update HOGDescriptor documentation

modules/objdetect/include/opencv2/objdetect.hpp

@@ -376,7 +376,7 @@ public:
          };
     enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
          };
-    /**@brief Creates the HOG descriptor and detector with default params.
+    /**@brief Creates the HOG descriptor and detector with default parameters.
 
     aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
     */
@@ -412,6 +412,8 @@ public:
     {}
 
     /** @overload
+
+    Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
     @param filename the file name containing  HOGDescriptor properties and coefficients of the trained classifier
     */
     CV_WRAP HOGDescriptor(const String& filename)
@@ -450,24 +452,24 @@ public:
     */
     CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
 
-    /** @brief Reads HOGDescriptor parameters from a file node.
+    /** @brief Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.
     @param fn File node
     */
     virtual bool read(FileNode& fn);
 
-    /** @brief Stores HOGDescriptor parameters in a file storage.
+    /** @brief Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.
     @param fs File storage
     @param objname Object name
     */
     virtual void write(FileStorage& fs, const String& objname) const;
 
-    /** @brief loads coefficients for the linear SVM classifier from a file
+    /** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
     @param filename Name of the file to read.
     @param objname The optional name of the node to read (if empty, the first top-level node will be used).
     */
     CV_WRAP virtual bool load(const String& filename, const String& objname = String());
 
-    /** @brief saves coefficients for the linear SVM classifier to a file
+    /** @brief saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file
     @param filename File name
     @param objname Object name
     */
@@ -535,13 +537,14 @@ public:
     @param winStride Window stride. It must be a multiple of block stride.
     @param padding Padding
     @param scale Coefficient of the detection window increase.
-    @param finalThreshold Final threshold
+    @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
+    by many rectangles. 0 means not to perform grouping.
     @param useMeanshiftGrouping indicates grouping algorithm
     */
     CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
                                   CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
                                   Size winStride = Size(), Size padding = Size(), double scale = 1.05,
-                                  double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
+                                  double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
 
     /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
     of rectangles.
@@ -553,13 +556,14 @@ public:
     @param winStride Window stride. It must be a multiple of block stride.
     @param padding Padding
     @param scale Coefficient of the detection window increase.
-    @param finalThreshold Final threshold
+    @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
+    by many rectangles. 0 means not to perform grouping.
     @param useMeanshiftGrouping indicates grouping algorithm
     */
     virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
                                   double hitThreshold = 0, Size winStride = Size(),
                                   Size padding = Size(), double scale = 1.05,
-                                  double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
+                                  double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
 
     /** @brief  Computes gradients and quantized gradient orientations.
     @param img Matrix contains the image to be computed

modules/objdetect/src/hog.cpp

@@ -1884,7 +1884,7 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
 void HOGDescriptor::detectMultiScale(
     InputArray _img, std::vector<Rect>& foundLocations, std::vector<double>& foundWeights,
     double hitThreshold, Size winStride, Size padding,
-    double scale0, double finalThreshold, bool useMeanshiftGrouping) const
+    double scale0, double groupThreshold, bool useMeanshiftGrouping) const
 {
     CV_INSTRUMENT_REGION();
 
@@ -1910,7 +1910,7 @@ void HOGDescriptor::detectMultiScale(
 
     CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 &&
         winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(),
-        ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector,
+        ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, groupThreshold, oclSvmDetector,
         blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient));
 
     std::vector<Rect> allCandidates;
@@ -1931,21 +1931,21 @@ void HOGDescriptor::detectMultiScale(
     std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));
 
     if ( useMeanshiftGrouping )
-        groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
+        groupRectangles_meanshift(foundLocations, foundWeights, foundScales, groupThreshold, winSize);
     else
-        groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
+        groupRectangles(foundLocations, foundWeights, (int)groupThreshold, 0.2);
     clipObjects(imgSize, foundLocations, 0, &foundWeights);
 }
 
 void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations,
     double hitThreshold, Size winStride, Size padding,
-    double scale0, double finalThreshold, bool useMeanshiftGrouping) const
+    double scale0, double groupThreshold, bool useMeanshiftGrouping) const
 {
     CV_INSTRUMENT_REGION();
 
     std::vector<double> foundWeights;
     detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
-                padding, scale0, finalThreshold, useMeanshiftGrouping);
+                padding, scale0, groupThreshold, useMeanshiftGrouping);
 }
 
 template<typename _ClsName> struct RTTIImpl