From 2e7a51f09975a8cd5056a13cd226949ee36d8703 Mon Sep 17 00:00:00 2001 From: miral Date: Mon, 12 Aug 2019 00:12:28 +0530 Subject: [PATCH] added definition of gaussian adaptive threshold closes #316 --- example/adaptive_threshold.cpp | 18 +++--- example/test_adaptive.png | Bin 26467 -> 150964 bytes .../boost/gil/image_processing/threshold.hpp | 54 ++++++++++++------ 3 files changed, 48 insertions(+), 24 deletions(-) diff --git a/example/adaptive_threshold.cpp b/example/adaptive_threshold.cpp index 6d953808d7..65f0f9ac14 100644 --- a/example/adaptive_threshold.cpp +++ b/example/adaptive_threshold.cpp @@ -7,6 +7,7 @@ // #include #include +#include using namespace boost::gil; @@ -16,14 +17,17 @@ int main() read_image("test_adaptive.png", img, png_tag{}); gray8_image_t img_out(img.dimensions()); - // performing binary threshold on each channel of the image - // if the pixel value is more than 150 than it will be set to 255 else to 0 - boost::gil::threshold_adaptive(const_view(img), view(img_out), 11); - write_view("out-threshold-adaptive.png", view(img_out), png_tag{}); + boost::gil::threshold_adaptive(const_view(img), view(img_out), 11, threshold_adaptive_method::mean, threshold_direction::regular, 2); + write_view("out-threshold-adaptive-mean.png", view(img_out), png_tag{}); + + boost::gil::threshold_adaptive(const_view(img), view(img_out), 11, threshold_adaptive_method::mean, threshold_direction::inverse, 2); + write_view("out-threshold-adaptive-mean-inv.png", view(img_out), png_tag{}); - // if the pixel value is more than 150 than it will be set to 150 else no change - boost::gil::threshold_adaptive(const_view(img), view(img_out), 11, threshold_adaptive_method::mean, threshold_direction::inverse); - write_view("out-threshold-adaptive-inv.png", view(img_out), png_tag{}); + boost::gil::threshold_adaptive(const_view(img), view(img_out), 7, threshold_adaptive_method::gaussian, threshold_direction::regular, 2); + write_view("out-threshold-adaptive-gaussian.png", view(img_out), png_tag{}); + + boost::gil::threshold_adaptive(const_view(img), view(img_out), 11, threshold_adaptive_method::gaussian, threshold_direction::inverse, 2); + write_view("out-threshold-adaptive-gaussian-inv.png", view(img_out), png_tag{}); return 0; } diff --git a/example/test_adaptive.png b/example/test_adaptive.png index 8aea767ab89b1d4f8f32a7f8f834d70ac63136ff..d72dd68f7b3d8cc4c56064fac7c85e85fbc62652 100644 GIT binary patch literal 150964 zcmeFabyQnh+wL8-xRv6?p+IqWhvHsb3jtEx-3rA@aSQHFad#+Eq!f23ER;-E*zqlr^)GK$wcsD|8eh6aWB#E-NFc1^~b^KRh^)9tr@k zpb_bZCuDmW9VY+)_30lE7(hxI0RR9;0FsbUQL(ghwsW$yvnQ98kRZ2rv@-`;0|5Z{ zc~fKKjPE;Z8-vuHohm9CY8#{*1ccax`JKKQ0Urav0e@^1Y)y@wY~-CGf5a>zED12r zSlL;t}RedA-7Ue-aiGldA87AG}AY6PN7DS+edyU8t7&<_QfP{)IjKl^n z!_34G_=E;75D*9(2one=0g#}S5bA*q&PB5$=p{5DkSEk)T{gurMR}9b=^UVw%opoH zaY~FQpNNsDSLRt(UUo6dI9E{RV~T09GLrpytoQgmN6aZ~64eZzt>Phqpqik%$9wk| z$H=FO>q)Cdj9FBb65n3`2v;fjhPi5p{Vb*}XEn#@Gi?}gm{CkobbOp3Z2}7ulaF|T 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zSTYAE6Ai3-orIjwamQ~ECuRbn^6Pv8d9^52Xca=ZMqEADmb`rPEXGLZEKT8Kxn!Pu=va}mAnT3H23{Jc zh(E*(Wh8!6K)^J@IYoLqr{K$gzj$OBQL;U;f%5#UzhWnr$<_Y~W+s~M)4HCF1D`iL+PUD29}>kGE*O1VD5yiLNgT#onB5Kvt`?i?%STW1?L1(?plG` ztmlkdQ;OoYELzCY$pfb%F5KVqUWB+X=F18&DoX7w80m#96_D8H5%4K@ zq#HlTCq!~rNqgKGxU2y?@|k`QV$KxId8{9Df O0000eB diff --git a/include/boost/gil/image_processing/threshold.hpp b/include/boost/gil/image_processing/threshold.hpp index 0a4f579499..7bb39b9874 100644 --- a/include/boost/gil/image_processing/threshold.hpp +++ b/include/boost/gil/image_processing/threshold.hpp @@ -16,10 +16,12 @@ #include #include +#include + #include #include #include -#include +#include namespace boost { namespace gil { @@ -403,31 +405,49 @@ void threshold_adaptive typedef typename channel_type::type source_channel_t; typedef typename channel_type::type result_channel_t; + image temp_img(src_view.width(), src_view.height()); + typename image::view_t temp_view = view(temp_img); + SrcView temp_conv(temp_view); + if (method == threshold_adaptive_method::mean) { std::vector mean_kernel_values(kernel_size, 1.0f/kernel_size); kernel_1d kernel(mean_kernel_values.begin(), kernel_size, kernel_size/2); - image temp_img(src_view.width(), src_view.height()); - typename image::view_t temp_view = view(temp_img); - SrcView temp_conv(temp_view); - convolve_1d>( src_view, kernel, temp_view ); + } + else if (method == threshold_adaptive_method::gaussian) + { + gray32f_image_t gaussian_kernel_values(kernel_size, kernel_size); + generate_gaussian_kernel(view(gaussian_kernel_values), 1.0); + + gray32f_view_t gaussian_kernel_view = view(gaussian_kernel_values); + kernel_2d kernel( + kernel_size, + kernel_size / 2, + kernel_size / 2 + ); - if (direction == threshold_direction::regular) - { - detail::adaptive_impl(src_view, temp_conv, dst_view, - [max_value, constant](source_channel_t px, source_channel_t threshold) -> result_channel_t - { return px > (threshold - constant) ? max_value : 0; }); - } - else - { - detail::adaptive_impl(src_view, temp_conv, dst_view, - [max_value, constant](source_channel_t px, source_channel_t threshold) -> result_channel_t - { return px > (threshold - constant) ? 0 : max_value; }); - } + std::transform(gaussian_kernel_view.begin(), gaussian_kernel_view.end(), kernel.begin(), + [](gray32f_pixel_t pixel) -> float {return pixel.at(std::integral_constant{}); } + ); + + convolve_2d(src_view, kernel, temp_view); + } + + if (direction == threshold_direction::regular) + { + detail::adaptive_impl(src_view, temp_conv, dst_view, + [max_value, constant](source_channel_t px, source_channel_t threshold) -> result_channel_t + { return px > (threshold - constant) ? max_value : 0; }); + } + else + { + detail::adaptive_impl(src_view, temp_conv, dst_view, + [max_value, constant](source_channel_t px, source_channel_t threshold) -> result_channel_t + { return px > (threshold - constant) ? 0 : max_value; }); } }