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416bf3253d
opencv/modules/core/test/test_mat.cpp
Vadim Pisarevsky 416bf3253d
attempt to add 0d/1d mat support to OpenCV (#23473) 
* attempt to add 0d/1d mat support to OpenCV

* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.

* a step towards 'green' tests

* another little step towards 'green' tests

* calib test failures seem to be fixed now

* more fixes _core & _dnn

* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!

* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core

* disabled failing GAPI tests - too complex to dig into this compiler pipeline

* hopefully fixed java tests

* trying to fix some more tests

* quick followup fix

* continue to fix test failures and warnings

* quick followup fix

* trying to fix some more tests

* partly fixed support for 0D/scalar UMat's

* use updated parseReduce() from upstream

* trying to fix the remaining test failures

* fixed [ch]aruco tests in Python

* still trying to fix tests

* revert "fix" in dnn's CUDA tensor

* trying to fix dnn+CUDA test failures

* fixed 1D umat creation

* hopefully fixed remaining cuda test failures

* removed training whitespaces
2023-09-21 18:24:38 +03:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
#ifdef HAVE_EIGEN
#include <Eigen/Core>
#include <Eigen/Dense>
#include "opencv2/core/eigen.hpp"
#endif
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/bindings_utils.hpp"
namespace opencv_test { namespace {
class Core_ReduceTest : public cvtest::BaseTest
{
public:
Core_ReduceTest() {}
protected:
void run( int) CV_OVERRIDE;
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, Mat& sum2, int dim )
{
CV_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 );
sum2.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 );
sum2.create( src.rows, 1, CV_64FC1 );
}
sum.setTo(Scalar(0));
max.setTo(Scalar(-DBL_MAX));
min.setTo(Scalar(DBL_MAX));
sum2.setTo(Scalar(0));
const Mat_<Type>& src_ = src;
Mat_<double>& sum_ = (Mat_<double>&)sum;
Mat_<double>& min_ = (Mat_<double>&)min;
Mat_<double>& max_ = (Mat_<double>&)max;
Mat_<double>& sum2_ = (Mat_<double>&)sum2;
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) );
sum2_(0, ci) += ((double)src_(ri, 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) );
sum2_(ri, 0) += ((double)src_(ri, ci))*((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 == REDUCE_SUM || opType == REDUCE_AVG || opType == REDUCE_SUM2 )
{
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 == 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 == 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 == REDUCE_SUM || opType == REDUCE_AVG || opType == REDUCE_SUM2 )
{
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;
}
CV_Assert( opRes.type() == CV_64FC1 );
Mat _dst, dst, diff;
cv::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 == REDUCE_SUM ? "REDUCE_SUM" :
opType == REDUCE_AVG ? "REDUCE_AVG" :
opType == REDUCE_MAX ? "REDUCE_MAX" :
opType == REDUCE_MIN ? "REDUCE_MIN" :
opType == REDUCE_SUM2 ? "REDUCE_SUM2" :
"unknown operation type";
string srcTypeStr, dstTypeStr;
getMatTypeStr( src.type(), srcTypeStr );
getMatTypeStr( dstType, dstTypeStr );
const char* dimStr = dim == 0 ? "ROWS" : "COLS";
snprintf( msg, sizeof(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, sum2;
src.create( sz, srcType );
randu( src, Scalar(0), Scalar(100) );
if( srcType == CV_8UC1 )
testReduce<uchar>( src, sum, avg, max, min, sum2, dim );
else if( srcType == CV_8SC1 )
testReduce<char>( src, sum, avg, max, min, sum2, dim );
else if( srcType == CV_16UC1 )
testReduce<unsigned short int>( src, sum, avg, max, min, sum2, dim );
else if( srcType == CV_16SC1 )
testReduce<short int>( src, sum, avg, max, min, sum2, dim );
else if( srcType == CV_32SC1 )
testReduce<int>( src, sum, avg, max, min, sum2, dim );
else if( srcType == CV_32FC1 )
testReduce<float>( src, sum, avg, max, min, sum2, dim );
else if( srcType == CV_64FC1 )
testReduce<double>( src, sum, avg, max, min, sum2, dim );
else
CV_Assert( 0 );
// 1. sum
tempCode = checkOp( src, dstType, REDUCE_SUM, sum, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 2. avg
tempCode = checkOp( src, dstType, REDUCE_AVG, avg, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 3. max
tempCode = checkOp( src, dstType, REDUCE_MAX, max, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 4. min
tempCode = checkOp( src, dstType, REDUCE_MIN, min, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 5. sum2
tempCode = checkOp( src, dstType, REDUCE_SUM2, sum2, 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
TEST(Core_PCA, accuracy)
{
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(12345);
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 );
cv::reduce( rPoints, avg, 0, 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-4;
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 | NORM_RELATIVE);
EXPECT_LE(err, eigenEps) << "bad accuracy of eigen(); i = " << i;
}
// check pca eigenvalues
evalEps = 1e-5, evecEps = 5e-3;
err = cvtest::norm(rPCA.eigenvalues, subEval, NORM_L2 | NORM_RELATIVE);
EXPECT_LE(err , evalEps) << "pca.eigenvalues is incorrect (CV_PCA_DATA_AS_ROW)";
// check pca eigenvectors
for(int i = 0; i < subEvec.rows; i++)
{
Mat r0 = rPCA.eigenvectors.row(i);
Mat r1 = subEvec.row(i);
// eigenvectors have normalized length, but both directions v and -v are valid
double err1 = cvtest::norm(r0, r1, NORM_L2 | NORM_RELATIVE);
double err2 = cvtest::norm(r0, -r1, NORM_L2 | NORM_RELATIVE);
err = std::min(err1, err2);
if (err > evecEps)
{
Mat tmp;
absdiff(rPCA.eigenvectors, subEvec, tmp);
double mval = 0; Point mloc;
minMaxLoc(tmp, 0, &mval, 0, &mloc);
EXPECT_LE(err, evecEps) << "pca.eigenvectors is incorrect (CV_PCA_DATA_AS_ROW) at " << i << " "
<< cv::format("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))
<< "r0=" << r0 << std::endl
<< "r1=" << r1 << std::endl
<< "err1=" << err1 << " err2=" << err2
;
}
}
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, NORM_L2 | NORM_RELATIVE);
if (err < prjEps)
{
EXPECT_LE(err, prjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_ROW)";
continue;
}
// check pca backProject
Mat backPrj = rPrjTestPoints.row(i) * subEvec + avg;
err = cvtest::norm(rBackPrjTestPoints.row(i), backPrj, NORM_L2 | NORM_RELATIVE);
if (err > backPrjEps)
{
EXPECT_LE(err, backPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_ROW)";
continue;
}
}
// 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()), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL)";
err = cvtest::norm(cPCA.backProject(ocvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL)";
// 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()), NORM_L2 | NORM_RELATIVE);
else
err = cvtest::norm(cv::abs(rvPrjTestPoints), cv::abs(rPrjTestPoints.colRange(0,cPCA.eigenvectors.rows).t()), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of project() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
err = cvtest::norm(cPCA.backProject(rvPrjTestPoints), rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of backProject() (CV_PCA_DATA_AS_COL); retainedVariance=" << retainedVariance;
#ifdef CHECK_C
// 4. check C PCA & ROW
_points = cvMat(rPoints);
_testPoints = cvMat(rTestPoints);
_avg = cvMat(avg);
_eval = cvMat(eval);
_evec = cvMat(evec);
prjTestPoints.create(rTestPoints.rows, maxComponents, rTestPoints.type() );
backPrjTestPoints.create(rPoints.size(), rPoints.type() );
_prjTestPoints = cvMat(prjTestPoints);
_backPrjTestPoints = cvMat(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, NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_ROW)";
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints, NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_ROW)";
// 5. check C PCA & COL
_points = cvMat(cPoints);
_testPoints = cvMat(cTestPoints);
avg = avg.t(); _avg = cvMat(avg);
eval = eval.t(); _eval = cvMat(eval);
evec = evec.t(); _evec = cvMat(evec);
prjTestPoints = prjTestPoints.t(); _prjTestPoints = cvMat(prjTestPoints);
backPrjTestPoints = backPrjTestPoints.t(); _backPrjTestPoints = cvMat(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()), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffPrjEps) << "bad accuracy of cvProjectPCA() (CV_PCA_DATA_AS_COL)";
err = cvtest::norm(backPrjTestPoints, rBackPrjTestPoints.t(), NORM_L2 | NORM_RELATIVE);
ASSERT_LE(err, diffBackPrjEps) << "bad accuracy of cvBackProjectPCA() (CV_PCA_DATA_AS_COL)";
#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, NORM_L2 | NORM_RELATIVE);
EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
err = cvtest::norm(rPCA.eigenvalues, lPCA.eigenvalues, NORM_L2 | NORM_RELATIVE);
EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
err = cvtest::norm(rPCA.mean, lPCA.mean, NORM_L2 | NORM_RELATIVE);
EXPECT_LE(err, 0) << "bad accuracy of write/load functions (YML)";
}
class Core_ArrayOpTest : public cvtest::BaseTest
{
public:
Core_ArrayOpTest();
~Core_ArrayOpTest();
protected:
void run(int) CV_OVERRIDE;
};
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++ )
{
snprintf(ptr, sizeof(buf) - (ptr - buf), "%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, "");
}
#if defined(__GNUC__) && (__GNUC__ == 11 || __GNUC__ == 12)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Warray-bounds"
#endif
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];
}
};
#if defined(__GNUC__) && (__GNUC__ == 11 || __GNUC__ == 12)
#pragma GCC diagnostic pop
#endif
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 = cvMatND(A), matB = cvMatND(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, cvScalar(val1));
cvSet3D(&matB, idx1[0], idx1[1], idx1[2], cvScalar(-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 % std::max(p/5,10);
nz0 = std::min(std::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 = cv/*test*/::norm(_all_vals, CV_C);
double _norm1 = cv/*test*/::norm(_all_vals, CV_L1);
double _norm2 = cv/*test*/::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 = cv/*test*/::norm(M, CV_C);
double norm1 = cv/*test*/::norm(M, CV_L1);
double norm2 = cv/*test*/::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 % std::max(p/5,10);
n = std::min(std::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);
cv::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;
}
cv::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 T>
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 T* mElem = &m.at<T>(y, x*mChannels);
size_t loc = 0;
for(size_t i = 0; i < mv.size(); i++)
{
const size_t mvChannel = mv[i].channels();
const T* mvElem = &mv[i].at<T>(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) CV_OVERRIDE
{
// 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;
int 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) CV_OVERRIDE
{
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
std::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) CV_OVERRIDE
{
Mat src(size, CV_MAKETYPE(depth, (int)channels));
rng.fill(src, RNG::UNIFORM, 0, 100, true);
vector<Mat> dst;
split(src, dst);
// check result
std::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_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).reshape(1, 5));
src.convertTo(ref_dst16, CV_16U);
src.convertTo(dst16, CV_16U);
ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst16, cv::Mat_<ushort>(dst16).reshape(1, 5));
}
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.);
}
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());
}
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(cvtest::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();
dst = dst.reshape(dst.channels(), dst.rows);
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));
}
TEST(Mat, regression_9720)
{
Mat mat(1, 1, CV_32FC1);
mat.at<float>(0) = 1.f;
const float a = 0.1f;
Mat me1 = (Mat)(mat.mul((a / mat)));
Mat me2 = (Mat)(mat.mul((Mat)(a / mat)));
Mat me3 = (Mat)(mat.mul((a * mat)));
Mat me4 = (Mat)(mat.mul((Mat)(a * mat)));
EXPECT_EQ(me1.at<float>(0), me2.at<float>(0));
EXPECT_EQ(me3.at<float>(0), me4.at<float>(0));
}
#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, REDUCE_MIN, CV_32S), cv::Exception);
EXPECT_THROW(cv::reduce(src, dst, 0, REDUCE_MAX, CV_32S), cv::Exception);
EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_SUM, CV_32S));
EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_AVG, CV_32S));
EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_SUM2, 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]);
}
TEST(Mat, regression_10507_mat_setTo)
{
Size sz(6, 4);
Mat test_mask(sz, CV_8UC1, cv::Scalar::all(255));
test_mask.at<uchar>(1,0) = 0;
test_mask.at<uchar>(0,1) = 0;
for (int cn = 1; cn <= 4; cn++)
{
cv::Mat A(sz, CV_MAKE_TYPE(CV_32F, cn), cv::Scalar::all(5));
A.setTo(cv::Scalar::all(std::numeric_limits<float>::quiet_NaN()), test_mask);
int nans = 0;
for (int y = 0; y < A.rows; y++)
{
for (int x = 0; x < A.cols; x++)
{
for (int c = 0; c < cn; c++)
{
float v = A.ptr<float>(y, x)[c];
nans += (v == v) ? 0 : 1;
}
}
}
EXPECT_EQ(nans, cn * (sz.area() - 2)) << "A=" << A << std::endl << "mask=" << test_mask << std::endl;
}
}
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] = Mat(10, 10, CV_8U, Scalar((double)(16 * (i + 1))));
}
Mat merged;
merge(src, merged);
std::array<cv::Mat, 3> dst;
split(merged, dst);
for (size_t i = 0; i < dst.size(); ++i)
{
EXPECT_EQ(0, cvtest::norm(src[i], dst[i], NORM_INF));
}
}
TEST(Mat, regression_8680)
{
Mat_<Point2i> mat(3,1);
ASSERT_EQ(mat.channels(), 2);
mat.release();
ASSERT_EQ(mat.channels(), 2);
}
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(cvtest::norm(img, ref, NORM_INF), 0.);
}
TEST(Mat, from_initializer_list)
{
Mat A({1.f, 2.f, 3.f});
int n = 3;
Mat_<float> B(1, &n); B << 1, 2, 3;
Mat_<float> C({3}, {1,2,3});
ASSERT_EQ(A.type(), CV_32F);
ASSERT_DOUBLE_EQ(cvtest::norm(A, B, NORM_INF), 0.);
ASSERT_DOUBLE_EQ(cvtest::norm(A, C, NORM_INF), 0.);
ASSERT_DOUBLE_EQ(cvtest::norm(B, C, NORM_INF), 0.);
auto D = Mat_<double>({2, 3}, {1, 2, 3, 4, 5, 6});
EXPECT_EQ(2, D.rows);
EXPECT_EQ(3, D.cols);
}
TEST(Mat_, from_initializer_list)
{
Mat_<float> A = {1, 2, 3};
int n = 3;
Mat_<float> B(1, &n); B << 1, 2, 3;
Mat_<float> C({3}, {1,2,3});
ASSERT_DOUBLE_EQ(cvtest::norm(A, B, NORM_INF), 0.);
ASSERT_DOUBLE_EQ(cvtest::norm(A, C, NORM_INF), 0.);
ASSERT_DOUBLE_EQ(cvtest::norm(B, C, NORM_INF), 0.);
}
TEST(Mat, template_based_ptr)
{
Mat mat = (Mat_<float>(2, 2) << 11.0f, 22.0f, 33.0f, 44.0f);
int idx[2] = {1, 0};
ASSERT_FLOAT_EQ(33.0f, *(mat.ptr<float>(idx)));
idx[0] = 1;
idx[1] = 1;
ASSERT_FLOAT_EQ(44.0f, *(mat.ptr<float>(idx)));
}
TEST(Mat_, template_based_ptr)
{
int dim[4] = {2, 2, 1, 2};
Mat_<float> mat = (Mat_<float>(4, dim) << 11.0f, 22.0f, 33.0f, 44.0f,
55.0f, 66.0f, 77.0f, 88.0f);
int idx[4] = {1, 0, 0, 1};
ASSERT_FLOAT_EQ(66.0f, *(mat.ptr<float>(idx)));
}
BIGDATA_TEST(Mat, push_back_regression_4158) // memory usage: ~10.6 Gb
{
Mat result;
Mat tail(100, 500000, CV_32FC2, Scalar(1, 2));
tail.copyTo(result);
for (int i = 1; i < 15; i++)
{
result.push_back(tail);
std::cout << "i = " << i << " result = " << result.size() << " used = " << (uint64)result.total()*result.elemSize()*(1.0 / (1 << 20)) << " Mb"
<< " allocated=" << (uint64)(result.datalimit - result.datastart)*(1.0 / (1 << 20)) << " Mb" << std::endl;
}
for (int i = 0; i < 15; i++)
{
Rect roi(0, tail.rows * i, tail.cols, tail.rows);
int nz = countNonZero(result(roi).reshape(1) == 2);
EXPECT_EQ(tail.total(), (size_t)nz) << "i=" << i;
}
}
TEST(Core_Merge, hang_12171)
{
Mat src1(4, 24, CV_8UC1, Scalar::all(1));
Mat src2(4, 24, CV_8UC1, Scalar::all(2));
Rect src_roi(0, 0, 23, 4);
Mat src_channels[2] = { src1(src_roi), src2(src_roi) };
Mat dst(4, 24, CV_8UC2, Scalar::all(5));
Rect dst_roi(1, 0, 23, 4);
cv::merge(src_channels, 2, dst(dst_roi));
EXPECT_EQ(5, dst.ptr<uchar>()[0]);
EXPECT_EQ(5, dst.ptr<uchar>()[1]);
EXPECT_EQ(1, dst.ptr<uchar>()[2]);
EXPECT_EQ(2, dst.ptr<uchar>()[3]);
EXPECT_EQ(5, dst.ptr<uchar>(1)[0]);
EXPECT_EQ(5, dst.ptr<uchar>(1)[1]);
EXPECT_EQ(1, dst.ptr<uchar>(1)[2]);
EXPECT_EQ(2, dst.ptr<uchar>(1)[3]);
}
TEST(Core_Split, hang_12171)
{
Mat src(4, 24, CV_8UC2, Scalar(1,2,3,4));
Rect src_roi(0, 0, 23, 4);
Mat dst1(4, 24, CV_8UC1, Scalar::all(5));
Mat dst2(4, 24, CV_8UC1, Scalar::all(10));
Rect dst_roi(0, 0, 23, 4);
Mat dst[2] = { dst1(dst_roi), dst2(dst_roi) };
cv::split(src(src_roi), dst);
EXPECT_EQ(1, dst1.ptr<uchar>()[0]);
EXPECT_EQ(1, dst1.ptr<uchar>()[1]);
EXPECT_EQ(2, dst2.ptr<uchar>()[0]);
EXPECT_EQ(2, dst2.ptr<uchar>()[1]);
EXPECT_EQ(1, dst1.ptr<uchar>(1)[0]);
EXPECT_EQ(1, dst1.ptr<uchar>(1)[1]);
EXPECT_EQ(2, dst2.ptr<uchar>(1)[0]);
EXPECT_EQ(2, dst2.ptr<uchar>(1)[1]);
}
TEST(Core_Split, crash_12171)
{
Mat src(4, 40, CV_8UC2, Scalar(1,2,3,4));
Rect src_roi(0, 0, 39, 4);
Mat dst1(4, 40, CV_8UC1, Scalar::all(5));
Mat dst2(4, 40, CV_8UC1, Scalar::all(10));
Rect dst_roi(0, 0, 39, 4);
Mat dst[2] = { dst1(dst_roi), dst2(dst_roi) };
cv::split(src(src_roi), dst);
EXPECT_EQ(1, dst1.ptr<uchar>()[0]);
EXPECT_EQ(1, dst1.ptr<uchar>()[1]);
EXPECT_EQ(2, dst2.ptr<uchar>()[0]);
EXPECT_EQ(2, dst2.ptr<uchar>()[1]);
EXPECT_EQ(1, dst1.ptr<uchar>(1)[0]);
EXPECT_EQ(1, dst1.ptr<uchar>(1)[1]);
EXPECT_EQ(2, dst2.ptr<uchar>(1)[0]);
EXPECT_EQ(2, dst2.ptr<uchar>(1)[1]);
}
TEST(Core_Merge, bug_13544)
{
Mat src1(2, 2, CV_8UC3, Scalar::all(1));
Mat src2(2, 2, CV_8UC3, Scalar::all(2));
Mat src3(2, 2, CV_8UC3, Scalar::all(3));
Mat src_arr[] = { src1, src2, src3 };
Mat dst;
merge(src_arr, 3, dst);
ASSERT_EQ(9, dst.channels()); // Avoid memory access out of buffer
EXPECT_EQ(3, (int)dst.ptr<uchar>(0)[6]);
EXPECT_EQ(3, (int)dst.ptr<uchar>(0)[7]);
EXPECT_EQ(3, (int)dst.ptr<uchar>(0)[8]);
EXPECT_EQ(1, (int)dst.ptr<uchar>(1)[0]);
EXPECT_EQ(1, (int)dst.ptr<uchar>(1)[1]);
EXPECT_EQ(1, (int)dst.ptr<uchar>(1)[2]);
EXPECT_EQ(2, (int)dst.ptr<uchar>(1)[3]);
EXPECT_EQ(2, (int)dst.ptr<uchar>(1)[4]);
EXPECT_EQ(2, (int)dst.ptr<uchar>(1)[5]);
EXPECT_EQ(3, (int)dst.ptr<uchar>(1)[6]);
EXPECT_EQ(3, (int)dst.ptr<uchar>(1)[7]);
EXPECT_EQ(3, (int)dst.ptr<uchar>(1)[8]);
}
struct CustomType // like cv::Keypoint
{
Point2f pt;
float size;
float angle;
float response;
int octave;
int class_id;
};
static void test_CustomType(InputArray src_, OutputArray dst_)
{
Mat src = src_.getMat();
ASSERT_EQ(sizeof(CustomType), src.elemSize());
CV_CheckTypeEQ(src.type(), CV_MAKETYPE(CV_8U, sizeof(CustomType)), "");
CustomType* kpt = NULL;
{
Mat dst = dst_.getMat();
for (size_t i = 0; i < dst.total(); i++)
{
kpt = dst.ptr<CustomType>(0) + i;
kpt->octave = (int)i;
}
}
const int N = (int)src.total();
dst_.create(1, N * 2, rawType<CustomType>());
Mat dst = dst_.getMat();
for (size_t i = N; i < dst.total(); i++)
{
kpt = dst.ptr<CustomType>(0) + i;
kpt->octave = -(int)i;
}
#if 0 // Compilation error
CustomType& kpt = dst.at<CustomType>(0, 5);
#endif
}
TEST(Core_InputArray, support_CustomType)
{
std::vector<CustomType> kp1(5);
std::vector<CustomType> kp2(3);
test_CustomType(rawIn(kp1), rawOut(kp2));
ASSERT_EQ((size_t)10, kp2.size());
for (int i = 0; i < 3; i++)
{
EXPECT_EQ(i, kp2[i].octave);
}
for (int i = 3; i < 5; i++)
{
EXPECT_EQ(0, kp2[i].octave);
}
for (int i = 5; i < 10; i++)
{
EXPECT_EQ(-i, kp2[i].octave);
}
}
TEST(Core_InputArray, fetch_MatExpr)
{
Mat a(Size(10, 5), CV_32FC1, 5);
Mat b(Size(10, 5), CV_32FC1, 2);
MatExpr expr = a * b.t(); // gemm expression
Mat dst;
cv::add(expr, Scalar(1), dst); // invoke gemm() here
void* expr_data = expr.a.data;
Mat result = expr; // should not call gemm() here again
EXPECT_EQ(expr_data, result.data); // expr data is reused
EXPECT_EQ(dst.size(), result.size());
}
class TestInputArrayRangeChecking {
static const char *kind2str(cv::_InputArray ia)
{
switch (ia.kind())
{
#define C(x) case cv::_InputArray::x: return #x
C(MAT);
C(UMAT);
C(MATX);
C(STD_VECTOR);
C(NONE);
C(STD_VECTOR_VECTOR);
C(STD_BOOL_VECTOR);
C(STD_VECTOR_MAT);
C(STD_ARRAY_MAT);
C(STD_VECTOR_UMAT);
C(CUDA_GPU_MAT);
C(STD_VECTOR_CUDA_GPU_MAT);
#undef C
default:
return "<unsupported>";
}
}
static void banner(cv::_InputArray ia, const char *label, const char *name)
{
std::cout << std::endl
<< label << " = " << name << ", Kind: " << kind2str(ia)
<< std::endl;
}
template<typename I, typename F>
static void testA(I ia, F f, const char *mfname)
{
banner(ia, "f", mfname);
EXPECT_THROW(f(ia, -1), cv::Exception)
<< "f(ia, " << -1 << ") should throw cv::Exception";
for (int i = 0; i < int(ia.size()); i++)
{
EXPECT_NO_THROW(f(ia, i))
<< "f(ia, " << i << ") should not throw an exception";
}
EXPECT_THROW(f(ia, int(ia.size())), cv::Exception)
<< "f(ia, " << ia.size() << ") should throw cv::Exception";
}
template<typename I, typename F>
static void testB(I ia, F f, const char *mfname)
{
banner(ia, "f", mfname);
EXPECT_THROW(f(ia, -1), cv::Exception)
<< "f(ia, " << -1 << ") should throw cv::Exception";
for (int i = 0; i < int(ia.size()); i++)
{
EXPECT_NO_THROW(f(ia, i))
<< "f(ia, " << i << ") should not throw an exception";
}
EXPECT_THROW(f(ia, int(ia.size())), cv::Exception)
<< "f(ia, " << ia.size() << ") should throw cv::Exception";
}
static void test_isContinuous()
{
auto f = [](cv::_InputArray ia, int i) { (void)ia.isContinuous(i); };
cv::Mat M;
cv::UMat uM;
std::vector<cv::Mat> vec = {M, M};
std::array<cv::Mat, 2> arr = {M, M};
std::vector<cv::UMat> uvec = {uM, uM};
testA(vec, f, "isContinuous");
testA(arr, f, "isContinuous");
testA(uvec, f, "isContinuous");
}
static void test_isSubmatrix()
{
auto f = [](cv::_InputArray ia, int i) { (void)ia.isSubmatrix(i); };
cv::Mat M;
cv::UMat uM;
std::vector<cv::Mat> vec = {M, M};
std::array<cv::Mat, 2> arr = {M, M};
std::vector<cv::UMat> uvec = {uM, uM};
testA(vec, f, "isSubmatrix");
testA(arr, f, "isSubmatrix");
testA(uvec, f, "isSubmatrix");
}
static void test_offset()
{
auto f = [](cv::_InputArray ia, int i) { return ia.offset(i); };
cv::Mat M;
cv::UMat uM;
cv::cuda::GpuMat gM;
std::vector<cv::Mat> vec = {M, M};
std::array<cv::Mat, 2> arr = {M, M};
std::vector<cv::UMat> uvec = {uM, uM};
std::vector<cv::cuda::GpuMat> gvec = {gM, gM};
testB(vec, f, "offset");
testB(arr, f, "offset");
testB(uvec, f, "offset");
testB(gvec, f, "offset");
}
static void test_step()
{
auto f = [](cv::_InputArray ia, int i) { return ia.step(i); };
cv::Mat M;
cv::UMat uM;
cv::cuda::GpuMat gM;
std::vector<cv::Mat> vec = {M, M};
std::array<cv::Mat, 2> arr = {M, M};
std::vector<cv::UMat> uvec = {uM, uM};
std::vector<cv::cuda::GpuMat> gvec = {gM, gM};
testB(vec, f, "step");
testB(arr, f, "step");
testB(uvec, f, "step");
testB(gvec, f, "step");
}
public:
static void run()
{
test_isContinuous();
test_isSubmatrix();
test_offset();
test_step();
}
};
TEST(Core_InputArray, range_checking)
{
TestInputArrayRangeChecking::run();
}
TEST(Core_Vectors, issue_13078)
{
float floats_[] = { 1, 2, 3, 4, 5, 6, 7, 8 };
std::vector<float> floats(floats_, floats_ + 8);
std::vector<int> ints(4);
Mat m(4, 1, CV_32FC1, floats.data(), sizeof(floats[0]) * 2);
m.convertTo(ints, CV_32S);
ASSERT_EQ(1, ints[0]);
ASSERT_EQ(3, ints[1]);
ASSERT_EQ(5, ints[2]);
ASSERT_EQ(7, ints[3]);
}
TEST(Core_Vectors, issue_13078_workaround)
{
float floats_[] = { 1, 2, 3, 4, 5, 6, 7, 8 };
std::vector<float> floats(floats_, floats_ + 8);
std::vector<int> ints(4);
Mat m(4, 1, CV_32FC1, floats.data(), sizeof(floats[0]) * 2);
m.convertTo(Mat(ints), CV_32S);
ASSERT_EQ(1, ints[0]);
ASSERT_EQ(3, ints[1]);
ASSERT_EQ(5, ints[2]);
ASSERT_EQ(7, ints[3]);
}
TEST(Core_MatExpr, issue_13926)
{
Mat M1 = (Mat_<double>(4,4,CV_64FC1) << 1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12,
13, 14, 15, 16);
Matx44d M2(1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12,
13, 14, 15, 16);
EXPECT_GE(1e-6, cvtest::norm(M1*M2, M1*M1, NORM_INF)) << Mat(M1*M2) << std::endl << Mat(M1*M1);
EXPECT_GE(1e-6, cvtest::norm(M2*M1, M2*M2, NORM_INF)) << Mat(M2*M1) << std::endl << Mat(M2*M2);
}
TEST(Core_MatExpr, issue_16655)
{
Mat a(Size(5, 5), CV_32FC3, Scalar::all(1));
Mat b(Size(5, 5), CV_32FC3, Scalar::all(2));
MatExpr ab_expr = a != b;
Mat ab_mat = ab_expr;
EXPECT_EQ(CV_8UC3, ab_expr.type())
<< "MatExpr: CV_8UC3 != " << typeToString(ab_expr.type());
EXPECT_EQ(CV_8UC3, ab_mat.type())
<< "Mat: CV_8UC3 != " << typeToString(ab_mat.type());
}
TEST(Core_MatExpr, issue_16689)
{
Mat a(Size(10, 5), CV_32FC1, 5);
Mat b(Size(10, 5), CV_32FC1, 2);
Mat bt(Size(5, 10), CV_32FC1, 3);
{
MatExpr r = a * bt; // gemm
EXPECT_EQ(Mat(r).size(), r.size()) << "[10x5] x [5x10] => [5x5]";
}
{
MatExpr r = a * b.t(); // gemm
EXPECT_EQ(Mat(r).size(), r.size()) << "[10x5] x [10x5].t() => [5x5]";
}
{
MatExpr r = a.t() * b; // gemm
EXPECT_EQ(Mat(r).size(), r.size()) << "[10x5].t() x [10x5] => [10x10]";
}
{
MatExpr r = a.t() * bt.t(); // gemm
EXPECT_EQ(Mat(r).size(), r.size()) << "[10x5].t() x [5x10].t() => [10x10]";
}
}
#ifdef HAVE_EIGEN
TEST(Core_Eigen, eigen2cv_check_Mat_type)
{
Mat A(4, 4, CV_32FC1, Scalar::all(0));
Eigen::MatrixXf eigen_A;
cv2eigen(A, eigen_A);
Mat_<float> f_mat;
EXPECT_NO_THROW(eigen2cv(eigen_A, f_mat));
EXPECT_EQ(CV_32FC1, f_mat.type());
Mat_<double> d_mat;
EXPECT_ANY_THROW(eigen2cv(eigen_A, d_mat));
//EXPECT_EQ(CV_64FC1, d_mat.type());
}
#endif // HAVE_EIGEN
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Core_Eigen, cv2eigen_check_tensor_conversion)
{
Mat A(2, 3, CV_32FC3);
float value = 0;
for(int row=0; row<A.rows; row++)
for(int col=0; col<A.cols; col++)
for(int ch=0; ch<A.channels(); ch++)
A.at<Vec3f>(row,col)[ch] = value++;
Eigen::Tensor<float, 3, Eigen::RowMajor> row_tensor;
cv2eigen(A, row_tensor);
float* mat_ptr = (float*)A.data;
float* tensor_ptr = row_tensor.data();
for (int i=0; i< row_tensor.size(); i++)
ASSERT_FLOAT_EQ(mat_ptr[i], tensor_ptr[i]);
Eigen::Tensor<float, 3, Eigen::ColMajor> col_tensor;
cv2eigen(A, col_tensor);
value = 0;
for(int row=0; row<A.rows; row++)
for(int col=0; col<A.cols; col++)
for(int ch=0; ch<A.channels(); ch++)
ASSERT_FLOAT_EQ(value++, col_tensor(row,col,ch));
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Core_Eigen, eigen2cv_check_tensor_conversion)
{
Eigen::Tensor<float, 3, Eigen::RowMajor> row_tensor(2,3,3);
Eigen::Tensor<float, 3, Eigen::ColMajor> col_tensor(2,3,3);
float value = 0;
for(int row=0; row<row_tensor.dimension(0); row++)
for(int col=0; col<row_tensor.dimension(1); col++)
for(int ch=0; ch<row_tensor.dimension(2); ch++)
{
row_tensor(row,col,ch) = value;
col_tensor(row,col,ch) = value;
value++;
}
Mat A;
eigen2cv(row_tensor, A);
float* tensor_ptr = row_tensor.data();
float* mat_ptr = (float*)A.data;
for (int i=0; i< row_tensor.size(); i++)
ASSERT_FLOAT_EQ(tensor_ptr[i], mat_ptr[i]);
Mat B;
eigen2cv(col_tensor, B);
value = 0;
for(int row=0; row<B.rows; row++)
for(int col=0; col<B.cols; col++)
for(int ch=0; ch<B.channels(); ch++)
ASSERT_FLOAT_EQ(value++, B.at<Vec3f>(row,col)[ch]);
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Core_Eigen, cv2eigen_tensormap_check_tensormap_access)
{
float arr[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
Mat a_mat(2, 2, CV_32FC3, arr);
Eigen::TensorMap<Eigen::Tensor<float, 3, Eigen::RowMajor>> a_tensor = cv2eigen_tensormap<float>(a_mat);
for(int i=0; i<a_mat.rows; i++) {
for (int j=0; j<a_mat.cols; j++) {
for (int ch=0; ch<a_mat.channels(); ch++) {
ASSERT_FLOAT_EQ(a_mat.at<Vec3f>(i,j)[ch], a_tensor(i,j,ch));
ASSERT_EQ(&a_mat.at<Vec3f>(i,j)[ch], &a_tensor(i,j,ch));
}
}
}
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Mat, regression_12943) // memory usage: ~4.5 Gb
{
applyTestTag(CV_TEST_TAG_MEMORY_6GB);
const int width = 0x8000;
const int height = 0x10001;
cv::Mat src(height, width, CV_8UC1, Scalar::all(128));
cv::Mat dst;
cv::flip(src, dst, 0);
}
TEST(Mat, empty_iterator_16855)
{
cv::Mat m;
EXPECT_NO_THROW(m.begin<uchar>());
EXPECT_NO_THROW(m.end<uchar>());
EXPECT_TRUE(m.begin<uchar>() == m.end<uchar>());
}
TEST(Mat, regression_18473)
{
std::vector<int> sizes(3);
sizes[0] = 20;
sizes[1] = 50;
sizes[2] = 100;
#if 1 // with the fix
std::vector<size_t> steps(2);
steps[0] = 50*100*2;
steps[1] = 100*2;
#else // without the fix
std::vector<size_t> steps(3);
steps[0] = 50*100*2;
steps[1] = 100*2;
steps[2] = 2;
#endif
std::vector<short> data(20*50*100, 0); // 1Mb
data[data.size() - 1] = 5;
// param steps Array of ndims-1 steps
Mat m(sizes, CV_16SC1, (void*)data.data(), (const size_t*)steps.data());
ASSERT_FALSE(m.empty());
EXPECT_EQ((int)5, (int)m.at<short>(19, 49, 99));
}
// FITIT: remove DISABLE_ when 1D Mat is supported
TEST(Mat1D, basic)
{
std::vector<int> sizes { 100 };
Mat m1(sizes, CV_8UC1, Scalar::all(5));
Mat m1_copy(sizes, CV_8UC1, Scalar::all(5));
m1.at<uchar>(50) = 10;
EXPECT_FALSE(m1.empty());
ASSERT_EQ(1, m1.dims);
ASSERT_EQ(1, m1.size.dims()); // hack map on .rows
EXPECT_EQ(Size(100, 1), m1.size());
{
SCOPED_TRACE("clone");
Mat m = m1.clone();
EXPECT_EQ(1, m.dims);
EXPECT_EQ(Size(100, 1), m.size());
}
{
SCOPED_TRACE("colRange()");
Mat m = m1.colRange(Range(10, 30));
EXPECT_EQ(1, m.dims);
EXPECT_EQ(Size(20, 1), m.size());
}
{
SCOPED_TRACE("reshape(1, 1)");
Mat m = m1.reshape(1, 1);
EXPECT_EQ(2, m.dims);
EXPECT_EQ(Size(100, 1), m.size());
}
{
SCOPED_TRACE("reshape(1, 100)");
Mat m = m1.reshape(1, 100);
EXPECT_EQ(2, m.dims);
EXPECT_EQ(Size(1, 100), m.size());
}
{
SCOPED_TRACE("reshape(1, {10, 10}).reshape(1, {100})");
std::vector<int> newsize={100};
Mat m2 = m1.reshape(1, {10, 10});
Mat m3 = m2.reshape(1, newsize);
EXPECT_EQ(1, m3.dims);
EXPECT_EQ(Size(100, 1), m3.size());
}
{
SCOPED_TRACE("copyTo(std::vector<uchar>)");
std::vector<uchar> dst;
m1.copyTo(dst);
EXPECT_EQ(100u, dst.size());
}
{
SCOPED_TRACE("copyTo(row2D)");
Mat m(5, 100, CV_8UC1, Scalar::all(0));
const Mat row2D = m.row(2);
EXPECT_NO_THROW(m1.copyTo(row2D));
EXPECT_NO_THROW(row2D.copyTo(m1_copy));
}
{
SCOPED_TRACE("convertTo(row2D)");
Mat m(5, 100, CV_32FC1, Scalar::all(0));
const Mat row2D = m.row(2);
EXPECT_NO_THROW(m1.convertTo(row2D, CV_32FC1));
}
{
SCOPED_TRACE("CvMat");
CvMat c_mat = cvMat(m1);
EXPECT_EQ(100, c_mat.cols);
EXPECT_EQ(1, c_mat.rows);
}
{
SCOPED_TRACE("CvMatND");
CvMatND c_mat = cvMatND(m1);
EXPECT_EQ(2, c_mat.dims);
EXPECT_EQ(1, c_mat.dim[0].size);
EXPECT_EQ(100, c_mat.dim[1].size);
}
{
SCOPED_TRACE("minMaxLoc");
Point pt;
minMaxLoc(m1, 0, 0, 0, &pt);
EXPECT_EQ(0, pt.y);
EXPECT_EQ(50, pt.x);
minMaxLoc(m1_copy, 0, 0, 0, &pt);
EXPECT_EQ(0, pt.y);
EXPECT_EQ(50, pt.x);
}
}
TEST(Mat, ptrVecni_20044)
{
Mat_<int> m(3,4); m << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12;
Vec2i idx(1,1);
uchar *u = m.ptr(idx);
EXPECT_EQ(int(6), *(int*)(u));
const uchar *cu = m.ptr(idx);
EXPECT_EQ(int(6), *(int*)(cu));
int *i = m.ptr<int>(idx);
EXPECT_EQ(int(6), *(i));
const int *ci = m.ptr<int>(idx);
EXPECT_EQ(int(6), *(ci));
}
TEST(Mat, VecMatx_4650)
{
// Makes sure the following compiles.
cv::Vec3b a;
a = cv::Vec3b::ones();
a = cv::Vec3b::zeros();
a = cv::Vec3b::randn(0, 10);
a = cv::Vec3b::randu(0, 10);
}
TEST(Mat, reverse_iterator_19967)
{
// empty iterator (#16855)
cv::Mat m_empty;
EXPECT_NO_THROW(m_empty.rbegin<uchar>());
EXPECT_NO_THROW(m_empty.rend<uchar>());
EXPECT_TRUE(m_empty.rbegin<uchar>() == m_empty.rend<uchar>());
// 1D test
std::vector<uchar> data{0, 1, 2, 3};
const std::vector<int> sizes_1d{4};
//Base class
cv::Mat m_1d(sizes_1d, CV_8U, data.data());
auto mismatch_it_pair_1d = std::mismatch(data.rbegin(), data.rend(), m_1d.rbegin<uchar>());
EXPECT_EQ(mismatch_it_pair_1d.first, data.rend()); // expect no mismatch
EXPECT_EQ(mismatch_it_pair_1d.second, m_1d.rend<uchar>());
//Templated derived class
cv::Mat_<uchar> m_1d_t(static_cast<int>(sizes_1d.size()), sizes_1d.data(), data.data());
auto mismatch_it_pair_1d_t = std::mismatch(data.rbegin(), data.rend(), m_1d_t.rbegin());
EXPECT_EQ(mismatch_it_pair_1d_t.first, data.rend()); // expect no mismatch
EXPECT_EQ(mismatch_it_pair_1d_t.second, m_1d_t.rend());
// 2D test
const std::vector<int> sizes_2d{2, 2};
//Base class
cv::Mat m_2d(sizes_2d, CV_8U, data.data());
auto mismatch_it_pair_2d = std::mismatch(data.rbegin(), data.rend(), m_2d.rbegin<uchar>());
EXPECT_EQ(mismatch_it_pair_2d.first, data.rend());
EXPECT_EQ(mismatch_it_pair_2d.second, m_2d.rend<uchar>());
//Templated derived class
cv::Mat_<uchar> m_2d_t(static_cast<int>(sizes_2d.size()),sizes_2d.data(), data.data());
auto mismatch_it_pair_2d_t = std::mismatch(data.rbegin(), data.rend(), m_2d_t.rbegin());
EXPECT_EQ(mismatch_it_pair_2d_t.first, data.rend());
EXPECT_EQ(mismatch_it_pair_2d_t.second, m_2d_t.rend());
// 3D test
std::vector<uchar> data_3d{0, 1, 2, 3, 4, 5, 6, 7};
const std::vector<int> sizes_3d{2, 2, 2};
//Base class
cv::Mat m_3d(sizes_3d, CV_8U, data_3d.data());
auto mismatch_it_pair_3d = std::mismatch(data_3d.rbegin(), data_3d.rend(), m_3d.rbegin<uchar>());
EXPECT_EQ(mismatch_it_pair_3d.first, data_3d.rend());
EXPECT_EQ(mismatch_it_pair_3d.second, m_3d.rend<uchar>());
//Templated derived class
cv::Mat_<uchar> m_3d_t(static_cast<int>(sizes_3d.size()),sizes_3d.data(), data_3d.data());
auto mismatch_it_pair_3d_t = std::mismatch(data_3d.rbegin(), data_3d.rend(), m_3d_t.rbegin());
EXPECT_EQ(mismatch_it_pair_3d_t.first, data_3d.rend());
EXPECT_EQ(mismatch_it_pair_3d_t.second, m_3d_t.rend());
// const test base class
const cv::Mat m_1d_const(sizes_1d, CV_8U, data.data());
auto mismatch_it_pair_1d_const = std::mismatch(data.rbegin(), data.rend(), m_1d_const.rbegin<uchar>());
EXPECT_EQ(mismatch_it_pair_1d_const.first, data.rend()); // expect no mismatch
EXPECT_EQ(mismatch_it_pair_1d_const.second, m_1d_const.rend<uchar>());
EXPECT_FALSE((std::is_assignable<decltype(m_1d_const.rend<uchar>()), uchar>::value)) << "Constness of const iterator violated.";
EXPECT_FALSE((std::is_assignable<decltype(m_1d_const.rbegin<uchar>()), uchar>::value)) << "Constness of const iterator violated.";
// const test templated dervied class
const cv::Mat_<uchar> m_1d_const_t(static_cast<int>(sizes_1d.size()), sizes_1d.data(), data.data());
auto mismatch_it_pair_1d_const_t = std::mismatch(data.rbegin(), data.rend(), m_1d_const_t.rbegin());
EXPECT_EQ(mismatch_it_pair_1d_const_t.first, data.rend()); // expect no mismatch
EXPECT_EQ(mismatch_it_pair_1d_const_t.second, m_1d_const_t.rend());
EXPECT_FALSE((std::is_assignable<decltype(m_1d_const_t.rend()), uchar>::value)) << "Constness of const iterator violated.";
EXPECT_FALSE((std::is_assignable<decltype(m_1d_const_t.rbegin()), uchar>::value)) << "Constness of const iterator violated.";
}
TEST(Mat, Recreate1DMatWithSameMeta)
{
std::vector<int> dims = {100};
auto depth = CV_8U;
cv::Mat m(dims, depth);
// By default m has dims: [1, 100]
m.dims = 1;
EXPECT_NO_THROW(m.create(dims, depth));
}
TEST(InputArray, dumpEmpty)
{
std::string s;
s = cv::utils::dumpInputArray(noArray());
EXPECT_EQ(s, "InputArray: noArray()");
s = cv::utils::dumpInputArray(Mat());
EXPECT_EQ(s, "InputArray: empty()=true kind=0x00010000 flags=0x01010000 total(-1)=0 dims(-1)=0 size(-1)=0x0 type(-1)=CV_8UC1");
}
}} // namespace
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