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

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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();