Trait opencv::optflow::prelude::DualTVL1OpticalFlow[−][src]

pub trait DualTVL1OpticalFlow: DenseOpticalFlow {
Show 26 methods
    fn as_raw_DualTVL1OpticalFlow(&self) -> *const c_void;
    fn as_raw_mut_DualTVL1OpticalFlow(&mut self) -> *mut c_void;

    fn get_tau(&self) -> Result<f64> { ... }
    fn set_tau(&mut self, val: f64) -> Result<()> { ... }
    fn get_lambda(&self) -> Result<f64> { ... }
    fn set_lambda(&mut self, val: f64) -> Result<()> { ... }
    fn get_theta(&self) -> Result<f64> { ... }
    fn set_theta(&mut self, val: f64) -> Result<()> { ... }
    fn get_gamma(&self) -> Result<f64> { ... }
    fn set_gamma(&mut self, val: f64) -> Result<()> { ... }
    fn get_scales_number(&self) -> Result<i32> { ... }
    fn set_scales_number(&mut self, val: i32) -> Result<()> { ... }
    fn get_warpings_number(&self) -> Result<i32> { ... }
    fn set_warpings_number(&mut self, val: i32) -> Result<()> { ... }
    fn get_epsilon(&self) -> Result<f64> { ... }
    fn set_epsilon(&mut self, val: f64) -> Result<()> { ... }
    fn get_inner_iterations(&self) -> Result<i32> { ... }
    fn set_inner_iterations(&mut self, val: i32) -> Result<()> { ... }
    fn get_outer_iterations(&self) -> Result<i32> { ... }
    fn set_outer_iterations(&mut self, val: i32) -> Result<()> { ... }
    fn get_use_initial_flow(&self) -> Result<bool> { ... }
    fn set_use_initial_flow(&mut self, val: bool) -> Result<()> { ... }
    fn get_scale_step(&self) -> Result<f64> { ... }
    fn set_scale_step(&mut self, val: f64) -> Result<()> { ... }
    fn get_median_filtering(&self) -> Result<i32> { ... }
    fn set_median_filtering(&mut self, val: i32) -> Result<()> { ... }
}

Expand description

“Dual TV L1” Optical Flow Algorithm.

The class implements the “Dual TV L1” optical flow algorithm described in Zach2007 and Javier2012 . Here are important members of the class that control the algorithm, which you can set after constructing the class instance:

member double tau Time step of the numerical scheme.
member double lambda Weight parameter for the data term, attachment parameter. This is the most relevant parameter, which determines the smoothness of the output. The smaller this parameter is, the smoother the solutions we obtain. It depends on the range of motions of the images, so its value should be adapted to each image sequence.
member double theta Weight parameter for (u - v)^2, tightness parameter. It serves as a link between the attachment and the regularization terms. In theory, it should have a small value in order to maintain both parts in correspondence. The method is stable for a large range of values of this parameter.
member int nscales Number of scales used to create the pyramid of images.
member int warps Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the method. It also affects the running time, so it is a compromise between speed and accuracy.
member double epsilon Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time. A small value will yield more accurate solutions at the expense of a slower convergence.
member int iterations Stopping criterion iterations number used in the numerical scheme.

C. Zach, T. Pock and H. Bischof, “A Duality Based Approach for Realtime TV-L1 Optical Flow”. Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. “TV-L1 Optical Flow Estimation”.