Adding upstream version 14.2.21.upstream/14.2.21upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 208 insertions, 0 deletions

diff --git a/src/boost/libs/gil/example/hessian.cpp b/src/boost/libs/gil/example/hessian.cpp
new file mode 100644
index 00000000..d12f51dd
--- /dev/null
+++ b/src/boost/libs/gil/example/hessian.cpp
@@ -0,0 +1,208 @@
+#include <boost/gil/image.hpp>
+#include <boost/gil/image_view.hpp>
+#include <boost/gil/image_processing/numeric.hpp>
+#include <boost/gil/image_processing/hessian.hpp>
+#include <boost/gil/extension/io/png.hpp>
+#include <vector>
+#include <functional>
+#include <set>
+#include <iostream>
+#include <fstream>
+
+namespace gil = boost::gil;
+
+// some images might produce artifacts
+// when converted to grayscale,
+// which was previously observed on
+// canny edge detector for test input
+// used for this example.
+// the algorithm here follows sRGB gamma definition
+// taken from here (luminance calculation):
+// https://en.wikipedia.org/wiki/Grayscale
+gil::gray8_image_t to_grayscale(gil::rgb8_view_t original)
+{
+    gil::gray8_image_t output_image(original.dimensions());
+    auto output = gil::view(output_image);
+    constexpr double max_channel_intensity = (std::numeric_limits<std::uint8_t>::max)();
+    for (long int y = 0; y < original.height(); ++y)
+    {
+        for (long int x = 0; x < original.width(); ++x)
+        {
+            // scale the values into range [0, 1] and calculate linear intensity
+            auto& p = original(x, y);
+            double red_intensity = p.at(std::integral_constant<int, 0>{})
+                / max_channel_intensity;
+            double green_intensity = p.at(std::integral_constant<int, 1>{})
+                / max_channel_intensity;
+            double blue_intensity = p.at(std::integral_constant<int, 2>{})
+                / max_channel_intensity;
+            auto linear_luminosity = 0.2126 * red_intensity
+                                    + 0.7152 * green_intensity
+                                    + 0.0722 * blue_intensity;
+
+            // perform gamma adjustment
+            double gamma_compressed_luminosity = 0;
+            if (linear_luminosity < 0.0031308)
+            {
+                gamma_compressed_luminosity = linear_luminosity * 12.92;
+            } else
+            {
+                gamma_compressed_luminosity = 1.055 * std::pow(linear_luminosity, 1 / 2.4) - 0.055;
+            }
+
+            // since now it is scaled, descale it back
+            output(x, y) = gamma_compressed_luminosity * max_channel_intensity;
+        }
+    }
+
+    return output_image;
+}
+
+void apply_gaussian_blur(gil::gray8_view_t input_view, gil::gray8_view_t output_view)
+{
+    constexpr static auto filter_height = 5ull;
+    constexpr static auto filter_width = 5ull;
+    constexpr static double filter[filter_height][filter_width] =
+    {
+        2,  4,  6,  4,  2,
+        4, 9, 12, 9,  4,
+        5, 12, 15, 12,  5,
+        4, 9, 12, 9,  4,
+        2,  4,  5,  4,  2,
+    };
+    constexpr double factor = 1.0 / 159;
+    constexpr double bias = 0.0;
+
+    const auto height = input_view.height();
+    const auto width = input_view.width();
+    for (std::ptrdiff_t x = 0; x < width; ++x)
+    {
+        for (std::ptrdiff_t y = 0; y < height; ++y)
+        {
+            double intensity = 0.0;
+            for (std::ptrdiff_t filter_y = 0; filter_y < filter_height; ++filter_y)
+            {
+                for (std::ptrdiff_t filter_x = 0; filter_x < filter_width; ++filter_x)
+                {
+                    int image_x = x - filter_width / 2 + filter_x;
+                    int image_y = y - filter_height / 2 + filter_y;
+                    if (image_x >= input_view.width() || image_x < 0 ||
+                        image_y >= input_view.height() || image_y < 0)
+                    {
+                        continue;
+                    }
+                    const auto& pixel = input_view(image_x, image_y);
+                    intensity += pixel.at(std::integral_constant<int, 0>{})
+                        * filter[filter_y][filter_x];
+                }
+            }
+            auto& pixel = output_view(gil::point_t(x, y));
+            pixel = (std::min)((std::max)(int(factor * intensity + bias), 0), 255);
+        }
+
+    }
+}
+
+std::vector<gil::point_t> suppress(
+    gil::gray32f_view_t harris_response,
+    double harris_response_threshold)
+{
+    std::vector<gil::point_t> corner_points;
+    for (gil::gray32f_view_t::coord_t y = 1; y < harris_response.height() - 1; ++y)
+    {
+        for (gil::gray32f_view_t::coord_t x = 1; x < harris_response.width() - 1; ++x)
+        {
+            auto value = [](gil::gray32f_pixel_t pixel) {
+                return pixel.at(std::integral_constant<int, 0>{});
+            };
+            double values[9] = {
+                value(harris_response(x - 1, y - 1)),
+                value(harris_response(x, y - 1)),
+                value(harris_response(x + 1, y - 1)),
+                value(harris_response(x - 1, y)),
+                value(harris_response(x, y)),
+                value(harris_response(x + 1, y)),
+                value(harris_response(x - 1, y + 1)),
+                value(harris_response(x, y + 1)),
+                value(harris_response(x + 1, y + 1))
+            };
+
+            auto maxima = *std::max_element(
+                values,
+                values + 9,
+                [](double lhs, double rhs)
+                {
+                    return lhs < rhs;
+                }
+            );
+
+            if (maxima == value(harris_response(x, y))
+                && std::count(values, values + 9, maxima) == 1
+                && maxima >= harris_response_threshold)
+            {
+                corner_points.emplace_back(x, y);
+            }
+        }
+    }
+
+    return corner_points;
+}
+
+int main(int argc, char* argv[]) {
+    if (argc != 5)
+    {
+        std::cout << "usage: " << argv[0] << " <input.png> <odd-window-size>"
+            " <hessian-response-threshold> <output.png>\n";
+        return -1;
+    }
+
+    std::size_t window_size = std::stoul(argv[2]);
+    long hessian_determinant_threshold = std::stol(argv[3]);
+
+    gil::rgb8_image_t input_image;
+
+    gil::read_image(argv[1], input_image, gil::png_tag{});
+
+    auto input_view = gil::view(input_image);
+    auto grayscaled = to_grayscale(input_view);
+    gil::gray8_image_t smoothed_image(grayscaled.dimensions());
+    auto smoothed = gil::view(smoothed_image);
+    apply_gaussian_blur(gil::view(grayscaled), smoothed);
+    gil::gray16s_image_t x_gradient_image(grayscaled.dimensions());
+    gil::gray16s_image_t y_gradient_image(grayscaled.dimensions());
+
+    auto x_gradient = gil::view(x_gradient_image);
+    auto y_gradient = gil::view(y_gradient_image);
+    auto scharr_x = gil::generate_dx_scharr();
+    gil::detail::convolve_2d(smoothed, scharr_x, x_gradient);
+    auto scharr_y = gil::generate_dy_scharr();
+    gil::detail::convolve_2d(smoothed, scharr_y, y_gradient);
+
+    gil::gray32f_image_t m11(x_gradient.dimensions());
+    gil::gray32f_image_t m12_21(x_gradient.dimensions());
+    gil::gray32f_image_t m22(x_gradient.dimensions());
+    gil::compute_hessian_entries(
+        x_gradient,
+        y_gradient,
+        gil::view(m11),
+        gil::view(m12_21),
+        gil::view(m22)
+    );
+
+    gil::gray32f_image_t hessian_response(x_gradient.dimensions());
+    auto gaussian_kernel = gil::generate_gaussian_kernel(window_size, 0.84089642);
+    gil::compute_hessian_responses(
+        gil::view(m11),
+        gil::view(m12_21),
+        gil::view(m22),
+        gaussian_kernel,
+        gil::view(hessian_response)
+    );
+
+    auto corner_points = suppress(gil::view(hessian_response), hessian_determinant_threshold);
+    for (auto point: corner_points) {
+        input_view(point) = gil::rgb8_pixel_t(0, 0, 0);
+        input_view(point).at(std::integral_constant<int, 1>{}) = 255;
+    }
+    gil::write_view(argv[4], input_view, gil::png_tag{});
+}