Delphi-OpenCV/source/ocv.contrib.pas

// --------------------------------- OpenCV license.txt ---------------------------
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  //                             License Agreement
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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.
  //  If you wish to allow use of your version of this file only under the terms
  //  of the LGPL License and not to allow others to use your version of this file
  //  under the MPL, indicate your decision by deleting  the provisions above and
  //  replace  them with the notice and other provisions required by the LGPL
  //  License.  If you do not delete the provisions above, a recipient may use
  //  your version of this file under either the MPL or the LGPL License.
  //
  //  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
  //  Q&A forum:   http://answers.opencv.org
  //  Dev zone:    http://code.opencv.org
  //  **************************************************************************************************
  //  Original file:
  //  opencv\modules\contrib\include\opencv2\contrib.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 ocv.contrib;

interface

uses
  ocv.core.types_c;

/// ****************************************************************************************\
// *                                   Adaptive Skin Detector                               *
// \****************************************************************************************/

Type
  TCvAdaptiveSkinDetector = class
  private const
    GSD_HUE_LT = 3;
    GSD_HUE_UT = 33;
    GSD_INTENSITY_LT = 15;
    GSD_INTENSITY_UT = 250;

  type
    THistogram = class
    private const
      HistogramSize = (GSD_HUE_UT - GSD_HUE_LT + 1);
    protected
      function findCoverageIndex(surfaceToCover: double; defaultValue: Integer = 0): Integer;
    public
      fHistogram: pCvHistogram;
      constructor create;
      destructor Destroy; override;
      procedure findCurveThresholds(Var x1, x2: Integer; percent: double = 0.05);
      procedure mergeWith(source: THistogram; weight: double);
    end;
  private
    nStartCounter, nFrameCount, nSkinHueLowerBound, nSkinHueUpperBound, nMorphingMethod, nSamplingDivider: Integer;
    fHistogramMergeFactor, fHuePercentCovered: double;
    histogramHueMotion, skinHueHistogram: THistogram;
    imgHueFrame, imgSaturationFrame, imgLastGrayFrame, imgMotionFrame, imgFilteredFrame: pIplImage;
    imgShrinked, imgTemp, imgGrayFrame, imgHSVFrame: pIplImage;

  protected
    procedure initData(src: pIplImage; widthDivider, heightDivider: Integer);
    procedure adaptiveFilter;
  public

    const
    MORPHING_METHOD_NONE = 0;
    MORPHING_METHOD_ERODE = 1;
    MORPHING_METHOD_ERODE_ERODE = 2;
    MORPHING_METHOD_ERODE_DILATE = 3;

    constructor create(samplingDivider: Integer = 1; morphingMethod: Integer = MORPHING_METHOD_NONE);
    destructor Destroy; override;
    procedure process(inputBGRImage: pIplImage; outputHueMask: pIplImage); virtual;
  end;

procedure ASD_INTENSITY_SET_PIXEL(ptr: PByte; qq: uchar); inline;
{ (*pointer) = (unsigned char)qq; }

function ASD_IS_IN_MOTION(ptr: PByte; v, threshold: uchar): Boolean;
// ((abs((*(pointer)) - (v)) > (threshold)) ? true : false)

/// ****************************************************************************************\
// *                                  Fuzzy MeanShift Tracker                               *
// \****************************************************************************************/
//
// class CV_EXPORTS CvFuzzyPoint {
// public:
// double x, y, value;
//
// CvFuzzyPoint(double _x, double _y);
// };
//
// class CV_EXPORTS CvFuzzyCurve {
// private:
// std::vector<CvFuzzyPoint> points;
// double value, centre;
//
// bool between(double x, double x1, double x2);
//
// public:
// CvFuzzyCurve();
// ~CvFuzzyCurve();
//
// void setCentre(double _centre);
// double getCentre();
// void clear();
// void addPoint(double x, double y);
// double calcValue(double param);
// double getValue();
// void setValue(double _value);
// };
//
// class CV_EXPORTS CvFuzzyFunction {
// public:
// std::vector<CvFuzzyCurve> curves;
//
// CvFuzzyFunction();
// ~CvFuzzyFunction();
// void addCurve(CvFuzzyCurve *curve, double value = 0);
// void resetValues();
// double calcValue();
// CvFuzzyCurve *newCurve();
// };
//
// class CV_EXPORTS CvFuzzyRule {
// private:
// CvFuzzyCurve *fuzzyInput1, *fuzzyInput2;
// CvFuzzyCurve *fuzzyOutput;
// public:
// CvFuzzyRule();
// ~CvFuzzyRule();
// void setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1);
// double calcValue(double param1, double param2);
// CvFuzzyCurve *getOutputCurve();
// };
//
// class CV_EXPORTS CvFuzzyController {
// private:
// std::vector<CvFuzzyRule*> rules;
// public:
// CvFuzzyController();
// ~CvFuzzyController();
// void addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1);
// double calcOutput(double param1, double param2);
// };
//
// class CV_EXPORTS CvFuzzyMeanShiftTracker
// {
// private:
// class FuzzyResizer
// {
// private:
// CvFuzzyFunction iInput, iOutput;
// CvFuzzyController fuzzyController;
// public:
// FuzzyResizer();
// int calcOutput(double edgeDensity, double density);
// };
//
// class SearchWindow
// {
// public:
// FuzzyResizer *fuzzyResizer;
// int x, y;
// int width, height, maxWidth, maxHeight, ellipseHeight, ellipseWidth;
// int ldx, ldy, ldw, ldh, numShifts, numIters;
// int xGc, yGc;
// long m00, m01, m10, m11, m02, m20;
// double ellipseAngle;
// double density;
// unsigned int depthLow, depthHigh;
// int verticalEdgeLeft, verticalEdgeRight, horizontalEdgeTop, horizontalEdgeBottom;
//
// SearchWindow();
// ~SearchWindow();
// void setSize(int _x, int _y, int _width, int _height);
// void initDepthValues(IplImage *maskImage, IplImage *depthMap);
// bool shift();
// void extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth);
// void getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
// void getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
// void getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
// bool meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth);
// };
//
// public:
// enum TrackingState
// {
// tsNone          = 0,
// tsSearching     = 1,
// tsTracking      = 2,
// tsSetWindow     = 3,
// tsDisabled      = 10
// };
//
// enum ResizeMethod {
// rmEdgeDensityLinear     = 0,
// rmEdgeDensityFuzzy      = 1,
// rmInnerDensity          = 2
// };
//
// enum {
// MinKernelMass           = 1000
// };
//
// SearchWindow kernel;
// int searchMode;
//
// private:
// enum
// {
// MaxMeanShiftIteration   = 5,
// MaxSetSizeIteration     = 5
// };
//
// void findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth);
//
// public:
// CvFuzzyMeanShiftTracker();
// ~CvFuzzyMeanShiftTracker();
//
// void track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass = MinKernelMass);
// };
//
//
// namespace cv
// {
//
// class CV_EXPORTS Octree
// {
// public:
// struct Node
// {
// Node() {}
// int begin, end;
// float x_min, x_max, y_min, y_max, z_min, z_max;
// int maxLevels;
// bool isLeaf;
// int children[8];
// };
//
// Octree();
// Octree( const std::vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
// virtual ~Octree();
//
// virtual void buildTree( const std::vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
// virtual void getPointsWithinSphere( const Point3f& center, float radius,
// std::vector<Point3f>& points ) const;
// const std::vector<Node>& getNodes() const { return nodes; }
// private:
// int minPoints;
// std::vector<Point3f> points;
// std::vector<Node> nodes;
//
// virtual void buildNext(size_t node_ind);
// };
//
//
// class CV_EXPORTS Mesh3D
// {
// public:
// struct EmptyMeshException {};
//
// Mesh3D();
// Mesh3D(const std::vector<Point3f>& vtx);
// ~Mesh3D();
//
// void buildOctree();
// void clearOctree();
// float estimateResolution(float tryRatio = 0.1f);
// void computeNormals(float normalRadius, int minNeighbors = 20);
// void computeNormals(const std::vector<int>& subset, float normalRadius, int minNeighbors = 20);
//
// void writeAsVrml(const String& file, const std::vector<Scalar>& colors = std::vector<Scalar>()) const;
//
// std::vector<Point3f> vtx;
// std::vector<Point3f> normals;
// float resolution;
// Octree octree;
//
// const static Point3f allzero;
// };
//
// class CV_EXPORTS SpinImageModel
// {
// public:
//
// /* model parameters, leave unset for default or auto estimate */
// float normalRadius;
// int minNeighbors;
//
// float binSize;
// int imageWidth;
//
// float lambda;
// float gamma;
//
// float T_GeometriccConsistency;
// float T_GroupingCorespondances;
//
// /* public interface */
// SpinImageModel();
// explicit SpinImageModel(const Mesh3D& mesh);
// ~SpinImageModel();
//
// void setLogger(std::ostream* log);
// void selectRandomSubset(float ratio);
// void setSubset(const std::vector<int>& subset);
// void compute();
//
// void match(const SpinImageModel& scene, std::vector< std::vector<Vec2i> >& result);
//
// Mat packRandomScaledSpins(bool separateScale = false, size_t xCount = 10, size_t yCount = 10) const;
//
// size_t getSpinCount() const { return spinImages.rows; }
// Mat getSpinImage(size_t index) const { return spinImages.row((int)index); }
// const Point3f& getSpinVertex(size_t index) const { return mesh.vtx[subset[index]]; }
// const Point3f& getSpinNormal(size_t index) const { return mesh.normals[subset[index]]; }
//
// const Mesh3D& getMesh() const { return mesh; }
// Mesh3D& getMesh() { return mesh; }
//
// /* static utility functions */
// static bool spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result);
//
// static Point2f calcSpinMapCoo(const Point3f& point, const Point3f& vertex, const Point3f& normal);
//
// static float geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1,
// const Point3f& pointModel1, const Point3f& normalModel1,
// const Point3f& pointScene2, const Point3f& normalScene2,
// const Point3f& pointModel2, const Point3f& normalModel2);
//
// static float groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1,
// const Point3f& pointModel1, const Point3f& normalModel1,
// const Point3f& pointScene2, const Point3f& normalScene2,
// const Point3f& pointModel2, const Point3f& normalModel2,
// float gamma);
// protected:
// void defaultParams();
//
// void matchSpinToModel(const Mat& spin, std::vector<int>& indeces,
// std::vector<float>& corrCoeffs, bool useExtremeOutliers = true) const;
//
// void repackSpinImages(const std::vector<uchar>& mask, Mat& spinImages, bool reAlloc = true) const;
//
// std::vector<int> subset;
// Mesh3D mesh;
// Mat spinImages;
// std::ostream* out;
// };
//
// class CV_EXPORTS TickMeter
// {
// public:
// TickMeter();
// void start();
// void stop();
//
// int64 getTimeTicks() const;
// double getTimeMicro() const;
// double getTimeMilli() const;
// double getTimeSec()   const;
// int64 getCounter() const;
//
// void reset();
// private:
// int64 counter;
// int64 sumTime;
// int64 startTime;
// };
//
// CV_EXPORTS std::ostream& operator<<(std::ostream& out, const TickMeter& tm);
//
// class CV_EXPORTS SelfSimDescriptor
// {
// public:
// SelfSimDescriptor();
// SelfSimDescriptor(int _ssize, int _lsize,
// int _startDistanceBucket=DEFAULT_START_DISTANCE_BUCKET,
// int _numberOfDistanceBuckets=DEFAULT_NUM_DISTANCE_BUCKETS,
// int _nangles=DEFAULT_NUM_ANGLES);
// SelfSimDescriptor(const SelfSimDescriptor& ss);
// virtual ~SelfSimDescriptor();
// SelfSimDescriptor& operator = (const SelfSimDescriptor& ss);
//
// size_t getDescriptorSize() const;
// Size getGridSize( Size imgsize, Size winStride ) const;
//
// virtual void compute(const Mat& img, std::vector<float>& descriptors, Size winStride=Size(),
// const std::vector<Point>& locations=std::vector<Point>()) const;
// virtual void computeLogPolarMapping(Mat& mappingMask) const;
// virtual void SSD(const Mat& img, Point pt, Mat& ssd) const;
//
// int smallSize;
// int largeSize;
// int startDistanceBucket;
// int numberOfDistanceBuckets;
// int numberOfAngles;
//
// enum { DEFAULT_SMALL_SIZE = 5, DEFAULT_LARGE_SIZE = 41,
// DEFAULT_NUM_ANGLES = 20, DEFAULT_START_DISTANCE_BUCKET = 3,
// DEFAULT_NUM_DISTANCE_BUCKETS = 7 };
// };
//
//
// typedef bool (*BundleAdjustCallback)(int iteration, double norm_error, void* user_data);
//
// class CV_EXPORTS LevMarqSparse {
// public:
// LevMarqSparse();
// LevMarqSparse(int npoints, // number of points
// int ncameras, // number of cameras
// int nPointParams, // number of params per one point  (3 in case of 3D points)
// int nCameraParams, // number of parameters per one camera
// int nErrParams, // number of parameters in measurement vector
// // for 1 point at one camera (2 in case of 2D projections)
// Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// // 1 - point is visible for the camera, 0 - invisible
// Mat& P0, // starting vector of parameters, first cameras then points
// Mat& X, // measurements, in order of visibility. non visible cases are skipped
// TermCriteria criteria, // termination criteria
//
// // callback for estimation of Jacobian matrices
// void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
// Mat& cam_params, Mat& A, Mat& B, void* data),
// // callback for estimation of backprojection errors
// void (CV_CDECL * func)(int i, int j, Mat& point_params,
// Mat& cam_params, Mat& estim, void* data),
// void* data, // user-specific data passed to the callbacks
// BundleAdjustCallback cb, void* user_data
// );
//
// virtual ~LevMarqSparse();
//
// virtual void run( int npoints, // number of points
// int ncameras, // number of cameras
// int nPointParams, // number of params per one point  (3 in case of 3D points)
// int nCameraParams, // number of parameters per one camera
// int nErrParams, // number of parameters in measurement vector
// // for 1 point at one camera (2 in case of 2D projections)
// Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// // 1 - point is visible for the camera, 0 - invisible
// Mat& P0, // starting vector of parameters, first cameras then points
// Mat& X, // measurements, in order of visibility. non visible cases are skipped
// TermCriteria criteria, // termination criteria
//
// // callback for estimation of Jacobian matrices
// void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
// Mat& cam_params, Mat& A, Mat& B, void* data),
// // callback for estimation of backprojection errors
// void (CV_CDECL * func)(int i, int j, Mat& point_params,
// Mat& cam_params, Mat& estim, void* data),
// void* data // user-specific data passed to the callbacks
// );
//
// virtual void clear();
//
// // useful function to do simple bundle adjustment tasks
// static void bundleAdjust(std::vector<Point3d>& points, // positions of points in global coordinate system (input and output)
// const std::vector<std::vector<Point2d> >& imagePoints, // projections of 3d points for every camera
// const std::vector<std::vector<int> >& visibility, // visibility of 3d points for every camera
// std::vector<Mat>& cameraMatrix, // intrinsic matrices of all cameras (input and output)
// std::vector<Mat>& R, // rotation matrices of all cameras (input and output)
// std::vector<Mat>& T, // translation vector of all cameras (input and output)
// std::vector<Mat>& distCoeffs, // distortion coefficients of all cameras (input and output)
// const TermCriteria& criteria=
// TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON),
// BundleAdjustCallback cb = 0, void* user_data = 0);
//
// public:
// virtual void optimize(CvMat &_vis); //main function that runs minimization
//
// //iteratively asks for measurement for visible camera-point pairs
// void ask_for_proj(CvMat &_vis,bool once=false);
// //iteratively asks for Jacobians for every camera_point pair
// void ask_for_projac(CvMat &_vis);
//
// CvMat* err; //error X-hX
// double prevErrNorm, errNorm;
// double lambda;
// CvTermCriteria criteria;
// int iters;
//
// CvMat** U; //size of array is equal to number of cameras
// CvMat** V; //size of array is equal to number of points
// CvMat** inv_V_star; //inverse of V*
//
// CvMat** A;
// CvMat** B;
// CvMat** W;
//
// CvMat* X; //measurement
// CvMat* hX; //current measurement extimation given new parameter vector
//
// CvMat* prevP; //current already accepted parameter.
// CvMat* P; // parameters used to evaluate function with new params
// // this parameters may be rejected
//
// CvMat* deltaP; //computed increase of parameters (result of normal system solution )
//
// CvMat** ea; // sum_i  AijT * e_ij , used as right part of normal equation
// // length of array is j = number of cameras
// CvMat** eb; // sum_j  BijT * e_ij , used as right part of normal equation
// // length of array is i = number of points
//
// CvMat** Yj; //length of array is i = num_points
//
// CvMat* S; //big matrix of block Sjk  , each block has size num_cam_params x num_cam_params
//
// CvMat* JtJ_diag; //diagonal of JtJ,  used to backup diagonal elements before augmentation
//
// CvMat* Vis_index; // matrix which element is index of measurement for point i and camera j
//
// int num_cams;
// int num_points;
// int num_err_param;
// int num_cam_param;
// int num_point_param;
//
// //target function and jacobian pointers, which needs to be initialized
// void (*fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data);
// void (*func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data);
//
// void* data;
//
// BundleAdjustCallback cb;
// void* user_data;
// };
//
// CV_EXPORTS_W int chamerMatching( Mat& img, Mat& templ,
// CV_OUT std::vector<std::vector<Point> >& results, CV_OUT std::vector<float>& cost,
// double templScale=1, int maxMatches = 20,
// double minMatchDistance = 1.0, int padX = 3,
// int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6,
// double orientationWeight = 0.5, double truncate = 20);
//
//
// class CV_EXPORTS_W StereoVar
// {
// public:
// // Flags
// enum {USE_INITIAL_DISPARITY = 1, USE_EQUALIZE_HIST = 2, USE_SMART_ID = 4, USE_AUTO_PARAMS = 8, USE_MEDIAN_FILTERING = 16};
// enum {CYCLE_O, CYCLE_V};
// enum {PENALIZATION_TICHONOV, PENALIZATION_CHARBONNIER, PENALIZATION_PERONA_MALIK};
//
// //! the default constructor
// CV_WRAP StereoVar();
//
// //! the full constructor taking all the necessary algorithm parameters
// CV_WRAP StereoVar(int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags);
//
// //! the destructor
// virtual ~StereoVar();
//
// //! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair
// CV_WRAP_AS(compute) virtual void operator()(const Mat& left, const Mat& right, CV_OUT Mat& disp);
//
// CV_PROP_RW int      levels;
// CV_PROP_RW double   pyrScale;
// CV_PROP_RW int      nIt;
// CV_PROP_RW int      minDisp;
// CV_PROP_RW int      maxDisp;
// CV_PROP_RW int      poly_n;
// CV_PROP_RW double   poly_sigma;
// CV_PROP_RW float    fi;
// CV_PROP_RW float    lambda;
// CV_PROP_RW int      penalization;
// CV_PROP_RW int      cycle;
// CV_PROP_RW int      flags;
//
// private:
// void autoParams();
// void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level);
// void VCycle_MyFAS(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
// void VariationalSolver(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
// };
//
// CV_EXPORTS void polyfit(const Mat& srcx, const Mat& srcy, Mat& dst, int order);
//
// class CV_EXPORTS Directory
// {
// public:
// static std::vector<String> GetListFiles  ( const String& path, const String & exten = "*", bool addPath = true );
// static std::vector<String> GetListFilesR ( const String& path, const String & exten = "*", bool addPath = true );
// static std::vector<String> GetListFolders( const String& path, const String & exten = "*", bool addPath = true );
// };
//
// /*
// * Generation of a set of different colors by the following way:
// * 1) generate more then need colors (in "factor" times) in RGB,
// * 2) convert them to Lab,
// * 3) choose the needed count of colors from the set that are more different from
// *    each other,
// * 4) convert the colors back to RGB
// */
// CV_EXPORTS void generateColors( std::vector<Scalar>& colors, size_t count, size_t factor=100 );
//
//
// /*
// *  Estimate the rigid body motion from frame0 to frame1. The method is based on the paper
// *  "Real-Time Visual Odometry from Dense RGB-D Images", F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011.
// */
// enum { ROTATION          = 1,
// TRANSLATION       = 2,
// RIGID_BODY_MOTION = 4
// };
// CV_EXPORTS bool RGBDOdometry( Mat& Rt, const Mat& initRt,
// const Mat& image0, const Mat& depth0, const Mat& mask0,
// const Mat& image1, const Mat& depth1, const Mat& mask1,
// const Mat& cameraMatrix, float minDepth=0.f, float maxDepth=4.f, float maxDepthDiff=0.07f,
// const std::vector<int>& iterCounts=std::vector<int>(),
// const std::vector<float>& minGradientMagnitudes=std::vector<float>(),
// int transformType=RIGID_BODY_MOTION );
//
// /**
// *Bilinear interpolation technique.
// *
// *The value of a desired cortical pixel is obtained through a bilinear interpolation of the values
// *of the four nearest neighbouring Cartesian pixels to the center of the RF.
// *The same principle is applied to the inverse transformation.
// *
// *More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
// */
// class CV_EXPORTS LogPolar_Interp
// {
// public:
//
// LogPolar_Interp() {}
//
// /**
// *Constructor
// *\param w the width of the input image
// *\param h the height of the input image
// *\param center the transformation center: where the output precision is maximal
// *\param R the number of rings of the cortical image (default value 70 pixel)
// *\param ro0 the radius of the blind spot (default value 3 pixel)
// *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
// *            \a 0 means that the retinal image is computed within the inscribed circle.
// *\param S the number of sectors of the cortical image (default value 70 pixel).
// *         Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
// *\param sp \a 1 (default value) means that the parameter \a S is internally computed.
// *          \a 0 means that the parameter \a S is provided by the user.
// */
// LogPolar_Interp(int w, int h, Point2i center, int R=70, double ro0=3.0,
// int interp=INTER_LINEAR, int full=1, int S=117, int sp=1);
// /**
// *Transformation from Cartesian image to cortical (log-polar) image.
// *\param source the Cartesian image
// *\return the transformed image (cortical image)
// */
// const Mat to_cortical(const Mat &source);
// /**
// *Transformation from cortical image to retinal (inverse log-polar) image.
// *\param source the cortical image
// *\return the transformed image (retinal image)
// */
// const Mat to_cartesian(const Mat &source);
// /**
// *Destructor
// */
// ~LogPolar_Interp();
//
// protected:
//
// Mat Rsri;
// Mat Csri;
//
// int S, R, M, N;
// int top, bottom,left,right;
// double ro0, romax, a, q;
// int interp;
//
// Mat ETAyx;
// Mat CSIyx;
//
// void create_map(int M, int N, int R, int S, double ro0);
// };
//
// /**
// *Overlapping circular receptive fields technique
// *
// *The Cartesian plane is divided in two regions: the fovea and the periphery.
// *The fovea (oversampling) is handled by using the bilinear interpolation technique described above, whereas in
// *the periphery we use the overlapping Gaussian circular RFs.
// *
// *More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
// */
// class CV_EXPORTS LogPolar_Overlapping
// {
// public:
// LogPolar_Overlapping() {}
//
// /**
// *Constructor
// *\param w the width of the input image
// *\param h the height of the input image
// *\param center the transformation center: where the output precision is maximal
// *\param R the number of rings of the cortical image (default value 70 pixel)
// *\param ro0 the radius of the blind spot (default value 3 pixel)
// *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
// *            \a 0 means that the retinal image is computed within the inscribed circle.
// *\param S the number of sectors of the cortical image (default value 70 pixel).
// *         Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
// *\param sp \a 1 (default value) means that the parameter \a S is internally computed.
// *          \a 0 means that the parameter \a S is provided by the user.
// */
// LogPolar_Overlapping(int w, int h, Point2i center, int R=70,
// double ro0=3.0, int full=1, int S=117, int sp=1);
// /**
// *Transformation from Cartesian image to cortical (log-polar) image.
// *\param source the Cartesian image
// *\return the transformed image (cortical image)
// */
// const Mat to_cortical(const Mat &source);
// /**
// *Transformation from cortical image to retinal (inverse log-polar) image.
// *\param source the cortical image
// *\return the transformed image (retinal image)
// */
// const Mat to_cartesian(const Mat &source);
// /**
// *Destructor
// */
// ~LogPolar_Overlapping();
//
// protected:
//
// Mat Rsri;
// Mat Csri;
// std::vector<int> Rsr;
// std::vector<int> Csr;
// std::vector<double> Wsr;
//
// int S, R, M, N, ind1;
// int top, bottom,left,right;
// double ro0, romax, a, q;
//
// struct kernel
// {
// kernel() { w = 0; }
// std::vector<double> weights;
// int w;
// };
//
// Mat ETAyx;
// Mat CSIyx;
// std::vector<kernel> w_ker_2D;
//
// void create_map(int M, int N, int R, int S, double ro0);
// };
//
// /**
// * Adjacent receptive fields technique
// *
// *All the Cartesian pixels, whose coordinates in the cortical domain share the same integer part, are assigned to the same RF.
// *The precision of the boundaries of the RF can be improved by breaking each pixel into subpixels and assigning each of them to the correct RF.
// *This technique is implemented from: Traver, V., Pla, F.: Log-polar mapping template design: From task-level requirements
// *to geometry parameters. Image Vision Comput. 26(10) (2008) 1354-1370
// *
// *More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
// */
// class CV_EXPORTS LogPolar_Adjacent
// {
// public:
// LogPolar_Adjacent() {}
//
// /**
// *Constructor
// *\param w the width of the input image
// *\param h the height of the input image
// *\param center the transformation center: where the output precision is maximal
// *\param R the number of rings of the cortical image (default value 70 pixel)
// *\param ro0 the radius of the blind spot (default value 3 pixel)
// *\param smin the size of the subpixel (default value 0.25 pixel)
// *\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
// *            \a 0 means that the retinal image is computed within the inscribed circle.
// *\param S the number of sectors of the cortical image (default value 70 pixel).
// *         Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
// *\param sp \a 1 (default value) means that the parameter \a S is internally computed.
// *          \a 0 means that the parameter \a S is provided by the user.
// */
// LogPolar_Adjacent(int w, int h, Point2i center, int R=70, double ro0=3.0, double smin=0.25, int full=1, int S=117, int sp=1);
// /**
// *Transformation from Cartesian image to cortical (log-polar) image.
// *\param source the Cartesian image
// *\return the transformed image (cortical image)
// */
// const Mat to_cortical(const Mat &source);
// /**
// *Transformation from cortical image to retinal (inverse log-polar) image.
// *\param source the cortical image
// *\return the transformed image (retinal image)
// */
// const Mat to_cartesian(const Mat &source);
// /**
// *Destructor
// */
// ~LogPolar_Adjacent();
//
// protected:
// struct pixel
// {
// pixel() { u = v = 0; a = 0.; }
// int u;
// int v;
// double a;
// };
// int S, R, M, N;
// int top, bottom,left,right;
// double ro0, romax, a, q;
// std::vector<std::vector<pixel> > L;
// std::vector<double> A;
//
// void subdivide_recursively(double x, double y, int i, int j, double length, double smin);
// bool get_uv(double x, double y, int&u, int&v);
// void create_map(int M, int N, int R, int S, double ro0, double smin);
// };
//
// CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
// CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
//
// class CV_EXPORTS LDA
// {
// public:
// // Initializes a LDA with num_components (default 0) and specifies how
// // samples are aligned (default dataAsRow=true).
// LDA(int num_components = 0) :
// _num_components(num_components) {};
//
// // Initializes and performs a Discriminant Analysis with Fisher's
// // Optimization Criterion on given data in src and corresponding labels
// // in labels. If 0 (or less) number of components are given, they are
// // automatically determined for given data in computation.
// LDA(InputArrayOfArrays src, InputArray labels,
// int num_components = 0) :
// _num_components(num_components)
// {
// this->compute(src, labels); //! compute eigenvectors and eigenvalues
// }
//
// // Serializes this object to a given filename.
// void save(const String& filename) const;
//
// // Deserializes this object from a given filename.
// void load(const String& filename);
//
// // Serializes this object to a given cv::FileStorage.
// void save(FileStorage& fs) const;
//
// // Deserializes this object from a given cv::FileStorage.
// void load(const FileStorage& node);
//
// // Destructor.
// ~LDA() {}
//
// //! Compute the discriminants for data in src and labels.
// void compute(InputArrayOfArrays src, InputArray labels);
//
// // Projects samples into the LDA subspace.
// Mat project(InputArray src);
//
// // Reconstructs projections from the LDA subspace.
// Mat reconstruct(InputArray src);
//
// // Returns the eigenvectors of this LDA.
// Mat eigenvectors() const { return _eigenvectors; };
//
// // Returns the eigenvalues of this LDA.
// Mat eigenvalues() const { return _eigenvalues; }
//
// protected:
// bool _dataAsRow;
// int _num_components;
// Mat _eigenvectors;
// Mat _eigenvalues;
//
// void lda(InputArrayOfArrays src, InputArray labels);
// };
//
// class CV_EXPORTS_W FaceRecognizer : public Algorithm
// {
// public:
// //! virtual destructor
// virtual ~FaceRecognizer() {}
//
// // Trains a FaceRecognizer.
// CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0;
//
// // Updates a FaceRecognizer.
// CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels);
//
// // Gets a prediction from a FaceRecognizer.
// virtual int predict(InputArray src) const = 0;
//
// // Predicts the label and confidence for a given sample.
// CV_WRAP virtual void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const = 0;
//
// // Serializes this object to a given filename.
// CV_WRAP virtual void save(const String& filename) const;
//
// // Deserializes this object from a given filename.
// CV_WRAP virtual void load(const String& filename);
//
// // Serializes this object to a given cv::FileStorage.
// virtual void save(FileStorage& fs) const = 0;
//
// // Deserializes this object from a given cv::FileStorage.
// virtual void load(const FileStorage& fs) = 0;
//
// };
//
// CV_EXPORTS_W Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
// CV_EXPORTS_W Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
// CV_EXPORTS_W Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
// int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
//
// enum
// {
// COLORMAP_AUTUMN = 0,
// COLORMAP_BONE = 1,
// COLORMAP_JET = 2,
// COLORMAP_WINTER = 3,
// COLORMAP_RAINBOW = 4,
// COLORMAP_OCEAN = 5,
// COLORMAP_SUMMER = 6,
// COLORMAP_SPRING = 7,
// COLORMAP_COOL = 8,
// COLORMAP_HSV = 9,
// COLORMAP_PINK = 10,
// COLORMAP_HOT = 11
// };
//
// CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
//
// CV_EXPORTS bool initModule_contrib();
// }

implementation

Uses
    ocv.core_c,
    ocv.imgproc_c,
    ocv.imgproc.types_c;

{ TCvAdaptiveSkinDetector.THistogram }

constructor TCvAdaptiveSkinDetector.THistogram.create;
Var
  _histogramSize: Integer;
  range: TSingleArray1D;
  ranges: TSingleArray2D;
begin
  _histogramSize := HistogramSize;
  range[0] := GSD_HUE_LT;
  range[1] := GSD_HUE_UT;
  ranges[0] := @range;
  fHistogram := cvCreateHist(1, @_histogramSize, CV_HIST_ARRAY, @ranges, 1);
  cvClearHist(fHistogram);
end;

destructor TCvAdaptiveSkinDetector.THistogram.Destroy;
begin
  cvReleaseHist(fHistogram);
  inherited;
end;

function TCvAdaptiveSkinDetector.THistogram.findCoverageIndex(surfaceToCover: double; defaultValue: Integer): Integer;
Var
  s: double;
  i: Integer;
begin
  s := 0;
  for i := 0 to HistogramSize - 1 do
  begin
    s := s + cvGetReal1D(fHistogram^.bins, i);
    if (s >= surfaceToCover) then
      Exit(i);
  end;
  Result := defaultValue;
end;

procedure TCvAdaptiveSkinDetector.THistogram.findCurveThresholds(var x1, x2: Integer; percent: double);
Var
  sum: double;
  i: Integer;
begin
  sum := 0;

  for i := 0 to HistogramSize - 1 do
    sum := sum + cvGetReal1D(fHistogram^.bins, i);

  x1 := findCoverageIndex(sum * percent, -1);
  x2 := findCoverageIndex(sum * (1 - percent), -1);

  if (x1 = -1) then
    x1 := GSD_HUE_LT
  else
    x1 := x1 + GSD_HUE_LT;

  if (x2 = -1) then
    x2 := GSD_HUE_UT
  else
    x2 := x2 + GSD_HUE_LT;
end;

procedure TCvAdaptiveSkinDetector.THistogram.mergeWith(source: THistogram; weight: double);
Var
  myweight, maxVal1, maxVal2, ff1, ff2: Single;
  f1, f2: PSingle;
  i: Integer;
begin
  myweight := 1 - weight;
  maxVal1 := 0;
  maxVal2 := 0;
  cvGetMinMaxHistValue(source.fHistogram, nil, @maxVal2);
  if (maxVal2 > 0) then
  begin
    cvGetMinMaxHistValue(fHistogram, nil, @maxVal1);
    if (maxVal1 <= 0) then
    begin
      for i := 0 to HistogramSize - 1 do
      begin
        f1 := cvPtr1D(fHistogram^.bins, i);
        f2 := cvPtr1D(source.fHistogram^.bins, i);
        f1^ := f2^;
      end;
    end
    else
    begin
      for i := 0 to HistogramSize - 1 do
      begin
        f1 := cvPtr1D(fHistogram^.bins, i);
        f2 := cvPtr1D(source.fHistogram^.bins, i);

        ff1 := (f1^ / maxVal1) * myweight;
        if (ff1 < 0) then
          ff1 := -ff1;

        ff2 := (f2^ / maxVal2) * weight;
        if (ff2 < 0) then
          ff2 := -ff2;
        f1^ := (ff1 + ff2);
      end;
    end;
  end;
end;

{ TCvAdaptiveSkinDetector }

procedure TCvAdaptiveSkinDetector.adaptiveFilter;
begin

end;

constructor TCvAdaptiveSkinDetector.create(samplingDivider, morphingMethod: Integer);
begin
  nSkinHueLowerBound := GSD_HUE_LT;
  nSkinHueUpperBound := GSD_HUE_UT;

  fHistogramMergeFactor := 0.05; // empirical result
  fHuePercentCovered := 0.95; // empirical result

  nMorphingMethod := morphingMethod;
  nSamplingDivider := samplingDivider;

  nFrameCount := 0;
  nStartCounter := 0;

  imgHueFrame := nil;
  imgMotionFrame := nil;
  imgTemp := nil;
  imgFilteredFrame := nil;
  imgShrinked := nil;
  imgGrayFrame := nil;
  imgLastGrayFrame := nil;
  imgSaturationFrame := nil;
  imgHSVFrame := nil;

  histogramHueMotion := THistogram.create;
  skinHueHistogram := THistogram.create;

end;

destructor TCvAdaptiveSkinDetector.Destroy;
begin
  cvReleaseImage(imgHueFrame);
  cvReleaseImage(imgSaturationFrame);
  cvReleaseImage(imgMotionFrame);
  cvReleaseImage(imgTemp);
  cvReleaseImage(imgFilteredFrame);
  cvReleaseImage(imgShrinked);
  cvReleaseImage(imgGrayFrame);
  cvReleaseImage(imgLastGrayFrame);
  cvReleaseImage(imgHSVFrame);
  histogramHueMotion.Free;
  skinHueHistogram.Free;
  inherited;
end;

procedure TCvAdaptiveSkinDetector.initData(src: pIplImage; widthDivider, heightDivider: Integer);
Var
  imageSize: TCvSize;
begin
  imageSize := cvSize(src^.width div widthDivider, src^.height div heightDivider);

  imgHueFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgShrinked := cvCreateImage(imageSize, IPL_DEPTH_8U, src^.nChannels);
  imgSaturationFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgMotionFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgTemp := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgFilteredFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgGrayFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgLastGrayFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 1);
  imgHSVFrame := cvCreateImage(imageSize, IPL_DEPTH_8U, 3);
end;

procedure TCvAdaptiveSkinDetector.process(inputBGRImage, outputHueMask: pIplImage);
Var
  src: pIplImage;
  h, v, i, l: Integer;
  isInit: Boolean;
  pShrinked, pHueFrame, pMotionFrame, pLastGrayFrame, pFilteredFrame, pGrayFrame: PByte;
begin
  src := inputBGRImage;

  isInit := false;

  Inc(nFrameCount);

  if (imgHueFrame = nil) then
  begin
    isInit := true;
    initData(src, nSamplingDivider, nSamplingDivider);
  end;

  pShrinked := imgShrinked^.imageData;
  pHueFrame := imgHueFrame^.imageData;
  pMotionFrame := imgMotionFrame^.imageData;
  pLastGrayFrame := imgLastGrayFrame^.imageData;
  pFilteredFrame := imgFilteredFrame^.imageData;
  pGrayFrame := imgGrayFrame^.imageData;

  if (src^.width <> imgHueFrame^.width) or (src^.height <> imgHueFrame^.height) then
  begin
    cvResize(src, imgShrinked);
    cvCvtColor(imgShrinked, imgHSVFrame, CV_BGR2HSV);
  end
  else
  begin
    cvCvtColor(src, imgHSVFrame, CV_BGR2HSV);
  end;

  cvSplit(imgHSVFrame, imgHueFrame, imgSaturationFrame, imgGrayFrame, 0);

  cvSetZero(imgMotionFrame);
  cvSetZero(imgFilteredFrame);

  l := imgHueFrame^.height * imgHueFrame^.width;

  for i := 0 to l - 1 do
  begin
    v := pGrayFrame^;
    if (v >= GSD_INTENSITY_LT) and (v <= GSD_INTENSITY_UT) then
    begin
      h := pHueFrame^;
      if (h >= GSD_HUE_LT) and (h <= GSD_HUE_UT) then
      begin
        if (h >= nSkinHueLowerBound) and (h <= nSkinHueUpperBound) then
          ASD_INTENSITY_SET_PIXEL(pFilteredFrame, h);

        if ASD_IS_IN_MOTION(pLastGrayFrame, v, 7) then
          ASD_INTENSITY_SET_PIXEL(pMotionFrame, h);
      end;
    end;
    pShrinked := pShrinked + 3;
    Inc(pGrayFrame);
    Inc(pLastGrayFrame);
    Inc(pMotionFrame);
    Inc(pHueFrame);
    Inc(pFilteredFrame);
  end;

  if (isInit) then
    cvCalcHist(imgHueFrame, skinHueHistogram.fHistogram);

  cvCopy(imgGrayFrame, imgLastGrayFrame);

  cvErode(imgMotionFrame, imgTemp); // eliminate disperse pixels, which occur because of the camera noise
  cvDilate(imgTemp, imgMotionFrame);

  cvCalcHist(&imgMotionFrame, histogramHueMotion.fHistogram);

  skinHueHistogram.mergeWith(&histogramHueMotion, fHistogramMergeFactor);

  skinHueHistogram.findCurveThresholds(nSkinHueLowerBound, nSkinHueUpperBound, 1 - fHuePercentCovered);

  case nMorphingMethod of
    MORPHING_METHOD_ERODE:
      begin
        cvErode(imgFilteredFrame, imgTemp);
        cvCopy(imgTemp, imgFilteredFrame);
      end;
    MORPHING_METHOD_ERODE_ERODE:
      begin
        cvErode(imgFilteredFrame, imgTemp);
        cvErode(imgTemp, imgFilteredFrame);
      end;
    MORPHING_METHOD_ERODE_DILATE:
      begin
        cvErode(imgFilteredFrame, imgTemp);
        cvDilate(imgTemp, imgFilteredFrame);
      end;
  end;

  if (outputHueMask <> nil) then
    cvCopy(imgFilteredFrame, outputHueMask);
end;

procedure ASD_INTENSITY_SET_PIXEL(ptr: PByte; qq: uchar); inline;
begin
  // (*pointer) = (unsigned char)qq;
  ptr[0] := qq;
end;

function ASD_IS_IN_MOTION(ptr: PByte; v, threshold: uchar): Boolean;
begin
  // ((abs((*(pointer)) - (v)) > (threshold)) ? true : false)
  Result := Abs(ptr[0] - v) > threshold;
end;

end.