Trait opencv::prelude::DenseRLOFOpticalFlow

pub trait DenseRLOFOpticalFlow: DenseOpticalFlow {
    pub fn as_raw_DenseRLOFOpticalFlow(&self) -> *const c_void;
    pub fn as_raw_mut_DenseRLOFOpticalFlow(&mut self) -> *mut c_void;

    pub fn set_rlof_optical_flow_parameter(
        &mut self, 
        val: Ptr<RLOFOpticalFlowParameter>
    ) -> Result<()> { ... }
    pub fn get_rlof_optical_flow_parameter(
        &self
    ) -> Result<Ptr<RLOFOpticalFlowParameter>> { ... }
    pub fn set_forward_backward(&mut self, val: f32) -> Result<()> { ... }
    pub fn get_forward_backward(&self) -> Result<f32> { ... }
    pub fn get_grid_step(&self) -> Result<Size> { ... }
    pub fn set_grid_step(&mut self, val: Size) -> Result<()> { ... }
    pub fn set_interpolation(&mut self, val: InterpolationType) -> Result<()> { ... }
    pub fn get_interpolation(&self) -> Result<InterpolationType> { ... }
    pub fn get_epick(&self) -> Result<i32> { ... }
    pub fn set_epick(&mut self, val: i32) -> Result<()> { ... }
    pub fn get_epic_sigma(&self) -> Result<f32> { ... }
    pub fn set_epic_sigma(&mut self, val: f32) -> Result<()> { ... }
    pub fn get_epic_lambda(&self) -> Result<f32> { ... }
    pub fn set_epic_lambda(&mut self, val: f32) -> Result<()> { ... }
    pub fn get_fgs_lambda(&self) -> Result<f32> { ... }
    pub fn set_fgs_lambda(&mut self, val: f32) -> Result<()> { ... }
    pub fn get_fgs_sigma(&self) -> Result<f32> { ... }
    pub fn set_fgs_sigma(&mut self, val: f32) -> Result<()> { ... }
    pub fn set_use_post_proc(&mut self, val: bool) -> Result<()> { ... }
    pub fn get_use_post_proc(&self) -> Result<bool> { ... }
    pub fn set_use_variational_refinement(&mut self, val: bool) -> Result<()> { ... }
    pub fn get_use_variational_refinement(&self) -> Result<bool> { ... }
    pub fn set_ricsp_size(&mut self, val: i32) -> Result<()> { ... }
    pub fn get_ricsp_size(&self) -> Result<i32> { ... }
    pub fn set_ricslic_type(&mut self, val: i32) -> Result<()> { ... }
    pub fn get_ricslic_type(&self) -> Result<i32> { ... }
}

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.

See also

optflow::calcOpticalFlowDenseRLOF(), optflow::RLOFOpticalFlowParameter

Required methods

pub fn as_raw_DenseRLOFOpticalFlow(&self) -> *const c_void

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

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Provided methods

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

Configuration of the RLOF alogrithm.

See also

optflow::RLOFOpticalFlowParameter, getRLOFOpticalFlowParameter

pub fn get_rlof_optical_flow_parameter(
    &self
) -> Result<Ptr<RLOFOpticalFlowParameter>>

Configuration of the RLOF alogrithm.

See also

optflow::RLOFOpticalFlowParameter, getRLOFOpticalFlowParameter optflow::RLOFOpticalFlowParameter, setRLOFOpticalFlowParameter

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

Threshold for the forward backward confidence check For each grid point a motion vector is computed. * If the forward backward error * is larger than threshold given by this function then the motion vector will not be used by the following * vector field interpolation. 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

pub fn get_forward_backward(&self) -> Result<f32>

Threshold for the forward backward confidence check For each grid point a motion vector is computed. * If the forward backward error * is larger than threshold given by this function then the motion vector will not be used by the following * vector field interpolation. 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. * getForwardBackward, setGridStep

See also

setForwardBackward

pub fn get_grid_step(&self) -> Result<Size>

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

pub 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

pub 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

pub fn get_interpolation(&self) -> Result<InterpolationType>

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 * see also: ximgproc::EdgeAwareInterpolator, setInterpolation

pub fn get_epick(&self) -> Result<i32>

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

pub 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

pub fn get_epic_sigma(&self) -> Result<f32>

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

pub 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

pub fn get_epic_lambda(&self) -> Result<f32>

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

pub 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

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

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

pub 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

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

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

pub 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

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

enables ximgproc::fastGlobalSmootherFilter

• see also: getUsePostProc

pub fn get_use_post_proc(&self) -> Result<bool>

enables ximgproc::fastGlobalSmootherFilter

• see also: getUsePostProc
  • see also: ximgproc::fastGlobalSmootherFilter, setUsePostProc

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

enables VariationalRefinement

• see also: getUseVariationalRefinement

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

enables VariationalRefinement

• see also: getUseVariationalRefinement
  • see also: ximgproc::fastGlobalSmootherFilter, setUsePostProc

pub 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

pub fn get_ricsp_size(&self) -> Result<i32>

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

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

pub 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).

See also

cv::ximgproc::createSuperpixelSLIC, cv::ximgproc::RICInterpolator

pub fn get_ricslic_type(&self) -> Result<i32>

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, cv::ximgproc::RICInterpolator * setRICSLICType

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Implementations

impl<'_> dyn DenseRLOFOpticalFlow + '_

pub fn create(
    rlof_param: Ptr<RLOFOpticalFlowParameter>,
    forward_backward_threshold: f32,
    grid_step: Size,
    interp_type: InterpolationType,
    epic_k: i32,
    epic_sigma: f32,
    epic_lambda: f32,
    ric_sp_size: i32,
    ric_slic_type: i32,
    use_post_proc: bool,
    fgs_lambda: f32,
    fgs_sigma: f32,
    use_variational_refinement: bool
) -> Result<Ptr<dyn DenseRLOFOpticalFlow>>

Creates instance of optflow::DenseRLOFOpticalFlow

Parameters

• rlofParam: see optflow::RLOFOpticalFlowParameter
• forwardBackwardThreshold: see setForwardBackward
• gridStep: see setGridStep
• interp_type: see setInterpolation
• epicK: see setEPICK
• epicSigma: see setEPICSigma
• epicLambda: see setEPICLambda
• ricSPSize: see setRICSPSize
• ricSLICType: see setRICSLICType
• use_post_proc: see setUsePostProc
• fgsLambda: see setFgsLambda
• fgsSigma: see setFgsSigma
• use_variational_refinement: see setUseVariationalRefinement

C++ default parameters

• rlof_param: Ptr()
• forward_backward_threshold: 1.f
• grid_step: Size(6,6)
• interp_type: InterpolationType::INTERP_EPIC
• epic_k: 128
• epic_sigma: 0.05f
• epic_lambda: 999.0f
• ric_sp_size: 15
• ric_slic_type: 100
• use_post_proc: true
• fgs_lambda: 500.0f
• fgs_sigma: 1.5f
• use_variational_refinement: false

Implementors

impl DenseRLOFOpticalFlow for PtrOfDenseRLOFOpticalFlow

pub fn as_raw_DenseRLOFOpticalFlow(&self) -> *const c_void

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

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