R/assign.size.R

In sclero: Measure Growth Patterns and Align Sampling Spots in Photographs

#' @title Assign spot sizes to 'rawDist' objects for estimating spatial extent of sample averaging error.
#' 
#' @description Assigns spot sizes to \code{\link[=convert.ijdata]{rawDist}} objects for \link[=spot.dist]{estimating averaging error}.
#' 
#' @param rawDist \code{\link[=convert.ijdata]{rawDist}} object to which the values should be assigned.
#' @param file optional. ImageJ .zip file containing the spot size information. If \code{NULL} (default), the file name is assumed to be the same than from where \code{rawDist} \link[=read.ijdata]{data was read from}.
#' @param path optional. A character argument specifying the location of the \code{file}. If \code{NULL} (default), the \code{file} is assumed to be located in the \link[=getwd]{working directory}. See \code{\link[base]{dir}} for further information.
#' @param names optional. A character argument specifying how the names of \code{spots} should be generated. See \code{\link{read.ijdata}} for details. Defaults to "generate.invalid".
#' @param types optional. A character vector specifying the \code{strType} of ROI objects to be considered as sample spots (see \code{\link{plot.ijroi}} for possible pattern types). Defaults to \code{c("oval", "freehand", "rect")} meaning that oval and freehand selections, as well as rectangle tool selections will be used to calculate the spatial extent of sample spots.
#' @return Returns a list of class 'rawDist' with a list of \code{\link{ppp}} objects containing locations of sample spot centroids and a list of \code{\link{hyperframe}}s containing spot size information.
#' @details If the .zip file containing spot size information is the same than from which the \code{rawDist} object was derived from and located in your working directory, assignment of spot sizes is simply specified by \code{assign.size(rawDist)}. Otherwise, use the \code{path} argument to specify the folder where the \code{file} is located.
#' @author Mikko Vihtakari
#' @seealso \code{\link{read.ijdata}} \code{\link{spot.dist}} \code{\link{assign.value}} \code{\link{plot.spotDist}}  
#' @examples data(shellspots)
#' shell <- convert.ijdata(shellspots)
#' path <- file.path(system.file("extdata", package = "sclero"))
#' sizes <- assign.size(shell, path = path)
#' sizes$spot.area
#' @import spatstat RImageJROI
#' @export
#' 

assign.size <- function(rawDist, file = NULL, path = NULL, names = "generate.invalid", types = c("oval", "freehand", "rect")){

Z <- rawDist

## 1. Read in the .zip file and convert to spatstat
  if(is.null(file)) file <- paste0(Z$sample.name, ".zip")
  if(is.null(path)) path <- getwd()
  if(!file %in% dir(path)) stop(paste0("Cannot find ", file, ". Check getwd() or define file"))
  X <-  read.ijzip(file.path(path,file), names = TRUE)
  spot.owins <- ij2spatstat(X, convert.only = types, scale = Z$scaling.factor, unitname = Z$unit)

## 2. Relate the spot areas with sample spots 
## Relate spot areas with spots
## Calculate centroids and find closest spot to each centroid. This will be related to spot area
centroids <- lapply(spot.owins, function(k) {tmp <- centroid.owin(k)
  ppp(x = tmp$x, y = tmp$y, window = k)})

spot.equivalent <- lapply(centroids, function(k) {
  dist2spots <- lapply(Z$spots, function(s) nncross(k, s))
  min.dist <- dist2spots[which.min(do.call(rbind, dist2spots)$dist)]
  min.dist[[1]]$type <- names(min.dist)
  min.dist[[1]]$spot.mark <- as.integer(as.character(marks(Z$spots[[names(min.dist)]])[min.dist[[1]]$which]))
  min.dist[[1]]})

G <- do.call(rbind, spot.equivalent)
G$owin.name <- rownames(G)
G <- G[with(G, order(type, spot.mark)),]

out <- lapply(names(Z$spots), function(k) {
  tmp <- G[G$type %in% k,]$owin.name
  tmp.dt <- G[G$owin.name %in% tmp,]
  areas <- unlist(lapply(spot.owins[tmp], function(k) area.owin(k)))
  diams <- unlist(lapply(spot.owins[tmp], function(k) diameter(k)))
hyperframe(spot = tmp.dt$spot.mark, dist2spot = tmp.dt$dist, spot.owin.name = tmp.dt$owin.name, spot.owins = spot.owins[tmp], spot.area = areas, spot.diameter = diams)
  })
names(out) <- names(Z$spots)

centroids <- lapply(names(Z$spots), function(k) {
  tmp <- G[G$type %in% k,]$owin.name
  centroid.name <- G[G$owin.name %in% tmp,]$spot.mark
  tmp2 <- c()
  for(i in seq_along(tmp)){
    tmp3 <- centroids[[tmp[i]]]
    marks(tmp3) <- factor(centroid.name[i])
    tmp2 <- superimpose(tmp2, tmp3, W = Z$window)
  }
  tmp2})
names(centroids) <- names(Z$spots)  

Z$spot.area <- list(centroids = centroids, spot.dat = out)
return(Z)
}