fix according to pull requests comments

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

@@ -540,7 +540,7 @@ public:
     // Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
     // Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
     // Param scales is a number of scales from minScale to maxScale.
-    // Param rejfactor is used for NMS.
+    // Param rejCriteria is used for NMS.
     CV_WRAP SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejCriteria = 1);
 
     CV_WRAP virtual ~SCascade();

modules/objdetect/src/softcascade.cpp

@@ -443,15 +443,8 @@ namespace {
 typedef cv::SCascade::Detection Detection;
 typedef std::vector<Detection>  dvector;
 
-struct NMS
-{
 
-    virtual ~NMS(){}
+struct ConfidenceGt
-    virtual void apply(dvector& objects) const = 0;
-};
-
-
-struct ConfidenceLess
 {
     bool operator()(const Detection& a, const Detection& b) const
     {
@@ -459,44 +452,40 @@ struct ConfidenceLess
     }
 };
 
-struct DollarNMS: public NMS
+static float overlap(const cv::Rect &a, const cv::Rect &b)
 {
-    virtual ~DollarNMS(){}
+    int w = std::min(a.x + a.width,  b.x + b.width)  - std::max(a.x, b.x);
+    int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y);
 
-    static float overlap(const cv::Rect &a, const cv::Rect &b)
+    return (w < 0 || h < 0)? 0.f : (float)(w * h);
+}
+
+void DollarNMS(dvector& objects)
+{
+    static const float DollarThreshold = 0.65f;
+    std::sort(objects.begin(), objects.end(), ConfidenceGt());
+
+    for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt)
     {
-        int w = std::min(a.x + a.width,  b.x + b.width)  - std::max(a.x, b.x);
+        const Detection &a = *dIt;
-        int h = std::min(a.y + a.height, b.y + b.height) - std::max(a.y, b.y);
+        for (dvector::iterator next = dIt + 1; next != objects.end(); )
-
-        return (w < 0 || h < 0)? 0.f : (float)(w * h);
-    }
-
-    virtual void apply(dvector& objects) const
-    {
-        std::sort(objects.begin(), objects.end(), ConfidenceLess());
-
-        for (dvector::iterator dIt = objects.begin(); dIt != objects.end(); ++dIt)
         {
-            const Detection &a = *dIt;
+            const Detection &b = *next;
-            for (dvector::iterator next = dIt + 1; next != objects.end(); )
-            {
-                const Detection &b = *next;
 
-                const float ovl =  overlap(a.bb, b.bb) / std::min(a.bb.area(), b.bb.area());
+            const float ovl =  overlap(a.bb, b.bb) / std::min(a.bb.area(), b.bb.area());
 
-                if (ovl > 0.65f)
+            if (ovl > DollarThreshold)
-                    next = objects.erase(next);
+                next = objects.erase(next);
-                else
+            else
-                    ++next;
+                ++next;
-            }
         }
     }
-};
+}
 
-cv::Ptr<NMS> createNMS(int type)
+static void suppress(int type, std::vector<Detection>& objects)
 {
     CV_Assert(type == cv::SCascade::DOLLAR);
-    return cv::Ptr<NMS>(new DollarNMS);
+    DollarNMS(objects);
 }
 
 }
@@ -522,8 +511,7 @@ void cv::SCascade::detectNoRoi(const cv::Mat& image, std::vector<Detection>& obj
         }
     }
 
-    if (rejCriteria != NO_REJECT)
+    if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
-        createNMS(rejCriteria)->apply(objects);
 }
 
 void cv::SCascade::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
@@ -572,8 +560,7 @@ void cv::SCascade::detect(cv::InputArray _image, cv::InputArray _rois, std::vect
          }
     }
 
-    if (rejCriteria != NO_REJECT)
+    if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
-    	createNMS(rejCriteria)->apply(objects);
 }
 
 void cv::SCascade::detect(InputArray _image, InputArray _rois,  OutputArray _rects, OutputArray _confs) const