Merge branch 'boost'

Scott Klum
2 parents a641da3a 6e0e624d
Showing 1 changed file with 83 additions and 227 deletions
openbr/core/boost.cpp
@@ -21,7 +21,6 @@ logRatio( double val )
 #define CV_CMP_NUM_IDX(i,j) (aux[i] < aux[j])
 static CV_IMPLEMENT_QSORT_EX( icvSortIntAux, int, CV_CMP_NUM_IDX, const float* )
-static CV_IMPLEMENT_QSORT_EX( icvSortUShAux, unsigned short, CV_CMP_NUM_IDX, const float* )
 #define CV_THRESHOLD_EPS (0.00001F)
@@ -73,7 +72,7 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
         if( idx_selected == 0 )
             CV_ERROR( CV_StsOutOfRange, "No components/input_variables is selected!" );
-    } else if (type == CV_32SC(channels)) {
+    } else if (type == CV_32SC1) {
         // idx_arr is array of integer indices of selected components
         if( idx_total > data_arr_size )
             CV_ERROR( CV_StsOutOfRange,
@@ -95,10 +94,10 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
                                            "(it should be 8uC1, 8sC1 or 32sC1)" );
     }
-    CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC(channels) ));
+    CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC1 ));
     dsti = idx->data.i;
-    if( type < CV_32SC(channels) )
+    if( type < CV_32SC1 )
     {
         for( i = 0; i < idx_total; i++ )
             if( srcb[i*step] )
@@ -181,7 +180,12 @@ struct CascadeBoostTrainData : CvDTreeTrainData
     virtual CvDTreeNode* subsample_data(const CvMat* _subsample_idx);
-    virtual const int* getBufferValues(CvDTreeNode* n, int* labelsBuf, uint64_t vi);
+    inline uint64_t getLength() const
+    {
+        return ((uint64_t)work_var_count + 1) * sample_count;
+    }
+
+    virtual const int* getBufferValues(CvDTreeNode* n, uint64_t vi);
     virtual const int* get_class_labels(CvDTreeNode* n, int* labelsBuf);
     virtual const int* get_cv_labels(CvDTreeNode* n, int* labelsBuf);
     virtual const int* get_sample_indices(CvDTreeNode* n, int* indicesBuf);
@@ -254,12 +258,12 @@ CvDTreeNode* CascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx 
         int* sidx = isubsample_idx->data.i;
         // co - array of count/offset pairs (to handle duplicated values in _subsample_idx)
         int* co, cur_ofs = 0;
-        int workVarCount = get_work_var_count();
+        uint64_t workVarCount = get_work_var_count();
         int count = isubsample_idx->rows + isubsample_idx->cols - 1;
         root = new_node( 0, count, 1, 0 );
-        CV_Assert( (subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC(channels) )) != 0);
+        CV_Assert( (subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 )) != 0);
         cvZero( subsample_co );
         co = subsample_co->data.i;
         for( int i = 0; i < count; i++ )
@@ -293,67 +297,29 @@ CvDTreeNode* CascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx 
             int num_valid = data_root->get_num_valid(vi);
             CV_Assert( num_valid == sample_count );
-            if (is_buf_16u)
+            int* idst_idx = buf->data.i + root->buf_idx*getLength() + (uint64_t)vi*sample_count + root->offset;
+            for( int i = 0; i < num_valid; i++ )
             {
-                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++ )
-                {
-                    idx = src_idx[i];
-                    count_i = co[idx*2];
-                    if( count_i )
-                        for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
-                            udst_idx[j] = (unsigned short)cur_ofs;
-                }
-            }
-            else
-            {
-                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++ )
-                {
-                    idx = src_idx[i];
-                    count_i = co[idx*2];
-                    if( count_i )
-                        for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
-                            idst_idx[j] = cur_ofs;
-                }
+                idx = src_idx[i];
+                count_i = co[idx*2];
+                if( count_i )
+                    for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ )
+                        idst_idx[j] = cur_ofs;
             }
         }
         // subsample cv_lables
         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*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*get_length_subbuf() +
-                (workVarCount-1)*sample_count + root->offset;
-            for( int i = 0; i < count; i++ )
-                idst[i] = src_lbls[sidx[i]];
-        }
+        int* idst = buf->data.i + root->buf_idx*getLength() +
+            (workVarCount-1)*sample_count + root->offset;
+        for( int i = 0; i < count; i++ )
+            idst[i] = src_lbls[sidx[i]];
         // subsample sample_indices
         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*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*get_length_subbuf() +
-                workVarCount*sample_count + root->offset;
-            for( int i = 0; i < count; i++ )
-                sample_idx_dst[i] = sample_idx_src[sidx[i]];
-        }
+        int* sample_idx_dst = buf->data.i + root->buf_idx*getLength() + workVarCount*sample_count + root->offset;
+        for( int i = 0; i < count; i++ )
+            sample_idx_dst[i] = sample_idx_src[sidx[i]];
         for( int vi = 0; vi < var_count; vi++ )
             root->set_num_valid(vi, count);
@@ -390,7 +356,7 @@ CascadeBoostTrainData::CascadeBoostTrainData(const FeatureEvaluator* _featureEva
 void CascadeBoostTrainData::initVarType()
 {
-    var_type = cvCreateMat( 1, var_count + 2, CV_32SC(channels) );
+    var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
     if ( featureEvaluator->getMaxCatCount() > 0 )
     {
         numPrecalcIdx = 0;
@@ -459,17 +425,15 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
     sample_count = _numSamples;
     is_buf_16u = false;
-    if (sample_count < 65536)
-        is_buf_16u = true;
     // 1048576 is the number of bytes in a megabyte
     numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count );
-    numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. / ((is_buf_16u ? sizeof(unsigned short) : sizeof (int))*sample_count)), var_count );
+    numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. / (sizeof (int)*sample_count)), var_count );
     assert( numPrecalcIdx >= 0 && numPrecalcVal >= 0 );
     valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
-    var_type = cvCreateMat( 1, var_count + 2, CV_32SC(channels) );
+    var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
     if ( featureEvaluator->getMaxCatCount() > 0 )
     {
@@ -500,7 +464,7 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
     buf_size = -1; // the member buf_size is obsolete
-    effective_buf_size = (work_var_count + 1)*sample_count * buf_count; // this is the total size of "CvMat buf" to be allocated
+    effective_buf_size = getLength()*buf_count;
     effective_buf_width = sample_count;
     effective_buf_height = work_var_count+1;
@@ -510,15 +474,11 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
         effective_buf_width *= buf_count;
     if (effective_buf_width * 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( effective_buf_height, effective_buf_width, CV_16UC(channels) );
-    else
-        buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC(channels) );
-    cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC(channels) );
+    buf = cvCreateMat( effective_buf_height, effective_buf_width, CV_32SC1 );
+
+    cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
     // precalculate valCache and set indices in buf
     precalculate();
@@ -542,10 +502,7 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
     data_root = new_node( 0, sample_count, 0, 0 );
     // set sample labels
-    if (is_buf_16u)
-        udst = (unsigned short*)(buf->data.s + (uint64_t)work_var_count*sample_count);
-    else
-        idst = buf->data.i + (uint64_t)work_var_count*sample_count;
+    idst = buf->data.i + (uint64_t)work_var_count*sample_count;
     for (int si = 0; si < sample_count; si++)
     {
@@ -568,9 +525,9 @@ void CascadeBoostTrainData::setData( const FeatureEvaluator* _featureEvaluator,
     priors = cvCreateMat( 1, get_num_classes(), CV_64F );
     cvSet(priors, cvScalar(1));
     priors_mult = cvCloneMat( priors );
-    counts = cvCreateMat( 1, get_num_classes(), CV_32SC(channels) );
-    direction = cvCreateMat( 1, sample_count, CV_8UC(channels) );
-    split_buf = cvCreateMat( 1, sample_count, CV_32SC(channels) );//TODO: make a pointer
+    counts = cvCreateMat( 1, get_num_classes(), CV_32SC1 );
+    direction = cvCreateMat( 1, sample_count, CV_8UC1 );
+    split_buf = cvCreateMat( 1, sample_count, CV_32SC1 );//TODO: make a pointer
 }
 void CascadeBoostTrainData::free_train_data()
@@ -594,29 +551,21 @@ const int* CascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsB
     return labelsBuf;
 }
-const int* CascadeBoostTrainData::getBufferValues(CvDTreeNode* n, int* indicesBuf, uint64_t vi)
+const int* CascadeBoostTrainData::getBufferValues(CvDTreeNode* n, uint64_t vi)
 {
-    const int* cat_values = 0;
-    if (!is_buf_16u)
-        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*get_length_subbuf() + vi*sample_count + n->offset);
-        for (int i = 0; i < n->sample_count; i++)
-            indicesBuf[i] = short_values[i];
-        cat_values = indicesBuf;
-    }
-
-    return cat_values;
+    return buf->data.i + n->buf_idx*getLength() + vi*sample_count + n->offset;
 }
-const int* CascadeBoostTrainData::get_sample_indices( CvDTreeNode* n, int* indicesBuf )
+const int* CascadeBoostTrainData::get_sample_indices(CvDTreeNode* n, int* indicesBuf)
 {
-    return getBufferValues(n,indicesBuf,get_work_var_count());
+    Q_UNUSED(indicesBuf)
+    return getBufferValues(n,get_work_var_count());
 }
-const int* CascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* indicesBuf )
+const int* CascadeBoostTrainData::get_cv_labels(CvDTreeNode* n, int* indicesBuf)
 {
-    return getBufferValues(n,indicesBuf,get_work_var_count()-1);
+    Q_UNUSED(indicesBuf)
+    return getBufferValues(n,get_work_var_count()-1);
 }
 void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf, const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf )
@@ -629,14 +578,7 @@ void CascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord
     // For this feature (this code refers to features as values, hence vi == value index),
     // have we precalculated (presorted) the training samples by their feature response?
     if (vi < numPrecalcIdx) {
-        if (!is_buf_16u)
-            *sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + (uint64_t)vi*sample_count + n->offset;
-        else {
-            const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() + (uint64_t)vi*sample_count + n->offset );
-            for (int i = 0; i < nodeSampleCount; i++)
-                sortedIndicesBuf[i] = shortIndices[i];
-            *sortedIndices = sortedIndicesBuf;
-        }
+        *sortedIndices = getBufferValues(n, vi);
         // For this feature, have we precalculated all of the feature responses?
         if (vi < numPrecalcVal) {
@@ -731,26 +673,20 @@ struct IndexPrecalc : Precalc
 {
     int* idst;
     unsigned short* udst;
-    bool isBufShort;
-    IndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, int sampleCount, bool isBufShort) :
+    IndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, int sampleCount) :
         Precalc(featureEvaluator, sampleCount),
-        isBufShort(isBufShort)
-    {
-        udst = (unsigned short*)buf->data.s;
-        idst = buf->data.i;
-    }
+        idst(buf->data.i)
+    {}
     void setBuffer(uint64_t fi, uint64_t si) const
     {
-        if (isBufShort) *(udst + fi*sampleCount + si) = (unsigned short)si;
-        else            *(idst + fi*sampleCount + si) = si;
+        *(idst + fi*sampleCount + si) = si;
     }
     void sortBuffer(uint64_t fi, float *valCachePtr) const
     {
-        if (isBufShort) icvSortUShAux(udst + fi*sampleCount, sampleCount, valCachePtr);
-        else            icvSortIntAux(idst + fi*sampleCount, sampleCount, valCachePtr);
+        icvSortIntAux(idst + fi*sampleCount, sampleCount, valCachePtr);
     }
     virtual void operator()(const Range& range) const
@@ -771,8 +707,8 @@ struct FeatureAndIndexPrecalc : IndexPrecalc
 {
     Mat *valCache;
-    FeatureAndIndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, Mat* valCache, int sampleCount, bool isBufShort) :
-        IndexPrecalc(featureEvaluator, buf, sampleCount, isBufShort),
+    FeatureAndIndexPrecalc(const FeatureEvaluator* featureEvaluator, CvMat* buf, Mat* valCache, int sampleCount) :
+        IndexPrecalc(featureEvaluator, buf, sampleCount),
         valCache(valCache)
     {}
@@ -816,11 +752,11 @@ void CascadeBoostTrainData::precalculate()
     // Compute features and sort training samples for feature indices we are not going to cache
     parallel_for_(Range(numPrecalcVal, numPrecalcIdx),
-                  IndexPrecalc(featureEvaluator, buf, sample_count, is_buf_16u!=0));
+                  IndexPrecalc(featureEvaluator, buf, sample_count));
     // Compute features and sort training samples for features indices we are going to cache
     parallel_for_(Range(0, minPrecalc),
-                  FeatureAndIndexPrecalc(featureEvaluator, buf, &valCache, sample_count, is_buf_16u!=0));
+                  FeatureAndIndexPrecalc(featureEvaluator, buf, &valCache, sample_count));
     // Compute feature values for feature indices for which we are not going to sort training samples
     parallel_for_(Range(minPrecalc, numPrecalcVal),
@@ -874,14 +810,13 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
     int* newIdx = data->split_buf->data.i;
     int newBufIdx = data->get_child_buf_idx( node );
     CvMat* buf = data->buf;
-    size_t length_buf_row = data->get_length_subbuf();
+    size_t length_buf_row = ((CascadeBoostTrainData*)data)->getLength();
     cv::AutoBuffer<uchar> inn_buf(nodeSampleCount*(3*sizeof(int)+sizeof(float)));
     int* tempBuf = (int*)(uchar*)inn_buf;
     complete_node_dir(node);
-    for (uint64_t i = nLeft = nRight = 0; i < nodeSampleCount; i++)
-    {
+    for (uint64_t i = nLeft = nRight = 0; i < nodeSampleCount; i++) {
         int d = dir[i];
         // initialize new indices for splitting ordered variables
         newIdx[i] = (nLeft & (d-1)) | (nRight & -d); // d ? ri : li
@@ -900,59 +835,27 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
         if( ci >= 0 || !splitInputData )
             continue;
-        int n1 = node->get_num_valid(vi);
-        float *src_val_buf = (float*)(tempBuf + nodeSampleCount);
-        int *src_sorted_idx_buf = (int*)(src_val_buf + nodeSampleCount);
-        int *src_sample_idx_buf = src_sorted_idx_buf + nodeSampleCount;
-        const int* src_sorted_idx = 0;
-        const float* src_val = 0;
-
-        data->get_ord_var_data(node, vi, src_val_buf, src_sorted_idx_buf, &src_val, &src_sorted_idx, src_sample_idx_buf);
-
+        const int* src_sorted_idx = ((CascadeBoostTrainData*)data)->getBufferValues(node, vi);
         for(uint64_t i = 0; i < nodeSampleCount; i++)
             tempBuf[i] = src_sorted_idx[i];
-        if (data->is_buf_16u) {
-            ushort *ldst, *rdst;
-            ldst = (ushort*)(buf->data.s + left->buf_idx*length_buf_row + vi*sampleCount + left->offset);
-            rdst = (ushort*)(ldst + nLeft);
-
-            // split sorted
-            for (int i = 0; i < n1; i++) {
-                int idx = tempBuf[i];
-                int d = dir[idx];
-                idx = newIdx[idx];
-                if (d) {
-                    *rdst = (ushort)idx;
-                    rdst++;
-                } else {
-                    *ldst = (ushort)idx;
-                    ldst++;
-                }
-            }
-        }
-        else
-        {
-            int *ldst, *rdst;
-            ldst = buf->data.i + left->buf_idx*length_buf_row + vi*sampleCount + left->offset;
-            rdst = buf->data.i + right->buf_idx*length_buf_row + vi*sampleCount + right->offset;
+        int *ldst, *rdst;
+        ldst = buf->data.i + left->buf_idx*length_buf_row + vi*sampleCount + left->offset;
+        rdst = buf->data.i + right->buf_idx*length_buf_row + vi*sampleCount + right->offset;
-            // split sorted
-            for( int i = 0; i < n1; i++ )
-            {
-                int idx = tempBuf[i];
-                int d = dir[idx];
-                idx = newIdx[idx];
-                if (d)
-                {
-                    *rdst = idx;
-                    rdst++;
-                }
-                else
-                {
-                    *ldst = idx;
-                    ldst++;
-                }
+        int n1 = node->get_num_valid(vi);
+
+        // split sorted
+        for (int i = 0; i < n1; i++) {
+            int idx = tempBuf[i];
+            int d = dir[idx];
+            idx = newIdx[idx];
+            if (d) {
+                *rdst = idx;
+                rdst++;
+            } else {
+                *ldst = idx;
+                ldst++;
             }
         }
     }
@@ -964,37 +867,16 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
     for(uint64_t i = 0; i < nodeSampleCount; i++)
         tempBuf[i] = src_lbls[i];
-    if (data->is_buf_16u) {
-        unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + left->offset);
-        unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + right->offset);
-
-        for( uint64_t i = 0; i < nodeSampleCount; i++ ) {
-            int idx = tempBuf[i];
-            if (dir[i]) {
-                *rdst = (unsigned short)idx;
-                rdst++;
-            } else {
-                *ldst = (unsigned short)idx;
-                ldst++;
-            }
-        }
-
-    }
-    else
     {
         int *ldst = buf->data.i + left->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + left->offset;
         int *rdst = buf->data.i + right->buf_idx*length_buf_row + (workVarCount-1)*sampleCount + right->offset;
-        for( uint64_t i = 0; i < nodeSampleCount; i++ )
-        {
+        for (uint64_t i = 0; i < nodeSampleCount; i++) {
             int idx = tempBuf[i];
-            if (dir[i])
-            {
+            if (dir[i]) {
                 *rdst = idx;
                 rdst++;
-            }
-            else
-            {
+            } else {
                 *ldst = idx;
                 ldst++;
             }
@@ -1008,35 +890,15 @@ void CascadeBoostTree::split_node_data( CvDTreeNode* node )
     for(uint64_t i = 0; i < nodeSampleCount; i++)
         tempBuf[i] = sampleIdx_src[i];
-    if (data->is_buf_16u) {
-        unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row + workVarCount*sampleCount + left->offset);
-        unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row + workVarCount*sampleCount + right->offset);
-        for (uint64_t i = 0; i < nodeSampleCount; i++) {
-            unsigned short idx = (unsigned short)tempBuf[i];
-            if (dir[i]) {
-                *rdst = idx;
-                rdst++;
-            } else {
-                *ldst = idx;
-                ldst++;
-            }
-        }
-    }
-    else
     {
         int* ldst = buf->data.i + left->buf_idx*length_buf_row + workVarCount*sampleCount + left->offset;
         int* rdst = buf->data.i + right->buf_idx*length_buf_row + workVarCount*sampleCount + right->offset;
-        for (uint64_t i = 0; i < nodeSampleCount; i++)
-        {
+        for (uint64_t i = 0; i < nodeSampleCount; i++) {
             int idx = tempBuf[i];
-            if (dir[i])
-            {
-
+            if (dir[i]) {
                 *rdst = idx;
                 rdst++;
-            }
-            else
-            {
+            } else {
                 *ldst = idx;
                 ldst++;
             }
@@ -1143,7 +1005,7 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
     }
     CvMat* buf = data->buf;
-    size_t length_buf_row = data->get_length_subbuf();
+    uint64_t length_buf_row = ((CascadeBoostTrainData*)data)->getLength();
     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);
@@ -1167,15 +1029,9 @@ void CascadeBoost::update_weights(CvBoostTree* tree)
         // set the labels to find (from within weak tree learning proc)
         // the particular sample weight, and where to store the response.
-        if (data->is_buf_16u) {
-            unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64_t)(data->work_var_count-1)*data->sample_count);
-            for (int i = 0; i < n; i++)
-                labels[i] = (unsigned short)i;
-        } else {
-            int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64_t)(data->work_var_count-1)*data->sample_count;
-            for( int i = 0; i < n; i++ )
-                labels[i] = i;
-        }
+        int* labels = buf->data.i + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (uint64_t)(data->work_var_count-1)*data->sample_count;
+        for( int i = 0; i < n; i++ )
+            labels[i] = i;
         for (int i = 0; i < n; i++) {
             // save original categorical responses {0,1}, convert them to {-1,1}