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// // Copyright 2019 Olzhas Zhumabek // // Use, modification and distribution are subject to the Boost Software License, // Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at // http://www.boost.org/LICENSE_1_0.txt) // #ifndef BOOST_GIL_IMAGE_PROCESSING_SCALING_HPP #define BOOST_GIL_IMAGE_PROCESSING_SCALING_HPP #include #include #include #include namespace boost { namespace gil { /// \defgroup ScalingAlgorithms /// \brief Algorithms suitable for rescaling /// /// These algorithms are used to improve image quality after image resizing is made. /// /// \defgroup DownScalingAlgorithms /// \ingroup ScalingAlgorithms /// \brief Algorithms suitable for downscaling /// /// These algorithms provide best results when used for downscaling. Using for upscaling will /// probably provide less than good results. /// /// \brief a single step of lanczos downscaling /// \ingroup DownScalingAlgorithms /// /// Use this algorithm to scale down source image into a smaller image with reasonable quality. /// Do note that having a look at the output once is a good idea, since it might have ringing /// artifacts. template void lanczos_at( ImageView input_view, ImageView output_view, typename ImageView::x_coord_t source_x, typename ImageView::y_coord_t source_y, typename ImageView::x_coord_t target_x, typename ImageView::y_coord_t target_y, std::ptrdiff_t a) { using x_coord_t = typename ImageView::x_coord_t; using y_coord_t = typename ImageView::y_coord_t; using pixel_t = typename std::remove_reference()(0, 0))>::type; // C++11 doesn't allow auto in lambdas using channel_t = typename std::remove_reference < decltype(std::declval().at(std::integral_constant{})) >::type; pixel_t result_pixel; static_transform(result_pixel, result_pixel, [](channel_t) { return static_cast(0); }); auto x_zero = static_cast(0); auto x_one = static_cast(1); auto y_zero = static_cast(0); auto y_one = static_cast(1); for (y_coord_t y_i = (std::max)(source_y - static_cast(a) + y_one, y_zero); y_i <= (std::min)(source_y + static_cast(a), input_view.height() - y_one); ++y_i) { for (x_coord_t x_i = (std::max)(source_x - static_cast(a) + x_one, x_zero); x_i <= (std::min)(source_x + static_cast(a), input_view.width() - x_one); ++x_i) { double lanczos_response = lanczos(source_x - x_i, a) * lanczos(source_y - y_i, a); auto op = [lanczos_response](channel_t prev, channel_t next) { return static_cast(prev + next * lanczos_response); }; static_transform(result_pixel, input_view(source_x, source_y), result_pixel, op); } } output_view(target_x, target_y) = result_pixel; } /// \brief Complete Lanczos algorithm /// \ingroup DownScalingAlgorithms /// /// This algorithm does full pass over resulting image and convolves pixels from /// original image. Do note that it might be a good idea to have a look at test /// output as there might be ringing artifacts. /// Based on wikipedia article: /// https://en.wikipedia.org/wiki/Lanczos_resampling /// with standardinzed cardinal sin (sinc) template void scale_lanczos(ImageView input_view, ImageView output_view, std::ptrdiff_t a) { double scale_x = (static_cast(output_view.width())) / static_cast(input_view.width()); double scale_y = (static_cast(output_view.height())) / static_cast(input_view.height()); using x_coord_t = typename ImageView::x_coord_t; using y_coord_t = typename ImageView::y_coord_t; for (y_coord_t y = 0; y < output_view.height(); ++y) { for (x_coord_t x = 0; x < output_view.width(); ++x) { lanczos_at(input_view, output_view, x / scale_x, y / scale_y, x, y, a); } } } }} // namespace boost::gil #endif