opencv学习-core-离散傅里叶变换

本文转于http://m.blog.csdn.net/blog/tianzhaixing/8741460

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0copyMakeBorder(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;}