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"""Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy
https://github.com/cheind/magic-texture
"""
from typing import Tuple, Optional
import numpy as np
def coordinate_grid(shape: Tuple[int, int], dtype=np.float32):
"""Returns a three-dimensional coordinate grid of given shape for use in `magic`."""
x = np.linspace(-1, 1, shape[1], endpoint=True, dtype=dtype)
y = np.linspace(-1, 1, shape[0], endpoint=True, dtype=dtype)
X, Y = np.meshgrid(x, y)
XYZ = np.stack((X, Y, np.ones_like(X)), -1)
return XYZ
def random_transform(coords: np.ndarray, rng: np.random.Generator = None):
"""Returns randomly transformed coordinates"""
H, W = coords.shape[:2]
rng = rng or np.random.default_rng()
m = rng.uniform(-1.0, 1.0, size=(3, 3)).astype(coords.dtype)
return (coords.reshape(-1, 3) @ m.T).reshape(H, W, 3)
def magic(
coords: np.ndarray,
depth: Optional[int] = None,
distortion: Optional[int] = None,
rng: np.random.Generator = None,
):
"""Returns color magic color texture.
The implementation is based on Blender's (https://www.blender.org/) magic
texture shader. The following adaptions have been made:
- we exchange the nested if-cascade by a probabilistic iterative approach
Kwargs
------
coords: HxWx3 array
Coordinates transformed into colors by this method. See
`magictex.coordinate_grid` to generate the default.
depth: int (optional)
Number of transformations applied. Higher numbers lead to more
nested patterns. If not specified, randomly sampled.
distortion: float (optional)
Distortion of patterns. Larger values indicate more distortion,
lower values tend to generate smoother patterns. If not specified,
randomly sampled.
rng: np.random.Generator
Optional random generator to draw samples from.
Returns
-------
colors: HxWx3 array
Three channel color image in range [0,1]
"""
rng = rng or np.random.default_rng()
if distortion is None:
distortion = rng.uniform(1, 4)
if depth is None:
depth = rng.integers(1, 5)
H, W = coords.shape[:2]
XYZ = coords
x = np.sin((XYZ[..., 0] + XYZ[..., 1] + XYZ[..., 2]) * distortion)
y = np.cos((-XYZ[..., 0] + XYZ[..., 1] - XYZ[..., 2]) * distortion)
z = -np.cos((-XYZ[..., 0] - XYZ[..., 1] + XYZ[..., 2]) * distortion)
if depth > 0:
x *= distortion
y *= distortion
z *= distortion
y = -np.cos(x - y + z)
y *= distortion
xyz = [x, y, z]
fns = [np.cos, np.sin]
for _ in range(1, depth):
axis = rng.choice(3)
fn = fns[rng.choice(2)]
signs = rng.binomial(n=1, p=0.5, size=4) * 2 - 1
xyz[axis] = signs[-1] * fn(
signs[0] * xyz[0] + signs[1] * xyz[1] + signs[2] * xyz[2]
)
xyz[axis] *= distortion
x, y, z = xyz
x /= 2 * distortion
y /= 2 * distortion
z /= 2 * distortion
c = 0.5 - np.stack((x, y, z), -1)
np.clip(c, 0, 1.0)
return c |