View Source Evision.KalmanFilter (Evision v0.1.21)

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Types

t()

Type that represents an Evision.KalmanFilter struct.

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@type t() :: %Evision.KalmanFilter{ref: reference()}

Type that represents an Evision.KalmanFilter struct.

  • ref. reference()

    The underlying erlang resource variable.

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correct(self, measurement)

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@spec correct(t(), Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}

Updates the predicted state from the measurement.

Positional Arguments
  • self: Evision.KalmanFilter.t()

  • measurement: Evision.Mat.

    The measured system parameters

Return

Python prototype (for reference only):

correct(measurement) -> retval
@spec get_controlMatrix(t()) :: Evision.Mat.t()
@spec get_errorCovPost(t()) :: Evision.Mat.t()
@spec get_errorCovPre(t()) :: Evision.Mat.t()
@spec get_gain(t()) :: Evision.Mat.t()
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get_measurementMatrix(self)

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@spec get_measurementMatrix(t()) :: Evision.Mat.t()
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get_measurementNoiseCov(self)

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@spec get_measurementNoiseCov(t()) :: Evision.Mat.t()
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get_processNoiseCov(self)

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@spec get_processNoiseCov(t()) :: Evision.Mat.t()
@spec get_statePost(t()) :: Evision.Mat.t()
@spec get_statePre(t()) :: Evision.Mat.t()
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get_transitionMatrix(self)

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@spec get_transitionMatrix(t()) :: Evision.Mat.t()
@spec kalmanFilter() :: t() | {:error, String.t()}

KalmanFilter

Return

Python prototype (for reference only):

KalmanFilter() -> <KalmanFilter object>
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kalmanFilter(dynamParams, measureParams)

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@spec kalmanFilter(integer(), integer()) :: t() | {:error, String.t()}

KalmanFilter

Positional Arguments
  • dynamParams: int.

    Dimensionality of the state.

  • measureParams: int.

    Dimensionality of the measurement.

Keyword Arguments
  • controlParams: int.

    Dimensionality of the control vector.

  • type: int.

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

Return

Has overloading in C++

Python prototype (for reference only):

KalmanFilter(dynamParams, measureParams[, controlParams[, type]]) -> <KalmanFilter object>
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kalmanFilter(dynamParams, measureParams, opts)

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@spec kalmanFilter(integer(), integer(), [{atom(), term()}, ...] | nil) ::
  t() | {:error, String.t()}

KalmanFilter

Positional Arguments
  • dynamParams: int.

    Dimensionality of the state.

  • measureParams: int.

    Dimensionality of the measurement.

Keyword Arguments
  • controlParams: int.

    Dimensionality of the control vector.

  • type: int.

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

Return

Has overloading in C++

Python prototype (for reference only):

KalmanFilter(dynamParams, measureParams[, controlParams[, type]]) -> <KalmanFilter object>
@spec predict(t()) :: Evision.Mat.t() | {:error, String.t()}

Computes a predicted state.

Positional Arguments
  • self: Evision.KalmanFilter.t()
Keyword Arguments
Return

Python prototype (for reference only):

predict([, control]) -> retval
@spec predict(t(), [{atom(), term()}, ...] | nil) ::
  Evision.Mat.t() | {:error, String.t()}

Computes a predicted state.

Positional Arguments
  • self: Evision.KalmanFilter.t()
Keyword Arguments
Return

Python prototype (for reference only):

predict([, control]) -> retval
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set_controlMatrix(self, prop)

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@spec set_controlMatrix(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_errorCovPost(self, prop)

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@spec set_errorCovPost(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_errorCovPre(self, prop)

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@spec set_errorCovPre(t(), Evision.Mat.maybe_mat_in()) :: t()
@spec set_gain(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_measurementMatrix(self, prop)

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@spec set_measurementMatrix(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_measurementNoiseCov(self, prop)

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@spec set_measurementNoiseCov(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_processNoiseCov(self, prop)

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@spec set_processNoiseCov(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_statePost(self, prop)

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@spec set_statePost(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_statePre(self, prop)

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@spec set_statePre(t(), Evision.Mat.maybe_mat_in()) :: t()
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set_transitionMatrix(self, prop)

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@spec set_transitionMatrix(t(), Evision.Mat.maybe_mat_in()) :: t()