高斯滤波算法python代码

以下是一个基于Python的高斯滤波算法代码示例：

import cv2
import numpy as np

def gaussian_filter(image, kernel_size, sigma):
    # define kernel
    kernel = np.zeros((kernel_size, kernel_size))
    center = kernel_size // 2
    for i in range(kernel_size):
        for j in range(kernel_size):
            x = i - center
            y = j - center
            kernel[i, j] = np.exp(-(x**2 + y**2)/(2*sigma**2))
    kernel /= (2*np.pi*sigma**2)
    kernel_sum = np.sum(kernel)

    # apply filter
    height, width = image.shape[:2]
    filtered = np.zeros_like(image)
    padded = np.pad(image, ((center, center), (center, center), (0, 0)), mode='constant')
    for i in range(center, height+center):
        for j in range(center, width+center):
            filtered[i-center, j-center] = np.sum(padded[i-center:i+center+1, j-center:j+center+1] * kernel) / kernel_sum
    return filtered

# read image
image = cv2.imread('lena.jpg')

# apply filter
filtered = gaussian_filter(image, 5, 1)

# show result
cv2.imshow('Original', image)
cv2.imshow('Filtered', filtered)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个示例中，我们首先定义了一个高斯核，并通过定义的高斯函数计算出每个像素的权重。然后，我们将这个核与输入图像进行卷积，并将结果返回为过滤图像。最后，我们使用OpenCV库的imshow函数显示原始图像和过滤后的图像。