Generative art from Daniel Eden
This site serves as a gallery for my favorite select pieces of generative art. These pieces have been generated using Processing, p5.js, or OpenFrameworks and are each accompanied by a description of the underlying logic that generated the results.
For more information about my foray into generative art, you can read a blog post I published.
Given an origin and a parallel destination, draw 1000 points of varying transparency between them. Using Perlin noise, calculate a delta vector for both origin and destination, with x coordinates between -0.2 and +0.5, and y coordinates between -1 and +2. Add the delta vectors to the origin and destination. Repeat until either the origin or destination points are at least 80px from the bottom of the canvas.
Pack as many circles as possible within another circle, ensuring they don't overlap. Draw a line through the middle of each of the sub-circles at a random angle.
Plot two kinds of objects around the canvas; attractors and particles. Particles are attracted to attractors, and their path/history is shown in white. Attractors are invisible. Within a certain radius, attractors reject particles with a force. Plot thousands of each type of object. Observe through the particle history how all particles are attracted to the center of the canvas; the center of mass, if you will.
Pick a random origin and destination vector, and a random delta vector. Draw a series of points between the origin and destination. On the next frame, there is a 30% chance the delta will be added to the origin, a 30% chance the delta will be added to the destination, and a 40% chance the delta will be added to both. The probability of the outcome is determined by Perlin noise. When either the origin or the destination is within 50px of the canvas edge, pick a new origin, destination, and delta.
Plot a series of connected points around the center of the canvas, using three-dimensional Perlin noise to vary the radius. Repeat this with an increasing base radius, stepping forward through the Perlin noise function to slightly vary the next shape.