fit_xform_brain | R Documentation |
Fit a single transform to a bridging registration between brains
fit_xform_brain(
sample,
reference,
via = NULL,
type = c("affine", "rigid", "similarity", "tps"),
pts = NULL,
npts = if (is.null(pts)) 1000 else NULL,
scale = c(1, 1),
...
)
sample |
Source template brain (e.g. IS2) that data is currently in.
Specified either as character vector or a |
reference |
Target template brain (e.g. IS2) that data should be transformed into. |
via |
(optional) intermediate template brain that the registration sequence must pass through. |
type |
A character string specifying the type of registration. See
|
pts |
Optional set of points to use for the fit. If they are not specified they will be randomly sampled (see details). |
npts |
Number of points to use for fit (defaults to 1000 when no
|
scale |
a 2-vector indicating the amount to scale the sample and
reference points. You can supply one vector if this is the same. If the
transform expects points in microns and returns points in microns then you
would need |
... |
Additional arguments passed to |
If pts
are supplied these will be used to construct the fit.
This might be useful if you are focussing on a particular brain region and
supply relevant neurons. But if you want to work with one registration to
use for the whole brain then it's better to use points across the brain.
When points are not provided then fit_xform_brain
will first check
to see if it can find a neuropil surface model for the sample
. If it
can, it will sample a number of points that lie inside the surface model.
If no surface model can be found, then it will look for a
templatebrain
object specifying a bounding box around the
sample brain. If successful, it will sample npts
within that
bounding box. If not there will be an error and you will have to supply the
points.
A homogeneous affine matrix or a nat::tpsreg
object
n.b. only in development nat (>= 1.10.1)
fit_xform
## Not run:
library(nat.flybrains)
t1=fit_xform_brain(sample='FCWB', reference="JFRC2", type='affine')
t1
# the same but for points in nm
fit_xform_brain(sample='FCWB', reference="JFRC2", type='affine', scale=1000)
# run the fit based on a particular group of Kenyon cells
t2=fit_xform_brain(sample='FCWB', reference="JFRC2",
pts=nat::kcs20, npts=300, type='affine')
nclear3d()
mfrow3d(1, 2, sharedMouse = TRUE)
plot3d(JFRC2)
plot3d(xform(FCWB.surf, t1))
next3d()
plot3d(JFRC2)
plot3d(xform(FCWB.surf, t2))
# the whole brain surfaces clearly match better when you fit on the whole brain
## End(Not run)
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