numpy 傅里叶变换与反变换高低通滤波与带通滤波

#coding=utf-8import cv2import numpy as npimport matplotlib.pyplot as pltimg=cv2.imread('test1-angle.jpg',cv2.IMREAD_GRAYSCALE)# f = np.fft.fft2(img)# fshift = np.fft.fftshift(f)# #取绝对值：将复数变化成实数# #取对数的目的为了将数据变化到较小的范围（比如0-255）# s1 = np.log(np.abs(f))# s2 = np.log(np.abs(fshift))# print(np.shape(s1))# print(s1[0:20,0:20])# cv2.imshow('s1',np.array(s1,dtype=int))# cv2.imshow('s2',s2)# cv2.waitKey()# plt.subplot(321),plt.imshow(s1,'gray'),plt.title('original')# plt.subplot(322),plt.imshow(s2,'gray'),plt.title('center')# ph_f = np.angle(f)# ph_fshift = np.angle(fshift)# # print(ph_f)# # print(ph_fshift)# plt.subplot(323),plt.imshow(ph_f,'gray'),plt.title('original')# plt.subplot(324),plt.imshow(ph_fshift,'gray'),plt.title('center')## # 逆变换# f1shift = np.fft.ifftshift(fshift)# img_back = np.fft.ifft2(f1shift)# # 出来的是复数，无法显示# img_back = np.abs(img_back)# plt.subplot(325), plt.imshow(img_back, 'gray'), plt.title('img back')# plt.show()plt.subplot(121),plt.imshow(img,'gray'),plt.title('origial')plt.xticks([]),plt.yticks([])#--------------------------------rows,cols = img.shape# mask = np.ones(img.shape,np.uint8)# mask[rows/2-30:rows/2+30,cols/2-30:cols/2+30] = 0 #高通滤波# mask = np.zeros(img.shape,np.uint8)# mask[rows/2-80:rows/2+80,cols/2-80:cols/2+80] = 1 #低通滤波#--------------------------------#--------------------------------理想的带通滤波器rows,cols = img.shapemask1 = np.ones(img.shape,np.uint8)mask1[rows/2-8:rows/2+8,cols/2-8:cols/2+8] = 0mask2 = np.zeros(img.shape,np.uint8)mask2[rows/2-80:rows/2+80,cols/2-80:cols/2+80] = 1mask = mask1*mask2#--------------------------------f1 = np.fft.fft2(img)f1shift = np.fft.fftshift(f1)f1shift = f1shift*maskf2shift = np.fft.ifftshift(f1shift) #对新的进行逆变换img_new = np.fft.ifft2(f2shift)#出来的是复数，无法显示img_new = np.abs(img_new)#调整大小范围便于显示img_new = 255-(img_new-np.amin(img_new))/(np.amax(img_new)-np.amin(img_new))plt.subplot(122),plt.imshow(img_new,'gray'),plt.title('Highpass')plt.xticks([]),plt.yticks([])plt.show()