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

Computes a foreground mask.

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

• image: Evision.Mat.

  Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.

Keyword Arguments

• learningRate: double.

  The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.

Return

• fgmask: Evision.Mat.

  The output foreground mask as an 8-bit binary image.

Python prototype (for reference only):

apply(image[, fgmask[, learningRate]]) -> fgmask

@spec apply(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  Evision.Mat.t() | {:error, String.t()}

Computes a foreground mask.

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

• image: Evision.Mat.

  Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.

Keyword Arguments

• learningRate: double.

  The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.

Return

• fgmask: Evision.Mat.

  The output foreground mask as an 8-bit binary image.

Python prototype (for reference only):

apply(image[, fgmask[, learningRate]]) -> fgmask

@spec getBackgroundRatio(t()) :: number() | {:error, String.t()}

Returns the "background ratio" parameter of the algorithm

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

If a foreground pixel keeps semi-constant value for about backgroundRatio*history frames, it's considered background and added to the model as a center of a new component. It corresponds to TB parameter in the paper.

Python prototype (for reference only):

getBackgroundRatio() -> retval

@spec getComplexityReductionThreshold(t()) :: number() | {:error, String.t()}

Returns the complexity reduction threshold

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the standard Stauffer&Grimson algorithm.

Python prototype (for reference only):

getComplexityReductionThreshold() -> retval

@spec getDetectShadows(t()) :: boolean() | {:error, String.t()}

Returns the shadow detection flag

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: bool

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.

Python prototype (for reference only):

getDetectShadows() -> retval

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

Returns the number of last frames that affect the background model

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: int

Python prototype (for reference only):

getHistory() -> retval

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

Returns the number of gaussian components in the background model

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: int

Python prototype (for reference only):

getNMixtures() -> retval

@spec getShadowThreshold(t()) :: number() | {:error, String.t()}

Returns the shadow threshold

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, Detecting Moving Shadows...*, IEEE PAMI,2003.

Python prototype (for reference only):

getShadowThreshold() -> retval

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

Returns the shadow value

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: int

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 in the mask always means background, 255 means foreground.

Python prototype (for reference only):

getShadowValue() -> retval

@spec getVarInit(t()) :: number() | {:error, String.t()}

Returns the initial variance of each gaussian component

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

Python prototype (for reference only):

getVarInit() -> retval

@spec getVarMax(t()) :: number() | {:error, String.t()}

getVarMax

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

Python prototype (for reference only):

getVarMax() -> retval

@spec getVarMin(t()) :: number() | {:error, String.t()}

getVarMin

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

Python prototype (for reference only):

getVarMin() -> retval

@spec getVarThreshold(t()) :: number() | {:error, String.t()}

Returns the variance threshold for the pixel-model match

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

The main threshold on the squared Mahalanobis distance to decide if the sample is well described by the background model or not. Related to Cthr from the paper.

Python prototype (for reference only):

getVarThreshold() -> retval

@spec getVarThresholdGen(t()) :: number() | {:error, String.t()}

Returns the variance threshold for the pixel-model match used for new mixture component generation

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()

Return

• retval: double

Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it is considered foreground or added as a new component. 3 sigma => Tg=3*3=9 is default. A smaller Tg value generates more components. A higher Tg value may result in a small number of components but they can grow too large.

Python prototype (for reference only):

getVarThresholdGen() -> retval

@spec setBackgroundRatio(t(), number()) :: :ok | {:error, String.t()}

Sets the "background ratio" parameter of the algorithm

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• ratio: double

Python prototype (for reference only):

setBackgroundRatio(ratio) -> None

@spec setComplexityReductionThreshold(t(), number()) :: :ok | {:error, String.t()}

Sets the complexity reduction threshold

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• ct: double

Python prototype (for reference only):

setComplexityReductionThreshold(ct) -> None

@spec setDetectShadows(t(), boolean()) :: :ok | {:error, String.t()}

Enables or disables shadow detection

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• detectShadows: bool

Python prototype (for reference only):

setDetectShadows(detectShadows) -> None

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

Sets the number of last frames that affect the background model

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• history: int

Python prototype (for reference only):

setHistory(history) -> None

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

Sets the number of gaussian components in the background model.

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• nmixtures: int

The model needs to be reinitalized to reserve memory.

Python prototype (for reference only):

setNMixtures(nmixtures) -> None

@spec setShadowThreshold(t(), number()) :: :ok | {:error, String.t()}

Sets the shadow threshold

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• threshold: double

Python prototype (for reference only):

setShadowThreshold(threshold) -> None

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

Sets the shadow value

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• value: int

Python prototype (for reference only):

setShadowValue(value) -> None

@spec setVarInit(t(), number()) :: :ok | {:error, String.t()}

Sets the initial variance of each gaussian component

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• varInit: double

Python prototype (for reference only):

setVarInit(varInit) -> None

@spec setVarMax(t(), number()) :: :ok | {:error, String.t()}

setVarMax

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• varMax: double

Python prototype (for reference only):

setVarMax(varMax) -> None

@spec setVarMin(t(), number()) :: :ok | {:error, String.t()}

setVarMin

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• varMin: double

Python prototype (for reference only):

setVarMin(varMin) -> None

@spec setVarThreshold(t(), number()) :: :ok | {:error, String.t()}

Sets the variance threshold for the pixel-model match

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• varThreshold: double

Python prototype (for reference only):

setVarThreshold(varThreshold) -> None

@spec setVarThresholdGen(t(), number()) :: :ok | {:error, String.t()}

Sets the variance threshold for the pixel-model match used for new mixture component generation

Positional Arguments

• self: Evision.BackgroundSubtractorMOG2.t()
• varThresholdGen: double

Python prototype (for reference only):

setVarThresholdGen(varThresholdGen) -> None