We'll use the house.png image as input and thus fft2() to get the real and imaginary components of the Fourier coefficients; after that, we'll compute the magnitude/spectrum and the phase and, finally, use ifft2() to reconstruct the image:
import numpy.fft as fpim1 = rgb2gray(imread('../images/house.png'))pylab.figure(figsize=(12,10))freq1 = fp.fft2(im1)im1_ = fp.ifft2(freq1).realpylab.subplot(2,2,1), pylab.imshow(im1, cmap='gray'), pylab.title('Original Image', size=20)pylab.subplot(2,2,2), pylab.imshow(20*np.log10( 0.01 + np.abs(fp.fftshift(freq1))), cmap='gray')pylab.title('FFT Spectrum Magnitude', size=20)pylab.subplot(2,2,3), pylab.imshow(np.angle(fp.fftshift(freq1)), cmap='gray')pylab.title('FFT ...
