Perlin Noise Generator using Bilinear Interpolation.
Python, 45 lines
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Copy to clipboard1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # Perlin Noise Generator
# http://en.wikipedia.org/wiki/Perlin_noise
# http://en.wikipedia.org/wiki/Bilinear_interpolation
# FB36 - 20130222
import random
import math
from PIL import Image, ImageDraw
imgx = 800; imgy = 600 # image size
image = Image.new("RGB", (imgx, imgy))
draw = ImageDraw.Draw(image)
pixels = image.load()
octaves = int(math.log(max(imgx, imgy), 2.0))
persistence = random.random()
imgAr = [[0.0 for i in range(imgx)] for j in range(imgy)] # image array
totAmp = 0.0
for k in range(octaves):
freq = 2 ** k
amp = persistence ** k
totAmp += amp
# create an image from n by m grid of random numbers (w/ amplitude)
# using Bilinear Interpolation
n = freq + 1; m = freq + 1 # grid size
ar = [[random.random() * amp for i in range(n)] for j in range(m)]
nx = imgx / (n - 1.0); ny = imgy / (m - 1.0)
for ky in range(imgy):
for kx in range(imgx):
i = int(kx / nx); j = int(ky / ny)
dx0 = kx - i * nx; dx1 = nx - dx0
dy0 = ky - j * ny; dy1 = ny - dy0
z = ar[j][i] * dx1 * dy1
z += ar[j][i + 1] * dx0 * dy1
z += ar[j + 1][i] * dx1 * dy0
z += ar[j + 1][i + 1] * dx0 * dy0
z /= nx * ny
imgAr[ky][kx] += z # add image layers together
# paint image
for ky in range(imgy):
for kx in range(imgx):
c = int(imgAr[ky][kx] / totAmp * 255)
pixels[kx, ky] = (c, c, c)
label = "Persistence = " + str(persistence)
draw.text((0, 0), label, (0, 255, 0)) # write to top-left using green color
image.save("PerlinNoise.png", "PNG")
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Perlin Noise is created from N images. Each image is created from a random grid using any interpolation method (bilinear, bicubic etc.). In each successive image, grid size (frequency) exponentially increases and max/average height (z) of grid points exponentially decreases. In the end, all images are added together.
The final image is pretty similar to a Plasma Fractal.