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 npimport cv2 as cvfrom matplotlib import pyplot as pltimg = cv.imread(’d:/paojie_g.jpg’,0)rows, cols = img.shapecrow, ccol = rows//2 , cols//2dft = 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 zerosmask = np.zeros((rows,cols,2),np.uint8)mask[crow-30:crow+31, ccol-30:ccol+31, :] = 1# apply mask and inverse DFTfshift = dft_shift*maskf_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()