View Source Evision.BgSegm (Evision v0.1.26-rc1)
Link to this section Summary
Functions
Creates a CNT Background Subtractor
Creates a CNT Background Subtractor
Creates a GMG Background Subtractor
Creates a GMG Background Subtractor
Creates an instance of BackgroundSubtractorGSOC algorithm.
Creates an instance of BackgroundSubtractorGSOC algorithm.
Creates an instance of BackgroundSubtractorLSBP algorithm.
Creates an instance of BackgroundSubtractorLSBP algorithm.
Creates mixture-of-gaussian background subtractor
Creates mixture-of-gaussian background subtractor
Creates an instance of SyntheticSequenceGenerator.
Creates an instance of SyntheticSequenceGenerator.
Link to this section Types
@type t() :: %Evision.BgSegm{ref: reference()}
Type that represents an BgSegm
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
@spec createBackgroundSubtractorCNT() :: Evision.BgSegm.BackgroundSubtractorCNT.t() | {:error, String.t()}
Creates a CNT Background Subtractor
Keyword Arguments
minPixelStability:
int
.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:
int
.maximum allowed credit for a pixel in history
isParallel:
bool
.determines if we're parallelizing the algorithm
Return
Python prototype (for reference only):
createBackgroundSubtractorCNT([, minPixelStability[, useHistory[, maxPixelStability[, isParallel]]]]) -> retval
@spec createBackgroundSubtractorCNT([{atom(), term()}, ...] | nil) :: Evision.BgSegm.BackgroundSubtractorCNT.t() | {:error, String.t()}
Creates a CNT Background Subtractor
Keyword Arguments
minPixelStability:
int
.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:
int
.maximum allowed credit for a pixel in history
isParallel:
bool
.determines if we're parallelizing the algorithm
Return
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:
int
.number of frames used to initialize the background models.
decisionThreshold:
double
.Threshold value, above which it is marked foreground, else background.
Return
Python prototype (for reference only):
createBackgroundSubtractorGMG([, initializationFrames[, decisionThreshold]]) -> retval
@spec createBackgroundSubtractorGMG([{atom(), term()}, ...] | nil) :: Evision.BgSegm.BackgroundSubtractorGMG.t() | {:error, String.t()}
Creates a GMG Background Subtractor
Keyword Arguments
initializationFrames:
int
.number of frames used to initialize the background models.
decisionThreshold:
double
.Threshold value, above which it is marked foreground, else background.
Return
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:
int
.Whether to use camera motion compensation.
nSamples:
int
.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:
int
.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
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([{atom(), term()}, ...] | nil) :: Evision.BgSegm.BackgroundSubtractorGSOC.t() | {:error, String.t()}
Creates an instance of BackgroundSubtractorGSOC algorithm.
Keyword Arguments
mc:
int
.Whether to use camera motion compensation.
nSamples:
int
.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:
int
.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
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:
int
.Whether to use camera motion compensation.
nSamples:
int
.Number of samples to maintain at each point of the frame.
lSBPRadius:
int
.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:
int
.Threshold for LSBP binary string.
minCount:
int
.Minimal number of matches for sample to be considered as foreground.
Return
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([{atom(), term()}, ...] | nil) :: Evision.BgSegm.BackgroundSubtractorLSBP.t() | {:error, String.t()}
Creates an instance of BackgroundSubtractorLSBP algorithm.
Keyword Arguments
mc:
int
.Whether to use camera motion compensation.
nSamples:
int
.Number of samples to maintain at each point of the frame.
lSBPRadius:
int
.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:
int
.Threshold for LSBP binary string.
minCount:
int
.Minimal number of matches for sample to be considered as foreground.
Return
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:
int
.Length of the history.
nmixtures:
int
.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
Python prototype (for reference only):
createBackgroundSubtractorMOG([, history[, nmixtures[, backgroundRatio[, noiseSigma]]]]) -> retval
@spec createBackgroundSubtractorMOG([{atom(), term()}, ...] | nil) :: Evision.BgSegm.BackgroundSubtractorMOG.t() | {:error, String.t()}
Creates mixture-of-gaussian background subtractor
Keyword Arguments
history:
int
.Length of the history.
nmixtures:
int
.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
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
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(), [{atom(), 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
Python prototype (for reference only):
createSyntheticSequenceGenerator(background, object[, amplitude[, wavelength[, wavespeed[, objspeed]]]]) -> retval