OpenCV 3.4.3

tested with NDK 17b (17.1.4828580)

3rdparty/ittnotify/src/ittnotify/ittnotify_config.h

@@ -335,7 +335,7 @@ ITT_INLINE long __itt_interlocked_increment(volatile long* ptr)
 #ifdef SDL_STRNCPY_S
 #define __itt_fstrcpyn(s1, b, s2, l) SDL_STRNCPY_S(s1, b, s2, l)
 #else
-#define __itt_fstrcpyn(s1, b, s2, l) strncpy(s1, s2, l)
+#define __itt_fstrcpyn(s1, b, s2, l) strncpy(s1, s2, b)
 #endif /* SDL_STRNCPY_S */
 
 #define __itt_fstrdup(s)          strdup(s)

3rdparty/openexr/CMakeLists.txt

@@ -47,6 +47,10 @@ ocv_warnings_disable(CMAKE_CXX_FLAGS -Wshadow -Wunused -Wsign-compare -Wundef -W
                                      -Wsuggest-override -Winconsistent-missing-override
                                      -Wimplicit-fallthrough
 )
+if(CV_GCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 8.0)
+  ocv_warnings_disable(CMAKE_CXX_FLAGS -Wclass-memaccess)
+endif()
+
 ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4018 /wd4099 /wd4100 /wd4101 /wd4127 /wd4189 /wd4245 /wd4305 /wd4389 /wd4512 /wd4701 /wd4702 /wd4706 /wd4800) # vs2005
 ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4334) # vs2005 Win64
 ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4244) # vs2008

3rdparty/protobuf/CMakeLists.txt

@@ -29,6 +29,9 @@ if(CV_ICC)
       -wd265 -wd858 -wd873 -wd2196
   )
 endif()
+if(CV_GCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 8.0)
+  ocv_warnings_disable(CMAKE_CXX_FLAGS -Wclass-memaccess)
+endif()
 
 # Easier to support different versions of protobufs
 function(append_if_exist OUTPUT_LIST)

CMakeLists.txt

@@ -276,7 +276,7 @@ OCV_OPTION(WITH_VA             "Include VA support"                          OFF
 OCV_OPTION(WITH_VA_INTEL       "Include Intel VA-API/OpenCL support"         OFF  IF (UNIX AND NOT ANDROID) )
 OCV_OPTION(WITH_MFX            "Include Intel Media SDK support"             OFF   IF ((UNIX AND NOT ANDROID) OR (WIN32 AND NOT WINRT AND NOT MINGW)) )
 OCV_OPTION(WITH_GDAL           "Include GDAL Support"                        OFF  IF (NOT ANDROID AND NOT IOS AND NOT WINRT) )
-OCV_OPTION(WITH_GPHOTO2        "Include gPhoto2 library support"             ON   IF (UNIX AND NOT ANDROID AND NOT IOS) )
+OCV_OPTION(WITH_GPHOTO2        "Include gPhoto2 library support"             OFF  IF (UNIX AND NOT ANDROID AND NOT IOS) )
 OCV_OPTION(WITH_LAPACK         "Include Lapack library support"              (NOT CV_DISABLE_OPTIMIZATION)  IF (NOT ANDROID AND NOT IOS) )
 OCV_OPTION(WITH_ITT            "Include Intel ITT support"                   ON   IF (NOT APPLE_FRAMEWORK) )
 OCV_OPTION(WITH_PROTOBUF       "Enable libprotobuf"                          ON )
@@ -1407,8 +1407,22 @@ if(WITH_HALIDE OR HAVE_HALIDE)
   status("    Halide:"     HAVE_HALIDE      THEN "YES (${HALIDE_LIBRARIES} ${HALIDE_INCLUDE_DIRS})" ELSE NO)
 endif()
 
-if(WITH_INF_ENGINE OR HAVE_INF_ENGINE)
-  status("    Inference Engine:"     HAVE_INF_ENGINE     THEN "YES (${INF_ENGINE_LIBRARIES} ${INF_ENGINE_INCLUDE_DIRS})" ELSE NO)
+if(WITH_INF_ENGINE OR INF_ENGINE_TARGET)
+  if(INF_ENGINE_TARGET)
+    set(__msg "YES (${INF_ENGINE_RELEASE} / ${INF_ENGINE_VERSION})")
+    get_target_property(_lib ${INF_ENGINE_TARGET} IMPORTED_LOCATION)
+    if(NOT _lib)
+      get_target_property(_lib_rel ${INF_ENGINE_TARGET} IMPORTED_IMPLIB_RELEASE)
+      get_target_property(_lib_dbg ${INF_ENGINE_TARGET} IMPORTED_IMPLIB_DEBUG)
+      set(_lib "${_lib_rel} / ${_lib_dbg}")
+    endif()
+    get_target_property(_inc ${INF_ENGINE_TARGET} INTERFACE_INCLUDE_DIRECTORIES)
+    status("    Inference Engine:" "${__msg}")
+    status("                libs:" "${_lib}")
+    status("            includes:" "${_inc}")
+  else()
+    status("    Inference Engine:"     "NO")
+  endif()
 endif()
 
 if(WITH_EIGEN OR HAVE_EIGEN)

apps/createsamples/utility.cpp

@@ -54,6 +54,10 @@
 #include "opencv2/highgui.hpp"
 #include "opencv2/calib3d.hpp"
 
+#if defined __GNUC__ && __GNUC__ >= 8
+#pragma GCC diagnostic ignored "-Wclass-memaccess"
+#endif
+
 using namespace cv;
 
 #ifndef PATH_MAX
@@ -1040,12 +1044,10 @@ void cvCreateTrainingSamples( const char* filename,
         output = fopen( filename, "wb" );
         if( output != NULL )
         {
-            int hasbg;
             int i;
             int inverse;
 
-            hasbg = 0;
-            hasbg = (bgfilename != NULL && icvInitBackgroundReaders( bgfilename,
+            const int hasbg = (bgfilename != NULL && icvInitBackgroundReaders( bgfilename,
                      Size( winwidth,winheight ) ) );
 
             Mat sample( winheight, winwidth, CV_8UC1 );
@@ -1372,7 +1374,7 @@ int icvGetTraininDataFromVec( Mat& img, CvVecFile& userdata )
 
     size_t elements_read = fread( &tmp, sizeof( tmp ), 1, userdata.input );
     CV_Assert(elements_read == 1);
-    elements_read = fread( vector, sizeof( short ), userdata.vecsize, userdata.input );
+    elements_read = fread(vector.data(), sizeof(short), userdata.vecsize, userdata.input);
     CV_Assert(elements_read == (size_t)userdata.vecsize);
 
     if( feof( userdata.input ) || userdata.last++ >= userdata.count )

apps/interactive-calibration/calibController.cpp

@@ -224,8 +224,10 @@ void calib::calibDataController::filterFrames()
         cv::Mat newErrorsVec = cv::Mat((int)numberOfFrames - 1, 1, CV_64F);
         std::copy(mCalibData->perViewErrors.ptr<double>(0),
                   mCalibData->perViewErrors.ptr<double>((int)worstElemIndex), newErrorsVec.ptr<double>(0));
-        std::copy(mCalibData->perViewErrors.ptr<double>((int)worstElemIndex + 1), mCalibData->perViewErrors.ptr<double>((int)numberOfFrames),
+        if((int)worstElemIndex < (int)numberOfFrames-1) {
+            std::copy(mCalibData->perViewErrors.ptr<double>((int)worstElemIndex + 1), mCalibData->perViewErrors.ptr<double>((int)numberOfFrames),
                     newErrorsVec.ptr<double>((int)worstElemIndex));
+        }
         mCalibData->perViewErrors = newErrorsVec;
     }
 }

apps/interactive-calibration/rotationConverters.cpp

@@ -16,7 +16,7 @@ void calib::Euler(const cv::Mat& src, cv::Mat& dst, int argType)
 {
     if((src.rows == 3) && (src.cols == 3))
     {
-        //convert rotaion matrix to 3 angles (pitch, yaw, roll)
+        //convert rotation matrix to 3 angles (pitch, yaw, roll)
         dst = cv::Mat(3, 1, CV_64F);
         double pitch, yaw, roll;
 
@@ -55,7 +55,7 @@ void calib::Euler(const cv::Mat& src, cv::Mat& dst, int argType)
     else if( (src.cols == 1 && src.rows == 3) ||
              (src.cols == 3 && src.rows == 1 ) )
     {
-        //convert vector which contains 3 angles (pitch, yaw, roll) to rotaion matrix
+        //convert vector which contains 3 angles (pitch, yaw, roll) to rotation matrix
         double pitch, yaw, roll;
         if(src.cols == 1 && src.rows == 3)
         {

apps/traincascade/HOGfeatures.cpp

@@ -165,7 +165,7 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
     Mat qangle(gradSize, CV_8U);
 
     AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
-    int* xmap = (int*)mapbuf + 1;
+    int* xmap = mapbuf.data() + 1;
     int* ymap = xmap + gradSize.width + 2;
 
     const int borderType = (int)BORDER_REPLICATE;
@@ -177,7 +177,7 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
 
     int width = gradSize.width;
     AutoBuffer<float> _dbuf(width*4);
-    float* dbuf = _dbuf;
+    float* dbuf = _dbuf.data();
     Mat Dx(1, width, CV_32F, dbuf);
     Mat Dy(1, width, CV_32F, dbuf + width);
     Mat Mag(1, width, CV_32F, dbuf + width*2);

apps/traincascade/boost.cpp

@@ -383,7 +383,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
             int ci = get_var_type(vi);
             CV_Assert( ci < 0 );
 
-            int *src_idx_buf = (int*)(uchar*)inn_buf;
+            int *src_idx_buf = (int*)inn_buf.data();
             float *src_val_buf = (float*)(src_idx_buf + sample_count);
             int* sample_indices_buf = (int*)(src_val_buf + sample_count);
             const int* src_idx = 0;
@@ -423,7 +423,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
         }
 
         // subsample cv_lables
-        const int* src_lbls = get_cv_labels(data_root, (int*)(uchar*)inn_buf);
+        const int* src_lbls = get_cv_labels(data_root, (int*)inn_buf.data());
         if (is_buf_16u)
         {
             unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
@@ -440,7 +440,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
         }
 
         // subsample sample_indices
-        const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
+        const int* sample_idx_src = get_sample_indices(data_root, (int*)inn_buf.data());
         if (is_buf_16u)
         {
             unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
@@ -815,7 +815,7 @@ struct FeatureIdxOnlyPrecalc : ParallelLoopBody
     void operator()( const Range& range ) const
     {
         cv::AutoBuffer<float> valCache(sample_count);
-        float* valCachePtr = (float*)valCache;
+        float* valCachePtr = valCache.data();
         for ( int fi = range.start; fi < range.end; fi++)
         {
             for( int si = 0; si < sample_count; si++ )
@@ -1084,7 +1084,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
     CvMat* buf = data->buf;
     size_t length_buf_row = data->get_length_subbuf();
     cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int)+sizeof(float)));
-    int* tempBuf = (int*)(uchar*)inn_buf;
+    int* tempBuf = (int*)inn_buf.data();
     bool splitInputData;
 
     complete_node_dir(node);
@@ -1398,7 +1398,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
     int inn_buf_size = ((params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? n*sizeof(int) : 0) +
                        ( !tree ? n*sizeof(int) : 0 );
     cv::AutoBuffer<uchar> inn_buf(inn_buf_size);
-    uchar* cur_inn_buf_pos = (uchar*)inn_buf;
+    uchar* cur_inn_buf_pos = inn_buf.data();
     if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
     {
         step = CV_IS_MAT_CONT(data->responses_copy->type) ?

apps/traincascade/old_ml_boost.cpp

@@ -168,7 +168,7 @@ CvBoostTree::try_split_node( CvDTreeNode* node )
         // store the responses for the corresponding training samples
         double* weak_eval = ensemble->get_weak_response()->data.db;
         cv::AutoBuffer<int> inn_buf(node->sample_count);
-        const int* labels = data->get_cv_labels( node, (int*)inn_buf );
+        const int* labels = data->get_cv_labels(node, inn_buf.data());
         int i, count = node->sample_count;
         double value = node->value;
 
@@ -191,7 +191,7 @@ CvBoostTree::calc_node_dir( CvDTreeNode* node )
     if( data->get_var_type(vi) >= 0 ) // split on categorical var
     {
         cv::AutoBuffer<int> inn_buf(n);
-        const int* cat_labels = data->get_cat_var_data( node, vi, (int*)inn_buf );
+        const int* cat_labels = data->get_cat_var_data(node, vi, inn_buf.data());
         const int* subset = node->split->subset;
         double sum = 0, sum_abs = 0;
 
@@ -210,7 +210,7 @@ CvBoostTree::calc_node_dir( CvDTreeNode* node )
     else // split on ordered var
     {
         cv::AutoBuffer<uchar> inn_buf(2*n*sizeof(int)+n*sizeof(float));
-        float* values_buf = (float*)(uchar*)inn_buf;
+        float* values_buf = (float*)inn_buf.data();
         int* sorted_indices_buf = (int*)(values_buf + n);
         int* sample_indices_buf = sorted_indices_buf + n;
         const float* values = 0;
@@ -260,7 +260,7 @@ CvBoostTree::find_split_ord_class( CvDTreeNode* node, int vi, float init_quality
     cv::AutoBuffer<uchar> inn_buf;
     if( !_ext_buf )
         inn_buf.allocate(n*(3*sizeof(int)+sizeof(float)));
-    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
+    uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
     float* values_buf = (float*)ext_buf;
     int* sorted_indices_buf = (int*)(values_buf + n);
     int* sample_indices_buf = sorted_indices_buf + n;
@@ -369,7 +369,7 @@ CvBoostTree::find_split_cat_class( CvDTreeNode* node, int vi, float init_quality
     cv::AutoBuffer<uchar> inn_buf((2*mi+3)*sizeof(double) + mi*sizeof(double*));
     if( !_ext_buf)
         inn_buf.allocate( base_size + 2*n*sizeof(int) );
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
 
     int* cat_labels_buf = (int*)ext_buf;
@@ -490,7 +490,7 @@ CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality,
     cv::AutoBuffer<uchar> inn_buf;
     if( !_ext_buf )
         inn_buf.allocate(2*n*(sizeof(int)+sizeof(float)));
-    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
+    uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
 
     float* values_buf = (float*)ext_buf;
     int* indices_buf = (int*)(values_buf + n);
@@ -559,7 +559,7 @@ CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality,
     cv::AutoBuffer<uchar> inn_buf(base_size);
     if( !_ext_buf )
         inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
 
     int* cat_labels_buf = (int*)ext_buf;
@@ -652,7 +652,7 @@ CvBoostTree::find_surrogate_split_ord( CvDTreeNode* node, int vi, uchar* _ext_bu
     cv::AutoBuffer<uchar> inn_buf;
     if( !_ext_buf )
         inn_buf.allocate(n*(2*sizeof(int)+sizeof(float)));
-    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
+    uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
     float* values_buf = (float*)ext_buf;
     int* indices_buf = (int*)(values_buf + n);
     int* sample_indices_buf = indices_buf + n;
@@ -733,7 +733,7 @@ CvBoostTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, uchar* _ext_bu
     cv::AutoBuffer<uchar> inn_buf(base_size);
     if( !_ext_buf )
         inn_buf.allocate(base_size + n*sizeof(int));
-    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
+    uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
     int* cat_labels_buf = (int*)ext_buf;
     const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf);
 
@@ -797,7 +797,7 @@ CvBoostTree::calc_node_value( CvDTreeNode* node )
     int i, n = node->sample_count;
     const double* weights = ensemble->get_weights()->data.db;
     cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int) + ( data->is_classifier ? sizeof(int) : sizeof(int) + sizeof(float))));
-    int* labels_buf = (int*)(uchar*)inn_buf;
+    int* labels_buf = (int*)inn_buf.data();
     const int* labels = data->get_cv_labels(node, labels_buf);
     double* subtree_weights = ensemble->get_subtree_weights()->data.db;
     double rcw[2] = {0,0};
@@ -1147,7 +1147,7 @@ CvBoost::update_weights( CvBoostTree* tree )
             _buf_size += data->get_length_subbuf()*(sizeof(float)+sizeof(uchar));
     }
     inn_buf.allocate(_buf_size);
-    uchar* cur_buf_pos = (uchar*)inn_buf;
+    uchar* cur_buf_pos = inn_buf.data();
 
     if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
     {

apps/traincascade/old_ml_tree.cpp

@@ -780,7 +780,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
             if( ci >= 0 || vi >= var_count )
             {
                 int num_valid = 0;
-                const int* src = CvDTreeTrainData::get_cat_var_data( data_root, vi, (int*)(uchar*)inn_buf );
+                const int* src = CvDTreeTrainData::get_cat_var_data(data_root, vi, (int*)inn_buf.data());
 
                 if (is_buf_16u)
                 {
@@ -810,7 +810,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
             }
             else
             {
-                int *src_idx_buf = (int*)(uchar*)inn_buf;
+                int *src_idx_buf = (int*)inn_buf.data();
                 float *src_val_buf = (float*)(src_idx_buf + sample_count);
                 int* sample_indices_buf = (int*)(src_val_buf + sample_count);
                 const int* src_idx = 0;
@@ -870,7 +870,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
             }
         }
         // sample indices subsampling
-        const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
+        const int* sample_idx_src = get_sample_indices(data_root, (int*)inn_buf.data());
         if (is_buf_16u)
         {
             unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
@@ -943,7 +943,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
         {
             float* dst = values + vi;
             uchar* m = missing ? missing + vi : 0;
-            const int* src = get_cat_var_data(data_root, vi, (int*)(uchar*)inn_buf);
+            const int* src = get_cat_var_data(data_root, vi, (int*)inn_buf.data());
 
             for( i = 0; i < count; i++, dst += var_count )
             {
@@ -962,7 +962,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
             float* dst = values + vi;
             uchar* m = missing ? missing + vi : 0;
             int count1 = data_root->get_num_valid(vi);
-            float *src_val_buf = (float*)(uchar*)inn_buf;
+            float *src_val_buf = (float*)inn_buf.data();
             int* src_idx_buf = (int*)(src_val_buf + sample_count);
             int* sample_indices_buf = src_idx_buf + sample_count;
             const float *src_val = 0;
@@ -999,7 +999,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
     {
         if( is_classifier )
         {
-            const int* src = get_class_labels(data_root, (int*)(uchar*)inn_buf);
+            const int* src = get_class_labels(data_root, (int*)inn_buf.data());
             for( i = 0; i < count; i++ )
             {
                 int idx = sidx ? sidx[i] : i;
@@ -1010,7 +1010,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
         }
         else
         {
-            float* val_buf = (float*)(uchar*)inn_buf;
+            float* val_buf = (float*)inn_buf.data();
             int* sample_idx_buf = (int*)(val_buf + sample_count);
             const float* _values = get_ord_responses(data_root, val_buf, sample_idx_buf);
             for( i = 0; i < count; i++ )
@@ -1780,7 +1780,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
     if( data->get_var_type(vi) >= 0 ) // split on categorical var
     {
         cv::AutoBuffer<int> inn_buf(n*(!data->have_priors ? 1 : 2));
-        int* labels_buf = (int*)inn_buf;
+        int* labels_buf = inn_buf.data();
         const int* labels = data->get_cat_var_data( node, vi, labels_buf );
         const int* subset = node->split->subset;
         if( !data->have_priors )
@@ -1824,7 +1824,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
         int split_point = node->split->ord.split_point;
         int n1 = node->get_num_valid(vi);
         cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int)*(data->have_priors ? 3 : 2) + sizeof(float)));
-        float* val_buf = (float*)(uchar*)inn_buf;
+        float* val_buf = (float*)inn_buf.data();
         int* sorted_buf = (int*)(val_buf + n);
         int* sample_idx_buf = sorted_buf + n;
         const float* val = 0;
@@ -1929,16 +1929,16 @@ void DTreeBestSplitFinder::operator()(const BlockedRange& range)
         if( data->is_classifier )
         {
             if( ci >= 0 )
-                res = tree->find_split_cat_class( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
+                res = tree->find_split_cat_class( node, vi, bestSplit->quality, split, inn_buf.data() );
             else
-                res = tree->find_split_ord_class( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
+                res = tree->find_split_ord_class( node, vi, bestSplit->quality, split, inn_buf.data() );
         }
         else
         {
             if( ci >= 0 )
-                res = tree->find_split_cat_reg( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
+                res = tree->find_split_cat_reg( node, vi, bestSplit->quality, split, inn_buf.data() );
             else
-                res = tree->find_split_ord_reg( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
+                res = tree->find_split_ord_reg( node, vi, bestSplit->quality, split, inn_buf.data() );
         }
 
         if( res && bestSplit->quality < split->quality )
@@ -1982,7 +1982,7 @@ CvDTreeSplit* CvDTree::find_split_ord_class( CvDTreeNode* node, int vi,
     cv::AutoBuffer<uchar> inn_buf(base_size);
     if( !_ext_buf )
       inn_buf.allocate(base_size + n*(3*sizeof(int)+sizeof(float)));
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
     float* values_buf = (float*)ext_buf;
     int* sorted_indices_buf = (int*)(values_buf + n);
@@ -2096,7 +2096,7 @@ void CvDTree::cluster_categories( const int* vectors, int n, int m,
     int iters = 0, max_iters = 100;
     int i, j, idx;
     cv::AutoBuffer<double> buf(n + k);
-    double *v_weights = buf, *c_weights = buf + n;
+    double *v_weights = buf.data(), *c_weights = buf.data() + n;
     bool modified = true;
     RNG* r = data->rng;
 
@@ -2201,7 +2201,7 @@ CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float in
     cv::AutoBuffer<uchar> inn_buf(base_size);
     if( !_ext_buf )
         inn_buf.allocate(base_size + 2*n*sizeof(int));
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
 
     int* lc = (int*)base_buf;
@@ -2383,7 +2383,7 @@ CvDTreeSplit* CvDTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init
     cv::AutoBuffer<uchar> inn_buf;
     if( !_ext_buf )
         inn_buf.allocate(2*n*(sizeof(int) + sizeof(float)));
-    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
+    uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
     float* values_buf = (float*)ext_buf;
     int* sorted_indices_buf = (int*)(values_buf + n);
     int* sample_indices_buf = sorted_indices_buf + n;
@@ -2443,7 +2443,7 @@ CvDTreeSplit* CvDTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init
     cv::AutoBuffer<uchar> inn_buf(base_size);
     if( !_ext_buf )
         inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
     int* labels_buf = (int*)ext_buf;
     const int* labels = data->get_cat_var_data(node, vi, labels_buf);
@@ -2534,7 +2534,7 @@ CvDTreeSplit* CvDTree::find_surrogate_split_ord( CvDTreeNode* node, int vi, ucha
     cv::AutoBuffer<uchar> inn_buf;
     if( !_ext_buf )
         inn_buf.allocate( n*(sizeof(int)*(data->have_priors ? 3 : 2) + sizeof(float)) );
-    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
+    uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
     float* values_buf = (float*)ext_buf;
     int* sorted_indices_buf = (int*)(values_buf + n);
     int* sample_indices_buf = sorted_indices_buf + n;
@@ -2658,7 +2658,7 @@ CvDTreeSplit* CvDTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, ucha
     cv::AutoBuffer<uchar> inn_buf(base_size);
     if( !_ext_buf )
         inn_buf.allocate(base_size + n*(sizeof(int) + (data->have_priors ? sizeof(int) : 0)));
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
 
     int* labels_buf = (int*)ext_buf;
@@ -2758,7 +2758,7 @@ void CvDTree::calc_node_value( CvDTreeNode* node )
     int base_size = data->is_classifier ? m*cv_n*sizeof(int) : 2*cv_n*sizeof(double)+cv_n*sizeof(int);
     int ext_size = n*(sizeof(int) + (data->is_classifier ? sizeof(int) : sizeof(int)+sizeof(float)));
     cv::AutoBuffer<uchar> inn_buf(base_size + ext_size);
-    uchar* base_buf = (uchar*)inn_buf;
+    uchar* base_buf = inn_buf.data();
     uchar* ext_buf = base_buf + base_size;
 
     int* cv_labels_buf = (int*)ext_buf;
@@ -2961,7 +2961,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node )
 
             if( data->get_var_type(vi) >= 0 ) // split on categorical var
             {
-                int* labels_buf = (int*)(uchar*)inn_buf;
+                int* labels_buf = (int*)inn_buf.data();
                 const int* labels = data->get_cat_var_data(node, vi, labels_buf);
                 const int* subset = split->subset;
 
@@ -2980,7 +2980,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node )
             }
             else // split on ordered var
             {
-                float* values_buf = (float*)(uchar*)inn_buf;
+                float* values_buf = (float*)inn_buf.data();
                 int* sorted_indices_buf = (int*)(values_buf + n);
                 int* sample_indices_buf = sorted_indices_buf + n;
                 const float* values = 0;
@@ -3042,7 +3042,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
     CvMat* buf = data->buf;
     size_t length_buf_row = data->get_length_subbuf();
     cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int) + sizeof(float)));
-    int* temp_buf = (int*)(uchar*)inn_buf;
+    int* temp_buf = (int*)inn_buf.data();
 
     complete_node_dir(node);
 

cmake/FindCUDA.cmake

@@ -141,7 +141,7 @@
 #   -- Same as CUDA_ADD_EXECUTABLE except that a library is created.
 #
 #   CUDA_BUILD_CLEAN_TARGET()
-#   -- Creates a convience target that deletes all the dependency files
+#   -- Creates a convenience target that deletes all the dependency files
 #      generated.  You should make clean after running this target to ensure the
 #      dependency files get regenerated.
 #
@@ -473,7 +473,7 @@ else()
 endif()
 
 # Propagate the host flags to the host compiler via -Xcompiler
-option(CUDA_PROPAGATE_HOST_FLAGS "Propage C/CXX_FLAGS and friends to the host compiler via -Xcompile" ON)
+option(CUDA_PROPAGATE_HOST_FLAGS "Propagate C/CXX_FLAGS and friends to the host compiler via -Xcompile" ON)
 
 # Enable CUDA_SEPARABLE_COMPILATION
 option(CUDA_SEPARABLE_COMPILATION "Compile CUDA objects with separable compilation enabled.  Requires CUDA 5.0+" OFF)

cmake/OpenCVCompilerOptimizations.cmake

@@ -700,12 +700,21 @@ macro(ocv_compiler_optimization_fill_cpu_config)
     list(APPEND __dispatch_modes ${CPU_DISPATCH_${OPT}_FORCE} ${OPT})
   endforeach()
   list(REMOVE_DUPLICATES __dispatch_modes)
-  set(OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE "")
   foreach(OPT ${__dispatch_modes})
     set(OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE "${OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE}
 #define CV_CPU_DISPATCH_COMPILE_${OPT} 1")
   endforeach()
 
+  set(OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE "${OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE}
+\n\n#define CV_CPU_DISPATCH_FEATURES 0 \\")
+  foreach(OPT ${__dispatch_modes})
+    if(NOT DEFINED CPU_${OPT}_FEATURE_ALIAS OR NOT "x${CPU_${OPT}_FEATURE_ALIAS}" STREQUAL "x")
+      set(OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE "${OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE}
+    , CV_CPU_${OPT} \\")
+    endif()
+  endforeach()
+  set(OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE "${OPENCV_CPU_DISPATCH_DEFINITIONS_CONFIGMAKE}\n")
+
   set(OPENCV_CPU_CONTROL_DEFINITIONS_CONFIGMAKE "// AUTOGENERATED, DO NOT EDIT\n")
   foreach(OPT ${CPU_ALL_OPTIMIZATIONS})
     if(NOT DEFINED CPU_${OPT}_FEATURE_ALIAS OR NOT "x${CPU_${OPT}_FEATURE_ALIAS}" STREQUAL "x")
@@ -740,7 +749,7 @@ macro(ocv_compiler_optimization_fill_cpu_config)
 ")
 
 
-  set(__file "${CMAKE_SOURCE_DIR}/modules/core/include/opencv2/core/cv_cpu_helper.h")
+  set(__file "${OpenCV_SOURCE_DIR}/modules/core/include/opencv2/core/cv_cpu_helper.h")
   if(EXISTS "${__file}")
     file(READ "${__file}" __content)
   endif()
@@ -752,24 +761,24 @@ macro(ocv_compiler_optimization_fill_cpu_config)
   endif()
 endmacro()
 
-macro(ocv_add_dispatched_file filename)
+macro(__ocv_add_dispatched_file filename target_src_var src_directory dst_directory precomp_hpp optimizations_var)
   if(NOT OPENCV_INITIAL_PASS)
     set(__codestr "
-#include \"${CMAKE_CURRENT_LIST_DIR}/src/precomp.hpp\"
-#include \"${CMAKE_CURRENT_LIST_DIR}/src/${filename}.simd.hpp\"
+#include \"${src_directory}/${precomp_hpp}\"
+#include \"${src_directory}/${filename}.simd.hpp\"
 ")
 
-    set(__declarations_str "#define CV_CPU_SIMD_FILENAME \"${CMAKE_CURRENT_LIST_DIR}/src/${filename}.simd.hpp\"")
+    set(__declarations_str "#define CV_CPU_SIMD_FILENAME \"${src_directory}/${filename}.simd.hpp\"")
     set(__dispatch_modes "BASELINE")
 
-    set(__optimizations "${ARGN}")
+    set(__optimizations "${${optimizations_var}}")
     if(CV_DISABLE_OPTIMIZATION OR NOT CV_ENABLE_INTRINSICS)
       set(__optimizations "")
     endif()
 
     foreach(OPT ${__optimizations})
       string(TOLOWER "${OPT}" OPT_LOWER)
-      set(__file "${CMAKE_CURRENT_BINARY_DIR}/${filename}.${OPT_LOWER}.cpp")
+      set(__file "${CMAKE_CURRENT_BINARY_DIR}/${dst_directory}${filename}.${OPT_LOWER}.cpp")
       if(EXISTS "${__file}")
         file(READ "${__file}" __content)
       else()
@@ -782,7 +791,11 @@ macro(ocv_add_dispatched_file filename)
       endif()
 
       if(";${CPU_DISPATCH};" MATCHES "${OPT}" OR __CPU_DISPATCH_INCLUDE_ALL)
-        list(APPEND OPENCV_MODULE_${the_module}_SOURCES_DISPATCHED "${__file}")
+        if(EXISTS "${src_directory}/${filename}.${OPT_LOWER}.cpp")
+          message(STATUS "Using overrided ${OPT} source: ${src_directory}/${filename}.${OPT_LOWER}.cpp")
+        else()
+          list(APPEND ${target_src_var} "${__file}")
+        endif()
       endif()
 
       set(__declarations_str "${__declarations_str}
@@ -794,9 +807,11 @@ macro(ocv_add_dispatched_file filename)
 
     set(__declarations_str "${__declarations_str}
 #define CV_CPU_DISPATCH_MODES_ALL ${__dispatch_modes}
+
+#undef CV_CPU_SIMD_FILENAME
 ")
 
-    set(__file "${CMAKE_CURRENT_BINARY_DIR}/${filename}.simd_declarations.hpp")
+    set(__file "${CMAKE_CURRENT_BINARY_DIR}/${dst_directory}${filename}.simd_declarations.hpp")
     if(EXISTS "${__file}")
       file(READ "${__file}" __content)
     endif()
@@ -808,6 +823,17 @@ macro(ocv_add_dispatched_file filename)
   endif()
 endmacro()
 
+macro(ocv_add_dispatched_file filename)
+  set(__optimizations "${ARGN}")
+  if(" ${ARGV1}" STREQUAL " TEST")
+    list(REMOVE_AT __optimizations 0)
+    __ocv_add_dispatched_file("${filename}" "OPENCV_MODULE_${the_module}_TEST_SOURCES_DISPATCHED" "${CMAKE_CURRENT_LIST_DIR}/test" "test/" "test_precomp.hpp" __optimizations)
+  else()
+    __ocv_add_dispatched_file("${filename}" "OPENCV_MODULE_${the_module}_SOURCES_DISPATCHED" "${CMAKE_CURRENT_LIST_DIR}/src" "" "precomp.hpp" __optimizations)
+  endif()
+endmacro()
+
+
 # Workaround to support code which always require all code paths
 macro(ocv_add_dispatched_file_force_all)
   set(__CPU_DISPATCH_INCLUDE_ALL 1)

cmake/OpenCVDetectCUDA.cmake

@@ -3,19 +3,28 @@ if(WIN32 AND NOT MSVC)
   return()
 endif()
 
-if(NOT APPLE AND CV_CLANG)
+if(NOT UNIX AND CV_CLANG)
   message(STATUS "CUDA compilation is disabled (due to Clang unsupported on your platform).")
   return()
 endif()
 
-set(CMAKE_MODULE_PATH "${OpenCV_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH})
 
-if(ANDROID)
-  set(CUDA_TARGET_OS_VARIANT "Android")
+if(((NOT CMAKE_VERSION VERSION_LESS "3.9.0")  # requires https://gitlab.kitware.com/cmake/cmake/merge_requests/663
+      OR OPENCV_CUDA_FORCE_EXTERNAL_CMAKE_MODULE)
+    AND NOT OPENCV_CUDA_FORCE_BUILTIN_CMAKE_MODULE)
+  ocv_update(CUDA_LINK_LIBRARIES_KEYWORD "LINK_PRIVATE")
+  find_host_package(CUDA "${MIN_VER_CUDA}" QUIET)
+else()
+  # Use OpenCV's patched "FindCUDA" module
+  set(CMAKE_MODULE_PATH "${OpenCV_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH})
+
+  if(ANDROID)
+    set(CUDA_TARGET_OS_VARIANT "Android")
+  endif()
+  find_host_package(CUDA "${MIN_VER_CUDA}" QUIET)
+
+  list(REMOVE_AT CMAKE_MODULE_PATH 0)
 endif()
-find_host_package(CUDA "${MIN_VER_CUDA}" QUIET)
-
-list(REMOVE_AT CMAKE_MODULE_PATH 0)
 
 if(CUDA_FOUND)
   set(HAVE_CUDA 1)
@@ -179,6 +188,13 @@ if(CUDA_FOUND)
     foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
       set(${var}_backup_in_cuda_compile_ "${${var}}")
 
+      if (CV_CLANG)
+        # we remove -Winconsistent-missing-override and -Qunused-arguments
+        # just in case we are compiling CUDA with gcc but OpenCV with clang
+        string(REPLACE "-Winconsistent-missing-override" "" ${var} "${${var}}")
+        string(REPLACE "-Qunused-arguments" "" ${var} "${${var}}")
+      endif()
+
       # we remove /EHa as it generates warnings under windows
       string(REPLACE "/EHa" "" ${var} "${${var}}")
 

cmake/OpenCVDetectInferenceEngine.cmake

@@ -1,71 +1,87 @@
 # The script detects Intel(R) Inference Engine installation
 #
-# Parameters:
-# INTEL_CVSDK_DIR - Path to Inference Engine root folder
-# IE_PLUGINS_PATH - Path to folder with Inference Engine plugins
+# Cache variables:
+# INF_ENGINE_OMP_DIR - directory with OpenMP library to link with (needed by some versions of IE)
+# INF_ENGINE_RELEASE - a number reflecting IE source interface (linked with OpenVINO release)
 #
-# On return this will define:
+# Detect parameters:
+# 1. Native cmake IE package:
+#    - enironment variable InferenceEngine_DIR is set to location of cmake module
+# 2. Custom location:
+#    - INF_ENGINE_INCLUDE_DIRS - headers search location
+#    - INF_ENGINE_LIB_DIRS     - library search location
+# 3. OpenVINO location:
+#    - environment variable INTEL_CVSDK_DIR is set to location of OpenVINO installation dir
+#    - INF_ENGINE_PLATFORM - part of name of library directory representing its platform (default ubuntu_16.04)
 #
-# HAVE_INF_ENGINE          - True if Intel Inference Engine was found
-# INF_ENGINE_INCLUDE_DIRS  - Inference Engine include folder
-# INF_ENGINE_LIBRARIES     - Inference Engine libraries and it's dependencies
+# Result:
+# INF_ENGINE_TARGET - set to name of imported library target representing InferenceEngine
 #
-macro(ie_fail)
-    set(HAVE_INF_ENGINE FALSE)
-    return()
-endmacro()
 
 if(NOT HAVE_CXX11)
-    ie_fail()
+    message(WARNING "DL Inference engine requires C++11. You can turn it on via ENABLE_CXX11=ON CMake flag.")
+    return()
 endif()
 
-if(NOT INF_ENGINE_ROOT_DIR OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}/include/inference_engine.hpp")
-    set(ie_root_paths "${INF_ENGINE_ROOT_DIR}")
-    if(DEFINED ENV{INTEL_CVSDK_DIR})
-        list(APPEND ie_root_paths "$ENV{INTEL_CVSDK_DIR}")
-        list(APPEND ie_root_paths "$ENV{INTEL_CVSDK_DIR}/inference_engine")
-    endif()
-    if(DEFINED INTEL_CVSDK_DIR)
-        list(APPEND ie_root_paths "${INTEL_CVSDK_DIR}")
-        list(APPEND ie_root_paths "${INTEL_CVSDK_DIR}/inference_engine")
-    endif()
+# =======================
 
-    if(NOT ie_root_paths)
-        list(APPEND ie_root_paths "/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/inference_engine")
-    endif()
+function(add_custom_ie_build _inc _lib _lib_rel _lib_dbg _msg)
+  if(NOT _inc OR NOT (_lib OR _lib_rel OR _lib_dbg))
+    return()
+  endif()
+  add_library(inference_engine UNKNOWN IMPORTED)
+  set_target_properties(inference_engine PROPERTIES
+    IMPORTED_LOCATION "${_lib}"
+    IMPORTED_IMPLIB_RELEASE "${_lib_rel}"
+    IMPORTED_IMPLIB_DEBUG "${_lib_dbg}"
+    INTERFACE_INCLUDE_DIRECTORIES "${_inc}"
+  )
+  find_library(omp_lib iomp5 PATHS "${INF_ENGINE_OMP_DIR}" NO_DEFAULT_PATH)
+  if(NOT omp_lib)
+    message(WARNING "OpenMP for IE have not been found. Set INF_ENGINE_OMP_DIR variable if you experience build errors.")
+  else()
+    set_target_properties(inference_engine PROPERTIES IMPORTED_LINK_INTERFACE_LIBRARIES "${omp_lib}")
+  endif()
+  set(INF_ENGINE_VERSION "Unknown" CACHE STRING "")
+  set(INF_ENGINE_TARGET inference_engine PARENT_SCOPE)
+  message(STATUS "Detected InferenceEngine: ${_msg}")
+endfunction()
 
-    find_path(INF_ENGINE_ROOT_DIR include/inference_engine.hpp PATHS ${ie_root_paths})
+# ======================
+
+find_package(InferenceEngine QUIET)
+if(InferenceEngine_FOUND)
+  set(INF_ENGINE_TARGET IE::inference_engine)
+  set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}" CACHE STRING "")
+  message(STATUS "Detected InferenceEngine: cmake package")
 endif()
 
-set(INF_ENGINE_INCLUDE_DIRS "${INF_ENGINE_ROOT_DIR}/include" CACHE PATH "Path to Inference Engine include directory")
-
-if(NOT INF_ENGINE_ROOT_DIR
-    OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}"
-    OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}/include/inference_engine.hpp"
-)
-    ie_fail()
+if(NOT INF_ENGINE_TARGET AND INF_ENGINE_LIB_DIRS AND INF_ENGINE_INCLUDE_DIRS)
+  find_path(ie_custom_inc "inference_engine.hpp" PATHS "${INF_ENGINE_INCLUDE_DIRS}" NO_DEFAULT_PATH)
+  find_library(ie_custom_lib "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH)
+  find_library(ie_custom_lib_rel "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Release" NO_DEFAULT_PATH)
+  find_library(ie_custom_lib_dbg "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Debug" NO_DEFAULT_PATH)
+  add_custom_ie_build("${ie_custom_inc}" "${ie_custom_lib}" "${ie_custom_lib_rel}" "${ie_custom_lib_dbg}" "INF_ENGINE_{INCLUDE,LIB}_DIRS")
 endif()
 
-set(INF_ENGINE_LIBRARIES "")
+set(_loc "$ENV{INTEL_CVSDK_DIR}")
+if(NOT INF_ENGINE_TARGET AND _loc)
+  set(INF_ENGINE_PLATFORM "ubuntu_16.04" CACHE STRING "InferenceEngine platform (library dir)")
+  find_path(ie_custom_env_inc "inference_engine.hpp" PATHS "${_loc}/deployment_tools/inference_engine/include" NO_DEFAULT_PATH)
+  find_library(ie_custom_env_lib "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH)
+  find_library(ie_custom_env_lib_rel "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Release" NO_DEFAULT_PATH)
+  find_library(ie_custom_env_lib_dbg "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Debug" NO_DEFAULT_PATH)
+  add_custom_ie_build("${ie_custom_env_inc}" "${ie_custom_env_lib}" "${ie_custom_env_lib_rel}" "${ie_custom_env_lib_dbg}" "OpenVINO (${_loc})")
+endif()
 
-set(ie_lib_list inference_engine)
+# Add more features to the target
 
-link_directories(
-  ${INTEL_CVSDK_DIR}/inference_engine/external/mkltiny_lnx/lib
-  ${INTEL_CVSDK_DIR}/inference_engine/external/cldnn/lib
-)
-
-foreach(lib ${ie_lib_list})
-    find_library(${lib}
-        NAMES ${lib}
-        # For inference_engine
-        HINTS ${IE_PLUGINS_PATH}
-        HINTS "$ENV{IE_PLUGINS_PATH}"
-    )
-    if(NOT ${lib})
-        ie_fail()
-    endif()
-    list(APPEND INF_ENGINE_LIBRARIES ${${lib}})
-endforeach()
-
-set(HAVE_INF_ENGINE TRUE)
+if(INF_ENGINE_TARGET)
+  if(NOT INF_ENGINE_RELEASE)
+    message(WARNING "InferenceEngine version have not been set, 2018R3 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
+  endif()
+  set(INF_ENGINE_RELEASE "2018030000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
+  set_target_properties(${INF_ENGINE_TARGET} PROPERTIES
+    INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
+  )
+endif()

cmake/OpenCVDownload.cmake

@@ -20,16 +20,19 @@ if(DEFINED ENV{OPENCV_DOWNLOAD_PATH})
 endif()
 set(OPENCV_DOWNLOAD_PATH "${OpenCV_SOURCE_DIR}/.cache" CACHE PATH "${HELP_OPENCV_DOWNLOAD_PATH}")
 set(OPENCV_DOWNLOAD_LOG "${OpenCV_BINARY_DIR}/CMakeDownloadLog.txt")
+set(OPENCV_DOWNLOAD_WITH_CURL "${OpenCV_BINARY_DIR}/download_with_curl.sh")
+set(OPENCV_DOWNLOAD_WITH_WGET "${OpenCV_BINARY_DIR}/download_with_wget.sh")
 
-# Init download cache directory and log file
+# Init download cache directory and log file and helper scripts
 if(NOT EXISTS "${OPENCV_DOWNLOAD_PATH}")
   file(MAKE_DIRECTORY ${OPENCV_DOWNLOAD_PATH})
 endif()
 if(NOT EXISTS "${OPENCV_DOWNLOAD_PATH}/.gitignore")
   file(WRITE "${OPENCV_DOWNLOAD_PATH}/.gitignore" "*\n")
 endif()
-file(WRITE "${OPENCV_DOWNLOAD_LOG}" "use_cache \"${OPENCV_DOWNLOAD_PATH}\"\n")
-
+file(WRITE "${OPENCV_DOWNLOAD_LOG}" "#use_cache \"${OPENCV_DOWNLOAD_PATH}\"\n")
+file(REMOVE "${OPENCV_DOWNLOAD_WITH_CURL}")
+file(REMOVE "${OPENCV_DOWNLOAD_WITH_WGET}")
 
 function(ocv_download)
   cmake_parse_arguments(DL "UNPACK;RELATIVE_URL" "FILENAME;HASH;DESTINATION_DIR;ID;STATUS" "URL" ${ARGN})
@@ -103,7 +106,7 @@ function(ocv_download)
   endif()
 
   # Log all calls to file
-  ocv_download_log("do_${mode} \"${DL_FILENAME}\" \"${DL_HASH}\" \"${DL_URL}\" \"${DL_DESTINATION_DIR}\"")
+  ocv_download_log("#do_${mode} \"${DL_FILENAME}\" \"${DL_HASH}\" \"${DL_URL}\" \"${DL_DESTINATION_DIR}\"")
   # ... and to console
   set(__msg_prefix "")
   if(DL_ID)
@@ -191,6 +194,9 @@ function(ocv_download)
 For details please refer to the download log file:
 ${OPENCV_DOWNLOAD_LOG}
 ")
+      # write helper scripts for failed downloads
+      file(APPEND "${OPENCV_DOWNLOAD_WITH_CURL}" "curl --output \"${CACHE_CANDIDATE}\" \"${DL_URL}\"\n")
+      file(APPEND "${OPENCV_DOWNLOAD_WITH_WGET}" "wget -O \"${CACHE_CANDIDATE}\" \"${DL_URL}\"\n")
       return()
     endif()
 

cmake/OpenCVFindVA.cmake

@@ -12,7 +12,9 @@ endif()
 
 if(VA_INCLUDE_DIR)
     set(HAVE_VA TRUE)
-    set(VA_LIBRARIES "-lva" "-lva-drm")
+    if(NOT DEFINED VA_LIBRARIES)
+      set(VA_LIBRARIES "va" "va-drm")
+    endif()
 else()
     set(HAVE_VA FALSE)
     message(WARNING "libva installation is not found.")

cmake/OpenCVModule.cmake

@@ -1132,7 +1132,7 @@ function(ocv_add_perf_tests)
       source_group("Src" FILES "${${the_target}_pch}")
       ocv_add_executable(${the_target} ${OPENCV_PERF_${the_module}_SOURCES} ${${the_target}_pch})
       ocv_target_include_modules(${the_target} ${perf_deps} "${perf_path}")
-      ocv_target_link_libraries(${the_target} LINK_PRIVATE ${perf_deps} ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_LINKER_LIBS})
+      ocv_target_link_libraries(${the_target} LINK_PRIVATE ${perf_deps} ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_LINKER_LIBS} ${OPENCV_PERF_${the_module}_DEPS})
       add_dependencies(opencv_perf_tests ${the_target})
 
       set_target_properties(${the_target} PROPERTIES LABELS "${OPENCV_MODULE_${the_module}_LABEL};PerfTest")
@@ -1175,7 +1175,7 @@ function(ocv_add_perf_tests)
 endfunction()
 
 # this is a command for adding OpenCV accuracy/regression tests to the module
-# ocv_add_accuracy_tests([FILES <source group name> <list of sources>] [DEPENDS_ON] <list of extra dependencies>)
+# ocv_add_accuracy_tests(<list of extra dependencies>)
 function(ocv_add_accuracy_tests)
   ocv_debug_message("ocv_add_accuracy_tests(" ${ARGN} ")")
 
@@ -1202,6 +1202,9 @@ function(ocv_add_accuracy_tests)
         set(OPENCV_TEST_${the_module}_SOURCES ${test_srcs} ${test_hdrs})
       endif()
 
+      if(OPENCV_MODULE_${the_module}_TEST_SOURCES_DISPATCHED)
+        list(APPEND OPENCV_TEST_${the_module}_SOURCES ${OPENCV_MODULE_${the_module}_TEST_SOURCES_DISPATCHED})
+      endif()
       ocv_compiler_optimization_process_sources(OPENCV_TEST_${the_module}_SOURCES OPENCV_TEST_${the_module}_DEPS ${the_target})
 
       if(NOT BUILD_opencv_world)
@@ -1211,7 +1214,10 @@ function(ocv_add_accuracy_tests)
       source_group("Src" FILES "${${the_target}_pch}")
       ocv_add_executable(${the_target} ${OPENCV_TEST_${the_module}_SOURCES} ${${the_target}_pch})
       ocv_target_include_modules(${the_target} ${test_deps} "${test_path}")
-      ocv_target_link_libraries(${the_target} LINK_PRIVATE ${test_deps} ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_LINKER_LIBS})
+      if(EXISTS "${CMAKE_CURRENT_BINARY_DIR}/test")
+        ocv_target_include_directories(${the_target} "${CMAKE_CURRENT_BINARY_DIR}/test")
+      endif()
+      ocv_target_link_libraries(${the_target} LINK_PRIVATE ${test_deps} ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_LINKER_LIBS} ${OPENCV_TEST_${the_module}_DEPS})
       add_dependencies(opencv_tests ${the_target})
 
       set_target_properties(${the_target} PROPERTIES LABELS "${OPENCV_MODULE_${the_module}_LABEL};AccuracyTest")

cmake/OpenCVPCHSupport.cmake

@@ -362,7 +362,7 @@ MACRO(ADD_NATIVE_PRECOMPILED_HEADER _targetName _input)
           endif()
         endforeach()
 
-        #also inlude ${oldProps} to have the same compile options
+        #also include ${oldProps} to have the same compile options
         GET_TARGET_PROPERTY(oldProps ${_targetName} COMPILE_FLAGS)
         if (oldProps MATCHES NOTFOUND)
             SET(oldProps "")

cmake/templates/OpenCVConfig.cmake.in

@@ -260,7 +260,7 @@ endif()
 set(OpenCV_LIBRARIES ${OpenCV_LIBS})
 
 #
-# Some macroses for samples
+# Some macros for samples
 #
 macro(ocv_check_dependencies)
   set(OCV_DEPENDENCIES_FOUND TRUE)

doc/js_tutorials/js_imgproc/js_grabcut/js_grabcut.markdown

@@ -29,7 +29,7 @@ What happens in background ?
     objects). Everything inside rectangle is unknown. Similarly any user input specifying
     foreground and background are considered as hard-labelling which means they won't change in
     the process.
--   Computer does an initial labelling depeding on the data we gave. It labels the foreground and
+-   Computer does an initial labelling depending on the data we gave. It labels the foreground and
     background pixels (or it hard-labels)
 -   Now a Gaussian Mixture Model(GMM) is used to model the foreground and background.
 -   Depending on the data we gave, GMM learns and create new pixel distribution. That is, the

doc/js_tutorials/js_setup/js_usage/js_usage.markdown

@@ -129,7 +129,7 @@ function onOpenCvReady() {
 </html>
 @endcode
 
-@note You have to call delete method of cv.Mat to free memory allocated in Emscripten's heap. Please refer to [Memeory management of Emscripten](https://kripken.github.io/emscripten-site/docs/porting/connecting_cpp_and_javascript/embind.html#memory-management) for details.
+@note You have to call delete method of cv.Mat to free memory allocated in Emscripten's heap. Please refer to [Memory management of Emscripten](https://kripken.github.io/emscripten-site/docs/porting/connecting_cpp_and_javascript/embind.html#memory-management) for details.
 
 Try it
 ------

doc/opencv.bib

@@ -1016,3 +1016,17 @@
   year = {2017},
   organization = {IEEE}
 }
+
+@ARTICLE{gonzalez,
+  title={Digital Image Fundamentals, Digital Imaging Processing},
+  author={Gonzalez, Rafael C and others},
+  year={1987},
+  publisher={Addison Wesley Publishing Company}
+}
+
+@ARTICLE{gruzman,
+  title={Цифровая обработка изображений в информационных системах},
+  author={Грузман, И.С. and Киричук, В.С. and Косых, В.П. and Перетягин, Г.И. and Спектор, А.А.},
+  year={2000},
+  publisher={Изд-во НГТУ Новосибирск}
+}

doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown

@@ -37,7 +37,7 @@ So what happens in background ?
     objects). Everything inside rectangle is unknown. Similarly any user input specifying
     foreground and background are considered as hard-labelling which means they won't change in
     the process.
--   Computer does an initial labelling depeding on the data we gave. It labels the foreground and
+-   Computer does an initial labelling depending on the data we gave. It labels the foreground and
     background pixels (or it hard-labels)
 -   Now a Gaussian Mixture Model(GMM) is used to model the foreground and background.
 -   Depending on the data we gave, GMM learns and create new pixel distribution. That is, the

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -16,7 +16,7 @@ In this tutorial is explained how to build a real time application to estimate t
 order to track a textured object with six degrees of freedom given a 2D image and its 3D textured
 model.
 
-The application will have the followings parts:
+The application will have the following parts:
 
 -   Read 3D textured object model and object mesh.
 -   Take input from Camera or Video.
@@ -426,16 +426,16 @@ Here is explained in detail the code for the real time application:
     @endcode
     OpenCV provides four PnP methods: ITERATIVE, EPNP, P3P and DLS. Depending on the application type,
     the estimation method will be different. In the case that we want to make a real time application,
-    the more suitable methods are EPNP and P3P due to that are faster than ITERATIVE and DLS at
+    the more suitable methods are EPNP and P3P since they are faster than ITERATIVE and DLS at
     finding an optimal solution. However, EPNP and P3P are not especially robust in front of planar
-    surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this this
-    tutorial is used ITERATIVE method due to the object to be detected has planar surfaces.
+    surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this
+    tutorial an ITERATIVE method is used due to the object to be detected has planar surfaces.
 
-    The OpenCV RANSAC implementation wants you to provide three parameters: the maximum number of
-    iterations until stop the algorithm, the maximum allowed distance between the observed and
-    computed point projections to consider it an inlier and the confidence to obtain a good result.
+    The OpenCV RANSAC implementation wants you to provide three parameters: 1) the maximum number of
+    iterations until the algorithm stops, 2) the maximum allowed distance between the observed and
+    computed point projections to consider it an inlier and 3) the confidence to obtain a good result.
     You can tune these parameters in order to improve your algorithm performance. Increasing the
-    number of iterations you will have a more accurate solution, but will take more time to find a
+    number of iterations will have a more accurate solution, but will take more time to find a
     solution. Increasing the reprojection error will reduce the computation time, but your solution
     will be unaccurate. Decreasing the confidence your algorithm will be faster, but the obtained
     solution will be unaccurate.

doc/tutorials/core/basic_linear_transform/basic_linear_transform.markdown

@@ -1,6 +1,9 @@
 Changing the contrast and brightness of an image! {#tutorial_basic_linear_transform}
 =================================================
 
+@prev_tutorial{tutorial_adding_images}
+@next_tutorial{tutorial_discrete_fourier_transform}
+
 Goal
 ----
 
@@ -53,48 +56,143 @@ Theory
 Code
 ----
 
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp)
+
 -   The following code performs the operation \f$g(i,j) = \alpha \cdot f(i,j) + \beta\f$ :
-@include BasicLinearTransforms.cpp
+    @include samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java)
+
+-   The following code performs the operation \f$g(i,j) = \alpha \cdot f(i,j) + \beta\f$ :
+    @include samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py)
+
+-   The following code performs the operation \f$g(i,j) = \alpha \cdot f(i,j) + \beta\f$ :
+    @include samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py
+@end_toggle
 
 Explanation
 -----------
 
--#  We begin by creating parameters to save \f$\alpha\f$ and \f$\beta\f$ to be entered by the user:
-    @snippet BasicLinearTransforms.cpp basic-linear-transform-parameters
+-   We load an image using @ref cv::imread and save it in a Mat object:
 
--#  We load an image using @ref cv::imread and save it in a Mat object:
-    @snippet BasicLinearTransforms.cpp basic-linear-transform-load
--#  Now, since we will make some transformations to this image, we need a new Mat object to store
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp basic-linear-transform-load
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java basic-linear-transform-load
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py basic-linear-transform-load
+@end_toggle
+
+-   Now, since we will make some transformations to this image, we need a new Mat object to store
     it. Also, we want this to have the following features:
 
     -   Initial pixel values equal to zero
     -   Same size and type as the original image
-    @snippet BasicLinearTransforms.cpp basic-linear-transform-output
-    We observe that @ref cv::Mat::zeros returns a Matlab-style zero initializer based on
-    *image.size()* and *image.type()*
 
--#  Now, to perform the operation \f$g(i,j) = \alpha \cdot f(i,j) + \beta\f$ we will access to each
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp basic-linear-transform-output
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java basic-linear-transform-output
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py basic-linear-transform-output
+@end_toggle
+
+We observe that @ref cv::Mat::zeros returns a Matlab-style zero initializer based on
+*image.size()* and *image.type()*
+
+-   We ask now the values of \f$\alpha\f$ and \f$\beta\f$ to be entered by the user:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp basic-linear-transform-parameters
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java basic-linear-transform-parameters
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py basic-linear-transform-parameters
+@end_toggle
+
+-   Now, to perform the operation \f$g(i,j) = \alpha \cdot f(i,j) + \beta\f$ we will access to each
     pixel in image. Since we are operating with BGR images, we will have three values per pixel (B,
     G and R), so we will also access them separately. Here is the piece of code:
-    @snippet BasicLinearTransforms.cpp basic-linear-transform-operation
-    Notice the following:
-    -   To access each pixel in the images we are using this syntax: *image.at\<Vec3b\>(y,x)[c]*
-        where *y* is the row, *x* is the column and *c* is R, G or B (0, 1 or 2).
-    -   Since the operation \f$\alpha \cdot p(i,j) + \beta\f$ can give values out of range or not
-        integers (if \f$\alpha\f$ is float), we use cv::saturate_cast to make sure the
-        values are valid.
 
--#  Finally, we create windows and show the images, the usual way.
-    @snippet BasicLinearTransforms.cpp basic-linear-transform-display
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp basic-linear-transform-operation
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java basic-linear-transform-operation
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py basic-linear-transform-operation
+@end_toggle
+
+Notice the following (**C++ code only**):
+-   To access each pixel in the images we are using this syntax: *image.at\<Vec3b\>(y,x)[c]*
+    where *y* is the row, *x* is the column and *c* is R, G or B (0, 1 or 2).
+-   Since the operation \f$\alpha \cdot p(i,j) + \beta\f$ can give values out of range or not
+    integers (if \f$\alpha\f$ is float), we use cv::saturate_cast to make sure the
+    values are valid.
+
+-   Finally, we create windows and show the images, the usual way.
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp basic-linear-transform-display
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/BasicLinearTransformsDemo.java basic-linear-transform-display
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py basic-linear-transform-display
+@end_toggle
 
 @note
     Instead of using the **for** loops to access each pixel, we could have simply used this command:
-    @code{.cpp}
-    image.convertTo(new_image, -1, alpha, beta);
-    @endcode
-    where @ref cv::Mat::convertTo would effectively perform *new_image = a*image + beta\*. However, we
-    wanted to show you how to access each pixel. In any case, both methods give the same result but
-    convertTo is more optimized and works a lot faster.
+
+@add_toggle_cpp
+@code{.cpp}
+image.convertTo(new_image, -1, alpha, beta);
+@endcode
+@end_toggle
+
+@add_toggle_java
+@code{.java}
+image.convertTo(newImage, -1, alpha, beta);
+@endcode
+@end_toggle
+
+@add_toggle_python
+@code{.py}
+new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)
+@endcode
+@end_toggle
+
+where @ref cv::Mat::convertTo would effectively perform *new_image = a*image + beta\*. However, we
+wanted to show you how to access each pixel. In any case, both methods give the same result but
+convertTo is more optimized and works a lot faster.
 
 Result
 ------
@@ -185,10 +283,31 @@ and are not intended to be used as a replacement of a raster graphics editor!**
 
 ### Code
 
+@add_toggle_cpp
 Code for the tutorial is [here](https://github.com/opencv/opencv/blob/3.4/samples/cpp/tutorial_code/ImgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.cpp).
+@end_toggle
+
+@add_toggle_java
+Code for the tutorial is [here](https://github.com/opencv/opencv/blob/3.4/samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/ChangingContrastBrightnessImageDemo.java).
+@end_toggle
+
+@add_toggle_python
+Code for the tutorial is [here](https://github.com/opencv/opencv/blob/3.4/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py).
+@end_toggle
+
 Code for the gamma correction:
 
-@snippet changing_contrast_brightness_image.cpp changing-contrast-brightness-gamma-correction
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.cpp changing-contrast-brightness-gamma-correction
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgProc/changing_contrast_brightness_image/ChangingContrastBrightnessImageDemo.java changing-contrast-brightness-gamma-correction
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py changing-contrast-brightness-gamma-correction
+@end_toggle
 
 A look-up table is used to improve the performance of the computation as only 256 values needs to be calculated once.
 

doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown

@@ -1,7 +1,7 @@
 Discrete Fourier Transform {#tutorial_discrete_fourier_transform}
 ==========================
 
-@prev_tutorial{tutorial_random_generator_and_text}
+@prev_tutorial{tutorial_basic_linear_transform}
 @next_tutorial{tutorial_file_input_output_with_xml_yml}
 
 Goal

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown

@@ -1,6 +1,9 @@
 File Input and Output using XML and YAML files {#tutorial_file_input_output_with_xml_yml}
 ==============================================
 
+@prev_tutorial{tutorial_discrete_fourier_transform}
+@next_tutorial{tutorial_interoperability_with_OpenCV_1}
+
 Goal
 ----
 

doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown

@@ -1,6 +1,9 @@
 How to scan images, lookup tables and time measurement with OpenCV {#tutorial_how_to_scan_images}
 ==================================================================
 
+@prev_tutorial{tutorial_mat_the_basic_image_container}
+@next_tutorial{tutorial_mat_mask_operations}
+
 Goal
 ----
 

doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown

@@ -1,6 +1,8 @@
 How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_use_OpenCV_parallel_for_}
 ==================================================================
 
+@prev_tutorial{tutorial_how_to_use_ippa_conversion}
+
 Goal
 ----
 

doc/tutorials/core/how_to_use_ippa_conversion/how_to_use_ippa_conversion.markdown

@@ -1,6 +1,9 @@
 Intel® IPP Asynchronous C/C++ library in OpenCV {#tutorial_how_to_use_ippa_conversion}
 ===============================================
 
+@prev_tutorial{tutorial_interoperability_with_OpenCV_1}
+@next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
+
 Goal
 ----
 

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown

@@ -1,6 +1,9 @@
 Interoperability with OpenCV 1 {#tutorial_interoperability_with_OpenCV_1}
 ==============================
 
+@prev_tutorial{tutorial_file_input_output_with_xml_yml}
+@next_tutorial{tutorial_how_to_use_ippa_conversion}
+
 Goal
 ----
 

doc/tutorials/core/mat_operations.markdown

@@ -1,31 +1,59 @@
 Operations with images {#tutorial_mat_operations}
 ======================
 
+@prev_tutorial{tutorial_mat_mask_operations}
+@next_tutorial{tutorial_adding_images}
+
 Input/Output
 ------------
 
 ### Images
 
 Load an image from a file:
-@code{.cpp}
-    Mat img = imread(filename)
-@endcode
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Load an image from a file
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Load an image from a file
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Load an image from a file
+@end_toggle
 
 If you read a jpg file, a 3 channel image is created by default. If you need a grayscale image, use:
 
-@code{.cpp}
-    Mat img = imread(filename, IMREAD_GRAYSCALE);
-@endcode
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Load an image from a file in grayscale
+@end_toggle
 
-@note format of the file is determined by its content (first few bytes) Save an image to a file:
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Load an image from a file in grayscale
+@end_toggle
 
-@code{.cpp}
-    imwrite(filename, img);
-@endcode
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Load an image from a file in grayscale
+@end_toggle
 
-@note format of the file is determined by its extension.
+@note Format of the file is determined by its content (first few bytes). To save an image to a file:
 
-@note use imdecode and imencode to read and write image from/to memory rather than a file.
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Save image
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Save image
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Save image
+@end_toggle
+
+@note Format of the file is determined by its extension.
+
+@note Use cv::imdecode and cv::imencode to read and write an image from/to memory rather than a file.
 
 Basic operations with images
 ----------------------------
@@ -35,49 +63,65 @@ Basic operations with images
 In order to get pixel intensity value, you have to know the type of an image and the number of
 channels. Here is an example for a single channel grey scale image (type 8UC1) and pixel coordinates
 x and y:
-@code{.cpp}
-    Scalar intensity = img.at<uchar>(y, x);
-@endcode
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 1
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Pixel access 1
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Pixel access 1
+@end_toggle
+
+C++ version only:
 intensity.val[0] contains a value from 0 to 255. Note the ordering of x and y. Since in OpenCV
 images are represented by the same structure as matrices, we use the same convention for both
 cases - the 0-based row index (or y-coordinate) goes first and the 0-based column index (or
-x-coordinate) follows it. Alternatively, you can use the following notation:
-@code{.cpp}
-    Scalar intensity = img.at<uchar>(Point(x, y));
-@endcode
+x-coordinate) follows it. Alternatively, you can use the following notation (**C++ only**):
+
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 2
+
 Now let us consider a 3 channel image with BGR color ordering (the default format returned by
 imread):
-@code{.cpp}
-    Vec3b intensity = img.at<Vec3b>(y, x);
-    uchar blue = intensity.val[0];
-    uchar green = intensity.val[1];
-    uchar red = intensity.val[2];
-@endcode
+
+**C++ code**
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 3
+
+**Python Python**
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Pixel access 3
+
 You can use the same method for floating-point images (for example, you can get such an image by
-running Sobel on a 3 channel image):
-@code{.cpp}
-    Vec3f intensity = img.at<Vec3f>(y, x);
-    float blue = intensity.val[0];
-    float green = intensity.val[1];
-    float red = intensity.val[2];
-@endcode
+running Sobel on a 3 channel image) (**C++ only**):
+
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 4
+
 The same method can be used to change pixel intensities:
-@code{.cpp}
-    img.at<uchar>(y, x) = 128;
-@endcode
-There are functions in OpenCV, especially from calib3d module, such as projectPoints, that take an
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Pixel access 5
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Pixel access 5
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Pixel access 5
+@end_toggle
+
+There are functions in OpenCV, especially from calib3d module, such as cv::projectPoints, that take an
 array of 2D or 3D points in the form of Mat. Matrix should contain exactly one column, each row
 corresponds to a point, matrix type should be 32FC2 or 32FC3 correspondingly. Such a matrix can be
-easily constructed from `std::vector`:
-@code{.cpp}
-    vector<Point2f> points;
-    //... fill the array
-    Mat pointsMat = Mat(points);
-@endcode
-One can access a point in this matrix using the same method Mat::at :
-@code{.cpp}
-Point2f point = pointsMat.at<Point2f>(i, 0);
-@endcode
+easily constructed from `std::vector` (**C++ only**):
+
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Mat from points vector
+
+One can access a point in this matrix using the same method `Mat::at` (**C++ only**):
+
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Point access
 
 ### Memory management and reference counting
 
@@ -85,91 +129,141 @@ Mat is a structure that keeps matrix/image characteristics (rows and columns num
 and a pointer to data. So nothing prevents us from having several instances of Mat corresponding to
 the same data. A Mat keeps a reference count that tells if data has to be deallocated when a
 particular instance of Mat is destroyed. Here is an example of creating two matrices without copying
-data:
-@code{.cpp}
-    std::vector<Point3f> points;
-    // .. fill the array
-    Mat pointsMat = Mat(points).reshape(1);
-@endcode
-As a result we get a 32FC1 matrix with 3 columns instead of 32FC3 matrix with 1 column. pointsMat
+data (**C++ only**):
+
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 1
+
+As a result, we get a 32FC1 matrix with 3 columns instead of 32FC3 matrix with 1 column. `pointsMat`
 uses data from points and will not deallocate the memory when destroyed. In this particular
-instance, however, developer has to make sure that lifetime of points is longer than of pointsMat.
+instance, however, developer has to make sure that lifetime of `points` is longer than of `pointsMat`
 If we need to copy the data, this is done using, for example, cv::Mat::copyTo or cv::Mat::clone:
-@code{.cpp}
-    Mat img = imread("image.jpg");
-    Mat img1 = img.clone();
-@endcode
-To the contrary with C API where an output image had to be created by developer, an empty output Mat
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 2
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Reference counting 2
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Reference counting 2
+@end_toggle
+
+To the contrary with C API where an output image had to be created by the developer, an empty output Mat
 can be supplied to each function. Each implementation calls Mat::create for a destination matrix.
 This method allocates data for a matrix if it is empty. If it is not empty and has the correct size
-and type, the method does nothing. If, however, size or type are different from input arguments, the
+and type, the method does nothing. If however, size or type are different from the input arguments, the
 data is deallocated (and lost) and a new data is allocated. For example:
-@code{.cpp}
-    Mat img = imread("image.jpg");
-    Mat sobelx;
-    Sobel(img, sobelx, CV_32F, 1, 0);
-@endcode
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Reference counting 3
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Reference counting 3
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Reference counting 3
+@end_toggle
 
 ### Primitive operations
 
 There is a number of convenient operators defined on a matrix. For example, here is how we can make
-a black image from an existing greyscale image \`img\`:
-@code{.cpp}
-    img = Scalar(0);
-@endcode
+a black image from an existing greyscale image `img`
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Set image to black
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Set image to black
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Set image to black
+@end_toggle
+
 Selecting a region of interest:
-@code{.cpp}
-    Rect r(10, 10, 100, 100);
-    Mat smallImg = img(r);
-@endcode
-A conversion from Mat to C API data structures:
-@code{.cpp}
-    Mat img = imread("image.jpg");
-    IplImage img1 = img;
-    CvMat m = img;
-@endcode
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Select ROI
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Select ROI
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Select ROI
+@end_toggle
+
+A conversion from Mat to C API data structures (**C++ only**):
+
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp C-API conversion
 
 Note that there is no data copying here.
 
-Conversion from color to grey scale:
-@code{.cpp}
-    Mat img = imread("image.jpg"); // loading a 8UC3 image
-    Mat grey;
-    cvtColor(img, grey, COLOR_BGR2GRAY);
-@endcode
+Conversion from color to greyscale:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp BGR to Gray
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java BGR to Gray
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py BGR to Gray
+@end_toggle
+
 Change image type from 8UC1 to 32FC1:
-@code{.cpp}
-    src.convertTo(dst, CV_32F);
-@endcode
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp Convert to CV_32F
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java Convert to CV_32F
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py Convert to CV_32F
+@end_toggle
 
 ### Visualizing images
 
 It is very useful to see intermediate results of your algorithm during development process. OpenCV
 provides a convenient way of visualizing images. A 8U image can be shown using:
-@code{.cpp}
-    Mat img = imread("image.jpg");
 
-    namedWindow("image", WINDOW_AUTOSIZE);
-    imshow("image", img);
-    waitKey();
-@endcode
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp imshow 1
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java imshow 1
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py imshow 1
+@end_toggle
 
 A call to waitKey() starts a message passing cycle that waits for a key stroke in the "image"
 window. A 32F image needs to be converted to 8U type. For example:
-@code{.cpp}
-    Mat img = imread("image.jpg");
-    Mat grey;
-    cvtColor(img, grey, COLOR_BGR2GRAY);
 
-    Mat sobelx;
-    Sobel(grey, sobelx, CV_32F, 1, 0);
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/core/mat_operations/mat_operations.cpp imshow 2
+@end_toggle
 
-    double minVal, maxVal;
-    minMaxLoc(sobelx, &minVal, &maxVal); //find minimum and maximum intensities
-    Mat draw;
-    sobelx.convertTo(draw, CV_8U, 255.0/(maxVal - minVal), -minVal * 255.0/(maxVal - minVal));
+@add_toggle_java
+@snippet samples/java/tutorial_code/core/mat_operations/MatOperations.java imshow 2
+@end_toggle
 
-    namedWindow("image", WINDOW_AUTOSIZE);
-    imshow("image", draw);
-    waitKey();
-@endcode
+@add_toggle_python
+@snippet samples/python/tutorial_code/core/mat_operations/mat_operations.py imshow 2
+@end_toggle
+
+@note Here cv::namedWindow is not necessary since it is immediately followed by cv::imshow.
+Nevertheless, it can be used to change the window properties or when using cv::createTrackbar

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown

@@ -1,6 +1,8 @@
 Mat - The Basic Image Container {#tutorial_mat_the_basic_image_container}
 ===============================
 
+@next_tutorial{tutorial_how_to_scan_images}
+
 Goal
 ----
 

doc/tutorials/core/table_of_content_core.markdown

@@ -36,6 +36,10 @@ understanding how to manipulate the images on a pixel level.
 
 -   @subpage tutorial_mat_operations
 
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
     Reading/writing images from file, accessing pixels, primitive operations, visualizing images.
 
 -   @subpage tutorial_adding_images
@@ -50,29 +54,13 @@ understanding how to manipulate the images on a pixel level.
 
 -   @subpage tutorial_basic_linear_transform
 
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will learn how to change our image appearance!
-
--   @subpage tutorial_basic_geometric_drawing
-
     *Languages:* C++, Java, Python
 
     *Compatibility:* \> OpenCV 2.0
 
     *Author:* Ana Huamán
 
-    We will learn how to draw simple geometry with OpenCV!
-
--   @subpage tutorial_random_generator_and_text
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will draw some *fancy-looking* stuff using OpenCV!
+    We will learn how to change our image appearance!
 
 -   @subpage tutorial_discrete_fourier_transform
 

doc/tutorials/dnn/dnn_android/dnn_android.markdown

@@ -12,7 +12,7 @@ Tutorial was written for the following versions of corresponding software:
 
 - Download and install Android Studio from https://developer.android.com/studio.
 
-- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-3.4.2-android-sdk.zip`).
+- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-3.4.3-android-sdk.zip`).
 
 - Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD. We need a configuration file `MobileNetSSD_deploy.prototxt` and weights `MobileNetSSD_deploy.caffemodel`.
 

doc/tutorials/imgproc/basic_geometric_drawing/basic_geometric_drawing.markdown

@@ -1,7 +1,6 @@
 Basic Drawing {#tutorial_basic_geometric_drawing}
 =============
 
-@prev_tutorial{tutorial_basic_linear_transform}
 @next_tutorial{tutorial_random_generator_and_text}
 
 Goals
@@ -82,20 +81,20 @@ Code
 
 @add_toggle_cpp
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp)
-    @include samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp
+    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp)
+    @include samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp
 @end_toggle
 
 @add_toggle_java
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java)
-    @include samples/java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java
+    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java)
+    @include samples/java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java
 @end_toggle
 
 @add_toggle_python
 -   This code is in your OpenCV sample folder. Otherwise you can grab it from
-    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py)
-    @include samples/python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py
+    [here](https://raw.githubusercontent.com/opencv/opencv/3.4/samples/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py)
+    @include samples/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py
 @end_toggle
 
 Explanation
@@ -104,42 +103,42 @@ Explanation
 Since we plan to draw two examples (an atom and a rook), we have to create two images and two
 windows to display them.
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp create_images
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp create_images
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java create_images
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java create_images
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py create_images
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py create_images
 @end_toggle
 
 We created functions to draw different geometric shapes. For instance, to draw the atom we used
 **MyEllipse** and **MyFilledCircle**:
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp draw_atom
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp draw_atom
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java draw_atom
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java draw_atom
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py draw_atom
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py draw_atom
 @end_toggle
 
 And to draw the rook we employed **MyLine**, **rectangle** and a **MyPolygon**:
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp draw_rook
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp draw_rook
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java draw_rook
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java draw_rook
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py draw_rook
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py draw_rook
 @end_toggle
 
 
@@ -149,15 +148,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyLine</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_line
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_line
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_line
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_line
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_line
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_line
 @end_toggle
 
 -   As we can see, **MyLine** just call the function **line()** , which does the following:
@@ -170,15 +169,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyEllipse</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_ellipse
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_ellipse
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_ellipse
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_ellipse
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_ellipse
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_ellipse
 @end_toggle
 
 -   From the code above, we can observe that the function **ellipse()** draws an ellipse such
@@ -194,15 +193,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyFilledCircle</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_filled_circle
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_filled_circle
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_filled_circle
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_filled_circle
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_filled_circle
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_filled_circle
 @end_toggle
 
 -   Similar to the ellipse function, we can observe that *circle* receives as arguments:
@@ -215,15 +214,15 @@ Let's check what is inside each of these functions:
 
 <H4>MyPolygon</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp my_polygon
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp my_polygon
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java my_polygon
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java my_polygon
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py my_polygon
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py my_polygon
 @end_toggle
 
 -   To draw a filled polygon we use the function **fillPoly()** . We note that:
@@ -235,15 +234,15 @@ Let's check what is inside each of these functions:
 
 <H4>rectangle</H4>
 @add_toggle_cpp
-@snippet cpp/tutorial_code/core/Matrix/Drawing_1.cpp rectangle
+@snippet cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp rectangle
 @end_toggle
 
 @add_toggle_java
-@snippet java/tutorial_code/core/BasicGeometricDrawing/BasicGeometricDrawing.java rectangle
+@snippet java/tutorial_code/ImgProc/BasicGeometricDrawing/BasicGeometricDrawing.java rectangle
 @end_toggle
 
 @add_toggle_python
-@snippet python/tutorial_code/core/BasicGeometricDrawing/basic_geometric_drawing.py rectangle
+@snippet python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py rectangle
 @end_toggle
 
 -   Finally we have the @ref cv::rectangle function (we did not create a special function for

doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown

@@ -1,6 +1,9 @@
 Eroding and Dilating {#tutorial_erosion_dilatation}
 ====================
 
+@prev_tutorial{tutorial_gausian_median_blur_bilateral_filter}
+@next_tutorial{tutorial_opening_closing_hats}
+
 Goal
 ----
 

doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown

@@ -1,6 +1,7 @@
 Smoothing Images {#tutorial_gausian_median_blur_bilateral_filter}
 ================
 
+@prev_tutorial{tutorial_random_generator_and_text}
 @next_tutorial{tutorial_erosion_dilatation}
 
 Goal

doc/tutorials/imgproc/histograms/back_projection/back_projection.markdown

@@ -1,6 +1,9 @@
 Back Projection {#tutorial_back_projection}
 ===============
 
+@prev_tutorial{tutorial_histogram_comparison}
+@next_tutorial{tutorial_template_matching}
+
 Goal
 ----
 

doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.markdown

@@ -1,6 +1,9 @@
 Histogram Calculation {#tutorial_histogram_calculation}
 =====================
 
+@prev_tutorial{tutorial_histogram_equalization}
+@next_tutorial{tutorial_histogram_comparison}
+
 Goal
 ----
 

doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.markdown

@@ -1,6 +1,9 @@
 Histogram Comparison {#tutorial_histogram_comparison}
 ====================
 
+@prev_tutorial{tutorial_histogram_calculation}
+@next_tutorial{tutorial_back_projection}
+
 Goal
 ----
 

doc/tutorials/imgproc/histograms/histogram_equalization/histogram_equalization.markdown

@@ -1,6 +1,9 @@
 Histogram Equalization {#tutorial_histogram_equalization}
 ======================
 
+@prev_tutorial{tutorial_warp_affine}
+@next_tutorial{tutorial_histogram_calculation}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.markdown

@@ -1,6 +1,9 @@
 Canny Edge Detector {#tutorial_canny_detector}
 ===================
 
+@prev_tutorial{tutorial_laplace_operator}
+@next_tutorial{tutorial_hough_lines}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown

@@ -1,6 +1,9 @@
 Image Segmentation with Distance Transform and Watershed Algorithm {#tutorial_distance_transform}
 =============
 
+@prev_tutorial{tutorial_point_polygon_test}
+@next_tutorial{tutorial_out_of_focus_deblur_filter}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/remap/remap.markdown

@@ -1,6 +1,9 @@
 Remapping {#tutorial_remap}
 =========
 
+@prev_tutorial{tutorial_hough_circle}
+@next_tutorial{tutorial_warp_affine}
+
 Goal
 ----
 

doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.markdown

@@ -1,6 +1,9 @@
 Affine Transformations {#tutorial_warp_affine}
 ======================
 
+@prev_tutorial{tutorial_remap}
+@next_tutorial{tutorial_histogram_equalization}
+
 Goal
 ----
 

doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.markdown

@@ -1,6 +1,9 @@
 More Morphology Transformations {#tutorial_opening_closing_hats}
 ===============================
 
+@prev_tutorial{tutorial_erosion_dilatation}
+@next_tutorial{tutorial_hitOrMiss}
+
 Goal
 ----
 

doc/tutorials/imgproc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.markdown

@@ -0,0 +1,114 @@
+Out-of-focus Deblur Filter {#tutorial_out_of_focus_deblur_filter}
+==========================
+
+@prev_tutorial{tutorial_distance_transform}
+
+Goal
+----
+
+In this tutorial you will learn:
+
+-   what is a degradation image model
+-   what is PSF of out-of-focus image
+-   how to restore a blurred image
+-   what is Wiener filter
+
+Theory
+------
+
+@note The explanation is based on the books @cite gonzalez and @cite gruzman. Also, you can refer to Matlab's tutorial [Image Deblurring in Matlab] and an article [SmartDeblur].
+@note An out-of-focus image on this page is a real world  image. An out-of-focus was done manually by camera optics.
+
+### What is a degradation image model?
+
+A mathematical model of the image degradation in frequency domain representation is:
+
+\f[S = H\cdot U + N\f]
+
+where
+\f$S\f$ is a spectrum of blurred (degraded) image,
+\f$U\f$ is a spectrum of original true (undegraded) image,
+\f$H\f$ is frequency response of point spread function (PSF),
+\f$N\f$ is a spectrum of additive noise.
+
+Circular PSF is a good approximation of out-of-focus distortion. Such PSF is specified by only one parameter - radius \f$R\f$. Circular PSF is used in this work.
+
+![Circular point spread function](psf.png)
+
+### How to restore an blurred image?
+
+The objective of restoration (deblurring) is to obtain an estimate of the original image. Restoration formula in frequency domain is:
+
+\f[U' = H_w\cdot S\f]
+
+where
+\f$U'\f$ is spectrum of estimation of original image \f$U\f$,
+\f$H_w\f$ is restoration filter, for example, Wiener filter.
+
+### What is Wiener filter?
+
+Wiener filter is a way to restore a blurred image. Let's suppose that PSF is a real and symmetric signal, a power spectrum of the original true image and noise are not known,
+then simplified Wiener formula is:
+
+\f[H_w = \frac{H}{|H|^2+\frac{1}{SNR}} \f]
+
+where
+\f$SNR\f$ is signal-to-noise ratio.
+
+So, in order to recover an out-of-focus image by Wiener filter, it needs to know \f$SNR\f$ and \f$R\f$ of circular PSF.
+
+
+Source code
+-----------
+
+You can find source code in the `samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp` of the OpenCV source code library.
+
+@include cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp
+
+Explanation
+-----------
+
+An out-of-focus image recovering algorithm consists of PSF generation, Wiener filter generation and filtering an blurred image in frequency domain:
+@snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp main
+
+A function calcPSF() forms an circular PSF according to input parameter radius \f$R\f$:
+@snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp calcPSF
+
+A function calcWnrFilter() synthesizes simplified Wiener filter \f$H_w\f$ according to formula described above:
+@snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp calcWnrFilter
+
+A function fftshift() rearranges PSF. This code was just copied from tutorial @ref tutorial_discrete_fourier_transform "Discrete Fourier Transform":
+@snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp fftshift
+
+A function filter2DFreq() filters an blurred image in frequency domain:
+@snippet samples/cpp/tutorial_code/ImgProc/out_of_focus_deblur_filter/out_of_focus_deblur_filter.cpp filter2DFreq
+
+Result
+------
+
+Below you can see real out-of-focus image:
+![Out-of-focus image](images/original.jpg)
+
+
+Below result was done by \f$R\f$ = 53 and \f$SNR\f$ = 5200 parameters:
+![The restored (deblurred) image](images/recovered.jpg)
+
+The Wiener filter was used, values of \f$R\f$ and \f$SNR\f$ were selected manually to give the best possible visual result.
+We can see that the result is not perfect, but it gives us a hint to the image content. With some difficulty, the text is readable.
+
+@note The parameter \f$R\f$ is the most important. So you should adjust \f$R\f$ first, then \f$SNR\f$.
+@note Sometimes you can observe the ringing effect in an restored image. This effect can be reduced by several methods. For example, you can taper input image edges.
+
+You can also find a quick video demonstration of this on
+[YouTube](https://youtu.be/0bEcE4B0XP4).
+@youtube{0bEcE4B0XP4}
+
+References
+------
+- [Image Deblurring in Matlab] - Image Deblurring in Matlab
+- [SmartDeblur] - SmartDeblur site
+
+<!-- invisible references list -->
+[Digital Image Processing]: http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/Digital_Image_Processing_2ndEd.pdf
+[Image Deblurring in Matlab]: https://www.mathworks.com/help/images/image-deblurring.html
+[SmartDeblur]: http://yuzhikov.com/articles/BlurredImagesRestoration1.htm

doc/tutorials/imgproc/random_generator_and_text/random_generator_and_text.markdown

@@ -1,6 +1,9 @@
 Random generator and text with OpenCV {#tutorial_random_generator_and_text}
 =====================================
 
+@prev_tutorial{tutorial_basic_geometric_drawing}
+@next_tutorial{tutorial_gausian_median_blur_bilateral_filter}
+
 Goals
 -----
 

doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.markdown

@@ -1,6 +1,9 @@
 Creating Bounding boxes and circles for contours {#tutorial_bounding_rects_circles}
 ================================================
 
+@prev_tutorial{tutorial_hull}
+@next_tutorial{tutorial_bounding_rotated_ellipses}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/bounding_rotated_ellipses/bounding_rotated_ellipses.markdown

@@ -1,6 +1,9 @@
 Creating Bounding rotated boxes and ellipses for contours {#tutorial_bounding_rotated_ellipses}
 =========================================================
 
+@prev_tutorial{tutorial_bounding_rects_circles}
+@next_tutorial{tutorial_moments}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/find_contours/find_contours.markdown

@@ -1,6 +1,9 @@
 Finding contours in your image {#tutorial_find_contours}
 ==============================
 
+@prev_tutorial{tutorial_template_matching}
+@next_tutorial{tutorial_hull}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/hull/hull.markdown

@@ -1,6 +1,9 @@
 Convex Hull {#tutorial_hull}
 ===========
 
+@prev_tutorial{tutorial_find_contours}
+@next_tutorial{tutorial_bounding_rects_circles}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/moments/moments.markdown

@@ -1,6 +1,9 @@
 Image Moments {#tutorial_moments}
 =============
 
+@prev_tutorial{tutorial_bounding_rotated_ellipses}
+@next_tutorial{tutorial_point_polygon_test}
+
 Goal
 ----
 

doc/tutorials/imgproc/shapedescriptors/point_polygon_test/point_polygon_test.markdown

@@ -1,6 +1,9 @@
 Point Polygon Test {#tutorial_point_polygon_test}
 ==================
 
+@prev_tutorial{tutorial_moments}
+@next_tutorial{tutorial_distance_transform}
+
 Goal
 ----
 

doc/tutorials/imgproc/table_of_content_imgproc.markdown

@@ -3,6 +3,24 @@ Image Processing (imgproc module) {#tutorial_table_of_content_imgproc}
 
 In this section you will learn about the image processing (manipulation) functions inside OpenCV.
 
+-   @subpage tutorial_basic_geometric_drawing
+
+    *Languages:* C++, Java, Python
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    We will learn how to draw simple geometry with OpenCV!
+
+-   @subpage tutorial_random_generator_and_text
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Ana Huamán
+
+    We will draw some *fancy-looking* stuff using OpenCV!
+
 -   @subpage tutorial_gausian_median_blur_bilateral_filter
 
     *Languages:* C++, Java, Python
@@ -292,3 +310,13 @@ In this section you will learn about the image processing (manipulation) functio
     *Author:* Theodore Tsesmelis
 
     Where we learn to segment objects using Laplacian filtering, the Distance Transformation and the Watershed algorithm.
+
+-   @subpage tutorial_out_of_focus_deblur_filter
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 2.0
+
+    *Author:* Karpushin Vladislav
+
+    You will learn how to recover an out-of-focus image by Wiener filter.

doc/tutorials/imgproc/threshold/threshold.markdown

@@ -1,6 +1,9 @@
 Basic Thresholding Operations {#tutorial_threshold}
 =============================
 
+@prev_tutorial{tutorial_pyramids}
+@next_tutorial{tutorial_threshold_inRange}
+
 Goal
 ----
 

doc/tutorials/imgproc/threshold_inRange/threshold_inRange.markdown

@@ -1,6 +1,9 @@
 Thresholding Operations using inRange {#tutorial_threshold_inRange}
 =====================================
 
+@prev_tutorial{tutorial_threshold}
+@next_tutorial{tutorial_filter_2d}
+
 Goal
 ----
 

doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown

@@ -36,14 +36,14 @@ Open your Doxyfile using your favorite text editor and search for the key
 `TAGFILES`. Change it as follows:
 
 @code
-TAGFILES = ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.2
+TAGFILES = ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.3
 @endcode
 
 If you had other definitions already, you can append the line using a `\`:
 
 @code
 TAGFILES = ./docs/doxygen-tags/libstdc++.tag=https://gcc.gnu.org/onlinedocs/libstdc++/latest-doxygen \
-           ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.2
+           ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.3
 @endcode
 
 Doxygen can now use the information from the tag file to link to the OpenCV

doc/tutorials/introduction/windows_install/windows_install.markdown

@@ -46,7 +46,7 @@ cd /c/lib
 myRepo=$(pwd)
 CMAKE_CONFIG_GENERATOR="Visual Studio 14 2015 Win64"
 if [  ! -d "$myRepo/opencv"  ]; then
-    echo "clonning opencv"
+    echo "cloning opencv"
     git clone https://github.com/opencv/opencv.git
     mkdir Build
     mkdir Build/opencv
@@ -58,7 +58,7 @@ else
     cd ..
 fi
 if [  ! -d "$myRepo/opencv_contrib"  ]; then
-    echo "clonning opencv_contrib"
+    echo "cloning opencv_contrib"
     git clone https://github.com/opencv/opencv_contrib.git
     mkdir Build
     mkdir Build/opencv_contrib

doc/tutorials/ml/introduction_to_pca/introduction_to_pca.markdown

@@ -91,37 +91,112 @@ __Find the eigenvectors and eigenvalues of the covariance matrix__
 Source Code
 -----------
 
-This tutorial code's is shown lines below. You can also download it from
-    [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp).
-@include cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java)
+
+-   **Code at glance:**
+    @include samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py
+@end_toggle
 
 @note Another example using PCA for dimensionality reduction while maintaining an amount of variance can be found at [opencv_source_code/samples/cpp/pca.cpp](https://github.com/opencv/opencv/tree/3.4/samples/cpp/pca.cpp)
 
 Explanation
 -----------
 
--#  __Read image and convert it to binary__
+-   __Read image and convert it to binary__
 
-    Here we apply the necessary pre-processing procedures in order to be able to detect the objects of interest.
-    @snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pre-process
+Here we apply the necessary pre-processing procedures in order to be able to detect the objects of interest.
 
--#  __Extract objects of interest__
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pre-process
+@end_toggle
 
-    Then find and filter contours by size and obtain the orientation of the remaining ones.
-    @snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp contours
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java pre-process
+@end_toggle
 
--#  __Extract orientation__
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py pre-process
+@end_toggle
 
-    Orientation is extracted by the call of getOrientation() function, which performs all the PCA procedure.
-    @snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pca
+-   __Extract objects of interest__
 
-    First the data need to be arranged in a matrix with size n x 2, where n is the number of data points we have. Then we can perform that PCA analysis. The calculated mean (i.e. center of mass) is stored in the _cntr_ variable and the eigenvectors and eigenvalues are stored in the corresponding std::vector’s.
+Then find and filter contours by size and obtain the orientation of the remaining ones.
 
--#  __Visualize result__
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp contours
+@end_toggle
 
-    The final result is visualized through the drawAxis() function, where the principal components are drawn in lines, and each eigenvector is multiplied by its eigenvalue and translated to the mean position.
-    @snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization
-    @snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization1
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java contours
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py contours
+@end_toggle
+
+-   __Extract orientation__
+
+Orientation is extracted by the call of getOrientation() function, which performs all the PCA procedure.
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pca
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java pca
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py pca
+@end_toggle
+
+First the data need to be arranged in a matrix with size n x 2, where n is the number of data points we have. Then we can perform that PCA analysis. The calculated mean (i.e. center of mass) is stored in the _cntr_ variable and the eigenvectors and eigenvalues are stored in the corresponding std::vector’s.
+
+-   __Visualize result__
+
+The final result is visualized through the drawAxis() function, where the principal components are drawn in lines, and each eigenvector is multiplied by its eigenvalue and translated to the mean position.
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java visualization
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py visualization
+@end_toggle
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization1
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java visualization1
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py visualization1
+@end_toggle
 
 Results
 -------

doc/tutorials/ml/introduction_to_svm/introduction_to_svm.markdown

@@ -96,25 +96,67 @@ Source Code
 
 @note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html#introductiontosvms)
 
-@include cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java)
+
+-   **Code at glance:**
+    @include samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py
+@end_toggle
 
 Explanation
 -----------
 
--#  **Set up the training data**
+-   **Set up the training data**
 
-    The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
-    two different classes; one of the classes consists of one point and the other of three points.
+The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
+two different classes; one of the classes consists of one point and the other of three points.
 
-    @snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup1
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup1
+@end_toggle
 
-    The function @ref cv::ml::SVM::train that will be used afterwards requires the training data to be
-    stored as @ref cv::Mat objects of floats. Therefore, we create these objects from the arrays
-    defined above:
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java setup1
+@end_toggle
 
-    @snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup2
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py setup1
+@end_toggle
 
--#  **Set up SVM's parameters**
+The function @ref cv::ml::SVM::train that will be used afterwards requires the training data to be
+stored as @ref cv::Mat objects of floats. Therefore, we create these objects from the arrays
+defined above:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup2
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java setup2
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py setup1
+@end_toggle
+
+-   **Set up SVM's parameters**
 
     In this tutorial we have introduced the theory of SVMs in the most simple case, when the
     training examples are spread into two classes that are linearly separable. However, SVMs can be
@@ -123,35 +165,55 @@ Explanation
     we have to define some parameters before training the SVM. These parameters are stored in an
     object of the class @ref cv::ml::SVM.
 
-    @snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp init
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp init
+@end_toggle
 
-    Here:
-    -   *Type of SVM*. We choose here the type @ref cv::ml::SVM::C_SVC "C_SVC" that can be used for
-        n-class classification (n \f$\geq\f$ 2). The important feature of this type is that it deals
-        with imperfect separation of classes (i.e. when the training data is non-linearly separable).
-        This feature is not important here since the data is linearly separable and we chose this SVM
-        type only for being the most commonly used.
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java init
+@end_toggle
 
-    -   *Type of SVM kernel*. We have not talked about kernel functions since they are not
-        interesting for the training data we are dealing with. Nevertheless, let's explain briefly now
-        the main idea behind a kernel function. It is a mapping done to the training data to improve
-        its resemblance to a linearly separable set of data. This mapping consists of increasing the
-        dimensionality of the data and is done efficiently using a kernel function. We choose here the
-        type @ref cv::ml::SVM::LINEAR "LINEAR" which means that no mapping is done. This parameter is
-        defined using cv::ml::SVM::setKernel.
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py init
+@end_toggle
 
-    -   *Termination criteria of the algorithm*. The SVM training procedure is implemented solving a
-        constrained quadratic optimization problem in an **iterative** fashion. Here we specify a
-        maximum number of iterations and a tolerance error so we allow the algorithm to finish in
-        less number of steps even if the optimal hyperplane has not been computed yet. This
-        parameter is defined in a structure @ref cv::TermCriteria .
+Here:
+-   *Type of SVM*. We choose here the type @ref cv::ml::SVM::C_SVC "C_SVC" that can be used for
+    n-class classification (n \f$\geq\f$ 2). The important feature of this type is that it deals
+    with imperfect separation of classes (i.e. when the training data is non-linearly separable).
+    This feature is not important here since the data is linearly separable and we chose this SVM
+    type only for being the most commonly used.
 
--#  **Train the SVM**
+-   *Type of SVM kernel*. We have not talked about kernel functions since they are not
+    interesting for the training data we are dealing with. Nevertheless, let's explain briefly now
+    the main idea behind a kernel function. It is a mapping done to the training data to improve
+    its resemblance to a linearly separable set of data. This mapping consists of increasing the
+    dimensionality of the data and is done efficiently using a kernel function. We choose here the
+    type @ref cv::ml::SVM::LINEAR "LINEAR" which means that no mapping is done. This parameter is
+    defined using cv::ml::SVM::setKernel.
+
+-   *Termination criteria of the algorithm*. The SVM training procedure is implemented solving a
+    constrained quadratic optimization problem in an **iterative** fashion. Here we specify a
+    maximum number of iterations and a tolerance error so we allow the algorithm to finish in
+    less number of steps even if the optimal hyperplane has not been computed yet. This
+    parameter is defined in a structure @ref cv::TermCriteria .
+
+-   **Train the SVM**
     We call the method @ref cv::ml::SVM::train to build the SVM model.
 
-    @snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp train
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp train
+@end_toggle
 
--#  **Regions classified by the SVM**
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java train
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py train
+@end_toggle
+
+-   **Regions classified by the SVM**
 
     The method @ref cv::ml::SVM::predict is used to classify an input sample using a trained SVM. In
     this example we have used this method in order to color the space depending on the prediction done
@@ -159,16 +221,36 @@ Explanation
     Cartesian plane. Each of the points is colored depending on the class predicted by the SVM; in
     green if it is the class with label 1 and in blue if it is the class with label -1.
 
-    @snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show
+@end_toggle
 
--#  **Support vectors**
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java show
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py show
+@end_toggle
+
+-   **Support vectors**
 
     We use here a couple of methods to obtain information about the support vectors.
     The method @ref cv::ml::SVM::getSupportVectors obtain all of the support
     vectors. We have used this methods here to find the training examples that are
     support vectors and highlight them.
 
-    @snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show_vectors
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show_vectors
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java show_vectors
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py show_vectors
+@end_toggle
 
 Results
 -------

doc/tutorials/ml/non_linear_svms/non_linear_svms.markdown

@@ -92,81 +92,175 @@ You may also find the source code in `samples/cpp/tutorial_code/ml/non_linear_sv
 @note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code
 using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/non_linear_svms/non_linear_svms.html#nonlinearsvms)
 
-@include cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java)
+
+-   **Code at glance:**
+    @include samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py
+@end_toggle
 
 Explanation
 -----------
 
--#  __Set up the training data__
+-   __Set up the training data__
 
-    The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
-    two different classes. To make the exercise more appealing, the training data is generated
-    randomly using a uniform probability density functions (PDFs).
+The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
+two different classes. To make the exercise more appealing, the training data is generated
+randomly using a uniform probability density functions (PDFs).
 
-    We have divided the generation of the training data into two main parts.
+We have divided the generation of the training data into two main parts.
 
-    In the first part we generate data for both classes that is linearly separable.
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup1
+In the first part we generate data for both classes that is linearly separable.
 
-    In the second part we create data for both classes that is non-linearly separable, data that
-    overlaps.
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup2
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup1
+@end_toggle
 
--#  __Set up SVM's parameters__
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java setup1
+@end_toggle
 
-    @note In the previous tutorial @ref tutorial_introduction_to_svm there is an explanation of the
-    attributes of the class @ref cv::ml::SVM that we configure here before training the SVM.
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py setup1
+@end_toggle
 
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp init
+In the second part we create data for both classes that is non-linearly separable, data that
+overlaps.
 
-    There are just two differences between the configuration we do here and the one that was done in
-    the previous tutorial (@ref tutorial_introduction_to_svm) that we use as reference.
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup2
+@end_toggle
 
-    -   _C_. We chose here a small value of this parameter in order not to punish too much the
-        misclassification errors in the optimization. The idea of doing this stems from the will of
-        obtaining a solution close to the one intuitively expected. However, we recommend to get a
-        better insight of the problem by making adjustments to this parameter.
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java setup2
+@end_toggle
 
-        @note In this case there are just very few points in the overlapping region between classes.
-        By giving a smaller value to __FRAC_LINEAR_SEP__ the density of points can be incremented and the
-        impact of the parameter _C_ explored deeply.
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py setup2
+@end_toggle
 
-    -   _Termination Criteria of the algorithm_. The maximum number of iterations has to be
-        increased considerably in order to solve correctly a problem with non-linearly separable
-        training data. In particular, we have increased in five orders of magnitude this value.
+-   __Set up SVM's parameters__
 
--#  __Train the SVM__
+@note In the previous tutorial @ref tutorial_introduction_to_svm there is an explanation of the
+attributes of the class @ref cv::ml::SVM that we configure here before training the SVM.
 
-    We call the method @ref cv::ml::SVM::train to build the SVM model. Watch out that the training
-    process may take a quite long time. Have patiance when your run the program.
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp init
+@end_toggle
 
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp train
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java init
+@end_toggle
 
--#  __Show the Decision Regions__
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py init
+@end_toggle
 
-    The method @ref cv::ml::SVM::predict is used to classify an input sample using a trained SVM. In
-    this example we have used this method in order to color the space depending on the prediction done
-    by the SVM. In other words, an image is traversed interpreting its pixels as points of the
-    Cartesian plane. Each of the points is colored depending on the class predicted by the SVM; in
-    dark green if it is the class with label 1 and in dark blue if it is the class with label 2.
+There are just two differences between the configuration we do here and the one that was done in
+the previous tutorial (@ref tutorial_introduction_to_svm) that we use as reference.
 
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show
+-   _C_. We chose here a small value of this parameter in order not to punish too much the
+    misclassification errors in the optimization. The idea of doing this stems from the will of
+    obtaining a solution close to the one intuitively expected. However, we recommend to get a
+    better insight of the problem by making adjustments to this parameter.
 
--#  __Show the training data__
+    @note In this case there are just very few points in the overlapping region between classes.
+    By giving a smaller value to __FRAC_LINEAR_SEP__ the density of points can be incremented and the
+    impact of the parameter _C_ explored deeply.
 
-    The method @ref cv::circle is used to show the samples that compose the training data. The samples
-    of the class labeled with 1 are shown in light green and in light blue the samples of the class
-    labeled with 2.
+-   _Termination Criteria of the algorithm_. The maximum number of iterations has to be
+    increased considerably in order to solve correctly a problem with non-linearly separable
+    training data. In particular, we have increased in five orders of magnitude this value.
 
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_data
+-   __Train the SVM__
 
--#  __Support vectors__
+We call the method @ref cv::ml::SVM::train to build the SVM model. Watch out that the training
+process may take a quite long time. Have patiance when your run the program.
 
-    We use here a couple of methods to obtain information about the support vectors. The method
-    @ref cv::ml::SVM::getSupportVectors obtain all support vectors. We have used this methods here
-    to find the training examples that are support vectors and highlight them.
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp train
+@end_toggle
 
-    @snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_vectors
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java train
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py train
+@end_toggle
+
+-   __Show the Decision Regions__
+
+The method @ref cv::ml::SVM::predict is used to classify an input sample using a trained SVM. In
+this example we have used this method in order to color the space depending on the prediction done
+by the SVM. In other words, an image is traversed interpreting its pixels as points of the
+Cartesian plane. Each of the points is colored depending on the class predicted by the SVM; in
+dark green if it is the class with label 1 and in dark blue if it is the class with label 2.
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java show
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py show
+@end_toggle
+
+-   __Show the training data__
+
+The method @ref cv::circle is used to show the samples that compose the training data. The samples
+of the class labeled with 1 are shown in light green and in light blue the samples of the class
+labeled with 2.
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_data
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java show_data
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py show_data
+@end_toggle
+
+-   __Support vectors__
+
+We use here a couple of methods to obtain information about the support vectors. The method
+@ref cv::ml::SVM::getSupportVectors obtain all support vectors. We have used this methods here
+to find the training examples that are support vectors and highlight them.
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_vectors
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java show_vectors
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py show_vectors
+@end_toggle
 
 Results
 -------

doc/tutorials/ml/table_of_content_ml.markdown

@@ -6,6 +6,8 @@ of data.
 
 -   @subpage tutorial_introduction_to_svm
 
+    *Languages:* C++, Java, Python
+
     *Compatibility:* \> OpenCV 2.0
 
     *Author:* Fernando Iglesias García
@@ -14,6 +16,8 @@ of data.
 
 -   @subpage tutorial_non_linear_svms
 
+    *Languages:* C++, Java, Python
+
     *Compatibility:* \> OpenCV 2.0
 
     *Author:* Fernando Iglesias García
@@ -23,6 +27,8 @@ of data.
 
 -   @subpage tutorial_introduction_to_pca
 
+    *Languages:* C++, Java, Python
+
     *Compatibility:* \> OpenCV 2.0
 
     *Author:* Theodore Tsesmelis

doc/tutorials/stitching/stitcher/stitcher.markdown

@@ -24,17 +24,7 @@ Explanation
 
 The most important code part is:
 
-@code{.cpp}
-Mat pano;
-Ptr<Stitcher> stitcher = Stitcher::create(mode, try_use_gpu);
-Stitcher::Status status = stitcher->stitch(imgs, pano);
-
-if (status != Stitcher::OK)
-{
-    cout << "Can't stitch images, error code = " << int(status) << endl;
-    return -1;
-}
-@endcode
+@snippet cpp/stitching.cpp stitching
 
 A new instance of stitcher is created and the @ref cv::Stitcher::stitch will
 do all the hard work.

doc/tutorials/tutorials.markdown

@@ -15,7 +15,7 @@ As always, we would be happy to hear your comments and receive your contribution
 -   @subpage tutorial_table_of_content_core
 
     Here you will learn
-    the about the basic building blocks of this library. A must read for understanding how
+    about the basic building blocks of this library. A must read for understanding how
     to manipulate the images on a pixel level.
 
 -   @subpage tutorial_table_of_content_imgproc

modules/calib3d/include/opencv2/calib3d.hpp

@@ -118,7 +118,7 @@ v = f_y*y'' + c_y
 tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion
 coefficients. Higher-order coefficients are not considered in OpenCV.
 
-The next figure shows two common types of radial distortion: barrel distortion (typically \f$ k_1 > 0 \f$ and pincushion distortion (typically \f$ k_1 < 0 \f$).
+The next figure shows two common types of radial distortion: barrel distortion (typically \f$ k_1 > 0 \f$) and pincushion distortion (typically \f$ k_1 < 0 \f$).
 
 ![](pics/distortion_examples.png)
 
@@ -307,11 +307,11 @@ optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .
  */
 CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() );
 
-/** @example pose_from_homography.cpp
-  An example program about pose estimation from coplanar points
+/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp
+An example program about pose estimation from coplanar points
 
-  Check @ref tutorial_homography "the corresponding tutorial" for more details
- */
+Check @ref tutorial_homography "the corresponding tutorial" for more details
+*/
 
 /** @brief Finds a perspective transformation between two planes.
 
@@ -526,11 +526,11 @@ CV_EXPORTS_W void projectPoints( InputArray objectPoints,
                                  OutputArray jacobian = noArray(),
                                  double aspectRatio = 0 );
 
-/** @example homography_from_camera_displacement.cpp
-  An example program about homography from the camera displacement
+/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp
+An example program about homography from the camera displacement
 
-  Check @ref tutorial_homography "the corresponding tutorial" for more details
- */
+Check @ref tutorial_homography "the corresponding tutorial" for more details
+*/
 
 /** @brief Finds an object pose from 3D-2D point correspondences.
 
@@ -1966,11 +1966,11 @@ CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, Out
                                   size_t maxIters = 2000, double confidence = 0.99,
                                   size_t refineIters = 10);
 
-/** @example decompose_homography.cpp
-  An example program with homography decomposition.
+/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp
+An example program with homography decomposition.
 
-  Check @ref tutorial_homography "the corresponding tutorial" for more details.
- */
+Check @ref tutorial_homography "the corresponding tutorial" for more details.
+*/
 
 /** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
 

modules/calib3d/misc/java/gen_dict.json

@@ -18,6 +18,9 @@
             ]
         }
     },
+    "namespaces_dict": {
+        "cv.fisheye": "fisheye"
+    },
     "func_arg_fix" : {
         "findFundamentalMat"  : { "points1" : {"ctype" : "vector_Point2f"},
                                   "points2" : {"ctype" : "vector_Point2f"} },

modules/calib3d/src/calib3d_c_api.cpp

@@ -0,0 +1,47 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// This file contains wrappers for legacy OpenCV C API
+
+#include "precomp.hpp"
+#include "opencv2/calib3d/calib3d_c.h"
+
+using namespace cv;
+
+CV_IMPL void
+cvDrawChessboardCorners(CvArr* _image, CvSize pattern_size,
+                        CvPoint2D32f* corners, int count, int found)
+{
+    CV_Assert(corners != NULL); //CV_CheckNULL(corners, "NULL is not allowed for 'corners' parameter");
+    Mat image = cvarrToMat(_image);
+    CV_StaticAssert(sizeof(CvPoint2D32f) == sizeof(Point2f), "");
+    drawChessboardCorners(image, pattern_size, Mat(1, count, traits::Type<Point2f>::value, corners), found != 0);
+}
+
+CV_IMPL int
+cvFindChessboardCorners(const void* arr, CvSize pattern_size,
+                        CvPoint2D32f* out_corners_, int* out_corner_count,
+                        int flags)
+{
+    if (!out_corners_)
+        CV_Error( CV_StsNullPtr, "Null pointer to corners" );
+
+    Mat image = cvarrToMat(arr);
+    std::vector<Point2f> out_corners;
+
+    if (out_corner_count)
+        *out_corner_count = 0;
+
+    bool res = cv::findChessboardCorners(image, pattern_size, out_corners, flags);
+
+    int corner_count = (int)out_corners.size();
+    if (out_corner_count)
+        *out_corner_count = corner_count;
+    CV_CheckLE(corner_count, Size(pattern_size).area(), "Unexpected number of corners");
+    for (int i = 0; i < corner_count; ++i)
+    {
+        out_corners_[i] = cvPoint2D32f(out_corners[i]);
+    }
+    return res ? 1 : 0;
+}

modules/calib3d/src/calibration.cpp

@@ -2336,10 +2336,13 @@ void cvStereoRectify( const CvMat* _cameraMatrix1, const CvMat* _cameraMatrix2,
     _uu[2] = 1;
     cvCrossProduct(&uu, &t, &ww);
     nt = cvNorm(&t, 0, CV_L2);
+    CV_Assert(fabs(nt) > 0);
     nw = cvNorm(&ww, 0, CV_L2);
+    CV_Assert(fabs(nw) > 0);
     cvConvertScale(&ww, &ww, 1 / nw);
     cvCrossProduct(&t, &ww, &w3);
     nw = cvNorm(&w3, 0, CV_L2);
+    CV_Assert(fabs(nw) > 0);
     cvConvertScale(&w3, &w3, 1 / nw);
     _uu[2] = 0;
 
@@ -3159,6 +3162,10 @@ static void collectCalibrationData( InputArrayOfArrays objectPoints,
     Point3f* objPtData = objPtMat.ptr<Point3f>();
     Point2f* imgPtData1 = imgPtMat1.ptr<Point2f>();
 
+#if defined __GNUC__ && __GNUC__ >= 8
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wclass-memaccess"
+#endif
     for( i = 0; i < nimages; i++, j += ni )
     {
         Mat objpt = objectPoints.getMat(i);
@@ -3176,6 +3183,9 @@ static void collectCalibrationData( InputArrayOfArrays objectPoints,
             memcpy( imgPtData2 + j, imgpt2.ptr(), ni*sizeof(imgPtData2[0]) );
         }
     }
+#if defined __GNUC__ && __GNUC__ >= 8
+#pragma GCC diagnostic pop
+#endif
 }
 
 static Mat prepareCameraMatrix(Mat& cameraMatrix0, int rtype)
@@ -3870,12 +3880,14 @@ float cv::rectify3Collinear( InputArray _cameraMatrix1, InputArray _distCoeffs1,
 
     int idx = fabs(t12(0,0)) > fabs(t12(1,0)) ? 0 : 1;
     double c = t12(idx,0), nt = norm(t12, CV_L2);
+    CV_Assert(fabs(nt) > 0);
     Mat_<double> uu = Mat_<double>::zeros(3,1);
     uu(idx, 0) = c > 0 ? 1 : -1;
 
     // calculate global Z rotation
     Mat_<double> ww = t12.cross(uu), wR;
     double nw = norm(ww, CV_L2);
+    CV_Assert(fabs(nw) > 0);
     ww *= acos(fabs(c)/nt)/nw;
     Rodrigues(ww, wR);
 

modules/calib3d/src/circlesgrid.cpp

@@ -224,7 +224,7 @@ void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f>
   CV_Assert(!corners.empty());
   outsideCorners.clear();
   //find two pairs of the most nearest corners
-  int i, j, n = (int)corners.size();
+  const size_t n = corners.size();
 
 #ifdef DEBUG_CIRCLES
   Mat cornersImage(1024, 1248, CV_8UC1, Scalar(0));
@@ -232,22 +232,22 @@ void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f>
   imshow("corners", cornersImage);
 #endif
 
-  std::vector<Point2f> tangentVectors(corners.size());
-  for(size_t k=0; k<corners.size(); k++)
+  std::vector<Point2f> tangentVectors(n);
+  for(size_t k=0; k < n; k++)
   {
-    Point2f diff = corners[(k + 1) % corners.size()] - corners[k];
+    Point2f diff = corners[(k + 1) % n] - corners[k];
     tangentVectors[k] = diff * (1.0f / norm(diff));
   }
 
   //compute angles between all sides
-  Mat cosAngles(n, n, CV_32FC1, 0.0f);
-  for(i = 0; i < n; i++)
+  Mat cosAngles((int)n, (int)n, CV_32FC1, 0.0f);
+  for(size_t i = 0; i < n; i++)
   {
-    for(j = i + 1; j < n; j++)
+    for(size_t j = i + 1; j < n; j++)
     {
       float val = fabs(tangentVectors[i].dot(tangentVectors[j]));
-      cosAngles.at<float>(i, j) = val;
-      cosAngles.at<float>(j, i) = val;
+      cosAngles.at<float>((int)i, (int)j) = val;
+      cosAngles.at<float>((int)j, (int)i) = val;
     }
   }
 
@@ -276,10 +276,10 @@ void CirclesGridClusterFinder::findOutsideCorners(const std::vector<cv::Point2f>
   const int bigDiff = 4;
   if(maxIdx - minIdx == bigDiff)
   {
-    minIdx += n;
+    minIdx += (int)n;
     std::swap(maxIdx, minIdx);
   }
-  if(maxIdx - minIdx != n - bigDiff)
+  if(maxIdx - minIdx != (int)n - bigDiff)
   {
     return;
   }

modules/calib3d/src/dls.cpp

@@ -206,6 +206,7 @@ void dls::run_kernel(const cv::Mat& pp)
 
 void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D)
 {
+    CV_Assert(!pp.empty() && N > 0);
     cv::Mat eye = cv::Mat::eye(3, 3, CV_64F);
 
     // build coeff matrix

modules/calib3d/src/fisheye.cpp

@@ -126,7 +126,8 @@ void cv::fisheye::projectPoints(InputArray objectPoints, OutputArray imagePoints
     {
         Vec3d Xi = objectPoints.depth() == CV_32F ? (Vec3d)Xf[i] : Xd[i];
         Vec3d Y = aff*Xi;
-
+        if (fabs(Y[2]) < DBL_MIN)
+            Y[2] = 1;
         Vec2d x(Y[0]/Y[2], Y[1]/Y[2]);
 
         double r2 = x.dot(x);
@@ -1186,6 +1187,7 @@ void cv::internal::ComputeExtrinsicRefine(const Mat& imagePoints, const Mat& obj
 {
     CV_Assert(!objectPoints.empty() && objectPoints.type() == CV_64FC3);
     CV_Assert(!imagePoints.empty() && imagePoints.type() == CV_64FC2);
+    CV_Assert(rvec.total() > 2 && tvec.total() > 2);
     Vec6d extrinsics(rvec.at<double>(0), rvec.at<double>(1), rvec.at<double>(2),
                     tvec.at<double>(0), tvec.at<double>(1), tvec.at<double>(2));
     double change = 1;
@@ -1365,9 +1367,13 @@ void cv::internal::InitExtrinsics(const Mat& _imagePoints, const Mat& _objectPoi
     double sc = .5 * (norm(H.col(0)) + norm(H.col(1)));
     H = H / sc;
     Mat u1 = H.col(0).clone();
-    u1  = u1 / norm(u1);
+    double norm_u1 = norm(u1);
+    CV_Assert(fabs(norm_u1) > 0);
+    u1  = u1 / norm_u1;
     Mat u2 = H.col(1).clone() - u1.dot(H.col(1).clone()) * u1;
-    u2 = u2 / norm(u2);
+    double norm_u2 = norm(u2);
+    CV_Assert(fabs(norm_u2) > 0);
+    u2 = u2 / norm_u2;
     Mat u3 = u1.cross(u2);
     Mat RRR;
     hconcat(u1, u2, RRR);

modules/calib3d/src/homography_decomp.cpp

@@ -194,6 +194,7 @@ void HomographyDecompZhang::decompose(std::vector<CameraMotion>& camMotions)
 {
     Mat W, U, Vt;
     SVD::compute(getHnorm(), W, U, Vt);
+    CV_Assert(W.total() > 2 && Vt.total() > 7);
     double lambda1=W.at<double>(0);
     double lambda3=W.at<double>(2);
     double lambda1m3 =  (lambda1-lambda3);

modules/calib3d/src/precomp.hpp

@@ -42,13 +42,15 @@
 #ifndef __OPENCV_PRECOMP_H__
 #define __OPENCV_PRECOMP_H__
 
-#include "opencv2/calib3d.hpp"
-#include "opencv2/imgproc.hpp"
-#include "opencv2/features2d.hpp"
 #include "opencv2/core/utility.hpp"
 
 #include "opencv2/core/private.hpp"
 
+#include "opencv2/calib3d.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/features2d.hpp"
+
+
 #include "opencv2/core/ocl.hpp"
 
 #ifdef HAVE_TEGRA_OPTIMIZATION

modules/calib3d/src/ptsetreg.cpp

@@ -104,7 +104,7 @@ public:
                     int maxAttempts=1000 ) const
     {
         cv::AutoBuffer<int> _idx(modelPoints);
-        int* idx = _idx;
+        int* idx = _idx.data();
         int i = 0, j, k, iters = 0;
         int d1 = m1.channels() > 1 ? m1.channels() : m1.cols;
         int d2 = m2.channels() > 1 ? m2.channels() : m2.cols;

modules/calib3d/src/stereosgbm.cpp

@@ -2451,7 +2451,7 @@ void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDis
     int minD = minDisparity, maxD = minDisparity + numberOfDisparities;
     int x, minX1 = std::max(maxD, 0), maxX1 = cols + std::min(minD, 0);
     AutoBuffer<int> _disp2buf(cols*2);
-    int* disp2buf = _disp2buf;
+    int* disp2buf = _disp2buf.data();
     int* disp2cost = disp2buf + cols;
     const int DISP_SHIFT = 4, DISP_SCALE = 1 << DISP_SHIFT;
     int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE;

modules/calib3d/test/test_cameracalibration.cpp

@@ -1618,7 +1618,8 @@ void CV_StereoCalibrationTest::run( int )
             bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]);
             if(!found1 || !found2)
             {
-                ts->printf( cvtest::TS::LOG, "The function could not detect boards on the images %s and %s, testcase %d\n",
+                ts->printf( cvtest::TS::LOG, "The function could not detect boards (%d x %d) on the images %s and %s, testcase %d\n",
+                    patternSize.width, patternSize.height,
                     imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase );
                 ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
                 return;

modules/calib3d/test/test_cameracalibration_badarg.cpp

@@ -489,7 +489,14 @@ protected:
     void run(int /* start_from */ )
     {
         CvMat zeros;
+#if defined __GNUC__ && __GNUC__ >= 8
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wclass-memaccess"
+#endif
         memset(&zeros, 0, sizeof(zeros));
+#if defined __GNUC__ && __GNUC__ >= 8
+#pragma GCC diagnostic pop
+#endif
 
         C_Caller caller, bad_caller;
         CvMat objectPoints_c, r_vec_c, t_vec_c, A_c, distCoeffs_c, imagePoints_c,

modules/calib3d/test/test_chesscorners.cpp

@@ -198,7 +198,7 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
 
     if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 )
     {
-        ts->printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() );
+        ts->printf( cvtest::TS::LOG, "%s can not be read or is not valid\n", (folder + filename).c_str() );
         ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n",
             fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2);
         ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
@@ -334,19 +334,19 @@ bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
 
             tmp = cv::norm(cur - mat(i + 1, j + 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
 
             tmp = cv::norm(cur - mat(i - 1, j + 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
 
             tmp = cv::norm(cur - mat(i + 1, j - 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
 
             tmp = cv::norm(cur - mat(i - 1, j - 1)); // TODO cvtest
             if (tmp < minNeibDist)
-                tmp = minNeibDist;
+                minNeibDist = tmp;
         }
 
     const double threshold = 0.25;

modules/calib3d/test/test_chesscorners_timing.cpp

@@ -85,7 +85,7 @@ void CV_ChessboardDetectorTimingTest::run( int start_from )
     if( !fs || !board_list || !CV_NODE_IS_SEQ(board_list->tag) ||
         board_list->data.seq->total % 4 != 0 )
     {
-        ts->printf( cvtest::TS::LOG, "chessboard_timing_list.dat can not be readed or is not valid" );
+        ts->printf( cvtest::TS::LOG, "chessboard_timing_list.dat can not be read or is not valid" );
         code = cvtest::TS::FAIL_MISSING_TEST_DATA;
         goto _exit_;
     }

modules/core/CMakeLists.txt

@@ -3,6 +3,10 @@ set(the_description "The Core Functionality")
 ocv_add_dispatched_file(mathfuncs_core SSE2 AVX AVX2)
 ocv_add_dispatched_file(stat SSE4_2 AVX2)
 
+# dispatching for accuracy tests
+ocv_add_dispatched_file_force_all(test_intrin128 TEST SSE2 SSE3 SSSE3 SSE4_1 SSE4_2 AVX FP16 AVX2)
+ocv_add_dispatched_file_force_all(test_intrin256 TEST AVX2)
+
 ocv_add_module(core
                OPTIONAL opencv_cudev
                WRAP java python js)
@@ -29,9 +33,14 @@ if(CV_TRACE AND HAVE_ITT AND BUILD_ITT)
   add_definitions(-DOPENCV_WITH_ITT=1)
 endif()
 
-file(GLOB lib_cuda_hdrs        "include/opencv2/${name}/cuda/*.hpp"        "include/opencv2/${name}/cuda/*.h")
-file(GLOB lib_cuda_hdrs_detail "include/opencv2/${name}/cuda/detail/*.hpp" "include/opencv2/${name}/cuda/detail/*.h")
-file(GLOB_RECURSE module_opencl_hdrs "include/opencv2/${name}/opencl/*")
+file(GLOB lib_cuda_hdrs
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/*.hpp"
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/*.h")
+file(GLOB lib_cuda_hdrs_detail
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/detail/*.hpp"
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/cuda/detail/*.h")
+file(GLOB_RECURSE module_opencl_hdrs
+    "${CMAKE_CURRENT_LIST_DIR}/include/opencv2/${name}/opencl/*")
 
 source_group("Include\\Cuda Headers"         FILES ${lib_cuda_hdrs})
 source_group("Include\\Cuda Headers\\Detail" FILES ${lib_cuda_hdrs_detail})

modules/core/include/opencv2/core.hpp

@@ -273,9 +273,11 @@ of p and len.
 */
 CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType);
 
-/** @example copyMakeBorder_demo.cpp
-An example using copyMakeBorder function
- */
+/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
+An example using copyMakeBorder function.
+Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details
+*/
+
 /** @brief Forms a border around an image.
 
 The function copies the source image into the middle of the destination image. The areas to the
@@ -474,9 +476,10 @@ The function can also be emulated with a matrix expression, for example:
 */
 CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
 
-/** @example AddingImagesTrackbar.cpp
+/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
+Check @ref tutorial_trackbar "the corresponding tutorial" for more details
+*/
 
- */
 /** @brief Calculates the weighted sum of two arrays.
 
 The function addWeighted calculates the weighted sum of two arrays as follows:
@@ -1978,10 +1981,20 @@ CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar,
 CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
                              OutputArray eigenvectors, int maxComponents = 0);
 
+/** wrap PCA::operator() and add eigenvalues output parameter */
+CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean,
+                                           OutputArray eigenvectors, OutputArray eigenvalues,
+                                           int maxComponents = 0);
+
 /** wrap PCA::operator() */
 CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
                              OutputArray eigenvectors, double retainedVariance);
 
+/** wrap PCA::operator() and add eigenvalues output parameter */
+CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean,
+                                           OutputArray eigenvectors, OutputArray eigenvalues,
+                                           double retainedVariance);
+
 /** wrap PCA::project */
 CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean,
                              InputArray eigenvectors, OutputArray result);
@@ -2517,14 +2530,18 @@ public:
     Mat mean; //!< mean value subtracted before the projection and added after the back projection
 };
 
-/** @example pca.cpp
-  An example using %PCA for dimensionality reduction while maintaining an amount of variance
- */
+/** @example samples/cpp/pca.cpp
+An example using %PCA for dimensionality reduction while maintaining an amount of variance
+*/
+
+/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
+Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details
+*/
 
 /**
-   @brief Linear Discriminant Analysis
-   @todo document this class
- */
+@brief Linear Discriminant Analysis
+@todo document this class
+*/
 class CV_EXPORTS LDA
 {
 public:
@@ -2840,7 +2857,7 @@ public:
     use explicit type cast operators, as in the a1 initialization above.
     @param a lower inclusive boundary of the returned random number.
     @param b upper non-inclusive boundary of the returned random number.
-      */
+    */
     int uniform(int a, int b);
     /** @overload */
     float uniform(float a, float b);
@@ -2902,7 +2919,7 @@ public:
 
 Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
 @todo document
- */
+*/
 class CV_EXPORTS RNG_MT19937
 {
 public:
@@ -2920,17 +2937,11 @@ public:
     unsigned operator ()(unsigned N);
     unsigned operator ()();
 
-    /** @brief returns uniformly distributed integer random number from [a,b) range
-
-*/
+    /** @brief returns uniformly distributed integer random number from [a,b) range*/
     int uniform(int a, int b);
-    /** @brief returns uniformly distributed floating-point random number from [a,b) range
-
-*/
+    /** @brief returns uniformly distributed floating-point random number from [a,b) range*/
     float uniform(float a, float b);
-    /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range
-
-*/
+    /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/
     double uniform(double a, double b);
 
 private:
@@ -2944,8 +2955,8 @@ private:
 //! @addtogroup core_cluster
 //!  @{
 
-/** @example kmeans.cpp
-  An example on K-means clustering
+/** @example samples/cpp/kmeans.cpp
+An example on K-means clustering
 */
 
 /** @brief Finds centers of clusters and groups input samples around the clusters.
@@ -3057,7 +3068,7 @@ etc.).
 
 Here is example of SimpleBlobDetector use in your application via Algorithm interface:
 @snippet snippets/core_various.cpp Algorithm
- */
+*/
 class CV_EXPORTS_W Algorithm
 {
 public:
@@ -3073,8 +3084,8 @@ public:
     virtual void write(FileStorage& fs) const { (void)fs; }
 
     /** @brief simplified API for language bindings
-     * @overload
-     */
+    * @overload
+    */
     CV_WRAP void write(const Ptr<FileStorage>& fs, const String& name = String()) const;
 
     /** @brief Reads algorithm parameters from a file storage
@@ -3082,20 +3093,20 @@ public:
     CV_WRAP virtual void read(const FileNode& fn) { (void)fn; }
 
     /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
-     */
+    */
     CV_WRAP virtual bool empty() const { return false; }
 
     /** @brief Reads algorithm from the file node
 
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
-     cv::FileStorage fsRead("example.xml", FileStorage::READ);
-     Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
-     @endcode
-     In order to make this method work, the derived class must overwrite Algorithm::read(const
-     FileNode& fn) and also have static create() method without parameters
-     (or with all the optional parameters)
-     */
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    @code
+    cv::FileStorage fsRead("example.xml", FileStorage::READ);
+    Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
+    @endcode
+    In order to make this method work, the derived class must overwrite Algorithm::read(const
+    FileNode& fn) and also have static create() method without parameters
+    (or with all the optional parameters)
+    */
     template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
     {
         Ptr<_Tp> obj = _Tp::create();
@@ -3105,16 +3116,16 @@ public:
 
     /** @brief Loads algorithm from the file
 
-     @param filename Name of the file to read.
-     @param objname The optional name of the node to read (if empty, the first top-level node will be used)
+    @param filename Name of the file to read.
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used)
 
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
-     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
-     @endcode
-     In order to make this method work, the derived class must overwrite Algorithm::read(const
-     FileNode& fn).
-     */
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    @code
+    Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
+    @endcode
+    In order to make this method work, the derived class must overwrite Algorithm::read(const
+    FileNode& fn).
+    */
     template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
     {
         FileStorage fs(filename, FileStorage::READ);
@@ -3128,14 +3139,14 @@ public:
 
     /** @brief Loads algorithm from a String
 
-     @param strModel The string variable containing the model you want to load.
-     @param objname The optional name of the node to read (if empty, the first top-level node will be used)
+    @param strModel The string variable containing the model you want to load.
+    @param objname The optional name of the node to read (if empty, the first top-level node will be used)
 
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
-     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
-     @endcode
-     */
+    This is static template method of Algorithm. It's usage is following (in the case of SVM):
+    @code
+    Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
+    @endcode
+    */
     template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
     {
         FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
@@ -3146,11 +3157,11 @@ public:
     }
 
     /** Saves the algorithm to a file.
-     In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
+    In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
     CV_WRAP virtual void save(const String& filename) const;
 
     /** Returns the algorithm string identifier.
-     This string is used as top level xml/yml node tag when the object is saved to a file or string. */
+    This string is used as top level xml/yml node tag when the object is saved to a file or string. */
     CV_WRAP virtual String getDefaultName() const;
 
 protected:

modules/core/include/opencv2/core/base.hpp

@@ -414,7 +414,7 @@ CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const ch
 // We need to use simplified definition for them.
 #define CV_Error(...) do { abort(); } while (0)
 #define CV_Error_( code, args ) do { cv::format args; abort(); } while (0)
-#define CV_Assert_1( expr ) do { if (!(expr)) abort(); } while (0)
+#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0)
 
 #else // CV_STATIC_ANALYSIS
 
@@ -444,7 +444,13 @@ for example:
 */
 #define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
 
-#define CV_Assert_1( expr ) if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
+/** @brief Checks a condition at runtime and throws exception if it fails
+
+The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
+raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
+configurations while CV_DbgAssert is only retained in the Debug configuration.
+*/
+#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
 
 //! @cond IGNORED
 #define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ )
@@ -454,8 +460,8 @@ for example:
 #define CV_Error CV__ErrorNoReturn
 #undef CV_Error_
 #define CV_Error_ CV__ErrorNoReturn_
-#undef CV_Assert_1
-#define CV_Assert_1( expr ) if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
+#undef CV_Assert
+#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
 #else
 // backward compatibility
 #define CV_ErrorNoReturn CV__ErrorNoReturn
@@ -465,6 +471,18 @@ for example:
 
 #endif // CV_STATIC_ANALYSIS
 
+//! @cond IGNORED
+
+#if defined OPENCV_FORCE_MULTIARG_ASSERT_CHECK && defined CV_STATIC_ANALYSIS
+#warning "OPENCV_FORCE_MULTIARG_ASSERT_CHECK can't be used with CV_STATIC_ANALYSIS"
+#undef OPENCV_FORCE_MULTIARG_ASSERT_CHECK
+#endif
+
+#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK
+#define CV_Assert_1( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
+#else
+#define CV_Assert_1 CV_Assert
+#endif
 #define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2)
 #define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3)
 #define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4)
@@ -475,21 +493,18 @@ for example:
 #define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9)
 #define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10)
 
-#define CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
-#define CV_VA_NUM_ARGS(...) CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
+#define CV_Assert_N(...) do { __CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0)
 
-/** @brief Checks a condition at runtime and throws exception if it fails
+#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK
+#undef CV_Assert
+#define CV_Assert CV_Assert_N
+#endif
+//! @endcond
 
-The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
-raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
-configurations while CV_DbgAssert is only retained in the Debug configuration.
-*/
-#define CV_Assert(...) do { CVAUX_CONCAT(CV_Assert_, CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0)
-
-/** replaced with CV_Assert(expr) in Debug configuration */
-#ifdef _DEBUG
+#if defined _DEBUG || defined CV_STATIC_ANALYSIS
 #  define CV_DbgAssert(expr) CV_Assert(expr)
 #else
+/** replaced with CV_Assert(expr) in Debug configuration */
 #  define CV_DbgAssert(expr)
 #endif
 

modules/core/include/opencv2/core/check.hpp

@@ -66,6 +66,7 @@ struct CheckContext {
             { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str }
 
 CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx);
@@ -73,6 +74,7 @@ CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, con
 CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx);
 
 CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx);
+CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx);
@@ -120,15 +122,35 @@ CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckCon
 
 #define CV_CheckChannelsEQ(c1, c2, msg)  CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg)
 
-
 /// Example: type == CV_8UC1 || type == CV_8UC3
 #define CV_CheckType(t, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg)
 
 /// Example: depth == CV_32F || depth == CV_64F
 #define CV_CheckDepth(t, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg)
 
+/// Example: v == A || v == B
+#define CV_Check(v, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
+
 /// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1")
-// TODO define pretty-printers: #define CV_Check(v, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
+// TODO define pretty-printers
+
+#ifndef NDEBUG
+#define CV_DbgCheck(v, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
+#define CV_DbgCheckEQ(v1, v2, msg)  CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckNE(v1, v2, msg)  CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckLE(v1, v2, msg)  CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckLT(v1, v2, msg)  CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckGE(v1, v2, msg)  CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
+#define CV_DbgCheckGT(v1, v2, msg)  CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
+#else
+#define CV_DbgCheck(v, test_expr, msg)  do { } while (0)
+#define CV_DbgCheckEQ(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckNE(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckLE(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckLT(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckGE(v1, v2, msg)  do { } while (0)
+#define CV_DbgCheckGT(v1, v2, msg)  do { } while (0)
+#endif
 
 } // namespace
 

modules/core/include/opencv2/core/cvdef.h

@@ -79,6 +79,8 @@ namespace cv { namespace debug_build_guard { } using namespace debug_build_guard
 #define __CV_CAT(x, y) __CV_CAT_(x, y)
 #endif
 
+#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
+#define __CV_VA_NUM_ARGS(...) __CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
 
 // undef problematic defines sometimes defined by system headers (windows.h in particular)
 #undef small
@@ -255,6 +257,7 @@ Cv64suf;
 
 #ifdef __OPENCV_BUILD
 #  define DISABLE_OPENCV_24_COMPATIBILITY
+#  define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY
 #endif
 
 #ifdef CVAPI_EXPORTS
@@ -346,7 +349,13 @@ Cv64suf;
 // We need to use simplified definition for them.
 #ifndef CV_STATIC_ANALYSIS
 # if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__)
-#   define CV_STATIC_ANALYSIS
+#   define CV_STATIC_ANALYSIS 1
+# endif
+#else
+# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1)  // defined and not empty
+#   if 0 == CV_STATIC_ANALYSIS
+#     undef CV_STATIC_ANALYSIS
+#   endif
 # endif
 #endif
 
@@ -405,6 +414,24 @@ Cv64suf;
 #endif
 
 
+/****************************************************************************************\
+*                                  CV_NODISCARD attribute                                *
+* encourages the compiler to issue a warning if the return value is discarded (C++17)    *
+\****************************************************************************************/
+#ifndef CV_NODISCARD
+#  if defined(__GNUC__)
+#    define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4
+#  elif defined(__clang__) && defined(__has_attribute)
+#    if __has_attribute(__warn_unused_result__)
+#      define CV_NODISCARD __attribute__((__warn_unused_result__))
+#    endif
+#  endif
+#endif
+#ifndef CV_NODISCARD
+#  define CV_NODISCARD /* nothing by default */
+#endif
+
+
 /****************************************************************************************\
 *                                    C++ 11                                              *
 \****************************************************************************************/

modules/core/include/opencv2/core/cvstd.inl.hpp

@@ -269,6 +269,11 @@ static inline std::ostream& operator << (std::ostream& out, const MatSize& msize
     return out;
 }
 
+static inline std::ostream &operator<< (std::ostream &s, cv::Range &r)
+{
+    return s << "[" << r.start << " : " << r.end << ")";
+}
+
 } // cv
 
 #ifdef _MSC_VER

modules/core/include/opencv2/core/hal/intrin.hpp

@@ -60,255 +60,83 @@
 // access from within opencv code more accessible
 namespace cv {
 
+namespace hal {
+
+enum StoreMode
+{
+    STORE_UNALIGNED = 0,
+    STORE_ALIGNED = 1,
+    STORE_ALIGNED_NOCACHE = 2
+};
+
+}
+
+template<typename _Tp> struct V_TypeTraits
+{
+};
+
+#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_, nlanes128_) \
+    template<> struct V_TypeTraits<type> \
+    { \
+        typedef type value_type; \
+        typedef int_type_ int_type; \
+        typedef abs_type_ abs_type; \
+        typedef uint_type_ uint_type; \
+        typedef w_type_ w_type; \
+        typedef q_type_ q_type; \
+        typedef sum_type_ sum_type; \
+        enum { nlanes128 = nlanes128_ }; \
+    \
+        static inline int_type reinterpret_int(type x) \
+        { \
+            union { type l; int_type i; } v; \
+            v.l = x; \
+            return v.i; \
+        } \
+    \
+        static inline type reinterpret_from_int(int_type x) \
+        { \
+            union { type l; int_type i; } v; \
+            v.i = x; \
+            return v.l; \
+        } \
+    }
+
+CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned, 16);
+CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int, 16);
+CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned, 8);
+CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int, 8);
+CV_INTRIN_DEF_TYPE_TRAITS(unsigned, int, unsigned, unsigned, uint64, void, unsigned, 4);
+CV_INTRIN_DEF_TYPE_TRAITS(int, int, unsigned, unsigned, int64, void, int, 4);
+CV_INTRIN_DEF_TYPE_TRAITS(float, int, unsigned, float, double, void, float, 4);
+CV_INTRIN_DEF_TYPE_TRAITS(uint64, int64, uint64, uint64, void, void, uint64, 2);
+CV_INTRIN_DEF_TYPE_TRAITS(int64, int64, uint64, uint64, void, void, int64, 2);
+CV_INTRIN_DEF_TYPE_TRAITS(double, int64, uint64, double, void, void, double, 2);
+
 #ifndef CV_DOXYGEN
 
 #ifdef CV_CPU_DISPATCH_MODE
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE)
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) {
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE)
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) {
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
 #else
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline {
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline {
+    #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
 #endif
 
-
 CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
 CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
 using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE;
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-#endif
-
-//! @addtogroup core_hal_intrin
-//! @{
-
-//! @cond IGNORED
-template<typename _Tp> struct V_TypeTraits
-{
-    typedef _Tp int_type;
-    typedef _Tp uint_type;
-    typedef _Tp abs_type;
-    typedef _Tp sum_type;
-
-    enum { delta = 0, shift = 0 };
-
-    static int_type reinterpret_int(_Tp x) { return x; }
-    static uint_type reinterpet_uint(_Tp x) { return x; }
-    static _Tp reinterpret_from_int(int_type x) { return (_Tp)x; }
-};
-
-template<> struct V_TypeTraits<uchar>
-{
-    typedef uchar value_type;
-    typedef schar int_type;
-    typedef uchar uint_type;
-    typedef uchar abs_type;
-    typedef int sum_type;
-
-    typedef ushort w_type;
-    typedef unsigned q_type;
-
-    enum { delta = 128, shift = 8 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<schar>
-{
-    typedef schar value_type;
-    typedef schar int_type;
-    typedef uchar uint_type;
-    typedef uchar abs_type;
-    typedef int sum_type;
-
-    typedef short w_type;
-    typedef int q_type;
-
-    enum { delta = 128, shift = 8 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<ushort>
-{
-    typedef ushort value_type;
-    typedef short int_type;
-    typedef ushort uint_type;
-    typedef ushort abs_type;
-    typedef int sum_type;
-
-    typedef unsigned w_type;
-    typedef uchar nu_type;
-
-    enum { delta = 32768, shift = 16 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<short>
-{
-    typedef short value_type;
-    typedef short int_type;
-    typedef ushort uint_type;
-    typedef ushort abs_type;
-    typedef int sum_type;
-
-    typedef int w_type;
-    typedef uchar nu_type;
-    typedef schar n_type;
-
-    enum { delta = 128, shift = 8 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<unsigned>
-{
-    typedef unsigned value_type;
-    typedef int int_type;
-    typedef unsigned uint_type;
-    typedef unsigned abs_type;
-    typedef unsigned sum_type;
-
-    typedef uint64 w_type;
-    typedef ushort nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<int>
-{
-    typedef int value_type;
-    typedef int int_type;
-    typedef unsigned uint_type;
-    typedef unsigned abs_type;
-    typedef int sum_type;
-
-    typedef int64 w_type;
-    typedef short n_type;
-    typedef ushort nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<uint64>
-{
-    typedef uint64 value_type;
-    typedef int64 int_type;
-    typedef uint64 uint_type;
-    typedef uint64 abs_type;
-    typedef uint64 sum_type;
-
-    typedef unsigned nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<int64>
-{
-    typedef int64 value_type;
-    typedef int64 int_type;
-    typedef uint64 uint_type;
-    typedef uint64 abs_type;
-    typedef int64 sum_type;
-
-    typedef int nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-
-template<> struct V_TypeTraits<float>
-{
-    typedef float value_type;
-    typedef int int_type;
-    typedef unsigned uint_type;
-    typedef float abs_type;
-    typedef float sum_type;
-
-    typedef double w_type;
-
-    static int_type reinterpret_int(value_type x)
-    {
-        Cv32suf u;
-        u.f = x;
-        return u.i;
-    }
-    static uint_type reinterpet_uint(value_type x)
-    {
-        Cv32suf u;
-        u.f = x;
-        return u.u;
-    }
-    static value_type reinterpret_from_int(int_type x)
-    {
-        Cv32suf u;
-        u.i = x;
-        return u.f;
-    }
-};
-
-template<> struct V_TypeTraits<double>
-{
-    typedef double value_type;
-    typedef int64 int_type;
-    typedef uint64 uint_type;
-    typedef double abs_type;
-    typedef double sum_type;
-    static int_type reinterpret_int(value_type x)
-    {
-        Cv64suf u;
-        u.f = x;
-        return u.i;
-    }
-    static uint_type reinterpet_uint(value_type x)
-    {
-        Cv64suf u;
-        u.f = x;
-        return u.u;
-    }
-    static value_type reinterpret_from_int(int_type x)
-    {
-        Cv64suf u;
-        u.i = x;
-        return u.f;
-    }
-};
-
-template <typename T> struct V_SIMD128Traits
-{
-    enum { nlanes = 16 / sizeof(T) };
-};
-
-//! @endcond
-
-//! @}
-
-#ifndef CV_DOXYGEN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
 #endif
 }
 
 #ifdef CV_DOXYGEN
+#   undef CV_AVX2
 #   undef CV_SSE2
 #   undef CV_NEON
 #   undef CV_VSX
+#   undef CV_FP16
 #endif
 
 #if CV_SSE2
@@ -325,27 +153,25 @@ CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
 
 #else
 
+#define CV_SIMD128_CPP 1
 #include "opencv2/core/hal/intrin_cpp.hpp"
 
 #endif
 
-//! @addtogroup core_hal_intrin
-//! @{
+// AVX2 can be used together with SSE2, so
+// we define those two sets of intrinsics at once.
+// Most of the intrinsics do not conflict (the proper overloaded variant is
+// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2),
+// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load().
+// Correspondingly, the wide intrinsics (which are mapped to the "widest"
+// available instruction set) will get vx_ prefix
+// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load())
+#if CV_AVX2
+
+#include "opencv2/core/hal/intrin_avx.hpp"
 
-#ifndef CV_SIMD128
-//! Set to 1 if current compiler supports vector extensions (NEON or SSE is enabled)
-#define CV_SIMD128 0
 #endif
 
-#ifndef CV_SIMD128_64F
-//! Set to 1 if current intrinsics implementation supports 64-bit float vectors
-#define CV_SIMD128_64F 0
-#endif
-
-//! @}
-
-//==================================================================================================
-
 //! @cond IGNORED
 
 namespace cv {
@@ -354,88 +180,208 @@ namespace cv {
 CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
 #endif
 
-template <typename R> struct V_RegTrait128;
+#ifndef CV_SIMD128
+#define CV_SIMD128 0
+#endif
 
-template <> struct V_RegTrait128<uchar> {
-    typedef v_uint8x16 reg;
-    typedef v_uint16x8 w_reg;
-    typedef v_uint32x4 q_reg;
-    typedef v_uint8x16 u_reg;
-    static v_uint8x16 zero() { return v_setzero_u8(); }
-    static v_uint8x16 all(uchar val) { return v_setall_u8(val); }
+#ifndef CV_SIMD128_64F
+#define CV_SIMD128_64F 0
+#endif
+
+#ifndef CV_SIMD256
+#define CV_SIMD256 0
+#endif
+
+#ifndef CV_SIMD256_64F
+#define CV_SIMD256_64F 0
+#endif
+
+#ifndef CV_SIMD512
+#define CV_SIMD512 0
+#endif
+
+#ifndef CV_SIMD512_64F
+#define CV_SIMD512_64F 0
+#endif
+
+#ifndef CV_SIMD128_FP16
+#define CV_SIMD128_FP16 0
+#endif
+
+#ifndef CV_SIMD256_FP16
+#define CV_SIMD256_FP16 0
+#endif
+
+#ifndef CV_SIMD512_FP16
+#define CV_SIMD512_FP16 0
+#endif
+
+//==================================================================================================
+
+#define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \
+    inline vtyp vx_setall_##short_typ(typ v) { return prefix##_setall_##short_typ(v); } \
+    inline vtyp vx_setzero_##short_typ() { return prefix##_setzero_##short_typ(); } \
+    inline vtyp vx_##loadsfx(const typ* ptr) { return prefix##_##loadsfx(ptr); } \
+    inline vtyp vx_##loadsfx##_aligned(const typ* ptr) { return prefix##_##loadsfx##_aligned(ptr); } \
+    inline vtyp vx_##loadsfx##_low(const typ* ptr) { return prefix##_##loadsfx##_low(ptr); } \
+    inline vtyp vx_##loadsfx##_halves(const typ* ptr0, const typ* ptr1) { return prefix##_##loadsfx##_halves(ptr0, ptr1); } \
+    inline void vx_store(typ* ptr, const vtyp& v) { return v_store(ptr, v); } \
+    inline void vx_store_aligned(typ* ptr, const vtyp& v) { return v_store_aligned(ptr, v); }
+
+#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \
+    inline wtyp vx_load_expand(const typ* ptr) { return prefix##_load_expand(ptr); }
+
+#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) \
+    inline qtyp vx_load_expand_q(const typ* ptr) { return prefix##_load_expand_q(ptr); }
+
+#define CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(typ, vtyp, short_typ, wtyp, qtyp, prefix, loadsfx) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \
+    CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \
+    CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix)
+
+#define CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(prefix) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(uchar, v_uint8, u8, v_uint16, v_uint32, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(schar, v_int8, s8, v_int16, v_int32, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(ushort, v_uint16, u16, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(ushort, v_uint32, prefix) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_int16, s16, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(short, v_int32, prefix) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(int, v_int32, s32, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(int, v_int64, prefix) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(unsigned, v_uint32, u32, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(unsigned, v_uint64, prefix) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(float, v_float32, f32, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(int64, v_int64, s64, prefix, load) \
+    CV_INTRIN_DEFINE_WIDE_INTRIN(uint64, v_uint64, u64, prefix, load)
+
+template<typename _Tp> struct V_RegTraits
+{
 };
 
-template <> struct V_RegTrait128<schar> {
-    typedef v_int8x16 reg;
-    typedef v_int16x8 w_reg;
-    typedef v_int32x4 q_reg;
-    typedef v_uint8x16 u_reg;
-    static v_int8x16 zero() { return v_setzero_s8(); }
-    static v_int8x16 all(schar val) { return v_setall_s8(val); }
-};
-
-template <> struct V_RegTrait128<ushort> {
-    typedef v_uint16x8 reg;
-    typedef v_uint32x4 w_reg;
-    typedef v_int16x8 int_reg;
-    typedef v_uint16x8 u_reg;
-    static v_uint16x8 zero() { return v_setzero_u16(); }
-    static v_uint16x8 all(ushort val) { return v_setall_u16(val); }
-};
-
-template <> struct V_RegTrait128<short> {
-    typedef v_int16x8 reg;
-    typedef v_int32x4 w_reg;
-    typedef v_uint16x8 u_reg;
-    static v_int16x8 zero() { return v_setzero_s16(); }
-    static v_int16x8 all(short val) { return v_setall_s16(val); }
-};
-
-template <> struct V_RegTrait128<unsigned> {
-    typedef v_uint32x4 reg;
-    typedef v_uint64x2 w_reg;
-    typedef v_int32x4 int_reg;
-    typedef v_uint32x4 u_reg;
-    static v_uint32x4 zero() { return v_setzero_u32(); }
-    static v_uint32x4 all(unsigned val) { return v_setall_u32(val); }
-};
-
-template <> struct V_RegTrait128<int> {
-    typedef v_int32x4 reg;
-    typedef v_int64x2 w_reg;
-    typedef v_uint32x4 u_reg;
-    static v_int32x4 zero() { return v_setzero_s32(); }
-    static v_int32x4 all(int val) { return v_setall_s32(val); }
-};
-
-template <> struct V_RegTrait128<uint64> {
-    typedef v_uint64x2 reg;
-    static v_uint64x2 zero() { return v_setzero_u64(); }
-    static v_uint64x2 all(uint64 val) { return v_setall_u64(val); }
-};
-
-template <> struct V_RegTrait128<int64> {
-    typedef v_int64x2 reg;
-    static v_int64x2 zero() { return v_setzero_s64(); }
-    static v_int64x2 all(int64 val) { return v_setall_s64(val); }
-};
-
-template <> struct V_RegTrait128<float> {
-    typedef v_float32x4 reg;
-    typedef v_int32x4 int_reg;
-    typedef v_float32x4 u_reg;
-    static v_float32x4 zero() { return v_setzero_f32(); }
-    static v_float32x4 all(float val) { return v_setall_f32(val); }
-};
+#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \
+    template<> struct V_RegTraits<_reg> \
+    { \
+        typedef _reg reg; \
+        typedef _u_reg u_reg; \
+        typedef _w_reg w_reg; \
+        typedef _q_reg q_reg; \
+        typedef _int_reg int_reg; \
+        typedef _round_reg round_reg; \
+    }
 
+#if CV_SIMD128 || CV_SIMD128_CPP
+    CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void);
+    CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void);
+    CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void);
+    CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void);
+    CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void);
+    CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void);
 #if CV_SIMD128_64F
-template <> struct V_RegTrait128<double> {
-    typedef v_float64x2 reg;
-    typedef v_int32x4 int_reg;
-    typedef v_float64x2 u_reg;
-    static v_float64x2 zero() { return v_setzero_f64(); }
-    static v_float64x2 all(double val) { return v_setall_f64(val); }
-};
+    CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4);
+#else
+    CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4);
+#endif
+    CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void);
+    CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void);
+#if CV_SIMD128_64F
+    CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4);
+#endif
+#if CV_SIMD128_FP16
+    CV_DEF_REG_TRAITS(v, v_float16x8, short, f16, v_float16x8, void, void, v_int16x8, v_int16x8);
+#endif
+#endif
+
+#if CV_SIMD256
+    CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void);
+    CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void);
+    CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void);
+    CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void);
+    CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void);
+    CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void);
+    CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8);
+    CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void);
+    CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void);
+    CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8);
+#if CV_SIMD256_FP16
+    CV_DEF_REG_TRAITS(v256, v_float16x16, short, f16, v_float16x16, void, void, v_int16x16, void);
+#endif
+#endif
+
+#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512)
+#define CV__SIMD_NAMESPACE simd512
+namespace CV__SIMD_NAMESPACE {
+    #define CV_SIMD 1
+    #define CV_SIMD_64F CV_SIMD512_64F
+    #define CV_SIMD_WIDTH 64
+    // TODO typedef v_uint8 / v_int32 / etc types here
+} // namespace
+using namespace CV__SIMD_NAMESPACE;
+#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256)
+#define CV__SIMD_NAMESPACE simd256
+namespace CV__SIMD_NAMESPACE {
+    #define CV_SIMD 1
+    #define CV_SIMD_64F CV_SIMD256_64F
+    #define CV_SIMD_FP16 CV_SIMD256_FP16
+    #define CV_SIMD_WIDTH 32
+    typedef v_uint8x32   v_uint8;
+    typedef v_int8x32    v_int8;
+    typedef v_uint16x16  v_uint16;
+    typedef v_int16x16   v_int16;
+    typedef v_uint32x8   v_uint32;
+    typedef v_int32x8    v_int32;
+    typedef v_uint64x4   v_uint64;
+    typedef v_int64x4    v_int64;
+    typedef v_float32x8  v_float32;
+    #if CV_SIMD256_64F
+    typedef v_float64x4  v_float64;
+    #endif
+    #if CV_FP16
+    #define vx_load_fp16_f32 v256_load_fp16_f32
+    #define vx_store_fp16 v_store_fp16
+    #endif
+    #if CV_SIMD256_FP16
+    typedef v_float16x16  v_float16;
+    CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_float16, f16, v256, load_f16)
+    #endif
+    CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v256)
+    CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v256, load)
+    inline void vx_cleanup() { v256_cleanup(); }
+} // namespace
+using namespace CV__SIMD_NAMESPACE;
+#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128)
+#define CV__SIMD_NAMESPACE simd128
+namespace CV__SIMD_NAMESPACE {
+    #define CV_SIMD CV_SIMD128
+    #define CV_SIMD_64F CV_SIMD128_64F
+    #define CV_SIMD_FP16 CV_SIMD128_FP16
+    #define CV_SIMD_WIDTH 16
+    typedef v_uint8x16  v_uint8;
+    typedef v_int8x16   v_int8;
+    typedef v_uint16x8  v_uint16;
+    typedef v_int16x8   v_int16;
+    typedef v_uint32x4  v_uint32;
+    typedef v_int32x4   v_int32;
+    typedef v_uint64x2  v_uint64;
+    typedef v_int64x2   v_int64;
+    typedef v_float32x4 v_float32;
+    #if CV_SIMD128_64F
+    typedef v_float64x2 v_float64;
+    #endif
+    #if CV_FP16
+    #define vx_load_fp16_f32 v128_load_fp16_f32
+    #define vx_store_fp16 v_store_fp16
+    #endif
+    #if CV_SIMD128_FP16
+    typedef v_float16x8  v_float16;
+    CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_float16, f16, v, load_f16)
+    #endif
+    CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v)
+    #if CV_SIMD128_64F
+    CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v, load)
+    #endif
+    inline void vx_cleanup() { v_cleanup(); }
+} // namespace
+using namespace CV__SIMD_NAMESPACE;
 #endif
 
 inline unsigned int trailingZeros32(unsigned int value) {
@@ -465,6 +411,19 @@ inline unsigned int trailingZeros32(unsigned int value) {
 CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
 #endif
 
+#ifndef CV_SIMD_64F
+#define CV_SIMD_64F 0
+#endif
+
+#ifndef CV_SIMD_FP16
+#define CV_SIMD_FP16 0  //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types
+#endif
+
+
+#ifndef CV_SIMD
+#define CV_SIMD 0
+#endif
+
 } // cv::
 
 //! @endcond