图像基础10 比较图片是否一样

方法一 使用cv2 矩阵减subtract

1. 两个图片完全一样时返回值为true
2. 如果把一个图片转了格式，则会被为不一样

import cv2import numpy as npfile2 = "1.jpg"file1 = "1.png"image1 = cv2.imread(file1)image2 = cv2.imread(file2)difference = cv2.subtract(image1, image2)result = not np.any(difference) #if difference is all zeros it will return Falseif result is True:     print("两张图片一样")else:     cv2.imwrite("result.jpg", difference)     print ("两张图片不一样")

测试一个jpg图片和转成png进行比较，两张图片不一样

方法二 按像素计算差异数

# -*- coding: utf-8 -*-#!/usr/bin/pythonimport numpy as np import cv2 global n n = 0 img1 = cv2.imread('1.jpg', cv2.IMREAD_GRAYSCALE) img2 = cv2.imread('1.png', cv2.IMREAD_GRAYSCALE) height, width = img1.shape for line in range(height):     for pixel in range(width):         if img1[line][pixel] != img2[line][pixel]:             n = n + 1 print (n)

方法三 计算灰度图hash值

• 不同类型的图片也可以比较

# -*- coding: utf-8 -*-import pytesseract  from pytesseract import *  from PIL import Image,ImageEnhance,ImageFilter  import os  import fnmatch  import re,time  import urllib, random  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 bitlsa=getImgHash("1.jpg")#图片地址自行替换  b=getImgHash("1.png")  compare=getMH(a,b)  print (u'相似度'+str(compare)+'%')  

计算一个jpg和转成png的图片比较相似度为100%

方法四 灰度直方图作为相似比较

图像识别中，常用的特征：
- 颜色特征（最常用）
- 纹理特征
- 形状特征
- 空间关系特征

颜色特征分为：
- 直方图
- 颜色集
- 颜色矩
- 聚合向量
- 相关图

# -*- coding: utf-8 -*-#!/usr/bin/pythonimport cv2 import numpy as np from matplotlib import pyplot as plt # 最简单的以灰度直方图作为相似比较的实现 def classify_gray_hist(image1,image2,size = (256,256)):     # 先计算直方图     # 几个参数必须用方括号括起来     # 这里直接用灰度图计算直方图，所以是使用第一个通道，     # 也可以进行通道分离后，得到多个通道的直方图     # bins 取为16     image1 = cv2.resize(image1,size)     image2 = cv2.resize(image2,size)     hist1 = cv2.calcHist([image1],[0],None,[256],[0.0,255.0])     hist2 = cv2.calcHist([image2],[0],None,[256],[0.0,255.0])     # 可以比较下直方图     plt.plot(range(256),hist1,'r')     plt.plot(range(256),hist2,'b')     #plt.show()     # 计算直方图的重合度     degree = 0    for i in range(len(hist1)):         if hist1[i] != hist2[i]:             degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))         else:             degree = degree + 1    degree = degree/len(hist1)     return degree # 计算单通道的直方图的相似值 def calculate(image1,image2):     hist1 = cv2.calcHist([image1],[0],None,[256],[0.0,255.0])     hist2 = cv2.calcHist([image2],[0],None,[256],[0.0,255.0])     # 计算直方图的重合度     degree = 0    for i in range(len(hist1)):         if hist1[i] != hist2[i]:             degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))         else:             degree = degree + 1    degree = degree/len(hist1)     return degree# 通过得到每个通道的直方图来计算相似度 def classify_hist_with_split(image1,image2,size = (256,256)):     # 将图像resize后，分离为三个通道，再计算每个通道的相似值     image1 = cv2.resize(image1,size)     image2 = cv2.resize(image2,size)     sub_image1 = cv2.split(image1)     sub_image2 = cv2.split(image2)     sub_data = 0    for im1,im2 in zip(sub_image1,sub_image2):         sub_data += calculate(im1,im2)     sub_data = sub_data/3    return sub_data # 平均哈希算法计算 def classify_aHash(image1,image2):     image1 = cv2.resize(image1,(8,8))     image2 = cv2.resize(image2,(8,8))     gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)     gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)     hash1 = getHash(gray1)     hash2 = getHash(gray2)     return Hamming_distance(hash1,hash2) def classify_pHash(image1,image2):     image1 = cv2.resize(image1,(32,32))     image2 = cv2.resize(image2,(32,32))     gray1 = cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)     gray2 = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)     # 将灰度图转为浮点型，再进行dct变换     dct1 = cv2.dct(np.float32(gray1))     dct2 = cv2.dct(np.float32(gray2))     # 取左上角的8*8，这些代表图片的最低频率     # 这个操作等价于c++中利用opencv实现的掩码操作     # 在python中进行掩码操作，可以直接这样取出图像矩阵的某一部分     dct1_roi = dct1[0:8,0:8]     dct2_roi = dct2[0:8,0:8]     hash1 = getHash(dct1_roi)     hash2 = getHash(dct2_roi)     return Hamming_distance(hash1,hash2) # 输入灰度图，返回hash def getHash(image):     avreage = np.mean(image)     hash = []     for i in range(image.shape[0]):         for j in range(image.shape[1]):             if image[i,j] > avreage:                 hash.append(1)             else:                 hash.append(0)     return hash# 计算汉明距离 def Hamming_distance(hash1,hash2):     num = 0    for index in range(len(hash1)):         if hash1[index] != hash2[index]:             num += 1    return num if __name__ == '__main__':     img1 = cv2.imread('1.jpg')     #cv2.imshow('img1',img1)     img2 = cv2.imread('1.png')     #cv2.imshow('img2',img2)     degree = classify_gray_hist(img1,img2)     #degree = classify_hist_with_split(img1,img2)     #degree = classify_aHash(img1,img2)     #degree = classify_pHash(img1,img2)     print( degree)     #cv2.waitKey(0)

测试一个jpg和转成png的结果比较，相似度为93.6%

参考：http://www.jb51.net/article/83315.htm?t=1479218868974