OpenCV学习笔记-\doc\tutorials\core\discrete_fourier_transform
一些必要的补充说明:
这一节很多函数作者并没有细讲每一个参数的作用和含义,下面作补充说明:
1.copyMakeBorder()
查阅API Reference::improc,可以得到以下信息:
从中可以看到源码中那几个参数的意思,就是图像顶部不补全,底部补全,左边不补全,右边补全,这下就很好理解扩展图像那一段代码了。
2.merge()
查阅API Reference::core,可以得到以下信息:
planes[ ]是一个Mat数组,是输入,2表示有两个通道,comlexI是输出。与其相反,
后面计算幅度值时要分析两个通道,使用split()函数也很好理解了。
3.dft()
这个函数看似使用简单,实际内部很复杂,它实际上有4个参数,输入输出也有多种情况,
后面将幅度图像的四个角点重叠使原点到图像中心,下面的代码示例将显示没有调整的幅度图像和调整过后的幅度图像,从对比中可以看到,具体关于dft()等变换的使用后面还需要深入学习。
和以前一样,我把代码稍作修改,用lena.jpg来演示一下:
从中可以看出象限调整前和调整后的区别。;-)具体为什么,以后会讲到。
#include "opencv2/core/core.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include <iostream> using namespace cv; using namespace std; int main(int argc, char ** argv) { const char* filename = argc >=2 ? argv[1] : "lena.jpg"; Mat I = imread(filename, CV_LOAD_IMAGE_GRAYSCALE); if( I.empty()) return -1; Mat padded; //expand input image to optimal size int m = getOptimalDFTSize( I.rows ); int n = getOptimalDFTSize( I.cols ); // on the border add zero values copyMakeBorder(I, padded, 0, m - I.rows, 0, n - I.cols, BORDER_CONSTANT, Scalar::all(0)); Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)}; Mat complexI; merge(planes, 2, complexI); // Add to the expanded another plane with zeros dft(complexI, complexI); // this way the result may fit in the source matrix // compute the magnitude and switch to logarithmic scale // => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2)) split(complexI, planes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I)) magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude Mat magI = planes[0]; magI += Scalar::all(1); // switch to logarithmic scale log(magI, magI); // crop the spectrum, if it has an odd number of rows or columns magI = magI(Rect(0, 0, magI.cols & -2, magI.rows & -2)); Mat unrearranged = magI.clone(); // rearrange the quadrants of Fourier image so that the origin is at the image center int cx = magI.cols/2; int cy = magI.rows/2; Mat q0(magI, Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant 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 Mat tmp; // swap quadrants (Top-Left with Bottom-Right) q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left) q2.copyTo(q1); tmp.copyTo(q2); normalize(magI, magI, 0, 1, CV_MINMAX); // Transform the matrix with float values into a // viewable image form (float between values 0 and 1). normalize(unrearranged,unrearranged,0,1,CV_MINMAX); namedWindow("Unrearranged"); imshow("Unrearranged",unrearranged); namedWindow("Input Image"); imshow("Input Image" , I ); // Show the result namedWindow("spectrum magnitude"); imshow("spectrum magnitude", magI); waitKey(0); return 0; }