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Fix a segment fault issue in cascade classfier

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work_var_count and sample_count are both 32bit integer, if the product of work_var_count and sample_count is bigger than 2^31, the compiler will treat (work_var_count*sample_count) as a negative number. Force work_var_count as an unsigned 64bit integer to avoid this issue.
This commit is contained in:
greensea 2015-03-31 17:28:40 +08:00
parent 864b4e3b26
commit 52c727f09a
3 changed files with 48 additions and 48 deletions
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56
apps/traincascade/boost.cpp
View File

@ -437,7 +437,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + data_root->offset);
(size_t)vi*sample_count + data_root->offset);
for( int i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
@ -450,7 +450,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
else
{
int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
(size_t)vi*sample_count + root->offset;
for( int i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
@ -467,14 +467,14 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
(workVarCount-1)*sample_count + root->offset);
(size_t)(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;
(size_t)(workVarCount-1)*sample_count + root->offset;
for( int i = 0; i < count; i++ )
idst[i] = src_lbls[sidx[i]];
}
@ -484,14 +484,14 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
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);
(size_t)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;
(size_t)workVarCount*sample_count + root->offset;
for( int i = 0; i < count; i++ )
sample_idx_dst[i] = sample_idx_src[sidx[i]];
}
@ -677,9 +677,9 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
// set sample labels
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
udst = (unsigned short*)(buf->data.s + (size_t)work_var_count*sample_count);
else
idst = buf->data.i + work_var_count*sample_count;
idst = buf->data.i + (size_t)work_var_count*sample_count;
for (int si = 0; si < sample_count; si++)
{
@ -747,11 +747,11 @@ 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*get_length_subbuf() + vi*sample_count + n->offset;
*sortedIndices = buf->data.i + n->buf_idx*get_length_subbuf() + (size_t)vi*sample_count + n->offset;
else
{
const unsigned short* shortIndices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset );
(size_t)vi*sample_count + n->offset );
for( int i = 0; i < nodeSampleCount; i++ )
sortedIndicesBuf[i] = shortIndices[i];
@ -862,14 +862,14 @@ struct FeatureIdxOnlyPrecalc : ParallelLoopBody
{
valCachePtr[si] = (*featureEvaluator)( fi, si );
if ( is_buf_16u )
*(udst + fi*sample_count + si) = (unsigned short)si;
*(udst + (size_t)fi*sample_count + si) = (unsigned short)si;
else
*(idst + fi*sample_count + si) = si;
*(idst + (size_t)fi*sample_count + si) = si;
}
if ( is_buf_16u )
std::sort(udst + fi*sample_count, udst + (fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCachePtr) );
std::sort(udst + (size_t)fi*sample_count, udst + (size_t)(fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCachePtr) );
else
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, int>(valCachePtr) );
std::sort(idst + (size_t)fi*sample_count, idst + (size_t)(fi + 1)*sample_count, LessThanIdx<float, int>(valCachePtr) );
}
}
const CvFeatureEvaluator* featureEvaluator;
@ -898,14 +898,14 @@ struct FeatureValAndIdxPrecalc : ParallelLoopBody
{
valCache->at<float>(fi,si) = (*featureEvaluator)( fi, si );
if ( is_buf_16u )
*(udst + fi*sample_count + si) = (unsigned short)si;
*(udst + (size_t)fi*sample_count + si) = (unsigned short)si;
else
*(idst + fi*sample_count + si) = si;
*(idst + (size_t)fi*sample_count + si) = si;
}
if ( is_buf_16u )
std::sort(udst + fi*sample_count, udst + (fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCache->ptr<float>(fi)) );
std::sort(udst + (size_t)fi*sample_count, udst + (size_t)(fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCache->ptr<float>(fi)) );
else
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, int>(valCache->ptr<float>(fi)) );
std::sort(idst + (size_t)fi*sample_count, idst + (size_t)(fi + 1)*sample_count, LessThanIdx<float, int>(valCache->ptr<float>(fi)) );
}
}
const CvFeatureEvaluator* featureEvaluator;
@ -1228,9 +1228,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*length_buf_row +
(workVarCount-1)*scount + left->offset);
(size_t)(workVarCount-1)*scount + left->offset);
unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*length_buf_row +
(workVarCount-1)*scount + right->offset);
(size_t)(workVarCount-1)*scount + right->offset);
for( int i = 0; i < n; i++ )
{
@ -1251,9 +1251,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
else
{
int *ldst = buf->data.i + left->buf_idx*length_buf_row +
(workVarCount-1)*scount + left->offset;
(size_t)(workVarCount-1)*scount + left->offset;
int *rdst = buf->data.i + right->buf_idx*length_buf_row +
(workVarCount-1)*scount + right->offset;
(size_t)(workVarCount-1)*scount + right->offset;
for( int i = 0; i < n; i++ )
{
@ -1281,9 +1281,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
if (data->is_buf_16u)
{
unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*length_buf_row +
workVarCount*scount + left->offset);
(size_t)workVarCount*scount + left->offset);
unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*length_buf_row +
workVarCount*scount + right->offset);
(size_t)workVarCount*scount + right->offset);
for (int i = 0; i < n; i++)
{
unsigned short idx = (unsigned short)tempBuf[i];
@ -1302,9 +1302,9 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
else
{
int* ldst = buf->data.i + left->buf_idx*length_buf_row +
workVarCount*scount + left->offset;
(size_t)workVarCount*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*length_buf_row +
workVarCount*scount + right->offset;
(size_t)workVarCount*scount + right->offset;
for (int i = 0; i < n; i++)
{
int idx = tempBuf[i];
@ -1473,7 +1473,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
if (data->is_buf_16u)
{
unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count);
data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count);
for( int i = 0; i < n; i++ )
{
// save original categorical responses {0,1}, convert them to {-1,1}
@ -1491,7 +1491,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
else
{
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;
data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count;
for( int i = 0; i < n; i++ )
{

4
apps/traincascade/old_ml_boost.cpp
View File

@ -1200,7 +1200,7 @@ 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*length_buf_row +
data->data_root->offset + (data->work_var_count-1)*data->sample_count);
data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count);
for( i = 0; i < n; i++ )
{
// save original categorical responses {0,1}, convert them to {-1,1}
@ -1218,7 +1218,7 @@ CvBoost::update_weights( CvBoostTree* tree )
else
{
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;
data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count;
for( i = 0; i < n; i++ )
{

36
apps/traincascade/old_ml_tree.cpp
View File

@ -424,9 +424,9 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
int* c_map;
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + vi*sample_count);
udst = (unsigned short*)(buf->data.s + (size_t)vi*sample_count);
else
idst = buf->data.i + vi*sample_count;
idst = buf->data.i + (size_t)vi*sample_count;
// copy data
for( i = 0; i < sample_count; i++ )
@ -540,9 +540,9 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
else if( ci < 0 ) // process ordered variable
{
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + vi*sample_count);
udst = (unsigned short*)(buf->data.s + (size_t)vi*sample_count);
else
idst = buf->data.i + vi*sample_count;
idst = buf->data.i + (size_t)vi*sample_count;
for( i = 0; i < sample_count; i++ )
{
@ -583,9 +583,9 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
// set sample labels
if (is_buf_16u)
udst = (unsigned short*)(buf->data.s + work_var_count*sample_count);
udst = (unsigned short*)(buf->data.s + (size_t)work_var_count*sample_count);
else
idst = buf->data.i + work_var_count*sample_count;
idst = buf->data.i + (size_t)work_var_count*sample_count;
for (i = 0; i < sample_count; i++)
{
@ -602,7 +602,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
if (is_buf_16u)
{
usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
usdst = (unsigned short*)(buf->data.s + (size_t)(get_work_var_count()-1)*sample_count);
for( i = vi = 0; i < sample_count; i++ )
{
usdst[i] = (unsigned short)vi++;
@ -619,7 +619,7 @@ void CvDTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
}
else
{
idst2 = buf->data.i + (get_work_var_count()-1)*sample_count;
idst2 = buf->data.i + (size_t)(get_work_var_count()-1)*sample_count;
for( i = vi = 0; i < sample_count; i++ )
{
idst2[i] = vi++;
@ -785,7 +785,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset);
(size_t)vi*sample_count + root->offset);
for( i = 0; i < count; i++ )
{
int val = src[sidx[i]];
@ -796,7 +796,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
else
{
int* idst = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
(size_t)vi*sample_count + root->offset;
for( i = 0; i < count; i++ )
{
int val = src[sidx[i]];
@ -822,7 +822,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if (is_buf_16u)
{
unsigned short* udst_idx = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
vi*sample_count + data_root->offset);
(size_t)vi*sample_count + data_root->offset);
for( i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
@ -846,7 +846,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
else
{
int* idst_idx = buf->data.i + root->buf_idx*get_length_subbuf() +
vi*sample_count + root->offset;
(size_t)vi*sample_count + root->offset;
for( i = 0; i < num_valid; i++ )
{
idx = src_idx[i];
@ -874,14 +874,14 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
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);
(size_t)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*get_length_subbuf() +
workVarCount*sample_count + root->offset;
(size_t)workVarCount*sample_count + root->offset;
for (i = 0; i < count; i++)
sample_idx_dst[i] = sample_idx_src[sidx[i]];
}
@ -1192,10 +1192,10 @@ void CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_valu
if( !is_buf_16u )
*sorted_indices = buf->data.i + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset;
(size_t)vi*sample_count + n->offset;
else {
const unsigned short* short_indices = (const unsigned short*)(buf->data.s + n->buf_idx*get_length_subbuf() +
vi*sample_count + n->offset );
(size_t)vi*sample_count + n->offset );
for( int i = 0; i < node_sample_count; i++ )
sorted_indices_buf[i] = short_indices[i];
*sorted_indices = sorted_indices_buf;
@ -1266,10 +1266,10 @@ 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*get_length_subbuf() +
vi*sample_count + n->offset;
(size_t)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);
(size_t)vi*sample_count + n->offset);
for( int i = 0; i < n->sample_count; i++ )
cat_values_buf[i] = short_values[i];
cat_values = cat_values_buf;