# Tk_perlin_noise2.py

# Tk_perlin_noise2.py

from Tkinter import *
from PIL import Image, ImageTk
import random
import math
import time

root = Tk()
root.title("Tk Perlin Noise")
ww = 400
hh = 400
root.geometry("%dx%d+-10+0"%(ww,hh))
canvas = Canvas(root, width=ww, height=hh)
canvas.pack()

dirs = [(math.cos(a * 2.0 * math.pi / 256),
         math.sin(a * 2.0 * math.pi / 256))
         for a in range(256)]

def noise(x, y, per):
    def surflet(gridX, gridY):
        distX, distY = abs(x-gridX), abs(y-gridY)
        polyX = 1 - 6*distX**5 + 15*distX**4 - 10*distX**3
        polyY = 1 - 6*distY**5 + 15*distY**4 - 10*distY**3
        hashed = perm[perm[int(gridX)%per] + int(gridY)%per]
        grad = (x-gridX)*dirs[hashed][0] + (y-gridY)*dirs[hashed][1]
        return polyX * polyY * grad
    intX, intY = int(x), int(y)
    return (surflet(intX+0, intY+0) + surflet(intX+1, intY+0) +
            surflet(intX+0, intY+1) + surflet(intX+1, intY+1))

def fBm(x, y, per, octs):
    val = 0
    for o in range(octs):
        val += 0.5**o * noise(x*2**o, y*2**o, per*2**o)
    return int(val*256)

freq, octs = 1/32.0, 5

perm = range(256)
perm += perm

while 1:
    data = []
    random.shuffle(perm)
    for y in range(hh):
        for x in range(ww):
            data.append(fBm(x*freq, y*freq, int(ww*hh*freq), octs))

    img = Image.new("L", (ww, hh))

    img.putdata(data)

    imgTk = ImageTk.PhotoImage(img)
    canvas.create_image(0, 0, anchor=NW, image=imgTk)
    canvas.update()