opencv从零开始——4. 离散傅里叶变换的体验

来源：互联网发布：西部数码域名编辑：程序博客网时间：2024/06/05 02:34

opencv的强大之处，从这里开始，越来越能和信号处理接轨了，赞一个。

代码:

#include <opencv2/core/core.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include <iostream>using namespace std;using namespace cv;// "/mnt/hgfs/code_for_Linux/code_opencv/test1/pic/"int main(){    Mat srcImage = imread("/mnt/hgfs/code_for_Linux/code_opencv/test1/pic/3.jpg", 0);    if (!srcImage.data) {        return -1;    }    imshow("[灰度图]", srcImage);    int m = getOptimalDFTSize(srcImage.rows);    int n = getOptimalDFTSize(srcImage.cols);    Mat padded;    copyMakeBorder(srcImage, padded, 0, m - srcImage.rows, 0, n - srcImage.cols,                   BORDER_CONSTANT, Scalar::all(0));    Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};    Mat complexI;    merge(planes, 2, complexI);    dft(complexI, complexI);    split(complexI, planes);    magnitude(planes[0], planes[1], planes[0]);    Mat magnitudeImage = planes[0];    magnitudeImage += Scalar::all(1);    log(magnitudeImage, magnitudeImage);    magnitudeImage = magnitudeImage(Rect(0,                                         0,                                         magnitudeImage.cols & - 2,                                         magnitudeImage.rows & - 2));    int cx = magnitudeImage.cols/2;    int cy = magnitudeImage.rows/2;    Mat q0(magnitudeImage, Rect(0, 0, cx, cy));    Mat q1(magnitudeImage, Rect(cx, 0, cx, cy));    Mat q2(magnitudeImage, Rect(0, cy, cx, cy));    Mat q3(magnitudeImage, Rect(cx, cy, cx, cy));    Mat tmp;    q0.copyTo(tmp);    q3.copyTo(q0);    tmp.copyTo(q3);    q1.copyTo(tmp);    q2.copyTo(q1);    tmp.copyTo(q2);    normalize(magnitudeImage, magnitudeImage, 0, 1, NORM_MINMAX);    imshow("频谱幅值", magnitudeImage);    waitKey(0);    return 0;}

效果如下：

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