opencv利用霍夫变换检测直线对图片进行校正 opencv利用霍夫变换检测直线进行图片校正

#include<opencv2\opencv.hpp>
#include <iostream>
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<Vec2f> 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;
}