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Merge pull request #3911 from vpisarev:core_tweaks

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This commit is contained in:
Vadim Pisarevsky
2015-04-07 19:03:48 +00:00
parent edb4a7ad03 259e41da73
commit 5b3f89df0a
34 changed files with 885 additions and 197 deletions
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modules/core/include/opencv2/core.hpp
+76 -1
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@@ -10,8 +10,10 @@
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2015, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@@ -2921,6 +2923,10 @@ public:
Algorithm();
virtual ~Algorithm();
/** @brief Clears the algorithm state
*/
CV_WRAP virtual void clear() {}
/** @brief Stores algorithm parameters in a file storage
*/
virtual void write(FileStorage& fs) const { (void)fs; }
@@ -2928,6 +2934,75 @@ public:
/** @brief Reads algorithm parameters from a file storage
*/
virtual void read(const FileNode& fn) { (void)fn; }
/** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
*/
virtual bool empty() const { return false; }
/** @brief Reads algorithm from the file node
This is static template method of Algorithm. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = Algorithm::read<SVM>(fn);
@endcode
In order to make this method work, the derived class must overwrite Algorithm::read(const
FileNode& fn) and also have static create() method without parameters
(or with all the optional parameters)
*/
template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
{
Ptr<_Tp> obj = _Tp::create();
obj->read(fn);
return !obj->empty() ? obj : Ptr<_Tp>();
}
/** @brief Loads algorithm from the file
@param filename Name of the file to read.
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
@endcode
In order to make this method work, the derived class must overwrite Algorithm::read(const
FileNode& fn).
*/
template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
{
FileStorage fs(filename, FileStorage::READ);
FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
Ptr<_Tp> obj = _Tp::create();
obj->read(fn);
return !obj->empty() ? obj : Ptr<_Tp>();
}
/** @brief Loads algorithm from a String
@param strModel The string variable containing the model you want to load.
@param objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
@endcode
*/
template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
{
FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
Ptr<_Tp> obj = _Tp::create();
obj->read(fn);
return !obj->empty() ? obj : Ptr<_Tp>();
}
/** Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
CV_WRAP virtual void save(const String& filename) const;
/** Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string. */
CV_WRAP virtual String getDefaultName() const;
};
struct Param {
modules/core/include/opencv2/core/ippasync.hpp
+44
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@@ -1,3 +1,47 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CORE_IPPASYNC_HPP__
#define __OPENCV_CORE_IPPASYNC_HPP__
modules/core/include/opencv2/core/mat.hpp
+45 -41
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@@ -185,39 +185,43 @@ public:
_InputArray(const UMat& um);
_InputArray(const std::vector<UMat>& umv);
virtual Mat getMat(int idx=-1) const;
virtual UMat getUMat(int idx=-1) const;
virtual void getMatVector(std::vector<Mat>& mv) const;
virtual void getUMatVector(std::vector<UMat>& umv) const;
virtual cuda::GpuMat getGpuMat() const;
virtual ogl::Buffer getOGlBuffer() const;
void* getObj() const;
Mat getMat(int idx=-1) const;
Mat getMat_(int idx=-1) const;
UMat getUMat(int idx=-1) const;
void getMatVector(std::vector<Mat>& mv) const;
void getUMatVector(std::vector<UMat>& umv) const;
cuda::GpuMat getGpuMat() const;
ogl::Buffer getOGlBuffer() const;
virtual int kind() const;
virtual int dims(int i=-1) const;
virtual int cols(int i=-1) const;
virtual int rows(int i=-1) const;
virtual Size size(int i=-1) const;
virtual int sizend(int* sz, int i=-1) const;
virtual bool sameSize(const _InputArray& arr) const;
virtual size_t total(int i=-1) const;
virtual int type(int i=-1) const;
virtual int depth(int i=-1) const;
virtual int channels(int i=-1) const;
virtual bool isContinuous(int i=-1) const;
virtual bool isSubmatrix(int i=-1) const;
virtual bool empty() const;
virtual void copyTo(const _OutputArray& arr) const;
virtual void copyTo(const _OutputArray& arr, const _InputArray & mask) const;
virtual size_t offset(int i=-1) const;
virtual size_t step(int i=-1) const;
int getFlags() const;
void* getObj() const;
Size getSz() const;
int kind() const;
int dims(int i=-1) const;
int cols(int i=-1) const;
int rows(int i=-1) const;
Size size(int i=-1) const;
int sizend(int* sz, int i=-1) const;
bool sameSize(const _InputArray& arr) const;
size_t total(int i=-1) const;
int type(int i=-1) const;
int depth(int i=-1) const;
int channels(int i=-1) const;
bool isContinuous(int i=-1) const;
bool isSubmatrix(int i=-1) const;
bool empty() const;
void copyTo(const _OutputArray& arr) const;
void copyTo(const _OutputArray& arr, const _InputArray & mask) const;
size_t offset(int i=-1) const;
size_t step(int i=-1) const;
bool isMat() const;
bool isUMat() const;
bool isMatVector() const;
bool isUMatVector() const;
bool isMatx() const;
virtual ~_InputArray();
~_InputArray();
protected:
int flags;
@@ -303,21 +307,21 @@ public:
_OutputArray(const UMat& m);
_OutputArray(const std::vector<UMat>& vec);
virtual bool fixedSize() const;
virtual bool fixedType() const;
virtual bool needed() const;
virtual Mat& getMatRef(int i=-1) const;
virtual UMat& getUMatRef(int i=-1) const;
virtual cuda::GpuMat& getGpuMatRef() const;
virtual ogl::Buffer& getOGlBufferRef() const;
virtual cuda::HostMem& getHostMemRef() const;
virtual void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
virtual void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
virtual void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
virtual void createSameSize(const _InputArray& arr, int mtype) const;
virtual void release() const;
virtual void clear() const;
virtual void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const;
bool fixedSize() const;
bool fixedType() const;
bool needed() const;
Mat& getMatRef(int i=-1) const;
UMat& getUMatRef(int i=-1) const;
cuda::GpuMat& getGpuMatRef() const;
ogl::Buffer& getOGlBufferRef() const;
cuda::HostMem& getHostMemRef() const;
void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
void createSameSize(const _InputArray& arr, int mtype) const;
void release() const;
void clear() const;
void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const;
void assign(const UMat& u) const;
void assign(const Mat& m) const;
modules/core/include/opencv2/core/mat.inl.hpp
+12 -1
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@@ -7,11 +7,13 @@
// copy or use the software.
//
//
// License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@@ -61,6 +63,8 @@ inline void _InputArray::init(int _flags, const void* _obj, Size _sz)
{ flags = _flags; obj = (void*)_obj; sz = _sz; }
inline void* _InputArray::getObj() const { return obj; }
inline int _InputArray::getFlags() const { return flags; }
inline Size _InputArray::getSz() const { return sz; }
inline _InputArray::_InputArray() { init(NONE, 0); }
inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); }
@@ -110,6 +114,13 @@ inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem)
inline _InputArray::~_InputArray() {}
inline Mat _InputArray::getMat(int i) const
{
if( kind() == MAT && i < 0 )
return *(const Mat*)obj;
return getMat_(i);
}
inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; }
inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; }
inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; }
modules/core/include/opencv2/core/operations.hpp
+2
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@@ -12,6 +12,8 @@
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
modules/core/include/opencv2/core/version.hpp
+3 -1
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@@ -10,8 +10,10 @@
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright( C) 2000, Intel Corporation, all rights reserved.
// Copyright( C) 2000-2015, Intel Corporation, all rights reserved.
// Copyright (C) 2011-2013, NVIDIA Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
modules/core/src/algorithm.cpp
+14
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@@ -53,6 +53,20 @@ Algorithm::~Algorithm()
{
}
void Algorithm::save(const String& filename) const
{
FileStorage fs(filename, FileStorage::WRITE);
fs << getDefaultName() << "{";
fs << "format" << (int)3;
write(fs);
fs << "}";
}
String Algorithm::getDefaultName() const
{
return String("my_object");
}
}
/* End of file. */
modules/core/src/matrix.cpp
+1 -1
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@@ -1113,7 +1113,7 @@ void scalarToRawData(const Scalar& s, void* _buf, int type, int unroll_to)
Input/Output Array
\*************************************************************************************************/
Mat _InputArray::getMat(int i) const
Mat _InputArray::getMat_(int i) const
{
int k = kind();
int accessFlags = flags & ACCESS_MASK;
modules/ml/include/opencv2/ml.hpp
+7 -48
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@@ -297,11 +297,12 @@ public:
COMPRESSED_INPUT=2,
PREPROCESSED_INPUT=4
};
CV_WRAP virtual void clear();
/** @brief Returns the number of variables in training samples */
CV_WRAP virtual int getVarCount() const = 0;
CV_WRAP virtual bool empty() const;
/** @brief Returns true if the model is trained */
CV_WRAP virtual bool isTrained() const = 0;
/** @brief Returns true if the model is classifier */
@@ -347,40 +348,6 @@ public:
*/
CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
/** @brief Loads model from the file
This is static template method of StatModel. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = StatModel::load<SVM>("my_svm_model.xml");
@endcode
In order to make this method work, the derived class must overwrite Algorithm::read(const
FileNode& fn).
*/
template<typename _Tp> static Ptr<_Tp> load(const String& filename)
{
FileStorage fs(filename, FileStorage::READ);
Ptr<_Tp> model = _Tp::create();
model->read(fs.getFirstTopLevelNode());
return model->isTrained() ? model : Ptr<_Tp>();
}
/** @brief Loads model from a String
@param strModel The string variable containing the model you want to load.
This is static template method of StatModel. It's usage is following (in the case of SVM):
@code
Ptr<SVM> svm = StatModel::loadFromString<SVM>(myStringModel);
@endcode
*/
template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel)
{
FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
Ptr<_Tp> model = _Tp::create();
model->read(fs.getFirstTopLevelNode());
return model->isTrained() ? model : Ptr<_Tp>();
}
/** @brief Create and train model with default parameters
The class must implement static `create()` method with no parameters or with all default parameter values
@@ -390,14 +357,6 @@ public:
Ptr<_Tp> model = _Tp::create();
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
}
/** Saves the model to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
CV_WRAP virtual void save(const String& filename) const;
/** Returns model string identifier.
This string is used as top level xml/yml node tag when model is saved to a file or string. */
CV_WRAP virtual String getDefaultModelName() const = 0;
};
/****************************************************************************************\
@@ -939,7 +898,7 @@ public:
/** Creates empty %EM model.
The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
can use one of the EM::train\* methods or load it from file using StatModel::load\<EM\>(filename).
can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
*/
CV_WRAP static Ptr<EM> create();
};
@@ -1127,7 +1086,7 @@ public:
The static method creates empty decision tree with the specified parameters. It should be then
trained using train method (see StatModel::train). Alternatively, you can load the model from
file using StatModel::load\<DTrees\>(filename).
file using Algorithm::load\<DTrees\>(filename).
*/
CV_WRAP static Ptr<DTrees> create();
};
@@ -1181,7 +1140,7 @@ public:
/** Creates the empty model.
Use StatModel::train to train the model, StatModel::train to create and train the model,
StatModel::load to load the pre-trained model.
Algorithm::load to load the pre-trained model.
*/
CV_WRAP static Ptr<RTrees> create();
};
@@ -1231,7 +1190,7 @@ public:
};
/** Creates the empty model.
Use StatModel::train to train the model, StatModel::load\<Boost\>(filename) to load the pre-trained model. */
Use StatModel::train to train the model, Algorithm::load\<Boost\>(filename) to load the pre-trained model. */
CV_WRAP static Ptr<Boost> create();
};
@@ -1416,7 +1375,7 @@ public:
/** @brief Creates empty model
Use StatModel::train to train the model, StatModel::load\<ANN_MLP\>(filename) to load the pre-trained model.
Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
Note that the train method has optional flags: ANN_MLP::TrainFlags.
*/
static Ptr<ANN_MLP> create();
modules/ml/src/ann_mlp.cpp
+1 -1
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@@ -1294,7 +1294,7 @@ public:
return layer_sizes.empty() ? 0 : layer_sizes[0];
}
String getDefaultModelName() const
String getDefaultName() const
{
return "opencv_ml_ann_mlp";
}
modules/ml/src/boost.cpp
+1 -1
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@@ -465,7 +465,7 @@ public:
CV_WRAP_SAME_PROPERTY(float, RegressionAccuracy, impl.params)
CV_WRAP_SAME_PROPERTY_S(cv::Mat, Priors, impl.params)
String getDefaultModelName() const { return "opencv_ml_boost"; }
String getDefaultName() const { return "opencv_ml_boost"; }
bool train( const Ptr<TrainData>& trainData, int flags )
{
modules/ml/src/em.cpp
+1 -1
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@@ -227,7 +227,7 @@ public:
return means.cols;
}
String getDefaultModelName() const
String getDefaultName() const
{
return "opencv_ml_em";
}
modules/ml/src/inner_functions.cpp
+1 -10
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@@ -50,7 +50,7 @@ ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
logStep = std::max(_logStep, 1.);
}
void StatModel::clear() {}
bool StatModel::empty() const { return !isTrained(); }
int StatModel::getVarCount() const { return 0; }
@@ -111,15 +111,6 @@ float StatModel::calcError( const Ptr<TrainData>& data, bool testerr, OutputArra
return (float)(err / n * (isclassifier ? 100 : 1));
}
void StatModel::save(const String& filename) const
{
FileStorage fs(filename, FileStorage::WRITE);
fs << getDefaultModelName() << "{";
fs << "format" << (int)3;
write(fs);
fs << "}";
}
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
static void Cholesky( const Mat& A, Mat& S )
{
modules/ml/src/knearest.cpp
+1 -1
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@@ -496,7 +496,7 @@ public:
return impl->train(data, flags);
}
String getDefaultModelName() const { return impl->getModelName(); }
String getDefaultName() const { return impl->getModelName(); }
protected:
void initImpl(int algorithmType)
modules/ml/src/lr.cpp
+1 -1
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@@ -104,7 +104,7 @@ public:
virtual int getVarCount() const { return learnt_thetas.cols; }
virtual bool isTrained() const { return !learnt_thetas.empty(); }
virtual bool isClassifier() const { return true; }
virtual String getDefaultModelName() const { return "opencv_ml_lr"; }
virtual String getDefaultName() const { return "opencv_ml_lr"; }
protected:
Mat calc_sigmoid(const Mat& data) const;
double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
modules/ml/src/nbayes.cpp
+1 -1
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@@ -443,7 +443,7 @@ public:
bool isTrained() const { return !avg.empty(); }
bool isClassifier() const { return true; }
int getVarCount() const { return nallvars; }
String getDefaultModelName() const { return "opencv_ml_nbayes"; }
String getDefaultName() const { return "opencv_ml_nbayes"; }
int nallvars;
Mat var_idx, cls_labels, c;
modules/ml/src/precomp.hpp
+1 -1
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@@ -290,7 +290,7 @@ namespace ml
virtual ~DTreesImpl();
virtual void clear();
String getDefaultModelName() const { return "opencv_ml_dtree"; }
String getDefaultName() const { return "opencv_ml_dtree"; }
bool isTrained() const { return !roots.empty(); }
bool isClassifier() const { return _isClassifier; }
int getVarCount() const { return varType.empty() ? 0 : (int)(varType.size() - 1); }
modules/ml/src/rtrees.cpp
+1 -1
View File
@@ -375,7 +375,7 @@ public:
RTreesImpl() {}
virtual ~RTreesImpl() {}
String getDefaultModelName() const { return "opencv_ml_rtrees"; }
String getDefaultName() const { return "opencv_ml_rtrees"; }
bool train( const Ptr<TrainData>& trainData, int flags )
{
modules/ml/src/svm.cpp
+1 -1
View File
@@ -2008,7 +2008,7 @@ public:
return var_count;
}
String getDefaultModelName() const
String getDefaultName() const
{
return "opencv_ml_svm";
}
modules/ml/test/test_emknearestkmeans.cpp
+1 -1
View File
@@ -576,7 +576,7 @@ protected:
// Read in
try
{
em = StatModel::load<EM>(filename);
em = Algorithm::load<EM>(filename);
}
catch(...)
{
modules/ml/test/test_lr.cpp
+1 -1
View File
@@ -179,7 +179,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
// and load to another
try
{
Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(filename);
Ptr<LogisticRegression> lr2 = Algorithm::load<LogisticRegression>(filename);
lr2->predict(tdata->getSamples(), responses2);
learnt_mat2 = lr2->get_learnt_thetas();
}
modules/ml/test/test_mltests2.cpp
+7 -7
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@@ -472,19 +472,19 @@ void CV_MLBaseTest::save( const char* filename )
void CV_MLBaseTest::load( const char* filename )
{
if( modelName == CV_NBAYES )
model = StatModel::load<NormalBayesClassifier>( filename );
model = Algorithm::load<NormalBayesClassifier>( filename );
else if( modelName == CV_KNEAREST )
model = StatModel::load<KNearest>( filename );
model = Algorithm::load<KNearest>( filename );
else if( modelName == CV_SVM )
model = StatModel::load<SVM>( filename );
model = Algorithm::load<SVM>( filename );
else if( modelName == CV_ANN )
model = StatModel::load<ANN_MLP>( filename );
model = Algorithm::load<ANN_MLP>( filename );
else if( modelName == CV_DTREE )
model = StatModel::load<DTrees>( filename );
model = Algorithm::load<DTrees>( filename );
else if( modelName == CV_BOOST )
model = StatModel::load<Boost>( filename );
model = Algorithm::load<Boost>( filename );
else if( modelName == CV_RTREES )
model = StatModel::load<RTrees>( filename );
model = Algorithm::load<RTrees>( filename );
else
CV_Error( CV_StsNotImplemented, "invalid stat model name");
}
modules/ml/test/test_save_load.cpp
+9 -9
View File
@@ -190,17 +190,17 @@ protected:
bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES;
Ptr<StatModel> model;
if (modelName == CV_BOOST)
model = StatModel::load<Boost>(filename);
model = Algorithm::load<Boost>(filename);
else if (modelName == CV_ANN)
model = StatModel::load<ANN_MLP>(filename);
model = Algorithm::load<ANN_MLP>(filename);
else if (modelName == CV_DTREE)
model = StatModel::load<DTrees>(filename);
model = Algorithm::load<DTrees>(filename);
else if (modelName == CV_NBAYES)
model = StatModel::load<NormalBayesClassifier>(filename);
model = Algorithm::load<NormalBayesClassifier>(filename);
else if (modelName == CV_SVM)
model = StatModel::load<SVM>(filename);
model = Algorithm::load<SVM>(filename);
else if (modelName == CV_RTREES)
model = StatModel::load<RTrees>(filename);
model = Algorithm::load<RTrees>(filename);
if (!model)
{
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
@@ -273,11 +273,11 @@ TEST(DISABLED_ML_SVM, linear_save_load)
{
Ptr<cv::ml::SVM> svm1, svm2, svm3;
svm1 = StatModel::load<SVM>("SVM45_X_38-1.xml");
svm2 = StatModel::load<SVM>("SVM45_X_38-2.xml");
svm1 = Algorithm::load<SVM>("SVM45_X_38-1.xml");
svm2 = Algorithm::load<SVM>("SVM45_X_38-2.xml");
string tname = tempfile("a.xml");
svm2->save(tname);
svm3 = StatModel::load<SVM>(tname);
svm3 = Algorithm::load<SVM>(tname);
ASSERT_EQ(svm1->getVarCount(), svm2->getVarCount());
ASSERT_EQ(svm1->getVarCount(), svm3->getVarCount());
modules/stitching/include/opencv2/stitching/detail/seam_finders.hpp
+2
View File
@@ -106,6 +106,8 @@ protected:
class CV_EXPORTS VoronoiSeamFinder : public PairwiseSeamFinder
{
public:
virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks);
virtual void find(const std::vector<Size> &size, const std::vector<Point> &corners,
std::vector<UMat> &masks);
private:
modules/stitching/include/opencv2/stitching/detail/warpers.hpp
+4
View File
@@ -186,14 +186,18 @@ public:
*/
PlaneWarper(float scale = 1.f) { projector_.scale = scale; }
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R);
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R, InputArray T);
virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap);
Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap);
Point warp(InputArray src, InputArray K, InputArray R,
int interp_mode, int border_mode, OutputArray dst);
virtual Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
OutputArray dst);
Rect warpRoi(Size src_size, InputArray K, InputArray R);
Rect warpRoi(Size src_size, InputArray K, InputArray R, InputArray T);
protected:
modules/stitching/src/seam_finders.cpp
+5
View File
@@ -82,6 +82,11 @@ void PairwiseSeamFinder::run()
}
}
void VoronoiSeamFinder::find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks)
{
PairwiseSeamFinder::find(src, corners, masks);
}
void VoronoiSeamFinder::find(const std::vector<Size> &sizes, const std::vector<Point> &corners,
std::vector<UMat> &masks)
modules/stitching/src/warpers.cpp
+21
View File
@@ -87,6 +87,13 @@ Point2f PlaneWarper::warpPoint(const Point2f &pt, InputArray K, InputArray R, In
return uv;
}
Point2f PlaneWarper::warpPoint(const Point2f &pt, InputArray K, InputArray R)
{
float tz[] = {0.f, 0.f, 0.f};
Mat_<float> T(3, 1, tz);
return warpPoint(pt, K, R, T);
}
Rect PlaneWarper::buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap)
{
return buildMaps(src_size, K, R, Mat::zeros(3, 1, CV_32FC1), xmap, ymap);
@@ -155,6 +162,13 @@ Point PlaneWarper::warp(InputArray src, InputArray K, InputArray R, InputArray T
return dst_roi.tl();
}
Point PlaneWarper::warp(InputArray src, InputArray K, InputArray R,
int interp_mode, int border_mode, OutputArray dst)
{
float tz[] = {0.f, 0.f, 0.f};
Mat_<float> T(3, 1, tz);
return warp(src, K, R, T, interp_mode, border_mode, dst);
}
Rect PlaneWarper::warpRoi(Size src_size, InputArray K, InputArray R, InputArray T)
{
@@ -166,6 +180,13 @@ Rect PlaneWarper::warpRoi(Size src_size, InputArray K, InputArray R, InputArray
return Rect(dst_tl, Point(dst_br.x + 1, dst_br.y + 1));
}
Rect PlaneWarper::warpRoi(Size src_size, InputArray K, InputArray R)
{
float tz[] = {0.f, 0.f, 0.f};
Mat_<float> T(3, 1, tz);
return warpRoi(src_size, K, R, T);
}
void PlaneWarper::detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br)
{
modules/superres/src/optical_flow.cpp
+1 -1
View File
@@ -224,7 +224,7 @@ namespace
void Farneback::impl(InputArray input0, InputArray input1, OutputArray dst)
{
calcOpticalFlowFarneback(input0, input1, (InputOutputArray)dst, pyrScale_,
calcOpticalFlowFarneback(input0, input1, InputOutputArray(dst), pyrScale_,
numLevels_, winSize_, numIters_,
polyN_, polySigma_, flags_);
}