org.bytedeco.javacpp

Class opencv_video.KalmanFilter

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.opencv_video.KalmanFilter

All Implemented Interfaces:

AutoCloseable

Enclosing class:

opencv_video

@Namespace(value="cv")
@NoOffset
public static class opencv_video.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. See the OpenCV sample kalman.cpp.

\note

- An example using the standard Kalman filter can be found at opencv_source_code/samples/cpp/kalman.cpp

Nested Class Summary

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

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

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

Method Summary

Methods

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

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, hashCode, isNull, limit, limit, maxBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, position, put, setNull, sizeof, toString, totalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

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

Constructor Detail

opencv_video.KalmanFilter

public opencv_video.KalmanFilter(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

opencv_video.KalmanFilter

public opencv_video.KalmanFilter(long size)

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

opencv_video.KalmanFilter

public opencv_video.KalmanFilter()

\brief The constructors.

\note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released with cvReleaseKalman(&kalmanFilter)

opencv_video.KalmanFilter

public opencv_video.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.

opencv_video.KalmanFilter

public opencv_video.KalmanFilter(int dynamParams,
                         int measureParams)

Method Detail

position

public opencv_video.KalmanFilter position(long position)

Overrides:

position 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 opencv_core.Mat predict(@Const@ByRef(nullValue="cv::Mat()")
                                  opencv_core.Mat control)

\brief Computes a predicted state.

Parameters:

control - The optional input control

predict

@Const
@ByRef
public opencv_core.Mat predict()

correct

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

\brief Updates the predicted state from the measurement.

Parameters:

measurement - The measured system parameters

statePre

@ByRef
public opencv_core.Mat statePre()

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

statePre

public opencv_video.KalmanFilter statePre(opencv_core.Mat statePre)

statePost

@ByRef
public opencv_core.Mat statePost()

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

statePost

public opencv_video.KalmanFilter statePost(opencv_core.Mat statePost)

transitionMatrix

@ByRef
public opencv_core.Mat transitionMatrix()

state transition matrix (A)

transitionMatrix

public opencv_video.KalmanFilter transitionMatrix(opencv_core.Mat transitionMatrix)

controlMatrix

@ByRef
public opencv_core.Mat controlMatrix()

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

controlMatrix

public opencv_video.KalmanFilter controlMatrix(opencv_core.Mat controlMatrix)

measurementMatrix

@ByRef
public opencv_core.Mat measurementMatrix()

measurement matrix (H)

measurementMatrix

public opencv_video.KalmanFilter measurementMatrix(opencv_core.Mat measurementMatrix)

processNoiseCov

@ByRef
public opencv_core.Mat processNoiseCov()

process noise covariance matrix (Q)

processNoiseCov

public opencv_video.KalmanFilter processNoiseCov(opencv_core.Mat processNoiseCov)

measurementNoiseCov

@ByRef
public opencv_core.Mat measurementNoiseCov()

measurement noise covariance matrix (R)

measurementNoiseCov

public opencv_video.KalmanFilter measurementNoiseCov(opencv_core.Mat measurementNoiseCov)

errorCovPre

@ByRef
public opencv_core.Mat errorCovPre()

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

errorCovPre

public opencv_video.KalmanFilter errorCovPre(opencv_core.Mat errorCovPre)

gain

@ByRef
public opencv_core.Mat gain()

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

gain

public opencv_video.KalmanFilter gain(opencv_core.Mat gain)

errorCovPost

@ByRef
public opencv_core.Mat errorCovPost()

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

errorCovPost

public opencv_video.KalmanFilter errorCovPost(opencv_core.Mat errorCovPost)

temp1

@ByRef
public opencv_core.Mat temp1()

temp1

public opencv_video.KalmanFilter temp1(opencv_core.Mat temp1)

temp2

@ByRef
public opencv_core.Mat temp2()

temp2

public opencv_video.KalmanFilter temp2(opencv_core.Mat temp2)

temp3

@ByRef
public opencv_core.Mat temp3()

temp3

public opencv_video.KalmanFilter temp3(opencv_core.Mat temp3)

temp4

@ByRef
public opencv_core.Mat temp4()

temp4

public opencv_video.KalmanFilter temp4(opencv_core.Mat temp4)

temp5

@ByRef
public opencv_core.Mat temp5()

temp5

public opencv_video.KalmanFilter temp5(opencv_core.Mat temp5)