Merge pull request #5922 from DarwinsBuddy:fix_no_py_load_svm_bug
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@@ -719,6 +719,15 @@ public:
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Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
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find the best parameters for your problem, it can be done with SVM::trainAuto. */
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CV_WRAP static Ptr<SVM> create();
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/** @brief Loads and creates a serialized svm from a file
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*
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* Use SVM::save to serialize and store an SVM to disk.
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* Load the SVM from this file again, by calling this function with the path to the file.
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*
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* @param filepath path to serialized svm
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*/
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CV_WRAP static Ptr<SVM> load(const String& filepath);
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};
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/****************************************************************************************\
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@@ -1389,6 +1398,16 @@ public:
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Note that the train method has optional flags: ANN_MLP::TrainFlags.
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*/
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CV_WRAP static Ptr<ANN_MLP> create();
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/** @brief Loads and creates a serialized ANN from a file
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*
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* Use ANN::save to serialize and store an ANN to disk.
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* Load the ANN from this file again, by calling this function with the path to the file.
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*
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* @param filepath path to serialized ANN
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*/
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CV_WRAP static Ptr<ANN_MLP> load(const String& filepath);
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};
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/****************************************************************************************\
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@@ -1317,6 +1317,18 @@ Ptr<ANN_MLP> ANN_MLP::create()
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return makePtr<ANN_MLPImpl>();
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}
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}}
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Ptr<ANN_MLP> ANN_MLP::load(const String& filepath)
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{
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FileStorage fs;
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fs.open(filepath, FileStorage::READ);
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Ptr<ANN_MLP> ann = makePtr<ANN_MLPImpl>();
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((ANN_MLPImpl*)ann.get())->read(fs.getFirstTopLevelNode());
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return ann;
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}
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}}
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/* End of file. */
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@@ -2261,6 +2261,17 @@ Ptr<SVM> SVM::create()
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return makePtr<SVMImpl>();
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}
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Ptr<SVM> SVM::load(const String& filepath)
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{
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FileStorage fs;
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fs.open(filepath, FileStorage::READ);
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Ptr<SVM> svm = makePtr<SVMImpl>();
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((SVMImpl*)svm.get())->read(fs.getFirstTopLevelNode());
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return svm;
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}
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Mat SVM::getUncompressedSupportVectors() const
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{
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const SVMImpl* this_ = dynamic_cast<const SVMImpl*>(this);
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