best similarity measure for images with different angles I want to compare different images (where the images are of the same setup but the angles with which the images are taken are different). I want to obtain some sort of similarity score. I tried using some libraries like
from image_similarity_measures.quality_metrics import rmse, psnr, fsim
in_img1 = cv2.imread(&quot;./IMG_4835.jpg&quot;)
in_img2= cv2.imread(&quot;./IMG_4836.jpg&quot;)
out_rmse = rmse(in_img1, in_img2)
out_fsim = fsim(in_img1, in_img2)
print(out_rmse)
print(out_fsim)

but none of the metrics seem to provide good results in my case. According to the description of different metrics given here, most of them check for contract/signal to error reconstruction ratio etc etc. https://up42.com/blog/tech/image-similarity-measures
What similarity metric or image comparison method in general would suit well in my case?
A brute-force method is simply to try all rotation angles and decide if 2 images are a rotation of one another.
However, there are features (eg fourier coefficients) which are rotation-invariant. So comparing these rotation-invariant features is a similarity metric for determining if 2 images are a rotation of one another.
References:

Rotation Invariance in Images
Rotation invariant indexing of shapes and line drawings
Which Transformation, Or Similarity Metric, Is Rotation, Shift and Scale Invariant?