Some code for making (multiple) dotprops objects from skeletonised images. Originally used with output of Nick Masse's image dimension reduction. Input would be matlab .mat file from the Masse code. Then tries to fine one or more connected components based on a radius threshold.

reduceddps.R

#' Make R dotprops from matlab output of Masse image_dimension_reduction()
#'
#' 
as.dotprops.masseidr<-function(f,subsample=0.1,reg=NULL,k=5,regopts=NULL,...) {
    stopifnot(require(R.matlab))
    x=readMat(f)
    dots=t(do.call(cbind,x$dots))
    if(subsample!=1){
        idxs=sample(nrow(dots),round(nrow(dots)*subsample))
        dots=dots[idxs,]
    }
    dp=DotProperties(dots,k=k,...)
    if(!is.null(reg)){
        dp=do.call("transform.dotprops",c(list(dp,reg,k=k),regopts))
    }
    dp
}

graph.dotprops<-function(x,radthresh=5,k=10,directed=FALSE){
    xyz=xyzmatrix(x)
    nnx=nn2(xyz,searchtype='radius',k=k,radius=radthresh)
    zz=sapply(seq(nrow(xyz)),function(i) {ninds=nnx$nn.idx[i,];cbind(i,ninds[ninds>0])})
    el=do.call(rbind,zz)
    g=graph.edgelist(el)
    g=set.graph.attribute(g,'radthresh',radthresh)
    g=set.graph.attribute(g,'k',k)
    if(!directed) g=as.undirected(g)
    g
}

plot3d.connected<-function(x,g,col='black',lwd=1,...){
    xyz=xyzmatrix(x)
    if(missing(g)){
        g=if(!is.null(x$g)) x$g else graph.dotprops(x,...)
    } 
    if(is.directed(g)) g=as.undirected(g) 
    el=get.edgelist(g)
    starts=xyz[el[,1],]
    stops=xyz[el[,2],]
    interleaved=matrix(t(cbind(starts,stops)),ncol=3,byrow=T)
    segments3d(interleaved,col=col,lwd=lwd)
    invisible(g)
}

plotbycluster.dotprops<-function(x, g, csizethresh=1,colfun=rainbow, pointfun=points3d, ...){
    if(missing(g)) g=graph.dotprops(x,...)
    cg=clusters(g)
    radthresh<-get.graph.attribute(g,'radthresh')
    if(!is.null(radthresh)) radthresh=5
    colpal=colfun(cg$no)
    colpal[cg$csize<csizethresh]='grey'
    pointfun(xyzmatrix(x),col=colpal[cg$membership],rad=radthresh/2)
    invisible(g)
}

subset.dotprops.connclusters<-function(x,g,clusternums,rankordered=T,csizethresh=1,...){
    if(missing(g)){
        g=if(!is.null(x$g)) x$g else graph.dotprops(x,...)
    } 
    cg=clusters(g)
    if(rankordered) clusternums=order(cg$csize,decreasing=T)[clusternums]
    # only keep clusters above theshold size
    clusternums=clusternums[cg$csize[clusternums]>=csizethresh]
    x$labels=cg$membership
    subset(x,labels%in%clusternums)
}