tpsreg creates an object encapsulating a thin plate spine transform mapping a paired landmark set.

xformpoints.tpsreg enables xform and friends to transform 3d vertices (or more complex objects containing 3d vertices) using a thin plate spline mapping stored in a tpsreg object.

tpsreg(sample, reference, ...)

# S3 method for tpsreg
xformpoints(reg, points, swap = NULL, ...)

Arguments

sample, reference

Matrices defining the sample (or floating) and reference (desired target after transformation) spaces. See details.

...

additional arguments passed to xformpoints.tpsreg

reg

The tpsreg registration object

points

The 3D points to transform

swap

Whether to change the direction of registration (default of NULL checks if reg has a attr('swap'=TRUE)) otherwise

Details

Note that we use the nat convention for naming the sample/reference space arguments but these actually clash with the nomenclature in the underlying Morpho::tps3d function.

  • refmat (Morpho3d) == sample (nat)

  • tarmat (Morpho3d) == reference (nat)

See also

Examples

if (FALSE) { ## A full worked example of using landmarks based registration to construct ## a mirroring registration from one side of the brain to the other. # read in set of landmarks defined in FAFB CATMAID library('catmaid') emlandmarks=catmaid::read.neurons.catmaid('annotation:^GJLandmark') # Match up L and R pairs library('stringr') emlandmarks[,'side']=stringr::str_match(emlandmarks[,'name'], "([LR]) Landmark")[,2] emlandmarks[,'shortname']=stringr::str_match(emlandmarks[,'name'], "(.*)([LR]) Landmark.*")[,2] emlandmarks[,'shortname']=sub("[_ ]+$", "", emlandmarks[,'shortname']) library('dplyr') lmpairs=dplyr::inner_join( dplyr::filter(emlandmarks[,], side=="L"), dplyr::filter(emlandmarks[,], side=="R"), by='shortname', suffix=c(".L",".R")) # find mean xyz position of each landmark (they are drawn as a little cross) lmxyz=t(sapply(emlandmarks, function(x) colMeans(xyzmatrix(x)))) # construct thin plate splines registration (here mapping the right side neurons to left side) mirror_reg=tpsreg( lmxyz[as.character(lmpairs$skid.R),], lmxyz[as.character(lmpairs$skid.L),] ) # map RHS DA2 PNs onto left and compare with LHS neurons da2pns.R=catmaid::read.neurons.catmaid('glomerulus DA2 right') da2pns.L=catmaid::read.neurons.catmaid('glomerulus DA2 left') da2pns.R.L=xform(da2pns.R, reg = mirror_reg) plot(da2pns.L, col='red') plot(da2pns.R.L, col='blue', add=TRUE) }