Perlin Noise is a kind of algorithmic random value generator that, unlike pure random methods where each value is unrelated to the previous or next, generates values in a way that whenever you look at one particular value, you know the adjecent values are not radically far of.
In fact - and without surprise - it was mostly the Coding Train that nudged me to get off my ass and start these experiments (done and redone by thousands of people throughout the internet).
In this particular exercise I’m mapping Perlin Noise onto a 2D space, so effectively you’re seeing the noise values generated as if we’re looking top down onto a surface. What you’re seeing is a highly detailed rendition of the perlin noise values in 2D , but if you click on the image I’ll start to iterate those values, as the rendering is slow (and until I know how to improve things) I’m evaluating and lowering resolution to try and not kill your computer :)
The code is shown bellow, it is literally the code that generates whatever image you’re seeing and I’ll try to work out more interesting experiments, so come back now and then.
All the code that runs this experiment is bellow, apart from any external libraries (i.e. p5.js)