Modulations, Modulations, Modulations

Here is yet another experiment in generating noisy images. This time the pattern is obvious, regular. The point of all this is to start with noise and modulate the noise until I get something "visually interesting." Below the code I wrote to generate the image is the resultant image itself.

	import time
	import random
	import numpy as np
	from PIL import Image
	def noisefield():
	im = Image.new("P", (256, 256))
	for x in range(256):
	for y in range(256):
	im.putpixel((x, y), nixel.next())
	im.save(FILEPATH+time.strftime('%Y%m%d%H%M%S')+".png")
	im.show()
	ls = [x**2 for x in range(17)]
	ls = ls*4096
	ls = ls[0:65536]
	l2 = []
	for i in range(65536):
	l2.append(random.choice([0,255]))
	len(l2)
	65536
	ls = np.array(ls)
	l2 = np.array(l2)
	l3 = ls-l2
	nixel = iter(l3)
	noisefield()

view raw noisebitons.py hosted with ❤ by GitHub

Noise field by A.G. (c) 2017. All Rights Reserved.