├── .gitignore ├── CMakeLists.txt ├── LICENSE ├── README.md ├── data ├── data_process.py ├── groundtruth.json └── selected test data │ ├── 0531 │ ├── 1492626292371547028 │ │ └── 20.jpg │ ├── 1492626388446057821 │ │ └── 20.jpg │ ├── 1492626718748019090 │ │ └── 20.jpg │ ├── 1492626726476963805 │ │ └── 20.jpg │ ├── 1492626749527113213 │ │ └── 20.jpg │ ├── 1492627128130967757 │ │ └── 20.jpg │ ├── 1492627317650154687 │ │ └── 20.jpg │ ├── 1492627349131844229 │ │ └── 20.jpg │ ├── 1492627471056566462 │ │ └── 20.jpg │ ├── 1492627515463990014 │ │ └── 20.jpg │ ├── 1492627525623470452 │ │ └── 20.jpg │ ├── 1492627607908463563 │ │ └── 20.jpg │ ├── 1492627610474600372 │ │ └── 20.jpg │ ├── 1492627805420764602 │ │ └── 20.jpg │ ├── 1492627871547619739 │ │ └── 20.jpg │ ├── 1492627958694436076 │ │ └── 20.jpg │ ├── 1492628003602776964 │ │ └── 20.jpg │ ├── 1492628026320478715 │ │ └── 20.jpg │ ├── 1492628031017373452 │ │ └── 20.jpg │ ├── 1492628364152947274 │ │ └── 20.jpg │ ├── 1492628367980837458 │ │ └── 20.jpg │ ├── 1492628392132013744 │ │ └── 20.jpg │ ├── 1492628544439813675 │ │ └── 20.jpg │ ├── 1492628717669110483 │ │ └── 20.jpg │ ├── 1492628748683581912 │ │ └── 20.jpg │ ├── 1492628753214604895 │ │ └── 20.jpg │ ├── 1492628756811316586 │ │ └── 20.jpg │ ├── 1492628759843106892 │ │ └── 20.jpg │ ├── 1492628785560580255 │ │ └── 20.jpg │ ├── 1492628791892679406 │ │ └── 20.jpg │ ├── 1492628894461588977 │ │ └── 20.jpg │ ├── 1492628898525855772 │ │ └── 20.jpg │ ├── 1492628915781811493 │ │ └── 20.jpg │ ├── 1492628925209797907 │ │ └── 20.jpg │ ├── 1492628946166107862 │ │ └── 20.jpg │ ├── 1492629493832252271 │ │ └── 20.jpg │ ├── 1492629626720641603 │ │ └── 20.jpg │ ├── 1492629646273459822 │ │ └── 20.jpg │ ├── 1492629700240878893 │ │ └── 20.jpg │ ├── 1492629744847348361 │ │ └── 20.jpg │ ├── 1492629764202222740 │ │ └── 20.jpg │ ├── 1492629772330681800 │ │ └── 20.jpg │ ├── 1492629968347062256 │ │ └── 20.jpg │ ├── 1492630120020607688 │ │ └── 20.jpg │ ├── 1492630278191641862 │ │ └── 20.jpg │ ├── 1492631377662145887 │ │ └── 20.jpg │ ├── 1492631797808463012 │ │ └── 20.jpg │ ├── 1492634561885337945 │ │ └── 20.jpg │ ├── 1492636918035773538 │ │ └── 20.jpg │ └── 1492637138669280283 │ │ └── 20.jpg │ └── 0601 │ ├── 1494452381594376146 │ └── 20.jpg │ ├── 1494452399585761304 │ └── 20.jpg │ ├── 1494452407582274573 │ └── 20.jpg │ ├── 1494452443567310647 │ └── 20.jpg │ ├── 1494452495543904834 │ └── 20.jpg │ ├── 1494452521532285259 │ └── 20.jpg │ ├── 1494452527529663225 │ └── 20.jpg │ ├── 1494452555517417669 │ └── 20.jpg │ ├── 1494452587503447885 │ └── 20.jpg │ ├── 1494452605495775275 │ └── 20.jpg │ ├── 1494452631484224411 │ └── 20.jpg │ ├── 1494452667468632483 │ └── 20.jpg │ ├── 1494452689458738349 │ └── 20.jpg │ ├── 1494452809905908227 │ └── 20.jpg │ ├── 1494452825898945556 │ └── 20.jpg │ ├── 1494452921856946234 │ └── 20.jpg │ ├── 1494452929853344267 │ └── 20.jpg │ ├── 1494452957841111858 │ └── 20.jpg │ ├── 1494452981830568996 │ └── 20.jpg │ ├── 1494452987828017539 │ └── 20.jpg │ ├── 1494453131765683584 │ └── 20.jpg │ ├── 1494453137763384771 │ └── 20.jpg │ ├── 1494453157754360152 │ └── 20.jpg │ ├── 1494453173747344679 │ └── 20.jpg │ ├── 1494453227726434936 │ └── 20.jpg │ ├── 1494453233720967019 │ └── 20.jpg │ ├── 1494453287696553635 │ └── 20.jpg │ ├── 1494453307691066696 │ └── 20.jpg │ ├── 1494453313685150979 │ └── 20.jpg │ ├── 1494453341673586257 │ └── 20.jpg │ ├── 1494453401646638002 │ └── 20.jpg │ ├── 1494453409643085329 │ └── 20.jpg │ ├── 1494453427635226083 │ └── 20.jpg │ ├── 1494453467617697485 │ └── 20.jpg │ ├── 1494453481611650216 │ └── 20.jpg │ ├── 1494453511598429183 │ └── 20.jpg │ ├── 1494453553580016137 │ └── 20.jpg │ ├── 1494453561576544114 │ └── 20.jpg │ ├── 1494453583566932856 │ └── 20.jpg │ ├── 1494453589564231638 │ └── 20.jpg │ ├── 1494453607556426762 │ └── 20.jpg │ ├── 1494453621551554547 │ └── 20.jpg │ ├── 1494453643540652356 │ └── 20.jpg │ ├── 1494453657537147475 │ └── 20.jpg │ ├── 1494453713511948219 │ └── 20.jpg │ ├── 1494453731502184768 │ └── 20.jpg │ ├── 1494453753492316007 │ └── 20.jpg │ ├── 1494453761488899826 │ └── 20.jpg │ ├── 1494453785478375673 │ └── 20.jpg │ └── 1494453809467806176 │ └── 20.jpg ├── evaluate.py ├── include ├── Edge.h ├── Hough.h ├── Img.hpp ├── Kernel.h └── SaveResult.h ├── main.cpp ├── result ├── image_of_readme │ ├── Sobel.png │ ├── binary.png │ ├── binary_failure.png │ ├── canny.png │ ├── hough.png │ ├── img.png │ ├── mask.png │ ├── nms.png │ ├── result.png │ ├── roi.png │ └── update_cmp.png └── predict.json └── source ├── Edge.cpp ├── Hough.cpp ├── Kernel.cpp └── SaveResult.cpp /.gitignore: -------------------------------------------------------------------------------- 1 | cmake-build-*/ 2 | .idea/ 3 | .vscode/ 4 | build/ 5 | debug/ 6 | release/ -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.17) 2 | project(lane_detection) 3 | 4 | set(CMAKE_CXX_STANDARD 14) 5 | 6 | set(OpenCV_DIR D:\\Envs\\opencv\\build\\x64\\mingw\\install) 7 | find_package(OpenCV REQUIRED) 8 | include_directories(${OpenCV_INCLUDE_DIRS}) 9 | set(OpenCV_LIBS opencv_core opencv_imgproc opencv_highgui opencv_imgcodecs) 10 | 11 | add_executable(lane_detection main.cpp include/Img.hpp include/Kernel.h source/Kernel.cpp include/Edge.h source/Edge.cpp include/Hough.h source/Hough.cpp include/SaveResult.h source/SaveResult.cpp) 12 | 13 | target_link_libraries(lane_detection ${OpenCV_LIBS}) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 XuZhengfei 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

车道线检测 （Lane Detection）

2 | 3 | ## 1、实验内容 4 | 5 | 本实验使用数字图像处理的基本方法，构建了一个车道线检测模型。该模型可以识别图像中所有的车道线，并得到完整的车道线信息。模型在tuSimple Lane Dataset大小为100的数据子集进行了测试，达到了较好的结果。 6 | 7 | ## 2、实现思路 8 | 9 | 实现车道线检测，主要包含两部分操作 10 | 11 | 1. 道路图像的处理，主要包括灰度图转换、基于高斯平滑的图像去噪、基于Canny算法的边缘提取 12 | 2. 车道线检测方法，主要包括获取感兴趣区域（ROI）、形态学闭运算、基于Hough变换的直线检测 13 | 14 | 模型的处理流程如下， 15 | 16 | image-20210214120515919 17 | 18 | ### 2.1 道路图像处理 19 | 20 | 通过对道路图像进行处理，突出图像中的车道线部分。模型将彩色图像转化成灰度图像进行处理，目的是简化模型的复杂度，提高运行效率。 21 | 22 | #### 2.1.1 高斯平滑 23 | 24 | 由于光照、路面情况、拍摄质量等问题，道路图像上存在很多噪声，通过高斯滤波使图像变得平滑，减弱图像中的噪声对结果的影响，提高车道线检测模型的鲁棒性。 25 | 26 | 高斯平滑就是使用高斯滤波器与原图像进行卷积，得到平滑图像。与均值滤波类似，它们都是取滤波器窗口内像素的加权均值作为输出。高斯滤波器的权值分布满足二维高斯函数。 27 | ![](https://latex.codecogs.com/gif.latex?h(x,y)=e^{-\frac{x^2+y^2}{2\sigma^2}}) 28 | 由于高斯平滑是线性离散滤波，因此离散形式的高斯滤波器为 29 | ![](https://latex.codecogs.com/gif.latex?H_{i,j}&space;=&space;\frac{1}{2&space;\pi&space;\sigma&space;^&space;2}e&space;^{-\frac{(i&space;-&space;k&space;-&space;1)^2&space;+&space;(j&space;-&space;k&space;-&space;1)^2}{2&space;\sigma&space;^&space;2}}) 30 | 本实验采用 $3\times3$ 的高斯滤波器。具体实现为定义 `Kernel` 类实现通用的卷积操作，定义派生类 `GaussianKernel` 实现不同 size 和 $\sigma$ 高斯滤波器的构建的运算，实现接口如下： 31 | 32 | ```c++ 33 | /* Kernel.h */ 34 | class Kernel 35 | { 36 | public: 37 | double **data; 38 | int size; 39 | Kernel(int size); // 空的卷积核 40 | Kernel(Kernel &cp); // 拷贝构造函数 41 | ~Kernel(); 42 | double *operator[](const int idx) const; 43 | // 卷积操作 44 | template 45 | void convolve(const Img &src, Img &dst, const bool is_clip = true) const; 46 | }; 47 | 48 | class GaussianKernel : public Kernel 49 | { 50 | public: 51 | double sigma; 52 | GaussianKernel(const int size, const double sigma); 53 | GaussianKernel(GaussianKernel &cp); 54 | }; 55 | ``` 56 | 57 | #### 2.1.2 边缘提取 58 | 59 | 在实验过程中，我曾尝试采用以下方法进行边缘提取的方法。由于在图像中车道线的灰度值较大，因此我设计了一种参数自适应的阈值分割算法，把车道线从图像中抽取出来。具体方法如下：统计图像的灰度分布，选取整体灰度分布相应比例对应的灰度值作为阈值，对图像进行二值化。效果如下： 60 | 61 | ![阈值分割效果](./result/image_of_readme/binary.png) 62 | 63 | 可以发现，通过阈值分割有效的过滤掉了大部分背景，如山脉、路面、车辆，这为下面的直线检测去除了一定的干扰。但是由于部分图像存在反光或较亮区域，这导致一些车道线丢失，或特征不再明显，如下图。 64 | 65 | ![阈值分割导致车道线信息丢失](./result/image_of_readme/binary_failure.png) 66 | 67 | 虽然可以通过求图像梯度的方法将大面积的高亮度区域滤除，但是直接将原图转成二值图像处理，会丢失车道线的细节信息导致结果车道线信息不完整。因此舍弃该方案。 68 | 69 | 最终采用基于图像梯度的边缘提取方法——Canny算法。Canny主要包含三个步骤： 70 | 71 | 1. Sobel算子：计算图像梯度 72 | 2. 非极大值抑制：去除非边缘的噪点，细化边缘 73 | 3. 双阈值：检测并连接边缘 74 | 75 | （1）Sobel 算子计算图像梯度 76 | 77 | 灰度图可以看做灰度值 $h(x,y)$ 关于 $(x,y)$ 坐标的二元函数，计算图像梯度可以通过Sobel算子计算得到。​ 78 | 79 | * $x$ 方向梯度： ${grad}_x(x,y) = \frac{\partial h(x,y)}{\partial x}$ 80 | * $y$ 方向梯度： ${grad}_y(x,y) = \frac{\partial h(x,y)}{\partial x}$ 81 | * 梯度幅度： $grad = \sqrt{{grad_x}^2 + {gard_y}^2}$ 82 | * 梯度方向：$gard_\theta = arctan(\frac{grad_y}{grad_x})$ 83 | 84 | 其中计算 $x,y$ 方向的梯度使用Sobel算子对图像进行卷积 85 | 86 | 87 | 88 | Sobel 算子计算梯度效果如下： 89 | 90 | ![Sobel计算图像梯度](./result/image_of_readme/Sobel.png) 91 | 92 | （2）非极大值抑制 93 | 94 | 分析上图发现，由于图像灰度存在起伏，所以有一些不是边缘的区域也存在较大的梯度。采用非极大值抑制（NMS）的方法，消除梯度图像中非边缘的噪声，并将边缘细化。 95 | 96 | NMS实现的思路如下：计算每个中心像素点沿梯度方向邻域内各点的梯度值，如果该中心像素点的梯度值是以上像素点梯度值的局部极大值，则保留梯度，否则梯度置为零。由于邻域内在梯度方向上的点不一定是在整数坐标位置，因此需要通过插值计算邻域内梯度方向点的梯度值。实现效果如下： 97 | 98 | ![非极大值抑制效果](./result/image_of_readme/nms.png) 99 | 100 | 一些非边缘的噪点得到了一定程度的抑制，边缘也得到细化。 101 | 102 | （3）双阈值检测和边缘连接 103 | 104 | 需要将得到的梯度图像进行阈值分割，得到二值图以便后续进行hough变换。采用双阈值对图像进行阈值分割，实现思路如下： 105 | 106 | * 当梯度值大于高阈值时，将其灰度值取为255。 107 | * 当梯度值小于低阈值时，将其灰度值取为0。 108 | * 当梯度介于两者之间是，如果该点邻域内有高阈值点，则取为255，否则取0。 109 | 110 | 双阈值处理中，高阈值将物体边缘和背景区分开，但是当高阈值较大时，可能导致边缘断断续续；此时低阈值平滑边缘轮廓，能实现较好的分割效果。同时借鉴之前尝试对灰度图做阈值分割的思路，采用整体灰度分布相应比例处的灰度值为高阈值，低阈值取高阈值的 $\frac{2}{3}$，实现效果如下： 111 | 112 | ![双阈值效果](./result/image_of_readme/canny.png) 113 | 114 | Canny 边缘提取的实现接口如下： 115 | 116 | ```c++ 117 | #ifndef LANE_DETECTION_EDGE_DETECTION_H 118 | #define LANE_DETECTION_EDGE_DETECTION_H 119 | 120 | #include "Img.hpp" 121 | #include "Kernel.h" 122 | // 阈值分割 123 | void TurnBinary(Img &src, const double weight); 124 | // 膨胀运算 125 | void Dilation(const Img &src, Img &dst, int kernel_size); 126 | // 腐蚀运算 127 | void Erosion(const Img &src, Img &dst, int kernel_size); 128 | // 遮盖无效部分 129 | void RoiMask(Img &src); 130 | // Sobel 算子计算梯度 131 | void Sobel(const Img &src, Img &dst, Img &theta); 132 | // 非极大值抑制 133 | void NonMaxSuppression(const Img &src, Img &dst, const Img &theta); 134 | // 双阈值处理 135 | void DoubleThreshold(Img &image, const double weight = 0.9); 136 | // Canny 边缘检测 137 | void Canny(Img &image, const double weight = 0.9); 138 | #endif //LANE_DETECTION_EDGE_DETECTION_H 139 | ``` 140 | 141 | ### 2.2 车道线检测 142 | 143 | #### 2.2.1 梯形 ROI mask 144 | 145 | 经过图像的边缘提取，车道线边缘已经从图像中抽取出来。观察边缘图像发现：道路两边的环境复杂，存在很多干扰车道线检测的直线边缘，如天际线、山脉边缘、电线杆、树丛等。同时考虑到道路图像中，车道线集中在图像的中间偏下区域，因此可以仅对感兴趣区域(ROI)进行处理和检测。根据车道线图像特点，采用梯形掩码获取ROI。 146 | 147 | 观察图像选取了(400, 0) (220, 420) (200, 860), (400, 1280)四个点作为 mask 的角点。mask图像如下 148 | 149 | ![梯形 mask](./result/image_of_readme/mask.png) 150 | 151 | ROI 如下： 152 | 153 | ![梯形 ROI](./result/image_of_readme/roi.png) 154 | 155 | #### 2.2.2 hough 变换检测直线 156 | 157 | hough变换是一种目标检测的方法，可以检测出有明确表达式的图形。hough 变换的基本原理：利用两个不同坐标系之间的变换来检测图像中的直线。将图像空间中的直线映射到参数空间的一个点，然后对参数空间中的点进行累计投票，进而得到参数空间中的峰值点，该峰值点就对应空间坐标系中的真实存在的直线参数。 158 | 159 | hough变换中，直线采用极坐标方程表示，因为参数$\theta$ 和 $r$ 的范围有限，便于以相同步长进行离散化 160 | 161 | 实现思路： 162 | 163 | * 初始化参数空间（二维矩阵） 164 | * 遍历空间坐标系的每个非零像素点，为所有可能经过该点的直线的参数进行投票。 165 | * 找出参数空间中大于指定阈值的参数点 166 | 167 | hough 变换效果如下图： 168 | 169 | ![hough变换效果](./result/image_of_readme/hough.png) 170 | 171 | 可以线由于车道线存在一定的弧度并非严格地直线，且存在一定宽度，导致每条车道线都会检测出多条对应直线。可以采用聚类的方法对检测出的直线进行聚类，以得到更精准的效果。 172 | 173 | #### 2.2.3 车道线聚类 174 | 175 | 由于 k-means 等聚类算法复杂度较高，影响车道线检测的实时性。所以我设计了一种高效的聚类方案。具体思路如下：根据以两个直线的角度参数距离为相似度函数，遍历hough变换检测出的所有直线参数，如果相似度高于阈值，则认为属于同一类别，该类别大小加一；如果相似度低于阈值，则认为属于不同类别，与下一个类中心点进行比较。如果没有相似的 176 | 177 | 伪代码如下： 178 | 179 | ```c++ 180 | params; // hough 变换得到参数列表 181 | clusters; // 聚类列表 182 | flag; // 标记是否新建类 183 | for param in params 184 | { 185 | flag = true; 186 | for cluster in clusters 187 | { 188 | if is_similar(param, cluster) // 如果相似则添加到该类中 189 | { 190 | flag = false; 191 | update(cluster); 192 | break; 193 | } 194 | } 195 | if flag // 与现有的所有类都不同 196 | clusters.append(param); // 添加新类 197 | } 198 | ``` 199 | 200 | 这里相似度函数采用两条直线的角度参数的差值。 201 | 202 | 一开始选择的更新聚类中心的方法，是取同一类别的平均值，效果不佳。经过尝试最后采用取每个类别的初始值为中心点，实现较好的效果。示例如下： 203 | 204 | ![聚类方法对比](./result/image_of_readme/update_cmp.png) 205 | 206 | 评测结果对比： 207 | 208 | | 中心点 | Accuracy | FP | FN | 209 | | ---------- | -------- | ------ | ------ | 210 | | 数据均值 | 0.5740 | 0.7058 | 0.7533 | 211 | | 聚类初始值 | 0.7539 | 0.5025 | 0.5242 | 212 | 213 | 分析原因：由于车道线有一定弧度，导致前半部分和后半部分的车道线参数差距较大。如果降低判定相似的标准，就会导致本不相似的直线求均值，从而使Accuracy较低；如果提高相似的标准就会，导致聚类得到类别很多，从而FP较大；因此采用加权均值更新聚类中心点并不理想。 214 | 215 | 按照车道线聚类结果中每个类别的大小，对聚类结果进行排序，选择所有聚类结果中规模最大的4个类作为最终确定的直线参数。 216 | 217 | 代码接口如下： 218 | 219 | ```c++ 220 | // 相似函数 221 | bool is_similar(pair &l1, pair &l2); 222 | // 更新类中心点 223 | void update_cluster(pair &line, pair, int> &cluster); 224 | // 直线聚类 225 | void lines_cluster(vector> &lines); 226 | // hough变换 227 | void HoughTransform(Img &src, vector> &lines, int threshold); 228 | ``` 229 | 230 | ### 2.3 输出结果 231 | 232 | 函数接口如下： 233 | 234 | ```c++ 235 | /* 根据车道线的参数，获取坐标向量 */ 236 | void GetLanes(Img &src, vector> ¶ms, vector> &lanes); 237 | /* 将检测结果写入json文件 */ 238 | void WriteJson(string &raw_file, vector> &lanes, double run_time, ofstream &of); 239 | /* 展示车道线检测结果 */ 240 | void polyLanes(const string &path, vector> &lanes, int delay); 241 | ``` 242 | 243 | 通过 `GetLanes` 将每个直线参数转换成直线坐标，`WriteJson` 函数将结果写入json文件，`polyLanes` 可视化展示车道线。 244 | 245 | 实现效果如下： 246 | 247 | ![输出结果](./result/image_of_readme/result.png) 248 | 249 | ## 3、 实现说明 250 | 251 | ### 3.1 Img 模板类存储图像 252 | 253 | 由于只允许使用OpenCV进行图像的读写操作，因此本实验构建了 `Img` 模板类，作为图像存储和操作的基本数据结构，代码接口如下： 254 | 255 | ```c++ 256 | template 257 | class Img 258 | { 259 | public: 260 | T **data; // 存放数据 261 | int rows; // 图像的行数 262 | int cols; // 图像的列数 263 | 264 | Img(int rows, int cols); /* 构造空值图像 */ 265 | Img(const char *path); /* 读入图像：灰度图 */ 266 | Img(Img &cp); /* Img类的复制构造函数 */ 267 | ~Img(); 268 | 269 | T *operator[](const int idx) const; 270 | Img &operator=(const Img &cp); 271 | 272 | cv::Mat toMat() const; /* 将图像转换成 cv::Mat */ 273 | void show(const char *name, int delay) const; /* 展示图片 */ 274 | }; 275 | ``` 276 | 277 | 展示图片的`plotLanes`函数也使用了 OpenCV 框架对图像进行展示。 278 | 279 | ### 3.2 main 函数 280 | 281 | ```c++ 282 | int main() 283 | { 284 | // 获取所有图片的路径 285 | vector file_names; 286 | get_image_names("../data/selected test data", file_names); 287 | // 输出文件流接口 288 | ofstream out; 289 | out.open("../result/predict.json", ios::out); 290 | // 记录 run_time 运行时间 291 | clock_t begin_time, end_time; 292 | // 车道线检测 293 | for (auto &path : file_names) 294 | { 295 | begin_time = clock(); 296 | Img src(path.data()); 297 | Img dst(src.rows, src.cols); 298 | Img dst_close(src.rows, src.cols); 299 | Img theta(src.rows, src.cols); 300 | 301 | // 高斯滤波 302 | GaussianKernel filter(3, 1); 303 | filter.convolve(src, dst, true); 304 | // Canny 边缘检测 305 | Canny(dst, 0.97); 306 | // 获取图像 Roi 307 | RoiMask(dst); 308 | // hough 变换 309 | vector> lanes_param; 310 | HoughTransform(dst, lanes_param, 100); 311 | // 将车道线转成标准格式 312 | vector> lanes; 313 | GetLanes(dst, lanes_param, lanes); 314 | end_time = clock(); 315 | // 将预测结果保存在 json 文件中 316 | string raw_path = path; 317 | raw_path.replace(raw_path.find("../data/selected test data"), 26, "clips"); 318 | WriteJson(raw_path, lanes, double(end_time - begin_time) / CLOCKS_PER_SEC, out); 319 | // 绘图展示检测出的直线 320 | polyLanes(path, lanes, lanes_param, 100); 321 | cout << lanes_param.size() << endl; 322 | } 323 | out.close(); 324 | return 0; 325 | } 326 | ``` 327 | 328 | ### 3.3 项目文件树结构： 329 | 330 | ```shell 331 | │ .gitignore 332 | │ CMakeLists.txt 333 | │ evaluate.py # 评测脚本 334 | │ main.cpp # 主函数 335 | │ README.md 336 | │ 337 | ├─result 338 | │ predict.json # 预测结果 339 | ├─data 340 | │ │ data_process.py # 数据预处理脚本 341 | │ │ groundtruth.json # 真实值 342 | │ │ 343 | │ └─selected test data # 待处理图像 344 | │ ├─0531 345 | │ └─0601 346 | │ 347 | ├─include 348 | │ Edge.h # 边缘检测 349 | │ Hough.h # hough 变换 350 | │ Img.hpp # Img 模板类 351 | │ Kernel.h # Kernel 滤波器 352 | │ SaveResult.h # 输出结果接口 353 | │ 354 | └─source 355 | Edge.cpp 356 | Hough.cpp 357 | Kernel.cpp 358 | SaveResult.cpp 359 | ``` 360 | 361 | ## 4、 实验结果 362 | 363 | 经过运行 TuSimple Lane Detection 项目的测评脚本，得到在数据子集上检测结果如下： 364 | 365 | | Accuracy | FP | FN | 366 | | -------- | ------ | ------ | 367 | | 0.7539 | 0.5025 | 0.5242 | 368 | 369 | 实现了较好的测评效果。同时检测每张图像约用时0.4秒。 370 | 371 | ## 5、 实验总结及改进 372 | 373 | 实验过程中尝试了很多方案，如采用形态学运算，提高车道线的完整性；通过阈值分割，去除背景和干扰物；采用均值作为聚类中心等。由于方案设计上的主观缺陷和检测任务的存在的光照不均、环境复杂等客观因素，以上方案均被舍弃。最终经过实践得到了一种鲁棒性较好，效果较优的车道线检测方案。 374 | 375 | 通过查阅相关资料，我了解到更多车道线检测的改进算法，例如可以通过最大类间方差法(OTSU)进行阈值分割、动态ROI区域等。可以通过以上算法进一步提高模型精度和性能。 376 | 377 | 378 | ## 6、 运行方法 379 | 1. **编译**（Windows 10）： 380 | * cmake version 3.19.3 381 | * GNU Make version 4.2.1 382 | * OpenCV version 4.5.1 383 | 384 | 在 powershell 中执行以下命令： 385 | （1）生成 debug 版本： 386 | ```shell 387 | mkdir debug # 创建编译目录 388 | cd debug 389 | cmake -G "MinGW Makefiles" .. # 生成 Makefile 390 | mingw32-make # 使用 MinGW 编译代码 391 | ``` 392 | （2）生成 release 版本 393 | ```shell 394 | mkdir release 395 | cd release 396 | cmake -G "MinGW Makefiles" -DCMAKE_BUILD_TYPE=Release .. 397 | mingw32-make 398 | ``` 399 | 2. **运行** 400 | > 在 debug / release 目录中双击 lane_detection.exe 即可运行 401 | 3. **测评** 402 | ```shell 403 | cd .. 404 | python evaluate.py ./result/predict.json ./data/groundtruth.json 405 | ``` -------------------------------------------------------------------------------- /data/data_process.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | # 读入 groundtruth 5 | gt_all = {} 6 | gt = [] 7 | with open("./groundtruth.json", 'r', encoding='utf-8') as f: 8 | for line in f.readlines(): 9 | line_dic = json.loads(line) 10 | gt_all[line_dic["raw_file"]] = line 11 | 12 | # 遍历图片 13 | find_img = os.walk(r"selected test data") 14 | 15 | 16 | # 创建文件夹保存测试图片 17 | count = 0 18 | 19 | for path, dir_list, file_list in find_img: 20 | for file_name in file_list: 21 | if file_name == "20.jpg": 22 | count += 1 23 | name = os.path.join(path, file_name) 24 | name = name.replace('\\', '/'); 25 | name = name.replace("./selected test data", "clips") 26 | gt.append(gt_all[name]) 27 | print(name) 28 | else: 29 | os.remove(os.path.join(path, file_name)) 30 | 31 | print("total image num: ", count) 32 | 33 | # 存储 test 图像的 groundtruth 34 | with open("groundtruth.json", 'w', encoding='utf8') as f: 35 | for sample in gt: 36 | f.write(sample) 37 | 38 | -------------------------------------------------------------------------------- /data/selected test data/0531/1492626292371547028/20.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuzf-git/lane_detection_by_DIP/04febcb9423288896b85a4321bf83da5d80cf146/data/selected test data/0531/1492626292371547028/20.jpg -------------------------------------------------------------------------------- /data/selected test data/0531/1492626388446057821/20.jpg: -------------------------------------------------------------------------------- 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| theta = 0 20 | return theta 21 | 22 | @staticmethod 23 | def line_accuracy(pred, gt, thresh): 24 | pred = np.array([p if p >= 0 else -100 for p in pred]) 25 | gt = np.array([g if g >= 0 else -100 for g in gt]) 26 | return np.sum(np.where(np.abs(pred - gt) < thresh, 1., 0.)) / len(gt) 27 | 28 | @staticmethod 29 | def bench(pred, gt, y_samples, running_time): 30 | if any(len(p) != len(y_samples) for p in pred): 31 | raise Exception('Format of lanes error.') 32 | if running_time > 200 or len(gt) + 2 < len(pred): 33 | return 0., 0., 1. 34 | angles = [LaneEval.get_angle(np.array(x_gts), np.array(y_samples)) for x_gts in gt] 35 | threshs = [LaneEval.pixel_thresh / np.cos(angle) for angle in angles] 36 | line_accs = [] 37 | fp, fn = 0., 0. 38 | matched = 0. 39 | for x_gts, thresh in zip(gt, threshs): 40 | accs = [LaneEval.line_accuracy(np.array(x_preds), np.array(x_gts), thresh) for x_preds in pred] 41 | max_acc = np.max(accs) if len(accs) > 0 else 0. 42 | if max_acc < LaneEval.pt_thresh: 43 | fn += 1 44 | else: 45 | matched += 1 46 | line_accs.append(max_acc) 47 | fp = len(pred) - matched 48 | if len(gt) > 4 and fn > 0: 49 | fn -= 1 50 | s = sum(line_accs) 51 | if len(gt) > 4: 52 | s -= min(line_accs) 53 | return s / max(min(4.0, len(gt)), 1.), fp / len(pred) if len(pred) > 0 else 0., fn / max(min(len(gt), 4.), 1.) 54 | 55 | @staticmethod 56 | def bench_one_submit(pred_file, gt_file): 57 | json_pred = [json.loads(line) for line in open(pred_file).readlines()] 58 | json_gt = [json.loads(line) for line in open(gt_file).readlines()] 59 | if len(json_gt) != len(json_pred): 60 | raise Exception('We do not get the predictions of all the test tasks') 61 | gts = {l['raw_file']: l for l in json_gt} 62 | accuracy, fp, fn = 0., 0., 0. 63 | for pred in json_pred: 64 | if 'raw_file' not in pred or 'lanes' not in pred or 'run_time' not in pred: 65 | raise Exception('raw_file or lanes or run_time not in some predictions.') 66 | raw_file = pred['raw_file'] 67 | pred_lanes = pred['lanes'] 68 | run_time = pred['run_time'] 69 | if raw_file not in gts: 70 | raise Exception('Some raw_file from your predictions do not exist in the test tasks.') 71 | gt = gts[raw_file] 72 | gt_lanes = gt['lanes'] 73 | y_samples = gt['h_samples'] 74 | try: 75 | a, p, n = LaneEval.bench(pred_lanes, gt_lanes, y_samples, run_time) 76 | except BaseException as e: 77 | raise Exception('Format of lanes error.') 78 | accuracy += a 79 | fp += p 80 | fn += n 81 | num = len(gts) 82 | # the first return parameter is the default ranking parameter 83 | return json.dumps([ 84 | {'name': 'Accuracy', 'value': accuracy / num, 'order': 'desc'}, 85 | {'name': 'FP', 'value': fp / num, 'order': 'asc'}, 86 | {'name': 'FN', 'value': fn / num, 'order': 'asc'} 87 | ]) 88 | 89 | 90 | if __name__ == '__main__': 91 | import sys 92 | if len(sys.argv) != 3: 93 | raise Exception('Invalid input arguments') 94 | print(LaneEval.bench_one_submit(sys.argv[1], sys.argv[2])) 95 | 96 | -------------------------------------------------------------------------------- /include/Edge.h: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/6. 3 | // 4 | 5 | #ifndef LANE_DETECTION_EDGE_DETECTION_H 6 | #define LANE_DETECTION_EDGE_DETECTION_H 7 | 8 | #include "Img.hpp" 9 | #include "Kernel.h" 10 | 11 | // 阈值分割 12 | void TurnBinary(Img &src, const double weight); 13 | 14 | // 膨胀运算 15 | void Dilation(const Img &src, Img &dst, int kernel_size); 16 | 17 | // 腐蚀运算 18 | void Erosion(const Img &src, Img &dst, int kernel_size); 19 | 20 | // 遮盖无效部分 21 | void RoiMask(Img &src); 22 | 23 | // Sobel 算子计算梯度 24 | void Sobel(const Img &src, Img &dst, Img &theta); 25 | 26 | // 非极大值抑制 27 | void NonMaxSuppression(const Img &src, Img &dst, const Img &theta); 28 | 29 | // 双阈值处理 30 | void DoubleThreshold(Img &image, const double weight = 0.9); 31 | 32 | // Canny 边缘检测 33 | void Canny(Img &image, const double weight = 0.9); 34 | 35 | #endif //LANE_DETECTION_EDGE_DETECTION_H -------------------------------------------------------------------------------- /include/Hough.h: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/8. 3 | // 4 | 5 | #ifndef LANE_DETECTION_HOUGH_H 6 | #define LANE_DETECTION_HOUGH_H 7 | #include 8 | #include 9 | #include "Img.hpp" 10 | using namespace std; 11 | 12 | void HoughTransform(Img &src, vector> &lines, int threshold); 13 | 14 | #endif //LANE_DETECTION_HOUGH_H 15 | -------------------------------------------------------------------------------- /include/Img.hpp: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/5. 3 | // 4 | 5 | #ifndef LANE_DETECTION_IMG_HPP 6 | #define LANE_DETECTION_IMG_HPP 7 | 8 | #include 9 | #include 10 | #include 11 | 12 | //typedef unsigned char uchar; 13 | const double PI = acos(-1.0); // 圆周率 14 | 15 | template 16 | class Img 17 | { 18 | public: 19 | T **data; // 存放数据 20 | int rows; // 图像的行数 21 | int cols; // 图像的列数 22 | 23 | Img(int rows, int cols); /* 构造空值图像 */ 24 | Img(const char *path); /* 读入图像：灰度图 */ 25 | Img(Img &cp); /* Img类的复制构造函数 */ 26 | ~Img(); 27 | 28 | T *operator[](const int idx) const; 29 | 30 | Img &operator=(const Img &cp); 31 | 32 | cv::Mat toMat() const; /* 将图像转换成 cv::Mat */ 33 | void show(const char *name, int delay) const; /* 展示图片 */ 34 | }; 35 | 36 | /* 构造空值图像 */ 37 | template 38 | Img::Img(int rows, int cols) : rows(rows), cols(cols) 39 | { 40 | data = new T *[rows]; 41 | for (int i = 0; i < rows; ++i) 42 | { 43 | data[i] = new T[cols]; 44 | std::memset(data[i], 0, sizeof(uchar) * cols); 45 | } 46 | } 47 | 48 | /* 读入真实图像：灰度图 */ 49 | template 50 | Img::Img(const char *path) : rows(0), cols(0) 51 | { 52 | cv::Mat img_mat = cv::imread(path, cv::IMREAD_GRAYSCALE); 53 | this->rows = img_mat.rows; 54 | this->cols = img_mat.cols; 55 | data = new uchar *[rows]; 56 | for (int i = 0; i < this->rows; i++) 57 | { 58 | uchar *p = img_mat.ptr(i); 59 | data[i] = new uchar[cols]; 60 | for (int j = 0; j < this->cols; j++) 61 | { 62 | data[i][j] = *p; 63 | p++; 64 | } 65 | } 66 | } 67 | 68 | /* 复制构造函数 */ 69 | template 70 | Img::Img(Img &cp) : rows(cp.rows), cols(cp.cols) 71 | { 72 | data = new T *[this->rows]; 73 | for (int i = 0; i < this->rows; i++) 74 | { 75 | data[i] = new T[this->cols]; 76 | for (int j = 0; j < this->cols; j++) 77 | { 78 | data[i][j] = cp[i][j]; 79 | } 80 | } 81 | } 82 | 83 | /* 析构函数 */ 84 | template 85 | Img::~Img() 86 | { 87 | for (int i = 0; i < rows; ++i) 88 | { 89 | delete[] data[i]; 90 | } 91 | delete[] data; 92 | } 93 | 94 | /* 访问图像的指定行 */ 95 | template 96 | T *Img::operator[](const int idx) const 97 | { 98 | return data[idx]; 99 | } 100 | 101 | template 102 | Img &Img::operator=(const Img &cp) 103 | { 104 | assert(this->rows == cp.rows); 105 | assert(this->cols == cp.cols); 106 | for (int i = 0; i < rows; ++i) 107 | { 108 | for (int j = 0; j < cols; ++j) 109 | { 110 | data[i][j] = cp[i][j]; 111 | } 112 | } 113 | return *this; 114 | } 115 | 116 | /* 将图像转换成 cv::Mat */ 117 | template 118 | cv::Mat Img::toMat() const 119 | { 120 | cv::Mat img_mat = cv::Mat::zeros(rows, cols, CV_8UC1); 121 | for (int i = 0; i < rows; ++i) 122 | { 123 | uchar *p = img_mat.ptr(i); 124 | for (int j = 0; j < cols; ++j) 125 | { 126 | *p = data[i][j]; 127 | p++; 128 | } 129 | } 130 | return img_mat; 131 | } 132 | 133 | /* 展示图片 */ 134 | template 135 | void Img::show(const char *name, int delay) const 136 | { 137 | cv::Mat src = this->toMat(); 138 | cv::Mat temp; 139 | 140 | cv::resize(src, temp, cv::Size(src.cols / 2.0, src.rows / 2.0), 0, 0, cv::INTER_LINEAR); 141 | cv::imshow(name, temp); 142 | cv::waitKey(delay); 143 | } 144 | 145 | #endif //LANE_DETECTION_IMG_HPP 146 | -------------------------------------------------------------------------------- /include/Kernel.h: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/5. 3 | // 4 | 5 | #ifndef LANE_DETECTION_KERNEL_H 6 | #define LANE_DETECTION_KERNEL_H 7 | 8 | #include "Img.hpp" 9 | 10 | class Kernel 11 | { 12 | public: 13 | double **data; 14 | int size; 15 | 16 | Kernel(int size); // 空的卷积核 17 | Kernel(Kernel &cp); // 拷贝构造函数 18 | ~Kernel(); 19 | 20 | double *operator[](const int idx) const; 21 | 22 | template 23 | void convolve(const Img &src, Img &dst, const bool is_clip = true) const; // 卷积操作 24 | }; 25 | 26 | class GaussianKernel : public Kernel 27 | { 28 | public: 29 | double sigma; 30 | 31 | GaussianKernel(const int size, const double sigma); 32 | 33 | GaussianKernel(GaussianKernel &cp); 34 | }; 35 | 36 | /* 实现卷积操作 */ 37 | template 38 | void Kernel::convolve(const Img &src, Img &dst, const bool is_clip) const 39 | { 40 | if (!src.data || src.rows != dst.rows || src.cols != dst.cols) 41 | return; 42 | 43 | int offset = size / 2; 44 | double val; 45 | int x, y; 46 | 47 | for (int i = 0; i < dst.rows; ++i) 48 | { 49 | for (int j = 0; j < dst.cols; ++j) 50 | { 51 | /* 计算目标图像上每个像素点的值 */ 52 | val = 0; 53 | for (int dx = -offset; dx <= offset; ++dx) 54 | { 55 | for (int dy = -offset; dy <= offset; ++dy) 56 | { 57 | x = i + dx; 58 | y = j + dy; 59 | if (x >= 0 && x < dst.rows && y >= 0 && y <= dst.cols) 60 | { 61 | val += double(src[x][y]) * double(data[offset + dx][offset + dy]); 62 | } 63 | } 64 | } 65 | if (is_clip) // 截断到 0 ~ 255 66 | { 67 | val = val > 255 ? 255 : val; 68 | val = val < 0 ? 0 : val; 69 | val = round(val); 70 | } 71 | dst[i][j] = T2(val); 72 | } 73 | } 74 | } 75 | 76 | #endif //LANE_DETECTION_KERNEL_H 77 | -------------------------------------------------------------------------------- /include/SaveResult.h: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/11. 3 | // 4 | 5 | #ifndef LANE_DETECTION_SAVE_RESULT_H 6 | #define LANE_DETECTION_SAVE_RESULT_H 7 | 8 | #include "Img.hpp" 9 | #include 10 | #include 11 | #include 12 | #include 13 | 14 | using namespace std; 15 | 16 | const double h_samples[56] = {160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 17 | 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 18 | 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710}; 19 | 20 | /* 根据车道线的参数，获取坐标向量 */ 21 | void GetLanes(Img &src, vector> ¶ms, vector > &lanes); 22 | 23 | /* 将检测结果写入json文件 */ 24 | void WriteJson(string &raw_file, vector > &lanes, double run_time, ofstream &of); 25 | 26 | /* 展示车道线检测结果 */ 27 | void polyLanes(const string &path, vector> &lanes, vector> ¶ms, int delay); 28 | #endif //LANE_DETECTION_SAVE_RESULT_H 29 | -------------------------------------------------------------------------------- /main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include "include/Img.hpp" 7 | #include "include/Kernel.h" 8 | #include "include/Edge.h" 9 | #include "include/Hough.h" 10 | #include "include/SaveResult.h" 11 | 12 | using namespace std; 13 | using namespace cv; 14 | 15 | /* 获取所有待处理的图片路径 */ 16 | void get_image_names(const string &inPath, vector &file_names) 17 | { 18 | string strFind = inPath + "/*"; 19 | _finddata_t fileInfo{}; 20 | intptr_t hFile = _findfirst(strFind.c_str(), &fileInfo); 21 | if (hFile == -1) 22 | return; 23 | do 24 | { 25 | // 如果为文件夹，则递归遍历 26 | if (fileInfo.attrib & _A_SUBDIR) 27 | { 28 | if ((strcmp(fileInfo.name, ".") != 0) && (strcmp(fileInfo.name, "..") != 0)) 29 | { 30 | get_image_names(inPath + "/" + fileInfo.name, file_names); 31 | } 32 | } 33 | // 如果为单个文件直接push_back 34 | else 35 | { 36 | file_names.emplace_back(inPath + "/" + fileInfo.name); 37 | } 38 | 39 | } while (!_findnext(hFile, &fileInfo)); 40 | 41 | _findclose(hFile); 42 | } 43 | 44 | int main() 45 | { 46 | // 获取所有图片的路径 47 | vector file_names; 48 | get_image_names("../data/selected test data", file_names); 49 | 50 | // 输出文件流接口 51 | ofstream out; 52 | // out.open("../result/best_predict.json", ios::out); 53 | out.open("../result/predict.json", ios::out); 54 | 55 | // 记录 run_time 运行时间 56 | clock_t begin_time, end_time; 57 | 58 | // 车道线检测 59 | for (auto &path : file_names) 60 | { 61 | begin_time = clock(); 62 | Img src(path.data()); 63 | Img dst(src.rows, src.cols); 64 | Img dst_close(src.rows, src.cols); 65 | Img theta(src.rows, src.cols); 66 | 67 | // 原图 68 | // src.show("origin", 10); 69 | 70 | // 高斯滤波 71 | GaussianKernel filter(3, 1); 72 | filter.convolve(src, dst, true); 73 | // dst.show("gaussian", 10); 74 | 75 | // Canny 边缘检测 76 | Canny(dst, 0.97); 77 | // dst.show("canny", 10); 78 | 79 | // 获取图像 Roi 80 | RoiMask(dst); 81 | // dst.show("roi mask", 10); 82 | 83 | // hough 变换 84 | vector> lanes_param; 85 | HoughTransform(dst, lanes_param, 100); 86 | 87 | // 将车道线转成标准格式 88 | vector> lanes; 89 | GetLanes(dst, lanes_param, lanes); 90 | end_time = clock(); 91 | 92 | // 将预测结果保存在 json 文件中 93 | string raw_path = path; 94 | raw_path.replace(raw_path.find("../data/selected test data"), 26, "clips"); 95 | WriteJson(raw_path, lanes, (double) (end_time - begin_time) / CLOCKS_PER_SEC, out); 96 | 97 | // 绘图展示检测出的直线 98 | // dst.show("final", 10); 99 | polyLanes(path, lanes, lanes_param, 50); 100 | cout << lanes_param.size() << endl; 101 | } 102 | out.close(); 103 | 104 | return 0; 105 | } 106 | -------------------------------------------------------------------------------- /result/image_of_readme/Sobel.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuzf-git/lane_detection_by_DIP/04febcb9423288896b85a4321bf83da5d80cf146/result/image_of_readme/Sobel.png -------------------------------------------------------------------------------- /result/image_of_readme/binary.png: -------------------------------------------------------------------------------- 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/result/image_of_readme/update_cmp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xuzf-git/lane_detection_by_DIP/04febcb9423288896b85a4321bf83da5d80cf146/result/image_of_readme/update_cmp.png -------------------------------------------------------------------------------- /result/predict.json: -------------------------------------------------------------------------------- 1 | {"lanes": [[-2, -2, -2, -2, -2, -2, -2, -2, 583, 571, 559, 547, 535, 524, 512, 500, 488, 476, 464, 452, 440, 428, 416, 404, 392, 381, 369, 357, 345, 333, 321, 309, 297, 285, 273, 261, 249, 238, 226, 214, 202, 190, 178, 166, 154, 142, 130, 118, 106, 95, 83, 71, 59, 47, 35, 23], [-2, -2, -2, -2, -2, -2, -2, -2, 589, 599, 609, 619, 629, 639, 649, 659, 669, 679, 689, 699, 709, 719, 729, 739, 749, 759, 769, 779, 789, 799, 809, 819, 829, 839, 849, 859, 869, 879, 889, 899, 909, 919, 929, 939, 949, 959, 969, 979, 989, 999, 1009, 1019, 1029, 1039, 1049, 1059], [-2, -2, -2, -2, -2, -2, -2, -2, 598, 635, 672, 710, 747, 784, 822, 859, 896, 934, 971, 1008, 1046, 1083, 1120, 1157, 1195, 1232, 1269, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2], [-2, -2, -2, -2, -2, -2, -2, -2, 563, 528, 493, 458, 423, 389, 354, 319, 284, 249, 214, 179, 144, 110, 75, 40, 5, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2]], "h_samples": [160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710], "raw_file": "clips/0531/1492626292371547028/20.jpg", "run_time": 0.545 } 2 | {"lanes": [[-2, -2, -2, -2, -2, -2, -2, -2, 577, 567, 557, 548, 538, 529, 519, 509, 500, 490, 480, 471, 461, 451, 442, 432, 422, 413, 403, 393, 384, 374, 364, 355, 345, 335, 326, 316, 306, 297, 287, 277, 268, 258, 248, 239, 229, 220, 210, 200, 191, 181, 171, 162, 152, 142, 133, 123], [-2, -2, -2, -2, -2, -2, -2, -2, 632, 644, 655, 667, 678, 690, 701, 713, 724, 736, 747, 759, 770, 782, 793, 805, 816, 828, 839, 851, 862, 874, 885, 897, 908, 920, 931, 943, 954, 966, 977, 989, 1000, 1012, 1023, 1035, 1046, 1058, 1069, 1081, 1092, 1104, 1115, 1127, 1138, 1150, 1161, 1173], [-2, -2, -2, -2, -2, -2, -2, -2, 730, 705, 680, 655, 631, 606, 581, 556, 532, 507, 482, 457, 433, 408, 383, 358, 334, 309, 284, 259, 235, 210, 185, 160, 136, 111, 86, 61, 37, 12, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2], [-2, -2, -2, -2, -2, -2, -2, -2, 661, 694, 727, 759, 792, 825, 858, 890, 923, 956, 988, 1021, 1054, 1087, 1119, 1152, 1185, 1217, 1250, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2]], "h_samples": [160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710], "raw_file": "clips/0531/1492626388446057821/20.jpg", "run_time": 0.485 } 3 | {"lanes": [[-2, -2, -2, -2, -2, -2, -2, -2, 606, 599, 592, 585, 577, 570, 563, 555, 548, 541, 534, 526, 519, 512, 505, 497, 490, 483, 476, 468, 461, 454, 446, 439, 432, 425, 417, 410, 403, 396, 388, 381, 374, 367, 359, 352, 345, 338, 330, 323, 316, 308, 301, 294, 287, 279, 272, 265], [-2, -2, -2, -2, -2, -2, -2, -2, 702, 739, 777, 814, 851, 889, 926, 963, 1001, 1038, 1075, 1113, 1150, 1187, 1224, 1262, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2], [-2, -2, -2, -2, -2, -2, -2, -2, 586, 566, 545, 525, 504, 484, 463, 443, 422, 402, 381, 361, 340, 320, 299, 279, 258, 238, 217, 197, 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| /* 阈值分割，得到二值图像 */ 10 | void TurnBinary(Img &src, double weight) 11 | { 12 | std::vector vec; 13 | // 高阈值取灰度分布图中 weight 对应的灰度值 14 | for (int i = 160; i < src.rows; ++i) 15 | { 16 | for (int j = 0; j < src.cols; ++j) 17 | { 18 | vec.push_back(src[i][j]); 19 | } 20 | } 21 | std::sort(vec.begin(), vec.end()); 22 | int threshold = vec[(src.rows - 160)* src.cols * weight]; 23 | for (int i = 0; i < src.rows; ++i) 24 | { 25 | for (int j = 0; j < src.cols; ++j) 26 | { 27 | if (src[i][j] > threshold && i >= 160) 28 | src[i][j] = 255; 29 | else 30 | src[i][j] = 0; 31 | } 32 | } 33 | } 34 | 35 | /* 形态学：膨胀运算 */ 36 | void Dilation(const Img &src, Img &dst, int kernel_size) 37 | { 38 | for (int i = kernel_size; i < src.rows - kernel_size; ++i) 39 | { 40 | for (int j = kernel_size; j < src.cols - kernel_size; ++j) 41 | { 42 | if (src[i][j] == 255) 43 | { 44 | for (int x = -kernel_size; x <= kernel_size; ++x) 45 | { 46 | for (int y = -kernel_size; y <= kernel_size; ++y) 47 | { 48 | dst[i + x][j + y] = 255; 49 | } 50 | } 51 | } 52 | else 53 | dst[i][j] = 0; 54 | } 55 | } 56 | } 57 | 58 | /* 形态学: 腐蚀运算 */ 59 | void Erosion(const Img &src, Img &dst, int kernel_size) 60 | { 61 | bool is_kept; 62 | for (int i = kernel_size; i < src.rows - kernel_size; ++i) 63 | { 64 | for (int j = kernel_size; j < src.cols - kernel_size; ++j) 65 | { 66 | is_kept = true; 67 | for (int x = -kernel_size; x <= kernel_size; ++x) 68 | { 69 | if (!is_kept) 70 | break; 71 | for (int y = -kernel_size; y <= kernel_size; ++y) 72 | { 73 | if (src[i + x][j + y] != 255) 74 | { 75 | is_kept = false; 76 | break; 77 | } 78 | } 79 | } 80 | dst[i][j] = is_kept ? 255 : 0; 81 | } 82 | } 83 | } 84 | 85 | /* 遮盖无效部分 */ 86 | void RoiMask(Img &src) 87 | { 88 | // 梯形 ROI 区域进行 mask (400, 0) (220, 420) (200, 860), (400, 1280) 89 | for (int i = 0; i < src.rows; ++i) 90 | { 91 | for (int j = 0; j < src.cols; ++j) 92 | { 93 | if (i <= 200) 94 | src[i][j] = 0; 95 | else if (i > 400) 96 | continue; 97 | else if (2.1 * (400 - i) > j || j > 1280 - 2.1 * (400 - i)) 98 | src[i][j] = 0; 99 | } 100 | } 101 | } 102 | 103 | /* Sobel 算子：计算图像梯度 */ 104 | void Sobel(const Img &src, Img &dst, Img &theta) 105 | { 106 | assert(src.rows == dst.rows); 107 | assert(src.cols == dst.cols); 108 | 109 | const double sobelX_arr[3][3] = { 110 | {-1, 0, 1}, 111 | {-2, 0, 2}, 112 | {-1, 0, 1} 113 | }; 114 | const double sobelY_arr[3][3] = { 115 | {1, 2, 1}, 116 | {0, 0, 0}, 117 | {-1, -2, -1} 118 | }; 119 | Kernel sobelX(3); 120 | Kernel sobelY(3); 121 | for (int i = 0; i < 3; ++i) 122 | { 123 | for (int j = 0; j < 3; ++j) 124 | { 125 | sobelX[i][j] = sobelX_arr[i][j]; 126 | sobelY[i][j] = sobelY_arr[i][j]; 127 | } 128 | } 129 | Img imgGradX(src.rows, src.cols); 130 | Img imgGradY(src.rows, src.cols); 131 | sobelX.convolve(src, imgGradX, false); 132 | sobelY.convolve(src, imgGradY, false); 133 | for (int i = 0; i < src.rows; ++i) 134 | { 135 | for (int j = 0; j < src.cols; ++j) 136 | { 137 | dst[i][j] = sqrt(imgGradX[i][j] * imgGradX[i][j] + imgGradY[i][j] * imgGradY[i][j]); 138 | if (fabs(imgGradX[i][j]) < 1e-10) // 防止除以0，导致溢出 139 | imgGradX[i][j] = 1e-10; 140 | theta[i][j] = atan((double) imgGradY[i][j] / imgGradX[i][j]); 141 | } 142 | } 143 | } 144 | 145 | /* 非极大值抑制 */ 146 | void NonMaxSuppression(const Img &src, Img &dst, const Img &theta) 147 | { 148 | assert(src.rows == dst.rows); 149 | assert(src.cols == dst.cols); 150 | 151 | // 将 src 的值拷贝到 dst 中 152 | dst = src; 153 | 154 | uchar local[3][3]; 155 | uchar temp1, temp2; 156 | double weight; 157 | const double PI_2 = PI / 2; 158 | const double PI_4 = PI / 4; 159 | 160 | for (int i = 1; i < src.rows - 1; ++i) 161 | { 162 | for (int j = 1; j < src.cols - 1; ++j) 163 | { 164 | // 记录考察点的局部值 165 | for (int x = 0; x < 3; ++x) 166 | { 167 | for (int y = 0; y < 3; ++y) 168 | { 169 | local[x][y] = src[i - 1 + x][j - 1 + y]; 170 | } 171 | } 172 | if (theta[i][j] > -PI_2 && theta[i][j] <= -PI_4) 173 | { 174 | weight = fabs(1 / tan(theta[i][j])); 175 | temp1 = uchar(weight * local[2][2] + (1 - weight) * local[2][1]); 176 | temp2 = uchar(weight * local[0][0] + (1 - weight) * local[0][1]); 177 | if (local[1][1] <= temp1 || local[1][1] <= temp2) 178 | dst[i][j] = 0; 179 | 180 | } else if (theta[i][j] > -PI_4 && theta[i][j] <= 0) 181 | { 182 | weight = fabs(tan(theta[i][j])); 183 | temp1 = uchar(weight * local[2][2] + (1 - weight) * local[1][2]); 184 | temp2 = uchar(weight * local[0][0] + (1 - weight) * local[1][0]); 185 | if (local[1][1] <= temp1 || local[1][1] <= temp2) 186 | dst[i][j] = 0; 187 | } else if (theta[i][j] > 0 && theta[i][j] <= PI_4) 188 | { 189 | weight = tan(theta[i][j]); 190 | temp1 = uchar(weight * local[0][2] + (1 - weight) * local[1][2]); 191 | temp2 = uchar(weight * local[2][0] + (1 - weight) * local[1][0]); 192 | if (local[1][1] <= temp1 || local[1][1] <= temp2) 193 | dst[i][j] = 0; 194 | } else if (theta[i][j] > PI_4 && theta[i][j] < PI_2) 195 | { 196 | weight = 1 / tan(theta[i][j]); 197 | temp1 = uchar(weight * local[0][2] + (1 - weight) * local[0][1]); 198 | temp2 = uchar(weight * local[2][0] + (1 - weight) * local[2][1]); 199 | if (local[1][1] <= temp1 || local[1][1] <= temp2) 200 | dst[i][j] = 0; 201 | } 202 | } 203 | } 204 | } 205 | 206 | /* 双阈值检测 & 连接边缘 */ 207 | void DoubleThreshold(Img &image, const double weight) 208 | { 209 | double highThreshold; 210 | double lowThreshold; 211 | bool flag = false; 212 | std::vector vec; 213 | 214 | // 高阈值取灰度分布图中 weight 对应的灰度值 215 | for (int i = 0; i < image.rows; ++i) 216 | { 217 | for (int j = 0; j < image.cols; ++j) 218 | vec.push_back(image[i][j]); 219 | } 220 | std::sort(vec.begin(), vec.end()); 221 | highThreshold = vec[weight * image.rows * image.cols]; 222 | // 低阈值为高阈值的 2/3 223 | lowThreshold = highThreshold / 1.5; 224 | 225 | for (int i = 1; i < image.rows - 1; ++i) 226 | { 227 | for (int j = 1; j < image.cols - 1; ++j) 228 | { 229 | if (image[i][j] < lowThreshold) // 检测低阈值 230 | image[i][j] = 0; 231 | else if (image[i][j] > highThreshold) // 检测高阈值 232 | image[i][j] = 255; 233 | else // 介于双阈值之间，连接边缘 234 | { 235 | // 检查邻域中是否有高于阈值点（排除孤立的局部极大值点) 236 | for (int x = -1; x < 2; ++x) 237 | { 238 | if (flag) break; 239 | for (int y = -1; y < 2; ++y) 240 | { 241 | if (image[i + x][j + y] > highThreshold) 242 | { 243 | image[i][j] = 255; 244 | flag = true; 245 | break; 246 | } 247 | } 248 | } 249 | if (!flag) 250 | image[i][j] = 0; 251 | } 252 | } 253 | } 254 | } 255 | 256 | /* Canny 边缘检测 */ 257 | void Canny(Img &image, const double weight) 258 | { 259 | Img theta(image.rows, image.cols); 260 | Img grad(image.rows, image.cols); 261 | 262 | Sobel(image, grad, theta); 263 | NonMaxSuppression(grad, image, theta); 264 | DoubleThreshold(image, weight); 265 | } 266 | -------------------------------------------------------------------------------- /source/Hough.cpp: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/8. 3 | // 4 | 5 | #include "../include/Hough.h" 6 | #include 7 | 8 | bool is_similar(pair &l1, pair &l2) 9 | { 10 | /*double theta1 = l1.first * PI / 180; 11 | double theta2 = l2.first * PI / 180; 12 | double delta_k = abs(tan(theta1) - tan(theta2)); 13 | double delta_b = abs(l1.second * (tan(theta1) * sin(theta1) + cos(theta1)) - 14 | l2.second * (tan(theta2) * sin(theta2) + cos(theta2)));*/ 15 | int delta_angle = abs(l1.first - l2.first); 16 | int delta_r = abs(l1.second - l2.second); 17 | // 一般相似直线 18 | if (delta_angle <= 20 || abs(delta_angle - 180) <= 20) 19 | return true; 20 | // if (delta_angle <= 20 && delta_r <= 70) 21 | // return true; 22 | return false; 23 | } 24 | 25 | void update_cluster(pair &line, pair, int> &cluster) 26 | { 27 | // 更新簇大小 28 | cluster.second++; 29 | // 更新中心点 30 | cluster.first.first = 31 | (cluster.first.first * (cluster.second - 1) + line.first) / cluster.second; 32 | cluster.first.second = 33 | (cluster.first.second * (cluster.second - 1) + line.second) / cluster.second; 34 | } 35 | 36 | bool cmp(const pair, int> &g1, const pair, int> &g2) 37 | { 38 | return g1.second > g2.second; 39 | } 40 | 41 | void lines_cluster(vector> &lines) 42 | { 43 | if (lines.empty()) 44 | return; 45 | // 检测结果聚类 46 | vector, int> > clusters; 47 | clusters.emplace_back(lines[0], 1); 48 | 49 | // 是否创建新的簇 50 | bool flag; 51 | 52 | // 聚类 53 | for (int i = 1; i < lines.size(); ++i) 54 | { 55 | flag = true; 56 | for (auto &cluster : clusters) 57 | { 58 | // 当前数据点与某簇的中心点相似，则将其添加到簇中 59 | if (is_similar(lines[i], cluster.first)) 60 | { 61 | // 更新簇 62 | // update_cluster(lines[i], cluster); 63 | cluster.second++; 64 | flag = false; 65 | break; 66 | } 67 | } 68 | if (flag) // 创建新的簇 69 | clusters.emplace_back(lines[i], 1); 70 | } 71 | 72 | 73 | // 合并相似参数 74 | for (int i = 0; i < clusters.size(); ++i) 75 | { 76 | if (clusters[i].second < 0) 77 | { 78 | continue; 79 | } 80 | for (int j = i + 1; j < clusters.size(); ++j) 81 | { 82 | if (clusters[j].second < 0) 83 | { 84 | continue; 85 | } 86 | if (is_similar(clusters[i].first, clusters[j].first)) 87 | { 88 | clusters[i].first.first = (clusters[i].first.first * clusters[i].second + 89 | clusters[j].first.first * clusters[j].second) / 90 | (clusters[i].second + clusters[j].second); 91 | clusters[i].first.second = (clusters[i].first.second * clusters[i].second + 92 | clusters[j].first.second * clusters[j].second) / 93 | (clusters[i].second + clusters[j].second); 94 | clusters[i].second += clusters[j].second; 95 | clusters[j].second = -1; 96 | } 97 | } 98 | } 99 | auto iter = clusters.begin(); 100 | while (iter != clusters.end()) 101 | { 102 | if ((*iter).second < 0) 103 | iter = clusters.erase(iter); 104 | else 105 | iter++; 106 | } 107 | 108 | // 按照簇的大小进行排序 109 | sort(clusters.begin(), clusters.end(), cmp); 110 | 111 | // 打印簇 112 | // cout << endl << "num of clusters: " << clusters.size() << " " << endl; 113 | // for (const auto &cluster : clusters) 114 | // { 115 | // cout << "num: " << cluster.second; 116 | // cout << '(' << cluster.first.first << ", " << cluster.first.second << ") "; 117 | // cout << endl; 118 | // } 119 | // cout << endl; 120 | 121 | lines.clear(); 122 | 123 | // 取最大的前四个簇的中心点（均值点） 124 | for (int i = 0; i < clusters.size(); ++i) 125 | { 126 | lines.push_back(clusters[i].first); 127 | cout << "final center " << lines[i].first << " " << lines[i].second << endl; 128 | } 129 | cout << endl; 130 | } 131 | 132 | void HoughTransform(Img &src, vector> &lines, int threshold) 133 | { 134 | // 参数空间的计数矩阵 135 | int **count; 136 | 137 | // 计数器初始化 138 | int rows = src.rows; 139 | int cols = src.cols; 140 | int r_max = 2 * (int) sqrt(rows * rows + cols * cols) + 1; 141 | count = new int *[181]; 142 | for (int i = 0; i < 181; ++i) 143 | { 144 | count[i] = new int[r_max]; 145 | memset(count[i], 0, r_max * sizeof(int)); 146 | } 147 | 148 | // 参数空间变量 149 | int theta, r; 150 | 151 | // 遍历图像为每组参数投票 152 | for (int row = 0; row < rows - 2; ++row) 153 | { 154 | for (int col = 2; col < cols - 2; ++col) 155 | { 156 | // 对边缘点进行统计 157 | if (src[row][col] == 255) 158 | { 159 | for (theta = 0; theta < 181; ++theta) 160 | { 161 | r = int(row * sin(theta * PI / 180.0) + col * cos(theta * PI / 180.0) + r_max / 2.0); 162 | count[theta][r]++; 163 | } 164 | } 165 | } 166 | } 167 | 168 | // 遍历计数矩阵，选出超出阈值的参数 169 | for (theta = 0; theta < 181; ++theta) 170 | { 171 | for (r = 0; r < r_max; ++r) 172 | { 173 | if (count[theta][r] >= threshold && abs(theta - 90) >= 15 && abs(theta) > 10 && 180 - theta > 10) 174 | // if (count[theta][r] >= threshold) 175 | { 176 | if (theta > 90 && (r - r_max / 2) < 0) 177 | lines.emplace_back(theta - 180, r_max / 2 - r); 178 | else 179 | lines.emplace_back(theta, r - r_max / 2); 180 | } 181 | } 182 | } 183 | 184 | // 直线参数聚类 185 | lines_cluster(lines); 186 | 187 | for (int i = 0; i < 181; ++i) 188 | { 189 | delete[] count[i]; 190 | } 191 | delete[] count; 192 | } -------------------------------------------------------------------------------- /source/Kernel.cpp: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/5. 3 | // 4 | #include "../include/Kernel.h" 5 | #include 6 | #include 7 | 8 | /********************************************************* 9 | * Kernel 类定义 10 | ********************************************************/ 11 | 12 | /* 构造空的滤波核 */ 13 | Kernel::Kernel(int size) 14 | { 15 | assert(size % 2 != 0); 16 | this->size = size; 17 | data = new double *[size]; 18 | for (int i = 0; i < size; ++i) 19 | { 20 | data[i] = new double[size]; 21 | memset(data[i], 0, sizeof(double) * size); 22 | } 23 | } 24 | 25 | /* 拷贝构造函数 */ 26 | Kernel::Kernel(Kernel &cp) 27 | { 28 | size = cp.size; 29 | data = new double *[size]; 30 | for (int i = 0; i < size; ++i) 31 | { 32 | data[i] = new double[size]; 33 | for (int j = 0; j < size; ++j) 34 | { 35 | data[i][j] = cp[i][j]; 36 | } 37 | } 38 | } 39 | 40 | /* 析构函数 */ 41 | Kernel::~Kernel() 42 | { 43 | for (int i = 0; i < size; ++i) 44 | { 45 | delete[] data[i]; 46 | } 47 | delete[] data; 48 | } 49 | 50 | /* 访问指定行 */ 51 | double *Kernel::operator[](const int idx) const 52 | { 53 | return data[idx]; 54 | } 55 | 56 | /********************************************************* 57 | * GaussianKernel 类定义 58 | ********************************************************/ 59 | 60 | /* 高斯 Kernel 的构造函数 */ 61 | GaussianKernel::GaussianKernel(const int size, const double sigma) : Kernel(size), sigma(sigma) 62 | { 63 | int center = size / 2; 64 | double sum = 0; 65 | 66 | for (int i = 0; i < size; i++) 67 | { 68 | for (int j = 0; j < size; j++) 69 | { 70 | data[i][j] = (1 / (2 * PI * pow(sigma, 2)) * 71 | exp(-(pow(i - center, 2) + pow(j - center, 2)) / (2 * pow(sigma, 2)))); 72 | sum += data[i][j]; 73 | } 74 | } 75 | // 归一化 76 | for (int i = 0; i < size; i++) 77 | { 78 | for (int j = 0; j < size; j++) 79 | { 80 | data[i][j] /= sum; 81 | } 82 | } 83 | } 84 | 85 | /* 高斯 Kernel 的拷贝构造函数 */ 86 | GaussianKernel::GaussianKernel(GaussianKernel &cp) : Kernel(cp) 87 | { 88 | sigma = cp.sigma; 89 | } 90 | -------------------------------------------------------------------------------- /source/SaveResult.cpp: -------------------------------------------------------------------------------- 1 | // 2 | // Created by xuzf on 2021/2/11. 3 | // 4 | 5 | #include "../include/SaveResult.h" 6 | 7 | void GetLanes(Img &src, vector> ¶ms, vector> &lanes) 8 | { 9 | double sin_val, cos_val, tan_val, r; 10 | int val; 11 | bool is_empty; 12 | for (auto & param : params) 13 | { 14 | vector lane; 15 | sin_val = sin(param.first * PI / 180); 16 | cos_val = cos(param.first * PI / 180); 17 | tan_val = sin_val / cos_val; 18 | r = param.second; 19 | is_empty = true; 20 | 21 | for (double h_sample : h_samples) 22 | { 23 | val = int((r * sin_val - h_sample) * tan_val + r * cos_val); 24 | if (val < 0 || val >= src.cols || h_sample <= 230) 25 | val = -2; 26 | lane.push_back(val); 27 | if (val != -2 && is_empty) 28 | is_empty = false; 29 | } 30 | if (!is_empty) 31 | lanes.push_back(lane); 32 | lane.clear(); 33 | } 34 | } 35 | 36 | void WriteJson(string &raw_file, vector > &lanes, double run_time, ofstream &of) 37 | { 38 | // 打印 lanes 39 | of << R"({"lanes": [)"; 40 | for (int i = 0; i < lanes.size() && i < 4; ++i) 41 | { 42 | of << "["; 43 | for (int j = 0; j < 55; ++j) 44 | { 45 | of << lanes[i][j] << ", "; 46 | } 47 | of << lanes[i][55] << "]"; 48 | if (i != lanes.size() - 1 && i != 3) 49 | of << ", "; 50 | } 51 | of << "], "; 52 | 53 | // 打印 h_samples 54 | of << R"("h_samples": [)"; 55 | for (int j = 0; j < 55; ++j) 56 | { 57 | of << h_samples[j] << ", "; 58 | } 59 | of << h_samples[55] << "], "; 60 | 61 | // 打印 raw_file 62 | of << R"("raw_file": ")" << raw_file << R"(", )"; 63 | 64 | // 打印 run_time 65 | of << R"("run_time": )" << run_time << " }" << endl; 66 | } 67 | 68 | void polyLanes(const string &path, vector> &lanes, vector> ¶ms, int delay) 69 | { 70 | cv::Mat src_mat = cv::imread(path); 71 | cv::Mat show_img; 72 | cv::Point2d last_point(-2, -2); 73 | cv::Point2d current_point; 74 | int k = 0; 75 | for (auto &lane : lanes) 76 | { 77 | if (k >= 4) 78 | break; 79 | k++; 80 | last_point.y = -2; 81 | last_point.x = -2; 82 | for (int j = 0; j < 56; ++j) 83 | { 84 | if (lane[j] < 0) 85 | continue; 86 | if (last_point.y < 0) 87 | { 88 | last_point.y = h_samples[j]; 89 | last_point.x = lane[j]; 90 | continue; 91 | } 92 | current_point.y = h_samples[j]; 93 | current_point.x = lane[j]; 94 | cv::line(src_mat, last_point, current_point, cv::Scalar(0, 255, 0), 2); 95 | last_point.x = current_point.x; 96 | last_point.y = current_point.y; 97 | } 98 | } 99 | // cv::Point2d pt1, pt2; 100 | // for (auto param : params) 101 | // { 102 | // // 极坐标中的r长度 103 | // float rho = param.second; 104 | // // 极坐标中的角度 105 | // float theta = param.first * PI / 180; 106 | // double a = cos(theta), b = sin(theta); 107 | // double x0 = a*rho, y0 = b*rho; 108 | // // 转换为平面坐标的四个点 109 | // pt1.x = cvRound(x0 + 2000 * (-b)); 110 | // pt1.y = cvRound(y0 + 2000 * (a)); 111 | // pt2.x = cvRound(x0 - 2000 * (-b)); 112 | // pt2.y = cvRound(y0 - 2000 * (a)); 113 | // line(src_mat, pt1, pt2, cv::Scalar(0, 0, 255), 2); 114 | // } 115 | // 缩放图片 116 | // cv::resize(src_mat, show_img, cv::Size(src_mat.cols / 2.0, src_mat.rows / 2.0), 0, 0, cv::INTER_LINEAR); 117 | cv::imshow("lines", src_mat); 118 | cv::waitKey(delay); 119 | } 120 | --------------------------------------------------------------------------------