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

Computes a foreground mask.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

• image: Evision.Mat.

  Next video frame.

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.t().

  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(), [{:learningRate, term()}] | nil) ::
  Evision.Mat.t() | {:error, String.t()}

Computes a foreground mask.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

• image: Evision.Mat.

  Next video frame.

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.t().

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

Python prototype (for reference only):

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

@spec clear(Keyword.t()) :: any() | {:error, String.t()}

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

Clears the algorithm state

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Python prototype (for reference only):

clear() -> None

@spec empty(Keyword.t()) :: any() | {:error, String.t()}

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

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getBackgroundImage(Keyword.t()) :: any() | {:error, String.t()}

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

Computes a background image.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• backgroundImage: Evision.Mat.t().

  The output background image.

Note: Sometimes the background image can be very blurry, as it contain the average background statistics.

Python prototype (for reference only):

getBackgroundImage([, backgroundImage]) -> backgroundImage

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

Computes a background image.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• backgroundImage: Evision.Mat.t().

  The output background image.

Note: Sometimes the background image can be very blurry, as it contain the average background statistics.

Python prototype (for reference only):

getBackgroundImage([, backgroundImage]) -> backgroundImage

@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

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

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

Returns the shadow detection flag

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: bool

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

Python prototype (for reference only):

getDetectShadows() -> retval

@spec getDist2Threshold(Keyword.t()) :: any() | {:error, String.t()}

@spec getDist2Threshold(t()) :: number() | {:error, String.t()}

Returns the threshold on the squared distance between the pixel and the sample

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: double

The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample.

Python prototype (for reference only):

getDist2Threshold() -> retval

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

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

Returns the number of last frames that affect the background model

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: integer()

Python prototype (for reference only):

getHistory() -> retval

@spec getkNNSamples(Keyword.t()) :: any() | {:error, String.t()}

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

Returns the number of neighbours, the k in the kNN.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: integer()

K is the number of samples that need to be within dist2Threshold in order to decide that that pixel is matching the kNN background model.

Python prototype (for reference only):

getkNNSamples() -> retval

@spec getNSamples(Keyword.t()) :: any() | {:error, String.t()}

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

Returns the number of data samples in the background model

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: integer()

Python prototype (for reference only):

getNSamples() -> retval

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

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

Returns the shadow threshold

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.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(Keyword.t()) :: any() | {:error, String.t()}

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

Returns the shadow value

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()

Return

• retval: integer()

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 read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

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

Enables or disables shadow detection

Positional Arguments

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

Python prototype (for reference only):

setDetectShadows(detectShadows) -> None

@spec setDist2Threshold(t(), number()) :: t() | {:error, String.t()}

Sets the threshold on the squared distance

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• dist2Threshold: double

Python prototype (for reference only):

setDist2Threshold(_dist2Threshold) -> None

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

Sets the number of last frames that affect the background model

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• history: integer()

Python prototype (for reference only):

setHistory(history) -> None

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

Sets the k in the kNN. How many nearest neighbours need to match.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• nkNN: integer()

Python prototype (for reference only):

setkNNSamples(_nkNN) -> None

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

Sets the number of data samples in the background model.

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• nN: integer()

The model needs to be reinitalized to reserve memory.

Python prototype (for reference only):

setNSamples(_nN) -> None

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

Sets the shadow threshold

Positional Arguments

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

Python prototype (for reference only):

setShadowThreshold(threshold) -> None

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

Sets the shadow value

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• value: integer()

Python prototype (for reference only):

setShadowValue(value) -> None

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• fs: Evision.FileStorage

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.BackgroundSubtractorKNN.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None