Sparse-to-dense OF algorithm is added

* Sparse match interpolator interface and EdgeAwareInterpolator were
added to the ximgproc module
* New optical flow algorithm, based on PyrLK sparse OF and sparse match
interpolation, is added to the optflow module

modules/optflow/CMakeLists.txt

 set(the_description "Optical Flow Algorithms")
-ocv_define_module(optflow opencv_core opencv_imgproc opencv_video opencv_highgui WRAP python)
+ocv_define_module(optflow opencv_core opencv_imgproc opencv_video opencv_highgui opencv_ximgproc WRAP python)

modules/optflow/include/opencv2/optflow.hpp

@@ -109,7 +109,30 @@ CV_EXPORTS_W void calcOpticalFlowSF( InputArray from, InputArray to, OutputArray
                                      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 );    
+                                     double upscale_sigma_color, double speed_up_thr );
+
+/** @brief Fast dense optical flow based on PyrLK sparse matches interpolation.
+
+@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 );
 
 /** @brief Read a .flo file
 
@@ -165,6 +188,9 @@ CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SimpleFlow();
 //! Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback()
 CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_Farneback();
 
+//! Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()
+CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_SparseToDense();
+
 //! @}
 
 } //optflow

modules/optflow/samples/optical_flow_evaluation.cpp

@@ -10,7 +10,7 @@ using namespace optflow;
 const String keys = "{help h usage ? |      | print this message   }"
         "{@image1        |      | image1               }"
         "{@image2        |      | image2               }"
-        "{@algorithm     |      | [farneback, simpleflow, tvl1 or deepflow] }"
+        "{@algorithm     |      | [farneback, simpleflow, tvl1, deepflow or sparsetodenseflow] }"
         "{@groundtruth   |      | path to the .flo file  (optional), Middlebury format }"
         "{m measure      |endpoint| error measure - [endpoint or angular] }"
         "{r region       |all   | region to compute stats about [all, discontinuities, untextured] }"
@@ -249,6 +249,8 @@ int main( int argc, char** argv )
         algorithm = createOptFlow_DualTVL1();
     else if ( method == "deepflow" )
         algorithm = createOptFlow_DeepFlow();
+    else if ( method == "sparsetodenseflow" )
+        algorithm = createOptFlow_SparseToDense();
     else
     {
         printf("Wrong method!\n");

modules/optflow/src/interfaces.cpp

@@ -177,5 +177,42 @@ Ptr<DenseOpticalFlow> createOptFlow_Farneback()
 {
     return makePtr<OpticalFlowFarneback>();
 }
+
+class OpticalFlowSparseToDense : public DenseOpticalFlow
+{
+public:
+    OpticalFlowSparseToDense(int _grid_step, int _k, float _sigma, bool _use_post_proc, float _fgs_lambda, float _fgs_sigma);
+    void calc(InputArray I0, InputArray I1, InputOutputArray flow);
+    void collectGarbage();
+protected:
+    int grid_step;
+    int k;
+    float sigma;
+    bool use_post_proc;
+    float fgs_lambda;
+    float fgs_sigma;
+};
+
+OpticalFlowSparseToDense::OpticalFlowSparseToDense(int _grid_step, int _k, float _sigma, bool _use_post_proc, float _fgs_lambda, float _fgs_sigma)
+{
+    grid_step     = _grid_step;
+    k             = _k;
+    sigma         = _sigma;
+    use_post_proc = _use_post_proc;
+    fgs_lambda    = _fgs_lambda;
+    fgs_sigma     = _fgs_sigma;
+}
+
+void OpticalFlowSparseToDense::calc(InputArray I0, InputArray I1, InputOutputArray flow)
+{
+    calcOpticalFlowSparseToDense(I0,I1,flow,grid_step,k,sigma,use_post_proc,fgs_lambda,fgs_sigma);
+}
+
+void OpticalFlowSparseToDense::collectGarbage() {}
+
+Ptr<DenseOpticalFlow> createOptFlow_SparseToDense()
+{
+    return makePtr<OpticalFlowSparseToDense>(8,128,0.05f,true,500.0f,1.5f);
+}
 }
 }

modules/optflow/src/sparsetodenseflow.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  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
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., 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:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's 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.
+//
+//   * The name of the copyright holders may not 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 the Intel Corporation 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.
+//
+//M*/
+
+#include "precomp.hpp"
+#include "opencv2/ximgproc/sparse_match_interpolator.hpp"
+using namespace std;
+
+namespace cv {
+namespace optflow {
+
+CV_EXPORTS_W void calcOpticalFlowSparseToDense(InputArray from, InputArray to, OutputArray flow,
+                                               int grid_step, int k,
+                                               float sigma, bool use_post_proc,
+                                               float fgs_lambda, float fgs_sigma)
+{
+    CV_Assert( grid_step>1 && k>3 && sigma>0.0001f && fgs_lambda>1.0f && fgs_sigma>0.01f );
+    CV_Assert( !from.empty() && from.depth() == CV_8U && (from.channels() == 3 || from.channels() == 1) );
+    CV_Assert( !to  .empty() && to  .depth() == CV_8U && (to  .channels() == 3 || to  .channels() == 1) );
+    CV_Assert( from.sameSize(to) );
+
+    Mat prev = from.getMat();
+    Mat cur  = to.getMat();
+    Mat prev_grayscale, cur_grayscale;
+
+    while( (prev.cols/grid_step)*(prev.rows/grid_step) > SHRT_MAX ) //ensure that the number matches is not too big
+        grid_step*=2;
+
+    if(prev.channels()==3)
+    {
+        cvtColor(prev,prev_grayscale,COLOR_BGR2GRAY);
+        cvtColor(cur, cur_grayscale, COLOR_BGR2GRAY);
+    }
+    else
+    {
+        prev.copyTo(prev_grayscale);
+        cur .copyTo(cur_grayscale);
+    }
+
+    vector<Point2f> points;
+    vector<Point2f> dst_points;
+    vector<unsigned char> status;
+    vector<float> err;
+    vector<Point2f> points_filtered, dst_points_filtered;
+
+    for(int i=0;i<prev.rows;i+=grid_step)
+        for(int j=0;j<prev.cols;j+=grid_step)
+            points.push_back(Point2f((float)j,(float)i));
+
+    calcOpticalFlowPyrLK(prev_grayscale,cur_grayscale,points,dst_points,status,err,Size(21,21));
+
+    for(unsigned int i=0;i<points.size();i++)
+    {
+        if(status[i]!=0)
+        {
+            points_filtered.push_back(points[i]);
+            dst_points_filtered.push_back(dst_points[i]);
+        }
+    }
+
+    flow.create(from.size(),CV_32FC2);
+    Mat dense_flow = flow.getMat();
+
+    Ptr<ximgproc::EdgeAwareInterpolator> gd = ximgproc::createEdgeAwareInterpolator();
+    gd->setK(k);
+    gd->setSigma(sigma);
+    gd->setUsePostProcessing(use_post_proc);
+    gd->setFGSLambda(fgs_lambda);
+    gd->setFGSSigma (fgs_sigma);
+    gd->interpolate(prev,points_filtered,cur,dst_points_filtered,dense_flow);
+}
+
+}
+}
\ No newline at end of file

modules/optflow/test/test_sparsetodenseflow.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  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.
+//
+//
+//                        Intel License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, 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:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's 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.
+//
+//   * The name of Intel Corporation may not 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 the Intel Corporation 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.
+//
+//M*/
+
+#include "test_precomp.hpp"
+
+#include <string>
+
+using namespace std;
+using namespace cv;
+
+/* ///////////////////// sparsetodenseflow_test ///////////////////////// */
+
+class CV_SparseToDenseFlowTest : public cvtest::BaseTest
+{
+protected:
+    void run(int);
+};
+
+static bool isFlowCorrect(float u) {
+  return !cvIsNaN(u) && (fabs(u) < 1e9);
+}
+
+static float calc_rmse(Mat flow1, Mat flow2) {
+  float sum = 0;
+  int counter = 0;
+  const int rows = flow1.rows;
+  const int cols = flow1.cols;
+
+  for (int y = 0; y < rows; ++y) {
+    for (int x = 0; x < cols; ++x) {
+      Vec2f flow1_at_point = flow1.at<Vec2f>(y, x);
+      Vec2f flow2_at_point = flow2.at<Vec2f>(y, x);
+
+      float u1 = flow1_at_point[0];
+      float v1 = flow1_at_point[1];
+      float u2 = flow2_at_point[0];
+      float v2 = flow2_at_point[1];
+
+      if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) {
+        sum += (u1-u2)*(u1-u2) + (v1-v2)*(v1-v2);
+        counter++;
+      }
+    }
+  }
+  return (float)sqrt(sum / (1e-9 + counter));
+}
+
+void CV_SparseToDenseFlowTest::run(int) {
+    const float MAX_RMSE = 0.6f;
+    const string frame1_path = ts->get_data_path() + "optflow/RubberWhale1.png";
+    const string frame2_path = ts->get_data_path() + "optflow/RubberWhale2.png";
+    const string gt_flow_path = ts->get_data_path() + "optflow/RubberWhale.flo";
+
+    Mat frame1 = imread(frame1_path);
+    Mat frame2 = imread(frame2_path);
+
+    if (frame1.empty()) {
+      ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+
+    if (frame2.empty()) {
+      ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+
+    if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) {
+      ts->printf(cvtest::TS::LOG, "images should be of equal sizes (%s and %s)",
+                 frame1_path.c_str(), frame2_path.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+
+    if (frame1.type() != 16 || frame2.type() != 16) {
+      ts->printf(cvtest::TS::LOG, "images should be of equal type CV_8UC3 (%s and %s)",
+                 frame1_path.c_str(), frame2_path.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+
+    Mat flow_gt = optflow::readOpticalFlow(gt_flow_path);
+    if(flow_gt.empty()) {
+      ts->printf(cvtest::TS::LOG, "error while reading flow data from file %s",
+                 gt_flow_path.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+
+    Mat flow;
+    optflow::calcOpticalFlowSparseToDense(frame1, frame2, flow);
+
+    float rmse = calc_rmse(flow_gt, flow);
+
+    ts->printf(cvtest::TS::LOG, "Optical flow estimation RMSE for SparseToDenseFlow algorithm : %lf\n",
+               rmse);
+
+    if (rmse > MAX_RMSE) {
+      ts->printf( cvtest::TS::LOG,
+                 "Too big rmse error : %lf ( >= %lf )\n", rmse, MAX_RMSE);
+      ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
+      return;
+    }
+}
+
+
+TEST(Video_OpticalFlowSparseToDenseFlow, accuracy) { CV_SparseToDenseFlowTest test; test.safe_run(); }

modules/ximgproc/doc/ximgproc.bib

@@ -77,3 +77,11 @@
   year={2008},
   organization={ACM}
 }
+
+@inproceedings{Revaud2015,
+  title={EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow},
+  author={Revaud, Jerome and Weinzaepfel, Philippe and Harchaoui, Zaid and Schmid, Cordelia},
+  booktitle={Computer Vision and Pattern Recognition (CVPR), IEEE Conference on},
+  pages={1164--1172},
+  year={2015}
+}

modules/ximgproc/include/opencv2/ximgproc.hpp

@@ -39,6 +39,7 @@
 
 #include "ximgproc/edge_filter.hpp"
 #include "ximgproc/disparity_filter.hpp"
+#include "ximgproc/sparse_match_interpolator.hpp"
 #include "ximgproc/structured_edge_detection.hpp"
 #include "ximgproc/seeds.hpp"
 

modules/ximgproc/include/opencv2/ximgproc/sparse_match_interpolator.hpp

+/*
+ *  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)
+ *
+ *  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_SPARSEMATCHINTERPOLATOR_HPP__
+#define __OPENCV_SPARSEMATCHINTERPOLATOR_HPP__
+#ifdef __cplusplus
+
+#include <opencv2/core.hpp>
+
+namespace cv {
+namespace ximgproc {
+
+//! @addtogroup ximgproc_filters
+//! @{
+
+/** @brief Main interface for all filters, that take sparse matches as an
+input and produce a dense per-pixel matching (optical flow) as an output.
+ */
+class CV_EXPORTS_W SparseMatchInterpolator : public Algorithm
+{
+public:
+    /** @brief Interpolate input sparse matches.
+
+    @param from_image first of the two matched images, 8-bit single-channel or three-channel.
+
+    @param from_points points of the from_image for which there are correspondences in the
+    to_image (Point2f vector, size shouldn't exceed 32767)
+
+    @param to_image second of the two matched images, 8-bit single-channel or three-channel.
+
+    @param to_points points in the to_image corresponding to from_points
+    (Point2f vector, size shouldn't exceed 32767)
+
+    @param dense_flow output dense matching (two-channel CV_32F image)
+     */
+    CV_WRAP virtual void interpolate(InputArray from_image, InputArray from_points,
+                                     InputArray to_image  , InputArray to_points,
+                                     OutputArray dense_flow) = 0;
+};
+
+/** @brief Sparse match interpolation algorithm based on modified locally-weighted affine
+estimator from @cite Revaud2015 and Fast Global Smoother as post-processing filter.
+ */
+class CV_EXPORTS_W EdgeAwareInterpolator : public SparseMatchInterpolator
+{
+public:
+    /** @brief K is a number of nearest-neighbor matches considered, when fitting a locally affine
+    model. Usually it should be around 128. However, lower values would make the interpolation
+    noticeably faster.
+     */
+    CV_WRAP virtual void setK(int _k) = 0;
+    /** @see setK */
+    CV_WRAP virtual int  getK() = 0;
+
+    /** @brief Sigma is a 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 noise in the
+    output flow.
+     */
+    CV_WRAP virtual void  setSigma(float _sigma) = 0;
+    /** @see setSigma */
+    CV_WRAP virtual float getSigma() = 0;
+
+    /** @brief Lambda is a parameter defining the weight of the edge-aware term in geodesic distance,
+    should be in the range of 0 to 1000.
+     */
+    CV_WRAP virtual void  setLambda(float _lambda) = 0;
+    /** @see setLambda */
+    CV_WRAP virtual float getLambda() = 0;
+
+    /** @brief Sets whether the fastGlobalSmootherFilter() post-processing is employed. It is turned on by
+    default.
+     */
+    CV_WRAP virtual void setUsePostProcessing(bool _use_post_proc) = 0;
+    /** @see setUsePostProcessing */
+    CV_WRAP virtual bool getUsePostProcessing() = 0;
+
+    /** @brief Sets the respective fastGlobalSmootherFilter() parameter.
+     */
+    CV_WRAP virtual void  setFGSLambda(float _lambda) = 0;
+    /** @see setFGSLambda */
+    CV_WRAP virtual float getFGSLambda() = 0;
+
+    /** @see setFGSLambda */
+    CV_WRAP virtual void  setFGSSigma(float _sigma) = 0;
+    /** @see setFGSLambda */
+    CV_WRAP virtual float getFGSSigma() = 0;
+};
+
+/** @brief Factory method that creates an instance of the
+EdgeAwareInterpolator.
+*/
+CV_EXPORTS_W
+Ptr<EdgeAwareInterpolator> createEdgeAwareInterpolator();
+
+//! @}
+}
+}
+#endif
+#endif

modules/ximgproc/src/sparse_match_interpolators.cpp

+/*
+ *  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)
+ *
+ *  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.
+ */
+
+#include "precomp.hpp"
+#include "opencv2/ximgproc/sparse_match_interpolator.hpp"
+#include <algorithm>
+#include <vector>
+
+using namespace std;
+
+#define INF 1E+20F
+
+namespace cv {
+namespace ximgproc {
+
+struct SparseMatch
+{
+    Point2f reference_image_pos;
+    Point2f target_image_pos;
+    SparseMatch(){}
+    SparseMatch(Point2f ref_point, Point2f target_point): reference_image_pos(ref_point), target_image_pos(target_point) {}
+};
+
+bool operator<(const SparseMatch& lhs,const SparseMatch& rhs);
+
+void weightedLeastSquaresAffineFit(short* labels, float* weights, int count, float lambda, SparseMatch* matches, Mat& dst);
+void generateHypothesis(short* labels, int count, RNG& rng, unsigned char* is_used, SparseMatch* matches, Mat& dst);
+void verifyHypothesis(short* labels, float* weights, int count, SparseMatch* matches, float eps, float lambda, Mat& hypothesis_transform, Mat& old_transform, float& old_weighted_num_inliers);
+
+struct node
+{
+    float dist;
+    short label;
+    node() {}
+    node(short l,float d): dist(d), label(l) {}
+};
+
+class EdgeAwareInterpolatorImpl : public EdgeAwareInterpolator
+{
+public:
+    static Ptr<EdgeAwareInterpolatorImpl> create();
+    void interpolate(InputArray from_image, InputArray from_points, InputArray to_image, InputArray to_points, OutputArray dense_flow);
+
+protected:
+    int w,h;
+    int match_num;
+
+    //internal buffers:
+    vector<node>* g;
+    Mat labels;
+    Mat NNlabels;
+    Mat NNdistances;
+
+    //tunable parameters:
+    float lambda;
+    int k;
+    float sigma;
+    bool use_post_proc;
+    float fgs_lambda;
+    float fgs_sigma;
+
+    // static parameters:
+    static const int distance_transform_num_iter   = 1;
+    static const int ransac_interpolation_num_iter = 1;
+    float regularization_coef;
+    static const int ransac_num_stripes = 4;
+    RNG rngs[ransac_num_stripes];
+
+    void init();
+    void preprocessData(Mat& src, vector<SparseMatch>& matches);
+    void computeGradientMagnitude(Mat& src, Mat& dst);
+    void geodesicDistanceTransform(Mat& distances, Mat& cost_map);
+    void buildGraph(Mat& distances, Mat& cost_map);
+    void ransacInterpolation(vector<SparseMatch>& matches, Mat& dst_dense_flow);
+
+protected:
+    struct GetKNNMatches_ParBody : public ParallelLoopBody
+    {
+        EdgeAwareInterpolatorImpl* inst;
+        int num_stripes;
+        int stripe_sz;
+
+        GetKNNMatches_ParBody(EdgeAwareInterpolatorImpl& _inst, int _num_stripes);
+        void operator () (const Range& range) const;
+    };
+
+    struct RansacInterpolation_ParBody : public ParallelLoopBody
+    {
+        EdgeAwareInterpolatorImpl* inst;
+        Mat* transforms;
+        float* weighted_inlier_nums;
+        float* eps;
+        SparseMatch* matches;
+        int num_stripes;
+        int stripe_sz;
+        int inc;
+
+        RansacInterpolation_ParBody(EdgeAwareInterpolatorImpl& _inst, Mat* _transforms, float* _weighted_inlier_nums, float* _eps, SparseMatch* _matches, int _num_stripes, int _inc);
+        void operator () (const Range& range) const;
+    };
+
+public:
+    void  setK(int _k) {k=_k;}
+    int   getK() {return k;}
+    void  setSigma(float _sigma) {sigma=_sigma;}
+    float getSigma() {return sigma;}
+    void  setLambda(float _lambda) {lambda=_lambda;}
+    float getLambda() {return lambda;}
+    void  setUsePostProcessing(bool _use_post_proc) {use_post_proc=_use_post_proc;}
+    bool  getUsePostProcessing() {return use_post_proc;}
+    void  setFGSLambda(float _lambda) {fgs_lambda=_lambda;}
+    float getFGSLambda() {return fgs_lambda;}
+    void  setFGSSigma(float _sigma) {fgs_sigma = _sigma;}
+    float getFGSSigma() {return fgs_sigma;}
+};
+
+void EdgeAwareInterpolatorImpl::init()
+{
+    lambda        = 999.0f;
+    k             = 128;
+    sigma         = 0.05f;
+    use_post_proc = true;
+    fgs_lambda    = 500.0f;
+    fgs_sigma     = 1.5f;
+    regularization_coef = 0.01f;
+}
+
+Ptr<EdgeAwareInterpolatorImpl> EdgeAwareInterpolatorImpl::create()
+{
+    EdgeAwareInterpolatorImpl *eai = new EdgeAwareInterpolatorImpl();
+    eai->init();
+    return Ptr<EdgeAwareInterpolatorImpl>(eai);
+}
+
+void EdgeAwareInterpolatorImpl::interpolate(InputArray from_image, InputArray from_points, InputArray, InputArray to_points, OutputArray dense_flow)
+{
+    CV_Assert( !from_image.empty() && (from_image.depth() == CV_8U) && (from_image.channels() == 3 || from_image.channels() == 1) );
+    CV_Assert( !from_points.empty() && from_points.isVector() &&
+               !to_points  .empty() && to_points  .isVector() &&
+               from_points.sameSize(to_points) );
+
+    w = from_image.cols();
+    h = from_image.rows();
+
+    vector<Point2f> from_vector = *(const vector<Point2f>*)from_points.getObj();
+    vector<Point2f> to_vector   = *(const vector<Point2f>*)to_points  .getObj();
+    vector<SparseMatch> matches_vector(from_vector.size());
+    for(unsigned int i=0;i<from_vector.size();i++)
+        matches_vector[i] = SparseMatch(from_vector[i],to_vector[i]);
+    sort(matches_vector.begin(),matches_vector.end());
+    match_num = (int)matches_vector.size();
+    CV_Assert(match_num<SHRT_MAX);
+
+    Mat src = from_image.getMat();
+    labels = Mat(h,w,CV_16S);
+    labels = Scalar(-1);
+    NNlabels = Mat(match_num,k,CV_16S);
+    NNlabels = Scalar(-1);
+    NNdistances = Mat(match_num,k,CV_32F);
+    NNdistances = Scalar(0.0f);
+    g = new vector<node>[match_num];
+
+    preprocessData(src,matches_vector);
+
+    dense_flow.create(from_image.size(),CV_32FC2);
+    Mat dst = dense_flow.getMat();
+    ransacInterpolation(matches_vector,dst);
+    if(use_post_proc)
+        fastGlobalSmootherFilter(src,dst,dst,fgs_lambda,fgs_sigma);
+
+    delete[] g;
+}
+
+void EdgeAwareInterpolatorImpl::preprocessData(Mat& src, vector<SparseMatch>& matches)
+{
+    Mat distances(h,w,CV_32F);
+    Mat cost_map (h,w,CV_32F);
+    distances = Scalar(INF);
+
+    int x,y;
+    for(unsigned int i=0;i<matches.size();i++)
+    {
+        x = min((int)(matches[i].reference_image_pos.x+0.5f),w-1);
+        y = min((int)(matches[i].reference_image_pos.y+0.5f),h-1);
+
+        distances.at<float>(y,x) = 0.0f;
+        labels.at<short>(y,x) = (short)i;
+    }
+
+    computeGradientMagnitude(src,cost_map);
+    cost_map = (1000.0f-lambda) + lambda*cost_map;
+
+    geodesicDistanceTransform(distances,cost_map);
+    buildGraph(distances,cost_map);
+    parallel_for_(Range(0,getNumThreads()),GetKNNMatches_ParBody(*this,getNumThreads()));
+}
+
+void EdgeAwareInterpolatorImpl::computeGradientMagnitude(Mat& src, Mat& dst)
+{
+    Mat dx,dy;
+    Sobel(src, dx, CV_16S, 1, 0);
+    Sobel(src, dy, CV_16S, 0, 1);
+    float norm_coef = src.channels()*4.0f*255.0f;
+
+    if(src.channels()==1)
+    {
+        for(int i=0;i<h;i++)
+        {
+            short* dx_row  = dx.ptr<short>(i);
+            short* dy_row  = dy.ptr<short>(i);
+            float* dst_row = dst.ptr<float>(i);
+
+            for(int j=0;j<w;j++)
+                dst_row[j] = ((float)abs(dx_row[j])+abs(dy_row[j]))/norm_coef;
+        }
+    }
+    else
+    {
+        for(int i=0;i<h;i++)
+        {
+            Vec3s* dx_row  = dx.ptr<Vec3s>(i);
+            Vec3s* dy_row  = dy.ptr<Vec3s>(i);
+            float* dst_row = dst.ptr<float>(i);
+
+            for(int j=0;j<w;j++)
+                dst_row[j] = (float)(abs(dx_row[j][0])+abs(dy_row[j][0])+
+                                     abs(dx_row[j][1])+abs(dy_row[j][1])+
+                                     abs(dx_row[j][2])+abs(dy_row[j][2]))/norm_coef;
+        }
+    }
+}
+
+void EdgeAwareInterpolatorImpl::geodesicDistanceTransform(Mat& distances, Mat& cost_map)
+{
+    float c1 = 1.0f/2.0f;
+    float c2 = sqrt(2.0f)/2.0f;
+    float d = 0.0f;
+    int i,j;
+    float *dist_row,      *cost_row;
+    float *dist_row_prev, *cost_row_prev;
+    short *label_row;
+    short *label_row_prev;
+
+#define CHECK(cur_dist,cur_label,cur_cost,prev_dist,prev_label,prev_cost,coef)\
+{\
+    d = prev_dist + coef*(cur_cost+prev_cost);\
+    if(cur_dist>d){\
+        cur_dist=d;\
+        cur_label = prev_label;}\
+}
+
+    for(int it=0;it<distance_transform_num_iter;it++)
+    {
+        //first pass (left-to-right, top-to-bottom):
+        dist_row  = distances.ptr<float>(0);
+        label_row = labels.ptr<short>(0);
+        cost_row  = cost_map.ptr<float>(0);
+        for(j=1;j<w;j++)
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j-1],label_row[j-1],cost_row[j-1],c1);
+
+        for(i=1;i<h;i++)
+        {
+            dist_row       = distances.ptr<float>(i);
+            dist_row_prev  = distances.ptr<float>(i-1);
+
+            label_row      = labels.ptr<short>(i);
+            label_row_prev = labels.ptr<short>(i-1);
+
+            cost_row      = cost_map.ptr<float>(i);
+            cost_row_prev = cost_map.ptr<float>(i-1);
+
+            j=0;
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j+1],label_row_prev[j+1],cost_row_prev[j+1],c2);
+            j++;
+            for(;j<w-1;j++)
+            {
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j-1]     ,label_row[j-1]     ,cost_row[j-1]     ,c1);
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j-1],label_row_prev[j-1],cost_row_prev[j-1],c2);
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j+1],label_row_prev[j+1],cost_row_prev[j+1],c2);
+            }
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j-1]     ,label_row[j-1]     ,cost_row[j-1]     ,c1);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j-1],label_row_prev[j-1],cost_row_prev[j-1],c2);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+        }
+
+        //second pass (right-to-left, bottom-to-top):
+        dist_row  = distances.ptr<float>(h-1);
+        label_row = labels.ptr<short>(h-1);
+        cost_row  = cost_map.ptr<float>(h-1);
+        for(j=w-2;j>=0;j--)
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j+1],label_row[j+1],cost_row[j+1],c1);
+
+        for(i=h-2;i>=0;i--)
+        {
+            dist_row       = distances.ptr<float>(i);
+            dist_row_prev  = distances.ptr<float>(i+1);
+
+            label_row      = labels.ptr<short>(i);
+            label_row_prev = labels.ptr<short>(i+1);
+
+            cost_row      = cost_map.ptr<float>(i);
+            cost_row_prev = cost_map.ptr<float>(i+1);
+
+            j=w-1;
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j-1],label_row_prev[j-1],cost_row_prev[j-1],c2);
+            j--;
+            for(;j>0;j--)
+            {
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j+1]     ,label_row[j+1]     ,cost_row[j+1]     ,c1);
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j+1],label_row_prev[j+1],cost_row_prev[j+1],c2);
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+                CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j-1],label_row_prev[j-1],cost_row_prev[j-1],c2);
+            }
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j+1]     ,label_row[j+1]     ,cost_row[j+1]     ,c1);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j+1],label_row_prev[j+1],cost_row_prev[j+1],c2);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+        }
+    }
+#undef CHECK
+}
+
+void EdgeAwareInterpolatorImpl::buildGraph(Mat& distances, Mat& cost_map)
+{
+    float *dist_row,      *cost_row;
+    float *dist_row_prev, *cost_row_prev;
+    short *label_row;
+    short *label_row_prev;
+    int i,j;
+    const float c1 = 1.0f/2.0f;
+    const float c2 = sqrt(2.0f)/2.0f;
+    float d;
+    bool found;
+
+#define CHECK(cur_dist,cur_label,cur_cost,prev_dist,prev_label,prev_cost,coef)\
+    if(cur_label!=prev_label)\
+    {\
+        d = prev_dist + cur_dist + coef*(cur_cost+prev_cost);\
+        found = false;\
+        for(unsigned int n=0;n<g[prev_label].size();n++)\
+        {\
+            if(g[prev_label][n].label==cur_label)\
+            {\
+                g[prev_label][n].dist = min(g[prev_label][n].dist,d);\
+                found=true;\
+                break;\
+            }\
+        }\
+        if(!found)\
+            g[prev_label].push_back(node(cur_label ,d));\
+    }
+
+    dist_row  = distances.ptr<float>(0);
+    label_row = labels.ptr<short>(0);
+    cost_row  = cost_map.ptr<float>(0);
+    for(j=1;j<w;j++)
+        CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j-1],label_row[j-1],cost_row[j-1],c1);
+
+    for(i=1;i<h;i++)
+    {
+        dist_row       = distances.ptr<float>(i);
+        dist_row_prev  = distances.ptr<float>(i-1);
+
+        label_row      = labels.ptr<short>(i);
+        label_row_prev = labels.ptr<short>(i-1);
+
+        cost_row      = cost_map.ptr<float>(i);
+        cost_row_prev = cost_map.ptr<float>(i-1);
+
+        j=0;
+        CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+        CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j+1],label_row_prev[j+1],cost_row_prev[j+1],c2);
+        j++;
+        for(;j<w-1;j++)
+        {
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j-1]     ,label_row[j-1]     ,cost_row[j-1]     ,c1);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j-1],label_row_prev[j-1],cost_row_prev[j-1],c2);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+            CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j+1],label_row_prev[j+1],cost_row_prev[j+1],c2);
+        }
+        CHECK(dist_row[j],label_row[j],cost_row[j],dist_row[j-1]     ,label_row[j-1]     ,cost_row[j-1]     ,c1);
+        CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j-1],label_row_prev[j-1],cost_row_prev[j-1],c2);
+        CHECK(dist_row[j],label_row[j],cost_row[j],dist_row_prev[j]  ,label_row_prev[j]  ,cost_row_prev[j]  ,c1);
+    }
+#undef CHECK
+
+    // force equal distances in both directions:
+    node* neighbors;
+    for(i=0;i<match_num;i++)
+    {
+        if(g[i].empty())
+            continue;
+        neighbors = &g[i].front();
+        for(j=0;j<(int)g[i].size();j++)
+        {
+            found = false;
+
+            for(unsigned int n=0;n<g[neighbors[j].label].size();n++)
+            {
+                if(g[neighbors[j].label][n].label==i)
+                {
+                    neighbors[j].dist = g[neighbors[j].label][n].dist = min(neighbors[j].dist,g[neighbors[j].label][n].dist);
+                    found = true;
+                    break;
+                }
+            }
+
+            if(!found)
+                g[neighbors[j].label].push_back(node((short)i,neighbors[j].dist));
+        }
+    }
+}
+
+struct nodeHeap
+{
+    // start indexing from 1 (root)
+    // children: 2*i, 2*i+1
+    // parent: i>>1
+    node* heap;
+    short* heap_pos;
+    node tmp_node;
+    short size;
+    short num_labels;
+
+    nodeHeap(short _num_labels)
+    {
+        num_labels = _num_labels;
+        heap = new node[num_labels+1];
+        heap[0] = node(-1,-1.0f);
+        heap_pos = new short[num_labels];
+        memset(heap_pos,0,sizeof(short)*num_labels);
+        size=0;
+    }
+
+    ~nodeHeap()
+    {
+        delete[] heap;
+        delete[] heap_pos;
+    }
+
+    void clear()
+    {
+        size=0;
+        memset(heap_pos,0,sizeof(short)*num_labels);
+    }
+
+    inline bool empty()
+    {
+        return (size==0);
+    }
+
+    inline void nodeSwap(short idx1, short idx2)
+    {
+        heap_pos[heap[idx1].label] = idx2;
+        heap_pos[heap[idx2].label] = idx1;
+
+        tmp_node   = heap[idx1];
+        heap[idx1] = heap[idx2];
+        heap[idx2] = tmp_node;
+    }
+
+    void add(node n)
+    {
+        size++;
+        heap[size] = n;
+        heap_pos[n.label] = size;
+        short i = size;
+        short parent_i = i>>1;
+        while(heap[i].dist<heap[parent_i].dist)
+        {
+            nodeSwap(i,parent_i);
+            i=parent_i;
+            parent_i = i>>1;
+        }
+    }
+
+    node getMin()
+    {
+        node res = heap[1];
+        heap_pos[res.label] = 0;
+
+        short i=1;
+        short left,right;
+        while( (left=i<<1) < size )
+        {
+            right = left+1;
+            if(heap[left].dist<heap[right].dist)
+            {
+                heap[i] = heap[left];
+                heap_pos[heap[i].label] = i;
+                i = left;
+            }
+            else
+            {
+                heap[i] = heap[right];
+                heap_pos[heap[i].label] = i;
+                i = right;
+            }
+        }
+
+        if(i==size)
+        {
+            size--;
+            return res;
+        }
+
+        heap[i] = heap[size];
+        heap_pos[heap[i].label] = i;
+
+        short parent_i = i>>1;
+        while(heap[i].dist<heap[parent_i].dist)
+        {
+            nodeSwap(i,parent_i);
+            i=parent_i;
+            parent_i = i>>1;
+        }
+
+        size--;
+        return res;
+    }
+
+    //checks if node is already in the heap
+    //if not - add it
+    //if it is - update it with the min dist of the two
+    void updateNode(node n)
+    {
+        if(heap_pos[n.label])
+        {
+            short i = heap_pos[n.label];
+            heap[i].dist = min(heap[i].dist,n.dist);
+            short parent_i = i>>1;
+            while(heap[i].dist<heap[parent_i].dist)
+            {
+                nodeSwap(i,parent_i);
+                i=parent_i;
+                parent_i = i>>1;
+            }
+        }
+        else
+            add(n);
+    }
+};
+
+EdgeAwareInterpolatorImpl::GetKNNMatches_ParBody::GetKNNMatches_ParBody(EdgeAwareInterpolatorImpl& _inst, int _num_stripes):
+inst(&_inst),num_stripes(_num_stripes)
+{
+    stripe_sz = (int)ceil(inst->match_num/(double)num_stripes);
+}
+
+void EdgeAwareInterpolatorImpl::GetKNNMatches_ParBody::operator() (const Range& range) const
+{
+    int start = std::min(range.start * stripe_sz, inst->match_num);
+    int end   = std::min(range.end   * stripe_sz, inst->match_num);
+    nodeHeap q((short)inst->match_num);
+    int num_expanded_vertices;
+    unsigned char* expanded_flag = new unsigned char[inst->match_num];
+    node* neighbors;
+
+    for(int i=start;i<end;i++)
+    {
+        if(inst->g[i].empty())
+            continue;
+
+        num_expanded_vertices = 0;
+        memset(expanded_flag,0,inst->match_num);
+        q.clear();
+        q.add(node((short)i,0.0f));
+        short* NNlabels_row    = inst->NNlabels.ptr<short>(i);
+        float* NNdistances_row = inst->NNdistances.ptr<float>(i);
+        while(num_expanded_vertices<inst->k && !q.empty())
+        {
+            node vert_for_expansion = q.getMin();
+            expanded_flag[vert_for_expansion.label] = 1;
+
+            //write the expanded vertex to the dst:
+            NNlabels_row[num_expanded_vertices] = vert_for_expansion.label;
+            NNdistances_row[num_expanded_vertices] = vert_for_expansion.dist;
+            num_expanded_vertices++;
+
+            //update the heap:
+            neighbors = &inst->g[vert_for_expansion.label].front();
+            for(int j=0;j<(int)inst->g[vert_for_expansion.label].size();j++)
+            {
+                if(!expanded_flag[neighbors[j].label])
+                    q.updateNode(node(neighbors[j].label,vert_for_expansion.dist+neighbors[j].dist));
+            }
+        }
+    }
+    delete[] expanded_flag;
+}
+
+void weightedLeastSquaresAffineFit(short* labels, float* weights, int count, float lambda, SparseMatch* matches, Mat& dst)
+{
+    double sa[6][6]={{0.}}, sb[6]={0.};
+    Mat A (6, 6, CV_64F, &sa[0][0]),
+        B (6, 1, CV_64F, sb),
+        MM(1, 6, CV_64F);
+    Point2f a,b;
+    float w;
+
+    for( int i = 0; i < count; i++ )
+    {
+        a = matches[labels[i]].reference_image_pos;
+        b = matches[labels[i]].target_image_pos;
+        w = weights[i];
+
+        sa[0][0] += w*a.x*a.x;
+        sa[0][1] += w*a.y*a.x;
+        sa[0][2] += w*a.x;
+        sa[1][1] += w*a.y*a.y;
+        sa[1][2] += w*a.y;
+        sa[2][2] += w;
+
+        sb[0] += w*a.x*b.x;
+        sb[1] += w*a.y*b.x;
+        sb[2] += w*b.x;
+        sb[3] += w*a.x*b.y;
+        sb[4] += w*a.y*b.y;
+        sb[5] += w*b.y;
+    }
+    sa[0][0] += lambda;
+    sa[1][1] += lambda;
+
+    sa[3][4] = sa[4][3] = sa[1][0] = sa[0][1];
+    sa[3][5] = sa[5][3] = sa[2][0] = sa[0][2];
+    sa[4][5] = sa[5][4] = sa[2][1] = sa[1][2];
+
+    sa[3][3] = sa[0][0];
+    sa[4][4] = sa[1][1];
+    sa[5][5] = sa[2][2];
+
+    sb[0] += lambda;
+    sb[4] += lambda;
+
+    solve(A, B, MM, DECOMP_EIG);
+    MM.reshape(2,3).convertTo(dst,CV_32F);
+}
+
+void generateHypothesis(short* labels, int count, RNG& rng, unsigned char* is_used, SparseMatch* matches, Mat& dst)
+{
+    int idx;
+    Point2f src_points[3];
+    Point2f dst_points[3];
+    memset(is_used,0,count);
+
+    // randomly get 3 distinct matches
+    idx = rng.uniform(0,count-2);
+    is_used[idx] = true;
+    src_points[0] = matches[labels[idx]].reference_image_pos;
+    dst_points[0] = matches[labels[idx]].target_image_pos;
+
+    idx = rng.uniform(0,count-1);
+    if (is_used[idx])
+        idx = count-2;
+    is_used[idx] = true;
+    src_points[1] = matches[labels[idx]].reference_image_pos;
+    dst_points[1] = matches[labels[idx]].target_image_pos;
+
+    idx = rng.uniform(0,count);
+    if (is_used[idx])
+        idx = count-1;
+    is_used[idx] = true;
+    src_points[2] = matches[labels[idx]].reference_image_pos;
+    dst_points[2] = matches[labels[idx]].target_image_pos;
+
+    // compute an affine transform:
+    getAffineTransform(src_points,dst_points).convertTo(dst,CV_32F);
+}
+
+void verifyHypothesis(short* labels, float* weights, int count, SparseMatch* matches, float eps, float lambda, Mat& hypothesis_transform, Mat& old_transform, float& old_weighted_num_inliers)
+{
+    float* tr = hypothesis_transform.ptr<float>(0);
+    Point2f a,b;
+    float weighted_num_inliers = -lambda*((tr[0]-1)*(tr[0]-1)+tr[1]*tr[1]+tr[3]*tr[3]+(tr[4]-1)*(tr[4]-1));
+
+    for(int i=0;i<count;i++)
+    {
+        a = matches[labels[i]].reference_image_pos;
+        b = matches[labels[i]].target_image_pos;
+        if(abs(tr[0]*a.x + tr[1]*a.y + tr[2] - b.x) +
+           abs(tr[3]*a.x + tr[4]*a.y + tr[5] - b.y) < eps)
+            weighted_num_inliers += weights[i];
+    }
+
+    if(weighted_num_inliers>=old_weighted_num_inliers)
+    {
+        old_weighted_num_inliers = weighted_num_inliers;
+        hypothesis_transform.copyTo(old_transform);
+    }
+}
+
+EdgeAwareInterpolatorImpl::RansacInterpolation_ParBody::RansacInterpolation_ParBody(EdgeAwareInterpolatorImpl& _inst, Mat* _transforms, float* _weighted_inlier_nums, float* _eps, SparseMatch* _matches, int _num_stripes, int _inc):
+inst(&_inst), transforms(_transforms), weighted_inlier_nums(_weighted_inlier_nums), eps(_eps), matches(_matches), num_stripes(_num_stripes), inc(_inc)
+{
+    stripe_sz = (int)ceil(inst->match_num/(double)num_stripes);
+}
+
+void EdgeAwareInterpolatorImpl::RansacInterpolation_ParBody::operator() (const Range& range) const
+{
+    if(range.end>range.start+1)
+    {
+        for(int n=range.start;n<range.end;n++)
+            (*this)(Range(n,n+1));
+        return;
+    }
+
+    int start = std::min(range.start * stripe_sz, inst->match_num);
+    int end   = std::min(range.end   * stripe_sz, inst->match_num);
+    if(inc<0)
+    {
+        int tmp = end;
+        end = start-1;
+        start = tmp-1;
+    }
+
+    short* KNNlabels;
+    float* KNNdistances;
+    unsigned char* is_used = new unsigned char[inst->k];
+    Mat hypothesis_transform;
+
+    short* inlier_labels    = new short[inst->k];
+    float* inlier_distances = new float[inst->k];
+    float* tr;
+    int num_inliers;
+    Point2f a,b;
+
+    for(int i=start;i!=end;i+=inc)
+    {
+        if(inst->g[i].empty())
+            continue;
+
+        KNNlabels    = inst->NNlabels.ptr<short>(i);
+        KNNdistances = inst->NNdistances.ptr<float>(i);
+        if(inc>0) //forward pass
+        {
+            hal::exp(KNNdistances,KNNdistances,inst->k);
+
+            Point2f average = Point2f(0.0f,0.0f);
+            for(int j=0;j<inst->k;j++)
+                average += matches[KNNlabels[j]].target_image_pos - matches[KNNlabels[j]].reference_image_pos;
+            average/=inst->k;
+            float average_dist = 0.0;
+            Point2f vec;
+            for(int j=0;j<inst->k;j++)
+            {
+                vec = (matches[KNNlabels[j]].target_image_pos - matches[KNNlabels[j]].reference_image_pos);
+                average_dist += abs(vec.x-average.x) + abs(vec.y-average.y);
+            }
+            eps[i] = min(0.5f*(average_dist/inst->k),2.0f);
+        }
+
+        for(int it=0;it<inst->ransac_interpolation_num_iter;it++)
+        {
+            generateHypothesis(KNNlabels,inst->k,inst->rngs[range.start],is_used,matches,hypothesis_transform);
+            verifyHypothesis(KNNlabels,KNNdistances,inst->k,matches,eps[i],inst->regularization_coef,hypothesis_transform,transforms[i],weighted_inlier_nums[i]);
+        }
+
+        //propagate hypotheses from neighbors:
+        node* neighbors = &inst->g[i].front();
+        for(int j=0;j<(int)inst->g[i].size();j++)
+        {
+            if((inc*neighbors[j].label)<(inc*i) && (inc*neighbors[j].label)>=(inc*start)) //already processed this neighbor
+                verifyHypothesis(KNNlabels,KNNdistances,inst->k,matches,eps[i],inst->regularization_coef,transforms[neighbors[j].label],transforms[i],weighted_inlier_nums[i]);
+        }
+
+        if(inc<0) //backward pass
+        {
+            // determine inliers and compute a least squares fit:
+            tr = transforms[i].ptr<float>(0);
+            num_inliers = 0;
+
+            for(int j=0;j<inst->k;j++)
+            {
+                a = matches[KNNlabels[j]].reference_image_pos;
+                b = matches[KNNlabels[j]].target_image_pos;
+                if(abs(tr[0]*a.x + tr[1]*a.y + tr[2] - b.x) +
+                   abs(tr[3]*a.x + tr[4]*a.y + tr[5] - b.y) < eps[i])
+                {
+                    inlier_labels[num_inliers]    = KNNlabels[j];
+                    inlier_distances[num_inliers] = KNNdistances[j];
+                    num_inliers++;
+                }
+            }
+
+            weightedLeastSquaresAffineFit(inlier_labels,inlier_distances,num_inliers,inst->regularization_coef,matches,transforms[i]);
+        }
+    }
+
+    delete[] inlier_labels;
+    delete[] inlier_distances;
+    delete[] is_used;
+}
+
+void EdgeAwareInterpolatorImpl::ransacInterpolation(vector<SparseMatch>& matches, Mat& dst_dense_flow)
+{
+    NNdistances *= (-sigma*sigma);
+
+    Mat* transforms = new Mat[match_num];
+    float* weighted_inlier_nums = new float[match_num];
+    float* eps = new float[match_num];
+    for(int i=0;i<match_num;i++)
+        weighted_inlier_nums[i] = -1E+10F;
+
+    for(int i=0;i<ransac_num_stripes;i++)
+        rngs[i] = RNG(0);
+
+    //forward pass:
+    parallel_for_(Range(0,ransac_num_stripes),RansacInterpolation_ParBody(*this,transforms,weighted_inlier_nums,eps,&matches.front(),ransac_num_stripes,1));
+    //backward pass:
+    parallel_for_(Range(0,ransac_num_stripes),RansacInterpolation_ParBody(*this,transforms,weighted_inlier_nums,eps,&matches.front(),ransac_num_stripes,-1));
+
+    //construct the final piecewise-affine interpolation:
+    short* label_row;
+    float* tr;
+    for(int i=0;i<h;i++)
+    {
+        label_row = labels.ptr<short>(i);
+        Point2f* dst_row = dst_dense_flow.ptr<Point2f>(i);
+        for(int j=0;j<w;j++)
+        {
+            tr = transforms[label_row[j]].ptr<float>(0);
+            dst_row[j] = Point2f(tr[0]*j+tr[1]*i+tr[2],tr[3]*j+tr[4]*i+tr[5]) - Point2f((float)j,(float)i);
+        }
+    }
+
+    delete[] transforms;
+    delete[] weighted_inlier_nums;
+    delete[] eps;
+}
+
+CV_EXPORTS_W
+Ptr<EdgeAwareInterpolator> createEdgeAwareInterpolator()
+{
+    return Ptr<EdgeAwareInterpolator>(EdgeAwareInterpolatorImpl::create());
+}
+
+bool operator<(const SparseMatch& lhs,const SparseMatch& rhs)
+{
+    if((int)(lhs.reference_image_pos.y+0.5f)!=(int)(rhs.reference_image_pos.y+0.5f))
+        return (lhs.reference_image_pos.y<rhs.reference_image_pos.y);
+    else
+        return (lhs.reference_image_pos.x<rhs.reference_image_pos.x);
+}
+
+}
+}
\ No newline at end of file

modules/ximgproc/test/test_sparse_match_interpolator.cpp

+/*
+ *  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)
+ *
+ *  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.
+ */
+
+#include "test_precomp.hpp"
+#include "opencv2/ximgproc/sparse_match_interpolator.hpp"
+#include <fstream>
+
+namespace cvtest
+{
+
+using namespace std;
+using namespace std::tr1;
+using namespace testing;
+using namespace perf;
+using namespace cv;
+using namespace cv::ximgproc;
+
+static string getDataDir()
+{
+    return cvtest::TS::ptr()->get_data_path();
+}
+
+const float FLOW_TAG_FLOAT = 202021.25f;
+Mat readOpticalFlow( const String& path )
+{
+//    CV_Assert(sizeof(float) == 4);
+    //FIXME: ensure right sizes of int and float - here and in writeOpticalFlow()
+
+    Mat_<Point2f> flow;
+    ifstream file(path.c_str(), std::ios_base::binary);
+    if ( !file.good() )
+        return flow; // no file - return empty matrix
+
+    float tag;
+    file.read((char*) &tag, sizeof(float));
+    if ( tag != FLOW_TAG_FLOAT )
+        return flow;
+
+    int width, height;
+
+    file.read((char*) &width, 4);
+    file.read((char*) &height, 4);
+
+    flow.create(height, width);
+
+    for ( int i = 0; i < flow.rows; ++i )
+    {
+        for ( int j = 0; j < flow.cols; ++j )
+        {
+            Point2f u;
+            file.read((char*) &u.x, sizeof(float));
+            file.read((char*) &u.y, sizeof(float));
+            if ( !file.good() )
+            {
+                flow.release();
+                return flow;
+            }
+
+            flow(i, j) = u;
+        }
+    }
+    file.close();
+    return flow;
+}
+
+CV_ENUM(GuideTypes, CV_8UC1, CV_8UC3)
+typedef tuple<Size, GuideTypes> InterpolatorParams;
+typedef TestWithParam<InterpolatorParams> InterpolatorTest;
+
+TEST(InterpolatorTest, ReferenceAccuracy)
+{
+    double MAX_DIF = 1.0;
+    double MAX_MEAN_DIF = 1.0 / 256.0;
+    string dir = getDataDir() + "cv/sparse_match_interpolator";
+
+    Mat src = imread(getDataDir() + "cv/optflow/RubberWhale1.png",IMREAD_COLOR);
+    ASSERT_FALSE(src.empty());
+
+    Mat ref_flow = readOpticalFlow(dir + "/RubberWhale_reference_result.flo");
+    ASSERT_FALSE(ref_flow.empty());
+
+    ifstream file((dir + "/RubberWhale_sparse_matches.txt").c_str());
+    float from_x,from_y,to_x,to_y;
+    vector<Point2f> from_points;
+    vector<Point2f> to_points;
+
+    while(file >> from_x >> from_y >> to_x >> to_y)
+    {
+        from_points.push_back(Point2f(from_x,from_y));
+        to_points.push_back(Point2f(to_x,to_y));
+    }
+
+    cv::setNumThreads(cv::getNumberOfCPUs());
+    Mat res_flow;
+
+    Ptr<EdgeAwareInterpolator> interpolator = createEdgeAwareInterpolator();
+    interpolator->setK(128);
+    interpolator->setSigma(0.05f);
+    interpolator->setUsePostProcessing(true);
+    interpolator->setFGSLambda(500.0f);
+    interpolator->setFGSSigma(1.5f);
+    interpolator->interpolate(src,from_points,Mat(),to_points,res_flow);
+
+    EXPECT_LE(cv::norm(res_flow, ref_flow, NORM_INF), MAX_DIF);
+    EXPECT_LE(cv::norm(res_flow, ref_flow, NORM_L1) , MAX_MEAN_DIF*res_flow.total());
+}
+
+TEST_P(InterpolatorTest, MultiThreadReproducibility)
+{
+    if (cv::getNumberOfCPUs() == 1)
+        return;
+
+    double MAX_DIF = 1.0;
+    double MAX_MEAN_DIF = 1.0 / 256.0;
+    int loopsCount = 2;
+    RNG rng(0);
+
+    InterpolatorParams params = GetParam();
+    Size size       = get<0>(params);
+    int guideType   = get<1>(params);
+
+    Mat from(size, guideType);
+    randu(from, 0, 255);
+
+    int num_matches = rng.uniform(5,SHRT_MAX-1);
+    vector<Point2f> from_points;
+    vector<Point2f> to_points;
+
+    for(int i=0;i<num_matches;i++)
+    {
+        from_points.push_back(Point2f(rng.uniform(0.01f,(float)size.width-1.01f),rng.uniform(0.01f,(float)size.height-1.01f)));
+        to_points.push_back(Point2f(rng.uniform(0.01f,(float)size.width-1.01f),rng.uniform(0.01f,(float)size.height-1.01f)));
+    }
+
+    for (int iter = 0; iter <= loopsCount; iter++)
+    {
+        int K = rng.uniform(4,512);
+        float sigma = rng.uniform(0.01f,0.5f);
+        float FGSlambda = rng.uniform(100.0f, 10000.0f);
+        float FGSsigma  = rng.uniform(0.5f, 100.0f);
+
+        Ptr<EdgeAwareInterpolator> interpolator = createEdgeAwareInterpolator();
+        interpolator->setK(K);
+        interpolator->setSigma(sigma);
+        interpolator->setUsePostProcessing(true);
+        interpolator->setFGSLambda(FGSlambda);
+        interpolator->setFGSSigma(FGSsigma);
+
+        cv::setNumThreads(cv::getNumberOfCPUs());
+        Mat resMultiThread;
+        interpolator->interpolate(from,from_points,Mat(),to_points,resMultiThread);
+
+        cv::setNumThreads(1);
+        Mat resSingleThread;
+        interpolator->interpolate(from,from_points,Mat(),to_points,resSingleThread);
+
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_INF), MAX_DIF);
+        EXPECT_LE(cv::norm(resSingleThread, resMultiThread, NORM_L1) , MAX_MEAN_DIF*resMultiThread.total());
+    }
+}
+INSTANTIATE_TEST_CASE_P(FullSet,InterpolatorTest, Combine(Values(szODD,szVGA), GuideTypes::all()));
+}
\ No newline at end of file