How do I compute similarity of two images using SIFT/evaluate SIFT results?

Question

I want to compute similarities between two images using SIFT. I have managed to compute matches and visualize it as seen in the image below. I have one image of the Eiffel tower and another image of a heavily modified Eiffel tower. To me this match looks good but I don't know what metrics, equations or algorithms to use to compute the similarity or to evaluate the match.

I am using the following code to compute the matching.

import cv2

# Read images
img1 = cv2.imread("eiffel_normal.jpeg")
img2 = cv2.imread("eiffel_rotated.jpeg")

#sift
sift = cv2.SIFT_create()

# Get keypoints and descriptors
keypoints_1, descriptors_1 = sift.detectAndCompute(img1, None)
keypoints_2, descriptors_2 = sift.detectAndCompute(img2, None)

#feature matching
bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True)

matches = bf.match(descriptors_1,descriptors_2)
matches = sorted(matches, key=lambda x:x.distance)

# Visualize the results
img3 = cv2.drawMatches(img1, keypoints_1, img2, keypoints_2, matches[:30], img2, flags=2)
plt.imshow(img3)
plt.show()

I've tried:

def calculateScore(matches, key_1_len, key_2_len):
    return 100 * (matches/min(key_1_len, key_2_len))

similar_regions = [i for i in matches if i.distance < 50]
sift_score= calculateScore(len(matches), len(keypoints_1), len(keypoints_2))
sift_acc = len(similar_regions)/len(matches)

but both sift_score and sift_acc gives bad results.

The evaluator must take in account: Scaling, Rotation and translation

Any ideas?