Class cv::BackgroundSubtractorKNN#

K-nearest neighbours - based Background/Foreground Segmentation Algorithm. View details

#include <opencv2/video/background_segm.hpp>

Collaboration diagram for cv::BackgroundSubtractorKNN:

Public Member Functions#

Public Member Functions inherited from cv::BackgroundSubtractor

Return	Name	Description
`void`	`apply(` `InputArray image,` `InputArray knownForegroundMask,` `OutputArray fgmask,` `double learningRate = -1 )`	Computes a foreground mask with known foreground mask input.
`void`	`apply(` `InputArray image,` `OutputArray fgmask,` `double learningRate = -1 )`	Computes a foreground mask.
`void`	`getBackgroundImage(OutputArray backgroundImage)`	Computes a background image.

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#

K-nearest neighbours - based Background/Foreground Segmentation Algorithm.

The class implements the K-nearest neighbours background subtraction described in Zivkovic2006 . Very efficient if number of foreground pixels is low.

Member Function Documentation#

getDetectShadows()#

bool cv::BackgroundSubtractorKNN::getDetectShadows()

Python:

cv.BackgroundSubtractorKNN.getDetectShadows() -> retval

Returns the shadow detection flag.

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

getDist2Threshold()#

double cv::BackgroundSubtractorKNN::getDist2Threshold()

Python:

cv.BackgroundSubtractorKNN.getDist2Threshold() -> retval

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

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

getHistory()#

int cv::BackgroundSubtractorKNN::getHistory()

Python:

cv.BackgroundSubtractorKNN.getHistory() -> retval

Returns the number of last frames that affect the background model.

getkNNSamples()#

int cv::BackgroundSubtractorKNN::getkNNSamples()

Python:

cv.BackgroundSubtractorKNN.getkNNSamples() -> retval

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

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.

getNSamples()#

int cv::BackgroundSubtractorKNN::getNSamples()

Python:

cv.BackgroundSubtractorKNN.getNSamples() -> retval

Returns the number of data samples in the background model.

getShadowThreshold()#

double cv::BackgroundSubtractorKNN::getShadowThreshold()

Python:

cv.BackgroundSubtractorKNN.getShadowThreshold() -> retval

Returns the shadow threshold.

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.

getShadowValue()#

int cv::BackgroundSubtractorKNN::getShadowValue()

Python:

cv.BackgroundSubtractorKNN.getShadowValue() -> retval

Returns the shadow value.

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.

setDetectShadows()#

void cv::BackgroundSubtractorKNN::setDetectShadows(bool detectShadows)

Python:

cv.BackgroundSubtractorKNN.setDetectShadows(detectShadows)

Enables or disables shadow detection.

setDist2Threshold()#

void cv::BackgroundSubtractorKNN::setDist2Threshold(double _dist2Threshold)

Python:

cv.BackgroundSubtractorKNN.setDist2Threshold(_dist2Threshold)

Sets the threshold on the squared distance.

setHistory()#

void cv::BackgroundSubtractorKNN::setHistory(int history)

Python:

cv.BackgroundSubtractorKNN.setHistory(history)

Sets the number of last frames that affect the background model.

setkNNSamples()#

void cv::BackgroundSubtractorKNN::setkNNSamples(int _nkNN)

Python:

cv.BackgroundSubtractorKNN.setkNNSamples(_nkNN)

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

setNSamples()#

void cv::BackgroundSubtractorKNN::setNSamples(int _nN)

Python:

cv.BackgroundSubtractorKNN.setNSamples(_nN)

Sets the number of data samples in the background model.

The model needs to be reinitialized to reserve memory.

setShadowThreshold()#

void cv::BackgroundSubtractorKNN::setShadowThreshold(double threshold)

Python:

cv.BackgroundSubtractorKNN.setShadowThreshold(threshold)

Sets the shadow threshold.

setShadowValue()#

void cv::BackgroundSubtractorKNN::setShadowValue(int value)

Python:

cv.BackgroundSubtractorKNN.setShadowValue(value)

Sets the shadow value.

Source file#

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

opencv2/video/background_segm.hpp