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Augmented Reality and 3D reconstruction library
/*
* Copyright (C) 2017 Alberto Irurueta Carro ([email protected])
*
* 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 com.irurueta.ar.epipolar.refiners;
import com.irurueta.algebra.AlgebraException;
import com.irurueta.algebra.Matrix;
import com.irurueta.ar.epipolar.FundamentalMatrix;
import com.irurueta.ar.epipolar.InvalidFundamentalMatrixException;
import com.irurueta.geometry.CoordinatesType;
import com.irurueta.geometry.Line2D;
import com.irurueta.geometry.Point2D;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
import com.irurueta.geometry.refiners.PairMatchesAndInliersDataRefiner;
import com.irurueta.geometry.refiners.RefinerException;
import com.irurueta.numerical.EvaluationException;
import com.irurueta.numerical.GradientEstimator;
import com.irurueta.numerical.MultiDimensionFunctionEvaluatorListener;
import com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter;
import com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFunctionEvaluator;
import com.irurueta.numerical.robust.InliersData;
import java.util.BitSet;
import java.util.List;
/**
* Refines a fundamental matrix by taking into account an initial estimation,
* inlier matches and their residuals.
* This class can be used to find a solution that minimizes error of inliers
* in LMSE terms.
* Typically a refiner is used by a robust estimator, however it can also be
* useful in some other situations.
*/
@SuppressWarnings("DuplicatedCode")
public class FundamentalMatrixRefiner extends
PairMatchesAndInliersDataRefiner {
/**
* Test line to compute epipolar residuals.
*/
private final Line2D mTestLine = new Line2D();
/**
* Standard deviation used for Levenberg-Marquardt fitting during
* refinement.
* Returned value gives an indication of how much variance each residual
* has.
* Typically this value is related to the threshold used on each robust
* estimation, since residuals of found inliers are within the range of
* such threshold.
*/
private double mRefinementStandardDeviation;
/**
* Constructor.
*/
public FundamentalMatrixRefiner() {
}
/**
* Constructor.
*
* @param initialEstimation initial estimation to be set.
* @param keepCovariance true if covariance of estimation must be kept after
* refinement, false otherwise.
* @param inliers set indicating which of the provided matches are inliers.
* @param residuals residuals for matched samples.
* @param numInliers number of inliers on initial estimation.
* @param samples1 1st set of paired samples.
* @param samples2 2nd set of paired samples.
* @param refinementStandardDeviation standard deviation used for
* Levenberg-Marquardt fitting.
*/
public FundamentalMatrixRefiner(
final FundamentalMatrix initialEstimation,
final boolean keepCovariance,
final BitSet inliers,
final double[] residuals,
final int numInliers,
final List samples1,
final List samples2,
final double refinementStandardDeviation) {
super(initialEstimation, keepCovariance, inliers, residuals, numInliers,
samples1, samples2);
mRefinementStandardDeviation = refinementStandardDeviation;
}
/**
* Constructor.
*
* @param initialEstimation initial estimation to be set.
* @param keepCovariance true if covariance of estimation must be kept after
* refinement, false otherwise.
* @param inliersData inlier data, typically obtained from a robust
* estimator.
* @param samples1 1st set of paired samples.
* @param samples2 2nd set of paired samples.
* @param refinementStandardDeviation standard deviation used for
* Levenberg-Marquardt fitting.
*/
public FundamentalMatrixRefiner(
final FundamentalMatrix initialEstimation,
final boolean keepCovariance,
final InliersData inliersData,
final List samples1,
final List samples2,
final double refinementStandardDeviation) {
super(initialEstimation, keepCovariance, inliersData, samples1,
samples2);
mRefinementStandardDeviation = refinementStandardDeviation;
}
/**
* Gets standard deviation used for Levenberg-Marquardt fitting during
* refinement.
* Returned value gives an indication of how much variance each residual
* has.
* Typically this value is related to the threshold used on each robust
* estimation, since residuals of found inliers are within the range of such
* threshold.
*
* @return standard deviation used for refinement.
*/
public double getRefinementStandardDeviation() {
return mRefinementStandardDeviation;
}
/**
* Sets standard deviation used for Levenberg-Marquardt fitting during
* refinement.
* Returned value gives an indication of how much variance each residual
* has.
* Typically this value is related to the threshold used on each robust
* estimation, since residuals of found inliers are within the range of such
* threshold.
*
* @param refinementStandardDeviation standard deviation used for
* refinement.
* @throws LockedException if estimator is locked.
*/
public void setRefinementStandardDeviation(
final double refinementStandardDeviation) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mRefinementStandardDeviation = refinementStandardDeviation;
}
/**
* Refines provided initial estimation.
*
* @return refined estimation.
* @throws NotReadyException if not enough input data has been provided.
* @throws LockedException if estimator is locked because refinement is
* already in progress.
* @throws RefinerException if refinement fails for some reason (e.g. unable
* to converge to a result).
*/
@Override
public FundamentalMatrix refine() throws NotReadyException,
LockedException, RefinerException {
final FundamentalMatrix result = new FundamentalMatrix();
refine(result);
return result;
}
/**
* Refines provided initial estimation.
* This method always sets a value into provided result instance regardless
* of the fact that error has actually improved in LMSE terms or not.
*
* @param result instance where refined estimation will be stored.
* @return true if result improves (decreases) in LMSE terms respect to
* initial estimation, false if no improvement has been achieved.
* @throws NotReadyException if not enough input data has been provided.
* @throws LockedException if estimator is locked because refinement is
* already in progress.
* @throws RefinerException if refinement fails for some reason (e.g. unable
* to converge to a result).
*/
@Override
public boolean refine(final FundamentalMatrix result) throws NotReadyException,
LockedException, RefinerException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
mLocked = true;
if (mListener != null) {
mListener.onRefineStart(this, mInitialEstimation);
}
mInitialEstimation.normalize();
final double initialTotalResidual = totalResidual(mInitialEstimation);
try {
final Matrix internalMatrix = mInitialEstimation.getInternalMatrix();
final double[] initParams = internalMatrix.getBuffer();
// output values to be fitted/optimized will contain residuals
final double[] y = new double[mNumInliers];
// input values will contain 2 points to compute residuals
final int nDims = 2 * Point2D.POINT2D_HOMOGENEOUS_COORDINATES_LENGTH;
final Matrix x = new Matrix(mNumInliers, nDims);
final int nSamples = mInliers.length();
int pos = 0;
Point2D leftPoint;
Point2D rightPoint;
for (int i = 0; i < nSamples; i++) {
if (mInliers.get(i)) {
// sample is inlier
leftPoint = mSamples1.get(i);
rightPoint = mSamples2.get(i);
leftPoint.normalize();
rightPoint.normalize();
x.setElementAt(pos, 0, leftPoint.getHomX());
x.setElementAt(pos, 1, leftPoint.getHomY());
x.setElementAt(pos, 2, leftPoint.getHomW());
x.setElementAt(pos, 3, rightPoint.getHomX());
x.setElementAt(pos, 4, rightPoint.getHomY());
x.setElementAt(pos, 5, rightPoint.getHomW());
y[pos] = mResiduals[i];
pos++;
}
}
final LevenbergMarquardtMultiDimensionFunctionEvaluator evaluator =
new LevenbergMarquardtMultiDimensionFunctionEvaluator() {
private final Point2D mLeftPoint = Point2D.create(
CoordinatesType.HOMOGENEOUS_COORDINATES);
private final Point2D mRightPoint = Point2D.create(
CoordinatesType.HOMOGENEOUS_COORDINATES);
private final FundamentalMatrix mFundMatrix =
new FundamentalMatrix();
private Matrix mInternalMatrix;
private final GradientEstimator mGradientEstimator =
new GradientEstimator(
new MultiDimensionFunctionEvaluatorListener() {
@Override
public double evaluate(final double[] params) {
try {
mInternalMatrix.fromArray(params);
mFundMatrix.setInternalMatrix(mInternalMatrix);
return residual(mFundMatrix, mLeftPoint,
mRightPoint);
} catch (final AlgebraException | InvalidFundamentalMatrixException e) {
return initialTotalResidual;
}
}
});
@Override
public int getNumberOfDimensions() {
return nDims;
}
@Override
public double[] createInitialParametersArray() {
return initParams;
}
@Override
public double evaluate(
final int i, final double[] point,
final double[] params, final double[] derivatives)
throws EvaluationException {
mLeftPoint.setHomogeneousCoordinates(point[0], point[1],
point[2]);
mRightPoint.setHomogeneousCoordinates(point[3],
point[4], point[5]);
double y;
try {
if (mInternalMatrix == null) {
mInternalMatrix = new Matrix(
FundamentalMatrix.FUNDAMENTAL_MATRIX_ROWS,
FundamentalMatrix.FUNDAMENTAL_MATRIX_COLS);
}
mInternalMatrix.fromArray(params);
mFundMatrix.setInternalMatrix(mInternalMatrix);
y = residual(mFundMatrix, mLeftPoint,
mRightPoint);
} catch (final AlgebraException | InvalidFundamentalMatrixException e) {
y = initialTotalResidual;
}
mGradientEstimator.gradient(params, derivatives);
return y;
}
};
final LevenbergMarquardtMultiDimensionFitter fitter =
new LevenbergMarquardtMultiDimensionFitter(evaluator, x, y,
getRefinementStandardDeviation());
fitter.fit();
// obtain estimated params
final double[] params = fitter.getA();
// update fundamental matrix
internalMatrix.fromArray(params);
result.setInternalMatrix(internalMatrix);
if (mKeepCovariance) {
// keep covariance
mCovariance = fitter.getCovar();
}
final double finalTotalResidual = totalResidual(result);
final boolean errorDecreased = finalTotalResidual < initialTotalResidual;
if (mListener != null) {
mListener.onRefineEnd(this, mInitialEstimation, result,
errorDecreased);
}
return errorDecreased;
} catch (final Exception e) {
throw new RefinerException(e);
} finally {
mLocked = false;
}
}
/**
* Computes the residual between a fundamental matrix and a pair of matched
* points.
*
* @param fundamentalMatrix a fundamental matrix.
* @param leftPoint left 2D point.
* @param rightPoint right 2D point.
* @return residual (distance of point to epipolar line).
*/
private double residual(final FundamentalMatrix fundamentalMatrix,
final Point2D leftPoint, final Point2D rightPoint) {
try {
leftPoint.normalize();
rightPoint.normalize();
fundamentalMatrix.normalize();
fundamentalMatrix.leftEpipolarLine(rightPoint, mTestLine);
final double leftDistance = Math.abs(mTestLine.signedDistance(
leftPoint));
fundamentalMatrix.rightEpipolarLine(leftPoint, mTestLine);
final double rightDistance = Math.abs(mTestLine.signedDistance(
rightPoint));
// return average distance as an error residual
return 0.5 * (leftDistance + rightDistance);
} catch (final NotReadyException e) {
return Double.MAX_VALUE;
}
}
/**
* Computes total residual among all provided inlier samples.
*
* @param fundamentalMatrix a fundamental matrix.
* @return total residual.
*/
private double totalResidual(final FundamentalMatrix fundamentalMatrix) {
double result = 0.0;
final int nSamples = mInliers.length();
Point2D leftPoint;
Point2D rightPoint;
for (int i = 0; i < nSamples; i++) {
if (mInliers.get(i)) {
// sample is inlier
leftPoint = mSamples1.get(i);
rightPoint = mSamples2.get(i);
leftPoint.normalize();
rightPoint.normalize();
result += residual(fundamentalMatrix, leftPoint, rightPoint);
}
}
return result;
}
}
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