本文主要使用DFT相关函数实现对水平文本和旋转文本的DFT变换,在幅度谱中识别文本的变换,从而为图像旋转的检测和校正做准备。
#include "opencv2/core/core.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include <iostream> using namespace cv; using namespace std; void help(char* progName) { cout << endl << "This program demonstrated the use of the discrete Fourier transform (DFT). " << endl << "The dft of an image is taken and it's power spectrum(功率谱) is displayed." << endl << "Usage:" << endl << progName << " [image_name -- default lena.jpg] " << endl << endl; } int main(int argc, char ** argv) { help(argv[0]); const char* filename = argc >=2 ? argv[1] : "lena.jpg"; /* Mat I = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);*/ Mat I = imread(filename, 0); if( I.empty()) { cout<<"Can't load image!"<<endl; return -1; } //填充输入图像到最优大小一般是2,3,5的倍数 Mat padded; int m = getOptimalDFTSize( I.rows ); int n = getOptimalDFTSize( I.cols ); //把填充边界置0 copyMakeBorder(I, padded, 0, m - I.rows, 0, n - I.cols, BORDER_CONSTANT, Scalar::all(0)); //因为图像的频域比空域范围更大,故把输入图像转换到浮点类型, //并用另一个通道扩展它。这样才可以存储复数值(实部和虚部) Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)}; Mat complexI; merge(planes, 2, complexI);//把0值添加到另一个扩充的平面 //这样处理的结果可以适合原来的矩阵 dft(complexI, complexI); //计算这个幅度并转换到log领域 //log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2)) //planes[0] = Re(DFT(I))实部, planes[1] = Im(DFT(I))虚部 split(complexI, planes); //planes[0] = magnitude幅度值 magnitude(planes[0], planes[1], planes[0]); Mat magI = planes[0]; //转换到log运算 magI += Scalar::all(1); log(magI, magI); //如果它由奇数个行或奇数个列,截取频谱 magI = magI(Rect(0, 0, magI.cols & -2, magI.rows & -2)); //重新分配傅里叶变换后图像的象限从而让图像原始(0,0)位置在图像中心 int cx = magI.cols/2; int cy = magI.rows/2; Mat q0(magI, Rect(0, 0, cx, cy)); // Top-Left -每个象限创建一个感兴趣区域 Mat q1(magI, Rect(cx, 0, cx, cy)); // Top-Right Mat q2(magI, Rect(0, cy, cx, cy)); // Bottom-Left Mat q3(magI, Rect(cx, cy, cx, cy)); // Bottom-Right //交换Ⅱ和Ⅳ象限位置(Top-Left with Bottom-Right) Mat tmp; q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); //交换Ⅰ和Ⅲ象限位置(Top-Right with Bottom-Left) q1.copyTo(tmp); q2.copyTo(q1); tmp.copyTo(q2); // Transform the matrix with float values into a // viewable image form (float between values 0 and 1). normalize(magI, magI, 0, 1, CV_MINMAX); imshow("Input Image" , I ); imshow("spectrum magnitude", magI); waitKey(); return 0; }