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fix for bug #3068 (PCA::computeVar for double input):

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The matrix g  can have CV_32F or CV_64F type,  but g.at uses only float template.
This fix adds specialization for double type.
This commit is contained in:
Vladislav Vinogradov 2013-06-14 12:53:44 +04:00
parent b84296c02e
commit a4750f49c6
1 changed files with 36 additions and 21 deletions
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57
modules/core/src/matmul.cpp
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@ -2855,9 +2855,9 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
if( _mean.data )
{
CV_Assert( _mean.size() == mean_sz );
CV_Assert( _mean.size() == mean_sz );
_mean.convertTo(mean, ctype);
covar_flags |= CV_COVAR_USE_AVG;
covar_flags |= CV_COVAR_USE_AVG;
}
calcCovarMatrix( data, covar, mean, covar_flags, ctype );
@ -2901,6 +2901,36 @@ PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComp
return *this;
}
template <typename T>
int computeCumulativeEnergy(const Mat& eigenvalues, double retainedVariance)
{
CV_DbgAssert( eigenvalues.type() == DataType<T>::type );
Mat g(eigenvalues.size(), DataType<T>::type);
for(int ig = 0; ig < g.rows; ig++)
{
g.at<T>(ig, 0) = 0;
for(int im = 0; im <= ig; im++)
{
g.at<T>(ig,0) += eigenvalues.at<T>(im,0);
}
}
int L;
for(L = 0; L < eigenvalues.rows; L++)
{
double energy = g.at<T>(L, 0) / g.at<T>(g.rows - 1, 0);
if(energy > retainedVariance)
break;
}
L = std::max(2, L);
return L;
}
PCA& PCA::computeVar(InputArray _data, InputArray __mean, int flags, double retainedVariance)
{
Mat data = _data.getMat(), _mean = __mean.getMat();
@ -2977,26 +3007,11 @@ PCA& PCA::computeVar(InputArray _data, InputArray __mean, int flags, double reta
}
// compute the cumulative energy content for each eigenvector
Mat g(eigenvalues.size(), ctype);
for(int ig = 0; ig < g.rows; ig++)
{
g.at<float>(ig,0) = 0;
for(int im = 0; im <= ig; im++)
{
g.at<float>(ig,0) += eigenvalues.at<float>(im,0);
}
}
int L;
for(L = 0; L < eigenvalues.rows; L++)
{
double energy = g.at<float>(L, 0) / g.at<float>(g.rows - 1, 0);
if(energy > retainedVariance)
break;
}
L = std::max(2, L);
if (ctype == CV_32F)
L = computeCumulativeEnergy<float>(eigenvalues, retainedVariance);
else
L = computeCumulativeEnergy<double>(eigenvalues, retainedVariance);
// use clone() to physically copy the data and thus deallocate the original matrices
eigenvalues = eigenvalues.rowRange(0,L).clone();