Color Perceptual Image Hashing
I found a couple of ways to do this.
I ended up using a Mean Squared Error function that I wrote myself:
def mse(reference, query):
    return (((reference).astype("double")-(query).astype("double"))**2).mean()

Until, upon later tinkering I found a function that seemed to do something similar (compare image similarity, bit by bit), but a good amount faster:
def linalg_norm(reference, query):
    return np.linalg.norm(reference-query)

I have no theoretical knowledge of what the second function does, however practically it doesn't matter. I am not averse to learning how it works.
I've been trying to write on a fast (ish) image matching program which doesn't match rotated or scale deformed image, in Python.
The goal is to be able to find small sections of an image that are similar to other images in color features, but dissimilar if rotated or warped.
I found out about perceptual image hashing, and I've had a look at the ImageHash module for Python and SSIM, however most of the things I've looked at do not have in color as a major factor, ie they average the color and only work in one channel, and phash in particular doesn't care if images are rotated.
I would like to be able to have an algorithm which would match images which at a distance would appear the same (but which would not necessarily need to be the same image).
Can anyone suggest how I would structure and write such an algorithm in python? or suggest a function which would be able to compare images in this manner?