Delphi-OpenCV/include/objdetect/objdetect.pas

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(* /  **************************************************************************************************
  //                                 Project Delphi-OpenCV
  //  **************************************************************************************************
  //  Contributor:
  //  laentir Valetov
  //  email:laex@bk.ru
  //  **************************************************************************************************
  //  You may retrieve the latest version of this file at the GitHub,
  //  located at git://github.com/Laex/Delphi-OpenCV.git
  //  **************************************************************************************************
  //  License:
  //  The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
  //  you may not use this file except in compliance with the License. You may obtain a copy of the
  //  License at http://www.mozilla.org/MPL/
  //
  //  Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
  //  ANY KIND, either express or implied. See the License for the specific language governing rights
  //  and limitations under the License.
  //
  //  Alternatively, the contents of this file may be used under the terms of the
  //  GNU Lesser General Public License (the  "LGPL License"), in which case the
  //  provisions of the LGPL License are applicable instead of those above.
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  //
  //  For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
  //  **************************************************************************************************
  //  Warning: Using Delphi XE3 syntax!
  //  **************************************************************************************************
  //  The Initial Developer of the Original Code:
  //  OpenCV: open source computer vision library
  //  Homepage:    http://opencv.org
  //  Online docs: http://docs.opencv.org
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  //  **************************************************************************************************
  //  Original file:
  //  opencv\modules\objdetect\include\opencv2\objdetect.hpp
  //  ************************************************************************************************* *)

{$IFDEF DEBUG}
{$A8,B-,C+,D+,E-,F-,G+,H+,I+,J-,K-,L+,M-,N+,O-,P+,Q+,R+,S-,T-,U-,V+,W+,X+,Y+,Z1}
{$ELSE}
{$A8,B-,C-,D-,E-,F-,G+,H+,I+,J-,K-,L-,M-,N+,O+,P+,Q-,R-,S-,T-,U-,V+,W-,X+,Y-,Z1}
{$ENDIF}
{$WARN SYMBOL_DEPRECATED OFF}
{$WARN SYMBOL_PLATFORM OFF}
{$WARN UNIT_PLATFORM OFF}
{$WARN UNSAFE_TYPE OFF}
{$WARN UNSAFE_CODE OFF}
{$WARN UNSAFE_CAST OFF}
Unit ObjDetect;

interface

Uses Core.types_c, haar, System.Types, Xml.XMLDoc;

(*
  //****************************************************************************************\
  //*                         Haar-like Object Detection functions                           *
  //****************************************************************************************/
*)
Const
  CV_HAAR_MAGIC_VAL = $42500000;
  CV_TYPE_NAME_HAAR = 'opencv-haar-classifier';

  // #define CV_IS_HAAR_CLASSIFIER( haar )                                                    \
  // ((haar) != NULL &&                                                                   \
  // (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)

type
  THaarFeature = record
    r: TCvRect;
    weight: Single;
  end;

  pCvHaarFeature = ^TCvHaarFeature;

  TCvHaarFeature = packed record
    tilted: Integer;
    rect: array [0 .. CV_HAAR_FEATURE_MAX - 1] of THaarFeature;
  end;

  pCvHaarClassifier = ^TCvHaarClassifier;

  TCvHaarClassifier = packed record
    count: Integer;
    haar_feature: pCvHaarFeature;
    threshold: pSingle;
    left: pInteger;
    right: pInteger;
    alpha: pSingle;
  end;

  pCvHaarStageClassifier = ^TCvHaarStageClassifier;

  TCvHaarStageClassifier = packed record
    count: Integer;
    threshold: Single;
    classifier: pCvHaarClassifier;
    next: Integer;
    child: Integer;
    parent: Integer;
  end;

  // TCvHidHaarClassifierCascade = TCvHidHaarClassifierCascade;
  pCvHidHaarClassifierCascade = ^TCvHidHaarClassifierCascade;

  pCvHaarClassifierCascade = ^TCvHaarClassifierCascade;

  TCvHaarClassifierCascade = packed record
    flags: Integer;
    count: Integer;
    orig_window_size: TCvSize;
    real_window_size: TCvSize;
    scale: Real;
    stage_classifier: pCvHaarStageClassifier;
    hid_cascade: pCvHidHaarClassifierCascade;
  end;

  TCvAvgComp = packed record
    rect: TCvRect;
    neighbors: Integer;
  end;

  {
    // Loads haar classifier cascade from a directory.
    // It is obsolete: convert your cascade to xml and use cvLoad instead
    CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade(
    const char* directory,
    CvSize orig_window_size);
  }
function cvLoadHaarClassifierCascade(const directory: pCVChar; orig_window_size: TCvSize)
  : pCvHaarClassifierCascade; cdecl;

// CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );

procedure cvReleaseHaarClassifierCascade(Var cascade: pCvHaarClassifierCascade); cdecl;

Const
  CV_HAAR_DO_CANNY_PRUNING = 1;
  CV_HAAR_SCALE_IMAGE = 2;
  CV_HAAR_FIND_BIGGEST_OBJECT = 4;
  CV_HAAR_DO_ROUGH_SEARCH = 8;

  // CVAPI(CvSeq*) cvHaarDetectObjectsForROC( const CvArr* image,
  // CvHaarClassifierCascade* cascade, CvMemStorage* storage,
  // CvSeq** rejectLevels, CvSeq** levelWeightds,
  // double scale_factor CV_DEFAULT(1.1),
  // int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
  // CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
  // bool outputRejectLevels = false );

  {
    CVAPI(CvSeq*) cvHaarDetectObjects(
    const CvArr* image,
    CvHaarClassifierCascade* cascade,
    CvMemStorage* storage,
    double scale_factor CV_DEFAULT(1.1),
    int min_neighbors CV_DEFAULT(3),
    int flags CV_DEFAULT(0),
    CvSize min_size CV_DEFAULT(cvSize(0,0)),
    CvSize max_size CV_DEFAULT(cvSize(0,0)));
  }

function cvHaarDetectObjects(
  { } const image: pIplImage;
  { } cascade: pCvHaarClassifierCascade;
  { } storage: pCvMemStorage;
  { } scale_factor: double { =1.1 };
  { } min_neighbors: Integer { =3 };
  { } flags: Integer { = 0 };
  { } min_size: TCvSize { =CV_DEFAULT(cvSize(0,0)) };
  { } max_size: TCvSize { =CV_DEFAULT(cvSize(0,0)) } ): pCvSeq; cdecl;

{
  /* sets images for haar classifier cascade */
  CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
  const CvArr* sum, const CvArr* sqsum,
  const CvArr* tilted_sum, double scale );
}
procedure cvSetImagesForHaarClassifierCascade(
  { } cascade: pCvHaarClassifierCascade;
  { } const sum: pCvArr;
  { } const sqsum: pCvArr;
  { } const tilted_sum: pCvArr;
  { } scale: double); cdecl;

//
/// * runs the cascade on the specified window */
// CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
// CvPoint pt, int start_stage CV_DEFAULT(0));
//
//
/// ****************************************************************************************\
// *                         Latent SVM Object Detection functions                          *
// \****************************************************************************************/
//
/// / DataType: STRUCT position
/// / Structure describes the position of the filter in the feature pyramid
/// / l - level in the feature pyramid
/// / (x, y) - coordinate in level l
// typedef struct CvLSVMFilterPosition
// {
// int x;
// int y;
// int l;
// } CvLSVMFilterPosition;
//
/// / DataType: STRUCT filterObject
/// / Description of the filter, which corresponds to the part of the object
/// / V               - ideal (penalty = 0) position of the partial filter
/// /                   from the root filter position (V_i in the paper)
/// / penaltyFunction - vector describes penalty function (d_i in the paper)
/// /                   pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
/// / FILTER DESCRIPTION
/// /   Rectangular map (sizeX x sizeY),
/// /   every cell stores feature vector (dimension = p)
/// / H               - matrix of feature vectors
/// /                   to set and get feature vectors (i,j)
/// /                   used formula H[(j * sizeX + i) * p + k], where
/// /                   k - component of feature vector in cell (i, j)
/// / END OF FILTER DESCRIPTION
// typedef struct CvLSVMFilterObject{
// CvLSVMFilterPosition V;
// float fineFunction[4];
// int sizeX;
// int sizeY;
// int numFeatures;
// float *H;
// } CvLSVMFilterObject;
//
/// / data type: STRUCT CvLatentSvmDetector
/// / structure contains internal representation of trained Latent SVM detector
/// / num_filters			- total number of filters (root plus part) in model
/// / num_components		- number of components in model
/// / num_part_filters		- array containing number of part filters for each component
/// / filters				- root and part filters for all model components
/// / b					- biases for all model components
/// / score_threshold		- confidence level threshold
// typedef struct CvLatentSvmDetector
// {
// int num_filters;
// int num_components;
// int* num_part_filters;
// CvLSVMFilterObject** filters;
// float* b;
// float score_threshold;
// }
// CvLatentSvmDetector;
//
/// / data type: STRUCT CvObjectDetection
/// / structure contains the bounding box and confidence level for detected object
/// / rect					- bounding box for a detected object
/// / score				- confidence level
// typedef struct CvObjectDetection
// {
// CvRect rect;
// float score;
// } CvObjectDetection;
//
/// /////////////// Object Detection using Latent SVM //////////////
//
//
/// *
/// / load trained detector from a file
/// /
/// / API
/// / CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
/// / INPUT
/// / filename				- path to the file containing the parameters of
// - trained Latent SVM detector
/// / OUTPUT
/// / trained Latent SVM detector in internal representation
// */
// CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
//
/// *
/// / release memory allocated for CvLatentSvmDetector structure
/// /
/// / API
/// / void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
/// / INPUT
/// / detector				- CvLatentSvmDetector structure to be released
/// / OUTPUT
// */
// CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
//
/// *
/// / find rectangular regions in the given image that are likely
/// / to contain objects and corresponding confidence levels
/// /
/// / API
/// / CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
/// /									CvLatentSvmDetector* detector,
/// /									CvMemStorage* storage,
/// /									float overlap_threshold = 0.5f,
/// /                                  int numThreads = -1);
/// / INPUT
/// / image				- image to detect objects in
/// / detector				- Latent SVM detector in internal representation
/// / storage				- memory storage to store the resultant sequence
/// /							of the object candidate rectangles
/// / overlap_threshold	- threshold for the non-maximum suppression algorithm
// = 0.5f [here will be the reference to original paper]
/// / OUTPUT
/// / sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
// */
// CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// float overlap_threshold CV_DEFAULT(0.5f),
// int numThreads CV_DEFAULT(-1));
//
// #ifdef __cplusplus
// }
//
// CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
// CvHaarClassifierCascade* cascade, CvMemStorage* storage,
// std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
// double scale_factor CV_DEFAULT(1.1),
// int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
// CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
// bool outputRejectLevels = false );
//
// namespace cv
// {
//
/// //////////////////////////// Object Detection ////////////////////////////
//
/// *
// * This is a class wrapping up the structure CvLatentSvmDetector and functions working with it.
// * The class goals are:
// * 1) provide c++ interface;
// * 2) make it possible to load and detect more than one class (model) unlike CvLatentSvmDetector.
// */
// class CV_EXPORTS LatentSvmDetector
// {
// public:
// struct CV_EXPORTS ObjectDetection
// {
// ObjectDetection();
// ObjectDetection( const Rect& rect, float score, int classID=-1 );
// Rect rect;
// float score;
// int classID;
// };
//
// LatentSvmDetector();
// LatentSvmDetector( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
// virtual ~LatentSvmDetector();
//
// virtual void clear();
// virtual bool empty() const;
// bool load( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
//
// virtual void detect( const Mat& image,
// std::vector<ObjectDetection>& objectDetections,
// float overlapThreshold=0.5f,
// int numThreads=-1 );
//
// const std::vector<String>& getClassNames() const;
// size_t getClassCount() const;
//
// private:
// std::vector<CvLatentSvmDetector*> detectors;
// std::vector<String> classNames;
// };
//
// CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT std::vector<Rect>& rectList, int groupThreshold, double eps=0.2);
// CV_EXPORTS_W void groupRectangles(CV_OUT 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));
//
//
// class CV_EXPORTS FeatureEvaluator
// {
// public:
// enum { HAAR = 0, LBP = 1, HOG = 2 };
// virtual ~FeatureEvaluator();
//
// virtual bool read(const FileNode& node);
// virtual Ptr<FeatureEvaluator> clone() const;
// virtual int getFeatureType() const;
//
// virtual bool setImage(const Mat& img, Size origWinSize);
// virtual bool setWindow(Point p);
//
// virtual double calcOrd(int featureIdx) const;
// virtual int calcCat(int featureIdx) const;
//
// static Ptr<FeatureEvaluator> create(int type);
// };
//
// template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
//
// enum
// {
// CASCADE_DO_CANNY_PRUNING=1,
// CASCADE_SCALE_IMAGE=2,
// CASCADE_FIND_BIGGEST_OBJECT=4,
// CASCADE_DO_ROUGH_SEARCH=8
// };
//
Type
  TCascadeClassifier = class
  public
    // CV_WRAP CascadeClassifier();
    constructor Create; overload;
    // CV_WRAP CascadeClassifier( const String& filename );
    constructor Create(const filename: String); overload;
    // virtual ~CascadeClassifier();
    destructor Destroy; override;
    // CV_WRAP virtual bool empty() const;
    function Empty: Boolean; virtual;
    // CV_WRAP bool load( const String& filename );
    function Load(const filename: String): Boolean;
    // virtual bool read( const FileNode& node );
    function Read(const node: TXMLNode): Boolean; virtual;
    // CV_WRAP virtual void detectMultiScale( const Mat& image,
    // CV_OUT std::vector<Rect>& objects,
    // double scaleFactor=1.1,
    // int minNeighbors=3, int flags=0,
    // Size minSize=Size(),
    // Size maxSize=Size() );
    procedure detectMultiScale(
      { } const image: pIplImage;
      { } const objects: TArray<TCvRect>;
      { } scaleFactor: double { =1.1 };
      { } minNeighbors: Integer { =3 };
      { } flags: Integer { =0 };
      { } minSize: TCvSize { =Size() };
      { } maxSize: TCvSize { =Size() } ); overload;
    // CV_WRAP virtual void detectMultiScale( const Mat& image,
    // CV_OUT std::vector<Rect>& objects,
    // std::vector<int>& rejectLevels,
    // std::vector<double>& levelWeights,
    // double scaleFactor=1.1,
    // int minNeighbors=3, int flags=0,
    // Size minSize=Size(),
    // Size maxSize=Size(),
    // bool outputRejectLevels=false );
    procedure detectMultiScale(
      { } const image: pIplImage;
      { } const objects: TArray<TCvRect>;
      { } const rejectLevels: TArray<Integer>;
      { } const levelWeights: TArray<double>;
      { } scaleFactor: double { =1.1 };
      { } minNeighbors: Integer { =3 };
      { } flags: Integer { =0 };
      { } minSize: TCvSize { =Size() };
      { } maxSize: TCvSize { =Size() };
      { } outputRejectLevels: Boolean { =false } ); overload;
    // bool isOldFormatCascade() const;
    function isOldFormatCascade: Boolean;
    // virtual Size getOriginalWindowSize() const;
    function getOriginalWindowSize: TCvSize; virtual;
    // int getFeatureType() const;
    function getFeatureType: Integer;
    // bool setImage( const Mat& );
    function setImage(const image: pIplImage): Boolean;

  protected
    // virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
    // int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
    // std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels=false);
    function detectSingleScale(const image: pIplImage; stripCount: Integer; processingRectSize: TCvSize;
      stripSize: Integer; yStep: Integer; factor: double; candidates: TArray<TCvRect>; rejectLevels: TArray<Integer>;
      levelWeights: TArray<double>; outputRejectLevels: Boolean = false): Boolean; virtual;

  const
    BOOST = 0;
    DO_CANNY_PRUNING = 1;
    SCALE_IMAGE = 2;
    FIND_BIGGEST_OBJECT = 4;
    DO_ROUGH_SEARCH = 8;
  end;

  // friend class CascadeClassifierInvoker;
  //
  // template<class FEval>
  // friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
  //
  // template<class FEval>
  // friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
  //
  // template<class FEval>
  // friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
  //
  // template<class FEval>
  // friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
  //
  // bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image);
  // virtual int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );
  //
  // class Data
  // {
  // public:
  // struct CV_EXPORTS DTreeNode
  // {
  // int featureIdx;
  // float threshold; // for ordered features only
  // int left;
  // int right;
  // };
  //
  // struct CV_EXPORTS DTree
  // {
  // int nodeCount;
  // };
  //
  // struct CV_EXPORTS Stage
  // {
  // int first;
  // int ntrees;
  // float threshold;
  // };
  //
  // bool read(const FileNode &node);
  //
  // bool isStumpBased;
  //
  // int stageType;
  // int featureType;
  // int ncategories;
  // Size origWinSize;
  //
  // std::vector<Stage> stages;
  // std::vector<DTree> classifiers;
  // std::vector<DTreeNode> nodes;
  // std::vector<float> leaves;
  // std::vector<int> subsets;
  // };
  //
  // Data data;
  // Ptr<FeatureEvaluator> featureEvaluator;
  // Ptr<CvHaarClassifierCascade> oldCascade;
  //
  // public:
  // class CV_EXPORTS MaskGenerator
  // {
  // public:
  // virtual ~MaskGenerator() {}
  // virtual cv::Mat generateMask(const cv::Mat& src)=0;
  // virtual void initializeMask(const cv::Mat& /*src*/) {};
  // };
  // void setMaskGenerator(Ptr<MaskGenerator> maskGenerator);
  // Ptr<MaskGenerator> getMaskGenerator();
  //
  // void setFaceDetectionMaskGenerator();
  //
  // protected:
  // Ptr<MaskGenerator> maskGenerator;
  // };


  //
  /// /////////////// 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
  // std::vector<cv::Point> locations;
  // // vector that will contain confidence values for each location
  // std::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)
  // {}
  //
  // 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 std::vector<float>& descriptors,
  // Size winStride=Size(), Size padding=Size(),
  // const std::vector<Point>& locations=std::vector<Point>()) const;
  // //with found weights output
  // CV_WRAP virtual void detect(const Mat& 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;
  // //without found weights output
  // virtual void detect(const Mat& 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;
  // //with result weights output
  // CV_WRAP virtual void detectMultiScale(const Mat& 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;
  // //without found weights output
  // virtual void detectMultiScale(const Mat& 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;
  //
  // 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 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 int histogramNormType;
  // CV_PROP double L2HysThreshold;
  // CV_PROP bool gammaCorrection;
  // CV_PROP std::vector<float> svmDetector;
  // CV_PROP int nlevels;
  //
  //
  // // evaluate specified ROI and return confidence value for each location
  // virtual void detectROI(const cv::Mat& 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;
  //
  // // evaluate specified ROI and return confidence value for each location in multiple scales
  // virtual 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(String modelfile);
  // };
  //
  //
  // CV_EXPORTS_W void findDataMatrix(InputArray image,
  // CV_OUT std::vector<String>& codes,
  // OutputArray corners=noArray(),
  // OutputArrayOfArrays dmtx=noArray());
  // CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image,
  // const std::vector<String>& codes,
  // InputArray corners);
  // }
  //
  /// ****************************************************************************************\
  // *                                Datamatrix                                              *
  // \****************************************************************************************/
  //
  // struct CV_EXPORTS CvDataMatrixCode {
  // char msg[4];
  // CvMat *original;
  // CvMat *corners;
  // };
  //
  // CV_EXPORTS std::deque<CvDataMatrixCode> cvFindDataMatrix(CvMat *im);
  //
  /// ****************************************************************************************\
  // *                                 LINE-MOD                                               *
  // \****************************************************************************************/
  //
  // namespace cv {
  // namespace linemod {
  //
  /// // @todo Convert doxy comments to rst
  //
  /// **
  // * \brief Discriminant feature described by its location and label.
  // */
  // struct CV_EXPORTS Feature
  // {
  // int x; ///< x offset
  // int y; ///< y offset
  // int label; ///< Quantization
  //
  // Feature() : x(0), y(0), label(0) {}
  // Feature(int x, int y, int label);
  //
  // void read(const FileNode& fn);
  // void write(FileStorage& fs) const;
  // };
  //
  // inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {}
  //
  // struct CV_EXPORTS Template
  // {
  // int width;
  // int height;
  // int pyramid_level;
  // std::vector<Feature> features;
  //
  // void read(const FileNode& fn);
  // void write(FileStorage& fs) const;
  // };
  //
  /// **
  // * \brief Represents a modality operating over an image pyramid.
  // */
  // class QuantizedPyramid
  // {
  // public:
  // // Virtual destructor
  // virtual ~QuantizedPyramid() {}
  //
  // /**
  // * \brief Compute quantized image at current pyramid level for online detection.
  // *
  // * \param[out] dst The destination 8-bit image. For each pixel at most one bit is set,
  // *                 representing its classification.
  // */
  // virtual void quantize(Mat& dst) const =0;
  //
  // /**
  // * \brief Extract most discriminant features at current pyramid level to form a new template.
  // *
  // * \param[out] templ The new template.
  // */
  // virtual bool extractTemplate(Template& templ) const =0;
  //
  // /**
  // * \brief Go to the next pyramid level.
  // *
  // * \todo Allow pyramid scale factor other than 2
  // */
  // virtual void pyrDown() =0;
  //
  // protected:
  // /// Candidate feature with a score
  // struct Candidate
  // {
  // Candidate(int x, int y, int label, float score);
  //
  // /// Sort candidates with high score to the front
  // bool operator<(const Candidate& rhs) const
  // {
  // return score > rhs.score;
  // }
  //
  // Feature f;
  // float score;
  // };
  //
  // /**
  // * \brief Choose candidate features so that they are not bunched together.
  // *
  // * \param[in]  candidates   Candidate features sorted by score.
  // * \param[out] features     Destination vector of selected features.
  // * \param[in]  num_features Number of candidates to select.
  // * \param[in]  distance     Hint for desired distance between features.
  // */
  // static void selectScatteredFeatures(const std::vector<Candidate>& candidates,
  // std::vector<Feature>& features,
  // size_t num_features, float distance);
  // };
  //
  // inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {}
  //
  /// **
  // * \brief Interface for modalities that plug into the LINE template matching representation.
  // *
  // * \todo Max response, to allow optimization of summing (255/MAX) features as uint8
  // */
  // class CV_EXPORTS Modality
  // {
  // public:
  // // Virtual destructor
  // virtual ~Modality() {}
  //
  // /**
  // * \brief Form a quantized image pyramid from a source image.
  // *
  // * \param[in] src  The source image. Type depends on the modality.
  // * \param[in] mask Optional mask. If not empty, unmasked pixels are set to zero
  // *                 in quantized image and cannot be extracted as features.
  // */
  // Ptr<QuantizedPyramid> process(const Mat& src,
  // const Mat& mask = Mat()) const
  // {
  // return processImpl(src, mask);
  // }
  //
  // virtual String name() const =0;
  //
  // virtual void read(const FileNode& fn) =0;
  // virtual void write(FileStorage& fs) const =0;
  //
  // /**
  // * \brief Create modality by name.
  // *
  // * The following modality types are supported:
  // * - "ColorGradient"
  // * - "DepthNormal"
  // */
  // static Ptr<Modality> create(const String& modality_type);
  //
  // /**
  // * \brief Load a modality from file.
  // */
  // static Ptr<Modality> create(const FileNode& fn);
  //
  // protected:
  // // Indirection is because process() has a default parameter.
  // virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
  // const Mat& mask) const =0;
  // };
  //
  /// **
  // * \brief Modality that computes quantized gradient orientations from a color image.
  // */
  // class CV_EXPORTS ColorGradient : public Modality
  // {
  // public:
  // /**
  // * \brief Default constructor. Uses reasonable default parameter values.
  // */
  // ColorGradient();
  //
  // /**
  // * \brief Constructor.
  // *
  // * \param weak_threshold   When quantizing, discard gradients with magnitude less than this.
  // * \param num_features     How many features a template must contain.
  // * \param strong_threshold Consider as candidate features only gradients whose norms are
  // *                         larger than this.
  // */
  // ColorGradient(float weak_threshold, size_t num_features, float strong_threshold);
  //
  // virtual String name() const;
  //
  // virtual void read(const FileNode& fn);
  // virtual void write(FileStorage& fs) const;
  //
  // float weak_threshold;
  // size_t num_features;
  // float strong_threshold;
  //
  // protected:
  // virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
  // const Mat& mask) const;
  // };
  //
  /// **
  // * \brief Modality that computes quantized surface normals from a dense depth map.
  // */
  // class CV_EXPORTS DepthNormal : public Modality
  // {
  // public:
  // /**
  // * \brief Default constructor. Uses reasonable default parameter values.
  // */
  // DepthNormal();
  //
  // /**
  // * \brief Constructor.
  // *
  // * \param distance_threshold   Ignore pixels beyond this distance.
  // * \param difference_threshold When computing normals, ignore contributions of pixels whose
  // *                             depth difference with the central pixel is above this threshold.
  // * \param num_features         How many features a template must contain.
  // * \param extract_threshold    Consider as candidate feature only if there are no differing
  // *                             orientations within a distance of extract_threshold.
  // */
  // DepthNormal(int distance_threshold, int difference_threshold, size_t num_features,
  // int extract_threshold);
  //
  // virtual String name() const;
  //
  // virtual void read(const FileNode& fn);
  // virtual void write(FileStorage& fs) const;
  //
  // int distance_threshold;
  // int difference_threshold;
  // size_t num_features;
  // int extract_threshold;
  //
  // protected:
  // virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
  // const Mat& mask) const;
  // };
  //
  /// **
  // * \brief Debug function to colormap a quantized image for viewing.
  // */
  // void colormap(const Mat& quantized, Mat& dst);
  //
  /// **
  // * \brief Represents a successful template match.
  // */
  // struct CV_EXPORTS Match
  // {
  // Match()
  // {
  // }
  //
  // Match(int x, int y, float similarity, const String& class_id, int template_id);
  //
  // /// Sort matches with high similarity to the front
  // bool operator<(const Match& rhs) const
  // {
  // // Secondarily sort on template_id for the sake of duplicate removal
  // if (similarity != rhs.similarity)
  // return similarity > rhs.similarity;
  // else
  // return template_id < rhs.template_id;
  // }
  //
  // bool operator==(const Match& rhs) const
  // {
  // return x == rhs.x && y == rhs.y && similarity == rhs.similarity && class_id == rhs.class_id;
  // }
  //
  // int x;
  // int y;
  // float similarity;
  // String class_id;
  // int template_id;
  // };
  //
  // inline  Match::Match(int _x, int _y, float _similarity, const String& _class_id, int _template_id)
  // : x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id)
  // {
  // }
  //
  /// **
  // * \brief Object detector using the LINE template matching algorithm with any set of
  // * modalities.
  // */
  // class CV_EXPORTS Detector
  // {
  // public:
  // /**
  // * \brief Empty constructor, initialize with read().
  // */
  // Detector();
  //
  // /**
  // * \brief Constructor.
  // *
  // * \param modalities       Modalities to use (color gradients, depth normals, ...).
  // * \param T_pyramid        Value of the sampling step T at each pyramid level. The
  // *                         number of pyramid levels is T_pyramid.size().
  // */
  // Detector(const std::vector< Ptr<Modality> >& modalities, const std::vector<int>& T_pyramid);
  //
  // /**
  // * \brief Detect objects by template matching.
  // *
  // * Matches globally at the lowest pyramid level, then refines locally stepping up the pyramid.
  // *
  // * \param      sources   Source images, one for each modality.
  // * \param      threshold Similarity threshold, a percentage between 0 and 100.
  // * \param[out] matches   Template matches, sorted by similarity score.
  // * \param      class_ids If non-empty, only search for the desired object classes.
  // * \param[out] quantized_images Optionally return vector<Mat> of quantized images.
  // * \param      masks     The masks for consideration during matching. The masks should be CV_8UC1
  // *                       where 255 represents a valid pixel.  If non-empty, the vector must be
  // *                       the same size as sources.  Each element must be
  // *                       empty or the same size as its corresponding source.
  // */
  // void match(const std::vector<Mat>& sources, float threshold, std::vector<Match>& matches,
  // const std::vector<String>& class_ids = std::vector<String>(),
  // OutputArrayOfArrays quantized_images = noArray(),
  // const std::vector<Mat>& masks = std::vector<Mat>()) const;
  //
  // /**
  // * \brief Add new object template.
  // *
  // * \param      sources      Source images, one for each modality.
  // * \param      class_id     Object class ID.
  // * \param      object_mask  Mask separating object from background.
  // * \param[out] bounding_box Optionally return bounding box of the extracted features.
  // *
  // * \return Template ID, or -1 if failed to extract a valid template.
  // */
  // int addTemplate(const std::vector<Mat>& sources, const String& class_id,
  // const Mat& object_mask, Rect* bounding_box = NULL);
  //
  // /**
  // * \brief Add a new object template computed by external means.
  // */
  // int addSyntheticTemplate(const std::vector<Template>& templates, const String& class_id);
  //
  // /**
  // * \brief Get the modalities used by this detector.
  // *
  // * You are not permitted to add/remove modalities, but you may dynamic_cast them to
  // * tweak parameters.
  // */
  // const std::vector< Ptr<Modality> >& getModalities() const { return modalities; }
  //
  // /**
  // * \brief Get sampling step T at pyramid_level.
  // */
  // int getT(int pyramid_level) const { return T_at_level[pyramid_level]; }
  //
  // /**
  // * \brief Get number of pyramid levels used by this detector.
  // */
  // int pyramidLevels() const { return pyramid_levels; }
  //
  // /**
  // * \brief Get the template pyramid identified by template_id.
  // *
  // * For example, with 2 modalities (Gradient, Normal) and two pyramid levels
  // * (L0, L1), the order is (GradientL0, NormalL0, GradientL1, NormalL1).
  // */
  // const std::vector<Template>& getTemplates(const String& class_id, int template_id) const;
  //
  // int numTemplates() const;
  // int numTemplates(const String& class_id) const;
  // int numClasses() const { return static_cast<int>(class_templates.size()); }
  //
  // std::vector<String> classIds() const;
  //
  // void read(const FileNode& fn);
  // void write(FileStorage& fs) const;
  //
  // String readClass(const FileNode& fn, const String &class_id_override = "");
  // void writeClass(const String& class_id, FileStorage& fs) const;
  //
  // void readClasses(const std::vector<String>& class_ids,
  // const String& format = "templates_%s.yml.gz");
  // void writeClasses(const String& format = "templates_%s.yml.gz") const;
  //
  // protected:
  // std::vector< Ptr<Modality> > modalities;
  // int pyramid_levels;
  // std::vector<int> T_at_level;
  //
  // typedef std::vector<Template> TemplatePyramid;
  // typedef std::map<String, std::vector<TemplatePyramid> > TemplatesMap;
  // TemplatesMap class_templates;
  //
  // typedef std::vector<Mat> LinearMemories;
  // // Indexed as [pyramid level][modality][quantized label]
  // typedef std::vector< std::vector<LinearMemories> > LinearMemoryPyramid;
  //
  // void matchClass(const LinearMemoryPyramid& lm_pyramid,
  // const std::vector<Size>& sizes,
  // float threshold, std::vector<Match>& matches,
  // const String& class_id,
  // const std::vector<TemplatePyramid>& template_pyramids) const;
  // };
  //
  /// **
  // * \brief Factory function for detector using LINE algorithm with color gradients.
  // *
  // * Default parameter settings suitable for VGA images.
  // */
  // CV_EXPORTS Ptr<Detector> getDefaultLINE();
  //
  /// **
  // * \brief Factory function for detector using LINE-MOD algorithm with color gradients
  // * and depth normals.
  // *
  // * Default parameter settings suitable for VGA images.
  // */
  // CV_EXPORTS Ptr<Detector> getDefaultLINEMOD();
  //
  // } // namespace linemod
  // } // namespace cv
  //
  // #endif
  //
  // #endif

implementation

uses
  uLibName;

function cvLoadHaarClassifierCascade; external objdetect_dll;
procedure cvReleaseHaarClassifierCascade; external objdetect_dll;
function cvHaarDetectObjects; external objdetect_dll;
procedure cvSetImagesForHaarClassifierCascade; external objdetect_dll;

{ TCascadeClassifier }

constructor TCascadeClassifier.Create;
begin

end;

constructor TCascadeClassifier.Create(const filename: String);
begin

end;

destructor TCascadeClassifier.Destroy;
begin

  inherited;
end;

procedure TCascadeClassifier.detectMultiScale(const image: pIplImage; const objects: TArray<TCvRect>;
  const rejectLevels: TArray<Integer>; const levelWeights: TArray<double>; scaleFactor: double;
  minNeighbors, flags: Integer; minSize, maxSize: TCvSize; outputRejectLevels: Boolean);
begin

end;

procedure TCascadeClassifier.detectMultiScale(const image: pIplImage; const objects: TArray<TCvRect>;
  scaleFactor: double; minNeighbors, flags: Integer; minSize, maxSize: TCvSize);
begin

end;

function TCascadeClassifier.detectSingleScale(const image: pIplImage; stripCount: Integer; processingRectSize: TCvSize;
  stripSize, yStep: Integer; factor: double; candidates: TArray<TCvRect>; rejectLevels: TArray<Integer>;
  levelWeights: TArray<double>; outputRejectLevels: Boolean): Boolean;
begin

end;

function TCascadeClassifier.Empty: Boolean;
begin

end;

function TCascadeClassifier.getFeatureType: Integer;
begin

end;

function TCascadeClassifier.getOriginalWindowSize: TCvSize;
begin

end;

function TCascadeClassifier.isOldFormatCascade: Boolean;
begin

end;

function TCascadeClassifier.Load(const filename: String): Boolean;
begin

end;

function TCascadeClassifier.Read(const node: TXMLNode): Boolean;
begin

end;

function TCascadeClassifier.setImage(const image: pIplImage): Boolean;
begin

end;

end.