generic-library/opencv
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1. Input/OutputArray optimizations;

Browse Source 
2. Algorithm::load/save added (moved from StatModel)
3. copyrights updated; added copyright/licensing info for ffmpeg
4. some warnings from Xcode 6.x are fixed
This commit is contained in:
Vadim Pisarevsky
2015-04-07 16:44:26 +03:00
parent 44f112a9de
commit 052593c760
34 changed files with 888 additions and 207 deletions
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55
modules/ml/include/opencv2/ml.hpp
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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();

2
modules/ml/src/ann_mlp.cpp
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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";
}

2
modules/ml/src/boost.cpp
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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 )
{

2
modules/ml/src/em.cpp
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@@ -227,7 +227,7 @@ public:
return means.cols;
}
String getDefaultModelName() const
String getDefaultName() const
{
return "opencv_ml_em";
}

11
modules/ml/src/inner_functions.cpp
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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 )
{

2
modules/ml/src/knearest.cpp
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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)

2
modules/ml/src/lr.cpp
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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);

2
modules/ml/src/nbayes.cpp
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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;

2
modules/ml/src/precomp.hpp
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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); }

2
modules/ml/src/rtrees.cpp
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@@ -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 )
{

2
modules/ml/src/svm.cpp
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@@ -2008,7 +2008,7 @@ public:
return var_count;
}
String getDefaultModelName() const
String getDefaultName() const
{
return "opencv_ml_svm";
}

2
modules/ml/test/test_emknearestkmeans.cpp
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@@ -576,7 +576,7 @@ protected:
// Read in
try
{
em = StatModel::load<EM>(filename);
em = Algorithm::load<EM>(filename);
}
catch(...)
{

2
modules/ml/test/test_lr.cpp
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@@ -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();
}

14
modules/ml/test/test_mltests2.cpp
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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");
}

18
modules/ml/test/test_save_load.cpp
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@@ -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());