基於opencv實現車道線檢測

基於opencv的車道線檢測,供大傢參考,具體內容如下

原理:

算法基本思想說明:

傳統的車道線檢測,多數是基於霍夫直線檢測,其實這個裡面有個很大的誤區,霍夫直線擬合容易受到各種噪聲幹擾,直接運用有時候效果不好,更多的時候通過霍夫直線檢測進行初步的篩選,然後再有針對性的進行直線擬合,根據擬合的直線四個點坐標,繪制出車道線,這種方式可以有效避免霍夫直線擬合不良後果,是一種更加穩定的車道線檢測方法,在實際項目中,可以選擇兩種方法並行,在計算出結果後進行疊加或者對比提取,今天分享的案例主要是繞開瞭霍夫直線檢測,通過對二值圖像進行輪廓分析與幾何分析,提取到相關的車道線信息、然後進行特定區域的像素掃描,擬合生成直線方程,確定四個點繪制出車道線,對連續的視頻來說,如果某一幀無法正常檢測,就可以通過緩存來替代繪制,從而實現在視頻車道線檢測中實時可靠。

原理圖:

代碼:

#include <opencv2/opencv.hpp>
#include <iostream>
#include <cmath>

using namespace cv;
using namespace std;

/**
**1、讀取視頻 
**2、二值化
**3、輪廓發現
**4、輪廓分析、面積就算,角度分析
**5、直線擬合
**6、畫出直線
**
*/

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) {
 //讀取視頻
 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<< " " <<count<< " " <<fps << endl;

 //循環讀取視頻
 Mat frame;
 while (true) {
 int ret = capture.read(frame);
 if (!ret) {
 break;
 }
 imshow("input", frame);
 process(frame, left_line, right_line);

 char c = waitKey(5);
 if (c == 27) {
 break;
 }
 
 
 }

}

void process(Mat &frame, Point *left_line, Point *right_line ){
 Mat gray,binary;
 /**灰度化*/
 cvtColor(frame, gray, COLOR_BGR2GRAY);
 
 //threshold(gray, binary, );
 //邊緣檢測
 Canny(gray, binary, 150, 300);
 //imshow("Canny", binary);
 for (size_t i = 0; i < (gray.rows/2+40); i++) {
 for (size_t j = 0; j < gray.cols; j++)
 {
 binary.at<uchar>(i, j) = 0;
 }
 }
 imshow("binary", binary);
 
 //尋找輪廓
 vector<vector<Point>> 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++)
 {
 
 //計算面積與周長
 double length = arcLength(contours[i], true);
 double area = contourArea(contours[i]);
 //cout << "周長 length:" << length << endl;
 //cout << "面積 area:" << area << endl;

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

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

 //最小包圍矩形
 RotatedRect rrt = minAreaRect(contours[i]);
 
 //cout << "最小包圍矩形 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) {
 //用橢圓擬合
 RotatedRect errt = fitEllipse(contours[i]);
 //cout << "用橢圓擬合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 << "開始繪制:" << 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);

}

//直線擬合
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<Point> left_pts;
 vector<Point> right_pts;
 Vec4f left;
 

 for (int i = 100; i < (cx-10); i++)
 {
 for (int j = cy; j < height; j++)
 {
 int pv = image.at<uchar>(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<uchar>(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;
}

結果圖片:

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