libs/gil/test/core/image_processing/histogram_equalization.cpp
// // Copyright 2020 Debabrata Mandal <mandaldebabrata123@gmail.com> // // 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) // #include <boost/gil/color_base_algorithm.hpp> #include <boost/gil/histogram.hpp> #include <boost/gil/image_view.hpp> #include <boost/gil/io/write_view.hpp> #include <boost/gil/image_processing/histogram_equalization.hpp> #include <boost/gil/extension/toolbox/metafunctions/get_pixel_type.hpp> #include <boost/core/lightweight_test.hpp> #include <vector> const int a = 5; const double epsilon = 0.005; // Decided by the value 1/255 i.e. an error of 1 px in 255 px boost::gil::gray8_image_t original(a, a); boost::gil::gray8_image_t processed_1(a, a), processed_2(a, a), expected(a, a); std::vector<std::vector<int> > test1_random{ { 1, 10, 10, 10, 10}, { 20, 25, 25, 55, 20}, { 0, 55, 55, 55, 20}, { 20, 255, 255, 255, 0}, { 100, 100, 100, 10, 0}}; std::vector<std::vector<int> > expected_test1{ { 40, 91, 91, 91, 91}, { 132, 153, 153, 193, 132}, { 30, 193, 193, 193, 132}, { 132, 255, 255, 255, 30}, { 224, 224, 224, 91, 30}}; std::vector<std::vector<int> > all_white{ {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}}; std::vector<std::vector<int> > expected_all_white{ {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}}; std::vector<std::vector<int> > binary_img{ {0 , 0 , 0 , 0 , 0 }, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}}; std::vector<std::vector<int> > expected_binary_img{ {51 , 51 , 51 , 51 , 51 }, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}, {255, 255, 255, 255, 255}}; std::vector<std::vector<int> > test2_uniform{ { 0, 10, 20, 30, 40}, { 50, 60, 70, 80, 90}, { 100, 110, 120, 130, 140}, { 150, 160, 170, 180, 190}, { 200, 210, 220, 230, 240}}; std::vector<std::vector<int> > expected_test2{ { 10, 20, 30, 40, 51}, { 61, 71, 81, 91, 102}, { 112, 122, 132, 142, 153}, { 163, 173, 183, 193, 204}, { 214, 224, 234, 244, 255}}; std::vector<std::vector<int> > test3_2peaks{ { 0, 0, 0, 0, 10}, { 40, 43, 44, 46, 50}, { 55, 56, 44, 46, 44}, { 200, 201, 202, 203, 200}, { 201, 202, 201, 201, 22}}; std::vector<std::vector<int> > expected_test3{ { 40, 40, 40, 40, 51}, { 71, 81, 112, 132, 142}, { 153, 163, 112, 132, 112}, { 183, 224, 244, 255, 183}, { 224, 244, 224, 224, 61}}; std::vector<std::vector<int> > test_mask{ {1, 10, 10, 10, 10}, {20, 25, 25, 25, 20}, {0, 25, 25, 25, 20}, {20, 25, 25, 25, 0}, {100, 100, 100, 10, 0}}; std::vector<std::vector<int> > expected_test_mask{ {1, 10, 10, 10, 10}, {20, 255, 255, 255, 20}, {0, 255, 255, 255, 20}, {20, 255, 255, 255, 0}, {100, 100, 100, 10, 0}}; std::vector<std::vector<bool> > mask{ {0, 0, 0, 0, 0}, {0, 1, 1, 1, 0}, {0, 1, 1, 1, 0}, {0, 1, 1, 1, 0}, {0, 0, 0, 0, 0}}; void vector_to_gray_image(boost::gil::gray8_image_t& img, std::vector<std::vector<int> >& grid) { for(std::ptrdiff_t y=0; y<grid.size(); ++y) { for(std::ptrdiff_t x=0; x<grid[0].size(); ++x) { boost::gil::view(img)(x,y) = boost::gil::gray8_pixel_t(grid[y][x]); } } } template<typename SrcView> bool equal_pixels(SrcView const& v1, SrcView const& v2, double threshold) { double sum=0.0; using pixel_t = typename boost::gil::get_pixel_type<SrcView>::type; using channel_t = typename boost::gil::channel_type<SrcView>::type; channel_t max_p = std::numeric_limits<channel_t>::max(); channel_t min_p = std::numeric_limits<channel_t>::min(); long int num_pixels = v1.width() * v1.height(); std::size_t num_channels = boost::gil::num_channels<SrcView>::value; for (std::ptrdiff_t y = 0; y < v1.height(); ++y) { auto it1 = v1.row_begin(y); auto it2 = v2.row_begin(y); for (std::ptrdiff_t x = 0; x < v2.width(); ++x) { for(std::ptrdiff_t c = 0; c < num_channels; ++c) { sum += abs(it1[x][c]-it2[x][c]); } } } return ( abs(sum) / (num_pixels * num_channels * (max_p - min_p)) < threshold ); } void test_random_image() { vector_to_gray_image(original,test1_random); vector_to_gray_image(expected,expected_test1); histogram_equalization(boost::gil::const_view(original),boost::gil::view(processed_1)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(expected), epsilon)); // Process image again to look for differences histogram_equalization(boost::gil::const_view(processed_1),boost::gil::view(processed_2)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(processed_2), epsilon)); // Test overloaded version boost::gil::histogram<unsigned char> hist, process_1, process_2; fill_histogram(boost::gil::const_view(original), hist, 1, false, false); histogram_equalization(hist, process_1); histogram_equalization(process_1, process_2); BOOST_TEST(process_1.equals(process_2)); } void test_random_image_with_mask() { vector_to_gray_image(original,test_mask); vector_to_gray_image(expected,expected_test_mask); histogram_equalization(boost::gil::const_view(original),boost::gil::view(processed_1), 1, true, mask); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(expected), epsilon)); // Process image again to look for differences histogram_equalization(boost::gil::const_view(processed_1),boost::gil::view(processed_2), 1, true, mask); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(processed_2), epsilon)); // Test overloaded version boost::gil::histogram<unsigned char> hist, process_1, process_2; fill_histogram(boost::gil::const_view(original), hist, 1, false, false); histogram_equalization(hist, process_1); histogram_equalization(process_1, process_2); BOOST_TEST(process_1.equals(process_2)); } void test_uniform_image() { vector_to_gray_image(original,test2_uniform); vector_to_gray_image(expected,expected_test2); histogram_equalization(boost::gil::const_view(original),boost::gil::view(processed_1)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(expected), epsilon)); // Process image again to look for differences histogram_equalization(boost::gil::const_view(processed_1),boost::gil::view(processed_2)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(processed_2), epsilon)); // Test overloaded version boost::gil::histogram<unsigned char> hist, process_1, process_2; fill_histogram(boost::gil::const_view(original), hist, 1, false, false); histogram_equalization(hist, process_1); histogram_equalization(process_1, process_2); BOOST_TEST(process_1.equals(process_2)); } void test_all_white_image() { vector_to_gray_image(original,all_white); vector_to_gray_image(expected,expected_all_white); histogram_equalization(boost::gil::const_view(original),boost::gil::view(processed_1)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(expected), epsilon)); // Process image again to look for differences histogram_equalization(boost::gil::const_view(processed_1),boost::gil::view(processed_2)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(processed_2), epsilon)); // Test overloaded version boost::gil::histogram<unsigned char> hist, process_1, process_2; fill_histogram(boost::gil::const_view(original), hist, 1, false, false); histogram_equalization(hist, process_1); histogram_equalization(process_1, process_2); BOOST_TEST(process_1.equals(process_2)); } void test_binary_image() { vector_to_gray_image(original,binary_img); vector_to_gray_image(expected,expected_binary_img); histogram_equalization(boost::gil::const_view(original),boost::gil::view(processed_1)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(expected), epsilon)); // Process image again to look for differences histogram_equalization(boost::gil::const_view(processed_1),boost::gil::view(processed_2)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(processed_2), epsilon)); // Test overloaded version boost::gil::histogram<unsigned char> hist, process_1, process_2; fill_histogram(boost::gil::const_view(original), hist, 1, false, false); histogram_equalization(hist, process_1); histogram_equalization(process_1, process_2); BOOST_TEST(process_1.equals(process_2)); } void test_double_peaked_image() { vector_to_gray_image(original,test3_2peaks); vector_to_gray_image(expected,expected_test3); histogram_equalization(boost::gil::const_view(original),boost::gil::view(processed_1)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(expected), epsilon)); // Process image again to look for differences histogram_equalization(boost::gil::const_view(processed_1),boost::gil::view(processed_2)); BOOST_TEST(equal_pixels(boost::gil::view(processed_1), boost::gil::view(processed_2), epsilon)); // Test overloaded version boost::gil::histogram<unsigned char> hist, process_1, process_2; fill_histogram(boost::gil::const_view(original), hist, 1, false, false); histogram_equalization(hist, process_1); histogram_equalization(process_1, process_2); BOOST_TEST(process_1.equals(process_2)); } int main() { //Basic tests for grayscale histogram_equalization test_random_image(); test_random_image_with_mask(); test_all_white_image(); test_binary_image(); test_uniform_image(); test_double_peaked_image(); return boost::report_errors(); }