void perform_dft(T *out_ptr, const T *mydata, int total_samples, int width, int height, std::string feature){
fftw_complex *data_in;
mycomplex *cI = new mycomplex[width*height]();
///initialize arrays for fftw operations
data_in = ( fftw_complex* ) fftw_malloc( sizeof( fftw_complex ) * width* height);
///Store data in the real component of the data_in array.
for(int r = 0; r < height; r++){
for(int c = 0; c < width; c++){
data_in[r*width+c][0] = mydata[r*width+c];
data_in[r*width+c][1] = 0.0;
}
}
fftw_complex *data_out;
fftw_plan plan_f;
data_out = ( fftw_complex* )fftw_malloc( sizeof( fftw_complex ) * width * height );
plan_f = fftw_plan_dft_2d(width, height, data_in, data_out, FFTW_FORWARD, FFTW_ESTIMATE);
fftw_execute( plan_f);
double maxMagnitude =-1e5;
double maxPower = -1e5;
int tempr = 0, tempc = 0;
for(int r = 0; r < height; r++){
for(int c = 0; c < width; c++){
/// normalize values
double realI = data_out[r*width + c][0];
double imagI = data_out[r*width + c][1];
double powerI = ((realI * realI) + (imagI * imagI)) / double(total_samples);
double magI = sqrt(powerI);
///Get the maximum value of the magnitude
if(maxMagnitude < magI)
maxMagnitude = magI;
if(maxPower < powerI){
tempr = r;
tempc = c;
maxPower = powerI;
}
std::complex<double> ci(realI, imagI);
double phaseI = arg(ci) + M_PI;
mycomplex z;
z.r = realI;
z.i = imagI;
z.mag = magI;/// double(total_samples);
z.phase = phaseI;
z.power =powerI;
cI[r*width+c] = z;
}
}
//std::cout << maxPower << " " << tempc << " " << tempr << std::endl;
double constantMagFactor = 1.0 / log10(1.0 + fabs(maxMagnitude));
double constantPowerLogScaling = 1.0 / log10(1.0 + fabs(maxPower));
double constantPowerScaling = 1.0 / fabs(maxPower);
for(int r = 0; r < height; r++){
for(int c = 0; c < width; c++){
double magnitude = cI[r*width + c].mag;
magnitude = (magnitude < 1e-4) ? 0.0 : magnitude;
magnitude = (constantMagFactor * log10(1.0 + magnitude));
double power = cI[r*width + c].power;
double powerLogScaling = constantPowerLogScaling * power;
double powerScaling = constantPowerScaling * power; //Simple range mapping to (0,1)
double phase = cI[r*width+c].phase;
if(feature == "magnitude" || feature == "mag")
out_ptr[r*width+c] = magnitude;
else if(feature == "power-logscaled")
out_ptr[r*width+c] = powerLogScaling;
else if(feature == "power-scaled")
out_ptr[r*width+c] = powerScaling;
else if(feature == "power")
out_ptr[r*width+c] = power;
else if(feature == "phase")
out_ptr[r*width+c] = phase / double(2 * M_PI);
else if(feature == "magphase")
out_ptr[r*width+c] = magnitude + phase;
else{
std::cerr << "Please specify the feature (magnitude or power) of the Fourier spectrum !!!" << std::endl;
exit(-2);
}
}
}
swapQuadrants(out_ptr, width, height);
fftw_destroy_plan( plan_f);
fftw_free( data_out);
fftw_free(data_in);
delete [] cI;
}
/**
* With the critical edge selection, initial kernel erstimation can be accomplished quickly.
* Objective function: E(k) = ||∇I^s ⊗ k - ∇B||² + γ||k||²
*
* @param selectionGrads array of x and y gradients of final selected edges (∇I^s) [-1, 1]
* @param blurredGrads array of x and y gradients of blurred image (∇B) [-1, 1]
* @param kernel energy preserving kernel (k)
*/
void fastKernelEstimation(const array<Mat,2>& selectionGrads,
const array<Mat,2>& blurredGrads, Mat& kernel,
const float weight = 1e-2) {
assert(selectionGrads[0].rows == blurredGrads[0].rows && "matrixes have to be of same size!");
assert(selectionGrads[0].cols == blurredGrads[0].cols && "matrixes have to be of same size!");
// based on Perseval's theorem, perform FFT
// __________ __________
// ( F(∂_x I^s) * F(∂_x B) + F(∂_y I^s) * F(∂_y B) )
// k = F^-1 * ( ---------------------------------------------- )
// ( F(∂_x I^s)² + F(∂_y I^s)² + γ )
// where * is pointwise multiplication
// __________
// and F(∂_x I^s)² = F(∂_x I^s) * F(∂_x I^s) ! because they mean the norm
//
// here: F(∂_x I^s) = xS
// F(∂_x B) = xB
// F(∂_y I^s) = yS
// F(∂_y B) = yB
// compute FFTs
// the result are stored as 2 channel matrices: Re(FFT(I)), Im(FFT(I))
Mat xS, xB, yS, yB;
deblur::dft(selectionGrads[0], xS);
deblur::dft(blurredGrads[0], xB);
deblur::dft(selectionGrads[1], yS);
deblur::dft(blurredGrads[1], yB);
complex<float> we(weight, 0.0);
// kernel in Fourier domain
Mat K = Mat::zeros(xS.size(), xS.type());
// pixelwise computation of kernel
for (int y = 0; y < K.rows; y++) {
for (int x = 0; x < K.cols; x++) {
// complex entries at the current position
complex<float> xs(xS.at<Vec2f>(y, x)[0], xS.at<Vec2f>(y, x)[1]);
complex<float> ys(yS.at<Vec2f>(y, x)[0], yS.at<Vec2f>(y, x)[1]);
complex<float> xb(xB.at<Vec2f>(y, x)[0], xB.at<Vec2f>(y, x)[1]);
complex<float> yb(yB.at<Vec2f>(y, x)[0], yB.at<Vec2f>(y, x)[1]);
// kernel entry in the Fourier space
complex<float> k = (conj(xs) * xb + conj(ys) * yb) /
(conj(xs) * xs + conj(ys) * ys + we);
// (abs(xs) * abs(xs) + abs(ys) * abs(ys) + we); // equivalent
K.at<Vec2f>(y, x) = { real(k), imag(k) };
}
}
// only use the real part of the complex output
Mat kernelBig;
dft(K, kernelBig, DFT_INVERSE | DFT_REAL_OUTPUT);
// FIXME: find kernel inside image (kind of bounding box) instead of force user to
// approximate a correct kernel-width (otherwise some information are lost)
// cut of kernel in middle of the temporary kernel
int x = kernelBig.cols / 2 - kernel.cols / 2;
int y = kernelBig.rows / 2 - kernel.rows / 2;
swapQuadrants(kernelBig);
Mat kernelROI = kernelBig(Rect(x, y, kernel.cols, kernel.rows));
// copy the ROI to the kernel to avoid that some OpenCV functions accidently
// uses the information outside of the ROI (like copyMakeBorder())
kernelROI.copyTo(kernel);
// threshold kernel to erease negative values
threshold(kernel, kernel, 0.0, -1, THRESH_TOZERO);
// // kernel has to be energy preserving
// // this means: sum(kernel) = 1
kernel /= sum(kernel)[0];
}
