Python+OpenCV 实现简单的高斯滤波(推荐)

# 高斯核生成函数 kernel_size:滤波核大小  sigma:高斯核函数的局部影响范围
def gauss(kernel_size, sigma):
    #定型0填充
    kernel = np.zeros((kernel_size, kernel_size))
    #确定正态分布中间值
    center = kernel_size // 2
    # s:方差 sum:记录总和
    s = sigma ** 2
    sum = 0
    for i in range(kernel_size):
        for j in range(kernel_size):
            #由于最后要进行归一化处理，此处根号下2Π计算可以省略
            x, y = i - center, j - center
            kernel[i, j] = np.exp(-(x ** 2 + y ** 2) / (2 * s))
            sum += kernel[i, j]
    #归一化处理后输出
    kernel = kernel / sum
    return kernel

# 高斯滤波实现，img:输入图像 kermel:输入高斯核函数
def myfilter(img,kernel):
    # 读取img行数核列数
    h = img.shape[0]
    w = img.shape[1]
    # 直接拷贝父对象
    img1 = copy.copy(img)
    # 去掉边缘
    for i in range(1,h-1):
        for j in range(1,w-1):
            # 三通道处理
            for c in range(0,2):
                sum = 0
                # 加权
                for k in range(-1,2):
                    for l in range(-1,2):
                        sum += img[i+k,j+l,c]*kernel[k+1,l+1]
                img1[i,j,c] = sum
    return img1

def myfilter2(img,kernel):
    # 读取img行数核列数
    h = img.shape[0]
    w = img.shape[1]
    # 直接拷贝父对象
    img1 = copy.copy(img)
    # 去掉边缘
    for i in range(1,h-1):
        for j in range(1,w-1):
            sum = 0
            for k in range(-1,2):
                for l in range(-1,2):
                    sum += img[i+k,j+l]*kernel[k+1,l+1]
            img1[i,j] = sum
    return img1

import copy
import cv2
import numpy as np
#图像读取
img_y = cv2.imread('p2.jpg')
# 选择高斯生成函数3*3，其中sigama = 3
kernel = gauss(3,3)
# 打印这个生成函数
print(kernel)
# 高斯滤波处理
img1 = myfilter(img_y, kernel)
cv2.imshow('P1_yuantu',img_y)
cv2.imshow('P1_gaussian', img1)
cv2.waitKey(0)