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Add semicolons after CV_INSTRUMENT macros

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This commit is contained in:
Hamdi Sahloul
2018-09-14 06:35:26 +09:00
parent dbfeb8892d
commit 5d54def264
161 changed files with 695 additions and 695 deletions
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modules/objdetect/src/cascadedetect.cpp
+17 -17
View File
@@ -60,7 +60,7 @@ template<typename _Tp> void copyVectorToUMat(const std::vector<_Tp>& v, UMat& um
void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps,
std::vector<int>* weights, std::vector<double>* levelWeights)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
if( groupThreshold <= 0 || rectList.empty() )
{
@@ -361,14 +361,14 @@ static void groupRectangles_meanshift(std::vector<Rect>& rectList, double detect
void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
groupRectangles(rectList, groupThreshold, eps, 0, 0);
}
void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& weights, int groupThreshold, double eps)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
groupRectangles(rectList, groupThreshold, eps, &weights, 0);
}
@@ -376,7 +376,7 @@ void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& weights, int
void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
std::vector<double>& levelWeights, int groupThreshold, double eps)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
groupRectangles(rectList, groupThreshold, eps, &rejectLevels, &levelWeights);
}
@@ -384,7 +384,7 @@ void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels
void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
std::vector<double>& foundScales, double detectThreshold, Size winDetSize)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
groupRectangles_meanshift(rectList, detectThreshold, foundWeights, foundScales, winDetSize);
}
@@ -483,7 +483,7 @@ bool FeatureEvaluator::updateScaleData( Size imgsz, const std::vector<float>& _s
bool FeatureEvaluator::setImage( InputArray _image, const std::vector<float>& _scales )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
Size imgsz = _image.size();
bool recalcOptFeatures = updateScaleData(imgsz, _scales);
@@ -632,7 +632,7 @@ Ptr<FeatureEvaluator> HaarEvaluator::clone() const
void HaarEvaluator::computeChannels(int scaleIdx, InputArray img)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
const ScaleData& s = scaleData->at(scaleIdx);
sqofs = hasTiltedFeatures ? sbufSize.area() * 2 : sbufSize.area();
@@ -676,7 +676,7 @@ void HaarEvaluator::computeChannels(int scaleIdx, InputArray img)
void HaarEvaluator::computeOptFeatures()
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
if (hasTiltedFeatures)
tofs = sbufSize.area();
@@ -929,7 +929,7 @@ void CascadeClassifierImpl::read(const FileNode& node)
int CascadeClassifierImpl::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, int scaleIdx, double& weight )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
assert( !oldCascade &&
(data.featureType == FeatureEvaluator::HAAR ||
@@ -999,7 +999,7 @@ public:
void operator()(const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
Ptr<FeatureEvaluator> evaluator = classifier->featureEvaluator->clone();
double gypWeight = 0.;
@@ -1244,7 +1244,7 @@ void CascadeClassifierImpl::detectMultiScaleNoGrouping( InputArray _image, std::
double scaleFactor, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
Size imgsz = _image.size();
Size originalWindowSize = getOriginalWindowSize();
@@ -1371,7 +1371,7 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
int flags, Size minObjectSize, Size maxObjectSize,
bool outputRejectLevels )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert( scaleFactor > 1 && _image.depth() == CV_8U );
@@ -1405,7 +1405,7 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
double scaleFactor, int minNeighbors,
int flags, Size minObjectSize, Size maxObjectSize)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
std::vector<int> fakeLevels;
std::vector<double> fakeWeights;
@@ -1418,7 +1418,7 @@ void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rec
int minNeighbors, int flags, Size minObjectSize,
Size maxObjectSize )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
Mat image = _image.getMat();
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
@@ -1693,7 +1693,7 @@ void CascadeClassifier::detectMultiScale( InputArray image,
Size minSize,
Size maxSize )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert(!empty());
cc->detectMultiScale(image, objects, scaleFactor, minNeighbors, flags, minSize, maxSize);
@@ -1707,7 +1707,7 @@ void CascadeClassifier::detectMultiScale( InputArray image,
int minNeighbors, int flags,
Size minSize, Size maxSize )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert(!empty());
cc->detectMultiScale(image, objects, numDetections,
@@ -1724,7 +1724,7 @@ void CascadeClassifier::detectMultiScale( InputArray image,
Size minSize, Size maxSize,
bool outputRejectLevels )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert(!empty());
cc->detectMultiScale(image, objects, rejectLevels, levelWeights,
modules/objdetect/src/cascadedetect.hpp
+4 -4
View File
@@ -484,7 +484,7 @@ template<class FEval>
inline int predictOrdered( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
@@ -526,7 +526,7 @@ template<class FEval>
inline int predictCategorical( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
int nstages = (int)cascade.data.stages.size();
int nodeOfs = 0, leafOfs = 0;
@@ -570,7 +570,7 @@ template<class FEval>
inline int predictOrderedStump( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert(!cascade.data.stumps.empty());
FEval& featureEvaluator = (FEval&)*_featureEvaluator;
@@ -609,7 +609,7 @@ template<class FEval>
inline int predictCategoricalStump( CascadeClassifierImpl& cascade,
Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert(!cascade.data.stumps.empty());
int nstages = (int)cascade.data.stages.size();
modules/objdetect/src/detection_based_tracker.cpp
+1 -1
View File
@@ -626,7 +626,7 @@ cv::DetectionBasedTracker::~DetectionBasedTracker()
void DetectionBasedTracker::process(const Mat& imageGray)
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert(imageGray.type()==CV_8UC1);
modules/objdetect/src/haar.cpp
+4 -4
View File
@@ -922,7 +922,7 @@ CV_IMPL int
cvRunHaarClassifierCascade( const CvHaarClassifierCascade* _cascade,
CvPoint pt, int start_stage )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
double stage_sum;
return cvRunHaarClassifierCascadeSum(_cascade, pt, stage_sum, start_stage);
@@ -959,7 +959,7 @@ public:
void operator()(const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
Size winSize0 = cascade->orig_window_size;
Size winSize(cvRound(winSize0.width*factor), cvRound(winSize0.height*factor));
@@ -1139,7 +1139,7 @@ public:
void operator()(const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
int iy, startY = range.start, endY = range.end;
const int *p0 = p[0], *p1 = p[1], *p2 = p[2], *p3 = p[3];
@@ -1216,7 +1216,7 @@ cvHaarDetectObjectsForROC( const CvArr* _img,
double scaleFactor, int minNeighbors, int flags,
CvSize minSize, CvSize maxSize, bool outputRejectLevels )
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
const double GROUP_EPS = 0.2;
CvMat stub, *img = (CvMat*)_img;
modules/objdetect/src/hog.cpp
+10 -10
View File
@@ -237,7 +237,7 @@ inline float32x4_t vsetq_f32(float f0, float f1, float f2, float f3)
void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
Size paddingTL, Size paddingBR) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
@@ -1587,7 +1587,7 @@ static bool ocl_compute(InputArray _img, Size win_stride, std::vector<float>& _d
void HOGDescriptor::compute(InputArray _img, std::vector<float>& descriptors,
Size winStride, Size padding, const std::vector<Point>& locations) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
if( winStride == Size() )
winStride = cellSize;
@@ -1654,7 +1654,7 @@ void HOGDescriptor::detect(const Mat& img,
std::vector<Point>& hits, std::vector<double>& weights, double hitThreshold,
Size winStride, Size padding, const std::vector<Point>& locations) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
hits.clear();
weights.clear();
@@ -1767,7 +1767,7 @@ void HOGDescriptor::detect(const Mat& img,
void HOGDescriptor::detect(const Mat& img, std::vector<Point>& hits, double hitThreshold,
Size winStride, Size padding, const std::vector<Point>& locations) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
std::vector<double> weightsV;
detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
@@ -2051,7 +2051,7 @@ void HOGDescriptor::detectMultiScale(
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
double scale = 1.;
int levels = 0;
@@ -2106,7 +2106,7 @@ void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLoc
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
std::vector<double> foundWeights;
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
@@ -3504,7 +3504,7 @@ public:
void operator()(const Range& range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
int i, i1 = range.start, i2 = range.end;
@@ -3548,7 +3548,7 @@ void HOGDescriptor::detectROI(const cv::Mat& img, const std::vector<cv::Point> &
CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
double hitThreshold, cv::Size winStride, cv::Size padding) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
foundLocations.clear();
confidences.clear();
@@ -3660,7 +3660,7 @@ void HOGDescriptor::detectMultiScaleROI(const cv::Mat& img,
CV_OUT std::vector<cv::Rect>& foundLocations, std::vector<DetectionROI>& locations,
double hitThreshold, int groupThreshold) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
std::vector<Rect> allCandidates;
Mutex mtx;
@@ -3780,7 +3780,7 @@ void HOGDescriptor::readALTModel(String modelfile)
void HOGDescriptor::groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const
{
CV_INSTRUMENT_REGION()
CV_INSTRUMENT_REGION();
if( groupThreshold <= 0 || rectList.empty() )
{