integrated parallel SVM prediction; fixed warnings after meanshift integration

modules/ml/include/opencv2/ml/ml.hpp

@@ -543,7 +543,8 @@ public:
         bool balanced=false );
 
     virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
-
+    virtual float predict( const CvMat* samples, 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(),
@@ -563,7 +564,7 @@ public:
                             CvParamGrid coeffGrid  = CvSVM::get_default_grid(CvSVM::COEF),
                             CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE),
                             bool balanced=false);
-    CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;    
+    CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
 #endif
     
     CV_WRAP virtual int get_support_vector_count() const;

modules/ml/src/svm.cpp

@@ -2081,7 +2081,7 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const
     CV_CALL( cvPreparePredictData( sample, var_all, var_idx,
                                    class_count, 0, &row_sample ));
     result = predict( row_sample, get_var_count(), returnDFVal );
-
+  
     __END__;
 
     if( sample && (!CV_IS_MAT(sample) || sample->data.fl != row_sample) )
@@ -2090,6 +2090,44 @@ float CvSVM::predict( const CvMat* sample, bool returnDFVal ) const
     return result;
 }
 
+struct predict_body {
+    predict_body(const CvSVM* _pointer, float* _result, const CvMat* _samples, CvMat* _results)
+    {
+        pointer = _pointer;
+        result = _result;
+        samples = _samples;
+        results = _results;
+    }
+    
+    const CvSVM* pointer;
+    float* result;
+    const CvMat* samples;
+    CvMat* results;
+  
+    void operator()( const cv::BlockedRange& range ) const
+    {
+        for(int i = range.begin(); i < range.end(); i++ )
+        {
+            CvMat sample;
+            cvGetRow( samples, &sample, i );
+            int r = (int)pointer->predict(&sample);
+            if (results)
+                results->data.fl[i] = r;
+            if (i == 0)
+                *result = r;
+	}
+    }
+};
+
+float CvSVM::predict(const CvMat* samples, CV_OUT CvMat* results) const
+{
+    float result = 0;
+    cv::parallel_for(cv::BlockedRange(0, samples->rows), 
+		     predict_body(this, &result, samples, results)
+    );
+    return result;
+}
+
 
 CvSVM::CvSVM( const Mat& _train_data, const Mat& _responses,
               const Mat& _var_idx, const Mat& _sample_idx, CvSVMParams _params )

modules/objdetect/src/matching.cpp

@@ -1396,7 +1396,7 @@ int createSchedule(const CvLSVMFeaturePyramid *H, const CvLSVMFilterObject **all
                    const int threadsNum, int *kLevels, int **processingLevels)
 {
     int rootFilterDim, sumPartFiltersDim, i, numLevels, dbx, dby, numDotProducts;
-    int averNumDotProd, j, minValue, argMin, tmp, lambda, maxValue, k;
+    int averNumDotProd, j, minValue, argMin, lambda, maxValue, k;
     int *dotProd, *weights, *disp;
     if (H == NULL || all_F == NULL)
     {

modules/objdetect/src/ms_grouping.cpp

@@ -44,11 +44,17 @@
 using namespace cv;
 
 MeanshiftGrouping::MeanshiftGrouping(const Point3d& densKer, const vector<Point3d>& posV, 
-					 const vector<double>& wV, double modeEps, int maxIter):
-	densityKernel(densKer), weightsV(wV), positionsV(posV), positionsCount(posV.size()),
-	meanshiftV(positionsCount), distanceV(positionsCount), modeEps(modeEps),
-	iterMax (maxIter)
+					 const vector<double>& wV, double modeEps, int maxIter)
 {
+	densityKernel = densKer;
+    weightsV = wV;
+    positionsV = posV;
+    positionsCount = posV.size();
+	meanshiftV.resize(positionsCount);
+    distanceV.resize(positionsCount);
+    modeEps = modeEps;
+	iterMax = maxIter;
+    
 	for (unsigned i=0; i<positionsV.size(); i++)
 	{
 		meanshiftV[i] = getNewValue(positionsV[i]);