example_12-01.cpp

// Using cv::dft() and cv::idft() to accelerate the computation of
// convolutions

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

using namespace std;

int main(int argc, char** argv) {

  if(argc != 2) {
    cout << "Fourier Transform\nUsage: " <<argv[0] <<" <imagename>" << endl;
    return -1;
  }

  cv::Mat A = cv::imread(argv[1],0);

  if( A.empty() ) { cout << "Cannot load " << argv[1] << endl; return -1; }

  cv::Size patchSize( 100, 100 );
  cv::Point topleft( A.cols/2, A.rows/2 );
  cv::Rect roi( topleft.x, topleft.y, patchSize.width, patchSize.height );
  cv::Mat B = A( roi );

  int dft_M = cv::getOptimalDFTSize( A.rows+B.rows-1 );
  int dft_N = cv::getOptimalDFTSize( A.cols+B.cols-1 );

  cv::Mat dft_A = cv::Mat::zeros( dft_M, dft_N, CV_32F );
  cv::Mat dft_B = cv::Mat::zeros( dft_M, dft_N, CV_32F );

  cv::Mat dft_A_part = dft_A( cv::Rect(0, 0, A.cols,A.rows) );
  cv::Mat dft_B_part = dft_B( cv::Rect(0, 0, B.cols,B.rows) );

  A.convertTo( dft_A_part, dft_A_part.type(), 1, -mean(A)[0] );
  B.convertTo( dft_B_part, dft_B_part.type(), 1, -mean(B)[0] );

  cv::dft( dft_A, dft_A, 0, A.rows );
  cv::dft( dft_B, dft_B, 0, B.rows );

  // set the last parameter to false to compute convolution instead of correlation
  //
  cv::mulSpectrums( dft_A, dft_B, dft_A, 0, true );
  cv::idft( dft_A, dft_A, cv::DFT_SCALE, A.rows + B.rows - 1 );

  cv::Mat corr = dft_A( cv::Rect(0, 0, A.cols + B.cols - 1, A.rows + B.rows - 1) );
  cv::normalize( corr, corr, 0, 1, cv::NORM_MINMAX, corr.type() );
  cv::pow( corr, 3., corr );

  B ^= cv::Scalar::all( 255 );

  cv::imshow( "Image", A );
  cv::imshow( "ROI", B );

  cv::imshow( "Correlation", corr );
  cv::waitKey();

  return 0;
}