带阻滤波器

import math
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
def bandstop_filter(image, radius, w, n=1):
    #对图像进行傅里叶变换(fft是一个三维数组，fft[:, :, 0]为实部，fft[:, :, 1]为虚部)
    fft=cv2.dft(np.float32(image),flags=cv2.DFT_COMPLEX_OUTPUT)
    #对fft进行中心化
    dshift=np.fft.fftshift(fft)
    #获到中心像素
    rows,cols=image.shape[:2]
    mid_row,mid_col=int(rows/2),int(cols/2)
    #构建掩模
    mask=np.zeros((rows,cols,2),np.float32)
    for i in range(0,rows):
        for j in range(0,cols):
            #计算(i,j)到中心点的距离
            d=math.sqrt(pow(i-mid_row,2)+pow(j-mid_col,2))
            if radius-w/2<d<radius+w/2:
                mask[i,j,0]=mask[i,j,1]=0
            else:
                mask[i,j,0]=mask[i,j,1]=1
    #傅里叶变换结果乘掩模
    fft_filtering=dshift*np.float32(mask)
    #傅里叶逆变换
    ishift=np.fft.ifftshift(fft_filtering)
    image_filtering=cv2.idft(ishift)
    image_filtering=cv2.magnitude(image_filtering[:,:,0],image_filtering[:,:,1])
    #对逆变换结果进行归一化
    cv2.normalize(image_filtering,image_filtering,0,1,cv2.NORM_MINMAX)
    return image_filtering
def put(path):
    image=cv2.imread(path,1)
    image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    image_bandstop_filtering5=bandstop_filter(image,30,35,1)
    plt.subplot(131)
    plt.axis('off')
    plt.title('原始图像')
    plt.imshow(image,cmap='gray')
    plt.subplot(132),
    plt.axis('off')
    plt.title('带阻图像')
    plt.imshow(image_bandstop_filtering5,'gray')
    plt.savefig('C:/Users/xpp/Desktop/result.png')
    plt.show()
put(r'C:/Users/xpp/Desktop/Lena.png')