OpenCV编程->HOG检测（2）

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<cv::Point> locations;    // vector that will contain confidence values for each location    vector<double> 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<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);    void groupRectangles(vector<cv::Rect>& rectList, vector<double>& weights, int groupThreshold, double eps) const; };   CV_EXPORTS_W void findDataMatrix(InputArray image,                                  CV_OUT vector<string>& codes,                                  OutputArray corners=noArray(),                                  OutputArrayOfArrays dmtx=noArray()); CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image,                                       const vector<string>& codes,                                       InputArray corners); }

从上面可以知道 有两种方式进行HOG特征检测。

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