Class BackgroundSubtractorMOG

java.lang.Object
org.opencv.core.Algorithm
org.opencv.video.BackgroundSubtractor
org.opencv.bgsegm.BackgroundSubtractorMOG
public class BackgroundSubtractorMOG extends BackgroundSubtractor
Gaussian Mixture-based Background/Foreground Segmentation Algorithm. The class implements the algorithm described in CITE: KB2001 .

  • Constructor Details

    • BackgroundSubtractorMOG

      protected BackgroundSubtractorMOG(long addr)

  • Method Details

    • __fromPtr__

      public static BackgroundSubtractorMOG __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.

    • apply

      public void apply(Mat image, Mat knownForegroundMask, Mat fgmask, double learningRate)
      Computes a foreground mask and skips known foreground in evaluation.
      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.
      knownForegroundMask - The mask for inputting already known foreground, allows model to ignore learning known pixels.
      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 knownForegroundMask, Mat fgmask)
      Computes a foreground mask and skips known foreground in evaluation.
      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.
      knownForegroundMask - The mask for inputting already known foreground, allows model to ignore learning known pixels. 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.
      fgmask - The output foreground mask as an 8-bit binary image.

    • getHistory

      public int getHistory()

    • setHistory

      public void setHistory(int nframes)

    • getNMixtures

      public int getNMixtures()

    • setNMixtures

      public void setNMixtures(int nmix)

    • getBackgroundRatio

      public double getBackgroundRatio()

    • setBackgroundRatio

      public void setBackgroundRatio(double backgroundRatio)

    • getNoiseSigma

      public double getNoiseSigma()

    • setNoiseSigma

      public void setNoiseSigma(double noiseSigma)