Merge pull request #395 from LeonidBeynenson:fix_ml_large_data_bug__2.4

apps/traincascade/boost.cpp

@@ -360,7 +360,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
 
             if (is_buf_16u)
             {
-                unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
+                unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                     vi*sample_count + data_root->offset);
                 for( int i = 0; i < num_valid; i++ )
                 {
@@ -373,7 +373,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
             }
             else
             {
-                int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
+                int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
                     vi*sample_count + root->offset;
                 for( int i = 0; i < num_valid; i++ )
                 {
@@ -390,14 +390,14 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
         const int* src_lbls = get_cv_labels(data_root, (int*)(uchar*)inn_buf);
         if (is_buf_16u)
         {
-            unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
+            unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                 (workVarCount-1)*sample_count + root->offset);
             for( int i = 0; i < count; i++ )
                 udst[i] = (unsigned short)src_lbls[sidx[i]];
         }
         else
         {
-            int* idst = buf->data.i + root->buf_idx*buf->cols +
+            int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
                 (workVarCount-1)*sample_count + root->offset;
             for( int i = 0; i < count; i++ )
                 idst[i] = src_lbls[sidx[i]];
@@ -407,14 +407,14 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
         const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
         if (is_buf_16u)
         {
-            unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
+            unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                 workVarCount*sample_count + root->offset);
             for( int i = 0; i < count; i++ )
                 sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
         }
         else
         {
-            int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
+            int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
                 workVarCount*sample_count + root->offset;
             for( int i = 0; i < count; i++ )
                 sample_idx_dst[i] = sample_idx_src[sidx[i]];
@@ -489,6 +489,10 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
     int* idst = 0;
     unsigned short* udst = 0;
 
+    uint64 effective_buf_size = 0;
+    int effective_buf_height = 0, effective_buf_width = 0;
+
+
     clear();
     shared = true;
     have_labels = true;
@@ -548,13 +552,28 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
     var_type->data.i[var_count] = cat_var_count;
     var_type->data.i[var_count+1] = cat_var_count+1;
     work_var_count = ( cat_var_count ? 0 : numPrecalcIdx ) + 1/*cv_lables*/;
-    buf_size = (work_var_count + 1) * sample_count/*sample_indices*/;
     buf_count = 2;
 
+    buf_size = -1; // the member buf_size is obsolete
+
+    effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
+    effective_buf_width = sample_count;
+    effective_buf_height = work_var_count+1;
+
+    if (effective_buf_width >= effective_buf_height)
+        effective_buf_height *= buf_count;
+    else
+        effective_buf_width *= buf_count;
+
+    if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
+    {
+        CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
+    }
+
     if ( is_buf_16u )
-        buf = cvCreateMat( buf_count, buf_size, CV_16UC1 );
+        buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 );
     else
-        buf = cvCreateMat( buf_count, buf_size, CV_32SC1 );
+        buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 );
 
     cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
 
@@ -609,7 +628,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
     priors_mult = cvCloneMat( priors );
     counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
     direction = cvCreateMat( 1, sample_count, CV_8UC1 );
-    split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );
+    split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );//TODO: make a pointer
 }
 
 void CvCascadeBoostTrainData::free_train_data()
@@ -652,10 +671,10 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
     if ( vi < numPrecalcIdx )
     {
         if( !is_buf_16u )
-            *sortedIndices = buf->data.i + n->buf_idx*buf->cols + vi*sample_count + n->offset;
+            *sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + vi*sample_count + n->offset;
         else
         {
-            const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
+            const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
                                                     vi*sample_count + n->offset );
             for( int i = 0; i < nodeSampleCount; i++ )
                 sortedIndicesBuf[i] = shortIndices[i];
@@ -1027,6 +1046,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
     int newBufIdx = data->get_child_buf_idx( node );
     int workVarCount = data->get_work_var_count();
     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;
     bool splitInputData;
@@ -1070,7 +1090,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
         if (data->is_buf_16u)
         {
             ushort *ldst, *rdst;
-            ldst = (ushort*)(buf->data.s + left->buf_idx*buf->cols +
+            ldst = (ushort*)(buf->data.s + left->buf_idx*length_buf_row +
                 vi*scount + left->offset);
             rdst = (ushort*)(ldst + nl);
 
@@ -1096,9 +1116,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
         else
         {
             int *ldst, *rdst;
-            ldst = buf->data.i + left->buf_idx*buf->cols +
+            ldst = buf->data.i + left->buf_idx*length_buf_row +
                 vi*scount + left->offset;
-            rdst = buf->data.i + right->buf_idx*buf->cols +
+            rdst = buf->data.i + right->buf_idx*length_buf_row +
                 vi*scount + right->offset;
 
             // split sorted
@@ -1131,9 +1151,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
 
     if (data->is_buf_16u)
     {
-        unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
+        unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
             (workVarCount-1)*scount + left->offset);
-        unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
+        unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
             (workVarCount-1)*scount + right->offset);
 
         for( int i = 0; i < n; i++ )
@@ -1154,9 +1174,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
     }
     else
     {
-        int *ldst = buf->data.i + left->buf_idx*buf->cols +
+        int *ldst = buf->data.i + left->buf_idx*length_buf_row +
             (workVarCount-1)*scount + left->offset;
-        int *rdst = buf->data.i + right->buf_idx*buf->cols +
+        int *rdst = buf->data.i + right->buf_idx*length_buf_row +
             (workVarCount-1)*scount + right->offset;
 
         for( int i = 0; i < n; i++ )
@@ -1184,9 +1204,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
 
     if (data->is_buf_16u)
     {
-        unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
+        unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
             workVarCount*scount + left->offset);
-        unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
+        unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
             workVarCount*scount + right->offset);
         for (int i = 0; i < n; i++)
         {
@@ -1205,9 +1225,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
     }
     else
     {
-        int* ldst = buf->data.i + left->buf_idx*buf->cols +
+        int* ldst = buf->data.i + left->buf_idx*length_buf_row +
             workVarCount*scount + left->offset;
-        int* rdst = buf->data.i + right->buf_idx*buf->cols +
+        int* rdst = buf->data.i + right->buf_idx*length_buf_row +
             workVarCount*scount + right->offset;
         for (int i = 0; i < n; i++)
         {
@@ -1352,6 +1372,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
         sampleIdx = data->get_sample_indices( data->data_root, sampleIdxBuf );
     }
     CvMat* buf = data->buf;
+    size_t length_buf_row = data->get_length_subbuf();
     if( !tree ) // before training the first tree, initialize weights and other parameters
     {
         int* classLabelsBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(classLabelsBuf + n);
@@ -1375,7 +1396,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
 
         if (data->is_buf_16u)
         {
-            unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*buf->cols +
+            unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row +
                 data->data_root->offset + (data->work_var_count-1)*data->sample_count);
             for( int i = 0; i < n; i++ )
             {
@@ -1393,7 +1414,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
         }
         else
         {
-            int* labels = buf->data.i + data->data_root->buf_idx*buf->cols +
+            int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row +
                 data->data_root->offset + (data->work_var_count-1)*data->sample_count;
 
             for( int i = 0; i < n; i++ )

modules/ml/include/opencv2/ml/ml.hpp

@@ -796,7 +796,7 @@ struct CV_EXPORTS CvDTreeTrainData
     const CvMat* responses;
     CvMat* responses_copy; // used in Boosting
 
-    int buf_count, buf_size;
+    int buf_count, buf_size; // buf_size is obsolete, please do not use it, use expression ((int64)buf->rows * (int64)buf->cols / buf_count) instead
     bool shared;
     int is_buf_16u;
 
@@ -806,6 +806,12 @@ struct CV_EXPORTS CvDTreeTrainData
 
     CvMat* counts;
     CvMat* buf;
+    inline size_t get_length_subbuf() const
+    {
+        size_t res = (size_t)(work_var_count + 1) * (size_t)sample_count;
+        return res;
+    }
+
     CvMat* direction;
     CvMat* split_buf;
 

modules/ml/src/boost.cpp

@@ -1130,13 +1130,13 @@ CvBoost::update_weights( CvBoostTree* tree )
     int *sample_idx_buf;
     const int* sample_idx = 0;
     cv::AutoBuffer<uchar> inn_buf;
-    size_t _buf_size = (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? data->sample_count*sizeof(int) : 0;
+    size_t _buf_size = (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? (size_t)(data->sample_count)*sizeof(int) : 0;
     if( !tree )
         _buf_size += n*sizeof(int);
     else
     {
         if( have_subsample )
-            _buf_size += data->buf->cols*(sizeof(float)+sizeof(uchar));
+            _buf_size += data->get_length_subbuf()*(sizeof(float)+sizeof(uchar));
     }
     inn_buf.allocate(_buf_size);
     uchar* cur_buf_pos = (uchar*)inn_buf;
@@ -1151,6 +1151,7 @@ CvBoost::update_weights( CvBoostTree* tree )
         sample_idx = data->get_sample_indices( data->data_root, sample_idx_buf );
     }
     CvMat* dtree_data_buf = data->buf;
+    size_t length_buf_row = data->get_length_subbuf();
     if( !tree ) // before training the first tree, initialize weights and other parameters
     {
         int* class_labels_buf = (int*)cur_buf_pos;
@@ -1189,7 +1190,7 @@ CvBoost::update_weights( CvBoostTree* tree )
 
         if (data->is_buf_16u)
         {
-            unsigned short* labels = (unsigned short*)(dtree_data_buf->data.s + data->data_root->buf_idx*dtree_data_buf->cols +
+            unsigned short* labels = (unsigned short*)(dtree_data_buf->data.s + data->data_root->buf_idx*length_buf_row +
                 data->data_root->offset + (data->work_var_count-1)*data->sample_count);
             for( i = 0; i < n; i++ )
             {
@@ -1207,7 +1208,7 @@ CvBoost::update_weights( CvBoostTree* tree )
         }
         else
         {
-            int* labels = dtree_data_buf->data.i + data->data_root->buf_idx*dtree_data_buf->cols +
+            int* labels = dtree_data_buf->data.i + data->data_root->buf_idx*length_buf_row +
                 data->data_root->offset + (data->work_var_count-1)*data->sample_count;
 
             for( i = 0; i < n; i++ )
@@ -1254,9 +1255,10 @@ CvBoost::update_weights( CvBoostTree* tree )
         if( have_subsample )
         {
             float* values = (float*)cur_buf_pos;
-            cur_buf_pos = (uchar*)(values + data->buf->cols);
+            cur_buf_pos = (uchar*)(values + data->get_length_subbuf());
             uchar* missing = cur_buf_pos;
-            cur_buf_pos = missing + data->buf->step;
+            cur_buf_pos = missing + data->get_length_subbuf() * (size_t)CV_ELEM_SIZE(data->buf->type);
+
             CvMat _sample, _mask;
 
             // invert the subsample mask

modules/ml/src/ertrees.cpp

@@ -80,6 +80,9 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
     char err[100];
     const int *sidx = 0, *vidx = 0;
 
+    uint64 effective_buf_size = 0;
+    int effective_buf_height = 0, effective_buf_width = 0;
+
     if ( _params.use_surrogates )
         CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits");
 
@@ -179,18 +182,34 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
     have_labels = cv_n > 0 || (ord_var_count == 1 && cat_var_count == 0) || _add_labels;
 
     work_var_count = cat_var_count + (is_classifier ? 1 : 0) + (have_labels ? 1 : 0);
-    buf_size = (work_var_count + 1)*sample_count;
+
     shared = _shared;
     buf_count = shared ? 2 : 1;
 
+    buf_size = -1; // the member buf_size is obsolete
+
+    effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
+    effective_buf_width = sample_count;
+    effective_buf_height = work_var_count+1;
+
+    if (effective_buf_width >= effective_buf_height)
+        effective_buf_height *= buf_count;
+    else
+        effective_buf_width *= buf_count;
+
+    if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
+    {
+        CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
+    }
+
     if ( is_buf_16u )
     {
-        CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
+        CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 ));
         CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
     }
     else
     {
-        CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
+        CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 ));
         CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
     }
 
@@ -293,13 +312,13 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
             for( i = 0; i < sample_count; i++ )
             {
                 int val = INT_MAX, si = sidx ? sidx[i] : i;
-                if( !mask || !mask[si*m_step] )
+                if( !mask || !mask[(size_t)si*m_step] )
                 {
                     if( idata )
-                        val = idata[si*step];
+                        val = idata[(size_t)si*step];
                     else
                     {
-                        float t = fdata[si*step];
+                        float t = fdata[(size_t)si*step];
                         val = cvRound(t);
                         if( val != t )
                         {
@@ -405,12 +424,12 @@ void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
             {
                 float val = ord_nan;
                 int si = sidx ? sidx[i] : i;
-                if( !mask || !mask[si*m_step] )
+                if( !mask || !mask[(size_t)si*m_step] )
                 {
                     if( idata )
-                        val = (float)idata[si*step];
+                        val = (float)idata[(size_t)si*step];
                     else
-                        val = fdata[si*step];
+                        val = fdata[(size_t)si*step];
 
                     if( fabs(val) >= ord_nan )
                     {
@@ -578,9 +597,9 @@ const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat
     int ci = get_var_type( vi);
     const int* cat_values = 0;
     if( !is_buf_16u )
-        cat_values = buf->data.i + n->buf_idx*buf->cols + ci*sample_count + n->offset;
+        cat_values = buf->data.i + n->buf_idx*get_length_subbuf() + ci*sample_count + n->offset;
     else {
-        const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
+        const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
             ci*sample_count + n->offset);
         for( int i = 0; i < n->sample_count; i++ )
             cat_values_buf[i] = short_values[i];
@@ -1333,6 +1352,7 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
     CvDTreeNode *left = 0, *right = 0;
     int new_buf_idx = data->get_child_buf_idx( node );
     CvMat* buf = data->buf;
+    size_t length_buf_row = data->get_length_subbuf();
     cv::AutoBuffer<int> temp_buf(n);
 
     complete_node_dir(node);
@@ -1385,9 +1405,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
 
         if (data->is_buf_16u)
         {
-            unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
+            unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
                 ci*scount + left->offset);
-            unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
+            unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
                 ci*scount + right->offset);
 
             for( i = 0; i < n; i++ )
@@ -1415,9 +1435,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
         }
         else
         {
-            int *ldst = buf->data.i + left->buf_idx*buf->cols +
+            int *ldst = buf->data.i + left->buf_idx*length_buf_row +
                 ci*scount + left->offset;
-            int *rdst = buf->data.i + right->buf_idx*buf->cols +
+            int *rdst = buf->data.i + right->buf_idx*length_buf_row +
                 ci*scount + right->offset;
 
             for( i = 0; i < n; i++ )
@@ -1460,9 +1480,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
 
         if (data->is_buf_16u)
         {
-            unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
+            unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
                 pos*scount + left->offset);
-            unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
+            unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
                 pos*scount + right->offset);
 
             for (i = 0; i < n; i++)
@@ -1483,9 +1503,9 @@ void CvForestERTree::split_node_data( CvDTreeNode* node )
         }
         else
         {
-            int* ldst = buf->data.i + left->buf_idx*buf->cols +
+            int* ldst = buf->data.i + left->buf_idx*length_buf_row +
                 pos*scount + left->offset;
-            int* rdst = buf->data.i + right->buf_idx*buf->cols +
+            int* rdst = buf->data.i + right->buf_idx*length_buf_row +
                 pos*scount + right->offset;
             for (i = 0; i < n; i++)
             {

modules/ml/src/tree.cpp

@@ -50,7 +50,8 @@ static const int block_size_delta = 1 << 10;
 CvDTreeTrainData::CvDTreeTrainData()
 {
     var_idx = var_type = cat_count = cat_ofs = cat_map =
-        priors = priors_mult = counts = buf = direction = split_buf = responses_copy = 0;
+        priors = priors_mult = counts = direction = split_buf = responses_copy = 0;
+    buf = 0;
     tree_storage = temp_storage = 0;
 
     clear();
@@ -64,7 +65,8 @@ CvDTreeTrainData::CvDTreeTrainData( const CvMat* _train_data, int _tflag,
                       bool _shared, bool _add_labels )
 {
     var_idx = var_type = cat_count = cat_ofs = cat_map =
-        priors = priors_mult = counts = buf = direction = split_buf = responses_copy = 0;
+        priors = priors_mult = counts = direction = split_buf = responses_copy = 0;
+    buf = 0;
 
     tree_storage = temp_storage = 0;
 
@@ -157,6 +159,9 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
     char err[100];
     const int *sidx = 0, *vidx = 0;
 
+    uint64 effective_buf_size = 0;
+    int effective_buf_height = 0, effective_buf_width = 0;
+
     if( _update_data && data_root )
     {
         data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx,
@@ -285,18 +290,35 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
     work_var_count = var_count + (is_classifier ? 1 : 0) // for responses class_labels
                                + (have_labels ? 1 : 0); // for cv_labels
 
-    buf_size = (work_var_count + 1 /*for sample_indices*/) * sample_count;
     shared = _shared;
     buf_count = shared ? 2 : 1;
 
+    buf_size = -1; // the member buf_size is obsolete
+
+    effective_buf_size = (uint64)(work_var_count + 1)*(uint64)sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
+    effective_buf_width = sample_count;
+    effective_buf_height = work_var_count+1;
+
+    if (effective_buf_width >= effective_buf_height)
+        effective_buf_height *= buf_count;
+    else
+        effective_buf_width *= buf_count;
+
+    if ((uint64)effective_buf_width * (uint64)effective_buf_height != effective_buf_size)
+    {
+        CV_Error(CV_StsBadArg, "The memory buffer cannot be allocated since its size exceeds integer fields limit");
+    }
+
+
+
     if ( is_buf_16u )
     {
-        CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
+        CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_16UC1 ));
         CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
     }
     else
     {
-        CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
+        CV_CALL( buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 ));
         CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
     }
 
@@ -356,7 +378,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
     {
         int ci;
         const uchar* mask = 0;
-        int m_step = 0, step;
+        int64 m_step = 0, step;
         const int* idata = 0;
         const float* fdata = 0;
         int num_valid = 0;
@@ -399,13 +421,13 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
             for( i = 0; i < sample_count; i++ )
             {
                 int val = INT_MAX, si = sidx ? sidx[i] : i;
-                if( !mask || !mask[si*m_step] )
+                if( !mask || !mask[(size_t)si*m_step] )
                 {
                     if( idata )
-                        val = idata[si*step];
+                        val = idata[(size_t)si*step];
                     else
                     {
-                        float t = fdata[si*step];
+                        float t = fdata[(size_t)si*step];
                         val = cvRound(t);
                         if( fabs(t - val) > FLT_EPSILON )
                         {
@@ -515,12 +537,12 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
             {
                 float val = ord_nan;
                 int si = sidx ? sidx[i] : i;
-                if( !mask || !mask[si*m_step] )
+                if( !mask || !mask[(size_t)si*m_step] )
                 {
                     if( idata )
-                        val = (float)idata[si*step];
+                        val = (float)idata[(size_t)si*step];
                     else
-                        val = fdata[si*step];
+                        val = fdata[(size_t)si*step];
 
                     if( fabs(val) >= ord_nan )
                     {
@@ -532,7 +554,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
                 }
 
                 if (is_buf_16u)
-                    udst[i] = (unsigned short)i;
+                    udst[i] = (unsigned short)i; // TODO: memory corruption may be here
                 else
                     idst[i] = i;
                 _fdst[i] = val;
@@ -751,7 +773,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
 
                 if (is_buf_16u)
                 {
-                    unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
+                    unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                         vi*sample_count + root->offset);
                     for( i = 0; i < count; i++ )
                     {
@@ -762,7 +784,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
                 }
                 else
                 {
-                    int* idst = buf->data.i + root->buf_idx*buf->cols +
+                    int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
                         vi*sample_count + root->offset;
                     for( i = 0; i < count; i++ )
                     {
@@ -788,7 +810,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
 
                 if (is_buf_16u)
                 {
-                    unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
+                    unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                         vi*sample_count + data_root->offset);
                     for( i = 0; i < num_valid; i++ )
                     {
@@ -812,7 +834,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
                 }
                 else
                 {
-                    int* idst_idx = buf->data.i + root->buf_idx*buf->cols +
+                    int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
                         vi*sample_count + root->offset;
                     for( i = 0; i < num_valid; i++ )
                     {
@@ -840,14 +862,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
         const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
         if (is_buf_16u)
         {
-            unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*buf->cols +
+            unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
                 workVarCount*sample_count + root->offset);
             for (i = 0; i < count; i++)
                 sample_idx_dst[i] = (unsigned short)sample_idx_src[sidx[i]];
         }
         else
         {
-            int* sample_idx_dst = buf->data.i + root->buf_idx*buf->cols +
+            int* sample_idx_dst = buf->data.i + root->buf_idx*get_length_subbuf() +
                 workVarCount*sample_count + root->offset;
             for (i = 0; i < count; i++)
                 sample_idx_dst[i] = sample_idx_src[sidx[i]];
@@ -1158,10 +1180,10 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
     const int* sample_indices = get_sample_indices(n, sample_indices_buf);
 
     if( !is_buf_16u )
-        *sorted_indices = buf->data.i + n->buf_idx*buf->cols +
+        *sorted_indices = buf->data.i + n->buf_idx*get_length_subbuf() +
         vi*sample_count + n->offset;
     else {
-        const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
+        const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
             vi*sample_count + n->offset );
         for( int i = 0; i < node_sample_count; i++ )
             sorted_indices_buf[i] = short_indices[i];
@@ -1232,10 +1254,10 @@ const int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_
 {
     const int* cat_values = 0;
     if( !is_buf_16u )
-        cat_values = buf->data.i + n->buf_idx*buf->cols +
+        cat_values = buf->data.i + n->buf_idx*get_length_subbuf() +
             vi*sample_count + n->offset;
     else {
-        const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
+        const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
             vi*sample_count + n->offset);
         for( int i = 0; i < n->sample_count; i++ )
             cat_values_buf[i] = short_values[i];
@@ -3004,6 +3026,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
     int new_buf_idx = data->get_child_buf_idx( node );
     int work_var_count = data->get_work_var_count();
     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;
 
@@ -3049,7 +3072,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
         {
             unsigned short *ldst, *rdst, *ldst0, *rdst0;
             //unsigned short tl, tr;
-            ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
+            ldst0 = ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
                 vi*scount + left->offset);
             rdst0 = rdst = (unsigned short*)(ldst + nl);
 
@@ -3095,9 +3118,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
         else
         {
             int *ldst0, *ldst, *rdst0, *rdst;
-            ldst0 = ldst = buf->data.i + left->buf_idx*buf->cols +
+            ldst0 = ldst = buf->data.i + left->buf_idx*length_buf_row +
                 vi*scount + left->offset;
-            rdst0 = rdst = buf->data.i + right->buf_idx*buf->cols +
+            rdst0 = rdst = buf->data.i + right->buf_idx*length_buf_row +
                 vi*scount + right->offset;
 
             // split sorted
@@ -3158,9 +3181,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
 
         if (data->is_buf_16u)
         {
-            unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
+            unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
                 vi*scount + left->offset);
-            unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
+            unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
                 vi*scount + right->offset);
 
             for( i = 0; i < n; i++ )
@@ -3188,9 +3211,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
         }
         else
         {
-            int *ldst = buf->data.i + left->buf_idx*buf->cols +
+            int *ldst = buf->data.i + left->buf_idx*length_buf_row +
                 vi*scount + left->offset;
-            int *rdst = buf->data.i + right->buf_idx*buf->cols +
+            int *rdst = buf->data.i + right->buf_idx*length_buf_row +
                 vi*scount + right->offset;
 
             for( i = 0; i < n; i++ )
@@ -3230,9 +3253,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
     int pos = data->get_work_var_count();
     if (data->is_buf_16u)
     {
-        unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
+        unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
             pos*scount + left->offset);
-        unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
+        unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
             pos*scount + right->offset);
         for (i = 0; i < n; i++)
         {
@@ -3252,9 +3275,9 @@ void CvDTree::split_node_data( CvDTreeNode* node )
     }
     else
     {
-        int* ldst = buf->data.i + left->buf_idx*buf->cols +
+        int* ldst = buf->data.i + left->buf_idx*length_buf_row +
             pos*scount + left->offset;
-        int* rdst = buf->data.i + right->buf_idx*buf->cols +
+        int* rdst = buf->data.i + right->buf_idx*length_buf_row +
             pos*scount + right->offset;
         for (i = 0; i < n; i++)
         {
@@ -3310,7 +3333,7 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
             float r = (float)predict( &sample, missing ? &miss : 0 )->value;
             if( pred_resp )
                 pred_resp[i] = r;
-            int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1;
+            int d = fabs((double)r - response->data.fl[(size_t)si*r_step]) <= FLT_EPSILON ? 0 : 1;
             err += d;
         }
         err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
@@ -3327,7 +3350,7 @@ float CvDTree::calc_error( CvMLData* _data, int type, vector<float> *resp )
             float r = (float)predict( &sample, missing ? &miss : 0 )->value;
             if( pred_resp )
                 pred_resp[i] = r;
-            float d = r - response->data.fl[si*r_step];
+            float d = r - response->data.fl[(size_t)si*r_step];
             err += d*d;
         }
         err = sample_count ? err / (float)sample_count : -FLT_MAX;
@@ -3633,8 +3656,8 @@ CvDTreeNode* CvDTree::predict( const CvMat* _sample,
             int vi = split->var_idx;
             int ci = vtype[vi];
             i = vidx ? vidx[vi] : vi;
-            float val = sample[i*step];
-            if( m && m[i*mstep] )
+            float val = sample[(size_t)i*step];
+            if( m && m[(size_t)i*mstep] )
                 continue;
             if( ci < 0 ) // ordered
                 dir = val <= split->ord.c ? -1 : 1;