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Commit da383e65 authored by Maksim Shabunin's avatar Maksim Shabunin
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Remove deprecated methods from cv::Algorithm

parent fdf31ec1
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Showing with 233 additions and 435 deletions
apps/traincascade/old_ml.hpp
View file @ da383e65
......@@ -122,7 +122,6 @@ CV_INLINE CvParamLattice cvDefaultParamLattice( void )
#define CV_TYPE_NAME_ML_SVM "opencv-ml-svm"
#define CV_TYPE_NAME_ML_KNN "opencv-ml-knn"
#define CV_TYPE_NAME_ML_NBAYES "opencv-ml-bayesian"
#define CV_TYPE_NAME_ML_EM "opencv-ml-em"
#define CV_TYPE_NAME_ML_BOOSTING "opencv-ml-boost-tree"
#define CV_TYPE_NAME_ML_TREE "opencv-ml-tree"
#define CV_TYPE_NAME_ML_ANN_MLP "opencv-ml-ann-mlp"
......@@ -562,100 +561,6 @@ private:
CvSVM& operator = (const CvSVM&);
};
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
namespace cv
{
class EM : public Algorithm
{
public:
// Type of covariation matrices
enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
// Default parameters
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
// The initial step
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
virtual ~EM();
CV_WRAP virtual void clear();
CV_WRAP virtual bool train(InputArray samples,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainE(InputArray samples,
InputArray means0,
InputArray covs0=noArray(),
InputArray weights0=noArray(),
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainM(InputArray samples,
InputArray probs0,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP Vec2d predict(InputArray sample,
OutputArray probs=noArray()) const;
CV_WRAP bool isTrained() const;
AlgorithmInfo* info() const;
virtual void read(const FileNode& fn);
protected:
virtual void setTrainData(int startStep, const Mat& samples,
const Mat* probs0,
const Mat* means0,
const std::vector<Mat>* covs0,
const Mat* weights0);
bool doTrain(int startStep,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs);
virtual void eStep();
virtual void mStep();
void clusterTrainSamples();
void decomposeCovs();
void computeLogWeightDivDet();
Vec2d computeProbabilities(const Mat& sample, Mat* probs) const;
// all inner matrices have type CV_64FC1
CV_PROP_RW int nclusters;
CV_PROP_RW int covMatType;
CV_PROP_RW int maxIters;
CV_PROP_RW double epsilon;
Mat trainSamples;
Mat trainProbs;
Mat trainLogLikelihoods;
Mat trainLabels;
CV_PROP Mat weights;
CV_PROP Mat means;
CV_PROP std::vector<Mat> covs;
std::vector<Mat> covsEigenValues;
std::vector<Mat> covsRotateMats;
std::vector<Mat> invCovsEigenValues;
Mat logWeightDivDet;
};
} // namespace cv
/****************************************************************************************\
* Decision Tree *
\****************************************************************************************/\
......@@ -2155,8 +2060,6 @@ typedef CvGBTreesParams GradientBoostingTreeParams;
typedef CvGBTrees GradientBoostingTrees;
template<> void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const;
bool initModule_ml(void);
}
#endif // __cplusplus
......
doc/Doxyfile.in
View file @ da383e65
......@@ -100,7 +100,7 @@ RECURSIVE = YES
EXCLUDE =
EXCLUDE_SYMLINKS = NO
EXCLUDE_PATTERNS = *.inl.hpp *.impl.hpp *_detail.hpp */cudev/**/detail/*.hpp
EXCLUDE_SYMBOLS = cv::DataType<*> int
EXCLUDE_SYMBOLS = cv::DataType<*> int void
EXAMPLE_PATH = @CMAKE_DOXYGEN_EXAMPLE_PATH@
EXAMPLE_PATTERNS = *
EXAMPLE_RECURSIVE = YES
......@@ -243,7 +243,8 @@ PREDEFINED = __cplusplus=1 \
CV_NORETURN= \
CV_DEFAULT(x)=" = x" \
CV_NEON=1 \
FLANN_DEPRECATED=
FLANN_DEPRECATED= \
"CV_PURE_PROPERTY(type, name)= /**\@{*/ virtual type get##name() const = 0; virtual void set##name(type _##name) = 0; /**\@}*/"
EXPAND_AS_DEFINED =
SKIP_FUNCTION_MACROS = YES
TAGFILES =
......
modules/calib3d/src/levmarq.cpp
View file @ da383e65
......@@ -200,8 +200,6 @@ public:
void setCallback(const Ptr<LMSolver::Callback>& _cb) { cb = _cb; }
AlgorithmInfo* info() const;
Ptr<LMSolver::Callback> cb;
double epsx;
......@@ -211,15 +209,8 @@ public:
};
CV_INIT_ALGORITHM(LMSolverImpl, "LMSolver",
obj.info()->addParam(obj, "epsx", obj.epsx);
obj.info()->addParam(obj, "epsf", obj.epsf);
obj.info()->addParam(obj, "maxIters", obj.maxIters);
obj.info()->addParam(obj, "printInterval", obj.printInterval))
Ptr<LMSolver> createLMSolver(const Ptr<LMSolver::Callback>& cb, int maxIters)
{
CV_Assert( !LMSolverImpl_info_auto.name().empty() );
return makePtr<LMSolverImpl>(cb, maxIters);
}
......
modules/calib3d/src/ptsetreg.cpp
View file @ da383e65
......@@ -256,8 +256,6 @@ public:
void setCallback(const Ptr<PointSetRegistrator::Callback>& _cb) { cb = _cb; }
AlgorithmInfo* info() const;
Ptr<PointSetRegistrator::Callback> cb;
int modelPoints;
bool checkPartialSubsets;
......@@ -378,25 +376,12 @@ public:
return result;
}
AlgorithmInfo* info() const;
};
CV_INIT_ALGORITHM(RANSACPointSetRegistrator, "PointSetRegistrator.RANSAC",
obj.info()->addParam(obj, "threshold", obj.threshold);
obj.info()->addParam(obj, "confidence", obj.confidence);
obj.info()->addParam(obj, "maxIters", obj.maxIters))
CV_INIT_ALGORITHM(LMeDSPointSetRegistrator, "PointSetRegistrator.LMeDS",
obj.info()->addParam(obj, "confidence", obj.confidence);
obj.info()->addParam(obj, "maxIters", obj.maxIters))
Ptr<PointSetRegistrator> createRANSACPointSetRegistrator(const Ptr<PointSetRegistrator::Callback>& _cb,
int _modelPoints, double _threshold,
double _confidence, int _maxIters)
{
CV_Assert( !RANSACPointSetRegistrator_info_auto.name().empty() );
return Ptr<PointSetRegistrator>(
new RANSACPointSetRegistrator(_cb, _modelPoints, _threshold, _confidence, _maxIters));
}
......@@ -405,7 +390,6 @@ Ptr<PointSetRegistrator> createRANSACPointSetRegistrator(const Ptr<PointSetRegis
Ptr<PointSetRegistrator> createLMeDSPointSetRegistrator(const Ptr<PointSetRegistrator::Callback>& _cb,
int _modelPoints, double _confidence, int _maxIters)
{
CV_Assert( !LMeDSPointSetRegistrator_info_auto.name().empty() );
return Ptr<PointSetRegistrator>(
new LMeDSPointSetRegistrator(_cb, _modelPoints, _confidence, _maxIters));
}
......
modules/calib3d/src/stereobm.cpp
View file @ da383e65
......@@ -1010,8 +1010,6 @@ public:
disp.convertTo(disp0, disp0.type(), 1./(1 << DISPARITY_SHIFT), 0);
}
AlgorithmInfo* info() const { return 0; }
int getMinDisparity() const { return params.minDisparity; }
void setMinDisparity(int minDisparity) { params.minDisparity = minDisparity; }
......
modules/calib3d/src/stereosgbm.cpp
View file @ da383e65
......@@ -865,8 +865,6 @@ public:
StereoMatcher::DISP_SCALE*params.speckleRange, buffer);
}
AlgorithmInfo* info() const { return 0; }
int getMinDisparity() const { return params.minDisparity; }
void setMinDisparity(int minDisparity) { params.minDisparity = minDisparity; }
......
modules/core/include/opencv2/core.hpp
View file @ da383e65
This diff is collapsed.
modules/core/include/opencv2/core/operations.hpp
View file @ da383e65
......@@ -412,84 +412,6 @@ int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout)
return print(Formatter::get()->format(cv::Mat(matx)), stream);
}
////////////////////////////////////////// Algorithm //////////////////////////////////////////
template<typename _Tp> inline
Ptr<_Tp> Algorithm::create(const String& name)
{
return _create(name).dynamicCast<_Tp>();
}
template<typename _Tp> inline
void Algorithm::set(const char* _name, const Ptr<_Tp>& value)
{
Ptr<Algorithm> algo_ptr = value. template dynamicCast<cv::Algorithm>();
if (!algo_ptr) {
CV_Error( Error::StsUnsupportedFormat, "unknown/unsupported Ptr type of the second parameter of the method Algorithm::set");
}
info()->set(this, _name, ParamType<Algorithm>::type, &algo_ptr);
}
template<typename _Tp> inline
void Algorithm::set(const String& _name, const Ptr<_Tp>& value)
{
this->set<_Tp>(_name.c_str(), value);
}
template<typename _Tp> inline
void Algorithm::setAlgorithm(const char* _name, const Ptr<_Tp>& value)
{
Ptr<Algorithm> algo_ptr = value. template ptr<cv::Algorithm>();
if (!algo_ptr) {
CV_Error( Error::StsUnsupportedFormat, "unknown/unsupported Ptr type of the second parameter of the method Algorithm::set");
}
info()->set(this, _name, ParamType<Algorithm>::type, &algo_ptr);
}
template<typename _Tp> inline
void Algorithm::setAlgorithm(const String& _name, const Ptr<_Tp>& value)
{
this->set<_Tp>(_name.c_str(), value);
}
template<typename _Tp> inline
typename ParamType<_Tp>::member_type Algorithm::get(const String& _name) const
{
typename ParamType<_Tp>::member_type value;
info()->get(this, _name.c_str(), ParamType<_Tp>::type, &value);
return value;
}
template<typename _Tp> inline
typename ParamType<_Tp>::member_type Algorithm::get(const char* _name) const
{
typename ParamType<_Tp>::member_type value;
info()->get(this, _name, ParamType<_Tp>::type, &value);
return value;
}
template<typename _Tp, typename _Base> inline
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, Ptr<_Tp>& value, bool readOnly,
Ptr<_Tp> (Algorithm::*getter)(), void (Algorithm::*setter)(const Ptr<_Tp>&),
const String& help)
{
//TODO: static assert: _Tp inherits from _Base
addParam_(algo, parameter, ParamType<_Base>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
template<typename _Tp> inline
void AlgorithmInfo::addParam(Algorithm& algo, const char* parameter, Ptr<_Tp>& value, bool readOnly,
Ptr<_Tp> (Algorithm::*getter)(), void (Algorithm::*setter)(const Ptr<_Tp>&),
const String& help)
{
//TODO: static assert: _Tp inherits from Algorithm
addParam_(algo, parameter, ParamType<Algorithm>::type, &value, readOnly,
(Algorithm::Getter)getter, (Algorithm::Setter)setter, help);
}
//! @endcond
/****************************************************************************************\
......
modules/core/include/opencv2/core/private.hpp
View file @ da383e65
......@@ -129,40 +129,6 @@ namespace cv
CV_EXPORTS const char* currentParallelFramework();
} //namespace cv
#define CV_INIT_ALGORITHM(classname, algname, memberinit) \
static inline ::cv::Algorithm* create##classname##_hidden() \
{ \
return new classname; \
} \
\
static inline ::cv::Ptr< ::cv::Algorithm> create##classname##_ptr_hidden() \
{ \
return ::cv::makePtr<classname>(); \
} \
\
static inline ::cv::AlgorithmInfo& classname##_info() \
{ \
static ::cv::AlgorithmInfo classname##_info_var(algname, create##classname##_hidden); \
return classname##_info_var; \
} \
\
static ::cv::AlgorithmInfo& classname##_info_auto = classname##_info(); \
\
::cv::AlgorithmInfo* classname::info() const \
{ \
static volatile bool initialized = false; \
\
if( !initialized ) \
{ \
initialized = true; \
classname obj; \
memberinit; \
} \
return &classname##_info(); \
}
/****************************************************************************************\
* Common declarations *
\****************************************************************************************/
......
modules/core/src/algorithm.cpp
View file @ da383e65
This diff is collapsed.
modules/cudaimgproc/src/histogram.cpp
View file @ da383e65
......@@ -140,8 +140,6 @@ namespace
public:
CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
cv::AlgorithmInfo* info() const;
void apply(cv::InputArray src, cv::OutputArray dst);
void apply(InputArray src, OutputArray dst, Stream& stream);
......@@ -167,11 +165,6 @@ namespace
{
}
CV_INIT_ALGORITHM(CLAHE_Impl, "CLAHE_CUDA",
obj.info()->addParam(obj, "clipLimit", obj.clipLimit_);
obj.info()->addParam(obj, "tilesX", obj.tilesX_);
obj.info()->addParam(obj, "tilesY", obj.tilesY_))
void CLAHE_Impl::apply(cv::InputArray _src, cv::OutputArray _dst)
{
apply(_src, _dst, Stream::Null());
......
modules/cudaoptflow/perf/perf_optflow.cpp
View file @ da383e65
......@@ -310,10 +310,10 @@ PERF_TEST_P(ImagePair, OpticalFlowDual_TVL1,
{
cv::Mat flow;
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->set("medianFiltering", 1);
alg->set("innerIterations", 1);
alg->set("outerIterations", 300);
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->setMedianFiltering(1);
alg->setInnerIterations(1);
alg->setOuterIterations(300);
TEST_CYCLE() alg->calc(frame0, frame1, flow);
CPU_SANITY_CHECK(flow);
......
modules/cudaoptflow/test/test_optflow.cpp
View file @ da383e65
......@@ -369,11 +369,11 @@ CUDA_TEST_P(OpticalFlowDual_TVL1, Accuracy)
cv::cuda::GpuMat d_flow;
d_alg->calc(loadMat(frame0), loadMat(frame1), d_flow);
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->set("medianFiltering", 1);
alg->set("innerIterations", 1);
alg->set("outerIterations", d_alg->getNumIterations());
alg->set("gamma", gamma);
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
alg->setMedianFiltering(1);
alg->setInnerIterations(1);
alg->setOuterIterations(d_alg->getNumIterations());
alg->setGamma(gamma);
cv::Mat flow;
alg->calc(frame0, frame1, flow);
......
modules/imgproc/src/clahe.cpp
View file @ da383e65
......@@ -320,8 +320,6 @@ namespace
public:
CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
cv::AlgorithmInfo* info() const;
void apply(cv::InputArray src, cv::OutputArray dst);
void setClipLimit(double clipLimit);
......@@ -351,11 +349,6 @@ namespace
{
}
CV_INIT_ALGORITHM(CLAHE_Impl, "CLAHE",
obj.info()->addParam(obj, "clipLimit", obj.clipLimit_);
obj.info()->addParam(obj, "tilesX", obj.tilesX_);
obj.info()->addParam(obj, "tilesY", obj.tilesY_))
void CLAHE_Impl::apply(cv::InputArray _src, cv::OutputArray _dst)
{
CV_Assert( _src.type() == CV_8UC1 || _src.type() == CV_16UC1 );
......
modules/shape/include/opencv2/shape.hpp
View file @ da383e65
......@@ -52,11 +52,6 @@
@defgroup shape Shape Distance and Matching
*/
namespace cv
{
CV_EXPORTS bool initModule_shape();
}
#endif
/* End of file. */
modules/shape/src/aff_trans.cpp
View file @ da383e65
......@@ -66,8 +66,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, std::vector<DMatch> &matches);
virtual float applyTransformation(InputArray input, OutputArray output=noArray());
......
modules/shape/src/haus_dis.cpp
View file @ da383e65
......@@ -60,8 +60,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual float computeDistance(InputArray contour1, InputArray contour2);
......
modules/shape/src/hist_cost.cpp
View file @ da383e65
......@@ -62,8 +62,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix);
......@@ -189,8 +187,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix);
......@@ -327,8 +323,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix);
......@@ -445,8 +439,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix);
......
modules/shape/src/sc_dis.cpp
View file @ da383e65
......@@ -79,8 +79,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operator
virtual float computeDistance(InputArray contour1, InputArray contour2);
......
modules/shape/src/tps_trans.cpp
View file @ da383e65
......@@ -68,8 +68,6 @@ public:
{
}
virtual AlgorithmInfo* info() const { return 0; }
//! the main operators
virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape, std::vector<DMatch> &matches);
virtual float applyTransformation(InputArray inPts, OutputArray output=noArray());
......
modules/superres/include/opencv2/superres.hpp
View file @ da383e65
......@@ -44,6 +44,7 @@
#define __OPENCV_SUPERRES_HPP__
#include "opencv2/core.hpp"
#include "opencv2/superres/optical_flow.hpp"
/**
@defgroup superres Super Resolution
......@@ -62,8 +63,6 @@ namespace cv
//! @addtogroup superres
//! @{
CV_EXPORTS bool initModule_superres();
class CV_EXPORTS FrameSource
{
public:
......@@ -105,6 +104,36 @@ namespace cv
*/
virtual void collectGarbage();
//! @name Scale factor
CV_PURE_PROPERTY(int, Scale)
//! @name Iterations count
CV_PURE_PROPERTY(int, Iterations)
//! @name Asymptotic value of steepest descent method
CV_PURE_PROPERTY(double, Tau)
//! @name Weight parameter to balance data term and smoothness term
CV_PURE_PROPERTY(double, Labmda)
//! @name Parameter of spacial distribution in Bilateral-TV
CV_PURE_PROPERTY(double, Alpha)
//! @name Kernel size of Bilateral-TV filter
CV_PURE_PROPERTY(int, KernelSize)
//! @name Gaussian blur kernel size
CV_PURE_PROPERTY(int, BlurKernelSize)
//! @name Gaussian blur sigma
CV_PURE_PROPERTY(double, BlurSigma)
//! @name Radius of the temporal search area
CV_PURE_PROPERTY(int, TemporalAreaRadius)
//! @name Dense optical flow algorithm
CV_PURE_PROPERTY_S(Ptr<cv::superres::DenseOpticalFlowExt>, OpticalFlow)
protected:
SuperResolution();
......@@ -139,7 +168,6 @@ namespace cv
*/
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1();
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_CUDA();
CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_OCL();
//! @} superres
......
modules/superres/include/opencv2/superres/optical_flow.hpp
View file @ da383e65
......@@ -60,20 +60,68 @@ namespace cv
virtual void collectGarbage() = 0;
};
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback_CUDA();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback_OCL();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Simple();
class CV_EXPORTS FarnebackOpticalFlow : public virtual DenseOpticalFlowExt
{
public:
CV_PURE_PROPERTY(double, PyrScale)
CV_PURE_PROPERTY(int, LevelsNumber)
CV_PURE_PROPERTY(int, WindowSize)
CV_PURE_PROPERTY(int, Iterations)
CV_PURE_PROPERTY(int, PolyN)
CV_PURE_PROPERTY(double, PolySigma)
CV_PURE_PROPERTY(int, Flags)
};
CV_EXPORTS Ptr<FarnebackOpticalFlow> createOptFlow_Farneback();
CV_EXPORTS Ptr<FarnebackOpticalFlow> createOptFlow_Farneback_CUDA();
// CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Simple();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1_CUDA();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1_OCL();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Brox_CUDA();
class CV_EXPORTS DualTVL1OpticalFlow : public virtual DenseOpticalFlowExt
{
public:
CV_PURE_PROPERTY(double, Tau)
CV_PURE_PROPERTY(double, Lambda)
CV_PURE_PROPERTY(double, Theta)
CV_PURE_PROPERTY(int, ScalesNumber)
CV_PURE_PROPERTY(int, WarpingsNumber)
CV_PURE_PROPERTY(double, Epsilon)
CV_PURE_PROPERTY(int, Iterations)
CV_PURE_PROPERTY(bool, UseInitialFlow)
};
CV_EXPORTS Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
CV_EXPORTS Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1_CUDA();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_CUDA();
CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_OCL();
class CV_EXPORTS BroxOpticalFlow : public virtual DenseOpticalFlowExt
{
public:
//! @name Flow smoothness
CV_PURE_PROPERTY(double, Alpha)
//! @name Gradient constancy importance
CV_PURE_PROPERTY(double, Gamma)
//! @name Pyramid scale factor
CV_PURE_PROPERTY(double, ScaleFactor)
//! @name Number of lagged non-linearity iterations (inner loop)
CV_PURE_PROPERTY(int, InnerIterations)
//! @name Number of warping iterations (number of pyramid levels)
CV_PURE_PROPERTY(int, OuterIterations)
//! @name Number of linear system solver iterations
CV_PURE_PROPERTY(int, SolverIterations)
};
CV_EXPORTS Ptr<BroxOpticalFlow> createOptFlow_Brox_CUDA();
class PyrLKOpticalFlow : public virtual DenseOpticalFlowExt
{
public:
CV_PURE_PROPERTY(int, WindowSize)
CV_PURE_PROPERTY(int, MaxLevel)
CV_PURE_PROPERTY(int, Iterations)
};
CV_EXPORTS Ptr<PyrLKOpticalFlow> createOptFlow_PyrLK_CUDA();
//! @}
......
modules/superres/perf/perf_superres.cpp
View file @ da383e65
......@@ -138,10 +138,10 @@ PERF_TEST_P(Size_MatType, SuperResolution_BTVL1,
{
Ptr<SuperResolution> superRes = createSuperResolution_BTVL1_CUDA();
superRes->set("scale", scale);
superRes->set("iterations", iterations);
superRes->set("temporalAreaRadius", temporalAreaRadius);
superRes->set("opticalFlow", opticalFlow);
superRes->setScale(scale);
superRes->setIterations(iterations);
superRes->setTemporalAreaRadius(temporalAreaRadius);
superRes->setOpticalFlow(opticalFlow);
superRes->setInput(makePtr<OneFrameSource_CUDA>(GpuMat(frame)));
......@@ -156,10 +156,10 @@ PERF_TEST_P(Size_MatType, SuperResolution_BTVL1,
{
Ptr<SuperResolution> superRes = createSuperResolution_BTVL1();
superRes->set("scale", scale);
superRes->set("iterations", iterations);
superRes->set("temporalAreaRadius", temporalAreaRadius);
superRes->set("opticalFlow", opticalFlow);
superRes->setScale(scale);
superRes->setIterations(iterations);
superRes->setTemporalAreaRadius(temporalAreaRadius);
superRes->setOpticalFlow(opticalFlow);
superRes->setInput(makePtr<OneFrameSource_CPU>(frame));
......@@ -198,10 +198,10 @@ OCL_PERF_TEST_P(SuperResolution_BTVL1 ,BTVL1,
Ptr<DenseOpticalFlowExt> opticalFlow(new ZeroOpticalFlow);
Ptr<SuperResolution> superRes = createSuperResolution_BTVL1();
superRes->set("scale", scale);
superRes->set("iterations", iterations);
superRes->set("temporalAreaRadius", temporalAreaRadius);
superRes->set("opticalFlow", opticalFlow);
superRes->setScale(scale);
superRes->setIterations(iterations);
superRes->setTemporalAreaRadius(temporalAreaRadius);
superRes->setOpticalFlow(opticalFlow);
superRes->setInput(makePtr<OneFrameSource_CPU>(frame));
......
modules/superres/src/btv_l1.cpp
View file @ da383e65
......@@ -460,7 +460,7 @@ namespace
func(_src, _dst, btvKernelSize, btvWeights);
}
class BTVL1_Base
class BTVL1_Base : public cv::superres::SuperResolution
{
public:
BTVL1_Base();
......@@ -470,6 +470,17 @@ namespace
void collectGarbage();
CV_IMPL_PROPERTY(int, Scale, scale_)
CV_IMPL_PROPERTY(int, Iterations, iterations_)
CV_IMPL_PROPERTY(double, Tau, tau_)
CV_IMPL_PROPERTY(double, Labmda, lambda_)
CV_IMPL_PROPERTY(double, Alpha, alpha_)
CV_IMPL_PROPERTY(int, KernelSize, btvKernelSize_)
CV_IMPL_PROPERTY(int, BlurKernelSize, blurKernelSize_)
CV_IMPL_PROPERTY(double, BlurSigma, blurSigma_)
CV_IMPL_PROPERTY(int, TemporalAreaRadius, temporalAreaRadius_)
CV_IMPL_PROPERTY_S(Ptr<cv::superres::DenseOpticalFlowExt>, OpticalFlow, opticalFlow_)
protected:
int scale_;
int iterations_;
......@@ -479,7 +490,8 @@ namespace
int btvKernelSize_;
int blurKernelSize_;
double blurSigma_;
Ptr<DenseOpticalFlowExt> opticalFlow_;
int temporalAreaRadius_; // not used in some implementations
Ptr<cv::superres::DenseOpticalFlowExt> opticalFlow_;
private:
bool ocl_process(InputArrayOfArrays src, OutputArray dst, InputArrayOfArrays forwardMotions,
......@@ -539,6 +551,7 @@ namespace
btvKernelSize_ = 7;
blurKernelSize_ = 5;
blurSigma_ = 0.0;
temporalAreaRadius_ = 0;
opticalFlow_ = createOptFlow_Farneback();
curBlurKernelSize_ = -1;
......@@ -781,12 +794,9 @@ namespace
////////////////////////////////////////////////////////////////////
class BTVL1 :
public SuperResolution, private BTVL1_Base
class BTVL1 : public BTVL1_Base
{
public:
AlgorithmInfo* info() const;
BTVL1();
void collectGarbage();
......@@ -799,8 +809,6 @@ namespace
bool ocl_processImpl(Ptr<FrameSource>& frameSource, OutputArray output);
private:
int temporalAreaRadius_;
void readNextFrame(Ptr<FrameSource>& frameSource);
bool ocl_readNextFrame(Ptr<FrameSource>& frameSource);
......@@ -841,18 +849,6 @@ namespace
#endif
};
CV_INIT_ALGORITHM(BTVL1, "SuperResolution.BTVL1",
obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor.");
obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count.");
obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method.");
obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term.");
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV.");
obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter.");
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."))
BTVL1::BTVL1()
{
temporalAreaRadius_ = 4;
......@@ -1101,7 +1097,7 @@ namespace
}
}
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1()
Ptr<cv::superres::SuperResolution> cv::superres::createSuperResolution_BTVL1()
{
return makePtr<BTVL1>();
}
modules/superres/src/btv_l1_cuda.cpp
View file @ da383e65
......@@ -207,7 +207,7 @@ namespace
funcs[src.channels()](src, dst, ksize);
}
class BTVL1_CUDA_Base
class BTVL1_CUDA_Base : public cv::superres::SuperResolution
{
public:
BTVL1_CUDA_Base();
......@@ -218,6 +218,17 @@ namespace
void collectGarbage();
CV_IMPL_PROPERTY(int, Scale, scale_)
CV_IMPL_PROPERTY(int, Iterations, iterations_)
CV_IMPL_PROPERTY(double, Tau, tau_)
CV_IMPL_PROPERTY(double, Labmda, lambda_)
CV_IMPL_PROPERTY(double, Alpha, alpha_)
CV_IMPL_PROPERTY(int, KernelSize, btvKernelSize_)
CV_IMPL_PROPERTY(int, BlurKernelSize, blurKernelSize_)
CV_IMPL_PROPERTY(double, BlurSigma, blurSigma_)
CV_IMPL_PROPERTY(int, TemporalAreaRadius, temporalAreaRadius_)
CV_IMPL_PROPERTY_S(Ptr<cv::superres::DenseOpticalFlowExt>, OpticalFlow, opticalFlow_)
protected:
int scale_;
int iterations_;
......@@ -227,7 +238,8 @@ namespace
int btvKernelSize_;
int blurKernelSize_;
double blurSigma_;
Ptr<DenseOpticalFlowExt> opticalFlow_;
int temporalAreaRadius_;
Ptr<cv::superres::DenseOpticalFlowExt> opticalFlow_;
private:
std::vector<Ptr<cuda::Filter> > filters_;
......@@ -272,6 +284,7 @@ namespace
#else
opticalFlow_ = createOptFlow_Farneback();
#endif
temporalAreaRadius_ = 0;
curBlurKernelSize_ = -1;
curBlurSigma_ = -1.0;
......@@ -401,11 +414,9 @@ namespace
////////////////////////////////////////////////////////////
class BTVL1_CUDA : public SuperResolution, private BTVL1_CUDA_Base
class BTVL1_CUDA : public BTVL1_CUDA_Base
{
public:
AlgorithmInfo* info() const;
BTVL1_CUDA();
void collectGarbage();
......@@ -415,8 +426,6 @@ namespace
void processImpl(Ptr<FrameSource>& frameSource, OutputArray output);
private:
int temporalAreaRadius_;
void readNextFrame(Ptr<FrameSource>& frameSource);
void processFrame(int idx);
......@@ -438,18 +447,6 @@ namespace
GpuMat finalOutput_;
};
CV_INIT_ALGORITHM(BTVL1_CUDA, "SuperResolution.BTVL1_CUDA",
obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor.");
obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count.");
obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method.");
obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term.");
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV.");
obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter.");
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."));
BTVL1_CUDA::BTVL1_CUDA()
{
temporalAreaRadius_ = 4;
......
modules/superres/src/optical_flow.cpp
View file @ da383e65
This diff is collapsed.
modules/superres/src/super_resolution.cpp
View file @ da383e65
......@@ -45,11 +45,6 @@
using namespace cv;
using namespace cv::superres;
bool cv::superres::initModule_superres()
{
return !createSuperResolution_BTVL1().empty();
}
cv::superres::SuperResolution::SuperResolution()
{
frameSource_ = createFrameSource_Empty();
......
modules/superres/test/test_superres.cpp
View file @ da383e65
......@@ -222,11 +222,11 @@ void SuperResolution::RunTest(cv::Ptr<cv::superres::SuperResolution> superRes)
ASSERT_FALSE( superRes.empty() );
const int btvKernelSize = superRes->getInt("btvKernelSize");
const int btvKernelSize = superRes->getKernelSize();
superRes->set("scale", scale);
superRes->set("iterations", iterations);
superRes->set("temporalAreaRadius", temporalAreaRadius);
superRes->setScale(scale);
superRes->setIterations(iterations);
superRes->setTemporalAreaRadius(temporalAreaRadius);
cv::Ptr<cv::superres::FrameSource> goldSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale));
cv::Ptr<cv::superres::FrameSource> lowResSource(new DegradeFrameSource(
......
modules/video/include/opencv2/video/tracking.hpp
View file @ da383e65
......@@ -380,6 +380,21 @@ public:
};
class CV_EXPORTS_W DenseOpticalFlow : public Algorithm
{
public:
/** @brief Calculates an optical flow.
@param I0 first 8-bit single-channel input image.
@param I1 second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.
*/
CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0;
/** @brief Releases all inner buffers.
*/
CV_WRAP virtual void collectGarbage() = 0;
};
/** @brief "Dual TV L1" Optical Flow Algorithm.
The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
......@@ -422,24 +437,38 @@ constructing the class instance:
C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
*/
class CV_EXPORTS_W DenseOpticalFlow : public Algorithm
class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow
{
public:
/** @brief Calculates an optical flow.
@param I0 first 8-bit single-channel input image.
@param I1 second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.
*/
CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0;
/** @brief Releases all inner buffers.
*/
CV_WRAP virtual void collectGarbage() = 0;
//! @name Time step of the numerical scheme
CV_PURE_PROPERTY(double, Tau)
//! @name Weight parameter for the data term, attachment parameter
CV_PURE_PROPERTY(double, Lambda)
//! @name Weight parameter for (u - v)^2, tightness parameter
CV_PURE_PROPERTY(double, Theta)
//! @name coefficient for additional illumination variation term
CV_PURE_PROPERTY(double, Gamma)
//! @name Number of scales used to create the pyramid of images
CV_PURE_PROPERTY(int, ScalesNumber)
//! @name Number of warpings per scale
CV_PURE_PROPERTY(int, WarpingsNumber)
//! @name Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time
CV_PURE_PROPERTY(double, Epsilon)
//! @name Inner iterations (between outlier filtering) used in the numerical scheme
CV_PURE_PROPERTY(int, InnerIterations)
//! @name Outer iterations (number of inner loops) used in the numerical scheme
CV_PURE_PROPERTY(int, OuterIterations)
//! @name Use initial flow
CV_PURE_PROPERTY(bool, UseInitialFlow)
//! @name Step between scales (<1)
CV_PURE_PROPERTY(double, ScaleStep)
//! @name Median filter kernel size (1 = no filter) (3 or 5)
CV_PURE_PROPERTY(int, MedianFiltering)
};
/** @brief Creates instance of cv::DenseOpticalFlow
*/
CV_EXPORTS_W Ptr<DenseOpticalFlow> createOptFlow_DualTVL1();
CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
//! @} video_track
......
modules/video/perf/opencl/perf_optflow_dualTVL1.cpp
View file @ da383e65
......@@ -87,11 +87,11 @@ OCL_PERF_TEST_P(OpticalFlowDualTVL1Fixture, OpticalFlowDualTVL1,
declare.in(uFrame0, uFrame1, WARMUP_READ).out(uFlow, WARMUP_READ);
//create algorithm
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
//set parameters
alg->set("scaleStep", scaleStep);
alg->setInt("medianFiltering", medianFiltering);
alg->setScaleStep(scaleStep);
alg->setMedianFiltering(medianFiltering);
if (useInitFlow)
{
......@@ -100,7 +100,7 @@ OCL_PERF_TEST_P(OpticalFlowDualTVL1Fixture, OpticalFlowDualTVL1,
}
//set flag to use initial flow
alg->setBool("useInitialFlow", useInitFlow);
alg->setUseInitialFlow(useInitFlow);
OCL_TEST_CYCLE()
alg->calc(uFrame0, uFrame1, uFlow);
......@@ -109,4 +109,4 @@ OCL_PERF_TEST_P(OpticalFlowDualTVL1Fixture, OpticalFlowDualTVL1,
}
} // namespace cvtest::ocl
#endif // HAVE_OPENCL
\ No newline at end of file
#endif // HAVE_OPENCL
modules/video/src/bgfg_KNN.cpp
View file @ da383e65
......@@ -160,8 +160,6 @@ public:
nNextLongUpdate = Scalar::all(0);
}
virtual AlgorithmInfo* info() const { return 0; }
virtual int getHistory() const { return history; }
virtual void setHistory(int _nframes) { history = _nframes; }
......
modules/video/src/bgfg_gaussmix2.cpp
View file @ da383e65
......@@ -230,8 +230,6 @@ public:
}
}
virtual AlgorithmInfo* info() const { return 0; }
virtual int getHistory() const { return history; }
virtual void setHistory(int _nframes) { history = _nframes; }
......
modules/video/src/tvl1flow.cpp
View file @ da383e65
......@@ -86,7 +86,7 @@ using namespace cv;
namespace {
class OpticalFlowDual_TVL1 : public DenseOpticalFlow
class OpticalFlowDual_TVL1 : public DualTVL1OpticalFlow
{
public:
OpticalFlowDual_TVL1();
......@@ -94,7 +94,18 @@ public:
void calc(InputArray I0, InputArray I1, InputOutputArray flow);
void collectGarbage();
AlgorithmInfo* info() const;
CV_IMPL_PROPERTY(double, Tau, tau)
CV_IMPL_PROPERTY(double, Lambda, lambda)
CV_IMPL_PROPERTY(double, Theta, theta)
CV_IMPL_PROPERTY(double, Gamma, gamma)
CV_IMPL_PROPERTY(int, ScalesNumber, nscales)
CV_IMPL_PROPERTY(int, WarpingsNumber, warps)
CV_IMPL_PROPERTY(double, Epsilon, epsilon)
CV_IMPL_PROPERTY(int, InnerIterations, innerIterations)
CV_IMPL_PROPERTY(int, OuterIterations, outerIterations)
CV_IMPL_PROPERTY(bool, UseInitialFlow, useInitialFlow)
CV_IMPL_PROPERTY(double, ScaleStep, scaleStep)
CV_IMPL_PROPERTY(int, MedianFiltering, medianFiltering)
protected:
double tau;
......@@ -1416,35 +1427,9 @@ void OpticalFlowDual_TVL1::collectGarbage()
dum.norm_buf.release();
}
CV_INIT_ALGORITHM(OpticalFlowDual_TVL1, "DenseOpticalFlow.DualTVL1",
obj.info()->addParam(obj, "tau", obj.tau, false, 0, 0,
"Time step of the numerical scheme");
obj.info()->addParam(obj, "lambda", obj.lambda, false, 0, 0,
"Weight parameter for the data term, attachment parameter");
obj.info()->addParam(obj, "theta", obj.theta, false, 0, 0,
"Weight parameter for (u - v)^2, tightness parameter");
obj.info()->addParam(obj, "nscales", obj.nscales, false, 0, 0,
"Number of scales used to create the pyramid of images");
obj.info()->addParam(obj, "warps", obj.warps, false, 0, 0,
"Number of warpings per scale");
obj.info()->addParam(obj, "medianFiltering", obj.medianFiltering, false, 0, 0,
"Median filter kernel size (1 = no filter) (3 or 5)");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Step between scales (<1)");
obj.info()->addParam(obj, "epsilon", obj.epsilon, false, 0, 0,
"Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time");
obj.info()->addParam(obj, "innerIterations", obj.innerIterations, false, 0, 0,
"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, "gamma", obj.gamma, false, 0, 0,
"coefficient for additional illumination variation term");
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow))
} // namespace
Ptr<DenseOpticalFlow> cv::createOptFlow_DualTVL1()
Ptr<DualTVL1OpticalFlow> cv::createOptFlow_DualTVL1()
{
return makePtr<OpticalFlowDual_TVL1>();
}
modules/video/test/ocl/test_optflow_tvl1flow.cpp
View file @ da383e65
......@@ -82,11 +82,11 @@ OCL_TEST_P(OpticalFlowTVL1, Mat)
cv::Mat flow; cv::UMat uflow;
//create algorithm
cv::Ptr<cv::DenseOpticalFlow> alg = cv::createOptFlow_DualTVL1();
cv::Ptr<cv::DualTVL1OpticalFlow> alg = cv::createOptFlow_DualTVL1();
//set parameters
alg->set("scaleStep", scaleStep);
alg->setInt("medianFiltering", medianFiltering);
alg->setScaleStep(scaleStep);
alg->setMedianFiltering(medianFiltering);
//create initial flow as result of algorithm calculation
if (useInitFlow)
......@@ -96,7 +96,7 @@ OCL_TEST_P(OpticalFlowTVL1, Mat)
}
//set flag to use initial flow as it is ready to use
alg->setBool("useInitialFlow", useInitFlow);
alg->setUseInitialFlow(useInitFlow);
OCL_OFF(alg->calc(frame0, frame1, flow));
OCL_ON(alg->calc(frame0, frame1, uflow));
......@@ -114,4 +114,4 @@ OCL_INSTANTIATE_TEST_CASE_P(Video, OpticalFlowTVL1,
} } // namespace cvtest::ocl
#endif // HAVE_OPENCL
\ No newline at end of file
#endif // HAVE_OPENCL
samples/cpp/tutorial_code/features2D/AKAZE_tracking/planar_tracking.cpp
View file @ da383e65
......@@ -138,7 +138,7 @@ int main(int argc, char **argv)
Stats stats, akaze_stats, orb_stats;
Ptr<AKAZE> akaze = AKAZE::create();
akaze->set("threshold", akaze_thresh);
akaze->setThreshold(akaze_thresh);
Ptr<ORB> orb = ORB::create();
orb->setMaxFeatures(stats.keypoints);
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
......@@ -163,7 +163,7 @@ int main(int argc, char **argv)
akaze_draw_stats = stats;
}
orb_tracker.getDetector()->set("nFeatures", stats.keypoints);
orb->setMaxFeatures(stats.keypoints);
orb_res = orb_tracker.process(frame, stats);
orb_stats += stats;
if(update_stats) {
......
samples/gpu/super_resolution.cpp
View file @ da383e65
......@@ -26,32 +26,32 @@ using namespace cv::superres;
cout << tm.getTimeSec() << " sec" << endl; \
}
static Ptr<DenseOpticalFlowExt> createOptFlow(const string& name, bool useGpu)
static Ptr<cv::superres::DenseOpticalFlowExt> createOptFlow(const string& name, bool useGpu)
{
if (name == "farneback")
{
if (useGpu)
return createOptFlow_Farneback_CUDA();
return cv::superres::createOptFlow_Farneback_CUDA();
else
return createOptFlow_Farneback();
return cv::superres::createOptFlow_Farneback();
}
/*else if (name == "simple")
return createOptFlow_Simple();*/
else if (name == "tvl1")
{
if (useGpu)
return createOptFlow_DualTVL1_CUDA();
return cv::superres::createOptFlow_DualTVL1_CUDA();
else
return createOptFlow_DualTVL1();
return cv::superres::createOptFlow_DualTVL1();
}
else if (name == "brox")
return createOptFlow_Brox_CUDA();
return cv::superres::createOptFlow_Brox_CUDA();
else if (name == "pyrlk")
return createOptFlow_PyrLK_CUDA();
return cv::superres::createOptFlow_PyrLK_CUDA();
else
cerr << "Incorrect Optical Flow algorithm - " << name << endl;
return Ptr<DenseOpticalFlowExt>();
return Ptr<cv::superres::DenseOpticalFlowExt>();
}
int main(int argc, const char* argv[])
......@@ -92,15 +92,15 @@ int main(int argc, const char* argv[])
else
superRes = createSuperResolution_BTVL1();
Ptr<DenseOpticalFlowExt> of = createOptFlow(optFlow, useCuda);
Ptr<cv::superres::DenseOpticalFlowExt> of = createOptFlow(optFlow, useCuda);
if (of.empty())
return EXIT_FAILURE;
superRes->set("opticalFlow", of);
superRes->setOpticalFlow(of);
superRes->set("scale", scale);
superRes->set("iterations", iterations);
superRes->set("temporalAreaRadius", temporalAreaRadius);
superRes->setScale(scale);
superRes->setIterations(iterations);
superRes->setTemporalAreaRadius(temporalAreaRadius);
Ptr<FrameSource> frameSource;
if (useCuda)
......
samples/gpu/surf_keypoint_matcher.cpp
View file @ da383e65
......@@ -62,19 +62,17 @@ int main(int argc, char* argv[])
cout << "FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl;
// matching descriptors
BFMatcher_CUDA matcher(surf.defaultNorm());
GpuMat trainIdx, distance;
matcher.matchSingle(descriptors1GPU, descriptors2GPU, trainIdx, distance);
Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(surf.defaultNorm());
vector<DMatch> matches;
matcher->match(descriptors1GPU, descriptors2GPU, matches);
// downloading results
vector<KeyPoint> keypoints1, keypoints2;
vector<float> descriptors1, descriptors2;
vector<DMatch> matches;
surf.downloadKeypoints(keypoints1GPU, keypoints1);
surf.downloadKeypoints(keypoints2GPU, keypoints2);
surf.downloadDescriptors(descriptors1GPU, descriptors1);
surf.downloadDescriptors(descriptors2GPU, descriptors2);
BFMatcher_CUDA::matchDownload(trainIdx, distance, matches);
// drawing the results
Mat img_matches;
......
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