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|---|---|---|---|
| 1 | #include "vdovin_a_gauss_block/seq/include/ops_seq.hpp" | ||
| 2 | |||
| 3 | #include <algorithm> | ||
| 4 | #include <array> | ||
| 5 | #include <cstdint> | ||
| 6 | #include <vector> | ||
| 7 | |||
| 8 | #include "vdovin_a_gauss_block/common/include/common.hpp" | ||
| 9 | |||
| 10 | namespace vdovin_a_gauss_block { | ||
| 11 | |||
| 12 | namespace { | ||
| 13 | constexpr int kChannels = 3; | ||
| 14 | constexpr int kKernelSize = 3; | ||
| 15 | constexpr int kKernelSum = 16; | ||
| 16 | constexpr std::array<std::array<int, kKernelSize>, kKernelSize> kKernel = {{{1, 2, 1}, {2, 4, 2}, {1, 2, 1}}}; | ||
| 17 | } // namespace | ||
| 18 | |||
| 19 | 80 | VdovinAGaussBlockSEQ::VdovinAGaussBlockSEQ(const InType &in) { | |
| 20 | SetTypeOfTask(GetStaticTypeOfTask()); | ||
| 21 | 80 | GetInput() = in; | |
| 22 | GetOutput() = 0; | ||
| 23 | 80 | } | |
| 24 | |||
| 25 | 80 | bool VdovinAGaussBlockSEQ::ValidationImpl() { | |
| 26 | 80 | return GetInput() >= 3; | |
| 27 | } | ||
| 28 | |||
| 29 | 80 | bool VdovinAGaussBlockSEQ::PreProcessingImpl() { | |
| 30 | 80 | width_ = GetInput(); | |
| 31 | 80 | height_ = GetInput(); | |
| 32 |
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80 | if (width_ < 3 || height_ < 3) { |
| 33 | input_image_.clear(); | ||
| 34 | output_image_.clear(); | ||
| 35 | ✗ | return false; | |
| 36 | } | ||
| 37 | 80 | int total = width_ * height_ * kChannels; | |
| 38 | 80 | input_image_.assign(total, 100); | |
| 39 | 80 | output_image_.assign(total, 0); | |
| 40 | 80 | return true; | |
| 41 | } | ||
| 42 | |||
| 43 | 105592 | void VdovinAGaussBlockSEQ::ApplyGaussianToPixel(int py, int px) { | |
| 44 |
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422368 | for (int ch = 0; ch < kChannels; ch++) { |
| 45 | int sum = 0; | ||
| 46 |
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1267104 | for (int ky = -1; ky <= 1; ky++) { |
| 47 |
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3801312 | for (int kx = -1; kx <= 1; kx++) { |
| 48 | 2850984 | int ny = std::clamp(py + ky, 0, height_ - 1); | |
| 49 | 2850984 | int nx = std::clamp(px + kx, 0, width_ - 1); | |
| 50 | 2850984 | sum += input_image_[(((ny * width_) + nx) * kChannels) + ch] * kKernel.at(ky + 1).at(kx + 1); | |
| 51 | } | ||
| 52 | } | ||
| 53 |
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633552 | output_image_[(((py * width_) + px) * kChannels) + ch] = static_cast<uint8_t>(std::clamp(sum / kKernelSum, 0, 255)); |
| 54 | } | ||
| 55 | 105592 | } | |
| 56 | |||
| 57 |
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80 | bool VdovinAGaussBlockSEQ::RunImpl() { |
| 58 |
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80 | if (input_image_.empty() || output_image_.empty()) { |
| 59 | return false; | ||
| 60 | } | ||
| 61 | |||
| 62 |
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80 | int block_height = std::max(1, height_ / 4); |
| 63 |
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80 | int block_width = std::max(1, width_ / 4); |
| 64 | |||
| 65 |
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456 | for (int block_y = 0; block_y < height_; block_y += block_height) { |
| 66 |
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2240 | for (int block_x = 0; block_x < width_; block_x += block_width) { |
| 67 | 1864 | int y_end = std::min(block_y + block_height, height_); | |
| 68 | 1864 | int x_end = std::min(block_x + block_width, width_); | |
| 69 |
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9440 | for (int py = block_y; py < y_end; py++) { |
| 70 |
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113168 | for (int px = block_x; px < x_end; px++) { |
| 71 | 105592 | ApplyGaussianToPixel(py, px); | |
| 72 | } | ||
| 73 | } | ||
| 74 | } | ||
| 75 | } | ||
| 76 | |||
| 77 | return true; | ||
| 78 | } | ||
| 79 | |||
| 80 |
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80 | bool VdovinAGaussBlockSEQ::PostProcessingImpl() { |
| 81 |
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80 | if (output_image_.empty()) { |
| 82 | return false; | ||
| 83 | } | ||
| 84 | auto total = static_cast<int64_t>(output_image_.size()); | ||
| 85 | if (total == 0) { | ||
| 86 | return false; | ||
| 87 | } | ||
| 88 | int64_t sum = 0; | ||
| 89 |
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316856 | for (int64_t idx = 0; idx < total; idx++) { |
| 90 | 316776 | sum += output_image_[idx]; | |
| 91 | } | ||
| 92 | 80 | GetOutput() = static_cast<int>(sum / total); | |
| 93 | 80 | return true; | |
| 94 | } | ||
| 95 | |||
| 96 | } // namespace vdovin_a_gauss_block | ||
| 97 |