darknet_2_5_python/include/opencv2/optflow/sparse_matching_gpc.hpp

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/**
 * @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/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 > &gt, 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 &params )
      : 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 &params ) : 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 CV_OVERRIDE;

  void read( const FileNode &fn ) CV_OVERRIDE;

  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 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 CV_OVERRIDE
    {
      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 > &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 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 CV_OVERRIDE
  {
    fs << "ntrees" << T << "trees"
       << "[";
    for ( int i = 0; i < T; ++i )
    {
      fs << "{";
      tree[i].write( fs );
      fs << "}";
    }
    fs << "]";
  }

  void read( const FileNode &fn ) CV_OVERRIDE
  {
    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