前言
最近整理图片发现,好多图片都非常相似,于是写如下代码去删除,有两种方法:
注:第一种方法只对于连续图片(例一个视频里截下的图片)准确率也较高,其效率高;第二种方法准确率高,但效率低
方法一:相邻两个文件比较相似度,相似就把第二个加到新列表里,然后进行新列表去重,统一删除。
例如:有文件1-10,首先1和2相比较,若相似,则把2加入到新列表里,再接着2和3相比较,若不相似,则继续进行3和4比较…一直比到最后,然后删除新列表里的图片
代码如下:
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import cv2
from skimage.measure import compare_ssim
# import shutil
# def yidong(filename1,filename2):
# shutil.move(filename1,filename2)
def delete(filename1):
os.remove(filename1)
if __name__ = = '__main__' :
path = r 'D:\camera_pic\test\rec_pic'
# save_path_img = r'E:\0115_test\rec_pic'
# os.makedirs(save_path_img, exist_ok=True)
img_path = path
imgs_n = []
num = []
img_files = [os.path.join(rootdir, file ) for rootdir, _, files in os.walk(path) for file in files if
( file .endswith( '.jpg' ))]
for currIndex, filename in enumerate (img_files):
if not os.path.exists(img_files[currIndex]):
print ( 'not exist' , img_files[currIndex])
break
img = cv2.imread(img_files[currIndex])
img1 = cv2.imread(img_files[currIndex + 1 ])
ssim = compare_ssim(img, img1, multichannel = True )
if ssim > 0.9 :
imgs_n.append(img_files[currIndex + 1 ])
print (img_files[currIndex], img_files[currIndex + 1 ], ssim)
else :
print ( 'small_ssim' ,img_files[currIndex], img_files[currIndex + 1 ], ssim)
currIndex + = 1
if currIndex > = len (img_files) - 1 :
break
for image in imgs_n:
# yidong(image, save_path_img)
delete(image)
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方法二:逐个去比较,若相似,则从原来列表删除,添加到新列表里,若不相似,则继续
例如:有文件1-10,首先1和2相比较,若相似,则把2在原列表删除同时加入到新列表里,再接着1和3相比较,若不相似,则继续进行1和4比较…一直比,到最后一个,再继续,正常应该再从2开始比较,但2被删除了,所以从3开始,继续之前的操作,最后把新列表里的删除。
代码如下:
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import cv2
from skimage.measure import compare_ssim
import shutil
import datetime
def yidong(filename1,filename2):
shutil.move(filename1,filename2)
def delete(filename1):
os.remove(filename1)
print ( 'real_time:' ,now_now - now)
if __name__ = = '__main__' :
path = r 'F:\temp\demo'
# save_path_img = r'F:\temp\demo_save'
# os.makedirs(save_path_img, exist_ok=True)
for (root, dirs, files) in os.walk(path):
for dirc in dirs:
if dirc = = 'rec_pic' :
pic_path = os.path.join(root, dirc)
img_path = pic_path
imgs_n = []
num = []
del_list = []
img_files = [os.path.join(rootdir, file ) for rootdir, _, files in os.walk(img_path) for file in files if
( file .endswith( '.jpg' ))]
for currIndex, filename in enumerate (img_files):
if not os.path.exists(img_files[currIndex]):
print ( 'not exist' , img_files[currIndex])
break
new_cur = 0
for i in range ( 10000000 ):
currIndex1 = new_cur
if currIndex1 > = len (img_files) - currIndex - 1 :
break
else :
size = os.path.getsize(img_files[currIndex1 + currIndex + 1 ])
if size < 512 :
# delete(img_files[currIndex + 1])
del_list.append(img_files.pop(currIndex1 + currIndex + 1 ))
else :
img = cv2.imread(img_files[currIndex])
img = cv2.resize(img, ( 46 , 46 ), interpolation = cv2.INTER_CUBIC)
img1 = cv2.imread(img_files[currIndex1 + currIndex + 1 ])
img1 = cv2.resize(img1, ( 46 , 46 ), interpolation = cv2.INTER_CUBIC)
ssim = compare_ssim(img, img1, multichannel = True )
if ssim > 0.9 :
# imgs_n.append(img_files[currIndex + 1])
print (img_files[currIndex], img_files[currIndex1 + currIndex + 1 ], ssim)
del_list.append(img_files.pop(currIndex1 + currIndex + 1 ))
new_cur = currIndex1
else :
new_cur = currIndex1 + 1
print ( 'small_ssim' ,img_files[currIndex], img_files[currIndex1 + currIndex + 1 ], ssim)
for image in del_list:
# yidong(image, save_path_img)
delete(image)
print ( 'delete' ,image)
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总结
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原文链接:https://blog.csdn.net/sinat_38682860/article/details/103498657