fixed warnings in gbt; added insertChannel() and extractChannel(); made the code…

fixed warnings in gbt; added insertChannel() and extractChannel(); made the code "rand{u|n}(arr, <number>, <number>)" work properly.

modules/core/include/opencv2/core/core.hpp

@@ -1961,7 +1961,7 @@ CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false,
 CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1);
 //! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage
 CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1);  
-    
+
 //! adds one matrix to another (dst = src1 + src2)
 CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst,
                       InputArray mask=noArray(), int dtype=-1);
@@ -2039,6 +2039,12 @@ CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts
 CV_EXPORTS void mixChannels(const vector<Mat>& src, vector<Mat>& dst,
                             const int* fromTo, size_t npairs);
 
+//! extracts a single channel from src (coi is 0-based index)
+CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi);
+
+//! inserts a single channel to dst (coi is 0-based index)
+CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi);
+    
 //! reverses the order of the rows, columns or both in a matrix
 CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);
 

modules/core/src/convert.cpp

@@ -501,7 +501,26 @@ void cv::mixChannels(const vector<Mat>& src, vector<Mat>& dst,
 {
     mixChannels(!src.empty() ? &src[0] : 0, src.size(),
                 !dst.empty() ? &dst[0] : 0, dst.size(), fromTo, npairs);
-}        
+}
+
+void cv::extractChannel(InputArray _src, OutputArray _dst, int coi)
+{
+    Mat src = _src.getMat();
+    CV_Assert( 0 <= coi && coi < src.channels() );
+    _dst.create(src.dims, &src.size[0], src.depth());
+    Mat dst = _dst.getMat();
+    int ch[] = { coi, 0 };
+    mixChannels(&src, 1, &dst, 1, ch, 1);
+}
+
+void cv::insertChannel(InputArray _src, InputOutputArray _dst, int coi)
+{
+    Mat src = _src.getMat(), dst = _dst.getMat();
+    CV_Assert( src.size == dst.size && src.depth() == dst.depth() );
+    CV_Assert( 0 <= coi && coi < dst.channels() && src.channels() == 1 );
+    int ch[] = { 0, coi };
+    mixChannels(&src, 1, &dst, 1, ch, 1);
+}
 
 /****************************************************************************************\
 *                                convertScale[Abs]                                       *

modules/core/src/rand.cpp

@@ -453,11 +453,11 @@ void RNG::fill( InputOutputArray _mat, int disttype, InputArray _param1arg, Inpu
     RandnScaleFunc scaleFunc = 0;
     
     CV_Assert(_param1.channels() == 1 && (_param1.rows == 1 || _param1.cols == 1) &&
-              (_param1.rows + _param1.cols - 1 == cn ||
+              (_param1.rows + _param1.cols - 1 == cn || _param1.rows + _param1.cols - 1 == 1 ||
                (_param1.size() == Size(1, 4) && _param1.type() == CV_64F && cn <= 4)));
     CV_Assert( _param2.channels() == 1 &&
                (((_param2.rows == 1 || _param2.cols == 1) && 
-                (_param2.rows + _param2.cols - 1 == cn ||
+                (_param2.rows + _param2.cols - 1 == cn || _param2.rows + _param2.cols - 1 == 1 ||
                 (_param1.size() == Size(1, 4) && _param1.type() == CV_64F && cn <= 4))) ||
                 (_param2.rows == cn && _param2.cols == cn && disttype == NORMAL)));
     
@@ -468,26 +468,34 @@ void RNG::fill( InputOutputArray _mat, int disttype, InputArray _param1arg, Inpu
     uchar* mean = 0;
     uchar* stddev = 0;
     bool stdmtx = false;
+    int n1 = (int)_param1.total();
+    int n2 = (int)_param2.total();
 
     if( disttype == UNIFORM )
     {
-        _parambuf.allocate(cn*8);
+        _parambuf.allocate(cn*8 + n1 + n2);
         double* parambuf = _parambuf;
-        const double* p1 = (const double*)_param1.data;
-        const double* p2 = (const double*)_param2.data;
+        double* p1 = (double*)_param1.data;
+        double* p2 = (double*)_param2.data;
         
-        if( !_param1.isContinuous() || _param1.type() != CV_64F )
+        if( !_param1.isContinuous() || _param1.type() != CV_64F || n1 != cn )
         {
             Mat tmp(_param1.size(), CV_64F, parambuf);
             _param1.convertTo(tmp, CV_64F);
             p1 = parambuf;
+            if( n1 < cn )
+                for( j = n1; j < cn; j++ )
+                    p1[j] = p1[j-n1];
         }
         
-        if( !_param2.isContinuous() || _param2.type() != CV_64F )
+        if( !_param2.isContinuous() || _param2.type() != CV_64F || n2 != cn )
         {
             Mat tmp(_param2.size(), CV_64F, parambuf + cn);
             _param2.convertTo(tmp, CV_64F);
             p2 = parambuf + cn;
+            if( n2 < cn )
+                for( j = n2; j < cn; j++ )
+                    p2[j] = p2[j-n2];
         }
         
         if( depth <= CV_32S )
@@ -559,10 +567,12 @@ void RNG::fill( InputOutputArray _mat, int disttype, InputArray _param1arg, Inpu
     }
     else if( disttype == CV_RAND_NORMAL )
     {
-        _parambuf.allocate(_param1.total() + _param2.total());
+        _parambuf.allocate(MAX(n1, cn) + MAX(n2, cn));
         double* parambuf = _parambuf;
         
         int ptype = depth == CV_64F ? CV_64F : CV_32F;
+        int esz = (int)CV_ELEM_SIZE(ptype);
+        
         if( _param1.isContinuous() && _param1.type() == ptype )
             mean = _param1.data;
         else
@@ -572,6 +582,10 @@ void RNG::fill( InputOutputArray _mat, int disttype, InputArray _param1arg, Inpu
             mean = (uchar*)parambuf;
         }
         
+        if( n1 < cn )
+            for( j = n1*esz; j < cn*esz; j++ )
+                mean[j] = mean[j - n1*esz];
+        
         if( _param2.isContinuous() && _param2.type() == ptype )
             stddev = _param2.data;
         else
@@ -581,6 +595,10 @@ void RNG::fill( InputOutputArray _mat, int disttype, InputArray _param1arg, Inpu
             stddev = (uchar*)(parambuf + cn);
         }
         
+        if( n1 < cn )
+            for( j = n1*esz; j < cn*esz; j++ )
+                stddev[j] = stddev[j - n1*esz];
+        
         stdmtx = _param2.rows == cn && _param2.cols == cn;
         scaleFunc = randnScaleTab[depth];
         CV_Assert( scaleFunc != 0 );

modules/ml/src/gbt.cpp

@@ -919,17 +919,17 @@ public:
 	Tree_predictor() : weak(0), sum(0), k(0), sample(0), missing(0), shrinkage(1.0f) {}
 	Tree_predictor(pCvSeq* _weak, const int _k, const float _shrinkage,
 				   const CvMat* _sample, const CvMat* _missing, float* _sum ) :
-				   weak(_weak), k(_k), sample(_sample),
-                   missing(_missing), sum(_sum), shrinkage(_shrinkage)
+				   weak(_weak), sum(_sum), k(_k), sample(_sample),
+                   missing(_missing), shrinkage(_shrinkage)
 	{}
 	
     Tree_predictor( const Tree_predictor& p, cv::Split ) :
-			weak(p.weak), k(p.k), sample(p.sample),
-            missing(p.missing), sum(p.sum), shrinkage(p.shrinkage)
+			weak(p.weak), sum(p.sum), k(p.k), sample(p.sample),
+            missing(p.missing), shrinkage(p.shrinkage)
 	{}
 
 	Tree_predictor& operator=( const Tree_predictor& )
-	{}
+	{ return *this; }
 	
     virtual void operator()(const cv::BlockedRange& range) const
 	{