vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e
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/*
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By downloading, copying, installing or using the software you agree to this
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license. If you do not agree to this license, do not download, install,
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copy or use the software.
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License Agreement
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For Open Source Computer Vision Library
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(3-clause BSD License)
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Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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Third party copyrights are property of their respective owners.
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice,
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this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the names of the copyright holders nor the names of the contributors
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may be used to endorse or promote products derived from this software
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without specific prior written permission.
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This software is provided by the copyright holders and contributors "as is" and
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any express or implied warranties, including, but not limited to, the implied
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warranties of merchantability and fitness for a particular purpose are
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disclaimed. In no event shall copyright holders or contributors be liable for
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any direct, indirect, incidental, special, exemplary, or consequential damages
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(including, but not limited to, procurement of substitute goods or services;
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loss of use, data, or profits; or business interruption) however caused
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and on any theory of liability, whether in contract, strict liability,
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or tort (including negligence or otherwise) arising in any way out of
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the use of this software, even if advised of the possibility of such damage.
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*/
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#ifndef __OPENCV_OPTFLOW_HPP__
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#define __OPENCV_OPTFLOW_HPP__
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#include "opencv2/core.hpp"
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#include "opencv2/video.hpp"
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/**
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@defgroup optflow Optical Flow Algorithms
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Dense optical flow algorithms compute motion for each point:
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- cv::optflow::calcOpticalFlowSF
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- cv::optflow::createOptFlow_DeepFlow
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Motion templates is alternative technique for detecting motion and computing its direction.
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See samples/motempl.py.
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- cv::motempl::updateMotionHistory
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- cv::motempl::calcMotionGradient
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- cv::motempl::calcGlobalOrientation
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- cv::motempl::segmentMotion
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Functions reading and writing .flo files in "Middlebury" format, see: <http://vision.middlebury.edu/flow/code/flow-code/README.txt>
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- cv::optflow::readOpticalFlow
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- cv::optflow::writeOpticalFlow
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*/
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#include "opencv2/optflow/pcaflow.hpp"
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#include "opencv2/optflow/sparse_matching_gpc.hpp"
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#include "opencv2/optflow/rlofflow.hpp"
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namespace cv
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{
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namespace optflow
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{
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//! @addtogroup optflow
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//! @{
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/** @overload */
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CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray flow,
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int layers, int averaging_block_size, int max_flow);
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/** @brief Calculate an optical flow using "SimpleFlow" algorithm.
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@param from First 8-bit 3-channel image.
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@param to Second 8-bit 3-channel image of the same size as prev
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@param flow computed flow image that has the same size as prev and type CV_32FC2
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@param layers Number of layers
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@param averaging_block_size Size of block through which we sum up when calculate cost function
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for pixel
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@param max_flow maximal flow that we search at each level
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@param sigma_dist vector smooth spatial sigma parameter
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@param sigma_color vector smooth color sigma parameter
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@param postprocess_window window size for postprocess cross bilateral filter
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@param sigma_dist_fix spatial sigma for postprocess cross bilateralf filter
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@param sigma_color_fix color sigma for postprocess cross bilateral filter
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@param occ_thr threshold for detecting occlusions
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@param upscale_averaging_radius window size for bilateral upscale operation
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@param upscale_sigma_dist spatial sigma for bilateral upscale operation
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@param upscale_sigma_color color sigma for bilateral upscale operation
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@param speed_up_thr threshold to detect point with irregular flow - where flow should be
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recalculated after upscale
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See @cite Tao2012 . And site of project - <http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/>.
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@note
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- An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp
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*/
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CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray flow, int layers,
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int averaging_block_size, int max_flow,
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double sigma_dist, double sigma_color, int postprocess_window,
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double sigma_dist_fix, double sigma_color_fix, double occ_thr,
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int upscale_averaging_radius, double upscale_sigma_dist,
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double upscale_sigma_color, double speed_up_thr );
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/** @brief Fast dense optical flow based on PyrLK sparse matches interpolation.
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@param from first 8-bit 3-channel or 1-channel image.
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@param to second 8-bit 3-channel or 1-channel image of the same size as from
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@param flow computed flow image that has the same size as from and CV_32FC2 type
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@param grid_step stride used in sparse match computation. Lower values usually
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result in higher quality but slow down the algorithm.
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@param k number of nearest-neighbor matches considered, when fitting a locally affine
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model. Lower values can make the algorithm noticeably faster at the cost of
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some quality degradation.
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@param sigma parameter defining how fast the weights decrease in the locally-weighted affine
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fitting. Higher values can help preserve fine details, lower values can help to get rid
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of the noise in the output flow.
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@param use_post_proc defines whether the ximgproc::fastGlobalSmootherFilter() is used
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for post-processing after interpolation
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@param fgs_lambda see the respective parameter of the ximgproc::fastGlobalSmootherFilter()
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@param fgs_sigma see the respective parameter of the ximgproc::fastGlobalSmootherFilter()
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*/
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CV_EXPORTS_W void calcOpticalFlowSparseToDense ( InputArray from, InputArray to, OutputArray flow,
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int grid_step = 8, int k = 128, float sigma = 0.05f,
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bool use_post_proc = true, float fgs_lambda = 500.0f,
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float fgs_sigma = 1.5f );
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/** @brief DeepFlow optical flow algorithm implementation.
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The class implements the DeepFlow optical flow algorithm described in @cite Weinzaepfel2013 . See
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also <http://lear.inrialpes.fr/src/deepmatching/> .
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Parameters - class fields - that may be modified after creating a class instance:
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- member float alpha
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Smoothness assumption weight
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- member float delta
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Color constancy assumption weight
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- member float gamma
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Gradient constancy weight
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- member float sigma
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Gaussian smoothing parameter
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- member int minSize
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Minimal dimension of an image in the pyramid (next, smaller images in the pyramid are generated
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until one of the dimensions reaches this size)
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- member float downscaleFactor
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Scaling factor in the image pyramid (must be \< 1)
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- member int fixedPointIterations
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How many iterations on each level of the pyramid
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- member int sorIterations
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Iterations of Succesive Over-Relaxation (solver)
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- member float omega
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Relaxation factor in SOR
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*/
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CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_DeepFlow();
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//! Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()
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CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SimpleFlow();
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//! Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback()
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CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_Farneback();
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//! Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()
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CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SparseToDense();
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/** @brief "Dual TV L1" Optical Flow Algorithm.
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The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
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@cite Javier2012 .
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Here are important members of the class that control the algorithm, which you can set after
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constructing the class instance:
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- member double tau
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Time step of the numerical scheme.
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- member double lambda
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Weight parameter for the data term, attachment parameter. This is the most relevant
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parameter, which determines the smoothness of the output. The smaller this parameter is,
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the smoother the solutions we obtain. It depends on the range of motions of the images, so
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its value should be adapted to each image sequence.
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- member double theta
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Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the
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attachment and the regularization terms. In theory, it should have a small value in order
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to maintain both parts in correspondence. The method is stable for a large range of values
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of this parameter.
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- member int nscales
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Number of scales used to create the pyramid of images.
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- member int warps
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Number of warpings per scale. Represents the number of times that I1(x+u0) and grad(
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I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the
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method. It also affects the running time, so it is a compromise between speed and
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accuracy.
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- member double epsilon
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Stopping criterion threshold used in the numerical scheme, which is a trade-off between
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precision and running time. A small value will yield more accurate solutions at the
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expense of a slower convergence.
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- member int iterations
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Stopping criterion iterations number used in the numerical scheme.
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C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
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Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
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*/
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class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow
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{
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public:
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//! @brief Time step of the numerical scheme
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/** @see setTau */
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CV_WRAP virtual double getTau() const = 0;
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/** @copybrief getTau @see getTau */
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CV_WRAP virtual void setTau(double val) = 0;
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//! @brief Weight parameter for the data term, attachment parameter
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/** @see setLambda */
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CV_WRAP virtual double getLambda() const = 0;
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/** @copybrief getLambda @see getLambda */
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CV_WRAP virtual void setLambda(double val) = 0;
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//! @brief Weight parameter for (u - v)^2, tightness parameter
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/** @see setTheta */
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CV_WRAP virtual double getTheta() const = 0;
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/** @copybrief getTheta @see getTheta */
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CV_WRAP virtual void setTheta(double val) = 0;
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//! @brief coefficient for additional illumination variation term
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/** @see setGamma */
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CV_WRAP virtual double getGamma() const = 0;
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/** @copybrief getGamma @see getGamma */
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CV_WRAP virtual void setGamma(double val) = 0;
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//! @brief Number of scales used to create the pyramid of images
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/** @see setScalesNumber */
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CV_WRAP virtual int getScalesNumber() const = 0;
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/** @copybrief getScalesNumber @see getScalesNumber */
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CV_WRAP virtual void setScalesNumber(int val) = 0;
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//! @brief Number of warpings per scale
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/** @see setWarpingsNumber */
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CV_WRAP virtual int getWarpingsNumber() const = 0;
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/** @copybrief getWarpingsNumber @see getWarpingsNumber */
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CV_WRAP virtual void setWarpingsNumber(int val) = 0;
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//! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time
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/** @see setEpsilon */
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CV_WRAP virtual double getEpsilon() const = 0;
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/** @copybrief getEpsilon @see getEpsilon */
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CV_WRAP virtual void setEpsilon(double val) = 0;
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//! @brief Inner iterations (between outlier filtering) used in the numerical scheme
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/** @see setInnerIterations */
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CV_WRAP virtual int getInnerIterations() const = 0;
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/** @copybrief getInnerIterations @see getInnerIterations */
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CV_WRAP virtual void setInnerIterations(int val) = 0;
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//! @brief Outer iterations (number of inner loops) used in the numerical scheme
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/** @see setOuterIterations */
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CV_WRAP virtual int getOuterIterations() const = 0;
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/** @copybrief getOuterIterations @see getOuterIterations */
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CV_WRAP virtual void setOuterIterations(int val) = 0;
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//! @brief Use initial flow
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/** @see setUseInitialFlow */
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CV_WRAP virtual bool getUseInitialFlow() const = 0;
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/** @copybrief getUseInitialFlow @see getUseInitialFlow */
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CV_WRAP virtual void setUseInitialFlow(bool val) = 0;
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//! @brief Step between scales (<1)
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/** @see setScaleStep */
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CV_WRAP virtual double getScaleStep() const = 0;
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/** @copybrief getScaleStep @see getScaleStep */
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CV_WRAP virtual void setScaleStep(double val) = 0;
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//! @brief Median filter kernel size (1 = no filter) (3 or 5)
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/** @see setMedianFiltering */
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CV_WRAP virtual int getMedianFiltering() const = 0;
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/** @copybrief getMedianFiltering @see getMedianFiltering */
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CV_WRAP virtual void setMedianFiltering(int val) = 0;
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/** @brief Creates instance of cv::DualTVL1OpticalFlow*/
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CV_WRAP static Ptr<DualTVL1OpticalFlow> create(
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double tau = 0.25,
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double lambda = 0.15,
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double theta = 0.3,
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int nscales = 5,
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int warps = 5,
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double epsilon = 0.01,
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int innnerIterations = 30,
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int outerIterations = 10,
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double scaleStep = 0.8,
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double gamma = 0.0,
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int medianFiltering = 5,
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bool useInitialFlow = false);
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};
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/** @brief Creates instance of cv::DenseOpticalFlow
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*/
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CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
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//! @}
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} //optflow
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}
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#include "opencv2/optflow/motempl.hpp"
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#endif
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