org.bytedeco.opencv.opencv_video

Class KalmanFilter

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_video.KalmanFilter

All Implemented Interfaces:

AutoCloseable

@Namespace(value="cv")
 @NoOffset
 @Properties(inherit=opencv_video.class)
public class KalmanFilter
extends Pointer

\brief Kalman filter class.

The class implements a standard Kalman filter , \cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality. \note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released with cvReleaseKalman(&kalmanFilter)

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
KalmanFilter()
KalmanFilter(int dynamParams, int measureParams)
KalmanFilter(int dynamParams, int measureParams, int controlParams, int type) \overload
KalmanFilter(long size) Native array allocator.
KalmanFilter(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
Mat	controlMatrix() control matrix (B) (not used if there is no control)
KalmanFilter	controlMatrix(Mat setter)
Mat	correct(Mat measurement) \brief Updates the predicted state from the measurement.
Mat	errorCovPost() posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
KalmanFilter	errorCovPost(Mat setter)
Mat	errorCovPre() priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)
KalmanFilter	errorCovPre(Mat setter)
Mat	gain() Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
KalmanFilter	gain(Mat setter)
KalmanFilter	getPointer(long i)
void	init(int dynamParams, int measureParams)
void	init(int dynamParams, int measureParams, int controlParams, int type) \brief Re-initializes Kalman filter.
Mat	measurementMatrix() measurement matrix (H)
KalmanFilter	measurementMatrix(Mat setter)
Mat	measurementNoiseCov() measurement noise covariance matrix (R)
KalmanFilter	measurementNoiseCov(Mat setter)
KalmanFilter	position(long position)
Mat	predict()
Mat	predict(Mat control) \brief Computes a predicted state.
Mat	processNoiseCov() process noise covariance matrix (Q)
KalmanFilter	processNoiseCov(Mat setter)
Mat	statePost() corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
KalmanFilter	statePost(Mat setter)
Mat	statePre() predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
KalmanFilter	statePre(Mat setter)
Mat	temp1()
KalmanFilter	temp1(Mat setter)
Mat	temp2()
KalmanFilter	temp2(Mat setter)
Mat	temp3()
KalmanFilter	temp3(Mat setter)
Mat	temp4()
KalmanFilter	temp4(Mat setter)
Mat	temp5()
KalmanFilter	temp5(Mat setter)
Mat	transitionMatrix() state transition matrix (A)
KalmanFilter	transitionMatrix(Mat setter)

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, getPointer, getPointer, getPointer, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

KalmanFilter

public KalmanFilter(Pointer p)

Pointer cast constructor. Invokes Pointer(Pointer).

KalmanFilter

public KalmanFilter(long size)

Native array allocator. Access with Pointer.position(long).

KalmanFilter

public KalmanFilter()

KalmanFilter

public KalmanFilter(int dynamParams,
                    int measureParams,
                    int controlParams,
                    int type)

\overload

Parameters:

dynamParams - Dimensionality of the state.

measureParams - Dimensionality of the measurement.

controlParams - Dimensionality of the control vector.

type - Type of the created matrices that should be CV_32F or CV_64F.

KalmanFilter

public KalmanFilter(int dynamParams,
                    int measureParams)

Method Detail

position

public KalmanFilter position(long position)

Overrides:

position in class Pointer

getPointer

public KalmanFilter getPointer(long i)

Overrides:

getPointer in class Pointer

init

public void init(int dynamParams,
                 int measureParams,
                 int controlParams,
                 int type)

\brief Re-initializes Kalman filter. The previous content is destroyed.

Parameters:

dynamParams - Dimensionality of the state.

measureParams - Dimensionality of the measurement.

controlParams - Dimensionality of the control vector.

type - Type of the created matrices that should be CV_32F or CV_64F.

init

public void init(int dynamParams,
                 int measureParams)

predict

@Const
 @ByRef
public Mat predict(@Const @ByRef(nullValue="cv::Mat()")
                                    Mat control)

\brief Computes a predicted state.

Parameters:

control - The optional input control

predict

@Const
 @ByRef
public Mat predict()

correct

@Const
 @ByRef
public Mat correct(@Const @ByRef
                                    Mat measurement)

\brief Updates the predicted state from the measurement.

Parameters:

measurement - The measured system parameters

statePre

@ByRef
public Mat statePre()

predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)

statePre

public KalmanFilter statePre(Mat setter)

statePost

@ByRef
public Mat statePost()

corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))

statePost

public KalmanFilter statePost(Mat setter)

transitionMatrix

@ByRef
public Mat transitionMatrix()

state transition matrix (A)

transitionMatrix

public KalmanFilter transitionMatrix(Mat setter)

controlMatrix

@ByRef
public Mat controlMatrix()

control matrix (B) (not used if there is no control)

controlMatrix

public KalmanFilter controlMatrix(Mat setter)

measurementMatrix

@ByRef
public Mat measurementMatrix()

measurement matrix (H)

measurementMatrix

public KalmanFilter measurementMatrix(Mat setter)

processNoiseCov

@ByRef
public Mat processNoiseCov()

process noise covariance matrix (Q)

processNoiseCov

public KalmanFilter processNoiseCov(Mat setter)

measurementNoiseCov

@ByRef
public Mat measurementNoiseCov()

measurement noise covariance matrix (R)

measurementNoiseCov

public KalmanFilter measurementNoiseCov(Mat setter)

errorCovPre

@ByRef
public Mat errorCovPre()

priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)

errorCovPre

public KalmanFilter errorCovPre(Mat setter)

gain

@ByRef
public Mat gain()

Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)

gain

public KalmanFilter gain(Mat setter)

errorCovPost

@ByRef
public Mat errorCovPost()

posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)

errorCovPost

public KalmanFilter errorCovPost(Mat setter)

temp1

@ByRef
public Mat temp1()

temp1

public KalmanFilter temp1(Mat setter)

temp2

@ByRef
public Mat temp2()

temp2

public KalmanFilter temp2(Mat setter)

temp3

@ByRef
public Mat temp3()

temp3

public KalmanFilter temp3(Mat setter)

temp4

@ByRef
public Mat temp4()

temp4

public KalmanFilter temp4(Mat setter)

temp5

@ByRef
public Mat temp5()

temp5

public KalmanFilter temp5(Mat setter)