void _DFT(){
 //1以灰度模式讀取原圖像并顯示
 Mat srcImage = imread("miFan.jpg",0);
 if (!srcImage.data){ cout << "Error\n"; }
 imshow("原圖像", srcImage);

 //2將輸入圖像擴展到最佳尺寸,邊界用0補充
 int m = getOptimalDFTSize(srcImage.rows);
 int n = getOptimalDFTSize(srcImage.cols);

 //將添加的像素初始化為0
 Mat padded;
 copyMakeBorder(srcImage, padded, 0, m - srcImage.rows,
  0, n - srcImage.cols, BORDER_CONSTANT, Scalar::all(0));

 //3為傅里葉變換的結果(實部和虛部)分配存儲空間
 //將數(shù)組組合合并為一個多通道數(shù)組
 Mat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) };
 Mat complexI;
 merge(planes, 2, complexI);

 //4進行傅里葉變換
 dft(complexI, complexI);

 //5將復數(shù)轉換為幅值，即=> log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
 //將多通道數(shù)組分離為幾個單通道數(shù)組
 split(complexI, planes);//planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
 magnitude(planes[0], planes[1], planes[0]);
 Mat magImage = planes[0];

 //6進行對數(shù)尺度縮放
 magImage += Scalar::all(1);
 log(magImage, magImage);//求自然對數(shù)

 //7剪切和重分布幅度圖象限
 //若有奇數(shù)行或奇數(shù)列，進行頻譜剪裁
 magImage = magImage(Rect(0, 0, magImage.cols&-2, magImage.rows&-2));
 //重新排列傅立葉圖像中的象限，使得原點位于圖像中心 
 int cx = magImage.cols / 2;
 int cy = magImage.rows / 2;
 Mat q0(magImage, Rect(0, 0, cx, cy));
 Mat q1(magImage, Rect(cx, 0, cx, cy));
 Mat q2(magImage, Rect(0,cy,cx,cy));
 Mat q3(magImage, Rect(cx,cy,cx,cy));
 //交換象限(左上與右下進行交換)
 Mat tmp;
 q0.copyTo(tmp);
 q3.copyTo(q0);
 tmp.copyTo(q3);
 //交換象限（右上與左下進行交換）
 q1.copyTo(tmp);
 q2.copyTo(q1);
 tmp.copyTo(q2);

 //8歸一化，用0到1的浮點值將矩陣變換為可視的圖像格式
 normalize(magImage, magImage, 0, 1, CV_MINMAX);

 //9顯示
 imshow("頻譜增幅", magImage);

 waitKey();
}