基于opencv实现车道线检测

基于opencv的车道线检测，供大家参考，具体内容如下

原理：

算法基本思想说明：

传统的车道线检测，多数是基于霍夫直线检测，其实这个里面有个很大的误区，霍夫直线拟合容易受到各种噪声干扰，直接运用有时候效果不好，更多的时候通过霍夫直线检测进行初步的筛选，然后再有针对性的进行直线拟合，根据拟合的直线四个点坐标，绘制出车道线，这种方式可以有效避免霍夫直线拟合不良后果，是一种更加稳定的车道线检测方法，在实际项目中，可以选择两种方法并行，在计算出结果后进行叠加或者对比提取，今天分享的案例主要是绕开了霍夫直线检测，通过对二值图像进行轮廓分析与几何分析，提取到相关的车道线信息、然后进行特定区域的像素扫描，拟合生成直线方程，确定四个点绘制出车道线，对连续的视频来说，如果某一帧无法正常检测，就可以通过缓存来替代绘制，从而实现在视频车道线检测中实时可靠。

原理图：

代码：

#include 
#include 
#include 

using namespace cv;
using namespace std;

/**
**1、读取视频 
**2、二值化
**3、轮廓发现
**4、轮廓分析、面积就算，角度分析
**5、直线拟合
**6、画出直线
**
*/

Point left_line[2];
Point right_line[2];

void process(Mat &frame, Point *left_line, Point *right_line);
Mat fitLines(Mat &image, Point *left_line, Point *right_line);

int main(int argc, char** argv) {
 //读取视频
 VideoCapture capture("E:/opencv/road_line.mp4");

 int height = capture.get(CAP_PROP_FRAME_HEIGHT);
 int width = capture.get(CAP_PROP_FRAME_WIDTH);
 int count = capture.get(CAP_PROP_FRAME_COUNT);
 int fps = capture.get(CAP_PROP_FPS);
 //初始化

 left_line[0] = Point(0,0);

 left_line[1] = Point(0, 0);
 
 right_line[0] = Point(0, 0);
 
 right_line[1] = Point(0, 0);

 cout << height<<" "<< width<< " " <(i, j) = 0;
 }
 }
 imshow("binary", binary);
 
 //寻找轮廓
 vector> contours;
 findContours(binary, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

 Mat out_image = Mat::zeros(gray.size(), gray.type());

 for (int i = 0; i < contours.size(); i++)
 {
 
 //计算面积与周长
 double length = arcLength(contours[i], true);
 double area = contourArea(contours[i]);
 //cout << "周长 length:" << length << endl;
 //cout << "面积 area:" << area << endl;

 //外部矩形边界
 Rect rect = boundingRect(contours[i]);
 int h = gray.rows - 50;

 //轮廓分析：
 if (length < 5.0 || area < 10.0) {
 continue;
 }
 if (rect.y > h) {
 continue;
 }

 //最小包围矩形
 RotatedRect rrt = minAreaRect(contours[i]);
 
 //cout << "最小包围矩形 angle:" << rrt.angle << endl;

 double angle = abs(rrt.angle);
 
 //angle < 50.0 || angle>89.0

 if (angle < 20.0 || angle>84.0) {

 continue;

 }
 

 if (contours[i].size() > 5) {
 //用椭圆拟合
 RotatedRect errt = fitEllipse(contours[i]);
 //cout << "用椭圆拟合err.angle:" << errt.angle << endl;

 if ((errt.angle<5.0) || (errt.angle>160.0))
 {
 if (80.0 < errt.angle && errt.angle < 100.0) {
 continue;
 }
 
 }
 }


 //cout << "开始绘制：" << endl;
 drawContours(out_image, contours, i, Scalar(255), 2, 8);
 imshow("out_image", out_image);

 }
 Mat result = fitLines(out_image, left_line, right_line);
 imshow("result", result);

 Mat dst;
 addWeighted(frame, 0.8, result, 0.5,0, dst);
 imshow("lane-lines", dst);

}

//直线拟合
Mat fitLines(Mat &image, Point *left_line, Point *right_line) {
 int height = image.rows;
 int width = image.cols;

 Mat out = Mat::zeros(image.size(), CV_8UC3);

 int cx = width / 2;
 int cy = height / 2;

 vector left_pts;
 vector right_pts;
 Vec4f left;
 

 for (int i = 100; i < (cx-10); i++)
 {
 for (int j = cy; j < height; j++)
 {
 int pv = image.at(j, i);
 if (pv == 255) 
 {
 left_pts.push_back(Point(i, j));
 }
 }
 }

 for (int i = cx; i < (width-20); i++)
 {
 for (int j = cy; j < height; j++)
 {
 int pv = image.at(j, i);
 if (pv == 255)
 {
 right_pts.push_back(Point(i, j));
 }
 }
 }

 if (left_pts.size() > 2)
 {
 fitLine(left_pts, left, DIST_L1, 0, 0.01, 0.01);
 
 double k1 = left[1] / left[0];
 double step = left[3] - k1 * left[2];

 int x1 = int((height - step) / k1);
 int y2 = int((cx - 25)*k1 + step);

 Point left_spot_1 = Point(x1, height);
 Point left_spot_end = Point((cx - 25), y2);
 

 line(out, left_spot_1, left_spot_end, Scalar(0, 0, 255), 8, 8, 0);
 left_line[0] = left_spot_1;
 left_line[1] = left_spot_end;

 }
 else
 {
 line(out, left_line[0], left_line[1], Scalar(0, 0, 255), 8, 8, 0);
 }



 if (right_pts.size()>2)
 {
 
 Point spot_1 = right_pts[0];
 Point spot_end = right_pts[right_pts.size()-1];

 int x1 = spot_1.x;
 
 int y1 = spot_1.y;

 int x2 = spot_end.x;
 int y2 = spot_end.y;

 

 line(out, spot_1, spot_end, Scalar(0, 0, 255), 8, 8, 0);
 right_line[0] = spot_1;
 right_line[1] = spot_end;

 }
 else
 {
 line(out, right_line[0], right_line[1], Scalar(0, 0, 255), 8, 8, 0);
 }

 return out;
}

结果图片：

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