# Tk_grays_perlin_noise.py

# Tk_grays_perlin_noise.py

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

wt = 256
ht = 256
root = Tk()
root.title("Grays Perlin Noise")
root.geometry("%dx%d+0+0"%(wt,ht))
canvas = Canvas(root, width=wt, height=ht)
canvas.grid()

# create new image
img = Image.new("RGB",(wt, ht), "white")


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):
    # Perlin noise is generated from a summation of little "surflets" which are the product of a randomly oriented
    # gradient and a separable polynomial falloff function.
    def surflet(gridX, gridY):
        distX, distY = abs(x-gridX), abs(y-gridY)
        polyX = 1 - 6*distX**5 + 15*distX**4 - 10*distX**3  # polynomial falloff function
        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 val

while 1:
    perm = range(256)
    random.shuffle(perm)
    perm += perm
    xy = []
    size, freq, octs = 256, 1/32.0, 5
    for y in range(size):
        for x in range(size):
            zzz = fBm(x*freq, y*freq, int(size*freq), octs)
            xy.extend([tuple([int(zzz*128)+128])*3])
    img.putdata(xy)
    imgTk = ImageTk.PhotoImage(img)
    #time.sleep(0.02)
    canvas.create_image(-2, 0, anchor=NW, image=imgTk)
    root.update()