diff --git a/3rdparty/include/opencl/1.2/CL/cl.hpp b/3rdparty/include/opencl/1.2/CL/cl.hpp
index 0480e3116..2502d4c52 100644
--- a/3rdparty/include/opencl/1.2/CL/cl.hpp
+++ b/3rdparty/include/opencl/1.2/CL/cl.hpp
@@ -210,7 +210,7 @@
#include <string>
#endif
-#if defined(linux) || defined(__APPLE__) || defined(__MACOSX)
+#if defined(__linux__) || defined(__APPLE__) || defined(__MACOSX)
#include <alloca.h>
#include <emmintrin.h>
diff --git a/modules/core/src/copy.cpp b/modules/core/src/copy.cpp
index 472de6e1e..3a712c4a9 100644
--- a/modules/core/src/copy.cpp
+++ b/modules/core/src/copy.cpp
@@ -247,10 +247,7 @@ void Mat::copyTo( OutputArray _dst ) const
const uchar* sptr = data;
uchar* dptr = dst.data;
- // to handle the copying 1xn matrix => nx1 std vector.
- Size sz = size() == dst.size() ?
- getContinuousSize(*this, dst) :
- getContinuousSize(*this);
+ Size sz = getContinuousSize(*this, dst);
size_t len = sz.width*elemSize();
for( ; sz.height--; sptr += step, dptr += dst.step )
@@ -301,6 +298,7 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
if( dims <= 2 )
{
+ CV_Assert( size() == mask.size() );
Size sz = getContinuousSize(*this, dst, mask, mcn);
copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz);
return;
diff --git a/modules/core/src/opencl/runtime/opencl_clamdblas.cpp b/modules/core/src/opencl/runtime/opencl_clamdblas.cpp
index e7ff79e3c..1f06446ae 100644
--- a/modules/core/src/opencl/runtime/opencl_clamdblas.cpp
+++ b/modules/core/src/opencl/runtime/opencl_clamdblas.cpp
@@ -67,7 +67,7 @@
#define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
#endif // _WIN32
-#if defined(linux)
+#if defined(__linux__)
#include <dlfcn.h>
#include <stdio.h>
diff --git a/modules/core/src/opencl/runtime/opencl_clamdfft.cpp b/modules/core/src/opencl/runtime/opencl_clamdfft.cpp
index d42bef9b8..a3a6eff0c 100644
--- a/modules/core/src/opencl/runtime/opencl_clamdfft.cpp
+++ b/modules/core/src/opencl/runtime/opencl_clamdfft.cpp
@@ -67,7 +67,7 @@
#define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
#endif // _WIN32
-#if defined(linux)
+#if defined(__linux__)
#include <dlfcn.h>
#include <stdio.h>
diff --git a/modules/core/src/opencl/runtime/opencl_core.cpp b/modules/core/src/opencl/runtime/opencl_core.cpp
index 238a5bdfb..fb82c0d93 100644
--- a/modules/core/src/opencl/runtime/opencl_core.cpp
+++ b/modules/core/src/opencl/runtime/opencl_core.cpp
@@ -125,7 +125,7 @@ static void* WinGetProcAddress(const char* name)
#define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
#endif // _WIN32
-#if defined(linux)
+#if defined(__linux__)
#include <dlfcn.h>
#include <stdio.h>
diff --git a/modules/core/test/test_mat.cpp b/modules/core/test/test_mat.cpp
index 3c8ae8bf9..6b7e5fe03 100644
--- a/modules/core/test/test_mat.cpp
+++ b/modules/core/test/test_mat.cpp
@@ -1139,3 +1139,24 @@ TEST(Core_Mat, reshape_1942)
);
ASSERT_EQ(1, cn);
}
+
+TEST(Core_Mat, copyNx1ToVector)
+{
+ cv::Mat_<uchar> src(5, 1);
+ cv::Mat_<uchar> ref_dst8;
+ cv::Mat_<ushort> ref_dst16;
+ std::vector<uchar> dst8;
+ std::vector<ushort> dst16;
+
+ src << 1, 2, 3, 4, 5;
+
+ src.copyTo(ref_dst8);
+ src.copyTo(dst8);
+
+ ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst8, cv::Mat_<uchar>(dst8));
+
+ src.convertTo(ref_dst16, CV_16U);
+ src.convertTo(dst16, CV_16U);
+
+ ASSERT_PRED_FORMAT2(cvtest::MatComparator(0, 0), ref_dst16, cv::Mat_<ushort>(dst16));
+}
diff --git a/modules/imgproc/doc/miscellaneous_transformations.rst b/modules/imgproc/doc/miscellaneous_transformations.rst
index df63c929b..5b3277581 100644
--- a/modules/imgproc/doc/miscellaneous_transformations.rst
+++ b/modules/imgproc/doc/miscellaneous_transformations.rst
@@ -110,6 +110,8 @@ But in case of a non-linear transformation, an input RGB image should be normali
If you use ``cvtColor`` with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.
+If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for ``CV_8U``, 65535 for ``CV_16U``, 1 for ``CV_32F``.
+
The function can do the following transformations:
*
@@ -124,7 +126,7 @@ The function can do the following transformations:
.. math::
- \text{Gray to RGB[A]:} \quad R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow 0
+ \text{Gray to RGB[A]:} \quad R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow \max (ChannelRange)
The conversion from a RGB image to gray is done with:
diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp
index 9f531ac4e..84de4cd79 100644
--- a/modules/ml/src/svm.cpp
+++ b/modules/ml/src/svm.cpp
@@ -2347,14 +2347,24 @@ void CvSVM::write_params( CvFileStorage* fs ) const
}
+static bool isSvmModelApplicable(int sv_total, int var_all, int var_count, int class_count)
+{
+ return (sv_total > 0 && var_count > 0 && var_count <= var_all && class_count >= 0);
+}
+
+
void CvSVM::write( CvFileStorage* fs, const char* name ) const
{
CV_FUNCNAME( "CvSVM::write" );
__BEGIN__;
- int i, var_count = get_var_count(), df_count, class_count;
+ int i, var_count = get_var_count(), df_count;
+ int class_count = class_labels ? class_labels->cols :
+ params.svm_type == CvSVM::ONE_CLASS ? 1 : 0;
const CvSVMDecisionFunc* df = decision_func;
+ if( !isSvmModelApplicable(sv_total, var_all, var_count, class_count) )
+ CV_ERROR( CV_StsParseError, "SVM model data is invalid, check sv_count, var_* and class_count tags" );
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_SVM );
@@ -2363,9 +2373,6 @@ void CvSVM::write( CvFileStorage* fs, const char* name ) const
cvWriteInt( fs, "var_all", var_all );
cvWriteInt( fs, "var_count", var_count );
- class_count = class_labels ? class_labels->cols :
- params.svm_type == CvSVM::ONE_CLASS ? 1 : 0;
-
if( class_count )
{
cvWriteInt( fs, "class_count", class_count );
@@ -2503,7 +2510,6 @@ void CvSVM::read_params( CvFileStorage* fs, CvFileNode* svm_node )
__END__;
}
-
void CvSVM::read( CvFileStorage* fs, CvFileNode* svm_node )
{
const double not_found_dbl = DBL_MAX;
@@ -2532,7 +2538,7 @@ void CvSVM::read( CvFileStorage* fs, CvFileNode* svm_node )
var_count = cvReadIntByName( fs, svm_node, "var_count", var_all );
class_count = cvReadIntByName( fs, svm_node, "class_count", 0 );
- if( sv_total <= 0 || var_all <= 0 || var_count <= 0 || var_count > var_all || class_count < 0 )
+ if( !isSvmModelApplicable(sv_total, var_all, var_count, class_count) )
CV_ERROR( CV_StsParseError, "SVM model data is invalid, check sv_count, var_* and class_count tags" );
CV_CALL( class_labels = (CvMat*)cvReadByName( fs, svm_node, "class_labels" ));
diff --git a/modules/ml/test/test_save_load.cpp b/modules/ml/test/test_save_load.cpp
index 9fd31b9f2..7300185b4 100644
--- a/modules/ml/test/test_save_load.cpp
+++ b/modules/ml/test/test_save_load.cpp
@@ -155,6 +155,14 @@ TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); }
TEST(ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
+TEST(ML_SVM, throw_exception_when_save_untrained_model)
+{
+ SVM svm;
+ string filename = tempfile("svm.xml");
+ ASSERT_THROW(svm.save(filename.c_str()), Exception);
+ remove(filename.c_str());
+}
+
TEST(DISABLED_ML_SVM, linear_save_load)
{
CvSVM svm1, svm2, svm3;
diff --git a/modules/ocl/perf/perf_haar.cpp b/modules/ocl/perf/perf_haar.cpp
index 8e69b7648..37c14e3d3 100644
--- a/modules/ocl/perf/perf_haar.cpp
+++ b/modules/ocl/perf/perf_haar.cpp
@@ -85,3 +85,69 @@ PERF_TEST(HaarFixture, Haar)
else
OCL_PERF_ELSE
}
+
+using namespace std;
+using namespace cv;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+typedef std::tr1::tuple<std::string, std::string, int> OCL_Cascade_Image_MinSize_t;
+typedef perf::TestBaseWithParam<OCL_Cascade_Image_MinSize_t> OCL_Cascade_Image_MinSize;
+
+PERF_TEST_P( OCL_Cascade_Image_MinSize, CascadeClassifier,
+ testing::Combine(
+ testing::Values( string("cv/cascadeandhog/cascades/haarcascade_frontalface_alt.xml") ),
+ testing::Values( string("cv/shared/lena.png"),
+ string("cv/cascadeandhog/images/bttf301.png"),
+ string("cv/cascadeandhog/images/class57.png") ),
+ testing::Values(30, 64, 90) ) )
+{
+ const string cascasePath = get<0>(GetParam());
+ const string imagePath = get<1>(GetParam());
+ const int min_size = get<2>(GetParam());
+ Size minSize(min_size, min_size);
+ vector<Rect> faces;
+
+ Mat img = imread(getDataPath(imagePath), IMREAD_GRAYSCALE);
+ ASSERT_TRUE(!img.empty()) << "Can't load source image: " << getDataPath(imagePath);
+ equalizeHist(img, img);
+ declare.in(img);
+
+ if (RUN_PLAIN_IMPL)
+ {
+ CascadeClassifier cc;
+ ASSERT_TRUE(cc.load(getDataPath(cascasePath))) << "Can't load cascade file: " << getDataPath(cascasePath);
+
+ while (next())
+ {
+ faces.clear();
+
+ startTimer();
+ cc.detectMultiScale(img, faces, 1.1, 3, 0, minSize);
+ stopTimer();
+ }
+ }
+ else if (RUN_OCL_IMPL)
+ {
+ ocl::oclMat uimg(img);
+ ocl::OclCascadeClassifier cc;
+ ASSERT_TRUE(cc.load(getDataPath(cascasePath))) << "Can't load cascade file: " << getDataPath(cascasePath);
+
+ while (next())
+ {
+ faces.clear();
+ ocl::finish();
+
+ startTimer();
+ cc.detectMultiScale(uimg, faces, 1.1, 3, 0, minSize);
+ stopTimer();
+ }
+ }
+ else
+ OCL_PERF_ELSE
+
+ //sort(faces.begin(), faces.end(), comparators::RectLess());
+ SANITY_CHECK_NOTHING();//(faces, min_size/5);
+ // using SANITY_CHECK_NOTHING() since OCL and PLAIN version may find different faces number
+}