Add a variant of detectMultiScale with an argument 'weights' that

receives the number of neighbors joined into each detected object

modules/objdetect/doc/cascade_classification.rst

@@ -189,6 +189,7 @@ CascadeClassifier::detectMultiScale
 Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
 
 .. ocv:function:: void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
+.. ocv:function:: void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& objects, vector<int>& weights, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size(), Size maxSize=Size())
 
 .. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) -> objects
 .. ocv:pyfunction:: cv2.CascadeClassifier.detectMultiScale(image, rejectLevels, levelWeights[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) -> objects
@@ -203,6 +204,8 @@ Detects objects of different sizes in the input image. The detected objects are
 
     :param objects: Vector of rectangles where each rectangle contains the detected object.
 
+    :param weights: Vector of weights of the corresponding objects. Weight is the number of neighboring positively classified rectangles that were joined into one object.
+
     :param scaleFactor: Parameter specifying how much the image size is reduced at each image scale.
 
     :param minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it.

modules/objdetect/include/opencv2/objdetect/objdetect.hpp

@@ -382,6 +382,14 @@ public:
                                    Size minSize=Size(),
                                    Size maxSize=Size() );
 
+    CV_WRAP virtual void detectMultiScale( const Mat& image,
+                                   CV_OUT vector<Rect>& objects,
+                                   vector<int>& weights,
+                                   double scaleFactor=1.1,
+                                   int minNeighbors=3, int flags=0,
+                                   Size minSize=Size(),
+                                   Size maxSize=Size() );
+
     CV_WRAP virtual void detectMultiScale( const Mat& image,
                                    CV_OUT vector<Rect>& objects,
                                    vector<int>& rejectLevels,
@@ -390,7 +398,8 @@ public:
                                    int minNeighbors=3, int flags=0,
                                    Size minSize=Size(),
                                    Size maxSize=Size(),
-                                   bool outputRejectLevels=false );
+                                   bool outputRejectLevels=false,
+                                   bool outputWeights=false );
 
 
     bool isOldFormatCascade() const;

modules/objdetect/src/cascadedetect.cpp

@@ -1023,6 +1023,7 @@ public:
 };
 
 struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
+struct getNeighbors { int operator ()(const CvAvgComp& e) const { return e.neighbors; } };
 
 
 bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
@@ -1092,11 +1093,12 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
                                           vector<double>& levelWeights,
                                           double scaleFactor, int minNeighbors,
                                           int flags, Size minObjectSize, Size maxObjectSize,
-                                          bool outputRejectLevels )
+                                          bool outputRejectLevels, bool outputWeights )
 {
     const double GROUP_EPS = 0.2;
 
     CV_Assert( scaleFactor > 1 && image.depth() == CV_8U );
+    CV_Assert( !( outputRejectLevels && outputWeights ) );
 
     if( empty() )
         return;
@@ -1111,6 +1113,12 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
         Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
         objects.resize(vecAvgComp.size());
         std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
+        if( outputWeights )
+        {
+            rejectLevels.resize(vecAvgComp.size());
+            std::transform(vecAvgComp.begin(), vecAvgComp.end(), rejectLevels.begin(),
+                    getNeighbors());
+        }
         return;
     }
 
@@ -1183,6 +1191,10 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
     {
         groupRectangles( objects, rejectLevels, levelWeights, minNeighbors, GROUP_EPS );
     }
+    else if( outputWeights )
+    {
+        groupRectangles( objects, rejectLevels, minNeighbors, GROUP_EPS );
+    }
     else
     {
         groupRectangles( objects, minNeighbors, GROUP_EPS );
@@ -1199,6 +1211,16 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
         minNeighbors, flags, minObjectSize, maxObjectSize, false );
 }
 
+void CascadeClassifier::detectMultiScale( const Mat& image, CV_OUT vector<Rect>& objects,
+                                          vector<int>& weights, double scaleFactor,
+                                          int minNeighbors, int flags, Size minObjectSize,
+                                          Size maxObjectSize )
+{
+    vector<double> fakeLevelWeights;
+    detectMultiScale( image, objects, weights, fakeLevelWeights, scaleFactor,
+            minNeighbors, flags, minObjectSize, maxObjectSize, false, true );
+}
+
 bool CascadeClassifier::Data::read(const FileNode &root)
 {
     static const float THRESHOLD_EPS = 1e-5f;