generic-library/opencv
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refactoring

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This commit is contained in:
marina.kolpakova
2012-12-07 17:55:58 +04:00
parent 883d691c2b
commit bfa26fd447
4 changed files with 92 additions and 86 deletions
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41
apps/sft/sft.cpp
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@@ -106,47 +106,34 @@ int main(int argc, char** argv)
// 3. Train all octaves
for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
{
// a. create rangom feature pool
int nfeatures = cfg.poolSize;
cv::Size model = cfg.model(it);
std::cout << "Model " << model << std::endl;
sft::FeaturePool pool(model, nfeatures);
nfeatures = pool.size();
int npositives = cfg.positives;
int nnegatives = cfg.negatives;
int shrinkage = cfg.shrinkage;
int octave = *it;
cv::Rect boundingBox = cfg.bbox(it);
std::cout << "Object bounding box" << boundingBox << std::endl;
sft::Octave boost(boundingBox, npositives, nnegatives, *it, shrinkage);
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))
if (boost.train(dataset, pool, cfg.weaks, cfg.treeDepth))
{
}
std::cout << "Octave " << octave << " was successfully trained..." << std::endl;
// // d. crain octave
// if (octave.train(pool, cfg.positives, cfg.negatives, cfg.weaks))
// {
std::cout << "Octave " << *it << " was successfully trained..." << std::endl;
// 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();