generic-library/opencv
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Removed remaining SWIG marks from headers

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This commit is contained in:
Andrey Kamaev 2012-09-05 01:15:00 +04:00
parent a5e37779fb
commit f268af8ef0
3 changed files with 0 additions and 20 deletions
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2
modules/highgui/src/makeswig.sh
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@ -1,2 +0,0 @@
swig -DSKIP_INCLUDES -python -small highgui.i
gcc -I/usr/include/python2.3/ -I../../cxcore/include -D CV_NO_BACKWARD_COMPATIBILITY -c highgui_wrap.c

2
modules/legacy/include/opencv2/legacy/legacy.hpp
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@ -1787,7 +1787,6 @@ public:
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
#ifndef SWIG
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams() );
@ -1806,7 +1805,6 @@ public:
CV_WRAP cv::Mat getProbs() const;
CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? logLikelihood : DBL_MAX; }
#endif
CV_WRAP virtual void clear();

16
modules/ml/include/opencv2/ml/ml.hpp
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@ -201,14 +201,12 @@ public:
virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
CV_WRAP virtual void clear();
#ifndef SWIG
CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
virtual void read( CvFileStorage* storage, CvFileNode* node );
@ -249,7 +247,6 @@ public:
virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
#ifndef SWIG
CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
@ -262,7 +259,6 @@ public:
cv::Mat* dist=0 ) const;
CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
#endif
virtual void clear();
int get_max_k() const;
@ -490,7 +486,6 @@ public:
virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
virtual float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
#ifndef SWIG
CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams() );
@ -511,7 +506,6 @@ public:
bool balanced=false);
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
CV_WRAP_AS(predict_all) virtual void predict( cv::InputArray samples, cv::OutputArray results ) const;
#endif
CV_WRAP virtual int get_support_vector_count() const;
virtual const float* get_support_vector(int i) const;
@ -868,7 +862,6 @@ public:
virtual CvDTreeNode* predict( const CvMat* sample, const CvMat* missingDataMask=0,
bool preprocessedInput=false ) const;
#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@ -878,7 +871,6 @@ public:
CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
bool preprocessedInput=false ) const;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
virtual const CvMat* get_var_importance();
CV_WRAP virtual void clear();
@ -1011,7 +1003,6 @@ public:
virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;
virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@ -1020,7 +1011,6 @@ public:
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
CV_WRAP virtual void clear();
@ -1107,13 +1097,11 @@ public:
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvRTParams params=CvRTParams());
#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvRTParams params=CvRTParams());
#endif
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
protected:
virtual std::string getName() const;
@ -1220,7 +1208,6 @@ public:
CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
bool raw_mode=false, bool return_sum=false ) const;
#ifndef SWIG
CV_WRAP CvBoost( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
@ -1237,7 +1224,6 @@ public:
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
const cv::Range& slice=cv::Range::all(), bool rawMode=false,
bool returnSum=false ) const;
#endif
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
@ -1904,7 +1890,6 @@ public:
int flags=0 );
virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
#ifndef SWIG
CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
@ -1919,7 +1904,6 @@ public:
int flags=0 );
CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
#endif
CV_WRAP virtual void clear();