使用python调用函数库，实现2种频域锐化算法。

以下是两种频域锐化算法的Python实现：

1. 傅里叶变换+高通滤波器

import cv2
import numpy as np
from matplotlib import pyplot as plt

def fourier_highpass(img, filter_size=30):
    f = np.fft.fft2(img)
    fshift = np.fft.fftshift(f)
    rows, cols = img.shape
    crow, ccol = rows//2, cols//2
    fshift[crow-filter_size:crow+filter_size, ccol-filter_size:ccol+filter_size] = 0
    f_ishift = np.fft.ifftshift(fshift)
    img_back = np.fft.ifft2(f_ishift)
    img_back = np.real(img_back)
    img_back = np.uint8(np.clip(img_back, 0, 255))
    return img_back

img = cv2.imread('lena.png', 0)
img_highpass = fourier_highpass(img)
plt.subplot(121), plt.imshow(img, cmap='gray'), plt.title('Original')
plt.subplot(122), plt.imshow(img_highpass, cmap='gray'), plt.title('Highpass Filter')
plt.show()

2. 巴特沃斯高通滤波器

import cv2
import numpy as np
from matplotlib import pyplot as plt

def butterworth_highpass(img, filter_size=30, order=2):
    rows, cols = img.shape
    crow, ccol = rows//2, cols//2
    x = np.linspace(-cols/2, cols/2-1, cols)
    y = np.linspace(-rows/2, rows/2-1, rows)
    xv, yv = np.meshgrid(x, y)
    d = np.sqrt(xv**2 + yv**2)
    H = 1 / (1+(d/filter_size)**(2*order))
    F = np.fft.fft2(img)
    Fshift = np.fft.fftshift(F)
    Gshift = H * Fshift
    G = np.fft.ifftshift(Gshift)
    g = np.fft.ifft2(G)
    g = np.real(g)
    g = np.uint8(np.clip(g, 0, 255))
    return g

img = cv2.imread('lena.png', 0)
img_highpass = butterworth_highpass(img)
plt.subplot(121), plt.imshow(img, cmap='gray'), plt.title('Original')
plt.subplot(122), plt.imshow(img_highpass, cmap='gray'), plt.title('Butterworth Highpass Filter')
plt.show()