Fourier transformation in frequency domain with opencv



I'm new to openCV and I'm trying to filter an image using a gaussian filter in frequency domain. But there is a run time error "assertion failed (type == srcB.type() && srcA.size() == srcB.size()) in cv::mulSpectrum" I know it is caused by the return type of my filter, the type doesn't match and I don't know how to make it right

here is the filter function (my guess is the return value from this function is wrong):

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.x, v - center.y, cutoffInPixels); //kernel utk gaussian filter yg 128x128
}
}

return ghpf;
}

and this is the main function:

Mat mask = createGaussianHighPassFilter(complexI.size(),16);
shift(mask);
Mat AX[] = {Mat::zeros(complexI.size(), CV_32F), Mat::zeros(complexI.size(), CV_32F)};
Mat kernel_spec;
AX[0] = mask; // real
AX[1] = mask; // imaginar
merge(AX, 2, kernel_spec);

cout<<complexI.type()<<endl<<kernel_spec.type(); //the result is 13 and 14, the type doesn't match

mulSpectrums(complexI, kernel_spec, complexI, DFT_ROWS); // only DFT_ROWS accepted

updateMag(complexI); // show spectrum
updateResult(complexI); // do inverse transform
c++ opencv fft
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Well of course they don't match. You are initializing kernel_spec as CV_32 but complexI is CV_64. Do a Mat::convertTo() and it should work.

HTH






OpenCV - Create Gaussian Filter on Frequency Domain

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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?



I'm new to openCV and I'm trying to filter an image using a gaussian filter in frequency domain. But there is a run time error "assertion failed (type == srcB.type() && srcA.size() == srcB.size()) in cv::mulSpectrum" I know it is caused by the return type of my filter, the type doesn't match and I don't know how to make it right

here is the filter function (my guess is the return value from this function is wrong):

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.x, v - center.y, cutoffInPixels); //kernel utk gaussian filter yg 128x128
    }
}

return ghpf;
}

and this is the main function:

Mat mask = createGaussianHighPassFilter(complexI.size(),16);
shift(mask);
Mat AX[] = {Mat::zeros(complexI.size(), CV_32F), Mat::zeros(complexI.size(), CV_32F)};
Mat kernel_spec;
AX[0] = mask; // real
AX[1] = mask; // imaginar
  merge(AX, 2, kernel_spec);

cout<<complexI.type()<<endl<<kernel_spec.type(); //the result is 13 and 14, the type doesn't match

mulSpectrums(complexI, kernel_spec, complexI, DFT_ROWS); // only DFT_ROWS accepted

updateMag(complexI);        // show spectrum
updateResult(complexI);     // do inverse transform
c++ opencv fft
shareimprove this question

Well of course they don't match. You are initializing kernel_spec as CV_32 but complexI is CV_64. Do a Mat::convertTo() and it should work.

HTH

OpenCV - Create Gaussian Filter on Frequency Domain

Ask Question

 

 
up vote

0

down vote

favorite

 

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?