特殊高通滤波器

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
#高频增强滤波器 (滤波器选择巴特沃斯高通滤波器)
def Enhance_PassFilter(image,d,n,s1):
    f=np.fft.fft2(image)
    fshift=np.fft.fftshift(f)
    def make_transform_matrix(d):
        transform_matrix=np.zeros(image.shape)
        center_point=tuple(map(lambda x:(x-1)/2,s1.shape))
        for i in range(transform_matrix.shape[0]):
            for j in range(transform_matrix.shape[1]):
                def cal_distance(pa,pb):
                    from math import sqrt
                    dis=sqrt((pa[0]-pb[0])**2+(pa[1]-pb[1])**2)
                    return dis
                dis=cal_distance(center_point,(i,j))
                transform_matrix[i,j]=1/(1+(d/dis)**(2*n))
        return transform_matrix
    d_matrix=make_transform_matrix(d)
    d_matrix=0.8*d_matrix+0.5
    new_img=np.abs(np.fft.ifft2(np.fft.ifftshift(fshift*d_matrix)))
    return new_img
#捕获异常
class imageSizeError(Exception):
    def __init__(self):
        self.value="图片大小错误"
    def __str__(self):
        return self.value
#矩阵减法
def decreaseArray(image1,image2):
    if image1.shape==image2.shape:
        image=image1.copy()
        for i in range(image1.shape[0]-1):
            for j in range(image1.shape[1]-1):
                image[i][j]=image1[i][j]-image2[i][j]
        return image
    else:
        raise imageSizeError()
#矩阵加法
def increaseArray(image1,image2):
    if image1.shape==image2.shape:
        image=image1.copy()
        for i in range(image1.shape[0]-1):
            for j in range(image1.shape[1]-1):
                image[i][j]=image1[i][j]+image2[i][j]
        return image
    else:
        raise imageSizeError()
def put(path):
    img=cv2.imread(path,1)
    img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    f=np.fft.fft2(img)
    fshift=np.fft.fftshift(f)
    #取绝对值后将复数变化为实数(取对数的目的是将数据变换到0~255)
    s1=np.log(np.abs(fshift))
    plt.subplot(231)
    plt.axis('off')
    plt.title('原始图像')
    plt.imshow(img, cmap='gray')
    plt.subplot(232)
    plt.axis('off')
    plt.title('高频增强滤波10')
    butter_100_1 = Enhance_PassFilter(img, 30, 1, s1)
    plt.imshow(butter_100_1, cmap='gray')
    #高频提升滤波
    imageAver3=cv2.blur(img,(3,3))#线性平滑滤波
    unsharpMask=decreaseArray(img,imageAver3)#非锐化掩模
    imageSharp=increaseArray(img,unsharpMask)#将模板加到原图像
    plt.subplot(233)
    plt.axis('off')
    plt.title('3×3平滑滤波结果图像')
    plt.imshow(imageAver3,cmap='gray')
    plt.subplot(234)
    plt.axis('off')
    plt.title('非锐化掩模')
    plt.imshow(unsharpMask,cmap='gray')
    plt.subplot(235)
    plt.axis('off')
    plt.title('高频提升滤波结果')
    plt.imshow(imageSharp,cmap='gray')
    plt.savefig('C:/Users/xpp/Desktop/result.png')
    plt.show()
put(r'C:/Users/xpp/Desktop/Lena.png')