I have a simple problem, but I cannot find a good solution to it.
I want to take a NumPy 2D array which represents a grayscale image, and convert it to an RGB PIL image while applying some of the matplotlib colormaps.
I can get a reasonable PNG output by using the pyplot.figure.figimage command:
dpi = 100.0
w, h = myarray.shape[1]/dpi, myarray.shape[0]/dpi
fig = plt.figure(figsize=(w,h), dpi=dpi)
fig.figimage(sub, cmap=cm.gist_earth)
plt.savefig('out.png')
Although I could adapt this to get what I want (probably using StringIO do get the PIL image), I wonder if there is not a simpler way to do that, since it seems to be a very natural problem of image visualization. Let’s say, something like this:
colored_PIL_image = magic_function(array, cmap)
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Answer
Quite a busy one-liner, but here it is:
- First ensure your NumPy array,
myarray, is normalised with the max value at1.0. - Apply the colormap directly to
myarray. - Rescale to the
0-255range. - Convert to integers, using
np.uint8(). - Use
Image.fromarray().
And you’re done:
from PIL import Image from matplotlib import cm im = Image.fromarray(np.uint8(cm.gist_earth(myarray)*255))
with plt.savefig():
with im.save():
