opencv_run_all_tests.sh script added to -tests package.

(cherry picked from commit d45350a06a)

.gitignore

@@ -1,7 +1,6 @@
 *.autosave
 *.pyc
 *.user
-*~
 .*.swp
 .DS_Store
 .sw[a-z]

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>

apps/haartraining/cvclassifier.h

@@ -338,7 +338,7 @@ typedef enum CvBoostType
     CV_LKCLASS  = 5, /* classification (K class problem)    */
     CV_LSREG    = 6, /* least squares regression            */
     CV_LADREG   = 7, /* least absolute deviation regression */
-    CV_MREG     = 8  /* M-regression (Huber loss)           */
+    CV_MREG     = 8, /* M-regression (Huber loss)           */
 } CvBoostType;
 
 /****************************************************************************************\

cmake/OpenCVDetectPython.cmake

@@ -81,39 +81,24 @@ if(PYTHON_EXECUTABLE)
     SET(PYTHON_PACKAGES_PATH "${_PYTHON_PACKAGES_PATH}" CACHE PATH "Where to install the python packages.")
 
     if(NOT PYTHON_NUMPY_INCLUDE_DIR)
-      if(CMAKE_CROSSCOMPILING)
-        message(STATUS "Cannot probe for Python/Numpy support (because we are cross-compiling OpenCV)")
-        message(STATUS "If you want to enable Python/Numpy support, set the following variables:")
-        message(STATUS "  PYTHON_INCLUDE_PATH")
-        message(STATUS "  PYTHON_LIBRARIES")
-        message(STATUS "  PYTHON_NUMPY_INCLUDE_DIR")
-      else()
-        # Attempt to discover the NumPy include directory. If this succeeds, then build python API with NumPy
-        execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import os; os.environ['DISTUTILS_USE_SDK']='1'; import numpy.distutils; print numpy.distutils.misc_util.get_numpy_include_dirs()[0]"
-                        RESULT_VARIABLE PYTHON_NUMPY_PROCESS
-                        OUTPUT_VARIABLE PYTHON_NUMPY_INCLUDE_DIR
-                        OUTPUT_STRIP_TRAILING_WHITESPACE)
+      # Attempt to discover the NumPy include directory. If this succeeds, then build python API with NumPy
+      execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import os; os.environ['DISTUTILS_USE_SDK']='1'; import numpy.distutils; print numpy.distutils.misc_util.get_numpy_include_dirs()[0]"
+                      RESULT_VARIABLE PYTHON_NUMPY_PROCESS
+                      OUTPUT_VARIABLE PYTHON_NUMPY_INCLUDE_DIR
+                      OUTPUT_STRIP_TRAILING_WHITESPACE)
 
-        if(NOT PYTHON_NUMPY_PROCESS EQUAL 0)
-          unset(PYTHON_NUMPY_INCLUDE_DIR)
-        endif()
+      if(PYTHON_NUMPY_PROCESS EQUAL 0)
+        file(TO_CMAKE_PATH "${PYTHON_NUMPY_INCLUDE_DIR}" _PYTHON_NUMPY_INCLUDE_DIR)
+        set(PYTHON_NUMPY_INCLUDE_DIR ${_PYTHON_NUMPY_INCLUDE_DIR} CACHE PATH "Path to numpy headers")
       endif()
     endif()
 
     if(PYTHON_NUMPY_INCLUDE_DIR)
-      file(TO_CMAKE_PATH "${PYTHON_NUMPY_INCLUDE_DIR}" _PYTHON_NUMPY_INCLUDE_DIR)
-      set(PYTHON_NUMPY_INCLUDE_DIR ${_PYTHON_NUMPY_INCLUDE_DIR} CACHE PATH "Path to numpy headers")
       set(PYTHON_USE_NUMPY TRUE)
-      if(CMAKE_CROSSCOMPILING)
-        if(NOT PYTHON_NUMPY_VERSION)
-          set(PYTHON_NUMPY_VERSION "undefined - cannot be probed because of the cross-compilation")
-        endif()
-      else()
-        execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import numpy; print numpy.version.version"
+      execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "import numpy; print numpy.version.version"
                         RESULT_VARIABLE PYTHON_NUMPY_PROCESS
                         OUTPUT_VARIABLE PYTHON_NUMPY_VERSION
                         OUTPUT_STRIP_TRAILING_WHITESPACE)
-      endif()
     endif()
   endif(NOT ANDROID AND NOT IOS)
 

doc/license.txt

@@ -0,0 +1,37 @@
+IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+ By downloading, copying, installing or using the software you agree to this license.
+ If you do not agree to this license, do not download, install,
+ copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+
+Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * The name of the copyright holders may not be used to endorse or promote products
+    derived from this software without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are disclaimed.
+In no event shall the Intel Corporation or contributors be liable for any direct,
+indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.

doc/tutorials/introduction/android_binary_package/O4A_SDK.rst

@@ -66,7 +66,7 @@ The structure of package contents looks as follows:
     |               |_ armeabi-v7a
     |               |_ x86
     |
-    |_ LICENSE
+    |_ license.txt
     |_ README.android
 
 * :file:`sdk` folder contains OpenCV API and libraries for Android:

doc/tutorials/introduction/linux_install/linux_install.rst

@@ -16,7 +16,7 @@ Required Packages
   * CMake 2.6 or higher;
   * Git;
   * GTK+2.x or higher, including headers (libgtk2.0-dev);
-  * pkg-config;
+  * pkgconfig;
   * Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy);
   * ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev;
   * [optional] libdc1394 2.x;

modules/calib3d/test/test_chessboardgenerator.hpp

@@ -34,7 +34,7 @@ private:
     Mat rvec, tvec;
 };
 
-}
+};
 
 
 #endif

modules/contrib/src/adaptiveskindetector.cpp

@@ -53,7 +53,7 @@ void CvAdaptiveSkinDetector::initData(IplImage *src, int widthDivider, int heigh
     imgGrayFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
     imgLastGrayFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
     imgHSVFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 3);
-}
+};
 
 CvAdaptiveSkinDetector::CvAdaptiveSkinDetector(int samplingDivider, int morphingMethod)
 {
@@ -78,7 +78,7 @@ CvAdaptiveSkinDetector::CvAdaptiveSkinDetector(int samplingDivider, int morphing
     imgLastGrayFrame = NULL;
     imgSaturationFrame = NULL;
     imgHSVFrame = NULL;
-}
+};
 
 CvAdaptiveSkinDetector::~CvAdaptiveSkinDetector()
 {
@@ -91,7 +91,7 @@ CvAdaptiveSkinDetector::~CvAdaptiveSkinDetector()
     cvReleaseImage(&imgGrayFrame);
     cvReleaseImage(&imgLastGrayFrame);
     cvReleaseImage(&imgHSVFrame);
-}
+};
 
 void CvAdaptiveSkinDetector::process(IplImage *inputBGRImage, IplImage *outputHueMask)
 {
@@ -188,7 +188,7 @@ void CvAdaptiveSkinDetector::process(IplImage *inputBGRImage, IplImage *outputHu
 
     if (outputHueMask != NULL)
         cvCopy(imgFilteredFrame, outputHueMask);
-}
+};
 
 
 //------------------------- Histogram for Adaptive Skin Detector -------------------------//
@@ -200,12 +200,12 @@ CvAdaptiveSkinDetector::Histogram::Histogram()
     float *ranges[] = { range };
     fHistogram = cvCreateHist(1, histogramSize, CV_HIST_ARRAY, ranges, 1);
     cvClearHist(fHistogram);
-}
+};
 
 CvAdaptiveSkinDetector::Histogram::~Histogram()
 {
     cvReleaseHist(&fHistogram);
-}
+};
 
 int CvAdaptiveSkinDetector::Histogram::findCoverageIndex(double surfaceToCover, int defaultValue)
 {
@@ -219,7 +219,7 @@ int CvAdaptiveSkinDetector::Histogram::findCoverageIndex(double surfaceToCover,
         }
     }
     return defaultValue;
-}
+};
 
 void CvAdaptiveSkinDetector::Histogram::findCurveThresholds(int &x1, int &x2, double percent)
 {
@@ -242,7 +242,7 @@ void CvAdaptiveSkinDetector::Histogram::findCurveThresholds(int &x1, int &x2, do
         x2 = GSD_HUE_UT;
     else
         x2 += GSD_HUE_LT;
-}
+};
 
 void CvAdaptiveSkinDetector::Histogram::mergeWith(CvAdaptiveSkinDetector::Histogram *source, double weight)
 {
@@ -283,4 +283,4 @@ void CvAdaptiveSkinDetector::Histogram::mergeWith(CvAdaptiveSkinDetector::Histog
             }
         }
     }
-}
+};

modules/contrib/src/ba.cpp

@@ -938,7 +938,7 @@ static void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, C
 
 #endif
 
-}
+};
 static void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* estim, void* /*data*/) {
   //just do projections
   CvMat _Mi;
@@ -977,17 +977,17 @@ static void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, C
   cvTranspose( _mp2, estim );
   cvReleaseMat( &_mp );
   cvReleaseMat( &_mp2 );
-}
+};
 
 static void fjac_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data) {
   CvMat _point_params = point_params, _cam_params = cam_params, _Al = A, _Bl = B;
   fjac(i,j, &_point_params, &_cam_params, &_Al, &_Bl, data);
-}
+};
 
 static void func_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data)  {
   CvMat _point_params = point_params, _cam_params = cam_params, _estim = estim;
   func(i,j,&_point_params,&_cam_params,&_estim,data);
-}
+};
 
 void LevMarqSparse::bundleAdjust( vector<Point3d>& points, //positions of points in global coordinate system (input and output)
           const vector<vector<Point2d> >& imagePoints, //projections of 3d points for every camera

modules/contrib/src/facerec.cpp

@@ -873,7 +873,7 @@ CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
                   obj.info()->addParam(obj, "labels", obj._labels, true);
                   obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
                   obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
-                  obj.info()->addParam(obj, "mean", obj._mean, true))
+                  obj.info()->addParam(obj, "mean", obj._mean, true));
 
 CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
                   obj.info()->addParam(obj, "ncomponents", obj._num_components);
@@ -882,7 +882,7 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
                   obj.info()->addParam(obj, "labels", obj._labels, true);
                   obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
                   obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
-                  obj.info()->addParam(obj, "mean", obj._mean, true))
+                  obj.info()->addParam(obj, "mean", obj._mean, true));
 
 CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
                   obj.info()->addParam(obj, "radius", obj._radius);
@@ -891,7 +891,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
                   obj.info()->addParam(obj, "grid_y", obj._grid_y);
                   obj.info()->addParam(obj, "threshold", obj._threshold);
                   obj.info()->addParam(obj, "histograms", obj._histograms, true);
-                  obj.info()->addParam(obj, "labels", obj._labels, true))
+                  obj.info()->addParam(obj, "labels", obj._labels, true));
 
 bool initModule_contrib()
 {

modules/contrib/src/fuzzymeanshifttracker.cpp

@@ -40,7 +40,7 @@ CvFuzzyPoint::CvFuzzyPoint(double _x, double _y)
 {
     x = _x;
     y = _y;
-}
+};
 
 bool CvFuzzyCurve::between(double x, double x1, double x2)
 {
@@ -50,37 +50,37 @@ bool CvFuzzyCurve::between(double x, double x1, double x2)
         return true;
 
     return false;
-}
+};
 
 CvFuzzyCurve::CvFuzzyCurve()
 {
     value = 0;
-}
+};
 
 CvFuzzyCurve::~CvFuzzyCurve()
 {
     // nothing to do
-}
+};
 
 void CvFuzzyCurve::setCentre(double _centre)
 {
     centre = _centre;
-}
+};
 
 double CvFuzzyCurve::getCentre()
 {
     return centre;
-}
+};
 
 void CvFuzzyCurve::clear()
 {
     points.clear();
-}
+};
 
 void CvFuzzyCurve::addPoint(double x, double y)
 {
     points.push_back(CvFuzzyPoint(x, y));
-}
+};
 
 double CvFuzzyCurve::calcValue(double param)
 {
@@ -101,41 +101,41 @@ double CvFuzzyCurve::calcValue(double param)
         }
     }
     return 0;
-}
+};
 
 double CvFuzzyCurve::getValue()
 {
     return value;
-}
+};
 
 void CvFuzzyCurve::setValue(double _value)
 {
     value = _value;
-}
+};
 
 
 CvFuzzyFunction::CvFuzzyFunction()
 {
     // nothing to do
-}
+};
 
 CvFuzzyFunction::~CvFuzzyFunction()
 {
     curves.clear();
-}
+};
 
 void CvFuzzyFunction::addCurve(CvFuzzyCurve *curve, double value)
 {
     curves.push_back(*curve);
     curve->setValue(value);
-}
+};
 
 void CvFuzzyFunction::resetValues()
 {
     int numCurves = (int)curves.size();
     for (int i = 0; i < numCurves; i++)
         curves[i].setValue(0);
-}
+};
 
 double CvFuzzyFunction::calcValue()
 {
@@ -152,7 +152,7 @@ double CvFuzzyFunction::calcValue()
         return s1/s2;
     else
         return 0;
-}
+};
 
 CvFuzzyCurve *CvFuzzyFunction::newCurve()
 {
@@ -160,14 +160,14 @@ CvFuzzyCurve *CvFuzzyFunction::newCurve()
     c = new CvFuzzyCurve();
     addCurve(c);
     return c;
-}
+};
 
 CvFuzzyRule::CvFuzzyRule()
 {
     fuzzyInput1 = NULL;
     fuzzyInput2 = NULL;
     fuzzyOutput = NULL;
-}
+};
 
 CvFuzzyRule::~CvFuzzyRule()
 {
@@ -179,14 +179,14 @@ CvFuzzyRule::~CvFuzzyRule()
 
     if (fuzzyOutput != NULL)
         delete fuzzyOutput;
-}
+};
 
 void CvFuzzyRule::setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
 {
     fuzzyInput1 = c1;
     fuzzyInput2 = c2;
     fuzzyOutput = o1;
-}
+};
 
 double CvFuzzyRule::calcValue(double param1, double param2)
 {
@@ -202,31 +202,31 @@ double CvFuzzyRule::calcValue(double param1, double param2)
     }
     else
         return v1;
-}
+};
 
 CvFuzzyCurve *CvFuzzyRule::getOutputCurve()
 {
     return fuzzyOutput;
-}
+};
 
 CvFuzzyController::CvFuzzyController()
 {
     // nothing to do
-}
+};
 
 CvFuzzyController::~CvFuzzyController()
 {
     int size = (int)rules.size();
     for(int i = 0; i < size; i++)
         delete rules[i];
-}
+};
 
 void CvFuzzyController::addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
 {
     CvFuzzyRule *f = new CvFuzzyRule();
     rules.push_back(f);
     f->setRule(c1, c2, o1);
-}
+};
 
 double CvFuzzyController::calcOutput(double param1, double param2)
 {
@@ -242,7 +242,7 @@ double CvFuzzyController::calcOutput(double param1, double param2)
     }
     v = list.calcValue();
     return v;
-}
+};
 
 CvFuzzyMeanShiftTracker::FuzzyResizer::FuzzyResizer()
 {
@@ -298,12 +298,12 @@ CvFuzzyMeanShiftTracker::FuzzyResizer::FuzzyResizer()
     fuzzyController.addRule(i1L, NULL, oS);
     fuzzyController.addRule(i1M, NULL, oZE);
     fuzzyController.addRule(i1H, NULL, oE);
-}
+};
 
 int CvFuzzyMeanShiftTracker::FuzzyResizer::calcOutput(double edgeDensity, double density)
 {
     return (int)fuzzyController.calcOutput(edgeDensity, density);
-}
+};
 
 CvFuzzyMeanShiftTracker::SearchWindow::SearchWindow()
 {
@@ -328,7 +328,7 @@ CvFuzzyMeanShiftTracker::SearchWindow::SearchWindow()
     depthLow = 0;
     depthHigh = 0;
     fuzzyResizer = NULL;
-}
+};
 
 CvFuzzyMeanShiftTracker::SearchWindow::~SearchWindow()
 {
@@ -354,7 +354,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::setSize(int _x, int _y, int _width,
 
     if (y + height > maxHeight)
         height = maxHeight - y;
-}
+};
 
 void CvFuzzyMeanShiftTracker::SearchWindow::initDepthValues(IplImage *maskImage, IplImage *depthMap)
 {
@@ -408,7 +408,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::initDepthValues(IplImage *maskImage,
         depthHigh = 32000;
         depthLow = 0;
     }
-}
+};
 
 bool CvFuzzyMeanShiftTracker::SearchWindow::shift()
 {
@@ -421,7 +421,7 @@ bool CvFuzzyMeanShiftTracker::SearchWindow::shift()
     {
         return false;
     }
-}
+};
 
 void CvFuzzyMeanShiftTracker::SearchWindow::extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth)
 {
@@ -527,7 +527,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::extractInfo(IplImage *maskImage, Ipl
         ellipseAngle = 0;
         density = 0;
     }
-}
+};
 
 void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh) {
     int x1 = horizontalEdgeTop;
@@ -571,7 +571,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityLinear(in
     } else {
         resizeDw = - resizeDx;
     }
-}
+};
 
 void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
 {
@@ -587,7 +587,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsInnerDensity(int &re
     resizeDy = (int)(py*dy);
     resizeDw = (int)((1-px)*dx);
     resizeDh = (int)((1-py)*dy);
-}
+};
 
 void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
 {
@@ -626,7 +626,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityFuzzy(int
         resizeDy = int(-dy1);
         resizeDh = int(dy1+dy2);
     }
-}
+};
 
 bool CvFuzzyMeanShiftTracker::SearchWindow::meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth)
 {
@@ -639,7 +639,7 @@ bool CvFuzzyMeanShiftTracker::SearchWindow::meanShift(IplImage *maskImage, IplIm
     } while (++numShifts < maxIteration);
 
     return false;
-}
+};
 
 void CvFuzzyMeanShiftTracker::findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth)
 {
@@ -679,17 +679,17 @@ void CvFuzzyMeanShiftTracker::findOptimumSearchWindow(SearchWindow &searchWindow
 
         searchWindow.setSize(searchWindow.x + resizeDx, searchWindow.y + resizeDy, searchWindow.width + resizeDw, searchWindow.height + resizeDh);
     }
-}
+};
 
 CvFuzzyMeanShiftTracker::CvFuzzyMeanShiftTracker()
 {
     searchMode = tsSetWindow;
-}
+};
 
 CvFuzzyMeanShiftTracker::~CvFuzzyMeanShiftTracker()
 {
     // nothing to do
-}
+};
 
 void CvFuzzyMeanShiftTracker::track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass)
 {
@@ -717,4 +717,4 @@ void CvFuzzyMeanShiftTracker::track(IplImage *maskImage, IplImage *depthMap, int
             else
                 searchMode = tsTracking;
     }
-}
+};

modules/contrib/src/retina.cpp

@@ -85,7 +85,7 @@ Retina::Retina(const cv::Size inputSz, const bool colorMode, RETINA_COLORSAMPLIN
 {
     _retinaFilter = 0;
     _init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
-}
+};
 
 Retina::~Retina()
 {

modules/contrib/src/spinimages.cpp

@@ -718,7 +718,7 @@ void cv::SpinImageModel::defaultParams()
 
     T_GeometriccConsistency = 0.25f;
     T_GroupingCorespondances = 0.25f;
-}
+};
 
 Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, size_t yCount) const
 {

modules/core/include/opencv2/core/mat.hpp

@@ -2401,7 +2401,7 @@ template<typename _Tp> inline SparseMat_<_Tp>::SparseMat_(const SparseMat& m)
     if( m.type() == DataType<_Tp>::type )
         *this = (const SparseMat_<_Tp>&)m;
     else
-        m.convertTo(*this, DataType<_Tp>::type);
+        m.convertTo(this, DataType<_Tp>::type);
 }
 
 template<typename _Tp> inline SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m)

modules/core/include/opencv2/core/version.hpp

@@ -50,7 +50,7 @@
 #define CV_VERSION_EPOCH    2
 #define CV_VERSION_MAJOR    4
 #define CV_VERSION_MINOR    8
-#define CV_VERSION_REVISION 0
+#define CV_VERSION_REVISION 1
 
 #define CVAUX_STR_EXP(__A)  #__A
 #define CVAUX_STR(__A)      CVAUX_STR_EXP(__A)

modules/core/src/algorithm.cpp

@@ -647,7 +647,7 @@ void AlgorithmInfo::set(Algorithm* algo, const char* parameter, int argType, con
             || argType == Param::FLOAT || argType == Param::UNSIGNED_INT || argType == Param::UINT64 || argType == Param::UCHAR)
     {
         if ( !( p->type == Param::INT || p->type == Param::REAL || p->type == Param::BOOLEAN
-                || p->type == Param::UNSIGNED_INT || p->type == Param::UINT64 || p->type == Param::FLOAT || p->type == Param::UCHAR
+                || p->type == Param::UNSIGNED_INT || p->type == Param::UINT64 || p->type == Param::FLOAT || argType == Param::UCHAR
                 || (p->type == Param::SHORT && argType == Param::INT)) )
         {
             string message = getErrorMessageForWrongArgumentInSetter(algo->name(), parameter, p->type, argType);

modules/core/src/arithm.cpp

@@ -1272,8 +1272,8 @@ static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
     bool haveScalar = false, swapped12 = false;
     int depth2 = src2.depth();
     if( src1.size != src2.size || src1.channels() != src2.channels() ||
-        (kind1 == _InputArray::MATX && (src1.size() == Size(1,4) || src1.size() == Size(1,1))) ||
-        (kind2 == _InputArray::MATX && (src2.size() == Size(1,4) || src2.size() == Size(1,1))) )
+        ((kind1 == _InputArray::MATX || kind2 == _InputArray::MATX) &&
+         src1.cols == 1 && src2.rows == 4) )
     {
         if( checkScalar(src1, src2.type(), kind1, kind2) )
         {

modules/core/src/convert.cpp

@@ -839,122 +839,122 @@ stype* dst, size_t dstep, Size size, double*) \
 }
 
 
-DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float)
-DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float)
-DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float)
-DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float)
-DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float)
-DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float)
-DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float)
+DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float);
+DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float);
+DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float);
+DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float);
+DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float);
+DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float);
+DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float);
 
-DEF_CVT_SCALE_FUNC(8u,     uchar, uchar, float)
-DEF_CVT_SCALE_FUNC(8s8u,   schar, uchar, float)
-DEF_CVT_SCALE_FUNC(16u8u,  ushort, uchar, float)
-DEF_CVT_SCALE_FUNC(16s8u,  short, uchar, float)
-DEF_CVT_SCALE_FUNC(32s8u,  int, uchar, float)
-DEF_CVT_SCALE_FUNC(32f8u,  float, uchar, float)
-DEF_CVT_SCALE_FUNC(64f8u,  double, uchar, float)
+DEF_CVT_SCALE_FUNC(8u,     uchar, uchar, float);
+DEF_CVT_SCALE_FUNC(8s8u,   schar, uchar, float);
+DEF_CVT_SCALE_FUNC(16u8u,  ushort, uchar, float);
+DEF_CVT_SCALE_FUNC(16s8u,  short, uchar, float);
+DEF_CVT_SCALE_FUNC(32s8u,  int, uchar, float);
+DEF_CVT_SCALE_FUNC(32f8u,  float, uchar, float);
+DEF_CVT_SCALE_FUNC(64f8u,  double, uchar, float);
 
-DEF_CVT_SCALE_FUNC(8u8s,   uchar, schar, float)
-DEF_CVT_SCALE_FUNC(8s,     schar, schar, float)
-DEF_CVT_SCALE_FUNC(16u8s,  ushort, schar, float)
-DEF_CVT_SCALE_FUNC(16s8s,  short, schar, float)
-DEF_CVT_SCALE_FUNC(32s8s,  int, schar, float)
-DEF_CVT_SCALE_FUNC(32f8s,  float, schar, float)
-DEF_CVT_SCALE_FUNC(64f8s,  double, schar, float)
+DEF_CVT_SCALE_FUNC(8u8s,   uchar, schar, float);
+DEF_CVT_SCALE_FUNC(8s,     schar, schar, float);
+DEF_CVT_SCALE_FUNC(16u8s,  ushort, schar, float);
+DEF_CVT_SCALE_FUNC(16s8s,  short, schar, float);
+DEF_CVT_SCALE_FUNC(32s8s,  int, schar, float);
+DEF_CVT_SCALE_FUNC(32f8s,  float, schar, float);
+DEF_CVT_SCALE_FUNC(64f8s,  double, schar, float);
 
-DEF_CVT_SCALE_FUNC(8u16u,  uchar, ushort, float)
-DEF_CVT_SCALE_FUNC(8s16u,  schar, ushort, float)
-DEF_CVT_SCALE_FUNC(16u,    ushort, ushort, float)
-DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float)
-DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float)
-DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float)
-DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float)
+DEF_CVT_SCALE_FUNC(8u16u,  uchar, ushort, float);
+DEF_CVT_SCALE_FUNC(8s16u,  schar, ushort, float);
+DEF_CVT_SCALE_FUNC(16u,    ushort, ushort, float);
+DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float);
+DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float);
+DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float);
+DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float);
 
-DEF_CVT_SCALE_FUNC(8u16s,  uchar, short, float)
-DEF_CVT_SCALE_FUNC(8s16s,  schar, short, float)
-DEF_CVT_SCALE_FUNC(16u16s, ushort, short, float)
-DEF_CVT_SCALE_FUNC(16s,    short, short, float)
-DEF_CVT_SCALE_FUNC(32s16s, int, short, float)
-DEF_CVT_SCALE_FUNC(32f16s, float, short, float)
-DEF_CVT_SCALE_FUNC(64f16s, double, short, float)
+DEF_CVT_SCALE_FUNC(8u16s,  uchar, short, float);
+DEF_CVT_SCALE_FUNC(8s16s,  schar, short, float);
+DEF_CVT_SCALE_FUNC(16u16s, ushort, short, float);
+DEF_CVT_SCALE_FUNC(16s,    short, short, float);
+DEF_CVT_SCALE_FUNC(32s16s, int, short, float);
+DEF_CVT_SCALE_FUNC(32f16s, float, short, float);
+DEF_CVT_SCALE_FUNC(64f16s, double, short, float);
 
-DEF_CVT_SCALE_FUNC(8u32s,  uchar, int, float)
-DEF_CVT_SCALE_FUNC(8s32s,  schar, int, float)
-DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float)
-DEF_CVT_SCALE_FUNC(16s32s, short, int, float)
-DEF_CVT_SCALE_FUNC(32s,    int, int, double)
-DEF_CVT_SCALE_FUNC(32f32s, float, int, float)
-DEF_CVT_SCALE_FUNC(64f32s, double, int, double)
+DEF_CVT_SCALE_FUNC(8u32s,  uchar, int, float);
+DEF_CVT_SCALE_FUNC(8s32s,  schar, int, float);
+DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float);
+DEF_CVT_SCALE_FUNC(16s32s, short, int, float);
+DEF_CVT_SCALE_FUNC(32s,    int, int, double);
+DEF_CVT_SCALE_FUNC(32f32s, float, int, float);
+DEF_CVT_SCALE_FUNC(64f32s, double, int, double);
 
-DEF_CVT_SCALE_FUNC(8u32f,  uchar, float, float)
-DEF_CVT_SCALE_FUNC(8s32f,  schar, float, float)
-DEF_CVT_SCALE_FUNC(16u32f, ushort, float, float)
-DEF_CVT_SCALE_FUNC(16s32f, short, float, float)
-DEF_CVT_SCALE_FUNC(32s32f, int, float, double)
-DEF_CVT_SCALE_FUNC(32f,    float, float, float)
-DEF_CVT_SCALE_FUNC(64f32f, double, float, double)
+DEF_CVT_SCALE_FUNC(8u32f,  uchar, float, float);
+DEF_CVT_SCALE_FUNC(8s32f,  schar, float, float);
+DEF_CVT_SCALE_FUNC(16u32f, ushort, float, float);
+DEF_CVT_SCALE_FUNC(16s32f, short, float, float);
+DEF_CVT_SCALE_FUNC(32s32f, int, float, double);
+DEF_CVT_SCALE_FUNC(32f,    float, float, float);
+DEF_CVT_SCALE_FUNC(64f32f, double, float, double);
 
-DEF_CVT_SCALE_FUNC(8u64f,  uchar, double, double)
-DEF_CVT_SCALE_FUNC(8s64f,  schar, double, double)
-DEF_CVT_SCALE_FUNC(16u64f, ushort, double, double)
-DEF_CVT_SCALE_FUNC(16s64f, short, double, double)
-DEF_CVT_SCALE_FUNC(32s64f, int, double, double)
-DEF_CVT_SCALE_FUNC(32f64f, float, double, double)
-DEF_CVT_SCALE_FUNC(64f,    double, double, double)
+DEF_CVT_SCALE_FUNC(8u64f,  uchar, double, double);
+DEF_CVT_SCALE_FUNC(8s64f,  schar, double, double);
+DEF_CVT_SCALE_FUNC(16u64f, ushort, double, double);
+DEF_CVT_SCALE_FUNC(16s64f, short, double, double);
+DEF_CVT_SCALE_FUNC(32s64f, int, double, double);
+DEF_CVT_SCALE_FUNC(32f64f, float, double, double);
+DEF_CVT_SCALE_FUNC(64f,    double, double, double);
 
-DEF_CPY_FUNC(8u,     uchar)
-DEF_CVT_FUNC(8s8u,   schar, uchar)
-DEF_CVT_FUNC(16u8u,  ushort, uchar)
-DEF_CVT_FUNC(16s8u,  short, uchar)
-DEF_CVT_FUNC(32s8u,  int, uchar)
-DEF_CVT_FUNC(32f8u,  float, uchar)
-DEF_CVT_FUNC(64f8u,  double, uchar)
+DEF_CPY_FUNC(8u,     uchar);
+DEF_CVT_FUNC(8s8u,   schar, uchar);
+DEF_CVT_FUNC(16u8u,  ushort, uchar);
+DEF_CVT_FUNC(16s8u,  short, uchar);
+DEF_CVT_FUNC(32s8u,  int, uchar);
+DEF_CVT_FUNC(32f8u,  float, uchar);
+DEF_CVT_FUNC(64f8u,  double, uchar);
 
-DEF_CVT_FUNC(8u8s,   uchar, schar)
-DEF_CVT_FUNC(16u8s,  ushort, schar)
-DEF_CVT_FUNC(16s8s,  short, schar)
-DEF_CVT_FUNC(32s8s,  int, schar)
-DEF_CVT_FUNC(32f8s,  float, schar)
-DEF_CVT_FUNC(64f8s,  double, schar)
+DEF_CVT_FUNC(8u8s,   uchar, schar);
+DEF_CVT_FUNC(16u8s,  ushort, schar);
+DEF_CVT_FUNC(16s8s,  short, schar);
+DEF_CVT_FUNC(32s8s,  int, schar);
+DEF_CVT_FUNC(32f8s,  float, schar);
+DEF_CVT_FUNC(64f8s,  double, schar);
 
-DEF_CVT_FUNC(8u16u,  uchar, ushort)
-DEF_CVT_FUNC(8s16u,  schar, ushort)
-DEF_CPY_FUNC(16u,    ushort)
-DEF_CVT_FUNC(16s16u, short, ushort)
-DEF_CVT_FUNC(32s16u, int, ushort)
-DEF_CVT_FUNC(32f16u, float, ushort)
-DEF_CVT_FUNC(64f16u, double, ushort)
+DEF_CVT_FUNC(8u16u,  uchar, ushort);
+DEF_CVT_FUNC(8s16u,  schar, ushort);
+DEF_CPY_FUNC(16u,    ushort);
+DEF_CVT_FUNC(16s16u, short, ushort);
+DEF_CVT_FUNC(32s16u, int, ushort);
+DEF_CVT_FUNC(32f16u, float, ushort);
+DEF_CVT_FUNC(64f16u, double, ushort);
 
-DEF_CVT_FUNC(8u16s,  uchar, short)
-DEF_CVT_FUNC(8s16s,  schar, short)
-DEF_CVT_FUNC(16u16s, ushort, short)
-DEF_CVT_FUNC(32s16s, int, short)
-DEF_CVT_FUNC(32f16s, float, short)
-DEF_CVT_FUNC(64f16s, double, short)
+DEF_CVT_FUNC(8u16s,  uchar, short);
+DEF_CVT_FUNC(8s16s,  schar, short);
+DEF_CVT_FUNC(16u16s, ushort, short);
+DEF_CVT_FUNC(32s16s, int, short);
+DEF_CVT_FUNC(32f16s, float, short);
+DEF_CVT_FUNC(64f16s, double, short);
 
-DEF_CVT_FUNC(8u32s,  uchar, int)
-DEF_CVT_FUNC(8s32s,  schar, int)
-DEF_CVT_FUNC(16u32s, ushort, int)
-DEF_CVT_FUNC(16s32s, short, int)
-DEF_CPY_FUNC(32s,    int)
-DEF_CVT_FUNC(32f32s, float, int)
-DEF_CVT_FUNC(64f32s, double, int)
+DEF_CVT_FUNC(8u32s,  uchar, int);
+DEF_CVT_FUNC(8s32s,  schar, int);
+DEF_CVT_FUNC(16u32s, ushort, int);
+DEF_CVT_FUNC(16s32s, short, int);
+DEF_CPY_FUNC(32s,    int);
+DEF_CVT_FUNC(32f32s, float, int);
+DEF_CVT_FUNC(64f32s, double, int);
 
-DEF_CVT_FUNC(8u32f,  uchar, float)
-DEF_CVT_FUNC(8s32f,  schar, float)
-DEF_CVT_FUNC(16u32f, ushort, float)
-DEF_CVT_FUNC(16s32f, short, float)
-DEF_CVT_FUNC(32s32f, int, float)
-DEF_CVT_FUNC(64f32f, double, float)
+DEF_CVT_FUNC(8u32f,  uchar, float);
+DEF_CVT_FUNC(8s32f,  schar, float);
+DEF_CVT_FUNC(16u32f, ushort, float);
+DEF_CVT_FUNC(16s32f, short, float);
+DEF_CVT_FUNC(32s32f, int, float);
+DEF_CVT_FUNC(64f32f, double, float);
 
-DEF_CVT_FUNC(8u64f,  uchar, double)
-DEF_CVT_FUNC(8s64f,  schar, double)
-DEF_CVT_FUNC(16u64f, ushort, double)
-DEF_CVT_FUNC(16s64f, short, double)
-DEF_CVT_FUNC(32s64f, int, double)
-DEF_CVT_FUNC(32f64f, float, double)
-DEF_CPY_FUNC(64s,    int64)
+DEF_CVT_FUNC(8u64f,  uchar, double);
+DEF_CVT_FUNC(8s64f,  schar, double);
+DEF_CVT_FUNC(16u64f, ushort, double);
+DEF_CVT_FUNC(16s64f, short, double);
+DEF_CVT_FUNC(32s64f, int, double);
+DEF_CVT_FUNC(32f64f, float, double);
+DEF_CPY_FUNC(64s,    int64);
 
 static BinaryFunc getCvtScaleAbsFunc(int depth)
 {

modules/core/src/copy.cpp

@@ -166,16 +166,16 @@ static void copyMask##suffix(const uchar* src, size_t sstep, const uchar* mask,
 }
 
 
-DEF_COPY_MASK(8u, uchar)
-DEF_COPY_MASK(16u, ushort)
-DEF_COPY_MASK(8uC3, Vec3b)
-DEF_COPY_MASK(32s, int)
-DEF_COPY_MASK(16uC3, Vec3s)
-DEF_COPY_MASK(32sC2, Vec2i)
-DEF_COPY_MASK(32sC3, Vec3i)
-DEF_COPY_MASK(32sC4, Vec4i)
-DEF_COPY_MASK(32sC6, Vec6i)
-DEF_COPY_MASK(32sC8, Vec8i)
+DEF_COPY_MASK(8u, uchar);
+DEF_COPY_MASK(16u, ushort);
+DEF_COPY_MASK(8uC3, Vec3b);
+DEF_COPY_MASK(32s, int);
+DEF_COPY_MASK(16uC3, Vec3s);
+DEF_COPY_MASK(32sC2, Vec2i);
+DEF_COPY_MASK(32sC3, Vec3i);
+DEF_COPY_MASK(32sC4, Vec4i);
+DEF_COPY_MASK(32sC6, Vec6i);
+DEF_COPY_MASK(32sC8, Vec8i);
 
 BinaryFunc copyMaskTab[] =
 {
@@ -232,7 +232,10 @@ void Mat::copyTo( OutputArray _dst ) const
             const uchar* sptr = data;
             uchar* dptr = dst.data;
 
-            Size sz = getContinuousSize(*this, dst);
+            // to handle the copying 1xn matrix => nx1 std vector.
+            Size sz = size() == dst.size() ?
+                getContinuousSize(*this, dst) :
+                getContinuousSize(*this);
             size_t len = sz.width*elemSize();
 
             for( ; sz.height--; sptr += step, dptr += dst.step )
@@ -283,7 +286,6 @@ 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/gpumat.cpp

@@ -620,19 +620,11 @@ void cv::gpu::GpuMat::copyTo(GpuMat& m) const
 void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const
 {
     if (mask.empty())
-    {
         copyTo(mat);
-    }
     else
     {
-        uchar* data0 = mat.data;
-
         mat.create(size(), type());
 
-        // do not leave dst uninitialized
-        if (mat.data != data0)
-            mat.setTo(Scalar::all(0));
-
         gpuFuncTable()->copyWithMask(*this, mat, mask);
     }
 }

modules/core/src/system.cpp

@@ -982,9 +982,7 @@ public:
 };
 
 #ifdef WIN32
-#ifdef _MSC_VER
 #pragma warning(disable:4505) // unreferenced local function has been removed
-#endif
 
 #ifdef HAVE_WINRT
     // using C++11 thread attribute for local thread data
@@ -1131,24 +1129,17 @@ public:
         }
     }
 };
-
-// This is a wrapper function that will ensure 'tlsContainerStorage' is constructed on first use.
-// For more information: http://www.parashift.com/c++-faq/static-init-order-on-first-use.html
-static TLSContainerStorage& getTLSContainerStorage()
-{
-    static TLSContainerStorage *tlsContainerStorage = new TLSContainerStorage();
-    return *tlsContainerStorage;
-}
+static TLSContainerStorage tlsContainerStorage;
 
 TLSDataContainer::TLSDataContainer()
     : key_(-1)
 {
-    key_ = getTLSContainerStorage().allocateKey(this);
+    key_ = tlsContainerStorage.allocateKey(this);
 }
 
 TLSDataContainer::~TLSDataContainer()
 {
-    getTLSContainerStorage().releaseKey(key_, this);
+    tlsContainerStorage.releaseKey(key_, this);
     key_ = -1;
 }
 
@@ -1173,7 +1164,7 @@ TLSStorage::~TLSStorage()
         void*& data = tlsData_[i];
         if (data)
         {
-            getTLSContainerStorage().destroyData(i, data);
+            tlsContainerStorage.destroyData(i, data);
             data = NULL;
         }
     }

modules/core/test/test_arithm.cpp

@@ -115,7 +115,7 @@ struct BaseAddOp : public BaseElemWiseOp
 
 struct AddOp : public BaseAddOp
 {
-    AddOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
+    AddOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         if( mask.empty() )
@@ -128,7 +128,7 @@ struct AddOp : public BaseAddOp
 
 struct SubOp : public BaseAddOp
 {
-    SubOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, -1, Scalar::all(0)) {}
+    SubOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, -1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         if( mask.empty() )
@@ -141,7 +141,7 @@ struct SubOp : public BaseAddOp
 
 struct AddSOp : public BaseAddOp
 {
-    AddSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 0, Scalar::all(0)) {}
+    AddSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 0, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         if( mask.empty() )
@@ -154,7 +154,7 @@ struct AddSOp : public BaseAddOp
 
 struct SubRSOp : public BaseAddOp
 {
-    SubRSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, -1, 0, Scalar::all(0)) {}
+    SubRSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, -1, 0, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         if( mask.empty() )
@@ -167,7 +167,7 @@ struct SubRSOp : public BaseAddOp
 
 struct ScaleAddOp : public BaseAddOp
 {
-    ScaleAddOp() : BaseAddOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    ScaleAddOp() : BaseAddOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         scaleAdd(src[0], alpha, src[1], dst);
@@ -181,7 +181,7 @@ struct ScaleAddOp : public BaseAddOp
 
 struct AddWeightedOp : public BaseAddOp
 {
-    AddWeightedOp() : BaseAddOp(2, REAL_GAMMA, 1, 1, Scalar::all(0)) {}
+    AddWeightedOp() : BaseAddOp(2, REAL_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         addWeighted(src[0], alpha, src[1], beta, gamma[0], dst);
@@ -194,7 +194,7 @@ struct AddWeightedOp : public BaseAddOp
 
 struct MulOp : public BaseElemWiseOp
 {
-    MulOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    MulOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void getValueRange(int depth, double& minval, double& maxval)
     {
         minval = depth < CV_32S ? cvtest::getMinVal(depth) : depth == CV_32S ? -1000000 : -1000.;
@@ -218,7 +218,7 @@ struct MulOp : public BaseElemWiseOp
 
 struct DivOp : public BaseElemWiseOp
 {
-    DivOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    DivOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::divide(src[0], src[1], dst, alpha);
@@ -235,7 +235,7 @@ struct DivOp : public BaseElemWiseOp
 
 struct RecipOp : public BaseElemWiseOp
 {
-    RecipOp() : BaseElemWiseOp(1, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    RecipOp() : BaseElemWiseOp(1, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::divide(alpha, src[0], dst);
@@ -252,7 +252,7 @@ struct RecipOp : public BaseElemWiseOp
 
 struct AbsDiffOp : public BaseAddOp
 {
-    AbsDiffOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, -1, Scalar::all(0)) {}
+    AbsDiffOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, -1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         absdiff(src[0], src[1], dst);
@@ -265,7 +265,7 @@ struct AbsDiffOp : public BaseAddOp
 
 struct AbsDiffSOp : public BaseAddOp
 {
-    AbsDiffSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA, 1, 0, Scalar::all(0)) {}
+    AbsDiffSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA, 1, 0, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         absdiff(src[0], gamma, dst);
@@ -278,7 +278,7 @@ struct AbsDiffSOp : public BaseAddOp
 
 struct LogicOp : public BaseElemWiseOp
 {
-    LogicOp(char _opcode) : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)), opcode(_opcode) {}
+    LogicOp(char _opcode) : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)), opcode(_opcode) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         if( opcode == '&' )
@@ -309,7 +309,7 @@ struct LogicOp : public BaseElemWiseOp
 struct LogicSOp : public BaseElemWiseOp
 {
     LogicSOp(char _opcode)
-    : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+(_opcode != '~' ? SUPPORT_MASK : 0), 1, 1, Scalar::all(0)), opcode(_opcode) {}
+    : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+(_opcode != '~' ? SUPPORT_MASK : 0), 1, 1, Scalar::all(0)), opcode(_opcode) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         if( opcode == '&' )
@@ -341,7 +341,7 @@ struct LogicSOp : public BaseElemWiseOp
 
 struct MinOp : public BaseElemWiseOp
 {
-    MinOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    MinOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::min(src[0], src[1], dst);
@@ -358,7 +358,7 @@ struct MinOp : public BaseElemWiseOp
 
 struct MaxOp : public BaseElemWiseOp
 {
-    MaxOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    MaxOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::max(src[0], src[1], dst);
@@ -375,7 +375,7 @@ struct MaxOp : public BaseElemWiseOp
 
 struct MinSOp : public BaseElemWiseOp
 {
-    MinSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
+    MinSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::min(src[0], gamma[0], dst);
@@ -392,7 +392,7 @@ struct MinSOp : public BaseElemWiseOp
 
 struct MaxSOp : public BaseElemWiseOp
 {
-    MaxSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
+    MaxSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::max(src[0], gamma[0], dst);
@@ -409,7 +409,7 @@ struct MaxSOp : public BaseElemWiseOp
 
 struct CmpOp : public BaseElemWiseOp
 {
-    CmpOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    CmpOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void generateScalars(int depth, RNG& rng)
     {
         BaseElemWiseOp::generateScalars(depth, rng);
@@ -437,7 +437,7 @@ struct CmpOp : public BaseElemWiseOp
 
 struct CmpSOp : public BaseElemWiseOp
 {
-    CmpSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
+    CmpSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
     void generateScalars(int depth, RNG& rng)
     {
         BaseElemWiseOp::generateScalars(depth, rng);
@@ -467,7 +467,7 @@ struct CmpSOp : public BaseElemWiseOp
 
 struct CopyOp : public BaseElemWiseOp
 {
-    CopyOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
+    CopyOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
     {
         src[0].copyTo(dst, mask);
@@ -490,7 +490,7 @@ struct CopyOp : public BaseElemWiseOp
 
 struct SetOp : public BaseElemWiseOp
 {
-    SetOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
+    SetOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>&, Mat& dst, const Mat& mask)
     {
         dst.setTo(gamma, mask);
@@ -651,7 +651,7 @@ static void inRangeS(const Mat& src, const Scalar& lb, const Scalar& rb, Mat& ds
 
 struct InRangeSOp : public BaseElemWiseOp
 {
-    InRangeSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {}
+    InRangeSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::inRange(src[0], gamma, gamma1, dst);
@@ -681,7 +681,7 @@ struct InRangeSOp : public BaseElemWiseOp
 
 struct InRangeOp : public BaseElemWiseOp
 {
-    InRangeOp() : BaseElemWiseOp(3, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    InRangeOp() : BaseElemWiseOp(3, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         Mat lb, rb;
@@ -707,7 +707,7 @@ struct InRangeOp : public BaseElemWiseOp
 
 struct ConvertScaleOp : public BaseElemWiseOp
 {
-    ConvertScaleOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)), ddepth(0) { }
+    ConvertScaleOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)), ddepth(0) { };
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         src[0].convertTo(dst, ddepth, alpha, gamma[0]);
@@ -742,7 +742,7 @@ struct ConvertScaleOp : public BaseElemWiseOp
 
 struct ConvertScaleAbsOp : public BaseElemWiseOp
 {
-    ConvertScaleAbsOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
+    ConvertScaleAbsOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>& src, Mat& dst, const Mat&)
     {
         cv::convertScaleAbs(src[0], dst, alpha, gamma[0]);
@@ -810,7 +810,7 @@ static void setIdentity(Mat& dst, const Scalar& s)
 
 struct FlipOp : public BaseElemWiseOp
 {
-    FlipOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    FlipOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void getRandomSize(RNG& rng, vector<int>& size)
     {
         cvtest::randomSize(rng, 2, 2, cvtest::ARITHM_MAX_SIZE_LOG, size);
@@ -836,7 +836,7 @@ struct FlipOp : public BaseElemWiseOp
 
 struct TransposeOp : public BaseElemWiseOp
 {
-    TransposeOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    TransposeOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void getRandomSize(RNG& rng, vector<int>& size)
     {
         cvtest::randomSize(rng, 2, 2, cvtest::ARITHM_MAX_SIZE_LOG, size);
@@ -857,7 +857,7 @@ struct TransposeOp : public BaseElemWiseOp
 
 struct SetIdentityOp : public BaseElemWiseOp
 {
-    SetIdentityOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {}
+    SetIdentityOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {};
     void getRandomSize(RNG& rng, vector<int>& size)
     {
         cvtest::randomSize(rng, 2, 2, cvtest::ARITHM_MAX_SIZE_LOG, size);
@@ -878,7 +878,7 @@ struct SetIdentityOp : public BaseElemWiseOp
 
 struct SetZeroOp : public BaseElemWiseOp
 {
-    SetZeroOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    SetZeroOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     void op(const vector<Mat>&, Mat& dst, const Mat&)
     {
         dst = Scalar::all(0);
@@ -954,7 +954,7 @@ static void log(const Mat& src, Mat& dst)
 
 struct ExpOp : public BaseElemWiseOp
 {
-    ExpOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    ExpOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     int getRandomType(RNG& rng)
     {
         return cvtest::randomType(rng, DEPTH_MASK_FLT, 1, ARITHM_MAX_CHANNELS);
@@ -981,7 +981,7 @@ struct ExpOp : public BaseElemWiseOp
 
 struct LogOp : public BaseElemWiseOp
 {
-    LogOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
+    LogOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
     int getRandomType(RNG& rng)
     {
         return cvtest::randomType(rng, DEPTH_MASK_FLT, 1, ARITHM_MAX_CHANNELS);
@@ -1564,19 +1564,3 @@ TEST(Core_round, CvRound)
     ASSERT_EQ(-2, cvRound(-2.5));
     ASSERT_EQ(-4, cvRound(-3.5));
 }
-
-
-typedef testing::TestWithParam<Size> Mul1;
-
-TEST_P(Mul1, One)
-{
-    Size size = GetParam();
-    cv::Mat src(size, CV_32FC1, cv::Scalar::all(2)), dst,
-            ref_dst(size, CV_32FC1, cv::Scalar::all(6));
-
-    cv::multiply(3, src, dst);
-
-    ASSERT_EQ(0, cv::norm(dst, ref_dst, cv::NORM_INF));
-}
-
-INSTANTIATE_TEST_CASE_P(Arithm, Mul1, testing::Values(Size(2, 2), Size(1, 1)));

modules/core/test/test_mat.cpp

@@ -897,24 +897,3 @@ 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/features2d/src/features2d_init.cpp

@@ -59,23 +59,23 @@ Ptr<Feature2D> Feature2D::create( const string& feature2DType )
 
 CV_INIT_ALGORITHM(BRISK, "Feature2D.BRISK",
                    obj.info()->addParam(obj, "thres", obj.threshold);
-                   obj.info()->addParam(obj, "octaves", obj.octaves))
+                   obj.info()->addParam(obj, "octaves", obj.octaves));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
 CV_INIT_ALGORITHM(BriefDescriptorExtractor, "Feature2D.BRIEF",
-                  obj.info()->addParam(obj, "bytes", obj.bytes_))
+                  obj.info()->addParam(obj, "bytes", obj.bytes_));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
 CV_INIT_ALGORITHM(FastFeatureDetector, "Feature2D.FAST",
                   obj.info()->addParam(obj, "threshold", obj.threshold);
-                  obj.info()->addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression))
+                  obj.info()->addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression));
 
 CV_INIT_ALGORITHM(FastFeatureDetector2, "Feature2D.FASTX",
                   obj.info()->addParam(obj, "threshold", obj.threshold);
                   obj.info()->addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
-                  obj.info()->addParam(obj, "type", obj.type))
+                  obj.info()->addParam(obj, "type", obj.type));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -84,7 +84,7 @@ CV_INIT_ALGORITHM(StarDetector, "Feature2D.STAR",
                   obj.info()->addParam(obj, "responseThreshold", obj.responseThreshold);
                   obj.info()->addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
                   obj.info()->addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
-                  obj.info()->addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize))
+                  obj.info()->addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -97,7 +97,7 @@ CV_INIT_ALGORITHM(MSER, "Feature2D.MSER",
                   obj.info()->addParam(obj, "maxEvolution", obj.maxEvolution);
                   obj.info()->addParam(obj, "areaThreshold", obj.areaThreshold);
                   obj.info()->addParam(obj, "minMargin", obj.minMargin);
-                  obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize))
+                  obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -109,7 +109,7 @@ CV_INIT_ALGORITHM(ORB, "Feature2D.ORB",
                   obj.info()->addParam(obj, "edgeThreshold", obj.edgeThreshold);
                   obj.info()->addParam(obj, "patchSize", obj.patchSize);
                   obj.info()->addParam(obj, "WTA_K", obj.WTA_K);
-                  obj.info()->addParam(obj, "scoreType", obj.scoreType))
+                  obj.info()->addParam(obj, "scoreType", obj.scoreType));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -117,7 +117,7 @@ CV_INIT_ALGORITHM(FREAK, "Feature2D.FREAK",
                   obj.info()->addParam(obj, "orientationNormalized", obj.orientationNormalized);
                   obj.info()->addParam(obj, "scaleNormalized", obj.scaleNormalized);
                   obj.info()->addParam(obj, "patternScale", obj.patternScale);
-                  obj.info()->addParam(obj, "nbOctave", obj.nOctaves))
+                  obj.info()->addParam(obj, "nbOctave", obj.nOctaves));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -126,7 +126,7 @@ CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT",
                   obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
                   obj.info()->addParam(obj, "minDistance", obj.minDistance);
                   obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
-                  obj.info()->addParam(obj, "k", obj.k))
+                  obj.info()->addParam(obj, "k", obj.k));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -146,7 +146,7 @@ CV_INIT_ALGORITHM(SimpleBlobDetector, "Feature2D.SimpleBlob",
                   obj.info()->addParam(obj, "maxInertiaRatio",  obj.params.maxInertiaRatio);
                   obj.info()->addParam(obj, "filterByConvexity", obj.params.filterByConvexity);
                   obj.info()->addParam(obj, "maxConvexity",     obj.params.maxConvexity);
-                  )
+                  );
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -167,7 +167,7 @@ CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS",
                   obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
                   obj.info()->addParam(obj, "minDistance", obj.minDistance);
                   obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
-                  obj.info()->addParam(obj, "k", obj.k))
+                  obj.info()->addParam(obj, "k", obj.k));
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -178,21 +178,21 @@ CV_INIT_ALGORITHM(DenseFeatureDetector, "Feature2D.Dense",
                   obj.info()->addParam(obj, "initXyStep", obj.initXyStep);
                   obj.info()->addParam(obj, "initImgBound", obj.initImgBound);
                   obj.info()->addParam(obj, "varyXyStepWithScale", obj.varyXyStepWithScale);
-                  obj.info()->addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale))
+                  obj.info()->addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale));
 
 CV_INIT_ALGORITHM(GridAdaptedFeatureDetector, "Feature2D.Grid",
                   obj.info()->addParam<FeatureDetector>(obj, "detector", obj.detector, false, 0, 0); // Extra params added to avoid VS2013 fatal error in opencv2/core.hpp (decl. of addParam)
                   obj.info()->addParam(obj, "maxTotalKeypoints", obj.maxTotalKeypoints);
                   obj.info()->addParam(obj, "gridRows", obj.gridRows);
-                  obj.info()->addParam(obj, "gridCols", obj.gridCols))
+                  obj.info()->addParam(obj, "gridCols", obj.gridCols));
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
 CV_INIT_ALGORITHM(BFMatcher, "DescriptorMatcher.BFMatcher",
                   obj.info()->addParam(obj, "normType", obj.normType);
-                  obj.info()->addParam(obj, "crossCheck", obj.crossCheck))
+                  obj.info()->addParam(obj, "crossCheck", obj.crossCheck));
 
-CV_INIT_ALGORITHM(FlannBasedMatcher, "DescriptorMatcher.FlannBasedMatcher",)
+CV_INIT_ALGORITHM(FlannBasedMatcher, "DescriptorMatcher.FlannBasedMatcher",);
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 

modules/flann/include/opencv2/flann/hierarchical_clustering_index.h

@@ -414,6 +414,12 @@ public:
 
     void loadIndex(FILE* stream)
     {
+        load_value(stream, branching_);
+        load_value(stream, trees_);
+        load_value(stream, centers_init_);
+        load_value(stream, leaf_size_);
+        load_value(stream, memoryCounter);
+
         free_elements();
 
         if (root!=NULL) {
@@ -424,12 +430,6 @@ public:
             delete[] indices;
         }
 
-        load_value(stream, branching_);
-        load_value(stream, trees_);
-        load_value(stream, centers_init_);
-        load_value(stream, leaf_size_);
-        load_value(stream, memoryCounter);
-
         indices = new int*[trees_];
         root = new NodePtr[trees_];
         for (int i=0; i<trees_; ++i) {

modules/gpu/include/opencv2/gpu/gpu.hpp

@@ -2443,7 +2443,7 @@ public:
         Uncompressed_YV12   = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,V,U (4:2:0)
         Uncompressed_NV12   = (('N'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,UV  (4:2:0)
         Uncompressed_YUYV   = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')),   // YUYV/YUY2 (4:2:2)
-        Uncompressed_UYVY   = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y'))    // UYVY (4:2:2)
+        Uncompressed_UYVY   = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')),   // UYVY (4:2:2)
     };
 
     enum ChromaFormat
@@ -2451,7 +2451,7 @@ public:
         Monochrome=0,
         YUV420,
         YUV422,
-        YUV444
+        YUV444,
     };
 
     struct FormatInfo

modules/gpu/perf/perf_imgproc.cpp

@@ -50,7 +50,7 @@ using namespace perf;
 // Remap
 
 enum { HALF_SIZE=0, UPSIDE_DOWN, REFLECTION_X, REFLECTION_BOTH };
-CV_ENUM(RemapMode, HALF_SIZE, UPSIDE_DOWN, REFLECTION_X, REFLECTION_BOTH)
+CV_ENUM(RemapMode, HALF_SIZE, UPSIDE_DOWN, REFLECTION_X, REFLECTION_BOTH);
 
 void generateMap(cv::Mat& map_x, cv::Mat& map_y, int remapMode)
 {
@@ -941,7 +941,7 @@ PERF_TEST_P(Sz_TemplateSz_Cn_Method, ImgProc_MatchTemplate8U,
 
         CPU_SANITY_CHECK(dst);
     }
-}
+};
 
 ////////////////////////////////////////////////////////////////////////////////
 // MatchTemplate32F
@@ -981,7 +981,7 @@ PERF_TEST_P(Sz_TemplateSz_Cn_Method, ImgProc_MatchTemplate32F,
 
         CPU_SANITY_CHECK(dst);
     }
-}
+};
 
 //////////////////////////////////////////////////////////////////////
 // MulSpectrums
@@ -1821,7 +1821,7 @@ PERF_TEST_P(Sz_Dp_MinDist, ImgProc_HoughCircles,
 //////////////////////////////////////////////////////////////////////
 // GeneralizedHough
 
-CV_FLAGS(GHMethod, GHT_POSITION, GHT_SCALE, GHT_ROTATION)
+CV_FLAGS(GHMethod, GHT_POSITION, GHT_SCALE, GHT_ROTATION);
 
 DEF_PARAM_TEST(Method_Sz, GHMethod, cv::Size);
 

modules/highgui/src/bitstrm.hpp

@@ -53,7 +53,7 @@ enum
     RBS_THROW_EOS=-123,  // <end of stream> exception code
     RBS_THROW_FORB=-124,  // <forrbidden huffman code> exception code
     RBS_HUFF_FORB=2047,  // forrbidden huffman code "value"
-    RBS_BAD_HEADER=-125 // invalid header
+    RBS_BAD_HEADER=-125, // invalid header
 };
 
 typedef unsigned long ulong;

modules/highgui/src/cap_ffmpeg_impl.hpp

@@ -2066,7 +2066,7 @@ enum
     VideoCodec_YV12   = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,V,U (4:2:0)
     VideoCodec_NV12   = (('N'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,UV  (4:2:0)
     VideoCodec_YUYV   = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')),   // YUYV/YUY2 (4:2:2)
-    VideoCodec_UYVY   = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y'))    // UYVY (4:2:2)
+    VideoCodec_UYVY   = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')),   // UYVY (4:2:2)
 };
 
 enum
@@ -2074,7 +2074,7 @@ enum
     VideoChromaFormat_Monochrome = 0,
     VideoChromaFormat_YUV420,
     VideoChromaFormat_YUV422,
-    VideoChromaFormat_YUV444
+    VideoChromaFormat_YUV444,
 };
 
 struct InputMediaStream_FFMPEG

modules/highgui/src/precomp.hpp

@@ -57,14 +57,6 @@
 #include <assert.h>
 
 #if defined WIN32 || defined WINCE
-    #if !defined _WIN32_WINNT
-        #ifdef HAVE_MSMF
-            #define _WIN32_WINNT 0x0600 // Windows Vista
-        #else
-            #define _WIN32_WINNT 0x0500 // Windows 2000
-        #endif
-    #endif
-
     #include <windows.h>
     #undef small
     #undef min

modules/highgui/src/window_w32.cpp

@@ -43,11 +43,27 @@
 
 #if defined WIN32 || defined _WIN32
 
+#define COMPILE_MULTIMON_STUBS // Required for multi-monitor support
+#ifndef _MULTIMON_USE_SECURE_CRT
+#  define _MULTIMON_USE_SECURE_CRT 0 // some MinGW platforms have no strncpy_s
+#endif
+
+#if defined SM_CMONITORS && !defined MONITOR_DEFAULTTONEAREST
+#  define MONITOR_DEFAULTTONULL       0x00000000
+#  define MONITOR_DEFAULTTOPRIMARY    0x00000001
+#  define MONITOR_DEFAULTTONEAREST    0x00000002
+#  define MONITORINFOF_PRIMARY        0x00000001
+#endif
+#ifndef __inout
+#  define __inout
+#endif
+
 #ifdef __GNUC__
 #  pragma GCC diagnostic ignored "-Wmissing-declarations"
 #endif
 
 #include <commctrl.h>
+#include <winuser.h>
 #include <stdlib.h>
 #include <string.h>
 #include <stdio.h>

modules/imgproc/doc/miscellaneous_transformations.rst

@@ -113,8 +113,6 @@ 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:
 
 *
@@ -129,7 +127,7 @@ 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 \max (ChannelRange)
+        \text{Gray to RGB[A]:} \quad R  \leftarrow Y, G  \leftarrow Y, B  \leftarrow Y, A  \leftarrow 0
 
     The conversion from a RGB image to gray is done with:
 

modules/imgproc/perf/perf_filter2d.cpp

@@ -8,7 +8,7 @@ using std::tr1::make_tuple;
 using std::tr1::get;
 
 
-CV_ENUM(BorderMode, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT_101)
+CV_ENUM(BorderMode, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT_101);
 
 typedef TestBaseWithParam< tr1::tuple<Size, int, BorderMode> > TestFilter2d;
 typedef TestBaseWithParam< tr1::tuple<String, int> > Image_KernelSize;

modules/imgproc/src/gcgraph.hpp

@@ -380,6 +380,6 @@ bool GCGraph<TWeight>::inSourceSegment( int i )
 {
     CV_Assert( i>=0 && i<(int)vtcs.size() );
     return vtcs[i].t == 0;
-}
+};
 
 #endif

modules/imgproc/src/generalized_hough.cpp

@@ -300,7 +300,7 @@ namespace
                       obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
                                            "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
                       obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
-                                           "Inverse ratio of the accumulator resolution to the image resolution."))
+                                           "Inverse ratio of the accumulator resolution to the image resolution."));
 
     GHT_Ballard_Pos::GHT_Ballard_Pos()
     {
@@ -466,7 +466,7 @@ namespace
                       obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
                                            "Maximal scale to detect.");
                       obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
-                                           "Scale step."))
+                                           "Scale step."));
 
     GHT_Ballard_PosScale::GHT_Ballard_PosScale()
     {
@@ -631,7 +631,7 @@ namespace
                       obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
                                            "Maximal rotation angle to detect in degrees.");
                       obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
-                                           "Angle step in degrees."))
+                                           "Angle step in degrees."));
 
     GHT_Ballard_PosRotation::GHT_Ballard_PosRotation()
     {
@@ -878,7 +878,7 @@ namespace
                       obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
                                            "Inverse ratio of the accumulator resolution to the image resolution.");
                       obj.info()->addParam(obj, "posThresh", obj.posThresh, false, 0, 0,
-                                           "Position threshold."))
+                                           "Position threshold."));
 
     GHT_Guil_Full::GHT_Guil_Full()
     {

modules/imgproc/src/imgwarp.cpp

@@ -2201,15 +2201,15 @@ struct RemapVec_8u
     int operator()( const Mat& _src, void* _dst, const short* XY,
                     const ushort* FXY, const void* _wtab, int width ) const
     {
-        int cn = _src.channels(), x = 0, sstep = (int)_src.step;
+        int cn = _src.channels();
 
-        if( (cn != 1 && cn != 3 && cn != 4) || !checkHardwareSupport(CV_CPU_SSE2) ||
-                sstep > 0x8000 )
+        if( (cn != 1 && cn != 3 && cn != 4) || !checkHardwareSupport(CV_CPU_SSE2) )
             return 0;
 
         const uchar *S0 = _src.data, *S1 = _src.data + _src.step;
         const short* wtab = cn == 1 ? (const short*)_wtab : &BilinearTab_iC4[0][0][0];
         uchar* D = (uchar*)_dst;
+        int x = 0, sstep = (int)_src.step;
         __m128i delta = _mm_set1_epi32(INTER_REMAP_COEF_SCALE/2);
         __m128i xy2ofs = _mm_set1_epi32(cn + (sstep << 16));
         __m128i z = _mm_setzero_si128();

modules/imgproc/src/smooth.cpp

@@ -718,7 +718,7 @@ void cv::boxFilter( InputArray _src, OutputArray _dst, int ddepth,
         ddepth = sdepth;
     _dst.create( src.size(), CV_MAKETYPE(ddepth, cn) );
     Mat dst = _dst.getMat();
-    if( borderType != BORDER_CONSTANT && normalize && (borderType & BORDER_ISOLATED) != 0 )
+    if( borderType != BORDER_CONSTANT && normalize )
     {
         if( src.rows == 1 )
             ksize.height = 1;

modules/imgproc/test/test_cvtyuv.cpp

@@ -603,7 +603,7 @@ CV_ENUM(YUVCVTS, CV_YUV2RGB_NV12, CV_YUV2BGR_NV12, CV_YUV2RGB_NV21, CV_YUV2BGR_N
                  CV_YUV2RGBA_YUY2, CV_YUV2BGRA_YUY2, CV_YUV2RGBA_YVYU, CV_YUV2BGRA_YVYU,
                  CV_YUV2GRAY_420, CV_YUV2GRAY_UYVY, CV_YUV2GRAY_YUY2,
                  CV_YUV2BGR, CV_YUV2RGB, CV_RGB2YUV_YV12, CV_BGR2YUV_YV12, CV_RGBA2YUV_YV12,
-                 CV_BGRA2YUV_YV12, CV_RGB2YUV_I420, CV_BGR2YUV_I420, CV_RGBA2YUV_I420, CV_BGRA2YUV_I420)
+                 CV_BGRA2YUV_YV12, CV_RGB2YUV_I420, CV_BGR2YUV_I420, CV_RGBA2YUV_I420, CV_BGRA2YUV_I420);
 
 typedef ::testing::TestWithParam<YUVCVTS> Imgproc_ColorYUV;
 

modules/imgproc/test/test_filter.cpp

@@ -1886,35 +1886,3 @@ protected:
 };
 
 TEST(Imgproc_Filtering, supportedFormats) { CV_FilterSupportedFormatsTest test; test.safe_run(); }
-
-TEST(Imgproc_Blur, borderTypes)
-{
-    Size kernelSize(3, 3);
-
-    /// ksize > src_roi.size()
-    Mat src(3, 3, CV_8UC1, cv::Scalar::all(255)), dst;
-    Mat src_roi = src(Rect(1, 1, 1, 1));
-    src_roi.setTo(cv::Scalar::all(0));
-
-    // should work like !BORDER_ISOLATED
-    blur(src_roi, dst, kernelSize, Point(-1, -1), BORDER_REPLICATE);
-    EXPECT_EQ(227, dst.at<uchar>(0, 0));
-
-    // should work like BORDER_ISOLATED
-    blur(src_roi, dst, kernelSize, Point(-1, -1), BORDER_REPLICATE | BORDER_ISOLATED);
-    EXPECT_EQ(0, dst.at<uchar>(0, 0));
-
-    /// ksize <= src_roi.size()
-    src = Mat(5, 5, CV_8UC1, cv::Scalar(255));
-    src_roi = src(Rect(1, 1, 3, 3));
-    src_roi.setTo(0);
-    src.at<uchar>(2, 2) = 255;
-
-    // should work like !BORDER_ISOLATED
-    blur(src_roi, dst, kernelSize, Point(-1, -1), BORDER_REPLICATE);
-    Mat expected_dst =
-            (Mat_<uchar>(3, 3) << 170, 113, 170, 113, 28, 113, 170, 113, 170);
-    EXPECT_EQ(expected_dst.type(), dst.type());
-    EXPECT_EQ(expected_dst.size(), dst.size());
-    EXPECT_DOUBLE_EQ(0.0, cvtest::norm(expected_dst, dst, NORM_INF));
-}

modules/java/generator/src/cpp/VideoCapture.cpp

@@ -329,10 +329,7 @@ JNIEXPORT jstring JNICALL Java_org_opencv_highgui_VideoCapture_n_1getSupportedPr
         VideoCapture* me = (VideoCapture*) self; //TODO: check for NULL
         union {double prop; const char* name;} u;
         u.prop = me->get(CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING);
-        // VideoCapture::get can return 0.0 or -1.0 if it doesn't support
-        // CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING
-        if (u.prop != 0.0 && u.prop != -1.0)
-            return env->NewStringUTF(u.name);
+        return env->NewStringUTF(u.name);
     } catch(const std::exception &e) {
         throwJavaException(env, &e, method_name);
     } catch (...) {

modules/legacy/src/blobtrack.cpp

@@ -205,7 +205,7 @@ double CvVSModule::GetParam(const char* name)
         if(p->pInt) return p->pInt[0];
     }
     return 0;
-}
+};
 
 const char* CvVSModule::GetParamStr(const char* name)
 {

modules/legacy/src/enteringblobdetection.cpp

@@ -209,7 +209,7 @@ public:
     CvBlobDetectorSimple();
    ~CvBlobDetectorSimple();
     int DetectNewBlob(IplImage* pImg, IplImage* pFGMask, CvBlobSeq* pNewBlobList, CvBlobSeq* pOldBlobList);
-    void Release(){delete this;}
+    void Release(){delete this;};
 
 protected:
     IplImage*       m_pMaskBlobNew;
@@ -219,7 +219,7 @@ protected:
 };
 
 /* Blob detector creator (sole interface function for this file) */
-CvBlobDetector* cvCreateBlobDetectorSimple(){return new CvBlobDetectorSimple;}
+CvBlobDetector* cvCreateBlobDetectorSimple(){return new CvBlobDetectorSimple;};
 
 /* Constructor of BlobDetector: */
 CvBlobDetectorSimple::CvBlobDetectorSimple()

modules/legacy/src/vecfacetracking.cpp

@@ -52,7 +52,7 @@ enum
 {
     MOUTH = 0,
     LEYE = 1,
-    REYE = 2
+    REYE = 2,
 };
 
 #define MAX_LAYERS      64

modules/ml/doc/neural_networks.rst

@@ -240,7 +240,6 @@ This method applies the specified training algorithm to computing/adjusting the
 
 The RPROP training algorithm is parallelized with the TBB library.
 
-If you are using the default ``cvANN_MLP::SIGMOID_SYM`` activation function then the output should be in the range [-1,1], instead of [0,1], for optimal results.
 
 CvANN_MLP::predict
 ------------------
@@ -258,8 +257,6 @@ Predicts responses for input samples.
 
 The method returns a dummy value which should be ignored.
 
-If you are using the default ``cvANN_MLP::SIGMOID_SYM`` activation function with the default parameter values fparam1=0 and fparam2=0 then the function used is y = 1.7159*tanh(2/3 * x), so the output will range from [-1.7159, 1.7159], instead of [0,1].
-
 CvANN_MLP::get_layer_count
 --------------------------
 Returns the number of layers in the MLP.

modules/ml/src/ml_init.cpp

@@ -52,7 +52,7 @@ CV_INIT_ALGORITHM(EM, "StatModel.EM",
                   obj.info()->addParam(obj, "epsilon", obj.epsilon);
                   obj.info()->addParam(obj, "weights", obj.weights, true);
                   obj.info()->addParam(obj, "means", obj.means, true);
-                  obj.info()->addParam(obj, "covs", obj.covs, true))
+                  obj.info()->addParam(obj, "covs", obj.covs, true));
 
 bool initModule_ml(void)
 {

modules/ml/src/svm.cpp

@@ -2298,24 +2298,14 @@ 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;
-    int class_count = class_labels ? class_labels->cols :
-                      params.svm_type == CvSVM::ONE_CLASS ? 1 : 0;
+    int i, var_count = get_var_count(), df_count, class_count;
     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 );
 
@@ -2324,6 +2314,9 @@ 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 );
@@ -2461,6 +2454,7 @@ 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;
@@ -2489,7 +2483,7 @@ 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( !isSvmModelApplicable(sv_total, var_all, var_count, class_count) )
+    if( sv_total <= 0 || var_all <= 0 || var_count <= 0 || var_count > var_all || class_count < 0 )
         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

@@ -155,14 +155,6 @@ 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/nonfree/src/nonfree_init.cpp

@@ -52,7 +52,7 @@ CV_INIT_ALGORITHM(SURF, "Feature2D.SURF",
                   obj.info()->addParam(obj, "nOctaves", obj.nOctaves);
                   obj.info()->addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
                   obj.info()->addParam(obj, "extended", obj.extended);
-                  obj.info()->addParam(obj, "upright", obj.upright))
+                  obj.info()->addParam(obj, "upright", obj.upright));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -61,7 +61,7 @@ CV_INIT_ALGORITHM(SIFT, "Feature2D.SIFT",
                   obj.info()->addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
                   obj.info()->addParam(obj, "contrastThreshold", obj.contrastThreshold);
                   obj.info()->addParam(obj, "edgeThreshold", obj.edgeThreshold);
-                  obj.info()->addParam(obj, "sigma", obj.sigma))
+                  obj.info()->addParam(obj, "sigma", obj.sigma));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 

modules/ocl/include/opencv2/ocl/ocl.hpp

@@ -204,7 +204,7 @@ namespace cv
             CACHE_NONE    = 0,        // do not cache OpenCL binary
             CACHE_DEBUG   = 0x1 << 0, // cache OpenCL binary when built in debug mode
             CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
-            CACHE_ALL     = CACHE_DEBUG | CACHE_RELEASE // cache opencl binary
+            CACHE_ALL     = CACHE_DEBUG | CACHE_RELEASE, // cache opencl binary
         };
         //! Enable or disable OpenCL program binary caching onto local disk
         // After a program (*.cl files in opencl/ folder) is built at runtime, we allow the

modules/ocl/include/opencv2/ocl/private/opencl_utils.hpp

@@ -108,7 +108,7 @@ inline cl_int getStringInfo(Functor f, ObjectType obj, cl_uint name, std::string
     }
 
     return CL_SUCCESS;
-}
+};
 
 } // namespace cl_utils
 

modules/ocl/perf/perf_haar.cpp

@@ -83,69 +83,3 @@ 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
-}

modules/ocl/src/cl_runtime/cl_runtime.cpp

@@ -45,7 +45,7 @@
     #define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
 #endif // _WIN32
 
-#if defined(__linux__)
+#if defined(linux)
     #include <dlfcn.h>
     #include <stdio.h>
 

modules/ocl/src/cl_runtime/clamdblas_runtime.cpp

@@ -27,7 +27,7 @@
     #define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
 #endif // _WIN32
 
-#if defined(__linux__)
+#if defined(linux)
     #include <dlfcn.h>
     #include <stdio.h>
 

modules/ocl/src/cl_runtime/clamdfft_runtime.cpp

@@ -27,7 +27,7 @@
     #define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
 #endif // _WIN32
 
-#if defined(__linux__)
+#if defined(linux)
     #include <dlfcn.h>
     #include <stdio.h>
 

modules/ocl/src/cl_runtime/generator/template/clamdblas_runtime.cpp.in

@@ -24,7 +24,7 @@
     #define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
 #endif // _WIN32
 
-#if defined(__linux__)
+#if defined(linux)
     #include <dlfcn.h>
     #include <stdio.h>
 

modules/ocl/src/cl_runtime/generator/template/clamdfft_runtime.cpp.in

@@ -24,7 +24,7 @@
     #define CV_CL_GET_PROC_ADDRESS(name) WinGetProcAddress(name)
 #endif // _WIN32
 
-#if defined(__linux__)
+#if defined(linux)
     #include <dlfcn.h>
     #include <stdio.h>
 

modules/ocl/test/test_canny.cpp

@@ -48,8 +48,8 @@
 
 ////////////////////////////////////////////////////////
 // Canny
-IMPLEMENT_PARAM_CLASS(AppertureSize, int)
-IMPLEMENT_PARAM_CLASS(L2gradient, bool)
+IMPLEMENT_PARAM_CLASS(AppertureSize, int);
+IMPLEMENT_PARAM_CLASS(L2gradient, bool);
 
 PARAM_TEST_CASE(Canny, AppertureSize, L2gradient)
 {

modules/ocl/test/test_match_template.cpp

@@ -50,7 +50,7 @@
 // MatchTemplate
 #define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF_NORMED))
 
-IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size)
+IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size);
 
 #define MTEMP_SIZES testing::Values(cv::Size(128, 256), cv::Size(1024, 768))
 

modules/ocl/test/test_objdetect.cpp

@@ -181,7 +181,7 @@ INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, HOG, testing::Combine(
                             testing::Values(MatType(CV_8UC1), MatType(CV_8UC4))));
 
 ///////////////////////////// Haar //////////////////////////////
-IMPLEMENT_PARAM_CLASS(CascadeName, std::string)
+IMPLEMENT_PARAM_CLASS(CascadeName, std::string);
 CascadeName cascade_frontalface_alt(std::string("haarcascade_frontalface_alt.xml"));
 CascadeName cascade_frontalface_alt2(std::string("haarcascade_frontalface_alt2.xml"));
 struct getRect

modules/ocl/test/utility.hpp

@@ -266,7 +266,7 @@ CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA)
 CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP)
 CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
 
-CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T)
+CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T);
 CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP)
 CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)
 

modules/superres/src/btv_l1.cpp

@@ -488,7 +488,7 @@ namespace
                       obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
                       obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
                       obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
-                      obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."))
+                      obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
 
     BTVL1::BTVL1()
     {

modules/superres/src/btv_l1_ocl.cpp

@@ -580,7 +580,7 @@ namespace
     obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
     obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
     obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
-    obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."))
+    obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
 
     BTVL1_OCL::BTVL1_OCL()
     {

modules/superres/src/optical_flow.cpp

@@ -149,7 +149,7 @@ namespace
                       obj.info()->addParam(obj, "numIters", obj.numIters_);
                       obj.info()->addParam(obj, "polyN", obj.polyN_);
                       obj.info()->addParam(obj, "polySigma", obj.polySigma_);
-                      obj.info()->addParam(obj, "flags", obj.flags_))
+                      obj.info()->addParam(obj, "flags", obj.flags_));
 
     Farneback::Farneback() : CpuOpticalFlow(CV_8UC1)
     {
@@ -217,7 +217,7 @@ namespace
                       obj.info()->addParam(obj, "upscaleAveragingRadius", obj.upscaleAveragingRadius_);
                       obj.info()->addParam(obj, "upscaleSigmaDist", obj.upscaleSigmaDist_);
                       obj.info()->addParam(obj, "upscaleSigmaColor", obj.upscaleSigmaColor_);
-                      obj.info()->addParam(obj, "speedUpThr", obj.speedUpThr_))
+                      obj.info()->addParam(obj, "speedUpThr", obj.speedUpThr_));
 
     Simple::Simple() : CpuOpticalFlow(CV_8UC3)
     {
@@ -300,7 +300,7 @@ namespace
                       obj.info()->addParam(obj, "warps", obj.warps_);
                       obj.info()->addParam(obj, "epsilon", obj.epsilon_);
                       obj.info()->addParam(obj, "iterations", obj.iterations_);
-                      obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_))
+                      obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
 
     DualTVL1::DualTVL1() : CpuOpticalFlow(CV_8UC1)
     {
@@ -471,7 +471,7 @@ namespace
                       obj.info()->addParam(obj, "scaleFactor", obj.scaleFactor_, false, 0, 0, "Pyramid scale factor");
                       obj.info()->addParam(obj, "innerIterations", obj.innerIterations_, false, 0, 0, "Number of lagged non-linearity iterations (inner loop)");
                       obj.info()->addParam(obj, "outerIterations", obj.outerIterations_, false, 0, 0, "Number of warping iterations (number of pyramid levels)");
-                      obj.info()->addParam(obj, "solverIterations", obj.solverIterations_, false, 0, 0, "Number of linear system solver iterations"))
+                      obj.info()->addParam(obj, "solverIterations", obj.solverIterations_, false, 0, 0, "Number of linear system solver iterations"));
 
     Brox_GPU::Brox_GPU() : GpuOpticalFlow(CV_32FC1), alg_(0.197f, 50.0f, 0.8f, 10, 77, 10)
     {
@@ -535,7 +535,7 @@ namespace
     CV_INIT_ALGORITHM(PyrLK_GPU, "DenseOpticalFlowExt.PyrLK_GPU",
                       obj.info()->addParam(obj, "winSize", obj.winSize_);
                       obj.info()->addParam(obj, "maxLevel", obj.maxLevel_);
-                      obj.info()->addParam(obj, "iterations", obj.iterations_))
+                      obj.info()->addParam(obj, "iterations", obj.iterations_));
 
     PyrLK_GPU::PyrLK_GPU() : GpuOpticalFlow(CV_8UC1)
     {
@@ -602,7 +602,7 @@ namespace
                       obj.info()->addParam(obj, "numIters", obj.numIters_);
                       obj.info()->addParam(obj, "polyN", obj.polyN_);
                       obj.info()->addParam(obj, "polySigma", obj.polySigma_);
-                      obj.info()->addParam(obj, "flags", obj.flags_))
+                      obj.info()->addParam(obj, "flags", obj.flags_));
 
     Farneback_GPU::Farneback_GPU() : GpuOpticalFlow(CV_8UC1)
     {
@@ -678,7 +678,7 @@ namespace
                       obj.info()->addParam(obj, "warps", obj.warps_);
                       obj.info()->addParam(obj, "epsilon", obj.epsilon_);
                       obj.info()->addParam(obj, "iterations", obj.iterations_);
-                      obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_))
+                      obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
 
     DualTVL1_GPU::DualTVL1_GPU() : GpuOpticalFlow(CV_8UC1)
     {
@@ -800,7 +800,7 @@ namespace
     CV_INIT_ALGORITHM(PyrLK_OCL, "DenseOpticalFlowExt.PyrLK_OCL",
         obj.info()->addParam(obj, "winSize", obj.winSize_);
     obj.info()->addParam(obj, "maxLevel", obj.maxLevel_);
-    obj.info()->addParam(obj, "iterations", obj.iterations_))
+    obj.info()->addParam(obj, "iterations", obj.iterations_));
 
     PyrLK_OCL::PyrLK_OCL() : oclOpticalFlow(CV_8UC1)
     {
@@ -869,7 +869,7 @@ namespace
     obj.info()->addParam(obj, "warps", obj.warps_);
     obj.info()->addParam(obj, "epsilon", obj.epsilon_);
     obj.info()->addParam(obj, "iterations", obj.iterations_);
-    obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_))
+    obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
 
     DualTVL1_OCL::DualTVL1_OCL() : oclOpticalFlow(CV_8UC1)
     {
@@ -946,7 +946,7 @@ namespace
     obj.info()->addParam(obj, "numIters", obj.numIters_);
     obj.info()->addParam(obj, "polyN", obj.polyN_);
     obj.info()->addParam(obj, "polySigma", obj.polySigma_);
-    obj.info()->addParam(obj, "flags", obj.flags_))
+    obj.info()->addParam(obj, "flags", obj.flags_));
 
     FarneBack_OCL::FarneBack_OCL() : oclOpticalFlow(CV_8UC1)
     {

modules/ts/include/opencv2/ts/ts_perf.hpp

@@ -258,7 +258,7 @@ typedef struct CV_EXPORTS performance_metrics
 enum PERF_STRATEGY
 {
     PERF_STRATEGY_BASE = 0,
-    PERF_STRATEGY_SIMPLE = 1
+    PERF_STRATEGY_SIMPLE = 1,
 };
 
 

modules/video/perf/perf_optflowpyrlk.cpp

@@ -178,7 +178,7 @@ PERF_TEST_P(Path_Idx_Cn_NPoints_WSize_Deriv, OpticalFlowPyrLK_self, testing::Com
     SANITY_CHECK(err, 2);
 }
 
-CV_ENUM(PyrBorderMode, BORDER_DEFAULT, BORDER_TRANSPARENT)
+CV_ENUM(PyrBorderMode, BORDER_DEFAULT, BORDER_TRANSPARENT);
 typedef tr1::tuple<std::string, int, bool, PyrBorderMode, bool> Path_Win_Deriv_Border_Reuse_t;
 typedef TestBaseWithParam<Path_Win_Deriv_Border_Reuse_t> Path_Win_Deriv_Border_Reuse;
 

modules/video/src/kalman.cpp

@@ -297,4 +297,4 @@ const Mat& KalmanFilter::correct(const Mat& measurement)
     return statePost;
 }
 
-}
+};

modules/video/src/tvl1flow.cpp

@@ -928,7 +928,7 @@ CV_INIT_ALGORITHM(OpticalFlowDual_TVL1, "DenseOpticalFlow.DualTVL1",
                                        "Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time");
                   obj.info()->addParam(obj, "iterations", obj.iterations, false, 0, 0,
                                        "Stopping criterion iterations number used in the numerical scheme");
-                  obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow))
+                  obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow));
 
 } // namespace
 

modules/video/src/video_init.cpp

@@ -52,7 +52,7 @@ CV_INIT_ALGORITHM(BackgroundSubtractorMOG, "BackgroundSubtractor.MOG",
     obj.info()->addParam(obj, "history", obj.history);
     obj.info()->addParam(obj, "nmixtures", obj.nmixtures);
     obj.info()->addParam(obj, "backgroundRatio", obj.backgroundRatio);
-    obj.info()->addParam(obj, "noiseSigma", obj.noiseSigma))
+    obj.info()->addParam(obj, "noiseSigma", obj.noiseSigma));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -68,7 +68,7 @@ CV_INIT_ALGORITHM(BackgroundSubtractorMOG2, "BackgroundSubtractor.MOG2",
     obj.info()->addParam(obj, "fVarMax", obj.fVarMax);
     obj.info()->addParam(obj, "fCT", obj.fCT);
     obj.info()->addParam(obj, "nShadowDetection", obj.nShadowDetection);
-    obj.info()->addParam(obj, "fTau", obj.fTau))
+    obj.info()->addParam(obj, "fTau", obj.fTau));
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -88,7 +88,7 @@ CV_INIT_ALGORITHM(BackgroundSubtractorGMG, "BackgroundSubtractor.GMG",
                   obj.info()->addParam(obj, "decisionThreshold", obj.decisionThreshold,false,0,0,
                                        "Threshold for FG decision rule. Pixel is FG if posterior probability exceeds threshold.");
                   obj.info()->addParam(obj, "updateBackgroundModel", obj.updateBackgroundModel,false,0,0,
-                                       "Perform background model update."))
+                                       "Perform background model update."));
 
 bool initModule_video(void)
 {

samples/c/adaptiveskindetector.cpp

@@ -126,12 +126,12 @@ ASDFrameHolder::ASDFrameHolder( )
 {
     image = NULL;
     timeStamp = 0;
-}
+};
 
 ASDFrameHolder::~ASDFrameHolder( )
 {
     cvReleaseImage(&image);
-}
+};
 
 void ASDFrameHolder::assignFrame(IplImage *sourceImage, double frameTime)
 {
@@ -143,22 +143,22 @@ void ASDFrameHolder::assignFrame(IplImage *sourceImage, double frameTime)
 
     image = cvCloneImage(sourceImage);
     timeStamp = frameTime;
-}
+};
 
 IplImage *ASDFrameHolder::getImage()
 {
     return image;
-}
+};
 
 double ASDFrameHolder::getTimeStamp()
 {
     return timeStamp;
-}
+};
 
 void ASDFrameHolder::setImage(IplImage *sourceImage)
 {
     image = sourceImage;
-}
+};
 
 
 //-------------------- ASDFrameSequencer -----------------------//
@@ -166,26 +166,26 @@ void ASDFrameHolder::setImage(IplImage *sourceImage)
 ASDFrameSequencer::~ASDFrameSequencer()
 {
     close();
-}
+};
 
 IplImage *ASDFrameSequencer::getNextImage()
 {
     return NULL;
-}
+};
 
 void ASDFrameSequencer::close()
 {
 
-}
+};
 
 bool ASDFrameSequencer::isOpen()
 {
     return false;
-}
+};
 
 void ASDFrameSequencer::getFrameCaption(char* /*caption*/) {
     return;
-}
+};
 
 IplImage* ASDCVFrameSequencer::getNextImage()
 {
@@ -201,7 +201,7 @@ IplImage* ASDCVFrameSequencer::getNextImage()
     {
         return NULL;
     }
-}
+};
 
 void ASDCVFrameSequencer::close()
 {
@@ -209,12 +209,12 @@ void ASDCVFrameSequencer::close()
     {
         cvReleaseCapture(&capture);
     }
-}
+};
 
 bool ASDCVFrameSequencer::isOpen()
 {
     return (capture != NULL);
-}
+};
 
 
 //-------------------- ASDFrameSequencerWebCam -----------------------//
@@ -233,7 +233,7 @@ bool ASDFrameSequencerWebCam::open(int cameraIndex)
     {
         return true;
     }
-}
+};
 
 
 //-------------------- ASDFrameSequencerVideoFile -----------------------//
@@ -251,7 +251,7 @@ bool ASDFrameSequencerVideoFile::open(const char *fileName)
     {
         return true;
     }
-}
+};
 
 
 //-------------------- ASDFrameSequencerImageFile -----------------------//
@@ -263,11 +263,11 @@ void ASDFrameSequencerImageFile::open(const char *fileNameMask, int startIndex,
     nEndIndex = endIndex;
 
     std::sprintf(sFileNameMask, "%s", fileNameMask);
-}
+};
 
 void ASDFrameSequencerImageFile::getFrameCaption(char *caption) {
     std::sprintf(caption, sFileNameMask, nCurrentIndex);
-}
+};
 
 IplImage* ASDFrameSequencerImageFile::getNextImage()
 {
@@ -283,23 +283,23 @@ IplImage* ASDFrameSequencerImageFile::getNextImage()
     IplImage* img = cvLoadImage(fileName);
 
     return img;
-}
+};
 
 void ASDFrameSequencerImageFile::close()
 {
     nCurrentIndex = nEndIndex+1;
-}
+};
 
 bool ASDFrameSequencerImageFile::isOpen()
 {
     return (nCurrentIndex <= nEndIndex);
-}
+};
 
 static void putTextWithShadow(IplImage *img, const char *str, CvPoint point, CvFont *font, CvScalar color = CV_RGB(255, 255, 128))
 {
     cvPutText(img, str, cvPoint(point.x-1,point.y-1), font, CV_RGB(0, 0, 0));
     cvPutText(img, str, point, font, color);
-}
+};
 
 #define ASD_RGB_SET_PIXEL(pointer, r, g, b) { (*pointer) = (unsigned char)b; (*(pointer+1)) = (unsigned char)g; (*(pointer+2)) = (unsigned char)r; }
 
@@ -336,7 +336,7 @@ static void displayBuffer(IplImage *rgbDestImage, IplImage *buffer, int rValue,
         destY = 0;
         destX += dx;
     }
-}
+};
 
 int main(int argc, char** argv )
 {

samples/cpp/calibration_artificial.cpp

@@ -46,7 +46,7 @@ private:
     Point3f generateChessBoardCenter(const Mat& camMat, const Size& imgSize) const;
     Mat rvec, tvec;
 };
-}
+};
 
 
 

samples/cpp/kmeans.cpp

@@ -33,10 +33,10 @@ int main( int /*argc*/, char** /*argv*/ )
     {
         int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
         int i, sampleCount = rng.uniform(1, 1001);
-        Mat points(sampleCount, 2, CV_32F), labels;
+        Mat points(sampleCount, 1, CV_32FC2), labels;
 
         clusterCount = MIN(clusterCount, sampleCount);
-        Mat centers;
+        Mat centers(clusterCount, 1, points.type());
 
         /* generate random sample from multigaussian distribution */
         for( k = 0; k < clusterCount; k++ )

samples/cpp/tutorial_code/ImgProc/Threshold.cpp

@@ -14,7 +14,7 @@ using namespace cv;
 /// Global variables
 
 int threshold_value = 0;
-int threshold_type = 3;
+int threshold_type = 3;;
 int const max_value = 255;
 int const max_type = 4;
 int const max_BINARY_value = 255;