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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/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
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