hog人体检测

opencv中的 HOGDescriptor 类

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)      {}        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;        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<float>& descriptors,                           Size winStride=Size(), Size padding=Size(),                           const vector<Point>& locations=vector<Point>()) const;      //with found weights output      CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,                          CV_OUT vector<double>& weights,                          double hitThreshold=0, Size winStride=Size(),                          Size padding=Size(),                          const vector<Point>& searchLocations=vector<Point>()) const;      //without found weights output      virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations,                          double hitThreshold=0, Size winStride=Size(),                          Size padding=Size(),                          const vector<Point>& searchLocations=vector<Point>()) const;      //with result weights output      CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations,                                    CV_OUT vector<double>& 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<Rect>& 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<float> getDefaultPeopleDetector();      CV_WRAP static vector<float> 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<float> svmDetector;      CV_PROP int nlevels;         // evaluate specified ROI and return confidence value for each location     void detectROI(const cv::Mat& img, const vector<cv::Point> &locations,                                     CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& 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<cv::Rect>& foundLocations,                                                         std::vector<DetectionROI>& locations,                                                         double hitThreshold = 0,                                                         int groupThreshold = 0) const;       // read/parse Dalal's alt model file     void readALTModel(std::string modelfile);  };  

其中hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());得到svm的检测算子。

hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);得到检测的结果。

人体检测代码：

vector<Rect> getBodyArea(Mat rawFrame){   vector<Rect> foundRect,foundFilteredRect;   HOGDescriptor peopleDectectHog;   peopleDectectHog .setSVMDetector (HOGDescriptor::getDefaultPeopleDetector());//默认人体检测器    peopleDectectHog.detectMultiScale (rawFrame ,foundRect, 0 ,Size(8,8),Size(32,32),1.05,2);   size_t i,j;   for(i=0;i<foundRect.size() ;i++)//下面的这个for语句是找出所有没有嵌套的矩形框r,并放入found_filtered中,   {//如果有嵌套的话,则取外面最大的那个矩形框放入found_filtered中       Rect r=foundRect[i];   for(j=0;j<foundRect.size();j++)   {   if(j!=i&&(r&foundRect[j])==r)   break ;   }   if(j==foundRect.size())   foundFilteredRect .push_back (r);   }   for(i=0;i<foundFilteredRect.size ();i++)//调整大小   {      Rect r=foundFilteredRect [i];  r.x+=cvRound (r.width *0.1);  r.width =cvRound(r.width *0.8);  r.y+=cvRound(r.height *0.07);  r.height =cvRound(r.height *0.8);  rectangle (rawFrame ,r.tl(),r.br(),Scalar(0,255,0),3);         }   return foundFilteredRect ;}

结果如下: