How to do alpha compositing with a list of RGBA data in numpy arrays?

问题

Following this formula for alpha blending two color values, I wish to apply this to n numpy arrays of rgba image data (though the expected use-case will, in practice, have a very low upper bound of arrays, probably > 5). In context, this process will be constrained to arrays of identical shape.

I could in theory achieve this through iteration, but expect that this would be computationally intensive and terribly inefficient.

What is the most efficient way to apply a function between two elements in the same position between two arrays across the entire array?

A loose example:

# in context, the numpy arrays come from here, as either numpy data in the 
# first place or a path
def import_data(source):
    # first test for an extant numpy array
    try:
        assert(type(source) is np.ndarray)
        data = source
    except AssertionError:
        try:
            exists(source)
            data = add_alpha_channel(np.array(Image.open(source)))
        except IOError:
            raise IOError("Cannot identify image data in file '{0}'".format(source))
        except TypeError:
                raise TypeError("Cannot identify image data from source.")

    return data

# and here is the in-progress method that will, in theory composite the stack of 
# arrays; it context this is a bit more elaborate; self.width & height are just what  
# they appear to be—-the final size of the composited output of all layers

def render(self):
        render_surface = np.zeros((self.height, self.width, 4))
        for l in self.__layers:  
            foreground = l.render() # basically this just returns an np array
            # the next four lines just find the regions between two layers to 
            # be composited
            l_x1, l_y1 = l.origin
            l_x2 = l_x1 + foreground.shape[1]
            l_y2 = l_y1 + foreground.shape[0]
            background = render_surface[l_y1: l_y2, l_x1: l_x2]

            # at this point, foreground & background contain two identically shaped 
            # arrays to be composited; next line is where the function i'm seeking 
            # ought to go
            render_surface[l_y1: l_y2, l_x1: l_x2] = ?

回答1:

Starting with these two RGBA images:

I implemented the formula you linked to and came up with this:

#!/usr/local/bin/python3
from PIL import Image
import numpy as np

# Open input images, and make Numpy array versions
src  = Image.open("a.png")
dst  = Image.open("b.png")
nsrc = np.array(src, dtype=np.float)
ndst = np.array(dst, dtype=np.float)

# Extract the RGB channels
srcRGB = nsrc[...,:3]
dstRGB = ndst[...,:3]

# Extract the alpha channels and normalise to range 0..1
srcA = nsrc[...,3]/255.0
dstA = ndst[...,3]/255.0

# Work out resultant alpha channel
outA = srcA + dstA*(1-srcA)

# Work out resultant RGB
outRGB = (srcRGB*srcA[...,np.newaxis] + dstRGB*dstA[...,np.newaxis]*(1-srcA[...,np.newaxis])) / outA[...,np.newaxis]

# Merge RGB and alpha (scaled back up to 0..255) back into single image
outRGBA = np.dstack((outRGB,outA*255)).astype(np.uint8)

# Make into a PIL Image, just to save it
Image.fromarray(outRGBA).save('result.png')

Output image

来源：https://stackoverflow.com/questions/60398939/how-to-do-alpha-compositing-with-a-list-of-rgba-data-in-numpy-arrays

标签

python

arrays

numpy

multidimensional-array

alphablending