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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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84
modules/objdetect/src/haar.cpp
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@@ -653,7 +653,7 @@ double icvEvalHidHaarClassifier( CvHidHaarClassifier* classifier,
}
CV_IMPL int
static int
cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
CvPoint pt, double& stage_sum, int start_stage )
{
@@ -759,7 +759,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
sum += calc_sum(node->feature.rect[1],p_offset) * node->feature.rect[1].weight;
if( node->feature.rect[2].p0 )
sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
stage_sum += classifier->alpha[sum >= t];
#else
// ayasin - NHM perf optim. Avoid use of costly flaky jcc
@@ -771,7 +771,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
if( node->feature.rect[2].p0 )
_sum += calc_sum(node->feature.rect[2],p_offset) * node->feature.rect[2].weight;
__m128d sum = _mm_set_sd(_sum);
t = _mm_cmpgt_sd(t, sum);
stage_sum = _mm_add_sd(stage_sum, _mm_blendv_pd(b, a, t));
#endif
@@ -823,7 +823,7 @@ struct HaarDetectObjects_ScaleImage_Invoker
HaarDetectObjects_ScaleImage_Invoker( const CvHaarClassifierCascade* _cascade,
int _stripSize, double _factor,
const Mat& _sum1, const Mat& _sqsum1, Mat* _norm1,
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec,
Mat* _mask1, Rect _equRect, ConcurrentRectVector& _vec,
std::vector<int>& _levels, std::vector<double>& _weights,
bool _outputLevels )
{
@@ -839,19 +839,19 @@ struct HaarDetectObjects_ScaleImage_Invoker
rejectLevels = _outputLevels ? &_levels : 0;
levelWeights = _outputLevels ? &_weights : 0;
}
void operator()( const BlockedRange& range ) const
{
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor));
int y1 = range.begin()*stripSize, y2 = min(range.end()*stripSize, sum1.rows - 1 - winSize0.height);
if (y2 <= y1 || sum1.cols <= 1 + winSize0.width)
return;
Size ssz(sum1.cols - 1 - winSize0.width, y2 - y1);
int x, y, ystep = factor > 2 ? 1 : 2;
#ifdef HAVE_IPP
if( cascade->hid_cascade->ipp_stages )
{
@@ -860,7 +860,7 @@ struct HaarDetectObjects_ScaleImage_Invoker
sqsum1.ptr<double>(y1), sqsum1.step,
norm1->ptr<float>(y1), norm1->step,
ippiSize(ssz.width, ssz.height), iequRect );
int positive = (ssz.width/ystep)*((ssz.height + ystep-1)/ystep);
if( ystep == 1 )
@@ -870,12 +870,12 @@ struct HaarDetectObjects_ScaleImage_Invoker
{
uchar* mask1row = mask1->ptr(y);
memset( mask1row, 0, ssz.width );
if( y % ystep == 0 )
for( x = 0; x < ssz.width; x += ystep )
mask1row[x] = (uchar)1;
}
for( int j = 0; j < cascade->count; j++ )
{
if( ippiApplyHaarClassifier_32f_C1R(
@@ -889,7 +889,7 @@ struct HaarDetectObjects_ScaleImage_Invoker
if( positive <= 0 )
break;
}
if( positive > 0 )
for( y = y1; y < y2; y += ystep )
{
@@ -929,11 +929,11 @@ struct HaarDetectObjects_ScaleImage_Invoker
{
if( result > 0 )
vec->push_back(Rect(cvRound(x*factor), cvRound(y*factor),
winSize.width, winSize.height));
winSize.width, winSize.height));
}
}
}
const CvHaarClassifierCascade* cascade;
int stripSize;
double factor;
@@ -943,7 +943,7 @@ struct HaarDetectObjects_ScaleImage_Invoker
std::vector<int>* rejectLevels;
std::vector<double>* levelWeights;
};
struct HaarDetectObjects_ScaleCascade_Invoker
{
@@ -960,7 +960,7 @@ struct HaarDetectObjects_ScaleCascade_Invoker
p = _p; pq = _pq;
vec = &_vec;
}
void operator()( const BlockedRange& range ) const
{
int iy, startY = range.begin(), endY = range.end();
@@ -968,14 +968,14 @@ struct HaarDetectObjects_ScaleCascade_Invoker
const int *pq0 = pq[0], *pq1 = pq[1], *pq2 = pq[2], *pq3 = pq[3];
bool doCannyPruning = p0 != 0;
int sstep = (int)(sumstep/sizeof(p0[0]));
for( iy = startY; iy < endY; iy++ )
{
int ix, y = cvRound(iy*ystep), ixstep = 1;
for( ix = xrange.start; ix < xrange.end; ix += ixstep )
{
int x = cvRound(ix*ystep); // it should really be ystep, not ixstep
if( doCannyPruning )
{
int offset = y*sstep + x;
@@ -987,7 +987,7 @@ struct HaarDetectObjects_ScaleCascade_Invoker
continue;
}
}
int result = cvRunHaarClassifierCascade( cascade, cvPoint(x, y), 0 );
if( result > 0 )
vec->push_back(Rect(x, y, winsize.width, winsize.height));
@@ -995,7 +995,7 @@ struct HaarDetectObjects_ScaleCascade_Invoker
}
}
}
const CvHaarClassifierCascade* cascade;
double ystep;
size_t sumstep;
@@ -1005,16 +1005,16 @@ struct HaarDetectObjects_ScaleCascade_Invoker
const int** pq;
ConcurrentRectVector* vec;
};
}
CvSeq*
cvHaarDetectObjectsForROC( const CvArr* _img,
cvHaarDetectObjectsForROC( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors, int flags,
double scaleFactor, int minNeighbors, int flags,
CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
const double GROUP_EPS = 0.2;
@@ -1044,13 +1044,13 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
if( CV_MAT_DEPTH(img->type) != CV_8U )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit images are supported" );
if( scaleFactor <= 1 )
CV_Error( CV_StsOutOfRange, "scale factor must be > 1" );
if( findBiggestObject )
flags &= ~CV_HAAR_SCALE_IMAGE;
if( maxSize.height == 0 || maxSize.width == 0 )
{
maxSize.height = img->rows;
@@ -1132,7 +1132,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
#else
const int stripCount = 1;
#endif
#ifdef HAVE_IPP
if( use_ipp )
{
@@ -1141,8 +1141,8 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
}
else
#endif
cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. );
cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. );
cv::Mat _norm1(&norm1), _mask1(&mask1);
cv::parallel_for(cv::BlockedRange(0, stripCount),
cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
@@ -1242,22 +1242,22 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
{
rectList.resize(allCandidates.size());
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
groupRectangles(rectList, std::max(minNeighbors, 1), GROUP_EPS);
if( !rectList.empty() )
{
size_t i, sz = rectList.size();
cv::Rect maxRect;
for( i = 0; i < sz; i++ )
{
if( rectList[i].area() > maxRect.area() )
maxRect = rectList[i];
}
allCandidates.push_back(maxRect);
scanROI = maxRect;
int dx = cvRound(maxRect.width*GROUP_EPS);
int dy = cvRound(maxRect.height*GROUP_EPS);
@@ -1265,7 +1265,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
scanROI.y = std::max(scanROI.y - dy, 0);
scanROI.width = std::min(scanROI.width + dx*2, img->cols-1-scanROI.x);
scanROI.height = std::min(scanROI.height + dy*2, img->rows-1-scanROI.y);
double minScale = roughSearch ? 0.6 : 0.4;
minSize.width = cvRound(maxRect.width*minScale);
minSize.height = cvRound(maxRect.height*minScale);
@@ -1277,7 +1277,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
rectList.resize(allCandidates.size());
if(!allCandidates.empty())
std::copy(allCandidates.begin(), allCandidates.end(), rectList.begin());
if( minNeighbors != 0 || findBiggestObject )
{
if( outputRejectLevels )
@@ -1291,11 +1291,11 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
}
else
rweights.resize(rectList.size(),0);
if( findBiggestObject && rectList.size() )
{
CvAvgComp result_comp = {{0,0,0,0},0};
for( size_t i = 0; i < rectList.size(); i++ )
{
cv::Rect r = rectList[i];
@@ -1322,14 +1322,14 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
}
CV_IMPL CvSeq*
cvHaarDetectObjects( const CvArr* _img,
cvHaarDetectObjects( const CvArr* _img,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scaleFactor,
int minNeighbors, int flags, CvSize minSize, CvSize maxSize )
{
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
return cvHaarDetectObjectsForROC( _img, cascade, storage, fakeLevels, fakeWeights,
scaleFactor, minNeighbors, flags, minSize, maxSize, false );
}
@@ -2091,7 +2091,7 @@ namespace cv
HaarClassifierCascade::HaarClassifierCascade() {}
HaarClassifierCascade::HaarClassifierCascade(const String& filename)
{ load(filename); }
bool HaarClassifierCascade::load(const String& filename)
{
cascade = Ptr<CvHaarClassifierCascade>((CvHaarClassifierCascade*)cvLoad(filename.c_str(), 0, 0, 0));