WIP: remove imageMath

R/basicInPaint.R

@@ -44,18 +44,16 @@ basicInPaint <- function(img, paintMask, speedimage = NA, its = 0, gparam = 0.05
   temp <- antsImageClone(paintMask)
   temp[temp == 1] <- 0
   temp[temp == 2] <- 1
-  imageMath(img@dimension, temp, "MD", temp, 1)
+  temp = iMath(temp,"MD",1)
   paintMaskUse[temp == 1 & paintMaskUse == 1] <- 2
-  # imageMath(img@dimension,paintMaskUse,'GD',paintMask,1)
   healthymask <- antsImageClone(paintMaskUse)
   healthymask[paintMaskUse == 2] <- 0
   if (is.na(speedimage)) {
     speedimage <- antsImageClone(img)
     upit <- mean(img[paintMaskUse == 2])
     speedimage[paintMaskUse == 2] <- speedimage[paintMaskUse == 2] + upit
   }
-  imageMath(inpainted@dimension, inpainted, "FastMarchingExtension", speedimage,
-    healthymask, img)
+  inpainted = iMath( speedimage,"FastMarchingExtension", healthymask, img )
   outimg <- antsImageClone(img)
   outimg[paintMaskUse == 2] <- inpainted[paintMaskUse == 2]
   if (its > 0) {

R/compcor.R

@@ -4,7 +4,6 @@
 #' @param ncompcor n compcor vectors
 #' @param variance_extreme high variance threshold e.g 0.95 for 95 percent
 #' @param mask optional mask for image
-#' @param useimagemath use the imagemath implementation instead
 #' @param randomSamples take this many random samples to speed things up
 #' @param returnv return the spatial vectors
 #' @param returnhighvarmat bool to return the high variance matrix
@@ -20,24 +19,14 @@
 #' @export compcor
 compcor <- function(fmri, ncompcor = 4,
   variance_extreme = 0.975,
-  mask = NA,
-  useimagemath = FALSE, randomSamples = 1,
+  mask = NA, randomSamples = 1,
   returnv = FALSE, returnhighvarmat = FALSE,
   returnhighvarmatinds = FALSE,
   highvarmatinds = NA) {
   if (nargs() == 0) {
     print("Usage:  compcorr_df<-compcor( fmri, mask ) ")
     return(1)
   }
-  if (useimagemath & !is.na(mask)) {
-    myoutfn <- tempfile(pattern = "file", tmpdir = tempdir(), fileext = ".nii.gz")
-    imageMath(4, myoutfn, "CompCorrAuto", fmri, mask, ncompcor)
-    mycsv <- sub(".nii.gz", "_compcorr.csv", myoutfn)
-    myvarimg <- sub(".nii.gz", "_variance.nii.gz", myoutfn)
-    varimage <- antsImageRead(myvarimg, 3)
-    mycompcorrdf <- read.csv(mycsv)
-    return(mycompcorrdf)
-  }
   if (class(fmri)[1] == "antsImage" & is.na(mask)) {
     print("Need to input a mask too")
     print(args(compcor))

R/getCentroids.R

@@ -28,9 +28,7 @@ getCentroids <- function(img, clustparam = 250, outprefix = NA) {
   if (is.na(outprefix)) {
     outprefix <- paste(tempdir(), "/Z", sep = "")
   }
-  pointfile <- paste(outprefix, "coords.csv", sep = "")
-  imageMath(imagedim, pointfile, "LabelStats", img, img, clustparam)
-  mypoints <- read.csv(pointfile)
+  mypoints <- labelStats( img, img, clustparam )
   centroids <- as.matrix(data.frame(x = mypoints$x, y = mypoints$y, z = mypoints$z, t=mypoints$t ))
   return( centroids )
 }

R/getTemplateCoordinates.R

@@ -88,10 +88,13 @@ getTemplateCoordinates <- function(
   milab <- imagePairToBeLabeled[[2]]
   mywarpedLimage <- antsApplyTransforms(fixed = fi, moving = milab, transformlist = mytx$fwdtransforms,
     interpolator = c("NearestNeighbor"))
-  pointfile <- paste(outprefix, "coords.csv", sep = "")
-  imageMath(milab@dimension, pointfile, "LabelStats",
-    mywarpedLimage, mywarpedLimage, 1)
-  mypoints <- read.csv(pointfile)
+  mypoints <- labelStats( mywarpedLimage, mywarpedLimage )
+  mypoints = data.frame(
+    x = mypoints$x
+    y = mypoints$y
+    z = mypoints$z
+    t = mypoints$t
+    )
   for (mylab in 2:length(templatePairWithLabels)) {
     filab <- templatePairWithLabels[[mylab]]
     if (class(filab)[[1]] != "antsImage") {

R/pairwiseImageDistanceMatrix.R

@@ -39,8 +39,6 @@ pairwiseImageDistanceMatrix <- function(dim,
         mytx <- antsRegistration(fixed = i1, moving = i2, typeofTransform = c("AffineFast"),
           outprefix = toutfn)
         mywarpedimage <- antsApplyTransforms(fixed = i1, moving = i2, transformlist = mytx$fwdtransforms)
-        # broken !!  metric <- capture.output(imageMath(dim, 'j', metrictype, i1,
-        # mywarpedimage))[1]
         wh <- (mywarpedimage > 0 & i1 > 0)
         if (metrictype == "PearsonCorrelation") {
           metric <- abs(cor.test(i1[wh], mywarpedimage[wh])$est)

R/projectImageAlongAxis.R

@@ -31,6 +31,6 @@ projectImageAlongAxis <- function(imageND,
   if (axis >= imageND@dimension)
     axis <- (imageND@dimension - 1)
   downimg <- antsImageClone(referenceImageNDminus1)
-  imageMath(imageND@dimension, downimg, "Project", imageND, axis, projtype)
+  downimg = iMath(imageND,"Project",axis,projtype)
   return(downimg)
 }