python检测图片是否有马赛克内容

首先是Canny边缘检测，将图片的边缘检测出来，参考博客https://www.cnblogs.com/techyan1990/p/7291771.html

原理讲的很清晰，给原博主一个赞

边缘检测之后按照正方形检索来判定是否是马赛克内容，参考博客https://blog.csdn.net/ZhanCF/article/details/49736823

原理知晓了之后就很好做了

话说MATLAB转化为python的过程还是很有趣的

另外做完这些才发现其实还有更好的解决方法

from PIL import Image
import numpy as np
import math
import warnings

#算法来源，博客https://www.cnblogs.com/techyan1990/p/7291771.html和https://blog.csdn.net/zhancf/article/details/49736823
highhold=200#高阈值
lowhold=40#低阈值
warnings.filterwarnings("ignore")
demo=Image.open("noise_check//23.jpg")
im=np.array(demo.convert('L'))#灰度化矩阵
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)]#梯度强度
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)]#强边缘、弱边缘、忽略判定值2,1,0
rm=np.array([[0 for i in range(width)]for j in range(height)])#输出矩阵
#高斯滤波平滑，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):#梯度强度和方向
    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):#非极大值抑制,双阈值监测
    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):#抑制孤立低阈值点
    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)#矩阵化为图片
#img.show()
#正方形法判定是否有马赛克
value=35
lowvalue=16
imgnumber=[0 for i in range(value)]
for i in range(1,height-1,1):#性价比高的8点判定法
    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>=continue
            if rm[i][j]!=0:count+=1#4个顶点
            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]))

结果图可以上一下了

可以看出在一定程度上能够检测出马赛克内容

原图

边缘图案

 正方形数量