/*
* Copyright (C) 2011-2015, Peter Abeles. All Rights Reserved.
*
* This file is part of Geometric Regression Library (GeoRegression).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package georegression.fitting.se;
import georegression.fitting.MotionTransformPoint;
import georegression.geometry.ConvertRotation3D_F64;
import georegression.geometry.UtilPoint3D_F64;
import georegression.misc.GrlConstants;
import georegression.misc.test.GeometryUnitTest;
import georegression.struct.EulerType;
import georegression.struct.point.Point3D_F64;
import georegression.struct.point.Vector3D_F64;
import georegression.struct.se.Se3_F64;
import georegression.transform.se.SePointOps_F64;
import org.ejml.data.DenseMatrix64F;
import org.junit.Test;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import static org.junit.Assert.assertTrue;
/**
* @author Peter Abeles
*/
public abstract class GeneralMotionSe3Tests_F64 {
Random rand = new Random( 434324 );
abstract MotionTransformPoint<Se3_F64, Point3D_F64> createAlg();
@Test
public void noiseless() {
for( int i = 0; i < 100; i++ ) {
DenseMatrix64F R = ConvertRotation3D_F64.eulerToMatrix(EulerType.XYZ, rand.nextGaussian(),
rand.nextGaussian(), rand.nextGaussian(), null);
Vector3D_F64 T = new Vector3D_F64( rand.nextGaussian(),
rand.nextGaussian(), rand.nextGaussian() );
Se3_F64 tran = new Se3_F64( R, T );
List<Point3D_F64> src = UtilPoint3D_F64.random(-10, 10, 30, rand);
List<Point3D_F64> dst = new ArrayList<Point3D_F64>();
for( Point3D_F64 p : src ) {
dst.add(SePointOps_F64.transform(tran, p, null));
}
MotionTransformPoint<Se3_F64, Point3D_F64> alg = createAlg();
assertTrue( alg.process( src, dst ) );
Se3_F64 foundSrcToDst = alg.getTransformSrcToDst();
checkTransform( src, dst, foundSrcToDst, GrlConstants.DOUBLE_TEST_TOL );
}
}
@Test
public void noiselessPlanar() {
for( int i = 0; i < 100; i++ ) {
DenseMatrix64F R = ConvertRotation3D_F64.eulerToMatrix(EulerType.XYZ, rand.nextGaussian(),
rand.nextGaussian(), rand.nextGaussian(), null );
Vector3D_F64 T = new Vector3D_F64( rand.nextGaussian(),
rand.nextGaussian(), rand.nextGaussian() );
Se3_F64 tran = new Se3_F64( R, T );
List<Point3D_F64> from = createPlanar(30);
List<Point3D_F64> to = new ArrayList<Point3D_F64>();
for( Point3D_F64 p : from ) {
to.add( SePointOps_F64.transform( tran, p, null ) );
}
MotionTransformPoint<Se3_F64, Point3D_F64> alg = createAlg();
assertTrue( alg.process( from, to ) );
Se3_F64 tranFound = alg.getTransformSrcToDst();
// R.print();
// tranFound.getR().print();
checkTransform( from, to, tranFound, GrlConstants.DOUBLE_TEST_TOL );
}
}
private List<Point3D_F64> createPlanar( int N ) {
List<Point3D_F64> ret = new ArrayList<Point3D_F64>();
for( int i = 0; i < N; i++ )
ret.add( new Point3D_F64((double) (rand.nextGaussian()*2),(double)(rand.nextGaussian()*2),3));
return ret;
}
public static void checkTransform( List<Point3D_F64> src, List<Point3D_F64> dst, Se3_F64 foundSrcToDst, double tol ) {
Point3D_F64 foundPt = new Point3D_F64();
for( int i = 0; i < src.size(); i++ ) {
Point3D_F64 p = src.get( i );
SePointOps_F64.transform( foundSrcToDst, p, foundPt );
GeometryUnitTest.assertEquals(dst.get(i), foundPt, tol);
}
}
}