move soft cascade octave to ml module

apps/sft/include/sft/octave.hpp

@@ -155,6 +155,9 @@ private:
 void write(cv::FileStorage& fs, const std::string&, const ICF& f);
 std::ostream& operator<<(std::ostream& out, const ICF& m);
 
+using cv::FeaturePool;
+using cv::Dataset;
+
 class ICFFeaturePool : public cv::FeaturePool
 {
 public:
@@ -184,79 +187,20 @@ private:
 };
 
 
-using cv::FeaturePool;
-
 
-
-class Dataset
+class ScaledDataset : public Dataset
 {
 public:
-    typedef enum {POSITIVE = 1, NEGATIVE = 2} SampleType;
-    Dataset(const sft::string& path, const int octave);
+    ScaledDataset(const sft::string& path, const int octave);
 
-    cv::Mat get(SampleType type, int idx) const;
-    int available(SampleType type) const;
+    virtual cv::Mat get(SampleType type, int idx) const;
+    virtual int available(SampleType type) const;
+    virtual ~ScaledDataset();
 
 private:
     svector pos;
     svector neg;
 };
-
-// used for traning single octave scale
-class Octave : 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 ~Octave();
-
-    virtual bool train(const Dataset& dataset, const FeaturePool* pool, int weaks, int treeDepth);
-
-    virtual float predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const;
-    virtual void setRejectThresholds(cv::Mat& thresholds);
-    virtual void write( CvFileStorage* fs, string name) const;
-
-    virtual void write( cv::FileStorage &fs, const FeaturePool* pool, const Mat& thresholds) const;
-
-    int logScale;
-
-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]);
-
-    cv::Rect boundingBox;
-
-    int npositives;
-    int nnegatives;
-
-    int shrinkage;
-
-    Mat integrals;
-    Mat responses;
-
-    CvBoostParams params;
-
-    Mat trainData;
-};
-
 }
 
 #endif
\ No newline at end of file

apps/sft/sft.cpp

@@ -127,12 +127,12 @@ int main(int argc, char** argv)
         cv::Rect boundingBox = cfg.bbox(it);
         std::cout << "Object bounding box" << boundingBox << std::endl;
 
-        sft::Octave boost(boundingBox, npositives, nnegatives, *it, shrinkage);
+        cv::Octave boost(boundingBox, npositives, nnegatives, *it, shrinkage);
 
         std::string path = cfg.trainPath;
-        sft::Dataset dataset(path, boost.logScale);
+        sft::ScaledDataset dataset(path, boost.logScale);
 
-        if (boost.train(dataset, &pool, cfg.weaks, cfg.treeDepth))
+        if (boost.train(&dataset, &pool, cfg.weaks, cfg.treeDepth))
         {
             CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
             boost.write(fout, cfg.cascadeName);

modules/ml/include/opencv2/ml/ml.hpp

@@ -2142,7 +2142,72 @@ public:
 
     virtual void preprocess(const Mat& frame, Mat& integrals) const = 0;
 
-    virtual ~FeaturePool() = 0;
+    virtual ~FeaturePool();
+};
+
+class 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 Octave : 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 ~Octave();
+
+    virtual bool train(const Dataset* dataset, const FeaturePool* pool, int weaks, int treeDepth);
+
+    virtual float predict( const Mat& _sample, Mat& _votes, bool raw_mode, bool return_sum ) const;
+    virtual void setRejectThresholds(cv::Mat& thresholds);
+    virtual void write( CvFileStorage* fs, string name) const;
+
+    virtual void write( cv::FileStorage &fs, const FeaturePool* pool, const Mat& thresholds) const;
+
+    int logScale;
+
+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]);
+
+    cv::Rect boundingBox;
+
+    int npositives;
+    int nnegatives;
+
+    int shrinkage;
+
+    Mat integrals;
+    Mat responses;
+
+    CvBoostParams params;
+
+    Mat trainData;
 };
 
 }