How To Find Correlation Between Two Images Using Numpy

This is inspired by this question.

I'm trying to find correlation between two grayscale images using Numpy. Using SciPy's correlate2d we can find this. I have found Numpy's corrcoef but results are different when I compared with correlate2d. Hence this question- Is there correlate2d equivalent in Numpy?

As far as I can tell, this produces the same result as scipy.correlate2d(), where img1 and img2 are 2d arrays representing greyscale (i.e. single-channel) images:

import numpy as np

pad = np.max(img1.shape) // 2
fft1 = np.fft.fft2(np.pad(img1, pad))
fft2 = np.fft.fft2(np.pad(img2, pad))
prod = fft1 * fft2.conj()
result_full = np.fft.fftshift(np.fft.ifft2(prod))
corr = result_full.real[1+pad:-pad+1, 1+pad:-pad+1]

The single-pixel cropping adjustment is not very elegant but that's FFTs for you: fiddly.

I just want to say that scipy is perfectly fine to use and I strongly recommend it. Having said that, this approach does seem to be a lot faster for the single case I tried.