BackgroundSubtractorMOG2 (OpenPnP OpenCV 4.5.1-0 API)

java.lang.Object
- org.opencv.core.Algorithm
- - org.opencv.video.BackgroundSubtractor
  - - org.opencv.video.BackgroundSubtractorMOG2

```
public class BackgroundSubtractorMOG2
extends BackgroundSubtractor
```
Gaussian Mixture-based Background/Foreground Segmentation Algorithm. The class implements the Gaussian mixture model background subtraction described in CITE: Zivkovic2004 and CITE: Zivkovic2006 .

Field Summary
- Fields inherited from class org.opencv.core.Algorithm
  nativeObj

Constructor Summary

Constructors
Modifier Constructor and Description

protected BackgroundSubtractorMOG2(long addr)

Constructors
Modifier	Constructor and Description
`protected`	`BackgroundSubtractorMOG2(long addr)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static BackgroundSubtractorMOG2`	`__fromPtr__(long addr)`
`void`	`apply(Mat image, Mat fgmask)` Computes a foreground mask.
`void`	`apply(Mat image, Mat fgmask, double learningRate)` Computes a foreground mask.
`protected void`	`finalize()`
`double`	`getBackgroundRatio()` Returns the "background ratio" parameter of the algorithm 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.
`double`	`getComplexityReductionThreshold()` Returns the complexity reduction threshold This parameter defines the number of samples needed to accept to prove the component exists.
`boolean`	`getDetectShadows()` Returns the shadow detection flag If true, the algorithm detects shadows and marks them.
`int`	`getHistory()` Returns the number of last frames that affect the background model
`int`	`getNMixtures()` Returns the number of gaussian components in the background model
`double`	`getShadowThreshold()` Returns the shadow threshold A shadow is detected if pixel is a darker version of the background.
`int`	`getShadowValue()` Returns the shadow value Shadow value is the value used to mark shadows in the foreground mask.
`double`	`getVarInit()` Returns the initial variance of each gaussian component
`double`	`getVarMax()`
`double`	`getVarMin()`
`double`	`getVarThreshold()` Returns the variance threshold for the pixel-model match The main threshold on the squared Mahalanobis distance to decide if the sample is well described by the background model or not.
`double`	`getVarThresholdGen()` Returns the variance threshold for the pixel-model match used for new mixture component generation Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to Tg in the paper).
`void`	`setBackgroundRatio(double ratio)` Sets the "background ratio" parameter of the algorithm
`void`	`setComplexityReductionThreshold(double ct)` Sets the complexity reduction threshold
`void`	`setDetectShadows(boolean detectShadows)` Enables or disables shadow detection
`void`	`setHistory(int history)` Sets the number of last frames that affect the background model
`void`	`setNMixtures(int nmixtures)` Sets the number of gaussian components in the background model.
`void`	`setShadowThreshold(double threshold)` Sets the shadow threshold
`void`	`setShadowValue(int value)` Sets the shadow value
`void`	`setVarInit(double varInit)` Sets the initial variance of each gaussian component
`void`	`setVarMax(double varMax)`
`void`	`setVarMin(double varMin)`
`void`	`setVarThreshold(double varThreshold)` Sets the variance threshold for the pixel-model match
`void`	`setVarThresholdGen(double varThresholdGen)` Sets the variance threshold for the pixel-model match used for new mixture component generation

Methods inherited from class org.opencv.video.BackgroundSubtractor
getBackgroundImage

Methods inherited from class org.opencv.core.Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object
clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - BackgroundSubtractorMOG2
```
protected BackgroundSubtractorMOG2(long addr)
```
- Method Detail
  - __fromPtr__
```
public static BackgroundSubtractorMOG2 __fromPtr__(long addr)
```
  - getHistory
```
public int getHistory()
```
    Returns the number of last frames that affect the background model
    
    Returns:
    
    automatically generated
  - setHistory
```
public void setHistory(int history)
```
    Sets the number of last frames that affect the background model
    
    Parameters:
    
    history - automatically generated
  - getNMixtures
```
public int getNMixtures()
```
    Returns the number of gaussian components in the background model
    
    Returns:
    
    automatically generated
  - setNMixtures
```
public void setNMixtures(int nmixtures)
```
    Sets the number of gaussian components in the background model. The model needs to be reinitalized to reserve memory.
    
    Parameters:
    
    nmixtures - automatically generated
  - getBackgroundRatio
```
public double getBackgroundRatio()
```
    Returns the "background ratio" parameter of the algorithm 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.
    
    Returns:
    
    automatically generated
  - setBackgroundRatio
```
public void setBackgroundRatio(double ratio)
```
    Sets the "background ratio" parameter of the algorithm
    
    Parameters:
    
    ratio - automatically generated
  - getVarThreshold
```
public double getVarThreshold()
```
    Returns the variance threshold for the pixel-model match 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.
    
    Returns:
    
    automatically generated
  - setVarThreshold
```
public void setVarThreshold(double varThreshold)
```
    Sets the variance threshold for the pixel-model match
    
    Parameters:
    
    varThreshold - automatically generated
  - getVarThresholdGen
```
public double getVarThresholdGen()
```
    Returns the variance threshold for the pixel-model match used for new mixture component generation 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.
    
    Returns:
    
    automatically generated
  - setVarThresholdGen
```
public void setVarThresholdGen(double varThresholdGen)
```
    Sets the variance threshold for the pixel-model match used for new mixture component generation
    
    Parameters:
    
    varThresholdGen - automatically generated
  - getVarInit
```
public double getVarInit()
```
    Returns the initial variance of each gaussian component
    
    Returns:
    
    automatically generated
  - setVarInit
```
public void setVarInit(double varInit)
```
    Sets the initial variance of each gaussian component
    
    Parameters:
    
    varInit - automatically generated
  - getVarMin
```
public double getVarMin()
```
  - setVarMin
```
public void setVarMin(double varMin)
```
  - getVarMax
```
public double getVarMax()
```
  - setVarMax
```
public void setVarMax(double varMax)
```
  - getComplexityReductionThreshold
```
public double getComplexityReductionThreshold()
```
    Returns the complexity reduction threshold 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.
    
    Returns:
    
    automatically generated
  - setComplexityReductionThreshold
```
public void setComplexityReductionThreshold(double ct)
```
    Sets the complexity reduction threshold
    
    Parameters:
    
    ct - automatically generated
  - getDetectShadows
```
public boolean getDetectShadows()
```
    Returns the shadow detection flag If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.
    
    Returns:
    
    automatically generated
  - setDetectShadows
```
public void setDetectShadows(boolean detectShadows)
```
    Enables or disables shadow detection
    
    Parameters:
    
    detectShadows - automatically generated
  - getShadowValue
```
public int getShadowValue()
```
    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.
    
    Returns:
    
    automatically generated
  - setShadowValue
```
public void setShadowValue(int value)
```
    Sets the shadow value
    
    Parameters:
    
    value - automatically generated
  - getShadowThreshold
```
public double getShadowThreshold()
```
    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.
    
    Returns:
    
    automatically generated
  - setShadowThreshold
```
public void setShadowThreshold(double threshold)
```
    Sets the shadow threshold
    
    Parameters:
    
    threshold - automatically generated
  - apply
```
public void apply(Mat image,
                  Mat fgmask,
                  double learningRate)
```
    Computes a foreground mask.
    
    Overrides:
    
    apply in class BackgroundSubtractor
    
    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.
    
    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
```
public void apply(Mat image,
                  Mat fgmask)
```
    Computes a foreground mask.
    
    Overrides:
    
    apply in class BackgroundSubtractor
    
    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. 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.
  - finalize
```
protected void finalize()
                 throws Throwable
```
    Overrides:
    
    finalize in class BackgroundSubtractor
    
    Throws:
    
    Throwable

Class BackgroundSubtractorMOG2

Field Summary

Fields inherited from class org.opencv.core.Algorithm

Constructor Summary

Method Summary

Methods inherited from class org.opencv.video.BackgroundSubtractor

Methods inherited from class org.opencv.core.Algorithm

Methods inherited from class java.lang.Object

Constructor Detail

BackgroundSubtractorMOG2

Method Detail

__fromPtr__

getHistory

setHistory

getNMixtures

setNMixtures

getBackgroundRatio

setBackgroundRatio

getVarThreshold

setVarThreshold

getVarThresholdGen

setVarThresholdGen

getVarInit

setVarInit

getVarMin

setVarMin

getVarMax

setVarMax

getComplexityReductionThreshold

setComplexityReductionThreshold

getDetectShadows

setDetectShadows

getShadowValue

setShadowValue

getShadowThreshold

setShadowThreshold

apply

apply

finalize

fromPtr