python实现高斯滤波器_均值滤波、高斯滤波python实现
首先编写卷积代码
保证可以实现各种size滤波
def image_convolution(image,kernel):
[img_height,img_width] = image.shape
[kernel_height,kernel_width] = kernel.shape
expand_width = int((kernel_width - 1)/2)
expand_height = int((kernel_height - 1)/2)
con_height = int(img_height + expand_height*2)
con_width = int(img_width + expand_width*2)
#给结果图像、用于卷积处理的矩阵创建空间
result_image = np.zeros(image.shape)
con_image = np.zeros((con_height, con_width))
#填入图片
con_image[expand_height:expand_height+img_height, expand_width:expand_width+img_width]=image[ : , :]
#对每个像素点进行处理
for i in range(expand_height,expand_height+img_height):
for j in range(expand_width,expand_width+img_width):
result_image[i-expand_height][j-expand_width] = int(np.sum(con_image[i-expand_height:i+expand_height+1, j-expand_width:j+expand_width+1]*kernel))
print(result_image)
return result_image
均值滤波
设计kernel很简单，初始化为1后遍历除以size平方即可
def mean_mask(size):
mask=np.ones((size, size))
return mask/size/size
高斯滤波
def gauss_mask(sigma):
mask_height = mask_width = sigma*2+1
mask = np.zeros((mask_height, mask_width))
sum = 0
for i in range(-sigma,sigma+1):
for j in range(-sigma,sigma+1):
mask[i+sigma][j+sigma] = np.exp(-0.5 * (i ** 2 + j ** 2) / sigma ** 2)
sum += mask[i+sigma][j+sigma]
return mask/sum
代码总和
import cv2 as cv2
import matplotlib.pyplot as plt
import numpy as np
def image_convolution(image,kernel):
[img_height,img_width] = image.shape
[kernel_height,kernel_width] = kernel.shape
expand_width = int((kernel_width - 1)/2)
expand_height = int((kernel_height - 1)/2)
con_height = int(img_height + expand_height*2)
con_width = int(img_width + expand_width*2)
#给结果图像、用于卷积处理的矩阵创建空间
result_image = np.zeros(image.shape)
con_image = np.zeros((con_height, con_width))
#填入图片
con_image[expand_height:expand_height+img_height, expand_width:expand_width+img_width]=image[ : , :]
#对每个像素点进行处理
for i in range(expand_height,expand_height+img_height):
for j in range(expand_width,expand_width+img_width):
result_image[i-expand_height][j-expand_width] = int(np.sum(con_image[i-expand_height:i+expand_height+1, j-expand_width:j+expand_width+1]*kernel))
print(result_image)
return result_image
def gauss_mask(sigma):
mask_height = mask_width = sigma*2+1
mask = np.zeros((mask_height, mask_width))
sum = 0
for i in range(-sigma,sigma+1):
for j in range(-sigma,sigma+1):
mask[i+sigma][j+sigma] = np.exp(-0.5 * (i ** 2 + j ** 2) / sigma ** 2)
sum += mask[i+sigma][j+sigma]
return mask/sum
def mean_mask(size):
mask=np.ones((size, size))
return mask/size/size
if __name__ == "__main__":
image = cv2.imread("dongcha.jpg", 0)
image = cv2.resize(image,(512,512))
kernel = mean_mask(5)
result = np.zeros(image.shape)
result = image_convolution(image = image,kernel = kernel)
print(result)
result=result.astype(np.uint8)
cv2.imshow("result", result)
cv2.imshow("orignal", image)
# mask =gauss_mask(1)
print(kernel)
# print(mask/sum(mask))
cv2.waitKey(0)
效果如下
原文：https://www.cnblogs.com/xinyuLee404/p/12719112.html