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

Conflicts:
	modules/ocl/src/cl_runtime/cl_runtime.cpp
	modules/ocl/src/cl_runtime/clamdblas_runtime.cpp
	modules/ocl/src/cl_runtime/clamdfft_runtime.cpp
	modules/ocl/src/cl_runtime/generator/template/clamdblas_runtime.cpp.in
	modules/ocl/src/cl_runtime/generator/template/clamdfft_runtime.cpp.in

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>

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;

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>
 

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>
 

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>
 

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));
+}

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:
 

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" ));

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;

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
+}