fftw + opencv inconsistent output - c++

Question

I recently tried to implement an FFT function for Opencv's Mat.
I inspired my implementation mainly from FFTW's code samples and from :
FFTW-OpenCV
I payed close attention to adapt the size of the input image in order to fasten the processing.
It seems that I did something wrong because the output is always a black image.
Here is my implementation:
void fft2_32f(const cv::Mat1f& _src, cv::Mat2f& dst)
{
cv::Mat2f src;
const int rows = cv::getOptimalDFTSize(_src.rows);
const int cols = cv::getOptimalDFTSize(_src.cols);
// const int total = cv::alignSize(rows*cols,steps);
if(_src.isContinuous() && _src.rows == rows && _src.cols == cols)
{
src = cv::Mat2f::zeros(src.size());
dst = cv::Mat2f::zeros(src.size());
// 1) copy the source into a complex matrix (the imaginary component is set to 0).
cblas_scopy(src.total(), _src.ptr<float>(), 1, src.ptr<float>(), 2);
// 2) prepare and apply the transform.
fftwf_complex* ptr_in = reinterpret_cast<fftwf_complex*>(src.ptr<float>());
fftwf_complex* ptr_out = reinterpret_cast<fftwf_complex*>(dst.ptr<float>());
// fftwf_plan fft = fftwf_plan_dft_1d(src.total(), ptr_in, ptr_out, FFTW_FORWARD, FFTW_ESTIMATE);
fftwf_plan fft = fftwf_plan_dft_2d(src.rows, src.cols, ptr_in, ptr_out, FFTW_FORWARD, FFTW_ESTIMATE);
fftwf_execute(fft);
fftwf_destroy_plan(fft);
// 3) normalize
cblas_saxpy(dst.rows * dst.step1(), 1.f/dst.total(), dst.ptr<float>(), 1, dst.ptr<float>(), 1);
}
else
{
src = cv::Mat2f::zeros(rows, cols);
dst = cv::Mat2f::zeros(rows, cols);
// 1) copy the source into a complex matrix (the imaginary component is set to 0).
support::parallel_for(cv::Range(0, _src.rows), [&src, &_src](const cv::Range& range)->void
{
for(int r=range.start; r<range.end; r++)
{
int c=0;
const float* it_src = _src[r];
float* it_dst = src.ptr<float>(r);
#if CV_ENABLE_UNROLLED
for(;c<=_src.cols-4; c+=4, it_src+=4, it_dst+=8)
{
*it_dst = *it_src;
*(it_dst+2) = *(it_src+1);
*(it_dst+4) = *(it_src+2);
*(it_dst+6) = *(it_src+3);
}
#endif
for(; c<_src.cols; c++, it_src++, it_dst+=2)
*it_dst = *it_src;
}
}, 0x80);
// 2) prepare and apply the transform.
fftwf_complex* ptr_in = reinterpret_cast<fftwf_complex*>(src.ptr<float>());
fftwf_complex* ptr_out = reinterpret_cast<fftwf_complex*>(dst.ptr<float>());
fftwf_plan fft = fftwf_plan_dft_2d(src.rows, src.cols, ptr_in, ptr_out, FFTW_FORWARD, FFTW_ESTIMATE);
fftwf_execute(fft);
fftwf_destroy_plan(fft);
double min(0.);
double max(0.);
// 3) normalize
cblas_saxpy(dst.rows * dst.step1(), 1.f/dst.total(), dst.ptr<float>(), 1, dst.ptr<float>(), 1);
}
}
Note:
The parallel_for implementation is inspired by: How to use lambda as a parameter to parallel_for_
Thanks in advance for any help.

Answer 1

I figure out my issue.
This function written as is does work perfectly (at least for the purpose I made it for).
My issue was that :
cv::Mat dst = cv::Mat::zeros(src.size(), CV_32FC2);
cv::Mat1f srcw = src;
cv::Mat1f dstw = dst;
fft2_32f(srcw, dstw); // realocate dstw to the optimal size for receive the output depending on the size of srcw. ... so the dstw is reallocate but not dst.
dst.copyTo(_outputVariable);
In that case the correct information is store in dstw but not in dst because of the reallocation inside the function.
So when I try to visualize my data I had a black image because of that.
The proper call use to be:
cv::Mat dst;
cv::Mat1f srcw = src;
cv::Mat1f dstw;
fft2_32f(srcw, dstw); // realocate dstw to the optimal size for receive the output depending on the size of srcw. ... so the dstw is reallocate but not dst.
dst = dstw;
dst.copyTo(_outputVariable); // or dstw.copyTo(_outputVariable);
With that code I got the proper output.
Note depending on the application a roi (take a look to the operator()(const cv::Rect&) of OpenCV's Mat container) corresponding to the size of the input may be usefull in order to preserve the dimensions.
Thank you for your help :).
Can someone help me to mark this topic as close ? please.