使用for Loop python遍历两个目录

我试图比较两个PDF。我的方法包括转换PDF和比较相应页面的图像。我最初编写了一个简单的代码，比较了与下面第一个代码相对应的两个图像，该代码工作时没有任何问题

import cv2
import numpy as np

original = cv2.imread("image_old/imageOld_1.jpg")
image_to_compare = cv2.imread("image_new/imageNew_1.jpg")

image1 = original.shape
image2 = image_to_compare.shape

if 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 * 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()

---

您需要合并这两个列表以比较相同的图像

您可以使用

zip

：

导入cv2
a=[1]
b=[1]
对于i，j（a，b）：
原件=cv2.imread（“图像旧/图像旧”+str（i）+.jpg”）
image\u to\u compare=cv2.imread（“image\u new/imageNew”+str（j）+.jpg”）
image1=原始形状
image2=图像与形状的比较
如果original.shape==image\u to\u compare.shape：
打印（“图像”+str（i）+“具有相同的大小和通道”）
打印（“两个PDF的差异页”+str（i）+”和“+str（j）+”）
差值=cv2.相减（原始、图像比较）
r、 g，b=cv2.分割（差异）
cv2.imshow（“差异”，cv2.resize（差异，无，fx=0.3，fy=0.3））
如果cv2.countNonZero（b）==0且cv2.countNonZero（g）==0且cv2.countNonZero（r）==0：
打印（“图像完全相等”）
其他：
打印（“图像不相等”）
sift=cv2.xfeature2d.sift_create（）
kp_1，desc_1=筛选、检测和计算（原始，无）
kp_2，desc_2=sift.detectAndCompute（图像比较，无）
打印（“第一张图像的关键点：+str（透镜（kp_1）））
打印（“第二幅图像的关键点：+str（len（kp_2）））
索引参数=dict（算法=0，树=5）
搜索参数=dict（）
flann=cv2.FlannBasedMatcher（索引参数、搜索参数）
匹配=法兰。knnMatch（描述1，描述2，k=2）
好分数=[]
对于匹配中的m，n：
如果m.距离<0.6*n.距离：
好的分数。追加（m）
打印（'图像有%d%s'（len（好点），“好点匹配”））
如果len（kp_1）
import cv2
import numpy as np

a=[1,2,3,4,5]
b=[1,2,3,4,5]

for i in a:
    original = cv2.imread("image_old/imageOld_"+str(i)+".jpg")
    for j in b:
        if a == b :
            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()

 DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
  if a == b :