下面开始看源码。

HOG特征检测源码在opencv/sources/modules/object/src/hog.cpp  和 object.h文件里。

object.h文件里HOG检测代码如下：

HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //  // struct for detection region of interest (ROI) struct DetectionROI {    // scale(size) of the bounding box    double scale;    // set of requrested locations to be evaluated    vector
     
       locations;    // vector that will contain confidence values for each location    vector
      
        confidences; };  struct CV_EXPORTS_W HOGDescriptor { public:     enum { L2Hys=0 };     enum { DEFAULT_NLEVELS=64 };      CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),         cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),         histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),         nlevels(HOGDescriptor::DEFAULT_NLEVELS)     {}      CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,                   Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,                   int _histogramNormType=HOGDescriptor::L2Hys,                   double _L2HysThreshold=0.2, bool _gammaCorrection=false,                   int _nlevels=HOGDescriptor::DEFAULT_NLEVELS)     : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),     nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),     histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),     gammaCorrection(_gammaCorrection), nlevels(_nlevels)     {}     //导入文本文件进行初始化     CV_WRAP HOGDescriptor(const String& filename)     {         load(filename);     }      HOGDescriptor(const HOGDescriptor& d)     {         d.copyTo(*this);     }      virtual ~HOGDescriptor() {}      CV_WRAP size_t getDescriptorSize() const;     CV_WRAP bool checkDetectorSize() const;     CV_WRAP double getWinSigma() const;     //virtual为虚函数，在指针或引用时起函数多态作用     CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);          virtual bool read(FileNode& fn);     virtual void write(FileStorage& fs, const String& objname) const;      CV_WRAP virtual bool load(const String& filename, const String& objname=String());     CV_WRAP virtual void save(const String& filename, const String& objname=String()) const;     virtual void copyTo(HOGDescriptor& c) const;      CV_WRAP virtual void compute(const Mat& img,                          CV_OUT vector
       
        & descriptors,                          Size winStride=Size(), Size padding=Size(),                          const vector
        
         & locations=vector
         
          ()) const;     //with found weights output     CV_WRAP virtual void detect(const Mat& img, CV_OUT vector
          
           & foundLocations, CV_OUT vector
           
            & weights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const vector
            
             & searchLocations=vector
             
              ()) const; //without found weights output virtual void detect(const Mat& img, CV_OUT vector
              
               & foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const vector
               
                & searchLocations=vector
                
                 ()) const; //with result weights output CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector
                 
                  & foundLocations, CV_OUT vector
                  
                   & foundWeights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), double scale=1.05, double finalThreshold=2.0,bool useMeanshiftGrouping = false) const; //without found weights output virtual void detectMultiScale(const Mat& img, CV_OUT vector
                   
                    & foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), double scale=1.05, double finalThreshold=2.0, bool useMeanshiftGrouping = false) const; CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, Size paddingTL=Size(), Size paddingBR=Size()) const; CV_WRAP static vector
                    
                      getDefaultPeopleDetector(); CV_WRAP static vector
                     
                       getDaimlerPeopleDetector(); CV_PROP Size winSize; CV_PROP Size blockSize; CV_PROP Size blockStride; CV_PROP Size cellSize; CV_PROP int nbins; CV_PROP int derivAperture; CV_PROP double winSigma; CV_PROP int histogramNormType; CV_PROP double L2HysThreshold; CV_PROP bool gammaCorrection; CV_PROP vector
                      
                        svmDetector; CV_PROP int nlevels; // evaluate specified ROI and return confidence value for each location void detectROI(const cv::Mat& img, const vector
                       
                         &locations, CV_OUT std::vector
                        
                         & foundLocations, CV_OUT std::vector
                         
                          & confidences, double hitThreshold = 0, cv::Size winStride = Size(), cv::Size padding = Size()) const; // evaluate specified ROI and return confidence value for each location in multiple scales void detectMultiScaleROI(const cv::Mat& img, CV_OUT std::vector
                          
                           & foundLocations, std::vector
                           
                            & locations, double hitThreshold = 0, int groupThreshold = 0) const; // read/parse Dalal's alt model file void readALTModel(std::string modelfile); void groupRectangles(vector
                            
                             & rectList, vector
                             
                              & weights, int groupThreshold, double eps) const; }; CV_EXPORTS_W void findDataMatrix(InputArray image, CV_OUT vector
                              
                               & codes, OutputArray corners=noArray(), OutputArrayOfArrays dmtx=noArray()); CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image, const vector
                               
                                & codes, InputArray corners); }
                               
                              
                             
                            
                           
                          
                         
                        
                       
                      
                     
                    
                   
                  
                 
                
               
              
             
            
           
          
         
        
       
      
     

1.导入HOG特征文本数据检测

2.直接调用opencv里的HOG特征数据检测