Struct BoostDesc

pub struct BoostDesc { /* private fields */ }

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.

Implementations

impl BoostDesc

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

C++ default parameters

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

pub fn create_def() -> Result<Ptr<BoostDesc>>

Note

This alternative version of BoostDesc::create function uses the following default values for its arguments:

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

Trait Implementations

impl AlgorithmTrait for BoostDesc

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

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for BoostDesc

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut impl FileStorageTrait) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut impl FileStorageTrait, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

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

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl BoostDescTrait for BoostDesc

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

fn set_use_scale_orientation( &mut self, use_scale_orientation: bool, ) -> Result<()>

fn set_scale_factor(&mut self, scale_factor: f32) -> Result<()>

impl BoostDescTraitConst for BoostDesc

fn as_raw_BoostDesc(&self) -> *const c_void

fn get_default_name(&self) -> Result<String>

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

fn get_scale_factor(&self) -> Result<f32>

impl Boxed for BoostDesc

unsafe fn from_raw(ptr: <BoostDesc as OpenCVFromExtern>::ExternReceive) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> <BoostDesc as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> <BoostDesc as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer.

fn as_raw_mut( &mut self, ) -> <BoostDesc as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer

impl Debug for BoostDesc

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for BoostDesc

fn drop(&mut self)

Executes the destructor for this type.

impl Feature2DTrait for BoostDesc

fn as_raw_mut_Feature2D(&mut self) -> *mut c_void

fn detect( &mut self, image: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, mask: &impl ToInputArray, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn detect_def( &mut self, image: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn detect_multiple( &mut self, images: &impl ToInputArray, keypoints: &mut Vector<Vector<KeyPoint>>, masks: &impl ToInputArray, ) -> Result<()>

Detects keypoints in an image (first variant) or image set (second variant).

fn detect_multiple_def( &mut self, images: &impl ToInputArray, keypoints: &mut Vector<Vector<KeyPoint>>, ) -> Result<()>

@overload

fn compute( &mut self, image: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, descriptors: &mut impl ToOutputArray, ) -> Result<()>

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

fn compute_multiple( &mut self, images: &impl ToInputArray, keypoints: &mut Vector<Vector<KeyPoint>>, descriptors: &mut impl ToOutputArray, ) -> Result<()>

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

fn detect_and_compute( &mut self, image: &impl ToInputArray, mask: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, descriptors: &mut impl ToOutputArray, use_provided_keypoints: bool, ) -> Result<()>

Detects keypoints and computes the descriptors

fn detect_and_compute_def( &mut self, image: &impl ToInputArray, mask: &impl ToInputArray, keypoints: &mut Vector<KeyPoint>, descriptors: &mut impl ToOutputArray, ) -> Result<()>

Detects keypoints and computes the descriptors

fn read(&mut self, file_name: &str) -> Result<()>

fn read_from_node(&mut self, unnamed: &impl FileNodeTraitConst) -> Result<()>

impl Feature2DTraitConst for BoostDesc

fn as_raw_Feature2D(&self) -> *const c_void

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

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

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

fn write(&self, file_name: &str) -> Result<()>

fn write_to_storage(&self, unnamed: &mut impl FileStorageTrait) -> Result<()>

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

Return true if detector object is empty

fn get_default_name(&self) -> Result<String>

fn write_to_storage_with_name( &self, fs: &mut impl FileStorageTrait, name: &str, ) -> Result<()>

fn write_to_storage_ptr_with_name( &self, fs: &Ptr<FileStorage>, name: &str, ) -> Result<()>

impl From<BoostDesc> for Algorithm

fn from(s: BoostDesc) -> Self

Converts to this type from the input type.

impl From<BoostDesc> for Feature2D

fn from(s: BoostDesc) -> Self

Converts to this type from the input type.

impl Send for BoostDesc