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calcCovarMatrix cupport fot std::vectors of cv::Mat (#494)

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This commit is contained in:
Marina Kolpakova 2012-04-12 04:21:32 +00:00
parent 38488cfdf1
commit c0f6e219bb
2 changed files with 117 additions and 1 deletions
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52
modules/core/src/matmul.cpp
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@ -2098,7 +2098,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
{
CV_Assert( data && nsamples > 0 );
Size size = data[0].size();
int sz = size.width*size.height, esz = (int)data[0].elemSize();
int sz = size.width * size.height, esz = (int)data[0].elemSize();
int type = data[0].type();
Mat mean;
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), _mean.depth()), CV_32F);
@ -2116,6 +2116,7 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
}
Mat _data(nsamples, sz, type);
for( int i = 0; i < nsamples; i++ )
{
CV_Assert( data[i].size() == size && data[i].type() == type );
@ -2135,6 +2136,55 @@ void cv::calcCovarMatrix( const Mat* data, int nsamples, Mat& covar, Mat& _mean,
void cv::calcCovarMatrix( InputArray _data, OutputArray _covar, InputOutputArray _mean, int flags, int ctype )
{
if(_data.kind() == _InputArray::STD_VECTOR_MAT)
{
std::vector<cv::Mat> src;
_data.getMatVector(src);
CV_Assert( src.size() > 0 );
Size size = src[0].size();
int type = src[0].type();
ctype = std::max(std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), _mean.depth()), CV_32F);
Mat _data(src.size(), size.area(), type);
int i = 0;
for(vector<cv::Mat>::iterator each = src.begin(); each != src.end(); each++, i++ )
{
CV_Assert( (*each).size() == size && (*each).type() == type );
Mat dataRow(size.height, size.width, type, _data.ptr(i));
(*each).copyTo(dataRow);
}
Mat mean;
if( (flags & CV_COVAR_USE_AVG) != 0 )
{
CV_Assert( _mean.size() == size );
if( mean.type() != ctype )
{
mean = _mean.getMat();
_mean.create(mean.size(), ctype);
Mat tmp = _mean.getMat();
mean.convertTo(tmp, ctype);
mean = tmp;
}
mean = _mean.getMat().reshape(1, 1);
}
calcCovarMatrix( _data, _covar, mean, (flags & ~(CV_COVAR_ROWS|CV_COVAR_COLS)) | CV_COVAR_ROWS, ctype );
if( (flags & CV_COVAR_USE_AVG) == 0 )
{
mean = mean.reshape(1, size.height);
mean.copyTo(_mean);
}
return;
}
Mat data = _data.getMat(), mean;
CV_Assert( ((flags & CV_COVAR_ROWS) != 0) ^ ((flags & CV_COVAR_COLS) != 0) );
bool takeRows = (flags & CV_COVAR_ROWS) != 0;

66
modules/core/test/test_math.cpp
View File

@ -2531,5 +2531,71 @@ protected:
TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(); }
TEST(CovariationMatrixVectorOfMat, accuracy)
{
unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;
cv::Mat src(vector_size, col_problem_size * row_problem_size, CV_32F);
int singleMatFlags = CV_COVAR_ROWS;
cv::Mat gold;
cv::Mat goldMean;
cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);
std::vector<cv::Mat> srcVec;
for(size_t i = 0; i < vector_size; i++)
{
srcVec.push_back(src.row(i).reshape(0,col_problem_size));
}
cv::Mat actual;
cv::Mat actualMean;
cv::calcCovarMatrix(srcVec, actual, actualMean,singleMatFlags,CV_32F);
cv::Mat diff;
cv::absdiff(gold, actual, diff);
cv::Scalar s = cv::sum(diff);
ASSERT_EQ(s.dot(s), 0.0);
cv::Mat meanDiff;
cv::absdiff(goldMean, actualMean.reshape(0,1), meanDiff);
cv::Scalar sDiff = cv::sum(meanDiff);
ASSERT_EQ(sDiff.dot(sDiff), 0.0);
}
TEST(CovariationMatrixVectorOfMatWithMean, accuracy)
{
unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;
cv::Mat src(vector_size, col_problem_size * row_problem_size, CV_32F);
int singleMatFlags = CV_COVAR_ROWS | CV_COVAR_USE_AVG;
cv::Mat gold;
cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
cv::Mat goldMean;
cv::reduce(src,goldMean,0 ,CV_REDUCE_AVG, CV_32F);
cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);
std::vector<cv::Mat> srcVec;
for(size_t i = 0; i < vector_size; i++)
{
srcVec.push_back(src.row(i).reshape(0,col_problem_size));
}
cv::Mat actual;
cv::Mat actualMean = goldMean.reshape(0, row_problem_size);
cv::calcCovarMatrix(srcVec, actual, actualMean,singleMatFlags,CV_32F);
cv::Mat diff;
cv::absdiff(gold, actual, diff);
cv::Scalar s = cv::sum(diff);
ASSERT_EQ(s.dot(s), 0.0);
cv::Mat meanDiff;
cv::absdiff(goldMean, actualMean.reshape(0,1), meanDiff);
cv::Scalar sDiff = cv::sum(meanDiff);
ASSERT_EQ(sDiff.dot(sDiff), 0.0);
}
/* End of file. */