How to colourize (blending between a set of RGBA colour points) large grayscale images efficiently using NumPy

Question

I have made a recipe to colorize and add an alpha channel to grayscale images. It is my first attempt at using numpy. It is efficient enough with a 0.3 mega pixel test image (takes only 2.3 seconds on my 1.8GHz machine):

but with 7 mega pixel images it is too slow (more like a minute per image). How can I make the code more efficient? It is making a 256 numpy masks for every image which I guess is probably not the best way.

#!/usr/bin/env python3

from PIL import Image
import numpy


# a test image is loaded and converted to a numpy array
img = Image.open( 'lena.jpg' ).convert( 'RGBA' )
img = numpy.array( img )

# color point objects represent how pixels with a specific luminescence are to colourized
class ColorPoint():
    def __init__( self, luminescence=0, red=0, green=0, blue=0, alpha=255 ):
        self.luminescence = luminescence
        self.red = red
        self.green = green
        self.blue = blue
        self.alpha = alpha

# colour points are stored in a list, defining the colorization
color_points = []
color_points += [ ColorPoint( luminescence=0, red=255 ) ]
color_points += [ ColorPoint( luminescence=85, green=255 ) ]
color_points += [ ColorPoint( luminescence=170, blue=255 ) ]
color_points += [ ColorPoint( luminescence=255, alpha=0 ) ]

if color_points[0].luminescence!=0 or color_points[-1].luminescence!=255:
    print( 'color points do not span full luminescence range!' )
    sys.exit()

# red, green and blue, alpha values are read in from the numpy array
red, green, blue, alpha = img[:,:,0], img[:,:,1], img[:,:,2], img[:,:,3]

for luminescence in range( 256 ):
    # the luminescence value is either equal to that of a colour point or falls inbetween two
    cp = next((x for x in color_points if x.luminescence==luminescence), None)

    if( cp ):
        # the current luminescence value matches a color point exactly
        new_red = cp.red
        new_green = cp.green
        new_blue = cp.blue
        new_alpha = cp.alpha

    else:
        # find the color points which the the current luminescence value lies between
        start_cp = next((x for x in reversed(color_points) if x.luminescence<luminescence), None)
        end_cp = next((x for x in color_points if x.luminescence>luminescence), None)

        # this factor represents the position between the two colour points
        factor = ( luminescence - start_cp.luminescence ) / ( end_cp.luminescence - start_cp.luminescence )

        # new RGBA values are set based on the two colour points and the position between the two       
        new_red = start_cp.red + (end_cp.red-start_cp.red)*factor
        new_green = start_cp.green + (end_cp.green-start_cp.green)*factor
        new_blue = start_cp.blue + (end_cp.blue-start_cp.blue)*factor
        new_alpha = start_cp.alpha + (end_cp.alpha-start_cp.alpha)*factor


    # a mask is created for the current luminescence value used to apply the new values
    mask = ( red == luminescence ) & ( green == luminescence ) & ( blue == luminescence )
    img[:,:,:4][mask] = [ new_red, new_green, new_blue, new_alpha ]

# convert back to PIL image and show
img = Image.fromarray( img )
img.show()

Answer 1

You should be able to use one of the interpolation routines such as griddata . Here's an example, where I've used a random look-up table which corresponds to color_points in your code.

>>> from scipy.interpolate import griddata
>>> LUT = np.random.random_sample((256, 3))
>>> griddata(np.arange(256), LUT, image)

To use this in your code, you will need to build a 1d array with the gray/luminescence values and another 2d array with the corresponding rgb values. These are the first two parameters to griddata .