com.irurueta.ar.calibration.estimators.ImageOfAbsoluteConicRobustEstimator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of irurueta-ar Show documentation
Show all versions of irurueta-ar Show documentation
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.calibration.estimators;
import com.irurueta.algebra.AlgebraException;
import com.irurueta.algebra.ArrayUtils;
import com.irurueta.algebra.Matrix;
import com.irurueta.ar.calibration.ImageOfAbsoluteConic;
import com.irurueta.geometry.ProjectiveTransformation2D;
import com.irurueta.geometry.Transformation2D;
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;
/**
* This is an abstract class for algorithms to robustly find the best
* ImageOfAbsoluteConic (IAC) for provided collection of 2D homographies.
* Implementions of this class should be able to detect and discard outliers
* in order to find the best solution.
*/
public abstract class ImageOfAbsoluteConicRobustEstimator {
/**
* Default robust estimator method when none is provided.
* In general for IAC estimation is best to use PROSAC or RANSAC than
* any other method, as it provides more robust results.
*/
public static final RobustEstimatorMethod DEFAULT_ROBUST_METHOD =
RobustEstimatorMethod.PROSAC;
/**
* 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;
/**
* Homographies to estimate image of absolute conic (IAC).
*/
protected List mHomographies;
/**
* Internal non robust estimator of IAC.
*/
protected final LMSEImageOfAbsoluteConicEstimator mIACEstimator;
/**
* Listener to be notified of events such as when estimation starts, ends or
* its progress significantly changes.
*/
protected ImageOfAbsoluteConicRobustEstimatorListener mListener;
/**
* Indicates if this estimator is locked because an estimation is being
* computed.
*/
protected 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;
// internal members to compute residuals
/**
* IAC matrix for one iteration of the robust estimator.
* This is used during residuals estimation.
* This instance is reused for performance reasons.
*/
private Matrix mIACMatrix;
/**
* Matrix representation of an homography.
* This is used during residuals estimation.
* This instance is reused for performance reasons.
*/
private Matrix mHomMatrix;
/**
* Sub-matrix of homography used as the left term on a matrix multiplication.
* This is used during residuals estimation.
* This instance is reused for performance reasons.
*/
private double[] mSubMatrixLeft;
/**
* Sub-matrix of homography used as the right term on a matrix
* multiplication.
* This is used during residuals estimation.
* This instance is reused for performance reasons.
*/
private Matrix mSubMatrixRight;
/**
* Product multiplication of IAC by the right term.
* This is used during residuals estimation.
* This instance is reused for performance reasons.
*/
private Matrix mMult1;
/**
* Constructor.
*/
protected ImageOfAbsoluteConicRobustEstimator() {
mProgressDelta = DEFAULT_PROGRESS_DELTA;
mConfidence = DEFAULT_CONFIDENCE;
mMaxIterations = DEFAULT_MAX_ITERATIONS;
mIACEstimator = new LMSEImageOfAbsoluteConicEstimator();
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when
* estimation starts, ends or its progress significantly changes.
*/
protected ImageOfAbsoluteConicRobustEstimator(
final ImageOfAbsoluteConicRobustEstimatorListener listener) {
this();
mListener = listener;
}
/**
* Constructor.
*
* @param homographies list of homographies (2D transformations) used to
* estimate the image of absolute conic (IAC), which can be used to obtain
* pinhole camera intrinsic parameters.
* @throws IllegalArgumentException if not enough homographies are provided
* for default settings. Hence, at least 1 homography must be provided.
*/
protected ImageOfAbsoluteConicRobustEstimator(
final List homographies) {
this();
internalSetHomographies(homographies);
}
/**
* Constructor.
*
* @param homographies list of homographies (2D transformations) used to
* estimate the image of absolute conic (IAC), which can be used to obtain
* pinhole camera intrinsic parameters.
* @param listener listener to be notified of events such as when estimation
* starts, ends or estimation progress changes.
* @throws IllegalArgumentException if not enough homographies are provided
* for default settings. Hence, at least 1 homography must be provided.
*/
protected ImageOfAbsoluteConicRobustEstimator(
final List homographies,
final ImageOfAbsoluteConicRobustEstimatorListener listener) {
this(listener);
internalSetHomographies(homographies);
}
/**
* Returns boolean indicating whether camera skewness is assumed to be zero
* or not.
* Skewness determines whether LCD sensor cells are properly aligned or not,
* where zero indicates perfect alignment.
* Typically skewness is a value equal or very close to zero.
*
* @return true if camera skewness is assumed to be zero, otherwise camera
* skewness is estimated.
*/
public boolean isZeroSkewness() {
return mIACEstimator.isZeroSkewness();
}
/**
* Sets boolean indicating whether camera skewness is assumed to be zero or
* not.
* Skewness determines whether LCD sensor cells are properly aligned or not,
* where zero indicates perfect alignment.
* Typically skewness is a value equal or very close to zero.
*
* @param zeroSkewness true if camera skewness is assumed to be zero,
* otherwise camera skewness is estimated.
* @throws LockedException if estimator is locked.
*/
public void setZeroSkewness(final boolean zeroSkewness) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mIACEstimator.setZeroSkewness(zeroSkewness);
}
/**
* Returns boolean indicating whether principal point is assumed to be at
* origin of coordinates or not.
* Typically principal point is located at image center (origin of
* coordinates), and usually matches the center of radial distortion if
* it is taken into account.
*
* @return true if principal point is assumed to be at origin of
* coordinates, false if principal point must be estimated.
*/
public boolean isPrincipalPointAtOrigin() {
return mIACEstimator.isPrincipalPointAtOrigin();
}
/**
* Sets boolean indicating whether principal point is assumed to be at
* origin of coordinates or not.
* Typically principal point is located at image center (origin of
* coordinates), and usually matches the center of radial distortion if it
* is taken into account.
*
* @param principalPointAtOrigin true if principal point is assumed to bet
* at origin of coordinates, false if principal point must be estimated
* @throws LockedException if estimator is locked.
*/
public void setPrincipalPointAtOrigin(final boolean principalPointAtOrigin)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mIACEstimator.setPrincipalPointAtOrigin(principalPointAtOrigin);
}
/**
* Returns boolean indicating whether aspect ratio of focal distances (i.e.
* vertical focal distance divided by horizontal focal distance) is known or
* not.
* Notice that focal distance aspect ratio is not related to image size
* aspect ratio. Typically LCD sensor cells are square and hence aspect
* ratio of focal distances is known and equal to 1.
* This value is only taken into account if skewness is assumed to be zero,
* otherwise it is ignored.
*
* @return true if focal distance aspect ratio is known, false otherwise.
*/
public boolean isFocalDistanceAspectRatioKnown() {
return mIACEstimator.isFocalDistanceAspectRatioKnown();
}
/**
* Sets boolean indicating whether aspect ratio of focal distances (i.e.
* vertical focal distance divided by horizontal focal distance) is known or
* not.
* Notice that focal distance aspect ratio is not related to image size
* aspect ratio. Typically LCD sensor cells are square and hence aspect
* ratio of focal distances is known and equal to 1.
* This value is only taken into account if skewness is assumed to be zero,
* otherwise it is ignored.
*
* @param focalDistanceAspectRatioKnown true if focal distance aspect ratio
* is known, false otherwise.
* @throws LockedException if estimator is locked.
*/
public void setFocalDistanceAspectRatioKnown(
final boolean focalDistanceAspectRatioKnown) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mIACEstimator.setFocalDistanceAspectRatioKnown(
focalDistanceAspectRatioKnown);
}
/**
* Returns aspect ratio of focal distances (i.e. vertical focal distance
* divided by horizontal focal distance).
* This value is only taken into account if skewness is assumed to be zero
* and focal distance aspect ratio is marked as known, otherwise it is
* ignored.
* By default this is 1.0, since it is taken into account that typically
* LCD sensor cells are square and hence aspect ratio focal distances is
* known and equal to 1.
* Notice that focal distance aspect ratio is not related to image size
* aspect ratio.
* Notice that a negative aspect ratio indicates that vertical axis is
* reversed. This can be useful in some situations where image vertical
* coordinates are reversed respect to the physical world (i.e. in computer
* graphics typically image vertical coordinates go downwards, while in
* physical world they go upwards).
*
* @return aspect ratio of focal distances.
*/
public double getFocalDistanceAspectRatio() {
return mIACEstimator.getFocalDistanceAspectRatio();
}
/**
* Sets aspect ratio of focal distances (i.e. vertical focal distance
* divided by horizontal focal distance).
* This value is only taken into account if skewness is assumed to be zero
* and focal distance aspect ratio is marked as known, otherwise it is
* ignored.
* By default this is 1.0, since it is taken into account that typically
* LCD sensor cells are square and hence aspect ratio focal distances is
* known and equal to 1.
* Notice that focal distance aspect ratio is not related to image size
* aspect ratio.
* Notice that a negative aspect ratio indicates that vertical axis is
* reversed. This can be useful in some situations where image vertical
* coordinates are reversed respect to the physical world (i.e. in computer
* graphics typically image vertical coordinates go downwards, while in
* physical world they go upwards).
*
* @param focalDistanceAspectRatio aspect ratio of focal distances to be set
* @throws LockedException if estimator is locked.
* @throws IllegalArgumentException if focal distance aspect ratio is too
* close to zero, as it might produce numerical instabilities.
*/
public void setFocalDistanceAspectRatio(final double focalDistanceAspectRatio)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mIACEstimator.setFocalDistanceAspectRatio(focalDistanceAspectRatio);
}
/**
* 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 ImageOfAbsoluteConicRobustEstimatorListener 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 ImageOfAbsoluteConicRobustEstimatorListener 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 if this instance is locked because estimation 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;
}
/**
* Gets list of homographies to estimate IAC.
*
* @return list of homographies to estimate IAC.
*/
public List getHomographies() {
return mHomographies;
}
/**
* Sets list of homographies to estimate IAC.
*
* @param homographies list of homographies to estimate IAC.
* @throws LockedException if estimator is locked.
* @throws IllegalArgumentException if provided list of homographies does
* not contain enough elements to estimate the DIAC using current settings.
*/
public void setHomographies(final List homographies)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetHomographies(homographies);
}
/**
* Returns minimum number of required homographies needed to estimate the
* Imag eof Absolute Conic (IAC).
* If no constraints are imposed, then at least 3 homographies are required.
* For each constraint imposed, one less equation will be required, hence
* if skewness is assumed to be known (by using its typical value of zero),
* then only 4 equations will be needed.
* If also the horizontal and vertical coordinates of the principal point
* are assumed to be known (by being placed at the 2D origin of coordinates,
* which is a typical value), then only 2 equations will be needed
* (1 homography).
*
* @return minimum number of required homographies required to estimate the
* IAC.
*/
public int getMinNumberOfRequiredHomographies() {
return mIACEstimator.getMinNumberOfRequiredHomographies();
}
/**
* Returns value indicating whether required data has been provided so that
* IAC estimation can start.
* If true, estimator is ready to compute the IAC, otherwise more data needs
* to be provided.
*
* @return true if estimator is ready, false otherwise.
*/
public boolean isReady() {
return mHomographies != null &&
mHomographies.size() >= getMinNumberOfRequiredHomographies();
}
/**
* Returns quality scores corresponding to each homography.
* The larger the score value the better the quality of the homography.
* This implementation always returns null.
* Subclasses using quality scores must implement proper behaviour.
*
* @return quality scores corresponding to each homography.
*/
public double[] getQualityScores() {
return null;
}
/**
* Sets quality scores corresponding to each homography.
* The larger the score value the better the quality of the homography.
* This implementation makes no action.
* Subclasses using quality scores must implement proper behaviour.
*
* @param qualityScores quality scores corresponding to each homography.
* @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 required number of homographies.
*/
public void setQualityScores(final double[] qualityScores) throws LockedException {
}
/**
* Estimates Image of Absolute Conic (IAC).
*
* @return estimated IAC.
* @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 ImageOfAbsoluteConic 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 image of absolute conic robust estimator using provided
* method.
*
* @param method method of a robust estimator algorithm to estimate best
* IAC.
* @return an instance of an image of absolute conic robust estimator.
*/
public static ImageOfAbsoluteConicRobustEstimator create(
final RobustEstimatorMethod method) {
switch (method) {
case LMedS:
return new LMedSImageOfAbsoluteConicRobustEstimator();
case MSAC:
return new MSACImageOfAbsoluteConicRobustEstimator();
case PROSAC:
return new PROSACImageOfAbsoluteConicRobustEstimator();
case PROMedS:
return new PROMedSImageOfAbsoluteConicRobustEstimator();
case RANSAC:
default:
return new RANSACImageOfAbsoluteConicRobustEstimator();
}
}
/**
* Creates an image of absolute conic robust estimator using provided
* homographies.
*
* @param homographies list of homographies.
* @param qualityScores quality scores corresponding to each homography.
* @param method method of a robust estimator algorithm to estimate
* best IAC.
* @return an instance of an image of absolute conic robust estimator.
* @throws IllegalArgumentException if provided list of homographies and
* quality scores don't have the same size or size is too short.
*/
public static ImageOfAbsoluteConicRobustEstimator create(
final List homographies, final double[] qualityScores,
final RobustEstimatorMethod method) {
switch (method) {
case LMedS:
return new LMedSImageOfAbsoluteConicRobustEstimator(
homographies);
case MSAC:
return new MSACImageOfAbsoluteConicRobustEstimator(
homographies);
case PROSAC:
return new PROSACImageOfAbsoluteConicRobustEstimator(
homographies, qualityScores);
case PROMedS:
return new PROMedSImageOfAbsoluteConicRobustEstimator(
homographies, qualityScores);
case RANSAC:
default:
return new RANSACImageOfAbsoluteConicRobustEstimator(
homographies);
}
}
/**
* Creates an image of absolute conic robust estimator using provided
* homographies.
*
* @param homographies list of homographies.
* @param method method of a robust estimator algorithm to estimate
* best IAC.
* @return an instance of an image of absolute conic robust estimator.
* @throws IllegalArgumentException if provided list of homographies is too
* short.
*/
public static ImageOfAbsoluteConicRobustEstimator create(
final List homographies, final RobustEstimatorMethod method) {
switch (method) {
case LMedS:
return new LMedSImageOfAbsoluteConicRobustEstimator(
homographies);
case MSAC:
return new MSACImageOfAbsoluteConicRobustEstimator(
homographies);
case PROSAC:
return new PROSACImageOfAbsoluteConicRobustEstimator(
homographies);
case PROMedS:
return new PROMedSImageOfAbsoluteConicRobustEstimator(
homographies);
case RANSAC:
default:
return new RANSACImageOfAbsoluteConicRobustEstimator(
homographies);
}
}
/**
* Creates an image of absolute conic robust estimator using default
* method.
*
* @return an instance of an image of absolute conic robust estimator.
*/
public static ImageOfAbsoluteConicRobustEstimator create() {
return create(DEFAULT_ROBUST_METHOD);
}
/**
* Creates an image of absolute conic robust estimator using provided
* homographies.
*
* @param homographies list of homographies.
* @param qualityScores quality scores corresponding to each point.
* @return an instance of an image of absolute conic robust estimator.
* @throws IllegalArgumentException if provided list of homographies and
* quality scores don't have the same size or size is too short.
*/
public static ImageOfAbsoluteConicRobustEstimator create(
final List homographies, final double[] qualityScores) {
return create(homographies, qualityScores, DEFAULT_ROBUST_METHOD);
}
/**
* Creates an image of absolute conic robust estimator using provided
* homographies.
*
* @param homographies list of homographies.
* @return an instance of an image of absolute conic robust estimator.
* @throws IllegalArgumentException if provided list of homographies and
* is too short.
*/
public static ImageOfAbsoluteConicRobustEstimator create(
final List homographies) {
return create(homographies, DEFAULT_ROBUST_METHOD);
}
/**
* Computes the residual between an image of absolute conic (IAC) and
* a 2D transformation (homography).
* Residual is derived from the fact that rotation matrices are
* orthonormal (i.e. h1'*IAC*h2 = 0 and
* h1'*IAC*h1 = h2'*IAC*h2 --< h1'*IAC*h1 - h2'*IAC*h2 = 0).
* The residual will be the average error of those two equations.
*
* @param iac the matrix of an image of absolute conic (IAC)
* @param homography 2D transformation (homography)
* @return residual.
*/
protected double residual(final ImageOfAbsoluteConic iac,
final Transformation2D homography) {
iac.normalize();
if (homography instanceof ProjectiveTransformation2D) {
((ProjectiveTransformation2D) homography).normalize();
}
try {
if (mIACMatrix == null) {
mIACMatrix = iac.asMatrix();
} else {
iac.asMatrix(mIACMatrix);
}
if (mHomMatrix == null) {
mHomMatrix = homography.asMatrix();
} else {
homography.asMatrix(mHomMatrix);
}
if (mSubMatrixLeft == null) {
mSubMatrixLeft = new double[
ProjectiveTransformation2D.HOM_COORDS];
}
if (mSubMatrixRight == null) {
mSubMatrixRight = new Matrix(
ProjectiveTransformation2D.HOM_COORDS, 1);
}
if (mMult1 == null) {
mMult1 = new Matrix(ProjectiveTransformation2D.HOM_COORDS, 1);
}
// 1st equation h1'*IAC*h2 = 0
// 1st column of homography
mHomMatrix.getSubmatrixAsArray(0, 0,
ProjectiveTransformation2D.HOM_COORDS - 1, 0,
mSubMatrixLeft);
// 2nd column of homography
mHomMatrix.getSubmatrix(0, 1,
ProjectiveTransformation2D.HOM_COORDS - 1, 1,
mSubMatrixRight);
// IAC * h2
mIACMatrix.multiply(mSubMatrixRight, mMult1);
// h1' * (IAC * h2)
final double error1 = Math.abs(ArrayUtils.dotProduct(mSubMatrixLeft,
mMult1.getBuffer()));
// 2nd equation h1'*IAC*h1 - h2'*IAC*h2 = 0
// 1st column of homography
mHomMatrix.getSubmatrixAsArray(0, 0,
ProjectiveTransformation2D.HOM_COORDS - 1, 0,
mSubMatrixLeft);
mHomMatrix.getSubmatrix(0, 0,
ProjectiveTransformation2D.HOM_COORDS - 1, 0,
mSubMatrixRight);
// IAC * h1
mIACMatrix.multiply(mSubMatrixRight, mMult1);
// h1' * (IAC * h1)
final double error2a = ArrayUtils.dotProduct(mSubMatrixLeft,
mMult1.getBuffer());
// 2nd column of homography
mHomMatrix.getSubmatrixAsArray(0, 1,
ProjectiveTransformation2D.HOM_COORDS - 1, 1,
mSubMatrixLeft);
mHomMatrix.getSubmatrix(0, 1,
ProjectiveTransformation2D.HOM_COORDS - 1, 1,
mSubMatrixRight);
// IAC * h2
mIACMatrix.multiply(mSubMatrixRight, mMult1);
// h2' * (IAC * h1)
final double error2b = ArrayUtils.dotProduct(mSubMatrixLeft,
mMult1.getBuffer());
final double error2 = Math.abs(error2a - error2b);
return 0.5 * (error1 + error2);
} catch (final AlgebraException e) {
return Double.MAX_VALUE;
}
}
/**
* Sets list of homographies.
* This method does not check whether estimator is locked.
*
* @param homographies list of homographies to estimate IAC.
* @throws IllegalArgumentException if provided list of homographies is null
* or too small.
*/
private void internalSetHomographies(final List homographies) {
if (homographies == null ||
homographies.size() < getMinNumberOfRequiredHomographies()) {
throw new IllegalArgumentException();
}
mHomographies = homographies;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy