opencv/apps/sft/sft.cpp

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// Trating application for Soft Cascades.

#include <sft/common.hpp>
#include <sft/octave.hpp>
#include <sft/config.hpp>

int main(int argc, char** argv)
{
    using namespace sft;

    const string keys =
        "{help h usage ? |      | print this message              }"
        "{config c       |      | path to configuration xml       }"
    ;

    cv::CommandLineParser parser(argc, argv, keys);
    parser.about("Soft cascade training application.");

    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }

    if (!parser.check())
    {
        parser.printErrors();
        return 1;
    }

    string configPath = parser.get<string>("config");
    if (configPath.empty())
    {
        std::cout << "Configuration file is missing or empty. Could not start training." << std::endl << std::flush;
        return 0;
    }

    std::cout << "Read configuration from file " << configPath << std::endl;
    cv::FileStorage fs(configPath, cv::FileStorage::READ);
    if(!fs.isOpened())
    {
        std::cout << "Configuration file " << configPath << " can't be opened." << std::endl << std::flush;
        return 1;
    }

    // 1. load config
    sft::Config cfg;
    fs["config"] >> cfg;
    std::cout << std::endl << "Training will be executed for configuration:" << std::endl << cfg << std::endl;

    // 2. check and open output file
    cv::FileStorage fso(cfg.outXmlPath, cv::FileStorage::WRITE);
    if(!fs.isOpened())
    {
        std::cout << "Training stopped. Output classifier Xml file " << cfg.outXmlPath << " can't be opened." << std::endl << std::flush;
        return 1;
    }

    // ovector strong;
    // strong.reserve(cfg.octaves.size());

    // fso << "softcascade" << "{" << "octaves" << "[";

    // 3. Train all octaves
    for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
    {
        int nfeatures  = cfg.poolSize;
        int npositives = cfg.positives;
        int nnegatives = cfg.negatives;

        int shrinkage  = cfg.shrinkage;
        int octave = *it;

        cv::Size model = cv::Size(cfg.modelWinSize.width / cfg.shrinkage, cfg.modelWinSize.height / cfg.shrinkage );
        std::string path = cfg.trainPath;

        cv::Rect boundingBox(cfg.offset.x / cfg.shrinkage, cfg.offset.y / cfg.shrinkage,
            cfg.modelWinSize.width / cfg.shrinkage, cfg.modelWinSize.height / cfg.shrinkage);

        sft::Octave boost(boundingBox, npositives, nnegatives, octave, shrinkage);

        sft::FeaturePool pool(model, nfeatures);
        sft::Dataset dataset(path, boost.logScale);

        if (boost.train(dataset, pool))
        {
        }
        std::cout << "Octave " << octave << " was successfully trained..." << std::endl;
    //     // d. crain octave
    //     if (octave.train(pool, cfg.positives, cfg.negatives, cfg.weaks))
    //     {
    //         strong.push_back(octave);
    //     }
    }

    // fso << "]" << "}";

//     // 3. create Soft Cascade
//     // sft::SCascade cascade(cfg.modelWinSize, cfg.octs, cfg.shrinkage);

//     // // 4. Generate feature pool
//     // std::vector<sft::ICF> pool;
//     // sft::fillPool(pool, cfg.poolSize, cfg.modelWinSize / cfg.shrinkage, cfg.seed);

//     // // 5. Train all octaves
//     // cascade.train(cfg.trainPath);

//     // // 6. Set thresolds
//     // cascade.prune();

//     // // 7. Postprocess
//     // cascade.normolize();

//     // // 8. Write result xml
//     // cv::FileStorage ofs(cfg.outXmlPath, cv::FileStorage::WRITE);
//     // ofs << cfg.cascadeName << cascade;

    std::cout << "Training complete..." << std::endl;
    return 0;
}