/*
* Copyright (c) 2011-2015, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* 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 boofcv.factory.geo;
import boofcv.abst.geo.Estimate1ofEpipolar;
import boofcv.abst.geo.Estimate1ofPnP;
import boofcv.abst.geo.TriangulateTwoViewsCalibrated;
import boofcv.alg.geo.DistanceModelMonoPixels;
import boofcv.alg.geo.pose.PnPDistanceReprojectionSq;
import boofcv.alg.geo.robust.DistanceHomographySq;
import boofcv.alg.geo.robust.DistanceSe3SymmetricSq;
import boofcv.alg.geo.robust.GenerateHomographyLinear;
import boofcv.alg.geo.robust.Se3FromEssentialGenerator;
import boofcv.struct.calib.IntrinsicParameters;
import boofcv.struct.geo.AssociatedPair;
import boofcv.struct.geo.Point2D3D;
import georegression.fitting.homography.ModelManagerHomography2D_F64;
import georegression.fitting.se.ModelManagerSe3_F64;
import georegression.struct.homography.Homography2D_F64;
import georegression.struct.se.Se3_F64;
import org.ddogleg.fitting.modelset.DistanceFromModel;
import org.ddogleg.fitting.modelset.ModelGenerator;
import org.ddogleg.fitting.modelset.ModelManager;
import org.ddogleg.fitting.modelset.lmeds.LeastMedianOfSquares;
import org.ddogleg.fitting.modelset.ransac.Ransac;
/**
* Factory for creating robust false-positive tolerant estimation algorithms in multi-view geometry. These
* algorithms tend to have a lot of boilerplate associated with them and the goal of this factory
* is to make their use much easier and less error prone.
*
* @author Peter Abeles
*/
public class FactoryMultiViewRobust {
/**
* Robust solution to PnP problem using {@link LeastMedianOfSquares LMedS}. Input observations are
* in normalized image coordinates.
*
*
* - Error units is pixels squared.
*
*
* See code for all the details.
*
* @param pnp PnP parameters. Can't be null.
* @param lmeds Parameters for LMedS. Can't be null.
* @return Robust Se3_F64 estimator
*/
public static LeastMedianOfSquares pnpLMedS( ConfigPnP pnp,
ConfigLMedS lmeds)
{
Estimate1ofPnP estimatorPnP = FactoryMultiView.computePnP_1(EnumPNP.P3P_FINSTERWALDER, -1, 1);
DistanceModelMonoPixels distance = new PnPDistanceReprojectionSq();
distance.setIntrinsic(pnp.intrinsic.fx,pnp.intrinsic.fy,pnp.intrinsic.skew);
ModelManagerSe3_F64 manager = new ModelManagerSe3_F64();
EstimatorToGenerator generator =
new EstimatorToGenerator(estimatorPnP);
return new LeastMedianOfSquares(lmeds.randSeed, lmeds.totalCycles, manager, generator, distance);
}
/**
* Robust solution to PnP problem using {@link Ransac}. Input observations are in normalized
* image coordinates.
*
* See code for all the details.
*
* @param pnp PnP parameters. Can't be null.
* @param ransac Parameters for RANSAC. Can't be null.
* @return Robust Se3_F64 estimator
*/
public static Ransac pnpRansac( ConfigPnP pnp,
ConfigRansac ransac)
{
Estimate1ofPnP estimatorPnP = FactoryMultiView.computePnP_1(pnp.which, -1, pnp.numResolve);
DistanceModelMonoPixels distance = new PnPDistanceReprojectionSq();
distance.setIntrinsic(pnp.intrinsic.fx,pnp.intrinsic.fy,pnp.intrinsic.skew);
ModelManagerSe3_F64 manager = new ModelManagerSe3_F64();
EstimatorToGenerator generator =
new EstimatorToGenerator(estimatorPnP);
// convert from pixels to pixels squared
double threshold = ransac.inlierThreshold*ransac.inlierThreshold;
return new Ransac(ransac.randSeed, manager, generator, distance,ransac.maxIterations,threshold);
}
/**
* Robust solution for estimating {@link Se3_F64} using epipolar geometry from two views with
* {@link LeastMedianOfSquares LMedS}. Input observations are in normalized image coordinates.
*
*
* - Error units is pixels squared times two
*
*
* See code for all the details.
*
* @param essential Essential matrix estimation parameters. Can't be null.
* @param lmeds Parameters for RANSAC. Can't be null.
* @return Robust Se3_F64 estimator
*/
public static LeastMedianOfSquares essentialLMedS( ConfigEssential essential,
ConfigLMedS lmeds ) {
essential.checkValidity();
Estimate1ofEpipolar essentialAlg = FactoryMultiView.
computeFundamental_1(essential.which, essential.numResolve);
TriangulateTwoViewsCalibrated triangulate = FactoryMultiView.triangulateTwoGeometric();
ModelManager manager = new ModelManagerSe3_F64();
ModelGenerator generateEpipolarMotion =
new Se3FromEssentialGenerator(essentialAlg, triangulate);
IntrinsicParameters intrinsic = essential.intrinsic;
DistanceFromModel distanceSe3 =
new DistanceSe3SymmetricSq(triangulate,
intrinsic.fx, intrinsic.fy, intrinsic.skew,
intrinsic.fx, intrinsic.fy, intrinsic.skew);
return new LeastMedianOfSquares
(lmeds.randSeed, lmeds.totalCycles, manager, generateEpipolarMotion, distanceSe3);
}
/**
* Robust solution for estimating {@link Se3_F64} using epipolar geometry from two views with
* {@link Ransac}. Input observations are in normalized image coordinates.
*
* See code for all the details.
*
* @param essential Essential matrix estimation parameters. Can't be null.
* @param ransac Parameters for RANSAC. Can't be null.
* @return Robust Se3_F64 estimator
*/
public static Ransac essentialRansac( ConfigEssential essential,
ConfigRansac ransac ) {
essential.checkValidity();
Estimate1ofEpipolar essentialAlg = FactoryMultiView.
computeFundamental_1(essential.which, essential.numResolve);
TriangulateTwoViewsCalibrated triangulate = FactoryMultiView.triangulateTwoGeometric();
ModelManager manager = new ModelManagerSe3_F64();
ModelGenerator generateEpipolarMotion =
new Se3FromEssentialGenerator(essentialAlg, triangulate);
IntrinsicParameters intrinsic = essential.intrinsic;
DistanceFromModel distanceSe3 =
new DistanceSe3SymmetricSq(triangulate,
intrinsic.fx, intrinsic.fy, intrinsic.skew,
intrinsic.fx, intrinsic.fy, intrinsic.skew);
double ransacTOL = ransac.inlierThreshold * ransac.inlierThreshold * 2.0;
return new Ransac(ransac.randSeed, manager, generateEpipolarMotion, distanceSe3,
ransac.maxIterations, ransacTOL);
}
/**
* Robust solution for estimating {@link Homography2D_F64} with {@link LeastMedianOfSquares LMedS}. Input
* observations are in pixel coordinates.
*
*
* - Four point linear is used internally
*
- inlierThreshold is in pixels
*
*
* See code for all the details.
*
* @param homography Homography estimation parameters. If null default is used.
* @param lmeds Parameters for LMedS. Can't be null.
* @return Homography estimator
*/
public static LeastMedianOfSquares
homographyLMedS( ConfigHomography homography , ConfigLMedS lmeds )
{
if( homography == null )
homography = new ConfigHomography();
ModelManager manager = new ModelManagerHomography2D_F64();
GenerateHomographyLinear modelFitter = new GenerateHomographyLinear(homography.normalize);
DistanceHomographySq distance = new DistanceHomographySq();
return new LeastMedianOfSquares
(lmeds.randSeed,lmeds.totalCycles,manager,modelFitter,distance);
}
/**
* Robust solution for estimating {@link Homography2D_F64} with {@link Ransac}. Input
* observations are in pixel coordinates.
*
*
* - Four point linear is used internally
*
- inlierThreshold is in pixels
*
*
* See code for all the details.
*
* @param homography Homography estimation parameters. If null default is used.
* @param ransac Parameters for RANSAC. Can't be null.
* @return Homography estimator
*/
public static Ransac
homographyRansac( ConfigHomography homography , ConfigRansac ransac )
{
if( homography == null )
homography = new ConfigHomography();
ModelManager manager = new ModelManagerHomography2D_F64();
GenerateHomographyLinear modelFitter = new GenerateHomographyLinear(homography.normalize);
DistanceHomographySq distance = new DistanceHomographySq();
double ransacTol = ransac.inlierThreshold*ransac.inlierThreshold;
return new Ransac
(ransac.randSeed,manager,modelFitter,distance,ransac.maxIterations,ransacTol);
}
}
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