Trait opencv::prelude::DenseRLOFOpticalFlow

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pub trait DenseRLOFOpticalFlow: DenseRLOFOpticalFlowConst + DenseOpticalFlow {
Show 14 methods    // Required method
    fn as_raw_mut_DenseRLOFOpticalFlow(&mut self) -> *mut c_void;

    // Provided methods
    fn set_rlof_optical_flow_parameter(
        &mut self,
        val: Ptr<RLOFOpticalFlowParameter>
    ) -> Result<()> { ... }
    fn set_forward_backward(&mut self, val: f32) -> Result<()> { ... }
    fn set_grid_step(&mut self, val: Size) -> Result<()> { ... }
    fn set_interpolation(&mut self, val: InterpolationType) -> Result<()> { ... }
    fn set_epick(&mut self, val: i32) -> Result<()> { ... }
    fn set_epic_sigma(&mut self, val: f32) -> Result<()> { ... }
    fn set_epic_lambda(&mut self, val: f32) -> Result<()> { ... }
    fn set_fgs_lambda(&mut self, val: f32) -> Result<()> { ... }
    fn set_fgs_sigma(&mut self, val: f32) -> Result<()> { ... }
    fn set_use_post_proc(&mut self, val: bool) -> Result<()> { ... }
    fn set_use_variational_refinement(&mut self, val: bool) -> Result<()> { ... }
    fn set_ricsp_size(&mut self, val: i32) -> Result<()> { ... }
    fn set_ricslic_type(&mut self, val: i32) -> Result<()> { ... }
}

Expand description

Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme.

The RLOF is a fast local optical flow approach described in Senst2012 Senst2013 Senst2014 and Senst2016 similar to the pyramidal iterative Lucas-Kanade method as proposed by Bouguet00. More details and experiments can be found in the following thesis Senst2019. The implementation is derived from optflow::calcOpticalFlowPyrLK().

The sparse-to-dense interpolation scheme allows for fast computation of dense optical flow using RLOF (see Geistert2016). For this scheme the following steps are applied: -# motion vector seeded at a regular sampled grid are computed. The sparsity of this grid can be configured with setGridStep -# (optinally) errornous motion vectors are filter based on the forward backward confidence. The threshold can be configured with setForwardBackward. The filter is only applied if the threshold >0 but than the runtime is doubled due to the estimation of the backward flow. -# Vector field interpolation is applied to the motion vector set to obtain a dense vector field.

For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details. Parameters have been described in Senst2012 Senst2013 Senst2014 and Senst2016.

Note: If the grid size is set to (1,1) and the forward backward threshold <= 0 than pixelwise dense optical flow field is computed by RLOF without using interpolation.

Note: Note that in output, if no correspondences are found between \a I0 and \a I1, the \a flow is set to 0.

Required Methods§

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

Provided Methods§

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fn set_rlof_optical_flow_parameter( &mut self, val: Ptr<RLOFOpticalFlowParameter> ) -> Result<()>

Configuration of the RLOF alogrithm.

fn set_forward_backward(&mut self, val: f32) -> Result<()>

Threshold for the forward backward confidence check For each grid point $inline formula$ a motion vector $inline formula$ is computed. * If the forward backward error $block formula$ * is larger than threshold given by this function then the motion vector will not be used by the following * vector field interpolation. $inline formula$ denotes the backward flow. Note, the forward backward test * will only be applied if the threshold > 0. This may results into a doubled runtime for the motion estimation. * see also: getForwardBackward, setGridStep

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fn set_grid_step(&mut self, val: Size) -> Result<()>

Size of the grid to spawn the motion vectors. For each grid point a motion vector is computed. Some motion vectors will be removed due to the forwatd backward * threshold (if set >0). The rest will be the base of the vector field interpolation. * see also: getForwardBackward, setGridStep * see also: getGridStep

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fn set_interpolation(&mut self, val: InterpolationType) -> Result<()>

Interpolation used to compute the dense optical flow. Two interpolation algorithms are supported * - INTERP_GEO applies the fast geodesic interpolation, see Geistert2016. * - INTERP_EPIC_RESIDUAL applies the edge-preserving interpolation, see Revaud2015,Geistert2016. * see also: ximgproc::EdgeAwareInterpolator, getInterpolation

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fn set_epick(&mut self, val: i32) -> Result<()>

see ximgproc::EdgeAwareInterpolator() K value. K is a number of nearest-neighbor matches considered, when fitting a locally affine * model. Usually it should be around 128. However, lower values would make the interpolation noticeably faster. * see also: ximgproc::EdgeAwareInterpolator, setEPICK * see also: ximgproc::EdgeAwareInterpolator, getEPICK

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fn set_epic_sigma(&mut self, val: f32) -> Result<()>

see ximgproc::EdgeAwareInterpolator() sigma value. Sigma is a parameter defining how fast the weights decrease in the locally-weighted affine * fitting. Higher values can help preserve fine details, lower values can help to get rid of noise in the * output flow. * see also: ximgproc::EdgeAwareInterpolator, setEPICSigma * see also: ximgproc::EdgeAwareInterpolator, getEPICSigma

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fn set_epic_lambda(&mut self, val: f32) -> Result<()>

see ximgproc::EdgeAwareInterpolator() lambda value. Lambda is a parameter defining the weight of the edge-aware term in geodesic distance, * should be in the range of 0 to 1000. * see also: ximgproc::EdgeAwareInterpolator, setEPICSigma * see also: ximgproc::EdgeAwareInterpolator, getEPICLambda

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fn set_fgs_lambda(&mut self, val: f32) -> Result<()>

see ximgproc::EdgeAwareInterpolator(). Sets the respective fastGlobalSmootherFilter() parameter. * see also: ximgproc::EdgeAwareInterpolator, setFgsLambda * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, getFgsLambda

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fn set_fgs_sigma(&mut self, val: f32) -> Result<()>

see ximgproc::EdgeAwareInterpolator(). Sets the respective fastGlobalSmootherFilter() parameter. * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, setFgsSigma * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, getFgsSigma

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fn set_use_post_proc(&mut self, val: bool) -> Result<()>

enables ximgproc::fastGlobalSmootherFilter

see also: getUsePostProc

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fn set_use_variational_refinement(&mut self, val: bool) -> Result<()>

enables VariationalRefinement

see also: getUseVariationalRefinement

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fn set_ricsp_size(&mut self, val: i32) -> Result<()>

Parameter to tune the approximate size of the superpixel used for oversegmentation.

see also: cv::ximgproc::createSuperpixelSLIC, cv::ximgproc::RICInterpolator

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fn set_ricslic_type(&mut self, val: 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).