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split public interface and realization for SoftCascadeOctave

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This commit is contained in:
marina.kolpakova
2013-01-29 17:44:21 +04:00
parent f3227c3f1a
commit 4ba8b53152
5 changed files with 92 additions and 55 deletions
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modules/softcascade/include/opencv2/softcascade/softcascade.hpp
+9 -36
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@@ -44,7 +44,6 @@
#define __OPENCV_SOFTCASCADE_HPP__
#include "opencv2/core/core.hpp"
#include "opencv2/ml/ml.hpp"
namespace cv {
@@ -90,7 +89,7 @@ public:
// ========================================================================== //
// Implementation of Integral Channel Feature.
// Public Interface for Integral Channel Feature.
// ========================================================================== //
class CV_EXPORTS_W ChannelFeatureBuilder : public Algorithm
@@ -155,12 +154,11 @@ private:
};
// ========================================================================== //
// Implementation of singe soft (stageless) cascade octave training.
// Public Interface for singe soft (stageless) cascade octave training.
// ========================================================================== //
class CV_EXPORTS SoftCascadeOctave : public cv::Boost
class CV_EXPORTS SoftCascadeOctave : public Algorithm
{
public:
enum
{
// Direct backward pruning. (Cha Zhang and Paul Viola)
@@ -171,39 +169,14 @@ public:
HEURISTIC = 4
};
SoftCascadeOctave(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 ~SoftCascadeOctave();
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());
static cv::Ptr<SoftCascadeOctave> create(cv::Rect boundingBox, int npositives, int nnegatives,
int logScale, int shrinkage);
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;
virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth) = 0;
virtual void setRejectThresholds(OutputArray thresholds) = 0;
virtual void write( cv::FileStorage &fs, const FeaturePool* pool, InputArray thresholds) const = 0;
virtual void write( CvFileStorage* fs, string name) const = 0;
};
CV_EXPORTS bool initModule_softcascade(void);