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Merge pull request #2146 from SpecLad:merge-2.4

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This commit is contained in:
Roman Donchenko 2014-01-17 16:28:29 +04:00 committed by OpenCV Buildbot
commit 79fcc06a4b
50 changed files with 323 additions and 312 deletions
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1
.gitignore vendored
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@ -1,6 +1,7 @@
*.autosave
*.pyc
*.user
*~
.*.swp
.DS_Store
.sw[a-z]

2
apps/haartraining/cvclassifier.h
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@ -340,7 +340,7 @@ typedef enum CvBoostType
CV_LKCLASS = 5, /* classification (K class problem) */
CV_LSREG = 6, /* least squares regression */
CV_LADREG = 7, /* least absolute deviation regression */
CV_MREG = 8, /* M-regression (Huber loss) */
CV_MREG = 8 /* M-regression (Huber loss) */
} CvBoostType;
/****************************************************************************************\

2
doc/tutorials/introduction/linux_install/linux_install.rst
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@ -16,7 +16,7 @@ Required Packages
* CMake 2.6 or higher;
* Git;
* GTK+2.x or higher, including headers (libgtk2.0-dev);
* pkgconfig;
* pkg-config;
* Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy);
* ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev;
* [optional] libdc1394 2.x;

2
modules/bioinspired/src/retina.cpp
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@ -312,7 +312,7 @@ RetinaImpl::RetinaImpl(const cv::Size inputSz, const bool colorMode, int colorSa
{
_retinaFilter = 0;
_init(inputSz, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrenght);
};
}
RetinaImpl::~RetinaImpl()
{

2
modules/calib3d/test/test_chessboardgenerator.hpp
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@ -34,7 +34,7 @@ private:
Mat rvec, tvec;
};
};
}
#endif

18
modules/contrib/src/adaptiveskindetector.cpp
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@ -55,7 +55,7 @@ void CvAdaptiveSkinDetector::initData(IplImage *src, int widthDivider, int heigh
imgGrayFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgLastGrayFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
imgHSVFrame = cvCreateImage(imageSize, IPL_DEPTH_8U, 3);
};
}
CvAdaptiveSkinDetector::CvAdaptiveSkinDetector(int samplingDivider, int morphingMethod)
{
@ -80,7 +80,7 @@ CvAdaptiveSkinDetector::CvAdaptiveSkinDetector(int samplingDivider, int morphing
imgLastGrayFrame = NULL;
imgSaturationFrame = NULL;
imgHSVFrame = NULL;
};
}
CvAdaptiveSkinDetector::~CvAdaptiveSkinDetector()
{
@ -93,7 +93,7 @@ CvAdaptiveSkinDetector::~CvAdaptiveSkinDetector()
cvReleaseImage(&imgGrayFrame);
cvReleaseImage(&imgLastGrayFrame);
cvReleaseImage(&imgHSVFrame);
};
}
void CvAdaptiveSkinDetector::process(IplImage *inputBGRImage, IplImage *outputHueMask)
{
@ -190,7 +190,7 @@ void CvAdaptiveSkinDetector::process(IplImage *inputBGRImage, IplImage *outputHu
if (outputHueMask != NULL)
cvCopy(imgFilteredFrame, outputHueMask);
};
}
//------------------------- Histogram for Adaptive Skin Detector -------------------------//
@ -202,12 +202,12 @@ CvAdaptiveSkinDetector::Histogram::Histogram()
float *ranges[] = { range };
fHistogram = cvCreateHist(1, histogramSize, CV_HIST_ARRAY, ranges, 1);
cvClearHist(fHistogram);
};
}
CvAdaptiveSkinDetector::Histogram::~Histogram()
{
cvReleaseHist(&fHistogram);
};
}
int CvAdaptiveSkinDetector::Histogram::findCoverageIndex(double surfaceToCover, int defaultValue)
{
@ -221,7 +221,7 @@ int CvAdaptiveSkinDetector::Histogram::findCoverageIndex(double surfaceToCover,
}
}
return defaultValue;
};
}
void CvAdaptiveSkinDetector::Histogram::findCurveThresholds(int &x1, int &x2, double percent)
{
@ -244,7 +244,7 @@ void CvAdaptiveSkinDetector::Histogram::findCurveThresholds(int &x1, int &x2, do
x2 = GSD_HUE_UT;
else
x2 += GSD_HUE_LT;
};
}
void CvAdaptiveSkinDetector::Histogram::mergeWith(CvAdaptiveSkinDetector::Histogram *source, double weight)
{
@ -285,4 +285,4 @@ void CvAdaptiveSkinDetector::Histogram::mergeWith(CvAdaptiveSkinDetector::Histog
}
}
}
};
}

8
modules/contrib/src/ba.cpp
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@ -940,7 +940,7 @@ static void fjac(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, C
#endif
};
}
static void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, CvMat* estim, void* /*data*/) {
//just do projections
CvMat _Mi;
@ -979,17 +979,17 @@ static void func(int /*i*/, int /*j*/, CvMat *point_params, CvMat* cam_params, C
cvTranspose( _mp2, estim );
cvReleaseMat( &_mp );
cvReleaseMat( &_mp2 );
};
}
static void fjac_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data) {
CvMat _point_params = point_params, _cam_params = cam_params, _Al = A, _Bl = B;
fjac(i,j, &_point_params, &_cam_params, &_Al, &_Bl, data);
};
}
static void func_new(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data) {
CvMat _point_params = point_params, _cam_params = cam_params, _estim = estim;
func(i,j,&_point_params,&_cam_params,&_estim,data);
};
}
void LevMarqSparse::bundleAdjust( std::vector<Point3d>& points, //positions of points in global coordinate system (input and output)
const std::vector<std::vector<Point2d> >& imagePoints, //projections of 3d points for every camera

6
modules/contrib/src/facerec.cpp
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@ -872,7 +872,7 @@ CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
obj.info()->addParam(obj, "mean", obj._mean, true))
CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
@ -881,7 +881,7 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
obj.info()->addParam(obj, "mean", obj._mean, true))
CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "radius", obj._radius);
@ -890,7 +890,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "grid_y", obj._grid_y);
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true));
obj.info()->addParam(obj, "labels", obj._labels, true))
bool initModule_contrib()
{

82
modules/contrib/src/fuzzymeanshifttracker.cpp
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@ -41,7 +41,7 @@ CvFuzzyPoint::CvFuzzyPoint(double _x, double _y)
{
x = _x;
y = _y;
};
}
bool CvFuzzyCurve::between(double x, double x1, double x2)
{
@ -51,37 +51,37 @@ bool CvFuzzyCurve::between(double x, double x1, double x2)
return true;
return false;
};
}
CvFuzzyCurve::CvFuzzyCurve()
{
value = 0;
};
}
CvFuzzyCurve::~CvFuzzyCurve()
{
// nothing to do
};
}
void CvFuzzyCurve::setCentre(double _centre)
{
centre = _centre;
};
}
double CvFuzzyCurve::getCentre()
{
return centre;
};
}
void CvFuzzyCurve::clear()
{
points.clear();
};
}
void CvFuzzyCurve::addPoint(double x, double y)
{
points.push_back(CvFuzzyPoint(x, y));
};
}
double CvFuzzyCurve::calcValue(double param)
{
@ -102,41 +102,41 @@ double CvFuzzyCurve::calcValue(double param)
}
}
return 0;
};
}
double CvFuzzyCurve::getValue()
{
return value;
};
}
void CvFuzzyCurve::setValue(double _value)
{
value = _value;
};
}
CvFuzzyFunction::CvFuzzyFunction()
{
// nothing to do
};
}
CvFuzzyFunction::~CvFuzzyFunction()
{
curves.clear();
};
}
void CvFuzzyFunction::addCurve(CvFuzzyCurve *curve, double value)
{
curves.push_back(*curve);
curve->setValue(value);
};
}
void CvFuzzyFunction::resetValues()
{
int numCurves = (int)curves.size();
for (int i = 0; i < numCurves; i++)
curves[i].setValue(0);
};
}
double CvFuzzyFunction::calcValue()
{
@ -153,7 +153,7 @@ double CvFuzzyFunction::calcValue()
return s1/s2;
else
return 0;
};
}
CvFuzzyCurve *CvFuzzyFunction::newCurve()
{
@ -161,14 +161,14 @@ CvFuzzyCurve *CvFuzzyFunction::newCurve()
c = new CvFuzzyCurve();
addCurve(c);
return c;
};
}
CvFuzzyRule::CvFuzzyRule()
{
fuzzyInput1 = NULL;
fuzzyInput2 = NULL;
fuzzyOutput = NULL;
};
}
CvFuzzyRule::~CvFuzzyRule()
{
@ -180,14 +180,14 @@ CvFuzzyRule::~CvFuzzyRule()
if (fuzzyOutput != NULL)
delete fuzzyOutput;
};
}
void CvFuzzyRule::setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
{
fuzzyInput1 = c1;
fuzzyInput2 = c2;
fuzzyOutput = o1;
};
}
double CvFuzzyRule::calcValue(double param1, double param2)
{
@ -203,31 +203,31 @@ double CvFuzzyRule::calcValue(double param1, double param2)
}
else
return v1;
};
}
CvFuzzyCurve *CvFuzzyRule::getOutputCurve()
{
return fuzzyOutput;
};
}
CvFuzzyController::CvFuzzyController()
{
// nothing to do
};
}
CvFuzzyController::~CvFuzzyController()
{
int size = (int)rules.size();
for(int i = 0; i < size; i++)
delete rules[i];
};
}
void CvFuzzyController::addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
{
CvFuzzyRule *f = new CvFuzzyRule();
rules.push_back(f);
f->setRule(c1, c2, o1);
};
}
double CvFuzzyController::calcOutput(double param1, double param2)
{
@ -243,7 +243,7 @@ double CvFuzzyController::calcOutput(double param1, double param2)
}
v = list.calcValue();
return v;
};
}
CvFuzzyMeanShiftTracker::FuzzyResizer::FuzzyResizer()
{
@ -299,12 +299,12 @@ CvFuzzyMeanShiftTracker::FuzzyResizer::FuzzyResizer()
fuzzyController.addRule(i1L, NULL, oS);
fuzzyController.addRule(i1M, NULL, oZE);
fuzzyController.addRule(i1H, NULL, oE);
};
}
int CvFuzzyMeanShiftTracker::FuzzyResizer::calcOutput(double edgeDensity, double density)
{
return (int)fuzzyController.calcOutput(edgeDensity, density);
};
}
CvFuzzyMeanShiftTracker::SearchWindow::SearchWindow()
{
@ -329,7 +329,7 @@ CvFuzzyMeanShiftTracker::SearchWindow::SearchWindow()
depthLow = 0;
depthHigh = 0;
fuzzyResizer = NULL;
};
}
CvFuzzyMeanShiftTracker::SearchWindow::~SearchWindow()
{
@ -355,7 +355,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::setSize(int _x, int _y, int _width,
if (y + height > maxHeight)
height = maxHeight - y;
};
}
void CvFuzzyMeanShiftTracker::SearchWindow::initDepthValues(IplImage *maskImage, IplImage *depthMap)
{
@ -409,7 +409,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::initDepthValues(IplImage *maskImage,
depthHigh = 32000;
depthLow = 0;
}
};
}
bool CvFuzzyMeanShiftTracker::SearchWindow::shift()
{
@ -422,7 +422,7 @@ bool CvFuzzyMeanShiftTracker::SearchWindow::shift()
{
return false;
}
};
}
void CvFuzzyMeanShiftTracker::SearchWindow::extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth)
{
@ -528,7 +528,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::extractInfo(IplImage *maskImage, Ipl
ellipseAngle = 0;
density = 0;
}
};
}
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh) {
int x1 = horizontalEdgeTop;
@ -572,7 +572,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityLinear(in
} else {
resizeDw = - resizeDx;
}
};
}
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
{
@ -588,7 +588,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsInnerDensity(int &re
resizeDy = (int)(py*dy);
resizeDw = (int)((1-px)*dx);
resizeDh = (int)((1-py)*dy);
};
}
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
{
@ -627,7 +627,7 @@ void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityFuzzy(int
resizeDy = int(-dy1);
resizeDh = int(dy1+dy2);
}
};
}
bool CvFuzzyMeanShiftTracker::SearchWindow::meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth)
{
@ -640,7 +640,7 @@ bool CvFuzzyMeanShiftTracker::SearchWindow::meanShift(IplImage *maskImage, IplIm
} while (++numShifts < maxIteration);
return false;
};
}
void CvFuzzyMeanShiftTracker::findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth)
{
@ -680,17 +680,17 @@ void CvFuzzyMeanShiftTracker::findOptimumSearchWindow(SearchWindow &searchWindow
searchWindow.setSize(searchWindow.x + resizeDx, searchWindow.y + resizeDy, searchWindow.width + resizeDw, searchWindow.height + resizeDh);
}
};
}
CvFuzzyMeanShiftTracker::CvFuzzyMeanShiftTracker()
{
searchMode = tsSetWindow;
};
}
CvFuzzyMeanShiftTracker::~CvFuzzyMeanShiftTracker()
{
// nothing to do
};
}
void CvFuzzyMeanShiftTracker::track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass)
{
@ -718,4 +718,4 @@ void CvFuzzyMeanShiftTracker::track(IplImage *maskImage, IplImage *depthMap, int
else
searchMode = tsTracking;
}
};
}

2
modules/contrib/src/spinimages.cpp
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@ -709,7 +709,7 @@ void cv::SpinImageModel::defaultParams()
T_GeometriccConsistency = 0.25f;
T_GroupingCorespondances = 0.25f;
};
}
Mat cv::SpinImageModel::packRandomScaledSpins(bool separateScale, size_t xCount, size_t yCount) const
{

2
modules/core/include/opencv2/core/mat.inl.hpp
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@ -1918,7 +1918,7 @@ SparseMat_<_Tp>::SparseMat_(const SparseMat& m)
if( m.type() == DataType<_Tp>::type )
*this = (const SparseMat_<_Tp>&)m;
else
m.convertTo(this, DataType<_Tp>::type);
m.convertTo(*this, DataType<_Tp>::type);
}
template<typename _Tp> inline

2
modules/core/src/algorithm.cpp
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@ -628,7 +628,7 @@ void AlgorithmInfo::set(Algorithm* algo, const char* parameter, int argType, con
|| argType == Param::FLOAT || argType == Param::UNSIGNED_INT || argType == Param::UINT64 || argType == Param::UCHAR)
{
if ( !( p->type == Param::INT || p->type == Param::REAL || p->type == Param::BOOLEAN
|| p->type == Param::UNSIGNED_INT || p->type == Param::UINT64 || p->type == Param::FLOAT || argType == Param::UCHAR) )
|| p->type == Param::UNSIGNED_INT || p->type == Param::UINT64 || p->type == Param::FLOAT || p->type == Param::UCHAR) )
{
String message = getErrorMessageForWrongArgumentInSetter(algo->name(), parameter, p->type, argType);
CV_Error(CV_StsBadArg, message);

4
modules/core/src/arithm.cpp
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@ -1434,8 +1434,8 @@ static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
bool haveScalar = false, swapped12 = false;
if( dims1 != dims2 || sz1 != sz2 || cn != cn2 ||
((kind1 == _InputArray::MATX || kind2 == _InputArray::MATX) &&
(sz1 == Size(1,4) || sz2 == Size(1,4))) )
(kind1 == _InputArray::MATX && (sz1 == Size(1,4) || sz1 == Size(1,1))) ||
(kind2 == _InputArray::MATX && (sz2 == Size(1,4) || sz2 == Size(1,1))) )
{
if( checkScalar(*psrc1, type2, kind1, kind2) )
{

204
modules/core/src/convert.cpp
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@ -1040,122 +1040,122 @@ stype* dst, size_t dstep, Size size, double*) \
}
DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float);
DEF_CVT_SCALE_ABS_FUNC(8u, cvtScaleAbs_, uchar, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtScaleAbs_, schar, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtScaleAbs_, ushort, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtScaleAbs_, short, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtScaleAbs_, int, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtScaleAbs_, float, uchar, float)
DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtScaleAbs_, double, uchar, float)
DEF_CVT_SCALE_FUNC(8u, uchar, uchar, float);
DEF_CVT_SCALE_FUNC(8s8u, schar, uchar, float);
DEF_CVT_SCALE_FUNC(16u8u, ushort, uchar, float);
DEF_CVT_SCALE_FUNC(16s8u, short, uchar, float);
DEF_CVT_SCALE_FUNC(32s8u, int, uchar, float);
DEF_CVT_SCALE_FUNC(32f8u, float, uchar, float);
DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float);
DEF_CVT_SCALE_FUNC(8u, uchar, uchar, float)
DEF_CVT_SCALE_FUNC(8s8u, schar, uchar, float)
DEF_CVT_SCALE_FUNC(16u8u, ushort, uchar, float)
DEF_CVT_SCALE_FUNC(16s8u, short, uchar, float)
DEF_CVT_SCALE_FUNC(32s8u, int, uchar, float)
DEF_CVT_SCALE_FUNC(32f8u, float, uchar, float)
DEF_CVT_SCALE_FUNC(64f8u, double, uchar, float)
DEF_CVT_SCALE_FUNC(8u8s, uchar, schar, float);
DEF_CVT_SCALE_FUNC(8s, schar, schar, float);
DEF_CVT_SCALE_FUNC(16u8s, ushort, schar, float);
DEF_CVT_SCALE_FUNC(16s8s, short, schar, float);
DEF_CVT_SCALE_FUNC(32s8s, int, schar, float);
DEF_CVT_SCALE_FUNC(32f8s, float, schar, float);
DEF_CVT_SCALE_FUNC(64f8s, double, schar, float);
DEF_CVT_SCALE_FUNC(8u8s, uchar, schar, float)
DEF_CVT_SCALE_FUNC(8s, schar, schar, float)
DEF_CVT_SCALE_FUNC(16u8s, ushort, schar, float)
DEF_CVT_SCALE_FUNC(16s8s, short, schar, float)
DEF_CVT_SCALE_FUNC(32s8s, int, schar, float)
DEF_CVT_SCALE_FUNC(32f8s, float, schar, float)
DEF_CVT_SCALE_FUNC(64f8s, double, schar, float)
DEF_CVT_SCALE_FUNC(8u16u, uchar, ushort, float);
DEF_CVT_SCALE_FUNC(8s16u, schar, ushort, float);
DEF_CVT_SCALE_FUNC(16u, ushort, ushort, float);
DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float);
DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float);
DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float);
DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float);
DEF_CVT_SCALE_FUNC(8u16u, uchar, ushort, float)
DEF_CVT_SCALE_FUNC(8s16u, schar, ushort, float)
DEF_CVT_SCALE_FUNC(16u, ushort, ushort, float)
DEF_CVT_SCALE_FUNC(16s16u, short, ushort, float)
DEF_CVT_SCALE_FUNC(32s16u, int, ushort, float)
DEF_CVT_SCALE_FUNC(32f16u, float, ushort, float)
DEF_CVT_SCALE_FUNC(64f16u, double, ushort, float)
DEF_CVT_SCALE_FUNC(8u16s, uchar, short, float);
DEF_CVT_SCALE_FUNC(8s16s, schar, short, float);
DEF_CVT_SCALE_FUNC(16u16s, ushort, short, float);
DEF_CVT_SCALE_FUNC(16s, short, short, float);
DEF_CVT_SCALE_FUNC(32s16s, int, short, float);
DEF_CVT_SCALE_FUNC(32f16s, float, short, float);
DEF_CVT_SCALE_FUNC(64f16s, double, short, float);
DEF_CVT_SCALE_FUNC(8u16s, uchar, short, float)
DEF_CVT_SCALE_FUNC(8s16s, schar, short, float)
DEF_CVT_SCALE_FUNC(16u16s, ushort, short, float)
DEF_CVT_SCALE_FUNC(16s, short, short, float)
DEF_CVT_SCALE_FUNC(32s16s, int, short, float)
DEF_CVT_SCALE_FUNC(32f16s, float, short, float)
DEF_CVT_SCALE_FUNC(64f16s, double, short, float)
DEF_CVT_SCALE_FUNC(8u32s, uchar, int, float);
DEF_CVT_SCALE_FUNC(8s32s, schar, int, float);
DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float);
DEF_CVT_SCALE_FUNC(16s32s, short, int, float);
DEF_CVT_SCALE_FUNC(32s, int, int, double);
DEF_CVT_SCALE_FUNC(32f32s, float, int, float);
DEF_CVT_SCALE_FUNC(64f32s, double, int, double);
DEF_CVT_SCALE_FUNC(8u32s, uchar, int, float)
DEF_CVT_SCALE_FUNC(8s32s, schar, int, float)
DEF_CVT_SCALE_FUNC(16u32s, ushort, int, float)
DEF_CVT_SCALE_FUNC(16s32s, short, int, float)
DEF_CVT_SCALE_FUNC(32s, int, int, double)
DEF_CVT_SCALE_FUNC(32f32s, float, int, float)
DEF_CVT_SCALE_FUNC(64f32s, double, int, double)
DEF_CVT_SCALE_FUNC(8u32f, uchar, float, float);
DEF_CVT_SCALE_FUNC(8s32f, schar, float, float);
DEF_CVT_SCALE_FUNC(16u32f, ushort, float, float);
DEF_CVT_SCALE_FUNC(16s32f, short, float, float);
DEF_CVT_SCALE_FUNC(32s32f, int, float, double);
DEF_CVT_SCALE_FUNC(32f, float, float, float);
DEF_CVT_SCALE_FUNC(64f32f, double, float, double);
DEF_CVT_SCALE_FUNC(8u32f, uchar, float, float)
DEF_CVT_SCALE_FUNC(8s32f, schar, float, float)
DEF_CVT_SCALE_FUNC(16u32f, ushort, float, float)
DEF_CVT_SCALE_FUNC(16s32f, short, float, float)
DEF_CVT_SCALE_FUNC(32s32f, int, float, double)
DEF_CVT_SCALE_FUNC(32f, float, float, float)
DEF_CVT_SCALE_FUNC(64f32f, double, float, double)
DEF_CVT_SCALE_FUNC(8u64f, uchar, double, double);
DEF_CVT_SCALE_FUNC(8s64f, schar, double, double);
DEF_CVT_SCALE_FUNC(16u64f, ushort, double, double);
DEF_CVT_SCALE_FUNC(16s64f, short, double, double);
DEF_CVT_SCALE_FUNC(32s64f, int, double, double);
DEF_CVT_SCALE_FUNC(32f64f, float, double, double);
DEF_CVT_SCALE_FUNC(64f, double, double, double);
DEF_CVT_SCALE_FUNC(8u64f, uchar, double, double)
DEF_CVT_SCALE_FUNC(8s64f, schar, double, double)
DEF_CVT_SCALE_FUNC(16u64f, ushort, double, double)
DEF_CVT_SCALE_FUNC(16s64f, short, double, double)
DEF_CVT_SCALE_FUNC(32s64f, int, double, double)
DEF_CVT_SCALE_FUNC(32f64f, float, double, double)
DEF_CVT_SCALE_FUNC(64f, double, double, double)
DEF_CPY_FUNC(8u, uchar);
DEF_CVT_FUNC(8s8u, schar, uchar);
DEF_CVT_FUNC(16u8u, ushort, uchar);
DEF_CVT_FUNC(16s8u, short, uchar);
DEF_CVT_FUNC(32s8u, int, uchar);
DEF_CVT_FUNC(32f8u, float, uchar);
DEF_CVT_FUNC(64f8u, double, uchar);
DEF_CPY_FUNC(8u, uchar)
DEF_CVT_FUNC(8s8u, schar, uchar)
DEF_CVT_FUNC(16u8u, ushort, uchar)
DEF_CVT_FUNC(16s8u, short, uchar)
DEF_CVT_FUNC(32s8u, int, uchar)
DEF_CVT_FUNC(32f8u, float, uchar)
DEF_CVT_FUNC(64f8u, double, uchar)
DEF_CVT_FUNC(8u8s, uchar, schar);
DEF_CVT_FUNC(16u8s, ushort, schar);
DEF_CVT_FUNC(16s8s, short, schar);
DEF_CVT_FUNC(32s8s, int, schar);
DEF_CVT_FUNC(32f8s, float, schar);
DEF_CVT_FUNC(64f8s, double, schar);
DEF_CVT_FUNC(8u8s, uchar, schar)
DEF_CVT_FUNC(16u8s, ushort, schar)
DEF_CVT_FUNC(16s8s, short, schar)
DEF_CVT_FUNC(32s8s, int, schar)
DEF_CVT_FUNC(32f8s, float, schar)
DEF_CVT_FUNC(64f8s, double, schar)
DEF_CVT_FUNC(8u16u, uchar, ushort);
DEF_CVT_FUNC(8s16u, schar, ushort);
DEF_CPY_FUNC(16u, ushort);
DEF_CVT_FUNC(16s16u, short, ushort);
DEF_CVT_FUNC(32s16u, int, ushort);
DEF_CVT_FUNC(32f16u, float, ushort);
DEF_CVT_FUNC(64f16u, double, ushort);
DEF_CVT_FUNC(8u16u, uchar, ushort)
DEF_CVT_FUNC(8s16u, schar, ushort)
DEF_CPY_FUNC(16u, ushort)
DEF_CVT_FUNC(16s16u, short, ushort)
DEF_CVT_FUNC(32s16u, int, ushort)
DEF_CVT_FUNC(32f16u, float, ushort)
DEF_CVT_FUNC(64f16u, double, ushort)
DEF_CVT_FUNC(8u16s, uchar, short);
DEF_CVT_FUNC(8s16s, schar, short);
DEF_CVT_FUNC(16u16s, ushort, short);
DEF_CVT_FUNC(32s16s, int, short);
DEF_CVT_FUNC(32f16s, float, short);
DEF_CVT_FUNC(64f16s, double, short);
DEF_CVT_FUNC(8u16s, uchar, short)
DEF_CVT_FUNC(8s16s, schar, short)
DEF_CVT_FUNC(16u16s, ushort, short)
DEF_CVT_FUNC(32s16s, int, short)
DEF_CVT_FUNC(32f16s, float, short)
DEF_CVT_FUNC(64f16s, double, short)
DEF_CVT_FUNC(8u32s, uchar, int);
DEF_CVT_FUNC(8s32s, schar, int);
DEF_CVT_FUNC(16u32s, ushort, int);
DEF_CVT_FUNC(16s32s, short, int);
DEF_CPY_FUNC(32s, int);
DEF_CVT_FUNC(32f32s, float, int);
DEF_CVT_FUNC(64f32s, double, int);
DEF_CVT_FUNC(8u32s, uchar, int)
DEF_CVT_FUNC(8s32s, schar, int)
DEF_CVT_FUNC(16u32s, ushort, int)
DEF_CVT_FUNC(16s32s, short, int)
DEF_CPY_FUNC(32s, int)
DEF_CVT_FUNC(32f32s, float, int)
DEF_CVT_FUNC(64f32s, double, int)
DEF_CVT_FUNC(8u32f, uchar, float);
DEF_CVT_FUNC(8s32f, schar, float);
DEF_CVT_FUNC(16u32f, ushort, float);
DEF_CVT_FUNC(16s32f, short, float);
DEF_CVT_FUNC(32s32f, int, float);
DEF_CVT_FUNC(64f32f, double, float);
DEF_CVT_FUNC(8u32f, uchar, float)
DEF_CVT_FUNC(8s32f, schar, float)
DEF_CVT_FUNC(16u32f, ushort, float)
DEF_CVT_FUNC(16s32f, short, float)
DEF_CVT_FUNC(32s32f, int, float)
DEF_CVT_FUNC(64f32f, double, float)
DEF_CVT_FUNC(8u64f, uchar, double);
DEF_CVT_FUNC(8s64f, schar, double);
DEF_CVT_FUNC(16u64f, ushort, double);
DEF_CVT_FUNC(16s64f, short, double);
DEF_CVT_FUNC(32s64f, int, double);
DEF_CVT_FUNC(32f64f, float, double);
DEF_CPY_FUNC(64s, int64);
DEF_CVT_FUNC(8u64f, uchar, double)
DEF_CVT_FUNC(8s64f, schar, double)
DEF_CVT_FUNC(16u64f, ushort, double)
DEF_CVT_FUNC(16s64f, short, double)
DEF_CVT_FUNC(32s64f, int, double)
DEF_CVT_FUNC(32f64f, float, double)
DEF_CPY_FUNC(64s, int64)
static BinaryFunc getCvtScaleAbsFunc(int depth)
{

20
modules/core/src/copy.cpp
View File

@ -166,16 +166,16 @@ static void copyMask##suffix(const uchar* src, size_t sstep, const uchar* mask,
}
DEF_COPY_MASK(8u, uchar);
DEF_COPY_MASK(16u, ushort);
DEF_COPY_MASK(8uC3, Vec3b);
DEF_COPY_MASK(32s, int);
DEF_COPY_MASK(16uC3, Vec3s);
DEF_COPY_MASK(32sC2, Vec2i);
DEF_COPY_MASK(32sC3, Vec3i);
DEF_COPY_MASK(32sC4, Vec4i);
DEF_COPY_MASK(32sC6, Vec6i);
DEF_COPY_MASK(32sC8, Vec8i);
DEF_COPY_MASK(8u, uchar)
DEF_COPY_MASK(16u, ushort)
DEF_COPY_MASK(8uC3, Vec3b)
DEF_COPY_MASK(32s, int)
DEF_COPY_MASK(16uC3, Vec3s)
DEF_COPY_MASK(32sC2, Vec2i)
DEF_COPY_MASK(32sC3, Vec3i)
DEF_COPY_MASK(32sC4, Vec4i)
DEF_COPY_MASK(32sC6, Vec6i)
DEF_COPY_MASK(32sC8, Vec8i)
BinaryFunc copyMaskTab[] =
{

2
modules/core/src/system.cpp
View File

@ -849,7 +849,9 @@ public:
};
#ifdef WIN32
#ifdef _MSC_VER
#pragma warning(disable:4505) // unreferenced local function has been removed
#endif
#ifdef HAVE_WINRT
// using C++11 thread attribute for local thread data

78
modules/core/test/test_arithm.cpp
View File

@ -115,7 +115,7 @@ struct BaseAddOp : public BaseElemWiseOp
struct AddOp : public BaseAddOp
{
AddOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {};
AddOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
if( mask.empty() )
@ -128,7 +128,7 @@ struct AddOp : public BaseAddOp
struct SubOp : public BaseAddOp
{
SubOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, -1, Scalar::all(0)) {};
SubOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, -1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
if( mask.empty() )
@ -141,7 +141,7 @@ struct SubOp : public BaseAddOp
struct AddSOp : public BaseAddOp
{
AddSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 0, Scalar::all(0)) {};
AddSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 0, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
if( mask.empty() )
@ -154,7 +154,7 @@ struct AddSOp : public BaseAddOp
struct SubRSOp : public BaseAddOp
{
SubRSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, -1, 0, Scalar::all(0)) {};
SubRSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, -1, 0, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
if( mask.empty() )
@ -167,7 +167,7 @@ struct SubRSOp : public BaseAddOp
struct ScaleAddOp : public BaseAddOp
{
ScaleAddOp() : BaseAddOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
ScaleAddOp() : BaseAddOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
scaleAdd(src[0], alpha, src[1], dst);
@ -181,7 +181,7 @@ struct ScaleAddOp : public BaseAddOp
struct AddWeightedOp : public BaseAddOp
{
AddWeightedOp() : BaseAddOp(2, REAL_GAMMA, 1, 1, Scalar::all(0)) {};
AddWeightedOp() : BaseAddOp(2, REAL_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
addWeighted(src[0], alpha, src[1], beta, gamma[0], dst);
@ -194,7 +194,7 @@ struct AddWeightedOp : public BaseAddOp
struct MulOp : public BaseElemWiseOp
{
MulOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
MulOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void getValueRange(int depth, double& minval, double& maxval)
{
minval = depth < CV_32S ? cvtest::getMinVal(depth) : depth == CV_32S ? -1000000 : -1000.;
@ -218,7 +218,7 @@ struct MulOp : public BaseElemWiseOp
struct DivOp : public BaseElemWiseOp
{
DivOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
DivOp() : BaseElemWiseOp(2, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::divide(src[0], src[1], dst, alpha);
@ -235,7 +235,7 @@ struct DivOp : public BaseElemWiseOp
struct RecipOp : public BaseElemWiseOp
{
RecipOp() : BaseElemWiseOp(1, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
RecipOp() : BaseElemWiseOp(1, FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::divide(alpha, src[0], dst);
@ -252,7 +252,7 @@ struct RecipOp : public BaseElemWiseOp
struct AbsDiffOp : public BaseAddOp
{
AbsDiffOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, -1, Scalar::all(0)) {};
AbsDiffOp() : BaseAddOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, -1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
absdiff(src[0], src[1], dst);
@ -265,7 +265,7 @@ struct AbsDiffOp : public BaseAddOp
struct AbsDiffSOp : public BaseAddOp
{
AbsDiffSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA, 1, 0, Scalar::all(0)) {};
AbsDiffSOp() : BaseAddOp(1, FIX_ALPHA+FIX_BETA, 1, 0, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
absdiff(src[0], gamma, dst);
@ -278,7 +278,7 @@ struct AbsDiffSOp : public BaseAddOp
struct LogicOp : public BaseElemWiseOp
{
LogicOp(char _opcode) : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)), opcode(_opcode) {};
LogicOp(char _opcode) : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)), opcode(_opcode) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
if( opcode == '&' )
@ -309,7 +309,7 @@ struct LogicOp : public BaseElemWiseOp
struct LogicSOp : public BaseElemWiseOp
{
LogicSOp(char _opcode)
: BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+(_opcode != '~' ? SUPPORT_MASK : 0), 1, 1, Scalar::all(0)), opcode(_opcode) {};
: BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+(_opcode != '~' ? SUPPORT_MASK : 0), 1, 1, Scalar::all(0)), opcode(_opcode) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
if( opcode == '&' )
@ -341,7 +341,7 @@ struct LogicSOp : public BaseElemWiseOp
struct MinOp : public BaseElemWiseOp
{
MinOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
MinOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::min(src[0], src[1], dst);
@ -358,7 +358,7 @@ struct MinOp : public BaseElemWiseOp
struct MaxOp : public BaseElemWiseOp
{
MaxOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
MaxOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::max(src[0], src[1], dst);
@ -375,7 +375,7 @@ struct MaxOp : public BaseElemWiseOp
struct MinSOp : public BaseElemWiseOp
{
MinSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
MinSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::min(src[0], gamma[0], dst);
@ -392,7 +392,7 @@ struct MinSOp : public BaseElemWiseOp
struct MaxSOp : public BaseElemWiseOp
{
MaxSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
MaxSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::max(src[0], gamma[0], dst);
@ -409,7 +409,7 @@ struct MaxSOp : public BaseElemWiseOp
struct CmpOp : public BaseElemWiseOp
{
CmpOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
CmpOp() : BaseElemWiseOp(2, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void generateScalars(int depth, RNG& rng)
{
BaseElemWiseOp::generateScalars(depth, rng);
@ -437,7 +437,7 @@ struct CmpOp : public BaseElemWiseOp
struct CmpSOp : public BaseElemWiseOp
{
CmpSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
CmpSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
void generateScalars(int depth, RNG& rng)
{
BaseElemWiseOp::generateScalars(depth, rng);
@ -467,7 +467,7 @@ struct CmpSOp : public BaseElemWiseOp
struct CopyOp : public BaseElemWiseOp
{
CopyOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {};
CopyOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
src[0].copyTo(dst, mask);
@ -490,7 +490,7 @@ struct CopyOp : public BaseElemWiseOp
struct SetOp : public BaseElemWiseOp
{
SetOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {};
SetOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+SUPPORT_MASK, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>&, Mat& dst, const Mat& mask)
{
dst.setTo(gamma, mask);
@ -651,7 +651,7 @@ static void inRangeS(const Mat& src, const Scalar& lb, const Scalar& rb, Mat& ds
struct InRangeSOp : public BaseElemWiseOp
{
InRangeSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {};
InRangeSOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::inRange(src[0], gamma, gamma1, dst);
@ -681,7 +681,7 @@ struct InRangeSOp : public BaseElemWiseOp
struct InRangeOp : public BaseElemWiseOp
{
InRangeOp() : BaseElemWiseOp(3, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
InRangeOp() : BaseElemWiseOp(3, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
Mat lb, rb;
@ -707,7 +707,7 @@ struct InRangeOp : public BaseElemWiseOp
struct ConvertScaleOp : public BaseElemWiseOp
{
ConvertScaleOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)), ddepth(0) { };
ConvertScaleOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)), ddepth(0) { }
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
src[0].convertTo(dst, ddepth, alpha, gamma[0]);
@ -742,7 +742,7 @@ struct ConvertScaleOp : public BaseElemWiseOp
struct ConvertScaleAbsOp : public BaseElemWiseOp
{
ConvertScaleAbsOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {};
ConvertScaleAbsOp() : BaseElemWiseOp(1, FIX_BETA+REAL_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>& src, Mat& dst, const Mat&)
{
cv::convertScaleAbs(src[0], dst, alpha, gamma[0]);
@ -810,7 +810,7 @@ static void setIdentity(Mat& dst, const Scalar& s)
struct FlipOp : public BaseElemWiseOp
{
FlipOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
FlipOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void getRandomSize(RNG& rng, vector<int>& size)
{
cvtest::randomSize(rng, 2, 2, cvtest::ARITHM_MAX_SIZE_LOG, size);
@ -836,7 +836,7 @@ struct FlipOp : public BaseElemWiseOp
struct TransposeOp : public BaseElemWiseOp
{
TransposeOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
TransposeOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void getRandomSize(RNG& rng, vector<int>& size)
{
cvtest::randomSize(rng, 2, 2, cvtest::ARITHM_MAX_SIZE_LOG, size);
@ -857,7 +857,7 @@ struct TransposeOp : public BaseElemWiseOp
struct SetIdentityOp : public BaseElemWiseOp
{
SetIdentityOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {};
SetIdentityOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA, 1, 1, Scalar::all(0)) {}
void getRandomSize(RNG& rng, vector<int>& size)
{
cvtest::randomSize(rng, 2, 2, cvtest::ARITHM_MAX_SIZE_LOG, size);
@ -878,7 +878,7 @@ struct SetIdentityOp : public BaseElemWiseOp
struct SetZeroOp : public BaseElemWiseOp
{
SetZeroOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
SetZeroOp() : BaseElemWiseOp(0, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
void op(const vector<Mat>&, Mat& dst, const Mat&)
{
dst = Scalar::all(0);
@ -954,7 +954,7 @@ static void log(const Mat& src, Mat& dst)
struct ExpOp : public BaseElemWiseOp
{
ExpOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
ExpOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
int getRandomType(RNG& rng)
{
return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_FLT, 1, ARITHM_MAX_CHANNELS);
@ -981,7 +981,7 @@ struct ExpOp : public BaseElemWiseOp
struct LogOp : public BaseElemWiseOp
{
LogOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {};
LogOp() : BaseElemWiseOp(1, FIX_ALPHA+FIX_BETA+FIX_GAMMA, 1, 1, Scalar::all(0)) {}
int getRandomType(RNG& rng)
{
return cvtest::randomType(rng, _OutputArray::DEPTH_MASK_FLT, 1, ARITHM_MAX_CHANNELS);
@ -1564,3 +1564,19 @@ TEST(Core_round, CvRound)
ASSERT_EQ(-2, cvRound(-2.5));
ASSERT_EQ(-4, cvRound(-3.5));
}
typedef testing::TestWithParam<Size> Mul1;
TEST_P(Mul1, One)
{
Size size = GetParam();
cv::Mat src(size, CV_32FC1, cv::Scalar::all(2)), dst,
ref_dst(size, CV_32FC1, cv::Scalar::all(6));
cv::multiply(3, src, dst);
ASSERT_EQ(0, cv::norm(dst, ref_dst, cv::NORM_INF));
}
INSTANTIATE_TEST_CASE_P(Arithm, Mul1, testing::Values(Size(2, 2), Size(1, 1)));

2
modules/cudaimgproc/perf/perf_match_template.cpp
View File

@ -90,7 +90,7 @@ PERF_TEST_P(Sz_TemplateSz_Cn_Method, MatchTemplate8U,
CPU_SANITY_CHECK(dst);
}
};
}
////////////////////////////////////////////////////////////////////////////////
// MatchTemplate32F

28
modules/features2d/src/features2d_init.cpp
View File

@ -58,19 +58,19 @@ Ptr<Feature2D> Feature2D::create( const String& feature2DType )
CV_INIT_ALGORITHM(BRISK, "Feature2D.BRISK",
obj.info()->addParam(obj, "thres", obj.threshold);
obj.info()->addParam(obj, "octaves", obj.octaves));
obj.info()->addParam(obj, "octaves", obj.octaves))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(BriefDescriptorExtractor, "Feature2D.BRIEF",
obj.info()->addParam(obj, "bytes", obj.bytes_));
obj.info()->addParam(obj, "bytes", obj.bytes_))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(FastFeatureDetector, "Feature2D.FAST",
obj.info()->addParam(obj, "threshold", obj.threshold);
obj.info()->addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
obj.info()->addParam(obj, "type", obj.type));
obj.info()->addParam(obj, "type", obj.type))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -79,7 +79,7 @@ CV_INIT_ALGORITHM(StarDetector, "Feature2D.STAR",
obj.info()->addParam(obj, "responseThreshold", obj.responseThreshold);
obj.info()->addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
obj.info()->addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
obj.info()->addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize));
obj.info()->addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -92,7 +92,7 @@ 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))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -104,7 +104,7 @@ CV_INIT_ALGORITHM(ORB, "Feature2D.ORB",
obj.info()->addParam(obj, "edgeThreshold", obj.edgeThreshold);
obj.info()->addParam(obj, "patchSize", obj.patchSize);
obj.info()->addParam(obj, "WTA_K", obj.WTA_K);
obj.info()->addParam(obj, "scoreType", obj.scoreType));
obj.info()->addParam(obj, "scoreType", obj.scoreType))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -112,7 +112,7 @@ CV_INIT_ALGORITHM(FREAK, "Feature2D.FREAK",
obj.info()->addParam(obj, "orientationNormalized", obj.orientationNormalized);
obj.info()->addParam(obj, "scaleNormalized", obj.scaleNormalized);
obj.info()->addParam(obj, "patternScale", obj.patternScale);
obj.info()->addParam(obj, "nbOctave", obj.nOctaves));
obj.info()->addParam(obj, "nbOctave", obj.nOctaves))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -121,7 +121,7 @@ CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT",
obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
obj.info()->addParam(obj, "minDistance", obj.minDistance);
obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
obj.info()->addParam(obj, "k", obj.k));
obj.info()->addParam(obj, "k", obj.k))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -141,7 +141,7 @@ CV_INIT_ALGORITHM(SimpleBlobDetector, "Feature2D.SimpleBlob",
obj.info()->addParam(obj, "maxInertiaRatio", obj.params.maxInertiaRatio);
obj.info()->addParam(obj, "filterByConvexity", obj.params.filterByConvexity);
obj.info()->addParam(obj, "maxConvexity", obj.params.maxConvexity);
);
)
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -162,7 +162,7 @@ CV_INIT_ALGORITHM(HarrisDetector, "Feature2D.HARRIS",
obj.info()->addParam(obj, "qualityLevel", obj.qualityLevel);
obj.info()->addParam(obj, "minDistance", obj.minDistance);
obj.info()->addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
obj.info()->addParam(obj, "k", obj.k));
obj.info()->addParam(obj, "k", obj.k))
////////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -173,21 +173,21 @@ CV_INIT_ALGORITHM(DenseFeatureDetector, "Feature2D.Dense",
obj.info()->addParam(obj, "initXyStep", obj.initXyStep);
obj.info()->addParam(obj, "initImgBound", obj.initImgBound);
obj.info()->addParam(obj, "varyXyStepWithScale", obj.varyXyStepWithScale);
obj.info()->addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale));
obj.info()->addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale))
CV_INIT_ALGORITHM(GridAdaptedFeatureDetector, "Feature2D.Grid",
obj.info()->addParam<FeatureDetector>(obj, "detector", obj.detector, false, 0, 0); // Extra params added to avoid VS2013 fatal error in opencv2/core.hpp (decl. of addParam)
obj.info()->addParam(obj, "maxTotalKeypoints", obj.maxTotalKeypoints);
obj.info()->addParam(obj, "gridRows", obj.gridRows);
obj.info()->addParam(obj, "gridCols", obj.gridCols));
obj.info()->addParam(obj, "gridCols", obj.gridCols))
////////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(BFMatcher, "DescriptorMatcher.BFMatcher",
obj.info()->addParam(obj, "normType", obj.normType);
obj.info()->addParam(obj, "crossCheck", obj.crossCheck));
obj.info()->addParam(obj, "crossCheck", obj.crossCheck))
CV_INIT_ALGORITHM(FlannBasedMatcher, "DescriptorMatcher.FlannBasedMatcher",);
CV_INIT_ALGORITHM(FlannBasedMatcher, "DescriptorMatcher.FlannBasedMatcher",)
///////////////////////////////////////////////////////////////////////////////////////////////////////////

12
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
View File

@ -413,12 +413,6 @@ public:
void loadIndex(FILE* stream)
{
load_value(stream, branching_);
load_value(stream, trees_);
load_value(stream, centers_init_);
load_value(stream, leaf_size_);
load_value(stream, memoryCounter);
free_elements();
if (root!=NULL) {
@ -429,6 +423,12 @@ public:
delete[] indices;
}
load_value(stream, branching_);
load_value(stream, trees_);
load_value(stream, centers_init_);
load_value(stream, leaf_size_);
load_value(stream, memoryCounter);
indices = new int*[trees_];
root = new NodePtr[trees_];
for (int i=0; i<trees_; ++i) {

2
modules/highgui/src/bitstrm.hpp
View File

@ -53,7 +53,7 @@ enum
RBS_THROW_EOS=-123, // <end of stream> exception code
RBS_THROW_FORB=-124, // <forrbidden huffman code> exception code
RBS_HUFF_FORB=2047, // forrbidden huffman code "value"
RBS_BAD_HEADER=-125, // invalid header
RBS_BAD_HEADER=-125 // invalid header
};
typedef unsigned long ulong;

4
modules/highgui/src/cap_ffmpeg_impl.hpp
View File

@ -2096,7 +2096,7 @@ enum
VideoCodec_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,V,U (4:2:0)
VideoCodec_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2')), // Y,UV (4:2:0)
VideoCodec_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')), // YUYV/YUY2 (4:2:2)
VideoCodec_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')), // UYVY (4:2:2)
VideoCodec_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')) // UYVY (4:2:2)
};
enum
@ -2104,7 +2104,7 @@ enum
VideoChromaFormat_Monochrome = 0,
VideoChromaFormat_YUV420,
VideoChromaFormat_YUV422,
VideoChromaFormat_YUV444,
VideoChromaFormat_YUV444
};
struct InputMediaStream_FFMPEG

8
modules/highgui/src/precomp.hpp
View File

@ -58,6 +58,14 @@
#include <assert.h>
#if defined WIN32 || defined WINCE
#if !defined _WIN32_WINNT
#ifdef HAVE_MSMF
#define _WIN32_WINNT 0x0600 // Windows Vista
#else
#define _WIN32_WINNT 0x0500 // Windows 2000
#endif
#endif
#include <windows.h>
#undef small
#undef min

16
modules/highgui/src/window_w32.cpp
View File

@ -44,27 +44,11 @@
#if defined WIN32 || defined _WIN32
#define COMPILE_MULTIMON_STUBS // Required for multi-monitor support
#ifndef _MULTIMON_USE_SECURE_CRT
# define _MULTIMON_USE_SECURE_CRT 0 // some MinGW platforms have no strncpy_s
#endif
#if defined SM_CMONITORS && !defined MONITOR_DEFAULTTONEAREST
# define MONITOR_DEFAULTTONULL 0x00000000
# define MONITOR_DEFAULTTOPRIMARY 0x00000001
# define MONITOR_DEFAULTTONEAREST 0x00000002
# define MONITORINFOF_PRIMARY 0x00000001
#endif
#ifndef __inout
# define __inout
#endif
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
#endif
#include <commctrl.h>
#include <winuser.h>
#include <stdlib.h>
#include <string.h>
#include <stdio.h>

2
modules/imgproc/perf/perf_filter2d.cpp
View File

@ -8,7 +8,7 @@ using std::tr1::make_tuple;
using std::tr1::get;
CV_ENUM(BorderMode, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT_101);
CV_ENUM(BorderMode, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT_101)
typedef TestBaseWithParam< tr1::tuple<Size, int, BorderMode> > TestFilter2d;
typedef TestBaseWithParam< tr1::tuple<string, int> > Image_KernelSize;

2
modules/imgproc/src/gcgraph.hpp
View File

@ -380,6 +380,6 @@ bool GCGraph<TWeight>::inSourceSegment( int i )
{
CV_Assert( i>=0 && i<(int)vtcs.size() );
return vtcs[i].t == 0;
};
}
#endif

6
modules/imgproc/src/imgwarp.cpp
View File

@ -2565,15 +2565,15 @@ struct RemapVec_8u
int operator()( const Mat& _src, void* _dst, const short* XY,
const ushort* FXY, const void* _wtab, int width ) const
{
int cn = _src.channels();
int cn = _src.channels(), x = 0, sstep = (int)_src.step;
if( (cn != 1 && cn != 3 && cn != 4) || !checkHardwareSupport(CV_CPU_SSE2) )
if( (cn != 1 && cn != 3 && cn != 4) || !checkHardwareSupport(CV_CPU_SSE2) ||
sstep > 0x8000 )
return 0;
const uchar *S0 = _src.data, *S1 = _src.data + _src.step;
const short* wtab = cn == 1 ? (const short*)_wtab : &BilinearTab_iC4[0][0][0];
uchar* D = (uchar*)_dst;
int x = 0, sstep = (int)_src.step;
__m128i delta = _mm_set1_epi32(INTER_REMAP_COEF_SCALE/2);
__m128i xy2ofs = _mm_set1_epi32(cn + (sstep << 16));
__m128i z = _mm_setzero_si128();

2
modules/imgproc/test/test_cvtyuv.cpp
View File

@ -603,7 +603,7 @@ CV_ENUM(YUVCVTS, CV_YUV2RGB_NV12, CV_YUV2BGR_NV12, CV_YUV2RGB_NV21, CV_YUV2BGR_N
CV_YUV2RGBA_YUY2, CV_YUV2BGRA_YUY2, CV_YUV2RGBA_YVYU, CV_YUV2BGRA_YVYU,
CV_YUV2GRAY_420, CV_YUV2GRAY_UYVY, CV_YUV2GRAY_YUY2,
CV_YUV2BGR, CV_YUV2RGB, CV_RGB2YUV_YV12, CV_BGR2YUV_YV12, CV_RGBA2YUV_YV12,
CV_BGRA2YUV_YV12, CV_RGB2YUV_I420, CV_BGR2YUV_I420, CV_RGBA2YUV_I420, CV_BGRA2YUV_I420);
CV_BGRA2YUV_YV12, CV_RGB2YUV_I420, CV_BGR2YUV_I420, CV_RGBA2YUV_I420, CV_BGRA2YUV_I420)
typedef ::testing::TestWithParam<YUVCVTS> Imgproc_ColorYUV;

2
modules/legacy/src/blobtrack.cpp
View File

@ -205,7 +205,7 @@ double CvVSModule::GetParam(const char* name)
if(p->pInt) return p->pInt[0];
}
return 0;
};
}
const char* CvVSModule::GetParamStr(const char* name)
{

4
modules/legacy/src/enteringblobdetection.cpp
View File

@ -209,7 +209,7 @@ public:
CvBlobDetectorSimple();
~CvBlobDetectorSimple();
int DetectNewBlob(IplImage* pImg, IplImage* pFGMask, CvBlobSeq* pNewBlobList, CvBlobSeq* pOldBlobList);
void Release(){delete this;};
void Release(){delete this;}
protected:
IplImage* m_pMaskBlobNew;
@ -219,7 +219,7 @@ protected:
};
/* Blob detector creator (sole interface function for this file) */
CvBlobDetector* cvCreateBlobDetectorSimple(){return new CvBlobDetectorSimple;};
CvBlobDetector* cvCreateBlobDetectorSimple(){return new CvBlobDetectorSimple;}
/* Constructor of BlobDetector: */
CvBlobDetectorSimple::CvBlobDetectorSimple()

2
modules/legacy/src/vecfacetracking.cpp
View File

@ -52,7 +52,7 @@ enum
{
MOUTH = 0,
LEYE = 1,
REYE = 2,
REYE = 2
};
#define MAX_LAYERS 64

2
modules/ml/src/ml_init.cpp
View File

@ -52,7 +52,7 @@ CV_INIT_ALGORITHM(EM, "StatModel.EM",
obj.info()->addParam(obj, "epsilon", obj.epsilon);
obj.info()->addParam(obj, "weights", obj.weights, true);
obj.info()->addParam(obj, "means", obj.means, true);
obj.info()->addParam(obj, "covs", obj.covs, true));
obj.info()->addParam(obj, "covs", obj.covs, true))
bool initModule_ml(void)
{

4
modules/nonfree/src/nonfree_init.cpp
View File

@ -52,7 +52,7 @@ CV_INIT_ALGORITHM(SURF, "Feature2D.SURF",
obj.info()->addParam(obj, "nOctaves", obj.nOctaves);
obj.info()->addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
obj.info()->addParam(obj, "extended", obj.extended);
obj.info()->addParam(obj, "upright", obj.upright));
obj.info()->addParam(obj, "upright", obj.upright))
///////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -61,7 +61,7 @@ CV_INIT_ALGORITHM(SIFT, "Feature2D.SIFT",
obj.info()->addParam(obj, "nOctaveLayers", obj.nOctaveLayers);
obj.info()->addParam(obj, "contrastThreshold", obj.contrastThreshold);
obj.info()->addParam(obj, "edgeThreshold", obj.edgeThreshold);
obj.info()->addParam(obj, "sigma", obj.sigma));
obj.info()->addParam(obj, "sigma", obj.sigma))
///////////////////////////////////////////////////////////////////////////////////////////////////////////

2
modules/ocl/include/opencv2/ocl.hpp
View File

@ -167,7 +167,7 @@ namespace cv
CACHE_NONE = 0, // do not cache OpenCL binary
CACHE_DEBUG = 0x1 << 0, // cache OpenCL binary when built in debug mode
CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode
CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE, // cache opencl binary
CACHE_ALL = CACHE_DEBUG | CACHE_RELEASE // cache opencl binary
};
//! Enable or disable OpenCL program binary caching onto local disk
// After a program (*.cl files in opencl/ folder) is built at runtime, we allow the

2
modules/ocl/include/opencv2/ocl/private/opencl_utils.hpp
View File

@ -108,7 +108,7 @@ inline cl_int getStringInfo(Functor f, ObjectType obj, cl_uint name, std::string
}
return CL_SUCCESS;
};
}
} // namespace cl_utils

4
modules/ocl/test/test_canny.cpp
View File

@ -48,8 +48,8 @@
////////////////////////////////////////////////////////
// Canny
IMPLEMENT_PARAM_CLASS(AppertureSize, int);
IMPLEMENT_PARAM_CLASS(L2gradient, bool);
IMPLEMENT_PARAM_CLASS(AppertureSize, int)
IMPLEMENT_PARAM_CLASS(L2gradient, bool)
PARAM_TEST_CASE(Canny, AppertureSize, L2gradient)
{

2
modules/ocl/test/test_match_template.cpp
View File

@ -50,7 +50,7 @@
// MatchTemplate
#define ALL_TEMPLATE_METHODS testing::Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR), TemplateMethod(cv::TM_CCOEFF), TemplateMethod(cv::TM_SQDIFF_NORMED), TemplateMethod(cv::TM_CCORR_NORMED), TemplateMethod(cv::TM_CCOEFF_NORMED))
IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size);
IMPLEMENT_PARAM_CLASS(TemplateSize, cv::Size)
#define MTEMP_SIZES testing::Values(cv::Size(128, 256), cv::Size(1024, 768))

2
modules/ocl/test/test_objdetect.cpp
View File

@ -180,7 +180,7 @@ INSTANTIATE_TEST_CASE_P(OCL_ObjDetect, HOG, testing::Combine(
///////////////////////////// Haar //////////////////////////////
IMPLEMENT_PARAM_CLASS(CascadeName, std::string);
IMPLEMENT_PARAM_CLASS(CascadeName, std::string)
CascadeName cascade_frontalface_alt(std::string("haarcascade_frontalface_alt.xml"));
CascadeName cascade_frontalface_alt2(std::string("haarcascade_frontalface_alt2.xml"));

2
modules/ocl/test/utility.hpp
View File

@ -273,7 +273,7 @@ CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA)
CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP)
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T);
CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T)
CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP)
CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)

2
modules/superres/src/btv_l1.cpp
View File

@ -487,7 +487,7 @@ namespace
obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."))
BTVL1::BTVL1()
{

2
modules/superres/src/btv_l1_ocl.cpp
View File

@ -582,7 +582,7 @@ namespace
obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."))
BTVL1_OCL::BTVL1_OCL()
{

20
modules/superres/src/optical_flow.cpp
View File

@ -148,7 +148,7 @@ namespace
obj.info()->addParam(obj, "numIters", obj.numIters_);
obj.info()->addParam(obj, "polyN", obj.polyN_);
obj.info()->addParam(obj, "polySigma", obj.polySigma_);
obj.info()->addParam(obj, "flags", obj.flags_));
obj.info()->addParam(obj, "flags", obj.flags_))
Farneback::Farneback() : CpuOpticalFlow(CV_8UC1)
{
@ -218,7 +218,7 @@ namespace
obj.info()->addParam(obj, "upscaleAveragingRadius", obj.upscaleAveragingRadius_);
obj.info()->addParam(obj, "upscaleSigmaDist", obj.upscaleSigmaDist_);
obj.info()->addParam(obj, "upscaleSigmaColor", obj.upscaleSigmaColor_);
obj.info()->addParam(obj, "speedUpThr", obj.speedUpThr_));
obj.info()->addParam(obj, "speedUpThr", obj.speedUpThr_))
Simple::Simple() : CpuOpticalFlow(CV_8UC3)
{
@ -301,7 +301,7 @@ namespace
obj.info()->addParam(obj, "warps", obj.warps_);
obj.info()->addParam(obj, "epsilon", obj.epsilon_);
obj.info()->addParam(obj, "iterations", obj.iterations_);
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_))
DualTVL1::DualTVL1() : CpuOpticalFlow(CV_8UC1)
{
@ -472,7 +472,7 @@ namespace
obj.info()->addParam(obj, "scaleFactor", obj.scaleFactor_, false, 0, 0, "Pyramid scale factor");
obj.info()->addParam(obj, "innerIterations", obj.innerIterations_, false, 0, 0, "Number of lagged non-linearity iterations (inner loop)");
obj.info()->addParam(obj, "outerIterations", obj.outerIterations_, false, 0, 0, "Number of warping iterations (number of pyramid levels)");
obj.info()->addParam(obj, "solverIterations", obj.solverIterations_, false, 0, 0, "Number of linear system solver iterations"));
obj.info()->addParam(obj, "solverIterations", obj.solverIterations_, false, 0, 0, "Number of linear system solver iterations"))
Brox_CUDA::Brox_CUDA() : GpuOpticalFlow(CV_32FC1), alg_(0.197f, 50.0f, 0.8f, 10, 77, 10)
{
@ -536,7 +536,7 @@ namespace
CV_INIT_ALGORITHM(PyrLK_CUDA, "DenseOpticalFlowExt.PyrLK_CUDA",
obj.info()->addParam(obj, "winSize", obj.winSize_);
obj.info()->addParam(obj, "maxLevel", obj.maxLevel_);
obj.info()->addParam(obj, "iterations", obj.iterations_));
obj.info()->addParam(obj, "iterations", obj.iterations_))
PyrLK_CUDA::PyrLK_CUDA() : GpuOpticalFlow(CV_8UC1)
{
@ -603,7 +603,7 @@ namespace
obj.info()->addParam(obj, "numIters", obj.numIters_);
obj.info()->addParam(obj, "polyN", obj.polyN_);
obj.info()->addParam(obj, "polySigma", obj.polySigma_);
obj.info()->addParam(obj, "flags", obj.flags_));
obj.info()->addParam(obj, "flags", obj.flags_))
Farneback_CUDA::Farneback_CUDA() : GpuOpticalFlow(CV_8UC1)
{
@ -679,7 +679,7 @@ namespace
obj.info()->addParam(obj, "warps", obj.warps_);
obj.info()->addParam(obj, "epsilon", obj.epsilon_);
obj.info()->addParam(obj, "iterations", obj.iterations_);
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_))
DualTVL1_CUDA::DualTVL1_CUDA() : GpuOpticalFlow(CV_8UC1)
{
@ -801,7 +801,7 @@ namespace
CV_INIT_ALGORITHM(PyrLK_OCL, "DenseOpticalFlowExt.PyrLK_OCL",
obj.info()->addParam(obj, "winSize", obj.winSize_);
obj.info()->addParam(obj, "maxLevel", obj.maxLevel_);
obj.info()->addParam(obj, "iterations", obj.iterations_));
obj.info()->addParam(obj, "iterations", obj.iterations_))
PyrLK_OCL::PyrLK_OCL() : oclOpticalFlow(CV_8UC1)
{
@ -870,7 +870,7 @@ namespace
obj.info()->addParam(obj, "warps", obj.warps_);
obj.info()->addParam(obj, "epsilon", obj.epsilon_);
obj.info()->addParam(obj, "iterations", obj.iterations_);
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_))
DualTVL1_OCL::DualTVL1_OCL() : oclOpticalFlow(CV_8UC1)
{
@ -947,7 +947,7 @@ namespace
obj.info()->addParam(obj, "numIters", obj.numIters_);
obj.info()->addParam(obj, "polyN", obj.polyN_);
obj.info()->addParam(obj, "polySigma", obj.polySigma_);
obj.info()->addParam(obj, "flags", obj.flags_));
obj.info()->addParam(obj, "flags", obj.flags_))
FarneBack_OCL::FarneBack_OCL() : oclOpticalFlow(CV_8UC1)
{

2
modules/ts/include/opencv2/ts/ts_perf.hpp
View File

@ -255,7 +255,7 @@ enum PERF_STRATEGY
{
PERF_STRATEGY_DEFAULT = -1,
PERF_STRATEGY_BASE = 0,
PERF_STRATEGY_SIMPLE = 1,
PERF_STRATEGY_SIMPLE = 1
};

2
modules/video/perf/perf_optflowpyrlk.cpp
View File

@ -178,7 +178,7 @@ PERF_TEST_P(Path_Idx_Cn_NPoints_WSize_Deriv, OpticalFlowPyrLK_self, testing::Com
SANITY_CHECK(err, 2);
}
CV_ENUM(PyrBorderMode, BORDER_DEFAULT, BORDER_TRANSPARENT);
CV_ENUM(PyrBorderMode, BORDER_DEFAULT, BORDER_TRANSPARENT)
typedef tr1::tuple<std::string, int, bool, PyrBorderMode, bool> Path_Win_Deriv_Border_Reuse_t;
typedef TestBaseWithParam<Path_Win_Deriv_Border_Reuse_t> Path_Win_Deriv_Border_Reuse;

2
modules/video/src/kalman.cpp
View File

@ -127,4 +127,4 @@ const Mat& KalmanFilter::correct(const Mat& measurement)
return statePost;
}
};
}

2
modules/video/src/tvl1flow.cpp
View File

@ -947,7 +947,7 @@ CV_INIT_ALGORITHM(OpticalFlowDual_TVL1, "DenseOpticalFlow.DualTVL1",
"inner iterations (between outlier filtering) used in the numerical scheme");
obj.info()->addParam(obj, "outerIterations", obj.outerIterations, false, 0, 0,
"outer iterations (number of inner loops) used in the numerical scheme");
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow));
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow))
} // namespace

46
samples/c/adaptiveskindetector.cpp
View File

@ -126,12 +126,12 @@ ASDFrameHolder::ASDFrameHolder( )
{
image = NULL;
timeStamp = 0;
};
}
ASDFrameHolder::~ASDFrameHolder( )
{
cvReleaseImage(&image);
};
}
void ASDFrameHolder::assignFrame(IplImage *sourceImage, double frameTime)
{
@ -143,22 +143,22 @@ void ASDFrameHolder::assignFrame(IplImage *sourceImage, double frameTime)
image = cvCloneImage(sourceImage);
timeStamp = frameTime;
};
}
IplImage *ASDFrameHolder::getImage()
{
return image;
};
}
double ASDFrameHolder::getTimeStamp()
{
return timeStamp;
};
}
void ASDFrameHolder::setImage(IplImage *sourceImage)
{
image = sourceImage;
};
}
//-------------------- ASDFrameSequencer -----------------------//
@ -166,26 +166,26 @@ void ASDFrameHolder::setImage(IplImage *sourceImage)
ASDFrameSequencer::~ASDFrameSequencer()
{
close();
};
}
IplImage *ASDFrameSequencer::getNextImage()
{
return NULL;
};
}
void ASDFrameSequencer::close()
{
};
}
bool ASDFrameSequencer::isOpen()
{
return false;
};
}
void ASDFrameSequencer::getFrameCaption(char* /*caption*/) {
return;
};
}
IplImage* ASDCVFrameSequencer::getNextImage()
{
@ -201,7 +201,7 @@ IplImage* ASDCVFrameSequencer::getNextImage()
{
return NULL;
}
};
}
void ASDCVFrameSequencer::close()
{
@ -209,12 +209,12 @@ void ASDCVFrameSequencer::close()
{
cvReleaseCapture(&capture);
}
};
}
bool ASDCVFrameSequencer::isOpen()
{
return (capture != NULL);
};
}
//-------------------- ASDFrameSequencerWebCam -----------------------//
@ -233,7 +233,7 @@ bool ASDFrameSequencerWebCam::open(int cameraIndex)
{
return true;
}
};
}
//-------------------- ASDFrameSequencerVideoFile -----------------------//
@ -251,7 +251,7 @@ bool ASDFrameSequencerVideoFile::open(const char *fileName)
{
return true;
}
};
}
//-------------------- ASDFrameSequencerImageFile -----------------------//
@ -263,11 +263,11 @@ void ASDFrameSequencerImageFile::open(const char *fileNameMask, int startIndex,
nEndIndex = endIndex;
std::sprintf(sFileNameMask, "%s", fileNameMask);
};
}
void ASDFrameSequencerImageFile::getFrameCaption(char *caption) {
std::sprintf(caption, sFileNameMask, nCurrentIndex);
};
}
IplImage* ASDFrameSequencerImageFile::getNextImage()
{
@ -283,23 +283,23 @@ IplImage* ASDFrameSequencerImageFile::getNextImage()
IplImage* img = cvLoadImage(fileName);
return img;
};
}
void ASDFrameSequencerImageFile::close()
{
nCurrentIndex = nEndIndex+1;
};
}
bool ASDFrameSequencerImageFile::isOpen()
{
return (nCurrentIndex <= nEndIndex);
};
}
static void putTextWithShadow(IplImage *img, const char *str, CvPoint point, CvFont *font, CvScalar color = CV_RGB(255, 255, 128))
{
cvPutText(img, str, cvPoint(point.x-1,point.y-1), font, CV_RGB(0, 0, 0));
cvPutText(img, str, point, font, color);
};
}
#define ASD_RGB_SET_PIXEL(pointer, r, g, b) { (*pointer) = (unsigned char)b; (*(pointer+1)) = (unsigned char)g; (*(pointer+2)) = (unsigned char)r; }
@ -336,7 +336,7 @@ static void displayBuffer(IplImage *rgbDestImage, IplImage *buffer, int rValue,
destY = 0;
destX += dx;
}
};
}
int main(int argc, char** argv )
{

2
samples/cpp/calibration_artificial.cpp
View File

@ -46,7 +46,7 @@ private:
Point3f generateChessBoardCenter(const Mat& camMat, const Size& imgSize) const;
Mat rvec, tvec;
};
};
}

4
samples/cpp/kmeans.cpp
View File

@ -33,10 +33,10 @@ int main( int /*argc*/, char** /*argv*/ )
{
int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
int i, sampleCount = rng.uniform(1, 1001);
Mat points(sampleCount, 1, CV_32FC2), labels;
Mat points(sampleCount, 2, CV_32F), labels;
clusterCount = MIN(clusterCount, sampleCount);
Mat centers(clusterCount, 1, points.type());
Mat centers;
/* generate random sample from multigaussian distribution */
for( k = 0; k < clusterCount; k++ )

2
samples/cpp/tutorial_code/ImgProc/Threshold.cpp
View File

@ -14,7 +14,7 @@ using namespace cv;
/// Global variables
int threshold_value = 0;
int threshold_type = 3;;
int threshold_type = 3;
int const max_value = 255;
int const max_type = 4;
int const max_BINARY_value = 255;