noise_perlin {ambient} | R Documentation |
Perlin noise generator
Description
This function generates either 2 or 3 dimensional perlin noise, with optional pertubation and fractality. Perlin noise is one of the most well known gradient noise algorithms and have been used extensively as the basis for generating landscapes and textures, as well as within generative art. The algorithm was developed by Ken Perlin in 1983.
Usage
noise_perlin(
dim,
frequency = 0.01,
interpolator = "quintic",
fractal = "fbm",
octaves = 3,
lacunarity = 2,
gain = 0.5,
pertubation = "none",
pertubation_amplitude = 1
)
gen_perlin(
x,
y = NULL,
z = NULL,
frequency = 1,
seed = NULL,
interpolator = "quintic",
...
)
Arguments
dim |
The dimensions (height, width, (and depth)) of the noise to be generated. The length determines the dimensionality of the noise. |
frequency |
Determines the granularity of the features in the noise. |
interpolator |
How should values between sampled points be calculated?
Either |
fractal |
The fractal type to use. Either |
octaves |
The number of noise layers used to create the fractal noise.
Ignored if |
lacunarity |
The frequency multiplier between successive noise layers
when building fractal noise. Ignored if |
gain |
The relative strength between successive noise layers when
building fractal noise. Ignored if |
pertubation |
The pertubation to use. Either |
pertubation_amplitude |
The maximal pertubation distance from the
origin. Ignored if |
x , y , z |
Coordinates to get noise value from |
seed |
The seed to use for the noise. If |
... |
ignored |
Value
For noise_perlin()
a matrix if length(dim) == 2
or an array if
length(dim) == 3
. For gen_perlin()
a numeric vector matching the length of
the input.
References
Perlin, Ken (1985). An Image Synthesizer. SIGGRAPH Comput. Graph. 19 (0097-8930): 287–296. doi:10.1145/325165.325247.
Examples
# Basic use
noise <- noise_perlin(c(100, 100))
plot(as.raster(normalise(noise)))
# Using the generator
grid <- long_grid(seq(1, 10, length.out = 1000), seq(1, 10, length.out = 1000))
grid$noise <- gen_perlin(grid$x, grid$y)
plot(grid, noise)