never has panel rationalization been so straighforward! k-means clustering to sort similar panels!
panels are sorted by two parameters:
- panel area
- surface normals
and then replaced with set panel dimensions (an average of each cluster) + 20mm offset. the results are pretty decent, with minimal overlap even at the steep bits of the surface.
number of clusters (types of rectangles) from 2 – 50, iterations = 3
k (number of clusters) = 25, iterations running from 1 – 30
EDIT : a little extra definition showing colour clustering to reduce the number of colour variations needed from 1124 to 10-50.
colour variations from 10-30, 30 iterations
10 colours, iterations from 10-30, showing how the clustering works in realtime
plugins used: OWL