有没有办法更快地运行OpenCV的SIFT？

Question

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

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

有人能提出解决方案吗？我的research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orb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

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")

Answer 1

我在我的计算机上运行了现有的实现，在100个图像上运行。这段代码运行了6小时31分钟。然后，我改变了我在评论中建议的实现，只为每个图像计算sift.detectAndCompute一次，缓存结果并在比较中使用缓存的结果。这将我的计算机在同一个100映像上的执行时间从6小时31分钟减少到6分29秒。我不知道这对你所有的图片来说是否足够快，但这是一个显著的减少

请参阅下面我修改的实现

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/**/*"
folder = "SiftImages/*"


siftOut = {}
for a in glob.iglob(folder,recursive=True):
    if not a.lower().endswith(('.jpg','.png','.tif','.tiff','.gif')):
        continue
    image1 = cv2.imread(a)
    kp_1, desc_1 = sift.detectAndCompute(image1, None)
    siftOut[a]=(kp_1,desc_1)



for a in glob.iglob(folder,recursive=True):
    if not a.lower().endswith(('.jpg','.png','.tif','.tiff','.gif')):
        continue

    (kp_1,desc_1) = siftOut[a]

    for b in glob.iglob(folder,recursive=True):


        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)

        (kp_2,desc_2) = siftOut[b]

        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")
dfObj.to_csv(r"DuplicateImages3.2.csv")