Is there a convenient way to apply a lookup table to a large array in numpy?

Question

I’ve got an image read into numpy with quite a few pixels in my resulting array.

I calculated a lookup table with 256 values. Now I want to do the following:

Answer 1

TheodrosZelleke's answer in correct, but I just wanted to add a little undocumented wisdom to it. Numpy provides a function, np.take, which according to the documentation "does the same thing as fancy indexing."

Well, almost, but not quite the same:

>>> import numpy as np
>>> lut = np.arange(256)
>>> image = np.random.randint(256, size=(5000, 5000))
>>> np.all(lut[image] == np.take(lut, image))
True
>>> import timeit
>>> timeit.timeit('lut[image]',
...               'from __main__ import lut, image', number=10)
4.369504285407089
>>> timeit.timeit('np.take(lut, image)',
...               'from __main__ import np, lut, image', number=10)
1.3678052776554637

np.take is about 3x faster! In my experience, when using 3D luts to convert images from RGB to other color spaces, adding logic to convert the 3D look-up to a 1D flattened look-up allows a x10 speed up.