| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | #pragma once | ||
| 2 | |||
| 3 | #include <algorithm> | ||
| 4 | #include <array> | ||
| 5 | #include <cstddef> | ||
| 6 | #include <cstdint> | ||
| 7 | #include <string> | ||
| 8 | #include <tuple> | ||
| 9 | #include <vector> | ||
| 10 | |||
| 11 | #include "task/include/task.hpp" | ||
| 12 | |||
| 13 | namespace sabirov_s_linear_filtering_block_partitioning { | ||
| 14 | |||
| 15 | // Структура для хранения данных изображения | ||
| 16 |
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255 | struct ImageData { |
| 17 | std::vector<uint8_t> pixels; // Пиксели в формате RGB | ||
| 18 | int width{0}; | ||
| 19 | int height{0}; | ||
| 20 | int channels{3}; // RGB = 3 канала | ||
| 21 | |||
| 22 |
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100 | ImageData() = default; |
| 23 |
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105 | ImageData(int w, int h, int ch = 3) : width(w), height(h), channels(ch) { |
| 24 |
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105 | pixels.resize(static_cast<std::size_t>(w) * static_cast<std::size_t>(h) * static_cast<std::size_t>(ch)); |
| 25 | 105 | } | |
| 26 | |||
| 27 | [[nodiscard]] std::size_t Size() const { | ||
| 28 | return pixels.size(); | ||
| 29 | } | ||
| 30 | |||
| 31 | [[nodiscard]] bool Empty() const { | ||
| 32 | return pixels.empty(); | ||
| 33 | } | ||
| 34 | }; | ||
| 35 | |||
| 36 | using InType = ImageData; | ||
| 37 | using OutType = ImageData; | ||
| 38 | using TestType = std::tuple<int, std::string>; | ||
| 39 | using BaseTask = ppc::task::Task<InType, OutType>; | ||
| 40 | |||
| 41 | // Ядро Гаусса 3x3 с использованием std::array | ||
| 42 | inline constexpr std::array<std::array<float, 3>, 3> kGaussianKernel = {{{1.0F / 16.0F, 2.0F / 16.0F, 1.0F / 16.0F}, | ||
| 43 | {2.0F / 16.0F, 4.0F / 16.0F, 2.0F / 16.0F}, | ||
| 44 | {1.0F / 16.0F, 2.0F / 16.0F, 1.0F / 16.0F}}}; | ||
| 45 | |||
| 46 | // Вспомогательная функция для применения ядра Гаусса к одному пикселю | ||
| 47 | 37206 | inline float ApplyGaussianKernel(const ImageData &input, int row, int col, int channel) { | |
| 48 | 37206 | const int width = input.width; | |
| 49 | 37206 | const int height = input.height; | |
| 50 | 37206 | const int channels = input.channels; | |
| 51 | |||
| 52 | float sum = 0.0F; | ||
| 53 | |||
| 54 |
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148824 | for (int ky = 0; ky < 3; ky++) { |
| 55 |
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446472 | for (int kx = 0; kx < 3; kx++) { |
| 56 | 334854 | int ny = row + ky - 1; | |
| 57 | 334854 | int nx = col + kx - 1; | |
| 58 | |||
| 59 | // Обрабатываем граничные пиксели методом ограничения координат | ||
| 60 | ny = std::max(ny, 0); | ||
| 61 | 334854 | ny = std::min(ny, height - 1); | |
| 62 | nx = std::max(nx, 0); | ||
| 63 | 334854 | nx = std::min(nx, width - 1); | |
| 64 | |||
| 65 | 334854 | auto input_idx = | |
| 66 | 334854 | ((static_cast<std::size_t>(ny) * static_cast<std::size_t>(width) + static_cast<std::size_t>(nx)) * | |
| 67 | 334854 | static_cast<std::size_t>(channels)) + | |
| 68 | 334854 | static_cast<std::size_t>(channel); | |
| 69 | 334854 | sum += static_cast<float>(input.pixels[input_idx]) * | |
| 70 | 334854 | kGaussianKernel.at(static_cast<std::size_t>(ky)).at(static_cast<std::size_t>(kx)); | |
| 71 | } | ||
| 72 | } | ||
| 73 | |||
| 74 | 37206 | return sum; | |
| 75 | } | ||
| 76 | |||
| 77 | } // namespace sabirov_s_linear_filtering_block_partitioning | ||
| 78 |