add Perlin noise as distribution

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julius 2024-08-22 15:52:32 +02:00
parent ba0834d729
commit dc3503a407
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2 changed files with 102 additions and 9 deletions

27
main.py
View File

@ -1,13 +1,11 @@
import io import io
import os from perlin import CoordsGenerator
import matplotlib
from typing import Literal from typing import Literal
import itertools import itertools
import logging import logging
import random import random
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import numpy as np import numpy as np
import uvicorn import uvicorn
from fastapi import FastAPI from fastapi import FastAPI
@ -90,12 +88,13 @@ marker_subs = {
@app.get("/speckles/") @app.get("/speckles/")
def make_wallpaper( def make_wallpaper(
speckle_colours: str, speckle_colours: str,
density: float | None = 0.12, density: float = 0.12,
size: float | None = 3, size: float = 3,
fileformat: str = "svg", fileformat: str = "svg",
orientation: str | None = "landscape", orientation: str = "landscape",
local: bool = False, local: bool = False,
markers: str | None = ".", markers: str = ".",
perlin: bool = True,
): ):
if fileformat not in MEDIA_TYPES: if fileformat not in MEDIA_TYPES:
return return
@ -126,15 +125,25 @@ def make_wallpaper(
ax.set_yticks([]) ax.set_yticks([])
ax.margins(0, 0) ax.margins(0, 0)
if perlin:
gen = CoordsGenerator(y, x)
for color, marker, size in itertools.product( for color, marker, size in itertools.product(
speckle_colours, speckle_colours,
markers, markers,
np.logspace(0, size, 10, base=np.exp(2)), np.logspace(0, size, 10, base=np.exp(2)),
): ):
marker = marker_subs.get(marker, marker) marker = marker_subs.get(marker, marker)
if perlin:
x_coords, y_coords = gen.pick(speckles_per_colour)
else:
x_coords, y_coords = (
[random.random() * x / 8 for _ in range(speckles_per_colour)],
[random.random() * y / 8 for _ in range(speckles_per_colour)],
)
ax.scatter( ax.scatter(
[random.random() * x / 8 for _ in range(speckles_per_colour)], x_coords,
[random.random() * y / 8 for _ in range(speckles_per_colour)], y_coords,
c=color, c=color,
s=size, s=size,
marker=marker, marker=marker,

84
perlin.py Normal file
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@ -0,0 +1,84 @@
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()