continuing refactoring ml samples; added "max vote" response to ANN_MLP.…

continuing refactoring ml samples; added "max vote" response to ANN_MLP. Probably, should make it in less hacky way

modules/ml/src/ann_mlp.cpp

@@ -228,9 +228,8 @@ public:
         int n = inputs.rows, dn0 = n;
 
         CV_Assert( (type == CV_32F || type == CV_64F) && inputs.cols == layer_sizes[0] );
-        _outputs.create(n, layer_sizes[l_count-1], type);
-
-        Mat outputs = _outputs.getMat();
+        int noutputs = layer_sizes[l_count-1];
+        Mat outputs;
 
         int min_buf_sz = 2*max_lsize;
         int buf_sz = n*min_buf_sz;
@@ -242,9 +241,20 @@ public:
             buf_sz = dn0*min_buf_sz;
         }
 
-        cv::AutoBuffer<double> _buf(buf_sz);
+        cv::AutoBuffer<double> _buf(buf_sz+noutputs);
         double* buf = _buf;
 
+        if( !_outputs.needed() )
+        {
+            CV_Assert( n == 1 );
+            outputs = Mat(n, noutputs, type, buf + buf_sz);
+        }
+        else
+        {
+            _outputs.create(n, noutputs, type);
+            outputs = _outputs.getMat();
+        }
+
         int dn = 0;
         for( int i = 0; i < n; i += dn )
         {
@@ -273,6 +283,13 @@ public:
             scale_output( layer_in, layer_out );
         }
 
+        if( n == 1 )
+        {
+            int maxIdx[] = {0, 0};
+            minMaxIdx(outputs, 0, 0, 0, maxIdx);
+            return maxIdx[0] + maxIdx[1];
+        }
+
         return 0.f;
     }
 

samples/cpp/points_classifier.cpp

@@ -229,22 +229,7 @@ static void find_decision_boundary_ANN( const Mat&  layer_sizes )
     Ptr<TrainData> tdata = TrainData::create(samples, ROW_SAMPLE, trainClasses);
 
     ann->train(tdata);
-
-    Mat testSample( 1, 2, CV_32FC1 );
-    Mat outputs;
-    for( int y = 0; y < img.rows; y += testStep )
-    {
-        for( int x = 0; x < img.cols; x += testStep )
-        {
-            testSample.at<float>(0) = (float)x;
-            testSample.at<float>(1) = (float)y;
-
-            ann->predict( testSample, outputs );
-            Point maxLoc;
-            minMaxLoc( outputs, 0, 0, 0, &maxLoc );
-            imgDst.at<Vec3b>(y, x) = classColors[maxLoc.x];
-        }
-    }
+    predict_and_paint(ann, imgDst);
 }
 #endif