opencv傅里叶变换

傅里叶变换

#include <iostream>
#include <opencv2/highgui.hpp>

using namespace std;
using namespace cv;

//计算两个二维实矩阵卷积
void convolveDFT(InputArray A, InputArray B, OutputArray C)
{
	C.create(abs(A.rows() - B.rows()) + 1, abs(A.cols() - B.cols()) + 1, A.type());
	Size dftSize;

	dftSize.width = getOptimalDFTSize(A.cols() + B.cols() - 1);
	dftSize.height = getOptimalDFTSize(A.rows() + B.rows() - 1);

	Mat tempA(dftSize, A.type(), Scalar::all(0));
	Mat tempB(dftSize, B.type(), Scalar::all(0));

	Mat roiA(tempA, Rect(0, 0, A.cols(), A.rows()));
	A.copyTo(roiA);
	Mat roiB(tempB, Rect(0, 0, B.cols(), B.rows()));
	B.copyTo(roiB);

	dft(tempA, tempA, 0, A.rows());
	dft(tempB, tempB, 0, B.rows());

	mulSpectrums(tempA, tempB, tempA, 0);
	dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows());
	tempA(Rect(0, 0, C.cols(), C.rows())).copyTo(C);
}
// 离散傅里叶变换
int main()
{
	//【1】以灰度模式读取原始图像并显示
	Mat srcImage = imread("1.jpg", 0);
	if (!srcImage.data) {
		printf("读取图片错误，请确定目录下是否有imread函数指定图片存在~！ \n");
		return false;
	}
	imshow("原始图像", srcImage);
	//【2】将输入图像延扩到最佳的尺寸，边界用0补充
	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));
	//【3】为傅里叶变换的结果（实部和虚部）分配存储空间
	Mat planes[] = { Mat_<float>(padded),Mat::zeros(padded.size(),CV_32F) };
	Mat complexI;
	merge(planes, 2, complexI);
	//【4】进行就地傅里叶变换
	dft(complexI, complexI);
	//【5】将复数转换为幅值
	split(complexI, planes);
	magnitude(planes[0], planes[1], planes[0]);
	Mat magnitudeImage = planes[0];
	//【6】进行对数尺度缩放
	magnitudeImage += Scalar::all(1);
	log(magnitudeImage, magnitudeImage);
	//【7】剪切和重分布幅度图象限
	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(q3);
	tmp.copyTo(q0);
	q1.copyTo(tmp);
	q2.copyTo(q1);
	tmp.copyTo(q2);
	//【8】归一化，用0到1之间的浮点值将矩阵变换为可视的图像格式
	normalize(magnitudeImage, magnitudeImage, 0, 1, NORM_MINMAX);
	//【9】显示效果图
	imshow("频谱幅值", magnitudeImage);
	waitKey();
	return 0;
}