Resolve "Adaptive downsampling for ICP" (5b791b26) · Commits · Jan Šmíd / Analyst 2

Comparison/src/main/java/cz/fidentis/analyst/icp/IcpTransformer.java

+48 −8

Original line number	Diff line number	Diff line
		@@ -9,6 +9,7 @@ import cz.fidentis.analyst.mesh.core.MeshPoint;
		import cz.fidentis.analyst.visitors.mesh.HausdorffDistance;
		import cz.fidentis.analyst.visitors.mesh.sampling.NoSampling;
		import cz.fidentis.analyst.visitors.mesh.sampling.PointSampling;
		import cz.fidentis.analyst.visitors.mesh.sampling.RandomSampling;
		import java.util.ArrayList;
		import java.util.Collections;
		import java.util.HashMap;
		@@ -81,6 +82,29 @@ public class IcpTransformer extends MeshVisitor {

		private final PointSampling samplingStrategy;

		/**
		* Constructor for random sampling, which is the best performing downsampling strategy.
		*
		* @param mainFacet Primary mesh facet. Must not be {@code null}.
		* Inspected facets are transformed toward this primary mesh.
		* @param maxIteration Maximal number of ICP iterations (it includes computing
		* new transformation and applying it). A number bigger than zero.
		* Reasonable number seems to be 10.
		* @param scale If {@code true}, then the scale factor is also computed.
		* @param error Acceptable error - a number bigger than or equal to zero.
		* Mean distance of vertices is computed for each ICP iteration.
		* If the difference between the previous and current mean distances is less than the error,
		* then the ICP computation stops. Reasonable number seems to be 0.05.
		* @param numSamples Number of samples for downsampling. Use 1000 for best performance. Zero = no downsampling
		* @throws IllegalArgumentException if some parameter is wrong
		*/
		public IcpTransformer(MeshFacet mainFacet, int maxIteration, boolean scale, double error, int numSamples) {
		this(new HashSet<>(Collections.singleton(mainFacet)), maxIteration, scale, error, (numSamples == 0) ? new NoSampling() : new RandomSampling(numSamples));
		if (mainFacet == null) {
		throw new IllegalArgumentException("mainFacet");
		}
		}

		/**
		* Constructor.
		*
		@@ -90,8 +114,10 @@ public class IcpTransformer extends MeshVisitor {
		* new transformation and applying it). A number bigger than zero.
		* Reasonable number seems to be 10.
		* @param scale If {@code true}, then the scale factor is also computed.
		* @param error Acceptable error. A number bigger than or equal to zero.
		* When reached, then the ICP stops. Reasonable number seems to be 0.05.
		* @param error Acceptable error - a number bigger than or equal to zero.
		* Mean distance of vertices is computed for each ICP iteration.
		* If the difference between the previous and current mean distances is less than the error,
		* then the ICP computation stops. Reasonable number seems to be 0.05.
		* @param strategy One of the reduction strategies. If {@code null}, then {@link NoUndersampling} is used.
		* @throws IllegalArgumentException if some parameter is wrong
		*/
		@@ -111,8 +137,10 @@ public class IcpTransformer extends MeshVisitor {
		* new transformation and applying it). A number bigger than zero.
		* Reasonable number seems to be 10.
		* @param scale If {@code true}, then the scale factor is also computed.
		* @param error Acceptable error. A number bugger than or equal to zero.
		* When reached, then the ICP stops. Reasonable number seems to be 0.05.
		* @param error Acceptable error - a number bigger than or equal to zero.
		* Mean distance of vertices is computed for each ICP iteration.
		* If the difference between the previous and current mean distances is less than the error,
		* then the ICP computation stops. Reasonable number seems to be 0.05.
		* @param strategy One of the reduction strategies. If {@code null}, then {@link NoUndersampling} is used.
		* @throws IllegalArgumentException if some parameter is wrong
		*/
		@@ -142,8 +170,10 @@ public class IcpTransformer extends MeshVisitor {
		* new transformation and applying it). A number bigger than zero.
		* Reasonable number seems to be 10.
		* @param scale If {@code true}, then the scale factor is also computed.
		* @param error Acceptable error. A number bugger than or equal to zero.
		* When reached, then the ICP stops. Reasonable number seems to be 0.05.
		* @param error Acceptable error - a number bigger than or equal to zero.
		* Mean distance of vertices is computed for each ICP iteration.
		* If the difference between the previous and current mean distances is less than the error,
		* then the ICP computation stops. Reasonable number seems to be 0.05.
		* @param strategy One of the reduction strategies. If {@code null}, then {@link NoUndersampling} is used.
		* @throws IllegalArgumentException if some parameter is wrong
		*/
		@@ -163,7 +193,10 @@ public class IcpTransformer extends MeshVisitor {
		* new transformation and applying it). A number bigger than zero.
		* Reasonable number seems to be 10.
		* @param scale If {@code true}, then the scale factor is also computed.
		* @param error Acceptable error. A number bugger than or equal to zero. When reached, then the ICP stops.
		* @param error Acceptable error - a number bigger than or equal to zero.
		* Mean distance of vertices is computed for each ICP iteration.
		* If the difference between the previous and current mean distances is less than the error,
		* then the ICP computation stops. Reasonable number seems to be 0.05.
		* @param strategy One of the reduction strategies. If {@code null}, then {@link NoUndersampling} is used.
		* @throws IllegalArgumentException if some parameter is wrong
		*/
		@@ -259,6 +292,8 @@ public class IcpTransformer extends MeshVisitor {
		false // auto crop
		);

		//int numSamples = samplingStrategy.getNumDownsampledPoints(transformedFacet.getNumberOfVertices());
		//samplingStrategy.setRequiredSamples(200);
		MeshFacet reducedFacet = new UndersampledMeshFacet(transformedFacet, samplingStrategy);

		int currentIteration = 0;
		@@ -290,6 +325,11 @@ public class IcpTransformer extends MeshVisitor {
		}

		currentIteration++;

		//if (currentIteration >= 3) {
		// samplingStrategy.setRequiredSamples(numSamples);
		// reducedFacet = new UndersampledMeshFacet(transformedFacet, samplingStrategy);
		//}
		}
		}

Comparison/src/main/java/cz/fidentis/analyst/visitors/mesh/sampling/PointSampling.java

+1 −1

Original line number	Diff line number	Diff line
		@@ -134,7 +134,7 @@ public abstract class PointSampling extends MeshVisitor {
		* @param origPoints Original number of vertices
		* @return number of points to be returned after downsampling.
		*/
		protected int getNumDownsampledPoints(int origPoints) {
		public int getNumDownsampledPoints(int origPoints) {
		switch (this.samplingType) {
		case PERCENTAGE:
		return (int) (origPoints * this.samplingLimit);

GUI/src/main/java/cz/fidentis/analyst/batch/BatchPanel.java

+1 −1

Original line number	Diff line number	Diff line
		@@ -66,7 +66,7 @@ public class BatchPanel extends ControlPanel {

		jButton1.addActionListener(createListener(action, ACTION_COMMAND_COMPUTE_ICP));

		spinSlider1.initInteger(2000, 0, 5000, 1);
		spinSlider1.initInteger(1000, 0, 5000, 1); // downsampling strength

		jComboBox2.addItem(SIMILARITY_APPROX_HD);
		jComboBox2.addItem(SIMILARITY_COMPLETE_HD);

GUI/src/main/java/cz/fidentis/analyst/batch/IcpTask.java

+6 −3

Original line number	Diff line number	Diff line
		@@ -28,6 +28,9 @@ import javax.swing.SwingWorker;
		*/
		public class IcpTask extends SwingWorker<MeshModel, HumanFace> {

		private static final double ICP_ERROR = 0.3; //0.05;
		private static final int ICP_ITERS = 100;

		private HumanFace avgFace;
		private final ProgressDialog progressDialog;
		private final BatchPanel controlPanel;
		@@ -121,14 +124,14 @@ public class IcpTask extends SwingWorker<MeshModel, HumanFace> {
		HumanFaceUtils.alignMeshes(
		initFace,
		face, // is transformed
		100, // max iterations
		ICP_ITERS, // max iterations
		controlPanel.scaleIcp(),
		0.3, // error
		ICP_ERROR, // error
		sampling,
		false // drop k-d tree, if exists
		);
		icpComputationTime.stop();
		System.out.println(sampling);
		//System.out.println(sampling);
		}

		if (computeAvgFace) { // AVG template face

GUI/src/main/java/cz/fidentis/analyst/tests/IcpDownsampling.java

+61 −31

Original line number	Diff line number	Diff line
		@@ -3,10 +3,9 @@ package cz.fidentis.analyst.tests;
		import cz.fidentis.analyst.batch.Stopwatch;
		import cz.fidentis.analyst.face.HumanFace;
		import cz.fidentis.analyst.face.HumanFaceUtils;
		import cz.fidentis.analyst.icp.UndersampledMeshFacet;
		import cz.fidentis.analyst.mesh.core.MeshFacet;
		import cz.fidentis.analyst.mesh.core.MeshModel;
		import cz.fidentis.analyst.symmetry.Plane;
		import cz.fidentis.analyst.symmetry.SymmetryEstimator;
		import cz.fidentis.analyst.symmetry.SymmetryEstimatorRobust;
		import cz.fidentis.analyst.visitors.mesh.HausdorffDistance;
		import cz.fidentis.analyst.visitors.mesh.sampling.CurvatureSampling;
		import cz.fidentis.analyst.visitors.mesh.sampling.NoSampling;
		@@ -19,6 +18,7 @@ import java.io.IOException;
		import java.nio.file.Files;
		import java.nio.file.Path;
		import java.util.ArrayList;
		import java.util.Collections;
		import java.util.List;
		import java.util.Map;
		import java.util.SortedMap;
		@@ -48,13 +48,13 @@ public class IcpDownsampling {
		.limit(MAX_SAMPLES)
		.collect(Collectors.toList());

		SortedMap<Integer, Stopwatch> efficiency = new TreeMap<>();
		SortedMap<Integer, List<Double>> precision = new TreeMap<>();
		SortedMap<Integer, Stopwatch> efficiency = new TreeMap<>(Collections.reverseOrder());
		SortedMap<Integer, List<Double>> precision = new TreeMap<>(Collections.reverseOrder());

		//String alg = "random";
		String alg = "random";
		//String alg = "gaussian";
		//String alg = "uniform space";
		String alg = "uniform mesh";
		//String alg = "uniform mesh";

		int counter = 1;
		for (int i = 0; i < faces.size(); i += 5) {
		@@ -78,7 +78,7 @@ public class IcpDownsampling {
		});
		System.out.println();
		System.out.println("Avg. Precision - " + sampling + " sampling:");
		System.out.println("# samples;average similarity;variance");
		System.out.println("# samples;dissimilarity;variance");
		precision.entrySet().forEach(e -> {
		double avg = e.getValue().stream()
		.mapToDouble(val -> val)
		@@ -100,64 +100,86 @@ public class IcpDownsampling {
		//double[] percs = new double[]{0.5,1,2,4,6,8,10,15,20,30,40,50,60,70,80,85,90,92,94,96,98,100};
		//double[] percs = new double[]{100,90,80,70,60,50,40,30,20,15,10,8,6,4,2,1,0.5};
		//int[] samples = new int[]{0,200,500,1000,2000,3000,5000,10000,20000,30000,40000};
		int[] samples = new int[]{0,200,500,1000,2000,3000,5000};
		//int[] samples = new int[]{0,200,500,1000,2000,3000,5000};
		//int[] samples = new int[]{50,100,150,300,400};
		//int[] samples = new int[]{0,50,100,150,200,300,400,500,1000,2000,3000,5000,10000,20000,30000};
		//int[] samples = new int[]{5,10,20,30,40,50};
		int[] samples = new int[]{0,40,50,100,150,200,300,400,500,1000,2000,3000,5000,10000,20000,30000};

		HumanFace priFace = new HumanFace(priFacePath.toFile());
		priFace.computeKdTree(false);
		SymmetryEstimator seVisitor = new SymmetryEstimatorRobust(new UniformSpaceSampling(), 100, 1000);
		priFace.getMeshModel().compute(seVisitor);
		priFace.setSymmetryPlane(seVisitor.getSymmetryPlane());

		HumanFace secFaceFromFile = new HumanFace(secFacePath.toFile());
		seVisitor = new SymmetryEstimatorRobust(new UniformSpaceSampling(), 100, 1000);
		secFaceFromFile.getMeshModel().compute(seVisitor);
		secFaceFromFile.setSymmetryPlane(seVisitor.getSymmetryPlane());


		System.out.println(secFaceFromFile.getShortName());
		for (int i: samples) {
		HumanFace secFace = new HumanFace(new MeshModel(secFaceFromFile.getMeshModel()), secFaceFromFile.getId());
		secFace.setSymmetryPlane(new Plane(secFaceFromFile.getSymmetryPlane()));

		PointSampling sampling;
		PointSampling secSampling;
		PointSampling priSampling;
		switch (samp) {
		case "random":
		sampling = new RandomSampling();
		secSampling = new RandomSampling();
		priSampling = new RandomSampling();
		break;
		case "gaussian":
		sampling = new CurvatureSampling(CurvatureSampling.CurvatureAlg.GAUSSIAN);
		secSampling = new CurvatureSampling(CurvatureSampling.CurvatureAlg.GAUSSIAN);
		priSampling = new CurvatureSampling(CurvatureSampling.CurvatureAlg.GAUSSIAN);
		break;
		case "uniform space":
		sampling = new UniformSpaceSampling();
		secSampling = new UniformSpaceSampling();
		priSampling = new UniformSpaceSampling();
		break;
		case "uniform mesh":
		sampling = new UniformSurfaceSampling();
		secSampling = new UniformSurfaceSampling();
		priSampling = new UniformSurfaceSampling();
		break;
		default:
		return;
		}

		/*
		if (samples[i] == 0) {
		secSampling = new NoSampling();
		priSampling = new NoSampling();
		} else {
		secSampling.setRequiredSamples(i);
		priSampling.setRequiredSamples(i);
		}
		*/

		if (i == 0) {
		sampling = new NoSampling();
		priSampling = new NoSampling();
		} else {
		//sampling.setRequiredSamples(i/100.0);
		sampling.setRequiredSamples(i);
		priSampling.setRequiredSamples(i);
		}
		secSampling.setRequiredSamples(5000);

		efficiency.computeIfAbsent(i, k-> new Stopwatch("")).start();
		HumanFaceUtils.alignSymmetryPlanes(priFace, secFace, false, true);
		HumanFaceUtils.alignMeshes(
		downsamplePriFace(priFace, priSampling),
		secFace, // is transformed
		100, // max iterations
		false,// scale
		0.05, // error
		secSampling,
		false // drop k-d tree of the secFace, if exists
		);
		/*
		HumanFaceUtils.alignMeshes( /// ORIG SETTING
		priFace,
		secFace, // is transformed
		100, // max iterations
		false,// scale
		0.3, // error
		sampling,
		false // drop k-d tree, if exists
		0.05, // error
		secSampling,
		false // drop k-d tree of the secFace, if exists
		);
		*/
		efficiency.get(i).stop();

		System.out.println("" + sampling);
		System.out.println(" Primary face: " + priSampling);
		System.out.println("Secondary face: " + secSampling);

		HausdorffDistance hd = new HausdorffDistance(
		priFace.getKdTree(),
		@@ -171,4 +193,12 @@ public class IcpDownsampling {
		}
		System.out.println();
		}

		private static HumanFace downsamplePriFace(HumanFace priFace, PointSampling sampling) {
		MeshFacet reducedFacet = new UndersampledMeshFacet(priFace.getMeshModel().getFacets().get(0), sampling);
		MeshModel model = new MeshModel();
		model.setFacets(List.of(reducedFacet));
		HumanFace ret = new HumanFace(model, priFace.getId());
		return ret;
		}
		}