namespace sft
{
using cv::Mat;
+ struct ICF;
+ typedef std::vector<ICF> Icfvector;
+
}
#endif
\ No newline at end of file
#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;
};
}
--- /dev/null
+#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
//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
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");
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