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|---|---|---|---|
| 1 | #include "terekhov_d_gauss_vert/seq/include/ops_seq.hpp" | ||
| 2 | |||
| 3 | #include <algorithm> | ||
| 4 | #include <cmath> | ||
| 5 | #include <cstddef> | ||
| 6 | #include <vector> | ||
| 7 | |||
| 8 | #include "terekhov_d_gauss_vert/common/include/common.hpp" | ||
| 9 | |||
| 10 | namespace terekhov_d_gauss_vert { | ||
| 11 | |||
| 12 |
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24 | TerekhovDGaussVertSEQ::TerekhovDGaussVertSEQ(const InType &in) { |
| 13 | SetTypeOfTask(GetStaticTypeOfTask()); | ||
| 14 | GetInput() = in; | ||
| 15 | 24 | } | |
| 16 | |||
| 17 | 24 | bool TerekhovDGaussVertSEQ::ValidationImpl() { | |
| 18 | const auto &input = GetInput(); | ||
| 19 | |||
| 20 |
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24 | if (input.width <= 0 || input.height <= 0) { |
| 21 | return false; | ||
| 22 | } | ||
| 23 | |||
| 24 |
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24 | if (static_cast<int>(input.data.size()) != input.width * input.height) { |
| 25 | ✗ | return false; | |
| 26 | } | ||
| 27 | |||
| 28 | return true; | ||
| 29 | } | ||
| 30 | |||
| 31 | 24 | bool TerekhovDGaussVertSEQ::PreProcessingImpl() { | |
| 32 | const auto &input = GetInput(); | ||
| 33 | 24 | width_ = input.width; | |
| 34 | 24 | height_ = input.height; | |
| 35 | |||
| 36 | 24 | GetOutput().width = width_; | |
| 37 | 24 | GetOutput().height = height_; | |
| 38 | 24 | GetOutput().data.resize(static_cast<size_t>(width_) * static_cast<size_t>(height_)); | |
| 39 | |||
| 40 | 24 | int padded_width = width_ + 2; | |
| 41 | 24 | int padded_height = height_ + 2; | |
| 42 | 24 | padded_image_.resize(static_cast<size_t>(padded_width) * static_cast<size_t>(padded_height)); | |
| 43 | |||
| 44 |
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488 | for (int row = 0; row < padded_height; ++row) { |
| 45 |
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12592 | for (int col = 0; col < padded_width; ++col) { |
| 46 | 12128 | int src_x = col - 1; | |
| 47 | 12128 | int src_y = row - 1; | |
| 48 | |||
| 49 | 12128 | if (src_x < 0) { | |
| 50 | src_x = -src_x - 1; | ||
| 51 | } | ||
| 52 |
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12128 | if (src_x >= width_) { |
| 53 | 464 | src_x = (2 * width_) - src_x - 1; | |
| 54 | } | ||
| 55 | 12128 | if (src_y < 0) { | |
| 56 | src_y = -src_y - 1; | ||
| 57 | } | ||
| 58 |
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12128 | if (src_y >= height_) { |
| 59 | 464 | src_y = (2 * height_) - src_y - 1; | |
| 60 | } | ||
| 61 | |||
| 62 | 12128 | size_t padded_idx = (static_cast<size_t>(row) * static_cast<size_t>(padded_width)) + static_cast<size_t>(col); | |
| 63 | 12128 | size_t src_idx = (static_cast<size_t>(src_y) * static_cast<size_t>(width_)) + static_cast<size_t>(src_x); | |
| 64 | 12128 | padded_image_[padded_idx] = input.data[src_idx]; | |
| 65 | } | ||
| 66 | } | ||
| 67 | |||
| 68 | 24 | return true; | |
| 69 | } | ||
| 70 | |||
| 71 | 10368 | void TerekhovDGaussVertSEQ::ProcessPixel(OutType &output, int padded_width, int row, int col) { | |
| 72 | 10368 | size_t idx = (static_cast<size_t>(row) * static_cast<size_t>(width_)) + static_cast<size_t>(col); | |
| 73 | |||
| 74 | float sum = 0.0F; | ||
| 75 | |||
| 76 |
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41472 | for (int ky = -1; ky <= 1; ++ky) { |
| 77 |
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124416 | for (int kx = -1; kx <= 1; ++kx) { |
| 78 | 93312 | int px = col + kx + 1; | |
| 79 | 93312 | int py = row + ky + 1; | |
| 80 | |||
| 81 | 93312 | int kernel_idx = ((ky + 1) * 3) + (kx + 1); | |
| 82 | 93312 | size_t padded_idx = (static_cast<size_t>(py) * static_cast<size_t>(padded_width)) + static_cast<size_t>(px); | |
| 83 | 93312 | int pixel_value = padded_image_[padded_idx]; | |
| 84 | |||
| 85 | 93312 | sum += static_cast<float>(pixel_value) * kGaussKernel[static_cast<size_t>(kernel_idx)]; | |
| 86 | } | ||
| 87 | } | ||
| 88 | |||
| 89 | 10368 | output.data[idx] = static_cast<int>(std::lround(sum)); | |
| 90 | 10368 | } | |
| 91 | |||
| 92 | 96 | void TerekhovDGaussVertSEQ::ProcessBand(OutType &output, int padded_width, int band, int band_width) { | |
| 93 | 96 | int start_x = band * band_width; | |
| 94 |
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96 | int end_x = (band == kNumBands - 1) ? width_ : ((band + 1) * band_width); |
| 95 | |||
| 96 |
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1760 | for (int row = 0; row < height_; ++row) { |
| 97 |
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12032 | for (int col = start_x; col < end_x; ++col) { |
| 98 | 10368 | ProcessPixel(output, padded_width, row, col); | |
| 99 | } | ||
| 100 | } | ||
| 101 | 96 | } | |
| 102 | |||
| 103 | 24 | void TerekhovDGaussVertSEQ::ProcessBands(OutType &output) { | |
| 104 | 24 | int padded_width = width_ + 2; | |
| 105 | |||
| 106 |
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24 | int band_width = width_ / kNumBands; |
| 107 | band_width = std::max(band_width, 1); | ||
| 108 | |||
| 109 |
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120 | for (int band = 0; band < kNumBands; ++band) { |
| 110 | 96 | ProcessBand(output, padded_width, band, band_width); | |
| 111 | } | ||
| 112 | 24 | } | |
| 113 | |||
| 114 |
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24 | bool TerekhovDGaussVertSEQ::RunImpl() { |
| 115 | const auto &input = GetInput(); | ||
| 116 | auto &output = GetOutput(); | ||
| 117 | |||
| 118 |
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24 | if (input.data.empty() || width_ <= 0 || height_ <= 0) { |
| 119 | return false; | ||
| 120 | } | ||
| 121 | |||
| 122 | 24 | ProcessBands(output); // 1 | |
| 123 | 24 | return true; | |
| 124 | } | ||
| 125 | |||
| 126 | 24 | bool TerekhovDGaussVertSEQ::PostProcessingImpl() { | |
| 127 | 24 | return GetOutput().data.size() == (static_cast<size_t>(GetOutput().width) * static_cast<size_t>(GetOutput().height)); | |
| 128 | } | ||
| 129 | |||
| 130 | } // namespace terekhov_d_gauss_vert | ||
| 131 |