[−][src]Trait opencv::video::prelude::KalmanFilterTrait
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)
Required methods
pub fn as_raw_KalmanFilter(&self) -> *const c_void
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pub fn as_raw_mut_KalmanFilter(&mut self) -> *mut c_void
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Provided methods
pub fn state_pre(&mut self) -> Mat
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predicted state (x'(k)): x(k)=Ax(k-1)+Bu(k)
pub fn set_state_pre(&mut self, val: Mat)
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predicted state (x'(k)): x(k)=Ax(k-1)+Bu(k)
pub fn state_post(&mut self) -> Mat
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corrected state (x(k)): x(k)=x'(k)+K(k)(z(k)-Hx'(k))
pub fn set_state_post(&mut self, val: Mat)
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corrected state (x(k)): x(k)=x'(k)+K(k)(z(k)-Hx'(k))
pub fn transition_matrix(&mut self) -> Mat
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state transition matrix (A)
pub fn set_transition_matrix(&mut self, val: Mat)
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state transition matrix (A)
pub fn control_matrix(&mut self) -> Mat
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control matrix (B) (not used if there is no control)
pub fn set_control_matrix(&mut self, val: Mat)
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control matrix (B) (not used if there is no control)
pub fn measurement_matrix(&mut self) -> Mat
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measurement matrix (H)
pub fn set_measurement_matrix(&mut self, val: Mat)
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measurement matrix (H)
pub fn process_noise_cov(&mut self) -> Mat
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process noise covariance matrix (Q)
pub fn set_process_noise_cov(&mut self, val: Mat)
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process noise covariance matrix (Q)
pub fn measurement_noise_cov(&mut self) -> Mat
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measurement noise covariance matrix (R)
pub fn set_measurement_noise_cov(&mut self, val: Mat)
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measurement noise covariance matrix (R)
pub fn error_cov_pre(&mut self) -> Mat
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priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)
pub fn set_error_cov_pre(&mut self, val: Mat)
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priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)
pub fn gain(&mut self) -> Mat
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Kalman gain matrix (K(k)): K(k)=P'(k)Htinv(H*P'(k)*Ht+R)
pub fn set_gain(&mut self, val: Mat)
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Kalman gain matrix (K(k)): K(k)=P'(k)Htinv(H*P'(k)*Ht+R)
pub fn error_cov_post(&mut self) -> Mat
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posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
pub fn set_error_cov_post(&mut self, val: Mat)
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posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
pub fn temp1(&mut self) -> Mat
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pub fn set_temp1(&mut self, val: Mat)
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pub fn temp2(&mut self) -> Mat
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pub fn set_temp2(&mut self, val: Mat)
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pub fn temp3(&mut self) -> Mat
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pub fn set_temp3(&mut self, val: Mat)
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pub fn temp4(&mut self) -> Mat
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pub fn set_temp4(&mut self, val: Mat)
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pub fn temp5(&mut self) -> Mat
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pub fn set_temp5(&mut self, val: Mat)
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pub fn init(
&mut self,
dynam_params: i32,
measure_params: i32,
control_params: i32,
typ: i32
) -> Result<()>
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&mut self,
dynam_params: i32,
measure_params: i32,
control_params: i32,
typ: i32
) -> Result<()>
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.
C++ default parameters
- control_params: 0
- typ: CV_32F
pub fn predict(&mut self, control: &Mat) -> Result<Mat>
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Computes a predicted state.
Parameters
- control: The optional input control
C++ default parameters
- control: Mat()
pub fn correct(&mut self, measurement: &Mat) -> Result<Mat>
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Updates the predicted state from the measurement.
Parameters
- measurement: The measured system parameters