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Diffstat (limited to 'thirdparty1/linux/include/opencv2/optflow')
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diff --git a/thirdparty1/linux/include/opencv2/optflow/motempl.hpp b/thirdparty1/linux/include/opencv2/optflow/motempl.hpp new file mode 100644 index 0000000..aeea9e8 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/optflow/motempl.hpp @@ -0,0 +1,147 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2013, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +#ifndef __OPENCV_OPTFLOW_MOTEMPL_HPP__ +#define __OPENCV_OPTFLOW_MOTEMPL_HPP__ + +#include "opencv2/core.hpp" + +namespace cv +{ +namespace motempl +{ + +//! @addtogroup optflow +//! @{ + +/** @brief Updates the motion history image by a moving silhouette. + +@param silhouette Silhouette mask that has non-zero pixels where the motion occurs. +@param mhi Motion history image that is updated by the function (single-channel, 32-bit +floating-point). +@param timestamp Current time in milliseconds or other units. +@param duration Maximal duration of the motion track in the same units as timestamp . + +The function updates the motion history image as follows: + +\f[\texttt{mhi} (x,y)= \forkthree{\texttt{timestamp}}{if \(\texttt{silhouette}(x,y) \ne 0\)}{0}{if \(\texttt{silhouette}(x,y) = 0\) and \(\texttt{mhi} < (\texttt{timestamp} - \texttt{duration})\)}{\texttt{mhi}(x,y)}{otherwise}\f] + +That is, MHI pixels where the motion occurs are set to the current timestamp , while the pixels +where the motion happened last time a long time ago are cleared. + +The function, together with calcMotionGradient and calcGlobalOrientation , implements a motion +templates technique described in @cite Davis97 and @cite Bradski00 . + */ +CV_EXPORTS_W void updateMotionHistory( InputArray silhouette, InputOutputArray mhi, + double timestamp, double duration ); + +/** @brief Calculates a gradient orientation of a motion history image. + +@param mhi Motion history single-channel floating-point image. +@param mask Output mask image that has the type CV_8UC1 and the same size as mhi . Its non-zero +elements mark pixels where the motion gradient data is correct. +@param orientation Output motion gradient orientation image that has the same type and the same +size as mhi . Each pixel of the image is a motion orientation, from 0 to 360 degrees. +@param delta1 Minimal (or maximal) allowed difference between mhi values within a pixel +neighborhood. +@param delta2 Maximal (or minimal) allowed difference between mhi values within a pixel +neighborhood. That is, the function finds the minimum ( \f$m(x,y)\f$ ) and maximum ( \f$M(x,y)\f$ ) mhi +values over \f$3 \times 3\f$ neighborhood of each pixel and marks the motion orientation at \f$(x, y)\f$ +as valid only if +\f[\min ( \texttt{delta1} , \texttt{delta2} ) \le M(x,y)-m(x,y) \le \max ( \texttt{delta1} , \texttt{delta2} ).\f] +@param apertureSize Aperture size of the Sobel operator. + +The function calculates a gradient orientation at each pixel \f$(x, y)\f$ as: + +\f[\texttt{orientation} (x,y)= \arctan{\frac{d\texttt{mhi}/dy}{d\texttt{mhi}/dx}}\f] + +In fact, fastAtan2 and phase are used so that the computed angle is measured in degrees and covers +the full range 0..360. Also, the mask is filled to indicate pixels where the computed angle is +valid. + +@note + - (Python) An example on how to perform a motion template technique can be found at + opencv_source_code/samples/python2/motempl.py + */ +CV_EXPORTS_W void calcMotionGradient( InputArray mhi, OutputArray mask, OutputArray orientation, + double delta1, double delta2, int apertureSize = 3 ); + +/** @brief Calculates a global motion orientation in a selected region. + +@param orientation Motion gradient orientation image calculated by the function calcMotionGradient +@param mask Mask image. It may be a conjunction of a valid gradient mask, also calculated by +calcMotionGradient , and the mask of a region whose direction needs to be calculated. +@param mhi Motion history image calculated by updateMotionHistory . +@param timestamp Timestamp passed to updateMotionHistory . +@param duration Maximum duration of a motion track in milliseconds, passed to updateMotionHistory + +The function calculates an average motion direction in the selected region and returns the angle +between 0 degrees and 360 degrees. The average direction is computed from the weighted orientation +histogram, where a recent motion has a larger weight and the motion occurred in the past has a +smaller weight, as recorded in mhi . + */ +CV_EXPORTS_W double calcGlobalOrientation( InputArray orientation, InputArray mask, InputArray mhi, + double timestamp, double duration ); + +/** @brief Splits a motion history image into a few parts corresponding to separate independent motions (for +example, left hand, right hand). + +@param mhi Motion history image. +@param segmask Image where the found mask should be stored, single-channel, 32-bit floating-point. +@param boundingRects Vector containing ROIs of motion connected components. +@param timestamp Current time in milliseconds or other units. +@param segThresh Segmentation threshold that is recommended to be equal to the interval between +motion history "steps" or greater. + +The function finds all of the motion segments and marks them in segmask with individual values +(1,2,...). It also computes a vector with ROIs of motion connected components. After that the motion +direction for every component can be calculated with calcGlobalOrientation using the extracted mask +of the particular component. + */ +CV_EXPORTS_W void segmentMotion( InputArray mhi, OutputArray segmask, + CV_OUT std::vector<Rect>& boundingRects, + double timestamp, double segThresh ); + + +//! @} + +} +} + +#endif diff --git a/thirdparty1/linux/include/opencv2/optflow/pcaflow.hpp b/thirdparty1/linux/include/opencv2/optflow/pcaflow.hpp new file mode 100644 index 0000000..6645363 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/optflow/pcaflow.hpp @@ -0,0 +1,149 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2016, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +/** + * @file pcaflow.hpp + * @author Vladislav Samsonov <vvladxx@gmail.com> + * @brief Implementation of the PCAFlow algorithm from the following paper: + * http://files.is.tue.mpg.de/black/papers/cvpr2015_pcaflow.pdf + * + * @cite Wulff:CVPR:2015 + * + * There are some key differences which distinguish this algorithm from the original PCAFlow (see paper): + * - Discrete Cosine Transform basis is used instead of basis extracted with PCA. + * Reasoning: DCT basis has comparable performance and it doesn't require additional storage space. + * Also, this decision helps to avoid overloading the algorithm with a lot of external input. + * - Usage of built-in OpenCV feature tracking instead of libviso. +*/ + +#ifndef __OPENCV_OPTFLOW_PCAFLOW_HPP__ +#define __OPENCV_OPTFLOW_PCAFLOW_HPP__ + +#include "opencv2/core.hpp" +#include "opencv2/video.hpp" + +namespace cv +{ +namespace optflow +{ + +//! @addtogroup optflow +//! @{ + +/** @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. + */ +class CV_EXPORTS_W PCAPrior +{ +private: + Mat L1; + Mat L2; + Mat c1; + Mat c2; + +public: + PCAPrior( const char *pathToPrior ); + + int getPadding() const { return L1.size().height; } + + int getBasisSize() const { return L1.size().width; } + + void fillConstraints( float *A1, float *A2, float *b1, float *b2 ) const; +}; + +/** @brief PCAFlow algorithm. + */ +class CV_EXPORTS_W OpticalFlowPCAFlow : public DenseOpticalFlow +{ +protected: + const Ptr<const PCAPrior> prior; + const Size basisSize; + const float sparseRate; // (0 .. 0.1) + const float retainedCornersFraction; // [0 .. 1] + const float occlusionsThreshold; + const float dampingFactor; + const float claheClip; + bool useOpenCL; + +public: + /** @brief Creates an instance of PCAFlow algorithm. + * @param _prior Learned prior or no prior (default). @see cv::optflow::PCAPrior + * @param _basisSize Number of basis vectors. + * @param _sparseRate Controls density of sparse matches. + * @param _retainedCornersFraction Retained corners fraction. + * @param _occlusionsThreshold Occlusion threshold. + * @param _dampingFactor Regularization term for solving least-squares. It is not related to the prior regularization. + * @param _claheClip Clip parameter for CLAHE. + */ + OpticalFlowPCAFlow( Ptr<const PCAPrior> _prior = Ptr<const PCAPrior>(), const Size _basisSize = Size( 18, 14 ), + float _sparseRate = 0.024, float _retainedCornersFraction = 0.2, + float _occlusionsThreshold = 0.0003, float _dampingFactor = 0.00002, float _claheClip = 14 ); + + void calc( InputArray I0, InputArray I1, InputOutputArray flow ); + void collectGarbage(); + +private: + void findSparseFeatures( UMat &from, UMat &to, std::vector<Point2f> &features, + std::vector<Point2f> &predictedFeatures ) const; + + void removeOcclusions( UMat &from, UMat &to, std::vector<Point2f> &features, + std::vector<Point2f> &predictedFeatures ) const; + + void getSystem( OutputArray AOut, OutputArray b1Out, OutputArray b2Out, const std::vector<Point2f> &features, + const std::vector<Point2f> &predictedFeatures, const Size size ); + + void getSystem( OutputArray A1Out, OutputArray A2Out, OutputArray b1Out, OutputArray b2Out, + const std::vector<Point2f> &features, const std::vector<Point2f> &predictedFeatures, + const Size size ); + + OpticalFlowPCAFlow& operator=( const OpticalFlowPCAFlow& ); // make it non-assignable +}; + +/** @brief Creates an instance of PCAFlow +*/ +CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_PCAFlow(); + +//! @} + +} +} + +#endif diff --git a/thirdparty1/linux/include/opencv2/optflow/sparse_matching_gpc.hpp b/thirdparty1/linux/include/opencv2/optflow/sparse_matching_gpc.hpp new file mode 100644 index 0000000..3127710 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/optflow/sparse_matching_gpc.hpp @@ -0,0 +1,380 @@ +/* +By downloading, copying, installing or using the software you agree to this +license. If you do not agree to this license, do not download, install, +copy or use the software. + + + License Agreement + For Open Source Computer Vision Library + (3-clause BSD License) + +Copyright (C) 2016, OpenCV Foundation, all rights reserved. +Third party copyrights are property of their respective owners. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +This software is provided by the copyright holders and contributors "as is" and +any express or implied warranties, including, but not limited to, the implied +warranties of merchantability and fitness for a particular purpose are +disclaimed. In no event shall copyright holders or contributors be liable for +any direct, indirect, incidental, special, exemplary, or consequential damages +(including, but not limited to, procurement of substitute goods or services; +loss of use, data, or profits; or business interruption) however caused +and on any theory of liability, whether in contract, strict liability, +or tort (including negligence or otherwise) arising in any way out of +the use of this software, even if advised of the possibility of such damage. +*/ + +/** + * @file sparse_matching_gpc.hpp + * @author Vladislav Samsonov <vvladxx@gmail.com> + * @brief Implementation of the Global Patch Collider. + * + * Implementation of the Global Patch Collider algorithm from the following paper: + * http://research.microsoft.com/en-us/um/people/pkohli/papers/wfrik_cvpr2016.pdf + * + * @cite Wang_2016_CVPR + */ + +#ifndef __OPENCV_OPTFLOW_SPARSE_MATCHING_GPC_HPP__ +#define __OPENCV_OPTFLOW_SPARSE_MATCHING_GPC_HPP__ + +#include "opencv2/core.hpp" +#include "opencv2/core/hal/intrin.hpp" +#include "opencv2/imgproc.hpp" + +namespace cv +{ +namespace optflow +{ + +//! @addtogroup optflow +//! @{ + +struct CV_EXPORTS_W GPCPatchDescriptor +{ + static const unsigned nFeatures = 18; //!< number of features in a patch descriptor + Vec< double, nFeatures > feature; + + double dot( const Vec< double, nFeatures > &coef ) const; + + void markAsSeparated() { feature[0] = std::numeric_limits< double >::quiet_NaN(); } + + bool isSeparated() const { return cvIsNaN( feature[0] ) != 0; } +}; + +struct CV_EXPORTS_W GPCPatchSample +{ + GPCPatchDescriptor ref; + GPCPatchDescriptor pos; + GPCPatchDescriptor neg; + + void getDirections( bool &refdir, bool &posdir, bool &negdir, const Vec< double, GPCPatchDescriptor::nFeatures > &coef, double rhs ) const; +}; + +typedef std::vector< GPCPatchSample > GPCSamplesVector; + +/** @brief Descriptor types for the Global Patch Collider. + */ +enum GPCDescType +{ + GPC_DESCRIPTOR_DCT = 0, //!< Better quality but slow + GPC_DESCRIPTOR_WHT //!< Worse quality but much faster +}; + +/** @brief Class encapsulating training samples. + */ +class CV_EXPORTS_W GPCTrainingSamples +{ +private: + GPCSamplesVector samples; + int descriptorType; + +public: + /** @brief This function can be used to extract samples from a pair of images and a ground truth flow. + * Sizes of all the provided vectors must be equal. + */ + static Ptr< GPCTrainingSamples > create( const std::vector< String > &imagesFrom, const std::vector< String > &imagesTo, + const std::vector< String > >, int descriptorType ); + + static Ptr< GPCTrainingSamples > create( InputArrayOfArrays imagesFrom, InputArrayOfArrays imagesTo, InputArrayOfArrays gt, + int descriptorType ); + + size_t size() const { return samples.size(); } + + int type() const { return descriptorType; } + + operator GPCSamplesVector &() { return samples; } +}; + +/** @brief Class encapsulating training parameters. + */ +struct GPCTrainingParams +{ + unsigned maxTreeDepth; //!< Maximum tree depth to stop partitioning. + int minNumberOfSamples; //!< Minimum number of samples in the node to stop partitioning. + int descriptorType; //!< Type of descriptors to use. + bool printProgress; //!< Print progress to stdout. + + GPCTrainingParams( unsigned _maxTreeDepth = 20, int _minNumberOfSamples = 3, GPCDescType _descriptorType = GPC_DESCRIPTOR_DCT, + bool _printProgress = true ) + : maxTreeDepth( _maxTreeDepth ), minNumberOfSamples( _minNumberOfSamples ), descriptorType( _descriptorType ), + printProgress( _printProgress ) + { + CV_Assert( check() ); + } + + GPCTrainingParams( const GPCTrainingParams ¶ms ) + : maxTreeDepth( params.maxTreeDepth ), minNumberOfSamples( params.minNumberOfSamples ), descriptorType( params.descriptorType ), + printProgress( params.printProgress ) + { + CV_Assert( check() ); + } + + bool check() const { return maxTreeDepth > 1 && minNumberOfSamples > 1; } +}; + +/** @brief Class encapsulating matching parameters. + */ +struct GPCMatchingParams +{ + bool useOpenCL; //!< Whether to use OpenCL to speed up the matching. + + GPCMatchingParams( bool _useOpenCL = false ) : useOpenCL( _useOpenCL ) {} + + GPCMatchingParams( const GPCMatchingParams ¶ms ) : useOpenCL( params.useOpenCL ) {} +}; + +/** @brief Class for individual tree. + */ +class CV_EXPORTS_W GPCTree : public Algorithm +{ +public: + struct Node + { + Vec< double, GPCPatchDescriptor::nFeatures > coef; //!< Hyperplane coefficients + double rhs; //!< Bias term of the hyperplane + unsigned left; + unsigned right; + + bool operator==( const Node &n ) const { return coef == n.coef && rhs == n.rhs && left == n.left && right == n.right; } + }; + +private: + typedef GPCSamplesVector::iterator SIter; + + std::vector< Node > nodes; + GPCTrainingParams params; + + bool trainNode( size_t nodeId, SIter begin, SIter end, unsigned depth ); + +public: + void train( GPCTrainingSamples &samples, const GPCTrainingParams params = GPCTrainingParams() ); + + void write( FileStorage &fs ) const; + + void read( const FileNode &fn ); + + unsigned findLeafForPatch( const GPCPatchDescriptor &descr ) const; + + static Ptr< GPCTree > create() { return makePtr< GPCTree >(); } + + bool operator==( const GPCTree &t ) const { return nodes == t.nodes; } + + int getDescriptorType() const { return params.descriptorType; } +}; + +template < int T > class CV_EXPORTS_W GPCForest : public Algorithm +{ +private: + struct Trail + { + unsigned leaf[T]; //!< Inside which leaf of the tree 0..T the patch fell? + Point2i coord; //!< Patch coordinates. + + bool operator==( const Trail &trail ) const { return memcmp( leaf, trail.leaf, sizeof( leaf ) ) == 0; } + + bool operator<( const Trail &trail ) const + { + for ( int i = 0; i < T - 1; ++i ) + if ( leaf[i] != trail.leaf[i] ) + return leaf[i] < trail.leaf[i]; + return leaf[T - 1] < trail.leaf[T - 1]; + } + }; + + class ParallelTrailsFilling : public ParallelLoopBody + { + private: + const GPCForest *forest; + const std::vector< GPCPatchDescriptor > *descr; + std::vector< Trail > *trails; + + ParallelTrailsFilling &operator=( const ParallelTrailsFilling & ); + + public: + ParallelTrailsFilling( const GPCForest *_forest, const std::vector< GPCPatchDescriptor > *_descr, std::vector< Trail > *_trails ) + : forest( _forest ), descr( _descr ), trails( _trails ){}; + + void operator()( const Range &range ) const + { + for ( int t = range.start; t < range.end; ++t ) + for ( size_t i = 0; i < descr->size(); ++i ) + trails->at( i ).leaf[t] = forest->tree[t].findLeafForPatch( descr->at( i ) ); + } + }; + + GPCTree tree[T]; + +public: + /** @brief Train the forest using one sample set for every tree. + * Please, consider using the next method instead of this one for better quality. + */ + void train( GPCTrainingSamples &samples, const GPCTrainingParams params = GPCTrainingParams() ) + { + for ( int i = 0; i < T; ++i ) + tree[i].train( samples, params ); + } + + /** @brief Train the forest using individual samples for each tree. + * It is generally better to use this instead of the first method. + */ + void train( const std::vector< String > &imagesFrom, const std::vector< String > &imagesTo, const std::vector< String > >, + const GPCTrainingParams params = GPCTrainingParams() ) + { + for ( int i = 0; i < T; ++i ) + { + Ptr< GPCTrainingSamples > samples = + GPCTrainingSamples::create( imagesFrom, imagesTo, gt, params.descriptorType ); // Create training set for the tree + tree[i].train( *samples, params ); + } + } + + void train( InputArrayOfArrays imagesFrom, InputArrayOfArrays imagesTo, InputArrayOfArrays gt, + const GPCTrainingParams params = GPCTrainingParams() ) + { + for ( int i = 0; i < T; ++i ) + { + Ptr< GPCTrainingSamples > samples = + GPCTrainingSamples::create( imagesFrom, imagesTo, gt, params.descriptorType ); // Create training set for the tree + tree[i].train( *samples, params ); + } + } + + void write( FileStorage &fs ) const + { + fs << "ntrees" << T << "trees" + << "["; + for ( int i = 0; i < T; ++i ) + { + fs << "{"; + tree[i].write( fs ); + fs << "}"; + } + fs << "]"; + } + + void read( const FileNode &fn ) + { + CV_Assert( T <= (int)fn["ntrees"] ); + FileNodeIterator it = fn["trees"].begin(); + for ( int i = 0; i < T; ++i, ++it ) + tree[i].read( *it ); + } + + /** @brief Find correspondences between two images. + * @param[in] imgFrom First image in a sequence. + * @param[in] imgTo Second image in a sequence. + * @param[out] corr Output vector with pairs of corresponding points. + * @param[in] params Additional matching parameters for fine-tuning. + */ + void findCorrespondences( InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i, Point2i > > &corr, + const GPCMatchingParams params = GPCMatchingParams() ) const; + + static Ptr< GPCForest > create() { return makePtr< GPCForest >(); } +}; + +class CV_EXPORTS_W GPCDetails +{ +public: + static void dropOutliers( std::vector< std::pair< Point2i, Point2i > > &corr ); + + static void getAllDescriptorsForImage( const Mat *imgCh, std::vector< GPCPatchDescriptor > &descr, const GPCMatchingParams &mp, + int type ); + + static void getCoordinatesFromIndex( size_t index, Size sz, int &x, int &y ); +}; + +template < int T > +void GPCForest< T >::findCorrespondences( InputArray imgFrom, InputArray imgTo, std::vector< std::pair< Point2i, Point2i > > &corr, + const GPCMatchingParams params ) const +{ + CV_Assert( imgFrom.channels() == 3 ); + CV_Assert( imgTo.channels() == 3 ); + + Mat from, to; + imgFrom.getMat().convertTo( from, CV_32FC3 ); + imgTo.getMat().convertTo( to, CV_32FC3 ); + cvtColor( from, from, COLOR_BGR2YCrCb ); + cvtColor( to, to, COLOR_BGR2YCrCb ); + + Mat fromCh[3], toCh[3]; + split( from, fromCh ); + split( to, toCh ); + + std::vector< GPCPatchDescriptor > descr; + GPCDetails::getAllDescriptorsForImage( fromCh, descr, params, tree[0].getDescriptorType() ); + std::vector< Trail > trailsFrom( descr.size() ), trailsTo( descr.size() ); + + for ( size_t i = 0; i < descr.size(); ++i ) + GPCDetails::getCoordinatesFromIndex( i, from.size(), trailsFrom[i].coord.x, trailsFrom[i].coord.y ); + parallel_for_( Range( 0, T ), ParallelTrailsFilling( this, &descr, &trailsFrom ) ); + + descr.clear(); + GPCDetails::getAllDescriptorsForImage( toCh, descr, params, tree[0].getDescriptorType() ); + + for ( size_t i = 0; i < descr.size(); ++i ) + GPCDetails::getCoordinatesFromIndex( i, to.size(), trailsTo[i].coord.x, trailsTo[i].coord.y ); + parallel_for_( Range( 0, T ), ParallelTrailsFilling( this, &descr, &trailsTo ) ); + + std::sort( trailsFrom.begin(), trailsFrom.end() ); + std::sort( trailsTo.begin(), trailsTo.end() ); + + for ( size_t i = 0; i < trailsFrom.size(); ++i ) + { + bool uniq = true; + while ( i + 1 < trailsFrom.size() && trailsFrom[i] == trailsFrom[i + 1] ) + ++i, uniq = false; + if ( uniq ) + { + typename std::vector< Trail >::const_iterator lb = std::lower_bound( trailsTo.begin(), trailsTo.end(), trailsFrom[i] ); + if ( lb != trailsTo.end() && *lb == trailsFrom[i] && ( ( lb + 1 ) == trailsTo.end() || !( *lb == *( lb + 1 ) ) ) ) + corr.push_back( std::make_pair( trailsFrom[i].coord, lb->coord ) ); + } + } + + GPCDetails::dropOutliers( corr ); +} + +//! @} + +} // namespace optflow + +CV_EXPORTS void write( FileStorage &fs, const String &name, const optflow::GPCTree::Node &node ); + +CV_EXPORTS void read( const FileNode &fn, optflow::GPCTree::Node &node, optflow::GPCTree::Node ); +} // namespace cv + +#endif |