本文整理汇总了C++中TrainingSet::normalizer方法的典型用法代码示例。如果您正苦于以下问题：C++ TrainingSet::normalizer方法的具体用法？C++ TrainingSet::normalizer怎么用？C++ TrainingSet::normalizer使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类TrainingSet的用法示例。
在下文中一共展示了TrainingSet::normalizer方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: makeFaceRecognizer
FaceRecognizer::FaceRecognizer(const TrainingSet& set, const double avgThRangeScale, const double maxThreshold):
imgNorm_( set.normalizer() ),
faceRecognizer_( makeFaceRecognizer() )
{
if( set.entries().size() < 2 )
throw Util::Exception{ UTIL_LOCSTRM << "training samples set needs to have at least 2 elements" };
int nextFreeLabel = 0;
std::vector<cv::Mat> faces;
std::vector<int> labels;
std::vector<cv::Mat> testSet;
// prepare space for the destination data
faces.reserve( set.samples() );
labels.reserve( set.samples() );
labMap_.reserve( set.samples() );
testSet.reserve( set.samples() );
// prepare data in the format used by the learning algorithm
for( const auto& e: set.entries() )
{
const std::string& name = e.first;
const cv::Mat& face = e.second;
// find/create id to assign
int id = -1;
// initially this vector is ordered by string names
const LabelMap::value_type searchValue(-1/*whatever*/, name);
const auto swo = [](const LabelMap::value_type& lhs, const LabelMap::value_type& rhs) -> bool { return lhs.second < rhs.second; };
const auto it = std::lower_bound( labMap_.begin(), labMap_.end(), searchValue, swo );
if( it==labMap_.end() || it->second!=name )
{
// assign new label
id = nextFreeLabel;
++nextFreeLabel;
labMap_.insert( it, LabelMap::value_type{id, name} );
// first image from every class keep as a test one
testSet.push_back(face);
continue;
}
else
{
// use already assigned id
assert( it->second==name );
id = it->first;
}
// add to the containers
faces.push_back(face);
labels.push_back(id);
}
// learn data
assert( faceRecognizer_.get() != nullptr );
assert( faces.size() == labels.size() );
//for(size_t i=0; i<faces.size(); ++i)
faceRecognizer_->train(faces, labels);
// set threshold according to what has been learned
double thMin = 99999999999999;
double thMax = 0;
for(const auto& face: testSet)
{
int label = -1;
double dist = -1;
faceRecognizer_->predict(face, label, dist);
if(dist<thMin)
thMin = dist;
if(dist>thMax)
thMax = dist;
}
const double diff = thMax - thMin;
threshold_ = thMin + diff * avgThRangeScale;
if( threshold_ > maxThreshold )
threshold_ = maxThreshold;
// final version needs to be sorted by int ids, for easier search
{
const auto swo = [](const LabelMap::value_type& lhs, const LabelMap::value_type& rhs) -> bool { return lhs.first < rhs.first; };
std::sort( labMap_.begin(), labMap_.end(), swo );
}
assert( labMap_.size() == static_cast<size_t>(nextFreeLabel) );
}