0031-在OpenCV环境下做旋转文本图像的矫正!
直接上代码和运行结果截图，具体的原理大家可以访问下面的链接查看!
链接：http://johnhany.net/2013/11/dft-based-text-rotation-correction/
如果上面的链接访问不了，可以下载我离线保存的网页文件查看，下载链接如下：
链接：http://pan.baidu.com/s/1gfxnfCR 密码：94pf代码如下：
代码中用到的图像下载链接:http://pan.baidu.com/s/1dFcYzMp 密码：tfae
图像处理开发资料、图像处理开发需求、图像处理接私活挣零花钱，可以搜索公众号"qxsf321"，并关注！
//opencv版本:OpenCV3.0
//VS版本:VS2013
//Author:qxsf321.net
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/imgproc/types_c.h>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/highgui/highgui_c.h>
#include <iostream>
using namespace cv;
using namespace std;
#define GRAY_THRESH 150
#define HOUGH_VOTE 100
//#define DEGREE 27
int main(int argc, char **argv)
{
//Read a single-channel image
const char* filename = "12_1.jpg";
Mat srcImg = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
if (srcImg.empty())
return -1;
imshow("source", srcImg);
Point center(srcImg.cols / 2, srcImg.rows / 2);
#ifdef DEGREE
//Rotate source image
Mat rotMatS = getRotationMatrix2D(center, DEGREE, 1.0);
warpAffine(srcImg, srcImg, rotMatS, srcImg.size(), 1, 0, Scalar(255, 255, 255));
imshow("RotatedSrc", srcImg);
//imwrite("imageText_R.jpg",srcImg);
#endif
//Expand image to an optimal size, for faster processing speed
//Set widths of borders in four directions
//If borderType==BORDER_CONSTANT, fill the borders with (0,0,0)
Mat padded;
int opWidth = getOptimalDFTSize(srcImg.rows);
int opHeight = getOptimalDFTSize(srcImg.cols);
copyMakeBorder(srcImg, padded, 0, opWidth - srcImg.rows, 0, opHeight - srcImg.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) };
Mat comImg;
//Merge into a double-channel image
merge(planes, 2, comImg);
//Use the same image as input and output,
//so that the results can fit in Mat well
dft(comImg, comImg);
//Compute the magnitude
//planes[0]=Re(DFT(I)), planes[1]=Im(DFT(I))
//magnitude=sqrt(Re^2+Im^2)
split(comImg, planes);
magnitude(planes[0], planes[1], planes[0]);
//Switch to logarithmic scale, for better visual results
//M2=log(1+M1)
Mat magMat = planes[0];
magMat += Scalar::all(1);
log(magMat, magMat);
//Crop the spectrum
//Width and height of magMat should be even, so that they can be divided by 2
//-2 is 11111110 in binary system, operator & make sure width and height are always even
magMat = magMat(Rect(0, 0, magMat.cols & -2, magMat.rows & -2));
//Rearrange the quadrants of Fourier image,
//so that the origin is at the center of image,
//and move the high frequency to the corners
int cx = magMat.cols / 2;
int cy = magMat.rows / 2;
Mat q0(magMat, Rect(0, 0, cx, cy));
Mat q1(magMat, Rect(0, cy, cx, cy));
Mat q2(magMat, Rect(cx, cy, cx, cy));
Mat q3(magMat, Rect(cx, 0, cx, cy));
Mat tmp;
q0.copyTo(tmp);
q2.copyTo(q0);
tmp.copyTo(q2);
q1.copyTo(tmp);
q3.copyTo(q1);
tmp.copyTo(q3);
//Normalize the magnitude to [0,1], then to[0,255]
normalize(magMat, magMat, 0, 1, CV_MINMAX);
Mat magImg(magMat.size(), CV_8UC1);
magMat.convertTo(magImg, CV_8UC1, 255, 0);
imshow("magnitude", magImg);
//imwrite("imageText_mag.jpg",magImg);
//Turn into binary image
threshold(magImg, magImg, GRAY_THRESH, 255, CV_THRESH_BINARY);
imshow("mag_binary", magImg);
//imwrite("imageText_bin.jpg",magImg);
//Find lines with Hough Transformation
vector<Vec2f> lines;
float pi180 = (float)CV_PI / 180;
Mat linImg(magImg.size(), CV_8UC3);
HoughLines(magImg, lines, 1, pi180, HOUGH_VOTE, 0, 0);
int numLines = lines.size();
for (int l = 0; l<numLines; l++)
{
float rho = lines[l][0], theta = lines[l][1];
Point pt1, pt2;
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));
line(linImg, pt1, pt2, Scalar(255, 0, 0), 3, 8, 0);
}
imshow("lines", linImg);
//imwrite("imageText_line.jpg",linImg);
if (lines.size() == 3){
cout << "found three angels:" << endl;
cout << lines[0][1] * 180 / CV_PI << endl << lines[1][1] * 180 / CV_PI << endl << lines[2][1] * 180 / CV_PI << endl << endl;
}
//Find the proper angel from the three found angels
float angel = 0;
float piThresh = (float)CV_PI / 90;
float pi2 = CV_PI / 2;
for (int l = 0; l<numLines; l++)
{
float theta = lines[l][1];
if (abs(theta) < piThresh || abs(theta - pi2) < piThresh)
continue;
else{
angel = theta;
break;
}
}
//Calculate the rotation angel
//The image has to be square,
//so that the rotation angel can be calculate right
angel = angel<pi2 ? angel : angel - CV_PI;
if (angel != pi2){
float angelT = srcImg.rows*tan(angel) / srcImg.cols;
angel = atan(angelT);
}
float angelD = angel * 180 / (float)CV_PI;
cout << "the rotation angel to be applied:" << endl << angelD << endl << endl;
//Rotate the image to recover
Mat rotMat = getRotationMatrix2D(center, angelD, 1.0);
Mat dstImg = Mat::ones(srcImg.size(), CV_8UC3);
warpAffine(srcImg, dstImg, rotMat, srcImg.size(), 1, 0, Scalar(255, 255, 255));
imshow("result", dstImg);
//imwrite("imageText_D.jpg",dstImg);
waitKey(0);
return 0;
}
运行结果截图如下：