opencv 图像傅里叶变换

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()

实验结果