{
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);
}
Mat _data(nsamples, sz, type);
+
for( int i = 0; i < nsamples; i++ )
{
CV_Assert( data[i].size() == size && data[i].type() == type );
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;
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. */