objdetect.hpp

/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                          License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
 
#ifndef OPENCV_OBJDETECT_HPP
#define OPENCV_OBJDETECT_HPP
 
#include "opencv2/core.hpp"
#include "opencv2/objdetect/aruco_detector.hpp"
#include "opencv2/objdetect/graphical_code_detector.hpp"
 
typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
 
namespace cv
{
 
 
 
class CV_EXPORTS SimilarRects
{
public:
    SimilarRects(double _eps) : eps(_eps) {}
    inline bool operator()(const Rect& r1, const Rect& r2) const
    {
        double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5;
        return std::abs(r1.x - r2.x) <= delta &&
            std::abs(r1.y - r2.y) <= delta &&
            std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
            std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
    }
    double eps;
};
 
CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
                                  int groupThreshold, double eps = 0.2);
CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
                                  double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
                                  std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
CV_EXPORTS   void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
                                            std::vector<double>& foundScales,
                                            double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
 
 
template<> struct DefaultDeleter<CvHaarClassifierCascade>{ CV_EXPORTS void operator ()(CvHaarClassifierCascade* obj) const; };
 
enum { CASCADE_DO_CANNY_PRUNING    = 1,
       CASCADE_SCALE_IMAGE         = 2,
       CASCADE_FIND_BIGGEST_OBJECT = 4,
       CASCADE_DO_ROUGH_SEARCH     = 8
     };
 
class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
{
public:
    virtual ~BaseCascadeClassifier();
    virtual bool empty() const CV_OVERRIDE = 0;
    virtual bool load( const String& filename ) = 0;
    virtual void detectMultiScale( InputArray image,
                           CV_OUT std::vector<Rect>& objects,
                           double scaleFactor,
                           int minNeighbors, int flags,
                           Size minSize, Size maxSize ) = 0;
 
    virtual void detectMultiScale( InputArray image,
                           CV_OUT std::vector<Rect>& objects,
                           CV_OUT std::vector<int>& numDetections,
                           double scaleFactor,
                           int minNeighbors, int flags,
                           Size minSize, Size maxSize ) = 0;
 
    virtual void detectMultiScale( InputArray image,
                                   CV_OUT std::vector<Rect>& objects,
                                   CV_OUT std::vector<int>& rejectLevels,
                                   CV_OUT std::vector<double>& levelWeights,
                                   double scaleFactor,
                                   int minNeighbors, int flags,
                                   Size minSize, Size maxSize,
                                   bool outputRejectLevels ) = 0;
 
    virtual bool isOldFormatCascade() const = 0;
    virtual Size getOriginalWindowSize() const = 0;
    virtual int getFeatureType() const = 0;
    virtual void* getOldCascade() = 0;
 
    class CV_EXPORTS MaskGenerator
    {
    public:
        virtual ~MaskGenerator() {}
        virtual Mat generateMask(const Mat& src)=0;
        virtual void initializeMask(const Mat& /*src*/) { }
    };
    virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
    virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
};
 
class CV_EXPORTS_W CascadeClassifier
{
public:
    CV_WRAP CascadeClassifier();
    CV_WRAP CascadeClassifier(const String& filename);
    ~CascadeClassifier();
    CV_WRAP bool empty() const;
    CV_WRAP bool load( const String& filename );
    CV_WRAP bool read( const FileNode& node );
 
    CV_WRAP void detectMultiScale( InputArray image,
                          CV_OUT std::vector<Rect>& objects,
                          double scaleFactor = 1.1,
                          int minNeighbors = 3, int flags = 0,
                          Size minSize = Size(),
                          Size maxSize = Size() );
 
    CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image,
                          CV_OUT std::vector<Rect>& objects,
                          CV_OUT std::vector<int>& numDetections,
                          double scaleFactor=1.1,
                          int minNeighbors=3, int flags=0,
                          Size minSize=Size(),
                          Size maxSize=Size() );
 
    CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
                                  CV_OUT std::vector<Rect>& objects,
                                  CV_OUT std::vector<int>& rejectLevels,
                                  CV_OUT std::vector<double>& levelWeights,
                                  double scaleFactor = 1.1,
                                  int minNeighbors = 3, int flags = 0,
                                  Size minSize = Size(),
                                  Size maxSize = Size(),
                                  bool outputRejectLevels = false );
 
    CV_WRAP bool isOldFormatCascade() const;
    CV_WRAP Size getOriginalWindowSize() const;
    CV_WRAP int getFeatureType() const;
    void* getOldCascade();
 
    CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
 
    void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
    Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
 
    Ptr<BaseCascadeClassifier> cc;
};
 
CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
 
 
struct DetectionROI
{
   double scale;
   std::vector<cv::Point> locations;
   std::vector<double> confidences;
};
 
struct CV_EXPORTS_W HOGDescriptor
{
public:
    enum HistogramNormType { L2Hys = 0 
         };
    enum { DEFAULT_NLEVELS = 64 
         };
    enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW };
 
    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),
        free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
    {}
 
    CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
                  Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
                  HOGDescriptor::HistogramNormType _histogramNormType=HOGDescriptor::L2Hys,
                  double _L2HysThreshold=0.2, bool _gammaCorrection=false,
                  int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
    : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
    nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
    histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
    gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
    {}
 
    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(InputArray img,
                         CV_OUT std::vector<float>& descriptors,
                         Size winStride = Size(), Size padding = Size(),
                         const std::vector<Point>& locations = std::vector<Point>()) const;
 
    CV_WRAP virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
                        CV_OUT std::vector<double>& weights,
                        double hitThreshold = 0, Size winStride = Size(),
                        Size padding = Size(),
                        const std::vector<Point>& searchLocations = std::vector<Point>()) const;
 
    virtual void detect(InputArray img, CV_OUT std::vector<Point>& foundLocations,
                        double hitThreshold = 0, Size winStride = Size(),
                        Size padding = Size(),
                        const std::vector<Point>& searchLocations=std::vector<Point>()) const;
 
    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 groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
 
    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 groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
 
    CV_WRAP virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray angleOfs,
                                 Size paddingTL = Size(), Size paddingBR = Size()) const;
 
    CV_WRAP static std::vector<float> getDefaultPeopleDetector();
 
    CV_WRAP static std::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 HOGDescriptor::HistogramNormType histogramNormType;
 
    CV_PROP double L2HysThreshold;
 
    CV_PROP bool gammaCorrection;
 
    CV_PROP std::vector<float> svmDetector;
 
    UMat oclSvmDetector;
 
    float free_coef;
 
    CV_PROP int nlevels;
 
    CV_PROP bool signedGradient;
 
    virtual void detectROI(InputArray img, const std::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;
 
    virtual void detectMultiScaleROI(InputArray img,
                                     CV_OUT std::vector<cv::Rect>& foundLocations,
                                     std::vector<DetectionROI>& locations,
                                     double hitThreshold = 0,
                                     int groupThreshold = 0) const;
 
    void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
};
 
 
class CV_EXPORTS_W QRCodeEncoder {
protected:
    QRCodeEncoder();  // use ::create()
public:
    virtual ~QRCodeEncoder();
 
    enum EncodeMode {
        MODE_AUTO              = -1,
        MODE_NUMERIC           = 1, // 0b0001
        MODE_ALPHANUMERIC      = 2, // 0b0010
        MODE_BYTE              = 4, // 0b0100
        MODE_ECI               = 7, // 0b0111
        MODE_KANJI             = 8, // 0b1000
        MODE_STRUCTURED_APPEND = 3  // 0b0011
    };
 
    enum CorrectionLevel {
        CORRECT_LEVEL_L = 0,
        CORRECT_LEVEL_M = 1,
        CORRECT_LEVEL_Q = 2,
        CORRECT_LEVEL_H = 3
    };
 
    enum ECIEncodings {
        ECI_UTF8 = 26
    };
 
    struct CV_EXPORTS_W_SIMPLE Params
    {
        CV_WRAP Params();
 
        CV_PROP_RW int version;
 
        CV_PROP_RW CorrectionLevel correction_level;
 
        CV_PROP_RW EncodeMode mode;
 
        CV_PROP_RW int structure_number;
    };
 
    static CV_WRAP
    Ptr<QRCodeEncoder> create(const QRCodeEncoder::Params& parameters = QRCodeEncoder::Params());
 
    CV_WRAP virtual void encode(const String& encoded_info, OutputArray qrcode) = 0;
 
    CV_WRAP virtual void encodeStructuredAppend(const String& encoded_info, OutputArrayOfArrays qrcodes) = 0;
 
};
class CV_EXPORTS_W_SIMPLE QRCodeDetector : public GraphicalCodeDetector
{
public:
    CV_WRAP QRCodeDetector();
 
    CV_WRAP QRCodeDetector& setEpsX(double epsX);
    CV_WRAP QRCodeDetector& setEpsY(double epsY);
 
    CV_WRAP QRCodeDetector& setUseAlignmentMarkers(bool useAlignmentMarkers);
 
    CV_WRAP cv::String decodeCurved(InputArray img, InputArray points, OutputArray straight_qrcode = noArray());
 
    CV_WRAP std::string detectAndDecodeCurved(InputArray img, OutputArray points=noArray(),
                                              OutputArray straight_qrcode = noArray());
};
 
class CV_EXPORTS_W_SIMPLE QRCodeDetectorAruco : public GraphicalCodeDetector {
public:
    CV_WRAP QRCodeDetectorAruco();
 
    struct CV_EXPORTS_W_SIMPLE Params {
        CV_WRAP Params();
 
        CV_PROP_RW float minModuleSizeInPyramid;
 
        CV_PROP_RW float maxRotation;
 
        CV_PROP_RW float maxModuleSizeMismatch;
 
        CV_PROP_RW float maxTimingPatternMismatch;
 
        CV_PROP_RW float maxPenalties;
 
        CV_PROP_RW float maxColorsMismatch;
 
        CV_PROP_RW float scaleTimingPatternScore;
    };
 
    CV_WRAP explicit QRCodeDetectorAruco(const QRCodeDetectorAruco::Params& params);
 
    CV_WRAP const QRCodeDetectorAruco::Params& getDetectorParameters() const;
 
    CV_WRAP QRCodeDetectorAruco& setDetectorParameters(const QRCodeDetectorAruco::Params& params);
 
    CV_WRAP const aruco::DetectorParameters& getArucoParameters() const;
 
    CV_WRAP void setArucoParameters(const aruco::DetectorParameters& params);
};
 
}
 
#include "opencv2/objdetect/detection_based_tracker.hpp"
#include "opencv2/objdetect/face.hpp"
#include "opencv2/objdetect/charuco_detector.hpp"
#include "opencv2/objdetect/barcode.hpp"
 
#endif