Module opencv::optflow [−][src]
Expand description
Optical Flow Algorithms
Dense optical flow algorithms compute motion for each point:
- cv::optflow::calcOpticalFlowSF
- cv::optflow::createOptFlow_DeepFlow
Motion templates is alternative technique for detecting motion and computing its direction. See samples/motempl.py.
- cv::motempl::updateMotionHistory
- cv::motempl::calcMotionGradient
- cv::motempl::calcGlobalOrientation
- cv::motempl::segmentMotion
Functions reading and writing .flo files in “Middlebury” format, see: http://vision.middlebury.edu/flow/code/flow-code/README.txt
- cv::optflow::readOpticalFlow
- cv::optflow::writeOpticalFlow
Modules
Structs
Class encapsulating matching parameters.
Class encapsulating training parameters.
Class encapsulating training samples.
Class for individual tree.
PCAFlow algorithm.
@brief This class can be used for imposing a learned prior on the resulting optical flow. Solution will be regularized according to this prior. You need to generate appropriate prior file with “learn_prior.py” script beforehand.
This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm.
Enums
Descriptor types for the Global Patch Collider.
Constants
Better quality but slow
Worse quality but much faster
< Edge-preserving interpolation using ximgproc::EdgeAwareInterpolator, see Revaud2015,Geistert2016.
< Fast geodesic interpolation, see Geistert2016
< SLIC based robust interpolation using ximgproc::RICInterpolator, see Hu2017.
< Apply a adaptive support region obtained by cross-based segmentation as described in Senst2014
< Apply a constant support region
< Apply optimized iterative refinement based bilinear equation solutions as described in Senst2013
< Apply standard iterative refinement
Traits
Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme.
“Dual TV L1” Optical Flow Algorithm.
Class encapsulating training samples.
Class for individual tree.
PCAFlow algorithm.
@brief This class can be used for imposing a learned prior on the resulting optical flow. Solution will be regularized according to this prior. You need to generate appropriate prior file with “learn_prior.py” script beforehand.
This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm.
Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms.
Functions
Calculates a global motion orientation in a selected region.
Calculates a gradient orientation of a motion history image.
Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme.
Calculate an optical flow using “SimpleFlow” algorithm.
Calculate an optical flow using “SimpleFlow” algorithm.
Calculates fast optical flow for a sparse feature set using the robust local optical flow (RLOF) similar to optflow::calcOpticalFlowPyrLK().
Fast dense optical flow based on PyrLK sparse matches interpolation.
DeepFlow optical flow algorithm implementation.
Additional interface to the Dense RLOF algorithm - optflow::calcOpticalFlowDenseRLOF()
Creates instance of cv::DenseOpticalFlow
Additional interface to the Farneback’s algorithm - calcOpticalFlowFarneback()
Creates an instance of PCAFlow
Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()
Additional interface to the Sparse RLOF algorithm - optflow::calcOpticalFlowSparseRLOF()
Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()
Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand).
Updates the motion history image by a moving silhouette.