From f268af8ef05712557aae949eda8055f6c673e544 Mon Sep 17 00:00:00 2001 From: Andrey Kamaev Date: Wed, 5 Sep 2012 01:15:00 +0400 Subject: [PATCH] Removed remaining SWIG marks from headers --- modules/highgui/src/makeswig.sh | 2 -- modules/legacy/include/opencv2/legacy/legacy.hpp | 2 -- modules/ml/include/opencv2/ml/ml.hpp | 16 ---------------- 3 files changed, 20 deletions(-) delete mode 100644 modules/highgui/src/makeswig.sh diff --git a/modules/highgui/src/makeswig.sh b/modules/highgui/src/makeswig.sh deleted file mode 100644 index 06e88e1..0000000 --- a/modules/highgui/src/makeswig.sh +++ /dev/null @@ -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 diff --git a/modules/legacy/include/opencv2/legacy/legacy.hpp b/modules/legacy/include/opencv2/legacy/legacy.hpp index f8ecb4d..1144131 100644 --- a/modules/legacy/include/opencv2/legacy/legacy.hpp +++ b/modules/legacy/include/opencv2/legacy/legacy.hpp @@ -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(); diff --git a/modules/ml/include/opencv2/ml/ml.hpp b/modules/ml/include/opencv2/ml/ml.hpp index 8def877..7ba97c8 100644 --- a/modules/ml/include/opencv2/ml/ml.hpp +++ b/modules/ml/include/opencv2/ml/ml.hpp @@ -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 *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(); -- 2.7.4