Point2DRobustEstimator.java
/*
* Copyright (C) 2015 Alberto Irurueta Carro (alberto@irurueta.com)
*
* 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.geometry.estimators;
import com.irurueta.algebra.Matrix;
import com.irurueta.geometry.CoordinatesType;
import com.irurueta.geometry.HomogeneousPoint2D;
import com.irurueta.geometry.InhomogeneousPoint2D;
import com.irurueta.geometry.Line2D;
import com.irurueta.geometry.Point2D;
import com.irurueta.geometry.refiners.HomogeneousPoint2DRefiner;
import com.irurueta.geometry.refiners.InhomogeneousPoint2DRefiner;
import com.irurueta.geometry.refiners.Point2DRefiner;
import com.irurueta.numerical.robust.InliersData;
import com.irurueta.numerical.robust.RobustEstimatorException;
import com.irurueta.numerical.robust.RobustEstimatorMethod;
import java.util.List;
/**
* This is an abstract class for algorithms to robustly find the best 3D point
* that intersects in a collection of 2D lines.
* Implementations of this class should be able to detect and discard outliers
* in order to find the best solution.
*/
@SuppressWarnings("DuplicatedCode")
public abstract class Point2DRobustEstimator {
/**
* Minimum number of 2D lines required to estimate a point.
*/
public static final int MINIMUM_SIZE = 2;
/**
* Default amount of progress variation before notifying a change in
* estimation progress. By default, this is set to 5%.
*/
public static final float DEFAULT_PROGRESS_DELTA = 0.05f;
/**
* Minimum allowed value for progress delta.
*/
public static final float MIN_PROGRESS_DELTA = 0.0f;
/**
* Maximum allowed value for progress delta.
*/
public static final float MAX_PROGRESS_DELTA = 1.0f;
/**
* Constant defining default confidence of the estimated result, which is
* 99%. This means that with a probability of 99% estimation will be
* accurate because chosen sub-samples will be inliers.
*/
public static final double DEFAULT_CONFIDENCE = 0.99;
/**
* Default maximum allowed number of iterations.
*/
public static final int DEFAULT_MAX_ITERATIONS = 5000;
/**
* Minimum allowed confidence value.
*/
public static final double MIN_CONFIDENCE = 0.0;
/**
* Maximum allowed confidence value.
*/
public static final double MAX_CONFIDENCE = 1.0;
/**
* Minimum allowed number of iterations.
*/
public static final int MIN_ITERATIONS = 1;
/**
* Default robust estimator method when none is provided.
*/
public static final RobustEstimatorMethod DEFAULT_ROBUST_METHOD = RobustEstimatorMethod.PROMEDS;
/**
* Indicates that result is refined by default using Levenberg-Marquardt
* fitting algorithm over found inliers.
*/
public static final boolean DEFAULT_REFINE_RESULT = true;
/**
* Indicates that covariance is not kept by default after refining result.
*/
public static final boolean DEFAULT_KEEP_COVARIANCE = false;
/**
* Listener to be notified of events such as when estimation starts, ends
* or its progress significantly changes.
*/
protected Point2DRobustEstimatorListener listener;
/**
* Indicates if this estimator is locked because an estimation is being
* computed.
*/
protected volatile boolean locked;
/**
* Amount of progress variation before notifying a progress change during
* estimation.
*/
protected float progressDelta;
/**
* Amount of confidence expressed as a value between 0.0 and 1.0 (which is
* equivalent to 100%). The amount of confidence indicates the probability
* that the estimated result is correct. Usually this value will be close
* to 1.0, but not exactly 1.0.
*/
protected double confidence;
/**
* Maximum allowed number of iterations. When the maximum number of
* iterations is exceeded, result will not be available, however an
* approximate result will be available for retrieval.
*/
protected int maxIterations;
/**
* List of lines to be used to estimate a 2D point. Provided list must have
* a size greater or equal than MINIMUM_SIZE.
*/
protected List<Line2D> lines;
/**
* Data related to inliers found after estimation.
*/
protected InliersData inliersData;
/**
* Indicates whether result must be refined using Levenberg-Marquardt
* fitting algorithm over found inliers.
* If true, inliers will be computed and kept in any implementation
* regardless of the settings.
*/
protected boolean refineResult;
/**
* Coordinates type to use for refinement. When using inhomogeneous
* coordinates a 3x3 covariance matrix is estimated. When using homogeneous
* coordinates a 4x4 covariance matrix is estimated.
*/
private CoordinatesType refinementCoordinatesType = CoordinatesType.INHOMOGENEOUS_COORDINATES;
/**
* Indicates whether covariance must be kept after refining result.
* This setting is only taken into account if result is refined.
*/
private boolean keepCovariance;
/**
* Estimated covariance of estimated 2D point.
* This is only available when result has been refined and covariance is
* kept.
*/
private Matrix covariance;
/**
* Constructor.
*/
protected Point2DRobustEstimator() {
progressDelta = DEFAULT_PROGRESS_DELTA;
confidence = DEFAULT_CONFIDENCE;
maxIterations = DEFAULT_MAX_ITERATIONS;
refineResult = DEFAULT_REFINE_RESULT;
keepCovariance = DEFAULT_KEEP_COVARIANCE;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
protected Point2DRobustEstimator(final Point2DRobustEstimatorListener listener) {
this.listener = listener;
progressDelta = DEFAULT_PROGRESS_DELTA;
confidence = DEFAULT_CONFIDENCE;
maxIterations = DEFAULT_MAX_ITERATIONS;
refineResult = DEFAULT_REFINE_RESULT;
keepCovariance = DEFAULT_KEEP_COVARIANCE;
}
/**
* Constructor with lines.
*
* @param lines 2D lines to estimate a 2D point.
* @throws IllegalArgumentException if provided list of lines don't have
* a size greater or equal than MINIMUM_SIZE.
*/
protected Point2DRobustEstimator(final List<Line2D> lines) {
progressDelta = DEFAULT_PROGRESS_DELTA;
confidence = DEFAULT_CONFIDENCE;
maxIterations = DEFAULT_MAX_ITERATIONS;
internalSetLines(lines);
refineResult = DEFAULT_REFINE_RESULT;
keepCovariance = DEFAULT_KEEP_COVARIANCE;
}
/**
* Constructor.
*
* @param lines 2D lines to estimate a 2D point.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided list of lines don't have
* a size greater or equal than MINIMUM_SIZE.
*/
protected Point2DRobustEstimator(final Point2DRobustEstimatorListener listener, final List<Line2D> lines) {
this.listener = listener;
progressDelta = DEFAULT_PROGRESS_DELTA;
confidence = DEFAULT_CONFIDENCE;
maxIterations = DEFAULT_MAX_ITERATIONS;
internalSetLines(lines);
refineResult = DEFAULT_REFINE_RESULT;
keepCovariance = DEFAULT_KEEP_COVARIANCE;
}
/**
* Returns reference to listener to be notified of events such as when
* estimation starts, ends or its progress significantly changes.
*
* @return listener to be notified of events.
*/
public Point2DRobustEstimatorListener getListener() {
return listener;
}
/**
* Sets listener to be notified of events such as when estimation starts,
* ends or its progress significantly changes.
*
* @param listener listener to be notified of events.
* @throws LockedException if robust estimator is locked.
*/
public void setListener(final Point2DRobustEstimatorListener listener) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
this.listener = listener;
}
/**
* Indicates whether listener has been provided and is available for
* retrieval.
*
* @return true if available, false otherwise.
*/
public boolean isListenerAvailable() {
return listener != null;
}
/**
* Indicates if this instance is locked because estimation is being computed.
*
* @return true if locked, false otherwise.
*/
public boolean isLocked() {
return locked;
}
/**
* Returns amount of progress variation before notifying a progress change
* during estimation.
*
* @return amount of progress variation before notifying a progress change
* during estimation.
*/
public float getProgressDelta() {
return progressDelta;
}
/**
* Sets amount of progress variation before notifying a progress change
* during estimation.
*
* @param progressDelta amount of progress variation before notifying a
* progress change during estimation.
* @throws IllegalArgumentException if progress delta is less than zero or
* greater than 1.
* @throws LockedException if this estimator is locked because an estimation
* is being computed.
*/
public void setProgressDelta(final float progressDelta) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (progressDelta < MIN_PROGRESS_DELTA || progressDelta > MAX_PROGRESS_DELTA) {
throw new IllegalArgumentException();
}
this.progressDelta = progressDelta;
}
/**
* Returns amount of confidence expressed as a value between 0.0 and 1.0
* (which is equivalent to 100%). The amount of confidence indicates the
* probability that the estimated result is correct. Usually this value will
* be close to 1.0, but not exactly 1.0.
*
* @return amount of confidence as a value between 0.0 and 1.0.
*/
public double getConfidence() {
return confidence;
}
/**
* Sets amount of confidence expressed as a value between 0.0 and 1.0 (which
* is equivalent to 100%). The amount of confidence indicates the
* probability that the estimated result is correct. Usually this value will
* be close to 1.0, but not exactly 1.0.
*
* @param confidence confidence to be set as a value between 0.0 and 1.0.
* @throws IllegalArgumentException if provided value is not between 0.0 and
* 1.0.
* @throws LockedException if this estimator is locked because an estimator
* is being computed.
*/
public void setConfidence(final double confidence) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (confidence < MIN_CONFIDENCE || confidence > MAX_CONFIDENCE) {
throw new IllegalArgumentException();
}
this.confidence = confidence;
}
/**
* Returns maximum allowed number of iterations. If maximum allowed number
* of iterations is achieved without converging to a result when calling
* estimate(), a RobustEstimatorException will be raised.
*
* @return maximum allowed number of iterations.
*/
public int getMaxIterations() {
return maxIterations;
}
/**
* Sets maximum allowed number of iterations. When the maximum number of
* iterations is exceeded, result will not be available, however an
* approximate result will be available for retrieval.
*
* @param maxIterations maximum allowed number of iterations to be set.
* @throws IllegalArgumentException if provided value is less than 1.
* @throws LockedException if this estimator is locked because an estimation
* is being computed.
*/
public void setMaxIterations(final int maxIterations) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (maxIterations < MIN_ITERATIONS) {
throw new IllegalArgumentException();
}
this.maxIterations = maxIterations;
}
/**
* Gets data related to inliers found after estimation.
*
* @return data related to inliers found after estimation.
*/
public InliersData getInliersData() {
return inliersData;
}
/**
* Indicates whether result must be refined using Levenberg-Marquardt
* fitting algorithm over found inliers.
* If true, inliers will be computed and kept in any implementation
* regardless of the settings.
*
* @return true to refine result, false to simply use result found by
* robust estimator without further refining.
*/
public boolean isResultRefined() {
return refineResult;
}
/**
* Specifies whether result must be refined using Levenberg-Marquardt
* fitting algorithm over found inliers.
*
* @param refineResult true to refine result, false to simply use result
* found by robust estimator without further refining.
* @throws LockedException if estimator is locked.
*/
public void setResultRefined(final boolean refineResult) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
this.refineResult = refineResult;
}
/**
* Gets coordinates type to use for refinement. When using inhomogeneous
* coordinates a 3x3 covariance matrix is estimated. When using homogeneous
* coordinates a 4x4 covariance matrix is estimated.
*
* @return coordinates type to use for refinement.
*/
public CoordinatesType getRefinementCoordinatesType() {
return refinementCoordinatesType;
}
/**
* Sets coordinates type to use for refinement. When using inhomogeneous
* coordinates a 3x3 covariance matrix is estimated. When using homogeneous
* coordinates a 4x4 covariance matrix is estimated.
*
* @param refinementCoordinatesType coordinates type to use for refinement.
* @throws LockedException if estimator is locked.
*/
public void setRefinementCoordinatesType(final CoordinatesType refinementCoordinatesType) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
this.refinementCoordinatesType = refinementCoordinatesType;
}
/**
* Indicates whether covariance must be kept after refining result.
* This setting is only taken into account if result is refined.
*
* @return true if covariance must be kept after refining result, false
* otherwise.
*/
public boolean isCovarianceKept() {
return keepCovariance;
}
/**
* Specifies whether covariance must be kept after refining result.
* This setting is only taken into account if result is refined.
*
* @param keepCovariance true if covariance must be kept after refining
* result, false otherwise.
* @throws LockedException if estimator is locked.
*/
public void setCovarianceKept(final boolean keepCovariance) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
this.keepCovariance = keepCovariance;
}
/**
* Returns list of lines to be used to estimate a 2D point.
* Provided list must have a size greater or equal than MINIMUM_SIZE
*
* @return list of lines to be used to estimate a 2D point.
*/
public List<Line2D> getLines() {
return lines;
}
/**
* Sets list of lines to be used to estimate a 2D point.
* Provided list must have a size greater or equal than MINIMUM_SIZE.
*
* @param lines list of lines to be used to estimate a 2D point.
* @throws IllegalArgumentException if provided list of lines don't have
* a size greater or equal than MINIMUM_SIZE.
* @throws LockedException if estimator is locked because a computation is
* already in progress.
*/
public void setLines(final List<Line2D> lines) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetLines(lines);
}
/**
* Indicates if estimator is ready to start the 2D point estimation.
* This is true when a minimum if MINIMUM_SIZE lines are available.
*
* @return true if estimator is ready, false otherwise.
*/
public boolean isReady() {
return lines != null && lines.size() >= MINIMUM_SIZE;
}
/**
* Returns quality scores corresponding to each line.
* The larger the score value the better the quality of the line measure.
* This implementation always returns null.
* Subclasses using quality scores must implement proper behaviour.
*
* @return quality scores corresponding to each point.
*/
public double[] getQualityScores() {
return null;
}
/**
* Sets quality scores corresponding to each line.
* The larger the score value the better the quality of the line measure.
* This implementation makes no action.
* Subclasses using quality scores must implement proper behaviour.
*
* @param qualityScores quality scores corresponding to each sampled line.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 2 samples).
*/
public void setQualityScores(final double[] qualityScores) throws LockedException {
}
/**
* Gets estimated covariance of estimated 3D point if available.
* This is only available when result has been refined and covariance is
* kept.
*
* @return estimated covariance or null.
*/
public Matrix getCovariance() {
return covariance;
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided robust estimator method.
*
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
*/
public static Point2DRobustEstimator create(final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator();
case MSAC -> new MSACPoint2DRobustEstimator();
case PROSAC -> new PROSACPoint2DRobustEstimator();
case PROMEDS -> new PROMedSPoint2DRobustEstimator();
default -> new RANSACPoint2DRobustEstimator();
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided lines and robust estimator method.
*
* @param lines 2D lines to estimate a 2D point.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have a
* size greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(final List<Line2D> lines, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator(lines);
case MSAC -> new MSACPoint2DRobustEstimator(lines);
case PROSAC -> new PROSACPoint2DRobustEstimator(lines);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(lines);
default -> new RANSACPoint2DRobustEstimator(lines);
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator(listener);
case MSAC -> new MSACPoint2DRobustEstimator(listener);
case PROSAC -> new PROSACPoint2DRobustEstimator(listener);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener);
default -> new RANSACPoint2DRobustEstimator(listener);
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener and lines.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param lines 2D lines to estimate a 2D point.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have a
* size greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final List<Line2D> lines,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator(listener, lines);
case MSAC -> new MSACPoint2DRobustEstimator(listener, lines);
case PROSAC -> new PROSACPoint2DRobustEstimator(listener, lines);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener, lines);
default -> new RANSACPoint2DRobustEstimator(listener, lines);
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided robust estimator method.
*
* @param qualityScores quality scores corresponding to each provided line.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 2 lines).
*/
public static Point2DRobustEstimator create(final double[] qualityScores, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator();
case MSAC -> new MSACPoint2DRobustEstimator();
case PROSAC -> new PROSACPoint2DRobustEstimator(qualityScores);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(qualityScores);
default -> new RANSACPoint2DRobustEstimator();
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided lines and robust estimator method.
*
* @param lines 2D lines to estimate a 2D point.
* @param qualityScores quality scores corresponding to each provided line.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have
* the same size as the list of provided quality scores, or it their size
* is not greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(
final List<Line2D> lines, final double[] qualityScores, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator(lines);
case MSAC -> new MSACPoint2DRobustEstimator(lines);
case PROSAC -> new PROSACPoint2DRobustEstimator(lines, qualityScores);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(lines, qualityScores);
default -> new RANSACPoint2DRobustEstimator(lines);
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param qualityScores quality scores corresponding to each provided line.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 2 lines).
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final double[] qualityScores,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator(listener);
case MSAC -> new MSACPoint2DRobustEstimator(listener);
case PROSAC -> new PROSACPoint2DRobustEstimator(listener, qualityScores);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener, qualityScores);
default -> new RANSACPoint2DRobustEstimator(listener);
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener and lines.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param lines 2D lines to estimate a 2D point.
* @param qualityScores quality scores corresponding to each provided point.
* @param method method of a robust estimator algorithm to estimate the best
* 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have
* the same size as the list of provided quality scores, or it their size
* is not greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final List<Line2D> lines, final double[] qualityScores,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSPoint2DRobustEstimator(listener, lines);
case MSAC -> new MSACPoint2DRobustEstimator(listener, lines);
case PROSAC -> new PROSACPoint2DRobustEstimator(listener, lines, qualityScores);
case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener, lines, qualityScores);
default -> new RANSACPoint2DRobustEstimator(listener, lines);
};
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* default robust estimator method.
*
* @return an instance of a 2D point robust estimator.
*/
public static Point2DRobustEstimator create() {
return create(DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided lines and default robust estimator method.
*
* @param lines 2D lines to estimate a 2D point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have a
* size greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(final List<Line2D> lines) {
return create(lines, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener and default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @return an instance of a 2D point robust estimator.
*/
public static Point2DRobustEstimator create(final Point2DRobustEstimatorListener listener) {
return create(listener, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener and lines and default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param lines 2D lines to estimate a point.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have a
* size greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final List<Line2D> lines) {
return create(listener, lines, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* default robust estimator method.
*
* @param qualityScores quality scores corresponding to each provided line
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 2 lines).
*/
public static Point2DRobustEstimator create(final double[] qualityScores) {
return create(qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided lines and default estimator method.
*
* @param lines 2D lines to estimate a 2D point.
* @param qualityScores quality scores corresponding to each provided line.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have
* the same size as the list of provided quality scores, or if their size
* is not greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(final List<Line2D> lines, final double[] qualityScores) {
return create(lines, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener and default estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param qualityScores quality scores corresponding to each provided line
* @return an instance of a circle robust estimator.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 2 lines).
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final double[] qualityScores) {
return create(listener, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a 2D point robust estimator based on 2D line samples and using
* provided listener and lines and default estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param lines 2D lines to estimate a 2D point.
* @param qualityScores quality scores corresponding to each provided line.
* @return an instance of a 2D point robust estimator.
* @throws IllegalArgumentException if provided list of lines don't have
* the same size as the list of provided quality scores, or if their size
* is not greater or equal than MINIMUM_SIZE.
*/
public static Point2DRobustEstimator create(
final Point2DRobustEstimatorListener listener, final List<Line2D> lines, final double[] qualityScores) {
return create(listener, lines, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Estimates a 2D point using a robust estimator and the best set of 2D
* lines that intersect into the estimated 2D point.
*
* @return a 2D point.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws NotReadyException if provided input data is not enough to start
* the estimation.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
public abstract Point2D estimate() throws LockedException, NotReadyException, RobustEstimatorException;
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
public abstract RobustEstimatorMethod getMethod();
/**
* Computes the residual between a 2D point and a line.
*
* @param p a 2D point.
* @param line a 2D line.
* @return residual.
*/
protected double residual(final Point2D p, final Line2D line) {
p.normalize();
line.normalize();
return Math.abs(line.signedDistance(p));
}
/**
* Attempts to refine provided solution if refinement is requested.
* This method returns a refined solution or the same provided solution
* if refinement is not requested or has failed.
* If refinement is enabled, and it is requested to keep covariance, this
* method will also keep covariance of refined point.
*
* @param point point estimated by a robust estimator without refinement.
* @return solution after refinement (if requested) or the provided non-refined
* solution if not requested or if refinement failed.
*/
protected Point2D attemptRefine(final Point2D point) {
if (refineResult) {
try {
final Point2DRefiner<? extends Point2D> refiner;
final Point2D result;
final boolean improved;
switch (refinementCoordinatesType) {
case HOMOGENEOUS_COORDINATES:
final HomogeneousPoint2D homP;
if (point.getType() == CoordinatesType.HOMOGENEOUS_COORDINATES) {
homP = (HomogeneousPoint2D) point;
} else {
homP = new HomogeneousPoint2D(point);
}
final var homRefiner = new HomogeneousPoint2DRefiner(homP, keepCovariance, getInliersData(),
lines, getRefinementStandardDeviation());
refiner = homRefiner;
final var homResult = new HomogeneousPoint2D();
improved = homRefiner.refine(homResult);
result = homResult;
break;
case INHOMOGENEOUS_COORDINATES:
default:
InhomogeneousPoint2D inhomP;
if (point.getType() == CoordinatesType.INHOMOGENEOUS_COORDINATES) {
inhomP = (InhomogeneousPoint2D) point;
} else {
inhomP = new InhomogeneousPoint2D(point);
}
final var inhomRefiner = new InhomogeneousPoint2DRefiner(inhomP, keepCovariance,
getInliersData(), lines, getRefinementStandardDeviation());
refiner = inhomRefiner;
final var inhomResult = new InhomogeneousPoint2D();
improved = inhomRefiner.refine(inhomResult);
result = inhomResult;
break;
}
if (keepCovariance) {
// keep covariance
covariance = refiner.getCovariance();
}
return improved ? result : point;
} catch (final Exception e) {
// refinement failed, so we return input value
return point;
}
} else {
return point;
}
}
/**
* 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.
*/
protected abstract double getRefinementStandardDeviation();
/**
* Internal method to set list of 2D lines to be used to estimate a 2D
* point.
* This method does not check whether estimator is locked or not
*
* @param lines list of lines to be used to estimate a 2D point
* @throws IllegalArgumentException if provided list of lines doesn't have
* a size greater or equal than MINIMUM_SIZE.
*/
private void internalSetLines(final List<Line2D> lines) {
if (lines.size() < MINIMUM_SIZE) {
throw new IllegalArgumentException();
}
this.lines = lines;
}
}