integrated parallel SVM prediction; fixed warnings after meanshift integration

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

@@ -543,6 +543,7 @@ 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,

modules/ml/src/svm.cpp

@@ -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):
+					 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)
 {
+	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]);

samples/cpp/letter_recog.cpp

@@ -9,7 +9,7 @@
 void help()
 {
 	printf("\nThe sample demonstrates how to train Random Trees classifier\n"
-	"(or Boosting classifier, or MLP, or Knearest, or Nbayes - see main()) using the provided dataset.\n"
+	"(or Boosting classifier, or MLP, or Knearest, or Nbayes, or Support Vector Machines - see main()) using the provided dataset.\n"
 	"\n"
 	"We use the sample database letter-recognition.data\n"
 	"from UCI Repository, here is the link:\n"
@@ -28,7 +28,7 @@ void help()
             "The usage: letter_recog [-data <path to letter-recognition.data>] \\\n"
             "  [-save <output XML file for the classifier>] \\\n"
             "  [-load <XML file with the pre-trained classifier>] \\\n"
-            "  [-boost|-mlp|-knearest|-nbayes] # to use boost/mlp/knearest classifier instead of default Random Trees\n" );
+            "  [-boost|-mlp|-knearest|-nbayes|-svm] # to use boost/mlp/knearest/SVM classifier instead of default Random Trees\n" );
 }
 
 // This function reads data and responses from the file <filename>
@@ -630,6 +630,78 @@ int build_nbayes_classifier( char* data_filename )
     return 0;
 }
 
+static
+int build_svm_classifier( char* data_filename )
+{
+    CvMat* data = 0;
+    CvMat* responses = 0;
+    CvMat train_data;
+    int nsamples_all = 0, ntrain_samples = 0;
+    int var_count;
+    CvSVM svm;
+
+    int ok = read_num_class_data( data_filename, 16, &data, &responses );
+    if( !ok )
+    {
+        printf( "Could not read the database %s\n", data_filename );
+        return -1;
+    }
+    ////////// SVM parameters ///////////////////////////////
+    CvSVMParams param;
+    param.kernel_type=CvSVM::LINEAR;
+    param.svm_type=CvSVM::C_SVC;
+    param.C=1;
+    ///////////////////////////////////////////////////////////
+
+    printf( "The database %s is loaded.\n", data_filename );
+    nsamples_all = data->rows;
+    ntrain_samples = (int)(nsamples_all*0.1);
+    var_count = data->cols;
+
+    // train classifier
+    printf( "Training the classifier (may take a few minutes)...\n");
+    cvGetRows( data, &train_data, 0, ntrain_samples );
+    CvMat* train_resp = cvCreateMat( ntrain_samples, 1, CV_32FC1);
+    for (int i = 0; i < ntrain_samples; i++)
+        train_resp->data.fl[i] = responses->data.fl[i];
+    svm.train(&train_data, train_resp, 0, 0, param);
+
+    // classification
+    float _sample[var_count * (nsamples_all - ntrain_samples)];
+    CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
+    float true_results[nsamples_all - ntrain_samples];
+    for (int j = ntrain_samples; j < nsamples_all; j++)
+    {
+        float *s = data->data.fl + j * var_count;
+        
+        for (int i = 0; i < var_count; i++)
+        {   
+            sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
+        }
+        true_results[j - ntrain_samples] = responses->data.fl[j];
+    }
+    CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
+    
+    printf("Classification (may take a few minutes)...\n");
+    (int)svm.predict(&sample, result);
+
+    int true_resp = 0;
+    for (int i = 0; i < nsamples_all - ntrain_samples; i++)
+    {
+        if (result->data.fl[i] == true_results[i])
+            true_resp++;
+    }
+    
+    printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
+    
+    cvReleaseMat( &train_resp );
+    cvReleaseMat( &result );
+    cvReleaseMat( &data );
+    cvReleaseMat( &responses );
+
+    return 0;
+}
+
 int main( int argc, char *argv[] )
 {
     char* filename_to_save = 0;
@@ -672,6 +744,10 @@ int main( int argc, char *argv[] )
 	{
 	    method = 4;
 	}
+	else if ( strcmp(argv[i], "-svm") == 0)
+	{
+	    method = 5;
+	}
         else
             break;
     }
@@ -687,6 +763,8 @@ int main( int argc, char *argv[] )
         build_knearest_classifier( data_filename, 10 ) :
         method == 4 ?
         build_nbayes_classifier( data_filename) :
+        method == 5 ?
+        build_svm_classifier( data_filename ):
         -1) < 0)
     {
     	help();