@spec createBackgroundSubtractorCNT() ::
  Evision.BgSegm.BackgroundSubtractorCNT.t() | {:error, String.t()}

Creates a CNT Background Subtractor

Keyword Arguments

• minPixelStability: integer().

  number of frames with same pixel color to consider stable

• useHistory: bool.

  determines if we're giving a pixel credit for being stable for a long time

• maxPixelStability: integer().

  maximum allowed credit for a pixel in history

• isParallel: bool.

  determines if we're parallelizing the algorithm

Return

• retval: Evision.BgSegm.BackgroundSubtractorCNT.t()

Python prototype (for reference only):

createBackgroundSubtractorCNT([, minPixelStability[, useHistory[, maxPixelStability[, isParallel]]]]) -> retval

@spec createBackgroundSubtractorCNT(Keyword.t()) :: any() | {:error, String.t()}

@spec createBackgroundSubtractorCNT(
  [
    isParallel: term(),
    maxPixelStability: term(),
    minPixelStability: term(),
    useHistory: term()
  ]
  | nil
) :: Evision.BgSegm.BackgroundSubtractorCNT.t() | {:error, String.t()}

Creates a CNT Background Subtractor

Keyword Arguments

• minPixelStability: integer().

  number of frames with same pixel color to consider stable

• useHistory: bool.

  determines if we're giving a pixel credit for being stable for a long time

• maxPixelStability: integer().

  maximum allowed credit for a pixel in history

• isParallel: bool.

  determines if we're parallelizing the algorithm

Return

• retval: Evision.BgSegm.BackgroundSubtractorCNT.t()

Python prototype (for reference only):

createBackgroundSubtractorCNT([, minPixelStability[, useHistory[, maxPixelStability[, isParallel]]]]) -> retval

@spec createBackgroundSubtractorGMG() ::
  Evision.BgSegm.BackgroundSubtractorGMG.t() | {:error, String.t()}

Creates a GMG Background Subtractor

Keyword Arguments

• initializationFrames: integer().

  number of frames used to initialize the background models.

• decisionThreshold: double.

  Threshold value, above which it is marked foreground, else background.

Return

• retval: Evision.BgSegm.BackgroundSubtractorGMG.t()

Python prototype (for reference only):

createBackgroundSubtractorGMG([, initializationFrames[, decisionThreshold]]) -> retval

@spec createBackgroundSubtractorGMG(Keyword.t()) :: any() | {:error, String.t()}

@spec createBackgroundSubtractorGMG(
  [decisionThreshold: term(), initializationFrames: term()]
  | nil
) ::
  Evision.BgSegm.BackgroundSubtractorGMG.t() | {:error, String.t()}

Creates a GMG Background Subtractor

Keyword Arguments

• initializationFrames: integer().

  number of frames used to initialize the background models.

• decisionThreshold: double.

  Threshold value, above which it is marked foreground, else background.

Return

• retval: Evision.BgSegm.BackgroundSubtractorGMG.t()

Python prototype (for reference only):

createBackgroundSubtractorGMG([, initializationFrames[, decisionThreshold]]) -> retval

@spec createBackgroundSubtractorGSOC() ::
  Evision.BgSegm.BackgroundSubtractorGSOC.t() | {:error, String.t()}

Creates an instance of BackgroundSubtractorGSOC algorithm.

Keyword Arguments

• mc: integer().

  Whether to use camera motion compensation.

• nSamples: integer().

  Number of samples to maintain at each point of the frame.

• replaceRate: float.

  Probability of replacing the old sample - how fast the model will update itself.

• propagationRate: float.

  Probability of propagating to neighbors.

• hitsThreshold: integer().

  How many positives the sample must get before it will be considered as a possible replacement.

• alpha: float.

  Scale coefficient for threshold.

• beta: float.

  Bias coefficient for threshold.

• blinkingSupressionDecay: float.

  Blinking supression decay factor.

• blinkingSupressionMultiplier: float.

  Blinking supression multiplier.

• noiseRemovalThresholdFacBG: float.

  Strength of the noise removal for background points.

• noiseRemovalThresholdFacFG: float.

  Strength of the noise removal for foreground points.

Return

• retval: Evision.BgSegm.BackgroundSubtractorGSOC.t()

Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.

Python prototype (for reference only):

createBackgroundSubtractorGSOC([, mc[, nSamples[, replaceRate[, propagationRate[, hitsThreshold[, alpha[, beta[, blinkingSupressionDecay[, blinkingSupressionMultiplier[, noiseRemovalThresholdFacBG[, noiseRemovalThresholdFacFG]]]]]]]]]]]) -> retval

@spec createBackgroundSubtractorGSOC(Keyword.t()) :: any() | {:error, String.t()}

@spec createBackgroundSubtractorGSOC(
  [
    alpha: term(),
    beta: term(),
    blinkingSupressionDecay: term(),
    blinkingSupressionMultiplier: term(),
    hitsThreshold: term(),
    mc: term(),
    nSamples: term(),
    noiseRemovalThresholdFacBG: term(),
    noiseRemovalThresholdFacFG: term(),
    propagationRate: term(),
    replaceRate: term()
  ]
  | nil
) :: Evision.BgSegm.BackgroundSubtractorGSOC.t() | {:error, String.t()}

Creates an instance of BackgroundSubtractorGSOC algorithm.

Keyword Arguments

• mc: integer().

  Whether to use camera motion compensation.

• nSamples: integer().

  Number of samples to maintain at each point of the frame.

• replaceRate: float.

  Probability of replacing the old sample - how fast the model will update itself.

• propagationRate: float.

  Probability of propagating to neighbors.

• hitsThreshold: integer().

  How many positives the sample must get before it will be considered as a possible replacement.

• alpha: float.

  Scale coefficient for threshold.

• beta: float.

  Bias coefficient for threshold.

• blinkingSupressionDecay: float.

  Blinking supression decay factor.

• blinkingSupressionMultiplier: float.

  Blinking supression multiplier.

• noiseRemovalThresholdFacBG: float.

  Strength of the noise removal for background points.

• noiseRemovalThresholdFacFG: float.

  Strength of the noise removal for foreground points.

Return

• retval: Evision.BgSegm.BackgroundSubtractorGSOC.t()

Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.

Python prototype (for reference only):

createBackgroundSubtractorGSOC([, mc[, nSamples[, replaceRate[, propagationRate[, hitsThreshold[, alpha[, beta[, blinkingSupressionDecay[, blinkingSupressionMultiplier[, noiseRemovalThresholdFacBG[, noiseRemovalThresholdFacFG]]]]]]]]]]]) -> retval

@spec createBackgroundSubtractorLSBP() ::
  Evision.BgSegm.BackgroundSubtractorLSBP.t() | {:error, String.t()}

Creates an instance of BackgroundSubtractorLSBP algorithm.

Keyword Arguments

• mc: integer().

  Whether to use camera motion compensation.

• nSamples: integer().

  Number of samples to maintain at each point of the frame.

• lSBPRadius: integer().

  LSBP descriptor radius.

• tlower: float.

  Lower bound for T-values. See @cite LGuo2016 for details.

• tupper: float.

  Upper bound for T-values. See @cite LGuo2016 for details.

• tinc: float.

  Increase step for T-values. See @cite LGuo2016 for details.

• tdec: float.

  Decrease step for T-values. See @cite LGuo2016 for details.

• rscale: float.

  Scale coefficient for threshold values.

• rincdec: float.

  Increase/Decrease step for threshold values.

• noiseRemovalThresholdFacBG: float.

  Strength of the noise removal for background points.

• noiseRemovalThresholdFacFG: float.

  Strength of the noise removal for foreground points.

• lSBPthreshold: integer().

  Threshold for LSBP binary string.

• minCount: integer().

  Minimal number of matches for sample to be considered as foreground.

Return

• retval: Evision.BgSegm.BackgroundSubtractorLSBP.t()

Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016

Python prototype (for reference only):

createBackgroundSubtractorLSBP([, mc[, nSamples[, LSBPRadius[, Tlower[, Tupper[, Tinc[, Tdec[, Rscale[, Rincdec[, noiseRemovalThresholdFacBG[, noiseRemovalThresholdFacFG[, LSBPthreshold[, minCount]]]]]]]]]]]]]) -> retval

@spec createBackgroundSubtractorLSBP(Keyword.t()) :: any() | {:error, String.t()}

@spec createBackgroundSubtractorLSBP(
  [
    lSBPRadius: term(),
    lSBPthreshold: term(),
    mc: term(),
    minCount: term(),
    nSamples: term(),
    noiseRemovalThresholdFacBG: term(),
    noiseRemovalThresholdFacFG: term(),
    rincdec: term(),
    rscale: term(),
    tdec: term(),
    tinc: term(),
    tlower: term(),
    tupper: term()
  ]
  | nil
) :: Evision.BgSegm.BackgroundSubtractorLSBP.t() | {:error, String.t()}

Creates an instance of BackgroundSubtractorLSBP algorithm.

Keyword Arguments

• mc: integer().

  Whether to use camera motion compensation.

• nSamples: integer().

  Number of samples to maintain at each point of the frame.

• lSBPRadius: integer().

  LSBP descriptor radius.

• tlower: float.

  Lower bound for T-values. See @cite LGuo2016 for details.

• tupper: float.

  Upper bound for T-values. See @cite LGuo2016 for details.

• tinc: float.

  Increase step for T-values. See @cite LGuo2016 for details.

• tdec: float.

  Decrease step for T-values. See @cite LGuo2016 for details.

• rscale: float.

  Scale coefficient for threshold values.

• rincdec: float.

  Increase/Decrease step for threshold values.

• noiseRemovalThresholdFacBG: float.

  Strength of the noise removal for background points.

• noiseRemovalThresholdFacFG: float.

  Strength of the noise removal for foreground points.

• lSBPthreshold: integer().

  Threshold for LSBP binary string.

• minCount: integer().

  Minimal number of matches for sample to be considered as foreground.

Return

• retval: Evision.BgSegm.BackgroundSubtractorLSBP.t()

Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016

Python prototype (for reference only):

createBackgroundSubtractorLSBP([, mc[, nSamples[, LSBPRadius[, Tlower[, Tupper[, Tinc[, Tdec[, Rscale[, Rincdec[, noiseRemovalThresholdFacBG[, noiseRemovalThresholdFacFG[, LSBPthreshold[, minCount]]]]]]]]]]]]]) -> retval

@spec createBackgroundSubtractorMOG() ::
  Evision.BgSegm.BackgroundSubtractorMOG.t() | {:error, String.t()}

Creates mixture-of-gaussian background subtractor

Keyword Arguments

• history: integer().

  Length of the history.

• nmixtures: integer().

  Number of Gaussian mixtures.

• backgroundRatio: double.

  Background ratio.

• noiseSigma: double.

  Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.

Return

• retval: Evision.BgSegm.BackgroundSubtractorMOG.t()

Python prototype (for reference only):

createBackgroundSubtractorMOG([, history[, nmixtures[, backgroundRatio[, noiseSigma]]]]) -> retval

@spec createBackgroundSubtractorMOG(Keyword.t()) :: any() | {:error, String.t()}

@spec createBackgroundSubtractorMOG(
  [
    backgroundRatio: term(),
    history: term(),
    nmixtures: term(),
    noiseSigma: term()
  ]
  | nil
) :: Evision.BgSegm.BackgroundSubtractorMOG.t() | {:error, String.t()}

Creates mixture-of-gaussian background subtractor

Keyword Arguments

• history: integer().

  Length of the history.

• nmixtures: integer().

  Number of Gaussian mixtures.

• backgroundRatio: double.

  Background ratio.

• noiseSigma: double.

  Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.

Return

• retval: Evision.BgSegm.BackgroundSubtractorMOG.t()

Python prototype (for reference only):

createBackgroundSubtractorMOG([, history[, nmixtures[, backgroundRatio[, noiseSigma]]]]) -> retval

@spec createSyntheticSequenceGenerator(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in()
) ::
  Evision.BgSegm.SyntheticSequenceGenerator.t() | {:error, String.t()}

Creates an instance of SyntheticSequenceGenerator.

Positional Arguments

• background: Evision.Mat.

  Background image for object.

• object: Evision.Mat.

  Object image which will move slowly over the background.

Keyword Arguments

• amplitude: double.

  Amplitude of wave distortion applied to background.

• wavelength: double.

  Length of waves in distortion applied to background.

• wavespeed: double.

  How fast waves will move.

• objspeed: double.

  How fast object will fly over background.

Return

• retval: Evision.BgSegm.SyntheticSequenceGenerator.t()

Python prototype (for reference only):

createSyntheticSequenceGenerator(background, object[, amplitude[, wavelength[, wavespeed[, objspeed]]]]) -> retval

@spec createSyntheticSequenceGenerator(
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [amplitude: term(), objspeed: term(), wavelength: term(), wavespeed: term()]
  | nil
) :: Evision.BgSegm.SyntheticSequenceGenerator.t() | {:error, String.t()}

Creates an instance of SyntheticSequenceGenerator.

Positional Arguments

• background: Evision.Mat.

  Background image for object.

• object: Evision.Mat.

  Object image which will move slowly over the background.

Keyword Arguments

• amplitude: double.

  Amplitude of wave distortion applied to background.

• wavelength: double.

  Length of waves in distortion applied to background.

• wavespeed: double.

  How fast waves will move.

• objspeed: double.

  How fast object will fly over background.

Return

• retval: Evision.BgSegm.SyntheticSequenceGenerator.t()

Python prototype (for reference only):

createSyntheticSequenceGenerator(background, object[, amplitude[, wavelength[, wavespeed[, objspeed]]]]) -> retval