像素对比两张图片相似度是否一样

方法一：

#!C:/Python27
#coding=utf-8

import pytesseract
from pytesser import *
from PIL import Image,ImageEnhance,ImageFilter
import os
import fnmatch
import re,time

import urllib, random


#import hashlib  
 
def getGray(image_file):
   tmpls=[]
   for h in range(0,  image_file.size[1]):#h
      for w in range(0, image_file.size[0]):#w
         tmpls.append( image_file.getpixel((w,h))  )
          
   return tmpls
 
def 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+1
   return dist
 
def 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 12px
   image_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):#h
      for w in range(1, image_file.size[0]-1):#w
         if image_file.getpixel((w,h))>=avg:#像素的值比较平均值 大于记为1 小于记为0
            bitls=bitls+'1'
         else:
            bitls=bitls+'0'
   return bitls
'''         
   m2 = hashlib.md5()   
   m2.update(bitls)
   print m2.hexdigest(),bitls
   return 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 Image


def 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_size
    w, h = image.size

    sub_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_list


def 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 = 0
    for 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 * 100


if __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+halfWindowSize0 else 0  
            right=j+halfWindowSize if j+halfWindowSize 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-8

import pytesseract
from pytesser import *
from PIL import Image,ImageEnhance,ImageFilter
import os
import fnmatch
import re,time

import urllib, random

onepng = ('.//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()