opencv检测图像直线

　　

 #include<opencv2/opencv.hpp>
 #include<iostream>
 using namespace std;
 using namespace cv;
 Mat src, dst;
 int main(void) {
     src = imread("..\\lineDetect.jpg");
     if (src.empty()) {
         cout << "Loading image failed!" << endl;
         return -;
     }

     //图片大小不合适，先对图片进行缩放
     resize(src, src, Size(src.rows/, src.cols/));
     imshow("Sources image", src);
     //图片的方向不对，用仿射变换调整至正确位置
     //图片需要逆时针旋转90度
     double angel = ;
     Point center = Point(src.cols/,src.rows/);
     //由于仿射变换后导致图像不全，用下面方法解决
     Rect mask = RotatedRect(center, Size(src.cols,src.rows), angel).boundingRect();
     //对仿射中心进行调整
     Mat h = getRotationMatrix2D(center, angel, );
     h.at<double>(, ) += mask.width /  - center.x;
     h.at<double>(, ) += mask.height /  - center.y;
     //获取仿射矩阵
     Mat temp;
     warpAffine(src, temp, h ,Size(src.rows,src.cols),,,Scalar(,,));
     copyTo(temp, dst, Mat());

     Mat gray;
     cvtColor(temp, gray, COLOR_BGR2GRAY);
     //反向二值化图像
     Mat binary;
     threshold(gray, binary, ,, THRESH_BINARY_INV);
     //通过开操作消除文字，保留直线
     Mat kernel = getStructuringElement(MORPH_RECT, Size(, ));
     morphologyEx(binary, binary, MORPH_OPEN, kernel);
     //通过膨胀操作使直线更明显
     Mat kernel1 = getStructuringElement(MORPH_RECT, Size(, ));
     dilate(binary, binary, kernel1);
     //imshow("temp image",binary);
     //霍夫直线检测
     vector<Vec4f> linesPoint;
     HoughLinesP(binary, linesPoint, , CV_PI / , , , );
     for (int i = ; i < linesPoint.size(); i++){
         line(dst, Point(linesPoint[i][], linesPoint[i][]), Point(linesPoint[i][], linesPoint[i][]), Scalar(,,), );
     }
     imshow("Object image", dst);
     waitKey();
     return ;
 }

　　　　结果：

　　代码中关于仿射变换，有不懂的可以去看看这篇博客：https://blog.csdn.net/u013105205/article/details/78826789