傅里叶变换

dft = cv.dft(np.float32(img),flags = cv.dft_complex_output)

傅里叶逆变换

img_back = cv.idft(f_ishift)

实验：将图像转换到频率域，低通滤波，将频率域转回到时域，显示图像

import numpy as np

import cv2 as cv

from matplotlib import pyplot as plt

img = cv.imread('d:/paojie_g.jpg',0)

rows, cols = img.shape

crow, ccol = rows//2 , cols//2

dft = cv.dft(np.float32(img),flags = cv.dft_complex_output)

dft_shift = np.fft.fftshift(dft)

# create a mask first, center square is 1, remaining all zeros

mask = np.zeros((rows,cols,2),np.uint8)

mask[crow-30:crow+31, ccol-30:ccol+31, :] = 1

# apply mask and inverse dft

fshift = dft_shift*mask

f_ishift = np.fft.ifftshift(fshift)

img_back = cv.idft(f_ishift)

img_back = cv.magnitude(img_back[:,:,0],img_back[:,:,1])

plt.subplot(121),plt.imshow(img, cmap = 'gray')

plt.title('input image'), plt.xticks([]), plt.yticks([])

plt.subplot(122),plt.imshow(img_back, cmap = 'gray')

plt.title('low pass filter'), plt.xticks([]), plt.yticks([])

plt.show()

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