显示sift的匹配时间和匹配正确率的代码

以下是一个简单的示例代码，用于显示SIFT的匹配时间和匹配正确率：

import cv2
import numpy as np

# Load images
img1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)

# Create SIFT detector
sift = cv2.xfeatures2d.SIFT_create()

# Detect keypoints and compute descriptors
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# Create BFMatcher object
bf = cv2.BFMatcher()

# Match descriptors
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

# Calculate matching time
start_time = cv2.getTickCount()
bf.knnMatch(des1, des2, k=2)
end_time = cv2.getTickCount()
matching_time = (end_time - start_time) / cv2.getTickFrequency()

# Calculate matching accuracy
accuracy = len(good_matches) / len(matches) * 100

# Draw matches
img3 = cv2.drawMatches(img1, kp1, img2, kp2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

# Display results
cv2.imshow('Matches', img3)
print('Matching time: %.2f seconds' % matching_time)
print('Matching accuracy: %.2f%%' % accuracy)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，匹配时间和匹配正确率都取决于所使用的图像和算法。在实际应用中，您可能需要对算法进行优化和调整，以实现更好的性能和准确性。