R/classes.R

In nucim: Nucleus imaging

classify<-function(f,N,cores=1)
{
orig<-getwd()
setwd(f)
  
library(bioimagetools)
if (cores>1)
  {
  library(parallel)
  options("mc.cores"=cores)
  }

files<-sample(list.files("blue"))
cat(paste(length(files),"files.\n"))
if(length(list.files(paste0("class",N)))==0)dir.create(paste0("class",N))
if(length(list.files(paste0("class",N,"-n")))==0)dir.create(paste0("class",N,"-n"))

if(cores>1)jobs <- mclapply(files, classify.file, N=N, mc.preschedule=FALSE)
if(cores==1)jobs <- lapply(files, classify.file, N=N)
setwd(orig)
}

classify.file<-function(file,N)
{
test<-try({
  mask<-readTIF(paste("dapimask/",file,sep=""))
  blau<-readTIF(paste("blue/",file,sep=""))
  blau<-array(blau,dim(blau))
  blau<-round(blau*2^16)
  storage.mode(blau)<-"integer"
    img.seg<-segment(blau,N,0.1,1/3,mask=(mask==1),maxit=50,varfixed=TRUE,
                     inforce.nclust=TRUE, start="equal")
    classes<-array(as.integer(img.seg$class),dim(blau))
    remove(img.seg)
    writeTIF(classes/N,paste("class",N,"/",file,sep=""),bps=8)
  
    classes<-classes
    classes<-classes[classes!=0]
    t=table(classes)
    print(file)
    print(t)
    write(t,file=paste0("class",N,"-n/",file,".txt"),ncolumns=N)
    write(t,file=paste0("class",N,"-n/",file,"-percent.txt"),ncolumns=N)
  
  remove(mask,blau,classes)
gc(verbose=FALSE)
},silent=TRUE)
if(class(test)=="try-error")cat(paste0(file,": ",attr(test,"condition"),"\n"))
else(cat(paste0(file," OK\n")))

}