opencv::mod_prelude

Trait BackgroundSubtractorMOG2Trait

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pub trait BackgroundSubtractorMOG2Trait: BackgroundSubtractorMOG2TraitConst + BackgroundSubtractorTrait {

Show 15 methods    // Required method
    fn as_raw_mut_BackgroundSubtractorMOG2(&mut self) -> *mut c_void;

    // Provided methods
    fn set_history(&mut self, history: i32) -> Result<()> { ... }
    fn set_n_mixtures(&mut self, nmixtures: i32) -> Result<()> { ... }
    fn set_background_ratio(&mut self, ratio: f64) -> Result<()> { ... }
    fn set_var_threshold(&mut self, var_threshold: f64) -> Result<()> { ... }
    fn set_var_threshold_gen(&mut self, var_threshold_gen: f64) -> Result<()> { ... }
    fn set_var_init(&mut self, var_init: f64) -> Result<()> { ... }
    fn set_var_min(&mut self, var_min: f64) -> Result<()> { ... }
    fn set_var_max(&mut self, var_max: f64) -> Result<()> { ... }
    fn set_complexity_reduction_threshold(&mut self, ct: f64) -> Result<()> { ... }
    fn set_detect_shadows(&mut self, detect_shadows: bool) -> Result<()> { ... }
    fn set_shadow_value(&mut self, value: i32) -> Result<()> { ... }
    fn set_shadow_threshold(&mut self, threshold: f64) -> Result<()> { ... }
    fn apply(
        &mut self,
        image: &impl ToInputArray,
        fgmask: &mut impl ToOutputArray,
        learning_rate: f64,
    ) -> Result<()> { ... }
    fn apply_def(
        &mut self,
        image: &impl ToInputArray,
        fgmask: &mut impl ToOutputArray,
    ) -> Result<()> { ... }

}
Expand description

Mutable methods for crate::video::BackgroundSubtractorMOG2

Required Methods§

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fn as_raw_mut_BackgroundSubtractorMOG2(&mut self) -> *mut c_void

Provided Methods§

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fn set_history(&mut self, history: i32) -> Result<()>

Sets the number of last frames that affect the background model

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fn set_n_mixtures(&mut self, nmixtures: i32) -> Result<()>

Sets the number of gaussian components in the background model.

The model needs to be reinitalized to reserve memory.

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fn set_background_ratio(&mut self, ratio: f64) -> Result<()>

Sets the “background ratio” parameter of the algorithm

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fn set_var_threshold(&mut self, var_threshold: f64) -> Result<()>

Sets the variance threshold for the pixel-model match

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fn set_var_threshold_gen(&mut self, var_threshold_gen: f64) -> Result<()>

Sets the variance threshold for the pixel-model match used for new mixture component generation

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fn set_var_init(&mut self, var_init: f64) -> Result<()>

Sets the initial variance of each gaussian component

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fn set_var_min(&mut self, var_min: f64) -> Result<()>

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fn set_var_max(&mut self, var_max: f64) -> Result<()>

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fn set_complexity_reduction_threshold(&mut self, ct: f64) -> Result<()>

Sets the complexity reduction threshold

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fn set_detect_shadows(&mut self, detect_shadows: bool) -> Result<()>

Enables or disables shadow detection

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fn set_shadow_value(&mut self, value: i32) -> Result<()>

Sets the shadow value

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fn set_shadow_threshold(&mut self, threshold: f64) -> Result<()>

Sets the shadow threshold

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fn apply( &mut self, image: &impl ToInputArray, fgmask: &mut impl ToOutputArray, learning_rate: f64, ) -> Result<()>

Computes a foreground mask.

§Parameters
  • image: Next video frame. Floating point frame will be used without scaling and should be in range inline formula.
  • 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.
§C++ default parameters
  • learning_rate: -1
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fn apply_def( &mut self, image: &impl ToInputArray, fgmask: &mut impl ToOutputArray, ) -> Result<()>

Computes a foreground mask.

§Parameters
  • image: Next video frame. Floating point frame will be used without scaling and should be in range inline formula.
  • 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.
§Note

This alternative version of BackgroundSubtractorMOG2Trait::apply function uses the following default values for its arguments:

  • learning_rate: -1

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§