GCC Code Coverage Report

Directory: ./
File: tasks/kapanova_s_image_smoothing/seq/src/ops_seq.cpp
Date: 2026-01-09 01:27:18
Exec Total Coverage
Lines: 61 62 98.4%
Functions: 7 7 100.0%
Branches: 26 38 68.4%
Line Branch Exec Source
1 #include "kapanova_s_image_smoothing/seq/include/ops_seq.hpp"
2
3 #include <algorithm>
4 #include <cmath>
5 #include <cstddef>
6 #include <cstdint>
7 #include <cstdio>
8 #include <vector>
9
10 #include "kapanova_s_image_smoothing/common/include/common.hpp"
11
12 namespace kapanova_s_image_smoothing {
13
14
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 24 KapanovaSImageSmoothingSEQ::KapanovaSImageSmoothingSEQ(const InType &in) {
15 SetTypeOfTask(GetStaticTypeOfTask());
16
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 24 if (!in.empty()) {
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 24 GetInput() = in;
18 } else {
19 ✗ GetInput() = InType();
20 }
21 24 width_ = 0;
22 24 height_ = 0;
23 24 }
24
25
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 24 bool KapanovaSImageSmoothingSEQ::ValidationImpl() {
26 const auto &input_data = GetInput();
27
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 24 return !input_data.empty() && !input_data[0].empty();
28 }
29
30
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 24 bool KapanovaSImageSmoothingSEQ::PreProcessingImpl() {
31 const auto &input_data = GetInput();
32
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 24 if (input_data.empty() || input_data[0].size() < 4) {
33 return false;
34 }
35
36 const auto &data = input_data[0];
37 24 width_ = static_cast<int>((data[1] << 8) | data[0]);
38 24 height_ = static_cast<int>((data[3] << 8) | data[2]);
39
40 24 const auto width_u = static_cast<size_t>(width_);
41 24 const auto height_u = static_cast<size_t>(height_);
42 24 const auto expected_size = 4U + (width_u * height_u * 3U);
43
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 24 if (data.size() < expected_size) {
44 return false;
45 }
46
47 24 input_.assign(data.begin() + 4, data.end());
48 24 result_.resize(width_u * height_u * 3U);
49
50 24 CreateKernel();
51 24 return true;
52 }
53
54 24 void KapanovaSImageSmoothingSEQ::CreateKernel() {
55 24 const int size = (2 * radius_) + 1;
56 24 const auto size_u = static_cast<size_t>(size);
57 24 kernel_.resize(size_u * size_u);
58 constexpr float kSigma = 1.5F;
59 constexpr float kSigmaSquared = kSigma * kSigma; // σ²
60 float norm = 0.0F;
61
62
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 96 for (int i = -radius_; i <= radius_; ++i) {
63
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 288 for (int j = -radius_; j <= radius_; ++j) {
64 216 const int temp_index = ((i + radius_) * size) + (j + radius_);
65 216 const auto kernel_index = static_cast<size_t>(temp_index);
66
67 // ПРАВИЛЬНАЯ формула Гаусса: exp(-(i² + j²) / (2σ²))
68 216 kernel_[kernel_index] = std::exp(-static_cast<float>((i * i) + (j * j)) / (2.0F * kSigmaSquared));
69
70 216 norm += kernel_[kernel_index];
71 }
72 }
73
74
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 240 for (auto &value : kernel_) {
75 216 value /= norm;
76 }
77 24 }
78
79 232 void KapanovaSImageSmoothingSEQ::SmoothPixel(int x_coord, int y_coord) {
80 232 const int stride = width_ * 3;
81 constexpr int kSize = (2 * 1) + 1; // radius_ = 1
82 float out_r = 0.0F;
83 float out_g = 0.0F;
84 float out_b = 0.0F;
85
86 auto clamp = [](int n, int lo, int hi) { return std::min(std::max(n, lo), hi); };
87
88
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 928 for (int ry = -radius_; ry <= radius_; ++ry) {
89
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 2784 for (int rx = -radius_; rx <= radius_; ++rx) {
90 2088 const int idx = clamp(x_coord + rx, 0, width_ - 1);
91 2088 const int idy = clamp(y_coord + ry, 0, height_ - 1);
92 2088 const int temp_pos = (idy * stride) + (idx * 3);
93 2088 const auto pos = static_cast<size_t>(temp_pos);
94 2088 const int temp_kernel_pos = ((ry + radius_) * kSize) + (rx + radius_);
95 2088 const auto kernel_pos = static_cast<size_t>(temp_kernel_pos);
96
97 2088 out_r += static_cast<float>(input_[pos]) * kernel_[kernel_pos];
98 2088 out_g += static_cast<float>(input_[pos + 1U]) * kernel_[kernel_pos];
99 2088 out_b += static_cast<float>(input_[pos + 2U]) * kernel_[kernel_pos];
100 }
101 }
102
103 232 const int temp_pos = (y_coord * stride) + (x_coord * 3);
104 232 const auto pos = static_cast<size_t>(temp_pos);
105
106 232 result_[pos] = static_cast<uint8_t>(std::clamp(static_cast<int>(std::round(out_r)), 0, 255));
107 232 result_[pos + 1U] = static_cast<uint8_t>(std::clamp(static_cast<int>(std::round(out_g)), 0, 255));
108 232 result_[pos + 2U] = static_cast<uint8_t>(std::clamp(static_cast<int>(std::round(out_b)), 0, 255));
109 232 }
110
111 24 bool KapanovaSImageSmoothingSEQ::RunImpl() {
112
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 96 for (int y_coord = 0; y_coord < height_; ++y_coord) {
113
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 304 for (int x_coord = 0; x_coord < width_; ++x_coord) {
114 232 SmoothPixel(x_coord, y_coord);
115 }
116 }
117 24 return true;
118 }
119
120
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 24 bool KapanovaSImageSmoothingSEQ::PostProcessingImpl() {
121 kernel_.clear();
122
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 24 kernel_.shrink_to_fit();
123
124 24 GetOutput() = result_;
125 24 return true;
126 }
127
128 } // namespace kapanova_s_image_smoothing
129