created abstract FeaturePool class

diff --git a/apps/sft/include/sft/octave.hpp b/apps/sft/include/sft/octave.hpp
index 0a5fc17..1ab5e40 100644 (file)
--- a/apps/sft/include/sft/octave.hpp
+++ b/apps/sft/include/sft/octave.hpp
@@ -102,28 +102,34 @@ private:
 void write(cv::FileStorage& fs, const string&, const ICF& f);
 std::ostream& operator<<(std::ostream& out, const ICF& m);
 
-class FeaturePool
+class ICFFeaturePool : public cv::FeaturePool
 {
 public:
-    FeaturePool(cv::Size model, int nfeatures);
+    ICFFeaturePool(cv::Size model, int nfeatures);
 
-    int size() const { return (int)pool.size(); }
-    float apply(int fi, int si, const Mat& integrals) const;
-    void write( cv::FileStorage& fs, int index) const;
+    virtual int size() const { return (int)pool.size(); }
+    virtual float apply(int fi, int si, const Mat& integrals) const;
+    virtual void write( cv::FileStorage& fs, int index) const;
+
+    virtual ~ICFFeaturePool();
 
 private:
+
     void fill(int desired);
 
     cv::Size model;
     int nfeatures;
 
-    Icfvector pool;
+    std::vector<ICF> pool;
 
     static const unsigned int seed = 0;
 
     enum { N_CHANNELS = 10 };
 };
 
+
+using cv::FeaturePool;
+
 // used for traning single octave scale
 class Octave : cv::Boost
 {
@@ -142,12 +148,13 @@ public:
     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 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;
+    virtual void write( cv::FileStorage &fs, const FeaturePool* pool, const Mat& thresholds) const;
 
     int logScale;
 
@@ -155,7 +162,7 @@ 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 processPositives(const Dataset& dataset, const FeaturePool* pool);
     void generateNegatives(const Dataset& dataset);
 
     float predict( const Mat& _sample, const cv::Range range) const;

diff --git a/apps/sft/octave.cpp b/apps/sft/octave.cpp
index 14f3016..693b7b9 100644 (file)
--- a/apps/sft/octave.cpp
+++ b/apps/sft/octave.cpp
@@ -197,14 +197,14 @@ public:
 };
 }
 
-void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& pool)
+void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool* pool)
 {
     Preprocessor prepocessor(shrinkage);
 
     int w = boundingBox.width;
     int h = boundingBox.height;
 
-    integrals.create(pool.size(), (w / shrinkage + 1) * (h / shrinkage * 10 + 1), CV_32SC1);
+    integrals.create(pool->size(), (w / shrinkage + 1) * (h / shrinkage * 10 + 1), CV_32SC1);
 
     int total = 0;
     for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it)
@@ -338,7 +338,7 @@ void sft::Octave::traverse(const CvBoostTree* tree, cv::FileStorage& fs, int& nf
     fs << "}";
 }
 
-void sft::Octave::write( cv::FileStorage &fso, const FeaturePool& pool, const Mat& thresholds) const
+void sft::Octave::write( cv::FileStorage &fso, const FeaturePool* pool, const Mat& thresholds) const
 {
     CV_Assert(!thresholds.empty());
     cv::Mat used( 1, weak->total * (pow(2, params.max_depth) - 1), CV_32SC1);
@@ -364,7 +364,7 @@ void sft::Octave::write( cv::FileStorage &fso, const FeaturePool& pool, const Ma
 
     fso << "features" << "[";
     for (int i = 0; i < nfeatures; ++i)
-        pool.write(fso, usedPtr[i]);
+        pool->write(fso, usedPtr[i]);
     fso << "]"
         << "}";
 }
@@ -376,7 +376,7 @@ void sft::Octave::initial_weights(double (&p)[2])
     p[1] =  n / (2. * (double)(npositives));
 }
 
-bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int weaks, int treeDepth)
+bool sft::Octave::train(const Dataset& dataset, const FeaturePool* pool, int weaks, int treeDepth)
 {
     CV_Assert(treeDepth == 2);
     CV_Assert(weaks > 0);
@@ -389,7 +389,7 @@ bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int wea
     generateNegatives(dataset);
 
     // 2. only sumple case (all features used)
-    int nfeatures = pool.size();
+    int nfeatures = pool->size();
     cv::Mat varIdx(1, nfeatures, CV_32SC1);
     int* ptr = varIdx.ptr<int>(0);
 
@@ -417,7 +417,7 @@ bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int wea
         float* dptr = trainData.ptr<float>(fi);
         for (int si = 0; si < nsamples; ++si)
         {
-            dptr[si] = pool.apply(fi, si, integrals);
+            dptr[si] = pool->apply(fi, si, integrals);
         }
     }
 
@@ -448,18 +448,19 @@ void sft::Octave::write( CvFileStorage* fs, string name) const
 }
 
 // ========= FeaturePool ========= //
-sft::FeaturePool::FeaturePool(cv::Size m, int n) : model(m), nfeatures(n)
+
+sft::ICFFeaturePool::ICFFeaturePool(cv::Size m, int n) : FeaturePool(), model(m), nfeatures(n)
 {
     CV_Assert(m != cv::Size() && n > 0);
     fill(nfeatures);
 }
 
-float sft::FeaturePool::apply(int fi, int si, const Mat& integrals) const
+float sft::ICFFeaturePool::apply(int fi, int si, const Mat& integrals) const
 {
     return pool[fi](integrals.row(si), model);
 }
 
-void sft::FeaturePool::write( cv::FileStorage& fs, int index) const
+void sft::ICFFeaturePool::write( cv::FileStorage& fs, int index) const
 {
     CV_Assert((index > 0) && (index < (int)pool.size()));
     fs << pool[index];
@@ -470,8 +471,9 @@ void sft::write(cv::FileStorage& fs, const string&, const ICF& f)
     fs << "{" << "channel" << f.channel << "rect" << f.bb << "}";
 }
 
+sft::ICFFeaturePool::~ICFFeaturePool(){}
 
-void sft::FeaturePool::fill(int desired)
+void sft::ICFFeaturePool::fill(int desired)
 {
     int mw = model.width;
     int mh = model.height;

diff --git a/apps/sft/sft.cpp b/apps/sft/sft.cpp
index 9da4c04..8c40039 100644 (file)
--- a/apps/sft/sft.cpp
+++ b/apps/sft/sft.cpp
@@ -117,7 +117,7 @@ int main(int argc, char** argv)
         int nfeatures  = cfg.poolSize;
         cv::Size model = cfg.model(it);
         std::cout << "Model " << model << std::endl;
-        sft::FeaturePool pool(model, nfeatures);
+        sft::ICFFeaturePool pool(model, nfeatures);
         nfeatures = pool.size();
 
 
@@ -132,7 +132,7 @@ int main(int argc, char** argv)
         std::string path = cfg.trainPath;
         sft::Dataset 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);
@@ -142,7 +142,7 @@ int main(int argc, char** argv)
             cv::Mat thresholds;
             boost.setRejectThresholds(thresholds);
 
-            boost.write(fso, pool, thresholds);
+            boost.write(fso, &pool, thresholds);
 
             cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
             tfs << "thresholds" << thresholds;

diff --git a/modules/ml/include/opencv2/ml/ml.hpp b/modules/ml/include/opencv2/ml/ml.hpp
index e8e069b..775fab5 100644 (file)
--- a/modules/ml/include/opencv2/ml/ml.hpp
+++ b/modules/ml/include/opencv2/ml/ml.hpp
@@ -2132,6 +2132,17 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
 
 CV_EXPORTS bool initModule_ml(void);
 
+CV_EXPORTS class 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 ~FeaturePool() = 0;
+};
+
 }
 
 #endif // __cplusplus

diff --git a/modules/ml/src/octave.cpp b/modules/ml/src/octave.cpp
new file mode 100644 (file)
index 0000000..a7b7179
--- /dev/null
+++ b/modules/ml/src/octave.cpp
@@ -0,0 +1,45 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+cv::FeaturePool::~FeaturePool(){}
\ No newline at end of file