Commit 16c9fcaa authored by Radek Ošlejšek's avatar Radek Ošlejšek
Browse files

Merge branch '180-adaptive-downsampling-for-icp' into 'master' 

Resolve "Adaptive downsampling for ICP"

Closes #180

See merge request grp-fidentis/analyst2!190
parents eacd7eef 5b791b26

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;

    }

}