[−][src]Trait opencv::hub_prelude::SparseRLOFOpticalFlow

pub trait SparseRLOFOpticalFlow: SparseOpticalFlow {
    pub fn as_raw_SparseRLOFOpticalFlow(&self) -> *const c_void;

    pub fn as_raw_mut_SparseRLOFOpticalFlow(&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> { ... }
}

Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms.

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().

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

Note: SIMD parallelization is only available when compiling with SSE4.1.

See also

optflow::calcOpticalFlowSparseRLOF(), optflow::RLOFOpticalFlowParameter

Required methods

pub fn as_raw_SparseRLOFOpticalFlow(&self) -> *const c_void[src]

pub fn as_raw_mut_SparseRLOFOpticalFlow(&mut self) -> *mut c_void[src]

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Provided methods

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

@copydoc DenseRLOFOpticalFlow::setRLOFOpticalFlowParameter

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

@copydoc DenseRLOFOpticalFlow::setRLOFOpticalFlowParameter

See also

setRLOFOpticalFlowParameter

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

Threshold for the forward backward confidence check For each feature point a motion vector inline formula is computed. * If the forward backward error block formula * is larger than threshold given by this function then the status 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: setForwardBackward

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

Threshold for the forward backward confidence check For each feature point a motion vector inline formula is computed. * If the forward backward error block formula * is larger than threshold given by this function then the status 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: setForwardBackward * see also: setForwardBackward

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Implementations

impl<'_> dyn SparseRLOFOpticalFlow + '_[src]

pub fn create(
    rlof_param: Ptr<RLOFOpticalFlowParameter>,
    forward_backward_threshold: f32
) -> Result<Ptr<dyn SparseRLOFOpticalFlow>>[src]

Creates instance of SparseRLOFOpticalFlow

Parameters

  • rlofParam: see setRLOFOpticalFlowParameter
  • forwardBackwardThreshold: see setForwardBackward

C++ default parameters

  • rlof_param: Ptr()
  • forward_backward_threshold: 1.f

Implementors

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