My plan for the typology is to continue my exploration of pennies from previous studios. Since this is a typology, I plan to focus on scanning a large number of pennies and trying to learn something about the ‘pennie space’ – ie the visual space pennies occupy.
^ penny
Below is my planned pipeline:
(1) scan pennies with flatbed scanner –> (2) python openCV edge detection to extract individual pennies –> (3) rotation correction (also pythonCV ?) –> (4) perform umap on penny images –> (5) interpolate the output to a grid (RasterFairy?)
^ gridded t.sne from ml4a
I also want to try and see if interesting patterns emerge from computing and visualizing the norm between each penny and the average penny, to visualize both individual wear on each penny and average wear across all pennies. This would use the same first 3 steps, then require a separate computing norms (which would be pretty easy I think).
I’ve written the openCV script for separating the pennies and plan on scanning some pennies and testing it this week, then have to write the rotation correction. I found and played around a bit with this open source umap python package and I think it’ll work well, but I still have to figure out how to do grid interpolation. That same package does have an aligned umap api but im not sure that’ll accomplish what I want.