I was trying to understand how convolutional network works with filters, I decided to replicate it.

ApplyFilter.py

from PIL import Image, ImageDraw
import random
from sys import argv

if len(argv) != 3:
    print("<<script>> Image outputDir")
    exit(1)
image_name = argv[1]
out = argv[2]


def random_vertical_filter():
    base = 1000
    start_range = 0.8
    to = int(start_range * base)
    a = (random.randint(0, to) + ((1 - start_range) * base)) / 1000
    b = (random.randint(0, to) + ((1 - start_range) * base)) / 1000
    c = (random.randint(0, to) + ((1 - start_range) * base)) / 1000
    return [
          [1-a, 0, -a],
          [1-b, 0, -b],
          [1-c, 0, -c]
          ]

def random_horizontal_filter():
    base = 1000
    start_range = 0.8
    to = int(start_range * base)
    a = (random.randint(0, to) + ((1 - start_range) * base)) / 1000
    b = (random.randint(0, to) + ((1 - start_range) * base)) / 1000
    c = (random.randint(0, to) + ((1 - start_range) * base)) / 1000
    return [
                     [1-a, 1-b, 1-c],
                     [0,0,0],
                     [-a, -b, -c]
                     ]

#filter = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
#filter = [[.5, .5, .5], [0, 0, 0], [-.5, -.5, -.5]]
#filter = [[-1, -1, -1], [0, 0, 0], [1, 1, 1]]
#filter = [[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]
filter = [[ 0, -1, -1],
          [ 1,  0, -1],
          [ 1,  1, 0]]

#filter = random_horizontal_filter()


im = Image.open(image_name)
pix = im.load()

outim = Image.new('RGB', im.size)
outdraw = ImageDraw.Draw(outim)
outpix = outim.load()
for y in range(1, im.size[1]-1):
    for x in range(1, im.size[0]-1):
        all = 0
        for iy in range(-1, 2, 1):
            for ix in range(-1, 2, 1):
                zx = int(sum(pix[x + ix, y + iy])/3)
                zx *= filter[ix][iy]
                all += int(zx)
        outpix[x, y] = (all, all, all)


outim.save(out + "/" + str(filter) + ".png")