Arbitrarily-sized image convolution using SciPy

convolve.py

import numpy

from PIL import Image
from sys import argv

from scipy import ndimage

target = Image.open(argv[1])
kernel = Image.open(argv[2])

# Kernel [must] be grayscale.
assert kernel.mode == 'L'

target_np = numpy.asarray(target)
kernel_np = numpy.asarray(kernel)

kernel_np = kernel_np / numpy.sum(numpy.sum(kernel_np, axis=0), axis=0)

for index, band in enumerate(target.getbands()):
    ndimage.convolve(
        target_np[:,:,index],
        kernel_np,
        output=target_np[:,:,index])
    
Image.fromarray(numpy.uint8(target_np)) \
     .save(argv[3])