[aktiur]

More generally, for an image im with shape (width, height, channels) and a square transformation matrix M of shape (channels, channels), you can do :

  res = np.dot(im, M.T)

It will work with affine transformation as well if you add a 1 component to every pixel. It will also work with higher dimensional images if I'm not mistaken.

[onalark]

Agreed, for some reason when I was looking at this last night I thought I couldn't use broadcasting, I've added the example to the gist: https://gist.github.com/ahmadia/c1f8be119f3cb2d2b8e5

Would you believe that Numba is 4 times faster for the sort of simple transformations described in the blog post?

(See aktiur's response below, some performance gains come from avoiding a copy)

[aktiur]

Numba is indeed pretty impressive, but you're not comparing exactly the same thing with this code.

In the Numba case, you're basically modifying the image in place: it means no allocating a new array, no full copying. However, your pure-numpy code basically creates a new array (the result of np.dot) before copying it back entirely in image.

If you write the two functions so that they both return a new numpy array and do not touch the original one, the time difference drops from 4 times faster to 2.5 times faster. That's still an impressive difference, but at the loss of a bit of flexibility.

https://gist.github.com/aktiur/e1cddee8f699ded49824

N.B.: numpy.dot does not use broadcasting, i.e. it does not allocate a temporary array to extend the smaller one. The function handles n-dimensional arrays by summing on the last index of the first array, and on the second last of the second array.