Added new method for training forest

modules/optflow/include/opencv2/optflow/sparse_matching_gpc.hpp

@@ -71,6 +71,27 @@ struct CV_EXPORTS_W GPCPatchDescriptor
 typedef std::pair< GPCPatchDescriptor, GPCPatchDescriptor > GPCPatchSample;
 typedef std::vector< GPCPatchSample > GPCSamplesVector;
 
+/** @brief Class encapsulating training samples.
+ */
+class CV_EXPORTS_W GPCTrainingSamples
+{
+private:
+  GPCSamplesVector samples;
+
+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 > &gt );
+
+  size_t size() const { return samples.size(); }
+
+  operator GPCSamplesVector() const { return samples; }
+
+  operator GPCSamplesVector &() { return samples; }
+};
+
 class CV_EXPORTS_W GPCTree : public Algorithm
 {
 public:
@@ -109,12 +130,27 @@ private:
   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( GPCSamplesVector &samples )
   {
     for ( int i = 0; i < T; ++i )
       tree[i].train( samples );
   }
 
+  /** @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 > &gt )
+  {
+    for ( int i = 0; i < T; ++i )
+    {
+      Ptr< GPCTrainingSamples > samples = GPCTrainingSamples::create( imagesFrom, imagesTo, gt ); // Create training set for the tree
+      tree[i].train( *samples );
+    }
+  }
+
   void write( FileStorage &fs ) const
   {
     fs << "ntrees" << T << "trees"
@@ -138,27 +174,6 @@ public:
 
   static Ptr< GPCForest > create() { return makePtr< GPCForest >(); }
 };
-
-/** @brief Class encapsulating training samples.
- */
-class CV_EXPORTS_W GPCTrainingSamples
-{
-private:
-  GPCSamplesVector samples;
-
-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> &gt );
-
-  size_t size() const { return samples.size(); }
-
-  operator GPCSamplesVector() const { return samples; }
-
-  operator GPCSamplesVector &() { return samples; }
-};
 }
 
 CV_EXPORTS void write( FileStorage &fs, const String &name, const optflow::GPCTree::Node &node );

modules/optflow/samples/gpc_train.cpp

@@ -23,12 +23,8 @@ int main( int argc, const char **argv )
     gt.push_back( argv[1 + i * 3 + 2] );
   }
 
-  cv::Ptr<cv::optflow::GPCTrainingSamples> ts = cv::optflow::GPCTrainingSamples::create( img1, img2, gt );
-
-  std::cout << "Got " << ts->size() << " samples." << std::endl;
-
   cv::Ptr< cv::optflow::GPCForest< nTrees > > forest = cv::optflow::GPCForest< nTrees >::create();
-  forest->train( *ts );
+  forest->train( img1, img2, gt );
   forest->save( "forest.dump" );
 
   return 0;

modules/optflow/src/sparse_matching_gpc.cpp

@@ -116,7 +116,8 @@ void getTrainingSamples( const Mat &from, const Mat &to, const Mat &gt, GPCSampl
     for ( int j = patchRadius; j + patchRadius < sz.width; ++j )
       mag.push_back( Magnitude( normL2Sqr( gt.at< Vec2f >( i, j ) ), i, j ) );
 
-  size_t n = mag.size() * thresholdMagnitudeFrac;
+  size_t n = mag.size() * thresholdMagnitudeFrac; // As suggested in the paper, we discard part of the training samples
+                                                  // with a small displacement and train to better distinguish hard pairs.
   std::nth_element( mag.begin(), mag.begin() + n, mag.end() );
   mag.resize( n );
   std::random_shuffle( mag.begin(), mag.end() );