OpenCV 3 Computer Vision with Python Cookbook

How to do it...

The following steps must be performed:

1. Import the required packages:

import cv2import numpy as npimport matplotlib.pyplot as plt

2. Read the image as grayscale and convert it to np.float32 datatype:

image = cv2.imread('../data/Lena.png', 0).astype(np.float32) / 255

3. Apply the discrete Fourier transform:

fft = cv2.dft(image, flags=cv2.DFT_COMPLEX_OUTPUT)

4. Visualize image spectrum:

shifted = np.fft.fftshift(fft, axes=[0, 1])magnitude = cv2.magnitude(shifted[:, :, 0], shifted[:, :, 1])magnitude = np.log(magnitude)plt.axis('off')plt.imshow(magnitude, cmap='gray')plt.tight_layout()plt.show()

5. Convert the image from the frequency spectrum back to spatial representation:

restored = cv2.idft(fft, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT) ...