使用 for 循环 python 循环遍历两个目录

Question

我正在尝试比较两个pdf。我的方法包括转换两个 pdf 并比较相应页面的图像。我最初编写了一个简单的代码，它比较与下面的第一个代码相对应的两个图像，该代码可以正常工作。import cv2import numpy as nporiginal = cv2.imread("image_old/imageOld_1.jpg")image_to_compare = cv2.imread("image_new/imageNew_1.jpg")image1 = original.shapeimage2 = image_to_compare.shapeif original.shape == image_to_compare.shape:    print("The images have same size and channels")    difference = cv2.subtract(original, image_to_compare)    r, g, b = cv2.split(difference)      cv2.imshow("difference", cv2.resize( difference, None, fx=0.3, fy=0.3))        print(cv2.countNonZero(b))    if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:       print("The images are completely Equal")    else:        print("The images are not equal")sift = cv2.xfeatures2d.SIFT_create()kp_1, desc_1 = sift.detectAndCompute(original, None)kp_2, desc_2 = sift.detectAndCompute(image_to_compare, None) print("Keypoints of 1st image: " + str(len(kp_1)))print("Keypoints of 2nd image: " + str(len(kp_2)))index_params = dict(algorithm=0, trees=5)search_params = dict()flann = cv2.FlannBasedMatcher(index_params, search_params)matches = flann.knnMatch(desc_1, desc_2, k=2)good_points = []for m, n in matches:    if m.distance < 0.6*n.distance:        good_points.append(m)print('The images have %d %s' %(len(good_points),"good points matches"))if len(kp_1) <= len(kp_2):    number_keypoints = len(kp_1)else:    number_keypoints = len(kp_2)percentage_similarity = len(good_points) / number_keypoints * 100print('Similarity %d %s' %(round((percentage_similarity)),"%\n"))result = cv2.drawMatches(original, kp_1, image_to_compare, kp_2, good_points, None)cv2.waitKey(0)cv2.destroyAllWindows()接下来，我添加了一个 for 循环，与上面的代码执行相同的操作，但针对 5 个图像。这个想法是，它获取 imageOld_1.jpg 并与 imageNew_1.jpg、imageOld_2.jpg 进行比较，并与 imageNew_2.jpg 进行比较...下面的代码比较第一对图像 imageOld_1.jpg 和 imageNew_1.jpg（我得到结果）但由于某种原因，它不会继续循环遍历其他图像，即比较 imageOld_2.jpg 和 imageNew_2.jpg、比较 imageOld_3.jpg 和 imageNew_3.jpg...我收到以下错误，我做错了什么？

Answer 1

您需要组合两个列表来比较相同的图像。

您可以使用zip：

import cv2

a = [1]

b = [1]

for i, j in zip(a, b):

    original = cv2.imread("image_old/imageOld_" + str(i) + ".jpg")

    image_to_compare = cv2.imread("image_new/imageNew_" + str(j) + ".jpg")

    image1 = original.shape

    image2 = image_to_compare.shape

    if original.shape == image_to_compare.shape:

        print("The images " + str(i) + " have same size and channels")

        print("Diffing page " + str(i) + " and " + str(j) + " of both pdfs")

        difference = cv2.subtract(original, image_to_compare)

        r, g, b = cv2.split(difference)

        cv2.imshow("difference", cv2.resize(difference, None, fx=0.3, fy=0.3))

        if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:

            print("The images are completely Equal")

        else:

            print("The images are not equal")

    sift = cv2.xfeatures2d.SIFT_create()

    kp_1, desc_1 = sift.detectAndCompute(original, None)

    kp_2, desc_2 = sift.detectAndCompute(image_to_compare, None)

    print("Keypoints of 1st image: " + str(len(kp_1)))

    print("Keypoints of 2nd image: " + str(len(kp_2)))

    index_params = dict(algorithm=0, trees=5)

    search_params = dict()

    flann = cv2.FlannBasedMatcher(index_params, search_params)

    matches = flann.knnMatch(desc_1, desc_2, k=2)

    good_points = []

    for m, n in matches:

        if m.distance < 0.6 * n.distance:

            good_points.append(m)

    print('The images have %d %s' % (len(good_points), "good points matches"))

    if len(kp_1) <= len(kp_2):

        number_keypoints = len(kp_1)

    else:

        number_keypoints = len(kp_2)

    percentage_similarity = len(good_points) / number_keypoints * 100

    print('Similarity %d %s' % (round((percentage_similarity)), "%\n"))

    result = cv2.drawMatches(original, kp_1, image_to_compare, kp_2, good_points, None)

cv2.waitKey(0)

cv2.destroyAllWindows()

结果：

The images 1 have same size and channels

Diffing page 1 and 1 of both pdfs

The images are completely Equal

Keypoints of 1st image: 8066

Keypoints of 2nd image: 8066

The images have 3117 good points matches

Similarity 39 %

imagesNew_1.jpg以下是我用于和的示例imagesOld_1.jpg