Convert numpy array to RGB img with custom pallete

Question

I want to convert a numpy.ndarray:

out = [[12 12 12 ..., 12 12 12]
     [12 12 12 ..., 12 12 12]
     [12 12 12 ..., 12 12 12]
     ...,
     [11 11 11 ..., 10 10 10]
     [11 11 11 ..., 10 10 10]
     [11 11 11 ..., 10 10 10]]

to RGB img.

The colors are taken from an array:

colors_pal = np.array(
           [0,0,128],      #ind 0
           [0,0,0],
           ....
           [255,255,255]], #ind 12
           dtype=np.float32)

So, for example, all the pixels with index 12 will be white (255,255,255).
The way I do it now is very slow (about 1.5 sec/img):

        data = np.zeros((out.shape[0],out.shape[1],3), dtype=np.uint8 )
        for x in range(0,out.shape[0]):
            for y in range(0,out.shape[1]):
                data[x,y] = colors_pal[out[x][y]]
        img = Image.fromarray(data)
        img.save(...)

what is the efficient way to do it faster?

filippo · Accepted Answer · 2018-05-14 10:01:22Z

2

You can just use the full image as index for the look-up table.

Something like data = colors_pal[out]

import numpy as np
import matplotlib.pyplot as plt
import skimage.data
import skimage.color

# sample image, grayscale 8 bits
img = skimage.color.rgb2gray(skimage.data.astronaut())
img = (img * 255).astype(np.uint8)
# sample LUT from matplotlib
lut = (plt.cm.jet(np.arange(256)) * 255).astype(np.uint8)

# apply LUT and display
plt.imshow(lut[img])
plt.show()

edited May 14, 2018 at 10:01

answered May 14, 2018 at 9:55

filippo

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