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Merge commit '43aec5ad' into merge-2.4

Browse Source 
Conflicts:
	cmake/OpenCVConfig.cmake
	cmake/OpenCVLegacyOptions.cmake
	modules/contrib/src/retina.cpp
	modules/gpu/doc/camera_calibration_and_3d_reconstruction.rst
	modules/gpu/doc/video.rst
	modules/gpu/src/speckle_filtering.cpp
	modules/python/src2/cv2.cv.hpp
	modules/python/test/test2.py
	samples/python/watershed.py
This commit is contained in:
Roman Donchenko
2013-08-27 13:26:44 +04:00
parent 4d06c4c7b6 43aec5ad4a
commit 2c4bbb313c
448 changed files with 984 additions and 1307 deletions
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modules/core/doc/basic_structures.rst
+2 -3
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@@ -544,7 +544,7 @@ Range
-----
.. ocv:class:: Range
Template class specifying a continuous subsequence (slice) of a sequence.
Template class specifying a continuous subsequence (slice) of a sequence.
::
@@ -854,7 +854,7 @@ Mat
---
.. ocv:class:: Mat
OpenCV C++ n-dimensional dense array class
OpenCV C++ n-dimensional dense array class
::
class CV_EXPORTS Mat
@@ -2941,4 +2941,3 @@ 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.
modules/core/doc/core.rst
-1
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@@ -16,4 +16,3 @@ core. The Core Functionality
clustering
utility_and_system_functions_and_macros
opengl_interop
modules/core/doc/dynamic_structures.rst
-1
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@@ -1580,4 +1580,3 @@ 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.
modules/core/doc/old_basic_structures.rst
-1
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@@ -1677,4 +1677,3 @@ 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).
modules/core/perf/perf_abs.cpp
-1
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@@ -24,4 +24,3 @@ PERF_TEST_P(Size_MatType, abs, TYPICAL_MATS_ABS)
SANITY_CHECK(c);
}
modules/core/perf/perf_bitwise.cpp
-1
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@@ -73,4 +73,3 @@ PERF_TEST_P(Size_MatType, bitwise_xor, TYPICAL_MATS_BITW_ARITHM)
SANITY_CHECK(c);
}
modules/core/perf/perf_merge.cpp
+1 -1
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@@ -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);
}
}
modules/core/src/convert.cpp
+1 -1
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@@ -1396,4 +1396,4 @@ 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. */
modules/core/src/lapack.cpp
+1 -1
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@@ -1823,4 +1823,4 @@ cvSVBkSb( const CvArr* warr, const CvArr* uarr,
cv::SVD::backSubst(w, u, v, rhs, dst);
CV_Assert( dst.data == dst0.data );
}
}
modules/core/src/stat.cpp
+111 -111
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@@ -454,45 +454,45 @@ 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 );
@@ -566,81 +566,81 @@ 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 );
modules/core/test/test_ds.cpp
-2
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@@ -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(); }
modules/core/test/test_dxt.cpp
-2
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@@ -866,5 +866,3 @@ protected:
};
TEST(Core_DFT, complex_output) { Core_DFTComplexOutputTest test; test.safe_run(); }
modules/core/test/test_io.cpp
+1 -1
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@@ -406,7 +406,7 @@ 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;
modules/core/test/test_math.cpp
-1
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@@ -2755,4 +2755,3 @@ TEST(CovariationMatrixVectorOfMatWithMean, accuracy)
}
/* End of file. */