Class cv::BackgroundSubtractor#

Base class for background/foreground segmentation. : View details

#include <opencv2/video/background_segm.hpp>

Collaboration diagram for cv::BackgroundSubtractor:

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

Base class for background/foreground segmentation. :

The class is only used to define the common interface for the whole family of background/foreground segmentation algorithms.

Member Function Documentation#

apply()#

void cv::BackgroundSubtractor::apply(
InputArray image,
InputArray knownForegroundMask,
OutputArray fgmask,
double learningRate = -1 )

Python:

cv.BackgroundSubtractor.apply(image[, fgmask[, learningRate]]) -> fgmask

cv.BackgroundSubtractor.apply(image, knownForegroundMask[, fgmask[, learningRate]]) -> fgmask

Computes a foreground mask with known foreground mask input.

Note

This method has a default virtual implementation that throws a “not impemented” error. Foreground masking may not be supported by all background subtractors.

Parameters

image — Next video frame. Floating point frame will be used without scaling and should be in range \([0,255]\).
fgmask — The output foreground mask as an 8-bit binary image.
knownForegroundMask — The mask for inputting already known foreground, allows model to ignore pixels.
learningRate — 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.

apply()#

void cv::BackgroundSubtractor::apply(
InputArray image,
OutputArray fgmask,
double learningRate = -1 )

Python:

cv.BackgroundSubtractor.apply(image[, fgmask[, learningRate]]) -> fgmask

cv.BackgroundSubtractor.apply(image, knownForegroundMask[, fgmask[, learningRate]]) -> fgmask

Computes a foreground mask.

Parameters

image — Next video frame.
fgmask — The output foreground mask as an 8-bit binary image.
learningRate — 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.

getBackgroundImage()#

void cv::BackgroundSubtractor::getBackgroundImage(OutputArray backgroundImage)

Python:

cv.BackgroundSubtractor.getBackgroundImage([, backgroundImage]) -> backgroundImage

Computes a background image.

Note

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

Parameters

backgroundImage — The output background image.

Source file#

The documentation for this class was generated from the following file:

opencv2/video/background_segm.hpp