RUn this code to get access to experimental imgSWT function

Stationary Wavelet Experimental

imgSWT <- function(input, filter=NULL, scales=6, cell.bodies = 3, processes = 5, family='db2', sigma=10, processLength=12, alim=c(200, 500), pch=21, bg="white", cex=2, lwd=2, illustrator=F, output='waveletoutput') {
    file <- as.character(input)[1]
    family <- as.character(family)[1]
    outputfile <- as.character(output)[1]
    scales<-as.integer(scales)
    cellBodies<-as.integer(cell.bodies)
    processes <-  as.integer(processes)
    sigmaR<-as.integer(sigma)
    processLength<-as.integer(processLength)
    areaSize<-alim
    # initialize the filter parameter list.
    if(is.null(filter)){
      noFilter<-1

      filter_minArea<-as.integer(0)
      filter_maxArea<-as.integer(0)
      filter_minThresh<-as.integer(0)
      filter_maxThresh<-as.integer(0)
      filter_eccentricity<-as.integer(0)
      filter_eccentricity<-as.integer(0)
      filter_alpha<-as.numeric(1.0)
      filter_beta<-as.integer(0)

    }else{
      noFilter<-0

      filter_minArea<-as.integer(filter$alim[1])
      filter_maxArea<-as.integer(filter$alim[2])
      filter_minThresh<-as.integer(filter$threshold.range[1])
      filter_maxThresh<-as.integer(filter$threshold.range[2])
      filter_eccentricity<-as.integer(filter$eccentricity)
      filter_alpha<-as.numeric(filter$alpha)
      filter_beta<-as.integer(filter$beta)
      filter_alpha<-as.numeric(1.0)
      filter_beta<-as.integer(0)
      filter_Max<-as.numeric(filter$Max)
      filter_Min<-as.integer(filter$Min)
    }


    ## check for existence
    if(!file.exists(file))
    stop(file, "not found")
    
    create.output.directory('approximations_coefficents')
    lapply(1:scales-1, function(x){create.output.directory( paste('d',x, sep='') ) } )
    create.output.directory('trace')
    create.output.directory('coherency')
    create.output.directory('orientation')
    create.output.directory('scharr')
    create.output.directory('cellbodies')
    create.output.directory('processes_labeled')
    create.output.directory('processes_anisotropic')
    create.output.directory('output')
    create.output.directory('blended')
    create.output.directory('analyzed')
    create.output.directory('rangecorrected')
    

    ## expand path
    file <- path.expand(file)
    #############################
    #           CALL            #
    #############################
    a <- .Call("filterImage", file, 
                              noFilter, 
                              filter_minArea, 
                              filter_maxArea, 
                              filter_minThresh, 
                              filter_maxThresh, 
                              filter_eccentricity, 
                              filter_alpha, 
                              filter_beta, 
                              filter_Max, 
                              filter_Min, 
                              scales, 
                              cellBodies, 
                              processes, 
                              family, 
                              sigmaR, 
                              areaSize, 
                              processLength, 
                              outputfile)
    cat("IMAGE PROCESSED\n")
    #############################
    #           PLOT            #
    #############################
    confocal.image<-readPNG(paste('output/d', cell.bodies, '_output_', output, '.png', sep='') )
    confocal.image<-as.raster(confocal.image[,,1:3])
    process.color<-rgb(a$process.r,  a$process.g, a$process.b, 255, maxColorValue = 255)

    if(illustrator){
      if(dim(confocal.image)[2]==dim(confocal.image)[1]){
        quartz(width=8, height=8)
        par(xaxs='i', yaxs='i', pty='s')
      }else{
        if(dim(confocal.image)[2]<dim(confocal.image)[1]){
          quartz(width=8*dim(confocal.image)[2]/dim(confocal.image)[1], height=8)
        }else{
          quartz(width=8, height=8*dim(confocal.image)[1]/dim(confocal.image)[2])
        }
        par(xaxs='i', yaxs='i', pty='m')
      }
      
      par(mar=c(0,0,0,0))
    
      plot(c(0,dim(confocal.image)[2]), c(0,dim(confocal.image)[1]), axes=F, ylab='', xlab='', col=0)
      rasterImage(confocal.image,0,0,dim(confocal.image)[2],dim(confocal.image)[1])
      points(a$process.x, dim(confocal.image)[1] - a$process.y, col=process.color, pch=16, cex=0.35) #cex=0.15
    
      dev.copy(png,  width = dim(confocal.image)[2], height = dim(confocal.image)[1], paste('analyzed/', output, '_illustrator.png', sep=''))
      dev.off()
    }

    if(dim(confocal.image)[2]==dim(confocal.image)[1]){
      quartz(width=8, height=8)
      par(xaxs='i', yaxs='i', pty='s')
    }else{
      if(dim(confocal.image)[2]<dim(confocal.image)[1]){
        quartz(width=8*dim(confocal.image)[2]/dim(confocal.image)[1], height=8)
      }else{
        quartz(width=8, height=8*dim(confocal.image)[1]/dim(confocal.image)[2])
      }
      par(xaxs='i', yaxs='i', pty='m')
    }

    par(mar=c(0,0,0,0))
    
    plot(c(0,dim(confocal.image)[2]), c(0,dim(confocal.image)[1]), axes=F, ylab='', xlab='', col=0)
    rasterImage(confocal.image,0,0,dim(confocal.image)[2],dim(confocal.image)[1])
    points(a$process.x, dim(confocal.image)[1] - a$process.y, col=process.color, pch=16, cex=0.15)  

    points(a$centroid.x[a$area>areaSize[1]], dim(confocal.image)[1] - a$centroid.y[a$area>areaSize[1]], bg=bg, pch=pch, cex=cex, lwd=lwd)
    dev.copy(png,  width = dim(confocal.image)[2], height = dim(confocal.image)[1], paste('analyzed/', output, '_analyzed.png', sep=''))
    dev.off()

    if(illustrator){
      confocal.image<-readPNG(paste('analyzed/', output, '_illustrator.png', sep='') )
      confocal.image<-as.raster(confocal.image[,,1:3])
      plot(c(0,dim(confocal.image)[2]), c(0,dim(confocal.image)[1]), axes=F, ylab='', xlab='', col=0)
      rasterImage(confocal.image,0,0,dim(confocal.image)[2],dim(confocal.image)[1])
      points(a$centroid.x[a$area>areaSize[1]], dim(confocal.image)[1] - a$centroid.y[a$area>areaSize[1]], bg=bg, pch=pch, cex=cex*1.1, lwd=lwd)
      dev.copy2pdf(file=paste('analyzed/', output, '_illustrator.pdf', sep=''))
    }  
    dev.off()

    a$process.type[which(a$process.type==1)]<-"slab"
    a$process.type[which(a$process.type==2)]<-"end.point"
    a$process.type[which(a$process.type==3)]<-"joint3"
    a$process.type[which(a$process.type==4)]<-"joint4"
    a$process.type[which(a$process.type==5)]<-"jointNA"

    a$eccentricity[which(a$eccentricity==0)]<-NA #contours with less than 5 pixels cannot have eccenticity therefore lets put them to NA instead of 0.

    #reorganize output
    somas<-list(centroid.x=a$centroid.x, centroid.y=a$centroid.y, area=a$area, intensity=a$intensity, eccentricity=a$eccentricity, id=a$id, contour.x=a$x, contour.y=a$y)
    processes<-list(id=a$process.id, type=a$process.type, orientation=a$process.theta, x=a$process.x,  y=a$process.y, col= process.color, coherence= a$process.coherence)
    b<-list(somas=somas, processes=processes)

    return(b)
}