将灰度值映射到图像中的RGB值

Question

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.

Answer 1

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]