map grayscale values to RGB values in image

Let us consider a grayscale value with values in the range of [0, 255]. How can we efficiently map each value to a RGB value?

So far, I have come up with the following implementation:

# function for colorizing a label image:
def label_img_to_color(img):
    label_to_color = {
    0: [128, 64,128],
    1: [244, 35,232],
    2: [ 70, 70, 70],
    3: [102,102,156],
    4: [190,153,153],
    5: [153,153,153],
    6: [250,170, 30],
    7: [220,220,  0],
    8: [107,142, 35],
    9: [152,251,152],
    10: [ 70,130,180],
    11: [220, 20, 60],
    12: [255,  0,  0],
    13: [  0,  0,142],
    14: [  0,  0, 70],
    15: [  0, 60,100],
    16: [  0, 80,100],
    17: [  0,  0,230],
    18: [119, 11, 32],
    19: [81,  0, 81]
    }

img_height, img_width = img.shape

img_color = np.zeros((img_height, img_width, 3))
for row in range(img_height):
    for col in range(img_width):
        label = img[row, col]
        img_color[row, col] = np.array(label_to_color[label])
return img_color

However, as you can see it is not efficient as there are two "for" loops.

This question was also asked in Convert grayscale value to RGB representation?, but no efficient implementation was suggested.

A more efficient way of doing that instead of a double for loop over all pixels could be:

rgb_img = np.zeros((*img.shape, 3)) 
for key in label_to_color.keys():
    rgb_img[img == key] = label_to_color[key]