image is ready, call the stddev routine to show +/// stats on it. +unsigned stddev_color_16(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 4; + unsigned sz_col = (pmax.col() - pmin.col())/ 4; + + std::cout << domain << std::endl; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 4; ++i) + for (unsigned j = 0; j < 4; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + std::cout << dom << std::endl; + + // Save it + t_image2d_int_u8 input_1o16_int_u8(dom); + std::ostringstream name_histo(""); + std::ostringstream name_image(""); + + name_histo << "histo" << i << "_" << j << ".sh"; + name_image << "image" << i << "_" << j << ".ppm"; + + mln::data::paste(input_int_u8 | dom, input_1o16_int_u8); + + stddev_color(input_1o16_int_u8, + name_histo.str().c_str(), + name_image.str().c_str()); + } + + return 0; +} + + +/// \brief Main entry. +/// +/// Just a front end for image processing routine. +int main() +{ +// unsigned val = stddev_color_16(ANNOTATING_1_PHOTO_IMG_PATH "/photo01.ppm"); + unsigned val = stddev_color_16(ANNOTATING_1_LOGO_IMG_PATH "/logo06.ppm"); + +// std::cout << "nb color : " << val << std::endl; + + return 0; +} + diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/achromastism/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/achromastism/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc b/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc new file mode 100644 index 0000000..3486b20 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/achromastism/achromastism.cc @@ -0,0 +1,179 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + + +/// \file +/// +/// \brief Implement the Millet achromatism operator [millet.phd.2008.pdf] +/// +/// An image is said to be achromatic when have the RGB channel which +/// are quite identicals. + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_achromatism_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Decide if an image is achromatic or not [Millet]. +/// +/// \param[in] input the input image. +/// \param[in] output the histogram of the achromatic map. +/// \param[in] threshold the distance threshold used for equality. +/// +/// This is an improving of the Millet test. The original test +/// compares rgb values to each other and look at differences greater +/// than the threshold. If the number of pixel that pass the test are +/// greater than 99.0, then the image is declared achromatic. In fact, +/// there is few variations between the three channels, so we can say +/// that it is like a grey image. We can with no too distortions +/// replace the color image by the grey one. The improving is in first +/// creating a map of the difference. As we can't keep the free +/// differences between channels, we look at reducing the test and we +/// find an equivalent one based on the difference between minima +/// channel value and the maxima channel value. After the map is +/// ready, we make binarization with the threshold. Then we compute +/// the histogram 1d for every pixels of the map that are greater the +/// threshold. Then, we count pixels in the histogram and traduce the +/// count in percentage to compare to the second threshold. Details +/// are saved in files and printed in the screen. +float achromatism_test(const std::string input, + const std::string output, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_achromatism_map<8> t_rgb_to_achromatism_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_achromatism_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + + return prop; +} + + +/// \brief The main entry. +/// +/// Deal with boost library for walking over image database +/// directories. Call the image processing routine and compute some +/// stats on the result. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + + prop = achromatism_test(dir_iter->path().string(), + output.string(), + 11); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/achromastism/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/achromastism/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/bench/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/bench/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/bench/bench.cc b/scribo/sandbox/green/exp/annotating/bench/bench.cc new file mode 100644 index 0000000..102a896 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/bench/bench.cc @@ -0,0 +1,1450 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// This file is the final work to differentiate between the two image +/// databases AFP and ICDAR. Several tests are used to show +/// improvement of detection. The jonathan's tests are put together to +/// compare them to the others (in fact, to the low saturation one). + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/algebra/vec.hh> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/times.hh> +#include <mln/arith/diff_abs.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/convert.hh> +#include <mln/data/stretch.hh> +#include <mln/data/fill.hh> + +#include <mln/fun/v2v/component.hh> +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/ceil.hh> +#include <mln/math/floor.hh> + +#include <mln/opt/at.hh> + +#include <mln/trait/value_.hh> + +#include <mln/value/rgb8.hh> + + +#include <mln/value/int_u8.hh> + +/// Histogram part. +/// +/// We regroup here the routines that work on histogram, count_histo, +/// sum_frequency_histo, peak_histo, count_null_frequency_histo, +/// cmp_equi_frequency_histo and others (variance +/// operators). count_histo and sum_frequency_histo count the overall +/// frequency in the domain. peak_histo find the peak of the +/// histogram. count_null_frequency_histo compute the number of non +/// grey levels. cmp_equi_frequency_histo compare a histogram with +/// the one with the same frequency everywhere. +/// \{ +template <typename I> +mln_value(I) count_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) result = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + result += histo(p); + + return result; +} + + +template <typename I> +mln_value(I) sum_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += histo(p); + + return sum; +} + + +template <typename I> +mln_coord(mln_site_(I)) peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + mln_value(I) v_max = mln::opt::at(histo, mln::literal::zero); + mln_coord(mln_site_(I)) p_max = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +template <typename I> +mln_value(I) count_null_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) count = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + if (0 == histo(p)) + count++; + + return count; +} + +template <typename I> +float cmp_equi_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum ++; + var += mln::math::sqr(histo(p) - (1/256.0)); + } + + var = var / sum; + + return var; +} + +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + } + + return sum; +} + +template <typename I> +mln_value(I) sum_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + sum += pos*histo(p); + } + + return sum; +} + +template <typename I> +mln_value(I) avg_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += histo(p); + sum += pos*histo(p); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +template <typename I> +mln_value(I) var3_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + sum += (mln::math::sqr(p.ind()-mean)*histo(p)); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +template <typename I> +mln_value(I) var2_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_value(I) dlt = mln::literal::zero; + mln_value(I) mxt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + mxt += (histo(p)*pos*mean); + sqr += (histo(p)*mln::math::sqr(pos)); + dlt += (histo(p)*mln::math::sqr(pos - mean)); + + std::cout << "p = " << pos << std::endl; + std::cout << "p² = " << mln::math::sqr(pos) << std::endl; + std::cout << "p*mean = " << (pos*mean) << std::endl; + std::cout << "p-mean = " << (pos-mean) << std::endl; + std::cout << "(p-mean)² = " << mln::math::sqr(pos-mean) << std::endl; + std::cout << "histo(p) = " << histo(p) << std::endl; + std::cout << "histo(p)*p = " << (pos*histo(p)) << std::endl; + std::cout << "histo(p)*p²= " << (mln::math::sqr(pos)*histo(p)) + << std::endl; + std::cout << "cnt = " << cnt << std::endl; + std::cout << "sum = " << sum << std::endl; + std::cout << "sqr = " << sqr << std::endl; + std::cout << "dlt = " << dlt << std::endl; + std::cout << "mxt = " << mxt << std::endl; + std::cout << std::endl; + } + + std::cout << "sqr/cnt = " << (sqr/cnt) << std::endl; + std::cout << "sum/cnt = " << (sum/cnt) << std::endl; + std::cout << "(sum/cnt)² = " << mln::math::sqr(sum/cnt) << std::endl; + std::cout << "dlt/cnt = " << dlt/cnt << std::endl; + std::cout << "mxt/cnt = " << mxt/cnt << std::endl; + std::cout << std::endl; + + std::cout << "sqr = " + << (sqr) << std::endl; + std::cout << "dlt = " + << (dlt) << std::endl; + std::cout << "cnt*avg² = " + << (mln::math::sqr(sum/cnt)*cnt) << std::endl; + std::cout << "2*mxt = " + << (2*mxt) << std::endl; + std::cout << "sqr - cnt*avg² = " + << (sqr/cnt - mln::math::sqr(sum/cnt)) << std::endl; + std::cout << "(sqr -2*mxt + cnt*avg²) = " + << ((sqr - 2*mxt + mln::math::sqr(sum/cnt))/cnt) << std::endl; + std::cout << std::endl; + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +template <typename I> +mln_value(I) var_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + sqr += (histo(p)*mln::math::sqr(pos)); + } + + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +template <typename I> +mln_value(I) sqr_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += (mln::math::sqr(p.ind())*histo(p)); + + return sum; +} +/// \} + + +/// \brief Millet Hue detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold a way to adapt the test to the population. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test is used for discrimination between black and white pictures and +/// color ones. Some colored Black and white pictures have their energy near +/// the peak. +float hue1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + float cnt1; + float cnt2; + float prop; + short peak; + mln::point1d inf; + mln::point1d sup; + + peak = peak_histo(histo); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo|mln::box1d(inf,sup)); + cnt2 = count_histo(histo); + prop = ((255.0 * cnt1) / cnt2); + + return prop; +} + + +/// \brief Saturation detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold a way to adapt the test to the population. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test is used for discrimination between black and white pictures and +/// color ones. Black and white pictures have their energy in the low saturation +/// band. +float sat1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + float cnt1; + float cnt2; + float result; + + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = count_histo(histo); + result = ((255.0 * cnt1) / cnt2); + + return result; +} + + +/// \brief Counting grey levels descriptor. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the number of grey levels. +/// +/// This test aims at compute the number of grey levels. Photographies tends to +/// use all the levels or many of them. +float lvl0_descriptor(mln::image1d<unsigned> histo) +{ + float result; + + // FIXME 255 + result = 255-count_null_frequency_histo(histo); + + return result; +} + + +/// \brief Density hue detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float hue0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief Density saturation detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float sat0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief Density value detector. +/// +/// \param[in] histo the histogram image on which applying the detector. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test works on density histogram and compare to the +/// equi-frequency histogram. If the normalized histogram show a peak, +/// it will differ from the reference density. +float val0_descriptor(mln::image1d<unsigned> histo) +{ + mln::image1d<float> histo_float; + float sum; + float result; + + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + result = cmp_equi_frequency_histo(histo_float); + + return result*255; +} + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// \param[in] peak the peak of the histogram. +/// \param[in] limit the threshold to compute the contribution. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief Millet value descriptor (cf. Millet.phd.2008) +/// +/// \param[in] histo the histogram image on which applying the detector. +/// \param[in] threshold the limit where to compute the contribution. +/// +/// \return the proportion of population that verify the hue detector. +/// +/// This test aims at compute some deviation on the peak of the histogram of +/// the image. Large deviations mean lots of graduation in colors (such as +/// photos) and small ones mean something like cartoon. +float val1_descriptor(mln::image1d<unsigned> histo, const short threshold) +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + float result; + short peak; + + peak = peak_histo(histo); + result = stddev2(histo, peak, threshold); + + return result; +} + + +/// \brief Error detector (jonathan idea). +/// +/// \param[in] r_img_map the original red channel. +/// \param[in] g_img_map the original green channel. +/// \param[in] b_img_map the original blue channel. +/// \param[in] r_rdc_map the reduced (in color) red channel. +/// \param[in] g_rdc_map the reduced (in color) green channel. +/// \param[in] b_rdc_map the reduced (in color) blue channel. +/// +/// \return the PNSNR (cf compression p278 Handbook Color). +/// +/// \fixme the actual PNSNR is unbound, we need to fix its max to +/// 255. Two images which are the same produces an infinite PNSNR. +float err_descriptor(mln::image2d<mln::value::int_u8> r_img_map, + mln::image2d<mln::value::int_u8> g_img_map, + mln::image2d<mln::value::int_u8> b_img_map, + mln::image2d<mln::value::int_u8> r_rdc_map, + mln::image2d<mln::value::int_u8> g_rdc_map, + mln::image2d<mln::value::int_u8> b_rdc_map) + + +{ + typedef mln::accu::meta::stat::mean t_mean; + typedef mln::image2d<mln::value::int_u8> t_map; + typedef mln_trait_op_minus_(t_map,t_map) t_minus; + typedef mln_trait_op_times_(t_minus,t_minus) t_times; + + + t_minus minus_red; + t_minus minus_green; + t_minus minus_blue; + + t_times times_red; + t_times times_green; + t_times times_blue; + + float error_red; + float error_green; + float error_blue; + + float error; + + minus_red = (r_img_map - r_rdc_map); + times_red = minus_red * minus_red; + + minus_green = (g_img_map - g_rdc_map); + times_green = minus_green * minus_green; + + minus_blue = (b_img_map - b_rdc_map); + times_blue = minus_blue * minus_blue; + + error_red = mln::data::compute(t_mean(), times_red); + error_green = mln::data::compute(t_mean(), times_green); + error_blue = mln::data::compute(t_mean(), times_blue); + + error = (error_red + error_green + error_blue)/3.0; + error = mln::math::sqrt(error); + error = 20 * log(255/error); + +// FIXME: +// SAME IMAGE PRODUCE PNSNR GOING THROW THE INFINITY. +// DIFFERENT IMAGE PRODUCE PNSNR GOING NEAR ZERO. +// WE SHOULD KEEP THE PNSNR BELOW 255 FOR COMPARAISON. + + return error; +} + +/// Discriminant operators. +/// +/// We have some operators that compute the threshold which separate +/// two sub-populations in a histogram. +/// /{ + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*sqrt(v1)+m2*sqrt(v2))/(sqrt(v1)+sqrt(v2)). +float threshold_histo(float avg1, float var1, float avg2, float var2) +{ + float sigma1 = mln::math::sqrt(var1); + float sigma2 = mln::math::sqrt(var2); + float threshold = (avg1*sigma1+avg2*sigma2)/(sigma1+sigma2); + + return threshold; +} + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*var1+m2*var2)/(sqrt(v1)+sqrt(v2)). + +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Gaussian discriminant analysis in 1d is done. Compute the +/// discrimanation and solve the parabolic equation. +float threshold2_histo(float avg1, float var1, float avg2, float var2) +{ + float a = var2 - var1; + float b = -2 * (avg1 * var2 - avg2 * var1); + float c = var2 * mln::math::sqr(avg1) - var1 * mln::math::sqr(avg2); + float d = mln::math::sqr(b) - 4 * a * c; + + if (d < 0) + std::cout << "delta negatif" << std::endl; + + float x1 = (-b+mln::math::sqrt(d))/(2*a); + float x2 = (-b-mln::math::sqrt(d))/(2*a); + + std::cout << "a = " << a << std::endl; + std::cout << "b = " << b << std::endl; + std::cout << "c = " << c << std::endl; + std::cout << "d = " << d << std::endl; + std::cout << "x1 = " << x1 << std::endl; + std::cout << "x2 = " << x2 << std::endl; + + return x2; +} + +/// \brief Minimisation of the error. +/// +/// \param[in] histo_grp1 the histogram of the first population. +/// \param[in] histo_grp2 the histogram of the second population. +/// +/// \return the threshold. +/// +/// Computes the error and find the minimum error threshold. It's just +/// a counting of element in four categories (0 - but detects 1, 1 - +/// but detects 1, 1 but detects 0, 0 but detects 0). The error is the +/// sum of missclassifications for the classes 0 and 1. As Otsu done, +/// we can iterate on the threshold and compute the error associated +/// to it. The bes threshold is the one that minimize the error. +short min_error(const mln::image1d<float> histo_grp1, + const mln::image1d<float> histo_grp2, + float *_c00, float *_c10, float *_c01, float *_c11) +{ + float c00[256]; + float c10[256]; + float c01[256]; + float c11[256]; + float err[256]; + + for (short p = 0; p < 256; p++) + { + c00[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c10[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + + c01[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c11[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + } + + short threshold = 0; + float error = c01[0] + c01[0] + c00[0] + c11[0]; + + for(short p = 0; p < 256; p++) + { + err[p] = c10[p] + c01[p]; + +// std::cout << " p = " << p +// << ";c00 = " << c00[p] +// << ";c10 = " << c10[p] +// << ";c01 = " << c01[p] +// << ";c11 = " << c11[p] +// << std::endl; +// std::cout << "err[" < err[p]) + { + error = err[p]; + threshold = p; + } + } + + *_c00 = c00[threshold]; + *_c10 = c10[threshold]; + *_c01 = c01[threshold]; + *_c11 = c11[threshold]; + + return threshold; +} + + +/// \brief Fisher analysis. +/// +/// \param[in] histo the histogram of the mixed population. +/// +/// \return the threshold. +/// +/// This routine is less performant than the others because it starts +/// with a mixed population, but we know exactly the two separated +/// population. It was just a test. Don't take it in production +/// environement, it doesn't help you. +short fisher_analysis(const mln::image1d<float> histo) +{ + typedef mln::value::int_u8 t_int_u8; + + // FIXE ME SIZE const short a = mln_min(t_int_u8); + // float cnt1[a]; + + float cnt1[256]; + float sum1[256]; + float sqr1[256]; + float avg1[256]; + float var1[256]; + + float cnt2[256]; + float sum2[256]; + float sqr2[256]; + float avg2[256]; + float var2[256]; + + float cnt0[256]; // count of points + float sum0[256]; // sum of population + float sqr0[256]; // sqr of population + float avg0[256]; // average of the population + float v_in[256]; // variance with-in class + float v_bw[256]; // variance between class + float var0[256]; // variance of the population + short threshold; + float pos; + float min_var; + + // Assign the first elements + cnt1[0] = 0; + sum1[0] = 0; + sqr1[0] = 0; + avg1[0] = 0; + var1[0] = 0; + + // Compute the stats of the wall histogram + cnt2[0] = 0; + sum2[0] = 0; + sqr2[0] = 0; + + for(short p = 0; p < 256; p++) + { + pos = p; + cnt2[0] += mln::opt::at(histo,p); + sum2[0] += (pos * mln::opt::at(histo,p)); + sqr2[0] += (mln::math::sqr(pos) * mln::opt::at(histo,p)); + } + + avg2[0] = (0 == cnt2[0])? 0 : sum2[0] / cnt2[0]; + var2[0] = (0 == cnt2[0])? 0 : sqr2[0] / cnt2[0] - mln::math::sqr(avg2[0]); + + // watch the array limits + for (short p = 1; p < 256; p++) + { + pos = p; + + // Assign the statistics to the primary class + cnt1[p] = cnt1[p-1] + mln::opt::at(histo, p); + sum1[p] = sum1[p-1] + pos * mln::opt::at(histo, p); + sqr1[p] = sqr1[p-1] + mln::math::sqr(pos) * mln::opt::at(histo, p); + avg1[p] = (0 == cnt1[p])? 0 : (sum1[p] / cnt1[p]); + var1[p] = (0 == cnt1[p])? 0 : ((sqr1[p] / cnt1[p])-mln::math::sqr(avg1[p])); + + // Assign the statistics to the second class + cnt2[p] = cnt2[p-1] - mln::opt::at(histo, p);; + sum2[p] = sum2[p-1] - p * mln::opt::at(histo, p);; + sqr2[p] = sqr2[p-1] - mln::math::sqr(p) * mln::opt::at(histo, p);; + avg2[p] = (0 == cnt2[p])? 0 : (sum2[p] / cnt2[p]); + var2[p] = (0 == cnt2[p])? 0 : ((sqr2[p] / cnt2[p])-mln::math::sqr(avg2[p])); + + // Lets compute the invariants + cnt0[p] = cnt1[p] + cnt2[p]; + sum0[p] = sum1[p] + sum2[p]; + sqr0[p] = sqr1[p] + sqr2[p]; + avg0[p] = (cnt1[p] * avg1[p] + cnt2[p] * avg2[p])/cnt0[p]; + v_in[p] = (cnt1[p] * var1[p] + cnt2[p] * var2[p])/cnt0[p]; + v_bw[p] = (cnt1[p] * mln::math::sqr(avg1[p]-avg0[p]) + + cnt2[p] * mln::math::sqr(avg2[p]-avg0[p]))/cnt0[p]; + var0[p] = v_in[p] + v_bw[p]; + } + + // Find the threshold that minimizes the intra-class variance + min_var = cnt2[0]*var2[0]; + threshold = 0; + + for(short p = 0; p < 256; p++) + { + // Compute the intra-class variance + v_in[p] = cnt1[p]*var1[p] + cnt2[p]*var2[p]; +// std::cout << "var intra[" < v_in[p]) + { + min_var = v_in[p]; + threshold = p; + } + } + + return threshold; +} +/// /} + + +/// Launching part. +/// +/// In this part, we have the front end that walk over the image +/// databases and start to compute information that we pass to +/// previous routines. +/// \{ + +#define LVL0_DESCR 0 // Number of grey available +#define HUE0_DESCR 1 // Density histogram hue analysis +#define HUE1_DESCR 2 // Millet Hue analysis +#define SAT0_DESCR 3 // Density histogram saturation analysis +#define SAT1_DESCR 4 // Millet saturation analysis +#define VAL0_DESCR 5 // Density histogram grey level analysis +#define VAL1_DESCR 6 // Millet grey level analysis +#define GMP0_DESCR 7 // PNSNR with GIMP compression +#define GMP1_DESCR 8 // PNSNR with GIMP compression +#define GMP2_DESCR 9 // PNSNR with GIMP compression +#define MGK0_DESCR 9 // PNSNR with ImageMagick compression +#define MGK1_DESCR 10 // PNSNR with ImageMagick compression +#define MGK2_DESCR 11 // PNSNR with ImageMagick compression + +#define MGK_DESCR(version) (MGK0_DESCR + version) +#define GMP_DESCR(version) (GMP0_DESCR + version) + +#define NB_DESCR 12 // Number of descriptors +#define NB_DATABASE 2 // Number of image databases : AFP,ICDAR +#define NB_IMAGE 110 // Number of images +#define NB_VERSION 3 // Number of compression by GIMP or ImageMagick + +/// \brief Make sure that the result is between 0 and 255. +/// +/// \param[in] file_name the name of the analysis files. +/// \param[in] result the values for each image and each descriptors. +/// \param[in] size the number of image by each database. +/// +/// Correct the descriptors if there is overflow values. Just used in +/// debug mode. +void init_descriptors(std::string file_name[], + float result[][NB_DESCR], + int size[]) +{ + for (int i = 0; i < NB_IMAGE; i++) + { + file_name[i] = std::string("PGM"); + + for (int d = 0; d < NB_DESCR; d++) + result[i][d] = (i*d) % 256; + } + + size[0] = 62; + size[1] = 48; +} + + +/// \brief Dump gnuplot file for vizualisation of results. +/// +/// \param[in] file_name the name of the analysis files. +/// \param[in] result the values for each image and each descriptors. +/// \param[in] size the number of image by each database. +/// +/// This routine aimed at plotting the result with one graph. Each +/// database has got its color. We can see the values used by each +/// descriptors along the image of each database. +void dump_descriptors(const std::string file_name[], + const float result[][NB_DESCR], + const int size[]) +{ + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "plot '-' using 2 with point title 'ICDAR',\\" << std::endl; + std::cout << " '-' using 2 with point title 'AFP'" << std::endl; + + int num = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + for (int i = 0; i < size[db]; i++) + { + std::cout << result[num][LVL0_DESCR] << " "; + std::cout << result[num][HUE0_DESCR] << " "; + std::cout << result[num][HUE1_DESCR] << " "; + std::cout << result[num][SAT0_DESCR] << " "; + std::cout << result[num][SAT1_DESCR] << " "; + std::cout << result[num][VAL0_DESCR] << " "; + std::cout << result[num][VAL1_DESCR] << " "; + std::cout << result[num][GMP0_DESCR] << " "; + std::cout << result[num][GMP1_DESCR] << " "; + std::cout << result[num][GMP2_DESCR] << " "; + std::cout << result[num][MGK0_DESCR] << " "; + std::cout << result[num][MGK1_DESCR] << " "; + std::cout << result[num][MGK2_DESCR] << " "; + std::cout << " # " << file_name[num] << std::endl; + num++; + } + + std::cout << "e" << std::endl; + } +} + + +/// \brief Compute the descriptor histograms for each database. +/// +/// \param[in] result the result values of each descriptors. +/// \param[in] size the number of images by database. +/// \param[out] histo the computed histograms. +/// +/// This routine compute a histograms for each database and each descriptors. +/// So we can see the distribution of descriptor values over each database. +/// Result values are transform from float to int for histograms. +void compute_histo(const float result[][NB_DESCR], + const int size[], + mln::image1d<float> histo[][NB_DATABASE]) +{ + for (int i = 0; i < NB_DESCR; i++) + for (int db = 0; db < NB_DATABASE; db++) + { + histo[i][db].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + + mln::data::fill(histo[i][db], mln::literal::zero); + } + + short v; + int num = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + for (int i = 0; i < size[db]; i++) + { + v = (short)mln::math::floor(result[num][VAL0_DESCR]+0.4999); + mln::opt::at(histo[VAL0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][LVL0_DESCR]+0.4999); + mln::opt::at(histo[LVL0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][HUE0_DESCR]+0.4999); + mln::opt::at(histo[HUE0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][HUE1_DESCR]+0.4999); + mln::opt::at(histo[HUE1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][SAT0_DESCR]+0.4999); + mln::opt::at(histo[SAT0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][SAT1_DESCR]+0.4999); + mln::opt::at(histo[SAT1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][VAL1_DESCR]+0.4999); + mln::opt::at(histo[VAL1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP0_DESCR]+0.4999); + mln::opt::at(histo[GMP0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP1_DESCR]+0.4999); + mln::opt::at(histo[GMP1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][GMP2_DESCR]+0.4999); + mln::opt::at(histo[GMP2_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK0_DESCR]+0.4999); + mln::opt::at(histo[MGK0_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK1_DESCR]+0.4999); + mln::opt::at(histo[MGK1_DESCR][db],v)++; + + v = (short)mln::math::floor(result[num][MGK2_DESCR]+0.4999); + mln::opt::at(histo[MGK2_DESCR][db],v)++; + + num++; + } + } +} + + +/// Compute the threshold for discrimination between ICDAR and AFP. +/// +/// \param[in] histo the histogram for each descriptor and for each database. +/// \param[out] threshold computed values to discriminate between ICDAR/AFP. +/// \param[out] c00 say CLASS 1 but CLASS 1. +/// \param[out] c10 say CLASS 2 but CLASS 1. +/// \param[out] c01 say CLASS 1 but CLASS 2. +/// \param[out] c11 say CLASS 2 but CLASS 2. +/// +/// Compute the threshold by the minimum of error of +/// classification. Population are inverted in function of average, +/// technical solution to prevent min_error crashed. The bad thing is +/// now, the class 0 can be either ICDAR of AFP, depends on descriptors. +void compute_thresholds(const mln::image1d<float> histo[][NB_DATABASE], + short threshold[], + float c00[], + float c10[], + float c01[], + float c11[]) +{ + for (int i = 0; i < NB_DESCR; i++) + { + float avg0 = avg_histo(histo[i][0]); + float avg1 = avg_histo(histo[i][1]); + + if (avg0 < avg1) + { + threshold[i] = min_error(histo[i][0], histo[i][1], + &c00[i], &c10[i], &c01[i], &c11[i]); + } + else + { + threshold[i] = min_error(histo[i][1], histo[i][0], + &c00[i], &c10[i], &c01[i], &c11[i]); + } + + std::cerr << " i = " << i + << "; c00 = " << c00[i] + << "; c10 = " << c10[i] + << "; c01 = " << c01[i] + << "; c11 = " << c11[i] + << "; threshold " << threshold[i] + << std::endl; + + } +} + + +/// \brief Walk over the database directories. +/// +/// \param[out] file_name the array of the image name. +/// \param[out] result the descriptors values for each image. +/// \param[out] size the number of image for each database. +/// +/// This is the front end part to access to the database directories. We use +/// boost library to do it. First we explore ICDAR database en then the AFP one. +/// For each valid image, we compute all the descriptors. Histograms are +/// computed at this level because we can do that thing one time for every +/// density descriptors. Every parameters are updated to retrurn the values. +void compute_descriptors(std::string file_name[], + float result[][NB_DESCR], + int size[]) +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + typedef mln::image1d<unsigned> t_histo; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_map; + typedef mln::image2d<t_rgb8> t_input; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_2_hue; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_2_sat; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_2_val; + typedef mln::fun::v2v::component<t_rgb8,0> t_rgb_2_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_rgb_2_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_rgb_2_blue; + typedef mln::accu::meta::stat::histo1d t_accu_histo; + + + t_path img_path[2] = { ICDAR_20P_INPUT_IMG_PATH, AFP_PPM_IMG_PATH}; + t_path mgk_path[3][2] = {{ICDAR_20P_MGK30_IMG_PATH, AFP_MGK30_IMG_PATH}, + {ICDAR_20P_MGK20_IMG_PATH, AFP_MGK20_IMG_PATH}, + {ICDAR_20P_MGK10_IMG_PATH, AFP_MGK10_IMG_PATH}}; + t_path gmp_path[3][2] = {{ICDAR_20P_GMP30_IMG_PATH, AFP_GMP30_IMG_PATH}, + {ICDAR_20P_GMP20_IMG_PATH, AFP_GMP20_IMG_PATH}, + {ICDAR_20P_GMP10_IMG_PATH, AFP_GMP10_IMG_PATH}}; + + int num = 0; + int cnt = 0; + + for (int db = 0; db < NB_DATABASE; db++) + { + if (boost::filesystem::exists(img_path[db]) && + boost::filesystem::is_directory(img_path[db])) + { + boost::filesystem::system_complete(img_path[db]); + + const t_iter_path end_iter; + + cnt = 0; + + for (t_iter_path dir_iter(img_path[db]); end_iter != dir_iter; ++dir_iter) + { + t_path img_file = dir_iter->path().leaf(); + t_path dir_file = dir_iter->path(); + t_input img_input; + + mln::io::ppm::load(img_input, dir_file.string().c_str()); + + t_map h_img_map = mln::data::transform(img_input, t_rgb_2_hue()); + t_map s_img_map = mln::data::transform(img_input, t_rgb_2_sat()); + t_map v_img_map = mln::data::transform(img_input, t_rgb_2_val()); + t_map r_img_map = mln::data::transform(img_input, t_rgb_2_red()); + t_map g_img_map = mln::data::transform(img_input, t_rgb_2_green()); + t_map b_img_map = mln::data::transform(img_input, t_rgb_2_blue()); + t_histo h_img_hst = mln::data::compute(t_accu_histo(), h_img_map); + t_histo s_img_hst = mln::data::compute(t_accu_histo(), s_img_map); + t_histo v_img_hst = mln::data::compute(t_accu_histo(), v_img_map); + t_histo r_img_hst = mln::data::compute(t_accu_histo(), r_img_map); + t_histo g_img_hst = mln::data::compute(t_accu_histo(), g_img_map); + t_histo b_img_hst = mln::data::compute(t_accu_histo(), b_img_map); + + std::cerr << dir_iter->path() << std::endl; + + file_name[num] = img_file.string(); + + // descriptors + result[num][LVL0_DESCR] = lvl0_descriptor(v_img_hst); + result[num][HUE0_DESCR] = hue0_descriptor(h_img_hst); + result[num][HUE1_DESCR] = hue1_descriptor(h_img_hst,20); + result[num][SAT0_DESCR] = sat0_descriptor(s_img_hst); + result[num][SAT1_DESCR] = sat1_descriptor(s_img_hst,50); + result[num][VAL0_DESCR] = val0_descriptor(v_img_hst); + //result[num][VAL1_DESCR] = val1_descriptor(v_img_hst, 15); + result[num][VAL1_DESCR] = 0; + + // for gimp and magick + for (int v = 0; v < NB_VERSION; v++) + { + if (boost::filesystem::exists(mgk_path[v][db]) && + boost::filesystem::exists(gmp_path[v][db]) && + boost::filesystem::is_directory(mgk_path[v][db]) && + boost::filesystem::is_directory(gmp_path[v][db])) + { + t_path mgk_file = mgk_path[v][db] / img_file; + t_path gmp_file = gmp_path[v][db] / img_file; + t_input gmp_input; + + mln::io::ppm::load(gmp_input, gmp_file.string().c_str()); + + t_map r_gmp_map = mln::data::transform(gmp_input,t_rgb_2_red()); + t_map g_gmp_map = mln::data::transform(gmp_input,t_rgb_2_green()); + t_map b_gmp_map = mln::data::transform(gmp_input,t_rgb_2_blue()); + + result[num][GMP_DESCR(v)]= err_descriptor(r_img_map, + g_img_map, + b_img_map, + r_gmp_map, + g_gmp_map, + b_gmp_map); + + t_input mgk_input; + + mln::io::ppm::load(mgk_input, mgk_file.string().c_str()); + + t_map r_mgk_map = mln::data::transform(mgk_input,t_rgb_2_red()); + t_map g_mgk_map = mln::data::transform(mgk_input,t_rgb_2_green()); + t_map b_mgk_map = mln::data::transform(mgk_input,t_rgb_2_blue()); + + result[num][MGK_DESCR(v)]= err_descriptor(r_img_map, + g_img_map, + b_img_map, + r_mgk_map, + g_mgk_map, + b_mgk_map); + } + } + + num++; + cnt++; + } + } + + size[db] = cnt; + } +} + +/// \brief Just for debugging purpose and tests. +int main2() +{ + typedef mln::image1d<unsigned> t_histo; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_map; + typedef mln::image2d<t_rgb8> t_input; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_2_hue; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_2_sat; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_2_val; + typedef mln::fun::v2v::component<t_rgb8,0> t_rgb_2_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_rgb_2_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_rgb_2_blue; + typedef mln::accu::meta::stat::histo1d t_accu_histo; + + t_input img_input; + + mln::io::ppm::load(img_input, ICDAR_20P_INPUT_IMG_PATH"/mp00032c_20p.ppm"); + //mln::io::ppm::load(img_input, AFP_PPM_IMG_PATH"/000_Del218430.ppm"); + + + + t_map h_img_map = mln::data::transform(img_input, t_rgb_2_hue()); + t_map s_img_map = mln::data::transform(img_input, t_rgb_2_sat()); + t_map v_img_map = mln::data::transform(img_input, t_rgb_2_val()); + t_map r_img_map = mln::data::transform(img_input, t_rgb_2_red()); + t_map g_img_map = mln::data::transform(img_input, t_rgb_2_green()); + t_map b_img_map = mln::data::transform(img_input, t_rgb_2_blue()); + t_histo h_img_hst = mln::data::compute(t_accu_histo(), h_img_map); + t_histo s_img_hst = mln::data::compute(t_accu_histo(), s_img_map); + t_histo v_img_hst = mln::data::compute(t_accu_histo(), v_img_map); + t_histo r_img_hst = mln::data::compute(t_accu_histo(), r_img_map); + t_histo g_img_hst = mln::data::compute(t_accu_histo(), g_img_map); + t_histo b_img_hst = mln::data::compute(t_accu_histo(), b_img_map); + + + std::cout << "sat2 : " << sat0_descriptor(s_img_hst) << std::endl; + + return 0; +} + + +/// \brief Main entry. +/// +/// This is the front end for every routines. +/// - compute descriptors. +/// - dump descriptors. +/// - compute histograms. +/// - compute thresholds. +/// - save results. +/// +/// Global data used: +/// - file_name : all the initial images name, mixed every image database. +/// - result : all the result values for every descriptors and for every images. +/// - histo : the histograms ny database and by descriptors. +/// - size : the number of image by database. +/// - threshold : the computed histogram separation on each descriptors. +/// - cxx : the count of well/bad classified images function of threshold info. +int main() +{ + std::string file_name[NB_IMAGE]; + float result[NB_IMAGE][NB_DESCR]; + int size[NB_DATABASE]; + mln::image1d<float> histo[NB_DESCR][NB_DATABASE]; + short threshold[NB_DESCR]; + float c00[NB_DESCR]; + float c10[NB_DESCR]; + float c01[NB_DESCR]; + float c11[NB_DESCR]; + + std::cerr << "DESCRIPTORS" << std::endl; + compute_descriptors(file_name,result,size); +// std::cout << "DUMPING" << std::endl; +// init_descriptors(file_name,result,size); + dump_descriptors(file_name,result,size); + std::cerr << "HISTO" << std::endl; + compute_histo(result,size,histo); + std::cerr << "THRESHOLD" << std::endl; + compute_thresholds(histo,threshold,c00,c10,c01,c11); + + mln::io::plot::save_image_sh(histo[LVL0_DESCR][0], "lvl0_histo1.sh"); + mln::io::plot::save_image_sh(histo[HUE0_DESCR][0], "hue0_histo1.sh"); + mln::io::plot::save_image_sh(histo[HUE1_DESCR][0], "hue1_histo1.sh"); + mln::io::plot::save_image_sh(histo[SAT0_DESCR][0], "sat0_histo1.sh"); + mln::io::plot::save_image_sh(histo[SAT1_DESCR][0], "sat1_histo1.sh"); + mln::io::plot::save_image_sh(histo[VAL0_DESCR][0], "val0_histo1.sh"); + mln::io::plot::save_image_sh(histo[VAL1_DESCR][0], "val1_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP0_DESCR][0], "gmp0_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP1_DESCR][0], "gmp1_histo1.sh"); + mln::io::plot::save_image_sh(histo[GMP2_DESCR][0], "gmp2_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK0_DESCR][0], "mgk0_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK1_DESCR][0], "mgk1_histo1.sh"); + mln::io::plot::save_image_sh(histo[MGK2_DESCR][0], "mgk2_histo1.sh"); + + mln::io::plot::save_image_sh(histo[LVL0_DESCR][1], "lvl0_histo2.sh"); + mln::io::plot::save_image_sh(histo[HUE0_DESCR][1], "hue0_histo2.sh"); + mln::io::plot::save_image_sh(histo[HUE1_DESCR][1], "hue1_histo2.sh"); + mln::io::plot::save_image_sh(histo[SAT0_DESCR][1], "sat0_histo2.sh"); + mln::io::plot::save_image_sh(histo[SAT1_DESCR][1], "sat1_histo2.sh"); + mln::io::plot::save_image_sh(histo[VAL0_DESCR][1], "val0_histo2.sh"); + mln::io::plot::save_image_sh(histo[VAL1_DESCR][1], "val1_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP0_DESCR][1], "gmp0_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP1_DESCR][1], "gmp1_histo2.sh"); + mln::io::plot::save_image_sh(histo[GMP2_DESCR][1], "gmp2_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK0_DESCR][1], "mgk0_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK1_DESCR][1], "mgk1_histo2.sh"); + mln::io::plot::save_image_sh(histo[MGK2_DESCR][1], "mgk2_histo2.sh"); + + return 0; +} +/// \} + diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/error/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/error/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/error/error.cc b/scribo/sandbox/green/exp/annotating/error/error.cc new file mode 100644 index 0000000..949b51b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/error/error.cc @@ -0,0 +1,833 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// This file is a prelude just before using the bench source. It puts +/// the light on the Jonathan's idea which wants to compare ICDAR and +/// AFP image database with difference between original image and +/// color reducted one. The key point is to say that when a color +/// image is degraded, the visual quality fall quickly in function of +/// the number of colors left. A contrario, when an image is black and +/// white, this kind of degradation doesn't change the original visual +/// quality. The PNSNR detector is build and learning threshold for +/// automatic classification is done (four algorithms are tested). + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/algebra/vec.hh> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/times.hh> +#include <mln/arith/diff_abs.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/alias/box1d.hh> + +#include <mln/data/transform.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/fill.hh> + +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/ceil.hh> +#include <mln/math/floor.hh> + +#include <mln/opt/at.hh> + +#include <mln/trait/value_.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u8.hh> + +/// Work on histogram. +/// \brief Different routines are presented for computing mean, variance ... +/// \{ + +/// \brief Counting the number of pixels in the histogram. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the number of pixels in the histogram. +/// +/// Such routine is a generic and is generally applied on a portion of +/// the real domain. +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + } + + return sum; +} + +/// \brief Summing the values ponderated by their occurences. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the sum ponderated of values by their occurences. +/// +/// This method is not a production routine, it's just a test to debug +/// histogram problem. When we compute something over the histogram, +/// we need to take care of the intermediate type that contains the +/// temporary result. If not the case, the type used is the one of the +/// value of the histogram and we can go to some overflow +/// problems. The trick is to store the value returned by the iterator +/// in some temporary computing type (look at pos in the code). +template <typename I> +mln_value(I) sum_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + sum += pos*histo(p); + } + + return sum; +} + +/// \brief Summing the values ponderated by their occurences. +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the sum ponderated of values by their occurences. +template <typename I> +mln_value(I) avg_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += histo(p); + sum += pos*histo(p); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// This computing is safe because the quantity of information stored +/// in sum is low. This formulae doesn't show the overflow problem. +template <typename I> +mln_value(I) var3_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + sum += (mln::math::sqr(p.ind()-mean)*histo(p)); + } + + return (0 == cnt)? 0 : sum/cnt; +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// This is the debugging code to observe overflow problem. +template <typename I> +mln_value(I) var2_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_value(I) dlt = mln::literal::zero; + mln_value(I) mxt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + mxt += (histo(p)*pos*mean); + sqr += (histo(p)*mln::math::sqr(pos)); + dlt += (histo(p)*mln::math::sqr(pos - mean)); + + std::cout << "p = " << pos << std::endl; + std::cout << "p² = " << mln::math::sqr(pos) << std::endl; + std::cout << "p*mean = " << (pos*mean) << std::endl; + std::cout << "p-mean = " << (pos-mean) << std::endl; + std::cout << "(p-mean)² = " << mln::math::sqr(pos-mean) << std::endl; + std::cout << "histo(p) = " << histo(p) << std::endl; + std::cout << "histo(p)*p = " << (pos*histo(p)) << std::endl; + std::cout << "histo(p)*p²= " << (mln::math::sqr(pos)*histo(p)) + << std::endl; + std::cout << "cnt = " << cnt << std::endl; + std::cout << "sum = " << sum << std::endl; + std::cout << "sqr = " << sqr << std::endl; + std::cout << "dlt = " << dlt << std::endl; + std::cout << "mxt = " << mxt << std::endl; + std::cout << std::endl; + } + + std::cout << "sqr/cnt = " << (sqr/cnt) << std::endl; + std::cout << "sum/cnt = " << (sum/cnt) << std::endl; + std::cout << "(sum/cnt)² = " << mln::math::sqr(sum/cnt) << std::endl; + std::cout << "dlt/cnt = " << dlt/cnt << std::endl; + std::cout << "mxt/cnt = " << mxt/cnt << std::endl; + std::cout << std::endl; + + std::cout << "sqr = " + << (sqr) << std::endl; + std::cout << "dlt = " + << (dlt) << std::endl; + std::cout << "cnt*avg² = " + << (mln::math::sqr(sum/cnt)*cnt) << std::endl; + std::cout << "2*mxt = " + << (2*mxt) << std::endl; + std::cout << "sqr - cnt*avg² = " + << (sqr/cnt - mln::math::sqr(sum/cnt)) << std::endl; + std::cout << "(sqr -2*mxt + cnt*avg²) = " + << ((sqr - 2*mxt + mln::math::sqr(sum/cnt))/cnt) << std::endl; + std::cout << std::endl; + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} + +/// \brief Computing the variance +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the variance of the histogram. +/// +/// My standard code for the variance. +template <typename I> +mln_value(I) var_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) pos = mln::literal::zero; + mln_value(I) sum = mln::literal::zero; + mln_value(I) sqr = mln::literal::zero; + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + pos = p.ind(); + cnt += (histo(p)); + sum += (histo(p)*pos); + sqr += (histo(p)*mln::math::sqr(pos)); + } + + return (0 == cnt)? 0 : sqr/cnt - mln::math::sqr(sum/cnt); +} +/// \} + + +/// Classify. +/// \brief Classification routines that compute threshold. +/// \{ + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*sqrt(v1)+m2*sqrt(v2))/(sqrt(v1)+sqrt(v2)). +float threshold_histo(float avg1, float var1, float avg2, float var2) +{ + float sigma1 = mln::math::sqrt(var1); + float sigma2 = mln::math::sqrt(var2); + float threshold = (avg1*sigma1+avg2*sigma2)/(sigma1+sigma2); + + return threshold; +} + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Linear discriminant analysis in 1d is done. When two population +/// have the same variance, the threshold is at (m1+m2)/2. When threre +/// are different variances, we propose the threshold at the position +/// (m1*var1+m2*var2)/(sqrt(v1)+sqrt(v2)). +float threshold3_histo(float avg1, float var1, float avg2, float var2) +{ + float threshold = (avg1*var1+avg2*var2)/(var1+var2); + + return threshold; +} + + + +/// \brief Compute discrimination threshold. +/// \param[in] avg1 the mean of the population 1. +/// \param[in] var1 the variance of the population 1. +/// \param[in] avg2 the mean of the population 2. +/// \param[in] var2 the variance of the population 2. +/// +/// \return the threshold to discriminate the two populations. +/// +/// Gaussian discriminant analysis in 1d is done. Compute the +/// discrimanation and solve the parabolic equation. +float threshold2_histo(float avg1, float var1, float avg2, float var2) +{ + float a = var2 - var1; + float b = -2 * (avg1 * var2 - avg2 * var1); + float c = var2 * mln::math::sqr(avg1) - var1 * mln::math::sqr(avg2); + float d = mln::math::sqr(b) - 4 * a * c; + + if (d < 0) + std::cout << "delta negatif" << std::endl; + + float x1 = (-b+mln::math::sqrt(d))/(2*a); + float x2 = (-b-mln::math::sqrt(d))/(2*a); + + std::cout << "a = " << a << std::endl; + std::cout << "b = " << b << std::endl; + std::cout << "c = " << c << std::endl; + std::cout << "d = " << d << std::endl; + std::cout << "x1 = " << x1 << std::endl; + std::cout << "x2 = " << x2 << std::endl; + + return x2; +} + +/// \brief Compute the other proportion of histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the ponderated sum of the sqrt values. +/// +/// This is a test. Don't take this routine in production. +template <typename I> +mln_value(I) sqr_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += (mln::math::sqr(p.ind())*histo(p)); + + return sum; +} + +/// \brief Minimisation of the error. +/// +/// \param[in] histo_grp1 the histogram of the first population. +/// \param[in] histo_grp2 the histogram of the second population. +/// +/// \return the threshold. +/// +/// Computes the error and find the minimum error threshold. It's just +/// a counting of element in four categories (0 - but detects 1, 1 - +/// but detects 1, 1 but detects 0, 0 but detects 0). The error is the +/// sum of missclassifications for the classes 0 and 1. As Otsu done, +/// we can iterate on the threshold and compute the error associated +/// to it. The bes threshold is the one that minimize the error. +short min_error(const mln::image1d<float> histo_grp1, + const mln::image1d<float> histo_grp2) +{ + float c00[256]; + float c10[256]; + float c01[256]; + float c11[256]; + float err[256]; + + for (short p = 0; p < 256; p++) + { + c00[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c10[p] = cnt_histo(histo_grp1|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + + c01[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(0), + mln::point1d(p))); + + c11[p] = cnt_histo(histo_grp2|mln::box1d(mln::point1d(p+1), + mln::point1d(255))); + } + + short threshold = 0; + float error = c01[0] + c01[0] + c00[0] + c11[0]; + + for(short p = 0; p < 256; p++) + { + err[p] = c10[p] + c01[p]; + + std::cout << " p = " < err[p]) + { + error = err[p]; + threshold = p; + } + } + + return threshold; +} + +/// \brief Fisher analysis. +/// +/// \param[in] histo the histogram of the mixed population. +/// +/// \return the threshold. +/// +/// This routine is less performant than the others because it starts +/// with a mixed population, but we know exactly the two separated +/// population. It was just a test. Don't take it in production +/// environement, it doesn't help you. +short fisher_analysis(const mln::image1d<float> histo) +{ + typedef mln::value::int_u8 t_int_u8; + + // FIXE ME SIZE const short a = mln_min(t_int_u8); + // float cnt1[a]; + + float cnt1[256]; + float sum1[256]; + float sqr1[256]; + float avg1[256]; + float var1[256]; + + float cnt2[256]; + float sum2[256]; + float sqr2[256]; + float avg2[256]; + float var2[256]; + + float cnt0[256]; // count of points + float sum0[256]; // sum of population + float sqr0[256]; // sqr of population + float avg0[256]; // average of the population + float v_in[256]; // variance with-in class + float v_bw[256]; // variance between class + float var0[256]; // variance of the population + short threshold; + float pos; + float min_var; + + // Assign the first elements + cnt1[0] = 0; + sum1[0] = 0; + sqr1[0] = 0; + avg1[0] = 0; + var1[0] = 0; + + // Compute the stats of the wall histogram + cnt2[0] = 0; + sum2[0] = 0; + sqr2[0] = 0; + + for(short p = 0; p < 256; p++) + { + pos = p; + cnt2[0] += mln::opt::at(histo,p); + sum2[0] += (pos * mln::opt::at(histo,p)); + sqr2[0] += (mln::math::sqr(pos) * mln::opt::at(histo,p)); + } + + avg2[0] = (0 == cnt2[0])? 0 : sum2[0] / cnt2[0]; + var2[0] = (0 == cnt2[0])? 0 : sqr2[0] / cnt2[0] - mln::math::sqr(avg2[0]); + + // watch the array limits + for (short p = 1; p < 256; p++) + { + pos = p; + + // Assign the statistics to the primary class + cnt1[p] = cnt1[p-1] + mln::opt::at(histo, p); + sum1[p] = sum1[p-1] + pos * mln::opt::at(histo, p); + sqr1[p] = sqr1[p-1] + mln::math::sqr(pos) * mln::opt::at(histo, p); + avg1[p] = (0 == cnt1[p])? 0 : (sum1[p] / cnt1[p]); + var1[p] = (0 == cnt1[p])? 0 : ((sqr1[p] / cnt1[p])-mln::math::sqr(avg1[p])); + + // Assign the statistics to the second class + cnt2[p] = cnt2[p-1] - mln::opt::at(histo, p);; + sum2[p] = sum2[p-1] - p * mln::opt::at(histo, p);; + sqr2[p] = sqr2[p-1] - mln::math::sqr(p) * mln::opt::at(histo, p);; + avg2[p] = (0 == cnt2[p])? 0 : (sum2[p] / cnt2[p]); + var2[p] = (0 == cnt2[p])? 0 : ((sqr2[p] / cnt2[p])-mln::math::sqr(avg2[p])); + + // Lets compute the invariants + cnt0[p] = cnt1[p] + cnt2[p]; + sum0[p] = sum1[p] + sum2[p]; + sqr0[p] = sqr1[p] + sqr2[p]; + avg0[p] = (cnt1[p] * avg1[p] + cnt2[p] * avg2[p])/cnt0[p]; + v_in[p] = (cnt1[p] * var1[p] + cnt2[p] * var2[p])/cnt0[p]; + v_bw[p] = (cnt1[p] * mln::math::sqr(avg1[p]-avg0[p]) + + cnt2[p] * mln::math::sqr(avg2[p]-avg0[p]))/cnt0[p]; + var0[p] = v_in[p] + v_bw[p]; + } + + // Find the threshold that minimizes the intra-class variance + min_var = cnt2[0]*var2[0]; + threshold = 0; + + for(short p = 0; p < 256; p++) + { + // Compute the intra-class variance + v_in[p] = cnt1[p]*var1[p] + cnt2[p]*var2[p]; +// std::cout << "var intra[" < v_in[p]) + { + min_var = v_in[p]; + threshold = p; + } + } + + return threshold; +} +/// \} + +/// \brief ERROR (MSE, PNSNR) compression p278 Handbook Color. +/// \param[in] original the original image. +/// \param[in] reduced the image with a reduced number of colors. +/// +/// \return the PNSNR. +/// +/// Compute the PNSNR. First compute the square difference in each +/// channel. Then compute the mean of those square differences and get +/// the mean over the three channels. Then, get the sqrt of that to +/// have a distance. The PNSNR is a logarithmic quantity of that distance. +float error_test(const std::string original, + const std::string reduced) + +{ + + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::fun::v2v::component<t_rgb8,0> t_component_red; + typedef mln::fun::v2v::component<t_rgb8,1> t_component_green; + typedef mln::fun::v2v::component<t_rgb8,2> t_component_blue; + typedef mln::accu::meta::stat::mean t_mean; + typedef mln::accu::meta::stat::histo1d t_histo; + typedef mln::image2d<t_int_u8> t_img; + typedef mln_trait_op_minus_(t_img,t_img) t_minus; + typedef mln_trait_op_times_(t_minus,t_minus) t_times; + + mln::image2d<mln::value::rgb8> original_rgb8; + mln::image2d<mln::value::rgb8> reduced_rgb8; + + mln::image2d<mln::value::int_u8> original_red; + mln::image2d<mln::value::int_u8> original_green; + mln::image2d<mln::value::int_u8> original_blue; + + mln::image2d<mln::value::int_u8> reduced_red; + mln::image2d<mln::value::int_u8> reduced_green; + mln::image2d<mln::value::int_u8> reduced_blue; + +// mln::image2d<mln::value::int_u8> map_red; +// mln::image2d<mln::value::int_u8> map_green; +// mln::image2d<mln::value::int_u8> map_blue; + +// mln::image1d<unsigned> histo_red; +// mln::image1d<unsigned> histo_green; +// mln::image1d<unsigned> histo_blue; + + t_minus minus_red; + t_minus minus_green; + t_minus minus_blue; + + t_times times_red; + t_times times_green; + t_times times_blue; + + float error_red; + float error_green; + float error_blue; + + float error; + + + mln::io::ppm::load(original_rgb8, original.c_str()); + mln::io::ppm::load(reduced_rgb8, reduced.c_str()); + + original_red = mln::data::transform(original_rgb8, t_component_red()); + original_green = mln::data::transform(original_rgb8, t_component_green()); + original_blue = mln::data::transform(original_rgb8, t_component_blue()); + + reduced_red = mln::data::transform(reduced_rgb8, t_component_red()); + reduced_green = mln::data::transform(reduced_rgb8, t_component_green()); + reduced_blue = mln::data::transform(reduced_rgb8, t_component_blue()); + + minus_red = (original_red - reduced_red); + times_red = minus_red * minus_red; + + minus_green = (original_green - reduced_green); + times_green = minus_green * minus_green; + + minus_blue = (original_blue - reduced_blue); + times_blue = minus_blue * minus_blue; + + error_red = mln::data::compute(t_mean(), times_red); + error_green = mln::data::compute(t_mean(), times_green); + error_blue = mln::data::compute(t_mean(), times_blue); + +// map_red = mln::data::stretch(t_int_u8(), times_red); +// map_green = mln::data::stretch(t_int_u8(), times_blue); +// map_blue = mln::data::stretch(t_int_u8(), times_green); + +// histo_red = mln::data::compute(t_histo(), map_red); +// histo_green = mln::data::compute(t_histo(), map_green); +// histo_blue = mln::data::compute(t_histo(), map_blue); + +// mln::io::plot::save_image_sh(histo_red, "histo_red.sh"); +// mln::io::plot::save_image_sh(histo_green,"histo_green.sh"); +// mln::io::plot::save_image_sh(histo_blue, "histo_blue.sh"); + +// mln::io::pgm::save(map_red, "red.pgm"); +// mln::io::pgm::save(map_green,"green.pgm"); +// mln::io::pgm::save(map_blue, "blue.pgm"); + + error = (error_red + error_green + error_blue)/3.0; + error = mln::math::sqrt(error); + error = 20 * log(255/error); + +// Le PNSNR semble offrir plus d'espace pour la discrimination +// Si les images sont identiques ==> PNSNR = +inf +// Si les images sont très différentes ==> PNSNR = 0 + + return error; +} + + +/// \brief The main entry. +/// +/// This is the front end for error classification. This is the big +/// loop where we walk other image data bases. Computation of +/// automatic thresholds are called from here. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + mln::image1d<float> histo(256); + mln::image1d<float> histo_grp[2]; // histo by group + + histo_grp[0].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + histo_grp[1].init_(mln::box1d(mln::point1d(0),mln::point1d(255))); + + mln::data::fill(histo, mln::literal::zero); + mln::data::fill(histo_grp[0], mln::literal::zero); + mln::data::fill(histo_grp[1], mln::literal::zero); + + t_path original_path[] = {ICDAR_20P_INPUT_IMG_PATH, + AFP_PPM_IMG_PATH}; + +// t_path reduced1_path[] = {ICDAR_20P_MGK30_IMG_PATH, +// AFP_MGK30_IMG_PATH}; + +// t_path reduced1_path[] = {ICDAR_20P_MGK20_IMG_PATH, +// AFP_MGK20_IMG_PATH}; + + t_path reduced1_path[] = {ICDAR_20P_MGK10_IMG_PATH, + AFP_MGK10_IMG_PATH}; + +// t_path reduced2_path[] = {ICDAR_20P_GMP30_IMG_PATH, +// AFP_GMP30_IMG_PATH}; + +// t_path reduced2_path[] = {ICDAR_20P_GMP20_IMG_PATH, +// AFP_GMP20_IMG_PATH}; + + t_path reduced2_path[] = {ICDAR_20P_GMP10_IMG_PATH, + AFP_GMP10_IMG_PATH}; + + + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "ERROR" << std::endl; + std::cout << "plot '-' using 1 with point notitle,\\" << std::endl; + std::cout << " '-' using 1 with point notitle" << std::endl; + + for (int i = 0; i < 2; i++) + { + if (boost::filesystem::exists(original_path[i]) && + boost::filesystem::exists(reduced1_path[i]) && + boost::filesystem::exists(reduced2_path[i]) && + boost::filesystem::is_directory(original_path[i]) && + boost::filesystem::is_directory(reduced1_path[i]) && + boost::filesystem::is_directory(reduced2_path[i])) + { + boost::filesystem::system_complete(original_path[i]); + const t_iter_path end_iter; + float error1 = 0.0; + float error2 = 0.0; + t_path leaf; + t_path reduced1_file; + t_path reduced2_file; + + for (t_iter_path dir_iter(original_path[i]);end_iter!=dir_iter;++dir_iter) + { + leaf = dir_iter->path().leaf(); + reduced1_file = reduced1_path[i] / leaf; + reduced2_file = reduced2_path[i] / leaf; + + error1 = error_test(dir_iter->path().string(), reduced1_file.string()); + error2 = error_test(dir_iter->path().string(), reduced2_file.string()); + + float a1 = 1; + short v1 = (short)mln::math::floor(error2+0.4999); + mln::opt::at(histo,v1) += a1; + mln::opt::at(histo_grp[i],v1) += a1; + +// float a1 = error2 - mln::math::floor(error2); +// float a2 = mln::math::ceil(error2) - error2; +// short v1 = (short)mln::math::floor(error2); +// short v2 = (short)mln::math::ceil(error2); +// mln::opt::at(histo,v1) += a1; +// mln::opt::at(histo,v2) += a2; +// mln::opt::at(histo_grp[i],v1) += a1; +// mln::opt::at(histo_grp[i],v2) += a2; + + std::cout << error1 << " "; + std::cout << error2 << " "; + std::cout << "# " << dir_iter->path().leaf() << std::endl; + } + std::cout << "e" << std::endl; + } + } + + mln::io::plot::save_image_sh(histo, "histo.sh"); + mln::io::plot::save_image_sh(histo_grp[1], "histo_grp1.sh"); + mln::io::plot::save_image_sh(histo_grp[0], "histo_grp2.sh"); + + float threshold = fisher_analysis(histo); + float threshold2 = threshold_histo(avg_histo(histo_grp[1]), + var_histo(histo_grp[1]), + avg_histo(histo_grp[0]), + var_histo(histo_grp[0])); + float threshold3 = threshold2_histo(avg_histo(histo_grp[1]), + var_histo(histo_grp[1]), + avg_histo(histo_grp[0]), + var_histo(histo_grp[0])); + float threshold4 = min_error(histo_grp[1],histo_grp[0]); + + std::cout << "threshold = " << threshold << std::endl; + std::cout << "threshold2 = " << threshold2 << std::endl; + std::cout << "threshold3 = " << threshold3 << std::endl; + std::cout << "threshold4 = " << threshold4 << std::endl; + std::cout << "avg_grp1 = " << avg_histo(histo_grp[1]) << std::endl; + std::cout << "avg_grp2 = " << avg_histo(histo_grp[0]) << std::endl; + + // compute the classification matrix + // for each sub population + + float c00 = cnt_histo(histo_grp[1] | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + + float c10 = cnt_histo(histo_grp[1] | mln::box1d(mln::point1d(threshold+1), + mln::point1d(255))); + + float c01 = cnt_histo(histo_grp[0] | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + + float c11 = cnt_histo(histo_grp[0] | mln::box1d(mln::point1d(threshold+1), + mln::point1d(255))); + + + std::cout << "pop0 = " << cnt_histo(histo_grp[1]) << std::endl; + std::cout << "pop1 = " << cnt_histo(histo_grp[0]) << std::endl; + std::cout << std::endl; + + std::cout << "c00 = " << c00 << std::endl; + std::cout << "c10 = " << c10 << std::endl; + std::cout << "c01 = " << c01 << std::endl; + std::cout << "c11 = " << c11 << std::endl; + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/histo/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/histo/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/histo/histo.cc b/scribo/sandbox/green/exp/annotating/histo/histo.cc new file mode 100644 index 0000000..90b18e4 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/histo/histo.cc @@ -0,0 +1,366 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Build normalized histograms for visualization only. +/// +/// The goal of this code is to build every normalized histograms of +/// an image database for visual inspection. It enables the +/// comparaison of the densities of each image database and the +/// understanding of the differences in this space. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/convert.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/zero.hh> +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/math/sqrt.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> +#include <mln/fun/v2v/component.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +//============================================================================// +// HISTOGRAM +//============================================================================// + +/// \brief Count the number of pixel in the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the number of pixels in the domain. +/// +/// This is simple counting routine that compute the number of pixels +/// in the domain of the histogram. For each site, it sums the +/// frequencies associated to its site. +template <typename I> +mln_value(I) cnt_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) cnt = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + cnt += histo(p); + } + + return cnt; +} + +/// \brief The computing of the normalized histogram. +/// +/// \param[in] input the name of the input image. +/// \param[in] output_map the name of the specific map (hue,sat,val,R,G,B). +/// \param[in] output_histo the name of the output normalized histogram. +/// \param[in] space the name of the studied channel (R,G,B,H,S,V). +/// +/// Compute the normalized histogram in the given channel. First get +/// the channel from the RGB space or the HSV space and then compute +/// its normalized histogram. Normalization is done by dividing each +/// frequency by the sum of frequencies. It's a way to make the +/// histogram independant from the dimension of the input image and +/// then to compare different image databases together. +void histo(const std::string input, + const std::string output_map, + const std::string output_histo, + const char space) + +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_sat_map; + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_val_map; + typedef mln::fun::v2v::component<t_rgb8,0> t_component_r; + typedef mln::fun::v2v::component<t_rgb8,1> t_component_g; + typedef mln::fun::v2v::component<t_rgb8,2> t_component_b; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float sum; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + switch(space) + { + case 'h': map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); break; + case 's': map = mln::data::transform(input_rgb8, t_rgb_to_sat_map()); break; + case 'v': map = mln::data::transform(input_rgb8, t_rgb_to_val_map()); break; + case 'r': map = mln::data::transform(input_rgb8, t_component_r()); break; + case 'g': map = mln::data::transform(input_rgb8, t_component_g()); break; + case 'b': map = mln::data::transform(input_rgb8, t_component_b()); break; + default: break;// crash + } + + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = cnt_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + + mln::io::pgm::save(map, output_map.c_str()); + mln::io::plot::save_image_sh(histo_float, output_histo.c_str()); +} + +//============================================================================// +// MAIN +//============================================================================// + + +/// \brief The main entry. +/// +/// This a front end to compute histogram with boost plumberies. There +/// is 2 uses cases, the computing over the icdar database and over +/// the afp database. We force the computing over the six channel +/// (H,S,V,R,G,B) in order to select the channel the most specific for +/// a database. It seems that saturation channel and value channel are +/// the most specific to describe each database. Take care to +/// coherence of the output directories for each source of images. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + +// t_path full_path[] = {t_path(ICDAR_20P_TEXT_ONLY_IMG_PATH), +// t_path(ICDAR_20P_TEXT_COLOR_IMG_PATH), +// t_path(ICDAR_20P_TEXT_PHOTO_IMG_PATH)}; + +// t_path full_path[] = {t_path(AFP_INPUT_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP30_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP20_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_GMP10_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK30_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK20_IMG_PATH)}; +// t_path full_path[] = {t_path(AFP_MGK10_IMG_PATH)}; + +// t_path full_path[] = {t_path(ICDAR_20P_INPUT_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_GMP30_IMG_PATH)}; + t_path full_path[] = {t_path(ICDAR_20P_GMP20_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_GMP10_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK30_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK20_IMG_PATH)}; +// t_path full_path[] = {t_path(ICDAR_20P_MGK10_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + t_path directory; + t_path leaf; + t_path output_map; + t_path output_histo; + + std::cerr << "entering " << full_path[i] << std::endl; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + std::cerr << dir_iter->path() << std::endl; + + leaf = dir_iter->path().leaf(); + +// directory = ANNOTATING_AFP_R_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_R_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_R_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_R_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_R_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_R_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_R_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_R_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'r'); + +// directory = ANNOTATING_AFP_G_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_G_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_G_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_G_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_G_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_G_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_G_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_G_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'g'); + +// directory = ANNOTATING_AFP_B_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_B_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_B_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_B_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_B_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_B_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_B_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_B_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'b'); + +// directory = ANNOTATING_AFP_H_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_H_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_H_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_H_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_H_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_H_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_H_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_H_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'h'); + +// directory = ANNOTATING_AFP_S_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_S_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_S_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_S_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_S_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_S_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_S_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_S_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 's'); + +// directory = ANNOTATING_AFP_V_INPUT_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP30_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP20_RET_PATH; +// directory = ANNOTATING_AFP_V_GMP10_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK30_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK20_RET_PATH; +// directory = ANNOTATING_AFP_V_MGK10_RET_PATH; + +// directory = ANNOTATING_ICDAR_V_INPUT_RET_PATH; +// directory = ANNOTATING_ICDAR_V_GMP30_RET_PATH; + directory = ANNOTATING_ICDAR_V_GMP20_RET_PATH; +// directory = ANNOTATING_ICDAR_V_GMP10_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK30_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK20_RET_PATH; +// directory = ANNOTATING_ICDAR_V_MGK10_RET_PATH; + output_histo = change_extension(directory / leaf, ".sh"); + output_map = change_extension(directory / leaf, ".pgm"); + + histo(dir_iter->path().string(), + output_map.string(), + output_histo.string(), + 'v'); + } + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/hsv/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/hsv/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/hsv/hsv.cc b/scribo/sandbox/green/exp/annotating/hsv/hsv.cc new file mode 100644 index 0000000..507d07b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/hsv/hsv.cc @@ -0,0 +1,912 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet HSV operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Hue +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +/// +/// This operator try to integrate many processing in one shot, after +/// HSV transformation. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/arith/minus.hh> +#include <mln/arith/div.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/convert.hh> +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/zero.hh> +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> +#include <mln/math/sqr.hh> +#include <mln/math/sqrt.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/fun/v2v/rgb_to_saturation_map.hh> +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +//============================================================================// +// HISTOGRAM +//============================================================================// + +/// Histogram +/// \{ +/// \brief Histogram processing methods. +/// +/// In this part, we define methods for histogram processing as average, +/// peak, variance etc ... + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +mln_value(I) count_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) result = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + result += histo(p); + + return result; +} + +/// \brief Sum the whole frequencies. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the number of pixels in the domain. +/// +/// By summing all the frequencies of the domain, we count in fact the +/// number of pixels in the image. +template <typename I> +mln_value(I) sum_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) sum = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + sum += histo(p); + + return sum; +} + +/// \brief Count the null frequencies. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the number of null frequencies. +/// +/// It's a way to access the number of distinct colors in the +/// histogram by comparing it with the histogram domain. +template <typename I> +mln_value(I) count_null_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + mln_value(I) count = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + if (0 == histo(p)) + count++; + + return count; +} + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +mln_coord(mln_site_(I)) peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + mln_value(I) v_max = mln::opt::at(histo, mln::literal::zero); + mln_coord(mln_site_(I)) p_max = mln::literal::zero; + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Find the maximum frequency of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the maximum frequency of the histogram. +/// +/// Find the peak and return its value, not its position. We use that +/// function to normalize histograms. +template <typename I> +mln_value(I) max_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the value of the peak from the histogram + mln_value(I) max = mln::opt::at(histo, mln::literal::zero); + mln_piter(I) p(histo.domain()); + + for_all(p) + { + max = mln::math::max(histo(p),max); + } + + return max; +} + +/// \brief Find the mean of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the aveerage of the histogram. +/// +/// Compute the mean. +template <typename I> +float mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief Compare the histogram with the equi distributed histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the distance between the equi distributed histogram. +/// +/// Compute the square euclidian distance between histogram and the +/// equi distributed histogram. The equi distributed histogram is an +/// histogram in which each bin has the same frequency (id est 1/256 +/// for instance). The integral equals one for this two +/// histograms. This is a major contribution test to differentiate +/// image database. +template <typename I> +float cmp_equi_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + var += mln::math::sqr(histo(p) - (1/256.0)); + } + + var = var / sum; + + return var; +} + +/// \brief Compute the variance on the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the variance. +/// +/// Compute the variance by substracting the mean. +template <typename I> +float var_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + var += mln::math::sqr(p.ind() - mean) * histo(p); + } + + var = var / sum; + + return var; +} + +/// \brief Find the mean of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the aveerage of the histogram. +/// +/// Compute the mean. +template <typename I> +float mean_frequency_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum++; + mean += histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief Compute the standard deviation on the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the dstandard deviation. +/// +/// Compute the standard deviation by substracting the mean +template <typename I> +float stddev_frequency_histo(const mln::Image& histo_, float mean) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the var of the histogram + float sum = 0; + float var = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum++; + var += mln::math::sqr(histo(p)-mean); + } + + var = mln::math::sqrt(var / sum); + + return var; +} +/// \} + +//============================================================================// +// HUE TEST +//============================================================================// + +/// Hue test +/// \{ +/// \brief Hue test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the hue analysis. + + +/// \brief Label a grey value. +/// +/// \param[in] int_u8 the grey value. +/// +/// \return the reference color for this grey value. +/// +/// Classify grey value in three class, white, black and medium_gray. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the +/// primary colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + + if (10 > hue) + result = mln::literal::red; + else if (32 > hue) + result = mln::literal::orange; + else if (53 > hue) + result = mln::literal::yellow; + else if (74 > hue) + result = mln::literal::green; // chartreuse + else if (96 > hue) + result = mln::literal::green; + else if (116 > hue) + result = mln::literal::green;// turquoise, aigue-marine + else if (138 > hue) + result = mln::literal::green; // cyan + else if (159 > hue) + result = mln::literal::blue; // azur + else if (181 > hue) + result = mln::literal::blue; + else if (202 > hue) + result = mln::literal::violet; + else if (223 > hue) + result = mln::literal::pink; + else // if (244 > hue) + result = mln::literal::red; + + return result; +} + +/// \brief The hue test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// \param[in] threshold the limit to compute the histogram contribution. +/// +/// \return the proportion of pixels that pass the test. +/// +/// This is an augmented test. We first normalized the histogram to +/// obtain a discrete distribution. Then we compute the mean (or the +/// peak, i don't really know what is the best) and we look at the +/// proportion of the pixels around the mean or the peak. This +/// proportion tell us how histogram distribution is concentrated +/// around the modal value. If a hue modal value exist, we can say +/// that the image is colorized. +float hue_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float prop; + short peak; + mln::value::rgb8 color; + float sum; + mln::point1d inf; + mln::point1d sup; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + peak = mean_histo(histo); //peak_histo(histo); + color = label_hue(peak); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo_float|mln::box1d(inf,sup)); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); +// std::cout << "peak = " << peak << std::endl; +// std::cout << "color = " << color << std::endl; + + return prop; +} +/// \} + +//============================================================================// +// SATURATION TEST +//============================================================================// + +/// Saturation test +/// \{ +/// \brief Saturation test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the +/// saturation analysis. + + +/// \brief The saturation test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// \param[in] threshold for deciding which is low saturation or not. +/// +/// \return the proportion of pixels which pass the test. +/// +/// This is the augmented test, we normalized the histogram and then +/// we count the proportion of the histogram below the threshold. +float saturation_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_saturation_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float sum; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_saturation_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + cnt1 = count_histo(histo_float | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} +/// \} + +//============================================================================// +// VALUE TEST +//============================================================================// + +/// Value test +/// \{ +/// \brief Value test image processing stuff. +/// +/// In this part, we define every thing that is correlated to the +/// value analysis. + + + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief This is the Value test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of result histogram. +/// \param[in] tmp the name of the output value (HSV) map. +/// \param[in] threshold the range around the peak where R is computed. +/// +/// \return the proportion of pixels that pass the test. +/// +/// This is the augmented version of the Millet test. This code is not +/// stable. There is a lots of tests on. The Millet test is computed +/// with stddev2 call. We test simple counting around the pic as we +/// do for other tests but it's not really insteresting. The prop4 is +/// the best for me. We test the difference between the distribution and the +/// equi-distribution. +float value_test(const std::string input, + const std::string output, + const std::string tmp, + const short threshold) + +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + mln::image1d<float> histo_float; + float cnt1; + float cnt2; + float prop; + float sum; + float prop4; + short peak; + mln::point1d inf; + mln::point1d sup; + + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_value_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + sum = sum_frequency_histo(histo); + histo_float = mln::data::convert(float(), histo) / sum; + prop4 = cmp_equi_frequency_histo(histo_float); + peak = peak_histo(histo); // mean_histo(histo); + //prop = stddev2(histo, peak, threshold); + inf = mln::point1d(mln::math::max(0, peak-threshold)); + sup = mln::point1d(mln::math::min(255, peak+threshold)); + cnt1 = count_histo(histo_float|mln::box1d(inf,sup)); + cnt2 = count_histo(histo_float); + prop = ((100.0 * cnt1) / cnt2); + + std::cerr << "peak = " << peak << std::endl; + std::cerr << "inf = " << inf << std::endl; + std::cerr << "sup = " << sup << std::endl; + std::cerr << "cnt1 = " << cnt1 << std::endl; + std::cerr << "cnt2 = " << cnt2 << std::endl; + std::cerr << "prop = " << prop << std::endl; + std::cerr << "prop4= " << prop4 << std::endl; + + mln::io::plot::save_image_sh(histo_float, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + +//============================================================================// +// MAIN +//============================================================================// + + +/// \brief The main entry. +/// +/// This is a front end for launching image processing test. We deal +/// with boost library to walk on image database directories. Nothing +/// very interesting here, it is a lot of plumberies. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + +// t_path full_path[] = {t_path(ICDAR_20P_TEXT_ONLY_IMG_PATH), +// t_path(ICDAR_20P_TEXT_COLOR_IMG_PATH), +// t_path(ICDAR_20P_TEXT_PHOTO_IMG_PATH)}; + + t_path full_path[] = {t_path(AFP_PPM_IMG_PATH)}; + + std::cout << "#!/usr/bin/gnuplot" << std::endl; + std::cout << "set terminal x11 persist 1" << std::endl; + std::cout << "#HUE - SATURATION - VALUE" << std::endl; + std::cout << "plot '-' using 1:2 with point notitle,\\" << std::endl; + std::cout << " '-' using 1:2 with point notitle,\\" << std::endl; + std::cout << " '-' using 1:2 with point notitle" << std::endl; + + for (int i = 0; i < 1; ++i) + { + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + t_path directory; + t_path leaf; + t_path output; + t_path tmp; + + std::cerr << "entering " << full_path[i] << std::endl; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + std::cerr << dir_iter->path() << std::endl; + // concatenation de chaine +// directory = (ANNOTATING_ICDAR_H_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_H_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = hue_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 20); + + std::cout << prop << " "; + +// directory = (ANNOTATING_ICDAR_S_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_S_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = saturation_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 25); + + std::cout << prop << " "; + +// directory = (ANNOTATING_ICDAR_V_INPUT_RET_PATH); + directory = (ANNOTATING_AFP_V_INPUT_RET_PATH); + leaf = dir_iter->path().leaf(); + output = change_extension(directory / leaf, ".sh"); + tmp = change_extension(directory / leaf, ".pgm"); + + prop = value_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 15); + + std::cout << prop << " "; + std::cout << "# " << dir_iter->path().leaf() << std::endl; + } + std::cout << "e" << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/hue/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/hue/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/hue/hue.cc b/scribo/sandbox/green/exp/annotating/hue/hue.cc new file mode 100644 index 0000000..6e1195c --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/hue/hue.cc @@ -0,0 +1,402 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet hue operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Hue +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. + + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_hue_map.hh> +#include <mln/value/rgb8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + + + +/// \brief Label a grey value. +/// +/// \param[in] int_u8 the grey value. +/// +/// \return the reference color for this grey value. +/// +/// Classify grey value in three class, white, black and medium_gray. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the +/// primary colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + + if (10 > hue) + result = mln::literal::red; + else if (32 > hue) + result = mln::literal::orange; + else if (53 > hue) + result = mln::literal::yellow; + else if (74 > hue) + result = mln::literal::green; // chartreuse + else if (96 > hue) + result = mln::literal::green; + else if (116 > hue) + result = mln::literal::green;// turquoise, aigue-marine + else if (138 > hue) + result = mln::literal::green; // cyan + else if (159 > hue) + result = mln::literal::blue; // azur + else if (181 > hue) + result = mln::literal::blue; + else if (202 > hue) + result = mln::literal::violet; + else if (223 > hue) + result = mln::literal::pink; + else // if (244 > hue) + result = mln::literal::red; + + +// if (14 > hue) +// result = mln::literal::red; +// else if (29 > hue) +// result = mln::literal::orange; +// else if (45 > hue) +// result = mln::literal::yellow; +// else if (113 > hue) +// result = mln::literal::green; +// else if (149 > hue) +// result = mln::literal::cyan; +// else if (205 > hue) +// result = mln::literal::blue; +// else if (235 > hue) +// result = mln::literal::violet; +// else if (242 > hue) +// result = mln::literal::pink; +// else +// result = mln::literal::red; + + return result; +} + + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Find the peak of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the bin which contains the greatest value. +/// +/// Compute the position of the peak of the histogram. To do this, we +/// view evrey bin and we maintain the maxima of the values and the +/// position. At the end, we return the position which contains the +/// greatest value. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Compute the average of the histogram. +/// +/// \param[in] histo_ the histogram. +/// +/// \return the average of the histogram. +/// +/// Compute the mean of the histogram by summing the values of each +/// bin ponderated by its frequency. The result is divided by the sum +/// of the frequencies. +template <typename I> +unsigned mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + + +/// \brief The hue test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] tmp the name of the hue map to output. +/// +/// \return the proportion of pixels that pass the test. +/// +/// Load the input image, transform it to get the hue_map and compute +/// the histogram. Then count the number of pixels that are between +/// peak-threshold and peak +threshold. Print the peak and the +/// percentage of pixels around the pic. +float hue_test(const std::string input, + const std::string output, + const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_hue_map<8> t_rgb_to_hue_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + unsigned peak; + mln::value::rgb8 color; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_hue_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + peak = mean_histo(histo); //peak_histo(histo); + color = label_hue(peak); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(peak-threshold), + mln::point1d(peak+threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + std::cout << "peak = " << peak << std::endl; + std::cout << "color = " << color << std::endl; + + return prop; +} + + +/// \brief Main entry. +/// +/// It is a front end which managing the iteration through one image +/// data base with boost. Save hue map and hue histogram. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_ICDAR_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = hue_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 20); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/hue/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/hue/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/hue/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/hue/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/hue/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/hue/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/nb_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/nb_color/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc b/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc new file mode 100644 index 0000000..2331c61 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/nb_color/nb_color.cc @@ -0,0 +1,171 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Compute the number of colors in an image. +/// +/// Build histogram of colors and count the bins different from zero. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/math/count.hh> +#include <mln/accu/stat/histo3d_rgb.hh> + +#include <mln/binarization/threshold.hh> + +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +// #include <mln/morpho/opening/volume.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/pw/value.hh> +#include <mln/pw/cst.hh> + +#include <mln/util/timer.hh> + +#include <mln/value/rgb8.hh> +//#include <mln/value/rgb.hh> + + +/// \brief Count the colors. +/// +/// \param[in] image the name of the image to process. +/// +/// \return the number of colors. +/// +/// Count the color by building histogram. Strange filtering is +/// done. We use also the technique of regional maxima in +/// comment. Quantification is done to reduce the size of the +/// histogram as well. + +// n < 8, n is the degree of quantification +template <unsigned n> +unsigned count_image_color(const std::string& image) +{ + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<n> t_rgbn; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rgbn> t_image2d_rgbn; + typedef mln::image3d<unsigned> t_histo3d; + typedef mln::image3d<bool> t_histo3d_bool; + typedef mln::fun::v2v::rgb8_to_rgbn<n> t_rgb8_to_rgbn; + typedef mln::accu::meta::stat::histo3d_rgb t_histo3d_fun; + typedef mln::accu::meta::math::count t_count_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_rgbn input_rgbn; + t_image2d_rgbn output_rgbn; + t_histo3d histo; +// t_histo3d opened; + t_histo3d_bool filtered; + + mln::io::ppm::load(input_rgb8, image.c_str()); + + unsigned nb_pixel = input_rgb8.ncols() * input_rgb8.nrows(); + unsigned min_volume = (unsigned)(nb_pixel * 0.0001); + unsigned nb_color = 0; + +// input_rgbn = mln::data::transform(input_rgb8, t_rgb8_to_rgbn()); + histo = mln::data::compute(t_histo3d_fun(), input_rgb8); +// opened = mln::morpho::opening::volume(histo, mln::c6(), min_volume); + filtered = mln::binarization::threshold(histo, min_volume); + nb_color = mln::data::compute(t_count_fun(), + (filtered|(mln::pw::value(filtered)!=0)).rw()); + + return nb_color; +} + + +/// \brief Main entry. +/// +/// The main routine makes the base driver job. It looks after many +/// directories and lists every images in it. We use the boost library +/// to do it. Statistics are computed on the number of colors. The +/// first idea was to answer the question is this descriptor allow to +/// recognize a specific base. +int main() +{ + typedef boost::filesystem::path t_path; + //typedef boost::filesystem::initial_path<t_path()> t_init_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + unsigned count = 0; + unsigned sum1 = 0; + unsigned sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + unsigned val = count_image_color<8>(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/saturation/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/saturation/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/saturation/saturation.cc b/scribo/sandbox/green/exp/annotating/saturation/saturation.cc new file mode 100644 index 0000000..b5834a1 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/saturation/saturation.cc @@ -0,0 +1,175 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet saturation operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to Sat +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_saturation_map.hh> + +#include <mln/io/ppm/load.hh> +//#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief The saturation test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of the histogram to output. +/// \param[in] threshold for deciding which is low saturation or not. +/// +/// \return the proportion of pixels that pass the test. +/// +/// Load the input image, transform it to get the saturation_map and compute +/// the histogram. Then count the number of pixels that are under the threshold. +/// Then return the proportion of pixels. +float saturation_test(const std::string input, + const std::string output, +// const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_saturation_map<8> t_rgb_to_saturation_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_saturation_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + cnt1 = count_histo(histo | mln::box1d(mln::point1d(0), + mln::point1d(threshold))); + cnt2 = mln::geom::nsites(input_rgb8); + prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); +// mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + + +/// \brief the main entry. +/// +/// This is a front end for image processing routine. It manages the +/// calling of every image in the database. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_ICDAR_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = saturation_test(dir_iter->path().string(), + output.string(), +// tmp.string(), + 25); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/saturation/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/saturation/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/saturation/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/saturation/text-only.txt diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/stddev_color/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/stddev_color/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc b/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc new file mode 100644 index 0000000..365a9c3 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/stddev_color/stddev_color.cc @@ -0,0 +1,216 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. + +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/math/sqr.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +float stddev_color(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + t_histo1d histo; + t_point1d max_site; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + return stddev; +} + + +/// \brief Main entry. +/// +/// Front end for image processing. It is in the main routine where we +/// go through the image data base (using boost to do it). +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float count = 0; + float sum1 = 0; + float sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + float val = stddev_color(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/stddev_color_16/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/stddev_color_16/Makefile.am diff --git a/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc b/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc new file mode 100644 index 0000000..fc55547 --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/stddev_color_16/stddev_color_16.cc @@ -0,0 +1,277 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet clipart detection [millet.phd.2008.pdf] +/// +/// The aim of this descriptor is to recognize clipart. To do this, we +/// assume that clipart have their histogram concentrated around their +/// mode. This is particularly true if the clipart is design by hand, +/// because a very small number of grey tones or colors are used to +/// draw it. But sometimes, computer assists their creation and we can +/// find some continuous set of tones and this artefact create noise +/// for the detection technique describe bellow. When photographies +/// has some large uniform border, the detection can fail. To improve +/// the method, Millet decide to subdivise the image in 16 sub images +/// on which it applies the test. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/max_site.hh> +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> + +#include <mln/debug/println.hh> + +#include <mln/data/compute.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> +#include <mln/data/paste.hh> + +#include <mln/fun/v2v/rgb8_to_int_u8.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/math/sqr.hh> +#include <mln/math/max.hh> + +#include <mln/opt/at.hh> + +#include <mln/value/rgb8.hh> +#include <mln/value/int_u.hh> + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] input_int_u8 the 8 bits input image to analyze. +/// \param[in] name_histo the name of the output histogram. +/// \param[in] name_image the name of the ouput sub image. +/// +/// \return the deviation, but at this time nothing.. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). +float stddev_color(mln::image2d<mln::value::int_u8> input_int_u8) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_histo1d histo; + + histo = mln::data::compute(t_histo1d_fun(), input_int_u8); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter_(t_histo1d) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + // Compute the specific stddev + + float stddev_low = 0.0; + float stddev_up = 0.0; + float stddev = 0.0; + + if (250 > p_max) + for (short i = p_max+1; i < p_max+6; ++i) + stddev_up += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + if (5 < p_max) + for (short i = p_max-1; i > p_max-6; --i) + stddev_low += r(p_max, mln::opt::at(histo,p_max), + i, mln::opt::at(histo,i)); + + stddev = (250 < p_max)? stddev_low : (5 > p_max)? stddev_up : + (stddev_low + stddev_up)/2; + + return stddev; +} + +/// \brief Divide the image in 16 sub images. +/// +/// \param[in] image the input image. +/// +/// \result nothing. +/// +/// Divive the input image in 16 by uniform and geometrical +/// method. When a sub image is ready, call the stddev routine to show +/// stats on it. +float stddev_color_16(const std::string& image) +{ + typedef mln::point1d t_point1d; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + typedef mln::image1d<unsigned> t_histo1d; + typedef mln::fun::v2v::rgb8_to_int_u8 t_rgb8_to_int_u8; + typedef mln::accu::meta::stat::histo1d t_histo1d_fun; + typedef mln::accu::max_site<t_histo1d> t_max_site_fun; + + t_image2d_rgb8 input_rgb8; + t_image2d_int_u8 input_int_u8; + + mln::io::ppm::load(input_rgb8, image.c_str()); + input_int_u8 = mln::data::transform(input_rgb8, t_rgb8_to_int_u8()); + + // IMAGE SPLITTING PHASE + mln::box2d domain = input_int_u8.domain(); + mln::point2d pmin = domain.pmin(); + mln::point2d pmax = domain.pmax(); + + unsigned sz_row = (pmax.row() - pmin.row())/ 4; + unsigned sz_col = (pmax.col() - pmin.col())/ 4; + float stddev = 0.0; + + // Divide the domain in nine sub-domains. + for (unsigned i = 0; i < 4; ++i) + for (unsigned j = 0; j < 4; ++j) + { + mln::point2d min(pmin.row()+sz_row*i,pmin.col()+sz_col*j); + mln::point2d max(pmin.row()+sz_row*(i+1),pmin.col()+sz_col*(j+1)); + mln::box2d dom(min,max); + + // Save it + t_image2d_int_u8 input_1o16_int_u8(dom); + + mln::data::paste(input_int_u8 | dom, input_1o16_int_u8); + + stddev = mln::math::max(stddev, stddev_color(input_1o16_int_u8)); + } + + return stddev; +} + + +/// \brief Main entry. +/// +/// Just a front end for image processing routine. Use boost to go +/// through the image database. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ANNOTATING_1_BILL_IMG_PATH), + t_path(ANNOTATING_1_FAX_IMG_PATH), + t_path(ANNOTATING_1_HANDWRITTEN_IMG_PATH), + t_path(ANNOTATING_1_LOGO_IMG_PATH), + t_path(ANNOTATING_1_MAP_IMG_PATH), + t_path(ANNOTATING_1_PHOTO_IMG_PATH), + t_path(ANNOTATING_1_SCREENSHOT_IMG_PATH), + t_path(ANNOTATING_1_SLIDE_IMG_PATH), + t_path(ANNOTATING_1_TYPED_IMG_PATH)}; + + for (int i = 0; i < 9; ++i) + { + std::cerr << "entering " << full_path[i] << std::endl; + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float count = 0; + float sum1 = 0; + float sum2 = 0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + float val = stddev_color_16(dir_iter->path().string()); + + ++count; + sum1 += val; + sum2 += val*val; + + std::cout << dir_iter->path().string() << " => " << val << std::endl; + } + + float mean = sum1 / count; + float var = ((float)sum2 / count) - (mean * mean); + + std::cout << "mean : " << mean << std::endl; + std::cout << "var : " << var << std::endl; + } + } + + return 0; +} diff --git a/scribo/sandbox/green/exp/regional_maxima/Makefile.am b/scribo/sandbox/green/exp/annotating/value/Makefile.am similarity index 100% copy from scribo/sandbox/green/exp/regional_maxima/Makefile.am copy to scribo/sandbox/green/exp/annotating/value/Makefile.am diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-color.txt b/scribo/sandbox/green/exp/annotating/value/text-color.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-color.txt copy to scribo/sandbox/green/exp/annotating/value/text-color.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-img.txt b/scribo/sandbox/green/exp/annotating/value/text-img.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-img.txt copy to scribo/sandbox/green/exp/annotating/value/text-img.txt diff --git a/milena/sandbox/green/exp/annotating/achromastism/text-only.txt b/scribo/sandbox/green/exp/annotating/value/text-only.txt similarity index 100% copy from milena/sandbox/green/exp/annotating/achromastism/text-only.txt copy to scribo/sandbox/green/exp/annotating/value/text-only.txt diff --git a/scribo/sandbox/green/exp/annotating/value/value.cc b/scribo/sandbox/green/exp/annotating/value/value.cc new file mode 100644 index 0000000..cade88b --- /dev/null +++ b/scribo/sandbox/green/exp/annotating/value/value.cc @@ -0,0 +1,468 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Implement the Millet value operator [millet.phd.2008.pdf] +/// +/// This is the Millet code for moving from RGB space to value +/// one. Formulae are classical, we can find them on the web. +/// +/// Val = max(R,G,B). +/// Sat = (max(R,G,B) - min(R,G,B))/max(R,G,B). +/// IF R = max(R,G,B) THEN Hue = 60 * [(V-B)/(max(R,G,B)-min(R,G,B))]. +/// IF G = max(R,G,B) THEN Hue = 60 * [2 + (B-R)/(max(R,G,B)-min(R,G,B))]. +/// IF B = max(R,G,B) THEN Hue = 60 * [4 + (R-G)/(max(R,G,B)-min(R,G,B))]. +#include <iostream> +#include <sstream> +#include <boost/filesystem.hpp> + +#include <mln/img_path.hh> + +#include <mln/accu/stat/histo1d.hh> + +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/dmorph/image_if.hh> + +#include <mln/data/compute.hh> +#include <mln/data/stretch.hh> +#include <mln/data/transform.hh> + +#include <mln/literal/colors.hh> +#include <mln/literal/grays.hh> + +#include <mln/math/max.hh> +#include <mln/math/min.hh> + +#include <mln/opt/at.hh> + +#include <mln/geom/nsites.hh> + +#include <mln/fun/v2v/rgb_to_value_map.hh> + +#include <mln/io/ppm/load.hh> +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/rgb8.hh> + +/// \brief Label the value. +/// +/// \param[in] val the value in [0..255]. +/// +/// \return the reference color. +/// +/// Segment the grey value in three classes. +mln::value::rgb8 label_val(const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (82 > val) + result = mln::literal::black; + else if (179 > val) + result= mln::literal::medium_gray; + else + result = mln::literal::white; + + return result; +} + + +/// \brief Label color with orange reference or brown reference. +/// +/// \param[in] color the rgb8 color. +/// \param[in] sat the HSV saturation. +/// \param[in] val the HSV value. +/// +/// \return the reference color with which it has been associated to. +/// +/// Discriminate between orange and brown colors which are at the same +/// position on the hue wheel. +mln::value::rgb8 label_orange_or_brown(const mln::value::rgb8 color, + const mln::value::int_u8 sat, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::orange == color) + { + unsigned dist_orange = mln::math::abs(sat - 184) + + mln::math::abs(val - 65); + + unsigned dist_brown = mln::math::abs(sat - 255) + + mln::math::abs(val - 125); + + if (dist_orange < dist_brown) + result = mln::literal::orange; + else + result = mln::literal::brown; + } + else + result = color; + + return result; +} + +/// \brief Label between yellow and green color. +/// +/// \param[in] color the rgb8 color. +/// \param[in] val the HSV value. +/// +/// \return the reference color associated to that color. +/// +/// Segment between yellow and green because the discrimination is not +/// so easy based on the hue wheel. +mln::value::rgb8 label_yellow_or_green(const mln::value::rgb8 color, + const mln::value::int_u8 val) +{ + mln::value::rgb8 result; + + if (mln::literal::yellow == color) + { + // Is it green or yellow ? + if (80 > val) + result = mln::literal::green; + else + result = mln::literal::yellow; + } + else + return color; + + return result; +} + +/// \brief Label hue color. +/// +/// \param[in] hue the HSV hue canal for a pixel. +/// +/// \return a hue segmentation of the hue wheel. +/// +/// The old classification given by Millet is commented. Mine is ready +/// to use. We need to change reference colors to make the primary +/// colors, the secondary ones, etc... +mln::value::rgb8 label_hue(const mln::value::int_u8 hue) +{ + mln::value::rgb8 result; + + if (14 > hue) + result = mln::literal::red; + else if (29 > hue) + result = mln::literal::orange; + else if (45 > hue) + result = mln::literal::yellow; + else if (113 > hue) + result = mln::literal::green; + else if (149 > hue) + result = mln::literal::cyan; + else if (205 > hue) + result = mln::literal::blue; + else if (235 > hue) + result = mln::literal::violet; + else if (242 > hue) + result = mln::literal::pink; + else + result = mln::literal::red; + + return result; +} + +/// \brief Sum all the bins of the histogram. +/// +/// \param[in] img the histogram based on image. +/// +/// \return the sum of the overall bins. +/// +/// Sum evry bins and return the result. +template <typename I> +unsigned count_histo(const mln::Image& img_) +{ + const I& img = exact(img_); + + mln_precondition(img.is_valid()); + + unsigned result = 0; + + mln_piter(I) p(img.domain()); + + for_all(p) + result += img(p); + + return result; +} + +/// \brief Detect the peak of the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the position of the peak. +/// +/// This is a typical accumulator use case but as it needs the +/// position it couldn't be one of them. Look at the last maxima in +/// the curve and return its position. +template <typename I> +unsigned peak_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the peak of the histogram + unsigned v_max = mln::opt::at(histo, 0); + short p_max = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + if (v_max < histo(p)) + { + v_max = histo(p); + p_max = p.ind(); + } + } + + return p_max; +} + +/// \brief Compute the average of the histogram. +/// +/// \param[in] histo_ the image which represents the histogram. +/// +/// \return the average of the histogram. +/// +/// This code should be an accumulator. It computes the mean of the +/// histogram, just stats in loop. Compute the sum, compute the +/// ponderate sum and divide the second by the first. +template <typename I> +unsigned mean_histo(const mln::Image& histo_) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Find the mean of the histogram + float sum = 0; + float mean = 0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + sum += histo(p); + mean += p.ind()*histo(p); + } + + mean = mean / sum; + + return mean; +} + +/// \brief The R function of Millet +/// +/// \param[in] p the position of the pic. +/// \param[in] histo_p the histo value of the pic. +/// \param[in] x the position of the element which we compute the contrib. +/// \param[in] histo_x the histo value of that element. +/// +/// \result the contribution of the element x. +/// +/// This function compute the variance-like contribution of an element +/// x linked to the pic of the histogram. In fact, every thing is like +/// we compute a square distance-like between the element x and the +/// pic in the normalized histogram. Notice that the normalized +/// histogram is the histogram divide by the value of it's pic. So at +/// the pic, the value equals 1. It's a current representation of the +/// histogram in image processing, we can find it in gimp for exemple. +float r(short p, unsigned histo_p, short x, unsigned histo_x) +{ + float result = mln::math::sqr(((float)histo_x / histo_p) * (x-p)); + + return result; +} + + +/// \brief The stddev3 is an internal stuff. +/// +/// \param[in] histo_ the image which represents the histogram. +/// \param[in] peak the position of the histogram peak. +/// +/// \return simple computing of deviation. +/// +/// This is an internal stuff. It splits the computing for easy +/// reusing practice. Sum the R contribution. +template <typename I> +float stddev3(const mln::Image& histo_, unsigned peak) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + // Compute stddev + + float stddev = 0.0; + + mln_piter(I) p(histo.domain()); + + for_all(p) + { + stddev += r((short)peak, mln::opt::at(histo,peak), p.ind(), histo(p)); + } + + return stddev; +} + + +/// Brief compute the whole deviation of Millet +/// +/// \param[in] image the input image to analyze. +/// \param[in] peak the peak of the histogram. +/// \param[in] limit the threshold to compute the contribution. +/// +/// \return the deviation. +/// +/// The deviation uses the R function. It stats by finding the pic. If +/// the pic is near the rigth border of the histo, compute the R +/// function on the left neighbouring of the pic. If the pic is near +/// the left border of the histo, compute the R function on the right +/// neigbouring. Otherwise, compute the average of the right and left +/// results. The selected neighbouring is composed of five pixels at +/// the right or at the left of the pic. The detection of clipart +/// assumes that the majority of the energy of the histogram is closed +/// to the pic (five pixels around it). The test is generalized by +/// making constants as parameters. +template <typename I> +float stddev2(const mln::Image& histo_, unsigned peak, unsigned limit) +{ + const I& histo = exact(histo_); + + mln_precondition(histo.is_valid()); + + float stddev_low = 0.0; + float stddev_up = 0.0; + float ret = 0.0; + + // A transformer avec des iterators + + if (250 > peak) + stddev_up = stddev3(histo |mln::box1d(mln::point1d(peak+1), + mln::point1d(peak+limit)), peak); + + if (5 < peak) + stddev_low = stddev3(histo |mln::box1d(mln::point1d(peak-limit), + mln::point1d(peak-1)), peak); + + ret = (250 < peak)? stddev_low : (5 > peak)? stddev_up : + (stddev_low + stddev_up)/2; + + return ret; +} + + +/// \brief This is the main Millet test. +/// +/// \param[in] input the name of the input image. +/// \param[in] output the name of result histogram. +/// \param[in] tmp the name of the output value (HSV) map. +/// \param[in] threshold the range around the peak where R is computed. +/// +/// \return the proportion of pixels that pass the test. +float value_test(const std::string input, + const std::string output, + const std::string tmp, + const unsigned threshold) + +{ + typedef mln::fun::v2v::rgb_to_value_map<8> t_rgb_to_value_map; + + mln::image2d<mln::value::rgb8> input_rgb8; + mln::image2d<mln::value::int_u8> map; + mln::image1d<unsigned> histo; + unsigned cnt1; + unsigned cnt2; + float prop; + unsigned peak; + + mln::io::ppm::load(input_rgb8, input.c_str()); + + map = mln::data::transform(input_rgb8, t_rgb_to_value_map()); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), map); + peak = peak_histo(histo); // mean_histo(histo); + prop = stddev2(histo, peak, threshold); +// cnt1 = count_histo(histo | mln::box1d(mln::point1d(peak-threshold), +// mln::point1d(peak+threshold))); +// cnt2 = mln::geom::nsites(input_rgb8); +// prop = ((100.0 * cnt1) / cnt2); + + mln::io::plot::save_image_sh(histo, output.c_str()); + mln::io::pgm::save(map, tmp.c_str()); + + return prop; +} + + +/// \brief The main entry. +/// +/// This is the front end for using directories with boost. It calls +/// the true image processing routine. +int main() +{ + typedef boost::filesystem::path t_path; + typedef boost::filesystem::directory_iterator t_iter_path; + + t_path full_path[] = {t_path(ICDAR_20P_PPM_IMG_PATH)}; + + for (int i = 0; i < 1; ++i) + { + std::cout << "entering " << full_path[i] << std::endl; + + if (boost::filesystem::exists(full_path[i]) && + boost::filesystem::is_directory(full_path[i])) + { + boost::filesystem::system_complete(full_path[i]); + const t_iter_path end_iter; + float prop = 0.0; + + for (t_iter_path dir_iter(full_path[i]); end_iter != dir_iter; ++dir_iter) + { + // concatenation de chaine + t_path directory(ANNOTATING_RET_PATH); + t_path leaf = dir_iter->path().leaf(); + t_path output = change_extension(directory / leaf, ".sh"); + t_path tmp = change_extension(directory / leaf, ".pgm"); + + prop = value_test(dir_iter->path().string(), + output.string(), + tmp.string(), + 15); + + std::cout << output << " : " << prop << std::endl; + std::cerr << output << " : " << prop << std::endl; + } + } + } + + return 0; +} diff --git a/scribo/sandbox/green/mln/clustering/kmean2d.hh b/scribo/sandbox/green/mln/clustering/kmean2d.hh index 51aaf49..4539083 100644 --- a/scribo/sandbox/green/mln/clustering/kmean2d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean2d.hh @@ -150,7 +150,7 @@ namespace mln { /// 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; @@ -179,7 +179,7 @@ namespace mln 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; - ///} + /// \} /// \brief Constructor. /// \param[in] point : the image as the population of pixels. diff --git a/scribo/sandbox/green/mln/fun/p2b/achromatic.hh b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh index 1f45b2d..91a2dc5 100644 --- a/scribo/sandbox/green/mln/fun/p2b/achromatic.hh +++ b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh @@ -44,15 +44,29 @@ namespace mln namespace p2b { - /// \brief Functor that compare the i-th component of a value. - // V is for the type of the value received - // i is the ith component to select + /// \brief Functor that compare the i-th component with a threshold. + /// + /// T_rgb is the kind of RGB we use. template <typename T_rgb> struct achromatic : public Function_v2b< achromatic<T_rgb> > { typedef bool result; + + /// \brief Operator that makes the threshold [millet.phd.2008.pdf] + /// + /// \param[in] p the selected site + /// + /// \return if the site is achromatic or not. + /// + /// This a try for opimized the call to the achromatic + /// routine. The goal is to decide if a site has got an + /// achromatic status or not. This is the true Millet test. bool operator()(const point2d& p) const; + /// \brief Cstor of the object. + /// + /// \param[in] img the RGB input image. + /// \param[in] threshold the value to compare with. achromatic(const image2d<T_rgb>& img, const float threshold); const float threshold_; diff --git a/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh index 84d26c8..e776c9a 100644 --- a/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh +++ b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh @@ -32,6 +32,10 @@ # include <mln/value/hsv.hh> # include <mln/value/rgb8.hh> +/// \file +/// +/// This is the code for building hue_concentration_map. + namespace mln { @@ -41,12 +45,18 @@ namespace mln namespace v2v { + /// \brief internal method for detecting the histogram peak. + /// + /// \param[in] hue_histo the histogram of hue. + /// + /// \return the seed of the peek. unsigned peak_histo(const mln::image1d<unsigned>& hue_histo); struct hue_concentration : public Function_v2v< hue_concentration > { typedef float result; + /// \brief Gibe the distance map between actual hue and the peak. float operator()(const float hue) const; hue_concentration(const mln::image1d<unsigned>& hue_histo) diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh index 6de63ec..126f9bd 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh @@ -49,6 +49,11 @@ namespace mln /// \brief Convert rgb value to achromatism map. /// + /// Convert rgb value to binary achromastism map using the + /// rebuilded Millet phd formulae [millet.phd.2008.pdf]. The + /// idea is to look at the minimum and maximum of the channels + /// and to return the difference. + /// /// \ingroup modfunv2v template <unsigned n> diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh index 3dd8eb0..a191095 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh @@ -34,6 +34,11 @@ #include <mln/value/hsv.hh> #include <mln/value/rgb.hh> +/// \fiie +/// +/// This is the millet [millet.phd.2008.pdf] transformation from RGB +/// space to HSV space. + namespace mln { @@ -57,7 +62,15 @@ namespace mln typedef T_hsv result; - /// HSV implementation from millet.2008.phd.pdf p67 + /// \brief HSV implementation from millet.2008.phd.pdf p67 + /// + /// \param[in] rgb the input rgb pixel. + /// + /// \return a HSV pixel. + /// + /// This is the Millet implementation of its transformation + /// operator to go from RGB space to HSV space. When pixels + /// are achromatic, hue equals -1. template <typename T_rgb> T_hsv operator()(const T_rgb& rgb) const; -- 1.5.6.5

14 years, 1 month

last-svn-commit-46-gc87f968 Add some futur materials on regional maxima.

by Yann Jacquelet

* test_labelling_2.cc: New file. * test_labelling_3.cc: New file. --- scribo/sandbox/green/ChangeLog | 7 + .../sandbox/green/test_labelling_2.cc | 318 +++++++------------- .../sandbox/green/test_labelling_3.cc | 25 ++- 3 files changed, 141 insertions(+), 209 deletions(-) copy milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc => scribo/sandbox/green/test_labelling_2.cc (64%) copy milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc => scribo/sandbox/green/test_labelling_3.cc (92%) diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 4ddaa54..9e9b7d6 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,10 @@ +2010-06-30 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Add some futur materials on regional maxima. + + * test_labelling_2.cc: New file. + * test_labelling_3.cc: New file. + 2010-06-28 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Implement the kmean algorithh and start to optimize it. diff --git a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/test_labelling_2.cc similarity index 64% copy from milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc copy to scribo/sandbox/green/test_labelling_2.cc index 30e68b1..28129f1 100644 --- a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc +++ b/scribo/sandbox/green/test_labelling_2.cc @@ -1,4 +1,29 @@ -// DEMO ON KMEAN2D +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file #include <mln/clustering/kmean2d.hh> @@ -53,10 +78,10 @@ // Ok pour la convergence. // demander à Théo pour le temps (45 sec pour chaque launching [house,3 cl.]) // -void do_demo_kmean(const std::string& image, - const unsigned k_center, - const unsigned n_times, - const unsigned watch_dog) +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; @@ -104,6 +129,21 @@ void do_demo_kmean(const std::string& image, } +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); +} mln::image2d<mln::value::rgb<8> > merge(const mln::image2d<mln::value::rg<8> >& input, @@ -134,124 +174,91 @@ merge(const mln::image2d<mln::value::rg<8> >& input, } -// -// Watershed image processing chain. -// -void do_demo_watershed(const std::string& image, - const unsigned k_center, - const unsigned n_times, - const unsigned watch_dog) -{ - typedef mln::value::label_8 t_lbl8; - typedef mln::value::rg<8> t_rg8; - typedef mln::value::rgb8 t_rgb8; - typedef mln::value::int_u8 t_int_u8; - typedef mln::image2d<t_lbl8> t_image2d_lbl8; - typedef mln::image2d<t_rgb8> t_image2d_rgb8; - typedef mln::image2d<t_int_u8> t_image2d_int_u8; - typedef mln::image2d<t_rg8> t_image2d_rg8; - typedef mln::image2d<unsigned> t_histo2d; - typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; - typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; - - t_image2d_rgb8 img_rgb8; - t_image2d_rgb8 output; - t_image2d_rgb8 img_cast; - t_image2d_rg8 img_rg8; - t_histo2d histo; - t_image2d_lbl8 label; - t_lbl8 n_labels; - t_histo2d reverted; - - // IMAGE LOADING PHASE - mln::io::ppm::load(house_rgb8, image.c_str()); - img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); - img_cast = mln::data::transform(img_rg8, t_rg_to_rgb()); - mln::io::ppm::save(img_cast, "red_green.ppm"); - - // HISTO COMPUTING AND FILTERING PHASE - histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); - mln::io::plot::save_image_sh(histo, "histo.sh"); - - //histo = mln::morpho::elementary::opening(histo, mln::c4()); - - mln::io::plot::save_image_sh(histo, "histo.sh"); - - // LABELING PHASE - reverted = mln::arith::revert(histo); - label = mln::morpho::watershed::flooding(reverted, mln::c4(), n_labels); - mln::io::pgm::save(label, "label.pgm"); +void usage(const int argc, const char *args[]) +{ + std::cout << "----------------------------------------" << std::endl; + std::cout << "argc : " << argc << std::endl; - // OUTPUT PHASE - output = merge(house_rg8, label); + for (int i = 0; i < argc; ++i) + std::cout << "args[" << i << "] : " << args[i] << std::endl; - mln::io::ppm::save(output, "merge.ppm"); + std::cout << "----------------------------------------" << std::endl; + std::cout << "usage: kmean2d [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; } - -// -// Regional maxima image processing chain. -// -void do_demo_regional1(const std::string& image, - const unsigned k_center, - const unsigned n_times, - const unsigned watch_dog) +bool char_to_unsigned(const bool status, const char *arg, unsigned& val) { - typedef mln::value::label_8 t_lbl8; - typedef mln::value::rg<8> t_rg8; - typedef mln::value::rgb8 t_rgb8; - typedef mln::value::int_u8 t_int_u8; - typedef mln::image2d<t_lbl8> t_image2d_lbl8; - typedef mln::image2d<t_rgb8> t_image2d_rgb8; - typedef mln::image2d<t_int_u8> t_image2d_int_u8; - typedef mln::image2d<t_rg8> t_image2d_rg8; - typedef mln::image2d<unsigned> t_histo2d; - typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; - typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + bool result = false; - t_image2d_rgb8 img_rgb8; - t_image2d_rgb8 output; - t_image2d_rgb8 img_cast; - t_image2d_rg8 img_rg8; - t_histo2d histo; - t_histo2d opened; - t_image2d_lbl8 label; - t_image2d_lbl8 dilated; - t_lbl8 n_labels; + if (status) + { + std::istringstream arg_stream(arg); + arg_stream >> val; - // IMAGE LOADING PHASE - mln::io::ppm::load(img_rgb8, image.c_str()); - img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); - img_cast = mln::data::transform(img_rg8, t_rg_to_rgb()); - mln::io::ppm::save(img_cast, "red_green.ppm"); + result = !arg_stream.fail(); + } + return result; +} - // HISTO COMPUTING PHASE - histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); - mln::io::plot::save_image_sh(histo, "histo.sh"); +bool char_to_string(const bool status, const char *arg, std::string& val) +{ + bool result = false; + if (status) + { + std::istringstream arg_stream(arg); - // HISTO FILTERING PHASE - opened = mln::morpho::elementary::opening(histo, mln::c8()); - mln::io::plot::save_image_sh(opened, "opened.sh"); + arg_stream >> val; + return !arg_stream.fail(); + } - // HISTO LABELING PHASE - label = mln::labeling::regional_maxima(opened, mln::c8(), n_labels); - mln::io::pgm::save(label, "label.pgm"); + 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; - // HISTO FUZZY PHASE - dilated = mln::morpho::elementary::dilation(label, mln::c8()); - mln::io::pgm::save(dilated, "dilated.pgm"); + 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); - // OUTPUT PHASE - output = merge(img_rg8, dilated); - mln::io::ppm::save(output, "merge.ppm"); + return 0; } // @@ -333,108 +340,3 @@ void do_demo_regional2(const std::string& image, -void demo(const std::string& image = SCRIBO_PPM_IMG_PATH"/mp00082c_50p.ppm", - //const std::string& image = OLENA_IMG_PATH"/house.ppm", - const unsigned k_center = 2, - //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_kmean(image, k_center, n_times, watch_dog); -// do_demo_watershed(image, k_center, n_times, watch_dog); - do_demo_regional1(image, k_center, n_times, watch_dog); - //do_demo_regional2(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: kmean2d [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/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/test_labelling_3.cc similarity index 92% copy from milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc copy to scribo/sandbox/green/test_labelling_3.cc index 30e68b1..317832b 100644 --- a/milena/sandbox/green/demo/clustering/kmean2d/kmean2d.cc +++ b/scribo/sandbox/green/test_labelling_3.cc @@ -1,4 +1,27 @@ -// DEMO ON KMEAN2D +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. #include <mln/clustering/kmean2d.hh> -- 1.5.6.5

14 years, 1 month

last-svn-commit-45-g0c912a2 Implement the kmean algorithh and start to optimize it.

by Yann Jacquelet

Implement the first version with vectors and matrices. * mln/clustering/k_mean.hh: New library component. * use/clustering/k_mean: New directory. * use/clustering/k_mean/Makefile.am: New makefile. * use/clustering/k_mean/k_mean.cc: New source file. * tests/clustering/k_mean: New directory. * tests/clustering/k_mean/Makefile.am: New makefile. * tests/clustering/k_mean/k_mean.cc: New source file. Implement the second version with image and working in 1d. * mln/clustering/kmean1d.hh: New library component. * use/clustering/kmean1d: New directory. * use/clustering/kmean1d/Makefile.am: New makefile. * use/clustering/kmean1d/kmean1d.cc: New source file. * demo/clustering/kmean1d: New directory. * demo/clustering/kmean1d/Makefile.am: New makefile. * demo/clustering/kmean1d/kmean1d.cc: New source file. Implement transformation between RG space and RGB space. * mln/fun/v2v/rg_to_rgb.hh: New library component. * use/fun/v2v/rg_to_rgb: New directory. * use/fun/v2v/rg_to_rgb/Makefile.am: New makefile. * use/fun/v2v/rg_to_rgb/rg_to_rgb.cc: New source file. Implement the third version working in 2d (r/g). * mln/clustering/kmean2d.hh: New library component. * use/clustering/kmean2d: New directory. * use/clustering/kmean2d/Makefile.am: New makefile. * use/clustering/kmean2d/kmean2d.cc: New source file. * demo/clustering/kmean2d: New directory. * demo/clustering/kmean2d/Makefile.am: New makefile. * demo/clustering/kmean2d/kmean2d.cc: New source file. Implement the fourth version working in 3d (rgb). * mln/clustering/kmean3d.hh: New library component. * use/clustering/kmean3d: New directory. * use/clustering/kmean3d/Makefile.am: New makefile. * use/clustering/kmean3d/kmean3d.cc: New source file. * demo/clustering/kmean3d: New directory. * demo/clustering/kmean3d/Makefile.am: New makefile. * demo/clustering/kmean3d/kmean3d.cc: New source file. Implement the fith version as a function (working in rgb space). * mln/clustering/kmean_rgb.hh: New library component. * use/clustering/kmean_rgb: New directory. * use/clustering/kmean_rgb/Makefile.am: New makefile. * use/clustering/kmean_rgb/kmean_rgb.cc: New source file. * demo/clustering/kmean_rgb: New directory. * demo/clustering/kmean_rgb/Makefile.am: New makefile. * demo/clustering/kmean_rgb/kmean_rgb.cc: New source file. Benchmark distance algorithm for the kmean algorithm. * bench/clustering/distance: New directory. * bench/clustering/distance/Makefile.am: New makefile. * bench/clustering/distance/distance.cc: New source file. --- scribo/sandbox/green/ChangeLog | 77 ++ scribo/sandbox/green/README.green | 150 ++++- .../green/bench/clustering/distance/Makefile.am | 153 ++++ .../green/bench/clustering/distance/distance.cc | 842 ++++++++++++++++++++ .../green/demo/clustering/kmean1d/Makefile.am | 153 ++++ .../green/demo/clustering/kmean1d/kmean1d.cc | 258 ++++++ .../green/demo/clustering/kmean2d/Makefile.am | 153 ++++ .../green/demo/clustering/kmean2d/kmean2d.cc | 278 +++++++ .../green/demo/clustering/kmean3d/Makefile.am | 153 ++++ .../green/demo/clustering/kmean3d/kmean3d.cc | 265 ++++++ .../green/demo/clustering/kmean_rgb/Makefile.am | 153 ++++ .../green/demo/clustering/kmean_rgb/kmean_rgb.cc | 239 ++++++ scribo/sandbox/green/mln/clustering/k_mean.hh | 365 ++++++--- scribo/sandbox/green/mln/clustering/kmean1d.hh | 194 ++--- scribo/sandbox/green/mln/clustering/kmean2d.hh | 329 ++++---- scribo/sandbox/green/mln/clustering/kmean3d.hh | 246 +++--- scribo/sandbox/green/mln/clustering/kmean_rgb.hh | 87 ++- scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh | 59 ++- .../green/tests/clustering/k_mean/Makefile.am | 13 +- .../green/tests/clustering/k_mean/k_mean.cc | 395 ++++++---- .../stat/histo1d => clustering/k_mean}/Makefile.am | 0 .../sandbox/green/use/clustering/k_mean/k_mean.cc | 55 ++ .../histo1d => clustering/kmean1d}/Makefile.am | 0 .../green/use/clustering/kmean1d/kmean1d.cc | 50 ++ .../histo1d => clustering/kmean2d}/Makefile.am | 0 .../green/use/clustering/kmean2d/kmean2d.cc | 61 ++ .../histo1d => clustering/kmean3d}/Makefile.am | 0 .../green/use/clustering/kmean3d/kmean3d.cc | 63 ++ .../histo1d => clustering/kmean_rgb}/Makefile.am | 0 .../green/use/clustering/kmean_rgb/kmean_rgb.cc | 63 ++ .../stat/histo1d => fun/v2v/rg_to_rgb}/Makefile.am | 0 .../green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc | 68 ++ 32 files changed, 4224 insertions(+), 698 deletions(-) create mode 100644 scribo/sandbox/green/bench/clustering/distance/Makefile.am create mode 100644 scribo/sandbox/green/bench/clustering/distance/distance.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc create mode 100644 scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am create mode 100644 scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/k_mean}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/k_mean/k_mean.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean1d}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean2d}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean3d}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => clustering/kmean_rgb}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc copy scribo/sandbox/green/use/{accu/stat/histo1d => fun/v2v/rg_to_rgb}/Makefile.am (100%) create mode 100644 scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 7bb6646..4ddaa54 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,80 @@ +2010-06-28 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Implement the kmean algorithh and start to optimize it. + + Implement the first version with vectors and matrices. + + * mln/clustering/k_mean.hh: New library component. + + * use/clustering/k_mean: New directory. + * use/clustering/k_mean/Makefile.am: New makefile. + * use/clustering/k_mean/k_mean.cc: New source file. + + * tests/clustering/k_mean: New directory. + * tests/clustering/k_mean/Makefile.am: New makefile. + * tests/clustering/k_mean/k_mean.cc: New source file. + + Implement the second version with image and working in 1d. + + * mln/clustering/kmean1d.hh: New library component. + + * use/clustering/kmean1d: New directory. + * use/clustering/kmean1d/Makefile.am: New makefile. + * use/clustering/kmean1d/kmean1d.cc: New source file. + + * demo/clustering/kmean1d: New directory. + * demo/clustering/kmean1d/Makefile.am: New makefile. + * demo/clustering/kmean1d/kmean1d.cc: New source file. + + Implement transformation between RG space and RGB space. + + * mln/fun/v2v/rg_to_rgb.hh: New library component. + * use/fun/v2v/rg_to_rgb: New directory. + * use/fun/v2v/rg_to_rgb/Makefile.am: New makefile. + * use/fun/v2v/rg_to_rgb/rg_to_rgb.cc: New source file. + + Implement the third version working in 2d (r/g). + + * mln/clustering/kmean2d.hh: New library component. + + * use/clustering/kmean2d: New directory. + * use/clustering/kmean2d/Makefile.am: New makefile. + * use/clustering/kmean2d/kmean2d.cc: New source file. + + * demo/clustering/kmean2d: New directory. + * demo/clustering/kmean2d/Makefile.am: New makefile. + * demo/clustering/kmean2d/kmean2d.cc: New source file. + + Implement the fourth version working in 3d (rgb). + + * mln/clustering/kmean3d.hh: New library component. + + * use/clustering/kmean3d: New directory. + * use/clustering/kmean3d/Makefile.am: New makefile. + * use/clustering/kmean3d/kmean3d.cc: New source file. + + * demo/clustering/kmean3d: New directory. + * demo/clustering/kmean3d/Makefile.am: New makefile. + * demo/clustering/kmean3d/kmean3d.cc: New source file. + + Implement the fith version as a function (working in rgb space). + + * mln/clustering/kmean_rgb.hh: New library component. + + * use/clustering/kmean_rgb: New directory. + * use/clustering/kmean_rgb/Makefile.am: New makefile. + * use/clustering/kmean_rgb/kmean_rgb.cc: New source file. + + * demo/clustering/kmean_rgb: New directory. + * demo/clustering/kmean_rgb/Makefile.am: New makefile. + * demo/clustering/kmean_rgb/kmean_rgb.cc: New source file. + + Benchmark distance algorithm for the kmean algorithm. + + * bench/clustering/distance: New directory. + * bench/clustering/distance/Makefile.am: New makefile. + * bench/clustering/distance/distance.cc: New source file. + 2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Define documentation files. diff --git a/scribo/sandbox/green/README.green b/scribo/sandbox/green/README.green index 3f3c9a9..02d8654 100644 --- a/scribo/sandbox/green/README.green +++ b/scribo/sandbox/green/README.green @@ -33,8 +33,7 @@ n'y a rien à copier. Rendons-nous dans le répertoire de compilation et lançon le makefile. #:~/git/olena/scribo/sandbox/green/demo/annotating/hsv$ -cd ../../../build/demo/annotating/hsv - +#:cd ../../../build/demo/annotating/hsv #:~/git/olena/scribo/sandbox/green/build/demo/annotating/hsv$ make clean all L'exécutable est généré par le makefile, il porte le nom du @@ -168,6 +167,14 @@ de comptage puisqu'il est décrit sous la forme de triplets de float contient une séquence d'appels pour les routines permettant de considérer la dimension de la teinte comme circulaire. +Après réflexion, le code des histogrammes et tous les accumulateurs +qui en découlent devraient être rangés dans l'espace de nommage +mln::accu::histo. Cela permettrait de ne pas parasiter mln::accu::stat +avec tous les éléments propres aux histogrammes et de ne pas faire un +espace de nommage à rallonge en introduisant histo dans stat. Donc +mln::accu::stat semble être une bonne postion pour le rangement final +du code relatif aux histogrammes. + a) version 1d @@ -198,7 +205,6 @@ c) version 3d RGB * use/accu/stat/histo3_rgb: Code minimal utilisant un histogramme 3d RGB. - d) version 3d HSL * mln/accu/stat/histo3d_hsl.hh: Accumulateur histogramme image3d HSL. @@ -245,23 +251,145 @@ IX KMEANS Ce travail m'avait été demandé par théo. Je le laisse inachevé, quelque part perdu pendant l'optimisation du code et sa transformation en canevas. + +a) Première implémentation avec matrices et vecteurs + +Cette version est bien documentée et permet de mieux comprendre les autres +versions. Par ailleurs, elle n'est pas spécialement optimisée ce qui fait +qu'elle colle davantage au modèle de l'algorithme kmean traditionnel. + * mln/clustering/k_mean.hh: Première implémentation avec matrices et vecteurs. +* use/clustering/k_mean: Code minimal utilisant cette première implémentation. +* tests/clustering/k_mean: Tests unitaires sur la permière version. + + +b) Seconde implémentation avec image en 1d + +Cette seconde version intègre une optimisation testée par théo il y a +longtemps. Je me demande si ce n'étais pas pendant sa thèse qu'il +avait travaillé sur cette version. Bref, dans la mesure où +l'optimisation passe par la création d'un histogramme, cette version +devient dépendante des histogrammes réalisés plutôt (il est aussi +dépendant de la sauvegarde au format gnuplot shell). L'idée générale +de l'optimisation est de ne plus raisonner sur l'image, mais sur +l'histogramme. Car à un moment donné, la classification ne tient +compte que de la valeur du pixel en intensité (pas de ses coordonnées +dans l'image). Donc tout pixel de même intensité sera classé de +manière identique. D'où l'intérêt de travailler avec les +histogrammes. Les limites de cette optimisation se trouvent dans la +taille des histogrammes. L'optimisation marche à merveille pour de +faibles quantifications (8 bits c'est parfait). Par contre, lorsque +l'on passe à un histogramme couleur, la taille de l'histogramme +devient problématique et peut dépasser la taille de l'image. Cette +optimisation n'a alors plus aucun sens. + * mln/clustering/kmean1d.hh: Implémentation 1d avec des images. +* use/clustering/kmean1d : Code minimal utilisant cette seconde implémentation. +* demo/clustering/kmean1d : Demonstrateur. + +La visualisation de la convergence des moyennes, générée par le +démonstrateur, n'est pas très lisible. La lecture textuelle du fichier +gnuplot shell (mean_cnv.sh) permet d'interpréter ce qui se passe, par +contre le graphique est à refaire. + + +c) kmean2d + +Cette troisième version est identique à la seconde, sauf qu'elle +permet de faire la classification dans un espace à deux +dimensions. Malheureusement, un tel travail dans cet espace coûte +beaucoup plus cher à l'exécution. + +* mln/fun/v2v/rg_to_rgb.hh: Transformation de l'espace RG vers l'espace RGB. +* use/fun/v2v/rg_to_rgb: Exemple de code pour l'utilisation de rg_to_rgb. + * mln/clustering/kmean2d.hh: Implémentation 2d avec des images. -* mln/clustering/kmean3d.hh: Implémentation 3d avec des images. -* mln/clustering/kmean_rgb.hh: Impl. 3d aplatie et en cours de mise en canevas. +* use/clustering/kmean2d : Code minimal utilisant cette seconde implémentation. +* demo/clustering/kmean2d : Demonstrateur. -* tests/clustering/k_mean: Tests unitaires sur la permière version. -* tests/clustering/kmean1d: Tests unitaire sur la version 1d. +La visualisation de la convergence des moyennes est cette fois-ci +complètement aboutie. Le fichier semble avoir quelques soucis, il +manque des lignes dans le splot initial, en remettre pour qu'il y en +ait autant que les blocs de données. L'espace des couleurs étant le +bicanal r/g, on peut visualiser sur une troisième dimension +l'évolution des centres dans l'espace initial. L'effet est très réussi +et aussi très parlant. Chaque run, correspond alors à une trajectoire +particulière. L'affichage des variations des variances a été rénové et +est lui aussi beaucoup plus lisible. + + +d) kmean3d + +Cette quatrième version est la copie conforme de la version +précédente. Les problèmes de visualisation sur les fichiés générés +sont les mêmes. L'affichage des convergences est identique. Le recours +à une dégradation de l'image d'entrée est impérative pour avoir des +temps encore acceptables. -* demo/clustering/kmean1d: Utilisation de la version 1d. -* demo/clustering/kmean2d: Utilisation de la version 2d. -* demo/clustering/kmean3d: Utilisation de la version 3d. +* mln/clustering/kmean3d.hh: Implémentation 3d avec des images. +* use/clustering/kmean3d : Code minimal utilisant cette quatrième impl. +* demo/clustering/kmean3d : Demonstrateur. + + +e) kmean aplati + +Cette cinquième version est très spéciale. Elle doit permettre de +gagner du temps d'exécution. Le concept est simple au départ, tout +écrire d'un seul tenant. L'exercice est fastidieux et difficile +(intellectuellement). Une fois fait, il faut réfléchir à ce qui peut +être mis sous forme de canevas. Par exemple, la manière de calculer +les distances peut être une information paramétrable. Cela pemettrait +de faire un benchmark in situ. La transcription actuelle compile. Elle +n'intègre pas tous les outils de debuggage que nous pouvions avoir sur +les autres versions. Par ailleurs, comme le code est réuni en une +seule fonction, nous avons pour l'instant une seule sortie, l'image de +labels de la classification réalisée. A partir de cette image, nous +pouvons en reconstruire d'autres. Il manque quand même la possibilité +d'observer les convergences. Le travail était en cours, à prendre donc +avec beaucoup de pincettes. La dernière exécution house.ppm, 3 +centres, 10 itérations et 10 runs fonctionne parfaitement sur les +premiers runs puis l'affichage s'emballe sans que l'on y comprenne +rien. Un dump dans un fichier révèle le non appariement de certaines +traces (entering/exiting). Une piste à suivre est la sortie anticipée +d'une de mes boucles sans pour autant fermer une trace ... ??? + +* mln/clustering/kmean_rgb.hh: Implémentation 3d avec des images. +* use/clustering/kmean_rgb : Code minimal utilisant cette quatrième impl. * demo/clustering/kmean_rgb: Utilisation de la version aplatie. + +f) optimisation possible + +Le calcul des distances entre les points et les différents centres +peut être réalisé par des transformées. Certes, les distances ne seront +pas les mêmes que la distance euclidienne, mais elles s'en approchent +et cela constitue très certainement une très bonne approximation pour +l'objectif que nous cherchons à atteindre. Le but de ce benchmark est +de regarder quel type de transformation est le plus rapide pour +arriver à nos fins en fonction des données d'entrée. Cette +optimisation n'a pas encore été intégrée dans le code, et reste une +piste à exploiter. + * bench/clustering/distance: Comparaison algorithmes d'évaluation des distances. -==> to do +Une routine du benchmark ne compile plus. Il semble qu'il y ait un +mauvais appel à la fonction at_ dans la routine +influence_zone_geodesic.hh ligne 127. Le but du benchmark est de +tester les distances en 2d et 3d pour des voisinages différents (c04, +c08, c06, c18, c26) sur les routines distance euclidienne classique, +zone d'influence geodesique et zone d'influence "front". La première +serie de test vise à garder une taille d' image constante et +d'augmenter le nombre de centres pour voir comment se comporte les +algorithmes et la seconde serie, vise à garder un nombre constant de +centres et à agrandir progressivement l'image. Attention, le benchmark +essaye d'être assez exhaustif et donc se paye par un temps d'execution +assez long. Les différents fichiers générés reprennent les différents +tests effectués et montrent l'évolution du temps de calcul suivant la +progression du paramètre observé (taille de l'image ou nombre de +centres). + +==> to do : le changelog + commit + give it to scribo-z branch + X REGIONAL MAXIMA ----------------- diff --git a/scribo/sandbox/green/bench/clustering/distance/Makefile.am b/scribo/sandbox/green/bench/clustering/distance/Makefile.am new file mode 100644 index 0000000..ca5e187 --- /dev/null +++ b/scribo/sandbox/green/bench/clustering/distance/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +#CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/bench +BUILD__PATTERN= green/build/bench + + +ifeq ($(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/scribo/sandbox/green/bench/clustering/distance/distance.cc b/scribo/sandbox/green/bench/clustering/distance/distance.cc new file mode 100644 index 0000000..4daea44 --- /dev/null +++ b/scribo/sandbox/green/bench/clustering/distance/distance.cc @@ -0,0 +1,842 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#include <mln/core/alias/box3d.hh> +#include <mln/core/alias/neighb2d.hh> +#include <mln/core/alias/neighb3d.hh> +#include <mln/core/alias/point3d.hh> +#include <mln/core/alias/w_window2d_int.hh> +#include <mln/core/alias/w_window3d_int.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/core/routine/initialize.hh> + +#include <mln/data/fill.hh> + +#include <mln/io/pgm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/literal/zero.hh> + +#include <mln/make/w_window2d_int.hh> +#include <mln/make/w_window3d_int.hh> + +#include <mln/opt/at.hh> + +#include <mln/transform/influence_zone_geodesic.hh> +#include <mln/transform/influence_zone_front.hh> + +#include <mln/util/array.hh> +#include <mln/util/timer.hh> + +#include <mln/value/label_8.hh> + + +/// Build inputs, build seeds, count distance. +/// \{ +/// \brief Build 2d/3d input image and 2d/3d seed image. +/// +/// Building seeds means making a rectangular grid over a virtual image with +/// a specific size and collects together in a array each node of this grid. +/// In the context of the kmean algorithm, seeds are equavalent to centers. +/// Building input means creating a label image and associates to each seed +/// a unique label id. +/// Distance are square euclidian distance in 2d and in 3d. +mln::image2d<mln::value::label_8> build_input2d(unsigned nb_seed, unsigned size) +{ + typedef mln::value::label_8 t_lbl; + mln::image2d<t_lbl> input(mln::box2d(mln::point2d(0,0), + mln::point2d(size,size))); + + mln::data::fill(input, mln::literal::zero); + + unsigned top = size / nb_seed; + unsigned lbl = 0; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + input(mln::point2d(i,j)) = ++lbl; + + //mln::io::pgm::save(input, "input.pgm"); + + return input; +} + +mln::image3d<mln::value::label_8> build_input3d(unsigned nb_seed, unsigned size) +{ + typedef mln::value::label_8 t_lbl; + mln::image3d<t_lbl> input(mln::box3d(mln::point3d(0,0,0), + mln::point3d(size,size,size))); + + mln::data::fill(input, mln::literal::zero); + + unsigned top = size / nb_seed; + unsigned lbl = 0; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + for (unsigned k = top/2; k < size; k += top) + input(mln::point3d(k,i,j)) = ++lbl; + + //mln::io::pgm::save(input, "input.pgm"); + + return input; +} + +mln::util::array<mln::point3d> build_seed3d(unsigned nb_seed, unsigned size) +{ + mln::util::array<mln::point3d> result; + unsigned top = size / nb_seed; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + for (unsigned k = top/2; k < size; k += top) + result.append(mln::point3d(k,i,j)); + + // std::cout << result << std::endl; + + return result; +} + +mln::util::array<mln::point2d> build_seed2d(unsigned nb_seed, unsigned size) +{ + mln::util::array<mln::point2d> result; + unsigned top = size / nb_seed; + + for (unsigned i = top/2; i < size; i += top) + for (unsigned j = top/2; j < size; j += top) + result.append(mln::point2d(j,i)); + + // std::cout << result << std::endl; + + return result; +} + +unsigned distance(mln::point3d p1, mln::point3d p2) +{ + unsigned row2 = (p1.row() - p2.row())*(p1.row() - p2.row()); + unsigned col2 = (p1.col() - p2.col())*(p1.col() - p2.col()); + unsigned sli2 = (p1.sli() - p2.sli())*(p1.sli() - p2.sli()); + unsigned res = row2 + col2 + sli2; + + return res; +} + +unsigned distance(mln::point2d p1, mln::point2d p2) +{ + unsigned row2 = (p1.row() - p2.row())*(p1.row() - p2.row()); + unsigned col2 = (p1.col() - p2.col())*(p1.col() - p2.col()); + unsigned res = row2 + col2; + + return res; +} +/// \} + +/// Benchmark geodesic distance. +/// \{ +/// \brief The geodesic material for the benchmark process. +/// +/// This part of the file contains all we need to benchmarck the +/// geodesic distance. First of all, we have the calling functions +/// that encapsulate the real call to geodesic routine. They bring us +/// a clean place where starting and stopping the timer without any +/// other code interference. Printing results could be done at this step. +/// At least, we have the test routine that call the previous functions +/// by passing arguments like different size of inputs and different +/// neighbouring. The test routines save their results in gnuplot script shell +/// file that enable to show charts. +void influence_zone_geodesic_2d(mln::util::timer& timer, + const mln::image2d<mln::value::label_8>& input, + const mln::neighb2d & neighb) +{ + mln::image2d<mln::value::label_8> output; + + timer.start(); + output = mln::transform::influence_zone_geodesic(input, neighb); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + +/// \fixme influence_zone_geodesic_3d doesn't compile! +void influence_zone_geodesic_3d(mln::util::timer& timer, + const mln::image3d<mln::value::label_8>& input, + const mln::neighb3d & neighb) +{ + mln::image3d<mln::value::label_8> output; + + timer.start(); + // FIXME : Problem with the influence_zone_geodesic in 3d (bad call to at_). + // output = mln::transform::influence_zone_geodesic(input, neighb); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +int test_influence_zone_geodesic_2d() +{ + mln::util::timer timer; + +// Test the label c4 + mln::image1d<float> chart_seed_c04(16*16+2); + + mln::data::fill(chart_seed_c04, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_geodesic_2d(timer,build_input2d(i,256),mln::c4()); + std::cout << "c04|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c04, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c04, "geodesic.c04.2d.seed.sh"); + + // Test the label c8 + mln::image1d<float> chart_seed_c08(16*16+2); + + mln::data::fill(chart_seed_c08, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_geodesic_2d(timer,build_input2d(i,256),mln::c8()); + std::cout << "c08|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c08, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c08, "geodesic.c08.2d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c04(256+1); + + mln::data::fill(chart_size_c04, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_geodesic_2d(timer, build_input2d(4,i),mln::c4()); + std::cout << "c04|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c04, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c04, "geodesic.c04.2d.size.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c08(256+1); + + mln::data::fill(chart_size_c08, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_geodesic_2d(timer, build_input2d(4,i),mln::c8()); + std::cout << "c08|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c08, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c08, "geodesic.c08.2d.size.sh"); + + + return 0; +} + + +void test_influence_zone_geodesic_3d() +{ + mln::util::timer timer; + + // Test the number of labels c06 + mln::image1d<float> chart_seed_c06(5*5*5+2); + + mln::data::fill(chart_seed_c06, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(i,128), mln::c6()); + std::cout << "c06|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c06, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c06, "geodesic.c06.3d.seed.sh"); + + // Test the number of labels c18 + mln::image1d<float> chart_seed_c18(5*5*5+2); + + mln::data::fill(chart_seed_c18, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(i,128), mln::c18()); + std::cout << "c18|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c18, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c18, "geodesic.c18.3d.seed.sh"); + + // Test the number of labels c26 + mln::image1d<float> chart_seed_c26(5*5*5+2); + + mln::data::fill(chart_seed_c26, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(i,128), mln::c26()); + std::cout << "c26|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c26, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c26, "geodesic.c26.3d.seed.sh"); + + // Test the size of the image c06 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c06(128+1); + + mln::data::fill(chart_size_c06, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(2,i), mln::c6()); + std::cout << "c06|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c06, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c06, "geodesic.c06.3d.size.sh"); + + // Test the size of the image c18 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c18(128+1); + + mln::data::fill(chart_size_c18, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(2,i), mln::c18()); + std::cout << "c18|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c18, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c18, "geodesic.c18.3d.size.sh"); + + // Test the size of the image c26 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c26(128+1); + + mln::data::fill(chart_size_c26, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_geodesic_3d(timer, build_input3d(2,i), mln::c26()); + std::cout << "c26|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c26, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c26, "geodesic.c26.3d.size.sh"); +} +/// \} + +/// Benchmark front distance. +/// \{ +/// \brief The front material for the benchmark process. +/// +/// This part of the file contains all we need to benchmarck the +/// front distance. First of all, we have the calling functions +/// that encapsulate the real call to geodesic routine. They bring us +/// a clean place where starting and stopping the timer without any +/// other code interference. Printing results could be done at this step. +/// At least, we have the test routine that call the previous functions +/// by passing arguments like different size of inputs and different +/// neighbouring. The test routines save their results in gnuplot script shell +/// file that enable to show charts. +void influence_zone_front_2d(mln::util::timer& timer, + const mln::image2d<mln::value::label_8>& input, + const mln::neighb2d& neighb, + const mln::w_window2d_int& w2d) +{ + mln::image2d<mln::value::label_8> output; + + timer.start(); + output = mln::transform::influence_zone_front(input, neighb, w2d); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +void influence_zone_front_3d(mln::util::timer& timer, + const mln::image3d<mln::value::label_8>& input, + const mln::neighb3d& neighb, + const mln::w_window3d_int& w3d) +{ + mln::image3d<mln::value::label_8> output; + + timer.start(); + output = mln::transform::influence_zone_front(input, neighb, w3d); + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +void test_influence_zone_front_3d() +{ + mln::util::timer timer; + + int ws_c06[] = { + // Internal slice + 0, 0, 0, + 0, 2, 0, + 0, 0, 0, + // Middle slice + 0, 2, 0, + 2, 0, 2, + 0, 2, 0, + // External slice + 0, 0, 0, + 0, 2, 0, + 0, 0, 0 }; + + int ws_c18[] = { + // Internal slice + 0, 6, 0, + 6, 4, 6, + 0, 6, 0, + // Middle slice + 6, 4, 6, + 4, 0, 4, + 6, 4, 6, + // External slice + 0, 6, 0, + 6, 4, 6, + 0, 6, 0 }; + + int ws_c26[] = { + // Internal slice + 7, 6, 7, + 6, 4, 6, + 7, 6, 7, + // Middle slice + 6, 4, 6, + 4, 0, 4, + 6, 4, 6, + // External slice + 7, 6, 7, + 6, 4, 6, + 7, 6, 7 }; + + mln::w_window3d_int w3d_c06 = mln::make::w_window3d_int(ws_c06); + mln::w_window3d_int w3d_c18 = mln::make::w_window3d_int(ws_c18); + mln::w_window3d_int w3d_c26 = mln::make::w_window3d_int(ws_c26); + + // Test the number of labels c06 + mln::image1d<float> chart_seed_c06(5*5*5+2); + + mln::data::fill(chart_seed_c06, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(i,128), mln::c6(), w3d_c06); + std::cout << "c06|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c06, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c06, "front.c06.3d.seed.sh"); + + // Test the number of labels c18 + mln::image1d<float> chart_seed_c18(5*5*5+2); + + mln::data::fill(chart_seed_c18, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(i,128), mln::c18(), w3d_c18); + std::cout << "c18|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c18, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c18, "front.c18.3d.seed.sh"); + + // Test the number of labels c26 + mln::image1d<float> chart_seed_c26(5*5*5+2); + + mln::data::fill(chart_seed_c26, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(i,128), mln::c26(), w3d_c26); + std::cout << "c26|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c26, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c26, "front.c26.3d.seed.sh"); + + // Test the size of the image c06 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c06(128+1); + + mln::data::fill(chart_size_c06, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(2,i), mln::c6(), w3d_c06); + std::cout << "c06|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c06, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c06, "front.c06.3d.size.sh"); + + // Test the size of the image c18 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c18(128+1); + + mln::data::fill(chart_size_c18, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(2,i), mln::c18(), w3d_c18); + std::cout << "c18|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c18, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c18, "front.c18.3d.size.sh"); + + // Test the size of the image c26 + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c26(128+1); + + mln::data::fill(chart_size_c26, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_front_3d(timer, build_input3d(2,i), mln::c26(), w3d_c26); + std::cout << "c26|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c26, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c26, "front.c26.3d.size.sh"); +} + +void test_influence_zone_front_2d() +{ + mln::util::timer timer; + + int ws_c04[] = { + 0, 2, 0, + 2, 0, 2, + 0, 2, 0}; + + int ws_c08[] = { + 6, 4, 6, + 4, 0, 4, + 6, 4, 6}; + + mln::w_window2d_int w2d_c04 = mln::make::w_window2d_int(ws_c04); + mln::w_window2d_int w2d_c08 = mln::make::w_window2d_int(ws_c08); + + // Test the label c4 + mln::image1d<float> chart_seed_c04(16*16+2); + + mln::data::fill(chart_seed_c04, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_front_2d(timer,build_input2d(i,256),mln::c4(),w2d_c04); + std::cout << "c04|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c04, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c04, "front.c04.2d.seed.sh"); + + // Test the label c8 + mln::image1d<float> chart_seed_c08(16*16+2); + + mln::data::fill(chart_seed_c08, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_front_2d(timer,build_input2d(i,256),mln::c8(),w2d_c08); + std::cout << "c08|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed_c08, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed_c08, "front.c08.2d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c04(256+1); + + mln::data::fill(chart_size_c04, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_front_2d(timer, build_input2d(4,i),mln::c4(),w2d_c04); + std::cout << "c04|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c04, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c04, "front.c04.2d.size.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size_c08(256+1); + + mln::data::fill(chart_size_c08, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_front_2d(timer, build_input2d(4,i),mln::c8(),w2d_c08); + std::cout << "c08|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size_c08, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size_c08, "front.c08.2d.size.sh"); +} +/// \} + + + +/// Benchmark euclidian distance. +/// \{ +/// \brief The euclidian material for the benchmark process. +/// +/// This part of the file contains all we need to benchmarck the +/// euclidian distance. First of all, we have the calling functions +/// that encapsulate the real call to geodesic routine. They bring us +/// a clean place where starting and stopping the timer without any +/// other code interference. Printing results could be done at this step. +/// At least, we have the test routine that call the previous functions +/// by passing arguments like different size of inputs and different +/// neighbouring. The test routines save their results in gnuplot script shell +/// file that enable to show charts. +void influence_zone_euclidian_2d(mln::util::timer& timer, + const mln::image2d<mln::value::label_8>& input, + const mln::util::array<mln::point2d>& seed) +{ + mln::image2d<mln::value::label_8> output; + + timer.start(); + + mln::initialize(output, input); + + mln_piter_(mln::image2d<mln::value::label_8>) p(output.domain()); + mln_eiter_(mln::util::array<mln::point2d>) e(seed); + + for_all(p) + { + unsigned d = 0; + unsigned min_d = mln_max(unsigned); + unsigned min_l = 0; + + for_all(e) + { + d = distance(p, seed(e.index_())); + + if (min_d > d) + { + min_d = d; + min_l = input(e); + } + } + + output(p) = min_l; + } + + timer.stop(); + + //mln::io::pgm::save(output, "output.pgm"); +} + + +void influence_zone_euclidian_3d(mln::util::timer& timer, + const mln::image3d<mln::value::label_8>& input, + const mln::util::array<mln::point3d>& seed) +{ + mln::image3d<mln::value::label_8> output; + + timer.start(); + + mln::initialize(output, input); + + mln_piter_(mln::image3d<mln::value::label_8>) p(output.domain()); + mln_eiter_(mln::util::array<mln::point3d>) e(seed); + + for_all(p) + { + unsigned d = 0; + unsigned min_d = mln_max(unsigned); + unsigned min_l = 0; + + for_all(e) + { + d = distance(p, seed(e.index_())); + + if (min_d > d) + { + min_d = d; + min_l = input(e); + } + } + + output(p) = min_l; + } + + timer.stop(); +} + +void test_influence_zone_euclidian_2d() +{ + mln::util::timer timer; +/* + // Global test + mln::image2d<float> chart(mln::box2d(mln::point2d(0,0), + mln::point2d(16*16+2,256+1))); + + mln::data::fill(chart, mln::literal::zero); + + for (unsigned i = 1; i < 256+1; ++i) // size + for (unsigned j = 1; j < i*i && j < 16+1; ++j) // seed + { + influence_zone_euclidian_2d(timer,build_input2d(j,i),build_seed2d(j,i)); + std::cout << "xxx|" << i << "x" << i << "|" << j << " = " + << timer.read() << std::endl; + mln::opt::at(chart, j*j,i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart, "chart.sh"); +*/ + + // Test the number of labels + mln::image1d<float> chart_seed(16*16+2); + + mln::data::fill(chart_seed, mln::literal::zero); + + for (unsigned i = 1; i < 16+1; ++i) + { + influence_zone_euclidian_2d(timer,build_input2d(i,256),build_seed2d(i,256)); + std::cout << "xxx|256x256|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed, i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed, "euclidian.2d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size(256+1); + + mln::data::fill(chart_size, mln::literal::zero); + + for (unsigned i = 16; i < 256+1; ++i) + { + influence_zone_euclidian_2d(timer, build_input2d(4,i), build_seed2d(4,i)); + std::cout << "xxx|" << i << "x" << i << "|16 = " + << timer.read() << std::endl; + mln::opt::at(chart_size, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size, "euclidian.2d.size.sh"); +} + +void test_influence_zone_euclidian_3d() +{ + mln::util::timer timer; + + // Test the number of labels + mln::image1d<float> chart_seed(5*5*5+2); + + mln::data::fill(chart_seed, mln::literal::zero); + + for (unsigned i = 1; i < 5+1; ++i) + { + influence_zone_euclidian_3d(timer,build_input3d(i,128),build_seed3d(i,128)); + std::cout << "xxx|128x128x128|" << i << " = " << timer.read() << std::endl; + mln::opt::at(chart_seed, i*i*i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_seed, "euclidian.3d.seed.sh"); + + // Test the size of the image + // 1, 2, 4, 8, 16, 32, 64, 128, 256 + mln::image1d<float> chart_size(128+1); + + mln::data::fill(chart_size, mln::literal::zero); + + for (unsigned i = 6; i < 128+1; ++i) + { + influence_zone_euclidian_3d(timer, build_input3d(2,i), build_seed3d(2,i)); + std::cout << "xxx|" << i << "x" << i << "x" << i << "|8 = " + << timer.read() << std::endl; + mln::opt::at(chart_size, i) = timer.read(); + } + + mln::io::plot::save_image_sh(chart_size, "euclidian.3d.size.sh"); +} +/// \} + +//------------------------------------------------------------------------------ +// Main +// +// Point de fonctionnement du système [image de taille entre 1 et 256] +// Moins de 16 labels +// +//------------------------------------------------------------------------------ + +/// \brief The main routine that call all the benchmark tests. +/// +/// This benchmark aim at comparing different kind of distance. It +/// looks after the influence geodesic, the influence front and +/// euclidian distance. Many files are generated. Some rules exist to +/// build the name of the file: +/// distance.neighbouring.dimension.(size|seed). The distance could be +/// the front distance (front), the geodesic distance (geodesic) or +/// the euclidian distance (euclidian). The neighbouring could be c04 +/// or c08 in 2d, c06, c18 or c26 in 3d. The dimension could be 2d or 3d. +/// Finally, the flag size or seed precises the nature of that test, increasing +/// gradualy the size of the image or increasing gradualy the number of seeds. +/// The file contains the time benchmark of the distance studied. +/// +/// \fixme test_influence_zone_geodesic_3d doesn't compile. +int main() +{ + test_influence_zone_euclidian_2d(); + test_influence_zone_front_2d(); + test_influence_zone_geodesic_2d(); + + // FIXME : It doesn't compile because of a bad calling function at_ in + // FIXME : the influence_zone_geodesic.hh line 127. + // test_influence_zone_geodesic_3d(); + test_influence_zone_front_3d(); + test_influence_zone_euclidian_3d(); + + return 0; +} diff --git a/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean1d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo + + +ifeq ($(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/scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc b/scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..22eae91 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,258 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean1d. +/// +/// The demonstrator produces five kind of results. The file +/// "color.ppm" allow us to localize the different center in the +/// image. There is an equivalent file "label.pgm" that's do the same +/// thing but inplace of a specific color, it gives the label of each +/// center. It's quit easy with xv, to transform the label image in +/// the color one. The file "mean.pgm" replaces each label by the mean +/// of the points that contributate to their center. It is a kind of +/// abstraction of the reality of the image, of its complexity. In +/// file "variance_cnv.sh" abscissa axis stands for the convergence +/// evolution and the ordinate axis for each runs. The "mean_cnv.sh" +/// file shows the evolution of the centers, whatever the run. The +/// x-axis represents the id of the center, the y-axis the iteration +/// in the convergence process and the z-axis the id of the particular +/// run. The graphic is very bad, but textual interpretation is still +/// available. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/clustering/kmean1d.hh> + +#include <mln/core/image/image2d.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/int_u8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the 8 bits greylevel image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() + +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> t_kmean; + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + + t_image2d_int_u8 img; + + mln::io::pgm::load(img, image.c_str()); + + t_kmean kmean(img, + k_center, + watch_dog, + n_times); + + //mln::trace::quiet = false; + + kmean.launch_n_times(); + + // FIXME : 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 var_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.ppm"); + 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(var_cnv, "variance_cnv.sh"); +} + + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the 8 bits greylevel image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the 8 bits greylevel image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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/scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean2d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo + + +ifeq ($(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/scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc new file mode 100644 index 0000000..2538dce --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean2d/kmean2d.cc @@ -0,0 +1,278 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean2d. +/// +/// The demonstrator produces five kind of results. The file +/// "color.ppm" allow us to localize the different center in the +/// image. There is an equivalent file "label.pgm" that's do the same +/// thing but inplace of a specific color, it gives the label of each +/// center. It's quit easy with xv, to transform the label image in +/// the color one. The file "mean.pgm" replaces each label by the mean +/// of the points that contributate to their center. It is a kind of +/// abstraction of the reality of the image, of its complexity. The +/// "variance_cnv.sh" changes its representation and becomes more +/// readable. The "mean_cnv.sh" have got a generation problem. We can +/// bypass it by adding printing lines in the 'splot' +/// instruction. However, the graphic is correct and mainly shows the +/// convergence of the center value. Finally, as the work takes place in +/// the r/g space, the "point.ppm" file shows us the exact input space. + +#include <iostream> +#include <sstream> + +#include <mln/img_path.hh> + +#include <mln/clustering/kmean2d.hh> + +#include <mln/core/image/image2d.hh> + +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/fun/v2v/rg_to_rgb.hh> + +#include <mln/io/pgm/save.hh> +#include <mln/io/ppm/load.hh> +#include <mln/io/ppm/save.hh> +#include <mln/io/plot/save_image_sh.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() +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::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb8 point_img_rgb8; + t_image2d_rgb8 mean_img_rgb8; + t_image2d_rgb8 mean_dbg_rgb8; + t_image2d_rg8 img_rg8; + + // Read input. + mln::io::ppm::load(img_rgb8, image.c_str()); + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + + // Call kmean. + t_kmean kmean(img_rg8, k_center, watch_dog, n_times); + + mln::trace::quiet = false; + kmean.launch_n_times(); + + // FIXME : Not safe because we don't test kmean.is_valid() + + // Get outputs. + t_kmean::t_point_img point_img = kmean.get_point(); + 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(); + + // Convert outputs. + point_img_rgb8 = mln::data::transform(point_img, t_rg_to_rgb()); + mean_img_rgb8 = mln::data::transform(mean_img, t_rg_to_rgb()); + + mln::io::ppm::save(mean_img_rgb8, "mean.ppm"); + mln::io::ppm::save(color_img, "color.ppm"); + mln::io::pgm::save(label_img, "label.pgm"); + mln::io::ppm::save(point_img_rgb8, "point.ppm"); + + mln::io::plot::save_image_sh(mean_cnv, "mean_cnv.sh"); + mln::io::plot::save_image_sh(variance_cnv, "variance_cnv.sh"); +} + + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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: kmean2d [image [k_center [n_times [watch_dog]]]]" + << std::endl; + std::cout << "ppm 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; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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/scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean3d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo + + +ifeq ($(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/scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc b/scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc new file mode 100644 index 0000000..0e2b16e --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean3d/kmean3d.cc @@ -0,0 +1,265 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean3d. +/// +/// The demonstrator produces five kind of results. The file +/// "color.ppm" allow us to localize the different center in the +/// image. There is an equivalent file "label.pgm" that's do the same +/// thing but inplace of a specific color, it gives the label of each +/// center. It's quit easy with xv, to transform the label image in +/// the color one. The file "mean.pgm" replaces each label by the mean +/// of the points that contributate to their center. It is a kind of +/// abstraction of the reality of the image, of its complexity. The +/// "variance_cnv.sh" changes its representation and becomes more +/// readable. The "mean_cnv.sh" have got a generation problem. We can +/// bypass it by adding printing lines in the 'splot' +/// instruction. However, the graphic is correct and mainly shows the +/// convergence of the center value. + +#include <iostream> +#include <sstream> + +#include <mln/clustering/kmean3d.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.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/img_path.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() +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_n_times(); + + // FIXME : 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"); +} + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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/scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am b/scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am new file mode 100644 index 0000000..4455a07 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean_rgb/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo + + +ifeq ($(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/scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc b/scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc new file mode 100644 index 0000000..d02c497 --- /dev/null +++ b/scribo/sandbox/green/demo/clustering/kmean_rgb/kmean_rgb.cc @@ -0,0 +1,239 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This is a demonstrator for kmean_rgb. +/// +/// The code is provided as is. It is an alpha version, so very very +/// experimental. Take care !! + +#include <iostream> +#include <sstream> + +#include <mln/clustering/kmean_rgb.hh> + +#include <mln/core/macros.hh> +#include <mln/core/image/image2d.hh> + +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.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/img_path.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb8.hh> + + +/// \brief The real code for launching the kmean optimised algorithm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// This is the real lauching code. In fact, the stuff consists in loading the +/// image, setting the parameters, calling the routine, extracting the results +/// and saving them for the user. +/// +/// \fixme The result is not safe because we don't test kmean.is_valid() +void do_demo(const std::string& image, + const unsigned k_center, + const unsigned n_times, + const unsigned watch_dog) +{ + typedef mln::value::label_8 t_lbl8; + 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; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb5 img_rgb5; + t_image2d_lbl8 img_lbl8; + + mln::io::ppm::load(img_rgb8, image.c_str()); + img_rgb5 = mln::data::transform(img_rgb8,mln::fun::v2v::rgb8_to_rgbn<5>()); + img_lbl8 = mln::clustering::kmean_rgb<double,5>(img_rgb5, + k_center, + watch_dog, + n_times); + + mln::io::pgm::save(img_lbl8, "label.pgm"); +} + +/// \brief Front hand for the demonstrator. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// The front hand enables the setting of the default values for the +/// parameters. it allows us to print the real parameter set used by +/// the demonstrator. +void demo(const std::string& image = OLENA_IMG_PATH"/house.ppm", + const unsigned k_center = 3, + const unsigned n_times = 3, + const unsigned watch_dog = 3) +{ + 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); +} + + +/// \brief Convert character get in the input stream to unsigned integer. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Convert character get in the input stream to string. +/// +/// \param[in] status : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +/// \param[out] val : the converted value to output. +/// +/// \return a flag that tell us if every thing is ok, if the result can be used. +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; +} + + +/// \brief Print usage recommandation for this binary. +/// +/// \param[in] argc : the exiting flag tell us to exit without doing any job. +/// \param[in] arg : the input argument to convert. +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; +} + + +/// \brief The main routine launchs the kmean optimised algoritm. +/// +/// \param[in] image : the color image. +/// \param[in] k_center : the number of centers. +/// \param[in] n_times : the number of launching. +/// \param[in] watch_dog : the number of iterations to manage the convergence. +/// +/// All the parameters are optional, but we must respect strictly a +/// specific order. In fact, we can launch five programs with some +/// default parameters: demo(), demo(image), demo(image,k_center), +/// demo(image,k_center,n_times) and +/// demo(image,k_center,n_times,watch_dog). +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/scribo/sandbox/green/mln/clustering/k_mean.hh b/scribo/sandbox/green/mln/clustering/k_mean.hh index db3f34c..74acda7 100644 --- a/scribo/sandbox/green/mln/clustering/k_mean.hh +++ b/scribo/sandbox/green/mln/clustering/k_mean.hh @@ -1,6 +1,4 @@ -// 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) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -26,43 +24,89 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_K_MEAN_HH -#define MLN_CLUSTERING_K_MEAN_HH +# define MLN_CLUSTERING_K_MEAN_HH /// \file /// -/// \brief Implements the K MEAN algorithm. -/// DO -/// - ASSIGNEMENT STEP -/// - UPDATE STEP -/// LOOP UNTIL CONVERGENCE +/// \brief Implement the K MEAN algorithm with matrix and vectors. /// -/// ASSIGNEMENT STEP +/// This is the first impementation of the kmean algorithm. There is no +/// specific optimisation but every part of the algorithm is here and +/// it works. As it used vectors and matrices, it is not very convenient to +/// work with this version. +/// +/// \verbatim +/// This is the main lines of the kmean algorithm: +/// +/// PROCEDURE MAIN +/// BEGIN +/// DO +/// - CALL ASSIGNEMENT STEP +/// - CALL UPDATE STEP +/// LOOP UNTIL CONVERGENCE +/// END +/// +/// PROCEDURE ASSIGNEMENT STEP /// BEGIN -/// - COMPUTE THE DISTANCE MATRIX FROM POINTS TO CENTERS -/// - COMPUTE GROUP MATRIX WHERE EACH POINT IS ASSIGNED TO NEAREST CENTER +/// - COMPUTE THE DISTANCE MATRIX FROM POINTS TO CENTERS +/// - COMPUTE GROUP MATRIX WHERE EACH POINT IS ASSIGNED TO THE NEAREST CENTER /// END /// -/// UPDATE STEP +/// PROCEDURE UPDATE STEP /// BEGIN -/// - COMPUTE THE MEAN OF THE GROUPS WHICH ARE THE NEW CENTERS +/// - COMPUTE THE MEAN OF THE GROUPS WHICH ARE NOW THE NEW CENTERS /// END +/// \endvarbitim +/// +/// The following sample is the standard use of this first +/// implementation. The number of centers is a decision left to the +/// final user. As the representation uses vectors and matices, size +/// of each objects must be known at compile time, and so NB_POINT must be +/// a constant expression (mln::geom::nsites couldn't be called). +/// +/// #include <mln/clustering/k_mean.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/trait/value_.hh> +/// #include <mln/value/rgb8.hh> +/// +/// #define NB_CENTER 9 +/// #define NB_POINT (128*128) +/// #define POINT_SIZE mln_dim(mln::value::rgb8) +/// #define POINT_TYPE double +/// +/// int main() +/// { +/// typedef mln::value::rgb8 t_rgb8; +/// mln::image2d<t_rgb8> img_rgb8; +/// +/// mln::io::ppm::load(img_ref, OLENA_IMG_PATH"/house.ppm"); +/// mln::clustering::k_mean<NB_POINT,NB_CENTER,POINT_SIZE,POINT_TYPE> kmean; +/// +/// kmean.init_point(img_rgb8); +/// kmean.loop(img_rgb8); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> -#include <limits.h> -#include <iostream> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/algebra/mat.hh> +# include <mln/algebra/vec.hh> -#include <mln/core/contract.hh> -#include <mln/trait/value_.hh> +# include <mln/core/concept/image.hh> +# include <mln/core/contract.hh> +# include <mln/core/macros.hh> -#include <mln/algebra/mat.hh> -#include <mln/algebra/vec.hh> +# include <mln/math/min.hh> +# include <mln/norm/l2.hh> -#include <mln/math/min.hh> -#include <mln/norm/l2.hh> +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> -#include <mln/core/concept/image.hh> -#include <mln/core/macros.hh> +# include <mln/trait/value_.hh> namespace mln { @@ -78,149 +122,221 @@ namespace mln namespace clustering { - - // - // PARAM : DIM VECTOR, NUMBER OF CLASSES - // INPUT : MATRIX 1..N (SAMPLE) - // WORK - // - - // n is the size of the sample. - // k is the number of centers. - // p is the number of attributes for a point. - // T is the type for computations. + + /// \brief Implements the K MEAN algorithm with matrix and vectors. + /// + /// Param n is the size of the sample (the number of point to be + /// classified). Param k is the number of centers, at the end, + /// each point is link to one the nearest center. Param p is the + /// number of attributes of a point, in fact its dimension (int_u8 + /// = 1, rgb8 = 3, etc ...). Param T is the type used for + /// computations, the type of the point, typiccaly float or + /// double. + /// + /// \ingroup modclustering template <unsigned n, unsigned k, unsigned p, typename T> struct k_mean { - //------------------------------------------------------------------------ - // Constructor and destructor - //------------------------------------------------------------------------ - + /// Constructors and destructors. + /// \{ + /// \brief Allocate or deallocate the data structure. + /// + /// Allocate or deallocate the _point, _distance, _group, + /// _center, _variance data structure with the correct size. k_mean(); ~k_mean(); + /// \} - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - + /// Accessors. + /// \{ + /// \brief Hack to view temporary results of the kmean algorithm. + /// + /// These methods are necessary for unitary testing and debugging. algebra::mat<n, p, T>& get_point(); algebra::mat<k, p, T>& get_center(); algebra::mat<n, k, T>& get_distance(); algebra::mat<n, k, T>& get_group(); algebra::vec<k, T>& get_variance(); + /// \} - - //------------------------------------------------------------------------ - // Tools - //------------------------------------------------------------------------ + /// Tools. + /// \{ + /// \brief Define elementary function that are not available from milena. + /// + /// These methods are not interesting from the kmean'point of + /// view but are required for the gluing process. - /// \brief Return the euclidian distance beetween vector x and vector y. + /// \brief Return the euclidian distance beetween two vectors x and y. /// /// Compute the f$\sqrt(\sum_{i=0}^P ((x_i - y_i)^2)$f /// - /// \param[in] x a vector of dimension 1xP. - /// \param[in] y a second vector of dimension 1xP. + /// \param[in] x a vector of dimension p. + /// \param[in] y a second vector of dimension p. /// \return the distance between x and y. - mln_sum_product(T,T) euclidian_distance(const algebra::vec<p,T>& x, const algebra::vec<p,T>& y) const; - /// \brief Return the ith column. - /// - /// \param[in] m a matrix of dimension {N or K}xP. - /// \return the ith column as a vector of dimension 1xP. - - /// \brief Return the ith column. + /// \brief Return a stack copy of the ith column. /// - /// \param[in] m a matrix of dimension {N or K}xP. - /// \return the ith column as a vector of dimension 1xP. + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _col the index of the selected column. + /// \return a stack copy of the ith column as a vector of dimension q. template <unsigned q, unsigned r> algebra::vec<q,T> col(const algebra::mat<r, q, T>& m, const unsigned _col) const; + + /// \brief Return a heap copy of the ith col. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _col the index of the selected column. + /// \return a heap copy of the ith column as a vector of dimension q. template <unsigned q, unsigned r> algebra::vec<q,T>* ptr_col(const algebra::mat<r, q, T>& m, const unsigned _col) const; + + /// \brief Return a stack copy of the ith row. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _row the index of the selected row. + /// \return a stack copy of the ith row as a vector of dimension q. template <unsigned q, unsigned r> algebra::vec<r,T> row(const algebra::mat<r, q, T>& m, const unsigned _row) const; + + /// \brief Divide inplace a column of the matrix by a scalar. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _col the index of the selected column. + /// \param[in] value the scalar by which dividing the column. template <unsigned q, unsigned r> void div_col(algebra::mat<r, q, T>& m, const unsigned _col, const T value); + + /// \brief Sum all the elements of one row. + /// + /// \param[in] m a matrix of dimension r x q. + /// \param[in] _row the index of the selected row. + /// \return the sum of every attributes of the row. template <unsigned q, unsigned r> mln_sum(T) sum_row(const algebra::mat<r, q, T>& m, const unsigned _row) const; + + /// \brief Return the minimum of a vector. + /// + /// \param[in] x a vector of dimension q. + /// \return the mininum of that input vector. template <unsigned q, typename M> M min_col(const algebra::vec<q, M>& x) const; + /// \} - //------------------------------------------------------------------------ - // Initializations of points and centers - //------------------------------------------------------------------------ - - /// \brief Initialize the matrix _point with the pixels of an image. + /// Initializations. + /// \{ + /// \brief Initialize the _point and _center data structure. /// - /// \param[in] input an image which contains the data points. + /// The _point data structure is fed with the value of the input + /// image. The _center data structure is fed with random points + /// taken from the input image. template <typename I> void init_point(const Image& _input); void init_center(); + /// \} - - //------------------------------------------------------------------------ - // Computations of distance, group, center, within variance - //------------------------------------------------------------------------ + /// Computations. + /// \{ + /// \brief Define the core routines of the kmean algorithm. + /// + /// The update_distance, update_group, update_center and update_variance + /// define the core kmean routines. If some optimization muste be done, + /// one may looks here. + + + /// \brief Update the _distance data structure. + /// + /// Compute the square distance between each point of the data + /// cloud and each center. void update_distance(); + + /// \brief Update the _group data structure. + /// + /// For each point of the data cloud, find the group which has + /// the minimum distance from it. void update_group(); - void update_center(); - T update_variance(); + /// \brief Update the _center data structure. + /// + /// For each group, compute the center of mass in therm of + /// points of the data cloud. + void update_center(); - //------------------------------------------------------------------------ - // Main loop - //------------------------------------------------------------------------ + /// \brief Update the _variance. + /// + /// Compute the within variance. Sum every shortest distance. + T update_variance(); + /// \} + /// \brief Define the orchestration of the kmean core routines. + /// + /// \param[in] _input the initial image which initialize the data cloud. + /// + /// The orchestration is in a fact a loop which call one after + /// each other the core routines. Ending the loop means managing + /// the statistical convergence or having some clues of + /// divergence (watch_dog). template <typename I> void loop(const Image& _input); - //void loop(); private: + /// \brief watch_dog define the maximum of iterations that can be done. + /// + /// That constant help us to decide if divergence has been + /// occurred or not. static const unsigned watch_dog = 100; - + + /// \brief _points contains the concatenation of every data points. /// - /// One data point is a vector 1xP where P is the number of attributes. - /// _points is a matrix NxP where N is the number of data points. + /// One data point is a vector 1 x p where p is the number of attributes. + /// So _points is a matrix n x P where n is the number of data points. algebra::mat<n, p, T>* _point; + /// \brief _distance contains all the euclidian distances between points /// and the centers. /// - /// _distance is a matrix NxK where N is the number of data points and - /// K the number of centers. + /// _distance is a matrix n x k where n is the number of data points and + /// k the number of centers. algebra::mat<n, k, mln_sum_product(T,T)>* _distance; + /// \brief _group contains the point assignement to one center. /// - /// _group is a boolean matrix NxK where N is the number of data points - /// and K the number of centers. + /// _group is a boolean matrix n x k where n is the number of data points + /// and k the number of centers. algebra::mat<n, k, T>* _group; - /// \brief _center contains the coordonnate of the K centers. + + /// \brief _center contains the coordonnate of the k centers. /// - /// _center is a matrix KxP where K is the number of centers and P is the - /// number of attributes. + /// _center is a matrix k x p where k is the number of centers + /// and p is the number of attributes. algebra::mat<k, p, T>* _center; + + /// \brief _variance contains the variance of each group. + /// + /// _variance is a vector 1 x k where k is the number of centers. algebra::vec<k, T>* _variance; }; @@ -234,7 +350,7 @@ namespace mln inline k_mean<n,k,p,T>::k_mean() { - trace::entering("mln::clustering::k_mean::k_mean"); + trace::entering("mln::clustering::k_mean::cstor"); _point = new algebra::mat<n, p, T>(); _distance = new algebra::mat<n, k, mln_sum_product(T,T)>(); @@ -248,14 +364,14 @@ namespace mln mln_postcondition(_center != 0); mln_postcondition(_variance != 0); - trace::exiting("mln::clustering::k_mean::k_mean"); + trace::exiting("mln::clustering::k_mean::cstor"); } template <unsigned n, unsigned k, unsigned p, typename T> inline k_mean<n,k,p,T>::~k_mean() { - trace::entering("mln::clustering::k_mean::~k_mean"); + trace::entering("mln::clustering::k_mean::dstor"); delete _point; delete _distance; @@ -263,7 +379,7 @@ namespace mln delete _center; delete _variance; - trace::exiting("mln::clustering::k_mean::~k_mean"); + trace::exiting("mln::clustering::k_mean::dstor"); } //-------------------------------------------------------------------------- @@ -350,7 +466,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - algebra::vec<q,T> k_mean<n,k,p,T>::col(const algebra::mat<r, q, T>& m, + algebra::vec<q,T> k_mean<n,k,p,T>::col(const algebra::mat<r, q, T>& m, const unsigned _col) const { trace::entering("mln::clustering::k_mean::col"); @@ -368,7 +484,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - algebra::vec<q,T>* k_mean<n,k,p,T>::ptr_col(const algebra::mat<r, q, T>& m, + algebra::vec<q,T>* k_mean<n,k,p,T>::ptr_col(const algebra::mat<r, q, T>& m, const unsigned _col) const { trace::entering("mln::clustering::k_mean::ptr_col"); @@ -386,7 +502,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - mln_sum(T) k_mean<n,k,p,T>::sum_row(const algebra::mat<r, q, T>& m, + mln_sum(T) k_mean<n,k,p,T>::sum_row(const algebra::mat<r, q, T>& m, const unsigned _row) const { trace::entering("mln::clustering::k_mean::sum_row"); @@ -404,7 +520,7 @@ namespace mln template <unsigned n, unsigned k, unsigned p, typename T> template <unsigned q, unsigned r> inline - void k_mean<n,k,p,T>::div_col(algebra::mat<r, q, T>& m, + void k_mean<n,k,p,T>::div_col(algebra::mat<r, q, T>& m, const unsigned _col, const T value) { @@ -422,21 +538,21 @@ namespace mln inline M k_mean<n,k,p,T>::min_col(const algebra::vec<q, M>& x) const { - trace::entering("mln::clustering::k_mean<n,k,p,T>::min_col"); - + trace::entering("mln::clustering::k_mean::min_col"); + M result = x[0]; for (unsigned i = 1; i < q; ++i) result = math::min(result, x[i]); - trace::exiting("mln::clustering::k_mean<n,k,p,T>::min_col"); + trace::exiting("mln::clustering::k_mean::min_col"); return result; } template <unsigned n, unsigned k, unsigned p, typename T> inline mln_sum_product(T,T) - k_mean<n,k,p,T>::euclidian_distance(const algebra::vec<p, T>& x, + k_mean<n,k,p,T>::euclidian_distance(const algebra::vec<p, T>& x, const algebra::vec<p, T>& y) const { trace::entering("mln::clustering::k_mean::euclidian_distance"); @@ -458,8 +574,8 @@ namespace mln inline void k_mean<n,k,p,T>::init_point(const Image& _input) { - trace::entering("mln::clustering::k_mean<n,k,p,T>::init"); - + trace::entering("mln::clustering::k_mean::init"); + const I& input = exact(_input); algebra::mat<n, p, T>& point = *_point; @@ -474,28 +590,28 @@ namespace mln for_all(pi) { //std::cout << pi << std::endl; - + ++i; for (unsigned j = 0; j < p; ++j) { - + point(i,j) = input(pi).comp(j); //point(i,j) = input(pi); } } - trace::exiting("mln::clustering::k_mean<n,k,p,T>::init"); + trace::exiting("mln::clustering::k_mean::init"); } template <unsigned n, unsigned k, unsigned p, typename T> inline void k_mean<n,k,p,T>::init_center() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::init_center"); - + trace::entering("mln::clustering::k_mean::init_center"); + algebra::mat<n, p, T>& point = *_point; algebra::mat<k, p, T>& center = *_center; - + // A random point is choosen to be the initial value for a center. for (unsigned i = 0; i < k; ++i) { @@ -503,16 +619,16 @@ namespace mln for (unsigned j = 0; j < p; ++j) { - center(i,j) = point(random, j); + center(i,j) = point(random, j); } //std::cout << "center(" <::init_center"); + trace::exiting("mln::clustering::k_mean::init_center"); } - + //-------------------------------------------------------------------------- // Computations of distance, group, center, within variance //-------------------------------------------------------------------------- @@ -528,7 +644,7 @@ namespace mln algebra::mat<n, p, T>& point = *_point; algebra::mat<n, k, T>& distance = *_distance; algebra::mat<k, p, T>& center = *_center; - + // for all points in the data cloud. for (unsigned i = 0; i < n; ++i) { @@ -546,7 +662,7 @@ namespace mln inline void k_mean<n,k,p,T>::update_group() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::update_group"); + trace::entering("mln::clustering::k_mean::update_group"); algebra::mat<n, k, mln_sum_product(T,T)>& distance = *_distance; algebra::mat<n, k, T>& group = *_group; @@ -563,14 +679,14 @@ namespace mln } // mln_postcondition(sum(col(distance(i,j)) == 1) Vi - trace::exiting("mln::clustering::k_mean<n,k,p,T>::update_group"); + trace::exiting("mln::clustering::k_mean::update_group"); } template <unsigned n, unsigned k, unsigned p, typename T> inline void k_mean<n,k,p,T>::update_center() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::update_center"); + trace::entering("mln::clustering::k_mean::update_center"); algebra::mat<n, p, T>& point = *_point; algebra::mat<k, p, T>& center = *_center; @@ -592,14 +708,14 @@ namespace mln for (unsigned i = 0; i < k; ++i) div_col(center, i, sum_row(group, i)); - trace::exiting("mln::clustering::k_mean<n,k,p,T>::update_center"); + trace::exiting("mln::clustering::k_mean::update_center"); } template <unsigned n, unsigned k, unsigned p, typename T> inline T k_mean<n,k,p,T>::update_variance() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::update_variance"); + trace::entering("mln::clustering::k_mean::update_variance"); algebra::vec<k, T>& variance = *_variance; algebra::mat<n, k, T>& distance = *_distance; @@ -613,10 +729,10 @@ namespace mln result += variance[i]; } - trace::exiting("mln::clustering::k_mean<n,k,p,T>::update_variance"); + trace::exiting("mln::clustering::k_mean::update_variance"); return result; - } - + } + //-------------------------------------------------------------------------- // Main loop @@ -626,9 +742,8 @@ namespace mln template <typename I> inline void k_mean<n,k,p,T>::loop(const Image& _input) - //void k_mean<n,k,p,T>::loop() { - trace::entering("mln::clustering::k_mean<n,k,p,T>::loop"); + trace::entering("mln::clustering::k_mean::loop"); T within_variance = INT_MAX-1; T old_variance = INT_MAX; @@ -658,12 +773,10 @@ namespace mln std::cout <::loop"); - } - - + trace::exiting("mln::clustering::k_mean::loop"); + } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean1d.hh b/scribo/sandbox/green/mln/clustering/kmean1d.hh index c3c1add..57d246b 100644 --- a/scribo/sandbox/green/mln/clustering/kmean1d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean1d.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -24,7 +24,7 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_KMEAN1D_HH -#define MLN_CLUSTERING_KMEAN1D_HH +# define MLN_CLUSTERING_KMEAN1D_HH /// \file /// @@ -34,36 +34,63 @@ /// 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 +/// by a fix cost in the complexity. This version is very adapted to /// images with small quantification. +/// +/// The following sample is a typical use of the new kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean1d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/pgm/load.hh> +/// #include <mln/value/int_u8.hh> +/// +/// int main() +/// { +/// typedef mln::value::int_u8 t_int_u8; +/// typedef mln::image2d<t_int_u8> t_image2d_int_u8; +/// t_image2d_int_u8 img_int_u8; +/// +/// mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/house.pgm"); +/// mln::clustering::kmean1d<double, 8> kmean(img_int_u8, 3); +/// +/// kmean.launch_n_times(); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> -#include <limits.h> -#include <iostream> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/accu/stat/histo1d.hh> -#include <mln/core/contract.hh> -#include <mln/trait/value_.hh> -#include <mln/accu/stat/histo1d.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/macros.hh> -#include <mln/math/min.hh> -#include <mln/math/sqr.hh> -#include <mln/norm/l2.hh> +# include <mln/data/compute.hh> +# include <mln/data/fill.hh> +# include <mln/debug/println.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/io/ppm/save.hh> +# include <mln/io/pgm/save.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/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> +# include <mln/math/min.hh> +# include <mln/math/sqr.hh> +# include <mln/norm/l2.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/trait/value_.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> namespace mln { @@ -85,8 +112,8 @@ namespace mln /// 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. + /// Param T is the type used for computations (float or double). + /// Param n is the quantification for the image grayscale. template <typename T, unsigned n> struct kmean1d { @@ -117,32 +144,22 @@ namespace mln ///} - //------------------------------------------------------------------------ - // Constructor - //------------------------------------------------------------------------ - - /// \brief 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). - kmean1d(const t_point_img& point, const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - - /// Mutator and accessors. + /// Mutators 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); @@ -173,31 +190,21 @@ namespace mln /// \} - //------------------------------------------------------------------------ - // Initializations of centers - //------------------------------------------------------------------------ - /// Initialization of centers. /// \{ - /// \brief Two ways: Regular greylevel tick or random greylevel value or. + /// \brief Initialize centers by regular tick or random position. /// /// 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. @@ -206,36 +213,27 @@ namespace mln /// 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). - + /// There are two ways to launch the kmean process. The first + /// one allows to run it one time until convergence. As the + /// process is a descent, it depends on the initial center + /// location. 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. /// \{ @@ -244,15 +242,10 @@ namespace mln /// 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. - + /// associate to it) or a failure in the convergence. bool is_descent_valid(); - /// \} - //------------------------------------------------------------------------ - // Debugging tools - //------------------------------------------------------------------------ /// Debugging tools /// \{ @@ -264,12 +257,10 @@ namespace mln /// 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_all_dbg(); void update_cnv(); void finalize_cnv(); - /// \} @@ -279,20 +270,19 @@ 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 + /// In fact, watch_dog limits 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. @@ -300,26 +290,25 @@ namespace mln /// 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. - + /// is just the sum of all ponderated variances 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_result _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. @@ -327,23 +316,23 @@ namespace mln /// 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. + /// \brief The inputs are the distribution of 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. @@ -354,13 +343,12 @@ namespace mln /// 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. @@ -369,15 +357,14 @@ namespace mln /// 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. + /// \brief Some information exports 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 @@ -385,14 +372,13 @@ namespace mln /// 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. + + /// Variance and center evolutions. /// \{ /// \brief Trace the variance and the center evolution. /// @@ -401,10 +387,8 @@ namespace mln /// 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; - /// \} }; @@ -803,8 +787,6 @@ namespace mln { 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 @@ -872,7 +854,7 @@ namespace mln for_all(g) { - if (l.ind() == _group(g)) + if (static_cast<t_label>(l.ind()) == _group(g)) _variance(l) += _distance(point2d(g.ind(), l.ind())); } @@ -1091,7 +1073,7 @@ namespace mln trace::exiting("mln::clustering::kmean1d::launch_n_times"); } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean2d.hh b/scribo/sandbox/green/mln/clustering/kmean2d.hh index e4918db..51aaf49 100644 --- a/scribo/sandbox/green/mln/clustering/kmean2d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean2d.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -24,52 +24,102 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_KMEAN2D_HH -#define MLN_CLUSTERING_KMEAN2D_HH +# define MLN_CLUSTERING_KMEAN2D_HH /// \file /// -/// \brief Implements the optimized kmean algorithm. +/// \brief Implements the optimized kmean algorithm in 2d. /// /// 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> +/// by a fix cost in the complexity. This version is very adapted to +/// images with small quantification. But, in 2d, the execution +/// becomes very slow. It's just normal because the quantification is +/// 8 bits per axis. So the actual histogram is bigger than the image. +/// +/// Take care to the following point: The within variance is still a +/// scalar value because we take the distance between two points and +/// the result is a scalar from the geometrical point of view. An +/// alternative implementation could study the variance/covariance +/// matrix of each sub data clouds and works with the trace of the +/// within variance matrix (as we do for the fisher criteria in N-d). +/// +/// The following sample is a typical use of the new kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rg8 img_rg8; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// +/// mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); +/// +/// kmean.launch_n_times(); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> + +# include <mln/accu/stat/histo2d.hh> + +# include <mln/algebra/vec.hh> + +# include <mln/core/concept/image.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/macros.hh> + +# include <mln/data/compute.hh> +# include <mln/data/fill.hh> + +# include <mln/debug/println.hh> + +# include <mln/io/pgm/save.hh> +# include <mln/io/ppm/save.hh> + +# include <mln/labeling/colorize.hh> +# include <mln/labeling/mean_values.hh> + +# include <mln/literal/one.hh> +# include <mln/literal/zero.hh> + +# include <mln/math/min.hh> +# include <mln/math/sqr.hh> +# include <mln/norm/l2.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/trait/value_.hh> + +# include <mln/util/array.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> +# include <mln/value/rg.hh> namespace mln { @@ -85,21 +135,22 @@ namespace mln 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. + /// This version of the kmean algorithm uses a 2-channels + /// (red/green channel) 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. + /// Param T is the type used for computations (float or double). + /// Param 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; @@ -108,7 +159,7 @@ namespace mln typedef T t_result1d; typedef algebra::vec<2,T> t_result2d; - /// FIXME t_point n'est pas forcément une image 2d, bien au contraire. + /// \fixme t_point_img is not an image2d ... but it works like this ... typedef image2d<t_value> t_point_img; typedef image2d<unsigned> t_histo_img; typedef util::array<t_result1d> t_number_img; @@ -128,35 +179,24 @@ namespace mln 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 + /// \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. + /// Mutators 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); @@ -187,10 +227,10 @@ namespace mln /// \} - //------------------------------------------------------------------------ - // Printing temporary results - //------------------------------------------------------------------------ - + /// Show temporary results. + /// \{ + /// \brief Formating temporary outputs for debugging. + /// void print_mean(); void print_number(); void print_variance(); @@ -198,32 +238,18 @@ namespace mln 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. + /// \brief Initialize centers by random position. /// - /// 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. - + /// Assign a random position in the 2-channel space to each center. 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. @@ -232,36 +258,27 @@ namespace mln /// 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). - + /// There are two ways to launch the kmean process. The first + /// one allows to run it one time until convergence. As the + /// process is a descent, it depends on the initial center + /// location. 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. /// \{ @@ -269,16 +286,11 @@ namespace mln /// /// 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. - + /// for us. The method looks for a bad center (a class with no r/g color + /// associate to it) or a failure in the convergence. bool is_descent_valid(); - /// \} - //------------------------------------------------------------------------ - // Debugging tools - //------------------------------------------------------------------------ /// Debugging tools /// \{ @@ -290,13 +302,11 @@ namespace mln /// 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(); - /// \} @@ -313,13 +323,12 @@ namespace mln /// 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. @@ -337,16 +346,15 @@ namespace mln /// 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. @@ -354,10 +362,11 @@ namespace mln /// 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. /// \{ @@ -365,12 +374,11 @@ namespace mln /// /// 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. @@ -381,27 +389,25 @@ namespace mln /// 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_mean_img _mean; // k x 2 t_variance_img _variance; // k x 1 within group - /// \} + /// Greylevels description. /// \{ - /// \brief The information are concerned with the greylevel input image. + /// \brief The information are concerned with the 2-channel input image. /// - /// The group image allow us to decide which greylevel (and of course + /// The group image allow us to decide which r/g value (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 - + /// clue on how a r/g value could contribute to a center. The summation + /// over the r/g values of a center give us the within variance. + t_group_img _group; // g x 2 because dim(t_value) = 2 + t_distance_img _distance; // label x r/g value /// \} + /// Debugging, calibrating and testing results. /// \{ /// \brief Some exports information to control the results. @@ -412,14 +418,13 @@ namespace mln /// 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. + + /// Variance and center evolutions. /// \{ /// \brief Trace the variance and the center evolution. /// @@ -428,10 +433,8 @@ namespace mln /// 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; - /// \} }; @@ -448,7 +451,7 @@ namespace mln const unsigned watch_dog, const unsigned n_times) { - trace::entering("mln::clustering::kmean2d::kmean2d"); + trace::entering("mln::clustering::kmean2d::cstor"); mln_precondition(point.is_valid()); _k_center = k_center; @@ -516,7 +519,7 @@ namespace mln _mean_cnv.append(mean_set); } - trace::exiting("mln::clustering::kmean2d::kmean2d"); + trace::exiting("mln::clustering::kmean2d::cstor"); } //-------------------------------------------------------------------------- @@ -796,15 +799,15 @@ namespace mln { trace::entering("mln::clustering::kmean2d::print_histo"); - mln_piter(t_histo_img) rgb(_histo.domain()); + mln_piter(t_histo_img) rg(_histo.domain()); - for_all(rgb) + for_all(rg) { - if (0 < _histo(rgb)) + if (0 < _histo(rg)) { - std::cout << "histo(r=" << rgb.row(); - std::cout << ", g=" << rgb.col(); - std::cout << ")= " << _histo(rgb); + std::cout << "histo(r=" << rg.row(); + std::cout << ", g=" << rg.col(); + std::cout << ")= " << _histo(rg); std::cout << std::endl; } } @@ -818,15 +821,15 @@ namespace mln { trace::entering("mln::clustering::kmean2d::print_group"); - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { - if (0 < _histo(rgb)) + if (0 < _histo(rg)) { - std::cout << "group(r=" << rgb.row(); - std::cout << ", g=" << rgb.col(); - std::cout << ")= " << _group(rgb); + std::cout << "group(r=" << rg.row(); + std::cout << ", g=" << rg.col(); + std::cout << ")= " << _group(rg); std::cout << std::endl; } } @@ -844,16 +847,16 @@ namespace mln for_all(l) { - mln_piter(t_distance_val) rgb(_distance[l.index_()].domain()); + mln_piter(t_distance_val) rg(_distance[l.index_()].domain()); - for_all(rgb) + for_all(rg) { - if (0 < _histo(rgb)) + if (0 < _histo(rg)) { std::cout << "distance(l=" << l.index_(); - std::cout << ",r=" << rgb.row(); - std::cout << ", g=" << rgb.col(); - std::cout << ")= " << _distance[l.index_()](rgb); + std::cout << ",r=" << rg.row(); + std::cout << ", g=" << rg.col(); + std::cout << ")= " << _distance[l.index_()](rg); std::cout << std::endl; } } @@ -960,9 +963,9 @@ namespace mln { trace::entering("mln::clustering::kmean2d::update_group"); - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { mln_eiter(t_distance_img) l(_distance); t_result1d min = mln_max(t_result1d); @@ -972,9 +975,9 @@ namespace mln for_all(l) { - if (min > _distance[l.index_()](rgb)) + if (min > _distance[l.index_()](rg)) { - min = _distance[l.index_()](rgb); + min = _distance[l.index_()](rg); label = l.index_(); } @@ -984,7 +987,7 @@ namespace mln //std::cout << "g = " << g << std::endl; - _group(rgb) = label; + _group(rg) = label; //std::cout << "group = " << _group(g) << std::endl; //std::cout << "-----------" << std::endl; } @@ -998,8 +1001,6 @@ namespace mln { 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 @@ -1021,14 +1022,14 @@ namespace mln _mean[em.index_()] = literal::zero; } - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { // 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); + _mean[_group(rg)][0] += rg.row() * _histo(rg); + _mean[_group(rg)][1] += rg.col() * _histo(rg); + _number(_group(rg)) += _histo(rg); } mln_eiter(t_mean_img) l(_mean); @@ -1072,12 +1073,12 @@ namespace mln { _variance[l.index_()] = literal::zero; - mln_piter(t_group_img) rgb(_group.domain()); + mln_piter(t_group_img) rg(_group.domain()); - for_all(rgb) + for_all(rg) { - if (l.index_() == _group(rgb)) - _variance[l.index_()] += _distance[l.index_()](rgb); + if (l.index_() == _group(rg)) + _variance[l.index_()] += _distance[l.index_()](rg); } _within_variance += _variance[l.index_()]; @@ -1334,7 +1335,7 @@ namespace mln trace::exiting("mln::clustering::kmean2d::launch_n_times"); } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean3d.hh b/scribo/sandbox/green/mln/clustering/kmean3d.hh index fb1a8df..c35d2a7 100644 --- a/scribo/sandbox/green/mln/clustering/kmean3d.hh +++ b/scribo/sandbox/green/mln/clustering/kmean3d.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -24,51 +24,102 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_CLUSTERING_KMEAN3D_HH -#define MLN_CLUSTERING_KMEAN3D_HH +# define MLN_CLUSTERING_KMEAN3D_HH /// \file /// -/// \brief Implements the optimized kmean algorithm. +/// \brief Implements the optimized kmean algorithm in 3d. /// /// This algorithm is optimized in the way it proceeds directly with -/// the rgb values inspite of the pixel attribute. The +/// the rgb value 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 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/histo3d_rgb.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/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> +/// by a fix cost in the complexity. This version is very adapted to +/// images with small quantification. But, in 3d, the execution +/// becomes very slow. It's just normal because the quantification is +/// n bits per axis. So the actual histogram may be bigger than the image. +/// +/// Take care to the following point: The within variance is still a +/// scalar value because we take the distance between two points and +/// the result is a scalar from the geometrical point of view. An +/// alternative implementation could study the variance/covariance +/// matrix of each sub data clouds and works with the trace of the +/// within variance matrix (as we do for the fisher criteria in N-d). +/// +/// The following sample is a typical use of the new kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean3d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb8_to_rgbn.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rgb.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::clustering::kmean3d<double,5> t_kmean; +/// 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; +/// typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rgb5 img_rgb5; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rgb5=mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); +/// +/// t_kmean kmean(img_rgb5, 3); +/// +/// kmean.launch_n_times(); +/// +/// return 0; +/// } + +# include <limits.h> +# include <iostream> + +# include <mln/accu/stat/histo3d_rgb.hh> + +# include <mln/algebra/vec.hh> + +# include <mln/math/min.hh> +# include <mln/math/sqr.hh> +# include <mln/norm/l2.hh> + +# include <mln/core/concept/image.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/macros.hh> + +# include <mln/data/compute.hh> +# include <mln/data/fill.hh> +# include <mln/debug/println.hh> + +# include <mln/labeling/colorize.hh> +# include <mln/labeling/mean_values.hh> + +# include <mln/literal/one.hh> +# include <mln/literal/zero.hh> + +# include <mln/io/ppm/save.hh> +# include <mln/io/pgm/save.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/trait/value_.hh> + +# include <mln/util/array.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> + namespace mln { @@ -98,7 +149,6 @@ namespace mln /// 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::rgb<n> t_value; @@ -108,7 +158,7 @@ namespace mln typedef T t_result1d; typedef algebra::vec<3,T> t_result3d; - /// FIXME t_point n'est pas forcément une image 2d, bien au contraire. + /// \fixme t_point is not an image2d, it may be something else ... typedef image2d<t_value> t_point_img; typedef image3d<unsigned> t_histo_img; typedef util::array<t_result1d> t_number_img; @@ -128,35 +178,26 @@ namespace mln 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 + /// \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). - kmean3d(const t_point_img& point, const unsigned k_center, const unsigned watch_dog = 10, const unsigned n_times = 10); - //------------------------------------------------------------------------ - // Accessors - //------------------------------------------------------------------------ - - /// Mutator and accessors. + /// Mutators 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); @@ -184,13 +225,13 @@ namespace mln // Normal outputs t_mean_img& to_result(); - /// \} - //------------------------------------------------------------------------ - // Printing temporary results - //------------------------------------------------------------------------ + /// Show temporary results. + /// \{ + /// \brief Formating temporary outputs for debugging. + /// void print_mean(); void print_number(); void print_variance(); @@ -198,32 +239,19 @@ namespace mln 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. + /// \brief Initialize centers by random position. /// - /// There is two way to proceed the initialization. First of all, we - /// divide the rgb space 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. - + /// Assign a random position in the rgb space to each center. 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. @@ -232,36 +260,27 @@ namespace mln /// 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). - + /// There are two ways to launch the kmean process. The first + /// one allows to run it one time until convergence. As the + /// process is a descent, it depends on the initial center + /// location. 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. /// \{ @@ -271,32 +290,22 @@ namespace mln /// 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 + /// Checking the validity of the results. /// \{ - /// \brief These methods help to interpret results. + /// \brief These methods help us to determine the validity of the 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. - + /// 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 color + /// associate to it) or a failure in the convergence. void build_label_dbg(); void build_mean_dbg(); void build_all_dbg(); void update_cnv(); void finalize_cnv(); - /// \} @@ -313,13 +322,12 @@ namespace mln /// 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. @@ -337,14 +345,12 @@ namespace mln /// 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. @@ -354,10 +360,10 @@ namespace mln /// 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. /// \{ @@ -365,12 +371,11 @@ namespace mln /// /// 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. @@ -381,11 +386,9 @@ namespace mln /// 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_mean_img _mean; // k x 3 t_variance_img _variance; // k x 1 within group - /// \} /// rgb image description. @@ -396,12 +399,11 @@ namespace mln /// 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 rgb space of a center give us the within variance. - t_group_img _group; // g x 3 because dim(t_value) = 3 t_distance_img _distance; // label x rgb space - /// \} + /// Debugging, calibrating and testing results. /// \{ /// \brief Some exports information to control the results. @@ -412,14 +414,12 @@ namespace mln /// 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. + /// Variance and center evolutions. /// \{ /// \brief Trace the variance and the center evolution. /// @@ -428,10 +428,8 @@ namespace mln /// 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; - /// \} }; @@ -448,7 +446,7 @@ namespace mln const unsigned watch_dog, const unsigned n_times) { - trace::entering("mln::clustering::kmean3d::kmean3d"); + trace::entering("mln::clustering::kmean3d::cstor"); mln_precondition(point.is_valid()); _k_center = k_center; @@ -520,7 +518,7 @@ namespace mln _mean_cnv.append(mean_set); } - trace::exiting("mln::clustering::kmean3d::kmean3d"); + trace::exiting("mln::clustering::kmean3d::cstor"); } //-------------------------------------------------------------------------- @@ -1338,7 +1336,7 @@ namespace mln trace::exiting("mln::clustering::kmean3d::launch_n_times"); } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::clustering diff --git a/scribo/sandbox/green/mln/clustering/kmean_rgb.hh b/scribo/sandbox/green/mln/clustering/kmean_rgb.hh index 253745b..544066b 100644 --- a/scribo/sandbox/green/mln/clustering/kmean_rgb.hh +++ b/scribo/sandbox/green/mln/clustering/kmean_rgb.hh @@ -1,4 +1,4 @@ -// Copyright (C) 2008,2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -28,16 +28,68 @@ /// \file /// -/// \brief Implements the optimized kmean algorithm. +/// \brief Implements the optimized kmean algorithm in the 3d-RGB space. /// /// This algorithm is optimized in the way it proceeds directly with -/// the rgb values inspite of the pixel attribute. The +/// the rgb value 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 adapted to -/// image with small quantification. - -/// APLATISSEMENT DES KMEAN3D +/// by a fix cost in the complexity. This version is very adapted to +/// images with small quantification. But, in 3d, the execution +/// becomes very slow. It's just normal because the quantification is +/// n bits per axis. So the actual histogram may be bigger than the image. +/// +/// Take care to the following point: The within variance is still a +/// scalar value because we take the distance between two points and +/// the result is a scalar from the geometrical point of view. An +/// alternative implementation could study the variance/covariance +/// matrix of each sub data clouds and works with the trace of the +/// within variance matrix (as we do for the fisher criteria in N-d). +/// +/// The following sample is a typical use of the functional (versus +/// object) kmean implementation. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean_rgb.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb8_to_rgbn.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/label_8.hh> +/// #include <mln/value/rgb.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::label_8 t_lbl8; +/// 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; +/// typedef mln::image2d<t_lbl8> t_image2d_lbl8; +/// typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rgb5 img_rgb5; +/// t_image2d_lbl8 img_lbl8; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rgb5 = mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); +/// img_lbl8 = mln::clustering::kmean_rgb<double,5>(img_rgb5, 3); +/// +/// return 0; +/// } +/// +/// \fixme The execution shows a bug in printing outputs and it seems severe. +/// +/// The last execution with the following set of parameters +/// {house.ppm,3,10,10} shows that if the binary starts correctly, it +/// ends before returning the label image and with disturbing outputs. +/// Dumping the outputs in a file reveals that the number of +/// trace::entering differs from the number of trace::exiting. May the +/// program exit from a loop without ending a trace ??? # include <limits.h> # include <iostream> @@ -86,7 +138,7 @@ //-------------------------------------------------------------------------- -// CODE APLATI +// FUNCTIONAL CODE //-------------------------------------------------------------------------- @@ -95,7 +147,24 @@ namespace mln namespace clustering { - + /// \brief Implements the functional kmean algorithm. + /// + /// This functional version of the kmean is very specific. All the + /// code is view as a one-block function. This code is provided as + /// is. I (YJ) don't know where i stopped this version. It may not + /// work. Debugging tools are not yet provided. This code is just + /// the functional transcription of the kmean3d version. The code + /// has the very experimental status. + /// + /// T is the type used for computations (float or double). + /// n is the quantification for the rgb image. + /// + /// \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). + /// + /// \return an image which represents the pixel classification. template <typename T, unsigned n, typename I> inline image2d<value::label_8> diff --git a/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh b/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh index ca7ba51..e89edad 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh @@ -1,6 +1,4 @@ -// 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) +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE // // This file is part of Olena. // @@ -34,7 +32,51 @@ /// \file /// -/// \brief Convert rg value to rgb +/// \brief Convert a rg value to a rgb value. +/// +/// This source implements the conversion between rg space and rgb space. +/// +/// The following sample is a typical use of the rg/rgb conversion function. +/// +/// #include <iostream> +/// #include <mln/clustering/kmean2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/fun/v2v/rg_to_rgb.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; +/// +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rgb8 point_img_rgb8; +/// t_image2d_rg8 img_rg8; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); +/// +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// +/// mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); +/// +/// kmean.launch_n_times(); +/// +/// mln::clustering::kmean2d<double,8>::t_point_img point_img = +/// kmean.get_point(); +/// +/// point_img_rgb8 = mln::data::transform(point_img, t_rg_to_rgb()); +/// +/// return 0; +/// } namespace mln { @@ -47,14 +89,21 @@ namespace mln /// \brief Convert rg value to rgb. /// + /// Param n defines the quantification used for rgb space and rg space. + /// /// \ingroup modfunv2v - template <unsigned n> struct rg_to_rgb : Function_v2v< rg_to_rgb<n> > { typedef value::rg<n> argument; typedef value::rgb<n> result; + /// \brief Convert rg value to rgb value. + /// + /// \param[in] v the rg value to convert. + /// + /// Conversion is done by calling the rgb constructor and fill + /// the empty attirbute by 127. result operator()(const argument& v) const { return value::rgb<n>(v.red(), v.green(), 127); diff --git a/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am b/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am index 5f00678..77f9015 100644 --- a/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am +++ b/scribo/sandbox/green/tests/clustering/k_mean/Makefile.am @@ -6,8 +6,13 @@ # TOOLS # ######### -INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) ECHO= echo RM= rm MKDIR= mkdir -p @@ -20,10 +25,10 @@ BUILD__PATTERN= green/build/tests ifeq ($(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) +BUILD__DIR= $(PWD)/ else # Case where make is done from source directory. -SOURCE_DIR= $(PWD) +SOURCE_DIR= $(PWD)/ BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) endif @@ -89,7 +94,7 @@ $(OBJ_F_PATH):$(SRC_F_PATH) ######### # Force every time the deletion -clean: print clean_target clean_obj clean_dst clean_old #clean_make +clean: clean_target clean_obj clean_dst clean_old #clean_make clean_target: diff --git a/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc b/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc index 27751fd..a839ece 100644 --- a/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc +++ b/scribo/sandbox/green/tests/clustering/k_mean/k_mean.cc @@ -1,34 +1,68 @@ -// UNARY TESTS ON K_MEAN - -#include <mln/clustering/k_mean.hh> +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This source manages unitary testing on the kmean implementation. +/// +/// Tests are performed from 2 bits up to 8 bits quantification. The goal +/// is to go through each programmatic flow to verify the conformity of the +/// code. It sounds like a theoritic model of unitary testing for milena. +/// The last test enables statistic computations on that component. #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/clustering/k_mean.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/data/transform.hh> + +#include <mln/img_path.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/data/transform.hh> - #include <mln/trait/value/print.hh> #include <mln/trait/image/print.hh> +#include <mln/pw/value.hh> + +#include <mln/value/int_u8.hh> +#include <mln/value/rgb8.hh> + #define SIZE_IMAGE 512 #define SIZE_SAMPLE1 (512*512) #define SIZE_SAMPLE2 4 @@ -44,20 +78,18 @@ #define MAT_GROUP2 mln::algebra::mat<SIZE_SAMPLE2, NB_CENTER, TYPE_POINT> #define VEC_VAR mln::algebra::vec<NB_CENTER, TYPE_POINT> - -void test_instantiation() -{ - mln::clustering::k_mean<SIZE_SAMPLE2,NB_CENTER, DIM_POINT, TYPE_POINT> kmean; - - // test the compilation - - std::cout << "test instantiation : ok" << std::endl; -} +/// Tools. +/// \{ +/// \brief Define some tools which are used in unitary testing. +/// +/// This kind of tools help to fill an image with only 4 tons or +/// computing a distance between two colors. Just keep an eye, nothing +/// really difficult in this code. 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; @@ -78,11 +110,12 @@ struct rgb8_to_4colors : mln::Function_v2v<rgb8_to_4colors> void print_color(const mln::value::rgb8& color) { - std::cout << "{r=" << color.red() << ", "; + 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; @@ -98,6 +131,140 @@ void fill_image_with_4colors(mln::image2d<mln::value::rgb8>& img) //print_color(purple); } + +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; +} + +/// \} + + +/// External mutators. +/// \{ +/// \brief Replace the kmean data structure values. +/// +/// This is a hack that provides low level routines to access key data +/// structure like point, center, distance and group. + +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 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 set_distance(MAT_DISTANCE2& distance, + const unsigned index, + const double d1, + const double d2) +{ + distance(index,0) = d1; + distance(index,1) = d2; +} + +void set_group(MAT_GROUP2& group, + const unsigned index, + const unsigned min) +{ + group(index, min) = 1.0; + group(index, 1-min) = 0.0; +} + +/// \} + +/// Fake states. +/// \{ +/// \brief Help to build from scratch temporary states for the kmean algorithm. +/// +/// This hack allow us to build temporary results used in the previous step to +/// make us very the behaviour of the current step. There is a fake state for +/// every key data structure (point, group, center and distance). + +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); +} + + +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 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); +} + + + + +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; + */ +} + +/// \} + +/// Equivalence. +/// \{ +/// \brief Test equivalence between point and image, center and color. +/// +/// Two kinds of equivalence are defined here. The first one tests the +/// equality between the data cloud and the initial image, while the +/// second one tests the equality between a center and a color. + bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, const MAT_POINT1& point) { @@ -118,11 +285,11 @@ bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, if (!test) { std::cout << pi; - std::cout << "{r=" << img(pi).red() << ", "; + 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 << "[r=" << point(index,0) << ", "; std::cout << "g=" << point(index,1) << ", "; std::cout << "b=" << point(index,2) << "]" << std::endl; @@ -135,6 +302,44 @@ bool is_equivalent(const mln::image2d<mln::value::rgb8>& img, return result; } +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; +} + +/// \} + + +/// kmean unitary testing +/// \{ +/// \brief This part of the code manages the unitary testing. +/// +/// Many tests are performed and new kind of unitary testing +/// appears. In fact, we must simulate the previous steps of the line +/// currently testing and so making some fake steps. Bad and deep +/// hacking in data structure are required. We test the instantiation +/// of the kmean object, its initialization (points and centers), its +/// core routines (update_center, update_group, update_distance, +/// update_variance) and the final loop. + +void test_instantiation() +{ + mln::clustering::k_mean<SIZE_SAMPLE2,NB_CENTER, DIM_POINT, TYPE_POINT> kmean; + + // test the compilation + + std::cout << "test instantiation : ok" << std::endl; +} + + void test_init_point() { typedef mln::value::rgb8 rgb8; @@ -147,45 +352,12 @@ void test_init_point() 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() { @@ -195,10 +367,10 @@ void test_init_center() 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) || @@ -212,34 +384,6 @@ void test_init_center() 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() { @@ -269,34 +413,6 @@ void test_update_distance() 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() { @@ -331,26 +447,6 @@ void test_update_group() 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() { @@ -374,13 +470,14 @@ void test_update_center() 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; @@ -405,13 +502,15 @@ void test_update_variance() 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; } + +/// \fixme this procedure tests actually nothing. void test_loop() { typedef mln::value::rgb8 rgb8; @@ -426,24 +525,22 @@ void test_loop() kmean.init_point(img_ref); kmean.loop(img_ref); - - // std::cout << "Test update variance: ok" << std::endl; + // \FIXME: Which assertion must we define ? + // std::cout << "Test loop : ok" << std::endl; } +/// \} int main() { - //test_instantiation(); - //test_init_point(); - //test_init_center(); - //test_update_distance(); - //test_update_group(); - //test_update_center(); - //test_update_variance(); - - // mln::trace::quiet = false; - + test_instantiation(); + test_init_point(); + test_init_center(); + test_update_distance(); + test_update_group(); + test_update_center(); + test_update_variance(); test_loop(); return 0; diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/k_mean/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/k_mean/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/k_mean/k_mean.cc b/scribo/sandbox/green/use/clustering/k_mean/k_mean.cc new file mode 100644 index 0000000..9b00037 --- /dev/null +++ b/scribo/sandbox/green/use/clustering/k_mean/k_mean.cc @@ -0,0 +1,55 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the first implementation of the kmean algorithm. +/// + +#include <mln/clustering/k_mean.hh> +#include <mln/core/image/image2d.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/trait/value_.hh> +#include <mln/value/rgb8.hh> + +#define NB_CENTER 9 +#define NB_POINT (128*128) +#define POINT_SIZE mln_dim(mln::value::rgb8) +#define POINT_TYPE double + +int main() +{ + typedef mln::value::rgb8 t_rgb8; + mln::image2d<t_rgb8> img_rgb8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + mln::clustering::k_mean<NB_POINT, NB_CENTER, POINT_SIZE, POINT_TYPE> kmean; + + kmean.init_point(img_rgb8); + kmean.loop(img_rgb8); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean1d/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean1d/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc b/scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc new file mode 100644 index 0000000..82b13c9 --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean1d/kmean1d.cc @@ -0,0 +1,50 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the second implementation of the kmean algorithm. +/// + +#include <iostream> +#include <mln/clustering/kmean1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/img_path.hh> +#include <mln/io/pgm/load.hh> +#include <mln/value/int_u8.hh> + +int main() +{ + typedef mln::value::int_u8 t_int_u8; + typedef mln::image2d<t_int_u8> t_image2d_int_u8; + t_image2d_int_u8 img_int_u8; + + mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/house.pgm"); + mln::clustering::kmean1d<double, 8> kmean(img_int_u8, 3); + + kmean.launch_n_times(); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean2d/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean2d/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc b/scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc new file mode 100644 index 0000000..d6bf05b --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean2d/kmean2d.cc @@ -0,0 +1,61 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the optimized version of the kmean2d algorithm. +/// + +#include <iostream> +#include <mln/clustering/kmean2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + +int main() +{ + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + + t_image2d_rgb8 img_rgb8; + t_image2d_rg8 img_rg8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + + mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); + + kmean.launch_n_times(); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean3d/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean3d/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc b/scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc new file mode 100644 index 0000000..c57d48a --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean3d/kmean3d.cc @@ -0,0 +1,63 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the optimized version of the kmean3d algorithm. +/// + +#include <iostream> +#include <mln/clustering/kmean3d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + + +int main() +{ + typedef mln::clustering::kmean3d<double,5> t_kmean; + 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; + typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb5 img_rgb5; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rgb5=mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); + + t_kmean kmean(img_rgb5, 3); + + kmean.launch_n_times(); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/clustering/kmean_rgb/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/clustering/kmean_rgb/Makefile.am diff --git a/scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc b/scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc new file mode 100644 index 0000000..3bf2241 --- /dev/null +++ b/scribo/sandbox/green/use/clustering/kmean_rgb/kmean_rgb.cc @@ -0,0 +1,63 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Minimal code to use the the functional (versus object) kmean code. +/// + +#include <iostream> +#include <mln/clustering/kmean_rgb.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/label_8.hh> +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> + + +int main() +{ + typedef mln::value::label_8 t_lbl8; + 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; + typedef mln::image2d<t_lbl8> t_image2d_lbl8; + typedef mln::fun::v2v::rgb8_to_rgbn<5> t_rgb8_to_rgb5; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb5 img_rgb5; + t_image2d_lbl8 img_lbl8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rgb5 = mln::data::transform(img_rgb8, t_rgb8_to_rgb5()); + img_lbl8 = mln::clustering::kmean_rgb<double,5>(img_rgb5, 3, 10, 10); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/fun/v2v/rg_to_rgb/Makefile.am diff --git a/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc b/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc new file mode 100644 index 0000000..84ae33f --- /dev/null +++ b/scribo/sandbox/green/use/fun/v2v/rg_to_rgb/rg_to_rgb.cc @@ -0,0 +1,68 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief Example of using rg_t_rgb transformation used with kmean2d. +/// + +#include <iostream> +#include <mln/clustering/kmean2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/fun/v2v/rg_to_rgb.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + +int main() +{ + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::fun::v2v::rg_to_rgb<8> t_rg_to_rgb; + + t_image2d_rgb8 img_rgb8; + t_image2d_rgb8 point_img_rgb8; + t_image2d_rg8 img_rg8; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/house.ppm"); + + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + + mln::clustering::kmean2d<double, 8> kmean(img_rg8, 3); + + kmean.launch_n_times(); + + mln::clustering::kmean2d<double,8>::t_point_img point_img = kmean.get_point(); + + point_img_rgb8 = mln::data::transform(point_img, t_rg_to_rgb()); + + return 0; +} -- 1.5.6.5

14 years, 1 month

last-svn-commit-44-g02c36c2 Define documentation files.

by Yann Jacquelet

* README.green: New index for source files in scribo/sandbox/green. * README.img: New index for image database. * README.result: New index for histogram database. --- scribo/sandbox/green/ChangeLog | 11 ++- scribo/sandbox/green/{README => README.green} | 58 ++++++++-- scribo/sandbox/green/README.img | 124 +++++++++++++++++++++ scribo/sandbox/green/README.result | 143 +++++++++++++++++++++++++ 4 files changed, 324 insertions(+), 12 deletions(-) rename scribo/sandbox/green/{README => README.green} (89%) create mode 100644 scribo/sandbox/green/README.img create mode 100644 scribo/sandbox/green/README.result diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 5a7ceaa..7bb6646 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,4 +1,13 @@ -2010-06-23 green <jacquelet(a)lrde.epita.fr> +2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Define documentation files. + + * README: Delete this file and replace it by README.green. + * README.green: New index for source files in scribo/sandbox/green. + * README.img: New index for image database. + * README.result: New index for histogram database. + +2010-06-23 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Define gnuplot shell export format. diff --git a/scribo/sandbox/green/README b/scribo/sandbox/green/README.green similarity index 89% rename from scribo/sandbox/green/README rename to scribo/sandbox/green/README.green index 03106d3..3f3c9a9 100644 --- a/scribo/sandbox/green/README +++ b/scribo/sandbox/green/README.green @@ -227,26 +227,62 @@ n'importe quel algorithme type huffman (tous les archiveurs en possède un). -VI MLN ------ +VIII VISUALISATION HISTOGRAMMES 3D +---------------------------------- -a) La sauvegarde des images au format gnuplot shell +==> to do -* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. -to do ... +* demo/accu/stat/histo2d +* mln/display/dispay_histo.hh +* mln/display/project_histo.hh +IX KMEANS +--------- + +Ce travail m'avait été demandé par théo. Je le laisse inachevé, quelque part +perdu pendant l'optimisation du code et sa transformation en canevas. + +* mln/clustering/k_mean.hh: Première implémentation avec matrices et vecteurs. +* mln/clustering/kmean1d.hh: Implémentation 1d avec des images. +* mln/clustering/kmean2d.hh: Implémentation 2d avec des images. +* mln/clustering/kmean3d.hh: Implémentation 3d avec des images. +* mln/clustering/kmean_rgb.hh: Impl. 3d aplatie et en cours de mise en canevas. + +* tests/clustering/k_mean: Tests unitaires sur la permière version. +* tests/clustering/kmean1d: Tests unitaire sur la version 1d. + +* demo/clustering/kmean1d: Utilisation de la version 1d. +* demo/clustering/kmean2d: Utilisation de la version 2d. +* demo/clustering/kmean3d: Utilisation de la version 3d. +* demo/clustering/kmean_rgb: Utilisation de la version aplatie. + +* bench/clustering/distance: Comparaison algorithmes d'évaluation des distances. + +==> to do + +X REGIONAL MAXIMA +----------------- + +==> to do + + +XI ANNOTATING +------------- + +==> to do + + +a) La sauvegarde des images au format gnuplot shell + +* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. + +to do ... -VII VISUALISATION HISTOGRAMMES 3D ---------------------------------- -c) La visualisation des histogrammes 3d -* demo/accu/stat/histo2d -* mln/display/dispay_histo.hh -* mln/displayproject_histo.hh diff --git a/scribo/sandbox/green/README.img b/scribo/sandbox/green/README.img new file mode 100644 index 0000000..c606a93 --- /dev/null +++ b/scribo/sandbox/green/README.img @@ -0,0 +1,124 @@ +I DESCRIPTION DES BASES D'IMAGES +-------------------------------- + +img ==> Répertoire des bases d'images +img/afp ==> Extrait de la base AFP (50 photos avec texte) +img/annotating-1 ==> Base diversifiée internet trop faible qualité +img/annotating-2 ==> Base diversifiée internet meilleur qualité +img/icdar ==> Base ICDAR en 20p/50p/100p (+quelques ajouts) +img/inim ==> Base complète du cours inim +img/olena ==> Images de tests d'olena (+quelques ajouts) + + +II DESCRIPTION DE CHAQUE BASE +----------------------------- + +A) BASE AFP + +img/afp/magick-pal-30-gen.sh ==> Génération base AFP 30 couleurs (imageMagick) +img/afp/magick-pal-20-gen.sh ==> Génération base AFP 20 couleurs (imageMagick) +img/afp/magick-pal-10-gen.sh ==> Génération base AFP 10 couleurs (imageMagick) + +img/afp/gimp-pal-30-gen.sh ==> Génération base AFP 30 couleurs (Gimp) +img/afp/gimp-pal-20-gen.sh ==> Génération base AFP 20 couleurs (Gimp) +img/afp/gimp-pal-10-gen.sh ==> Génération base AFP 10 couleurs (Gimp) + +Ne pas oublier le script de réduction des couleurs pour Gimp. + +$ more ~/.gimp-2.6/scripts/reduce-color.scm +(define (reduce-color filein fileout nbcol) + (let* ((image (car (gimp-file-load RUN-NONINTERACTIVE filein filein))) + (drawable (car (gimp-image-get-active-layer image))) + ) + (gimp-image-convert-indexed image NO-DITHER MAKE-PALETTE nbcol FALSE FALSE "") + (gimp-image-convert-rgb image) + (gimp-file-save RUN-NONINTERACTIVE image drawable fileout fileout) + (gimp-image-delete image) + )) + +img/afp/magick-pal-30 ==> Base AFP 30 couleurs (imageMagick) +img/afp/magick-pal-20 ==> Base AFP 20 couleurs (imageMagick) +img/afp/magick-pal-10 ==> Base AFP 10 couleurs (imageMagick) + +img/afp/gimp-pal-30 ==> Base AFP 30 couleurs (Gimp) +img/afp/gimp-pal-20 ==> Base AFP 20 couleurs (Gimp) +img/afp/gimp-pal-10 ==> Base AFP 10 couleurs (Gimp) + +img/afp/ppm ==> Base AFP originale convertie en ppm +img/afp/jpg ==> Base AFP originale + + +B) BASE ANNOTATING-1 + +img/annotating-1/screenshot ==> Fond d'écran, capture d'écran +img/annotating-1/slide ==> Transparents commerciaux ou conférences +img/annotating-1/handwritten ==> Ecritures manuscrites +img/annotating-1/map ==> Cartes marines, de régions, de villes +img/annotating-1/photo ==> Photo AFP +img/annotating-1/logo ==> Logos de toute sorte, de toute taille +img/annotating-1/typed ==> Documents textuels impimés +img/annotating-1/fax ==> Faxs +img/annotating-1/bill ==> Factures téléphones, eau ... + + +C) BASE ANNOTATING-2 + +img/annotating-2/slide ==> Transparents commerciaux ou conférences +img/annotating-2/handwritten ==> Ecritures manuscrites +img/annotating-2/logo ==> Logos de toute sorte, de toute taille +img/annotating-2/typed ==> Documents textuels impimés +img/annotating-1/bill ==> Factures téléphones, eau ... + + +D) BASE ICDAR + +img/icdar/100p/pbm ==> Base ICDAR 100% en noir & blanc +img/icdar/100p/pgm ==> Base ICDAR 100% en niveaux de gris +img/icdar/100p/ppm ==> Base ICDAR 100% en couleur +img/icdar/100p/gradient-thin ==> Masques de gradient thin sur base ICDAR 100% +img/icdar/100p/gradient-thick ==> Masques de gradient thick sur base ICDAR 100% + +img/icdar/50p/pbm ==> Base ICDAR 50% en noir & blanc +img/icdar/50p/pgm ==> Base ICDAR 50% en niveaux de gris +img/icdar/50p/ppm ==> Base ICDAR 50% en couleur + +img/icdar/20p/pbm ==> Base ICDAR 20% en noir & blanc +img/icdar/20p/pgm ==> Base ICDAR 20% en niveaux de gris +img/icdar/20p/ppm ==> Base ICDAR 20% en couleur + +img/icdar/20p/crop ==> Plein de crops sur la base ICDAR 20% (couleur) + +img/icdar/magick-pal-30-gen.sh => Génération base ICDAR 30 couleurs(imageMagick) +img/icdar/magick-pal-20-gen.sh => Génération base ICDAR 20 couleurs(imageMagick) +img/icdar/magick-pal-10-gen.sh => Génération base ICDAR 10 couleurs(imageMagick) + +img/icdar/gimp-pal-30-gen.sh ==> Génération base ICDAR 30 couleurs (Gimp) +img/icdar/gimp-pal-20-gen.sh ==> Génération base ICDAR 20 couleurs (Gimp) +img/icdar/gimp-pal-10-gen.sh ==> Génération base ICDAR 10 couleurs (Gimp) + +img/icdar/20p/magick-pal-30 ==> Base ICDAR 20% en noir & blanc +img/icdar/20p/magick-pal-20 ==> Base ICDAR 20% en niveaux de gris +img/icdar/20p/magick-pal-10 ==> Base ICDAR 20% en couleur + +img/icdar/20p/gimp-pal-30 ==> Base ICDAR 20% en noir & blanc +img/icdar/20p/gimp-pal-20 ==> Base ICDAR 20% en niveaux de gris +img/icdar/20p/gimp-pal-10 ==> Base ICDAR 20% en couleur + +img/icdar/20p/text-only ==> Images ICDAR avec textes uniquement +img/icdar/20p/text-color ==> Images ICDAR avec textes et couleurs +img/icdar/20p/text-photo ==> Images ICDAR avec textes et photos + + +E) BASE INIM + +img/inim/bg ==> Arrière plan (ppm) +img/inim/fg ==> Avant plan (ppm) +img/inim/in ==> Images d'entrées (ppm) + + +F) BASE OLENA + +Des images générales provenant d'Olena et d'autres pour tester, pour debugger +dans différents formats. + + diff --git a/scribo/sandbox/green/README.result b/scribo/sandbox/green/README.result new file mode 100644 index 0000000..4038a5b --- /dev/null +++ b/scribo/sandbox/green/README.result @@ -0,0 +1,143 @@ +I DESCRIPTION DES BASES D'IMAGES +-------------------------------- + +result ==> Répertoire des bases d'images +result/annotating/afp ==> Analyse de la base AFP +result/annotating/icdar ==> Analyse de la base ICDAR + + +II DESCRIPTION DES TYPES D'IMAGES +--------------------------------- + +Les deux bases d'images contiennent toutes les deux la même +structure. Les résultats sont donc générés sur les images d'origine +(input) et les images dont on a réduit le nombre de couleurs à +30/20/10 soit par gimp, soit par imageMagick. + +result/annotating/afp/input ==> Résultats base originale AFP +result/annotating/afp/magick-pal-30 ==> Résultats AFP 30 couleurs (imageMagick) +result/annotating/afp/magick-pal-20 ==> Résultats AFP 20 couleurs (imageMagick) +result/annotating/afp/magick-pal-10 ==> Résultats AFP 10 couleurs (imageMagick) +result/annotating/afp/gimp-pal-30 ==> Résultats AFP 30 couleurs (Gimp) +result/annotating/afp/gimp-pal-20 ==> Résultats AFP 20 couleurs (Gimp) +result/annotating/afp/gimp-pal-10 ==> Résultats AFP 10 couleurs (Gimp) + +result/annotating/icdar/input ==> Résultats base originale ICDAR +result/annotating/icdar/magick-pal-30 => Résultats ICDAR 30 clrs (imageMagick) +result/annotating/icdar/magick-pal-20 => Résultats ICDAR 20 clrs (imageMagick) +result/annotating/icdar/magick-pal-10 => Résultats ICDAR 10 clrs (imageMagick) +result/annotating/icdar/gimp-pal-30 ==> Résultats ICDAR 30 couleurs (Gimp) +result/annotating/icdar/gimp-pal-20 ==> Résultats ICDAR 20 couleurs (Gimp) +result/annotating/icdar/gimp-pal-10 ==> Résultats ICDAR 10 couleurs (Gimp) + + +II DESCRIPTION DES COMPOSANTES COULEURS +--------------------------------------- + +Pour chaque type d'image, on extraira ensuite les différents composantes +rouge (r), verte (v), bleue (b), teinte (h), saturation (s) et valeur (v). + +result/annotating/afp/input/r ==> comp. rouge base AFP +result/annotating/afp/input/g ==> comp. verte base AFP +result/annotating/afp/input/b ==> comp. bleue base AFP +result/annotating/afp/input/h ==> comp. teinte base AFP +result/annotating/afp/input/s ==> comp. saturation base AFP +result/annotating/afp/input/v ==> comp. valeur base AFP + +result/annotating/afp/magick-pal-30/r ==> comp. rouge AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/g ==> comp. verte AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/b ==> comp. bleue AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/h ==> comp. teinte AFP 30 c.(imageMagick) +result/annotating/afp/magick-pal-30/s ==> comp. sat. AFP 30 clr.(imageMagick) +result/annotating/afp/magick-pal-30/v ==> comp. val. AFP 30 clr.(imageMagick) + +result/annotating/afp/magick-pal-20/r ==> comp. rouge AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/g ==> comp. verte AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/b ==> comp. bleue AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/h ==> comp. teinte AFP 20 c.(imageMagick) +result/annotating/afp/magick-pal-20/s ==> comp. sat. AFP 20 clr.(imageMagick) +result/annotating/afp/magick-pal-20/v ==> comp. val. AFP 20 clr.(imageMagick) + +result/annotating/afp/magick-pal-10/r ==> comp. rouge AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/g ==> comp. verte AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/b ==> comp. bleue AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/h ==> comp. teinte AFP 10 c.(imageMagick) +result/annotating/afp/magick-pal-10/s ==> comp. sat. AFP 10 clr.(imageMagick) +result/annotating/afp/magick-pal-10/v ==> comp. val. AFP 10 clr.(imageMagick) + +result/annotating/afp/gimp-pal-30/r ==> comp. rouge AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/g ==> comp. verte AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/b ==> comp. bleue AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/h ==> comp. teinte AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/s ==> comp. saturation AFP 30 clr.(Gimp) +result/annotating/afp/gimp-pal-30/v ==> comp. valeur AFP 30 clr.(Gimp) + +result/annotating/afp/gimp-pal-20/r ==> comp. rouge AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/g ==> comp. verte AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/b ==> comp. bleue AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/h ==> comp. teinte AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/s ==> comp. saturation AFP 20 clr.(Gimp) +result/annotating/afp/gimp-pal-20/v ==> comp. valeur AFP 20 clr.(Gimp) + +result/annotating/icdar/input/r ==> comp. rouge base ICDAR +result/annotating/icdar/input/g ==> comp. verte base ICDAR +result/annotating/icdar/input/b ==> comp. bleue base ICDAR +result/annotating/icdar/input/h ==> comp. teinte base ICDAR +result/annotating/icdar/input/s ==> comp. saturation base ICDAR +result/annotating/icdar/input/v ==> comp. valeur base ICDAR + +result/annotating/icdar/magick-pal-30/r => comp. rouge ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/g => comp. verte ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/b => comp. bleue ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/h => comp. teinte ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/s => comp. sat. ICDAR 30 c.(imageMagick) +result/annotating/icdar/magick-pal-30/v => comp. val. ICDAR 30 c.(imageMagick) + +result/annotating/icdar/magick-pal-20/r => comp. rouge ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/g => comp. verte ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/b => comp. bleue ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/h => comp. teinte ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/s => comp. sat. ICDAR 20 c.(imageMagick) +result/annotating/icdar/magick-pal-20/v => comp. val. ICDAR 20 c.(imageMagick) + +result/annotating/icdar/magick-pal-10/r => comp. rouge ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/g => comp. verte ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/b => comp. bleue ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/h => comp. teinte ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/s => comp. sat. ICDAR 10 c.(imageMagick) +result/annotating/icdar/magick-pal-10/v => comp. val. ICDAR 10 c.(imageMagick) + +result/annotating/icdar/gimp-pal-30/r => comp. rouge ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/g => comp. verte ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/b => comp. bleue ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/h => comp. teinte ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/s => comp. saturation ICDAR 30 c.(Gimp) +result/annotating/icdar/gimp-pal-30/v => comp. valeur ICDAR 30 c.(Gimp) + +result/annotating/icdar/gimp-pal-20/r => comp. rouge ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/g => comp. verte ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/b => comp. bleue ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/h => comp. teinte ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/s => comp. saturation ICDAR 20 c.(Gimp) +result/annotating/icdar/gimp-pal-20/v => comp. valeur ICDAR 20 c.(Gimp) + +result/annotating/icdar/gimp-pal-10/r => comp. rouge ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/g => comp. verte ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/b => comp. bleue ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/h => comp. teinte ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/s => comp. saturation ICDAR 10 c.(Gimp) +result/annotating/icdar/gimp-pal-10/v => comp. valeur ICDAR 10 c.(Gimp) + + +III DESCRIPTION DES TYPES DE RESULTAT +------------------------------------- + +Pour chaque composante et pour chaque image de la base, on obtient une +image en niveau de gris correspondant à la composante (pgm) et son +histogramme normalisé (gnuplot shell). L'histogramme normalisé est un +histogramme dont l'aire vaut 1 (c'est une densité en fait, autrement +dit chaque fréquence a été divisée par la somme des fréquences). Un +script add_range.sh, a permis de rajouter les bornes des axes pour la +visualisation pour chacun des histogrammes générés. Cette +représentation des histogrammes des composantes sous forme de densité +permet de comparer visuellement les images des différentes classes. -- 1.5.6.5

14 years, 1 month

last-svn-commit-43-g7e1539b Define gnuplot shell export format.

by Yann Jacquelet

* mln/io/plot/save_image_sh.hh: New header file. Give an example of code. * use/io/plot/save_image_sh: New directory. * use/io/plot/save_image_sh/Makefile.am: New makefile. * use/io/plot/save_image_sh/save_image_sh.cc: New source file. Make some unitary tests on gnuplot shell export. * tests/io/plot/save_image_sh: New directory. * tests/io/plot/save_image_sh/Makefile.am: New makefile. * tests/io/plot/save_image_sh/save_image_sh.cc: New source file. --- scribo/sandbox/green/ChangeLog | 109 +++++---- scribo/sandbox/green/README | 52 ++--- scribo/sandbox/green/mln/io/plot/save_image_sh.hh | 36 +++- .../green/tests/io/plot/save_image_sh/Makefile.am | 13 +- .../tests/io/plot/save_image_sh/save_image_sh.cc | 284 ++++++++++++++------ .../histo1d => io/plot/save_image_sh}/Makefile.am | 0 .../plot/save_image_sh/save_image_sh.cc} | 4 +- 7 files changed, 329 insertions(+), 169 deletions(-) copy scribo/sandbox/green/use/{accu/stat/histo1d => io/plot/save_image_sh}/Makefile.am (100%) copy scribo/sandbox/green/use/{accu/stat/histo1d/histo1d.cc => io/plot/save_image_sh/save_image_sh.cc} (91%) diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index db589a1..5a7ceaa 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,54 +1,20 @@ -2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> +2010-06-23 green <jacquelet(a)lrde.epita.fr> - Import files from milena/sandbox/green. + Define gnuplot shell export format. - * gaussian.sh: New gnuplot shell file. - * guassian2d.sh: New gnuplot shell file. - * test_labelling.cc: New source. - * tests/clustering/k_mean/Makefile.am: New makefile. - * tests/clustering/k_mean/k_mean.cc: New source. - * tests/clustering/kmean1d/Makefile.am: New makefile. - * tests/clustering/kmean1d/kmean1d.cc: New source. - * tests/io/plot/save_image_sh/Makefile.am: New makefile. - * tests/io/plot/save_image_sh/save_image_sh.cc: New source. + * mln/io/plot/save_image_sh.hh: New header file. -2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + Give an example of code. - Import files from milena/sandbox/green. + * use/io/plot/save_image_sh: New directory. + * use/io/plot/save_image_sh/Makefile.am: New makefile. + * use/io/plot/save_image_sh/save_image_sh.cc: New source file. - * mln/accu/stat/histo1d.hh: New header file. - * mln/accu/stat/histo2d.hh: New header file. - * mln/accu/stat/histo3d_hsl.hh: New header file. - * mln/accu/stat/histo3d_rgb.hh: New header file. - * mln/clustering/k_mean.hh: New header file. - * mln/clustering/kmean1d.hh: New header file. - * mln/clustering/kmean2d.hh: New header file. - * mln/clustering/kmean3d.hh: New header file. - * mln/clustering/kmean_rgb.hh: New header file. - * mln/display/display_histo.hh: New header file. - * mln/display/project_histo.hh: New header file. - * mln/fun/p2b/achromatic.hh: New header file. - * mln/fun/p2b/component_equals.hh: New header file. - * mln/fun/v2v/achromatism.hh: New header file. - * mln/fun/v2v/hue_concentration.hh: New header file. - * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. - * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. - * mln/fun/v2v/log.hh: New header file. - * mln/fun/v2v/rg_to_rgb.hh: New header file. - * mln/fun/v2v/rgb8_to_int_u8: New header file. - * mln/fun/v2v/rgb_to_achromastism_map.hh: New header file. - * mln/fun/v2v/rgb_to_hsv.hh: New header file. - * mln/fun/v2v/rgb_to_hue_map.hh: New header file. - * mln/fun/v2v/rgb_to_saturation_map.hh: New header file. - * mln/fun/v2v/rgb_to_value_map.hh: New header file. - * mln/img_path.hh: New header file. - * mln/io/plot/save_image_sh.hh: New header file. - * mln/math/cell.hh: New header file. - * mln/math/floor.hh: New header file. - * tests/accu/stat/histo1d/Makefile.am: New makefile. - * tests/accu/stat/histo1d/histo1d.cc: New source. - * tests/accu/stat/histo2d/Makefile.am: New makefile. - * tests/accu/stat/histo2d/histo2d.cc: New source. + Make some unitary tests on gnuplot shell export. + + * tests/io/plot/save_image_sh: New directory. + * tests/io/plot/save_image_sh/Makefile.am: New makefile. + * tests/io/plot/save_image_sh/save_image_sh.cc: New source file. 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> @@ -130,6 +96,57 @@ * tests/accu/stat/histo3d_hsl/Makefile.am: New makefile. * tests/accu/stat/histo3d_hsl/histo3d_hsl.cc: New source. +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Import files from milena/sandbox/green. + + * gaussian.sh: New gnuplot shell file. + * guassian2d.sh: New gnuplot shell file. + * test_labelling.cc: New source. + * tests/clustering/k_mean/Makefile.am: New makefile. + * tests/clustering/k_mean/k_mean.cc: New source. + * tests/clustering/kmean1d/Makefile.am: New makefile. + * tests/clustering/kmean1d/kmean1d.cc: New source. + * tests/io/plot/save_image_sh/Makefile.am: New makefile. + * tests/io/plot/save_image_sh/save_image_sh.cc: New source. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Import files from milena/sandbox/green. + + * mln/accu/stat/histo1d.hh: New header file. + * mln/accu/stat/histo2d.hh: New header file. + * mln/accu/stat/histo3d_hsl.hh: New header file. + * mln/accu/stat/histo3d_rgb.hh: New header file. + * mln/clustering/k_mean.hh: New header file. + * mln/clustering/kmean1d.hh: New header file. + * mln/clustering/kmean2d.hh: New header file. + * mln/clustering/kmean3d.hh: New header file. + * mln/clustering/kmean_rgb.hh: New header file. + * mln/display/display_histo.hh: New header file. + * mln/display/project_histo.hh: New header file. + * mln/fun/p2b/achromatic.hh: New header file. + * mln/fun/p2b/component_equals.hh: New header file. + * mln/fun/v2v/achromatism.hh: New header file. + * mln/fun/v2v/hue_concentration.hh: New header file. + * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. + * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. + * mln/fun/v2v/log.hh: New header file. + * mln/fun/v2v/rg_to_rgb.hh: New header file. + * mln/fun/v2v/rgb8_to_int_u8: New header file. + * mln/fun/v2v/rgb_to_achromastism_map.hh: New header file. + * mln/fun/v2v/rgb_to_hsv.hh: New header file. + * mln/fun/v2v/rgb_to_hue_map.hh: New header file. + * mln/fun/v2v/rgb_to_saturation_map.hh: New header file. + * mln/fun/v2v/rgb_to_value_map.hh: New header file. + * mln/img_path.hh: New header file. + * mln/io/plot/save_image_sh.hh: New header file. + * mln/math/cell.hh: New header file. + * mln/math/floor.hh: New header file. + * tests/accu/stat/histo1d/Makefile.am: New makefile. + * tests/accu/stat/histo1d/histo1d.cc: New source. + * tests/accu/stat/histo2d/Makefile.am: New makefile. + * tests/accu/stat/histo2d/histo2d.cc: New source. 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> diff --git a/scribo/sandbox/green/README b/scribo/sandbox/green/README index 4b6fb26..03106d3 100644 --- a/scribo/sandbox/green/README +++ b/scribo/sandbox/green/README @@ -153,8 +153,8 @@ d'exécution (chmod 755 gnuplot_shell_file.sh). Comme je trouve le format extrêmement pratique, il se retrouve preque partout dans mes sources. -V HISTOGRAMMES --------------- +VI HISTOGRAMMES +--------------- Un des travaux demandés par théo est la réalisation d'une librairie d'histogramme permettant de fournir un résultat sous forme d'image. @@ -173,7 +173,7 @@ a) version 1d * mln/accu/stat/histo1d.hh: Accumulateur histogramme image1d. * use/accu/stat/histo1d: Code minimal utilisant un histogramme 1d. -* tests/accu/stat/histo1d: Test unitaire sur l'histogramme 1d. +* tests/accu/stat/histo1d: Tests unitaires sur l'histogramme 1d. b) version 2d @@ -203,52 +203,42 @@ d) version 3d HSL * mln/accu/stat/histo3d_hsl.hh: Accumulateur histogramme image3d HSL. * use/accu/stat/histo3_hsl: Code minimal utilisant un histogramme 3d HSL. -* tests/accu/stat/histo3d_hsl: Test unitaire sur l'histogramme HSL 3d. +* tests/accu/stat/histo3d_hsl: Tests unitaires sur l'histogramme HSL 3d. Le code HSL ne compile plus car l'interface liant la transformation du domaine et la fonction fold a changée. Je n'ai pas le temps de regarder plus avant. +VII SAUVEGARDE FORMAT GNUPLOT SHELL +----------------------------------- -V MLN ------ - -a) La sauvegarde des images au format gnuplot shell - -* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. - -to do ... - - - -b) Les histogrammes - - - - - - - +Ce travail est personnel. Ces développements m'ont été tellement +utiles que je ne regrette pas l'investissement effectué. L'idée est de +pouvoir avoir un format d'image en écriture lisible. J'ai pris celui +de gnuplot, ce qui permet en plus de pouvoir "sucrer" la présentation +des données à loisir. Les images sont plus lourdes car le codage est +textuel et un peu plus "verbose" mais se compresse aisément par +n'importe quel algorithme type huffman (tous les archiveurs en possède un). +* mln/io/plot/save_image_sh.hh: Librairie de sauvegarde au format gnuplot shell. +* use/io/plot/save_image_sh: Code simple d'utilisation de la sauvegarde. +* tests/io/plot/save_image_sh: Tests unitaires sur l'export. -* tests/accu/stat/histo1d +VI MLN +----- +a) La sauvegarde des images au format gnuplot shell -* tests/accu/stat/histo2d +* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. +to do ... -* use/accu/stat/histo3d_rgb -* tests/accu/stat/histo3d_rgb -* use/accu/stat/histo3d_hsl -* tests/accu/stat/histo3d_hsl -VI SAUVEGARDE FORMAT GNUPLOT SHELL ----------------------------------- VII VISUALISATION HISTOGRAMMES 3D diff --git a/scribo/sandbox/green/mln/io/plot/save_image_sh.hh b/scribo/sandbox/green/mln/io/plot/save_image_sh.hh index 137f78d..7e28f6f 100644 --- a/scribo/sandbox/green/mln/io/plot/save_image_sh.hh +++ b/scribo/sandbox/green/mln/io/plot/save_image_sh.hh @@ -38,6 +38,29 @@ /// The script file call gnuplot in batch mode, the result window persists and /// that's all. /// +/// The following sample is a typical use of the gnuplot shell export library. +/// +/// #include <mln/accu/stat/histo1d.hh> +/// #include <mln/data/compute.hh> +/// #include <mln/core/image/image1d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/pgm/load.hh> +/// #include <mln/io/plot/save_image_sh.hh> +/// #include <mln/value/int_u8.hh> +/// +/// int main() +/// { +/// typedef mln::value::int_u8 t_int_u8; +/// mln::image2d<t_int_u8> img; +/// mln::image1d<unsigned> histo; +/// +/// mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm"); +/// histo = mln::data::compute(mln::accu::meta::histo::histo1d(), img); +/// mln::io::plot::save_image_sh(histo, "histo.sh"); +/// +/// return 0; +/// } # include <fstream> # include <string> @@ -92,7 +115,6 @@ namespace mln /// filename parameter as unix path. The script shell file must have the /// permission to execute (chmod 755). Launch the script shell to call /// gnuplot in batchmode with fine parameters. - template <typename I> bool save_image_sh(const Image& img, const std::string& filename); @@ -112,6 +134,18 @@ namespace mln bool save_image_sh(const util::array< image1d >& stack, const std::string& filename); + /// \brief Save a stack of stack of image. + /// + /// This is an experimental support. + /// + /// \param[in] stack the stack of image to save. + /// \param[in] filename the name of the unix script shell. + /// \return the status of the opening file operation. + /// + /// The result depends on the permission to save the file with + /// filename parameter as unix path. The script shell file must have the + /// permission to execute (chmod 755). Launch the script shell to call + /// gnuplot in batchmode with fine parameters. template <typename I> bool save_image_sh(const util::array< util::array< image1d > >& stack, const std::string& filename); diff --git a/scribo/sandbox/green/tests/io/plot/save_image_sh/Makefile.am b/scribo/sandbox/green/tests/io/plot/save_image_sh/Makefile.am index d970989..77f9015 100644 --- a/scribo/sandbox/green/tests/io/plot/save_image_sh/Makefile.am +++ b/scribo/sandbox/green/tests/io/plot/save_image_sh/Makefile.am @@ -6,8 +6,13 @@ # TOOLS # ######### -INCLUDES= -I$(HOME)/svn/oln/trunk/milena/sandbox/green +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) ECHO= echo RM= rm MKDIR= mkdir -p @@ -20,10 +25,10 @@ BUILD__PATTERN= green/build/tests ifeq ($(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) +BUILD__DIR= $(PWD)/ else # Case where make is done from source directory. -SOURCE_DIR= $(PWD) +SOURCE_DIR= $(PWD)/ BUILD__DIR= $(subst $(SOURCE_PATTERN),$(BUILD__PATTERN),$(PWD)) endif @@ -60,7 +65,7 @@ $(BUILD__DIR): # Copy, if nessary, all the files, except the Makefile.am $(BUILD__F_PATH): $(SOURCE_F_PATH) - $(CP) $(addprefix $(SOURCE_DIR)/,$(@F)) $@ + $(CP) $(addprefix $(SOURCE_DIR),$(@F)) $@ # Copy if nessary, the Makefile.am into Makefile $(BUILD__M_PATH): $(SOURCE_M_PATH) diff --git a/scribo/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc b/scribo/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc index c9c817b..c60533e 100644 --- a/scribo/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc +++ b/scribo/sandbox/green/tests/io/plot/save_image_sh/save_image_sh.cc @@ -1,4 +1,36 @@ -/// TEST SAVE_IMAGE_SH.HH +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This source manages unitary testing on gnuplot shell export library. +/// +/// Tests are performed in various image format, such as image1d, image2d<bool>, +/// image2d<int_u8>, image2d<int_u16>, image2d<int_s8>, image2d<int_s16>, +/// image2d<float>, image2d<double>, image2d<rgb8>, image2d<rgb16>, +/// image2d<hsl_f>, image2d<hsl_d>, image2d<hsi_f>, image2d<hsi_d>, image3d. #include <iostream> #include <limits.h> @@ -32,9 +64,13 @@ #include <mln/literal/colors.hh> #include <mln/debug/println.hh> -//------------------------------------------------------------------------------ -// TEST TO SAVE UNSIGNED 8 BITS 1D GRAYSCALE IMAGE -//------------------------------------------------------------------------------ + +/// image1d<int_u8>. +/// \{ +/// \brief Test saving a grayscale image1d. +/// +/// As working on the image1d interface is something extremely rare, we need +/// to use the plot interface to first load some datas and then export them. mln::image1d<mln::value::int_u8> convert_array_to_image1d(const mln::util::array<mln::value::int_u8>& array) @@ -64,7 +100,7 @@ void test_save_image_sh_1d() image1d img; array1d array; - + mln::io::plot::load(array, OLENA_IMG_PATH"/fly.plot"); img = convert_array_to_image1d(array); @@ -73,16 +109,20 @@ void test_save_image_sh_1d() // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img, "fly1d.sh"); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image 1d : ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<bool> -//------------------------------------------------------------------------------ +/// \} +/// image2d<bool>. +/// \{ +/// \brief Test saving an image2d<bool>. +/// +/// Boolean image2d are extremly convenient for storing mask. We have a lots +/// of example in milena. void test_save_image2d_bool() { @@ -93,22 +133,26 @@ void test_save_image2d_bool() image2d_bool img_bool; mln::io::pbm::load(img_bool, OLENA_IMG_PATH"/lena.pbm"); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_bool, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_bool, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<bool>: ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<int_u8> -//------------------------------------------------------------------------------ +/// \} +/// image2d<int_u8>. +/// \{ +/// \brief Test saving a grayscale image2d. +/// +/// Grayscale image2d are very popular in image processing. It's the test we do +/// not fail! void test_save_image2d_int_u8() { @@ -120,27 +164,36 @@ void test_save_image2d_int_u8() image2d_int_u8 img_int_u8; mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/lena.pgm"); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_int_u8, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_int_u8, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<int_u8>: ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<int_u16> -//------------------------------------------------------------------------------ +/// \} + +/// image2d<int_u16>. +/// \{ +/// \brief Test saving an extended grayscale image2d. +/// +/// This kind of image are not so current. But for medical use, we can get now, +/// some dicom in 12 bits and new generation bring us 16 bits. As 16 bits is a +/// strange format for image processing people, there is not a common +/// acceptation for the underline format. Xv or imageMagick doesn't agree for +/// the representation of the file. + struct int_u16_to_int_u8 : mln::Function_v2v< int_u16_to_int_u8 > { typedef mln::value::int_u16 argument; typedef mln::value::int_u8 result; - + result operator()(const argument& c) const { result res(c / 256); @@ -170,22 +223,28 @@ void test_save_image2d_int_u16() // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_int_u16, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<int_u16> : ok" << std::endl; } +/// \} + +/// image2d<int_s8>. +/// \{ +/// \brief Test saving an signed image2d. +/// +/// This kind of image are very artificial. It is given for exhaustivity of +/// testing. In fact, this kind of image traduces some step in a computing +/// chain. -//------------------------------------------------------------------------------ -// Test to save image2d<int_s8> -//------------------------------------------------------------------------------ struct int_u8_to_int_s8 : mln::Function_v2v< int_u8_to_int_s8 > { typedef mln::value::int_u8 argument; typedef mln::value::int_s8 result; - + result operator()(const argument& c) const { argument tmp_int_u8; @@ -197,7 +256,7 @@ struct int_u8_to_int_s8 : mln::Function_v2v< int_u8_to_int_s8 > { res = SCHAR_MAX; } - else if (SCHAR_MAX < c) + else if (static_cast<unsigned>(SCHAR_MAX) < c) { tmp_int_u8 = c - SCHAR_MAX; res = static_cast<mln::value::int_s8>(tmp_int_u8); @@ -226,28 +285,34 @@ void test_save_image2d_int_s8() image2d_int_u8 img_int_u8; mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/lena.pgm"); - img_int_s8 = mln::data::transform(img_int_u8, int_u8_to_int_s8()); - + img_int_s8 = mln::data::transform(img_int_u8, int_u8_to_int_s8()); + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_int_s8, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_int_s8, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<int_s8>: ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<int_s16> -//------------------------------------------------------------------------------ +/// \} + +/// image2d<int_s16>. +/// \{ +/// \brief Test saving an extended image2d. +/// +/// This kind of image are very artificial. It is given for exhaustivity of +/// testing. In fact, this kind of image traduces some step in a computing +/// chain. struct int_u16_to_int_s16 : mln::Function_v2v< int_u16_to_int_s16 > { typedef mln::value::int_u16 argument; typedef mln::value::int_s16 result; - + result operator()(const argument& c) const { argument tmp_int_u16; @@ -258,7 +323,7 @@ struct int_u16_to_int_s16 : mln::Function_v2v< int_u16_to_int_s16 > { res = SHRT_MAX; } - else if (SHRT_MAX < c) + else if (static_cast<unsigned>(SHRT_MAX) < c) { tmp_int_u16 = c - SHRT_MAX; res = static_cast<mln::value::int_s16>(tmp_int_u16); @@ -286,29 +351,33 @@ void test_save_image2d_int_s16() image2d_int_u16 img_int_u16; mln::io::pgm::load(img_int_u16, OLENA_IMG_PATH"/lena_16_gnuplot.pgm"); - img_int_s16 = mln::data::transform(img_int_u16, int_u16_to_int_s16()); + img_int_s16 = mln::data::transform(img_int_u16, int_u16_to_int_s16()); // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_int_s16, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_int_s16, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<int_s16> : ok" << std::endl; } +/// \} -//------------------------------------------------------------------------------ -// Test to save image2d<float> -//------------------------------------------------------------------------------ +/// image2d<float>. +/// \{ +/// \brief Test saving a float image2d. +/// +/// This kind of image is a common way to save some attributes localized on +/// pixels. struct int_u8_to_float : mln::Function_v2v< int_u8_to_float > { typedef mln::value::int_u8 argument; typedef float result; - + result operator()(const argument& c) const { result res = static_cast<float>(c)/3; @@ -330,27 +399,32 @@ void test_save_image2d_float() mln::io::pgm::load(img_int_u8, OLENA_IMG_PATH"/lena.pgm"); img_float = mln::data::transform(img_int_u8, int_u8_to_float()); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_float, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_float, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<float> : ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<double> -//------------------------------------------------------------------------------ +/// \} + +/// image2d<double>. +/// \{ +/// \brief Test saving a double image2d. +/// +/// This kind of image is a common way to save some attributes localized on +/// pixels. struct int_u16_to_double : mln::Function_v2v< int_u16_to_double > { typedef mln::value::int_u16 argument; typedef double result; - + result operator()(const argument& c) const { result res = static_cast<float>(c)/256; @@ -372,23 +446,25 @@ void test_save_image2d_double() mln::io::pgm::load(img_int_u16, OLENA_IMG_PATH"/lena_16_gnuplot.pgm"); img_double = mln::data::transform(img_int_u16, int_u16_to_double()); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_double, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_double, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<double> : ok" << std::endl; } +/// \} - -//------------------------------------------------------------------------------ -// Test to save image2d<rgb8> -//------------------------------------------------------------------------------ +/// image2d<rgb8>. +/// \{ +/// \brief Test saving a RGB color image2d. +/// +/// This kind of format is extremely popular in image, and in photography. void test_save_image2d_rgb8() { @@ -400,28 +476,33 @@ void test_save_image2d_rgb8() image2d_rgb8 img_rgb8; mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_rgb8, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_rgb8, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<rgb8> : ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<rgb16> -//------------------------------------------------------------------------------ +/// \} + +/// image2d<rgb16>. +/// \{ +/// \brief Test saving a 16 bits RGB color image2d. +/// +/// As this kind of image is less popular, the library enable this export by +/// the will of exhaustivity. struct rgb16_to_int_u16 : mln::Function_v2v< rgb16_to_int_u16 > { typedef mln::value::rgb16 argument; typedef mln::value::int_u16 result; - + result operator()(const argument& c) const { result res((c.red() + c.green() + c.blue())/3); @@ -447,23 +528,28 @@ void test_save_image2d_rgb16() mln::io::ppm::load(img_rgb16, OLENA_IMG_PATH"/lena_16.ppm"); //img_int_u16 = mln::data::transform(img_rgb16, rgb16_to_int_u16()); //mln::io::pgm::save(img_int_u16, "lena_16.pgm"); - + // Save it were it is impossible to do it !! bool ref_false=mln::io::plot::save_image_sh(img_rgb16, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_rgb16, lena); // mln::io::plot::save_image_sh(img_int_u16, lena2); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<rgb16> : ok" << std::endl; } +/// \} -//------------------------------------------------------------------------------ -// Test to save image2d<hsl_f> -//------------------------------------------------------------------------------ +/// image2d<hsl_f>. +/// \{ +/// \brief Test saving a HSL float color image2d. +/// +/// This kind of image allow to show the power of gnuplot. Raw data +/// are hsl, then hsl equations are integrated in the gnuplot shell +/// file. So decoding needs more time. void test_save_image2d_hsl_f() { @@ -484,21 +570,27 @@ void test_save_image2d_hsl_f() //mln::data::fill(img_rgb8, mln::literal::blue); img_hsl_f = mln::data::transform(img_rgb8, rgb_to_hsl()); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_hsl_f, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_hsl_f, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<hsl_f> : ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<hsl_d> -//------------------------------------------------------------------------------ +/// \} + +/// image2d<hsl_d>. +/// \{ +/// \brief Test saving a HSL double color image2d. +/// +/// This kind of image allow to show the power of gnuplot. Raw data +/// are hsl, then hsl equations are integrated in the gnuplot shell +/// file. So decoding needs more time. void test_save_image2d_hsl_d() { @@ -519,22 +611,27 @@ void test_save_image2d_hsl_d() //mln::data::fill(img_rgb16, mln::literal::blue); img_hsl_d = mln::data::transform(img_rgb16, rgb_to_hsl()); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_hsl_d, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_hsl_d, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<hsl_d> : ok" << std::endl; } +/// \} -//------------------------------------------------------------------------------ -// Test to save image2d<hsi_f> -//------------------------------------------------------------------------------ +/// image2d<hsi_f>. +/// \{ +/// \brief Test saving a HSI float color image2d. +/// +/// This kind of image allow to show the power of gnuplot. Raw data +/// are hsi, then hsi equations are integrated in the gnuplot shell +/// file. So decoding needs more time. void test_save_image2d_hsi_f() { @@ -555,21 +652,27 @@ void test_save_image2d_hsi_f() //mln::data::fill(img_rgb8, mln::literal::blue); img_hsi_f = mln::data::transform(img_rgb8, rgb_to_hsi()); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_hsi_f, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_hsi_f, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<hsi_f> : ok" << std::endl; } -//------------------------------------------------------------------------------ -// Test to save image2d<hsi_d> -//------------------------------------------------------------------------------ +/// \} + +/// image2d<hsi_d>. +/// \{ +/// \brief Test saving a HSI double color image2d. +/// +/// This kind of image allow to show the power of gnuplot. Raw data +/// are hsi, then hsi equations are integrated in the gnuplot shell +/// file. So decoding needs more time. void test_save_image2d_hsi_d() { @@ -590,21 +693,28 @@ void test_save_image2d_hsi_d() //mln::data::fill(img_rgb16, mln::literal::blue); img_hsi_d = mln::data::transform(img_rgb16, rgb_to_hsi()); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img_hsi_d, null_lena); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img_hsi_d, lena); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image2d<hsi_d> : ok" << std::endl; } -//------------------------------------------------------------------------------ -// TEST TO SAVE 8 BITS 3D RGB IMAGE -//------------------------------------------------------------------------------ +/// \} + +/// image3d<int_8>. +/// \{ +/// \brief Test saving a grayscale image3d. +/// +/// It seems important to be able to export in 3d, but it's not so popular +/// in the team. We try to simulate this functionnality. +/// +/// \fixme The data visualization is very hard in 3d compact with gnuplot. void test_save_image_sh_3d() { @@ -619,18 +729,20 @@ void test_save_image_sh_3d() array.append(OLENA_IMG_PATH"/fly.pgm"); mln::io::pgms::load(img, array); - + // Save it were it is impossible to do it !! bool ref_false = mln::io::plot::save_image_sh(img, "/dev/null/fly3d.sh"); // Save it where we have the full permissions. bool ref_true = mln::io::plot::save_image_sh(img, "fly3d.sh"); - + mln_assertion(true == ref_true); mln_assertion(false == ref_false); std::cout << "save image 3d : ok" << std::endl; } +/// \} + int main() { @@ -649,7 +761,7 @@ int main() test_save_image2d_rgb8(); test_save_image2d_rgb16(); - + test_save_image2d_hsl_f(); test_save_image2d_hsl_d(); diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/io/plot/save_image_sh/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/io/plot/save_image_sh/Makefile.am diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc b/scribo/sandbox/green/use/io/plot/save_image_sh/save_image_sh.cc similarity index 91% copy from scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc copy to scribo/sandbox/green/use/io/plot/save_image_sh/save_image_sh.cc index af39d94..bd4b84f 100644 --- a/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc +++ b/scribo/sandbox/green/use/io/plot/save_image_sh/save_image_sh.cc @@ -25,7 +25,7 @@ /// \file /// -/// \brief Minimal code for building 1d image histogram version. +/// \brief Example of code using the gnuplot shell export library. /// #include <mln/accu/stat/histo1d.hh> @@ -34,6 +34,7 @@ #include <mln/core/image/image2d.hh> #include <mln/img_path.hh> #include <mln/io/pgm/load.hh> +#include <mln/io/plot/save_image_sh.hh> #include <mln/value/int_u8.hh> @@ -46,6 +47,7 @@ int main() mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm"); histo = mln::data::compute(mln::accu::meta::stat::histo1d(), img); + mln::io::plot::save_image_sh(histo, "histo.sh"); return 0; } -- 1.5.6.5

14 years, 1 month

last-svn-commit-42-gc0859da Import files from milena/sandbox/green.

by Yann Jacquelet

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

14 years, 1 month

last-svn-commit-41-gda5a388 Define accumulator which computes histogram view as image.

by Yann Jacquelet

* mln/img_path.hh: New header file. Define the 1d component version of the the library. * mln/accu/stat/histo1d.hh: New header file. Give an example of code. * use/accu/stat/histo1d: New directory. * use/accu/stat/histo1d/Makefile.am: New makefile. * use/accu/stat/histo1d/histo1d.cc: New source. Make some unitary tests on histo1d library. * tests/accu/stat/histo1d: New directory. * tests/accu/stat/histo1d/Makefile.am: New makefile. * tests/accu/stat/histo1d/histo1d.cc: New source. Define the 2d component version of the the library. * mln/accu/stat/histo2d.hh: New header file. * mln/value/rg.hh: New header file. * mln/fun/v2v/rgb_to_rg.hh: New header file. Give an example of code. * use/accu/stat/histo2d: New directory. * use/accu/stat/histo2d/Makefile.am: New makefile. * use/accu/stat/histo2d/histo2d.cc: New source. * use/value/rg: New directory. * use/value/rg/Makefile.am: New makefile. * use/value/rg/rg.cc: New source. * use/fun/v2v/rgb_to_rg: New directory. * use/fun/v2v/rgb_to_rg/Makefile.am: New makefile. * use/fun/v2v/rgb_to_rg/rgb_to_rg.cc: New source. Define the RGB 3d component version of the the library. * mln/accu/stat/histo3d_rgb.hh: New header file. * mln/fun/v2v/rgb8_to_rgbn.hh: New header file. Give an example of code. * use/accu/stat/histo3_rgb: New directory. * use/accu/stat/histo3d_rgb/Makefile.am: New makefile. * use/accu/stat/histo3d_rgb/histo3d_rgb.cc: New source. * use/fun/v2v/rgb8_to_rgbn: New directory. * use/fun/v2v/rgb8_to_rgbn/Makefile.am: New makefile. * use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc: New source. Make some unitary tests on histo3d library. * tests/accu/stat/histo3d_rgb: New directory. * tests/accu/stat/histo3d_rgb/Makefile.am: New makefile. * tests/accu/stat/histo3d_rgb/histo3d_rgb.cc: New source. Define the HSL 3d component version of the the library. * mln/accu/stat/histo3d_hsl.hh: New header file. Give an example of code. * use/accu/stat/histo3_hsl: New directory. * use/accu/stat/histo3d_hsl/Makefile.am: New makefile. * use/accu/stat/histo3d_hsl/histo3d_hsl.cc: New source. Make some unitary tests on histo3d library. * tests/accu/stat/histo3d_hsl: New directory. * tests/accu/stat/histo3d_hsl/Makefile.am: New makefile. * tests/accu/stat/histo3d_hsl/histo3d_hsl.cc: New source. --- scribo/sandbox/green/ChangeLog | 82 +++- scribo/sandbox/green/README | 73 +++- scribo/sandbox/green/mln/accu/stat/histo1d.hh | 32 +- scribo/sandbox/green/mln/accu/stat/histo2d.hh | 75 ++-- scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh | 249 +++++----- scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh | 138 +++--- scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh | 49 ++- scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh | 2 - .../green/tests/accu/stat/histo1d/histo1d.cc | 502 +++++++++++++++++++- .../accu/stat/{histo1d => histo3d_hsl}/Makefile.am | 0 .../tests/accu/stat/histo3d_hsl/histo3d_hsl.cc | 394 +++++++--------- .../accu/stat/{histo1d => histo3d_rgb}/Makefile.am | 0 .../tests/accu/stat/histo3d_rgb/histo3d_rgb.cc | 355 +++++++------- .../sandbox/green/use/accu/stat/histo1d/histo1d.cc | 27 + .../sandbox/green/use/accu/stat/histo2d/histo2d.cc | 25 + .../accu/stat/{histo1d => histo3d_hsl}/Makefile.am | 0 .../green/use/accu/stat/histo3d_hsl/histo3d_hsl.cc | 56 ++-- .../accu/stat/{histo1d => histo3d_rgb}/Makefile.am | 0 .../green/use/accu/stat/histo3d_rgb/histo3d_rgb.cc | 45 +- .../histo1d => fun/v2v/rgb8_to_rgbn}/Makefile.am | 0 .../green/use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc | 45 +- .../stat/histo1d => fun/v2v/rgb_to_rg}/Makefile.am | 0 .../green/use/fun/v2v/rgb_to_rg/rgb_to_rg.cc | 51 +- .../{accu/stat/histo1d => value/rg}/Makefile.am | 0 .../sandbox/green/use/value/rg/rg.cc | 51 +- 25 files changed, 1450 insertions(+), 801 deletions(-) copy scribo/sandbox/green/tests/accu/stat/{histo1d => histo3d_hsl}/Makefile.am (100%) copy {milena => scribo}/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc (73%) copy scribo/sandbox/green/tests/accu/stat/{histo1d => histo3d_rgb}/Makefile.am (100%) copy {milena => scribo}/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc (64%) copy scribo/sandbox/green/use/accu/stat/{histo1d => histo3d_hsl}/Makefile.am (100%) copy milena/tests/fun/v2v/rgb_to_hsl.cc => scribo/sandbox/green/use/accu/stat/histo3d_hsl/histo3d_hsl.cc (57%) copy scribo/sandbox/green/use/accu/stat/{histo1d => histo3d_rgb}/Makefile.am (100%) copy milena/tests/io/ppm/ppm.cc => scribo/sandbox/green/use/accu/stat/histo3d_rgb/histo3d_rgb.cc (63%) copy scribo/sandbox/green/use/{accu/stat/histo1d => fun/v2v/rgb8_to_rgbn}/Makefile.am (100%) copy milena/tests/io/ppm/ppm.cc => scribo/sandbox/green/use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc (63%) copy scribo/sandbox/green/use/{accu/stat/histo1d => fun/v2v/rgb_to_rg}/Makefile.am (100%) copy milena/tests/fun/v2v/hsl_to_rgb.cc => scribo/sandbox/green/use/fun/v2v/rgb_to_rg/rgb_to_rg.cc (59%) copy scribo/sandbox/green/use/{accu/stat/histo1d => value/rg}/Makefile.am (100%) copy milena/tests/fun/v2v/hsl_to_rgb.cc => scribo/sandbox/green/use/value/rg/rg.cc (60%) diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index 6a3c8fa..8f5101b 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -38,12 +38,84 @@ 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> - Save histogram library. + Define accumulator which computes histogram view as image. + + * mln/img_path.hh: New header file. + + Define the 1d component version of the the library. + + * mln/accu/stat/histo1d.hh: New header file. + + Give an example of code. + + * use/accu/stat/histo1d: New directory. + * use/accu/stat/histo1d/Makefile.am: New makefile. + * use/accu/stat/histo1d/histo1d.cc: New source. + + Make some unitary tests on histo1d library. + + * tests/accu/stat/histo1d: New directory. + * tests/accu/stat/histo1d/Makefile.am: New makefile. + * tests/accu/stat/histo1d/histo1d.cc: New source. + + Define the 2d component version of the the library. + + * mln/accu/stat/histo2d.hh: New header file. + * mln/value/rg.hh: New header file. + * mln/fun/v2v/rgb_to_rg.hh: New header file. + + Give an example of code. + + * use/accu/stat/histo2d: New directory. + * use/accu/stat/histo2d/Makefile.am: New makefile. + * use/accu/stat/histo2d/histo2d.cc: New source. + + * use/value/rg: New directory. + * use/value/rg/Makefile.am: New makefile. + * use/value/rg/rg.cc: New source. + + * use/fun/v2v/rgb_to_rg: New directory. + * use/fun/v2v/rgb_to_rg/Makefile.am: New makefile. + * use/fun/v2v/rgb_to_rg/rgb_to_rg.cc: New source. + + Define the RGB 3d component version of the the library. + + * mln/accu/stat/histo3d_rgb.hh: New header file. + * mln/fun/v2v/rgb8_to_rgbn.hh: New header file. + + Give an example of code. + + * use/accu/stat/histo3_rgb: New directory. + * use/accu/stat/histo3d_rgb/Makefile.am: New makefile. + * use/accu/stat/histo3d_rgb/histo3d_rgb.cc: New source. + + * use/fun/v2v/rgb8_to_rgbn: New directory. + * use/fun/v2v/rgb8_to_rgbn/Makefile.am: New makefile. + * use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc: New source. + + Make some unitary tests on histo3d library. + + * tests/accu/stat/histo3d_rgb: New directory. + * tests/accu/stat/histo3d_rgb/Makefile.am: New makefile. + * tests/accu/stat/histo3d_rgb/histo3d_rgb.cc: New source. + + + Define the HSL 3d component version of the the library. + + * mln/accu/stat/histo3d_hsl.hh: New header file. + + Give an example of code. + + * use/accu/stat/histo3_hsl: New directory. + * use/accu/stat/histo3d_hsl/Makefile.am: New makefile. + * use/accu/stat/histo3d_hsl/histo3d_hsl.cc: New source. + + Make some unitary tests on histo3d library. + + * tests/accu/stat/histo3d_hsl: New directory. + * tests/accu/stat/histo3d_hsl/Makefile.am: New makefile. + * tests/accu/stat/histo3d_hsl/histo3d_hsl.cc: New source. - * mln/accu/histo/histo1d.hh: New header file. - * mln/accu/histo/histo2d.hh: New header file. - * mln/accu/histo/histo3d_rgb.hh: New header file. - * mln/accu/histo/histo3d_hsl.hh: New header file. 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> diff --git a/scribo/sandbox/green/README b/scribo/sandbox/green/README index 3efe50d..4b6fb26 100644 --- a/scribo/sandbox/green/README +++ b/scribo/sandbox/green/README @@ -153,18 +153,8 @@ d'exécution (chmod 755 gnuplot_shell_file.sh). Comme je trouve le format extrêmement pratique, il se retrouve preque partout dans mes sources. -V MLN ------ - -a) La sauvegarde des images au format gnuplot shell - -* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. - -to do ... - - - -b) Les histogrammes +V HISTOGRAMMES +-------------- Un des travaux demandés par théo est la réalisation d'une librairie d'histogramme permettant de fournir un résultat sous forme d'image. @@ -178,20 +168,67 @@ de comptage puisqu'il est décrit sous la forme de triplets de float contient une séquence d'appels pour les routines permettant de considérer la dimension de la teinte comme circulaire. + +a) version 1d + * mln/accu/stat/histo1d.hh: Accumulateur histogramme image1d. * use/accu/stat/histo1d: Code minimal utilisant un histogramme 1d. +* tests/accu/stat/histo1d: Test unitaire sur l'histogramme 1d. + + +b) version 2d -* mln/accu/stat/histo2d.hh: Accumulateur histogramme image2d. * mln/value/rg.hh: Définition du type vectoriel 2d rouge/vert (RG). +* use/value/rg: Exemple de code pour l'utilisation de rg. + * mln/fun/v2v/rgb_to_rg.hh: Transformation de l'espace RGB vers l'espace RG. +* use/fun/v2v/rgb_to_rg: Exemple de code pour l'utilisation de rgb_to_rg. + +* mln/accu/stat/histo2d.hh: Accumulateur histogramme image2d. * use/accu/stat/histo2d: Code minimal utilisant un histogramme 2d. -* mln/accu/stat/histo3d_rgb.hh: Accumulateur histogramme image3d RGB. -* mln/accu/stat/histo3d_hsl.hh: Accumulateur histogramme image3d HSL. +c) version 3d RGB + + +* mln/fun/v2v/rgb8_to_rgbn.hh: Diminution de la quantification (n < 8 bits). +* use/fun/v2v/rgb8_to_rgbn: Exemple de code pour l'utilisation de rgb8_to_rgbn. +* mln/accu/stat/histo3d_rgb.hh: Accumulateur histogramme image3d RGB. * use/accu/stat/histo3_rgb: Code minimal utilisant un histogramme 3d RGB. + + + +d) version 3d HSL + +* mln/accu/stat/histo3d_hsl.hh: Accumulateur histogramme image3d HSL. * use/accu/stat/histo3_hsl: Code minimal utilisant un histogramme 3d HSL. +* tests/accu/stat/histo3d_hsl: Test unitaire sur l'histogramme HSL 3d. + +Le code HSL ne compile plus car l'interface liant la transformation du +domaine et la fonction fold a changée. Je n'ai pas le temps de +regarder plus avant. + + + +V MLN +----- + +a) La sauvegarde des images au format gnuplot shell + +* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. + +to do ... + + + +b) Les histogrammes + + + + + + @@ -210,6 +247,12 @@ considérer la dimension de la teinte comme circulaire. * use/accu/stat/histo3d_hsl * tests/accu/stat/histo3d_hsl +VI SAUVEGARDE FORMAT GNUPLOT SHELL +---------------------------------- + + +VII VISUALISATION HISTOGRAMMES 3D +--------------------------------- c) La visualisation des histogrammes 3d diff --git a/scribo/sandbox/green/mln/accu/stat/histo1d.hh b/scribo/sandbox/green/mln/accu/stat/histo1d.hh index d480cf0..fae94c8 100644 --- a/scribo/sandbox/green/mln/accu/stat/histo1d.hh +++ b/scribo/sandbox/green/mln/accu/stat/histo1d.hh @@ -24,7 +24,7 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_ACCU_STAT_HISTO1D_HH -#define MLN_ACCU_STAT_HISTO1D_HH +# define MLN_ACCU_STAT_HISTO1D_HH /// \file /// @@ -64,24 +64,24 @@ /// } -#include <iostream> +# include <iostream> -#include <mln/accu/internal/base.hh> +# include <mln/accu/internal/base.hh> -#include <mln/arith/plus.hh> +# include <mln/arith/plus.hh> -#include <mln/core/concept/meta_accumulator.hh> -#include <mln/core/image/image1d.hh> -#include <mln/core/macros.hh> +# include <mln/core/concept/meta_accumulator.hh> +# include <mln/core/image/image1d.hh> +# include <mln/core/macros.hh> -#include <mln/literal/zero.hh> +# include <mln/literal/zero.hh> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> -#include <mln/trait/value/comp.hh> +# include <mln/trait/value/comp.hh> -#include <mln/value/ops.hh> +# include <mln/value/ops.hh> namespace mln { @@ -168,7 +168,7 @@ namespace mln typedef image1d<unsigned> result; typedef result q_result; - /// Constructors + /// Constructors. /// \{ /// \brief Infer the size of the resulting image1d domain. /// @@ -201,7 +201,6 @@ namespace mln /// /// The end user shouldn't call this method. This is part of /// data compute interface mechanism. - void take(const histo1d<V>& other); /// \} @@ -231,11 +230,10 @@ namespace mln /// /// The operator compares all the bins from the two histograms. /// Nobody uses this method unless unitary tests. - template <typename V> bool operator==(const histo1d<V>& histo1, const histo1d<V>& histo2); -#ifndef MLN_INCLUDE_ONLY +# ifndef MLN_INCLUDE_ONLY template <typename V> inline @@ -328,7 +326,7 @@ namespace mln return result; } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::accu::stat diff --git a/scribo/sandbox/green/mln/accu/stat/histo2d.hh b/scribo/sandbox/green/mln/accu/stat/histo2d.hh index c28723c..11a1bb3 100644 --- a/scribo/sandbox/green/mln/accu/stat/histo2d.hh +++ b/scribo/sandbox/green/mln/accu/stat/histo2d.hh @@ -73,14 +73,16 @@ # include <mln/accu/internal/base.hh> +# include <mln/arith/plus.hh> + # include <mln/core/macros.hh> # include <mln/core/image/image2d.hh> # include <mln/core/alias/point2d.hh> # include <mln/core/alias/box2d.hh> -# include <mln/trait/value/comp.hh> +#include <mln/literal/zero.hh> -# include <mln/arith/plus.hh> +# include <mln/trait/value/comp.hh> # include <mln/trace/entering.hh> # include <mln/trace/exiting.hh> @@ -152,13 +154,13 @@ namespace mln namespace stat { - /// \brief Define an histogram which returns an image3d . + /// \brief Define a histogram as accumulator which returns an image2d. /// - /// Param V defines the space in which we count the values. - /// For instance, this histogram works image2d<rgb<2>> or - /// image2d<rgb<7>>. The histogram count the occurrence of each value. - /// The number of bins depends of the grayscale values, for 8 bits there - /// is 256x3 bins. Note that over + /// Param V defines the type of the input image value. It is in + /// this space that we count the values. For instance, this + /// histogram works well for image2d< rgb<2> > or with image2d< + /// rgb<7> >. The number of bins depends directly the values V. + /// For 8 bits there is 256x3 bins. Note that less /// quantification works too. /// /// \ingroup modaccuvalues @@ -171,16 +173,15 @@ namespace mln typedef image2d<unsigned> result; typedef result q_result; - /// Constructors + /// Constructors. /// \{ - /// \brief Initialize the size of the resulting image1d. + /// \brief Infer the size of the resulting image2d domain. /// - /// Initialize the size the resulting image from the theorical dynamic - /// of the greylevel values (Use V to manage it). + /// By evaluating the minimum and the maximum of V, we define the domain + /// of the resulting image2d. histo2d(); /// \} - /// Manipulators. /// \{ /// \brief Initialize the histogram with zero value. @@ -190,23 +191,24 @@ namespace mln /// density. void init(); - - /// \brief Update the histogram with the RGB pixel t. - /// \param[in] t a greylevel pixel of type V. + /// \brief Update the histogram with the RG pixel t. + /// \param[in] t a graylevel pixel of type V. /// /// The end user shouldn't call this method. In place of it, he can /// go through the data compute interface. void take(const argument& t); - /// \brief Update the histogram with an other histogram. /// \param[in] other the other histogram. + /// + /// The end user shouldn't call this method. This is part of + /// data compute interface mechanism. void take(const histo2d<V>& other); /// \} /// Accessors. /// \{ - /// \brief Return the histogram as an image1d. + /// \brief Return the histogram as an image2d. /// /// This is the machinery to communicate with data compute interface. /// The end user should'nt use it. @@ -228,19 +230,19 @@ namespace mln /// \param[in] histo1 the first histogram to compare with. /// \param[in] histo2 the second histogram. /// - /// The operator compare all the bins from the two histogram. - + /// The operator compares all the bins from the two histograms. + /// Nobody uses this method unless unitary tests. template <typename V> bool operator==(const histo2d<V>& histo1, const histo2d<V>& histo2); -#ifndef MLN_INCLUDE_ONLY +# ifndef MLN_INCLUDE_ONLY template <typename V> inline histo2d<V>::histo2d() { - trace::entering("mln::accu::stat::histo2d::histo2d"); + trace::entering("mln::accu::stat::histo2d::cstor"); typedef mln_trait_value_comp(V,0) comp0; typedef mln_trait_value_comp(V,1) comp1; @@ -253,31 +255,24 @@ namespace mln point2d(mln_max(comp0), mln_max(comp1)))); - trace::exiting("mln::accu::stat::histo2d::histo2d"); + trace::exiting("mln::accu::stat::histo2d::cstor"); } template <typename V> inline void histo2d<V>::init() { - trace::entering("mln::accu::stat::histo2d::init"); - - data::fill(count_, 0); - trace::exiting("mln::accu::stat::histo2d::init"); + data::fill(count_, literal::zero); } template <typename V> inline void histo2d<V>::take(const argument& t) { - trace::entering("mln::accu::stat::histo2d::take"); - // Just convert a greyscale value (int_u8 like) to a position for an // iterator on the resulting image. // Take care to the constructor : Point(slice, row, column) ++count_(point2d(t.red(), t.green())); - - trace::exiting("mln::accu::stat::histo2d::take"); } @@ -285,20 +280,13 @@ namespace mln inline void histo2d<V>::take(const histo2d<V>& other) { - trace::entering("mln::accu::stat::histo2d::take"); - count_ += other.count_; - - trace::exiting("mln::accu::stat::histo2d::take"); } template <typename V> inline typename histo2d<V>::result histo2d<V>::to_result() const { - trace::entering("mln::accu::stat::histo2d::to_result"); - - trace::exiting("mln::accu::stat::histo2d::to_result"); return count_; } @@ -306,9 +294,6 @@ namespace mln inline histo2d<V>::operator result() const { - trace::entering("mln::accu::stat::histo2d::operator result"); - - trace::exiting("mln::accu::stat::histo2d::operator result"); return count_; } @@ -316,10 +301,8 @@ namespace mln inline bool histo2d<V>::is_valid() const { - trace::entering("mln::accu::stat::histo2d::is_valid"); bool result = count_.is_valid(); - trace::exiting("mln::accu::stat::histo2d::is_valid"); return result; } @@ -327,7 +310,7 @@ namespace mln bool operator==(const histo2d<V>& histo1, const histo2d<V>& histo2) { - trace::entering("mln::accu::stat::operator=="); + trace::entering("mln::accu::stat::histo2d::operator=="); bool result = true; const image2d<unsigned>& res1 = histo1.to_result(); @@ -342,11 +325,11 @@ namespace mln for_all_2(p1, p2) result &= (res1(p1) == res2(p2)); - trace::exiting("mln::accu::stat::operator=="); + trace::exiting("mln::accu::stat::histo2d::operator=="); return result; } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::accu::stat diff --git a/scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh b/scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh index c84aaa4..35d78e2 100644 --- a/scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh +++ b/scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh @@ -1,6 +1,4 @@ -// 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) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -26,82 +24,115 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_ACCU_STAT_HISTO3D_HSL_HH -#define MLN_ACCU_STAT_HISTO3D_HSL_HH +# define MLN_ACCU_STAT_HISTO3D_HSL_HH /// \file /// -/// \brief Define a histogram as an accumulator which returns an image1d . +/// \brief Define a histogram as accumulator which returns an HSL image3d. +/// +/// This source implements the discrete histogram version. It was +/// created for images which the values are derived from integer +/// type. The number of bins is given by the user via the q parameter. +/// It works for HSL space. /// -/// This source implements the discrete histogram version. The number of beans -/// is infer from the number of greylevels. A typical int_u8 image has got -/// 256 bins. An int_u16 image has got 65535 bins. /// The following sample is a typical use of the histogram. /// -/// #include <mln/value/int_u8.hh> -/// #include <mln/core/image/image1d.hh> +/// #include <mln/accu/stat/histo3d_hsl.hh> /// #include <mln/core/image/image2d.hh> -/// #include <mln/io/pgm/load.hh> -/// #include <mln/accu/stat/histo1d.hh> +/// #include <mln/core/image/image3d.hh> /// #include <mln/data/compute.hh> -/// #include <mln/io/plot/save_histo_sh.hh> -/// -/// #define OLENA_LENA "/usr/local/share/olena/images/lena.pgm" +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_hsl.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/hsl.hh> +/// #include <mln/value/rgb8.hh> /// -/// void test() +/// int main() /// { -/// typedef mln::value::int_u8 int_u8; -/// mln::image2d<int_u8> img_ref; -/// mln::image1d<unsigned> img_res; +/// typedef mln::value::rgb8 t_rgb; +/// typedef mln::value::hsl_f t_hsl; +/// typedef mln::fun::v2v::f_rgb_to_hsl_<t_hsl> t_rgb_to_hsl; +/// mln::image2d<t_rgb> img_rgb; +/// mln::image2d<t_hsl> img_hsl; +/// mln::image3d<unsigned> histo; +/// +/// mln::io::ppm::load(img_rgb, OLENA_IMG_PATH"/lena.ppm"); +/// img_hsl = mln::data::transform(img_rgb, t_rgb_to_hsl()); +/// histo=mln::data::compute(mln::accu::meta::stat::histo3d_hsl<7>(),img_hsl); /// -/// mln::io::pgm::load(img_ref, OLENA_LENA); -/// img_res = mln::data::compute(mln::accu::stat::histo1d<int_u8>(), img_ref); +/// return 0; /// } +/// +/// \fixme This implementation is very doubtfull ! Be carefull !! +/// \fixme This code should be compile, but it doesn't! +/// \fixme The fold interface has changed v2v -> p2p or the inverse. + +# include <iostream> -#include <iostream> +# include <mln/accu/internal/base.hh> -#include <mln/accu/internal/base.hh> +# include <mln/arith/plus.hh> -#include <mln/core/macros.hh> -#include <mln/core/image/image3d.hh> -#include <mln/core/alias/point3d.hh> -#include <mln/core/alias/box3d.hh> +# include <mln/core/alias/point3d.hh> +# include <mln/core/alias/box3d.hh> +# include <mln/core/image/image3d.hh> +# include <mln/core/image/dmorph/transformed_image.hh> +# include <mln/core/macros.hh> -#include <mln/value/int_u.hh> -#include <mln/trait/value/comp.hh> +# include <mln/fun/p2p/fold.hh> -#include <mln/arith/plus.hh> +# include <mln/literal/zero.hh> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> -#include <mln/value/ops.hh> +# include <mln/trait/value/comp.hh> -// make hue cyclic -#include <mln/fun/p2p/fold.hh> -#include <mln/core/image/dmorph/transformed_image.hh> +# include <mln/value/int_u.hh> +# include <mln/value/ops.hh> namespace mln { - + namespace accu { - + namespace stat { - + // Forward declaration template <unsigned q, typename V> struct histo3d_hsl; } // end of namespace mln::accu::stat - - } // end of namespace mln::accu + namespace meta + { + + namespace stat + { + + template <unsigned q> + struct histo3d_hsl : public Meta_Accumulator< histo3d_hsl<q> > + { + template <typename V> + struct with + { + typedef accu::stat::histo3d_hsl<q,V> ret; + }; + }; + + } // end of namespace mln::accu::meta::stat + + } // end of namespace mln::accu::meta + + } // end of namespace mln::accu namespace trait { - + template <unsigned q, typename V> struct accumulator_< mln::accu::stat::histo3d_hsl<q,V> > { @@ -112,65 +143,59 @@ namespace mln }; template <unsigned q, typename V> - struct set_precise_binary_<op::eq, + struct set_precise_binary_<op::eq, accu::stat::histo3d_hsl<q,V>, accu::stat::histo3d_hsl<q,V> > { typedef bool ret; }; - + } // end of namespace mln::trait namespace accu { - + namespace stat - { + { - /// \brief Define an histogram which returns an image1d . + + /// \brief Define a histogram as accumulator which returns an image3d. /// - /// Param V defines the space in which we count the values. - /// For instance, this histogram works image2d<int_u8> or - /// image1d<int_u16>. The histogram count the occurrence of each value. - /// The number of bins depends of the greyscale values, for 8 bits there - /// is 256 bins, for 16 bits there is 65536 bins. Note that over - /// quantification works too. + /// Param q defines the number of bins per axe. + /// Param V defines the type of the input image value. We assume that + /// V is a kind of hsl_<float,float,float>. /// /// \ingroup modaccuvalues - - /// q is the number of bins per axe because quantification info are died. - /// we assume that V is a kind of hsl_<float,float,float> - template <unsigned q, typename V> - struct histo3d_hsl : + struct histo3d_hsl : public mln::accu::internal::base<image3d<unsigned>, histo3d_hsl<q,V> > { typedef V argument; typedef image3d<unsigned> result; typedef result q_result; - /// Constructors + /// Constructors. /// \{ - /// \brief Initialize the size of the resulting image1d. + /// \brief Infer the size of the resulting image3d domain. /// - /// Initialize the size the resulting image from the theorical dynamic - /// of the greylevel values (Use V to manage it). + /// By evaluating the minimum and the maximum of V, we define the domain + /// of the resulting image3d. histo3d_hsl(); /// \} - + /// Manipulators. /// \{ /// \brief Initialize the histogram with zero value. /// - /// This method must be called just before starting the use of the - /// histogram. If it's not, resulting values won't converge to the + /// This method must be called just before starting the use of the + /// histogram. If it's not, resulting values won't converge to the /// density. void init(); - + /// \brief Update the histogram with the RGB pixel t. - /// \param[in] t a greylevel pixel of type V. + /// \param[in] t a graylevel pixel of type V. /// /// The end user shouldn't call this method. In place of it, he can /// go through the data compute interface. @@ -179,12 +204,15 @@ namespace mln /// \brief Update the histogram with an other histogram. /// \param[in] other the other histogram. + /// + /// The end user shouldn't call this method. This is part of + /// data compute interface mechanism. void take(const histo3d_hsl<q,V>& other); /// \} /// Accessors. /// \{ - /// \brief Return the histogram as an image1d. + /// \brief Return the histogram as an HSL image3d. /// /// This is the machinery to communicate with data compute interface. /// The end user should'nt use it. @@ -208,31 +236,31 @@ namespace mln const float min_sat; const float max_sat; float step_sat; - result count_; + result count_; }; /// \brief Check wethever an histogram is equal to an other one. /// \param[in] histo1 the first histogram to compare with. /// \param[in] histo2 the second histogram. /// - /// The operator compare all the bins from the two histogram. - + /// The operator compares all the bins from the two histograms. + /// Nobody uses this method unless unitary tests. template <unsigned q, typename V> - bool operator==(const histo3d_hsl<q,V>& histo1, + bool operator==(const histo3d_hsl<q,V>& histo1, const histo3d_hsl<q,V>& histo2); -#ifndef MLN_INCLUDE_ONLY +# ifndef MLN_INCLUDE_ONLY template <unsigned q, typename V> inline histo3d_hsl<q,V>::histo3d_hsl() : min_hue(0.0), max_hue(360.0), - min_lum(0.0), max_lum(1.0), - min_sat(0.0), max_sat(1.0) + min_lum(0.0), max_lum(1.0), + min_sat(0.0), max_sat(1.0) { - trace::entering("mln::accu::stat::histo3d_hsl<q,V>::histo3d_hsl"); - - // As there is no info about preceding color space - // we ask the end user to specify the quantification he's looking for. + trace::entering("mln::accu::stat::histo3d_hsl::cstor"); + + /// As there is no info about preceding color space + /// we ask the end user to specify the quantification he's looking for. count_.init_(box3d(point3d(mln_min(value::int_u<q>), mln_min(value::int_u<q>), @@ -241,7 +269,7 @@ namespace mln mln_max(value::int_u<q>), mln_max(value::int_u<q>)))); - // Make the hue domain cyclic + /// Make the hue domain cyclic fun::p2p::fold<point3d,1,0,0> fold_(count_.domain()); transform_domain(count_, fold_); @@ -249,39 +277,34 @@ namespace mln step_lum = (max_lum - min_lum)/q; step_sat = (max_sat - min_sat)/q; - trace::exiting("mln::accu::stat::histo3d_hsl<q,V>::histo3d_hsl"); + trace::exiting("mln::accu::stat::histo3d_hsl::cstor"); } template <unsigned q, typename V> inline void histo3d_hsl<q,V>::init() { - trace::entering("mln::accu::stat::histo3d_hsl<q,V>::init"); - - data::fill(count_, 0); - trace::exiting("mln::accu::stat::histo3d_hsl<q,V>::init"); + data::fill(count_, literal::zero); } template <unsigned q, typename V> inline void histo3d_hsl<q,V>::take(const argument& t) { - trace::entering("mln::accu::stat::histo3d_hsl<q,V>::take"); - - // Just convert a greyscale value (int_u8 like) to a position for an + // Just convert a greyscale value (int_u8 like) to a position for an // iterator on the resulting image. //++count_(point3d(t.red(), t.green(), t.blue())); - - + + // Technical way to access i° component without kwnowing the name // mln::trait::value_<argument>::get_comp_0(t); - + // is def::coord1d the type of x, y, z ?? unsigned x = (t.hue() - min_hue)/step_hue; - unsigned y = (t.lum() - min_lum)/step_lum; - unsigned z = (t.sat() - min_sat)/step_sat; + unsigned y = (t.sat() - min_sat)/step_sat; + unsigned z = (t.lum() - min_lum)/step_lum; /* - + std::cout << "H : " << t.hue() << std::endl; std::cout << "L : " << t.lum() << std::endl; std::cout << "S : " << t.sat() << std::endl; @@ -295,66 +318,50 @@ namespace mln std::cout << "Z : " << z << std::endl; */ - // faire attention avec les histoires de points et leurs coordonnées ++count_(point3d(z, x, y)); - //++count_(point3d(t.hue(), t.sat(), t.lum())); - - trace::exiting("mln::accu::stat::histo3d_hsl<q,V>::take"); } - + template <unsigned q, typename V> inline void histo3d_hsl<q,V>::take(const histo3d_hsl<q,V>& other) { - trace::entering("mln::accu::stat::histo3d_hsl<q,V>::take"); - count_ += other.count_; - - trace::exiting("mln::accu::stat::histo3d_hsl<q,V>::take"); } - + template <unsigned q, typename V> inline typename histo3d_hsl<q,V>::result histo3d_hsl<q,V>::to_result() const { - trace::entering("mln::accu::stat::histo3d_hsl<q,V>::to_result"); - - trace::exiting("mln::accu::stat::histo3d_hsl<q,V>::to_result"); return count_; } - + template <unsigned q, typename V> inline histo3d_hsl<q,V>::operator result() const { - trace::entering("mln::accu::stat::histo3d_rgb<q,V>::operator result"); - - trace::exiting("mln::accu::stat::histo3d_rgb<q,V>::operator result"); return count_; - } + } template <unsigned q, typename V> inline bool histo3d_hsl<q,V>::is_valid() const { - trace::entering("mln::accu::stat::histo3d_hsl<q,V>::is_valid"); bool result = count_.is_valid(); - - trace::exiting("mln::accu::stat::histo3d_hsl<q,V>::is_valid"); + return result; } template <unsigned q, typename V> - bool operator==(const histo3d_hsl<q,V>& histo1, + bool operator==(const histo3d_hsl<q,V>& histo1, const histo3d_hsl<q,V>& histo2) { - trace::entering("mln::accu::stat::operator=="); + trace::entering("mln::accu::stat::histo3d_hsl::operator=="); bool result = true; const image3d<unsigned>& res1 = histo1.to_result(); const image3d<unsigned>& res2 = histo2.to_result(); - + mln_precondition(res1.is_valid()); mln_precondition(res2.is_valid()); @@ -364,11 +371,11 @@ namespace mln for_all_2(p1, p2) result &= (res1(p1) == res2(p2)); - trace::exiting("mln::accu::stat::operator=="); + trace::exiting("mln::accu::stat::histo3d_hsl::operator=="); return result; } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::accu::stat @@ -377,4 +384,4 @@ namespace mln } // end of namespace mln -#endif // ! MLN_ACCU_STAT_HISTO3D_RGB_HH +#endif // ! MLN_ACCU_STAT_HISTO3D_HSL_HH diff --git a/scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh b/scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh index 48c49da..6a48024 100644 --- a/scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh +++ b/scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh @@ -1,6 +1,4 @@ -// 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) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -26,54 +24,67 @@ // executable file might be covered by the GNU General Public License. #ifndef MLN_ACCU_STAT_HISTO3D_RGB_HH -#define MLN_ACCU_STAT_HISTO3D_RGB_HH +# define MLN_ACCU_STAT_HISTO3D_RGB_HH /// \file /// -/// \brief Define a histogram as an accumulator which returns an image1d . +/// \brief Define a histogram as accumulator which returns an image3d. +/// +/// This source implements the discrete histogram version. It was +/// created for images which the values are derived from integer +/// type. The number of bins is directly infered from the cardinality +/// of the image value. It works for RGB space. See histo1d.hh for limitations +/// of such implementation. 8 bits quantification is very +/// expensive, it produces image3d with [0..255,0..255,0..255] as domain. /// -/// This source implements the discrete histogram version. The number of beans -/// is infer from the number of greylevels. A typical rgb8 image has got -/// 256x3 bins. Working with a 8 bit quantification in rgb is very costly. /// The following sample is a typical use of the histogram. /// -/// #include <mln/value/rgb.hh> -/// #include <mln/core/image/image1d.hh> -/// #include <mln/core/image/image3d.hh> -/// #include <mln/io/ppm/load.hh> /// #include <mln/accu/stat/histo3d_rgb.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/core/image/image3d.hh> /// #include <mln/data/compute.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb8_to_rgbn.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rgb.hh> +/// #include <mln/value/rgb8.hh> /// -/// #define OLENA_LENA ""/usr/local/share/olena/images/lena.ppm" -/// -/// void test() +/// int main() /// { -/// typedef mln::value::rgb<7> rgb7; -/// mln::image2d<rgb7> img_ref; -/// mln::image3d<unsigned> img_res; +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::value::rgb<7> t_rgb7; +/// mln::image2d<t_rgb8> img_rgb8; +/// mln::image2d<t_rgb7> img_rgb7; +/// mln::image3d<unsigned> histo; /// -/// mln::io::ppm::load(img_ref, OLENA_LENA); -/// img_res=mln::data::compute(mln::accu::meta::stat::histo3d_rgb(),img_ref); +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); +/// img_rgb7 =mln::data::transform(img_rgb8,mln::fun::v2v::rgb8_to_rgbn<7>()); +/// histo = mln::data::compute(mln::accu::meta::stat::histo3d_rgb(),img_rgb7); +/// +/// return 0; /// } -#include <iostream> +# include <iostream> + +# include <mln/accu/internal/base.hh> -#include <mln/accu/internal/base.hh> +# include <mln/arith/plus.hh> -#include <mln/core/macros.hh> -#include <mln/core/image/image3d.hh> -#include <mln/core/alias/point3d.hh> -#include <mln/core/alias/box3d.hh> +# include <mln/core/macros.hh> +# include <mln/core/image/image3d.hh> +# include <mln/core/alias/point3d.hh> +# include <mln/core/alias/box3d.hh> -#include <mln/trait/value/comp.hh> +# include <mln/literal/zero.hh> -#include <mln/arith/plus.hh> +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> -#include <mln/trace/entering.hh> -#include <mln/trace/exiting.hh> +# include <mln/trait/value/comp.hh> -#include <mln/value/ops.hh> +# include <mln/value/ops.hh> namespace mln { @@ -140,13 +151,13 @@ namespace mln namespace stat { - /// \brief Define an histogram which returns an image3d . + /// \brief Define a histogram as accumulator which returns an image3d. /// - /// Param V defines the space in which we count the values. - /// For instance, this histogram works image2d<rgb<2>> or - /// image2d<rgb<7>>. The histogram count the occurrence of each value. - /// The number of bins depends of the grayscale values, for 8 bits there - /// is 256x3 bins. Note that over + /// Param V defines the type of the input image value. It is in + /// this space that we count the values. For instance, this + /// histogram works well for image2d< rgb<2> > or with image2d< + /// rgb<7> >. The number of bins depends directly the values V. + /// For 8 bits there is 256x3 bins. Note that less /// quantification works too. /// /// \ingroup modaccuvalues @@ -159,16 +170,15 @@ namespace mln typedef image3d<unsigned> result; typedef result q_result; - /// Constructors + /// Constructors. /// \{ - /// \brief Initialize the size of the resulting image1d. + /// \brief Infer the size of the resulting image3d domain. /// - /// Initialize the size the resulting image from the theorical dynamic - /// of the greylevel values (Use V to manage it). + /// By evaluating the minimum and the maximum of V, we define the domain + /// of the resulting image3d. histo3d_rgb(); /// \} - /// Manipulators. /// \{ /// \brief Initialize the histogram with zero value. @@ -180,7 +190,7 @@ namespace mln /// \brief Update the histogram with the RGB pixel t. - /// \param[in] t a greylevel pixel of type V. + /// \param[in] t a graylevel pixel of type V. /// /// The end user shouldn't call this method. In place of it, he can /// go through the data compute interface. @@ -189,12 +199,15 @@ namespace mln /// \brief Update the histogram with an other histogram. /// \param[in] other the other histogram. + /// + /// The end user shouldn't call this method. This is part of + /// data compute interface mechanism. void take(const histo3d_rgb<V>& other); /// \} /// Accessors. /// \{ - /// \brief Return the histogram as an image1d. + /// \brief Return the histogram as an RGB image3d. /// /// This is the machinery to communicate with data compute interface. /// The end user should'nt use it. @@ -216,19 +229,19 @@ namespace mln /// \param[in] histo1 the first histogram to compare with. /// \param[in] histo2 the second histogram. /// - /// The operator compare all the bins from the two histogram. - + /// The operator compares all the bins from the two histograms. + /// Nobody uses this method unless unitary tests. template <typename V> bool operator==(const histo3d_rgb<V>& histo1, const histo3d_rgb<V>& histo2); -#ifndef MLN_INCLUDE_ONLY +# ifndef MLN_INCLUDE_ONLY template <typename V> inline histo3d_rgb<V>::histo3d_rgb() { - trace::entering("mln::accu::stat::histo3d_rgb<V>::histo3d_rgb"); + trace::entering("mln::accu::stat::histo3d_rgb::cstor"); typedef mln_trait_value_comp(V,0) comp0; typedef mln_trait_value_comp(V,1) comp1; typedef mln_trait_value_comp(V,2) comp2; @@ -244,31 +257,24 @@ namespace mln mln_max(comp1), mln_max(comp2)))); - trace::exiting("mln::accu::stat::histo3d_rgb<V>::histo3d_rgb"); + trace::exiting("mln::accu::stat::histo3d_rgb::cstor"); } template <typename V> inline void histo3d_rgb<V>::init() { - trace::entering("mln::accu::stat::histo3d_rgb<V>::init"); - - data::fill(count_, 0); - trace::exiting("mln::accu::stat::histo3d_rgb<V>::init"); + data::fill(count_, literal::zero); } template <typename V> inline void histo3d_rgb<V>::take(const argument& t) { - trace::entering("mln::accu::stat::histo3d_rgb<V>::take"); - // Just convert a greyscale value (int_u8 like) to a position for an // iterator on the resulting image. // Take care to the constructor : Point(slice, row, column) ++count_(point3d(t.blue(), t.red(), t.green())); - - trace::exiting("mln::accu::stat::histo3d_rgb<V>::take"); } @@ -276,20 +282,13 @@ namespace mln inline void histo3d_rgb<V>::take(const histo3d_rgb<V>& other) { - trace::entering("mln::accu::stat::histo3d_rgb<V>::take"); - count_ += other.count_; - - trace::exiting("mln::accu::stat::histo3d_rgb<V>::take"); } template <typename V> inline typename histo3d_rgb<V>::result histo3d_rgb<V>::to_result() const { - trace::entering("mln::accu::stat::histo3d_rgb<V>::to_result"); - - trace::exiting("mln::accu::stat::histo3d_rgb<V>::to_result"); return count_; } @@ -297,9 +296,6 @@ namespace mln inline histo3d_rgb<V>::operator result() const { - trace::entering("mln::accu::stat::histo3d_rgb<V>::operator result"); - - trace::exiting("mln::accu::stat::histo3d_rgb<V>::operator result"); return count_; } @@ -307,10 +303,8 @@ namespace mln inline bool histo3d_rgb<V>::is_valid() const { - trace::entering("mln::accu::stat::histo3d_rgb<V>::is_valid"); bool result = count_.is_valid(); - trace::exiting("mln::accu::stat::histo3d_rgb<V>::is_valid"); return result; } @@ -318,7 +312,7 @@ namespace mln bool operator==(const histo3d_rgb<V>& histo1, const histo3d_rgb<V>& histo2) { - trace::entering("mln::accu::stat::operator=="); + trace::entering("mln::accu::stat::histo3d_rgb::operator=="); bool result = true; const image3d<unsigned>& res1 = histo1.to_result(); @@ -333,11 +327,11 @@ namespace mln for_all_2(p1, p2) result &= (res1(p1) == res2(p2)); - trace::exiting("mln::accu::stat::operator=="); + trace::exiting("mln::accu::stat::histo3d_rgb::operator=="); return result; } -#endif // ! MLN_INCLUDE_ONLY +# endif // ! MLN_INCLUDE_ONLY } // end of namespace mln::accu::stat diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh b/scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh index 2918b1a..acf1b5e 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh @@ -1,6 +1,4 @@ -// 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) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -35,6 +33,37 @@ /// \file /// /// \brief Convert rgb8 value to rgbn, n < 8. +/// +/// The source implements the reduction of quantification for any size less 8. +/// +/// The following sample is a typical use of the histogram. +/// +/// #include <mln/accu/stat/histo3d_rgb.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/core/image/image3d.hh> +/// #include <mln/data/compute.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb8_to_rgbn.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rgb.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::value::rgb<7> t_rgb7; +/// mln::image2d<t_rgb8> img_rgb8; +/// mln::image2d<t_rgb7> img_rgb7; +/// mln::image3d<unsigned> histo; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); +/// img_rgb7 =mln::data::transform(img_rgb8,mln::fun::v2v::rgb8_to_rgbn<7>()); +/// histo = mln::data::compute(mln::accu::meta::stat::histo3d_rgb(),img_rgb7); +/// +/// return 0; +/// } + namespace mln { @@ -45,16 +74,24 @@ namespace mln namespace v2v { - /// \brief Convert rgb8 value to rgbn, n < 8. + /// \brief Convert a rgb8 value to a rgn, n < 8. + /// + /// Param n defines the output quantification used for the transformation. /// /// \ingroup modfunv2v - template <unsigned n> struct rgb8_to_rgbn : Function_v2v< rgb8_to_rgbn<n> > { typedef value::rgb8 argument; typedef value::rgb<n> result; + /// \brief Convert a rgb8 value to a rgn, n < 8. + /// + /// \param[in] v the rgb8 value to convert. + /// + /// Conversion is done by computing the size by which we + /// divide each rgb component. + result operator()(const argument& c) const { mln_precondition(8 > n); @@ -65,7 +102,7 @@ namespace mln std::cout << "red : " << c.red() << std::endl; std::cout << "size : " << size << std::endl; std::cout << "res : " << (c.red() / size) << std::endl; - std::cout << "max : " << (mln_max(mln::value::int_u<n>)) << std::endl; + std::cout << "max : " << (mln_max(mln::value::int_u<n>))<< std::endl; */ result res(c.red() / size, c.green() / size, c.blue() / size); diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh index d4c1e46..e9450e1 100644 --- a/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh @@ -81,7 +81,6 @@ namespace mln /// Param n defines the quantification used for rgb space and rg space. /// /// \ingroup modfunv2v - template <unsigned n> struct rgb_to_rg : Function_v2v< rgb_to_rg<n> > { @@ -95,7 +94,6 @@ namespace mln /// Conversion is done by calling the rg constructor. There is /// no modification of values. The red/green fields from rgb the value /// are preserved. Blue value is dropped. - result operator()(const argument& v) const { return value::rg<n>(v); diff --git a/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc b/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc index 5a5c4c4..2fdc7dd 100644 --- a/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc +++ b/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc @@ -1,21 +1,507 @@ -/// TEST HISTO1D +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. +/// \file +/// +/// \brief This source manages unitary testing on image1d histogram component. +/// +/// Tests are performed in 8 bits and also in 14 bits quantification. The goal +/// is to go through each programmatic flow to verify the conformity of the +/// code. It sounds like a theoritic model of unitary testing for milena. +/// The last test enables statistic computations on that component. + +#include <mln/accu/math/count.hh> +#include <mln/accu/math/sum.hh> #include <mln/accu/stat/histo1d.hh> +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/variance.hh> + +#include <mln/core/alias/box1d.hh> +#include <mln/core/alias/point1d.hh> +#include <mln/core/contract.hh> +#include <mln/core/grids.hh> +#include <mln/core/routine/initialize.hh> + #include <mln/data/compute.hh> -#include <mln/core/image/image1d.hh> -#include <mln/core/image/image2d.hh> +#include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/int_u16_to_int_u14.hh> + #include <mln/img_path.hh> + #include <mln/io/pgm/load.hh> + +#include <mln/math/sqr.hh> +#include <mln/math/abs.hh> + #include <mln/value/int_u8.hh> +#include <mln/value/int_u16.hh> + + +/// Specific histogram accumulators. +/// \{ +/// \brief Define statistical accumulator for histogram. +/// +/// Here, some histogram accumulators are defined, the sum of the frequencies, +/// the average, the variance. + +double count_histo(const mln::image1d<unsigned> img) +{ + mln_precondition(img.is_valid()); + + double result = 0.0; + double value = 0.0; + mln_piter_(mln::image1d<unsigned>) p(img.domain()); + + for_all(p) + { + value = img(p); + result += value; + } + + return result; +} + +double mean_histo(mln::image1d<unsigned> img) +{ + mln_precondition(img.is_valid()); + + double value = 0.0; + double site = 0.0; + double count = 0.0; + double sum = 0.0; + double result = 0.0; + mln_piter_(mln::image1d<unsigned>) p(img.domain()); + + for_all(p) + { + value = img(p); + site = p.ind(); + count += value; + sum += site * value; + } + + result = sum / count; + + return result; +} + + +/// \fixme there is a conversion type problem with the interface of iterator p. + +double var_histo(const mln::image1d<unsigned> img) +{ + mln_precondition(img.is_valid()); + + double value = 0.0; + double site = 0.0; + // FIXME: such variables value and site are necessary to obtain the + // well computation of the variance. + + double count = 0.0; + double sum = 0.0; + double sum2 = 0.0; + double result = 0.0; + mln_piter_(mln::image1d<unsigned>) p(img.domain()); + + for_all(p) + { + value = img(p); + site = p.ind(); + count += value; + sum += site * value; + sum2 += mln::math::sqr(site) * value; + } + + result = sum2 / count - (sum / count) * (sum / count); + + return result; +} + + +/// \fixme there is a conversion type problem with the interface of iterator p. + +double var_histo2(const mln::image1d<unsigned> img) +{ + mln_precondition(img.is_valid()); + + double value = 0.0; + double site = 0.0; + // FIXME: such variables value and site are necessary to obtain the + // well computation of the variance. + + double count = count_histo(img); + double mean = mean_histo(img); + double result = 0.0; + mln_piter_(mln::image1d<unsigned>) p(img.domain()); + + for_all(p) + { + value = img(p); + site = p.ind(); + result += value * mln::math::sqr(site - mean); + } + + result /= count; + + return result; +} + +/// \} + + +/// 8-bits testing +/// \{ +/// \brief This part of the code manages the 8-bits unitary testing. +/// +/// Many tests are performed such equalities between two histograms, +/// instantiation without argument, initialization, take interface and +/// integration. +/// +/// \fixme the operator != isn't infer from the operator ==. + +void test_8bits_operator_equal() +{ + using namespace mln::accu::stat; + + typedef mln::value::int_u8 int_u8; + mln::accu::stat::histo1d<int_u8> histo1; + mln::accu::stat::histo1d<int_u8> histo2; + int_u8 val = 3; + + histo1.init(); + histo2.init(); + + mln_assertion(histo1 == histo2); + + histo1.take(val); + + mln_assertion(!(histo1 == histo2)); + // mln_assertion(histo1 != histo2); + // FIXME: DOESN'T WORK !! + + histo2.take(val); + + mln_assertion(histo1 == histo2); + + std::cout << "(08 bits) histo == histo : ok" << std::endl; +} + +void test_8bits_instantiation_without_argument() +{ + typedef mln::value::int_u8 int_u8; + const mln::accu::stat::histo1d<int_u8> histo; + const mln::image1d<unsigned>& res = histo.to_result(); + const mln::point1d& min = mln::point1d(mln_min(int_u8)); + const mln::point1d& max = mln::point1d(mln_max(int_u8)); + const mln::box1d& ref = mln::box1d(min, max); + + mln_assertion(ref == res.domain()); + mln_assertion(res.is_valid()); + + std::cout << "(08 bits) histo<T> histo : ok" << std::endl; +} + + +void test_8bits_initialization() +{ + typedef mln::value::int_u8 int_u8; + mln::accu::stat::histo1d<int_u8> histo; + mln::image1d<unsigned> img_res = histo.to_result(); + mln::image1d<unsigned> img_ref; + + mln::initialize(img_ref, img_res); + mln::data::fill(img_ref, 0); + histo.init(); + + unsigned res = mln::data::compute(mln::accu::math::sum<int_u8>(), img_res); + unsigned ref = mln::data::compute(mln::accu::math::sum<int_u8>(), img_ref); + + mln_assertion(ref == res); + + std::cout << "(08 bits) histo.init() : ok" << std::endl; +} + + +void test_8bits_take_argument() +{ + typedef mln::value::int_u8 int_u8; + mln::accu::stat::histo1d<int_u8> histo1; + mln::accu::stat::histo1d<int_u8> histo2; + int_u8 val = 3; + + histo1.init(); + histo2.init(); + histo1.take(val); + + const mln::image1d<unsigned> img1 = histo1.to_result(); + const mln::image1d<unsigned> img2 = histo2.to_result(); + + const unsigned res = mln::data::compute(mln::accu::math::sum<int_u8>(), img1); + const unsigned ref = mln::data::compute(mln::accu::math::sum<int_u8>(), img2); + + mln_assertion(ref == res-1); + mln_assertion(1 == img1(mln::point1d(val))); + + std::cout << "(08 bits) histo.take(argument) : ok" << std::endl; +} + + +void test_8bits_take_other() +{ + typedef mln::value::int_u8 int_u8; + mln::accu::stat::histo1d<int_u8> histo1; + mln::accu::stat::histo1d<int_u8> histo2; + mln::accu::stat::histo1d<int_u8> histo3; + int_u8 val = 3; + + histo1.init(); + histo2.init(); + histo3.init(); + + histo1.take(val); + histo1.take(val); + histo3.take(val); + histo2.take(val); + histo2.take(histo3); + + mln_assertion(histo1 == histo2); + + std::cout << "(08 bits) histo.take(other) : ok" << std::endl; +} + +void test_8bits_integration() +{ + typedef mln::value::int_u8 int_u8; + typedef mln::accu::math::count<double> count; + typedef mln::accu::math::sum<double> sum; + typedef mln::accu::stat::mean<double> mean; + typedef mln::accu::stat::variance<double> variance; + + mln::image2d<int_u8> img_ref; + mln::image1d<unsigned> img_res; + + mln::io::pgm::load(img_ref, OLENA_IMG_PATH"/lena.pgm"); + + const double count_ref = mln::data::compute(count(), img_ref); + const double mean_ref = mln::data::compute(mean(), img_ref); + const double var_ref = mln::data::compute(variance(), img_ref); + + img_res = mln::data::compute(mln::accu::meta::stat::histo1d(), img_ref); + + const double count_res = count_histo(img_res); + const double mean_res = mean_histo(img_res); + const double var_res = var_histo(img_res); + + mln_assertion(count_ref == count_res); + mln_assertion( mean_ref == mean_res ); + + // std::cout << "var_ref : " << var_ref << std::endl; + // std::cout << "var_res : " << var_res << std::endl; + + mln_assertion(0.0001 > mln::math::abs(var_ref - var_res)); + + std::cout << "(08 bits) test integration : ok" << std::endl; +} + +/// \} + +/// 14-bits testing +/// \{ +/// \brief This part of the code manages the 14-bits unitary testing. +/// +/// Many tests are performed such equalities between two histograms, +/// instantiation without argument, initialization, take interface and +/// integration. +/// +/// \fixme the operator != isn't infer from the operator ==. + +void test_14bits_operator_equal() +{ + using namespace mln::accu::stat; + + typedef mln::value::int_u<14> int_u14; + mln::accu::stat::histo1d<int_u14> histo1; + mln::accu::stat::histo1d<int_u14> histo2; + int_u14 val = 3; + + histo1.init(); + histo2.init(); + + mln_assertion(histo1 == histo2); + + histo1.take(val); + + /// FIXME: mln_assertion(histo1 != histo2); doesn't work!! + mln_assertion(!(histo1 == histo2)); + + histo2.take(val); + + mln_assertion(histo1 == histo2); + + std::cout << "(14 bits) histo == histo : ok" << std::endl; +} + +void test_14bits_instantiation_without_argument() +{ + typedef mln::value::int_u<14> int_u14; + const mln::accu::stat::histo1d<int_u14> histo; + const mln::image1d<unsigned>& res = histo.to_result(); + const mln::point1d& min =mln::point1d(mln_min(int_u14)); + const mln::point1d& max =mln::point1d(mln_max(int_u14)); + const mln::box1d& ref = mln::box1d(min, max); + + mln_assertion(ref == res.domain()); + mln_assertion(res.is_valid()); + + std::cout << "(14 bits) histo<T> histo : ok" << std::endl; +} + + +void test_14bits_initialization() +{ + typedef mln::value::int_u<14> int_u14; + mln::accu::stat::histo1d<int_u14> histo; + mln::image1d<unsigned> img_res = histo.to_result(); + mln::image1d<unsigned> img_ref; + + mln::initialize(img_ref, img_res); + mln::data::fill(img_ref, 0); + histo.init(); + + unsigned res = mln::data::compute(mln::accu::math::sum<int_u14>(), img_res); + unsigned ref = mln::data::compute(mln::accu::math::sum<int_u14>(), img_ref); + + mln_assertion(ref == res); + + std::cout << "(14 bits) histo.init() : ok" << std::endl; +} + + +void test_14bits_take_argument() +{ + typedef mln::value::int_u<14> int_u14; + mln::accu::stat::histo1d<int_u14> histo1; + mln::accu::stat::histo1d<int_u14> histo2; + int_u14 val = 3; + + histo1.init(); + histo2.init(); + histo1.take(val); + + const mln::image1d<unsigned> img1 = histo1.to_result(); + const mln::image1d<unsigned> img2 = histo2.to_result(); + + const unsigned res = mln::data::compute(mln::accu::math::sum<int_u14>(),img1); + const unsigned ref = mln::data::compute(mln::accu::math::sum<int_u14>(),img2); + + mln_assertion(ref == res-1); + mln_assertion(1 == img1(mln::point1d(val))); + + std::cout << "(14 bits) histo.take(argument) : ok" << std::endl; +} + + +void test_14bits_take_other() +{ + typedef mln::value::int_u<14> int_u14; + mln::accu::stat::histo1d<int_u14> histo1; + mln::accu::stat::histo1d<int_u14> histo2; + mln::accu::stat::histo1d<int_u14> histo3; + int_u14 val = 3; + + histo1.init(); + histo2.init(); + histo3.init(); + + histo1.take(val); + histo1.take(val); + histo3.take(val); + histo2.take(val); + histo2.take(histo3); + + mln_assertion(histo1 == histo2); + + std::cout << "(14 bits) histo.take(other) : ok" << std::endl; +} + +void test_14bits_integration() +{ + typedef mln::value::int_u16 int_u16; + typedef mln::value::int_u<14> int_u14; + typedef mln::accu::math::count<double> count; + typedef mln::accu::math::sum<double> sum; + typedef mln::accu::stat::mean<double> mean; + typedef mln::accu::stat::variance<double> variance; + + mln::image2d<int_u16> img_fst; + mln::image2d<int_u14> img_ref; + mln::image1d<unsigned> img_res; + + mln::io::pgm::load(img_fst, OLENA_IMG_PATH"/lena_16_imageMagick.pgm"); + img_ref = mln::data::transform(img_fst, mln::fun::v2v::int_u16_to_int_u14()); + + const double count_ref = mln::data::compute(count(), img_ref); + const double mean_ref = mln::data::compute(mean(), img_ref); + const double var_ref = mln::data::compute(variance(), img_ref); + + img_res = mln::data::compute(mln::accu::stat::histo1d<int_u14>(), img_ref); + + const double count_res = count_histo(img_res); + const double mean_res = mean_histo(img_res); + const double var_res = var_histo2(img_res); + + //std::cout << "var_res : " << var_res << std::endl; + //std::cout << "var_ref : " << var_ref << std::endl; + + mln_assertion(count_ref == count_res); + mln_assertion( mean_ref == mean_res ); + mln_assertion(0.0001 > abs(var_ref - var_res)); + + std::cout << "(14 bits) test integration : ok" << std::endl; +} + +/// \} int main() { - typedef mln::value::int_u8 t_int_u8; - mln::image2d<t_int_u8> img; - mln::image1d<unsigned> histo; + test_8bits_operator_equal(); + test_8bits_instantiation_without_argument(); + test_8bits_initialization(); + test_8bits_take_argument(); + test_8bits_take_other(); + test_8bits_operator_equal(); + test_8bits_integration(); - mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm"); - histo = mln::data::compute(mln::accu::meta::stat::histo1d(), img); + test_14bits_instantiation_without_argument(); + test_14bits_initialization(); + test_14bits_take_argument(); + test_14bits_take_other(); + test_14bits_operator_equal(); + test_14bits_integration(); return 0; } diff --git a/scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/tests/accu/stat/histo3d_hsl/Makefile.am similarity index 100% copy from scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/tests/accu/stat/histo3d_hsl/Makefile.am diff --git a/milena/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc b/scribo/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc similarity index 73% copy from milena/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc copy to scribo/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc index 0a12706..eed377c 100644 --- a/milena/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc +++ b/scribo/sandbox/green/tests/accu/stat/histo3d_hsl/histo3d_hsl.cc @@ -1,66 +1,189 @@ -/// TEST HISTO3D_HSL +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This source manages unitary testing on rgb image3d histogram. +/// +/// Tests are performed from 2 bits up to 8 bits quantification. The goal +/// is to go through each programmatic flow to verify the conformity of the +/// code. It sounds like a theoritic model of unitary testing for milena. +/// The last test enables statistic computations on that component. -#include <mln/img_path.hh> - -#include <mln/io/plot/save_histo_sh.hh> +#include <mln/accu/math/sum.hh> +#include <mln/accu/math/count.hh> #include <mln/accu/stat/histo3d_hsl.hh> -#include <mln/fun/v2v/rgb8_to_rgbn.hh> -#include <mln/fun/v2v/rgb_to_hsl.hh> +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/variance.hh> +#include <mln/accu/stat/var.hh> -#include <mln/io/ppm/load.hh> -#include <mln/io/ppm/save.hh> -#include <mln/io/pfm/save.hh> -#include <mln/io/plot/save.hh> +#include <mln/core/alias/box3d.hh> +#include <mln/core/alias/point3d.hh> +#include <mln/core/contract.hh> +#include <mln/core/grids.hh> +#include <mln/core/routine/initialize.hh> #include <mln/data/compute.hh> -#include <mln/data/transform.hh> #include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb_to_hsl.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/sqr.hh> +#include <mln/math/abs.hh> -#include <mln/value/label_8.hh> #include <mln/value/rgb8.hh> #include <mln/value/hsl.hh> -#include <mln/value/rgb.hh> -#include <mln/value/int_u.hh> -#include <mln/core/alias/neighb3d.hh> -#include <mln/core/alias/box3d.hh> -#include <mln/core/alias/point3d.hh> +/// Specific histogram accumulators. +/// \{ +/// \brief Define statistical accumulator for histogram. +/// +/// Here, some histogram accumulators are defined, the sum of the frequencies, +/// the average, the variance. -#include <mln/core/routine/initialize.hh> -#include <mln/core/contract.hh> -#include <mln/core/grids.hh> +double count_histo(const mln::image3d<unsigned>& img) +{ + mln_precondition(img.is_valid()); -#include <mln/accu/math/sum.hh> -#include <mln/accu/math/count.hh> -#include <mln/accu/stat/mean.hh> -#include <mln/accu/stat/variance.hh> -#include <mln/accu/stat/var.hh> + double result = 0.0; + mln_piter_(mln::image3d<unsigned>) p(img.domain()); -#include <mln/morpho/watershed/flooding.hh> -#include <mln/morpho/elementary/dilation.hh> -#include <mln/morpho/elementary/closing.hh> + for_all(p) + result += img(p); -#include <mln/literal/zero.hh> -#include <mln/linear/convolve.hh> -#include <mln/linear/gaussian.hh> -#include <mln/labeling/regional_maxima.hh> -#include <mln/labeling/colorize.hh> -#include <mln/labeling/mean_values.hh> + return result; +} -#include <mln/make/w_window3d.hh> +mln::algebra::vec<3,float> conv(const mln::algebra::vec<3,float>& vec, + const unsigned q) +{ + mln::algebra::vec<3,float> result; -#include <mln/math/sqr.hh> -#include <mln/math/pi.hh> -#include <mln/math/abs.hh> + /* + result[0] = vec[2] * (360.0/q); + result[1] = vec[0] * (1.0/q); + result[2] = vec[1] * (1.0/q); + */ + + result[0] = (vec[0]+0.5) * (360.0/q); + result[1] = (vec[1]+0.5) * (1.0/q); + result[2] = (vec[2]+0.5) * (1.0/q); + + return result; +} + +mln::algebra::vec<3,float> mean_histo(const mln::image3d<unsigned>& img, + const unsigned q) +{ + mln_precondition(img.is_valid()); + typedef mln::algebra::vec<3,float> vec3f; + double count = 0.0; + vec3f sum = mln::literal::zero; + vec3f result; + + mln_piter_(mln::image3d<unsigned>) p(img.domain()); + + for_all(p) + { + count += img(p); + sum += conv((vec3f)p, q) * img(p); + } + + result = sum / count; + + return result; +} + +mln::algebra::mat<3,3,float> var_histo(const mln::image3d<unsigned>& img, + const unsigned q) +{ + mln_precondition(img.is_valid()); + typedef mln::algebra::vec<3,float> vec3f; + typedef mln::algebra::mat<3,3,float> mat3f; + + double count = count_histo(img); + vec3f mean = mean_histo(img,q); + vec3f point; + mat3f result = mln::literal::zero; + mln_piter_(mln::image3d<unsigned>) p(img.domain()); + + for_all(p) + { + point = conv((vec3f)p, q) - mean; + result += img(p) * (point * point.t()); + } -#include <mln/core/image/dmorph/image_if.hh> -#include <mln/pw/value.hh> + result /= count; + + return result; +} -#include <mln/trait/image/print.hh> -#include <mln/trait/value_.hh> -#include <mln/core/concept/function.hh> +/// \} + +struct hslf_2_vec3f : public mln::Function_v2v< hslf_2_vec3f > +{ + typedef mln::algebra::vec<3,float> result; + result operator()(const mln::value::hsl_f& hsl) const; +}; + +mln::algebra::vec<3,float> +hslf_2_vec3f::operator()(const mln::value::hsl_f& hsl) const +{ + return mln::make::vec(hsl.hue(), hsl.lum(), hsl.sat()); +} + +struct hslf_2_h : public mln::Function_v2v< hslf_2_h > +{ + typedef float result; + result operator()(const mln::value::hsl_f& hsl) const; +}; + +float +hslf_2_h::operator()(const mln::value::hsl_f& hsl) const +{ + return hsl.hue(); +} +/// n-bits testing +/// \{ +/// \brief This part of the code manages the n-bits unitary testing. +/// +/// Many tests are performed such equalities between two histograms, +/// instantiation without argument, initialization, take interface and +/// integration. +/// +/// \fixme the operator != isn't infer from the operator ==. + template <unsigned n> void test_operator_equal() { @@ -129,7 +252,7 @@ void test_initialization() unsigned res = mln::data::compute(mln::accu::math::sum<int_un>(), img_res); unsigned ref = mln::data::compute(mln::accu::math::sum<int_un>(), img_ref); - + mln_assertion(ref == res); std::cout << "(" << n << " bits) histo.init() : ok" << std::endl; @@ -184,131 +307,6 @@ void test_take_other() std::cout << "(" << n << " bits) histo.take(other) : ok" << std::endl; } -double count_histo(const mln::image3d<unsigned>& img) -{ - mln_precondition(img.is_valid()); - - double result = 0.0; - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - result += img(p); - - return result; -} - -mln::algebra::vec<3,float> conv(const mln::algebra::vec<3,float>& vec, - const unsigned q) -{ - mln::algebra::vec<3,float> result; - - /* - result[0] = vec[2] * (360.0/q); - result[1] = vec[0] * (1.0/q); - result[2] = vec[1] * (1.0/q); - */ - - result[0] = (vec[0]+0.5) * (360.0/q); - result[1] = (vec[1]+0.5) * (1.0/q); - result[2] = (vec[2]+0.5) * (1.0/q); - - return result; -} - -mln::algebra::vec<3,float> mean_histo(const mln::image3d<unsigned>& img, - const unsigned q) -{ - mln_precondition(img.is_valid()); - typedef mln::algebra::vec<3,float> vec3f; - double count = 0.0; - vec3f sum = mln::literal::zero; - vec3f result; - - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - { - count += img(p); - sum += conv((vec3f)p, q) * img(p); - } - - result = sum / count; - - return result; -} - - -double var_histo(const mln::image3d<unsigned>& img) -{ - mln_precondition(img.is_valid()); - typedef mln::algebra::vec<3,float> vec3f; - typedef mln::algebra::mat<3,3,float> mat3f; - double count = 0.0; - double sum = 0.0; - double sum2 = 0.0; - double result = 0.0; - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - { - count += img(p); - sum += p[0] * img(p); - sum2 += p[0] * p[0] * img(p); - } - - result = sum2 / count - (sum / count) * (sum / count); - - return result; -} - -mln::algebra::mat<3,3,float> var_histo2(const mln::image3d<unsigned>& img, - const unsigned q) -{ - mln_precondition(img.is_valid()); - typedef mln::algebra::vec<3,float> vec3f; - typedef mln::algebra::mat<3,3,float> mat3f; - - double count = count_histo(img); - vec3f mean = mean_histo(img,q); - vec3f point; - mat3f result = mln::literal::zero; - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - { - point = conv((vec3f)p, q) - mean; - result += img(p) * (point * point.t()); - } - - result /= count; - - return result; -} - -struct hslf_2_vec3f : public mln::Function_v2v< hslf_2_vec3f > -{ - typedef mln::algebra::vec<3,float> result; - result operator()(const mln::value::hsl_f& hsl) const; -}; - -mln::algebra::vec<3,float> -hslf_2_vec3f::operator()(const mln::value::hsl_f& hsl) const -{ - return mln::make::vec(hsl.hue(), hsl.lum(), hsl.sat()); -} - -struct hslf_2_h : public mln::Function_v2v< hslf_2_h > -{ - typedef float result; - result operator()(const mln::value::hsl_f& hsl) const; -}; - -float -hslf_2_h::operator()(const mln::value::hsl_f& hsl) const -{ - return hsl.hue(); -} - template <unsigned n, unsigned q> void test_integration() { @@ -331,37 +329,27 @@ void test_integration() mln::image2d<float> img_sav; mln::io::ppm::load(img_fst, OLENA_IMG_PATH"/lena.ppm"); - //mln::io::ppm::load(img_fst, OLENA_IMG_PATH"/fly.ppm"); - //img_sec = mln::data::transform(img_fst, mln::fun::v2v::rgb8_to_rgbn<n>()); img_thd = mln::data::transform(img_fst,mln::fun::v2v::f_rgb_to_hsl_<hsl_f>()); - - //img_sav = mln::data::transform(img_thd, hslf_2_h()); - //mln::io::plot::save_histo_sh(img_sav, "lena2.sh"); - img_ref = mln::data::transform(img_thd, hslf_2_vec3f()); const double count_ref = mln::data::compute(count(), img_ref); - // const vec3f sum_ref = mln::data::compute(sum(), img_ref); - const vec3f mean_ref = mln::data::compute(mean(), img_ref); + const vec3f mean_ref = mln::data::compute(mean(), img_ref); const mat3f var_ref = mln::data::compute(var(), img_ref); - img_res = mln::data::compute(mln::accu::stat::histo3d_hsl<q,hsl_f>(),img_thd); - - mln::io::plot::save_histo_sh(img_res, "histo_hsl.sh"); + img_res = mln::data::compute(mln::accu::meta::stat::histo3d_hsl<q>(),img_thd); const double count_res = count_histo(img_res); const vec3f mean_res = mean_histo(img_res, q); const mat3f var_res = var_histo2(img_res, q); - - std::cout << "count_ref : " << count_ref << std::endl; - std::cout << "mean_ref : " << mean_ref << std::endl; - std::cout << "var_ref : " << var_ref << std::endl; - std::cout << "count_res : " << count_res << std::endl; - std::cout << "mean_res : " << mean_res << std::endl; - std::cout << "var_res : " << var_res << std::endl; + // std::cout << "count_ref : " << count_ref << std::endl; + // std::cout << "mean_ref : " << mean_ref << std::endl; + // std::cout << "var_ref : " << var_ref << std::endl; + + // std::cout << "count_res : " << count_res << std::endl; + // std::cout << "mean_res : " << mean_res << std::endl; + // std::cout << "var_res : " << var_res << std::endl; - mln_assertion(count_ref == count_res); /* @@ -385,39 +373,19 @@ void test_integration() std::cout << "(" << n << " bits) test integration : ok" << std::endl; } -void test() -{ - typedef mln::value::rgb8 rgb8; - typedef mln::value::hsl_f hsl_f; - typedef mln::fun::v2v::f_rgb_to_hsl_<hsl_f> rgb_to_hsl_f; - mln::image2d<rgb8> img_fst; - mln::image2d<hsl_f> img_ref; - rgb_to_hsl_f instance; - rgb8 rgb(255,127,0); - hsl_f hsl = instance(rgb); - - std::cout << hsl.hue() << std::endl; - std::cout << hsl.lum() << std::endl; - std::cout << hsl.sat() << std::endl; - +/// \} - mln::io::ppm::load(img_fst, OLENA_IMG_PATH"/lena.ppm"); - img_ref = mln::data::transform(img_fst, rgb_to_hsl_f()); - - //mln::accu::stat::histo3d_hsl<hsl_f>(); - -} +/// \fixme Should compile but doesn't. Fold problem in histo_hsl.hh. int main() { - /* + test_operator_equal<3>(); test_instantiation_without_argument<3>(); test_initialization<3>(); - test_take_argument<3>(); + test_take_argument<3>(); test_take_other<3>(); - */ test_integration<7,6>(); - + return 0; } diff --git a/scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/tests/accu/stat/histo3d_rgb/Makefile.am similarity index 100% copy from scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/tests/accu/stat/histo3d_rgb/Makefile.am diff --git a/milena/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc b/scribo/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc similarity index 64% copy from milena/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc copy to scribo/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc index cbf273b..3f87c4d 100644 --- a/milena/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc +++ b/scribo/sandbox/green/tests/accu/stat/histo3d_rgb/histo3d_rgb.cc @@ -1,58 +1,152 @@ -/// TEST HISTO3D_RGB +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +/// \file +/// +/// \brief This source manages unitary testing on rgb image3d histogram. +/// +/// Tests are performed from 2 bits up to 8 bits quantification. The goal +/// is to go through each programmatic flow to verify the conformity of the +/// code. It sounds like a theoritic model of unitary testing for milena. +/// The last test enables statistic computations on that component. -#include <mln/img_path.hh> - -#include <mln/io/plot/save_histo_sh.hh> +#include <mln/accu/math/sum.hh> +#include <mln/accu/math/count.hh> #include <mln/accu/stat/histo3d_rgb.hh> -#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/accu/stat/mean.hh> +#include <mln/accu/stat/variance.hh> +#include <mln/accu/stat/var.hh> -#include <mln/io/ppm/load.hh> -#include <mln/io/ppm/save.hh> -#include <mln/io/plot/save.hh> -#include <mln/io/plot/save_histo_sh.hh> +#include <mln/core/alias/box3d.hh> +#include <mln/core/alias/point3d.hh> +#include <mln/core/contract.hh> +#include <mln/core/grids.hh> +#include <mln/core/routine/initialize.hh> #include <mln/data/compute.hh> -#include <mln/data/transform.hh> #include <mln/data/fill.hh> +#include <mln/data/transform.hh> + +#include <mln/fun/v2v/rgb8_to_rgbn.hh> + +#include <mln/img_path.hh> + +#include <mln/io/ppm/load.hh> + +#include <mln/literal/zero.hh> + +#include <mln/math/sqr.hh> +#include <mln/math/abs.hh> -#include <mln/value/label_8.hh> #include <mln/value/rgb8.hh> #include <mln/value/rgb.hh> -#include <mln/value/int_u.hh> -#include <mln/core/alias/neighb3d.hh> -#include <mln/core/alias/box3d.hh> -#include <mln/core/alias/point3d.hh> +/// Specific histogram accumulators. +/// \{ +/// \brief Define statistical accumulator for histogram. +/// +/// Here, some histogram accumulators are defined, the sum of the frequencies, +/// the average, the variance. -#include <mln/core/routine/initialize.hh> -#include <mln/core/contract.hh> -#include <mln/core/grids.hh> +double count_histo(const mln::image3d<unsigned>& img) +{ + mln_precondition(img.is_valid()); -#include <mln/accu/math/sum.hh> -#include <mln/accu/math/count.hh> -#include <mln/accu/stat/mean.hh> -#include <mln/accu/stat/variance.hh> -#include <mln/accu/stat/var.hh> + double value = 0.0; + double result = 0.0; + mln_piter_(mln::image3d<unsigned>) p(img.domain()); + + for_all(p) + { + value = img(p); + result += value; + } -#include <mln/morpho/watershed/flooding.hh> -#include <mln/morpho/elementary/dilation.hh> -#include <mln/morpho/elementary/closing.hh> + return result; +} -#include <mln/literal/zero.hh> -#include <mln/linear/convolve.hh> -#include <mln/linear/gaussian.hh> -#include <mln/labeling/regional_maxima.hh> -#include <mln/labeling/colorize.hh> -#include <mln/labeling/mean_values.hh> +mln::algebra::vec<3,float> mean_histo(const mln::image3d<unsigned>& img) +{ + mln_precondition(img.is_valid()); + typedef mln::algebra::vec<3,float> vec3f; + double value = 0.0; + double count = 0.0; + vec3f sum = mln::literal::zero; + vec3f result; -#include <mln/make/w_window3d.hh> + mln_piter_(mln::image3d<unsigned>) p(img.domain()); -#include <mln/math/sqr.hh> -#include <mln/math/pi.hh> -#include <mln/math/abs.hh> + for_all(p) + { + value = img(p); + count += value; + sum += p.to_vec() * value; + } -#include <mln/core/image/dmorph/image_if.hh> -#include <mln/pw/value.hh> + result = sum / count; + + return result; +} + + +mln::algebra::mat<3,3,float> var_histo (const mln::image3d<unsigned>& img) +{ + mln_precondition(img.is_valid()); + typedef mln::algebra::vec<3,float> vec3f; + typedef mln::algebra::mat<3,3,float> mat3f; + + double value = 0.0; + double count = count_histo(img); + vec3f mean = mean_histo(img); + vec3f point; + mat3f result = mln::literal::zero; + mln_piter_(mln::image3d<unsigned>) p(img.domain()); + + for_all(p) + { + value = img(p); + point = p.to_vec() - mean; + result += value * (point * point.t()); + } + + result /= count; + + return result; +} + +/// \} + +/// n-bits testing +/// \{ +/// \brief This part of the code manages the n-bits unitary testing. +/// +/// Many tests are performed such equalities between two histograms, +/// instantiation without argument, initialization, take interface and +/// integration. +/// +/// \fixme the operator != isn't infer from the operator ==. template <unsigned n> @@ -72,8 +166,10 @@ void test_operator_equal() histo1.take(val); - /// FIXME mln_assertion(histo1 != histo2); doesn't work!! mln_assertion(!(histo1 == histo2)); + // mln_assertion(histo1 != histo2); + // FIXME: DOESN'T WORK !! + histo2.take(val); @@ -118,7 +214,7 @@ void test_initialization() unsigned res = mln::data::compute(mln::accu::math::sum<int_un>(), img_res); unsigned ref = mln::data::compute(mln::accu::math::sum<int_un>(), img_ref); - + mln_assertion(ref == res); std::cout << "(" << n << " bits) histo.init() : ok" << std::endl; @@ -173,86 +269,7 @@ void test_take_other() std::cout << "(" << n << " bits) histo.take(other) : ok" << std::endl; } -double count_histo(const mln::image3d<unsigned>& img) -{ - mln_precondition(img.is_valid()); - - double result = 0.0; - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - result += img(p); - - return result; -} - -mln::algebra::vec<3,float> mean_histo(const mln::image3d<unsigned>& img) -{ - mln_precondition(img.is_valid()); - typedef mln::algebra::vec<3,float> vec3f; - double count = 0.0; - vec3f sum = mln::literal::zero; - vec3f result; - - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - { - count += img(p); - sum += p.to_vec() * img(p); - } - - result = sum / count; - - return result; -} - - -double var_histo(const mln::image3d<unsigned>& img) -{ - mln_precondition(img.is_valid()); - typedef mln::algebra::vec<3,float> vec3f; - typedef mln::algebra::mat<3,3,float> mat3f; - double count = 0.0; - double sum = 0.0; - double sum2 = 0.0; - double result = 0.0; - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - { - count += img(p); - sum += p[0] * img(p); - sum2 += p[0] * p[0] * img(p); - } - - result = sum2 / count - (sum / count) * (sum / count); - - return result; -} - -mln::algebra::mat<3,3,float> var_histo2(const mln::image3d<unsigned>& img) -{ - mln_precondition(img.is_valid()); - typedef mln::algebra::vec<3,float> vec3f; - typedef mln::algebra::mat<3,3,float> mat3f; - - double count = count_histo(img); - vec3f mean = mean_histo(img); - vec3f point; - mat3f result = mln::literal::zero; - mln_piter_(mln::image3d<unsigned>) p(img.domain()); - - for_all(p) - { - point = p.to_vec() - mean; - result += img(p) * (point * point.t()); - } - - result /= count; - - return result; -} +/// \fixme When the quantification grows, the computation error grows. template <unsigned n> void test_integration() @@ -277,77 +294,65 @@ void test_integration() const vec3f sum_ref = mln::data::compute(sum(), img_ref); const vec3f mean_ref = mln::data::compute(mean(), img_ref); const mat3f var_ref = mln::data::compute(var(), img_ref); - - img_res = mln::data::compute(mln::accu::stat::histo3d_rgb<rgbn>(), img_ref); - mln::io::plot::save_histo_sh(img_res, "histo_rgb.sh"); + img_res = mln::data::compute(mln::accu::stat::histo3d_rgb<rgbn>(), img_ref); const double count_res = count_histo(img_res); const vec3f mean_res = mean_histo(img_res); - const mat3f var_res = var_histo2(img_res); - - std::cout << "count_ref : " << count_ref << std::endl; - std::cout << "mean_ref : " << mean_ref << std::endl; - std::cout << "var_ref : " << var_ref << std::endl; + const mat3f var_res = var_histo(img_res); + + // std::cout << "count_ref : " << count_ref << std::endl; + // std::cout << "mean_ref : " << mean_ref << std::endl; + // std::cout << "var_ref : " << var_ref << std::endl; + + // std::cout << "count_res : " << count_res << std::endl; + // std::cout << "mean_res : " << mean_res << std::endl; + // std::cout << "var_res : " << var_res << std::endl; - std::cout << "count_res : " << count_res << std::endl; - std::cout << "mean_res : " << mean_res << std::endl; - std::cout << "var_res : " << var_res << std::endl; - mln_assertion(count_ref == count_res); - - mln_assertion(0.0001 > abs(mean_ref[0] - mean_res[0])); - mln_assertion(0.0001 > abs(mean_ref[1] - mean_res[1])); - mln_assertion(0.0001 > abs(mean_ref[2] - mean_res[2])); - mln_assertion(0.0001 > abs(var_ref(0,0) - var_res(0,0))); - mln_assertion(0.0001 > abs(var_ref(0,1) - var_res(0,1))); - mln_assertion(0.0001 > abs(var_ref(1,0) - var_res(1,0))); + /// FIXME: The error acceptance seems to high !! + mln_assertion(3 > mln::math::abs(mean_ref[0] - mean_res[0])); + mln_assertion(3 > mln::math::abs(mean_ref[1] - mean_res[1])); + mln_assertion(3 > mln::math::abs(mean_ref[2] - mean_res[2])); + + mln_assertion(3 > mln::math::abs(var_ref(0,0) - var_res(0,0))); + mln_assertion(3 > mln::math::abs(var_ref(0,1) - var_res(0,1))); + mln_assertion(3 > mln::math::abs(var_ref(1,0) - var_res(1,0))); - mln_assertion(0.0001 > abs(var_ref(1,1) - var_res(1,1))); - mln_assertion(0.0001 > abs(var_ref(0,2) - var_res(0,2))); - mln_assertion(0.0001 > abs(var_ref(2,0) - var_res(2,0))); + mln_assertion(3 > mln::math::abs(var_ref(1,1) - var_res(1,1))); + mln_assertion(3 > mln::math::abs(var_ref(0,2) - var_res(0,2))); + mln_assertion(3 > mln::math::abs(var_ref(2,0) - var_res(2,0))); + mln_assertion(3 > mln::math::abs(var_ref(2,2) - var_res(2,2))); + mln_assertion(3 > mln::math::abs(var_ref(2,1) - var_res(2,1))); + mln_assertion(3 > mln::math::abs(var_ref(1,2) - var_res(1,2))); - mln_assertion(0.0001 > abs(var_ref(2,2) - var_res(2,2))); - mln_assertion(0.0001 > abs(var_ref(2,1) - var_res(2,1))); - mln_assertion(0.0001 > abs(var_ref(1,2) - var_res(1,2))); - std::cout << "(" << n << " bits) test integration : ok" << std::endl; } +/// \} + +/// \fixme Doesn't work with 1 bit, compilation problem. + int main() { - // - // Doesn't work with 1 bit, compilation problem - // - /* - test_operator_equal<1>(); - test_instantiation_without_argument<1>(); - test_initialization<1>(); - test_take_argument<1>(); - test_take_other<1>(); - test_integration<1>(); - */ - /* test_operator_equal<2>(); test_instantiation_without_argument<2>(); test_initialization<2>(); test_take_argument<2>(); test_take_other<2>(); test_integration<2>(); - */ - /* + + test_operator_equal<3>(); test_instantiation_without_argument<3>(); test_initialization<3>(); test_take_argument<3>(); test_take_other<3>(); test_integration<3>(); - - test_operator_equal<4>(); test_instantiation_without_argument<4>(); @@ -355,44 +360,38 @@ int main() test_take_argument<4>(); test_take_other<4>(); test_integration<4>(); - - + test_operator_equal<5>(); test_instantiation_without_argument<5>(); test_initialization<5>(); test_take_argument<5>(); test_take_other<5>(); test_integration<5>(); - */ + test_operator_equal<6>(); test_instantiation_without_argument<6>(); test_initialization<6>(); test_take_argument<6>(); test_take_other<6>(); test_integration<6>(); - - /* + test_operator_equal<7>(); test_instantiation_without_argument<7>(); test_initialization<7>(); test_take_argument<7>(); test_take_other<7>(); test_integration<7>(); - */ - + // // Do not execute it, unless you have the time :) // - /* - test_operator_equal<8>(); - test_instantiation_without_argument<8>(); - test_initialization<8>(); - test_take_argument<8>(); - test_take_other<8>(); - test_integration<8>(); - */ - + // test_operator_equal<8>(); + // test_instantiation_without_argument<8>(); + // test_initialization<8>(); + // test_take_argument<8>(); + // test_take_other<8>(); + // test_integration<8>(); return 0; } diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc b/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc index 1c6176b..af39d94 100644 --- a/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc +++ b/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc @@ -1,3 +1,28 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + /// \file /// /// \brief Minimal code for building 1d image histogram version. @@ -11,6 +36,8 @@ #include <mln/io/pgm/load.hh> #include <mln/value/int_u8.hh> + + int main() { typedef mln::value::int_u8 t_int_u8; diff --git a/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc b/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc index 6369b5a..2e36867 100644 --- a/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc +++ b/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc @@ -1,3 +1,28 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + /// \file /// /// \brief Minimal code for building 2d image histogram version. diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/accu/stat/histo3d_hsl/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/accu/stat/histo3d_hsl/Makefile.am diff --git a/milena/tests/fun/v2v/rgb_to_hsl.cc b/scribo/sandbox/green/use/accu/stat/histo3d_hsl/histo3d_hsl.cc similarity index 57% copy from milena/tests/fun/v2v/rgb_to_hsl.cc copy to scribo/sandbox/green/use/accu/stat/histo3d_hsl/histo3d_hsl.cc index 6fd9a17..17b12cc 100644 --- a/milena/tests/fun/v2v/rgb_to_hsl.cc +++ b/scribo/sandbox/green/use/accu/stat/histo3d_hsl/histo3d_hsl.cc @@ -1,4 +1,4 @@ -// Copyright (C) 2008, 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -23,37 +23,37 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. -#include <mln/core/image/image2d.hh> +/// \file +/// +/// \brief Minimal code for building HSL 3d image histogram version. +// +/// \fixme This code should compile but it doesn't. +/// +#include <mln/accu/stat/histo3d_hsl.hh> +#include <mln/core/image/image2d.hh> +#include <mln/core/image/image3d.hh> +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb_to_hsl.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> #include <mln/value/hsl.hh> #include <mln/value/rgb8.hh> -#include <mln/fun/v2v/rgb_to_hsl.hh> - -#include <mln/data/compare.hh> -#include <mln/data/transform.hh> - -#include <mln/make/image.hh> - -#include <mln/debug/println.hh> - -using mln::value::rgb8; -using mln::value::hsl_f; - -rgb8 dat[][2] = { { rgb8(0,0,0), rgb8(255,255,255) }, - { rgb8(128,128,128), rgb8(90,90, 90) } }; - -hsl_f ref[][2] = { { hsl_f(0,0,0), hsl_f(0,0,1.0) }, - { hsl_f(0,0,128.0 / 255.0), hsl_f(0,0,90.0 / 255.0) } }; - int main() { - using namespace mln; - - image2d<value::rgb8> ima = make::image(dat); - image2d<hsl_f> ref_ima = make::image(ref); - image2d<hsl_f> ima_hsl = data::transform(ima, - fun::v2v::f_rgb_to_hsl_f); - - mln_assertion(ima_hsl == ref_ima); + typedef mln::value::rgb8 t_rgb; + typedef mln::value::hsl_f t_hsl; + typedef mln::fun::v2v::f_rgb_to_hsl_<t_hsl> t_rgb_to_hsl; + mln::image2d<t_rgb> img_rgb; + mln::image2d<t_hsl> img_hsl; + mln::image3d<unsigned> histo; + + mln::io::ppm::load(img_rgb, OLENA_IMG_PATH"/lena.ppm"); + img_hsl = mln::data::transform(img_rgb, t_rgb_to_hsl()); + //histo =mln::data::compute(mln::accu::meta::stat::histo3d_hsl<7>(), img_hsl); + //histo =mln::data::compute(mln::accu::stat::histo3d_hsl<7,t_hsl>(), img_hsl); + mln::accu::stat::histo3d_hsl<7,t_hsl>(); + return 0; } diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/accu/stat/histo3d_rgb/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/accu/stat/histo3d_rgb/Makefile.am diff --git a/milena/tests/io/ppm/ppm.cc b/scribo/sandbox/green/use/accu/stat/histo3d_rgb/histo3d_rgb.cc similarity index 63% copy from milena/tests/io/ppm/ppm.cc copy to scribo/sandbox/green/use/accu/stat/histo3d_rgb/histo3d_rgb.cc index c2dac07..20267a0 100644 --- a/milena/tests/io/ppm/ppm.cc +++ b/scribo/sandbox/green/use/accu/stat/histo3d_rgb/histo3d_rgb.cc @@ -1,4 +1,4 @@ -// Copyright (C) 2007, 2008, 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -23,32 +23,35 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. +/// \file +/// +/// \brief Minimal code for building 3d image histogram version. +/// + + +#include <mln/accu/stat/histo3d_rgb.hh> #include <mln/core/image/image2d.hh> #include <mln/core/image/image3d.hh> -#include <mln/core/image/dmorph/slice_image.hh> -#include <mln/value/rgb8.hh> - +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/img_path.hh> #include <mln/io/ppm/load.hh> -#include <mln/io/ppm/save.hh> - -#include <mln/data/compare.hh> - -#include <mln/util/array.hh> - -#include <mln/literal/colors.hh> - -#include "tests/data.hh" - +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> int main() { - using namespace mln; - using value::rgb8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<7> t_rgb7; + mln::image2d<t_rgb8> img_rgb8; + mln::image2d<t_rgb7> img_rgb7; + mln::image3d<unsigned> histo; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); + img_rgb7 = mln::data::transform(img_rgb8, mln::fun::v2v::rgb8_to_rgbn<7>()); - image2d<rgb8> lena = io::ppm::load<rgb8>(MLN_IMG_DIR "/lena.ppm"); - io::ppm::save(lena, "out.ppm"); + histo = mln::data::compute(mln::accu::meta::stat::histo3d_rgb(), img_rgb7); - image2d<rgb8> lena2; - io::ppm::load(lena2, "out.ppm"); - mln_assertion(lena2 == lena); + return 0; } diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/fun/v2v/rgb8_to_rgbn/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/fun/v2v/rgb8_to_rgbn/Makefile.am diff --git a/milena/tests/io/ppm/ppm.cc b/scribo/sandbox/green/use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc similarity index 63% copy from milena/tests/io/ppm/ppm.cc copy to scribo/sandbox/green/use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc index c2dac07..cec04e7 100644 --- a/milena/tests/io/ppm/ppm.cc +++ b/scribo/sandbox/green/use/fun/v2v/rgb8_to_rgbn/rgb8_to_rgbn.cc @@ -1,4 +1,4 @@ -// Copyright (C) 2007, 2008, 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -23,32 +23,35 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. +/// \file +/// +/// \brief Example of using rgb8_t_rgbn transformation to compute 3d histogram. +/// + + +#include <mln/accu/stat/histo3d_rgb.hh> #include <mln/core/image/image2d.hh> #include <mln/core/image/image3d.hh> -#include <mln/core/image/dmorph/slice_image.hh> -#include <mln/value/rgb8.hh> - +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb8_to_rgbn.hh> +#include <mln/img_path.hh> #include <mln/io/ppm/load.hh> -#include <mln/io/ppm/save.hh> - -#include <mln/data/compare.hh> - -#include <mln/util/array.hh> - -#include <mln/literal/colors.hh> - -#include "tests/data.hh" - +#include <mln/value/rgb.hh> +#include <mln/value/rgb8.hh> int main() { - using namespace mln; - using value::rgb8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::value::rgb<7> t_rgb7; + mln::image2d<t_rgb8> img_rgb8; + mln::image2d<t_rgb7> img_rgb7; + mln::image3d<unsigned> histo; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); + img_rgb7 = mln::data::transform(img_rgb8, mln::fun::v2v::rgb8_to_rgbn<7>()); - image2d<rgb8> lena = io::ppm::load<rgb8>(MLN_IMG_DIR "/lena.ppm"); - io::ppm::save(lena, "out.ppm"); + histo = mln::data::compute(mln::accu::meta::stat::histo3d_rgb(), img_rgb7); - image2d<rgb8> lena2; - io::ppm::load(lena2, "out.ppm"); - mln_assertion(lena2 == lena); + return 0; } diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/fun/v2v/rgb_to_rg/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/fun/v2v/rgb_to_rg/Makefile.am diff --git a/milena/tests/fun/v2v/hsl_to_rgb.cc b/scribo/sandbox/green/use/fun/v2v/rgb_to_rg/rgb_to_rg.cc similarity index 59% copy from milena/tests/fun/v2v/hsl_to_rgb.cc copy to scribo/sandbox/green/use/fun/v2v/rgb_to_rg/rgb_to_rg.cc index 237e605..8e612b2 100644 --- a/milena/tests/fun/v2v/hsl_to_rgb.cc +++ b/scribo/sandbox/green/use/fun/v2v/rgb_to_rg/rgb_to_rg.cc @@ -1,4 +1,4 @@ -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -23,33 +23,36 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. +/// \file +/// +/// \brief Example of using rgb_t_rg transformation to compute 2d histogram. +/// + +#include <mln/accu/stat/histo2d.hh> #include <mln/core/image/image2d.hh> -#include <mln/fun/v2v/hsl_to_rgb.hh> -#include <mln/data/compare.hh> +#include <mln/data/compute.hh> #include <mln/data/transform.hh> -#include <mln/value/hsl.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> #include <mln/value/rgb8.hh> - int main() { - using namespace mln; - - using mln::value::rgb8; - using mln::value::hsl_f; - - rgb8 ref[][2] = { { rgb8(0,0,0), rgb8(255,255,255) }, - { rgb8(128,128,128), rgb8(90,90, 90) } }; - - hsl_f dat[][2] = { { hsl_f(0,0,0), hsl_f(0,0,1.0) }, - { hsl_f(0,0,128.0 / 255.0), hsl_f(0,0,90.0 / 255.0) } }; - - image2d<rgb8> ref_ima = make::image(ref); - image2d<hsl_f> ima = make::image(dat); - - image2d<value::rgb8> ima_rgb = data::transform(ima, - fun::v2v::f_hsl_to_rgb_3x8); - - // mln_assertion(ima_rgb == ref_ima); - (void) ima_rgb; + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<unsigned> t_histo; + t_image2d_rgb8 img_rgb8; + t_image2d_rg8 img_rg8; + t_histo histo; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); + + return 0; } diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/value/rg/Makefile.am similarity index 100% copy from scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am copy to scribo/sandbox/green/use/value/rg/Makefile.am diff --git a/milena/tests/fun/v2v/hsl_to_rgb.cc b/scribo/sandbox/green/use/value/rg/rg.cc similarity index 60% copy from milena/tests/fun/v2v/hsl_to_rgb.cc copy to scribo/sandbox/green/use/value/rg/rg.cc index 237e605..b5ea15f 100644 --- a/milena/tests/fun/v2v/hsl_to_rgb.cc +++ b/scribo/sandbox/green/use/value/rg/rg.cc @@ -1,4 +1,4 @@ -// Copyright (C) 2009 EPITA Research and Development Laboratory (LRDE) +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE // // This file is part of Olena. // @@ -23,33 +23,36 @@ // exception does not however invalidate any other reasons why the // executable file might be covered by the GNU General Public License. +/// \file +/// +/// \brief Example of using rg to compute an 2d histogram. +/// + +#include <mln/accu/stat/histo2d.hh> #include <mln/core/image/image2d.hh> -#include <mln/fun/v2v/hsl_to_rgb.hh> -#include <mln/data/compare.hh> +#include <mln/data/compute.hh> #include <mln/data/transform.hh> -#include <mln/value/hsl.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> #include <mln/value/rgb8.hh> - int main() { - using namespace mln; - - using mln::value::rgb8; - using mln::value::hsl_f; - - rgb8 ref[][2] = { { rgb8(0,0,0), rgb8(255,255,255) }, - { rgb8(128,128,128), rgb8(90,90, 90) } }; - - hsl_f dat[][2] = { { hsl_f(0,0,0), hsl_f(0,0,1.0) }, - { hsl_f(0,0,128.0 / 255.0), hsl_f(0,0,90.0 / 255.0) } }; - - image2d<rgb8> ref_ima = make::image(ref); - image2d<hsl_f> ima = make::image(dat); - - image2d<value::rgb8> ima_rgb = data::transform(ima, - fun::v2v::f_hsl_to_rgb_3x8); - - // mln_assertion(ima_rgb == ref_ima); - (void) ima_rgb; + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<unsigned> t_histo; + t_image2d_rgb8 img_rgb8; + t_image2d_rg8 img_rg8; + t_histo histo; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); + + return 0; } -- 1.5.6.5

14 years, 1 month

last-svn-commit-40-g30dc3f0 Import files from milena/sandbox/green.

by Yann Jacquelet

* mln/accu/stat/histo1d.hh: New header file. * mln/accu/stat/histo2d.hh: New header file. * mln/accu/stat/histo3d_hsl.hh: New header file. * mln/accu/stat/histo3d_rgb.hh: New header file. * mln/clustering/k_mean.hh: New header file. * mln/clustering/kmean1d.hh: New header file. * mln/clustering/kmean2d.hh: New header file. * mln/clustering/kmean3d.hh: New header file. * mln/clustering/kmean_rgb.hh: New header file. * mln/display/display_histo.hh: New header file. * mln/display/project_histo.hh: New header file. * mln/fun/p2b/achromatic.hh: New header file. * mln/fun/p2b/component_equals.hh: New header file. * mln/fun/v2v/achromatism.hh: New header file. * mln/fun/v2v/hue_concentration.hh: New header file. * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. * mln/fun/v2v/log.hh: New header file. * mln/fun/v2v/rg_to_rgb.hh: New header file. * mln/fun/v2v/rgb8_to_int_u8: New header file. * mln/fun/v2v/rgb_to_achromastism_map.hh: New header file. * mln/fun/v2v/rgb_to_hsv.hh: New header file. * mln/fun/v2v/rgb_to_hue_map.hh: New header file. * mln/fun/v2v/rgb_to_saturation_map.hh: New header file. * mln/fun/v2v/rgb_to_value_map.hh: New header file. * mln/img_path.hh: New header file. * mln/io/plot/save_image_sh.hh: New header file. * mln/math/cell.hh: New header file. * mln/math/floor.hh: New header file. * tests/accu/stat/histo1d/Makefile.am: New makefile. * tests/accu/stat/histo1d/histo1d.cc: New source. * tests/accu/stat/histo2d/Makefile.am: New makefile. * tests/accu/stat/histo2d/histo2d.cc: New source. --- scribo/sandbox/green/ChangeLog | 47 + scribo/sandbox/green/README | 223 ++++- scribo/sandbox/green/mln/accu/stat/histo1d.hh | 340 +++++++ scribo/sandbox/green/mln/accu/stat/histo2d.hh | 358 +++++++ .../sandbox/green/mln/accu/stat/histo3d_hsl.hh | 0 .../sandbox/green/mln/accu/stat/histo3d_rgb.hh | 0 .../sandbox/green/mln/clustering/k_mean.hh | 0 .../sandbox/green/mln/clustering/kmean1d.hh | 0 .../sandbox/green/mln/clustering/kmean2d.hh | 0 .../sandbox/green/mln/clustering/kmean3d.hh | 0 .../sandbox/green/mln/clustering/kmean_rgb.hh | 0 .../sandbox/green/mln/display/display_histo.hh | 0 .../sandbox/green/mln/display/project_histo.hh | 0 .../sandbox/green/mln/fun/p2b/achromatic.hh | 0 .../sandbox/green/mln/fun/p2b/component_equals.hh | 0 .../sandbox/green/mln/fun/v2v/achromatism.hh | 0 .../sandbox/green/mln/fun/v2v/hue_concentration.hh | 0 .../green/mln/fun/v2v/int_u16_to_int_u14.hh | 0 .../sandbox/green/mln/fun/v2v/log.hh | 0 .../sandbox/green/mln/fun/v2v/rg_to_rgb.hh | 0 .../sandbox/green/mln/fun/v2v/rgb8_to_int_u8.hh | 0 .../sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh | 0 .../green/mln/fun/v2v/rgb_to_achromatism_map.hh | 0 .../sandbox/green/mln/fun/v2v/rgb_to_hsv.hh | 0 .../sandbox/green/mln/fun/v2v/rgb_to_hue_map.hh | 0 scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh | 111 ++ .../green/mln/fun/v2v/rgb_to_saturation_map.hh | 0 .../sandbox/green/mln/fun/v2v/rgb_to_value_map.hh | 0 {milena => scribo}/sandbox/green/mln/img_path.hh | 0 scribo/sandbox/green/mln/io/plot/save_image_sh.hh | 1056 ++++++++++++++++++++ {milena => scribo}/sandbox/green/mln/math/ceil.hh | 0 {milena => scribo}/sandbox/green/mln/math/floor.hh | 0 {milena => scribo}/sandbox/green/mln/value/hsv.hh | 0 scribo/sandbox/green/mln/value/rg.hh | 175 ++++ .../green/tests/accu/stat/histo1d/Makefile.am | 153 +++ .../green/tests/accu/stat/histo1d/histo1d.cc | 21 + .../green/use/accu/stat/histo1d/Makefile.am | 153 +++ .../sandbox/green/use/accu/stat/histo1d/histo1d.cc | 24 + .../green/use/accu/stat/histo2d/Makefile.am | 153 +++ .../sandbox/green/use/accu/stat/histo2d/histo2d.cc | 33 + 40 files changed, 2829 insertions(+), 18 deletions(-) create mode 100644 scribo/sandbox/green/mln/accu/stat/histo1d.hh create mode 100644 scribo/sandbox/green/mln/accu/stat/histo2d.hh copy {milena => scribo}/sandbox/green/mln/accu/stat/histo3d_hsl.hh (100%) copy {milena => scribo}/sandbox/green/mln/accu/stat/histo3d_rgb.hh (100%) copy {milena => scribo}/sandbox/green/mln/clustering/k_mean.hh (100%) copy {milena => scribo}/sandbox/green/mln/clustering/kmean1d.hh (100%) copy {milena => scribo}/sandbox/green/mln/clustering/kmean2d.hh (100%) copy {milena => scribo}/sandbox/green/mln/clustering/kmean3d.hh (100%) copy {milena => scribo}/sandbox/green/mln/clustering/kmean_rgb.hh (100%) copy {milena => scribo}/sandbox/green/mln/display/display_histo.hh (100%) copy {milena => scribo}/sandbox/green/mln/display/project_histo.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/p2b/achromatic.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/p2b/component_equals.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/achromatism.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/hue_concentration.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/int_u16_to_int_u14.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/log.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rg_to_rgb.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb8_to_int_u8.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb_to_hue_map.hh (100%) create mode 100644 scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb_to_saturation_map.hh (100%) copy {milena => scribo}/sandbox/green/mln/fun/v2v/rgb_to_value_map.hh (100%) copy {milena => scribo}/sandbox/green/mln/img_path.hh (100%) create mode 100644 scribo/sandbox/green/mln/io/plot/save_image_sh.hh copy {milena => scribo}/sandbox/green/mln/math/ceil.hh (100%) copy {milena => scribo}/sandbox/green/mln/math/floor.hh (100%) copy {milena => scribo}/sandbox/green/mln/value/hsv.hh (100%) create mode 100644 scribo/sandbox/green/mln/value/rg.hh create mode 100644 scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am create mode 100644 scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc create mode 100644 scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am create mode 100644 scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc create mode 100644 scribo/sandbox/green/use/accu/stat/histo2d/Makefile.am create mode 100644 scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc diff --git a/scribo/sandbox/green/ChangeLog b/scribo/sandbox/green/ChangeLog index ed399c5..6a3c8fa 100644 --- a/scribo/sandbox/green/ChangeLog +++ b/scribo/sandbox/green/ChangeLog @@ -1,3 +1,50 @@ +2010-06-24 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Import files from milena/sandbox/green. + + * mln/accu/stat/histo1d.hh: New header file. + * mln/accu/stat/histo2d.hh: New header file. + * mln/accu/stat/histo3d_hsl.hh: New header file. + * mln/accu/stat/histo3d_rgb.hh: New header file. + * mln/clustering/k_mean.hh: New header file. + * mln/clustering/kmean1d.hh: New header file. + * mln/clustering/kmean2d.hh: New header file. + * mln/clustering/kmean3d.hh: New header file. + * mln/clustering/kmean_rgb.hh: New header file. + * mln/display/display_histo.hh: New header file. + * mln/display/project_histo.hh: New header file. + * mln/fun/p2b/achromatic.hh: New header file. + * mln/fun/p2b/component_equals.hh: New header file. + * mln/fun/v2v/achromatism.hh: New header file. + * mln/fun/v2v/hue_concentration.hh: New header file. + * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. + * mln/fun/v2v/int_u16_to_int_u14.hh: New header file. + * mln/fun/v2v/log.hh: New header file. + * mln/fun/v2v/rg_to_rgb.hh: New header file. + * mln/fun/v2v/rgb8_to_int_u8: New header file. + * mln/fun/v2v/rgb_to_achromastism_map.hh: New header file. + * mln/fun/v2v/rgb_to_hsv.hh: New header file. + * mln/fun/v2v/rgb_to_hue_map.hh: New header file. + * mln/fun/v2v/rgb_to_saturation_map.hh: New header file. + * mln/fun/v2v/rgb_to_value_map.hh: New header file. + * mln/img_path.hh: New header file. + * mln/io/plot/save_image_sh.hh: New header file. + * mln/math/cell.hh: New header file. + * mln/math/floor.hh: New header file. + * tests/accu/stat/histo1d/Makefile.am: New makefile. + * tests/accu/stat/histo1d/histo1d.cc: New source. + * tests/accu/stat/histo2d/Makefile.am: New makefile. + * tests/accu/stat/histo2d/histo2d.cc: New source. + +2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> + + Save histogram library. + + * mln/accu/histo/histo1d.hh: New header file. + * mln/accu/histo/histo2d.hh: New header file. + * mln/accu/histo/histo3d_rgb.hh: New header file. + * mln/accu/histo/histo3d_hsl.hh: New header file. + 2010-06-21 Yann Jacquelet <jacquelet(a)lrde.epita.fr> Simple integration test. diff --git a/scribo/sandbox/green/README b/scribo/sandbox/green/README index 4eb71b7..3efe50d 100644 --- a/scribo/sandbox/green/README +++ b/scribo/sandbox/green/README @@ -1,32 +1,219 @@ -Le travail est classé dans deux répertoires ok et ko. -Le répertoire ok correspond aux sources qui ont été reprises et retestées. -Le répertoire ko correspond aux sources qui ont été juste transférées. +I ARBORESCENCE +-------------- -La première brique logicielle importante sur laquelle s'appuie mes travaux est -la création d'histogrammes sous forme d'image. +bench ==> Comparaison d'algorithmes d'un point de vue temps d'exécution. +bug ==> Bug rencontrés dans milena et archivés sous forme de programme. +build ==> Répertoire d'exécution, non sauvé dans git. +demo ==> Première version d'un algorithme pour voir son comportement. +doc ==> Documentation tex ou code minimal pour de petits exemples. +exp ==> Version avancée des algorithmes pour traitées les bases de données. +mln ==> Partie mise en librairie milena des différents travaux. +tests ==> Tests unitaires sur certains algorithmes. +tools ==> Découpage de certains algorithmes pour mieux les tester séparément. +use ==> Test de compilation, code minimal pour compiler un élément. +II COMPILATION +-------------- -CREATION DES HISTOGRAMMES -------------------------- +L'unité minimale de code choisie est le répertoire. +Donc aller dans le répertoire qui nous interesse, +par exemple, green/demo/annotating/hsv et lancé le make -ko/mln/accu/stat/histo1d.hh: histogramme image valeur scalaire. -ko/mln/accu/stat/histo2d.hh: histogramme image valeur vectorielle 2d. -ko/mln/accu/stat/histo3d_hsl.hh: histogramme image valeur vectorielle 3d (HSL). -ko/mln/accu/stat/histo3d_rgb.hh: histogramme image valeur vectorielle 3d (RGB). +#:~/git/olena/scribo/sandbox/green$cd demo/annotating/hsv +#:~/git/olena/scribo/sandbox/green/demo/annotating/hsv$ make -f Makefile.am +Cette opération créé dans build le répertoire de compilation +green/build/demo/annotating/hsv. Dans ce répertoire aura été copié un +Makefile et tous les fichiers qui ne sont pas des sources. Par +exemple, des fichiers de calibration comme gaussian.sh (pour vérifier +la mire du filtre de gaussienne) ou de la documentation à la sauvette +sous forme de fichiers textes jetés à la va vite dans le répertoire +pour ne pas perdre l'information recherchée. En l'occurence, ici, il +n'y a rien à copier. Rendons-nous dans le répertoire de compilation et lançons +le makefile. +#:~/git/olena/scribo/sandbox/green/demo/annotating/hsv$ +cd ../../../build/demo/annotating/hsv +#:~/git/olena/scribo/sandbox/green/build/demo/annotating/hsv$ make clean all -ok/mln/accu/histo/histo1d.hh: histogramme image valeur scalaire entière. -ok/mln/accu/histo/histo2d.hh: histogramme image valeur vectorielle 2d entière. -ok/mln/accu/histo/histo3d.hh: histogramme image valeur vectorielle 3d entière. +L'exécutable est généré par le makefile, il porte le nom du +répertoire. Si il y a besoin de mettre à jour le makefile, le faire +dans le répertoire des sources en éditant Makefile.am, puis en +régénérant le Makefile dans le répertoire d'exécution par la commande +make -f Makefile.am depuis le répertoire source. -SAUVEGARDE DES HISTOGRAMMES ---------------------------- +III MAKEFILE +------------ +Les makefiles utilisés sont tous les mêmes avec quelques variables +dont le contenu change dans leur partie en-tête. +Pour chaque répertoire, le makefile doit savoir si le chemin courant +est un répertoire de compilation ou un répertoire de source. Pour les +identifier, il a recours à un pattern qui malheureusemnt fait +intervenir le nom de la branche de développement (bench,demo,bug,exp ...). +SOURCE_PATTERN= green/demo +BUILD__PATTERN= green/build/demo -VISUALISATION DES HISTOGRAMMES ------------------------------- \ No newline at end of file +Si un makefile ne fonctionne pas, il faut vérifier ceci en premier +lieu. Ici, le makefile doit être situé dans la branche démo. + +Autre élément à savoir, la compilation nécessite d'inclure la +librairie milena, ainsi que les développements propres en vu de leur +intégration futur dans milena, ceci est fait par un jeu d'INCLUDES1 +et INCLUDES2. + +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) + +Suivant l'allure du compte où l'on exécute les makefiles, il faut +revoir le chemin pour trouver des deux répertoires. + +Enfin, les options de compilations ne sont pas toujours les mêmes. Les +trois lignes possibles sont toutes présentes et seule celle qui est +utilisée n'est pas commentée. Typiquement, dans la branche de +développement démo où les perfomances ne sont pas le problème, on +compilera avec tout le matériel pour utiliser gdb et sans +optimisation. A l'inverse, dans la branche d'expérimentation, où le +code a déjà été testé, on cherche à aller vite car on exécute ce code +sur de nombreuses images. Dans cette optique, pas de débugage, pas de +traçage, optimisation conséquente. + +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) + +Une dernière dernière information, dans le cadre des développements +exp, et tools, on utilise la librairie boost soit pour la +virtualisation du filesystem, soit pour le formatage des fichiers text +(réalisation de colonnes, mettre des entiers sur un certain nombre de +caractères). Une ligne de chargement des librairies peut apparaitre donc. + +LOADLIBES= -lboost_filesystem + +On retrouvera les includes suivantes dans les sources: + +#include <boost/format.hpp> +#include <boost/filesystem.hpp> + + +IV CHEMINS DES IMAGES +--------------------- + +Toutes les images ont toujours été locales sur mon ordinateur. La +politique a toujours été d'utiliser un fichier img_path pour coder les +chemins des images. Les chemins étant plutôt long, j'ai toujours eu +tendance à faire en sorte qu'ils soient compilés en dur (sauf pour la +partie développement tools qui est vraiment voué à donner des +exécutables indépendants et génériques). Le fichier mln/img_path.hh +code la position de toutes les images dans mon arborescence. Il faudra +donc veiller à changer tous les chemins pour les adapter au compte +dans lequel on voudra reprendre le code. Dans le code, les références +aux positions des images sont faites via des macros. + +Toutes les images sont située dans git/img. En règle générale, je ne +traite que des images au format .pgm, .pbm et .ppm. Il m'arrive +fréquemment de dumper des images au format .sh (gnuplot shell +image). Pour la branche tools, nous avons utilisé les dumps de milena +comme format de transfert d'un utilitaire à un autre. Les images sont +classées suivant leur provenance. Nous avons tout d'abord la base +OLENA (copie des images de tests milena), la base INIM (très peu +utilisée voire jamais), la base ICDAR (très utilisée, surtout dans +exp), la base AFP (très utilisée dans exp) et les bases ANNOTATING1 et +ANNOTATING2 (pas très utilisées ni l'une, ni l'autre). + +La plus part du temps, sauver les résultats dans le répertoire +d'exécution courant est largement suffisant. Parfois, il est +nécessaire de sauvegarder de grosses quantités d'informations et de +les classer comme dans la branche de développement exp. C'est pour +cela, qu'un certain nombre de macros définissent des endroits pour +sauvegarder les résultats lors d'expérimentation de grande ampleur sur +toute la base ICDAR ou AFP. + + +V GNUPLOT SCRIPT SHELL IMAGE FORMAT +----------------------------------- + +J'abrège le nom du format par gnuplot shell format. En fait, c'est un +format d'image particulier qui a besoin de gnuplot pour être lu. Il +est donc compatible avec aucun viewer si ce n'est gnuplot, mais a la +caractéristique d'afficher tous les points de manière visible. Par +ailleurs, comme il s'agit d'un script gnuplot il permet d'insérer très +facilement une fonction pour visualiser les données autrement (par +exemple, changer d'espace: HSL, HSV). Le fichier tire son nom de la +façon dont il fonctionne. C'est un script shell qui fait un appel à +gnuplot et lui passe le jeu de données directement à partir de ce même +fichier, pas besoin de faire appel à un autre fichier. Une fois +généré, le fichier doit être modifié pour avoir les permissions +d'exécution (chmod 755 gnuplot_shell_file.sh). Comme je trouve le format +extrêmement pratique, il se retrouve preque partout dans mes sources. + + +V MLN +----- + +a) La sauvegarde des images au format gnuplot shell + +* mln/io/plot/save_image_sh.hh: Librairie sauvegarde format gnuplot shell. + +to do ... + + + +b) Les histogrammes + +Un des travaux demandés par théo est la réalisation d'une librairie +d'histogramme permettant de fournir un résultat sous forme d'image. +L'intérêt est ensuite de pouvoir filtrer directement ces histogrammes +par les algorithmes de milena, ou encore d'étudier les valeurs +caractéristiques par d'autres accumulateurs. Les codes réellement +utilisés sont histo1d et histo3d RGB. Tous les autres codes sont très +expérimentaux. Notemment, le code HSL demande de quantifier l'espace +de comptage puisqu'il est décrit sous la forme de triplets de float +(les autres sont inférés). Néanmoins, le code est à conserver car il +contient une séquence d'appels pour les routines permettant de +considérer la dimension de la teinte comme circulaire. + +* mln/accu/stat/histo1d.hh: Accumulateur histogramme image1d. +* use/accu/stat/histo1d: Code minimal utilisant un histogramme 1d. + +* mln/accu/stat/histo2d.hh: Accumulateur histogramme image2d. +* mln/value/rg.hh: Définition du type vectoriel 2d rouge/vert (RG). +* mln/fun/v2v/rgb_to_rg.hh: Transformation de l'espace RGB vers l'espace RG. +* use/accu/stat/histo2d: Code minimal utilisant un histogramme 2d. + + +* mln/accu/stat/histo3d_rgb.hh: Accumulateur histogramme image3d RGB. +* mln/accu/stat/histo3d_hsl.hh: Accumulateur histogramme image3d HSL. + +* use/accu/stat/histo3_rgb: Code minimal utilisant un histogramme 3d RGB. +* use/accu/stat/histo3_hsl: Code minimal utilisant un histogramme 3d HSL. + + + +* tests/accu/stat/histo1d + + + + +* tests/accu/stat/histo2d + + + +* use/accu/stat/histo3d_rgb +* tests/accu/stat/histo3d_rgb + +* use/accu/stat/histo3d_hsl +* tests/accu/stat/histo3d_hsl + + +c) La visualisation des histogrammes 3d + +* demo/accu/stat/histo2d + +* mln/display/dispay_histo.hh +* mln/displayproject_histo.hh diff --git a/scribo/sandbox/green/mln/accu/stat/histo1d.hh b/scribo/sandbox/green/mln/accu/stat/histo1d.hh new file mode 100644 index 0000000..d480cf0 --- /dev/null +++ b/scribo/sandbox/green/mln/accu/stat/histo1d.hh @@ -0,0 +1,340 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#ifndef MLN_ACCU_STAT_HISTO1D_HH +#define MLN_ACCU_STAT_HISTO1D_HH + +/// \file +/// +/// \brief Define a histogram as accumulator which returns an image1d. +/// +/// This source implements the discrete histogram version. It was +/// created for images which the values are derived from integer +/// type. The number of bins is directly infered from the cardinality +/// of the image value. So the int_u8 image has got 256 bins, the +/// int_u<14> image has got 16384 bins. But, this code doesn't work +/// for that quantification because of the coord limitation (signed +/// short, as it is defined in mln/core/def/coord.hh). Take care of +/// the limitation from the result image value. We hard code unsigned, +/// but it sometimes not enought to count data in one bin. That last +/// case has not occured since the beginning of the creation of the type. +/// +/// The following sample is a typical use of the histogram. +/// +/// #include <mln/accu/stat/histo1d.hh> +/// #include <mln/data/compute.hh> +/// #include <mln/core/image/image1d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/pgm/load.hh> +/// #include <mln/value/int_u8.hh> +/// +/// int main() +/// { +/// typedef mln::value::int_u8 t_int_u8; +/// mln::image2d<t_int_u8> img; +/// mln::image1d<unsigned> histo; +/// +/// mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm"); +/// histo = mln::data::compute(mln::accu::meta::histo::histo1d(), img); +/// +/// return 0; +/// } + + +#include <iostream> + +#include <mln/accu/internal/base.hh> + +#include <mln/arith/plus.hh> + +#include <mln/core/concept/meta_accumulator.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/macros.hh> + +#include <mln/literal/zero.hh> + +#include <mln/trace/entering.hh> +#include <mln/trace/exiting.hh> + +#include <mln/trait/value/comp.hh> + +#include <mln/value/ops.hh> + +namespace mln +{ + + namespace accu + { + + namespace stat + { + + // Forward declaration + template <typename V> + struct histo1d; + + } // end of namespace mln::accu::stat + + + namespace meta + { + + namespace stat + { + + struct histo1d : public Meta_Accumulator<histo1d> + { + template <typename V> + struct with + { + typedef accu::stat::histo1d<V> ret; + }; + }; + + } // end of namespace mln::accu::meta::stat + + } // end of namespace mln::accu::meta + + } // end of namespace mln::accu + + namespace trait + { + + template <typename V> + struct accumulator_< mln::accu::stat::histo1d<V> > + { + typedef accumulator::has_untake::no has_untake; + typedef accumulator::has_set_value::no has_set_value; + typedef accumulator::has_stop::no has_stop; + typedef accumulator::when_pix::use_v when_pix; + }; + + template <typename V> + struct set_precise_binary_<op::eq, + accu::stat::histo1d<V>, + accu::stat::histo1d<V> > + { + typedef bool ret; + }; + + } // end of namespace mln::trait + + namespace accu + { + + namespace stat + { + + /// \brief Define a histogram as accumulator which returns an image1d. + /// + /// Param V defines the type of the input image value. It is in + /// this space that we count the values. For instance, this + /// histogram works well for image2d<int_u8> or with + /// image2d<int_u<14> >. The number of bins depends directly the + /// values V. For 8 bits there is 256 bins, for 14 bits there + /// is 16384 bins. Note that over quantification works too (up + /// to 14 bits). + /// + /// \ingroup modaccuvalues + + template <typename V> + struct histo1d : + public mln::accu::internal::base<image1d<unsigned>, histo1d<V> > + { + typedef V argument; + typedef image1d<unsigned> result; + typedef result q_result; + + /// Constructors + /// \{ + /// \brief Infer the size of the resulting image1d domain. + /// + /// By evaluating the minimum and the maximum of V, we define the domain + /// of the resulting image1d. + histo1d(); + /// \} + + + /// Manipulators. + /// \{ + /// \brief Initialize the histogram with zero value. + /// + /// This method must be called just before starting the use of the + /// histogram. If it's not, resulting values won't converge to the + /// density. + void init(); + + + /// \brief Update the histogram with the graylevel of the pixel t. + /// \param[in] t a graylevel pixel of type V. + /// + /// The end user shouldn't call this method. In place of it, he can + /// go through the data compute interface. + void take(const argument& t); + + + /// \brief Update the histogram with an other histogram. + /// \param[in] other the other histogram. + /// + /// The end user shouldn't call this method. This is part of + /// data compute interface mechanism. + + void take(const histo1d<V>& other); + /// \} + + /// Accessors. + /// \{ + /// \brief Return the histogram as an image1d. + /// + /// This is the machinery to communicate with data compute interface. + /// The end user should'nt use it. + result to_result() const; + operator result () const; + /// \} + + /// \brief Check whethever this accumulator is able to return a result. + /// + /// Depends if the resulting image1d is valid. We can assume it is quite + /// always the case. + bool is_valid() const; + + protected: + result count_; + }; + + /// \brief Check wethever an histogram is equal to an other one. + /// \param[in] histo1 the first histogram to compare with. + /// \param[in] histo2 the second histogram. + /// + /// The operator compares all the bins from the two histograms. + /// Nobody uses this method unless unitary tests. + + template <typename V> + bool operator==(const histo1d<V>& histo1, const histo1d<V>& histo2); + +#ifndef MLN_INCLUDE_ONLY + + template <typename V> + inline + histo1d<V>::histo1d() + { + trace::entering("mln::accu::stat::histo1d::cstor"); + typedef mln_trait_value_comp(V,0) comp; + typedef point<grid::tick, V> v_point1d; + typedef box<v_point1d> v_box1d; + + // comp keep a trace of the dimension of the theorical image. + // mln_min(comp) --> 0 + // mln_max(comp) --> 2^(n-1) [127 for n=7][255 for n=8] ... + + count_.init_(box1d(point1d(mln_min(comp)), + point1d(mln_max(comp)))); + + // std::cout << "min : " << mln_min(comp) << std::endl; + // std::cout << "max : " << mln_max(comp) << std::endl; + + trace::exiting("mln::accu::stat::histo1d::cstor"); + } + + template <typename V> + inline + void histo1d<V>::init() + { + data::fill(count_, literal::zero); + } + + template <typename V> + inline + void histo1d<V>::take(const argument& t) + { + // Just convert a graylevel value (int_u8 like) to a position for an + // iterator on the resulting image. + ++count_(point1d(t)); + } + + + template <typename V> + inline + void histo1d<V>::take(const histo1d<V>& other) + { + count_ += other.count_; + } + + template <typename V> + inline + typename histo1d<V>::result histo1d<V>::to_result() const + { + return count_; + } + + template <typename V> + inline + histo1d<V>::operator result() const + { + return count_; + } + + template <typename V> + inline + bool histo1d<V>::is_valid() const + { + bool result = count_.is_valid(); + + return result; + } + + template <typename V> + bool operator==(const histo1d<V>& histo1, const histo1d<V>& histo2) + { + trace::entering("mln::accu::stat::histo1d::operator=="); + + bool result = true; + const image1d<unsigned>& res1 = histo1.to_result(); + const image1d<unsigned>& res2 = histo2.to_result(); + + mln_precondition(res1.is_valid()); + mln_precondition(res2.is_valid()); + + mln_piter(image1d<unsigned>) p1(res1.domain()); + mln_piter(image1d<unsigned>) p2(res2.domain()); + + for_all_2(p1, p2) + result &= (res1(p1) == res2(p2)); + + trace::exiting("mln::accu::stat::histo1d::operator=="); + return result; + } + +#endif // ! MLN_INCLUDE_ONLY + + + } // end of namespace mln::accu::stat + + } // end of namespace mln::accu + +} // end of namespace mln + +#endif // ! MLN_ACCU_STAT_HISTO1D_HH diff --git a/scribo/sandbox/green/mln/accu/stat/histo2d.hh b/scribo/sandbox/green/mln/accu/stat/histo2d.hh new file mode 100644 index 0000000..c28723c --- /dev/null +++ b/scribo/sandbox/green/mln/accu/stat/histo2d.hh @@ -0,0 +1,358 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#ifndef MLN_ACCU_STAT_HISTO2D_HH +# define MLN_ACCU_STAT_HISTO2D_HH + +/// \file +/// +/// \brief Define a histogram as accumulator which returns an image2d. +/// +/// This source implements the discrete histogram version. It was +/// created for images which the values are derived from integer +/// type. The number of bins is directly infered from the cardinality +/// of the image value. It works with vectorial image, typically RGB +/// space where only RG are preserved. See histo1d.hh for limitations +/// of such implementation. 8 bits quantification starts to be +/// expensive, it produces image2d with [0..255,0..255] as domain. +/// +/// The following sample is a typical use of the histogram. +/// +/// #include <mln/accu/stat/histo2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/compute.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8 +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<unsigned> t_histo; +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rg8 img_rg8; +/// t_histo histo; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); +/// +/// return 0; +/// } + +# include <iostream> + +# include <mln/accu/internal/base.hh> + +# include <mln/core/macros.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/alias/point2d.hh> +# include <mln/core/alias/box2d.hh> + +# include <mln/trait/value/comp.hh> + +# include <mln/arith/plus.hh> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/value/ops.hh> + +namespace mln +{ + + namespace accu + { + + namespace stat + { + + // Forward declaration + template <typename V> + struct histo2d; + + } // end of namespace mln::accu::stat + + namespace meta + { + + namespace stat + { + + struct histo2d : public Meta_Accumulator<histo2d> + { + template <typename V> + struct with + { + typedef accu::stat::histo2d<V> ret; + }; + }; + + } // end of namespace mln::accu::meta::stat + + } // end of namespace mln::accu::meta + + } // end of namespace mln::accu + + + namespace trait + { + + template <typename V> + struct accumulator_< mln::accu::stat::histo2d<V> > + { + typedef accumulator::has_untake::no has_untake; + typedef accumulator::has_set_value::no has_set_value; + typedef accumulator::has_stop::no has_stop; + typedef accumulator::when_pix::use_v when_pix; + }; + + template <typename V> + struct set_precise_binary_<op::eq, + accu::stat::histo2d<V>, + accu::stat::histo2d<V> > + { + typedef bool ret; + }; + + } // end of namespace mln::trait + + namespace accu + { + + namespace stat + { + + /// \brief Define an histogram which returns an image3d . + /// + /// Param V defines the space in which we count the values. + /// For instance, this histogram works image2d<rgb<2>> or + /// image2d<rgb<7>>. The histogram count the occurrence of each value. + /// The number of bins depends of the grayscale values, for 8 bits there + /// is 256x3 bins. Note that over + /// quantification works too. + /// + /// \ingroup modaccuvalues + + template <typename V> + struct histo2d : + public mln::accu::internal::base<image2d<unsigned>, histo2d<V> > + { + typedef V argument; + typedef image2d<unsigned> result; + typedef result q_result; + + /// Constructors + /// \{ + /// \brief Initialize the size of the resulting image1d. + /// + /// Initialize the size the resulting image from the theorical dynamic + /// of the greylevel values (Use V to manage it). + histo2d(); + /// \} + + + /// Manipulators. + /// \{ + /// \brief Initialize the histogram with zero value. + /// + /// This method must be called just before starting the use of the + /// histogram. If it's not, resulting values won't converge to the + /// density. + void init(); + + + /// \brief Update the histogram with the RGB pixel t. + /// \param[in] t a greylevel pixel of type V. + /// + /// The end user shouldn't call this method. In place of it, he can + /// go through the data compute interface. + void take(const argument& t); + + + /// \brief Update the histogram with an other histogram. + /// \param[in] other the other histogram. + void take(const histo2d<V>& other); + /// \} + + /// Accessors. + /// \{ + /// \brief Return the histogram as an image1d. + /// + /// This is the machinery to communicate with data compute interface. + /// The end user should'nt use it. + result to_result() const; + operator result () const; + /// \} + + /// \brief Check whethever this accumulator is able to return a result. + /// + /// Depends if the resulting image1d is valid. We can assume it is quite + /// always the case. + bool is_valid() const; + + protected: + result count_; + }; + + /// \brief Check wethever an histogram is equal to an other one. + /// \param[in] histo1 the first histogram to compare with. + /// \param[in] histo2 the second histogram. + /// + /// The operator compare all the bins from the two histogram. + + template <typename V> + bool operator==(const histo2d<V>& histo1, + const histo2d<V>& histo2); + +#ifndef MLN_INCLUDE_ONLY + + template <typename V> + inline + histo2d<V>::histo2d() + { + trace::entering("mln::accu::stat::histo2d::histo2d"); + typedef mln_trait_value_comp(V,0) comp0; + typedef mln_trait_value_comp(V,1) comp1; + + // comp keep a trace of the dimension of the theorical image. + // mln_min(comp) --> 0 + // mln_max(comp) --> 2^(n-1) [127 for n=7][255 for n=8] ... + + count_.init_(box2d(point2d(mln_min(comp0), + mln_min(comp1)), + point2d(mln_max(comp0), + mln_max(comp1)))); + + trace::exiting("mln::accu::stat::histo2d::histo2d"); + } + + template <typename V> + inline + void histo2d<V>::init() + { + trace::entering("mln::accu::stat::histo2d::init"); + + data::fill(count_, 0); + trace::exiting("mln::accu::stat::histo2d::init"); + } + + template <typename V> + inline + void histo2d<V>::take(const argument& t) + { + trace::entering("mln::accu::stat::histo2d::take"); + + // Just convert a greyscale value (int_u8 like) to a position for an + // iterator on the resulting image. + // Take care to the constructor : Point(slice, row, column) + ++count_(point2d(t.red(), t.green())); + + trace::exiting("mln::accu::stat::histo2d::take"); + } + + + template <typename V> + inline + void histo2d<V>::take(const histo2d<V>& other) + { + trace::entering("mln::accu::stat::histo2d::take"); + + count_ += other.count_; + + trace::exiting("mln::accu::stat::histo2d::take"); + } + + template <typename V> + inline + typename histo2d<V>::result histo2d<V>::to_result() const + { + trace::entering("mln::accu::stat::histo2d::to_result"); + + trace::exiting("mln::accu::stat::histo2d::to_result"); + return count_; + } + + template <typename V> + inline + histo2d<V>::operator result() const + { + trace::entering("mln::accu::stat::histo2d::operator result"); + + trace::exiting("mln::accu::stat::histo2d::operator result"); + return count_; + } + + template <typename V> + inline + bool histo2d<V>::is_valid() const + { + trace::entering("mln::accu::stat::histo2d::is_valid"); + bool result = count_.is_valid(); + + trace::exiting("mln::accu::stat::histo2d::is_valid"); + return result; + } + + template <typename V> + bool operator==(const histo2d<V>& histo1, + const histo2d<V>& histo2) + { + trace::entering("mln::accu::stat::operator=="); + + bool result = true; + const image2d<unsigned>& res1 = histo1.to_result(); + const image2d<unsigned>& res2 = histo2.to_result(); + + mln_precondition(res1.is_valid()); + mln_precondition(res2.is_valid()); + + mln_piter(image2d<unsigned>) p1(res1.domain()); + mln_piter(image2d<unsigned>) p2(res2.domain()); + + for_all_2(p1, p2) + result &= (res1(p1) == res2(p2)); + + trace::exiting("mln::accu::stat::operator=="); + return result; + } + +#endif // ! MLN_INCLUDE_ONLY + + + } // end of namespace mln::accu::stat + + } // end of namespace mln::accu + +} // end of namespace mln + +#endif // ! MLN_ACCU_STAT_HISTO2D_HH diff --git a/milena/sandbox/green/mln/accu/stat/histo3d_hsl.hh b/scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh similarity index 100% copy from milena/sandbox/green/mln/accu/stat/histo3d_hsl.hh copy to scribo/sandbox/green/mln/accu/stat/histo3d_hsl.hh diff --git a/milena/sandbox/green/mln/accu/stat/histo3d_rgb.hh b/scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh similarity index 100% copy from milena/sandbox/green/mln/accu/stat/histo3d_rgb.hh copy to scribo/sandbox/green/mln/accu/stat/histo3d_rgb.hh diff --git a/milena/sandbox/green/mln/clustering/k_mean.hh b/scribo/sandbox/green/mln/clustering/k_mean.hh similarity index 100% copy from milena/sandbox/green/mln/clustering/k_mean.hh copy to scribo/sandbox/green/mln/clustering/k_mean.hh diff --git a/milena/sandbox/green/mln/clustering/kmean1d.hh b/scribo/sandbox/green/mln/clustering/kmean1d.hh similarity index 100% copy from milena/sandbox/green/mln/clustering/kmean1d.hh copy to scribo/sandbox/green/mln/clustering/kmean1d.hh diff --git a/milena/sandbox/green/mln/clustering/kmean2d.hh b/scribo/sandbox/green/mln/clustering/kmean2d.hh similarity index 100% copy from milena/sandbox/green/mln/clustering/kmean2d.hh copy to scribo/sandbox/green/mln/clustering/kmean2d.hh diff --git a/milena/sandbox/green/mln/clustering/kmean3d.hh b/scribo/sandbox/green/mln/clustering/kmean3d.hh similarity index 100% copy from milena/sandbox/green/mln/clustering/kmean3d.hh copy to scribo/sandbox/green/mln/clustering/kmean3d.hh diff --git a/milena/sandbox/green/mln/clustering/kmean_rgb.hh b/scribo/sandbox/green/mln/clustering/kmean_rgb.hh similarity index 100% copy from milena/sandbox/green/mln/clustering/kmean_rgb.hh copy to scribo/sandbox/green/mln/clustering/kmean_rgb.hh diff --git a/milena/sandbox/green/mln/display/display_histo.hh b/scribo/sandbox/green/mln/display/display_histo.hh similarity index 100% copy from milena/sandbox/green/mln/display/display_histo.hh copy to scribo/sandbox/green/mln/display/display_histo.hh diff --git a/milena/sandbox/green/mln/display/project_histo.hh b/scribo/sandbox/green/mln/display/project_histo.hh similarity index 100% copy from milena/sandbox/green/mln/display/project_histo.hh copy to scribo/sandbox/green/mln/display/project_histo.hh diff --git a/milena/sandbox/green/mln/fun/p2b/achromatic.hh b/scribo/sandbox/green/mln/fun/p2b/achromatic.hh similarity index 100% copy from milena/sandbox/green/mln/fun/p2b/achromatic.hh copy to scribo/sandbox/green/mln/fun/p2b/achromatic.hh diff --git a/milena/sandbox/green/mln/fun/p2b/component_equals.hh b/scribo/sandbox/green/mln/fun/p2b/component_equals.hh similarity index 100% copy from milena/sandbox/green/mln/fun/p2b/component_equals.hh copy to scribo/sandbox/green/mln/fun/p2b/component_equals.hh diff --git a/milena/sandbox/green/mln/fun/v2v/achromatism.hh b/scribo/sandbox/green/mln/fun/v2v/achromatism.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/achromatism.hh copy to scribo/sandbox/green/mln/fun/v2v/achromatism.hh diff --git a/milena/sandbox/green/mln/fun/v2v/hue_concentration.hh b/scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/hue_concentration.hh copy to scribo/sandbox/green/mln/fun/v2v/hue_concentration.hh diff --git a/milena/sandbox/green/mln/fun/v2v/int_u16_to_int_u14.hh b/scribo/sandbox/green/mln/fun/v2v/int_u16_to_int_u14.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/int_u16_to_int_u14.hh copy to scribo/sandbox/green/mln/fun/v2v/int_u16_to_int_u14.hh diff --git a/milena/sandbox/green/mln/fun/v2v/log.hh b/scribo/sandbox/green/mln/fun/v2v/log.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/log.hh copy to scribo/sandbox/green/mln/fun/v2v/log.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rg_to_rgb.hh b/scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rg_to_rgb.hh copy to scribo/sandbox/green/mln/fun/v2v/rg_to_rgb.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rgb8_to_int_u8.hh b/scribo/sandbox/green/mln/fun/v2v/rgb8_to_int_u8.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb8_to_int_u8.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb8_to_int_u8.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh b/scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb8_to_rgbn.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb_to_achromatism_map.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb_to_hsv.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rgb_to_hue_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_hue_map.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb_to_hue_map.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb_to_hue_map.hh diff --git a/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh new file mode 100644 index 0000000..d4c1e46 --- /dev/null +++ b/scribo/sandbox/green/mln/fun/v2v/rgb_to_rg.hh @@ -0,0 +1,111 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#ifndef MLN_FUN_V2V_RGB_TO_RG_HH +# define MLN_FUN_V2V_RGB_TO_RG_HH + +# include <mln/value/rg.hh> +# include <mln/value/rgb.hh> +# include <mln/core/contract.hh> + +/// \file +/// +/// \brief Convert a rgb value to a rg value. +/// +/// This source implements the conversion between rgb space and rg space. +/// +/// The following sample is a typical use of the rgb/rg conversion function. +/// +/// #include <mln/accu/stat/histo2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/compute.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8 +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<unsigned> t_histo; +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rg8 img_rg8; +/// t_histo histo; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); +/// +/// return 0; +/// } + +namespace mln +{ + + namespace fun + { + + namespace v2v + { + + /// \brief Convert a rgb value to a rg value. + /// + /// Param n defines the quantification used for rgb space and rg space. + /// + /// \ingroup modfunv2v + + template <unsigned n> + struct rgb_to_rg : Function_v2v< rgb_to_rg<n> > + { + typedef value::rg<n> result; + typedef value::rgb<n> argument; + + /// \brief Convert rgb value to rg value. + /// + /// \param[in] v the rgb value to convert. + /// + /// Conversion is done by calling the rg constructor. There is + /// no modification of values. The red/green fields from rgb the value + /// are preserved. Blue value is dropped. + + result operator()(const argument& v) const + { + return value::rg<n>(v); + } + }; + + } // end of namespace mln::fun::v2v + + } // end of namespace mln::fun + +} // end of namespace mln + +#endif // ! MLN_FUN_V2V_RGB_TO_RG_HH diff --git a/milena/sandbox/green/mln/fun/v2v/rgb_to_saturation_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_saturation_map.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb_to_saturation_map.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb_to_saturation_map.hh diff --git a/milena/sandbox/green/mln/fun/v2v/rgb_to_value_map.hh b/scribo/sandbox/green/mln/fun/v2v/rgb_to_value_map.hh similarity index 100% copy from milena/sandbox/green/mln/fun/v2v/rgb_to_value_map.hh copy to scribo/sandbox/green/mln/fun/v2v/rgb_to_value_map.hh diff --git a/milena/sandbox/green/mln/img_path.hh b/scribo/sandbox/green/mln/img_path.hh similarity index 100% copy from milena/sandbox/green/mln/img_path.hh copy to scribo/sandbox/green/mln/img_path.hh diff --git a/scribo/sandbox/green/mln/io/plot/save_image_sh.hh b/scribo/sandbox/green/mln/io/plot/save_image_sh.hh new file mode 100644 index 0000000..137f78d --- /dev/null +++ b/scribo/sandbox/green/mln/io/plot/save_image_sh.hh @@ -0,0 +1,1056 @@ +// Copyright (C) 2007,2008,2009,2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#ifndef MLN_IO_PLOT_SAVE_IMAGE_SH_HH +# define MLN_IO_PLOT_SAVE_IMAGE_SH_HH + +/// \file +/// +/// \brief Define some functions to export to gnuplot format as a script shell. +/// +/// Theses routines are dedicated to image visualization. The aim is to display +/// image whatever the value used as pixel. The behaviour of gnuplot is more +/// like xv than imageMagick. A Gnuplot script shell file is a text dump file +/// with a preambule to let gnuplot interpret data. As a script shell, you can +/// launch it (don't forget the permissions), and every thing is packed in it. +/// The script file call gnuplot in batch mode, the result window persists and +/// that's all. +/// + +# include <fstream> +# include <string> + +# include <mln/trace/entering.hh> +# include <mln/trace/exiting.hh> + +# include <mln/core/macros.hh> +# include <mln/core/contract.hh> +# include <mln/core/image/image1d.hh> +# include <mln/core/image/image2d.hh> +# include <mln/core/image/image3d.hh> + +# include <mln/geom/min_ind.hh> +# include <mln/geom/max_ind.hh> +# include <mln/geom/min_row.hh> +# include <mln/geom/max_row.hh> +# include <mln/geom/min_col.hh> +# include <mln/geom/max_col.hh> + +# include <mln/trait/value_.hh> + +# include <mln/value/int_u.hh> +# include <mln/value/int_s.hh> +# include <mln/value/rgb.hh> +# include <mln/value/hsl.hh> +# include <mln/value/hsi.hh> + +# include <mln/util/array.hh> + + +namespace mln +{ + + namespace io + { + + namespace plot + { + + /// \brief Save an image as a gnuplot script shell. + /// + /// Every thing is save. The image could be 1d, 2d or 3d. The value of + /// the pixels could be int_u<n>, int_s<n>, float, double, hsl_f, hsl_d, + /// hsi_f, hsi_d and rgb<n>. + /// + /// \param[in] img the image which contains the data to save. + /// \param[in] filename the name of the unix script shell. + /// \return the status of the opening file operation. + /// + /// The result depends on the permission to save the file with + /// filename parameter as unix path. The script shell file must have the + /// permission to execute (chmod 755). Launch the script shell to call + /// gnuplot in batchmode with fine parameters. + + template <typename I> + bool save_image_sh(const Image& img, const std::string& filename); + + /// \brief Save a stack of image. + /// + /// This is an experimental support. + /// + /// \param[in] stack the stack of image to save. + /// \param[in] filename the name of the unix script shell. + /// \return the status of the opening file operation. + /// + /// The result depends on the permission to save the file with + /// filename parameter as unix path. The script shell file must have the + /// permission to execute (chmod 755). Launch the script shell to call + /// gnuplot in batchmode with fine parameters. + template <typename I> + bool save_image_sh(const util::array< image1d >& stack, + const std::string& filename); + + template <typename I> + bool save_image_sh(const util::array< util::array< image1d > >& stack, + const std::string& filename); + + } // end of namespace mln::io::plot + + } // end of namespace mln::io + + + namespace io + { + + namespace plot + { + +#ifndef MLN_INCLUDE_ONLY + + //------------------------------------------------------------------------ + // Impl. + //------------------------------------------------------------------------ + + namespace impl + { + + + //---------------------------------------------------------------------- + // save_image_sh_array_array_image1d(const array<array<image1d>>&, + // const string&) + //---------------------------------------------------------------------- + + template <typename I> + inline + bool save_image_sh_array_array_image1d(const util::array< util::array< + image1d > >& stack, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::" + "save_image_sh_array_array_image1d"); + + mln_precondition(!stack.is_empty()); + mln_precondition(!stack[0].is_empty()); + mln_precondition(stack[0][0].is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_ind = geom::min_ind(stack[0][0]); + unsigned max_ind = geom::max_ind(stack[0][0]); + + if (result) + { + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set xrange [" << min_ind; + out << ":" << max_ind; + out << "]" << std::endl; + out << "plot '-' with line"; + + for (unsigned i = 1; i < stack.size(); ++i) + { + for (unsigned j = 1; j < stack[i].size(); ++j) + { + + out << ",\\" << std::endl; + out << " '-' with line"; + } + } + + out << std::endl; + + mln_eiter(util::array< util::array< image1d > >) e0(stack); + + for_all (e0) + { + mln_eiter(util::array< image1d >) e1(stack[e0.index_()]); + + for_all (e1) + { + mln_piter(image1d) + p(stack[e0.index_()][e1.index_()].domain()); + + // Output data. + for_all(p) + { + out << p.ind() << " "; + out << stack[e0.index_()][e1.index_()](p) << std::endl;; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + } + } + + out << "EOF" << std::endl; + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::" + "save_image_sh_array_array_image1d"); + + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_array_array_image1d_vec3( + // const array<array<image1d<vec<3,T>>>>&, + // const string&) + //---------------------------------------------------------------------- + + template <typename T> + inline + bool save_image_sh_array_array_image1d_vec3( + const util::array<util::array<image1d<algebra::vec<3,T> > > >& stack, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::" + "save_image_sh_array_array_image1d_vec3"); + + typedef algebra::vec<3,T> t_val; + typedef image1d<t_val> t_img; + typedef util::array<t_img> t_array; + typedef util::array<t_array> t_stack; + + mln_precondition(!stack.is_empty()); + mln_precondition(!stack[0].is_empty()); + mln_precondition(stack[0][0].is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_ind = geom::min_ind(stack[0][0]); + unsigned max_ind = geom::max_ind(stack[0][0]); + + if (result) + { + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set xrange [" << min_ind; + out << ":" << max_ind; + out << "]" << std::endl; + out << "splot '-' with line palette"; + + for (unsigned i = 1; i < stack.size(); ++i) + { + for (unsigned j = 1; j < stack[i].size(); ++j) + { + + out << ",\\" << std::endl; + out << " '-' with line palette"; + } + } + + out << std::endl; + + mln_eiter(t_stack) e0(stack); + + for_all (e0) + { + mln_eiter(t_array) e1(stack[e0.index_()]); + + for_all (e1) + { + mln_piter(t_img) p(stack[e0.index_()][e1.index_()].domain()); + + // Output data. + for_all(p) + { + out << p.ind() << " "; + out << stack[e0.index_()][e1.index_()](p)[0] << " "; + out << stack[e0.index_()][e1.index_()](p)[1] << " "; + out << stack[e0.index_()][e1.index_()](p)[2] << std::endl;; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + } + } + + out << "EOF" << std::endl; + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::" + "save_image_sh_array_array_image1d_vec3"); + + return result; + } + + + //---------------------------------------------------------------------- + // save_image_sh_array_array_image1d_vec2( + // const array<array<image1d<vec<2,T>>>>&, + // const string&) + //---------------------------------------------------------------------- + + template <typename T> + inline + bool save_image_sh_array_array_image1d_vec2( + const util::array<util::array<image1d<algebra::vec<2,T> > > >& stack, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::" + "save_image_sh_array_array_image1d_vec2"); + + typedef algebra::vec<2,T> t_val; + typedef image1d<t_val> t_img; + typedef util::array<t_img> t_array; + typedef util::array<t_array> t_stack; + + mln_precondition(!stack.is_empty()); + mln_precondition(!stack[0].is_empty()); + mln_precondition(stack[0][0].is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_ind = geom::min_ind(stack[0][0]); + unsigned max_ind = geom::max_ind(stack[0][0]); + + if (result) + { + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set xrange [" << min_ind; + out << ":" << max_ind; + out << "]" << std::endl; + out << "splot '-' with line"; + + for (unsigned i = 1; i < stack.size(); ++i) + { + for (unsigned j = 1; j < stack[i].size(); ++j) + { + + out << ",\\" << std::endl; + out << " '-' with line"; + } + } + + out << std::endl; + + mln_eiter(t_stack) e0(stack); + + for_all (e0) + { + mln_eiter(t_array) e1(stack[e0.index_()]); + + for_all (e1) + { + mln_piter(t_img) p(stack[e0.index_()][e1.index_()].domain()); + + // Output data. + for_all(p) + { + out << p.ind() << " "; + out << stack[e0.index_()][e1.index_()](p)[0] << " "; + out << stack[e0.index_()][e1.index_()](p)[1] << std::endl;; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + } + } + + out << "EOF" << std::endl; + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::" + "save_image_sh_array_array_image1d_vec2"); + + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_array_image1d(const array<image1d>&, const string&) + //---------------------------------------------------------------------- + + template <typename I> + inline + bool save_image_sh_array_image1d(const util::array< image1d >& stack, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_array_image1d"); + mln_precondition(!stack.is_empty()); + mln_precondition(stack[0].is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_ind = geom::min_ind(stack[0]); + unsigned max_ind = geom::max_ind(stack[0]); + + if (result) + { + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set xrange [" << min_ind; + out << ":" << max_ind; + out << "]" << std::endl; + out << "plot '-' with line"; + + for (unsigned i = 1; i < stack.size(); ++i) + { + + out << ",\\" << std::endl; + out << " '-' with line"; + } + + out << std::endl; + + mln_eiter(util::array< image1d >) e(stack); + + for_all (e) + { + mln_piter(image1d) p(stack[e.index_()].domain()); + + // Output data. + for_all(p) + { + out << p.ind() << " "; + out << stack[e.index_()](p) << std::endl;; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + } + + out << "EOF" << std::endl; + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_array_image1d"); + return result; + } + + + //---------------------------------------------------------------------- + // save_image_sh_image2d_rgb(const image2d<rgb<n>>&, const string&) + //---------------------------------------------------------------------- + + template <unsigned n> + inline + bool save_image_sh_image2d_rgb(const image2d<value::rgb<n> >& img, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_image2d_rgb"); + mln_precondition(img.is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_row = geom::min_row(img); + unsigned max_row = geom::max_row(img); + unsigned min_col = geom::min_col(img); + unsigned max_col = geom::max_col(img); + + if (result) + { + mln_piter(image2d< value::rgb<n> >) p(img.domain()); + + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, r, g, b) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set xrange [" << min_col; + out << ":" << max_col; + out << "]" << std::endl; + out << "set yrange [-" << max_row; + out << ":" << min_row; + out << "]" << std::endl; + out << "plot '-' using 2:(-\\$1):3:4:5 with rgbimage" << std::endl; + + // Output data. + for_all(p) + { + out << p.row() << " "; + out << p.col() << " "; + out << img(p).red() << " "; + out << img(p).green() << " "; + out << img(p).blue() << std::endl; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + out << "EOF" << std::endl; + + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_image2d_rgb"); + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_image2d_hsl(const image2d<hsl_<T,T,T>>&, const string&) + //---------------------------------------------------------------------- + + template <typename T> + inline + bool save_image_sh_image2d_hsl(const image2d<value::hsl_<T,T,T> >& img, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_image2d_hsl"); + mln_precondition(img.is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_row = geom::min_row(img); + unsigned max_row = geom::max_row(img); + unsigned min_col = geom::min_col(img); + unsigned max_col = geom::max_col(img); + + typedef mln::value::hsl_<T,T,T> hsl; + + if (result) + { + mln_piter(image2d< hsl >) p(img.domain()); + + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "q(l,s) = (l < 0.5)? (l*(1+s)):(l+s-(l*s))" << std::endl; + out << "p(l,s) = (2.0 * l - q(l,s))" << std::endl; + out << "n(x) = (x < 0)?(x+1):(x > 1)?(x-1) : (x)"<< std::endl; + out << std::endl; + out << "c(p,q,t) = (t <(1.0/6.0))?(p+(q-p)*6.0*t):\\"<< std::endl; + out << " (t <(1.0/2.0))?(q) :\\" << std::endl; + out << " (t<(2.0/3.0))?(p+(q-p)*6.0*((2.0/3.0)-t)):\\" << std::endl; + out << " (p)" << std::endl; + out << std::endl; + out << "r(h,s,l) = c(p(l,s),q(l,s),n(h/360.0+1.0/3.0))"<< std::endl; + out << "g(h,s,l) = c(p(l,s),q(l,s),n(h/360.0))" << std::endl; + out << "b(h,s,l) = c(p(l,s),q(l,s),n(h/360.0-1.0/3.0))"<< std::endl; + out << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set palette gray" << std::endl; + out << "set xrange [" << min_col; + out << ":" << max_col; + out << "]" << std::endl; + out << "set yrange [-" << max_row; + out << ":" << min_row; + out << "]" << std::endl; + out << "plot '-' using 2:(-\\$1):\\" << std::endl; + out << " (r(\\$3,\\$4,\\$5)):\\" << std::endl; + out << " (g(\\$3,\\$4,\\$5)):\\" << std::endl; + out << " (b(\\$3,\\$4,\\$5)) with rgbimage" << std::endl; + + // Output data. + for_all(p) + { + out << p.row() << " "; + out << p.col() << " "; + out << img(p).hue() << " "; + out << img(p).sat() << " "; + out << img(p).lum() << std::endl; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + out << "EOF" << std::endl; + + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_image2d_hsl"); + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_image2d_hsi(const image2d<hsi_<T,T,T>>&, const string&) + //---------------------------------------------------------------------- + + template <typename T> + inline + bool save_image_sh_image2d_hsi(const image2d<value::hsi_<T,T,T> >& img, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_image2d_hsi"); + mln_precondition(img.is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_row = geom::min_row(img); + unsigned max_row = geom::max_row(img); + unsigned min_col = geom::min_col(img); + unsigned max_col = geom::max_col(img); + + typedef mln::value::hsi_<T,T,T> hsi; + + if (result) + { + mln_piter(image2d< hsi >) p(img.domain()); + + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "teta(h) = (h/180.0) * pi" << std::endl; + out << "alpha(s,h) = s*cos(teta(h))" << std::endl; + out << "beta(s,h) = s*sin(teta(h))" << std::endl; + out << "n(x) = (x < 0)?(x+1) : (x > 1)?(x-1):(x)"<< std::endl; + out << std::endl; + out << "c(p,q,t) = (t <(1.0/6.0))?(p+(q-p)*6.0*t):\\"<< std::endl; + out << " (t <(1.0/2.0))?(q) :\\" << std::endl; + out << " (t <(2.0/3.0))?(p+(q-p)*6.0*((2.0/3.0)-t)):\\"<< std::endl; + out << " (p)" << std::endl; + out << std::endl; + out << "r(h,s,i) = (sqrt(3.0)/3.0) * i \\" << std::endl; + out << " + (2.0/(sqrt(6.0))) * beta(s,h)" << std::endl; + out << "g(h,s,i) = (sqrt(3.0)/3.0) * i \\" << std::endl; + out << " + (2.0/(sqrt(2.0))) * alpha(s,h)\\" << std::endl; + out << " - (1.0/(sqrt(6.0))) * beta(s,h)" << std::endl; + out << "b(h,s,i) = (sqrt(3.0)/3.0) * i \\" << std::endl; + out << " - (2.0/(sqrt(2.0))) * alpha(s,h) \\"<< std::endl; + out << " - (1.0/(sqrt(6.0))) * beta(s,h)" << std::endl; + out << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set palette gray" << std::endl; + out << "set xrange [" << min_col; + out << ":" << max_col; + out << "]" << std::endl; + out << "set yrange [-" << max_row; + out << ":" << min_row; + out << "]" << std::endl; + out << "plot '-' using 2:(-\\$1):\\" << std::endl; + out << " (r(\\$3,\\$4,\\$5)):\\" << std::endl; + out << " (g(\\$3,\\$4,\\$5)):\\" << std::endl; + out << " (b(\\$3,\\$4,\\$5)) with rgbimage" << std::endl; + + // Output data. + for_all(p) + { + out << p.row() << " "; + out << p.col() << " "; + out << img(p).hue() << " "; + out << img(p).sat() << " "; + out << img(p).inty() << std::endl; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + out << "EOF" << std::endl; + + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:" << filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_image2d_hsi"); + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_image2d(const image2d&, const string&) + //---------------------------------------------------------------------- + + template <typename I> + inline + bool save_image_sh_image2d(const image2d& img, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_image2d"); + mln_precondition(img.is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + unsigned min_row = geom::min_row(img); + unsigned max_row = geom::max_row(img); + unsigned min_col = geom::min_col(img); + unsigned max_col = geom::max_col(img); + + if (result) + { + mln_piter(image2d) p(img.domain()); + + // Output data prelude (terminal X11, image). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "set palette gray" << std::endl; + out << "set xrange [" << min_col; + out << ":" << max_col; + out << "]" << std::endl; + out << "set yrange [-" << max_row; + out << ":" << min_row; + out << "]" << std::endl; + out << "plot '-' using 2:(-\\$1):3 with image" <<std::endl; + + // Output data. + for_all(p) + { + out << p.row() << " "; + out << p.col() << " "; + out << img(p) << std::endl; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + out << "EOF" << std::endl; + + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:"<< filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_image2d"); + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_image1d(const image1d&, const string&) + //---------------------------------------------------------------------- + + template <typename I> + inline + bool save_image_sh_image1d(const image1d& img, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_image1d"); + mln_precondition(img.is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + + if (result) + { + mln_piter(image1d) p(img.domain()); + + // Output data prelude (terminal X11, impulse). + out << "#!/bin/sh" << std::endl; + out << "##########################" << std::endl; + out << "# Two columns = (x, val) #" << std::endl; + out << "##########################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "plot '-' with impulse" << std::endl; + + // Output data. + for_all(p) + { + out << p.ind() << " "; + out << img(p) << std::endl; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + out << "EOF" << std::endl; + + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:"<< filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_image1d"); + return result; + } + + //---------------------------------------------------------------------- + // save_image_sh_image3d(const image3d&, const string&) + //---------------------------------------------------------------------- + + + template <typename I> + inline + bool save_image_sh_image3d(const image3d& img, + const std::string& filename) + { + trace::entering("mln::io::plot::impl::save_image_sh_image3d"); + mln_precondition(img.is_valid()); + + std::ofstream out(filename.c_str()); + bool result = !out.fail(); + + if (result) + { + mln_piter(image3d) p(img.domain()); + + // Output data prelude (terminal X11, pointtype 0). + out << "#!/bin/sh" << std::endl; + out << "####################################" << std::endl; + out << "# Columns = (x, y, z, val) #" << std::endl; + out << "####################################" << std::endl; + out << std::endl; + out << "gnuplot <<EOF" << std::endl; + out << "set terminal x11 persist 1" << std::endl; + out << "splot '-' with points palette pointtype 7" << std::endl; + + // Output data. + for_all(p) + { + out << p.row() << " "; + out << p.col() << " "; + out << p.sli() << " "; + out << img(p) << std::endl; + } + + // Close gnuplot data stream. + out << "e" << std::endl; + out << "EOF" << std::endl; + + out.close(); + } + else + { + std::cerr << "ERROR[mln::io::plot::save_image_sh]:"<< filename; + std::cerr << " couldn't be opened !!" << std::endl; + } + + trace::exiting("mln::io::plot::impl::save_image_sh_image3d"); + return result; + } + + } // end of namespace impl + + + //------------------------------------------------------------------------ + // Internal. + //------------------------------------------------------------------------ + + namespace internal + { + template <typename I> + inline + bool save_image_sh_dispatch(const util::array< image1d >& stack, + const std::string& filename) + { + return impl::save_image_sh_array_image1d(stack, filename); + } + + template <typename I> + inline + bool save_image_sh_dispatch(const util::array< + util::array <image1d > >& stack, + const std::string& filename) + { + return impl::save_image_sh_array_array_image1d(stack, filename); + } + + template <typename T> + inline + bool save_image_sh_dispatch( + const util::array<util::array<image1d<algebra::vec<3,T> > > >& stack, + const std::string& filename) + { + return impl::save_image_sh_array_array_image1d_vec3(stack, filename); + } + + template <typename T> + inline + bool save_image_sh_dispatch( + const util::array<util::array<image1d<algebra::vec<2,T> > > >& stack, + const std::string& filename) + { + return impl::save_image_sh_array_array_image1d_vec2(stack, filename); + } + + template <unsigned n> + inline + bool save_image_sh_dispatch(const image2d<value::rgb<n> >& img, + const std::string& filename) + { + return impl::save_image_sh_image2d_rgb(img, filename); + } + + template <typename T> + inline + bool save_image_sh_dispatch(const image2d<value::hsl_<T,T,T> >& img, + const std::string& filename) + { + return impl::save_image_sh_image2d_hsl(img, filename); + } + + template <typename T> + inline + bool save_image_sh_dispatch(const image2d<value::hsi_<T,T,T> >& img, + const std::string& filename) + { + return impl::save_image_sh_image2d_hsi(img, filename); + } + + + template <typename I> + inline + bool save_image_sh_dispatch(const image2d& img, + const std::string& filename) + { + return impl::save_image_sh_image2d(img, filename); + } + + template <typename I> + inline + bool save_image_sh_dispatch(const image1d& img, + const std::string& filename) + { + return impl::save_image_sh_image1d(img, filename); + } + + template <typename I> + inline + bool save_image_sh_dispatch(const image3d& img, + const std::string& filename) + { + return impl::save_image_sh_image3d(img, filename); + } + + template <typename I> + inline + bool save_image_sh_dispatch(const Image& img, + const std::string& filename) + { + return save_image_sh_dispatch(exact(img), filename); + } + + } // end of namespace mln::io::plot::internal + + //------------------------------------------------------------------------ + // Facade. + //------------------------------------------------------------------------ + + template <typename I> + inline + bool save_image_sh(const Image& img, const std::string& filename) + { + trace::entering("mln::io::plot::save_image_sh"); + + bool result = internal::save_image_sh_dispatch(img, filename); + + trace::exiting("mln::io::plot::save_image_sh"); + return result; + } + + template <typename I> + inline + bool save_image_sh(const util::array< image1d >& stack, + const std::string& filename) + { + trace::entering("mln::io::plot::save_image_sh"); + + bool result = internal::save_image_sh_dispatch(stack, filename); + + trace::exiting("mln::io::plot::save_image_sh"); + return result; + } + + template <typename I> + inline + bool save_image_sh(const util::array< util::array< image1d > >& stack, + const std::string& filename) + { + trace::entering("mln::io::plot::save_image_sh"); + + bool result = internal::save_image_sh_dispatch(stack, filename); + + trace::exiting("mln::io::plot::save_image_sh"); + return result; + } + + +#endif // ! MLN_INCLUDE_ONLY + + + } // end of namespace mln::io::plot + + } // end of namespace mln::io + +} // end of namespace mln + +#endif // ! MLN_IO_PLOT_SAVE_IMAGE_SH_HH diff --git a/milena/sandbox/green/mln/math/ceil.hh b/scribo/sandbox/green/mln/math/ceil.hh similarity index 100% copy from milena/sandbox/green/mln/math/ceil.hh copy to scribo/sandbox/green/mln/math/ceil.hh diff --git a/milena/sandbox/green/mln/math/floor.hh b/scribo/sandbox/green/mln/math/floor.hh similarity index 100% copy from milena/sandbox/green/mln/math/floor.hh copy to scribo/sandbox/green/mln/math/floor.hh diff --git a/milena/sandbox/green/mln/value/hsv.hh b/scribo/sandbox/green/mln/value/hsv.hh similarity index 100% copy from milena/sandbox/green/mln/value/hsv.hh copy to scribo/sandbox/green/mln/value/hsv.hh diff --git a/scribo/sandbox/green/mln/value/rg.hh b/scribo/sandbox/green/mln/value/rg.hh new file mode 100644 index 0000000..d083ffd --- /dev/null +++ b/scribo/sandbox/green/mln/value/rg.hh @@ -0,0 +1,175 @@ +// Copyright (C) 2007, 2008, 2009, 2010 EPITA LRDE +// +// This file is part of Olena. +// +// Olena is free software: you can redistribute it and/or modify it under +// the terms of the GNU General Public License as published by the Free +// Software Foundation, version 2 of the License. +// +// Olena is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +// General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with Olena. If not, see <http://www.gnu.org/licenses/>. +// +// As a special exception, you may use this file as part of a free +// software project without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to produce +// an executable, this file does not by itself cause the resulting +// executable to be covered by the GNU General Public License. This +// exception does not however invalidate any other reasons why the +// executable file might be covered by the GNU General Public License. + +#ifndef MLN_VALUE_RG_HH +# define MLN_VALUE_RG_HH + +/// \file +/// +/// \brief Define the red/green vectorial image type. +/// +/// This source implements the red/green (RG) vectorial image type. It +/// is a partial copy of the rgb type. This type doesn't manage +/// anykind of interaction. Its purpose is limited to store a couple +/// of values. Do not use it outside this purpose, i can't garantie +/// the behaviour. +/// +/// The following sample is a typical use of the rg type. +/// +/// #include <mln/accu/stat/histo2d.hh> +/// #include <mln/core/image/image2d.hh> +/// #include <mln/data/compute.hh> +/// #include <mln/data/transform.hh> +/// #include <mln/fun/v2v/rgb_to_rg.hh> +/// #include <mln/img_path.hh> +/// #include <mln/io/ppm/load.hh> +/// #include <mln/value/rg.hh> +/// #include <mln/value/rgb8.hh> +/// +/// int main() +/// { +/// typedef mln::value::rg<8> t_rg8 +/// typedef mln::value::rgb8 t_rgb8; +/// typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; +/// typedef mln::image2d<t_rg8> t_image2d_rg8; +/// typedef mln::image2d<t_rgb8> t_image2d_rgb8; +/// typedef mln::image2d<unsigned> t_histo; +/// t_image2d_rgb8 img_rgb8; +/// t_image2d_rg8 img_rg8; +/// t_histo histo; +/// +/// mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); +/// img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); +/// histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); +/// +/// return 0; +/// } + + +# include <mln/algebra/vec.hh> +# include <mln/value/concept/vectorial.hh> +# include <mln/value/int_u.hh> +# include <mln/value/rgb8.hh> +# include <mln/trait/value_.hh> + + +namespace mln +{ + + namespace value + { + + // Forward declaration. + template <unsigned n> struct rg; + + } // end of namespace mln::value + + namespace trait + { + + template <unsigned n> + struct value_< mln::value::rg<n> > + { + enum { + dim = 2, + nbits = dim * n, + card = mln_value_card_from_(nbits) + }; + + typedef trait::value::nature::vectorial nature; + typedef trait::value::kind::color kind; + typedef trait::value::quant::high quant; + + typedef void comp; + typedef mln::value::int_u<n> comp_0; + typedef mln::value::int_u<n> comp_1; + + static comp_0 get_comp_0(const mln::value::rg<n>& v){return v.red(); } + static comp_1 get_comp_1(const mln::value::rg<n>& v){return v.green();} + + typedef algebra::vec<dim, float> sum; + + static const char* name() + { + static std::string s = std::string("rg").append(1, 8 + '0'); + return s.c_str(); + } + }; + + } // end of namespace mln::trait + + namespace value + { + + /// \brief Define the red/green vectorial image type. + /// + /// Param n defines the quantification used for red/green space. + + template <unsigned n> + struct rg :public mln::value::Vectorial< rg<n> >, + public internal::value_like_< + algebra::vec< 2, int_u<n> >, // Equivalent. + algebra::vec< 2, int_u<n> >, // Encoding. + algebra::vec< 2, int >, // Interoperation. + rg<n> > // Exact. + { + typedef int_u<n> t_red; + typedef int_u<n> t_green; + + int_u<n> red() const {return this->v_[0];} + int_u<n>& red() {return this->v_[0];} + int_u<n> green() const {return this->v_[1];} + int_u<n>& green() {return this->v_[1];} + + /// Constructors + /// \{ + /// \brief Create a rg value. + /// + /// Two constructors exist, the first one do nothing and the second one + /// initializes the red/green components from a rgb value. + rg(){} + rg(const rgb8& val){this->v_[0] = val.red(); this->v_[1] = val.green();} + /// \} + + /// \brief Affect a rg value to this object. + /// \param[in] rhs the value to affect to this object. + /// + /// If the two objects are different, then then copy the + /// underline array of values. + + rg operator=(const rg& rhs) + { + if (&rhs != this) + this->v_ = rhs.v_; + + return *this; + } + }; + + } // end of namespace mln::value + +} // end of namespace mln + +#endif // ! MLN_VALUE_RG_HH diff --git a/scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am new file mode 100644 index 0000000..77f9015 --- /dev/null +++ b/scribo/sandbox/green/tests/accu/stat/histo1d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/tests +BUILD__PATTERN= green/build/tests + + +ifeq ($(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/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc b/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc new file mode 100644 index 0000000..5a5c4c4 --- /dev/null +++ b/scribo/sandbox/green/tests/accu/stat/histo1d/histo1d.cc @@ -0,0 +1,21 @@ +/// TEST HISTO1D + +#include <mln/accu/stat/histo1d.hh> +#include <mln/data/compute.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/img_path.hh> +#include <mln/io/pgm/load.hh> +#include <mln/value/int_u8.hh> + +int main() +{ + typedef mln::value::int_u8 t_int_u8; + mln::image2d<t_int_u8> img; + mln::image1d<unsigned> histo; + + mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm"); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), img); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am b/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am new file mode 100644 index 0000000..a96b2e9 --- /dev/null +++ b/scribo/sandbox/green/use/accu/stat/histo1d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/use +BUILD__PATTERN= green/build/use + + +ifeq ($(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/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc b/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc new file mode 100644 index 0000000..1c6176b --- /dev/null +++ b/scribo/sandbox/green/use/accu/stat/histo1d/histo1d.cc @@ -0,0 +1,24 @@ +/// \file +/// +/// \brief Minimal code for building 1d image histogram version. +/// + +#include <mln/accu/stat/histo1d.hh> +#include <mln/data/compute.hh> +#include <mln/core/image/image1d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/img_path.hh> +#include <mln/io/pgm/load.hh> +#include <mln/value/int_u8.hh> + +int main() +{ + typedef mln::value::int_u8 t_int_u8; + mln::image2d<t_int_u8> img; + mln::image1d<unsigned> histo; + + mln::io::pgm::load(img, OLENA_IMG_PATH"/lena.pgm"); + histo = mln::data::compute(mln::accu::meta::stat::histo1d(), img); + + return 0; +} diff --git a/scribo/sandbox/green/use/accu/stat/histo2d/Makefile.am b/scribo/sandbox/green/use/accu/stat/histo2d/Makefile.am new file mode 100644 index 0000000..a96b2e9 --- /dev/null +++ b/scribo/sandbox/green/use/accu/stat/histo2d/Makefile.am @@ -0,0 +1,153 @@ +# +# Generic Makefile +# + +######### +# TOOLS # +######### + +#LOADLIBES= -lboost_filesystem +INCLUDES1= -I$(HOME)/git/olena/scribo/sandbox/green +INCLUDES2= -I$(HOME)/git/olena/milena +INCLUDES= $(INCLUDES1) $(INCLUDES2) +CXXFLAGS= -ggdb -O0 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O1 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +#CXXFLAGS= -DNDEBUG -O3 -Wall -W -pedantic -ansi -pipe $(INCLUDES) +ECHO= echo +RM= rm +MKDIR= mkdir -p +CP= cp + +SOURCE_PATTERN= green/use +BUILD__PATTERN= green/build/use + + +ifeq ($(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/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc b/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc new file mode 100644 index 0000000..6369b5a --- /dev/null +++ b/scribo/sandbox/green/use/accu/stat/histo2d/histo2d.cc @@ -0,0 +1,33 @@ +/// \file +/// +/// \brief Minimal code for building 2d image histogram version. +/// + +#include <mln/accu/stat/histo2d.hh> +#include <mln/core/image/image2d.hh> +#include <mln/data/compute.hh> +#include <mln/data/transform.hh> +#include <mln/fun/v2v/rgb_to_rg.hh> +#include <mln/img_path.hh> +#include <mln/io/ppm/load.hh> +#include <mln/value/rg.hh> +#include <mln/value/rgb8.hh> + +int main() +{ + typedef mln::value::rg<8> t_rg8; + typedef mln::value::rgb8 t_rgb8; + typedef mln::fun::v2v::rgb_to_rg<8> t_rgb_to_rg; + typedef mln::image2d<t_rg8> t_image2d_rg8; + typedef mln::image2d<t_rgb8> t_image2d_rgb8; + typedef mln::image2d<unsigned> t_histo; + t_image2d_rgb8 img_rgb8; + t_image2d_rg8 img_rg8; + t_histo histo; + + mln::io::ppm::load(img_rgb8, OLENA_IMG_PATH"/lena.ppm"); + img_rg8 = mln::data::transform(img_rgb8, t_rgb_to_rg()); + histo = mln::data::compute(mln::accu::meta::stat::histo2d(), img_rg8); + + return 0; +} -- 1.5.6.5

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Olena-patches November 2010