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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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6
modules/objdetect/doc/cascade_classification.rst
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@ -86,12 +86,14 @@ FeatureEvaluator::setImage
------------------------------
Assigns an image to feature evaluator.
.. ocv:function:: bool FeatureEvaluator::setImage(const Mat& img, Size origWinSize)
.. ocv:function:: bool FeatureEvaluator::setImage(InputArray img, Size origWinSize, Size sumSize)
:param img: Matrix of the type ``CV_8UC1`` containing an image where the features are computed.
:param img: Matrix of the type ``CV_8UC1`` containing an image where the features are computed.
:param origWinSize: Size of training images.
:param sumSize: The requested size of integral images (so if the integral image is smaller, it resides in the top-left corner of the larger image of requested size). Because the features are represented using offsets from the image origin, using the same sumSize for all scales helps to avoid constant readjustments of the features to different scales.
The method assigns an image, where the features will be computed, to the feature evaluator.

12
modules/objdetect/src/cascadedetect.cpp
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@ -116,9 +116,9 @@ 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)
@ -600,7 +600,7 @@ 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;
@ -1150,8 +1150,8 @@ bool CascadeClassifierImpl::ocl_detectSingleScale( InputArray _image, Size proce
if( ustages.empty() )
{
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.stumps, ustumps);
copyVectorToUMat(data.stages, ustages);
copyVectorToUMat(data.stumps, ustumps);
}
std::vector<UMat> bufs;

21
modules/objdetect/src/cascadedetect_convert.cpp
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@ -121,16 +121,15 @@ static bool convert(const String& oldcascade, const String& newcascade)
FileNode sznode = oldroot[ICV_HAAR_SIZE_NAME];
if( sznode.empty() )
return false;
int maxdepth = 0;
Size cascadesize;
cascadesize.width = (int)sznode[0];
cascadesize.height = (int)sznode[1];
std::vector<HaarFeature> features;
size_t i, j, k, n;
int i, j, k, n;
FileNode stages_seq = oldroot[ICV_HAAR_STAGES_NAME];
size_t nstages = stages_seq.size();
int nstages = (int)stages_seq.size();
std::vector<HaarStageClassifier> stages(nstages);
for( i = 0; i < nstages; i++ )
@ -139,14 +138,14 @@ static bool convert(const String& oldcascade, const String& newcascade)
HaarStageClassifier& stage = stages[i];
stage.threshold = (double)stagenode[ICV_HAAR_STAGE_THRESHOLD_NAME];
FileNode weaks_seq = stagenode[ICV_HAAR_TREES_NAME];
size_t nweaks = weaks_seq.size();
int nweaks = (int)weaks_seq.size();
stage.weaks.resize(nweaks);
for( j = 0; j < nweaks; j++ )
{
HaarClassifier& weak = stage.weaks[j];
FileNode weaknode = weaks_seq[j];
size_t nnodes = weaknode.size();
int nnodes = (int)weaknode.size();
for( n = 0; n < nnodes; n++ )
{
@ -157,7 +156,7 @@ static bool convert(const String& oldcascade, const String& newcascade)
node.f = (int)features.size();
f.tilted = (int)fnode[ICV_HAAR_TILTED_NAME] != 0;
FileNode rects_seq = fnode[ICV_HAAR_RECTS_NAME];
size_t nrects = rects_seq.size();
int nrects = (int)rects_seq.size();
for( k = 0; k < nrects; k++ )
{
@ -199,9 +198,9 @@ static bool convert(const String& oldcascade, const String& newcascade)
if( !newfs.isOpened() )
return false;
size_t maxWeakCount = 0, nfeatures = features.size();
int maxWeakCount = 0, nfeatures = (int)features.size();
for( i = 0; i < nstages; i++ )
maxWeakCount = std::max(maxWeakCount, stages[i].weaks.size());
maxWeakCount = std::max(maxWeakCount, (int)stages[i].weaks.size());
newfs << "cascade" << "{:opencv-cascade-classifier"
<< "stageType" << "BOOST"
@ -219,7 +218,7 @@ static bool convert(const String& oldcascade, const String& newcascade)
for( i = 0; i < nstages; i++ )
{
size_t nweaks = stages[i].weaks.size();
int nweaks = (int)stages[i].weaks.size();
newfs << "{" << "maxWeakCount" << (int)nweaks
<< "stageThreshold" << stages[i].threshold
<< "weakClassifiers" << "[";
@ -227,7 +226,7 @@ static bool convert(const String& oldcascade, const String& newcascade)
{
const HaarClassifier& c = stages[i].weaks[j];
newfs << "{" << "internalNodes" << "[";
size_t nnodes = c.nodes.size(), nleaves = c.leaves.size();
int nnodes = (int)c.nodes.size(), nleaves = (int)c.leaves.size();
for( k = 0; k < nnodes; k++ )
newfs << c.nodes[k].left << c.nodes[k].right
<< c.nodes[k].f << c.nodes[k].threshold;
@ -246,7 +245,7 @@ static bool convert(const String& oldcascade, const String& newcascade)
{
const HaarFeature& f = features[i];
newfs << "{" << "rects" << "[";
for( j = 0; j < (size_t)HaarFeature::RECT_NUM; j++ )
for( j = 0; j < HaarFeature::RECT_NUM; j++ )
{
if( j >= 2 && fabs(f.rect[j].weight) < FLT_EPSILON )
break;

1
modules/objdetect/src/opencl/cascadedetect.cl
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@ -183,4 +183,3 @@ __kernel void runLBPClassifierStump(
}
}
#endif