Python用哈希算法查找相似图片（包括不同分辨率，不同大小，不同格式的图片）

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# -*- coding: utf-8 -*- ''' Python用哈希算法查找相似图片并放入[_df]的文件夹中   相似图片包括不同分辨率，不同大小，不同格式，只要图片相似就会算重复文件     安装cv2 pip install opencv-python   ''' import os import cv2 import numpy as np import shutil import random   class DuplicateFiles (object):     dir = ''     def __init__(self, dir):         self.dir = dir  # 实例属性       # 均值哈希算法     def aHash(self,img,shape=(10,10)):         # 缩放为10*10         img = cv2.resize(img, shape)         # 转换为灰度图         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)         # s为像素和初值为0，hash_str为hash值初值为''         s = 0         hash_str = ''         # 遍历累加求像素和         for i in range(shape[0]):             for j in range(shape[1]):                 s = s + gray[i, j]         # 求平均灰度         avg = s / 100         # 灰度大于平均值为1相反为0生成图片的hash值         for i in range(shape[0]):             for j in range(shape[1]):                 if gray[i, j] > avg:                     hash_str = hash_str + '1'                 else:                     hash_str = hash_str + '0'         return hash_str       # 差值感知算法     def dHash(self,img,shape=(10,10)):         # 缩放10*11         img = cv2.resize(img, (shape[0]+1, shape[1]))         # 转换灰度图         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)         hash_str = ''         # 每行前一个像素大于后一个像素为1，相反为0，生成哈希         for i in range(shape[0]):             for j in range(shape[1]):                 if gray[i, j] > gray[i, j + 1]:                     hash_str = hash_str + '1'                 else:                     hash_str = hash_str + '0'         return hash_str       # 感知哈希算法(pHash)     def pHash(self,img,shape=(10,10)):         # 缩放32*32         img = cv2.resize(img, (32, 32))  # , interpolation=cv2.INTER_CUBIC           # 转换为灰度图         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)         # 将灰度图转为浮点型，再进行dct变换         dct = cv2.dct(np.float32(gray))         # opencv实现的掩码操作         dct_roi = dct[0:10, 0:10]           hash = []         avreage = np.mean(dct_roi)         for i in range(dct_roi.shape[0]):             for j in range(dct_roi.shape[1]):                 if dct_roi[i, j] > avreage:                     hash.append(1)                 else:                     hash.append(0)         return hash       # 通过得到RGB每个通道的直方图来计算相似度     def classify_hist_with_split(self,image1, image2, size=(256, 256)):         # 将图像resize后，分离为RGB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 += self.calculate(im1, im2)         sub_data = sub_data / 3         return sub_data       # 计算单通道的直方图的相似值     def calculate(self,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       # Hash值对比     def cmpHash(self,hash1, hash2,shape=(10,10)):         n = 0         # hash长度不同则返回-1代表传参出错         if len(hash1)!=len(hash2):             return -1         # 遍历判断         for i in range(len(hash1)):             # 相等则n计数+1，n最终为相似度             if hash1[i] == hash2[i]:                 n = n + 1         return n/(shape[0]*shape[1])       def mymovefile(self,srcfile,dstpath,ffname):           # 移动函数         if not os.path.isfile(srcfile):             print ("%s not exist!"%(srcfile))         else:             fpath,fname=os.path.split(srcfile)             # 分离文件名和路径             if(ffname):fname=ffname             if not os.path.exists(dstpath):                 os.makedirs(dstpath)                       # 创建路径             shutil.move(srcfile, dstpath + fname)          # 移动文件             #print ("move %s -> %s"%(srcfile, dstpath + fname))       # 定义函数     def list_all_files(self,rootdir):         _files = []         # 列出文件夹下所有的目录与文件         list = os.listdir(rootdir)         for i in range(0, len(list)):             # 构造路径             path = os.path.join(rootdir, list[i])             # 判断路径是否为文件目录或者文件             # 如果是目录则继续递归             if os.path.isdir(path):                 _files.extend(list_all_files(path))             if os.path.isfile(path):                 _files.append(path)         return _files       #处理文件     def mvPhoto(self):                   photoList = self.list_all_files(self.dir)         #print(photoList)           for i,photo in enumerate(photoList):             mvPhoto = False #是否移动主文件             #如果不是文件则跳出             if(not os.path.isfile(photo)):                 continue             fpath,fname=os.path.split(photo)             print('Master:'+fname)             ffname = fname.split('.')               #不是下列文件形式跳出             if(ffname[1] not in {'jpg', 'bmp', 'png', 'jpeg', 'gif'}):                 continue               img1 = cv2.imdecode(np.fromfile(photo,dtype=np.uint8),cv2.IMREAD_COLOR)             for j in range(i+1,len(photoList)):                 #print('  ',j,photoList[j])                 if(not os.path.isfile(photo) or not os.path.isfile(photoList[j])):                     continue                 spath,sname=os.path.split(photoList[j])                 #print(sname)                 ssname = sname.split('.')                 if(ssname[1] not in {'jpg', 'bmp', 'png', 'jpeg', 'jfif'}):                     continue                                   #img1 = cv2.imread(photo)                 img2 = cv2.imdecode(np.fromfile(photoList[j],dtype=np.uint8),cv2.IMREAD_COLOR)                                   #hash1 = aHash(img1)                 #hash2 = aHash(img2)                 n1 = self.cmpHash(self.aHash(img1), self.aHash(img2))                 n2 = self.cmpHash(self.dHash(img1), self.dHash(img2))                 n3 = self.cmpHash(self.pHash(img1), self.pHash(img2))                 n4 = self.classify_hist_with_split(img1, img2)                 n5 = self.calculate(img1, img2)                 #print('    ',n1,n2,n3,n4,n5)                 if(n1>0.90 or n2>0.90 or n3>0.90 or n4>0.90 or n5>0.90):                     mvPhoto = True                     print('    move file:'+photoList[j])                     if(os.path.isfile(photoList[j])):                         print('ffname[0]:'+ffname[0])                         #mymovefile(photoList[j],dir+'_重复'+'/',ffname[0]+'_'+str(random.randint(10,99))+'.'+ffname[1])                         self.mymovefile(photoList[j],dir+'_df'+'/',ffname[0]+'_'+sname)                           #最后移动主文件             if(mvPhoto==True):                    self.mymovefile(photo,dir+'_df'+'/',fname)   if __name__ == "__main__":     #指定路径     #dir = r'E:\python\photoCompare\328' #指定目录地址     dir = os.getcwd()                    #当前文件所在目录     duplicateFiles = DuplicateFiles(dir)     duplicateFiles.mvPhoto()