matchedLandmarks | R Documentation |
Convert match output to landmarks in physical space
matchedLandmarks( matchObject, referenceImage, movingImage, patchSize, whichK = 1 )
matchObject |
object, the output of |
referenceImage |
the fixed image |
movingImage |
the image that will be matched to the fixed image |
patchSize |
size of patch features |
whichK |
which matched point set (e.g. 1 gives the best, 2 second best and so on) |
output list contains fixed and matched points
## Not run: library( keras ) library( ANTsR ) layout( matrix(1:2,nrow=1) ) nP1 = 50 nP2 = 200 psz = 32 img <- ri( 1 ) %>% iMath( "Normalize" ) %>% resampleImage( c( 2, 2 ) ) img2 <- ri( 2 ) %>% iMath( "Normalize" ) %>% resampleImage( c( 2, 2 ) ) mask = randomMask( getMask( img ), nP1 ) mask2 = randomMask( getMask( img2 ), nP2 ) matchO = deepPatchMatch( img2, img, mask, mask2 ) mlm = matchedLandmarks( matchO, img, img2, c(psz,psz) ) ct = 0 mxct = 18 lmImage1 = img * 0 lmImage2 = img2 * 0 for ( k in 1:nrow( mlm$fixedPoints ) ) { if ( ct < mxct ) { pt1i = makePointsImage( matrix(mlm$fixedPoints[k,],ncol=2), img, radius = 2 ) * k pt2i = makePointsImage( matrix(mlm$movingPoints[k,],ncol=2), img2, radius = 2 ) * k lmImage1 = lmImage1 + pt1i lmImage2 = lmImage2 + pt2i } ct = ct + 1 } plot( img, lmImage1 ) plot( img2, lmImage2 ) ## End(Not run)
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