73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
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import random
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import math
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from PIL import Image
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""" Produce a series of Perlin noise images that tile with each other
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and wrap at the end of the sequence. The pixel value are interpreted as
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heights and colour values then calculated to represent a normal map.
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"""
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# values used by the noise function
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perm = list(range(256))
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random.seed(1)
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random.shuffle(perm)
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perm += perm
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dirs = [(math.cos(a * 2.0 * math.pi / 256),
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math.cos((a+85) * 2.0 * math.pi / 256),
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math.cos((a+170) * 2.0 * math.pi / 256))
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for a in range(256)]
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def noise(x, y, z, per):
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def surflet(gridX, gridY, gridZ):
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distX, distY, distZ = abs(x-gridX), abs(y-gridY), abs(z-gridZ)
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polyX = 1 - 6*distX**5 + 15*distX**4 - 10*distX**3
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polyY = 1 - 6*distY**5 + 15*distY**4 - 10*distY**3
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polyZ = 1 - 6*distZ**5 + 15*distZ**4 - 10*distZ**3
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hashed = perm[perm[perm[int(gridX)%per] + int(gridY)%per] + int(gridZ)%per]
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grad = (x-gridX)*dirs[hashed][0] + (y-gridY)*dirs[hashed][1] + (z-gridZ)*dirs[hashed][2]
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return polyX * polyY * polyZ * grad
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intX, intY, intZ = int(x), int(y), int(z)
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return (surflet(intX+0, intY+0, intZ+0) + surflet(intX+0, intY+0, intZ+1) + surflet(intX+0, intY+1, intZ+0) +
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surflet(intX+0, intY+1, intZ+1) + surflet(intX+1, intY+0, intZ+0) + surflet(intX+1, intY+0, intZ+1) +
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surflet(intX+1, intY+1, intZ+0) + surflet(intX+1, intY+1, intZ+1))
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def fBm(x, y, z, per, octs):
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val = 0
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for o in range(octs):
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val += 0.5**o * noise(x*2**o, y*2**o, z*2**o, per*2**o)
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return val
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""" variables used by the noise file generator:
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num -- images in sequence
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size -- size to resize each image to
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freq -- frequency of noise
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octs -- octaves of noise
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"""
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num = 128
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size = 32
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freq = 1/8.0
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octs = 5
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###################################################
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for z in range (num):
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data = []
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print z
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for y in range(num):
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for x in range(num):
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data.append(fBm(x*freq, y*freq, z*freq, int(num*freq), octs))
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im_data = []
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for y in range(num):
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for x in range(num):
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rVal = 0.5 + (data[((y+1)%num)*num + x] - data[y*num + x])*0.5
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gVal = 0.5 + (data[y*num + (x+1)%num] - data[y*num + x])*0.5
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bVal = 1.0/math.sqrt(1.0 + rVal**2 + gVal**2)
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im_data.append((int(255*rVal), int(255*gVal), int(255*bVal), 255))
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im = Image.new("RGBA", (num, num))
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im.putdata(im_data, 1.0, 0.0)
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im = im.resize((size, size))
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im.save("n_norm"+format(z, '03d')+".png")
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print "finished doing it"
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