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：
#!/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)
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：
#!/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)
总结
