Fixed mingw build warnings

diff --git a/apps/traincascade/features.cpp b/apps/traincascade/features.cpp
index 8ecdfcc..effa4dc 100644 (file)
--- a/apps/traincascade/features.cpp
+++ b/apps/traincascade/features.cpp
@@ -24,7 +24,7 @@ CvParams::CvParams() : name( "params" ) {}
 void CvParams::printDefaults() const
 { cout << "--" << name << "--" << endl; }
 void CvParams::printAttrs() const {}
-bool CvParams::scanAttr( const String prmName, const String val ) { return false; }
+bool CvParams::scanAttr( const String, const String ) { return false; }
 
 
 //---------------------------- FeatureParams --------------------------------------

diff --git a/modules/contrib/include/opencv2/contrib/hybridtracker.hpp b/modules/contrib/include/opencv2/contrib/hybridtracker.hpp
index 418a7b8..3a1f722 100644 (file)
--- a/modules/contrib/include/opencv2/contrib/hybridtracker.hpp
+++ b/modules/contrib/include/opencv2/contrib/hybridtracker.hpp
@@ -59,151 +59,151 @@ namespace cv
 // To add Kalman filter
 struct CV_EXPORTS CvMotionModel
 {
-       enum {LOW_PASS_FILTER = 0, KALMAN_FILTER = 1, EM = 2};
+    enum {LOW_PASS_FILTER = 0, KALMAN_FILTER = 1, EM = 2};
 
-       CvMotionModel()
-       {
-       }
+    CvMotionModel()
+    {
+    }
 
-       float low_pass_gain;    // low pass gain
+    float low_pass_gain;    // low pass gain
 };
 
 // Mean Shift Tracker parameters for specifying use of HSV channel and CamShift parameters.
 struct CV_EXPORTS CvMeanShiftTrackerParams
 {
-       enum {  H = 0, HS = 1, HSV = 2  };
-       CvMeanShiftTrackerParams(int tracking_type = CvMeanShiftTrackerParams::HS,
-                       CvTermCriteria term_crit = CvTermCriteria());
-
-       int tracking_type;
-       vector<float> h_range;
-       vector<float> s_range;
-       vector<float> v_range;
-       CvTermCriteria term_crit;
+    enum {  H = 0, HS = 1, HSV = 2  };
+    CvMeanShiftTrackerParams(int tracking_type = CvMeanShiftTrackerParams::HS,
+            CvTermCriteria term_crit = CvTermCriteria());
+
+    int tracking_type;
+    vector<float> h_range;
+    vector<float> s_range;
+    vector<float> v_range;
+    CvTermCriteria term_crit;
 };
 
 // Feature tracking parameters
 struct CV_EXPORTS CvFeatureTrackerParams
 {
-       enum {  SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 };
-       CvFeatureTrackerParams(int featureType = 0, int windowSize = 0)
-       {
-               featureType = 0;
-               windowSize = 0;
-       }
-
-       int feature_type; // Feature type to use
-       int window_size; // Window size in pixels around which to search for new window
+    enum {  SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 };
+    CvFeatureTrackerParams(int featureType = 0, int windowSize = 0)
+    {
+        feature_type = featureType;
+        window_size = windowSize;
+    }
+
+    int feature_type; // Feature type to use
+    int window_size; // Window size in pixels around which to search for new window
 };
 
 // Hybrid Tracking parameters for specifying weights of individual trackers and motion model.
 struct CV_EXPORTS CvHybridTrackerParams
 {
-       CvHybridTrackerParams(float ft_tracker_weight = 0.5, float ms_tracker_weight = 0.5,
-                       CvFeatureTrackerParams ft_params = CvFeatureTrackerParams(),
-                       CvMeanShiftTrackerParams ms_params = CvMeanShiftTrackerParams(),
-                       CvMotionModel model = CvMotionModel());
-
-       float ft_tracker_weight;
-       float ms_tracker_weight;
-       CvFeatureTrackerParams ft_params;
-       CvMeanShiftTrackerParams ms_params;
-       int motion_model;
-       float low_pass_gain;
+    CvHybridTrackerParams(float ft_tracker_weight = 0.5, float ms_tracker_weight = 0.5,
+            CvFeatureTrackerParams ft_params = CvFeatureTrackerParams(),
+            CvMeanShiftTrackerParams ms_params = CvMeanShiftTrackerParams(),
+            CvMotionModel model = CvMotionModel());
+
+    float ft_tracker_weight;
+    float ms_tracker_weight;
+    CvFeatureTrackerParams ft_params;
+    CvMeanShiftTrackerParams ms_params;
+    int motion_model;
+    float low_pass_gain;
 };
 
 // Performs Camshift using parameters from MeanShiftTrackerParams
 class CV_EXPORTS CvMeanShiftTracker
 {
 private:
-       Mat hsv, hue;
-       Mat backproj;
-       Mat mask, maskroi;
-       MatND hist;
-       Rect prev_trackwindow;
-       RotatedRect prev_trackbox;
-       Point2f prev_center;
+    Mat hsv, hue;
+    Mat backproj;
+    Mat mask, maskroi;
+    MatND hist;
+    Rect prev_trackwindow;
+    RotatedRect prev_trackbox;
+    Point2f prev_center;
 
 public:
-       CvMeanShiftTrackerParams params;
-
-       CvMeanShiftTracker();
-       explicit CvMeanShiftTracker(CvMeanShiftTrackerParams _params);
-       ~CvMeanShiftTracker();
-       void newTrackingWindow(Mat image, Rect selection);
-       RotatedRect updateTrackingWindow(Mat image);
-       Mat getHistogramProjection(int type);
-       void setTrackingWindow(Rect _window);
-       Rect getTrackingWindow();
-       RotatedRect getTrackingEllipse();
-       Point2f getTrackingCenter();
+    CvMeanShiftTrackerParams params;
+
+    CvMeanShiftTracker();
+    explicit CvMeanShiftTracker(CvMeanShiftTrackerParams _params);
+    ~CvMeanShiftTracker();
+    void newTrackingWindow(Mat image, Rect selection);
+    RotatedRect updateTrackingWindow(Mat image);
+    Mat getHistogramProjection(int type);
+    void setTrackingWindow(Rect _window);
+    Rect getTrackingWindow();
+    RotatedRect getTrackingEllipse();
+    Point2f getTrackingCenter();
 };
 
 // Performs SIFT/SURF feature tracking using parameters from FeatureTrackerParams
 class CV_EXPORTS CvFeatureTracker
 {
 private:
-       Ptr<Feature2D> dd;
-       Ptr<DescriptorMatcher> matcher;
-       vector<DMatch> matches;
+    Ptr<Feature2D> dd;
+    Ptr<DescriptorMatcher> matcher;
+    vector<DMatch> matches;
 
-       Mat prev_image;
-       Mat prev_image_bw;
-       Rect prev_trackwindow;
-       Point2d prev_center;
+    Mat prev_image;
+    Mat prev_image_bw;
+    Rect prev_trackwindow;
+    Point2d prev_center;
 
-       int ittr;
-       vector<Point2f> features[2];
+    int ittr;
+    vector<Point2f> features[2];
 
 public:
-       Mat disp_matches;
-       CvFeatureTrackerParams params;
-
-       CvFeatureTracker();
-       explicit CvFeatureTracker(CvFeatureTrackerParams params);
-       ~CvFeatureTracker();
-       void newTrackingWindow(Mat image, Rect selection);
-       Rect updateTrackingWindow(Mat image);
-       Rect updateTrackingWindowWithSIFT(Mat image);
-       Rect updateTrackingWindowWithFlow(Mat image);
-       void setTrackingWindow(Rect _window);
-       Rect getTrackingWindow();
-       Point2f getTrackingCenter();
+    Mat disp_matches;
+    CvFeatureTrackerParams params;
+
+    CvFeatureTracker();
+    explicit CvFeatureTracker(CvFeatureTrackerParams params);
+    ~CvFeatureTracker();
+    void newTrackingWindow(Mat image, Rect selection);
+    Rect updateTrackingWindow(Mat image);
+    Rect updateTrackingWindowWithSIFT(Mat image);
+    Rect updateTrackingWindowWithFlow(Mat image);
+    void setTrackingWindow(Rect _window);
+    Rect getTrackingWindow();
+    Point2f getTrackingCenter();
 };
 
 // Performs Hybrid Tracking and combines individual trackers using EM or filters
 class CV_EXPORTS CvHybridTracker
 {
 private:
-       CvMeanShiftTracker* mstracker;
-       CvFeatureTracker* fttracker;
+    CvMeanShiftTracker* mstracker;
+    CvFeatureTracker* fttracker;
 
-       CvMat* samples;
-       CvMat* labels;
+    CvMat* samples;
+    CvMat* labels;
 
-       Rect prev_window;
-       Point2f prev_center;
-       Mat prev_proj;
-       RotatedRect trackbox;
+    Rect prev_window;
+    Point2f prev_center;
+    Mat prev_proj;
+    RotatedRect trackbox;
 
-       int ittr;
-       Point2f curr_center;
+    int ittr;
+    Point2f curr_center;
 
-       inline float getL2Norm(Point2f p1, Point2f p2);
-       Mat getDistanceProjection(Mat image, Point2f center);
-       Mat getGaussianProjection(Mat image, int ksize, double sigma, Point2f center);
-       void updateTrackerWithEM(Mat image);
-       void updateTrackerWithLowPassFilter(Mat image);
+    inline float getL2Norm(Point2f p1, Point2f p2);
+    Mat getDistanceProjection(Mat image, Point2f center);
+    Mat getGaussianProjection(Mat image, int ksize, double sigma, Point2f center);
+    void updateTrackerWithEM(Mat image);
+    void updateTrackerWithLowPassFilter(Mat image);
 
 public:
-       CvHybridTrackerParams params;
-       CvHybridTracker();
-       explicit CvHybridTracker(CvHybridTrackerParams params);
-       ~CvHybridTracker();
-
-       void newTracker(Mat image, Rect selection);
-       void updateTracker(Mat image);
-       Rect getTrackingWindow();
+    CvHybridTrackerParams params;
+    CvHybridTracker();
+    explicit CvHybridTracker(CvHybridTrackerParams params);
+    ~CvHybridTracker();
+
+    void newTracker(Mat image, Rect selection);
+    void updateTracker(Mat image);
+    Rect getTrackingWindow();
 };
 
 typedef CvMotionModel MotionModel;

diff --git a/modules/flann/include/opencv2/flann/any.h b/modules/flann/include/opencv2/flann/any.h
index e1c78b6..dc0b948 100644 (file)
--- a/modules/flann/include/opencv2/flann/any.h
+++ b/modules/flann/include/opencv2/flann/any.h
@@ -12,6 +12,7 @@
  * Adapted for FLANN by Marius Muja
  */
 
+#include "defines.h"
 #include <stdexcept>
 #include <ostream>
 #include <typeinfo>
@@ -95,6 +96,16 @@ struct big_any_policy : typed_base_any_policy<T>
     virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast<T const*>(*src); }
 };
 
+template<> inline void big_any_policy<flann_centers_init_t>::print(std::ostream& out, void* const* src)
+{
+    out << int(*reinterpret_cast<flann_centers_init_t const*>(*src));
+}
+
+template<> inline void big_any_policy<flann_algorithm_t>::print(std::ostream& out, void* const* src)
+{
+    out << int(*reinterpret_cast<flann_algorithm_t const*>(*src));
+}
+
 template<typename T>
 struct choose_policy
 {

diff --git a/modules/flann/src/flann.cpp b/modules/flann/src/flann.cpp
index 36ee669..fa1fdaf 100644 (file)
--- a/modules/flann/src/flann.cpp
+++ b/modules/flann/src/flann.cpp
@@ -36,7 +36,7 @@ namespace cvflann
     */
     flann_distance_t flann_distance_type_ = FLANN_DIST_L2;
     flann_distance_t flann_distance_type() { return flann_distance_type_; }
-    
+
     /**
      * Set distance type to used
      * \deprecated
@@ -52,6 +52,6 @@ namespace cvflann
         }
         flann_distance_type_ = distance_type;
     }
-    
+
     void dummyfunc() {}
 }
\ No newline at end of file

diff --git a/modules/flann/src/precomp.hpp b/modules/flann/src/precomp.hpp
index 88df36b..72731af 100644 (file)
--- a/modules/flann/src/precomp.hpp
+++ b/modules/flann/src/precomp.hpp
@@ -1,7 +1,3 @@
-#ifdef __GNUC__\r
-#  pragma GCC diagnostic ignored "-Wsign-promo"\r
-#endif\r
-\r
 #ifndef _OPENCV_FLANN_PRECOMP_HPP_\r
 #define _OPENCV_FLANN_PRECOMP_HPP_\r
 \r

diff --git a/modules/highgui/src/grfmt_jpeg.cpp b/modules/highgui/src/grfmt_jpeg.cpp
index 24318ca..5b4c9e8 100644 (file)
--- a/modules/highgui/src/grfmt_jpeg.cpp
+++ b/modules/highgui/src/grfmt_jpeg.cpp
@@ -542,8 +542,6 @@ bool  JpegEncoder::write( const Mat& img, const vector<int>& params )
     };
     bool result = false;
     fileWrapper fw;
-    int _channels = img.channels();
-    int channels = _channels > 1 ? 3 : 1;
     int width = img.cols, height = img.rows;
 
     vector<uchar> out_buf(1 << 12);
@@ -580,6 +578,9 @@ bool  JpegEncoder::write( const Mat& img, const vector<int>& params )
     {
         cinfo.image_width = width;
         cinfo.image_height = height;
+
+        int _channels = img.channels();
+        int channels = _channels > 1 ? 3 : 1;
         cinfo.input_components = channels;
         cinfo.in_color_space = channels > 1 ? JCS_RGB : JCS_GRAYSCALE;
 

diff --git a/modules/ml/test/test_mltests2.cpp b/modules/ml/test/test_mltests2.cpp
index a5dfbe8..80776b4 100644 (file)
--- a/modules/ml/test/test_mltests2.cpp
+++ b/modules/ml/test/test_mltests2.cpp
@@ -52,7 +52,7 @@ void nbayes_check_data( CvMLData* _data )
         CV_Error( CV_StsBadArg, "missing values are not supported" );
     const CvMat* var_types = _data->get_var_types();
     bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL;
-    if( ( fabs( cvNorm( var_types, 0, CV_L1 ) - 
+    if( ( fabs( cvNorm( var_types, 0, CV_L1 ) -
         (var_types->rows + var_types->cols - 2)*CV_VAR_ORDERED - CV_VAR_CATEGORICAL ) > FLT_EPSILON ) ||
         !is_classifier )
         CV_Error( CV_StsBadArg, "incorrect types of predictors or responses" );
@@ -89,7 +89,7 @@ float nbayes_calc_error( CvNormalBayesClassifier* nbayes, CvMLData* _data, int t
     {
         CvMat sample;
         int si = sidx ? sidx[i] : i;
-        cvGetRow( values, &sample, si ); 
+        cvGetRow( values, &sample, si );
         float r = (float)nbayes->predict( &sample, 0 );
         if( pred_resp )
             pred_resp[i] = r;
@@ -151,7 +151,7 @@ float knearest_calc_error( CvKNearest* knearest, CvMLData* _data, int k, int typ
         {
             CvMat sample;
             int si = sidx ? sidx[i] : i;
-            cvGetRow( &predictors, &sample, si ); 
+            cvGetRow( &predictors, &sample, si );
             float r = knearest->find_nearest( &sample, k );
             if( pred_resp )
                 pred_resp[i] = r;
@@ -166,14 +166,14 @@ float knearest_calc_error( CvKNearest* knearest, CvMLData* _data, int k, int typ
         {
             CvMat sample;
             int si = sidx ? sidx[i] : i;
-            cvGetRow( &predictors, &sample, si ); 
+            cvGetRow( &predictors, &sample, si );
             float r = knearest->find_nearest( &sample, k );
             if( pred_resp )
                 pred_resp[i] = r;
             float d = r - response->data.fl[si*r_step];
             err += d*d;
         }
-        err = sample_count ? err / (float)sample_count : -FLT_MAX;    
+        err = sample_count ? err / (float)sample_count : -FLT_MAX;
     }
     return err;
 }
@@ -239,7 +239,7 @@ bool svm_train_auto( CvSVM* svm, CvMLData* _data, CvSVMParams _params,
     const CvMat* _responses = _data->get_responses();
     const CvMat* _var_idx = _data->get_var_idx();
     const CvMat* _sample_idx = _data->get_train_sample_idx();
-    return svm->train_auto( _train_data, _responses, _var_idx, 
+    return svm->train_auto( _train_data, _responses, _var_idx,
         _sample_idx, _params, k_fold, C_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
 }
 float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp )
@@ -268,7 +268,7 @@ float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp
         {
             CvMat sample;
             int si = sidx ? sidx[i] : i;
-            cvGetRow( values, &sample, si ); 
+            cvGetRow( values, &sample, si );
             float r = svm->predict( &sample );
             if( pred_resp )
                 pred_resp[i] = r;
@@ -290,7 +290,7 @@ float svm_calc_error( CvSVM* svm, CvMLData* _data, int type, vector<float> *resp
             float d = r - response->data.fl[si*r_step];
             err += d*d;
         }
-        err = sample_count ? err / (float)sample_count : -FLT_MAX;    
+        err = sample_count ? err / (float)sample_count : -FLT_MAX;
     }
     return err;
 }
@@ -395,7 +395,7 @@ float ann_calc_error( CvANN_MLP* ann, CvMLData* _data, map<int, int>& cls_map, i
     {
         CvMat sample;
         int si = sidx ? sidx[i] : i;
-        cvGetRow( &predictors, &sample, si ); 
+        cvGetRow( &predictors, &sample, si );
         ann->predict( &sample, &_output );
         CvPoint best_cls = {0,0};
         cvMinMaxLoc( &_output, 0, 0, 0, &best_cls, 0 );
@@ -417,7 +417,7 @@ int str_to_boost_type( string& str )
     if ( !str.compare("DISCRETE") )
         return CvBoost::DISCRETE;
     if ( !str.compare("REAL") )
-        return CvBoost::REAL;    
+        return CvBoost::REAL;
     if ( !str.compare("LOGIT") )
         return CvBoost::LOGIT;
     if ( !str.compare("GENTLE") )
@@ -480,7 +480,7 @@ CV_MLBaseTest::~CV_MLBaseTest()
         validationFS.release();
     if( nbayes )
         delete nbayes;
-    if( knearest ) 
+    if( knearest )
         delete knearest;
     if( svm )
         delete svm;
@@ -519,15 +519,14 @@ int CV_MLBaseTest::read_params( CvFileStorage* _fs )
     return cvtest::TS::OK;;
 }
 
-void CV_MLBaseTest::run( int start_from )
+void CV_MLBaseTest::run( int )
 {
     string filename = ts->get_data_path();
     filename += get_validation_filename();
     validationFS.open( filename, FileStorage::READ );
     read_params( *validationFS );
-    
+
     int code = cvtest::TS::OK;
-    start_from = 0;
     for (int i = 0; i < test_case_count; i++)
     {
         int temp_code = run_test_case( i );
@@ -594,7 +593,7 @@ string& CV_MLBaseTest::get_validation_filename()
 int CV_MLBaseTest::train( int testCaseIdx )
 {
     bool is_trained = false;
-    FileNode modelParamsNode = 
+    FileNode modelParamsNode =
         validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"];
 
     if( !modelName.compare(CV_NBAYES) )
@@ -651,7 +650,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
         modelParamsNode["max_categories"] >> MAX_CATEGORIES;
         modelParamsNode["cv_folds"] >> CV_FOLDS;
         modelParamsNode["is_pruned"] >> IS_PRUNED;
-        is_trained = dtree->train( &data, 
+        is_trained = dtree->train( &data,
             CvDTreeParams(MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY, USE_SURROGATE,
             MAX_CATEGORIES, CV_FOLDS, false, IS_PRUNED, 0 )) != 0;
     }
@@ -683,7 +682,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
         modelParamsNode["is_pruned"] >> IS_PRUNED;
         modelParamsNode["nactive_vars"] >> NACTIVE_VARS;
         modelParamsNode["max_trees_num"] >> MAX_TREES_NUM;
-        is_trained = rtrees->train( &data, CvRTParams( MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
+        is_trained = rtrees->train( &data, CvRTParams(  MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
             USE_SURROGATE, MAX_CATEGORIES, 0, true, // (calc_var_importance == true) <=> RF processes variable importance
             NACTIVE_VARS, MAX_TREES_NUM, OOB_EPS, CV_TERMCRIT_ITER)) != 0;
     }
@@ -713,7 +712,7 @@ int CV_MLBaseTest::train( int testCaseIdx )
     return cvtest::TS::OK;
 }
 
-float CV_MLBaseTest::get_error( int testCaseIdx, int type, vector<float> *resp )
+float CV_MLBaseTest::get_error( int /*testCaseIdx*/, int type, vector<float> *resp )
 {
     float err = 0;
     if( !modelName.compare(CV_NBAYES) )
@@ -721,8 +720,8 @@ float CV_MLBaseTest::get_error( int testCaseIdx, int type, vector<float> *resp )
     else if( !modelName.compare(CV_KNEAREST) )
     {
         assert( 0 );
-        testCaseIdx = 0;
-        /*int k = 2;
+        /*testCaseIdx = 0;
+        int k = 2;
         validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"]["k"] >> k;
         err = knearest->calc_error( &data, k, type, resp );*/
     }

diff --git a/modules/python/src2/cv2.cpp b/modules/python/src2/cv2.cpp
index ebef099..4924e9b 100644 (file)
--- a/modules/python/src2/cv2.cpp
+++ b/modules/python/src2/cv2.cpp
@@ -181,7 +181,7 @@ public:
         datastart = data = (uchar*)PyArray_DATA(o);
     }
 
-    void deallocate(int* refcount, uchar* datastart, uchar* data)
+    void deallocate(int* refcount, uchar*, uchar*)
     {
         PyEnsureGIL gil;
         if( !refcount )
@@ -349,6 +349,7 @@ static PyObject* pyopencv_from(bool value)
 
 static bool pyopencv_to(PyObject* obj, bool& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     int _val = PyObject_IsTrue(obj);
@@ -365,6 +366,7 @@ static PyObject* pyopencv_from(size_t value)
 
 static bool pyopencv_to(PyObject* obj, size_t& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     value = (int)PyLong_AsUnsignedLong(obj);
@@ -376,8 +378,19 @@ static PyObject* pyopencv_from(int value)
     return PyInt_FromLong(value);
 }
 
+static PyObject* pyopencv_from(cvflann_flann_algorithm_t value)
+{
+    return PyInt_FromLong(int(value));
+}
+
+static PyObject* pyopencv_from(cvflann_flann_distance_t value)
+{
+    return PyInt_FromLong(int(value));
+}
+
 static bool pyopencv_to(PyObject* obj, int& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     value = (int)PyInt_AsLong(obj);
@@ -391,6 +404,7 @@ static PyObject* pyopencv_from(uchar value)
 
 static bool pyopencv_to(PyObject* obj, uchar& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     int ivalue = (int)PyInt_AsLong(obj);
@@ -405,6 +419,7 @@ static PyObject* pyopencv_from(double value)
 
 static bool pyopencv_to(PyObject* obj, double& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     if(PyInt_CheckExact(obj))
@@ -421,6 +436,7 @@ static PyObject* pyopencv_from(float value)
 
 static bool pyopencv_to(PyObject* obj, float& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     if(PyInt_CheckExact(obj))
@@ -442,6 +458,7 @@ static PyObject* pyopencv_from(const string& value)
 
 static bool pyopencv_to(PyObject* obj, string& value, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     char* str = PyString_AsString(obj);
@@ -453,6 +470,7 @@ static bool pyopencv_to(PyObject* obj, string& value, const char* name = "<unkno
 
 static inline bool pyopencv_to(PyObject* obj, Size& sz, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     return PyArg_ParseTuple(obj, "ii", &sz.width, &sz.height) > 0;
@@ -465,6 +483,7 @@ static inline PyObject* pyopencv_from(const Size& sz)
 
 static inline bool pyopencv_to(PyObject* obj, Rect& r, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     return PyArg_ParseTuple(obj, "iiii", &r.x, &r.y, &r.width, &r.height) > 0;
@@ -477,6 +496,7 @@ static inline PyObject* pyopencv_from(const Rect& r)
 
 static inline bool pyopencv_to(PyObject* obj, Range& r, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     if(PyObject_Size(obj) == 0)
@@ -494,6 +514,7 @@ static inline PyObject* pyopencv_from(const Range& r)
 
 static inline bool pyopencv_to(PyObject* obj, CvSlice& r, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     if(PyObject_Size(obj) == 0)
@@ -511,6 +532,7 @@ static inline PyObject* pyopencv_from(const CvSlice& r)
 
 static inline bool pyopencv_to(PyObject* obj, Point& p, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     if(PyComplex_CheckExact(obj))
@@ -525,6 +547,7 @@ static inline bool pyopencv_to(PyObject* obj, Point& p, const char* name = "<unk
 
 static inline bool pyopencv_to(PyObject* obj, Point2f& p, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj || obj == Py_None)
         return true;
     if(PyComplex_CheckExact(obj))
@@ -549,6 +572,7 @@ static inline PyObject* pyopencv_from(const Point2f& p)
 
 static inline bool pyopencv_to(PyObject* obj, Vec3d& v, const char* name = "<unknown>")
 {
+    (void)name;
     if(!obj)
         return true;
     return PyArg_ParseTuple(obj, "ddd", &v[0], &v[1], &v[2]) > 0;
@@ -792,6 +816,7 @@ template<> struct pyopencvVecConverter<string>
 
 static inline bool pyopencv_to(PyObject *obj, CvTermCriteria& dst, const char *name="<unknown>")
 {
+    (void)name;
     if(!obj)
         return true;
     return PyArg_ParseTuple(obj, "iid", &dst.type, &dst.max_iter, &dst.epsilon) > 0;
@@ -804,6 +829,7 @@ static inline PyObject* pyopencv_from(const CvTermCriteria& src)
 
 static inline bool pyopencv_to(PyObject *obj, TermCriteria& dst, const char *name="<unknown>")
 {
+    (void)name;
     if(!obj)
         return true;
     return PyArg_ParseTuple(obj, "iid", &dst.type, &dst.maxCount, &dst.epsilon) > 0;
@@ -816,6 +842,7 @@ static inline PyObject* pyopencv_from(const TermCriteria& src)
 
 static inline bool pyopencv_to(PyObject *obj, RotatedRect& dst, const char *name="<unknown>")
 {
+    (void)name;
     if(!obj)
         return true;
     return PyArg_ParseTuple(obj, "(ff)(ff)f", &dst.center.x, &dst.center.y, &dst.size.width, &dst.size.height, &dst.angle) > 0;
@@ -847,6 +874,7 @@ static inline PyObject* pyopencv_from(const CvDTreeNode* node)
 
 static bool pyopencv_to(PyObject *o, cv::flann::IndexParams& p, const char *name="<unknown>")
 {
+    (void)name;
     bool ok = false;
     PyObject* keys = PyObject_CallMethod(o,(char*)"keys",0);
     PyObject* values = PyObject_CallMethod(o,(char*)"values",0);
@@ -927,7 +955,7 @@ static void OnMouse(int event, int x, int y, int flags, void* param)
     PyGILState_Release(gstate);
 }
 
-static PyObject *pycvSetMouseCallback(PyObject *self, PyObject *args, PyObject *kw)
+static PyObject *pycvSetMouseCallback(PyObject*, PyObject *args, PyObject *kw)
 {
     const char *keywords[] = { "window_name", "on_mouse", "param", NULL };
     char* name;
@@ -961,7 +989,7 @@ static void OnChange(int pos, void *param)
     PyGILState_Release(gstate);
 }
 
-static PyObject *pycvCreateTrackbar(PyObject *self, PyObject *args)
+static PyObject *pycvCreateTrackbar(PyObject*, PyObject *args)
 {
     PyObject *on_change;
     char* trackbar_name;
@@ -983,6 +1011,11 @@ static PyObject *pycvCreateTrackbar(PyObject *self, PyObject *args)
 
 #define MKTYPE2(NAME) pyopencv_##NAME##_specials(); if (!to_ok(&pyopencv_##NAME##_Type)) return
 
+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wunused-parameter"
+#  pragma GCC diagnostic ignored "-Wmissing-field-initializers"
+#endif
+
 #include "pyopencv_generated_types.h"
 #include "pyopencv_generated_funcs.h"
 

diff --git a/modules/stitching/perf/perf_stich.cpp b/modules/stitching/perf/perf_stich.cpp
index c7c3f62..bf62f86 100644 (file)
--- a/modules/stitching/perf/perf_stich.cpp
+++ b/modules/stitching/perf/perf_stich.cpp
@@ -1,9 +1,4 @@
 #include "perf_precomp.hpp"\r
-\r
-#ifdef __GNUC__\r
-#  pragma GCC diagnostic ignored "-Wsign-promo"\r
-#endif\r
-\r
 #include "opencv2/highgui/highgui.hpp"\r
 #include "opencv2/core/internal.hpp"\r
 #include "opencv2/flann/flann.hpp"\r

diff --git a/samples/gpu/cascadeclassifier_nvidia_api.cpp b/samples/gpu/cascadeclassifier_nvidia_api.cpp
index bc12bf5..349476d 100644 (file)
--- a/samples/gpu/cascadeclassifier_nvidia_api.cpp
+++ b/samples/gpu/cascadeclassifier_nvidia_api.cpp
@@ -18,7 +18,7 @@ using namespace cv;
 \r
 \r
 #if !defined(HAVE_CUDA)\r
-int main( int argc, const char** argv )\r
+int main( int, const char** )\r
 {\r
     cout << "Please compile the library with CUDA support" << endl;\r
     return -1;\r

diff --git a/samples/gpu/opticalflow_nvidia_api.cpp b/samples/gpu/opticalflow_nvidia_api.cpp
index e40f059..f48b13a 100644 (file)
--- a/samples/gpu/opticalflow_nvidia_api.cpp
+++ b/samples/gpu/opticalflow_nvidia_api.cpp
@@ -20,7 +20,7 @@
 #endif\r
 \r
 #if !defined(HAVE_CUDA)\r
-int main( int argc, const char** argv )\r
+int main( int, const char** )\r
 {\r
     std::cout << "Please compile the library with CUDA support" << std::endl;\r
     return -1;\r