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Set stricter warning rules for gcc

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This commit is contained in:
Andrey Kamaev
2012-06-07 17:21:29 +00:00
parent 0395f7c63f
commit 49a1ba6038
241 changed files with 9054 additions and 8947 deletions
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6
modules/features2d/src/brief.cpp
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@@ -61,7 +61,7 @@ inline int smoothedSum(const Mat& sum, const KeyPoint& pt, int y, int x)
+ sum.at<int>(img_y - HALF_KERNEL, img_x - HALF_KERNEL);
}
void pixelTests16(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
static void pixelTests16(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
{
for (int i = 0; i < (int)keypoints.size(); ++i)
{
@@ -71,7 +71,7 @@ void pixelTests16(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& d
}
}
void pixelTests32(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
static void pixelTests32(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
{
for (int i = 0; i < (int)keypoints.size(); ++i)
{
@@ -82,7 +82,7 @@ void pixelTests32(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& d
}
}
void pixelTests64(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
static void pixelTests64(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
{
for (int i = 0; i < (int)keypoints.size(); ++i)
{

8
modules/features2d/src/descriptors.cpp
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@@ -56,7 +56,7 @@ DescriptorExtractor::~DescriptorExtractor()
{}
void DescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const
{
{
if( image.empty() || keypoints.empty() )
{
descriptors.release();
@@ -102,7 +102,7 @@ Ptr<DescriptorExtractor> DescriptorExtractor::create(const string& descriptorExt
string type = descriptorExtractorType.substr(pos);
return new OpponentColorDescriptorExtractor(DescriptorExtractor::create(type));
}
return Algorithm::create<DescriptorExtractor>("Feature2D." + descriptorExtractorType);
}
@@ -117,7 +117,7 @@ OpponentColorDescriptorExtractor::OpponentColorDescriptorExtractor( const Ptr<De
CV_Assert( !descriptorExtractor.empty() );
}
void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, vector<Mat>& opponentChannels )
static void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, vector<Mat>& opponentChannels )
{
if( bgrImage.type() != CV_8UC3 )
CV_Error( CV_StsBadArg, "input image must be an BGR image of type CV_8UC3" );
@@ -227,7 +227,7 @@ void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector<
Mat mergedDescriptors( maxKeypointsCount, 3*descriptorSize, descriptorExtractor->descriptorType() );
int mergedCount = 0;
// cp - current channel position
size_t cp[] = {0, 0, 0};
size_t cp[] = {0, 0, 0};
while( cp[0] < channelKeypoints[0].size() &&
cp[1] < channelKeypoints[1].size() &&
cp[2] < channelKeypoints[2].size() )

24
modules/features2d/src/detectors.cpp
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@@ -45,7 +45,7 @@ using namespace std;
namespace cv
{
/*
* FeatureDetector
*/
@@ -95,19 +95,19 @@ Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
return new GridAdaptedFeatureDetector(FeatureDetector::create(
detectorType.substr(strlen("Grid"))));
}
if( detectorType.find("Pyramid") == 0 )
{
return new PyramidAdaptedFeatureDetector(FeatureDetector::create(
detectorType.substr(strlen("Pyramid"))));
}
if( detectorType.find("Dynamic") == 0 )
{
return new DynamicAdaptedFeatureDetector(AdjusterAdapter::create(
detectorType.substr(strlen("Dynamic"))));
}
if( detectorType.compare( "HARRIS" ) == 0 )
{
Ptr<FeatureDetector> fd = FeatureDetector::create("GFTT");
@@ -149,13 +149,13 @@ void GFTTDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, co
/*
* DenseFeatureDetector
*/
DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels,
float _featureScaleMul, int _initXyStep,
int _initImgBound, bool _varyXyStepWithScale,
bool _varyImgBoundWithScale ) :
initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels,
float _featureScaleMul, int _initXyStep,
int _initImgBound, bool _varyXyStepWithScale,
bool _varyImgBoundWithScale ) :
initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
{}
@@ -203,7 +203,7 @@ struct ResponseComparator
}
};
void keepStrongest( int N, vector<KeyPoint>& keypoints )
static void keepStrongest( int N, vector<KeyPoint>& keypoints )
{
if( (int)keypoints.size() > N )
{

44
modules/features2d/src/features2d_init.cpp
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@@ -42,8 +42,7 @@
#include "precomp.hpp"
namespace cv
{
using namespace cv;
/////////////////////// AlgorithmInfo for various detector & descriptors ////////////////////////////
@@ -54,7 +53,7 @@ namespace cv
CV_INIT_ALGORITHM(BriefDescriptorExtractor, "Feature2D.BRIEF",
obj.info()->addParam(obj, "bytes", obj.bytes_));
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(FastFeatureDetector, "Feature2D.FAST",
@@ -69,7 +68,7 @@ CV_INIT_ALGORITHM(StarDetector, "Feature2D.STAR",
obj.info()->addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
obj.info()->addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
obj.info()->addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize));
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(MSER, "Feature2D.MSER",
@@ -81,8 +80,8 @@ CV_INIT_ALGORITHM(MSER, "Feature2D.MSER",
obj.info()->addParam(obj, "maxEvolution", obj.maxEvolution);
obj.info()->addParam(obj, "areaThreshold", obj.areaThreshold);
obj.info()->addParam(obj, "minMargin", obj.minMargin);
obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize));
obj.info()->addParam(obj, "edgeBlurSize", obj.edgeBlurSize));
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(ORB, "Feature2D.ORB",
@@ -96,7 +95,7 @@ CV_INIT_ALGORITHM(ORB, "Feature2D.ORB",
obj.info()->addParam(obj, "scoreType", obj.scoreType));
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT",
obj.info()->addParam(obj, "nfeatures", obj.nfeatures);
obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
@@ -105,7 +104,7 @@ CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT",
obj.info()->addParam(obj, "k", obj.k));
///////////////////////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS HarrisDetector : public GFTTDetector
{
public:
@@ -113,7 +112,7 @@ public:
int blockSize=3, bool useHarrisDetector=true, double k=0.04 )
: GFTTDetector( maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k ) {}
AlgorithmInfo* info() const;
};
};
CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS",
obj.info()->addParam(obj, "nfeatures", obj.nfeatures);
@@ -122,7 +121,7 @@ CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS",
obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
obj.info()->addParam(obj, "k", obj.k));
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(DenseFeatureDetector, "Feature2D.Dense",
obj.info()->addParam(obj, "initFeatureScale", obj.initFeatureScale);
@@ -134,22 +133,23 @@ CV_INIT_ALGORITHM(DenseFeatureDetector, "Feature2D.Dense",
obj.info()->addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale));
CV_INIT_ALGORITHM(GridAdaptedFeatureDetector, "Feature2D.Grid",
obj.info()->addParam(obj, "detector", (Ptr<Algorithm>&)obj.detector);
//obj.info()->addParam(obj, "detector", (Ptr<Algorithm>&)obj.detector);
obj.info()->addParam(obj, "maxTotalKeypoints", obj.maxTotalKeypoints);
obj.info()->addParam(obj, "gridRows", obj.gridRows);
obj.info()->addParam(obj, "gridCols", obj.gridCols));
bool initModule_features2d(void)
bool cv::initModule_features2d(void)
{
Ptr<Algorithm> brief = createBriefDescriptorExtractor(), orb = createORB(),
star = createStarDetector(), fastd = createFastFeatureDetector(), mser = createMSER(),
dense = createDenseFeatureDetector(), gftt = createGFTTDetector(),
harris = createHarrisDetector(), grid = createGridAdaptedFeatureDetector();
return brief->info() != 0 && orb->info() != 0 && star->info() != 0 &&
fastd->info() != 0 && mser->info() != 0 && dense->info() != 0 &&
gftt->info() != 0 && harris->info() != 0 && grid->info() != 0;
}
bool all = true;
all &= !BriefDescriptorExtractor_info_auto.name().empty();
all &= !FastFeatureDetector_info_auto.name().empty();
all &= !StarDetector_info_auto.name().empty();
all &= !MSER_info_auto.name().empty();
all &= !ORB_info_auto.name().empty();
all &= !GFTTDetector_info_auto.name().empty();
all &= !HarrisDetector_info_auto.name().empty();
all &= !DenseFeatureDetector_info_auto.name().empty();
all &= !GridAdaptedFeatureDetector_info_auto.name().empty();
return all;
}

2
modules/features2d/src/matchers.cpp
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@@ -174,7 +174,7 @@ int DescriptorMatcher::DescriptorCollection::size() const
/*
* DescriptorMatcher
*/
void convertMatches( const vector<vector<DMatch> >& knnMatches, vector<DMatch>& matches )
static void convertMatches( const vector<vector<DMatch> >& knnMatches, vector<DMatch>& matches )
{
matches.clear();
matches.reserve( knnMatches.size() );

2
modules/features2d/src/precomp.hpp
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@@ -43,7 +43,7 @@
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if _MSC_VER >= 1200
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4251 4512 4710 4711 4514 4996 )
#endif