randomImageTransformParametersBatchGenerator | R Documentation |
This R6 class can be used to generate parameters to affine and other
transformations applied to an input image population.
The class calls randomImageTransformParametersAugmentation
.
bgen = randomImageTransformParametersBatchGenerator$new( ... ) bgen$generate( batchSize = 32L )
imageDomain
defines the spatial domain for all images.
imageList
List contains k images.
transformType
random transform type to generate;
one of the following options
c("Translation","Rigid","ScaleShear","Affine","DeformationBasis" )
NOTE: if the input images do not match the spatial domain of the domain
image, we internally resample the target to the domain. This may have
unexpected consequences if you are not aware of this.
This operation will test
antsImagePhysicalSpaceConsistency
then call
resampleImageToTarget
upon failure.
spatialSmoothing
spatial smoothing for simulated deformation
numberOfCompositions
number of compositions
deformationBasis
list of basis deformations
txParamMeans
vector of basis deformations means
txParamSDs
vector of basis deformations sds
center
center the parameters before passing to Y
$new()
Initialize the class in default empty or filled form.
$generate
generate the batch of samples with given batch size
randomImageTransformParametersAugmentation
library( ANTsR )
i1 = antsImageRead( getANTsRData( "r16" ) )
i2 = antsImageRead( getANTsRData( "r64" ) )
s1 = thresholdImage( i1, "Otsu", 3 )
s2 = thresholdImage( i2, "Otsu", 3 )
# see ANTsRNet randomImageTransformAugmentation
predictors = list( i1, i2, i2, i1 )
trainingData <- randomImageTransformParametersBatchGenerator$new(
imageList = predictors,
transformType = "Affine",
imageDomain = i1, txParamMeans=c(1,0,0,1,0,0), txParamSDs=diag(6)*0.01
)
testBatchGenFunction = trainingData$generate( 2 )
myout = testBatchGenFunction( )
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