Struct opencv::video::KalmanFilter

pub struct KalmanFilter { /* fields omitted */ }

Kalman filter class.

The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, 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)

Implementations

impl KalmanFilter

pub fn as_raw_KalmanFilter(&self) -> *const c_void

pub fn as_raw_mut_KalmanFilter(&mut self) -> *mut c_void

impl KalmanFilter

pub fn default() -> Result<KalmanFilter>

pub fn new(
    dynam_params: i32,
    measure_params: i32,
    control_params: i32,
    typ: i32
) -> Result<KalmanFilter>

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.

C++ default parameters

• control_params: 0
• typ: CV_32F

Trait Implementations

impl Boxed for KalmanFilter

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for KalmanFilter

fn drop(&mut self)

Executes the destructor for this type.

impl KalmanFilterTrait for KalmanFilter

fn as_raw_KalmanFilter(&self) -> *const c_void

fn as_raw_mut_KalmanFilter(&mut self) -> *mut c_void

fn state_pre(&mut self) -> Mat

predicted state (x'(k)): x(k)=Ax(k-1)+Bu(k)

fn set_state_pre(&mut self, val: Mat)

predicted state (x'(k)): x(k)=Ax(k-1)+Bu(k)

fn state_post(&mut self) -> Mat

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

fn set_state_post(&mut self, val: Mat)

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

fn transition_matrix(&mut self) -> Mat

state transition matrix (A)

fn set_transition_matrix(&mut self, val: Mat)

state transition matrix (A)

fn control_matrix(&mut self) -> Mat

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

fn set_control_matrix(&mut self, val: Mat)

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

fn measurement_matrix(&mut self) -> Mat

measurement matrix (H)

fn set_measurement_matrix(&mut self, val: Mat)

measurement matrix (H)

fn process_noise_cov(&mut self) -> Mat

process noise covariance matrix (Q)

fn set_process_noise_cov(&mut self, val: Mat)

process noise covariance matrix (Q)

fn measurement_noise_cov(&mut self) -> Mat

measurement noise covariance matrix (R)

fn set_measurement_noise_cov(&mut self, val: Mat)

measurement noise covariance matrix (R)

fn error_cov_pre(&mut self) -> Mat

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

fn set_error_cov_pre(&mut self, val: Mat)

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

fn gain(&mut self) -> Mat

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

fn set_gain(&mut self, val: Mat)

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

fn error_cov_post(&mut self) -> Mat

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

fn set_error_cov_post(&mut self, val: Mat)

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

fn temp1(&mut self) -> Mat

fn set_temp1(&mut self, val: Mat)

fn temp2(&mut self) -> Mat

fn set_temp2(&mut self, val: Mat)

fn temp3(&mut self) -> Mat

fn set_temp3(&mut self, val: Mat)

fn temp4(&mut self) -> Mat

fn set_temp4(&mut self, val: Mat)

fn temp5(&mut self) -> Mat

fn set_temp5(&mut self, val: Mat)

fn init(
    &mut self,
    dynam_params: i32,
    measure_params: i32,
    control_params: i32,
    typ: i32
) -> Result<()>

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

fn predict(&mut self, control: &Mat) -> Result<Mat>

Computes a predicted state.

fn correct(&mut self, measurement: &Mat) -> Result<Mat>

Updates the predicted state from the measurement.

impl Send for KalmanFilter