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Perlin noise in PyTorch
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# ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py | |
import torch | |
import math | |
def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3): | |
delta = (res[0] / shape[0], res[1] / shape[1]) | |
d = (shape[0] // res[0], shape[1] // res[1]) | |
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1 | |
angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1) | |
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1) | |
tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1) | |
dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1] ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1) | |
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]) | |
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]) | |
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]) | |
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]) | |
t = fade(grid[:shape[0], :shape[1]]) | |
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]) | |
def rand_perlin_2d_octaves(shape, res, octaves=1, persistence=0.5): | |
noise = torch.zeros(shape) | |
frequency = 1 | |
amplitude = 1 | |
for _ in range(octaves): | |
noise += amplitude * rand_perlin_2d(shape, (frequency*res[0], frequency*res[1])) | |
frequency *= 2 | |
amplitude *= persistence | |
return noise | |
if __name__ == '__main__': | |
import matplotlib.pyplot as plt | |
noise = rand_perlin_2d((256, 256), (8, 8)) | |
plt.figure() | |
plt.imshow(noise, cmap='gray', interpolation='lanczos') | |
plt.colorbar() | |
plt.savefig('perlin.png') | |
plt.close() | |
noise = rand_perlin_2d_octaves((256, 256), (8, 8), 5) | |
plt.figure() | |
plt.imshow(noise, cmap='gray', interpolation='lanczos') | |
plt.colorbar() | |
plt.savefig('perlino.png') | |
plt.close() |
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