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Open Source Computer Vision Library
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1113 lines
37 KiB
1113 lines
37 KiB
#include "test_precomp.hpp" |
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using namespace cv; |
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using namespace std; |
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class Core_ReduceTest : public cvtest::BaseTest |
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{ |
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public: |
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Core_ReduceTest() {}; |
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protected: |
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void run( int); |
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int checkOp( const Mat& src, int dstType, int opType, const Mat& opRes, int dim ); |
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int checkCase( int srcType, int dstType, int dim, Size sz ); |
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int checkDim( int dim, Size sz ); |
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int checkSize( Size sz ); |
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}; |
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template<class Type> |
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void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim ) |
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{ |
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assert( src.channels() == 1 ); |
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if( dim == 0 ) // row |
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{ |
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sum.create( 1, src.cols, CV_64FC1 ); |
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max.create( 1, src.cols, CV_64FC1 ); |
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min.create( 1, src.cols, CV_64FC1 ); |
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} |
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else |
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{ |
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sum.create( src.rows, 1, CV_64FC1 ); |
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max.create( src.rows, 1, CV_64FC1 ); |
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min.create( src.rows, 1, CV_64FC1 ); |
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} |
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sum.setTo(Scalar(0)); |
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max.setTo(Scalar(-DBL_MAX)); |
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min.setTo(Scalar(DBL_MAX)); |
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const Mat_<Type>& src_ = src; |
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Mat_<double>& sum_ = (Mat_<double>&)sum; |
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Mat_<double>& min_ = (Mat_<double>&)min; |
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Mat_<double>& max_ = (Mat_<double>&)max; |
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if( dim == 0 ) |
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{ |
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for( int ri = 0; ri < src.rows; ri++ ) |
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{ |
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for( int ci = 0; ci < src.cols; ci++ ) |
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{ |
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sum_(0, ci) += src_(ri, ci); |
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max_(0, ci) = std::max( max_(0, ci), (double)src_(ri, ci) ); |
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min_(0, ci) = std::min( min_(0, ci), (double)src_(ri, ci) ); |
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} |
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} |
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} |
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else |
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{ |
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for( int ci = 0; ci < src.cols; ci++ ) |
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{ |
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for( int ri = 0; ri < src.rows; ri++ ) |
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{ |
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sum_(ri, 0) += src_(ri, ci); |
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max_(ri, 0) = std::max( max_(ri, 0), (double)src_(ri, ci) ); |
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min_(ri, 0) = std::min( min_(ri, 0), (double)src_(ri, ci) ); |
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} |
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} |
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} |
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sum.convertTo( avg, CV_64FC1 ); |
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avg = avg * (1.0 / (dim==0 ? (double)src.rows : (double)src.cols)); |
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} |
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void getMatTypeStr( int type, string& str) |
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{ |
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str = type == CV_8UC1 ? "CV_8UC1" : |
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type == CV_8SC1 ? "CV_8SC1" : |
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type == CV_16UC1 ? "CV_16UC1" : |
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type == CV_16SC1 ? "CV_16SC1" : |
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type == CV_32SC1 ? "CV_32SC1" : |
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type == CV_32FC1 ? "CV_32FC1" : |
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type == CV_64FC1 ? "CV_64FC1" : "unsupported matrix type"; |
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} |
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int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat& opRes, int dim ) |
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{ |
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int srcType = src.type(); |
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bool support = false; |
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if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG ) |
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{ |
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if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) ) |
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support = true; |
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if( srcType == CV_16U && (dstType == CV_32F || dstType == CV_64F) ) |
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support = true; |
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if( srcType == CV_16S && (dstType == CV_32F || dstType == CV_64F) ) |
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support = true; |
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if( srcType == CV_32F && (dstType == CV_32F || dstType == CV_64F) ) |
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support = true; |
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if( srcType == CV_64F && dstType == CV_64F) |
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support = true; |
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} |
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else if( opType == CV_REDUCE_MAX ) |
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{ |
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if( srcType == CV_8U && dstType == CV_8U ) |
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support = true; |
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if( srcType == CV_32F && dstType == CV_32F ) |
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support = true; |
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if( srcType == CV_64F && dstType == CV_64F ) |
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support = true; |
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} |
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else if( opType == CV_REDUCE_MIN ) |
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{ |
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if( srcType == CV_8U && dstType == CV_8U) |
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support = true; |
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if( srcType == CV_32F && dstType == CV_32F) |
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support = true; |
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if( srcType == CV_64F && dstType == CV_64F) |
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support = true; |
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} |
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if( !support ) |
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return cvtest::TS::OK; |
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double eps = 0.0; |
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if ( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG ) |
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{ |
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if ( dstType == CV_32F ) |
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eps = 1.e-5; |
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else if( dstType == CV_64F ) |
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eps = 1.e-8; |
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else if ( dstType == CV_32S ) |
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eps = 0.6; |
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} |
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assert( opRes.type() == CV_64FC1 ); |
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Mat _dst, dst, diff; |
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reduce( src, _dst, dim, opType, dstType ); |
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_dst.convertTo( dst, CV_64FC1 ); |
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absdiff( opRes,dst,diff ); |
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bool check = false; |
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if (dstType == CV_32F || dstType == CV_64F) |
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check = countNonZero(diff>eps*dst) > 0; |
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else |
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check = countNonZero(diff>eps) > 0; |
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if( check ) |
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{ |
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char msg[100]; |
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const char* opTypeStr = opType == CV_REDUCE_SUM ? "CV_REDUCE_SUM" : |
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opType == CV_REDUCE_AVG ? "CV_REDUCE_AVG" : |
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opType == CV_REDUCE_MAX ? "CV_REDUCE_MAX" : |
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opType == CV_REDUCE_MIN ? "CV_REDUCE_MIN" : "unknown operation type"; |
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string srcTypeStr, dstTypeStr; |
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getMatTypeStr( src.type(), srcTypeStr ); |
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getMatTypeStr( dstType, dstTypeStr ); |
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const char* dimStr = dim == 0 ? "ROWS" : "COLS"; |
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sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s", |
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srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr ); |
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ts->printf( cvtest::TS::LOG, msg ); |
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return cvtest::TS::FAIL_BAD_ACCURACY; |
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} |
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return cvtest::TS::OK; |
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} |
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int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz ) |
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{ |
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int code = cvtest::TS::OK, tempCode; |
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Mat src, sum, avg, max, min; |
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src.create( sz, srcType ); |
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randu( src, Scalar(0), Scalar(100) ); |
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if( srcType == CV_8UC1 ) |
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testReduce<uchar>( src, sum, avg, max, min, dim ); |
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else if( srcType == CV_8SC1 ) |
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testReduce<char>( src, sum, avg, max, min, dim ); |
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else if( srcType == CV_16UC1 ) |
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testReduce<unsigned short int>( src, sum, avg, max, min, dim ); |
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else if( srcType == CV_16SC1 ) |
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testReduce<short int>( src, sum, avg, max, min, dim ); |
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else if( srcType == CV_32SC1 ) |
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testReduce<int>( src, sum, avg, max, min, dim ); |
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else if( srcType == CV_32FC1 ) |
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testReduce<float>( src, sum, avg, max, min, dim ); |
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else if( srcType == CV_64FC1 ) |
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testReduce<double>( src, sum, avg, max, min, dim ); |
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else |
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assert( 0 ); |
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// 1. sum |
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tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// 2. avg |
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tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// 3. max |
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tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// 4. min |
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tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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return code; |
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} |
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int Core_ReduceTest::checkDim( int dim, Size sz ) |
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{ |
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int code = cvtest::TS::OK, tempCode; |
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// CV_8UC1 |
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tempCode = checkCase( CV_8UC1, CV_8UC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkCase( CV_8UC1, CV_32SC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkCase( CV_8UC1, CV_32FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkCase( CV_8UC1, CV_64FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// CV_16UC1 |
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tempCode = checkCase( CV_16UC1, CV_32FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkCase( CV_16UC1, CV_64FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// CV_16SC1 |
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tempCode = checkCase( CV_16SC1, CV_32FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkCase( CV_16SC1, CV_64FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// CV_32FC1 |
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tempCode = checkCase( CV_32FC1, CV_32FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkCase( CV_32FC1, CV_64FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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// CV_64FC1 |
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tempCode = checkCase( CV_64FC1, CV_64FC1, dim, sz ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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return code; |
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} |
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int Core_ReduceTest::checkSize( Size sz ) |
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{ |
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int code = cvtest::TS::OK, tempCode; |
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tempCode = checkDim( 0, sz ); // rows |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkDim( 1, sz ); // cols |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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return code; |
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} |
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void Core_ReduceTest::run( int ) |
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{ |
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int code = cvtest::TS::OK, tempCode; |
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tempCode = checkSize( Size(1,1) ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkSize( Size(1,100) ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkSize( Size(100,1) ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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tempCode = checkSize( Size(1000,500) ); |
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code = tempCode != cvtest::TS::OK ? tempCode : code; |
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ts->set_failed_test_info( code ); |
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} |
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#define CHECK_C |
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class Core_PCATest : public cvtest::BaseTest |
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{ |
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public: |
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Core_PCATest() {} |
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protected: |
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void run(int) |
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{ |
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const Size sz(200, 500); |
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double diffPrjEps, diffBackPrjEps, |
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prjEps, backPrjEps, |
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evalEps, evecEps; |
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int maxComponents = 100; |
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double retainedVariance = 0.95; |
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Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1); |
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RNG& rng = ts->get_rng(); |
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rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); |
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rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) ); |
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PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA; |
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// 1. check C++ PCA & ROW |
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Mat rPrjTestPoints = rPCA.project( rTestPoints ); |
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Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints ); |
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Mat avg(1, sz.width, CV_32FC1 ); |
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reduce( rPoints, avg, 0, CV_REDUCE_AVG ); |
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Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec; |
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Q = Qt * Q; |
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Q = Q /(float)rPoints.rows; |
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eigen( Q, eval, evec ); |
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/*SVD svd(Q); |
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evec = svd.vt; |
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eval = svd.w;*/ |
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Mat subEval( maxComponents, 1, eval.type(), eval.data ), |
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subEvec( maxComponents, evec.cols, evec.type(), evec.data ); |
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#ifdef CHECK_C |
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Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t(); |
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CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints; |
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#endif |
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// check eigen() |
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double eigenEps = 1e-6; |
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double err; |
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for(int i = 0; i < Q.rows; i++ ) |
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{ |
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Mat v = evec.row(i).t(); |
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Mat Qv = Q * v; |
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Mat lv = eval.at<float>(i,0) * v; |
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err = norm( Qv, lv ); |
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if( err > eigenEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of eigen(); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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} |
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// check pca eigenvalues |
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evalEps = 1e-6, evecEps = 1e-3; |
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err = norm( rPCA.eigenvalues, subEval ); |
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if( err > evalEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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// check pca eigenvectors |
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for(int i = 0; i < subEvec.rows; i++) |
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{ |
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Mat r0 = rPCA.eigenvectors.row(i); |
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Mat r1 = subEvec.row(i); |
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err = norm( r0, r1, CV_L2 ); |
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if( err > evecEps ) |
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{ |
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r1 *= -1; |
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double err2 = norm(r0, r1, CV_L2); |
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if( err2 > evecEps ) |
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{ |
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Mat tmp; |
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absdiff(rPCA.eigenvectors, subEvec, tmp); |
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double mval = 0; Point mloc; |
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minMaxLoc(tmp, 0, &mval, 0, &mloc); |
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ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err ); |
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ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n", |
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mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x), |
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subEvec.at<float>(mloc.y, mloc.x)); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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} |
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} |
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prjEps = 1.265, backPrjEps = 1.265; |
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for( int i = 0; i < rTestPoints.rows; i++ ) |
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{ |
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// check pca project |
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Mat subEvec_t = subEvec.t(); |
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Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t; |
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err = norm(rPrjTestPoints.row(i), prj, CV_RELATIVE_L2); |
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if( err > prjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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// check pca backProject |
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Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg; |
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err = norm( rBackPrjTestPoints.row(i), backPrj, CV_RELATIVE_L2 ); |
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if( err > backPrjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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} |
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// 2. check C++ PCA & COL |
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cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents ); |
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diffPrjEps = 1, diffBackPrjEps = 1; |
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Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t()); |
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err = norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); |
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if( err > diffPrjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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err = norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 ); |
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if( err > diffBackPrjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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// 3. check C++ PCA w/retainedVariance |
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cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance ); |
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diffPrjEps = 1, diffBackPrjEps = 1; |
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Mat rvPrjTestPoints = cPCA.project(rTestPoints.t()); |
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if( cPCA.eigenvectors.rows > maxComponents) |
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err = norm(cv::abs(rvPrjTestPoints.rowRange(0,maxComponents)), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); |
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else |
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err = norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), CV_RELATIVE_L2 ); |
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if( err > diffPrjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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err = norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 ); |
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if( err > diffBackPrjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
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#ifdef CHECK_C |
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// 4. check C PCA & ROW |
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_points = rPoints; |
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_testPoints = rTestPoints; |
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_avg = avg; |
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_eval = eval; |
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_evec = evec; |
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prjTestPoints.create(rTestPoints.rows, maxComponents, rTestPoints.type() ); |
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backPrjTestPoints.create(rPoints.size(), rPoints.type() ); |
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_prjTestPoints = prjTestPoints; |
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_backPrjTestPoints = backPrjTestPoints; |
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cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW ); |
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cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); |
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cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); |
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err = norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2); |
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if( err > diffPrjEps ) |
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{ |
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ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
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return; |
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} |
|
err = norm(backPrjTestPoints, rBackPrjTestPoints, CV_RELATIVE_L2); |
|
if( err > diffBackPrjEps ) |
|
{ |
|
ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err ); |
|
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
|
return; |
|
} |
|
|
|
// 5. check C PCA & COL |
|
_points = cPoints; |
|
_testPoints = cTestPoints; |
|
avg = avg.t(); _avg = avg; |
|
eval = eval.t(); _eval = eval; |
|
evec = evec.t(); _evec = evec; |
|
prjTestPoints = prjTestPoints.t(); _prjTestPoints = prjTestPoints; |
|
backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = backPrjTestPoints; |
|
|
|
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_COL ); |
|
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints ); |
|
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints ); |
|
|
|
err = norm(cv::abs(prjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 ); |
|
if( err > diffPrjEps ) |
|
{ |
|
ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err ); |
|
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
|
return; |
|
} |
|
err = norm(backPrjTestPoints, rBackPrjTestPoints.t(), CV_RELATIVE_L2); |
|
if( err > diffBackPrjEps ) |
|
{ |
|
ts->printf( cvtest::TS::LOG, "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL); err = %f\n", err ); |
|
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); |
|
return; |
|
} |
|
#endif |
|
} |
|
}; |
|
|
|
class Core_ArrayOpTest : public cvtest::BaseTest |
|
{ |
|
public: |
|
Core_ArrayOpTest(); |
|
~Core_ArrayOpTest(); |
|
protected: |
|
void run(int); |
|
}; |
|
|
|
|
|
Core_ArrayOpTest::Core_ArrayOpTest() |
|
{ |
|
} |
|
Core_ArrayOpTest::~Core_ArrayOpTest() {} |
|
|
|
static string idx2string(const int* idx, int dims) |
|
{ |
|
char buf[256]; |
|
char* ptr = buf; |
|
for( int k = 0; k < dims; k++ ) |
|
{ |
|
sprintf(ptr, "%4d ", idx[k]); |
|
ptr += strlen(ptr); |
|
} |
|
ptr[-1] = '\0'; |
|
return string(buf); |
|
} |
|
|
|
static const int* string2idx(const string& s, int* idx, int dims) |
|
{ |
|
const char* ptr = s.c_str(); |
|
for( int k = 0; k < dims; k++ ) |
|
{ |
|
int n = 0; |
|
sscanf(ptr, "%d%n", idx + k, &n); |
|
ptr += n; |
|
} |
|
return idx; |
|
} |
|
|
|
static double getValue(SparseMat& M, const int* idx, RNG& rng) |
|
{ |
|
int d = M.dims(); |
|
size_t hv = 0, *phv = 0; |
|
if( (unsigned)rng % 2 ) |
|
{ |
|
hv = d == 2 ? M.hash(idx[0], idx[1]) : |
|
d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); |
|
phv = &hv; |
|
} |
|
|
|
const uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], false, phv) : |
|
d == 3 ? M.ptr(idx[0], idx[1], idx[2], false, phv) : |
|
M.ptr(idx, false, phv); |
|
return !ptr ? 0 : M.type() == CV_32F ? *(float*)ptr : M.type() == CV_64F ? *(double*)ptr : 0; |
|
} |
|
|
|
static double getValue(const CvSparseMat* M, const int* idx) |
|
{ |
|
int type = 0; |
|
const uchar* ptr = cvPtrND(M, idx, &type, 0); |
|
return !ptr ? 0 : type == CV_32F ? *(float*)ptr : type == CV_64F ? *(double*)ptr : 0; |
|
} |
|
|
|
static void eraseValue(SparseMat& M, const int* idx, RNG& rng) |
|
{ |
|
int d = M.dims(); |
|
size_t hv = 0, *phv = 0; |
|
if( (unsigned)rng % 2 ) |
|
{ |
|
hv = d == 2 ? M.hash(idx[0], idx[1]) : |
|
d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); |
|
phv = &hv; |
|
} |
|
|
|
if( d == 2 ) |
|
M.erase(idx[0], idx[1], phv); |
|
else if( d == 3 ) |
|
M.erase(idx[0], idx[1], idx[2], phv); |
|
else |
|
M.erase(idx, phv); |
|
} |
|
|
|
static void eraseValue(CvSparseMat* M, const int* idx) |
|
{ |
|
cvClearND(M, idx); |
|
} |
|
|
|
static void setValue(SparseMat& M, const int* idx, double value, RNG& rng) |
|
{ |
|
int d = M.dims(); |
|
size_t hv = 0, *phv = 0; |
|
if( (unsigned)rng % 2 ) |
|
{ |
|
hv = d == 2 ? M.hash(idx[0], idx[1]) : |
|
d == 3 ? M.hash(idx[0], idx[1], idx[2]) : M.hash(idx); |
|
phv = &hv; |
|
} |
|
|
|
uchar* ptr = d == 2 ? M.ptr(idx[0], idx[1], true, phv) : |
|
d == 3 ? M.ptr(idx[0], idx[1], idx[2], true, phv) : |
|
M.ptr(idx, true, phv); |
|
if( M.type() == CV_32F ) |
|
*(float*)ptr = (float)value; |
|
else if( M.type() == CV_64F ) |
|
*(double*)ptr = value; |
|
else |
|
CV_Error(CV_StsUnsupportedFormat, ""); |
|
} |
|
|
|
void Core_ArrayOpTest::run( int /* start_from */) |
|
{ |
|
int errcount = 0; |
|
|
|
// dense matrix operations |
|
{ |
|
int sz3[] = {5, 10, 15}; |
|
MatND A(3, sz3, CV_32F), B(3, sz3, CV_16SC4); |
|
CvMatND matA = A, matB = B; |
|
RNG rng; |
|
rng.fill(A, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10)); |
|
rng.fill(B, CV_RAND_UNI, Scalar::all(-10), Scalar::all(10)); |
|
|
|
int idx0[] = {3,4,5}, idx1[] = {0, 9, 7}; |
|
float val0 = 130; |
|
Scalar val1(-1000, 30, 3, 8); |
|
cvSetRealND(&matA, idx0, val0); |
|
cvSetReal3D(&matA, idx1[0], idx1[1], idx1[2], -val0); |
|
cvSetND(&matB, idx0, val1); |
|
cvSet3D(&matB, idx1[0], idx1[1], idx1[2], -val1); |
|
Ptr<CvMatND> matC = cvCloneMatND(&matB); |
|
|
|
if( A.at<float>(idx0[0], idx0[1], idx0[2]) != val0 || |
|
A.at<float>(idx1[0], idx1[1], idx1[2]) != -val0 || |
|
cvGetReal3D(&matA, idx0[0], idx0[1], idx0[2]) != val0 || |
|
cvGetRealND(&matA, idx1) != -val0 || |
|
|
|
Scalar(B.at<Vec4s>(idx0[0], idx0[1], idx0[2])) != val1 || |
|
Scalar(B.at<Vec4s>(idx1[0], idx1[1], idx1[2])) != -val1 || |
|
Scalar(cvGet3D(matC, idx0[0], idx0[1], idx0[2])) != val1 || |
|
Scalar(cvGetND(matC, idx1)) != -val1 ) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "one of cvSetReal3D, cvSetRealND, cvSet3D, cvSetND " |
|
"or the corresponding *Get* functions is not correct\n"); |
|
errcount++; |
|
} |
|
} |
|
|
|
RNG rng; |
|
const int MAX_DIM = 5, MAX_DIM_SZ = 10; |
|
// sparse matrix operations |
|
for( int si = 0; si < 10; si++ ) |
|
{ |
|
int depth = (unsigned)rng % 2 == 0 ? CV_32F : CV_64F; |
|
int dims = ((unsigned)rng % MAX_DIM) + 1; |
|
int i, k, size[MAX_DIM]={0}, idx[MAX_DIM]={0}; |
|
vector<string> all_idxs; |
|
vector<double> all_vals; |
|
vector<double> all_vals2; |
|
string sidx, min_sidx, max_sidx; |
|
double min_val=0, max_val=0; |
|
|
|
int p = 1; |
|
for( k = 0; k < dims; k++ ) |
|
{ |
|
size[k] = ((unsigned)rng % MAX_DIM_SZ) + 1; |
|
p *= size[k]; |
|
} |
|
SparseMat M( dims, size, depth ); |
|
map<string, double> M0; |
|
|
|
int nz0 = (unsigned)rng % max(p/5,10); |
|
nz0 = min(max(nz0, 1), p); |
|
all_vals.resize(nz0); |
|
all_vals2.resize(nz0); |
|
Mat_<double> _all_vals(all_vals), _all_vals2(all_vals2); |
|
rng.fill(_all_vals, CV_RAND_UNI, Scalar(-1000), Scalar(1000)); |
|
if( depth == CV_32F ) |
|
{ |
|
Mat _all_vals_f; |
|
_all_vals.convertTo(_all_vals_f, CV_32F); |
|
_all_vals_f.convertTo(_all_vals, CV_64F); |
|
} |
|
_all_vals.convertTo(_all_vals2, _all_vals2.type(), 2); |
|
if( depth == CV_32F ) |
|
{ |
|
Mat _all_vals2_f; |
|
_all_vals2.convertTo(_all_vals2_f, CV_32F); |
|
_all_vals2_f.convertTo(_all_vals2, CV_64F); |
|
} |
|
|
|
minMaxLoc(_all_vals, &min_val, &max_val); |
|
double _norm0 = norm(_all_vals, CV_C); |
|
double _norm1 = norm(_all_vals, CV_L1); |
|
double _norm2 = norm(_all_vals, CV_L2); |
|
|
|
for( i = 0; i < nz0; i++ ) |
|
{ |
|
for(;;) |
|
{ |
|
for( k = 0; k < dims; k++ ) |
|
idx[k] = (unsigned)rng % size[k]; |
|
sidx = idx2string(idx, dims); |
|
if( M0.count(sidx) == 0 ) |
|
break; |
|
} |
|
all_idxs.push_back(sidx); |
|
M0[sidx] = all_vals[i]; |
|
if( all_vals[i] == min_val ) |
|
min_sidx = sidx; |
|
if( all_vals[i] == max_val ) |
|
max_sidx = sidx; |
|
setValue(M, idx, all_vals[i], rng); |
|
double v = getValue(M, idx, rng); |
|
if( v != all_vals[i] ) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%d. immediately after SparseMat[%s]=%.20g the current value is %.20g\n", |
|
i, sidx.c_str(), all_vals[i], v); |
|
errcount++; |
|
break; |
|
} |
|
} |
|
|
|
Ptr<CvSparseMat> M2 = (CvSparseMat*)M; |
|
MatND Md; |
|
M.copyTo(Md); |
|
SparseMat M3; SparseMat(Md).convertTo(M3, Md.type(), 2); |
|
|
|
int nz1 = (int)M.nzcount(), nz2 = (int)M3.nzcount(); |
|
double norm0 = norm(M, CV_C); |
|
double norm1 = norm(M, CV_L1); |
|
double norm2 = norm(M, CV_L2); |
|
double eps = depth == CV_32F ? FLT_EPSILON*100 : DBL_EPSILON*1000; |
|
|
|
if( nz1 != nz0 || nz2 != nz0) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "%d: The number of non-zero elements before/after converting to/from dense matrix is not correct: %d/%d (while it should be %d)\n", |
|
si, nz1, nz2, nz0 ); |
|
break; |
|
} |
|
|
|
if( fabs(norm0 - _norm0) > fabs(_norm0)*eps || |
|
fabs(norm1 - _norm1) > fabs(_norm1)*eps || |
|
fabs(norm2 - _norm2) > fabs(_norm2)*eps ) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "%d: The norms are different: %.20g/%.20g/%.20g vs %.20g/%.20g/%.20g\n", |
|
si, norm0, norm1, norm2, _norm0, _norm1, _norm2 ); |
|
break; |
|
} |
|
|
|
int n = (unsigned)rng % max(p/5,10); |
|
n = min(max(n, 1), p) + nz0; |
|
|
|
for( i = 0; i < n; i++ ) |
|
{ |
|
double val1, val2, val3, val0; |
|
if(i < nz0) |
|
{ |
|
sidx = all_idxs[i]; |
|
string2idx(sidx, idx, dims); |
|
val0 = all_vals[i]; |
|
} |
|
else |
|
{ |
|
for( k = 0; k < dims; k++ ) |
|
idx[k] = (unsigned)rng % size[k]; |
|
sidx = idx2string(idx, dims); |
|
val0 = M0[sidx]; |
|
} |
|
val1 = getValue(M, idx, rng); |
|
val2 = getValue(M2, idx); |
|
val3 = getValue(M3, idx, rng); |
|
|
|
if( val1 != val0 || val2 != val0 || fabs(val3 - val0*2) > fabs(val0*2)*FLT_EPSILON ) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "SparseMat M[%s] = %g/%g/%g (while it should be %g)\n", sidx.c_str(), val1, val2, val3, val0 ); |
|
break; |
|
} |
|
} |
|
|
|
for( i = 0; i < n; i++ ) |
|
{ |
|
double val1, val2; |
|
if(i < nz0) |
|
{ |
|
sidx = all_idxs[i]; |
|
string2idx(sidx, idx, dims); |
|
} |
|
else |
|
{ |
|
for( k = 0; k < dims; k++ ) |
|
idx[k] = (unsigned)rng % size[k]; |
|
sidx = idx2string(idx, dims); |
|
} |
|
eraseValue(M, idx, rng); |
|
eraseValue(M2, idx); |
|
val1 = getValue(M, idx, rng); |
|
val2 = getValue(M2, idx); |
|
if( val1 != 0 || val2 != 0 ) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "SparseMat: after deleting M[%s], it is =%g/%g (while it should be 0)\n", sidx.c_str(), val1, val2 ); |
|
break; |
|
} |
|
} |
|
|
|
int nz = (int)M.nzcount(); |
|
if( nz != 0 ) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "The number of non-zero elements after removing all the elements = %d (while it should be 0)\n", nz ); |
|
break; |
|
} |
|
|
|
int idx1[MAX_DIM], idx2[MAX_DIM]; |
|
double val1 = 0, val2 = 0; |
|
M3 = SparseMat(Md); |
|
minMaxLoc(M3, &val1, &val2, idx1, idx2); |
|
string s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims); |
|
if( val1 != min_val || val2 != max_val || s1 != min_sidx || s2 != max_sidx ) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "%d. Sparse: The value and positions of minimum/maximum elements are different from the reference values and positions:\n\t" |
|
"(%g, %g, %s, %s) vs (%g, %g, %s, %s)\n", si, val1, val2, s1.c_str(), s2.c_str(), |
|
min_val, max_val, min_sidx.c_str(), max_sidx.c_str()); |
|
break; |
|
} |
|
|
|
minMaxIdx(Md, &val1, &val2, idx1, idx2); |
|
s1 = idx2string(idx1, dims), s2 = idx2string(idx2, dims); |
|
if( (min_val < 0 && (val1 != min_val || s1 != min_sidx)) || |
|
(max_val > 0 && (val2 != max_val || s2 != max_sidx)) ) |
|
{ |
|
errcount++; |
|
ts->printf(cvtest::TS::LOG, "%d. Dense: The value and positions of minimum/maximum elements are different from the reference values and positions:\n\t" |
|
"(%g, %g, %s, %s) vs (%g, %g, %s, %s)\n", si, val1, val2, s1.c_str(), s2.c_str(), |
|
min_val, max_val, min_sidx.c_str(), max_sidx.c_str()); |
|
break; |
|
} |
|
} |
|
|
|
ts->set_failed_test_info(errcount == 0 ? cvtest::TS::OK : cvtest::TS::FAIL_INVALID_OUTPUT); |
|
} |
|
|
|
|
|
template <class ElemType> |
|
int calcDiffElemCountImpl(const vector<Mat>& mv, const Mat& m) |
|
{ |
|
int diffElemCount = 0; |
|
const int mChannels = m.channels(); |
|
for(int y = 0; y < m.rows; y++) |
|
{ |
|
for(int x = 0; x < m.cols; x++) |
|
{ |
|
const ElemType* mElem = &m.at<ElemType>(y,x*mChannels); |
|
int loc = 0; |
|
for(size_t i = 0; i < mv.size(); i++) |
|
{ |
|
const size_t mvChannel = mv[i].channels(); |
|
const ElemType* mvElem = &mv[i].at<ElemType>(y,x*(int)mvChannel); |
|
for(size_t li = 0; li < mvChannel; li++) |
|
if(mElem[loc + li] != mvElem[li]) |
|
diffElemCount++; |
|
loc += mvChannel; |
|
} |
|
CV_Assert(loc == mChannels); |
|
} |
|
} |
|
return diffElemCount; |
|
} |
|
|
|
static |
|
int calcDiffElemCount(const vector<Mat>& mv, const Mat& m) |
|
{ |
|
int depth = m.depth(); |
|
switch (depth) |
|
{ |
|
case CV_8U: |
|
return calcDiffElemCountImpl<uchar>(mv, m); |
|
case CV_8S: |
|
return calcDiffElemCountImpl<char>(mv, m); |
|
case CV_16U: |
|
return calcDiffElemCountImpl<unsigned short>(mv, m); |
|
case CV_16S: |
|
return calcDiffElemCountImpl<short int>(mv, m); |
|
case CV_32S: |
|
return calcDiffElemCountImpl<int>(mv, m); |
|
case CV_32F: |
|
return calcDiffElemCountImpl<float>(mv, m); |
|
case CV_64F: |
|
return calcDiffElemCountImpl<double>(mv, m); |
|
} |
|
|
|
return INT_MAX; |
|
} |
|
|
|
class Core_MergeSplitBaseTest : public cvtest::BaseTest |
|
{ |
|
protected: |
|
virtual int run_case(int depth, size_t channels, const Size& size, RNG& rng) = 0; |
|
|
|
virtual void run(int) |
|
{ |
|
// m is Mat |
|
// mv is vector<Mat> |
|
const int minMSize = 1; |
|
const int maxMSize = 100; |
|
const size_t maxMvSize = 10; |
|
|
|
RNG& rng = theRNG(); |
|
Size mSize(rng.uniform(minMSize, maxMSize), rng.uniform(minMSize, maxMSize)); |
|
size_t mvSize = rng.uniform(1, maxMvSize); |
|
|
|
int res = cvtest::TS::OK, curRes = res; |
|
curRes = run_case(CV_8U, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
curRes = run_case(CV_8S, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
curRes = run_case(CV_16U, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
curRes = run_case(CV_16S, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
curRes = run_case(CV_32S, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
curRes = run_case(CV_32F, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
curRes = run_case(CV_64F, mvSize, mSize, rng); |
|
res = curRes != cvtest::TS::OK ? curRes : res; |
|
|
|
ts->set_failed_test_info(res); |
|
} |
|
}; |
|
|
|
class Core_MergeTest : public Core_MergeSplitBaseTest |
|
{ |
|
public: |
|
Core_MergeTest() {} |
|
~Core_MergeTest() {} |
|
|
|
protected: |
|
virtual int run_case(int depth, size_t matCount, const Size& size, RNG& rng) |
|
{ |
|
const int maxMatChannels = 10; |
|
|
|
vector<Mat> src(matCount); |
|
int channels = 0; |
|
for(size_t i = 0; i < src.size(); i++) |
|
{ |
|
Mat m(size, CV_MAKETYPE(depth, rng.uniform(1,maxMatChannels))); |
|
rng.fill(m, RNG::UNIFORM, 0, 100, true); |
|
channels += m.channels(); |
|
src[i] = m; |
|
} |
|
|
|
Mat dst; |
|
merge(src, dst); |
|
|
|
// check result |
|
stringstream commonLog; |
|
commonLog << "Depth " << depth << " :"; |
|
if(dst.depth() != depth) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s incorrect depth of dst (%d instead of %d)\n", |
|
commonLog.str().c_str(), dst.depth(), depth); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
if(dst.size() != size) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s incorrect size of dst (%d x %d instead of %d x %d)\n", |
|
commonLog.str().c_str(), dst.rows, dst.cols, size.height, size.width); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
if(dst.channels() != channels) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s: incorrect channels count of dst (%d instead of %d)\n", |
|
commonLog.str().c_str(), dst.channels(), channels); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
|
|
int diffElemCount = calcDiffElemCount(src, dst); |
|
if(diffElemCount > 0) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s: there are incorrect elements in dst (part of them is %f)\n", |
|
commonLog.str().c_str(), static_cast<float>(diffElemCount)/(channels*size.area())); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
|
|
return cvtest::TS::OK; |
|
} |
|
}; |
|
|
|
class Core_SplitTest : public Core_MergeSplitBaseTest |
|
{ |
|
public: |
|
Core_SplitTest() {} |
|
~Core_SplitTest() {} |
|
|
|
protected: |
|
virtual int run_case(int depth, size_t channels, const Size& size, RNG& rng) |
|
{ |
|
Mat src(size, CV_MAKETYPE(depth, (int)channels)); |
|
rng.fill(src, RNG::UNIFORM, 0, 100, true); |
|
|
|
vector<Mat> dst; |
|
split(src, dst); |
|
|
|
// check result |
|
stringstream commonLog; |
|
commonLog << "Depth " << depth << " :"; |
|
if(dst.size() != channels) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s incorrect count of matrices in dst (%d instead of %d)\n", |
|
commonLog.str().c_str(), dst.size(), channels); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
for(size_t i = 0; i < dst.size(); i++) |
|
{ |
|
if(dst[i].size() != size) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s incorrect size of dst[%d] (%d x %d instead of %d x %d)\n", |
|
commonLog.str().c_str(), i, dst[i].rows, dst[i].cols, size.height, size.width); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
if(dst[i].depth() != depth) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s: incorrect depth of dst[%d] (%d instead of %d)\n", |
|
commonLog.str().c_str(), i, dst[i].depth(), depth); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
if(dst[i].channels() != 1) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s: incorrect channels count of dst[%d] (%d instead of %d)\n", |
|
commonLog.str().c_str(), i, dst[i].channels(), 1); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
} |
|
|
|
int diffElemCount = calcDiffElemCount(dst, src); |
|
if(diffElemCount > 0) |
|
{ |
|
ts->printf(cvtest::TS::LOG, "%s: there are incorrect elements in dst (part of them is %f)\n", |
|
commonLog.str().c_str(), static_cast<float>(diffElemCount)/(channels*size.area())); |
|
return cvtest::TS::FAIL_INVALID_OUTPUT; |
|
} |
|
|
|
return cvtest::TS::OK; |
|
} |
|
}; |
|
|
|
TEST(Core_PCA, accuracy) { Core_PCATest test; test.safe_run(); } |
|
TEST(Core_Reduce, accuracy) { Core_ReduceTest test; test.safe_run(); } |
|
TEST(Core_Array, basic_operations) { Core_ArrayOpTest test; test.safe_run(); } |
|
|
|
TEST(Core_Merge, shape_operations) { Core_MergeTest test; test.safe_run(); } |
|
TEST(Core_Split, shape_operations) { Core_SplitTest test; test.safe_run(); } |
|
|
|
|
|
TEST(Core_IOArray, submat_assignment) |
|
{ |
|
Mat1f A = Mat1f::zeros(2,2); |
|
Mat1f B = Mat1f::ones(1,3); |
|
|
|
EXPECT_THROW( B.colRange(0,3).copyTo(A.row(0)), cv::Exception ); |
|
|
|
EXPECT_NO_THROW( B.colRange(0,2).copyTo(A.row(0)) ); |
|
|
|
EXPECT_EQ( 1.0f, A(0,0) ); |
|
EXPECT_EQ( 1.0f, A(0,1) ); |
|
} |
|
|
|
void OutputArray_create1(OutputArray m) { m.create(1, 2, CV_32S); } |
|
void OutputArray_create2(OutputArray m) { m.create(1, 3, CV_32F); } |
|
|
|
TEST(Core_IOArray, submat_create) |
|
{ |
|
Mat1f A = Mat1f::zeros(2,2); |
|
|
|
EXPECT_THROW( OutputArray_create1(A.row(0)), cv::Exception ); |
|
EXPECT_THROW( OutputArray_create2(A.row(0)), cv::Exception ); |
|
} |
|
|
|
TEST(Core_Mat, reshape_1942) |
|
{ |
|
cv::Mat A = (cv::Mat_<float>(2,3) << 3.4884074, 1.4159607, 0.78737736, 2.3456569, -0.88010466, 0.3009364); |
|
int cn = 0; |
|
ASSERT_NO_THROW( |
|
cv::Mat_<float> M = A.reshape(3); |
|
cn = M.channels(); |
|
); |
|
ASSERT_EQ(1, cn); |
|
}
|
|
|