I’m pretty happy with how my final project turned out. We were asked to bring in a few generative animation examples to use as creative inspiration for our final project. I brought in this example, and it didn’t take long for us to crack the code of how it was accomplished in class. I took this resampled code and started working on expanding it’s premise which was pretty basic. Understanding classes and how to combine them with arrays was pivotal for developing this project.
Chris showed us how to detect image attachments in p5js by telling a particle to sense the image by color percentages. This opened up a new possibility to animate a ton of pixels and mask them until they sensed certain images that I preloaded in the sketch. Here are the 4 images I designed, each had a different class of particles interacting with them to create a multilayered effect:
By using variable speeds that were linked to perlin noise I was able to create some pretty fun effects. Perlin noise is very sensitive, so dialing in the right numbers took a very long time. There are 4000 total pixels at work in this animation so it gets a little bogged down, as they are constantly searching for the red to trace in the image examples I posted above. I’m happy with my progress in p5 for this quarter and am excited to keep digging into programming.