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AmbergRegister_modified.R
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AmbergRegister_modified.R
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#' @importFrom Rvcg vcgClean vcgClost vcgUpdateNormals
#' @importFrom Morpho meshcube applyTransform computeTransform pcAlign
AmbergRegister_modified <- function(x, mesh2, lm1=NULL, lm2=NULL, k=1, lambda=1, iterations=15, rho=pi/2, dist=2, border=FALSE, smooth=TRUE, smoothit=1, smoothtype="t", tol=1e-10, useiter=TRUE, minclost=50, distinc=1,affine=NULL, rigid=NULL, similarity=NULL, tps=FALSE, pcAlign=FALSE,nn=20, silent=FALSE, useConstrained=TRUE, forceLM=FALSE,visualize=FALSE, folder=NULL,noinc=FALSE,
bboxCrop=NULL,threads=0,iterations4pcAlign=150, mc.cores4pcAlign=2)
{
if (inherits(x, "mesh3d")) {
mesh1 <- x
Bayes <- NULL
} else if (inherits(x, "BayesDeform"))
Bayes <- x
else
stop("x must be an object of class mesh3d or BayesDeform")
if (!is.null(Bayes)) {
if (!requireNamespace("RvtkStatismo"))
stop("for using the option Bayes, please install RvtkStatismo from https://github.com/zarquon42b/RvtkStatismo")
mesh1 <- RvtkStatismo::DrawMean(Bayes$model)
}
#mesh1 <- rmUnrefVertex(mesh1, silent=TRUE)
mesh1 <- vcgUpdateNormals(mesh1)
mesh2 <- vcgUpdateNormals(mesh2)
meshbord <- vcgBorder(mesh2)
count <- 0
if (iterations < 1)
iterations <- 1e10
if (length(lambda) == 1)
lambda <- rep(lambda,iterations)
else if (length(lambda) != iterations)
stop("lambda must be vector of length 'iterations'")
k <- round(k)# make sure k is integer - otherwise RAM overkill
if (length(k) == 1)
k <- rep(k,iterations)
else if (length(k) != iterations)
stop("k must be vector of length 'iterations'")
affinemat <- NULL
meshorig <- mesh1
stopit <- FALSE
hasLM <- FALSE
if (!is.null(lm1) && !is.null(lm2)) {
hasLM <- TRUE
bary <- vcgClost(lm1,mesh1,barycentric = T)
}
if (!is.null(Bayes$initparams)) {
mesh1 <- RvtkStatismo::DrawSample(Bayes$model,Bayes$initparams)
if (hasLM)
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
#wire3d(mesh1);spheres3d(lm1)
#return(1)
}
if (hasLM) {## case: landmarks are provided
if (!is.null(Bayes) && hasLM) {
##register landmarks on model and constrain reference
lm2tmp <- rotonto(lm1,lm2,scale=Bayes$model@scale,reflection=FALSE)$yrot
constMod <- RvtkStatismo::statismoConstrainModel(Bayes$model,lm2tmp,lm1,Bayes$ptValueNoise)
if (useConstrained) {
Bayes$model <- constMod
mesh1 <- vcgUpdateNormals(RvtkStatismo::DrawMean(Bayes$model))
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
}
}
if (tps) {
mesh1 <- tps3d(mesh1,lm1,lm2,threads=threads)
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
} else {
if (is.null(affine) && is.null(rigid) && is.null(similarity) && !pcAlign) {
if (is.null(Bayes)) {
rigid <- list(iterations=0)
if (!silent)
cat("\n landmarks but no transform specified, performing rigid transform\n")
} else if (Bayes$align) {
rigid <- list(iterations=0)
if (!silent)
cat("\n landmarks but no transform specified, performing rigid transform\n")
}
}
if (pcAlign) {
mesh1 <- pcAlign(mesh1,mesh2,optim = TRUE,iterations = iterations4pcAlign,mc.cores = mc.cores4pcAlign)
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
}
if (!is.null(affine)) {##similarity matching
if (!is.null(rigid) && is.null(similarity)) {
affine$lm1 <- lm1
affine$lm2 <- lm2
}
mesh1 <- rigSimAff(mesh1,mesh2,affine,type="a",silent = silent,threads=threads)
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
}
if (!is.null(rigid)) { ##perform rigid icp-matching
if (!pcAlign) {
rigid$lm1 <- lm1
rigid$lm2 <- lm2
}
mesh1 <- rigSimAff(mesh1,mesh2,rigid,type="r",silent = silent,threads=threads)
if (hasLM)
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
}
if (!is.null(similarity)) {##similarity matching
if (is.null(rigid)) {
similarity$lm1 <- lm1
similarity$lm2 <- lm2
}
mesh1 <- rigSimAff(mesh1,mesh2,similarity,type="s",silent = silent,threads=threads)
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
}
}
affinemat <- computeTransform(vert2points(mesh1),vert2points(meshorig))
tmp <- list()
tmp$mesh <- mesh1
if (!useiter && !forceLM)
tmp$S <- createS(mesh1)
if (forceLM && hasLM) {
lm1 <- bary2point(bary$barycoords,bary$faceptr,mesh1)
tmp <- AmbergDeformSpam(mesh1,lm1,lm2,k0=k[1],lambda=lambda[1])
count <- count+1
if (iterations == 1)
stopit <- TRUE
}
verts0 <- vert2points(mesh1)
} else if (pcAlign || !is.null(affine) || !is.null(similarity) || !is.null(rigid)) {
if (pcAlign) {
mesh1 <- pcAlign(mesh1,mesh2,optim = TRUE,iterations = iterations4pcAlign,mc.cores = mc.cores4pcAlign)
}
if (!is.null(affine)) {##similarity matching
mesh1 <- rigSimAff(mesh1,mesh2,affine,type="a",silent = silent)
}
if (!is.null(similarity)) {##similarity matching
mesh1 <- rigSimAff(mesh1,mesh2,similarity,type="s",silent = silent)
}
if (!is.null(rigid)){ ##perform rigid icp-matching
mesh1 <- rigSimAff(mesh1,mesh2,rigid,type="r",silent = silent)
}
affinemat <- computeTransform(vert2points(mesh1),vert2points(meshorig))
tmp <- list(mesh=mesh1)
if (!useiter)
tmp$S <- createS(mesh1)
verts0 <- vert2points(mesh1)
} else { ## case: meshes are already aligned
affinemat <- diag(4)
tmp <- list()
tmp$mesh <- mesh1
if (!useiter)
tmp$S <- createS(mesh1)
verts0 <- vert2points(mesh1)
}
if (!is.null(bboxCrop)) {
mesh2 <- cropOutsideBBox(mesh1,mesh2,extend=bboxCrop)
if (!silent)
cat("cropping target mesh\n")
}
if (visualize) {
rglid <- NULL
if (!length(rgl.ids()$id))
open3d(windowRect=c(80,80,800,800))
else {
rgl.bringtotop()
rgl.clear()
}
points3d(meshcube(tmp$mesh),col="white",alpha=0)
shade3d(mesh2,col=2,specular=1)
if (!is.null(rglid))
rgl.pop(id=rglid)
rglid <- shade3d(tmp$mesh,col="white",front="lines", back="lines")
if (!is.null(folder)) {
if (substr(folder,start=nchar(folder),stop=nchar(folder)) != "/")
folder <- paste(folder,"/",sep="")
dir.create(folder,showWarnings=F)
movie <- paste(folder,"deformation",sep="")
npics <- nchar(iterations+1)
ndec <- paste0("%s%0",npics,"d.png")
}
#if (interactive())
#readline("please select viewpoint\n")
if (!is.null(folder)) {
filename <- sprintf("%s%04d.png", movie, 1)
rgl.snapshot(filename,fmt="png")
movcount <- 2
}
}
if (stopit) {
## set error and counter appropriately
distance <- 1e12
error <- 1e12
count <- count+1
while (count <= iterations && error > tol) {
time0 <- Sys.time()
if (useiter) {
verts0 <- vert2points(tmp$mesh)
mesh1 <- tmp$mesh
}
vert_old <- vert2points(tmp$mesh)
clost <- vcgClostKD(tmp$mesh,mesh2,k=nn,threads=threads)
verts1 <- vert2points(clost)
nc <- normcheck(clost,tmp$mesh,threads = threads)
## find valid hits
normgood <- as.logical(nc < rho)
distgood <- as.logical(abs(clost$quality) <= dist)
bordergood <- 1
if (!border)
bordgood <- as.logical(!meshbord$borderit[clost$faceptr])
#dupes <- !(as.logical(vcgClean(clost)$remvert))
dupes <- TRUE
good <- sort(which(as.logical(normgood*distgood*bordergood*dupes)))
### in case no good hit is found within the given distance we increase the distance by 1mm until valid references are found:
increase <- distinc
while (length(good) < minclost) {
distgood <- as.logical(abs(clost$quality) <= (dist+increase))
good <- sort(which(as.logical(normgood*distgood*bordergood)))
increase <- increase+distinc
cat(paste("distance increased to",dist+increase,"\n"))
}
## update reference points
lmtmp1 <- verts0[good,]
lmtmp2 <- verts1[good,]
## map it according to new reference points
#points3d(lmtmp2,col=count)
if (useiter)
tmp$S <- NULL
tmpold <- tmp
chk <- try(tmp <- AmbergDeformSpam(mesh1,lmtmp1,lmtmp2,k0=k[count],lambda=lambda[count],S=tmp$S),silent = TRUE)
if (inherits(chk,"try-error")) {
tmp <- tmpold
cat(paste("iteration failed with:",chk,"previous iteration used\n"))
}
gc()
if (smooth)
tmp$mesh <- vcgSmooth(tmp$mesh,iteration = smoothit,type=smoothtype)
## calculate error
if (!is.null(Bayes) && length(Bayes$sdmax) >= count) {
if (!is.null(Bayes$wt)) {
wt <- Bayes$wt[count]
wts <- c(1,wt)
wts <- wts/sum(wts)
tmpmesh <- RvtkStatismo::PredictSample(Bayes$model,tmp$mesh,TRUE, sdmax=Bayes$sdmax[count],align=Bayes$align,mahaprob=Bayes$mahaprob)
tmp$mesh$vb[1:3,] <- wts[1]*tmp$mesh$vb[1:3,]+wts[2]*tmpmesh$vb[1:3,]
} else {
tmp$mesh <- RvtkStatismo::PredictSample(Bayes$model,tmp$mesh,TRUE, sdmax=Bayes$sdmax[count],align=Bayes$align,mahaprob=Bayes$mahaprob)
}
}
distance_old <- distance
distance <- mean(vcgClostKD(mesh2,tmp$mesh,k0=10,sign=F,threads=threads)$quality)
if (distance > distance_old && !is.null(Bayes) && noinc) {
cat("\n=========================================\n")
message(paste(" Info: Distance is increasing, matching stopped after ",count,"iterations\n"))
count <- 1e10
tmp <- tmpold
}
tmp$mesh <- vcgUpdateNormals(tmp$mesh)
if (visualize) {
if (!is.null(rglid))
rgl.pop(id=rglid)
rglid <- shade3d(tmp$mesh,col="white",front="lines",back="lines")
if (!is.null(folder)) {
filename <- sprintf("%s%04d.png", movie, movcount)
movcount <- movcount+1
rgl.snapshot(filename,fmt="png")
}
}
error <- sum((vert2points(tmp$mesh)-vert_old)^2)/nrow(vert_old)
time1 <- Sys.time()
if (!silent && count < 1e10) {
cat(paste("-> finished iteration",count,"in",round(as.numeric(time1-time0,unit="secs"),2), "seconds\n"))
cat(paste(" Info: MSE between iterations:",error,"\n"))
cat(paste(" Info: Average distance to target:",distance,"\n"))
if (error < tol)
cat(paste("***\n==> Convergence threshold reached after",count,"iterations\n"))
}
count <- count+1
}
}
lm1map <- NULL
if (!is.null(lm1))
lm1map <- lm1 <- bary2point(bary$barycoords,bary$faceptr,tmp$mesh)
return(list(mesh=tmp$mesh,affine=affinemat,lm1=lm1map))
}
rigSimAff <- function(mesh1,mesh2,args,type="r",silent=TRUE,threads=1) {
iterations <- args$iterations; if (is.null(iterations)) iterations <- 3
lm1=args$lm1
lm2 <- args$lm2
uprange <- args$uprange; if (is.null(uprange)) uprange <- 0.9
maxdist <- args$maxdist
minclost <- args$minclost; if (is.null(minclost)) minclost <- 50
distinc <- args$distinc;
rhotol <- args$rhotol; if (is.null(rhotol)) rhotol <- pi
k <- args$k; if (is.null(k)) k <- 50
reflection <- args$reflection; if (is.null(reflection)) reflection <- FALSE
subsample <- args$subsample
out <- icp(mesh1, mesh2, iterations=iterations, lm1=lm1, lm2=lm2, uprange=uprange ,maxdist=maxdist, minclost=minclost, distinc=distinc, rhotol=rhotol, k=k, reflection=reflection, silent = silent,subsample=subsample, type=type,threads=threads)
return(out)
}