[−][src]Trait opencv::hub_prelude::WBDetector

pub trait WBDetector {
    pub fn as_raw_WBDetector(&self) -> *const c_void;

    pub fn as_raw_mut_WBDetector(&mut self) -> *mut c_void;

    pub fn read(&mut self, node: &FileNode) -> Result<()> { ... }

    pub fn write(&self, fs: &mut FileStorage) -> Result<()> { ... }

    pub fn train(&mut self, pos_samples: &str, neg_imgs: &str) -> Result<()> { ... }

    pub fn detect(
        &mut self, 
        img: &Mat, 
        bboxes: &mut Vector<Rect>, 
        confidences: &mut Vector<f64>
    ) -> Result<()> { ... }
}

WaldBoost detector

Required methods

pub fn as_raw_WBDetector(&self) -> *const c_void[src]

pub fn as_raw_mut_WBDetector(&mut self) -> *mut c_void[src]

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Provided methods

pub fn read(&mut self, node: &FileNode) -> Result<()>[src]

Read detector from FileNode.

Parameters

  • node: FileNode for input

pub fn write(&self, fs: &mut FileStorage) -> Result<()>[src]

Write detector to FileStorage.

Parameters

  • fs: FileStorage for output

pub fn train(&mut self, pos_samples: &str, neg_imgs: &str) -> Result<()>[src]

Train WaldBoost detector

Parameters

  • pos_samples: Path to directory with cropped positive samples
  • neg_imgs: Path to directory with negative (background) images

pub fn detect(
    &mut self,
    img: &Mat,
    bboxes: &mut Vector<Rect>,
    confidences: &mut Vector<f64>
) -> Result<()>[src]

Detect objects on image using WaldBoost detector

Parameters

  • img: Input image for detection
  • bboxes: Bounding boxes coordinates output vector
  • confidences: Confidence values for bounding boxes output vector
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Implementations

impl<'_> dyn WBDetector + '_[src]

pub fn create() -> Result<Ptr<dyn WBDetector>>[src]

Create instance of WBDetector

Implementors

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