Type Definition opencv::types::PtrOfLearningBasedWB

pub type PtrOfLearningBasedWB = Ptr<dyn LearningBasedWB>;

Implementations

impl PtrOfLearningBasedWB

pub fn as_raw_PtrOfLearningBasedWB(&self) -> *const c_void

pub fn as_raw_mut_PtrOfLearningBasedWB(&mut self) -> *mut c_void

Trait Implementations

impl AlgorithmTrait for PtrOfLearningBasedWB

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

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

Clears the algorithm state

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

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for PtrOfLearningBasedWB

fn as_raw_Algorithm(&self) -> *const c_void

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

Stores algorithm parameters in a file storage

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

simplified API for language bindings Stores algorithm parameters in a file storage

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 LearningBasedWB for PtrOfLearningBasedWB

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

fn extract_simple_features(
    &mut self,
    src: &dyn ToInputArray,
    dst: &mut dyn ToOutputArray
) -> Result<()>

Implements the feature extraction part of the algorithm.

fn set_range_max_val(&mut self, val: i32) -> Result<()>

Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images)

fn set_saturation_threshold(&mut self, val: f32) -> Result<()>

Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds

inline formula

are ignored.

fn set_hist_bin_num(&mut self, val: i32) -> Result<()>

Defines the size of one dimension of a three-dimensional RGB histogram that is used internally by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth (e.g. 256 bins for a 12 bit image).

impl LearningBasedWBConst for PtrOfLearningBasedWB

fn as_raw_LearningBasedWB(&self) -> *const c_void

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

Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images)

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

Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds

inline formula

are ignored.

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

Defines the size of one dimension of a three-dimensional RGB histogram that is used internally by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth (e.g. 256 bins for a 12 bit image).

impl WhiteBalancer for PtrOfLearningBasedWB

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

fn balance_white(
    &mut self,
    src: &dyn ToInputArray,
    dst: &mut dyn ToOutputArray
) -> Result<()>

Applies white balancing to the input image

impl WhiteBalancerConst for PtrOfLearningBasedWB

fn as_raw_WhiteBalancer(&self) -> *const c_void