Trait opencv::hub_prelude::RICInterpolatorTrait

source ·

pub trait RICInterpolatorTrait: RICInterpolatorTraitConst + SparseMatchInterpolatorTrait {
Show 28 methods    // Required method
    fn as_raw_mut_RICInterpolator(&mut self) -> *mut c_void;

    // Provided methods
    fn set_k(&mut self, k: i32) -> Result<()> { ... }
    fn set_k_def(&mut self) -> Result<()> { ... }
    fn set_cost_map(&mut self, cost_map: &Mat) -> Result<()> { ... }
    fn set_superpixel_size(&mut self, sp_size: i32) -> Result<()> { ... }
    fn set_superpixel_size_def(&mut self) -> Result<()> { ... }
    fn set_superpixel_nn_cnt(&mut self, sp_nn: i32) -> Result<()> { ... }
    fn set_superpixel_nn_cnt_def(&mut self) -> Result<()> { ... }
    fn set_superpixel_ruler(&mut self, ruler: f32) -> Result<()> { ... }
    fn set_superpixel_ruler_def(&mut self) -> Result<()> { ... }
    fn set_superpixel_mode(&mut self, mode: i32) -> Result<()> { ... }
    fn set_superpixel_mode_def(&mut self) -> Result<()> { ... }
    fn set_alpha(&mut self, alpha: f32) -> Result<()> { ... }
    fn set_alpha_def(&mut self) -> Result<()> { ... }
    fn set_model_iter(&mut self, model_iter: i32) -> Result<()> { ... }
    fn set_model_iter_def(&mut self) -> Result<()> { ... }
    fn set_refine_models(&mut self, refine_modles: bool) -> Result<()> { ... }
    fn set_refine_models_def(&mut self) -> Result<()> { ... }
    fn set_max_flow(&mut self, max_flow: f32) -> Result<()> { ... }
    fn set_max_flow_def(&mut self) -> Result<()> { ... }
    fn set_use_variational_refinement(
        &mut self,
        use_variational_refinement: bool
    ) -> Result<()> { ... }
    fn set_use_variational_refinement_def(&mut self) -> Result<()> { ... }
    fn set_use_global_smoother_filter(&mut self, use_fgs: bool) -> Result<()> { ... }
    fn set_use_global_smoother_filter_def(&mut self) -> Result<()> { ... }
    fn set_fgs_lambda(&mut self, lambda: f32) -> Result<()> { ... }
    fn set_fgs_lambda_def(&mut self) -> Result<()> { ... }
    fn set_fgs_sigma(&mut self, sigma: f32) -> Result<()> { ... }
    fn set_fgs_sigma_def(&mut self) -> Result<()> { ... }
}

Expand description

Mutable methods for crate::ximgproc::RICInterpolator

Required Methods§

source

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

Provided Methods§

source

fn set_k(&mut self, k: i32) -> Result<()>

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

source

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

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.

Note

This alternative version of [set_k] function uses the following default values for its arguments:

k: 32

source

fn set_cost_map(&mut self, cost_map: &Mat) -> Result<()>

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.

fn set_superpixel_size(&mut self, sp_size: i32) -> Result<()>

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

C++ default parameters

sp_size: 15

source

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

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

Note

This alternative version of [set_superpixel_size] function uses the following default values for its arguments:

sp_size: 15

source

fn set_superpixel_nn_cnt(&mut self, sp_nn: i32) -> Result<()>

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

C++ default parameters

sp_nn: 150

source

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

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

Note

This alternative version of [set_superpixel_nn_cnt] function uses the following default values for its arguments:

sp_nn: 150

source

fn set_superpixel_ruler(&mut self, ruler: f32) -> Result<()>

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

C++ default parameters

ruler: 15.f

source

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

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

Note

This alternative version of [set_superpixel_ruler] function uses the following default values for its arguments:

ruler: 15.f

source

fn set_superpixel_mode(&mut self, mode: i32) -> Result<()>

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).

C++ default parameters

mode: 100

source

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

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).

Note

This alternative version of [set_superpixel_mode] function uses the following default values for its arguments:

mode: 100

source

fn set_alpha(&mut self, alpha: f32) -> Result<()>

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

C++ default parameters

alpha: 0.7f

source

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

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

Note

This alternative version of [set_alpha] function uses the following default values for its arguments:

alpha: 0.7f

source

fn set_model_iter(&mut self, model_iter: i32) -> Result<()>

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

C++ default parameters

model_iter: 4

source

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

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

Note

This alternative version of [set_model_iter] function uses the following default values for its arguments:

model_iter: 4

source

fn set_refine_models(&mut self, refine_modles: bool) -> Result<()>

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

C++ default parameters

refine_modles: true

source

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

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

Note

This alternative version of [set_refine_models] function uses the following default values for its arguments:

refine_modles: true

source

fn set_max_flow(&mut self, max_flow: f32) -> Result<()>

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

source

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

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.