EuclideanTransformation2DRobustEstimator.java
/*
* Copyright (C) 2017 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.EuclideanTransformation2D;
import com.irurueta.geometry.Point2D;
import com.irurueta.geometry.refiners.EuclideanTransformation2DRefiner;
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 to robustly find the best Euclidean transformation
* for collections of matching 2D points.
* Implementations of this class should be able to detect and discard outliers
* in order to find the best solution.
*/
@SuppressWarnings("DuplicatedCode")
public abstract class EuclideanTransformation2DRobustEstimator {
/**
* Minimum number of matched points required to estimate an Euclidean 2D
* transformation.
*/
public static final int MINIMUM_SIZE = EuclideanTransformation2DEstimator.MINIMUM_SIZE;
/**
* For some point configurations a solution can be found with only 2 points.
*/
public static final int WEAK_MINIMUM_SIZE = EuclideanTransformation2DEstimator.WEAK_MINIMUM_SIZE;
/**
* 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;
/**
* Indicates that 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;
/**
* Default robust estimator method when none is provided.
*/
public static final RobustEstimatorMethod DEFAULT_ROBUST_METHOD = RobustEstimatorMethod.PROMEDS;
/**
* Listener to be notified of events such as when estimation starts, ends
* or its progress significantly changes.
*/
protected EuclideanTransformation2DRobustEstimatorListener listener;
/**
* Indicates if this estimator is locked because an estimation is being
* computed.
*/
protected 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;
/**
* 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;
/**
* 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 Euclidean transformation.
* This is only available when result has been refined and covariance is
* kept.
*/
private Matrix covariance;
/**
* List of points to be used to estimate an Euclidean 2D transformation.
* Each point in the list of input points must be matched with the
* corresponding point in the list of output points located at the same
* position. Hence, both input points and output points must have the same
* size, and their size must be greater or equal than MINIMUM_SIZE.
*/
protected List<Point2D> inputPoints;
/**
* List of points to be used to estimate an Euclidean 2D transformation.
* Each point in the lis tof output points must be matched with the
* corresponding point in the list of input points located at the same
* position. Hence, both input points and output points must have the same
* size, and their size must be greater or equal than MINIMUM_SIZE.
*/
protected List<Point2D> outputPoints;
/**
* Indicates whether estimation can start with only 2 points or not.
* True allows 2 points, false requires 3.
*/
private boolean weakMinimumSizeAllowed;
/**
* Constructor.
*/
protected EuclideanTransformation2DRobustEstimator() {
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 EuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener) {
this();
this.listener = listener;
}
/**
* Constructor with lists of points to be used to estimate an Euclidean 2D
* transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
protected EuclideanTransformation2DRobustEstimator(
final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
this();
internalSetPoints(inputPoints, outputPoints);
}
/**
* Constructor with listener and lists of points to be used to estimate an
* Euclidean 2D transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
protected EuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints) {
this(listener);
internalSetPoints(inputPoints, outputPoints);
}
/**
* Constructor.
*
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
*/
protected EuclideanTransformation2DRobustEstimator(final boolean weakMinimumSizeAllowed) {
this();
this.weakMinimumSizeAllowed = weakMinimumSizeAllowed;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
*/
protected EuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener, final boolean weakMinimumSizeAllowed) {
this();
this.listener = listener;
this.weakMinimumSizeAllowed = weakMinimumSizeAllowed;
}
/**
* Constructor with lists of points to be used to estimate an Euclidean 2D
* transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
protected EuclideanTransformation2DRobustEstimator(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
this();
this.weakMinimumSizeAllowed = weakMinimumSizeAllowed;
internalSetPoints(inputPoints, outputPoints);
}
/**
* Constructor with listener and lists of points to be used to estimate an
* Euclidean 2D transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
protected EuclideanTransformation2DRobustEstimator(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
this(listener);
this.weakMinimumSizeAllowed = weakMinimumSizeAllowed;
internalSetPoints(inputPoints, outputPoints);
}
/**
* Returns list of input points to be used to estimate an Euclidean 2D
* transformation.
* Each point in the list of input points must be matched with the
* corresponding point in the list of output points located at the same
* position. Hence, both input points and output points must have the same
* size, and their size must be greater or equal than MINIMUM_SIZE.
*
* @return list of input points to be used to estimate an Euclidean 2D
* transformation.
*/
public List<Point2D> getInputPoints() {
return inputPoints;
}
/**
* Returns list of output points to be used to estimate an Euclidean 2D
* transformation.
* Each point in the list of output points must be matched with the
* corresponding point in the list of input points located at the same
* position. Hence, both input points and output points must have the same
* size, and their size must be greater or equal than MINIMUM_SIZE.
*
* @return list of output points to be used to estimate an Euclidean 2D
* transformation.
*/
public List<Point2D> getOutputPoints() {
return outputPoints;
}
/**
* Sets list of points to be used to estimate an Euclidean 2D
* transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
* @throws LockedException if estimator is locked because a computation is
* already in progress.
*/
public void setPoints(final List<Point2D> inputPoints, final List<Point2D> outputPoints) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetPoints(inputPoints, outputPoints);
}
/**
* Indicates if estimator is ready to start the Euclidean 2D transformation
* estimation.
* This is true when input data (i.e. lists of matched points) are provided
* and a minimum of MINIMUM_SIZE points are available.
*
* @return true if estimator is ready, false otherwise.
*/
public boolean isReady() {
return inputPoints != null && outputPoints != null && inputPoints.size() == outputPoints.size()
&& inputPoints.size() >= getMinimumPoints();
}
/**
* Returns quality scores corresponding to each pair of matched points.
* The larger the score value the better the quality of the matching.
* This implementation always returns null.
* Subclasses using quality scores must implement proper behaviour.
*
* @return quality scores corresponding to each pair of matched points.
*/
public double[] getQualityScores() {
return null;
}
/**
* Sets quality scores corresponding to each pair of matched points.
* The larger the score value the better the quality of the matching.
* This implementation makes no action.
* Subclasses using quality scores must implement proper behaviour.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @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. 3 samples).
*/
public void setQualityScores(final double[] qualityScores) throws LockedException {
}
/**
* 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 EuclideanTransformation2DRobustEstimatorListener 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 EuclideanTransformation2DRobustEstimatorListener 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 whether estimation can start with only 2 points or not.
*
* @return true allows 2 points, false requires 3.
*/
public boolean isWeakMinimumSizeAllowed() {
return weakMinimumSizeAllowed;
}
/**
* Specifies whether estimation can start with only 2 points or not.
*
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @throws LockedException if estimator is locked.
*/
public void setWeakMinimumSizeAllowed(final boolean weakMinimumSizeAllowed) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
this.weakMinimumSizeAllowed = weakMinimumSizeAllowed;
}
/**
* Required minimum number of point correspondences to start the estimation.
* Can be either 2 or 3.
*
* @return minimum number of point correspondences.
*/
public int getMinimumPoints() {
return weakMinimumSizeAllowed ? WEAK_MINIMUM_SIZE : MINIMUM_SIZE;
}
/**
* 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 ture, 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;
}
/**
* 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;
}
/**
* 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;
}
/**
* Estimates an Euclidean 2D transformation using a robust estimator and the
* best set of matched 2D point correspondences found using the robust
* estimator.
*
* @return an Euclidean 2D transformation.
* @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 EuclideanTransformation2D estimate() throws LockedException, NotReadyException,
RobustEstimatorException;
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
public abstract RobustEstimatorMethod getMethod();
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param method method of a robust estimator algorithm to estimate the
* best Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator();
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator();
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator();
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator();
default -> new RANSACEuclideanTransformation2DRobustEstimator();
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
default -> new RANSACEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @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
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(listener);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(listener);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(listener);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(listener);
default -> new RANSACEuclideanTransformation2DRobustEstimator(listener);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
default -> new RANSACEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 3 matched points).
*/
public static EuclideanTransformation2DRobustEstimator create(
final double[] qualityScores, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator();
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator();
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(qualityScores);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(qualityScores);
default -> new RANSACEuclideanTransformation2DRobustEstimator();
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points or scores
* don't have the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints, qualityScores);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, qualityScores);
default -> new RANSACEuclideanTransformation2DRobustEstimator(inputPoints, outputPoints);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust 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 pair of matched
* points.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided quality scores don't have
* the required minimum size.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final double[] qualityScores,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(listener);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(listener);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(listener, qualityScores);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(listener, qualityScores);
default -> new RANSACEuclideanTransformation2DRobustEstimator(listener);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size of their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final double[] qualityScores, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, qualityScores);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, qualityScores);
default -> new RANSACEuclideanTransformation2DRobustEstimator(listener, inputPoints, outputPoints);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate
* the best Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final boolean weakMinimumSizeAllowed, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final boolean weakMinimumSizeAllowed,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MINIMUM_SIZE (i.e. 3 matched points).
*/
public static EuclideanTransformation2DRobustEstimator create(
final double[] qualityScores, final boolean weakMinimumSizeAllowed, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(qualityScores, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(qualityScores, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points or scores
* don't have the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores,
final boolean weakMinimumSizeAllowed, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, qualityScores, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, qualityScores, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(
inputPoints, outputPoints, weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust 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 pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided quality scores don't have
* the required minimum size.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final double[] qualityScores,
final boolean weakMinimumSizeAllowed, final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(
listener, qualityScores, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
listener, qualityScores, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(listener, weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using provided robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @param method method of a robust estimator algorithm to estimate the best
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size of their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final double[] qualityScores, final boolean weakMinimumSizeAllowed,
final RobustEstimatorMethod method) {
return switch (method) {
case LMEDS -> new LMedSEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
case MSAC -> new MSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
case PROSAC -> new PROSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, qualityScores, weakMinimumSizeAllowed);
case PROMEDS -> new PROMedSEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, qualityScores, weakMinimumSizeAllowed);
default -> new RANSACEuclideanTransformation2DRobustEstimator(
listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
};
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create() {
return create(DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size of their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
return create(inputPoints, outputPoints, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using 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 Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener) {
return create(listener, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints) {
return create(listener, inputPoints, outputPoints, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(final double[] qualityScores) {
return create(qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points ot be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of points.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores) {
return create(inputPoints, outputPoints, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust 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 pair of matched
* points.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final double[] qualityScores) {
return create(listener, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points ot be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final double[] qualityScores) {
return create(listener, inputPoints, outputPoints, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(final boolean weakMinimumSizeAllowed) {
return create(weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size of their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
return create(inputPoints, outputPoints, weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final boolean weakMinimumSizeAllowed) {
return create(listener, weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
return create(listener, inputPoints, outputPoints, weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final double[] qualityScores, final boolean weakMinimumSizeAllowed) {
return create(qualityScores, weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points ot be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores,
final boolean weakMinimumSizeAllowed) {
return create(inputPoints, outputPoints, qualityScores, weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust 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 pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final double[] qualityScores,
final boolean weakMinimumSizeAllowed) {
return create(listener, qualityScores, weakMinimumSizeAllowed, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an Euclidean 2D transformation estimator based on 2D point
* correspondences and using default robust estimator method.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points ot be used to estimate an
* Euclidean 2D transformation.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
* @return an instance of Euclidean 2D transformation estimator.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public static EuclideanTransformation2DRobustEstimator create(
final EuclideanTransformation2DRobustEstimatorListener listener, final List<Point2D> inputPoints,
final List<Point2D> outputPoints, final double[] qualityScores, final boolean weakMinimumSizeAllowed) {
return create(listener, inputPoints, outputPoints, qualityScores, weakMinimumSizeAllowed,
DEFAULT_ROBUST_METHOD);
}
/**
* Internal method to set lists of points to be used to estimate an
* Euclidean 2D transformation.
* This method does not check whether estimator is locked or not.
*
* @param inputPoints list of input points to be used to estimate an
* Euclidean 2D transformation.
* @param outputPoints list of output points to be used to estimate an
* Euclidean 2D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
private void internalSetPoints(final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
if (inputPoints.size() < getMinimumPoints()) {
throw new IllegalArgumentException();
}
if (inputPoints.size() != outputPoints.size()) {
throw new IllegalArgumentException();
}
this.inputPoints = inputPoints;
this.outputPoints = outputPoints;
}
/**
* Attempts to refine provided solution if refinement is requested.
* This method returns a refined solution of 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 transformation.
*
* @param transformation transformation estimated by a robust estimator
* without refinement.
* @return solution after refinement (if requested) or the provided
* non-refined solution if not requested or refinement failed.
*/
protected EuclideanTransformation2D attemptRefine(final EuclideanTransformation2D transformation) {
if (refineResult) {
final var refiner = new EuclideanTransformation2DRefiner(transformation, keepCovariance, getInliersData(),
inputPoints, outputPoints, getRefinementStandardDeviation());
try {
final var result = new EuclideanTransformation2D();
final var improved = refiner.refine(result);
if (keepCovariance) {
// keep covariance
covariance = refiner.getCovariance();
}
return improved ? result : transformation;
} catch (final Exception e) {
// refinement failed, so we return input value
return transformation;
}
} else {
return transformation;
}
}
/**
* 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();
}