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perlin.py
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perlin.py
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import torch
import math
import numpy as np
def lerp_np(x,y,w):
fin_out = (y-x)*w + x
return fin_out
def generate_fractal_noise_2d(shape, res, octaves=1, persistence=0.5):
noise = np.zeros(shape)
frequency = 1
amplitude = 1
for _ in range(octaves):
noise += amplitude * generate_perlin_noise_2d(shape, (frequency*res[0], frequency*res[1]))
frequency *= 2
amplitude *= persistence
return noise
def generate_perlin_noise_2d(shape, res):
def f(t):
return 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 = np.mgrid[0:res[0]:delta[0], 0:res[1]:delta[1]].transpose(1, 2, 0) % 1
# Gradients
angles = 2 * np.pi * np.random.rand(res[0] + 1, res[1] + 1)
gradients = np.dstack((np.cos(angles), np.sin(angles)))
g00 = gradients[0:-1, 0:-1].repeat(d[0], 0).repeat(d[1], 1)
g10 = gradients[1:, 0:-1].repeat(d[0], 0).repeat(d[1], 1)
g01 = gradients[0:-1, 1:].repeat(d[0], 0).repeat(d[1], 1)
g11 = gradients[1:, 1:].repeat(d[0], 0).repeat(d[1], 1)
# Ramps
n00 = np.sum(grid * g00, 2)
n10 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1])) * g10, 2)
n01 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1] - 1)) * g01, 2)
n11 = np.sum(np.dstack((grid[:, :, 0] - 1, grid[:, :, 1] - 1)) * g11, 2)
# Interpolation
t = f(grid)
n0 = n00 * (1 - t[:, :, 0]) + t[:, :, 0] * n10
n1 = n01 * (1 - t[:, :, 0]) + t[:, :, 0] * n11
return np.sqrt(2) * ((1 - t[:, :, 1]) * n0 + t[:, :, 1] * n1)
def rand_perlin_2d_np(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 = np.mgrid[0:res[0]:delta[0], 0:res[1]:delta[1]].transpose(1, 2, 0) % 1
angles = 2 * math.pi * np.random.rand(res[0] + 1, res[1] + 1)
gradients = np.stack((np.cos(angles), np.sin(angles)), axis=-1)
tt = np.repeat(np.repeat(gradients,d[0],axis=0),d[1],axis=1)
tile_grads = lambda slice1, slice2: np.repeat(np.repeat(gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]],d[0],axis=0),d[1],axis=1)
dot = lambda grad, shift: (
np.stack((grid[:shape[0], :shape[1], 0] + shift[0], grid[:shape[0], :shape[1], 1] + shift[1]),
axis=-1) * grad[:shape[0], :shape[1]]).sum(axis=-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) * lerp_np(lerp_np(n00, n10, t[..., 0]), lerp_np(n01, n11, t[..., 0]), t[..., 1])
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