opencv/apps/sft/octave.cpp

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#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

#include <glob.h>

// ============ Octave ============ //
sft::Octave::Octave(int ls) : logScale(ls) {}

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);
    }
}

// ============ Dataset ============ //
namespace {
using namespace sft;

string itoa(long i)
{
    char s[65];
    sprintf(s, "%ld", i);
    return std::string(s);
}

void glob(const string& path, svector& ret)
{
    glob_t glob_result;
    glob(path.c_str(), GLOB_TILDE, 0, &glob_result);

    ret.clear();
    ret.reserve(glob_result.gl_pathc);

    for(uint i = 0; i < glob_result.gl_pathc; ++i)
    {
        ret.push_back(std::string(glob_result.gl_pathv[i]));
        // dprintf("%s\n", ret[i].c_str());
    }

    globfree(&glob_result);
}
}
// in the default case data folders should be alligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
Dataset::Dataset(const string& path, const int oct)
{
    // dprintf("%s\n", "get dataset file names...");

    // dprintf("%s\n", "Positives globbing...");
    glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);

    // dprintf("%s\n", "Negatives globbing...");
    glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);

    // Check: files not empty
    CV_Assert(pos.size() != size_t(0));
    CV_Assert(neg.size() != size_t(0));
}