Merge pull request #11719 from alalek:update_autobuffer_api

apps/createsamples/utility.cpp

@@ -1372,7 +1372,7 @@ 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/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);
 

modules/calib3d/src/calibinit.cpp

@@ -1264,7 +1264,7 @@ icvCleanFoundConnectedQuads( int quad_count, CvCBQuad **quad_group, CvSize patte
             // get bounding rectangle
             CvPoint2D32f temp = centers[skip]; // temporarily make index 'skip' the same as
             centers[skip] = center;            // pattern center (so it is not counted for convex hull)
-            CvMat pointMat = cvMat(1, quad_count, CV_32FC2, centers);
+            CvMat pointMat = cvMat(1, quad_count, CV_32FC2, centers.data());
             CvSeq *hull = cvConvexHull2( &pointMat, temp_storage, CV_CLOCKWISE, 1 );
             centers[skip] = temp;
             double hull_area = fabs(cvContourArea(hull, CV_WHOLE_SEQ));

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/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

@@ -255,6 +255,7 @@ Cv64suf;
 
 #ifdef __OPENCV_BUILD
 #  define DISABLE_OPENCV_24_COMPATIBILITY
+#  define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY
 #endif
 
 #ifdef CVAPI_EXPORTS

modules/core/include/opencv2/core/utility.hpp

@@ -143,9 +143,21 @@ public:
     //! returns the current buffer size
     size_t size() const;
     //! returns pointer to the real buffer, stack-allocated or heap-allocated
-    operator _Tp* ();
+    inline _Tp* data() { return ptr; }
     //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated
-    operator const _Tp* () const;
+    inline const _Tp* data() const { return ptr; }
+
+#if !defined(OPENCV_DISABLE_DEPRECATED_COMPATIBILITY) // use to .data() calls instead
+    //! returns pointer to the real buffer, stack-allocated or heap-allocated
+    operator _Tp* () { return ptr; }
+    //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated
+    operator const _Tp* () const { return ptr; }
+#else
+    //! returns a reference to the element at specified location. No bounds checking is performed in Release builds.
+    inline _Tp& operator[] (size_t i) { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; }
+    //! returns a reference to the element at specified location. No bounds checking is performed in Release builds.
+    inline const _Tp& operator[] (size_t i) const { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; }
+#endif
 
 protected:
     //! pointer to the real buffer, can point to buf if the buffer is small enough
@@ -1064,14 +1076,6 @@ template<typename _Tp, size_t fixed_size> inline size_t
 AutoBuffer<_Tp, fixed_size>::size() const
 { return sz; }
 
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::operator _Tp* ()
-{ return ptr; }
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::operator const _Tp* () const
-{ return ptr; }
-
 template<> inline std::string CommandLineParser::get<std::string>(int index, bool space_delete) const
 {
     return get<String>(index, space_delete);

modules/core/src/arithm.cpp

@@ -282,7 +282,7 @@ static void binary_op( InputArray _src1, InputArray _src2, OutputArray _dst,
         {
             blocksize = std::min(blocksize, blocksize0);
             _buf.allocate(blocksize*esz);
-            maskbuf = _buf;
+            maskbuf = _buf.data();
         }
 
         for( size_t i = 0; i < it.nplanes; i++, ++it )
@@ -312,7 +312,7 @@ static void binary_op( InputArray _src1, InputArray _src2, OutputArray _dst,
         size_t total = it.size, blocksize = std::min(total, blocksize0);
 
         _buf.allocate(blocksize*(haveMask ? 2 : 1)*esz + 32);
-        scbuf = _buf;
+        scbuf = _buf.data();
         maskbuf = alignPtr(scbuf + blocksize*esz, 16);
 
         convertAndUnrollScalar( src2, src1.type(), scbuf, blocksize);
@@ -754,7 +754,7 @@ static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
             blocksize = std::min(blocksize, blocksize0);
 
         _buf.allocate(bufesz*blocksize + 64);
-        buf = _buf;
+        buf = _buf.data();
         if( cvtsrc1 )
             buf1 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
         if( cvtsrc2 )
@@ -818,7 +818,7 @@ static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
         size_t total = it.size, blocksize = std::min(total, blocksize0);
 
         _buf.allocate(bufesz*blocksize + 64);
-        buf = _buf;
+        buf = _buf.data();
         if( cvtsrc1 )
             buf1 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
         buf2 = buf; buf = alignPtr(buf + blocksize*wsz, 16);
@@ -1309,7 +1309,7 @@ void cv::compare(InputArray _src1, InputArray _src2, OutputArray _dst, int op)
         size_t total = it.size, blocksize = std::min(total, blocksize0);
 
         AutoBuffer<uchar> _buf(blocksize*esz);
-        uchar *buf = _buf;
+        uchar *buf = _buf.data();
 
         if( depth1 > CV_32S )
             convertAndUnrollScalar( src2, depth1, buf, blocksize );
@@ -1700,7 +1700,7 @@ static bool ocl_inRange( InputArray _src, InputArray _lowerb,
         size_t blocksize = 36;
 
         AutoBuffer<uchar> _buf(blocksize*(((int)lbScalar + (int)ubScalar)*esz + cn) + 2*cn*sizeof(int) + 128);
-        uchar *buf = alignPtr(_buf + blocksize*cn, 16);
+        uchar *buf = alignPtr(_buf.data() + blocksize*cn, 16);
 
         if( ldepth != sdepth && sdepth < CV_32S )
         {
@@ -1806,7 +1806,7 @@ void cv::inRange(InputArray _src, InputArray _lowerb,
     size_t total = it.size, blocksize = std::min(total, blocksize0);
 
     AutoBuffer<uchar> _buf(blocksize*(((int)lbScalar + (int)ubScalar)*esz + cn) + 2*cn*sizeof(int) + 128);
-    uchar *buf = _buf, *mbuf = buf, *lbuf = 0, *ubuf = 0;
+    uchar *buf = _buf.data(), *mbuf = buf, *lbuf = 0, *ubuf = 0;
     buf = alignPtr(buf + blocksize*cn, 16);
 
     if( lbScalar && ubScalar )

modules/core/src/batch_distance.cpp

@@ -179,7 +179,7 @@ struct BatchDistInvoker : public ParallelLoopBody
     void operator()(const Range& range) const CV_OVERRIDE
     {
         AutoBuffer<int> buf(src2->rows);
-        int* bufptr = buf;
+        int* bufptr = buf.data();
 
         for( int i = range.start; i < range.end; i++ )
         {

modules/core/src/channels.cpp

@@ -104,7 +104,7 @@ void cv::mixChannels( const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, cons
     int depth = dst[0].depth();
 
     AutoBuffer<uchar> buf((nsrcs + ndsts + 1)*(sizeof(Mat*) + sizeof(uchar*)) + npairs*(sizeof(uchar*)*2 + sizeof(int)*6));
-    const Mat** arrays = (const Mat**)(uchar*)buf;
+    const Mat** arrays = (const Mat**)(uchar*)buf.data();
     uchar** ptrs = (uchar**)(arrays + nsrcs + ndsts);
     const uchar** srcs = (const uchar**)(ptrs + nsrcs + ndsts + 1);
     uchar** dsts = (uchar**)(srcs + npairs);
@@ -294,7 +294,7 @@ void cv::mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
 
     CV_Assert(nsrc > 0 && ndst > 0);
     cv::AutoBuffer<Mat> _buf(nsrc + ndst);
-    Mat* buf = _buf;
+    Mat* buf = _buf.data();
     for( i = 0; i < nsrc; i++ )
         buf[i] = src.getMat(src_is_mat ? -1 : i);
     for( i = 0; i < ndst; i++ )
@@ -327,7 +327,7 @@ void cv::mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
 
     CV_Assert(fromTo.size()%2 == 0 && nsrc > 0 && ndst > 0);
     cv::AutoBuffer<Mat> _buf(nsrc + ndst);
-    Mat* buf = _buf;
+    Mat* buf = _buf.data();
     for( i = 0; i < nsrc; i++ )
         buf[i] = src.getMat(src_is_mat ? -1 : i);
     for( i = 0; i < ndst; i++ )

modules/core/src/check.cpp

@@ -101,6 +101,10 @@ void check_failed_auto(const int v1, const int v2, const CheckContext& ctx)
 {
     check_failed_auto_<int>(v1, v2, ctx);
 }
+void check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx)
+{
+    check_failed_auto_<size_t>(v1, v2, ctx);
+}
 void check_failed_auto(const float v1, const float v2, const CheckContext& ctx)
 {
     check_failed_auto_<float>(v1, v2, ctx);
@@ -147,6 +151,10 @@ void check_failed_auto(const int v, const CheckContext& ctx)
 {
     check_failed_auto_<int>(v, ctx);
 }
+void check_failed_auto(const size_t v, const CheckContext& ctx)
+{
+    check_failed_auto_<size_t>(v, ctx);
+}
 void check_failed_auto(const float v, const CheckContext& ctx)
 {
     check_failed_auto_<float>(v, ctx);

modules/core/src/conjugate_gradient.cpp

@@ -52,7 +52,7 @@ namespace cv
         double eps = getGradientEps();
         int i, n = getDims();
         AutoBuffer<double> x_buf(n);
-        double* x_ = x_buf;
+        double* x_ = x_buf.data();
         for( i = 0; i < n; i++ )
             x_[i] = x[i];
         for( i = 0; i < n; i++ )

modules/core/src/copy.cpp

@@ -531,7 +531,7 @@ Mat& Mat::setTo(InputArray _value, InputArray _mask)
     int blockSize0 = std::min(totalsz, (int)((BLOCK_SIZE + esz-1)/esz));
     blockSize0 -= blockSize0 % mcn;    // must be divisible without remainder for unrolling and advancing
     AutoBuffer<uchar> _scbuf(blockSize0*esz + 32);
-    uchar* scbuf = alignPtr((uchar*)_scbuf, (int)sizeof(double));
+    uchar* scbuf = alignPtr((uchar*)_scbuf.data(), (int)sizeof(double));
     convertAndUnrollScalar( value, type(), scbuf, blockSize0/mcn );
 
     for( size_t i = 0; i < it.nplanes; i++, ++it )
@@ -559,7 +559,7 @@ flipHoriz( const uchar* src, size_t sstep, uchar* dst, size_t dstep, Size size,
 {
     int i, j, limit = (int)(((size.width + 1)/2)*esz);
     AutoBuffer<int> _tab(size.width*esz);
-    int* tab = _tab;
+    int* tab = _tab.data();
 
     for( i = 0; i < size.width; i++ )
         for( size_t k = 0; k < esz; k++ )
@@ -960,7 +960,7 @@ void copyMakeBorder_8u( const uchar* src, size_t srcstep, cv::Size srcroi,
     }
 
     cv::AutoBuffer<int> _tab((dstroi.width - srcroi.width)*cn);
-    int* tab = _tab;
+    int* tab = _tab.data();
     int right = dstroi.width - srcroi.width - left;
     int bottom = dstroi.height - srcroi.height - top;
 
@@ -1031,7 +1031,7 @@ void copyMakeConstBorder_8u( const uchar* src, size_t srcstep, cv::Size srcroi,
 {
     int i, j;
     cv::AutoBuffer<uchar> _constBuf(dstroi.width*cn);
-    uchar* constBuf = _constBuf;
+    uchar* constBuf = _constBuf.data();
     int right = dstroi.width - srcroi.width - left;
     int bottom = dstroi.height - srcroi.height - top;
 
@@ -1224,10 +1224,10 @@ void cv::copyMakeBorder( InputArray _src, OutputArray _dst, int top, int bottom,
             CV_Assert( value[0] == value[1] && value[0] == value[2] && value[0] == value[3] );
             cn1 = 1;
         }
-        scalarToRawData(value, buf, CV_MAKETYPE(src.depth(), cn1), cn);
+        scalarToRawData(value, buf.data(), CV_MAKETYPE(src.depth(), cn1), cn);
         copyMakeConstBorder_8u( src.ptr(), src.step, src.size(),
                                 dst.ptr(), dst.step, dst.size(),
-                                top, left, (int)src.elemSize(), (uchar*)(double*)buf );
+                                top, left, (int)src.elemSize(), (uchar*)buf.data() );
     }
 }
 

modules/core/src/dxt.cpp

@@ -908,7 +908,7 @@ DFT(const OcvDftOptions & c, const Complex<T>* src, Complex<T>* dst)
             int p, q, factor2 = (factor - 1)/2;
             int d, dd, dw_f = c.tab_size/factor;
             AutoBuffer<Complex<T> > buf(factor2 * 2);
-            Complex<T>* a = buf;
+            Complex<T>* a = buf.data();
             Complex<T>* b = a + factor2;
 
             for( i = 0; i < c.n; i += n )
@@ -2895,7 +2895,7 @@ protected:
             uchar* dptr = dptr0;
 
             if( needBufferA )
-                dptr = tmp_bufA;
+                dptr = tmp_bufA.data();
 
             contextA->apply(sptr, dptr);
 
@@ -2921,12 +2921,12 @@ protected:
         const uchar* sptr0 = src_data;
         uchar* dptr0 = dst_data;
 
-        dbuf0 = buf0, dbuf1 = buf1;
+        dbuf0 = buf0.data(), dbuf1 = buf1.data();
 
         if( needBufferB )
         {
-            dbuf1 = tmp_bufB;
-            dbuf0 = buf1;
+            dbuf1 = tmp_bufB.data();
+            dbuf0 = buf1.data();
         }
 
         if( real_transform )
@@ -2937,42 +2937,42 @@ protected:
             b = (count+1)/2;
             if( !inv )
             {
-                memset( buf0, 0, len*complex_elem_size );
-                CopyColumn( sptr0, src_step, buf0, complex_elem_size, len, elem_size );
+                memset( buf0.data(), 0, len*complex_elem_size );
+                CopyColumn( sptr0, src_step, buf0.data(), complex_elem_size, len, elem_size );
                 sptr0 += stage_dst_channels*elem_size;
                 if( even )
                 {
-                    memset( buf1, 0, len*complex_elem_size );
+                    memset( buf1.data(), 0, len*complex_elem_size );
                     CopyColumn( sptr0 + (count-2)*elem_size, src_step,
-                                buf1, complex_elem_size, len, elem_size );
+                                buf1.data(), complex_elem_size, len, elem_size );
                 }
             }
             else if( stage_src_channels == 1 )
             {
-                CopyColumn( sptr0, src_step, buf0, elem_size, len, elem_size );
-                ExpandCCS( buf0, len, elem_size );
+                CopyColumn( sptr0, src_step, buf0.data(), elem_size, len, elem_size );
+                ExpandCCS( buf0.data(), len, elem_size );
                 if( even )
                 {
                     CopyColumn( sptr0 + (count-1)*elem_size, src_step,
-                                buf1, elem_size, len, elem_size );
-                    ExpandCCS( buf1, len, elem_size );
+                                buf1.data(), elem_size, len, elem_size );
+                    ExpandCCS( buf1.data(), len, elem_size );
                 }
                 sptr0 += elem_size;
             }
             else
             {
-                CopyColumn( sptr0, src_step, buf0, complex_elem_size, len, complex_elem_size );
+                CopyColumn( sptr0, src_step, buf0.data(), complex_elem_size, len, complex_elem_size );
                 if( even )
                 {
                     CopyColumn( sptr0 + b*complex_elem_size, src_step,
-                                   buf1, complex_elem_size, len, complex_elem_size );
+                                   buf1.data(), complex_elem_size, len, complex_elem_size );
                 }
                 sptr0 += complex_elem_size;
             }
 
             if( even )
-                contextB->apply(buf1, dbuf1);
-            contextB->apply(buf0, dbuf0);
+                contextB->apply(buf1.data(), dbuf1);
+            contextB->apply(buf0.data(), dbuf0);
 
             if( stage_dst_channels == 1 )
             {
@@ -3019,13 +3019,13 @@ protected:
         {
             if( i+1 < b )
             {
-                CopyFrom2Columns( sptr0, src_step, buf0, buf1, len, complex_elem_size );
-                contextB->apply(buf1, dbuf1);
+                CopyFrom2Columns( sptr0, src_step, buf0.data(), buf1.data(), len, complex_elem_size );
+                contextB->apply(buf1.data(), dbuf1);
             }
             else
-                CopyColumn( sptr0, src_step, buf0, complex_elem_size, len, complex_elem_size );
+                CopyColumn( sptr0, src_step, buf0.data(), complex_elem_size, len, complex_elem_size );
 
-            contextB->apply(buf0, dbuf0);
+            contextB->apply(buf0.data(), dbuf0);
 
             if( i+1 < b )
                 CopyTo2Columns( dbuf0, dbuf1, dptr0, dst_step, len, complex_elem_size );
@@ -3134,9 +3134,9 @@ public:
             if (len != prev_len || (!inplace_transform && opt.isInverse && real_transform))
             {
                 wave_buf.allocate(opt.n*complex_elem_size);
-                opt.wave = wave_buf;
+                opt.wave = wave_buf.data();
                 itab_buf.allocate(opt.n);
-                opt.itab = itab_buf;
+                opt.itab = itab_buf.data();
                 DFTInit( opt.n, opt.nf, opt.factors, opt.itab, complex_elem_size,
                          opt.wave, stage == 0 && opt.isInverse && real_transform );
             }
@@ -4152,31 +4152,31 @@ public:
                 bool inplace_transform = opt.factors[0] == opt.factors[opt.nf-1];
 
                 wave_buf.allocate(len*complex_elem_size);
-                opt.wave = wave_buf;
+                opt.wave = wave_buf.data();
                 itab_buf.allocate(len);
-                opt.itab = itab_buf;
+                opt.itab = itab_buf.data();
                 DFTInit( len, opt.nf, opt.factors, opt.itab, complex_elem_size, opt.wave, isInverse );
 
                 dct_wave.allocate((len/2 + 1)*complex_elem_size);
                 src_buf.allocate(len*elem_size);
-                src_dft_buf = src_buf;
+                src_dft_buf = src_buf.data();
                 if(!inplace_transform)
                 {
                     dst_buf.allocate(len*elem_size);
-                    dst_dft_buf = dst_buf;
+                    dst_dft_buf = dst_buf.data();
                 }
                 else
                 {
-                    dst_dft_buf = src_buf;
+                    dst_dft_buf = src_buf.data();
                 }
-                DCTInit( len, complex_elem_size, dct_wave, isInverse);
+                DCTInit( len, complex_elem_size, dct_wave.data(), isInverse);
                 prev_len = len;
             }
             // otherwise reuse the tables calculated on the previous stage
             for(unsigned i = 0; i < static_cast<unsigned>(count); i++ )
             {
                 dct_func( opt, sptr + i*sstep0, sstep1, src_dft_buf, dst_dft_buf,
-                          dptr + i*dstep0, dstep1, dct_wave);
+                          dptr + i*dstep0, dstep1, dct_wave.data());
             }
             src = dst;
             src_step = dst_step;

modules/core/src/kmeans.cpp

@@ -330,7 +330,7 @@ double cv::kmeans( InputArray _data, int K,
                 else
                 {
                     for (int k = 0; k < K; k++)
-                        generateRandomCenter(dims, box, centers.ptr<float>(k), rng);
+                        generateRandomCenter(dims, box.data(), centers.ptr<float>(k), rng);
                 }
             }
             else
@@ -429,14 +429,14 @@ double cv::kmeans( InputArray _data, int K,
             if (isLastIter)
             {
                 // don't re-assign labels to avoid creation of empty clusters
-                parallel_for_(Range(0, N), KMeansDistanceComputer<true>(dists, labels, data, centers), (double)divUp((size_t)(dims * N), CV_KMEANS_PARALLEL_GRANULARITY));
+                parallel_for_(Range(0, N), KMeansDistanceComputer<true>(dists.data(), labels, data, centers), (double)divUp((size_t)(dims * N), CV_KMEANS_PARALLEL_GRANULARITY));
                 compactness = sum(Mat(Size(N, 1), CV_64F, &dists[0]))[0];
                 break;
             }
             else
             {
                 // assign labels
-                parallel_for_(Range(0, N), KMeansDistanceComputer<false>(dists, labels, data, centers), (double)divUp((size_t)(dims * N * K), CV_KMEANS_PARALLEL_GRANULARITY));
+                parallel_for_(Range(0, N), KMeansDistanceComputer<false>(dists.data(), labels, data, centers), (double)divUp((size_t)(dims * N * K), CV_KMEANS_PARALLEL_GRANULARITY));
             }
         }
 

modules/core/src/lapack.cpp

@@ -401,7 +401,7 @@ JacobiSVDImpl_(_Tp* At, size_t astep, _Tp* _W, _Tp* Vt, size_t vstep,
 {
     VBLAS<_Tp> vblas;
     AutoBuffer<double> Wbuf(n);
-    double* W = Wbuf;
+    double* W = Wbuf.data();
     int i, j, k, iter, max_iter = std::max(m, 30);
     _Tp c, s;
     double sd;
@@ -778,7 +778,7 @@ double cv::determinant( InputArray _mat )
         {
             size_t bufSize = rows*rows*sizeof(float);
             AutoBuffer<uchar> buffer(bufSize);
-            Mat a(rows, rows, CV_32F, (uchar*)buffer);
+            Mat a(rows, rows, CV_32F, buffer.data());
             mat.copyTo(a);
 
             result = hal::LU32f(a.ptr<float>(), a.step, rows, 0, 0, 0);
@@ -801,7 +801,7 @@ double cv::determinant( InputArray _mat )
         {
             size_t bufSize = rows*rows*sizeof(double);
             AutoBuffer<uchar> buffer(bufSize);
-            Mat a(rows, rows, CV_64F, (uchar*)buffer);
+            Mat a(rows, rows, CV_64F, buffer.data());
             mat.copyTo(a);
 
             result = hal::LU64f(a.ptr<double>(), a.step, rows, 0, 0, 0);
@@ -846,7 +846,7 @@ double cv::invert( InputArray _src, OutputArray _dst, int method )
         int nm = std::min(m, n);
 
         AutoBuffer<uchar> _buf((m*nm + nm + nm*n)*esz + sizeof(double));
-        uchar* buf = alignPtr((uchar*)_buf, (int)esz);
+        uchar* buf = alignPtr((uchar*)_buf.data(), (int)esz);
         Mat u(m, nm, type, buf);
         Mat w(nm, 1, type, u.ptr() + m*nm*esz);
         Mat vt(nm, n, type, w.ptr() + nm*esz);
@@ -865,7 +865,7 @@ double cv::invert( InputArray _src, OutputArray _dst, int method )
     if( method == DECOMP_EIG )
     {
         AutoBuffer<uchar> _buf((n*n*2 + n)*esz + sizeof(double));
-        uchar* buf = alignPtr((uchar*)_buf, (int)esz);
+        uchar* buf = alignPtr((uchar*)_buf.data(), (int)esz);
         Mat u(n, n, type, buf);
         Mat w(n, 1, type, u.ptr() + n*n*esz);
         Mat vt(n, n, type, w.ptr() + n*esz);
@@ -1063,7 +1063,7 @@ double cv::invert( InputArray _src, OutputArray _dst, int method )
 
    int elem_size = CV_ELEM_SIZE(type);
     AutoBuffer<uchar> buf(n*n*elem_size);
-    Mat src1(n, n, type, (uchar*)buf);
+    Mat src1(n, n, type, buf.data());
     src.copyTo(src1);
     setIdentity(dst);
 
@@ -1267,7 +1267,7 @@ bool cv::solve( InputArray _src, InputArray _src2arg, OutputArray _dst, int meth
         bufsize += n*5*esz + n*vstep + nb*sizeof(double) + 32;
 
     buffer.allocate(bufsize);
-    uchar* ptr = alignPtr((uchar*)buffer, 16);
+    uchar* ptr = alignPtr(buffer.data(), 16);
 
     Mat a(m_, n, type, ptr, astep);
 
@@ -1445,7 +1445,7 @@ bool cv::eigen( InputArray _src, OutputArray _evals, OutputArray _evects )
 
     size_t elemSize = src.elemSize(), astep = alignSize(n*elemSize, 16);
     AutoBuffer<uchar> buf(n*astep + n*5*elemSize + 32);
-    uchar* ptr = alignPtr((uchar*)buf, 16);
+    uchar* ptr = alignPtr(buf.data(), 16);
     Mat a(n, n, type, ptr, astep), w(n, 1, type, ptr + astep*n);
     ptr += astep*n + elemSize*n;
     src.copyTo(a);
@@ -1489,7 +1489,7 @@ static void _SVDcompute( InputArray _aarr, OutputArray _w,
     int urows = full_uv ? m : n;
     size_t esz = src.elemSize(), astep = alignSize(m*esz, 16), vstep = alignSize(n*esz, 16);
     AutoBuffer<uchar> _buf(urows*astep + n*vstep + n*esz + 32);
-    uchar* buf = alignPtr((uchar*)_buf, 16);
+    uchar* buf = alignPtr(_buf.data(), 16);
     Mat temp_a(n, m, type, buf, astep);
     Mat temp_w(n, 1, type, buf + urows*astep);
     Mat temp_u(urows, m, type, buf, astep), temp_v;
@@ -1568,11 +1568,11 @@ void SVD::backSubst( InputArray _w, InputArray _u, InputArray _vt,
     if( type == CV_32F )
         SVBkSb(m, n, w.ptr<float>(), wstep, u.ptr<float>(), u.step, false,
                vt.ptr<float>(), vt.step, true, rhs.ptr<float>(), rhs.step, nb,
-               dst.ptr<float>(), dst.step, buffer);
+               dst.ptr<float>(), dst.step, buffer.data());
     else if( type == CV_64F )
         SVBkSb(m, n, w.ptr<double>(), wstep, u.ptr<double>(), u.step, false,
                vt.ptr<double>(), vt.step, true, rhs.ptr<double>(), rhs.step, nb,
-               dst.ptr<double>(), dst.step, buffer);
+               dst.ptr<double>(), dst.step, buffer.data());
     else
         CV_Error( CV_StsUnsupportedFormat, "" );
 }

modules/core/src/mathfuncs.cpp

@@ -586,7 +586,7 @@ void polarToCart( InputArray src1, InputArray src2,
     if( depth == CV_64F )
     {
         _buf.allocate(blockSize*2);
-        buf[0] = _buf;
+        buf[0] = _buf.data();
         buf[1] = buf[0] + blockSize;
     }
 
@@ -1278,8 +1278,8 @@ void pow( InputArray _src, double power, OutputArray _dst )
         if( src.ptr() == dst.ptr() )
         {
             buf.allocate(blockSize*esz1);
-            fbuf = (float*)(uchar*)buf;
-            dbuf = (double*)(uchar*)buf;
+            fbuf = (float*)buf.data();
+            dbuf = (double*)buf.data();
         }
 
         for( size_t i = 0; i < it.nplanes; i++, ++it )
@@ -1901,7 +1901,7 @@ double cv::solvePoly( InputArray _coeffs0, OutputArray _roots0, int maxIters )
     Mat roots0 = _roots0.getMat();
 
     AutoBuffer<C> buf(n*2+2);
-    C *coeffs = buf, *roots = coeffs + n + 1;
+    C *coeffs = buf.data(), *roots = coeffs + n + 1;
     Mat coeffs1(coeffs0.size(), CV_MAKETYPE(CV_64F, coeffs0.channels()), coeffs0.channels() == 2 ? coeffs : roots);
     coeffs0.convertTo(coeffs1, coeffs1.type());
     if( coeffs0.channels() == 1 )

modules/core/src/matmul.cpp

@@ -165,7 +165,7 @@ GEMMSingleMul( const T* a_data, size_t a_step,
         if( a_step > 1 && n > 1 )
         {
             _a_buf.allocate(n);
-            a_buf = _a_buf;
+            a_buf = _a_buf.data();
         }
     }
 
@@ -177,7 +177,7 @@ GEMMSingleMul( const T* a_data, size_t a_step,
         if( a_step > 1 && a_size.height > 1 )
         {
             _a_buf.allocate(drows);
-            a_buf = _a_buf;
+            a_buf = _a_buf.data();
             for( k = 0; k < drows; k++ )
                 a_buf[k] = a_data[a_step*k];
             a_data = a_buf;
@@ -186,7 +186,7 @@ GEMMSingleMul( const T* a_data, size_t a_step,
         if( b_step > 1 )
         {
             _b_buf.allocate(d_size.width);
-            b_buf = _b_buf;
+            b_buf = _b_buf.data();
             for( j = 0; j < d_size.width; j++ )
                 b_buf[j] = b_data[j*b_step];
             b_data = b_buf;
@@ -326,7 +326,7 @@ GEMMSingleMul( const T* a_data, size_t a_step,
     else
     {
         cv::AutoBuffer<WT> _d_buf(m);
-        WT* d_buf = _d_buf;
+        WT* d_buf = _d_buf.data();
 
         for( i = 0; i < drows; i++, _a_data += a_step0, _c_data += c_step0, d_data += d_step )
         {
@@ -404,7 +404,7 @@ GEMMBlockMul( const T* a_data, size_t a_step,
         CV_SWAP( a_step0, a_step1, t_step );
         n = a_size.height;
         _a_buf.allocate(n);
-        a_buf = _a_buf;
+        a_buf = _a_buf.data();
     }
 
     if( flags & GEMM_2_T )
@@ -1354,7 +1354,7 @@ static void gemmImpl( Mat A, Mat B, double alpha,
         }
 
         buf.allocate(d_buf_size + b_buf_size + a_buf_size);
-        d_buf = (uchar*)buf;
+        d_buf = buf.data();
         b_buf = d_buf + d_buf_size;
 
         if( is_a_t )
@@ -2098,7 +2098,7 @@ void cv::transform( InputArray _src, OutputArray _dst, InputArray _mtx )
     if( !m.isContinuous() || m.type() != mtype || m.cols != scn + 1 )
     {
         _mbuf.allocate(dcn*(scn+1));
-        mbuf = (double*)_mbuf;
+        mbuf = _mbuf.data();
         Mat tmp(dcn, scn+1, mtype, mbuf);
         memset(tmp.ptr(), 0, tmp.total()*tmp.elemSize());
         if( m.cols == scn+1 )
@@ -2273,17 +2273,16 @@ void cv::perspectiveTransform( InputArray _src, OutputArray _dst, InputArray _mt
 
     const int mtype = CV_64F;
     AutoBuffer<double> _mbuf;
-    double* mbuf = _mbuf;
+    double* mbuf = m.ptr<double>();
 
     if( !m.isContinuous() || m.type() != mtype )
     {
         _mbuf.allocate((dcn+1)*(scn+1));
-        Mat tmp(dcn+1, scn+1, mtype, (double*)_mbuf);
+        mbuf = _mbuf.data();
+        Mat tmp(dcn+1, scn+1, mtype, mbuf);
         m.convertTo(tmp, mtype);
         m = tmp;
     }
-    else
-        mbuf = m.ptr<double>();
 
     TransformFunc func = depth == CV_32F ?
         (TransformFunc)perspectiveTransform_32f :
@@ -2612,7 +2611,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
         const float* src2 = v2.ptr<float>();
         size_t step1 = v1.step/sizeof(src1[0]);
         size_t step2 = v2.step/sizeof(src2[0]);
-        double* diff = buf;
+        double* diff = buf.data();
         const float* mat = icovar.ptr<float>();
         size_t matstep = icovar.step/sizeof(mat[0]);
 
@@ -2622,7 +2621,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
                 diff[i] = src1[i] - src2[i];
         }
 
-        diff = buf;
+        diff = buf.data();
         for( i = 0; i < len; i++, mat += matstep )
         {
             double row_sum = 0;
@@ -2643,7 +2642,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
         const double* src2 = v2.ptr<double>();
         size_t step1 = v1.step/sizeof(src1[0]);
         size_t step2 = v2.step/sizeof(src2[0]);
-        double* diff = buf;
+        double* diff = buf.data();
         const double* mat = icovar.ptr<double>();
         size_t matstep = icovar.step/sizeof(mat[0]);
 
@@ -2653,7 +2652,7 @@ double cv::Mahalanobis( InputArray _v1, InputArray _v2, InputArray _icovar )
                 diff[i] = src1[i] - src2[i];
         }
 
-        diff = buf;
+        diff = buf.data();
         for( i = 0; i < len; i++, mat += matstep )
         {
             double row_sum = 0;
@@ -2705,7 +2704,7 @@ MulTransposedR( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scal
         buf_size *= 5;
     }
     buf.allocate(buf_size);
-    col_buf = (dT*)(uchar*)buf;
+    col_buf = (dT*)buf.data();
 
     if( delta && delta_cols < size.width )
     {
@@ -2834,7 +2833,7 @@ MulTransposedL( const Mat& srcmat, Mat& dstmat, const Mat& deltamat, double scal
         dT delta_buf[4];
         int delta_shift = delta_cols == size.width ? 4 : 0;
         AutoBuffer<uchar> buf(size.width*sizeof(dT));
-        dT* row_buf = (dT*)(uchar*)buf;
+        dT* row_buf = (dT*)buf.data();
 
         for( i = 0; i < size.height; i++, tdst += dststep )
         {

modules/core/src/matrix.cpp

@@ -410,7 +410,7 @@ Mat::Mat(const Mat& m, const Range& _rowRange, const Range& _colRange)
         rs[1] = _colRange;
         for( int i = 2; i < m.dims; i++ )
             rs[i] = Range::all();
-        *this = m(rs);
+        *this = m(rs.data());
         return;
     }
 
@@ -897,7 +897,7 @@ Mat Mat::reshape(int _cn, int _newndims, const int* _newsz) const
 
         Mat hdr = *this;
         hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((_cn-1) << CV_CN_SHIFT);
-        setSize(hdr, _newndims, (int*)newsz_buf, NULL, true);
+        setSize(hdr, _newndims, newsz_buf.data(), NULL, true);
 
         return hdr;
     }

modules/core/src/matrix_c.cpp

@@ -169,7 +169,7 @@ Mat cvarrToMat(const CvArr* arr, bool copyData,
         if( abuf )
         {
             abuf->allocate(((size_t)total*esz + sizeof(double)-1)/sizeof(double));
-            double* bufdata = *abuf;
+            double* bufdata = abuf->data();
             cvCvtSeqToArray(seq, bufdata, CV_WHOLE_SEQ);
             return Mat(total, 1, type, bufdata);
         }

modules/core/src/matrix_decomp.cpp

@@ -206,7 +206,7 @@ QRImpl(_Tp* A, size_t astep, int m, int n, int k, _Tp* b, size_t bstep, _Tp* hFa
     cv::AutoBuffer<_Tp> buffer;
     size_t buf_size = m ? m + n : hFactors != NULL;
     buffer.allocate(buf_size);
-    _Tp* vl = buffer;
+    _Tp* vl = buffer.data();
     if (hFactors == NULL)
         hFactors = vl + m;
 

modules/core/src/matrix_operations.cpp

@@ -606,7 +606,7 @@ reduceR_( const Mat& srcmat, Mat& dstmat )
     Size size = srcmat.size();
     size.width *= srcmat.channels();
     AutoBuffer<WT> buffer(size.width);
-    WT* buf = buffer;
+    WT* buf = buffer.data();
     ST* dst = dstmat.ptr<ST>();
     const T* src = srcmat.ptr<T>();
     size_t srcstep = srcmat.step/sizeof(src[0]);
@@ -1125,7 +1125,6 @@ namespace cv
 template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
 {
     AutoBuffer<T> buf;
-    T* bptr;
     int n, len;
     bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
     bool inplace = src.data == dst.data;
@@ -1138,7 +1137,7 @@ template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
         n = src.cols, len = src.rows;
         buf.allocate(len);
     }
-    bptr = (T*)buf;
+    T* bptr = buf.data();
 
     for( int i = 0; i < n; i++ )
     {
@@ -1223,7 +1222,7 @@ static bool ipp_sort(const Mat& src, Mat& dst, int flags)
 
         for(int i = 0; i < dst.rows; i++)
         {
-            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)dst.ptr(i), dst.cols, buffer) < 0)
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)dst.ptr(i), dst.cols, buffer.data()) < 0)
                 return false;
         }
     }
@@ -1248,7 +1247,7 @@ static bool ipp_sort(const Mat& src, Mat& dst, int flags)
             dstSub = Mat(dst, subRect);
             srcSub.copyTo(row);
 
-            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)row.ptr(), dst.rows, buffer) < 0)
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadix_I, (void*)row.ptr(), dst.rows, buffer.data()) < 0)
                 return false;
 
             row = row.reshape(1, dstSub.rows);
@@ -1286,8 +1285,8 @@ template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
         buf.allocate(len);
         ibuf.allocate(len);
     }
-    T* bptr = (T*)buf;
-    int* _iptr = (int*)ibuf;
+    T* bptr = buf.data();
+    int* _iptr = ibuf.data();
 
     for( int i = 0; i < n; i++ )
     {
@@ -1365,7 +1364,7 @@ static bool ipp_sortIdx( const Mat& src, Mat& dst, int flags )
 
         for(int i = 0; i < src.rows; i++)
         {
-            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(i), (Ipp32s)src.step[1], (Ipp32s*)dst.ptr(i), src.cols, buffer) < 0)
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(i), (Ipp32s)src.step[1], (Ipp32s*)dst.ptr(i), src.cols, buffer.data()) < 0)
                 return false;
         }
     }
@@ -1388,7 +1387,7 @@ static bool ipp_sortIdx( const Mat& src, Mat& dst, int flags )
             subRect.x = i;
             dstSub = Mat(dst, subRect);
 
-            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(0, i), srcStep, (Ipp32s*)dstRow.ptr(), src.rows, buffer) < 0)
+            if(CV_INSTRUMENT_FUN_IPP(ippsSortRadixIndex, (const void*)src.ptr(0, i), srcStep, (Ipp32s*)dstRow.ptr(), src.rows, buffer.data()) < 0)
                 return false;
 
             dstRow = dstRow.reshape(1, dstSub.rows);

modules/core/src/mean.cpp

@@ -135,7 +135,7 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask )
         intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
         blockSize = std::min(blockSize, intSumBlockSize);
         _buf.allocate(cn);
-        buf = _buf;
+        buf = _buf.data();
 
         for( k = 0; k < cn; k++ )
             buf[k] = 0;
@@ -789,7 +789,7 @@ void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, Input
     int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
     int j, count = 0, nz0 = 0;
     AutoBuffer<double> _buf(cn*4);
-    double *s = (double*)_buf, *sq = s + cn;
+    double *s = (double*)_buf.data(), *sq = s + cn;
     int *sbuf = (int*)s, *sqbuf = (int*)sq;
     bool blockSum = depth <= CV_16S, blockSqSum = depth <= CV_8S;
     size_t esz = 0;

modules/core/src/merge.cpp

@@ -496,7 +496,7 @@ void cv::merge(const Mat* mv, size_t n, OutputArray _dst)
     size_t esz = dst.elemSize(), esz1 = dst.elemSize1();
     size_t blocksize0 = (int)((BLOCK_SIZE + esz-1)/esz);
     AutoBuffer<uchar> _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16);
-    const Mat** arrays = (const Mat**)(uchar*)_buf;
+    const Mat** arrays = (const Mat**)_buf.data();
     uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16);
 
     arrays[0] = &dst;

modules/core/src/ocl.cpp

@@ -617,12 +617,12 @@ public:
                 if (fileSourceSignatureSize == sourceSignatureSize_)
                 {
                     cv::AutoBuffer<char> fileSourceSignature(fileSourceSignatureSize + 1);
-                    f.read((char*)fileSourceSignature, fileSourceSignatureSize);
+                    f.read(fileSourceSignature.data(), fileSourceSignatureSize);
                     if (f.eof())
                     {
                         CV_LOG_ERROR(NULL, "Unexpected EOF");
                     }
-                    else if (memcmp(sourceSignature, (const char*)fileSourceSignature, fileSourceSignatureSize) == 0)
+                    else if (memcmp(sourceSignature, fileSourceSignature.data(), fileSourceSignatureSize) == 0)
                     {
                         isValid = true;
                     }
@@ -696,10 +696,10 @@ public:
             {
                 if (entry.keySize > 0)
                 {
-                    f.read((char*)fileKey, entry.keySize);
+                    f.read(fileKey.data(), entry.keySize);
                     CV_Assert(!f.fail());
                 }
-                if (memcmp((const char*)fileKey, key.c_str(), entry.keySize) == 0)
+                if (memcmp(fileKey.data(), key.c_str(), entry.keySize) == 0)
                 {
                     buf.resize(entry.dataSize);
                     f.read(&buf[0], entry.dataSize);
@@ -786,10 +786,10 @@ public:
             {
                 if (entry.keySize > 0)
                 {
-                    f.read((char*)fileKey, entry.keySize);
+                    f.read(fileKey.data(), entry.keySize);
                     CV_Assert(!f.fail());
                 }
-                if (0 == memcmp((const char*)fileKey, key.c_str(), entry.keySize))
+                if (0 == memcmp(fileKey.data(), key.c_str(), entry.keySize))
                 {
                     // duplicate
                     CV_LOG_VERBOSE(NULL, 0, "Duplicate key ignored: " << fileName_);
@@ -1634,7 +1634,7 @@ inline cl_int getStringInfo(Functor f, ObjectType obj, cl_uint name, std::string
     if (required > 0)
     {
         AutoBuffer<char> buf(required + 1);
-        char* ptr = (char*)buf; // cleanup is not needed
+        char* ptr = buf.data(); // cleanup is not needed
         err = f(obj, name, required, ptr, NULL);
         if (err != CL_SUCCESS)
             return err;
@@ -2002,7 +2002,7 @@ struct Context::Impl
         CV_OCL_DBG_CHECK(clGetDeviceIDs(pl, dtype, 0, 0, &nd0));
 
         AutoBuffer<void*> dlistbuf(nd0*2+1);
-        cl_device_id* dlist = (cl_device_id*)(void**)dlistbuf;
+        cl_device_id* dlist = (cl_device_id*)dlistbuf.data();
         cl_device_id* dlist_new = dlist + nd0;
         CV_OCL_DBG_CHECK(clGetDeviceIDs(pl, dtype, nd0, dlist, &nd0));
         String name0;
@@ -2465,12 +2465,12 @@ static void get_platform_name(cl_platform_id id, String& name)
 
     // get platform name string
     AutoBuffer<char> buf(sz + 1);
-    CV_OCL_CHECK(clGetPlatformInfo(id, CL_PLATFORM_NAME, sz, buf, 0));
+    CV_OCL_CHECK(clGetPlatformInfo(id, CL_PLATFORM_NAME, sz, buf.data(), 0));
 
     // just in case, ensure trailing zero for ASCIIZ string
     buf[sz] = 0;
 
-    name = (const char*)buf;
+    name = buf.data();
 }
 
 /*
@@ -3654,7 +3654,7 @@ struct Program::Impl
         {
             buffer.resize(retsz + 16);
             log_retval = clGetProgramBuildInfo(handle, deviceList[0],
-                                               CL_PROGRAM_BUILD_LOG, retsz+1, (char*)buffer, &retsz);
+                                               CL_PROGRAM_BUILD_LOG, retsz+1, buffer.data(), &retsz);
             if (log_retval == CL_SUCCESS)
             {
                 if (retsz < buffer.size())
@@ -3668,7 +3668,7 @@ struct Program::Impl
             }
         }
 
-        errmsg = String(buffer);
+        errmsg = String(buffer.data());
         printf("OpenCL program build log: %s/%s\nStatus %d: %s\n%s\n%s\n",
                 sourceModule_.c_str(), sourceName_.c_str(),
                 result, getOpenCLErrorString(result),
@@ -3701,7 +3701,7 @@ struct Program::Impl
         {
             size_t n = ctx.ndevices();
             AutoBuffer<cl_device_id, 4> deviceListBuf(n + 1);
-            cl_device_id* deviceList = deviceListBuf;
+            cl_device_id* deviceList = deviceListBuf.data();
             for (size_t i = 0; i < n; i++)
             {
                 deviceList[i] = (cl_device_id)(ctx.device(i).ptr());
@@ -3770,9 +3770,9 @@ struct Program::Impl
         AutoBuffer<const uchar*> binaryPtrs_(ndevices);
         AutoBuffer<size_t> binarySizes_(ndevices);
 
-        cl_device_id* devices = devices_;
-        const uchar** binaryPtrs = binaryPtrs_;
-        size_t* binarySizes = binarySizes_;
+        cl_device_id* devices = devices_.data();
+        const uchar** binaryPtrs = binaryPtrs_.data();
+        size_t* binarySizes = binarySizes_.data();
         for (size_t i = 0; i < ndevices; i++)
         {
             devices[i] = (cl_device_id)ctx.device(i).ptr();
@@ -3781,7 +3781,7 @@ struct Program::Impl
         }
 
         cl_int result = 0;
-        handle = clCreateProgramWithBinary((cl_context)ctx.ptr(), (cl_uint)ndevices, (cl_device_id*)devices_,
+        handle = clCreateProgramWithBinary((cl_context)ctx.ptr(), (cl_uint)ndevices, devices_.data(),
                                            binarySizes, binaryPtrs, NULL, &result);
         if (result != CL_SUCCESS)
         {
@@ -3798,7 +3798,7 @@ struct Program::Impl
         }
         // call clBuildProgram()
         {
-            result = clBuildProgram(handle, (cl_uint)ndevices, (cl_device_id*)devices_, buildflags.c_str(), 0, 0);
+            result = clBuildProgram(handle, (cl_uint)ndevices, devices_.data(), buildflags.c_str(), 0, 0);
             CV_OCL_DBG_CHECK_RESULT(result, cv::format("clBuildProgram(binary: %s/%s)", sourceModule_.c_str(), sourceName_.c_str()).c_str());
             if (result != CL_SUCCESS)
             {
@@ -6318,7 +6318,7 @@ struct Image2D::Impl
         AutoBuffer<cl_image_format> formats(numFormats);
         err = clGetSupportedImageFormats(context, CL_MEM_READ_WRITE,
                                          CL_MEM_OBJECT_IMAGE2D, numFormats,
-                                         formats, NULL);
+                                         formats.data(), NULL);
         CV_OCL_DBG_CHECK_RESULT(err, "clGetSupportedImageFormats(CL_MEM_OBJECT_IMAGE2D, formats)");
         for (cl_uint i = 0; i < numFormats; ++i)
         {

modules/core/src/persistence_cpp.cpp

@@ -222,7 +222,7 @@ String FileStorage::getDefaultObjectName(const String& _filename)
     if( ptr == ptr2 )
         CV_Error( CV_StsBadArg, "Invalid filename" );
 
-    char* name = name_buf;
+    char* name = name_buf.data();
 
     // name must start with letter or '_'
     if( !cv_isalpha(*ptr) && *ptr!= '_' ){
@@ -237,7 +237,7 @@ String FileStorage::getDefaultObjectName(const String& _filename)
         *name++ = c;
     }
     *name = '\0';
-    name = name_buf;
+    name = name_buf.data();
     if( strcmp( name, "_" ) == 0 )
         strcpy( name, stubname );
     return String(name);

modules/core/src/rand.cpp

@@ -542,7 +542,7 @@ void RNG::fill( InputOutputArray _mat, int disttype,
     if( disttype == UNIFORM )
     {
         _parambuf.allocate(cn*8 + n1 + n2);
-        double* parambuf = _parambuf;
+        double* parambuf = _parambuf.data();
         double* p1 = _param1.ptr<double>();
         double* p2 = _param2.ptr<double>();
 
@@ -651,7 +651,7 @@ void RNG::fill( InputOutputArray _mat, int disttype,
     else if( disttype == CV_RAND_NORMAL )
     {
         _parambuf.allocate(MAX(n1, cn) + MAX(n2, cn));
-        double* parambuf = _parambuf;
+        double* parambuf = _parambuf.data();
 
         int ptype = depth == CV_64F ? CV_64F : CV_32F;
         int esz = (int)CV_ELEM_SIZE(ptype);
@@ -701,7 +701,7 @@ void RNG::fill( InputOutputArray _mat, int disttype,
     if( disttype == UNIFORM )
     {
         buf.allocate(blockSize*cn*4);
-        param = (uchar*)(double*)buf;
+        param = (uchar*)(double*)buf.data();
 
         if( depth <= CV_32S )
         {
@@ -738,7 +738,7 @@ void RNG::fill( InputOutputArray _mat, int disttype,
     else
     {
         buf.allocate((blockSize*cn+1)/2);
-        nbuf = (float*)(double*)buf;
+        nbuf = (float*)(double*)buf.data();
     }
 
     for( size_t i = 0; i < it.nplanes; i++, ++it )

modules/core/src/split.cpp

@@ -485,7 +485,7 @@ void cv::split(const Mat& src, Mat* mv)
     size_t esz = src.elemSize(), esz1 = src.elemSize1();
     size_t blocksize0 = (BLOCK_SIZE + esz-1)/esz;
     AutoBuffer<uchar> _buf((cn+1)*(sizeof(Mat*) + sizeof(uchar*)) + 16);
-    const Mat** arrays = (const Mat**)(uchar*)_buf;
+    const Mat** arrays = (const Mat**)_buf.data();
     uchar** ptrs = (uchar**)alignPtr(arrays + cn + 1, 16);
 
     arrays[0] = &src;

modules/core/src/sum.cpp

@@ -617,7 +617,7 @@ cv::Scalar cv::sum( InputArray _src )
         intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
         blockSize = std::min(blockSize, intSumBlockSize);
         _buf.allocate(cn);
-        buf = _buf;
+        buf = _buf.data();
 
         for( k = 0; k < cn; k++ )
             buf[k] = 0;

modules/core/src/system.cpp

@@ -807,7 +807,7 @@ String format( const char* fmt, ... )
         va_list va;
         va_start(va, fmt);
         int bsize = static_cast<int>(buf.size());
-        int len = cv_vsnprintf((char *)buf, bsize, fmt, va);
+        int len = cv_vsnprintf(buf.data(), bsize, fmt, va);
         va_end(va);
 
         CV_Assert(len >= 0 && "Check format string for errors");
@@ -817,7 +817,7 @@ String format( const char* fmt, ... )
             continue;
         }
         buf[bsize - 1] = 0;
-        return String((char *)buf, len);
+        return String(buf.data(), len);
     }
 }
 

modules/core/src/umatrix.cpp

@@ -502,7 +502,7 @@ UMat::UMat(const UMat& m, const Range& _rowRange, const Range& _colRange)
         rs[1] = _colRange;
         for( int i = 2; i < m.dims; i++ )
             rs[i] = Range::all();
-        *this = m(rs);
+        *this = m(rs.data());
         return;
     }
 
@@ -805,7 +805,7 @@ UMat UMat::reshape(int _cn, int _newndims, const int* _newsz) const
 
         UMat hdr = *this;
         hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((_cn-1) << CV_CN_SHIFT);
-        setSize(hdr, _newndims, (int*)newsz_buf, NULL, true);
+        setSize(hdr, _newndims, newsz_buf.data(), NULL, true);
 
         return hdr;
     }

modules/core/src/utils/filesystem.cpp

@@ -158,13 +158,13 @@ cv::String getcwd()
 #else
     DWORD sz = GetCurrentDirectoryA(0, NULL);
     buf.allocate((size_t)sz);
-    sz = GetCurrentDirectoryA((DWORD)buf.size(), (char*)buf);
-    return cv::String((char*)buf, (size_t)sz);
+    sz = GetCurrentDirectoryA((DWORD)buf.size(), buf.data());
+    return cv::String(buf.data(), (size_t)sz);
 #endif
 #elif defined __linux__ || defined __APPLE__ || defined __HAIKU__
     for(;;)
     {
-        char* p = ::getcwd((char*)buf, buf.size());
+        char* p = ::getcwd(buf.data(), buf.size());
         if (p == NULL)
         {
             if (errno == ERANGE)
@@ -176,7 +176,7 @@ cv::String getcwd()
         }
         break;
     }
-    return cv::String((char*)buf, (size_t)strlen((char*)buf));
+    return cv::String(buf.data(), (size_t)strlen(buf.data()));
 #else
     return cv::String();
 #endif

modules/core/test/test_rand.cpp

@@ -374,9 +374,9 @@ TEST(Core_Rand, Regression_Stack_Corruption)
     int bufsz = 128; //enough for 14 doubles
     AutoBuffer<uchar> buffer(bufsz);
     size_t offset = 0;
-    cv::Mat_<cv::Point2d> x(2, 3, (cv::Point2d*)(buffer+offset)); offset += x.total()*x.elemSize();
-    double& param1 = *(double*)(buffer+offset); offset += sizeof(double);
-    double& param2 = *(double*)(buffer+offset); offset += sizeof(double);
+    cv::Mat_<cv::Point2d> x(2, 3, (cv::Point2d*)(buffer.data()+offset)); offset += x.total()*x.elemSize();
+    double& param1 = *(double*)(buffer.data()+offset); offset += sizeof(double);
+    double& param2 = *(double*)(buffer.data()+offset); offset += sizeof(double);
     param1 = -9; param2 = 2;
 
     cv::theRNG().fill(x, cv::RNG::NORMAL, param1, param2);

modules/dnn/src/layers/convolution_layer.cpp

@@ -586,7 +586,7 @@ public:
             float* data_out0_ = output_->ptr<float>();
             size_t rowbufsz = (size_t)karea*BLK_SIZE_CN*BLK_SIZE;
             AutoBuffer<float> rowbuf0_(rowbufsz + valign);
-            float* rowbuf0 = alignPtr((float*)rowbuf0_, (int)(valign*sizeof(float)));
+            float* rowbuf0 = alignPtr(rowbuf0_.data(), (int)(valign*sizeof(float)));
 
             // we clear the buffer once; ultimately, it lets us to avoid
             // tail processing after running the unrolled/vectorized loop.

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -182,7 +182,7 @@ public:
             size_t stripeEnd = r.end == nstripes ? total : std::min(r.end*stripeSize, total);
             size_t wstep = weights->step1();
             AutoBuffer<float> srcbuf(vecsize_aligned + valign);
-            float* sptr = alignPtr((float*)srcbuf, (int)(valign*sizeof(float)));
+            float* sptr = alignPtr(srcbuf.data(), (int)(valign*sizeof(float)));
 
             for( k = vecsize; k < vecsize_aligned; k++ )
                 sptr[k] = 0.f;

modules/dnn/src/layers/lrn_layer.cpp

@@ -211,7 +211,7 @@ public:
             int k, channels = channels_, ksize = ksize_;
 
             AutoBuffer<float> buf_((channels + ksize + 1)*2);
-            float* acc = (float*)buf_;
+            float* acc = buf_.data();
             float* buf = acc + channels + ksize + 1;
             for( k = 0; k <= ksize; k++ )
                 buf[-k-1] = buf[channels + k] = 0.f;

modules/dnn/src/torch/torch_importer.cpp

@@ -370,8 +370,8 @@ struct TorchImporter
         int ndims = readInt();
         AutoBuffer<int64, 4> sizes(ndims);
         AutoBuffer<int64, 4> steps(ndims);
-        THFile_readLongRaw(file, sizes, ndims);
-        THFile_readLongRaw(file, steps, ndims);
+        THFile_readLongRaw(file, sizes.data(), ndims);
+        THFile_readLongRaw(file, steps.data(), ndims);
         long offset = readLong() - 1;
 
         //read Storage
@@ -411,7 +411,7 @@ struct TorchImporter
         }
 
         //allocate Blob
-        Mat srcMat(ndims, (int*)isizes, typeTensor , storages[indexStorage].ptr() + offset*CV_ELEM_SIZE(typeTensor), (size_t*)ssteps);
+        Mat srcMat(ndims, isizes.data(), typeTensor , storages[indexStorage].ptr() + offset*CV_ELEM_SIZE(typeTensor), ssteps.data());
         int dstType = CV_32F;
 
         Mat blob;

modules/features2d/src/fast.cpp

@@ -83,7 +83,7 @@ void FAST_t(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bo
 
     AutoBuffer<uchar> _buf((img.cols+16)*3*(sizeof(int) + sizeof(uchar)) + 128);
     uchar* buf[3];
-    buf[0] = _buf; buf[1] = buf[0] + img.cols; buf[2] = buf[1] + img.cols;
+    buf[0] = _buf.data(); buf[1] = buf[0] + img.cols; buf[2] = buf[1] + img.cols;
     int* cpbuf[3];
     cpbuf[0] = (int*)alignPtr(buf[2] + img.cols, sizeof(int)) + 1;
     cpbuf[1] = cpbuf[0] + img.cols + 1;

modules/features2d/src/orb.cpp

@@ -143,7 +143,7 @@ HarrisResponses(const Mat& img, const std::vector<Rect>& layerinfo,
     float scale_sq_sq = scale * scale * scale * scale;
 
     AutoBuffer<int> ofsbuf(blockSize*blockSize);
-    int* ofs = ofsbuf;
+    int* ofs = ofsbuf.data();
     for( int i = 0; i < blockSize; i++ )
         for( int j = 0; j < blockSize; j++ )
             ofs[i*blockSize + j] = (int)(i*step + j);

modules/flann/include/opencv2/flann/kmeans_index.h

@@ -726,7 +726,7 @@ private:
         }
 
         cv::AutoBuffer<int> centers_idx_buf(branching);
-        int* centers_idx = (int*)centers_idx_buf;
+        int* centers_idx = centers_idx_buf.data();
         int centers_length;
         (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
 
@@ -739,7 +739,7 @@ private:
 
 
         cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
-        Matrix<double> dcenters((double*)dcenters_buf,branching,veclen_);
+        Matrix<double> dcenters(dcenters_buf.data(), branching, veclen_);
         for (int i=0; i<centers_length; ++i) {
             ElementType* vec = dataset_[centers_idx[i]];
             for (size_t k=0; k<veclen_; ++k) {
@@ -749,7 +749,7 @@ private:
 
         std::vector<DistanceType> radiuses(branching);
         cv::AutoBuffer<int> count_buf(branching);
-        int* count = (int*)count_buf;
+        int* count = count_buf.data();
         for (int i=0; i<branching; ++i) {
             radiuses[i] = 0;
             count[i] = 0;
@@ -757,7 +757,7 @@ private:
 
         //	assign points to clusters
         cv::AutoBuffer<int> belongs_to_buf(indices_length);
-        int* belongs_to = (int*)belongs_to_buf;
+        int* belongs_to = belongs_to_buf.data();
         for (int i=0; i<indices_length; ++i) {
 
             DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);

modules/imgcodecs/src/grfmt_bmp.cpp

@@ -223,7 +223,7 @@ bool  BmpDecoder::readData( Mat& img )
         }
         _bgr.allocate(m_width*3 + 32);
     }
-    uchar *src = _src, *bgr = _bgr;
+    uchar *src = _src.data(), *bgr = _bgr.data();
 
     CV_TRY
     {

modules/imgcodecs/src/grfmt_exr.cpp

@@ -199,7 +199,7 @@ bool  ExrDecoder::readData( Mat& img )
     if( !justcopy )
     {
         copy_buffer.allocate(sizeof(float) * m_width * 3);
-        buffer = copy_buffer;
+        buffer = copy_buffer.data();
         ystep = 0;
     }
     else

modules/imgcodecs/src/grfmt_jpeg.cpp

@@ -681,7 +681,7 @@ bool JpegEncoder::write( const Mat& img, const std::vector<int>& params )
 
         if( channels > 1 )
             _buffer.allocate(width*channels);
-        buffer = _buffer;
+        buffer = _buffer.data();
 
         for( int y = 0; y < height; y++ )
         {

modules/imgcodecs/src/grfmt_pam.cpp

@@ -496,9 +496,7 @@ bool  PAMDecoder::readData( Mat& img )
 
     /* setting buffer to max data size so scaling up is possible */
     AutoBuffer<uchar> _src(src_elems_per_row * 2);
-    uchar* src = _src;
-    AutoBuffer<uchar> _gray_palette;
-    uchar* gray_palette = _gray_palette;
+    uchar* src = _src.data();
 
     if( m_offset < 0 || !m_strm.isOpened())
         return false;
@@ -544,10 +542,7 @@ bool  PAMDecoder::readData( Mat& img )
             if (bit_mode) {
                 if( target_channels == 1 )
                 {
-                    _gray_palette.allocate(2);
-                    gray_palette = _gray_palette;
-                    gray_palette[0] = 0;
-                    gray_palette[1] = 255;
+                    uchar gray_palette[2] = {0, 255};
                     for( y = 0; y < m_height; y++, data += imp_stride )
                     {
                         m_strm.getBytes( src, src_stride );
@@ -683,7 +678,7 @@ bool PAMEncoder::write( const Mat& img, const std::vector<int>& params )
         bufsize = tmp;
 
     AutoBuffer<char> _buffer(bufsize);
-    char* buffer = _buffer;
+    char* buffer = _buffer.data();
 
     /* write header */
     tmp = 0;

modules/imgcodecs/src/grfmt_png.cpp

@@ -225,7 +225,7 @@ bool  PngDecoder::readData( Mat& img )
 {
     volatile bool result = false;
     AutoBuffer<uchar*> _buffer(m_height);
-    uchar** buffer = _buffer;
+    uchar** buffer = _buffer.data();
     int color = img.channels() > 1;
 
     png_structp png_ptr = (png_structp)m_png_ptr;
@@ -426,7 +426,7 @@ bool  PngEncoder::write( const Mat& img, const std::vector<int>& params )
                     for( y = 0; y < height; y++ )
                         buffer[y] = img.data + y*img.step;
 
-                    png_write_image( png_ptr, buffer );
+                    png_write_image( png_ptr, buffer.data() );
                     png_write_end( png_ptr, info_ptr );
 
                     result = true;

modules/imgcodecs/src/grfmt_pxm.cpp

@@ -245,7 +245,7 @@ bool PxMDecoder::readData( Mat& img )
             if( !m_binary )
             {
                 AutoBuffer<uchar> _src(m_width);
-                uchar* src = _src;
+                uchar* src = _src.data();
 
                 for (int y = 0; y < m_height; y++, data += img.step)
                 {
@@ -261,7 +261,7 @@ bool PxMDecoder::readData( Mat& img )
             else
             {
                 AutoBuffer<uchar> _src(src_pitch);
-                uchar* src = _src;
+                uchar* src = _src.data();
 
                 for (int y = 0; y < m_height; y++, data += img.step)
                 {
@@ -281,7 +281,7 @@ bool PxMDecoder::readData( Mat& img )
         case 24:
         {
             AutoBuffer<uchar> _src(std::max<size_t>(width3*2, src_pitch));
-            uchar* src = _src;
+            uchar* src = _src.data();
 
             for (int y = 0; y < m_height; y++, data += img.step)
             {
@@ -463,7 +463,7 @@ bool PxMEncoder::write(const Mat& img, const std::vector<int>& params)
         bufferSize = lineLength;
 
     AutoBuffer<char> _buffer(bufferSize);
-    char* buffer = _buffer;
+    char* buffer = _buffer.data();
 
     // write header;
     const int code = ((mode == PXM_TYPE_PBM) ? 1 : (mode == PXM_TYPE_PGM) ? 2 : 3)

modules/imgcodecs/src/grfmt_sunras.cpp

@@ -174,7 +174,7 @@ bool  SunRasterDecoder::readData( Mat& img )
         return false;
 
     AutoBuffer<uchar> _src(src_pitch + 32);
-    uchar* src = _src;
+    uchar* src = _src.data();
 
     if( !color && m_maptype == RMT_EQUAL_RGB )
         CvtPaletteToGray( m_palette, gray_palette, 1 << m_bpp );

modules/imgcodecs/src/grfmt_tiff.cpp

@@ -355,7 +355,7 @@ bool  TiffDecoder::readData( Mat& img )
             }
             const size_t buffer_size = (bpp/bitsPerByte) * ncn * tile_height0 * tile_width0;
             AutoBuffer<uchar> _buffer( buffer_size );
-            uchar* buffer = _buffer;
+            uchar* buffer = _buffer.data();
             ushort* buffer16 = (ushort*)buffer;
             float* buffer32 = (float*)buffer;
             double* buffer64 = (double*)buffer;
@@ -834,7 +834,7 @@ bool TiffEncoder::writeLibTiff( const std::vector<Mat>& img_vec, const std::vect
         // row buffer, because TIFFWriteScanline modifies the original data!
         size_t scanlineSize = TIFFScanlineSize(pTiffHandle);
         AutoBuffer<uchar> _buffer(scanlineSize + 32);
-        uchar* buffer = _buffer;
+        uchar* buffer = _buffer.data();
         if (!buffer)
         {
             TIFFClose(pTiffHandle);

modules/imgproc/src/approx.cpp

@@ -63,7 +63,7 @@ CvSeq* icvApproximateChainTC89( CvChain* chain, int header_size,
     cv::AutoBuffer<_CvPtInfo> buf(chain->total + 8);
 
     _CvPtInfo       temp;
-    _CvPtInfo       *array = buf, *first = 0, *current = 0, *prev_current = 0;
+    _CvPtInfo       *array = buf.data(), *first = 0, *current = 0, *prev_current = 0;
     int             i, j, i1, i2, s, len;
     int             count = chain->total;
 
@@ -475,14 +475,14 @@ namespace cv
 
 template<typename T> static int
 approxPolyDP_( const Point_<T>* src_contour, int count0, Point_<T>* dst_contour,
-              bool is_closed0, double eps, AutoBuffer<Range>* _stack )
+              bool is_closed0, double eps, AutoBuffer<Range>& _stack )
 {
     #define PUSH_SLICE(slice) \
         if( top >= stacksz ) \
         { \
-            _stack->resize(stacksz*3/2); \
-            stack = *_stack; \
-            stacksz = _stack->size(); \
+            _stack.resize(stacksz*3/2); \
+            stack = _stack.data(); \
+            stacksz = _stack.size(); \
         } \
         stack[top++] = slice
 
@@ -504,8 +504,8 @@ approxPolyDP_( const Point_<T>* src_contour, int count0, Point_<T>* dst_contour,
     int             i = 0, j, pos = 0, wpos, count = count0, new_count=0;
     int             is_closed = is_closed0;
     bool            le_eps = false;
-    size_t top = 0, stacksz = _stack->size();
-    Range*          stack = *_stack;
+    size_t top = 0, stacksz = _stack.size();
+    Range*          stack = _stack.data();
 
     if( count == 0  )
         return 0;
@@ -689,13 +689,13 @@ void cv::approxPolyDP( InputArray _curve, OutputArray _approxCurve,
 
     AutoBuffer<Point> _buf(npoints);
     AutoBuffer<Range> _stack(npoints);
-    Point* buf = _buf;
+    Point* buf = _buf.data();
     int nout = 0;
 
     if( depth == CV_32S )
-        nout = approxPolyDP_(curve.ptr<Point>(), npoints, buf, closed, epsilon, &_stack);
+        nout = approxPolyDP_(curve.ptr<Point>(), npoints, buf, closed, epsilon, _stack);
     else if( depth == CV_32F )
-        nout = approxPolyDP_(curve.ptr<Point2f>(), npoints, (Point2f*)buf, closed, epsilon, &_stack);
+        nout = approxPolyDP_(curve.ptr<Point2f>(), npoints, (Point2f*)buf, closed, epsilon, _stack);
     else
         CV_Error( CV_StsUnsupportedFormat, "" );
 
@@ -783,7 +783,7 @@ cvApproxPoly( const void* array, int header_size,
             {
             int npoints = src_seq->total, nout = 0;
             _buf.allocate(npoints*2);
-            cv::Point *src = _buf, *dst = src + npoints;
+            cv::Point *src = _buf.data(), *dst = src + npoints;
             bool closed = CV_IS_SEQ_CLOSED(src_seq);
 
             if( src_seq->first->next == src_seq->first )
@@ -792,10 +792,10 @@ cvApproxPoly( const void* array, int header_size,
                 cvCvtSeqToArray(src_seq, src);
 
             if( CV_SEQ_ELTYPE(src_seq) == CV_32SC2 )
-                nout = cv::approxPolyDP_(src, npoints, dst, closed, parameter, &stack);
+                nout = cv::approxPolyDP_(src, npoints, dst, closed, parameter, stack);
             else if( CV_SEQ_ELTYPE(src_seq) == CV_32FC2 )
                 nout = cv::approxPolyDP_((cv::Point2f*)src, npoints,
-                                         (cv::Point2f*)dst, closed, parameter, &stack);
+                                         (cv::Point2f*)dst, closed, parameter, stack);
             else
                 CV_Error( CV_StsUnsupportedFormat, "" );
 

modules/imgproc/src/canny.cpp

@@ -390,21 +390,21 @@ public:
         {
             dxMax.allocate(2 * dx.cols);
             dyMax.allocate(2 * dy.cols);
-            _dx_a = (short*)dxMax;
+            _dx_a = dxMax.data();
             _dx_n = _dx_a + dx.cols;
-            _dy_a = (short*)dyMax;
+            _dy_a = dyMax.data();
             _dy_n = _dy_a + dy.cols;
         }
 
         // _mag_p: previous row, _mag_a: actual row, _mag_n: next row
 #if CV_SIMD128
         AutoBuffer<int> buffer(3 * (mapstep * cn + CV_MALLOC_SIMD128));
-        _mag_p = alignPtr((int*)buffer + 1, CV_MALLOC_SIMD128);
+        _mag_p = alignPtr(buffer.data() + 1, CV_MALLOC_SIMD128);
         _mag_a = alignPtr(_mag_p + mapstep * cn, CV_MALLOC_SIMD128);
         _mag_n = alignPtr(_mag_a + mapstep * cn, CV_MALLOC_SIMD128);
 #else
         AutoBuffer<int> buffer(3 * (mapstep * cn));
-        _mag_p = (int*)buffer + 1;
+        _mag_p = buffer.data() + 1;
         _mag_a = _mag_p + mapstep * cn;
         _mag_n = _mag_a + mapstep * cn;
 #endif

modules/imgproc/src/clahe.cpp

@@ -230,7 +230,7 @@ namespace
             src_(src), dst_(dst), lut_(lut), tileSize_(tileSize), tilesX_(tilesX), tilesY_(tilesY)
         {
             buf.allocate(src.cols << 2);
-            ind1_p = (int *)buf;
+            ind1_p = buf.data();
             ind2_p = ind1_p + src.cols;
             xa_p = (float *)(ind2_p + src.cols);
             xa1_p = xa_p + src.cols;

modules/imgproc/src/color_lab.cpp

@@ -1398,7 +1398,7 @@ static LABLUVLUT_s16_t initLUTforLABLUVs16(const softfloat & un, const softfloat
                 for (int p_ = 0; p_ < 2; ++p_)
                     for (int q_ = 0; q_ < 2; ++q_)
                         for (int r_ = 0; r_ < 2; ++r_)
-                            fill_one(RGB2LabLUT_s16, RGB2Labprev, RGB2LuvLUT_s16, RGB2Luvprev, p, q, r, p_, q_, r_);
+                            fill_one(RGB2LabLUT_s16, RGB2Labprev.data(), RGB2LuvLUT_s16, RGB2Luvprev.data(), p, q, r, p_, q_, r_);
     LABLUVLUT_s16_t res;
     res.RGB2LabLUT_s16 = RGB2LabLUT_s16;
     res.RGB2LuvLUT_s16 = RGB2LuvLUT_s16;

modules/imgproc/src/convhull.cpp

@@ -147,11 +147,11 @@ void convexHull( InputArray _points, OutputArray _hull, bool clockwise, bool ret
     bool is_float = depth == CV_32F;
     AutoBuffer<Point*> _pointer(total);
     AutoBuffer<int> _stack(total + 2), _hullbuf(total);
-    Point** pointer = _pointer;
+    Point** pointer = _pointer.data();
     Point2f** pointerf = (Point2f**)pointer;
     Point* data0 = points.ptr<Point>();
-    int* stack = _stack;
-    int* hullbuf = _hullbuf;
+    int* stack = _stack.data();
+    int* hullbuf = _hullbuf.data();
 
     CV_Assert(points.isContinuous());
 

modules/imgproc/src/corner.cpp

@@ -542,7 +542,7 @@ static bool ipp_cornerMinEigenVal( InputArray _src, OutputArray _dst, int blockS
                 if (ok >= 0)
                 {
                     AutoBuffer<uchar> buffer(bufferSize);
-                    ok = CV_INSTRUMENT_FUN_IPP(ippiMinEigenVal_C1R, src.ptr(), (int) src.step, dst.ptr<Ipp32f>(), (int) dst.step, srcRoi, kerType, kerSize, blockSize, buffer);
+                    ok = CV_INSTRUMENT_FUN_IPP(ippiMinEigenVal_C1R, src.ptr(), (int) src.step, dst.ptr<Ipp32f>(), (int) dst.step, srcRoi, kerType, kerSize, blockSize, buffer.data());
                     CV_SUPPRESS_DEPRECATED_START
                     if (ok >= 0) ok = CV_INSTRUMENT_FUN_IPP(ippiMulC_32f_C1IR, norm_coef, dst.ptr<Ipp32f>(), (int) dst.step, srcRoi);
                     CV_SUPPRESS_DEPRECATED_END

modules/imgproc/src/demosaicing.cpp

@@ -976,7 +976,7 @@ static void Bayer2RGB_VNG_8u( const Mat& srcmat, Mat& dstmat, int code )
     int N = size.width, N2 = N*2, N3 = N*3, N4 = N*4, N5 = N*5, N6 = N*6, N7 = N*7;
     int i, bufstep = N7*bcn;
     cv::AutoBuffer<ushort> _buf(bufstep*brows);
-    ushort* buf = (ushort*)_buf;
+    ushort* buf = _buf.data();
 
     bayer += bstep*2;
 

modules/imgproc/src/distransform.cpp

@@ -458,7 +458,7 @@ struct DTColumnInvoker : ParallelLoopBody
         int m = src->rows;
         size_t sstep = src->step, dstep = dst->step/sizeof(float);
         AutoBuffer<int> _d(m);
-        int* d = _d;
+        int* d = _d.data();
 
         for( i = i1; i < i2; i++ )
         {
@@ -503,7 +503,7 @@ struct DTRowInvoker : ParallelLoopBody
         int i, i1 = range.start, i2 = range.end;
         int n = dst->cols;
         AutoBuffer<uchar> _buf((n+2)*2*sizeof(float) + (n+2)*sizeof(int));
-        float* f = (float*)(uchar*)_buf;
+        float* f = (float*)_buf.data();
         float* z = f + n;
         int* v = alignPtr((int*)(z + n + 1), sizeof(int));
 
@@ -564,7 +564,7 @@ trueDistTrans( const Mat& src, Mat& dst )
 
     cv::AutoBuffer<uchar> _buf(std::max(m*2*sizeof(float) + (m*3+1)*sizeof(int), n*2*sizeof(float)));
     // stage 1: compute 1d distance transform of each column
-    float* sqr_tab = (float*)(uchar*)_buf;
+    float* sqr_tab = (float*)_buf.data();
     int* sat_tab = cv::alignPtr((int*)(sqr_tab + m*2), sizeof(int));
     int shift = m*2;
 

modules/imgproc/src/drawing.cpp

@@ -2398,8 +2398,8 @@ void cv::fillPoly(InputOutputArray _img, InputArrayOfArrays pts,
         return;
     AutoBuffer<Point*> _ptsptr(ncontours);
     AutoBuffer<int> _npts(ncontours);
-    Point** ptsptr = _ptsptr;
-    int* npts = _npts;
+    Point** ptsptr = _ptsptr.data();
+    int* npts = _npts.data();
 
     for( i = 0; i < ncontours; i++ )
     {
@@ -2426,8 +2426,8 @@ void cv::polylines(InputOutputArray _img, InputArrayOfArrays pts,
         return;
     AutoBuffer<Point*> _ptsptr(ncontours);
     AutoBuffer<int> _npts(ncontours);
-    Point** ptsptr = _ptsptr;
-    int* npts = _npts;
+    Point** ptsptr = _ptsptr.data();
+    int* npts = _npts.data();
 
     for( i = 0; i < ncontours; i++ )
     {

modules/imgproc/src/emd.cpp

@@ -359,7 +359,7 @@ static int icvInitEMD( const float* signature1, int size1,
     /* allocate buffers */
     _buffer.allocate(buffer_size);
 
-    state->buffer = buffer = _buffer;
+    state->buffer = buffer = _buffer.data();
     buffer_end = buffer + buffer_size;
 
     state->idx1 = (int*) buffer;

modules/imgproc/src/filter.cpp

@@ -1444,7 +1444,7 @@ private:
                 return 0;
         }
         AutoBuffer<uchar> buf(bufsz + 64);
-        uchar* bufptr = alignPtr((uchar*)buf, 32);
+        uchar* bufptr = alignPtr(buf.data(), 32);
         int step = (int)(width*sizeof(dst[0])*cn);
         float borderValue[] = {0.f, 0.f, 0.f};
         // here is the trick. IPP needs border type and extrapolates the row. We did it already.

modules/imgproc/src/geometry.cpp

@@ -524,7 +524,7 @@ float cv::intersectConvexConvex( InputArray _p1, InputArray _p2, OutputArray _p1
     }
 
     AutoBuffer<Point2f> _result(n*2 + m*2 + 1);
-    Point2f *fp1 = _result, *fp2 = fp1 + n;
+    Point2f *fp1 = _result.data(), *fp2 = fp1 + n;
     Point2f* result = fp2 + m;
     int orientation = 0;
 

modules/imgproc/src/hough.cpp

@@ -165,11 +165,11 @@ HoughLinesStandard( InputArray src, OutputArray lines, int type,
     AutoBuffer<float> _tabSin(numangle);
     AutoBuffer<float> _tabCos(numangle);
     int *accum = _accum.ptr<int>();
-    float *tabSin = _tabSin, *tabCos = _tabCos;
+    float *tabSin = _tabSin.data(), *tabCos = _tabCos.data();
 
     // create sin and cos table
     createTrigTable( numangle, min_theta, theta,
-                     irho, tabSin, tabCos );
+                     irho, tabSin, tabCos);
 
     // stage 1. fill accumulator
     for( i = 0; i < height; i++ )
@@ -963,7 +963,7 @@ void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, i
     AutoBuffer<float> _tabSin(numangle);
     AutoBuffer<float> _tabCos(numangle);
     int *accum = _accum.ptr<int>();
-    float *tabSin = _tabSin, *tabCos = _tabCos;
+    float *tabSin = _tabSin.data(), *tabCos = _tabCos.data();
 
     // create sin and cos table
     createTrigTable( numangle, min_theta, theta_step,
@@ -1408,8 +1408,8 @@ protected:
         int nBins = cvRound((maxRadius - minRadius)/dr*nBinsPerDr);
         AutoBuffer<int> bins(nBins);
         AutoBuffer<float> distBuf(nzSz), distSqrtBuf(nzSz);
-        float *ddata = distBuf;
-        float *dSqrtData = distSqrtBuf;
+        float *ddata = distBuf.data();
+        float *dSqrtData = distSqrtBuf.data();
 
         bool singleThread = (boundaries == Range(0, centerSz));
         int i = boundaries.start;
@@ -1434,7 +1434,7 @@ protected:
                 Mat_<float> distSqrtMat(1, nzCount, dSqrtData);
                 sqrt(distMat, distSqrtMat);
 
-                memset(bins, 0, sizeof(bins[0])*bins.size());
+                memset(bins.data(), 0, sizeof(bins[0])*bins.size());
                 for(int k = 0; k < nzCount; k++)
                 {
                     int bin = std::max(0, std::min(nBins-1, cvRound((dSqrtData[k] - minRadius)/dr*nBinsPerDr)));

modules/imgproc/src/imgwarp.cpp

@@ -228,7 +228,7 @@ static const void* initInterTab2D( int method, bool fixpt )
     {
         AutoBuffer<float> _tab(8*INTER_TAB_SIZE);
         int i, j, k1, k2;
-        initInterTab1D(method, _tab, INTER_TAB_SIZE);
+        initInterTab1D(method, _tab.data(), INTER_TAB_SIZE);
         for( i = 0; i < INTER_TAB_SIZE; i++ )
             for( j = 0; j < INTER_TAB_SIZE; j++, tab += ksize*ksize, itab += ksize*ksize )
             {

modules/imgproc/src/linefit.cpp

@@ -360,7 +360,7 @@ static void fitLine2D( const Point2f * points, int count, int dist,
     }
 
     AutoBuffer<float> wr(count*2);
-    float *w = wr, *r = w + count;
+    float *w = wr.data(), *r = w + count;
 
     for( k = 0; k < 20; k++ )
     {
@@ -495,7 +495,7 @@ static void fitLine3D( Point3f * points, int count, int dist,
     }
 
     AutoBuffer<float> buf(count*2);
-    float *w = buf, *r = w + count;
+    float *w = buf.data(), *r = w + count;
 
     for( k = 0; k < 20; k++ )
     {

modules/imgproc/src/phasecorr.cpp

@@ -607,7 +607,7 @@ void cv::createHanningWindow(OutputArray _dst, cv::Size winSize, int type)
     int rows = dst.rows, cols = dst.cols;
 
     AutoBuffer<double> _wc(cols);
-    double * const wc = (double *)_wc;
+    double* const wc = _wc.data();
 
     double coeff0 = 2.0 * CV_PI / (double)(cols - 1), coeff1 = 2.0f * CV_PI / (double)(rows - 1);
     for(int j = 0; j < cols; j++)

modules/imgproc/src/pyramids.cpp

@@ -860,10 +860,10 @@ pyrDown_( const Mat& _src, Mat& _dst, int borderType )
     int cn = _src.channels();
     int bufstep = (int)alignSize(dsize.width*cn, 16);
     AutoBuffer<WT> _buf(bufstep*PD_SZ + 16);
-    WT* buf = alignPtr((WT*)_buf, 16);
+    WT* buf = alignPtr((WT*)_buf.data(), 16);
     int tabL[CV_CN_MAX*(PD_SZ+2)], tabR[CV_CN_MAX*(PD_SZ+2)];
     AutoBuffer<int> _tabM(dsize.width*cn);
-    int* tabM = _tabM;
+    int* tabM = _tabM.data();
     WT* rows[PD_SZ];
     CastOp castOp;
     VecOp vecOp;
@@ -984,9 +984,9 @@ pyrUp_( const Mat& _src, Mat& _dst, int)
     int cn = _src.channels();
     int bufstep = (int)alignSize((dsize.width+1)*cn, 16);
     AutoBuffer<WT> _buf(bufstep*PU_SZ + 16);
-    WT* buf = alignPtr((WT*)_buf, 16);
+    WT* buf = alignPtr((WT*)_buf.data(), 16);
     AutoBuffer<int> _dtab(ssize.width*cn);
-    int* dtab = _dtab;
+    int* dtab = _dtab.data();
     WT* rows[PU_SZ];
     T* dsts[2];
     CastOp castOp;

modules/imgproc/src/resize.cpp

@@ -686,18 +686,18 @@ public:
         {
             last_eval = 1 - interp_y_len;
             evalbuf_start = 1;
-            hResize((ET*)src, cn, xoffsets, xcoeffs, (fixedpoint*)linebuf, min_x, max_x, dst_width);
+            hResize((ET*)src, cn, xoffsets, xcoeffs, linebuf.data(), min_x, max_x, dst_width);
         }
         int dy = range.start;
         for (; dy < rmin_y; dy++)
-            vlineSet<ET, FT>((fixedpoint*)linebuf, (ET*)(dst + dst_step * dy), dst_width*cn);
+            vlineSet<ET, FT>(linebuf.data(), (ET*)(dst + dst_step * dy), dst_width*cn);
         for (; dy < rmax_y; dy++)
         {
             int &iy = yoffsets[dy];
 
             int i;
             for (i = max(iy, last_eval + interp_y_len); i < min(iy + interp_y_len, src_height); i++, evalbuf_start = (evalbuf_start + 1) % interp_y_len)
-                hResize((ET*)(src + i * src_step), cn, xoffsets, xcoeffs, (fixedpoint*)linebuf + evalbuf_start*(dst_width * cn), min_x, max_x, dst_width);
+                hResize((ET*)(src + i * src_step), cn, xoffsets, xcoeffs, linebuf.data() + evalbuf_start*(dst_width * cn), min_x, max_x, dst_width);
             evalbuf_start = (evalbuf_start + max(iy, src_height - interp_y_len) - max(last_eval, src_height - interp_y_len)) % interp_y_len;
             last_eval = iy;
 
@@ -707,9 +707,9 @@ public:
             for (; i < interp_y_len; i++)
                 curcoeffs[i] = ycoeffs[ dy*interp_y_len - evalbuf_start + i];
 
-            vlineResize<ET, FT, interp_y_len>((fixedpoint*)linebuf, dst_width*cn, curcoeffs, (ET*)(dst + dst_step * dy), dst_width*cn);
+            vlineResize<ET, FT, interp_y_len>(linebuf.data(), dst_width*cn, curcoeffs, (ET*)(dst + dst_step * dy), dst_width*cn);
         }
-        fixedpoint *endline = (fixedpoint*)linebuf;
+        fixedpoint *endline = linebuf.data();
         if (last_eval + interp_y_len > src_height)
             endline += dst_width*cn*((evalbuf_start + src_height - 1 - last_eval) % interp_y_len);
         else
@@ -757,7 +757,7 @@ void resize_bitExact(const uchar* src, size_t src_step, int src_width, int src_h
                            dst_height * sizeof(int) +
                            dst_width * interp_x.len*sizeof(fixedpoint) +
                            dst_height * interp_y.len * sizeof(fixedpoint) );
-    int* xoffsets = (int*)((uchar*)buf);
+    int* xoffsets = (int*)buf.data();
     int* yoffsets = xoffsets + dst_width;
     fixedpoint* xcoeffs = (fixedpoint*)(yoffsets + dst_height);
     fixedpoint* ycoeffs = xcoeffs + dst_width * interp_x.len;
@@ -950,7 +950,7 @@ resizeNN( const Mat& src, Mat& dst, double fx, double fy )
 {
     Size ssize = src.size(), dsize = dst.size();
     AutoBuffer<int> _x_ofs(dsize.width);
-    int* x_ofs = _x_ofs;
+    int* x_ofs = _x_ofs.data();
     int pix_size = (int)src.elemSize();
     int pix_size4 = (int)(pix_size / sizeof(int));
     double ifx = 1./fx, ify = 1./fy;
@@ -2226,7 +2226,7 @@ public:
         for(int k = 0; k < ksize; k++ )
         {
             prev_sy[k] = -1;
-            rows[k] = (WT*)_buffer + bufstep*k;
+            rows[k] = _buffer.data() + bufstep*k;
         }
 
         const AT* beta = _beta + ksize * range.start;
@@ -3039,7 +3039,7 @@ public:
         AutoBuffer<WT> _buffer(dsize.width*2);
         const DecimateAlpha* xtab = xtab0;
         int xtab_size = xtab_size0;
-        WT *buf = _buffer, *sum = buf + dsize.width;
+        WT *buf = _buffer.data(), *sum = buf + dsize.width;
         int j_start = tabofs[range.start], j_end = tabofs[range.end], j, k, dx, prev_dy = ytab[j_start].di;
 
         for( dx = 0; dx < dsize.width; dx++ )
@@ -3322,7 +3322,7 @@ static bool ocl_resize( InputArray _src, OutputArray _dst, Size dsize,
         if (depth == CV_8U && ((void)0, 0))
         {
             AutoBuffer<uchar> _buffer((dsize.width + dsize.height)*(sizeof(int) + sizeof(short)*2));
-            int* xofs = (int*)(uchar*)_buffer, * yofs = xofs + dsize.width;
+            int* xofs = (int*)_buffer.data(), * yofs = xofs + dsize.width;
             short* ialpha = (short*)(yofs + dsize.height), * ibeta = ialpha + dsize.width*2;
             float fxx, fyy;
             int sx, sy;
@@ -3357,7 +3357,7 @@ static bool ocl_resize( InputArray _src, OutputArray _dst, Size dsize,
 
             int wdepth = std::max(depth, CV_32S), wtype = CV_MAKETYPE(wdepth, cn);
             UMat coeffs;
-            Mat(1, static_cast<int>(_buffer.size()), CV_8UC1, (uchar *)_buffer).copyTo(coeffs);
+            Mat(1, static_cast<int>(_buffer.size()), CV_8UC1, _buffer.data()).copyTo(coeffs);
 
             k.create("resizeLN", ocl::imgproc::resize_oclsrc,
                      format("-D INTER_LINEAR_INTEGER -D depth=%d -D T=%s -D T1=%s "
@@ -3440,17 +3440,17 @@ static bool ocl_resize( InputArray _src, OutputArray _dst, Size dsize,
 
             AutoBuffer<int> _xymap_tab(xytab_size), _xyofs_tab(tabofs_size);
             AutoBuffer<float> _xyalpha_tab(xytab_size);
-            int * xmap_tab = _xymap_tab, * ymap_tab = _xymap_tab + (ssize.width << 1);
-            float * xalpha_tab = _xyalpha_tab, * yalpha_tab = _xyalpha_tab + (ssize.width << 1);
-            int * xofs_tab = _xyofs_tab, * yofs_tab = _xyofs_tab + dsize.width + 1;
+            int * xmap_tab = _xymap_tab.data(), * ymap_tab = _xymap_tab.data() + (ssize.width << 1);
+            float * xalpha_tab = _xyalpha_tab.data(), * yalpha_tab = _xyalpha_tab.data() + (ssize.width << 1);
+            int * xofs_tab = _xyofs_tab.data(), * yofs_tab = _xyofs_tab.data() + dsize.width + 1;
 
             ocl_computeResizeAreaTabs(ssize.width, dsize.width, inv_fx, xmap_tab, xalpha_tab, xofs_tab);
             ocl_computeResizeAreaTabs(ssize.height, dsize.height, inv_fy, ymap_tab, yalpha_tab, yofs_tab);
 
             // loading precomputed arrays to GPU
-            Mat(1, xytab_size, CV_32FC1, (void *)_xyalpha_tab).copyTo(alphaOcl);
-            Mat(1, xytab_size, CV_32SC1, (void *)_xymap_tab).copyTo(mapOcl);
-            Mat(1, tabofs_size, CV_32SC1, (void *)_xyofs_tab).copyTo(tabofsOcl);
+            Mat(1, xytab_size, CV_32FC1, _xyalpha_tab.data()).copyTo(alphaOcl);
+            Mat(1, xytab_size, CV_32SC1, _xymap_tab.data()).copyTo(mapOcl);
+            Mat(1, tabofs_size, CV_32SC1, _xyofs_tab.data()).copyTo(tabofsOcl);
         }
 
         ocl::KernelArg srcarg = ocl::KernelArg::ReadOnly(src), dstarg = ocl::KernelArg::WriteOnly(dst);
@@ -3856,7 +3856,7 @@ void resize(int src_type,
                 int area = iscale_x*iscale_y;
                 size_t srcstep = src_step / src.elemSize1();
                 AutoBuffer<int> _ofs(area + dsize.width*cn);
-                int* ofs = _ofs;
+                int* ofs = _ofs.data();
                 int* xofs = ofs + area;
                 ResizeAreaFastFunc func = areafast_tab[depth];
                 CV_Assert( func != 0 );
@@ -3881,13 +3881,13 @@ void resize(int src_type,
             CV_Assert( func != 0 && cn <= 4 );
 
             AutoBuffer<DecimateAlpha> _xytab((src_width + src_height)*2);
-            DecimateAlpha* xtab = _xytab, *ytab = xtab + src_width*2;
+            DecimateAlpha* xtab = _xytab.data(), *ytab = xtab + src_width*2;
 
             int xtab_size = computeResizeAreaTab(src_width, dsize.width, cn, scale_x, xtab);
             int ytab_size = computeResizeAreaTab(src_height, dsize.height, 1, scale_y, ytab);
 
             AutoBuffer<int> _tabofs(dsize.height + 1);
-            int* tabofs = _tabofs;
+            int* tabofs = _tabofs.data();
             for( k = 0, dy = 0; k < ytab_size; k++ )
             {
                 if( k == 0 || ytab[k].di != ytab[k-1].di )
@@ -3922,7 +3922,7 @@ void resize(int src_type,
     CV_Assert( func != 0 );
 
     AutoBuffer<uchar> _buffer((width + dsize.height)*(sizeof(int) + sizeof(float)*ksize));
-    int* xofs = (int*)(uchar*)_buffer;
+    int* xofs = (int*)_buffer.data();
     int* yofs = xofs + width;
     float* alpha = (float*)(yofs + dsize.height);
     short* ialpha = (short*)alpha;

modules/imgproc/src/rotcalipers.cpp

@@ -96,7 +96,7 @@ static void rotatingCalipers( const Point2f* points, int n, int mode, float* out
     char buffer[32] = {};
     int i, k;
     AutoBuffer<float> abuf(n*3);
-    float* inv_vect_length = abuf;
+    float* inv_vect_length = abuf.data();
     Point2f* vect = (Point2f*)(inv_vect_length + n);
     int left = 0, bottom = 0, right = 0, top = 0;
     int seq[4] = { -1, -1, -1, -1 };

modules/imgproc/src/shapedescr.cpp

@@ -318,7 +318,7 @@ cv::RotatedRect cv::fitEllipse( InputArray _points )
     const Point2f* ptsf = points.ptr<Point2f>();
 
     AutoBuffer<double> _Ad(n*5), _bd(n);
-    double *Ad = _Ad, *bd = _bd;
+    double *Ad = _Ad.data(), *bd = _bd.data();
 
     // first fit for parameters A - E
     Mat A( n, 5, CV_64F, Ad );

modules/imgproc/src/smooth.cpp

@@ -3614,9 +3614,9 @@ public:
     virtual void operator() (const Range& range) const CV_OVERRIDE
     {
         AutoBuffer<FT> _buf(width*cn*kylen);
-        FT* buf = _buf;
+        FT* buf = _buf.data();
         AutoBuffer<FT*> _ptrs(kylen*2);
-        FT** ptrs = _ptrs;
+        FT** ptrs = _ptrs.data();
 
         if (kylen == 1)
         {

modules/imgproc/src/sumpixels.cpp

@@ -216,7 +216,7 @@ void integral_( const T* src, size_t _srcstep, ST* sum, size_t _sumstep,
     else
     {
         AutoBuffer<ST> _buf(width+cn);
-        ST* buf = _buf;
+        ST* buf = _buf.data();
         ST s;
         QT sq;
         for( k = 0; k < cn; k++, src++, sum++, tilted++, buf++ )

modules/imgproc/test/test_convhull.cpp

@@ -1584,7 +1584,7 @@ int CV_FitLineTest::prepare_test_case( int test_case_idx )
 void CV_FitLineTest::run_func()
 {
     if(!test_cpp)
-        cvFitLine( points, dist_type, 0, reps, aeps, line );
+        cvFitLine( points, dist_type, 0, reps, aeps, line.data());
     else if(dims == 2)
         cv::fitLine(cv::cvarrToMat(points), (cv::Vec4f&)line[0], dist_type, 0, reps, aeps);
     else

modules/ml/src/ann_mlp.cpp

@@ -434,7 +434,7 @@ public:
         }
 
         cv::AutoBuffer<double> _buf(buf_sz+noutputs);
-        double* buf = _buf;
+        double* buf = _buf.data();
 
         if( !_outputs.needed() )
         {
@@ -996,7 +996,7 @@ public:
             _idx[i] = i;
 
         AutoBuffer<double> _buf(max_lsize*2);
-        double* buf[] = { _buf, (double*)_buf + max_lsize };
+        double* buf[] = { _buf.data(), _buf.data() + max_lsize };
 
         const double* sw = _sw.empty() ? 0 : _sw.ptr<double>();
 

modules/ml/src/boost.cpp

@@ -205,7 +205,7 @@ public:
         int nvars = (int)varIdx.size();
         double sumw = 0., C = 1.;
         cv::AutoBuffer<double> buf(n + nvars);
-        double* result = buf;
+        double* result = buf.data();
         float* sbuf = (float*)(result + n);
         Mat sample(1, nvars, CV_32F, sbuf);
         int predictFlags = bparams.boostType == Boost::DISCRETE ? (PREDICT_MAX_VOTE | RAW_OUTPUT) : PREDICT_SUM;

modules/ml/src/data.cpp

@@ -335,7 +335,7 @@ public:
         CatMapHash ofshash;
 
         AutoBuffer<uchar> buf(nsamples);
-        Mat non_missing(layout == ROW_SAMPLE ? Size(1, nsamples) : Size(nsamples, 1), CV_8U, (uchar*)buf);
+        Mat non_missing(layout == ROW_SAMPLE ? Size(1, nsamples) : Size(nsamples, 1), CV_8U, buf.data());
         bool haveMissing = !missing.empty();
         if( haveMissing )
         {

modules/ml/src/kdtree.cpp

@@ -285,13 +285,13 @@ int KDTree::findNearest(InputArray _vec, int K, int emax,
     CV_Assert(K > 0 && (normType == NORM_L2 || normType == NORM_L1));
 
     AutoBuffer<uchar> _buf((K+1)*(sizeof(float) + sizeof(int)));
-    int* idx = (int*)(uchar*)_buf;
+    int* idx = (int*)_buf.data();
     float* dist = (float*)(idx + K + 1);
     int i, j, ncount = 0, e = 0;
 
     int qsize = 0, maxqsize = 1 << 10;
     AutoBuffer<uchar> _pqueue(maxqsize*sizeof(PQueueElem));
-    PQueueElem* pqueue = (PQueueElem*)(uchar*)_pqueue;
+    PQueueElem* pqueue = (PQueueElem*)_pqueue.data();
     emax = std::max(emax, 1);
 
     for( e = 0; e < emax; )
@@ -433,7 +433,7 @@ void KDTree::findOrthoRange(InputArray _lowerBound,
 
     std::vector<int> idx;
     AutoBuffer<int> _stack(MAX_TREE_DEPTH*2 + 1);
-    int* stack = _stack;
+    int* stack = _stack.data();
     int top = 0;
 
     stack[top++] = 0;

modules/ml/src/knearest.cpp

@@ -149,7 +149,7 @@ public:
         int k = std::min(k0, nsamples);
 
         AutoBuffer<float> buf(testcount*k*2);
-        float* dbuf = buf;
+        float* dbuf = buf.data();
         float* rbuf = dbuf + testcount*k;
 
         const float* rptr = responses.ptr<float>();

modules/ml/src/nbayes.cpp

@@ -241,8 +241,8 @@ public:
             }
             // allocate memory and initializing headers for calculating
             cv::AutoBuffer<double> _buffer(nvars*2);
-            double* _diffin = _buffer;
-            double* _diffout = _buffer + nvars;
+            double* _diffin = _buffer.data();
+            double* _diffout = _buffer.data() + nvars;
             Mat diffin( 1, nvars, CV_64FC1, _diffin );
             Mat diffout( 1, nvars, CV_64FC1, _diffout );
 

modules/ml/src/svm.cpp

@@ -1579,7 +1579,7 @@ public:
             return;
 
         AutoBuffer<double> vbuf(var_count);
-        double* v = vbuf;
+        double* v = vbuf.data();
         Mat new_sv(df_count, var_count, CV_32F);
 
         vector<DecisionFunc> new_df;
@@ -1914,7 +1914,7 @@ public:
             int class_count = !svm->class_labels.empty() ? (int)svm->class_labels.total() : svmType == ONE_CLASS ? 1 : 0;
 
             AutoBuffer<float> _buffer(sv_total + (class_count+1)*2);
-            float* buffer = _buffer;
+            float* buffer = _buffer.data();
 
             int i, j, dfi, k, si;
 

modules/ml/src/tree.cpp

@@ -417,7 +417,7 @@ int DTreesImpl::findBestSplit( const vector<int>& _sidx )
     int splitidx = -1;
     int vi_, nv = (int)activeVars.size();
     AutoBuffer<int> buf(w->maxSubsetSize*2);
-    int *subset = buf, *best_subset = subset + w->maxSubsetSize;
+    int *subset = buf.data(), *best_subset = subset + w->maxSubsetSize;
     WSplit split, best_split;
     best_split.quality = 0.;
 
@@ -488,7 +488,7 @@ void DTreesImpl::calcValue( int nidx, const vector<int>& _sidx )
         //    misclassified samples with cv_labels(*)==j.
 
         // compute the number of instances of each class
-        double* cls_count = buf;
+        double* cls_count = buf.data();
         double* cv_cls_count = cls_count + m;
 
         double max_val = -1, total_weight = 0;
@@ -592,7 +592,7 @@ void DTreesImpl::calcValue( int nidx, const vector<int>& _sidx )
         }
         else
         {
-            double *cv_sum = buf, *cv_sum2 = cv_sum + cv_n;
+            double *cv_sum = buf.data(), *cv_sum2 = cv_sum + cv_n;
             double* cv_count = (double*)(cv_sum2 + cv_n);
 
             for( j = 0; j < cv_n; j++ )
@@ -646,7 +646,7 @@ DTreesImpl::WSplit DTreesImpl::findSplitOrdClass( int vi, const vector<int>& _si
     const int* sidx = &_sidx[0];
     const int* responses = &w->cat_responses[0];
     const double* weights = &w->sample_weights[0];
-    double* lcw = (double*)(uchar*)buf;
+    double* lcw = (double*)buf.data();
     double* rcw = lcw + m;
     float* values = (float*)(rcw + m);
     int* sorted_idx = (int*)(values + n);
@@ -717,7 +717,7 @@ void DTreesImpl::clusterCategories( const double* vectors, int n, int m, double*
     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((uint64)-1);
 
@@ -819,12 +819,12 @@ DTreesImpl::WSplit DTreesImpl::findSplitCatClass( int vi, const vector<int>& _si
         base_size += mi;
     AutoBuffer<double> buf(base_size + n);
 
-    double* lc = (double*)buf;
+    double* lc = buf.data();
     double* rc = lc + m;
     double* _cjk = rc + m*2, *cjk = _cjk;
     double* c_weights = cjk + m*mi;
 
-    int* labels = (int*)(buf + base_size);
+    int* labels = (int*)(buf.data() + base_size);
     w->data->getNormCatValues(vi, _sidx, labels);
     const int* responses = &w->cat_responses[0];
     const double* weights = &w->sample_weights[0];
@@ -991,7 +991,7 @@ DTreesImpl::WSplit DTreesImpl::findSplitOrdReg( int vi, const vector<int>& _sidx
 
     AutoBuffer<uchar> buf(n*(sizeof(int) + sizeof(float)));
 
-    float* values = (float*)(uchar*)buf;
+    float* values = (float*)buf.data();
     int* sorted_idx = (int*)(values + n);
     w->data->getValues(vi, _sidx, values);
     const double* responses = &w->ord_responses[0];
@@ -1053,7 +1053,7 @@ DTreesImpl::WSplit DTreesImpl::findSplitCatReg( int vi, const vector<int>& _sidx
     int mi = getCatCount(vi);
 
     AutoBuffer<double> buf(3*mi + 3 + n);
-    double* sum = (double*)buf + 1;
+    double* sum = buf.data() + 1;
     double* counts = sum + mi + 1;
     double** sum_ptr = (double**)(counts + mi);
     int* cat_labels = (int*)(sum_ptr + mi);
@@ -1148,7 +1148,7 @@ int DTreesImpl::calcDir( int splitidx, const vector<int>& _sidx,
     if( mi <= 0 ) // split on an ordered variable
     {
         float c = split.c;
-        float* values = buf;
+        float* values = buf.data();
         w->data->getValues(vi, _sidx, values);
 
         for( i = 0; i < n; i++ )
@@ -1169,7 +1169,7 @@ int DTreesImpl::calcDir( int splitidx, const vector<int>& _sidx,
     else
     {
         const int* subset = &w->wsubsets[split.subsetOfs];
-        int* cat_labels = (int*)(float*)buf;
+        int* cat_labels = (int*)buf.data();
         w->data->getNormCatValues(vi, _sidx, cat_labels);
 
         for( i = 0; i < n; i++ )
@@ -1372,7 +1372,7 @@ float DTreesImpl::predictTrees( const Range& range, const Mat& sample, int flags
     int i, ncats = (int)catOfs.size(), nclasses = (int)classLabels.size();
     int catbufsize = ncats > 0 ? nvars : 0;
     AutoBuffer<int> buf(nclasses + catbufsize + 1);
-    int* votes = buf;
+    int* votes = buf.data();
     int* catbuf = votes + nclasses;
     const int* cvidx = (flags & (COMPRESSED_INPUT|PREPROCESSED_INPUT)) == 0 && !varIdx.empty() ? &compVarIdx[0] : 0;
     const uchar* vtype = &varType[0];

modules/objdetect/src/cascadedetect.cpp

@@ -1346,7 +1346,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
 
         size_t i, nscales = scales.size();
         cv::AutoBuffer<int> stripeSizeBuf(nscales);
-        int* stripeSizes = stripeSizeBuf;
+        int* stripeSizes = stripeSizeBuf.data();
         const FeatureEvaluator::ScaleData* s = &featureEvaluator->getScaleData(0);
         Size szw = s->getWorkingSize(data.origWinSize);
         int nstripes = cvCeil(szw.width/32.);

modules/objdetect/src/hog.cpp

@@ -297,7 +297,7 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
 #endif
 
     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_REFLECT_101;
@@ -312,7 +312,7 @@ void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
     // x- & y- derivatives for the whole row
     int width = gradsize.width;
     AutoBuffer<float> _dbuf(width*4);
-    float* const dbuf = _dbuf;
+    float* const 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);
@@ -656,7 +656,7 @@ void HOGCache::init(const HOGDescriptor* _descriptor,
 
     {
         AutoBuffer<float> di(blockSize.height), dj(blockSize.width);
-        float* _di = (float*)di, *_dj = (float*)dj;
+        float* _di = di.data(), *_dj = dj.data();
         float bh = blockSize.height * 0.5f, bw = blockSize.width * 0.5f;
 
         i = 0;

modules/objdetect/test/test_cascadeandhog.cpp

@@ -1193,7 +1193,7 @@ void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle
            _lut(0,i) = (float)i;
 
     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_REFLECT_101;
@@ -1208,7 +1208,7 @@ void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle
     // x- & y- derivatives for the whole row
     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);

modules/python/src2/cv2.cpp

@@ -189,7 +189,7 @@ public:
             _sizes[i] = sizes[i];
         if( cn > 1 )
             _sizes[dims++] = cn;
-        PyObject* o = PyArray_SimpleNew(dims, _sizes, typenum);
+        PyObject* o = PyArray_SimpleNew(dims, _sizes.data(), typenum);
         if(!o)
             CV_Error_(Error::StsError, ("The numpy array of typenum=%d, ndims=%d can not be created", typenum, dims));
         return allocate(o, dims0, sizes, type, step);

modules/ts/src/ts_perf.cpp

@@ -2166,7 +2166,7 @@ void perf::sort(std::vector<cv::KeyPoint>& pts, cv::InputOutputArray descriptors
     for (int i = 0; i < desc.rows; ++i)
         idxs[i] = i;
 
-    std::sort((int*)idxs, (int*)idxs + desc.rows, KeypointComparator(pts));
+    std::sort(idxs.data(), idxs.data() + desc.rows, KeypointComparator(pts));
 
     std::vector<cv::KeyPoint> spts(pts.size());
     cv::Mat sdesc(desc.size(), desc.type());

modules/video/src/bgfg_gaussmix2.cpp

@@ -575,7 +575,7 @@ public:
 
         for( int y = y0; y < y1; y++ )
         {
-            const float* data = buf;
+            const float* data = buf.data();
             if( src->depth() != CV_32F )
                 src->row(y).convertTo(Mat(1, ncols, CV_32FC(nchannels), (void*)data), CV_32F);
             else

modules/video/src/lkpyramid.cpp

@@ -67,7 +67,7 @@ static void calcSharrDeriv(const cv::Mat& src, cv::Mat& dst)
 
     int x, y, delta = (int)alignSize((cols + 2)*cn, 16);
     AutoBuffer<deriv_type> _tempBuf(delta*2 + 64);
-    deriv_type *trow0 = alignPtr(_tempBuf + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);
+    deriv_type *trow0 = alignPtr(_tempBuf.data() + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);
 
 #if CV_SIMD128
     v_int16x8 c3 = v_setall_s16(3), c10 = v_setall_s16(10);
@@ -191,8 +191,8 @@ void cv::detail::LKTrackerInvoker::operator()(const Range& range) const
     cv::AutoBuffer<deriv_type> _buf(winSize.area()*(cn + cn2));
     int derivDepth = DataType<deriv_type>::depth;
 
-    Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (deriv_type*)_buf);
-    Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (deriv_type*)_buf + winSize.area()*cn);
+    Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), _buf.data());
+    Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), _buf.data() + winSize.area()*cn);
 
     for( int ptidx = range.start; ptidx < range.end; ptidx++ )
     {

modules/video/src/optflowgf.cpp

@@ -121,10 +121,10 @@ FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma )
     int width = src.cols;
     int height = src.rows;
     AutoBuffer<float> kbuf(n*6 + 3), _row((width + n*2)*3);
-    float* g = kbuf + n;
+    float* g = kbuf.data() + n;
     float* xg = g + n*2 + 1;
     float* xxg = xg + n*2 + 1;
-    float *row = (float*)_row + n*3;
+    float *row = _row.data() + n*3;
     double ig11, ig03, ig33, ig55;
 
     FarnebackPrepareGaussian(n, sigma, g, xg, xxg, ig11, ig03, ig33, ig55);
@@ -322,7 +322,7 @@ FarnebackUpdateFlow_Blur( const Mat& _R0, const Mat& _R1,
     double scale = 1./(block_size*block_size);
 
     AutoBuffer<double> _vsum((width+m*2+2)*5);
-    double* vsum = _vsum + (m+1)*5;
+    double* vsum = _vsum.data() + (m+1)*5;
 
     // init vsum
     const float* srow0 = matM.ptr<float>();
@@ -416,10 +416,10 @@ FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
 
     AutoBuffer<float> _vsum((width+m*2+2)*5 + 16), _hsum(width*5 + 16);
     AutoBuffer<float> _kernel((m+1)*5 + 16);
-    AutoBuffer<float*> _srow(m*2+1);
-    float *vsum = alignPtr((float*)_vsum + (m+1)*5, 16), *hsum = alignPtr((float*)_hsum, 16);
-    float* kernel = (float*)_kernel;
-    const float** srow = (const float**)&_srow[0];
+    AutoBuffer<const float*> _srow(m*2+1);
+    float *vsum = alignPtr(_vsum.data() + (m+1)*5, 16), *hsum = alignPtr(_hsum.data(), 16);
+    float* kernel = _kernel.data();
+    const float** srow = _srow.data();
     kernel[0] = (float)s;
 
     for( i = 1; i <= m; i++ )

modules/videoio/src/cap_mfx_common.cpp

@@ -96,7 +96,7 @@ SurfacePool::SurfacePool(ushort width_, ushort height_, ushort count, const mfxF
     for(int i = 0; i < count; ++i)
     {
         mfxFrameSurface1 &surface = surfaces[i];
-        uint8_t * dataPtr = buffers + oneSize * i;
+        uint8_t * dataPtr = buffers.data() + oneSize * i;
         memset(&surface, 0, sizeof(mfxFrameSurface1));
         surface.Info = frameInfo;
         surface.Data.Y = dataPtr;

modules/videoio/src/cap_msmf.cpp

@@ -1029,8 +1029,8 @@ bool CvCapture_MSMF::open(const cv::String& _filename)
             srAttr->SetUnknown(MF_SOURCE_READER_D3D_MANAGER, D3DMgr.Get());
 #endif
         cv::AutoBuffer<wchar_t> unicodeFileName(_filename.length() + 1);
-        MultiByteToWideChar(CP_ACP, 0, _filename.c_str(), -1, unicodeFileName, (int)_filename.length() + 1);
-        if (SUCCEEDED(MFCreateSourceReaderFromURL(unicodeFileName, srAttr.Get(), &videoFileSource)))
+        MultiByteToWideChar(CP_ACP, 0, _filename.c_str(), -1, unicodeFileName.data(), (int)_filename.length() + 1);
+        if (SUCCEEDED(MFCreateSourceReaderFromURL(unicodeFileName.data(), srAttr.Get(), &videoFileSource)))
         {
             isOpen = true;
             sampleTime = 0;
@@ -2081,8 +2081,8 @@ bool CvVideoWriter_MSMF::open( const cv::String& filename, int fourcc,
     {
         // Create the sink writer
         cv::AutoBuffer<wchar_t> unicodeFileName(filename.length() + 1);
-        MultiByteToWideChar(CP_ACP, 0, filename.c_str(), -1, unicodeFileName, (int)filename.length() + 1);
-        HRESULT hr = MFCreateSinkWriterFromURL(unicodeFileName, NULL, spAttr.Get(), &sinkWriter);
+        MultiByteToWideChar(CP_ACP, 0, filename.c_str(), -1, unicodeFileName.data(), (int)filename.length() + 1);
+        HRESULT hr = MFCreateSinkWriterFromURL(unicodeFileName.data(), NULL, spAttr.Get(), &sinkWriter);
         if (SUCCEEDED(hr))
         {
             // Configure the sink writer and tell it start to start accepting data