Merge pull request #8116 from mrquorr:master

modules/ml/include/opencv2/ml.hpp

@@ -1206,6 +1206,17 @@ public:
      */
     CV_WRAP virtual Mat getVarImportance() const = 0;
 
+    /** Returns the result of each individual tree in the forest.
+    In case the model is a regression problem, the method will return each of the trees'
+    results for each of the sample cases. If the model is a classifier, it will return
+    a Mat with samples + 1 rows, where the first row gives the class number and the
+    following rows return the votes each class had for each sample.
+        @param samples Array containg the samples for which votes will be calculated.
+        @param results Array where the result of the calculation will be written.
+        @param flags Flags for defining the type of RTrees.
+    */
+    CV_WRAP void getVotes(InputArray samples, OutputArray results, int flags) const;
+
     /** Creates the empty model.
     Use StatModel::train to train the model, StatModel::train to create and train the model,
     Algorithm::load to load the pre-trained model.

modules/ml/src/rtrees.cpp

@@ -349,6 +349,60 @@ public:
         }
     }
 
+    void getVotes( InputArray input, OutputArray output, int flags ) const
+    {
+        CV_Assert( !roots.empty() );
+        int nclasses = (int)classLabels.size(), ntrees = (int)roots.size();
+        Mat samples = input.getMat(), results;
+        int i, j, nsamples = samples.rows;
+
+        int predictType = flags & PREDICT_MASK;
+        if( predictType == PREDICT_AUTO )
+        {
+            predictType = !_isClassifier || (classLabels.size() == 2 && (flags & RAW_OUTPUT) != 0) ?
+                PREDICT_SUM : PREDICT_MAX_VOTE;
+        }
+
+        if( predictType == PREDICT_SUM )
+        {
+            output.create(nsamples, ntrees, CV_32F);
+            results = output.getMat();
+            for( i = 0; i < nsamples; i++ )
+            {
+                for( j = 0; j < ntrees; j++ )
+                {
+                    float val = predictTrees( Range(j, j+1), samples.row(i), flags);
+                    results.at<float> (i, j) = val;
+                }
+            }
+        } else
+        {
+            vector<int> votes;
+            output.create(nsamples+1, nclasses, CV_32S);
+            results = output.getMat();
+
+            for ( j = 0; j < nclasses; j++)
+            {
+                results.at<int> (0, j) = classLabels[j];
+            }
+
+            for( i = 0; i < nsamples; i++ )
+            {
+                votes.clear();
+                for( j = 0; j < ntrees; j++ )
+                {
+                    int val = (int)predictTrees( Range(j, j+1), samples.row(i), flags);
+                    votes.push_back(val);
+                }
+
+                for ( j = 0; j < nclasses; j++)
+                {
+                    results.at<int> (i+1, j) = (int)std::count(votes.begin(), votes.end(), classLabels[j]);
+                }
+            }
+        }
+    }
+
     RTreeParams rparams;
     double oobError;
     vector<float> varImportance;
@@ -401,6 +455,11 @@ public:
         impl.read(fn);
     }
 
+    void getVotes_( InputArray samples, OutputArray results, int flags ) const
+    {
+      impl.getVotes(samples, results, flags);
+    }
+
     Mat getVarImportance() const { return Mat_<float>(impl.varImportance, true); }
     int getVarCount() const { return impl.getVarCount(); }
 
@@ -427,6 +486,14 @@ Ptr<RTrees> RTrees::load(const String& filepath, const String& nodeName)
     return Algorithm::load<RTrees>(filepath, nodeName);
 }
 
+void RTrees::getVotes(InputArray input, OutputArray output, int flags) const
+{
+    const RTreesImpl* this_ = dynamic_cast<const RTreesImpl*>(this);
+    if(!this_)
+        CV_Error(Error::StsNotImplemented, "the class is not RTreesImpl");
+    return this_->getVotes_(input, output, flags);
+}
+
 }}
 
 // End of file.

modules/ml/test/test_mltests.cpp

@@ -172,4 +172,49 @@ TEST(ML_NBAYES, regression_5911)
     EXPECT_EQ(sum(P1 == P3)[0], 255 * P3.total());
 }
 
+TEST(ML_RTrees, getVotes)
+{
+    int n = 12;
+    int count, i;
+    int label_size = 3;
+    int predicted_class = 0;
+    int max_votes = -1;
+    int val;
+    // RTrees for classification
+    Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
+
+    //data
+    Mat data(n, 4, CV_32F);
+    randu(data, 0, 10);
+
+    //labels
+    Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
+
+    rt->train(data, ml::ROW_SAMPLE, labels);
+
+    //run function
+    Mat test(1, 4, CV_32F);
+    Mat result;
+    randu(test, 0, 10);
+    rt->getVotes(test, result, 0);
+
+    //count vote amount and find highest vote
+    count = 0;
+    const int* result_row = result.ptr<int>(1);
+    for( i = 0; i < label_size; i++ )
+    {
+        val = result_row[i];
+        //predicted_class = max_votes < val? i;
+        if( max_votes < val )
+        {
+            max_votes = val;
+            predicted_class = i;
+        }
+        count += val;
+    }
+
+    EXPECT_EQ(count, (int)rt->getRoots().size());
+    EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
+}
+
 /* End of file. */