Fast way to recolour an indexed image based on another array

Question

I have a an indexed image bins consisting of multiple regions. 0 is background and other positive value is a region.

I want to fill in values for each region based on another array, eg:

bins = # image of shape (height, width), type int
ids = np.array([1, 5, ... ]) # region ids
values = np.array([0.1, ...]) # Values for each region, same shape as ids
img = np.empty(bins.shape, 'float32')
img[:] = np.nan
for i, val in zip(ids, values):
    img[bins == i + 1] = val

but this loop is super slow in python. Is there a way to write it in a nice numpy way?

Thanks in advance!

Answer 1

Here's an approach -

out = np.take(values, np.searchsorted(ids, bins-1))
out.ravel()[~np.in1d(bins,ids+1)] = np.nan

Please note that this assumes ids to be sorted. If that's not the case, we need to use the optional argument sorter with np.searchsorted .

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Here's another one with a very similar idea, but as a minor tweak using initialization and limiting the use of np.searchsorted only on the valid elements -

out = np.full(bins.shape, np.nan)
mask = np.in1d(bins,ids+1)
out.ravel()[mask] = np.take(values, np.searchsorted(ids+1, bins.ravel()[mask]))