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Augmented Reality and 3D reconstruction library
/*
* Copyright (C) 2015 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.sfm;
import com.irurueta.geometry.PinholeCamera;
import com.irurueta.geometry.Point2D;
import com.irurueta.geometry.Point3D;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
import com.irurueta.numerical.robust.RobustEstimatorException;
import com.irurueta.numerical.robust.RobustEstimatorMethod;
import java.util.List;
/**
* Abstract class for algorithms to robustly triangulate 3D points from matched
* 2D points and their corresponding cameras on several views.
* Robust estimators can be used to estimate a more precise triangulation when
* there are more than 2 views where points have been matched.
*/
public abstract class RobustSinglePoint3DTriangulator {
/**
* Default robust estimator method when none is provided.
*/
public static final RobustEstimatorMethod DEFAULT_ROBUST_METHOD =
RobustEstimatorMethod.PROMedS;
/**
* 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 chose 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;
/**
* Minimum required number of views to triangulate 3D points.
*/
public static final int MIN_REQUIRED_VIEWS = 2;
/**
* Indicates whether by default a solution to an homogeneous system of
* equations should be found.
*/
public static final boolean DEFAULT_USE_HOMOGENEOUS_SOLUTION = true;
/**
* Matched 2D points. Each point in the list is assumed to be projected by
* the corresponding camera in the list.
*/
protected List mPoints2D;
/**
* List of cameras associated to the matched 2D point on the same position
* as the camera on the list.
*/
protected List mCameras;
/**
* Listener to be notified of events such as when estimation starts, ends or
* its progress significantly changes.
*/
protected RobustSinglePoint3DTriangulatorListener mListener;
/**
* Indicates whether a solution to an homogeneous system of equations should
* be found. Typically this should be true, since even points and cameras
* at infinity can be used. If points are close and geometry is well
* defined, false can be used to solve an inhomogeneous system of equations
* and obtain a slightly better accuracy.
*/
protected boolean mUseHomogeneousSolution;
/**
* Indicates if this estimator is locked because an estimation is being
* computed.
*/
protected volatile boolean mLocked;
/**
* Amount of progress variation before notifying a progress change during
* estimation.
*/
protected float mProgressDelta;
/**
* 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 mConfidence;
/**
* 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 mMaxIterations;
/**
* Constructor.
*/
protected RobustSinglePoint3DTriangulator() {
mUseHomogeneousSolution = DEFAULT_USE_HOMOGENEOUS_SOLUTION;
mProgressDelta = DEFAULT_PROGRESS_DELTA;
mConfidence = DEFAULT_CONFIDENCE;
mMaxIterations = DEFAULT_MAX_ITERATIONS;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
protected RobustSinglePoint3DTriangulator(
final RobustSinglePoint3DTriangulatorListener listener) {
this();
mListener = listener;
}
/**
* Constructor.
*
* @param points Matched 2D points. Each point in the list is assumed to be
* projected by the corresponding camera in the list.
* @param cameras List of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
protected RobustSinglePoint3DTriangulator(final List points,
final List cameras) {
this();
internalSetPointsAndCameras(points, cameras);
}
/**
* Constructor.
*
* @param points Matched 2D points. Each point in the list is assumed to be
* projected by the corresponding camera in the list.
* @param cameras List of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
protected RobustSinglePoint3DTriangulator(final List points,
final List cameras,
final RobustSinglePoint3DTriangulatorListener listener) {
this(points, cameras);
mListener = listener;
}
/**
* 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 RobustSinglePoint3DTriangulatorListener getListener() {
return mListener;
}
/**
* 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 RobustSinglePoint3DTriangulatorListener listener)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mListener = listener;
}
/**
* Indicates whether listener has been provided and is available for
* retrieval.
*
* @return true if available, false otherwise.
*/
public boolean isListenerAvailable() {
return mListener != null;
}
/**
* Indicates whether a solution to an homogeneous system of equations should
* be found. Typically this should be true, since even points and cameras
* at infinity can be used. If points are close and geometry is well
* defined, false can be used to solve an inhomogeneous system of equations
* and obtain a slightly better accuracy.
*
* @return true if an homogeneous solution must be found (default value),
* false otherwise.
*/
public boolean isUseHomogeneousSolution() {
return mUseHomogeneousSolution;
}
/**
* Sets boolean indicating whether a solution to an homogeneous system of
* equations should be found. Typically this should be true, since even
* points and cameras at infinity can be used. If points are close and
* geometry is well defined, false can be used to solve an inhomogeneous
* system of equations and obtain a slightly better accuracy.
*
* @param useHomogeneousSolution true if an homogeneous solution will be
* found, false if an inhomogeneous solution will be found instead.
* @throws LockedException if this instance is locked.
*/
public void setUseHomogeneousSolution(final boolean useHomogeneousSolution)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mUseHomogeneousSolution = useHomogeneousSolution;
}
/**
* Indicates if this instance is locked because triangulation is being
* computed.
*
* @return true if locked, false otherwise.
*/
public boolean isLocked() {
return mLocked;
}
/**
* 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 mProgressDelta;
}
/**
* 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();
}
mProgressDelta = 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 mConfidence;
}
/**
* 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();
}
mConfidence = 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 mMaxIterations;
}
/**
* 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();
}
mMaxIterations = maxIterations;
}
/**
* Sets list of matched 2D points for each view and their corresponding
* cameras used to project them.
*
* @param points2D list of matched 2D points on each view. Each point in the
* list is assumed to be projected by the corresponding camera in the list.
* @param cameras cameras for each view where 2D points are represented.
* @throws LockedException if this instance is locked.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
public void setPointsAndCameras(final List points2D,
final List cameras) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetPointsAndCameras(points2D, cameras);
}
/**
* Returns list of matched 2D points on each view. Each point in the list is
* assumed to be projected by the corresponding camera.
*
* @return list of matched 2D points on each view.
*/
public List getPoints2D() {
return mPoints2D;
}
/**
* Returns cameras for each view where 2D points are represented.
*
* @return cameras for each view where 2D points are represented.
*/
public List getCameras() {
return mCameras;
}
/**
* Returns quality scores corresponding to each view.
* The larger the score value the better the quality of the view measure.
* This implementation always returns null.
* Subclasses using quality scores must implement proper behaviour.
*
* @return quality scores corresponding to each view.
*/
public double[] getQualityScores() {
return null;
}
/**
* Sets quality scores corresponding to each view.
* The larger the score value the better the quality of the view sample.
* This implementation makes no action.
* Subclasses using quality scores must implement proper behaviour.
*
* @param qualityScores quality scores corresponding to each view.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than MIN_REQUIRED_VIEWS (i.e. 2 views).
*/
public void setQualityScores(final double[] qualityScores) throws LockedException {
}
/**
* Indicates whether this instance is ready to start the triangulation.
* An instance is ready when both lists of 2D points and cameras are
* provided, both lists have the same length and at least data for 2 views
* is provided.
*
* @return true if this instance is ready, false otherwise.
*/
public boolean isReady() {
return SinglePoint3DTriangulator.areValidPointsAndCameras(mPoints2D,
mCameras);
}
/**
* Triangulates provided matched 2D points being projected by each
* corresponding camera into a single 3D point.
* At least 2 matched 2D points and their corresponding 2 cameras are
* required to compute triangulation. If more views are provided, an
* averaged solution can be found.
*
* @return computed triangulated 3D point.
* @throws LockedException if this instance is locked.
* @throws NotReadyException if lists of points and cameras don't have the
* same length or less than 2 views are provided.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
public abstract Point3D triangulate() throws LockedException,
NotReadyException, RobustEstimatorException;
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
public abstract RobustEstimatorMethod getMethod();
/**
* Creates a robust single 3D point triangulator using provided robust
* method.
*
* @param method method of a robust estimator algorithm to estimate best
* triangulation.
* @return an instance of a robust single 3D point triangulator.
*/
public static RobustSinglePoint3DTriangulator create(
final RobustEstimatorMethod method) {
switch (method) {
case RANSAC:
return new RANSACRobustSinglePoint3DTriangulator();
case LMedS:
return new LMedSRobustSinglePoint3DTriangulator();
case MSAC:
return new MSACRobustSinglePoint3DTriangulator();
case PROSAC:
return new PROSACRobustSinglePoint3DTriangulator();
case PROMedS:
default:
return new PROMedSRobustSinglePoint3DTriangulator();
}
}
/**
* Creates a robust single 3D point triangulator using provided points,
* cameras and robust method.
*
* @param points matched 2D points. Each point in the list is assumed to
* be projected by the corresponding camera in the list.
* @param cameras list of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @param method method of a robust estimator algorithm to estimate best
* triangulation.
* @return an instance of a robust single 3D point triangulator.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
public static RobustSinglePoint3DTriangulator create(
final List points, final List cameras,
final RobustEstimatorMethod method) {
switch (method) {
case RANSAC:
return new RANSACRobustSinglePoint3DTriangulator(points,
cameras);
case LMedS:
return new LMedSRobustSinglePoint3DTriangulator(points,
cameras);
case MSAC:
return new MSACRobustSinglePoint3DTriangulator(points, cameras);
case PROSAC:
return new PROSACRobustSinglePoint3DTriangulator(points,
cameras);
case PROMedS:
default:
return new PROMedSRobustSinglePoint3DTriangulator(points,
cameras);
}
}
/**
* Creates a robust single 3D point triangulator using provided points,
* cameras and robust method.
*
* @param points matched 2D points. Each point in the list is assumed to
* be projected by the corresponding camera in the list.
* @param cameras list of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @param qualityScores quality scores corresponding to each point.
* @param method method of a robust estimator algorithm to estimate best
* triangulation.
* @return an instance of a robust single 3D point triangulator.
* @throws IllegalArgumentException if provided lists or quality scores
* don't have the same length or their length is less than 2 views, which
* is the minimum required to compute triangulation.
*/
public static RobustSinglePoint3DTriangulator create(
final List points,
final List cameras,
final double[] qualityScores,
final RobustEstimatorMethod method) {
switch (method) {
case RANSAC:
return new RANSACRobustSinglePoint3DTriangulator(points,
cameras);
case LMedS:
return new LMedSRobustSinglePoint3DTriangulator(points,
cameras);
case MSAC:
return new MSACRobustSinglePoint3DTriangulator(points, cameras);
case PROSAC:
return new PROSACRobustSinglePoint3DTriangulator(points,
cameras, qualityScores);
case PROMedS:
default:
return new PROMedSRobustSinglePoint3DTriangulator(points,
cameras, qualityScores);
}
}
/**
* Creates a robust single 3D point triangulator using default robust
* method.
*
* @return an instance of a robust single 3D point triangulator.
*/
public static RobustSinglePoint3DTriangulator create() {
return create(DEFAULT_ROBUST_METHOD);
}
/**
* Creates a robust single 3D point triangulator using provided points,
* cameras and default robust method.
*
* @param points matched 2D points. Each point in the list is assumed to
* be projected by the corresponding camera in the list.
* @param cameras list of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @return an instance of a robust single 3D point triangulator.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
public static RobustSinglePoint3DTriangulator create(
final List points,
final List cameras) {
return create(points, cameras, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a robust single 3D point triangulator using provided points,
* cameras and default robust method.
*
* @param points matched 2D points. Each point in the list is assumed to
* be projected by the corresponding camera in the list.
* @param cameras list of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @param qualityScores quality scores corresponding to each point.
* @return an instance of a robust single 3D point triangulator.
* @throws IllegalArgumentException if provided lists or quality scores
* don't have the same length or their length is less than 2 views, which
* is the minimum required to compute triangulation.
*/
public static RobustSinglePoint3DTriangulator create(
final List points,
final List cameras,
final double[] qualityScores) {
return create(points, cameras, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Internal method to sets list of matched 2D points for each view and their
* corresponding cameras used to project them.
* This method does not check whether instance is locked.
*
* @param points2D list of matched 2D points on each view. Each point in the
* list is assumed to be projected by the corresponding camera in the list.
* @param cameras cameras for each view where 2D points are represented.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
private void internalSetPointsAndCameras(
final List points2D,
final List cameras) {
if (!SinglePoint3DTriangulator.areValidPointsAndCameras(points2D, cameras)) {
throw new IllegalArgumentException();
}
mPoints2D = points2D;
mCameras = cameras;
}
}
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