這篇文章主要介紹了python 如何檢測圖片是否有馬賽克，幫助大家更好的理解和使用python處理圖片，感興趣的朋友可以瞭解下
首先是Canny邊緣檢測，將圖片的邊緣檢測出來，參考部落格https://www.cnblogs.com/techyan1990/p/7291771.html
原理講的很清晰，給原博主一個贊
邊緣檢測之後按照正方形檢索來判定是否是馬賽克內容
原理知曉了之後就很好做了
話說MATLAB轉化為python的過程還是很有趣的。
from PIL import Image
import numpy as np
import math
import warnings
highhold=200#高閾值
lowhold=40#低閾值
warnings.filterwarnings("ignore")
demo=Image.open("noise_check//23.jpg")
im=np.array(demo.convert('L'))#灰度化矩陣
print(im.shape)
print(im.dtype)
height=im.shape[0]#尺寸
width=im.shape[1]
gm=[[0 for i in range(width)]for j in range(height)]#梯度強度
gx=[[0 for i in range(width)]for j in range(height)]#梯度x
gy=[[0 for i in range(width)]for j in range(height)]#梯度y
theta=0#梯度方向角度360度
dirr=[[0 for i in range(width)]for j in range(height)]#0,1,2,3方位判定值
highorlow=[[0 for i in range(width)]for j in range(height)]#強邊緣、弱邊緣、忽略判定值2,1,0
rm=np.array([[0 for i in range(width)]for j in range(height)])#輸出矩陣
#高斯濾波平滑，3x3
for i in range(1,height-1,1):
for j in range(1,width-1,1):
rm[i][j]=im[i-1][j-1]*0.0924+im[i-1][j]*0.1192+im[i-1][j+1]*0.0924+im[i][j-1]*0.1192+im[i][j]*0.1538+im[i][j+1]*0.1192+im[i+1][j-1]*0.0924+im[i+1][j]*0.1192+im[i+1][j+1]*0.0924
for i in range(1,height-1,1):#梯度強度和方向
for j in range(1,width-1,1):
gx[i][j]=-rm[i-1][j-1]+rm[i-1][j+1]-2*rm[i][j-1]+2*rm[i][j+1]-rm[i+1][j-1]+rm[i+1][j+1]
gy[i][j]=rm[i-1][j-1]+2*rm[i-1][j]+rm[i-1][j+1]-rm[i+1][j-1]-2*rm[i+1][j]-rm[i+1][j+1]
gm[i][j]=pow(gx[i][j]*gx[i][j]+gy[i][j]*gy[i][j],0.5)
theta=math.atan(gy[i][j]/gx[i][j])*180/3.1415926
if theta>=0 and theta<45:
dirr[i][j]=2
elif theta>=45 and theta<90:
dirr[i][j]=3
elif theta>=90 and theta<135:
dirr[i][j]=0
else:
dirr[i][j]=1
for i in range(1,height-1,1):#非極大值抑制,雙閾值監測
for j in range(1,width-1,1):
NW=gm[i-1][j-1]
N=gm[i-1][j]
NE=gm[i-1][j+1]
W=gm[i][j-1]
E=gm[i][j+1]
SW=gm[i+1][j-1]
S=gm[i+1][j]
SE=gm[i+1][j+1]
if dirr[i][j]==0:
d=abs(gy[i][j]/gx[i][j])
gp1=(1-d)*E+d*NE
gp2=(1-d)*W+d*SW
elif dirr[i][j]==1:
d=abs(gx[i][j]/gy[i][j])
gp1=(1-d)*N+d*NE
gp2=(1-d)*S+d*SW
elif dirr[i][j]==2:
d=abs(gx[i][j]/gy[i][j])
gp1=(1-d)*N+d*NW
gp2=(1-d)*S+d*SE
elif dirr[i][j]==3:
d=abs(gy[i][j]/gx[i][j])
gp1=(1-d)*W+d*NW
gp2=(1-d)*E+d*SE
if gm[i][j]>=gp1 and gm[i][j]>=gp2:
if gm[i][j]>=highhold:
highorlow[i][j]=2
rm[i][j]=1
elif gm[i][j]>=lowhold:
highorlow[i][j]=1
else:
highorlow[i][j]=0
rm[i][j]=0
else:
highorlow[i][j]=0
rm[i][j]=0
for i in range(1,height-1,1):#抑制孤立低閾值點
for j in range(1,width-1,1):
if highorlow[i][j]==1 and (highorlow[i-1][j-1]==2 or highorlow[i-1][j]==2 or highorlow[i-1][j+1]==2 or highorlow[i][j-1]==2 or highorlow[i][j+1]==2 or highorlow[i+1][j-1]==2 or highorlow[i+1][j]==2 or highorlow[i+1][j+1]==2):
#highorlow[i][j]=2
rm[i][j]=1
#img=Image.fromarray(rm)#矩陣化為圖片
#img.show()
#正方形法判定是否有馬賽克
value=35
lowvalue=16
imgnumber=[0 for i in range(value)]
for i in range(1,height-1,1):#價效比高的8點判定法
for j in range(1,width-1,1):
for k in range(lowvalue,value):
count=0
if i+k-1>=height or j+k-1>=width:continue
if rm[i][j]!=0:count+=1#4個頂點
if rm[i+k-1][j]!=0:count+=1
if rm[i][j+k-1]!=0:count+=1
if rm[i+k-1][j+k-1]!=0:count+=1
e=(k-1)//2
if rm[i+e][j]!=0:count+=1
if rm[i][j+e]!=0:count+=1
if rm[i+e][j+k-1]!=0:count+=1
if rm[i+k-1][j+e]!=0:count+=1
if count>=6:
imgnumber[k]+=1
for i in range(lowvalue,value):
print("length:{} number:{}".format(i,imgnumber[i]))
結果圖可以上一下了
可以看出在一定程度上能夠檢測出馬賽克內容
原圖
邊緣圖案
正方形數量