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Code Issues Releases Wiki Activity

Added new method for training forest

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This commit is contained in:
Vladislav Samsonov
2016-07-25 03:22:31 +03:00
parent 7f93d951d3
commit 17831add02
3 changed files with 79 additions and 67 deletions
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73
modules/optflow/include/opencv2/optflow/sparse_matching_gpc.hpp
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@@ -63,20 +63,41 @@ namespace optflow
struct CV_EXPORTS_W GPCPatchDescriptor
{
static const unsigned nFeatures = 18; // number of features in a patch descriptor
Vec<double, nFeatures> feature;
Vec< double, nFeatures > feature;
GPCPatchDescriptor( const Mat *imgCh, int i, int j );
};
typedef std::pair<GPCPatchDescriptor, GPCPatchDescriptor> GPCPatchSample;
typedef std::vector<GPCPatchSample> GPCSamplesVector;
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:
struct Node
{
Vec<double, GPCPatchDescriptor::nFeatures> coef; // hyperplane coefficients
Vec< double, GPCPatchDescriptor::nFeatures > coef; // hyperplane coefficients
double rhs;
unsigned left;
unsigned right;
@@ -87,7 +108,7 @@ public:
private:
typedef GPCSamplesVector::iterator SIter;
std::vector<Node> nodes;
std::vector< Node > nodes;
bool trainNode( size_t nodeId, SIter begin, SIter end, unsigned depth );
@@ -98,23 +119,38 @@ public:
void read( const FileNode &fn );
static Ptr<GPCTree> create() { return makePtr<GPCTree>(); }
static Ptr< GPCTree > create() { return makePtr< GPCTree >(); }
bool operator==( const GPCTree &t ) const { return nodes == t.nodes; }
};
template <int T> class CV_EXPORTS_W GPCForest : public Algorithm
template < int T > class CV_EXPORTS_W GPCForest : public Algorithm
{
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"
@@ -136,28 +172,7 @@ public:
tree[i].read( *it );
}
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; }
static Ptr< GPCForest > create() { return makePtr< GPCForest >(); }
};
}