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move training to softcascade module

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rename Octave -> SoftCascadeOctave
This commit is contained in:
marina.kolpakova
2013-01-29 13:32:21 +04:00
parent 61441a1014
commit 716a9ccb71
5 changed files with 90 additions and 89 deletions
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74
modules/ml/include/opencv2/ml/ml.hpp
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@@ -2131,80 +2131,6 @@ typedef CvGBTrees GradientBoostingTrees;
template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
CV_EXPORTS bool initModule_ml(void);
class CV_EXPORTS FeaturePool
{
public:
virtual int size() const = 0;
virtual float apply(int fi, int si, const Mat& integrals) const = 0;
virtual void write( cv::FileStorage& fs, int index) const = 0;
virtual void preprocess(InputArray frame, OutputArray integrals) const = 0;
virtual ~FeaturePool();
};
class CV_EXPORTS Dataset
{
public:
typedef enum {POSITIVE = 1, NEGATIVE = 2} SampleType;
virtual cv::Mat get(SampleType type, int idx) const = 0;
virtual int available(SampleType type) const = 0;
virtual ~Dataset();
};
// used for traning single octave scale
class CV_EXPORTS Octave : public cv::Boost
{
public:
enum
{
// Direct backward pruning. (Cha Zhang and Paul Viola)
DBP = 1,
// Multiple instance pruning. (Cha Zhang and Paul Viola)
MIP = 2,
// Originally proposed by L. bourdev and J. brandt
HEURISTIC = 4
};
Octave(cv::Rect boundingBox, int npositives, int nnegatives, int logScale, int shrinkage);
virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth);
virtual void setRejectThresholds(OutputArray thresholds);
virtual void write( CvFileStorage* fs, string name) const;
virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const;
virtual float predict( InputArray _sample, InputArray _votes, bool raw_mode, bool return_sum ) const;
virtual ~Octave();
protected:
virtual bool train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(), const cv::Mat& missingDataMask=cv::Mat());
void processPositives(const Dataset* dataset, const FeaturePool* pool);
void generateNegatives(const Dataset* dataset, const FeaturePool* pool);
float predict( const Mat& _sample, const cv::Range range) const;
private:
void traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nfeatures, int* used, const double* th) const;
virtual void initial_weights(double (&p)[2]);
int logScale;
cv::Rect boundingBox;
int npositives;
int nnegatives;
int shrinkage;
Mat integrals;
Mat responses;
CvBoostParams params;
Mat trainData;
};
}
#endif // __cplusplus