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Set stricter warning rules for gcc

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This commit is contained in:
Andrey Kamaev
2012-06-07 17:21:29 +00:00
parent 0395f7c63f
commit 49a1ba6038
241 changed files with 9054 additions and 8947 deletions
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119
apps/traincascade/boost.cpp
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@@ -1,3 +1,6 @@
#include "opencv2/core/core.hpp"
#include "opencv2/core/internal.hpp"
#include "boost.h"
#include "cascadeclassifier.h"
#include <queue>
@@ -139,7 +142,7 @@ static CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, b
//----------------------------- CascadeBoostParams -------------------------------------------------
CvCascadeBoostParams::CvCascadeBoostParams() : minHitRate( 0.995F), maxFalseAlarm( 0.5F )
{
{
boost_type = CvBoost::GENTLE;
use_surrogates = use_1se_rule = truncate_pruned_tree = false;
}
@@ -157,7 +160,7 @@ CvCascadeBoostParams::CvCascadeBoostParams( int _boostType,
void CvCascadeBoostParams::write( FileStorage &fs ) const
{
String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
boost_type == CvBoost::REAL ? CC_REAL_BOOST :
boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : String();
@@ -197,7 +200,7 @@ bool CvCascadeBoostParams::read( const FileNode &node )
void CvCascadeBoostParams::printDefaults() const
{
cout << "--boostParams--" << endl;
cout << " [-bt <{" << CC_DISCRETE_BOOST << ", "
cout << " [-bt <{" << CC_DISCRETE_BOOST << ", "
<< CC_REAL_BOOST << ", "
<< CC_LOGIT_BOOST ", "
<< CC_GENTLE_BOOST << "(default)}>]" << endl;
@@ -210,7 +213,7 @@ void CvCascadeBoostParams::printDefaults() const
void CvCascadeBoostParams::printAttrs() const
{
String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
String boostTypeStr = boost_type == CvBoost::DISCRETE ? CC_DISCRETE_BOOST :
boost_type == CvBoost::REAL ? CC_REAL_BOOST :
boost_type == CvBoost::LOGIT ? CC_LOGIT_BOOST :
boost_type == CvBoost::GENTLE ? CC_GENTLE_BOOST : String();
@@ -259,7 +262,7 @@ bool CvCascadeBoostParams::scanAttr( const String prmName, const String val)
else
res = false;
return res;
return res;
}
CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_idx )
@@ -440,7 +443,7 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea
set_params( _params );
max_c_count = MAX( 2, featureEvaluator->getMaxCatCount() );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
if ( featureEvaluator->getMaxCatCount() > 0 )
if ( featureEvaluator->getMaxCatCount() > 0 )
{
numPrecalcIdx = 0;
cat_var_count = var_count;
@@ -448,7 +451,7 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea
for( int vi = 0; vi < var_count; vi++ )
{
var_type->data.i[vi] = vi;
}
}
}
else
{
@@ -457,8 +460,8 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea
for( int vi = 1; vi <= var_count; vi++ )
{
var_type->data.i[vi-1] = -vi;
}
}
}
}
var_type->data.i[var_count] = cat_var_count;
var_type->data.i[var_count+1] = cat_var_count+1;
@@ -467,7 +470,7 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea
treeBlockSize = MAX(treeBlockSize + BlockSizeDelta, MinBlockSize);
tree_storage = cvCreateMemStorage( treeBlockSize );
node_heap = cvCreateSet( 0, sizeof(node_heap[0]), sizeof(CvDTreeNode), tree_storage );
split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage );
split_heap = cvCreateSet( 0, sizeof(split_heap[0]), maxSplitSize, tree_storage );
}
CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
@@ -477,15 +480,15 @@ CvCascadeBoostTrainData::CvCascadeBoostTrainData( const CvFeatureEvaluator* _fea
{
setData( _featureEvaluator, _numSamples, _precalcValBufSize, _precalcIdxBufSize, _params );
}
void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples,
int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params )
{
const CvDTreeParams& _params )
{
int* idst = 0;
unsigned short* udst = 0;
clear();
shared = true;
have_labels = true;
@@ -503,16 +506,16 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
_resp = featureEvaluator->getCls();
responses = &_resp;
// TODO: check responses: elements must be 0 or 1
if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0)
if( _precalcValBufSize < 0 || _precalcIdxBufSize < 0)
CV_Error( CV_StsOutOfRange, "_numPrecalcVal and _numPrecalcIdx must be positive or 0" );
var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
var_count = var_all = featureEvaluator->getNumFeatures() * featureEvaluator->getFeatureSize();
sample_count = _numSamples;
is_buf_16u = false;
if (sample_count < 65536)
is_buf_16u = true;
is_buf_16u = false;
if (sample_count < 65536)
is_buf_16u = true;
numPrecalcVal = min( cvRound((double)_precalcValBufSize*1048576. / (sizeof(float)*sample_count)), var_count );
numPrecalcIdx = min( cvRound((double)_precalcIdxBufSize*1048576. /
@@ -522,8 +525,8 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
valCache.create( numPrecalcVal, sample_count, CV_32FC1 );
var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 );
if ( featureEvaluator->getMaxCatCount() > 0 )
if ( featureEvaluator->getMaxCatCount() > 0 )
{
numPrecalcIdx = 0;
cat_var_count = var_count;
@@ -531,7 +534,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
for( int vi = 0; vi < var_count; vi++ )
{
var_type->data.i[vi] = vi;
}
}
}
else
{
@@ -540,14 +543,14 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
for( int vi = 1; vi <= var_count; vi++ )
{
var_type->data.i[vi-1] = -vi;
}
}
}
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;
if ( is_buf_16u )
buf = cvCreateMat( buf_count, buf_size, CV_16UC1 );
else
@@ -556,7 +559,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
cat_count = cvCreateMat( 1, cat_var_count + 1, CV_32SC1 );
// precalculate valCache and set indices in buf
precalculate();
precalculate();
// now calculate the maximum size of split,
// create memory storage that will keep nodes and splits of the decision tree
@@ -574,7 +577,7 @@ void CvCascadeBoostTrainData::setData( const CvFeatureEvaluator* _featureEvaluat
tempBlockSize = MAX( tempBlockSize + BlockSizeDelta, MinBlockSize );
temp_storage = cvCreateMemStorage( tempBlockSize );
nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nvSize, temp_storage );
data_root = new_node( 0, sample_count, 0, 0 );
// set sample labels
@@ -617,7 +620,7 @@ void CvCascadeBoostTrainData::free_train_data()
const int* CvCascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* labelsBuf)
{
int nodeSampleCount = n->sample_count;
int nodeSampleCount = n->sample_count;
int rStep = CV_IS_MAT_CONT( responses->type ) ? 1 : responses->step / CV_ELEM_SIZE( responses->type );
int* sampleIndicesBuf = labelsBuf; //
@@ -626,7 +629,7 @@ const int* CvCascadeBoostTrainData::get_class_labels( CvDTreeNode* n, int* label
{
int sidx = sampleIndices[si];
labelsBuf[si] = (int)responses->data.fl[sidx*rStep];
}
}
return labelsBuf;
}
@@ -643,9 +646,9 @@ const int* CvCascadeBoostTrainData::get_cv_labels( CvDTreeNode* n, int* labels_b
void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf )
{
int nodeSampleCount = n->sample_count;
int nodeSampleCount = n->sample_count;
const int* sampleIndices = get_sample_indices(n, sampleIndicesBuf);
if ( vi < numPrecalcIdx )
{
if( !is_buf_16u )
@@ -659,7 +662,7 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
*sortedIndices = sortedIndicesBuf;
}
if( vi < numPrecalcVal )
{
for( int i = 0; i < nodeSampleCount; i++ )
@@ -705,10 +708,10 @@ void CvCascadeBoostTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* o
ordValuesBuf[i] = (&sampleValues[0])[sortedIndicesBuf[i]];
*sortedIndices = sortedIndicesBuf;
}
*ordValues = ordValuesBuf;
}
const int* CvCascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf )
{
int nodeSampleCount = n->sample_count;
@@ -739,8 +742,8 @@ const int* CvCascadeBoostTrainData::get_cat_var_data( CvDTreeNode* n, int vi, in
float CvCascadeBoostTrainData::getVarValue( int vi, int si )
{
if ( vi < numPrecalcVal && !valCache.empty() )
return valCache.at<float>( vi, si );
return (*featureEvaluator)( vi, si );
return valCache.at<float>( vi, si );
return (*featureEvaluator)( vi, si );
}
@@ -858,7 +861,7 @@ CvDTreeNode* CvCascadeBoostTree::predict( int sampleIdx ) const
CvDTreeNode* node = root;
if( !node )
CV_Error( CV_StsError, "The tree has not been trained yet" );
if ( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getMaxCatCount() == 0 ) // ordered
{
while( node->left )
@@ -946,7 +949,7 @@ void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble,
int maxCatCount = ((CvCascadeBoostTrainData*)_data)->featureEvaluator->getMaxCatCount();
int subsetN = (maxCatCount + 31)/32;
int step = 3 + ( maxCatCount>0 ? subsetN : 1 );
queue<CvDTreeNode*> internalNodesQueue;
FileNodeIterator internalNodesIt, leafValsuesIt;
CvDTreeNode* prntNode, *cldNode;
@@ -986,11 +989,11 @@ void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble,
{
prntNode->right = cldNode = data->new_node( 0, 0, 0, 0 );
*leafValsuesIt >> cldNode->value; leafValsuesIt--;
cldNode->parent = prntNode;
cldNode->parent = prntNode;
}
else
{
prntNode->right = internalNodesQueue.front();
prntNode->right = internalNodesQueue.front();
prntNode->right->parent = prntNode;
internalNodesQueue.pop();
}
@@ -999,7 +1002,7 @@ void CvCascadeBoostTree::read( const FileNode &node, CvBoost* _ensemble,
{
prntNode->left = cldNode = data->new_node( 0, 0, 0, 0 );
*leafValsuesIt >> cldNode->value; leafValsuesIt--;
cldNode->parent = prntNode;
cldNode->parent = prntNode;
}
else
{
@@ -1089,7 +1092,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
}
CV_Assert( n1 == n );
}
}
else
{
int *ldst, *rdst;
@@ -1116,7 +1119,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
}
CV_Assert( n1 == n );
}
}
}
// split cv_labels using newIdx relocation table
@@ -1171,7 +1174,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
}
}
// split sample indices
int *sampleIdx_src_buf = tempBuf + n;
const int* sampleIdx_src = data->get_sample_indices(node, sampleIdx_src_buf);
@@ -1181,9 +1184,9 @@ 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*buf->cols +
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*buf->cols +
workVarCount*scount + right->offset);
for (int i = 0; i < n; i++)
{
@@ -1202,9 +1205,9 @@ 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*buf->cols +
workVarCount*scount + left->offset;
int* rdst = buf->data.i + right->buf_idx*buf->cols +
int* rdst = buf->data.i + right->buf_idx*buf->cols +
workVarCount*scount + right->offset;
for (int i = 0; i < n; i++)
{
@@ -1229,10 +1232,10 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
}
// deallocate the parent node data that is not needed anymore
data->free_node_data(node);
data->free_node_data(node);
}
void auxMarkFeaturesInMap( const CvDTreeNode* node, Mat& featureMap)
static void auxMarkFeaturesInMap( const CvDTreeNode* node, Mat& featureMap)
{
if ( node && node->split )
{
@@ -1265,7 +1268,7 @@ bool CvCascadeBoost::train( const CvFeatureEvaluator* _featureEvaluator,
set_params( _params );
if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) )
data->do_responses_copy();
update_weights( 0 );
cout << "+----+---------+---------+" << endl;
@@ -1316,7 +1319,7 @@ bool CvCascadeBoost::set_params( const CvBoostParams& _params )
minHitRate = ((CvCascadeBoostParams&)_params).minHitRate;
maxFalseAlarm = ((CvCascadeBoostParams&)_params).maxFalseAlarm;
return ( ( minHitRate > 0 ) && ( minHitRate < 1) &&
( maxFalseAlarm > 0 ) && ( maxFalseAlarm < 1) &&
( maxFalseAlarm > 0 ) && ( maxFalseAlarm < 1) &&
CvBoost::set_params( _params ));
}
@@ -1364,7 +1367,7 @@ 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*buf->cols +
data->data_root->offset + (data->work_var_count-1)*data->sample_count);
for( int i = 0; i < n; i++ )
{
@@ -1382,7 +1385,7 @@ 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*buf->cols +
data->data_root->offset + (data->work_var_count-1)*data->sample_count;
for( int i = 0; i < n; i++ )
@@ -1425,7 +1428,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
{
// invert the subsample mask
cvXorS( subsample_mask, cvScalar(1.), subsample_mask );
// run tree through all the non-processed samples
for( int i = 0; i < n; i++ )
if( subsample_mask->data.ptr[i] )
@@ -1565,7 +1568,7 @@ bool CvCascadeBoost::isErrDesired()
int sCount = data->sample_count,
numPos = 0, numNeg = 0, numFalse = 0, numPosTrue = 0;
vector<float> eval(sCount);
for( int i = 0; i < sCount; i++ )
if( ((CvCascadeBoostTrainData*)data)->featureEvaluator->getCls( i ) == 1.0F )
eval[numPos++] = predict( i, true );
@@ -1625,7 +1628,7 @@ bool CvCascadeBoost::read( const FileNode &node,
set_params( _params );
node[CC_STAGE_THRESHOLD] >> threshold;
FileNode rnode = node[CC_WEAK_CLASSIFIERS];
FileNode rnode = node[CC_WEAK_CLASSIFIERS];
storage = cvCreateMemStorage();
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );