[−][src]Trait opencv::ximgproc::prelude::RICInterpolator

pub trait RICInterpolator: SparseMatchInterpolator {
    pub fn as_raw_RICInterpolator(&self) -> *const c_void;

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

    pub fn set_k(&mut self, k: i32) -> Result<()> { ... }

    pub fn get_k(&self) -> Result<i32> { ... }

    pub fn set_cost_map(&mut self, cost_map: &Mat) -> Result<()> { ... }

    pub fn set_superpixel_size(&mut self, sp_size: i32) -> Result<()> { ... }

    pub fn get_superpixel_size(&self) -> Result<i32> { ... }

    pub fn set_superpixel_nn_cnt(&mut self, sp_nn: i32) -> Result<()> { ... }

    pub fn get_superpixel_nn_cnt(&self) -> Result<i32> { ... }

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

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

    pub fn set_superpixel_mode(&mut self, mode: i32) -> Result<()> { ... }

    pub fn get_superpixel_mode(&self) -> Result<i32> { ... }

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

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

    pub fn set_model_iter(&mut self, model_iter: i32) -> Result<()> { ... }

    pub fn get_model_iter(&self) -> Result<i32> { ... }

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

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

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

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

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

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

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

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

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

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

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

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

Sparse match interpolation algorithm based on modified piecewise locally-weighted affine estimator called Robust Interpolation method of Correspondences or RIC from Hu2017 and Variational and Fast Global Smoother as post-processing filter. The RICInterpolator is a extension of the EdgeAwareInterpolator. Main concept of this extension is an piece-wise affine model based on over-segmentation via SLIC superpixel estimation. The method contains an efficient propagation mechanism to estimate among the pieces-wise models.

Required methods

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

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

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Provided methods

pub fn set_k(&mut self, k: i32) -> Result<()>[src]

K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of Hu2017 uses 32.

C++ default parameters

  • k: 32

pub fn get_k(&self) -> Result<i32>[src]

K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of Hu2017 uses 32. * see also: setK

pub fn set_cost_map(&mut self, cost_map: &Mat) -> Result<()>[src]

Interface to provide a more elaborated cost map, i.e. edge map, for the edge-aware term. This implementation is based on a rather simple gradient-based edge map estimation. To used more complex edge map estimator (e.g. StructuredEdgeDetection that has been used in the original publication) that may lead to improved accuracies, the internal edge map estimation can be bypassed here.

Parameters

  • costMap: a type CV_32FC1 Mat is required.

See also

cv::ximgproc::createSuperpixelSLIC

pub fn set_superpixel_size(&mut self, sp_size: i32) -> Result<()>[src]

Get the internal cost, i.e. edge map, used for estimating the edge-aware term.

See also

setCostMap

C++ default parameters

  • sp_size: 15

pub fn get_superpixel_size(&self) -> Result<i32>[src]

Get the internal cost, i.e. edge map, used for estimating the edge-aware term.

See also

setCostMap * setSuperpixelSize

pub fn set_superpixel_nn_cnt(&mut self, sp_nn: i32) -> Result<()>[src]

Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.

C++ default parameters

  • sp_nn: 150

pub fn get_superpixel_nn_cnt(&self) -> Result<i32>[src]

Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model. * see also: setSuperpixelNNCnt

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

Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.

See also

cv::ximgproc::createSuperpixelSLIC

C++ default parameters

  • ruler: 15.f

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

Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.

See also

cv::ximgproc::createSuperpixelSLIC * setSuperpixelRuler

pub fn set_superpixel_mode(&mut self, mode: i32) -> Result<()>[src]

Parameter to choose superpixel algorithm variant to use:

  • cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100)
  • cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101)
  • cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).

See also

cv::ximgproc::createSuperpixelSLIC

C++ default parameters

  • mode: 100

pub fn get_superpixel_mode(&self) -> Result<i32>[src]

Parameter to choose superpixel algorithm variant to use:

  • cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100)
  • cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101)
  • cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).

See also

cv::ximgproc::createSuperpixelSLIC * setSuperpixelMode

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

Alpha is a parameter defining a global weight for transforming geodesic distance into weight.

C++ default parameters

  • alpha: 0.7f

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

Alpha is a parameter defining a global weight for transforming geodesic distance into weight.

See also

setAlpha

pub fn set_model_iter(&mut self, model_iter: i32) -> Result<()>[src]

Parameter defining the number of iterations for piece-wise affine model estimation.

C++ default parameters

  • model_iter: 4

pub fn get_model_iter(&self) -> Result<i32>[src]

Parameter defining the number of iterations for piece-wise affine model estimation.

See also

setModelIter

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

Parameter to choose wether additional refinement of the piece-wise affine models is employed.

C++ default parameters

  • refine_modles: true

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

Parameter to choose wether additional refinement of the piece-wise affine models is employed.

See also

setRefineModels

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

MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.

C++ default parameters

  • max_flow: 250.f

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

MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead. * see also: setMaxFlow

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

Parameter to choose wether the VariationalRefinement post-processing is employed.

C++ default parameters

  • use_variational_refinement: false

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

Parameter to choose wether the VariationalRefinement post-processing is employed.

See also

setUseVariationalRefinement

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

Sets whether the fastGlobalSmootherFilter() post-processing is employed.

C++ default parameters

  • use_fgs: true

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

Sets whether the fastGlobalSmootherFilter() post-processing is employed.

See also

setUseGlobalSmootherFilter

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

Sets the respective fastGlobalSmootherFilter() parameter.

C++ default parameters

  • lambda: 500.f

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

Sets the respective fastGlobalSmootherFilter() parameter.

See also

setFGSLambda

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

Sets the respective fastGlobalSmootherFilter() parameter.

C++ default parameters

  • sigma: 1.5f

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

Sets the respective fastGlobalSmootherFilter() parameter.

See also

setFGSSigma

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Implementors

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