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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
View File

@@ -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 )

14
modules/objdetect/src/cascadedetect.hpp
View File

@@ -103,7 +103,7 @@ public:
protected:
Size origWinSize;
Ptr<vector<Feature> > features;
Ptr<std::vector<Feature> > features;
Feature* featuresPtr; // optimization
bool hasTiltedFeatures;
@@ -194,7 +194,7 @@ public:
{ return (*this)(featureIdx); }
protected:
Size origWinSize;
Ptr<vector<Feature> > features;
Ptr<std::vector<Feature> > features;
Feature* featuresPtr; // optimization
Mat sum0, sum;
Rect normrect;
@@ -247,7 +247,7 @@ public:
{
Feature();
float calc( int offset ) const;
void updatePtrs( const vector<Mat>& _hist, const Mat &_normSum );
void updatePtrs( const std::vector<Mat>& _hist, const Mat &_normSum );
bool read( const FileNode& node );
enum { CELL_NUM = 4, BIN_NUM = 9 };
@@ -274,12 +274,12 @@ public:
}
private:
virtual void integralHistogram( const Mat& srcImage, vector<Mat> &histogram, Mat &norm, int nbins ) const;
virtual void integralHistogram( const Mat& srcImage, std::vector<Mat> &histogram, Mat &norm, int nbins ) const;
Size origWinSize;
Ptr<vector<Feature> > features;
Ptr<std::vector<Feature> > features;
Feature* featuresPtr;
vector<Mat> hist;
std::vector<Mat> hist;
Mat normSum;
int offset;
};
@@ -300,7 +300,7 @@ inline float HOGEvaluator::Feature :: calc( int _offset ) const
return res;
}
inline void HOGEvaluator::Feature :: updatePtrs( const vector<Mat> &_hist, const Mat &_normSum )
inline void HOGEvaluator::Feature :: updatePtrs( const std::vector<Mat> &_hist, const Mat &_normSum )
{
int binIdx = featComponent % BIN_NUM;
int cellIdx = featComponent / BIN_NUM;

35
modules/objdetect/src/datamatrix.cpp
View File

@@ -1,17 +1,8 @@
#include "precomp.hpp"
#if CV_SSE2
#include <xmmintrin.h>
#endif
#include <deque>
#include <algorithm>
using namespace std;
#undef NDEBUG
#include <assert.h>
class Sampler {
public:
CvMat *im;
@@ -310,7 +301,7 @@ static int decode(Sampler &sa, code &cc)
}
}
static deque<CvPoint> trailto(CvMat *v, int x, int y, CvMat *terminal)
static std::deque<CvPoint> trailto(CvMat *v, int x, int y, CvMat *terminal)
{
CvPoint np;
/* Return the last 10th of the trail of points following v from (x,y)
@@ -319,7 +310,7 @@ static deque<CvPoint> trailto(CvMat *v, int x, int y, CvMat *terminal)
int ex = x + ((short*)cvPtr2D(terminal, y, x))[0];
int ey = y + ((short*)cvPtr2D(terminal, y, x))[1];
deque<CvPoint> r;
std::deque<CvPoint> r;
while ((x != ex) || (y != ey)) {
np.x = x;
np.y = y;
@@ -338,7 +329,7 @@ static deque<CvPoint> trailto(CvMat *v, int x, int y, CvMat *terminal)
}
#endif
deque <CvDataMatrixCode> cvFindDataMatrix(CvMat *im)
std::deque <CvDataMatrixCode> cvFindDataMatrix(CvMat *im)
{
#if CV_SSE2
int r = im->rows;
@@ -386,7 +377,7 @@ deque <CvDataMatrixCode> cvFindDataMatrix(CvMat *im)
cfollow(vc, cxy);
cfollow(vcc, ccxy);
deque <CvPoint> candidates;
std::deque <CvPoint> candidates;
{
int x, y;
int rows = cxy->rows;
@@ -437,13 +428,13 @@ deque <CvDataMatrixCode> cvFindDataMatrix(CvMat *im)
}
}
deque <code> codes;
std::deque <code> codes;
size_t i, j, k;
while (!candidates.empty()) {
CvPoint o = candidates.front();
candidates.pop_front();
deque<CvPoint> ptc = trailto(vc, o.x, o.y, cxy);
deque<CvPoint> ptcc = trailto(vcc, o.x, o.y, ccxy);
std::deque<CvPoint> ptc = trailto(vc, o.x, o.y, cxy);
std::deque<CvPoint> ptcc = trailto(vcc, o.x, o.y, ccxy);
for (j = 0; j < ptc.size(); j++) {
for (k = 0; k < ptcc.size(); k++) {
code cc;
@@ -476,7 +467,7 @@ endo: ; // end search for this o
cvReleaseMat(&cxy);
cvReleaseMat(&ccxy);
deque <CvDataMatrixCode> rc;
std::deque <CvDataMatrixCode> rc;
for (i = 0; i < codes.size(); i++) {
CvDataMatrixCode cc;
strcpy(cc.msg, codes[i].msg);
@@ -487,7 +478,7 @@ endo: ; // end search for this o
return rc;
#else
(void)im;
deque <CvDataMatrixCode> rc;
std::deque <CvDataMatrixCode> rc;
return rc;
#endif
}
@@ -498,13 +489,13 @@ namespace cv
{
void findDataMatrix(InputArray _image,
vector<string>& codes,
std::vector<std::string>& codes,
OutputArray _corners,
OutputArrayOfArrays _dmtx)
{
Mat image = _image.getMat();
CvMat m(image);
deque <CvDataMatrixCode> rc = cvFindDataMatrix(&m);
std::deque <CvDataMatrixCode> rc = cvFindDataMatrix(&m);
int i, n = (int)rc.size();
Mat corners;
@@ -522,7 +513,7 @@ void findDataMatrix(InputArray _image,
for( i = 0; i < n; i++ )
{
CvDataMatrixCode& rc_i = rc[i];
codes[i] = string(rc_i.msg);
codes[i] = std::string(rc_i.msg);
if( corners.data )
{
@@ -544,7 +535,7 @@ void findDataMatrix(InputArray _image,
}
void drawDataMatrixCodes(InputOutputArray _image,
const vector<string>& codes,
const std::vector<std::string>& codes,
InputArray _corners)
{
Mat image = _image.getMat();

4
modules/objdetect/src/haar.cpp
View File

@@ -854,7 +854,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
cascade->pq2[pq_offset] + cascade->pq3[pq_offset];
variance_norm_factor = variance_norm_factor*cascade->inv_window_area - mean*mean;
if( variance_norm_factor >= 0. )
variance_norm_factor = sqrt(variance_norm_factor);
variance_norm_factor = std::sqrt(variance_norm_factor);
else
variance_norm_factor = 1.;
@@ -1305,7 +1305,7 @@ public:
{
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor));
int y1 = range.start*stripSize, y2 = min(range.end*stripSize, sum1.rows - 1 - winSize0.height);
int y1 = range.start*stripSize, y2 = std::min(range.end*stripSize, sum1.rows - 1 - winSize0.height);
if (y2 <= y1 || sum1.cols <= 1 + winSize0.width)
return;

54
modules/objdetect/src/hog.cpp
View File

@@ -120,7 +120,7 @@ bool HOGDescriptor::read(FileNode& obj)
return true;
}
void HOGDescriptor::write(FileStorage& fs, const String& objName) const
void HOGDescriptor::write(FileStorage& fs, const std::string& objName) const
{
if( !objName.empty() )
fs << objName;
@@ -142,14 +142,14 @@ void HOGDescriptor::write(FileStorage& fs, const String& objName) const
fs << "}";
}
bool HOGDescriptor::load(const String& filename, const String& objname)
bool HOGDescriptor::load(const std::string& filename, const std::string& objname)
{
FileStorage fs(filename, FileStorage::READ);
FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
return read(obj);
}
void HOGDescriptor::save(const String& filename, const String& objName) const
void HOGDescriptor::save(const std::string& filename, const std::string& objName) const
{
FileStorage fs(filename, FileStorage::WRITE);
write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename));
@@ -409,11 +409,11 @@ struct HOGCache
const float* getBlock(Point pt, float* buf);
virtual void normalizeBlockHistogram(float* histogram) const;
vector<PixData> pixData;
vector<BlockData> blockData;
std::vector<PixData> pixData;
std::vector<BlockData> blockData;
bool useCache;
vector<int> ymaxCached;
std::vector<int> ymaxCached;
Size winSize, cacheStride;
Size nblocks, ncells;
int blockHistogramSize;
@@ -791,9 +791,9 @@ Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const
}
void HOGDescriptor::compute(const Mat& img, vector<float>& descriptors,
void HOGDescriptor::compute(const Mat& img, std::vector<float>& descriptors,
Size winStride, Size padding,
const vector<Point>& locations) const
const std::vector<Point>& locations) const
{
if( winStride == Size() )
winStride = cellSize;
@@ -854,8 +854,8 @@ void HOGDescriptor::compute(const Mat& img, vector<float>& descriptors,
void HOGDescriptor::detect(const Mat& img,
vector<Point>& hits, vector<double>& weights, double hitThreshold,
Size winStride, Size padding, const vector<Point>& locations) const
std::vector<Point>& hits, std::vector<double>& weights, double hitThreshold,
Size winStride, Size padding, const std::vector<Point>& locations) const
{
hits.clear();
if( svmDetector.empty() )
@@ -882,7 +882,7 @@ void HOGDescriptor::detect(const Mat& img,
size_t dsize = getDescriptorSize();
double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
vector<float> blockHist(blockHistogramSize);
std::vector<float> blockHist(blockHistogramSize);
for( size_t i = 0; i < nwindows; i++ )
{
@@ -932,10 +932,10 @@ void HOGDescriptor::detect(const Mat& img,
}
}
void HOGDescriptor::detect(const Mat& img, vector<Point>& hits, double hitThreshold,
Size winStride, Size padding, const vector<Point>& locations) const
void HOGDescriptor::detect(const Mat& img, std::vector<Point>& hits, double hitThreshold,
Size winStride, Size padding, const std::vector<Point>& locations) const
{
vector<double> weightsV;
std::vector<double> weightsV;
detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
}
@@ -965,8 +965,8 @@ public:
double minScale = i1 > 0 ? levelScale[i1] : i2 > 1 ? levelScale[i1+1] : std::max(img.cols, img.rows);
Size maxSz(cvCeil(img.cols/minScale), cvCeil(img.rows/minScale));
Mat smallerImgBuf(maxSz, img.type());
vector<Point> locations;
vector<double> hitsWeights;
std::vector<Point> locations;
std::vector<double> hitsWeights;
for( i = i1; i < i2; i++ )
{
@@ -1019,14 +1019,14 @@ public:
void HOGDescriptor::detectMultiScale(
const Mat& img, vector<Rect>& foundLocations, vector<double>& foundWeights,
const Mat& img, std::vector<Rect>& foundLocations, std::vector<double>& foundWeights,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
double scale = 1.;
int levels = 0;
vector<double> levelScale;
std::vector<double> levelScale;
for( levels = 0; levels < nlevels; levels++ )
{
levelScale.push_back(scale);
@@ -1064,11 +1064,11 @@ void HOGDescriptor::detectMultiScale(
}
}
void HOGDescriptor::detectMultiScale(const Mat& img, vector<Rect>& foundLocations,
void HOGDescriptor::detectMultiScale(const Mat& img, std::vector<Rect>& foundLocations,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
vector<double> foundWeights;
std::vector<double> foundWeights;
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
padding, scale0, finalThreshold, useMeanshiftGrouping);
}
@@ -1078,7 +1078,7 @@ typedef RTTIImpl<HOGDescriptor> HOGRTTI;
CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance,
HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);
vector<float> HOGDescriptor::getDefaultPeopleDetector()
std::vector<float> HOGDescriptor::getDefaultPeopleDetector()
{
static const float detector[] = {
0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
@@ -1886,11 +1886,11 @@ vector<float> HOGDescriptor::getDefaultPeopleDetector()
-0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
-0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
-0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
//This function renurn 1981 SVM coeffs obtained from daimler's base.
//To use these coeffs the detection window size should be (48,96)
vector<float> HOGDescriptor::getDaimlerPeopleDetector()
std::vector<float> HOGDescriptor::getDaimlerPeopleDetector()
{
static const float detector[] = {
0.294350f, -0.098796f, -0.129522f, 0.078753f,
@@ -2389,7 +2389,7 @@ vector<float> HOGDescriptor::getDaimlerPeopleDetector()
-0.025054f, -0.093026f, -0.035372f, -0.233209f,
-0.049869f, -0.039151f, -0.022279f, -0.065380f,
-9.063785f};
return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
return std::vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
class HOGConfInvoker : public ParallelLoopBody
@@ -2415,7 +2415,7 @@ public:
Size maxSz(cvCeil(img.cols/(*locations)[0].scale), cvCeil(img.rows/(*locations)[0].scale));
Mat smallerImgBuf(maxSz, img.type());
vector<Point> dets;
std::vector<Point> dets;
for( i = i1; i < i2; i++ )
{
@@ -2451,7 +2451,7 @@ public:
Mutex* mtx;
};
void HOGDescriptor::detectROI(const cv::Mat& img, const vector<cv::Point> &locations,
void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
double hitThreshold, cv::Size winStride,
cv::Size padding) const
@@ -2489,7 +2489,7 @@ void HOGDescriptor::detectROI(const cv::Mat& img, const vector<cv::Point> &locat
size_t dsize = getDescriptorSize();
double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
vector<float> blockHist(blockHistogramSize);
std::vector<float> blockHist(blockHistogramSize);
for( size_t i = 0; i < nwindows; i++ )
{

16
modules/objdetect/src/latentsvmdetector.cpp
View File

@@ -158,7 +158,7 @@ LatentSvmDetector::ObjectDetection::ObjectDetection( const Rect& _rect, float _s
LatentSvmDetector::LatentSvmDetector()
{}
LatentSvmDetector::LatentSvmDetector( const vector<string>& filenames, const vector<string>& _classNames )
LatentSvmDetector::LatentSvmDetector( const std::vector<std::string>& filenames, const std::vector<std::string>& _classNames )
{
load( filenames, _classNames );
}
@@ -182,7 +182,7 @@ bool LatentSvmDetector::empty() const
return detectors.empty();
}
const vector<string>& LatentSvmDetector::getClassNames() const
const std::vector<std::string>& LatentSvmDetector::getClassNames() const
{
return classNames;
}
@@ -192,13 +192,13 @@ size_t LatentSvmDetector::getClassCount() const
return classNames.size();
}
static string extractModelName( const string& filename )
static std::string extractModelName( const std::string& filename )
{
size_t startPos = filename.rfind('/');
if( startPos == string::npos )
if( startPos == std::string::npos )
startPos = filename.rfind('\\');
if( startPos == string::npos )
if( startPos == std::string::npos )
startPos = 0;
else
startPos++;
@@ -210,7 +210,7 @@ static string extractModelName( const string& filename )
return filename.substr(startPos, substrLength);
}
bool LatentSvmDetector::load( const vector<string>& filenames, const vector<string>& _classNames )
bool LatentSvmDetector::load( const std::vector<std::string>& filenames, const std::vector<std::string>& _classNames )
{
clear();
@@ -218,7 +218,7 @@ bool LatentSvmDetector::load( const vector<string>& filenames, const vector<stri
for( size_t i = 0; i < filenames.size(); i++ )
{
const string filename = filenames[i];
const std::string filename = filenames[i];
if( filename.length() < 5 || filename.substr(filename.length()-4, 4) != ".xml" )
continue;
@@ -239,7 +239,7 @@ bool LatentSvmDetector::load( const vector<string>& filenames, const vector<stri
}
void LatentSvmDetector::detect( const Mat& image,
vector<ObjectDetection>& objectDetections,
std::vector<ObjectDetection>& objectDetections,
float overlapThreshold,
int numThreads )
{