deepFeatures | R Documentation |
High-level function for extracting features based on a pretrained network.
deepFeatures( x, mask, patchSize = 64, featureSubset, block_name = "block2_conv2", vggmodel, subtractor = 127.5, patchVarEx = 0.95, meanCenter = FALSE )
x |
input input image |
mask |
defines the object of interest in the fixedImage |
patchSize |
vector or scalar defining patch dimensions |
featureSubset |
a vector that selects a subset of features |
block_name |
name of vgg feature block, either block2_conv2 or integer. use the former for smaller patch sizes. Or try ripmmarc. |
vggmodel |
prebuilt feature model |
subtractor |
value to subtract when scaling image intensity; should be chosen to match training paradigm eg 127.5 for vgg and 0.5 for resnet like. |
patchVarEx |
patch variance explained for ripmmarc |
meanCenter |
boolean mean center the patch for ripmmarc |
feature array, patches and patch coordinates
Avants BB
library(ANTsR) img <- ri( 1 ) %>% iMath( "Normalize" ) mask = randomMask( getMask( img ), 20 ) features = deepFeatures( img, mask, patchSize = 32 )
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.