当涉及到特征匹配时，存在若干特征匹配算法。ORB（Oriented FAST and Rotated BRIEF）算法比SIFT特征匹配算法快两个数量级。为了比较2个图像的特征，您可以使用它如下，

import numpy as np
import cv2

    
# Read the query image as query_img
# and train image This query image
# is what you need to find in train image
# Save it in the same directory
# with the name image.jpg
query_img = cv2.imread('query.jpg')
train_img = cv2.imread('train.jpg')

# Convert it to grayscale
query_img_bw = cv2.cvtColor(query_img,cv2.COLOR_BGR2GRAY)
train_img_bw = cv2.cvtColor(train_img, cv2.COLOR_BGR2GRAY)

# Initialize the ORB detector algorithm
orb = cv2.ORB_create()

# Now detect the keypoints and compute
# the descriptors for the query image
# and train image
queryKeypoints, queryDescriptors = orb.detectAndCompute(query_img_bw,None)
trainKeypoints, trainDescriptors = orb.detectAndCompute(train_img_bw,None)

# Initialize the Matcher for matching
# the keypoints and then match the
# keypoints
matcher = cv2.BFMatcher()
matches = matcher.match(queryDescriptors,trainDescriptors)

# draw the matches to the final image
# containing both the images the drawMatches()
# function takes both images and keypoints
# and outputs the matched query image with
# its train image
final_img = cv2.drawMatches(query_img, queryKeypoints,
train_img, trainKeypoints, matches[:20],None)

final_img = cv2.resize(final_img, (1000,650))

# Show the final image
cv2.imshow("Matches", final_img)
cv2.waitKey(3000)

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