A little bit off topic but useful is the pythonic numpy approach. Its robust and fast but just does compare pixels and not the objects or data the picture contains (and it requires images of same size and shape):
A very simple and fast approach to do this without openCV and any library for computer vision is to norm the picture arrays by
import numpy as np
picture1 = np.random.rand(100,100)
picture2 = np.random.rand(100,100)
picture1_norm = picture1/np.sqrt(np.sum(picture1**2))
picture2_norm = picture2/np.sqrt(np.sum(picture2**2))
After defining both normed pictures (or matrices) you can just sum over the multiplication of the pictures you like to compare:
1) If you compare similar pictures the sum will return 1:
In[1]: np.sum(picture1_norm**2)
Out[1]: 1.0
2) If they aren't similar, you'll get a value between 0 and 1 (a percentage if you multiply by 100):
In[2]: np.sum(picture2_norm*picture1_norm)
Out[2]: 0.75389941124629822
Please notice that if you have colored pictures you have to do this in all 3 dimensions or just compare a greyscaled version. I often have to compare huge amounts of pictures with arbitrary content and that's a really fast way to do so.