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opencv/modules/core/test/test_mat.cpp

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#include "test_precomp.hpp"
#include <map>
using namespace cv;
using namespace std;
class Core_ReduceTest : public cvtest::BaseTest
{
public:
Core_ReduceTest() {}
protected:
void run( int);
int checkOp( const Mat& src, int dstType, int opType, const Mat& opRes, int dim );
int checkCase( int srcType, int dstType, int dim, Size sz );
int checkDim( int dim, Size sz );
int checkSize( Size sz );
};
template<class Type>
void testReduce( const Mat& src, Mat& sum, Mat& avg, Mat& max, Mat& min, int dim )
{
assert( src.channels() == 1 );
if( dim == 0 ) // row
{
sum.create( 1, src.cols, CV_64FC1 );
max.create( 1, src.cols, CV_64FC1 );
min.create( 1, src.cols, CV_64FC1 );
}
else
{
sum.create( src.rows, 1, CV_64FC1 );
max.create( src.rows, 1, CV_64FC1 );
min.create( src.rows, 1, CV_64FC1 );
}
sum.setTo(Scalar(0));
max.setTo(Scalar(-DBL_MAX));
min.setTo(Scalar(DBL_MAX));
const Mat_<Type>& src_ = src;
Mat_<double>& sum_ = (Mat_<double>&)sum;
Mat_<double>& min_ = (Mat_<double>&)min;
Mat_<double>& max_ = (Mat_<double>&)max;
if( dim == 0 )
{
for( int ri = 0; ri < src.rows; ri++ )
{
for( int ci = 0; ci < src.cols; ci++ )
{
sum_(0, ci) += src_(ri, ci);
max_(0, ci) = std::max( max_(0, ci), (double)src_(ri, ci) );
min_(0, ci) = std::min( min_(0, ci), (double)src_(ri, ci) );
}
}
}
else
{
for( int ci = 0; ci < src.cols; ci++ )
{
for( int ri = 0; ri < src.rows; ri++ )
{
sum_(ri, 0) += src_(ri, ci);
max_(ri, 0) = std::max( max_(ri, 0), (double)src_(ri, ci) );
min_(ri, 0) = std::min( min_(ri, 0), (double)src_(ri, ci) );
}
}
}
sum.convertTo( avg, CV_64FC1 );
avg = avg * (1.0 / (dim==0 ? (double)src.rows : (double)src.cols));
}
void getMatTypeStr( int type, string& str)
{
str = type == CV_8UC1 ? "CV_8UC1" :
type == CV_8SC1 ? "CV_8SC1" :
type == CV_16UC1 ? "CV_16UC1" :
type == CV_16SC1 ? "CV_16SC1" :
type == CV_32SC1 ? "CV_32SC1" :
type == CV_32FC1 ? "CV_32FC1" :
type == CV_64FC1 ? "CV_64FC1" : "unsupported matrix type";
}
int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat& opRes, int dim )
{
int srcType = src.type();
bool support = false;
if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
{
if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_16U && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_16S && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_32F && (dstType == CV_32F || dstType == CV_64F) )
support = true;
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
else if( opType == CV_REDUCE_MAX )
{
if( srcType == CV_8U && dstType == CV_8U )
support = true;
if( srcType == CV_32F && dstType == CV_32F )
support = true;
if( srcType == CV_64F && dstType == CV_64F )
support = true;
}
else if( opType == CV_REDUCE_MIN )
{
if( srcType == CV_8U && dstType == CV_8U)
support = true;
if( srcType == CV_32F && dstType == CV_32F)
support = true;
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
if( !support )
return cvtest::TS::OK;
double eps = 0.0;
if ( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
{
if ( dstType == CV_32F )
eps = 1.e-5;
else if( dstType == CV_64F )
eps = 1.e-8;
else if ( dstType == CV_32S )
eps = 0.6;
}
assert( opRes.type() == CV_64FC1 );
Mat _dst, dst, diff;
reduce( src, _dst, dim, opType, dstType );
_dst.convertTo( dst, CV_64FC1 );
absdiff( opRes,dst,diff );
bool check = false;
if (dstType == CV_32F || dstType == CV_64F)
check = countNonZero(diff>eps*dst) > 0;
else
check = countNonZero(diff>eps) > 0;
if( check )
{
char msg[100];
const char* opTypeStr = opType == CV_REDUCE_SUM ? "CV_REDUCE_SUM" :
opType == CV_REDUCE_AVG ? "CV_REDUCE_AVG" :
opType == CV_REDUCE_MAX ? "CV_REDUCE_MAX" :
opType == CV_REDUCE_MIN ? "CV_REDUCE_MIN" : "unknown operation type";
string srcTypeStr, dstTypeStr;
getMatTypeStr( src.type(), srcTypeStr );
getMatTypeStr( dstType, dstTypeStr );
const char* dimStr = dim == 0 ? "ROWS" : "COLS";
sprintf( msg, "bad accuracy with srcType = %s, dstType = %s, opType = %s, dim = %s",
srcTypeStr.c_str(), dstTypeStr.c_str(), opTypeStr, dimStr );
ts->printf( cvtest::TS::LOG, msg );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
return cvtest::TS::OK;
}
int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz )
{
int code = cvtest::TS::OK, tempCode;
Mat src, sum, avg, max, min;
src.create( sz, srcType );
randu( src, Scalar(0), Scalar(100) );
if( srcType == CV_8UC1 )
testReduce<uchar>( src, sum, avg, max, min, dim );
else if( srcType == CV_8SC1 )
testReduce<char>( src, sum, avg, max, min, dim );
else if( srcType == CV_16UC1 )
testReduce<unsigned short int>( src, sum, avg, max, min, dim );
else if( srcType == CV_16SC1 )
testReduce<short int>( src, sum, avg, max, min, dim );
else if( srcType == CV_32SC1 )
testReduce<int>( src, sum, avg, max, min, dim );
else if( srcType == CV_32FC1 )
testReduce<float>( src, sum, avg, max, min, dim );
else if( srcType == CV_64FC1 )
testReduce<double>( src, sum, avg, max, min, dim );
else
assert( 0 );
// 1. sum
tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 2. avg
tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 3. max
tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 4. min
tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
}
int Core_ReduceTest::checkDim( int dim, Size sz )
{
int code = cvtest::TS::OK, tempCode;
// CV_8UC1
tempCode = checkCase( CV_8UC1, CV_8UC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_8UC1, CV_32SC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_8UC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_8UC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_16UC1
tempCode = checkCase( CV_16UC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_16UC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_16SC1
tempCode = checkCase( CV_16SC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_16SC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_32FC1
tempCode = checkCase( CV_32FC1, CV_32FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkCase( CV_32FC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// CV_64FC1
tempCode = checkCase( CV_64FC1, CV_64FC1, dim, sz );
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
}
int Core_ReduceTest::checkSize( Size sz )
{
int code = cvtest::TS::OK, tempCode;
tempCode = checkDim( 0, sz ); // rows
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkDim( 1, sz ); // cols
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
}
void Core_ReduceTest::run( int )
{
int code = cvtest::TS::OK, tempCode;
tempCode = checkSize( Size(1,1) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkSize( Size(1,100) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkSize( Size(100,1) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
tempCode = checkSize( Size(1000,500) );
code = tempCode != cvtest::TS::OK ? tempCode : code;
ts->set_failed_test_info( code );
}
#define CHECK_C
class Core_PCATest : public cvtest::BaseTest
{
public:
Core_PCATest() {}
protected:
void run(int)
{
const Size sz(200, 500);
double diffPrjEps, diffBackPrjEps,
prjEps, backPrjEps,
evalEps, evecEps;
int maxComponents = 100;
double retainedVariance = 0.95;
Mat rPoints(sz, CV_32FC1), rTestPoints(sz, CV_32FC1);
RNG& rng = ts->get_rng();
rng.fill( rPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
rng.fill( rTestPoints, RNG::UNIFORM, Scalar::all(0.0), Scalar::all(1.0) );
PCA rPCA( rPoints, Mat(), CV_PCA_DATA_AS_ROW, maxComponents ), cPCA;
// 1. check C++ PCA & ROW
Mat rPrjTestPoints = rPCA.project( rTestPoints );
Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints );
Mat avg(1, sz.width, CV_32FC1 );
reduce( rPoints, avg, 0, CV_REDUCE_AVG );
Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec;
Q = Qt * Q;
Q = Q /(float)rPoints.rows;
eigen( Q, eval, evec );
/*SVD svd(Q);
evec = svd.vt;
eval = svd.w;*/
Mat subEval( maxComponents, 1, eval.type(), eval.ptr() ),
subEvec( maxComponents, evec.cols, evec.type(), evec.ptr() );
#ifdef CHECK_C
Mat prjTestPoints, backPrjTestPoints, cPoints = rPoints.t(), cTestPoints = rTestPoints.t();
CvMat _points, _testPoints, _avg, _eval, _evec, _prjTestPoints, _backPrjTestPoints;
#endif
// check eigen()
double eigenEps = 1e-6;
double err;
for(int i = 0; i < Q.rows; i++ )
{
Mat v = evec.row(i).t();
Mat Qv = Q * v;
Mat lv = eval.at<float>(i,0) * v;
err = cvtest::norm( Qv, lv, NORM_L2 );
if( err > eigenEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of eigen(); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
}
// check pca eigenvalues
evalEps = 1e-6, evecEps = 1e-3;
err = cvtest::norm( rPCA.eigenvalues, subEval, NORM_L2 );
if( err > evalEps )
{
ts->printf( cvtest::TS::LOG, "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// check pca eigenvectors
for(int i = 0; i < subEvec.rows; i++)
{
Mat r0 = rPCA.eigenvectors.row(i);
Mat r1 = subEvec.row(i);
err = cvtest::norm( r0, r1, CV_L2 );
if( err > evecEps )
{
r1 *= -1;
double err2 = cvtest::norm(r0, r1, CV_L2);
if( err2 > evecEps )
{
Mat tmp;
absdiff(rPCA.eigenvectors, subEvec, tmp);
double mval = 0; Point mloc;
minMaxLoc(tmp, 0, &mval, 0, &mloc);
ts->printf( cvtest::TS::LOG, "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->printf( cvtest::TS::LOG, "max diff is %g at (i=%d, j=%d) (%g vs %g)\n",
mval, mloc.y, mloc.x, rPCA.eigenvectors.at<float>(mloc.y, mloc.x),
subEvec.at<float>(mloc.y, mloc.x));
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
}
}
prjEps = 1.265, backPrjEps = 1.265;
for( int i = 0; i < rTestPoints.rows; i++ )
{
// check pca project
Mat subEvec_t = subEvec.t();
Mat prj = rTestPoints.row(i) - avg; prj *= subEvec_t;
err = cvtest::norm(rPrjTestPoints.row(i), prj, CV_RELATIVE_L2);
if( err > prjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// check pca backProject
Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg;
err = cvtest::norm( rBackPrjTestPoints.row(i), backPrj, CV_RELATIVE_L2 );
if( err > backPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
}
// 2. check C++ PCA & COL
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, maxComponents );
diffPrjEps = 1, diffBackPrjEps = 1;
Mat ocvPrjTestPoints = cPCA.project(rTestPoints.t());
err = cvtest::norm(cv::abs(ocvPrjTestPoints), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
if( err > diffPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
if( err > diffBackPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
// 3. check C++ PCA w/retainedVariance
cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
diffPrjEps = 1, diffBackPrjEps = 1;
Mat rvPrjTestPoints = cPCA.project(rTestPoints.t());
if( cPCA.eigenvectors.rows > maxComponents)
err = cvtest::norm(cv::abs(rvPrjTestPoints.rowRange(0,maxComponents)), cv::abs(rPrjTestPoints.t()), CV_RELATIVE_L2 );
else
err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), CV_RELATIVE_L2 );
if( err > diffPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), CV_RELATIVE_L2 );
if( err > diffBackPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=0.95; err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
#ifdef CHECK_C
// 4. check C PCA & ROW
_points = rPoints;
_testPoints = rTestPoints;
_avg = avg;
_eval = eval;
_evec = evec;
prjTestPoints.create(rTestPoints.rows, maxComponents, rTestPoints.type() );
backPrjTestPoints.create(rPoints.size(), rPoints.type() );
_prjTestPoints = prjTestPoints;
_backPrjTestPoints = backPrjTestPoints;
cvCalcPCA( &_points, &_avg, &_eval, &_evec, CV_PCA_DATA_AS_ROW );
cvProjectPCA( &_testPoints, &_avg, &_evec, &_prjTestPoints );
cvBackProjectPCA( &_prjTestPoints, &_avg, &_evec, &_backPrjTestPoints );
err = cvtest::norm(prjTestPoints, rPrjTestPoints, CV_RELATIVE_L2);
if( err > diffPrjEps )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
err = cvtest::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 = cvtest::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 = cvtest::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
// Test read and write
FileStorage fs( "PCA_store.yml", FileStorage::WRITE );
rPCA.write( fs );
fs.release();
PCA lPCA;
fs.open( "PCA_store.yml", FileStorage::READ );
lPCA.read( fs.root() );
err = cvtest::norm( rPCA.eigenvectors, lPCA.eigenvectors, CV_RELATIVE_L2 );
if( err > 0 )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
err = cvtest::norm( rPCA.eigenvalues, lPCA.eigenvalues, CV_RELATIVE_L2 );
if( err > 0 )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
err = cvtest::norm( rPCA.mean, lPCA.mean, CV_RELATIVE_L2 );
if( err > 0 )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of write/load functions (YML); err = %f\n", err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
}
};
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, "");
}
template<typename Pixel>
struct InitializerFunctor{
/// Initializer for cv::Mat::forEach test
void operator()(Pixel & pixel, const int * idx) const {
pixel.x = idx[0];
pixel.y = idx[1];
pixel.z = idx[2];
}
};
template<typename Pixel>
struct InitializerFunctor5D{
/// Initializer for cv::Mat::forEach test (5 dimensional case)
void operator()(Pixel & pixel, const int * idx) const {
pixel[0] = idx[0];
pixel[1] = idx[1];
pixel[2] = idx[2];
pixel[3] = idx[3];
pixel[4] = idx[4];
}
};
template<typename Pixel>
struct EmptyFunctor
{
void operator()(const Pixel &, const int *) const {}
};
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++;
}
}
// test cv::Mat::forEach
{
const int dims[3] = { 101, 107, 7 };
typedef cv::Point3i Pixel;
cv::Mat a = cv::Mat::zeros(3, dims, CV_32SC3);
InitializerFunctor<Pixel> initializer;
a.forEach<Pixel>(initializer);
uint64 total = 0;
bool error_reported = false;
for (int i0 = 0; i0 < dims[0]; ++i0) {
for (int i1 = 0; i1 < dims[1]; ++i1) {
for (int i2 = 0; i2 < dims[2]; ++i2) {
Pixel& pixel = a.at<Pixel>(i0, i1, i2);
if (pixel.x != i0 || pixel.y != i1 || pixel.z != i2) {
if (!error_reported) {
ts->printf(cvtest::TS::LOG, "forEach is not correct.\n"
"First error detected at (%d, %d, %d).\n", pixel.x, pixel.y, pixel.z);
error_reported = true;
}
errcount++;
}
total += pixel.x;
total += pixel.y;
total += pixel.z;
}
}
}
uint64 total2 = 0;
for (size_t i = 0; i < sizeof(dims) / sizeof(dims[0]); ++i) {
total2 += ((dims[i] - 1) * dims[i] / 2) * dims[0] * dims[1] * dims[2] / dims[i];
}
if (total != total2) {
ts->printf(cvtest::TS::LOG, "forEach is not correct because total is invalid.\n");
errcount++;
}
}
// test cv::Mat::forEach
// with a matrix that has more dimensions than columns
// See https://github.com/opencv/opencv/issues/8447
{
const int dims[5] = { 2, 2, 2, 2, 2 };
typedef cv::Vec<int, 5> Pixel;
cv::Mat a = cv::Mat::zeros(5, dims, CV_32SC(5));
InitializerFunctor5D<Pixel> initializer;
a.forEach<Pixel>(initializer);
uint64 total = 0;
bool error_reported = false;
for (int i0 = 0; i0 < dims[0]; ++i0) {
for (int i1 = 0; i1 < dims[1]; ++i1) {
for (int i2 = 0; i2 < dims[2]; ++i2) {
for (int i3 = 0; i3 < dims[3]; ++i3) {
for (int i4 = 0; i4 < dims[4]; ++i4) {
const int i[5] = { i0, i1, i2, i3, i4 };
Pixel& pixel = a.at<Pixel>(i);
if (pixel[0] != i0 || pixel[1] != i1 || pixel[2] != i2 || pixel[3] != i3 || pixel[4] != i4) {
if (!error_reported) {
ts->printf(cvtest::TS::LOG, "forEach is not correct.\n"
"First error detected at position (%d, %d, %d, %d, %d), got value (%d, %d, %d, %d, %d).\n",
i0, i1, i2, i3, i4,
pixel[0], pixel[1], pixel[2], pixel[3], pixel[4]);
error_reported = true;
}
errcount++;
}
total += pixel[0];
total += pixel[1];
total += pixel[2];
total += pixel[3];
total += pixel[4];
}
}
}
}
}
uint64 total2 = 0;
for (size_t i = 0; i < sizeof(dims) / sizeof(dims[0]); ++i) {
total2 += ((dims[i] - 1) * dims[i] / 2) * dims[0] * dims[1] * dims[2] * dims[3] * dims[4] / dims[i];
}
if (total != total2) {
ts->printf(cvtest::TS::LOG, "forEach is not correct because total is invalid.\n");
errcount++;
}
}
// test const cv::Mat::forEach
{
const Mat a(10, 10, CV_32SC3);
Mat b(10, 10, CV_32SC3);
const Mat & c = b;
a.forEach<Point3i>(EmptyFunctor<Point3i>());
b.forEach<Point3i>(EmptyFunctor<const Point3i>());
c.forEach<Point3i>(EmptyFunctor<Point3i>());
// tests compilation, no runtime check is needed
}
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 = cvtest::norm(_all_vals, CV_C);
double _norm1 = cvtest::norm(_all_vals, CV_L1);
double _norm2 = cvtest::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(cvCreateSparseMat(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);
size_t 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 == (size_t)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, issue4457_pass_null_ptr)
{
ASSERT_ANY_THROW(cv::Mat mask(45, 45, CV_32F, 0));
}
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);
}
static void check_ndim_shape(const cv::Mat &mat, int cn, int ndims, const int *sizes)
{
EXPECT_EQ(mat.channels(), cn);
EXPECT_EQ(mat.dims, ndims);
if (mat.dims != ndims)
return;
for (int i = 0; i < ndims; i++)
EXPECT_EQ(mat.size[i], sizes[i]);
}
TEST(Core_Mat, reshape_ndims_2)
{
const cv::Mat A(8, 16, CV_8UC3);
cv::Mat B;
{
int new_sizes_mask[] = { 0, 3, 4, 4 };
int new_sizes_real[] = { 8, 3, 4, 4 };
ASSERT_NO_THROW(B = A.reshape(1, 4, new_sizes_mask));
check_ndim_shape(B, 1, 4, new_sizes_real);
}
{
int new_sizes[] = { 16, 8 };
ASSERT_NO_THROW(B = A.reshape(0, 2, new_sizes));
check_ndim_shape(B, 3, 2, new_sizes);
EXPECT_EQ(B.rows, new_sizes[0]);
EXPECT_EQ(B.cols, new_sizes[1]);
}
{
int new_sizes[] = { 2, 5, 1, 3 };
cv::Mat A_sliced = A(cv::Range::all(), cv::Range(0, 15));
ASSERT_ANY_THROW(A_sliced.reshape(4, 4, new_sizes));
}
}
TEST(Core_Mat, reshape_ndims_4)
{
const int sizes[] = { 2, 6, 4, 12 };
const cv::Mat A(4, sizes, CV_8UC3);
cv::Mat B;
{
int new_sizes_mask[] = { 0, 864 };
int new_sizes_real[] = { 2, 864 };
ASSERT_NO_THROW(B = A.reshape(1, 2, new_sizes_mask));
check_ndim_shape(B, 1, 2, new_sizes_real);
EXPECT_EQ(B.rows, new_sizes_real[0]);
EXPECT_EQ(B.cols, new_sizes_real[1]);
}
{
int new_sizes_mask[] = { 4, 0, 0, 2, 3 };
int new_sizes_real[] = { 4, 6, 4, 2, 3 };
ASSERT_NO_THROW(B = A.reshape(0, 5, new_sizes_mask));
check_ndim_shape(B, 3, 5, new_sizes_real);
}
{
int new_sizes_mask[] = { 1, 1 };
ASSERT_ANY_THROW(A.reshape(0, 2, new_sizes_mask));
}
{
int new_sizes_mask[] = { 4, 6, 3, 3, 0 };
ASSERT_ANY_THROW(A.reshape(0, 5, new_sizes_mask));
}
}
TEST(Core_Mat, push_back)
{
Mat a = (Mat_<float>(1,2) << 3.4884074f, 1.4159607f);
Mat b = (Mat_<float>(1,2) << 0.78737736f, 2.3456569f);
a.push_back(b);
ASSERT_EQ(2, a.cols);
ASSERT_EQ(2, a.rows);
ASSERT_FLOAT_EQ(3.4884074f, a.at<float>(0, 0));
ASSERT_FLOAT_EQ(1.4159607f, a.at<float>(0, 1));
ASSERT_FLOAT_EQ(0.78737736f, a.at<float>(1, 0));
ASSERT_FLOAT_EQ(2.3456569f, a.at<float>(1, 1));
Mat c = (Mat_<float>(2,2) << -0.88010466f, 0.3009364f, 2.22399974f, -5.45933905f);
ASSERT_EQ(c.rows, a.cols);
a.push_back(c.t());
ASSERT_EQ(2, a.cols);
ASSERT_EQ(4, a.rows);
ASSERT_FLOAT_EQ(3.4884074f, a.at<float>(0, 0));
ASSERT_FLOAT_EQ(1.4159607f, a.at<float>(0, 1));
ASSERT_FLOAT_EQ(0.78737736f, a.at<float>(1, 0));
ASSERT_FLOAT_EQ(2.3456569f, a.at<float>(1, 1));
ASSERT_FLOAT_EQ(-0.88010466f, a.at<float>(2, 0));
ASSERT_FLOAT_EQ(2.22399974f, a.at<float>(2, 1));
ASSERT_FLOAT_EQ(0.3009364f, a.at<float>(3, 0));
ASSERT_FLOAT_EQ(-5.45933905f, a.at<float>(3, 1));
a.push_back(Mat::ones(2, 2, CV_32FC1));
ASSERT_EQ(6, a.rows);
for(int row=4; row<a.rows; row++) {
for(int col=0; col<a.cols; col++) {
ASSERT_FLOAT_EQ(1.f, a.at<float>(row, col));
}
}
}
TEST(Core_Mat, copyNx1ToVector)
{
cv::Mat_<uchar> src(5, 1);
cv::Mat_<uchar> ref_dst8;
cv::Mat_<ushort> ref_dst16;
std::vector<uchar> dst8;
std::vector<ushort> dst16;
src << 1, 2, 3, 4, 5;
src.copyTo(ref_dst8);
src.copyTo(dst8);
ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst8, cv::Mat_<uchar>(dst8));
src.convertTo(ref_dst16, CV_16U);
src.convertTo(dst16, CV_16U);
ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst16, cv::Mat_<ushort>(dst16));
}
TEST(Core_Matx, fromMat_)
{
Mat_<double> a = (Mat_<double>(2,2) << 10, 11, 12, 13);
Matx22d b(a);
ASSERT_EQ( cvtest::norm(a, b, NORM_INF), 0.);
}
#ifdef CV_CXX11
TEST(Core_Matx, from_initializer_list)
{
Mat_<double> a = (Mat_<double>(2,2) << 10, 11, 12, 13);
Matx22d b = {10, 11, 12, 13};
ASSERT_EQ( cvtest::norm(a, b, NORM_INF), 0.);
}
TEST(Core_Mat, regression_9507)
{
cv::Mat m = Mat::zeros(5, 5, CV_8UC3);
cv::Mat m2{m};
EXPECT_EQ(25u, m2.total());
}
#endif // CXX11
TEST(Core_InputArray, empty)
{
vector<vector<Point> > data;
ASSERT_TRUE( _InputArray(data).empty() );
}
TEST(Core_CopyMask, bug1918)
{
Mat_<unsigned char> tmpSrc(100,100);
tmpSrc = 124;
Mat_<unsigned char> tmpMask(100,100);
tmpMask = 255;
Mat_<unsigned char> tmpDst(100,100);
tmpDst = 2;
tmpSrc.copyTo(tmpDst,tmpMask);
ASSERT_EQ(sum(tmpDst)[0], 124*100*100);
}
TEST(Core_SVD, orthogonality)
{
for( int i = 0; i < 2; i++ )
{
int type = i == 0 ? CV_32F : CV_64F;
Mat mat_D(2, 2, type);
mat_D.setTo(88.);
Mat mat_U, mat_W;
SVD::compute(mat_D, mat_W, mat_U, noArray(), SVD::FULL_UV);
mat_U *= mat_U.t();
ASSERT_LT(norm(mat_U, Mat::eye(2, 2, type), NORM_INF), 1e-5);
}
}
TEST(Core_SparseMat, footprint)
{
int n = 1000000;
int sz[] = { n, n };
SparseMat m(2, sz, CV_64F);
int nodeSize0 = (int)m.hdr->nodeSize;
double dataSize0 = ((double)m.hdr->pool.size() + (double)m.hdr->hashtab.size()*sizeof(size_t))*1e-6;
printf("before: node size=%d bytes, data size=%.0f Mbytes\n", nodeSize0, dataSize0);
for (int i = 0; i < n; i++)
{
m.ref<double>(i, i) = 1;
}
double dataSize1 = ((double)m.hdr->pool.size() + (double)m.hdr->hashtab.size()*sizeof(size_t))*1e-6;
double threshold = (n*nodeSize0*1.6 + n*2.*sizeof(size_t))*1e-6;
printf("after: data size=%.0f Mbytes, threshold=%.0f MBytes\n", dataSize1, threshold);
ASSERT_LE((int)m.hdr->nodeSize, 32);
ASSERT_LE(dataSize1, threshold);
}
// Can't fix without dirty hacks or broken user code (PR #4159)
TEST(Core_Mat_vector, DISABLED_OutputArray_create_getMat)
{
cv::Mat_<uchar> src_base(5, 1);
std::vector<uchar> dst8;
src_base << 1, 2, 3, 4, 5;
Mat src(src_base);
OutputArray _dst(dst8);
{
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
EXPECT_EQ(src.dims, dst.dims);
EXPECT_EQ(src.cols, dst.cols);
EXPECT_EQ(src.rows, dst.rows);
}
}
TEST(Core_Mat_vector, copyTo_roi_column)
{
cv::Mat_<uchar> src_base(5, 2);
std::vector<uchar> dst1;
src_base << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;
Mat src_full(src_base);
Mat src(src_full.col(0));
#if 0 // Can't fix without dirty hacks or broken user code (PR #4159)
OutputArray _dst(dst1);
{
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
EXPECT_EQ(src.dims, dst.dims);
EXPECT_EQ(src.cols, dst.cols);
EXPECT_EQ(src.rows, dst.rows);
}
#endif
std::vector<uchar> dst2;
src.copyTo(dst2);
std::cout << "src = " << src << std::endl;
std::cout << "dst = " << Mat(dst2) << std::endl;
EXPECT_EQ((size_t)5, dst2.size());
EXPECT_EQ(1, (int)dst2[0]);
EXPECT_EQ(3, (int)dst2[1]);
EXPECT_EQ(5, (int)dst2[2]);
EXPECT_EQ(7, (int)dst2[3]);
EXPECT_EQ(9, (int)dst2[4]);
}
TEST(Core_Mat_vector, copyTo_roi_row)
{
cv::Mat_<uchar> src_base(2, 5);
std::vector<uchar> dst1;
src_base << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;
Mat src_full(src_base);
Mat src(src_full.row(0));
OutputArray _dst(dst1);
{
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
EXPECT_EQ(src.dims, dst.dims);
EXPECT_EQ(src.cols, dst.cols);
EXPECT_EQ(src.rows, dst.rows);
}
std::vector<uchar> dst2;
src.copyTo(dst2);
std::cout << "src = " << src << std::endl;
std::cout << "dst = " << Mat(dst2) << std::endl;
EXPECT_EQ((size_t)5, dst2.size());
EXPECT_EQ(1, (int)dst2[0]);
EXPECT_EQ(2, (int)dst2[1]);
EXPECT_EQ(3, (int)dst2[2]);
EXPECT_EQ(4, (int)dst2[3]);
EXPECT_EQ(5, (int)dst2[4]);
}
TEST(Mat, regression_5991)
{
int sz[] = {2,3,2};
Mat mat(3, sz, CV_32F, Scalar(1));
ASSERT_NO_THROW(mat.convertTo(mat, CV_8U));
EXPECT_EQ(sz[0], mat.size[0]);
EXPECT_EQ(sz[1], mat.size[1]);
EXPECT_EQ(sz[2], mat.size[2]);
EXPECT_EQ(0, cvtest::norm(mat, Mat(3, sz, CV_8U, Scalar(1)), NORM_INF));
}
#ifdef OPENCV_TEST_BIGDATA
TEST(Mat, regression_6696_BigData_8Gb)
{
int width = 60000;
int height = 10000;
Mat destImageBGR = Mat(height, width, CV_8UC3, Scalar(1, 2, 3, 0));
Mat destImageA = Mat(height, width, CV_8UC1, Scalar::all(4));
vector<Mat> planes;
split(destImageBGR, planes);
planes.push_back(destImageA);
merge(planes, destImageBGR);
EXPECT_EQ(1, destImageBGR.at<Vec4b>(0)[0]);
EXPECT_EQ(2, destImageBGR.at<Vec4b>(0)[1]);
EXPECT_EQ(3, destImageBGR.at<Vec4b>(0)[2]);
EXPECT_EQ(4, destImageBGR.at<Vec4b>(0)[3]);
EXPECT_EQ(1, destImageBGR.at<Vec4b>(height-1, width-1)[0]);
EXPECT_EQ(2, destImageBGR.at<Vec4b>(height-1, width-1)[1]);
EXPECT_EQ(3, destImageBGR.at<Vec4b>(height-1, width-1)[2]);
EXPECT_EQ(4, destImageBGR.at<Vec4b>(height-1, width-1)[3]);
}
#endif
TEST(Reduce, regression_should_fail_bug_4594)
{
cv::Mat src = cv::Mat::eye(4, 4, CV_8U);
std::vector<int> dst;
EXPECT_THROW(cv::reduce(src, dst, 0, CV_REDUCE_MIN, CV_32S), cv::Exception);
EXPECT_THROW(cv::reduce(src, dst, 0, CV_REDUCE_MAX, CV_32S), cv::Exception);
EXPECT_NO_THROW(cv::reduce(src, dst, 0, CV_REDUCE_SUM, CV_32S));
EXPECT_NO_THROW(cv::reduce(src, dst, 0, CV_REDUCE_AVG, CV_32S));
}
TEST(Mat, push_back_vector)
{
cv::Mat result(1, 5, CV_32FC1);
std::vector<float> vec1(result.cols + 1);
std::vector<int> vec2(result.cols);
EXPECT_THROW(result.push_back(vec1), cv::Exception);
EXPECT_THROW(result.push_back(vec2), cv::Exception);
vec1.resize(result.cols);
for (int i = 0; i < 5; ++i)
result.push_back(cv::Mat(vec1).reshape(1, 1));
ASSERT_EQ(6, result.rows);
}
TEST(Mat, regression_5917_clone_empty)
{
Mat cloned;
Mat_<Point2f> source(5, 0);
ASSERT_NO_THROW(cloned = source.clone());
}
TEST(Mat, regression_7873_mat_vector_initialize)
{
std::vector<int> dims;
dims.push_back(12);
dims.push_back(3);
dims.push_back(2);
Mat multi_mat(dims, CV_32FC1, cv::Scalar(0));
ASSERT_EQ(3, multi_mat.dims);
ASSERT_EQ(12, multi_mat.size[0]);
ASSERT_EQ(3, multi_mat.size[1]);
ASSERT_EQ(2, multi_mat.size[2]);
std::vector<Range> ranges;
ranges.push_back(Range(1, 2));
ranges.push_back(Range::all());
ranges.push_back(Range::all());
Mat sub_mat = multi_mat(ranges);
ASSERT_EQ(3, sub_mat.dims);
ASSERT_EQ(1, sub_mat.size[0]);
ASSERT_EQ(3, sub_mat.size[1]);
ASSERT_EQ(2, sub_mat.size[2]);
}
#ifdef CV_CXX_STD_ARRAY
TEST(Core_Mat_array, outputArray_create_getMat)
{
cv::Mat_<uchar> src_base(5, 1);
std::array<uchar, 5> dst8;
src_base << 1, 2, 3, 4, 5;
Mat src(src_base);
OutputArray _dst(dst8);
{
_dst.create(src.rows, src.cols, src.type());
Mat dst = _dst.getMat();
EXPECT_EQ(src.dims, dst.dims);
EXPECT_EQ(src.cols, dst.cols);
EXPECT_EQ(src.rows, dst.rows);
}
}
TEST(Core_Mat_array, copyTo_roi_column)
{
cv::Mat_<uchar> src_base(5, 2);
src_base << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;
Mat src_full(src_base);
Mat src(src_full.col(0));
std::array<uchar, 5> dst1;
src.copyTo(dst1);
std::cout << "src = " << src << std::endl;
std::cout << "dst = " << Mat(dst1) << std::endl;
EXPECT_EQ((size_t)5, dst1.size());
EXPECT_EQ(1, (int)dst1[0]);
EXPECT_EQ(3, (int)dst1[1]);
EXPECT_EQ(5, (int)dst1[2]);
EXPECT_EQ(7, (int)dst1[3]);
EXPECT_EQ(9, (int)dst1[4]);
}
TEST(Core_Mat_array, copyTo_roi_row)
{
cv::Mat_<uchar> src_base(2, 5);
std::array<uchar, 5> dst1;
src_base << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;
Mat src_full(src_base);
Mat src(src_full.row(0));
OutputArray _dst(dst1);
{
_dst.create(5, 1, src.type());
Mat dst = _dst.getMat();
EXPECT_EQ(src.dims, dst.dims);
EXPECT_EQ(1, dst.cols);
EXPECT_EQ(5, dst.rows);
}
std::array<uchar, 5> dst2;
src.copyTo(dst2);
std::cout << "src = " << src << std::endl;
std::cout << "dst = " << Mat(dst2) << std::endl;
EXPECT_EQ(1, (int)dst2[0]);
EXPECT_EQ(2, (int)dst2[1]);
EXPECT_EQ(3, (int)dst2[2]);
EXPECT_EQ(4, (int)dst2[3]);
EXPECT_EQ(5, (int)dst2[4]);
}
TEST(Core_Mat_array, SplitMerge)
{
std::array<cv::Mat, 3> src;
for(size_t i=0; i<src.size(); ++i) {
src[i].create(10, 10, CV_8U);
src[i] = 127 * i;
}
Mat merged;
merge(src, merged);
std::array<cv::Mat, 3> dst;
split(merged, dst);
Mat diff;
for(size_t i=0; i<dst.size(); ++i) {
absdiff(src[i], dst[i], diff);
EXPECT_EQ(0, countNonZero(diff));
}
}
#endif
TEST(Mat, regression_8680)
{
Mat_<Point2i> mat(3,1);
ASSERT_EQ(mat.channels(), 2);
mat.release();
ASSERT_EQ(mat.channels(), 2);
}
#ifdef CV_CXX11
TEST(Mat_, range_based_for)
{
Mat_<uchar> img = Mat_<uchar>::zeros(3, 3);
for(auto& pixel : img)
{
pixel = 1;
}
Mat_<uchar> ref(3, 3);
ref.setTo(Scalar(1));
ASSERT_DOUBLE_EQ(norm(img, ref), 0.);
}
TEST(Mat, from_initializer_list)
{
Mat A({1.f, 2.f, 3.f});
Mat_<float> B(3, 1); B << 1, 2, 3;
ASSERT_EQ(A.type(), CV_32F);
ASSERT_DOUBLE_EQ(norm(A, B, NORM_INF), 0.);
}
TEST(Mat_, from_initializer_list)
{
Mat_<float> A = {1, 2, 3};
Mat_<float> B(3, 1); B << 1, 2, 3;
ASSERT_DOUBLE_EQ(norm(A, B, NORM_INF), 0.);
}
#endif