Trait opencv::prelude::DISOpticalFlow[−][src]

pub trait DISOpticalFlow: DenseOpticalFlow {
Show 22 methods
    fn as_raw_DISOpticalFlow(&self) -> *const c_void;
    fn as_raw_mut_DISOpticalFlow(&mut self) -> *mut c_void;

    fn get_finest_scale(&self) -> Result<i32> { ... }
    fn set_finest_scale(&mut self, val: i32) -> Result<()> { ... }
    fn get_patch_size(&self) -> Result<i32> { ... }
    fn set_patch_size(&mut self, val: i32) -> Result<()> { ... }
    fn get_patch_stride(&self) -> Result<i32> { ... }
    fn set_patch_stride(&mut self, val: i32) -> Result<()> { ... }
    fn get_gradient_descent_iterations(&self) -> Result<i32> { ... }
    fn set_gradient_descent_iterations(&mut self, val: i32) -> Result<()> { ... }
    fn get_variational_refinement_iterations(&self) -> Result<i32> { ... }
    fn set_variational_refinement_iterations(&mut self, val: i32) -> Result<()> { ... }
    fn get_variational_refinement_alpha(&self) -> Result<f32> { ... }
    fn set_variational_refinement_alpha(&mut self, val: f32) -> Result<()> { ... }
    fn get_variational_refinement_delta(&self) -> Result<f32> { ... }
    fn set_variational_refinement_delta(&mut self, val: f32) -> Result<()> { ... }
    fn get_variational_refinement_gamma(&self) -> Result<f32> { ... }
    fn set_variational_refinement_gamma(&mut self, val: f32) -> Result<()> { ... }
    fn get_use_mean_normalization(&self) -> Result<bool> { ... }
    fn set_use_mean_normalization(&mut self, val: bool) -> Result<()> { ... }
    fn get_use_spatial_propagation(&self) -> Result<bool> { ... }
    fn set_use_spatial_propagation(&mut self, val: bool) -> Result<()> { ... }
}

Expand description

DIS optical flow algorithm.

This class implements the Dense Inverse Search (DIS) optical flow algorithm. More details about the algorithm can be found at Kroeger2016 . Includes three presets with preselected parameters to provide reasonable trade-off between speed and quality. However, even the slowest preset is still relatively fast, use DeepFlow if you need better quality and don’t care about speed.

This implementation includes several additional features compared to the algorithm described in the paper, including spatial propagation of flow vectors (@ref getUseSpatialPropagation), as well as an option to utilize an initial flow approximation passed to @ref calc (which is, essentially, temporal propagation, if the previous frame’s flow field is passed).

Required methods

[src]

fn as_raw_DISOpticalFlow(&self) -> *const c_void

[src]

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

Provided methods

[src]

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

Finest level of the Gaussian pyramid on which the flow is computed (zero level corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.