像素对比两张图片相似度是否一样

方法一：

#!C:/Python27
#coding=utf-8import pytesseract
from pytesser import *
from PIL import Image,ImageEnhance,ImageFilter
import os
import fnmatch
import re,timeimport urllib, random#import hashlib  def getGray(image_file):tmpls=[]for h in range(0,  image_file.size[1]):#hfor w in range(0, image_file.size[0]):#wtmpls.append( image_file.getpixel((w,h))  )return tmplsdef getAvg(ls):#获取平均灰度值return sum(ls)/len(ls)def getMH(a,b):#比较100个字符有几个字符相同dist = 0;for i in range(0,len(a)):if a[i]==b[i]:dist=dist+1return distdef getImgHash(fne):image_file = Image.open(fne) # 打开image_file=image_file.resize((12, 12))#重置图片大小我12px X 12pximage_file=image_file.convert("L")#转256灰度图Grayls=getGray(image_file)#灰度集合avg=getAvg(Grayls)#灰度平均值bitls=''#接收获取0或1#除去变宽1px遍历像素for h in range(1,  image_file.size[1]-1):#hfor w in range(1, image_file.size[0]-1):#wif image_file.getpixel((w,h))>=avg:#像素的值比较平均值 大于记为1 小于记为0bitls=bitls+'1'else:bitls=bitls+'0'return bitls
'''         m2 = hashlib.md5()   m2.update(bitls)print m2.hexdigest(),bitlsreturn m2.hexdigest()
'''a=getImgHash(".//testpic//001n.bmp")#图片地址自行替换
files = os.listdir(".//testpic")#图片文件夹地址自行替换
for file in files:b=getImgHash(".//testpic//"+str(file))compare=getMH(a,b)print file,u'相似度',str(compare)+'%'

方法二

#!C:/Python27
#coding=utf-8# 原作者发布在GitHub上的一些列图片对比的方法。有兴趣研究的可以访问链接如下：
# https://github.com/MashiMaroLjc/Learn-to-identify-similar-images
# coding : utf-8
from PIL import Imagedef calculate(image1, image2):g = image1.histogram()s = image2.histogram()assert len(g) == len(s), "error"data = []for index in range(0, len(g)):if g[index] != s[index]:data.append(1 - abs(g[index] - s[index]) / max(g[index], s[index]))else:data.append(1)return sum(data) / len(g)def split_image(image, part_size):pw, ph = part_sizew, h = image.sizesub_image_list = []assert w % pw == h % ph == 0, "error"for i in range(0, w, pw):for j in range(0, h, ph):sub_image = image.crop((i, j, i + pw, j + ph)).copy()sub_image_list.append(sub_image)return sub_image_listdef classfiy_histogram_with_split(image1, image2, size=(256, 256), part_size=(64, 64)):''''image1' 和 'image2' 都是Image 对象.可以通过'Image.open(path)'进行创建。'size' 重新将 image 对象的尺寸进行重置，默认大小为256 * 256 .'part_size' 定义了分割图片的大小.默认大小为64*64 .返回值是 'image1' 和 'image2'对比后的相似度，相似度越高，图片越接近，达到100.0说明图片完全相同。'''img1 = image1.resize(size).convert("RGB")sub_image1 = split_image(img1, part_size)img2 = image2.resize(size).convert("RGB")sub_image2 = split_image(img2, part_size)sub_data = 0for im1, im2 in zip(sub_image1, sub_image2):sub_data += calculate(im1, im2)x = size[0] / part_size[0]y = size[1] / part_size[1]pre = round((sub_data / (x * y)), 6)print u"相似度为:",(pre * 100)return pre * 100if __name__ == '__main__':image1 = Image.open(".//testpic//new.png")image2 = Image.open(".//testpic//new.png")classfiy_histogram_with_split(image1, image2)

方法三：需要安装第三方包，针对window用户，切换到python安装目录下的script目录下，按住shift+右键，在此处打开cmd命令窗口
输入命令：pip install requests 来安装request 模块，其他模块也一样
pip install numpy
pip install matplotlib

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import cv2
from math import log
from PIL import Image
import datetime
import pywt  # 以下强行用Python宏定义变量
halfWindowSize=9
src1_path = './/testpic//new.png'
src2_path = './/testpic//new.png'  '''''
来自敬忠良，肖刚，李振华《图像融合——理论与分析》P85：基于像素清晰度的融合规则
1，用Laplace金字塔或者是小波变换，将图像分解成高频部分和低频部分两个图像矩阵
2，以某个像素点为中心开窗，该像素点的清晰度定义为窗口所有点((高频/低频)**2).sum()
3，目前感觉主要的问题在于低频
4，高频取清晰度图像中较大的那个图的高频图像像素点
5，算法优化后速度由原来的2min.44s.变成9s.305ms.
补充：书上建议开窗大小10*10，DWT取3层，Laplace金字塔取2层
'''  def imgOpen(img_src1,img_src2):  apple=Image.open(img_src1).convert('L')  orange=Image.open(img_src2).convert('L')  appleArray=np.array(apple)  orangeArray=np.array(orange)  return appleArray,orangeArray  # 严格的变换尺寸
def _sameSize(img_std,img_cvt):  x,y=img_std.shape  pic_cvt=Image.fromarray(img_cvt)  pic_cvt.resize((x,y))  return np.array(pic_cvt)  # 小波变换的层数不能太高，Image模块的resize不能变换太小的矩阵，不相同大小的矩阵在计算对比度时会数组越界
def getWaveImg(apple,orange):  appleWave=pywt.wavedec2(apple,'haar',level=4)  orangeWave=pywt.wavedec2(orange,'haar',level=4)  lowApple=appleWave[0];lowOrange=orangeWave[0]  # 以下处理低频  lowAppleWeight,lowOrangeWeight = getVarianceWeight(lowApple,lowOrange)  lowFusion = lowAppleWeight*lowApple + lowOrangeWeight*lowOrange  # 以下处理高频  for hi in range(1,5):  waveRec=[]  for highApple,highOrange in zip(appleWave[hi],orangeWave[hi]):  highFusion = np.zeros(highApple.shape)  contrastApple = getContrastImg(lowApple,highApple)  contrastOrange = getContrastImg(lowOrange,highOrange)  row,col = highApple.shape  for i in xrange(row):  for j in xrange(col):  if contrastApple[i,j] > contrastOrange[i,j]:  highFusion[i,j] = highApple[i,j]  else:  highFusion[i,j] = highOrange[i,j]  waveRec.append(highFusion)  recwave=(lowFusion,tuple(waveRec))  lowFusion=pywt.idwt2(recwave,'haar')  lowApple=lowFusion;lowOrange=lowFusion  return lowFusion  # 求Laplace金字塔
def getLaplacePyr(img):  firstLevel=img.copy()  secondLevel=cv2.pyrDown(firstLevel)  lowFreq=cv2.pyrUp(secondLevel)  highFreq=cv2.subtract(firstLevel,_sameSize(firstLevel,lowFreq))  return lowFreq,highFreq  # 计算对比度，优化后不需要这个函数了，扔在这里看看公式就行
def _getContrastValue(highWin,lowWin):  row,col = highWin.shape  contrastValue = 0.00  for i in xrange(row):  for j in xrange(col):  contrastValue += (float(highWin[i,j])/lowWin[i,j])**2  return contrastValue  # 先求出每个点的(hi/lo)**2，再用numpy的sum（C语言库）求和
def getContrastImg(low,high):  row,col=low.shape  if low.shape!=high.shape:  low=_sameSize(high,low)  contrastImg=np.zeros((row,col))  contrastVal=(high/low)**2  for i in xrange(row):  for j in xrange(col):  up=i-halfWindowSize if i-halfWindowSize>0 else 0  down=i+halfWindowSize if i+halfWindowSize<row else row  left=j-halfWindowSize if j-halfWindowSize>0 else 0  right=j+halfWindowSize if j+halfWindowSize<col else col  contrastWindow=contrastVal[up:down,left:right]  contrastImg[i,j]=contrastWindow.sum()  return contrastImg  # 计算方差权重比
def getVarianceWeight(apple,orange):  appleMean,appleVar=cv2.meanStdDev(apple)  orangeMean,orangeVar=cv2.meanStdDev(orange)  appleWeight=float(appleVar)/(appleVar+orangeVar)  orangeWeight=float(orangeVar)/(appleVar+orangeVar)  return appleWeight,orangeWeight  # 函数返回融合后的图像矩阵
def getPyrFusion(apple,orange):  lowApple,highApple = getLaplacePyr(apple)  lowOrange,highOrange = getLaplacePyr(orange)  contrastApple = getContrastImg(lowApple,highApple)  contrastOrange = getContrastImg(lowOrange,highOrange)  row,col = lowApple.shape  highFusion = np.zeros((row,col))  lowFusion = np.zeros((row,col))  # 开始处理低频  # appleWeight,orangeWeight=getVarianceWeight(lowApple,lowOrange)  for i in xrange(row):  for j in xrange(col):  # lowFusion[i,j]=lowApple[i,j]*appleWeight+lowOrange[i,j]*orangeWeight  lowFusion[i,j] = lowApple[i,j] if lowApple[i,j]<lowOrange[i,j] else lowOrange[i,j]  # 开始处理高频  for i in xrange(row):  for j in xrange(col):  highFusion[i,j] = highApple[i,j] if contrastApple[i,j] > contrastOrange[i,j] else highOrange[i,j]  # 开始重建  fusionResult = cv2.add(highFusion,lowFusion)  return fusionResult  # 绘图函数
def getPlot(apple,orange,result):  plt.subplot(131)  plt.imshow(apple,cmap='gray')  plt.title('src1')  plt.axis('off')  plt.subplot(132)  plt.imshow(orange,cmap='gray')  plt.title('src2')  plt.axis('off')  plt.subplot(133)  plt.imshow(result,cmap='gray')  plt.title('result')  plt.axis('off')  plt.show()  # 画四张图的函数，为了方便同时比较
def cmpPlot(apple,orange,wave,pyr):  plt.subplot(221)  plt.imshow(apple,cmap='gray')  plt.title('SRC1')  plt.axis('off')  plt.subplot(222)  plt.imshow(orange,cmap='gray')  plt.title('SRC2')  plt.axis('off')  plt.subplot(223)  plt.imshow(wave,cmap='gray')  plt.title('WAVELET')  plt.axis('off')  plt.subplot(224)  plt.imshow(pyr,cmap='gray')  plt.title('LAPLACE PYR')  plt.axis('off')  plt.show()  def runTest(src1=src1_path,src2=src2_path,isplot=True):  apple,orange=imgOpen(src1,src2)  beginTime=datetime.datetime.now()  print(beginTime)  waveResult=getWaveImg(apple,orange)  pyrResult=getPyrFusion(apple,orange)  endTime=datetime.datetime.now()  print(endTime)  print('Runtime: '+str(endTime-beginTime))  if isplot:  cmpPlot(apple,orange,waveResult,pyrResult)  return waveResult,pyrResult  if __name__=='__main__':  runTest()

方法四：最简单的对比方法

#!C:/Python27
#coding=utf-8import pytesseract
from pytesser import *
from PIL import Image,ImageEnhance,ImageFilter
import os
import fnmatch
import re,timeimport urllib, randomonepng = ('.//testpic//001n.bmp')
twopng = ('.//testpic//001n.png')def fixed_size():"""按照固定尺寸处理图片"""im1 = Image.open('.//testpic//001n.bmp')im2 = Image.open('.//testpic//000n.bmp') width, height = im1.size diff = [(x, y) for x in xrange(width) for y in xrange(height) if im1.getpixel((x, y)) != im2.getpixel((x, y))] print u"打印值:",len(diff)fixed_size()

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