Perlin noise is best explained in visual terms: if a 2D slice of truly random noise looks like even and harsh static, then a random 2D slice of Perlin noise will have a natural-looking blotchy structure, with smooth gradients. [Jacob Stanton] used Perlin noise as the starting point for creating some interesting generative vector art that shows off all kinds of different visuals. [Jacob] found that his results often exhibited a natural quality, with the visuals evoking a sense of things like moss, scales, hills, fur, and “other things too strange to describe.”
The art project [Jacob] created from it all is a series of posters showcasing some of the more striking examples, each of which displays an “A” modified in a different way. A few are shown here, and a collection of other results is also available.
Perlin noise was created by Ken Perlin while working on the original Tron movie in the early 80s, and came from a frustration with the look of computer generated imagery of the time. His work had a tremendous and lasting impact, and was instrumental to artists creating more natural-looking textures. Processing has a Perlin noise function, which was in fact [Jacob]’s starting point for this whole project.
Noise, after all, is a wide and varied term. From making generative art to a cone of silence for smart speakers, it has many practical and artistic applications.
Have you ever wrapped up a nice blinky project only to be disappointed by the predictability of the light or the color patterns? When it came to lighting this LED candle, so was [fungus amungus]. But there’s a better way, and it involves noise.
Perlin noise was created in the early 80s by Ken Perlin while he was working on the movie Tron. Frustrated by the current state of computer graphics and too limited on space to use images, he devised an algorithm for generating natural-looking textures. Basically, you generate a bunch of numbers between 0 and 1, then assign values to those numbers, such as a range of greyscale values from black (0) to white (1), or the values from the color wheel. The result is much prettier than random numbers because the neighboring values for any given number aren’t radically different. You get nice randomness with hardly any overhead.
[fungus amungus] is using the FastLED’s noise function to generate the numbers, but there’s a whole lot more going on here. As he explains in the excellent video after the break, if you want to animate these values, you just add another dimension of them. Although [fungus amungus] is using a Trinket Pro and a NeoPixel ring, we think a simplified version could be done with a Circuit Playground Express using the built-in LEDs.
If you want to do it the hard way, start by making your own NeoPixel ring.
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