GCC Code Coverage Report

Directory: ./
File: tasks/vlasova_a_image_smoothing/seq/src/ops_seq.cpp
Date: 2026-01-27 01:59:34
Exec Total Coverage
Lines: 41 43 95.3%
Functions: 6 6 100.0%
Branches: 25 34 73.5%
Line Branch Exec Source
1 #include "vlasova_a_image_smoothing/seq/include/ops_seq.hpp"
2
3 #include <algorithm>
4 #include <cstddef>
5 #include <cstdint>
6 #include <iterator>
7 #include <vector>
8
9 #include "vlasova_a_image_smoothing/common/include/common.hpp"
10
11 namespace vlasova_a_image_smoothing {
12
13
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 24 VlasovaAImageSmoothingSEQ::VlasovaAImageSmoothingSEQ(const InType &in) {
14 SetTypeOfTask(GetStaticTypeOfTask());
15 GetInput() = in;
16 24 }
17
18 24 bool VlasovaAImageSmoothingSEQ::ValidationImpl() {
19 const auto &input = GetInput();
20
21
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 24 if (input.width <= 0 || input.height <= 0) {
22 return false;
23 }
24
25
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 24 if (input.data.empty()) {
26 return false;
27 }
28
29 24 const std::size_t expected_size = static_cast<std::size_t>(input.width) * input.height;
30 24 return input.data.size() == expected_size;
31 }
32
33 24 bool VlasovaAImageSmoothingSEQ::PreProcessingImpl() {
34 const auto &input = GetInput();
35
36 24 width_ = input.width;
37 24 height_ = input.height;
38 24 input_image_ = input.data;
39 24 output_image_.resize(input_image_.size());
40
41 24 return true;
42 }
43
44 24 bool VlasovaAImageSmoothingSEQ::RunImpl() {
45 98304 auto calculate_pixel_median = [this](int col_idx, int row_idx) -> std::uint8_t {
46 98304 const int radius = window_size_ / 2;
47 98304 std::vector<std::uint8_t> neighbors;
48
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 98304 neighbors.reserve(static_cast<std::size_t>(window_size_) * window_size_);
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50
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 393216 for (int dy = -radius; dy <= radius; ++dy) {
51
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 1179648 for (int dx = -radius; dx <= radius; ++dx) {
52 884736 const int neighbor_x = col_idx + dx;
53 884736 const int neighbor_y = row_idx + dy;
54
55
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 884736 if (neighbor_x >= 0 && neighbor_x < width_ && neighbor_y >= 0 && neighbor_y < height_) {
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 866400 const std::size_t index = (static_cast<std::size_t>(neighbor_y) * width_) + neighbor_x;
57 neighbors.push_back(input_image_[index]);
58 }
59 }
60 }
61
62
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 98304 if (!neighbors.empty()) {
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 98304 auto middle = std::next(neighbors.begin(), static_cast<std::ptrdiff_t>(neighbors.size() / 2));
64 98304 std::nth_element(neighbors.begin(), middle, neighbors.end());
65 98304 return *middle;
66 }
67
68 ✗ const std::size_t index = (static_cast<std::size_t>(row_idx) * width_) + col_idx;
69 ✗ return input_image_[index];
70 24 };
71
72
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 1560 for (int row_idx = 0; row_idx < height_; ++row_idx) {
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 99840 for (int col_idx = 0; col_idx < width_; ++col_idx) {
74 98304 const std::size_t output_index = (static_cast<std::size_t>(row_idx) * width_) + col_idx;
75 98304 output_image_[output_index] = calculate_pixel_median(col_idx, row_idx);
76 }
77 }
78
79 24 return true;
80 }
81
82 24 bool VlasovaAImageSmoothingSEQ::PostProcessingImpl() {
83 24 GetOutput().width = width_;
84 24 GetOutput().height = height_;
85 24 GetOutput().data = output_image_;
86
87
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 24 if (GetOutput().data.empty()) {
88 return false;
89 }
90
91 24 const std::size_t expected_size = static_cast<std::size_t>(width_) * height_;
92 24 return GetOutput().data.size() == expected_size;
93 }
94
95 } // namespace vlasova_a_image_smoothing
96