Trait opencv::prelude::BackgroundSubtractorMOG2Const

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pub trait BackgroundSubtractorMOG2Const: BackgroundSubtractorConst {

Show 13 methods    // Required method
    fn as_raw_BackgroundSubtractorMOG2(&self) -> *const c_void;

    // Provided methods
    fn get_history(&self) -> Result<i32> { ... }
    fn get_n_mixtures(&self) -> Result<i32> { ... }
    fn get_background_ratio(&self) -> Result<f64> { ... }
    fn get_var_threshold(&self) -> Result<f64> { ... }
    fn get_var_threshold_gen(&self) -> Result<f64> { ... }
    fn get_var_init(&self) -> Result<f64> { ... }
    fn get_var_min(&self) -> Result<f64> { ... }
    fn get_var_max(&self) -> Result<f64> { ... }
    fn get_complexity_reduction_threshold(&self) -> Result<f64> { ... }
    fn get_detect_shadows(&self) -> Result<bool> { ... }
    fn get_shadow_value(&self) -> Result<i32> { ... }
    fn get_shadow_threshold(&self) -> Result<f64> { ... }

}
Expand description

Constant methods for crate::video::BackgroundSubtractorMOG2

Required Methods§

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fn as_raw_BackgroundSubtractorMOG2(&self) -> *const c_void

Provided Methods§

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fn get_history(&self) -> Result<i32>

Returns the number of last frames that affect the background model

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fn get_n_mixtures(&self) -> Result<i32>

Returns the number of gaussian components in the background model

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fn get_background_ratio(&self) -> Result<f64>

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.

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fn get_var_threshold(&self) -> Result<f64>

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.

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fn get_var_threshold_gen(&self) -> Result<f64>

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.

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fn get_var_init(&self) -> Result<f64>

Returns the initial variance of each gaussian component

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fn get_var_min(&self) -> Result<f64>

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fn get_var_max(&self) -> Result<f64>

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fn get_complexity_reduction_threshold(&self) -> Result<f64>

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.

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fn get_detect_shadows(&self) -> Result<bool>

Returns the shadow detection flag

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.

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fn get_shadow_value(&self) -> Result<i32>

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.

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fn get_shadow_threshold(&self) -> Result<f64>

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.

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