python 实现算术均值滤波，几何均值滤波，谐波均值滤波，逆谐波均值滤波

import numpy as np
from cv2 import cv2#算术均值滤波器：
def a_mean(img,kernel_size):G_mean_img = np.zeros(img.shape)#print(G_mean_img[0][0])#print(img)k = int((kernel_size-1)/2)#print(k)for i in range(img.shape[0]):for j in range(img.shape[1]):if i <k or i>(img.shape[0]-k-1) or j <k or j>(img.shape[1]-k-1):G_mean_img[i][j]=img[i][j]else:for n in range(kernel_size):for m in range(kernel_size):G_mean_img[i][j] +=np.float(1/(kernel_size*kernel_size)*img[i-k+n][j-k+m])#G_mean_img[i][j]=1/9*(img[i-1][j-1]+img[i-1][j]+img[i-1][j+1]+img[i][j-1]+img[i][j]+img[i][j+1]+img[i+1][j-1]+img[i+1][j]+img[i+1][j+1])G_mean_img = np.uint8(G_mean_img)return G_mean_img#几何均值滤波器：
def G_mean(img,kernel_size):G_mean_img = np.ones(img.shape)#print(G_mean_img[0][0])#print(img)k = int((kernel_size-1)/2)#print(k)for i in range(img.shape[0]):for j in range(img.shape[1]):if i <k or i>(img.shape[0]-k-1) or j <k or j>(img.shape[1]-k-1):G_mean_img[i][j]=img[i][j]else:for n in range(kernel_size):for m in range(kernel_size):G_mean_img[i][j] *=np.float(img[i-k+n][j-k+m])G_mean_img[i][j] = pow(G_mean_img[i][j],1/(kernel_size*kernel_size))#G_mean_img[i][j]=1/9*(img[i-1][j-1]+img[i-1][j]+img[i-1][j+1]+img[i][j-1]+img[i][j]+img[i][j+1]+img[i+1][j-1]+img[i+1][j]+img[i+1][j+1])G_mean_img = np.uint8(G_mean_img)return G_mean_img#谐波均值滤波均值滤波器：
def H_mean(img,kernel_size):G_mean_img = np.zeros(img.shape)#print(G_mean_img[0][0])#print(img)k = int((kernel_size-1)/2)#print(k)for i in range(img.shape[0]):for j in range(img.shape[1]):if i <k or i>(img.shape[0]-k-1) or j <k or j>(img.shape[1]-k-1):G_mean_img[i][j]=img[i][j]else:for n in range(kernel_size):for m in range(kernel_size):if img[i-k+n][j-k+m] ==0:G_mean_img[i][j] = 0breakelse:G_mean_img[i][j] +=1/np.float(img[i-k+n][j-k+m])else:continuebreakif G_mean_img[i][j]!=0:G_mean_img[i][j] = (kernel_size*kernel_size)/G_mean_img[i][j]#G_mean_img[i][j]=1/9*(img[i-1][j-1]+img[i-1][j]+img[i-1][j+1]+img[i][j-1]+img[i][j]+img[i][j+1]+img[i+1][j-1]+img[i+1][j]+img[i+1][j+1])G_mean_img = np.uint8(G_mean_img)return G_mean_img#逆谐波均值滤波均值滤波器：
def HT_mean(img,kernel_size,Q):G_mean_img = np.zeros(img.shape)#print(G_mean_img[0][0])#print(img)k = int((kernel_size-1)/2)#print(k)for i in range(img.shape[0]):for j in range(img.shape[1]):if i <k or i>(img.shape[0]-k-1) or j <k or j>(img.shape[1]-k-1):G_mean_img[i][j]=img[i][j]else:result_top = 0result_down = 0for n in range(kernel_size):for m in range(kernel_size):if Q>0:result_top +=pow(np.float(img[i-k+n][j-k+m]),Q+1)result_down +=pow(np.float(img[i-k+n][j-k+m]),Q)else:if img[i-k+n][j-k+m]==0:G_mean_img[i][j] = 0breakelse:result_top +=pow(np.float(img[i-k+n][j-k+m]),Q+1)result_down +=pow(np.float(img[i-k+n][j-k+m]),Q)else:continuebreakelse:if result_down !=0:G_mean_img[i][j] = result_top/result_down#G_mean_img[i][j]=1/9*(img[i-1][j-1]+img[i-1][j]+img[i-1][j+1]+img[i][j-1]+img[i][j]+img[i][j+1]+img[i+1][j-1]+img[i+1][j]+img[i+1][j+1])G_mean_img = np.uint8(G_mean_img)return G_mean_imgif __name__ == "__main__":img = cv2.imread('data.jpg',0)G_mean_img_3 = HT_mean(img,kernel_size = 3,Q=-1.5)G_mean_img_5 = HT_mean(img,kernel_size = 5,Q=-1.5)G_mean_img_9 = HT_mean(img,kernel_size = 9,Q=-1.5)print(G_mean_img_3.max())cv2.imshow("show1",img)cv2.imshow("show2_3",G_mean_img_3)cv2.imshow("show3_5",G_mean_img_5)cv2.imshow("show3_9",G_mean_img_9)cv2.waitKey(0)print("test_end")

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