update to new OpenCV 3.0 ml API

modules/text/src/erfilter.cpp

@@ -61,6 +61,7 @@ namespace text
 {
 
 using namespace std;
+using namespace cv::ml;
 
 // Deletes a tree of ERStat regions starting at root. Used only
 // internally to this implementation.
@@ -178,7 +179,7 @@ public:
     double eval(const ERStat& stat);
 
 private:
-    CvBoost boost;
+    Ptr<Boost> boost;
 };
 
 // default 2nd stage classifier
@@ -194,7 +195,7 @@ public:
     double eval(const ERStat& stat);
 
 private:
-    CvBoost boost;
+    Ptr<Boost> boost;
 };
 
 
@@ -1016,22 +1017,27 @@ ERClassifierNM1::ERClassifierNM1(const string& filename)
 {
 
     if (ifstream(filename.c_str()))
-        boost.load( filename.c_str(), "boost" );
+    {
+        boost = StatModel::load<Boost>( filename.c_str() );
+        if( boost.empty() )
+        {
+            cout << "Could not read the classifier " << filename.c_str() << endl;
+            CV_Error(Error::StsBadArg, "Could not read the default classifier!");
+        }
+    }
     else
-        CV_Error(CV_StsBadArg, "Default classifier file not found!");
+        CV_Error(Error::StsBadArg, "Default classifier file not found!");
 }
 
 double ERClassifierNM1::eval(const ERStat& stat)
 {
     //Classify
-    float arr[] = {0,(float)(stat.rect.width)/(stat.rect.height), // aspect ratio
+    Mat sample = (Mat_<float>(1,4) <<  (float)(stat.rect.width)/(stat.rect.height), // aspect ratio
                      sqrt((float)(stat.area))/stat.perimeter, // compactness
                      (float)(1-stat.euler), //number of holes
-                     stat.med_crossings};
+                     stat.med_crossings);
 
-    vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
-
-    float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
+    float votes = boost->predict( sample, noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
 
     // Logistic Correction returns a probability value (in the range(0,1))
     return (double)1-(double)1/(1+exp(-2*votes));
@@ -1042,23 +1048,28 @@ double ERClassifierNM1::eval(const ERStat& stat)
 ERClassifierNM2::ERClassifierNM2(const string& filename)
 {
     if (ifstream(filename.c_str()))
-        boost.load( filename.c_str(), "boost" );
+    {
+        boost = StatModel::load<Boost>( filename.c_str() );
+        if( boost.empty() )
+        {
+            cout << "Could not read the classifier " << filename.c_str() << endl;
+            CV_Error(Error::StsBadArg, "Could not read the default classifier!");
+        }
+    }
     else
-        CV_Error(CV_StsBadArg, "Default classifier file not found!");
+        CV_Error(Error::StsBadArg, "Default classifier file not found!");
 }
 
 double ERClassifierNM2::eval(const ERStat& stat)
 {
     //Classify
-    float arr[] = {0,(float)(stat.rect.width)/(stat.rect.height), // aspect ratio
+    Mat sample = (Mat_<float>(1,7) << (float)(stat.rect.width)/(stat.rect.height), // aspect ratio
                      sqrt((float)(stat.area))/stat.perimeter, // compactness
                      (float)(1-stat.euler), //number of holes
                      stat.med_crossings, stat.hole_area_ratio,
-                     stat.convex_hull_ratio, stat.num_inflexion_points};
+                     stat.convex_hull_ratio, stat.num_inflexion_points);
 
-    vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
-
-    float votes = boost.predict( Mat(sample), Mat(), Range::all(), false, true );
+    float votes = boost->predict( sample, noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
 
     // Logistic Correction returns a probability value (in the range(0,1))
     return (double)1-(double)1/(1+exp(-2*votes));
@@ -1397,7 +1408,7 @@ static double NFA(int n, int k, double p, double logNT)
     /* check parameters */
     if( n<0 || k<0 || k>n || p<=0.0 || p>=1.0 )
     {
-        CV_Error(CV_StsBadArg, "erGrouping wrong n, k or p values in NFA call!");
+        CV_Error(Error::StsBadArg, "erGrouping wrong n, k or p values in NFA call!");
     }
 
     /* trivial cases */
@@ -2137,15 +2148,15 @@ static int linkage_vector(double *X, int N, int dim, double * Z, unsigned char m
     } // try
     catch (const bad_alloc&)
     {
-        CV_Error(CV_StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
+        CV_Error(Error::StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
     }
     catch(const exception&)
     {
-        CV_Error(CV_StsError, "Uncaught exception in erGrouping!");
+        CV_Error(Error::StsError, "Uncaught exception in erGrouping!");
     }
     catch(...)
     {
-        CV_Error(CV_StsError, "C++ exception (unknown reason) in erGrouping!");
+        CV_Error(Error::StsError, "C++ exception (unknown reason) in erGrouping!");
     }
     return 0;
 }
@@ -2206,7 +2217,7 @@ public:
 
 private:
     double minProbability;
-    CvBoost group_boost;
+    Ptr<Boost> group_boost;
     vector<ERFeatures> &regions;
     Size imsize;
 
@@ -2230,9 +2241,16 @@ MaxMeaningfulClustering::MaxMeaningfulClustering(unsigned char _method, unsigned
     minProbability = _minProbability;
 
     if (ifstream(filename.c_str()))
-        group_boost.load( filename.c_str(), "boost" );
+    {
+        group_boost = StatModel::load<Boost>( filename.c_str() );
+        if( group_boost.empty() )
+        {
+            cout << "Could not read the classifier " << filename.c_str() << endl;
+            CV_Error(Error::StsBadArg, "Could not read the default classifier!");
+        }
+    }
     else
-        CV_Error(CV_StsBadArg, "erGrouping: Default classifier file not found!");
+        CV_Error(Error::StsBadArg, "erGrouping: Default classifier file not found!");
 }
 
 
@@ -2242,7 +2260,7 @@ void MaxMeaningfulClustering::operator()(double *data, unsigned int num, int dim
 
     double *Z = (double*)malloc(((num-1)*4) * sizeof(double)); // we need 4 floats foreach sample merge.
     if (Z == NULL)
-        CV_Error(CV_StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
+        CV_Error(Error::StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
 
     linkage_vector(data, (int)num, dim, Z, method, metric);
 
@@ -2541,7 +2559,6 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
         return 0.;
 
     vector<float> sample;
-    sample.push_back(0);
     sample.push_back((float)cluster.size());
 
     Mat diameters      ( (int)cluster.size(), 1, CV_32F, 1 );
@@ -2723,7 +2740,7 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
     sample.push_back((float)mean[0]);
     sample.push_back((float)std[0]);
 
-    float votes_group = group_boost.predict( Mat(sample), Mat(), Range::all(), false, true );
+    float votes_group = group_boost->predict( Mat(sample), noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
 
     return (double)1-(double)1/(1+exp(-2*votes_group));
 }
@@ -3021,7 +3038,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect
         // assert correct image type
         CV_Assert( channel.type() == CV_8UC1 );
 
-        CV_Assert( !regions.at(c).empty() );
+        //CV_Assert( !regions.at(c).empty() );
 
         if ( regions.at(c).size() < 3 )
             continue;
@@ -3039,7 +3056,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect
         int dim = 7; //dimensionality of feature space
         double *data = (double*)malloc(dim*N * sizeof(double));
         if (data == NULL)
-            CV_Error(CV_StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
+            CV_Error(Error::StsNoMem, "Not enough Memory for erGrouping hierarchical clustering structures!");
 
         //Learned weights
         float weight_param1 = 1.00f;