利用python对图像进行傅里叶变换_python用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()