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
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