Create Gaussian Filter on Frequency Domain



I've done anything that i got, I just want to create a Gaussian Filter from DFT code that I've done. Here is the code :

//#include <stdafx.h>#include "opencv2/core/core.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <stdio.h>#include <iostream>using namespace cv;using namespace std;double pixelDistance(double u, double v){    return cv::sqrt(u*u + v*v);}double gaussianCoeff(double u, double v, double d0){    double d = pixelDistance(u, v);    return cv::exp((-d*d) / (2 * d0*d0));}cv::Mat createGaussianHighPassFilter(cv::Size size, double cutoffInPixels){    Mat ghpf(size, CV_64F);    cv::Point center(size.width / 2, size.height / 2);    for (int u = 0; u < ghpf.rows; u++)    {        for (int v = 0; v < ghpf.cols; v++)        {            ghpf.at<double>(u, v) = gaussianCoeff(u - center.y, v - center.x, cutoffInPixels);        }    }    return ghpf;}void translateImg(Mat& imgIn, Mat& imgOut){    int i, j;    for (i = 0; i < imgIn.rows; i++)        for (j = 0; j < imgIn.cols; j++)            imgOut.at<double>(i, j) = imgIn.at<double>(i, j) * pow(-1.0, i + j);}void scaleImg(Mat& imgIn, Mat& imgOut, float scaleFactor){    int i, j;    for (i = 0; i < imgIn.rows; i++)        for (j = 0; j < imgIn.cols; j++)            imgOut.at<double>(i, j) = (double)scaleFactor * log(1.0 + imgIn.at<double>(i, j));}void consoleOut(cv::Mat outMat, int rows = 5, int cols = 5){    rows = ((rows == -1 || rows >= outMat.rows) ? outMat.rows : rows);    cols = ((cols == -1 || cols >= outMat.cols) ? outMat.cols : cols);    for (int i = 0; i < rows; i++)    {        for (int j = 0; j < cols; j++)        {            cout << outMat.at<double>(i, j);            cout << " ";        }        cout << endl;    }}double calcMSE(Mat& imgOrig, Mat& imgReconst){    int valOrig = 0, valReconst = 0;    double MSE = 0.0;    for (int i = 0; i < imgOrig.rows; i++)    {        for (int j = 0; j < imgOrig.cols; j++)        {            valOrig = imgOrig.at<unsigned char>(i, j);            valReconst = imgReconst.at<unsigned char>(i, j);            MSE += pow((double)(valOrig - valReconst), 2.0);        }    }    return (MSE / (imgOrig.rows * imgOrig.cols));}string convertInt(int number) // converts integer to string{    stringstream ss;    ss << number;    return ss.str();}int main(unsigned int argc, char* const argv[]){    int dftH, dftW;    cv::Mat imgIn;    imgIn = cv::imread("fri.pgm", 0); //grayscale    imshow("Original Image", imgIn);    waitKey();    dftH = cv::getOptimalDFTSize(imgIn.rows);    dftW = cv::getOptimalDFTSize(imgIn.cols);    Mat imgMod;    Mat imgPrecFFT(dftH, dftW, CV_64FC1, Scalar::all(0));    imgIn.convertTo(imgMod, CV_64FC1);    imgPrecFFT = imgMod(cv::Range::all(), cv::Range::all()).clone();    // translate image    std::vector<Mat> imgsTrans;    imgsTrans.push_back(Mat_<double>(imgIn.size(), CV_64FC1));    imgsTrans.push_back(Mat_<double>(imgIn.size(), CV_64FC1));    imgsTrans[1].setTo(Scalar::all(0), Mat());    translateImg(imgPrecFFT, imgsTrans[0]);    Mat imgPrecTransFFT(imgIn.size(), CV_64FC2, Scalar::all(0));    cv::merge(imgsTrans, imgPrecTransFFT);    // dft    cv::Mat imgFFT;    dft(imgPrecTransFFT, imgFFT, DFT_COMPLEX_OUTPUT);    cv::Mat imgDispFFT;    // gaussian filter    Mat ghpf = createGaussianHighPassFilter(Size(128, 128), 16.0);    imshow("Gaussian Filter", ghpf);    mulSpectrums(imgFFT, ghpf, imgFFT, 0, 0);    waitKey();    // calculate magnitude    Mat imgMagnitude(imgIn.size(), CV_64FC1);    std::vector<Mat> chans;    cv::split(imgFFT, chans);    cv::magnitude(chans[0], chans[1], imgMagnitude);    // scale magnitude image    Mat imgMagnitudeScaled(imgIn.size(), CV_64FC1);    scaleImg(imgMagnitude, imgMagnitudeScaled, 10.0);    // display magnitude image    cv::Mat imgDisp;    cv::convertScaleAbs(imgMagnitudeScaled, imgDisp);    imshow("Magnitude Output", imgDisp);    waitKey();    // inverse dft    cv::split(imgFFT, chans);    chans[1].zeros(imgIn.size(), CV_64FC1);    cv::merge(chans, imgFFT);    cv::Mat invFFT;    cv::idft(imgFFT, invFFT, DFT_REAL_OUTPUT + DFT_SCALE);    // translate image back to original location    cv::split(invFFT, imgsTrans);    Mat imgAfterTrans(imgIn.size(), CV_64FC1);    translateImg(imgsTrans[0], imgAfterTrans);    imgAfterTrans.convertTo(imgDisp, CV_8UC1);    imshow("After Inverse Output", imgDisp);    waitKey();    // calculate and output mean-squared error between input/output images    double MSE = calcMSE(imgIn, imgDisp);    cout << endl << "MSE: " << MSE << endl;    waitKey();    return 0;}

I've try mulspectrum but it said it's error, then how can I merge the Gaussian Filter with the DFT one?