#include<opencv2\opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
#define ERROR 1234

//度數(shù)轉(zhuǎn)換
double DegreeTrans(double theta)
{
 double res = theta / CV_PI * 180;
 return res;
}

//逆時針旋轉(zhuǎn)圖像degree角度（原尺寸） 
void rotateImage(Mat src, Mat& img_rotate, double degree)
{
 //旋轉(zhuǎn)中心為圖像中心 
 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);
 //計算二維旋轉(zhuǎn)的仿射變換矩陣 
 Mat M = getRotationMatrix2D(center, degree, 1);
 warpAffine(src, img_rotate, M, Size(length, length), 1, 0, Scalar(255, 255, 255));//仿射變換，背景色填充為白色 
}

//通過霍夫變換計算角度
double CalcDegree(const Mat &srcImage, Mat &dst)
{
 Mat midImage, dstImage;

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

 //通過霍夫變換檢測直線
 vector<Vec2f> lines;
 HoughLines(midImage, lines, 1, CV_PI / 180, 300, 0, 0);//第5個參數(shù)就是閾值，閾值越大，檢測精度越高
 //cout << lines.size() << endl;

 //由于圖像不同，閾值不好設(shè)定，因為閾值設(shè)定過高導致無法檢測直線，閾值過低直線太多，速度很慢
 //所以根據(jù)閾值由大到小設(shè)置了三個閾值，如果經(jīng)過大量試驗后，可以固定一個適合的閾值。

 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 << "沒有檢測到直線！" << endl;
  return ERROR;
 }
 float sum = 0;
 //依次畫出每條線段
 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));
  //只選角度最小的作為旋轉(zhuǎn)角度
  sum += theta;
  line(dstImage, pt1, pt2, Scalar(55, 100, 195), 1, CV_AA); //Scalar函數(shù)用于調(diào)節(jié)線段顏色
  imshow("直線探測效果圖", dstImage);
 }
 float average = sum / lines.size(); //對所有角度求平均，這樣做旋轉(zhuǎn)效果會更好
 cout << "average theta:" << average << endl;
 double angle = DegreeTrans(average) - 90;
 rotateImage(dstImage, dst, angle);
 //imshow("直線探測效果圖2", dstImage);
 return angle;
}

void ImageRecify(const char* pInFileName, const char* pOutFileName)
{
 double degree;
 Mat src = imread(pInFileName);
 imshow("原始圖", src);
 int srcWidth, srcHight;
 srcWidth = src.cols;
 srcHight = src.rows;
 cout << srcWidth << " " << srcHight << endl;
 Mat dst;
 src.copyTo(dst);
 //傾斜角度矯正
 degree = CalcDegree(src, dst);
 if (degree == ERROR)
 {
  cout << "矯正失敗！" << endl;
  return;
 }
 rotateImage(src, dst, degree);
 cout << "angle:" << degree << endl;
 imshow("旋轉(zhuǎn)調(diào)整后", dst);

 Mat resulyImage = dst(Rect(0, 0, srcWidth, srcHight)); //根據(jù)先驗知識，估計好文本的長寬，再裁剪下來
 imshow("裁剪之后", resulyImage);
 imwrite("recified.jpg", resulyImage);
}


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