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Remove all using directives for STL namespace and members

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Made all STL usages explicit to be able automatically find all usages of
particular class or function.
This commit is contained in:
Andrey Kamaev
2013-02-24 20:14:01 +04:00
parent f783f34e0b
commit 2a6fb2867e
310 changed files with 5744 additions and 5964 deletions
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112
modules/objdetect/src/cascadedetect.cpp
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@@ -132,7 +132,7 @@ public:
};
void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights)
void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights)
{
if( groupThreshold <= 0 || rectList.empty() )
{
@@ -146,13 +146,13 @@ void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vec
return;
}
vector<int> labels;
std::vector<int> labels;
int nclasses = partition(rectList, labels, SimilarRects(eps));
vector<Rect> rrects(nclasses);
vector<int> rweights(nclasses, 0);
vector<int> rejectLevels(nclasses, 0);
vector<double> rejectWeights(nclasses, DBL_MIN);
std::vector<Rect> rrects(nclasses);
std::vector<int> rweights(nclasses, 0);
std::vector<int> rejectLevels(nclasses, 0);
std::vector<double> rejectWeights(nclasses, DBL_MIN);
int i, j, nlabels = (int)labels.size();
for( i = 0; i < nlabels; i++ )
{
@@ -236,8 +236,8 @@ void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps, vec
class MeanshiftGrouping
{
public:
MeanshiftGrouping(const Point3d& densKer, const vector<Point3d>& posV,
const vector<double>& wV, double eps, int maxIter = 20)
MeanshiftGrouping(const Point3d& densKer, const std::vector<Point3d>& posV,
const std::vector<double>& wV, double eps, int maxIter = 20)
{
densityKernel = densKer;
weightsV = wV;
@@ -256,7 +256,7 @@ public:
}
}
void getModes(vector<Point3d>& modesV, vector<double>& resWeightsV, const double eps)
void getModes(std::vector<Point3d>& modesV, std::vector<double>& resWeightsV, const double eps)
{
for (size_t i=0; i <distanceV.size(); i++)
{
@@ -284,14 +284,14 @@ public:
}
protected:
vector<Point3d> positionsV;
vector<double> weightsV;
std::vector<Point3d> positionsV;
std::vector<double> weightsV;
Point3d densityKernel;
int positionsCount;
vector<Point3d> meanshiftV;
vector<Point3d> distanceV;
std::vector<Point3d> meanshiftV;
std::vector<Point3d> distanceV;
int iterMax;
double modeEps;
@@ -305,8 +305,8 @@ protected:
Point3d bPt = inPt;
Point3d sPt = densityKernel;
sPt.x *= exp(aPt.z);
sPt.y *= exp(aPt.z);
sPt.x *= std::exp(aPt.z);
sPt.y *= std::exp(aPt.z);
aPt.x /= sPt.x;
aPt.y /= sPt.y;
@@ -338,8 +338,8 @@ protected:
Point3d aPt = positionsV[i];
Point3d sPt = densityKernel;
sPt.x *= exp(aPt.z);
sPt.y *= exp(aPt.z);
sPt.x *= std::exp(aPt.z);
sPt.y *= std::exp(aPt.z);
aPt -= inPt;
@@ -370,8 +370,8 @@ protected:
double getDistance(Point3d p1, Point3d p2) const
{
Point3d ns = densityKernel;
ns.x *= exp(p2.z);
ns.y *= exp(p2.z);
ns.x *= std::exp(p2.z);
ns.y *= std::exp(p2.z);
p2 -= p1;
p2.x /= ns.x;
p2.y /= ns.y;
@@ -380,12 +380,12 @@ protected:
}
};
//new grouping function with using meanshift
static void groupRectangles_meanshift(vector<Rect>& rectList, double detectThreshold, vector<double>* foundWeights,
vector<double>& scales, Size winDetSize)
static void groupRectangles_meanshift(std::vector<Rect>& rectList, double detectThreshold, std::vector<double>* foundWeights,
std::vector<double>& scales, Size winDetSize)
{
int detectionCount = (int)rectList.size();
vector<Point3d> hits(detectionCount), resultHits;
vector<double> hitWeights(detectionCount), resultWeights;
std::vector<Point3d> hits(detectionCount), resultHits;
std::vector<double> hitWeights(detectionCount), resultWeights;
Point2d hitCenter;
for (int i=0; i < detectionCount; i++)
@@ -409,7 +409,7 @@ static void groupRectangles_meanshift(vector<Rect>& rectList, double detectThres
for (unsigned i=0; i < resultHits.size(); ++i)
{
double scale = exp(resultHits[i].z);
double scale = std::exp(resultHits[i].z);
hitCenter.x = resultHits[i].x;
hitCenter.y = resultHits[i].y;
Size s( int(winDetSize.width * scale), int(winDetSize.height * scale) );
@@ -424,23 +424,23 @@ static void groupRectangles_meanshift(vector<Rect>& rectList, double detectThres
}
}
void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps)
void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps)
{
groupRectangles(rectList, groupThreshold, eps, 0, 0);
}
void groupRectangles(vector<Rect>& rectList, vector<int>& weights, int groupThreshold, double eps)
void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& weights, int groupThreshold, double eps)
{
groupRectangles(rectList, groupThreshold, eps, &weights, 0);
}
//used for cascade detection algorithm for ROC-curve calculating
void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels, vector<double>& levelWeights, int groupThreshold, double eps)
void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels, std::vector<double>& levelWeights, int groupThreshold, double eps)
{
groupRectangles(rectList, groupThreshold, eps, &rejectLevels, &levelWeights);
}
//can be used for HOG detection algorithm only
void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights,
vector<double>& foundScales, double detectThreshold, Size winDetSize)
void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
std::vector<double>& foundScales, double detectThreshold, Size winDetSize)
{
groupRectangles_meanshift(rectList, detectThreshold, &foundWeights, foundScales, winDetSize);
}
@@ -483,7 +483,7 @@ bool HaarEvaluator::Feature :: read( const FileNode& node )
HaarEvaluator::HaarEvaluator()
{
features = new vector<Feature>();
features = new std::vector<Feature>();
}
HaarEvaluator::~HaarEvaluator()
{
@@ -578,7 +578,7 @@ bool HaarEvaluator::setWindow( Point pt )
double nf = (double)normrect.area() * valsqsum - (double)valsum * valsum;
if( nf > 0. )
nf = sqrt(nf);
nf = std::sqrt(nf);
else
nf = 1.;
varianceNormFactor = 1./nf;
@@ -598,7 +598,7 @@ bool LBPEvaluator::Feature :: read(const FileNode& node )
LBPEvaluator::LBPEvaluator()
{
features = new vector<Feature>();
features = new std::vector<Feature>();
}
LBPEvaluator::~LBPEvaluator()
{
@@ -678,7 +678,7 @@ bool HOGEvaluator::Feature :: read( const FileNode& node )
HOGEvaluator::HOGEvaluator()
{
features = new vector<Feature>();
features = new std::vector<Feature>();
}
HOGEvaluator::~HOGEvaluator()
@@ -745,7 +745,7 @@ bool HOGEvaluator::setWindow(Point pt)
return true;
}
void HOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const
void HOGEvaluator::integralHistogram(const Mat &img, std::vector<Mat> &histogram, Mat &norm, int nbins) const
{
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
int x, y, binIdx;
@@ -854,7 +854,7 @@ CascadeClassifier::CascadeClassifier()
{
}
CascadeClassifier::CascadeClassifier(const string& filename)
CascadeClassifier::CascadeClassifier(const std::string& filename)
{
load(filename);
}
@@ -868,7 +868,7 @@ bool CascadeClassifier::empty() const
return oldCascade.empty() && data.stages.empty();
}
bool CascadeClassifier::load(const string& filename)
bool CascadeClassifier::load(const std::string& filename)
{
oldCascade.release();
data = Data();
@@ -948,7 +948,7 @@ class CascadeClassifierInvoker : public ParallelLoopBody
{
public:
CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
vector<Rect>& _vec, vector<int>& _levels, vector<double>& _weights, bool outputLevels, const Mat& _mask, Mutex* _mtx)
std::vector<Rect>& _vec, std::vector<int>& _levels, std::vector<double>& _weights, bool outputLevels, const Mat& _mask, Mutex* _mtx)
{
classifier = &_cc;
processingRectSize = _sz1;
@@ -969,7 +969,7 @@ public:
Size winSize(cvRound(classifier->data.origWinSize.width * scalingFactor), cvRound(classifier->data.origWinSize.height * scalingFactor));
int y1 = range.start * stripSize;
int y2 = min(range.end * stripSize, processingRectSize.height);
int y2 = std::min(range.end * stripSize, processingRectSize.height);
for( int y = y1; y < y2; y += yStep )
{
for( int x = 0; x < processingRectSize.width; x += yStep )
@@ -1012,12 +1012,12 @@ public:
}
CascadeClassifier* classifier;
vector<Rect>* rectangles;
std::vector<Rect>* rectangles;
Size processingRectSize;
int stripSize, yStep;
double scalingFactor;
vector<int> *rejectLevels;
vector<double> *levelWeights;
std::vector<int> *rejectLevels;
std::vector<double> *levelWeights;
Mat mask;
Mutex* mtx;
};
@@ -1026,8 +1026,8 @@ struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } }
bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
int stripSize, int yStep, double factor, vector<Rect>& candidates,
vector<int>& levels, vector<double>& weights, bool outputRejectLevels )
int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
std::vector<int>& levels, std::vector<double>& weights, bool outputRejectLevels )
{
if( !featureEvaluator->setImage( image, data.origWinSize ) )
return false;
@@ -1041,9 +1041,9 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
currentMask=maskGenerator->generateMask(image);
}
vector<Rect> candidatesVector;
vector<int> rejectLevels;
vector<double> levelWeights;
std::vector<Rect> candidatesVector;
std::vector<int> rejectLevels;
std::vector<double> levelWeights;
Mutex mtx;
if( outputRejectLevels )
{
@@ -1087,9 +1087,9 @@ bool CascadeClassifier::setImage(const Mat& image)
return featureEvaluator->setImage(image, data.origWinSize);
}
void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects,
vector<int>& rejectLevels,
vector<double>& levelWeights,
void CascadeClassifier::detectMultiScale( const Mat& image, std::vector<Rect>& objects,
std::vector<int>& rejectLevels,
std::vector<double>& levelWeights,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
@@ -1107,7 +1107,7 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
CvMat _image = image;
CvSeq* _objects = cvHaarDetectObjectsForROC( &_image, oldCascade, storage, rejectLevels, levelWeights, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, outputRejectLevels );
vector<CvAvgComp> vecAvgComp;
std::vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
objects.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
@@ -1133,7 +1133,7 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
}
Mat imageBuffer(image.rows + 1, image.cols + 1, CV_8U);
vector<Rect> candidates;
std::vector<Rect> candidates;
for( double factor = 1; ; factor *= scaleFactor )
{
@@ -1194,12 +1194,12 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
}
}
void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects,
void CascadeClassifier::detectMultiScale( const Mat& image, std::vector<Rect>& objects,
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize)
{
vector<int> fakeLevels;
vector<double> fakeWeights;
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
detectMultiScale( image, objects, fakeLevels, fakeWeights, scaleFactor,
minNeighbors, flags, minObjectSize, maxObjectSize, false );
}
@@ -1209,13 +1209,13 @@ bool CascadeClassifier::Data::read(const FileNode &root)
static const float THRESHOLD_EPS = 1e-5f;
// load stage params
string stageTypeStr = (string)root[CC_STAGE_TYPE];
std::string stageTypeStr = (std::string)root[CC_STAGE_TYPE];
if( stageTypeStr == CC_BOOST )
stageType = BOOST;
else
return false;
string featureTypeStr = (string)root[CC_FEATURE_TYPE];
std::string featureTypeStr = (std::string)root[CC_FEATURE_TYPE];
if( featureTypeStr == CC_HAAR )
featureType = FeatureEvaluator::HAAR;
else if( featureTypeStr == CC_LBP )