fast kmeans clustering for 2D or 3D point clouds
fast kmeans clustering for 2D or 3D point clouds - with the primary purpose to get a spatially equally distributed samples
fastKmeans(x, k, iter.max = 10, project = TRUE, threads = 0)
x |
matrix containing coordinates or mesh3d |
k |
number of clusters |
iter.max |
maximum number of iterations |
project |
logical: if x is a triangular mesh, the centers will be projected onto the surface. |
threads |
integer number of threads to use |
returns a list containing
selected |
coordinates closest to the final centers |
centers |
cluster center |
class |
vector with cluster association for each coordinate |
require(Rvcg) data(humface) set.seed(42) clust <- fastKmeans(humface,k=1000,threads=1) ## Not run: require(rgl) ## plot the cluster centers spheres3d(clust$centers) ## now look at the vertices closest to the centers wire3d(humface) spheres3d(vert2points(humface)[clust$selected,],col=2) ## End(Not run)
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