BING bb sorting modified and SuBSENSE compiling error fixed

modules/saliency/src/BING/objectnessBING.cpp

@@ -177,9 +177,9 @@ void ObjectnessBING::predictBBoxSII( ValStructVec<float, Vec4i> &valBoxes, const
     valBoxes( i ) = valBoxes( i ) * svmIIw[0] + svmIIw[1];
   }
   //valBoxes.sort();
-  // Ascending order. At the top there are the values with lower
-  // values of ​​objectness, ie more likely to have objects in the their corresponding rectangles.
-  valBoxes.sort( false );
+  // Descending order. At the top there are the values with higher
+  // values, ie more likely to have objects in the their corresponding rectangles.
+  valBoxes.sort( true );
 }
 
 // Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY).
@@ -481,8 +481,8 @@ bool ObjectnessBING::computeSaliencyImpl( const InputArray image, OutputArray ob
   ValStructVec<float, Vec4i> finalBoxes;
   getObjBndBoxesForSingleImage( image.getMat(), finalBoxes, 250 );
 
-  // List of rectangles returned by objectess function in ascending order.
-  // At the top there are the rectangles with lower values of ​​objectness, ie more
+  // List of rectangles returned by objectess function in descending order.
+  // At the top there are the rectangles with higher values, ie more
   // likely to have objects in them.
   vector<Vec4i> sortedBB = finalBoxes.getSortedStructVal();
   Mat( sortedBB ).copyTo( objBoundingBox );

modules/saliency/src/SuBSENSE/BackgroundSubtractorLBSP.cpp

@@ -25,9 +25,9 @@ void BackgroundSubtractorLBSP::initialize(const cv::Mat& oInitImg) {
 	this->initialize(oInitImg,std::vector<cv::KeyPoint>());
 }
 
-cv::AlgorithmInfo* BackgroundSubtractorLBSP::info() const {
-	return nullptr;
-}
+//cv::AlgorithmInfo* BackgroundSubtractorLBSP::info() const {
+//	//return nullptr;
+//}
 
 void BackgroundSubtractorLBSP::getBackgroundDescriptorsImage(cv::OutputArray backgroundDescImage) const {
 	CV_Assert(LBSP::DESC_SIZE==2);

modules/saliency/src/SuBSENSE/BackgroundSubtractorLBSP.h

@@ -1,7 +1,7 @@
 #pragma once
 
 #include <opencv2/features2d.hpp>
-#include <opencv2/video/background_segm.hpp>
+//#include <opencv2/video/background_segm.hpp>
 #include "LBSP.h"
 
 /*!
@@ -13,31 +13,32 @@
 
  This algorithm is currently NOT thread-safe.
  */
-class BackgroundSubtractorLBSP  /* : public cv::BackgroundSubtractor */ {
-public:
+class BackgroundSubtractorLBSP /* : public cv::BackgroundSubtractor */
+{
+ public:
   //! full constructor
-	BackgroundSubtractorLBSP(float fRelLBSPThreshold, size_t nDescDistThreshold, size_t nLBSPThresholdOffset=0);
+  BackgroundSubtractorLBSP( float fRelLBSPThreshold, size_t nDescDistThreshold, size_t nLBSPThresholdOffset = 0 );
   //! default destructor
   virtual ~BackgroundSubtractorLBSP();
   //! (re)initiaization method; needs to be called before starting background subtraction
-	virtual void initialize(const cv::Mat& oInitImg);
+  virtual void initialize( const cv::Mat& oInitImg );
   //! (re)initiaization method; needs to be called before starting background subtraction (note: also reinitializes the keypoints vector)
-	virtual void initialize(const cv::Mat& oInitImg, const std::vector<cv::KeyPoint>& voKeyPoints)=0;
+  virtual void initialize( const cv::Mat& oInitImg, const std::vector<cv::KeyPoint>& voKeyPoints )=0;
   //! primary model update function; the learning param is used to override the internal learning speed (ignored when <= 0)
-	virtual void operator()(cv::InputArray image, cv::OutputArray fgmask, double learningRate=0)=0;
+  virtual void operator()( cv::InputArray image, cv::OutputArray fgmask, double learningRate = 0 )=0;
   //! unused, always returns nullptr
-	virtual cv::AlgorithmInfo* info() const;
+  cv::AlgorithmInfo* info() const;
   //! returns a copy of the latest reconstructed background descriptors image
-	virtual void getBackgroundDescriptorsImage(cv::OutputArray backgroundDescImage) const;
+  virtual void getBackgroundDescriptorsImage( cv::OutputArray backgroundDescImage ) const;
   //! returns the keypoints list used for descriptor extraction (note: by default, these are generated from the DenseFeatureDetector class, and the border points are removed)
   virtual std::vector<cv::KeyPoint> getBGKeyPoints() const;
   //! sets the keypoints to be used for descriptor extraction, effectively setting the BGModel ROI (note: this function will remove all border keypoints)
-	virtual void setBGKeyPoints(std::vector<cv::KeyPoint>& keypoints);
+  virtual void setBGKeyPoints( std::vector<cv::KeyPoint>& keypoints );
 
   // ######## DEBUG PURPOSES ONLY ##########
   int nDebugCoordX, nDebugCoordY;
 
-protected:
+ protected:
   //! background model descriptors samples (tied to m_voKeyPoints but shaped like the input frames)
   std::vector<cv::Mat> m_voBGDescSamples;
   //! background model keypoints used for LBSP descriptor extraction (specific to the input image size)
@@ -57,7 +58,7 @@ protected:
   //! LBSP relative internal threshold (kept here since we don't keep an LBSP object)
   const float m_fRelLBSPThreshold;
   //! pre-allocated internal LBSP threshold values for all possible 8-bit intensity values
-	size_t m_anLBSPThreshold_8bitLUT[UCHAR_MAX+1];
+  size_t m_anLBSPThreshold_8bitLUT[UCHAR_MAX + 1];
   //! defines whether or not the subtractor is fully initialized
   bool m_bInitialized;
 };