Add robust local optical flow (RLOF) implementations #1940
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| /* | ||
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| License Agreement | ||
| For Open Source Computer Vision Library | ||
| (3-clause BSD License) | ||
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| Copyright (C) 2013, OpenCV Foundation, all rights reserved. | ||
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| This software is provided by the copyright holders and contributors "as is" and | ||
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| */ | ||
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| #ifndef __OPENCV_OPTFLOW_HPP__ | ||
| #define __OPENCV_OPTFLOW_HPP__ | ||
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| #include "opencv2/core.hpp" | ||
| #include "opencv2/video.hpp" | ||
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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 | ||
| - cv::optflow::createOptFlow_DeepFlow | ||
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| Motion templates is alternative technique for detecting motion and computing its direction. | ||
| See samples/motempl.py. | ||
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| - cv::motempl::updateMotionHistory | ||
| - cv::motempl::calcMotionGradient | ||
| - cv::motempl::calcGlobalOrientation | ||
| - 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 | ||
| - cv::optflow::writeOpticalFlow | ||
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| */ | ||
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| #include "opencv2/optflow/pcaflow.hpp" | ||
| #include "opencv2/optflow/sparse_matching_gpc.hpp" | ||
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| namespace cv | ||
| { | ||
| namespace optflow | ||
| { | ||
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| //! @addtogroup optflow | ||
| //! @{ | ||
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| /** @overload */ | ||
| CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray flow, | ||
| 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. | ||
| @param to Second 8-bit 3-channel image of the same size as prev | ||
| @param flow computed flow image that has the same size as prev and type CV_32FC2 | ||
| @param layers Number of layers | ||
| @param averaging_block_size Size of block through which we sum up when calculate cost function | ||
| for pixel | ||
| @param max_flow maximal flow that we search at each level | ||
| @param sigma_dist vector smooth spatial sigma parameter | ||
| @param sigma_color vector smooth color sigma parameter | ||
| @param postprocess_window window size for postprocess cross bilateral filter | ||
| @param sigma_dist_fix spatial sigma for postprocess cross bilateralf filter | ||
| @param sigma_color_fix color sigma for postprocess cross bilateral filter | ||
| @param occ_thr threshold for detecting occlusions | ||
| @param upscale_averaging_radius window size for bilateral upscale operation | ||
| @param upscale_sigma_dist spatial sigma for bilateral upscale operation | ||
| @param upscale_sigma_color color sigma for bilateral upscale operation | ||
| @param speed_up_thr threshold to detect point with irregular flow - where flow should be | ||
| 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 | ||
| - An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp | ||
| */ | ||
| CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray flow, int layers, | ||
| int averaging_block_size, int max_flow, | ||
| double sigma_dist, double sigma_color, int postprocess_window, | ||
| double sigma_dist_fix, double sigma_color_fix, double occ_thr, | ||
| int upscale_averaging_radius, double upscale_sigma_dist, | ||
| 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. | ||
| @param to second 8-bit 3-channel or 1-channel image of the same size as from | ||
| @param flow computed flow image that has the same size as from and CV_32FC2 type | ||
| @param grid_step stride used in sparse match computation. Lower values usually | ||
| result in higher quality but slow down the algorithm. | ||
| @param k number of nearest-neighbor matches considered, when fitting a locally affine | ||
| model. Lower values can make the algorithm noticeably faster at the cost of | ||
| some quality degradation. | ||
| @param sigma 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 the noise in the output flow. | ||
| @param use_post_proc defines whether the ximgproc::fastGlobalSmootherFilter() is used | ||
| for post-processing after interpolation | ||
| @param fgs_lambda see the respective parameter of the ximgproc::fastGlobalSmootherFilter() | ||
| @param fgs_sigma see the respective parameter of the ximgproc::fastGlobalSmootherFilter() | ||
| */ | ||
| CV_EXPORTS_W void calcOpticalFlowSparseToDense ( InputArray from, InputArray to, OutputArray flow, | ||
| int grid_step = 8, int k = 128, float sigma = 0.05f, | ||
| bool use_post_proc = true, float fgs_lambda = 500.0f, | ||
| 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 | ||
| also <http://lear.inrialpes.fr/src/deepmatching/> . | ||
| Parameters - class fields - that may be modified after creating a class instance: | ||
| - member float alpha | ||
| Smoothness assumption weight | ||
| - member float delta | ||
| Color constancy assumption weight | ||
| - member float gamma | ||
| Gradient constancy weight | ||
| - member float sigma | ||
| Gaussian smoothing parameter | ||
| - member int minSize | ||
| Minimal dimension of an image in the pyramid (next, smaller images in the pyramid are generated | ||
| until one of the dimensions reaches this size) | ||
| - member float downscaleFactor | ||
| Scaling factor in the image pyramid (must be \< 1) | ||
| - member int fixedPointIterations | ||
| How many iterations on each level of the pyramid | ||
| - member int sorIterations | ||
| Iterations of Succesive Over-Relaxation (solver) | ||
| - member float omega | ||
| Relaxation factor in SOR | ||
| */ | ||
| CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_DeepFlow(); | ||
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| //! Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF() | ||
| CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SimpleFlow(); | ||
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| //! Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback() | ||
| CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_Farneback(); | ||
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| //! Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense() | ||
| 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 | ||
| @cite Javier2012 . | ||
| Here are important members of the class that control the algorithm, which you can set after | ||
| constructing the class instance: | ||
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| - member double tau | ||
| Time step of the numerical scheme. | ||
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| - member double lambda | ||
| Weight parameter for the data term, attachment parameter. This is the most relevant | ||
| parameter, which determines the smoothness of the output. The smaller this parameter is, | ||
| the smoother the solutions we obtain. It depends on the range of motions of the images, so | ||
| its value should be adapted to each image sequence. | ||
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| - member double theta | ||
| Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the | ||
| attachment and the regularization terms. In theory, it should have a small value in order | ||
| to maintain both parts in correspondence. The method is stable for a large range of values | ||
| of this parameter. | ||
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| - member int nscales | ||
| Number of scales used to create the pyramid of images. | ||
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| - member int warps | ||
| Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( | ||
| I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the | ||
| method. It also affects the running time, so it is a compromise between speed and | ||
| accuracy. | ||
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| - member double epsilon | ||
| Stopping criterion threshold used in the numerical scheme, which is a trade-off between | ||
| precision and running time. A small value will yield more accurate solutions at the | ||
| expense of a slower convergence. | ||
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| - member int iterations | ||
| 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". | ||
| Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". | ||
| */ | ||
| class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow | ||
| { | ||
| public: | ||
| //! @brief Time step of the numerical scheme | ||
| /** @see setTau */ | ||
| CV_WRAP virtual double getTau() const = 0; | ||
| /** @copybrief getTau @see getTau */ | ||
| CV_WRAP virtual void setTau(double val) = 0; | ||
| //! @brief Weight parameter for the data term, attachment parameter | ||
| /** @see setLambda */ | ||
| CV_WRAP virtual double getLambda() const = 0; | ||
| /** @copybrief getLambda @see getLambda */ | ||
| CV_WRAP virtual void setLambda(double val) = 0; | ||
| //! @brief Weight parameter for (u - v)^2, tightness parameter | ||
| /** @see setTheta */ | ||
| CV_WRAP virtual double getTheta() const = 0; | ||
| /** @copybrief getTheta @see getTheta */ | ||
| CV_WRAP virtual void setTheta(double val) = 0; | ||
| //! @brief coefficient for additional illumination variation term | ||
| /** @see setGamma */ | ||
| CV_WRAP virtual double getGamma() const = 0; | ||
| /** @copybrief getGamma @see getGamma */ | ||
| CV_WRAP virtual void setGamma(double val) = 0; | ||
| //! @brief Number of scales used to create the pyramid of images | ||
| /** @see setScalesNumber */ | ||
| CV_WRAP virtual int getScalesNumber() const = 0; | ||
| /** @copybrief getScalesNumber @see getScalesNumber */ | ||
| CV_WRAP virtual void setScalesNumber(int val) = 0; | ||
| //! @brief Number of warpings per scale | ||
| /** @see setWarpingsNumber */ | ||
| CV_WRAP virtual int getWarpingsNumber() const = 0; | ||
| /** @copybrief getWarpingsNumber @see getWarpingsNumber */ | ||
| CV_WRAP virtual void setWarpingsNumber(int val) = 0; | ||
| //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time | ||
| /** @see setEpsilon */ | ||
| CV_WRAP virtual double getEpsilon() const = 0; | ||
| /** @copybrief getEpsilon @see getEpsilon */ | ||
| CV_WRAP virtual void setEpsilon(double val) = 0; | ||
| //! @brief Inner iterations (between outlier filtering) used in the numerical scheme | ||
| /** @see setInnerIterations */ | ||
| CV_WRAP virtual int getInnerIterations() const = 0; | ||
| /** @copybrief getInnerIterations @see getInnerIterations */ | ||
| CV_WRAP virtual void setInnerIterations(int val) = 0; | ||
| //! @brief Outer iterations (number of inner loops) used in the numerical scheme | ||
| /** @see setOuterIterations */ | ||
| CV_WRAP virtual int getOuterIterations() const = 0; | ||
| /** @copybrief getOuterIterations @see getOuterIterations */ | ||
| CV_WRAP virtual void setOuterIterations(int val) = 0; | ||
| //! @brief Use initial flow | ||
| /** @see setUseInitialFlow */ | ||
| CV_WRAP virtual bool getUseInitialFlow() const = 0; | ||
| /** @copybrief getUseInitialFlow @see getUseInitialFlow */ | ||
| CV_WRAP virtual void setUseInitialFlow(bool val) = 0; | ||
| //! @brief Step between scales (<1) | ||
| /** @see setScaleStep */ | ||
| CV_WRAP virtual double getScaleStep() const = 0; | ||
| /** @copybrief getScaleStep @see getScaleStep */ | ||
| CV_WRAP virtual void setScaleStep(double val) = 0; | ||
| //! @brief Median filter kernel size (1 = no filter) (3 or 5) | ||
| /** @see setMedianFiltering */ | ||
| CV_WRAP virtual int getMedianFiltering() const = 0; | ||
| /** @copybrief getMedianFiltering @see getMedianFiltering */ | ||
| CV_WRAP virtual void setMedianFiltering(int val) = 0; | ||
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| /** @brief Creates instance of cv::DualTVL1OpticalFlow*/ | ||
| CV_WRAP static Ptr<DualTVL1OpticalFlow> create( | ||
| double tau = 0.25, | ||
| double lambda = 0.15, | ||
| double theta = 0.3, | ||
| int nscales = 5, | ||
| int warps = 5, | ||
| double epsilon = 0.01, | ||
| int innnerIterations = 30, | ||
| int outerIterations = 10, | ||
| double scaleStep = 0.8, | ||
| double gamma = 0.0, | ||
| int medianFiltering = 5, | ||
| bool useInitialFlow = false); | ||
| }; | ||
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| /** @brief Creates instance of cv::DenseOpticalFlow | ||
| */ | ||
| CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1(); | ||
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| //! @} | ||
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| } //optflow | ||
| } | ||
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| #include "opencv2/optflow/motempl.hpp" | ||
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| #endif | ||
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Please fix indentation (replace tabs)