opencv中利用霍夫变换检测直线对图片进行校正

图片校正
利用霍夫变换检测直线，校正拍摄倾斜的图片

#include
#include 
using namespace cv;
using namespace std;
#define ERROR 1234

//度数转换
double DegreeTrans(double theta)
{
    double res = theta / CV_PI * 180;
    return res;
}

//逆时针旋转图像degree角度（原尺寸）    
void rotateImage(Mat src, Mat& img_rotate, double degree)
{
    //旋转中心为图像中心    
    Point2f center;
    center.x = float(src.cols / 2.0);
    center.y = float(src.rows / 2.0);
    int length = 0;
    length = sqrt(src.cols*src.cols + src.rows*src.rows);
    //计算二维旋转的仿射变换矩阵  
    Mat M = getRotationMatrix2D(center, degree, 1);
    warpAffine(src, img_rotate, M, Size(length, length), 1, 0, Scalar(255, 255, 255));//仿射变换，背景色填充为白色  
}

//通过霍夫变换计算角度
double CalcDegree(const Mat &srcImage, Mat &dst)
{
    Mat midImage, dstImage;

    Canny(srcImage, midImage, 50, 200, 3);
    cvtColor(midImage, dstImage, CV_GRAY2BGR);

    //通过霍夫变换检测直线
    vector lines;
    HoughLines(midImage, lines, 1, CV_PI / 180, 300, 0, 0);//第5个参数就是阈值，阈值越大，检测精度越高
    //cout << lines.size() << endl;

    //由于图像不同，阈值不好设定，因为阈值设定过高导致无法检测直线，阈值过低直线太多，速度很慢
    //所以根据阈值由大到小设置了三个阈值，如果经过大量试验后，可以固定一个适合的阈值。

    if (!lines.size())
    {
        HoughLines(midImage, lines, 1, CV_PI / 180, 200, 0, 0);
    }
    //cout << lines.size() << endl;

    if (!lines.size())
    {
        HoughLines(midImage, lines, 1, CV_PI / 180, 150, 0, 0);
    }
    //cout << lines.size() << endl;
    if (!lines.size())
    {
        cout << "没有检测到直线！" << endl;
        return ERROR;
    }
    float sum = 0;
    //依次画出每条线段
    for (size_t i = 0; i < lines.size(); i++)
    {
        float rho = lines[i][0];
        float theta = lines[i][1];
        Point pt1, pt2;
        //cout << theta << endl;
        double a = cos(theta), b = sin(theta);
        double x0 = a*rho, y0 = b*rho;
        pt1.x = cvRound(x0 + 1000 * (-b));
        pt1.y = cvRound(y0 + 1000 * (a));
        pt2.x = cvRound(x0 - 1000 * (-b));
        pt2.y = cvRound(y0 - 1000 * (a));
        //只选角度最小的作为旋转角度
        sum += theta;
        line(dstImage, pt1, pt2, Scalar(55, 100, 195), 1, CV_AA); //Scalar函数用于调节线段颜色
        imshow("直线探测效果图", dstImage);
    }
    float average = sum / lines.size(); //对所有角度求平均，这样做旋转效果会更好
    cout << "average theta:" << average << endl;
    double angle = DegreeTrans(average) - 90;
    rotateImage(dstImage, dst, angle);
    //imshow("直线探测效果图2", dstImage);
    return angle;
}

void ImageRecify(const char* pInFileName, const char* pOutFileName)
{
    double degree;
    Mat src = imread(pInFileName);
    imshow("原始图", src);
    int srcWidth, srcHight;
    srcWidth = src.cols;
    srcHight = src.rows;
    cout << srcWidth << "   " << srcHight << endl;
    Mat dst;
    src.copyTo(dst);
    //倾斜角度矫正
    degree = CalcDegree(src, dst);
    if (degree == ERROR)
    {
        cout << "矫正失败！" << endl;
        return;
    }
    rotateImage(src, dst, degree);
    cout << "angle:" << degree << endl;
    imshow("旋转调整后", dst);

    Mat resulyImage = dst(Rect(0, 0, srcWidth, srcHight)); //根据先验知识，估计好文本的长宽，再裁剪下来
    imshow("裁剪之后", resulyImage);
    imwrite("recified.jpg", resulyImage);
}


int main()
{
    ImageRecify("jiao.jpg", "FinalImage.jpg");
    waitKey();
    return 0;
}

效果图如下所示：