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Merge pull request #1318 from SpecLad:wow

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This commit is contained in:
Roman Donchenko
2013-08-22 11:49:43 +04:00
committed by OpenCV Buildbot
parent 9c60a2c24c 3c137f7a04
commit 43aec5ad4a
628 changed files with 2623 additions and 3042 deletions
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5
modules/core/doc/basic_structures.rst
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@@ -553,7 +553,7 @@ Range
-----
.. ocv:class:: Range
Template class specifying a continuous subsequence (slice) of a sequence.
Template class specifying a continuous subsequence (slice) of a sequence.
::
@@ -773,7 +773,7 @@ Mat
---
.. ocv:class:: Mat
OpenCV C++ n-dimensional dense array class
OpenCV C++ n-dimensional dense array class
::
class CV_EXPORTS Mat
@@ -2892,4 +2892,3 @@ The above methods are usually enough for users. If you want to make your own alg
* Add public virtual method ``AlgorithmInfo* info() const;`` to your class.
* Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take http://code.opencv.org/projects/opencv/repository/revisions/master/entry/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add some public function (e.g. ``initModule_<mymodule>()``) that calls info() of your algorithm and put it into the same source file as ``info()`` implementation. This is to force C++ linker to include this object file into the target application. See ``Algorithm::create()`` for details.

4
modules/core/doc/clustering.rst
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@@ -80,8 +80,8 @@ Splits an element set into equivalency classes.
:param vec: Set of elements stored as a vector.
:param labels: Output vector of labels. It contains as many elements as ``vec``. Each label ``labels[i]`` is a 0-based cluster index of ``vec[i]`` .
:param labels: Output vector of labels. It contains as many elements as ``vec``. Each label ``labels[i]`` is a 0-based cluster index of ``vec[i]`` .
:param predicate: Equivalence predicate (pointer to a boolean function of two arguments or an instance of the class that has the method ``bool operator()(const _Tp& a, const _Tp& b)`` ). The predicate returns ``true`` when the elements are certainly in the same class, and returns ``false`` if they may or may not be in the same class.
The generic function ``partition`` implements an

1
modules/core/doc/core.rst
View File

@@ -15,4 +15,3 @@ core. The Core Functionality
clustering
utility_and_system_functions_and_macros
opengl_interop

5
modules/core/doc/drawing_functions.rst
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@@ -416,8 +416,8 @@ The number of pixels along the line is stored in ``LineIterator::count`` . The m
for(int i = 0; i < it.count; i++, ++it)
buf[i] = *(const Vec3b)*it;
// alternative way of iterating through the line
// alternative way of iterating through the line
for(int i = 0; i < it2.count; i++, ++it2)
{
Vec3b val = img.at<Vec3b>(it2.pos());
@@ -531,4 +531,3 @@ The function ``putText`` renders the specified text string in the image.
Symbols that cannot be rendered using the specified font are
replaced by question marks. See
:ocv:func:`getTextSize` for a text rendering code example.

1
modules/core/doc/dynamic_structures.rst
View File

@@ -1584,4 +1584,3 @@ Gathers all node pointers to a single sequence.
:param storage: Container for the sequence
The function puts pointers of all nodes reachable from ``first`` into a single sequence. The pointers are written sequentially in the depth-first order.

4
modules/core/doc/intro.rst
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@@ -91,8 +91,8 @@ you can use::
Ptr<T> ptr = new T(...);
That is, ``Ptr<T> ptr`` encapsulates a pointer to a ``T`` instance and a reference counter associated with the pointer. See the
:ocv:class:`Ptr`
That is, ``Ptr<T> ptr`` encapsulates a pointer to a ``T`` instance and a reference counter associated with the pointer. See the
:ocv:class:`Ptr`
description for details.
.. _AutomaticAllocation:

1
modules/core/doc/old_basic_structures.rst
View File

@@ -1751,4 +1751,3 @@ For example, `NumPy <http://numpy.scipy.org/>`_ arrays support the array interfa
(480, 640, 3) 1
.. note:: In the new Python wrappers (**cv2** module) the function is not needed, since cv2 can process Numpy arrays (and this is the only supported array type).

1
modules/core/include/opencv2/core/eigen.hpp
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@@ -278,4 +278,3 @@ void cv2eigen( const Matx<_Tp, 1, _cols>& src,
#endif
#endif

22
modules/core/include/opencv2/core/operations.hpp
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@@ -3002,55 +3002,55 @@ static inline void read(const FileNode& node, string& value, const string& defau
}
template<typename _Tp> static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
}
template<typename _Tp> static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
saturate_cast<_Tp>(temp[2]));
}
template<typename _Tp> static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
}
template<typename _Tp> static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
}
template<typename _Tp> static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3]));
}
template<typename _Tp, int cn> static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]);
}
template<typename _Tp> static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value)
{
{
vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3]));
}
static inline void read(const FileNode& node, Range& value, const Range& default_value)
{
Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end);
{
Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end);
read(node, temp, default_temp);
value.start = temp.x; value.end = temp.y;
value.start = temp.x; value.end = temp.y;
}
CV_EXPORTS_W void read(const FileNode& node, Mat& mat, const Mat& default_mat=Mat() );

1
modules/core/perf/perf_abs.cpp
View File

@@ -24,4 +24,3 @@ PERF_TEST_P(Size_MatType, abs, TYPICAL_MATS_ABS)
SANITY_CHECK(c);
}

1
modules/core/perf/perf_bitwise.cpp
View File

@@ -73,4 +73,3 @@ PERF_TEST_P(Size_MatType, bitwise_xor, TYPICAL_MATS_BITW_ARITHM)
SANITY_CHECK(c);
}

2
modules/core/perf/perf_merge.cpp
View File

@@ -34,4 +34,4 @@ PERF_TEST_P( Size_SrcDepth_DstChannels, merge,
TEST_CYCLE_MULTIRUN(runs) merge( (vector<Mat> &)mv, dst );
SANITY_CHECK(dst, 1e-12);
}
}

8
modules/core/src/arithm.cpp
View File

@@ -1252,14 +1252,14 @@ static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
Mat src1 = _src1.getMat(), src2 = _src2.getMat();
bool haveMask = !_mask.empty();
bool reallocate = false;
bool src1Scalar = checkScalar(src1, src2.type(), kind1, kind2);
bool src2Scalar = checkScalar(src2, src1.type(), kind2, kind1);
bool src1Scalar = checkScalar(src1, src2.type(), kind1, kind2);
bool src2Scalar = checkScalar(src2, src1.type(), kind2, kind1);
if( (kind1 == kind2 || src1.channels() == 1) && src1.dims <= 2 && src2.dims <= 2 &&
src1.size() == src2.size() && src1.type() == src2.type() &&
!haveMask && ((!_dst.fixedType() && (dtype < 0 || CV_MAT_DEPTH(dtype) == src1.depth())) ||
(_dst.fixedType() && _dst.type() == _src1.type())) &&
(_dst.fixedType() && _dst.type() == _src1.type())) &&
((src1Scalar && src2Scalar) || (!src1Scalar && !src2Scalar)) )
{
_dst.create(src1.size(), src1.type());

2
modules/core/src/convert.cpp
View File

@@ -1391,4 +1391,4 @@ CV_IMPL void cvNormalize( const CvArr* srcarr, CvArr* dstarr,
cv::normalize( src, dst, a, b, norm_type, dst.type(), mask );
}
/* End of file. */
/* End of file. */

2
modules/core/src/lapack.cpp
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@@ -1832,4 +1832,4 @@ cvSVBkSb( const CvArr* warr, const CvArr* uarr,
cv::SVD::backSubst(w, u, v, rhs, dst);
CV_Assert( dst.data == dst0.data );
}
}

1
modules/core/src/out.cpp
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@@ -304,4 +304,3 @@ Formatted::Formatted(const Mat& _m, const Formatter* _fmt, const int* _params)
}
}

222
modules/core/src/stat.cpp
View File

@@ -453,45 +453,45 @@ cv::Scalar cv::sum( InputArray _src )
{
Mat src = _src.getMat();
int k, cn = src.channels(), depth = src.depth();
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
size_t total_size = src.total();
int rows = src.size[0], cols = (int)(total_size/rows);
if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
{
IppiSize sz = { cols, rows };
int type = src.type();
typedef IppStatus (CV_STDCALL* ippiSumFunc)(const void*, int, IppiSize, double *, int);
ippiSumFunc ippFunc =
type == CV_8UC1 ? (ippiSumFunc)ippiSum_8u_C1R :
type == CV_8UC3 ? (ippiSumFunc)ippiSum_8u_C3R :
type == CV_8UC4 ? (ippiSumFunc)ippiSum_8u_C4R :
type == CV_16UC1 ? (ippiSumFunc)ippiSum_16u_C1R :
type == CV_16UC3 ? (ippiSumFunc)ippiSum_16u_C3R :
type == CV_16UC4 ? (ippiSumFunc)ippiSum_16u_C4R :
type == CV_16SC1 ? (ippiSumFunc)ippiSum_16s_C1R :
type == CV_16SC3 ? (ippiSumFunc)ippiSum_16s_C3R :
type == CV_16SC4 ? (ippiSumFunc)ippiSum_16s_C4R :
type == CV_32FC1 ? (ippiSumFunc)ippiSum_32f_C1R :
type == CV_32FC3 ? (ippiSumFunc)ippiSum_32f_C3R :
type == CV_32FC4 ? (ippiSumFunc)ippiSum_32f_C4R :
0;
if( ippFunc )
{
Ipp64f res[4];
if( ippFunc(src.data, src.step[0], sz, res, ippAlgHintAccurate) >= 0 )
{
Scalar sc;
for( int i = 0; i < cn; i++ )
{
sc[i] = res[i];
}
return sc;
}
}
}
#endif
size_t total_size = src.total();
int rows = src.size[0], cols = (int)(total_size/rows);
if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
{
IppiSize sz = { cols, rows };
int type = src.type();
typedef IppStatus (CV_STDCALL* ippiSumFunc)(const void*, int, IppiSize, double *, int);
ippiSumFunc ippFunc =
type == CV_8UC1 ? (ippiSumFunc)ippiSum_8u_C1R :
type == CV_8UC3 ? (ippiSumFunc)ippiSum_8u_C3R :
type == CV_8UC4 ? (ippiSumFunc)ippiSum_8u_C4R :
type == CV_16UC1 ? (ippiSumFunc)ippiSum_16u_C1R :
type == CV_16UC3 ? (ippiSumFunc)ippiSum_16u_C3R :
type == CV_16UC4 ? (ippiSumFunc)ippiSum_16u_C4R :
type == CV_16SC1 ? (ippiSumFunc)ippiSum_16s_C1R :
type == CV_16SC3 ? (ippiSumFunc)ippiSum_16s_C3R :
type == CV_16SC4 ? (ippiSumFunc)ippiSum_16s_C4R :
type == CV_32FC1 ? (ippiSumFunc)ippiSum_32f_C1R :
type == CV_32FC3 ? (ippiSumFunc)ippiSum_32f_C3R :
type == CV_32FC4 ? (ippiSumFunc)ippiSum_32f_C4R :
0;
if( ippFunc )
{
Ipp64f res[4];
if( ippFunc(src.data, src.step[0], sz, res, ippAlgHintAccurate) >= 0 )
{
Scalar sc;
for( int i = 0; i < cn; i++ )
{
sc[i] = res[i];
}
return sc;
}
}
}
#endif
SumFunc func = getSumFunc(depth);
CV_Assert( cn <= 4 && func != 0 );
@@ -565,81 +565,81 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask )
CV_Assert( mask.empty() || mask.type() == CV_8U );
int k, cn = src.channels(), depth = src.depth();
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
size_t total_size = src.total();
int rows = src.size[0], cols = (int)(total_size/rows);
if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
{
IppiSize sz = { cols, rows };
int type = src.type();
if( !mask.empty() )
{
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC1)(const void *, int, void *, int, IppiSize, Ipp64f *);
ippiMaskMeanFuncC1 ippFuncC1 =
type == CV_8UC1 ? (ippiMaskMeanFuncC1)ippiMean_8u_C1MR :
type == CV_16UC1 ? (ippiMaskMeanFuncC1)ippiMean_16u_C1MR :
type == CV_32FC1 ? (ippiMaskMeanFuncC1)ippiMean_32f_C1MR :
0;
if( ippFuncC1 )
{
Ipp64f res;
if( ippFuncC1(src.data, src.step[0], mask.data, mask.step[0], sz, &res) >= 0 )
{
return Scalar(res);
}
}
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC3)(const void *, int, void *, int, IppiSize, int, Ipp64f *);
ippiMaskMeanFuncC3 ippFuncC3 =
type == CV_8UC3 ? (ippiMaskMeanFuncC3)ippiMean_8u_C3CMR :
type == CV_16UC3 ? (ippiMaskMeanFuncC3)ippiMean_16u_C3CMR :
type == CV_32FC3 ? (ippiMaskMeanFuncC3)ippiMean_32f_C3CMR :
0;
if( ippFuncC3 )
{
Ipp64f res1, res2, res3;
if( ippFuncC3(src.data, src.step[0], mask.data, mask.step[0], sz, 1, &res1) >= 0 &&
ippFuncC3(src.data, src.step[0], mask.data, mask.step[0], sz, 2, &res2) >= 0 &&
ippFuncC3(src.data, src.step[0], mask.data, mask.step[0], sz, 3, &res3) >= 0 )
{
return Scalar(res1, res2, res3);
}
}
}
else
{
typedef IppStatus (CV_STDCALL* ippiMeanFunc)(const void*, int, IppiSize, double *, int);
ippiMeanFunc ippFunc =
type == CV_8UC1 ? (ippiMeanFunc)ippiMean_8u_C1R :
type == CV_8UC3 ? (ippiMeanFunc)ippiMean_8u_C3R :
type == CV_8UC4 ? (ippiMeanFunc)ippiMean_8u_C4R :
type == CV_16UC1 ? (ippiMeanFunc)ippiMean_16u_C1R :
type == CV_16UC3 ? (ippiMeanFunc)ippiMean_16u_C3R :
type == CV_16UC4 ? (ippiMeanFunc)ippiMean_16u_C4R :
type == CV_16SC1 ? (ippiMeanFunc)ippiMean_16s_C1R :
type == CV_16SC3 ? (ippiMeanFunc)ippiMean_16s_C3R :
type == CV_16SC4 ? (ippiMeanFunc)ippiMean_16s_C4R :
type == CV_32FC1 ? (ippiMeanFunc)ippiMean_32f_C1R :
type == CV_32FC3 ? (ippiMeanFunc)ippiMean_32f_C3R :
type == CV_32FC4 ? (ippiMeanFunc)ippiMean_32f_C4R :
0;
if( ippFunc )
{
Ipp64f res[4];
if( ippFunc(src.data, src.step[0], sz, res, ippAlgHintAccurate) >= 0 )
{
Scalar sc;
for( int i = 0; i < cn; i++ )
{
sc[i] = res[i];
}
return sc;
}
}
}
}
size_t total_size = src.total();
int rows = src.size[0], cols = (int)(total_size/rows);
if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
{
IppiSize sz = { cols, rows };
int type = src.type();
if( !mask.empty() )
{
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC1)(const void *, int, void *, int, IppiSize, Ipp64f *);
ippiMaskMeanFuncC1 ippFuncC1 =
type == CV_8UC1 ? (ippiMaskMeanFuncC1)ippiMean_8u_C1MR :
type == CV_16UC1 ? (ippiMaskMeanFuncC1)ippiMean_16u_C1MR :
type == CV_32FC1 ? (ippiMaskMeanFuncC1)ippiMean_32f_C1MR :
0;
if( ippFuncC1 )
{
Ipp64f res;
if( ippFuncC1(src.data, src.step[0], mask.data, mask.step[0], sz, &res) >= 0 )
{
return Scalar(res);
}
}
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC3)(const void *, int, void *, int, IppiSize, int, Ipp64f *);
ippiMaskMeanFuncC3 ippFuncC3 =
type == CV_8UC3 ? (ippiMaskMeanFuncC3)ippiMean_8u_C3CMR :
type == CV_16UC3 ? (ippiMaskMeanFuncC3)ippiMean_16u_C3CMR :
type == CV_32FC3 ? (ippiMaskMeanFuncC3)ippiMean_32f_C3CMR :
0;
if( ippFuncC3 )
{
Ipp64f res1, res2, res3;
if( ippFuncC3(src.data, src.step[0], mask.data, mask.step[0], sz, 1, &res1) >= 0 &&
ippFuncC3(src.data, src.step[0], mask.data, mask.step[0], sz, 2, &res2) >= 0 &&
ippFuncC3(src.data, src.step[0], mask.data, mask.step[0], sz, 3, &res3) >= 0 )
{
return Scalar(res1, res2, res3);
}
}
}
else
{
typedef IppStatus (CV_STDCALL* ippiMeanFunc)(const void*, int, IppiSize, double *, int);
ippiMeanFunc ippFunc =
type == CV_8UC1 ? (ippiMeanFunc)ippiMean_8u_C1R :
type == CV_8UC3 ? (ippiMeanFunc)ippiMean_8u_C3R :
type == CV_8UC4 ? (ippiMeanFunc)ippiMean_8u_C4R :
type == CV_16UC1 ? (ippiMeanFunc)ippiMean_16u_C1R :
type == CV_16UC3 ? (ippiMeanFunc)ippiMean_16u_C3R :
type == CV_16UC4 ? (ippiMeanFunc)ippiMean_16u_C4R :
type == CV_16SC1 ? (ippiMeanFunc)ippiMean_16s_C1R :
type == CV_16SC3 ? (ippiMeanFunc)ippiMean_16s_C3R :
type == CV_16SC4 ? (ippiMeanFunc)ippiMean_16s_C4R :
type == CV_32FC1 ? (ippiMeanFunc)ippiMean_32f_C1R :
type == CV_32FC3 ? (ippiMeanFunc)ippiMean_32f_C3R :
type == CV_32FC4 ? (ippiMeanFunc)ippiMean_32f_C4R :
0;
if( ippFunc )
{
Ipp64f res[4];
if( ippFunc(src.data, src.step[0], sz, res, ippAlgHintAccurate) >= 0 )
{
Scalar sc;
for( int i = 0; i < cn; i++ )
{
sc[i] = res[i];
}
return sc;
}
}
}
}
#endif
SumFunc func = getSumFunc(depth);
CV_Assert( cn <= 4 && func != 0 );

2
modules/core/test/test_arithm.cpp
View File

@@ -1563,4 +1563,4 @@ TEST(Core_round, CvRound)
ASSERT_EQ(4, cvRound(3.5));
ASSERT_EQ(-2, cvRound(-2.5));
ASSERT_EQ(-4, cvRound(-3.5));
}
}

2
modules/core/test/test_ds.cpp
View File

@@ -2118,5 +2118,3 @@ TEST(Core_DS_Seq, sort_invert) { Core_SeqSortInvTest test; test.safe_run(); }
TEST(Core_DS_Set, basic_operations) { Core_SetTest test; test.safe_run(); }
TEST(Core_DS_Graph, basic_operations) { Core_GraphTest test; test.safe_run(); }
TEST(Core_DS_Graph, scan) { Core_GraphScanTest test; test.safe_run(); }

2
modules/core/test/test_dxt.cpp
View File

@@ -866,5 +866,3 @@ protected:
};
TEST(Core_DFT, complex_output) { Core_DFTComplexOutputTest test; test.safe_run(); }

2
modules/core/test/test_io.cpp
View File

@@ -405,7 +405,7 @@ protected:
Vec<int, 5> v1(15, 16, 17, 18, 19), ov1;
Scalar sc1(20.0, 21.1, 22.2, 23.3), osc1;
Range g1(7, 8), og1;
FileStorage fs(fname, FileStorage::WRITE);
fs << "mi" << mi;
fs << "mv" << mv;

3
modules/core/test/test_math.cpp
View File

@@ -2457,7 +2457,7 @@ TEST(Core_Invert, small)
{
cv::Mat a = (cv::Mat_<float>(3,3) << 2.42104644730331, 1.81444796521479, -3.98072565304758, 0, 7.08389214348967e-3, 5.55326770986007e-3, 0,0, 7.44556154284261e-3);
//cv::randu(a, -1, 1);
cv::Mat b = a.t()*a;
cv::Mat c, i = Mat_<float>::eye(3, 3);
cv::invert(b, c, cv::DECOMP_LU); //std::cout << b*c << std::endl;
@@ -2643,4 +2643,3 @@ TEST(CovariationMatrixVectorOfMatWithMean, accuracy)
}
/* End of file. */