[−][src]Trait opencv::xfeatures2d::BoostDesc

pub trait BoostDesc: Feature2DTrait {
    pub fn as_raw_BoostDesc(&self) -> *const c_void;

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

    pub fn set_use_scale_orientation(
        &mut self, 
        use_scale_orientation: bool
    ) -> Result<()> { ... }

    pub fn get_use_scale_orientation(&self) -> Result<bool> { ... }

    pub fn set_scale_factor(&mut self, scale_factor: f32) -> Result<()> { ... }

    pub fn get_scale_factor(&self) -> Result<f32> { ... }
}

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in Trzcinski13a and Trzcinski13b.

Parameters

  • desc: type of descriptor to use, BoostDesc::BINBOOST_256 is default (256 bit long dimension) Available types are: BoostDesc::BGM, BoostDesc::BGM_HARD, BoostDesc::BGM_BILINEAR, BoostDesc::LBGM, BoostDesc::BINBOOST_64, BoostDesc::BINBOOST_128, BoostDesc::BINBOOST_256
  • use_orientation: sample patterns using keypoints orientation, enabled by default
  • scale_factor: adjust the sampling window of detected keypoints 6.25f is default and fits for KAZE, SURF detected keypoints window ratio 6.75f should be the scale for SIFT detected keypoints window ratio 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints window ratio 0.75f should be the scale for ORB keypoints ratio 1.50f was the default in original implementation

Note: BGM is the base descriptor where each binary dimension is computed as the output of a single weak learner. BGM_HARD and BGM_BILINEAR refers to same BGM but use different type of gradient binning. In the BGM_HARD that use ASSIGN_HARD binning type the gradient is assigned to the nearest orientation bin. In the BGM_BILINEAR that use ASSIGN_BILINEAR binning type the gradient is assigned to the two neighbouring bins. In the BGM and all other modes that use ASSIGN_SOFT binning type the gradient is assigned to 8 nearest bins according to the cosine value between the gradient angle and the bin center. LBGM (alias FP-Boost) is the floating point extension where each dimension is computed as a linear combination of the weak learner responses. BINBOOST and subvariants are the binary extensions of LBGM where each bit is computed as a thresholded linear combination of a set of weak learners. BoostDesc header files (boostdesc_*.i) was exported from original binaries with export-boostdesc.py script from samples subfolder.

Required methods

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

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

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Provided methods

pub fn set_use_scale_orientation(
    &mut self,
    use_scale_orientation: bool
) -> Result<()>[src]

pub fn get_use_scale_orientation(&self) -> Result<bool>[src]

pub fn set_scale_factor(&mut self, scale_factor: f32) -> Result<()>[src]

pub fn get_scale_factor(&self) -> Result<f32>[src]

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Implementations

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

pub fn create(
    desc: i32,
    use_scale_orientation: bool,
    scale_factor: f32
) -> Result<Ptr<dyn BoostDesc>>[src]

C++ default parameters

  • desc: BoostDesc::BINBOOST_256
  • use_scale_orientation: true
  • scale_factor: 6.25f

Implementors

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