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<p>我有一个图像目录,其中包含许多未识别的副本。我的目标是识别重复项。由于重复项已被裁剪、调整大小或转换为不同的图像格式,因此无法通过比较其哈希来检测它们</p>
<p>我编写了一个脚本,可以成功地检测重复项,但有一个主要缺点:脚本速度慢。在一个包含60个项目的文件夹的试驾上,运行了五个小时(这也可能反映了我的电脑越来越有问题,速度越来越慢)。由于我的目录中有大约66000个图像,我估计脚本需要229天才能完成</p>
<p>有人能提出解决方案吗?我的<a href="https://answers.opencv.org/question/14285/how-to-free-memory-through-cvmat/" rel="nofollow noreferrer">research</a>揭示了在循环完成时,可以通过“释放”存储在变量中的图像来释放内存,但是所有关于如何做到这一点的信息似乎都是用C编写的,而不是用python编写的。我也在考虑尝试使用<a href="https://docs.opencv.org/3.4/d1/d89/tutorial_py_orb.html" rel="nofollow noreferrer">orb</a>而不是sift,但担心它的准确性。有人对这两种选择中的哪一种更适合提出建议吗?还是重写脚本以减少内存占用的方法?非常感谢</p>
<pre><code>from __future__ import division
import cv2
import numpy as np
import glob
import pandas as pd
listOfTitles1 = []
listOfTitles2 = []
listOfSimilarities = []
# Sift and Flann
sift = cv2.xfeatures2d.SIFT_create()
index_params = dict(algorithm=0, trees=5)
search_params = dict()
flann = cv2.FlannBasedMatcher(index_params, search_params)
# Load all the images1
countInner = 0
countOuter = 1
folder = r"/Downloads/images/**/*"
for a in glob.iglob(folder,recursive=True):
for b in glob.iglob(folder,recursive=True):
if not a.lower().endswith(('.jpg','.png','.tif','.tiff','.gif')):
continue
if not b.lower().endswith(('.jpg','.png','.tif','.tiff','.gif')):
continue
if b.lower().endswith(('.jpg','.png','.tif','.tiff','.gif')):
countInner += 1
print(countInner, "", countOuter)
if countInner <= countOuter:
continue
image1 = cv2.imread(a)
kp_1, desc_1 = sift.detectAndCompute(image1, None)
image2 = cv2.imread(b)
kp_2, desc_2 = sift.detectAndCompute(image2, None)
matches = flann.knnMatch(desc_1, desc_2, k=2)
good_points = []
if good_points == 0:
continue
for m, n in matches:
if m.distance < 0.6*n.distance:
good_points.append(m)
number_keypoints = 0
if len(kp_1) >= len(kp_2):
number_keypoints = len(kp_1)
else:
number_keypoints = len(kp_2)
percentage_similarity = float(len(good_points)) / number_keypoints * 100
listOfSimilarities.append(str(int(percentage_similarity)))
listOfTitles2.append(b)
listOfTitles1.append(a)
countInner = 0
if a.lower().endswith(('.jpg','.png','.tif','.tiff','.gif')):
countOuter += 1
zippedList = list(zip(listOfTitles1,listOfTitles2, listOfSimilarities))
print(zippedList)
dfObj = pd.DataFrame(zippedList, columns = ['Original', 'Title' , 'Similarity'])
dfObj.to_csv(r"/Downloads/images/DuplicateImages3.csv")
</code></pre>