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On Tue, May 12, 2020 at 10:56 PM . <aasth...@gmail.com> wrote:
Hello--I was trying to perform registration between my aligned meshes and reference, and while I got no error in the code, I get the following when I run it:I googled the error, and found this problem might be caused due to the Java heap memory being too low, and how to increase the memory. However, I am not able to perform the indicated steps either. What could be going wrong here?
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On Tue, May 12, 2020 at 10:56 PM . <aasth...@gmail.com> wrote:
Hello--I was trying to perform registration between my aligned meshes and reference, and while I got no error in the code, I get the following when I run it:I googled the error, and found this problem might be caused due to the Java heap memory being too low, and how to increase the memory. However, I am not able to perform the indicated steps either. What could be going wrong here?
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Hello--I changed the code a bit, and the problem still persists. I am getting a 'GC overhead limit exceeded' error, even though I have already increased the heap memory to maximum. How do I resolve this?
On Wednesday, 13 May 2020 00:06:19 UTC+5:30, Behzad@UofA wrote:See if these work:On Tue, May 12, 2020 at 10:56 PM . <aasth...@gmail.com> wrote:Hello--I was trying to perform registration between my aligned meshes and reference, and while I got no error in the code, I get the following when I run it:I googled the error, and found this problem might be caused due to the Java heap memory being too low, and how to increase the memory. However, I am not able to perform the indicated steps either. What could be going wrong here?
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I'm using the dataset provided as a part of the femur project in the online courseᐧ
HiI realised where the problem was. I had updated the memory size of the application instead of the IDE. Doing the needful takes away the previous error. However, now I get another error 'java.lang.OutOfMemoryError: Java heap space'. Could you help me resolve this? Would you have some time to look at my code?Thanks in advanceᐧOn Thu, 14 May 2020 at 00:28, Aastha Jain <aasth...@gmail.com> wrote:Tutorial 12.ᐧ
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There must be some error in the loop. When i tried establishing correspondence for a single example mesh with the reference, it worked out fine.Best,AasthaᐧOn Thu, 14 May 2020 at 12:27, Aastha Jain <aasth...@gmail.com> wrote:Sure, got it. I am using the 64 bit version only. Here is a snippet of the code which is creating the problem. Any suggestions would be appreciated. Thanks!val refMesh = MeshIO.readMesh(alignedMeshFiles(0)).get
val targetMeshes: Array[TriangleMesh[_3D]] = alignedMeshFiles.map(meshFile => MeshIO.readMesh(meshFile).get)
val LMFiles = new java.io.File("C:/Users/Home/Desktop/jjn/internship/Courses_SSM2016_Training_step2/step2/landmarks").listFiles()
val LMs: Array[scala.Seq[Landmark[_3D]]] =LMFiles.map(f => LandmarkIO.readLandmarksJson[_3D](f).get)
val refLMs = LMs(0)
val refLMNodeIds = refLMs.map(eachLM => refMesh.pointSet.findClosestPoint(eachLM.point).id)
val knownDeformations = (1 until 49).map(i => {
LMs(0).indices.map(j => LMs(i + 1)(j).point - refMesh.pointSet.point(refLMNodeIds(j)))
})
val refLMpoints = refLMNodeIds.toIndexedSeq.map(eachId => refMesh.pointSet.point(eachId))
val refLMdomain = UnstructuredPointsDomain(refLMpoints)
val knownDeformationFields = knownDeformations.map(eachDefSet => {
DiscreteField[_3D, UnstructuredPointsDomain[_3D], EuclideanVector[_3D]](refLMdomain, eachDefSet)
})
val noise = MultivariateNormalDistribution(DenseVector.zeros[Double](3), 0.001 * DenseMatrix.eye[Double](3))
def RegressionDataGenerator(dataPoints: IndexedSeq[Point[_3D]], deformations: IndexedSeq[EuclideanVector[_3D]], noise: MultivariateNormalDistribution) = {
val regressionDataSet = dataPoints.indices.map(i => (dataPoints(i), deformations(i), noise))
regressionDataSet
}
val regressionDataSets = (1 until 49).map(i => RegressionDataGenerator(refLMpoints, knownDeformations(i), noise))
for (n <- 1 until 49) {
val posteriorGP = reflowRankGP.posterior(regressionDataSets(n))
val initialCoefficients = DenseVector.zeros[Double](posteriorGP.rank)
val registrationParameters = Seq(
RegistrationParameters(regularizationWeight = 1e-1, numberOfIterations = 20, numberOfSampledPoints = 1000),
RegistrationParameters(regularizationWeight = 1e-2, numberOfIterations = 30, numberOfSampledPoints = 1000),
RegistrationParameters(regularizationWeight = 1e-4, numberOfIterations = 40, numberOfSampledPoints = 2000),
RegistrationParameters(regularizationWeight = 1e-6, numberOfIterations = 50, numberOfSampledPoints = 4000)
)
val finalCoefficients = registrationParameters.foldLeft(initialCoefficients)((modelCoefficients, regParameters) =>
doRegistration(posteriorGP, refMesh, targetMeshes(n), regParameters, modelCoefficients))
val transformationSpace = GaussianProcessTransformationSpace(posteriorGP)
val registrationTransformation = transformationSpace.transformForParameters(finalCoefficients)
val fittedMesh = refMesh.transform(registrationTransformation)
val fittedMeshGroup = ui.createGroup("fittedMeshGroup")
ui.show(fittedMeshGroup, fittedMesh, "fittedMesh")
ui.show(fittedMeshGroup, targetMeshes(n), s"TargetMesh $n")ᐧ