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>=width: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
更优秀的方案：
github地址https://github.com/summer4an/mosaic_detector
作者Qiita地址https://qiita.com/summer4an/items/306acc5d38169f880ba8
我这个相当于从原理走了一遍，所以慢。。。
想要更快的实现请参考这个日本大佬的文章。。。
