├── .gitignore ├── LICENSE ├── LaneDetector.sln ├── LaneDetector ├── DBSCAN.hpp ├── LaneDetector.cpp ├── LaneDetector.hpp ├── LaneDetector.vcxproj ├── LaneDetector.vcxproj.filters ├── LaneMarkDetector.hpp ├── LaneModels.cpp ├── LaneModels.hpp ├── OpenCV_release.props ├── PointsToLanesConverter.hpp ├── RANSAC.hpp ├── main.cpp └── random_unique.hpp └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Compiled Object files 2 | *.slo 3 | *.lo 4 | *.o 5 | *.obj 6 | 7 | # Precompiled Headers 8 | *.gch 9 | *.pch 10 | 11 | # Compiled Dynamic libraries 12 | *.so 13 | *.dylib 14 | *.dll 15 | 16 | # Fortran module files 17 | *.mod 18 | *.smod 19 | 20 | # Compiled Static libraries 21 | *.lai 22 | *.la 23 | *.a 24 | *.lib 25 | 26 | # Executables 27 | *.exe 28 | *.out 29 | *.app 30 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /LaneDetector.sln: -------------------------------------------------------------------------------- 1 |  2 | Microsoft Visual Studio Solution File, Format Version 12.00 3 | # Visual Studio 14 4 | VisualStudioVersion = 14.0.23107.0 5 | MinimumVisualStudioVersion = 10.0.40219.1 6 | Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LaneDetector", "LaneDetector\LaneDetector.vcxproj", "{D8E1B78A-2FE3-47CF-B51D-88F345C13C57}" 7 | EndProject 8 | Global 9 | GlobalSection(SolutionConfigurationPlatforms) = preSolution 10 | Debug|x64 = Debug|x64 11 | Debug|x86 = Debug|x86 12 | Release|x64 = Release|x64 13 | Release|x86 = Release|x86 14 | EndGlobalSection 15 | GlobalSection(ProjectConfigurationPlatforms) = postSolution 16 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Debug|x64.ActiveCfg = Debug|x64 17 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Debug|x64.Build.0 = Debug|x64 18 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Debug|x86.ActiveCfg = Debug|Win32 19 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Debug|x86.Build.0 = Debug|Win32 20 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Release|x64.ActiveCfg = Release|x64 21 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Release|x64.Build.0 = Release|x64 22 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Release|x86.ActiveCfg = Release|Win32 23 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57}.Release|x86.Build.0 = Release|Win32 24 | EndGlobalSection 25 | GlobalSection(SolutionProperties) = preSolution 26 | HideSolutionNode = FALSE 27 | EndGlobalSection 28 | EndGlobal 29 | -------------------------------------------------------------------------------- /LaneDetector/DBSCAN.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | 6 | 7 | class DBScan 8 | { 9 | public: 10 | 11 | const int NOISE = -1; 12 | const int UNKNOW = 0; 13 | 14 | DBScan(float eps, int minSamples) : eps_(eps), minSamples_(minSamples) 15 | { 16 | 17 | } 18 | 19 | void fit(std::vector input) 20 | { 21 | labels.resize(input.size(), UNKNOW); 22 | visited.resize(input.size(), false); 23 | 24 | int cluster = UNKNOW; 25 | for (int i = 0; i < input.size(); ++i) 26 | { 27 | if (visited[i]) continue; 28 | 29 | visited[i] = true; 30 | auto neighbours = regionQuery(input, i); 31 | if (neighbours.size() < minSamples_) 32 | { 33 | labels[i] = NOISE; 34 | } 35 | else 36 | { 37 | expandCluster(input, i, neighbours, ++cluster); 38 | } 39 | } 40 | 41 | estimatedClusterNumber = cluster; 42 | } 43 | 44 | std::vector regionQuery(std::vector input, int i) 45 | { 46 | std::vector neighbours; 47 | auto p = input[i]; 48 | for (int j = 0; j < input.size(); ++j) 49 | { 50 | auto dst = p - input[j]; 51 | dst.y *= 0.5; // more eliptical distance 52 | if (dst.dot(dst) < eps_) 53 | neighbours.emplace_back(j); 54 | } 55 | return neighbours; 56 | } 57 | 58 | void expandCluster(std::vector input, int i, std::vector neighbours, const int cluster) 59 | { 60 | labels[i] = cluster; 61 | 62 | for (size_t k = 0; k < neighbours.size(); ++k) 63 | { 64 | int p = neighbours[k]; 65 | 66 | if (!visited[p]) 67 | { 68 | visited[p] = true; 69 | auto newNeighbours = regionQuery(input, p); 70 | if (newNeighbours.size() > minSamples_) 71 | { 72 | neighbours.insert(neighbours.end(), newNeighbours.begin(), newNeighbours.end()); 73 | } 74 | } 75 | 76 | if (labels[p] == UNKNOW) 77 | labels[p] = cluster; 78 | } 79 | } 80 | 81 | const std::vector& GetLabels() const 82 | { 83 | return labels; 84 | } 85 | 86 | const int GetEstimatedClusterNumber() const 87 | { 88 | return estimatedClusterNumber; 89 | } 90 | 91 | private: 92 | const float eps_; 93 | const int minSamples_; 94 | int estimatedClusterNumber; 95 | std::vector labels; 96 | std::vector visited; 97 | }; -------------------------------------------------------------------------------- /LaneDetector/LaneDetector.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include "LaneDetector.hpp" 4 | #include "LaneModels.hpp" 5 | #include 6 | #include 7 | #include "PointsToLanesConverter.hpp" 8 | #include "LaneMarkDetector.hpp" 9 | 10 | namespace vision 11 | { 12 | 13 | LaneDetector::LaneDetector() 14 | { 15 | 16 | } 17 | 18 | void LaneDetector::ComputePerspectiveTransformationMatrix(const int width, const int height) 19 | { 20 | cv::Point2f src[] = 21 | { 22 | { 0, 470 }, 23 | { 1280, 470 }, 24 | { 0, static_cast(height) }, 25 | { 1280 - 100, static_cast(height) } 26 | }; 27 | 28 | const float offset = width / 4; 29 | const float wo = width - offset; 30 | 31 | cv::Point2f dst[] = 32 | { 33 | { (offset - 800), 0 }, 34 | { (wo + 900), 0 }, 35 | { (offset + 120), static_cast(height) }, 36 | { (wo - 160 ), static_cast(height) } 37 | }; 38 | 39 | perspectiveTransform = cv::getPerspectiveTransform(src, dst); 40 | invPerspectiveTransform = cv::getPerspectiveTransform(dst, src); 41 | } 42 | 43 | cv::Mat LaneDetector::GetRoadOnlyImage(const cv::Mat& input) 44 | { 45 | cv::Mat road(input.clone()); 46 | const int width = input.cols; 47 | const int height = input.rows; 48 | 49 | static bool inited = false; 50 | 51 | if (!inited) 52 | { 53 | ComputePerspectiveTransformationMatrix(width, height); 54 | inited = true; 55 | } 56 | cv::warpPerspective(input, road, perspectiveTransform, { width, height }); 57 | 58 | return road; 59 | } 60 | 61 | cv::Mat LaneDetector::FindPixelsThatMayBelongToLane(const cv::Mat& input) 62 | { 63 | cv::Mat grayscale; 64 | cv::cvtColor(input, grayscale, CV_BGR2GRAY); 65 | 66 | cv::Mat out(cv::Size(grayscale.cols, grayscale.rows), CV_8U); 67 | LaneMarkDetector laneMarkDetector; 68 | laneMarkDetector.tau_ = 12; 69 | laneMarkDetector.verticalOffset_ = 150; 70 | laneMarkDetector.Process(grayscale, out); 71 | 72 | return out; 73 | } 74 | 75 | std::vector ConvertImageToPoints(const cv::Mat& input) 76 | { 77 | std::vector output; 78 | 79 | unsigned char *raw = (unsigned char*)(input.data); 80 | 81 | int aux = 0; 82 | int x = 0, y = 0; 83 | const int w = input.cols; 84 | 85 | for (y = 0; y < input.rows; ++y) 86 | { 87 | const auto raw = input.ptr(y); 88 | for (x = 0; x < w; ++x) 89 | { 90 | if (raw[x]) 91 | { 92 | output.emplace_back(cv::Point2f(x, y)); 93 | } 94 | } 95 | } 96 | 97 | return output; 98 | } 99 | 100 | bool InRange(lane_model::Parabola a, const int starty, const int endy, const int xstart, const int xend, const int dy = 2) 101 | { 102 | for (int y = starty; y <= endy; y += dy) 103 | { 104 | const auto x = 2 * a(y * 0.5); 105 | if (x >= xstart && x <= xend) return true; 106 | } 107 | return false; 108 | } 109 | 110 | void DetectCurrentLane(RoadModel& roadModel) 111 | { 112 | std::copy_if(roadModel.lanes_.begin(), roadModel.lanes_.end(), std::back_inserter(roadModel.current_lane_), [](const lane_model::Parabola& a) { return InRange(a, 720 - 210, 720 - 100, 1280/2 - 160, 1280/2 + 200 ); }); 113 | } 114 | 115 | void BuildCurrentLaneModel(RoadModel& roadModel) 116 | { 117 | roadModel.current_lane_model_.valid = false; 118 | 119 | if (roadModel.current_lane_.size() > 2 || roadModel.current_lane_.size() < 1) 120 | { 121 | return; 122 | } 123 | 124 | const int CENTER_PROBE = 720 - 200; 125 | const int CAR_CENTER = 1280 / 2; 126 | const int MIN_ROAD_WIDTH = 100; 127 | const int ROAD_WIDTH = 200 / 2; 128 | 129 | if (roadModel.current_lane_.size() == 2) 130 | { 131 | auto x0 = 2 * roadModel.current_lane_[0](CENTER_PROBE * 0.5); 132 | auto x1 = 2 * roadModel.current_lane_[1](CENTER_PROBE * 0.5); 133 | 134 | if (abs(x0 - x1) < MIN_ROAD_WIDTH) 135 | { 136 | return; 137 | } 138 | 139 | if (x1 < x0) std::swap(roadModel.current_lane_[0], roadModel.current_lane_[1]); 140 | 141 | roadModel.current_lane_model_.left_ = roadModel.current_lane_[0]; 142 | roadModel.current_lane_model_.right_ = roadModel.current_lane_[1]; 143 | 144 | roadModel.current_lane_model_.center = lane_model::Parabola((roadModel.current_lane_[0].a + roadModel.current_lane_[1].a) * 0.5, 145 | (roadModel.current_lane_[0].b + roadModel.current_lane_[1].b) * 0.5, 146 | (roadModel.current_lane_[0].c + roadModel.current_lane_[1].c) * 0.5); 147 | 148 | } 149 | else 150 | { 151 | auto x0 = 2 * roadModel.current_lane_[0](CENTER_PROBE * 0.5); 152 | 153 | if (x0 < CAR_CENTER) 154 | { 155 | roadModel.current_lane_model_.left_ = roadModel.current_lane_[0]; 156 | //roadModel.current_lane_model_.right_ = lane_model::Parabola(roadModel.current_lane_[0].a, roadModel.current_lane_[0].b, roadModel.current_lane_[0].c + ROAD_WIDTH); 157 | roadModel.current_lane_model_.center = lane_model::Parabola(roadModel.current_lane_[0].a, roadModel.current_lane_[0].b, roadModel.current_lane_[0].c + ROAD_WIDTH / 2); 158 | } 159 | else 160 | { 161 | //roadModel.current_lane_model_.left_ = lane_model::Parabola(roadModel.current_lane_[0].a, roadModel.current_lane_[0].b, roadModel.current_lane_[0].c - ROAD_WIDTH); 162 | roadModel.current_lane_model_.center = lane_model::Parabola(roadModel.current_lane_[0].a, roadModel.current_lane_[0].b, roadModel.current_lane_[0].c - ROAD_WIDTH / 2); 163 | roadModel.current_lane_model_.right_ = roadModel.current_lane_[0]; 164 | } 165 | } 166 | 167 | 168 | roadModel.current_lane_model_.valid = true; 169 | } 170 | 171 | void DrawParabola(cv::Mat& image, const lane_model::Parabola& parabola, const cv::Vec3b color) 172 | { 173 | std::vector parabolaLine; 174 | 175 | auto p = lane_model::Parabola(parabola.a * 0.25 * 2, parabola.b * 0.5 * 2, parabola.c * 2); // upscale 176 | for (int y = 0; y < image.rows; ++y) 177 | { 178 | const auto x = p(y); 179 | if (x > 0 && x < 1280 && y > 100 && y < 700) 180 | { 181 | parabolaLine.emplace_back(x - 3, y); 182 | parabolaLine.emplace_back(x - 2, y); 183 | parabolaLine.emplace_back(x - 1, y); 184 | parabolaLine.emplace_back(x, y); 185 | parabolaLine.emplace_back(x + 1, y); 186 | parabolaLine.emplace_back(x + 2, y); 187 | parabolaLine.emplace_back(x + 3, y); 188 | } 189 | } 190 | 191 | for (auto p : parabolaLine) 192 | { 193 | if (p.x > 0 && p.x < 1280 && p.y > 0 && p.y < 720) 194 | image.at(p) = color; 195 | } 196 | } 197 | 198 | RoadModel LaneDetector::BuildRoadModelFromPoints(const std::vector& points) 199 | { 200 | RoadModel roadModel; 201 | 202 | PointsToLanesConverter pointsToLanesCovnerter; 203 | roadModel.lanes_ = pointsToLanesCovnerter.Convert(points); 204 | roadModel.invPerspTransform = invPerspectiveTransform; 205 | 206 | DetectCurrentLane(roadModel); 207 | BuildCurrentLaneModel(roadModel); 208 | 209 | return roadModel; 210 | } 211 | 212 | cv::Mat LaneDetector::DownsampleImageByHalf(const cv::Mat& input) 213 | { 214 | cv::Mat minified; 215 | cv::resize(input, minified, cv::Size(input.cols / 2, input.rows / 2)); 216 | return minified; 217 | } 218 | 219 | RoadModel LaneDetector::DetectLane(cv::Mat& inputFrame) 220 | { 221 | auto road = GetRoadOnlyImage(inputFrame); 222 | auto lanesPixels = FindPixelsThatMayBelongToLane(road); 223 | auto points = ConvertImageToPoints(DownsampleImageByHalf(lanesPixels)); 224 | auto roadModel = BuildRoadModelFromPoints(points); 225 | 226 | ///////////////////////////////////////// 227 | cv::Mat tocolor[] = { lanesPixels, lanesPixels, lanesPixels }; 228 | cv::merge(tocolor, 3, road); 229 | 230 | 231 | //cv::rectangle(road, cv::Rect(road.cols / 2 - 100, road.rows - 200, 200, 200), cv::Scalar(255, 0, 0), -1); 232 | 233 | //cv::rectangle(road, cv::Rect(road.cols / 2 - 10, road.rows - 200, 20, 20), cv::Scalar(0, 0, 255), -1); 234 | 235 | //cv::rectangle(road, cv::Rect(road.cols / 2 - 150, road.rows - 200, 400 - 50, 10), cv::Scalar(0, 255, 0), -1); 236 | 237 | 238 | //190, 210, 1280 / 2 - 160, 1280 / 2 + 200 239 | //cv::rectangle(road, cv::Point(1280/2 - 160, 720 - 210), cv::Point(1280/2 + 200, 720 - 100), cv::Scalar(255, 255, 0), -1); 240 | 241 | //DBScan dbscan(50 * 50, 100); 242 | //dbscan.fit(points); 243 | 244 | //const std::vector colorPallette = 245 | //{ 246 | // { 226 ,43, 138 }, 247 | // { 0, 0, 255 }, 248 | // { 0, 255, 0 }, 249 | // { 255, 0, 0 }, 250 | // {255, 255, 0}, 251 | // {0, 255, 255}, 252 | // {255, 0, 255}, 253 | // {128, 0, 128}, 254 | // {0, 0, 128}, 255 | // {0, 100, 0}, 256 | // {128, 0, 0} 257 | //}; 258 | //const cv::Vec3b black = { 0, 0, 0 }; 259 | 260 | //int i = 0; 261 | //std::transform(points.begin(), points.end(), points.begin(), [](cv::Point2f & p) { return p * 2; }); 262 | ////if (points.size() > 0) cv::perspectiveTransform(points, points, invPerspectiveTransform); 263 | //const size_t colorsNumber = colorPallette.size(); 264 | //for (auto pt : points) 265 | //{ 266 | // int label = dbscan.GetLabels()[i++]; 267 | // if (label == dbscan.NOISE) 268 | // road.at(pt) = black; 269 | // else 270 | // { 271 | // label--; 272 | // road.at(pt) = colorPallette[label % colorsNumber]; 273 | // } 274 | //} 275 | 276 | //for (int y = 0; y < inputFrame.rows; ++y) 277 | //{ 278 | // road.at(y, inputFrame.cols / 2) = { 255, 0, 0 }; 279 | //} 280 | 281 | if (roadModel.current_lane_model_.valid) 282 | { 283 | DrawParabola(road, roadModel.current_lane_model_.center, { 0,255,0 }); 284 | DrawParabola(road, roadModel.current_lane_model_.left_, { 255,0,0 }); 285 | DrawParabola(road, roadModel.current_lane_model_.right_, { 0,0,255}); 286 | } 287 | 288 | //inputFrame = road; 289 | cv::Mat warped; 290 | cv::warpPerspective(road, warped, roadModel.invPerspTransform, inputFrame.size()); 291 | 292 | //inputFrame = warped; 293 | double alpha = 0.5; 294 | cv::addWeighted(warped, alpha, inputFrame, 1 - alpha, 0, inputFrame); 295 | /////////////////////////////////////// 296 | 297 | return roadModel; 298 | } 299 | 300 | } // namespace vision 301 | -------------------------------------------------------------------------------- /LaneDetector/LaneDetector.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | #include 3 | #include 4 | 5 | #include "LaneModels.hpp" 6 | 7 | namespace vision 8 | { 9 | 10 | struct CurrentLaneModel 11 | { 12 | CurrentLaneModel() : valid(false) {} 13 | 14 | lane_model::Parabola left_; 15 | lane_model::Parabola right_; 16 | lane_model::Parabola center; 17 | 18 | bool valid = false; 19 | }; 20 | 21 | struct RoadModel 22 | { 23 | RoadModel() {} 24 | 25 | std::vector lanes_; 26 | std::vector current_lane_; 27 | CurrentLaneModel current_lane_model_; 28 | 29 | cv::Mat invPerspTransform; 30 | }; 31 | 32 | class LaneDetector 33 | { 34 | public: 35 | LaneDetector(); 36 | RoadModel DetectLane(cv::Mat& inputFrame); 37 | private: 38 | cv::Mat FindPixelsThatMayBelongToLane(const cv::Mat& input); 39 | cv::Mat GetRoadOnlyImage(const cv::Mat& input); 40 | void ComputePerspectiveTransformationMatrix(const int width, const int height); 41 | cv::Mat LaneDetector::DownsampleImageByHalf(const cv::Mat& input); 42 | RoadModel BuildRoadModelFromPoints(const std::vector& points); 43 | 44 | cv::Mat perspectiveTransform; 45 | cv::Mat invPerspectiveTransform; 46 | }; 47 | 48 | 49 | } // namespace vision 50 | -------------------------------------------------------------------------------- /LaneDetector/LaneDetector.vcxproj: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | Debug 6 | Win32 7 | 8 | 9 | Release 10 | Win32 11 | 12 | 13 | Debug 14 | x64 15 | 16 | 17 | Release 18 | x64 19 | 20 | 21 | 22 | {D8E1B78A-2FE3-47CF-B51D-88F345C13C57} 23 | Win32Proj 24 | LaneDetector 25 | 8.1 26 | 27 | 28 | 29 | Application 30 | true 31 | v140 32 | Unicode 33 | 34 | 35 | Application 36 | false 37 | v140 38 | true 39 | Unicode 40 | 41 | 42 | Application 43 | true 44 | v140 45 | Unicode 46 | 47 | 48 | Application 49 | false 50 | v140 51 | true 52 | Unicode 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | true 77 | 78 | 79 | true 80 | 81 | 82 | false 83 | 84 | 85 | false 86 | 87 | 88 | 89 | 90 | 91 | Level3 92 | Disabled 93 | WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) 94 | true 95 | 96 | 97 | Console 98 | true 99 | 100 | 101 | 102 | 103 | 104 | 105 | Level3 106 | Disabled 107 | _DEBUG;_CONSOLE;%(PreprocessorDefinitions) 108 | true 109 | 110 | 111 | Console 112 | true 113 | 114 | 115 | 116 | 117 | Level3 118 | 119 | 120 | MaxSpeed 121 | true 122 | true 123 | WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) 124 | true 125 | 126 | 127 | Console 128 | true 129 | true 130 | true 131 | 132 | 133 | 134 | 135 | Level3 136 | 137 | 138 | MaxSpeed 139 | true 140 | true 141 | NDEBUG;_CONSOLE;%(PreprocessorDefinitions) 142 | true 143 | 144 | 145 | Console 146 | true 147 | true 148 | true 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | -------------------------------------------------------------------------------- /LaneDetector/LaneDetector.vcxproj.filters: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | {4FC737F1-C7A5-4376-A066-2A32D752A2FF} 6 | cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx 7 | 8 | 9 | {93995380-89BD-4b04-88EB-625FBE52EBFB} 10 | h;hh;hpp;hxx;hm;inl;inc;xsd 11 | 12 | 13 | {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} 14 | rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms 15 | 16 | 17 | 18 | 19 | Source Files 20 | 21 | 22 | Source Files 23 | 24 | 25 | Source Files 26 | 27 | 28 | 29 | 30 | Header Files 31 | 32 | 33 | Header Files 34 | 35 | 36 | Header Files 37 | 38 | 39 | Header Files 40 | 41 | 42 | Header Files 43 | 44 | 45 | Header Files 46 | 47 | 48 | Header Files 49 | 50 | 51 | -------------------------------------------------------------------------------- /LaneDetector/LaneMarkDetector.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | #include 3 | 4 | class LaneMarkDetector 5 | { 6 | public: 7 | int tau_ = 10; 8 | int verticalOffset_ = 440; 9 | int threshold_ = 160; 10 | 11 | void Process(cv::Mat& img, cv::Mat& out) 12 | { 13 | unsigned char *raw = (unsigned char*)(img.data); 14 | out.setTo(0); 15 | 16 | int aux = 0; 17 | int x = 0, y = 0; 18 | const int w = img.cols - tau_ - 1; 19 | 20 | for (y = verticalOffset_; y < img.rows; ++y) 21 | { 22 | auto raw = img.ptr(y); 23 | for (x = tau_; x < w; ++x) 24 | { 25 | aux = 2 * raw[x]; 26 | aux -= raw[x - tau_]; 27 | aux -= raw[x + tau_]; 28 | 29 | aux -= abs(raw[x - tau_] - raw[x + tau_]); 30 | 31 | aux *= 4;// more contrast 32 | 33 | if (aux >= threshold_) 34 | { 35 | out.at(y, x) = 255; 36 | } 37 | } 38 | } 39 | 40 | cv::erode(out, out, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 5))); 41 | //cv::dilate(out, out, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3))); 42 | 43 | ////cv::Mat line = cv::Mat::ones(10, 1, CV_8UC1); 44 | ////now apply the morphology open operation 45 | cv::Mat line2 = cv::Mat::ones(1, 3, CV_8UC1); 46 | morphologyEx(out, out, cv::MORPH_OPEN, line2, cv::Point(-1, -1)); 47 | } 48 | }; -------------------------------------------------------------------------------- /LaneDetector/LaneModels.cpp: -------------------------------------------------------------------------------- 1 | #include "LaneModels.hpp" 2 | #include 3 | 4 | namespace vision 5 | { 6 | namespace lane_model 7 | { 8 | 9 | Parabola fit(std::vector& points, const int count) 10 | { 11 | // Sjk = sum of p.x^j * p.y^k 12 | long double 13 | s00 = count, 14 | s01 = 0, 15 | s10 = 0, 16 | s11 = 0, 17 | s21 = 0, 18 | s20 = 0, 19 | s30 = 0, 20 | s40 = 0; 21 | long double x, xx, y; 22 | 23 | for (int i = 0; i < count; ++i) 24 | { 25 | x = points[i].y; // swap x and y to better model line 26 | y = points[i].x; 27 | xx = x * x; 28 | 29 | //s00 += 1; //s00 = input.Count 30 | s10 += x; 31 | s20 += xx; 32 | s30 += xx * x; 33 | s40 += xx * xx; 34 | 35 | s21 += xx * y; 36 | s11 += x * y; 37 | s01 += y; 38 | } 39 | 40 | // Cramer method: 41 | long double s20s00_s10s10 = s20 * s00 - s10 * s10; 42 | long double s30s00_s10s20 = s30 * s00 - s10 * s20; 43 | long double s30s10_s20s20 = s30 * s10 - s20 * s20; 44 | 45 | long double D = s40 * s20s00_s10s10 - s30 * s30s00_s10s20 + s20 * s30s10_s20s20; 46 | if (D == 0) 47 | return Parabola(0, 0, 0); 48 | long double Da = s21 * s20s00_s10s10 - s11 * s30s00_s10s20 + s01 * s30s10_s20s20; // <- blad!!!! 49 | long double Db = s40 * (s11 * s00 - s01 * s10) - s30 * (s21 * s00 - s01 * s20) + s20 * (s21 * s10 - s11 * s20); 50 | long double Dc = s40 * (s20 * s01 - s10 * s11) - s30 * (s30 * s01 - s10 * s21) + s20 * (s30 * s11 - s20 * s21); 51 | 52 | return Parabola(Da / D, Db / D, Dc / D); 53 | } 54 | } // namespace lane_models 55 | } // namespace vision -------------------------------------------------------------------------------- /LaneDetector/LaneModels.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | #include 6 | 7 | namespace vision 8 | { 9 | namespace lane_model 10 | { 11 | 12 | struct Parabola 13 | { 14 | double a, b, c; 15 | 16 | Parabola(double A = 0, double B = 0, double C = 0) : a(A), b(B), c(C) 17 | { } 18 | 19 | double value(double x) const 20 | { 21 | return ((a * x) + b) * x + c; 22 | } 23 | 24 | double operator()(double x) const 25 | { 26 | return value(x); 27 | } 28 | 29 | bool IsValid() const 30 | { 31 | return !(a == 0 && b == 0 && c == 0); 32 | } 33 | 34 | std::string ToString() const 35 | { 36 | return "f(y) = " + std::to_string(a) + " y^2 + " + std::to_string(b) + " y + " + std::to_string(c); 37 | } 38 | }; // class Parabola 39 | 40 | Parabola fit(std::vector& points, const int count); 41 | 42 | 43 | } // namespace lane_model 44 | } // namespace vision 45 | -------------------------------------------------------------------------------- /LaneDetector/OpenCV_release.props: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | 6 | F:\dev\opencv\opencv\build\include;$(IncludePath) 7 | F:\dev\opencv\opencv\build\x64\vc12\lib;$(LibraryPath) 8 | 9 | 10 | 11 | F:\dev\opencv\opencv\build\include;%(AdditionalIncludeDirectories) 12 | 13 | 14 | opencv_ts300.lib;opencv_world300.lib;%(AdditionalDependencies) 15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /LaneDetector/PointsToLanesConverter.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include "LaneModels.hpp" 5 | #include "DBSCAN.hpp" 6 | #include "RANSAC.hpp" 7 | 8 | namespace vision 9 | { 10 | 11 | class PointsToLanesConverter 12 | { 13 | public: 14 | std::vector Convert(std::vector points) 15 | { 16 | std::vector model; 17 | 18 | const int EPS = 50; 19 | const int MIN_DENSITY = 100; 20 | DBScan dbscan(EPS*EPS, MIN_DENSITY); 21 | dbscan.fit(points); 22 | auto& labels = dbscan.GetLabels(); 23 | 24 | for (int i = 1; i <= dbscan.GetEstimatedClusterNumber(); ++i) 25 | { 26 | decltype(points) oneGroup; 27 | for (int j = 0; j < points.size(); ++j) 28 | { 29 | if (labels[j] == i) oneGroup.emplace_back(points[j]); 30 | } 31 | 32 | if (oneGroup.size() < 250) 33 | continue; 34 | 35 | auto parabola = RANSAC_Parabola(RANSAC_ITERATIONS, RANSAC_MODEL_SIZE, static_cast(RANSAC_INLINERS * oneGroup.size()), RANSAC_ERROR_THRESHOLD, oneGroup); 36 | model.emplace_back(parabola); 37 | } 38 | 39 | RemoveSimilarOnes(model); 40 | 41 | return model; 42 | } 43 | 44 | void RemoveSimilarOnes(std::vector& input) 45 | { 46 | const int MIN_DIFFERENCE_BETWEEN_LANES = 60; 47 | for (int i = 0; i < input.size(); ++i) 48 | { 49 | for (int j = i + 1; j < input.size(); ++j) 50 | { 51 | if (MinimumDistance(input[i], input[j], 150, 650) < MIN_DIFFERENCE_BETWEEN_LANES) 52 | { 53 | //input[i] = lane_model::Parabola((input[i].a + input[j].a) / 2, (input[i].b + input[j].b) / 2, (input[i].c + input[j].c) / 2); 54 | if (abs(input[i].a) < abs(input[j].a)) // remove the more curved one 55 | { 56 | input.erase(input.begin() + j); 57 | } 58 | else 59 | { 60 | input.erase(input.begin() + i); 61 | break; 62 | } 63 | } 64 | } 65 | } 66 | } 67 | 68 | private: 69 | const int RANSAC_ITERATIONS = 100; 70 | const int RANSAC_MODEL_SIZE = 3; 71 | const int RANSAC_ERROR_THRESHOLD = 30; 72 | const double RANSAC_INLINERS = 0.55; 73 | 74 | float MinimumDistance(lane_model::Parabola a, lane_model::Parabola b, const int starty, const int endy, const int dy = 2) 75 | { 76 | double min = std::numeric_limits ::max(); 77 | for (int i = starty; i <= endy; i += dy) 78 | { 79 | min = std::min(abs(a(i) - b(i)), min); 80 | } 81 | return static_cast(min); 82 | } 83 | }; 84 | 85 | } // namespace vision -------------------------------------------------------------------------------- /LaneDetector/RANSAC.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | #include "LaneModels.hpp" 3 | #include "random_unique.hpp" 4 | 5 | /// Model is valid when it's not degenerated (all 0's) or is too much curved 6 | inline bool IsModelValid(vision::lane_model::Parabola& parabola) 7 | { 8 | return abs(parabola.a) <= 0.0007 && (parabola.a != 0 && parabola.b != 0 && parabola.c != 0); 9 | } 10 | 11 | vision::lane_model::Parabola RANSAC_Parabola(int iterations, int init_samples, int n, double error_threshold, std::vector inputData) 12 | { 13 | auto best_fit = vision::lane_model::Parabola(); 14 | double best_error = std::numeric_limits::max(); 15 | int consensus_set; 16 | 17 | for (int i = 0; i < iterations; ++i) 18 | { 19 | random_unique(inputData.begin(), inputData.end(), init_samples); 20 | auto model = vision::lane_model::fit(inputData, init_samples); 21 | auto straight_model = model; 22 | straight_model.a = 0; 23 | 24 | if (!IsModelValid(model)) continue; 25 | 26 | consensus_set = 0; 27 | auto model_error = model.a*model.a * 10000; // bias towards straight lines 28 | auto straight_model_error = 0; 29 | for (auto p : inputData) 30 | { 31 | const auto err = abs(model(p.y) - p.x); 32 | if (err < error_threshold) { 33 | consensus_set += 1; 34 | model_error += err; 35 | straight_model_error += abs(straight_model(p.y) - p.x); 36 | } 37 | } 38 | 39 | if (consensus_set >= n) 40 | { 41 | if (straight_model_error < model_error) 42 | { 43 | model_error = straight_model_error; 44 | model = straight_model; 45 | } 46 | 47 | if (model_error < best_error) 48 | { 49 | best_fit = model; 50 | best_error = model_error; 51 | } 52 | } 53 | } 54 | 55 | return best_fit; 56 | } 57 | -------------------------------------------------------------------------------- /LaneDetector/main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "LaneDetector.hpp" 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | struct Frame 10 | { 11 | cv::Mat frame; 12 | unsigned long seqNo; 13 | }; 14 | 15 | class SafeQueue 16 | { 17 | public: 18 | 19 | SafeQueue() : counter_(0) 20 | { 21 | 22 | } 23 | 24 | void push(cv::Mat frame) 25 | { 26 | std::lock_guard lock(mtx_); 27 | frames_.emplace(frame); 28 | } 29 | 30 | bool pop(cv::Mat& frame) 31 | { 32 | std::lock_guard lock(mtx_); 33 | if (!frames_.empty()) 34 | { 35 | frame = frames_.front(); 36 | frames_.pop(); 37 | return true; 38 | } 39 | return false; 40 | } 41 | 42 | private: 43 | std::queue frames_; 44 | unsigned long counter_; 45 | std::mutex mtx_; 46 | }; 47 | 48 | SafeQueue queueFrames; 49 | SafeQueue queueOutput; 50 | 51 | 52 | cv::Mat DrawRoadModel(const vision::RoadModel& roadModel, const cv::Mat& inputFrame) 53 | { 54 | cv::Mat color{ inputFrame }; 55 | const cv::Vec3b a = { 0, 255, 0 }; 56 | const cv::Vec3b b = { 0, 255, 255 }; 57 | //for (auto& parabola : roadModel.lanes_) 58 | //{ 59 | // std::vector parabolaLine; 60 | // //for (int y = 140; y < 650; ++y)// color.rows; ++y) 61 | // for (int y = 0; y < color.rows; ++y) 62 | // { 63 | // const auto x = 2 * parabola(y*0.5); 64 | // if (x > 0 && x < 1280 && y > 0 && y < 720) 65 | // parabolaLine.emplace_back(x, y); 66 | // } 67 | 68 | // if (parabolaLine.size() > 0) cv::perspectiveTransform(parabolaLine, parabolaLine, roadModel.invPerspTransform); 69 | // for (auto p : parabolaLine) 70 | // { 71 | // if (p.x > 0 && p.x < 1280 && p.y > 0 && p.y < 720) 72 | // color.at(p) = b; 73 | // } 74 | 75 | // //std::cout << parabola.ToString() << std::endl; 76 | //} 77 | 78 | //for (auto& parabola : roadModel.current_lane_) 79 | //{ 80 | // std::vector parabolaLine; 81 | // for (int y = 0; y < color.rows; ++y) 82 | // { 83 | // const auto x = 2 * parabola(y*0.5); 84 | // if (x > 1 && x < 1280-1 && y > 0 && y < 720) 85 | // { 86 | // parabolaLine.emplace_back(x-1, y); 87 | // parabolaLine.emplace_back(x, y); 88 | // parabolaLine.emplace_back(x+1, y); 89 | // } 90 | // } 91 | 92 | // if (parabolaLine.size() > 0) cv::perspectiveTransform(parabolaLine, parabolaLine, roadModel.invPerspTransform); 93 | // for (auto p : parabolaLine) 94 | // { 95 | // if (p.x > 0 && p.x < 1280 && p.y > 0 && p.y < 720) 96 | // color.at(p) = a; 97 | // } 98 | 99 | //} 100 | 101 | //std::cout << "----" << std::endl; 102 | 103 | return color; 104 | } 105 | 106 | #include 107 | 108 | std::atomic end = false; 109 | 110 | void processFrame() 111 | { 112 | vision::LaneDetector laneDetector; 113 | cv::Mat frame; 114 | 115 | unsigned long frameNumber = 0; 116 | unsigned long avg = 0; 117 | while (end == false) 118 | { 119 | if (queueFrames.pop(frame) == false) 120 | { 121 | std::this_thread::sleep_for(std::chrono::milliseconds(1)); 122 | continue; 123 | } 124 | 125 | auto beg = std::chrono::high_resolution_clock::now(); 126 | 127 | auto local = frame.clone(); 128 | auto roadModel = laneDetector.DetectLane(local); 129 | auto output = DrawRoadModel(roadModel, local); 130 | queueOutput.push(output); 131 | 132 | ++frameNumber; 133 | auto diff = std::chrono::duration_cast(std::chrono::high_resolution_clock::now() - beg).count(); 134 | avg += diff; 135 | std::cout << "frame:\t" << frameNumber << "\ttime:\t" << diff << "\tms\t" << avg / frameNumber << "\tms\t" << 1000 / diff << std::endl; 136 | } 137 | } 138 | 139 | 140 | void grabFrames() 141 | { 142 | cv::VideoCapture videoCapture("F://highway.avi");// F://road.wmv"); 143 | 144 | if (videoCapture.isOpened() == false) 145 | end = true; 146 | cv::Mat frame; 147 | while(end == false) 148 | { 149 | videoCapture >> frame; 150 | if (frame.empty()) 151 | end = true; 152 | else 153 | queueFrames.push(frame); 154 | 155 | std::this_thread::sleep_for(std::chrono::milliseconds(5)); 156 | } 157 | 158 | end = true; 159 | } 160 | 161 | void displayResult() 162 | { 163 | //cv::VideoWriter writer; 164 | //writer.open("F://output4.avi", writer.fourcc('M', 'J', 'P', 'G'), 25, cv::Size(1280, 720)); 165 | 166 | cv::namedWindow("LaneDetector"); 167 | 168 | cv::Mat frame; 169 | while (end == false) 170 | { 171 | if (queueOutput.pop(frame) == false) 172 | { 173 | std::this_thread::sleep_for(std::chrono::milliseconds(1)); 174 | continue; 175 | } 176 | 177 | //writer.write(frame); 178 | cv::imshow("LaneDetector", frame); 179 | 180 | if (cv::waitKey(3) == 27) 181 | { 182 | break; 183 | } 184 | } 185 | end = true; 186 | cv::destroyAllWindows(); 187 | } 188 | 189 | int main(int argc, char**argv) 190 | { 191 | std::vector threads; 192 | 193 | threads.emplace_back(displayResult); 194 | threads.emplace_back(grabFrames); 195 | threads.emplace_back(processFrame); 196 | 197 | for (auto& t : threads) t.join(); 198 | 199 | return 0; 200 | } -------------------------------------------------------------------------------- /LaneDetector/random_unique.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | #include 3 | 4 | // Fisher - Yates shuffle 5 | template 6 | bidiiter random_unique(bidiiter begin, bidiiter end, size_t num_random) { 7 | size_t left = std::distance(begin, end); 8 | while (num_random--) { 9 | bidiiter r = begin; 10 | std::advance(r, rand() % left); 11 | std::swap(*begin, *r); 12 | ++begin; 13 | --left; 14 | } 15 | return begin; 16 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Curved Lane Detection 2 | An old, experimental implementation of curved lane detection using OpenCV, RANSAC, DBSCAN, perspective projection, some filters etc. 3 | The resulted curves are in transformed space (a birdview of road, that could be possible to scale in cm) but it is displayed 4 | in argument reality by inverse transformation. 5 | 6 | This is hardcoded (perspective transformation) for some random video, but not optimized for it. It is developed mostly for white lanes, 7 | but sometimes can work for yellow as you can see in example video. 8 | It runs real time on my machine. 9 | It is quite robust (can estimate road center even if only one lane is visible, the lanes can be partially visible). 10 | 11 | The algorithm is quite simple oldschool pipeline processing that would need to be improved and probably would perform worse than using DNN. 12 | 13 | # Example 14 | The code effect can be seen in below video (click to watch): 15 | 16 | [![Code ran for sample video](http://img.youtube.com/vi/H-GIlXoNKI0/0.jpg)](http://www.youtube.com/watch?v=H-GIlXoNKI0) 17 | --------------------------------------------------------------------------------