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Merge remote-tracking branch 'origin/2.4' into merge-2.4

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Conflicts:
	doc/tutorials/introduction/linux_install/linux_install.rst
	modules/core/doc/operations_on_arrays.rst
	modules/core/include/opencv2/core/core.hpp
	modules/core/src/system.cpp
	modules/gpu/src/cuda/resize.cu
	modules/imgproc/doc/miscellaneous_transformations.rst
	modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
	modules/video/src/bgfg_gaussmix2.cpp
This commit is contained in:
Roman Donchenko
2014-05-19 18:39:20 +04:00
parent c6113c6cb3 0b4eb6a964
commit 3d7a457c97
26 changed files with 248 additions and 96 deletions
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2
modules/core/doc/drawing_functions.rst
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@@ -585,7 +585,7 @@ Draws a text string.
:param font: ``CvFont`` structure initialized using :ocv:cfunc:`InitFont`.
:param fontFace: Font type. One of ``FONT_HERSHEY_SIMPLEX``, ``FONT_HERSHEY_PLAIN``, ``FONT_HERSHEY_DUPLEX``, ``FONT_HERSHEY_COMPLEX``, ``FONT_HERSHEY_TRIPLEX``, ``FONT_HERSHEY_COMPLEX_SMALL``, ``FONT_HERSHEY_SCRIPT_SIMPLEX``, or ``FONT_HERSHEY_SCRIPT_COMPLEX``,
where each of the font ID's can be combined with ``FONT_HERSHEY_ITALIC`` to get the slanted letters.
where each of the font ID's can be combined with ``FONT_ITALIC`` to get the slanted letters.
:param fontScale: Font scale factor that is multiplied by the font-specific base size.

4
modules/core/doc/operations_on_arrays.rst
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@@ -1216,9 +1216,9 @@ gemm
----
Performs generalized matrix multiplication.
.. ocv:function:: void gemm( InputArray src1, InputArray src2, double alpha, InputArray src3, double gamma, OutputArray dst, int flags=0 )
.. ocv:function:: void gemm( InputArray src1, InputArray src2, double alpha, InputArray src3, double beta, OutputArray dst, int flags=0 )
.. ocv:pyfunction:: cv2.gemm(src1, src2, alpha, src3, gamma[, dst[, flags]]) -> dst
.. ocv:pyfunction:: cv2.gemm(src1, src2, alpha, src3, beta[, dst[, flags]]) -> dst
.. ocv:cfunction:: void cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, const CvArr* src3, double beta, CvArr* dst, int tABC=0)

2
modules/core/include/opencv2/core.hpp
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@@ -389,7 +389,7 @@ CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0);
//! implements generalized matrix product algorithm GEMM from BLAS
CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
InputArray src3, double gamma, OutputArray dst, int flags = 0);
InputArray src3, double beta, OutputArray dst, int flags = 0);
//! multiplies matrix by its transposition from the left or from the right
CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa,

14
modules/core/src/matrix.cpp
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@@ -4050,16 +4050,18 @@ double cv::kmeans( InputArray _data, int K,
int flags, OutputArray _centers )
{
const int SPP_TRIALS = 3;
Mat data = _data.getMat();
bool isrow = data.rows == 1 && data.channels() > 1;
int N = !isrow ? data.rows : data.cols;
int dims = (!isrow ? data.cols : 1)*data.channels();
int type = data.depth();
Mat data0 = _data.getMat();
bool isrow = data0.rows == 1 && data0.channels() > 1;
int N = !isrow ? data0.rows : data0.cols;
int dims = (!isrow ? data0.cols : 1)*data0.channels();
int type = data0.depth();
attempts = std::max(attempts, 1);
CV_Assert( data.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
CV_Assert( N >= K );
Mat data(N, dims, CV_32F, data0.data, isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
_bestLabels.create(N, 1, CV_32S, -1, true);
Mat _labels, best_labels = _bestLabels.getMat();

91
modules/core/test/test_math.cpp
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@@ -2627,6 +2627,15 @@ TEST(Core_SVD, flt)
// TODO: eigenvv, invsqrt, cbrt, fastarctan, (round, floor, ceil(?)),
enum
{
MAT_N_DIM_C1,
MAT_N_1_CDIM,
MAT_1_N_CDIM,
MAT_N_DIM_C1_NONCONT,
MAT_N_1_CDIM_NONCONT,
VECTOR
};
class CV_KMeansSingularTest : public cvtest::BaseTest
{
@@ -2634,7 +2643,7 @@ public:
CV_KMeansSingularTest() {}
~CV_KMeansSingularTest() {}
protected:
void run(int)
void run(int inVariant)
{
int i, iter = 0, N = 0, N0 = 0, K = 0, dims = 0;
Mat labels;
@@ -2646,20 +2655,70 @@ protected:
for( iter = 0; iter < maxIter; iter++ )
{
ts->update_context(this, iter, true);
dims = rng.uniform(1, MAX_DIM+1);
dims = rng.uniform(inVariant == MAT_1_N_CDIM ? 2 : 1, MAX_DIM+1);
N = rng.uniform(1, MAX_POINTS+1);
N0 = rng.uniform(1, MAX(N/10, 2));
K = rng.uniform(1, N+1);
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
if (inVariant == VECTOR)
{
dims = 2;
Mat data(N, dims, CV_32F);
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
std::vector<cv::Point2f> data0(N0);
rng.fill(data0, RNG::UNIFORM, -1, 1);
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
5, KMEANS_PP_CENTERS);
std::vector<cv::Point2f> data(N);
for( i = 0; i < N; i++ )
data[i] = data0[rng.uniform(0, N0)];
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
5, KMEANS_PP_CENTERS);
}
else
{
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
Mat data;
switch (inVariant)
{
case MAT_N_DIM_C1:
data.create(N, dims, CV_32F);
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
break;
case MAT_N_1_CDIM:
data.create(N, 1, CV_32FC(dims));
for( i = 0; i < N; i++ )
memcpy(data.ptr(i), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
case MAT_1_N_CDIM:
data.create(1, N, CV_32FC(dims));
for( i = 0; i < N; i++ )
memcpy(data.data + i * dims * sizeof(float), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
case MAT_N_DIM_C1_NONCONT:
data.create(N, dims + 5, CV_32F);
data = data(Range(0, N), Range(0, dims));
for( i = 0; i < N; i++ )
data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
break;
case MAT_N_1_CDIM_NONCONT:
data.create(N, 3, CV_32FC(dims));
data = data.colRange(0, 1);
for( i = 0; i < N; i++ )
memcpy(data.ptr(i), data0.ptr(rng.uniform(0, N0)), dims * sizeof(float));
break;
}
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
5, KMEANS_PP_CENTERS);
}
Mat hist(K, 1, CV_32S, Scalar(0));
for( i = 0; i < N; i++ )
@@ -2683,7 +2742,19 @@ protected:
}
};
TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(); }
TEST(Core_KMeans, singular) { CV_KMeansSingularTest test; test.safe_run(MAT_N_DIM_C1); }
CV_ENUM(KMeansInputVariant, MAT_N_DIM_C1, MAT_N_1_CDIM, MAT_1_N_CDIM, MAT_N_DIM_C1_NONCONT, MAT_N_1_CDIM_NONCONT, VECTOR)
typedef testing::TestWithParam<KMeansInputVariant> Core_KMeans_InputVariants;
TEST_P(Core_KMeans_InputVariants, singular)
{
CV_KMeansSingularTest test;
test.safe_run(GetParam());
}
INSTANTIATE_TEST_CASE_P(AllVariants, Core_KMeans_InputVariants, KMeansInputVariant::all());
TEST(CovariationMatrixVectorOfMat, accuracy)
{