85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import numpy as np
|
|
from scipy.special import softmax
|
|
|
|
|
|
def interpolant(t):
|
|
return t * t * t * (t * (t * 6 - 15) + 10)
|
|
|
|
|
|
def generate_perlin_noise_2d(
|
|
shape, res, tileable=(False, False), interpolant=interpolant
|
|
):
|
|
"""Generate a 2D numpy array of perlin noise.
|
|
|
|
Args:
|
|
shape: The shape of the generated array (tuple of two ints).
|
|
This must be a multple of res.
|
|
res: The number of periods of noise to generate along each
|
|
axis (tuple of two ints). Note shape must be a multiple of
|
|
res.
|
|
tileable: If the noise should be tileable along each axis
|
|
(tuple of two bools). Defaults to (False, False).
|
|
interpolant: The interpolation function, defaults to
|
|
t*t*t*(t*(t*6 - 15) + 10).
|
|
|
|
Returns:
|
|
A numpy array of shape shape with the generated noise.
|
|
|
|
Raises:
|
|
ValueError: If shape is not a multiple of res.
|
|
"""
|
|
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)))
|
|
if tileable[0]:
|
|
gradients[-1, :] = gradients[0, :]
|
|
if tileable[1]:
|
|
gradients[:, -1] = gradients[:, 0]
|
|
gradients = gradients.repeat(d[0], 0).repeat(d[1], 1)
|
|
g00 = gradients[: -d[0], : -d[1]]
|
|
g10 = gradients[d[0] :, : -d[1]]
|
|
g01 = gradients[: -d[0], d[1] :]
|
|
g11 = gradients[d[0] :, d[1] :]
|
|
# Ramps
|
|
n00 = np.sum(np.dstack((grid[:, :, 0], grid[:, :, 1])) * 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 = interpolant(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)
|
|
|
|
|
|
class CoordsGenerator:
|
|
def __init__(self, x: int, y: int, factor: int = 500):
|
|
print(x, y, x // factor, y // factor)
|
|
self.noise = generate_perlin_noise_2d((x, y), (x // factor, y // factor))
|
|
self.noise_distribution = softmax(self.noise, axis=1)
|
|
|
|
def pick(self, n):
|
|
x, y = self.noise.shape
|
|
x = np.random.choice(x, size=n, replace=False)
|
|
y = [
|
|
np.random.choice(y, size=1, p=self.noise_distribution[x_, :], replace=False) for x_ in x
|
|
]
|
|
return x, y
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import matplotlib.pyplot as plt
|
|
|
|
# gen = CoordsGenerator(1920, 1080, threshold=0.85)
|
|
# x, y = gen.pick(1000)
|
|
# plt.scatter(x, y)
|
|
factor = 500
|
|
noise = generate_perlin_noise_2d((1080, 1920), (1080 // factor, 1920 // factor))
|
|
|
|
plt.matshow(noise, cmap="bwr")
|
|
plt.colorbar()
|
|
plt.show()
|