Trait opencv::xphoto::prelude::LearningBasedWB

pub trait LearningBasedWB: WhiteBalancer {
    pub fn as_raw_LearningBasedWB(&self) -> *const c_void;
    pub fn as_raw_mut_LearningBasedWB(&mut self) -> *mut c_void;

    pub fn extract_simple_features(
        &mut self, 
        src: &dyn ToInputArray, 
        dst: &mut dyn ToOutputArray
    ) -> Result<()> { ... }
    pub fn get_range_max_val(&self) -> Result<i32> { ... }
    pub fn set_range_max_val(&mut self, val: i32) -> Result<()> { ... }
    pub fn get_saturation_threshold(&self) -> Result<f32> { ... }
    pub fn set_saturation_threshold(&mut self, val: f32) -> Result<()> { ... }
    pub fn get_hist_bin_num(&self) -> Result<i32> { ... }
    pub fn set_hist_bin_num(&mut self, val: i32) -> Result<()> { ... }
}

More sophisticated learning-based automatic white balance algorithm.

As @ref GrayworldWB, this algorithm works by applying different gains to the input image channels, but their computation is a bit more involved compared to the simple gray-world assumption. More details about the algorithm can be found in Cheng2015 .

To mask out saturated pixels this function uses only pixels that satisfy the following condition:

Currently supports images of type @ref CV_8UC3 and @ref CV_16UC3.

Required methods

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

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

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Provided methods

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

Implements the feature extraction part of the algorithm.

In accordance with Cheng2015 , computes the following features for the input image:

1. Chromaticity of an average (R,G,B) tuple
2. Chromaticity of the brightest (R,G,B) tuple (while ignoring saturated pixels)
3. Chromaticity of the dominant (R,G,B) tuple (the one that has the highest value in the RGB histogram)
4. Mode of the chromaticity palette, that is constructed by taking 300 most common colors according to the RGB histogram and projecting them on the chromaticity plane. Mode is the most high-density point of the palette, which is computed by a straightforward fixed-bandwidth kernel density estimator with a Epanechnikov kernel function.

Parameters

• src: Input three-channel image (BGR color space is assumed).
• dst: An array of four (r,g) chromaticity tuples corresponding to the features listed above.

pub 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)

See also

setRangeMaxVal

pub 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)

See also

setRangeMaxVal getRangeMaxVal

pub 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 are ignored.

See also

setSaturationThreshold

pub 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 are ignored.

See also

setSaturationThreshold getSaturationThreshold

pub 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).

See also

setHistBinNum

pub 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).

See also

setHistBinNum getHistBinNum

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Implementors

impl LearningBasedWB for PtrOfLearningBasedWB

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

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

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