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fixed various warnings from the “doc” and other builders

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This commit is contained in:
Vadim Pisarevsky
2013-12-19 16:31:30 +04:00
parent a1784b7320
commit f41f633d2d
8 changed files with 105 additions and 105 deletions
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106
modules/objdetect/src/cascadedetect.cpp
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@@ -113,12 +113,12 @@ struct Logger
namespace cv
{
template<typename _Tp> void copyVectorToUMat(const std::vector<_Tp>& v, UMat& um)
{
if(v.empty())
um.release();
Mat(1, (int)(v.size()*sizeof(v[0])), CV_8U, (void*)&v[0]).copyTo(um);
if(v.empty())
um.release();
Mat(1, (int)(v.size()*sizeof(v[0])), CV_8U, (void*)&v[0]).copyTo(um);
}
void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights)
@@ -528,24 +528,24 @@ bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize, Size _sumSiz
{
Size imgsz = _image.size();
int cols = imgsz.width, rows = imgsz.height;
if (imgsz.width < origWinSize.width || imgsz.height < origWinSize.height)
return false;
origWinSize = _origWinSize;
normrect = Rect(1, 1, origWinSize.width-2, origWinSize.height-2);
int rn = _sumSize.height, cn = _sumSize.width, rn_scale = hasTiltedFeatures ? 2 : 1;
int sumStep, tofs = 0;
CV_Assert(rn >= rows+1 && cn >= cols+1);
if( _image.isUMat() )
{
usum0.create(rn*rn_scale, cn, CV_32S);
usqsum0.create(rn, cn, CV_32S);
usum = UMat(usum0, Rect(0, 0, cols+1, rows+1));
usqsum = UMat(usqsum0, Rect(0, 0, cols, rows));
if( hasTiltedFeatures )
{
UMat utilted(usum0, Rect(0, _sumSize.height, cols+1, rows+1));
@@ -556,7 +556,7 @@ bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize, Size _sumSiz
{
integral(_image, usum, noArray(), noArray(), CV_32S);
}
sqrBoxFilter(_image, usqsum, CV_32S,
Size(normrect.width, normrect.height),
Point(0, 0), false);
@@ -572,7 +572,7 @@ bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize, Size _sumSiz
sqsum0.create(rn, cn, CV_32S);
sum = sum0(Rect(0, 0, cols+1, rows+1));
sqsum = sqsum0(Rect(0, 0, cols, rows));
if( hasTiltedFeatures )
{
Mat tilted = sum0(Rect(0, _sumSize.height, cols+1, rows+1));
@@ -591,7 +591,7 @@ bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize, Size _sumSiz
size_t fi, nfeatures = features->size();
const std::vector<Feature>& ff = *features;
if( sumSize0 != _sumSize )
{
optfeatures->resize(nfeatures);
@@ -600,13 +600,13 @@ bool HaarEvaluator::setImage( InputArray _image, Size _origWinSize, Size _sumSiz
optfeaturesPtr[fi].setOffsets( ff[fi], sumStep, tofs );
}
if( _image.isUMat() && (sumSize0 != _sumSize || ufbuf.empty()) )
copyVectorToUMat(*optfeatures, ufbuf);
copyVectorToUMat(*optfeatures, ufbuf);
sumSize0 = _sumSize;
return true;
}
bool HaarEvaluator::setWindow( Point pt )
{
if( pt.x < 0 || pt.y < 0 ||
@@ -628,12 +628,12 @@ bool HaarEvaluator::setWindow( Point pt )
return true;
}
Rect HaarEvaluator::getNormRect() const
{
return normrect;
}
void HaarEvaluator::getUMats(std::vector<UMat>& bufs)
{
bufs.clear();
@@ -1100,14 +1100,14 @@ bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processin
std::vector<Rect> candidatesVector;
std::vector<int> rejectLevels;
std::vector<double> levelWeights;
int stripCount, stripSize;
const int PTS_PER_THREAD = 1000;
stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
stripCount = std::min(std::max(stripCount, 1), 100);
stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
if( outputRejectLevels )
{
parallel_for_(Range(0, stripCount), CascadeClassifierInvoker( *this, processingRectSize, stripSize, yStep, factor,
@@ -1129,7 +1129,7 @@ bool CascadeClassifierImpl::detectSingleScale( InputArray _image, Size processin
return true;
}
bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size processingRectSize,
int yStep, double factor, Size sumSize0 )
{
@@ -1137,9 +1137,9 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
Ptr<HaarEvaluator> haar = featureEvaluator.dynamicCast<HaarEvaluator>();
if( haar.empty() )
return false;
haar->setImage(_image, data.origWinSize, sumSize0);
if( cascadeKernel.empty() )
{
cascadeKernel.create("runHaarClassifierStump", ocl::objdetect::cascadedetect_oclsrc,
@@ -1147,31 +1147,31 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
if( cascadeKernel.empty() )
return false;
}
if( ustages.empty() )
{
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.stumps, ustumps);
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.stumps, ustumps);
}
std::vector<UMat> bufs;
haar->getUMats(bufs);
CV_Assert(bufs.size() == 3);
Rect normrect = haar->getNormRect();
//processingRectSize = Size(yStep, yStep);
size_t globalsize[] = { (processingRectSize.width/yStep + VECTOR_SIZE-1)/VECTOR_SIZE, processingRectSize.height/yStep };
cascadeKernel.args(ocl::KernelArg::ReadOnlyNoSize(bufs[0]), // sum
ocl::KernelArg::ReadOnlyNoSize(bufs[1]), // sqsum
ocl::KernelArg::PtrReadOnly(bufs[2]), // optfeatures
// cascade classifier
(int)data.stages.size(),
ocl::KernelArg::PtrReadOnly(ustages),
ocl::KernelArg::PtrReadOnly(ustumps),
ocl::KernelArg::PtrWriteOnly(ufacepos), // positions
processingRectSize,
yStep, (float)factor,
@@ -1219,7 +1219,7 @@ static void detectMultiScaleOldFormat( const Mat& image, Ptr<CvHaarClassifierCas
std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
}
void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::vector<Rect>& candidates,
std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
double scaleFactor, Size minObjectSize, Size maxObjectSize,
@@ -1227,16 +1227,16 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
{
Size imgsz = _image.size();
int imgtype = _image.type();
Mat grayImage, imageBuffer;
candidates.clear();
rejectLevels.clear();
levelWeights.clear();
if( maxObjectSize.height == 0 || maxObjectSize.width == 0 )
maxObjectSize = imgsz;
bool use_ocl = ocl::useOpenCL() &&
getFeatureType() == FeatureEvaluator::HAAR &&
!isOldFormatCascade() &&
@@ -1244,7 +1244,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
maskGenerator.empty() &&
!outputRejectLevels &&
tryOpenCL;
if( !use_ocl )
{
Mat image = _image.getMat();
@@ -1270,9 +1270,9 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
uimage.copyTo(ugrayImage);
uimageBuffer.create(imgsz.height + 1, imgsz.width + 1, CV_8U);
}
Size sumSize0((imgsz.width + SUM_ALIGN) & -SUM_ALIGN, imgsz.height+1);
if( use_ocl )
{
ufacepos.create(1, MAX_FACES*4 + 1, CV_32S);
@@ -1295,7 +1295,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
break;
if( windowSize.width < minObjectSize.width || windowSize.height < minObjectSize.height )
continue;
int yStep;
if( getFeatureType() == cv::FeatureEvaluator::HOG )
{
@@ -1310,14 +1310,14 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
{
UMat uscaledImage(uimageBuffer, Rect(0, 0, scaledImageSize.width, scaledImageSize.height));
resize( ugrayImage, uscaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
if( ocl_detectSingleScale( uscaledImage, processingRectSize, yStep, factor, sumSize0 ) )
continue;
/////// if the OpenCL branch has been executed but failed, fall back to CPU: /////
tryOpenCL = false; // for this cascade do not try OpenCL anymore
// since we may already have some partial results from OpenCL code (unlikely, but still),
// we just recursively call the function again, but with tryOpenCL==false it will
// go with CPU route, so there is no infinite recursion
@@ -1330,13 +1330,13 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
{
Mat scaledImage( scaledImageSize, CV_8U, imageBuffer.data );
resize( grayImage, scaledImage, scaledImageSize, 0, 0, INTER_LINEAR );
if( !detectSingleScale( scaledImage, processingRectSize, yStep, factor, candidates,
rejectLevels, levelWeights, sumSize0, outputRejectLevels ) )
break;
}
}
if( use_ocl && tryOpenCL )
{
Mat facepos = ufacepos.getMat(ACCESS_READ);
@@ -1424,12 +1424,12 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
}
}
CascadeClassifierImpl::Data::Data()
{
stageType = featureType = ncategories = maxNodesPerTree = 0;
}
bool CascadeClassifierImpl::Data::read(const FileNode &root)
{
static const float THRESHOLD_EPS = 1e-5f;
@@ -1503,7 +1503,7 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
DTree tree;
tree.nodeCount = (int)internalNodes.size()/nodeStep;
maxNodesPerTree = std::max(maxNodesPerTree, tree.nodeCount);
classifiers.push_back(tree);
nodes.reserve(nodes.size() + tree.nodeCount);
@@ -1538,7 +1538,7 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
leaves.push_back((float)*internalNodesIter);
}
}
if( isStumpBased() )
{
int nodeOfs = 0, leafOfs = 0;
@@ -1546,7 +1546,7 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
for( size_t stageIdx = 0; stageIdx < nstages; stageIdx++ )
{
const Stage& stage = stages[stageIdx];
int ntrees = stage.ntrees;
for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
{
@@ -1560,7 +1560,7 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
return true;
}
bool CascadeClassifierImpl::read_(const FileNode& root)
{
tryOpenCL = true;