Fixed windows build problems of BackgroundSubtractorGMG but code still need more work.

modules/video/include/opencv2/video/background_segm.hpp

@@ -272,7 +272,7 @@ private:
      *  Used internally to represent a single feature in a histogram.
      *  Feature is a color and an associated likelihood (weight in the histogram).
      */
-	struct HistogramFeatureGMG
+    struct CV_EXPORTS HistogramFeatureGMG
     {
         /**
          * Default constructor.
@@ -316,11 +316,11 @@ private:
      *  Representation of the statistical model of a single pixel for use in the background subtraction
      *  algorithm.
      */
-	class PixelModelGMG
+    class CV_EXPORTS PixelModelGMG
     {
     public:
         PixelModelGMG();
-		virtual ~PixelModelGMG();
+        ~PixelModelGMG();
 
         /**
          *  Incorporate the last observed feature into the statistical model.
@@ -359,7 +359,7 @@ private:
         PixelModelGMG& operator *=(const float &rhs);
         //friend class BackgroundSubtractorGMG;
         //friend class HistogramFeatureGMG;
-	protected:
+    private:
         size_t numFeatures; //!< number of features in histogram
         size_t maxFeatures; //!< max allowable features in histogram
         std::list<HistogramFeatureGMG> histogram; //!< represents the histogram as a list of features
@@ -444,8 +444,6 @@ protected:
     Mat                         fgMaskImage;   //!< Foreground mask image.
 };
 
-bool initModule_BackgroundSubtractorGMG(void);
-
 }
 
 #endif

modules/video/src/bgfg_gmg.cpp

@@ -237,14 +237,14 @@ void BackgroundSubtractorGMG::operator()(InputArray _image, OutputArray _fgmask,
 
         if (frameNum > numInitializationFrames)  // typical operation
         {
-			newFeature.likelihood = learningRate;
+            newFeature.likelihood = float(learningRate);
             /*
              * (1) Query histogram to find posterior probability of feature under model.
              */
             float likelihood = (float)pixel->getLikelihood(newFeature);
 
             // see Godbehere, Matsukawa, Goldberg (2012) for reasoning behind this implementation of Bayes rule
-			float posterior = (likelihood*backgroundPrior)/(likelihood*backgroundPrior+(1-likelihood)*(1-backgroundPrior));
+            float posterior = float((likelihood*backgroundPrior)/(likelihood*backgroundPrior+(1-likelihood)*(1-backgroundPrior)));
 
             /*
              * (2) feed posterior probability into the posterior image
@@ -252,7 +252,7 @@ void BackgroundSubtractorGMG::operator()(InputArray _image, OutputArray _fgmask,
             int row,col;
             col = i%imWidth;
             row = (i-col)/imWidth;
-			posteriorImage.at<float>(row,col) = (1.0-posterior);
+            posteriorImage.at<float>(row,col) = (1.0f-posterior);
         }
         pixel->setLastObservedFeature(newFeature);
     }
@@ -393,8 +393,8 @@ void BackgroundSubtractorGMG::PixelModelGMG::insertFeature(double learningRate)
          * (3) Check if feature already represented. If so, simply add.
          * (4) If feature is not represented, remove old feature, distribute weight evenly among existing features, add in new feature.
          */
-		*this *= (1.0-learningRate);
-		lastObservedFeature.likelihood = learningRate;
+        *this *= float(1.0-learningRate);
+        lastObservedFeature.likelihood = float(learningRate);
 
         for (feature = histogram.begin(); feature != last_feature; ++feature)
         {
@@ -453,7 +453,7 @@ void BackgroundSubtractorGMG::PixelModelGMG::normalizeHistogram()
     for (feature = histogram.begin(); feature != last_feature; ++feature)
     {
         if (total != 0.0)
-			feature->likelihood /= total;
+            feature->likelihood = float(feature->likelihood / total);
     }
 }