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|---|---|---|---|
| 1 | #include "fatehov_k_gaussian/seq/include/ops_seq.hpp" | ||
| 2 | |||
| 3 | #include <algorithm> | ||
| 4 | #include <cmath> | ||
| 5 | #include <cstddef> | ||
| 6 | #include <cstdint> | ||
| 7 | |||
| 8 | #include "fatehov_k_gaussian/common/include/common.hpp" | ||
| 9 | |||
| 10 | namespace fatehov_k_gaussian { | ||
| 11 | |||
| 12 |
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24 | FatehovKGaussianSEQ::FatehovKGaussianSEQ(const InType &in) { |
| 13 | SetTypeOfTask(GetStaticTypeOfTask()); | ||
| 14 | GetInput() = in; | ||
| 15 | 24 | } | |
| 16 | |||
| 17 | 24 | bool FatehovKGaussianSEQ::ValidationImpl() { | |
| 18 | const auto &input = GetInput(); | ||
| 19 |
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24 | return input.image.width > 0 && input.image.height > 0 && input.image.channels > 0 && !input.image.data.empty() && |
| 20 |
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24 | input.sigma > 0.0F; |
| 21 | } | ||
| 22 | |||
| 23 | 24 | bool FatehovKGaussianSEQ::PreProcessingImpl() { | |
| 24 | const auto &input = GetInput(); | ||
| 25 | 24 | float sigma = input.sigma; | |
| 26 | |||
| 27 | 24 | kernel_size_ = (2 * static_cast<int>(std::ceil(3.0F * sigma))) + 1; | |
| 28 | 24 | kernel_.resize(static_cast<size_t>(kernel_size_) * kernel_size_); | |
| 29 | |||
| 30 | 24 | int half = kernel_size_ / 2; | |
| 31 | float sum = 0.0F; | ||
| 32 | 24 | float two_sigma_sq = 2.0F * sigma * sigma; | |
| 33 | |||
| 34 |
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192 | for (int i = -half; i <= half; i++) { |
| 35 |
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1344 | for (int j = -half; j <= half; j++) { |
| 36 | 1176 | float val = std::exp(-(static_cast<float>((i * i) + (j * j))) / two_sigma_sq); | |
| 37 | 1176 | kernel_[((i + half) * kernel_size_) + (j + half)] = val; | |
| 38 | 1176 | sum += val; | |
| 39 | } | ||
| 40 | } | ||
| 41 | |||
| 42 |
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1200 | for (float &val : kernel_) { |
| 43 | 1176 | val /= sum; | |
| 44 | } | ||
| 45 | |||
| 46 | auto &output = GetOutput(); | ||
| 47 | 24 | output = Image(input.image.width, input.image.height, input.image.channels); | |
| 48 | |||
| 49 | 24 | return true; | |
| 50 | } | ||
| 51 | |||
| 52 | 24 | bool FatehovKGaussianSEQ::RunImpl() { | |
| 53 | const auto &input = GetInput(); | ||
| 54 | auto &output = GetOutput(); | ||
| 55 | 24 | const int w = static_cast<int>(input.image.width); | |
| 56 | 24 | const int h = static_cast<int>(input.image.height); | |
| 57 | 24 | const int ch = static_cast<int>(input.image.channels); | |
| 58 | 24 | const int half = kernel_size_ / 2; | |
| 59 | |||
| 60 |
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384 | for (int y_coord = 0; y_coord < h; y_coord++) { |
| 61 |
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6160 | for (int x_coord = 0; x_coord < w; x_coord++) { |
| 62 |
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23200 | for (int c_coord = 0; c_coord < ch; c_coord++) { |
| 63 | 17400 | float res = 0.0F; | |
| 64 |
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139200 | for (int ky = -half; ky <= half; ky++) { |
| 65 |
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974400 | for (int kx = -half; kx <= half; kx++) { |
| 66 | 852600 | int ny = std::clamp(y_coord + ky, 0, h - 1); | |
| 67 | 852600 | int nx = std::clamp(x_coord + kx, 0, w - 1); | |
| 68 | |||
| 69 | 852600 | float weight = kernel_[((ky + half) * kernel_size_) + (kx + half)]; | |
| 70 | 852600 | res += static_cast<float>(input.image.data[((ny * w + nx) * ch) + c_coord]) * weight; | |
| 71 | } | ||
| 72 | } | ||
| 73 | 17400 | output.data[((y_coord * w + x_coord) * ch) + c_coord] = static_cast<uint8_t>(std::clamp(res, 0.0F, 255.0F)); | |
| 74 | } | ||
| 75 | } | ||
| 76 | } | ||
| 77 | 24 | return true; | |
| 78 | } | ||
| 79 | |||
| 80 | 24 | bool FatehovKGaussianSEQ::PostProcessingImpl() { | |
| 81 | 24 | return true; | |
| 82 | } | ||
| 83 | |||
| 84 | } // namespace fatehov_k_gaussian | ||
| 85 |