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Fixed java wrappers

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This commit is contained in:
mshabunin 2016-09-30 16:21:04 +03:00
parent 55e3deac46
commit 7a7a2749e0
2 changed files with 7 additions and 7 deletions
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4
modules/java/generator/gen_java.py
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@ -1347,7 +1347,7 @@ class JavaWrapperGenerator(object):
ret = "return (jlong) new %s(_retval_);" % self.fullTypeName(fi.ctype)
elif fi.ctype.startswith('Ptr_'):
c_prologue.append("typedef Ptr<%s> %s;" % (self.fullTypeName(fi.ctype[4:]), fi.ctype))
ret = "%(ctype)s* curval = new %(ctype)s(_retval_);return (jlong)curval->get();" % { 'ctype':fi.ctype }
ret = "return (jlong)(new %(ctype)s(_retval_));" % { 'ctype':fi.ctype }
elif self.isWrapped(ret_type): # pointer to wrapped class:
ret = "return (jlong) _retval_;"
elif type_dict[fi.ctype]["jni_type"] == "jdoubleArray":
@ -1538,7 +1538,7 @@ JNIEXPORT void JNICALL Java_org_opencv_%(module)s_%(j_cls)s_delete
'''
Check if class stores Ptr<T>* instead of T* in nativeObj field
'''
return False
return self.isWrapped(classname)
def smartWrap(self, name, fullname):
'''

10
modules/ml/include/opencv2/ml.hpp
View File

@ -289,7 +289,7 @@ public:
<number_of_variables_in_responses>`, containing types of each input and output variable. See
ml::VariableTypes.
*/
CV_WRAP static Ptr<cv::ml::TrainData> create(InputArray samples, int layout, InputArray responses,
CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
InputArray sampleWeights=noArray(), InputArray varType=noArray());
};
@ -324,7 +324,7 @@ public:
@param flags optional flags, depending on the model. Some of the models can be updated with the
new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
*/
CV_WRAP virtual bool train( const Ptr<cv::ml::TrainData>& trainData, int flags=0 );
CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
/** @brief Trains the statistical model
@ -347,7 +347,7 @@ public:
The method uses StatModel::predict to compute the error. For regression models the error is
computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
*/
CV_WRAP virtual float calcError( const Ptr<cv::ml::TrainData>& data, bool test, OutputArray resp ) const;
CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
/** @brief Predicts response(s) for the provided sample(s)
@ -361,7 +361,7 @@ public:
The class must implement static `create()` method with no parameters or with all default parameter values
*/
template<typename _Tp> static Ptr<_Tp> train(const Ptr<cv::ml::TrainData>& data, int flags=0)
template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
{
Ptr<_Tp> model = _Tp::create();
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
@ -671,7 +671,7 @@ public:
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
*/
virtual bool trainAuto( const Ptr<cv::ml::TrainData>& data, int kFold = 10,
virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
ParamGrid pGrid = SVM::getDefaultGrid(SVM::P),