BackgroundSubtractorGMG (JavaCPP Presets for OpenCV 4.11.0-1.5.12 API)

java.lang.Object
- org.opencv.core.Algorithm
- - org.opencv.video.BackgroundSubtractor
  - - org.opencv.bgsegm.BackgroundSubtractorGMG

```
public class BackgroundSubtractorGMG
extends BackgroundSubtractor
```
Background Subtractor module based on the algorithm given in CITE: Gold2012 . Takes a series of images and returns a sequence of mask (8UC1) images of the same size, where 255 indicates Foreground and 0 represents Background. This class implements an algorithm described in "Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.

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

Constructor Summary

Constructors
Modifier Constructor and Description

protected BackgroundSubtractorGMG(long addr)

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

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static BackgroundSubtractorGMG`	`__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()`
`void`	`getBackgroundImage(Mat backgroundImage)` Computes a background image.
`double`	`getBackgroundPrior()` Returns the prior probability that each individual pixel is a background pixel.
`double`	`getDecisionThreshold()` Returns the value of decision threshold.
`double`	`getDefaultLearningRate()` Returns the learning rate of the algorithm.
`int`	`getMaxFeatures()` Returns total number of distinct colors to maintain in histogram.
`double`	`getMaxVal()` Returns the maximum value taken on by pixels in image sequence.
`double`	`getMinVal()` Returns the minimum value taken on by pixels in image sequence.
`int`	`getNumFrames()` Returns the number of frames used to initialize background model.
`int`	`getQuantizationLevels()` Returns the parameter used for quantization of color-space.
`int`	`getSmoothingRadius()` Returns the kernel radius used for morphological operations
`boolean`	`getUpdateBackgroundModel()` Returns the status of background model update
`void`	`setBackgroundPrior(double bgprior)` Sets the prior probability that each individual pixel is a background pixel.
`void`	`setDecisionThreshold(double thresh)` Sets the value of decision threshold.
`void`	`setDefaultLearningRate(double lr)` Sets the learning rate of the algorithm.
`void`	`setMaxFeatures(int maxFeatures)` Sets total number of distinct colors to maintain in histogram.
`void`	`setMaxVal(double val)` Sets the maximum value taken on by pixels in image sequence.
`void`	`setMinVal(double val)` Sets the minimum value taken on by pixels in image sequence.
`void`	`setNumFrames(int nframes)` Sets the number of frames used to initialize background model.
`void`	`setQuantizationLevels(int nlevels)` Sets the parameter used for quantization of color-space
`void`	`setSmoothingRadius(int radius)` Sets the kernel radius used for morphological operations
`void`	`setUpdateBackgroundModel(boolean update)` Sets the status of background model update

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
  - BackgroundSubtractorGMG
```
protected BackgroundSubtractorGMG(long addr)
```
- Method Detail
  - __fromPtr__
```
public static BackgroundSubtractorGMG __fromPtr__(long addr)
```
  - apply
```
public void apply(Mat image,
                  Mat fgmask,
                  double learningRate)
```
    Computes a foreground mask.
    
    Overrides:
    
    apply in class BackgroundSubtractor
    
    Parameters:
    
    image - Next video frame of type CV_8UC(n),CV_8SC(n),CV_16UC(n),CV_16SC(n),CV_32SC(n),CV_32FC(n),CV_64FC(n), where n is 1,2,3,4.
    
    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 of type CV_8UC(n),CV_8SC(n),CV_16UC(n),CV_16SC(n),CV_32SC(n),CV_32FC(n),CV_64FC(n), where n is 1,2,3,4.
    
    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.
  - getBackgroundImage
```
public void getBackgroundImage(Mat backgroundImage)
```
    Description copied from class: BackgroundSubtractor
    
    Computes a background image.
    
    Overrides:
    
    getBackgroundImage in class BackgroundSubtractor
    
    Parameters:
    
    backgroundImage - The output background image. Note: Sometimes the background image can be very blurry, as it contain the average background statistics.
  - getMaxFeatures
```
public int getMaxFeatures()
```
    Returns total number of distinct colors to maintain in histogram.
    
    Returns:
    
    automatically generated
  - setMaxFeatures
```
public void setMaxFeatures(int maxFeatures)
```
    Sets total number of distinct colors to maintain in histogram.
    
    Parameters:
    
    maxFeatures - automatically generated
  - getDefaultLearningRate
```
public double getDefaultLearningRate()
```
    Returns the learning rate of the algorithm. It lies between 0.0 and 1.0. It determines how quickly features are "forgotten" from histograms.
    
    Returns:
    
    automatically generated
  - setDefaultLearningRate
```
public void setDefaultLearningRate(double lr)
```
    Sets the learning rate of the algorithm.
    
    Parameters:
    
    lr - automatically generated
  - getNumFrames
```
public int getNumFrames()
```
    Returns the number of frames used to initialize background model.
    
    Returns:
    
    automatically generated
  - setNumFrames
```
public void setNumFrames(int nframes)
```
    Sets the number of frames used to initialize background model.
    
    Parameters:
    
    nframes - automatically generated
  - getQuantizationLevels
```
public int getQuantizationLevels()
```
    Returns the parameter used for quantization of color-space. It is the number of discrete levels in each channel to be used in histograms.
    
    Returns:
    
    automatically generated
  - setQuantizationLevels
```
public void setQuantizationLevels(int nlevels)
```
    Sets the parameter used for quantization of color-space
    
    Parameters:
    
    nlevels - automatically generated
  - getBackgroundPrior
```
public double getBackgroundPrior()
```
    Returns the prior probability that each individual pixel is a background pixel.
    
    Returns:
    
    automatically generated
  - setBackgroundPrior
```
public void setBackgroundPrior(double bgprior)
```
    Sets the prior probability that each individual pixel is a background pixel.
    
    Parameters:
    
    bgprior - automatically generated
  - getSmoothingRadius
```
public int getSmoothingRadius()
```
    Returns the kernel radius used for morphological operations
    
    Returns:
    
    automatically generated
  - setSmoothingRadius
```
public void setSmoothingRadius(int radius)
```
    Sets the kernel radius used for morphological operations
    
    Parameters:
    
    radius - automatically generated
  - getDecisionThreshold
```
public double getDecisionThreshold()
```
    Returns the value of decision threshold. Decision value is the value above which pixel is determined to be FG.
    
    Returns:
    
    automatically generated
  - setDecisionThreshold
```
public void setDecisionThreshold(double thresh)
```
    Sets the value of decision threshold.
    
    Parameters:
    
    thresh - automatically generated
  - getUpdateBackgroundModel
```
public boolean getUpdateBackgroundModel()
```
    Returns the status of background model update
    
    Returns:
    
    automatically generated
  - setUpdateBackgroundModel
```
public void setUpdateBackgroundModel(boolean update)
```
    Sets the status of background model update
    
    Parameters:
    
    update - automatically generated
  - getMinVal
```
public double getMinVal()
```
    Returns the minimum value taken on by pixels in image sequence. Usually 0.
    
    Returns:
    
    automatically generated
  - setMinVal
```
public void setMinVal(double val)
```
    Sets the minimum value taken on by pixels in image sequence.
    
    Parameters:
    
    val - automatically generated
  - getMaxVal
```
public double getMaxVal()
```
    Returns the maximum value taken on by pixels in image sequence. e.g. 1.0 or 255.
    
    Returns:
    
    automatically generated
  - setMaxVal
```
public void setMaxVal(double val)
```
    Sets the maximum value taken on by pixels in image sequence.
    
    Parameters:
    
    val - automatically generated
  - finalize
```
protected void finalize()
                 throws Throwable
```
    Overrides:
    
    finalize in class BackgroundSubtractor
    
    Throws:
    
    Throwable

Class BackgroundSubtractorGMG

Field Summary

Fields inherited from class org.opencv.core.Algorithm

Constructor Summary

Method Summary

Methods inherited from class org.opencv.core.Algorithm

Methods inherited from class java.lang.Object

Constructor Detail

BackgroundSubtractorGMG

Method Detail

__fromPtr__

apply

apply

getBackgroundImage

getMaxFeatures

setMaxFeatures

getDefaultLearningRate

setDefaultLearningRate

getNumFrames

setNumFrames

getQuantizationLevels

setQuantizationLevels

getBackgroundPrior

setBackgroundPrior

getSmoothingRadius

setSmoothingRadius

getDecisionThreshold

setDecisionThreshold

getUpdateBackgroundModel

setUpdateBackgroundModel

getMinVal

setMinVal

getMaxVal

setMaxVal

finalize

fromPtr