feature pool generation:

    - use random from tr1 extension
    - extend cv::Boost

apps/sft/include/sft/common.hpp

@@ -48,6 +48,9 @@
 namespace sft
 {
     using cv::Mat;
+    struct ICF;
+    typedef std::vector<ICF>   Icfvector;
+
 }
 
 #endif
\ No newline at end of file

apps/sft/include/sft/octave.hpp

@@ -44,18 +44,63 @@
 #define __SFT_OCTAVE_HPP__
 
 #include <opencv2/ml/ml.hpp>
+#include <sft/common.hpp>
 
 namespace sft
 {
 
+struct ICF
+{
+    ICF(int x, int y, int w, int h, int ch) : bb(cv::Rect(x, y, w, h)), channel(ch) {}
+
+    bool operator ==(ICF b)
+    {
+        return bb == b.bb && channel == b.channel;
+    }
+
+    bool operator !=(ICF b)
+    {
+        return bb != b.bb || channel != b.channel;
+    }
+
+private:
+    cv::Rect bb;
+    int channel;
+};
+
+class FeaturePool
+{
+public:
+    FeaturePool(cv::Size model, int nfeatures);
+    ~FeaturePool();
+private:
+    void fill(int desired);
+
+    cv::Size model;
+    int nfeatures;
+
+    Mat integrals;
+    Mat responces;
+
+    Icfvector pool;
+
+    static const unsigned int seed = 0;
+
+    enum { N_CHANNELS = 10 };
+};
+
 // used for traning single octave scale
-class Octave : public cv::Boost
+class Octave : cv::Boost
 {
 public:
     Octave();
-    ~Octave();
+    virtual ~Octave();
+
+    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());
 
 private:
+    CvBoostParams params;
 };
 
 }

apps/sft/include/sft/random.hpp

+#ifndef __SFT_RANDOM_HPP__
+#define __SFT_RANDOM_HPP__
+
+#if defined(_MSC_VER) && _MSC_VER >= 1600
+
+# include <random>
+namespace sft {
+struct Random
+{
+    typedef std::mt19937 engine;
+    typedef std::uniform_int<int> uniform;
+};
+}
+
+#elif (__GNUC__) && __GNUC__ > 3 && __GNUC_MINOR__ > 1
+
+# if defined (__cplusplus) && __cplusplus > 201100L
+#  include <random>
+namespace sft {
+struct Random
+{
+    typedef std::mt19937 engine;
+    typedef std::uniform_int<int> uniform;
+};
+}
+# else
+#   include <tr1/random>
+
+namespace sft {
+struct Random
+{
+    typedef std::tr1::mt19937 engine;
+    typedef std::tr1::uniform_int<int> uniform;
+};
+}
+# endif
+
+#else
+#include <opencv2/core/core.hpp>
+namespace rnd {
+
+typedef cv::RNG engine;
+
+template<typename T>
+struct uniform_int
+{
+    uniform_int(const int _min, const int _max) : min(_min), max(_max) {}
+    T operator() (engine& eng, const int bound) const
+    {
+        return (T)eng.uniform(min, bound);
+    }
+
+    T operator() (engine& eng) const
+    {
+        return (T)eng.uniform(min, max);
+    }
+
+private:
+    int min;
+    int max;
+};
+
+}
+
+namespace sft {
+struct Random
+{
+    typedef rnd::engine engine;
+    typedef rnd::uniform_int<int> uniform;
+};
+}
+
+#endif
+
+#endif
\ No newline at end of file

apps/sft/octave.cpp

@@ -41,7 +41,84 @@
 //M*/
 
 #include <sft/octave.hpp>
+#include <sft/random.hpp>
 
+#if defined VISUALIZE_GENERATION
+# include <opencv2/highgui/highgui.hpp>
+# define show(a, b)  \
+    do {             \
+    cv::imshow(a,b); \
+    cv::waitkey(0);  \
+    } while(0)
+#else
+# define show(a, b)
+#endif
+
+// ============ Octave ============ //
 sft::Octave::Octave(){}
 
-sft::Octave::~Octave(){}
\ No newline at end of file
+sft::Octave::~Octave(){}
+
+bool sft::Octave::train( const cv::Mat& trainData, const cv::Mat& responses, const cv::Mat& varIdx,
+       const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
+{
+    bool update = false;
+    return cv::Boost::train(trainData, CV_COL_SAMPLE, responses, varIdx, sampleIdx, varType, missingDataMask, params,
+    update);
+}
+
+// ========= FeaturePool ========= //
+sft::FeaturePool::FeaturePool(cv::Size m, int n) : model(m), nfeatures(n)
+{
+    CV_Assert(m != cv::Size() && n > 0);
+    fill(nfeatures);
+}
+
+sft::FeaturePool::~FeaturePool(){}
+
+
+void sft::FeaturePool::fill(int desired)
+{
+
+    int mw = model.width;
+    int mh = model.height;
+
+    int maxPoolSize = (mw -1) * mw / 2 * (mh - 1) * mh / 2 * N_CHANNELS;
+
+    nfeatures = std::min(desired, maxPoolSize);
+
+    pool.reserve(nfeatures);
+
+    sft::Random::engine eng(seed);
+    sft::Random::engine eng_ch(seed);
+
+    sft::Random::uniform chRand(0, N_CHANNELS - 1);
+
+    sft::Random::uniform xRand(0, model.width  - 2);
+    sft::Random::uniform yRand(0, model.height - 2);
+
+    sft::Random::uniform wRand(1, model.width  - 1);
+    sft::Random::uniform hRand(1, model.height - 1);
+
+    while (pool.size() < size_t(nfeatures))
+    {
+        int x = xRand(eng);
+        int y = yRand(eng);
+
+        int w = 1 + wRand(eng, model.width  - x - 1);
+        int h = 1 + hRand(eng, model.height - y - 1);
+
+        CV_Assert(w > 0);
+        CV_Assert(h > 0);
+
+        CV_Assert(w + x < model.width);
+        CV_Assert(h + y < model.height);
+
+        int ch = chRand(eng_ch);
+
+        sft::ICF f(x, y, w, h, ch);
+
+        if (std::find(pool.begin(), pool.end(),f) == pool.end())
+            pool.push_back(f);
+    }
+}
\ No newline at end of file

apps/sft/sft.cpp

@@ -52,63 +52,65 @@ int main(int argc, char** argv)
     int npositives = 10;
     int nnegatives = 10;
     int nsamples = npositives + nnegatives;
+    cv::Size model(64, 128);
 
     sft::Octave boost;
     cv::Mat train_data(nfeatures, nsamples, CV_32FC1);
 
-    // cv::RNG rng;
+    sft::FeaturePool pool(model, nfeatures);
 
-    // for (int y = 0; y < nfeatures; ++y)
-    //     for (int x = 0; x < nsamples; ++x)
-    //         train_data.at<float>(y, x) = rng.uniform(0.f, 1.f);
+    cv::RNG rng;
 
-    // int tflag = CV_COL_SAMPLE;
-    // Mat responses(nsamples, 1, CV_32FC1);
-    // for (int y = 0; y < nsamples; ++y)
-    //     responses.at<float>(y, 0) = (y < npositives) ? 1.f : 0.f;
+    for (int y = 0; y < nfeatures; ++y)
+        for (int x = 0; x < nsamples; ++x)
+            train_data.at<float>(y, x) = rng.uniform(0.f, 1.f);
 
+    int tflag = CV_COL_SAMPLE;
+    cv::Mat responses(nsamples, 1, CV_32FC1);
+    for (int y = 0; y < nsamples; ++y)
+        responses.at<float>(y, 0) = (y < npositives) ? 1.f : 0.f;
 
-    // Mat var_idx(1, nfeatures, CV_32SC1);
-    // for (int x = 0; x < nfeatures; ++x)
-    //     var_idx.at<int>(0, x) = x;
 
-    // // Mat sample_idx;
-    // Mat sample_idx(1, nsamples, CV_32SC1);
-    // for (int x = 0; x < nsamples; ++x)
-    //     sample_idx.at<int>(0, x) = x;
+    cv::Mat var_idx(1, nfeatures, CV_32SC1);
+    for (int x = 0; x < nfeatures; ++x)
+        var_idx.at<int>(0, x) = x;
 
-    // Mat var_type(1, nfeatures + 1, CV_8UC1);
-    // for (int x = 0; x < nfeatures; ++x)
-    //     var_type.at<uchar>(0, x) = CV_VAR_ORDERED;
+    // Mat sample_idx;
+    cv::Mat sample_idx(1, nsamples, CV_32SC1);
+    for (int x = 0; x < nsamples; ++x)
+        sample_idx.at<int>(0, x) = x;
 
-    // var_type.at<uchar>(0, nfeatures) = CV_VAR_CATEGORICAL;
+    cv::Mat var_type(1, nfeatures + 1, CV_8UC1);
+    for (int x = 0; x < nfeatures; ++x)
+        var_type.at<uchar>(0, x) = CV_VAR_ORDERED;
 
-    // Mat missing_mask;
+    var_type.at<uchar>(0, nfeatures) = CV_VAR_CATEGORICAL;
 
-    // CvBoostParams params;
-    // {
-    //     params.max_categories       = 10;
-    //     params.max_depth            = 2;
-    //     params.min_sample_count     = 2;
-    //     params.cv_folds             = 0;
-    //     params.truncate_pruned_tree = false;
+    cv::Mat missing_mask;
 
-    //     /// ??????????????????
-    //     params.regression_accuracy = 0.01;
-    //     params.use_surrogates      = false;
-    //     params.use_1se_rule        = false;
+    CvBoostParams params;
+    {
+        params.max_categories       = 10;
+        params.max_depth            = 2;
+        params.min_sample_count     = 2;
+        params.cv_folds             = 0;
+        params.truncate_pruned_tree = false;
 
-    //     ///////// boost params
-    //     params.boost_type       = CvBoost::GENTLE;
-    //     params.weak_count       = 1;
-    //     params.split_criteria   = CvBoost::SQERR;
-    //     params.weight_trim_rate = 0.95;
-    // }
+        /// ??????????????????
+        params.regression_accuracy = 0.01;
+        params.use_surrogates      = false;
+        params.use_1se_rule        = false;
 
-    // bool update = false;
+        ///////// boost params
+        params.boost_type       = CvBoost::GENTLE;
+        params.weak_count       = 1;
+        params.split_criteria   = CvBoost::SQERR;
+        params.weight_trim_rate = 0.95;
+    }
 
-    // boost.train(train_data, tflag, responses,
-    //     var_idx, sample_idx, var_type, missing_mask, params, update);
+    bool update = false;
+
+    boost.train(train_data, responses, var_idx, sample_idx, var_type, missing_mask);
 
     // CvFileStorage* fs = cvOpenFileStorage( "/home/kellan/train_res.xml", 0, CV_STORAGE_WRITE );
     // boost.write(fs, "test_res");