diff --git a/modules/legacy/test/test_em.cpp b/modules/legacy/test/test_em.cpp index 411fc9ebd4..26be3feffe 100644 --- a/modules/legacy/test/test_em.cpp +++ b/modules/legacy/test/test_em.cpp @@ -45,45 +45,45 @@ using namespace std; using namespace cv; static -void defaultDistribs( Mat& means, vector& covs ) +void defaultDistribs( Mat& means, vector& covs, int type=CV_32FC1 ) { float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f}; float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f}; float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f}; - means.create(3, 2, CV_32FC1); + means.create(3, 2, type); Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 ); Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 ); Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 ); means.resize(3), covs.resize(3); Mat mr0 = means.row(0); - m0.copyTo(mr0); - c0.copyTo(covs[0]); + m0.convertTo(mr0, type); + c0.convertTo(covs[0], type); Mat mr1 = means.row(1); - m1.copyTo(mr1); - c1.copyTo(covs[1]); + m1.convertTo(mr1, type); + c1.convertTo(covs[1], type); Mat mr2 = means.row(2); - m2.copyTo(mr2); - c2.copyTo(covs[2]); + m2.convertTo(mr2, type); + c2.convertTo(covs[2], type); } // generate points sets by normal distributions static -void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& _means, const vector& covs, int labelType ) +void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& _means, const vector& covs, int dataType, int labelType ) { vector::const_iterator sit = sizes.begin(); int total = 0; for( ; sit != sizes.end(); ++sit ) total += *sit; - assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() ); - assert( !data.empty() && data.rows == total ); - assert( data.type() == CV_32FC1 ); - + CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() ); + CV_Assert( !data.empty() && data.rows == total ); + CV_Assert( data.type() == dataType ); + labels.create( data.rows, 1, labelType ); - randn( data, Scalar::all(0.0), Scalar::all(1.0) ); + randn( data, Scalar::all(-1.0), Scalar::all(1.0) ); vector means(sizes.size()); for(int i = 0; i < _means.rows; i++) means[i] = _means.row(i); @@ -98,8 +98,8 @@ void generateData( Mat& data, Mat& labels, const vector& sizes, const Mat& assert( cit->rows == data.cols && cit->cols == data.cols ); for( int i = bi; i < ei; i++, p++ ) { - Mat r(1, data.cols, CV_32FC1, data.ptr(i)); - r = r * (*cit) + *mit; + Mat r = data.row(i); + r = r * (*cit) + *mit; if( labelType == CV_32FC1 ) labels.at(p, 0) = (float)l; else if( labelType == CV_32SC1 ) @@ -129,7 +129,7 @@ int maxIdx( const vector& count ) } static -bool getLabelsMap( const Mat& labels, const vector& sizes, vector& labelsMap ) +bool getLabelsMap( const Mat& labels, const vector& sizes, vector& labelsMap, bool checkClusterUniq=true ) { size_t total = 0, nclusters = sizes.size(); for(size_t i = 0; i < sizes.size(); i++) @@ -158,21 +158,25 @@ bool getLabelsMap( const Mat& labels, const vector& sizes, vector& lab startIndex += sizes[clusterIndex]; int cls = maxIdx( count ); - CV_Assert( !buzy[cls] ); + CV_Assert( !checkClusterUniq || !buzy[cls] ); labelsMap[clusterIndex] = cls; buzy[cls] = true; } - for(size_t i = 0; i < buzy.size(); i++) - if(!buzy[i]) - return false; + + if(checkClusterUniq) + { + for(size_t i = 0; i < buzy.size(); i++) + if(!buzy[i]) + return false; + } return true; } static -bool calcErr( const Mat& labels, const Mat& origLabels, const vector& sizes, float& err, bool labelsEquivalent = true ) +bool calcErr( const Mat& labels, const Mat& origLabels, const vector& sizes, float& err, bool labelsEquivalent, bool checkClusterUniq ) { err = 0; CV_Assert( !labels.empty() && !origLabels.empty() ); @@ -186,7 +190,7 @@ bool calcErr( const Mat& labels, const Mat& origLabels, const vector& sizes bool isFlt = labels.type() == CV_32FC1; if( !labelsEquivalent ) { - if( !getLabelsMap( labels, sizes, labelsMap ) ) + if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) ) return false; for( int i = 0; i < labels.rows; i++ ) @@ -234,7 +238,7 @@ int CV_CvEMTest::runCase( int caseIndex, const CvEMParams& params, em.train( trainData, Mat(), params, &labels ); // check train error - if( !calcErr( labels, trainLabels, sizes, err , false ) ) + if( !calcErr( labels, trainLabels, sizes, err , false, false ) ) { ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex ); code = cvtest::TS::FAIL_INVALID_OUTPUT; @@ -252,7 +256,7 @@ int CV_CvEMTest::runCase( int caseIndex, const CvEMParams& params, Mat sample = testData.row(i); labels.at(i,0) = (int)em.predict( sample, 0 ); } - if( !calcErr( labels, testLabels, sizes, err, false ) ) + if( !calcErr( labels, testLabels, sizes, err, false, false ) ) { ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex ); code = cvtest::TS::FAIL_INVALID_OUTPUT; @@ -279,11 +283,11 @@ void CV_CvEMTest::run( int /*start_from*/ ) // train data Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels; vector sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) ); - generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 ); + generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32SC1 ); // test data Mat testData( pointsCount, 2, CV_32FC1 ), testLabels; - generateData( testData, testLabels, sizes, means, covs, CV_32SC1 ); + generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32SC1 ); CvEMParams params; params.nclusters = 3;