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#if !defined(ANDROID)

#include <test_precomp.hpp>
#include <string>
#include <fstream>
#include <vector>

using namespace std;

namespace {

using namespace cv::softcascade;

typedef vector<cv::String> svector;
class ScaledDataset : public Dataset
{
public:
    ScaledDataset(const string& path, const int octave);

    virtual cv::Mat get(SampleType type, int idx) const;
    virtual int available(SampleType type) const;
    virtual ~ScaledDataset();

private:
    svector pos;
    svector neg;
};

ScaledDataset::ScaledDataset(const string& path, const int oct)
{
    cv::glob(path + cv::format("/octave_%d/*.png", oct), pos);
    cv::glob(path + "/*.png", neg);

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

cv::Mat ScaledDataset::get(SampleType type, int idx) const
{
    const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
    return cv::imread(src);
}

int ScaledDataset::available(SampleType type) const
{
    return (int)((type == POSITIVE)? pos.size():neg.size());
}

ScaledDataset::~ScaledDataset(){}

}

TEST(SoftCascade, training)
{
    // // 2. check and open output file
    string outXmlPath = cv::tempfile(".xml");
    cv::FileStorage fso(outXmlPath, cv::FileStorage::WRITE);

    ASSERT_TRUE(fso.isOpened());

    std::vector<int> octaves;
    {
        octaves.push_back(-1);
        octaves.push_back(0);
    }

    fso << "regression-cascade"
        << "{"
        << "stageType"   << "BOOST"
        << "featureType" << "ICF"
        << "octavesNum"  << 2
        << "width"       << 64
        << "height"      << 128
        << "shrinkage"   << 4
        << "octaves"     << "[";

    for (std::vector<int>::const_iterator it = octaves.begin(); it != octaves.end(); ++it)
    {
        int nfeatures  = 100;
        int shrinkage = 4;
        float octave = powf(2.f, (float)(*it));
        cv::Size model = cv::Size( cvRound(64 * octave) / shrinkage, cvRound(128 * octave) / shrinkage );

        cv::Ptr<FeaturePool> pool = FeaturePool::create(model, nfeatures, 10);
        nfeatures = pool->size();
        int npositives = 10;
        int nnegatives = 20;

        cv::Rect boundingBox = cv::Rect( cvRound(20 * octave), cvRound(20  * octave),
                                         cvRound(64 * octave), cvRound(128 * octave));

        cv::Ptr<ChannelFeatureBuilder> builder = ChannelFeatureBuilder::create("HOG6MagLuv");
        cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, builder);

        std::string path = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sample_training_set";
        ScaledDataset dataset(path, *it);

        if (boost->train(&dataset, pool, 3, 2))
        {
            cv::Mat thresholds;
            boost->setRejectThresholds(thresholds);
            boost->write(fso, pool, thresholds);
        }
    }

    fso << "]" << "}";
    fso.release();


    cv::FileStorage actual(outXmlPath, cv::FileStorage::READ);
    cv::FileNode root = actual.getFirstTopLevelNode();

    cv::FileNode fn = root["octaves"];
    ASSERT_FALSE(fn.empty());
}

#endif