基于Python与OpenCV实现目录图片去重：感知哈希算法实战

苏见山

发布时间：2026-10-13 | 5人浏览过

原创

本文介绍利用Python和OpenCV库，通过感知哈希算法对指定目录下的图片进行自动去重。详细阐述了环境配置、算法原理（缩放、灰度化、指纹生成及汉明距离计算），并提供了完整的Python脚本与递归遍历的Shell脚本示例，帮助开发者高效清理重复图片。

版本信息

运行平台为Ubuntu 14系统，硬件配置为Intel I5处理器及4GB内存。

Python解释器版本指定为python2.7。

OpenCV库版本采用2.13.4。

环境依赖配置

若系统中未预装Python，需先执行安装操作。

执行命令：sudo apt-get install python

执行命令：sudo apt-get install python-dev

执行命令：sudo apt-get install python-pip

执行命令：sudo pip install numpy mathplotlib

执行命令：sudo apt-get install libcv-dev

执行命令：sudo apt-get install python-opencv

采用感知哈希算法执行图片去重操作。

算法逻辑是对目录内所有文件进行遍历比对，因此图片数量越多处理速度越慢，但能显著减少手动筛选的工作量。

感知哈希算法的具体原理如下：

第一步，将所有待比较的图片统一缩放至8*8像素的灰度图像。

第二步，计算每个像素值与图像平均灰度值的差异，从而生成代表图片特征的指纹序列。

第三步，基于生成的指纹序列计算汉明距离。

第四步，若计算出的不同像素点数量小于5，则判定为相同或高度相似的图片。

#!/usr/bin/python
# -*- coding: UTF-8 -*-
 
import cv2
import numpy as np
import os,sys,types

def cmpandremove2(path):
 dirs = os.listdir(path)
 dirs.sort()
 if len(dirs) <= 0:
  return
 dict={}
 for i in dirs:
  prepath = path + "/" + i
  preimg = cv2.imread(prepath)
  if type(preimg) is types.NoneType:
   continue
  preresize = cv2.resize(preimg, (8,8))
  pregray = cv2.cvtColor(preresize, cv2.COLOR_BGR2GRAY)
  premean = cv2.mean(pregray)[0]
  prearr = np.array(pregray.data)
  for j in range(0,len(prearr)):
   if prearr[j] >= premean:
    prearr[j] = 1
   else:
    prearr[j] = 0
  print "get", prepath
  dict[i] = prearr
 dictkeys = dict.keys()
 dictkeys.sort()
 index = 0
 while True:
  if index >= len(dictkeys):
   break
  curkey = dictkeys[index]
  dellist=[]
  print curkey
  index2 = index
  while True:
   if index2 >= len(dictkeys):
    break
   j = dictkeys[index2]
   if curkey == j:
    index2 = index2 + 1
    continue
   arr1 = dict[curkey]
   arr2 = dict[j]
   diff = 0
   for k in range(0,len(arr2)):
    if arr1[k] != arr2[k]:
     diff = diff + 1
   if diff <= 5:
    dellist.append(j)
   index2 = index2 + 1
  if len(dellist) > 0:
   for j in dellist:
    file = path + "/" + j
    print "remove", file
    os.remove(file)
    dict.pop(j)
   dictkeys = dict.keys()
   dictkeys.sort()
  index = index + 1

def cmpandremove(path):
 index = 0
 flag = 0
 dirs = os.listdir(path)
 dirs.sort()
 if len(dirs) <= 0:
  return 0
 while True:
  if index >= len(dirs):
   break
  prepath = path + dirs[index]
  print prepath
  index2 = 0
  preimg = cv2.imread(prepath)
  if type(preimg) is types.NoneType:
   index = index + 1
   continue
  preresize = cv2.resize(preimg,(8,8))
  pregray = cv2.cvtColor(preresize, cv2.COLOR_BGR2GRAY)
  premean = cv2.mean(pregray)[0]
  prearr = np.array(pregray.data)
  for i in range(0,len(prearr)):
   if prearr[i] >= premean:
    prearr[i] = 1
   else:
    prearr[i] = 0
  removepath = []
  while True:
   if index2 >= len(dirs):
    break
   if index2 != index:
    curpath = path + dirs[index2]
    #print curpath
    curimg = cv2.imread(curpath)
    if type(curimg) is types.NoneType:
     index2 = index2 + 1
     continue
    curresize = cv2.resize(curimg, (8,8))
    curgray = cv2.cvtColor(curresize, cv2.COLOR_BGR2GRAY)
    curmean = cv2.mean(curgray)[0]
    curarr = np.array(curgray.data)
    for i in range(0,len(curarr)):
     if curarr[i] >= curmean:
      curarr[i] = 1
     else:
      curarr[i] = 0
    diff = 0
    for i in range(0,len(curarr)):
     if curarr[i] != prearr[i] :
      diff = diff + 1
    if diff <= 5:
     print 'the same'
     removepath.append(curpath)
     flag = 1
   index2 = index2 + 1
  index = index + 1
  if len(removepath) > 0:
   for file in removepath:
    print "remove", file
    os.remove(file)
   dirs = os.listdir(path)
   dirs.sort()
   if len(dirs) <= 0:
    return 0
   #index = 0
 return flag
  
def main(argv):
 if len(argv) <= 1:
  print "command error"
  return -1
 if os.path.exists(argv[1]) is False:
  return -1
 path = argv[1]
 '''
 while True:
  if cmpandremove(path) == 0:
   break
 '''
 cmpandremove(path)
 return 0
   
if __name__ == '__main__':
 main(sys.argv)

为简化操作流程，脚本会遍历指定目录及其子目录，对需要去重的文件夹执行批量处理。

#!/bin/bash
indir=$1
addcount=0
function intest()
{
 
 for file in $1/*
 do
  echo $file
  if test -d $file 
  then
   ~/similar.py $file/
   intest $file
  fi
 done
}

intest $indir

以上即为使用Python和OpenCV对目录下图片进行去重的完整方法分享，供读者参考与实践，欢迎支持。